Determining environmental covariates which explain genotype environment interaction in winter wheat through probe genotypes and biadditive factorial regression

Genotype-environment interaction has been analyzed in a winter-wheat breeding network using bi-additive factorial regression models. This family of models generalizes both factorial regression and biadditive (or AMMI) models; it fits especially well when abundant external information is available on genotypes and/or environments. Our approach, focused on environmental characterization, was performed with two kinds of covariates: (1) deviations of yield components measured on four probe genotypes and (2) usual indicators of yield-limiting factors. The first step was based on the analysis of a crop diagnosis on four probe genotypes. Difference of kernel number to a threshold number (DKN) and reduction of thousand-kernel weight from a potential value (RTKW) were used to characterize the grain-number formation and the grain-filling periods, respectively. Grain yield was analyzed according to a biadditive factorial regression model using eight environmental covariates (DKN and RTKW measured on each of four probe genotypes). In the second step, the usual indicators of yield-limiting factors were too numerous for the analysis of grain yield. Thus a selection of a subset of environmental covariates was performed on the analysis of DKN and RTKW for the four probe genotypes. Biadditive factorial regression models involved environmental covariates related to each deviation and included environmental main effect, sum of water deficits, an indicator of nitrogen stress, sum of daily radiation, high temperature, pressure of powdery mildew and lodging. The correlations of each environmental covariate to the synthetic variates helped to discard those poorly involved in interaction (with | correlation | <0.3). The grain yield of 12 genotypes was interpreted with the retained covariates using biadditive factorial regression. The models explained about 75% of the interaction sums of squares. In addition, the biadditive factorial regression biplot gave relevant information about the interaction of the genotypes (interaction pattern and sensitivities to environmental covariates) with respect to the environmental covariates and proved to be interesting for such an approach. Received: 8 March 1999 / Accepted: 29 July 1999     

Choosing probe genotypes for the analysis of genotype-environment interaction in winter wheat trials

Genotype-environment interaction was analyzed in French multi-environment wheat (Triticum aestivum L.) trials using probe genotypes and bi-additive factorial regression. Probe genotypes are specific genotypes in which the comparisons of yield components to reference values describe the most-important environmental factors that limited grain yield. The time-period until flowering was described by the deviation of kernel number from a threshold number while the grain-filling period was described by the reduction of thousand-kernel weight from a potential value. The aim of this paper was to determine the convenient number and the characteristics of probe genotypes to include in wheat breeding trials. Two sets of genotypes were used to model genotype-environment interaction: set 1 with 12 varieties tested in 18 environments and set 2 with ten lines tested in 14 environments. Set 2 was used for validation. Seven probe genotypes described the environments by providing environmental covariates, namely differences in yield components, for further analysis of interaction in set 1 and set 2. Interaction was modelled with bi-additive factorial regressions including differences in yield components. Several rounds of models were fitted to determine the optimal number of probe genotypes (i.e. environmental covariates) to introduce. From the seven probe genotypes, all the possible combinations including one to seven genotypes were studied. Significance of the combinations was tested with critical values obtained from simulations through 1,000 random permutations. Taking into account the information available on the probe genotypes, one would think that two, three or four probe genotypes would be sufficient, otherwise the number should reach four or five genotypes. In all cases, these numbers will provide models more-parsimonious than the classical AMMI model. The important information to be known on the probe genotypes prior their first multilocation experiment is: interaction pattern, earliness, and differences in yield component. Tested for the first time, a quadruplet is better than a triplet because the probability of choosing complementary genotypes increases with their number.     

Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions

Key message

Development of models to predict genotype by environment interactions, in unobserved environments, using environmental covariates, a crop model and genomic selection. Application to a large winter wheat dataset.

