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20 October 2018, Volume 39 Issue 10

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Meteorological Influencing Factors on Variation in Winter Wheat Yield in the North China Plain

WU Bing-jie, WANG Jing, TANG Jian-zhao, WANG Na, XU Lin, BAI Hui-qing, ZHENG Jun-qing, WANG Na, LI Yang

2018, 39(10):  623-635.  doi:10.3969/j.issn.1000-6362.2018.10.001

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The meteorological influencing factors on variation in winter wheat yield in the North China Plain (NCP) were investigated by developing yield?meteorological factor relationship based on statistical yield of 46 cities and daily meteorological data from 46 meteorological stations from 1988 to 2015. The results showed that statistical yield of winter wheat ranged from 3200 to 6800kg·ha?1 in 1988?2015 with higher yield in the central NCP and higher yield variation in the southern NCP. The variation in growing season main meteorological factors including sunshine hours, temperature and precipitation could account for 17%?78% of variation in the climatic yield of winter wheat with statistical significance at 54% of the cities (P<0.05). The regions with high influencing degree were mainly concentrated in the southern Hebei, southern Shandong and northeastern Henan. Precipitation during the growing period of sowing to turning-green had significant impacts on meteorological yield of winter wheat. Meteorological yield of winter wheat increased by 13-74kg·ha-1 in Tianjin, Zhumadian and northwestern Shandong if amount of precipitation during the period increased by 1%, while the meteorological yield in southern Henan and southern Shandong decreased by 16-80kg·ha-1. Minimum temperature during the growing period of turning-green to maturity had significant impacts on variation in meteorological yield of winter wheat. Average minimum temperature during the period increased by 1-C would increase meteorological yield by 50-295kg·ha-1 in Tianjin, Shijiazhuang, the eastern and western Shandong, and the western Henan, but would decrease meteorological yield by 76-124kg·ha-1 in Beijing, Tangshan and Zaozhuang. In general, the temperature had significant impact on the variation of winter wheat yield at more sites than sunshine hours and precipitation. However, there was a large spatial difference in meteorological influencing factors on variation in winter wheat yield due to complex interaction of meteorological factors, cultivar and agronomic management practices.

Total and Labile Organic Carbon in Soils of Three Subalpine Forest Types in Gongga Mountain, Western Sichuan

GUO Lu-lu, LI An-di, SHANG Hong-li, SUN Shou-qin

2018, 39(10):  636-643.  doi:10.3969/j.issn.1000-6362.2018.10.002

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In this study the concentration and distribution of soil organic carbon (SOC), as well as the labile SOC in soil of three forest types including an evergreen-deciduous broad-leaved forest, a mixed broadleaf-coniferous forest and a subalpine dark coniferous forest were investigated in western Sichuan, China. Results indicated that SOC concentration in the surface soil (0-15cm) across three forest types was 44.21-179.98g·kg-1, with the highest value in the mixed broadleaf?coniferous forest, followed by the evergreen-deciduous broad-leaved forest, and then the coniferous forest. In all of the three forests the soil of 0-5cm layer relative to that of 5?15cm layer had a higher SOC concentration, indicating a surface gathering characteristic of SOC in the forests. The SOC density did not differ among the three forests, while significant differences in light of the vertical variation of SOC density along soil depth were detected among the three forests, where the mixed broadleaf-conifer forest compared to the other two forests had a higher SOC density in the 5?15cm soil depth. Although the concentrations of dissolved organic carbon (DOC), light fraction organic carbon (LFOC) and microbial biomass carbon (MBC) were highest in the mixed broadleaf-conifer forest, the coniferous forest among the three forests was highest in the ratios of these parameters to total SOC content, indicating a higher accumulation of labile SOC in forest with a higher elevation. The results suggest that ecosystem with a higher elevation may have a higher risk of CO2 emission under the circumstance of the climate warming.

