عنوان مقاله [English]
Background and objectives
Rice (Oryza sativa L.) is the staple food of more than half of the world’s population and has an obvious effect in feeding, income and job creation of people in the world especially, Iran. Yield gap analysis is providing a little estimation of increased production capacity which is one important component in designing food providing strategy in regional, national scale and world-wide surface. It seemed that by defining the effectiveness of each management parameters on the amount of presented yield gap and consequently 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 are investigated.
Material and methods
Research was done in 100 paddy fields in the 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 includes all main production procedure with variation in management view point. For defining yield model, relationship between all measured variables and final model was designed by controlled trial and error method, which can 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 as yield gap.
Data analysis revealed that from 155 variables under study, the final model in the Amol and Rasht regions with seven and six independent variables was chosen. In the 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 the Rasht yield model, maximum and average yield were 4443 and 6377 kg ha-1, respectively that estimated yield gap was 1934 kg ha-1. In the Amol region, the amount of increased yield has been related to transplanting date, top-dressing and nitrogen after flowering variable equal’s 364, 292 and 416 kg ha-1 includes 21, 17 and 24 percent of total yield increase. In the Rasht region, the yield increasing related to the effect of potassium, nitrogen before transplanting and N after flowering was 644, 325 and 730 kg ha-1 equals 33, 17 and 38 percent.
According to the finding, it is expressed that the model precision is appropriate and can be applied for both estimation of the quantity of yield gap and determining the portion of each constraints yield variables. Furthermore, regarding the fact that calculated yield potential is reached through actual data in each paddy field, it has been stated that this yield potential is attainable.