We use and develop spatial data for livestock, nutrient production, and agricultural systems at multiple resolutions (low to high) and spatial extent (regional, national, and global). Spatial data are important to us because they help us better understand the drivers of agriculture at the local and global scale. Spatial data analysis is a principal component of many of our projects that address decision making at multiple locations.
We use spatial data to investigate nutrient production and nutrient diversity. For example, we produced global maps of nutrient production and nutrient diversity for work by Herrero et al. (2017). We also use spatial data to map multiple cropping systems and mixed crop-livestock systems at a global scale: see our work on Agricultural systems classification and mapping for impact and vulnerability studies.
We produce and continually update global livestock datasets containing information on biomass use, production, feed efficiency, excretion, and greenhouse gas emissions. For example, Herrero et al. (2013) presented a unique, biologically consistent, spatially disaggregated livestock dataset for 28 regions, eight livestock production systems, four animal species (cattle, small ruminants, pigs, and poultry), and three livestock products (milk, meat, and eggs). As underlying information, we used the livestock demographics for 2010 (Nicolas et al. 2016; Robinson et al. 2014) and updated global distribution of livestock production systems (Gilbert et al. 2015). These data provide critical information for developing targeted, sustainable solutions for challenges facing agricultural systems.
Contact Mario Herrero for more information.