Intensive organic vegetable production increases soil organic carbon but with a lower carbon conversion efficiency than integrated management

Intensive vegetable production in greenhouses has rapidly expanded in China since the 1990s and increased to 1.3 million ha of farmland by 2016, which is the highest in the world. We conducted an 11‐year greenhouse vegetable production experiment from 2002 to 2013 to observe soil organic carbon (SOC) dynamics under three management systems, i.e., conventional (CON), integrated (ING), and intensive organic (ORG) farming. Soil samples (0–20 and 20–40 cm depth) were collected in 2002 and 2013 and separated into four particle‐size fractions, i.e., coarse sand (> 250 µm), fine sand (250–53 µm), silt (53–2 µm), and clay (< 2 µm). The SOC contents and δ¹³C values of the whole soil and the four particle‐size fractions were analyzed. After 11 years of vegetable farming, ORG and ING significantly increased SOC stocks (0–20 cm) by 4008 ± 36.6 and 2880 ± 365 kg C ha^?1 y^?1, respectively, 8.1‐ and 5.8‐times that of CON (494 ± 42.6 kg C ha^?1 y^?1). The SOC stock increase in ORG at 20–40 cm depth was 245 ± 66.4 kg C ha^?1 y^?1, significantly higher than in ING (66 ± 13.4 kg C ha^?1 y^?1) and CON (109 ± 44.8 kg C ha^?1 y^?1). Analyses of ¹³C revealed a significant increase in newly produced SOC in both soil layers in ORG. However, the carbon conversion efficiency (CE: increased organic carbon in soil divided by organic carbon input) was lower in ORG (14.4%–21.7%) than in ING (18.2%–27.4%). Among the four particle‐sizes in the 0–20 cm layer, the silt fraction exhibited the largest proportion of increase in SOC content (57.8% and 55.4% of the SOC increase in ORG and ING, respectively). A similar trend was detected in the 20–40 cm soil layer. Over all, intensive organic (ORG) vegetable production increases soil organic carbon but with a lower carbon conversion efficiency than integrated (ING) management.     

Improving wood volume predictions in dry tropical forest in the semi-arid Brazil

The volumetric variability of dry tropical forests in Brazil and the scarcity of studies on the subject show the need for the development of techniques that make it possible to obtain adequate and accurate wood volume estimates. In this study, we analyzed a database of thinning trees from a forest management plan in the Contendas de Sincorá National Forest, southwestern Bahia State, Brazil. The data set included a total of 300 trees with a trunk diameter ranging from 5 to 52 cm. Adjustments, validation and statistical selection of four volumetric models were performed. Due to the difference in height values for the same diameter and the low correlation between both variables, we do not suggest models which only use the diameter at breast height (DBH) variable as a predictor because they accommodate the largest estimation errors. In comparing the best single entry model (Hohenald-Krenn) with the Spurr model (best fit model), it is noted that the exclusion of height as a predictor causes the values of 136.44 and 0.93 for Akaike information criterion (AIC) and adjusted determination coefficient (R2 adj), which are poorer than the second best model (Schumacher-Hall). Regarding the minimum sample size, errors in estimation (root mean square error (RMSE) and bias) of the best model decrease as the sample size increases, especially when a larger number of trees with DBH≥15.0 cm are randomly sampled. Stratified sampling by diameter class produces smaller volume prediction errors than random sampling, especially when considering all trees. In summary, the Spurr and Schumacher-Hall models perform better. These models suggest that the total variance explained in the estimates is not less than 95%, producing reliable forecasts of the total volume with shell. Our estimates indicate that the bias around the average is not greater than 7%. Our results support the decision to use regression methods to build models and estimate their parameters, seeking stratification strategies in diameter classes for the sample trees. Volume estimates with valid confidence intervals can be obtained using the Spurr model for the studied dry forest. Stratified sampling of the data set for model adjustment and selection is necessary, since we find significant results with mean error square root values and bias of up to 70% of the total database.     

蜂群箱体关键参数在线监测系统与性能测试

随着信息技术的发展，利用大数据分析、物联网监控、传感器感知、无线通信等技术构建一种蜂箱蜂群实时在线监测系统，是减少因开箱检查造成蜂群应激反应的可行解决方案。本研究针对蜂箱封闭环境进行实时监测困难的现状，利用STM32F103VBT6 32位微控制器，同时融合了温湿度传感器、微麦克风以及激光对射传感器，开发了一套低功耗、可连续工作的蜂群箱体关键参数在线监测系统，实现了养蜂生产过程中多参数信息获取以及蜂箱内蜂群的环境参数和生活状态的实时在线监测。系统主要包括核心处理模块、数据采集模块、数据发送模块以及数据库服务器等。数据采集模块包括蜂箱内部温湿度采集单元、蜂群声音采集单元、蜜蜂进出巢数量计数单元等，通过接入移动通信网络进行数据传输。系统现场部署性能测试结果表明，研制的系统能够实时监测蜂箱内温湿度，有效区别进出蜂箱的蜜蜂并记录进出巢门的蜜蜂数量，且自动获取的蜂群声音与标准的蜂群声音分布相吻合。本系统符合设计要求，采集参数准确可靠，可以作为蜂群相关研究的数据采集方法。     

