Target Population Of Environments Research Articles

Genomic prediction for targeted populations of environments in oat (Avena sativa)

Context Long-term multi-environment trials (METs) could improve genomic prediction models for plant breeding programs by better representing the target population of environments (TPE). However, METs are generally highly unbalanced because genotypes are routinely dropped from trials after a few years. Furthermore, in the presence of genotype × environment interaction (GEI), selection of the environments to include in a prediction set becomes critical to represent specific TPEs. Aims The goals of this study were to compare strategies for modelling GEI in genomic prediction, using large METs from oat (Avena sativa L.) breeding programs in the Midwest United States, and to develop a variety decision tool for farmers and plant breeders. Methods The performance of genotypes in TPEs was predicted by using different strategies for handling GEI in genomic prediction models including systematic and/or random GEI components. These strategies were also used to build the variety decision tool for farmers. Key results Genomic prediction for unknown genotypes, locations and years within TPEs had moderate to high predictive ability, accuracy and reliability. Modelling GEI was beneficial in small, but not in large, mega-environments. The latest 3 years were highly predictive of performance in an upcoming year for most years but not for years with unusual weather patterns. High predictive ability, accuracy and reliability were obtained when large datasets were used in TPEs. Conclusions Deployment of historical datasets can be accomplished through meaningful delineation and prediction for TPEs. Implications We have shown the performance of a simple modelling strategy for handling prediction for TPEs when deploying large historical datasets.

GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting.

We propose an "enviromics" prediction model for recommending cultivars based on thematic maps aimed at decision-makers. Parsimonious methods that capture genotype-by-environment interaction (GEI) in multi-environment trials (MET) are important in breeding programs. Understanding the causes and factors of GEI allows the utilization of genotype adaptations in the target population of environments through environmental features and factor-analytic (FA) models. Here, we present a novel predictive breeding approach called GIS-FA, which integrates geographic information systems (GIS) techniques, FA models, partial least squares (PLS) regression, and enviromics to predict phenotypic performance in untested environments. The GIS-FA approach enables: (i) the prediction of the phenotypic performance of tested genotypes in untested environments, (ii) the selection of the best-ranking genotypes based on their overall performance and stability using the FA selection tools, and (iii) the creation of thematic maps showing overall or pairwise performance and stability for decision-making. We exemplify the usage of the GIS-FA approach using two datasets of rice [Oryza sativa (L.)] and soybean [Glycine max (L.) Merr.] in MET spread over tropical areas. In summary, our novel predictive method allows the identification of new breeding scenarios by pinpointing groups of environments where genotypes demonstrate superior predicted performance. It also facilitates and optimizes cultivar recommendations by utilizing thematic maps.

A new framework for predicting and understanding flowering time for crop breeding

Societal Impact StatementAs the growing season changes, the development of climate resilient crop varieties has emerged as a crucial adaptation in agricultural systems. Breeding new varieties for a changing climate requires enhanced capacity to predict the complex interactions between genotype and environment that determine flowering time. Hundreds of experiments with observations of flowering, the environment and plant genetics were used to build a model that can predict when a variety of common bean is going to flower. This model will help breeders to explore the phenological characteristics of their germplasm, speeding up selection for climate adaptation.Summary There is an urgent need to accelerate crop breeding for adaptation to a changing climate. As the growing season changes, crop improvement programmes must ensure that the phenological characteristics of the varieties they develop remain well suited to their target population of environments. Meeting this challenge will require a clear understanding of how existing germplasm behave across Genotype ∗ Environment (G ∗ E) to enhance the efficiency of selection. Recent work calls for the development of simple models that can accurately simulate genotypic variation in key traits across target population of environments. Accordingly, we develop a simple machine learning framework for modelling time to flowering across G ∗ E and apply this to common bean in an equatorial target population of environments. Within this framework, we test three machine learning models and find that the best performing models display high levels of accuracy across G ∗ E. We advance understanding of the environmental drivers of flowering time in equatorial conditions by showing that thermal time and accumulated evaporation are powerful predictors of flowering time across all three models.

Characterizing patterns of seasonal drought stress for use in common bean breeding in East Africa under present and future climates

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.

