安徽沿江地区油麦后直播棉的品种选用策略

在目前安徽沿江地区不能实现棉花机采的情况下,提出了“植株高度既适于机采也适于人工采收,含絮力适中(不易落花便于集中采收),生育期适中(播种到采收为180天左右),纤维品质及产量性状较优(纤维长度30.0 mm以上,断裂比强度30.0 cN/tex以上,马克隆值5.0以下,籽棉产量3750 kg/hm 2以上,衣分在40%左右)”的油(或麦)后直播棉模式的人工采收品种选用策略。旨在促进安徽沿江地区棉花生产稳定发展。     

电商环境下新中式木家具外贸市场分析

随着全球电子商务蓬勃发展,新中式木家具突破传统的国内营销环境,在“一带一路”沿线国家提高外贸市场占有率越来越重要。简述了电商环境与传统营销环境异同点,对新中式木家具外贸市场进行初步研判,提出了拓展电商环境下新中式木家具外贸市场措施。     

Analysis of residuals for stability assessment of rice genotypes

The analysis of residuals computes general adaptabilities (GAs) and specific adaptabilities (SAs) of genotypes similar to combining ability analysis. The residual analysis is simple and robust being based on the first-degree statistics, and is assumption-free. A unique statistic of ratio of variances (VR) of residuals for individual genotypes allows comparing their stability. The residuals analysis of genotype × environment (GE) interactions was performed for grain yield (t ha^?1) of 25 rice (Oryza sativa L.) genotypes, including two developed by marker-assisted selection for root traits quantitative trait loci (QTL), tested across 16 sites. Results were compared with the linear regression analysis for (i) full set of 16 sites showing adequacy of linear model and (ii) subset of nine-poor sites showing inadequacy of linear model. There was agreement between the two analyses for situation (i) but not for situation (ii) when the regression analysis fails because of nonlinearity but the residual analysis retains its validity.     

农业科研单位外事管理工作的思考——以丹东农业科学院为例

外事管理工作对于农业科研单位而言,既是加强国际交流与合作的桥梁,又是吸收国外先进农业技术、管理经验的重要渠道之一。文章阐述了丹东农业科学院外事管理工作取得的成效,分析了农业科研单位外事管理工作存在的问题,并针对问题提出了解决对策,在此基础上,进一步提出了建立并完善外事管理规章制度、积极开展多领域的对外交往活动、促进面向国际市场的科研成果转化等促进农业科研单位外事管理水平提升的建议。     

播期对直播水稻产量、花后各器官干物质和氮素积累及转运的影响

为探究播期对直播水稻产量、花后干物质和氮素积累与转运的影响,以水稻品种白粳1号、长白9号和龙粳31号为供试材料,设置3个播期（SD1、SD2和SD3）,比较不同播期条件下3个品种的产量、抽穗期和成熟期各器官干物质及氮素积累与转运特点。3个品种抽穗期和成熟期各器官干物质及氮素积累量、抽穗至成熟期各器官干物质及氮素转运量和产量均表现为SD2>SD3>SD1。相关分析表明,产量与成熟期穗干物质及氮素积累量呈显著正相关,与叶片干物质和氮素转运量呈显著正相关。在本试验条件下,播期的推迟提高了3个品种有效穗数及结实率,进而提高了产量,同时促进了各器官物质转运量与转运率的提高。各品种SD2处理的产量及花后干物质、氮素积累量最高,SD2为适宜播期。     

Rapid screening of genetically modified ingredients in soybean and cotton processing by-product and waste using direct qPCR

To develop a simple and fast method for screening genetically modified ingredients from processing by-product and waste, direct quantitative PCR (qPCR) kit-Taqman which omitting multi genomic DNA preparing steps was developed in this study. A total of 18 oil crop processing by-products and wastes including 10 soybean and 8 cotton materials were collected from food processing factories. Compared with 2 commercial direct qPCR kits, conditions of DNA releasing procedure and PCR amplification were optimized. Element screening was performed at the initial step of genetically modified (GM) ingredient testing procedure via direct qPCR. GM event identification was carried out in positive samples by initial screening. Totally 5 screening elements (P–35S, T-NOS, Cp4-epsps, bar and pat) for soybean materials and 6 screening elements (P–35S, T-NOS, NPTII, Cry1Ac, bar and pat) for cotton samples were detected. In GM event identification, MON531 and MON1445 were found in cotton materials. Results were further confirmed by real-time PCR with DNA extraction and purification. The direct qPCR system proposed by this research was convenient for rapid screening and identification of GM ingredients in oil crop primary by-product and waste.     

