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.
2023
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Forfattere
Anne Muola Traci Birge Marjo Helander Suni Mathew Vili Harazinova Kari Saikkonen Benjamin FuchsSammendrag
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Sammendrag
The durability against decay organisms is an essential material property for wood in outdoor use. A jack of all trades method for above-ground wood durability testing has been sought for decades, but until now no method has found its way into European standardization. The method of choice shall be applicable for untreated and treated wood—ideally also for wood composites. It shall further be reproducible, objective, fast, easy, and inexpensive. Finally, it shall provide high predictive power. This study was aimed at a review of results and practical experience with the Bundle test method which could serve as a standard procedure for above-ground field tests of wood-based materials. The method allows for water-trapping, creates a moderate moisture-induced decay risk typical for UC 3 situations, and was found applicable for a wide range of wood materials. The method allows for rapid infestation and failure of non-durable reference species within five years in Central Europe. Based on results from Bundle tests with different modifications and performed at different locations, a guideline has been developed. The method is recommended as a suitable tool for determining the durability of various wood-based materials including modified and preservative-treated wood and can provide data for durability classification.
Sammendrag
Active canopy sensors (ACSs) are great tools for diagnosing crop nitrogen (N) status and grain yield prediction to support precision N management strategies. Different commercial ACSs are available and their performances in crop N status diagnosis and recommendation may vary. The objective of this study was to determine the potential to minimize the differences of two commonly used ACSs (GreenSeeker and Crop Circle ACS-430) in maize (Zea mays L.) N status diagnosis and recommendation with multi-source data fusion and machine learning. The regression model was based on simple regression or machine learning regression including ancillary information of soil properties, weather conditions, and crop management information. Results of simple regression models indicated that Crop Circle ACS-430 with red-edge based vegetation indices performed better than GreenSeeker in estimating N nutrition index (NNI) (R2 = 0.63 vs. 0.50–0.51) and predicting grain yield (R2 = 0.56–0.57 vs. 0.49). The random forest regression (RFR) models using vegetation indices and ancillary data greatly improved the prediction of NNI (R2 = 0.81–0.82) and grain yield (R2 = 0.87–0.89), regardless of the sensor type or the vegetation index used. Using RFR models, moderate degree of accuracy in N status diagnosis was achieved based on either GreenSeeker or Crop Circle ACS-430. In comparison, using simple regression models based on spectral data only, the accuracy was significantly lower. When these two ACSs were used independently, they performed similarly in N fertilizer recommendation (R2 = 0.57–0.60). Hybrid RFR models were established using vegetation indices from both ACSs and ancillary data, which could be used to diagnose maize N status (moderate accuracy) and make side-dress N recommendations (R2 = 0.62–0.67) using any of the two ACSs. It is concluded that the use of multi-source data fusion with machine learning model could improve the accuracy of ACS-based N status diagnosis and recommendation and minimize the performance differences of different active sensors. The results of this research indicated the potential to develop machine learning models using multi-sensor and multi-source data fusion for more universal applications.
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Forfattere
Trond MæhlumSammendrag
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