Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2018
Forfattere
Gry AlfredsenSammendrag
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Sammendrag
The estimated potential yield losses caused by plant pathogens is up to 16% globally (Oerke 2006) and most research in plant pathology aims to reduce yield loss in our crops directly or indirectly. Yield losses caused by a certain disease depend not only on disease severity, but also on the weather factors, the pathogen’s aggressiveness, and the ability of the crop to compensate for reduced photosynthetic area. The yield loss-disease relationship in a certain host-pathogen system might therefore change from year to year, making predictions for yield loss very difficult at the regional or even at the farmer’s level. However, estimating yield losses is essential to determine disease management thresholds at which acute control measures such as fungicide applications, or strategic measures such as crop rotation or use of resistant cultivars are economically and environmentally sensible. Legislation in many countries enforces implementation of integrated pest management (IPM), based on economic thresholds at which the costs due to a disease justify the costs for its management. Without a better understanding of the relationship between disease epidemiology and yield loss, we remain insufficiently equipped to design adequate IPM strategies that will be widely adapted in agriculture. Crop loss studies are resource demanding and difficult to interpret for one particular disease, as crops are usually not invaded by only one pest or pathogen at a time. Combining our knowledge on disease epidemiology, crop physiology, yield development, damage mechanisms involved, and the effect of management practices can help us to increase our understanding of the disease-crop loss relationship. The main aim of this paper is to review and analyze the literature on a representative host-pathogen relationship in an important staple food crop to identify knowledge gaps and research areas to better assess yield loss and design management strategies based on economic thresholds. Wheat is one of the most important staple foods worldwide and is susceptible to several important plant diseases. In our article, we focus on Septoria nodorum blotch (SNB) or Glume blotch of wheat as an example for a stubble-borne, seed-transmitted disease with a worldwide distribution causing considerable and regular yield losses. In their review on yield losses due to wheat pathogens in Australia, Murray and Brennan (2009) estimated the current annual economic loss due to SNB as high as $108 × 106, with potential costs as high as $230 × 106. The causal fungus, Parastagonospora nodorum, is currently serving as a model organism for molecular studies of the intimate relationship between necrotic effector-producing fungal strains and their corresponding susceptibility genes present in wheat cultivars (Oliver et al. 2012). In this paper, we analyze the literature on the biology of this common wheat pathogen, the yield loss it reportedly has caused, and the effect of control strategies to reduce this loss. Based on this analysis, we will evaluate the use of common management practices to reduce disease-related yield loss and identify related research needs.
Sammendrag
Undersøkelse av antibiotikaresistensmarkørgenet neomycin fosfotransferase II (nptII) i prøver fra 12 ville arter fra Norge I et prosjekt fra Miljødirektoratet har vi testa for tilstedeværelse av nptII genet i 219 prøver fra 12 ulike ville arter fra hele Norge. Utvalget av prøver inkluderte planter (løvetann, rødkløver og markjordbær), insekter (skogmaur, rognebærmøll og liten høstmåler), snegl (brunsnegl), fisk (ørret og rognkjeks) og pattedyr (rødrev, brunbjørn og isbjørn). Vi brukte to ulike sanntids-PCR (Real-Time-PCR) tester for å undersøke fo tilstedeværelsen av kopier av nptII-genet i de 219 prøvene. Vi fant at nesten alle prøvene var negative (99%), mens kun tre enkeltprøver (løvetann, rødkløver og skogmaur) viste et svært lavt nivå av nptII (3-4 kopier). De positive prøvene kan være naturlige varianter eller kontaminering fra forskningslaboratorier. Vi konkluderer med at der er behov for utvida undersøkelser innenen for dette fagfeltet.
Sammendrag
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Forfattere
Paul Eric AspholmSammendrag
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Sammendrag
In many applications, estimates are required for small sub-populations with so few (or no) sample plots that direct estimators that do not utilize auxiliary variables (e.g. remotely sensed data) are not applicable or result in low precision. This problem is overcome in small area estimation (SAE) by linking the variable of interest to auxiliary variables using a model. Two types of models can be distinguished based on the scale on which they operate: i) Unit-level models are applied in the well-known area-based approach (ABA) and are commonly used in forest inventories supported by fine-resolution 3D remote sensing data such as airborne laser scanning (ALS) or digital aerial photogrammetry (AP); ii) Area-level models, where the response is a direct estimate based on a sample within the domain and the explanatory variables are aggregated auxiliary variables, are less frequently applied. Estimators associated with these two model types can make use of sample plots within domains if available and reduce to so-called synthetic estimators in domains where no sample plots are available. We used both model types and their associated model-based estimators in the same study area with AP data as auxiliary variables. Heteroscedasticity, i.e. for continuous dependent variables typically an increasing dispersion of re- siduals with increasing predictions, is often observed in models linking field- and remotely sensed data. This violates the model assumption that the distribution of the residual errors is constant. Complying with model assumptions is required for model-based methods to result in reliable estimates. Addressing heteroscedasticity in models had considerable impacts on standard errors. When complying with model assumptions, the precision of estimates based on unit-level models was, on average, considerably greater (29%–31% smaller standard errors) than those based on area-level models. Area-level models may nonetheless be attractive because they allow the use of sampling designs that do not easily link to remotely sensed data, such as variable radius plots.
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Sammendrag
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Forfattere
Mohsen Ghanbari Vahid AkbariSammendrag
Increased discrimination capability provided by polarimetric synthetic aperture radar (PolSAR) sensors compared to single and dual polarization synthetic aperture radar (SAR) sensors can improve land use monitoring and change detection. This necessitates reliable change detection methods in multitemporal PolSAR datasets. This paper proposes an unsupervised change detection algorithm for multilook PolSAR data. In the first step of the method, the Hotelling-Lawley trace (HLT) statistic is applied to measure the similarity of two multilook covariance matrices. As a result of this step, a scalar test statistic image is generated. Then, in the second step, a generalized Kittler and Illingworth (K&I) minimum-error thresholding algorithm is developed to perform on the test statistic image and discriminate between changed and unchanged areas. The K&I thresholding algorithm is based on the generalized Gamma distribution for statistical modeling of change and no-change classes. The proposed methodology is tested on a simulated PolSAR data and two C-band fully PolSAR datasets acquired by the uninhabited aerial vehicle SAR and RADARSAT-2 SAR satellites. The experiments show that the proposed algorithm accurately discriminates between change and no-change areas providing detection results with noticeably lower error rates and higher detection accuracy values compared to those of a CFAR-type thresholding of the HLT statistic. Also, the performance of the HLT statistic compared to the other statistics applied on the multilook polarimetric SAR data is assessed. Goodness-of-fit test results prove that the estimated generalized Gamma class conditional models adequately fit the corresponding change and no-change classes.
Forfattere
Trond MæhlumSammendrag
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