Timely and efficient land-cover mapping is of high interest, especially in agricultural landscapes. Classification based on satellite images over the season, while important for cropland monitoring, remains challenging in subtropical agricultural areas due to the high diversity of management systems and seasonal cloud cover variations. This work presents supervised object-based classifications over the year at 2-month time-steps in a heterogeneous region of 12,000 km2 in the Sao Paulo region of Brazil. Different methods and remote-sensing datasets were tested with the random forest algorithm, including optical and radar data, time series of images, and cloud gap-filling methods. The final selected method demonstrated an overall accuracy of approximately 0.84, which was stable throughout the year, at the more detailed level of classification; confusion mainly occurred among annual crop classes and soil classes. We showed in this study that the use of time series was useful in this context, mainly by including a small number of highly discriminant images. Such important images were eventually distant in time from the prediction date, and they corresponded to a high-quality image with low cloud cover. Consequently, the final classification accuracy was not sensitive to the cloud gap-filling method, and simple median gap-filling or linear interpolations with time were sufficient. Sentinel-1 images did not improve the classification results in this context. For within-season dynamic classes, such as annual crops, which were more difficult to classify, field measurement efforts should be densified and planned during the most discriminant window, which may not occur during the crop vegetation peak.

To meet rising demands for agricultural products, existing agricultural lands must either produce more or expand in area. Yield gaps (YGs)—the difference between current and potential yield of agricultural systems—indicate the ability to increase output while holding land area constant. Here, we assess YGs in global grazed‐only permanent pasture lands using a climate binning approach. We create a snapshot of circa 2000 empirical yields for meat and milk production from cattle, sheep, and goats by sorting pastures into climate bins defined by total annual precipitation and growing degree‐days. We then estimate YGs from intra‐bin yield comparisons. We evaluate YG patterns across three FAO definitions of grazed livestock agroecosystems (arid, humid, and temperate), and groups of animal production systems that vary in animal types and animal products. For all subcategories of grazed‐only permanent pasture assessed, we find potential to increase productivity several‐fold over current levels. However, because productivity of grazed pasture systems is generally low, even large relative increases in yield translated to small absolute gains in global protein production. In our dataset, milk‐focused production systems were found to be seven times as productive as meat‐focused production systems regardless of animal type, while cattle were four times as productive as sheep and goats regardless of animal output type. Sustainable intensification of pasture is most promising for local development, where large relative increases in production can substantially increase incomes or “spare” large amounts of land for other uses. Our results motivate the need for further studies to target agroecological and economic limitations on productivity to improve YG estimates and identify sustainable pathways toward intensification.

Fast and accurate quantification of the available pasture biomass is essential to support grazing management decisions in intensively managed fields. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely sensed data. Here, we assessed the feasibility of using spectral and textural information derived from PlanetScope imagery for estimating pasture aboveground biomass (AGB) and canopy height (CH) in intensively managed fields and the potential for enhanced accuracy by applying the extreme gradient boosting (XGBoost) algorithm. Our results demonstrated that the texture measures enhanced AGB and CH estimations compared to the performance obtained using only spectral bands or vegetation indices. The best results were found by employing the XGBoost models based only on texture measures. These models achieved moderately high accuracy to predict pasture AGB and CH, explaining 65% and 89% of AGB (root mean square error (RMSE) = 26.52%) and CH (RMSE = 20.94%) variability, respectively. This study demonstrated the potential of using texture measures to improve the prediction accuracy of AGB and CH models based on high spatiotemporal resolution PlanetScope data in intensively managed mixed pastures.

