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Determination of Mega-Environments for Peanut Breeding Using the CSM-CROPGRO-Peanut Model

^a Dep. of Plant Science and Agricultural Resources, Faculty of Agriculture, Khon Kaen Univ., Khon Kaen 40002, Thailand
^b Dep. of Biological and Agricultural Engineering, Univ. of Georgia, Griffin, GA 30223-1797, USA

^* Corresponding author (aran{at}kku.ac.th).

	ABSTRACT

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS REFERENCES

 
Breeding for locally adapted cultivars requires a subdivision of the target region into mega-environments. Crop models could assist in generating the required data for mega-environment determination. The objective of this study was to determine whether subdividing the peanut (Arachis hypogaea L.) production areas in Thailand into mega-environments using a crop simulation model would be justified. The Cropping System Model (CSM) CROPGRO-Peanut was used to simulate pod yield of 17 diverse peanut lines for 130 locations covering all peanut production areas in Thailand. The data were statistically analyzed, and the genotype and genotype x environment (GGE) biplot method was used to subdivide the peanut production areas into subregions. The results reveal that the genotype x location interaction accounted for only a small proportion of total yield variation for all years. The analyses of yearly data by the GGE biplot shows inconsistent results across years for location grouping as well as for the winning genotypes of the individual location-groups. The GGE biplot analysis of the mean data over 30 yr also indicates a similarity in genotype discrimination for all the locations. The results from this study show that the subdivision of peanut production areas into mega-environments is not justified for Thailand. Therefore, for peanut breeding, Thailand should be considered as one mega-environment.

Abbreviations: AMMI, additive main effects and multiplicative interaction • CSM, Cropping System Model • DSSAT, Decision Support System for Agrotechnology Transfer • G, genotype • GGE, genotype and genotype x environment • L, location • MET, multienvironment trial • PC, principal component • PCA, principal components analysis • Y, year

	INTRODUCTION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS REFERENCES

 
IN A CROP BREEDING PROGRAM, the first strategy is to define the geographical areas that will be the target environments for newly released cultivars. Normally, plant breeders try to develop broadly adapted cultivars for a wide target region. However, there is now increasing interest to breed crop cultivars for a particular environment to take advantage of specific adaptations (Annicchiarico et al., 2005; Samonte et al., 2005). Breeding for a specific adaptation of a particular crop, however, requires a subdivision of all production areas into mega-environments that are the target environments for breeding. A mega-environment is a group of growing areas that are similar in terms of genotype response and show a repeatable relative performance of crop genotypes across years (Gauch and Zobel, 1997; Yan and Rajcan, 2002; Yan and Tinker, 2005). It is generally identified through the analysis of multienvironment trials (METs) of crop breeding lines. The process involves analyzing the environmental responses of the test genotypes and then grouping the test environments on the basis of their similarity in genotypic responses. Environmental grouping determines the relationship among diverse yielding environments and their degrees of association. Several studies have been conducted to determine the associations among international yield test sites for bread wheat (Triticum aestivum L.) (Yau et al., 1991; Trethowan et al., 2001; Trethowan et al., 2003; Lillemo et al., 2004), spring durum wheat (Triticum durum Desf.) (DeLacy et al., 1994; Abdalla et al., 1996), and winter wheat (Peterson and Pfeiffer, 1989; Peterson, 1992). Different subregions have been identified not only within large or transnational regions but also within relatively small regions, as shown for cereals in Italy (Annicchiarico, 1997) and for barley (Hordeum vulgare L.), soybean [Glycine max (L.) Merr.], and winter wheat in Ontario, Canada (Yan et al., 2000; Yan and Rajcan, 2002; Yan and Tinker, 2005).

Several methods exist for analyzing responses of crop genotypes to environments, including the joint regression model, the additive main effects and multiplicative interaction (AMMI) model, the factorial regression model, and pattern analysis (Annicchiarico, 2002). For environmental grouping, cluster analysis was previously the most widely used method (Van Oosterom et al., 1993; Collaku et al., 2002). However, the AMMI model has lately become popular and has been used for determining mega-environments in a number of studies (Gauch and Zobel, 1997; Ebdon and Gauch, 2002; Gauch, 2006). Recently, the genotype main effects and genotype x environment interaction (GGE) model with a biplot display has gained in popularity for analyzing MET data (Casanoves et al., 2005; Dehghani et al., 2006). The method can also be used for determining mega-environments for crop breeding (Yan et al., 2000; Yan and Rajcan, 2002; Yan and Tinker, 2005; Dehghani et al., 2006). Although there have been discussions between proponents of the AMMI and the GGE biplot methods on the superiority of one over the other for analyzing multi-environment trial data (Gauch, 2006; Yan et al., 2007), the two methods often have provided similar results in determining mega-environments (Gauch, 2006).

