Integrating Innovative Sensing and Ground Measurements for Forage Mass Estimation in Rye Pasture

Authors

Pedro Henrique Jota Fernandes, University of Nebraska-LincolnFollow

First Advisor

Yijie Xiong

Second Advisor

Mary E. Drewnoski

Committee Members

James C. MacDonald, Wei-Zhen Liang

Date of this Version

12-2025

Document Type

Thesis

Citation

A thesis presented to the faculty of the Graduate College at the University of Nebraska in partial fulfillment of requirements for the degree of Master of Science

Major: Animal Science

Under the supervision of Professors Yijie Xiong and Mary E. Drewnoski

Lincoln, Nebraska, December 2025

Comments

Copyright 2025, Pedro Henrique Jota Fernandes. Used by permission

Abstract

The United States beef industry relies on forages to provide up to 80% of total lifetime dry matter intake, making accurate forage mass estimation essential for determining stocking rates, improving grazing efficiency, and supporting economic sustainability. Traditional methods to quantify forage mass, however, are time-consuming and labor-intensive. This thesis presents research efforts to achieve accurate forage mass estimation in cereal rye pasture by integrating novel sensing technologies.

Chapter 1 reviews the importance and evolution of forage mass estimation methods, from destructive clipping to indirect approaches based on canopy measurements. It then introduces remote sensing, from proximal tools to drone- and satellite-based imagery as scalable and efficient alternatives for quantifying forage availability. Practical implications of remote sensing for the beef industry are meaningful for informed grazing management and improved decision-making.

Chapter 2 evaluates two proximal sensing tools, Canopeo and the Crop Canopy Image Analyzer (CCIA), which use smartphone-based imagery to estimate forage mass. Using 400 paired forage and image samples, canopy cover derived from these tools was regressed against dried forage mass. Both tools showed strong predictive potential, with CCIA being simpler to operate and exhibited slightly superior performance (R² = 0.78; RMSE = 405 DM kg/ha) compared with Canopeo (R² = 0.72; RMSE = 500 DM kg/ha). Our results demonstrate that low-cost, close-range imagery can provide reliable estimates when properly calibrated.

Chapter 3 extends close-range proximal approach to spaceborne remote sensing using multispectral satellite imagery to assess forage mass across larger areas. Vegetation indices including NDVI, GNDVI and SFDVI were calculated compared with field clipping data, yielding high predictive accuracy (R² ≈ 0.86), which outperformed the traditional indirect method of standing height (R² = 0.44). These findings confirm the potential of satellite sensing to estimate forage mass at the pasture scale.

Collectively, this work demonstrates that proximal and satellite remote sensing can generate accurate forage mass estimates without labor demands of destructive sampling. The findings provide practical pathways for integrating sensing technologies into precision grazing systems, offering producers and scientists rapid and scalable tools to quantify forage availability, optimize stocking decisions, and support more efficient and sustainable beef production.

Advisors: Yijie Xiong and Mary E. Drewnoski