The availability of dense molecular markers has made possible the use of genomic selection (GS) for plant breeding. However, the evaluation of models for GS in real plant populations is very limited. This article evaluates the performance of parametric and semiparametric models for GS using wheat (Triticum aestivum L.) and maize (Zea mays) data in which different traits were measured in several environmental conditions. The findings, based on extensive cross-validations, indicate that models including marker information had higher predictive ability than pedigree-based models. In the wheat data set, and relative to a pedigree model, gains in predictive ability due to inclusion of markers ranged from 7.7 to 35.7%. Correlation between observed and predictive values in the maize data set achieved values up to 0.79. Estimates of marker effects were different across environmental conditions, indicating that genotype 3 environment interaction is an important component of genetic variability. These results indicate that GS in plant breeding can be an effective strategy for selecting among lines whose phenotypes have yet to be observed.
Drought is the main abiotic constraint on cereal yield. Analysing physiological determinants of yield responses to water may help in breeding for higher yield and stability under drought conditions. The traits to select (either for stress escape, avoidance or tolerance) and the framework where breeding for drought stress is addressed will depend on the level and timing of stress in the targeted area. If the stress is severe, breeding under stress-free conditions may be unsuccessful and traits that confer survival may become a priority. However, selecting for yield itself under stress-alleviated conditions appears to produce superior cultivars, not only for optimum environments, but also for those characterized by frequent mild and moderate stress conditions. This implies that broad avoidance/tolerance to mild-moderate stresses is given by constitutive traits also expressed under stress-free conditions. In this paper, we focus on physiological traits that contribute to improved productivity under mild-moderate drought. Increased crop performance may be achieved through improvements in water use, water-use efficiency and harvest index. The first factor is relevant when soil water remains available at maturity or when deep-rooted genotypes access water in the soil profile that is not normally available; the two latter conditions become more important when all available water is exhausted by the end of the crop cycle. Independent of the mechanism operating, a canopy able to use more water than another would have more open stomata and therefore higher canopy temperature depression, and 13C discrimination (delta13C) in plant matter. The same traits would also seem to be relevant when breeding for hot, irrigated environments. Where additional water is not available to the crop, higher water-use efficiency (WUE) appears to be an alternative strategy to improve crop performance. In this context delta13C constitutes a simple but reliable measure of WUE. However, in contrast to lines performing better because of increased access to water, lines producing greater biomass due to superior WUE will have lower delta13C values. WUE may be modified not only through a decrease in stomatal conductance, but also through an increase in photosynthetic capacity. Harvest index is strongly reduced by terminal drought (i.e. drought during grain filling). Thus, phenological traits increasing the relative amount of water used during grain filling, or adjusting the crop cycle to the seasonal pattern of rainfall may be useful. Augmenting the contribution of carbohydrate reserves accumulated during vegetative growth to grain filling may also be worthwhile in harsh environmcnts. Alternatively, extending the duration of stem elongation without changing the timing of anthesis would increase the number of grains per spike and the harvest index without changing the amount of water utilized by the crop.
Inability to efficiently implement high-throughput field phenotyping is increasingly perceived as a key component that limits genetic gain in breeding programs. Field phenotyping must be integrated into a wider context than just choosing the correct selection traits, deployment tools, evaluation platforms, or basic data-management methods. Phenotyping means more than conducting such activities in a resource-efficient manner; it also requires appropriate trial management and spatial variability handling, definition of key constraining conditions prevalent in the target population of environments, and the development of more comprehensive data management, including crop modeling. This review will provide a wide perspective on how field phenotyping is best implemented. It will also outline how to bridge the gap between breeders and ‘phenotypers’ in an effective manner.
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