Publikasjoner
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2024
Forfattere
Inger MartinussenSammendrag
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Sammendrag
Seasonal pollen allergy is a major public health concern, with many different pollen aeroallergens being present in the atmosphere at varying levels during the season. In Norway, information about spatiotemporal variation of pollen aeroallergens is currently lacking, leading to reduced ability to manage and treat seasonal allergies. Seven pollen aeroallergens (alder, hazel, willow, birch, pine, grass and mugwort) were monitored daily for 16 years from 12 regions and coalesced to create regional pollen calendars. Seasonal statistics, such as seasonal pollen integral (SPIn), onset, duration and periods of high and very high concentrations, were calculated for all pollen types and regions. High days were further modelled with SPIn in a linear regression framework to investigate the connection between the strength of the season and number of days above high pollen thresholds. The tree pollen season occurred between January and mid-July, with the pollen aeroallergens birch and pine being the most prominent in all regions. The herb pollen season was observed to occur between June and mid-August, although mugwort was almost completely absent. The grass pollen season was mostly mild on average but more severe in some regions, primarily Kristiansand. South-east regions of Oslo, Kristiansand and Lillehammer had the overall highest pollen load, while northern regions of Bodø, Tromsø and Kirkenes had the overall lowest pollen loads. SPIn and days above high pollen thresholds had positive highly significant relationships (R2 > 0.85) for all pollen types, bar mugwort. Regional pollen calendars and seasonal statistics contribute to reliable information that can be used by medical professionals to effectively and timely manage and treat seasonal pollen allergies in Norway. Further research is needed to determine sensitization profiles of pine and willow.
Sammendrag
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Sammendrag
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Sammendrag
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Sammendrag
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Forfattere
Lise GrøvaSammendrag
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Forfattere
Lise GrøvaSammendrag
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Sammendrag
Increasing planting densities and nitrogen (N) application rates are two practices commonly used in high-yield maize (Zea mays L.) production systems to increase crop yield, but have resulted in lower N use efficiency, increased lodging, and negative environmental problems. Crop sensing-based precision N management (PNM) strategies have been developed to optimize maize yield, N use efficiency, and reduce environmental footprints, however, PNM strategies to balance grain yield and lodging risks are still very limited. The objectives of this study were to: (1) propose a N nutrition index (NNI)-based algorithm for in-season estimation of maize N demand; and (2) develop a sensor-based PNM strategy to balance grain yield and lodging risk for maize. Field experiments were conducted in Northeast China from 2017 to 2019, using a split-plot design with three planting densities (5.5, 7.0 and 8.5 plants m−2) as main plots and six N rates (0–300 kg ha−1) as subplots. Based on previous studies, a leaf fluorescence sensor Dualex 4 good for estimating plant N concentration and a canopy reflectance sensor Crop Circle ACS 430 good for estimating plant aboveground biomass were used to estimate maize NNI and predict lodging risk. Total N rates to achieve low lodging risk were determined based on wind velocity causing maize stalk lodging and historical actual natural wind speed, as well as the response of a lodging risk indicator (stem failure moment, Bs) to N supply. In-season side-dress N rates were determined based on theoretical amount of preplant N fertilizer estimated using NNI and a target total N rate. The final recommended sidedress N rates were adjusted based on the sensor-predicted lodging risk. The results indicated that NNI could be used for estimating the theoretical amount of preplant N fertilizer required to reach the current N status. It’s feasible to estimate maize side-dress N demand based on the difference of a target total N rate (to achieve an optimal grain yield or low lodging risk) and the current theoretical N supply. Total N rate to ensure low lodging risk was suggested to be adopted under low and medium planting densities. Medium planting density of 70,000 plants ha−1 matched with the corresponding optimal N rate would be recommended for the study area to balance economic return and lodging risk. In general, high planting density is not recommended because it has high lodging risk. More studies are needed to further improve the developed crop sensing-based PNM strategy with more site-years of data and multi-source data fusion using machine learning models for practical on-farm applications.
Forfattere
Sidhi Soman Agnethe Christiansen Roman Florinski Girija Bharat Eirik Hovland Steindal Luca Nizzetto Paromita ChakrabortySammendrag
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