Evaluation of alternative algorithms used to simulate pasture intake in grazing dairy cows.

Four algorithms used to simulate pasture intake in grazing dairy cows in a dairy decision support system were proposed and evaluated with data from the literature. The algorithms proposed were: 1) an algorithm combining the approach used in a published model to determine dry matter intake based on neutral detergent fiber intake as a percentage of the BW, energy requirements, pasture availability and a standard supplementation (PIest), 2) the previous algorithm modified to consider the type and amount of supplementation (PIsup), 3) an algorithm which considers the effect of selection of pasture (PIsel), and 4) the combination of algorithms 2 and 3 (PIsupsel). Pasture intake data of 27 grazing experiments from the literature were used to evaluate those algorithms. Two methods of evaluation were used: 1) simple linear regression between reported and simulated values, and 2) analysis of variance for the difference between reported and simulated values considering pasture availability and type of supplementation. The R2 of the linear regression and average proportional bias between reported values and simulated values were 0.24 and 19% for PIest, 0.42, and 23% for PIsup, 0.45 and 2% for PIsel and 0.41 and 10% for PIsupsel. Those results showed that PIsel had the lower variability and the values closer to pasture intake. The algorithm PIsup had low variability but tended to underpredict pasture intake. The algorithm PIest values were closer to reported values for low pasture availability. The modeling results show the influence of pasture selection in grazing systems.

Factors affecting pasture intake and total dry matter intake in grazing dairy cows.

We investigated the most relevant variables for estimating pasture intake and total dry matter (DM) intake in grazing dairy cows using 27 previously published studies. Variables compared were pasture allowance, days in milk, amount of forage, amount of concentrate and total supplementation, pasture allowance and supplementation interaction, fat-corrected milk, body weight (BW), metabolic BW, daily change in BW, percentage of legumes in pasture, neutral detergent fiber (NDF) contents of pasture, and NDF in pasture selected. The variables were selected using stepwise regression analysis for total DM intake and pasture DM intake. Variables selected in the total DM intake regression equation (R2 = 0.95) were pasture allowance, total supplementation, interaction of pasture allowance and supplementation, fat-corrected milk, BW, daily change in BW, percentage of legumes and pasture NDF content. Pasture DM intake regression equation (R2 = 0.90) was similar to total DM intake equation, but supplementation coefficient was negative, showing substitution effect in supplementing grazing cows. The intake of NDF as a percentage of BW was higher than 1.3% when considering NDF content of the pasture allowance. Low pasture allowance groups had values higher than 1.3%.