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
Sammendrag
A process-based model was developed to predict dry matter yields and amounts of harvested nitrogen in conventionally cropped grassland fields, accounting for within-field variation by a node network design and utilizing remotely sensed information from a drone-borne system for increased accuracy. The model, named NORNE, was kept as simple as possible regarding required input variables, but with sufficient complexity to handle central processes and minimize prediction errors. The inputs comprised weather data, soil information, management data related to fertilization, and a visual estimate of clover proportion in the aboveground biomass. A sensitivity analysis was included to apportioning variation in dry matter yield outputs to variation in model parameter settings. Using default parameter values from the literature, the model was evaluated on data from a two-year study (2016–2017, 264 research plots in total each year) conducted at two locations in Norway (i.e. in South-East and in Central Norway) with contrasting climatic conditions and with internal variation in soil characteristics. The results showed that the model could estimate dry matter yields with a relatively high accuracy without any corrections based on remote sensing, compared with published results from comparable model studies. To further improve the results, the model was calibrated shortly before harvest, using predictions of above ground dry matter biomass obtained from a drone-borne remote sensing system. The only parameters which were hereby adjusted in the NORNE model were the starting values of nitrogen content in soil (first cut) and the plant available water capacity (second cut). The calibration based on the remotely sensed information improved the predictive performance of the model significantly. At first cut, the root mean square error (RMSE) of dry matter yield prediction was reduced by 20% to a mean value of 58 g m−2, corresponding to a relative value (rRMSE) of 0.12. For the second cut, the RMSE decreased by 13% to 66 g m−2 (rRMSE: 0.18). The model was also evaluated in terms of the predictions of amounts of nitrogen in the harvested crop. Here, the calibration reduced the RMSE of the first cut by 38%, obtaining a mean RMSE value of 2.1 g N m−2 (rRMSE: 0.28). For the second cut, the RMSE reduction for simulated harvested N was 16%, corresponding to a mean RMSE value of 2.3 g N m−2 (rRMSE: 0.33). The large improvements in model accuracy for simulated dry matter and nitrogen yields obtained through calibration by utilizing remotely sensed information, indicate the importance of considering spatial variability when applying models under Nordic conditions, both for yield predictions and for decision support for nitrogen application.
Sammendrag
There is an increased interest in the hydroponic production of strawberries in protected cultivation systems, and it is, therefore, urgent to develop new, more sustainable growing media alternatives. This study investigated the physical properties of wood fiber produced from Norway spruce (Picea abies (L.) H. Karst.) and peat:wood fiber substrate blends as well as the performance of the wood fiber in comparison to the industry standards, i.e., peat and coconut coir in the cultivation of hydroponic strawberry. Tray plants of the June-bearing strawberry (Fragaria × ananassa Duch.) cultivar ‘Malling Centenary’ were transplanted into five different growing media: a peat (80%) and perlite (20%) mixture, stand-alone (100%) coconut coir and three stand-alone (100%) Norway spruce wood fiber substrates (including coarse textured fibers with compact and loose packing density and compacted fine-textured fibers). Ripe strawberries were harvested and registered throughout the production season. The overall marketable yield was comparable across all the tested growing media; however, after 4 weeks of harvest, both coarse wood fiber and fine wood fiber showed better fruiting performance than the peat-perlite mixture. A trend for earlier berry maturation was observed for all wood fiber-based substrates. Plant parameters recorded after the end of production showed that plant height, number of leaves, and biomass production were higher in coarse wood fiber than in the peat-perlite mixture. Moreover, plants grown in wood fiber-based substrates had less unripe berries and flowers not harvested in comparison to both the peat and coir treatments.
