Multi-environment Trials Research Articles

Estimation of Additive and Dominance Effects in an Acacia crassicarpa Multi-Environment Progeny Trial Using Genomic Pedigree Reconstruction

Abstract Acacia crassicarpa is an important tree species in Southeast Asia, where hundreds of thousands of hectares of planted forests are supported by advancements in silviculture and genetic improvement. Although possible, controlled pollination is impractical for advancing breeding populations, requiring an unreasonable effort to produce more than a few crosses per year. For this reason, breeding populations often are bred by open pollination. This study used large-scale pedigree reconstruction in multi-environment trials to assess full-sib families to model the genetics of the quantitative traits survival, straightness, height, diameter at breast height, tree volume, mean annual increment (MAI), and basic density. The traits were predominantly controlled by additive effects, with heritabilities between 0.09 for survival and 0.45 for basic density. The genetic correlation across sites was high for all traits, showing the low impact of genotype-by-environment interaction. The trait-trait correlation showed that straightness was independent of any other traits, survival was only correlated with MAI, and growth traits were highly correlated among themselves. Basic density was positively correlated with growth traits and MAI. Study Implications: Parentage analysis using an informative single nucleotide polymorphism panel was used to reconstruct pedigree and allow a full-sib family model to estimate additive and dominance effects and genetic correlations across sites and among important traits in an open-pollinated population. The genetic control of all traits assessed in this study was mainly additive. In this scenario, the recommended breeding strategy is forward selection of outstanding progeny for advanced generation breeding and backward selection of outstanding parents to produce seed for deployment via family forestry. Full-sib families can be identified by pedigree reconstruction at a seedling stage, followed by tissue culture multiplication, rooted cutting propagation, and plantation establishment.

ProbBreed: a novel tool for calculating the risk of cultivar recommendation in multienvironment trials.

Neglecting genotype-by-environment interactions in multienvironment trials (MET) increases the risk of flawed cultivar recommendations for growers. Recent advancements in probability theory coupled with cutting-edge software offer a more streamlined decision-making process for selecting suitable candidates across diverse environments. Here, we present the user-friendly ProbBreed package in R, which allows breeders to calculate the probability of a given genotype outperforming competitors under a Bayesian framework. This article outlines the package's basic workflow and highlights its key features, ranging from MET model fitting to estimating the per se and pairwise probabilities of superior performance and stability for selection candidates. Remarkably, only the selection intensity is required to compute these probabilities. By democratizing this complex yet efficient methodology, ProbBreed aims to enhance decision-making and ultimately contribute to more accurate cultivar recommendations in breeding programs.

Genomic prediction in multi-environment trials in maize using statistical and machine learning methods

In the context of multi-environment trials (MET), genomic prediction is proposed as a tool that allows the prediction of the phenotype of single cross hybrids that were not tested in field trials. This approach saves time and costs compared to traditional breeding methods. Thus, this study aimed to evaluate the genomic prediction of single cross maize hybrids not tested in MET, grain yield and female flowering time. We also aimed to propose an application of machine learning methodologies in MET in the prediction of hybrids and compare their performance with Genomic best linear unbiased prediction (GBLUP) with non-additive effects. Our results highlight that both methodologies are efficient and can be used in maize breeding programs to accurately predict the performance of hybrids in specific environments. The best methodology is case-dependent, specifically, to explore the potential of GBLUP, it is important to perform accurate modeling of the variance components to optimize the prediction of new hybrids. On the other hand, machine learning methodologies can capture non-additive effects without making any assumptions at the outset of the model. Overall, predicting the performance of new hybrids that were not evaluated in any field trials was more challenging than predicting hybrids in sparse test designs.

Mr.Bean: a comprehensive statistical and visualization application for modeling agricultural field trials data.

