放牧和氮添加对半干旱沙质草地优势种糙隐子草及群落功能性状的影响

为探究氮沉降背景下放牧对半干旱沙质草地植物功能性状的影响,本试验探究了放牧和短期氮添加(20 g·m^-2·a^-1)对半干旱沙质草地优势种糙隐子草(Cleistogenes squarrosa)和群落功能性状的影响。结果表明：放牧和氮添加增加了糙隐子草的重要值,放牧降低了糙隐子草叶面积,氮添加显著降低了围封草地中糙隐子草叶干物质含量,增加了围封和放牧草地中糙隐子草叶氮含量(P<0.05);相同氮素水平下放牧降低了植物群落高度,氮添加增加了围封样地植物叶面积和叶氮含量,增加了放牧样地植物比叶面积和叶氮含量,降低了叶干物质含量(P<0.05);回归分析表明,糙隐子草叶面积和叶氮含量能较好地解释群落水平植物叶面积和叶氮含量的变化。半干旱沙质草地植物群落通过改变植株大小和主要叶性状来响应放牧和氮沉降,对预测未来草地生态系统结构与功能具有重要意义。     

施肥对割草地羊草功能性状和化学计量学的影响

本研究以羊草（Leymus chinensis）为研究对象,测定羊草植物功能性状、营养元素和化学计量等多个指标,进行多角度综合系统的研究,阐释羊草割草地羊草种群对不同改良措施的响应特点及变化规律。运用单、双因素方差分析、相关分析和主成分分析方法对相关指标进行统计分析。结果表明：大多数羊草功能性状对施肥处理较为敏感,但随着施肥年份增加,部分指标敏感程度减弱;高浓度（N 10.5 g·m^-2+P 5.1 g·m^-2）施肥处理显著增加了株高和茎长,茎长成为羊草株高增长的主要因素;高浓度（N 10.5 g·m^-2+P 5.1 g·m^-2）施肥处理有利于羊草种群地上生物量的增加;羊草氮含量随着施肥年份增加逐渐增加,羊草氮含量与羊草C：N存在高度负相关,施肥（N 10.5 g·m^-2+P 5.1 g·m^-2）处理下羊草叶片和茎的C：N,羊草个体和叶片C：P最低。     

基于HHT变换的PSO-LSSVM耦合模型估算气象资料短缺地区ET_0

为准确估算气象资料短缺地区参考作物腾发量,构建了一种基于HHT变换的PSO-LSSVM耦合模型,并利用新疆和田气象站2000—2009年单日数据做训练、双日数据做验证。结果表明,该模型估算ET0方法明显优于常规的PSO-LSSVM和GRNN,预测精度较二者分别提高了15.7%~85.6%和15.8%~93.7%;该方法预测ET0的气象要素重要性为RsTmaxTminRHWn,利用该方法对气象要素组合为Tmax/Tmin/RH/Wn、Tmax/RH/Wn、Tmin/Wn、Wn条件下的ET0预测,MSE分别为0.407、0.185、0.149、0.135,说明该方法可以很好地估算资料缺失地区ET0。     

基于神经网络红枣红外辐射干燥预测模型建立

针对红枣红外辐射干燥含水率的变化具有非线性和时变性、很难利用现有的模型构造一个数学模型来描述其变化规律的问题,利用Mat Lab神经网络工具箱和红枣红外辐射干燥特性试验数据建立了神经网络预测模型。通过对实测值和模型预测值进行分析研究,得出利用BP神经网络可以较快速、准确地建立模型来描述含水率的变化规律,且模型的预测值与试验测试值误差较小,能很好地实现在线预测的效果。     

Cloning and Bioinformatic Analysis of L1 Gene of BPV from Guizhou Province

In order to study and analyze L1 gene of bovine papillomavirus（BPV）in Guizhou province,the L1 gene of BPV-GZ01 strain was amplified,cloned and sequenced using bioinformatic softwares and methods,and the secondary structure,tertiary structure,B-cell preponderant epitope,conserved domains analysis, transmembrane domain and signal peptide of L1 gene were predicted.The results showed that the length of L1 gene was 1 494 bp,encoding 497 amino acids.The L1 gene of BPV-GZ01 strain shared an amino acid identities of 98.6%,99.4%,98.4%,94.4% and 91.3%,and a nucleotide identities of 99.1%,99.8%,99.4%,87.6% and 82.8% with those of BPV2,BPV2-SW01,BPV2-AKS01,BPV13 and BPV1 strains,respectively.The results of phylogenetic tree analysis indicated that there was a close relationship between BPV-GZ01 and BPV2-SW01 strains.The prediction of secondary structure of L1 protein indicated that the random coil,extended strand and alphahelix took a higher percentage.The L1 protein was supposed contain 6 potential antigen epitopes.And no transmembrane domains and no signal peptide were found.The tertiary structure of L1 protein was curved spiral structure.These results provided a theoretical basis for immunologic diagnosis and further research of nucleic acid vaccine of BPV.     

