Meta-Regression to Develop Predictive Equations for Urinary Nitrogen Excretion of Lactating Dairy Cows.

Dairy cows' urinary nitrogen (N) excretion (UN; g/d) represents a significant environmental concern due to their contribution to nitrate leaching, nitrous oxide (a potent greenhouse gas), and ammonia emissions (contributor to N deposition). The first objective of the current study was to determine the adequacy of existing models to predict UN from total mixed ration (TMR)-fed and fresh forage (FF)-fed cows. Next, we aimed to develop equations to predict UN based on animal factors [milk urea nitrogen (MUN; mg/dL) and body weight (BW, kg)] and to explore how these equations are improved when dietary factors, such as diet type, dry matter intake (DMI), or dietary characteristics [neutral detergent fiber (NDF) and crude protein (CP) content], are considered. A dataset was obtained from 51 published experiments composed of 174 treatment means. The whole dataset was used to evaluate the mean and linear biases of three existing equations including diet type as an interaction term; all models had significant linear and mean biases and two of the three models had poor predictive capabilities as indicated by their large relative prediction error (RPE; root mean square error of prediction as a percent of the observed mean). Next, the complete data set was split into training and test sets, which were used to develop and to evaluate new models, respectively. The first model included MUN and BW, and there was a significant interaction between diet type and the coefficients. This model had the worst 1:1 agreement [Lin's concordance correlation coefficient (CCC) = 0.50] and largest RPE (24.7%). Models that included both animal and dietary factors performed the best, and when included in the model, the effect of diet type was no longer significant (p > 0.10). These models all had very good agreement (CCC ≥ 0.86) and relatively low RPE (≤13.1%). This meta-analysis developed precise and accurate equations to predict UN from dairy cows in both confined and pasture-based systems.

Review and update of a Nutrient Transfer model used for estimating nitrous oxide emissions from complex grazed landscapes, and implications for nationwide accounting.

In New Zealand, nitrous oxide emissions from grazed hill pastures are estimated using different emission factors for urine and dung deposited on different slope classes. Allocation of urine and dung to each slope class needs to consider the distribution of slope classes within a landscape and animal behavior. The Nutrient Transfer (NT) model has recently been incorporated into the New Zealand Agricultural GHG Inventory Model to account for the allocation of excretal nitrogen (N) to each slope class. In this study, the predictive ability of the transfer function within the NT model was explored using urine deposition datasets collected with urine sensor and GPS tracker technology. Data were collected from three paddocks that had areas in low (<12°), medium (12-24°), and high slopes (>24°). The NT model showed a good overall predictive ability for two of the three datasets. However, if the urine emission factors (% of urine N emitted as N2 O-N) were to be further disaggregated to assess emissions from all three slope classes or slope gradients, more precise data would be required to accurately represent the range of landscapes found on farms. We have identified the need for more geospatial data on urine deposition and animal location for farms that are topographically out of the range used to develop the model. These new datasets would provide livestock urine deposition on a more continuous basis across slopes (as opposed to broad ranges), a unique opportunity to improve the performance of the NT model.

The Nutrient Transfer model allocates urine from grazing livestock to different slope classes. The predictive ability of the model was explored using urine sensor and tracker data of grazing livestock. The model showed a good overall predictive ability for two of the three datasets explored. There is a need for more geospatial urine deposition and animal location data on complex land.

Challenges and opportunities to capture dietary effects in on-farm greenhouse gas emissions models of ruminant systems.

This paper reviews existing on-farm GHG accounting models for dairy cattle systems and their ability to capture the effect of dietary strategies in GHG abatement. The focus is on methane (CH4) emissions from enteric and manure (animal excreta) sources and nitrous oxide (N2O) emissions from animal excreta. We identified three generic modelling approaches, based on the degree to which models capture diet-related characteristics: from 'none' (Type 1) to 'some' by combining key diet parameters with emission factors (EF) (Type 2) to 'many' by using process-based modelling (Type 3). Most of the selected on-farm GHG models have adopted a Type 2 approach, but a few hybrid Type 2 / Type 3 approaches have been developed recently that combine empirical modelling (through the use of CH4 and/or N2O emission factors; EF) and process-based modelling (mostly through rumen and whole tract fermentation and digestion). Empirical models comprising key dietary inputs (i.e., dry matter intake and organic matter digestibility) can predict CH4 and N2O emissions with reasonable accuracy. However, the impact of GHG mitigation strategies often needs to be assessed in a more integrated way, and Type 1 and Type 2 models frequently lack the biological foundation to do this. Only Type 3 models represent underlying mechanisms such as ruminal and total-tract digestive processes and excreta composition that can capture dietary effects on GHG emissions in a more biological manner. Overall, the better a model can simulate rumen function, the greater the opportunity to include diet characteristics in addition to commonly used variables, and thus the greater the opportunity to capture dietary mitigation strategies. The value of capturing the effect of additional animal feed characteristics on the prediction of on-farm GHG emissions needs to be carefully balanced against gains in accuracy, the need for additional input and activity data, and the variability encountered on-farm.

