Potential productive of Brazilian agricultural soils
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da USP |
Texto Completo: | https://www.teses.usp.br/teses/disponiveis/11/11140/tde-12072022-164028/ |
Resumo: | The main issue for the world\'s good is food security. Brazil plays an important role in global agriculture and can help meet global food demand in the future. The chemical, physical and biological properties of the soil directly influence the production of cultivated plants. Understanding the productive potential of Brazilian agricultural land is of great importance. Therefore, the objective of this study is to develop a strategy to map and quantify the productive potential of Brazilian agricultural soils through digital soil mapping techniques. Approximately 70,000 soil samples (0 - 1.0m) georeferenced with information on chemical, physical and biological properties of Brazilian agricultural soils were used. Each soil attribute was evaluated using literary information. Then, a weighted artifice equation was developed through the analysis of principal components of the soil samples. Therefore, each sample received a score ranging from 0 to 100 referring to its soil potential to produce plant biomass, that is, the higher the score, the higher its potential. Sample scores were spatialized via machine learning. 80% (205 million hectares) of Brazilian agricultural areas were mapped, considering areas of cultivated plants and pastures. Through the territorial quantification of the productive potential of soils, the possibility of seeing the best and worst categories of agricultural soils in each Brazilian biome. The best agricultural soils in Brazil that belong to the very high and high SoilPP categories were found in the Atlantic Forest (11.8 Mha), Ama zon (7.6 Mha) and Cerrado (4.4 Mha) biomes. On the other hand, soils that varied from medium/high to very low potential were found in the Cerrado (77.9 Mha), Caatinga (28.9 Mha) and Atlantic Forest (34.5 Mha) biomes. Through the evaluation of agricultural land and average productivity values, we observed that of 2304 soybean producing municipalities, 896 have the capacity to increase average soybean productivity to a certain level. For sugarcane, out of 2468 municipalities evaluated, 1056 can increase their average productivity. Therefore, this technique can assist in the development of global food and soil security policies and be replicated in other countries, regions, municipalities or farms. The SoilPPmap for Brazilian agricultural areas had as main limitations the spatial representativeness for all types of agricultural soils in Brazil and also the low accuracy of the prediction model, possibly related to the evaluation of soil chemical attributes. However, the strategy used was effective in mapping and quantifying the productive potential of Brazilian agricultural soils. |
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Potential productive of Brazilian agricultural soilsPotencial produtivo dos solos agrícolas brasileirosAprendizado de máquinasAtributos do soloDigital soil mappingMachine learningMapeamento digital de solosMonitoramento do soloSoil attributesSoil monitoringThe main issue for the world\'s good is food security. Brazil plays an important role in global agriculture and can help meet global food demand in the future. The chemical, physical and biological properties of the soil directly influence the production of cultivated plants. Understanding the productive potential of Brazilian agricultural land is of great importance. Therefore, the objective of this study is to develop a strategy to map and quantify the productive potential of Brazilian agricultural soils through digital soil mapping techniques. Approximately 70,000 soil samples (0 - 1.0m) georeferenced with information on chemical, physical and biological properties of Brazilian agricultural soils were used. Each soil attribute was evaluated using literary information. Then, a weighted artifice equation was developed through the analysis of principal components of the soil samples. Therefore, each sample received a score ranging from 0 to 100 referring to its soil potential to produce plant biomass, that is, the higher the score, the higher its potential. Sample scores were spatialized via machine learning. 