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I rapporten beskrives hvordan arealbruksendringene ved etablering av bakkemonterte solkraftverk påvirker opptak og utslipp av klimagasser. Det redegjøres også for hvordan ulike grader av skygge påvirker produksjonen av gras i ulike regioner i Norge. I tillegg beskrives driftstekniske utfordringer ved samproduksjon av strøm og jordbruksvekster. Rapporten belyser også hvilke konsekvenser fire planlagte solkraftverk kan få på opptak og utslipp av klimagasser fra arealbrukssektoren.

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Pandora neoaphidis is a common entomopathogenic fungus on Sitobion avenae, which is an important aphid pest on cereals in Europe. Pandora neoaphidis is known to cause epizootics (i.e. an unusually high prevalence of infected hosts) and the rapid collapse of aphid populations. We developed a weather-driven mechanistic model of the winter wheat-S. avenae-P. neoaphidis system to simulate the dynamics from spring to harvest. Aphid immigration was fixed at a rate that would lead to a pest outbreak, if not controlled by the fungus. We estimated the biocontrol efficacy by running pair-wise simulations, one with and one without the fungus. Uncertainty in model parameters and variation in weather was included, resulting in a range of simulation outcomes, and a global sensitivity analysis was performed. We identified two key understudied parameters that require more extensive experimental data collection to better assess the fungus biocontrol, namely the fungus transmission efficiency and the decay of cadaver, which defines the time window for possible disease transmission. The parameters with the largest influence on the improvement in yield were the weather, the lethal time of exposed aphids, the fungus transmission efficiency, and the humidity threshold for fungus development, while the fungus inoculum in the chosen range (between 10 and 70% of immigrant aphids carrying the fungus) was less influential. The model suggests that epizootics occurring early, around Zadoks growth stage (GS) 61, would lead to successful biocontrol, while later epizootics (GS 73) were a necessary but insufficient condition for success. These model predictions were based on the prevalence of cadavers only, not of exposed (i.e. infected but yet non-symptomatic) aphids, which in practice would be costly to monitor. The model suggests that practical Integrated Pest Management could thus benefit from including the cadavers prevalence in a monitoring program. We argue for further research to experimentally estimate these cadaver thresholds.

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Pandora neoaphidis is a common entomopathogenic fungus on Sitobion avenae, which is an important aphid pest on cereals in Europe. Pandora neoaphidis is known to cause epizootics (i.e. an unusually high prevalence of infected hosts) and the rapid collapse of aphid populations. We developed a weather-driven mechanistic model of the winter wheat-S. avenae-P. neoaphidis system to simulate the dynamics from spring to harvest. Aphid immigration was fixed at a rate that would lead to a pest outbreak, if not controlled by the fungus. We estimated the biocontrol efficacy by running pair-wise simulations, one with and one without the fungus. Uncertainty in model parameters and variation in weather was included, resulting in a range of simulation outcomes, and a global sensitivity analysis was performed. We identified two key understudied parameters that require more extensive experimental data collection to better assess the fungus biocontrol, namely the fungus transmission efficiency and the decay of cadaver, which defines the time window for possible disease transmission. The parameters with the largest influence on the improvement in yield were the weather, the lethal time of exposed aphids, the fungus transmission efficiency, and the humidity threshold for fungus development, while the fungus inoculum in the chosen range (between 10 and 70% of immigrant aphids carrying the fungus) was less influential. The model suggests that epizootics occurring early, around Zadoks growth stage (GS) 61, would lead to successful biocontrol, while later epizootics (GS 73) were a necessary but insufficient condition for success. These model predictions were based on the prevalence of cadavers only, not of exposed (i.e. infected but yet non-symptomatic) aphids, which in practice would be costly to monitor. The model suggests that practical Integrated Pest Management could thus benefit from including the cadavers prevalence in a monitoring program. We argue for further research to experimentally estimate these cadaver thresholds.

