In 2020, global agricultural emissions were 16 billion tonnes of carbon dioxide equivalent, according to the Food and Agriculture Organization of the United Nations (FAO) and other FAO data shows that cattle – including meat and milk – contribute around 3.8 billion tonnes of carbon dioxide equivalent. Increasing the efficiency and output of cattle grazing (like increasing milk production or a larger number of animals) without adding a larger environmental footprint is a key goal in reducing these emissions.
In a 2024 paper Pixels to pasture: Using machine learning and multispectral remote sensing to predict biomass and nutrient quality in tropical grasslands published in the international journal Remote Sensing Applications: Society and Environment, researchers from the University of Glasgow and the Alliance of Bioversity International and CIAT lay out a how-to guide on taking information from satellites and using predictive models to evaluate grazing pastures in terms of quantity (how much biomass) and quality (crude protein, digestibility, and ash content).
Juan Andrés Cardoso Arango, a co-author of the paper and a plant eco-physiologist focusing on tropical forages at the Alliance of Bioversity International and CIAT, explains that today, analyzing all the factors that determine quantity and quality are hard to scale: using a small drone, you can only sample nine hectares or so at a time and even less with hand-held instruments.
