Genotype-by-environment interaction effect on yield and its physiological bases in short-duration pigeonpea

Abstract

Short-duration pigeonpea [Cajanus cajan (L.) Millsp.] is being targeted for commercial cultivation in more diverse environments than traditional cultivars used in subsistence agriculture. As it is a relatively new crop, information on performance of recently evolved lines and hybrids across a range of environments is lacking. Thirty lines were compared for grain yield in 20 environments representing major areas of cultivation [Patancheru (178N and 728E), Gwalior (268N and 788E) and Hisar (298N and 758E)], soil types and sowing times. Grain yield across environments varied widely from 0.36 to 2.09 t ha À1. Average yield was highest, 1.53 t ha À1 , for hybrid ICPH 8. The genotype-by-environment interaction (GEI) effect analyzed using the additive main effects and multiplicative interaction (AMMI) statistical model was highly signi®cant and was three times more important than the line effects. Line ICPL 83006 was highest yielding in one, ICPL 87101 in two, hybrids ICPH 9 in four and ICPH 8 in 13 environments. Based on the range of adaptation of the highest yielding lines, the 20 environments were grouped into four fairly homogeneous crop growing environments in which the same genotypes performed best (mega-environments). The average yield with the highest-yielding lines planted in the respective mega-environment was 12.5% higher than when ICPH 8 was planted across all the environments. Signi®cant correlations of crop growth rate, duration of reproductive phase, partitioning and growth habits of lines with GEI patterns suggest that variable performance of the lines across the 20 environments could be due to variation in the physiological components of yield. The results suggest the presence of signi®cant GEI and that its systematic exploitation through selection of the appropriate physiological components for each mega-environment can result in higher average yield across growing environments.

Simultaneous selection of yield and yield stability in chickpea genotypes using the GGE biplot technique

“…The effect of the G × E interaction on grain yield has been reported in wheat, chickpea, maize, sorghum and other crops by various authors (Heinrich et al, 1982;Chauhan et al, 1998;Arshad et al, 2003;Masood et al, 2003;Ali et al, 2005;Dehghani et al, 2006;Farshadfar and Sutka, 2006;Mohammad, 2009;Zali et al, 2011) using different approaches for the identification of stable cultivars.…”

Yield Response of Winter Wheat to Agro‐Ecological Conditions Using Additive Main Effects and Multiplicative Interaction and Cluster Analysis

“…The response of subgroup in a specific environment was calculated using the sum of the estimates of the grand mean, the mean of the main effects for the genotypes in the subgroup, and the mean of the GL interaction effects of the genotypes in that subgroup (and equal to interaction effects of group G1–G3) and that location. The locations on the horizontal axis are arranged according to IPC1 scores (Gauch, 1992; Gauch and Zobel, 1997; Chauhan et al, 1998; Samonte et al, 2005).…”

Accomplishments and challenges of pigeonpea breeding research in India

“…However, Sheldrake and Naraynan (1979) showed that both determinate and indeterminate types had similar inefficiencies with respect to partitioning. However, the indeterminate types had greater advantage over determinates in the environments where growing period was longer, and less so in shorter growing environments (Chauhan et al 1998). The yields in shorter growing season are often similar to that obtained in the longer season even with half the dry matter production in the former.…”