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A process-based model was developed to predict dry matter yields and amounts of harvested nitrogen in conventionally cropped grassland fields, accounting for within-field variation by a node network design and utilizing remotely sensed information from a drone-borne system for increased accuracy. The model, named NORNE, was kept as simple as possible regarding required input variables, but with sufficient complexity to handle central processes and minimize prediction errors. The inputs comprised weather data, soil information, management data related to fertilization, and a visual estimate of clover proportion in the aboveground biomass. A sensitivity analysis was included to apportioning variation in dry matter yield outputs to variation in model parameter settings. Using default parameter values from the literature, the model was evaluated on data from a two-year study (2016–2017, 264 research plots in total each year) conducted at two locations in Norway (i.e. in South-East and in Central Norway) with contrasting climatic conditions and with internal variation in soil characteristics. The results showed that the model could estimate dry matter yields with a relatively high accuracy without any corrections based on remote sensing, compared with published results from comparable model studies. To further improve the results, the model was calibrated shortly before harvest, using predictions of above ground dry matter biomass obtained from a drone-borne remote sensing system. The only parameters which were hereby adjusted in the NORNE model were the starting values of nitrogen content in soil (first cut) and the plant available water capacity (second cut). The calibration based on the remotely sensed information improved the predictive performance of the model significantly. At first cut, the root mean square error (RMSE) of dry matter yield prediction was reduced by 20% to a mean value of 58 g m−2, corresponding to a relative value (rRMSE) of 0.12. For the second cut, the RMSE decreased by 13% to 66 g m−2 (rRMSE: 0.18). The model was also evaluated in terms of the predictions of amounts of nitrogen in the harvested crop. Here, the calibration reduced the RMSE of the first cut by 38%, obtaining a mean RMSE value of 2.1 g N m−2 (rRMSE: 0.28). For the second cut, the RMSE reduction for simulated harvested N was 16%, corresponding to a mean RMSE value of 2.3 g N m−2 (rRMSE: 0.33). The large improvements in model accuracy for simulated dry matter and nitrogen yields obtained through calibration by utilizing remotely sensed information, indicate the importance of considering spatial variability when applying models under Nordic conditions, both for yield predictions and for decision support for nitrogen application.

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Young children have unique nutritional requirements, and breastfeeding is the best option to support healthy growth and development. Concerns have been raised around the increasing use of milk-based infant formulas in replacement of breastfeeding, in regards to health, social, economic and environmental factors. However, literature on the environmental impact of infant formula feeding and breastfeeding is scarce. In this study we estimated the environmental impact of four months exclusive feeding with infant formula compared to four months exclusive breastfeeding in a Norwegian setting. We used life-cycle assessment (LCA) methodology, including the impact categories global warming potential, terrestrial acidification, marine and freshwater eutrophication, and land use. We found that the environmental impact of four months exclusive feeding with infant formula was 35–72% higher than that of four months exclusive breastfeeding, depending on the impact category. For infant formula, cow milk was the main contributor to total score for all impact categories. The environmental impact of breastfeeding was dependant on the composition of the lactating mother’s diet. In conclusion, we found that breastfeeding has a lower environmental impact than feeding with infant formula. A limitation of the study is the use of secondary LCA data for raw ingredients and processes.

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The availability of fresh vegetables grown in greenhouses under controlled conditions throughout the year has given rise to concerns about their impact on the environment. In high latitude countries such as Norway, greenhouse vegetable production requires large amounts of energy for heat and light, especially during the winter. The use of renewable energy such as hydroelectricity and its effect on the environment has not been well documented. Neither has the effect of different production strategies on the environment been studied to a large extent. We conducted a life cycle assessment (LCA) of greenhouse tomato production for mid-March to mid-October (seasonal production), 20th January to 20th November (extended seasonal) production, and year-round production including the processes from raw material extraction to farm gate. Three production seasons and six greenhouse designs were included, at one location in southwestern and one in northern Norway. The SimaPro software was used to calculate the environmental impact. Across the three production seasons, the lowest global warming (GW) potential (600 g CO2-eq per 1 kg tomatoes) was observed during year-round production in southwestern Norway for the design NDSFMLLED + LED, while the highest GW potential (3100 g CO2-eq per 1 kg tomatoes) was observed during seasonal production in northern Norway for the design NS. The choice of artificial lighting (HPS (High Pressure Sodium) or LED (Light Emitting Diodes)), heating system and the production season was found to have had a considerable effect on the environmental impact. Moreover, there was a significant reduction in most of the impact categories including GW potential, terrestrial acidification, and fossil resource scarcity from seasonal to year-round production. Overall, year-round production in southwestern Norway had the lowest environmental impact of the evaluated production types. Heating of the greenhouse using natural gas and electricity was the biggest contributor to most of the impact categories. The use of an electric heat pump and LED lights during extended seasonal and year-round production both decreased the environmental impact. However, while replacing natural gas with electricity resulted in decreased GW potential, it increased the ecotoxicity potential.

