• JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
 
  Bookmark and Share
 
 
Master's Dissertation
DOI
https://doi.org/10.11606/D.91.2021.tde-25032021-183834
Document
Author
Full name
Gabriela Maria Leme Trivellato
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
Piracicaba, 2021
Supervisor
Committee
Sarries, Gabriel Adrian (President)
Ferreira, Iuri Emmanuel de Paula
Molina, Rodrigo Sarruge
Sorrentino, Marcos
Title in Portuguese
Sistema de avaliação ponderada da multifuncionalidade da agricultura: seres humanos e serviços ecossistêmicos
Keywords in Portuguese
Machine Learning
Índices de sustentabilidade ambiental
Multifuncionalidade da agricultura
Serviços ecossistêmicos
Abstract in Portuguese
Este trabalho centrou-se no desenvolvimento do "Sistema de Avaliação Ponderada da Multifuncionalidade da Agricultura (MFA)". Este sistema de avaliação, ou índice inspirou-se em três índices de sustentabilidade ambiental: o APOIA-Novo Rural, o Ambitec-Agro e o método francês IDEA. Destina-se a avaliar as quatro principais funções da MFA na realidade rural brasileira propostas por Maria José Carneiro e Renato Maluf: 1. reprodução socioeconômica das famílias rurais; 2. promoção da segurança alimentar das famílias rurais e da sociedade; 3. manutenção do tecido social e cultural; 4. preservação dos recursos naturais e da paisagem rural. Este índice foi testado a partir dos resultados preliminares do Censo Agropecuário 2017, do IBGE, considerando os 26 estados brasileiros e o Distrito Federal. Um banco de dados foi construído; analisado por Machine Learning nos softwares Weka e R-Action Stat e; por estatística não paramétrica, uni e multivariada, no SAS e no R. Ferramenta desenvolvida para análise quantitativa da MFA, o índice permitiu identificar distinções entre desempenhos de estados e regiões em termos de favorecimento da MFA. Acreditamos no seu potencial para valorizar a preservação ambiental e cultural na gestão de estabelecimentos agropecuários, ou agroecossistemas, podendo viabilizar: a) comparações mais precisas entre performances agrícolas; b) definição de benchmarks e locais-problema; c) norteamento de políticas públicas, favorecendo a MFA e o equilíbrio ambiental, principalmente nas áreas mais susceptíveis.
Title in English
Agriculture multifunctionality weighted evaluation system: human beings and ecosystem services
Keywords in English
Machine Learning
Agriculture multifunctionality
Ecosystem services
Environmental sustainability indexes
Abstract in English
This work focused on the development of the "Weighted Assessment System for the Multifunctionality of Agriculture (MFA)". This assessment system, or index, was inspired by three environmental sustainability indexes: APOIA-Novo Rural, Ambitec-Agro and the French method IDEA. It aims to evaluate the four main functions of MFA in the Brazilian rural reality, proposed by Maria José Carneiro and Renato Maluf: 1. rural families socioeconomic reproduction; 2. rural families and society food security; 3. social and cultural relations maintenance; 4. natural resources and rural landscape preservation. This index was tested based on IBGE's 2017 Agricultural Census preliminary results, considering the 26 Brazilian states and the Federal District. A database was built; analyzed by Machine Learning on Weka and R-Action Stat softwares, and; by non-parametric, uni and multivariate statistical methods, on SAS and R. As a tool developed to analyze MFA quantitatively, this index identified distinctions among states and regions performances in terms of favoring MFA. We believe in this index's potential to strengthen environmental and cultural preservation in agricultural establishments - or agro-ecosystems - management. Therefore, we believe this index could enable: a) more accurate comparisons among agricultural performances; b) definition of benchmarks and problematic locations; c) guiding public policies, favoring MFA and environmental balance, especially in the most susceptible areas.
 
WARNING - Viewing this document is conditioned on your acceptance of the following terms of use:
This document is only for private use for research and teaching activities. Reproduction for commercial use is forbidden. This rights cover the whole data about this document as well as its contents. Any uses or copies of this document in whole or in part must include the author's name.
Publishing Date
2021-03-30
 
WARNING: Learn what derived works are clicking here.
All rights of the thesis/dissertation are from the authors
CeTI-SC/STI
Digital Library of Theses and Dissertations of USP. Copyright © 2001-2024. All rights reserved.