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.
2018
Authors
Björn Ringselle Therese With Berge Daniel Stout Tor Arvid Breland Paul E. Hatcher Espen Haugland Matthias Koesling Kjell Mangerud Tor Lunnan Lars Olav BrandsæterAbstract
Docks (Rumex spp.) are a considerable problem in grassland production worldwide. We investigated how different cultural management techniques affected dock populations during grassland renewal: (I) renewal time, (II) companion crop, (III) false seedbed, (IV) taproot cutting (V), plough skimmer and (VI) ploughing depth. Three factorial split-split plot experiments were carried out in Norway in 2007–2008 (three locations), 2008–2009 (one location) and 2009 (one location). After grassland renewal, more dock plants emerged from seeds than from roots. Summer renewal resulted in more dock seed and root plants than spring renewal. Adding a spring barley companion crop to the grassland crop often reduced dock density and biomass. A false seedbed resulted in 71% fewer dock seed plants following summer renewal, but tended to increase the number of dock plants after spring renewal. In some instances, taproot cutting resulted in less dock biomass, but the effect was weak and inconsistent, and if ploughing was shallow (16 cm) or omitted, it instead increased dock root plant emergence. Fewer root plants emerged after deep ploughing (24 cm) compared to shallow ploughing, and a plough skimmer tended to reduce the number further. We conclude that a competitive companion crop can assist in controlling both dock seed and root plants, but it is more important that the renewal time is favourable to the main crop. Taproot cutting in conjunction with ploughing is not an effective way to reduce dock root plants, but ploughing is more effective if it is deep and a skimmer is used.
2017
Abstract
No abstract has been registered
Authors
Matthias KoeslingAbstract
To improve environmental sustainability it is important that all sectors in a society contribute to improving the utilization of inputs as energy and nutrients. In Norway, dairy farming contributes with an important share to the added value from the agricultural sector, although there is little information available about utilization of energy and nitrogen (N). Many results on sustainability have been published on dairy farming. However, due to Norway’s Nordic climatic conditions, mountainous and rugged topography and an agricultural policy that can design its own prices and subsidies, results from other countries are hardly representative for Norwegian conditions. To bridge this gap, the objective of this study was to analyse if the utilisation of nitrogen and energy in dairy farming in Norway can be improved to strengthen its environmental sustainability. Data were collected from 2010 to 2012 on 10 conventional and 10 organic farms in a region in central Norway with dairy farming as the main enterprise. The farms varied in area, number of dairy cows and milk yield. For nitrogen, a farm gate balance was applied and supplemented with nitrogen fixation by clover and atmospheric N-deposition. The total farm area was broken down into three categories: dairy farm area utilized directly by the farm, off-farm area needed to produce imported roughages and concentrates, and free rangeland that only can be used for grazing.
Abstract
Reduced N-surpluses in dairy farming is a strategy to reduce the environmental pollution from this production. This study was designed to analyse the important variables influencing nitrogen (N) surplus per hectare and per unit of N in produce for dairy farms and dairy systems across 10 certified organic and 10 conventional commercial dairy farms in Møre og Romsdal County, Norway, between 2010 and 2012. The N-surplus per hectare was calculated as N-input (net N-purchase and inputs from biological N-fixation, atmospheric deposition and free rangeland) minus N in produce (sold milk and meat gain), and the N-surplus per unit of N-produce as net Ninput divided by N in produce. On average, the organic farms produced milk and meat with lower N-surplus per hectare (88 ± 25 kg N·ha−1) than did conventional farms (220 ± 56 kg N·ha−1). Also, the N-surplus per unit of N-produce was on average lower on organic than on conventional farms, 4.2 ± 1.2 kg N·kg N−1 and 6.3 ± 0.9 kg N·kg N−1, respectively. All farms included both fully-cultivated land and native grassland. Nsurplus was found to be higher on the fully cultivated land than on native grassland. N-fertilizers (43%) and concentrates (30%) accounted for most of the N input on conventional farms. On organic farms, biological Nfixation and concentrates contributed to 32% and 36% of the N-input (43 ± 18 N·kg N−1 and 48 ± 11 N·kg N−1), respectively. An increase in N-input per hectare increased the amount of N-produce in milk and meat per hectare, but, on average for all farms, only 11% of the N-input was utilised as N-output; however, the N-surplus per unit of N in produce (delivered milk and meat gain) was not correlated to total N-input. This surplus was calculated for the dairy system, which also included the N-surplus on the off-farm area. Only 16% and 18% of this surplus on conventional and organic farms, respectively, was attributed to surplus derived from off-farm production of purchased feed and animals. Since the dairy farm area of conventional and organic farms comprised 52% and 60% of the dairy system area, respectively, it is crucial to relate production not only to dairy farm area but also to the dairy system area. On conventional dairy farms, the N-surplus per unit of N in produce decreased with increasing milk yield per cow. Organic farms tended to have lower N-surpluses than conventional farms with no correlation between the milk yield and the N-surplus. For both dairy farm and dairy system area, N-surpluses increased with increasing use of fertilizer N per hectare, biological N-fixation, imported concentrates and roughages and decreased with higher production per area. This highlights the importance of good agronomy that well utilize available nitrogen.
