Irrigation is an important human activity that may impact local and regional climate, but current climate model simulations and data assimilation systems generally do not explicitly include it. The European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) shows more irrigation signal in surface evapotranspiration (ET) than the Modern-Era Retrospective Analysis for Research and Applications (MERRA) because ERA-Interim adjusts soil moisture according to the observed surface temperature and humidity while MERRA has no explicit consideration of irrigation at the surface. But, when compared with the results from a hydrological model with detailed considerations of agriculture, the ET from both reanalyses show large deficiencies in capturing the impact of irrigation. Here, a back-trajectory method is used to estimate the contribution of irrigation to precipitation over local and surrounding regions, using MERRA with observation-based corrections and added irrigation-caused ET increase from the hydrological model. Results show substantial contributions of irrigation to precipitation over heavily irrigated regions in Asia, but the precipitation increase is much less than the ET increase over most areas, indicating that irrigation could lead to water deficits over these regions. For the same increase in ET, precipitation increases are larger over wetter areas where convection is more easily triggered, but the percentage increase in precipitation is similar for different areas. There are substantial regional differences in the patterns of irrigation impact, but, for all the studied regions, the highest percentage contribution to precipitation is over local land.

There is a growing concern about limited water supply and water scarcity in many river basins across the world. The agricultural sector is the largest user of freshwater on the planet, with a growing amount of water extracted for livestock systems. Here, we use data from the GLEAM model to advance previous studies that estimated livestock water footprints by quantifying water use for feed production, animal drinking water, and animal service water. We additionally account for the role of trade in accounting for feed water allocations to different animals in different countries and make use of a hydrologic model to estimate feed irrigation water requirements for individual crops at a high spatial resolution. Lastly, we estimate the contribution of livestock water abstractions to water stress at a small river basin scale for the entire globe. We find that feed production water accounts for the majority (>90%) of global livestock water withdrawals, though there is regional variation. Similarly, we find large regional variation in the water consumption per head by livestock species. Despite consuming >200 km³ of water per year, we find that reducing water use in the livestock system alone will rarely reduce water stress in high-stress basins. This study highlights the need for quantifying locally relevant water use and water stress metrics for individual livestock systems.

Peste des petits ruminants (PPR) is a burdensome viral disease primarily affecting small ruminants, which is currently targeted for eradication by 2030 through the implementation of a Global Control and Eradication Strategy (PPR GCES). The PPR GCES, launched in 2015, has strongly encouraged countries to participate in Regional PPR Roadmaps, designated according to the Food and Agricultural Organization of the United Nations (FAO) and World Organisation for Animal Health (WOAH) regions and epidemiological considerations, with each targeted by dedicated meetings and activities. Following the conclusion of the first phase of the PPR Global Eradication Program (PPR GEP) (2017–2021), the present work focuses on the disease situation and status of the eradication campaign in the fourteen countries of the PPR GCES Middle Eastern Roadmap as well as Egypt. PPR is endemic to or suspected to be present in most of the region, except for Bahrain, which, as of 2021, is preparing to apply for official recognition as being free of PPR. Some substantial shortcomings are observed in surveillance and disease reporting, as well as in the implemented control strategies, most notably vaccination. Since many of these limitations are shared by many of the investigated countries, the international cooperation and harmonization of control efforts appears crucial to making PPR eradication attainable in the Middle East.