Crop improvement efforts have exploited new methods for modeling spatial trends using the arrangement of the experimental units in the field. These methods have shown improvement in predicting the genetic potential of evaluated genotypes. However, the use of these tools may be limited by the exposure and accessibility to these products. In addition, these new methodologies often require plant scientists to be familiar with the programming environment used to implement them; constraints that limit data analysis efficiency for decision-making. These challenges have led to the development of Mr.Bean, an accessible and user-friendly tool with a comprehensive graphical visualization interface. The application integrates descriptive analysis, measures of dispersion and centralization, linear mixed model fitting, multi-environment trial analysis, factor analytic models, and genomic analysis. All these capabilities are designed to help plant breeders and scientist working with agricultural field trials make informed decisions more quickly. Mr.Bean is available for download at https://github.com/AparicioJohan/MrBeanApp.

Modeling spatial trends and selecting tropical wheat genotypes in multi-environment trials

Modeling spatial trends and selecting tropical wheat genotypes in multi-environment trials

Application of factor analytic and spatial mixed models for the analysis of multi-environment trials in common bean (Phaseolus vulgaris L.) in Ethiopia.

Common bean (Phaseolus vulgaris L.) is one of the most important grain legumes consumed globally, especially in Ethiopia, for its edible seeds, cash crops, and supply of protein for farmers. Efficient statistical methods must be employed for the evaluation of common bean varieties to accurately select superior varieties that contribute to agricultural productivity. The objective of this study was to identify promising large mottled bean varieties through analysis of multi-environment trials (MET) data using multiplicative spatial mixed models. In this study, 16-18 large mottled common bean varieties, including one check, were sown across nine growing environments in Ethiopia using lattice and alpha lattice designs, with three replications laid out in a square or rectangular (row by column) array of plots, respectively during the main cropping season from 2015 to 2018. We present a linear mixed model analysis that integrates spatial and factor analytic (FA) models, and the heritability measure was used to evaluate the efficiency of these models with the conventional analysis. The analysis of the spatial model, and more significantly, the spatial+FA model, revealed a notable enhancement in heritability. With the exception of a trial conducted at Kobo, a genotype DAP 292, found to be good performing for days to flowering and maturity, but for yield only across four clusters of trials, C2, C3, C5 and C7, formed with trials of relatively high genetic variance. Across these clusters, the yield advantage of this variety over the check ranged from 10-32%. This genotype also has a yield that is somewhat comparable to the check across the remaining clusters. Overall, both the spatial and factor analytic models proved to be effective approaches for analyzing the data in this study. The analysis of multi-environment trial data through the use of more efficient statistical models can provide a more robust platform for evaluating common bean varieties with greater confidence in selecting superior varieties across a range of environments. Hence, scaling up the use of this efficient analysis method is indispensable for enhancing the selection of superior varieties.

Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers

BackgroundGenomewide prediction estimates the genomic breeding values of selection candidates which can be utilized for population improvement and cultivar development. Ridge regression and deep learning-based selection models were implemented for yield and agronomic traits of 204 chile pepper genotypes evaluated in multi-environment trials in New Mexico, USA.ResultsAccuracy of prediction differed across different models under ten-fold cross-validations, where high prediction accuracy was observed for highly heritable traits such as plant height and plant width. No model was superior across traits using 14,922 SNP markers for genomewide selection. Bayesian ridge regression had the highest average accuracy for first pod date (0.77) and total yield per plant (0.33). Multilayer perceptron (MLP) was the most superior for flowering time (0.76) and plant height (0.73), whereas the genomic BLUP model had the highest accuracy for plant width (0.62). Using a subset of 7,690 SNP loci resulting from grouping markers based on linkage disequilibrium coefficients resulted in improved accuracy for first pod date, ten pod weight, and total yield per plant, even under a relatively small training population size for MLP and random forest models. Genomic and ridge regression BLUP models were sufficient for optimal prediction accuracies for small training population size. Combining phenotypic selection and genomewide selection resulted in improved selection response for yield-related traits, indicating that integrated approaches can result in improved gains achieved through selection.ConclusionsAccuracy values for ridge regression and deep learning prediction models demonstrate the potential of implementing genomewide selection for genetic improvement in chile pepper breeding programs. Ultimately, a large training data is relevant for improved genomic selection accuracy for the deep learning models.

