Yield gap analysis in dairy production systems using the mechanistic model LiGAPS-Dairy

Area of Science:

• Animal Science
• Agricultural Engineering
• Dairy Production Systems

Background:

• The yield gap represents the difference between potential and actual milk production in dairy cows.
• Understanding and quantifying this gap is crucial for optimizing dairy farm efficiency and resource utilization.
• Existing models often lack the specificity to accurately analyze yield gaps in dairy production.

Purpose of the Study:

• To develop and evaluate a mechanistic model, Livestock simulator for Generic analysis of Animal Production Systems-Dairy cattle (LiGAPS-Dairy), for dairy cow yield gap analysis.
• To identify the primary factors contributing to the yield gap in Dutch dairy production systems.
• To assess the model's performance in simulating milk production and feed intake.

Main Methods:

• Adapted an existing beef cattle model to create the LiGAPS-Dairy model, incorporating factors like genotype, climate, and feed.
• Parameterized the model for Holstein-Friesian cows and calibrated it using experimental farm data from the Netherlands.
• Evaluated model performance by comparing simulated outputs (milk production, feed intake) against measured data from independent experimental farms.

Main Results:

• The LiGAPS-Dairy model demonstrated acceptable performance, with mean absolute errors of 12% for milk production and 10% for feed intake per lactation.
• The average yield gap was found to be 11% of potential milk production (Yp).
• Key factors limiting milk production (Yl) included feed intake capacity (33%), protein deficiency (25%), initial cow weight (23%), and heat stress (19%).

Conclusions:

• The LiGAPS-Dairy model is a valuable tool for analyzing yield gaps in dairy cows under specific conditions (permanently housed, Dutch environment).
• The model can pinpoint critical periods and factors limiting milk production, guiding targeted interventions.
• Further validation across diverse production systems, countries, and breeds is recommended to broaden its applicability for enhancing resource use efficiency.