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Estimating crop states accurately and reliably through climate sensing is a promising alternative for high tech crop sensing investments. This paper explores and demonstrates the applicability of joint crop parameter and crop state estimation through indoor climate monitoring in a lettuce greenhouse system via Ensemble Kalman filtering combined with a nonlinear observability analysis via the empirical observability Gramian. The observability analysis indicated that crop dry-weight can be estimated from the indoor CO2concentration, temperature and humidity, while simultaneously the parameter that represents the light use efficiency can be estimated and even corrected for. These outcomes were confirmed by a simulation study. This showed that the method is robust against one level of process and measurement noise, and a 50 % error in the model parameter that represents the light use efficiency. More precisely, it has been shown that improvements of 50 % of the dry-weight estimation in terms of average root mean squared error can be achieved with respect to the case where no Ensemble Kalman filtering and parameter update is used.

Key messageWe evaluate self-organizing maps (SOM) to identify adaptation zones and visualize multi-environment genotypic responses. We apply SOM to multiple traits and crop growth model output of large-scale European sunflower data.AbstractGenotype-by-environment interactions (G × E) complicate the selection of well-adapted varieties. A possible solution is to group trial locations into adaptation zones with G × E occurring mainly between zones. By selecting for good performance inside those zones, response to selection is increased. In this paper, we present a two-step procedure to identify adaptation zones that starts from a self-organizing map (SOM). In the SOM, trials across locations and years are assigned to groups, called units, that are organized on a two-dimensional grid. Units that are further apart contain more distinct trials. In an iterative process of reweighting trial contributions to units, the grid configuration is learnt simultaneously with the trial assignment to units. An aggregation of the units in the SOM by hierarchical clustering then produces environment types, i.e. trials with similar growing conditions. Adaptation zones can subsequently be identified by grouping trial locations with similar distributions of environment types across years. For the construction of SOMs, multiple data types can be combined. We compared environment types and adaptation zones obtained for European sunflower from quantitative traits like yield, oil content, phenology and disease scores with those obtained from environmental indices calculated with the crop growth model Sunflo. We also show how results are affected by input data organization and user-defined weights for genotypes and traits. Adaptation zones for European sunflower as identified by our SOM-based strategy captured substantial genotype-by-location interaction and pointed to trials in Spain, Turkey and South Bulgaria as inducing different genotypic responses.

Summary Broadening the genetic base of crops is crucial for developing varieties to respond to global agricultural challenges such as climate change. Here, we analysed a diverse panel of 371 domesticated lines of the model crop barley to explore the genetics of crop adaptation. We first collected exome sequence data and phenotypes of key life history traits from contrasting multi‐environment common garden trials. Then we applied refined statistical methods, including some based on exomic haplotype states, for genotype‐by‐environment (G×E) modelling. Sub‐populations defined from exomic profiles were coincident with barley's biology, geography and history, and explained a high proportion of trial phenotypic variance. Clear G×E interactions indicated adaptation profiles that varied for landraces and cultivars. Exploration of circadian clock‐related genes, associated with the environmentally adaptive days to heading trait (crucial for the crop's spread from the Fertile Crescent), illustrated complexities in G×E effect directions, and the importance of latitudinally based genic context in the expression of large‐effect alleles. Our analysis supports a gene‐level scientific understanding of crop adaption and leads to practical opportunities for crop improvement, allowing the prioritisation of genomic regions and particular sets of lines for breeding efforts seeking to cope with climate change and other stresses., Significance Statement We analysed exome sequence data and life history traits measured in multi‐environment trials of a diverse panel of 371 domesticated barley lines to explore the genetics of crop adaptation. G×E interactions indicated adaptation profiles that varied for sub‐populations defined from exomic data. Exploration of circadian clock‐related genes associated with days to heading revealed complexities in G×E effect directions, and the importance of latitudinally based genic context in the expression of large effect alleles. Analysis provides practical information for future crop breeding.