Abstract
Due to the limited resources of fossil fuels and the need to mitigate climate change, energy utilisation for all human activity has to be improved. The objective of this study was to analyse the correlation between energy intensity on dairy farms and production mode, to examine the influence of machinery and buildings on energy intensity, and to find production related solutions for conventional and organic dairy farms to reduce energy intensity. Data from ten conventional and ten organic commercial dairy farms in Norway from 2010 to 2012 were used to calculate the amount of embodied energy as the sum of primary energy used for production of inputs from cradle-to-farm gates using a life cycle assessment (LCA) approach. Energy intensities of dairy farms were used to show the amount of embodied energy needed to produce the inputs per metabolizable energy in the output. Energy intensities allow to easily point out the contribution of different inputs. The results showed that organic farms produced milk and meat with lower energy intensities on average than the conventional ones. On conventional farms, the energy intensity on all inputs was 2.6 ± 0.4 (MJMJ?1) and on organic farms it was significantly lower at 2.1 ± 0.3 (MJ MJ?1). On conventional farms, machinery and buildings contributed 18% ± 4%, on organic farms 29% ± 4% to the overall energy use. The high relative contribution of machinery and buildings to the overall energy consumption underlines the importance of considering them when developing solutions to reduce energy consumption in dairy production. For conventional and organic dairy farms, different strategies are recommend to reduce the energy intensity on all inputs. Conventional farms can reduce energy intensity by reducing the tractor weight and on most of them, it should be possible to reduce the use of nitrogen fertilisers without reducing yields. On organic dairy farms, energy intensity can be reduced by reducing embodied energy in barns and increasing yields. The embodied energy in existing barns can be reduced by a higher milk production per cow and by a longer use of the barns than the estimated lifetime. In the long run, new barns should be built with a lower amount of embodied energy. The high variation of energy intensity on all inputs from 1.6 to 3.3 (MJ MJ?1) (corresponding to the energy use of 4.5e9.3 MJ kg-1 milk) found on the 20 farms shows a potential for producing milk and meat with lower energy intensity on many farms. Based on the results, separate recommendations were provided for conventional and organic farms for reducing energy intensity.
Authors
Anne-Grete Roer Hjelkrem Mats Höglind Marcel van Oijen Jürgen Schellberg Thomas Gaiser Frank EwertAbstract
Proper parameterisation and quantification of model uncertainty are two essential tasks in improvement and assessment of model performance. Bayesian calibration is a method that combines both tasks by quantifying probability distributions for model parameters and outputs. However, the method is rarely applied to complex models because of its high computational demand when used with high-dimensional parameter spaces. We therefore combined Bayesian calibration with sensitivity analysis, using the screening method by Morris (1991), in order to reduce model complexity by fixing parameters to which model output was only weakly sensitive to a nominal value. Further, the robustness of the model with respect to reduction in the number of free parameters were examined according to model discrepancy and output uncertainty. The process-based grassland model BASGRA was examined in the present study on two sites in Norway and in Germany, for two grass species (Phleum pratense and Arrhenatherum elatius). According to this study, a reduction of free model parameters from 66 to 45 was possible. The sensitivity analysis showed that the parameters to be fixed were consistent across sites (which differed in climate and soil conditions), while model calibration had to be performed separately for each combination of site and species. The output uncertainty decreased slightly, but still covered the field observations of aboveground biomass. Considering the training data, the mean square error for both the 66 and the 45 parameter model was dominated by errors in timing (phase shift), whereas no general pattern was found in errors when using the validation data. Stronger model reduction should be avoided, as the error term increased and output uncertainty was underestimated.