Optimizing multi-environment testing in potato breeding: using heritability estimates to determine number of replications, sites, and years for field trials

Multi-environment trials (METs) of potato breeding clones and cultivars allow to precisely determine their performance across testing sites over years. However, these METs may be affected by the genotype × environment interaction (GEI) as noted in tuber yield. Furthermore, trials are replicated several times to optimize the predictive value of the data collected because knowledge on spatial and temporal variability of testing environments is often lacking. Hence, the objectives of this research were to use components of variance from METs to estimate broad sense heritability (H2) based on best linear unbiased predictors and use these estimates to determine the optimum number of sites, years, and replications for testing potato breeding clones along with cultivars. The data were taken from METs in southern and northern Sweden comprising up to 256 breeding clones and cultivars that underwent testing using a simple lattice design of 10-plant plots across three sites over 2 years. Percentage starch in the tuber flesh had the largest H2 in each testing environment (0.850–0.976) or across testing environments (0.905–0.921). Total tuber weight per plot also exhibited high H2 (0.720–0.919) in each testing environment or across them (0.726–0.852), despite a significant GEI. Reducing sugar content in the tuber flesh had the lowest, but still medium H2 (0.426–0.883 in each testing environment; 0.718–0.818 across testing environments). The H2 estimates were smaller when their variance components were disaggregated by year and site, instead of lumping them as environments. Simulating H2 with genetic, site, year, site × year, genetic × site, genetic × year, genetic × site × year, and residual variance components led to establish that two replicates at each of two sites in 2-year trials will suffice for testing tuber yield, starch and reducing sugars. This article provides a methodology to optimize the number of testing size and years for METs of potato breeding materials, as well as tabulated information for choosing the appropriate number of trials in same target population of environments.

Inferring multilocal typologies of agrosystems and farmers’ practices: A methodological basis for the setting of participatory breeding designs

Family farms play an essential role in agroecological transition in Sahelian countries and worldwide. They present diversified features in terms of socio-economic organization, agrobiodiversity management and cropping systems diversity. Decentralized participatory breeding approaches aim to sustain the diversity of varieties adapted for such smallholder farmers' contexts. However the lack of clear target population of environments limits the focus and the efficiency of these approaches given the large diversification of the local contexts. In this study, we surveyed variables linked to agrosystems, crop management options and farmers’ criteria of varietal evaluation from 254 family farms sampled along 13 locations spanning the target area of a decentralized participatory breeding program of cowpea crop (Vigna unguiculata L. Walp) in Niger. The objective of our study was to infer typologies of family farms in the study area based on relevant variables supporting the setting of target population of environments (TPEs) to be considered in the breeding program. We used factorial analysis of mixed data (FAMD) and the Discriminant Analysis of Principal Components (DAPC) to infer the clusters. Chi square test, analysis of variance and generalized linear model were used to identify key variables discriminating the clusters. These clusters were geographically mapped to analyze their multilocal distribution. So, we identified and characterized four clusters structuring the diversity of the local agrosystems (Typologie G), five clusters structuring the diversity of cowpea cropping management options (Typologie C) and five clusters structuring the diversity of criteria used by farmers to evaluate the performance of cowpea varieties in the local contexts (Typologie P). Typology G distinguished farms based on discriminating variables linked for instance to secondary activities, cultivated species, soil fertility management practices and farm resources including land and livestock. Typology C distinguished farms based on cowpea management pratices including the secondary crop intercropped with cowpea (sorghum, Guinea sorrel, sesame or groundnut) and the use of cowpea harvest products (seeds, haulms, hulls). Typology P was based on discriminating performance criteria including cycle length, insect resistance, striga resistance, drought resistance, haulm production and economic value of cowpea variety. This methodology provides a robust and replicable way for the definition of clusters capturing the diversity of farms and local contexts. We discussed the perspective of using these clusters to set target population of environments for decentralized breeding programs.

Extending the breeder's equation to take aim at the target population of environments.

A major focus for genomic prediction has been on improving trait prediction accuracy using combinations of algorithms and the training data sets available from plant breeding multi-environment trials (METs). Any improvements in prediction accuracy are viewed as pathways to improve traits in the reference population of genotypes and product performance in the target population of environments (TPE). To realize these breeding outcomes there must be a positive MET-TPE relationship that provides consistency between the trait variation expressed within the MET data sets that are used to train the genome-to-phenome (G2P) model for applications of genomic prediction and the realized trait and performance differences in the TPE for the genotypes that are the prediction targets. The strength of this MET-TPE relationship is usually assumed to be high, however it is rarely quantified. To date investigations of genomic prediction methods have focused on improving prediction accuracy within MET training data sets, with less attention to quantifying the structure of the TPE and the MET-TPE relationship and their potential impact on training the G2P model for applications of genomic prediction to accelerate breeding outcomes for the on-farm TPE. We extend the breeder's equation and use an example to demonstrate the importance of the MET-TPE relationship as a key component for the design of genomic prediction methods to realize improved rates of genetic gain for the target yield, quality, stress tolerance and yield stability traits in the on-farm TPE.

