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.

2022

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Rapid methods allowing for non-destructive crop monitoring are imperative for accurate in-season nitrogen (N) status assessment and precision N management. The objectives of this paper were to (1) compare the performance of a leaf fluorescence sensor Dualex 4 and an active canopy reflectance sensor Crop Circle ACS-430 for estimating maize (Zea mays L.) N status indicators across growth stages; (2) evaluate the potential of N status prediction across growth stages using the reflectance parameters acquired from the canopy sensor at an early growth stage; and, (3) investigate the prospect of combining the active canopy sensor and leaf fluorescence sensor data to estimate N nutrition index (NNI) indirectly using a general model across growth stages. The results indicated that data from both sensors were closely related to NNI across stages. However, using the direct NNI estimation method, among the tested indices, only the N balance index (NBI) could diagnose N status satisfactorily, based on the Kappa statistics. The effect of growth stages on proximal sensing was reduced by incorporating the information of days after sowing. It was found that the leaf fluorescence sensor performed relatively better in estimating plant N concentration whereas the canopy reflectance sensor performed better in aboveground biomass estimation. Their combination significantly improved the reliability of N diagnosis, including NNI prediction. In addition, the study confirmed that N status can be assessed by predicting aboveground biomass at the later stages using the canopy reflectance measurements at an early stage. Furthermore, the integrated NBI was verified to be a more robust and sensitive N status indicator than the chlorophyll concentration index. It is concluded that combining active canopy sensor data, of an early growth stage (e.g. V8), with leaf fluorescence sensor data, modified using days after sowing, can improve the accuracy of corn N status diagnosis across growth stages.

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The application of numerical models to understand the behavioural pattern of a flood is widely found in the literature. However, the selection of an appropriate hydraulic model is highly essential to conduct reliable predictions. Predicting flood discharges and inundation extents are the two most important outcomes of flood simulations to stakeholders. Precise topographical data and channel geometries along a suitable hydraulic model are required to accurately predict floods. One-dimensional (1D) hydraulic models are now replaced by two-dimensional (2D) or combined 1D/2D models for higher performances. The Hydraulic Engineering Centre’s River Analysis System (HEC-RAS) has been widely used in all three forms for predicting flood characteristics. However, comparison studies among the 1D, 2D to 1D/2D models are limited in the literature to identify the better/best approach. Therefore, this research was carried out to identify the better approach using an example case study of the Kelani River basin in Sri Lanka. Two flood events (in 2016 and 2018) were separately simulated and tested for their accuracy using observed inundations and satellite-based inundations. It was found that the combined 1D/2D HEC-RAS hydraulic model outperforms other models for the prediction of flows and inundation for both flood events. Therefore, the combined model can be concluded as the better hydraulic model to predict flood characteristics of the Kelani River basin in Sri Lanka. With more flood studies, the conclusions can be more generalized.

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Heat Field Deformation (HFD) is a widely used method to measure sap flow of trees based on empirical relationships between heat transfer within tree stems and the sap flow rates. As an alternative, the Linear Heat Balance (LHB) method implements the same instrumental configuration as HFD but calculates the sap flow rates using analytical equations that are derived from fundamental conduction-convection heat transfer theories. In this study, we systematically compared the sap flow calculated using the two methods based on data that were recorded using the same instrument. The measurements were conducted on four Norway spruce trees. We aimed to evaluate the discrepancies between the sap flow estimates from the two methods and determine the underlying causes. Diurnal and day-to-day patterns were consistent between the sap flow estimates from the two methods. However, the magnitudes of the estimated sap flow were different between them, where LHB resulted in much lower estimates in three trees and slightly higher estimates in one compared to HFD. We also observed larger discrepancies in negative (reversed flow) than in positive sap flow, where the LHB resulted in lower reversed flow than HFD. Consequently, the seasonal budget estimated by LHB can be as low as ∼20% of that estimated by HFD. The discrepancies can be mainly attributed to the low wood thermal conductivities for the studied trees that lead to substantial underestimations using the LHB method. In addition, the sap flow estimates were very sensitive to the value changes of the empirical parameters in the calculations and, thus, using a proper case-specific value is recommended, especially for the LHB method. Overall, we suggest that, despite the strong theoretical support, the correctness of LHB outputs depends largely on the tree individuals and should be carefully evaluated.

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The variability in the emergence process of different populations was confirmed for two Echinochloa crus-galli populations, one from Italy (IT) and the second from Norway (NO). Seeds were sown in 12 localities over Europe and the Middle East, and the emergence patterns of IT and NO were compared with those of several local populations at each location. Seeds of each population were sown in pots buried to the ground level. The base temperature (Tb) for emergence was estimated by (1) analysing logistic models applied to the field emergence of IT and NO, and (2) a germination assay set in winter 2020 at constant temperatures (8, 11, 14, 17, 20, 26, 29°C) with newly collected seeds in 2019 from the same fields where IT and NO had previously been harvested in 2015. The logistic models developed for IT and NO in each location showed that the emergence pattern of IT was similar to that of the local populations in Poland, Italy, Spain, Turkey South and Iran, while NO fitted better to those in Sweden and Latvia. No germination was obtained for IT in a germination chamber, but the estimated Tb with the logistic model was 11.2°C. For NO, the estimated Tb was 8.8°C in the germination chamber and 8.1°C in the field. Results suggest that adaptation to local environmental conditions has led to inter-population differences in Tb and parameter estimates of thermal-time models to predict the emergence of E. crus-galli should only be used for populations with similar climatic and habitat conditions.

