Investigating the yield gap and it's influencing factors in irrigated wheat fields of Azna county, Lorestan province, Iran, using comparative performance analysis (CPA)

Document Type : Research Paper

Author

Assistant Professor, Department of Agriculture, Payame Noor University, Tehran, Iran

10.22124/cr.2026.31499.1877

Abstract

Introduction: Wheat is one of the most important plant affecting human life due to various application. Diverse management practices of farmers is caused the potential of the agricultural lands are often not optimally utilized, resulting in low yields per unit area. The comparative performance analysis (CPA) method effectively estimates yield differences based on the potential of the land and the yield resulting from farmer’s management. This study was planned and implemented to investigate the yield gap and its contributing factors in the irrigated wheat fields of Azna county, Lorestan province, Iran, using the CPA method.

Materials and Methods: In this study, data from 74 irrigated wheat farms in Azna county were used to estimate the yield gap and its contributing factors through the CPA method. The data from the wheat fields were collected using a questionnaire including farm characteristics, planting, maintenance, and harvesting operations. Topographic and soil information for each wheat field were also obtained from the GIS-prepared layers based on their geographical locations. To determine the yield model (output), the relationship between all measured variables and yield was examined using stepwise regression analysis with SAS software. Finally, using the derived production equation, the yield gap and its contributing factors, along with the share of each, were identified.

Results and Discussion: The results of this study showed that the minimum and maximum observed yield of wheat fields were 2500 and 8500 kg.ha-1, respectively, with the average of 5943 kg.ha-1. Also, the average, minimum and maximum yield estimated by the model were 5906.62, 3248.22 and 11289.24 kg.ha-1, respectively, and the total yield gap was 5382.62 kg.ha-1. The correlation coefficient between the estimated yield and the actual yield of farmers was 0.81, and the residual root mean square (RMSE) and the coefficient of variation (CV) of the model were obtained 842.71 kg.ha-1 and 14.27%, respectively. The analysis of the factors contributing to the yield gap indicated the role of six variables, including low organic matter in the soil (26.8%), lack of row planting (3.60%), low seed usage (10.96%), reduced number of irrigations in the fall (18.88%), reduced amounts of nitrogen applied as top-dressing fertilizer (17.28%), and untimely harvesting (22.43%), in creating the yield gap in irrigated wheat fields in Azna county.

Conclusion: This study confirms the acceptable capability of the CPA method in estimating the yield gap and the factors affecting it in the wheat fields of Azna county. The results showed that with timely, targeted, and intelligent management, the yield gap can be reduced by approximately 48%. Implementing appropriate crop rotation, managing wheat and other crop residues, use of animal manure, promoting the use of row planters and providing them to farmers, managing seedbed preparation, selecting suitable varieties, and strongly recommending fall irrigation, soil testing to determine appropriate nitrogen fertilizer levels, and distributing it according to the growth stages of wheat, along with precise planning to avoid overlap in spring crop cultivation with wheat harvesting and timely entry of harvesting machinery into wheat fields are recommended to reduce the yield gap based on the influencing factors.

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References

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Cereal Research
Volume 15, Issue 4 - Serial Number 57
December 2025
Pages 369-381

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