茭白纹枯病发生规律及产量损失测定

茭白纹枯病发生规律与产量损失的研究结果表明:高温高湿、种植过密、偏施氮肥、缺乏钾素是病害加重的主要原因;田间发病盛期在7月中旬至8月中旬;单茭重减低率与病级之间呈极显著线性相关;始病期越早,危害越重,产量损失也越大;病害危害程度与有效分蘖数(孕茭数)减少率、单茭重减低率、产量损失率之间呈高度正相关。在产量损失构成中,有效分蘖数的减少为主要因素。     

夏大豆成熟期害鼠防治指标的研究

夏大豆成熟期害鼠防治指标的研究潘学锋（安徽省霍邱县植保站２３７４００）刘同群（霍邱县城西湖乡农技站）摘要本文通过大田调查夏大豆田的害鼠发生种类及其密度与产量损失的关系，结果表明，沿淮地区夏大豆田的害鼠种类为黑线姬鼠、黑线仓鼠、褐家鼠、小家鼠等，其中以...     

优质牦牛肉肉质的综合评价

2000-2002年对大通牛场40头6月龄不同性别的全哺乳牦牛的肉质进行了综合评价。对供试牦牛肌肉pH、嫩度、肌纤维细度、系水率、肉的贮存损失、肉的煮熟时间、熟肉率和烹饪损失率及肉色等进行了评价，分析了肌肉的化学成分，氨基酸及微量元素含量。结果表明该牦牛肉系水率强，肉的煮熟时间短，肉的贮存损失及烹饪损失较小，肌纤维较细，肉色深红。并具有蛋白质含量高，脂肪含量低及富含各种氨基酸的特点。     

长期施肥条件下菜田—蔬菜生态系统变化的研究( Ⅳ) 蔬菜生态系统的变化

 以2002 年设施番茄栽培为代表计算其N、P、K投入量与携出量之间的表观平衡状态, 其结果基本上和14.5 年后的土壤实测值与设施栽培本底值之间纵向比较的增长关系一致, 前者只能说明系统养分变化的趋势, 后者才是实际的养分盈亏状态, 养分盈亏主要取决于有机肥的施用与养分元素的供给。5个代表性处理模拟了蔬菜施肥的不同现状, 从土壤肥力及产量等方面的变化真实反映出各自的系统变化及发展趋势, 在本试验条件下以ANK施肥下的系统最优。对有机肥施用、化学肥料的配施以及测土施肥等问题进行了探讨。     

新扬州鸡IGF-1基因多态性与早期生长速度关系的研究

以150只非同胞新扬州鸡为材料，采用PCR—RFLP法检测了该基因5’调控区DNA序列多态性，并运用线性模型统计方法分析了多态性与初生重和12周龄体重的关系。结果显示：新扬州鸡IGF-1基因5、调控区自然存在两种不同DNA序列，经。PstⅠ酶切后出现3种基因型(“-／-”、“-／ ”、“ ／ ”)，基因型分布符合哈代一温伯格定律。各基因型个体的初生重、12周龄体重的最小二乘均数存在“-／-”＞“ ／-”＞“ ／ ”的趋势，且“-／-”型个体的12周龄体重显著高于“ ／ ”型个体(P＜0．05)。     

美系杜洛克种猪体尺、体重测定与体型评定研究

统计分析了2001年引进的新美系杜洛克种猪及繁殖后代在6月龄、12月龄、24月龄和36月龄四个阶段的体尺、体重数据，结合品种特征、体型外貌总结研究出杜洛克种猪的百分制评分及等级标准。     

高海拔地区鸡胚发育关键因素研究

对藏鸡(T)、饲养在北京的藏鸡(TB)、矮小隐性白(D)、农大小型蛋鸡商品代种蛋(M)、寿光鸡(S)、藏鸡×矮小隐性白(TD)、藏鸡×寿光鸡(TS)7组种蛋在西藏林芝进行孵化研究,通过阶段死亡率、出雏率、失水率数据分析了影响高海拔地区胚胎发育的关键因素。结果表明胚胎本身的高原生活力是鸡蛋高海拔孵化主要因素,其次是蛋壳传导力。     

Changes in technical efficiency of the broiler production in Poland, 1994–2013

1. The effects of changes in technical efficiency on the increase of broiler production are presented for the period 1994–2013 based on the panel data from seven farms located in southern and central Poland. A total of 766 cycles were analysed.

2. The Cobb–Douglas production function was used to assess the changes of output elasticities as well as technical changes in broiler production, for 5-year sub-periods separately.

3. Technical indices of broiler production significantly improved between years 1994–2013: feed conversion ratio decreased from 2.50 kg/kg to 1.78 kg/kg, mortality rate from 8.8% to 4.0% and daily weight gain increased from 37.1 g/d to 58.7 g/d, respectively.

4. Before accession to the EU, there was a substantial increase of fixed capital connected with modernisation of buildings and equipment. In the period 1994–2013, inputs of fixed capital per kilogram of livestock increased by 72% and at the same time the input of labour decreased by 56%.

5. Technical changes in years 1994–1998 contributed to a rapid production increase at a rate of 4.6% annually and only by up to 0.7% annually during 2009–2013. The slowdown of production rate increase after 2009 was partially caused by decreasing the stocking density.     

Multimarker and rare variants genomewide association studies for bone weight in Simmental cattle

Bone weight, defined as the total weight of the bones in all the forequarter and hindquarter joints, can reflect somebody conformation traits and skeletal diseases. To gain a better understanding of the genetic determinants of bone weight, we used a composite strategy including multimarker and rare‐marker association to perform genomewide association studies (GWAS) for that character in Simmental cattle. Our strategy consisted of three models: (i) A traditional linear mixed model (LMM) was applied (Q+K‐LMM); (ii) single nucleotide polymorphisms (SNPs) with p‐values less than .05 from the LMM were selected to undergo the least absolute shrinkage and selector operator (Lasso) in the second stage (LMM‐Lasso); (iii) genes containing two or more rare SNPs were examined by performing the sequence kernel association test (gene‐based SKAT). A total of 1,225 cattle were genotyped with an Illumina BovineHD BeadChip containing 770,000 SNPs. After the quality‐control procedures, 1,217 individuals with 608,696 common SNPs and 105,787 rare SNPs (with 0.001 < minor allele frequency [MAF] <0.05) remained in the sample for analysis. A traditional LMM successfully mapped three genes associated with bone weight, while LMM‐Lasso identified nine genes, which included all genes found by traditional LMM. Only a single gene, EPHB3, surpassed the significance threshold after Bonferroni correction in gene‐based SKAT. In conclusion, based on functional annotation and results from previous endeavours, we believe that LCORL, RIMS2, LAP3, PRKAR2B, CHSY1, MAP2K6 and EPHB3 are candidate genes for bone weight. In general, such a comprehensive strategy for GWAS may be useful for researchers seeking to probe the full genetic architecture underlying economic traits in livestock.