Quantifying the impact of climate variability on wheat productivity using simulation modelling technique

Climate variability is a big threat to wheat productivity. An experiment was conducted at Agronomic Research Farm, University of Agriculture, Faisalabad to study the impact of climatic variability on wheat. The treatments consisted of two varieties (Punjab-2011 and Lasani-2008) and three sowing dates i.e. 26th October, 26th November and 26th December with three replications under Randomized Complete Block Design (RCBD) with factorial arrangements. Climate variability on wheat was assessed by simulation model-CERES Wheat. To check the significance of treatments mean, Least Significant Difference (LSD) test was applied at 5% probability level. Results indicated that maximum total dry matter, leaf area index and net assimilation rate was recorded in S1 (26 October). Whereas, variety Lasani-2008 (V1) showed maximum crop growth rate at S1 (26 October) as compared to other treatments. Results indicated that the maximum number of productive tillers in S2 (26th Oct.) while spike length in both sowing time (October-26) and (26-November) compared to S3 (26 Dec.), Significant spikelet per spike in S1 were observed. At 23% and 35% more grains per spike in S1 than S2 and S3. Regarding yield components, biological yield and grain yield S1 gave maximum value; both V1 and V2 influenced the grain yield in interaction with sowings of S1. Highest grain yield in V2 and HI was observed in treatment (S1). Response of wheat to planting dates and cultivars were evaluated using Crop Environment Resource Synthesis (CERES-Wheat) model. CERES-Wheat model was calibrated with treatments (S1V1, S1V2) which aimed to determine the genetic coefficient of Lasani-2009 and Punjab-2011. Results indicated that (RMSEs) were 1, 0.0 for days to anthesis and maturity, 0.0 for LAI, 9 and 615 kg per ha for grain yield and biological yield for Lasani-2009. Punjab-2011 have the RMSEs 1, 1, 0.1, 81 kg per ha and 180 kg per ha for days to anthesis, maturity, LAI grain and biological yield respectively. Regression analysis showed that climate variability strongly affected the yield in wheat because it has the good relationship with maximum, minimum temperature, solar radiation, and day length with R2 0.68, 0.75, 0.77, 0.77 but for precipitation 0.13. The CERES-Wheat model provided the good and promising results for both verities Lasani-2008 and Punjab-2011 in three sowing times. Therefore, the model could be useful tool for climate variability impact quantification, sowing time of different varieties in precision agriculture.