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Despite substantial efforts to control locusts they remain periodically a major burden in Africa, causing severe yield loss and hence loss of food and income. Distribution maps indicating the value of the basic reproduction number R0 was used to identify areas where an insect pest can be controlled by a natural enemy. A dynamic process-based mathematical model integrating essential features of a natural enemy and its interaction with the pest is used to generate R0 risk maps for insect pest outbreaks, using desert locust and the entomopathogenic fungus Metarhizium acridum (Synn. Metarhizium anisoliae var. acridum) as a case study. This approach provides a tool for evaluating the impact of climatic variables such as temperature and relative humidity and mapping spatial variability on the efficacy of M. acridum as a biocontrol agent against desert locust invasion in Africa. Applications of M. acridum against desert locust in a few selected African countries including Morocco, Kenya, Mali, and Mauritania through monthly spatial projection of R0 maps for the prevailing climatic condition are illustrated. By combining mathematical modeling with a geographic information system in a spatiotemporal projection as we do in this study, the field implementation of microbial control against locust in an integrated pest management system may be improved. Finally, the practical utility of this model provides insights that may improve the timing of pesticide application in a selected area where efficacy is highly expected.

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Fusarium graminearum is regarded as the main deoxynivalenol (DON) producer in Norwegian oats, and high levels of DON are occasionally recorded in oat grains. Weather conditions in the period around flowering are reported to have a high impact on the development of Fusarium head blight (FHB) and DON in cereal grains. Thus, it would be advantageous if the risk of DON contamination of oat grains could be predicted based on weather data. We conducted a functional data analysis of weather-based time series data linked to DON content in order to identify weather patterns associated with increased DON levels. Since flowering date was not recorded in our dataset, a mathematical model was developed to predict phenological growth stages in Norwegian spring oats. Through functional data analysis, weather patterns associated with DON content in the harvested grain were revealed mainly from about three weeks pre-flowering onwards. Oat fields with elevated DON levels generally had warmer weather around sowing, and lower temperatures and higher relative humidity or rain prior to flowering onwards, compared to fields with low DON levels. Our results are in line with results from similar studies presented for FHB epidemics in wheat. Functional data analysis was found to be a useful tool to reveal weather patterns of importance for DON development in oats.

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This paper describes a tool that enables farmers to time harvests and target nitrogen (N) inputs in their forage production, according to the prevailing yield potential. Based on an existing grass growth model for forage yield estimation, a more detailed process-based model was developed, including a new nitrogen module. The model was tested using data from an experiment conducted in a grassland-rich region in central Norway and showed promising accuracy with estimated root mean square error (RMSE) of 50 and 130 g m-2 for dry matter yield in the trial. Three parameters were detected as highly sensitive to model output: initial value of organic N in the soil, fraction of humus in the initial organic N in the soil, and fraction of decomposed N mineralized. By varying these parameters within a range from 0.5 to 1.5 of their respective initial value, most of the within-field variation was captured. In a future step, remotely sensed information on model output will be included, and in-season model correction will be performed through re-calibration of the highly sensitive parameters.