Purpose Animal emissions account for nearly 60% of total greenhouse gas emissions from the livestock sector. To estimate these emissions, the Food and Agriculture Organization of the United Nations (FAO) developed a dedicated module within the Global Livestock Environmental Assessment Model (GLEAM). Although previous studies have explored selected inputs for specific animals and emission types, a comprehensive analysis of all 92 inputs (parameters and emission factors) had not been conducted. This study aimed to identify the most influential inputs affecting ruminant emissions in GLEAM. Methods Using global data from GLEAM to build representative samples, a one-at-a-time (OAT) sensitivity analysis was conducted by varying each input individually while holding the others constant. Parameters-specific ranges were defined, and sensitivity was assessed using regression coefficients for methane, nitrous oxide, and their sum as total emissions. Results Sensitivity was determined for 70 of the 92 inputs, based on a high R² between each input and the predicted emissions. Three parameters: gross energy of the diet, diet digestibility, and age at first calving, were the most influential with a negative correlation to animal emission, with diet digestibility emerging as the most sensitive. In contrast, parameters related to animal weight and two emissions factors: the methane producing capacity of manure (Bo) and urinary energy as a fraction of gross energy (UE), were the most influential with a positive correlation, mainly due to their impact on methane, which accounts for nearly 90% of total animal emissions. Nitrous oxide emissions were highly sensitive and positively correlated with the nitrogen content of the diet, while showing moderate sensitivity with a positive correlation to the emission factors for direct N₂O emissions from manure (EF3), for nitrogen volatilization and redeposition (EF4) and for N₂O from leaching/runoff (EF5). Regarding manure management systems, methane emissions were most affected and positively correlated with manure managed in liquid systems, while nitrous oxide emissions were most influenced with a positive correlation to manure managed as dry lot and deep litter. In contrast, changing manure management to compost, burned for fuel, or daily spreading showed the greatest potential to reduce animal emissions. Conclusions The study identified the most and least influential parameters and emission factors based on individual effects but did not evaluate interactions between them. The findings support prioritizing data quality improvements for the most influential inputs while using default values for less influential ones, helping to improve the accuracy and efficiency of livestock emission assessments.

Improved rangeland grazing could mitigate climate change through carbon dioxide (CO ₂ ) sequestration in soils and vegetation. However, altering grazing practices to increase ecosystem carbon storage may also decrease livestock production and/or increase greenhouse gas emissions through the supply chain, such that the net emissions impacts remain unclear. Here, we assess the global net mitigation potential of improving grazing intensity by quantifying potential CO ₂ sequestration alongside systems-level impacts of plant productivity changes, livestock emissions, feed requirements, and production constraints. Improving grazing intensity in global rangelands could sequester 2.2 ± 0.43 gigatons of carbon dioxide equivalent (Gt/CO ₂ eq) per year in the near term, but maintaining livestock production through supplemental feeding would reduce net mitigation by 2 to 31% (to 1.8 ± 0.45 GT/CO ₂ eq per year). Our results suggest that neglecting systems-level emissions impacts may substantially overestimate the global climate benefits of improved grazing.

The paper describes the production and evaluation of annual livestock densities and headcounts of cattle, horses, sheep, goats and buffaloes (including 95% probability prediction intervals) at 1~km spatial resolution for the 2000--2022 period using spatiotemporal machine learning. A compilation of subnational livestock census data has been imported, harmonized and used as reference data (55,336 census polygons and 939,257 individual data entries; covering 147 countries) to build predictive models. A large stack of multi-source harmonized raster data sets (128 individual layers) were used as features. Models were fitted using scikit-map and scikit-learn libraries with recursive feature elimination and Poisson criteria to represent the distribution of the target variable. Intermediate rasters estimating potential land for livestock production based on grassland and cropland extent, along with biophysical features, were used to estimate the spatial domain of livestock. The final predictions at 1~km were further adjusted to annual headcounts based on FAOSTAT national statistics to ensure consistency. Model benchmarking based on 10% test samples (with spatial blocking) shows that Random Forest outperforms Gradient Boosting Tree for predicting livestock densities, with CCC values of 0.603, 0.547, 0.598, 0.622, 0.689, and RMSE values of 104.59, 6.06, 64.09, 67.57, 30.37 (heads per km2) for cattle, horses, sheep, goats and buffaloes. Feature importance analysis shows that the key variables include climate and socio-economic layers, such as water vapour, aridity index, land surface temperature, travel time to the nearest cities, and religious population distribution. Further evaluation of the output layers shows similar distributions to existing global livestock products (FAO Gridded Livestock of The World --- GLW, and Annual Gridded Livestock of the World — AGLW). The spatial domain of livestock (active grazing/forage areas) is often difficult to validate, with many countries having very specific management cultures that can not be seamlessly represented using existing global raster layers, hence modeling distribution of livestock per country using local country-specific features (instead of using global models) could help increase accuracy, specially for regional/local applications. The modeling pipeline is open source and available on Github (https://github.com/wri/global-pasture-watch) with output layers (both original ML predictions and FAOSTAT-adjusted values) publicly available under CC-BY license on Zenodo (https://doi.org/10.5281/zenodo.17491242).