刘宝海, Li Baohai, 聂守军, Nie Shoujun, 高世伟, Gao Shiwei, 刘晴, Liu Qing, 刘宇强, Liu Yuqiang, 常汇琳, Chang Huilin, 马成, M. Cheng, 唐铭, Tang Ming, 薛英会, Xu Yinghui, 白瑞, Bai Rui
{"title":"基于压力-状态-响应模型的寒地粳稻杂交育种后代选择与实现","authors":"刘宝海, Li Baohai, 聂守军, Nie Shoujun, 高世伟, Gao Shiwei, 刘晴, Liu Qing, 刘宇强, Liu Yuqiang, 常汇琳, Chang Huilin, 马成, M. Cheng, 唐铭, Tang Ming, 薛英会, Xu Yinghui, 白瑞, Bai Rui","doi":"10.13930/J.CNKI.CJEA.200776","DOIUrl":"https://doi.org/10.13930/J.CNKI.CJEA.200776","url":null,"abstract":"为提高育种杂交后代选择效果，引入压力-状态-响应（PSR）模型对影响寒地粳稻杂交育种后代的遗传、环境和选择因素进行探讨。构建1个目标、3个准则和18个指标组成的寒地粳稻杂交育种后代选择概念模型与评价体系，并采用客观熵权和功效评分相组合方法进行综合指数评价。结果表明：在PSR模型设计环境下，‘绥粳18’杂交育种9个世代杂交后代均表现出穗颈瘟权重值最大，其次是倒伏级别，再次是空壳率，寒地生态环境下抗穗颈瘟发病指数、抗倒伏级别和空壳率水平是水稻育种杂交后代选择最重要的考虑指标。PSR系统评价中，各子系统的影响力大小依次是响应子系统（权重为0.6867）>状态子系统（权重为0.2651）>压力子系统（权重为0.0482）；各指标值变异系数为0~200.4%，大范围变异利于提高后代选择育种效果。与目前多依据株型理论选择杂交后代系谱相比，运用PSR模型理论与评价体系方法，创建动态压力选择环境，客观评价指标特征，并引入专家决策管理，能够有效克服单纯依靠育种经验、定性定量不结合、多注重性状选择以及响应决策不系统而导致多优性状聚合难、鉴定难、选择效率低等问题，具有较好可行性、可靠性和实用性，可以获得更加合理的寒地水稻育种杂交后代选择方案。本研究结果可为加快寒地优质高产多抗广适突破性水稻新品种选育提供有益参考和技术依据。","PeriodicalId":10032,"journal":{"name":"Chinese Journal of Eco-agriculture","volume":"29 1","pages":"738-750"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44170415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Y. Zhang, X. Tan, F. Li, H. Ruan, J. Yu, Y. Gao, X. Zhai
{"title":"Spatial variation in major water quality types and its relationships with land cover in the middle and lower reaches of Aral Sea Basin","authors":"Y. Zhang, X. Tan, F. Li, H. Ruan, J. Yu, Y. Gao, X. Zhai","doi":"10.13930/J.CNKI.CJEA.200429","DOIUrl":"https://doi.org/10.13930/J.CNKI.CJEA.200429","url":null,"abstract":"Water resources and environmental issues in the Aral Sea Basin of Central Asia are global concerns. In this study, the water quality variables (i.e., basic physical and chemical attributes, different forms of nutrients, other elements, cations, and anions) from 21 sampling sites in the middle and lower reaches of Aral Sea Basin were measured in 2019 to explore water environmental variations and their causes. Spatial variation in 20 water quality variables was investigated, and the representative water quality types, spatial differences, and their causes were identified via multivariate analysis methods (i.e., principal component analysis and cluster analysis). Furthermore, the effects of land cover on the spatial variation in water quality types were explored. The results showed that: 1) the values of electronic conductivity (EC) and total dissolved solids (TDS) increased from the middle to the lower reaches, and the highest values were in the Aral Sea. This indicates that the concentrations of anions and cations increased from the middle to the lower reaches. For the nutrient variables, high phosphorous concentrations were in the middle reaches of Amu Darya, and high nitrate-nitrogen concentrations were in the Syr Darya. For the different forms of carbon, the highest concentrations were in the Amu Darya, particularly in the delta area of lower reaches. 2) The water quality at all sampling sites can be divided into three water quality types according to the similarity classification of water quality variables. The first type had low concentrations for most water quality variables, which were distributed in the middle reaches of Syr Darya and the Aral Sea. The second type had high concentrations of different forms of nitrogen and phosphorus, which were distributed in the middle and lower reaches of Amu Darya. The third type had high concentrations of carbon, anions, and cations, which were distributed in the Aral Sea. The water quality concentrations of the first and second types were mainly due to rock weathering processes on bare land, and the anions and cations were mainly derived from the weathering of silicates and evaporites. The concentrations of the third type were mainly due to the evaporation and crystallization processes of a dry climate, and the anions and cations were mainly derived from the weathering of silicates and evaporites, which may also be affected by carbonate weathering. 