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Copyright of Geography & Geographic Information Science is the property of Geography & Geo-Information Science Editorial Office 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.)

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.)

设施 蔬菜 水分 氮素 WHCNS_Veg 模型评&#2 Excessive water and nitrogen (N) input and shallow root systems have led to serious N loss in greenhouse vegetable production system (GVPS), thereby threatening the soil and water-body environments. Identifying the fates of water and N is crucial to develop best management strategies in intensive GVPS. Soil-crop models are important as a water and N management tool to trade off crop yield and environmental cost. The objectives of this study were to develop a scientific water and N management tool for intensive GVPS in China, and evaluate the model performance in the simulating water dynamic, N fate and vegetable growth under different water and N management practices in China. In this study, based on the EU-Rotate_N model, a vegetable growth module was developed and fully incorporated into a soil water heat carbon nitrogen simulator (WHCNS). The coupled model, i.e., WHCNS_Veg, combined the soil module of the WHCNS model with the vegetable growth module of the EU-Rotate_N model. The key processes included soil movement, soil water evaporation, crop transpiration, soil N transport and transformation (net N mineralization, nitrification, ammonia volatilization, and denitrification) and vegetable growth. Two field experiments conducted in Shouguang city, Shandong province, and Tianjin city, Hebei province were used to test the coupled WHCNS_Veg model. Cucumber and tomato were planted in solar greenhouses at Shouguang and Tianjing experiments, respectively. Similar irrigation and fertilization management practices were both setup in the two experiments: 1) Furrow irrigation with conventional N fertilizer (farmer’s practice), FP; 2) Drip irrigation with optimal N fertilizer, OPT; 3) FP plus crop residues, FPR; and 4) OPT plus crop residues, DOR. Field experiment data including soil water (soil water content and matrix potential), soil N (nitrate concentration and nitrate leaching), plant N uptake, and marketable fresh yield under different water and N management practices were collected in the experiments. Results showed that the WHCNS_Veg model performed better in crop growth simulation than soil water and N dynamic simulation, with normalized root mean square error (nRMSE) not higher than 12.1%, index of agreement (IA) not less than 0.934 and Nash-Sutcliffe efficiency (NSE) not less than 0.829 for simulation of cucumber and tomato yield and plant N uptake. Soil water content had the highest simulation accuracy. The nRMSE, IA and NSE values for soil water content simulation were 6.2%-9.1%, 0.851-0.960 and 0.477-0.846, respectively. For soil nitrate concentration and nitrate leaching, the nRMSE values were 22.2%-40.1% and 4.6-26.0%, respectively, and the NSE values were −0.810-0.636 and 0.442-0.956, respectively. It indicated that the model simulation results are within the acceptable range. However, WHCNS_Veg model showed relative low simulation accuracy in soil matrix potential under drip irrigation condition, with nRMSE, IA and NSE values ranged from 22.9%-30.1%, 0.223-0.846, and -6.344-0.113, respectively. It indicated that the model has to be improved to simulate soil matrix potential under drip condition. In general, the coupled WHCNS_Veg model gives satisfactory results for soil water and N dynamics simulations as well as vegetable growth simulations. It has great potential to simulate and analyze water and N fates, and vegetable growth for the intensive greenhouse vegetable production in China. 0215; [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.)

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 Journal of Agricultural Resources & Environment / Nongye Ziyuan yu Huanjing Xuebao is the property of Journal of Agricultural Resources & Environment Editorial Board 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.)

Agricultural production management system models usually require numerous input parameters and the calibration and validation of the parameters are time-consuming, which significantly limit the use of models. This study aimed at improving the efficiency and accuracy of a soil-crop system model (soil water heat carbon and nitrogen simulator, WHCNS) using a model-independent optimization tools (parameter estimation, PEST) and data from field experiments. A two-year field experiment was conducted from October 2009 to October 2011 in Tai'an City, Shandong Province in North China Plain. The crop rotation was winter wheat-summer maize, and three fields with high, middle and low productivity levels based on the wheat yields (named T1, T2 and T3 treatments, respectively) were selected to test the WHCNS model. The dynamics of soil water content and soil nitrate concentration in different soil depths were monitored, crop dry matter and leaf area index at the key crop growth stages and yield data were measured. PEST was used to optimize model parameters and to calculate the relative composite sensitivity (RCS) of each input parameter for WHCNS model. The optimization parameters involved the majority modules of the model, such as soil water dynamic, nitrogen transformation and crop growth. The objective function of the optimization model were consist of four different groups of field data, including soil water content, soil nitrate concentration, crop yield and leaf area index. And the inverse solution was obtained through minimizing the object function using PEST program base on Gauss-Marquardt-Levevberg algorithm. The results of PEST were then compared with the simulations based on measured soil hydraulic parameters and the trial-and-error method. The statistical analysis (root mean square error, model efficiency, and agreement index) indicated that the PEST optimization method provided better accuracy and efficiency than the other two methods. For example, PEST method significantly decreased RMSE of soil water content, nitrate concentration, crop yield and leaf area index by 61.8%, 23.5%, 73.6% and 23.3%, respectively. Furthermore, the accuracy of simulated water content, nitrate concentration and crop yields were significantly improved by using PEST method. However, there were no significant improvements for the soil nitrogen concentrations and leaf area index, compared to the trial-and-error method. With sensitivity analysis, we identified 18 key parameters that had relatively higher sensitivity. Among these 18 parameters, soil water hydraulic parameters and crop genetic parameters had higher sensitivity than soil nitrogen transformation parameters. Among soil water hydraulic parameters, the soil saturated water content had the highest sensitivity; among crop parameters, the total cumulative available temperature and maximum specific leaf area showed the highest sensitivity; and among soil nitrogen transformation parameters, the maximum soil nitrification rate showed the highest sensitivity. Overall, the sensitivity of nitrogen transformation parameters was generally lower compared with those of soil hydraulic parameters and crop parameters. The sensitivity of crop parameters was significantly different between wheat (C3 crop) and maize (C4 crop), e.g., the maximum root depth and the maximum assimilation rate for maize showed a higher sensitivity than those of wheat, suggesting that model calibration and validation should be crop specific. The PEST method not only greatly saved time for model calibration, but also achieved significant higher simulation accuracy than that by trial-and-error method. In conclusion, the PEST parameter optimization program is a useful tool and should be adopted in calibration and application of soil-crop models. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agro-Environment Science is the property of Journal of Agro-Environment Science Editorial Board 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.)