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- Kätterer, Thomas
(författare)
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The impact of agroforestry combined with water harvesting on soil carbon and nitrogen stocks in central Chile evaluated using the ICBM/N model
- 2011
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Ingår i: Agriculture, Ecosystems and Environment. - : Elsevier BV. - 0167-8809 .- 1873-2305. ; 140, s. 123–136-
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Tidskriftsartikel (refereegranskat)abstract
- Soil organic carbon (SOC) and total nitrogen (N) stocks in an agroforestry system with water harvesting were analysed in a field experiment and the results compared with those of other crop management systems in the Mediterranean zone of central Chile. Agroforestry with water harvesting showed higher positive effects on N stocks, mainly in the upper soil layer, than the other crop management systems. However, soil analysis revealed a lack of differences between treatments, a fact that might be related mainly to the short study time (12 years) and the high spatial variability in these soil properties at the experimental site. In addition, the Introductory Carbon Balance Model that simulates N processes (ICBM/N) was evaluated for simulating trends in SOC and N stocks in the field experiment. Soil data collected between 1996 and 2008 in the field experiment and primarily literature data sets were used to test ICBM/N and its performance was evaluated by considering uncertainty in model inputs using Generalised Likelihood Uncertainty Estimation (GLUE) methodology. The GLUE estimates (5% and 95%) and measured SOC and N stocks were in satisfactory agreement. The observed SOC and N stocks were bracketed by the uncertainty bands in 70% and 80% of the simulations, respectively. Sensitivity analysis showed the model to be most sensitive to C parameters, such as the humification coefficient (h). The results of this study show that ICBM/N can be an effective tool for estimating SOC and N stocks from agroforestry combined with water harvesting systems in the Mediterranean zone of central Chile over the medium term. However, they also indicate that additional data sets are needed to redefine the parameter distributions in the model and thus to predict trends in SOC and N stocks in the future
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