Monitoring Complex Integrated Crop–Livestock Systems at Regional Scale in Brazil: A Big Earth Observation Data Approach
2022
Kuchler, Patrick Calvano | Simões, Margareth | Ferraz, Rodrigo | Arvor, Damien | de Almeida Machado, Pedro Luiz Oliveira | Rosa, Marcos | Gaetano, Raffaele | Bégué, Agnès | Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Universidade do Estado do Rio de Janeiro [Brasil] = Rio de Janeiro State University [Brazil] = Université d'État de Rio de Janeiro [Brésil] (UERJ) | Brazilian Agricultural Research Corporation = Empresa Brasileira de Pesquisa Agropecuária (Embrapa) | Embrapa Solos ; Ministério da Agricultura | Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes) ; Université de Brest (UBO)-Université de Rennes 2 (UR2)-Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG) ; Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (Nantes Univ - IGARUN) ; Nantes Université - pôle Humanités ; Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Humanités ; Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (Nantes Univ - IGARUN) ; Nantes Université - pôle Humanités ; Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Humanités ; Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ) | Universidade Estadual de Feira de Santana [Bahia]=State University of Feira de Santana (UEFS) | Département Environnements et Sociétés (Cirad-ES) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad) | The main author received a scholar fellowship from the Capes (Coordenacao de Aperfeicoamento de Pessoal de Nivel)-Cofecub (Cooperation Universitaire et Scientifique avec le Bresil) GeoABC Project (Methodologies and technological innovation for satellite monitoring of low carbon agriculture in support of Brazil's ABC Plan, project No. 845/15). The ground visits were partly supported by the H2020- MSCA-RISE-2015 ODYSSEA European project (Project Reference: 691053) and the French Agricultural Research Centre for International Development (CIRAD).
International audience
Show more [+] Less [-]English. Due to different combinations of agriculture, livestock and forestry managed by rotation, succession and intercropping practices, integrated agriculture production systems such as integrated crop–livestock systems (iCL) constitute a very complex target and a challenge for automatic mapping of cropping practices based on remote sensing data. The overall objective of this study was to develop a classification strategy for the annual mapping of integrated Crop–Livestock systems (iCL) at a regional scale. This strategy was designed and tested in the six agro-climatic regions of Mato Grosso, the largest Brazilian soybean producer state, using MODIS satellite time-series images acquired between 2012 and 2019, ground data with heterogeneous distribution in space and time and a Random Forest classifier. The results showed that: 1. the use of unbalanced training samples with a class composition close to the real one was the right classifier training strategy; 2. the use of a single training database (pooling samples from different years and regions) to classify each region and year individually proved to be robust enough to provide similar classification accuracies in comparison to those based on the use of a database acquired for each region and for each year. The final hierarchical classification overall accuracy was 0.89 for Level 1, the cropping pattern level (single and double crops DC); 0.84 for Level 2, the DC category level (integrated system iCL soy-pasture/brachiaria, soy-cotton and soy-cereal); 0.77 for Level 3, the iCL level (iCL1 soy-pasture and iCL2 soy-pasture mixed with corn). The F-scores for DC, iCL and iCL1 cropping systems presented high accuracy (0.89, 0.85 and 0.84), while iCL2 was more difficult to classify (0.63). This approach will next be applied across the entire Brazilian soybean corridor, leading to an operational tool for monitoring the adoption of sustainable intensification practices recognized by Brazil’s Agriculture Low Carbon Plan (ABC PLAN).
Show more [+] Less [-]AGROVOC Keywords
Bibliographic information
This bibliographic record has been provided by Institut national de la recherche agronomique