Soil, Crop and Climate Sciences
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Browsing Soil, Crop and Climate Sciences by Author "Barnard, J. H."
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Item Open Access Quantifying soil fertility parameters with electromagnetic induction, infrared reflectance spectroscopy and conventional chemistry procedures for wheat and maize under irrigation in arid climate(University of the Free State, 2021-04) Gura, Isaac; Du Preez, C. C.; Van Rensburg, L. D.; Barnard, J. H.Current global challenges, such as food security and soil quality, cannot be solved without up-to-date, high-quality, high-resolution, spatio-temporal, and continuous soil and environmental data that characterize soil and cropping ecosystems. Therefore, accurate and precise assessments of soil and crop characteristics are critical for site-specific management, vibrant soil condition and environmental sustainability. The inability to evaluate soil and crop characteristics quickly and inexpensively remains one of the main challenges of precision agriculture. Therefore, the ultimate aim of this study was to evaluate the use of soil sensors, viz the mid-infrared (MIR) sensor and the apparent electrical conductivity (ECa) sensor, in quantifying multiple soil fertility properties and their variability under irrigation. The study also attempted to apply the sensor data fusion approach to improve the assessment of multiple soil quality indicators and the overall soil quality under irrigation using the Soil Management Assessment Framework (SMAF). The established international ECa-directed soil sampling design approach was employed at each of the seven fields of interest by measuring apparent soil electrical conductivity (ECa) with a Geonics EM38-MK2 sensor (non-invasive geophysical electromagnetic induction, EMI). A “Response Surface Sampling Design” (RSSD) sampling methodology in the “Electrical Conductivity Sampling Assessment and Prediction” (ESAP) software was used to direct soil and crop sampling based on the degree of ECa variability. This methodology reduced sampling points from each field to 12 sampling points after an initial ECa survey. Soil samples from each field were analysed in the laboratory for various soil properties that are related to soil fertility. Wheat and maize were also sampled from the ECa directed sampling points at each field at the end of the winter 2016 season and 2016/17 summer season, respectively. The MIR spectra was obtained in the laboratory from the soil and crop samples from each field using a sensor iS50 Nicolet Fourier Transform Infrared (FTIR) (Thermo Fisher Scientific Inc., Waltham, MA) equipped with an accessory for attenuated reflectance acquisition (iS50 FTIR-ATR). The MIR sensor and EM38 sensor showed different levels of accuracy with respect to predicting soil fertility properties under irrigation. The study results demonstrated the effectiveness and usefulness of the MIR attenuated total reflectance (ATR) technique coupled with partial squares least regression (PLSR) in quantitative analysis of soil fertility properties. In contrast, the EM38 sensor modelled accurately only a few soil properties per site at a given sampling time. Comparatively, the model results from both sensors show that the MIR sensor produced better prediction models for most of the measured soil fertility properties than the EM38 sensor. For quantifying nutrient accumulation in wheat and maize, the MIR sensor technique produced more excellent predictive models for the nutrient concentrations in wheat samples than in maize. The results from the in-field spatial characterization of plant nutrient levels and crop yields at the study sites showed that although ECa readings may be useful for the spatial characterization of some soil fertility properties in non-saline and non-sodic soils in South Africa, the results showed many inconsistencies between sites and between the centre pivots. The limitations of quantifying soil properties and overall soil quality using a single soil sensor can be overcome by integrating data from conceptually different sensing techniques to improve model accuracy and robustness. The findings in this study demonstrated that models for most of the soil properties obtained based on step-wise multiple linear regression (SMLR) fusion of data from MIR sensor and EM38 sensor measurements were more robust as compared to models from individual sensors. The SMLR sensor fusion technique failed to improve the models of some soil properties at the selected fields as well as the overall SMAF soil quality index at the Douglas 40 ha field. A more robust fusion technique such as PLSR can be used to implement the data fusion for these properties. The sensor data fusion results demonstrate the superiority and efficiency of the sensor data fusion approach in the measurement of soil fertility properties and overall soil quality in irrigation systems of South Africa. Based on the findings of this study, for soil fertility evaluation and quantification, it is recommended to use the MIR technique coupled with PLSR and alternatively, the ECa measurements as complementary information to provide extended attribute coverage and increased capacity of the sensor data fusion.Item Open Access Quantifying spatio-temporal soil water content using electromagnetic induction(University of the Free State, 2017) Edeh, Judith Amarachukwu; Barnard, J. H.; Van Rensburg, L. D.Water scarcity is still a global concern, and the fact that water is not evenly distributed within the soil remains a case study in agriculture. Apparent electrical conductivity (ECₐ) measured with the EM38 devices have been consequently used to distinguished areas of water management in precision agriculture, before irrigation planning. However, to efficiently use EM38 and its newer model “EM38-MK2” required site specific calibration. This involves collecting soil samples for volumetric water content the same time the device is used. Repeated soil sampling over time series have been reportedly stated to be time consuming and destructive. Therefore, this thesis proposed to use DFM capacitance probes that only need to be installed once in the soil to continuously record water content. The study presented three main objectives to: (i) examine the influence of DFM probes, and other possible obstructions including neutron water meter galvanized-steel access tubes and profile pits on ECₐ measurements with the EM38-MK2, (ii) calibrate the EM38-MK2 using DFM probes installed in the field, and (iii) spatially characterize soil water content estimated from multiple EM38-MK2 surveys. On relative homogenous soils of Kenilworth Experimental Farm and with DFM probes, steel access