Mining a yield-trial database to identify high-yielding cultivars by simulation modeling: A case study for rice

Abstract

Genotypes with high potential yield would improve crop production. Trials that examined potential yield have been conducted around the world, producing databases that can be mined to reveal “hidden” high-yield cultivars. However, yield data from different times and locations are not comparable because yield integrates the effects of cultivar-specific potential with the effects of weather and management practices. Here, we hypothesized that cultivar-specific yield can be expressed as a function of the climatic potential yield, which is calculated using a model based on daily solar radiation, temperature, and phenology data. To test this hypothesis, we used a rice (Oryza sativa L.) yield database from Japan, including only data from years with normal climatic conditions and trials with optimal N fertilization. For cv. ‘Sasanishiki’, which is widely grown in northern Japan, data from four prefectures and 20 years showed yield. The yield variations could be expressed by a single unique statistically significant regression across prefectures and years as a function of the climatic potential yield. This method demonstrated that ‘Koshihikari’ produced 10% less and ‘Fukuhibiki’ produced 19% more than ‘Sasanishiki’ for a given climatic potential yield (1000 g m‒2). We confirmed this ranking by direct comparisons of the cultivars in identical years and at the same locations. Our method can be used for data mining to identify high-yield cultivars through data from previous yield research. We discuss the limitations and advantages of this method, its potential for other crop species, and its potential for determining responses to abiotic and biotic stresses.