Summary 1.Root life span regulates the quantity and quality of root-derived organic matter transferred to the soil organic matter pool. However, poor understanding of the rates and controls of root life span has hindered the prediction of carbon (C) flow and nutrient cycling dynamics at the ecosystem scale. 2. We examined the effects of root branch order, root diameter, mycorrhizal colonization, season of birth, depth in the soil, nitrogen (N) fertilization and foliage removal on root life span in a longleaf pine ( Pinus palustris Mill.) forest from 2001 to 2004 using minirhizotron and soil monolith sampling. 3 . Among all factors, root branch order had the strongest and most consistent effect on life span, with higher order roots having a 46% longer life span than roots one order lower. 4 . Within first order roots, mycorrhizal colonization significantly increased root life span by > 45% in 2 of 3 years. 5 . Roots born in winter and spring generally lived longer than roots born in summer and autumn. Root life span was positively correlated with depth in the soil and root diameter, but the correlations were weaker than with order, year and season. Neither N fertilization nor foliage removal had a significant impact on root life span. 6 . When biomass mortality and associated N flux were estimated based on order-specific mean life span, N concentration and ecosystem-scale biomass estimates, first order roots constituted approximately 50% of the total biomass mortality and > 60% of the N flux for the first three root orders combined. 7. Synthesis. Our results show that (i) root branch order was the strongest predictor of life span among all covariates and can effectively partition the distal longleaf pine root systems into three or more populations with different turnover rates; (ii) only a fraction of fine roots turns over annually, whereas models of C cycles assume an annual turnover for the entire fine root system. We conclude that an order-based approach holds greater promise than the traditional diameter class approach for evaluating the role of different fine root populations in C flow and nutrient cycling.

Among tree fine roots, the distal small-diameter lateral branches comprising first- and second-order roots lack secondary (wood) development. Therefore, these roots are expected to decompose more rapidly than higher order woody roots. But this prediction has not been tested and may not be correct. Current evidence suggests that lower order roots may decompose more slowly than higher order roots in tree species associated with ectomycorrhizal (EM) fungi because they are preferentially colonized by fungi and encased by a fungal sheath rich in chitin (a recalcitrant compound). In trees associated with arbuscular mycorrhizal (AM) fungi, lower order roots do not form fungal sheaths, but they may have poorer C quality, e.g. lower concentrations of soluble carbohydrates and higher concentrations of acid-insolubles than higher order roots, thus may decompose more slowly. In addition, litter with high concentrations of acid insolubles decomposes more slowly under higher N concentrations (such as lower order roots). Therefore, we propose that in both AM and EM trees, lower order roots decompose more slowly than higher order roots due to the combination of poor C quality and high N concentrations. To test this hypothesis, we examined decomposition of the first six root orders in Fraxinus mandshurica (an AM species) and Larix gmelinii (an EM species) using litterbag method in northeastern China. We found that lower order roots of both species decomposed more slowly than higher order roots, and this pattern appears to be associated mainly with initial C quality and N concentrations. Because these lower order roots have short life spans and thus dominate root mortality, their slow decomposition implies that a substantial fraction of the stable soil organic matter pool is derived from these lower order roots, at least in the two species we studied.

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White jute (Corchorus capsularis) and dark jute (Corchorus olitorius) are two important cultivated crops that are used for natural fibre production. Some genetic maps have been developed for dark jute, but the genetic map information for white jute (C. capsularis) is limited. In this study, a linkage map comprising 44 sequence‐related amplified polymorphisms (SRAPs), 57 intersimple sequence repeats (ISSRs) and 18 randomly amplified polymorphic DNA (RAPD) covering 2185.7 cM with a mean density of 18.7 cM per locus was constructed in an F2 population consisting of 185 individuals derived from a cross between two diverse genotypes of ‘Xinxuan No. 1’ and ‘Qiongyueqing’ in white jute. These markers were evenly distributed in the linkage groups without any clustering. This genetic linkage map construction will facilitate the mapping of agronomic traits and marker‐assisted selection breeding in white jute.

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This work investigated the effect of counter-ions and interfacial turbulence on oxygen transfer from gas to liquid phase containing ionic surfactant, and experiments were performed in a mechanically stirred reactor with flat gas-liquid interface. Counter-ions in terms of hydration ability and polarizability influence the interfacial coverage of ionic surfactants (i.e. cetytrimethylammonium bromide (CTAB) and cetytrimethylammonium chloride) with the same hydrocarbon chain length, producing hindrance but in different extent on oxygen transfer. The addition of electrolyte (NH 4 Br) substantially reduced the interfacial tension and surface charge of micelles (zeta potential) in CTAB system, and this salt effect greatly compressed interfacial double layer leading to gas transfer inhibition. The surface charge, aggregation number as well as stability of micelles formed above the critical micelle concentration could also alter interfacial configuration of surfactant layer reflected by gas absorption rate. Liquid turbulence was analyzed to decide the role of surfactant present in water on gas-liquid mass transfer, since Marangoni instability effect playing positive role should be taken into consideration under moderate liquid flow, while in turbulent system, contribution of Marangoni effect became overshadowed and consequently surfactant pose 'barrier' effect on gas transfer due to its surface active nature.

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Changes in land cover will cause the changes in the climate and environmental characteristics, which has an important influence on the social economy and ecosystem. The main form of land cover is different types of soil. Compared with traditional methods, visible and near-infrared spectroscopy technology can classify different types of soil rapidly, effectively, and nondestructively. Based on the visible near-infrared spectroscopy technology, this paper takes the soil of six different land cover types in Qingdao, China orchards, woodlands, tea plantations, farmlands, bare lands, and grasslands as examples and establishes a convolutional neural network classification model. The classification results of different number of training samples are analyzed and compared with the support vector machine algorithm. Under the condition that Kennard–Stone algorithm divides the calibration set, the classification results of six different soil types and single six soil types by convolutional neural network are better than those by the support vector machine. Under the condition of randomly dividing the calibration set according to the proportion of 1/3 and 1/4, the classification results by convolutional neural network are also better. The aim of this study is to analyze the feasibility of land cover classification with small samples by convolutional neural network and, according to the deep learning algorithm, to explore new methods for rapid, nondestructive, and accurate classification of the land cover.