Precipitation forecasting by large-scale climate indices and machine learning techniques

Global warming is one of the most complicated challenges of our time causing considerable tension on our societies and on the environment. The impacts of global warming are felt unprecedentedly in a wide variety of ways from shifting weather patterns that threatens food production, to rising sea levels that deteriorates the risk of catastrophic flooding. Among all aspects related to global warming, there is a growing concern on water resource management. This field is targeted at preventing future water crisis threatening human beings. The very first stage in such management is to recognize the prospective climate parameters influencing the future water resource conditions. Numerous prediction models, methods and tools, in this case, have been developed and applied so far. In line with trend, the current study intends to compare three optimization algorithms on the platform of a multilayer perceptron (MLP) network to explore any meaningful connection between large-scale climate indices (LSCIs) and precipitation in the capital of Iran, a country which is located in an arid and semi-arid region and suffers from severe water scarcity caused by mismanagement over years and intensified by global warming. This situation has propelled a great deal of population to immigrate towards more developed cities within the country especially towards Tehran. Therefore, the current and future environmental conditions of this city especially its water supply conditions are of great importance. To tackle this complication an outlook for the future precipitation should be provided and appropriate forecasting trajectories compatible with this region's characteristics should be developed. To this end, the present study investigates three training methods namely backpropagation (BP), genetic algorithms (GAs), and particle swarm optimization (PSO) algorithms on a MLP platform. Two frameworks distinguished by their input compositions are denoted in this study: Concurrent Model Framework (CMF) and Integrated Model Framework (IMF). Through these two frameworks, 13 cases are generated: 12 cases within CMF, each of which contains all selected LSCIs in the same lead-times, and one case within IMF that is constituted from the combination of the most correlated LSCIs with Tehran precipitation in each lead-time. Following the evaluation of all model performances through related statistical tests, Taylor diagram is implemented to make comparison among the final selected models in all three optimization algorithms, the best of which is found to be MLP-PSO in IMF.     

China's alfalfa market and imports: Development,trends, and potential impacts of the U.S.–China trade dispute and retaliations

This study examines the development and trends of China's alfalfa market and imports, identifies key factors for the rapid increase in China's alfalfa imports, and discusses potential impacts of the U.S.–China trade dispute and retaliations on the alfalfa markets and trade in both nations. China's rapid transition toward larger-scale commercial dairy production, with enhanced feed and cost management as well as quality and safety control, and its limited resources for high-quality alfalfa production are key factors for the dramatic increase in its alfalfa imports, from 19 601 metric tons in 2008 to 1.38 million metric tons (mmt) in 2018. While the United States dominated China's alfalfa imports with an average share of 97.01% from 2007 to 2017, the share dropped to 83.76% in 2018 and 63.28% in January 2019 due to the trade dispute and retaliations started in 2018. China will likely remain a large importer of alfalfa because of both its growing demand and the comparative advantages of imported alfalfa in quality and price, but the imports from the United States will be highly affected by the ongoing trade dispute and negotiations. China is also expected to make more efforts to reduce its dependence on U.S. alfalfa through increased investment in domestic alfalfa production and identification of alternative sources of alfalfa and other hay imports.     

四种短体线虫的形态和分子生物学鉴定

 采用形态学和分子生物学方法对来自荷兰的藏橐吾和铃兰、泰国的高山榕、缅甸的香蕉等种苗(球)分别鉴定出玻利维亚短体线虫Pratylenchus bolivianus、铃兰短体线虫P. convallariae、咖啡短体线虫P. coffeae和斯佩奇短体线虫 P. speijeri。对这4种线虫的形态特征进行较为详细的描述后,认为头环、口针、侧区、生殖系统和尾形等形态特征是种类鉴定的重要依据;进一步利用引物28S-D2A/28S-D3Br扩增测序得到上述4种线虫的28S rRNA基因D2/D3区序列,序列分析发现玻利维亚短体线虫从欧洲到美洲的不同种群之间的遗传距离变异较小,仅为0~0.007;铃兰短体线虫同一种群不同个体之间的遗传变异较大,为0.006~0.029;咖啡短体线虫不同种群之间的平均遗传距离为0.013;斯佩奇短体线虫不同种群之间的平均遗传距离为0.010;咖啡短体线虫P. coffeae和斯佩奇短体线虫P. speijeri亲缘关系很近,二者种间平均遗传距离仅为0.026。本研究再次证明线虫28S rRNA基因D2/D3区基因序列可作为短体线虫种间鉴定的依据。     

