Integrating agri-environmental indicators, ecosystem services assessment, life cycle assessment and yield gap analysis to assess the environmental sustainability of agriculture

Agriculture's primary function is the production of food, feed, fibre and fuel for the fast-growing world population. However, it also affects human health and ecosystem integrity. Policymakers make policies in order to avoid harmful impacts. How to assess such policies is a challenge. In this paper, we propose a conceptual framework to help evaluate the impacts of agricultural policies on the environment. Our framework represents the global system as four subsystems and their interactions. These four components are the cells of a 2 by 2 matrix [Agriculture, Rest of the word]; [Socio-eco system, Ecological system]. We then developed a set of indicators for environmental issues and positioned these issues in the framework. To assess these issues, we used four well-known existing approaches: Life Cycle Assessment, Ecosystem Services Analysis, Yield Gap Analysis and Agro-Environmental Indicators. Using these four approaches together provided a more holistic view of the impacts of a given policy on the system. We then applied our framework on existing cover crop policies using an extensive literature survey and analysing the different environmental issues mobilised by the four assessment approaches. This demonstration case shows that our framework may be of help for a full systemic assessment. Despite their differences (aims, scales, standardization, data requirements, etc.), it is possible and profitable to use the four approaches together. This is a significant step forward, though more work is needed to produce a genuinely operational tool. © 2022 The Author

Comparison between dynamic programming and reinforcement learning: A case study on maize irrigation management

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Using a genetic algorithm to define worst-best and best-worst options of a DEXi-type model: Application to the MASC model of cropping-system sustainability

International audienceDEXi-type models have been used recently to assess specific problems in agricultural systems and to assess cropping-system scenarios. Finding the set of the ‘‘worst-best’’ (lowest scores for basic attributes that lead to the highest score for the root attribute) and ‘‘best-worst’’ (highest scores for basic attributes that lead to the lowest score for the root attribute) options are of interest for improving current cropping systems. As DEXi-type models revealed a monotonicity property, we used a genetic algorithm to find these two sets. These sets are small and show that only a few attributes need to have low scores to reach the best-worst options or high scores to reach the worst-best options. These attributes are those with ahigh sensitivity index

Moderato: A decision tool for designing maize irrigation schedules

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