Accuracy of genomic prediction using imputed whole‐genome sequence data in white layers

There is an increasing interest in using whole‐genome sequence data in genomic selection breeding programmes. Prediction of breeding values is expected to be more accurate when whole‐genome sequence is used, because the causal mutations are assumed to be in the data. We performed genomic prediction for the number of eggs in white layers using imputed whole‐genome resequence data including ~4.6 million SNPs. The prediction accuracies based on sequence data were compared with the accuracies from the 60 K SNP panel. Predictions were based on genomic best linear unbiased prediction (GBLUP) as well as a Bayesian variable selection model (BayesC). Moreover, the prediction accuracy from using different types of variants (synonymous, non‐synonymous and non‐coding SNPs) was evaluated. Genomic prediction using the 60 K SNP panel resulted in a prediction accuracy of 0.74 when GBLUP was applied. With sequence data, there was a small increase (~1%) in prediction accuracy over the 60 K genotypes. With both 60 K SNP panel and sequence data, GBLUP slightly outperformed BayesC in predicting the breeding values. Selection of SNPs more likely to affect the phenotype (i.e. non‐synonymous SNPs) did not improve the accuracy of genomic prediction. The fact that sequence data were based on imputation from a small number of sequenced animals may have limited the potential to improve the prediction accuracy. A small reference population (n = 1004) and possible exclusion of many causal SNPs during quality control can be other possible reasons for limited benefit of sequence data. We expect, however, that the limited improvement is because the 60 K SNP panel was already sufficiently dense to accurately determine the relationships between animals in our data.     

生物多样性与地形及土壤因素对坡面产流产沙的耦合影响

生物多样性关系到生态系统的功能,而坡度和土壤因素是影响坡面产流产沙的重要因子。本研究基于黄土丘陵区生物多样性,并引入坡度、降雨前土壤含水量和土壤容重,探究其与坡面产流产沙总量的耦合关系。通过野外人工降雨试验,记录草本、灌木和乔木群落下的产流时间和产流产沙总量,并开展土壤植被调查。结果表明,与草本和乔木群落相比,灌木群落在延长产流开始时间和降低产沙总量上表现更优;植被群落功能离散度指数(FDiv)对产沙总量影响最大,Simpson指数(D)对产流总量影响最大,坡度仍然是不可忽视的第二重要影响因素;产流总量与坡度、土壤容重、D和功能丰富度指数(FRic)的回归关系优于产流总量分别与其的回归关系;产沙总量与坡度和FDiv指数的回归关系优于产沙总量分别与其的回归关系。植被保护与坡度改善将是该区水土保持规划工作的重点。     

Genomic prediction for meat and carcass traits in Nellore cattle using a Markov blanket algorithm

This study was carried out to evaluate the advantage of preselecting SNP markers using Markov blanket algorithm regarding the accuracy of genomic prediction for carcass and meat quality traits in Nellore cattle. This study considered 3675, 3680, 3660 and 524 records of rib eye area (REA), back fat thickness (BF), rump fat (RF), and Warner–Bratzler shear force (WBSF), respectively, from the Nellore Brazil Breeding Program. The animals have been genotyped using low-density SNP panel (30 k), and subsequently imputed for arrays with 777 k SNPs. Four Bayesian specifications of genomic regression models, namely Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression methods were compared in terms of prediction accuracy using a five folds cross-validation. Prediction accuracy for REA, BF and RF was all similar using the Bayesian Alphabet models, ranging from 0.75 to 0.95. For WBSF, the predictive ability was higher using Bayes B (0.47) than other methods (0.39 to 0.42). Although the prediction accuracies using Markov blanket of SNP markers were lower than those using all SNPs, for WBSF the relative gain was lower than 13%. With a subset of informative SNPs markers, identified using Markov blanket, probably, is possible to capture a large proportion of the genetic variance for WBSF. The development of low-density and customized arrays using Markov blanket might be cost-effective to perform a genomic selection for this trait, increasing the number of evaluated animals, improving the management decisions based on genomic information and applying genomic selection on a large scale.     

黑龙江省水稻单产增长潜力预测方法研究

水稻是黑龙江省的主要粮食作物。加入WTO后,水稻生产、贸易、进出口等方面都面临新的形势,同时水稻产量的稳定和增长对于国家粮食安全、人民生活水平提高以及农民奔小康都有着重要意义。因此,探讨了应用Peal曲线函数模型对黑龙江省水稻单产进行预测的方法。