可持续营养管理业务决策的混合整数框架

A. Christensen, Hongbo Dong, J. Ramakrishnan, M. Sharara, M. Ferris
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引用次数: 0

摘要

全球人口和收入趋势继续增加世界粮食需求和“升级”的饮食,包括更多的动物蛋白。为此,以成本和效率为驱动的种植业和畜牧业集约化经营造成了实质性的环境问题,包括森林砍伐、单一栽培与多样化生产系统、碳密集型化学品使用增加、温室气体排放增加、病原体和抗生素耐药性健康问题以及导致大规模富营养化和藻华的养分流失。本文表明,在商品作物和畜牧生产中进行养分管理可以提高农业的可持续性。具体而言,优化和数据驱动模型用于通过结合养分循环,减少化肥施用,以及由于粪肥分离和精确养分混合/施用技术而增强的物流增强来提高经济和环境绩效。通过使用复杂的、大规模的、混合整数规划方法,可以将来自监管工具的农场现场数据纳入过程模型基础,从而生成一个丰富的、相互关联的决策空间,用于评估经济和环境绩效权衡。本文还详细介绍了如何加强该操作模型,使其包含更符合土地、空气和水供应长期健康的新环境约束,并进一步说明了如何使用该模型来量化在优化系统中实施新政策的成本。特别是,该模型可以阐明关键的战略权衡,可用于了解分离的成本和效果,并可以证明这些方法在处理有机氮和干物质的增加调节方面的效用。它还为政策制定者提供了基于科学和数据的机制,以评估特定法规对典型和特定农场环境的影响,这种方式可以直接用于监管环境。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Mixed-Integer Framework for Operational Decision-Making in Sustainable Nutrient Management
Global population and income trends continue to increase world food demand and an “upscaling” of diets to include more animal proteins. In response, cost and efficiency driven intensification of cropping and livestock operations has created substantive environmental concerns including deforestation, mono-culture versus diversified production systems, increased use of carbon intensive chemicals, increased greenhouse gas emissions, pathogen and antibiotic resistance health concerns, and nutrient runoff leading to large scale eutrophication and algal blooms. This paper shows that management of nutrients within commodity crop and livestock production can provide improved agricultural sustainability. Specifically, optimization and data driven models are used to improve economic and environmental performance using a combination of nutrient cycling, reduced chemical fertilizer application, and logistical enhancements due to manure separation and precision nutrient blending/application technology. Farm field level data from regulatory instruments can be incorporated into a process model foundation using a sophisticated, large scale, mixed integer programming approach to generate a rich, linked decision space for evaluating economic and environmental performance tradeoffs. The paper also details how the operational model can be enhanced to include new environmental constraints that are more in line with the long term health of the land, air and water supply, and furthermore shows how the model can be used to quantify the costs of implementing new policies within an optimized system. In particular, the model can elucidate key strategic tradeoffs that can be used to understand the costs and effects of separation, and can demonstrate the utility of these approaches in dealing with increased regulation of organic nitrogen and dry matter. It also provides policy makers with science and data-based mechanisms to value the impact of specific regulations on both typical and specific farm setups, in a way that can be used directly in a regulatory setting.
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