Estimation of greenhouse gas emissions from agriculture in the eastern Free State, South Africa RESEARCH PROJECT SUBMITTED IN THE FULLFILLMENT OF REQUIREMENTS FOR DEGREE OF MASTERS OF SCIENCE IN GEOGRAPHY BY SEWELA FRANCINAH MALAKA 2013079443 FACULTY OF NATURAL AND AGRICULTURAL SCIENCE GEOGRAPHY DEPARTMENT UNIVERSITY OF THE FREE STATE, QWAQWA CAMPUS SUPERVISOR: Prof. G. MUKWADA SUPERVISOR: Dr. ME. MOELETSI December 2017 i PREFACE The research contained in this dissertation was completed by the candidate while based in the Discipline of geography, faculty of natural and agricultural science, University of the Free State, QwaQwa campus, South Africa. The research was financially supported by Agricultural Research Council and Department of Agriculture Forestry and Fisheries (DAFF) (Project no: 57/011). The contents of this work have not been submitted in any form to another university and, except where the work of others is acknowledged in the text, the results reported are due to investigations by the candidate. Professor G Mukwada (Supervisor) Signed:………………………………………… Date:……………………………………………. Dr ME Moeletsi (Supervisor) Signed:………………………………………… Date:……………………………………………. ii DECLARATION I, Sewela Francinah Malaka, declare that: (i) the research reported in this dissertation, except where otherwise indicated or acknowledged, is my original work; (ii) this dissertation has not been submitted in full or in part for any degree or examination to any other university; (iii) this dissertation does not contain other persons’ data, pictures, graphs or other information, unless specifically acknowledged as being sourced from other persons; (iv) this dissertation does not contain other persons’ writing, unless specifically acknowledged as being sourced from other researchers. Where other written sources have been quoted, then: a) their words have been re-written but the general information attributed to them has been referenced; b) where their exact words have been used, their writing has been placed inside quotation marks, and referenced; (v) where I have used material for which publications followed, I have indicated in detail my role in the work; (vi) this dissertation is primarily a collection of material, prepared by myself, published as journal articles or presented as a poster and oral presentations at conferences. In some cases, additional material has been included; iii (vii) this dissertation does not contain text, graphics or tables copied and pasted from the Internet, unless specifically acknowledged, and the source being detailed in the dissertation and in the References sections. Signed:…………………………………………………. Date:…………………………………………………….. iv ACKNOWLEDGEMENTS My special praise and thanks to the almighty God, for providing me with the health and strength and for wisdom and knowledge to complete this study Special thanks to Agricultural Research Council – institute for Soil, Climate and water (ARC – ISCW) and Department of Agriculture Forestry and Fisheries (DAFF) for funding and supplying resources for this project I would also like to thank ARC – ISCW staff, especially Agrometeorology division for their support and advice on data sources and technical issues I would like to express my deepest gratitude and thanks to both my supervisors Dr M.E Moeletsi and Prof G. Mukwada for their interest, useful criticism, helpful guidance, their generous assistance and continuous encouragement Thanks to farmers at Tshiame Ward for providing with their farm agricultural data I wish to express my gratitude to my family especially my mom (N. Kosotumba) and my siblings for their support I am grateful to have an understanding friend and also my son (Ankonisaho Trinity Malaka) for understanding and love he showed when I spent most of my days without him I would never have been able to finish my dissertation without the guidance and support of the above mentioned people. v ABSTRACT The agriculture sector is responsible for global emissions and the emissions continue to grow rapidly. The food agriculture organization (FAO) reported emissions with 7.1 gigatonnes CO2eq per annum, representing 14.5 % of human-induced GHG emissions; the livestock sector plays an important role in climate change. Beef and cattle milk production account for the majority of emissions, respectively contributing 41 and 20 % of the sector’s emissions. While pig meat and poultry meat and eggs contribute respectively 9 % and 8 % to the sector’s emissions. Feed production and processing, and enteric fermentation from ruminants are the two main sources of emissions, representing 45 and 39 % of sector emissions, respectively. Manure storage and processing represent 10 % in 2013. Contribution of agriculture sector to South Africa’s total CO2eq emissions was 11.6 % in 1990, 9.3 % in 1994 and 4.9 % in 2000. The livestock category was the major contributor to the Agriculture, Forestry and Other Land Use (AFOLU) sector, providing the average of 54.1 % to the total CH4 emissions in 2010. The contribution from livestock has declined by 11.8 % over the 2000 -2010 period. The department of environmental affairs (DEA) reported that, the total enteric CH4 emissions attained for the years (2000, 2004, and 2010) were 903.23 Gg, 1183.56 Gg and 1172.95 Gg. The contributions of dairy cattle to the total cattle emissions in 2004 was 14.3 % and 13.5 % in 2010. The overall objective of this research study was to estimate GHG emissions (CO2, CH4 and N2O) resulting from agricultural farms in Tshiame Ward in the eastern Free State region of South Africa for the years 2010 to 2014. The importance of this research was to assess GHG emissions in agricultural farms for purposes of developing mitigation options. The available data allowed Tier 2 method to calculate all the GHG emission factors (EFs) and emissions from cattle, sheep and cropland farming. However, Tier 1 method was used to estimate EFs and vi emissions from other livestock categories. Emissions were estimated from the agricultural sources including CH4 emissions from enteric fermentation, CH4 emissions from manure management, N2O emissions from manure management, non-CO2 emissions from biomass burning, Soil N2O emissions from managed soils, and emissions from fuel use. The results have shown relatively high CH4 EFs from enteric fermentation for mature female beef cattle (95- 109 kg/head/year) at all farms. The dairy mature females followed with 71-105 kg/head/animal, dairy mature bulls (63-96 kg/head/animal), beef mature bulls (53-89 kg/head/animal), beef heifers (37-52 kg/head/animal), dairy heifers (33-56 kg/head/animal), dairy calves (10-25 kg/head/animal), and beef calves (10-24 kg/head/year). Ewes recorded an enteric CH4 EF of about 7 kg CH4/head/year, heifers 0.86 kg CH4/head/year, rams with about 9 kg CH4/head/year and lambs were calculated to have an enteric CH4 EF of about 0.22 kg CH4/head/year. The manure CH4 EFs for MMSs varied per animal subcategories. Beef mature females had the highest average CH4 manure EFs ranging from 1.2 to 1.5 kg CH4/animal/year at all farms, followed by the dairy mature females with CH4 manure EFs ranging from 0.8 to 2.2 kg CH4/animal/year. The beef mature bulls had the CH4 manure EFs of 0.9 to 1.2 kg CH4/animal/year which was higher than the dairy mature bulls which ranged from 0.9 to 1 kg CH4/animal/year. The other animal subcategories had the manure CH4 EFs ranging from 0.1 to 1 kg CH4/animal/year by MMSs. In summary, manure CH4 EFs for beef category were higher than the dairy category at all animal subcategories. The livestock EFs in this study were higher than the EFs found in most studies and this might be due to the lower quality of the feeding situation used in the study area. However, the cropland EFs were consistent with those in literature for most of the studies. It was estimated that farm total emissions in the year 2010 ranged from (69220-580877 kg CO2eq), (70977-585732 kg CO2eq) in the 2011, (45338-676245 vii kg CO2eq) in 2012, (54731-485264 kg CO2eq) in 2013, and (36270-464119 kg CO2eq) in 2014 at all farms. CH4 enteric fermentation was the highest contributor to the total farm emissions at all farms by approximately 50% in all years, followed by CH4 and N2O from manure management respectively. GHG emissions from cropland farming were lower than the emissions produced during livestock farming. In this study, the mitigation options were analysed and evaluated, and as a result, six (6) mitigation options were regarded as the potential mitigation options for Tshiame farms. The six (6) potential mitigation options met the requirements of sustainability, environmental friendly as well as the profitability of farmers. Managing manure as solid storage had reduced the total emitted manure emissions by 21-75% in all years at all farms. Feeding manure to anaerobic digester had resulted in the reduction of manure emissions emitted by 9-24% at all farms. Manure left on pasture had reduced the manure emissions by 20-75%. However, the dry lot reduced the manure emissions by 20-74% in all years. Addition of supplements in feeding situations had reduced the emitted enteric emissions ranging from 81 to 92 percent. viii TABLE OF CONTENTS PREFACE ..................................................................................................................................... ii DECLARATION ............................................................................................................................ iii ACKNOWLEDGEMENTS .............................................................................................................. v ABSTRACT .................................................................................................................................. vi TABLE OF CONTENTS ..................................................................................................................ix LIST OF TABLES .......................................................................................................................... xii LIST OF FIGURES ..................................................................................................................... xviii CHAPTER 1: INTRODUCTION ...................................................................................................... 1 1.1 Research problem and research questions ....................................................................... 3 1.1.1 Problem statement ................................................................................................... 3 1.1.2 Research questions of the study .............................................................................. 4 1.2 Research aim and objectives of the study ........................................................................ 4 1.2.1. Aim of the study....................................................................................................... 4 1.2.2 Objectives of the study ............................................................................................. 4 1.3 Motivation of the study..................................................................................................... 5 1.4 Design of the study ............................................................................................................ 7 CHAPTER 2: LITERATURE REVIEW .............................................................................................. 9 2.1 Introduction ....................................................................................................................... 9 2.2 Greenhouse effect ........................................................................................................... 11 2.3 Climate change ................................................................................................................ 13 2.4 IPCC Methodology for GHG estimation and assessment reports ................................... 19 2.5 Greenhouse gas (GHG) emissions ................................................................................... 21 2.5.1 Agricultural GHG emissions .................................................................................... 25 2.5.2 Farm GHG emissions ............................................................................................... 41 2.6 Modeling agricultural GHG emissions ............................................................................. 43 CHAPTER 3: MATERIALS AND METHODOLOGY ........................................................................ 52 3.1 Introduction ..................................................................................................................... 52 3.2 Study area ........................................................................................................................ 52 3.2.1 The map of the study area ...................................................................................... 53 3.2.2 Sampling size for farms selected ............................................................................ 57 3.3 Data collection ................................................................................................................. 59 3.3.1 Soil sampling ........................................................................................................... 60 ix 3.4 Calculation of agriculture related GHG emissions .......................................................... 62 3.4.1 CH4 from enteric fermentation ............................................................................... 64 3.4.2 Methane from manure management..................................................................... 69 3.4.3 N2O emissions from manure management ............................................................ 71 3.4.4 N2O emissions from managed soils ........................................................................ 74 3.4.5 Biomass burning ..................................................................................................... 79 3.4.6 CO2 emissions emanating from the use of tractors ................................................ 81 3.5 Conversion factor of emissions to CO2 equivalent .......................................................... 82 3.6 Calculation of emission intensity .................................................................................... 82 3.7 Investigation of Potential mitigation options ................................................................. 83 CHAPTER 4: RESULTS AND DISCUSSION ................................................................................... 85 4.1 Agricultural emissions and emission factors ................................................................... 85 4.1.1 Enteric fermentation .............................................................................................. 85 4.1.2 Manure management systems ............................................................................... 95 4.1.3 Non-CO2 biomass burning emissions .................................................................... 107 4.1.4 Agricultural soil management N2O emissions ...................................................... 112 4.1.5 CO2 from diesel-tractor emissions ........................................................................ 121 4.2 Total farm emissions ..................................................................................................... 127 4.3 Emission intensity .......................................................................................................... 128 4.4 Potential Mitigation options ......................................................................................... 131 4.4.1 Mitigation 1: Solid storage manure management system ................................... 132 4.4.2 Mitigation 2: Anaerobic digester manure management system ......................... 133 4.4.3 Mitigation 3: Pasture - based manure management system ............................... 134 4.4.4 Mitigation 4: Drylot spread manure management system .................................. 135 4.4.5 Mitigation 5: Feeding system (50% Pasture and 50% supplements (TMR).......... 136 4.4.6 Mitigation 6: feeding system (TMR based 100%) ................................................. 137 CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS ........................................................ 139 5.1 Conclusions .................................................................................................................... 139 5.2 Recommendations ........................................................................................................ 141 REFERENCES ........................................................................................................................... 144 APPENDICES............................................................................................................................ 185 Appendix A: Inputs data ...................................................................................................... 185 Appendix B: Gross energy intake and emission results per livestock category .................. 192 Appendix C: Uncertainty results .......................................................................................... 196 x Appendix D: Questionnaire survey ..................................................................................... 218 xi LIST OF TABLES Table Page Table 2.1 Description of the anthropogenic GHG emission sectors by the IPCC (2014) ......... 22 Table 2.2 The various tools to estimate the greenhouse gas emissions. (Legend: + to ++++; from slowest (>1 month) and most difficult (formal training required) to the fastest (<1 day) and easiest to use.) .................................................................................................................. 45 Table 3.1 Geographical data of farms used for the study ....................................................... 58 Table 3.2 The dates in which animal weights were taken from farms .................................... 59 Table 3.3 Categorisation of livestock ....................................................................................... 60 Table 3.4 Various soil conditions in Tshiame farms (Data source: ARC, 2013) ........................ 61 Table 3.5 Description of various soil conditions in Tshiame farms (Data source: ARC, 2013) 61 Table 3.6 The various agricultural GHG sources that were estimated from livestock and cropland farming systems ........................................................................................................ 63 Table 3.7 Burned area data ...................................................................................................... 80 Table 3.8 various manure management systems and feeding systems that were evaluated for the study ................................................................................................................................... 84 Table 4.1 Enteric CH4 emission factors for dairy cattle ........................................................... 86 Table 4.2 Methane enteric fermentation emission factors for beef cattle ............................. 89 Table 4.3 Total Methane enteric emissions per farm from 2010 to 2014 ............................... 91 Table 4.4 Uncertainty for CH4 emissions by non-dairy cattle .................................................. 93 Table 4.5 Uncertainty for CH4 emissions by dairy cows ........................................................... 93 Table 4.6 Manure management system for different animal categories in percentages (applicable for all farms except farm 1, 2 and 14) ................................................................... 95 xii Table 4.7 Manure management system for different animal categories in percentages (applicable for farm 1 only) ...................................................................................................... 96 Table 4.8 Manure management system for different animal categories in percentages (applicable for farm 2 only) ...................................................................................................... 96 Table 4.9 Manure management system for different animal categories in percentages (applicable for farm 14 only) .................................................................................................... 97 Table 4.10 Emission factors for manure management systems per farm (Dairy cattle) ......... 98 Table 4.11 Emission factors for manure management systems per farm (Beef cattle) .......... 99 Table 4.12 Annual farm manure methane emissions ............................................................ 101 Table 4.13 Total manure nitrous oxide emissions per farm .................................................. 104 Table 4.14 Uncertainty for manure CH4 emissions by non-dairy ........................................... 106 Table 4.15 Uncertainty for N2O emissions from manure management by non-dairy for 2010 ................................................................................................................................................ 106 Table 4.16 Total methane emissions from biomass burning ................................................. 109 Table 4.17 Total nitrous oxide emissions from biomass burning .......................................... 110 Table 4.18 Uncertainty for grassland biomass burning CH4 emissions.................................. 111 Table 4.19 Uncertainty for grassland biomass burning N2O emissions ................................. 111 Table 4.20 Soil nitrous oxide from Manure N in pasture ....................................................... 113 Table 4.21 Soil nitrous oxide from Manure amendments ..................................................... 114 Table 4.22 Soil nitrous oxide from application of synthetic N fertilizers ............................... 115 Table 4.23 Nitrous oxide from retained crop residues .......................................................... 117 Table 4.24 Indirect N2O emissions by Atmospheric deposition, leaching and runoff ........... 119 Table 4.25 Uncertainty for soil nitrous oxide emissions ........................................................ 120 Table 4.26 CO2 from diesel-tractor emissions........................................................................ 122 xiii Table 4.27 The total amount of diesel, operation time and energy used per year per activity ................................................................................................................................................ 123 Table 4.28 The uncertainty for CO2 emissions from diesel tractor ........................................ 126 Table 4.29 Potential management practices for the study .................................................... 131 Table 4.30 Reduction of emissions by mitigation 1 ............................................................... 133 Table 4.31 Reduction of emissions by mitigation 2 ............................................................... 134 Table 4.32 Reduction of emissions by mitigation 3 ............................................................... 135 Table 4.33 Reduction of emissions by mitigation 4 ............................................................... 136 Table 4.34 Reduction of emissions by mitigation 5 (50% pasture 50% supplements) .......... 137 Table 4.35 Reduction of emissions by mitigation 6 (100% TMR) .......................................... 138 Table A.1 Productivity for dairy cattle for 2010-2014 .......................................................... 185 Table A.2 Average animal weight (kg) for dairy cattle ........................................................... 186 Table A.3 Annual milk production for dairy cattle for 2010-2014 ......................................... 186 Table A.4 Average animal weight (kg) for beef cattle ............................................................ 187 Table A.5 Productivity data for beef cattle for 2010-2014 .................................................... 187 Table A.6 Average weight for sheep sub-categories .............................................................. 188 Table A.7 Coefficients for calculating energy for maintenance (NEm) .................................. 188 Table A.8 Activity coefficients corresponding to animal s feeding situation ......................... 189 Table A.9 Constants for use in calculating net energy needed for growth (NEg) for sheep .. 189 Table A.10 Constants for use in calculating net energy required for pregnancy (NEp) ........ 189 Table A.11 The Africa default VS values for livestock categories .......................................... 189 Table A.12 The Bo values for all livestock categories ............................................................ 190 Table A.13 Cattle and sheep CH4 conversion factors (Ym) .................................................... 190 xiv Table A.14 The EF default used for goats, pigs and horses.................................................... 190 Table A.15 Data required for calculating N2O emissions from manure management .......... 191 Table A.16 Feeding systems for different animal categories in percentages (applicable to all farms) ..................................................................................................................................... 191 Table B.1 Gross energy intake by dairy cattle ...................................................................... 192 Table B.2 Gross energy intake by beef cattle ......................................................................... 193 Table B.3 Gross energy intake by sheep livestock category .................................................. 194 Table B.4 Total emissions per farm ........................................................................................ 194 Table B.5 Emission intensity ................................................................................................... 195 Table C.1 Uncertainty for CH4 emissions from enteric fermentation by non-dairy for 2011196 Table C.2 Uncertainty for CH4 emissions from enteric fermentation by non-dairy for 2012 196 Table C.3 Uncertainty for CH4 emissions from enteric fermentation by non-dairy 2013 ..... 197 Table C.4 Uncertainty for CH4 emissions from enteric fermentation by non-dairy 2014 ..... 197 Table C.5 Uncertainty for CH4 emissions from enteric fermentation by dairy cows for 2011 ................................................................................................................................................ 198 Table C.6 Uncertainty for CH4 emissions from enteric fermentation by dairy cows 2012 .... 198 Table C.7 Uncertainty for CH4 emissions from enteric fermentation by dairy cows 2013 .... 199 Table C.8 Uncertainty for CH4 emissions from enteric fermentation by dairy cows for 2014 ................................................................................................................................................ 200 Table C.9 Uncertainty for CH4 emissions from manure management by non-dairy for 2011200 Table C.10 Uncertainty for CH4 emissions from manure management by non-dairy for 2012 ................................................................................................................................................ 201 Table C.11 Uncertainty for CH4 emissions from manure management by non-dairy for 2013 ................................................................................................................................................ 201 xv Table C.12 Uncertainty for CH4 emissions from manure management by non-dairy for 2014 ................................................................................................................................................ 202 Table C.13 Uncertainty for CH4 emissions from manure management by dairy cows for 2010 ................................................................................................................................................ 202 Table C.14 Uncertainty for CH4 emissions from manure management by dairy cows for 2011 ................................................................................................................................................ 203 Table C.15 Uncertainty for CH4 emissions from manure management by dairy cows for 2012 ................................................................................................................................................ 203 Table C.16 Uncertainty for CH4 emissions from manure management by dairy cows for 2013 ................................................................................................................................................ 204 Table C.17 Uncertainty for CH4 emissions from manure management by dairy cows for 2014 ................................................................................................................................................ 204 Table C.18 Uncertainty for N2O emissions from manure management by non-dairy 2011 . 205 Table C.19 Uncertainty for N2O emissions from manure management by non-dairy for 2012 ................................................................................................................................................ 205 Table C.20 Uncertainty for N2O emissions from manure management by non-dairy for 2013 ................................................................................................................................................ 206 Table C.21 Uncertainty for N2O emissions from manure management by non-dairy for 2014 ................................................................................................................................................ 206 Table C.22 Uncertainty for N2O emissions from manure management by dairy cows for 2010 ................................................................................................................................................ 207 Table C.23 Uncertainty for N2O emissions from manure management by dairy cows for 2011 ................................................................................................................................................ 207 Table C.24 Uncertainty for N2O emissions from manure management by dairy cows for 2012 ................................................................................................................................................ 208 Table C.25 Uncertainty for N2O emissions from manure management by dairy cows for 2013 ................................................................................................................................................ 208 xvi Table C.26 Uncertainty for N2O emissions from manure management by dairy cows for 2014 ................................................................................................................................................ 209 Table C.27 Uncertainty for CH4 emissions from biomass burning for 2011 .......................... 209 Table C.28 Uncertainty for CH4 emissions from biomass burning for 2012........................... 210 Table C.29 Uncertainty for CH4 emissions from biomass burning for 2013 .......................... 210 Table C.30 Uncertainty for CH4 emissions from biomass burning for 2014 .......................... 211 Table C.31 Uncertainty for N2O emissions from biomass burning for 2011 .......................... 211 Table C.32 Uncertainty for N2O emissions from biomass burning 2012 ............................... 212 Table C.33 Uncertainty for N2O emissions from biomass burning for 2013 .......................... 212 Table C.34 Uncertainty for N2O emissions from biomass burning for 2014 .......................... 213 Table C.35 Uncertainty for N2O emissions from agricultural managed soils for 2011 .......... 213 Table C.36 Uncertainty for N2O emissions from agricultural managed soils for 2012 .......... 214 Table C.37 Uncertainty for N2O emissions from agricultural managed soils for 2013 .......... 214 Table C.38 Uncertainty for N2O emissions from agricultural managed soils for 2014 .......... 215 Table C.39 Uncertainty for CO2 from diesel tractor for 2011 ................................................ 215 Table C.40 Uncertainty for CO2 from diesel tractor for 2012 ................................................ 216 Table C.41 Uncertainty for CO2 from diesel tractor for 2013 ................................................ 216 Table C.42 Uncertainty for CO2 from diesel tractor for 2014 ................................................ 217 xvii LIST OF FIGURES Figure Page Figure 1.1 Design of the study .................................................................................................... 8 Figure 2.1 A simplified model of the greenhouse effect (IPCC, 2007, 115) ............................. 12 Figure 2.2 Separating human and natural influences on climate (Walsh et al., 2014, 803) .... 14 Figure 2.3 Concentration of GHGs CO2, CH4 and N2O from the year 0 – 2000 (IPCC Forth Assessment Report: Climate change 2007; Parry et al., 2007). ............................................... 15 Figure 2.4 The 1990 projections with the observed GHG changes (IPCC, 1990; IEA, 2011; USGS, 2012; WSA, 2012; NOAA, 2012). .............................................................................................. 16 Figure 2.5 The diagram showing the process of enteric fermentation by ruminant animals (adopted from Beil, 2015) ........................................................................................................ 28 Figure 2.6 Anaerobic digestion of organic matter (adapted from Melanie, 2011) ................. 31 Figure 2.7 The diagram showing the main greenhouse gas emission sources, removals and processes from managed agricultural soil (adapted from IPCC, 2006, page 16) ..................... 35 Figure 3.1 (a) The map of the study area ................................................................................ 53 Figure 3.1 (b) The map showing Tshiame in Maluti - A - Phofung municipality ..................... 54 Figure 3.2 The farm boundaries ............................................................................................... 55 Figure 3.3 (a) Average monthly temperature for Tshiame Ward (blue line) maximum temperature and (red line) minimum temperature for the study area (Data source: ARC, 2014) .................................................................................................................................................. 56 Figure 3.3 (b) Average rainfall (mm) for Tshiame Ward (Data source: ARC, 2014) ................. 57 Figure 4.1 The commercial VS subsistence farming scale ...................................................... 130 xviii CHAPTER 1: INTRODUCTION Estimating GHG emissions is an essential first step toward managing emissions. However, a complete, accurate, consistent, comparable and transparent GHG database is an essential tool for informing policy decisions and for understanding emissions and trends, projecting future emissions and identifying sectors for cost-effective emission reduction opportunities. Furthermore, this helps in preparing a national inventory as a core element of national communication reports to the United Nations Framework Convention on Climate Change (UNFCCC) by countries that are signatory parties to the treaty agreement. Agricultural activities contribute directly and indirectly to emissions of GHGs through a variety of processes. These processes emit to the atmosphere significant amounts of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) (Cole et al., 1997; IPCC, 2001a; Paustian et al., 2004). CO2 is largely released from microbial decay or burning of plant litter and soil organic matter (Janzen, 2004; Smith, 2004), while CH4 is produced when organic materials decompose in oxygen-deprived conditions, notably from fermentative digestion by ruminant livestock, and manure management (Smith et al., 2007). N2O is generated by the microbial transformation of nitrogen in soils and animal dung, and is often enhanced where available nitrogen exceeds plant requirements, especially under wet conditions (Smith and Conen, 2004; Oenema et al., 2005). Agriculture is the largest emitter of N2O and second largest emitter of CH4 (Tubiello et al., 2013). Agriculture alone contributes between 10% and 25% of the global GHG emissions annually through production practices, land-use changes and land management (Scialabba and Muller-Lindelauf, 2010; Smith et al., 2007). FAO (2006) reported that, agriculture emits 1 more GHGs than the transport sector worldwide. In livestock farming, particularly the high amount of GHGs is released from ruminants feed digestion (Rotz et al., 2010; Scialabba and Muller-Lindenlauf, 2010; Smith et al., 2007). Most field studies have reported cropland as a negligible source or sink of CO2 (Chianese et al., 2009). Agricultural practices at farm level are typically more complex than industrial agricultural practices or production systems (Henry et al., 2009). New data are often collected through farmer knowledge or records and field sampling in order to complement data on national level applications (FAO, 2009). At farm level detailed data may be available whereas for larger areas, it will be very hard to obtain the statistics required (Colomb et al., 2013). Many farmers are not familiar with the provision of detailed activity data concerning how their practices contribute towards the GHG emissions. Thus, it is unusual for farmers to monitor and hold detailed records of the input and output activity data (Keller et al., 2011). This is a huge challenge in the emerging and small-scale farming communities in South Africa and Africa as a whole. Even in the commercial farming sector, it is impossible to have records of all data required for a farm specific GHG assessment (NRC, 2003). Rotz et al (2010) and Seebauer (2014) argue that, estimation and monitoring emissions of agricultural GHG on farms is difficult because of the complexity of integrated crop-livestock production. Crop and livestock farming are responsible for a significant fraction of GHG emissions (Tubiello et al., 2013). Agricultural GHG emissions can be estimated in separate or through combinations of different approaches (Montzka et al., 2011). It is important to study and examine the management practices for crop and livestock farming in a farm rather than quantifying GHGs from one component because farm emission sources are interconnected as a system (Stewart et al., 2009). The Intergovernmental Panel on Climate Change (IPCC) 2 guidelines are used to guide users in estimating annual GHG emissions at different scales (IPCC, 1996; 2006). 