James Biney

A scientist with a demonstrated record of research success. Self-directed professional with a highly ethical, meticulous, and thorough approach, eager to leverage knowledge gaps in other fields. A team player with strong organizational skills and the ability to handle multiple research tasks simultaneously with a high degree of accuracy. Published research findings through high-peer-reviewed articles and analyzed results tailoring to possible future applications. Postdoctoral Fellow with one year in fellowship at University of Manitoba. Published research findings through peer-reviewed articles and analyzed results tailoring to possible future applications. Smart Postdoctoral Fellow with extensive knowledge of proximal and remote sensing skills in the field of spectroscopy. Hardworking with strong understanding of research processes. Adaptable and hardworking individual with excellent problem-solving skills.

• 5th Global workshop on Proximal Soil Sensing, Columbia, Missouri, USA, 05/28/2019, 05/31/2019
• 2023 CSSS Annual Meeting of the Canadian Society of Soil Science, 06/26/2023, 06/29/2023
• GLOSOLAN Soil Spectroscopy Workshop, http://www.fao.org/global-soilpartnership/glosolan/soil-analysis/capacity-development/en/, 09/06/21
• GLOSOLAN Soil Spectroscopy Workshop, http://www.fao.org/global-soilpartnership/glosolan/soil-analysis/capacity-development/en/, 10/28/21
• Department of Soil science seminar
• Machine learning algorithm for the prediction of soil organic carbon, Soil Science Department, University of Manitoba, 11/28/2023

• Comparison of Field and Laboratory Wet Soil Spectra in the Vis-NIR Range for Soil Organic Carbon Prediction in the Absence of Laboratory Dry Measurements, Remote sensing mdpi, Biney et al., 2020, https://doi.org/10.3390/rs12183082
• Exploring the Suitability of UAS-Based Multispectral Images for Estimating Soil Organic Carbon: Comparison with Proximal Soil Sensing and Spaceborne Imagery, Remote sensing mdpi, Biney et al., 2021, https://doi.org/10.3390/rs13020308
• Does the Limited use of Orthogonal Signal Correction Pre-treatment approach to improve the prediction accuracy of Soil Organic Carbon need attention?, Geoderma, Biney et al., 2021, https://doi.org/10.1016/j.geoderma.2021.114945
• Using an Ensemble model coupled with portable X-ray Fluorescence and visible near-infrared Spectroscopy to explore the viability of Mapping and Estimating Arsenic in an Agricultural Soil, Science of the Total Environment, Biney et al., 2021, https://doi.org/10.1016/j.scitotenv.2021.151805
• Verifying the predictive performance for soil organic carbon when employing field Vis-NIR spectroscopy and satellite imagery obtained using two different sampling methods, Computers and Electronics in Agriculture, Biney, 2022, https://doi.org/10.1016/j.compag.2022.106796
• Prediction of topsoil organic carbon content with Sentinel-2 imagery and spectroscopic measurements under different conditions using an ensemble model approach with multiple pre-treatment combinations, Soil and Tillage Research, Biney et al., 2022, https://doi.org/10.1016/j.still.2022.105379
• Can in situ spectral measurements under disturbance-reduced environmental conditions help improve soil organic carbon estimation?, Science of the Total Environment, Biney et al., 2021, https://doi.org/10.1016/j.scitotenv.2022.156304
• Examining the influence of bare soil UAV imagery combined with auxiliary datasets to estimate and map soil organic carbon distribution in an erosion-prone agricultural field, Science of the Total Environment, Biney et al., 2021, https://doi.org/10.1016/j.scitotenv.2023.161973
• Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview, Remote Sensing, Vaudour et al., 2022, https://doi.org/10.3390/rs14122917
• Optimized modelling of countrywide soil organic carbon levels via an interpretable decision tree, Smart Agricultural Technology, Kebonye et al., 2022, https://doi.org/10.1016/j.atech.2022.100106
• Estimating the spatial distribution of soil volumetric water content in an agricultural field employing remote sensing and other auxiliary data under different tillage management practices, Soil Use and Management, Abebrese et al., 2023, https://doi.org/10.1111/sum.12981