Inbred-hybrid relationship and heterosis on agronomic traits in bi-color sweet and waxy corn

Abstract. Patthawaro P, Dermail A, Chankaew S, Simla S, Lomthaisong K, Lertrat K, Suriharn K. 2023. Inbred-hybrid relationship and heterosis on agronomic traits in bi-color sweet and waxy corn. Biodiversitas 24: 6351-6359. Maize hybrids become more acceptable than other cultivar types since they promise high yield and uniformity by exploiting the heterosis advantages. Numerous lines are to be crossed to obtain the promising hybrids, and the progenies must be tested in multi-environment trials. Handling large number of genotypes in routine work is resource intensive; thus, the question arises if hybrid prediction based on the mid-parent (MP) value is feasible to reduce the workload. This study aimed to investigate the reliability of MP values in hybrid prediction through simple linear correlation and to estimate the magnitude of heterosis on given traits in sweet-waxy corn. Six parental lines and 10 F1 hybrids were evaluated in Khon Kaen in the dry and rainy seasons (2021/2022). Genotype and the interaction between genotype and season were significant for all observed traits. Waxy corn genotype 8A3-B was a good combiner for yield and yield components. Heterosis was trait-dependent, and it could imply parental adaptation when the estimation was done in contrasting environments. We found that the MP value can be used to predict the hybrid performance for flowering times only, but this approach was not effective for the rest agronomic traits. Since commonly, maize improvements are regarding multiple favorable traits, the strategy to use MP as single factor in hybrid prediction was not reliable. Other approaches, either SCA alone or the sum of mid-parent GCA and SCA, could be implemented for hybrid prediction in future studies.

Genetic Diversity and Association of Yield-Related Traits in Taro (Colocasia esculenta (L.) Schott) Sourced from Different Agroecological Origins of Nigeria

Taro’s production is characterized by low productivity due to a lack of sufficient improved cultivars suited to the different growing areas coupled with biotic and abiotic stresses. The first step in plant breeding program is to examine and quantify variations for traits of interest in a given set of genotypes so those variations can be exploited in breeding programmes. One hundred taro accessions were evaluated for 16 quantitative traits in a simple lattice design with the objective of estimating variability and determine and quantify association of characters with yield. Multienvironment trial analysis combined over seasons showed highly significant differences among taro accessions for several traits. Yield per plant showed a strong, positive, and highly significant genotypic correlation with plant height (0.99), leaf area (0.82), corm weight (0.99), and cormel weight (0.99). Genotypic path coefficient analysis showed plant height (1.49), leaf area (0.97), corm weight (0.76), and cormel weight (0.21) exerted a high positive direct effect on yield per plant indicating true relationship between the characters. Accessions EBNFC054, EBNFC045, EBNFC100, EBNFC046, EBNFC032, EBNFC075, EBNFC084, EBNFC057, and EBNFC037 were superior and could be promoted to field evaluation. This assessment of variability and associations can assist breeders to tap the potential of the genotypes for certain traits and identify major traits that could be used as a basis for the selection of superior taro genotypes.

A simulation-based assessment of the efficiency of QTL mapping under environment and genotype x environment interaction effects.

