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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.

2022

Abstract

Through the joint project Climate Smart Agriculture, the agricultural sector in Norway have successfully implemented the whole-farm models HolosNor models as farm advisory tools for milk, beef, pig, sheep, poultry, and crop production. The HolosNor modes are empirical models based on the methodology of the Intergovernmental Panel on Climate Change with modifications to Norwegian conditions. The models estimate direct emissions of methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2) from on-farm livestock production and includes indirect emissions of N2O and CO2 associated with inputs used on the farm in addition to including soil carbon balance through the ICBM model. The digital GHG Calculator automatically collects data from sources the farmer already uses for farm management, such as herd recording systems, manure planning systems, farm accounts, concentrate invoice, dairy, slaughterhouse, in addition to site-specific soil and weather data. Based on the collected data, both total emissions from the production and emission intensities for the different products are estimated. The emission intensities are shown by source relative to a reference group consisting of farms with the same type of production and production volume. Using the GHG Calculator, the farmers have the unique opportunity to have tailor-made mitigation plans to reduce emissions from the farm trough certified climate advisors. Participation and results from the GHG Calculator will be presented in addition to experiences from implementation of a GHG model as a farm advisory tool for commercial farms.

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Abstract

Interactions among fungi and insects involve hundreds of thousands of species. While insect communities on plants have formed some of the classic model systems in ecology, fungus-based communities and the forces structuring them remain poorly studied by comparison. We characterize the arthropod communities associated with fruiting bodies of eight mycorrhizal basidiomycete fungus species from three different orders along a 1200-km latitudinal gradient in northern Europe. We hypothesized that, matching the pattern seen for most insect taxa on plants, we would observe a general decrease in fungal-associated species with latitude. Against this backdrop, we expected local communities to be structured by host identity and phylogeny, with more closely related fungal species sharing more similar communities of associated organisms. As a more unique dimension added by the ephemeral nature of fungal fruiting bodies, we expected further imprints generated by successional change, with younger fruiting bodies harboring communities different from older ones. Using DNA metabarcoding to identify arthropod communities from fungal fruiting bodies, we found that latitude left a clear imprint on fungus-associated arthropod community composition, with host phylogeny and decay stage of fruiting bodies leaving lesser but still-detectable effects. The main latitudinal imprint was on a high arthropod species turnover, with no detectable pattern in overall species richness. Overall, these findings paint a new picture of the drivers of fungus-associated arthropod communities, suggesting that latitude will not affect how many arthropod species inhabit a fruiting body but, rather, what species will occur in it and at what relative abundances (as measured by sequence read counts). These patterns upset simplistic predictions regarding latitudinal gradients in species richness and in the strength of biotic interactions.

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Abstract

Accurate and non-destructive diagnosis of crop nitrogen (N) surplus and deficit status based on N nutrition index (NNI) is crucially important for the success of precision N management to improve N use efficiency (NUE) and reduce negative environmental impacts. However, due to the variability of the reflectance data obtained from different active crop sensors and complexity of the environmental and management conditions for regional applications, accurate determination of crop N status and topdressing N rate only using active canopy sensor data is very challenging. The objectives of this study were to (1) develop an in-season N status diagnosis and recommendation model based on NNI prediction using multi-source data fusion with machine learning, and (2) evaluate the accuracy of N diagnosis and recommendation in terms of rice yield and NUE under diverse on-farm conditions. Thirty plot experiments and thirteen on-farm experiments were conducted in Qixing Farm, Jiansanjiang, Northeast China from 2008 to 2018, and the dataset was used for the model calibration, validation, and evaluation. Two indirect and one direct NNI prediction methods using simple regression, stepwise multiple linear regression (SMLR) and random forest regression (RFR) were compared for N diagnosis and then integrated into N recommendation model. The results indicated that combining environmental and agronomic variables with crop sensor data improved the SMLR and RFR model performance by 1–16% and 9–40% over the corresponding models only using crop sensor data, respectively. The direct NNI prediction approach achieved slightly better N status diagnostic accuracy (areal agreement = 84% and Kappa statistics = 0.71) than indirect NNI prediction strategies based on plant N uptake and ΔN estimation (areal agreement = 81% and Kappa statistics = 0.67) or aboveground biomass and plant N uptake estimation (areal agreement = 77% and Kappa statistics = 0.58) across plot experiments and diverse on-farm conditions, based on multi-source data fusion with random forest regression models. About 82% of recommended N rates by the developed integrated in-season rice N diagnosis and recommendation model were within ±10 kg ha−1 of the measured economic optimum N rate across different varieties, environmental conditions and transplanting densities. Precision rice management based on in-season N diagnosis and recommendation decreased N rates and increased N partial factor productivity (PFPN) by 23% over regional optimum rice management, and significantly increased yield (7–11%) and PFPN (33–77%) over farmer's management. More studies are needed to develop in-season N diagnosis and recommendation strategies for applications across different regions and combine them with integrated precision rice management strategies for food security and sustainable development.

