1 Identifying Isoyield Environments for Field Pea Production 2 3 4 5 Rong-Cai Yang*, Stanford F. Blade, Jose Crossa, Daniel Stanton, and Manjula S. Bandara 6 7 8 9 Rong-Cai Yang and Daniel Stanton, Policy Secretariat, Alberta Agriculture, Food and Rural 10 Development, Room 300, 7000 – 113 Street, Edmonton, AB, Canada T6H 5T6 and Dep. of 11 Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada T6G 12 2P5; Stanford F. Blade, Crop Diversification Centre North, Alberta Agriculture, Food and Rural 13 Development, RR6, 17507 Fort Road, Edmonton, AB, Canada T5B 4K3; Jose Crossa, 14 Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), 15 Apdo. Postal 6-641, 06600 Mexico D.F., México; Manjula S. Bandara, Crop Diversification 16 Centre South, S.S. #4, Alberta Agriculture, Food and Rural Development, Brooks, AB, Canada 17 T1R 1E6. Received _______________. *Corresponding author ([email protected]) 18 19 Abbreviations: AFPRVT, Alberta Field Pea Regional Variety Test; CV, coefficient of variation; 20 GEI, genotype-environment interaction; UPGMA, unweighted pair-group method using 21 arithmetic averages. 22 1 1 ABSTRACT 2 Cultivars are often recommended to producers based on their averaged yields across sites 3 within a geographic region. However, this geography-based approach gives little regard to the 4 fact that not all sites in a given region have the same level of production capacity. The objective 5 of this paper was to describe a performance-based approach to identifying groups of sites with 6 similar yielding ability (i.e., ‘isoyield’ groups), but not necessarily contiguous, and its use for 7 analyzing the yield data from field pea (Pisum sativum L.) cultivar trials conducted across the 8 Province of Alberta, Canada from 1997 to 2001. Of 34 sites tested over the five years, 11 were 9 in 1997, 20 in 1998 and 2000, 22 in 1999 and 21 in 2001. The consecutive use of regression 10 analysis and cluster analysis allowed for classification of test sites in individual years into 11 different isoyield groups: six in 1997, 10 in 1998, 2000 and 2001 and 12 in 1999. However, the 12 most meaningful isoyield groups were those based on the data across the five years through a 13 normalization procedure developed for averaging the multi-year unbalanced data. The use of 14 such averages significantly lessens the impact of random year-to-year variation on the sites, 15 resulting in only seven isoyield groups for the 34 test sites. The identification of isoyield 16 environments (i) facilitates choosing appropriate cultivars for specific environments and (ii) 17 provides a basis for scaling down the cultivar testing program in Alberta. 18 19 20 2 1 The evaluation of registered cultivars or advanced breeding lines at different sites and in 2 different years is essential for selecting superior cultivars for local producers. Such evaluation 3 usually requires a large number of test sites to cover a wide range of regional climatic and 4 edaphic characteristics. However, it has been difficult to strike a balance between a need for 5 reasonable coverage of the regional agro-geoclimatic characteristics and a necessity for 6 economizing on the number of test sites in the face of (i) shrinking resources and (ii) a growing 7 demand for improving the quality of cultivar testing. The difficulty arises largely from 8 inconsistent performance of genotypes in different environments, i.e., genotype environment 9 interaction) (GEI). One widely used approach to lessening the GEI impact is to stratify the data 10 for homogeneous subsets of test sites through various clustering techniques (Horner and Frey, 11 1957; Abou-El-Fittouh et al., 1969; Ghaderi et al., 1980; Brown et al., 1983; Collaku et al., 12 2002;). The key outcome of such data stratification is that GEI is minimized within identified 13 groups, but maximized among the groups. While these studies have effectively reduced the 14 magnitude of GEI for clustered groups, they have one or more of the following drawbacks. First, 15 no consideration is given to the performance of a site or group. In reality, producers need to 16 know whether a selected cultivar would perform well in a ‘good’ or ‘bad’ environment (Helm et 17 al., 2002). Second, dendrograms by most cluster analyses only show topography of relative 18 similarities among sites, but there are no objective criteria for determining the number of clusters 19 from these dendrograms. Such criteria do exist, including those based on whether or not sites 20 within a cluster have similar linear responses (Lin and Butler, 1990) or those based on whether 21 or not crossover GEI within a cluster is negligible (Crossa and Cornelius, 1997; Russell et al., 22 2003), but they have not been widely used. Third, complications arising from the analysis of 23 multi-year data (e.g., unbalanced data and inconsistency of GEI patterns across years) have been 3 1 generally ignored. Thus, results of data stratification will be more useful when these issues are 2 resolved. 