Environment Interaction For Yield Research Articles

AMMI and GGE biplot analysis of genotype by environment interaction for yield and yield contributing traits in confectionery groundnut

The global market has a high demand for premium edible grade groundnut, particularly for table use. India, in particular, exhibits significant potential for exporting confectionary grade large seeded groundnut. The environment plays a significant impact in influencing the expression of seed traits, which subsequently affects the confectionary quality of groundnut genotypes. The states of Gujarat and Rajasthan in India are prominent producers of high-quality groundnuts specifically used for confectionary purposes. The current study was conducted with 43 confectionery groundnut genotypes at Junagadh, Gujarat, and Bikaner, Rajasthan, with the goals of understanding genotype-by-environment interaction (GEI) effects and identifying stable, high yielding confectionery quality groundnut genotypes using AMMI and GGE biplot models. Pod yield per plant (PYP), number of pods per plant (NPP), hundred kernel weight (HKW), and shelling percent (SP) were estimated. The interplay between the environment and genotype has had a notable impact on the manifestation of confectionary grade characteristics in peanuts. The results from the Interaction Principal Component Analysis (IPCA) indicate that HKW contributed 76.68% and 18.95% towards the Global Environmental Index (GEI) through IPCA1 and IPCA2, respectively. Similarly, NPP contributed 87.52% and 8.65%, PYP contributed 95.87% and 2.1%, and SP contributed 77.4% and 16.22% towards GEI through IPCA1 and IPCA2, respectively. Based on the ranking of genotypes, the ideal genotypes were PBS 29079B for HKW, PBS 29230 for NPP. The genotypes PBS 29233 and PBS 29230 exhibited superior performance and stability in terms of pod yield, hundred kernel weight, number of pods per plant, and shelling percentage across various sites. These breeding lines have the potential to be developed for the purpose of producing confectionary grade groundnut with larger seeds, in order to fulfil the growing demand for export.

Genotype by Environment Interaction for Yield And Yield Contributing Traits of Finger Millet (Eleusine Coracana) In Bangladesh

Stability of for yield and yield contributing traits of finger millet is an important consideration for identification of superior genotypes, which is highly influenced by agro-climatic conditions. The present study was conducted to determine stability for grain yield and yield contributing traits of four finger millet genotypes at three different locations viz; Gazipur, Jamalpur, and Rangpur during 2019-20. In AMMI (Additive Main and Multiplicative Interaction) model, G × E interaction analysis of grain yield and yield contributing traits showed differential interaction of the genotypes in the different environmental conditions. Rangpur and Gazipur were rich for finger millet production while the environment of Jamalpur was poor. Among the genotypes, IE-501 produced the maximum grain yield (5.81 t/ha), followed by IE-2043 (4.69 t/ha) in the favorable environment. Genotypes IE-2043 and IE- 3392 exhibited higher yielding as well as stable over all environments. Considering the AMMI model and mean, bi and S2di, the genotypes IE-2043 and IE-3392 would be suitable across environment whereas genotype IE-501 would be suitable under favorable environmental. For all of the traits evaluated, none of the genotypes were found stable across locations. The genotypes IE- 2043 and IE-3392 with high mean grain yield could be utilized for developing high yielding stable finger millet genotypes.
 Bangladesh J. Agril. Res. 46(4): 445-455, December 2021

Identification of Eco-Climatic Factors Driving Yields and Genotype by Environment Interactions for Yield in Early Maturity Soybean Using Crop Simulation

