Environmental impacts of innovative dairy farming systems aiming at improved internal nutrient cycling: A multi-scale assessment.

Several dairy farms in the Netherlands aim at reducing environmental impacts by improving the internal nutrient cycle (INC) on their farm by optimizing the use of available on-farm resources. This study evaluates the environmental performance of selected INC farms in the Northern Friesian Woodlands in comparison to regular benchmark farms using a Life Cycle Assessment. Regular farms were selected on the basis of comparability in terms of milk production per farm and per hectare, soil type and drainage conditions. In addition, the environmental impacts of INC farming at landscape level were evaluated with the integrated modelling system INITIATOR, using spatially explicit input data on animal numbers, land use, agricultural management, meteorology and soil, assuming that all farms practised the principle of INC farming. Impact categories used at both farm and landscape levels were global warming potential, acidification potential and eutrophication potential. Additional farm level indicators were land occupation and non-renewable energy use, and furthermore all farm level indicators were also expressed per kg fat and protein corrected milk. Results showed that both on-farm and off-farm non-renewable energy use was significantly lower at INC farms as compared with regular farms. Although nearly all other environmental impacts were numerically lower, both on-farm and off-farm, differences were not statistically significant. Nitrogen losses to air and water decreased by on average 5 to 10% when INC farming would be implemented for the whole region. The impact of INC farming on the global warming potential and eutrophication potential was, however, almost negligible (<2%) at regional level. This was due to a negligible impact on the methane emissions and on the surplus and thereby on the soil accumulation and losses of phosphorus to water at INC farms, illustrating the focus of these farms on closing the nitrogen cycle.

Strategies to mitigate nitrous oxide emissions from herbivore production systems.

Herbivores are a significant source of nitrous oxide (N(2)O) emissions. They account for a large share of manure-related N(2)O emissions, as well as soil-related N(2)O emissions through the use of grazing land, and land for feed and forage production. It is widely acknowledged that mitigation measures are necessary to avoid an increase in N(2)O emissions while meeting the growing global food demand. The production and emissions of N(2)O are closely linked to the efficiency of nitrogen (N) transfer between the major components of a livestock system, that is, animal, manure, soil and crop. Therefore, mitigation options in this paper have been structured along these N pathways. Mitigation technologies involving diet-based intervention include lowering the CP content or increasing the condensed tannin content of the diet. Animal-related mitigation options also include breeding for improved N conversion and high animal productivity. The main soil-based mitigation measures include efficient use of fertilizer and manure, including the use of nitrification inhibitors. In pasture-based systems with animal housing facilities, reducing grazing time is an effective option to reduce N(2)O losses. Crop-based options comprise breeding efforts for increased N-use efficiency and the use of pastures with N(2)-fixing clover. It is important to recognize that all N(2)O mitigation options affect the N and carbon cycles of livestock systems. Therefore, care should be taken that reductions in N(2)O emissions are not offset by unwanted increases in ammonia, methane or carbon dioxide emissions. Despite the abundant availability of mitigation options, implementation in practice is still lagging. Actual implementation will only follow after increased awareness among farmers and greenhouse gases targeted policies. So far, reductions in N(2)O emissions that have been achieved are mostly a positive side effect of other N-targeted policies.

Quantifying the effect of heat stress on daily milk yield and monitoring dynamic changes using an adaptive dynamic model.

Automation and use of robots are increasingly being used within dairy farming and result in large amounts of real time data. This information provides a base for the new management concept of precision livestock farming. From 2003 to 2006, time series of herd mean daily milk yield were collected on 6 experimental research farms in the Netherlands. These time series were analyzed with an adaptive dynamic model following a Bayesian method to quantify the effect of heat stress. The effect of heat stress was quantified in terms of critical temperature above which heat stress occurred, duration of heat stress periods, and resulting loss in milk yield. In addition, dynamic changes in level and trend were monitored, including the estimation of a weekly pattern. Monitoring comprised detection of potential outliers and other deteriorations. The adaptive dynamic model fitted the data well; the root mean squared error of the forecasts ranged from 0.55 to 0.99 kg of milk/d. The percentages of potential outliers and signals for deteriorations ranged from 5.5 to 9.7%. The Bayesian procedure for time series analysis and monitoring provided a useful tool for process control. Online estimates (based on past and present only) and retrospective estimates (determined afterward from all data) of level and trend in daily milk yield showed an almost yearly cycle that was in agreement with the calving pattern: most cows calved in winter and early spring versus summer and autumn. Estimated weekly patterns in terms of weekday effects could be related to specific management actions, such as change of pasture during grazing. For the effect of heat stress, the mean estimated critical temperature above which heat stress was expected was 17.8±0.56°C. The estimated duration of the heat stress periods was 5.5±1.03 d, and the estimated loss was 31.4±12.2 kg of milk/cow per year. Farm-specific estimates are helpful to identify management factors like grazing, housing and feeding, that affect the impact of heat stress. The effect of heat stress can be decreased by modifying these factors.