Yield Gap Assessment in Rice-Grown Fields Using CPA and BLA Approaches in Northern Iran

Abstract

Narrowing the yield gaps is one of the major concerns in developing countries. Closing yield gap to obtain attainable yield is a viable option for providing information regarding the reason of yield loss. Hence, accurate estimation of the yield gap has many practical applications for enhancing production of crops. This research was conducted for assessing the yield gap of rice-grown fields using boundary-line analysis (BLA) and comparative performance analysis (CPA) methods. Collection of 100 rice-grown fields data were done in Sari region, Mazandaran province, one of the major rice production areas in northern Iran from 2015 to 2016. All paddy field management operations from preparation of nursery to harvest of yield has been recorded for local rice varieties. The CPA model calculate the potential yield and factors causing yield gap. In contrast, BLA model were fitted to the edge of data cloud of rice yield versus field managing variables from monitoring. Analysis of data in 100 monitored paddy fields demonstrated that rice yield varied from 3100 to 5430 kg ha−1. Prediction of potential yield for CPA and BLA methods were 5703 and 5369 kg ha−1, respectively. The yield gaps calculated by CPA and BLA methods in 1212 and 881 kg ha−1, respectively. In the CPA, the share of yield gap for variables entered in the model were 5% for cover crop of canola, 18% for legumes before rice cultivation, 4% for seed disinfection, 10% for seeding date in nursery, 11% for seedling age, 11% for seedling growth stage for transplanting, 5% for mechanized transplanting, 4% for fertilizer top-dressing, 27% for number of top-dressing and 6% for foliar application of nutrients. In the BLA, an average attainable yield, based on the optimum level of the 12 studied variables, was 5369 kg ha−1 with an 881 kg ha−1 yield gap. Regarding the fact that calculated yield in CPA and BLA, it has been stated that this potential yield is attainable. CPA and BLA are cheap and simple tools that, without the need for expensive experimentation, is able to detect yield gap and its causes in a district. Therefore, these methods can be used effectively in developing countries where the highest yield gaps exist.