Abstract

Genotype by environment interaction (G*E) is one of the key issues when analyzing phenotypes. The use of environment data to model G*E has long been a subject of interest but is limited by the same problems as those addressed by genomic selection methods: a large number of correlated predictors each explaining a small amount of the total variance. In addition, non-linear responses of genotypes to stresses are expected to further complicate the analysis. Using a crop model to derive stress covariates from daily weather data for predicted crop development stages, we propose an extension of the factorial regression model to genomic selection. This model is further extended to the marker level, enabling the modeling of quantitative trait loci (QTL) by environment interaction (Q*E), on a genome-wide scale. A newly developed ensemble method, soft rule fit, was used to improve this model and capture non-linear responses of QTL to stresses. The method is tested using a large winter wheat dataset, representative of the type of data available in a large-scale commercial breeding program. Accuracy in predicting genotype performance in unobserved environments for which weather data were available increased by 11.1 % on average and the variability in prediction accuracy decreased by 10.8 %. By leveraging agronomic knowledge and the large historical datasets generated by breeding programs, this new model provides insight into the genetic architecture of genotype by environment interactions and could predict genotype performance based on past and future weather scenarios.     

Maize Kernel Hardness,Endosperm Zein Profiles,and Ethanol Production

Maize (Zea mays L.) grain is an important feedstock for the ethanol-producing industry. However, little is known about the optimum grain quality for optimizing ethanol yielding efficiencies. We specifically investigated the response of ethanol yields (L Mg^?1) to kernel hardness, and its physiological determinant endosperm zein protein profiles, as affected by genotype selection, field nitrogen (N) fertilization, and crop growth environment. We measured ethanol yield and related this to different kernel hardness indicators, kernel composition, and zein profiles. We also described changes in field ethanol yield (L ha^?1), by taking into account the crop yield (Mg ha^?1). Hard endosperm genotypes always yielded less ethanol than softer endosperm ones per grain mass (L Mg^?1). Higher N fertilization rates increased kernel hardness and decreased ethanol yield (L Mg^?1) on soft endosperm dented genotypes but had no effect on hard endosperm ones. Ethanol yield was negatively correlated with kernel density, kernel protein concentration, and Z1 and Z2 zein fractions. Within Z2, 15 kDa β-zein explained the largest ethanol yield variation generated by genotypes, N fertilizations, and growth environments. However, and although these differences were as large as 10%, ethanol field yield (L ha^?1) was mainly driven by crop yields (r ² 0.98) due to the large crop yield (Mg ha^?1) differences observed across treatments. Together, our results helped describe the magnitude that changes in maize kernel hardness can have over ethanol yield, both through genotype selection or crop management. A particular Z2 zein protein rises as relevant for future genetic manipulations of maize ethanol yield determination.     

Deciphering the environmental impact on spike architectural traits for grain yield consolidation in bread wheat (T. aestivum L.)

The realization of grain yield in wheat is decided by source-sink balance under prevailing environmental conditions. Management conditions like changing the sowing time influence the source-sink capacity through modification in agronomic traits. Therefore, this experiment was conducted to decipher the influence of spike architectural traits (SATs) on grain yield and to open avenues for further grain yield enhancement. Comparatively early sowing over timely sowing gives the advantage of realizing higher grain yield with a positive relationship with SATs namely spike length, spikelets per spike, individual spike weight, individual grain weight, number of grains per spikelet, grain length, and grain width of upper and lower spike portion. Confirmatory factorial analysis revealed that spike length, spikelets per spike, individual spike weight, grains per spikelet were having a significant effect in deciding grain yield in early sown. The presence of a significant effect of genotype by environment interaction over grain yield and SATs allows the exploitation of available genotypic and environmental variability for further yield enhancement. GGE analysis on transformed and standardized grain yield-trait (GY-trait) combinations was used in the selection of genotypes having high GY-trait combinations for both sowing times. In early sowing, WG 11 was the best for high GY with high individual spike weight; grain length and grain width at lower and upper parts of the spike; and shorter days to 50% flowering. Genotypes exclusively having the high GY-trait combination along with low values of remaining GY-trait combinations were also selected with genotype focused GGE approach.     