Deploying crops in regions bordering their initial distribution area requires adapting existing cultivars to particular environmental constraints. In this study, we revealed the main Eco-climatic Factors (EFs)—climatic factors recorded over specific phenological periods—impacting both yields and Genotype by Environment Interactions (GEI) for yield in early maturity soybeans (Glycine max (L.) Merrill) under high latitudes. A multi-year (2017–2021) and multi-environment (n = 112) database was built based on the official post-inscription French soybean trial network “SOJA Terres Inovia-GEVES-Partenaires”. Yields of 57 cultivars covering MG00 and MG000 maturity groups were considered. For each environment, 126 EFs were calculated using a Crop Growth Model (CGM) based on observed weather data and simulated developmental stages. Partial Least Square (PLS) regression analyses using the Variable Importance in Projection (VIP) score were used to sort out the most relevant EFs for their impact on yield levels on the one side and on GEI for yield on the other side. Our results confirmed that yield levels for both maturity groups were greatly influenced by climatic factors from the seed filling phenophases, mainly End of Pod to Physiological Maturity. The cumulative potential evapotranspiration during the End of Pod to Physiological Maturity period was the main EF affecting yield levels positively for both maturity groups (VIP = 2.86; R2 = 0.64). Interestingly, EFs explaining yield levels strongly differed from those explaining GEI, in terms of both climatic factors and phenophases. GEI were mostly influenced by climatic factors from First Flower to End of Pod; these factors were maximum temperatures and solar radiation intensity. Cold stress from Sowing to First Seed also appeared to be a critical driver for GEI in MG00 soybeans. The contrasted responses of several cultivars to the main GEI-drivers highlighted a potential genetic variability that could be exploited in early maturity soybean breeding. This study revealed the complexity of GEI ecophysiology, and our results should help breeding strategies to deliver germplasm that outperforms the existing genetic material for expanding the crop to northern European regions.

Orange-fleshed sweet potato [Ipomoea batatas (L.) Lam] genotype by environment interaction for yield and yield components and SPVD resistance under arid and semi-arid climate of northern Ethiopia

Worldwide, sweet potato (Ipomoea batatas (L.) Lam) ranks the sixth most important crop valued for food security, income, and nutritious diet for poor farming communities. Despite its importance, there is lack of information and knowledge to introduce the crop to Afar region where malnourishment is high. Six orange-fleshed sweet potato genotypes (Amelia, Erica, Gloria, Ininda, Kulfo, and Melinda were evaluated for sweet potato viral disease (SPVD), yield and yield components in three contrasting locations, namely Aba’ala, Mekelle 1, and Mekelle 2 using a Randomized Complete Block Design to produce variance components. Joint regression, and additive main effects and multiplicative interactions were used to test the Genotype × Location Interactions (GEI). Means for significant traits were separated by Least Significant Difference (p<0.05). Results showed ample genetic variability for total tuberous yield (TTY), and SPVD resistance. Genotypes Ininda, Gloria, Amelia, and Kulfo were superior for TTY with high SPVD resistance. Aba’ala was a hotspot for SPVD, Mekelle 2 was with low SPVD and recorded a high mean TTY. Amelia and Kulfo exhibited static stability, Ininda and Gloria displayed dynamic stability, high harvest index, and fresh vine yield, and were recommended for further stability investigations. The information generated in this study may be useful as preliminary data however, the experiment has to be repeated for practical conclusions and recommendation of stable varieties for the studied agro-climatic zones.

Exploiting heterosis of semi-hybrids and heterogeneity of cultivar mixtures to enhance alfalfa crop performance

Alfalfa semi-hybrids (alias free-hybrids or chance-hybrids) of genetically-distant populations may display heterosis, but their actual value is substantially unknown. Higher biomass yield may also derive from heterogeneity in cultivar mixtures. This study aimed to compare three two-way and one three-way semi-hybrids and three two-way physical mixtures issued from putative heterotic populations selected from elite Italian germplasm, Egyptian germplasm and a semi-erect pool from Eastern Europe, Canada and Spanish Mielga, their source populations, and a reference synthetic variety selected from the same pool as the Italian population. We assessed three-year dry-matter biomass yield, final persistence, adaptability and yield stability across eight densely-planted managed environments obtained by the factorial combination of two summer water amounts by two soil types by pure stand (PS) or mixed stand (MS) with forage grasses. Heterogeneity increased yield by 17.0% on average while affecting persistence inconsistently. The advantage over mid-parent value averaged 35.1% for yield and 16.3% for persistence of two-way semi-hybrids, while being 35.6% for yield and 21.1% for persistence of the three-way semi-hybrid. The top-yielding semi-hybrid, issued from Italian and Egyptian material, out-yielded both parent populations, and exhibited 7.4% higher mean yield and greater competitive ability than the reference synthetic variety. PS or MS condition affected plant material × environment interaction for yield or persistence more than summer water availability or soil type, but interaction patterns were mainly quantitative with increasing variation in MS. No manifest plant material variation emerged for yield stability. Based on our results, a semi-hybrid variety type can be valuable for alfalfa.