Yield is a function of environmental quality and the sensitivity with which genotypes react to that. Environmental quality is characterized by meteorological data, soil and agronomic management, whereas genotypic sensitivity is embodied by combinations of physiological traits that determine the crop capture and partitioning of environmental resources over time. This paper illustrates how environmental quality and genotype responses can be studied by a combination of crop simulation and statistical modelling. We characterized the genotype by environment interaction for grain yield of a wheat population segregating for flowering time by simulating it using the the Agricultural Production Systems sIMulator (APSIM) cropping systems model. For sites in the NE Australian wheat-belt, we used meteorological information as integrated by APSIM to classify years according to water, heat and frost stress. Results highlight that the frequency of years with more severe water and temperature stress has largely increased in recent years. Consequently, it is likely that future varieties will need to cope with more stressful conditions than in the past, making it important to select for flowering habits contributing to temperature and water-stress adaptation. Conditional on year types, we fitted yield response surfaces as functions of genotype, latitude and longitude to virtual multi-environment trials. Response surfaces were fitted by two-dimensional P-splines in a mixed-model framework to predict yield at high spatial resolution. Predicted yields demonstrated how relative genotype performance changed with location and year type and how genotype by environment interactions can be dissected. Predicted response surfaces for yield can be used for performance recommendations, quantification of yield stability and environmental characterization.

Yield as a complex trait may either be genetically improved directly, by identifying QTLs contributing to yield, or indirectly via improvement of underlying components, where parents contribute complementary alleles to different components. We investigated the utility of two yield dissection models in tomato for identifying promising yield components and corresponding QTLs. In a harvest dissection, marketable yield was the product of number of fruits and individual fruit fresh weight. In a biomass dissection, total yield was the product of fruit fresh-dry weight ratio and total fruit dry weight. Data came from a greenhouse experiment with a population of hybrids formed from four-way RILs. Trade-offs were observed between the component traits in both dissections. Genetic improvements were possible by increasing the number of fruits and the total fruit dry weight to offset losses in fruit fresh weight and fruit fresh-dry weight ratio. Most yield QTLs colocalized with component QTLs, offering options for the construction of high-yielding genotypes. An analysis of QTL allelic effects in relation to parental origin emphasized the complementary role of the parents in the construction of desired genotypes. Multi-QTL models were used for the comparison of yield predictions from yield QTLs and predictions from the products of components following multi-QTL models for those components. Component QTLs underlying dissection models were able to predict yield with the same accuracy as yield QTLs in direct predictions. Harvest and biomass yield dissection models may serve as useful tools for yield improvement in tomato by either or both of combining individual component QTLs and multi-QTL component predictions.

Genetic variance of a phenotypic trait can originate from direct genetic effects, or from indirect effects, i.e., through genetic effects on other traits, affecting the trait of interest. This distinction is often of great importance, for example, when trying to improve crop yield and simultaneously control plant height. As suggested by Sewall Wright, assessing contributions of direct and indirect effects requires knowledge of (1) the presence or absence of direct genetic effects on each trait, and (2) the functional relationships between the traits. Because experimental validation of such relationships is often unfeasible, it is increasingly common to reconstruct them using causal inference methods. However, most current methods require all genetic variance to be explained by a small number of quantitative trait loci (QTL) with fixed effects. Only a few authors have considered the “missing heritability” case, where contributions of many undetectable QTL are modeled with random effects. Usually, these are treated as nuisance terms that need to be eliminated by taking residuals from a multi-trait mixed model (MTM). But fitting such an MTM is challenging, and it is impossible to infer the presence of direct genetic effects. Here, we propose an alternative strategy, where genetic effects are formally included in the graph. This has important advantages: (1) genetic effects can be directly incorporated in causal inference, implemented via our PCgen algorithm, which can analyze many more traits; and (2) we can test the existence of direct genetic effects, and improve the orientation of edges between traits. Finally, we show that reconstruction is much more accurate if individual plant or plot data are used, instead of genotypic means. We have implemented the PCgen-algorithm in the R-package pcgen.

Genomic prediction of complex traits, say yield, benefits from including information on correlated component traits. Statistical criteria to decide which yield components to consider in the prediction model include the heritability of the component traits and their genetic correlation with yield. Not all component traits are easy to measure. Therefore, it may be attractive to include proxies to yield components, where these proxies are measured in (high-throughput) phenotyping platforms during the growing season. Using the Agricultural Production Systems Simulator (APSIM)-wheat cropping systems model, we simulated phenotypes for a wheat diversity panel segregating for a set of physiological parameters regulating phenology, biomass partitioning, and the ability to capture environmental resources. The distribution of the additive quantitative trait locus effects regulating the APSIM physiological parameters approximated the same distribution of quantitative trait locus effects on real phenotypic data for yield and heading date. We use the crop growth model APSIM-wheat to simulate phenotypes in three Australian environments with contrasting water deficit patterns. The APSIM output contained the dynamics of biomass and canopy cover, plus yield at the end of the growing season. Each water deficit pattern triggered different adaptive mechanisms and the impact of component traits differed between drought scenarios. We evaluated multiple phenotyping schedules by adding plot and measurement error to the dynamics of biomass and canopy cover. We used these trait dynamics to fit parametric models and P-splines to extract parameters with a larger heritability than the phenotypes at individual time points. We used those parameters in multi-trait prediction models for final yield. The combined use of crop growth models and multi-trait genomic prediction models provides a procedure to assess the efficiency of phenotyping strategies and compare methods to model trait dynamics. It also allows us to quantify the impact of yield components on yield prediction accuracy even in different environment types. In scenarios with mild or no water stress, yield prediction accuracy benefitted from including biomass and green canopy cover parameters. The advantage of the multi-trait model was smaller for the early-drought scenario, due to the reduced correlation between the secondary and the target trait. Therefore, multi-trait genomic prediction models for yield require scenario-specific correlated traits.

