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.
2023
Abstract
Soil management is important for sustainable agriculture, playing a vital role in food production and maintaining ecological functions in the agroecosystem. Effective soil management depends on highly accurate soil property estimation. Machine learning (ML) is an effective tool for data mining, selection of key soil properties, modeling the non-linear relationship between different soil properties. Through coupling with spectral imaging, ML algorithms have been extensively used to estimate physical, chemical, and biological properties quickly and accurately for more effective soil management. Most of the soil properties are estimated by either near infrared (NIR), Vis-NIR, or mid-infrared (MIR) in combination with different ML algorithms. Spectroscopy is widely used in estimation of chemical properties of soil samples. Spectral imaging from both UAV and satellite platforms should be taken to improve the spatial resolution of different soil properties. Spectral image super-resolution should be taken to generate spectral images in high spatial, spectral, and temporal resolutions; more advanced algorithms, especially deep learning (DL) should be taken for soil properties’ estimation based on the generated ‘super’ images. Using hyperspectral modeling, soil water content, soil organic matter, total N, total K, total P, clay and sand were found to be successfully predicted. Generally, MIR produced better predictions than Vis-NIR, but Vis-NIR outperformed MIR for a number of properties. An advantage of Vis-NIR is instrument portability although a new range of MIR portable devices is becoming available. In-field predictions for water, total organic C, extractable phosphorus, and total N appear similar to laboratory methods, but there are issues regarding, for example, sample heterogeneity, moisture content, and surface roughness. More precise and detailed soil property estimation will facilitate future soil management.
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Fride Høistad ScheiAbstract
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Cecilie Marie Mejdell Knut Egil Bøe Grete H. Meisfjord Jørgensen Turid BuvikAbstract
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Arne SteffenremAbstract
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Emmanuel Oladeji Alamu Njoloma Joyce Akello Juliet Ngumayo Joel Ray Chazangwe Mehreteab Tesfai Chikoye David Nyoka Isaac Dale Lewis Sekhar Udaya NagothuAbstract
Agroforestry practices improve soil health which in turn improves crop nutrient concentrations and quality. This study examined how the agroforestry tree Gliricidia sepium intercropped with soybean, groundnuts, or maize affects crop nutrient compositions. The study was conducted in five Zambian chiefdoms for three crop-growing seasons (2019–2022) on 13 farmer-led demonstration trial sites. Seven treatments were tested that included maize, soybean, and groundnut plots with and without Gliricidia interventions. Grain samples were analyzed for crop nutrient contents using standard laboratory methods. Results showed that the treatments significantly (P < 0.05) improved maize nutritional properties except for crude fiber, total carbohydrate, and metabolizable energy. G. sepium intercropping with maize and soybean decreased the antinutritional contents and displayed better functional qualities. All elemental mineral components (except potassium, calcium, and sodium) were higher in the Gliricidia + maize intercrop than in the control treatment. The Gliricidia+soybean intercrop had lower mean mineral concentrations than the control (soybean only) except for Mg, Cu, and Zn. The Giliricidia+groundnut intercrop significantly increased groundnut mineral components except for Nitrogen, Phosphorus, Potassium, and Iron. It can be concluded that G. sepium intercropped with maize, soybean, and groundnuts significantly improved the crops’ nutritional quality.
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