No abstract

No abstract

Sunflower (Helianthus annuus L.) has emerged as an economically important crop in Pakistan due to its significant share in vegetable oil production. The plant metabolic processes require protein to increase the vegetative, reproductive growth and yield of the crop. The protein is wholly dependent upon the amount of nitrogen fertilization available for plant use. ) and grain yield (3809 kg·ha -1 ) compared to the other N rates. The maximum oil content (46.2%) was observed in Hysun-38 without application of N fertilizer (N 1 ), while the minimum oil content (40.6%) was observed from N 5 treatment. In conclusion, the application of 180 kg·ha -1 N to Hysun-38 provided the best combination for good yield in sunflower crop under the prevailing sub-humid conditions of Pakistan.

No abstract

No abstract

Maize is one of the main cereal crops in Pakistan with sensitivity to drought at various developmental stages known to influence the yield. The impact of variable weather conditions on maize yield can be analyzed with crop simulation models. The CSM-CERES-Maize model has been widely used to assess irrigation strategies for maize. This research was conducted to test the CSM-CERES-Maize model for its ability to accurately predict maize biomass and grain yield under water limiting and non-limiting conditions in semiarid conditions. Four growth stage-based irrigation treatments and two potential soil moisture deficit-based treatments were defined. During model calibration, the simulated maximum leaf area index (LAI), total dry matter (TDM), and grain yield were all within 10% of observed values. During model evaluation, there was generally satisfactory agreement between observed and simulated values for two hybrids (Monsanto-919 and Pioneer-30Y87) with the model showing variability of −17.9–20.0%, −9.2–14.3%, and −19.6–19.9% for maximum LAI, TDM, and grain yield, respectively, for the two hybrids among various treatments. The CERES-Maize model was useful in providing information to decision-making regarding diverse irrigation regimes at the farm level in a semiarid environment.

No abstract

Decision support systems are key for yield improvement in modern agriculture. Crop models are decision support tools for crop management to increase crop yield and reduce production risks. Decision Support System for Agrotechnology Transfer (DSSAT) and an Agricultural System simulator (APSIM), intercomparisons were done to evaluate their performance for wheat simulation. Two-year field experimental data were used for model parameterization. The first year was used for calibration and the second-year data were used for model evaluation and intercomparison. Calibrated models were then evaluated with 155 farmers’ fields surveyed for data in rice-wheat cropping systems. Both models simulated crop phenology, leaf area index (LAI), total dry matter and yield with high goodness of fit to the measured data during both years of evaluation. DSSAT better predicted yield compared to APSIM with a goodness of fit of 64% and 37% during evaluation of 155 farmers’ data. Comparison of individual farmer’s yields showed that the model simulated wheat yield with percent differences (PDs) of −25% to 17% and −26% to 40%, Root Mean Square Errors (RMSEs) of 436 and 592 kg ha−1 with reasonable d-statistics of 0.87 and 0.72 for DSSAT and APSIM, respectively. Both models were used successfully as decision support system tools for crop improvement under vulnerable environments.