Modern information technologies have facilitated the collection of data to assess various aspects of rice production such as yield, quality, soil properties and growth conditions. Currently, farmers can identify any variation of these indicators within a field, between fields or with other farmers. However, a comprehensive analytical method to identify the determinants of variability has not been developed, and the data collected are not efficiently utilized to diagnose and improve the production skills of farmers. Our study focused on the development of an analytical method that can identify the determinants of rice yield and quality. The analytical method used applied cluster analysis (Ward method) to assess the data from 82 paddy fields where rice is produced in various environments and with various management styles. Initially, the 82 paddy fields were classified into 11 clusters based on five indicators of yield components and rice quality; number of panicles, number of spikelets, percentage of ripened grains, 1000-grain weight (GW) and protein content of brown rice. Then, 9 of 11 clusters (two clusters were excluded due to insufficient data to form a cluster) were divided into four groups based on yield capacity. As a result, common characteristics of fertilizer application, meteorological environment and growth conditions were extracted from each cluster. Furthermore, determinants of yield components and protein content were efficiently identified based on the common characteristics extracted.
All Science Journal Classification (ASJC) codes
- Agronomy and Crop Science
- Computer Science Applications