Genotype × Environment Interaction and Stability Analysis for Watermelon Fruit Yield in the United States

Abstract

One of the major breeding objectives for watermelon (Citrullus lanatus [Thumb.] Matsum & Nakai) is improved fruit yield. High yielding genotypes have been identified, so we measured their stability for fruit yield and yield components over diverse environments. The objectives of this study were to (i) evaluate the yield of watermelon genotypes over years and locations, (ii) identify genotypes with high stability for yield, and (iii) measure the correlations among univariate and multivariate stability statistics. A diverse set of 40 genotypes was evaluated over 3 yr (2009, 2010, and 2011) and eight locations across the southern United States in replicated trials. Yield traits were evaluated over multiple harvests, and measured as marketable yield, fruit count, percentage cull fruit, percentage early fruit, and fruit size. There were strong effects of environment as well as genotype ´ environment interaction (G´E) on watermelon yield traits. Based on multiple stability measures, genotypes were classified as stable or unstable for yield. There was an advantage of hybrids over inbreds for yield components in both performance and responsiveness to favorable environments. Cultivars Big Crimson and Legacy are inbred lines with high yield and stability. A significant (P < 0.001) and positive correlation was measured for Shukla's stability variance (s i 2 ), Shukla's squared hat (sˆi 2 ), Wricke's ecovalence (W i ), and deviation from regression (S 2 d ) for all the traits evaluated in this study.

Impacts of Heat Stress on Wheat: A Critical Review

“…These characteristics usually are influenced by genotype and genotype × environment interactions [12]. Genotype × environment interactions often result into scale or rank shift of genotype overall performance [13,14]. Understanding impenetrable interactions among genotype (crop bio-system) system with the soil, the atmosphere and the environment that plants live in it is important in planning key frame decisions including cultivar selection, sustainable farm management, and economic planning [15,16].…”

RGxE: An R Program for Genotype x Environment Interaction Analysis

“…Additionally, studies published on genotype stability [27] and location value [28] used SASGxE program [17], which is equivalent to RGxE program.…”

Predicting Yield and Stability Analysis of Wheat under Different Crop Management Systems across Agro-Ecosystems in India

“…The unbalanced grain yield GxMxLxY data were analyzed for genotype, environment, management, and genotype x environment x management interactions with the SASGxE [6] [43] [44] and RGxE [45] [46] programs using SAS and R programming language, respectively, in two steps. During the first stage, we im-puted the missing values using the mice() function of the mice (multivariate imputation by chained equations) package [47] of R. Parameters maximum iteration 50, predictive mean matching (pmm) method and random generator seed value 500 were used in the mice() function to generate five imputed datasets.…”

“…Improved agronomic practices played an important role in enhancing the dependability and sustainability of yields [3] [4]. Yield is a complex quantitative trait and greatly influenced by external environment, which results in scale or rank shift in its performance [5] [6]. This relative shift of genotype performance from one environment (location x year combination) to another is known as genotype x environment interaction (GxE) [7] [8].…”

“…Each statistical method reflects different aspects of the GxE, and no single method adequately explains genotype performance across environments [6]. Stability statistics are best used in combination with trait performance (mean or BLUP:…”