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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
Authors:Osvaldo Salazar  Manuel Casanova  Thomas Kätterer
Affiliation:1. Departamento de Ingeniería y Suelos, Facultad de Ciencias Agronómicas, Universidad de Chile, Casilla 1004, Santiago, Chile;2. Swedish University of Agricultural Sciences, Department of Soil and Environment, PO Box 7014, Uppsala SE-750 07, Sweden;1. CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Menglun 666303, China;2. School of Biological and Environmental Engineering, Xi’an University, Xi’an 710065, China;3. CAS Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, China;4. Nanjing Institution of Vegetable Science, Nanjing 210042, China;5. Center for Integrative Conservation, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Menglun, 666303, China;1. Université de Toulouse, UPS GET, 14 avenue E. Belin, F-31400 Toulouse, France;2. CNRS, GET, 14 avenue E. Belin, F-31400 Toulouse, France;3. IRD, UR 234, GET, 14 avenue E. Belin, F-31400 Toulouse, France;4. Normandie University, France;5. UCBN, M2C, F-14000 Caen, France;6. CNRS, UMR 6143 M2C, F-14000 Caen, France;7. CNRS, ISTerre, Université Joseph Fourier, Grenoble, France;8. Université de Lille 1, CNRS, Géosystèmes, Villeneuve D’Ascq, France;9. Département de Géologie, EA4098 LaRGe Labo. de Rech. en Géosciences, Université des Antilles et de la Guyane, Campus de Fouillole – 97159 Pointe à Pitre Cedex, Guadeloupe (FWI), France;1. Escuela de Geografía, Universidad Academia de Humanismo Cristiano, Condell 343. Edificio A, quinto piso. Providencia, Santiago, Chile;2. Departamento de Geografía, University of Chile, Av. Portugal 84, Región Metropolitana, Santiago, Chile;3. Faculty of Agronomic Sciences, University of Chile, Av. Santa Rosa N° 11315. La Pintana, Santiago, Chile;4. Center for Climate Resilience Research (CR2), University of Chile, Santiago, Chile;1. Departamento de Farmacia, Universidad de Guanajuato, Noria Alta, Guanajuato 36050, Mexico;2. Facultad de Medicina, Universidad Autónoma de Querétaro, Mexico;3. UNAM, Instituto de Neurobiología, Querétaro, Mexico;4. Departamento de Química, Universidad de Guanajuato, Guanajuato, Mexico
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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