Dynamic patterns of components of genotype × environment interaction for pod yield of peanut over multiple years: A simulation approach

Abstract

The relative importance of the genotype × year (G × Y), genotype × location (G × L) and genotype × location × year (G × L × Y) interactions has significant implications on the testing strategy of crop breeding lines. The goal of this study was to examine the dynamic patterns of these three interactions for pod yield of peanut using a crop simulation model. Pod yields of 17 peanut lines in the early-rainy, mid-rainy and dry seasons at 112 locations covering all peanut production areas in Thailand were simulated for 30 years (1972–2002) with the Cropping System Model (CSM)-CROPGRO-Peanut. Combined analyses of variance were preformed for individual seasons and for overall seasons, with the number of year incrementally increasing from 2 to 30, and the relative contributions of the individual sources of variation were determined. This procedure was repeated four times with different starting years. The results showed that the environmental effects accounted for the major proportion of the total yield variation, followed by the genotype effects, while the genotype × environment (G × E) effects were rather small. The contributions of the individual sources changed as the number of years in the analysis changed. Increasing number of years in the analyses resulted in an increase in the magnitude of the G × Y and G × L × Y interactions, but a decline in the G × L contribution. The contributions of the G × Y and G × L interactions were greater and more fluctuated in the dry season, while those of the G × L × Y interactions were greater in the mid-rainy season. Notable increases in the G × Y interaction in the dry season were observed in certain years. The decline in the G × L interaction with increasing number of years was closely associated with the increase in the G × L × Y interaction, and both became stable when 6 or more years were included. Several cross-over in the ranks of peanut lines for mean pod yield in two contrasting years were also observed for the mid-rainy season. These results raise a question on the effectiveness of the strategy for using locations to replace years in varietal testing that is normally employed by breeders. The practical limit of multi-year evaluation of crop breeding lines could be overcome by the use of a crop simulation model.

Introduction

Differential responses of crop genotypes to varying environmental conditions are common for crop yield and other quantitative traits. These differential genotypic responses, or genotype × environment (G × E) interaction, complicate the effective identification of superior genotypes, as performance ranking of the test genotypes may change in different environments (Kang, 1990, Cooper and DeLacy, 1994). In general, G × E interaction is considered a hindrance to crop improvement in a target region (Kang, 1998). However, it can be viewed as a reflection of the differences in genotype adaptation which may be exploited by selection and/or by adjustments in the testing strategy (Basford et al., 1996). To be able to assess the responses of crop breeding lines to different environmental factors, multi-environment trials (METs) are needed. The information provided by these trials may also help breeding programs in gaining a better understanding of the type and size of G × E interactions that can be expected in a given region and the reasons underlying its occurrence, and in defining, if necessary, a breeding strategy to cope with them successfully (Annicchiarico, 2002a).

Allocation of resources for testing is an important decision for breeders, as METs have become an essential part of all crop breeding programs. An understanding of the magnitudes of the components of G × E interaction is useful in this regard. Generally, an effective allocation of resources for testing of genotypes across locations and years is based on the relative importance of genotype × location (G × L), genotype × year (G × Y), and genotype × location × year (G × L × Y) interactions (Fehr, 1987). This information could be obtained from the analysis of data from METs that were conducted over multiple locations and years. To be reliable, the test genotypes should be relatively diverse, and the number of locations and years should be sufficiently large. In reality, such METs data are essentially not available, as there are practical limits to the number of locations and years of testing that can be done by a crop breeding program. For a given target region, it is also not known how the relative importance of G × L, G × Y and G × L × Y interactions would change with increasing number of locations and years. Such information will have significant implication on the testing strategy of crop breeding lines.

Presently, physiologically based crop simulation models have been developed as a multipurpose tool for applications in agricultural research (Boote et al., 1996, Jones et al., 1998, Hoogenboom et al., 1999). The ability of these models to simulate growth and yields of crop cultivars for different environmental conditions and management scenarios opens up a great opportunity for their use in studying the nature of G × E interactions (Aggarwal et al., 1997, White, 1998, Piper et al., 1998, Chapman et al., 2002). For peanut, the Cropping System Model (CSM) CROPGRO-Peanut is one of the crop simulation models that encompasses the Decision Support System for Agrotechnology Transfer (DSSAT) (Tsuji et al., 1994, Hoogenboom et al., 1999, Hoogenboom et al., 2004, Jones et al., 2003). The model is physiologically based and can simulate the productivity of different peanut cultivars under various management and environmental conditions. It can, therefore, be used to simulate METs data for a large number of locations and years that are needed to examine the nature of the G × E interaction components. The model has been evaluated for its application in assisting with the multi-environment evaluation of peanut breeding lines, and has been shown to capture the differential responses to environments of peanut genotypes (Banterng et al., 2006). The objective of the present study was to investigate the dynamic patterns of the G × Y, G × L and G × L × Y interactions for pod yield of peanut over multiple years using the CSM-CROPGRO-Peanut model.