Assessing the impact of climate variability on maize using simulation modeling under semi-arid environment of Punjab, Pakistan

Abstract

Climate change and variability are major threats to crop productivity. Crop models are being used worldwide for decision support system for crop management under changing climatic scenarios. Two-year field experiments were conducted at the Water Management Research Center (WMRC), University of Agriculture Faisalabad, Pakistan, to evaluate the application of CERES-Maize model for climate variability assessment under semi-arid environment. Experimental treatments included four sowing dates (27 January, 16 February, 8 March, and 28 March) with three maize hybrids (Pioneer-1543, Mosanto-DK6103, Syngenta-NK8711), adopted at farmer fields in the region. Model was calibrated with each hybrid independently using data of best sowing date (27 January) during the year 2015 and then evaluated with the data of 2016 and remaining sowing dates. Performance of model was evaluated by statistical indices. Model showed reliable information with phenological stages. Model predicted days to anthesis and maturity with lower RMSE (< 2 days) during both years. Model prediction for biological yield and grain yield were reasonably good with RMSE values of 963 and 451 kg ha⁻¹, respectively. Model was further used to assess climate variability. Historical climate data (1980–2016) were used as input to simulate the yield for each year. Results showed that days to anthesis and maturity were negatively correlated with increase in temperature and coefficient of regression ranged from 0.63 to 0.85, while its values were 0.76 to 0.89 kg ha⁻¹ for grain yield and biological yield, respectively. Sowing of maize hybrids (Pioneer-1543 and Mosanto-DK6103) can be recommended for the sowing on 17 January to 6 February at the farmer field for general cultivation in the region. Early sowing before 17 January should be avoided due to severe reduction in grain yield of all hybrids. A good calibrated CERES-Maize model can be used in decision-making for different management practices and assessment of climate variability in the region.

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