A Study on Genotype-by-Environment Interaction Analysis for Agronomic Traits of Maize Genotypes Across Huang-Huai-Hai Region in China

Facing the trend of increasing population, how to increase maize grain yield is a very important issue to ensure food security. In this study, 28 nationally approved maize hybrids were evaluated across 24 different climatic conditions for two consecutive years (2018–2019). The purpose of this study was to select high-yield with stable genotypes and identify important agronomic traits for maize breeding program improvement. The results of this study showed that the genotype ╳ environment interaction effects of the 12 evaluated agronomic traits was highly significant (P < 0.001). We introduced a novel multi-trait genotype-ideotype distance index (MGIDI) to select genotypes based on multiple agronomic traits. The selection process exhibited by this method is unique and easy to understand, so the MGIDI index will have more and more important applications in future multi-environment trials (METs) research. The genotypes selected by the MGIDI index were G22, G10, G12 and G1 as the high yielding and stable genotypes. The parents of these selected genotypes have the ability to play a greater role as the basic germplasm in the breeding process. A new form of genotype (G) main effects and genotype (G) -by-environment (E) interaction (GGE) technician, genotype*yield*trait (GYT) biplot, based on multiple traits for genotypes selection was also applied in this study. The GYT biplot ranked genotypes by combining grain yield with other evaluated agronomic traits, and displayed the distribution of their traits, namely strengths and weaknesses.     

Genotypic variability in physiological,biomass and yield response to drought stress?in pigeonpea

Three pigeonpea (Cajanus cajan L. Millsp.) genotypes- GT-1, AKP-1 and PRG-158 with varying crop duration, growth habit and flowering pattern were evaluated for variability in their response for drought stress. Drought stress was imposed at initiation of flowering and the observations on biomass and seed yield parameters were recorded at harvest. The magnitude of response of individual component to drought stress was found to be genotype specific. Drought stress significantly decreased photosynthetic rate (P_N), transpiration rate (Tr) and relative water content (RWC) in all the genotypes, however the magnitude of reduction differed with genotype. With drought stress, the reduction of P_N was highest in GT-1 while reduction in Tr was highest in PRG-158. The genotype AKP-1, accumulated significantly higher concentrations of osmotic solutes especially proline under water deficit stress, this facilitated it to maintain higher relative water content (RWC) and lower malondialdehyde (MDA) content as compared to other genotypes. Drought stress also impacted biomass production and their partitioning to vegetative and reproductive components at harvest. There was significant variability between the genotypes for seed yield under drought stress while it was non-significant under well-watered condition. Drought stress enhanced flower drop and decreased flower to pod conversion resulting in reduced pod number and seed number in PRG-158 and GT-1. The genotype AKP-1 recorded superior performance for seed yield under stress environment due to its ability in maintaining pod and seed number as well as improved test weight (100 seed weight). Under drought stress, significant positive association of seed yield with proline, seed number, pod number and test weight clearly indicating their role in drought tolerance.     

Grain yield and kernel weight of two maize genotypes differing in nitrogen use efficiency at various levels of nitrogen and carbohydrate availability during flowering and grain filling

Grain yield per plant (GYP) and mean kernel weight (KW) of maize (Zea mays L.) are sensitive to changes in the environment during the lag phase of kernel growth (the time after pollination in which the potential kernel size is determined), and during the phase of linear kernel growth. The aim of this study was to assess genotypic differences in the response to environmental stresses associated with N and/or carbohydrate shortage at different phases during plant development. The rate and timing of N and carbohydrate supply were modified by application of fertilizer, shading, and varying the plant density at sowing, at silking or at 14 d after silking. The effects of these treatments on the photosynthetic capacity, grain yield and mean kernel weight were investigated in two hybrids differing in N use efficiency. The total above-ground biomass and grain yield per plant of the efficient hybrid responded little to altered environmental conditions such as suboptimal N supply, enhanced inter-plant competition, and shading for 14 d during flowering, when compared to the less efficient genotype. We conclude that grain yields in the efficient genotype are less sensitive not only to N stress, but also to carbohydrate shortage before grain filling. Shading of N deficient plants from 14 d after silking to maturity did not significantly reduce grain yield in the non-efficient genotype, indicating complete sink limitation of grain yield during grain filling. In the efficient genotype, in contrast, grain yield of N-deficient plants was significantly reduced by shading during grain filling. The rate of photosynthesis declined with decreasing foliar N content. No genotypic differences in photosynthesis were observed at high or low foliar N contents. However, at high plant density and low N supply, the leaf chlorophyll content after flowering in the efficient genotype was higher than that in the non-efficient genotype. Obviously, the higher source capacity of the efficient genotype was not due to higher photosynthetic N use efficiency but due to maintenance of high chlorophyll contents under stressful conditions. In the efficient genotype, the harvest index was not significantly affected by N fertilization, plant density, or shading before the grain filling period. In contrast, in the non-efficient genotype the harvest index was diminished by N deficiency and shading during flowering. We conclude that the high yielding ability of the efficient genotype under stressful conditions was associated with formation of a high sink capacity of the grains under conditions of low carbohydrate and N availability during flowering and with maintenance of high source strength during grain filling under conditions of high plant density and low N availability.     

