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41.
本文介绍了籼型恢复系郴R92-225的杂交和辐射选育过程、特征特性及配制杂交组合应用于生产的情况。对选育的郴R92-225的有利变异和选育方法及应用进行了讨论。 相似文献
42.
针对雪茄烟叶晾制过程含水率人工判断主观性强、准确度低等不足,以及对影响雪茄烟叶晾制过程含水率预测的重要表观特征尚不明确等问题,该研究基于图像特征提取以及机器学习技术实现雪茄烟叶晾制过程含水率的预测。试验以雪茄烟品种“云雪2号”为试验材料,获取晾制过程的烟叶图像的颜色、轮廓、纹理以及部位四类特征并筛选出雪茄烟叶含水率预测的优选图像特征子集。在此基础上,构建了随机森林(random forest, RF)、支持向量机(support vector regression, SVR)与反向传播神经网络(back propagation neural network, BPNN)模型,并利用遗传算法(genetic algorithm, GA)对各模型超参数进行优化,将原始图像特征集与优选图像特征集输入3个机器学习模型,构建出6种模型-特征组合方案,依据晾制时期对原始数据集进行划分,并对测试集进行预测。最终结果显示:GA-SVR模型+优选图像特征子集的组合方案在测试集上表现最优,其决定系数(coefficient of determination,r2)与均方误差(mean square error,MSE)分别为0.980和0.001,且运行时间最短(运行时长=0.128 s)。研究结果可为雪茄烟叶晾制过程智能化控制提供理论依据。 相似文献
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Use of genomic recursions and algorithm for proven and young animals for single‐step genomic BLUP analyses – a simulation study 下载免费PDF全文
B.O. Fragomeni D.A.L. Lourenco S. Tsuruta Y. Masuda I. Aguilar I. Misztal 《Zeitschrift für Tierzüchtung und Züchtungsbiologie》2015,132(5):340-345
The purpose of this study was to examine accuracy of genomic selection via single‐step genomic BLUP (ssGBLUP) when the direct inverse of the genomic relationship matrix ( G ) is replaced by an approximation of G ?1 based on recursions for young genotyped animals conditioned on a subset of proven animals, termed algorithm for proven and young animals (APY). With the efficient implementation, this algorithm has a cubic cost with proven animals and linear with young animals. Ten duplicate data sets mimicking a dairy cattle population were simulated. In a first scenario, genomic information for 20k genotyped bulls, divided in 7k proven and 13k young bulls, was generated for each replicate. In a second scenario, 5k genotyped cows with phenotypes were included in the analysis as young animals. Accuracies (average for the 10 replicates) in regular EBV were 0.72 and 0.34 for proven and young animals, respectively. When genomic information was included, they increased to 0.75 and 0.50. No differences between genomic EBV (GEBV) obtained with the regular G ?1 and the approximated G ?1 via the recursive method were observed. In the second scenario, accuracies in GEBV (0.76, 0.51 and 0.59 for proven bulls, young males and young females, respectively) were also higher than those in EBV (0.72, 0.35 and 0.49). Again, no differences between GEBV with regular G ?1 and with recursions were observed. With the recursive algorithm, the number of iterations to achieve convergence was reduced from 227 to 206 in the first scenario and from 232 to 209 in the second scenario. Cows can be treated as young animals in APY without reducing the accuracy. The proposed algorithm can be implemented to reduce computing costs and to overcome current limitations on the number of genotyped animals in the ssGBLUP method. 相似文献
45.
2004年8~9月采用定点观察法、GPS定位法和样方法对内蒙古扎赉特旗图牧吉国家级自然保护区白鹤的秋季停歇期的觅食地生境进行了调查研究,数据分析结果显示:在201个实验样方和201个对照样方中,苔草高度、芦苇高度、香蒲高度、植被盖度和植被密度的t值均小于0.05,存在显著或极显著差异;另外,主成分分析结果显示,前4个主成分的累积贡献率达到56.652%,在第一主成分中,水稗草高度和水流状况的贡献率较高;在第二主成分中,水深和水透明度的贡献率较高;在第三主成分中,香蒲高度和植被密度的贡献率较高:在第4主成分中,苔草高度的贡献率较高。 相似文献
46.
