Premium
Comparing Nondestructive Sampling Techniques for Predicting Forage Mass in Alfalfa–Tall Wheatgrass Pasture
Author(s) -
Baxter Lisa L.,
West Charles P.,
Brown C. Philip,
Green Paul E.
Publication year - 2017
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj2016.12.0738
Subject(s) - forage , sampling (signal processing) , pasture , mathematics , normalized difference vegetation index , environmental science , vegetation (pathology) , agronomy , statistics , leaf area index , remote sensing , computer science , geography , biology , filter (signal processing) , medicine , pathology , computer vision
Core Ideas To compare five nondestructive sampling techniques for predicting forage mass. Procedures: pasture ruler, rising plate meter, ImageJ, PowerPoint photo point count, and normalized difference vegetation index. PowerPoint model was the best option if restricted to one sampling procedure. Combined (Height + ImageJ) model was the best option for predicting forage mass. These measurements require simple equipment, are adaptable, and can be automated.Producers rely on subjective visual assessments to estimate forage mass in their pastures, which often are inaccurate and lead to poor stocking decisions. The objective of this trial was to compare five nondestructive sampling techniques for predicting forage mass in three alfalfa–tall wheatgrass [ Medicago sativa L.; Thinopyrum ponticum (Host) Beauv.] pastures in the southern High Plains. Procedures included canopy height measured with a pasture ruler and rising plate meter (RPM), percentage of green pixels from ImageJ analyses, percentage of green points from photo point count in PowerPoint, and normalized difference vegetation index (NDVI). Height and RPM were linearly regressed on measured forage mass while the remaining were linearly regressed on the natural log of measured forage mass. Considering their limitations, stepwise regression was used to find the best combination of digital and physical procedure (Height + ImageJ) that reduced model error. Calibration models were then applied to external data to determine predictive ability of each procedure. The PowerPoint model was the best, most precise option if restricted to a single sampling procedure, whereas the combined model possessed the superior combination of high R 2 and low model error. The combined and PowerPoint models possessed the highest R 2 pred , but the combined model would be more applicable since it did not saturate when measured forage mass exceeded 1200 kg DM ha −1 . The use of ImageJ with canopy height measurements for forage mass prediction requires simple equipment, lends itself to automation, and is adaptable to various forage systems.