Matthias Koesling
Forsker
(+47) 943 74 616
matthias.koesling@nibio.no
Sted
Tingvoll
Besøksadresse
Gunnars veg 6, 6630 Tingvoll
Biografi
- Livsløpsanalyse (Life Cycle Assessment - LCA)
- Melkeproduksjon
- Sauehold
- Fôrvekster og korn
- Makroalgedyrking
- inkludering av maskiner og bygninger
- innføring i LCA for studenter og elever
- FARMnor (Flow Analysis and Resource Management): vedlikehold og videreutvikling av LCA-modellen
- Klimagassutslipp ved bruk av GWP, GWP* og GTP; vanligvis for en 100-års horisont
- Kombinasjon av LCA og økonomisk analyse
- Økologisk produksjon
- Spørreundersøkelser; kvantitativ og kvalitativ
- Forsøksarbeid; korn og fôrvekster; sorter, gjødsling, urasregulering
- Dr. agr. (Doctor of Agricultural Sciences) ved fakultetet for økologisk landbruk, Universität Kassel (2017).
- Diplom-Agraringenieur (tilsvarer master of science) ved fakultetet for landbruk og ernæring, Christian Albrechts Universität Kiel (1993).
- Fagbrev som agronom, ved Landwirtschaftskammer Schleswig-Holstein (1986).
Medlem:
Medlem av EGTOP (Expert group for technical advice on organic production) for EU-kommisjonen under direktoratet for landbruk og distriktsutvikling.
Forfattere
Kristian 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.
Sammendrag
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.
Forfattere
Micheloni, Cristina Oudshoorn, Frank Willem Raigon Jimenez, Maria Dolores Autio, Sari Beste, Andrea Blanco Penedo, María Isabel Bourin, Marie-Christine Goracci, Jacopo Matthias Koesling Kretzschmar, Ursula Malusá, Eligio Speiser, Bernhard van der Blom, Jan Wäckers, Felix Amador, Alberto Auranmaa, Kirsi Brugger, Harald Burkart, Crinna Cozzo, Riccardo Hansen, Max Milan, Marlene Molteni, Roberto Perez Calvo, Maria del Mar Raffaitin, Pauline Rauch, Thomas Sossidou, Evangelia Waidelich, AnneSammendrag
Considering the most recent technical and scientific information available to the experts, the Group is requested: (5) to agree on the criteria for evaluation of substances for cleaning and disinfection to be applied to all fields of organic production where the use of such agents is necessary to maintain a high level of food hygiene. (6) to make a proposal for a negative list of substances with unwanted properties based on the above defined criteria. (7) to carry on some worked examples of evaluation of prioritized dossiers submitted by the Member States on products for cleaning and disinfection based on the criteria agreed: i. Hydrogen peroxide (DK) ii. Sodium percarbonate (hydrogen peroxide released from sodium percarbonate, DK) iii. Sodium hydroxide (NL) iv. Glutaraldehyde (SE) v. Chlorine dioxide (NO) vi. Calcium hypochlorite (FR) vii. Peracetic acid (FR) viii. Formic acid (FR) ix. Sodium hypochlorite (FR) x. Iodophors (FR) xi. Dipotassium peroxodisulfate + potassium peroxomonosulfate (SE) xii. Fatty acid potassium salt (SE) xiii. Methane sulfonic acid (probably GER, the request was made by BASF Ludwigshafen) (8) to schedule the work for evaluating the rest of the substances on the Commission priority list. For the preparation of its report the Group was invited to examine technical dossiers provided to the Commission by the Member States and suggest amendments to the Annex IV to the Regulation (EU) 2021/1165.
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