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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). Made available in DSpace on 2022-04-04T15:00:45Z (GMT). No. of bitstreams: 1 Monitoring-complex-integrated-crop-livestock-systems-at-regional-scale-in-Brazil-2022.pdf: 8358810 bytes, checksum: 91d7d9fad47d513abb47458e709f1d4c (MD5) Previous issue date: 2022

Ecosystem service (ES) models can only inform policy design adequately if they incorporate ecological processes. We used the Lund-Potsdam-Jena managed Land (LPJmL) model, to address following questions for Mexico, Bolivia and Brazilian Amazon: (i) How different are C stocks and C sequestration quantifications under standard (when soil and litter C and heterotrophic respiration are not considered) and comprehensive (including all C stock and heterotrophic respiration) approach? and (ii) How does the valuation of C stock and C sequestration differ in national payments for ES and global C funds or markets when comparing both approach? We found that up to 65% of C stocks have not been taken into account by neglecting to include C stored in soil and litter, resulting in gross underpayments (up to 500 times lower). Since emissions from heterotrophic respiration of organic material offset a large proportion of C gained through growth of living matter, we found that markets and decision-makers are inadvertently overestimating up to 100 times C sequestrated. New approaches for modelling C services relevant ecological process-based can help accounting for C in soil, litter and heterotrophic respiration and become important for the operationalization of agreements on climate change mitigation following the COP21 in 2015. Made available in DSpace on 2018-11-28T23:41:43Z (GMT). No. of bitstreams: 1 2018056.pdf: 4636292 bytes, checksum: f0c3c2bb494dc8dc92d947d954894047 (MD5) Previous issue date: 2018-11-28

The availability of crop type reference datasets for satellite image classification is very limited for complex agricultural systems as observed in developing and emerging countries. Indeed, agricultural land use is very dynamic, agricultural censuses are often poorly georeferenced and crop types are difficult to interpret directly from satellite imagery. In this paper, we present a database made of 24 datasets collected in a standardized manner over nine sites within the framework of the international JECAM (Joint Experiment for Crop Assessment and Monitoring) initiative; the sites were spread over seven countries of the tropical belt, and the number of data collection years depended on the site (from 1 to 7 years between 2013 and 2020). These quality-controlled datasets are distinguished by in situ data collected at the field scale by local experts, with precise geographic coordinates, and following a common protocol. Altogether, the datasets completed 27 074 polygons (20 257 crops and 6817 noncrops, ranging from 748 plots in 2013 (one site visited) to 5515 in 2015 (six sites visited)) documented by detailed keywords. These datasets can be used to produce and validate agricultural land use maps in the tropics. They can also be used to assess the performances and robustness of classification methods of cropland and crop types/practices in a large range of tropical farming systems. The dataset is available at https://doi.org/10.18167/DVN1/P7OLAP (Jolivot et al., 2021). Made available in DSpace on 2022-01-06T12:00:36Z (GMT). No. of bitstreams: 1 Harmonized-in-situ-datasets-for-agricultural-land-use-2021.pdf: 1589665 bytes, checksum: d254577e188593e533093fe816e3fe00 (MD5) Previous issue date: 2021

International audience; The adoption of new cropping practices such as integrated Crop-Livestock systems (iCL) aims at improving the land use sustainability of the agricultural sector in the Brazilian Amazon. The emergence of such integrated systems, based on crop and pasture rotations over and within years, challenges the remote sensing community who needs to implement accurate and efficient methods to process satellite image time series (SITS) in order to come up with a monitoring protocol. These methods generally include a SITS preprocessing step which can be time consuming. The aim of this study is to assess the importance of preprocessing operations such as temporal smoothing and computation of phenological metrics on the mapping of main cropping systems (i.e. pasture, single cropping, double cropping and iCL), with a special emphasis on the iCL class. The study area is located in the state of Mato Grosso, an important producer of agriculture commodities located in the Southern Brazilian Amazon. SITS were composed of a set of 16-day composites of MODIS Vegetation Indices (MOD13Q1 product) covering a one year period between 2014 and 2015. Two widely used classifiers, i.e. Random Forest (RF) and Support Vector Machine (SVM), were tested using five data sets issued from a same SITS but with different preprocessing levels: (i) raw NDVI; (ii) raw NDVI + raw EVI; (iii) smoothed NDVI; (iv) NDVI-derived phenometrics; (v) raw NDVI + phenometrics. Both RF and SVM classification results showed that the "raw NDVI + raw EVI" data set achieved the highest performance (RF OA = 0.96, RF Kappa = 0.94, SVM OA = 0.95, SVM Kappa = 0.93), followed closely by the "raw NDVI" and the "raw NDVI + phenometrics" datasets. The "NDVIderived phenometrics" alone achieved the lowest accuracies (RF OA = 0.58 and SVM OA = 0.66). Considering that the implementation of preprocessing steps is computationally expensive and does not provide significant gains in terms of classification accuracy, we recommend to use raw vegetation indices for mapping cropping practices in Mato Grosso, including the integrated Crop-Livestock systems.