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Late blight caused by Phytophthora infestans is a serious, worldwide disease on potato (Solanum tuberosum). Phytophthora infestans normally reproduces in a clonal manner, but in some areas, as the Nordic Countries, sexual reproduction has become the major determinant of the population structure. To improve the late blight forecasting in Norway, the process-based Nærstad model was developed. The model includes the structure of the underlying processes in the disease development, including spore production, spore release, spore survival and infection of P. infestans. It needs hourly weather records of air temperature, precipitation, relative humidity, leaf wetness and global radiation. The model contained 19 uncertain parameters, and from a sensitivity analysis, 12 were detected as weakly sensitive to model outputs and fixed to a nominal value within their prior boundaries. The remaining seven parameters were detected as more sensitive to model outputs and were parameterized using maximum a'posteriori (MAP) estimates, calculated through Bayesian calibration. The model was developed based on literature combined with field data of daily observed number of lesions on trap plants of the Bintje cultivar (late blight susceptible) at Ås during the seasons 2006-2008 and 2010-2011. It was further tested on daily observed number of lesions on trap plants of the cultivars Bintje, Saturna (medium susceptible) and Peik (medium resistant) at Ås during the seasons 2012-2015. For all three cultivars, the Nærstad model improved with a higher model accuracy compared to the existing HOSPO-model and the Førsund rules that both have shown relatively good correlation with blight development in field evaluations in Norway. The best accuracy was found for Bintje (0.83) closely followed by Saturna (0.79), whereas a much lower accuracy was detected for Peik (0.66).

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Leaf blotch diseases (LBD), such as Septoria nodorum bloch (Parastagnospora nodorum), Septoria tritici blotch (Zymoseptoria tritici) and Tan spot (Pyrenophora tritici-repentis) can cause severe yield losses (up to 50%) in Norwegian spring wheat (Triticum aestivum) and are mainly controlled by fungicide applications. A forecasting model to predict disease risk can be an important tool to optimize disease control. The association between specific weather variables and the development of LBD differs between wheat growth stages. In this study, a mathematical model to estimate phenological development of spring wheat was derived based on sowing date, air temperature and photoperiod. Weather factors associated with LBD severity were then identified for selected phenological growth stages by a correlation study of LBD severity data (17 years). Although information regarding host resistance and previous crop were added to the identified weather factors, two purely weather-based risk prediction models (CART, classification and regression tree algorithm) and one black box model (KNN, based on K nearest neighbor algorithm) were most accurate to predict moderate to high LBD severity (>5% infection). The predictive accuracy of these models (76–83%) was compared to that of two existing models used in Norway and Denmark (60 and 61% accuracy, respectively). The newly developed models performed better than the existing models, but still had the tendency to overestimate disease risk. Specificity of the new models varied between 49 and 74% compared to 40 and 37% for the existing models. These new models are promising decision tools to improve integrated LBD management of spring wheat in Norway.

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Aksfusariose er en kornsjukdom som kan angripe alle kornarter. Sjukdommen forårsakes av sopparter innen slekta Fusarium. Ulike Fusarium-arter kan produsere en rekke forskjellige mykotoksiner (soppgifter). Grenseverdier for innhold av enkelte mykotoksiner i korn og kornprodukter til mat og fôr er fastsatt av Mattilsynet (i henhold til EU’s regelverk). Denne dyrkningsveiledningen gir, på bakgrunn av dagens kunnskap, råd om hvordan en kan redusere risikoen for utvikling av mykotoksiner i korn.

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En har i denne studien undersøkt potensialet for å erstatte fossilt drivstoff med elektrisk energi fra batterier og/eller hydrogenbrenselceller i traktorarbeidet på norske gårder. Dette ble gjort med utgangspunkt i seksten små og store modellgårder på Østlandet, i Trøndelag og i Rogaland. Disse var korngårder med og uten husdyr, og melkeproduksjonsbruk. Det årlige dieselforbruket i alle traktordrevne arbeidsoperasjoner ble beregnet og videre tidfestet og fordelt gjennom året. For alle brukstyper var det høye topper med mye traktorarbeid knyttet til pløying og/eller spredning av husdyrgjødsel om våren og til innhøsting og pløying om høsten...

