The development of a decision support tool is necessary for producers to recognize the conditions that impact growth, yield and nutritional quality of alfalfa, but also to evaluate the anticipated gains after correction of problems. This project aims to develop this digital tool to improve alfalfa production. To do so, artificial intelligence, the expertise of researchers and the participation of 20 advisors specialized in forage production and 80 producers from Quebec and the Maritime provinces will be combined. The application will be easy to use. It will allow the valorization of data on the nutritional value of forages obtained on the farm (by near infrared analysis), the current analysis of soils, the evaluation of the health of a soil profile in the field and finally, the prediction of the nutritional quality and the yield of alfalfa.
This project aims to develop a digital decision support tool to improve the nutritional quality and yeld of alfalfa from analyses of the nutritional quality of forages, based on its relationship with soil fertility and health, while including the other pedoclimatic parameters that define alfalfa production conditions.
From 2020 to 2024
Field crops, Livestock production
Association canadienne pour les plantes fourragères (ACPF) | Coordination des services-conseils (CSC) | Lactanet | Agritel | Laval University | Polytechnique Montréal
Demonstration project to showcase the ability of undersown clover cover crops to reduce nitrogen fertilizer requirements in crops.
Researcher: Marc-Olivier Gasser
Experimenting narrow-row crop weed control strategy on three crops: green beans, peas, and soybeans.
Researcher: Élise Smedbol
Showcase that will present and compare, in an impartial manner, a number of decision-support tools and technologies.
Researcher: Carl Boivin