The objective of this simulation-based study was to assess how genes, environments, and genotype x environment (GxE) interaction affect the quantitative trait loci (QTL) mapping efficiency. The simulation software performed 50 samplings of 300 recombinant inbred lines (RILs) from a F2, which were assessed in six environments. The RILs were genotyped for 977 single nucleotide polymorphisms (SNP) and phenotyped for grain yield. The average SNP density was 2 cM. We defined six QTLs and 190 minor genes. The trait heritability ranged from 30 to 80%. We fitted the single QTL model and the multiple QTL model on multiple phenotypes. The environment and complex GxE interaction effects led to a low correlation between the QTL heritability and power. The single- and across-environment analyses allowed all QTLs be declared, with an average power of 28 to 100%. In the across-environment analysis, five QTLs showed average power in the range 46 to 82%. Both models provided a good control of the false positive rate (6%, on average) and a precise localization of the QTLs (bias of 2 cM, on average). The QTL power in each environment has a high positive correlation with the range between QTL genotypes for the sum of the additive, environment, and GxE interaction effects (0.76 to 0.96). The uncertainty about the magnitude and sign of the environment and GxE interaction effects makes QTL mapping in multi-environment trials unpredictable. Unfortunately, this uncertainty has no solution because the geneticist has no control over the magnitude and sign of the environment and GxE interaction effects. However, the single- and across-environment analyses are efficient even under a low correlation between QTL heritability and power.

Models to estimate genetic gain of soybean seed yield from annual multi-environment field trials

Key messageSimulations demonstrated that estimates of realized genetic gain from linear mixed models using regional trials are biased to some degree. Thus, we recommend multiple selected models to obtain a range of reasonable estimates.Genetic improvements of discrete characteristics are obvious and easy to demonstrate, while quantitative traits require reliable and accurate methods to disentangle the confounding genetic and non-genetic components. Stochastic simulations of soybean [Glycine max (L.) Merr.] breeding programs were performed to evaluate linear mixed models to estimate the realized genetic gain (RGG) from annual multi-environment trials (MET). True breeding values were simulated under an infinitesimal model to represent the genetic contributions to soybean seed yield under various MET conditions. Estimators were evaluated using objective criteria of bias and linearity. Covariance modeling and direct versus indirect estimation-based models resulted in a substantial range of estimated values, all of which were biased to some degree. Although no models produced unbiased estimates, the three best-performing models resulted in an average bias of ±7.41\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm\\, 7.41$$\\end{document} kg/ha-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}/yr-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document} (±0.11\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm\\, 0.11$$\\end{document} bu/ac-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}/yr-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}). Rather than relying on a single model to estimate RGG, we recommend the application of several models with minimal and directional bias. Further, based on the parameters used in the simulations, we do not think it is appropriate to use any single model to compare breeding programs or quantify the efficiency of proposed new breeding strategies. Lastly, for public soybean programs breeding for maturity groups II and III in North America, the estimated RGG values ranged from 18.16 to 39.68 kg/ha-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}/yr-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document} (0.27–0.59 bu/ac-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}/yr-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}) from 1989 to 2019. These results provide strong evidence that public breeders have significantly improved soybean germplasm for seed yield in the primary production areas of North America.

Selection of elite genotypes deploying AMMI, GGE and MTSI in okra [Abelmoschus esculentus (L.) Moench

Okra, a versatile vegetable known as lady's finger or gumbo, is highly prized for its tender, flavorful pods, particularly in tropical and subtropical climates worldwide. Improving okra yield poses challenges due to complex genotype-environment (G×E) interactions. The success of enhancing okra is hindered by these interactions and necessitates thorough investigation. In this study, multivariate selection strategies like AMMI, GGE biplots and MTSI were employed to visually represent the relationships between genotypes and tested environments, aiding in the identification of okra genotypes with consistent and high yields. The study evaluated 24 okra hybrids and 10 parents for 13 traits in multi-environment trials. Notably, hybrids H23 (NOL 17–09 × Kashi Kranti) and H10 (AOL-16–04 × Arka Abhay) stood out for their promising performance, demonstrating elevated and adaptable fruit yields, along with traits highly favored by farmers. MTSI analysis confirmed substantial and desirable selection gains across all traits for these selected genotypes.

