Håvard Steinshamn
Seniorforskar
(+47) 906 82 643
havard.steinshamn@nibio.no
Sted
Tingvoll
Besøksadresse
Gunnars veg 6, 6630 Tingvoll
Biografi
Forfattere
Kristian Nikolai Jæger Hansen Håvard Steinshamn Sissel Hansen Matthias Koesling Tommy Dalgaard Bjørn Gunnar HansenSammendrag
To evaluate the environmental impact across multiple dairy farms cost-effectively, the methodological frame- work for environmental assessments may be redefined. This article aims to assess the ability of various statistical tools to predict impact assessment made from a Life Cyle Assessment (LCA). The different models predicted estimates of Greenhouse Gas (GHG) emissions, Energy (E) and Nitrogen (N) intensity. The functional unit in the study was defined as 2.78 MJMM human-edible energy from milk and meat. This amount is equivalent to the edible energy in one kg of energy-corrected milk but includes energy from milk and meat. The GHG emissions (GWP100) were calculated as kg CO2-eq per number of FU delivered, E intensity as fossil and renewable energy used divided by number of FU delivered, and N intensity as kg N imported and produced divided by kg N delivered in milk or meat (kg N/kg N). These predictions were based on 24 independent variables describing farm characteristics, management, use of external inputs, and dairy herd characteristics. All models were able to moderately estimate the results from the LCA calculations. However, their precision was low. Artificial Neural Network (ANN) was best for predicting GHG emissions on the test dataset, (RMSE = 0.50, R2 = 0.86), followed by Multiple Linear Regression (MLR) (RMSE = 0.68, R2 = 0.74). For E intensity, the Supported Vector Machine (SVM) model was performing best, (RMSE = 0.68, R2 = 0.73), followed by ANN (RMSE = 0.55, R2 = 0.71,) and Gradient Boosting Machine (GBM) (RMSE = 0.55, R2 = 0.71). For N intensity predictions the Multiple Linear Regression (MLR) (RMSE = 0.36, R2 = 0.89) and Lasso regression (RMSE = 0.36, R2 = 0.88), followed by the ANN (RMSE = 0.41, R2 = 0.86,). In this study, machine learning provided some benefits in prediction of GHG emission, over simpler models like Multiple Linear Regressions with backward selection. This benefit was limited for N and E intensity. The precision of predictions improved most when including the variables “fertiliser import nitrogen” (kg N/ha) and “proportion of milking cows” (number of dairy cows/number of all cattle) for predicting GHG emission across the different models. The inclusion of “fertiliser import nitrogen” was also important across the different models and prediction of E and N intensity.
Forfattere
Martha Irene Grøseth Linda Karlsson Håvard Steinshamn Marianne Johansen Alemayehu Kidane Sagaye Egil PrestløkkenSammendrag
Det er ikke registrert sammendrag
Forfattere
Håvard SteinshamnSammendrag
Svak samanheng mellom restriktiv gjæring og uRP=utnyttbart protein (unntatt ved låg TS). uRP var positivt korrelert med fordøyelegheit av organisk stoff (OMD) og RP-innhaldet. Svak samsvar mellom uRP og AAT20 (NorFor) men god samsvar mellom uRP og omsetteleg protein=MP (Luke, finske fôrevalueringssystemet)
Divisjon for skog og utmark
#Amazing grazing - bærekraftig kjøtt og ull fra sau som beiter i norsk utmark
Kjøtt og ull fra norske sauer kommer fra gårder med ulikt ressursgrunnlag, ulike driftsopplegg og ulik ressursbruk. I dette prosjektet skal vi undersøke sauebonden sitt driftsopplegg, forbrukeren sin innsikt, og rammevilkårene som både bonden og forbrukeren må forholde seg til. Hvordan kan produksjonen forbedres, og hvordan kan forbrukeren få mer kunnskap og nærhet til hva beitebruk bidrar med gjennom produktene?
Divisjon for matproduksjon og samfunn
Potential of biorefining fresh and preserved forages for year-round green protein supply in Norway
This YeRoP-project (Potential of biorefining fresh and preserved forages for
Divisjon for skog og utmark
#Amazing grazing - bærekraftig kjøtt og ull fra sau som beiter i norsk utmark
Kjøtt og ull fra norske sauer kommer fra gårder med ulikt ressursgrunnlag, ulike driftsopplegg og ulik ressursbruk. I dette prosjektet skal vi undersøke sauebonden sitt driftsopplegg, forbrukeren sin innsikt, og rammevilkårene som både bonden og forbrukeren må forholde seg til. Hvordan kan produksjonen forbedres, og hvordan kan forbrukeren få mer kunnskap og nærhet til hva beitebruk bidrar med gjennom produktene?
Divisjon for matproduksjon og samfunn
Visions and the consequences - analysing visions for Norwegian agriculture and its consequences for food security
In the FOSIP project (Visions and the consequences - analysing visions for Norwegian agriculture and its consequences for food security) we will assess and evaluate the foundation, support, opportunities, and limitations for the goal of increased agri-food self-sufficiency in Norway and assess how far an increase will contribute to improved national food security.
Divisjon for matproduksjon og samfunn
Visions and the consequences - analysing visions for Norwegian agriculture and its consequences for food security
In the FOSIP project (Visions and the consequences - analysing visions for Norwegian agriculture and its consequences for food security) we will assess and evaluate the foundation, support, opportunities, and limitations for the goal of increased agri-food self-sufficiency in Norway and assess how far an increase will contribute to improved national food security.
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Divisjon for matproduksjon og samfunn
Cultivating sustainable changes in livestock feed production and feeding practices (Feed&Feeding)
The project will evaluate various strategies for feed production and feeding practices to enhance the sustainability of Norway's food system and support national agricultural policy goals. These strategies include adjusting livestock diets, improving breeding and animal health, and introducing new protein sources for feed. The project will assess environmental impacts, such as land use changes, greenhouse gas emissions, soil carbon levels, nutrient balances, and biodiversity, as well as socioeconomic impacts, including food security, economic and social sustainability, and the viability of rural communities.