Soil carbon stocks under grazed pasture and pasture-tree systems.

There is increasing interest in the potential of trees to sequester carbon (C) in above- and below-ground stocks to mitigate against increasing concentrations of greenhouse gases (GHG). This study determined whether pasture-tree (PT) systems influence soil C stocks compared with open pasture (OP) by sampling four sites with trees aged 14 to16 years. Poplars (Populus spp.) at Tikokino and Woodville and alders (Alnus spp.) at Poukawa and Ruakura were planted on contrasting soils (Haplustands, Endoaquepts, Durustalfs and Humaquepts, respectively). Trees at all four sites were arranged in partial-Nelder radial planting designs, with five stem densities ranging from 67 to 1276 stems ha-1. Soils were sampled at five stem density classes, along with adjacent OP areas in the same paddock, to a depth of 1 m (0-75, 75-150, 150-300, 300-600, 600-1000 mm). At three of the four sites, root mass density was greater (P < 0.05) in PT than in OP systems. At Woodville, estimates of total soil C mass to 1 m tended to be greater (P = 0.08) in the OP than in the PT system (200 vs. 163 Mg C ha-1, respectively), whereas no significant differences in total soil C masses between OP and PT were shown at the remaining sites (P > 0.10). Despite the limited statistical significance, estimates of total soil C mass at Tikokino and Woodville (sites with poplars) were 11 and 18% greater in OP than in PT systems, whereas estimates at Poukawa and Ruakura (sites with alders) were 2 and 6% greater in PT than in OP systems. Under the current conditions, our study suggests that tree species may be an additional factor influencing the C cycle and C accumulation in soils and need to be considered in the building of our soil C inventories.

Evaluation of a sheep rumen model with fresh forages of diverse chemical composition.

The sheep rumen submodel MollyRum14 was evaluated on its methane and VFA predictions against data from respiration-chamber trials conducted with sheep fed perennial ryegrass, white clover, chicory, forage rape, turnip (leafy and bulb varieties), swedes, kale, or forage radish. We assessed the model's response to substrate degradation rate (settings that affect the rate of cellulose and hemicellulose digestion) and to fermentation stoichiometry (settings that alter nonglucogenic to glucogenic short-chain fatty acid ratios). Model predictions were evaluated against data for methane production (pCH4: g/d), methane yield (yCH4: g/kg DMI), and acetate to propionate ratio (A:P). The predictive ability of the model for both pCH4 and yCH4 was superior for perennial ryegrass than for other forages. Except for swedes and chicory, predictions for yCH4 were correctly ranked across the forages evaluated. Except for forage rape, robust predictions were obtained for all forages using fast degradation kinetics and a predominantly acetogenic stoichiometry. Model predictions for forage rape were enhanced using slow degradation kinetics and a predominantly propionic stoichiometry. These results indicate that MollyRum14 is suitable to predict methane emissions from sheep fed a variety of fresh forages including annual fodder crops. However, a clear understanding of degradation rates and stoichiometries is needed to enhance the utility of the model as a predictive tool. This would allow continuous adjustment of digestion rates and stoichiometries to be potentially tailored to individual forage species.

Effect of inter-annual variability in pasture growth and irrigation response on farm productivity and profitability based on biophysical and farm systems modelling.

Farm system and nutrient budget models are increasingly being used in analysis to inform on farm decision making and evaluate land use policy options at regional scales. These analyses are generally based on the use of average annual pasture yields. In New Zealand (NZ), like in many countries, there is considerable inter-annual variation in pasture growth rates, due to climate. In this study a modelling approach was used to (i) include inter-annual variability as an integral part of the analysis and (ii) test the approach in an economic analysis of irrigation in a case study within the Hawkes Bay Region of New Zealand. The Agricultural Production Systems Simulator (APSIM) was used to generate pasture dry matter yields (DMY) for 20 different years and under both dryland and irrigation. The generated DMY were linked to outputs from farm-scale modelling for both Sheep and Beef Systems (Farmaxx Pro) and Dairy Systems (Farmax® Dairy Pro) to calculate farm production over 20 different years. Variation in DMY and associated livestock production due to inter-annual variation in climate was large, with a coefficient of variations up to 20%. Irrigation decreased this inter-annual variation. On average irrigation, with unlimited available water, increased income by $831 to 1195/ha, but when irrigation was limited to 250mm/ha/year income only increased by $525 to 883/ha. Using pasture responses in individual years to capturing the inter-annual variation, rather than the pasture response averaged over 20years resulted in lower financial benefits. In the case study income from irrigation based on an average year were 10 to >20% higher compared with those obtained from individual years.

A regional assessment of the cost and effectiveness of mitigation measures for reducing nutrient losses to water and greenhouse gas emissions to air from pastoral farms.

