26
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Global alterations in areas of suitability for maize production from climate change and using a mechanistic species distribution model (CLIMEX)

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          At the global level, maize is the third most important crop on the basis of harvested area. Given its importance, an assessment of the variation in regional climatic suitability under climate change is critical. CliMond 10′ data were used to model the potential current and future climate distribution of maize at the global level using the CLIMEX distribution model with climate data from two general circulation models, CSIRO-Mk3.0 and MIROC-H, assuming an A2 emissions scenario for 2050 and 2100. The change in area under future climate was analysed at continental level and for major maize-producing countries of the world. Regions between the tropics of Cancer and Capricorn indicate the highest loss of climatic suitability, contrary to poleward regions that exhibit an increase of suitability. South America shows the highest loss of climatic suitability, followed by Africa and Oceania. Asia, Europe and North America exhibit an increase in climatic suitability. This study indicates that globally, large areas that are currently suitable for maize cultivation will suffer from heat and dry stresses that may constrain production. For the first time, a model was applied worldwide, allowing for a better understanding of areas that are suitable and that may remain suitable for maize.

          Related collections

          Most cited references41

          • Record: found
          • Abstract: found
          • Article: not found

          Prioritizing climate change adaptation needs for food security in 2030.

          Investments aimed at improving agricultural adaptation to climate change inevitably favor some crops and regions over others. An analysis of climate risks for crops in 12 food-insecure regions was conducted to identify adaptation priorities, based on statistical crop models and climate projections for 2030 from 20 general circulation models. Results indicate South Asia and Southern Africa as two regions that, without sufficient adaptation measures, will likely suffer negative impacts on several crops that are important to large food-insecure human populations. We also find that uncertainties vary widely by crop, and therefore priorities will depend on the risk attitudes of investment institutions.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            CliMond: global high-resolution historical and future scenario climate surfaces for bioclimatic modelling

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              How do various maize crop models vary in their responses to climate change factors?

              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.
                Bookmark

                Author and article information

                Contributors
                nramirez@myune.edu.au
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                19 July 2017
                19 July 2017
                2017
                : 7
                : 5910
                Affiliations
                [1 ]ISNI 0000 0004 1936 7371, GRID grid.1020.3, Ecosystem Management. School of Environmental and Rural Science, , University of New England, ; Armidale, NSW 2351 Australia
                [2 ]ISNI 0000 0001 2170 5278, GRID grid.473362.7, , INIFAP, Campo Experimental Zacatecas, ; Km, 24.5 Carretera Zacatecas-Fresnillo, 98500 Calera de V.R., Zacatecas Mexico
                Author information
                http://orcid.org/0000-0002-9205-756X
                Article
                5804
                10.1038/s41598-017-05804-0
                5517596
                28127051
                c6fa01cd-e982-4f9e-9a11-374ff2bbb2a5
                © The Author(s) 2017

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 10 February 2017
                : 2 June 2017
                Categories
                Article
                Custom metadata
                © The Author(s) 2017

                Uncategorized
                Uncategorized

                Comments

                Comment on this article