The determination of mega-environments, nonetheless, is generally limited by a lack of the required MET data. It should cover a wide range of geographical locations throughout the intended region and over several seasons and years. Crop simulation models have been developed to the level that they can simulate growth and development of crop cultivars for different environments and agronomic management practices (Boote et al., 1998; Tsuji et al., 1998; Chapman et al., 2002). For peanut (Arachis hypogaea L.), the Cropping System Model (CSM) CROPGRO-Peanut has been developed and is part of the Decision Support System for Agrotechnology Transfer (DSSAT) (Hoogenboom et al., 2004). The model is process oriented and can predict yield of peanut cultivars for a range of management practices and environmental conditions (Boote et al., 1998; Jones et al., 2003). The CSM-CROPGRO-Peanut model has been evaluated extensively in Thailand, particularly for assisting with multienvironment assessment of peanut breeding lines (Banterng et al., 2003, 2006; Suriharn, 2006; Suriharn et al., 2007). The availability of crop simulation models provides an opportunity for generating the required MET data to determine the mega-environments for a crop breeding program.

Peanut is grown in all regions across Thailand. However, it has been so far unknown whether peanut production areas in Thailand are sufficiently diverse in environmental conditions to justify a subdivision into mega-environments for breeding purpose. To examine the above question, several years of MET data for all major production areas are required. This information is not readily available from actual variety trials but could be generated by the CSM-CROPGRO-Peanut model. The objective of this study was to investigate whether peanut production areas in Thailand could be subdivided into mega-environments for specific adaptation breeding using yield data simulated with a crop simulation model.

	MATERIALS AND METHODS

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS REFERENCES

 
The main procedure used in the present study consisted of determining peanut production areas in different regions of Thailand, gathering the required input data for model simulation, simulating pod yields of a selected set of peanut lines for 30 years for all locations identified, grouping the peanut production areas on the basis of their similarity in genotypic responses, and evaluating whether the derived location-groups would satisfy the criteria of being different mega-environments.

Determining Peanut Production Areas
To determine the locations for peanut yield simulation, data on peanut production areas (organized by district in Thailand) for the 2002–2003 crop year were obtained from the Thai Department of Agricultural Extension. Only districts with a considerable peanut acreage were selected. This resulted in a total of 44 districts in 25 provinces, with a latitude ranging from 12°27' to 19°57' N and a longitude ranging from 98°33' to 104°43' E. Eleven of these provinces are in the northeast, one is in the west, eight are in the north, one is in the east, and two are in the central region of Thailand (Fig. 1 ). Questionnaires were sent to the district extension agents requesting identification of the main peanut-producing villages in their districts and information on agronomic management in the individual villages. The requested information included the growing seasons, range of planting dates, local soil characteristics, and irrigation practices. Once the production villages in the individual districts were identified, soil types of the individual villages were determined using the soil map and associated database of the Department of Land Development. The weather station that was located in or adjacent to each growing area was also determined; in total, 24 weather stations were identified. The basic units for model simulation, designated as locations in this study, were then determined using a combination of weather stations and soil types. This was done by overlaying the areas of weather stations as determined by Thiessen polygons onto the soil series map (Hartkamp et al., 1999), resulting in 130 unique locations. These included 57 locations for the early-rainy season, 26 locations for the mid-rainy season and 47 locations for the dry season.

View larger version (79K): [in this window] [in a new window]   Figure 1. Locations of the peanut production districts in Thailand for the individual growing seasons.

Model simulations for pod yield of the 17 peanut lines were conducted using the seasonal analysis option of the CSM-CROPGRO-Peanut model in DSSAT (Thornton and Hoogenboom, 1994; Hoogenboom et al., 2004). For each of the 130 locations, pod yield for each line was simulated for 30 yr (1972–2002). A model feature called automatic planting was used to obtain the planting date for the rainy season, with planting condition requirements set to 80% of extractable soil moisture for the top 30 cm of the soil profile. The planting date ranged from 1 May to 30 June for the early-rainy season and from 1 July to 30 August for the mid-rainy season. For the dry season, the crop was assumed to be irrigated. Thus, the planting date was fixed and set to 15 December. As peanut is a nitrogen fixing crop, it was assumed that there were no nitrogen limitations. With respect to water management, rainfed conditions were used for the early-rainy and the mid-rainy seasons, and full irrigation was used for the dry season. The harvesting dates were set using the prediction of the maturity date by the model.