1.1 Research problem and research questions 1.1.1 Problem statement GHG emissions are rising more rapidly than predicted and the world is warming more quickly in response (IPCC, 2006). Despite compelling scientific evidence, governments and businesses have responded with painful slowness on measures to reduce the emissions (IPCC, 2007). In South Africa, there is lack of literature published on GHG emissions estimated on a small scale (provincially or at farm level), wherein estimates are made about agriculture based emissions. In South Africa, there is therefore a need to establish a searchable literature database on agriculture based GHG emissions on farms. There is lack of agricultural data as agricultural census data does not have detailed farm inventories. Municipal or farm inventories are needed in order to determine the actual sources of emissions from agriculture activities, and therefore allowing each municipality to most effectively set targets for its emissions reduction policies. Godfray et al. (2010) predicted that the global population would reach 9 billion by 2050. Population growth will lead to a high demand of food consumption and this will consequently lead to increased GHG emissions (FAO, 2011), unless there is an improvement in production management practices. Agricultural GHG fluxes are complex and heterogeneous, but the active management of agriculture and land use activities offers possibilities for mitigation. Critical activity data (what crops or livestock are managed in what way) is poor or lacking for many agricultural systems, especially in developing countries including South Africa (Tubiello et al., 2012). In South Africa, as is the case with most developing countries, there is a scarcity 3 of data on GHG sources and sinks (DEA, 2011), to quantify agricultural emissions and reductions using IPCC Tier 2 emissions factors (EFs) (Smith and Conen 2004; Oenema et al., 2005). In addition, most of the currently available methods for quantifying emissions are often too expensive or complex, and also not sufficiently user friendly for widespread use (Olander et al 2013). Consequentially, there is no reliable information on the agricultural GHG budgets at the farm level. 1.1.2 Research questions of the study a) In Tshiame Ward, what are the emission factors for GHG emissions from agricultural sources at farm level? b) What are the estimates for the GHG emissions from the agricultural activities practiced in different parts of Tshiame Ward? c) Within Tshiame Ward, how can GHG emissions from the agricultural sector be mitigated? 1.2 Research aim and objectives of the study 1.2.1. Aim of the study The aim of this research study is to estimate GHG emissions resulting from agriculture in the Tshiame Ward. The importance of this research is to assess GHG emissions in agricultural farms for purposes of developing mitigation options. 1.2.2 Objectives of the study a) To estimate the emission factors for the agriculture GHG sources at farm level in the Tshiame Ward. 4 b) To estimate the GHG emissions from the agriculture in different parts of the Tshiame Ward. c) To investigate potential mitigation options that can reduce GHG emissions in the agriculture sector within Tshiame Ward. 1.3 Motivation of the study The anthropogenic GHG emissions should be estimated to provide advice and emission trends to decision makers in order to improve policy-relevant knowledge. Comparing the previous and current GHG emission trends is a crucial step for both science and emissions reduction policies (Tubiello et al., 2015). In addition, trends will also help in assessing progress in reducing the anthropogenic GHG emissions. Accurate measurements of GHG emissions also assist in improving the classification of anthropogenic climate forcing, resulting in a more profound understanding of climate change while also raising awareness and providing support for national action via policy instruments. However, climate change is a worldwide issue and successful potential mitigation options do require the concerted efforts of many governments (IPCC, 2007). Agriculture is the major source of emissions in many developing countries (Olander et al., 2013), and agriculture contribute approximately 30% of total global anthropogenic emissions (Vermeulen et al. 2012). Most studies attribute 10-35% of all global anthropogenic GHG emissions to agriculture (Denman et al. 2007, EPA 2006, McMichael 2007, Stern 2006). Providing food security while at the same time reducing GHG emissions from agriculture to mitigate climate change will be a major challenge with a global population predicted by some 5 sources to reach 9 billion by 2050 (Godfray et al. 2010). Therefore, better quantification and reporting capacity is needed for tracking emission trends and managing viable mitigation responses (Hansen et al., 2012). Improved estimation of GHG emissions and their evolution are needed to evaluate mitigation strategies (Houghton et al., 2012; Hansen et al., 2012). However, determining mitigation potential strategies requires an understanding of current emission trends and the influence of alternative land use and management practices on future emissions (Colomb et al., 2013). Smallholder farmers will receive benefits for GHG mitigation based on the adoption of sustainable agricultural and management practices (Seebauer, 2014). On a smaller scale, researchers suggested that management practices aimed at environmental sustainability in agriculture are similar to those required to reduce agricultural GHG emissions at farm level (Janzen, 1999). On-farm GHG estimation surveys promote the exchange of information based on farmers’ experience on management practices and assessing mitigation options. Paustian et al. (2013) noted that, there is a growing research demand for integrated assessment of GHG issues on farms. Farm scale GHG emissions data are needed for various purposes, such as guiding national planning for low emissions development and ensuring sustainable agricultural practices. Such data also informs consumer’s choice with regard to reducing their carbon footprints and supporting farmers in adopting farming practices that reduce emissions (Olander et al., 2013 and Tubiello et al., 2013). Furthermore, field sampling can be costly especially for large areas (Olander et al., 2013). However, when moving to a smaller scale, lack of local activity data and relevant emission factors can reduce accuracy, therefore, estimation at farm scale can help aggregate changes in emissions across diverse land uses and enhance flexibility in mitigation options 6 (Olander et al., 2013). Farm GHG EFs will also help improve the country’s annual GHG inventories in order to submit precise and regularly updated inventories to the UNFCCC as part of the Kyoto protocol. 1.4 Design of the study The research design adopted in this study provides the scope for organizing the quantification of GHG study from the initial identification of objectives, through planning and implementation of fieldwork, data management and analysis, to reporting outcomes and promoting full and effective use of the outputs of the study. The design contains five sections (Figure 1.1): Chapter 1 sets out the conceptual and theoretical background to the practical guidance presented in other studies made on quantification of agricultural GHG emissions. This is followed by Chapter 2, provides an overview of the principles and methods for agricultural activity data collection and of the constituent elements of GHG emissions characterization. Chapter 2 also covers the descriptions and the background literature on how emission factors were calculated and how the emissions were estimated. In chapter 3, the focus shifts to the preparatory activities for GHG quantification, it describes the methodology. The tasks of collecting background information and clarification of the objectives of the study are undertaken. Chapter 3 describes the data collection activities from agricultural source categories. Chapter 4, provides the results and the discussions. It describes data management (including checking data quality, data entry and processing), as well as data analysis, including a discussion of the resources and statistical packages used and the critical steps followed in the process of analyzing and interpretation of results. Chapter 5 provides the conclusions of the study and the recommendations. 7 Figure 1.1 Design of the study 8 CHAPTER 2: LITERATURE REVIEW 2.1 Introduction The major GHGs emitted into the atmosphere through human activities are CO2, CH4, N2O and fluorinated gases (Smith et al., 2008; Lokupitiya and Paustian, 2006). Globally, CH4, N2O and CO2 are considered to be the most important GHG emitted from agriculture (Cole et al., 1997; Huffman, 2010; IPCC 2001; Paustian et al., 2004; Smith et al., 2007; Smith et al., 2010). In South Africa CO2 is the most significant of the three main GHGs (CO2, CH4, and N2O). CO2 emissions increased by 24.3% between 2000 and 2010, however, the energy sector was the most contributor to CO2 emissions in South Africa with 89.1% contribution between 2000 and 2010 (DEA, 2014). Globally, agricultural CH4 and N2O are the main agricultural GHG emissions and have increased by nearly 17% from 1990 to 2005 (US-EPA, 2006; Smith et al., 2008). GHGs vary in their ability to absorb and hold heat in the atmosphere, for example, N2O absorbs 270 times more heat per molecule than CO2, and CH4 absorbs 21 times more heat per molecule than CO2 (IPCC, 2014). Emissions of non-CO2 GHGs contribute significantly to radiative forcing since they are more effective at trapping heat than CO2 (IPCC, 2007). However, CO2 contributes the most, since its level in the atmosphere is the highest (Massey and Ulmer, 2010). A common measure, termed the global warming potential (GWP), is used to equate the effect of different GHGs on a mass basis (Forster et al., 2007). By convention, the effect of CO2 is assigned a value of one (1) and the GWP of other gases are expressed relative to CO2-eq basis as a standard (IPCC, 2006; Ramaswamy et al., 2001). Different types of GHGs have different impacts on the climate, depending on such factors as how much of the gas is produced, how long it stays in the atmosphere, and how much heat it traps (Scialabba and Muller-Lindelauf, 2010). GHG emissions and their effect on the 9 environment is now a national and international issue (Rots et al., 2010). Among other sectors agriculture, forestry and other land use (AFOLU) presents a unique challenge to the inventory compilers, especially from developing countries, due to the lack of national data in most developing countries (DEA, 2014; FAOSTAT, 2014). It is also a challenge in modelling agricultural emissions at farm level due to lack of specific farm data (Keller et al., 2011). Dave et al., (2012), also concluded that the GHGs from agriculture are difficult to measure due to shortage of activity data for sources. The availability of activity data for compiling the national GHG inventory continued to be a challenge in South Africa (DEA, 2014). Vermeulen et al. (2012) reported that food systems contribute 19-29% of global anthropogenic GHG emissions since crop and livestock farming are responsible for a significant fraction of GHG emissions (Tubiello et al., 2013). The underlying cause for an increase in GHG emissions is perceived to be an ever increasing demand for agricultural products due to a growing population (Alexandratos and Bruinsma, 2012). Crop, dairy and beef production caused emissions were estimated to increase on an average of 2.2% to 6.4% annually from 1961 to 2010 FAO (2012). However, Godfray et al. (2010) predicted that the global population will reach 9 billion by 2050. Population growth will lead to a high demand of food consumption and this will consequently lead to an increased GHG emissions (FAO, 2011), unless there is an improvement in production management practices. Therefore, it is important to study and examine the management practices for crop and livestock farming as a whole farm rather than quantifying GHGs from one component in a farm (Stewart et al., 2009). GHG quantification is essential for emission reductions and the opportunity for mitigation in agriculture is thus significant, and, if realized, would contribute to making this sector carbon neutral and GHGs will be minimized (Olander et al., 2013). 10 South Africa has a large extent and intensive management system of agricultural lands and because of that, it has a significant impact on GHG emissions (Stern, 2006). South Africa was also reported as one of the world’s most carbon-intensive economies contributing to 1.49% of the total global emissions and a bigger emitter of CO2 than all other Sub-Saharan African (SSA) countries combined (Du plooy and Jooste, 2011). However, the contribution of agricultural GHG emissions from a country depends mainly on the structure of the economy (Van der werf et al., 2009). In South Africa, production activities that use large quantities of coal or electricity and the transportation sector generate the most CO2 emissions than all other sectors (Stern, 2006). Furthermore, the agriculture sector’s direct contribution of less than 5% to gross domestic product (GDP) and 13% to employment appears low but increases to 12% and 30% respectively when agribusinesses income and labour are included (DAFF, 2010). 2.2 Greenhouse effect Greenhouse effect is the phenomenon whereby the earth's atmosphere traps solar radiation, caused by the presence in the atmosphere of GHGs that allow incoming sunlight to pass through but absorb heat radiated back from the earth's surface (Turner et al., 2007). GHGs effectively absorb thermal infrared radiation, emitted by the Earth’s surface, by the atmosphere itself due to the same gases, and by clouds (IPCC, 2007). The greenhouse effect is primarily a function of the concentration of water vapor, CO2, CH4, (N2O), and other trace gases in the atmosphere that absorb the terrestrial radiation leaving the surface of the Earth (IPCC 2013). Changes in the atmospheric concentrations of these GHGs does alter the balance of energy transferred between the atmosphere, space, land, and the oceans (NRC, 2001). Therefore, GHGs in the atmosphere keep the earth warm through 11 the greenhouse effect (Metz et al., 2005, IPCC, 2013). Figure 2.1 below illustrate how the process of greenhouse effect takes place. Figure 2.1 A simplified model of the greenhouse effect (IPCC, 2007, 115) Energy radiated by the sun is converted to heat when it reaches the earth's surface. Some of the heat is reflected back through the atmosphere, while some is absorbed by atmospheric gases and radiated back to earth (Lockwood, 2009). Solar energy, mostly in the form of short- wavelength visible radiation, penetrates the atmosphere and is absorbed by the Earth's surface (UNFCCC, 2005). The heated surface then radiates some of that energy into the atmosphere in the form of longer-wavelength infrared radiation (Aldy, 2006). Although some of this radiation escapes into space, much of it is absorbed by GHGs in the lower atmospheres, which in turn re-radiate a portion back to the Earth's surface (Hovi and Holtsmark, 2006). 12 The physics of the greenhouse effect are similar for all GHGs; however, they differ in their overall effect on the earth’s radiation balance, depending on the concentration of a gas, its residence time in the atmosphere, and its physical properties with respect to absorbing and emitting radiant energy (Keller et al., 2011; Le Treut et al., 2007). Increased concentrations of GHGs in the atmosphere has contributed to an increase in the global surface temperature (IPCC, 2001a). GHGs have the ability to trap heat over a given period of time (Forster et al., 2007). However, the intensification of greenhouse effect due to increased levels of GHGs in the atmosphere is considered the main contributing factor to global warming (IPCC, 2007). This is because human activities such as agricultural practices that produce GHGs modify the earth’s energy balance between incoming solar radiation and the heat released back into space, resulting in climate change (EPA, 2010). 2.3 Climate change The IPCC defines climate change as any variation in climate over time whether due to natural variability or as a result of human activity (IPCC, 2007). Furthermore, it is a long-term shift in the statistics of the weather (including its averages), e.g. change in climate normal (expected average values for temperature and precipitation) for a given place and time of the year, from one decade to the next (IPCC, 2012; NOAA, 2007; OECD, 2011). The Earth’s climate has varied considerably in the past, as shown by the geological evidence of ice ages and sea-level changes, and by the records of human history over many hundreds of years (Taylor, 2001). However, climate changes prior to the Industrial Revolution in the 1700s is explained by the natural causes, such as changes in solar energy, changes in ocean currents, volcanic eruptions, natural changes in GHGs concentrations and other natural factors (IPCC, 2007 and 2014; Taylor, 2001). Though, climate changes since 1950 cannot be explained by natural factors, 13 and can only be explained by human factors (Huber and Knutti, 2012). In addition, recent rapid increases of GHG emissions are thought to have resulted due to the anthropogenic GHG emissions (IPCC, 2007; 2014; UNFCCC, 2012). The IPCC (2001, 2007; 2013; 2014) also concluded in their assessment reports with the compelling scientific evidence that the activities of human activities are responsible for changing the earth ‘s climate (Figure 2.2). Figure 2.2 Separating human and natural influences on climate (Walsh et al., 2014, 803) Figure 2.2 illustrate the factors of climate change including the natural and human factors. The atmospheric concentrations of the main GHGs (CO2, CH4, and N2O) long term, for 2000 years have increased since the industrial era (around 1750) due to human activities (IPCC, 2007; Parry et al., 2007). The results are presented on (Figure 2.3) expressed in parts per million (ppm) or parts per billion (ppb) with the number of molecules of GHGs in an 14 atmospheric sample given per million or billion air molecules, respectively. These gases accumulated in the atmosphere with increasing concentration over time (IPCC, 2007). The concentration has increased gradually during the industrial era (Figure 2.3). Figure 2.3 Concentration of GHGs CO2, CH4 and N2O from the year 0 – 2000 (IPCC Forth Assessment Report: Climate change 2007; Parry et al., 2007). The fifth assessment report of the IPCC (2014) recently concluded that GHG emissions from human activity between 2000 and 2010 were the highest in history, contributing to levels in the atmosphere record in at least 800.000 years. As the levels of GHGs rise due to natural and manmade causes, more heat is trapped and global temperatures increases (IPCC, 2007; Rotz et al., 2010). The global average temperature increased by 0.6 to 0.9 °C (degrees Celsius) between 1906 and 2005 and the rate of temperature growth has nearly doubled in the last 15 50 years (IPCC, 2007). Therefore, the earth‘s surface temperature would have been - 18° C if there were no trace of atmospheric GHGs (IPCC, 2007). The IPCC (1990) projected the global temperature increasing simultaneously with the concentration of GHGs during the period of 1990 to 2010, however, their projections (IPCC, 1990) were consistent with the observed global temperatures (IEA, 2011; USGS, 2012; WSA, 2012; NOAA, 2012) (Figure 2.4). Figure 2. 4 The 1990 projections with the observed GHG changes (IPCC, 1990; IEA, 2011; USGS, 2012; WSA, 2012; NOAA, 2012). 16 Continued GHG emissions will cause further warming and long-lasting changes in all components of the climate system (IPCC, 2014). The IPCC (2014) recently reported further climate change to be certain in the coming decades regardless of future emissions. Therefore, the world is expected to experience further warming. The UNEP (2007) stated that the major impacts and threats of global warming are widespread. The IPCC (2007) added that, climate change occurs on a global scale, but the ecological impacts are often local and vary from place to place. Globally, the year 2014 was the warmest year since the record began in 1880, though there were no EL Nino conditions, which would have caused higher temperatures (IPCC, 2014; NOAA, 2015). According to the fifth assessment report of the IPCC (2014), the effects of anthropogenic GHG emissions have been detected throughout the climate system. In 2007, scientists predicted the warming oceans and melting glaciers due to global warming and that climate change could cause sea levels to rise by 18 to 58 (cm) by the year 2100 (IPCC, 2007). Developing countries are the most vulnerable to climate change impacts due to fewer resources available to adapt socially, technologically and financially (IPCC, 2007). Africa will become more vulnerable, and extreme weather events are expected to be more frequent and severe with increasing risk to health and life (DEA, 2004; Few et al., 2004; Christensen et al., 2007). In addition, Africa will face increasing water scarcity and stress with a subsequent potential increase of water conflicts as almost all of the 50 river basinsins in Africa are transboundary (Ashton 2002; De wit and jacek 2006). Changes in the amount of rainfall will also affect how crops grow leading to some African countries not having enough food, and many people could suffer from hunger (IPCC, 2007). Under climate change much 17 agricultural land will be lost, with shorter growing seasons and lower yields (Fischer et al. 2002). Agricultural production relies mainly on rainfall for irrigation, therefore, it will be severely compromised in many African countries, particularly for subsistence farmers and in Sub-Saharan Africa. South Africa would generally also get drier and experience more extreme weather conditions due to global warming (DFID, 2004). In addition, climate change will have a wide range of impacts, including more extreme heat events, fires and drought, more extreme storms, heavy rainfall and floods in South Africa (DEAT, 2004). Many official policy documents in South Africa also openly acknowledged that a large number of sectors in the country are extremely vulnerable to the effects and impacts of climate change (DEA, 2010). For example, agriculture has been identified as the most vulnerable, and thus appropriate for special mitigation and adaptation interventions (Blignaut et al., 2009). Atmospheric scientists also concluded that these impacts will continue and in some cases they will lead to significant risks to agricultural sector which is vital to South Africa’s economy (Blignaut et al., 2009; Du Toit et al., 2002). However, despite the international scientific community's consensus on climate change, a small number of critics continue to deny that climate change exists or that humans are causing it (Begley et al., 2007; Oreskes and Conway, 2010). So far, there has been a lot of interventions internationally and nationally through signing treaties and policy making as well as other interventions (IPCC, 2007 and 2014). Worldwide, many measures have been undertaken to address climate change (IPCC, 2014). 18 2.4 IPCC Methodology for GHG estimation and assessment reports The Intergovernmental Panel on Climate Change (IPCC) is the leading international body for the assessment of climate change and was formed in 1988 by two United Nations organizations, the United Nations Environment Programme (UNEP) and the World Meteorological Organization (WMO) (IPCC, 2007), to assess the state of scientific knowledge about the human role in climate change. The IPCC provide information for the public and policy makers concerning climate change issues and publish guidelines and good practices references for GHG accounting (IPCC, 2006). The IPCC also prepares at regular intervals comprehensive assessment reports of scientific, technical and socio-economic information relevant for the understanding of human induced climate change, potential impacts of climate change and options for mitigation and adaptation (IPCC, 2014). The first assessment report (FAR) of the IPCC was completed in 1990 and it served as the basis for negotiating the United Nations Framework Convention on Climate Change (UNFCCC) (IPCC, 1990). The report was issued in three main sections, corresponding to the three working groups of scientists that the IPCC had established. Working group I (Scientific Assessment of Climate Change), working group II (Impacts Assessment of Climate Change), and working group III (The IPCC Response Strategies) (IPCC, 1990). Each section included a summary for policymakers and this format was adopted in subsequent assessment reports (IPCC, 1990). The second assessment report (SAR) of the IPCC was published in 1996 and it was an assessment of the available scientific and socio-economic information on climate change (IPCC, 1996). However, the second assessment report was superseded by the third assessment report (TAR) in 2001 (IPCC, 2001a). The IPCC third assessment report (TAR) 19 assessed the available scientific and socio-economic information on climate change. It was the third of a series of assessments. However, it was replaced by the IPCC fourth assessment report (AR4), which was released in 2007 (IPCC, 2007). Climate change 2007, the fourth assessment report (AR4), is the fourth in a series of reports which was intended to assess scientific, technical and socio-economic information concerning climate change, its potential effects, and options for adaptation and mitigation (IPCC, 2007). The fifth assessment report (AR5) was finalized in 2014 and it provided a clear and up to date view of the current state of scientific knowledge relevant to climate change. It consisted of three working group reports and a synthesis report (SYR) which integrated and synthesized material in the working group reports for policymakers (IPCC, 2014). The outline of the AR5 was developed through a scoping process which involved climate change experts from all relevant disciplines and users of IPCC reports; in particular representatives from governments. Governments and organizations involved in the fourth report were asked to submit comments and observations in writing with the submissions analyzed by the panel (IPCC, 2013). The IPCC Guidelines also provides the methodology for national and sub-national estimation of emissions (IPCC, 1996; 2006). The IPCC uses the tiered approach (tier 1, 2 and 3) to estimate GHG emissions and a choice of a tier depends on the availability of relevant activity data and indigenous emission factors (IPCC 1996 and 2006; NIES, 2006; Kis-Kovacs et al., 2010). Tier 1 is the basic method, where activity data usually aggregates national statistics and the emission factors are default values representing typical process conditions (IPCC, 2006). In addition tier 1 relies on a universal emission factor combined with activity data. The tier 2 method is more accurate than the tier 1 method and is recommended e.g. for estimating CH4 20 emissions for countries with large cattle populations. The key challenge of using IPCC tier 2 method lies on data collection. Generally, collecting data for tier 2 requires a high level of effort (IPCC, 2006; Crutzen et al., 2007; Montzka et al., 2011) and tier 2 utilizes a country- specific emission factor. Tier 3 involves direct measurement or modeling approaches. It was realized by the IPCC that when quantifying emissions from the agricultural sector, tier 3 estimates are rarely available and IPCC emission factor database factors are often employed (IPCC, 2006). Higher tier methodologies are more demanding, in terms of complexity and data requirements as they depend on availability of country-specific information (Bader and Bleischwitz, 2009; Kis-Kovacs et al., 2010). In South Africa agricultural emissions are usually estimated using IPCC Tier 1 mostly, while tier 2 and 3 are rarely employed (DEA, 2011). 2.5 Greenhouse gas (GHG) emissions The anthropogenic GHGs (CO2, CH4, N2O and fluorinated gases) generally originate from various sources of several sectors and the IPCC (1990, 1996, 2001, 2007 and 2014) assessment reports categorized those sectors into energy supply, transport, buildings, industry, solvent and other product use, agriculture, forestry and waste management, and others. The GHG emission sectors are described on table 2.1 below. 21 Table 2.1 Description of the anthropogenic GHG emission sectors by the IPCC (2014) SECTORS DESCRIPTION OF ACTIVITIES INCLUDED ENERGY SUPPLY Total emission of all GHGs from stationary and mobile energy activities (fuel combustion as well as fugitive fuel emissions). TRANSPORT The total GHGs emissions from the combustion of fuel for all transport activities INDUSTRIAL PROCESSES Emissions within this sector comprise by-product or fugitive emissions of GHGs from industrial processes. Emissions from fuel combustion in industry should be reported under Energy. BUILDINGS Total emissions from residential and commercial (including institutional) buildings, often called the residential and service sectors. SOLVENT AND OTHER PRODUCT USE This category pertains mainly to non-methane volatile compounds (NMVOCs) emissions resulting from the use of solvents and other products containing volatile compounds. AGRICULTURE Describes all anthropogenic emissions from this sector, except for fuel combustion emissions and sewage emissions, which are covered in Energy and Waste modules. LAND-USE CHANGE & FORESTRY Total emissions and removals from forest and land use change activities. WASTE Total emissions from waste management. OTHER Any other anthropogenic source or sink not referred to above The energy supply sector comprises activities of the primary energy sources including fossil carbon fuels; geothermal heat; fissionable, fertile and fusionable nuclides (UNEP, 2009; EPA, 2014). However, these must be extracted, collected, concentrated, transformed, transported, distributed and stored (if necessary) using technologies that consume some energy at every step of the supply chain, as a result during all this activities GHGs such as CO2, CH4, N2O fluorinated gases are emitted (Sims et al., 2006; IEA, 2014). This also includes all emissions from the energy sector which are directly associated with electricity or heat production, such as fuel extraction, refining, processing, and transportation (EPA, 2014). In addition, the burning of coal, natural gas, and oil for electricity and heat is the largest from these sector for global GHGs (IEA, 2015). 22 GHG emissions from the industry sector includes emissions from chemical, metallurgical, and mineral transformation processes not associated with energy consumption and emissions from waste management activities (Boden et al., 2013; EPA, 2012 and EPA, 2014). Industrial processes produce GHGs, including hydrofluorocarbons (HFC-23) from the manufacture of (HCFC-22); perfluorocarbons (PFCs) from aluminium smelting and semiconductor processing; sulfur hexafluoride (SF6) from use in flat panel screens (liquid crystal display) and semi- conductors, magnesium die casting, electrical equipment, aluminium melting, etc., and CH4 and N2O from chemical industry sources and food-industry waste streams (Duoba et al., 2005). However, emissions from industrial electricity use are excluded and are instead covered in the electricity and heat Production under energy sector (IPCC, 2007). The industry sector also includes GHG emission sources such as the energy-intensive industries, iron and steel, non-ferrous metals, chemicals and fertilizer, petroleum-refining, cement, pulp and paper. The direct route by which the transport sector contributes to GHGs emissions is through the combustion of fossil fuels (IPCC, 2007). Fossil fuels contain a substantial amount of carbon, and when these fuels are burned in the presence of oxygen they form CO2, the most extensive GHG by volume (IPCC, 2007; Duoba et al., 2005). The transport sector also contributes small amounts of CH4 and N2O emissions from fuel combustion and F-gases from vehicle air- conditioning. Methane emissions range between 0.1–0.3% of percentage of the total GHG emitted for transport sector, while N2O ranges between 2.0 and 2.8% (IEA, 2014). GHG emissions from the transportation sector contains emissions from the combustion of fuel for all transport activity (IEA, 2014) and it primarily involves fossil fuels burned for road, rail, air, and marine transportation (Boden et al., 2013). About 95% of the world's transportation 23 energy comes from petroleum-based fuels, largely gasoline and diesel (Moorhead and Nixon, 2014). GHG emissions from buildings sector arise from onsite energy generation and burning fuels for heat in buildings or cooking in homes. However, the emissions from electricity use in buildings are excluded and are instead covered in the electricity and heat production under energy sector (FAO, 2014). The disposal and treatment of waste from buildings sector can produce emissions of several GHGs (IPCC, 1996; EPA, 2012). The major GHG emissions from the waste sector are landfill CH4 and, secondarily, wastewater CH4 and N2O (IEA, 2013). CH4 is also released during the breakdown of organic matter in landfills (Bogner et al., 2007). The most significant GHG produced from waste management is CH4 (IPCC, 1996; EPA, 2012). In addition, the other forms of waste disposal also produce other GHGs but these are mainly in the form of CO2 (FAO, 2014). Even the recycling of waste produces some emissions (although these are offset by the reduction in fossil fuels that would be required to obtain new raw materials) (Boden et al., 2013). In addition, the waste treatment process that involves the combustion of organic substances contained in waste materials or the incineration of fossil carbon results in less emissions of CO2 (Ackerman, 2000; IPCC, 2001b). This building sector addresses the GHG emissions for residential and commercial (including institutional) buildings, often called the residential and service sectors (IPCC, 2001b). CO2 emissions from fossil fuel energy used directly or as electricity to power equipment and condition the air (including both heating and cooling) within these buildings is by far the largest source of GHG emissions in this sector (IEA, 2013). Other sources include HFCs from the production of foam insulation and for use in residential and commercial refrigeration and 24 air conditioning, and a variety of GHGs produced through combustion of biomass in cook stoves (IPCC, 2001b). GHG emissions from Agriculture, Forestry, and Other Land Use (AFOLU) sector emerge mostly from agriculture (cultivation of crops and livestock) and deforestation. The AFOLU sector does not include the CO2 that ecosystems remove from the atmosphere by sequestering carbon in biomass, dead organic matter, and soils (Tubiello et al., 2014). Agriculture, Forestry and Other Land Use activities produce GHG emissions by sources as well as removals by sinks, caused by the oxidation and fixation of organic matter via photosynthesis and complex microbial processes associated to human management and disturbance of ecosystems. They comprise non-CO2 emissions by sources from agriculture, CO2, CH4 and N2O emissions by sources from Forestry and Other Land Use (FOLU), and CO2 removals by FOLU sink (Tubiello et al., 2014). 