Forfattere
Siv Mari Aurdal Tomasz Leszek Woznicki Trond Haraldsen Krzysztof Kusnierek Anita Sønsteby Siv Fagertun RembergSammendrag
Cultivation of strawberries in greenhouses and polytunnels is increasing, and new sustainable growing media are needed to replace peat and coconut coir. This study investigated the effect of wood fiber and compost as growing media on hydroponically cultivated strawberries. Two experiments were conducted, where the everbearing cultivar ‘Murano’ was grown in mixtures of wood fiber and compost (Experiment 1) and the seasonal flowering cultivar ‘Malling Centenary’ was grown in mixtures of wood fiber and peat (Experiment 2). Additionally, in Experiment 2, the effect of adding start fertilizer was assessed. The yield potential of ‘Murano’ plants was maintained in all substrates compared to the coconut coir control. However, a mixture of 75% wood fiber and 25% compost produced the highest yield, suggesting that mixtures of nutritious materials with wood fiber may improve plant performance. The chemical composition of the berries was not affected by the substrate composition; however, berries from plants grown in the best performing blend had a lower firmness than those grown in coconut coir. ‘Malling Centenary’ plants produced higher yields in substrates enriched with start fertilizer. Generally, the productivity of ‘Malling Centenary’ plants was maintained in blends containing up to 75% of wood fiber mixture even without start fertilizer.
2022
Forfattere
Jakob Geipel El Houssein Chouaib Harik Kristian Rindal Anne-Grete Roer Hjelkrem Krzysztof KusnierekSammendrag
Det er ikke registrert sammendrag
Sammendrag
The remote sensing of the biophysical and biochemical parameters of crops facilitates the preparation of application maps for variable-rate nitrogen fertilization. According to comparative studies of machine learning algorithms, Gaussian process regression (GPR) can outperform more popular methods in the prediction of crop status from hyperspectral data. The present study evaluates GPR model accuracy in the context of spring wheat dry matter, nitrogen content, and nitrogen uptake estimation. Models with the squared exponential covariance function were trained on images from two hyperspectral cameras (a frenchFabry–Pérot interferometer camera and a push-broom scanner). The most accurate predictions were obtained for nitrogen uptake (R2=0.75–0.85, RPDP=2.0–2.6). Modifications of the basic workflow were then evaluated: the removal of soil pixels from the images prior to the training, data fusion with apparent soil electrical conductivity measurements, and replacing the Euclidean distance in the GPR covariance function with the spectral angle distance. Of these, the data fusion improved the performance while predicting nitrogen uptake and nitrogen content. The estimation accuracy of the latter parameter varied considerably across the two hyperspectral cameras. Satisfactory nitrogen content predictions (R2>0.8, RPDP>2.4) were obtained only in the data-fusion scenario, and only with a high spectral resolution push-broom device capable of capturing longer wavelengths, up to 1000 nm, while the full-frame camera spectral limit was 790 nm. The prediction performance and uncertainty metrics indicated the suitability of the models for precision agriculture applications. Moreover, the spatial patterns that emerged in the generated crop parameter maps accurately reflected the fertilization levels applied across the experimental area as well as the background variation of the abiotic growth conditions, further corroborating this conclusion.
Forfattere
Malin Larsen GræsdahlSammendrag
Denne rapporten er utarbeidet som en del av prosjektet PRESIS og gir en enkel innføring i hvordan gjøre gode målinger ved bruk av drone. I PRESIS-prosjektet bruker vi dronen DJI Phantom 4 Multispectral – derfor brukes den også som referanse i denne rapporten. Prinsippene for å gjøre gode målinger vil også gjelde ved bruk av andre typer droner.
Forfattere
Jiangsan ZhaoSammendrag
Det er ikke registrert sammendrag
Forfattere
Jiangsan ZhaoSammendrag
Det er ikke registrert sammendrag
Forfattere
Kjersti Balke HveemSammendrag
Det er ikke registrert sammendrag
Forfattere
Kjersti Balke HveemSammendrag
Denne rapporten beskriver resultatene fra en gjennomgang av forskjellige såkalte Farm Management Information Systems (FMIS) som er tilgjengelig for bruk for norske gårdbrukere. Arbeidet startet med en kartlegging av hvilke FMIS som finnes tilgjengelig. Deretter ble det mest relevante utvalget av disse testet med hensikt å svare på en rekke spørsmål knyttet til funksjonalitet og bruk. Basert på gjennomgangen som er gjort, gis et sett med råd til den som skal ta i bruk et FMIS.