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In many areas where spring is wet, fungicides are applied in relation to rain events that trigger ejection of ascospores of Venturia inaequalis, which cause primary infections of apple scab. Past studies established the rate of ejection during rain in relation to light and temperature, and determined the wetting time required for infection. Simulation software uses this information to calculate risk and help time sprays accordingly. However, the distribution of the infection time required by a population of spores landed on leaves was never studied, and assumptions were used. To estimate this, we inoculated ascospores of V. inaequalis on potted trees at different temperatures for specific wetting times. Lesions were enumerated after incubation. Lesions increased with wetness time and leveled off once the slowest spores infected the host, closely matching the monomolecular model. Wetness hours were best adjusted for temperature using the Yin equation. The minimum infection time on the youngest leaves was about 5 h, matching results from previous studies, whereas half the lesions appeared after 7 h of infection. Infection times for leaves with ontogenic resistance were longer. Our results improve current software estimates and may improve spraying decisions.

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We studied the effect of three Pandora neoaphidis isolates from one Sitobion avenae population, three temperatures, and two aphid species namely S. avenae and Rhopalosiphum padi on (i) aphid mortality, (ii) time needed to kill aphids, and (iii) aphid average daily and lifetime fecundity. A total of 38% of S. avenae and 7% of R. padi died and supported fungus sporulation. S. avenae was killed 30% faster than R. padi. Average daily fecundity was negatively affected only in S. avenae inoculated with, but not killed by, P. neoaphidis. Nevertheless, lifetime fecundity of both aphid species inoculated and sporulating with P. neoaphidis was halved compared to lifetime fecundity of surviving aphids in the control. Increased temperature resulted in higher mortality rates but did not consistently affect lethal time or fecundity. Results suggest that (i) temperature effects on virulence differ between isolates, even when obtained within the same host population, and (ii) even though an isolate does not kill a host it may reduce its fecundity. Our findings are important for the understanding of P. neoaphidis epizootiology and for use in pest-natural enemy modelling.

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Occasionally, high mycotoxin levels are observed in Norwegian oat grain lots. The development of moderate resistant oat cultivars is therefore highly valued in order to increase the share of high quality grain into the food and feed industry. The Norwegian SafeOats project (2016-2020) aims to develop resistance screening methods to facilitate the phase-out of Fusarium-susceptible oat germplasm. Furthermore, SafeOats will give new insight into the biology of F. langsethiae and HT2+T2 accumulation in oats. The relative ranking of oat varieties according to F. graminearum/DON versus F. langsethiae/HT2+T2 content has been explored in naturally infested as well as in inoculated field trials. Routine testing of the resistance to F. graminearum in oat cultivars and breeding lines has been conducted in Norway since 2007. We are currently working on ways to scale up the inoculum production and fine tune the methodology of F. langsethiae inoculation of field trials to be routinely applied in breeding programs. Through greenhouse studies, we have analysed the content of Fusarium DNA and mycotoxins in grains of selected oat varieties inoculated at different development stages. Furthermore, we are studying the transcriptome during F. langsethiae and F. graminearum infestation of oats. The project also focus on the occurrence of F. langsethiae in oat seeds and possible influence of the fungus on seedling development in a selection of oat varieties. On average, the fungus was observed on 5% of the kernels in 168 seed lots tested during 2016-2018. No indication of transmission of F. langsethiae from germinating seed to seedlings was found in a study with germination of naturally infected seeds. So far, the studies have shown that the ranking of oat varieties according to HT2+T2 content in non-inoculated field trials resembles the ranking observed in inoculated field trials. The ranking of oat varieties according to DON content is similar in non-inoculated and F. graminearum inoculated field trials. However, the ranking of oat varieties according to DON content does not resemble the ranking for HT2+T2. The results from SafeOats will benefit consumers nationally and internationally by providing tools to increase the share of high quality grain into the food and feed industry. The project is financed by The Foundation for Research Levy on Agricultural Products/Agricultural Agreement Research Fund/Research Council of Norway with support from the industry partners Graminor, Lantmännen, Felleskjøpet Agri, Felleskjøpet Rogaland & Agder, Fiskå Mølle Moss, Norgesmøllene, Strand Unikorn/Norgesfôr and Kimen Seed Laboratory.