For agronomic, environmental, and economic reasons, the need for spatialized information about agricultural practices is expected to rapidly increase. In this context, we reviewed the literature on remote sensing for mapping cropping practices. The reviewed studies were grouped into three categories of practices: crop succession (crop rotation and fallowing), cropping pattern (single tree crop planting pattern, sequential cropping, and intercropping/agroforestry), and cropping techniques (irrigation, soil tillage, harvest and post-harvest practices, crop varieties, and agro-ecological infrastructures). We observed that the majority of the studies were exploratory investigations, tested on a local scale with a high dependence on ground data, and used only one type of remote sensing sensor. Furthermore, to be correctly implemented, most of the methods relied heavily on local knowledge on the management practices, the environment, and the biological material. These limitations point to future research directions, such as the use of land stratification, multi-sensor data combination, and expert knowledge-driven methods. Finally, the new spatial technologies, and particularly the Sentinel constellation, are expected to improve the monitoring of cropping practices in the challenging context of food security and better management of agro-environmental issues. Fil: Bégué, Agnès. Institut National de la Recherche Agronomique; Francia. Université Montpellier II; Francia Fil: Arvor, Damien. Centre National de la Recherche Scientifique; Francia Fil: Bellon, Beatriz. Institut National de la Recherche Agronomique; Francia. Université Montpellier II; Francia Fil: Betbeder, Julie. Institut National de la Recherche Agronomique; Francia Fil: de Abelleyra, Diego. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires; Argentina. Instituto de Clima y Agua; Argentina Fil: Ferraz, Rodrigo P. D.. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; Brasil Fil: Lebourgeois, Valentine. Institut National de la Recherche Agronomique; Francia Fil: Lelong, Camille. Institut National de la Recherche Agronomique; Francia Fil: Simões, Margareth. Universidade do Estado de Rio do Janeiro; Brasil. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; Brasil Fil: Verón, Santiago Ramón. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires; Argentina

O país vem trabalhando no Plano ABC que incorpora bem o conceito de "intensificação sustentável", por meio da indução à adoção de sistemas de Integração Lavoura-Pecuária-Floresta (ILPF). O plano prevê o incremento da adoção de sistemas ILPF em 4 milhões de hectares até 2020. A ratificação do Acordo de Paris sobre mudança do clima pelo governo brasileiro, em 2016, fortaleceu ações desse plano, incluindo em sua "Contribuição Nacionalmente Determinada- INDC" (Intended Nationally Determined Contribution - INDC) o incremento de mais 5 milhões de hectares até 2030. Os sistemas ILPFs não dizem respeito a um sistema de produção em particular, mas sim a uma estratégia que pode incorporar diversas tecnologias e mesmo diversos sistemas de produção. Como pressuposto básico, preconiza a adoção do Sistema de Plantio Direto (SPD). Made available in DSpace on 2018-01-12T23:14:49Z (GMT). No. of bitstreams: 1 2017042.pdf: 453945 bytes, checksum: d128f51b7fe8a56536c1718ba49c430f (MD5) Previous issue date: 2017-11-23

O presente artigo objetivou apresentar os procedimentos metodológicos e as técnicas de geoprocessamento utilizadas para a extração de dados de bases cartográficas temáticas e de modelos hídricos espacializados requeridos para o cálculo de indicadores de sustentabilidade hídrica da cultura canavieira em quatro Microrregiões do Estado de Goiás: Sudoeste de Goiás, Vale do Rio dos Bois, Quirinópolis e Meia Ponte. Os diversos dados espaciais em formato shape, adquiridos de fontes secundárias e de modelos especificamente elaborados para este fim foram introduzidos em um Sistema de Informações Geográficas (SIG), no qual, por meio de técnicas simples de geoprocessamento, foram ajustados e integrados para a obtenção dos dados (grandezas físicas) utilizados para a determinação de indicadores (índices) propostos pelo Sistema de Indicadores para a Avaliação do Potencial de Sustentabilidade Hídrica da Atividade Canavieira - SISH-Cana (FERRAZ, 2012). Os resultados demonstram a pertinência das técnicas utilizadas e do nível de precisão e detalhamento dos modelos espaciais adotados, considerando que os índices aplicam-se para a avaliação de unidades territoriais de analise em nível de microrregião com vistas a planejamentos territoriais e/ou setoriais em nível estratégico. Made available in DSpace on 2020-09-15T04:40:02Z (GMT). No. of bitstreams: 1 Revista-Geotecnia-artigo-pedogeo.pdf: 3804451 bytes, checksum: 84f467ef524074a8d5df616d4dc9216b (MD5) Previous issue date: 2013