This study investigated the effect of restricting grazing time on circulating concentrations of ghrelin, nonesterified fatty acids (NEFA), and glucose before, and foraging behavior of dairy cows during, the first grazing session of the day (GS, 0800-1200 h). Forty-eight Holstein-Friesian cows (470 +/- 47 kg of BW; 35 +/- 9 d in milk) were strip-grazed on a perennial ryegrass pasture for either 4 h after each milking (2 x 4), 8 h between milkings (1 x 8), or the 24-h period excluding milking times (CTL). Cows were bled before the GS; plasma was analyzed for ghrelin and serum for glucose and NEFA. Herbage mass was measured pregrazing (0730 h), during and at the end of the GS (1200 h), and postgrazing (24 h after the first measurement). Herbage mass data were fitted to a model to estimate herbage disappearance rates. Herbage intake and bite mass were calculated using herbage mass disappearance and behavioral measurements. Bite rate, eating, searching, ruminating, and idling time were determined during the GS for each cow. No difference in glucose concentration was found between treatments. Concentrations of NEFA and ghrelin were the greatest for cows in the 1 x 8 treatment. Daily herbage intake did not differ between treatments; however, during the GS 1 x 8 had a greater herbage intake than 2 x 4 and CTL. Bite mass differed between treatments and throughout the GS. Bite mass was smallest for CTL during the first 60 min and greatest during the last 90 min, when cows in the 2 x 4 treatment had the smallest bite mass. Cows in 1 x 8 spent the longest time eating and the least time searching and ruminating. Eating time was greatest for 1 x 8 during the first 60 and last 90 min of the GS. Searching time only differed in the second 60 min, when it was the lowest for 1 x 8. Cows from all treatments did not ruminate during the first 120 min. Cows in CTL had the greatest rumination time during the last 90 min. The model fitted to represent dynamics of herbage mass disappearance presented differences in the fractional herbage disappearance rate. There was an interaction between treatment and time in herbage depletion rate. The results of this study present a fuller picture of foraging dynamics during the first 4 h of grazing and its potential relationship with physiological markers of hunger as affected by grazing management.

Decision support models have been developed to assist management in dairy systems. This paper describes Farmax Dairy Pro (a pastoral grazing model of a dairy farm) and presents an evaluation of it using two independent farmlet studies carried out in Hamilton and Palmerston North, New Zealand with spring-calving dairy cows. Farmax Dairy Pro predicted, to a high degree of accuracy, mean annual yields (per cow and per hectare) for milk, fat, protein and milksolids (MS; fat'protein) and mean annual concentrations of MS. Monthly predictions were predicted with less accuracy than whole lactation values, but still with moderate degrees of accuracy compared with other comparable models. The general trajectory over time of yield and MS concentration was predicted well for all datasets, but in some instances the model over or under predicted the degree of variation between months. The trajectory of body condition score over time was reliably simulated in early lactation but with some discrepancies in late lactation. The model was then used to determine if it was possible to achieve 1750 kg MS/cow per ha using forages grown within the milking area for the Hamilton study. Managerial changes represented in the model, which included earlier calving dates, use of a chicory crop and additional intakes of pasture in summer, predicted increases in performance of 50Á190 kg MS/ha, still at least 81 kg MS/ha short of the target level of production. Farmax Dairy Pro can be used to predict animal, farm and financial performance for different management scenarios.

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On New Zealand dairy farms, it is the nitrogen excreted directly onto pasture, particularly urine, that drives nitrogen (N) leaching from the farm. A new framework (UPF: Urine Patch Framework) is presented that post-processes the results of a whole farm model and runs a mechanistic soil model to simulate the urine patches. Two alternative methods to simulate the spatial distribution of urine patches were implemented and compared (Grid: spatially explicit, and Probabilistic: based on the probability of different temporal urination patterns). This paper describes the implementation of these two methods in connection with a Whole Farm Model; and compares the N leaching predictions with observed data. Two examples are provided, one analyzing the impact of urine patch overlap and another, the relative risk of N leaching at different times of urinary N deposition. The model showed good correlation and predictive ability between simulated annual N leaching results and observed data [R 2 = 94 %, mean relative prediction error (MRPE) = 10 % for Grid and R 2 = 72 %, MRPE = 20 % for Probabilistic]. The two methods produced similar results across an 8-year period for monthly and annual N leaching (R 2 = 96 %, MRPE = 10 % and R 2 = 86 %, MRPE = 8 %; respectively). Only 8 % of the paddock area was covered with multiple urinations during 1 year, but as much as 39 % of the total urine volume was deposited on overlapped patches. Systematically removing all urinary N for 1 month in either May or June reduced N leaching by approximately 20 %. Avoiding urinary N deposition during autumn or early winter could be highly effective in mitigating N leached during the following winter.

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