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The use of pedo-transfer functions for estimating soil organic carbon contents in maize cropland ecosystem in the Coastal Plains of Tanzania

Mwango, S.B., Wickama, J., Msanya, B.M., Kimaro, D.N., Mbogoni, J.D., Meliyo, J.L.
Catena 2019 v.172 pp. 163-169
clay, climate change, coastal plains, corn, cropland, ecosystems, laboratory experimentation, model validation, models, prediction, regression analysis, sand, sand fraction, silt, soil organic carbon, soil sampling, soil texture, Tanzania
Soil organic carbon (OC) plays a vital role on physico-chemical and biological properties of soils and on climate change regulation. The use of pedo-transfer functions from easily available soil properties for estimating soil OC could be fast and cheap when considering field and laboratory work implications especially in Sub Saharan Africa including Tanzania. This paper attempts to develop a model for estimating soil OC contents under maize croplands ecosystem using pedo-transfer functions from soil texture. A total of 100 epipedon data entries were randomly collected from the previous soil sampling works that were conducted under maize croplands in coastal plains of Tanzania. Eighty percent of the collected data were used for training the model by using multiple regression analysis while the remained 20% were used to validate the model. The results indicated that, clay and silt had significant (p < 0.001) positive correlation with soil OC while sand contents in soils had negative (p < 0.001) correlation with soil OC. All together clay, sand and silt were revealed powerful predictors (p < 0.001, R2 = 0.82) of OC content in soils. On validation, the soil OC predicted agreed by 81.2% with the soil OC determined by laboratory test. These results imply that pedo-transfer functions for predicting soil OC based on soil texture is not only fast and cheap but also is an effective option for estimating OC content in soils.