Authors
Anne-Grete Roer Hjelkrem Torfinn Torp Einar Strand Guro Brodal Heidi Udnes Aamot Berit Nordskog Ruth Dill-Macky Simon G. Edwards Ingerd Skow HofgaardAbstract
No abstract has been registered
Authors
Anne-Grete Roer Hjelkrem Heidi Udnes Aamot Guro Brodal Einar Strand Torfinn Torp Ruth Dill-Macky Simon G. Edwards Berit Nordskog Ingerd Skow HofgaardAbstract
No abstract has been registered
Authors
Tomas Persson Henrik Eckersten Oleif Elen Anne-Grete Roer Hjelkrem Joel Markgren Mats Söderström Thomas BörjessonAbstract
Deoxynivalenol (DON) in cereals, produced by Fusarium fungi, cause poisoning in humans and animals. Fusarium infections in cereals are favoured by humid conditions. Host species are susceptible mainly during the anthesis stage. Infections are also positively correlated with a regional history of Fusarium infections, frequent cereal production and non-tillage field management practices. Here, previously developed process-based models based on relative air humidity, rain and temperature conditions, Fusarium sporulation, host phenology and mycelium growth in host tissue were adapted and tested on oats. Model outputs were used to calculate risk indices. Statistical multivariate models, where independent variables were constructed from weather data, were also developed. Regressions of the risk indices obtained against DON concentrations in field experiments on oats in Sweden and Norway 2012–14 had coefficient of determination values (R2) between 0.84 and 0.88. Regressions of the same indices against DON concentrations in oat samples averaged for 11 × 11 km grids in farmers’ fields in Sweden 2012–14 resulted in R2 values between 0.27 and 0.41 for randomly selected grids and between 0.31 and 0.62 for grids with average DON concentration above 1000 μg kg–1 grain in the previous year. When data from all three years were evaluated together, a cross-validated statistical partial least squares model resulted in R2 = 0.70 and a standard error of cross-validation (SECV) = 522 μg kg–1 grain for the period 1 April–28 August in the construction of independent variables and R2 = 0.54 and SECV = 647 μg kg–1 grain for 1 April–23 June. Factors that were not accounted for in this study probably explain large parts of the variation in DON among samples and make further model development necessary before these models can be used practically. DON prediction in oats could potentially be improved by combining weather-based risk index outputs with agronomic factors.
Authors
Anne Kjersti Bakken Kristin Daugstad Astrid Johansen Anne-Grete Roer Hjelkrem Gustav Fystro Anders Hammer Strømman Audun KorsæthAbstract
The aim of the study was to explore whether and how intensification would contribute to more environmentally friendly dairy production in Norway. Three typical farms were envisaged, representing intensive production strategies with regard to milk yield both per cow and per hectare in the three most important regions for dairy production in Norway. The scores on six impact categories for produced milk and meat were compared with corresponding scores obtained with a medium production intensity at a base case farm. Further, six scenario farms were derived from the base case. They were either intensified or made more extensive with regard to management practices that were likely to be varied and implemented under northern temperate conditions. The practices covered the proportion and composition of concentrates in animal diets and the production and feeding of forages with different energy concentration. Processes from cradle to farm gate were incorporated in the assessments, including on-farm activities, capital goods, machinery and production inputs. Compared to milk produced in a base case with an annual yield of 7250 kg energy corrected milk (ECM) per cow, milk from farms with yields of 9000 kg ECM or higher, scored better in terms of global warming potential (GWP). The milk from intensive farms scored more favourably also for terrestrial acidification (TA), fossil depletion (FD) and freshwater eutrophication (FE). However, this was not in all cases directly related to animal yield, but rather to lower burden from forage production. Production of high yields of energy-rich forage contributed substantially to the better scores on farms with higher-yielding animals. The ranking of farms according to score on agricultural land occupation (ALO) depended upon assumptions set for land use in the production of concentrate ingredients. When the Ecoinvent procedure of weighting according to the length of the cropping period was applied, milk and meat produced on diets with a high proportion of concentrates, scored better than milk and meat based on a diet dominated by forages. With regards to terrestrial ecotoxicity (TE), the score was mainly a function of the amount of concentrates fed per functional unit produced, and not of animal yield per se. Overall, the results indicated that an intensification of dairy production by means of higher yields per animal would contribute to more environment-friendly production. For GWP this was also the case when higher yields per head also resulted in higher milk yields and higher N inputs per area of land.