Soybean (Glycine max L. Merr.) Yield Gap Analysis using Boundary Line Method in Gorgan and Aliabad Katul

Introduction Increasing the production of crops has been a necessity to reach food security for growing population. Since "expanding acreage" is almost impossible, "increasing the yield per unit of area", is the only possible option. Closing the gap between actual yield and potential yield (yield gap) is one of the important methods to increase yield per unit of area. It is necessary to increase yield to primarily identify the factors that contributing in the yield gap in each area. Recognizing potentials as well as the impact of each limiting factor on yield individually, plays an important role in determining the alternative management strategies to achieve maximum performance. Therefore, the present study was conducted in Gorgan and Aliabad Katul county for simultaneous recognition of best management practices, percentage of the affected fields, estimation of soybean yield potential and gaps using boundary line analysis. Material and Methods To quantify the production and estimation of soybean yield gap in Gorgan and Aliabad Katul, Farm management information of 224 soybean farms in the years 2010, 2011, 2013 and 2014 were collected. This information was collected through continuous farm monitoring during the growing season as along with face to face interviews with the farmers. Farms were selected by consulting with agricultural service centers expert in Gorgan and Aliabad districts. Based on the available information at the service centers, only farms , which is different in terms of acreage, cultural practices and harvesting operations were selected. In this study, by plotting the distribution of the yield obtained in each field as the dependent variable against the independent variables (crop management activities), using SAS software and an appropriate function was fitted on the upper edge of the data distribution. Results and Discussion The results showed that the average yield on the farms surveyed was 3507 Kg.ha-1 and by improving crop management, this productivity can increase to as high as 5355 Kg.ha-1. Most evaluated soybean yield responses in terms of the value of the nitrogen fertilizer showed that the data points follow a two-segmented function in a way that by increasing the amount of nitrogen fertilizer to 48 Kg.ha-1, the yield increased and then, the addition of the amount of nitrogen fertilizer had no effect on grain yield. Most evaluated soybean yield responses in terms of the amount of phosphorous fertilizer showed that the data points follow a two-segmented function in a way that by increasing the amount of phosphorous fertilizer to 43 kg per hectare, the yield increased and then, the addition of the amount of fertilizer had no effect on grain yield. Most evaluated soybean yield responses in terms of the irrigation frequency showed that the data points follow a two-segmented function in a way that by increasing irrigation frequencies up to four times, the yield increased and then, by increasing the number of irrigations no effect on grain yield has been observed. Data distribution of grain yield against the amount of seed used showed that the boundary line follows dent-like function. Accordingly, to reach a yield potential of 5780 Kg.ha-1, 53 to 67 kg of seeds per hectare is needed. The distribution of yield data against the distance between the rows showed that the boundary line follows a quadratic function. Hence, to reach a yield potential of 4048 Kg.ha-1, a 40 cm rows distance must be considered . Data distribution of grain yield against the inter-crop distance showed that the boundary line follows a dent-like function and inter-crop distance must be kept at a range of 5-7cm to reach a 5102 Kg.ha-1 grain yield. Conclusion In this study by examining several important management factors in growing soybean, optimal requirements of each factor to achieve the highest yield was determined by boundary analysis. In addition, the percentage of farms that had poor management as well as soybean yield potential and gaps in Gorgan and Aliabad. Yield responses to management practices were evaluated and studied by means of borderline analysis. Best management practices could be devised using study findings to realize the highest yield potential