Evaluating Remote Sensing Resolutions and Machine Learning Methods for Biomass Yield Prediction in Northern Great Plains Pastures

Srinivasagan N. Subhashree, C. Igathinathane (), John Hendrickson, David Archer, Mark Liebig, Jonathan Halvorson, Scott Kronberg, David Toledo and Kevin Sedivec
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Srinivasagan N. Subhashree: Department of Agricultural and Biosystems Engineering, North Dakota State University, 1231 Albrecht Boulevard, Fargo, ND 58102, USA
C. Igathinathane: Department of Agricultural and Biosystems Engineering, North Dakota State University, 1231 Albrecht Boulevard, Fargo, ND 58102, USA
John Hendrickson: Northern Great Plains Research Laboratory, USDA-ARS, 1701 10th Avenue SW, Mandan, ND 58554, USA
David Archer: Northern Great Plains Research Laboratory, USDA-ARS, 1701 10th Avenue SW, Mandan, ND 58554, USA
Mark Liebig: Northern Great Plains Research Laboratory, USDA-ARS, 1701 10th Avenue SW, Mandan, ND 58554, USA
Jonathan Halvorson: Northern Great Plains Research Laboratory, USDA-ARS, 1701 10th Avenue SW, Mandan, ND 58554, USA
Scott Kronberg: Northern Great Plains Research Laboratory, USDA-ARS, 1701 10th Avenue SW, Mandan, ND 58554, USA
David Toledo: Northern Great Plains Research Laboratory, USDA-ARS, 1701 10th Avenue SW, Mandan, ND 58554, USA
Kevin Sedivec: Central Grasslands Research Extension Center, 4824 48th Ave SE, Streeter, ND 58483, USA

Agriculture, 2025, vol. 15, issue 5, 1-24

Abstract: Predicting forage biomass yield is critical in managing livestock since it impacts livestock stocking rates, hay procurement, and livestock marketing strategies. Only a few biomass yield prediction studies on pasture and rangeland exist despite the need. Therefore, this study focused on developing a biomass yield prediction methodology through remote sensing satellite imagery (multispectral bands) and climate data, employing open-source software technologies. Biomass ground truth data were obtained from local pastures, where Kentucky bluegrass is the predominant species among other forages. Remote sensing data included spatial bands (6), vegetation indices (30), and climate data (16). The top-ranked features (52 tested) from recursive feature elimination (RFE) were short-wave infrared 2, normalized difference moisture index, and average turf soil temperature in the machine learning (ML) model developed. The random forest (RF) model produced the highest accuracy ( R 2 = 0.83 ) among others tested for biomass yield prediction. Applications of the developed methodology revealed that (i) the methodology applies to other unseen pasters ( R 2 = 0.79 ), (ii) finer satellite spatial resolution (e.g., CubeSat; 3 m) better-predicted pasture biomass, and (iii) the methodology successfully developed for a combination of Kentucky bluegrass and other forages, extended to high-value alfalfa hay crop with excellent yield prediction accuracy ( R 2 = 0.95 ). The developed methodology of RFE for feature selection and RF for biomass yield modeling is recommended for biomass and hay forage yield prediction.

Keywords: biomass; climate; forage; machine learning; modeling; remote sensing (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: 2025
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