Agro 4.0: A Green Information System for Sustainable Agroecosystem Management

TL;DR

This paper presents a Green Information System for sustainable agroecosystem management, utilizing data science and a set of sustainability indicators to efficiently assess and improve agricultural practices.

Contribution

It introduces a novel information system based on ISA methodology, demonstrating that a subset of indicators can effectively evaluate sustainability, reducing data collection efforts.

Findings

Using only 7 of 21 indicators achieves over 90% accuracy in sustainability assessment.

The system enables data collection, visualization, and analysis for sustainable agriculture.

Potential to simplify data requirements for sustainability evaluation.

Abstract

Agriculture is one of the most critical activities developed today by humankind and is in constant technical evolution to supply food and other essential products to everlasting and increasing demand. New machines, seeds, and fertilizers were developed to increase the productivity of cultivated areas. It is estimated that by 2050 we will have a population of 9 billion people and the production of food to meet this demand must occur sustainably. To achieve this goal, it is paramount the adoption of sustainable management techniques for agroecosystems. However, this is a complex task due to a large number of variables involved. One of the solutions for the handling and treatment of such diverse data is the use of Green IS. In this work, we adopt a methodology called Indicators of Sustainability in Agroecosystems (Indicadores de Sustentabilidade em Agroecossistemas -- ISA), implement an information system based on it and apply Data Science techniques over the gathered data - from 100 real rural properties - to compute which are the most relevant ISA Indicators for the final ISA Sustainability Index Score. As a result, we have developed a set of tools for data collection, processing, visualization, and analysis of the sustainability of a rural property or region, following the ISA methodology. We also have that with only 7 of the 21 Indicators present in ISA we can identify the level of sustainability in more than 90% of cases, allowing for a new discussion about shrinking the amount of data needed for the computation of ISA, or remodelling the final computation of the Sustainability Index so other Indicators can be more expressive. Users of the solutions developed in this work can identify best practices for sustainability in participating agroecosystems.