Modelling soil erodibility in mountain rangelands of southern Kyrgyzstan

Soil erosion in mountain rangelands in Kyrgyzstan is an emerging problem due to vegetation loss caused by overgrazing. It is further exacerbated by mountain terrain and high precipitation values in Fergana range in the south of Kyrgyzstan. The main objective of this study was to map soil erodibility in the mountainous rangelands of Kyrgyzstan. The results of this effort are expected to contribute to the development of soil erodibility modelling approaches for mountainous areas. In this study, we mapped soil erodibility at two sites, both representing grazing rangelands in the mountains of Kyrgyzstan and having potentially different levels of grazing pressure. We collected a total of 232 soil samples evenly distributed in geographical space and feature space. Then we analyzed the samples in laboratory for grain size distribution and calculated soil erodibility values from these data using the Revised Universal Soil Loss Equation (RUSLE) K-factor formula. After that, we derived different terrain indices and ratios of frequency bands from ASTER GDEM and LANDSAT images to use as auxiliary data because they are among the main soil forming factors and widely used for prediction of various soil properties. Soil erodibility was significantly correlated with channel network base level (geographically extrapolated altitude of water channels), remotely sensed indices of short-wave infrared spectral bands, exposition, and slope degree. We applied multiple regression analysis to predict soil erodibility from spatially explicit terrain and remotely sensed indices. The final soil erodibility model was developed using the spatially explicit predictors and the regression equation and then improved by adding the residuals. The spatial resolution of the model was 30 m, and the estimated mean adjusted coefficient of determination was 0.47. The two sites indicated different estimated and predicted means of soil erodibility values (0.035 and 0.039) with a 0.05 significance level, which is attributed mainly to the considerable difference in elevation.     

基于变量优选与机器学习的干旱区湿地土壤盐渍化数字制图

土壤盐渍化是导致土壤退化和生态系统恶化的主要原因之一，对干旱区的可持续发展构成主要威胁。为了尽可能精确地监测土壤盐渍化的空间变异性，该研究收集新疆艾比湖湿地78个典型样点，其中选取54个样本作为训练集，24个样本作为独立验证集。基于Sientinel-2 多光谱传感器（Multi-Spectral Instrument，MSI）、数字高程模型（Digital Elevation Model，DEM）数据提取3类指数（红边光谱指数、植被指数和地形指数），经过极端梯度提升（Extreme Gradient Boosting，XGBoost）算法筛选有效特征变量，构建了关于土壤电导率（Electrical Conductivity，EC）的随机森林（Random Forest，RF）、极限学习机（Extra Learning Machine，ELM）和偏最小二乘回归（Partial Least Squares Regression，PLSR）预测模型，并选择最优模型绘制了艾比湖湿地盐渍化分布图。结果表明：优选的红边光谱指数基本能够预测EC的空间变化；红边光谱指数与植被指数组合建模效果总体上优于其与地形指数的组合，3类指数组合的建模取得了较为理想的预测精度，其中RF模型表现最优（验证集R2=0.83，RMSE=4.81 dS/m，RPD=3.11）；在整个研究区内，中部和东部地区土壤盐渍化程度尤为严重。因此，XGBoost所筛选出的环境因子结合机器学习算法可以实现干旱区土壤盐渍化的监测。     

人工合成小麦紫色胚芽鞘的遗传定位分析

为探明小麦花青素色素沉积的关键位点,以167份重组自交系群体为材料,利用已构建的高密度遗传图谱对来源于硬粒小麦(AABB)和节节麦亚种tauschii(DD)杂交后所得的人工合成小麦SHW-L1中控制紫色胚芽鞘的基因进行遗传定位。同时利用目标性状关联遗传区段对SHW-L1及其亲本进行基因型鉴定。结果表明:目标性状在重组自交系群体中表现为单基因遗传,且被定位于7D染色体,其关联遗传区段在异源六倍化过程中有遗传位点缺失。     

基于景观视角的防城港万尾金滩滨海护岸探析

防城港东兴市万尾金滩约10km的滨海护岸公路是生态防护、景观步道、旅游集散、防灾避险和渔船停泊等多种功能协调统于一体的滨海防护工程。通过实地调查测绘,基于园林景观的角度从海岸生态防护、水文特征、景观特性三个方面探讨了万尾金滩滨海护岸公路的合理性与不足。指出了滨海护岸工程如何在抵御风浪侵蚀的基础上体现场地的观赏性、便利性、生态性和安全性,以实现滨海护岸工程景观、功能和生态的有机结合。     

