Factors Underlying Spatial Variability in Rice (Oryza sativa L.) Grain Quality at Field and Regional Level

Abstract

Accounting for the spatial variability of resources and yields has become both important and feasible in agricultural systems research. Such variability can be detected and addressed at various scales, from that of a small field to a whole region, and completely different problems arise at each scale. Traditionally, agricultural issues have been studied at a small scale (field plots) and then extrapolated in an ad hoc manner to a larger scale (field or region). Results of this process are not always accurate due to the intrinsic differences between the local and the regional level of analysis. In this paper we pursue different approaches as an example of a means of dealing with a particular issue, rice grain quality, at two scales, field and region. At the field level, we test a model that relates head rice (HR) to grain moisture content (GMC) and GMC pattern before harvest. At the region level, we propose a model to predict optimum harvest time for rice varieties in California, based on a degree days (DD) approach. Practical results obtained aid in reducing the risk of loosing HR grain quality at harvest.