(506c) A Life-Cycle Modeling Framework for Dynamic Energy Andwater Footprints of Agriculture Systems | AIChE

(506c) A Life-Cycle Modeling Framework for Dynamic Energy Andwater Footprints of Agriculture Systems


Lan, K. - Presenter, North Carolina State University
Yao, Y., Yale University
Given the rapid increase in food demand, it is critical to develop effective policy and management strategies to enhance the sustainability of agricultural activities. System evaluations for different strategies and their effects on agricultural productivity and environmental impacts are needed for decision making and strategy implementation. Quantifying the energy and water footprints of those strategies are particularly important as such information will be helpful for synergetic management and optimization of food-energy-water (FEW) nexus. Previous studies have used Life Cycle Assessment (LCA) and Techno-Economic Analysis (TEA) to evaluate the environmental impacts and economic gains of different farming activities and strategic scenarios.1–3 However, most of the previous models are static without considering geographic and temporal dynamics andindividual variabilities of farm decisions, both of which could have large impacts on the productivity of agriculture systems and environmental footprints.

To fill the gap, this study developed a modeling framework integrating LCA, TEA, and agent-based modeling (ABM).4 ABM is a practical simulation tool to model dynamic systems that consist of autonomous agents. In this study, each farm was modeled as an autonomous agent who could make decisions on crop selection and fertilizer usage according to interactions with other farms and different attributes. These attributed included farm size, profitability, environmental awareness, soil quality, and forecasting accuracy. More attributes could be added in the future given the flexibility of the modeling framework. A stochastic approach was adapted to model the parameter uncertainties related to farmers’ decision making. TEA was built upon dynamic simulation models of crop cultivation costs, crop selling prices, and crop yields. TEA, LCA, and ABM were linked to allow dynamic simulations of yields, profitability of plantation activities and life-cycle environmental impacts including primary energy consumption and water footprints. A case study of 1,000 farms in a 30-year period in North Carolina was conducted for demonstration. Different scenarios were developed to investigate the impacts of farms’ attributes, socio-economic factors, and intervention strategies on farmers’ decision-making and the environmental impacts of overall agriculture systems.

The results of the case study indicated that information exchanges among farmers, environmental awareness of farmers, access to environmental footprint information, and farm size are the key factors driving the system-level environmental impacts. Enhancing the environmental awareness of farmers has the potential to significantly reduce life-cycle primary energy consumption by 10–29% and water footprint by 8–35%. Switching to more environmentally friendly options generally reduced profits compared to the baseline (farms without environmental awareness). However, enhancing farmers’ environmental awareness can mitigate such profit decrease due to the significant cost benefits of reduced chemical usage. In general, farms with higher environmental awareness and more information exchange with other farmers have less profit loss than those with lower environmental awareness with limited information sharing with others. Another finding of the case study is that providing farms with timely feedback on the environmental impacts of farmer’s past decisions could affect farmers’ choices in the following years and as a result leads to more reductions in energy and water consumption.

The results of this study can provide a variety of stakeholders (e.g., policymakers, nonprofits, agriculture companies) with better understanding of FEW interactions and useful information for effectively managing the large-scale agriculture systems . Although this study mainly used the data in North Carolina for 1,000 farms, this modeling framework can be applied to other regions and different sizes of agricultural systems. In addition, the framework can be integrated with other agricultural, energy, water, and socio-economic models to assist the understanding of complex FEW systems with different geographic variations, climate conditions, crop species, and system boundaries.


(1) Kim, S.; Dale, B. E. Life Cycle Assessment of Various Cropping Systems Utilized for Producing Biofuels: Bioethanol and Biodiesel. Biomass and Bioenergy 2005, 29 (6), 426–439.

(2) Roy, P.; Nei, D.; Orikasa, T.; Xu, Q.; Okadome, H.; Nakamura, N.; Shiina, T. A Review of Life Cycle Assessment (LCA) on Some Food Products. Journal of Food Engineering 2009, 90 (1), 1–10.

(3) Chavas, J. P.; Shi, G. An Economic Analysis of Risk, Management, and Agricultural Technology. Journal of Agricultural and Resource Economics 2015, 40 (1), 63–79.

(4) Lan, K.; Yao, Y. Integrating Life Cycle Assessment and Agent-Based Modeling: A Dynamic Modeling Framework for Sustainable Agricultural Systems. Journal of Cleaner Production 2019, 238, 117853.