川西高山树线交错带海拔梯度上土壤有机碳稳定性特征

高山树线交错带是高海拔地带对环境变化最为敏感的生态系统之一，土壤有机碳库及其稳定性在生态系统变化中具有显著指示作用。通过物理-化学方法对土壤不同活性有机碳进行连续分级分离，研究土壤有机碳不同组分在海拔环境梯度下分布特征及稳定性。结果表明，高山树线交错带土壤有机碳主要集中在物理分级组分（≥0.02 mm），且粒径越大有机碳含量越高，即主要以颗粒态有机碳形式存在，占比均高于98%；在<0.02 mm有机-无机复合体中有机碳采用化学分级分离，这部分有机碳占比低于土壤总有机碳2%，且主要以腐殖质（胡敏素）形式存在（占土壤总有机碳0.6%~0.8%），腐殖质占有机碳比例远低于一般土壤；随着海拔升高，土壤有机碳总量上升，颗粒态有机碳（物理分级组分）比例升高，胡敏素类腐殖质比例下降。因此，川西高海拔树线交错带的高寒土壤有机碳，主要以不稳定有机碳（POC）形式存在，随着温度上升（海拔降低）将导致土壤矿质化和腐殖化加剧，有机碳总量下降，土壤不稳定性组分（物理分级组分）降低，土壤稳定性（腐殖化比例）上升。     

基于流式细胞术的桫椤基因组大小测定

以买麻藤(Gnetum montanum)为内参,测定5种常用裂解液(Tris·MgCl_2、LB01、WPB、Otto’s、Galbraith’s)对桫椤(Alsophila spinulosa)细胞核的裂解效果,并通过比较桫椤和买麻藤流式细胞仪测定的荧光峰值,计算出桫椤的基因组大小。结果表明:5种裂解液中,WPB为桫椤最佳裂解液;桫椤基因组大小为6 003.25Mb,即2C DNA含量为6.138 pg。     

绵羊Izumo1基因多态性及其与产羔数关联分析

为研究Izumo1基因多态性及与产羔数关系,本研究利用PCR和直接测序法对小尾寒羊和苏尼特羊的Izumo1基因DNA序列进行扩增、测序和BLAST分析,并和本课题组前期绵羊重测序数据进行比对,筛选出Izumo1基因SNP位点。同时,采用Sequenom MassARRAY~?技术进行基因分型,并对其SNP位点的基因型和等位基因频率在各群体中的分布进行研究。结果表明:小尾寒羊Izumo1基因DNA全长序列3 385 bp,苏尼特羊Izumo1基因DNA全长序列3 382 bp;筛选出8个Izumo1基因SNP位点,经过初步筛选,将在小尾寒羊、苏尼特羊、滩羊、萨福克羊、杜泊羊、草原藏羊这6个绵羊品种中位点分布没有差异的SNP位点排除,最终筛选获得的g.54412135AG和g.54412107CA 2个位点。Izumo1基因g.54412135AG位点在多羔和单羔绵羊品种中存在GG、GA和AA 3种基因型,G基因为优势等位基因;g.54412107CA在多羔品种中存在CC和CA 2种基因型,而在单羔品种中存在CC、CA和AA 3种基因型,C基因为优势等位基因,其基因型频率和基因频率在单、多羔绵羊群体间的分布差异均极显著(P0.01);群体遗传学分析得出g.54412135AG多态位点在6个品种中的多态信息含量(PIC)都属于低度多态(PIC0.25);g.54412107CA多态位点在杜泊羊中属于中度多态(0.25PIC0.5),在其他5个品种中都属于低度多态(PIC0.25),然而,关联分析发现Izumo1基因g.54412135AG和g.54412107CA位点的不同基因型与小尾寒羊不同胎次产羔数之间不存在显著关联(P0.05)。     

小麦光腥黑粉菌转化子最适固体培养基的筛选

为了筛选小麦光腥黑粉菌转化子最适培养基, 以提高小麦光腥黑粉菌转化子生长速度, 选取了3个菌落形态不同的转化子(ZHZ-1, ZHZ-2, ZHZ-3)进行培养试验?用6 mm打孔器打取菌饼, 将菌饼置于9种培养基上16℃避光培养?观察?测量小麦光腥黑粉菌转化子的菌丝生长情况?菌落直径等主要指标, 结果表明, 9种培养基中, 3种转化子都是在完全培养基(complete medium, CM)上生长状况最好, 菌丝生长速度最快?ZHZ-1转化子在CM培养基上菌落圆形, 有褶皱, 产生大量白色菌丝, 生长速度快?ZHZ-2转化子菌落圆形, 产生大量白色菌丝, 生长速度快?ZHZ-3转化子菌落云纹状, 有褶皱, 产生大量白色菌丝, 生长速度快?