Genotype × environment interaction and stability analysis for seed yield and yield components in sesame (Sesamum indicum L.) in Benin Republic using AMMI, GGE biplot and MTSI

Increasing production requires the development of high yielding cultivars adapted to various environments. Multi-environment trials (MET) remains the best approach to assess the performance of accessions across environments. The objective of this study was to select the best performing and stable accessions of sesame across different environments in Benin Republic. Nineteen sesame accessions were evaluated across eight environments using a randomized complete block design with four replicates. The accessions were evaluated for three traits: days to 50% flowering (D50F), thousand-seed weight (TSW), and seed yield (SY) during 2020 growing season. The stable and top-performing accessions across environments were determined using AMMI (Additive main effects and multiplicative interaction), GGE (Genotype main effect and genotype × environment interaction), and MTSI (Multi-trait stability index). AMMI analysis of variance showed a significant difference across environments for the three traits. The accessions were affected by environmental conditions for the three traits. The broad-sense heritability estimates were high (>0.60) for all the traits, indicating the improvement is achievable through selection. AMMI1 and AMMI2 biplots identified G10 and G13 as high seed yielding accessions adapted to environments E1, E2. The GGE biplot showed two mega-environments for TSW and three mega-environments for D50F and SY. For SY, G11 and G13 were the best accessions in the first mega-environment, G10 the best accession in the second mega-environment; G3 and G8 were the best accessions in the third mega-environment. AMMI and GGE analyses identified G10, G5, G12 as high seed yielding and stable accessions across environments. GGE biplot revealed that E1 and E2 were the most suitable environments for multi-location trials based on their discriminating ability and representativeness. MTSI indicated G10, G13, G19 as promising germplasm to be recommended for breeding program.

Assessment of yield performances for grain sorghum varieties by AMMI and GGE biplot analyses

Grain sorghum is an exceptional source of dietary nutrition with outstanding economic values. Breeding of grain sorghum can be slowed down by the occurrence of genotype × environment interactions (GEI) causing biased estimation of yield performance in multi-environments and therefore complicates direct phenotypic selection of superior genotypes. Multi-environment trials by randomized complete block design with three replications were performed on 13 newly developed grain sorghum varieties at seven test locations across China for two years. Additive main effects and multiplicative interaction (AMMI) and genotype + genotype × environment (GGE) biplot models were adopted to uncover GEI patterns and effectively identify high-yielding genotypes with stable performance across environments. Yield (YLD), plant height (PH), days to maturity (DTM), thousand seed weight (TSW), and panicle length (PL) were measured. Statistical analysis showed that target traits were influenced by significant GEI effects (p < 0.001), that broad-sense heritability estimates for these traits varied from 0.40 to 0.94 within the medium to high range, that AMMI and GGE biplot models captured more than 66.3% of total variance suggesting sufficient applicability of both analytic models, and that two genotypes, G3 (Liaoza No.52) and G10 (Jinza 110), were identified as the superior varieties while one genotype, G11 (Jinza 111), was the locally adapted variety. G3 was the most stable variety with highest yielding potential and G10 was second to G3 in average yield and stability whereas G11 had best adaptation only in one test location. We recommend G3 and G10 for the production in Shenyang, Chaoyang, Jinzhou, Jinzhong, Yulin, and Pingliang, while G11 for Yili.

A Multi-Model Based Stability Analysis Employing Multi-Environmental Trials (METs) Data for Discerning Heat Tolerance in Chickpea (Cicer arietinum L.) Landraces.