80% (205 million hectares) of Brazilian agricultural areas were mapped, considering areas of cultivated plants and pastures. Through the territorial quantification of the productive potential of soils, the possibility of seeing the best and worst categories of agricultural soils in each Brazilian biome. The best agricultural soils in Brazil that belong to the very high and high SoilPP categories were found in the Atlantic Forest (11.8 Mha), Ama zon (7.6 Mha) and Cerrado (4.4 Mha) biomes. On the other hand, soils that varied from medium/high to very low potential were found in the Cerrado (77.9 Mha), Caatinga (28.9 Mha) and Atlantic Forest (34.5 Mha) biomes. Through the evaluation of agricultural land and average productivity values, we observed that of 2304 soybean producing municipalities, 896 have the capacity to increase average soybean productivity to a certain level. For sugarcane, out of 2468 municipalities evaluated, 1056 can increase their average productivity. Therefore, this technique can assist in the development of global food and soil security policies and be replicated in other countries, regions, municipalities or farms. The SoilPPmap for Brazilian agricultural areas had as main limitations the spatial representativeness for all types of agricultural soils in Brazil and also the low accuracy of the prediction model, possibly related to the evaluation of soil chemical attributes. However, the strategy used was effective in mapping and quantifying the productive potential of Brazilian agricultural soils.A principal questão para o bem mundial é a segurança alimentar. O Brasil desempenha um papel importante na agricultura global, podendo auxiliar o suprimento da demanda alimentar global no futuro. As propriedades químicas, físicas e biológicas do solo influenciam diretamente a produção de plantas cultivadas. Compreender o potencial produtivo das terras agrícolas brasileiras é de grande importância. Portanto, o objetivo desse estudo é desenvolver uma estratégia para mapear e quantificar o potencial produtivo dos solos agrícolas brasileiros através de técnicas de mapeamento digital de solos. Foram utilizadas aproximadamente 70.000 amostras de solo (0 - 1,0m) georreferenciadas com informações de propriedades químicas, físicas e biológicas de solos agrícolas brasileiros. Cada atributo do solo foi avaliado por meio de informações literárias. Em seguida foi desenvolvida uma equação de artifício ponderado através da análise de componentes principais das amostras de solo. Portanto, cada amostra de recebeu uma nota variado de 0 a 100 referente ao seu potencial do solo em produzir biomassa vegetal, ou seja, quanto maior é a nota, consequentemente maior é o seu potencial. As pontuações das amostras foram preditas via aprendizado de máquina. Foram mapeados 80% (205 milhões de hectares) das áreas agrícolas brasileiras, considerando áreas de plantas cultivadas e pastagens. Através da quantificação territorial do potencial produtivo dos solos, a possibilidade de enxergar as melhores e piores categorias de solos agrícolas em cada bioma brasileiro. Os melhores solos agrícolas do Brasil que pertencem às categorias de SoilPP muito alto e alto, foram encontrados nos biomas Mata Atlântica (11,8 Mha), Amazônia (7,6 Mha) e Cerrado (4,4 Mha). Por outro lado, os solos que variaram de potencial médio/alto a muito baixo foram encontrados nos biomas Cerrado (77,9 Mha), Caatinga (28,9 Mha) e Mata Atlântica (34,5 Mha). Através da avaliação das terras agrícolas e de valores médios de produtividade, observamos que de 2304 municípios produtores de soja, 896 possuem capacidade para aumentar a produtividade média da soja até um determinado nível. Para a cana de açúcar, de 2468 municípios avaliados, 1056 podem elevar sua produtividade média. Portanto, essa técnica pode auxiliar no desenvolvimento de políticas globais de segurança alimentar e do solo e ser replicada em outros países, regiões, municípios ou fazendas. O SoilPPmap para as áreas agrícolas brasileiras teve por principais limitações a representatividade espacial para todos os tipos de solos agrícolas do Brasil e também a baixa acurácia do modelo de predição, possivelmente relacionada a avaliação de atributos químicos do solo. Entretanto, a estratégia utilizada foi efetiva para mapear e quantificar o potencial produtivo dos solos agrícolas brasileiros.Biblioteca Digitais de Teses e Dissertações da USPDematte, Jose Alexandre MeloGreschuk, Lucas Tadeu2022-06-28info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://www.teses.usp.br/teses/disponiveis/11/11140/tde-12072022-164028/reponame:Biblioteca Digital de Teses e Dissertações da USPinstname:Universidade de São Paulo (USP)instacron:USPLiberar o conteúdo para acesso público.info:eu-repo/semantics/openAccesseng2022-07-15T19:50:49Zoai:teses.usp.br:tde-12072022-164028Biblioteca Digital de Teses e Dissertaçõeshttp://www.teses.usp.br/PUBhttp://www.teses.usp.br/cgi-bin/mtd2br.plvirginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.bropendoar:27212022-07-15T19:50:49Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Potential productive of Brazilian agricultural soils Potencial produtivo dos solos agrícolas brasileiros |
title |
Potential productive of Brazilian agricultural soils |
spellingShingle |
Potential productive of Brazilian agricultural soils Greschuk, Lucas Tadeu Aprendizado de máquinas Atributos do solo Digital soil mapping Machine learning Mapeamento digital de solos Monitoramento do solo Soil attributes Soil monitoring |
