MAB: Agronomic and Economic Analysis of Progressive Green- house Warming: Impacts on Grain Yields, Cropping Patterns, and Farm Profitability

农业生态系统是所有发达社会生命支持系统的关键要素。这些系统是如此的人为因素的主导，合理的理解他们的反应大规模的变化，在物理环境，必然需要考虑经济影响人类决策，调节农业生产的生物物理方面。经济学反过来又受到生物对物理环境的反应的严重影响，而物理环境决定了产量潜力和商品供应。可信地处理这些相互作用需要综合物理、生物和社会科学的方法和工具。该研究项目将共同评估温室气体浓度逐渐增加造成的气候变化对经济和农业的预测影响。最近开发的一种方法，侧重于农场一级的决定对物理环境变化的反应，将成为研究的基础。该项目将考虑北美的主要粮食作物，这些作物有相当全面的农艺、气候和经济数据。拟议的研究将有助于科学地了解气候变化背景下人类和生物物理因素对粮食生产和价格的影响。它将通过一套生理作物模型跟踪逐渐气候变化的影响，并通过农场一级决策的顺序线性规划模型，因此是真正的跨学科性质。研究结果将提供工具，有助于就能源和农业政策对景观格局、粮食生产和价格的影响作出明智的决定。

Agricultural ecosystems are key elements of the life support systems of all developed societies. These systems are so dominated by human factors that a reasonable understanding of their responses to large-scale changes in the physical environment necessarily requires consideration of economic influences on human decision making, which modulate the biophysical aspects of agricultural production. The economics, in turn, are critically influenced by the biological responses to the physical environment, which determine yield potential and commodity supplies. A credible treatment of these interactions requires integration of physical, biological and social science approaches and tools. This research project will jointly assess the predicted economic and agricultural impacts of evolving climate changes caused by gradually increasing greenhouse gas concentrations. A recently developed methodology, which focuses on the response of farm-level decisions to changes in the physical environment, will form the basis of the study. The project will consider the major grain crops of North America, for which reasonably comprehensive agronomic, climatological and economic data are available. The proposed research will contribute to the scientific understanding of interactions between human and biophysical determinants of food production and prices in the context of a changing climate. It will track the effects of gradual climate changes through a set of physiological crop models to a sequential linear programming model of farm-level decision making, and is thus truly interdisciplinary in nature. The results will provide tools which can contribute usefully to informed decisions regarding the consequences of both energy and agricultural policy on landscape patterns, food production and prices.