PREDICTING INTERACTIVE EFFECTS OF CO2, TEMPERATURE, AND OTHER ENVIRONMENTAL FACTORS ON AGRICULTUAL PRODUCTIVITIY
Location: Plant Physiology and Genetics Research
Title: Global Change -- What Future for Wheat?
Submitted to: CIMMYT Wheat Technical Bulletin
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: September 10, 2007
Publication Date: June 17, 2009
Citation: Hodson, D.P., White, J.W. 2009. Global Change -- What Future for Wheat?. CIMMYT Wheat Technical Bulletin, (J. Dixon, H. Braun, P. Kosina, J. Crouch eds.) p. 52-61.
Interpretive Summary: Climate change and increasing atmospheric CO2 are expected to have a major impact on wheat yields, including where or what types of wheat are grown. Given the importance of wheat to US growers, consumers and grain dealers, it is imperative to examine how global change may affect wheat production around the world, including what prospects exist for mitigating the impacts of warming. This paper reviews various lines of evidence for impacts of warming and increased CO2 on wheat, using the International Maize and Wheat Improvement Center (CIMMYT) classification of wheat “megaenvironments” to guide the discussion. Regions in the tropics and sub-tropics are expected to suffer from effects of warming, but elevated CO2 may compensate by increasing productivity 10 to 20% as well as decreasing the amount of water required to achieve a given level of productivity. Likely, some regions, especially in Pakistan, India and Bangladesh, will become too hot for wheat. For higher latitudes, the most important changes may involve shifts from spring wheat to winter wheat and opening of new areas for spring wheat production as a result of longer growing seasons. Pests and diseases also may become more problematic. Challenges for wheat breeding include to increase heat tolerance and to re-match timing of flowering and maturity to growing seasons as warming alters season lengths and speeds up crop development. Agronomic practices such as conservation tillage also may give growers greater flexibility in dealing with changing growing seasons. Due to the complexity of the processes involved, it is very difficult to predict quantitative yield trends or changes in cropping area.
Grain yield and quality in cereals are often strongly influenced by flowering date. Ecophysiological models of bread wheat (Triticum aestivum L.) simulate the number of days to heading or anthesis by assuming that an intrinsic rate of development is modified by vernalization and photoperiodism. Cultivar differences are accounted for through parameters for vernalization requirement, photoperiod sensitivity, and earliness per se. These parameters are usually estimated by optimization based on comparison of simulated and observed data on development. In other crops, similar parameters have been estimated based on the genetic makeup of individual cultivars. For bread wheat, available data on the Vrn and Ppd loci, which affect vernalization and photoperiodism, appeared sufficient to estimate two parameters in the CSM-Cropsim-CERES using cultivar haplotypes. The parameters, which affect vernalization and photoperiodism, were first estimated using conventional procedures and then re-estimated using linear regressions for effects of the Vrn and Ppd loci. Flowering data were obtained for 29 cultivars tested in the International Winter Wheat Performance Nursery, which was distributed from 1968 to 1981 by the University of Nebraska. For our analyses, the experimental data were divided into geographically distinct sets, one for calibration (14 locations, 540 observations) and one evaluation (34 locations, 1499 observations). Using gene-based estimates of two parameters determining vernalization requirement and photoperiodism in the model, 96% of the variation in flowering was explained for the calibration dataset and 90% for the evaluation set, with root mean squared errors of 9 days and 10 days for the two datasets, respectively. The conventional coefficients explained an additional 1% of variation for each dataset. Recent advances in characterizing the wheat genome give promise for enabling rapid, reliable determination of haplotypes. Gene-based prediction of phenology appears feasible for improving predictions of how wheat genotypes respond to environment, allowing more accurate targeting of germplasm and analysis of issues such as crop response to climate risk or global warming.