Egill Gautason Goutam Sahana Guosheng Su Baldur Helgi Benjamínsson Gumundur Jhannesson Bernt Guldbrandtsen 《Journal of animal science》2021,99(7)
Icelandic Cattle is a local dairy cattle breed in Iceland. With about 26,000 breeding females, it is by far the largest among the indigenous Nordic cattle breeds. The objective of this study was to investigate the feasibility of genomic selection in Icelandic Cattle. Pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP) were compared. Accuracy, bias, and dispersion of estimated breeding values (EBV) for milk yield (MY), fat yield (FY), protein yield (PY), and somatic cell score (SCS) were estimated in a cross validation-based design. Accuracy () was estimated by the correlation between EBV and corrected phenotype in a validation set. The accuracy () of predictions using ssGBLUP increased by 13, 23, 19, and 20 percentage points for MY, FY, PY, and SCS for genotyped animals, compared with PBLUP. The accuracy of nongenotyped animals was not improved for MY and PY, but increased by 0.9 and 3.5 percentage points for FY and SCS. We used the linear regression (LR) method to quantify relative improvements in accuracy, bias (), and dispersion () of EBV. Using the LR method, the relative improvements in accuracy of validation from PBLUP to ssGBLUP were 43%, 60%, 50%, and 48% for genotyped animals for MY, FY, PY, and SCS. Single-step GBLUP EBV were less underestimated (), and less overdispersed () than PBLUP EBV for FY and PY. Pedigree-based BLUP EBV were close to unbiased for MY and SCS. Single-step GBLUP underestimated MY EBV but overestimated SCS EBV. Based on the average accuracy of 0.45 for ssGBLUP EBV obtained in this study, selection intensities according to the breeding scheme of Icelandic Cattle, and assuming a generation interval of 2.0 yr for sires of bulls, sires of dams and dams of bulls, genetic gain in Icelandic Cattle could be increased by about 50% relative to the current breeding scheme. 相似文献
47.
我国白羽肉鸡育种中,通过遗传途径提高产蛋数和控制合适的蛋重是培育优良品系的一个重要方面。为探索适合我国白羽肉鸡育种中的基因组选择模型,本研究以2 474只白羽肉鸡品系的产蛋性状为研究对象,主要分析了机器学习算法KAML、BLUP(包括:PBLUP、GBLUP、SSGBLUP)和Bayes(包括:Bayes A、Bayes B和Bayes Cπ)方法对产蛋数和蛋重性状的预测准确性,准确性以5倍交叉验证进行评估。利用系谱以及基因组信息估计了产蛋数和蛋重性状的遗传力和遗传相关。结果表明,产蛋数性状遗传力为0.061~0.16,属于低遗传力性状;蛋重遗传力为0.28~0.39,属于中等遗传力性状;产蛋数与蛋重是中等遗传负相关(-0.518~-0.184),不同阶段产蛋数之间是强的遗传正相关(0.736~0.998)。不同模型预测43周产蛋数和52周蛋重的育种值估计准确性结果表明,KAML方法对两者的预测准确性分别为0.115和0.266,与GBLUP方法(准确性分别为0.118和0.283)和SSGBLUP方法(准确性分别为0.136和0.259)的准确性差异显著,同时显著低于Bayes方法(准确性分别为0.230~0.239、0.336~0.340)的预测准确性, PBLUP方法预测准确性最低(准确性分别为0.095和0.246)。因此,在白羽肉鸡产蛋数和蛋重性状中应用Bayes方法将获得最高的育种值估计准确性。 相似文献
48.
草坪草引种观察与筛选 总被引:12,自引:5,他引:12
为满足绿化和水土保持对优良草坪草的需求,引进了草坪草种进行田间种植,并对其生物性状和抗逆性能进行观察,以从中筛选出优良的草坪草种。经过2年的观察,初步筛选出优良草坪草种-台湾草坪草;既可饲用、又可固土的优良草种-百喜草;饲用、草坪兼用的优良草种-早熟禾;饲用、草坪、固土多用型的优良草种-苇状羊茅。这几种草坪草种均具有绿化和水土保持作用。 相似文献
49.
Salvatore Mastrangelo Slim Ben Jemaa Elena Ciani Gianluca Sottile Angelo Moscarelli Mekki Boussaha Marina Montedoro Fabio Pilla Martino Cassandro 《Zeitschrift für Tierzüchtung und Züchtungsbiologie》2020,137(6):609-621
The Valdostana is a local dual purpose cattle breed developed in Italy. Three populations are recognized within this breed, based on coat colour, production level, morphology and temperament: Valdostana Red Pied (VPR), Valdostana Black Pied (VPN) and Valdostana Chestnut (VCA). Here, we investigated putative genomic regions under selection among these three populations using the Bovine 50K SNP array by combining three different statistical methods based either on allele frequencies (FST) or extended haplotype homozygosity (iHS and Rsb). In total, 8, 5 and 8 chromosomes harbouring 13, 13 and 16 genomic regions potentially under selection were identified by at least two approaches in VPR, VPN and VCA, respectively. Most of these candidate regions were population-specific but we found one common genomic region spanning 2.38 Mb on BTA06 which either overlaps or is located close to runs of homozygosity islands detected in the three populations. This region included inter alia two well-known genes: KDR, a well-established coat colour gene, and CLOCK, which plays a central role in positive regulation of inflammatory response and in the regulation of the mammalian circadian rhythm. The other candidate regions identified harboured genes associated mainly with milk and meat traits as well as genes involved in immune response/inflammation or associated with behavioural traits. This last category of genes was mainly identified in VCA, which is selected for fighting ability. Overall, our results provide, for the first time, a glimpse into regions of the genome targeted by selection in Valdostana cattle. Finally, this study illustrates the relevance of using multiple complementary approaches to identify genomic regions putatively under selection in livestock. 相似文献
50.