Genetic variability, Genotype × Environment interaction for grain yield of wheat (Triticum aestivum) backcross inbred lines population under different moisture regimes

Drought stress is well known phenomenon that affects the productivity of wheat (Triticum aestivum L). Knowledge on genetic variation, genotype × environment interaction and association between physiological and yield component traits is crucial for the development of improved varieties having high yield and water use efficiency. The present study consists of 280 backcross inbred lines (BILs) population evaluated for grain yield and morpho-physiological traits for two years at three locations. Combined ANOVA unfolded significant variability among traits in BILs population for yield and morpho-physiological traits.Grain yield showed significant association with normalized difference vegetation index (NDVI), soil plant analysis development (SPAD), thousand grain weight (TGW), and canopy temperature (CT). The genotype, environment and genotype ×environment interaction for yield was highly significant (p< 0.01). ASV (AMMI stability value) was calculated and top 29 genotypes were selected and further analyzed with AMMI and GGE biplot analysis for dissecting out genotype × environment interaction. The results classified genotypes G82, G202, G234, G263, G6, G192 and G77 are most stable and high yielding genotypes.

Genotype × Environment Interactions and Stability in Organic Wheat

Optimizing wheat (Triticum aestivum L.) genotypes for organic production can improve lagging yields worldwide. New genetic improvement programs, such as for organic wheat, require an understanding of genotype × environment interactions. Over 6 yr at 10 organically managed sites in the northeastern and north‐central United States, we assessed genotype × environment interactions for yield, test weight, protein, and falling number. We used an additive main effects and multiplicative interactions model to identify mega‐environments with distinct variety performance throughout the northeastern United States. Results indicate that organic wheat breeding and variety testing programs should decentralize selection into multiple locations. After determining the impact of changes in genotype performance across years, we assessed genotypes for stability in yield, test weight, protein, and falling number. Breeding for stability should be prioritized for the northeastern and north‐central United States, due to the large contribution of genotype × year interactions.

Genotype × Environment Interaction for Yield and Associated Traits in Rice

Genotype × Environment Interaction for Yield and Associated Traits in Rice

Evaluation of Genotype x Environment Interaction and Stability of Grain Yield and Related Yield Components in Pearl Millet (Pennisetum glaucum (L.) R.Br.)

Thirty six pearl millet genotypes were evaluated in randomized complete block design with two replications during 2011/2012 at two locations to study the magnitude of genotype by environment interaction for yield and yield related traits and identify the most stable high yielding genotypes. ANOVA of data at individual locations revealed significant differences among genotypes at Marigat and Koibatek for all yield components. Combined mean analysis of variance showed that the Genotype and location main effects and the genotype by environment interaction were highly significant (P≤0.01) for grain yield and other traits, indicating differential response of genotypes across testing locations and the need for stability analysis. Marigat was the most suitable environment and gave highest mean grain yield of 3620 kg/ha. The lowest yield 870 Kg/ha was recorded at Koibatek. Genotypes EUP 32, EUP 35, EUP 19 and EUP 10 produced high mean yield of 3530, 3080, 2690 and 2590 kg/ha respectively. The lowest grain 1290 kg/ha was obtained from genotype EUP 4.Based on the parameters of stability, three stable (widely adapted) and high yielding genotypes (EUP 34, EUP 18, and EUP 9) were identified. They also out-yield the standard open pollinated variety (OPV) check, Kat PM2. Genotypes EUP 32 was the highest yielding across all sites followed by EUP 35 and could be recommended for further multi-location evaluation in warmer environment and possible release for commercial production. The findings of this study showed that pearl millet hybrids have high potential for commercial production in Kenya than the OPVs. The ANOVA results showed that the effects of environments, genotypes and genotype x environment interaction (GE) were important in trait expression and performance of genotypes. In addition, it was observed that amount of rainfall received at both vegetative and post-anthesis phases and temperature had an effect on grain yield. The GGE biplot analysis characterised the environments in terms of stability and productivity, where Marigat was the best for grain yield; implying that environment-specific selection should be adopted. Genotypes EUP 34, EUP 18, and EUP 9 were the best performing since they out yielded the standard OPV check. These stable high yielding genotypes can be evaluated further in varied agro-ecologies and recommended for release as commercial hybrid varieties in ASALs of Kenya.