河南省肉鸡4株禽传染性支气管炎病毒的分离鉴定及S1基因序列分析

通过病料接种SPF鸡胚和鸡胚尿囊液的RT-PCR鉴定,于2017至2018年从河南省不同发病鸡场分离到4株鸡传染性支气管炎病毒(IBV),分别命名为HB/L1/201711、ZMD/L2/201704、SQ/L3/201803、LY/L4/201710,并对4株毒株S1基因进行克隆和序列分析。结果:HB/L1/201711、SQ/L3/201803 S1基因全长1 620 nt,编码540 aa,其裂解位点分别为HRRRR,分属于基因型V、Ⅰ分支;ZMD/L2/201704、LY/L4/201710株S1基因全长1 617 nt,编码539 aa,其裂解位点为RRSRR,属于基因型Ⅱ分支。ZMD/L2/201704与LY/L4/201710分离株核苷酸序列及其推导的氨基酸序列同源性较高,分别为97.6%、95.4%,与另外2株分离株之间核苷酸序列同源性为76.5%、76.8%。4株分离株与国内外参考毒株及疫苗株的氨基酸序列同源性在58.5%~98%之间,具有较大的差异性。其中:ZMD/L2/201704、LY/L4/201710与491型传支疫苗氨基酸同源性较高,可达95.4%、95.9%;SQ/L3/201803与CHI分支参考毒株氨基酸同源性在94.6%~98.9%之间;HB/L1/201711与TWⅠ型毒株2575/98、3468/07有较高同源性,分别为94.8%和93.5%。本研究表明河南省肉鸡鸡群鸡传染性支气管炎病毒基因型相对复杂,推测存在重组与突变。     

田菁种子内生菌的分离及其对萌发的影响

植物-微生物联合修复是改良盐碱地的重要生物策略，其中获得有效的微生物菌株是关键所在。为了获得更多的植物促生菌菌种资源，以滩涂耐盐植物田菁种子为材料分离可培养内生菌。分离纯化得到了79株菌，主要集中分布在Bacillus、Azorhizobium、Cellulosimicrobium三个属。菌株SC45、SC59、SC60、SC24、SC5、SC17六株菌可分泌吲哚乙酸（indole-3-acetic acid，IAA）。SC45、SC59、SC60具有溶解无机磷能力，其中SC60分解无机磷能力最高为（207.9±14.29 ）mg·L-1，且SC45具有产铁载体能力。种子萌发试验结果显示，与对照相比，SC45、SC60、SC59、SC17对田菁种子的活力系数分别可提高13.3%、14.1%、29.9%、26.7%。且SC60和SC17菌株浸种可显著促进胚根的发育，胚根占比提高了29.5%和13%。上述结果表明，Bacillus SC60可分泌IAA和溶解无机磷，提高种子活力，促进胚根发育，可作为田菁促生菌应用于农业生产。     

利用广亲和基因S5-n的功能标记鉴定特殊配组类型杂交种纯度研究

“粳不育系/广亲和恢复系”配组方式在浙江省得到了越来越多的应用。广亲和基因S5-n的功能标记可快速检测种子纯度。为验证水稻广亲和基因S5-n功能标记鉴定粳不育系/广亲和恢复系配组方式杂种一代的效果,我们分别将长粳1A和6个恢复系获得的F₁代进行大田统计和分子标记鉴定,结果显示,由于广亲和基因S5-n分子标记不能特异性检测出串粉形成的异形株,导致其鉴定结果较大田统计鉴定结果高1.0~2.0百分点。未来还需要进一步优化广亲和基因分子标记技术的特异性,进而提高分子检测的准确性,这将有助于杂交稻的安全生产。     

青土湖干涸湖底2种典型固沙植物群落土壤粒径分布分形特征与养分关系研究

以石羊河流域下游青土湖干涸湖底人工梭梭林和白刺灌丛沙包土壤为研究对象，通过分析2种植物群落5种立地条件土壤粒径分布和分形特征及其与土壤养分的关系，揭示了湖底2种典型植物群落土壤粒度的分布特征。结果表明:1）研究区固沙植被0~10 cm土壤颗粒以细砂粒为主，没有粗砂粒，其中白刺灌丛沙包表层极细砂含量、土壤颗粒物分形维数较梭梭林和对照区高。2）土壤颗粒分形维数与各粒级颗粒百分含量的关系差异较大，分形维数与黏粒、极细砂含量呈显著正相关，与细砂、中砂含量呈显著负相关，但是与粉粒含量关系不显著。土壤颗粒分形维数越大，土壤所含的细粒物质越多。3）土壤分形维数D值、黏粒、粉粒含量与有机质（SOM）、全N（TN）呈正相关性，土壤颗粒物的分形维数越大、土壤黏粒、粉粒含量越高，则土壤有机质、全N含量越高。综合分析，固定白刺灌丛表层土壤沙化程度相对较低，其他类型风蚀严重，因此，应积极采取生物措施和工程措施开展生态修复防治风蚀和盐尘暴的发生。