Genotype by environment interaction (G×E) for the target trait, e.g. yield, is an emerging property of agricultural systems and results from the interplay between a hierarchy of secondary traits involving the capture and allocation of environmental resources during the growing season. This hierarchy of secondary traits ranges from basic traits that correspond to response mechanisms/sensitivities, to intermediate traits that integrate a larger number of processes over time and therefore show a larger amount of G×E. Traits underlying yield differ in their contribution to adaptation across environmental conditions and have different levels of G×E. Here, we provide a framework to study the performance of genotype to phenotype (G2P) modeling approaches. We generate and analyze response surfaces, or adaptation landscapes, for yield and yield related traits, emphasizing the organization of the traits in a hierarchy and their development and interactions over time. We use the crop growth model APSIM-wheat with genotype-dependent parameters as a tool to simulate non-linear trait responses over time with complex trait dependencies and apply it to wheat crops in Australia. For biological realism, APSIM parameters were given a genetic basis of 300 QTLs sampled from a gamma distribution whose shape and rate parameters were estimated from real wheat data. In the simulations, the hierarchical organization of the traits and their interactions over time cause G×E for yield even when underlying traits do not show G×E. Insight into how G×E arises during growth and development helps to improve the accuracy of phenotype predictions within and across environments and to optimize trial networks. We produced a tangible simulated adaptation landscape for yield that we first investigated for its biological credibility by statistical models for G×E that incorporate genotypic and environmental covariables. Subsequently, the simulated trait data were used to evaluate statistical genotype-to-phenotype models for multiple traits and environments and to characterize relationships between traits over time and across environments, as a way to identify traits that could be useful to select for specific adaptation. Designed appropriately, these types of simulated landscapes might also serve as a basis to train other, more deep learning methodologies in order to transfer such network models to real-world situations.

New types of phenotyping tools generate large amounts of data on many aspects of plant physiology and morphology with high spatial and temporal resolution. These new phenotyping data are potentially useful to improve understanding and prediction of complex traits, like yield, that are characterized by strong environmental context dependencies, i.e., genotype by environment interactions. For an evaluation of the utility of new phenotyping information, we will look at how this information can be incorporated in different classes of genotype-to-phenotype (G2P) models. G2P models predict phenotypic traits as functions of genotypic and environmental inputs. In the last decade, access to high-density single nucleotide polymorphism markers (SNPs) and sequence information has boosted the development of a class of G2P models called genomic prediction models that predict phenotypes from genome wide marker profiles. The challenge now is to build G2P models that incorporate simultaneously extensive genomic information alongside with new phenotypic information. Beyond the modification of existing G2P models, new G2P paradigms are required. We present candidate G2P models for the integration of genomic and new phenotyping information and illustrate their use in examples. Special attention will be given to the modelling of genotype by environment interactions. The G2P models provide a framework for model based phenotyping and the evaluation of the utility of phenotyping information in the context of breeding programs. ispartof: Plant Science vol:282 pages:23-39 ispartof: location:MEXICO, Int Maize & Wheat Improvement Ctr status: Published online