3 With recent interest in diversification of crops, aiming at enhancing the long-term 4 sustainability of agriculture in western Canada, non-traditional crops such as field pea have been 5 increasingly incorporated into the farming system in the Canadian Prairies. In the Province of 6 Alberta, field pea is the most cultivated non-traditional crop, accounting for about 55% of the 7 total acreage for these crops (Olson et al., 2001). As field pea production has been expanded to 8 all possible growing areas of the Province, demand for new cultivars with high and stable yields 9 is increasing. Since 1987, Alberta Agriculture, Food and Rural Development (AAFRD) has 10 coordinated the Alberta Field Pea Regional Variety Test (AFPRVT) Program to conduct multi- 11 year and multi-site testing to recommend cultivars to pea producers across the province. These 12 multi-environment data are routinely averaged on a regional (geographic) basis over years (Park 13 and Lopetinsky, 1999). Clearly, this geography-based criterion for cultivar selection does not 14 address the three issues described above and, thus, may not be reliable for choosing appropriate 15 cultivars according to site production levels. 16 In this study, we propose a performance-based approach to grouping test sites for cultivar 17 recommendation. We coin the term ‘isoyield environments’ to describe those sites that are 18 homogeneous in their yielding ability, but not necessarily contiguous in their geography. The 19 concept of isoyield environments is very similar to that of ‘mega-environments’ (Gauch and 20 Zobel, 1997), but with a focus on the site performance in terms of yielding ability. We use this 21 approach to examine patterns of isoyield groups for the field pea trials conducted from 1997 to 22 2001. 23 24 4 1 MATERIALS AND METHODS 2 Data Sets 3 Yield data used for this study were taken from the field pea cultivar trials conducted by 4 AFPRVT collaborators from 1997 to 2001. The yield data prior to 1997 were cultivar means 5 over replications only and, thus, were not included in the present study. A total of 34 sites were 6 used for the trials over the five years: 11 sites in 1997, 20 in 1998, 22 in 1999, 20 in 2000 and 21 7 in 2001 (Table 1). Twenty-eight to 32 registered cultivars or advanced breeding lines from 8 public or private breeding programs were included in all test sites in a given year, but different 9 cultivars except for check cultivars were usually used in different years either due to a turnover 10 to newly registered cultivars or to unavailability of pedigree seed of older cultivars. Two types of 11 field pea cultivars, green and yellow, were grown in the same trials in 1997 and 1998, but in 12 separate trials at the same test sites from 1999 to 2001. The test sites were distributed over four 13 regions, delineated by their geographical and soil characteristics: 1. Southern Alberta, 2. East 14 central Alberta, 3. West central Alberta and 4. Peace River Region (Fig. 1). The Southern 15 Alberta region was further divided into irrigated and non-irrigated areas. The Peace River Region 16 included some neighboring sites in the Province of British Columbia. All trials were conducted 17 using a randomized complete block design with three or four replications. Yang et al. (2004) 18 detailed trial layout and maintenance. 19 Statistical Analysis 20 Let yij be the average yield of the ith (i = 1, 2, …, g) field pea cultivar over 3 or 4 21 replications in the jth (j = 1, 2, …, e) test site in a given year. We first conducted the baseline 22 analysis that partitions the value of yij into the effect of the ith cultivar ( i ), the effect of the jth 5 1 test site ( j ) and the interaction between these two effects ( ij ) under the classic two-way fixed 2 effects model, 3 yij i j ij ij [1] 4 where is the grand mean and the residual errors, ij ’s, are assumed to be normally and 5 independently distributed with mean zero and variance 2 / n (where n is the number of 6 replicates which, in this case, is n =3 or 4). The GEI effect ( ij ) could be further studied by 7 means of different statistical analyses, including stability analysis based on regression models 8 (Finlay and Wilkinson, 1963) or linear-bilinear models (Zobel et al., 1988; Cornelius et al., 1992; 9 Crossa and Cornelius, 1997) and likelihood analysis based on mixed models (Piepho, 1999; 10 Yang, 2002). 11 For our subsequent cluster analysis, we chose the regression-based stability analysis for 12 deriving dissimilarity indexes between pairs of sites, using a modification of method 1 of Lin and 13 Butler (1990), with the roles of cultivars and sites being swapped.