Adaptive responses of quinoa to diverse agro-ecological environments along an altitudinal gradient in North West Argentina

Quinoa is an important Andean grain crop grown in a wide range of tropical and temperate environments. Time to flowering is an important trait determining grain yield. This work aimed to understand how responses to photoperiod and temperature might alter plant leaf and floral development. To assess the likely degree of G×E interactions, eleven quinoa accessions from a wide range of environments of origin within Northwest Argentina were grown in several sowing dates over two seasons at a high altitude site. In a third season at a low altitude site, a subset of six accessions planted in pots in the field was exposed to two artificially extended and a control (natural) photoperiod. Time to the appearance of floral buds and anthesis were recorded as was leaf number. A photothermal model developed for quinoa was used to compare responses to photoperiod. Plant development rates to visible floral buds and anthesis stages and phyllochron varied widely amongst accessions and across environments within a short day response though phyllochron varied mostly during the reproductive phase. There was a very strong association between time to flowering and altitude of origin (r=−0.98), mean temperature of the wettest quarter (r=0.98) and Normalized Difference Vegetation Index values (r=0.73). Photoperiod sensitivity was higher for accessions from the lowlands (normally late flowering), while temperature sensitivity was greatest for accessions from the highlands (early flowering); most variation for these traits detected at the species level was found in North West Argentina. Genotype by environment interactions for yield were related to the traits examined in this study and considering their high heritability it is suggested that quinoa breeding programs targeted for specific adaptation to a wide range of environments can be developed from this germplasm.

Genetic background effects on QTL and QTL × environment interaction for yield and its component traits as revealed by reciprocal introgression lines in rice

QTLs for quantitative traits are influenced by genetic background (GB) and environment. Identification of QTL with GB independency and environmental stability is prerequisite for effective marker-assisted selection (MAS). In this study, QTLs and QTL×environment interactions affecting grain yield per plant (GY) and its component traits, filled grain number per panicle (FGN), panicle number per plant (PN) and 1000-grain weight (TGW) across six environments were dissected using two sets of reciprocal introgression lines (ILs) derived from the cross Lemont×Teqing and SNP genotypic data. ANOVA indicated that the differences among genotypes and environments within each set of ILs were highly significant for all traits. A total of 72 distinct QTLs for GY and its component traits including 15 for GY, 25 for FGN, 18 for PN, and 29 for TGW were detected over the six environments. Most QTLs (87.4%) showed significant QTL×environment interactions (QEIs) and appeared to be more or less environment-specific. Among 72 QTLs, 15 (20.8%) QTLs and 12 (16.7%) QEIs were commonly identified in both backgrounds, indicating QTL especially QEI for yield and its component traits had strong GB effects. Four QTL regions affecting GY and its component traits, including S1269707–S4288071, S16661497–S17511092, and S35861863–S36341768 on chromosome 3, and S4134205–S7643153 on chromosome 5, were detected in both backgrounds and coincided with cloned genes for yield-related traits. These regions can be the targeted in rice breeding for high yield potential through MAS. Application of QTL main effects and their environmental interaction effects in MAS was discussed in detail.

Genotype by environment interaction effects on yield and curcumin in turmeric (Curcuma longa L.)