我国旱地春小麦产量及主要农艺指标的变异分析

采用4年、13个品种(系)、18个试点组成的全国旱地春小麦区域试验产量资料,通过联合方差分析和基因型及其与环境互作（GGE）双标图分析,研究了基因型、环境、基因型与环境互作效应（GEI）对产量变异的影响及品种的产量稳定性.结果表明：环境对产量变异的影响远大于基因型和GEI,环境引起的产量变异占87.5%～92.0%．互作因素中以地点×基因型的互作效应最大,基因型×年份的互作效应最小.我国旱地春小麦基因型多年多点的平均产量水平为2550 kg·hm^-2.产量三要素中,千粒重受环境的影响最小.影响产量变异的主要环境因子有：≥10 ℃年积温、生育期降雨量、平均气温、海拔、年降雨量和无霜期.产量与单位面积穗数（0.675^**）、穗粒数（0.581^**）、千粒重（0.456^**）呈极显著正相关,产量三要素间也呈正相关（0.244～0.480^**）,处于可同步提高范围.     

Use of genotype × environment interactions to understand rooting depth and the ability of wheat to penetrate hard soils

Background

Root systems are well-recognized as complex and a variety of traits have been identified as contributing to plant adaptation to the environment. A significant proportion of soil in south-western Australia is prone to the formation of hardpans of compacted soil that limit root exploration and thus access to nutrients and water for plant growth. Genotypic variation has been reported for root-penetration ability of wheat in controlled conditions, which has been related to field performance in these environments. However, research on root traits in field soil is recognized as difficult and labour intensive. Pattern analysis of genotype × environment (G × E) interactions is one approach that enables interpretation of these complex relationships, particularly when undertaken with probe genotypes with well-documented traits, in this case, for the ability to penetrate a wax layer. While the analytical approach is well-established in the scientific literature, there are very few examples of pattern analysis for G × E interactions applied to root traits of cereal crops.

Scope

In this viewpoint, we aim to review the approach of pattern analysis for G × E interaction and the importance of environment and genotype characterization, with a focus on root traits. We draw on our research on G × E interaction for root depth and related studies on genotypic evaluation for root-penetration ability. In doing so, we wish to explore how pattern analysis can aid in the interpretation of complex root traits and their interaction with the environment and how this may explain patterns of adaptation and inform future research.

Conclusions

With appropriate characterization of environments and genotypes, the G × E approach can be used to aid in the interpretation of the complex interactions of root systems with the environment, inform future research and therefore provide supporting evidence for selecting specific root traits for target environments in a crop breeding programme.     

Kernel set in maize genotypes differing in nitrogen use efficiency in response to resource availability around flowering