Characterizing the Potato Growing Regions in India Using Meteorological Parameters.

Currently, the multi-location testing of advanced hybrids in India is carried out at 25 centers under the All India Co-ordinated Research Project on Potato (AICRP-P), which is spread across the country. These centres have been chosen to represent different potato growing regions based on soil and agronomic features. However, the reliable deployment of the newly bred varieties in different regions requires a scientific delineation of potato growing zones with homogenous climates. The present study was undertaken to develop homogenous zones in the Indian sub-continent based on the environmental parameters of the potato growing season. A total of 1253 locations were identified across the country as having a plausible potato growing season of at least 70 days with suitable thermal limits. Six variables including five meteorological parameters including Physiological days (P days), Growing degree days (GDD), Mean daily temperature, Mean night temperature and Mean daily incident solar radiation, together with altitude as the sixth variable, were used for Agglomerative Hierarchical Clustering (AHC) and the Principal Component Analysis by Multidimensional Scaling (MDS) technique to derive identical classes. The thematic map of the classes was overlaid on potato growing districts of India using ArcGIS 9.1 software. The study clearly depicted that the clustering technique can effectively delineate the target population of environments (TPE) for potato genotypes performing well at different testing environments in India. The study also identifies target locations for future focus on breeding strategies, especially the high night temperature class having a large expanse in India. This is also vital in view of the impending climate change situation.

Modeling drought stress impacts under current and future climate for peanut in the semiarid pampas region of Argentina

In the semiarid pampas region of Argentina, peanut ( Arachis hypogaea L.) crop undergoes frequent and unpredictable drought stress periods, with high probability of occurrence under future climate change projections. However, the overall frequency of occurrence and timing of different drought stress patterns under current and future climates has not been well investigated for the main peanut region in central Argentina. The aims of this study were to: (i) define the main peanut drought stress patterns and their occurrence (frequency) during the peanut growing season, via a crop growth modeling approach, (ii) test seed yield stability of the formerly defined (in i) drought stress patterns under future climate scenarios, and (iii) analyze the effect of sowing dates on peanut seed yield as a management strategy to mitigate the impact of future climate scenarios for peanut seed yield in the semiarid pampas region of Argentina. The APSIM-peanut growth model was calibrated and validated for local genotypes and production environments. Our study simulated seasonal drought stress patterns at five representative locations across the region under current and future climate scenarios, and tested six sowing dates as a practice to mitigate the impact of climate change. Clustering analysis identified two contrasting environment types (ETs). The high stress ET showed greater frequency of occurrence (>50%) at the southern locations. Future climate conditions increased the frequency of high stress ET by roughly 6% and reduced peanut seed yield by 12%, as average across locations. For both current and future climates, earlier sowing dates maximized peanut seed yield, regardless the locations. Changing sowing date was not an effective practice to mitigate the negative impact of climate change on peanut seed yield. The defined ETs allowed identifying a target population of environments (TPE) having implications for optimizing peanut breeding and management strategies. The projected increase in the frequency of drought stress for all tested locations provides a challenging scenario for sustaining peanut productivity in this region. • The aims were to determine drought patterns under current and future climate, and to test sowing date as a mitigation tool • The defined drought stress patterns changed the frequency across locations, identifying a target population of environments • ·Future climate increased the frequency of drought stress by ~6% and reduced peanut seed yield by ~12% • Sowing date did not mitigate climate change impact. • This study provides insights to optimize breeding and management, and assists policy makers to address climate change.

Exploring genotype by environment interaction on cassava yield and yield related traits using classical statistical methods.

