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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.

The impact of land cover change across the planet continues to necessitate accurate methods to detect and monitor evolving processes from satellite imagery. In this context, regional and global land cover mapping over time has largely treated time as independent and addressed temporal map consistency as a post-classification endeavor. However, we argue that time can be better modeled as codependent during the model classification stage to produce more consistent land cover estimates over long time periods and gradual change events. To produce temporally-dependent land cover estimates—meaning land cover is predicted over time in connected sequences as opposed to predictions made for a given time period without consideration of past land cover—we use structured learning with conditional random fields (CRFs), coupled with a land cover augmentation method to produce time series training data and bi-weekly Landsat imagery over 20 years (1999–2018) across the Southern Cone region of South America. A CRF accounts for the natural dependencies of land change processes. As a result, it is able to produce land cover estimates over time that better reflect real change and stability by reducing pixel-level annual noise. Using CRF, we produced a twenty-year dataset of land cover over the region, depicting key change processes such as cropland expansion and tree cover loss at the Landsat scale. The augmentation and CRF approach introduced here provides a more temporally consistent land cover product over traditional mapping methods.

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.

Land transformation is one of the most significant human changes on the Earth’s surface processes. Therefore, land use land cover time series are a key input for environmental monitoring, natural resources management, territorial planning enforcement at national scale. We here capitalize from the MapBiomas initiative to characterize land use land cover (LULC) change in the Gran Chaco between 2010 and 2017. Specifically we sought to a) quantify annual changes in the main LULC classes; b) identify the main LULC transitions and c) relate these transitions to current land use policies. Within the MapBiomas project, Landsat based annual maps depicting natural woody vegetation, natural herbaceous vegetation, dispersed natural vegetation, cropland, pastures, bare areas and water. We used Random Forest machine learning algorithms trained by samples produced by visual interpretation of high resolution images. Annual overall accuracy ranged from 0,73 to 0,74. Our results showed that, between 2010 and 2017, agriculture and pasture lands increased ca. 3.7 Mha while natural forestry decreased by 2.3 Mha. Transitions from forests to agriculture accounted for 1.14% of the overall deforestation while 86% was associated to pastures and natural herbaceous vegetation. In Argentina, forest loss occurred primarily (39%) on areas non considered by the territorial planning Law, followed by medium (33%), high (19%) and low (9%) conservation priority classes. These results illustrate the potential contribution of remote sensing to characterize complex human environmental interactions occurring over extended areas and timeframes.

The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that the WCM can accurately estimate LAI if the model is effectively calibrated. However, calibration of this model requires access to field measures of LAI as well as soil moisture. In contrast, machine learning (ML) algorithms can be trained to estimate LAI from satellite data, even if field moisture measures are not available. In this study, a support vector machine (SVM) was trained to estimate the LAI for corn, soybeans, rice, and wheat crops. These results were compared to LAI estimates from the WCM. To complete this comparison, in situ and satellite data were collected from seven Joint Experiment for Crop Assessment and Monitoring (JECAM) sites located in Argentina, Canada, Germany, India, Poland, Ukraine and the United States of America (U.S.A.). The models used C-Band backscatter intensity for two polarizations (like-polarization (VV) and cross-polarization (VH)) acquired by the RADARSAT-2 and Sentinel-1 SAR satellites. Both the WCM and SVM models performed well in estimating the LAI of corn. For the SVM, the correlation (R) between estimated LAI for corn and LAI measured in situ was reported as 0.93, with a root mean square error (RMSE) of 0.64 m2m−2 and mean absolute error (MAE) of 0.51 m2m−2. The WCM produced an R-value of 0.89, with only slightly higher errors (RMSE of 0.75 m2m−2 and MAE of 0.61 m2m−2) when estimating corn LAI. For rice, only the SVM model was tested, given the lack of soil moisture measures for this crop. In this case, both high correlations and low errors were observed in estimating the LAI of rice using SVM (R of 0.96, RMSE of 0.41 m2m−2 and MAE of 0.30 m2m−2). However, the results demonstrated that when the calibration points were limited (in this case for soybeans), the WCM outperformed the SVM model. This study demonstrates the importance of testing different modeling approaches over diverse agro-ecosystems to increase confidence in model performance.