Environmental conditions affect grain yield in maize (Zea mays L.) mainly by altering the kernel number per plant (KNP). This number is determined during a critical period of about 2 weeks around silking. The objectives of this study were to assess how the rate and timing of nitrogen (N) fertilizer applications affect biomass partitioning and KNP in two genotypes with different N use efficiency, and to compare kernel set of these genotypes under varying regimes of carbohydrate and N availability during the critical period for kernel set. In the first field experiment, plant density and the rate of N supply per plant were varied independently. In the second field experiment, N availability was controlled via the application of N fertilizer, and carbohydrate availability was controlled by shading or thinning at silking. In both experiments, low rates of N supply reduced KNP more strongly in the non-efficient genotype when compared to the efficient genotype. The genotypic differences in kernel set were neither associated with N uptake into the above-ground biomass at maturity, nor above-ground biomass at silking. In the non-efficient genotype, application of N fertilizer at silking increased KNP. This increase was not associated with an increase in plant growth but with increased partitioning of biomass towards the reproductive organs during the critical period for kernel set. The genotype which had been selected for its high N use efficiency also showed higher kernel set at high plant density and shading during flowering when compared to the non-efficient genotype. Under conditions of restricted resource availability per plant, plant and ear growth rates during the critical period of about 14 days after onset of flowering declined compared with non-limiting conditions. However, these growth rates were less reduced in the efficient genotype. Pooling treatments of different plant density and different available N, each hybrid showed linear responses of KNP to plant growth rate and to ear growth rate. Furthermore, in the efficient genotype KNP was reduced to a lesser extent in response to decreasing growth rates. We conclude that higher kernel set of the efficient genotype compared to the non-efficient genotype under stressful conditions was associated with low sensitivity of plant growth and dry matter distribution towards reproductive organs to low assimilate availability during the critical period of kernel set, and particularly with low sensitivity of kernel set to decreasing plant and ear growth rates.     

Evaluation of breeding strategies for drought tolerance in potato by means of crop growth simulation

Breeding strategies for drought tolerance in potato were evaluated by means of a crop growth model, in which seasonal courses of crop dry matter accumulation and soil moisture availability were simulated in dependence of plant characteristics and weather and soil data.Several plant characteristics substantially influenced the simulated instantaneous water consumption of the genotype. However, effects of genotypic differences on final tuber yield were much smaller because of the close relationship between transpiration and growth. Hence, a lower water consumption not only saved water for later use, but was also at the expense of the actual growth rate. Selection for low-transpiration types, at unchanged water use efficiency, would result in lower yields under optimum conditions.Short periods of drought, in general, reduced tuber yield of late genotypes less than that of early genotypes. Late genotypes had a surplus of leaf area for full light interception giving a lower impact of leaf area reduction. Late drought affected early genotypes less because of escape.The simulation results emphasized the complexity of selection for drought tranrance caused by the many plant processes involved, the contrast between instantaneous and cumulative reactions and the strong genotype × environment interaction for drought tolerance.     

Stability of performance of okra as influenced by planting date

Summary Fifteen selected okra genotypes, consisting of six from a pedigree breeding programme and nine established varieties as checks, were evaluated in five different environments for stability of performance. Performance was measured by pod yield per plant, number of days to flowering, final plant height, number of branches per plant, number of pods per plant and edible pod weight. A regression method and a genotype grouping technique were employed in the evaluation. The results showed significant genotype × environment interaction only with respect to number of days to flowering and number of branches per plant. Additive environmental effect was significant for all characters. Line UI 313 was considered stable with respect to pod yield per plant and edible pod weight. One line resulting from the pedigree breeding programme was also considered stable by the genotype-grouping technique.     

Phenological responses of upland rice grown along an altitudinal gradient

High altitude upland rice (Oryza sativa L.) production systems are expected to benefit from climate change induced increase in temperatures. The potential yield of rice genotypes is governed by the thermal environment experienced during crop development phases when yield components are determined. Thus, knowledge on genotypic variability in phenotypic responses to variable temperature is required for assessing the adaptability of rice production to changing climate. Although, several crop models are available for this task, genotypic thermal constants used to simulate crop phenology vary strongly among the models and are under debate. Therefore, we conducted field trials with ten contrasting upland rice (O. sativa L.) genotypes on three locations along an altitudinal gradient with five monthly staggered sowing dates for two years in Madagascar with the aim to study phenological responses at different temperature regimes. We found that, crop duration is equally influenced by genotype selection, sowing date and year in the high altitude. In contrast, in mid altitudes genotype has no effect on crop duration. At low altitudes crop duration is more affected by sowing date. Grain yield is strongly affected by low temperatures at high altitudes and severly influenced by frequent tropical cyclones at low altitudes. In high altitude, genotype explained 68% of variation in spikelet sterility, whereas in mid and low altitudes environment explained more than 70% of the variation. The phenological responses determining crop duration and yield, the basic genotypic thermal constants, and the analyses of genotypic thermal responses with regard to spikelet sterility reported here, provide valuable information for the improvement of rice phenological models urgently needed to develop new genotypes and better adapted cropping calendars.     

