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}
马力文, Ma Liwen, 李剑萍, Liao Jianping, 韩颖娟, Han Yingjuan, 李万春, Li Wanchun
{"title":"贺兰山东麓‘赤霞珠’品质形成气象条件与评级方法研究","authors":"马力文, Ma Liwen, 李剑萍, Liao Jianping, 韩颖娟, Han Yingjuan, 李万春, Li Wanchun","doi":"10.13930/J.CNKI.CJEA.170579","DOIUrl":"https://doi.org/10.13930/J.CNKI.CJEA.170579","url":null,"abstract":"葡萄品质很大程度上决定了葡萄酒品质，其与气象条件关系密切。在正常管理和农业技术水平下，衡量葡萄生长气象条件是否适宜品质形成的方法称为酿酒葡萄品质气象评价技术。研究酿酒葡萄品质形成的气象评级技术和气象指标，可为葡萄酒商业评级、年份酒鉴定和窖藏提供气候参考依据。利用贺兰山东麓2003-2011年多点采样化验的101份‘赤霞珠’总糖、总酸、糖酸比、pH和单宁资料，选取其中的51份进行相关普查，确定了有生物学意义的显著气象因子，以各因子的决定系数分别建立了5项品质指标的权重模型，通过单项品质指标对综合品质的贡献构建了综合品质气象评价模型。参照前人对葡萄酒品质、酿酒葡萄品质的研究和葡萄酒和葡萄品质标准，根据酿造葡萄酒所需的总糖、总酸、糖酸比、pH和单宁，确定‘赤霞珠’各项品质指标的阈值，利用模型反推出与葡萄酒品质相对应的气象因子阈值和‘赤霞珠’品质气象分类标准。回代检验显示5项品质指标均通过了0.001的R检验和F检验，与实测值接近且变化趋势一致。总糖、总酸、糖酸比和单宁的模拟效果较好，R ≥ 0.59且RMSE较小，但pH误差相对较大。用50份未参与建模的样本估算了‘赤霞珠’品质等级。总糖、总酸与实测接近；个别样本的糖酸比、单宁误差相对较大，但变化趋势与实测一致；样本pH均在适宜范围内且较稳定。从综合评分和等级来看，28个样本与实况等级相同，18个样本误差在1级内，仅4个样本相差2级，准确反映出原料的质量。‘赤霞珠’品质气象评价指标和模型为评价贺兰山东麓酿酒葡萄品质提供了一套可行的方法。","PeriodicalId":10032,"journal":{"name":"Chinese Journal of Eco-agriculture","volume":"26 1","pages":"453-466"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48048113","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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