Simulation Model of Hourly Air Temperature inside Glass Greenhouse and Plastic Greenhouse

WEI Ting-ting, YANG Zai-qiang, WANG Lin, ZHAO He-li, LI Jia-shuai

2018, 39(10):  644-655.  doi:10.3969/j.issn.1000-6362.2018.10.003

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In 2014-2016, plastic greenhouses and glass greenhouses in different districts of Jiangsu Province were selected for monitoring. Cosine segmentation function, sinusoidal piecewise function, sine-exponential piecewise function, first-order function and neural network model were used to simulate inside hourly temperature in different seasons and different weather conditions (clear day and rainy day). The results showed that all five models can simulate hourly air temperature inside greenhouse through the highest and lowest temperature of the day. The neural network simulation accuracy was the higher (RMSE=0.69℃) and was less affected by the type of greenhouse, weather conditions, and seasonal changes, the universality was higher. The sinusoidal-exponential piecewise function had the best accuracy (RMSE=0.43℃) and was less affected by weather and seasons, but it was affected by the characteristics of the greenhouse itself and the region. The cosine piecewise function (RMSE=0.8℃) and the sinusoidal function (RMSE=0.78℃) had similar simulation results and was affected by the weather and region. The accuracy of a piecewise function is low (RMSE=0.90℃) and the error varies greatly. Models had higher simulation accuracy in plastic greenhouses then it in glass greenhouses. Seasonal variation of the model's simulation accuracy was different between the models and the types of greenhouse, but usually, the simulation error in spring and winter was greater than in autumn, and the error in summer was the smallest.

Effects of Climate Variability during the Dormancy Period on Productivity in Typical Grassland at Yunwushan in Ningxia

ZHENG Zhou-min, LUO Rui-min, CHENG Ji-min, GUO Liang

2018, 39(10):  656-663.  doi:10.3969/j.issn.1000-6362.2018.10.004

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Based on long-term records of grassland productivity and the corresponding climatic data on the Loess Plateau during 1992-2011, the change trends of temperature and precipitation during the dormancy period (November to next March) in Yunwushan area were analyzed. To reveal the response patterns of grassland productivity to dormancy climate change on the Loess Plateau, the effects of variation in climatic factors at monthly and daily scale on grassland productivity were investigated by using simple linear regression and Partial Least Squares (PLS) regression, respectively. The results indicated that: (1) in the past 20 years, mean temperature during the dormancy period in the study area has increased significantly (P<0.01), with a rate of 0.09℃·y-1, while the strongest increasing trend occurred in January and March. (2) The precipitation during the dormancy period was few, but its inter-annual variation was large. The change trend of monthly precipitation was also not significant. (3) Different impacts of temperature during different periods on productivity were found. However, overall, increased temperature during the dormancy period was negatively correlated with grassland productivity. Impacts of precipitation were negligible. (4) The negative correlations between increased dormancy temperature and grassland productivity could be properly explained by decreased snow depth, increased freezing and thawing processes, and variations in soil micro-environment caused by winter warming. The rare precipitation during the dormancy period did not exert significant effects on productivity. However, snow, as a solid form of winter precipitation, its impacts on grassland productivity need further investigations.

Determination Methods of Weight Coefficient in Spring Maize Yield Prediction Based on Climatic Suitability Index

QIU Mei-juan, LIU Bu-chun, YUAN Fu-xiang, LIU Yuan, ZHANG Yue-ying, WU Xin-yue, XIAO Nan-shu

