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Rice breeding programs globally have worked to release increasingly productive and climate-smart cultivars, but the genetic gains have been limited for some reasons. One is the capacity for field phenotyping, which presents elevated costs and an unclear approach to defining the number and allocation of multi-environmental trials (MET). To address this challenge, we used soil information and ten years of historical weather data from the USA rice belt, which was translated into rice response based on the rice cardinal temperatures and crop stages. Next, we eliminated those highly correlated Environmental Covariates (ECs) (>0.95) and applied a supervised algorithm for feature selection using two years of data (2021-22) and 25 genotypes evaluated for grain yield in 18 representative locations in the Southern USA. To test the trials' optimization, we performed the joint analysis using prediction-based models in four different scenarios: i) considering trials as non-related, ii) including the environmental relationship matrix calculated from ECs, iii) within clusters; iv) sampling one location per cluster. Finally, we weigh the trial's allocation considering the counties' economic importance and the environmental group to which they belong. Our findings show that eight ECs explained 58% of grain yield variation across sites and 53% of the observed genotype-by-environment interaction. Moreover, it is possible to reduce 28% the number of locations without significant loss in accuracy. Furthermore, the US Rice belt comprises four clusters, with economic importance varying from 13 to 45%. These results will help us better allocate trials in advance and reduce costs without penalizing accuracy. [ABSTRACT FROM AUTHOR]

Finlay–Wilkinson regression is a popular method for modeling genotype–environment interaction in plant breeding and crop variety testing. When environment is a random factor, this model may be cast as a factor‐analytic variance–covariance structure, implying a regression on random latent environmental variables. This paper reviews such models with a focus on their use in the analysis of multi‐environment trials for the purpose of making predictions in a target population of environments. We investigate the implication of random versus fixed effects assumptions, starting from basic analysis‐of‐variance models, then moving on to factor‐analytic models and considering the transition to models involving observable environmental covariates, which promise to provide more accurate and targeted predictions than models with latent environmental variables. [ABSTRACT FROM AUTHOR]

Rice breeding programs globally have worked to release increasingly productive and climate-smart cultivars, but the genetic gains have been limited for some reasons. One is the capacity for field phenotyping, which presents elevated costs and an unclear approach to defining the number and allocation of multi-environmental trials (MET). To address this challenge, we used soil information and ten years of historical weather data from the USA rice belt, which was translated into rice response based on the rice cardinal temperatures and crop stages. Next, we eliminated those highly correlated Environmental Covariates (ECs) (>0.95) and applied a supervised algorithm for feature selection using two years of data (2021-22) and 25 genotypes evaluated for grain yield in 18 representative locations in the Southern USA. To test the trials’ optimization, we performed the joint analysis using prediction-based models in four different scenarios: i) considering trials as non-related, ii) including the environmental relationship matrix calculated from ECs, iii) within clusters; iv) sampling one location per cluster. Finally, we weigh the trial’s allocation considering the counties’ economic importance and the environmental group to which they belong. Our findings show that eight ECs explained 58% of grain yield variation across sites and 53% of the observed genotype-by-environment interaction. Moreover, it is possible to reduce 28% the number of locations without significant loss in accuracy. Furthermore, the US Rice belt comprises four clusters, with economic importance varying from 13 to 45%. These results will help us better allocate trials in advance and reduce costs without penalizing accuracy.

This research introduces a novel framework for enhancing soybean cultivation in North America by categorizing growing environments into distinct ecological and maturity-based zones. Using an integrated analysis of long-term climatic data and records of soybean varietal trials, this research generates a zonal environmental characterization which captures major components of the growing environment which affect the range of adaptation of soybean varieties. These findings have immediate applications for optimizing multi-environment soybean trials. This characterization allows breeders to assess the environmental representation of a multi-environmental trial of soybean varieties, and to strategize the distribution of testing and the placement of test sites accordingly. This application is demonstrated with a historical scenario of a soybean multi-environment trial, using two resource allocation models: one targeted towards improving the general adaptation of soybean varieties, which focuses on widely cultivated areas, and one targeted towards specific adaptation, which captures diverse environmental conditions. Ultimately, the study aims to improve the efficiency and impact of soybean breeding programs, leading to the development of cultivars resilient to variable and changing climates. [ABSTRACT FROM AUTHOR]