Using a novel approach that links geospatial land resource information with individual farm-scale simulation, we conducted a regional assessment of nitrogen (N) and phosphorous (P) losses to water and greenhouse gas (GHG) emissions to air from the predominant mix of pastoral industries in Southland, New Zealand. An evaluation of the cost-effectiveness of several nutrient loss mitigation strategies applied at the farm-scale, set primarily for reducing N and P losses and grouped by capital cost and potential ease of adoption, followed an initial baseline assessment. Grouped nutrient loss mitigation strategies were applied on an additive basis on the assumption of full adoption, and were broadly identified as 'improved nutrient management' (M1), 'improved animal productivity' (M2), and 'restricted grazing' (M3). Estimated annual nitrate-N leaching losses occurring under representative baseline sheep and beef (cattle) farms, and representative baseline dairy farms for the region were 10 ± 2 and 32 ± 6 kg N/ha (mean ± standard deviation), respectively. Both sheep and beef and dairy farms were responsive to N leaching loss mitigation strategies in M1, at a low cost per kg N-loss mitigated. Only dairy farms were responsive to N leaching loss abatement from adopting M2, at no additional cost per kg N-loss mitigated. Dairy farms were also responsive to N leaching loss abatement from adopting M3, but this reduction came at a greater cost per kg N-loss mitigated. Only dairy farms were responsive to P-loss mitigation strategies, in particular by adopting M1. Only dairy farms were responsive to GHG abatement; greater abatement was achieved by the most intensified dairy farm system simulated. Overall, M1 provided for high levels of regional scale N- and P-loss abatement at a low cost per farm without affecting overall farm production, M2 provided additional N-loss abatement but only marginal P-loss abatement, whereas M3 provided the greatest N-loss abatement, but delivered no additional P abatement, and came at a large financial cost to farmers, sheep and beef farmers in particular. The modelling approach provides a farm-scale framework that can be extended to other regions to accommodate different farm production systems and performances, capturing the interactions between farm types, land use capabilities and production levels, as these influence nutrient losses and GHG emissions, and the effectiveness of mitigation strategies.

Modelling pastoral farm systems--scaling from farm to region.

Farm system and nutrient budget models are increasingly being used to inform and evaluate policy options on the impacts of land use change on regional environmental and economic performance. In this study, the common approach of up-scaling representative farm systems to a regional scale, with a limited input of resource information, was compared with a new approach that links a geospatial land resource information data base (NZLRI, Agribase™) that includes independent estimates of the productive capacity of land parcels, with individual farm-scale simulation (Farmax® Pro and Farmax® Dairy Pro) and nutrient budgeting models (Overseer®). The Southland region of New Zealand, which is currently undergoing enormous land use change, was used as a case study. Model outputs from the new approach showed increased profit of about 75% for the region if the current land area under dairying increases from 16% to 45%, with the shift to dairy constrained to high pasture production classes only. Environmental impacts associated with the change were substantial, with nitrate leaching estimated to increase by 35% and greenhouse gas emissions by 25%. Up-scaling of representative farm systems to the regional scale with limited input of resource information predicted lower potential regional profit and higher N leaching from dairy conversion. The new approach provides a farm scale framework that could easily be extended to include different systems, different levels of farming performance and the use of mitigation technologies.

Performance of lactating dairy cows fed varying levels of total mixed ration and pasture.

Two, 8-week experiments, each using 30 lactating Holstein cows, were conducted to examine performance of animals offered combinations of total mixed ration (TMR) and high-quality pasture. Experiment 1 was initiated in mid October 2004 and Experiment 2 was initiated in late March 2005. Cows were assigned to either a 100% TMR diet (100:00, no access to pasture) or one of the following three formulated partial mixed rations (PMR) targeted at (1) 85% TMR and 15% pasture, (2) 70% TMR and 30% pasture and (3) 55% TMR and 45% pasture. Based on actual TMR and pasture intake, the dietary TMR and pasture proportions of the three PMR in Experiment 1 were 79% TMR and 21% pasture (79:21), 68% TMR and 32% pasture (68:32), and 59% TMR and 41% pasture (59:41), respectively. Corresponding proportions in Experiment 2 were 89% TMR and 11% pasture (89:11), 79% TMR and 21% pasture (79:21) and 65% TMR and 35% pasture (65:35), respectively. Reducing the proportion of TMR in the diets increased pasture consumption of cows on all PMR, but reduced total dry matter intake compared with cows on 100:00. An increase in forage from pasture increased the concentration of conjugated linoleic acids and decreased the concentration of saturated fatty acids in milk. Although milk and milk protein yields from cows grazing spring pastures (Experiment 2) increased with increasing intakes of TMR, a partial mixed ration that was composed of 41% pasture grazed in the fall (Experiment 1) resulted in a similar overall lactation performance with increased feed efficiency compared to an all-TMR ration.