Eleven cultivars were evaluated for fresh yield (10 environments), curing per cent, cucumin and dry yield (five environments) across India, four each in North and South India and two in North East India, ranging from 43 to 893m above mean sea level. Combined analyses showed significant differences among cultivars, environments, and cultivar by environment interactions for yield, curing per cent and curcumin contents. A large proportion (70.8%) of variation on fresh yield was attributed to environments; however, for curing per cent, curcumin content and dry yield, genotype effect accounted for 31.2%, 17.7% and 15.7% of variation, respectively. Mega Turmeric was the most stable for fresh yield with above average yield per plant across all environments. Rajendra Sonia was performing well at specific locations as the fresh yield was high and was highly responsive to favorable environments. Results on curcumin and curing per cent showed that, IISR Kedaram performed consistently across five environments with regression values almost equal to one and non-significant deviation from regression was adjudged to be the most stable cultivar for curcumin production. High curcumin cultivar Narendra Tumeric-1 was least responsive at environments with regression values less than one and significant deviation from regression. Mega Turmeric, IISR Prathiba and IISR Kedaram showed high stability for dry yield across environments. Three varieties, Mega Turmeric, IISR Kedaram and IISR Prathiba could serve as a good genetic source for stability in breeding programs for high dry yield and curcumin content.

10.5937/ratpov51-5326 = Yield stability in pigeon pea hybrids [Cajanus cajan (L.) Millsp.] under varying agro-climatic regions

Twelve pigeon pea genotypes comprising ten hybrids and two controls were evaluated at three environments during rainy season of 2012-13, to study genotype × environment interaction for yield and related traits. Analysis of variance in each environment and on a pooled basis expressed a significant difference among genotypes for yield and yield contributing characters except for 100 seed weight. Genotype × environment (linear) interactions were found significant for days to 50% flowering, plant height, primary and secondary branches per plant, pods per plant, seeds per pod, grain yield per plant and yield (kg/ha) except days to maturity and 100 seed weight. The regression and GGE biplot methods were used for analysis. Both methods identified ICPH 2671, ICPH 2740, ICPH 3933 and ICPH 3762 as stable hybrids. The GGE biplot explained higher proportion of the mean sum of squares of the G×E interaction and was more informative with regards to environments and their genotypic performance than the regression method. Hybrids ICPH 2671, ICPH 2740, ICPH 3762 and ICPH 3933 showed stability for the yield and yield components and could be recommended for cultivation.

Genotype × environment interaction for yield and reaction to leaf spot infections in groundnut in semiarid West Africa

Capitalizing on the yield potential in available groundnut germplasm, and high stability of kernel yield are important requirements for groundnut producers in semiarid environments. Forty-seven groundnut genotypes were evaluated from 2003 to 2005 at 4 locations representative of the Guinea and Sudan savanna ecologies in Ghana. The objectives were to assess genotypic differences in reaction to early and late leaf spot infections under natural field conditions, assess the extent of genotype × environment (G × E) interaction for kernel yield, and determine the relationship between yield potential and yield stability. Genotypes differed significantly in their reaction to leaf spot infections indicated by the area under disease progress curve (AUDPC). Genotypic AUDPC was negatively correlated with maturity period (P < 0.01), with kernel yield (P < 0.05) at each of the 3 locations in the Guinea savanna ecology but not in the Sudan savanna ecology and with each of four stability parameters (P < 0.05). High or low yielding genotypes were grouped based on Dunnett’s test at P < 0.10. High yielding groups had significantly low AUDPC, high biomass, high partitioning of dry matter for kernel growth, and were later in maturity compared to low yielding genotypes. Significant G × E interaction effect for kernel yield was dominated mainly by the lack of correlation among environments variance (76–78%) relative to the heterogeneity of genotypic variance component (22–24%). Stability of yield assessed through the among-environment variance, Wricke’s ecovalence, and Finlay-Wilkinson regression coefficient revealed that genotypes in the higher yielding group were relatively unstable compared to the low yielding group. Indicated by the Kataoka’s index of yield reliability, however, relatively unstable genotypes in the high yielding group are expected to be more productive even under assumptions of high risk aversion (P = 0.75–0.95) compared to the more stable, low yielding genotypes. The findings indicate that deploying these recently developed germplasm in semiarid regions in West Africa provides a better match to farmers’ risk-averse strategies compared with the use of existing earlier maturing cultivars.

Assessment of genotype × environment interactions for yield and morphine content in opium poppy ( Papaver somniferum L.)

