عنوان مقاله [English]
Pulses play an important role in supplying human food and are a major source of protein in developing countries, so they have a special role in producing food in these countries. Peas (Pisum sativum L.) belongs to legume family and is well adapted to the cold climate. This plant has a lot of crude protein and starch and is therefore high in energy. Evaluating the status of pulses production is essential because of their importance in feeding the people of the world and their role in the design of cultivation patterns. Eliminating the gap between the yield currently achieved on farms and the yield that can be achieved by using the best environmentally adapted varieties and the optimum water, soil and plant management techniques is a key strategy to overcome the nutritional challenge of a growing global population. In recent years, due to concerns about food safety issues, studies on the issue of yield gap have also been increasing worldwide and it is necessary to estimate the yield gap and its causes using appropriate methods. Therefore, the present study was conducted to determine the yield gap of peas and determine the limiting factors and their contribution to the yield gap in pea farms in Gonbad kavus.
Materials and Methods
This study was carried out in pea farms (40 farms) during 2017-18 in Gonbad Kavus, Golestan province. The required information about farms was completed through observation, questioning of farmers or measurement. Information on soil properties was obtained using digital maps available at the Agricultural Jihad Office of Gonbad Kavus. Comparative performance analysis (CPA) method was used to determine the yield gap rate and to identify its causes. In this method, the relationship between all measured variables (quantitative and qualitative) and yield was evaluated using multiple regression. In this section, first, we used stepwise method to determine which variables should be included in the final production model. The average yield was calculated by placing the observed variables (x) of the studied farms in the yield model. The maximum yield obtained was then calculated by placing the best observed value of the variables in the model. The discrepancy between these two functions was considered as the performance gap. The ratio of yield gap for each variable to total yield gap represents its contribution in the yield gap and was expressed as a percentage.
Results and Discussion
The actual and potential yield calculated by the model were estimated to be 7941 and 17708 kg/ha, respectively, and yield gap was 10078.7 kg/ha. The reasons for this yield gap were as follows: Seed rate (19.56%), Nitrogen rate (17.04%), Frequency of herbicide application (15.02%), amount of available potassium in soil (34 (13%), soil organic matter (12.81%), amount of available phosphorus in soil (11.87%), farmer experience (10.36%). Therefore, it seems that with proper management of farms and taking into account the yield gap factors mentioned above, the yield of peas in Gonbad kavus can be increased to about 10078.7 kg/ha in comparison with current yield.
Based on the results in 40 farms studied, out of 45 variables studied, the final model was selected with seven independent variables. In the yield model, the actual and potential yield of farms calculated by the model were 7941 and 17708 kg/ha, respectively, and the yield gap was 10078.7 kg/ha. The recommendations of this research are based on the findings of the mentioned years in the region. Obviously, these recommendations may change in the future with changing farming systems (such as extension of conservation agriculture), agricultural management and possibly climatic conditions. In addition, by modifying the first-order causing factors of the yield gap discussed in this study, the second-order factors will emerge. Therefore, monitoring and evaluation of crop management in farms should be carried out on a continuous basis and the yield gap and its causes identified and resolved. In this study, among all the common agricultural management of farms, the cases that have the most impact on the yield gap and need to be modified and improved in the first stage are identified.