2.5.1 Agricultural GHG emissions In agriculture, GHGs are emitted from various sources which include various agricultural management practices. The largest source of CH4 emissions from agriculture sector is enteric fermentation (Bull et al., 2005; Chhabra et al., 2009; Eagle et al., 2012; Smith et al., 2007 and Smith et al., 2008). However, agricultural sources of N2O have probably been substantially underestimated due to incomplete analysis of increased nitrogen flows in the environment (Tubiello et al., 2013). These sources are poorly understood regarding their magnitude and geographic distribution and quantifying net emissions represents a major undertaking (Nelson, 2009; IPCC, 2006). Agricultural science for GHG emission is complicated because agricultural land acts both as a source and a sink for GHGs (Smith et al., 2007). 25 The IPCC further divided the agriculture sector with several sub-sectors including cropland management, grazing land management/pasture improvement, management of agricultural organic soils, restoration of degraded lands, livestock management, manure management, and bio energy production (IPCC, 2014). Within the AFOLU sector, the GHG emission sources and sinks are disaggregated into several components such as non-CO2 emissions including enteric fermentation (CH4), manure management (CH4 and N2O), rice cultivation (CH4 and N2O), agricultural soils (N2O), burning of biomass (N2O); and CO2 emissions or emission removals such as carbon stock changes in biomass (above- and below-ground biomass, litter, deadwood, harvested wood products) and carbon stock changes in soil organic carbon (SOC) (IPCC, 2006). Cropland management comprise of all systems used to produce food, feed and fiber commodities, furthermore the feedstock for bioenergy production are also included (U.S. EPA, 2013). However, croplands are used for the production of crops cultivated (close-grown crops, such as hay, perennial crops e.g., orchards and vineyards, and horticultural crops (CAST, 2004). Wetlands can also be drained for crop production, which again is considered a cropland since it is used for crop production. Croplands also include agroforestry systems that are a mixture of crops and trees (Smith et al., 2008). Grasslands are composed of grasses, grass-like plants, forbs, or shrubs suitable for grazing and browsing, included is both pastures and native rangelands (Smith et al., 2008). Grazing land systems include managed pastures that may require periodic clearing, burning, chaining and chemicals to maintain the grass vegetation and native rangelands that requires limited management to maintain but may be degraded if overused (Smith et al., 2008). However, croplands, livestock and grazing land management practices influences GHG emissions (Smith et al., 2008). 26 Methane emissions from enteric fermentation Enteric fermentation is the process in which livestock produce CH4 through digestion (Smith et al., 2008; Chhabra et al., 2009) by ruminant animals (Smith et al., 2008). Ruminant animals consist of the fore-stomach or rumen, and this is the largest component of the stomach where food is stored temporarily before returning to the mouth for chewing (Chhabra et al., 2009). Rumen is characterized as a large fermentation vat where about 200 species and strains of micro-organisms are present (Chhabra et al., 2009). This micro-organisms ferment the plant material consumed by the animal through a process of enteric fermentation (EPA, 1995). The ruminant then chews the cud and when the food is sufficiently chewed it is swallowed and passed to the reticulum (Figure 2.5). The microbial fermentation breaks down food into soluble products that can be effectively used by the animal (Smith et al., 2008). However, the products of this process provide the animal with the nutrients it needs to survive which make it possible for ruminant animals to maintain on rough plant material. As a byproduct of enteric fermentation CH4 is produced and is forced out of the body (Gibbs ET AL., 1999). Most of the CH4 is emitted through an animal’s mouth as burbs and belches, whereas some is also emitted while the animal is chewing its cud and some through the lungs. However, a small amount is also produced in the intestine and emitted through the rectum as a flatulence (Ripple et al., 2014). Examples of ruminant animals include Goats, Sheep, Cattle, Antelopes and Buffalos (Smith et al., 2008). Cattle, sheep, and goats are the primary ruminant livestock found in South Africa (Du Toit et al., 2013a, b). 27 Figure 2.5 The diagram showing the process of enteric fermentation by ruminant animals (adopted from Beil, 2015) Enteric fermentation is the largest source of CH4 emissions of agricultural emissions overall in the world (Eagle et al., 2012). Animals with a ruminant digestive system produce more CH4 per unit of feed consumed than non-ruminant digestive systems for example monogastric, avian, and pseudo-ruminant (Smith et al., 2008). The main difference between ruminants and non-ruminants is that ruminants have a stomach with four chambers that release nutrients from food by fermenting it before digestion, while non-ruminant have a single stomach. Ruminant chew cud and ptyalin is absent in the saliva while non-ruminant do not chew cud and ptyalin is present in their saliva. Most digestion and absorption takes place in the stomach by ruminant animals and ruminants can digest cellulose with the help of cellulase from 28 bacteria while in non-ruminant most digestion and absorption takes place in the ileum and cannot digest cellulose (Chhabra et al., 2009). However, monogastric digestive system has one simple stomach and the stomach secretes acid, resulting in a low pH of 1.5 to 2.5. However, the low pH destroys most bacteria and begins to break down the feed materials. Examples of monogastric animals are hogs, cats, dogs, and humans (Bull et al., 2005). A pseudo-ruminant is an animal that eats large amounts of roughage but does not have a stomach with several compartments (Boadi et al., 2002). The digestive system does some of the same functions as those of ruminants, for example, in the horse, the cecum ferments forages (Freibauer et al., 2011). Besides horses, examples of pseudo-ruminants are rabbits, guinea pigs, and hamsters (Smith et al., 2008). Pseudo-ruminant animals produce less CH4 than ruminant livestock and more CH4 than monogastric animals. Pseudo-ruminants do not have a rumen, but feed is fermented during digestion (Bull et al., 2005; IPCC, 2006). Monogastric animals Produce less CH4 per animal as compared with the ruminants and pseudo-ruminants as less CH4 producing fermentation takes place in their digestive systems (Bull et al., 2005; IPCC, 2006). The amount of CH4 that is released depends upon the type, age and weight of the animal and the quantity and quality of the feed consumed (Reynolds, 2013). The type of digestive system has a significant influence on the rate of CH4 emission and the livestock fed higher-quality feed produce less CH4 than those fed low-quality feed (Smith et al., 2008). Feed intake is positively related to animal size, growth rate, and production (e.g., milk production, growth, or pregnancy) (AgDM Newsletter, Aug.2007). Within livestock, the most prominent source category is enteric fermentation of dairy cows, contributing more than 50 % of the overall agricultural CH4 emissions in the world (Freibauer 29 et al., 2011). Even if beef cattle represent 50–60% of livestock emissions, this translates roughly into a figure close to 30–35% of all agricultural emissions (UNEP, 2012). The primary reason for high CH4 emissions is mostly the low quality feed with fibrous contents (De Vries and de Boer, 2010). Heifers emit less CH4 as compared to non-lactating cows and steers (Boadi and Wittenberg, 2002; Boadi et al., 2002). Methane emissions from manure management Methane from animal manure management occurs as a result of manure decomposition under anaerobic conditions through anaerobic digestion (Reynolds, 2013; Smith et al., 2008). Anaerobic digestion occurs when bacteria produce biogas by decomposing organic matter, such as manure without oxygen (Smith et al., 2007). The process consists of four main phases such as hydrolysis, acidogenesis, acetogenesis and methanogenesis involving different microorganism consortia at each step (Figure 2.6) (Gujer and Zehnder, 1983; Demirel, 2005). Hydrolysis is an extracellular step, while the rest processes are intracellular (biological process) (Batstone et al., 2002). Firstly, the hydrolytic bacteria convert complex particulate matter into dissolved compounds with low molecular weight (Demirel, 2005). In this stage the volatile solids in manure are initially broken down to a series of fatty acids (Ward, 2008). During the hydrolysis process of the polymerized, mostly insoluble organic compounds, such as carbohydrates and proteins, fats are decomposed into soluble monomers and dimers, that is, monosaccharides, amino acids, and fatty acids (Pind et al., 2003; Ward, 2008;Taherzadeh and Karimi, 2008). During solid wastes digestion, only 50% of organic compounds undergo biodegradation (Chandra et al., 2012; Gerardi, 2003). The remaining part of the compounds remains in their primary state because of the lack of enzymes participating in their degradation (Ferrer et al., 2010; Parawira, 2008). 30 Secondly, the acidongenic or acetogenic bacteria convert the dissolved compounds into organic acids and hydrogen (Gerardi, 2003). Alcohols, for instance ethanol, and volatile fatty acids (VFAs) with more than two carbon atoms are degraded by acetate-forming bacteria with acetate, hydrogen and CO2 as the main products (Parawira, 2008; Gerardi, 2003). Furthermore, hydrogen and CO2 are constantly reduced to acetate by homoacetogenic microorganisms (Chandra et al., 2012). The methanogenic bacteria finally consume the acids or hydrogen to generate CH4 (Parawira, 2008). During this phase, CO2-reducing and hydrogen- oxidizing methanogens convert hydrogen and CO2 to producing CH4, while acetoclastic methanogens utilize acetate to produce CH4 (De Vrieze et al., 2012). Figure 2.6 depict the four stages of anaerobic digestion of manure (Khanal, 2008). Figure 2.6 Anaerobic digestion of organic matter (adapted from Melanie, 2011) 31 The CH4 production potential of manure depends on the specific composition of the manure, which in turn depends on the composition and digestibility of the animal diet (Smith et al., 2007). The management system determines key factors that affect CH4 production, including contact with oxygen, water content, pH levels, and nutrient availability. The amount of manure produced and the portion of the manure that decomposes anaerobically; also depend on temperature and moisture content (Boadi et al., 2004; Reynolds, 2013). Monteny et al. (2001) indicated that the manure storage temperature and the retention time of the storage has an effect upon CH4 emissions and this is due to the different types of bacteria which had adapted their activity to different temperature ranges (Sommer et al., 2000). High temperatures preferably between 35°C to 45°C, high moisture level and neutral pH conditions result in high CH4 production (Boadi et al., 2004; Bull et al., 2005; EPA, 2010). It also depends on the rate of waste production per animal and the number of animals, and the system on how the manure is managed (Eagle et al., 2012). The major problems with the utilisation of manure in the anaerobic digestion process are a high water content (Hamelin et al., 2011) and low biodegradability of animal manure due to a high biofibre content that mainly consists of lignocellulosic material (Knudsen et al., 2004). Manure stored or treated as a liquid (e.g., in lagoons, ponds, tanks, or pits) decomposes anaerobically and can produce a significant quantity of CH4 due to moisture content (Jacobson et al., 2000). However, when manure is handled as a solid (e.g., in stacks or piles) or when it is deposited on pastures and rangelands, it tends to decompose under more aerobic conditions and less CH4 is produced (Monteny et al., 2001). 32 Nitrous oxide from manure management N2O from manure management is produced during the decomposition of nitrogen contained in the livestock waste (IPCC, 2006; USEPA, 1992). N2O is produced in two ways, directly and indirectly during the storage and treatment of manure (IPCC, 2006; Smith et al., 2008). Direct emissions occur through the processes of nitrification and denitrification while indirect emissions occur through volatilization, leaching and runoff (IPCC, 2006; Bull et al., 2005). Nitrites and nitrates are converted to N2O and dinitrogen (N2) during the aerobic processes of nitrification, and the equations below indicate the chemical reactions of the microbial processes for Nitrification and denitrification (IPCC, 2006; Metay et al., 2007; Olander, 2013): 𝑁𝑖𝑡𝑟𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛: 𝑁𝐻 + 4 + 𝑂2 → 𝐻 + + 𝐻2𝑂 + 𝑁𝑂 − 2 𝑁𝑂 − 2 + 𝑂2 → 𝑁𝑂 − 3 (2.1) 𝐷𝑒𝑛𝑖𝑡𝑟𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛: 𝑁𝑂 − 3 → 𝑁𝑂 − 2 → 𝑁𝑂 → 𝑁2𝑂 → 𝑁2 (2.2) Most of nitrogen in manure is in ammonia (NH3) form, so nitrification occurs aerobically to converts this ammonia into nitrate (Bremner and Blackmer, 1978; IPCC, 2006), whereas denitrification occurs anaerobically to convert the nitrate to N2O (Firestone and Davidson, 1989). N2O production is affected by temperature, pH, biochemical oxygen demand (BOD) which is the amount of dissolved oxygen used by aerobic microorganisms to completely consume the available organic matter, and nitrogen concentration (Sommer et al., 2000). Increasing aeration initiates the nitrification-denitrification reactions, and hence it results to the release of N2O (Chadwick et al., 2000; Burton and Turner, 2003; Fangueiro et al., 2008; Anon., 2010). Therefore, as fresh dung and slurry is highly anoxic and well-buffered with near neutral pH, N2O production is expected to increase with increasing aeration (Chadwick et al., 2000; Sherlock et al., 2002; Fangueiro et al., 2008, 2010; Singurindy et al., 2009; Smith et al., 33 2008). Currently, there is not enough quantitative data to derive a relationship between the degree of aeration and N2O emissions, and this makes N2O emissions estimates from this source highly uncertain (Smith et al., 2008). Volatilization occurs when nitrogen is in the organic form of urea, most commonly from animal manure (Smith et al., 2008). When this happens the nitrogen is changed to ammonia gas (NH3) and lost into the atmosphere (Smith et al., 2008). This is more likely to take place when soils are warm and moist and the source of urea is near the surface (Chadwick et al., 2000; Sherlock et al., 2002; Fangueiro et al., 2008, 2010; Singurindy et al., 2009). N2O from manure depends on a large number of variables including organic carbon availability, O2 partial pressure, soil moisture content, pH, and temperature (Bouwman et al., 1993). High soil pH and high temperatures cause higher rates of volatilization (Engel et al., 2010) since they increase soil concentrations of ammonia dissolved in soil water and warm soil water cannot hold as much ammonia gas (Batstone et al., 2002). Manure deposited and left on pasture is a major source of N2O emissions because of its high nitrogen content and N2O is a by-product generated by the microbial breakdown of nitrogen in soils and manure (Smith et al., 2007). Manure stored for long periods of time generally results in relatively high emissions of N2O (Moeletsi and Tongwane, 2015). When more nitrogen is added to the soil than is needed, soil bacteria convert the extra nitrogen into N2O and emit it into the atmosphere (Smith et al., 2007). Lague (2003) noted that there is a needs to be careful management of these systems in order to mitigate N2O emissions. Soil nitrous oxide (direct and indirect emissions) Agricultural soil management practices produce GHGs such as N2O which as a result contribute to global warming (Omonode et al., 2007). This activities or practices includes 34 application of chemical fertilizers, organic manure and retaining crop residues to soil (Smith et al., 2007). According to the IPCC (2006), emissions of N2O that result from nitrogen inputs to soils occur through direct and indirect pathways. Direct pathway includes N2O emission directly from the agricultural managed soils, whereas, N2O emission through nitrogen leaching and runoff, as well as through volatilization (NH3, NOx) and subsequent redisposition refer to indirect pathways (Eggleston et al., 2006; Skiba and Smith, 2000; Flechard et al., 2007). N2O emissions from managed agricultural soils are produced through the microbial processes of nitrification and denitrification after application of nitrogen inputs in soil as shown in Figure 2.7 below. Figure 2.7 The diagram showing the main greenhouse gas emission sources, removals and processes from managed agricultural soil (adapted from IPCC, 2006, page 16) 35 The microbial reaction as well as the N2O produced from denitrification is largely influenced by soil properties such as soil temperature, moisture, substrate availability and pH level (FAO, 2001; Velthof et al., 1996). In most agricultural soils, formation of N2O is enhanced by an increase in available mineral N, which in turn increases nitrification and denitrification rates (Stehfest & Bouwman, 2006). Different processes influencing emissions interact with each other, as well as with the climate and soil making it difficult to predict their overall effect (Baldock, 2012; NRC, 2003; Boehm et al., 2004; FAO, 2011). Moreover, there remains a great deal of scientific doubt about how to control emissions from agriculture, since many factors are at play, such as local climate, soil type, and production practices (FAO, 2011; Batjes, 1992; Batjes and Bridges, 1992). In other words, there is no simple relationship between the quantity of production and emissions (OECD, 2008). Addition of fertilizer N or manure and wastes containing inorganic or readily mineralizable N, will stimulate N2O emission, as modified by soil conditions at the time of application (Skiba and Smith, 2000). The large increase in the use of nitrogen fertilizer for the production of high nitrogen consuming crops increases N2O emissions (Grant et al., 2006; Davidson, 2009; AgDM, 2007; EPA, 2010; Van Groenigen et al., 2010). However, for given soil and climate conditions, N2O emissions are likely to scale with the nitrogen fertilizer inputs (Soussana et al., 2010). Residue retention improves soil physical chemical and biological quality (Govaerts et al., 2007; West and Marland, 2002). Retaining crop residue in the soil also improves water use efficiency, decreases soil erosion and temperature, improves soil quality and increases yields (Govaerts et al., 2006, 2007, 2008; Lichter et al., 2008; Lal and Pimentel, 2007). The quantity and quality of residues retained also affect N2O emissions, legume residues can result in higher N2O–N losses (Baggs et al., 2000; Huang et al., 2004; Millar et al., 2004) than those 36 from non-legume, low N residues (Aulakh et al., 2001; Millar et al., 2004; Yao et al., 2009). Crop residue decomposition is mainly regulated by climatic conditions, soil nutrient availability and the biochemical composition of the residues, such as lignin and alkaloid contents and C/N ratio (Amado et al., 2003; De Bona et al., 2006). N2O production is particularly high in cases where the nitrogen available in soils exceeds that required by plants to grow, which often occurs when nitrogen-rich synthetic fertilizers are applied (Reynolds, 2013). Surplus of nitrogen or excessive rainfall promote nitrogen lost through leaching and surface runoff, producing high N2O concentrations in the subsurface (Reynolds, 2013). Emissions from leaching and runoff are calculated from leaching-runoff fraction of nitrogen that represents the share of nitrogen losses compared to total applied nitrogen (IPCC, 2006). The diversity of climate, soil types, and agricultural practices in a particular given farm makes it difficult to define generic scenarios for GHG emissions (Baldock 2012). However, more research has been done during the last few decades to identify and measure the production of GHGs and the changing carbon stocks due to global warming (Shaver et al., 2000; Grace, 2004; and Lal, 2004). Biomass burning non-CO2 greenhouse gases Biomass burning in agriculture comprises the burning of crop and grassland residues and this includes burning of living and dead grasses as well as crop biomass after harvesting (Smith et al., 2007). This also includes the human-initiated burning of vegetation for land-use change as well as natural, lightning-induced fires (Smith et al., 2007). Biomass may also be burned to clear forests for agriculture and grazing, control grass, weeds, and litter, eliminate agricultural waste, and serve as domestic fuels (e.g. wood and dung) (Crutzen and Andreae, 1990; Levine 37 et al., 1995). Biomass burning is a source of GHGs (CO2, CH4 and N2O), and is a source of chemically active gases, including carbon monoxide, non-CH4 hydrocarbons, and nitric oxide (Levine, 1996). In addition, it releases large amounts of particulates (solid carbon combustion particles) and gases, including GHGs such as CH4 and N2O. The IPCC (2006) suggested that only non-CO2 (CH4 and N2O) biomass burning GHGs should be accounted. According to the IPCC guidelines CO2 emissions from biomass burning for annual crops do not have to be reported, since the carbon released during the combustion process is assumed to be reabsorbed by the vegetation during the next growing season (IPCC, 2007). Biomass burning has two main phases including flaming and smoldering (Cofer et al., 1990). During the flaming stage, the fuel is well mixed with the surrounding air, and combustion is rapid and more efficient and the products include oxidized gases such as CO2 and N2O (Lobert et al., 1991; Laursen, et al., 1992, Ward et al., 1992). Flaming combustion often dominates when grasses burn. Thermal convection and vertical transport can accompany flaming and some smoldering combustion (Hurst et al., 1994). The smoldering stage generally has a lower combustion efficiency and lasts much longer. The products include a larger fraction of reduced gases such as CO, NMHCs, and NH3 (Lobert et al., 1991; Laursen et al., 1992, Ward et al., 1992) than is the case for flaming combustion. Combustion can also be a combination of the flaming and smouldering phases (Hurst et al., 1994). Scientists projected that humans are responsible for about 90% of biomass burning with only a small percentage of natural fires contributing to the total amount of vegetation burned (Cole, 1997). The percentage of the agricultural crop residues burnt on-site, which is the mass of fuel available for burning is estimated by taking into account the fractions removed before burning due to animal consumption, decay in the field, and use in other sectors (e.g., biofuel, 38 domestic livestock feed, building materials, etc.) (Smith et al., 2007). The information on the amount of emissions is important for accurate estimates of the environmental impacts of these GHGs (Cole, 1997). Some studies have reported that biomass burning has increased on a global scale over the last 100 years, and computer calculations indicated that a hotter earth resulting from global warming will lead to more frequent and larger fires (Cole, 1997). Increased emissions in the last decades were largely because of increasing rates of deforestation (Houghton, 1991). The burning of grasslands, savannas, and agricultural lands has increased over the last century since the rarely burned ecosystems, such as forests, have been converted to frequently burned ecosystems, such as grasslands, savannas, and agricultural lands (Houghton, 1991; Levine, 1996). The production of gases and particulates from fires varies with the type of ecosystem burned, the fire's characteristics, and the vegetation's moisture content (DeAngelo, 2006). CO2 emissions from the usage of agricultural machinery and tractors The use of agricultural machinery and tractors produce the GHG emissions through the combustion of fuel, generally diesel fuel since is mostly used for tractors in agriculture (FAO, 2015). During the use of the tractor, the internal combustion of diesel will take place in a tractor engine and the name internal combustion also refers to the machinery gas turbines (Zhao, 2010). The diesel engine is an internal combustion engine in which the ignition of the fuel that has been injected into the combustion chamber is caused by the high temperature which a gas attains (i.e. the air) when greatly compressed (adiabatic compression) (Zhao, 2010). Diesel engines by compressing only the air; increase the air temperature inside the cylinder to such a high degree that it then ignites the diesel fuel in the combustion chamber 39 (Mitsubishi, 2010). The major part of combustion is controlled by fuel air mixing process and mixing is dominated by flow field formed by fuel jet interacting with combustion chamber walls during injection. Highly luminous flame includes the substantial soot formation in the fuel rich zone by pyrolysis, followed by substantial subsequent oxidation and this lead to the production of CO2 (West and Marland, 2002). The equation below simple illustrate how CO2 is formed during the diesel combustion by tractor engine: 𝐹𝑢𝑒𝑙 + 𝑂2 → 𝐶𝑂2 + 𝐻2𝑂 (2.3) The fuel and the oxidizer are reactants, i.e., the substances present before the reaction takes place. Combustion takes place when fuel, most commonly a fossil fuel, reacts with the oxygen in air to produce heat. The heat created by the burning of a fossil fuel is used in the operation of equipment such as boilers, furnaces, kilns, and engines. Along with heat, CO2 and H2O are created as by-products of the exothermic reaction. In agriculture, the machinery and tractors generally use diesel fuel during various activities in production of crops (West and Marland, 2002). Those activities include ploughing, disking, planting, spraying fertilizers or lime and crop harvesting (FAO, 2015). It is a common practice worldwide that when using equipment for spraying pesticides or fertilizer and also harvesting diesel engine tractors are required to pull the machine and to provide power (FAO, 2015). In South Africa, cultivated crops, more especially maize, wheat and drybeans are highly mechanised and thus are reliant on fossil fuel such as diesel (Marland et al., 2003). Energy use efficient has become an emerging issue for crop farming (EIA, 2015). Quantifying the operational energy costs for different crop production systems through the development of an on-farm energy assessment is fundamental in identifying strategies to reduce energy 40 inputs (Grisso et al., 2004). According to the U.S. Energy Information Administration, the agricultural sector consumed about 69% more diesel than the construction sector in 2013 (EIA, 2015). Energy use efficient is an emerging issue for crop farming (EIA, 2015). Machinery and tractor operations during crop growing season on farm uses more fuel, therefore the ability to predict tractor fuel consumption is very useful for farm budgeting and management and also for reducing GHG emissions (Grisso et al., 2004). 2.5.2 Farm GHG emissions Agricultural practices at farm level are typically more complex than industrial agricultural practices (Henry et al., 2009). Lack of agricultural GHG emission database at farm level is one of the factors contributing to limited availability of data and the variability in agricultural emissions due to the dynamic nature of farm ecosystems (Henry et al., 2009). Colomb et al. (2013) concluded that at farm level detailed data might be available whereas for larger areas, it will be very hard to obtain the accurate statistics required for GHG assessment. However, Olander et al. (2013) argued that the activity data and emission factors at national level are the basis for smaller scale (e.g farm level) applications. To better target interventions aimed at reducing GHG emissions from agricultural systems, there is a need for information on GHG balances and the GHG intensity of agricultural products (e.g. emissions per unit product) at levels where liveliwood and environmental impacts occur and land management decisions are being made (Vermeulen et al., 2012). However, even for smallholder farming systems where decisions are taken in fields and farms that are usually less than one hectare, this decision scale is substantially greater than the scale at which changes in GHG fluxes take place or are measured (Rosenstock et al., 2013). In local government there are no defined protocols to monitor and report agricultural GHG emissions (SEA, 2017). New data are often collected 41 through farmer knowledge or records and field sampling in order to complement data on national level applications FAO (2009). Huge differences can occur among farms since agricultural emissions depend on specific farm management practices (Rotz et al., 2010). Many farmers are not familiar with provision of detailed activity data concerning the GHG impact of their systems and practices. Though, it is not unusual for farmers to monitor and hold detailed records of the input and output activity data as it is important for managing the whole-farm nutrient balance and maximizing system productivity, as well as being an important exercise for fiscal management (Keller et al., 2011). As a result, there is no field study that can feasible record all of the data needed for a farm specific GHG emission assessment (NRC, 2003). Rotz et al., (2010) argue that, measuring the assimilation and agricultural GHG on farms is difficult. Thus, there is a shortage of a defined and consistent methodology for GHG emissions quantification to enable comparability from different farm systems and management practices (Keller et al., 2011; Branca et al., 2013). Estimation of GHG emissions at farm level is also relevant as agriculture activities are often interdependent within an area (Milne et al., 2012). At farm level, most activity data can be provided by farmers whereas at national level activity data is based on statistics or expert knowledge (Milne et al., 2013). Complex interactions occur between the processes undertaken for crop and livestock farming which impact more than one GHG. Therefore, estimating only one GHG ignores those interactions (Robertson and Grace 2004; Gregorich et al., 2005; Schils et al., 2005). No baseline data is available for farm level and consequently this leads to challenges and uncertainties for obtaining agricultural emission data (Henry et al., 2009). Moreover, farmers make decisions based on the entire farm because of the subsequent effect that changes in 42 management practices may have on net farm GHG removals (Collas and Liang, 2007; Stewart et al., 2009). A whole system approach is important for evaluating the practices that best reduce GHG emissions at farm level (Stewart et al., 2009). For farmers or farm managers to be motivated to act on reducing GHG emissions, it is important for them to understand the issue of climate change and also the potential opportunities of reducing GHG emissions (Smith et al., 2007). However, quantification of GHG emissions at farm level in SA is scarce (Devarajan et al., 2009). 2.6 Modeling agricultural GHG emissions According to Denef et al. (2012), accounting tools for GHGs can be divided into three main categories including calculators, protocols and guidelines, and process-based models. Basically, calculators and protocols use models (process-based and/or empirical models) often in combination with IPCC default values as emission factors (Denef et al., 2012). Globally, a variety of models have been developed to model CO2, CH4 and N2O emissions from agriculture, mostly from countries where agriculture is an important contributor of GHG emissions (Crosson et al 2011), and most models are based on the IPCC guidelines which are developed to assist quantification and assessing GHGs (Denef et al., 2012; Colomb et al., 2013). Models can be used to scale up measurements and fill data gaps on GHG emission trends (Milne et al., 2012). Consequently, several tools that aim to narrow the farm level GHG data gap were developed (e.g. DNDC and DAYCENT), many of which require a strong firm hold of agri-ecosystem processes for effective use, unlike CALM and CFF carbon calculators which adapted national inventory data into tools for farm use in UK (Keller et al., 2011). Many current models for agricultural emissions fail to take into account the differences in farming 43 practices (Keller et al., 2011). Table 2.2 below shows the differences in the tools to calculate the GHG emissions and their similarities. 