Journal Article

Yield From Iowa's First Commercial Miscanthus Fields: Implications of Spatial Variability for Productivity and Sustainability Beyond Research Plots

VanLoocke, Andy, Hedrick, Travis, Emran, Shah‐Al, Varela, Sebastian, Heaton, Emily A., Leakey, Andrew D. B., Petersen, Bryan M., Masters, Michael David, Roney, Heather Elizabeth

2026

Overview

The cultivation of sterile giant miscanthus (Miscanthus × giganteus, M × g) for bioenergy and bioproducts has expanded into grain‐cropped land in the United States (US) as local markets developed for this high‐yielding perennial grass (10–30 Mg DM ha−1). However, the magnitude of spatial and temporal variability in yield within US Corn Belt fields, along with impacts on economic return and sustainable land management, is poorly understood. This study established a diagnostic model relating remote sensing‐derived vegetation indices to ground truth data from 105 hand‐harvested stem biomass samples, which were strategically selected to represent the full range of vegetation index observations. The high‐resolution satellite‐sensed vegetation indices captured > 90% of the yield variation measured within fields. This model was then used to predict yield variability and assess economic performance across four of the first commercial M × g fields in the Corn Belt state of Iowa, US. Significant spatial variability in biomass dry matter (DM) yields (9.3–18.1 Mg DM ha−1) and net profits ( $83 to $ 1211.5 ha−1) was observed. All fields were profitable in all site‐years. When low profit occurred, it was explained by limited management experience of the crop in Iowa. The breakeven yield at a selling price of$130 Mg−1 varied from 9.0–12.1 Mg ha−1 at 15% moisture content (7.6–10.3 Mg DM ha−1). Breakeven prices ranged from $ 73 to $122.4 Mg−1, matching ranges used in the Department of Energy Billion Ton Report (US Department of Energy, 2023). Notably, M × g yield and profits were commensurate with grain crops particularly with favorable precipitation. This study provides insight on the M × g management “learning curve”, performance on marginal land and in drought conditions, and demonstrates that addressing yield gaps, reducing costs, and implementing precision agriculture strategies can enhance profitability. These findings emphasize the value of remote sensing technologies in guiding sustainable and competitive commercial‐scale M × g production. We developed, tested, and used a satellite remote sensing method to predict the productivity and profitability of sterile Miscanthus × giganteus. Using high‐resolution imagery and ground measurements from Iowa's first commercial miscanthus fields, we made a robust model that accurately estimated yield over 8 site‐years. We found strong profitability and identified where better management could further boost returns. This is the first study with actual yield, costs, and returns of commercial miscanthus in the US. It demonstrates that satellite‐based tools can guide efficient and sustainable biomass production, supporting both farmers and industry.

• Publisher: John Wiley & Sons, Inc,Wiley
• Subject: Agricultural production / Algorithms / Artificial intelligence / Biomass / Corn / Corn belt / Drought / Dry matter / Economics / Energy consumption / Federal agencies / Grain / Grain crops / Ground truth / in‐field management / Land management / Learning curves / Machine learning / Miscanthus / miscanthus × giganteus / Moisture content / M × g / potential yield / Precipitation / Precision agriculture / Production costs / Productivity / Profitability / Profits / Remote sensing / Satellites / spatial variability / Sustainability management / Variability / Vegetation / Vegetation index / Water content / yield gap / yield prediction