方法对比

	Food-System Life Cycle Assessment	Crop Simulation Modeling
领域	Food Agriculture Studies	Food Agriculture Studies
方法族	Process / pipeline	Process / pipeline
起源年份	2018	2003
提出者	ISO 14040/14044 LCA framework; food-system synthesis by Joseph Poore & Thomas Nemecek	James W. Jones et al. (DSSAT); Dean Holzworth et al. (APSIM)
类型	Cradle-to-grave environmental modelling pipeline for foods and diets	Process-based dynamic simulation pipeline for crop growth and yield
开创性文献	Poore, J., & Nemecek, T. (2018). Reducing food's environmental impacts through producers and consumers. Science, 360(6392), 987-992.	Jones, J. W., Hoogenboom, G., Porter, C. H., Boote, K. J., Batchelor, W. D., Hunt, L. A., Wilkens, P. W., Singh, U., Gijsman, A. J., & Ritchie, J. T. (2003). The DSSAT cropping system model. European Journal of Agronomy, 18(3-4), 235-265.
别名	Food LCA, Agri-food Life Cycle Assessment, Dietary Life Cycle Assessment, Cradle-to-Grave Food Footprinting	Crop Growth Simulation, Process-Based Crop Modeling, Crop Systems Modeling, Dynamic Crop Modeling
相关	4	4
摘要	Food-system life cycle assessment (LCA) quantifies the environmental footprint of a food, meal or diet across its entire life cycle — from agricultural inputs on the farm, through processing, packaging, transport, retail and cooking, to waste disposal. Following the ISO 14040/14044 framework, an analyst defines a functional unit (such as one kilogram of food, 100 grams of protein, or 1000 kilocalories), compiles a life-cycle inventory of all inputs and emissions at each stage, characterises those flows into impact indicators (greenhouse-gas emissions, land and water use, eutrophication and acidification), and interprets the result with sensitivity and uncertainty analysis. Poore and Nemecek's 2018 Science synthesis, covering tens of thousands of farms worldwide, showed that impacts vary as much as fifty-fold among producers of the same product and that even the lowest-impact animal foods typically exceed plant substitutes — establishing LCA as the central tool for comparing the sustainability of foods and diets.	Crop simulation modeling uses process-based, dynamic computer models to predict how a crop grows and yields under specified weather, soil, and management, by numerically integrating mechanistic equations for development, photosynthesis, and water and nutrient balances on a daily time step. The two most widely used platforms are DSSAT, documented by James Jones and colleagues in 2003, and APSIM, whose modern architecture was described by Dean Holzworth and colleagues in 2014. Rather than fitting a statistical curve to yield data, these models encode the underlying biophysics — temperature-driven phenology, radiation-use efficiency, soil water and nitrogen dynamics — so they can extrapolate to weather, soils, and management combinations never directly observed. This makes crop models powerful tools for in silico experimentation, scenario analysis, and climate-change and management impact assessment where field trials alone would be impossibly slow or costly.