Volume 16, Issue 3 (10-2026)
J Crop Prod Process. 2026, 16(3): 1-22 |
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pourhadian H. Investigating the Effect of Farm Size on Yield Gap and Its Determinants in Irrigated wheat lands of Lorestan using CPA method (Case study: Azna County). J Crop Prod Process. 2026; 16 (3) :1-22
URL: http://jcpp.iut.ac.ir/article-1-3404-en.html
URL: http://jcpp.iut.ac.ir/article-1-3404-en.html
Payame Noor University, Tehran, Iran , hpoorhadian@pun.ac.ir
Abstract: (40 Views)
Introduction: Accurate knowledge of farmers’ cultivation practices is a prerequisite for effective agricultural extension and evidence-based research. Variations in management practices lead to yield disparities across similar land areas, often resulting in the underutilization of potential productivity and the emergence of a yield gap. Analyzing this gap and its underlying drivers is crucial for optimizing system efficiency. The Comparative Performance Analysis (CPA) method is well suited for this purpose. This study employed CPA to investigate the effect of farm size on the yield gap and its determinants in irrigated wheat fields of Azna County, Lorestan Province, Iran.
Materials and Methods: To conduct this research, data from 85 irrigated wheat farms in Azna County was used to investigate the size of the farm area in estimating the yield gap and its contributing factors using the CPA method. All necessary information was obtained through structured questionnaires face-to-face interviews, including data on rural area, farmer's experience and literacy, field area and rotation, planting date, plowing, subsoiler, cultivator, disk-harrowing dates and frequencies, planting method, fungicide application, variety, seed quantity, and nitrogen, phosphorus, and potash fertilizer management (method and timing), amount and type of livestock manure amended, type of irrigation, number of irrigations, number of irrigations in autumn, date of first irrigation in spring, amount, time and method of applying nitrogen fertilizer, pest and weed control, harvest date and length of wheat growth period. Topographic and soil data were derived from Geographic Information System (GIS) layers based on farm locations. Farms were stratified into small (0.5–2.5 ha), medium (2.5–5.5 ha), and large (>5.5 ha) groups. To determine the yield (production) model for each group, the relationship between all measured variables (Quantitative and qualitative; the qualitative variables were coded based on the number of their categorical levels) and yield was examined using the stepwise regression method by SAS 9.3 software. Subsequently, the yield gap and the contribution of each factor were determined using the derived production models. Graphs were generated using Microsoft Excel, and the results were subsequently analyzed.
Results: The results of this study showed that the mean actual yield of small, medium and large farms were 5587.4, 5729.2 and 6357.9 kg/ha, respectively (with a range of changes in all three groups between 2500 and 8500 kg/ha). The corresponding yield gaps were 5567.0 (49.59%), 4403.7 (44.31%), and 4000.4 (38.62%) kg/ha. The correlation coefficient between the estimated yield and the actual yield of farmers, the root mean square (RMSE) and the coefficient of variation of the model were 0.88, 851.64 kg/ha and 14.96% in small farms, 0.87, 865.05 kg/ha and 9.93% in medium farms and 0.90, 557.15 kg/ha and 6.64% in large farms, respectively. Factors affecting yield gap in small farms included soil available potassium (23.71%), amount of seed sown (19.68%), topdressed nitrogen (18.94%), number of irrigations (18.64%), number of disk-harrowings (12.14%) and planting method (6.89%). Factors affecting yield gap in medium farms included organic matter (34.57%), secondary bed preparation operations (25.53%), number of irrigations (24.86%), amount of seed sown (17.68%) and carrot root plant as a preceding crop (7.96%); the attributing factors in large farms included potato tuber plant as a preceding crop (43.54%), irrigations (20.22%), topdressed nitrogen (18.43%), and harvest
date (16.38%). With the increase in farm size, the yield gap decreased, and the analysis of the contribution of factors affecting the yield gap across the three farm groups revealed differences both in the types of influencing variables and in the coefficients of impact for the common variables.
