Understanding the reasons for the yield gap between potential and actual yield can provide insights for enhancing canola production by adapting measures for ensuring food security. The canola yield gap under different management practices (e.g. water, nitrogen, N- and sowing dates) was quantified using research trials that were conducted at on-station and historical data (1980–2016) and the CROPGRO-Canola model for Punjab, Pakistan. The integrated approach revealed that low inputs of N, the amount of irrigation, sowing date and the use of seeds from home stocks were the principal causes for a low yield. The CROPGRO-Canola model was able to simulate the canola yield from research trials (R2 = >0.90) and farm survey data (R2 = 0.63). The average yield gap between potential (YP), N-limited (YNL), water-limited (YWL), N- and water-limited (YNWL), and overall farmer field yield (YOFF) was 50, 46, 62 and 72%, respectively. The yield-gap with achievable yield (YA) for YNL, YWL, YNWL and YOFF was 34, 28, 49 and 63%, respectively. Overall, the results showed that a high canola yield for farmers’ fields can be obtained by selecting appropriate varieties and sowing dates with N rate of 120 kg/ha and efficient irrigation management. However, further studies are necessary to fully comprehend the underlying causes for the low actual yield and the high yield variability of farmers’ fields.

Soybean is one of the key oil crops in global food security. The objective of the current study was to determine the magnitude of soybean yield and yield gaps (Yg) in the main producing regions in Iran, the main causes and possible solutions to reduce these gaps and improve yields. This study uses an integrated approach of crop simulation and on-farm information. The SSM-iCrop2 model was used to calculate the potential yield (Yp). Furthermore, management information of soybean farms (the number of monitored farms was 224) was collected and analysed with two methods, including stepwise regression (a production model was created and based on it, the yield-limiting factors were determined) and boundary line analysis (show the optimum level of crop management and simultaneously the percentage of farms that were out of the optimal range of a specific management procedure). The results showed a Yp of 4681 kg/ha while actual yield (Ya) was around 2257 kg/ha. The main factors causing Yg of soybean in Iran were irrigation, nitrogen fertilizer, phosphorus fertilizer and sowing date. Altering soybean sowing date to late June or early July, irrigating at least five times during the growing season, applying at least 50 kg/ha nitrogen and 45 kg/ha phosphorus base application are foremost management practices that could shrink the soybean yield gap in Iran. The results presented in this study can bring relevant transferable information to other soybean production areas sharing the same latitudes and climate, and the approach can be used for other crops worldwide.

The effect of irrigation system uniformity on the selection of risk-efficient irrigation strategies is evaluated using crop simulation and stochastic dominance procedures. Alternative strategies are evaluated under assumptions of both uniform and non-uniform application. Results indicate that the variability of net returns resulting from the employment of a specified schedule increases when irrigation uniformity is explicitly represented. Solutions derived using economic efficiency and stochastic dominance criteria indicate that the uniformity with which irrigations are applied contributes to the application of water-intensive irrigation schedules.