Variety advancement decisions for root quality and yield-related traits in cassava are complex due to the variable patterns of genotype-by-environment interactions (GEI). Therefore, studies focused on the dissection of the existing patterns of GEI using linear-bilinear models such as Finlay-Wilkinson (FW), additive main effect and multiplicative interaction (AMMI), and genotype and genotype-by-environment (GGE) interaction models are critical in defining the target population of environments (TPEs) for future testing, selection, and advancement. This study assessed 36 elite cassava clones in 11 locations over three cropping seasons in the cassava breeding program of IITA based in Nigeria to quantify the GEI effects for root quality and yield-related traits. Genetic correlation coefficients and heritability estimates among environments found mostly intermediate to high values indicating high correlations with the major TPE. There was a differential clonal ranking among the environments indicating the existence of GEI as also revealed by the likelihood ratio test (LRT), which further confirmed the statistical model with the heterogeneity of error variances across the environments fit better. For all fitted models, we found the main effects of environment, genotype, and interaction significant for all observed traits except for dry matter content whose GEI sensitivity was marginally significant as found using the FW model. We identified TMS14F1297P0019 and TMEB419 as two topmost stable clones with a sensitivity values of 0.63 and 0.66 respectively using the FW model. However, GGE and AMMI stability value in conjunction with genotype selection index revealed that IITA-TMS-IBA000070 and TMS14F1036P0007 were the top-ranking clones combining both stability and yield performance measures. The AMMI-2 model clustered the testing environments into 6 mega-environments based on winning genotypes for fresh root yield. Alternatively, we identified 3 clusters of testing environments based on genotypic BLUPs derived from the random GEI component.

Genetic gains in potato breeding as measured by field testing of cultivars released during the last 200 years in the Nordic Region of Europe

AbstractGenetic gains (ΔG) are determined by the breeders' equation ΔG = [(ck σ2G)/(y σP)], 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 σ2G and are σP 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.

Environment Characterization in Sorghum (Sorghum bicolor L.) by Modeling Water-Deficit and Heat Patterns in the Great Plains Region, United States.

Environmental characterization for defining the target population of environments (TPE) is critical to improve the efficiency of breeding programs in crops, such as sorghum (Sorghum bicolor L.). The aim of this study was to characterize the spatial and temporal variation for a TPE for sorghum within the United States. APSIM-sorghum, included in the Agricultural Production Systems sIMulator software platform, was used to quantify water-deficit and heat patterns for 15 sites in the sorghum belt. Historical weather data (∼35 years) was used to identify water (WSP) and heat (HSP) stress patterns to develop water–heat clusters. Four WSPs were identified with large differences in the timing of onset, intensity, and duration of the stress. In the western region of Kansas, Oklahoma, and Texas, the most frequent WSP (∼35%) was stress during grain filling with late recovery. For northeast Kansas, WSP frequencies were more evenly distributed, suggesting large temporal variation. Three HSPs were defined, with the low HSP being most frequent (∼68%). Field data from Kansas State University sorghum hybrid yield performance trials (2006–2013 period, 6 hybrids, 10 sites, 46 site × year combinations) were classified into the previously defined WSP and HSP clusters. As the intensity of the environmental stress increased, there was a clear reduction on grain yield. Both simulated and observed yield data showed similar yield trends when the level of heat or water stressed increased. Field yield data clearly separated contrasting clusters for both water and heat patterns (with vs. without stress). Thus, the patterns were regrouped into four categories, which account for the observed genotype by environment interaction (GxE) and can be applied in a breeding program. A better definition of TPE to improve predictability of GxE could accelerate genetic gains and help bridge the gap between breeders, agronomists, and farmers.

Heritable Variation, Genetic and Phenotypic Correlations for Tuber Traits and Host Plant Resistance to Late Blight for Potato Breeding in Scandinavian Testing Sites

Potato breeding aims to improve crop productivity, quality and resilience based on heritable characteristics. Estimating the trait heritability and correlations—both genetic and phenotypic—among characteristics in a target population of environments allows us to define the best breeding method that leads to selection gains. Breeding clones (47) and released cultivars (209) were grown using simple lattice designs at three testing sites in northern and southern Sweden to estimate the best linear unbiased predictors (BLUPs) derived from mixed linear models for characteristics such as tuber weight (total and according to sizes), host plant resistance to late blight (caused by the oomycete Phytophthora infestans) and tuber quality (starch percentage based on specific gravity measurements and reducing sugars). There was significant heritable variation for all the characteristics investigated. Tuber starch percentage and total tuber weight were the traits with the highest broad-sense heritability (H2), while the weight for the smallest size (<40 mm) had the highest H2 among the different tuber categories. These results show the potential for further improving these traits for Scandinavia through recombination and selection in segregating offspring. The genetic and phenotypic correlations among the tuber weight characteristics were significant (p ≤ 0.05) irrespective of their sizes, but none were significant (p > 0.05) with tuber starch percentage. Host plant resistance to late blight was negatively and significantly associated with tuber weight and starch percentage, thereby showing the strong effects of this disease on the productivity and quality of the potatoes.