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Abstract

A study was conducted to investigate the effect of different storage periods and temperatures on pollen viability in vitro and in vivo in plum genotypes ‘Valerija’, ‘Čačanska Lepotica’ and ‘Valjevka’. In vitro pollen viability was tested at day 0 (fresh dry pollen) and after 3, 6, 9 and 12 months of storage at four different temperatures (4, −20, −80 and −196 °C), and in vivo after 12 months of storage at distinct temperatures. In vitro germination and fluorescein diacetate (FDA) staining methods were used to test pollen viability, while aniline blue staining was used for observing in vivo pollen tube growth. Fresh pollen germination and viability ranged from 42.35 to 63.79% (‘Valjevka’ and ‘Čačanska Lepotica’, respectively) and 54.58 to 62.15%, (‘Valjevka’ and ‘Valerija’, respectively). With storage at 4 °C, pollen viability and germination decreased over the period, with the lowest value after 12 months of storage. Pollen germination and viability for the other storage temperatures (−20, −80 and −196 °C) were higher than 30% by the end of the 12 months. Pollination using pollen stored at 4 °C showed that pollen tube growth mostly ended in the lower part of the style. With the other storage temperatures, pollen tube growth was similar, ranging between 50 and 100% of the pistils with pollen tubes penetrated into the nucellus of the ovule in the genotype ‘Čačanska Lepotica’. The results of these findings will have implications for plum pollen breeding and conservation.

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Abstract

Clarireedia spp., Fusarium culmorum, and Microdochium nivale are destructive and widespread fungal pathogens causing turfgrass disease. Chemical control is a key tool for managing these diseases on golf greens but are most effective when used in a manner that reduces overall inputs, maximizes fungicide efficacy, and minimizes the risk of fungicide resistance. In this study, sensitivity to eight commonly used fungicides was tested in 13 isolates of Clarireedia spp., F. culmorum, and M. nivale via in vitro toxicity assays. Fungicide sensitivity varied significantly among the three species, with isolates of F. culmorum showing the least sensitivity. The sensitivity of M. nivale to all tested fungicides was high (with the exception of tebuconazole), but only four fungicides (Banner Maxx®, Instrata® Elite, Medallion TL, and Switch® 62,5 WG) suppressed the growth of M. nivale completely at a concentration of 1% of the recommended dose. All three fludioxonil-containing fungicides either alone (Medallion TL) or in combination with difeconazole (Instrata® Elite) or cyprodinil (Switch® 62,5 WG) had the same high efficacy against isolates of both M. nivale and Clarireedia spp. On average, the Clarireedia isolates tested in this study showed high sensitivity to the tested fungicides, except for Heritage (azoxystrobin). The observed variation in sensitivity among isolates within the same fungal species to different fungicides needs further investigation, as an analysis of the differences in fungal growth within each fungal group revealed a significant isolate × fungicide interaction (p < .001).

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Abstract

The success of the mollusc-parasitic nematode, Phasmarhabditis hermaphrodita (Schneider) Andrássy (Rhabditida: Rhabditidae), as a biological control agent in Europe has led to worldwide interest in phasmarhabditids as biocontrol agents. In this study, the mass culture potential of three phasmarhabditids, namely Phasmarhabditis papillosa, Phasmarhabditis kenyaensis and Phasmarhabditis bohemica, was assessed. In addition, ten bacterial candidates, consisting of seven associated with slugs and three associated with entomopathogenic nematodes, were investigated. The bacteria were tested for their ability to cause mortality to Deroceras invadens, as well as to support nematode growth. Initial mortality studies demonstrated that Kluyvera, Aeromonas and Pseudomonas spp. (AP3) caused 100% mortality when they were injected into the haemocoel of D. invadens. However, in growth studies, Pseudomonas sp. (AP4) was found to be the most successful bacterium, leading to recovery and reproduction in almost all nematode species, except for P. kenyaensis. In flask studies, P. bohemica, which showed exceptional growth with Pseudomonas sp. (AP1), was chosen for further investigation. The effect of inoculating flasks with different concentrations of Pseudomonas sp. (AP1), as well as with different concentrations of P. bohemica, was evaluated by assessing the nematode populations for 14 days. The results indicated that the lowest, 1% (v/v), bacteria inoculation led to higher total nematode and to infective juvenile (IJ) yield, with flasks with the highest IJ inoculum (3000 IJs/ml) having a positive effect on the total number of nematodes and IJs in cultures of P. bohemica. This study presents improvements for the mass-culturing of nematodes associated with molluscs.