Summary
Nitrogen (N) is an essential nutrient of food production, but its extended use led to the N cascade phenomenon with considerable effects on atmospheric, freshwater and marine systems. The increased efficiency of N use in dairy production is proposed as key action to reduce N contamination, and mathematical models provide a powerful tool to help with this. We are planning to use two models: The Cornell Net Carbohydrate and Protein System (CNCPS; a cow based model) and the Lifetime Nitrogen Efficiency (LNE; a herd based model). The models will be updated on amino acid metabolism (CNCPS), heifer growth and cattle fertility and economics (LNE) increasing not only their accuracy but also their commercial potential. In parallel, an intensive outreach program will incorporate farms in each country to describe N balance situation. This step will provide the necessary time to familiarize and accommodate farms with current model use. The last phase of the project consists on the implementation of the upgraded models in our pilot farms. This will provide a real life example that can generate a shift in agricultural practice towards more efficient and less polluting dairy industry. The European Union has been taken several initiatives either at a research level (e.g. FP-7 project RedNex) or at a legislative level (e.g. the National Emission Ceilings directive 2001/81/EC; currently under revision) to reduce N pollution from dairy production. However, few strategies have been converted into agricultural practice because of the lack of an applied step linking research strategies with the dairy industry. This is the strength of the current project that includes four non-academic partners that consult on thousands of European dairy farms and six European and American academic institutions, two of which developed the models, but all highly prestigious in their countries and internationally.
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More information & hyperlinks
Web resources: | https://cordis.europa.eu/project/id/777974 |
Start date: | 01-01-2018 |
End date: | 30-06-2023 |
Total budget - Public funding: | 1 102 500,00 Euro - 994 500,00 Euro |
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Original description
Nitrogen (N) is an essential nutrient of food production, but its extended use led to the N cascade phenomenon with considerable effects on atmospheric, freshwater and marine systems. The increased efficiency of N use in dairy production is proposed as key action to reduce N contamination, and mathematical models provide a powerful tool to help with this. We are planning to use two models: The Cornell Net Carbohydrate and Protein System (CNCPS; a cow based model) and the Lifetime Nitrogen Efficiency (LNE; a herd based model). The models will be updated on amino acid metabolism (CNCPS), heifer growth and cattle fertility and economics (LNE) increasing not only their accuracy but also their commercial potential. In parallel, an intensive outreach program will incorporate farms in each country to describe N balance situation. This step will provide the necessary time to familiarize and accommodate farms with current model use. The last phase of the project consists on the implementation of the upgraded models in our pilot farms. This will provide a real life example that can generate a shift in agricultural practice towards more efficient and less polluting dairy industry. The European Union has been taken several initiatives either at a research level (e.g. FP-7 project RedNex) or at a legislative level (e.g. the National Emission Ceilings directive 2001/81/EC; currently under revision) to reduce N pollution from dairy production. However, few strategies have been converted into agricultural practice because of the lack of an applied step linking research strategies with the dairy industry. This is the strength of the current project that includes four non-academic partners that consult on thousands of European dairy farms and six European and American academic institutions, two of which developed the models, but all highly prestigious in their countries and internationally.Status
CLOSEDCall topic
MSCA-RISE-2017Update Date
28-04-2024
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