{"title":"O-AMIE: A tool combining systems engineering and life cycle assessment to eco-design agricultural practices and assess their environmental impacts","authors":"","doi":"10.1016/j.compag.2024.109558","DOIUrl":null,"url":null,"abstract":"<div><div>Agricultural practices are responsible for several environmental impacts; thus, sustainable practices need to be promoted. Environmental impacts of agricultural practices can be estimated using life cycle assessment (LCA). However, since agricultural systems are complex due to their strong interactions with the environment, specific methods need to be used to assess them. To meet this objective, this study combined systems engineering and life cycle assessment, supported by development of the tool O-AMIE (<em>Outil d’Analyse et de Management des Impacts Environnementaux</em> in French; Environmental impact analysis and management tool, in English), using Matlab®, Simulink® and the platform PhiSim (Sherpa Engineering). This tool was developed to help users design and validate operating systems used in agriculture (e.g., tractors, robots), calculate environmental impacts of process systems that use these operating systems (e.g., a complete set of crop management, field operations and cropping techniques) and provide a framework for standardizing agricultural LCAs. Here, we specifically focused on the conceptual framework and model of O-AMIE as well as the model-based design used to build high-level field-operation models.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7000,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers and Electronics in Agriculture","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0168169924009499","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Agricultural practices are responsible for several environmental impacts; thus, sustainable practices need to be promoted. Environmental impacts of agricultural practices can be estimated using life cycle assessment (LCA). However, since agricultural systems are complex due to their strong interactions with the environment, specific methods need to be used to assess them. To meet this objective, this study combined systems engineering and life cycle assessment, supported by development of the tool O-AMIE (Outil d’Analyse et de Management des Impacts Environnementaux in French; Environmental impact analysis and management tool, in English), using Matlab®, Simulink® and the platform PhiSim (Sherpa Engineering). This tool was developed to help users design and validate operating systems used in agriculture (e.g., tractors, robots), calculate environmental impacts of process systems that use these operating systems (e.g., a complete set of crop management, field operations and cropping techniques) and provide a framework for standardizing agricultural LCAs. Here, we specifically focused on the conceptual framework and model of O-AMIE as well as the model-based design used to build high-level field-operation models.
期刊介绍:
Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.