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High concentrations of the mycotoxins HT-2 and T-2 (HT2 + T2), primarily produced by Fusarium langsethiae, have occasionally been detected in Norwegian oat grains. In this study, we identified weather variables influencing accumulation of HT2 + T2 in Norwegian oat grains. Oat grain samples from farmers’ fields were collected together with weather data (2004–2013). Spearman rank correlation coefficients were calculated between the HT2 + T2 contamination in oats at harvest and a range of weather summarisations within estimated phenological windows of growth stages in oats (tillering, flowering etc.). Furthermore, we developed a mathematical model to predict the risk of HT2 + T2 in oat grains. Our data show that adequate predictions of the risk of HT2 + T2 in oat grains at harvest can be achieved, based upon weather data observed during the growing season. Humid and cool conditions, in addition to moderate temperatures during booting, were associated with increased HT2 + T2 accumulation in harvested oat grains, whereas warm and humid weather during stem elongation and inflorescence emergence, or cool weather and absence of rain during booting reduced the risk of HT2 + T2 accumulation. Warm and humid weather immediately after flowering increased the risk, while moderate to warm temperatures and absence of rain during dough development, reduced the risk of HT2 + T2 accumulation in oat grains. Our data indicated that HT2 + T2 contamination in oats is influenced by weather conditions both pre- and post-flowering. These findings are in contrast with a previous study examining the risk of deoxynivalenol contamination in oat reporting that toxin accumulation was mostly influenced by weather conditions from flowering onwards.

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I dette studiet har vi ved hjelp av livsløpsanalyse (LCA) analysert miljøeffektane av å produsere norsk svinekjøtt. Utgangspunktet for analysa har vore eit fiktivt gardsbruk, plassert i Stange kommune, med kombinert svineproduksjon (både smågris-og slaktegrisproduksjon) og med kornproduksjon (bygg, vårkveite og havre) der gjødsla frå svinebesetninga blir utnytta. Som utgangspunkt analyserte vi eit tradisjonelt opplegg der grisane fekk kraftfôrblandingar tilpassa behovet som einaste fôr. Soya utgjorde 8% kraftfôrblandinga på råvektbasis. Vi analyserte svineproduksjonen under to ulike alternativ: a) At dei norske kornråvarene i kraftfôret var produsert på garden eller på ein tilsvarande gard, b) At dei norske kornråvarene i kraftfôret kom frå husdyrfrie gardar med mineralgjødsel som einaste gjødselslag. I tillegg analyserte vi på tilsvarande måte svineproduksjonen på garden i ein situajson der arealgrunnlaget blei utvida til også å omfatte eng, og der engavlinga blei brukt i ein bioraffineringsprosess til å produsere grassaft som proteinfôr til slaktegrisane i besetninga. Pressresten (pulp) blei selt som grovfôr til lokale storfeprodusentar. I tillegg til grassaft fekk slaktegrisane kraftfôr med redusert innhald av soya (6%) samanlikna med standardblandinga. Samla ga denne fôrrasjonen dekning av slaktegrisane sitt næringsbehov, slik at tilvekst og produksjonsresultat var det same i begge produksjonsopplegga.....

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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.

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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.

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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.

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High concentrations of the mycotoxin deoxynivalenol (DON), produced by Fusarium graminearum have occurred frequently in Norwegian oats recently. Early prediction of DON levels is important for farmers, authorities and the Cereal Industry. In this study, the main weather factors influencing mycotoxin accumulation were identified and two models to predict the risk of DON in oat grains in Norway were developed: (1) as a warning system for farmers to decide if and when to treat with fungicide, and (2) for authorities and industry to use at harvest to identify potential food safety problems. Oat grain samples from farmers’ fields were collected together with weather data (2004–2013). A mathematical model was developed and used to estimate phenology windows of growth stages in oats (tillering, flowering etc.). Weather summarisations were then calculated within these windows, and the Spearman rank correlation factor calculated between DON-contamination in oats at harvest and the weather summarisations for each phenological window. DON contamination was most clearly associated with the weather conditions around flowering and close to harvest. Warm, rainy and humid weather during and around flowering increased the risk of DON accumulation in oats, as did dry periods during germination/seedling growth and tillering. Prior to harvest, warm and humid weather conditions followed by cool and dry conditions were associated with a decreased risk of DON accumulation. A prediction model, including only pre-flowering weather conditions, adequately forecasted risk of DON contamination in oat, and can aid in decisions about fungicide treatments.

