Evaluation of Potential Yield and Yield Gap Associated with Crop Management in Improved Rice Cultivars in Neka Region

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. The rice cultivation area in the world during the past years has been from 145 million hectares to over 160 million hectares. The last global statistics showed that paddy yield and white rice production were 742 and 492.2 million tons respectively in 2014. The same amount is predicted for 2016. 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. Due to the existing anxiety about discussions of food security, studies are also increasing globally and in Iran is necessary to estimate the quantity of yield gap and the reasons behind it by appropriate statistical methods, or in other words, detecting the restricting parameters of potential yield. As it was mentioned several factors prevent farmers to reach attainable yield in many crops. 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 yield gap is investigated. The research was done in 100 paddy fields between the Alborz Mountains range and the Caspian Sea in 2016. In this research, all managerial operations from nursery preparation to harvest for modified rice cultivars were recorded through field studies in Neka, Mazandaran, Iran from 2015-2016. All farm cases are pertaining to improved cultivars. The improved rice cultivars were Shiroodi, Neda, Fajr, Ghaem, Khazar, and Nemat, respectively.
Field identifications were done in a way that includes all main production procedure in a specific region with variation in management viewpoint. For defining the yield model (production model), the relationship between all measured variables and the 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. Data analysis revealed that seed consumption was varied from 30 to 120 kg.ha-1. The range of seedling age variable was from 20 to 60 days old. In 100 paddy fields planting density were 16 to 40 plants per m2. Nitrogen usage by 26% of farmers was among 69 to 92 kg.ha-1 and 16% of the farmers consumed 92 to 115 kg N per hectare. Potassium application was varied from 0 to 100 kg K ha-1 which within 60% of the field’s potassium usage was less than 35 kg K ha-1. The range of paddy yield in 100 paddy fields was varied from 6100 to 8200 kg.ha-1 that in 40% of the studied fields, the paddy yield was from 7000 to 7600 kg.ha-1. In the CPA model, the paddy yield increasing related to the effect of N top dressing, K usage and N usage after flowering was 327, 674 and 324 kg.ha-1. Therefore, the actual yield and yield potential were estimated to be 7194 and 9241 kg.ha-1, respectively and the yield gap was 2047 kg.ha-1. Therefore, regarding the fact that calculated potential yield was reached through actual data in each paddy field, it has been stated that this yield potential is attainable.