The analysis of the genotype × environmental interaction, which indicates the stability of genotypes, has always been part of plant breeding programmes before the release of varieties for commercial cultivation. In the present investigation the stability of seed yield, opium yield and morphine content in 11 advanced breeding lines of opium poppy was evaluated over five years. Combined ANOVA showed that both the main effects and interactions were significant, indicating the presence of genotype × environment interactions. The average seed yield and opium yield ranged from 10.41–16.92 q/ha and 45.21–59.85 kg/ha, respectively. Stability analysis involving the parameters bi, S 2 di, λi, s 2 , δ 2 , Wi, r 2 and CV identified four genotypes (BR330, BR333, BR229 and BR243) as ideal and stable for the traits studied. The level of association among different parameters obtained using Spearman’s rank correlation showed that Eberhart and Russell’s deviation from regression (S 2 di) was significantly and positively associated with s 2 , λi, δ 2 i and CV and negatively with r 2 . The parameters λi, s 2 , δ 2 i and CV also showed positive mutual association.

Genetic Variation and Genotype × Environment Interaction for Yield and Other Agronomic Traits in Cassava in Nigeria

The identification of superior genotypes and mega‐environments on the basis of multiple traits is a key objective of multi‐environment trials in cassava (Manihot esculenta Crantz). The objective of this study was to examine the genetic variation and genotype × environment interaction (GEI) effects for fresh root yield, six other agronomic traits, and severity ratings for cassava mosaic disease (CMD) and cassava green mite (CGM) in 40 genotypes of cassava. Experiments were conducted using a randomized complete‐block design with four replications for 2 yr in three representative agro‐ecological zones in Nigeria. Site regression (SREG) analysis revealed that GEI was a major source of fresh root yield variation and the different testing sites discriminated among the genotypes. Genotypes TMS 98/0581, TMS 97/4763, TMS 98/0002, TMS 99/3073, and M98/0068 were highest yielding at Otobi and Umudike, whereas TMS 98/0510, TMS 97/4779, and TMS 92B/00068 yielded the most at Ishiagu. TMS 98/2226, TMS 92/0325, and M98/0028 had the poorest performance across all locations. Genotypes with the highest yield showed the lowest CMD scores, whereas very tall (well above 2 m) plants had low harvest index on the basis of multiple trait analysis. We identified optimally adapted genotypes for commercial cassava production in different areas in Nigeria.

Dissecting gene × environmental effects on wheat yields via QTL and physiological analysis

Crops frequently display genotype x environment interaction for yield and end-use quality in response to different environments, particularly when stresses such as water limitation and temperature are components of the interaction. Plant breeders usually exploit this variation via phenotypic selection to develop varieties with both general and specific adaptation. However the individual genes and physiological processes underlying the basis of general and specific adaptation have rarely been elucidated. We are combining large-scale QTL analysis of several doubled haploid populations of wheat, grown over different environments and seasons, with detailed physiological analysis, to dissect the genes and mechanisms responsible for yield and yield x environment variation in adapted European winter germplasm. Analysis of populations grown under irrigated and non-irrigated conditions on drought-prone soils has revealed individual genes showing stable and differential expression over environments, and the analysis has also identified physiological traits that contribute to differential yield performance. Genes on the homoeologous group 2 chromosomes were associated with flag leaf senescence (stay-green) variation and were the most significant in drought interactions. Variation for stem soluble carbohydrate reserves was associated with the 1RS arm of the 1BL/1RS translocated chromosome, and was positively correlated with yield under both irrigated and non-irrigated conditions, and thus general adaptability. Separate analyses of populations grown over three seasons in England, Scotland, France and Germany revealed QTL for yield performance showing both general and specific effects. A stable QTL on chromosome 6A, consistent in different populations, showed significant effects over seasons and environments, whilst other QTL were specific to season and/or environments.

