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Bassu, S., Brisson, N., Durand, J. - L., Boote, K., Lizaso, J., Jones, J. W., et al. (2014). How do various maize crop models vary in their responses to climate change factors. Glob. Chang. Biol., 20(7), 2301–2320.
Abstract: Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 μmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.
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Ramirez-Villegas, J., Watson, J., & Challinor, A. J. (2015). Identifying traits for genotypic adaptation using crop models. J. Experim. Bot., 66(12), 3451–3462.
Abstract: Genotypic adaptation involves the incorporation of novel traits in crop varieties so as to enhance food productivity and stability and is expected to be one of the most important adaptation strategies to future climate change. Simulation modelling can provide the basis for evaluating the biophysical potential of crop traits for genotypic adaptation. This review focuses on the use of models for assessing the potential benefits of genotypic adaptation as a response strategy to projected climate change impacts. Some key crop responses to the environment, as well as the role of models and model ensembles for assessing impacts and adaptation, are first reviewed. Next, the review describes crop-climate models can help focus the development of future-adapted crop germplasm in breeding programmes. While recently published modelling studies have demonstrated the potential of genotypic adaptation strategies and ideotype design, it is argued that, for model-based studies of genotypic adaptation to be used in crop breeding, it is critical that modelled traits are better grounded in genetic and physiological knowledge. To this aim, two main goals need to be pursued in future studies: (i) a better understanding of plant processes that limit productivity under future climate change; and (ii) a coupling between genetic and crop growth models-perhaps at the expense of the number of traits analysed. Importantly, the latter may imply additional complexity (and likely uncertainty) in crop modelling studies. Hence, appropriately constraining processes and parameters in models and a shift from simply quantifying uncertainty to actually quantifying robustness towards modelling choices are two key aspects that need to be included into future crop model-based analyses of genotypic adaptation.
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Dockter, C., & Hansson, M. (2015). Improving barley culm robustness for secured crop yield in a changing climate. J. Experim. Bot., 66(12), 3499–3509.
Abstract: The Green Revolution combined advancements in breeding and agricultural practice, and provided food security to millions of people. Daily food supply is still a major issue in many parts of the world and is further challenged by future climate change. Fortunately, life science research is currently making huge progress, and the development of future crop plants will be explored. Today, plant breeding typically follows one gene per trait. However, new scientific achievements have revealed that many of these traits depend on different genes and complex interactions of proteins reacting to various external stimuli. These findings open up new possibilities for breeding where variations in several genes can be combined to enhance productivity and quality. In this review we present an overview of genes determining plant architecture in barley, with a special focus on culm length. Many genes are currently known only through their mutant phenotypes, but emerging genomic sequence information will accelerate their identification. More than 1000 different short-culm barley mutants have been isolated and classified in different phenotypic groups according to culm length and additional pleiotropic characters. Some mutants have been connected to deficiencies in biosynthesis and reception of brassinosteroids and gibberellic acids. Still other mutants are unlikely to be connected to these hormones. The genes and corresponding mutations are of potential interest for development of stiff-straw crop plants tolerant to lodging, which occurs in extreme weather conditions with strong winds and heavy precipitation.
Keywords: Climate Change; Crops, Agricultural/*anatomy & histology/genetics/*growth & development; Edible Grain/anatomy & histology/genetics/growth & development; Genes, Plant; Hordeum/*anatomy & histology/genetics/*growth & development; Phenotype; Barley (Hordeum vulgare); Green Revolution; brassinosteroid; gibberellin; lodging; plant architecture; short culm
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Martre, P., He, J., Le Gouis, J., & Semenov, M. A. (2015). In silico system analysis of physiological traits determining grain yield and protein concentration for wheat as influenced by climate and crop management. J. Experim. Bot., 66(12), 3581–3598.
Abstract: Genetic improvement of grain yield (GY) and grain protein concentration (GPC) is impeded by large genotype×environment×management interactions and by compensatory effects between traits. Here global uncertainty and sensitivity analyses of the process-based wheat model SiriusQuality2 were conducted with the aim of identifying candidate traits to increase GY and GPC. Three contrasted European sites were selected and simulations were performed using long-term weather data and two nitrogen (N) treatments in order to quantify the effect of parameter uncertainty on GY and GPC under variable environments. The overall influence of all 75 plant parameters of SiriusQuality2 was first analysed using the Morris method. Forty-one influential parameters were identified and their individual (first-order) and total effects on the model outputs were investigated using the extended Fourier amplitude sensitivity test. The overall effect of the parameters was dominated by their interactions with other parameters. Under high N supply, a few influential parameters with respect to GY were identified (e.g. radiation use efficiency, potential duration of grain filling, and phyllochron). However, under low N, >10 parameters showed similar effects on GY and GPC. All parameters had opposite effects on GY and GPC, but leaf and stem N storage capacity appeared as good candidate traits to change the intercept of the negative relationship between GY and GPC. This study provides a system analysis of traits determining GY and GPC under variable environments and delivers valuable information to prioritize model development and experimental work.
Keywords: Climate; *Computer Simulation; Crops, Agricultural/*growth & development/physiology; Edible Grain/*growth & development; Models, Biological; Nitrogen/metabolism; Plant Proteins/*metabolism; Plant Transpiration; Probability; *Quantitative Trait, Heritable; Soil/chemistry; Triticum/growth & development/metabolism/*physiology; Water/chemistry; Crop growth model; genetic adaptation; grain protein concentration; grain yield; interannual variability; sensitivity analysis; wheat (Triticum aestivum L.); yield stability
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McKersie, B. (2015). Planning for food security in a changing climate. J. Experim. Bot., 66(12), 3435–3450.
Abstract: The Intergovernmental Panel on Climate Change and other international agencies have concluded that global crop production is at risk due to climate change, population growth, and changing food preferences. Society expects that the agricultural sciences will innovate solutions to these problems and provide food security for the foreseeable future. My thesis is that an integrated research plan merging agronomic and genetic approaches has the greatest probability of success. I present a template for a research plan based on the lessons we have learned from the Green Revolution and from the development of genetically engineered crops that may guide us to meet this expectation. The plan starts with a vision of how the crop management system could change, and I give a few examples of innovations that are very much in their infancy but have significant potential. The opportunities need to be conceptualized on a regional basis for each crop to provide a target for change. The plan gives an overview of how the tools of plant biotechnology can be used to create the genetic diversity needed to implement the envisioned changes in the crop management system, using the development of drought tolerance in maize (Zea mays L.) as an example that has led recently to the commercial release of new hybrids in the USA. The plan requires an interdisciplinary approach that integrates and coordinates research on plant biotechnology, genetics, physiology, breeding, agronomy, and cropping systems to be successful.
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