PubMedID: 31043760 For more than 10,000 years, the selection of plant and animal traits that are better tailored for human use has shaped the development of civilizations. During this period, bread wheat (Triticum aestivum) emerged as one of the world’s most important crops. We use exome sequencing of a worldwide panel of almost 500 genotypes selected from across the geographical range of the wheat species complex to explore how 10,000 years of hybridization, selection, adaptation and plant breeding has shaped the genetic makeup of modern bread wheats. We observe considerable genetic variation at the genic, chromosomal and subgenomic levels, and use this information to decipher the likely origins of modern day wheats, the consequences of range expansion and the allelic variants selected since its domestication. Our data support a reconciled model of wheat evolution and provide novel avenues for future breeding improvement. © 2019, The Author(s), under exclusive licence to Springer Nature America, Inc. 339728 Kyoto University California Department of Fish and Game: SFB924 Wageningen UR University of Dundee 2819103915 Région Auvergne-Rhône-Alpes European Research Council 23000816 SRESRI 2015 Seventh Framework Programme: FP7-613556, FP7/ 2007–2013 23000892 SYMBIOSE 2016 Agence Nationale de la Recherche: 14-CE02-0002 Çukurova Üniversitesi: FUA-2016–6033 The authors wish to thank the INRA Biological Resources Center on small grain cereals (https://www6.ara.inra.fr/umr1095_eng/Teams/Research/Biological-Resources-Centre) for providing seeds and passport data, and for establishing a wheat biorepository. The authors thank the Federal ex situ Genbank Gatersleben, Germany (IPK), the N. I. Vavilov All-Russian Research Institute of Plant Industry, Russia (VIR), Centre for Genetic Resources, WUR, Netherlands (CGN), Kyoto University, National Bioresource Project, Japan (NBRP), the Australian Winter Cereal Collection Tamworth, Australia (AWCC), the National Plant Germplasm System, USA (USDA-ARS), the International Center for Agriculture Research in the Dry Areas (ICARDA), the Max Planck Institute for Plant Breeding Research Cologne, Germany (MPIPZ), Germplasm Resource Unit at the John Innes Centre UK (JIC) and the Wheat and Barley Legacy for Breeding Improvement (WHEALBI) consortium for providing plant material and passport data. The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/ 2007–2013) under grant agreement FP7-613556, Whealbi project (http://www.whealbi.eu/project/). R.W. and J.R. also acknowledge support from the Scottish Government Research Program and R.W. from the University of Dundee. H.O. acknowledges support from Çukurova University (FUA-2016–6033). K.F.X.M. acknowledges support from the German Federal Ministry of Food and Agriculture (2819103915) and the DFG (SFB924). T.L. acknowledges supports from the Agence Nationale pour la Recherche (BirdIslandGenomic project 14-CE02-0002), European Research Council (TREEPEACE project, grant agreement 339728) and the bioinformatics platform from Toulouse Midi-Pyrénées (Bioinfo Genotoul) for providing computing and storage resources. J.S. acknowledges support from the Région Auvergne-Rhône-Alpes and FEDER Fonds Européens de Développement Régional (23000816 SRESRI 2015), the CPER contrat de plan État-région (23000892 SYMBIOSE 2016) and AgreenSkills fellowship (applicant ID 4146).

Selection processes in plant breeding depend critically on the quality of phenotype predictions. The phenotype is classically predicted as a function of genotypic and environmental information. Models for phenotype prediction contain a mixture of statistical, genetic and physiological elements. In this chapter, we discuss prediction from linear mixed models (LMMs), with an emphasis on statistics, and prediction from crop growth models (CGMs), with an emphasis on physiology. Three modalities of prediction are distinguished: predictions for new genotypes under known environmental conditions, predictions for known genotypes under new environmental conditions, and predictions for new genotypes under new environmental conditions. For LMMs, the genotypic input information includes molecular marker variation, while the environmental input can consist of meteorological, soil and management variables. However, integrated types of environmental characterizations obtained from CGMs can also serve as environmental covariable in LMMs. LMMs consist of a fixed part, corresponding to the mean for a particular genotype in a particular environment, and a random part defined by genotypic and environmental variances and correlations. For prediction via the fixed part, genotypic and/or environmental covariables are required as in classical regression. For predictions via the random part, correlations need to be estimated between observed and new genotypes, between observed and new environments, or both. These correlations can be based on similarities calculated from genotypic and environmental covariables. A simple type of covariable assigns genotypes to sub-populations and environments to regions. Such groupings can improve phenotype prediction. For a second type of phenotype prediction, we consider CGMs. CGMs predict a target phenotype as a non-linear function of underlying intermediate phenotypes. The intermediate phenotypes are outcomes of functions defined on genotype dependent CGM parameters and classical environmental descriptors. While the intermediate phenotypes may still show some genotype by environment interaction, the genotype dependent CGM parameters should be consistent across environmental conditions. The CGM parameters are regressed on molecular marker information to allow phenotype prediction from molecular marker information and standard physiologically relevant environmental information. Both LMMs and CGMs require extensive characterization of genotypes and environments. High-throughput technologies for genotyping and phenotyping provide new opportunities for upscaling phenotype prediction and increasing the response to selection in the breeding process.

