Resumen
Farmers and ranchers depend on annual forage production for grassland livestock enterprises. Many regression and machine learning (ML) prediction models have been developed to understand the seasonal variability in grass and forage production, improve management practices, and adjust stocking rates. Moreover, decision support tools help farmers compare management practices and develop forecast scenarios. Although numerous individual studies on forage growth, modeling, prediction, economics, and related tools are available, these technologies have not been comprehensively reviewed. Therefore, a systematic literature review was performed to synthesize current knowledge, identify research gaps, and inform stakeholders. Input features (vegetation index [VI], climate, and soil parameters), models (regression and ML), relevant tools, and economic factors related to grass and forage production were analyzed. Among 85 peer-reviewed manuscripts selected, Moderating Resolution Imaging Spectrometer for remote sensing satellite platforms and normalized difference vegetation index (NDVI), precipitation, and soil moisture for input features were most frequently used. Among ML models, the random forest model was the most widely used for estimating grass and forage yield. Four existing tools used inputs of precipitation, evapotranspiration, and NDVI for large spatial-scale prediction and monitoring of grass and forage dynamics. Most tools available for forage economic analysis were spreadsheet-based and focused on alfalfa. Available studies mostly used coarse spatial resolution satellites and VI or climate features for larger-scale yield prediction. Therefore, further studies should evaluate the use of high-resolution satellites; VI and climate features; advanced ML models; field-specific prediction tools; and interactive, user-friendly, web-based tools and smartphone applications in this field.