This research introduces a novel framework for enhancing soybean cultivation in North America by categorizing growing environments into distinct ecological and maturity-based zones. Using an integrated analysis of long-term climatic data and records of soybean varietal trials, this research generates a zonal environmental characterization which captures major components of the growing environment which affect the range of adaptation of soybean varieties. These findings have immediate applications for optimizing multi-environment soybean trials. This characterization allows breeders to assess the environmental representation of a multi-environmental trial of soybean varieties, and to strategize the distribution of testing and the placement of test sites accordingly. This application is demonstrated with a historical scenario of a soybean multi-environment trial, using two resource allocation models: one targeted towards improving the general adaptation of soybean varieties, which focuses on widely cultivated areas, and one targeted towards specific adaptation, which captures diverse environmental conditions. Ultimately, the study aims to improve the efficiency and impact of soybean breeding programs, leading to the development of cultivars resilient to variable and changing climates.

The cultivation of pearl millet in India is experiencing important transformations. Here, we propose a new characterization of the pearl millet production environment using the latest available district level data (1998–2017), principal component analysis, and large-scale crop model simulations. Pearl millet cultivation environment can be divided in up to five environments (TPEs). The eastern part of the country (Rajasthan, Haryana, Uttar Pradesh) emerges as the only region where pearl millet cultivation has grown (+0.4 Kha/year), with important yield increase (+51 kg/ha/year), and potential surplus that are likely exported. Important reductions of pearl millet cultivated area in Gujarat (−4.5 Kha/year), Maharashtra and Karnataka (−4 Kha/year) are potentially due to economy-driven transition to other more profitable crops, such as cotton or maize. The potential rain increase could also accelerate this transition. With R 2 ∈ [0.15–0.61], the tested crop models reflected reasonably well the pearl millet production system in the A1 (North Radjasthan) and AE1 (South Rajastan and Haryana) TPEs covering the largest area (66%) and production share (59%), especially after the use of a new strategy for environment and management parameters calibration. Those results set the base for in silico system design and optimization in future climatic scenarios. [ABSTRACT FROM AUTHOR]

Genetic gains (ΔG) are determined by the breeders' equation ΔG = [(ck σ 2G)/(y σ P)], where c , k and y are the parental control, a function of the selection intensity and number of years to complete one selection cycle, respectively, while σ 2G and are σ P the genetic variance and the square root of the phenotypic variance. Plant breeding programs should deliver above 1% of annual genetic gains after testing and selection. The aim of this research was to estimate genetic gains in potato breeding after testing of cultivars released in western Europe in the last 200 years under high yield potential, and stress-prone environments affected by a pest (late blight) or daylength. The annual genetic gains for tuber yield and flesh's starch content for potato breeding in Europe were about 0.3 and −0.1%, respectively, thus telling that the realized genetic gains of foreign cultivars for both traits are small or negative, respectively, in the Nordic testing sites. The national annual productivity gains in potato grown in Sweden were on average 0.7% in the last 60 years while the genetic gains for tuber yield considering only the table cultivars released after the 2nd World War were about 0.36%, thus showing that breeding contributed just above ½ of it. Furthermore, genetic gains for breeding low reducing sugars in the tuber flesh, and high host plant resistance to late blight were small (<0.2% per year). These results highlight that genetic gains are small when testing bred germplasm outside their target population of environments. [ABSTRACT FROM AUTHOR]

The cultivation of pearl millet in India is experiencing important transformations. Here, we propose a new characterization of the pearl millet production environment using the latest available district level data (1998–2017), principal component analysis, and large-scale crop model simulations. Pearl millet cultivation environment can be divided in up to five environments (TPEs). The eastern part of the country (Rajasthan, Haryana, Uttar Pradesh) emerges as the only region where pearl millet cultivation has grown (+0.4 Kha/year), with important yield increase (+51 kg/ha/year), and potential surplus that are likely exported. Important reductions of pearl millet cultivated area in Gujarat (−4.5 Kha/year), Maharashtra and Karnataka (−4 Kha/year) are potentially due to economy-driven transition to other more profitable crops, such as cotton or maize. The potential rain increase could also accelerate this transition. With R2∈ [0.15–0.61], the tested crop models reflected reasonably well the pearl millet production system in the A1 (North Radjasthan) and AE1 (South Rajastan and Haryana) TPEs covering the largest area (66%) and production share (59%), especially after the use of a new strategy for environment and management parameters calibration. Those results set the base for in silico system design and optimization in future climatic scenarios.