春小麦抗旱耐热性QTL分析

为发掘控制小麦抗旱耐热基因位点,以小麦重组自交系(RILs)群体为材料,于2015-2017年小麦灌浆期进行干旱胁迫、热胁迫、旱热胁迫处理,并对抗旱耐热相关性状QTL进行分析。结果表明,在干旱胁迫、热胁迫、旱热胁迫下分别检测到22、36和30个QTL,其中控制旗叶叶绿素含量、旗叶含水量、穗粒重、千粒重的QTL数分别为26、21、22和19个。抗旱相关性状QTL位于2A、3B、4B、7B、3D、4D和7D染色体上,表型贡献率为9.38%～30.81%;耐热相关性状QTL位于2A、2B、3A、3B、4B、4D、5D、6D、7A和7B染色体上,表型贡献率为9.03%～34.97%。抗旱性共定位区间2个,分别位于2A(gwm294-wmc644)和7B(barc140-gwm297)染色体上;耐热性共定位区间2个,分别位于5D(cfd67-cfd40)和6D(barc196-barc54)染色体上;抗旱耐热性共定位区间3个,分别位于2A(gwm294-wmc644)、2B(wmc441-wmc317)和7B(wmc83-wmc276)染色体上,其中共定位区间gwm294-wmc644( Qcc-2A.2、 Qgw-2A.1)的贡献率超过10%,遗传距离较小(6.49 cM)。     

水稻半外卷叶突变体sol1的表型分析与基因定位

适度卷曲有利于提高水稻叶片的光合效率,增加植株光合产物的有效积累量。我们利用甲基磺酸乙酯(EMS)处理籼型水稻保持系西农1B,获得一个稳定遗传的水稻半外卷叶突变体。该突变体从十叶期开始各叶片逐渐向外卷曲直至半卷状,并伴随茎秆半矮化和叶片披垂,暂被命名为semi-outcurved leaf 1(sol1)。与野生型(WT)相比,sol1的叶片卷曲指数均达到30%以上(P<0.01);倒一、倒二、倒三、倒四节节间长度和穗长极显著缩短,倒一、倒二、倒三叶的叶夹角显著或极显著增加;有效穗数、千粒重、每穗实粒数、结实率显著或极显著下降,一次枝梗数则增加11.3%(P<0.05)。sol1的蒸腾速率、胞间CO2浓度、气孔导度显著高于野生型。石蜡切片显示,sol1倒一叶的泡状细胞体积变小,数量显著增多,表皮细胞体积略微增大。遗传分析表明,sol1的半外卷叶性状受1对隐性核基因调控,定位于6号染色体标记JY6-3和JY6-10之间165 kb的物理范围内,共含15个注释基因。qRT-PCR结果表明,与泡状细胞相关的内卷基因和外卷叶基因RL14、Roc5、REL1在突变体sol1中呈不同程度的上调,NRL、BRD1、OsHox32、ADL1、LC2则呈不同程度的下调。研究结果为SOL1基因的克隆和功能研究奠定了基础。     

Construction of high-density linkage map and identification of QTLs associated with resistance to black rot in radish (Raphanus sativus) by RAD sequencing

High-density marker-based QTL mapping can serve as an effective strategy to identify novel genomic information to facilitate crop improvement. In this study, we genotyped an F₂ population (KB12-1 × PP12-1) using a RAD-seq approach and constructed a high-density linkage map for radish. After a series of filtering procedures were performed, 17,124 SNPs and 3,336 indels with aa × bb genotyping were retained to obtain bin markers. Then, a linkage map comprising a total of 1,221 bin markers in nine linkage groups spanning 1,467.3 cM with an average marker interval of 1.2 cM was constructed. We evaluated the resistance of the F₂ mapping population to black rot using F₃ progeny, and two major QTLs related to black rot resistance were identified based on this map. Among these QTLs, qBRR2 on Chr.2 explained 26.97% of the phenotypic variation with a LOD score of 11.93, and qBRR7 on Chr.7 accounted for 27.06% of the phenotypic variation with a LOD score of 11.83. The additive effect of qBRR2 was positive (14.97); however, qBRR7 had the opposite effect (−11.99). The high-density linkage map and the major QTLs qBRR2 and qBRR7 provide new important information for disease resistance gene discovery and utilization in genetic improvement.     

越冬栽培稻产量性状相关QTL定位

越冬栽培稻是一类能越过自然冷冬季节并在第2年春季萌芽、正常开花结实、收获稻谷的水稻品种。本文通过对越冬栽培稻产量性状QTL分析,明确产量相关性状的遗传规律,旨在进一步解析越冬栽培稻产量性状的遗传机制,为育种创新利用提供理论依据。以3份越冬栽培稻构建的3个半同胞F2群体为材料。各考察15个产量相关性状,利用Excel 2003、GraphPad Prism 5.0和QTL IciMapping 4.10软件分析数据、绘制遗传图谱、定位QTL和联合分析。结果表明,产量性状表型值在3群体中呈连续正态分布,表现为数量性状遗传。共检测到37个QTL和26对上位性QTL,贡献率分别介于2.32%～36.31%和1.04%～2.05%;检测到9个同时影响2个及以上产量性状(一因多效)QTL标记区间;以联合分析检测到13个产量性状相关QTL,其中4个QTL区间与单群体检测QTL区间重叠;越冬栽培稻产量相关性状QTL以加–显性效应遗传为主、上位性遗传效应为辅。本研究将为越冬栽培稻产量相关基因挖掘及育种创新利用奠定基础。