Developing a Decision-Support Tool to Improve the Performance and Sustainability of Cow-Calf Grazing Systems Using Satellite Remote Sensing and Mechanistic Nutrition Models.

Sustainable cow-calf production requires balancing animal performance, economic returns, and environmental impacts under highly variable forage conditions. This study presents a conceptual model, CattleSat, whose decision-support framework integrates satellite-derived forage biomass with mechanistic ruminant nutrition models to simulate the effects of herd size and stocking strategies on animal performance, greenhouse gas emissions, and economic outcomes. A case study simulation using data from a Texas grazing system was conducted to demonstrate the application and behavior of the model under variable herd sizes. Results showed that increasing herd size reduced forage allowance, leading to decreased cow dry matter intake and, consequently, individual animal performance, particularly milk yield and weaning weight, while total calf production exhibited a curvilinear response. Economic outcomes followed similar patterns, with total net return increasing but net return per cow declining as herd size increased. Based on the assumptions and parameterization adopted in this simulation, a critical transition point was identified where system-level profitability and individual efficiency were balanced. Additionally, carbon emission intensity increased at higher stocking rates, indicating reduced environmental efficiency. Overall, forage dynamics were relevant drivers of system variability. These findings highlight the importance of adaptive, data-driven stocking strategies and demonstrate the potential of integrating remote sensing with mechanistic models to improve the sustainability of grazing systems. Future studies and model improvements should be incorporated to expand the applicability of the framework across diverse grazing systems.