Abstract
 
Background and Objectives
Rice (Oryza sativa L.) is the staple food of more than half of the world’s population and has an important role in feeding, income and job creation in the world, especially Iran. Yield gap analysis provides little estimation of increased production capacity which is an important component in designing food-providing strategy in regional, national and global scales. The existing heated discussions regarding food security have increased the number of necessary studies that estimate the quantity of yield gap and the reasons behind it through appropriate statistical methods in Iran and the rest of the world. It seems that by defining the effectiveness of each management parameter regarding the amount of presented yield gap and, consequently, the farmer’s knowledge on that matter the distance between actual yield and attainable yield can be reduced. In this research, estimation of potential yield, yield gap, and determining yield restricting factors and each of their portions in creating rice yield gap have been investigated.
 
Materials and Methods
Research was done in 100 paddy fields in Amol and Rasht regions in 2016 and 2017. All managerial practices from nursery preparation to harvest for local rice cultivars were recorded through fields monitoring. Field selection was done in a way that included all main production procedures with variation in management viewpoint. To define yield model, the relationship between all measured variables and the final model was designed by controlled trial and error method, which could quantify the effect of yield limitations. The average yield was calculated 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 yield gap. Different procedures of the software SAS version 9.1 were used for analysis.
 
Results
Data analysis revealed that from 155 variables under study, the final model in Amol and Rasht regions with seven and six independent variables was chosen. In Amol yield model, maximum and average yield were 4798 and 6505 kg ha^-1, respectively that estimated yield gap was 1707 kg ha^-1. In Rasht yield model, maximum and average yield were 4443 and 6377 kg ha^-1, respectively, which estimated that yield gap was 1934 kg ha^-1. In Amol region, the amount of increased yield related to transplanting date, top-dressing and nitrogen after flowering variable was 364, 292 and 416 kg ha^-1 which equals to 21, 17 and 24 percent of total yield increase. In Rasht region, the yield increase related to the effect of potassium and nitrogen before transplanting, and N after flowering was 644, 325 and 730 kg ha^-1 which equals to 33, 17 and 38 percent.
 
Discussion
According to the findings, we suggest that the model precision is appropriate and can be applied for both estimating the quantity of yield gap and determining the portion of each constraints yield variables. Furthermore, considering the fact that the calculated yield potential is reached through actual data in each paddy field, it is suggested that this yield potential is attainable.