Assessment of Soil Carbon Turnover in the Long Term Fertilizer Experiment and Validation of RothC -26.3 Model.
Ganesh S. S, Dhakshinamoorthy M, Kumaraperumal R, Anandakumar G, and Devarajan S. Tamil Nadu Agricultural Univ, Dept of Soil Science and Agricultural Chemistry, Coimbatore, 641003, India
In recent years the use of models to assess the turnover of soil organic matter is increasingly being used because the turnover of soil organic matter is a slow process and requires years and decades of measurements to quantify the changes induced by fertilization, tillage, management options, etc. The predictive modeling exercise offers a tool to quantify and validate these changes, assess carbon sequestration potential in various ecosystems and study the impact of carbon sequestration by land-use change. The idea behind the use of RothC-26.3 model in the present study was to assess the turnover of soil organic carbon caused by cropping system (Finger millet – Maize) and fertilization for the period 1992 - 2003 and to evaluate the performance of models with datasets from long-term fertilizer experiment under ten different static fertilization at Tamil Nadu Agricultural University, Tamil Nadu, India. The measured soil organic carbon pool agreed satisfactorily with the modeled soil organic carbon pool for all the ten static fertilizer and manurial application. The deviation in the soil carbon content between measured and modeled carbon pools was observed starting from the year 2001 to 2002. This shows that the model could not able to predict the carbon inputs by plants for the increased dry matter production caused by the effect of fertilizer dose. Hence, a plant production sub-model should be included giving due consideration to account for the fertilizer impact on the dry matter production and its consequent release of carbon into the soil by way of root exudates, lysates, etc., which would help RothC-26.3 in predicting precise estimates of plant derived carbon inputs and to overcome the discrepancies observed in this study. In general, the performance of RothC-26.3 model was good across all datasets and can be used for future prediction of the carbon estimates across India. Keywords: RothC-26.3 model, soil organic carbon turnover, simulation, plant carbon inputs, agricultural soils, long-term fertilizer experiment.