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The Genome-to-Phenome (G2P) problem is one of the highest-priority challenges in applied biology. Ecophysiological crop models (ECM) and genomic prediction (GP) models are quantitative algorithms, which, when given information on a genotype and environment, can produce an accurate estimate of a phenotype of interest. In this article, we discuss how the GP algorithms can be used to estimate genotype-specific parameters (GSPs) in ECMs to develop robust prediction methods. In this approach, the numerical constants (GSPs) that ECMs use to distinguish and characterize crop cultivars/varieties are treated as quantitative traits to be predicted by genomic prediction models from underlying genetic information. In this article we provide information on which GP methods appear favorable for predicting different types of GSPs, such as vernalization sensitivity or potential radiation use efficiency. For each example GSP, we assess a number of GP methods in terms of their suitability using a set of three criteria grounded in genetic architecture, computational requirements, and the use of prior information. In general, we conclude that the most useful algorithms were dependent on both the nature of the particular GSP and the GP methods considered.

Phenotypic assessment of breeding population is important to identify robust lines for incorporating into future breeding programs. The objective of this study was to identify potential lines from a wheat (Triticum aestivum L.) population, based on their morpho-physiological traits, for improved heat tolerance. A subset of 100 lines of the double haploid (DH) population named "Buster", developed from two successful Oklahoma wheat varieties (Billings and Duster), was used in the study. Two experiments were conducted one in a greenhouse and the other in growth chambers. Data on plant height, tiller number, leaf number, and photosynthetic pigments were collected from the greenhouse; whereas the data on physiological parameters (leaf net photosynthesis (Pn), transpiration (T), stomatal conductance (g s ), intercellular carbon dioxide concentration (C i ), electron transport rate (ETR), Photosystem II efficiency (Fv'/Fm') and instantaneous water use efficiency (IWUE)) were collected from the growth chambers. Buster lines were significantly (P < 0.05) different both morphologically and physiologically. A wide range of observations among genotypes for different morphological and physiological characteristics was found. For example, the Chlorophyll A:B ratio ranged from 1.8 to 4.3, average plant height ranged from 8.4 to 13.3 cm, and the net photosynthesis under heat stress ranged from 11.29 to 25.28 µmol CO 2 m −2 •s −1 . The differences in leaf physiological parameters were more discernible under heat stress. This study provides a piece of baseline information on morpho-physiological characteristics of Buster lines, and identified lines can be used in future breeding programs for incorporating heat stress tolerance.

Yield components are widely recognized as drivers of wheat (Triticum aestivum L.) yield across environments and genotypes. In this study, we used a hierarchical Bayesian approach to model wheat grain yield in Oklahoma on an eco‐physiological basis using yield component traits thousand kernel weight (TKW) and nonyield biomass (NYB). The Bayesian approach allowed us to quantify uncertainties around the parameter values rather than obtaining a single value estimate for a parameter. The main objectives of this study were to (a) explain wheat yield as a function of component traits TKW and NYB, and thereby examine the implications for source‐sink balance; and (b) assess their association with weather conditions during key stages of wheat development. A secondary objective was to introduce Bayesian estimation for eco‐physiological modeling. Fifteen wheat genotypes planted in three locations in Oklahoma (Altus, Chickasha, and Lahoma) were evaluated across three harvest years (2017 to 2019), whereby the combination of location and year defined an environment. Results indicate that the environment explained a greater proportion of the variability in yield than genotypes or than genotype × environment (G × E) interaction; however, evidence for G × E was substantial. Yield was expected to increase with increasing TKW and NYB, which would suggest a source limitation to achieve potential yield. Yet, the contribution of early reproductive stage weather variables to the relationship between yield and NYB pointed in the direction of sink strength being compromised. In summary, our approach provides evidence for source‐sink co‐limitation in grain yield of this sample of hard red winter wheat genotypes.

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