44 Table 2.2 The various tools to estimate the greenhouse gas emissions (Legend: + to ++++; from slowest (>1 month) and most difficult (formal training required) to the fastest (<1 day) and easiest to use) Calculators/ Speed of Usability Purpose The scope Scale / extension Method/ Algorithms GHG Availability tools assessment approach emissions Agricultural + + Reporting Developed for National Accommodates Based on Estimate Available Land Use LULUCF and Sub-national, IPCC Tier 1 IPCC CO2, CH4 for free or (ALU) Agriculture Project, & methods but methods and N2O after Sectors Local/Community allows (1996 & registration level) compilers to 2006 GL advance and 2000- inventory with 2003 GPG) the Tier 2 method capability USAID ++++ ++++ Reporting Developed for National Accommodates Based on Estimate Available AFOLU Agriculture, Sub-national, IPCC Tier 1 IPCC (1996) CO2 for free or Forestry and Project, & methods but after Other Land Use Local/Community allows registration level) compilers to advance inventory with the Tier 2 method capability Carbon ++ ++ Project evaluation Developed for National Accommodates Based on Estimate Available Benefit LULUCF and Land Sub-national, IPCC Tier 1 IPCC (1996) CO2, CH4 for free or Project (CBP) Management Project, & methods but and N2O after Local/Community allows registration level) compilers to advance inventory with the Tier 2 method capability Ex-Ante ++++ ++++ Project evaluation Developed for National Accommodates Based on Estimate Available Carbon- Agriculture, Sub-national, IPCC Tier 1 IPCC (2006) CO2 for free or Forestry, Project, & methods but 45 balance Tool Fisheries, & Land Local/Community allows after (EX-ACT) Management level) compilers to registration advance inventory with the Tier 2 method capability CALM +++ +++ Reporting Developed for CALM focuses on Accommodates Based on Estimate Available Agriculture, land the farm IPCC Tier 1 IPCC (1996 CO2, CH4 for free or use change and activities, not on methods but & 2006) and N2O after forestry whole life-cycles allows registration compilers to advance inventory with the Tier 2 method capability CFF carbon +++ +++ Reporting Developed for Accommodates Based on Available calculator Grassland, IPCC Tier 1 IPCC (1996 for free or livestock, crops methods but & 2006) after and forests allows registration compilers to advance inventory with the Tier 2 method capability Holos ++ ++ Project evaluation Developed for National Accommodates Based on Estimate Available temperate crops, Sub-national, IPCC Tier 1 IPCC (1996 CO2, CH4 for free or livestock, Project, & methods but & 2006) and N2O after grassland, Local/Community allows registration agroforestry and level) compilers to crop production advance inventory with the Tier 2 method capability FullCAM ++ ++ Reporting Developed for National Accommodates Based on Estimate Available crop production, Sub-national, IPCC Tier 1 IPCC (1996 change in for free or Project, & methods but & 2006) soil and 46 grassland and Local/Community allows biomass C after forest level) compilers to stock registration advance change inventory with for direct the Tier 2 LUC and method due to capability tillage residues FarmGAS ++ ++ Project evaluation Developed for National Accommodates Based on Estimate Available crop production, Sub-national, IPCC Tier 1 IPCC (1996 CO2, CH4 for free or grassland, and Project, & methods but & 2006) and N2O after livestock Local/Community allows registration production. level) compilers to advance inventory with the Tier 2 method capability Legend for Table 2.2 Speed of assessment - the rate at which the assessment is able to move or operate. + to ++++ - (from slowest (>1 month) and (most difficult (formal training required) to the fastest (<1 day) and easiest to use.) Usability - is the degree to which a tool can be used by specified users to achieve quantified objectives with effectiveness, efficiency, and satisfaction in a quantified context of use. Purpose - The aim of the tool. The scope - the extent of the area or subject matter that something deals with or to which it is relevant. Scale / extension - this is to explain to what extent the software estimates the emissions on a small scale (farm level) or large scale (national). Method/ approach - Does the software accommodates IPCC Tier 1 methods or allows compilers to advance inventory with the Tier 2 or Tier 3 method capability. Algorithms - a process or set of rules to be followed in emission calculations by the tool. GHG emissions - the estimated greenhouse gas emissions by the tool. Availability - the availability of the tool, whether is purchased or free. 47 The IPCC method can be used at regional or small scale (Colomb et al., 2013). Presently, the Tier 3 process-based models are only available for a small number of emission sources and are limited to specific regions (Crosson et al 2011). Colomb et al. (2013) assessed eighteen models and found that models were designed for various aims and to be used in different geographical areas and also use slightly different methodologies. However, methods differ in their cost, sophistication and geographic and temporal coverage (Colomb et al., 2013). Due to high costs, quantifying GHGs will likely incorporate modeling for scaling up and for projection (Conant et al., 2010). Models have an essential role to play in a small scale assessments and GHG quantification (IPCC, 2006; Conant et al., 2010). It was noted that most of these GHG emission models are developed for managing activity data and emissions factors other than developing a sustainable GHG estimates (Colomb et al., 2013). However, other tools have been developed for this purpose e.g. the US-EPA inventory management template workbook for developing a national GHG inventory system (US-EPA 2011). Many models are from the US, Australia and New Zealand (Milne et al., 2013). In developing countries most models are not used, for example, the Agricultural Land Use (ALU), United States Agency for International Development Agriculture Forestry and Other Land Use (USAID AFOLU) carbon calculator, the Carbon Benefit Project (CBP) simple and detailed assessments and the Ex-Ante Carbon-balance Tool (EX-ACT). Most of the models allow the user to input their own management practices data and emission factors (Milne et al., 2013). The EX-ACT tool has been widely used for large scale assessments of two rural development projects in Brazil dominated by smallholder farmers (Branca et al., 2013). EX-ACT allows the user to analyze any mosaic of land as the inputs and outputs are not spatially explicit. The USAID AFOLU carbon calculator carries out the analysis of specific administrative units, although 48 data for a different scale can be entered by the user (Milne et al., 2013). The CBP’s tools allow a more spatially explicit approach as the user can divide a landscape into numerous adjacent sub-units and enter detailed land management information for each of these before carrying out an integrated analysis which gives spatially explicit output (Milne et al., 2013). Most of those models have not been published in peer reviewed Journals (Turner et al., 2007). However, besides the IPCC based models, there are also the Dynamic ecosystem models. These are also known as the processed based models (Turner et al., 2007). The benefit of using processed based models for scaling purposes is their ability to estimate several measurable variables at the same time (Turner et al., 2007). Moreover, they have shown to decrease uncertainties in estimates, compared to estimations made using the IPCC equations (Del Grosso et al., 2010). Debate exists as to whether the focus should be on measurement or modeling. However, both will be needed, and with appropriate coordination the two approaches can be used to inform and enhance the value of each other (Baldock, 2012). Models which cover developing countries and those that can be applied at a small scale have been developed for a range of purposes and have different strengths and weaknesses (Milne et al., 2012). However, a number of tools have now been developed that aim to narrow this farm level GHG data gap (Hillier et al., 2011). Orlander et al. (2013) noted that there are common challenges for GHG quantification and in order to understand these challenges it is helpful to assess the existing supporting infrastructure and systems for GHGs quantification. Other models combine measurement at the farm-level with an evaluation of management practice to promote GHG saving changes (Keller et al., 2011). However, some models estimate emissions without the need for a data beyond farmer common knowledge of the interactions between management practices (Keller et al., 2011). Component models for predicting all 49 important sources and sinks of CH4, N2O, and CO2 from primary and secondary sources in dairy production were integrated in a software tool called Dairy GHG model (Rotz et al., 2010). Most models require management inputs and they use a year as the unit of time and a hectare as the unit of area (Kitzes et al., 2008). Almost all models estimate emissions from cropland, livestock farming and also grassland (Kitzes et al., 2008). Also, almost all models account for soil N2O from fertilizers, enteric CH4 and manure CH4 as the major sources (Orlander et al., 2013). Mostly, the results are expressed in tons of CO2 equivalent per year, per unit area or per unit of production and some models use several units to associate the emissions with the production (Rotz et al., 2010). A comparison of the estimations using various models is limited by differences between the scopes for each model (Conant et al., 2010). It is difficult to give a precise estimates of the time necessary for each model, because models are dependent on the level of accuracy and reliability and availability of the data required (Conant et al., 2010). Validation of most models is not possible because any approach of calculating GHG emissions is just an estimation (Rotz et al., 2010). The measurement, reporting and verification of GHG emissions are important for management and mitigations because it quantifies emission rates and provides the baseline data (Del Grosso et al., 2012). Nevertheless, measurements are an essential element of GHG assessments at any scale (Conant et al., 2010). Developing countries such as South Africa and Chile adapt some of the models and are also developing their own models for some sectors, except the agriculture sector (Winkler et al., 2014). At the farm level, complex models appear to be the best method to quantify the management impacts on emissions because extensive measuring is too expensive and simple models are not reliable at this scale (Del Grosso et al., 2012). But the ability of the models to represent how available land 50 management options interact with environmental conditions to control soil GHG emissions is incomplete (Del Grosso et al., 2012). Many recent models have been developed to estimate the farm GHG balance (Schils et al., 2007). Most models have used fixed emission factors both for indoor and outdoor emissions e.g. FARM GHG, (Olesen et al., 2006, Lovett et al., 2006). Moreover, as static factors are used rather than dynamic simulations, the environmental dependency of the GHG fluxes is not captured by these models. A dynamic farm-scale model (FarmSim) has been coupled to mechanistic simulation models of grasslands (PASIM, [Riedo et al., 1998; Vuichard et al., 2007]) and croplands (CERES ECC). Therefore, C sequestration by grasslands can be simulated (Soussana et al., 2004) and is included in the farm budget. Models are available for most activities to be assessed in every part of the world. The accuracy level is still restricted but active research is on going and most model developers are frequently updating their models. There is a lack of homogeneity in methodologies; therefore, it is impossible to do a straight comparison between studies done using different models. Indeed all models refer to IPCC but this does not ensure homogenous approach as IPCC provide a general framework including many methodologies with different levels of details. Only detailed comparative study would enable to evaluate precisely the variability of results depending on the calculator. 51 CHAPTER 3: MATERIALS AND METHODOLOGY 3.1 Introduction This chapter explains how the study was conducted, including data collection and analysis. The aim of the study was to estimate GHG emissions resulting from agriculture on selected farms in Tshiame Ward, in Maluti - a - Phofung municipality of the eastern Free State Region of South Africa. The study was the first step towards investigating the potential mitigation options that can reduce GHG emissions at farm level in the region. 3.2 Study area The study was conducted at different selected farms in the Tshiame Ward of Maluti- a - Phofung municipality (Figure 3.1(a) and (b)). The main agricultural activities undertaken in Tshiame Ward include mixed farming, comprising both livestock and crop farming. Tshiame Ward has high agricultural potential with adequate land and suitable growing conditions for crops such as maize, wheat, and dry bean. Farm boundaries are shown in Figure 3.2. Various soil types were recorded on different farms, including sandy soil, sandy loam soil, loam soil, clay soil, and clay loam soil (ARC, 2014). 52 3.2.1 The map of the study area Figure 3.1 (a) The map of the study area 53 Figure 3.1 (b) The map showing Tshiame in Maluti - A - Phofung municipality 54 The map below (Figure 3.2) represent the farm boundaries of the sixteen selected farms. Figure 3.2 The farm boundaries Tshiame Ward experiences hot weather in summer, but it can also be very cold in winter. Heavy frost and snow are often recorded along mountainous eastern part of the ward (Moeletsi et al., 2016). Tshiame Ward experiences four seasons: Summer (December, January, February), autumn (March, April, May), winter (June, July, August), and spring (Sep, 55 Oct, Nov). Figure 3.3 (a-b) presents a summary of the climatic conditions that prevail in Tshiame Ward. The graphs are based on the data averaged for the period between 1995 and 2014. Figure 3.3 (a) Average monthly temperature for Tshiame Ward (blue line) maximum temperature and (red line) minimum temperature for the study area (Data source: ARC, 2014) 56 Figure 3.3 (b) Average rainfall (mm) for Tshiame Ward (Data source: ARC, 2014) The graphs show how temperature and rainfall generally vary from month to month, showing a high degree of seasonality. During the spring season the minimum temperature rises to 14 °C, while the maximum temperature also rises to 25 °C. Figure 3.3(b) shows that the lowest rainfall is received during winter in July (5 mm), while the highest rainfall is received in December (120 mm). This shows that during summer, temperatures, and rainfall are higher than those experienced in winter. The area also experiences many frost days during autumn and winter seasons. 3.2.2 Sampling size for farms selected Farms were selected using the stratified method on the basis of their production levels. This approach was used to ensure that each category of farmers was represented and to minimise the sample selection bias. Tshiame Ward has 29 farms and their production levels were categorised as commercial or subsistence. The selection ensured that each category of 57 farmers was represented. A total of 8 commercial farms (28 %) and 21 subsistence farms (72 %) exist in Tshiame Ward. Therefore, of all farms that were selected, 50 % of the total selected farms consisted of commercial farms and 50 % subsistence farms. The selected sixteen farms were named and numbered as Farm 1 to Farm 16. The naming and numbering remained consistent throughout the study, so that all results presented for each farm relate to the same farms. The farm data are presented on Table 3.1 below. Table 3.1 Geographical data of farms used for the study Farms Latitude Longitude Altitude Farm Production Production systems size levels Farm 1 -28.4312 28.89669 1746 220 Subsistence Dairy, beef, sheep, and crop production Farm 2 Dairy, sheep, pig and crop -28.473 28.87731 1723 154 Subsistence production Farm 3 -28.3757 28.93692 1747 429.7 Subsistence Dairy, beef and crop production Farm 4 -28.3817 28.92372 1735 386 Commercial Dairy, beef and crop production Farm 5 -28.3652 28.87208 1649 150 Subsistence Dairy and crop production Farm 6 Dairy, sheep, pig and crop -28.3027 28.89167 1639 207 Commercial production Farm 7 -28.3261 28.90947 1660 350 Subsistence Dairy and beef production Farm 8 -28.2824 28.87769 1683 480 Subsistence Dairy, beef and crop production Farm 9 -28.2765 28.89753 1672 335 Subsistence Dairy, beef, pig and crop production Farm 10 -28.2775 28.83475 1634 107 Subsistence Dairy and crop production Farm 11 -28.2756 28.52009 1702 310 Commercial Dairy, beef and crop production Farm 12 -28.2806 28.52161 1711 386 Commercial Dairy and crop production Farm 13 -28.1938 28.54305 1650 209 Commercial Beef, goat and crop production Farm 14 -28.2850 28.5301 1767 207 Commercial Dairy and crop production Farm 15 -28.1613 28.53101 1678 520 Commercial Beef, pig and crop production Farm 16 Dairy, beef, pig, horse and crop -28.1531 28.54384 1703 1000 Commercial production 58 3.3 Data collection The data used in this study emanated from two key source categories, namely livestock and cropland farming, as noted below. Data was collected for each year from 2010 to 2014. Activity data for different years for various sources on Appendix A were collected from farmers through a questionnaire survey (Appendix D). Other sources of activity data included data that were recorded on livestock and crop production. Such data were obtained from agricultural censuses, expert knowledge (e.g. animal scientists and agronomists, researchers, crop grower or livestock associations) and previous surveys. This activity data included animal and crop data such as animal population number, animal weights, feeding situation for animals, cultivated crop types, and all agricultural management practices. The animal weight of all livestock was obtained by weighing each animal using weighing scale at randomly selected farms both in summer and winter. Table 3.4 below shows the dates in which animal weights were recorded from farms. Table 3.2 The dates in which animal weights were taken from farms Summer season Winter season 25/03/2014 28/07/2014 26/03/2014 29/07/2014 27/03/2014 30/07/2014 31/07/2014 Livestock data were categorised in line with Otter et al. (2010) recommendations, as noted in the report “The South African Agricultural GHG Inventory for 2004”, as shown in Table 3.3. 59 Table 3.3 Categorisation of livestock Livestock category Livestock sub - categories Calculation method Dairy cattle Mature female cows (< 2 years) Tier 2 Heifers (1 – 2 years) Tier 2 Calves (> 1 year) Tier 2 Bulls Tier 2 Beef cattle Mature female cows Tier 2 Heifers (1 – 2 years) Tier 2 Young oxens (> 1 year) Tier 2 Bulls Tier 2 Sheep Mature females (ewes) Tier 2 Rams Tier 2 Heifer Tier 2 Lambs Tier 2 Goats Mature females Tier 2 Rams Tier 2 Calves Tier 2 Pigs Boars Tier 1 Sows Tier 1 Growers Tier 1 Horses Mature horses Tier 1 3.3.1 Soil sampling For soil sampling, the equipment used included sampling tube, an Edelman soil auger, spade and clean plastic bags. A depth of 15 cm was used when collecting soil samples. Four soil samples were collected from each farm and the sample bags were labelled and numbered and the soil samples were collected for physical analysis. This included the determination of nutrient content, composition, as well as acidity or pH level. This was done with the purpose of obtaining the soil texture so as to determine the soil type per farm as required for GHG emission analysis. The various soil types representing various farms of Tshiame Ward were used for analysis. 60 Table 3.4 Various soil conditions in Tshiame farms (Data source: ARC, 2013) Farm Soil type F1 Loam soil and Sandy Loam soil F2 Clay soil F3 Clay soil and Sandy Loam soil F4 Sandy Loam soil F5 Clay soil and Sandy soil F6 Loam soil F7 Loam soil F8 Clay soil and Sandy soil F9 Clay soil and Sandy Loam soil F10 Sandy Loam soil F11 Loam soil and Sandy soil F12 Loam soil F13 Loam soil F14 Loam soil and Sandy Loam soil F15 Sandy Loam soil F16 Sandy soil Table 3.5 Description of various soil conditions in Tshiame farms (Data source: ARC, 2013) Soil Name Acronym Description Sandy Soil SS Soil with large particles that drains quickly but holds nutrients poorly Sandy Loam Soil SLS Soil material with 80% or more silt and less than 12% clay Loam Soil LS Soil composed mostly of sand and silt, and a smaller amount of clay Clay Soil CS A fine-grained soil or water-soaked earth or fine grain soil Clay Loam Soil CLS A fine-textured soil that breaks into clods or lumps that are hard when dry 61 The variability of climate and soil in Maluti-a-Phofung municipality were described and classified according to the requirements of the IPCC guidelines (IPCC, 1996 and 2006). 3.4 Calculation of agriculture related GHG emissions The quantification methodology used to estimate GHG emissions was based on the 1996 and 2006 IPCC guidelines and tier 2 methods were used in categories where data was available and where it permitted such calculations. Tier 1 was applied where the basic data was applicable and potential emissions are projected to be minimal. The Agricultural land use (ALU) software was used to quantify all emissions from various sources that are included in the study. The program is developed based on revised 1996 IPCC guidelines, 2000 IPCC Good Practice Guidance, 2003 IPCC Good Practice Guidance and 2006 IPCC guidelines (Colomb et al., 2013). The ALU calculation process has three steps to complete the farm GHG estimation, including activity data entry, assignment of emission factors and emission calculations. It was Developed in Colorado State University and designed to make the inventory process easier to implement and consistent with guidelines provided by the Intergovernmental Panel on Climate Change. The choice of ALU over other models was due to the fact that, Agriculture and Land Use (ALU) Software guides an inventory compiler through the process of estimating greenhouse gas emissions and removals related to agricultural and forestry activities. The software also has internal checks to ensure data integrity. This software program is designed to support an evaluation of mitigation potentials using the inventory data as a baseline for projecting emission trends associated with management alternatives. ALU version 6.0 (2012) was used, compared to other versions this version can develop an enhanced characterization for livestock, which is the major contributor source of greenhouse gas emissions in agriculture sector. ALU accommodates Tier 1 and 2 methods as defined by the IPCC. 62 Activity data (AD) was collected and emission factors (EFs) were then calculated in order to estimate emissions. Activity data, according to the Revised 1996 IPCC Guidelines for National GHG Inventories, are defined as data on the magnitude of human activity resulting in emissions or removals taking place during a given period of time. An emission factor is defined as the average emission rate of a given GHG for a given source, relative to units of activity (IPCC 1996 and 2006). In other words this refers to the rate of emission per unit of activity, output or input. Data output from ALU was further analysed using STATISTICA. The GHGs estimated included CH4, N2O and CO2 emanating from various agricultural sources of emissions due to livestock and cropland farming (Table 3.4). Table 3.6 The various agricultural GHG sources that were estimated from livestock and cropland farming systems Sources Greenhouse gasses (GHGs) Livestock Enteric fermentation Methane (CH4) Manure management Methane (CH4) and Nitrous oxide (N2O) Cropland Biomass burning non-CO2 GHG Methane (CH4) and Nitrous oxide (N2O) Synthetic fertilizer application Nitrous oxide (N2O) Organic fertilizer application Nitrous oxide (N2O) Crop residue retained in the soil Nitrous oxide (N2O) Tractor usage in the fields (ALU was not used for this Carbon dioxide (CO2) source, Its methodology is provided on section 3.4.6) 63 3.4.1 CH4 from enteric fermentation Enteric fermentation CH4 emissions from ruminant animals (dairy cattle, beef cattle and sheep) were calculated using tier 2 approaches. Ruminant animals produce CH4 emissions through the process of enteric fermentation which is the digestion of food. In some cases expert opinions were used to compensate for lack of agricultural data as farmers did not always have all the data requirements of the tier 2 calculations. The activity data that were estimated by use of expert opinions included the type of feeding system, feed quality, feed intake, diet and feed digestibility. CH4 emissions from non-ruminant animals (Goats, Pigs, and Horses) were also calculated. The IPCC defaults emission factor values were used for goats (5 kg CH4/head/day), horses (18 kg/CH4/head/day) and pigs (1 kg/CH4/head/day) (IPCC, 1996). The average daily weight gains (WG) were calculated from heifers, calves and lambs subcategories (See appendix A, Table 1, Table 5 and Table 6), since it was assumed that mature cows or animals don’t grow Otter et al. (2010). The WG data for calves and lambs was calculated from average birth weights of all the breeds’ type of animal livestock. The average weight for all animal subcategories were obtained from the averages of all the breeds that were weighed in 2013 for two seasons (summer and winter). The collected data included the average weights of the mature female cow, heifers, bulls, and calves, young and mature oxen, ewes, rams and lambs (See appendix A, Table 2, Table 4, Table 6). All other productivity data such as feed intake, and diet data were obtained from each farm through questionnaires (See appendix A, Table 1 and Table 5). However, the sheep diet data, milk productivity data, as well as the fat content were based on literature. The daily milk production, milk protein content, and fat content for dairy livestock were obtained from the monthly results of the tests made by the local milk company (See Appendix 64 A, Table 3). The percentage of lactating mature females for both beef and dairy cattle ranged from 90% to 100% at most farms. The swine livestock category were fed on concentrates which are high in energy, low in fiber and consist of < 20% protein. The feeding situations were considerably similar at all farms. In this study dairy cattle were 70% pasture-based and 30% TMR-based for feeding situation, while the beef cattle were 100% pasture-based, and this was employed at all farms. Nevertheless, the quality of the pasture-based feeding system was different from the TMR-based diet. The pasture-based animals had higher EFs than TMR- based feeding system. This is due to the variations in the digestibility energy various feeding system, for pasture-based is higher than the TMR-based. Dairy mature females at all farms were based on the TMR with 30% of the feeding situation. Methane emission factors Emission factors for dairy, beef cattle and sheep were determined using Tier 2 approach in ALU software, which is made of the algorithms from the 1996 and 2006 guidelines of the IPCC. The equation below was used: 𝑌𝑚 𝐸𝐹 = (𝐺𝐸 ∗ ( ) ∗ 365 ÷ 55.65) (3.1) 100 Where EF is the average emission rate for a given GHG for a given source relative to activity, GE (MJ day-1) is the gross energy for cattle and sheep, and Ym is the methane conversion factor. For Tier 2 approach the gross energy (GE) was calculated through ALU (IPCC, 1996 and 2006), and the equation below was used: 𝐺𝐸 = ((𝑁𝐸𝑚 + 𝑁𝐸𝑎 + 𝑁𝐸𝐿 + 𝑁𝐸𝑤𝑜𝑟𝑘 + 𝑁𝐸𝑝 ÷ 𝑅𝐸𝑀) + (𝑁𝐸𝑔 ÷ 𝑅𝐸𝐺)) ÷ 𝐷𝐸 %/ 100 (3.2) 65 Where GE is the gross energy for cattle and sheep, NE m is the Net energy for maintenance (MJ day-1), NEa is the net energy for activity (MJ day-1), NEg is the net energy required for growth (MJ day-1), NEL is the net energy required for lactation (MJ day-1), NEwork is the net energy required for work (MJ day -1), NEp is the net energy required for pregnancy (MJ day-1), REM is the ratio of net energy available in a diet for maintenance to digestible energy consumed (MJ day-1), REG is a net energy available for growth in a diet to digestible energy consumed (MJ day-1), DE is the digestible energy expressed as a percentage of gross energy. IPCC (2006) default values were used for digestibility (DE) percentage. For animals fed with > 90% concentrate diet the digestibility of 75 – 85% was used, for pasture based animals 55 – 75% was used and for animal fed low quality forage 45 – 55% was used (IPCC, 2006). The net energy for maintenance was calculated using: 𝑁𝐸𝑚 = 𝐶𝑓𝑖 ∗ (𝑊𝑒𝑖𝑔ℎ𝑡) 0.75 (3.3) Where NEm is the Net energy for maintenance (MJ day-1), Cfi is the coefficients for calculating net energy for maintenance and it varies for each animal category (MJ day-1 kg-1), weight is the live weight of animal (kg) (IPCC, 1996 and 2006). Net energy for activity (for dairy and beef cattle) was calculated using: 𝑁𝐸𝑎 = 𝐶𝑎 ∗ 𝑁𝐸𝑚 (3.4) Where NEa is the net energy for activity (MJ day-1), Ca is the coefficient corresponding to animal s feeding situation, NEm is the net energy required by animal for maintenance (MJ day- 1). Net energy for activity (for sheep) was calculated using: 𝑁𝐸𝑎 = 𝐶𝑎 ∗ (𝑤𝑒𝑖𝑔ℎ𝑡) (3.5) 66 Where NEa is the net energy for activity (MJ day-1), Ca is the coefficient corresponding to animal‘s feeding situation, weight is the live weight of animal (kg) (IPCC, 2006). Net energy for growth (for dairy and beef cattle) was calculated using: 𝐵𝑊 0.75 𝑁𝐸𝑔 = 22.02 ∗ ( ∗ 𝑀𝑊) ∗ 𝑊𝐺 1.097 (3.6) 𝐶 Where NEg is the net energy required for growth (MJ day-1), BW is the average live body weight of the animals in the population (Kg), C is a coefficient with a value of 0.8 for females, 1.0 for castrates and 1.2 for bulls (NRC, 2003), MW is the mature live body weight of an adult female in moderate body condition (kg), WG is the average daily weight gain of the animals in the population (kg day-1). Net energy for growth (for sheep) was calculated using: 𝑁𝐸𝑔 = 𝑊𝐺𝑙𝑎𝑚𝑏 ∗ (𝑎 + 0.5𝑏 (𝐵𝑊𝑖 + 𝐵𝑊𝑓)) ÷ 365 (3.7) Where NEg is the net energy required for growth (MJ day-1), WGlamb is the average daily weight gain of the animals in the population (kg day-1) (only for lamb), a and b are constants for use in calculating net energy needed for growth for sheep as shown in Appendix A, Table 9. BWi is the average live body weight of the animals in the population (Kg), BW is the live bodyweight (live – weight) (kg). Net energy for lactation for beef and dairy cattle (lactating mature females only) was calculated using: 𝑁𝐸𝐿 = 𝑀𝑖𝑙𝑘 ∗ (1.47 + 0.40 ∗ 𝐹𝑎𝑡) (3.8) Where NEL is the net energy required for lactation (MJ day-1), milk is the amount of milk produced (kg of milk day-1), fat is the fat content of milk (% by weight). Net energy for lactation for sheep (Milk production) was calculated using: 5∗𝑊𝐺 𝑁𝐸 = ( 𝑤𝑒𝑎𝑛𝐿 ) ∗ 𝐸𝑉𝑚𝑖𝑙𝑘 (3.9) 365 67 Where NEL is the net energy required for lactation (MJ day-1), WGwean is the weight gain of the lamb between birth and weaning (kg), EVmilk is the energy required to produce 1 kg of milk, MJ kg-1. A default value of 4.6 MJ kg-1 was used (AFRC, 1993) was used. Net energy for work (for dairy and beef cattle) was calculated using: 𝑁𝐸𝑤𝑜𝑟𝑘 = 0.10 ∗ 𝑁𝐸𝑚 ∗ 𝐻𝑜𝑢𝑟𝑠 (3.10) Where NEwork is the net energy required for work (MJ day -1), NEm is the net energy required for maintenance (MJ day-1), hours is the number of hours of work per day. Net energy to produce wool (for sheep) was calculated using: 𝑁𝐸𝑤𝑜𝑜𝑙 = (𝐸𝑉𝑤𝑜𝑜𝑙 ∗ 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑤𝑜𝑜𝑙 ÷ 365) (3.11) Where NEwool is the net energy required to produce wool (MJ day-1), EVwool is the energy value of each kg of wool produced (weighed after drying but before scoring), MJ kg-1 (A default value of 24 MJ kg -1 was used) (AFRC, 1993), Productionwool is the annual wool production per sheep (kg-1). Net energy for pregnancy (for dairy, beef cattle and sheep) was calculated using: 𝑁𝐸𝑝 = 𝐶𝑝𝑟𝑒𝑔𝑛𝑎𝑛𝑐𝑦 ∗ 𝑁𝐸𝑚 (3.12) Where NEp is the net energy required for pregnancy (MJ day-1), Cpregnancy is the pregnancy coefficient used for calculating NEp, and NEm is the net energy required for maintenance. Ratio of net energy available in a diet for maintenance (REM) to digestible energy consumed was calculated using the following equation: 𝑅𝐸𝑀 = (1.123 – (4.092 ∗ 10−3 ∗ 𝐷𝐸 %) + ( 1.126 ∗ 10 − 5 ∗ (𝐷𝐸 %)2) − (25.4/ 𝐷𝐸 %)) (3.13) 68 Where REM is the ratio of net energy available in a diet for maintenance to digestible energy consumed (MJ day-1), DE is the digestible energy expressed as a percentage of gross energy. Ratio of net energy available for growth (REG) in a diet to digestible energy consumed was calculated using: 𝑅𝐸𝐺 = (1.164 − (5.160 ∗ 10 − 3 ∗ 𝐷𝐸 %) + ( 1.308 ∗ 10 − 5 ∗ (𝐷𝐸 %) 2) − (37.4/ 𝐷𝐸 %)) (3.14) Where REG is a net energy available for growth in a diet to digestible energy consumed and DE is the digestible energy expressed as a percentage of gross energy. Enteric CH4 emissions for other animal categories Default CH4 emission factors from the IPCC 2006 Guidelines were used for all other livestock, since there was a shortage of data and less number for those animal population categories and the following equation was used: 𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠 = 𝐸𝐹(𝑇) ∗ (𝑁 /10 6 (𝑇) ) (3.15) Where emissions is the CH4 emissions from enteric fermentation Gg CH yr-14 , EF (T) is the CH4 emission factor for the defined livestock population (kg CH4 head-1 yr-1), N(T) is the number of head of livestock species/ category T, and T is the species/ category of livestock. 3.4.2 Methane from manure management CH4 emissions from manure management were calculated from animal population, activity data and manure management system (MMS) data (IPCC, 2006). CH4 from all livestock manure management was calculated using the tier 2 approach in ALU software. 69 Activity data and Emission factors The weights for livestock categories were obtained from the averages of all animals per farm per sub-category. Volatile solids (VS) for cattle were calculated using ALU software, the DE% values were used (IPCC, 2006). The typical value of 0.04 was used for urinary energy (UE) and 0.08 was the ASH value used (IPCC recommended values, IPCC 2006). The Africa default VS values were used for all other livestock categories in ALU software (See Appendix A, Table 13). The methane producing capacity (Bo) values for all livestock categories (See Appendix A, Table 11) were obtained from the default values of the 2006 guidelines. Animal data and MMS data was used to calculate the annual CH4 emission factor manure management per farm. The CH4 EFs from manure management were calculated using: 𝐸𝐹 (𝑇) = (𝑉𝑆𝑇 ∗ 365) ∗ (𝐵 𝑜(𝑇) ∗ 0.67/𝑚 3 ∗ ∑ 𝑀𝐶𝐹𝑆,𝐾 /100 ∗ 𝑀𝑆 (𝑇,𝑆,𝐾)) (3.16) Where EF (T) is the annual methane emission factor for livestock category T (kg CH animal-14 yr-1), VS is the volatile solid excretion per day on a dry organic matter basis (kg VS. day-1), Bo is the maximum methane producing capacity for manure produced by livestock category T (M3 CH4 kg-1) of VS excreted, 0.67 is the conversion factor of M3 CH4 to kilograms CH4, MCF(s,k) is the methane conversion factors for each manure management system S by climate region k (%), MS(T,S,k) is the fraction of livestock category TS manure handled using manure management system S in climate region k (dimensionless). Volatile solid excretion rates were calculated using: 𝑉𝑆 = (𝐺𝐸 ∗ (1 − 𝐷𝐸%/100) + (𝑈𝐸 ∗ 𝐺𝐸)) ∗ ((1 − 𝐴𝑆𝐻 ÷ 18.45)) (3.17) 70 Where VS is the volatile solid excretion per day on a dry organic matter basis (kg VS. day-1), GE is the gross energy intake (MJ day-1), DE% is the digestibility of the feed in percent, (UE x GE) is the urinary energy expressed as a fraction of GE, ASH is the ash content of manure calculated as a fraction of the dry matter feed intake and 18.45 is the conversion factor for dietary GE per kg of dry matter (MJ day-1), this value is relatively constant across a wide range of forage and grain-based feeds commonly consumed by livestock. Then the total emissions were estimated using: 𝐿𝑚𝑚 = (𝑃𝑜𝑝 ∗ (%𝑀𝑀𝑆/100) ∗ 𝐸𝐹𝑒/1000 (3.18) Where Lmm is the enhanced manure methane emissions (kg CH4), Pop is the population number (head), %MMS is the percent in manure management system (%), EFe is the enhanced manure CH4 emission factor (kg CH4/head/yr). The default EFs obtained from the IPCC guidelines were used for goats, pigs, and horses (IPCC, 2006) (See Appendix A, Table 14). 3.4.3 N2O emissions from manure management N2O emissions from manure management for cattle were calculated using Tier 2 approach, and N2O emissions from manure management were calculated for all livestock categories per farm for the period of 2010-2014. Activity data and Emission factors N2O emissions from manure management were estimated from animal population data, activity data and MMS data. Nitrogen excretion rate (Nrate), and annual N excretion per head of livestock (Nex) were used for all animal categories (See Appendix A, Table 15) including the values of animal weight. The Nrate was obtained from the Africa default values in 2006 IPCC 71 guidelines while the Nex was estimated using ALU based on the guidelines (IPCC, 2006). The defaults N2O EFs were used for the various manure management systems by ALU (IPCC, 2006). Direct N2O emissions due to leaching from manure management The direct N2O emissions due to leaching from manure management were estimated using the equation below: ∑ ∑ 𝑁2𝑂𝐷 (𝑚𝑚) = ((𝑠 (𝑇(𝑁(𝑇) ∗ 𝑁𝑒𝑥 (𝑇) ∗ 𝑀𝑆(𝑆,𝑇)) ∗ 𝐸𝐹3) ∗ 44/28 (3.19) Where N2OD(mm) is the direct N2O emissions from Manure Management in the farm (kg N2O yr-1), N(T) is the number of head of livestock species/category T in the farm, Nex(T) is the annual average N excretion per head of species/category T in the farm (kg N animal-1 yr-1), MS(T,S) is the fraction of total annual nitrogen excretion for each livestock species/category T that is managed in manure management system S in the country, dimensionless EF3(S) is the emission factor for direct N2O emissions from manure management system S in the country (kg N2O-N/kg N) in manure management system, S is the manure management system, T is the species/category of livestock 44/28 is the conversion of (N2O-N)(mm) emissions to N2O(mm) emissions. Annual N excretion rates were calculated using: 𝑁𝑒𝑥 (𝑇) = 𝑁𝑖𝑛𝑡𝑎𝑘𝑒 (𝑇) ∗ (1 − 𝑁𝑟𝑒𝑡𝑒𝑛𝑡𝑖𝑜𝑛 (𝑇)) (3.20) Where Nex is the annual N excretion rates (Kg N animal-1 yr-1(T) ), N intake (T) is the annual N intake per head of animal of species/category T (Kg N animal-1 yr-1), Nretention (T) is the fraction of annual N intake that is retained by animal of species/category T (dimensionless). N intake rates for cattle were calculated using: 72 𝑁_(𝑖𝑛𝑡𝑎𝑘𝑒 (𝑇)) = 𝐺𝐸/18.45 ∗ (𝐶𝑃% / 100 / 6.25) (3.21) Where N intake (T) is the annual N intake per head of animal of species/category T (Kg N animal- 1 yr-1), GE is the gross energy intake of the animal (MJ day-1), 18.45 is the conversion factor for dietary GE per kg of dry matter (MJ day-1), CP% is the percentage of crude protein in a diet, 6.25 is the conversion from Kg of dietary protein to Kg of dietary N, Kg feed protein (Kg N)-1. N retained rates for cattle were calculated using: 𝑁_(𝑟𝑒𝑡𝑒𝑛𝑡𝑖𝑜𝑛 (𝑇)) = (𝑀𝑖𝑙𝑘 ∗ (𝑀𝑖𝑙𝑘 𝑃𝑅% /100) ÷ 6.38) + (𝑊𝐺 ∗ (268 – (7.03 ∗ 𝑁𝐸 𝑔 / 𝑊𝐺 ÷ 6.25))) (3.22) Where Nretention (T) is the fraction of annual N intake that is retained by animal of species/category T (dimensionless), milk is the milk production (Kg animal-1)(applicable to dairy and beef mature female (MF) cows only), milk PR% is the percentage of protein in milk, calculated as (1.9+0.4*% fat), where %fat is an input (applicable to dairy MF cows) (see Appendix A, Table 1) and beef MF (see Appendix A, table 5), 6.38 is the conversion from milk protein to milk N, Kg protein (Kg N)-1, NEg is the net energy required for growth (MJ day-1), WG is the average daily weight gain of the animals in the population (kg day-1), 268 and 7.03 are constants (NRC, 2003), 6.25 is the conversion from Kg dietary protein to Kg dietary N, Kg protein (Kg N-1). Indirect N2O emissions due to leaching from manure management The indirect N2O emissions due to leaching from manure management were estimated using: 𝑁2𝑂𝐿 (𝑚𝑚) = (𝑁𝑙𝑒𝑎𝑐ℎ𝑖𝑛𝑔 – 𝑀𝑀𝑆 ∗ 𝐸𝐹5) ∗ 44/28 (3.23) 73 Where N2OL (mm) is the indirect N2O emissions due to leaching and runoff from manure management in the farm (kg N O y-12 ), Nleaching – MMS is the amount of manure nitrogen that leached from manure management system (kg N yr-1), EF5 is the emission factor for N2O emissions from nitrogen leaching and runoff (kg N2O-N/kg N leached and runoff (default value 0.0075 Kg N2O-N (Kg N leaching/runoff)-1. An N loss due to leaching from manure management systems was calculated using: 𝑁𝑙𝑒𝑎𝑐ℎ𝑖𝑛𝑔 − 𝑀𝑀𝑆 = ∑ (∑ ((𝑁(𝑇) ∗ 𝑁𝑒𝑥 (𝑇) ∗ 𝑀𝑆 (𝑆, 𝑇)) ∗ (𝐹𝑟𝑎𝑐𝑙𝑒𝑎𝑐ℎ𝑀𝑆 /100) (𝑇, 𝑆) )) (3.24) Where Nleaching-MMS is the amount of manure nitrogen that leached from MMS (kg N yr-1), N(T) is the number of head of livestock species/category T in the country (kg N animal-1 yr-1), Nex(T) is the annual average N excretion per head of species/category T in the country (kg N animal- 1 yr-1), MS(S,T) is the fraction of total annual nitrogen excretion for each livestock species/category T that is managed in manure MMS S in the farm (dimensionless), FracleachMS is the percentage of managed manure nitrogen losses for livestock category T due to runoff and leaching during solid and liquid storage of manure (typical range 1-2%). Annual N excretion rates were calculated using equation 3.20, N intake rates for cattle were calculated using equation 3.21 and N retained rates for cattle were calculated using equation 3.22. 