Global environmental problems such as climate change are not bounded by national borders or scientific disciplines, and therefore require international, interdisciplinary teamwork to develop understandings of their causes and solutions. Interdisciplinary scientific work is difficult enough, but these challenges are often magnified when teams also work across national boundaries. The literature on the challenges of interdisciplinary research is extensive. However, research on international, interdisciplinary teams is nearly non-existent. Our objective is to fill this gap by reporting on results from a study of a large interdisciplinary, international National Science Foundation Partnerships for International Research and Education (NSF-PIRE) research project across the Americas. We administered a structured questionnaire to team members about challenges they faced while working together across disciplines and outside of their home countries in Argentina, Brazil, and Mexico. Analysis of the responses indicated five major types of barriers to conducting interdisciplinary, international research: integration, language, fieldwork logistics, personnel and relationships, and time commitment. We discuss the causes and recommended solutions to the most common barriers. Our findings can help other interdisciplinary, international research teams anticipate challenges, and develop effective solutions to minimize the negative impacts of these barriers to their research.

1. Desertification is of critical concern because it may affect 40% of the global land area inhabited by more than 1 billion people. During the process of desertification, defined as land degradation in arid, semi‐arid and dry subhumid areas, drylands shift to a state of reduced biological productivity that may lead to widespread loss of human well‐being. Despite recent advances, we need a better understanding of the response of ecosystems to desertification to improve the assessment and monitoring of desertification. 2. We used a published physiognomic description, MODIS monthly NDVI data for 2000-2005 and rain gauge data to characterize the long‐term effects of degradation for an area of 128 000 ha located in western Patagonia. 3. We focused on three aspects of vegetation dynamics: radiation interception, precipitation use efficiency (PUE) and the sensitivity of vegetation to interannual changes in precipitation (i.e. the slope of the relationship between the above‐ground net primary productivity and precipitation, the precipitation marginal response, PMR). In particular, we analysed the response of PMR and PUE to long‐term changes in vegetation structure due to grazing. 4. On average, NDVI decreased by 28%, ranging between 35% (grass or grass-shrub steppes to semi‐deserts) and 22% (grass or grass‐shrub steppes to low cover grass steppes) suggesting that, in Patagonia, desertification may imply a reduction in the above‐ground net primary productivity. 5. Additionally, PMR and PUE captured the functional modifications associated with vegetation structure caused by desertification. In general, grass and grass‐shrub steppes had the highest average PUE and PMR. Shrub steppes and semi‐deserts had the lowest PMR and PUE. These results support the hypothesis that PUE is more sensitive to changes in total plant cover and PMR to changes in plant functional type composition. 6. Synthesis and applications. Our results indicate that the precipitation marginal response could complement current desertification assessments based only on precipitation use efficiency thereby improving our ability to monitor desertification. Enhanced monitoring programmes could provide an early warning signal for the onset of desertification allowing for timely management action.

The description of the relationship between temperature (T) and electricity consumption (EC) is key to improving our understanding of a potential climate change amplification feedback and, thus, energy planning. We sought to characterize the relationship between the EC and daily T of different regions of Argentina and use these historical relationships to estimate expected EC under T future scenarios. We used a time series approach to model EC, removing trends and seasonality and accounting for breaks and discontinuities. EC and T data were obtained from Argentine Wholesale Market Administrator Company and global databases, respectively. We evaluate the T-EC model for the period between 1997 and 2014 and two sub-periods: 1997–2001 and 2011–2014. We use modeled temperature projections for the 2027–2044 period based on the Representative Pathway Concentration 4.5 together with our region-specific T-EC models to predict changes in EC due to T changes. The shape of the T-EC relationships is quite stable between periods and regions but varies according to the temperature gradient. We find large increases in EC in warm days (from 40 to 126 Wh/cap/°C) and a region-specific response to cold days (from flat to steep responses). The T at which EC was at minimum varies between 14 and 20 °C and increase in time as mean daily T also increase. Estimated temperature projections translate into an average increase factor of 7.2 in EC with contrasting relative importance between regions of Argentina. Results highlight potential sensitivity of EC to T in the developing countries.

