渭北地区富士苹果不同砧穗组合幼树内源激素含量与早花性和易成形性关系的研究

以3~4年生5个不同砧穗组合富士苹果幼树为材料,研究植物内源激素含量与早花性和易成形性间的关系。结果表明:乔化砧组合树体高大、生长旺盛,短枝比例和花序数量均显著低于矮化砧组合。矮化砧木组合中,M26组合虽然短枝比例很高,但单株成花量低,树体长势弱;SH16组合次之;T337组合长势中庸、单株成花量大。一年中,春、秋梢开始生长期,幼叶IAA、ZR含量出现高峰,根系第2次生长高峰期,细根IAA、ZR、GA_3含量出现高峰。幼叶ZR和细根IAA含量呈负相关,IAA、GA_3分别在地上部和地下部之间存在正相关。5月5日,幼叶IAA和ZR含量分别与当年生枝量呈正相关;5月20日,ZR、ZR/GA_3分别与短枝比例呈正相关,GA_3与花序数量呈负相关;根系第2次生长高峰期,苹果幼树开花相关指标分别与细根中的IAA、ZR、GA_3、IAA/ZR呈正相关,说明内源激素在地上部成花与地下部根系生长过程中具有重要调节作用。渭北地区,T337自根砧木富士苹果组合体现了早花性和树体易成形性的统一。     

基于Lasso和支持向量机的组合预测及其应用

针对Lasso方法与支持向量机两者的联系与各自的优势，给出了基于Lasso与支持向量机的串联型、并联型和嵌入型三种组合预测，并将它们运用到我国粮食价格预测中.实证结果表明，与单一预测方法的预测结果相比，基于Lasso方法与支持向量机的串联型组合预测和嵌入型组合预测具有更高的预测精度.     

营业税改征增值税的税率评估——基于可计算一般均衡模型的研究

营业税改征增值税是完善我国税制的一个重要举措，确定各个产业部门改征增值税后的税率是其中一个关键的环节。运用可计算一般均衡模型可以评估营业税改征增值税之后不同税率的选择对宏观经济和产业结构的影响。政策模拟的结果显示，目前正在实施的营业税改征增值税的试点方案权衡了对财政收入和经济增长以及经济结构的影响，是对经济运行影响较小的稳健选择。     

德州驴生产性能的初步研究

德州驴是优秀的皮肉兼用驴,包括“三粉驴”和“乌头驴”两个品系,但是关于德州驴的研究较少。为了向驴产业提供可靠的德州驴生产性能数据,该研究以40头三粉驴和9头乌头驴为研究对象,系统研究了德州驴的体尺性状、屠宰性能、脏器系数及胸腰椎数等性状特点。结果表明,三粉驴的屠宰率为54.02%,净肉率为42.43%,净皮率为8.12%;乌头驴的屠宰率为53.71%,净肉率为40.32%,净皮率为9.41%。表明三粉驴更适合于向肉用驴品系进行培育,乌头驴更适合于向皮用驴品系进行培育。与乌头驴相比,三粉驴的肝脏系数较高,并且小肠、盲肠显著较长(P<0.01),说明三粉驴具有更加优良的消化及代谢能力,可能是其具有更优异产肉性能的原因之一。调查发现,德州驴群体内存在胸腰椎数的变异,胸椎数为17~19个,腰椎数为5~6个;胸椎数与腰椎数存在不同形式的组合,18胸椎5腰椎占比为73.47%,18胸椎6腰椎占比为12.24%,19胸椎5腰椎占比为2.04%,17胸椎6腰椎占比为12.24%。统计发现,多一个胸椎或者腰椎可使驴皮产量增加。该试验结果可为德州驴产业健康可持续发展提供重要理论支撑,为品种及新品系培育提供数据支持。     

