不同叶龄蘖、穗氮肥组合对粳稻产量及氮素利用的影响

以主茎叶片数不同的粳稻品种吉粳88 (14片)、沈农265 (15片)和沈农1401 (16片)为试材,采用大田筒栽方式,在总施氮量225kghm–2及轻简施肥(基肥、蘖肥、穗肥)模式基础上,设置基蘖肥∶穗肥6∶4和8∶2两种施肥比例,并分设不同源、库叶龄期施氮组合即不同叶龄蘖、穗肥精确施氮组合。分析了不同源库期氮肥运筹模式对水稻农艺性状、产量及氮素利用特性的影响。结果表明:(1)在有效穗数、分化颖花数、产量和氮素利用率方面,吉粳88、沈农265、沈农1401不同氮肥运筹下最佳蘖、穗肥叶龄组合均为6∶4显著高于8∶2。(2)不同氮肥运筹下,吉粳88在8叶(叶龄指数57.1%)、沈农265在9叶(叶龄指数60.0%)、沈农1401在10叶(叶龄指数62.5%)时,即叶龄指数在60%左右时,施用蘖肥效果最佳,最终穗数最多,对保蘖起主要作用;吉粳88在11叶(叶龄指数78.6%)、沈农265在12叶(叶龄指数80.0%)、沈农1401在13叶(叶龄指数81.3%)时,即叶龄指数在80%左右时,施用穗肥效果最佳,最终穗粒数最多,对促花起主要作用。(3)吉粳88-6∶4 (8, 11),沈农265-6∶4 (9, 12),沈农1401-6∶4 (10, 13) 3组处理,在产量、氮素积累量、氮素吸收利用率、农学利用率及偏生产力等方面,显著高于同品种不同叶龄蘖、穗氮肥组合中的其他处理。因此,适当延迟蘖肥施用叶龄期(叶龄指数60%左右)、提前穗肥施用叶龄期(叶龄指数80%左右)同时增加穗肥施用比例,既可以显著提高氮素积累量、氮素吸收利用率、农学利用率及偏生产力,又能显著促进成穗率的提高和颖花数的分化,达到保蘖促花的双重作用,实现优源、扩库、充实的目标,从而获得高产。     

水产类上市公司经营绩效评价及影响因素分析——基于DEA-Tobit模型

2012年以来水产类上市公司经营面临困局.该研究根据11家水产类上市公司2012～2014年的面板数据,首先运用DEA模型进行生产效率的评价,研究发现水产类上市公司的技术效率、纯技术效率和规模效率3年平均值都较高,但2014年整体绩效有走低的趋势;然后运用Tobit模型,分析经营绩效的影响因素,发现公司总资产周转率和高等教育人员比例与技术效率正相关,而研发人员比例与技术效率不相关.最后,从资产整合、加强营销、重视人才和发展休闲渔业等方面提出了提高水产类上市公司经营绩效的对策建议.     

动态细分饲粮营养供给对肉鸡生产性能、肉品质、氮与磷代谢率及血清生化指标的影响

本试验旨在根据肉鸡生长曲线及动态营养需要,研究动态细分饲粮营养供给对肉鸡生产性能、肉品质、氮、磷代谢率及血清生化指标的影响。选取1日龄爱拔益加(AA)肉鸡240只(公母各占1/2),随机分为3个组,分别为A组:每3.5 d 1个营养标准(1~7日龄1个营养标准);B组:每7 d 1个营养标准;C组:每14 d 1个营养标准。每组8个重复,每个重复10只鸡,试验期42 d。结果表明:1)C组肉鸡的平均日增重(ADG)显著高于A、B组(P0.05),料重比(F/G)显著低于A组(P0.05)。A、B组肉鸡生产性能指标差异均不显著(P0.05)。不同动态细分饲粮营养供给方式对肉鸡肉品质的影响无显著差异(P0.05)。2)不同动态细分饲粮营养供给方式对肉鸡氮、磷代谢率的影响无显著差异(P0.05)。3)肉鸡28日龄时,C组肉鸡血清尿素氮(UREA)含量显著高于A组(P0.05);42日龄时,C组肉鸡血清UREA含量显著高于A、B组(P0.05),但A、B组肉鸡血清UREA含量差异不显著(P0.05)。4)不同动态细分饲粮营养供给方式对肉鸡血清皮质酮(CORT)含量及总抗氧化能力(T-AOC)的影响差异不显著(P0.05)。综上所述,每14 d 1个营养标准可使肉鸡的生产性能达到最佳,从实际操作的复杂性考虑,每7 d 1个营养标准可更好地满足饲粮的氨基酸平衡。     

