Sammendrag

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.

Sammendrag

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

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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Sammendrag

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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Sammendrag

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.

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.

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Division of Biotechnology and Plant Health

Algae to Future (A2F) - Alger for fremtiden


Med mikroalger som utgangspunkt vil forskerprosjektet «Alger for fremtiden - A2F» undersøke algenes potensial til å produsere høykvalitets proteiner, flerumettede fettsyrer og karbohydrater til fremtidens matfat. Vi vil kombinere fagkunnskap om dyrking og optimalisering av mikroalgers biomassekomposisjon i lab- og pilotskala, med erfaringen til bl.a. profesjonelle bakere, ølbryggere og fiskefôrprodusenter. Slik legger vi grunnlaget for en felles innovasjonsplattform for fremtidens bærekraftige norske algeindustri.

Active Updated: 10.08.2017
End: mars 2021
Start: apr 2017