瘦肉型猪组合配套杂交效果研究

为探索瘦肉型猪组合配套杂交方法和效果而进行试验。试验采用HN121（温氏杜洛克猪）、HN212（法系皮特兰♂×温氏杜洛克♀）为父本,以HN204（温氏大白♂×美系长白♀）为母本,对HN323（HN121×HN204）和HN424（HN212×HN204）两个组合配套进行研究。结果表明,HN424组合与HN323组合相比母猪的窝产仔数、窝产活仔数、仔猪初生窝重、21日龄仔猪成活率、21日龄窝重等繁殖性状差异不显著（P>0.05）;HN424组合仔猪的体重、背膘厚降低5.63%（P<0.05）,瘦肉率提高4.95%（P<0.05）,眼肌面积增大33.08%（P<0.01）,肉色评分降低7.19%（P<0.05）,大理石纹评分降低11.30%（P<0.01）,滴水损失率提高10.63%（P<0.01）,其他遗传性状差异不显著;HN424组合与HN204二元母系相比,日增重提高9.26%（P<0.05）,背膘厚减少16.41%（P<0.01）,料肉比减少8.02%（P<0.05）;HN323组合与HN204二元杂交母系相比,日增重提高7.20%（P<0.05）,背膘厚减少11.43%（P<0.01）,料肉比降低5.35%（P<0.05）。说明采取四元杂组合和三元杂交组合均对“大×长”（HN204）二元杂交有显著的遗传改良作用,“皮×杜×长×大”四元杂组合可显著提高猪的瘦肉率、眼肌面积,但肉色、大理石纹、滴水损失率等肉质指标变差,建议在“皮×杜×长×大”四系组合配套杂交中加入能改良肉质基因的猪种。     

Heavy Metal Immobilization in Contaminated Soils using Phosphogypsum and Rice Straw Compost

The application of soil amendments to immobilize heavy metals is a promising technology to meet the requirements for environmentally sound and cost‐effective remediation. The present study was carried out to evaluate the result of phosphogypsum (PG) used alone and in combination with compost (CP) at a mix ratio of 1:1 wet weight ratio (PG + CP) at 10 and 20 g dry weight kg⁻¹ dry soil, on heavy metal immobilization in contaminated soil and on canola growth. The results revealed that the Pb, Cd and Zn uptake of canola plants was reduced by the application of PG alone and when it was mixed with CP as compared with untreated soil. At an application rate of 10 g dry weight kg⁻¹ dry soil of (PG + CP) the dry weight of canola plants increased by 66·8% was increased in comparison with its weight in the untreated soil. The addition of PG alone resulted in more pronounced immobilization of heavy metal as compared with PG mixed with CP. Plant growth was improved with CP addition, but heavy metals immobilization was the greatest in PG alone treatments. Results suggest that PG may be useful for the immobilization of heavy metals in contaminated soils. Copyright © 2014 John Wiley & Sons, Ltd.     

豫西丘陵地区果牧结合立体生态园设计的探讨

根据豫西丘陵地区生态、经济、社会条件以及目前农牧业生产中存在的突出问题，提出果牧结合立体生态园建设是该地区生态农业发展的重要途径。并在调查和初步研究基础上设计了“畜（禽）—果”、“畜（禽）—沼—果”等果牧结合模式，分析了生态园建设研究中要探讨的具体内容。     

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.     

Analysis of Cleanroom Technology Through Angle of Design

During the implementation of Cleanroom Model, there are a lot of particular problems need to be resolved, which make it indispensable to grasp the essence and the principles of CRM. The article is on the essential research work in the design procedure of the Cleanroom technology. Through the analysis, it proposes a general architecture, based on which the entry point of the solving process and the distribution of the reducing procedure are introduced.     

Determination to Weighting Coefficient of Combination Forecast Based on Rough Set Theory

The problem of determination to weighting coefficient is a key and difficulty for combination forecast. A method of determining weighting coefficient based on rough set theory is showed in this paper. Determining weighting coefficient is translated into estimating significance of attributes among rough set. A relation data model about combination forecast is established. Knowledge systems are built through making attribute value into eigenvalue. Under data moving, the weighting coefficients of a combination forecast model are computed by analyzing the dependence and significance of forecasting method for the predicted object. The proposed approach overcomes the subjectivity of traditional determination to weighting coefficient, avoids computing linear or nonlinear extremum problem and makes combination forecast more objective. The validity of the proposed approach is verified with a case.