tubes and profile pits consecutively inserted into the soil, EM38-MK2 was moved towards these interferences, over it and away from it without zeroing the EM-device. Results showed that while trenches had no effect, both DFM and steel tubes influenced ECₐ readings when the EM-device was closer than 1 m to these instruments. This effect was inconsistent with large values that were either negative or positive. After encountering the interferences and without EM zeroing, ECₐ readings were either less stable (only at vertical mode for the DFM) or reduced. Although the instability was statistically significant, the mean ECₐ before and after the probe-interference were not significantly different. The decreases in mean ECₐ values at horizontal mode for DFM and at both modes for steel tubes were all relatively small (< 2 mS m-1). This study concluded that the EM38-MK2 can be used together with DFM probes, but keeping the EM-device at least 1 m away from the probes. On a practical level, there should be no need to re-zero the EM38-MK2 after an encounter with such metal-containing interferences. Rather, re-zeroing is advised after extended use in the field as suggested by other researchers. On the heterogeneous soils of Paradys Experimental Farm comprising of four diverse soil forms, field calibration of DFM probes and EM38-MK2 were performed under both dry and wet conditions. The calibrated capacitance probes accurately predicted water content that spatially explained on average, up to 96% of the observed water content. The DFM estimated soil water values on individual plots were consistent and were used for site-specific calibration of EM38-MK2. ECₐ-based estimated water content for individual plot models explained on average, 97% and 90% of variation in soil water content, at 0.38 m and 0.75 m depth, respectively. With the general models ECₐ values could predict 74% and 69% of the volumetric soil water content at 0.38 m and 0.75 m, respectively. This was regarded as satisfactory, especially considering the heterogeneity of the soils on the experimental site. Therefore, the models developed in this study, performed well both at individual plot and over spatial scales. When the general models were applied on spatial scale, ECₐ-based estimated water content was temporally stable. The spatio-temporal soil water maps produced an accurate representation of topographical effects on soil water distribution over the area. Therefore, the proposed use of the DFM capacitance probe method for site specific-calibration of EM38-MK2 was successful and could be adopted for future research.Item Open Access Sampling and extraction methods for soil inorganic N determination to calibrate the EM38 in irrigated fields(University of the Free State, 2019-06) Steenekamp, Diandra; Van Rensburg, L. D.; Du Preez, C. C.; Barnard, J. H.Precise management of N variability in crop fields are required to increase yields and ensure sustainable and economic crop production, whilst not having a negative impact on the environment. A popular type of sensor for characterizing soil variability is the EM38-MK2 that measures apparent electrical conductivity (ECa), operating on the principle of electromagnetic induction (EMI). After analysis, inorganic N results can be calibrated to ECa measurements. It has been established that NH4+-N and NO3ˉ-N can be predicted from ECa. This study presented three main research aims to: i) compare single and composite samples for the determination of NH4ˉ-N, NO3ˉ-N, and total inorganic N (TIN), ii) determine if the saturated paste extract (SATe) could replace the standard 2.0 M KCl extraction for determination of NH4ˉ-N and NO3ˉ-N, and iii) determine what combination of single or composite ECa measurements and inorganic N at different sampling depths would produce the most statistically significant inorganic N prediction model. EMI surveys were conducted on four study sites under centre pivots, located on commercial irrigation farms in the districts of Douglas, Luckhoff, Hofmeyr and Empangeni. Using ECa data with the “Electrical Conductivity Sampling Assessment and Prediction” (ESAP) software and it‟s featured “Response Surface Sampling Design” (RSSD) sampling methodology, soil sampling points were identified based on the degree of ECa variability. Before sample collection, additional ECa readings were taken at each sampling point, one in the centre and one on each corner of a 1 m2 area. Afterwards soil samples were collected in the same manner in 300 mm depth increments up to 1500 mm. Samples collected in the centre were considered single, while those from the corners were composited. Concentrations of NH4+-N and NO3ˉ-N in KCl and SATe soil extracts were simultaneously determined colorimetrically. For the first aim, inorganic N concentrations in KCl extracts was loge transformed and pooled to compare sampling methods irrespective of study site, sampling point, and depth. For the second aim, data of inorganic N concentrations determined in KCl and SATe extracts were transformed and pooled for comparison, irrespective of site, sampling point, sampling method, and depth. The third aim was divided into three parts, determining agreement between single and composite ECa measurements, determining what inorganic N values to use, i.e. what sampling method and extract, and finally model calibration. Statistical analysis focused on assessing agreement using the Bland-Altman method for assessing agreement on a 95% confidence interval and multiple-linear regression calibration models were developed in Microsoft Excel. Results revealed poor agreement between single and composite samples for NO3ˉ-N and TIN. A composite sample taken in a 1 m2 area was more suitable when investigating NO3ˉ-N or TIN. Good agreement was found for NH4ˉ-N and a single sample proved sufficient. Agreement between the two extracts was poor for both NH4+-N and NO3ˉ-N and it was concluded that SATe cannot replace KCl for inorganic N determination. Agreement between single and composite ECa measurements was good and one ECa measurement was sufficient per 1 m2 sampling location. Based on the conclusions from the previous research questions, inorganic N results used for model development were those from composite samples extracted using KCl and an average between the single and composite ECa measurements was used. Values of inorganic N, ECa and elevation were loge transformed. Results showed that the majority of the calibration models were statistically insignificant except for one sampling depth (900 to 1200 mm) at Douglas (ECa 0 to 750 mm; R2=0.54 and ECa 0 to 1500 mm; R2=0.57). It was concluded that for the sites investigated, inorganic N was not the dominant soil property influencing ECa.