Recently, near infrared reflectance （NIR） and mid-infrared （MIR） spectroscopy techniques are increasingly introduced as convenient and simple non-destructive techniques for quantifying several soil properties. Thi...Recently, near infrared reflectance （NIR） and mid-infrared （MIR） spectroscopy techniques are increasingly introduced as convenient and simple non-destructive techniques for quantifying several soil properties. This study uses MIR method to predict pH, soil organic C, total N, AI, Ca, Mg and K, CEC and soil texture for soil samples collected in Sud-Kivu, Congo. A total of 536 composite soil samples were taken from two locations （Burhale and Luhihi） at two depths （0-20 cm and 20-40 cm） using a spatially-stratified random sampling design within an area of 200 km2. Differences in characteristics were evaluated between the two locations, land use （cultivated vs. non-cultivated land） with soil depths. A random subset of the samples （10%） were analyzed using standard wet chemistry methods, and calibration models developed by MIR data to estimate soil properties for the full soil sample set. Partial least squares regression （PLS） method gave acceptable coefficients of determination between 0.71 and 0.93 for all parameters. Soil organic matter levels were higher in cultivated plots in Luhihi （3.9% C） than in Burhale （3.0% C）, suggesting lower levels of soil fertility in the later area. This indicates high levels of acidity, which are likely to limit crop production in the area. Phosphorus deficiency is acute in Burhale （2.4 mg P/kg） but less in Luhihi （5.4 mg P/kg）. In both locations, low levels of Ca and Mg indicate that soils may be susceptible to deficiencies in both elements.These findings provide new opportunities for monitoring soil quality in the region which can benefit multiple actors and scientists involved in the agricultural and environmental sectors.展开更多
文摘Recently, near infrared reflectance （NIR） and mid-infrared （MIR） spectroscopy techniques are increasingly introduced as convenient and simple non-destructive techniques for quantifying several soil properties. This study uses MIR method to predict pH, soil organic C, total N, AI, Ca, Mg and K, CEC and soil texture for soil samples collected in Sud-Kivu, Congo. A total of 536 composite soil samples were taken from two locations （Burhale and Luhihi） at two depths （0-20 cm and 20-40 cm） using a spatially-stratified random sampling design within an area of 200 km2. Differences in characteristics were evaluated between the two locations, land use （cultivated vs. non-cultivated land） with soil depths. A random subset of the samples （10%） were analyzed using standard wet chemistry methods, and calibration models developed by MIR data to estimate soil properties for the full soil sample set. Partial least squares regression （PLS） method gave acceptable coefficients of determination between 0.71 and 0.93 for all parameters. Soil organic matter levels were higher in cultivated plots in Luhihi （3.9% C） than in Burhale （3.0% C）, suggesting lower levels of soil fertility in the later area. This indicates high levels of acidity, which are likely to limit crop production in the area. Phosphorus deficiency is acute in Burhale （2.4 mg P/kg） but less in Luhihi （5.4 mg P/kg）. In both locations, low levels of Ca and Mg indicate that soils may be susceptible to deficiencies in both elements.These findings provide new opportunities for monitoring soil quality in the region which can benefit multiple actors and scientists involved in the agricultural and environmental sectors.