title_short |
Potential productive of Brazilian agricultural soils |
title_full |
Potential productive of Brazilian agricultural soils |
title_fullStr |
Potential productive of Brazilian agricultural soils |
title_full_unstemmed |
Potential productive of Brazilian agricultural soils |
title_sort |
Potential productive of Brazilian agricultural soils |
author |
Greschuk, Lucas Tadeu |
author_facet |
Greschuk, Lucas Tadeu |
author_role |
author |
dc.contributor.none.fl_str_mv |
Dematte, Jose Alexandre Melo |
dc.contributor.author.fl_str_mv |
Greschuk, Lucas Tadeu |
dc.subject.por.fl_str_mv |
Aprendizado de máquinas Atributos do solo Digital soil mapping Machine learning Mapeamento digital de solos Monitoramento do solo Soil attributes Soil monitoring |
topic |
Aprendizado de máquinas Atributos do solo Digital soil mapping Machine learning Mapeamento digital de solos Monitoramento do solo Soil attributes Soil monitoring |
description |
The main issue for the world\'s good is food security. Brazil plays an important role in global agriculture and can help meet global food demand in the future. The chemical, physical and biological properties of the soil directly influence the production of cultivated plants. Understanding the productive potential of Brazilian agricultural land is of great importance. Therefore, the objective of this study is to develop a strategy to map and quantify the productive potential of Brazilian agricultural soils through digital soil mapping techniques. Approximately 70,000 soil samples (0 - 1.0m) georeferenced with information on chemical, physical and biological properties of Brazilian agricultural soils were used. Each soil attribute was evaluated using literary information. Then, a weighted artifice equation was developed through the analysis of principal components of the soil samples. Therefore, each sample received a score ranging from 0 to 100 referring to its soil potential to produce plant biomass, that is, the higher the score, the higher its potential. Sample scores were spatialized via machine learning. 80% (205 million hectares) of Brazilian agricultural areas were mapped, considering areas of cultivated plants and pastures. Through the territorial quantification of the productive potential of soils, the possibility of seeing the best and worst categories of agricultural soils in each Brazilian biome. The best agricultural soils in Brazil that belong to the very high and high SoilPP categories were found in the Atlantic Forest (11.8 Mha), Ama zon (7.6 Mha) and Cerrado (4.4 Mha) biomes. On the other hand, soils that varied from medium/high to very low potential were found in the Cerrado (77.9 Mha), Caatinga (28.9 Mha) and Atlantic Forest (34.5 Mha) biomes. Through the evaluation of agricultural land and average productivity values, we observed that of 2304 soybean producing municipalities, 896 have the capacity to increase average soybean productivity to a certain level. For sugarcane, out of 2468 municipalities evaluated, 1056 can increase their average productivity. Therefore, this technique can assist in the development of global food and soil security policies and be replicated in other countries, regions, municipalities or farms. The SoilPPmap for Brazilian agricultural areas had as main limitations the spatial representativeness for all types of agricultural soils in Brazil and also the low accuracy of the prediction model, possibly related to the evaluation of soil chemical attributes. However, the strategy used was effective in mapping and quantifying the productive potential of Brazilian agricultural soils. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-06-28 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://www.teses.usp.br/teses/disponiveis/11/11140/tde-12072022-164028/ |
url |
https://www.teses.usp.br/teses/disponiveis/11/11140/tde-12072022-164028/ |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
|
dc.rights.driver.fl_str_mv |
Liberar o conteúdo para acesso público. info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Liberar o conteúdo para acesso público. |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.coverage.none.fl_str_mv |
|
dc.publisher.none.fl_str_mv |
Biblioteca Digitais de Teses e Dissertações da USP |
publisher.none.fl_str_mv |
Biblioteca Digitais de Teses e Dissertações da USP |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações da USP instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
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USP |
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USP |
reponame_str |
Biblioteca Digital de Teses e Dissertações da USP |
collection |
Biblioteca Digital de Teses e Dissertações da USP |
repository.name.fl_str_mv |
Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
virginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.br |
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