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.
2024
Abstract
In this study, the influence of riverbed silting on the groundwater regime in a lowland area was investigated. The study area is situated at the Rye Island (Žitný Ostrov) in Slovakia, along the Gabčíkovo – Topoľníky canal, which is part of the drainage-irrigation canal system constructed in this locality. The Rye Island is an area with very low slope (0.25 10–4) and good climatic conditions for aquatic vegetation, therefore the canals are influenced by intensive silting processes. The spatial and temporal patterns of surface water – groundwater exchange are significantly influenced by the thickness of riverbed sediments and their permeability. The aim of this study was to evaluate the thickness and hydraulic conductivity of bed sediments in the Gabčíkovo – Topoľníky canal and to examine their influence on the groundwater – surface water interaction in the area. The hydraulic conductivity of the sediments was assessed from undisturbed samples by the falling head method. The obtained data were used for numerical simulations of groundwater heads by the TRIWACO model for different drainage and infiltration resistance conditions in the area of interest. The results of this study can support the planning of canal maintenance.
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Authors
Annette Folkedal SchjøllAbstract
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Kalkidan Mulatu Ayele An Notenbaert Shimelis Gizachew Raji Bimrew Asmare Niklas Wickander Solomon Mwendia Peter Dörsch W. A. Worku Caroline Brophy Karl Thunes Marit JørgensenAbstract
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Jorunn BørveAbstract
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Jorunn Børve Theresa Weigl Emily Follett Ingunn Øvsthus Hanne Larsen Torbjørn Haukås Erlend Indergård Siv Fagertun Remberg Dalphy Ondine Camira Harteveld Arne StensvandAbstract
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Christian Wilhelm Mohr Johannes Breidenbach Gunnhild Søgaard Oliver Moen Snoksrud Rune EriksenAbstract
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Lingwei Dong Yuxin Miao Xinbing Wang Krzysztof Kusnierek Hainie Zha Min Pan William D. BatchelorAbstract
Efficient and accurate in-season diagnosis of crop nitrogen (N) status is crucially important for precision N management. The main objective of this study was to develop a strategy for in-season dynamic diagnosis of maize (Zea mays L.) N status across the growing season by integrating proximal sensing and crop growth modeling. In this study, we integrated plant N concentration (PNC) derived from leaf fluorescence sensor data and aboveground biomass (AGB) based on the best-performing spectral index calculated from active canopy reflectance sensor data with simulated PNC and AGB using a crop growth model, DSSAT-CERES-Maize, for dynamic in-season maize N status diagnosis across the growing season. The results confirmed the applicability of leaf fluorescence sensing for PNC estimation and active canopy reflectance sensing for AGB estimation, respectively. The calibrated DSSAT CERES-Maize model performed well for simulating AGB (R2 = 0.96), which could be used for calculating the N status indicator, N nutrition index (NNI). However, the model did not perform satisfactorily for PNC simulation, with significant discrepancies between the simulated and measured PNC values. The data integration method using both proximal sensing and crop growth modeling produced accurate predictions of NNI (R2 = 0.95) and N status diagnostic outcomes (Kappa statistics = 0.64) for key growth stages in this study and could be used to simulate maize N status across the growing season, showing the potential for in-season dynamic N status diagnosis and management decision support. More studies are needed to further improve this approach by multi-sensor and multi-source data fusion using machine learning models.
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No abstract has been registered