3.4.4 N2O emissions from managed soils Soil N2O emissions from managed soils were estimated based on cropland data and this included crop residue retained, synthetic fertilizer and organic amendments data. The data required for N2O estimation from crop residue retained included the crop type, crop management strategies and the amount of residues retained. Data required for direct soil 74 N2O estimation from organic manure included the annual amount of animal manure nitrogen intentionally applied to soils adjusted to account for the amount that volatilises as NH3 and NOx, the total amount of animal manure produced, and the fraction of animal manure N that volatilises as NH3 and NOx (kg NH3-N and NOx-N/kg of N excreted). Synthetic fertilizer data included the amount of fertilizer applied to soil, type of synthetic fertilizer applied, and N fertilizer application rate. Direct and indirect N2O emissions from agricultural managed soils were calculated through ALU and Tier 2 approach. Activity data and Emission factors The quantity of crop residues left or retained after harvesting was calculated based on the crop yield through ALU. Estimates for N in crop residues were based on the IPCC equations (IPCC, 2006). The amount and the type of N fertilizers applied and crop types cultivated per farm per hectare were obtained through a questionnaire survey. Default EFs IPCC (2006) in ALU were used for the fraction of manure N for volatilized and leached/ runoff. And it was used for animal manure amendments, animal manure deposited on pastures, rangeland and paddocks. Direct N2O emissions from agricultural soils The direct N2O emissions from agricultural soils were calculated using the following equation: 𝑁2𝑂𝐷𝑖𝑟𝑒𝑐𝑡 − 𝑁 = 𝛴𝑖 (((𝐹𝑆𝑁 + 𝐹𝐴𝑀 )𝑖 ∗ 𝐸𝐹𝑖] + ((𝐹𝐵𝑁 + 𝐹𝐶𝑅) ∗ 𝐸𝐹1 ) + ( 𝐹𝑂𝑆 ∗ 𝐸𝐹2)) (3.25) Where N2ODirect -N is the emission of N2O in units of Nitrogen that volatilises as NH3 and NOx, FSN is the annual amount of synthetic nitrogen fertiliser applied to soils adjusted to account 75 for the amount that volatilises as NH3 and NOx, FAM is the annual amount of animal manure nitrogen intentionally applied to soils adjusted to account for the amount that volatilises as NH3 and NOx, FBN is the amount of nitrogen fixed by N-fixing crops cultivated annually, FCR is the amount of nitrogen in crop residues returned to soils annually, EFi is the emission factors developed for N2O emissions from synthetic fertiliser and animal manure application under different conditions i, FOS is the area of organic soils cultivated annually, EF1 is the emission factor for emissions from N inputs (kg N2O-N/kg N input), EF2 is the emission factor for emissions from organic soil cultivation (kg N2O-N/ha-yr), Conversion of N2O-N emissions to N2O emissions for reporting purposes is performed by using the following equation: 𝑁2𝑂 = 𝑁2𝑂 − 𝑁 ∗ 44/28 (3.26) N2O emissions from N from synthetic fertiliser application were calculated using the following equation: 𝐹𝑆𝑁 = 𝑁𝐹𝐸𝑅𝑇 ∗ (1 – 𝐹𝑟𝑎𝑐𝐺𝐴𝑆𝐹) (3.27) Where FSN is the annual amount of synthetic nitrogen fertiliser applied to soils after adjusting to account for the amount that volatilises, NFERT is the total amount of synthetic fertiliser consumed annually, FRACGASF is the volatilised as NH3 and NOx. N2O emissions from N from animal manure application were calculated using the following equation: 𝐹𝐴𝑀 = 𝛴𝑇(𝑁(𝑇) ∗ 𝑁𝑒𝑥(𝑇)) (1 – 𝐹𝑟𝑎𝑐𝐺𝐴𝑆𝑀) ∗ (1 – ( 𝐹𝑟𝑎𝑐𝐹𝑈𝐸𝐿−𝐴𝑀) (3.28) Where FAM is the annual amount of animal manure nitrogen intentionally applied to soils adjusted to account for the amount that volatilises as NH3 and NOx, ΣT (N(T) )* Nex(T)) is used 76 for the total amount of animal manure produced, FracGASM is the fraction of animal manure N that volatilises as NH3 and NOx (kg NH3-N and NOx-N/kg of N excreted) and FRACFUEL-AM is the fraction of animal manure used as fuel. N2O emissions from Residue returned to soils were calculated using: 𝐹_𝐶𝑅 = 𝛴_𝑖 [(𝐶𝑟𝑜𝑝𝑂𝑖 ∗ 𝑅𝑒𝑠𝑂𝑖 /𝐶𝑟𝑜𝑝𝑂𝑖 ∗ 𝐹𝑟𝑎𝑐𝐷𝑀𝑖 ∗ 𝐹𝑟𝑎𝑐𝑁𝐶𝑅𝑂𝑖 ) ∗ (1 – 𝐹𝑟𝑎𝑐𝐵𝑈𝑅𝑁𝑖 – 𝐹𝑟𝑎𝑐𝐹𝑈𝐸𝐿 − 𝐶𝑅𝑖 – 𝐹𝑟𝑎𝑐𝐶𝑁𝑆𝑇 − 𝐶𝑅𝑖 – 𝐹𝑟𝑎𝑐𝐹𝑂𝐷𝑖 )] + 𝛴𝑗 [(𝐶𝑟𝑜𝑝𝐵𝐹𝑗 ∗ 𝑅𝑒𝑠𝐵𝐹𝑗 /𝐶𝑟𝑜𝑝𝐵𝐹𝑗 ∗ 𝐹𝑟𝑎𝑐𝐷𝑀𝑗 ∗ 𝐹𝑟𝑎𝑐𝑁𝐶𝑅𝐵𝐹𝑗 ) ∗ (1 – 𝐹𝑟𝑎𝑐𝐵𝑈𝑅𝑁𝑗 – 𝐹𝑟𝑎𝑐𝐹𝑈𝐸𝐿 − 𝐶𝑅𝑗 – 𝐹𝑟𝑎𝑐𝐶𝑁𝑆𝑇 − 𝐶𝑅𝑗 – 𝐹𝑟𝑎𝑐𝐹𝑂𝐷𝑗 )] (3.29) Where FCR is the N2O emissions from Residue returned to soils, ResOi/CropOi and ResBFj/CropBFj is the residue to crop product mass ratio, FracDMi and FracDMj is the dry matter content of the aboveground biomass, FracNCROi and FracNCRBFj is the nitrogen content of the aboveground biomass, FracBURNi and FracBURNj is the fraction of residue burned in the field before and after harvest, FracFUEL-CRi and FracFUEL-CRj is the fraction of residue used as fuel, FracCNST-CRi and FracCNST-CRj is the fraction of residue used for construction, FracFODi and FracFODj is the fraction of residue used as fodder. Indirect N2O emissions The indirect N2O emissions were calculated using the following equation: 𝑁2𝑂𝑖𝑛𝑑𝑖𝑟𝑒𝑐𝑡 − 𝑁 = 𝑁2𝑂 (𝐺) + 𝑁2𝑂(𝐿) (3.30) Where N2Oindirect-N is the emissions of N2O in units of nitrogen (kg N/year), N2O(G) is the N2O produced from volatilisation of applied synthetic fertiliser and animal manure N, and its 77 subsequent atmospheric deposition as NOx and NH4 (kg N/yr), N2O(L) is the N2O produced from leaching and runoff of applied fertiliser and animal manure N (kg N/yr), and conversion of N2O-N emissions to N2O emissions for reporting purposes was performed by using the following equation: 𝑁2𝑂 = 𝑁2𝑂 − 𝑁 ∗ 44/28 (3.31) N2O from deposited n from leaching/runoff (from synthetic fertilizer and organic manure application) was calculated using: 𝑁2𝑂 (𝐿) − 𝑁 = 𝑁𝐹𝐸𝑅𝑇 + {𝛴𝑇 (𝑁(𝑇) ∗ 𝑁𝑒𝑥(𝑇)) ∗ [1 – (𝐹𝑟𝑎𝑐𝐹𝑈𝐸𝐿 − 𝐴𝑀)]} ∗ 𝐹𝑟𝑎𝑐𝐿𝐸𝐴𝐶𝐻 ∗ 𝐸𝐹5 (3.32) Where N2O (L) is the N2O produced from leaching and runoff of applied fertiliser and animal manure N (kg N/yr), NFERT is the total amount of synthetic fertiliser consumed annually, (ΣT (N(T) )* Nex(T)) is used for the total amount of animal manure produced, FRACFUEL-AM is the fraction of animal manure used as fuel, FracLEACH is the fraction of N input that is lost through leaching and runoff and EF5 is the emission factor for leaching/runoff. N2O from atmospheric deposition of N was calculated using: 𝑁2𝑂 (𝐺) − 𝑁 = {(𝑁𝐹𝐸𝑅𝑇 ∗ 𝐹𝑟𝑎𝑐𝐺𝐴𝑆𝐹) + [𝛴𝑇 (𝑁(𝑇) ∗ 𝑁𝑒𝑥(𝑇))] ∗ 𝐹𝑟𝑎𝑐𝐺𝐴𝑆𝑀} ∗ 𝐸𝐹4 (3.33) Where N2O (G) is the N2O produced from atmospheric deposition of N (kg N/yr), NFERT is the total amount of synthetic nitrogen fertiliser applied to soils (kg N/yr), (ΣT(N(T) * Nex(T)) is the total amount of animal manure nitrogen excreted in a country, kg N/yr, FracGASF is the fraction of synthetic N fertiliser that volatilises as NH3 and NOx, kg NH3-N and NOx-N/kg of N input, 78 FracGASM is the fraction of animal manure N that volatilises as NH3 and NOx, kg NH3-N and NOx-N/kg of N excreted and EF4 is the emission factor for N2O emissions from atmospheric deposition of N on soils and water surfaces, kg N2O-N/kg NH3-N and NOx-N emitted. 3.4.5 Biomass burning Non-CO2 emissions from biomass burning (CH4 and N2O) are calculated based on cropland and grassland data. However, this study had calculated only biomass burning from grassland biomass since it is a common practice by farmers to graze or collect crop residues after harvesting. Grassland biomass burning emissions were calculated through ALU. The method used to calculate the biomass burning from grasslands had utilized the amount of area burned per farm per year. Estimated emissions from biomass burning included CH4 and N2O (IPCC, 2006). Activity data and Emission factors The estimation of GHG emissions for grasslands fire included the percentage of grass residues burnt which is the mass of fuel available for burning, grass type, and management systems. It also required information on area burnt (A), mass of fuel available for combustion, combustion factor and emission factors, the equation used was obtained from ALU. Burnt area (A) was collected from farmers through questionnaires and the results are presented in Table 3.5 below. 79 Table 3.7 Burned area data Farms Annual burned area (ha) 2010 2011 2012 2013 2014 F1 80 - - 120 - F2 - - - - - F3 191 - 13 10 - F4 30 126 - - - F5 122 - - 122 - F6 113 70 90 50 60 F7 - 200 150 - - F8 - - 12 - - F9 106 - - 106 107 F10 281 140 - - - F11 - 50 - 239 20 F12 4 - - - - F13 89 20 - 25 - F14 20 - 165 165 - F15 50 50 50 50 - F16 261 - - - - - Represents the unavailability of grassland biomass burning in other farms To determine mass of fuel available for combustion for grasslands, biomass data was acquired by using the area burned per farm per year. The combustion factor and EFs were estimated through ALU. The biomass burned were calculated using: 𝐵𝐵 = 𝐴 ∗ 𝐵𝐷 ∗ 𝐹𝐵 (3.34) Where BB is the biomass burned (tonnes dm), A is the Area burned (ha), BD is the aboveground biomass density (tonnes dm/ha), FB is the fraction actually burned. Biomass CH4 emissions were calculated using: 𝐿 (𝐶𝐻4) = ((𝐶𝑅𝐼 ∗ 𝐸𝑅𝐼 (𝐶𝐻4)) + (𝐶𝑅𝑑 ∗ 𝐸𝑅𝑑 (𝐶𝐻4))) ∗ (16/12) (3.35) Where L (CH4) is the CH4 emissions (tonnes CH4), CRI is the carbon released from biomass (tonnes C), ERI (CH4) is the CH4 emission ratio for live biomass (tonnes CH4-C/tonnes C), CRd 80 is the carbon release from dead biomass (tonnes C), and ERd (CH4) is the CH4 emission ratio for dead biomass (tonnes CH4-C/ tonnes C). The N2O emissions from grassland biomass burning was calculated using: 44 𝐿(𝑁2𝑂) = ((𝐶𝑅𝐼 ∗ 𝐸𝑅𝐼 (𝑁2𝑂) ∗ 𝑁𝐶𝐼) + (𝐶𝑅𝑑 ∗ 𝐸𝑅𝑑 (𝑁2𝑂) ∗ 𝑁𝐶𝑑)) ∗ ( ) (3.36) 28 Where L(N2O) is the N2O Emissions (tonnes N2O), CRl is the carbon Released from Live biomass (tonnes C), ERl(N2O) is the N2O emission ratio for live biomass (tonnes N2O- N/tonnes N), NCl is the N/C ratio of live biomass (tonnes N/tonnes C), CRd is the carbon released from dead biomass (tonnes C), ERd(N2O) is the N2O emission ratio for dead biomass (tonnes N2O-N/tonnes N), NCd is the N/C ratio of dead biomass (tonnes N/tonnes C). 3.4.6 CO2 emissions emanating from the use of tractors Estimation of CO2 emissions resulting from the use of tractors included the activities undertaken, frequency of the activity in the growing season, operation time per hectare, Mean fuel consumption (MFC), and the density of diesel used. To estimate direct energy use and emissions by tractor engines during cultivation activities, the following equations based on Nemecek and Kagi (2007) and FAO (2006) were used per farm per activity per ha: 𝐷𝑖𝑒𝑠𝑒𝑙 − 𝑢𝑠𝑒 (𝑘𝑔/ℎ𝑎) = 𝑓𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦 ∗ 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑡𝑖𝑚𝑒 ∗ 𝑀𝐹𝐶 ∗ 𝑑𝑒𝑛𝑠𝑖𝑡𝑦 (𝑑𝑖𝑒𝑠𝑒𝑙) (3.37) Where Frequency is the frequency of the activity in the growing season, operation time is the time required to complete an activity (hour/ha), MFC is the Mean Fuel Consumption, the characteristic fuel consumption for a specific activity with a tractor (liters/hour), density (diesel) is the density of diesel (kg per liter), and these data was acquired through a 81 questionnaire survey as shown in Table 4.27. The energy use was calculated using the following equation: 𝐸𝑛𝑒𝑟𝑔𝑦 − 𝑢𝑠𝑒 (𝑚𝑗/ℎ𝑎) = 𝐷𝑖𝑒𝑠𝑒𝑙 − 𝑢𝑠𝑒 (𝑘𝑔/ℎ𝑎) ∗ 𝑀𝐽𝑑𝑖𝑒𝑠𝑒𝑙 (𝑚𝑗/𝑘𝑔) (3.38) Where MJdiesel is the energy density diesel (mj/kg). The total direct GHG emissions were estimated using: 𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠 (𝐾𝑔 𝐶𝑂2/ℎ𝑎) = 𝐸𝑛𝑒𝑟𝑔𝑦 − 𝑢𝑠𝑒 (𝑚𝑗/ℎ𝑎) ∗ 𝐶𝑂2𝑑𝑖𝑒𝑠𝑒𝑙𝑀𝐽 (𝑘𝑔 𝐶𝑂2/ 𝑀𝐽 𝑑𝑖𝑒𝑠𝑒𝑙) (3.39) Where CO2dieselMJ is the direct and indirect GHG emissions per MJ of diesel (in g/MJ). 3.5 Conversion factor of emissions to CO2 equivalent Estimated GHG emissions (CH4 and N2O) were converted to CO2 equivalent emissions as required by the international panel on climate change guidelines (IPCC, 2006). The 100 years global warming potentials were used to change CH4 and N2O emissions into their CO2 equivalents; 1 for CO2, 21 for CH4 and 310 for N2O as used by Forster et al. (2007); Scheutz et al. (2009). This is also based on the Fourth Assessment Report of the IPCC released in 2007. Carbon dioxide equivalent (CO2eq) is referred to as a unit of measurement that allows the effects of different GHGs to be compared using CO2 as a standard unit for reference (IPCC, 2007). 3.6 Calculation of emission intensity Emission intensity is the average emission rate of a given pollutant from a given source relative to the intensity of a specific activity; for example grams of carbon dioxide released 82 per mega joule of energy produced. In this study the emission intensity was calculated using the following equation: 𝑇𝑜𝑡𝑎𝑙 𝑓𝑎𝑟𝑚 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠 (𝑘𝑔 𝐶𝑂 𝑒𝑞/𝑓𝑎𝑟𝑚) 𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 = 2 (3.40) 𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 ℎ𝑒𝑐𝑡𝑎𝑟𝑒𝑠/𝑓𝑎𝑟𝑚 (ℎ𝑎) 3.7 Investigation of Potential mitigation options The evaluation of potential mitigation options was done through ALU software. The methodology used in this study was in agreement with recommendations from literature such as Smith et al. (2014), where it is suggested that GHGs can be reduced by supply-side mitigation options (e.g., by reducing GHG emissions per unit of land/animal, or per unit of product), or by demand-side options (e.g., by changing demand for food and fibre products, reducing waste). However, the fourth assessment report of the IPCC, the forestry chapter Nabuurs et al. (2007) considers some demand-side options, but the agriculture chapter focuses on supply-side options only (Nabuurs et al., 2007; Smith et al., 2007). The IPCC Fourth Assessment Report (2007) concluded that, the options that both reduce GHG emissions and increase productivity should be adopted more than those which only reduce emissions. The various management practices were analyzed in order to investigate the potential mitigation strategies which were recommended for the study area with the view to reduce the GHG emissions. The sixteen (16) farms, with the same climate of temperate and representing various soil conditions in Tshiame Ward (See Figure 3.3) were used to provide the basis for evaluating mitigation for potential options to reduce GHG emissions from agriculture. The data on farming methods of the study such as manure management and feeding systems were used in ALU software for evaluation purposes. For each of these farms, the baseline 83 established for active management systems on the farms were compared against the mitigation scenarios or proposed changes. The mitigated emissions were obtained by calculating the difference between the actual and the mitigation results from ALU. All management practice scenarios, regardless of location, were designed with some assumptions to support the same number and type of animal that were used to allow comparisons among the management practices. The recommendations proposed were based on the estimated emissions resulting from various management strategies employed. The management systems which reduced GHG emissions, which are profitable to farming, and were environmentally friendly, which also promoted sustainability were recommended as the potential management practices for the study. Table 3.8 various manure management systems and feeding systems that were evaluated for the study Designation Description Manure storage Aerobic treatment The biological oxidation of manure collected as a liquid with either forced or natural aeration. Anaerobic Digester Animal excreta with or without straw are collected and anaerobically digested in a large containment vessel or covered lagoon. Anaerobic lagoon A type of liquid storage system designed and operated to combine waste stabilization and storage. Burned for fuel The dung and urine are excreted on fields. The sun dried dung cakes are burned for fuel. Cattle/swine deep litter < 1 month As manure accumulates, bedding is continually added to absorb moisture over a production cycle and possibly for less than a month. Cattle/swine deep litter > 1 month As manure accumulates, bedding is continually added to absorb moisture over a production cycle and possibly for more than a month. Compost extensive Composting in windrows with infrequent turning for mixing and aeration. Compost intensive Composting in windrows with regular (at least daily) turning for mixing and aeration. Daily Spread Manure is routinely removed from a confinement facility and is applied to cropland or pasture within 24 hours of excretion. Dry lot A paved or unpaved open confinement area without any significant vegetative cover where accumulating manure may be removed periodically. Liquid/slurry Manure is stored as excreted or with some minimal addition of water in either tanks or earthen ponds outside the animal housing, usually for periods less than one year. Open pit storage < 1 month Collection and storage of manure usually with little or no added water typically below a slatted floor in an enclosed animal confinement facility, usually for periods less than one year. Open pit storage > 1 month Collection and storage of manure usually with little or no added water typically below a slatted floor in an enclosed animal confinement facility, usually for periods more than one year. Pasture/Range/Paddock The manure from pasture and range grazing animals is allowed to lie as deposited, and is not managed. Solid storage The storage of manure, typically for a period of several months, in unconfined piles or stacks. Feeding situation Large area grazing Animals are confined to a small area (i.e. tethered, pen, barn) with the result that they expend very little or no energy to acquire feed. Pasture Animals are confined in areas with sufficient forage requiring modest energy expense to acquire feed. Stall Animals are confined to a small area (i.e. tethered, pen, barn) with the result that they expend very little or no energy to acquire feed. 84 CHAPTER 4: RESULTS AND DISCUSSION 4.1 Agricultural emissions and emission factors This chapter presents the GHG emissions emitted by agricultural management practices at farm level in the Tshiame Ward of the eastern Free State, South Africa, over the period of five (5) years (2010 – 2014). Emission factors and emissions are estimated from each agriculture source of GHG emission from the farms and the potential mitigation options are evaluated. Trends of emissions are outlined and comparisons of emissions per farm are made, as well as with existing literature. Uncertainties associated with the emissions are also presented per agricultural source. Emission factors are influenced by farm activity data which contributed to the total farm emissions. 4.1.1 Enteric fermentation Enteric fermentation CH4 emission factors The results of the enteric CH4 EFs estimates by cattle livestock category are shown in Tables 4.1 and 4.2, respectively. The calculated gross energy intake (GEI) also varied per animal sub- category per farm (See Appendix B, Table 1), and this is the reason for variations in the calculated EFs among the farms. For example, the dairy mature females from Farm 1 had the highest EF, with 105 kg/head/year as compared to other farms, while mature females from Farm 5 and 6 had the lowest EFs of 71 kg/head/year. Dairy mature females at Farms 1 and 2 consumed the highest daily GEI of about 246 and 255 MJ/head and this explains the high EF at Farm 1, while, Farm 6 had the lowest, about 166 MJ/head/day. Dairy mature females consumed the highest GEI compared to other subcategories at all farms and this is due to their requirements for more energy needed for lactating or milk production. The lower EFs 85 for other animal subcategories were influenced by the consumed lower daily GEI (See Appendix B, Table 1). The EFs for dairy calves were more dispersed than all other animal subcategories with more Coefficient of Variation (CV) as shown in Table 4.1. The large variations by calves might be due to the rapid rate of growth at some farms and also might have been caused by the differences in GEI consumed per animal subcategory. Table 4.1 below represents the enteric CH4 EFs calculated for all dairy cattle. Table 4.1 Enteric CH4 emission factors for dairy cattle Lvstk categ Mature female(D) Heifers(D) Mature Bulls(D) Calves(D) Calves(D) Young bulls (D) LvstkSub Mature Females Young Females - Mature Bulls Young Females - Young Intact Males - Young intact males Age 1-2 Age 0-1 Age 0-1 Age 1-2 Units (kgCH4/head/year) (kg CH4/head/year) (kg CH4/head/year) (kg CH4/head/year) (kg CH4/head/year) (kg CH4/head/year) F1 105 43 87 22 25 55 F2 109 41 71 14 15 51 F3 89 56 70 18 20 60 F4 92 43 63 20 23 - F5 71 41 70 18 21 43 F6 71 33 70 14 20 - F7 72 49 96 10 15 51 F8 74 41 91 16 12 45 F9 89 48 68 18 19 - F10 73 50 70 13 15 - Standard 12 6 11 3 4 6 deviation(SD) Mean 84 44 76 17 19 51 Coefficient 15 13 14 20 22 11 variation(CV) - Represents the unavailability of other animal sub-categories during certain periods The higher enteric CH4 EFs for beef animal subcategories at Farms were also due to higher GEI consumed (See Appendix B, Table 2). The lower variations in EFs by beef cattle subcategories might be due to the same feeding strategies at all Farms. The higher GEI noted among beef MFs also led to higher EFs. The calculated EFs for dairy and beef animal subcategories have shown a proportional relationship with the consumed GEI per animal subcategory and this shows that the calculated EFs were strongly influenced by the GEI consumed. 86 The beef cattle subcategories such as mature females and bulls for all farms had higher average EFs compared to the dairy cattle subcategories, while the beef heifers, young bulls and calves had the EFs which are lower than that of dairy (Table 4.1 and 4.2). The higher EFs recorded among beef cattle subcategories are explained by the feed intake, as well as the GEI, which included 100% of pasture-based feeding system. In contrast, dairy cattle were fed 30% total mixed ration (TMR)- based and 70% pasture – based feeding systems at all farms and this might have caused a slightly differences in the calculated EFs between dairy and beef cattle. Therefore, the pasture based production system had higher EFs than TMR-based production system, as shown in Tables 4.1 and 4.2. This can be explained by the lower digestibility of pasture - based diets, as well as the high intakes attained by animals feeding on pasture-based diets. The beef mature females based on 100% pasture feeding system had the highest CH4 EFs, ranging from 95-109 kg/head/year with the average of 103 kg/head/year, while the dairy mature females based on TMR had the CH4 EFs ranging from 71-105 kg/head/year per farm with an average of 84 kg/head/year. These results are similar to those from a study by Stewart et al. (2009) where pasture feeding cattle were shown to have higher gross energy intake (GEI) which led to higher EFs as compared to confined cattle. However, this was different from the study by Du Toit et al. (2013a) since cattle production systems based on concentrate feeds (TMR-based) had higher EFs than pasture-based production systems. The EFs calculated for MF lactating dairy cattle in this study were comparable with the IPCC default values for North America with 128 kg/head/year, Western Europe with 117 kg/head/year and Oceania with 90 Kg/head/year (IPCC, 2006). The calculated enteric CH4 EFs for MF beef cattle in this study were higher (103 Kg/head/year) than dairy cattle EFs in other 87 developing countries such as Brazil and India, with 62 and 36 Kg/head/year respectively as reported by Chhabra et al., (2009). The IPCC (2006) reported enteric CH4 emission default factor for Africa as 46 kg/head/year for dairy cattle and this was also lower than the results found in this study. This also differed from the findings by Du Toit et al., (2013a), where enteric CH4 the EF of 130 Kg/head/year for lactating animals was recorded. Lactating MF CH4 EFs for South Africa by Du Toit et al., (2013a) were consistent with the results from Tshiame farms since both are higher than the IPCC default EFs and other developing countries. This implies that lactating MF cattle EFs for enteric CH4 for Tshiame Ward are higher across all cattle sub- categories, when compared with other developing countries. This is due to the use of country- specific activity data with relatively higher productivity and higher weights than values used to derive the default values. In addition, results from research by Du Toit et al. (2013a) and Otter et al. (2010) are comparable with the results from this study, since the mature cows and bulls had the highest CH4 EFs for enteric fermentation. Table 4.2 represent the EFs for beef cattle per subcategory per farm. 88 Table 4.2 Methane enteric fermentation emission factors for beef cattle Lvstk categ Mature female(B) Heifers(B) Mature Bulls(B) Calves(B) Calves(B) Young bulls (B) LvstkSub Mature Females Young Females - Mature Bulls Young Females - Young Intact Young intact Age 1-2 Age 0-1 Males - Age 0-1 males Age 1-2 Units (kgCH4/head/year) (kgCH4/head/year) (kgCH4/head/year) (kgCH4/head/year) (kgCH4/head/year) (kgCH4/head/year) F1 109 52 86 17 21 38 F2 - - - - - - F3 95 37 70 17 20 - F4 102 40 78 17 24 58 F5 - - - - - - F6 101 - - - - - F7 107 52 89 13 15 60 F8 - 44 - 8 10 - F9 - 39 75 - - 42 F10 - - - - - - Standard 5 6 7 3 5 10 deviation(SD) Mean 103 44 80 15 18 50 Coefficient 5 14 9 24 28 19 variation(CV) - Represents the unavailability of other animal sub-categories during certain periods The enteric CH4 EFs calculated for sheep livestock category varied per animal subcategory and were calculated at Farm 1 only. Ewes recorded the enteric CH4 EF of 7 kg CH4/head/year, young females (1-2 years) 0.86 kg CH4/head/year, rams with about 9 kg CH4/head/year and lambs had calculated the enteric CH4 EF of about 0.22 kg CH4/head/year. However, these were higher than the IPCC default value of 5 kg/head/year for developing countries (IPCC, 1996). Du Toit et al. (2013b) reported that the enteric CH4 EF for communal sheep was 6.1 kg/head/year which was comparable to the sheep EFs recorded in this study. However, the high EF registered by ewes was due to high daily GEI of 64 MJ/animal/day consumed which was higher than other animal subcategories. The sheep category were based on 100% grazing on hilly pasture feeding system. 89 Total CH4 enteric fermentation emissions The annual total farm CH4 emissions from enteric fermentation per farm ranged from 7056 to 339801 Kg CO2eq over a period of 5 years (Table 4.3). The production of CH4 by enteric fermentation included emissions from ruminant (dairy, beef cattle, and sheep) and non- ruminant animals (pigs, goats, and horses). The results have shown that the ruminant animals in this study emitted more enteric CH4 emissions than non-ruminant animals per farm and this is due to variations in their digestive systems. The total farm CH4 emission results show less variability across the years per farm as presented on Table 4.3 below. This might be due to less differences in animal populations and feeding systems within a farm across the years. Higher enteric CH4 emissions by Farm 11 were due to the higher population of ruminant animals at the farm compared to other farms. The lactating beef and dairy mature female cattle at all farms were responsible for higher enteric CH4 emissions. Higher CH4 emissions by lactating beef and dairy mature females were due to higher calculated EFs as shown in Table 4.1 and Table 4.2 above. Table 4.3 below represent the total farm CH4 emissions from enteric fermentation by ruminant and non-ruminant animals. 90 Table 4.3 Total Methane enteric emissions per farm from 2010 to 2014 Year 2010 2011 2012 2013 2014 Standard Mean Coefficient deviation variation(CV) Farm E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- % eq)/Year eq)/Year eq)/Year eq)/Year eq)/Year eq)/Year eq)/Year F1 189567 181545 197652 203427 164199 13705 187278 0,07 F2 86919 91665 115731 285642 269094 88517 169810 0,52 F3 88452 143871 175875 59325 68733 45131 107251 0,42 F4 73668 65394 64638 66192 91245 10045 72227 0,14 F5 55104 38451 70434 67662 42798 12816 54890 0,23 F6 31206 35154 25011 33243 37548 4262 32432 0,13 F7 101640 111468 103383 106428 105588 3337 105701 0,03 F8 40488 45549 31458 33117 37086 5091 37540 0,14 F9 139566 100590 43932 288603 68166 86401 128171 0,67 F10 27258 103467 19047 24990 21462 32235 39245 0,82 F11 313404 322224 335748 339801 154560 69934 293147 0,24 F12 48069 38724 21609 21672 16212 12081 29257 0,41 F13 73605 115899 129276 180243 211596 48636 142124 0,34 F14 144207 138642 49140 76251 75726 37778 96793 0,39 F15 19299 22155 7056 31563 23982 7987 20811 0,38 F16 161427 93744 73668 111741 151893 33545 118495 0,28 Enteric CH4 emissions from the sheep livestock category was the second highest emitter after the cattle livestock category, and this sequence was the same at all farms (See Appendix B, Table 6). The total enteric CH4 emissions from goats and horses were produced from Farm 13 and Farm 16 respectively, and they were less than those of other livestock categories (See Appendix B, Table 7). The swine livestock category also produced enteric CH4 emissions (See Appendix B, Table 8). CH4 emissions by ruminant animals were significantly higher than those produced by non- ruminant animals and this was the case for all farms in Tshiame Ward. Dairy cattle at all farms were the largest emitters in all years with about 60-67% of total farm enteric CH4 emissions. Beef cattle were the second largest emitter with about 30-39% of the total farm enteric CH4 produced over the period of five years at all farms. The sheep category was the third emitter 91 with 1-3% of the total farm enteric CH4, while the non-ruminant animals emitted the least (less than 1 percent) at all farms. Just like the results from the study by Du Toit et al. (2013a), the high amount of emissions from enteric fermentation were from ruminants’ animals such as dairy and beef cattle at all farms in this study. In studies undertaken by Du Toit et al. (2013a) and O’ mara (2011), enteric CH4 emissions were closely related to ruminant numbers. This has been confirmed by the results of this study. The lower amount of enteric CH4 produced by non-ruminant livestock is due to the lower animal populations per farm. This is similar to findings from most studies, for example, in a study by Stevens and Hume, (1995), and another by Wang and Huang, (2005), it was found that ruminant animals contribute higher CH4 emissions than non-ruminant animals. It was also emphasized in the study by Du Toit et al. (2013a) that the non-ruminant sector is a minor GHG contributor compared with ruminant CH4 emissions. Uncertainty analysis The uncertainty analysis was done for the estimated CH4 emissions from enteric fermentation. The quantitative analysis for this source category and the subsequent categories was done using the ALU software. The analysis was carried out at 95% confidence interval as recommended by IPCC (IPCC, 1996). Uncertainty was determined for each of the activity data entered into the software based on the overall collected dataset and the understanding of associated bias. The process started at animal annual population data up to the determination of emission factors. The results showed the uncertainty ranging from 8.44 to 18.82% below and above estimated values and this varied per farm per year as shown in Tables 4.4, 4.5 and in Appendix C, Table 1- Table 8. 92 Table 4.4 Uncertainty for CH4 emissions by non-dairy cattle Farms 2010 estimate Uncertainty range and percentage (Kg CO2eq) Lower bound Upper bound Uncertainty percentage Farm 1 47443 43438 51447 8.44 Farm 2 18374 15683 21066 14.65 Farm 3 87244 76129 98359 12.74 Farm 4 57880 49256 66504 14.9 Farm 5 34997 29005 40988 17.12 Farm 6 15684 13930 17437 11.18 Farm 7 77420 68447 86393 11.59 Farm 8 12338 10518 14158 14.75 Farm 9 44317 36614 52019 17.38 Farm 10 9582 8251 10913 13.89 Farm 11 191320 166123 216517 13.17 Farm 12 14864 13146 16582 11.56 Farm 13 89918 76763 103073 14.63 Farm 14 37179 32558 41800 12.43 Farm 15 20853 16929 24778 18.82 Farm 16 45051 40415 49686 10.29 Table 4.5 Uncertainty for CH4 emissions by dairy cows Farms 2010 estimate Uncertainty range and percentage (Kg CO2eq) Lower bound Upper bound Uncertainty percentage Farm 1 105518 85998 125039 18.50 Farm 2 45707 37224 54190 18.56 Farm 3 - - - - Farm 4 23244 19011 27476 18.21 Farm 5 17843 14397 21288 19.31 Farm 6 14841 11955 17728 19.45 Farm 7 37551 30191 44911 19.60 Farm 8 29526 23653 35398 19.89 Farm 9 108897 88054 129740 19.14 Farm 10 15308 12381 18235 19.12 Farm 11 83449 68053 98845 18.45 Farm 12 37652 31790 43515 15.57 Farm 13 - - - - Farm 14 118271 95586 140955 19.18 Farm 15 - - - - Farm 16 134086 108958 159213 18.74 - Represents the unavailability of other animal sub-categories during certain periods 93 Quality control and quality assurance Quality control of activity data was done while populating the Agriculture and Land Use (ALU) software. Various checks like consistency were done to ensure that data was correct and there were no errors. Quality assurance of the activity data was done by the supervisor. The main quality control measures were centred on the activity data and emission factors obtained. Activity data checks included:  Animal population data was discussed with the supervisor and the checks that the data was entered correctly was performed by the supervisor.  All activity was quality controlled through the utilization of ALU QA/QC functionality. This function was operated after populating the database. Emission factors and emissions QC included the following activities:  Emission factors obtained in all the animal sub-categories were checked against the IPCC recommended default emission factors and their corresponding activity data and reasons for disparity had to be documented.  Emission factors were also compared with emission factors obtained from the literature for consistencies and the explanations for any deviations were also documented.  The utilization of updated global warming potentials from the fifth assessment report for different GHG emissions was performed outside ALU software. 94 4.1.2 Manure management systems Methane emissions from animal manure depended on the manure management system (MMS) applied as well as the conditions and the manner in which the system operates. Manure management systems employed in the study included cattle/swine deep litter >1 month (CSDL), manure left on pasture during grazing, manure stored in an open pit, dry lot spread and feeding anaerobic digester. As a result, the employed MMS remained the same at all farms for the period of 5 years as shown in Tables 4.6, 4.7, 4.8, and 4.9. Table 4.6 Manure management systems for different animal categories in percentages (applicable for all farms except farm 1, 2 and 14) Livestock category Sub-category Cattle/Swine Pasture (%) Anaerobic Deep Litter < 1 digester (%) Month (CSDL) (%) Dairy cattle Lactating cows 75 20 5 Non-lactating dairy cattle 80 20 0 Beef cattle All Beef 80 20 0 Sheep All sheep 80 20 0 Goats All goats 80 20 0 Horses All horses 80 20 0 Pigs All pigs 80 20 0 95 Table 4.7 Manure management system for different animal categories in percentages (applicable for farm 1 only) Livestock category Sub-category Cattle/Swine Drylot Pasture (%) Deep Litter < 1 spread (%) Month (CSDL) (%) Dairy cattle Lactating cows 75 5 20 Non-lactating dairy cattle 75 5 20 Beef cattle All Beef 75 5 20 Sheep All sheep 75 5 20 Table 4.8 Manure management system for different animal categories in percentages (applicable for farm 2 only) Livestock category Sub-category Cattle/Swine Pasture (%) Anaerobic Composting - Deep Litter < 1 digester (%) static pile (%) Month (CSDL) (%) Dairy cattle Lactating cows 30 60 10 Non-lactating dairy cattle 0 100 0 Sheep All sheep 0 80 0 20 Pigs All pigs 80 0 20 96 Table 4.9 Manure management system for different animal categories in percentages (applicable for farm 14 only) Livestock category Sub-category Cattle/Swine Pasture (%) Open pit Deep Litter < 1 storage (%) Month (CSDL) (%) Dairy cattle Lactating cows 75 20 5 Non-lactating dairy cattle 80 20 0 Beef cattle All Beef 80 20 0 All sheep 80 20 0 Manure Methane Manure Methane emission factors Manure CH4 EFs were dependent on the methane conversion factor (MCF) which also differed for each MMS per livestock category and the livestock differed for each animal subcategory per farm. The MCF values applied were those of the IPCC default with pasture (0.015), CSDL (0.45), dry lot spread (0.015), open pit storage > 1 month (0.45) and anaerobic digester (1). The EFs for MMSs varied per animal subcategories. In summary, manure CH4 EFs for beef category were consistent with the dairy category at all animal subcategories as shown in Table 4.10 and 4.11 below. The CV per animal subcategories across the farms were less due to similar MMS used within the farms. 