The performance of an efficient dairy system using a combination of nitrogen leaching mitigation strategies in a variable climate.

An efficient dairy system, that implemented a combination of nitrogen (N) leaching mitigation strategies including lower N fertilizer input, standing cows off pasture for part of the day in autumn and winter (stand-off), and importing limited amounts of low protein supplements was evaluated over four consecutive years of a farmlet study. This efficient system consistently demonstrated a lower measured annual N leaching of 40 to 50% compared with a baseline system representing current practice with no mitigations. To maximize return from this system fewer cows but of higher genetic merit were used resulting in an average decrease in milk production of 2% and operating profit by 5% compared with the baseline system. The magnitude of the N leaching reduction from mitigation strategies was predicted in pre-trial modelling. Using similar mechanistic models in a post-trial study, we were able to satisfactorily predict the trends in the observed N leaching data over the four years. This enabled us to use the calibrated models to explore the contributions of the different mitigation strategies to the overall leaching reduction in the efficient system. In one of the years half of the leaching reduction was achieved by the 'input' component of the strategy (less feed N flowing through the herd from lower fertilizer use, less grass grown, and low-protein supplement use), while the other half was achieved by the stand-off strategy. However, these contributions are determined by the weather of a particular year. We estimate that on average stand-off would contribute 60% and 'input' 40% to the reduction. The implication is that farmers facing nutrient loss limitations have some current and some future technologies available to them for meeting these limitations. A shift towards the mitigations described here can result in a downward trend in their own N-loss metrics. The challenge will be to negate any reductions in production and profit, and remain competitive.

Short communication: grazing pattern of dairy cows that were selected for divergent residual feed intake as calves.

The aim of this study was to investigate and assess differences in the grazing pattern of 2 groups of mature dairy cows selected as calves for divergent residual feed intake (RFI). Sixteen Holstein-Friesian cows (471±31kg of body weight, 100 d in milk), comprising 8 cows selected as calves (6-8 mo old) for low (most efficient: CSCLowRFI) and 8 cows selected as calves for high (least efficient: CSCHighRFI) RFI, were used for the purpose of this study. Cows (n=16) were managed as a single group, and strip-grazed (24-h pasture allocation at 0800h) a perennial ryegrass sward for 31 d, with measurements taken during the last 21 d. All cows were equipped with motion sensors for the duration of the study, and jaw movements were measured for three 24-h periods during 3 random nonconsecutive days. Measurements included number of steps and jaw movements during grazing and rumination, plus fecal particle size distribution. Jaw movements were analyzed to identify bites, mastication (oral processing of ingesta) during grazing bouts, chewing during rumination, and to calculate grazing and rumination times for 24-h periods. Grazing and walking behavior were also analyzed in relation to the first meal of the day after the new pasture was allocated. Measured variables were subjected to multivariate analysis. Cows selected for low RFI as calves appeared to (a) prioritize grazing and rumination over idling; (b) take fewer steps, but with a higher proportion of grazing steps at the expense of nongrazing steps; and (c) increase the duration of the first meal and commenced their second meal earlier than CSCHighRFI. The CSCLowRFI had fewer jaw movements during eating (39,820 vs. 45,118 for CSCLowRFI and CSCHighRFI, respectively), more intense rumination (i.e., 5 more chews per bolus), and their feces had 30% less large particles than CSCHighRFI. These results suggest that CSCLowRFI concentrate their grazing activity to the time when fresh pasture is allocated, and graze more efficiently by walking and masticating less, hence they are more efficient grazers than CSCHighRFI.

Incorporating a prediction of postgrazing herbage mass into a whole-farm model for pasture-based dairy systems.

The DairyNZ whole-farm model (WFM; DairyNZ, Hamilton, New Zealand) consists of a framework that links component models for animal, pastures, crops, and soils. The model was developed to assist with analysis and design of pasture-based farm systems. New (this work) and revised (e.g., cow, pasture, crops) component models can be added to the WFM, keeping the model flexible and up to date. Nevertheless, the WFM does not account for plant-animal relationships determining herbage-depletion dynamics. The user has to preset the maximum allowable level of herbage depletion [i.e., postgrazing herbage mass (residuals)] throughout the year. Because residuals have a direct effect on herbage regrowth, the WFM in its current form does not dynamically simulate the effect of grazing pressure on herbage depletion and consequent effect on herbage regrowth. The management of grazing pressure is a key component of pasture-based dairy systems. Thus, the main objective of the present work was to develop a new version of the WFM able to predict residuals, and thereby simulate related effects of grazing pressure dynamically at the farm scale. This objective was accomplished by incorporating a new component model into the WFM. This model represents plant-animal relationships, for example sward structure and herbage intake rate, and resulting level of herbage depletion. The sensitivity of the new version of the WFM was evaluated and then the new WFM was tested against an experimental data set previously used to evaluate the WFM and to illustrate the adequacy and improvement of the model development. Key outputs variables of the new version pertinent to this work (milk production, herbage dry matter intake, intake rate, harvesting efficiency, and residuals) responded acceptably to a range of input variables. The relative prediction errors for monthly and mean annual residual predictions were 20 and 5%, respectively. Monthly predictions of residuals had a line bias (1.5%), with a proportion of square root of mean square prediction error (RMSPE) due to random error of 97.5%. Predicted monthly herbage growth rates had a line bias of 2%, a proportion of RMSPE due to random error of 96%, and a concordance correlation coefficient of 0.87. Annual herbage production was predicted with an RMSPE of 531 (kg of herbage dry matter/ha per year), a line bias of 11%, a proportion of RMSPE due to random error of 80%, and relative prediction errors of 2%. Annual herbage dry matter intake per cow and hectare, both per year, were predicted with RMSPE, relative prediction error, and concordance correlation coefficient of 169 and 692kg of dry matter, 3 and 4%, and 0.91 and 0.87, respectively. These results indicate that predictions of the new WFM are relatively accurate and precise, with a conclusion that incorporating a plant-animal relationship model into the WFM allows for dynamic predictions of residuals and more realistic simulations of the effect of grazing pressure on herbage production and intake at the farm level without the intervention from the user.

