Smallholder farmers in sub‐Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low‐input systems is currently lacking. We evaluated the impact of varying [CO2], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi‐arid Rwanda, hot subhumid Ghana and hot semi‐arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in‐season soil water content from 2‐year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2], temperature and rainfall. Without N fertilizer input, maize (a) benefited less from an increase in atmospheric [CO2]; (b) was less affected by higher temperature or decreasing rainfall; and (c) was more affected by increased rainfall because N leaching was more critical. The model intercomparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low‐input systems. Climate change and N input interactions have strong implications for the design of robust adaptation approaches across SSA, because the impact of climate change in low input systems will be modified if farmers intensify maize production with balanced nutrient management.
Nitrogen (N) is an essential nutrient for sorghum growth and development but often becomes limiting due to low availability and loss. The effects of N fertilization on water use efficiency (WUE) and physiological and yield traits of sorghum were investigated in two locations over two cropping seasons (2014 and 2015) in the Sudan Savanna zone of Nigeria. Three sorghum varieties were evaluated under six (6) N-levels (0, 20, 40, 60, 80, and 100 kg ha−1) at a constant phosphorus and potassium level of 30 kg ha−1. Results showed that N increased grain yield by 35–64% at the Bayero University Kano (BUK) and 23–78% at Minjibir. The highest mean grain yield in the N-fertilizer treatments (2709 kg ha−1 and 1852 kg ha−1 at BUK and Minjibir, resp.) was recorded at 80 kg N ha−1. ICSV400 produced the highest mean grain yields (2677 kg ha−1 and 1848 kg ha−1 at BUK and Minjibir, resp.). Significant differences were observed among the N-levels as well as among the sorghum varieties for estimated water use efficiency (WUE). The highest mean value coincided with the highest mean grain yield at an optimum application rate of 80 kg ha−1. N-fertilizer treatments increased WUE by 48–55% at BUK and increased WUE by 54–76% at Minjibir over control treatment. Maturity and physiological trait have a significant effect on WUE. The extra early maturing variety (ICSV400) recorded the highest mean WUE while late maturing variety (CSR01) recorded the lowest WUE.
Better defining niches for the photoperiod sensitive sorghum (Sorghum bicolor L. Moench) varieties of West Africa into the local cropping system might help to improve the resilience of food production in the region. In particular, crop models are key tools to assess the growth and development of such varieties against climate and soil variability. In this study, we compared the performance of three process-based crop models (APSIM, DSSAT and Samara) for prediction of diverse sorghum germplasm having widely varying photoperiod sensitivity (PPS) using detailed growth and development observations from field trials conducted in West Africa semi-arid region. Our results confirmed the capability of each selected model to reproduce growth and development for varieties of diverse sensitivities to photoperiod. Simulated phenology and morphology organs during calibration and validation were within the closet range of measured values with the evaluation of model error statistics (RMSE and R 2). With the exception of highly sensitive variety (IS15401), APSIM and Samara estimates indicate the lowest value of RMSE (<7days) against the observed values for phenology events (flowering and maturity) compared to DSSAT model. Across the varieties, there was over-estimation for simulated leaf area index (LAI) while total leaf number (TLN) fitted well with the observed values. Samara estimates were found to be the closet with the lowest RMSE values (<3 leaves for TLN and <1.0 m 2 /m 2 for LAI) followed by DSSAT and APSIM respectively. Prediction of grain yield and biomass was less accurate for both calibration and validation. The predictions using APSIM were found to be closest to the observed followed by DSSAT and Samara models respectively. Based on detailed field observations, this study showed that crop models captured well the phenology and leaf development of the photoperiod sensitive (PPS) varieties of West Africa, but failed to estimate accurately partitioning of assimilates during grain filling. APSIM and SAMARA as more mechanistic crop models, have a higher sensitivity of the adjustment of key parameters, notably the specific leaf area for APSIM in low PPS varieties, while SAMARA shows a higher response to parameters changes for high PPS varieties.
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