97 Table 4.10 Emission factors for manure management systems per farm (Dairy cattle) Lvstk categ Mature Heifers(D) Bulls(D) Calves(D) Calves(D) Bulls (D) Females(D) LvstkSub Mature Females Young Females - Mature Bulls Young Females - Young Intact Young bulls Age 1-2 Age 0-1 Males - Age 0-1 Units Kg Kg Kg Kg Kg Kg CH4/animal/year CH4/animal/year CH4/animal/year CH4/animal/year CH4/animal/year CH4/animal/year F1 0.8 0.6 1 0.3 0.4 0.6 F2 2.2 1 - 0.5 0.5 0.8 F3 - - 1 - - 0.8 F4 1.1 0.6 0.9 0.3 0.3 - F5 0.8 0.6 1 0.2 0.3 0.8 F6 0.8 0.5 1 0.3 0.3 - F7 0.8 0.7 1 0.2 0.2 - F8 0.9 0.6 1 0.1 0.2 0.9 F9 1.1 0.7 0.9 0.2 0.3 - F10 0.9 0.7 1 0.2 - - F11 1.2 0.6 0.9 0.2 0.2 - F12 1.1 0.6 1 0.2 0.3 - F13 - - - - - - F14 1.0 0.6 1 0.1 0.2 - F15 - - - - - - F16 1.0 0.7 1 0.2 0.2 - Standard 0.3 0.2 0.1 0.1 0.1 0.1 deviation(SD) Mean 1.0 0.7 1.0 0.2 0.3 0.8 Coefficient 33 26 13 33 30 14 variation(CV) - Represents the unavailability of other animal sub-categories during certain periods 98 Table 4.11 Emission factors for manure management systems per farm (Beef cattle) Lvstk categ Mature Heifers(B) Bulls(B) Calves(B) Calves(B) Mature Oxen Bulls (B) Females(B) (B) LvstkSub Mature Young Mature Bulls Young Young Intact Mature oxen Young bulls Females Females - Age Females - Age Males - Age 0- 1-2 0-1 1 Units Kg Kg Kg Kg Kg Kg Kg CH4/animal/y CH4/animal/y CH4/animal/y CH4/animal/y CH4/animal/y CH4/animal/y CH4/animal/y ear ear ear ear ear ear ear F1 1.5 0.7 1.2 0.2 0.3 - - F2 - - - - - - 0.5 F3 1.3 0.2 1.0 0.2 0.3 - - F4 1.4 - 1.1 0.2 0.3 - - F5 - - - - - - 1.1 F6 - - - - - 0.7 - F7 1.5 0.7 1.2 0.2 0.2 - - F8 - 0.6 - 0.1 - - 0.8 F9 - 0.5 1.2 - - - - F10 - - - - - - 0.6 F11 1.2 0.5 0.9 0.2 0.2 - - F12 - - - - - - - F13 1.4 0.7 1.2 0.2 0.2 - - F14 - - - - - - - F15 1.4 - - - - - - F16 1.4 - - 0.1 0.1 - - Standard 0.10 0.16 0.12 0.05 0.06 0 0.22 deviation(S D) Mean 1.4 0.6 1.1 0.2 0.2 0.7 0.8 Coefficient 7 27 11 25 26 0 29 variation(C V) - Represents the unavailability of other animal sub-categories during certain periods The lactating mature females had the highest manure CH4 EFs across all farms in this study. Similarly, lactating animals had the highest manure CH4 EFs due to different diets and MMS as compared to other animal sub-categories (Du Toit, 2013a). In addition, the study shows relatively lower manure CH4 EFs for mature female dairy cow as compared to the results found by Moeletsi and Tongwane (2015) with about 40.98 kg/year. This difference might be due to the different MMSs employed. However, the results are consistent with the IPCC (2006) manure CH4 EFs. The IPCC (2006) reported manure CH4 emission default factors for Africa to be around 1 kg/head/year. Manure CH4 EFs by sheep differed per MMS per farm and this included manure left on pasture/range/paddock and composting - static pile. Ewes had calculated the manure CH4 EFs 99 of about 0.29 and 0.44 kg/animal/day for manure left on pasture/range/paddock and composting - static pile respectively. The manure CH4 EFs for heifers were between 0.01 and 0.016, rams between 0.16 and 0.26, and female lambs (0.16 and 0.24), male lambs (0.15 and 0.23). The manure CH4 EFs were dependent on the volatile solid (VS), the biodegradability of manure (Bo), the methane conversion factors (MCFs) and the MMS % per farm. The VS for dairy mature females ranged from 2.05 (by Farm 5) to 4.62 kg dry solid/head/day (by Farm 15). Dairy heifers had the VS ranging from 1.01 (Farm 16 and Farm 4 each) to 4.26 kg dry solid/head/day (Farm 10). Dairy bulls ranged from 1.27 to 3.85 kg dry solid/head/day, with Farm 4 as the lowest and Farm 1 the highest. Dairy calves ranged from 0.82 to 2.35 kg dry solid/head/day, with Farm 12 as the lowest and Farm 2 as the highest. However, the beef mature females had the VS ranging from 2.71 to 7.36 kg dry solid/head/day with Farm 16 as the lowest and Farm 9 as the highest. Farm 16 had the lowest VS of about 0.64 and Farm 3 had 2.58 kg dry solid/head/day VS for heifers. Farm 13 had the lowest VS (about 1.19) and Farm 3 had the highest (3.46 kg dry solid/head/day). The young bulls had 1-2 kg dry solid/head/day, while the calves had the lowest VS of 0.64 and Farm 1 had the highest of 1.19 kg dry solid/head/day. Manure methane emissions Over the five year period, the farm total manure CH4 emissions ranged from 1890 to 446040 kg CO2eq/year at all farms (Table 4.12). The CV across the years per farm were less due to similar MMS used within the farms across the years. This might also be due to similar farm animal populations feeding systems. 100 Table 4.12 Annual farm manure methane emissions Year 2010 2011 2012 2013 2014 Standard Mean Coefficient deviation variation(CV) Farms E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- % eq)/Year eq)/Year eq)/Year eq)/Year eq)/Year eq)/Year eq)/Year F1 22260 56490 65520 67410 33810 17965 49098 0,37 F2 8820 9030 14280 63210 31920 20681 25452 0,81 F3 51030 84420 446040 21420 24990 161811 125580 1,29 F4 38010 32970 39690 33810 49980 6087 38892 0,16 F5 26040 12810 24570 15960 15120 5351 18900 0,28 F6 13860 10710 8820 11760 13860 1927 11802 0,16 F7 49560 53340 50400 55440 95760 17556 60900 0,29 F8 17430 16380 11130 12810 15330 2322 14616 0,16 F9 59850 36330 15960 122220 25830 38005 52038 0,73 F10 12600 37800 6510 9030 7140 11784 14616 0,81 F11 165690 158760 158970 32797 52290 58480 113701 0,51 F12 20580 13860 7560 7560 5250 5598 10962 0,51 F13 35490 67830 75810 105630 123690 30654 81690 0,38 F14 61320 5670 1890 2730 2730 23261 14868 1,56 F15 11130 13020 4200 18690 14280 4738 12264 0,39 F16 68250 38010 28770 44730 40530 13179 44058 0,30 The CSDL MMS had contributed the highest to the total farm manure CH4 emissions compared to the other MMSs followed by the CH4 from manure left on pasture during grazing and this resulted at all farms. High amount of manure were stored as cattle/Swine Deep Litter < 1 Month system at all farms in the study. Although the open pit storage and dry lot MMSs were not a usual practice in Tshiame farms, manure stored in an open pit contributed to fewer emissions from Farm 14, with 1% of the total manure CH4 emissions during the year 2010 and 2011. The amount of manure treated as dry lot spread and anaerobic digester were less. Dairy manure contributed the highest followed by beef manure. Manure CH4 by swine livestock category were produced from Farm 2, 6, 7, 9, 13, 15, and 16 and this increased the total farm manure CH4 emissions in those farms. Manure from swine livestock category were managed as cattle/Swine Deep Litter < 1 Month and by its nature it contribute to the 101 production of CH4 emissions. Goat livestock category produced less manure CH4 emissions to the total farm at Farm 13 only and the horse subcategory also produced less to the total farm manure CH4 emissions at farm 16 only. The results of this study, show that the liquid manure produced high CH4 emissions and this was due to the nature of liquid manure management which influenced more CH4 emissions (Du Toit, 2013a; Moeletsi and Tongwane, 2015). In addition, the MMS which are based on slurry, and CSDL are sensitive to temperature variations and have higher MCFs which lead to high EFs to produce more CH4 emissions (Monteny et al., 2001; Smith et al., 2007 and 2008; Du Toit, 2013a; Moeletsi and Tongwane, 2015). Cattle owners grazed their livestock on pastureland. However, the cattle were confined overnight in a cattle pen (kraal) and it is known that more manure is produced during the night. The results show that most of the manure produced on farms in Tshiame Ward are left in the kraal and managed as CSDL, and this contributes the most with approximately 98% of the farm totals of manure CH4 emissions in 2010, 2011 and 2014 and 99% in the year 2012 and 2013. This observation is consistent with the findings in a study by Sappo (2011), where it was found that a high percentage of manure in South Africa was managed as liquid, contributing to 93.5 % of CH4 emissions. Manure nitrous oxide Manure nitrous oxide emission factors Manure N2O EFs for livestock varied per MMS and were the same at all farms and this included the IPCC default value of 0.01 kg /head/year for manure managed as CSDL and for manure managed as dry lot and pasture (0.02 kg/head/year). However, manure used for feeding 102 anaerobic digester, as well as the manure stored in an open pit contributed to the N2O EF of 0.001 kg/head/year. The default EFs used in this study were consistent with the manure EFs used by Tubiello et al., (2013). Contrary to findings from this study, Amon et al. (2001) found that N2O EFs for manure deposited directly on pasture were greater than that for manure kept in storage and this was due to the aeration in pasture-deposited manure. In a study by Zhang and Han (2008), N2O EFs for manure deposited on pasture in China was 0.35% while EF for pasture for Netherlands was 0.17 (Schils et al., 2008). In another study by IPCC (2006) the New Zealand country had the specific EF values for urine and dung of 0.01 and 0.0025, respectively to estimate direct N2O emissions from manure. This was different from results of this study since the EF values for urine and excreta used were combined. However, the N2O EFs for this study were similar with the EFs in the study by IPCC (2006), wherein the EFs were the same for farms located at medium and high slope. The EF for pasture in Neitherlands was 0.29% (Schils et al., 2008), whereas, the EF for pasture in the Amozon in Brazil was 2.80% (Neli et al., 2005). The less N2O EFs from manure in this study might be due to the lower quality of feeding. Jungbluth et al. (2001) cited results from Amon et al. (2001) which, when recalculated using IPCC defaults for Nex, suggest an emissions factor of 0.14 percent. Manure nitrous oxide emissions The total farm N2O emissions from manure management ranged from 1240 to 65100 kg CO2eq/year, as shown in Table 4.13. The CV in manure CH4 emissions were less across the years per farm and the less variability were due to similar MMS used at per farm in all years. This might also be due to the comparable farm animal populations and feeding systems across the years. 103 Table 4.13 Total manure nitrous oxide emissions per farm Year 2010 2011 2012 2013 2014 Standard Mean Coefficient deviation variation(CV) Farm E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- % eq)/Year eq)/Year eq)/Year eq)/Year eq)/Year eq)/Year eq)/Year F1 9610 31310 33170 32860 20150 9250 25420 0,36 F2 3720 4960 8680 18600 50220 17300 17236 1,00 F3 14880 23250 32240 12400 47740 12866 26102 0,49 F4 15190 14260 11780 22630 53320 15374 23436 0,66 F5 11780 11470 26040 13950 18290 5443 16306 0,33 F6 8370 10540 8370 11160 15810 2724 10850 0,25 F7 25730 25110 22940 24490 34410 4045 26536 0,15 F8 12090 14880 14880 10850 12710 1586 13082 0,12 F9 35960 30690 13020 16740 21390 8566 23560 0,36 F10 6820 6510 6510 7750 9300 1063 7378 0,14 F11 56110 56110 65100 32860 11943 52545 0,23 F12 12400 10540 6200 6200 4340 3022 7936 0,38 F13 14880 20460 22630 31620 40920 9163 26102 0,35 F14 32860 33170 10850 17050 17050 9120 22196 0,41 F15 3100 4030 1240 6200 6200 1897 4154 0,46 F16 45880 28520 23250 31620 31620 7503 32178 0,23 Cattle/swine deep litter > 1 month system contributed the highest with 80% of the total N2O emissions in all years while dry lot and manure left on pasture contributed minimally to the total farm N2O emissions with 20 percent only from cattle livestock. Manure N2O emissions from the non- ruminants’ animals did not have any effect on total farm N2O production due to the lower quantity of the manure owing to the lower population number of animals. Manure stored in an open pit and anaerobic digester MMSs did not contribute to N2O emissions also due to less amount of manure. High manure N2O emissions resulted from the CSDL MMS and followed by manure left on pasture at all farms. Manure left on pasture did not have much effect on N2O emissions since most of the manure were managed as CSDL at all farms. This was different from other studies since manure stored in solid form usually produces more N2O emissions than manure stored 104 in liquid form (Chadwick et al., 2000; Sherlock et al., 2002; Fangueiro et al., 2008, 2010; Singurindy et al., 2009; Smith et al., 2008). N2O emissions from manure management were related to cattle population data; including nitrogen excretion rate and annual N excretion per head of cattle. The Nrate was 60 for dairy mature females and 40 for all other dairy subcategories and all beef cattle (IPCC, 2006). N2O is produced by nitrification and denitrification and the rate of both these processes increases with increasing temperature (Sommer, 2000). N2O emissions from slurry systems were assumed to be zero as a result of the anaerobic conditions (Monteny et al., 2001). Uncertainty analysis To estimate the uncertainty analysis for N2O and CH4 emissions from manure management, the level of uncertainty was determined at 95% confidence interval for each of the activity data and where possible expert opinion on observed variation was utilized for qualitative data such as MMSs. This was done in accordance with the IPCC recommendations (IPCC, 2006). The process was done for all the activity data and ALU already had predefined uncertainty ranges for all the default IPCC values imbedded in the software. The uncertainty results produced were ranging from 18.22% to 35.72% for CH4 and 33.77% to 92.61% for N2O from manure management respectively. High uncertainties for CH4 manure management is attributed to high error estimate attached to MMSs and utilization of default values which carries uncertainties exceeding 50%. In estimating N2O, most of the data utilized for estimating EFs were obtained from the IPCC default values hence the extremely high error estimate. The uncertainty results are shown in Tables 4.14, 4.15 and also in the Appendix C, Table 9 – Table 26. 105 Table 4.14 Uncertainty for manure CH4 emissions by non-dairy Farms 2010 estimate Uncertainty range and percentage (Kg CO2eq) Lower bound Upper bound Uncertainty percentage Farm 1 672 528 815 21.34 Farm 2 538 434 642 19.35 Farm 3 1759 1394 2124 20.75 Farm 4 801 655 947 18.22 Farm 5 484 388 581 20 Farm 6 217 169 265 22.03 Farm 7 1130 897 1364 20.67 Farm 8 171 136 206 20.65 Farm 9 614 491 736 20 Farm 10 133 107 158 19.36 Farm 11 2649 2013 3284 23.98 Farm 12 206 157 254 23.58 Farm 13 1245 923 1567 25.87 Farm 14 515 390 640 24.25 Farm 15 289 227 350 21.36 Farm 16 783 611 956 22.05 Table 4.15 Uncertainty for N2O emissions from manure management by non-dairy for 2010 Farms 2010 estimate Uncertainty range and percentage (Kg CO2eq) Lower bound Upper bound Uncertainty percentage Farm 1 64839 40913 88765 36.9 Farm 2 309 113 505 63.42 Farm 3 17095 7356 26834 56.97 Farm 4 15575 5484 25666 64.79 Farm 5 9417 2784 16051 70.44 Farm 6 4220 2185 6256 48.23 Farm 7 21672 10596 32749 51.11 Farm 8 6640 2471 10809 62.79 Farm 9 11925 3983 19868 66.6 Farm 10 2579 1024 4133 60.29 Farm 11 51483 20465 82502 60.25 Farm 12 4000 1905 6094 52.36 Farm 13 24196 8733 39660 63.91 Farm 14 10005 4451 15558 55.51 Farm 15 9085 1279 16891 85.92 Farm 16 15226 8458 21994 44.45 106 Quality control and quality assurance To ensure that collected data on manure management was understood, training of the data collection was done most specifically by the supervisors to gain knowledge on the differences between the livestock MMSs. Understanding of these systems had ensured that when asking farmers, they probe intelligently. MMSs collected were then compared with the previous findings and where possible individual farmers were also contacted to verify their choice of MMSs. The data from the survey was complemented by reports from the experts with different animal commodities. Quality control and quality assurance on activity data outlined in the previous section (Enteric CH4) also apply to this source. 4.1.3 Non-CO2 biomass burning emissions Biomass burning emission factors and emissions Biomass burning in agriculture at farms comprises the burning of grassland residues, including the burning of both living and dead grasses (Smith et al., 2007). Between 2010 and 2014, CH4 emission ratio for both life and dead biomass at all farms resulted into a similar factor value of 0.004 (tonnes CH4-C/tonnes) which is equivalent to 4 kg-1 dry fuel. However, the N2O emission ratio for both live and dead biomass were also similar by 0.007 (tonnes N2O- N/tonnes) in all years at all farms which is corresponding to 7 kg-1 dry fuel. A default combustion factor (Cf) value of 1 was used in this study. The amount of CH4 and N2O EFs from biomass burning were influenced by grassland data. This data included the percentage of grassland burnt on site which is the mass of fuel available for burning, grass type, and management systems (Smith et al., 2007). This also depended on the aboveground biomass 107 density and the fraction actually burned (Crutzen and Andreae, 1990; Levine et al., 1995; Smith et al., 2007). Methane and N2O EFs in this study were similar to the EFs noted in other studies in literature (Christian et al., 2004; Bertchi et al., 2003; Keene et al., 2006; Yokelson et al., 2007). The fuel consumption used in the study by Christian et al., (2004) was 5% with the calculated CH4 EF ranging from 1.21 to 17.6 Kg-1 dry fuel. However, the N2O EF estimated in the study by Bertchi et al. (2003) was 0.49 Kg-1 dry fuel. The CH4 estimated EF by Keene et al. (2006) was 11.0 Kg-1 dry fuel, whereas, Yokelson et al. (2007) estimated CH4 EF which were ranging from 1.50 to 15.7 Kg-1 dry fuel. Bertschi et al. (2003) estimated the average CH4 EF of 17.1 Kg-1 dry fuel. The farm total CH4 emissions from biomass burning ranged from 945 to 66339 kg CO2eq/year, when all farms were taken into consideration. The results of CH4 emissions from biomass burning are presented in Table 4.16 below. Taking all farms into consideration, the total farm N2O emissions from grassland biomass burning ranged from 310 to 12090 kg CO2eq as shown in Table 4.17. Emissions depended on the amount of biomass burned and the area burned (ha). The CV across the years within the farm were less and the less variability in CH4 and N2O emissions were due to slightly the same annual area burned (ha). During some years there were no grassland burning, at some farms while at Farm 2 there were no occurrence of fires in all years. It was found that grassland burning in Tshiame Ward is caused by either natural or unintended man-made fires. The percentage of grassland burnt differed per farm. Much of the large expanses of grassland fires were caused by periodic fires initiated by hunting and the sizes of the broadcast fires were often uncontrolled. The higher contributions of CH4 and N2O emissions from fires were from farms which does not implement fire mitigation strategies. In contrast, the lowest emissions occurred at farms where fire was 108 controlled, for example through fire belts or fire breaks. Fire patterns at some farms were managed by human actions such as controlled burning, back-burning and firebreaks, as reported by Yokelson et al. (2007). Table 4.16 Total methane emissions from biomass burning Year 2010 2011 2012 2013 2014 Standard Mean Coefficient deviation variation(CV) Farm E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- % eq)/Year eq)/Year eq)/Year eq)/Year eq)/Year eq)/Year eq)/Year F1 18879 - - 28329 - 4725 23604 0,2 F2 - - - - - - - - F3 45087 3066 2352 - 19979 16835 1,2 F4 7077 29757 - - - 11340 18417 0,6 F5 - - - 28812 - 0 28812 0 F6 26670 16527 21252 11802 12180 5652 17686 0,3 F7 - 47208 - 47208 - 0 47208 0 F8 - 2835 - - 0 2835 0 F9 25032 - - 25032 25074 20 25046 0 F10 66339 33054 - - - 16643 49697 0,3 F11 - - - - 4725 0 4725 0 F12 945 - - - - 0 945 0 F13 21021 4725 - 5901 - 7420 10549 0,7 F14 4725 - 38955 - - 17115 21840 0,8 F15 11802 11802 11802 11802 - 0 11802 0 F16 11802 - - - - 0 11802 0 - Represents the unavailability of grassland residue burning during certain periods 109 Table 4.17 Total nitrous oxide emissions from biomass burning Year 2010 2011 2012 2013 2014 Standard Mean Coefficient deviation variation(CV) Farm E(kgCO2- E(kgCO2- E(kgCO2- E(kgCO2- E(kgCO2- E(kgCO2- E(kgCO2- % eq)/Year eq)/Year eq)/Year eq)/Year eq)/Year eq)/Year eq)/Year F1 3410 - - 5270 - 930 4340 0,2 F2 - - - - - - - - F3 8370 - 620 310 3729 3100 1,2 F4 1240 5580 - - - 2170 3410 0,6 F5 - - - 5270 - - 5270 0,0 F6 4960 3100 4030 2170 2790 980 3410 0,3 F7 - 8680 - 8680 - - 8680 0,0 F8 - - 620 - - - 620 0,0 F9 4650 - - 4650 5890 585 5063 0,1 F10 12090 5890 - - - 3100 8990 0,3 F11 - - - - 930 - 930 0,0 F12 310 - - - - - 310 0,0 F13 3720 930 - 930 - 1315 1860 0,7 F14 930 - 7130 - - 3100 4030 0,8 F15 2170 2170 2170 2170 - - 2170 0,0 F16 2170 - - - - - 2170 0,0 - Represents the unavailability of grassland residue burning during certain periods Uncertainty analysis The causes of uncertainty from grassland biomass burning emissions arise from many sources such as the use of global or national average rates of conversion and coarse estimates of land areas converted to grassland, estimation of the area converted that is burnt as part of a management practice, mass of available fuel and combustion factors. The large uncertainty in the input data required for the calculations increased the uncertainties in the CH4 and N2O emission estimates as shown in Tables 4.18 and 4.19 below. The uncertainty results for 2011- 2014 are presented in the Appendix C, Table 27 – Table 34. 110 Table 4.18 Uncertainty for grassland biomass burning CH4 emissions Farms 2010 estimate Uncertainty range and percentage (Kg CO2/animal/year) Lower bound Upper bound Uncertainty percentage Farm 1 18879 7387 30371 60.87 Farm 2 - - - - Farm 3 45087 17643 72531 60.87 Farm 4 7077 2769 11385 60.87 Farm 5 - - - - Farm 6 26670 10436 42904 60.87 Farm 7 - - - - Farm 8 - - - - Farm 9 25032 9795 40269 60.87 Farm 10 66339 25958 106720 60.87 Farm 11 - - - - Farm 12 945 370 1520 60.87 Farm 13 21021 8226 33816 60.87 Farm 14 4725 1849 7601 60.87 Farm 15 11802 4618 18986 60.87 Farm 16 11802 4618 18986 60.87 - Represents the unavailability of grassland biomass burning during certain periods Table 4.19 Uncertainty for grassland biomass burning N2O emissions Farms 2010 estimate Uncertainty range and percentage Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 3410 1423 5397 58.26 Farm 2 - - - - Farm 3 8370 3494 13246 58.26 Farm 4 1240 518 1962 58.26 Farm 5 - - - - Farm 6 4960 2070 7850 58.26 Farm 7 - - - - Farm 8 - - - - Farm 9 4650 1941 7359 58.26 Farm 10 12090 5046 19134 58.26 Farm 11 - - - - Farm 12 310 129 491 58.26 Farm 13 3720 1553 5887 58.26 Farm 14 930 388 1472 58.26 Farm 15 2170 906 3434 58.26 Farm 16 2170 906 3434 58.26 - Represents the unavailability of grassland biomass burning during certain periods 111 Quality control and quality assurance The quality of ALU in estimating CH4 and N2O emissions from grassland biomass burning often depend on the quality of data input. Data on the annual total area burnt per farm and the mass of available fuel used were collected from farmers. The combustion factors used were from the IPCC (2006). 4.1.4 Agricultural soil management N2O emissions Direct soil N2O from manure N in pasture The IPCC default (2006) 0.02 (kg N2O-N/kg N) was used for N2O EFs by cattle manure left on pasture. The sheep animal category had the manure CH4 EFs of about 0.29 kg/head/year for ewes, 0.01 kg/head/year for young females (1-2 years), 0.16 kg/head/year for rams and 0.16 kg/head/year for lambs. However, ewes manure had the Nex rate of about 9 kg N/animal/year which was higher than all other sheep categories which had 5 kg N/animal/year each and this explained higher EFs associated with this animal category. The direct soil N2O emissions from manure left on pasture were reliant on the annual amount of animal manure nitrogen applied to soils adjusted to account for the amount that volatilises as ammonia (NH3) and nitrogen oxide (NOx). This is the fraction of animal manure N that volatilises as NH3 and NOx (kg NH3-N and NOx-N/kg of N excreted). The farm direct soil N2O emissions from the applied manure N on pasture ranged from 930 to 29450 kg CO2eq in all years at all farms. The CV across the years within the farms were less and the less variability in CH4 and N2O emissions were due to slightly the same amount of manure left on pasture as shown in Table 4.20. 112 Table 4.20 Soil nitrous oxide from Manure N in pasture Year 2010 2011 2012 2013 2014 Standard Mean Coefficient deviation variation(CV) Farm E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- % eq)/Year eq)/Year eq)/Year eq)/Year eq)/Year eq)/Year eq)/Year F1 29450 31310 32550 32550 32550 1215 31682 0,04 F2 12090 15190 24800 32860 48360 13089 26660 0,49 F3 3720 5890 8370 3410 3720 1895 5022 0,38 F4 3410 3410 2480 5580 5580 1262 4092 0,31 F5 4960 2790 6200 3720 3410 1218 4216 0,29 F6 2480 2480 1860 2790 3100 411 2542 0,16 F7 6200 6200 5580 5890 8680 1109 6510 0,17 F8 2790 3720 2480 2170 2480 533 2728 0,20 F9 8990 7750 3100 4340 4960 2196 5828 0,38 F10 1550 6820 1550 1860 1550 2080 2666 0,78 F11 14570 14570 17050 37200 8990 9727 18476 0,53 F12 3410 2480 1240 16430 1240 5793 4960 1,17 F13 4340 5270 5890 8060 10540 2227 6820 0,33 F14 8060 8990 3410 4650 4340 2209 5890 0,38 F15 930 930 310 1550 1550 464 1054 0,44 F16 11780 7130 5580 7440 8060 2060 7998 0,26 Direct soil N2O from application of organic fertilizers The organic fertilizers that were applied on the farms were estimated for the period of 5 years taking into account only the N2O from manure amendments and this was employed only on Farm 1. The IPCC default N2O EF of 0.01 kg N2O-N/kg N from manure amendment was used. The results are presented in Table 4.21 below. 113 Table 4.21 Soil nitrous oxide from Manure amendments Year 2010 2011 2012 2013 2014 Standard Mean Coefficient deviation variation(CV) Farm E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- % eq)/Year eq)/Year eq)/Year eq)/Year eq)/Year eq)/Year eq)/Year F1 1550 1581 1674 1674 1612 50 1618 0,03 The direct soil N2O from organic fertilizers were reliant on the total annual amount of animal manure nitrogen intentionally applied to soils adjusted to account for the amount that volatilises as NH3 and NOx, the total amount of animal manure produced, and the fraction of animal manure N that volatilises as NH3 and NOx (kg NH3-N and NOx-N/kg of N excreted) and the EF. Variations in total annual emissions across the years were less and there was an annual average N2O emissions of 1618 kgCO2eq/ha from organic fertilizers. N2O emissions from manure amendments depended on the amount of nitrogen manure (NM) applied, population number of animals, Nex and Nadj. The animal live weights differed per animal subcategories at all farms. The nitrogen excretion rate (Nex) also varied for animal subcategories per MMS at all farms. Direct soil N2O from synthetic fertilizers The estimation of soil N2O from synthetic fertilizers was based on the annual amount of synthetic fertiliser N applied to soils after adjusting to account for the amount that volatilises, the total amount of synthetic fertiliser applied to soil annually, the volatilised as NH3 and NOX, and also the calculated N2O fertilised EF per farm. The direct soil N2O default EF of 0.01 (kg N2O-N/kg N) for synthetic fertilizers was used. The CV across the years were less per farm and the less variability in N2O emissions were due to similar amount of fertilizers applied annually. The annual amount of synthetic fertiliser nitrogen applied to soils varied per farm, per crop, per kg, as well as per hectare. The amount of nitrogen in synthetic fertilizer applied 114 varied per fertilizer type per farm. Taking all farms into consideration, the amount of nitrogen from the synthetic fertilizer applied in percentages ranged from 6% to 22% for all years. Concisely, the total farm N2O emissions from the fertilizer application per hectare were consistent with the amount of nitrogen fertilizers applied per farm per hectare. However, the total nitrogen per kg per hectare per farm were less at all farms and this explains the less N2O produced at all farms. The results of N2O from synthetic fertilizer applied are presented in Table 4.22 below. Table 4.22 Soil nitrous oxide from application of synthetic N fertilizers Year 2010 2011 2012 2013 2014 Standard Mean Coefficient deviation variation(CV) Farm E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- % eq)/Year eq)/Year eq)/Year eq)/Year eq)/Year eq)/Year eq)/Year F1 3410 1550 1550 1550 13640 4705 4340 1,08 F2 8370 8370 8370 8370 8370 0 8370 0 F3 1860 4030 5270 3410 5270 1277 3968 0,32 F4 2790 930 1550 2480 739 1938 0,38 F5 0 1860 1860 19530 39060 15083 12462 1,21 F6 620 620 620 39060 16645 10230 1,63 F7 11160 8370 8370 1315 9300 0,14 F8 1550 1550 1550 1550 1550 0 1550 0 F9 1550 1550 1550 1550 0 1550 0 F10 3410 3410 3410 3410 15190 4712 5766 0,82 F11 4030 4030 4030 4030 2790 496 3782 0,13 F12 930 1550 1860 4030 1168 2093 0,56 F13 15190 15190 15190 15190 15190 0 15190 0 F14 3410 1550 1550 1550 1550 744 1922 0,39 F15 6200 6200 6200 4340 6200 744 5828 0,13 F16 23250 23250 3410 4960 4960 9231 11966 0,77 - Represents the unavailability of grassland biomass burning during certain periods Direct soil N2O from returned crop residues The direct N2O default EF for crop residues retained was used (0.01 tonnes N2O-N/tonnes) 10 kg N2O-N/kg) in all years at all farms. The N2O emissions from retained crop residues are presented in Table 4.23. There is a less variability across the years per farm and this could be 115 due to less quantities of crop residues retained annually. The total farm N2O emissions from residue returned to soil depended on the amount of residues returned in the field, residue to crop product mass ratio, the dry matter content of the aboveground biomass, and the nitrogen content of the aboveground biomass. This also depended on the crop residue N (Ncr) and Ncr was determined using the amount of residues retained, dry matter fraction of residue (DMF), carbon fraction (CF), and N/C ratio - crop residue and the crop type. Gomes et al (2009) observed that the biochemical composition of plant residues added to the soil was responsible for higher or lower N2O emissions from retained residues. Similarly, Liu et al. (2011) reported that wheat straw incorporation increased N2O emissions in the subsequent maize season, while the incorporation of maize straw did not influence the emissions. Results from this study are unique, since residues from maize contributed higher emissions and this is due to higher amount of maize residues retained than other crop residues. Toma & Hatano (2007) observed greater N2O emissions in plots receiving low C/N ratio residues, possibly because these residues are easily decomposable. In a no-till crop system, in southern Brazil, soil N2O emissions were lower for maize (higher C/N ratio) than for soybean (lower C/N ratio) (Escobar et al., 2010). Siqueira Neto et al. (2009) measured greater N2O emissions in areas cultivated with corn-wheat than in areas cultivated with soybean-wheat. They explained this by the high amount of N applied to the corn field, in contrast to the biological N (via microorganism fixation) used as N source by the soybean crop. Therefore, the total farm N2O emissions from retained residues depend on the amount of residues returned in the field and residue to crop product mass ratio as indicated in this study. Jantalia et al. (2008) reported that by using N-fixing in legume crops, N2O emissions were not altered in subsequent crops. In agreement with such findings, Siqueira Neto et al. (2009) 116 suggested that legume crops could be used as a N source in agricultural systems, with the advantage of decreasing N2O emissions, in comparison to N-fertilizers. An interaction between straw C/N ratio and mineral N-fertilizer addition may occur, increasing nitrification and denitrification rates and, therefore, increasing the N2O production and emission in agricultural soils. Table 4.23 Nitrous oxide from retained crop residues Year 2010 2011 2012 2013 2014 Standard Mean Coefficient deviation variation(CV) Farm E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- E(KgCO2- % eq)/Year eq)/Year eq)/Year eq)/Year eq)/Year eq)/Year eq)/Year Farm 1 - - 310 - 310 - 310 0 Farm 2 930 - - - - - 930 0 Farm 3 1550 310 - - - 620 930 0,67 Farm 4 - - - - - - - - Farm 5 - - - - - - - - Farm 6 310 310 - - - - 310 0 Farm 7 5890 5270 6510 620 - 2324 4573 0,51 Farm 8 - - - - - - - - Farm 9 310 310 310 310 310 0 Farm 10 620 620 1240 620 310 304 682 0,45 Farm 11 310 310 310 0 Farm 12 - 930 6510 310 2788 2583 1,08 Farm 13 1240 620 1240 2480 620 679 1240 0,55 Farm 14 - 620 - - - - 620 0 Farm 15 - - - - - - - - Farm 16 46810 25110 2170 2170 18555 19065 0,97 - Represents the unavailability of crop residue retained during certain periods 117 Indirect soil N2O emissions from organic and synthetic fertilizer The indirect N2O emissions from atmospheric N deposition depended on the total fertilizer nitrogen (N), the fraction of fertilizer volatilized (FNv), and indirect emission factor for volatilized N (EFv). The fraction of fertilizer volatilized and the indirect default emission factor for volatilized N were 0.1 (kg N volatilized) and 0.01 (kg N2O-N/kg N), respectively regardless of farm. However, the total fertilizer N varied slightly per farm. The indirect N2O emissions from leaching/runoff depended on the total fertilizer N, the fraction of fertilizer N leached/runoff (FNlr), the indirect emission factor for N leached/runoff (EFlr). The fraction of fertilizer N leached/runoff and indirect emission factor for N leached/runoff were 0.3 (kg N leached and runoff) and 0.007 (kg N2O-N/KG N leached), respectively. The study resulted in higher indirect N2O emissions through leaching and runoff than indirect N2O emissions through atmospheric deposition N2O emissions and this occurred at all farms. For the whole 5 year period of study, the total farm indirect soil N2O emissions ranged from 310 to 12710 kg CO2eq at all farms. The CV across the years were less per farm and this was due to slightly the same amount of leaching and runoff of the applied organic and synthetic fertilizers annually as shown in Table 4.24. 118 Table 4.24 Indirect N2O emissions by Atmospheric deposition, leaching and runoff Year 2010 2011 2012 2013 2014 Standard Mean Coefficient deviation variation(CV) Farms Kg CO2eq Kg CO2eq Kg CO2eq Kg CO2eq Kg CO2eq Kg CO2eq Kg CO2eq % F1 8680 7750 8370 8060 11160 1218 8804 0,14 F2 4650 5890 7750 9610 12400 2745 8060 0,34 F3 1240 1860 3410 1550 2480 769 2108 0,36 F4 1550 0 310 930 1860 707 930 0,76 F5 310 620 1860 6510 12710 4711 4402 1,07 F6 0 620 620 620 620 248 496 0,50 F7 4960 4030 1240 1240 4030 1556 3100 0,50 F8 930 930 930 930 930 0 930 0,00 F9 1860 1550 930 930 930 392 1240 0,32 F10 930 930 930 930 4650 1488 1674 0,89 F11 3720 3720 4030 3720 2170 662 3472 0,19 F12 930 930 620 310 310 277 620 0,45 F13 5270 5270 5270 5890 6510 496 5642 0,09 F14 2170 2170 930 930 930 607 1426 0,43 F15 1860 1860 1860 1550 2170 196 1860 0,11 F16 9300 8060 2170 3410 2790 2938 5146 0,57 Uncertainty analysis Uncertainties associated with emissions of N2O from managed soils were higher as compared to other sources of emissions. This is due to high uncertainty levels of the emission factors and activity data. Uncertainty levels of the IPCC default emission factors were used in this inventory. These high uncertainty levels are consistent with the results of Del Grosso et al. (2010) and Monni et al. (2007). High uncertainty of the emissions result from both large natural variability and lack of knowledge of emission-generating processes (Monni et al., 2007). Uptake of N2O in agricultural soils is difficult to quantify due to constraints such as instrumental precision and methodological uncertainties (Cowan et al., 2014). N2O emissions from agricultural soils are highly uncertain because they depend very much on both the exact conditions of the soil, the application of fertilizers and meteorological condition. The 119 uncertainties from this source were higher and are presented in Table 4.25. The uncertainty results for 2011-2014 are presented in the Appendix C, Table 35 – Table 38. Table 4.25 Uncertainty for soil nitrous oxide emissions Farms 2010 estimate Uncertainty range and percentage Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 43090 -8338 94518 119.35 Farm 2 26040 -12377 64457 147.53 Farm 3 9920 -1025 20865 110.33 Farm 4 7750 -2713 18213 135.00 Farm 5 5270 -1850 12390 135.11 Farm 6 3410 -885 7705 125.96 Farm 7 28210 -15039 71459 153.31 Farm 8 5270 -5477 16017 203.93 Farm 9 12710 -5362 30782 142.19 Farm 10 6510 -2292 15312 135.20 Farm 11 22630 -2109 47369 109.32 Farm 12 5270 -3215 13755 161.01 Farm 13 26040 -9143 61223 135.11 Farm 14 13640 -2555 29835 118.73 Farm 15 8990 -1130 19110 112.57 Farm 16 91140 -23104 205384 125.35 Quality control and quality assurance The amount of the applied fertilizers that was obtained from farmers was checked against other farms and were generally seen as similar. The data on manure management that was obtained from the survey was compared with limited available information from the literature and expert opinions within agricultural research council (ARC) was sought. Cropland management data that was collected from the farmers was compared against average practices and values according to the literature to remove the outliers. Data quality of the cropland areas and other management practices was checked by crop type against various 120 statistics including official reports, published data and expert judgement where information was lacking. 