Sward Factors Influence on Pasture Dry Matter Intake of Grazing Dairy Cows: A Review

Resumen

Successful pasture-based milk production systems pivot on balancing dairy cows’ feed requirements with seasonal and annual fluctuations in pasture production. In order to maximise cow production from grazing dairy systems, it is necessary to reach an efficient utilization of grazed grass for feeding cows and the development of appropriate grazing management systems designed to maximize daily pasture dry matter intake (PDMI) per cow and per hectare, while maintaining high sward quality over the grazing season by keeping high pasture levels of crude protein, water soluble carbohydrates and digestibility of organic matter and low levels of acid and neutral detergent fibers in the swards. To maximize PDMI, cows need to consume plants that have characteristics that allow rapid consumption and lead to fast rates of passage through the rumen. This review considers the role of sward factors which affect the short-term feed budget of cows at pasture and, therefore, condition cow feed requirements at grazing and influence on PDMI. Furthermore, it highlights the relevance that havefor the development of pasture-based milk production systems the study of the species of grasses and / or legumes that integrate the pastures, the changes on sward structure, the seasonality in grass production, the pasture chemical constituents, the sward botanical composition, the grass feeding value, the digestibility of pastures, the intensity of sward defoliation, and the importance of sward height and herbage mass in relation to maximizing PDMI. The amount of herbage consumed is the major determinant of cow production but it is yet one of the most difficult aspects of forage quality to predict. In this review, three methods for PDMI calculation are presented as faecal output/diet digestibility method, sward difference method and the grazing-behaviour method. Also, three equations for PDMI estimation are described considering different sward and animal variables.