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Echinochloa crus-galli (L.) P. Beauv. is one of the most important weeds. It is distributed worldwide and has adapted to diverse habitats and climatic conditions. This study aimed to compare the emergence patterns of two populations of E. crus-galli from different environments at 11 locations across Europe and the Middle East. Seeds of the two populations were collected from maize in Italy and from spring barley in Norway and were then buried in soil in autumn 2015. In the spring of 2016, the soil was disturbed around the usual seedbed preparation date in each location and emergence was recorded. The soil was again disturbed a year later and emergence was recorded for a second season. Total emergence, the times of onset, end and to 50% emergence and the period between 25% and 75% of emergence were analysed by two-way ANOVA and principal components analysis. The Italian population showed a higher emergence than the Norwegian population in Southern locations, while the ranking was reversed in Northern locations. In almost all locations, a tendency to emerge earlier was recorded for the Norwegian population, but the periods from 25% to 75% emergence were similar for both populations. Total emergence, and the times of onset and end of emergence seemed to be mainly under genotypic (plus maternal) control, suggesting there were different temperature thresholds for seedling emergence in each population. Conversely, the duration of emergence seemed to be mainly under environmental control. This research confirms the high variability between populations and suggests the need to continue identifying key characteristics for the development of efficient models for seedling emergence in specific climates and/or latitudes.

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Validation of models for plant disease management is a crucial part in the development of decision support systems in plant protection. Bespoke field trials are usually conducted to determine the performance of a model under practical conditions. However, field trials are very resource-demanding, and the use of already existing field trial data could significantly reduce costs for model validation. In this study, we took this novel approach to verify the performance of models for determining the need of fungicide applications against leaf blotch diseases in wheat by utilising historical weather data and yield data available from fungicide efficacy field trials. Two models based on humidity factors were used in the study. To estimate how specific humidity settings in the two models affect the number of recommended fungicide treatments per season, historical weather data from a 5-year period from weather stations in Denmark, Sweden, Norway, Finland, and Lithuania was used. The model output shows major differences between seasons and regions, typically recommending between one and three treatments per season. To determine the prediction potential of the models, data on yield gains from either one or two fungicide applications in fungicide efficacy trials conducted in wheat over a 5-year period in the five countries was utilised. The yield responses from fungicide treatments in the efficacy trials varied considerably between years and countries, as did the proportion of predictions of profitable treatments. In general, there was a tendency for the models to overestimate the need to apply fungicides (low specificity), but they rarely failed to recommend an application that was needed (high sensitivity). Despite the importance of having specific trials across regions in order to adjust models to local cropping and weather conditions, our study shows that historical weather data and existing field trial data have the potential to be used in model validation.

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In the present study, the streamflow simulation capacities between the Soil and Water Assessment Tool (SWAT) and the Hydrologic Engineering Centre-Hydrologic Modelling System (HEC-HMS) were compared for the Huai Bang Sai (HBS) watershed in northeastern Thailand. During calibration (2007–2010) and validation (2011–2014), the SWAT model demonstrated a Coefficient of Determination (R2) and a Nash Sutcliffe Efficiency (NSE) of 0.83 and 0.82, and 0.78 and 0.77, respectively. During the same periods, the HEC-HMS model demonstrated values of 0.80 and 0.79, and 0.84 and 0.82. The exceedance probabilities at 10%, 40%, and 90% were 144.5, 14.5, and 0.9 mm in the flow duration curves (FDCs) obtained for observed flow. From the HEC-HMS and SWAT models, these indices yielded 109.0, 15.0, and 0.02 mm, and 123.5, 16.95, and 0.02 mm. These results inferred those high flows were captured well by the SWAT model, while medium flows were captured well by the HEC-HMS model. It is noteworthy that the low flows were accurately simulated by both models. Furthermore, dry and wet seasonal flows were simulated reasonably well by the SWAT model with slight under-predictions of 2.12% and 13.52% compared to the observed values. The HEC-HMS model under-predicted the dry and wet seasonal flows by 10.76% and 18.54% compared to observed flows. The results of the present study will provide valuable recommendations for the stakeholders of the HBS watershed to improve water usage policies. In addition, the present study will be helpful to select the most appropriate hydrologic model for humid tropical watersheds in Thailand and elsewhere in the world.

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Prediction of the relative phosphorus (P) fertiliser value of bio-based fertiliser products is agronomically important, but previous attempts to develop prediction models have often failed due to the high chemical complexity of bio-based fertilisers and the limited number of products included in analyses. In this study, regression models for prediction were developed using independently produced data from 10 different studies on crop growth responses to P applied with bio-based fertiliser products, resulting in a dataset with 69 products. The 69 fertiliser products were organised into four sub-groups, based on the inorganic P compounds most likely to be present in each product. Within each product group, multiple regression was conducted using mineral fertiliser equivalents (MFE) as response variable and three potential explanatory variables derived from chemical analysis, all reflecting inorganic P binding in the fertiliser products: i) NaHCO3-soluble P, ii) molar ratio of calcium (Ca):P and iii) molar ratio of aluminium+iron (Al+Fe):P. The best regression model fit was achieved for sewage sludges with Al-/Fe-bound P (n = 20; R2 = 79.2%), followed by sewage sludges with Ca-bound P (n = 11; R2 = 71.1%); fertiliser products with Ca-bound P (n = 29; R2 = 58.2%); and thermally treated sewage sludge products (n=9;R2=44.9%). Even though external factors influencing P fertiliser values (e.g. fertiliser shape, application form, soil characteristics) differed between the underlying studies and were not considered, the suggested prediction models provide potential for more efficient P recycling in practice.