Tools for predicting forage growth in rangelands and economic analyses — A systematic review

Authors: SUBHASHREE, SRINIVASAGAN - North Dakota State University; IGATHINATHANE, CANNAYEN - North Dakota State University; AKYUZ, ADNAN - North Dakota State University; BORHAN, MD - North Dakota State University; Hendrickson, John; Archer, David; Liebig, Mark; Toledo, David; SEDIVEC, KEVIN - North Dakota State University; Kronberg, Scott; Halvorson, Jonathan

Submitted to: Agriculture
Publication Type: Review Article
Publication Acceptance Date: 2/10/2023
Publication Date: 2/15/2023
Citation: Subhashree, S.N., Igathinathane, C., Akyuz, A., Borhan, M., Hendrickson, J.R., Archer, D.W., Liebig, M.A., Toledo, D.N., Sedivec, K., Kronberg, S.L., Halvorson, J.J. 2023. Tools for predicting forage growth in rangelands and economic analyses — A systematic review. Agriculture. 13(2). Article 455. https://doi.org/10.3390/agriculture13020455.
DOI: https://doi.org/10.3390/agriculture13020455

Interpretive Summary: Grasslands cover over 60% of the western US and producers and land managers need better tools for managing these areas. While there have been multiple decision support tools developed to help farmers and ranchers manage their forage and grassland resources, they have not been extensively reviewed. This review focused on reviewing input variables, the underlying models and economic factors relevant to forage and grass production. The most commonly used input was normalized difference vegetation index (NDVI), precipitation and soil moisture. Random forest was the machine learning model most commonly used. Further decision tool development should focus on the use of high-resolution satellites, advanced machine learning and the development of interactive, user-friendly, web-based tools and smartphone applications.

Technical Abstract: 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.