Matthias Koesling
Research Scientist
(+47) 943 74 616
matthias.koesling@nibio.no
Place
Tingvoll
Visiting address
Gunnars veg 6, 6630 Tingvoll
Biography
- Life Cycle Assessment - LCA
- Dairy production
- Sheep production
- Feed and grain production
- Macro-algae farming
- Inclusion of machinery and buildings
- introduction to use of LCA for students and pupils
- FARMnor (Flow Analysis and Resource Management): maintenance and further development of the LCA-model
- Evaluation of climate gasses using GWP, GWP* and GTP; usually on a 100-years horizon
- Combination of LCA and econommic analysis
- Organic production
- Questionaires, qualitative and quantitative
- Field trials; forage and grain: varieties, fertilizing and weed-control
- Dr. agr. (Doctor of Agricultural Sciences) at the Faculty of Organic Agriculture of Kassel University, Germany (2017).
- Diplom-Agraringenieur (corresponds to master of science) at the Faculty of Agricultural and Nutritional Sciences of Kiel University, Germany (1993).
- Certification as agronomist, Landwirtschaftskammer Schleswig-Holstein, Germany (1986).
Member:
Member of EGTOP (Expert group for technical advice on organic production) for the EU-Commission under Directorate-General Agriculture and Rural Development.
Authors
Kristian 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.
Abstract
Context Dairy farming contributes approximately 2.5 % of annual global anthropogenic greenhouse gas (GHG) emissions, necessitating effective mitigation strategies. Two approaches are often discussed: low-intensity, low-cost production with minimal reliance on purchased inputs; and high-intensity production with higher-yielding cows to reduce land use and reduce methane emissions per unit of milk. Objective The objective was to identify management factors and farm characteristics that explain variations in GHG emissions, environmental, and economic performance. Indicators included were GHG emissions, land use occupation, energy intensity, nitrogen intensity, and gross margin. Methods Life Cycle Assessment (LCA) was used to calculate the environmental impacts for 200 commercial dairy farms in Central Norway based on farm activities, purchased inputs, machinery, and buildings from 2014 to 2016. A multiple regression analysis with backward elimination was conducted to highlight important variables for environmental impact and economic outcome. Results and conclusions A higher share of dairy cows was found to be the most important factor in reducing GHG emissions, energy and nitrogen intensity, and land use but also to decrease gross margin. Additional key factors for reducing environmental impact included less purchased nitrogen fertiliser, and higher forage yield. There were no statistical correlations between GHG emissions and gross margin per MJ of human-edible energy delivered. Significance Conducting LCA for many dairy farms allows to highlight important factors influencing environmental impact and economic outcome. Using the delivery of human-edible energy from milk and meat as a functional unit allows for a combined evaluation of milk and meat production on a farm.
Authors
Cristina Micheloni Frank Willem Oudshoorn María Isabel Blanco Penedo Sari Autio Andrea Beste Jacopo Goracci Matthias Koesling Ursula Kretzschmar Eligio Malusá Maria Dolores Raigon Jimenez Bernhard Speiser Jan van der Blom Felix WäckersAbstract
No abstract has been registered
