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.
2021
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Sammendrag
Many greenkeepers and authorities are concerned about the environmental risks resulting from pesticide use on golf courses. We studied leaching and surface runoff of fungicides and metabolites during two winter seasons after fall application of boscalid, pyraclostrobin, prothioconazole, trifloxystrobin and fludioxonil in field lysimeters at NIBIO Landvik, Norway. The applications were made on creeping bentgrass greens (5% slope) that had been established from seed or sod (26 mm mat) on USGA‐spec. root zones amended with Sphagnum peat or garden compost, both with 0.3‐0.4% organic carbon in the root zone. The proportions of the winter precipitation recovered as surface and drainage water varied from 3 and 91% in 2016‐17 to 33 and 55% in 2017‐18 due to differences in soil freezing, rainfall intensity and snow and ice cover. Detections of fungicides and their metabolites in drainage water were mostly within the Environmental Risk Limits (ERLs) for aquatic organisms. In contrast, concentrations in surface runoff exceeded ERLs by up to 1000 times. Greens established from sod usually had higher fungicide losses in surface runoff but lower losses in drainage water than greens established from seed. Presumably because of higher microbial activity and a higher pH that made prothioconazole‐desthio more polar, fungicide and metabolite losses in drainage water were usually higher from greens containing compost that from greens containing peat. Leaching of fungicides and metabolites occurred even from frozen greens. The results are discussed in a practical context aiming for reduced environmental risks from spraying fungicides against turfgrass winter diseases.
Sammendrag
Sustainability learning is gaining popularity as an important field within sustainability research, where farm sustainability can be understood as a learning process. In this study, we seek to reveal the sustainability learning process of farmers, utilizing a framework distinguishing contextual factors (where? and when?), knowledge (what?), motivation (why?), and process (how?). The article presents a participatory inquiry mixed-methods approach, utilizing results from sustainability assessments on five farms with the SMART-farm tool as a unifying starting point for further discussions on sustainability learning in farmers' interviews and stakeholder workshops. Empirically the study is set in the horticultural production in Arctic Norway, where few studies on sustainability have been undertaken. The study shows how both the complexity of the concept of farm sustainability and contextual factors influence the sustainability learning process, for instance by giving rise to a vast number of conflicting issues while working toward farm sustainability. The sustainability learning process is found to be predominantly a social learning process. The theoretic contribution of the study lies in its novel framework that can be used to reveal important aspects of the sustainability learning process, as well as to contribute to the literature on how to proceed from sustainability assessments to implementation. A key finding from the study is that farmers will require continuous assistance in their processes toward farm sustainability, but for this to be possible, knowledge, sources of knowledge, and learning platforms for holistic sustainability need to be established.
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Forfattere
Owusu Fordjour Aidoo Sarah Cunze Ritter Atoundem Guimapi Linda Arhin Fred Kormla Ablormeti Elizabeth Tettey Frank Dampare Yayra Afram Osei Bonsu Joshua Obeng Hanif Lutuf Matthew Dickinson Ndede YankeySammendrag
Coconut is recognized for its popularity in contributing to food and nutritional security. It generates income and helps to improve rural livelihood. However, these benefits are constrained by lethal yellowing disease (LYD). A clear understanding of climate suitable areas for disease invasion is essential for implementing quarantine measures. Therefore, we used a machine learning algorithm based on maximum entropy to model and map habitat suitability of LYD and coconut under current and future climate change scenarios using three Shared Socio-economic Pathways (SSPs) (1.26, 3.70 and 5.85) for three time periods (2041–2060, 2061–2080 and 2081–2100). Outside its current range, the model projected habitat suitability of LYD in Australia, Asia and South America. The distribution of coconut exceeded that of LYD. The area under the curve value of 0.98 was recorded for LYD, whereas 0.87 was obtained for the coconut model. The predictor variables that most influenced LYD projections were minimum temperature of the coldest month (88.4%) and precipitation of the warmest quarter (7.3%), whereas minimum temperature of the coldest month (85.9%) and temperature seasonality (8.7%) contributed most to the coconut model. Our study highlights potential climate suitable areas of LYD and coconut, and provides useful information for increasing quarantine measures and developing resistant or tolerant coconut varieties against the disease. Also, our study establishes an approach to model the climatic suitability for surveillance and monitoring of the disease, especially in areas that the disease has not been reported.
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