Håvard Steinshamn
Research Professor
(+47) 906 82 643
havard.steinshamn@nibio.no
Place
Tingvoll
Visiting address
Gunnars veg 6, 6630 Tingvoll
Biography
Authors
Kristian Nikolai Jæger Hansen Håvard Steinshamn Sissel Hansen Matthias Koesling Tommy Dalgaard Bjørn Gunnar HansenAbstract
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.
Authors
Martha Irene Grøseth Linda Karlsson Håvard Steinshamn Marianne Johansen Alemayehu Kidane Sagaye Egil PrestløkkenAbstract
No abstract has been registered
Authors
Berit Marie Blomstrand SM Thamsborg Håvard Steinshamn Heidi Larsen Enemark Inga Marie Aasen Karl-Christian Mahnert Kristin Sørheim Francesca Shepherd Jos Houdijk Spiridoula AthanasiadouAbstract
Plant secondary metabolites (PSMs) may improve gastrointestinal health by exerting immunomodulatory, anti-inflammatory and/or antiparasitic effects. Bark extracts from coniferous tree species have previously been shown to reduce the burden of a range of parasite species in the gastrointestinal tract, with condensed tannins as the potential active compounds. In the present study, the impact of an acetone extract of pine bark (Pinus sylvestris) on the resistance, performance and tolerance of genetically diverse mice (Mus musculus) was assessed. Mice able to clear an infection quickly (fast responders, BALB/c) or slowly (slow responders, C57BL/6) were infected orally with 200 infective third-stage larvae (L3) of the parasitic nematode Heligmosomoides bakeri or remained uninfected (dosed with water only). Each infection group of mice was gavaged for 3 consecutive days from day 19 post-infection with either bark extract or dimethyl sulphoxide (5%) as vehicle control. Oral administration of pine bark extract did not have an impact on any of the measured parasitological parameter. It did, however, have a positive impact on the performance of infected, slow-responder mice, through an increase in body weight (BW) and carcase weight and reduced feed intake by BW ratio. Importantly, bark extract administration had a negative impact on the fast responders, by reducing their ability to mediate the impact of parasitism through reducing their performance and tolerance. The results indicate that the impact of PSMs on parasitized hosts is affected by host's genetic susceptibility, with susceptible hosts benefiting more from bark extract administration compared to resistant ones.
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Division of Food Production and Society
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Division of Food Production and Society
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