Assessing the potential of conservation agriculture to off-set the effects of climate change on crop productivity using crop simulations model (APSIM)

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Mwansa, Besa, Fredrick
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University of Zambia
Agriculture in sub-Saharan African region has depended mainly on rainfall since 1990s and crop production has faced negative impacts of extreme climate events which are believed to be manifestations of long term climate change. In addition, maize (Zea mays L.) productivity has continued to decline over the past years from 2.5 tons ha-1 in 1964 to 1.5 tons ha-1 in 2013. This is largely due to continuous cultivation, often in mono-cropping with little or no inputs and absence of effective Conservation Agriculture (CA). A field experiment for this study was setup on the already established CA long-term trial at Msekera Research Station in Chipata Eastern Province of Zambia. The experiment comprised of different tillage techniques; zero tillage (dibble stick), minimum tillage (animal traction direct seeding), basins (Chaka hoe) and conventional practices (mouldboard ploughing and, ridges and furrow) that were compared on productivity maize (Zea mays L.). The experimental design used was a split plot with CA and CT treatments as main. During the 2014/15 season CA long-term trial was used with the above mentioned experimental design with fertilizer application rates of 165 kg ha-1 for Compound D (10N:20P2O5:10K2O) at planting and 200 kg ha-1 of Urea (46%N) with two (2) split applications. There was a significant difference of 1802 kg ha-1 on observed grain yield in 2014/15 season compared between Conventional Tillage (CPM2) ridge and furrow and Conservation Agriculture (DS-MC) treatments. CA treatments had maize leaves with greener phenological appearances from 24 to 60 days after planting (DAP). Agricultural Production Simulation Model (APSIM) was used to simulate the long-term effect of climate change on maize productivity using temperature rise at +1.0 oC, +2.0 oC, and +3.0 oC and rainfall increase and decrease of 11.3% as climate change scenarios. Calibration of APSIM model was done using Sc501 maize cultivar and data on soil N and water, bulk density, crop phenology, weather, and management information. While validation of model was done using crop phenology, soil water and N, Stover yield, and economic maize grain yield using long-term trials for 2014/15 season. Statistically, Root Mean Square Error (RMSE) and Normalized Root Mean Square Error (NRME) was used to assess the performance of the model and the prediction were 22.57% for maize grain yield and 8.6% for soil water results for both measured and simulated outputs and that represented fair to excellent performance of the model. On the contrary, the model over predicted the biomass yield compared to observed results with an average of 73% RMSE that represented poor performance of the model. Soil water simulation was used in this study in relation to crop yield. The model also predicted that 28 growing seasons out of 85 will have below average maize grain yield mostly to affect the conventional tillage practices. The APSIM model further simulated that crop yield will not be affected by decrease in rainfall but increase in temperature as a climate change scenario. In addition, the model simulated that decreasing annual rainfall by 11.3% as climate scenario increased maize grain yield under CA treatments by 4% (171 kg ha-1). While increasing temperature by 3.0 oC reduced maize grain yield by 31% (1278 kg ha-1) for CT treatments. Generally, results from both observed and simulated outputs revealed that CA increased crop yields, water infiltration and storage. Furthermore, the study proved that CA has the potential to off-set the effects of climate change on crop productivity both from measured observations and through crop simulations model.
MSc - Agriculture
Crops and climate. , Crop yields.