Increased rates of sexually transmitted infections (STIs) have been reported among men who have sex with men. We aimed to assess whether post-exposure prophylaxis (PEP) with doxycycline could reduce the incidence of STIs. All participants attending their scheduled visit in the open-label extension of the ANRS IPERGAY trial in France (men aged 18 years or older having condomless sex with men and using pre-exposure prophylaxis for HIV with tenofovir disoproxil fumarate plus emtricitabine) were eligible for inclusion in this open-label randomised study. Participants were randomly assigned (1:1) at a central site to take a single oral dose of 200 mg doxycycline PEP within 24 h after sex or no prophylaxis. The primary endpoint was the occurrence of a first STI (gonorrhoea, chlamydia, or syphilis) during the 10-month follow-up. The cumulative probability of occurrence of the primary endpoint was estimated in each group with the Kaplan-Meier method and compared with the log-rank test. The primary efficacy analysis was done on the intention-to-treat population, comprising all randomised participants. All participants received risk-reduction counselling and condoms, and were tested regularly for HIV. This trial is registered with ClinicalTrials.gov number, NCT01473472. Between July 20, 2015, and Jan 21, 2016, we randomly assigned 232 participants (n=116 in the doxycycline PEP group and n=116 in the no-PEP group) who were followed up for a median of 8·7 months (IQR 7·8–9·7). Participants in the PEP group used a median of 680 mg doxycycline per month (IQR 280–1450). 73 participants presented with a new STI during follow-up, 28 in the PEP group (9-month probability 22%, 95% CI 15–32) and 45 in the no-PEP group (42%, 33–53; log-rank test p=0·007). The occurrence of a first STI in participants taking PEP was lower than in those not taking PEP (hazard ratio [HR] 0·53; 95% CI 0·33–0·85; p=0·008). Similar results were observed for the occurrence of a first episode of chlamydia (HR 0·30; 95% CI 0·13–0·70; p=0·006) and of syphilis (0·27; 0·07–0·98; p=0·047); for a first episode of gonorrhoea the results did not differ significantly (HR 0·83; 0·47–1·47; p=0·52). No HIV seroconversion was observed, and 72 (71%) of all 102 STIs were asymptomatic. Rates of serious adverse events were similar in the two study groups. Gastrointestinal adverse events were reported in 62 (53%) participants in the PEP group and 47 (41%) in the no-PEP group (p=0·05). Doxycycline PEP reduced the occurrence of a first episode of bacterial STI in high-risk men who have sex with men. France Recherche Nord & Sud Sida-HIV Hépatites (ANRS) and Bill & Melinda Gates Foundation.

Degradation processes often lead to species loss. Such losses would impact on ecosystem functioning depending on the extinction order and the functional and structural aspects of species. For the Patagonian arid steppe, we used a simulation model to study the effects of species loss on the rate and variability (i.e. stability) of transpiration as a key attribute of ecosystem functioning. We addressed (1) the differences between the overgrazing extinction order and other potential orders, and (2) the role of biomass abundance, biomass distribution, and functional diversity on the effect of species loss due to overgrazing. We considered a community composed of ten species which were assigned an order of extinction due to overgrazing based on their preference by livestock. We performed four model simulations to test for overgrazing effects through different combinations of species loss, and reductions of biomass and functional diversity. In general, transpiration rate and variability were positively associated to species richness and remained fairly constant until half the species were lost by overgrazing. The extinction order by overgrazing was the most conservative of all possible orders. The amount of biomass was more important than functional diversity in accounting for the impacts of species richness on transpiration. Our results suggest that, to prevent Patagonian steppes from shifting to stable, low-production systems (by overgrazing), maintaining community biomass is more important than preserving species richness or species functional diversity.