2018, 39(10):  664-673.  doi:10.3969/j.issn.1000-6362.2018.10.005

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The climatic suitability models of each 10-day in the growth season of spring maize were constructed by using crop data of spring maize from 1980 to 2016 and daily meteorological data of 50 meteorological stations in Jilin province. In order to calculate climatic suitability index in different times (from early April to the 10-day before forecast day), methods of absolute, normalization, and correlation were used respectively to determine the weight coefficient of climatic suitability of each 10-day. Then, the relevance between meteorological influence index for maize yield and climatic suitability index obtained by different methods has been analyzed. A yield dynamic prediction model was established by regression analysis, and was used to forecast the spring maize yield in Jilin province. The results showed that, there were some differences between the three weight coefficient determination methods, but on the whole, the variation trend with the growth period were basically the same. The yield bumper or poor harvest prediction model established by regression analysis using materials from 1981 to 2012 most passed the 0.05 level effective test and the historical fitting average accuracy was all above 85.0%, the normalized root mean square error NRMSE was all less than 17.0%, and the accuracy of the bumper or poor harvest trend was generally in 60.0%-80.0%.The difference between the three methods was not obvious. The results of maize yield extrapolation forecast from 2013 to 2016 showed that, the yield prediction accuracy in each forecast times had fluctuant, but the average accuracy of methods of absolute, normalization, and correlation was 93.5%, 90.8% and 87.2%, respectively，and the standard deviation of the forecast results was 32.6, 69.4 and 116.1, respectively. Moreover, the average accuracy of each prediction time for the method of absolute was above 85.0%. It showed that the accuracy and stability of the prediction result of absolute were all high, which could meet the needs of business services.

Spatial Interpolation of Regional Precipitation Based on Mixed Geographical Weighted Regression Combined with Kriging Interpolation

LI Hao, LIU Tao, XU Jing-wen

2018, 39(10):  674-684.  doi:10.3969/j.issn.1000-6362.2018.10.006

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Based on the precipitation data of 1981?2010 from 144 meteorological stations in Sichuan province, using mixed geographical weighted regression Kriging interpolation (MGWRK) model, and considering the impact of topographic factors, the spatial distribution of the average annual precipitation was obtained in this paper. The effect of interpolation value was compared with those values from OK, GRK, and GWRK methods. The result showed that the optimal influencing factors combination was longitude, latitude and slope, determined by using the stepwise regression method, could decrease the multi-collinearity among the explanatory variables significantly. The types of spatial variability of the explanatory variables were analyzed quantitatively based on the index ΔAICc, which was the difference between the value of AICc (Corrected Akaike Information Criterion) of the same variable calculated by GWR model and by GR model. Then set the slope variable as global variable, and the longitude and latitude variables as local variables, the interpolation of the average annual precipitation in Sichuan province was conducted by the MGWRK model. The MGWRK method presented in this paper showed higher accuracy than those of the ordinary Kriging (OK) and global regression Kriging (GRK), because the method has taken into consideration of various influence factors of the spatial position and topography, and the variability of the relationship between these factors and precipitation.

Effect of Dust Stress on the Photosynthetic and Chlorophyll Fluorescence Characteristics of Cydonia oblonga Mill

WANG Meng-hui，Batur BAKE，KANG Li-juan，XUE Ya-rong，Sajida ABDUKIRIM，Zulkeya MANAP

2018, 39(10):  685-692.  doi:10.3969/j.issn.1000-6362.2018.10.007

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Taking Cydonia oblonga Mill as an experimental material to examine the effects of dust on the photosynthesis and chlorophyll fluorescence parameters, three levels of dust treatments during 40 days which comprise non-dust cover（CK）, mild dust treatment （5mg·cm-2）and serious dust treatment（12mg·cm?2）. The results showed that net photosynthetic rate（Pn）, stomatal conductance（Gs）, and transpiration rate（Tr）of the leaves under the dust treatment showed a downward trend with the treatment time. Intercellular CO2 concentration（Ci）showed a trend of increasing first and then decreasing, The maximum light quantum efficiency（Fv/Fm）decreased significantly on the 10th day of treatment, and then gradually increased to the control level with the prolonged treatment time. For two dust treatments, maximal fluorescence（Fm）, non-photochemical quenching under stable state（NPQ_Lss）, potential activity（Fv/Fo）and photochemical quenching under stable state（qP_Lss）increased with treatment time, but minimal fluorescence（Fo）decreased with treatment time .These results indicated that the non-stomatal inhibition had a significant on the photosynthetic rate greater at the beginning, the dust stress reduced the primary photochemical efficiency of PSⅡto cause photoinhibition. In later stage, Cydonia oblonga Mill promoted the efficiencies of photosynthetic electron transport due to increase such as qP_Lss，Fm and NPQ_Lss. On the other hand, Cydonia oblonga Mill increased non photochemical dissipation to protect the photosynthetic organs.