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.

DairyWise, a whole-farm dairy model.

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.

Evaluating farm performance using agri-environmental indicators: recent experiences for nitrogen management in The Netherlands.

Intensive agriculture, characterized by high inputs, has serious implications on the environment. Monitoring and evaluation of projects aiming at designing, testing and applying more sustainable practices require instruments to asses agronomic as well as environmental performance. Guidelines for Good Agricultural Practice (GAP) or Good Farming Practice (GFP) define sustainable practices but give limited insight into their environmental performance. Agri-environmental indicators (AEIs) provide information on environmental as well as agronomic performance, which allows them to serve as analytical instruments in research and provide thresholds for legislation purposes. Effective AEIs are quantifiable and scientifically sound, relevant, acceptable to target groups, easy to interpret and cost-effective. This paper discusses application of four AEIs for nitrogen (N) management in three Dutch research projects: 'De Marke', 'Cows and Opportunities' and 'Farming with a future'. 'De Marke' applied Nitrogen Surplus and Groundwater Nitrate Concentration in the design and testing of environmentally sound dairy systems. 'Cows and Opportunities', testing and disseminating dairy systems designed at 'De Marke', mainly applied Nitrogen Surplus, while 'Farming with a future' used Nitrogen Surplus, Groundwater Nitrate Concentration and Residual Mineral Soil Nitrogen to support arable farmers in complying with Dutch legislation (MINAS). Nitrogen Surplus is quantifiable, appealing and easy to interpret, but lacks scientific soundness or a good relationship with groundwater quality. Nitrogen Use Efficiency is sensitive to changes in management, while Residual Mineral Soil Nitrogen is appealing and cheap, but has difficulties in scaling. Groundwater Nitrate Concentration lacks clear rules for sampling, is labor consuming, expensive and mainly used in combination with other indicators. AEIs enhanced improvements in N management by facilitating (i) definition of project goals, (ii) design of desired systems, (iii) evaluation of applied systems and (iv) improving effective communication. AEI applications in other countries show a similar pattern as found in The Netherlands. Limitations to AEI application relate to inconsistencies between different indicators, heterogeneity of soil characteristics and linkages of N, carbon and water management. AEIs should be applied in an integrated evaluation, at a scale that reflects the farm's spatial variability. Simple AEIs like Nitrogen Surplus should be supported by other indicators and/or model calculations. The paper concludes that AEIs proved their value in design, implementation and testing of farming systems, but they should be used with care, always keeping in mind that indicators are simplifications of complex and variable processes.