Tropical ecosystems are essential for irrigated rice production and are vital for ensuring food security in countries like Brazil. However, the uncertainty stemming from numerous environmental constraints limits current productivity levels and presents challenges for plant breeding research in developing productive and stable cultivars. To overcome these challenges, environmental characterization is essential to support breeding decision-making and help design and develop superior cultivars capable of capitalizing on genotype x environment interactions (GxE). In this study, we characterize the tropical rice zone in Brazil using crop modeling outcomes, machine learning tools (unsupervised clustering and supervised classification), envirotyping, and long-term observed yield trials from the irrigated rice-breeding program. Three target population of environments (TPE) were detected across diverse geographic regions and planting dates, named after LFE (Least Favorable Environments, 44 % of occurrence), FE (Favorable Environments, 33 %), and HFE (Highly Favorable Environments, 23 %). For each TPE, we identified the key climate drivers for achieving higher yields, which involved a combination of lower temperatures (average maximum and minimum temperature: 30 °C and 19 °C), while higher radiation in vegetative and reproductive phases (692 and 723 MJ m−2) and precipitation (663 mm). The inclusion of TPE information to support the GXE analysis spanning 18 years of advanced yield trials for cultivar targeting increased the model's heritability, even reducing the number of trials by 40 %. Environmental characterization pipelines in tropical rice could be leveraged by combining crop models and machine learning. Virtual simulations of historical climate trends across locations and planting dates could extend the spectrum of growing conditions screened to compose target population of environments for breeding. This is vital to support the design of new breeding programs, exploring alternative planting dates, or when breeders want to characterize a new growing region without any prior field trial data. For an extensive region, this helps breeders either improve cultivar testing analysis or identify the most prone locations to optimize seed production at commercial phases. Further studies should investigate the impact of regional climatic drivers on the genetic gains and how the genetic gains observed in the nursery breeding locations are expected in the target population of environments (TPE). • Connecting breeding and agronomy research by integrating crop modeling and machine learning for TPE discover. • Crop model-based TPEs supports the genotype x environment analysis in yield trials. • Crop models provide an annual calendar of sowing dates based on target population of environments. [ABSTRACT FROM AUTHOR]

The diverse consequences of genotype-by-environment (GxE) interactions determine trait phenotypes across levels of biological organization for crops, challenging our ambition to predict trait phenotypes from genomic information alone. GxE interactions have many implications for optimizing both genetic gain through plant breeding and crop productivity through on-farm agronomic management. Advances in genomics technologies have provided many suitable predictors for the genotype dimension of GxE interactions. Emerging advances in high-throughput proximal and remote sensor technologies have stimulated the development of "enviromics" as a community of practice, which has the potential to provide suitable predictors for the environment dimension of GxE interactions. Recently, several bespoke examples have emerged demonstrating the nascent potential for enhancing the prediction of yield and other complex trait phenotypes of crop plants through including effects of GxE interactions within prediction models. These encouraging results motivate the development of new prediction methods to accelerate crop improvement. If we can automate methods to identify and harness suitable sets of coordinated genotypic and environmental predictors, this will open new opportunities to upscale and operationalize prediction of the consequences of GxE interactions. This would provide a foundation for accelerating crop improvement through integrating the contributions of both breeding and agronomy. Here we draw on our experience from improvement of maize productivity for the range of water-driven environments across the US corn-belt. We provide perspectives from the maize case study to prioritize promising opportunities to further develop and automate "enviromics" methodologies to accelerate crop improvement through integrated breeding and agronomic approaches for a wider range of crops and environmental targets. [ABSTRACT FROM AUTHOR]

New crop varieties are extensively tested in multi-environment trials in order to obtain a solid empirical basis for recommendations to farmers. When the target population of environments is large and heterogeneous, a division into sub-regions is often advantageous. When designing such trials, the question arises how to allocate trials to the different sub-regions. We consider a solution to this problem assuming a linear mixed model. We propose an analytical approach for computation of optimal designs for best linear unbiased prediction of genotype effects and their pairwise linear contrasts and illustrate the obtained results by a real data example from Indian nation-wide maize variety trials. It is shown that, except in simple cases such as a compound symmetry model, the optimal allocation depends on the variance–covariance structure for genotypic effects nested within sub-regions. [ABSTRACT FROM AUTHOR]

