With the anticipated growth in the global population and the increasing concern over future food security, closing the yield gap has become a major focus in agricultural research. Estimating the yield gap and identifying the factors contributing to it require robust analytical methods. Boundary line analysis is a statistical approach that quantifies crop yield responses to environmental or management factors, even when other factors vary and are not fixed. This method specifically determines yield responses to the desired factor under optimal conditions for other variables. The objective of this study was to introduce boundary line analysis as a practical tool for addressing crop yield gap issues and to apply this method to identify the best management practices while estimating the potential yield and yield gap of soybean in Babolsar.

Materials and Methods

This study investigated the limiting factors affecting soybean yield in Babolsar during the 2017-2018 and 2018-2019 cropping seasons, using boundary line analysis to estimate the yield gap. Data collection involved fieldwork and face-to-face interviews with farmers. At least 60 soybean fields within the study area were monitored each year. Weekly observations were conducted during the growing season, and all data were recorded systematically. Cumulative and relative frequency distributions were used to analyze the data. The scope of variations and the methods employed for each management practice in the soybean fields were identified. Additionally, the proportion of farmers adopting different practices was determined. Using boundary line analysis, the relationships between key factors and soybean yield were analyzed with SAS software. By plotting yield (dependent variable) against various agricultural management practices (independent variables), the highest yields at different levels of input or management practices were identified. A function was fitted to the upper boundary of the yield distribution, and its parameters were determined using the SAS software’s PROC NLIN procedure.



Results and Discussion

The study findings revealed that farmers in the investigated region had an average farming experience of 19.5 years, with mean application rates of 54.15 kg/ha for potassium, 56.75 kg/ha for phosphorus, and 77.25 kg/ha for nitrogen fertilizer. The average seed rate and plant density were recorded as 76 kg/ha and 47.34 plants/m², respectively. While a minimum of 16.71 years of cultivation experience was required to achieve the potential yield of 3,000 kg/ha, 34.31% of farms failed to reach this target. Analysis indicated that suboptimal potassium and phosphorus application resulted in yield gaps of 719 kg/ha (23.97%) and 727 kg/ha (24.23%), respectively. For nitrogen, the potential yield of 3,003 kg/ha was attainable within an optimal range of 23–45 kg of pure nitrogen; however, 38% of farms applied nitrogen outside this range. Notably, 79.49% of farms used seed rates below the optimum, leading to a substantial yield gap of 1,186 kg/ha (33.89%). Although 81.9% of farms maintained plant densities within the optimal range, fields with non-optimal densities consistently exhibited yields below the potential yield.



Conclusion

This study demonstrated the effectiveness of boundary line analysis in identifying the factors contributing to the soybean yield gap. By addressing the limiting factors, such as suboptimal fertilizer use, seed rates, and plant density, the difference between actual and potential yields can be significantly reduced. Boundary line analysis proves to be a practical tool for improving agricultural productivity and ensuring food security.