Global nitrous oxide emissions from livestock manure during 1890-2020: An IPCC tier 2 inventory.

Nitrous oxide (N2O) emissions from livestock manure contribute significantly to the growth of atmospheric N2O, a powerful greenhouse gas and dominant ozone-depleting substance. Here, we estimate global N2O emissions from livestock manure during 1890-2020 using the tier 2 approach of the 2019 Refinement to the 2006 IPCC Guidelines. Global N2O emissions from livestock manure increased by ~350% from 451 [368-556] Gg N year-1 in 1890 to 2042 [1677-2514] Gg N year-1 in 2020. These emissions contributed ~30% to the global anthropogenic N2O emissions in the decade 2010-2019. Cattle contributed the most (60%) to the increase, followed by poultry (19%), pigs (15%), and sheep and goats (6%). Regionally, South Asia, Africa, and Latin America dominated the growth in global emissions since the 1990s. Nationally, the largest emissions were found in India (329 Gg N year-1), followed by China (267 Gg N year-1), the United States (163 Gg N year-1), Brazil (129 Gg N year-1) and Pakistan (102 Gg N year-1) in the 2010s. We found a substantial impact of livestock productivity, specifically animal body weight and milk yield, on the emission trends. Furthermore, a large spread existed among different methodologies in estimates of global N2O emission from livestock manure, with our results 20%-25% lower than those based on the 2006 IPCC Guidelines. This study highlights the need for robust time-variant model parameterization and continuous improvement of emissions factors to enhance the precision of emission inventories. Additionally, urgent mitigation is required, as all available inventories indicate a rapid increase in global N2O emissions from livestock manure in recent decades.

CROPGRIDS: a global geo-referenced dataset of 173 crops.

CROPGRIDS is a comprehensive global geo-referenced dataset providing area information for 173 crops for the year 2020, at a resolution of 0.05° (about 5.6 km at the equator). It represents a major update of the Monfreda et al. (2008) dataset (hereafter MRF), the most widely used geospatial dataset previously available, covering 175 crops with reference year 2000 at 10 km spatial resolution. CROPGRIDS builds on information originally provided in MRF and expands it using 27 selected published gridded datasets, subnational data of 52 countries obtained from National Statistical Offices, and the 2020 national-level statistics from FAOSTAT, providing more recent harvested and crop (physical) areas for 173 crops at regional, national, and global levels. The CROPGRIDS data advance the current state of knowledge on the spatial distribution of crops, providing useful inputs for modelling studies and sustainability analyses relevant to national and international processes.

Using Random Forest to Improve the Downscaling of Global Livestock Census Data.

Large scale, high-resolution global data on farm animal distributions are essential for spatially explicit assessments of the epidemiological, environmental and socio-economic impacts of the livestock sector. This has been the major motivation behind the development of the Gridded Livestock of the World (GLW) database, which has been extensively used since its first publication in 2007. The database relies on a downscaling methodology whereby census counts of animals in sub-national administrative units are redistributed at the level of grid cells as a function of a series of spatial covariates. The recent upgrade of GLW1 to GLW2 involved automating the processing, improvement of input data, and downscaling at a spatial resolution of 1 km per cell (5 km per cell in the earlier version). The underlying statistical methodology, however, remained unchanged. In this paper, we evaluate new methods to downscale census data with a higher accuracy and increased processing efficiency. Two main factors were evaluated, based on sample census datasets of cattle in Africa and chickens in Asia. First, we implemented and evaluated Random Forest models (RF) instead of stratified regressions. Second, we investigated whether models that predicted the number of animals per rural person (per capita) could provide better downscaled estimates than the previous approach that predicted absolute densities (animals per km2). RF models consistently provided better predictions than the stratified regressions for both continents and species. The benefit of per capita over absolute density models varied according to the species and continent. In addition, different technical options were evaluated to reduce the processing time while maintaining their predictive power. Future GLW runs (GLW 3.0) will apply the new RF methodology with optimized modelling options. The potential benefit of per capita models will need to be further investigated with a better distinction between rural and agricultural populations.

