Hand-held NIR devices used to predict grass % in alfalfa-grass mixtures to improve nutrient management and whole-farm sustainability in dairy systems

View the project final report

Commodities

• Agronomic: grass (misc. perennial), medics/alfalfa

Practices

• Animal Production: feed management, feed rations, feed/forage
• Crop Production: continuous cropping, crop improvement and selection, crop rotation, cropping systems, nutrient management
• Education and Training: decision support system, extension, farmer to farmer, networking, on-farm/ranch research, participatory research, workshop
• Farm Business Management: farm succession, risk management, whole farm planning
• Production Systems: agroecosystems
• Sustainable Communities: sustainability measures

Estimating grass content of mixtures has not yet been attempted on any hand-held NIR instrument. The introduction of near infrared (NIR) technology in a small portable unit has the potential for a cost-effective solution to the problem of evaluating and managing variability in alfalfa-grass composition. The technology could improve the ability for alfalfa-grass producers to optimize field management and reduce variability in dairy rations, resulting in more environmentally and economically sustainable farming systems. Harvest management and rotation of alfalfa-grass stands is often a function of the grass percentage of the mixture, and an on-farm NIR unit can provide a record of stand composition, required for correct nutrient management decisions. This proposal includes a focused evaluation of the Neo Spectra Scanner where the main objective is to collect spectra and develop calibrations of alfalfa-grass compositions using wet, chopped samples. Farmers and farm consultants are very interested in the collection of independent research data on the functionality and feasibility of on-farm hand-held NIRS devices and this project will provide the research needed urgently in the agricultural industry. Outreach activities are essential to disseminate results to the agricultural community and will involve publication of articles in regional farming magazines, presentations at local field days and national conferences, and publication in a peer-reviewed journal.

# 1. Create a database of NIR spectra with the Neo Spectra Scanner, using alfalfa-grass samples with the full range of grass% from 0 to 100%.

# 2. Develop calibrations for the Neo Spectra Scanner for estimating grass percentage in alfalfa-grass fresh mixtures.