Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2020
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Lars Sandved DalenAbstract
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Cornelya KlutschAbstract
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Cornelya KlutschAbstract
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Florencia A. Yannelli Chloe MacLaren Johannes KollmannAbstract
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Forest inventories provide predictions of stand means on a routine basis from models with auxiliary variables from remote sensing as predictors and response variables from field data. Many forest inventory sampling designs do not afford a direct estimation of the among-stand variance. As consequence, the confidence interval for a model-based prediction of a stand mean is typically too narrow. We propose a new method to compute (from empirical regression residuals) an among-stand variance under sample designs that stratify sample selections by an auxiliary variable, but otherwise do not allow a direct estimation of this variance. We test the method in simulated sampling from a complex artificial population with an age class structure. Two sampling designs are used (one-per-stratum, and quasi systematic), neither recognize stands. Among-stand estimates of variance obtained with the proposed method underestimated the actual variance by 30-50%, yet 95% confidence intervals for a stand mean achieved a coverage that was either slightly better or at par with the coverage achieved with empirical linear best unbiased estimates obtained under less efficient two-stage designs.
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submittedVersion
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Liz Veerman Karsten Kalbitz Per Gundersen O. Janne Kjønaas Filip Moldan Patrick Schleppi Emiel E. van Loon Jorien Schoorl Wim Wessel Albert TietemaAbstract
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Ryan Bright Micky Allen Clara Antón-Fernández Helmer Belbo Lise Dalsgaard Stephanie Eisner Aksel Granhus O. Janne Kjønaas Gunnhild Søgaard Rasmus AstrupAbstract
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Abstract
The aim of this work was to calculate farm specific LCAs for milk-production on 200 dairy farms in Central Norway, where 185 farmed conventional and 15 according to organic standards. We assume that there are variations in environmental emission drivers between farms and therefore also variation in indicators. We think that information can be utilized to find management improvements on individual farms. Farm specific data on inputs and production for the calendar years 2014 to 2016 were used. The LCAs were calculated for purchased products and on farm-emissions, including atmospheric deposition, biological nitrogen fixation, use of fertilizer and manure. The enteric methane emission from digestion was calculated for different animal groups. The functional unit was one kg energy- corrected milk (ECM) delivered at farm-gate. For the 200 dairy farms there were huge variations of farm characteristics, environmental per- formance and economic outcome. On average, the organic farms produced milk with a lower carbon footprint (1.2 kg CO2 eq./kg ECM) than the conventional ones (1.4 kg CO2 eq./kg ECM). The organic farms had also a lower energy intensity (3.1 MJ/kg ECM) and nitrogen intensity (5.0 kg N/kg N) than their conventional colleagues (4.1 MJ/kg ECM and 6.9 kg N/kg N respectively). The contribution margin was better on the organic farms with 6.6 NOK/kg ECM compared to the conventional with 5.9 NOK/kg ECM. The average levels of the environmental indicators were comparable but slightly higher than findings in other international studies. The current study proved that the FARMnor model allows to calculate LCAs for large number of individual farms. The results show that the environmental performance and economic outcome vary between farms. We recommend that farm specific LCA-results are used to unveil what needs to be changed for improving a farm’s environmental performance.