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Dairy farms with intensive grazing systems combine grazing with supplemental feeding, which can be challenging because an incorrect balance between fresh grass allowance and feed supplementation results in inefficient use of the pasture, lower feed efficiency, and potential decreases in animal production. When estimating fresh grass allowance, we currently do not correct for the formation of rejected patches (RP) surrounding excreta, which can lead to overestimation of the potential fresh grass intake and hampers optimal grazing. In this study, therefore, we aim to quantify the formation of RP in intensive grazing systems and improve the quantification of fresh grass allowance. To do so, we studied 2 grazing systems (i.e., compartmented continuous grazing and strip grazing) that differ in key grazing characteristics, such as pre- and post-grazing heights and period of regrowth. The experiment was performed from April to October in 2016 and 2017 with 60 dairy cows at a fixed stocking rate of 7.5 cows/ha. Average pre-grazing grass height was measured with a rising plate meter. To quantify the formation of RP after grazing, individual grass height measurements were conducted after grazing and classified as RP or not, based on visual assessment. Our analysis showed that the average percentage of grassland covered with RP increased from around 22% at the end of May to around 43% at the end of July/beginning of August, and these percentages do not differ across grazing systems. The percentage of grassland covered with RP should be subtracted from the total grazed area to better estimate true fresh grass allowance., (Copyright © 2019 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The rising plate meter (RPM) is used to measure grass height, which subsequently is used in a calibration equation to estimate herbage mass (HM), an important parameter for optimization of feed management in grazing systems. The RPM is placed on the sward and measures the resistance of the sward toward the plate, which depends not only on grass length, but also on sward structure. The accuracy of the calibration equation for the RPM to estimate HM across grazing systems, however, has not yet been evaluated. Therefore, our aim was to analyze the effect of intensive grazing systems on RPM calibration for perennial ryegrass pastures. To do so, we studied 2 grazing systems: compartmented continuous grazing (CCG) and strip grazing (SG), which differ in key grazing characteristics, such as pre- and post-grazing heights and period of regrowth, that may influence tiller density and vertical flexibility of the sward. The experiment was performed from April until October in 2016 and 2017 with 60 dairy cows, at a fixed stocking rate of 7.5 cows per hectare. To calibrate the RPM, 256 direct measurements of HM >4 cm (i.e., above stubble) were collected by cutting and weighing plots of grass for CCG and SG. Our main interest was in the HM above stubble because this is consumed by cows. Herbage mass <4 cm represents the stubble left after grazing. Differences in HM <4 cm may (partially) explain differences in HM >4 cm between the grazing systems. Therefore, HM <4 cm was additionally measured on 4 out of every 8 plots per grazing system by cutting out quadrats to 0 cm with an electric grass trimmer. Our results showed an average error margin in our calibration equations of 25 to 31%, expressed as the root mean square error of prediction (RMSEP) as a percentage of the observed HM >4 cm. Differences between grazing systems were relatively small, and including grazing system as a factor in the regression model to explain the increase in HM per centimeter of grass did not reduce the RMSEP of the model to any relevant extent. On the other hand, HM <4 cm was significantly greater on CCG compared with SG, with 2,042 kg of DM per hectare for CCG and 1,676 kg of DM per hectare for SG. The HM <4 cm, however, is not used for grazing, and this difference was not reflected in the HM >4 cm. Our results indicate that we can use one region-specific calibration equation for perennial ryegrass pastures across intensive grazing systems, despite relatively large differences in pre- and post-grazing heights and period of regrowth., (Copyright © 2019 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

A whole-farm dairy model was developed and evaluated. The DairyWise model is an empirical model that simulated technical, environmental, and financial processes on a dairy farm. The central component is the FeedSupply model that balanced the herd requirements, as generated by the DairyHerd model, and the supply of homegrown feeds, as generated by the crop models for grassland and corn silage. The output of the FeedSupply model was used as input for several technical, environmental, and economic submodels. The submodels simulated a range of farm aspects such as nitrogen and phosphorus cycling, nitrate leaching, ammonia emissions, greenhouse gas emissions, energy use, and a financial farm budget. The final output was a farm plan describing all material and nutrient flows and the consequences on the environment and economy. Evaluation of DairyWise was performed with 2 data sets consisting of 29 dairy farms. The evaluation showed that DairyWise was able to simulate gross margin, concentrate intake, nitrogen surplus, nitrate concentration in ground water, and crop yields. The variance accounted for ranged from 37 to 84%, and the mean differences between modeled and observed values varied between -5 to +3% per set of farms. We conclude that DairyWise is a powerful tool for integrated scenario development and evaluation for scientists, policy makers, extension workers, teachers and farmers.