Reduction in seed set upon exposure to high night temperature during flowering in maize

High temperature reduces crop production; however, little is known about the effects of high night temperature (HNT) on the development of male and female reproductive organs, pollination, kernel formation and grain yield in maize (Zea mays L.). Therefore, a temperature-controlled experiment was carried out using heat-sensitive maize hybrid and including three temperature treatments of 32/22°C (day/night; control), 32/26°C and 32/30°C during 14 consecutive days encompassing the flowering stage. When exposed to 30°C night temperature, grain yield and kernel number reduced by 23.8 and 25.1%, respectively, compared with the control. The decrease in grain yield was mainly because of the lower kernel number rather than change in kernel weight under HNT exposure around flowering. No significant differences in grain yield and kernel number were found between 22 and 26°C night temperatures. HNT had no significant effects on the onset of flowering time and anthesis-silking interval but significantly reduced time period of pollen shedding duration and pollen viability, and increased leaf night respiration. Different from high daytime temperature, HNT had no lasting effects on daytime leaf photosynthesis, biomass production and assimilate transportation. From the perspective of source–flow–sink relationship, the unchanged source and flow capacities during daytime are supposed to alleviate the adverse effects on sink strength caused by HNT compared with daytime heat stress. These new findings commendably filled the knowledge gaps concerning heat stress in maize.     

The morpho-agronomic characterization study of Lens culinaris germplasm under salt marsh habitat in Swat,Pakistan

The present research study evaluate and identify the most suitable and high yielding genotypes of Lens culinaris for the salt marsh habitat of Swat in moist temperate sort of agro climatic environment of Pakistan. A total of fourteen genotypes were cultivated and analyzed through Randomized Complete Block Design (RCBD). These genotypes were AZRC-4, NL-2, NL4, NL-5, NL-6, NARC-11-1, NARC-11-2, NARC-11-3, NARC-11-4, 09503, 09505, 09506, P.Masoor-09 and Markaz-09. Different parameters i.e., germination rate, flowering, physiological maturity, plant height, biological grain yield, seed weight, pods formation and its height, pods per plants and protein content were focused specially throughout the study. Preliminary the Lentil genotypes have significant variability in all the major morpho-agronomic traits. The days to germination, 50% flowering and 100 seed weight ranged from 7 to 9, 110 to 116 days, and from 5.4 to 7.3 gm respectively. Biological yield and grain yield ranged from 5333 to 9777 kg ha⁻¹ and 1933 to 3655 kg ha⁻¹ respectively. Whereas, protein contents ranged from 23.21% to 28.45%. It was concluded that the genotype AZRC-4 is better varity in terms of grain yield plus in 100 seed weight and moreover, 09506 genotype was significant under salt marsh habitat in early maturing for the Swat Valley, Pakistan.     

Proteomic response of barley leaves to salinity

Drought and salinity stresses are adverse environmental factors that affect crop growth and yield. Proteomic analysis offers a new approach to identify a broad spectrum of genes that are expressed in living system. We applied this technique to investigate protein changes that were induced by salinity in barley genotypes (Hordeum vulgare L.), Afzal, as a salt-tolerant genotype and L-527, as a salt-sensitive genotype. The seeds of two genotypes were sown in pot under controlled condition of greenhouse, using a factorial experiment based on a randomized complete block design with three replications. Salt stress was imposed at seedling stage and leaves were collected from control and salt-stressed plant. The Na⁺ and K⁺ concentrations in leaves changed significantly in response to short-term stress. About 850 spots were reproducibly detected and analyzed on 2-DE gels. Of these, 117 proteins showed significant change under salinity condition in at least one of the genotypes. Mass spectrometry analysis using MALDI-TOF/TOF led to the identification some proteins involved in several salt responsive mechanisms which may increase plant adaptation to salt stress including higher constitutive expression level and upregulation of antioxidant, upregulation of protein involved in signal transduction, protein biosynthesis, ATP generation and photosynthesis. These findings may enhance our understanding of plant molecular response to salinity.