3) With an increase in the buffer zone radius for each sampling point (0.5 km to 10 km), the significant land cover changed from bare land to water, shrubland, grassland, mixed farmland, and vegetation for the first water quality type; the most significant land cover was water. There were no significant relationships between the second water quality type and land cover. For the third water quality type, the significant land cover changed from water to water, mixed farmland, and vegetation - the most significant land cover was water. Therefore, spatial varia","PeriodicalId":10032,"journal":{"name":"Chinese Journal of Eco-agriculture","volume":"29 1","pages":"299-311"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66581673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Corn disease recognition based on the Convolutional Neural Network with a small sampling size","authors":"Ming-tao Yang, Yao Zhang, Tao Liu","doi":"10.13930/J.CNKI.CJEA.200375","DOIUrl":"https://doi.org/10.13930/J.CNKI.CJEA.200375","url":null,"abstract":"Crop disease management influences yield and quality, yet identifying corn diseases is still difficult. High labor costs, small number of sample, and uneven disease distributions contribute to the difficulty. We propose an improved Convolutional Neural Network (CNN) model based on the transfer learning method for disease identification. The sample image set was enhanced by rotation and roll-over, then the migrated MobileNetV2 model was used to train the image data set for corn diseases. The Focal Loss function was used to improve the neural network loss function, and the Softmax classification method was used for corn disease image recognition. The training set accuracy, validation set accuracy, weight, run time, and the number of parameter in six models were experimentally compared. The verification set accuracy rates were 93.88% (AlexNet), 95.48% (GoogleNet), 91.69% (Vgg16), 97.67% (RestNet34), 96.21% (MobileNetV2), and 97.23% (migrated MobileNetV2). The migrated MobileNetV2 was 97.23% accurate and weighed 8.69 MB. Confounding the MobileNetV2 model improved the recognition accuracy by 1.02% and reduced the training speed by 6 350 seconds compared to the unconfounded model. The migrated MobileNetV2 model had the best corn disease recognition ability with a small sampling size; improved convergence speed, reduced model calculations, and greatly improved the recognition time.","PeriodicalId":10032,"journal":{"name":"Chinese Journal of Eco-agriculture","volume":"28 1","pages":"1924-1931"},"PeriodicalIF":0.0,"publicationDate":"2020-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42414967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"不忘初心 砥砺奋进 植根农业40年历程:祝贺中国科学院遗传与发育生物学研究所农业资源研究中心成立40周年","authors":"胡春胜, Hu Chunsheng","doi":"10.13930/J.CNKI.CJEA.180797","DOIUrl":"https://doi.org/10.13930/J.CNKI.CJEA.180797","url":null,"abstract":"本文回顾总结了中国科学院遗传与发育生物学研究所农业资源研究中心（原石家庄农业现代化研究所）成立40年来的主要科研历程与业绩。40年来，不忘初心，不断探索我国农业现代化发展道路与模式，20世纪70年代末探索了农业机械化示范模式，80年代开展了恢复型生态农业模式示范，90年代开展了资源节约型农业模式示范，21世纪初探索了智慧农业和生态循环农业模式；砥砺奋进，不断创新农业系统调控理论与技术体系，创建了农田SAPC水分传输与界面调控理论，量化了农田氮素通量过程，建立了农业面源污染防控理论与技术，发展了咸水安全灌溉理论，建立了林业生态工程理论，创建了食物链模型，创新小麦育种体系；扎根农业，组织了渤海粮仓科技示范工程等大规模区域农业示范，不断引领开展区域示范服务；放眼世界，不断拓展国际合作与创新平台，为我国农业绿色发展做出重大贡献。","PeriodicalId":10032,"journal":{"name":"Chinese Journal of Eco-agriculture","volume":"26 1","pages":"1423-1428"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43336447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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