Identifying a congenially targeted production environment and understanding the effects of genotype by environmental interactions on the adaption of chickpea genotypes is essential for achieving an optimal yield stability. Different models like additive main effect and multiplicative interactions (AMMI 1, AMM2), weighted average absolute scores of BLUPs (WAASB), and genotype plus genotype-environment (GGE) interactions were used to understand their suitability in the precise estimation of variance and their interaction. Our experiment used genotypes that represent the West Asia-North Africa (WANA) region. This trial involved two different sowing dates, two distinct seasons, and three different locations, resulting in a total of 12 environments. Genotype IG 5871(G1) showed a lower heat susceptibility index (HSI) across environments under study. The first four interactions principal component axis (IPCA) explain 93.2% of variations with significant genotype-environment interactions. Considering the AMMI stability value (ASV), the genotypes IG5862(G7), IG5861(G6), ILC239(G40), IG6002(G26), and ILC1932(G39), showing ASV scores of 1.66, 1.80, 2.20, 2.60, and 2.84, respectively, were ranked as the most stable and are comparable to the weighted average absolute scores of BLUPs (WAASB) ranking of genotypes. The which-won-where pattern of genotype plus genotype-environment (GGE) interactions suggested that the target environment consists of one mega environment. IG5866(G10), IG5865(G9), IG5884(G14), and IG5862(G7) displayed higher stability, as they were nearer to the origin. The genotypes that exhibited a superior performance in the tested environments can serve as ideal parental lines for heat-stress tolerance breeding programs. The weighted average absolute scores of BLUPs (WAASB) serve as an ideal tool to discern the variations and identify the stable genotype among all methods.

Incorporating environmental covariates to explore genotype × environment × management (G × E × M) interactions: A one-stage predictive model

ContextEvaluating the genotype (G) by management practice (M) interaction in agronomic experimentation is essential to help grain growers optimise the desired trait of interest (e.g. grain yield). However, the approach is complicated by interaction effects with environmental factors that differ across sites and seasons. Popular statistical methods for modelling the genotype by environment (G × E) interaction are limited as they neither provide a biological understanding of how environmental factors impact on the G × E interaction, nor assess how different management practices influence the G × E interaction. These limitations may be addressed by incorporating environmental covariates (ECs) into the modelling process to better explain why differences exist in the optimal genotype by management practice combination across environments. ObjectiveA novel statistical methodology is proposed that incorporates ECs to explore genotype by environment by management practice (G × E × M) interactions in agronomic multi-environment trial studies. MethodsA predictive linear mixed model is proposed that incorporates site and season specific ECs into a commonly used G × E interaction framework. The model is extended to include the effect of continuously varying agronomic management practices, whilst allowing for non-linear trait responses and complex variance structures. The methodology is applied to a multi-environment dataset exploring yield response to established plant density in a series of sorghum agronomy trials. ResultsResults indicated that the grain yield of sorghum genotypes would be optimised in environments that have (i) high total plant available water and photo-thermal quotient around flowering, (ii) low pre-flowering radiation and evapotranspiration and (iii) achieved flowering at an optimal time. Under this set of optimal G × E conditions, a high established plant density further optimised grain yield. ConclusionsThe proposed methodology successfully incorporated ECs to better understand G × E × M interactions in agronomic field trials, enabling predictions to be made in an untested or future environment and linking the statistical analysis to crop-ecophysiology principles. ImplicationsThis work will improve the generalisations agronomists can draw from experimental studies, enhancing the biological understanding of the analysis results and allowing for the development of more targeted and robust recommendations for agronomic practices.

Identification of Wheat Ideotype under Multiple Abiotic Stresses and Complex Environmental Interplays by Multivariate Analysis Techniques.

Multiple abiotic stresses negatively impact wheat production all over the world. We need to increase productivity by 60% to provide food security to the world population of 9.6 billion by 2050; it is surely time to develop stress-tolerant genotypes with a thorough comprehension of the genetic basis and the plant's capacity to tolerate these stresses and complex environmental reactions. To approach these goals, we used multivariate analysis techniques, the additive main effects and multiplicative interaction (AMMI) model for prediction, linear discriminant analysis (LDA) to enhance the reliability of the classification, multi-trait genotype-ideotype distance index (MGIDI) to detect the ideotype, and the weighted average of absolute scores (WAASB) index to recognize genotypes with stability that are highly productive. Six tolerance multi-indices were used to test twenty wheat genotypes grown under multiple abiotic stresses. The AMMI model showed varying differences with performance indices, which disagreed with the trait and genotype differences used. The G01, G12, G16, and G02 were selected as the appropriate and stable genotypes using the MGIDI with the six tolerance multi-indices. The biplot features the genotypes (G01, G03, G11, G16, G17, G18, and G20) that were most stable and had high tolerance across the environments. The pooled analyses (LDA, MGIDI, and WAASB) showed genotype G01 as the most stable candidate. The genotype (G01) is considered a novel genetic resource for improving productivity and stabilizing wheat programs under multiple abiotic stresses. Hence, these techniques, if used in an integrated manner, strongly support the plant breeders in multi-environment trials.