In the context of the European protein deficit and the need for climate change mitigation by agriculture, soybean (Glycine max L. Merr) is of major interest to farmers and breeders. Expanding the crop to new cultivation areas requires understanding and control of the Genotype by Environment Interactions (GEI) that impede the genetic gain. New envirotyping methods, including the Target Population of Environments (TPE) characterisation, are key to the development of efficient breeding programs for specific or broad adaptations. The objectives were to (i) determine the environment types describing the European early soybean TPE, (ii) characterise the distribution and repeatability of the environment types across the TPE to identify the best suited adaptation strategy and (iii) demonstrate the importance of assessing the alignment between multi-environmental trials and the TPE for breeding decisions. In this study, 602 environments from France to Russia were clustered into five environment types using twelve eco-climatic factors, i.e. environmental variables that were calculated over specific phenological periods, such as the number of days below 15 °C between flower induction and first flower stages or the solar radiation quantity between first pod and first seed stages. These factors were previously identified as main drivers of GEI for yield in early maturity soybean (maturity groups '000′ and '00′). The environmental clustering explained 88% of the GEI effect on soybean yield. The five environment types that composed the TPE, mainly contrasted in the intensity and timing of stresses related to temperature (cold stress during the vegetative growth and heat stress during the reproductive growth) and water availability (precipitation amount, evapotranspiration and drought throughout the crop cycle). Interestingly, we observed geographical and temporal variations in the environment types distributions across the TPE as well as in their repeatability. These variations attested to the TPE heterogeneity and thus suggested that selection strategies based on either specific or broad adaptations should be combined. For example, specific adaptations to the third and fourth environment types were best suited in Eastern Europe while the broad adaptation to all environment types could be recommended in Western Europe. When broad adaptation was required, we demonstrated the need to assess the alignments between the environment types frequencies in the TPE and those observed when multi-environmental trials were conducted. This work will contribute to improving the existing soybean germplasm by considering the risks linked with weather variations and unpredictability so as to design elite soybean according to environment type. • Northward soybean deployment requires environmental characterisation using eco-climatic factors. • Envirotyping explained 88% of the genotype by environment interactions. • Five environment types contrasted by the intensity and timing of stresses. • Combine specific and broad adaptation is best suited in the target population of environments. [ABSTRACT FROM AUTHOR]

Common bean ( Phaseolus vulgaris L.) is the second most important source of dietary protein and the third most important source of calories in Africa, especially for the poor. In East Africa, drought is an important constraint to bean production. Therefore, breeding programs in East Africa have been trying to develop drought resistant varieties of common bean. To do this, breeders need information about seasonal drought stress patterns including their onset, intensity, and duration in the target area of the breeding program, so that they can mimic this pattern during field trials. Using the Decision Support for Agrotechnology Transfer (DSSAT) v4.7 model together with historical and future (Coupled Model Inter-comparison Project 6, CMIP6) climate data, this study categorized Ethiopia, Tanzania, and Uganda into different target population of environments (TPEs) based on historical and future seasonal drought stress patterns. We find that stress-free conditions generally dominate across the three countries under historical conditions (50-80% frequency). These conditions are projected to increase in frequency in Ethiopia by 2-10% but the converse is true for Tanzania (2-8% reduction) and Uganda (17-20% reduction) by 2050 depending on the Shared Socioeconomic Pathway (SSP). Accordingly, by 2050, terminal drought stresses of various intensities (moderate, severe, extreme) are prevalent in 34% of Uganda, around a quarter of Ethiopia, and 40% of the bean growing environments in Tanzania. The TPEs identified in each country serve as a basis for prioritizing breeding activities in national programs. However, to optimize resource use in international breeding programs to develop genotypes that are resilient to future projected stress patterns, we argue that common bean breeding programs should focus primarily on identifying genotypes with tolerance to severe terminal drought, with co-benefits in relation to adaptation to moderate and extreme terminal drought. Little to no emphasis on heat stress is warranted by 2050s., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Prakash Kumar Jha reports financial support was provided by Accelerated Varietal Improvement and Seed Delivery of Legumes and Cereals in Africa (AVISA). Prakash Kumar Jha reports a relationship with International Center for Tropical Agriculture that includes: employment. Steve Beebe reports a relationship with International center for Tropical Agriculture (CIAT) that includes: employment. Patricia Alvarez-Toro reports a relationship with International center for Tropical Agriculture (CIAT) that includes: employment. Clare Mukankusi reports a relationship with International center for Tropical Agriculture (CIAT) that includes: employment. Julian Ramirez-Villegas reports a relationship with International center for Tropical Agriculture (CIAT) that includes: employment. None, (© 2023 The Authors.)