Grouping of Locations and Determination of Mega-Environments
In Thailand peanut is grown in all seasons. As harvested seed from one season is used as seed for planting in the next season, breeding for specific adaptation to each season is considered impractical. Therefore, simulated pod yield for all locations of the individual peanut lines was used in grouping the peanut production locations into subregions. These location groupings were made for each of the 30 yr from 1972 to 2001 and for the 30 combined years. The GGE biplot technique as described by Yan et al. (2000) was used for grouping the peanut production locations that were identified. This method was selected because of the availability of the GGE biplot software program (Yan et al., 2000), which could handle the very large data set of the present study, and because it is an effective visual tool for mega-environment analysis. With this method, the GGE biplot was constructed from the first two principal components (PC1 and PC2) that were derived from exposing environment-centered yield data to singular value decomposition, based on the following formula:

[1]

where Y_ij is the yield of genotype i in environment j, β_j is the average yield over all genotypes in environment j, ₁ and ₂ are the singular values for PC1 and PC2, respectively, _i1 and _i2 are the eigenvectors of genotype i for PC1 and PC2, respectively, _j1 and _j2 are the eigenvectors of environment j for PC1 and PC2, respectively, and _ij is the residual of the model associated with the genotype i in environment j.

The evaluation as to whether these location-groups could be considered mega-environments was based on the consistency of location groupings and of the winning genotypes in the individual location-groups across years (Yan et al., 2000, 2007).

	RESULTS

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS REFERENCES

 
Diversity of Locations and Genotypes
The 130 locations in the present study cover all peanut production areas in Thailand. These locations extend from 12°27' to 19°57' N latitude and 98°33' to 104°43' E longitude (Fig. 1). Climatic conditions for these locations were based on records at 24 weather stations, for which the numbers of stations covering production areas in the early-rainy, mid-rainy and dry seasons were 18, 9, and 15, respectively (Table 2 ). During the growing periods, average maximum temperatures ranged from 28.1 to 33.6°C, 30.7 to 32.5°C, and 29.4 to 33.2°C, while average minimum temperatures varied from 21.3 to 24.9°C, 22.4 to 24.4°C, and 14.2 to 21.7°C for the early-rainy, mid-rainy and dry seasons, respectively. Average solar radiation ranged from 14.1 to 18.6, 13.8 to 17.4 and 17.2 to 18.4 MJ m^–2 d^–1, respectively, for the early-rainy, mid-rainy, and dry seasons. Rainfall during the early-rainy season for the different locations ranged from 548 to 1073 mm and the number of rainy days ranged from 48 to 61, while rainfall during the mid-rainy season was not much different from the early-rainy season, ranging from 589 to 1074 mm with 34 to 62 rainy days. Much less rainfall occurred during the dry season, but simulations for this season were conducted with full irrigation. The 130 locations in the present study also encompassed a considerable range of soil types. The ranges of important soil parameters covered by these locations are shown in Table 3 . In terms of crop productivity, average simulated yield for these locations over 30 yr (1972–2001) ranged from 0.50 to 2.16 t ha^–1, 0.98 to 2.50 t ha^–1, and 1.34 to 3.43 t ha^–1 for the early-rainy season, mid-rainy season, and dry seasons, respectively. The locations with high yield in the early-rainy and mid-rainy seasons were those that experienced high rainfall and vice versa (data not shown).

Relative Contributions of Individual Sources of Variation
The analysis of variance for the individual years showed that the location (L) main effect was quite prominent, accounting for 50 to 80% of the total variation, while the genotype (G) main effect contributed 15 to 46% to the total variation. The GxL interaction effect was rather small, accounting for only 4 to 5% of the total variation (Fig. 2 ). The GxL interaction was inversely associated with the genotype main effect; that is, the year with a high genotype main effect generally had a low GxL interaction and vice versa (Fig. 2).

View larger version (39K): [in this window] [in a new window]   Figure 2. Relative contributions of location (L), genotype (G) and GxL interaction effects expressed as percentage of total sum of squares in the ANOVAs for the years 1972 to 2001. (SS = sum of squares.)

View larger version (21K): [in this window] [in a new window]   Figure 3. Genotype and genotype x environment biplots for simulated pod yields of 17 peanut lines at 130 locations for the years 1972 (A) and 1974 (B). The locations are identified by letter and numbers, e.g., E1, D1, M1, and the genotypes are represented by numbers ranging from 1 to 17 (see descriptions in Table 1). (PC = principal component.)

View larger version (18K): [in this window] [in a new window]   Figure 4. Genotype and genotype x environment biplot for mean simulated pod yields over 30 yr (1972–2001) of 17 peanut lines at 130 locations. The locations are identified by letter and numbers, e.g., E1, D1, M1, and the genotypes are represented by numbers ranging from 1 to 17 (see descriptions in Table 1). (PC = principal component.)

