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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

O GeoInfo é um repositório que disponibiliza os dados de pesquisa espacial gerados pela Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) para compreender a dinâmica da agricultura no território brasileiro. Considerando os esforços necessários para a implantação e institucionalização do repositório, a ausência de estudos da Ciência da Informação relacionados à investigação do impacto dos usos feitos a partir dos dados e informações disponibilizados em repositórios de dados de pesquisa, bem como o processo de avaliação de impactos utilizado pela EMBRAPA desde 1989, é oportuno investigar o impacto do repositório para seu público-alvo, qual seja, especialistas de geociências. Assim, o objetivo deste trabalho é apresentar os resultados de uma avaliação de impactos realizada com o GeoInfo, a partir da metodologia - Ambitec-TICs: Módulo de critérios e indicadores de impactos para Tecnologias de Informação e Comunicação -, especificamente para a dimensão social, existente no Ambitec-TICs. Made available in DSpace on 2021-05-06T02:11:16Z (GMT). No. of bitstreams: 1 5899.pdf: 589235 bytes, checksum: 4985b2b0753250e89f4c9a3f1e10121d (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.

Human pressure on ecosystems has undesirable impacts on human well-being. After the Millennium Project, much interdisciplinary research has been developed worldwide aiming to understand these impacts on ecosystem flows and processes, and to learn about the costs and the benefits of ecosystem services for production. Soil provides many ecosystem services, since its multi-functionality is the basis for food production, water filtration, nutrient cycling, and other goods essential to life. This article presents the main concepts and classifications of soil ecosystem services and of its functions; the indicators and the methods for assessment, modeling, and valuation of ecosystem services; some recent applications to assess and evaluate impacts of agricultural management practices on soil ecosystem services; as well as challenges and opportunities for research and for development of public policies related to agro-environmental sustainability in Brazil. Although the role of soil in supplying ecosystem services is yet undervalued, scientists are gradually recognizing soil processes and functions as fundamental to assess ecosystem services and the effects of land use and management on them. Interdisciplinary approaches to integrate science and public policies are necessary to build governance based on ecosystem services. Resumo A pressão humana sobre os serviços ecossistêmicos tem resultado em impactos indesejáveis sobre o bem-estar humano. Com o Projeto Millennium, várias pesquisas interdisciplinares têm sido desenvolvidas em todo o mundo com o objetivo de entender esses impactos sobre os fluxos e os processos dos ecossistemas e internalizar os custos e os benefícios dos serviços ecossistêmicos para a produção. O solo fornece muitos serviços ecossistêmicos, uma vez que sua multifuncionalidade é a base para a produção de alimentos, filtração de água, ciclagem de nutrientes e outros bens essenciais à vida. Este artigo apresenta os principais conceitos e classificações dos serviços ecossistêmicos do solo e de suas funções; os indicadores e os métodos de avaliação, modelagem e valoração dos serviços ecossistêmicos; algumas aplicações recentes para avaliar impactos de práticas de manejo agrícola sobre os serviços ecossistêmicos do solo; bem como os desafios e as oportunidades para a pesquisa e para o desenvolvimento de políticas públicas relacionadas à sustentabilidade agroambiental no Brasil. Apesar de o papel do solo para prestação de serviços ecossistêmicos ainda ser subestimado, os cientistas têm gradualmente reconhecido os processos e as funções do solo como fundamentais para avaliar os serviços ecossistêmicos e os efeitos do uso e manejo da terra sobre eles. Abordagens interdisciplinares que integrem ciência e políticas públicas são necessárias para construir uma governança com base em serviços ecossistêmicos.

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

Resumo. A geoinformação é essencial para o planejamento e monitoramento das atividades agropecuárias, justificando o emprego de esforços para reuni-la e padronizá-la de acordo com as diretrizes governamentais e possibiltando sua disponibilização à sociedade em geral. O objetivo deste trabalho é apresentar a experiência da Embrapa em construir sua Infraestrutura de Dados Espaciais, denominada GeoInfo, uma iniciativa para organizar, preservar, documentar e ofertar dados geoespaciais abertos produzidos nas pesquisas da empresa, ampliando o potencial de aplicação dessa informação na produção e difusão de conhecimento e inovação. O GeoInfo implementa os padrões da Infraestrutura Nacional de Dados Espaciais e viabiliza a interoperabilidade de dados geoespaciais provenientes de diferentes fontes, inclusive sob o aspecto semântico. Essa iniciativa possibilita a integração das informações geoespaciais produzidas na Embrapa com diversas informações disponíveis em nosso país e permite que a redundância de esforços na obtenção e produção de dados geoespaciais sejam evitados. Made available in DSpace on 2017-12-21T23:35:59Z (GMT). No. of bitstreams: 1 GeoInfo.pdf: 1278892 bytes, checksum: 2cd5fb0bd47329844a25d32038e1f36b (MD5) Previous issue date: 2017-12-21 Suplemento. Trabalhos apresentados na 8a Conferência Luso Brasileira sobre Acesso Aberto - ConfOA. Na publicação: Davi Oliveira Custódio, Bibiana Teixeira Almeida, Margareth Simões, Gustavo Bayma-Silva.

