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Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The key problems for realizing precision agriculture include integrating heterogeneous and multi-source agricultural information, developing localization agricultural resources, and providing personalized and active information services for individual farmers. In this paper, we present an approach to precision farming in citrus production management by using semantic technology. In our work, the first step was to transfer the expert knowledge existing in technical reports and books into the citrus fertilization and irrigation ontology that could be understood and directly computed by computer systems. By developing the ontology based on the semantic technology such as the resource framework description triples graph, heterogeneous and multi-source information was integrated into computable localization resources. We described how knowledge in the form of texts, pictures, and tables were encoded into resource description framework triples respectively. In addition, we also discussed how to establish properties, which is a difficulty in ontology development. Our ontology development process was supported by a set of professional tools: TopBraid Composer, the world's most powerful modeling environment, and Gruff, a graphical triple-store browser. We created 31 properties in total and used another five standard properties from the Semantic Web standards. As our aim for building the ontology was to support the decision making for citrus production management, our citrus ontology was not just taxonomy for the citrus management knowledge compared to the existing agricultural ontologies. Then we developed a personalized decision support system for citrus fertilization and irrigation management, based on the ontology. Different from the existing agricultural information services, our system can actively provide personalized production management instructions for individual farmers via multiple terminal devices, including mobile phones and Web browsers. The demo application was implemented using JSP/Servlet. The citrus ontology was loaded into a high performance semantic database system: AllegroGraph 4.10. Applications accessed semantic data via the Java Sesame application programming interface. The real time data from orchards, such as soil sensor records, were stored in relational databases. The demo decision support system had three major functionalities: fertilization management, physiological disease control, and irrigation management. The fertilization management functionality helps farmers fertilize their citrus orchards in the four major lifecycles of citrus. The physiological disease control functionality helps farmers react to nutrient deficiency or excess. The irrigation management functionality provides a real time monitoring and instructions for orchards watering and drainage. We have conducted system validation for the three modules. By testing the fertilization module with 216 data, it generated correct outputs according to the 5 factors: age, lifecycle, soil type, terrain and yield. The physiological disease control module was validated by 100 data, which covered 12 nutrients excess and deficiency diseases. The results showed that the system could correctly detect diseases. The drainage and irrigation monitor module was tested using historical soil moisture data, and the results showed that the system could correctly alarm for abnormal conditions. Finally, the system performance was tested with 361 inputs. The average response time is within 0.23 s for Web browser application and 0.58 for mobile application. This research provides a feasible solution to the problems of precision agriculture. We have demonstrated how expert knowledge in different forms can be transformed into a computable localization resource -- semantic ontology. This kind of transformation solves the problem of integrating and modeling heterogeneous and multi-source agricultural data. In addition, the semantic ontology and triple-store have a good scalability that can be continuously extended during the interaction with farmers and experts. The developed demo system can provide active and personalized instructions for individual farmers to guide them in orchard management with expert knowledge. Our research puts forward a solution to intelligent agriculture. Although this research focused on Chongqing citrus farming, the presented results can be applied to other crops and regions in China. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Water and fertilizer affect a range of physiological processes in the cotton crop and are key factors controlling cotton productivity and yield. Two-year experiments (2012 and 2013) were conducted to evaluate the effects of different fertigation regimes on cotton growth, yield, water use efficiency and partial factor productivity of fertilizers in Shihezi areas of Xinjiang. The fertilization treatment was based on different ratios of N - P2O5 -K2O and the irrigation treatment was based on the crop evapotranspiration (ETC). Five fertilizer treatments were designed including 60% (F0.6), 80 (F0.8), 100% (F1.0), 120% (F1.2) and 140% (F1.4) of recommended rates with N (250)-P2O5 (100) -K2O (50) kg/hm2, and three irrigation treatments included 60% ETC (W1), 80% ETC (W2) and 100% ETC (W3). Irrigation amount was decided by a 20-cm inner diameter evaporation pan. Evaporation pan-crop coefficient in seedling, budding period and flowering period, boll opening period were 0.2, 0.4, 0.7 and 0.25, respectively. During the experiment, plant height, leaf area index and dry mass accumulation, and yield components were measured. Harvest index (HI), water use efficiency (ratio between yield/irrigation amounts, WUE) and partial factor productivity of fertilizer (PFP) were calculated. The results showed that compared to W3F1.0, plant height, LAI, effective bolls number per plant, seed cotton yield and quality of dry matter and WUE of W1F0.6, W1F0.8, W1F1.0, W1F1.2 and W1F1.4 treatments have dropped significantly. Besides W2F0.6 in 2013, W2 and W3 irrigation level under various fertilizer treatments, plant height were between 60 and 71 and LAI were between 3.4 and 4.2. The effective bolls number per plant of W3F1.0, W3F1.2 and W3F1.4 treatments were above 6.8 per plant. Cotton effective bolls number per plant was more sensitive than the 100-boll weight. In 2012 and 2013, the irrigation amount ranged from 291 to 445 mm and from 267 to 392 mm, cotton ET ranged from 277 to 419 mm and from 316 to 438 mm, WUE ranged from 1.27 to 1.65 kg/m3 and from 1.27 to 1.52 kg/m3 in 2012 and 2013, respectively. The highest seed cotton yield was obtained in the combination treatments of W3F1.2 and W3F1.0 with the value of 5 896 and 5 888 kg/hm2 in 2012 and 2013, respectively. W2F1.0 combination treatment had the highest WUE. PFP decreased with the increase of fertilization rate but increased with the increased irrigation amount and W3F0.6 combination treatment had the highest PFP. Considering the water shortage condition in the northern Xinjiang Uygur Autonomous Region, the treatment of 80% ETC combined with 250-100-50 kg/hm2 (N-P2O5-K2O) was suggested to be optimal for cotton production since the treatment could save 63 to 77 mm irrigation amount while keeping 95% seed cotton yields compared with the treatment of 100% ETC combined with 250-100-50 kg/hm2 (N-P2O5-K2O). The saved water accounted for more than half amount of the averaged precipitation in this area. The study can provide valuable information for improving management of cotton crop in North of Xinjiang. [ABSTRACT FROM AUTHOR]