Evaluation of Yield Gap Associated with Crop Management in Canola Production by CPA method in Neka Region

Abstract
Introduction
World food security in the future depends on the generation of enough food for the world's population, which predicts that the world's population will reach more than nine billion by 2050. Achieving food security in the current environment depends on realizing the potential yield achieving in the agricultural land (Hochman et al., 2016; Guilpar et al., 2017). Hence, improving the crop yield is necessary in view of the increasing pressure and global demands for food. On the other hand, loss of high quality land, annual decline in crop yield, increased use of chemical fertilizers and the adverse environmental impact of chemical inputs indicate that the development of new strategies to increase yield with minimum environmental impact is necessary (Chapagain & Good, 2015). Moreover, ensuring environmental sustainability leads to research into changing agricultural management practices (Gaydon et al., 2017). As noted, many factors prevent farmers from achieving the crop attainable yield. It seems that, by determining the effect of each managing factors that affected on the amount of yield gap and, consequently, the knowledge of the farmers, it is possible to minimize the yield gap between the actual yield and the achievable yield. Therefore, this research was conducted with the aim of determining and ranking the factors causing the canola yield gap in the climate of eastern province of Mazandaran in Iran.
Material and Methods
Research was done in 100 canola fields in Neka, Mazandaran, Iran from 2015-16 and 2016-17. All managerial operations from seedbed preparation to harvest were recorded through field studies. Field identifications were done in a way that includes all main production procedure in specific region with variation in management view point. For defining yield model (production model), relationship between all measured variables and final model was designed by controlled trial and error method. The final model was obtained through the controlled trial and error method, which can quantify the effect of yield limitations. The average paddy yield was calculated by the model by placing the observed average variables (Xs) in the fields under study in the yield model. Thereafter, by putting the best observed value of the variables in the yield model, the maximum obtainable yield was calculated. The difference between these two has been considered as yield gap. Different procedures of the software SAS version 9.1 were used for analysis.
Results and Discussion: With approximately 150 variables under study, the final model with seven independent variables including soybean pre-sowing, rice pre-sowing, top dressing, K2O usage, nitrogen usage in vegetative stage, herbicide frequency usage and weed problem were considered as independent variables was chosen against depended variable of paddy yield. The yield gap caused by top dressing and K2O usage variables were equal to 462 and 294 kg.ha-1 equals 27 and 17% of the total yield gap. The yield gap related to the effect of soybean pre-sowing and herbicide frequency was 170 and 411 kg.ha-1, respectively, and equal to 10 and 24% of the total yield variation. In yield model by CPA method, the actual yield and calculated potential yield were equal to 2394 and 4119 kg.ha-1, respectively. The amount of yield gap equals 1725 kg.ha-1.
Conclusion: Among the five variables entered in the model, the effects of potassium consumption, soybean pre-sowing, top dressing and herbicide frequency usage were remarkable, which can compensate for a significant part of the yield gap in the farmers’ fields by managing potassium consumption and using integrative pest control. Based on the finding, it is expressed that the model precision (production equation) is appropriate and can be applied for both estimation of the quantity of yield gap and determining the portion of each restricting yield variables.