Apresenta uma breve discussão sobre como as diversas tecnologias de sensores remotos, modelos computacionais ou digitais podem estar a serviço da sustentabilidade, e ainda como tais modelos são úteis em avaliar impactos de políticas públicas voltadas à conservação dos solos e eficiência da produção agrícola do Brasil. Made available in DSpace on 2018-01-12T23:15:13Z (GMT). No. of bitstreams: 1 2017044.pdf: 367133 bytes, checksum: 1fd0268c3aa123f637de39fae87207dc (MD5) Previous issue date: 2017-11-23

En région tropicale, où les précipitations sont principalement d'origines convectives et où les réseaux d'observations au sol présentent de nombreuses lacunes, les données satellitaires TRMM (Tropical Rainfall Measuring Mission) représentent une alternative efficace pour l'étude de la variabilité spatiotemporelle des précipitations. Celle-ci est abordée au moyen d'une analyse en composantes principales des données TRMM 3B42 (au pas de temps mensuel) de 1998 à 2006 qui permet de mettre en évidence la variabilité saisonnière et interannuelle des pluies du Mato Grosso (Brésil), situé au sud du bassin amazonien. Les produits TRMM 3B42 sont ensuite validés à partir d'indices statistiques pour permettre de définir une résolution temporelle optimale qui autorise l'analyse de paramètres tels que le début, la fin et la durée de la saison pluvieuse, décisifs pour les cultures réalisées dans cette région, notamment pour celle du soja. Pour calculer ces paramètres, la méthode « Anomalous Accumulation » proposée par Liebmann (2006) est choisie. Le début de la saison des pluies est repérée au mois d'octobre mais gagne plus tardivement le sud-est que le nord-ouest. La date de fin des pluies intervient au mois de mars et progresse par contre vers le nord selon un axe N-S. La durée de la saison des pluies est en conséquence de 4 à 5 mois. Il apparaît que la variabilité de la durée de la saison des pluies est surtout liée aux variations des dates de début des pluies (plus de 2 mois de différence entre 2 années extrêmes sur 8 ans), alors que la fin de la saison des pluies est bien plus régulière. Par ailleurs, le début de la saison des pluies varie de plus de 15 jours en moyenne entre les grandes régions de production de soja, ce qui permet de comprendre les variantes dans leurs calendriers agricoles et donne des premières indications sur la vulnérabilité des producteurs. In tropical regions where convective rainfall is predominant while raingauge networks are deficient, satellite estimates as proposed by the Tropical Rainfall Measuring Mission (TRMM) can represent an efficient alternative way of studying rainfall spatiotemporal variability. A Principal Component Analysis based on monthly data allows mapping the various rainy regimes encountered in the south Amazonian state of Mato Grosso (Brazil). The TRMM 3B42 products are then validated based on statistical indexes. It permits determining an optimal 5-days temporal scale, which is then used to calculate rainy season parameters such as date of onset and end and duration. Those parameters are established based on the "Anomalous Accumulation" method proposed by Liebmann (2006). The date of onset occurs in October and occurs later in the south-east than in the north-west. The date of end is detected in March and progresses according to a N-S orientation. Consequently, the rainy season duration lasts from four to five months. It appears that the duration variability is especially linked to the onset variability (more than 2 months between 2 extreme years in 8 years) while the end of the rainy season is much more regular. Moreover, the onset date displays a 15 days amplitude between the main soybean productions regions. Thus, it explains the various agricultural calendars applied in Mato Grosso and help explaining the farmer's vulnerability. Made available in DSpace on 2022-03-27T07:05:11Z (GMT). No. of bitstreams: 1 Apport-des-donnees-TRMM-3B42-a-letude-des-precipitations-au-Mato-Grosso-2008.pdf: 2763228 bytes, checksum: a9ac5550d4da1975914eac1f4cb3ece0 (MD5) Previous issue date: 2008

This paper describes a methodology for systematic land use/land cover classification on a regional scale, with emphasis on a low cost and highly automatized approach. This methodology is based on multitemporal analyses of surface reflectance data from the Moderate Resolution Imaging Spectroradiometer (MODIS), which is located on board NASA's Terra and Aqua satellites and features high temporal frequency, extensive coverage, and extremely low costs for data acquisition. A sequence of automatized procedures were developed for MODIS data pre-processing, as well as for the training and execution of a supervised classification algorithm, where temporal profiles are fitted to smooth polynomial curves and intelligent curve features are then computed in order to reduce data dimensionality and improve profile interpretability, thus providing a more robust classification approach. A case study was performed in the High Taquari Basin, in the states of Mato Grosso do Sul and Mato Grosso, Brazil, which showed that the method was indeed capable of generalizing well over the entire region of study (over 25,000km2), effectively discriminating between areas of agriculture, pasture, and savannah. The methodology was also seen to be quite successful in identifying areas of deforestation, which is of particular interest for the monitoring of land use and land use change in the region. Made available in DSpace on 2021-10-15T02:07:20Z (GMT). No. of bitstreams: 1 Regional-scale-land-use-2006.pdf: 263717 bytes, checksum: 1fdc8481372cbad5b2691dd1bda21d45 (MD5) Previous issue date: 2006