The adaptation of genotypes to environmental conditions is one of the main levers for maintaining an acceptable level of production, both in terms of quality and quantity. To breed suitable genotypes and for the farmers to choose the most adapted one to his farm conditions, the factors affecting production must be precisely characterized. Here, we analyzed the impacts of the climatic factors on winter barley yield in 35 départements (French geographic units) over 25 years, by partial least squares (PLS) regression analysis. Using ascendant hierarchical clustering based on PLS results, we defined the main combinations of climatic factors affecting yield in the French barley belt, hereafter referred as “climatic-stress patterns” (CPs). Four CPs captured 27% of total yield variability and widely differ in term of yield. Crops experienced low winter rainfall and few days with heat stress (31.8% of environments- mean yield of 7.2 t ha −1 ), high winter frost levels and high amounts of precipitation during stem elongation (34.7% of environments- mean yield of 6.5 t ha −1 ), high temperature during grain filling either with low vernalization temperatures (28.9% of environments- mean yield of 6.2 t ha −1 ), or high winter rainfall (4.6% of environments –mean yield of 5.5 t ha −1 ). Two-thirds of the French départements experienced all the four CPS over the years studied. Three clusters of regions with homogeneous frequencies of the CPs were identified. The regions with similar climatic-stress patterns could help breeders to design genotypes better adapted to the different local French growing conditions. It could also help farmers to choose the most appropriate cultivars to grow. [ABSTRACT FROM AUTHOR]

To develop higher yielding and better adapted chickpeas, various breeding programs currently use a limited number of multi-location trials as a surrogate for the target population of environments (TPE). These TPEs have, however, not been adequately characterised, resulting in some uncertainty about the true representativeness of these surrogate locations as selection environments. We used the Agricultural Production Systems sIMulator (APSIM) model to characterise the Northern Grains Region of Australia, which is a major TPE of chickpea, for drought and thermal regimes. The model was first evaluated for its ability to simulate phenology, dry matter and yield of three new commercially-relevant chickpea varieties including PBA Boundary, PBA HatTrick and PBA Seamer. The model was then used to simulate dynamic changes in water stress quantified through the supply demand ratio, and yield of the highest yielding genotype PBA Boundary from 1900 to 2014 at 45 locations within the region. Water stress, and maximum, minimum and mean temperature patterns were derived through cluster analysis of respective averages computed for every 100 °Cd from 900 °Cd before flowering, to 900 °Cd after flowering. The Northern Grains Region TPE was characterised by four types of water stress patterns and five types each of maximum, minimum and mean temperatures patterns. Ward’s cluster analysis of the percentile ranks of simulated seasonal yield resulting from agro-climatic variability of the different locations enabled identification of eight unique agro-ecological regions within the TPE. Locations within each agro-ecological region were geographically contiguous and had highly harmonised annual variability in yield compared to locations of other agro-ecological regions. Overall, the identified agro-ecological regions were fairly homogenous with respect to drought and thermal regimes and could be treated as separate sub-TPEs. We argue that selecting locations within an agro-ecological region should assist breeding for locally adapted genotypes. In contrast, selecting locations distributed across agro-ecological regions could improve the broad adaptation of chickpea. [ABSTRACT FROM AUTHOR]

This paper is a section of the book ‘Drought phenotyping in crops: from theory to practice (Monneveux Philippe and Ribaut Jean-Marcel eds, published by CGIAR Generation Challenge Programme. Texcoco, Mexico). The section describes recent experience in drought phenotyping in rice which is one of the most drought susceptible crops. The section contains genetic and genomic resources for drought adaptation and methods for selection of drought resistant varieties in rice. In appendix, there is experience from Thailand on integration of direct selection for grain yield and physiological traits to confer drought resistance.

