Mats Söderström
Saint Louis University
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Featured researches published by Mats Söderström.
Precision Agriculture | 2008
Johanna Wetterlind; Bo Stenberg; Mats Söderström
The creation of fine resolution soil maps is hampered by the increasing costs associated with conventional laboratory analyses of soil. In this study, near infrared (NIR) reflectance spectroscopy was used to reduce the number of conventional soil analyses required by the use of calibration models at the farm scale. Soil electrical conductivity and mid infrared reflection (MIR) from a satellite image were used and compared as ancillary data to guide the targeting of soil sampling. About 150 targeted samples were taken over a 97 hectare farm (approximately 1.5 samples per hectare) for each type of ancillary data. A sub-set of 25 samples was selected from each of the targeted data sets (150 points) to measure clay and soil organic matter (SOM) contents for calibration with NIR. For the remaining 125 samples only their NIR-spectra needed to be determined. The NIR calibration models for both SOM and clay contents resulted in predictions with small errors. Maps derived from the calibrated data were compared with a map based on 0.5 samples per hectare representing a conventional farm-scale soil map. The maps derived from the NIR-calibrated data are promising, and the potential for developing a cost-effective strategy to map soil from NIR-calibrated data at the farm-scale is considerable.
Sensors | 2016
Kristin Piikki; Mats Söderström; Jan Eriksson; Jamleck Muturi John; Patrick Ireri Muthee; Johanna Wetterlind; Eric Lund
Four proximal soil sensors were tested at four smallholder farms in Embu County, Kenya: a portable X-ray fluorescence sensor (PXRF), a mobile phone application for soil color determination by photography, a dual-depth electromagnetic induction (EMI) sensor, and a LED-based soil optical reflectance sensor. Measurements were made at 32–43 locations at each site. Topsoil samples were analyzed for plant-available nutrients (N, P, K, Mg, Ca, S, B, Mn, Zn, Cu, and Fe), pH, total nitrogen (TN) and total carbon (TC), soil texture, cation exchange capacity (CEC), and exchangeable aluminum (Al). Multivariate prediction models of each of the lab-analyzed soil properties were parameterized for 576 sensor-variable combinations. Prediction models for K, N, Ca and S, B, Zn, Mn, Fe, TC, Al, and CEC met the setup criteria for functional, robust, and accurate models. The PXRF sensor was the sensor most often included in successful models. We concluded that the combination of a PXRF and a portable soil reflectance sensor is a promising combination of handheld soil sensors for the development of in situ soil assessments as a field-based alternative or complement to laboratory measurements.
Geoderma | 2010
Johanna Wetterlind; Bo Stenberg; Mats Söderström
Precision agriculture '07. Papers presented at the 6th European Conference on Precision Agriculture, Skiathos, Greece, 3-6 June, 2007. | 2007
Johanna Wetterlind; Bo Stenberg; Mats Söderström; J. V. Stafford
Archive | 2013
Kristin Piikki; Johanna Wetterlind; Mats Söderström; Bo Stenberg
Archive | 2010
Jan Eriksson; Lennart Mattsson; Mats Söderström
Sustainability | 2018
Ravic P. Nijbroek; Kristin Piikki; Mats Söderström; Bas Kempen; Katrine Turner; Simeon Hengari; John Mutua
Archive | 2017
Ravic P. Nijbroek; Bas Kempen; John Mutua; Mats Söderström; Kristin Piikki; Simeon Hengari; Amon Andreas
Archive | 2017
Ravic P. Nijbroek; John Mutua; Mats Söderström; Kristin Piikki; Bas Kempen; Simeon Hengari
Archive | 2016
Johanna Wetterlind; Mats Söderström