Tools for Predicting Forage Growth in Rangelands and Economic Analyses—A Systematic Review

Srinivasagan N. Subhashree, C. Igathinathane (), Adnan Akyuz, Md. Borhan, John Hendrickson, David Archer, Mark Liebig, David Toledo, Kevin Sedivec, Scott Kronberg and Jonathan Halvorson
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Srinivasagan N. Subhashree: Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA
C. Igathinathane: Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA
Adnan Akyuz: North Dakota State Climate Office, North Dakota State University, Fargo, ND 58102, USA
Md. Borhan: Texas Department of Transportation, Brownwood, TX 76802, USA
John Hendrickson: Northern Great Plains Research Laboratory, USDA-ARS, Mandan, ND 58554, USA
David Archer: Northern Great Plains Research Laboratory, USDA-ARS, Mandan, ND 58554, USA
Mark Liebig: Northern Great Plains Research Laboratory, USDA-ARS, Mandan, ND 58554, USA
David Toledo: Northern Great Plains Research Laboratory, USDA-ARS, Mandan, ND 58554, USA
Kevin Sedivec: School of Natural Resource Sciences—Range Program, North Dakota State University, Fargo, ND 58108, USA
Scott Kronberg: Northern Great Plains Research Laboratory, USDA-ARS, Mandan, ND 58554, USA
Jonathan Halvorson: Northern Great Plains Research Laboratory, USDA-ARS, Mandan, ND 58554, USA

Agriculture, 2023, vol. 13, issue 2, 1-30

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.

Keywords: decision support tools; economic analysis; machine learning; modeling; remote sensing; systematic literature review (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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