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United States Department of Agriculture

Agricultural Research Service

Research Project: PREDICTING INTERACTIVE EFFECTS OF CO2, TEMPERATURE, AND OTHER ENVIRONMENTAL FACTORS ON AGRICULTUAL PRODUCTIVITIY

Location: Plant Physiology and Genetics Research

Title: Adapting Cropping Patterns to Climate Change

Author
item White, Jeffrey

Submitted to: Workshop Proceedings
Publication Type: Proceedings
Publication Acceptance Date: October 5, 2009
Publication Date: December 15, 2009
Citation: White, J.W. (2009). Adapting Cropping Patterns to Climate Change. Proceedings of the twenty-first annual conference of the National Agricultural Biotechnology Council hosted by the College of Agriculture and Bioresources, University of Saskatchewan, Saskatoon, Saskatchewan, June 24 -26, 2009, pp. 104 - 115.

Interpretive Summary: Many studies on the potential impacts of climate change in agriculture have focused primarily on yield of individual crops at specific locations rather than considering how farmers might adapt to changing conditions. Such adaptations likely would include changes both where and how crops are grown. This paper illustrates the use of two research approaches for analyzing possible climate change effects. In ecological niche modeling, locations where a crop is known to be grown are linked to a database of climatic data such as for maximum temperatures in the warmest quarter of the year or total annual precipitation. For a given crop, variables are selected that are thought to delimit the crop’s distribution. Using the BIOCLIM tool of the DIVA-GIS (www.diva-gis.org), distribution of spring wheats in North America were mapped, recognizing two zones, winter-sown spring wheats, found mainly in Arizona and California, and conventional spring wheats, which occur in northern regions where survival of winter wheats would be too low. The impact of climate change on distribution of the two spring wheat regions was then estimated by applying the same rules used to map the current wheat distributions to climate data representing conditions in 2100. Both wheat regions moved northward with global warming. Notably, regions suitable for conventional spring wheats largely disappeared from the US. Presumably, however, the US regions would become suitable for winter wheat, so the impact on total wheat area is unclear. Crop simulation models describe how a crop grows and develops, considering diverse effects of the environment and how the crop is managed. Responses of cotton, sorghum and wheat crops to planting date were simulated for Maricopa, AZ using weather data from 1987 to 2008 plus modified weather data representing a +1.5°/+3.0°C day/night warming regime with 580 ppm CO2. This regime approximates a “business as usual” climate change scenario for 2100. For cotton, the simulations suggested that with warming early or late plantings would allow the highest yields, while plantings from mid-March to late May would have lower yields. A mid-June cotton planting date offered high yield with an increased possibility of planting a winter-sown wheat crop after the cotton, with the wheat reaching maturity before the next cotton crop would be planted. Thus, warming might allow a cotton-wheat rotation. Both types of analyses showed that attempts to understand potential impacts of climate change should consider effects over regions and potential interactions among crops grown in rotation. While the analyses may seem far removed from plant biotechnology, there are interesting options for plant biology to improve how cultivars described in simulation models and to improve how accurately physiological processes are described in these models.

Technical Abstract: Many studies on the potential impacts of climate change in agriculture have focused primarily on productivity of individual crops at specific locations rather than considering how cropping patterns may evolve adaptively. These adaptations likely would include both geographic and temporal changes. This paper illustrates the use of ecological niche modeling to estimate possible changes in crop geography and of simulation modeling to analyze changes in cropping sequences. In niche modeling, known locations of a crop are linked to climatic variables that are thought to delimit the crop’s distribution. Using the BIOCLIM tool of the DIVA-GIS (www.diva-gis.org), distribution of spring wheats in North America were modeled, recognizing two zones, winter-sown spring wheats, found mainly in Arizona and California, and conventional spring wheats, which occur in northern regions where survival of winter wheats would be too low. The impact of climate change on distribution of the two spring wheat regions was estimated by applying the niche criteria to modeled climate data for 2100. Both wheat regions moved northward with warming. Notably, regions suitable for conventional spring wheats largely disappeared from the US. Presumably, however, the US regions would become suitable for winter wheat, so the impact on total wheat area is unclear. Crop simulations describe processes of plant growth and development as affected by environment and management. Responses of cotton, sorghum and wheat crops to planting date were simulated for Maricopa, AZ using weather data from 1987 to 2008 plus modified weather data representing a +1.5°/+3.0°C day/night warming regime with 580 ppm CO2. This regime approximates a “business as usual” scenario for 2100. For cotton, the simulations suggested that warming would result in a bimodal yield response to planting date, with planting dates from mid-March to late May having lower yields. A mid-June cotton planting date offered high yield with an increased possibility of harvesting a winter-sown wheat crop. Thus, warming might allow a cotton-wheat rotation. Both types of analyses showed that attempts to understand potential impacts of climate change should examine adaptations that extend beyond assumptions of fixed locations and that ignore potential interactions among crops. While the analyses may seem far removed from plant biotechnology, there are interesting options for plant biology to improve characterizations of cultivars and to improve how accurately physiological processes are described in the simulation models. [GRACEnet Publication].

Last Modified: 7/23/2014
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