The relationship between milk production and farm-gate nitrogen surplus for the Waikato region, New Zealand.

As the scope and scale of New Zealand (NZ) dairy farming increases, farmers and the industry are being challenged by Government and the New Zealand public to address growing environmental concerns. Dairying has come under increasing scrutiny from local authorities tasked with sustainable resource management. Despite recent efforts of farmers and industry to improve resource use efficiency, there is increasing likelihood of further regulatory constraints on water use and nutrient management. This study uses available data on farm-gate nitrogen (N) surpluses and milk production from the Waikato, New Zealand's largest dairying region, together with a farm scale modeling exercise, to provide a perspective on the current situation compared to dairy farms in Europe. It also aims to provide relevant guidelines for N surpluses and efficiencies under NZ conditions. Waikato dairy farms compare favorably with farms in Europe in terms of N use efficiency expressed as L milk/kg farm-gate N surplus. Achievable and realistic good practice objectives for Waikato dairy farmers could be 15,000 L milk/ha (1200 kg milk fat plus protein/ha) with a farm-gate N surplus of 100 kg/ha giving an eco-efficiency (L milk/kg N surplus) of 150, and long-term average nitrate leaching losses of approximately 25-30 kg/ha/yr. This can be achieved by increasing the N conversion efficiency through lower replacement rates (16 versus 22%), lower stocked (< 3 cows/ha) high genetic merit cows (30 L milk/day at peak) milked for longer (277 versus 240 days), feeding effluent-irrigated, home-grown, low-protein supplements to cows on high-protein, grass-clover pastures to dilute N concentration in the diet, removing some of the urinary N from the paddocks during critical times by standing cows on a loafing pad for part of the day, and through lower N fertilizer rates (50-70 kg/ha/yr compared to the norm of 170-200 kg/ha/yr) and using a nitrification inhibitor and gibberellins to boost pasture growth and the former to reduce N leaching.

Technical note: a simple model to estimate changes in dietary composition of strip-grazed cattle during progressive pasture defoliations.

Methodological problems occur in measuring herbage intake and diet quality during short-term (4-24h) progressive defoliations by grazing. Several models were developed to describe pasture component selection by grazing ruminants, particularly sheep. These models contain empirical coefficients to determine preferences that require laborious and data-demanding calibration. The objective was to develop a simple and practical model of changes in diet composition (green:dead) of pastures strip-grazed by dairy cows. The model was based on 3 premises when cows are strip-grazed in relatively homogeneous swards: 1) cows eat dead material only when green leaf and uncontaminated material have been removed; 2) dead material increases toward the bottom of the sward canopy; and 3) cows progressively defoliate pasture in layers. The main simplification in this model was assuming a linear decrease of green mass from the top to the bottom of the sward canopy. Thus, the proportion of green mass in the stratum eaten depended on the proportion of green in the entire sward canopy and its vertical profile. The model offers a simple solution to estimate changes in dietary compositions in pastures strip-grazed by dairy cattle during progressive pasture defoliations. It uses 2 inputs, the green mass proportion of the total herbage mass and the proportion of total herbage mass eaten during grazing. This can be optionally complemented with inputs of herbage chemical composition. The main outputs of the model are the proportions of green and dead herbage mass in the diet. For example, if the green proportion in the sward was 0.5 and the proportion of herbage mass eaten was 0.5, then the diet would be 0.75 green:0.25 dead; assuming 0.8 and 0.4 digestibility for green and dead material, respectively, the diet digestibility would be 0.7.

Restricting time at pasture: effects on dairy cow herbage intake, foraging behavior, hunger-related hormones, and metabolite concentration during the first grazing session.

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.