Projected impact of future climate on water-stress patterns across the Australian wheatbelt

Watson, James; Zheng, Bangyou; Chapman, Scott C.; Chenu, Karine

doi:10.1093/jxb/erx368

Cited by 45 publications

(20 citation statements)

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“…Water availability is one of the primary limiting factors of yield for bread wheat ( Triticum aestivum L.). With projected increase in water-stress events in some regions due to climate change [ 1 , 2 ] and continuing global population growth, greater food production is needed. This can be achieved, in part, through greater crop yield and more efficient use of limited resources, such as water.…”

Section: Introductionmentioning

confidence: 99%

A low-cost method to rapidly and accurately screen for transpiration efficiency in wheat

et al. 2018

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BackgroundWheat (Triticum aestivum L.) productivity is commonly limited by the availability of water. Increasing transpiration efficiency (biomass produced per unit of water used, TE) can potentially lead to increased grain yield in water-limited environments (‘more crop per drop’). Currently, the ability to screen large populations for TE is limited by slow, low-throughput and/or expensive screening procedures. Here, we propose a low-cost, low-technology, rapid, and scalable method to screen for TE. The method uses a Pot-in-Bucket system that allows continuous watering of the pots and frequent monitoring of water use. To investigate the robustness of the method across environments, and to determine the shortest trial duration required to get accurate and repeatable TE estimates in wheat, plants from 11 genotypes varying in phenology were sown at three dates and grown for different durations in a polyhouse with partial environmental control.ResultsThe method revealed significant genotypic variations in TE among the 11 studied wheat genotypes. Genotype rankings for TE were consistent when plants were harvested the same day, at the flag-leaf stage or later. For these harvests, genotype rankings were consistent across experiments despite changes in environmental conditions, such as evaporative demand.ConclusionsThese results indicate that (1) the Pot-In-Bucket system is suitable to screen TE for breeding purposes in populations with varying phenology, (2) multiple short trials can be carried out within a season to allow increased throughput of genotypes for TE screening, and (3) root biomass measurement is not required to screen for TE, as whole-plant TE and shoot-only TE are highly correlated, at least in wheat. The method is particularly relevant in developing countries where low-cost and relatively high labour input may be most applicable.Electronic supplementary materialThe online version of this article (10.1186/s13007-018-0339-y) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

A low-cost method to rapidly and accurately screen for transpiration efficiency in wheat

et al. 2018

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“…The G×E patterns and partitioning of the phenotypic variance observed for this simulated combination of samples from TPG and TPE were comparable to the ones reported for real field trials (Cullis et al, 2000; Chenu et al, 2011). The large GLY ijk and the clear impact of water deficit patterns on G×E supports the convenience of focusing on the analysis of ETs (water-deficit patterns) in place of environments defined by years and locations (Chapman et al, 2002a; Chapman, 2008; Chenu et al, 2011; Chenu et al, 2013; Hammer et al, 2014; Watson et al, 2017). Such simulated data are useful to investigate phenotyping and breeding strategies across the Australian TPE.…”

Section: Discussionmentioning

confidence: 78%

From QTLs to Adaptation Landscapes: Using Genotype-To-Phenotype Models to Characterize G×E Over Time

et al. 2019

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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.

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“…Even though both these locations are within southeast Queensland, the region is known for highly variable climatic conditions (Chenu et al, 2013), and large genotype × environment interactions are common between wheat trials (Brennan et al, 1981). Consideration of these factors is increasingly important with projections indicating increased water stress not only within this region (Watson et al, 2017), but also in other important wheat‐producing regions worldwide (Ray et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Multivariate Genomic Selection and Potential of Rapid Indirect Selection with Speed Breeding in Spring Wheat

et al. 2019

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Genomic selection (GS) can be effective in breeding for quantitative traits, such as yield, by reducing the selection cycle duration. Speed breeding (SB) uses extended photoperiod and temperature control to enable rapid generation advancement. Together, GS and SB can synergistically reduce the breeding cycle by quickly producing recombinant inbred lines (RILs) and enabling indirect phenotypic selection to improve for key traits, such as height and flowering time, prior to field trials. In addition, traits measured under SB (SB traits) correlated with field‐based yield could improve yield prediction in multivariate GS. A 193‐line spring wheat (Triticum aestivum L.) training population (TP), tested for grain yield in the field in multiple environments, was used to predict grain yield of a 350‐line selection candidate (SC) population, across multiple environments. Four SB traits measured on the TP and SC populations were used to derive principal components, which were incorporated into multivariate GS models. Predictive ability was significantly increased by multivariate GS, in some cases being twice as high as univariate GS. Based on these results, an efficient breeding strategy is proposed combining SB and multivariate GS using yield‐correlated SB traits for yield prediction. The potential for early indirect SB phenotypic selection for targeted population improvement prior to trials was also investigated. Plant height and flowering time showed strong relative predicted efficiency to indirect selection, in some cases as high as direct field selection. The higher selection intensity and rate of generation turnover under SB may enable a greater rate of genetic gain than direct field phenotyping.

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Projected impact of future climate on water-stress patterns across the Australian wheatbelt

Abstract: The expected increase in temperatures and rainfall variability will challenge crop productivity. Unexpectedly, the frequency of severe water stress is projected to decrease in major wheat-producing regions, while increasing in others.

Cited by 45 publications

References 69 publications

A low-cost method to rapidly and accurately screen for transpiration efficiency in wheat

A low-cost method to rapidly and accurately screen for transpiration efficiency in wheat

From QTLs to Adaptation Landscapes: Using Genotype-To-Phenotype Models to Characterize G×E Over Time

Multivariate Genomic Selection and Potential of Rapid Indirect Selection with Speed Breeding in Spring Wheat

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