4.1.5 CO2 from diesel-tractor emissions CO2 EFs from diesel- tractor resulted to be the same at all farms with 0.073 Kg CO2e/L of diesel. The EFs from the use of agricultural diesel machines or tractors in this study was lower (0.073 kg CO2 MJfuel -1) as compared to EFs in other studies. Rajaniemi et al. (2011) recorded the EF of 98.48 g CO2 MJfuel-1 which is equivalent to 0.09848 kg CO2 MJfuel -1 with the energy content of 36.3 MJ-1. This study has used the energy density of 42.8 MJ/ha (Bridges and Smith, 1979) which is higher compared to that by Rajaniemi et al. (2011). In addition the EF for heavy- duty diesel vehicles was recorded as 2730 g CO2/L which is equivalent to 2.73 kg CO2/L which is also higher than the EF used in this study. The variations in the calculated EFs were due to differences in the type of tractor engine used and the the density of diesel (kg/liter). In this study the type of tractor engine used was neglected since the equation used did not include the type of engine tractor used, and the same amount of 0.84 density of diesel (kg/liter) was used at all farms. Agricultural tractor emission (Revised March 2004) reported the EFs of 0.41886 and 0.5856 Kg/Mj for two categories of tractor engines and which was also higher than the EF used in this study. However, this implies that, the EFs depend mostly on the type of the engine used as well as the density of diesel. The total farm CO2 emissions from diesel tractor-engine used for crop production ranged from 596 to 87884 kg CO2eq at all farms for the year 2010 to 2014. Higher total farm CO2 emissions from this sub source were due to high area of hectares planted per farm as it required more energy for crop production and this also led to less variations of emissions across the years per farm as shown by less CV in Table 4.26. 121 Table 4.26 CO2 from diesel-tractor emissions Yea 2010 2011 2012 2013 2014 Standard Mean Coefficie r deviation nt variation (CV) Far E(Kg E(Kg E(Kg E(Kg E(Kg E(Kg E(Kg % m CO2eq/farm/ CO2eq/farm/ CO2eq/farm/ CO2eq/farm/ CO2eq/farm/ CO2eq/farm/ CO2eq/farm/ year) year) year) year) year) year) year) F1 14330 12220 18707 18057 15080 2407 15679 0,15 F2 8005 7795 8005 7406 7795 219 7801 0,03 F3 3438 2299 1354 861 5795 1759 2749 0,64 F4 3701 4317 6168 6168 12577 3153 6586 0,48 F5 3128 2976 2661 1850 2661 442 2655 0,17 F6 882 903 1129 596 4514 1465 1605 0,91 F7 20471 22571 21495 25904 28741 3057 23836 0,13 F8 14802 14802 14802 7598 4724 4330 11346 0,38 F9 2997 2871 2997 3163 3107 101 3027 0,03 F10 7401 6141 6141 6141 6960 528 6557 0,08 F11 23043 26318 20755 22597 14435 3931 21430 0,18 F12 2047 10236 1024 3330 8608 3681 5049 0,73 F13 28366 28366 30234 30234 24938 1935 28427 0,07 F14 5984 3464 6394 5748 5427 1019 5403 0,19 F15 12729 12729 12729 12939 8635 1661 11952 0,14 F16 50015 45858 60804 87884 12708 24287 51454 0,47 A previous study by Stajnko et al (2009) showed that, higher CO2 emissions were recorded from conventional tillage system followed by the minimum tillage system. All crop production activities were undertaken the same way at all farms and this included activities such as disc ploughing, harrowing, fertilizer spraying, planting and harvesting. The total amount of diesel, operation time and energy used per year varied per activity as shown in Table 4.27 below. 122 Table 4.27 The total amount of diesel, operation time and energy used per year per activity Activity Diesel used (L/ha) Operation Time Energy used (T/hrs) (MJ/ha/farm) 1 Disc ploughing 8 0.50 287 2 Harrowing 4 0.25 143 3 Spraying 2 0.12 71 4 Planting 10 0.67 359 5 Harvesting 13 0.92 467 The farm total energy used for crop production varied per farm depending on the crops produced per growing season. The farm total energy consumed for crop production ranged from 15460 to 5253388 MJ when all farms were considered. In 2010 Farm 16 had consumed the highest farm total energy for production of crops with about 4008804 MJ and farm 6 used the lowest total farm energy of about 24736 MJ. In 2011 Farm 16 reduced its farm total energy use. However, it remained the highest with 3466802 MJ, whereas, Farm 6 remained constant with energy use of about 24736 MJ becoming the lowest as compared to other farms. In 2012 Farm 16 continued to use the most energy (2514720 MJ) though it decreased its farm total energy used. Farm 12 had utilized the smallest energy of about 14020 MJ in the same period. In 2013 Farm 16 increased its total farm energy and it remained the highest with 5253388 MJ, while Farm 6 had utilized less energy (15460 MJ) as compared to other farms. In 2014 Farm 16 remained the highest in energy consumption with 1480440 MJ and Farm 5 utilized the least energy of 36452 MJ. The high amount of energy used at Farm 16 in all years were due to more hectares planted than at other farms. However, at farms where more hectares of dry bean and soybean were planted more energy was used as these crops need to be planted on cleaner soil than other crops. This crops require thoroughly soil preparations and seed-bed of a depth of six to eight inches, loose soil which allows beans roots to stretch rapidly and take in water and oxygen easily. However, the full tillage system was used for production of soya 123 and dry beans which required more energy of about 1690 MJ/ha/crop at all farms and this included disc ploughing X2, harrowing X1, fertilizer spraying X2, planting X1 and harvesting X1. The results have shown that the production of crops such as maize, maize silage, sunflower, wheat and barley consumed the energy of about 1402 MJ/crop/ha. A full tillage system was also employed and this included disc ploughing X1, harrowing X1, fertilizer spraying X2, planting X1 and harvesting X1. Crops such as rhubarb, oats, teff grass and Lucerne hay required lesser energy (1186 MJ/crop/ha) than other crops. A minimum tillage system was employed and this included activities such as disc ploughing X1, fertilizer spraying X1, planting X1 and harvesting X1. Farm 16 and Farm 7 had applied the full tillage system the most while Farm 6, Farm 12 and Farm 5 had applied the minimum tillage system more regularly. The fertilization and planting activities in this study required more energy than other activities with about 10 L/ha and this was consistent with the energy used in other studies (Maraseni et al., 2009). The fuel consumption for spraying in all crops in this study were almost the same as that which was reported in a study by Maraseni et al (2009). Harvesting fuel consumption in this study are consistent with harvesting fuel consumption under irrigated land as found in a study by Maraseni et al (2009). The fuel consumption for harvesting by Maraseni et al (2009) were lower than the fuel consumption in this study. Among all the farming activities, harvesting energy use was found to be the highest and accounted for 52% of total energy used per farm. Energy use for planting was also significant, accounting for approximately 24% of the overall direct energy use. Farmers in the study area used minimum tillage for fodder crops whereas conventional tillage was applied on crops such as maize, drybean, soybean, wheat and barley. However, 124 conventional tillage required more energy than minimum tillage system. At some farms harvesting was done using diesel operated machines and the harvesting of other crops utilized several units of work involving mechanical harvesting of crops such as drybeans. However, this implies that where they practiced minimum tillage there was a potential saving of the overall fuel use on the farm unlike where conventional tillage was applied. For all farms growing maize, the diesel used was around 39 Liters/ha, broadly consistent with that reported in literature and from the experiences for other farmers (Gholani et al., 2013). From the results, it can be seen that the (direct) fuel use for spraying is significantly lower than that used for other activities. In a study by Gholani et al (2013), it was found that emissions of specific tractor engine depended mainly on engine speed. It was also found in the study by Gholani et al (2013) that emissions increased as the engine oil temperature increased. Results from this study differ since engine type and speed were not considered due to lack of data. The highest fuel consumption was recorded for conventional tillage with 82 L/ha and 68.38 L ha-1 at two different areas of the Eastern Slovenia, as reported in a study by Stajnko et al., (2009), showing higher emissions, as compared to those reported in this study. The minimum tillage system diesel consumption level were 35.09 L/ha and 38.93 L/ha, respectively, lower than the conventional tillage in the Eastern Slovenia. However, the lowest diesel oil consumption of all tillage systems were recorded with the direct seeding after Gliphosat spraying (DS-G system) on both location with 16.11 L/ha and 14.76 L/ha respectively (Stajnko et al., 2009). 125 Uncertainty analysis In general, use of the fuel-based methods produces less uncertainty than use of the distance based methods (IPCC, 2006c). However, this study used the fuel - based methods to reduce uncertainties and the use of locally estimated data at farm level reduced uncertainties as recommended by the IPCC (IPCC, 2006). The less uncertainty for CO2 emission factors from this source was also due to the uncertainty in the diesel fuel composition as shown in Table 4.28 below. The uncertainty results for 2011-2014 are presented in the Appendix C, Table 39 - Table 42. Table 4.28 The uncertainty for CO2 emissions from diesel tractor Farms 2010 estimate Uncertainty range and percentage Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 14329.75 12410 16250 13.4 Farm 2 8004.713 6932 9077 13.4 Farm 3 3438.09 2977 3899 13.4 Farm 4 3700.539 3205 4196 13.4 Farm 5 3128.399 2709 3548 13.4 Farm 6 881.8307 764 1000 13.4 Farm 7 20471.07 17728 23214 13.4 Farm 8 14802.16 12819 16786 13.4 Farm 9 2997.174 2596 3399 13.4 Farm 10 7401.079 6409 8393 13.4 Farm 11 23043.07 19955 26131 13.4 Farm 12 2047.107 1773 2321 13.4 Farm 13 28365.55 24565 32167 13.4 Farm 14 5983.851 5182 6786 13.4 Farm 15 12728.81 11023 14434 13.4 Farm 16 50015.02 43313 56717 13.4 126 Quality control and quality assurance The activity data such as the frequency of the activity in the growing season and the time required to complete an activity (hour/ha) were collected from farmers. However, this data was compared with data from literature. The Mean Fuel Consumption was calculated based on the data collected from farmers and was compared with the values from literature. The characteristic fuel consumption for a specific activity with a tractor (litres/hour) and the density of diesel (kg per litre) used was derived from the IPCC (2006). 4.2 Total farm emissions The total farm emission results are presented in the Appendix B, Table 4. The total emission results for each farm were directly correlated with total farm inputs. For example, in 2010, Farm 11 contributed the highest total amount of farm emissions with 580877 kg CO2eq. Farm 16 was the second emitter with 430684 kg CO2eq, while Farm 9 was the third contributor. All the remaining farms emitted less, whereas Farm 15 emitted the lowest with 69220 kg CO2eq. Therefore, the total farm GHG emissions produced depend on the farm total inputs. The highest total farm GHG emissions at some farms such as Farm 11 were due to higher inputs, especially livestock and cropland farming inputs. Livestock inputs included the stock rate which were higher at Farm 11 as compared to other farms. Meanwhile, this led to high demand of energy for production at farm level, including the feeding situations, as well as the intensive livestock management strategies for higher amount of manure. Higher stocking rates also affected Farm 2, Farm 13 and Farm 1 where farm inputs were high in all years. Furthermore, cropland production also required a high energy use for intensive farming management systems but emissions were also high due to use of high amount of synthetic 127 fertilizers. Conversely, the lowest total farm GHG emissions at some farms as described above were due to lower amount of farm inputs used for livestock and cropland farming. 4.3 Emission intensity The sizes of the farms included in this study ranged from about 107 to 1000 hectares. The sizes of the farms remained the same for all the sixteen farms from 2010 to 2014. Farm 16 with 1000 hectares was the largest farm covering more than 9 times the area of the smallest farm, which is Farm 10, with 107 hectares. Farm 16 as the largest farm should have required more inputs, and therefore contributed the highest GHG emissions, compared to the other 15 farms. However, this was not the case since farmers did not utilize all the available land. The area under cropping (e.g. the total area sown for grain or hay production, not including fallow or pasture area) fluctuated between 2010 and 2014 for all of the farms, except for Farm 15 where the area under cropping remained the same. On the other hand the area under cropping on Farms 4, 7, 9, and 13 increased during the same period. Table 5 (Appendix B) indicates the annual emissions per hectare per farm for the 5 year period (2010-2014) which varied per year. The average annual emissions per hectare for each farm ranged from 133 to 1832 kg CO2eq/year/ha/farm. Farm 2 had emitted the highest average annual emissions per hectare with 1832 kg CO2eq/year/ha. Farm 11 was the second highest with 1633 kg CO2eq/year/ha, Farm 1 the third (1524 kg CO2eq), and Farm 13 fourth (1506 kg CO2eq/year/ha). Farm 15 emitted the lowest amount with 133 kg CO2eq/year/ha, compared to all other farms. The farm total emissions was not related to the total farm size, since the largest farm did not produce the higher emissions, neither did the smaller farm emit the least. Emissions varied according to activities undertaken on the farm. However, emission intensities varied widely across the farms. The farming practices that contributed the lowest 128 emission intensity were from crop farming, while livestock related activities contributed the highest emission intensity in all years at all farms and this included enteric fermentation and manure management practices. Compared to commercial farming, in 2010, 2011 and 2014 commercial farming contributed the most to GHG emissions with 57, 52 and 51% respectively. However, in 2012 and 2013 subsistence farming contributed the most with 52 and 53% in that order. However, emissions compared per unit area of land (ha) had shown that commercial farming contributed higher than subsistence farming. This might be due to that, commercial farms use the intensive systems contrasting to smallholder farms, which generally implement the extensive systems. There were differences in emission intensity between producers. For ruminant products especially, but also for crop management systems, emission intensities vary greatly among producers (Figure 4.1). 129 Figure 4.1 The commercial VS subsistence farming scale 130 Difference agro-ecological conditions and farming practices explain this heterogeneity, observed both within and across production systems. It is within this variability between producers with highest emission intensity and those with lowest emission intensity. 4.4 Potential Mitigation options The mitigation analysis focused on mitigation measures that have the greatest capacity to reduce CH4 and N2O from enteric fermentation and manure management since livestock related activities contributed the most to the farm total GHG emissions in all years at all farms. The potential mitigation management practices used in the study are presented in Table 4.29 below. The results show that the potential management practices were the same at all farms though the mitigated emissions varied per farm, depending on the size of the farm. The potential mitigation options were chosen based on their ability to reduce GHG emissions, profitability to farming, and were environmentally friendly, and they promoted sustainability. Table 4.29 Potential management practices for the study Designation Description and input Mitigation 1: Solid storage manure management system Changing manure management system (From cattle/swine deep litter>1 month to solid storage) Mitigation 2: Anaerobic digester manure management Changing manure management system (From cattle/swine deep litter>1 system month to anaerobic digester) Mitigation 3: Pasture manure management system Changing manure management system (From cattle/swine deep litter>1 month to pasture) Mitigation 4: Drylot manure management system Changing manure management system (From cattle/swine deep litter>1 month to drylot) Mitigation 5: Feeding situation Decreasing pasture and Increasing supplements (TMR) to 50% pasture and 50% supplements (TMR), from approximately 80% pasture and 20% supplements (TMR). Mitigation 6: Feeding situation 100% Supplements (TMR) 131 4.4.1 Mitigation 1: Solid storage manure management system As a routine, farmers at the study area managed manure through the following systems: cattle/swine deep litter for greater than 1 month, manure left on pasture during grazing, manure stored in an open pit, dry lot spread and lastly manure feeding on anaerobic digester. However, cattle/swine deep litter >1 month has contributed more CH4 and N2O emissions from all farms and in all years of the study compared with the other active manure management. Therefore, mitigation of manure CH4 and N2O emissions was done with more focus on reducing emissions from the system that contributed the most. Hence, when famers practice solid manure storage instead of treating manure as cattle/swine deep litter for greater than one (1) month, total CH4 emission was reduced, while N2O emission increased. However, the total farm manure emissions (kgCO2eq) were reduced by 21% to 75% in all years at all farms. In 2010, Farm 3 had reduced more than other farms by 60% of the total manure emissions and in the year 2011, Farm 3 reduced more by 64% of the emitted manure emissions. In 2012 the farm manure emissions were reduced by 75% (Farm 3) which was more than at other farms. In 2013, Farm 11 reduced more by 69% of farm manure emissions and in the year 2014, farm 3 reduced its manure emissions by 70% and it was the highest. In view of that, solid storage reduced emissions as compared to that of cattle/swine deep litter for greater than 1 month and this indicated that solid storage can be considered instead of cattle/swine deep litter >1 month. The higher reductions at farms were due to the nature of solid manure management which influence less emissions (kgCO2eq) than liquid manure management. The results on manure emission reduction on mitigation 1 are presented in Table 4.30. 132 Table 4.30 Reduction of emissions by mitigation 1 Year 2010 2011 2012 2013 2014 Farms Kg CO2eq Kg CO2eq Kg CO2eq Kg CO2eq Kg CO2eq F1 -14672 -41031 -51387 -51433 -23687 F2 -5269 -5005 -8756 -43106 -41892 F3 -39720 -68424 -357673 -10489 -51010 F4 -22603 -22149 -27480 -19488 -28120 F5 -18785 -5398 -12595 -9416 -13146 F6 -8519 -10220 -4368 -5352 -8748 F7 -33513 -38384 -34831 -38886 -53336 F8 -6117 -6609 -12738 -6051 -7361 F9 -40498 -19858 -8490 -70991 -16003 F10 -8891 -25539 -3328 -5518 -6083 F11 -108089 -111490 -117900 -22647 -30081 F12 -12194 -8628 -4900 -4040 -3117 F13 -21721 -52139 -58280 -65094 -96162 F14 -38579 -18004 -7519 -9426 -8906 F15 -7468 -8976 -2756 -11019 -10063 F16 -44599 -21252 -12328 -24185 -17512 4.4.2 Mitigation 2: Anaerobic digester manure management system When all farmers were advised to use biogas digester instead of managing manure as cattle/swine deep litter for greater than one (1) month, total emissions reduction amount ranged from 9% to 24% in all years. This manure mitigation option showed less potential in reducing farm total manure emissions compared to mitigation 1. In 2010 Farms 1, 2 and 11 reduced their total farm manure emissions by 22% which was more than other farms. In 2011 Farm 14 reduced their manure emissions by 24% per farm and in 2012 Farms 1 and 8 reduced the highest of 23 percent. In 2013 Farm 8 reduced the highest by 24% and in 2014 there were more reduction of manure emissions from Farm 7 by 24%. The results on manure emission reduction on mitigation 2 are presented in Table 4.31. 133 Table 4.31 Reduction of emissions by mitigation 2 Year 2010 2011 2012 2013 2014 Farms Kg CO2eq Kg CO2eq Kg CO2eq Kg CO2eq Kg CO2eq F1 -6891 -21280 -22330 -21259 -11499 F2 -2788 -3410 -3018 -14625 -7959 F3 -7900 -19869 -56870 -6399 -12800 F4 -9780 -10470 -8630 -12720 -18078 F5 -7129 -2279 -10480 -6210 -3990 F6 -2109 -2872 -2644 -2660 -4190 F7 -13549 -10210 -9700 -13900 -31710 F8 -4679 -5000 -5909 -5729 -4779 F9 -8570 -13600 -4300 -18880 -9990 F10 -2159 -6978 -2239 -3674 -2506 F11 -49479 -49429 -44550 -5147 -10526 F12 -7425 -4678 -2906 -2130 -1068 F13 -10800 -11269 -14269 -31588 -24289 F14 -8968 -9450 -2980 -2859 -2239 F15 -1910 -2516 -1120 -4560 -3820 F16 -13308 -15489 -11350 -12840 -7530 4.4.3 Mitigation 3: Pasture - based manure management system Replacing manure management of cattle/swine deep litter for greater than one (1) month with pasture/range/paddock resulted in emission reduction ranging 20% – 75 percent. The total CH4 emission was reduced, while N2O emission increased and the sequence was similar to that of mitigation 1. In the year 2010, the highest mitigated farm manure emissions were from Farm 3 by 61 percent. However, in the year 2011 Farm 10 reduced more by 71 percent and in the year 2012 farm 3 had reduced more of its manure emissions by 75% of the total emitted manure emissions. In the year 2013 Farm 10 had reduced the highest manure emissions by 74% and in 2014 Farm 11 had reduced more (69%) of its total manure emissions 134 as compared to other farms. The results for pasture manure management as a mitigation option are presented in table 4.32 below. Table 4.32 Reduction of emissions by mitigation 3 Year 2010 2011 2012 2013 2014 Farms Kg CO2eq Kg CO2eq Kg CO2eq Kg CO2eq Kg CO2eq F1 -14158 -35590 -44745 -45080 -21889 F2 -6504 -7348 -7881 -33559 -39878 F3 -40389 -58076 -359060 -11680 -37300 F4 -23463 -21500 -26868 -17979 58232 F5 -17010 -4880 -16120 -10680 -14150 F6 -9349 -4999 -5100 -5909 -15030 F7 -26000 -24480 -27448 -39140 -67439 F8 -11999 -15229 -12852 -4766 -6809 F9 -44012 -16940 -13072 -98708 -10022 F10 -6701 -31490 -3438 -12392 -5848 F11 -122569 -108730 -100130 -23954 -58607 F12 -10490 -5066 -3478 -4525 -3016 F13 -21350 -49848 -41765 -72959 -97648 F14 -31515 -19712 -3440 -8791 -5718 F15 -8359 -9355 -2205 -14409 -11099 F16 -35723 -19091 -10678 -25940 -19557 4.4.4 Mitigation 4: Drylot spread manure management system Drylot spread manure management system is rarely used in the study area since it is practiced by only one farmer (Farm 1). However, changing manure management from cattle/swine deep litter for greater than one (1) month to drylot spread manure management system resulted in the emission reduction ranging from 20 to 74% in all years. The total CH4 emission was reduced, while N2O emission increased. In 2010, Farm 11 reduced more by 64%, while in 2011 Farm 3 had reduced about 61% of their farm total manure emissions. In 2012, the highest reduction was recorded from Farm 4 by 68% and in 2013, Farm 11 had reduced higher 135 than other farms by 74 percent. In 2014, Farm 3 reduced 60% of farm manure emissions and it was the highest. The results on manure emission reduction on mitigation 4 are presented in Table 4.33. Table 4.33 Reduction of emissions by mitigation 4 Year 2010 2011 2012 2013 2014 Farms Kg CO2eq Kg CO2eq Kg CO2eq Kg CO2eq Kg CO2eq F1 -15968 -26730 -37300 -46322 -12470 F2 -5898 -6269 -8208 -48912 -39278 F3 -32920 -65199 -306180 -8679 -43403 F4 -23530 -16480 -35110 -18065 -58403 F5 -12895 -14219 -30400 -6828 -13450 F6 -5259 -5831 -6308 -11180 -14800 F7 -33641 -35370 -34448 -41830 -50070 F8 -7389 -8730 -15229 -4709 -6089 F9 -32710 -15860 -6670 -87630 -11330 F10 -7999 -25568 -2989 -5497 -5870 F11 -141628 -122052 -90320 -24367 -36225 F12 -10870 -6458 -4206 -3889 -2339 F13 -19590 -50357 -56364 -78885 -85299 F14 -42347 -17120 -5120 -6819 -7590 F15 -6980 -7386 -2210 -13080 -8250 F16 -37040 -22744 -17378 -23495 -22110 4.4.5 Mitigation 5: Feeding system (50% Pasture and 50% supplements (TMR) When farmers were advised to decrease pasture and increase supplements in a feeding situation (50 % pasture and 50 % supplements), there was a decrease in enteric CH4 emissions for all years compared with the baseline data. There was a CH4 reduction of approximately 27% - 45% in all years at all farms. Consequently, complementary of supplements and grazing pastures led to reduction of enteric CH4 emissions as compared to the high percent of livestock grazing on pastures as practiced in Tshiame farms. The results on reduced CH4 emissions by mitigation 5 are presented in Table 4.34 below. 136 Table 4.34 Reduction of emissions by mitigation 5 (50% pasture 50% supplements) Year 2010 2011 2012 2013 2014 Farm Kg CO2eq Kg CO2eq Kg CO2eq Kg CO2eq Kg CO2eq F1 -69799 -82812 -88143 -67033 -74788 F2 -26672 -33673 -69399 -132008 -105756 F3 -27211 -45935 -79153 -22183 -24011 F4 -23425 -23956 -32891 -30771 -46693 F5 -19896 -13800 -37369 -28290 -16018 F6 -11522 -22767 -15120 -11449 -13440 F7 -28251 -38566 -38821 -41060 -44763 F8 -18944 -20035 -10887 -10996 -12584 F9 -49773 -119651 -24580 -31843 -33616 F10 -10935 -5789 -7672 -8300 -10141 F11 -110954 -110177 -122611 -132133 -53620 F12 -21640 -16622 -11059 -7805 -9460 F13 -32397 -52393 -47663 -87481 -110097 F14 -54184 -73925 -21766 -32882 -30471 F15 -9074 -10364 -2676 -11895 -10864 F16 -77208 -48333 -33142 -42635 -56487 4.4.6 Mitigation 6: feeding system (TMR based 100%) This feeding system had reduced more CH4 emissions from enteric fermentation by 81 to 92% than the previous mitigation option and the results are shown in Table 4.35. More reduction in CH4 enteric emission was due to the higher digestibility of TMR-based diets as well as the higher intakes attained by animals feeding on confined based diets. 137 Table 4.35 Reduction of emissions by mitigation 6 (100% TMR) Year 2010 2011 2012 2013 2014 Farm Kg CO2eq Kg CO2eq Kg CO2eq Kg CO2eq Kg CO2eq F1 -146770 -149845 -144710 -139102 -122416 F2 -77233 -95038 -211284 -226809 -215085 F3 -79705 -109870 -118983 -45226 -65471 F4 -74380 -73743 -69252 -186500 -113695 F5 -46106 -35203 -76837 -51793 -52645 F6 -27353 -49992 -27634 -33661 -38814 F7 -98852 -109213 -79294 -107874 -114847 F8 -33771 -44250 -29043 -29763 -33789 F9 -143023 -108078 -44949 -66061 -74481 F10 -22295 -11618 -18811 -22788 -22307 F11 -223464 -222377 -225111 -296639 -109614 F12 -44144 -39936 -22464 -22042 -11622 F13 -87400 -159286 -115325 -120962 -227810 F14 -130629 -110434 -54363 -82964 -77150 F15 -18139 -20618 -6753 -33789 -33869 F16 -146425 -109122 -85031 -110276 -113062 138 CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS 5.1 Conclusions The GHG EFs and emissions were calculated for sixteen (16) farms in the Ward, for the period of 2010 to 2014. The study used the Agricultural Land Use (ALU) software, which is developed based on revised 1996 IPCC guidelines, 2000 IPCC Good Practice Guidance, 2003 IPCC Good Practice Guidance and 2006 IPCC guidelines to estimate EFs and emissions. Emissions produced from all sources were estimated per year and per farm. The annual farm total emissions for all sixteen (16) farms ranged between 36270 to 676245 Kg CO2eq. The total livestock emissions were higher than cropland total emissions in all years. The livestock emissions ranged from 76 to 83 % while cropland emissions ranged from 17 to 24 % per year at all farms. Enteric fermentation contributed the highest to GHG emissions while manure amendments contributed the lowest and this was the case in all years at all farms. In all years, emissions from enteric fermentation remained the highest, ranging from 7499 to 983776 Kg CO2/year/farm and were directly related to stocking rate on the farms. However, the manure CH4 emissions remained the second highest for all years, and it ranged from 230 to 46181 Kg CO2/year/farm, while manure N2O emissions remained the third highest at all farms ranging from 2018 to 77790 Kg CO2/year. As a result CH4 and N2O manure emissions were directly related to the number of animals and the manure management systems practiced at the farms. The CH4 emissions from grassland biomass burning were the fourth highest, ranging from 945 to 66339 Kg CO2/year. These emissions were directly related to the number of hectares 139 burned per farm. The next highest were CO2 emissions from the engine tractor usage which ranged from 596 to 87884 Kg CO2/year/farm. However, the emissions were related to the amount of diesel used per activity per hectare per farm. The sixth highest were the N2O emissions from N pasture, which ranged from 310 to 48360 Kg CO2/year/farm. These emissions were related to the amount of N manure left on pasture per year per farm. In the seventh place were the N2O emissions from the synthetic fertilizer application, ranging from 620 to 39060 Kg CO2/year/farm. The N2O emissions were related to the amount of N fertilizers applied per hectare per year per farm. N2O emissions from synthetic fertilizer and manure left on pasture were the highest contributors to the total soil N2O emissions. During the period between 2010 and 2012, the higher contributions of emissions were from synthetic fertilizers. Synthetic fertilizer emissions were the highest, followed by N derived from pasture, and N from manure amendments emissions unlike in 2013 and 2014 when emissions from manure left on pasture were the highest, followed by those from synthetic fertilizer and manure amendments, which contributed the least to the total of N2O emissions. Indirect soil N2O emissions were ranked the eight, ranging from 310 to 12710 Kg CO2/year/farm. These emissions were produced from the crop residue retained, synthetic and organic fertilizer application, as well as from the manure N pasture. N2O emissions from crop residue management ranged from 310 to 46810 Kg CO2eq. N2O emissions from grassland biomass burning ranged between 310 and 12090 Kg CO2eq. Both the CH4 and N2O emissions from grassland biomass burning were related to the total number of hectares of grassland burned annually at all farms. N2O emissions from manure amendments ranged from 1550 to 1674 Kg CO2-eq, making them the lowest among all sources. In this study much higher emissions were generally found on Farm 11 and the total emissions from this farm were 140 directly related to the higher inputs. The lowest emitter was by Farm 15, whose emissions were directly related to the lower inputs as compared to other farms. In conclusion, the livestock emission factors assessed in this study were higher than the emission factors assessed in most previous studies and this might be due to the lower quality of the feeding situation used in the study area. However, the cropland emission factors were consistent with those cited in literature from most of the studies. There is a need for the development of emission factors at farm level or on a small scale. Each farm should have its own emission factors per GHG source, depending on the farm management practices. However, the results of this study have shown that, the activity data can be improved by replacing actual data collected with the assumptions as a way of evaluating the mitigation strategies. The mitigation analysis focused on mitigation measures that have the greatest capacity to reduce CH4 and N2O from enteric fermentation and manure management. Emissions from livestock related activities contributed the most to the farm total GHG emissions in all years at all farms. In this study, the mitigation options were analysed and evaluated, and as a result, six (6) mitigation options were regarded as the potential mitigation options for Tshiame farms. The six (6) potential mitigation options met the requirements of sustainability, environmental friendliness as well as profitability to farmers. 5.2 Recommendations The conclusions of this research suggest five recommendations. First, farmers should be advised to adopt application of both synthetic (50%) and organic fertilizers (50%). Since farmers in Tshiame Ward only use synthetic fertilizers, it is highly recommended that farmers should adopt the strategy of applying both synthetic (50%) and organic fertilizers (50%). 141 Manure which accumulates throughout the year in a kraal can be spread on the fields during the dry season to avoid the run-off of Nitrogen manure. Inorganic fertilizers provides nutrients or minerals for crop production without adding any organic matter to the soil. However, in comparison, waste typically contains half as much organic matter, and minerals are concentrated within the waste. There are many benefits of adding organic matter to the soil with manure application, if organic matter is depleted in the soil. Thus, effluent can be as good of a crop fertilizer as the original manure, depending on soil properties of the land. Secondly, it is suggested that farmers should move from conventional tillage to minimum and/ no tillage for a potential saving of energy and also for reduction of CO2 emissions from diesel-tractor. No-tillage is recommended as a strategy to improve soil organic carbon content and reduce erosion, minimize agricultural energy use, and decrease CO2 emission to the atmosphere. The third recommendation is related to preparation for accidental fires, farmers should be advised to prepare for these fires. In Tshiame Ward, since in most cases fires are accidentally started by human beings, farmers should be advised to adopt strategies that reduce chances of their fields or grasslands catching fire, including the creation of fire belts. Therefore if all farmers can adapt to those strategies, there will be a huge decrease in CH4 and N2O emissions from this source. The fourth recommendation is on complementary use of supplements. Farmers should be advised to use complementary supplements (TMR) and grazing pasture feeding situations for their animals. The complementary of supplements (TMR) and grazing pasture feeding situations have proved to meet the requirements of sustainability, environmental 142 friendliness, as well as profitability to farmers. This feeding situation has the potential to reduce the GHG emissions from pastures only. The fifth recommendation relates to the storage and treatment of manure. Farmers should be advised to store or treat the manure as solid. 