Experimental plan for oilseed flax in multi-environment trials

The importance of the cultivation of oilseed flax has increased in the Brazilian agricultural scenario, and therefore, research must be carried out to have better recommendations for cultivation. For research to be reliable, there is a need for adequate experimental planning. In this context, the objective of this work is to propose an experimental plan, with estimates of plot size, sample size, and the number of replicates, to increase the precision and reliability of experiments carried out with the oilseed flax crop. The experiments were carried out in three environments and two agricultural years, in a randomized block design with four replicates and two varieties of oilseed flax, totaling eleven experiments in six different environments. In both years, plants were sampled in the plot and measured for: total height, technical length, productive length, main branch diameter, number of capsules per plant, number of seeds per capsule, and grain mass per plant. From these data, the plot size was estimated using the Pimentel-Gomes method, and it was verified that 30 plants, arranged in 6 rows of cultivation, are needed to compose the usable area of the plot, with 2 border rows, totaling 90 plants per plot. Differently, with the grain yield data, in which the effects of the parameters of the mathematical model of each experimental trial were removed, the plot size was estimated through the Paranaíba method, and it was identified that the plots must be 9.86 m². For sampling, an optimal size of 5.4 m² was observed, considering a semi-amplitude of the confidence interval (D%) equal to 20% of the mean. Also, the need to use five replicates per plot was verified, considering a least significant difference in the Tukey test expressed as a percentage of the mean of 25%.

Genetic regions determine tolerance to nitrogen deficiency in European elite bread wheats grown under contrasting nitrogen stress scenarios.

Clustering 24 environments in four contrasting nitrogen stress scenarios enabled the detection of genetic regions determining tolerance to nitrogen deficiency in European elite bread wheats. Increasing the nitrogen use efficiency of wheat varieties is an important goal for breeding. However, most genetic studies of wheat grown at different nitrogen levels in the field report significant interactions with the genotype. The chromosomal regions possibly involved in these interactions are largely unknown. The objective of this study was to quantify the response of elite bread wheat cultivars to different nitrogen field stress scenarios and identify genomic regions involved in this response. For this purpose, 212 elite bread wheat varieties were grown in a multi-environment trial at different nitrogen levels. Genomic regions associated with grain yield, protein concentration and grain protein deviation responses to nitrogen deficiency were identified. Environments were clustered according to adjusted means for grain yield, yield components and grain protein concentration. Four nitrogen availability scenarios were identified: optimal condition, moderate early deficiency, severe late deficiency, and severe continuous deficiency. A large range of tolerance to nitrogen deficiency was observed among varieties, which were ranked differently in different nitrogen deficiency scenarios. The well-known negative correlation between grain yield and grain protein concentration also existed between their respective tolerance indices. Interestingly, the tolerance indices for grain yield and grain protein deviation were either null or weakly positive meaning that breeding for the two traits should be less difficult than expected. Twenty-two QTL regions were identified for the tolerance indices. By selecting associated markers, these regions may be selected separately or combined to improve the tolerance to N deficiency within a breeding programme.

Evaluation of maize varieties through multi-environment trials: application of multiplicative mixed models

Evaluation of maize varieties through multi-environment trials: application of multiplicative mixed models