油草混播种类对油菜生产和草地生产性能的影响

为筛选与门源油菜混播效益最高的牧草种类,解决油菜连作障碍问题,提高青藏高原高海拔区土地的生产能力,本研究采用油菜混播箭筈豌豆、青稞和燕麦的方式,分时段取样,探究了油菜混播不同牧草对在不同时段牧草自身生产能力的影响,并通过土地当量比判断各混播组合的优劣。研究表明油菜-燕麦混播处理在7月6日时表现干重最高,单位面积达174.48 g；在7月21日表现为鲜重和鲜干比最高,在8月15日时表现为株高显著高于单作油菜(p＜0.05),较单作油菜高97.02%；鲜重最高,达2817.00 g。在9月11日,油菜-青稞和油菜-燕麦混播处理的株高显著高于单作油菜(p＜0.05),分别较单作油菜高64.33%和99.68%,以油菜-青稞混播处理的干重最高,达589.00 g,较单作油菜处理高147.51%。油菜-箭筈豌豆混播和油菜-燕麦混播有产量优势,油菜-燕麦混播的土地当量比最高,为1.11,土地利用效果最好。     

关于PA-250感应电机效率提高的研究

随着社会经济的飞速发展,对于节能环保的要求越来越高,传统的G/M代码已经成为现代先进制造技术的瓶颈。而基于STEP标准的STEP-NC将为现代CNC的高速和高精度加工提供条件。     

广东省农地边际化特征及其影响因素的地理探测

为了明确经济发达地区广东省农地边际化现象及其主要影响因素。本研究采用“单位成本纯收益指标”、“农地利用集约度指标”和“农作物播种面积指标”三大主要指标,对广东省2000—2015年农地边际化特征进行提取。选择影响农地边际化特征的19个因子,利用地理探测器方法对农地边际化现象的核心影响因素进行挖掘。结果表明：2000—2015年广东省共发生3次比较明显的农地边际化现象,分别在2001—2002年、2005—2006年、2010—2013年,且第3次边际化持续时间最长,情况最严重。社会经济发展水平与结构指标和农户家庭资源要素结构变化指标对农地边际化的影响程度较大,其中第三产业产值比重、农村农户固定资产投资、粮食总播种面积对农地边际化现象有较强的解释力,q值分别为0.8067、0.8055、0.7916。任何两因子相互交互作用都能增强对农地边际化现象的解释力。     

Selecting the hologenome to breed for an improved feed efficiency in pigs—A novel selection index

Most traits in animal breeding, including feed efficiency traits in pigs, are affected by many genes with small effect and have a moderately high heritability between 0.1 and 0.5, which enables efficient selection. Since the microbiota composition in the gastrointestinal tract is also partly heritable and was shown to have a substantial effect on feed efficiency, the host genes affect the phenotype not only directly by altering metabolic pathways, but also indirectly by changing the microbiota composition. The effect of the microbiota composition on the breeding value of an animal is the conditional expectation of its breeding value, given the vector with microbiota frequencies, that is The breeding value of an animal can therefore be decomposed into a heritable contribution that arises from an altered microbiota composition and a heritable contribution that arises from altered metabolic pathways within the animal, so Instead of selecting for breeding value , an index comprising the two components and with appropriate weights, that is , can be used. The present study shows how this breeding strategy can be applied in pig genomic selection breeding scheme for two feed efficiency traits and daily gain.     

Time‐lapse monitoring of soil water content using electromagnetic conductivity imaging

The volumetric soil water content (θ) is fundamental to agriculture because its spatiotemporal variation in soil affects the growth of plants. Unfortunately, the universally accepted thermogravimetric method for estimating volumetric soil water content is very labour intensive and time‐consuming for use in field‐scale monitoring. Electromagnetic (EM) induction instruments have proven to be useful in mapping the spatiotemporal variation of θ. However, depth‐specific variation in θ, which is important for irrigation management, has been little explored. The objective of this study was to develop a relationship between θ and estimates of true electrical conductivity (σ) and to use this relationship to develop time‐lapse images of soil θ beneath a centre‐pivot irrigated alfalfa (Medicago sativa L.) crop in San Jacinto, California, USA. We first measured the bulk apparent electrical conductivity (EC_a – mS/m) using a DUALEM‐421 over a period of 12 days after an irrigation event (i.e. days 1, 2, 3, 4, 6, 8 and 12). We used EM4Soil to generate EM conductivity images (EMCIs). We used a physical model to estimate θ from σ, accounting for soil tortuosity and pore water salinity, with a cross‐validation RMSE of 0.04 cm³/cm³. Testing the scenario where no soil information is available, we used a three‐parameter exponential model to relate θ to σ and then to map θ along the transect on different days. The results allowed us to monitor the spatiotemporal variations of θ across the surveyed area, over the 12‐day period. In this regard, we were able to map the soil close to field capacity (0.27 cm³/cm³) and approaching permanent wilting point (0.03 cm³/cm³). The time‐lapse θ monitoring approach, developed using EMCI, has implications for soil and water use and management and will potentially allow farmers and consultants to identify inefficiencies in water application rates and use. It can also be used as a research tool to potentially assist precision irrigation practices and to test the efficacy of different methods of irrigation in terms of water delivery and efficiency in water use in near real time.