Monitoring and Estimation of Sugarcane Burning in the Middle Paranapanema Basin, Brazil, Using Linear Mixed Models

Jéssica Alves Silva (), Edinéia Aparecida Santos Galvanin () and Daniela Fernanda Silva Fuzzo ()
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Jéssica Alves Silva: Mestranda pela Paulista State University – UNESP
Edinéia Aparecida Santos Galvanin: São Paulo State University – UNESP
Daniela Fernanda Silva Fuzzo: State University of Minas Gerais – UEMG

A chapter in Information and Communication Technologies for Agriculture—Theme III: Decision, 2022, pp 251-264 from Springer

Abstract: Abstract Studies on sugarcane burning demonstrate that the use of fire in agriculture has been condemned for centuries by soil conservation manuals since it increases the temperature and decreases the natural moisture of the soil, leading to greater compaction, loss of porosity, erosion, and consequently soil infertility. The objective of research was to evaluate the spatial and temporal distribution of fire incidences in the period from 2000 to 2018 in the Water Resources Management Unit of the Middle Paranapanema, located in the state of São Paulo—Brazil, and to carry out the future estimate of this activity through mixed linear models. For this purpose, images from the Landsat 5/TM (year 2000), 7/TM (years 2006 and 2012), and 8/OLI (year 2018) satellites and 2018 were used. Numerical data (regarding area and fire incidences) and categorical data (terrain slope) were also employed. Statistical model was used to evaluate data and was possible to identify a decrease in fires in smooth undulating terrains, corresponding to 99.9% per year, and characterized by the increase in agricultural machinery in these areas. In these lands, the model made it possible to carry out the forecast for the next 6 years, in which timeframe, considering causes/effects, there would be a decrease over 100%. On the other hand, in strong undulating terrain there was an increase of 2.07% per year, which in the next 6 years represents an increase of 12.45%, a result contrary to what the established laws provide.

Keywords: Agriculture; Statistical modeling; Remote sensing (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-030-84152-2_12

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DOI: 10.1007/978-3-030-84152-2_12

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