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Advanced Direct Injection Combustion Engine Technologies and Development: Diesel Engines. Woodhead Publishing Limited. p. 8.ISBN 9781845697457. Zoz, F. M., and R.D. Grisso. (2003). Traction and tractor performance. ASAE Distinguished lecture #27, Agricultural equipment Technology conference, 9-11 february 2003. Louisville, Kentucky.USA. 184 APPENDICES Appendix A: Inputs data Table A.1 Productivity for dairy cattle for 2010-2014 Farms Fat content (%) Average daily weight gain (kg day-1) Mature females Calves Heifers F1 4 0.0714 0.0714 F2 4 0.0714 0.0714 F3 3.9 0.0308 0.0308 F4 3 0.0487 0.0487 F5 3 0.052 0.052 F6 3.1 0.052 0.052 F7 3 0.0551 0.0551 F8 3 0.0308 0.0308 F9 3.2 0.0238 0.0238 F10 3.1 0.0975 0.0975 F11 3.6 0.065 0.065 F12 3.2 0.0238 0.0238 F13 n/a n/a n/a F14 3.8 0.0157 0.0157 F15 n/a n/a n/a F16 3 0.0157 0.0157 Standard deviation(SD) 0.4 0.021 0.020 Mean 3 0.052 0.035 Coefficient variation(CV) 12 41 56 185 Table A.2 Average animal weight (kg) for dairy cattle Farms Mature females (< Heifers (1-2 Mature Male Calves Female Young bulls 2 yrs) yrs) Bulls (0-1 yrs) calves (0-1 (1-2 yrs) yrs) F1 558 316 728 134 132 274 F2 400 297 - 76 74 171 F3 546 469 540 102 100 228 F4 472 327 500 62 62 307 F5 465 301 546 103 99 332 F6 459 243 546 102 102 - F7 494 382 631 70 70 238 F8 502 314 772 48 48 314 F9 428 386 521 96 89 - F10 460 375 540 93 90 - Standard deviation(SD) 46 60 92 24 23 53 Mean 478 341 592 89 87 266 Coefficient variation(CV) 10 18 15 27 26 20 - Represents the unavailability of the weights of other animal sub-categories Table A.3 Annual milk production for dairy cattle for 2010-2014 Farms Annual milk production per cow (kg year-1)a 2010 2011 2012 2013 2014 F1 4500 4500 4500 4500 4500 F2 6000 6000 6000 6000 6000 F3 3213 3213 3213 3213 3213 F4 4500 4500 4500 4500 4500 F5 1500 1500 1500 1500 1500 F6 1800 1800 1800 1800 1800 F7 2100 2100 2100 2100 2100 F8 2400 2400 2400 2400 2400 F9 4500 4500 4500 4500 4500 F10 2400 2400 2400 2400 2400 F11 5400 5400 5400 5400 5400 F12 4200 4200 4200 4200 4200 F13 - - - - - F14 6000 6000 6000 6000 6000 F15 - - - - - F16 3600 3600 3600 3600 3600 Standard deviation(SD) 1535 1535 1535 1535 1535 Mean 3609 3609 3609 3609 3609 Coefficient variation(CV) 43 43 43 43 43 - Represents the unavailability of the mature females animal sub-category in other farms 186 Table A.4 Average animal weight (kg) for beef cattle Farms Mature Heifers Bulls Male Calve Fem calve Young bull females F1 534 430 713 108 100 244 F2 - - - - - - F3 409 446 540 99 99 - F4 475 304 632 129 100 318 F5 - - - - - - F6 425 243 420 106 106 - F7 523 414 753 67 67 238 F8 438 314 632 38 38 - F9 341 274 500 91 91 276 F10 461 385 705 82 82 - Standard deviation(SD) 59 72 108 26 21 32 Mean 451 351 612 90 85 269 Coefficient variation(CV) 13 21 18 29 25 12 - Represents the unavailability of the weights of other animal sub-categories Table A.5 Productivity data for beef cattle for 2010-2014 Farms Fat content Annual milk production Average daily weight gain (kg day-1) (kg year-1) Calves Heifers F1 3 500 0.04 0.07 F2 3 500 0.032 0.0317 F3 3 500 0.01 0.041 F4 3 500 0.036 0.0234 F5 3 500 0.0247 0.03 F6 3 500 0.02 0.0813 F7 3 500 0.031 0.052 F8 3 500 0.064 0.062 F9 3 500 0.012 0.028 F10 3 500 0.054 0.065 F11 3 500 0.0243 0.055 F12 3 500 0.039 0.049 F13 3 500 0.055 0.077 F14 3 500 0.047 0.029 F15 3 500 0.015 0.0561 F16 3 500 0.07 0.081 Standard deviation(SD) 0 0 0 0 Mean 3 500 0 0 Coefficient variation(CV) 0 0 45 35 187 Table A.6 Average weight for sheep sub-categories Farms Livestock Average weight (kg) Average daily gain (kg) category Mature Rams Heifers Lambs Lambs sheep (Ewes) F1 80 102 48 36 0.09 F2 76 84 55 22 0.07 F5 69 67 60 28 0.06 F6 80 102 48 36 0.01 F7 65 70 55 24 0.06 F9 73 70 49 31 0.02 F11 58 80 56 20 0.05 F13 80 102 48 36 0.036 Standard 8 14 4 6 0 deviation(SD) Mean 73 85 52 29 0 Coefficient 10 17 8 21 50 variation(CV) Table A.7 Coefficients for calculating energy for maintenance (NEm) Animal category Cfi (MJ day-1 kg-1) Comments Cattle (non-lactating cows) 0.322 Cattle (Lactating cows) 0.386 This value is 20% higher for maintenance during lactation Cattle (bulls) 0.370 This value is 15% higher for maintenance of intact males Sheep (Lamb to 1 year) 0.236 This value can be increased by 15% for intact males Sheep (Older than 1 year) 0.217 This value can be increased by 15% for intact males 188 Table A.8 Activity coefficients corresponding to animal s feeding situation Situation Defination Ca Cattle (unit for Ca is dimensionless) Stall Animals are confined to a small area with 0.00 the result that they expend very little or no energy to acquire feed Pasture Animals are confined in areas with 0.17 sufficient forage requiring modest energy expense to acquire feed Sheep (unit for Ca = MJ-1 kg-1) Housed ewes Animals are confined due to pregnancy in 0.0090 final trimester (50 days) Grazing flat pasture Animals walk up to 1000 meters per day 0.0107 and expend very little energy to acquire feed Housed fattening lambs Animals are housed for fattening 0.0067 Table A.9 Constants for use in calculating net energy needed for growth (NEg) for sheep Animal species/category a (MJ kg-1) b (MJ kg-2) Intact males 2.5 0.35 Castrates 4.4 0.32 Females 2.1 0.45 Table A.10 Constants for use in calculating net energy required for pregnancy (NEp) Animal category Cpregnancy Cattle 0.10 Sheep Single birth 0.077 Double birth (twins) 0.126 Triplets birth or more (Triplets) 0.150 Table A.11 The Africa default VS values for livestock categories Animal VS (kg VS day-1) Pigs 0.50 Goats 0.35 Horses 1.72 189 Table A.12 The Bo values for all livestock categories Animal Sub-category B (m3 CH kg-1o 4 of VS excreted) Dairy cattle Mature females 0.17 Heifers 0.17 Bulls 0.17 Calves 0.17 Young oxen 0.17 Mature Oxen 0.17 Beef cattle Mature cow 0.10 Heifers 0.10 Bulls 0.10 Calves 0.10 Young oxen 0.10 Mature oxen 0.10 Sheep 0.13 Pigs 0.45 Goats 0.13 Horses 0.26 Table A.13 Cattle and sheep CH4 conversion factors (Ym) Livestock category Ymb Dairy cows and their young 6.5% Other cattle that are primarily fed low quality crop residues and 6.5% by-product Other cattle grazing 6.5% Lambs (< 1 year old) 4.5% Mature sheep 6.5% Table A.14 The EF default used for goats, pigs and horses Livestock Emission Factors (EF) Live weight Goats 5 40 kg Horses 18 550 kg Swine 1 - 190 Table A.15 Data required for calculating N2O emissions from manure management Animal Sub-category Nrate Nex (kg N (1000 kg animal mass)-1 d-1) (kg N animal-1 yr-1) Dairy cattle Mature females 0.6 109.06 Heifers 0.6 77.75 Bulls 0.63 228.34 Calves 0.63 28.51 Young oxen 0.63 106.24 Mature Oxen 0.63 126.47 Beef cattle Mature cow 0.63 84.85 Heifers 0.63 48.98 Bulls 0.63 134.52 Calves 0.63 19.55 Young oxen 0.63 68.99 Mature oxen 0.63 92.21 Sheep 1.17 17.08 Pigs 0.55 43.76 Goats 1.37 18 Horses 0.46 99.9 Table A.16 Feeding systems for different animal categories in percentages (applicable to all farms) Livestock category Sub-category Pasture-based TMR-based Dairy cattle Mixed-Lactating cows 70 30 Non-lactating dairy cattle 70 30 Beef cattle All Beef 100 0 Sheep All sheep 100 0 Goats All goats 100 0 Horses All horses 100 0 191 Appendix B: Gross energy intake and emission results per livestock category Table B.1 Gross energy intake by dairy cattle LvsCategory Mature Bulls(D) Heifers(D) Calves(D) Calves(D) female(D) LvstkSub Mature Mature Bulls Young Females - Age 1- Young Females - Age 0- Young Intact Males - Age 0- Females 2 1 1 Units MJ/head/da MJ/head/da MJ/head/day MJ/head/day MJ/head/day y y F1 246 177 122 155 153 F2 226 - - 108 106 F3 141 117 175 44 43 F4 199 128 102 49 49 F5 151 117 108 45 44 F6 122 109 94 49 48 F7 155 159 196 38 37 F8 157 159 82 53 52 F9 163 138 110 64 64 F10 152 116 196 44 44 F11 222 150 142 55 54 F12 185 121 116 32 31 F13 - - - - - F14 232 105 102 40 40 F15 160 - - - - F16 158 120 - - - SD 37 22 38 33 32 Mean 179 133 129 60 59 CV 21 17 29 55 55 - Represents the unavailability of other animal sub-categories in other farms during certain periods 192 Table B.2 Gross energy intake by beef cattle LvsCategory Mature Bulls(B) Heifers(B) Calves(B) Calves(B) females(B) LvstkSub Mature Mature Bulls Young Females - Age 1- Young Females - Age 0- Young Intact Males - Age 0- Females 2 1 1 Units MJ/head/day MJ/head/day MJ/head/day MJ/head/day MJ/head/day F1 261 166 131 51 51 F2 - - - - 40 F3 232 142 118 73 71 F4 212 128 119 46 46 F5 - - - - - F6 - - - - - F7 107 159 119 32 31 F8 - - - - 38 F9 - 102 90 71 72 F10 - - - - - F11 234 128 123 73 72 F12 - - - - - F13 195 111 109 - 37 F14 - - - - - F15 193 - 118 32 - F16 179 - 107 26 25 SD 46 22 11 17 16 Mean 205 134 116 54 51 CV 22 16 10 32 31 - Represents the unavailability of other animal sub-categories in other farms during certain periods 193 Table B.3 Gross energy intake by sheep livestock category LvstkSub Ewes Heifers Rams Lambs Farms MJ/animal/day MJ/animal/day MJ/animal/day MJ/animal/day F1 64 39 47 30 F2 64 39 47 30 F5 64 39 47 30 F6 64 39 47 30 F7 64 39 47 30 F9 64 39 47 30 F11 64 39 47 30 Table B.4 Total emissions per farm Emissions kg kg kg kg kg CO2eq/year CO2eq/year CO2eq/year CO2eq/year CO2eq/year Farms 2010 2011 2012 2013 2014 F1 313868 338582 374875 415740 289719 F2 152580 173436 202610 446945 430693 F3 219627 265930 676245 105038 127728 F4 146636 155688 125996 136860 217042 F5 98683 68264 131616 161952 130192 F6 87540 97414 71712 74761 126382 F7 225921 292320 211728 276021 282536 F8 90080 97811 80685 69025 71710 F9 280765 181761 81799 467538 134049 F10 139018 204642 45338 54731 66562 F11 581295 585732 605683 440144 274342 F12 89621 79250 46623 59532 36270 F13 203122 264560 285540 386178 434004 F14 263666 194276 120249 108909 107753 F15 69220 74896 47567 90804 63017 F16 430684 269682 197652 293955 254731 194 Table B.5 Emission intensity Total emissions per farm per hectare Farms Kg CO2eq/farm/ha Kg CO2eq/farm/ha Kg CO2eq/farm/ha Kg CO2eq/farm/ha Kg CO2eq/farm/ha 2010 2011 2012 2013 2014 F1 2266 1242 1358 1477 1284 F2 858 1103 2000 2898 2711 F3 399 465 697 176 246 F4 316 373 275 725 546 F5 500 432 785 940 854 F6 362 441 344 326 560 F7 555 724 416 693 706 F8 158 176 139 111 130 F9 725 502 227 382 427 F10 1149 767 375 460 571 F11 1263 1246 1406 1540 726 F12 189 189 114 145 89 F13 931 5170 1130 1537 1872 F14 1057 1075 645 615 587 F15 128 160 86 165 136 F16 380 238 184 288 202 195 Appendix C: Uncertainty results Table C.1 Uncertainty for CH4 emissions from enteric fermentation by non-dairy for 2011 Farms 2011 estimate Uncertainty range and percentage (Kg CO2eq) Lower bound Upper bound Uncertainty percentage Farm 1 43451 39840 47062 8.31 Farm 2 17775 15787 19762 11.18 Farm 3 119553 100293 138813 16.11 Farm 4 62668 54371 70965 13.24 Farm 5 29970 24731 35209 17.48 Farm 6 12731 11221 14241 11.86 Farm 7 85956 75238 96675 12.47 Farm 8 10496 9075 11918 13.54 Farm 9 18164 15548 20780 14.4 Farm 10 3941 3477 4404 11.76 Farm 11 161688 142479 180897 11.88 Farm 12 16239 13351 19126 17.78 Farm 13 983776 842801 1124751 14.33 Farm 14 30588 25626 35549 16.22 Farm 15 23551 19608 27493 16.74 Farm 16 27169 23192 31147 14.64 Table C.2 Uncertainty for CH4 emissions from enteric fermentation by non-dairy for 2012 Farms 2012 estimate Uncertainty range and percentage (Kg CO2eq) Lower bound Upper bound Uncertainty percentage Farm 1 51282 46564 56000 9.2 Farm 2 120636 100056 141217 17.06 Farm 3 157782 137523 178041 12.84 Farm 4 60630 51445 69816 15.15 Farm 5 13746 12042 15449 12.39 Farm 6 13118 11442 14795 12.78 Farm 7 54970 48698 61242 11.41 Farm 8 3981 3516 4446 11.68 Farm 9 13776 11806 15746 14.3 Farm 10 5209 4598 5821 11.74 Farm 11 179085 157612 200557 11.99 Farm 12 11571 10127 13015 12.48 Farm 13 139045 119537 158553 14.03 Farm 14 16826 14900 18753 11.45 Farm 15 7499 6478 8519 13.61 Farm 16 18225 16307 20142 10.52 196 Table C.3 Uncertainty for CH4 emissions from enteric fermentation by non-dairy 2013 Farms 2013 estimate Uncertainty range and percentage (Kg CO2eq) Lower bound Upper bound Uncertainty percentage Farm 1 43844 40433 47255 7.78 Farm 2 147396 123223 171569 16.4 Farm 3 22598 19721 25474 12.73 Farm 4 217197 187028 247366 13.89 Farm 5 43074 35019 51128 18.7 Farm 6 13158 11805 14510 10.28 Farm 7 96711 84438 108983 12.69 Farm 8 6721 6124 7318 8.88 Farm 9 26508 23545 29472 11.18 Farm 10 12484 11182 13786 10.43 Farm 11 200462 174923 226000 12.74 Farm 12 7207 6352 8062 11.86 Farm 13 196778 166828 226727 15.22 Farm 14 28938 24366 33510 15.8 Farm 15 40292 32874 47710 18.41 Farm 16 69966 62648 77284 10.46 Table C.4 Uncertainty for CH4 emissions from enteric fermentation by non-dairy 2014 Farms 2014 estimate Uncertainty range and percentage (Kg CO2eq) Lower bound Upper bound Uncertainty percentage Farm 1 68337 63212 73462 7.5 Farm 2 45121 40392 49849 10.48 Farm 3 16803 14800 18806 11.92 Farm 4 133821 117093 150548 12.5 Farm 5 21305 18097 24514 15.06 Farm 6 11374 9945 12802 12.56 Farm 7 138554 127054 150054 8.3 Farm 8 18523 15810 21237 14.65 Farm 9 30985 27413 34558 11.53 Farm 10 9642 8401 10884 12.88 Farm 11 29358 24191 34525 17.6 Farm 12 6388 5389 7387 15.64 Farm 13 259889 220646 299132 15.1 Farm 14 22565 18857 26272 16.43 Farm 15 40858 33438 48278 18.16 Farm 16 51648 45352 57943 12.19 197 Table C.5 Uncertainty for CH4 emissions from enteric fermentation by dairy cows for 2011 Farms 2011 estimate Uncertainty range and percentage (Kg CO2eq) Lower bound Upper bound Uncertainty percentage Farm 1 114312 93164 135459 18.50 Farm 2 63989 53988 73991 15.63 Farm 3 - - - - Farm 4 19370 15701 23038 18.94 Farm 5 14869 11998 17740 19.31 Farm 6 23746 19128 28365 19.45 Farm 7 33045 26568 39522 19.60 Farm 8 40404 32367 48440 19.89 Farm 9 108897 88054 129740 19.14 Farm 10 16839 13619 20058 19.12 Farm 11 108484 88468 128499 18.45 Farm 12 30122 24549 35694 18.50 Farm 13 - - - - Farm 14 135166 109241 161091 19.18 Farm 15 - - - - Farm 16 91178 74091 108265 18.74 - Represents the unavailability of the animal sub-category at farms Table C.6 Uncertainty for CH4 emissions from enteric fermentation by dairy cows 2012 Farms 2012 estimate Uncertainty range and percentage (Kg CO2eq) Lower bound Upper bound Uncertainty percentage Farm 1 114312 93164 135459 18.50 Farm 2 109696 89337 130056 18.56 Farm 3 26104 21210 30999 18.75 Farm 4 15496 12559 18432 18.95 Farm 5 72859 58790 86928 19.31 Farm 6 20778 16737 24819 19.45 Farm 7 33045 26568 39522 19.60 Farm 8 31080 24898 37262 19.89 Farm 9 41306 33400 49212 19.14 Farm 10 16839 13619 20058 19.12 Farm 11 133518 108884 158153 18.45 Farm 12 15061 12275 17847 18.50 Farm 13 - - - - Farm 14 43446 35113 51779 19.18 Farm 15 - - - - Farm 16 73300 59564 87037 18.74 - Represents the unavailability of the animal sub-category at farms 198 Table C.7 Uncertainty for CH4 emissions from enteric fermentation by dairy cows 2013 Farms 2013 estimate Uncertainty range and percentage (Kg CO2eq) Lower bound Upper bound Uncertainty percentage Farm 1 118708 100237 137179 15.56 Farm 2 165698 134961 196435 18.55 Farm 3 28877 23283 34470 19.37 Farm 4 13559 10989 16128 18.95 Farm 5 17843 14397 21288 19.31 Farm 6 26714 21518 31910 19.45 Farm 7 22531 18115 26947 19.60 Farm 8 26418 21163 31672 19.89 Farm 9 45061 36436 53685 19.14 Farm 10 16839 14096 19582 16.29 Farm 11 127260 103780 150739 18.45 Farm 12 18826 15343 22309 18.50 Farm 13 - - - - Farm 14 62756 50719 74792 19.18 Farm 15 - - - - Farm 16 73300 59564 87037 18.74 - Represents the unavailability of the animal sub-category at farms 199 Table C.8 Uncertainty for CH4 emissions from enteric fermentation by dairy cows for 2014 Farms 2014 estimate Uncertainty range and percentage (Kg CO2eq) Lower bound Upper bound Uncertainty percentage Farm 1 101122 82414 119829 18.50 Farm 2 235389 191701 279078 18.56 Farm 3 55851 45167 66535 19.13 Farm 4 13559 11016 16101 18.75 Farm 5 35686 28795 42577 19.31 Farm 6 32651 26300 39002 19.45 Farm 7 15021 12077 17965 19.60 Farm 8 23310 18674 27946 19.89 Farm 9 52571 42509 62633 19.14 Farm 10 15308 12381 18235 19.12 Farm 11 127260 103780 150739 18.45 Farm 12 13178 10740 15616 18.50 Farm 13 - - - - Farm 14 65170 52670 77669 19.18 Farm 15 - - - - Farm 16 85815 69733 101896 18.74 - Represents the unavailability of the animal sub-category at farms Table C.9 Uncertainty for CH4 emissions from manure management by non-dairy for 2011 Farms 2011 estimate Uncertainty range and percentage Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 615 484 746 21.34 Farm 2 238 192 285 19.35 Farm 3 1655 1179 2131 28.77 Farm 4 - - - - Farm 5 415 332 498 20.00 Farm 6 175 136 213 22.03 Farm 7 1213 943 1484 22.32 Farm 8 145 115 175 20.65 Farm 9 251 201 302 20.00 Farm 10 36 29 43 19.36 Farm 11 2238 1755 2721 21.58 Farm 12 225 172 278 23.58 Farm 13 1776 1314 2237 25.99 Farm 14 423 321 526 24.25 Farm 15 326 256 396 21.36 Farm 16 376 293 459 22.05 - Represents the unavailability of the animal category at farms 200 Table C.10 Uncertainty for CH4 emissions from manure management by non-dairy for 2012 Farms 2012 estimate Uncertainty range and percentage Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 726 558 894 23.17 Farm 2 3315 2486 4145 25.01 Farm 3 45871 35344 56399 22.95 Farm 4 839 686 992 18.22 Farm 5 190 152 228 20.00 Farm 6 182 142 222 22.03 Farm 7 761 591 931 22.32 Farm 8 55 44 66 20.65 Farm 9 191 153 229 20.00 Farm 10 72 58 86 19.36 Farm 11 2487 1945 3029 21.8 Farm 12 160 122 198 23.58 Farm 13 1925 1448 2402 24.79 Farm 14 233 176 289 24.25 Farm 15 104 82 126 21.36 Farm 16 247 192 301 22.05 Table C.11 Uncertainty for CH4 emissions from manure management by non-dairy for 2013 Farms 2013 estimate Uncertainty range and percentage Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 429 329 528 23.17 Farm 2 2219 1664 2774 25.01 Farm 3 313 241 385 22.95 Farm 4 162 132 191 18.22 Farm 5 1935 1548 2321 20.00 Farm 6 182 142 222 22.03 Farm 7 108 84 132 22.32 Farm 8 1361 1080 1642 20.65 Farm 9 255 204 306 20.00 Farm 10 321 259 383 19.36 Farm 11 586 450 722 23.17 Farm 12 2289 1749 2828 23.58 Farm 13 - - - - Farm 14 3125 2367 3883 24.25 Farm 15 - - - - Farm 16 1146 893 1398 22.05 - Represents the unavailability of the animal category at farms 201 Table C.12 Uncertainty for CH4 emissions from manure management by non-dairy for 2014 Farms 2014 estimate Uncertainty range and percentage Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 532 371 692 30.19 Farm 2 1149 862 1437 25.01 Farm 3 235 181 289 22.95 Farm 4 31 14 48 55.05 Farm 5 2277 1821 2732 20.00 Farm 6 157 123 192 22.03 Farm 7 45 11 79 76.25 Farm 8 2194 1741 2647 20.65 Farm 9 435 348 522 20.00 Farm 10 307 248 367 19.36 Farm 11 406 312 501 23.17 Farm 12 88 68 109 23.58 Farm 13 - - - - Farm 14 3910 2962 4859 24.25 Farm 15 - - - - Farm 16 785 612 958 22.05 - Represents the unavailability of the animal category at farms Table C.13 Uncertainty for CH4 emissions from manure management by dairy cows for 2010 Farms 2010 estimate Uncertainty range and percentage Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 1138 750 1526 34.12 Farm 2 1084 786 1383 27.53 Farm 3 - - - - Farm 4 276 219 332 20.52 Farm 5 212 164 259 22.33 Farm 6 176 137 215 22.00 Farm 7 405 302 508 25.43 Farm 8 350 255 445 27.11 Farm 9 1292 830 1753 35.72 Farm 10 182 143 220 21.25 Farm 11 990 750 1230 24.21 Farm 12 447 354 539 20.65 Farm 13 - - - - Farm 14 1403 1035 1771 26.23 Farm 15 - - - - Farm 16 1591 1251 1930 21.36 - Represents the unavailability of the animal category at farms 202 Table C.14 Uncertainty for CH4 emissions from manure management by dairy cows for 2011 Farms 2011 estimate Uncertainty range and percentage Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 1387 948 1826 31.67 Farm 2 759 550 968 27.53 Farm 3 - - - - Farm 4 230 183 277 20.52 Farm 5 176 137 216 22.33 Farm 6 282 220 344 22.00 Farm 7 392 292 492 25.43 Farm 8 479 349 609 27.11 Farm 9 1292 830 1753 35.72 Farm 10 200 157 242 21.25 Farm 11 1287 975 1598 24.21 Farm 12 357 284 431 20.65 Farm 13 - - - - Farm 14 1603 1183 2024 26.23 Farm 15 - - - - Farm 16 1082 851 1313 21.36 - Represents the unavailability of the animal category at farms Table C.15 Uncertainty for CH4 emissions from manure management by dairy cows for 2012 Farms 2012 estimate Uncertainty range and percentage Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 1387 948 1826 31.67 Farm 2 2602 1886 3319 27.53 Farm 3 310 228 392 26.44 Farm 4 184 146 222 20.52 Farm 5 864 671 1057 22.33 Farm 6 246 192 301 22.00 Farm 7 392 292 492 25.43 Farm 8 369 269 469 27.11 Farm 9 490 315 665 35.72 Farm 10 200 157 242 21.25 Farm 11 1584 1200 1967 24.21 Farm 12 179 142 216 20.65 Farm 13 - - - - Farm 14 515 380 651 26.23 Farm 15 - - - - Farm 16 869 684 1055 21.36 - Represents the unavailability of the animal category at farms 203 Table C.16 Uncertainty for CH4 emissions from manure management by dairy cows for 2013 Farms 2013 estimate Uncertainty range and percentage Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 1408 962 1854 31.67 Farm 2 1965 1424 2507 27.53 Farm 3 343 252 433 26.44 Farm 4 161 128 194 20.52 Farm 5 212 164 259 22.33 Farm 6 317 247 387 22.00 Farm 7 267 199 335 25.43 Farm 8 313 228 398 27.11 Farm 9 535 344 725 35.72 Farm 10 200 157 242 21.25 Farm 11 1510 1144 1875 24.21 Farm 12 223 177 269 20.65 Farm 13 - - - - Farm 14 744 549 940 26.23 Farm 15 - - - - Farm 16 869 684 1055 21.36 - Represents the unavailability of the animal category at farms Table C.17 Uncertainty for CH4 emissions from manure management by dairy cows for 2014 Farms 2014 estimate Uncertainty range and percentage Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 1227 838 1615 31.67 Farm 2 2792 2023 3561 27.53 Farm 3 662 487 838 26.44 Farm 4 161 128 194 20.52 Farm 5 423 329 518 22.33 Farm 6 387 302 473 22.00 Farm 7 178 133 223 25.43 Farm 8 276 202 351 27.11 Farm 9 624 401 846 35.72 Farm 10 182 143 220 21.25 Farm 11 1510 1144 1875 24.21 Farm 12 156 124 189 20.65 Farm 13 - - - - Farm 14 773 570 976 26.23 Farm 15 - - - - Farm 16 1018 800 1235 21.36 - Represents the unavailability of the animal category at farms 204 Table C.18 Uncertainty for N2O emissions from manure management by non-dairy 2011 Farms 2011 estimate Uncertainty range and percentage (Kg CO2eq) Lower bound Upper bound Uncertainty percentage Farm 1 9585 6348 12822 33.77 Farm 2 11198 5208 17188 53.49 Farm 3 64342 17951 110733 72.1 Farm 4 15085 6051 24119 59.89 Farm 5 8065 2266 13863 71.9 Farm 6 3599 1811 5388 49.68 Farm 7 24269 11222 37316 53.76 Farm 8 2825 1134 4515 59.85 Farm 9 4888 2160 7616 55.81 Farm 10 1060 506 1615 52.33 Farm 11 43509 19936 67083 54.18 Farm 12 4370 931 7808 78.69 Farm 13 34518 12033 57004 65.14 Farm 14 8231 2324 14137 71.76 Farm 15 6337 1504 11171 76.27 Farm 16 7311 2429 12193 66.78 Table C.19 Uncertainty for N2O emissions from manure management by non-dairy for 2012 Farms 2012 estimate Uncertainty range and percentage (Kg CO2eq) Lower bound Upper bound Uncertainty percentage Farm 1 11654 7351 15956 36.92 Farm 2 14820 4713 24928 68.2 Farm 3 42458 18083 66833 57.41 Farm 4 16315 4994 27636 69.39 Farm 5 3699 1794 5603 51.49 Farm 6 3530 1480 5580 58.07 Farm 7 14792 7211 22373 51.25 Farm 8 1071 543 1600 49.34 Farm 9 3707 1662 5752 55.17 Farm 10 1402 672 2132 52.08 Farm 11 48191 21806 74575 54.75 Farm 12 3114 1393 4834 55.26 Farm 13 37416 14506 60326 61.23 Farm 14 4528 2239 6817 50.56 Farm 15 2018 552 3484 72.64 Farm 16 4796 2520 7072 47.45 205 Table C.20 Uncertainty for N2O emissions from manure management by non-dairy for 2013 Farms 2013 estimate Uncertainty range and percentage (Kg CO2eq) Lower bound Upper bound Uncertainty percentage Farm 1 6725 4029 9420 40.08 Farm 2 42208 14574 69842 65.47 Farm 3 6081 2617 9545 56.97 Farm 4 3144 1170 5118 62.8 Farm 5 37602 8690 66515 76.89 Farm 6 3541 1934 5148 45.39 Farm 7 2094 877 3311 58.13 Farm 8 26458 16332 36583 38.27 Farm 9 4951 2660 7242 46.27 Farm 10 6244 3215 9272 48.51 Farm 11 11394 4763 18026 58.2 Farm 12 44488 21141 67836 52.48 Farm 13 - - - - Farm 14 60739 18367 103110 69.76 Farm 15 - - - - Farm 16 22534 12153 32916 46.07 - Represents the unavailability of the animal category at farms Table C.21 Uncertainty for N2O emissions from manure management by non-dairy for 2014 Farms 2014 estimate Uncertainty range and percentage (Kg CO2eq) Lower bound Upper bound Uncertainty percentage Farm 1 4333 2242 6425 48.27 Farm 2 12234 6520 17949 46.71 Farm 3 4556 2108 7004 53.74 Farm 4 602 44 1159 92.61 Farm 5 41416 13630 69202 67.09 Farm 6 3061 1364 4757 55.42 Farm 7 867 -151 1885 117.48 Farm 8 42749 14890 70609 65.17 Farm 9 4806 2501 7110 47.95 Farm 10 7689 3601 11777 53.17 Farm 11 7900 2387 13413 69.78 Farm 12 1719 529 2909 69.22 Farm 13 - - - - Farm 14 76007 20856 131157 72.56 Farm 15 - - - - Farm 16 15260 6824 23696 55.28 - Represents the unavailability of the animal category at farms 206 Table C.22 Uncertainty for N2O emissions from manure management by dairy cows for 2010 Farms 2010 estimate Uncertainty range and percentage (Kg CO2eq) Lower bound Upper bound Uncertainty percentage Farm 1 173013 22717 323309 86.87 Farm 2 3212 428 5996 86.68 Farm 3 - - - - Farm 4 4356 576 8135 86.77 Farm 5 3344 440 6247 86.85 Farm 6 2781 356 5206 87.20 Farm 7 7037 1773 12301 74.81 Farm 8 6509 857 12162 86.84 Farm 9 20406 2692 38120 86.81 Farm 10 2869 378 5359 86.81 Farm 11 15637 2086 29189 86.66 Farm 12 7056 1676 12435 76.24 Farm 13 - - - - Farm 14 26074 3476 48671 86.67 Farm 15 - - - - Farm 16 25126 3337 46915 86.72 - Represents the unavailability of the animal category at farms Table C.23 Uncertainty for N2O emissions from manure management by dairy cows for 2011 Farms 2011 estimate Uncertainty range and percentage (Kg CO2eq) Lower bound Upper bound Uncertainty percentage Farm 1 20082 4589 35575 77.15 Farm 2 11991 1684 22298 85.96 Farm 3 - - - - Farm 4 3630 480 6779 86.77 Farm 5 2786 366 5206 86.85 Farm 6 4450 570 8330 87.20 Farm 7 6192 811 11574 86.91 Farm 8 8907 1172 16642 86.84 Farm 9 20406 2692 38120 86.81 Farm 10 3155 416 5895 86.81 Farm 11 20329 2712 37945 86.66 Farm 12 5644 752 10536 86.67 Farm 13 - - - - Farm 14 29798 3972 55624 86.67 Farm 15 17086 2278 31894 86.67 Farm 16 - - - - - Represents the unavailability of the animal category at farms 207 Table C.24 Uncertainty for N2O emissions from manure management by dairy cows for 2012 Farms 2012 estimate Uncertainty range and percentage (Kg CO2eq) Lower bound Upper bound Uncertainty percentage Farm 1 20082 4589 35575 77.15 Farm 2 7708 1038 14379 86.54 Farm 3 4892 656 9127 86.58 Farm 4 2904 384 5423 86.77 Farm 5 13653 1795 25510 86.85 Farm 6 3894 498 7289 87.20 Farm 7 6192 811 11574 86.91 Farm 8 6852 902 12802 86.84 Farm 9 7740 1021 14459 86.81 Farm 10 3155 416 5895 86.81 Farm 11 25020 3338 46702 86.66 Farm 12 2822 376 5268 86.67 Farm 13 - - - - Farm 14 9578 1277 17879 86.67 Farm 15 - - - - Farm 16 13736 1824 25647 86.72 - Represents the unavailability of the animal category at farms Table C.25 Uncertainty for N2O emissions from manure management by dairy cows for 2013 Farms 2013 estimate Uncertainty range and percentage (Kg CO2eq) Lower bound Upper bound Uncertainty percentage Farm 1 22244 3112 41377 86.01 Farm 2 31050 7222 54877 76.74 Farm 3 5411 664 10158 87.72 Farm 4 2541 336 4745 86.77 Farm 5 3344 440 6247 86.85 Farm 6 5006 641 9371 87.20 Farm 7 4222 553 7891 86.91 Farm 8 5824 766 10882 86.84 Farm 9 8444 1114 15774 86.81 Farm 10 3155 745 5566 76.39 Farm 11 23847 3181 44513 86.66 Farm 12 3528 470 6585 86.67 Farm 13 - - - - Farm 14 13835 1844 25826 86.67 Farm 15 - - - - Farm 16 13736 1824 25647 86.72 - Represents the unavailability of the animal category at farms 208 Table C.26 Uncertainty for N2O emissions from manure management by dairy cows for 2014 Farms 2014 estimate Uncertainty range and percentage (Kg CO2eq) Lower bound Upper bound Uncertainty percentage Farm 1 17765 4059 31470 77.15 Farm 2 44109 5937 82281 86.54 Farm 3 10466 1396 19536 86.66 Farm 4 2541 336 4745 86.77 Farm 5 6687 879 12495 86.85 Farm 6 6118 783 11454 87.20 Farm 7 2815 368 5261 86.91 Farm 8 5139 676 9601 86.84 Farm 9 9851 1299 18403 86.81 Farm 10 2869 378 5359 86.81 Farm 11 23847 5361 42333 77.52 Farm 12 2469 329 4610 86.67 Farm 13 - - - - Farm 14 14367 1915 26819 86.67 Farm 15 - - - - Farm 16 16081 2136 30026 86.72 - Represents the unavailability of the animal category at farms Table C.27 Uncertainty for CH4 emissions from biomass burning for 2011 Farms 2011 estimate Uncertainty range and percentage Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 - - - - Farm 2 - - - - Farm 3 - - - - Farm 4 29757 11644 47870 60.87 Farm 5 - - - - Farm 6 16527 6467 26587 60.87 Farm 7 47208 18472 75944 60.87 Farm 8 - - - - Farm 9 - - - - Farm 10 33054 12934 53174 60.87 Farm 11 - - - - Farm 12 - - - - Farm 13 4725 1849 7601 60.87 Farm 14 - - - - Farm 15 11802 4618 18986 60.87 Farm 16 - - - - - Represents the unavailability of fire at farms 209 Table C.28 Uncertainty for CH4 emissions from biomass burning for 2012 Farms 2012 estimate Uncertainty range and percentage Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 - - - - Farm 2 - - - - Farm 3 3066 1200 4932 60.87 Farm 4 - - - - Farm 5 - - - - Farm 6 21252 8316 34188 60.87 Farm 7 - - - - Farm 8 2835 1109 4561 60.87 Farm 9 - - - - Farm 10 - - - - Farm 11 - - - - Farm 12 - - - - Farm 13 - - - - Farm 14 38955 15243 62667 60.87 Farm 15 11802 4618 18986 60.87 Farm 16 - - - - - Represents the unavailability of fire at farms Table C.29 Uncertainty for CH4 emissions from biomass burning for 2013 Farms 2013 estimate Uncertainty range and percentage Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 28329 11085 45573 60.87 Farm 2 - - - - Farm 3 2352 920 3784 60.87 Farm 4 - - - - Farm 5 28812 11274 46350 60.87 Farm 6 11802 4618 18986 60.87 Farm 7 47208 18472 75944 60.87 Farm 8 - - - - Farm 9 25032 9795 40269 60.87 Farm 10 - - - - Farm 11 - - - - Farm 12 - - - - Farm 13 5901 2309 9493 60.87 Farm 14 - - - - Farm 15 11802 4618 18986 60.87 Farm 16 - - - - - Represents the unavailability of fire at farms 210 Table C.30 Uncertainty for CH4 emissions from biomass burning for 2014 Farms 2014 estimate Uncertainty range and percentage Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 - - - - Farm 2 - - - - Farm 3 - - - - Farm 4 - - - - Farm 5 - - - - Farm 6 12180 4766 19594 60.87 Farm 7 - - - - Farm 8 - - - - Farm 9 25074 9811 40337 60.87 Farm 10 - - - - Farm 11 4725 1849 7601 60.87 Farm 12 - - - - Farm 13 - - - - Farm 14 - - - - Farm 15 - - - - Farm 16 - - - - - Represents the unavailability of fire at farms Table C.31 Uncertainty for N2O emissions from biomass burning for 2011 Farms 2011 estimate Uncertainty range and percentage Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 - - - - Farm 2 - - - - Farm 3 - - - - Farm 4 5580 2329 8831 58.26 Farm 5 - - - - Farm 6 3100 1294 4906 58.26 Farm 7 8680 3623 13737 58.26 Farm 8 - - - - Farm 9 - - - - Farm 10 5890 2458 9322 58.26 Farm 11 - - - - Farm 12 - - - - Farm 13 930 388 1472 58.26 Farm 14 - - - - Farm 15 2170 906 3434 58.26 Farm 16 - - - - - Represents the unavailability of fire at farms 211 Table C.32 Uncertainty for N2O emissions from biomass burning 2012 Farms 2012 estimate Uncertainty range and percentage Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 - - - - Farm 2 - - - - Farm 3 620 259 981 58.26 Farm 4 - - - - Farm 5 - - - - Farm 6 4030 1682 6378 58.26 Farm 7 - - - - Farm 8 620 259 981 58.26 Farm 9 - - - - Farm 10 - - - - Farm 11 - - - - Farm 12 - - - - Farm 13 - - - - Farm 14 7130 2976 11284 58.26 Farm 15 2170 906 3434 58.26 Farm 16 - - - - - Represents the unavailability of fire at farms Table C.33 Uncertainty for N2O emissions from biomass burning for 2013 Farms 2013 estimate Uncertainty range and percentage Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 5270 2200 8340 58.26 Farm 2 - - - - Farm 3 310 129 491 58.26 Farm 4 - - - - Farm 5 5270 2200 8340 58.26 Farm 6 2170 906 3434 58.26 Farm 7 8680 3623 13737 58.26 Farm 8 - - - - Farm 9 4650 1941 7359 58.26 Farm 10 - - - - Farm 11 - - - - Farm 12 - - - - Farm 13 930 388 1472 58.26 Farm 14 - - - - Farm 15 2170 906 3434 58.26 Farm 16 - - - - - Represents the unavailability of fire at farms 212 Table C.34 Uncertainty for N2O emissions from biomass burning for 2014 Farms 2014 estimate Uncertainty range and percentage Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 - - - - Farm 2 - - - - Farm 3 - - - - Farm 4 - - - - Farm 5 - - - - Farm 6 2790 1165 4415 58.26 Farm 7 - - - - Farm 8 - - - - Farm 9 5890 2458 9322 58.26 Farm 10 - - - - Farm 11 930 388 1472 58.26 Farm 12 - - - - Farm 13 - - - - Farm 14 - - - - Farm 15 - - - - Farm 16 - - - - - Represents the unavailability of fire at farms Table C.35 Uncertainty for N2O emissions from agricultural managed soils for 2011 Farms 2011 Uncertainty range and percentage estimate Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 42191 -8164 92546 119.35 Farm 2 29450 -13998 72898 147.53 Farm 3 13671 -1412 28754 110.33 Farm 4 3410 -1194 8014 135.00 Farm 5 5270 -1850 12390 135.11 Farm 6 3410 -885 7705 125.96 Farm 7 23870 -12725 60465 153.31 Farm 8 6200 -6444 18844 203.93 Farm 9 10850 -4578 26278 142.19 Farm 10 11780 -4147 27707 135.2 Farm 11 22320 -2080 46720 109.32 Farm 12 5890 -3593 15373 161.01 Farm 13 26350 -9251 61951 135.11 Farm 14 13330 -2497 29157 118.73 Farm 15 8990 -1130 19110 112.57 Farm 16 63550 -16110 143210 125.35 213 Table C.36 Uncertainty for N2O emissions from agricultural managed soils for 2012 Farms 2012 estimate Uncertainty range and percentage Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 44454 -8602 97510 119.35 Farm 2 40920 -19449 101289 147.53 Farm 3 18724 -1934 39382 110.33 Farm 4 3720 -1302 8742 135.00 Farm 5 9920 -3483 23323 135.11 Farm 6 3100 -805 7005 125.96 Farm 7 13330 -7106 33766 153.31 Farm 8 4960 -5155 15075 203.93 Farm 9 5890 -2485 14265 142.19 Farm 10 7130 -2510 16770 135.2 Farm 11 25110 -2340 52560 109.32 Farm 12 10230 -6241 26701 161.01 Farm 13 27590 -9687 64867 135.11 Farm 14 5890 -1103 12883 118.73 Farm 15 8370 -1052 17792 112.57 Farm 16 11160 -2829 25149 125.35 Table C.37 Uncertainty for N2O emissions from agricultural managed soils for 2013 Farms 2013 estimate Uncertainty range and percentage Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 43834 -8482 96150 119.35 Farm 2 50840 -24164 125844 147.53 Farm 3 10044 -1038 21126 110.33 Farm 4 8060 -2821 18941 135.00 Farm 5 29760 -10449 69969 135.11 Farm 6 4030 -1046 9106 125.96 Farm 7 7750 -4132 19632 153.31 Farm 8 4650 -4833 14133 203.93 Farm 9 7130 -3008 17268 142.19 Farm 10 6820 -2401 16041 135.2 Farm 11 44950 -4189 94089 109.32 Farm 12 20770 -12672 54212 161.01 Farm 13 31620 -11102 74342 135.11 Farm 14 7130 -1335 15595 118.73 Farm 15 7440 -935 15815 112.57 Farm 16 17980 -4558 40518 125.35 214 Table C.38 Uncertainty for N2O emissions from agricultural managed soils for 2014 Farms 2014 estimate Uncertainty range and percentage Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 59272 -11469 130013 119.35 Farm 2 69130 -32857 171117 147.53 Farm 3 13082 -1351 27515 110.33 Farm 4 9920 -3472 23312 135.00 Farm 5 55180 -19374 129734 135.11 Farm 6 42780 -11106 96666 125.96 Farm 7 21080 -11238 53398 153.31 Farm 8 4960 -5155 15075 203.93 Farm 9 6200 -2616 15016 142.19 Farm 10 21700 -7638 51038 135.2 Farm 11 14260 -1329 29849 109.32 Farm 12 1860 -1135 4855 161.01 Farm 13 32860 -11537 77257 135.11 Farm 14 6820 -1277 14917 118.73 Farm 15 9920 -1247 21087 112.57 Farm 16 17980 -4558 40518 125.35 Table C.39 Uncertainty for CO2 from diesel tractor for 2011 Farms 2011 estimate Uncertainty range and percentage Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 12220 10582 13857 13.4 Farm 2 7795 6750 8839 13.4 Farm 3 2299 1991 2607 13.4 Farm 4 4317 3739 4896 13.4 Farm 5 2976 2577 3375 13.4 Farm 6 903 782 1024 13.4 Farm 7 22571 19546 25595 13.4 Farm 8 14802 12819 16786 13.4 Farm 9 2871 2486 3256 13.4 Farm 10 6141 5318 6964 13.4 Farm 11 26318 22792 29845 13.4 Farm 12 10236 8864 11607 13.4 Farm 13 28366 24565 32167 13.4 Farm 14 3464 3000 3929 13.4 Farm 15 12729 11023 14434 13.4 Farm 16 45858 39713 52003 13.4 215 Table C.40 Uncertainty for CO2 from diesel tractor for 2012 Farms 2012 estimate Uncertainty range and percentage Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 18707 16201 21214 13.4 Farm 2 8005 6932 9077 13.4 Farm 3 1354 1173 1536 13.4 Farm 4 6168 5341 6994 13.4 Farm 5 2661 2305 3018 13.4 Farm 6 1129 977 1280 13.4 Farm 7 21495 18614 24375 13.4 Farm 8 14802 12819 16786 13.4 Farm 9 2997 2596 3399 13.4 Farm 10 6141 5318 6964 13.4 Farm 11 20755 17973 23536 13.4 Farm 12 1024 886 1161 13.4 Farm 13 30234 26183 34286 13.4 Farm 14 6394 5537 7251 13.4 Farm 15 12729 11023 14434 13.4 Farm 16 60804 52657 68952 13.4 Table C.41 Uncertainty for CO2 from diesel tractor for 2013 Farms 2013 estimate Uncertainty range and percentage Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 18057 15637 20476 13.4 Farm 2 7406 6414 8399 13.4 Farm 3 861 745 976 13.4 Farm 4 6168 5341 6994 13.4 Farm 5 1850 1602 2098 13.4 Farm 6 596 516 676 13.4 Farm 7 25904 22433 29375 13.4 Farm 8 7598 6580 8616 13.4 Farm 9 3163 2739 3586 13.4 Farm 10 6141 5318 6964 13.4 Farm 11 22597 19569 25625 13.4 Farm 12 3330 2884 3777 13.4 Farm 13 30234 26183 34286 13.4 Farm 14 5748 4977 6518 13.4 Farm 15 12939 11205 14673 13.4 Farm 16 87884 76107 99660 13.4 216 Table C.42 Uncertainty for CO2 from diesel tractor for 2014 Farms 2014 estimate Uncertainty range and percentage Kg CO2eq Lower bound Upper bound Uncertainty percentage Farm 1 15080 13060 17101 13.4 Farm 2 7795 6750 8839 13.4 Farm 3 5795 5018 6571 13.4 Farm 4 12577 10891 14262 13.4 Farm 5 2661 2305 3018 13.4 Farm 6 4514 3909 5119 13.4 Farm 7 28741 24890 32592 13.4 Farm 8 4724 4091 5357 13.4 Farm 9 3107 2691 3524 13.4 Farm 10 6960 6028 7893 13.4 Farm 11 14435 12500 16369 13.4 Farm 12 8608 7455 9762 13.4 Farm 13 24938 21596 28280 13.4 Farm 14 5427 4700 6155 13.4 Farm 15 8635 7478 9792 13.4 Farm 16 12708 11005 14411 13.4 217 Appendix D: Questionnaire survey The purpose of this questionnaire survey is to assess the livestock and crop farming management practices in the eastern Free State. The results of the survey will assist in estimating GHG emissions and also evaluating the potential mitigation strategies that can reduce the GHG emissions from livestock and crop production. Any information provided by the farmers will be used for research purposes only and will be treated confidentially. When completing the questionnaire indicate your response by filling the blank spaces provided. Background on Interviewee Farm no: Geographical co-ordinates: Farmer ‘s Name: Contact: Date: 218 Land use categories Land use category Area (ha) Percent (%) Cropland Grassland Settlements Farm soil type Soil name Acronym Organic Description 219 Area burned Year Burned Area (ha) Percentage (%) Annual Area burned Burn Frequency (ha) 2010 2011 2012 2013 2014 220 SECTION A: LIVESTOCK DATA Year: Feeding situation Category Sub-categories Population data Feeding situation per sub-category Live weights Confined % Grazing % Pasture % Dairy Mature female cattle cows Heifers Calves Bulls Beef cattle Feedlot Mature cows Heifers Young oxens Mature oxens Bulls Calves Sheep 221 Ewes Ewe - lamb Lambs Goats Pigs Boar Sows Growers 222 Milk production Year: Category Sub- Population Mature females only Milk production categories data % of females pregnant % of females lactating Average daily production (kg day-1) Total annual production Dairy Mature cattle female cows Beef cattle Mature female cows Sheep Ewes 223 Manure management system usage (%) Year: Category Sub-categories Population data Manure management system usage (%) Lagoon Drylot Daily spread Compost Pasture Dairy cattle Mature female cows Heifers Calves Bulls Beef cattle Feedlot Mature cows Heifers Young oxens Mature oxens Bulls Calves Sheep Ewes Heifers Lambs Goats 224 Pigs Boar Sows Growers 225 SECTION B: CROPLAND DATA Management practices Year: Crop Area Production Management practices types planted(ha) (kilo Tillage practices Mineral fertilizer Organic amendments Lime application Irrigation practices tonnes) Full Minimum No till till till 1. 2. 3. 226 Residues management Year: Crop Area Production Residues management Fertilizer types planted(ha) (kilo tonnes) application (kg/ha) Retained (%) Burned (%) Collected (%) Grazed (%) 1. 2. 3. 4. 227 Cropping systems Year: Crop Area Production Cropping systems Cropping systems combination types planted(ha) (kilo Continous Rotation Single Double Inter-crop tonnes) 1. 2. 3. 4. 5. 228