Evaluating maize production potential of selected semi-arid ecotopes using a water balance model

English: The quantitative evaluation of crop production potential is important for sustainable and wise land use as well as for food security where subsistence farmers are involved. It is of particular importance in arid and semi-arid areas where rainfall is marginal and variable. This study aims at making a quantitative evaluation of the maize production potential of the Glen/Hutton and Glen/Oakleaf ecotopes which are located at the Glen Agricultural Research Station in the semi-arid Free State Province of South Africa. The objective was to characterize the ecotopes, and to make long-term yield predictions with a yield prediction model using long-term climate data. A detailed profile description, soil analyses and an in situ drainage curve were made for the Glen/Oakleaf ecotope. Similar data for the Glen/Hutton ecotope was obtained from previous research work (Hensley et al., 1993; Hattingh, 1993; Hensley, personal communication, 2002). A neutron water meter (NWM) was calibrated for each horizon of the Oakleaf soil on the Glen/Oakleaf ecootpe. The plant available water (PAW), defined as the differences between the drained upper limit (DUL) and the lower limit (LL), for maize grown on the Glen/Hutton and Glen/Oakleaf ecotopes was 133 mm and 120 mm respectively. Considering a mature maize crop growing in summer on these two ecotopes, PAW can be defined as the difference between the crop modified upper limit (CMUL) and LL. Results for this parameter were 183 mm and 192 mm for the Glen/Hutton and Glen/Oakleaf ecotopes respectively. The reason for the relatively high value of the latter is its slower drainage rate, which enables the crop to extract more water while drainage proceeds between field saturation and DUL than in the rapidly draining Hutton soil. Yields measured on experiments on the two ecotopes for 12 seasons on the Glen/Hutton and 10 seasons on the Glen/Oakleaf ecotope indicate that these two ecotopes have similar production potentials. For the development of a yield prediction model it was necessary to find a way to estimate daily crop evapotranspiration (ET). Based on the semi-arid climate, soil morphological observations and results of soil analyses, deep drainage from these two maize ecotopes was considered to be negligible. Equations for predicting runoff from rainfall (P) were developed based on long-term runoff measurements made at nearby sites (Du Plessis and Mostert, 1965; Hensley, personal communication, 2002). Because of fairly good r² values (0.84 and 0.82) the equations can be considered as reliable enough for the purpose of this study. A procedure for estimating soil water content at planting, from the rainfall pattern during preceding fallow period and grain yield in the preceding season, was also developed based on measurements from previous research work (De Jager and Hensley, 1988; Hattingh, 1993). Using all this information it was possible to make a fairly reliable estimation of daily ET. Climate data was used to calculate daily potential evaporation (Eo) values. This enabled the degree of crop water stress to be defined as ET/Eo , on a daily basis. The maize growing season was divided into three stages i.e. the vegetative, flowering and seed filling stages. A stress index (SI), defined as the average ET/Eo value for each period, was then calculated. To develop an integrated stress index (ISI) for the growing season eight different methods of integrating the three SI values were formulated. Measured maize yields from experimental plots on the two ecotopes were available for 22 seasons (De Wet and Engelbrecht, 1962; De Bruyn, 1974; De Jager and Hensley, 1988; Hattingh, 1993). Integrated stress index values were then calculated for these seasons and correlated with the biomass yields. This made it possible to choose the best method of calculating the ISI value from the individual SI's. The ISI with the best correlation (r² = 0.69) was the one with formula ISI = (2A + 3B + 2C)/7, where A, B and C are the SI values of the three growth periods respectively. The equation to predict total biomass (Yb) is Yb = 15238 ISI + 1067 kg ha¹. The biomass prediction equation was used to generate maize yields for 80 seasons (1922/23 - 2001/02). Yb was converted to grain yield using a harvest index regression equation based on 38 yields from Glen for which both total biomass and grain yield had been measured. Four production techniques were compared, i.e., November planting with conventional tillage (CTN), January planting with conventional tillage (CTJ), November planting with in-field water harvesting and basin tillage (WHBN), and January planting with water harvesting and basin tillage (WHBJ). Cumulative probability functions (CPF's) of yields were computed for the four different production techniques. The CPF's indicated that the long-term mean yields (at 50% probability) were 2653, 2 685, 3 108, and 3 355 kg ha¹ for CTN, CTJ, WHBN and WHBJ respectively. The CPF's were compared using the stochastic dominance and the Kolmogorov-Smimov (K-S) tests (Anderson et al., 1977; Steel et al., 1997). Stochastic dominance results indicated that the WHBJ and WHBN production techniques have well defined first degree stochastic dominance over the CTN and CTJ techniques. January planting showed only second degree stochastic dominance over November planting. The K-S test indicated that the CPF's of the water harvesting techniques were significantly different from those of the conventional production techniques. No statistical significant difference was observed with the K-S test between the November and January plantings.