Effects of ageing and successive slash-and-burn practice on the chemical composition of charcoal and yields of stable carbon

7 páginas.-- 2 figuras.-- 3 tablas.-- 50 referenciasChinese fir (Cunninghamia lanceolata) plantations cover over 12 Mha in the southeast provinces of China. The traditional conditioning of fields of this conifer involves the slash-and-burn practice. As a result of this practice, pyrogenic carbon (PyC) is produced by the incomplete combustion of organic matter; this includes a continuum of materials ranging from partially charred biomass and charcoal to soot. Owing to the structure and composition of PyC, it has traditionally been considered to have high chemical recalcitrance and resistance to degradation. Thus, PyC produced during slash-and-burn cultivation practices may profoundly alter the terrestrial carbon (C) cycle and soil chemical composition. Hence, this study aims to investigate the quantitative and qualitative composition of charcoal from the oldest Chinese fir plantation in China. Charcoal was sampled in soils from plots of different stand ages after one to four slash-and-burn rotations. This approach permitted an assessment of the effects of ageing and consecutive slash-and-burn rotations on the chemical composition of charcoal and the estimation of the stocks of stable C. The chemical composition of the PyC fraction was examined via elemental (CHN) and 13C nuclear magnetic resonance spectroscopy (13C NMR) analyses. These revealed some signs of degradation, such as a decrease in relative abundance of aryl-C and an increase in H/Cat values over time. The stocks of charcoal and yield of stable C in soil slightly increased with the number of fires, reaching a maximum of 1164 kg of PyC per hectare after four prescribed burns. Nevertheless, charcoal stocks decreased sharply with increasing stand age after each slash-and-burn event. In fact, over 25% of the charcoal stock was lost during the period from 12- to 21-years after the first slash-and-burn, and 85% was lost in the 97-year old stand. Our results together indicate that there has been a substantial loss of charcoal in soils following slash-and-burn rotations, on a time scale of decades associated with gentler slopes. Our results question the long-term persistence of charcoal in soils and highlight the necessity for periodic slash-and-burn rotation to maintain PyC stocksWe thank the economic support given by the National Natural Science Foundation of China ( U1405211 ) and the 12 th Five year plan of National Science and Technology Plan Project in Rural Areas ( 2015BAD09B010102 ). J.M. De la Rosa thanks the Spanish Ministry of Economy and Competitiveness (MINECO) ( RYC2014-16338 ) for a Ramón y Cajal contract. Our gratitude is extended to the field assistants of Wangtai town, for providing details about the area of study and their help during the soil sampling and to the forest managers for providing the management history of the plantation.Peer reviewe

Geospatial information on geographical and human factors improved anthropogenic fire occurrence modeling in the Chinese boreal forest

We applied a classic logistic regression (LR) together with a geographically weighted logistic regression (GWLR) to determine the relationship between anthropogenic fire occurrence and potential driving factors in the Chinese boreal forest, and to test whether the explanatory power of the LR model could be increased by considering geospatial information of geographical and human factors using a GWLR model. Three tests, "all variables", "significant variables" and "cross-validation", were applied to compare model performance between the LR and GWLR models. Our results confirmed the importance of distance to railway, elevation, length of fire line and vegetation cover on fire occurrence in the Chinese boreal forest. In addition, GWLR model performs better than LR in terms of model prediction accuracy, model residual reduction and spatial parameter estimation by considering geospatial information of explanatory variables. This indicates that the global LR model is incapable of identifying underlying causal factors for wildfire modeling sufficiently. The GWLR model helped identify spatial variation between driving factors and fire occurrence, which can contribute better understanding of forest fire occurrence over large geographic areas and the forest fire management practices may be improved based on it.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author