Crop traits enabling yield gains under more frequent extreme climatic events

Climate change (CC) in central China will change seasonal patterns of agricultural production through increasingly frequent extreme climatic events (ECEs). Breeding climate-resilient wheat (Triticum aestivum L.) genotypes may mitigate adverse effects of ECEs on crop productivity. To reveal crop traits conducive to long-term yield improvement in the target population of environments, we created 8,192 virtual genotypes with contrasting but realistic ranges of phenology, productivity and waterlogging tolerance. Using these virtual genotypes, we conducted a genotype (G) by environment (E) by management (M) factorial analysis (G×E×M) using locations distributed across the entire cereal cropping zone in mid-China. The G×E×M invoked locally-specific sowing dates under future climates that were premised on shared socioeconomic pathways SSP5-8.5, with a time horizon centred on 2080. Across the simulated adaptation landscape, productivity was primarily driven by yield components and phenology (average grain yield increase of 6-69% across sites with optimal combinations of these traits). When incident solar radiation was not limiting carbon assimilation, ideotypes with higher grain yields were characterised by earlier flowering, higher radiation-use efficiency and larger maximum kernel size. At sites with limited solar radiation, crops required longer growing periods to realise genetic yield potential, although higher radiation-use efficiency and larger maximum kernel size were again prospective traits enabling higher rates of yield gains. By 2080, extreme waterlogging stress in some regions of mid-China will impact substantially on productivity, with yield penalties of up to 1,010 kg ha−1. Ideotypes with optimal G×M could mitigate yield penalty caused by waterlogging by up to 15% under future climates. These results help distil promising crop trait by best management practice combinations that enable higher yields and robust adaptation to future climates and more frequent extreme climatic events, including flash flooding and soil waterlogging.

Target Population of Environments for Wheat Breeding in India: Definition, Prediction and Genetic Gains.

In this study, we defined the target population of environments (TPE) for wheat breeding in India, the largest wheat producer in South Asia, and estimated the correlated response to the selection and prediction ability of five selection environments (SEs) in Mexico. We also estimated grain yield (GY) gains in each TPE. Our analysis used meteorological, soil, and GY data from the international Elite Spring Wheat Yield Trials (ESWYT) distributed by the International Maize and Wheat Improvement Center (CIMMYT) from 2001 to 2016. We identified three TPEs: TPE 1, the optimally irrigated Northwestern Plain Zone; TPE 2, the optimally irrigated, heat-stressed North Eastern Plains Zone; and TPE 3, the drought-stressed Central-Peninsular Zone. The correlated response to selection ranged from 0.4 to 0.9 within each TPE. The highest prediction accuracies for GY per TPE were derived using models that included genotype-by-environment interaction and/or meteorological information and their interaction with the lines. The highest prediction accuracies for TPEs 1, 2, and 3 were 0.37, 0.46, and 0.51, respectively, and the respective GY gains were 118, 46, and 123 kg/ha/year. These results can help fine-tune the breeding of elite wheat germplasm with stable yields to reduce farmers’ risk from year-to-year environmental variation in India’s wheat lands, which cover 30 million ha, account for 100 million tons of grain or more each year, and provide food and livelihoods for hundreds of millions of farmers and consumers in South Asia.

Projecting results of zoned multi-environment trials to new locations using environmental covariates with random coefficient models: accuracy and precision

Key messageWe propose the utilisation of environmental covariates in random coefficient models to predict the genotype performances in new locations.Multi-environment trials (MET) are conducted to assess the performance of a set of genotypes in a target population of environments. From a grower’s perspective, MET results must provide high accuracy and precision for predictions of genotype performance in new locations, i.e. the grower’s locations, which hardly ever coincide with the locations at which the trials were conducted. Linear mixed modelling can provide predictions for new locations. Moreover, the precision of the predictions is of primary concern and should be assessed. Besides, the precision can be improved when auxiliary information is available to characterize the targeted locations. Thus, in this study, we demonstrate the benefit of using environmental information (covariates) for predicting genotype performance in some new locations for Swedish winter wheat official trials. Swedish MET locations can be stratified into zones, allowing borrowing information between zones when best linear unbiased prediction (BLUP) is used. To account for correlations between zones, as well as for intercepts and slopes for the regression on covariates, we fitted random coefficient (RC) models. The results showed that the RC model with appropriate covariate scaling and model for covariate terms improved the precision of predictions of genotypic performance for new locations. The prediction accuracy of the RC model was competitive compared to the model without covariates. The RC model reduced the standard errors of predictions for individual genotypes and standard errors of predictions of genotype differences in new locations by 30–38% and 12–40%, respectively.