	DISCUSSION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS REFERENCES

 
The present study used pod yield of peanut lines obtained from model simulation by the CSM-CROPGRO-Peanut model for the determination of mega-environments. The model is responsive to only certain abiotic factors including air temperature, solar radiation, rainfall and irrigation, and soil characteristics related to water availability in the profile and nitrogen in the soil. The model, however, does not respond to biotic factors such as diseases, insects, and weeds and other abiotic factors such as phosphorus, potassium, water logging, and microvariability within the field (Boote et al., 1996; Hoogenboom et al., 1999; Jones et al., 2003). The GxE interaction in the present study, therefore, represents only the interaction of the test genotypes with the weather and soil factors that were taken into account by the CSM-CROPGRO-Peanut model. However, it is the genotypic responses to these environmental factors that are of concern in mega-environment determination, as they would reflect the adaptability of genotypes to the inherent and persistent natural environments of the different production areas.

The use of simulated yield data for mega-environment determination has an advantage in that they mainly represent the real signal for genotypic responses to relevant environmental factors. It is well known that observed data from actual yield trials are noisy, consisting of both real signal and mere noise. The noise gives yield trial results a spurious complexity and is irrelevant for mega-environment determination, as it is unrepeatable. Reducing noise will help in mega-environment identification (Gauch and Zobel, 1997). Most of the noise goes into GxE and is recovered by the late components of the principal components analysis (PCA), where as the early components selectively recover signal (Gauch, 2006). Therefore, the early PCA components are of primary concern for mega-environment determination. In the present study, the relative contribution of GxE interaction to total yield variation was rather small compared with those of the E and G main effects. The GGE biplot analyses also showed that PC1 and PC2, the early PCA components, accounted for almost all of the yield variation due to G and GxE. These results show that with simulated pod yield, most of the noise has been eliminated, and that simulated pod yield represents the real signal, which would be most relevant for mega-environment determination.

It has been widely accepted that only GxL interaction is useful for depicting adaptation patterns, as only this interaction can be exploited by selecting for specific adaptation (Annicchiarico, 2002). In the present study, despite a large number of test locations and a relatively diverse set of genotypes, the relative contribution of the GxL interaction was rather small compared with those of the L and G main effects, as shown by both the yearly data analyses and the combined 30-yr data analysis. This suggests that there would not be much advantage in breeding for specific adaptation, as pointed out by Annicchiarico (2002). In the GGE biplot analysis, the GxE was essentially the GxL for both the analysis of yearly data and the analysis of the mean data over 30 yr. Results of the GGE biplot analyses also indicate that subdividing peanut production areas in Thailand into mega-environments for breeding peanut to a specific adaptation is unwarranted. Therefore, Thailand should be considered as one mega-environment for peanut breeding.

Determining mega-environment is the strategy that should be used by all breeding programs, as it would set the target area for breeding and cultivar release. However, not much has been done in actual practice because the required yield trial data for multiple locations and multiple years are normally not available; long-term yield trials of crop genotypes are not a common approach. The present study has demonstrated that this constraint could be overcome by the use of a crop simulation model; crop yield of different genotypes could be simulated for as many locations and as many years as necessary. However, a difficulty would still remain with the availability of the cultivar coefficients for the diverse range of genotypes that are required for model simulation. They are typically not available, and their determination with the standard procedure entails specific field experiments with intensive data collection (Hoogenboom et al., 1999). A recent study by Anothai et al. (2007) demonstrated that the minimum data required for cultivar coefficient determination of the CSM-CROPGRO-Peanut model could be much reduced without sacrificing the accuracy of the estimates. Mavromatis et al. (2001, 2002) also showed that cultivar coefficients of soybean cultivars could be derived from crop performance trials. These procedures provide the opportunity for deriving the cultivar coefficients of crop genotypes for model simulation to generate the necessary data for mega-environment determination. This approach, therefore, could be extended to other areas and to other crops for which crop simulation models are available.

	CONCLUSIONS

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS REFERENCES

 
Simulated pod yield of 17 peanut lines at 130 locations covering all peanut production areas in Thailand for 30 yr was used in determining the mega-environments for peanut breeding using the GGE biplot method. The analyses of the yearly data showed inconsistent results across years for location grouping as well as for the winning genotypes of the individual location-groups. The GGE biplot analysis of the mean data over 30 yr also indicated a similarity in genotype discrimination for all locations. We conclude that subdivision of peanut production areas in Thailand into mega-environments is unjustified and that Thailand should be considered as one mega-environment for peanut breeding. The study also demonstrated the usefulness of a crop simulation model as a tool for mega-environment determination.

	ACKNOWLEDGMENTS

 
This study was supported by the Thailand Research Fund through the Royal Golden Jubilee Ph.D. Program (Grant No. PHD/0134/2546) and the Senior Research Scholar Project of Dr. Aran Patanothai. Assistance was also received from the Peanut Project, Department of Plant Science and Agricultural Resources, Faculty of Agriculture, Khon Kaen University, Khon Kaen, Thailand.

	NOTES

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS REFERENCES

 
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Received for publication January 3, 2008.

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