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    20 March 2019, Volume 40 Issue 03
    Uncertainty Evaluation of Rice Gridded Crop Model in China
    SUN Qing, YANG Zai-qiang, CHE Xiang-hong, YANG Shi-qiong, WANG Lin, ZHENG Xiao-hui
    2019, 40(03):  135-148.  doi:10.3969/j.issn.1000-6362.2019.03.001
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    Crop model is a widely-used strategy to evaluate the impact of climate change imposed on agriculture. The inter-comparison of gridded crop model is still at initial stage in China, thus making it crucial to comprehensively evaluate the performance of each global gridded crop model (GGCM). Using the statistics derived from Food and Agricultura Organization of the United Nations(FAO) and Ministry of Agriculture and Rural Affairs of the People’s Republic of China(SYB) statistical rice yearly mean yield, this paper compared the simulated rice yields of 7 GGCMs (i.e. CGMS-WOFOST, CLM-CROP, EPIC-BOKU, GEPIC, LPJML, PDSSAT and PEPIC), which are driven by 2 climate datasets (AgMERRA and WFDEI-GPCC) and 3 management scenarios (Default, Fullharm and Harmnon) from 1980 to 2009 in China. The comparisons show that the simulated rice yields from different GGCMs have significant discrepancy in different regions of China. Each GGCM has different response and sensitiveness to climate datasets and management scenarios. The majority of simulations underestimate rice yield in China even though different statistical rice yield data will affect evaluation results. To some degree, GGCMs are able to simulate the inter-annual variation of rice yield and climate change effects, but hardly simulate the pattern of rice yield increase of statistics. The analyses of rice yield fluctuation on temporal and spatial aspect demonstrate LPJML and PDSSAT perform better among 7 GGCMs using 2 skill score approaches, and are most sensitive to different climate datasets and management scenarios, while CLM-CROP have lowest stimulation accuracy. In terms of management scenarios, the simulation on Default scenario performs dramatically better than that of Fullharm and Harmnon scenarios. In addition, Multi-gridded crop model ensemble (MME) could reduce simulation error compared to single GGCM, but requires suitable members to precisely perform MME.
    Modelling of Thermal Climate in a Large-scale Insulation Solar Greenhouse
    FANG Hui, ZHANG Yi, WU Gang, CHENG Rui-feng, ZHOU Bo, YANG Qi-chang
    2019, 40(03):  149-158.  doi:10.3969/j.issn.1000-6362.2019.03.002
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    Large-scale insulation solar greenhouse, with wide span, steel frame and built in south-north orientation, was a tunnel type greenhouse. The distance between greenhouses was only 2m and the land utilization efficiency can be increased up to 91%, but it still has the characteristics of energy saving compared with traditional solar greenhouse. A greenhouse climate model was developed in order to predict the inside air temperature and root zone temperature to assess the greenhouse insulation and heat storage ability based on the physical processes of heat conduction, heat convection, solar radiation distribution, sky radiation, crop transpiration and air exchanged by natural ventilation. The model was established with Matlab software to calculate the temperature of the different parts. The variables, include inside air temperature and plant root zone temperature, were also measured during four successive days with time span of 10min. The results showed that the absolute error of the air temperature inside large-scale insulation solar greenhouse was ±1.3℃, the simulated air temperature agreed well with the measured data. The determination coefficient of linear equation (R2) (n=576), root mean squared error (RMSE) and relative prediction error (RE) between simulated and measured air temperature was 0.99, 1.6℃ and 16.4%, respectively. The absolute error of the root zone temperature was ±0.6℃. The determination coefficient of linear equation (R2) (n=576), RMSE and RE between simulated and measured root zone temperature was 0.91, 0.76℃ and 6.7%, respectively. It was concluded that the model was robust and could be used for the optimization of the large-scale insulation solar greenhouse as well as the climate control.
    Research on the Factors of Xihulongjing Tea Picking Date and Its Prediction Model
    ZHU Lan-juan, JIN Zhi-feng, ZHANG Yu-jing, WANG Zhi-hai, LIU Min, FAN Liao-sheng
    2019, 40(03):  159-169.  doi:10.3969/j.issn.1000-6362.2019.03.003
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    Based on the picking date of the main species of Xihulongjing Tea (Longjing43 and Longjingqunti) and its meteorological data, the accumulated temperature prediction model and the stepwise regression prediction model of Xihulongjing tea were constructed by using accumulated temperature and stepwise regression method, also the prediction of multiple regression ensemble method was integrated by these two prediction results, using the multiple linear regression method. The results showed that the accumulated temperature prediction model, stepwise regression prediction model and integrated prediction model all passed the significance test of P<0.01. The simulated mean absolute error(MAE) of accumulated temperature prediction model were 3.6d and 2.8d, while the prediction MAE of 2-year test prediction were 2.5d and 1.0d for Longjing43 and Longjingqunti respectively. In addition, the simulated MAE of stepwise regression analysis were 0.9d and 1.4d, the prediction MAE of 2-year test prediction were 1.6d and 0.8d for Longjing43 and Longjingqunti separately. The prediction of multiple regression ensemble method was more accurate than single method with the simulated MAE value were 0.7d and 1.1d ,while the prediction MAE of 2-year test prediction were 1.3d and 0.8d, the prediction of multiple regression ensemble method would provide more scientific support for guiding tea production. These three forecasting models are of practical value for the prediction of the picking up period of Xihulongjing tea. The prediction of multiple regression ensemble method is more ideal and with more practical application value than accumulated temperature forecasting model and stepwise regression analysis model.
