Pub Date : 2021-04-01DOI: 10.13930/J.CNKI.CJEA.200776
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
To improve the selection effect of hybrid offspring in breeding, a pressure state response (PSR) model was introduced to explore the genetic, environmental, and selection factors that affect the offspring of cold region japonica rice hybrid breeding. Construct a conceptual model and evaluation system for offspring selection in cold region japonica hybrid rice breeding, consisting of 1 objective, 3 criteria, and 18 indicators, and use a combination of objective entropy weight and efficacy score method for comprehensive index evaluation. The results showed that under the PSR model design environment, all 9 generations of hybrid offspring of 'Suijing 18' showed the highest weight value of ear neck blast, followed by lodging level and empty shell rate. The index of resistance to ear neck blast, lodging level, and empty shell rate in cold ecological environment are the most important consideration indicators for selecting hybrid offspring in rice breeding. In the evaluation of the PSR system, the order of influence of each subsystem is the response subsystem (with a weight of 0.6867), the state subsystem (with a weight of 0.2651), and the pressure subsystem (with a weight of 0.0482); The coefficient of variation of each indicator value is 0~20.4%, and a wide range of variation is beneficial for improving the breeding effect of offspring selection. Compared with the current selection of hybrid offspring genealogy based on plant type theory, using PSR model theory and evaluation system methods to create a dynamic pressure selection environment, objectively evaluate indicator characteristics, and introduce expert decision-making management can effectively overcome the problems of relying solely on breeding experience, not combining qualitative and quantitative analysis, paying more attention to trait selection, and responding to decision-making not systematically, resulting in difficult aggregation, identification, and low selection efficiency of multiple excellent traits, It has good feasibility, reliability, and practicality, and can obtain a more reasonable selection plan for hybrid offspring in cold region rice breeding. The results of this study can provide useful reference and technical basis for accelerating the breeding of breakthrough rice varieties with high quality, high yield, multi resistance, and wide adaptability in cold regions.
{"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":null,"pages":null},"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}
In order to explore the effects of the combination of rice ridge cultivation and rice fish chicken symbiotic mode on rice stem lodging, ear traits, and yield, this study conducted field comparative experiments on conventional rice ridge cultivation (CK), rice ridge fish farming (RF), rice ridge chicken farming (RC), and rice ridge chicken fish farming (RFC) to study the changes in rice stem lodging, ear traits, and actual yield under the rice fish chicken symbiotic mode. The results showed that there was no significant overall difference in the mean values of rice plant height, fresh weight, center of gravity height, and internode length among the four treatments over the course of two years. Compared with CK treatment, RFC and RC treatment showed an increasing trend in the internode outer diameter, internode wall thickness, spike length, and fresh weight of rice stems, but there was no significant difference in the overall mean; The rice yield has also remained stable. The outer diameter, wall thickness, panicle length, and fresh weight of rice stem internodes under RF treatment showed a decreasing trend, and the fresh weight of panicles significantly decreased in 2019 (P<0.05); The average decrease in rice yield over the past two years was 29.98% (P<0.05), and there was no significant difference in the other mean values as a whole. Over the course of 2 years, the average increase in internode stem bending resistance of rice treated with RFC and RC compared to CK was 19.69% and 8.10%, and the 4th and 5th internode stem bending resistance of RFC significantly increased (P<0.05); However, the bending section modulus and bending strength of the stems treated with RF showed a decreasing trend overall, but the overall change in mean was not significant. Compared with CK treatment, RFC and RC treatments reduced the average maximum stress between rice stem nodes by 17.85% and 15.08%, while the average lodging index decreased by 4.35% and 4.26%, but did not reach a significant level; The lodging index of stem internodes under RF treatment increased by an average of 11.47%, and significant differences were observed between the 3rd and 2nd to 5th internodes in 2018 and 2019 (P<0.05). In summary, the ridge farming rice fish chicken symbiosis and ridge farming rice chicken symbiosis models can improve the length and fresh weight of rice panicles, stabilize rice yield, increase the outer diameter and wall thickness of rice stem internodes, improve stem bending resistance and bending section modulus, reduce the maximum stress and lodging index of the stem, and thus have a certain stem strengthening effect and lodging resistance.
Pub Date : 2021-01-01DOI: 10.13930/J.CNKI.CJEA.200429
Y. Zhang, X. Tan, F. Li, H. Ruan, J. Yu, Y. Gao, X. Zhai
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
中亚咸海盆地的水资源和环境问题是全球关注的问题。本研究对2019年咸海盆地中下游21个采样点的水质变量(即基本理化属性、不同形式的营养物质、其他元素、阳离子和阴离子)进行了测量，探讨了水环境的变化及其原因。研究了20个水质变量的空间变异，通过主成分分析和聚类分析等多变量分析方法，确定了具有代表性的水质类型、空间差异及其成因。此外，还探讨了土地覆被对水质类型空间变化的影响。结果表明:1)电子电导率(EC)和总溶解固形物(TDS)由中下游逐渐增大，以咸海最高;这说明阴离子和阳离子的浓度由中游向下游递增。在养分变量上，阿姆河中游磷含量较高，锡尔河中游硝态氮含量较高。对于不同形式的碳，阿姆河的浓度最高，特别是在下游的三角洲地区。2)根据水质变量的相似性分类，将各采样点的水质分为三种水质类型。第一类水质变量浓度低，主要分布在锡尔河中游和咸海。第二类土壤中氮、磷含量较高，分布在阿姆河中下游。第三种类型碳、阴离子和阳离子浓度较高，分布在咸海。第一类和第二类水质浓度主要来源于裸地岩石风化作用，阴离子和阳离子主要来源于硅酸盐和蒸发岩的风化作用。第三类主要受干燥气候的蒸发结晶作用的影响，阴离子和阳离子主要来源于硅酸盐和蒸发岩的风化作用，也可能受碳酸盐风化作用的影响。3)随着各采样点缓冲区半径的增大(0.5 km ~ 10 km)，第一类水质类型的显著土地覆被由裸地变为水域、灌丛、草地、混交田和植被;最重要的土地覆盖是水。第二类水质类型与土地覆被之间无显著相关。第三种水质类型的显著土地覆被由水变为水、混合农田和植被，其中最显著的土地覆被是水。因此，水质变量的空间变化主要受局地气候条件(即气候干旱和集约蒸散)和大陆覆被类型(即裸地、水域、农田、草地和城市)的影响。为了改善咸海盆地中下游的水环境条件，应增加径流以补充咸海，并减弱咸海下游的蒸发和结晶过程。河岸地带的植被恢复和退耕还林还草也应加强，特别是在阿姆河、锡尔河和咸海的中下游。
{"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":null,"pages":null},"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}
Pub Date : 2020-05-20DOI: 10.13930/J.CNKI.CJEA.200375
Ming-tao Yang, Yao Zhang, Tao Liu
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.
{"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":null,"pages":null},"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":null,"pages":null},"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}
{"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":null,"pages":null},"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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