| Summary: | This report was compiled in cooperation between the research organisations responsible for manure data in the Baltic Sea countries and the HELCOM Group on Sustainable Agricultural Practices (HELCOM Agri group). All Baltic Sea countries have their own methods for determining manure data, i.e. manure nutrient content and also its other characteristics and quantity. As this data is a prerequisite for all manure use and manure-related regulation and it affects the emission reduction targets set, it is important to understand the differences between manure data provision between the countries. Moreover, to share the burden of emission reduction in manure use as equally as possible between the Baltic Sea countries, a more harmonised system might be called for. In order to develop such a system, the state-of-the-art in the countries now (August 2016) is required as the stepping stone towards jointly agreed methodologies and tools for determining manure quality and quantity. In the following, the current national systems are shortly summarised with more detailed descriptions to be found in the chapters of this report. In Denmark, a calculation system called “normative manure” is used for determining manure quantity and quality in three steps: as excreted by the animal (ex animal), as collected from housing (ex housing) and after storage (ex storage). The system includes several animal categories, housing systems and manure types, leading to more than 150 possible combinations. The resulting standards are based on average recordings of e.g. feed nutrient content, feed intake and production of meat, milk, eggs and embryos of each type of farm animal in Denmark, and they include nitrogen, phosphorus, potassium, dry matter and volume. The standards are used as the basis of manure fertilisation on all farms, though minor farm-specific corrections with own production data are possible. It is noted that actual manure analysis shows a significant variation in nutrient content compared to the standards. This is often a case of water use and thus dilution. The standards are updated annually by University of Aarhus as based on a contract with the relevant Danish ministry. The updating is done in cooperation with the Danish advisory service. In Estonia, manure nutrient content is also calculated in three steps (ex animal, ex housing, ex storage) as the calculation system is based on the Danish normative manure system. However, the Estonian system contains less animal categories. The existing system works well with large-scale farms, e.g. cattle farms without grazing. There is also an unofficial online calculation tool available for farmers. Still, the system requires development e.g. in quantifying additives, such as precipitation and process water, and in losses during storage. The system is not updated on a regular basis. In Finland, manure nutrient content is currently based on sampling and analysis. Farm-specific analysis must be made minimum every five years. Still, farmers have a choice in manure fertilisation: they can use the analysis result or they can use table values. The table values are based on a large database of analysed manure samples. The standards for manure quantity are calculated. Still, a calcuation system for normative manure has also been developed with its first version published during the autumn of 2016. The calculated values are seen necessary e.g. due to the heterogeneous nature of manure and the need to know manure quality and quantity in different steps of the manure management chain and for more detailed animal categories. The results will be immediately used in policymaking and research, including e.g. emission inventories and agri-environmental indicators. Their use in manure fertilisation and nutrient bookkeeping will be discussed prior to the next Rural Development Programme period (2021>). In Germany, manure standards are based on calculated mass flow analysis. It is not based on manure sampling because manure is heterogenous and reliable sampling requires considerable professional experience. The calculation system works well and is also well accepted. The values take into account ammonia losses in housing, storage and spread. The values are used for fertiliser planning and nutrient bookkeeping by farmers where analyses are not available. The system is updated every five years. In Latvia, legislation concerning manure standards entered into force in the end of 2014 (previously only used as guidelines). The new system includes 18 animal categories and is based on scientific research for most categories and literature for a minority of categories. Livestock feeding, housing system and productivity are the basis of the system. Standards are used by farmers, governmental organizations (e.g. control purposes), advisory service, constructors for manure storages and other stakeholders. Still, there is a need for new / updated standards due to changes in livestock feeding, growing livestock productivity and new technologies. In Poland, the current system is based on a nutrient mass flow model, which deals with all stages of animal production and manure storage (gases, emissions accounted). The model is validated with farm monitoring data and takes into account different productivities and housing systems, such as grazing and straw bedding. The Polish system is currently under revision and could be developed further to include monitoring of 7000 farms where manure samples will be collected and analyzed twice a year. Background information from the farms (crops, animal number, bedding system etc.) will be collected as well. Manure sampling and enquiries are also vital for the stakeholders’ point of view and considered very useful for them. In Russia, farms are not required to keep account of manure nutrient content, but Regional Centres offer manure analysis. However, there are reference manuals used for fertilisation purposes. Document RC-APC 1.10.15.02 contains information for 56 animal categories on manure nutrient content. The data is collected from whole Russia. Regional data for North-West Russia is needed. In Sweden, a planning tool VERA (mass flow model) calculates manure nutrient content in the different handling steps. In the tool e.g. standard feeding or actual feeding can be used in order to have some flexibility. The planning tool is used in advisory purposes especially in nutrient vulnerable zones. The data is updated but not on a regular basis. There are default values for some parameters like ammonia emissions. The system could be developed as regards more frequent update, validation of errors and dilution of manure. Swedish inventory of fertiliser use is done every second year. Lithuania did not take part in the discussions or this report. However, they are engaged in the work towards a Baltic project in which joint guidelines for determination of manure quantity and quality will be developed and tested. Conclusions: The Baltic Sea countries agree that there is a need for enhancing manure utilisation in the BSR and this could be reached via more precise nutrient balances. In order to make them more precise and to ensure a level playing field for all Baltic Sea countries, unifying the procedures of collecting manure data is seen a way forward. All countries have national protocols for manure data provision and they are mostly based on sampling and analysis or modelled calculations. All countries report needs for improving and updating their protocols and the resulting manure data. Many also refer to similar challenges in data provision. There is a great chance of learning from each other via determining the suggested joint guidelines for manure data provision in close cooperation. The resulting tool, the more precise manure data, would become more effective and give all its users, farmers, advisory, authorities, policymakers, research and also companies developing and providing technologies for manure management and processing, a more solid database from which to plan the most effective ways to make the most of manure and its valuable nutrients (and energy). This is foreseen to also decrease the risk for environmentally harmful impacts from agriculture. 2016
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