Wheat (Triticum aestivum) adaptability evaluation in a semi-arid region of Central Morocco using APSIM model

Abstract

In this study, we evaluated the suitability of semi-arid region of Central Morocco for wheat production using Agricultural Production Systems sIMulator (APSIM) considering weather, soil properties and crop management production factors. Model calibration was carried out using data collected from field trials. A quantitative statistics, i.e., root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and index of agreement (d) were used in model performance evaluation. Furthermore, series of simulations were performed to simulate the future scenarios of wheat productivity based on climate projection; the optimum sowing date under water deficit condition and selection of appropriate wheat varieties. The study showed that the performance of the model was fairly accurate as judged by having RMSE = 0.13, NSE = 0.95, and d = 0.98. The realization of future climate data projection and their integration into the APSIM model allowed us to obtain future scenarios of wheat yield that vary between 0 and 2.33 t/ha throughout the study period. The simulated result confirmed that the yield obtained from plots seeded between 25 October and 25 November was higher than that of sown until 05 January. From the several varieties tested, Hartog, Sunstate, Wollaroi, Batten and Sapphire were yielded comparatively higher than the locale variety Marzak. In conclusion, APSIM-Wheat model could be used as a promising tool to identify the best management practices such as determining the sowing date and selection of crop variety based on the length of the crop cycle for adapting and mitigating climate change.

Responses of wheat kernel weight to diverse allelic combinations under projected climate change conditions

“…Process-based physiological and ecological models simulate crop growth, development, and water and nutrient uptake. They can explore the dynamic relationships among atmosphere, crop, and soil and enable the prediction of crop responses to climate, genotype, farm management, and their interactions ( Briak and Kebede, 2021 ). Prior studies focused on crop variety parameters.…”

“…Process-based crop models are often used to study the effects of genetics, field management, and their interactions on wheat growth and development as well as the impact of climate warming on wheat productivity. Briak and Kebede (2021) used their experimental data to calibrate and evaluate the Agricultural Production Systems Simulator (APSIM-Wheat) model and demonstrated its accuracy. Their simulation experiments showed that optimal sowing dates for suitable wheat varieties improve crop adaptation to climate warming.…”

Predicting the Production and Depletion of Rare Earth Elements and Their Influence on Energy Sector Sustainability through the Utilization of Multilevel Linear Prediction Mixed-Effects Models with R Software

“…A mixed model is a powerful statistical tool that examines the relationships between observed responses and explanatory covariates while accounting for two types of varia-tions [80]. By incorporating fixed and random effects, it captures systematic influences and inherent data variability [81].…”

Optimizing Wheat Yield and Profitability Through Conservation Tillage and Supplementary Irrigation During Terminal Heat Stress in North Bihar

“…The study focuses on evaluating different sowing mechanical tillage methods for wheat production, where conventionally chosen methods depend on input costs such as tillage and fuel costs, and grain yield, to control economic bene ts Briak et al, 2021;Chellappa et al, 2021;Daloz et al, 2021;Poudel et al, 2021;Radmehr et al, 2022). To reduce tillage costs and increase pro tability, zero tillage technique with rice residue mulching was implemented for timely sowing of wheat.…”