Conclusion: The results confirm an inverse relationship between farm size and yield gap, with larger farms exhibiting lower gaps. Number of irrigations and topdressed nitrogen (the latter mediated by soil organic matter in medium-sized farms) were identified as common variables across all three groups of farm size. Optimal management of the mentioned variables could reduce the overall yield gap by an average of 47.36%. Key management variables explained a significant portion of the yield gap: 60% in small farms (seed rate, irrigation frequency, topdressed N), 73% in medium farms (organic matter, secondary tillage, irrigation frequency), and 82% in large farms (potato as a preceding crop, irrigation frequency, topdressed N). Therefore, by prioritizing these variables in a calculated management method based on previous wheat cultivation, soil nitrogen levels and its consumption based on soil conditions and wheat growth and development stages, using appropriate and efficient irrigation methods based on regional conditions and plant needs, and using bed preparation tools based on soil texture and previous cultivation, an acceptable reduction in the yield gap may be achieved in the studied area.
Materials and Methods: To conduct this research, data from 85 irrigated wheat farms in Azna County was used to investigate the size of the farm area in estimating the yield gap and its contributing factors using the CPA method. All necessary information was obtained through structured questionnaires face-to-face interviews, including data on rural area, farmer's experience and literacy, field area and rotation, planting date, plowing, subsoiler, cultivator, disk-harrowing dates and frequencies, planting method, fungicide application, variety, seed quantity, and nitrogen, phosphorus, and potash fertilizer management (method and timing), amount and type of livestock manure amended, type of irrigation, number of irrigations, number of irrigations in autumn, date of first irrigation in spring, amount, time and method of applying nitrogen fertilizer, pest and weed control, harvest date and length of wheat growth period. Topographic and soil data were derived from Geographic Information System (GIS) layers based on farm locations. Farms were stratified into small (0.5–2.5 ha), medium (2.5–5.5 ha), and large (>5.5 ha) groups. To determine the yield (production) model for each group, the relationship between all measured variables (Quantitative and qualitative; the qualitative variables were coded based on the number of their categorical levels) and yield was examined using the stepwise regression method by SAS 9.3 software. Subsequently, the yield gap and the contribution of each factor were determined using the derived production models. Graphs were generated using Microsoft Excel, and the results were subsequently analyzed.
Results: The results of this study showed that the mean actual yield of small, medium and large farms were 5587.4, 5729.2 and 6357.9 kg/ha, respectively (with a range of changes in all three groups between 2500 and 8500 kg/ha). The corresponding yield gaps were 5567.0 (49.59%), 4403.7 (44.31%), and 4000.4 (38.62%) kg/ha. The correlation coefficient between the estimated yield and the actual yield of farmers, the root mean square (RMSE) and the coefficient of variation of the model were 0.88, 851.64 kg/ha and 14.96% in small farms, 0.87, 865.05 kg/ha and 9.93% in medium farms and 0.90, 557.15 kg/ha and 6.64% in large farms, respectively. Factors affecting yield gap in small farms included soil available potassium (23.71%), amount of seed sown (19.68%), topdressed nitrogen (18.94%), number of irrigations (18.64%), number of disk-harrowings (12.14%) and planting method (6.89%). Factors affecting yield gap in medium farms included organic matter (34.57%), secondary bed preparation operations (25.53%), number of irrigations (24.86%), amount of seed sown (17.68%) and carrot root plant as a preceding crop (7.96%); the attributing factors in large farms included potato tuber plant as a preceding crop (43.54%), irrigations (20.22%), topdressed nitrogen (18.43%), and harvest
date (16.38%). With the increase in farm size, the yield gap decreased, and the analysis of the contribution of factors affecting the yield gap across the three farm groups revealed differences both in the types of influencing variables and in the coefficients of impact for the common variables.
Conclusion: The results confirm an inverse relationship between farm size and yield gap, with larger farms exhibiting lower gaps. Number of irrigations and topdressed nitrogen (the latter mediated by soil organic matter in medium-sized farms) were identified as common variables across all three groups of farm size. Optimal management of the mentioned variables could reduce the overall yield gap by an average of 47.36%. Key management variables explained a significant portion of the yield gap: 60% in small farms (seed rate, irrigation frequency, topdressed N), 73% in medium farms (organic matter, secondary tillage, irrigation frequency), and 82% in large farms (potato as a preceding crop, irrigation frequency, topdressed N). Therefore, by prioritizing these variables in a calculated management method based on previous wheat cultivation, soil nitrogen levels and its consumption based on soil conditions and wheat growth and development stages, using appropriate and efficient irrigation methods based on regional conditions and plant needs, and using bed preparation tools based on soil texture and previous cultivation, an acceptable reduction in the yield gap may be achieved in the studied area.
Keywords: Number of irrigations, Preceding cropping, Production model, Amount of seed used, Topdressed nitrogen.
Type of Study: Applicable |
Subject:
General
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