Sammendrag

I dette studiet analyserte vi miljøeffekter av å produsere erter og åkerbønner i et korndominert vekstskifte på en gård ved Oslofjorden ved hjelp av livsløpsanalyse (LCA). Miljøeffekter av høsthvetedyrking (samme gård) ble tatt med som referanse. Miljøeffektene ble uttrykt gjennom følgende ni miljøindikatorer; globalt oppvarmingspotensial, eutrofiering av ferskvann, eutrofiering av marine miljøer, økotoksisitet i ferskvann, terrestrisk forsuring, forbruk av fossil energi, human toksisitet, økotoksisitet i marine miljø og terrestrisk økotoksisitet. Systemgrensen ble definert til å være lik gårdens fysiske grense og analysen dekket alle de viktigste prosessene inkludert i omvandlingen fra råstoff til produkt leveringsklart ved gårdsgrinda («cradle to farmgate»). Studien omfattet også prosesser som ofte utelates i LCA-studier, slik som produksjon av maskiner, bygninger og produksjon og bruk av plantevernmidler, samt humusmineralisering og utslipp av NOx fra mineralgjødsel. Tidsperioden for analysen var ett fullt produksjonsår, og for alle data brukte vi gjennomsnittsverdier for tiåret 2001-2010. Funksjonell enhet var enten ett kilo lagringsklart produkt (85% tørrstoff) eller ett kilo protein. Når funksjonell enhet var per kg produkt ble det globale oppvarmingspotensialet for henholdsvis erter og åkerbønner 0,94 og 0,80 kg CO2-ekvivalenter, og dermed på nivå med det vi har funnet tidligere for norskprodusert korn. Med 1 kg protein som funksjonell enhet var tilsvarende verdier 5,0 og 3,1 kg CO2-ekvivalenter. Hvis dette proteinet i stedet skulle blitt produsert i form av melk eller kjøtt, ville oppvarmingspotensialet blitt vesentlig større. Basert på tall fra noen av våre tidligere studier med tilsvarende metodikk, kom vi fram til at oppvarmingspotensialet per kg protein er 9-15 ganger høyere for melk og 14-29 ganger høyere for kjøtt (fra melkeproduksjonen) enn tilsvarende for de to proteinvekstene analysert her. Når alle de ni miljøindikatorene ble sett under ett viste resultatene at proteinet i åkerbønner ble produsert med et gjennomgående lavere miljøforavtrykk enn tilsvarende i høsthvete. Erter var delvis bedre, delvis dårligere enn høsthveten. En gjennomgang av proteinvekstene og deres vekstpotensial i Norge viste at potensialet for erter og åkervekster ligger på omtrent 230 000 daa til sammen. Det må også nevnes at oljevekster representerer en potensielt stor proteinkilde, med en proteinkonsentrasjon i frøet på 20-25% og et potensielt dyrkingsareal på ca. 380 000 daa. Proteinet i oljevekster brukes i dag nærmest utelukkende til fôr. Den volummessig viktigste vekstgruppen i Norge for produksjon av protein nyttbart for mennesker er imidlertid korn, som har et proteininnhold på 11-15% og et potensielt dyrkbart areal på godt over 3,3 mill. daa. Lokalklima og vær utgjør den mest begrensende faktoren for produksjon av vegetabilsk protein her til lands i dag.

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Sammendrag

Studying the winter survival of forage grasses under a changing climate requires models that can simulate the dynamics of soil conditions at low temperatures. We developed a simple model that simulates depth of snow cover, the lower frost boundary of the soil and the freezing of surface puddles. We calibrated the model against independent data from four locations in Norway, capturing climatic variation from south to north (Arctic) and from coastal to inland areas. We parameterized the model by means of Bayesian calibration, and identified the least important model parameters using the sensitivity analysis method of Morris. Verification of the model suggests that the results are reasonable. Because of the simple model structure, some overestimation occurs in snow and frost depth. Both the calibration and the sensitivity analysis suggested that the snow cover module could be simplified with respect to snowmelt and liquid water content. The soil frost module should be kept unchanged, whereas the surface ice module should be changed when more detailed topographical data become available, such as better estimates of the fraction of the land area where puddles may form.