Income Disparities and the Global Distribution of Intensively Farmed Chicken and Pigs.

The rapid transformation of the livestock sector in recent decades brought concerns on its impact on greenhouse gas emissions, disruptions to nitrogen and phosphorous cycles and on land use change, particularly deforestation for production of feed crops. Animal and human health are increasingly interlinked through emerging infectious diseases, zoonoses, and antimicrobial resistance. In many developing countries, the rapidity of change has also had social impacts with increased risk of marginalisation of smallholder farmers. However, both the impacts and benefits of livestock farming often differ between extensive (backyard farming mostly for home-consumption) and intensive, commercial production systems (larger herd or flock size, higher investments in inputs, a tendency towards market-orientation). A density of 10,000 chickens per km2 has different environmental, epidemiological and societal implications if these birds are raised by 1,000 individual households or in a single industrial unit. Here, we introduce a novel relationship that links the national proportion of extensively raised animals to the gross domestic product (GDP) per capita (in purchasing power parity). This relationship is modelled and used together with the global distribution of rural population to disaggregate existing 10 km resolution global maps of chicken and pig distributions into extensive and intensive systems. Our results highlight countries and regions where extensive and intensive chicken and pig production systems are most important. We discuss the sources of uncertainties, the modelling assumptions and ways in which this approach could be developed to forecast future trajectories of intensification.

Mapping global cropland and field size.

A new 1 km global IIASA-IFPRI cropland percentage map for the baseline year 2005 has been developed which integrates a number of individual cropland maps at global to regional to national scales. The individual map products include existing global land cover maps such as GlobCover 2005 and MODIS v.5, regional maps such as AFRICOVER and national maps from mapping agencies and other organizations. The different products are ranked at the national level using crowdsourced data from Geo-Wiki to create a map that reflects the likelihood of cropland. Calibration with national and subnational crop statistics was then undertaken to distribute the cropland within each country and subnational unit. The new IIASA-IFPRI cropland product has been validated using very high-resolution satellite imagery via Geo-Wiki and has an overall accuracy of 82.4%. It has also been compared with the EarthStat cropland product and shows a lower root mean square error on an independent data set collected from Geo-Wiki. The first ever global field size map was produced at the same resolution as the IIASA-IFPRI cropland map based on interpolation of field size data collected via a Geo-Wiki crowdsourcing campaign. A validation exercise of the global field size map revealed satisfactory agreement with control data, particularly given the relatively modest size of the field size data set used to create the map. Both are critical inputs to global agricultural monitoring in the frame of GEOGLAM and will serve the global land modelling and integrated assessment community, in particular for improving land use models that require baseline cropland information. These products are freely available for downloading from the http://cropland.geo-wiki.org website.

Mapping the global distribution of livestock.

Livestock contributes directly to the livelihoods and food security of almost a billion people and affects the diet and health of many more. With estimated standing populations of 1.43 billion cattle, 1.87 billion sheep and goats, 0.98 billion pigs, and 19.60 billion chickens, reliable and accessible information on the distribution and abundance of livestock is needed for a many reasons. These include analyses of the social and economic aspects of the livestock sector; the environmental impacts of livestock such as the production and management of waste, greenhouse gas emissions and livestock-related land-use change; and large-scale public health and epidemiological investigations. The Gridded Livestock of the World (GLW) database, produced in 2007, provided modelled livestock densities of the world, adjusted to match official (FAOSTAT) national estimates for the reference year 2005, at a spatial resolution of 3 minutes of arc (about 5×5 km at the equator). Recent methodological improvements have significantly enhanced these distributions: more up-to date and detailed sub-national livestock statistics have been collected; a new, higher resolution set of predictor variables is used; and the analytical procedure has been revised and extended to include a more systematic assessment of model accuracy and the representation of uncertainties associated with the predictions. This paper describes the current approach in detail and presents new global distribution maps at 1 km resolution for cattle, pigs and chickens, and a partial distribution map for ducks. These digital layers are made publically available via the Livestock Geo-Wiki (http://www.livestock.geo-wiki.org), as will be the maps of other livestock types as they are produced.