Genome-enabled prediction provides breeders with the means to increase the number of genotypes that can be evaluated for selection. One of the major challenges in genome-enabled prediction is how to construct a training set of genotypes from a calibration set that represents the target population of genotypes, where the calibration set is composed of a training and validation set. A random sampling protocol of genotypes from the calibration set will lead to low quality coverage of the total genetic space by the training set when the calibration set contains population structure. As a consequence, predictive ability will be affected negatively, because some parts of the genotypic diversity in the target population will be under-represented in the training set, whereas other parts will be over-represented. Therefore, we propose a training set construction method that uniformly samples the genetic space spanned by the target population of genotypes, thereby increasing predictive ability. To evaluate our method, we constructed training sets alongside with the identification of corresponding genomic prediction models for four genotype panels that differed in the amount of population structure they contained (maize Flint, maize Dent, wheat, and rice). Training sets were constructed using uniform sampling, stratified-uniform sampling, stratified sampling and random sampling. We compared these methods with a method that maximizes the generalized coefficient of determination (CD). Several training set sizes were considered. We investigated four genomic prediction models: multi-locus QTL models, GBLUP models, combinations of QTL and GBLUPs, and Reproducing Kernel Hilbert Space (RKHS) models. For the maize and wheat panels, construction of the training set under uniform sampling led to a larger predictive ability than under stratified and random sampling. The results of our methods were similar to those of the CD method. For the rice panel, all training set construction methods led to similar predictive ability, a reflection of the very strong population structure in this panel.

A good statistical analysis of genotype × environment interactions (G × E) is a key requirement for progress in any breeding program. Data for G × E analyses traditionally come from multi-environment trials. In recent years, increasingly data are generated from managed stress trials, phenotyping platforms, and high throughput phenotyping techniques in the field. Simultaneously, and complementary to the phenotyping, more elaborate genotyping and envirotyping occur. All of these developments further increase the importance of a sound statistical framework for analyzing G × E. This paper presents considerations on such a framework from the point of view of the choices that need to be made with respect to the content of short academic courses on statistical methods for G × E. Based on our experiences in teaching statistical methods to plant breeders, for specialized G × E courses between three and 5 d are reserved. The audience in such courses includes MSc students, PhD students, postdocs, and researchers at breeding companies. For such specialized courses, we propose a collection of topics to be covered. Our outlook on G × E analyses is two-fold. On the one hand, we see the G × E problem as the building of predictive models for genotype-specific reaction norms. On the other hand, the G × E problem consists in the identification of suitable variance-covariance models to describe heterogeneity of genetic variance and correlations across environments. Our preferred class of statistical models is the class of mixed linear-bilinear models. These statistical models allow us to answer breeding questions on adaptation, adaptability, stability, and the identification and subdivision of the target population of environments. By a citation analysis of the literature on G × E, we show that our preference for mixed linear-bilinear models for analyzing G × E is supported by recent trends in the types of methods for G × E analysis that are most frequently cited.

Prediction of the phenotypes for a set of genotypes across multiple environments is a fundamental task in any plant breeding program. Genomic prediction (GP) can assist selection decisions by combining incomplete phenotypic information over multiple environments (MEs) with dense sets of markers. We compared a range of ME-GP models differing in the way environment-specific genetic effects were modeled. Information among environments was shared either implicitly via the response variable, or by the introduction of explicit environmental covariables. We discuss the models not only in the light of their accuracy, but also in their ability to predict the different parts of the incomplete genotype × environment interaction (G × E) table: (G t ; E t ), (G u ; E t ), (G t ; E u ), and (G u ; E u ), where G is genotype, E is environment, both tested (t; in one or more instances) and untested (u). Using the ‘Steptoe’ × ‘Morex’ barley (Hordeum vulgare L.) population as an example, we show the advantage of ME-GP models that account for G × E. In addition, for our example data set, we show that for prediction in the most challenging scenario of untested environments (E u ), the use of explicit environmental information is preferable over the simpler approach of predicting from a main effects model. Besides producing the most general ME-GP model, the use of environmental covariables naturally links with ecophysiological and crop-growth models (CGMs) for G × E. We conclude with a list of future research topics in ME-GP, where we see CGMs playing a central role.