This paper is a section of the book "Drought phenotyping in crops: from theory to practice" (Monneveux Philippe and Ribaut Jean-Marcel eds, published by CGIAR Generation Challenge Programme. Texcoco, Mexico). The section describes recent experience in drought phenotyping in rice which is one of the most drought-susceptible crops. The section contains genetic and genomic resources for drought adaptation and methods for selection of drought-resistant varieties in rice. In appendix, there is experience from Thailand on integration of direct selection for grain yield and physiological traits to confer drought resistance. [ABSTRACT FROM AUTHOR]

Drought stresses arise when the combination of rainfall and soil water supply are insufficient to meet the transpiration needs of the crop. In the Cerrado region of Goiás state, Brazil, summer rainfall is typically greater than 1000 mm. However, drought stress can occur during rain-free periods of only 1–3 weeks, since roots are frequently restricted to shallow depths due to Al-induced acidity in deeper soil layers. If these droughts are frequent, then plant breeding programs need to consider how to develop suitable germplasm for the target population of environments (TPE). A crop simulation model was used to determine patterns of drought stress for 12 locations and >30 environments (6 years × 5–6 planting dates) for short and medium duration rice crops (planted in early summer), and for maize grown either as a 1st or 2nd crop in the summer cycle. Regression analysis of the simulations confirmed the greater yield impact in both crops of drought stress (quantified as the ratio of water-limited to potential transpiration) when it occurred around the time of flowering and early grain-filling. For rice, mild mid-season droughts occurred 40–60% of the time in virgin (0.4 m deep for rice or 0.5 m for maize) soils and improved (0.8 m for rice or 1.0 m for maize) soils, with a yield reduction of <30%. More severe reproductive and grain-filling stress (yield reductions of 50% for rice to 90% for maize) occurred less frequently in rice (<30% of time) and 1st maize crop (< 10% of time). The 2nd maize crop experienced the greatest proportion (75–90%) of drought stresses that reduced yield to <50% of potential, with most of these occasions associated with later planting. The rice breeding station (CNPAF) experiences the same pattern of different drought types as for the TPE, and is largely suitable for early-stage selection of adapted germplasm based on yield potential. However, selection for virgin soil types could be augmented by evaluation on some less-improved soils in the slightly drier parts of the TPE region. Similarly, the drought patterns at the maize research station (CNPMS) and the other maize screening locations are better suited to selection of lines for the improved soil types. Development of lines for the 2nd crop and on more virgin (acidic) soils would require more targeted selection at late planting dates in drier sites. [ABSTRACT FROM AUTHOR]

Spring maize area has emerged as a niche market in South Asia. Production of maize during this post-rainy season is often challenged due to heat stress. Therefore, incorporating heat stress resilience is an important trait for incorporation in maize hybrids selected for deployment in this season. However, due to the significant genotype × environment interaction (GEI) effects under heat stress, the major challenge lies in identifying maize genotypes with improved stable performance across locations and years. In the present study, we attempted to identify the key weather variables responsible for significant GEI effects, and identify maize hybrids with stable performance under heat stress across locations/years. The study details the evaluation of a set of prereleased advanced maize hybrids across heat stress vulnerable locations in South Asia during the spring seasons of 2015, 2016 and 2017. Using factorial regression, we identified that relative humidity (RH) and vapor pressure deficit (VPD) as the two most important environmental covariates contributing to the large GEI observed on grain yield under heat stress. The study also identified reproductive stage, starting from tassel emergence to early grain-filling stage, as the most critical crop stage highly susceptible to heat stress. Across-site/year evaluation resulted in identification of six high yielding heat stress resilient hybrids. [ABSTRACT FROM AUTHOR]

Multi-environment trials (METs) are used in plant breeding programmes to evaluate genotypes (lines/families) as a basis for selection on expected performance (yield and/or quality) in a target population of environments (TPE). When a large component of the genotype–environment (G × E) interactions results from crossover interactions, samples of environments in METs that deviate from the TPE provide a suboptimal basis for selection of genotypes on performance expected in the TPE. To adjust for the negative effects of these deviations, a selection strategy that weights the data from the MET according to their expected frequency of occurrence in the TPE (i.e. a weighted selection strategy) was investigated. Computer simulation methodology was used to obtain preliminary information on the weighted selection strategy and compare it to the traditional unweighted selection strategy for a range of MET scenarios and G × E interaction models. The evaluation of the weighted selection strategy was conducted in context with the germplasm enhancement programme (GEP) of the Northern Wheat Improvement Programme in Australia. The results indicated that when the environments sampled in the MET matched those expected in the TPE, the unweighted and weighted selection strategies achieved a similar response to selection in the TPE. However, when the environments sampled in the MET did not match the expectations in the TPE and a large component of the G × E interactions resulted from crossover interactions, the weighted selection strategy achieved a greater response to selection in the TPE. The advantage of the weighted strategy increased as the amount of crossover G × E interaction increased or fewer environments were sampled in the METs. [ABSTRACT FROM AUTHOR]