    Effects of Sand Dust and Shading Combined Stress on Photosynthesis of Prunus domestica L. Leaves
    XUE Ya-rong, Batur BAKE
    2019, 40(03):  170-179.  doi:10.3969/j.issn.1000-6362.2019.03.004
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    In order to investigate the change mechanism of photosynthetic characteristics of Prunus domestica L. under long-term dust and shading combined stress, the five-year-old Prunus domestica L. was used as research material to lightly dust and shade the leaves. Composite (1mg·cm-2 dust + white gauze 1 layer) and heavy dust and shading composite (5mg·cm-2 dust + white gauze 3 layers) were treated, and their photosynthesis, chlorophyll fluorescence, etc. were measured at 10, 20, 30 and 40 days of stress, respectively. The results showed that the contents of total chlorophyll(Chl), chlorophyll a(Chl-a) and chlorophyll b(Chl-b) decreased with the increase of stress time. Net photosynthetic rate(Pn), transpiration rate(Tr) and stomatal conductance(Gs), water use efficiency(WUE) decreased with the prolongation of treatment time, and intercellular CO2 concentration(Ci) showed a trend of decreasing first and then increasing. Fluorescence(Fm), PSII maximum photochemical efficiency(Fv/Fm), potential activity(Fv/F0), Photochemical quenching(qP) showed a downward trend, initial fluorescence(F0), Non-photochemical quenching(NPQ) increased trend. It is indicated that the stomatal factors in the early stage of sand and shading combined treatment are the main limiting factors for photosynthesis of prune. The latent treatment causes damage to photosynthetic apparatus, and non-stomatal factors become the main limiting factor. The compound stress affected the photosynthetic parameters of Prunus domestica L. leaves, but did not cause irreversible damage to the photoreaction system of Prunus domestica L. leaves.

    Effect of Increasing Night Temperature on Floret Opening and Grain Setting of Rice
    ZHANG Wen-qian, WANG Ya-liang, ZHU De-feng, CHEN Hui-zhe, XIANG Jing, ZHANG Yi-kai, ZHANG Yu-ping
    2019, 40(03):  180-185.  doi:10.3969/j.issn.1000-6362.2019.03.005
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    Taking indica rice varieties HHZ and HSZ as potting experimental materials, three day/night temperature treatments, namely 36℃/25℃ (T1), 36 ℃/29℃ (T2) and 36℃/33℃ (T3), were set up in artificial climate chamber, which means the daily maximum increasing temperature by 4℃ and the increasing temperature at night by 4℃ and 8℃, respectively, and 32℃/25℃ as CK. Continuous processing for 7 days from the day of heading and flowering, the flowering number, pollen vigor and anther dehiscence rate were observed hourly from 9:00 am to 11:00 am. After treatment, they were moved to the outside and were observed the fruiting rate after ripening. The results showed that, under the condition of increasing daily temperature, the increase night temperature at flowering stage put forward one hour in the flowering peak both for two varieties. Meanwhile, the increase night temperature at flowering stage caused the vitality of pollen, when night temperature increased by 4℃ (T2) and 8℃ (T3), the HHZ vitality of pollen dropped by 13.6 and 17.6 percentage point, HSZ dropped by 1.1 and 4.5 percentage point, which means the pollen vigor of HHZ was more sensitive to the increase night temperature than HSZ. Under the same conditions, the anther cracking rate of rice decreased significantly with the increasing night temperature at flowering stage (P < 0.05), the longer treatment, the greater the effect. The average rate of anther cracking was 73.2% in HHZ and 79.0% in HSZ with treatment T3. The increasing night temperature also caused the decreasing rice seed setting rate, both HHZ and HSZ showed the same tendency, but HHZ was more sensitive to the increasing night temperature than HSZ. The decreasing seed setting rate was mainly due to the decreasing anther dehiscence rate and pollen vigor.
    Establishment of Sugarcane Development Simulation Model Based on Clock Model Method
    CHEN Xiao, FENG Li-ping, PENG Ming-xi, CHEN Yan-li
    2019, 40(03):  186-194.  doi:10.3969/j.issn.1000-6362.2019.03.006
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    Based on the principle of theoretical model of crop development dynamics and clock model method, a sugarcane development simulation model (SDSM) was constructed to simulate the different development stages of new planting and perennial sugarcane. The data of sugarcane development stages from five major production sites (Yizhou, Shatang, Laibin, Fusui, Guigang) with three verities (Taitang 16, Taitang 22 and Guitang 25) and related meteorological data from 2003 to 2012 provided by Guangxi Meteorological Information Center were used. The whole growing period was divided as four development stages: sowing to emergence, emergence to tillering, tillering to stem elongation, and stem elongation to technical maturity. The parameters of SDSM model were determined by trial and error method. The simulation results of sugarcane development simulation model were evaluated by comparing the simulated and measured values. For new planting sugarcane, the NRMSE of each development stage was 5.2%-26.3%, the RMSE of simulated result and measured value were 8.1 days in the stage of seeding to emergence, 7.4 days in emergence to tillering, 4.6 days in tillering to stem elongation, and 7.4 days in stem elongation to technical maturity. For perennial sugarcane, the NRMSE of each developmental stage was 6.5%-21.7%, the RMSE of simulated result and measured value were 8.8 days in the stage of technical maturity to regrowth, 8.7 days in regrowth to tillering, 7.6 days in tillering to stem elongation, 9.9 days in stem elongation to technical maturity. It showed good consistency and correlation between the simulated and measured values. The model could effectively simulate the development period of sugarcane.