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.

Accurate cropland information is of paramount importance for crop monitoring. This study compares five existing cropland mapping methodologies over five contrasting Joint Experiment for Crop Assessment and Monitoring (JECAM) sites of medium to large average field size using the time series of 7-day 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) mean composites (red and near-infrared channels). Different strategies were devised to assess the accuracy of the classification methods: confusion matrices and derived accuracy indicators with and without equalizing class proportions, assessing the pairwise difference error rates and accounting for the spatial resolution bias. The robustness of the accuracy with respect to a reduction of the quantity of calibration data available was also assessed by a bootstrap approach in which the amount of training data was systematically reduced. Methods reached overall accuracies ranging from 85% to 95%, which demonstrates the ability of 250 m imagery to resolve fields down to 20 ha. Despite significantly different error rates, the site effect was found to persistently dominate the method effect. This was confirmed even after removing the share of the classification due to the spatial resolution of the satellite data (from 10% to 30%). This underlines the effect of other agrosystems characteristics such as cloudiness, crop diversity, and calendar on the ability to perform accurately. All methods have potential for large area cropland mapping as they provided accurate results with 20% of the calibration data, e.g. 2% of the study area in Ukraine. To better address the global cropland diversity, results advocate movement towards a set of cropland classification methods that could be applied regionally according to their respective performance in specific landscapes. (Résumé d'auteur

Understanding regional impacts of recent climate trends can help anticipate how further climate change will affect agricultural productivity. We here used panel models to estimate the contribution of growing season precipitation (P), average temperature (T) and diurnal temperature range (DTR) on wheat, maize and soy yield and yield trends between 1971 and 2012 from 33 counties of the Argentine Pampas. A parallel analysis was conducted on a per county basis by adjusting a linear model to the first difference (i.e., subtracting from each value the previous year value) in yield and first difference in weather variables to estimate crop sensitivity to interannual changes in P, T, and DTR. Our results show a relatively small but significant negative impact of climate trends on yield which is consistent with the estimated crop and county specific sensitivity of yield to interannual changes in P, T and DTR and their temporal trends. Median yield loss from climate trends for the 1971−2012 period amounted to 5.4 % of average yields for maize, 5.1 % for wheat, and 2.6 % for soy. Crop yield gains for this time period could have been 15-20 % higher if climate remained without directional changes in the Pampas. On average, crop yield responded more to trends in T and DTR than in P. Translated into economic terms the observed reductions in maize, wheat, and soy yields due to climate trends in the Pampas would equal $1.1 B using 2013 producer prices. These results add to the increasing evidence that climate trends are slowing yield increase. IntroductionIt is increasingly important to understand the mechanisms responsible for the variability in agricultural production of Argentina as it becomes a significant national and international food

Developing better agricultural monitoring capabilities based on Earth Observation data is critical for strengthening food production information and market transparency. The Sentinel-2 2015,7, mission has the optimal capacity for regional to global agriculture monitoring in terms of resolution (10-20 meter), revisit frequency (five days) and coverage (global). In this context, the European Space Agency launched in 2014 the "Sentinel-2 for Agriculture" project, which aims to prepare the exploitation of Sentinel-2 data for agriculture monitoring through the development of open source processing chains for relevant products. The project generated an unprecedented data set, made of "Sentinel-2 like" time series and in situ data acquired in 2013 over 12 globally distributed sites. Earth Observation time series were mostly built on the SPOT4 (Take 5) data set, which was specifically designed to simulate Sentinel-2. They also included Landsat 8 and RapidEye imagery as complementary data sources. Images were pre-processed to Level 2A and the quality of the resulting time series was assessed. In situ data about cropland, crop type and biophysical variables were shared by site managers, most of them belonging to the "Joint Experiment for Crop Assessment and Monitoring" network. This data set allowed testing and comparing across sites the methodologies that will be at the core of the future "Sentinel-2 for Agriculture" system.