Genotype by environment studies demonstrate the critical role of phenology in adaptation of chickpea ( Cicer arietinum L.) to high and low yielding environments of India

In order to investigate specific and general adaptation of chickpea in India, a wide range of sub-continental, Australian and Mediterranean genotypes were grown across seven sites characterizing the major chickpea growing areas over 3 years, and extensive data on plant stand, early vigour, phenology, productivity and yield components collected. High and low yielding sites were clearly separated by a range of physical and biological characters, low yield being associated with low latitude and pre-season rainfall, high temperature, early phenology, short crop duration, low biomass and fecundity. Genotype by environment interactions for yield were highly significant ( P < 0.001), and accounted for more variance than that attributed to genotypes alone. Ward's hierarchical clustering indicated that the genotypes could be separated into discrete groups, comprising material specifically adapted to the north (Clusters 2 and 3) or south (Cluster 5), widely or consistently poorly adapted germplasm (Clusters 1 and 4, respectively). Cluster 5, comprising germplasm from southern and central India, was characterized by early phenology, confirming the role of drought escape in southern India. With increasing latitude Cluster 5 genotypes remained early, but had the capacity to delay maturity considerably, resulting in average, and occasionally above average yields. However, compared to well-adapted material in the north, Cluster 5 biomass was low, and the time interval between flowering and podding up to 50 days, representing repeated cycles of flowering and subsequent abortion. Clusters 2 and 3, dominated by northern Indian genotypes, were characterized by later phenology, and were able to delay the onset of flowering significantly more than the remaining germplasm at late flowering northern sites. In Cluster 3, the second highest yielding group overall, this increased both source and sink potential at productive northern sites. Cluster 2 was uniformly later than Cluster 3, and lower yielding at most sites. Cluster 1 was characterized by intermediate flowering and relatively early, responsive maturity, a phenological compromise responsible for wide adaptation, by providing sufficient drought escape in the south, and enough biomass in the north to produce above average yields in these contrasting environments. ICCV 10 from the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), and 2 Indian Agricultural Research Institute (IARI) lines, BG 391 and BG 1006, were the most consistently high yielding, ranking in the top 10 at 10 and 8 sites, respectively. Cluster 4, comprising largely Australian cultivars, was characterized by late, unresponsive phenology and the lowest yield at each site.

Assessment of genotype × environment interactions for yield and fiber quality in cotton performance trials

Plant breeding programs involving a wide range of crop plants routinely practice selection (directly or indirectly) for genotypes that display stability for a given trait or set of traits across testing environments through the genotype evaluation process. Genotype stability for trait performance is a direct measure of the presence and effect of genotype × environment interactions, which result from the differential performance of a genotype or cultivar across environments. The genotype evaluation process also requires selection of the proper field trial locations that best represent the target environments the breeding program is directed toward. In this study, we assessed the extent to which genotype × environment interactions affected agronomic performance (lint yield, gin turnout) and fiber quality (fiber length, fiber strength, uniformity index, micronaire, fiber elongation) in a series of cotton (Gossypium hirsutum) performance trials in 12 location–year environments in South Carolina. Genotype × environment interactions affecting lint yield were larger in higher yielding environments, while interactions for fiber strength were greater for genotypes with lower mean fiber strength values. Two regions within the South Carolina cotton production areas were identified as proper testing locations for lint yield performance, while testing for fiber strength can be accomplished in any location within the statewide cotton production areas.

Mixed models including environmental covariables for studying QTL by environment interaction

The study of the phenotypic responses of a set of genotypes in their dependence on the environment has always been an important area of research in plant breeding. Non-parallelism of those responses is called genotype by environment interaction (GEI). GEI especially affects plant breeding strategies, when the phenotypic superiority of genotypes changes in relation to the environment. The study of the genetic basis of GEI involves the modelling of quantitative trait locus (QTL) expression in its dependence on environmental factors. We present a modelling framework for studying the interaction between QTL and environment, using regression models in a mixed model context. We integrate regression models for QTL main effect expression with factorial regression models for genotype by environment interaction, and, in addition, take care to model adequately the residual genetic variation. Factorial regression models describe GEI as differential genotypic sensitivity to one or more environmental covariables. We show how factorial regression models can be generalized to make also QTL expression dependent on environmental covariables. As an illustrative example, we reanalyzed yield data from the North American Barley Genome Project. QTL by environment interaction for yield, as identified at the 2H chromosome could be described as QTL expression in relation to the magnitude of the temperature range during heading.