Optimizing the Allocation of Trials to Sub-regions in Multi-environment Crop Variety Testing

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.

Mapping consistent additive and epistatic QTLs for plant production traits under drought in target populations of environment using locally adapted landrace in rice (Oryza sativa L.)

ABSTRACT A total of 36 colocated QTLs associated with plant production traits were mapped using IR62266/Norungan recombinant inbred lines under drought and non-stress conditions in target populations of environment. Multi-model composite interval mapping and multi-environment analysis detected seven consistent QTLs for plant production traits on chromosomes 1, 2, 3, 6, 10 and 11. QTLs for plant height, number of tillers, biomass, spikelet fertility and grain yield under drought identified on chr. 1 (RM5389-RM11943-RM5794), chr. 2 (RM324-RM5390 and RM12460-RM423-RM5345), chr. 3 (RM6329-RM5475-RM16030), chr. 6 (RM508-RM585-RM217), chr. 10 (RM7361-RM8207) and chr. 11 (RM209-RM6499) in this study have been reported as meta-QTLs for yield under drought. Consistent positive allelic contribution from the landrace, Norungan was shown at the two QTLs on chr. 2, while IR62266 showed consistent positive allelic effect at the QTL on chr. 10. At other four regions the effect was confounded with the stress condition, for instance the region on chr. 3 identified for straw yield showed additive effect from Norungan in all stress trials, while in the non-stress trials the positive allele came from IR62266. Epistasis for grain yield was detected in the regions of non-additive effects and its relative contribution was small. Relatively small percentage of grain yield variation was explained by additive x environment interaction (0.28 - 1.77%), but was larger than that explained by additive effect QTL across environments (0.05 - 0.35%). These consistent QTLs may have genes evolutionarily conserved in response to drought and could be ideal candidates for rice yield improvement in water-limited environments.

Modelling selection response in plant-breeding programs using crop models as mechanistic gene-to-phenotype (CGM-G2P) multi-trait link functions

AbstractPlant-breeding programs are designed and operated over multiple cycles to systematically change the genetic makeup of plants to achieve improved trait performance for a Target Population of Environments (TPE). Within each cycle, selection applied to the standing genetic variation within a structured reference population of genotypes (RPG) is the primary mechanism by which breeding programs make the desired genetic changes. Selection operates to change the frequencies of the alleles of the genes controlling trait variation within the RPG. The structure of the RPG and the TPE has important implications for the design of optimal breeding strategies. The breeder’s equation, together with the quantitative genetic theory behind the equation, informs many of the principles for design of breeding programs. The breeder’s equation can take many forms depending on the details of the breeding strategy. Through the genetic changes achieved by selection, the cultivated varieties of crops (cultivars) are improved for use in agriculture. From a breeding perspective, selection for specific trait combinations requires a quantitative link between the effects of the alleles of the genes impacted by selection and the trait phenotypes of plants and their breeding value. This gene-to-phenotype link function provides the G2P map for one to many traits. For complex traits controlled by many genes, the infinitesimal model for trait genetic variation is the dominant G2P model of quantitative genetics. Here we consider motivations and potential benefits of using the hierarchical structure of crop models as CGM-G2P trait link functions in combination with the infinitesimal model for the design and optimization of selection in breeding programs.

Combining focused identification of germplasm and core collection strategies to identify genebank accessions for central European soybean breeding.

Environmental adaptation of crops is essential for reliable agricultural production and an important breeding objective. Genebanks provide genetic variation for the improvement of modern varieties, but the selection of suitable germplasm is frequently impeded by incomplete phenotypic data. We address this bottleneck by combining a Focused Identification of Germplasm Strategy (FIGS) with core collection methodology to select soybean (Glycine max) germplasm for Central European breeding from a collection of >17,000 accessions. By focussing on adaptation to high-latitude cold regions, we selected an "environmental precore" of 3,663 accessions using environmental data and compared the Donor opulation of Environments (DPE) in Asia and the Target Population of Environments (TPE) in Central Europe in the present and 2070. Using single nucleotide polymorphisms, we reduced the precore into two diverse core collections of 183 and 366 accessions to serve as diversity panels for evaluation in the TPE. Genetic differentiation between precore and non-precore accessions revealed genomic regions that control maturity, and novel candidate loci for environmental adaptation, demonstrating the potential of diversity panels for studying adaptation. Objective-driven core collections have the potential to increase germplasm utilization for abiotic adaptation by breeding for a rapidly changing climate, or de novo adaptation of crops to expand cultivation ranges.