No abstract

Grasslands are the largest contributor of nitrous oxide (N2O) emissions in the agriculture sector due to livestock excreta and nitrogen fertilizers applied to the soil. Nitrification inhibitors (NIs) added to N input have reduced N2O emissions, but can show a range of efficiencies depending on climate, soil, and management conditions. A meta-analysis study was conducted to investigate the factors that influence the efficiency of NIs added to fertilizer and excreta in reducing N2O emissions, focused on grazing systems. Data from peer-reviewed studies comprising 2164 N2O emission factors (EFs) of N inputs with and without NIs addition were compared. The N2O EFs varied according to N source (0.0001–8.25%). Overall, NIs reduced the N2O EF from N addition by 56.6% (51.1–61.5%), with no difference between NI types (Dicyandiamide—DCD; 3,4-Dimethylpyrazole phosphate—DMPP; and Nitrapyrin) or N source (urine, dung, slurry, and fertilizer). The NIs were more efficient in situations of high N2O emissions compared with low; the reduction was 66.0% when EF > 1.5% of N applied compared with 51.9% when EF ≤ 0.5%. DCD was more efficient when applied at rates > 10 kg ha−1. NIs were less efficient in urine with lower N content (≤ 7 g kg−1). NI efficiency was negatively correlated with soil bulk density, and positively correlated with soil moisture and temperature. Better understanding and management of NIs can optimize N2O mitigation in grazing systems, e.g., by mapping N2O risk and applying NI at variable rate, contributing to improved livestock sustainability.

Despite the recent discoveries of considerable fossil fuel reserves, Brazil is one of the only great economic and industrial powers with very high amounts of renewable energy in its electricity matrix. Approximately 79.3% of the electric energy supply comes from renewable resources, of which hydroelectric power represents 70.6%. The two primary concerns regarding hydroelectricity are the damage caused to the environment by the construction of dams and the uncertainty of the supply in cases of long drought seasons. This article presents an analysis on the availability and energy exploitation of sugarcane straw and forest residues derived from eucalyptus for decentralized generation using a Geographic Information System–based model. The potential bioelectricity and bioethanol production from sugarcane and eucalyptus biomass in the Administrative Region of Campinas (ARC) is higher than the demand in this region. The results provide guidelines for designing alternatives to the intended Nationally Determined Contributions in Brazil within the scope of the ARC, and they can be used to provide energy empowerment, electric matrix diversification, and new policies that address the residue availability and demand.

Vegetation indices are widely used to monitor crop development and generally used as input data in models to forecast yield. The first step of this study consisted of using monthly . We compared the ability of forecasting crop yield based on correlation maps and crop specific masks. We ran a preliminary regression model to test its ability on yield estimation for four municipalities during the soybean growing season. A regression model was developed for both methodologies to forecast soybean crop yield using leave-one-out cross validation.The Root Mean Squared Error (RMSE) values in the implementation of the model ranged from 0.037 t ha −1 to 0.19 t ha −1 using correlation maps, while for crop specific masks, it varied from 0.21 t ha −1 to 0.35 t ha −1 . The model was able to explain 96 % to 98 % of the variance in estimated yield from correlation maps, while it was able to explain only 2 % to 67 % for crop specific mask approach. The results showed that the correlation maps could be used to predict crop yield more effectively than crop specific masks. In addition, this method can provide an indication of soybean yield prior to harvesting.

Abstract. Pastures are complex land covers with a variety of land use systems. This land cover occupies large areas in the globe and is mainly used for livestock production. Brazil is one of the largest livestock producers and has extensive pasture areas. We analyzed the pasture land cover change of the São Paulo State between the years 2000 to 2015. São Paulo was chosen as study case due to its large industrial and agricultural importance and its expressive land cover changes over past decades. It was analyzed land covers databases generated by the Brazilian Annual Land Use and Land Cover Mapping Project (MapBiomas Project) – Collection 4. Transition matrix was generated to analyze the land cover change during the period. Gain, loss, total change, net change and swap were calculated in terms of area. Total pasture area decreased but continues the largest land cover of the São Paulo State; with 79.5% of persistence in the area. Main changes were from losses of pastures and gains in agriculture. Most of the changes to pasture came from other non vegetated areas and grassland categories. These results demonstrated the relevance of pastures areas in land cover change dynamics to address land use policy and plan future land use scenarios.