Genetic parameters for litter size,piglet growth and sow's early growth and body composition in the Chinese–European line Tai Zumu

Genetics of piglet growth in association with sow's early growth and body composition were estimated in the Tai Zumu line. Piglet and sow's litter growth traits were calculated from individual weights collected at birth and at 3 weeks of age. Sow's litter traits included the number of piglets born alive (NBA), the mean piglet weight (MW) and the standard deviation of weights within the litter (SDW). Sow's early growth was measured by the age at 100 kg (A₁₀₀), and body composition included backfat thickness (BF₁₀₀). A main objective of this study was to estimate separately the direct genetic effect (d) and the maternal genetic effect (m) on piglet weight and daily weight gain during lactation. Variance components were estimated using the restricted maximum likelihood methodology based on animal models. The heritability estimates were 0.19 for NBA, 0.15 and 0.26 for SDW and MW at 3 weeks and 0.42 and 0.70 for A₁₀₀ and BF₁₀₀. The NBA was almost independent from SDW. Conversely, the A₁₀₀ and BF₁₀₀ were correlated unfavourably with SDW (r_g<−0.24, SE<0.12). A stronger selection for litter size should have little effect on litter homogeneity in weights. Selection for lean growth rate tends to favour heterogeneity in weights. The direct effect on piglet weight at birth and daily weight gain accounted for 12% (h² (d) = 0.02) and 50% (h² (d) = 0.11) of the genetic variance, respectively. The association between d and m for piglet weight was not different from zero at birth (r_g = 0.19, SE = 0.27), but a strong antagonism between d and m for daily weight gain from birth to 3 weeks was found (r_g = −0.41, SE = 0.17). Substantial direct and maternal genetic effects influenced piglet growth until weaning in opposite way.     

Evaluation of WARM for different establishment techniques in Jiangsu (China)

WARM is a model for rice simulation accounting for key biotic and a-biotic factors affecting quantitative and qualitative (e.g., amylose content, chalkiness) aspects of production. Although the model is used in different international contexts for yield forecasts (e.g., the EC monitoring and forecasting system) and climate change studies, it was never explicitly evaluated for transplanting, the most widespread rice establishment method especially in tropical and sub-tropical Asia. In this study, WARM was tested for its ability to reproduce nursery growth and transplanting shock, using data on direct sown and transplanted (both manual and mechanical) rice collected in 24 dedicated field experiments performed at eight sites in Jiangsu in 2011, 2012 and 2013. The agreement between measured and simulated aboveground biomass data was satisfactory for both direct sowing and transplanting: average R² of the linear regression between observed and simulated values was 0.97 for mechanical transplanting and direct sowing, and 0.99 for manual transplanting. RRMSE values ranged from 5.26% to 30.89%, with Nash and Sutcliffe modelling efficiency always higher than 0.78; no notable differences in the performance achieved for calibration and validation datasets were observed. The new transplanting algorithm – derived by extending the Oryza2000 one – allowed WARM to reproduce rice growth and development for direct sown and transplanted datasets (i) with comparable accuracy and (ii) using the same values for the parameters describing morphological and physiological plant traits. This demonstrates the reliability of the proposed transplanting simulation approach and the suitability of the WARM model for simulating rice biomass production even for production contexts where rice is mainly transplanted.     

基于风险与效益模型的政府农业科技投入范围界定

在分析农业科技研发层次与演化流程的基础上,提出了不同研究阶段的农业技术类型和特征,构建了农业科技研发投入风险与效益模型,揭示了政府农业科技投入领域的选择行为,并以此界定了农业科技投入的合理范围。