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20 April 2022, Volume 43 Issue 04

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Variation of Extreme Precipitation over Guizhou under the Global Warming of 1.5℃ and 2.0℃

ZHANG Jiao-yan, LI Xiao, CHEN Zao-yang, LI Yang, ZHOU Tao

2022, 43(04):  251-261.  doi:10.3969/j.issn.1000-6362.2022.04.001

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Guizhou province suffered frequently from natural hazards induced by severe weathers. It was necessary to investigate the features of extreme precipitation under the global warming scenarios quantitatively and scientifically to keep the relocated groups away from returning to the poverty due to disasters. Based on the daily precipitation of historical simulation from 1961 to 2005 and under RCP2.6/RCP4.5 emission scenarios during 2006−2098 from CCSM4/ IPSL-CM5A-MR modes, as well as daily precipitation data of 84 meteorological stations over Guizhou from 1961 to 2005, the characteristics of extreme precipitation changes in Guizhou under the global warming of 1.5℃ and 2.0℃ were explored through 9 indicators including precipitation intensity, daily maximum precipitation and heavy precipitation, using bias correction to improve the simulation capabilities. It was demonstrated that the extreme precipitation indices in RCP 2.6 and RCP 4.5 scenarios over Guizhou had a large fluctuation range, but uprising trend could be found. Of more interest was that the increase approximately doubled under the scenario of 2.0℃-warming comparing to 1.5℃-warming, which were based on extreme precipitation indices from 1986−2005. The tail ends of the probability density curves of the 9 indies all extended to the right at 2.0℃, indicating that more extreme precipitation events might occur under the scenario of 2.0℃-warming. Therefore, it was essential to make control on the global warming and keep the warming within 1.5℃.

Predicting Potential Suitable Planting Area of Rice in China under Future Climate Change Scenarios Using the MaxEnt Model

LV Tong, GUO Qian, DING Yong-xia, LIU Li, PENG Shou-zhang

2022, 43(04):  262-275.  doi:10.3969/j.issn.1000-6362.2022.04.002

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To provide a scientific basis for reasonably planting rice in China, this study investigated the major climatic factors affecting the rice distribution and predicted the changes of rice suitable areas in the past and future periods, using the distribution data of rice, the high-spatial-resolution historical (1970−2000) and future (2081−2100) climate data, and the MaxEnt model. The results showed: (1) the main climatic factors affecting the distribution of early rice and late rice were precipitation of driest month, mean temperature of warmest quarter, and precipitation of driest quarter, and those of single-season rice were annual mean temperature and precipitation of warmest quarter; (2) In the historical period, the suitable planting areas for early rice and late rice in China were mainly in the middle and lower reaches of the Yangtze River and the south of the Yangtze River, accounting for 14.26% and 13.01%, respectively, where most of the regions were slight suitable areas, accounting for 7.66% and 6.62%, respectively. The area of the suitable planting area for one season rice accounted for 45.46%, and most of the regions were slight suitable areas and suitable areas, accounting for 23.47% and 18.86%, respectively; (3) Compared with the historical period, the future suitable planting areas of early rice under the SSP126, SSP245, and SSP585 scenarios increased by 6.27, 9.26, and 16.66 percentage points, respectively; the future suitable planting areas of late rice increased by 4.26, 5.55, and 10.97 percentage points, respectively; and the suitable planting area of one season rice increased by 11.34, 18.46 and 28.31 percentage points, respectively. To the end of the century, the suitable planting areas for early rice would expand to Sichuan, Chongqing and Huang-Huai area, the suitable planting areas for late rice would expand to Sichuan, Chongqing and a small area of the north of the middle and lower reaches of the Yangtze River, and the optimum suitable areas for one-cropping rice showed spatial expansion to the North China Plain and Northeast China. In general, future climate change will contribute to the expansion of suitable rice planting areas over China.

Study on the Growth and Decline of Aboveground Biomass of Winter Wheat in North China Prior and Post Overwintering

TAN Kai-yan, ZHANG Xin-ru, GENG Jing-jian, CUI Zhao-yun

2022, 43(04):  276-284.  doi:10.3969/j.issn.1000-6362.2022.04.003

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There is a long overwintering process in winter wheat growth period in Northern China, when aboveground biomass of wheat usually wilts partially or completely, that was a lack of consideration in crop growth models such as WOFOST and CERES-Wheat. In order to explore a revision method of biomass simulation during overwintering process of winter wheat, an extensive field investigation was conducted in 15 counties in the main winter wheat producing provinces of China, such as Hebei, Henan, Shandong and Tianjin, to observe spatial variation of wheat withering degree during winter. Meanwhile field experiments by means of divided sowing date and controlling base fertilizer were carried out in Gucheng and Hengshui agrometeorological experimental stations located in Baoding city and Hengshui city of Hebei province respectively, the impact of seedling growth state before winter on the post winter growth rate was investigated. The results showed that the aboveground biomass wilt degrees of winter wheat in Northern China were closely related to the meteorological conditions during the overwintering period. The withering rate of aboveground biomass by turning green increased linearly with decline of extreme minimum temperature over the overwintering period. The extreme minimum temperature could explain 86% of causes of aboveground biomass withering in the experimental years and regions. Under partial or complete withering of wheat leaves during the overwintering period, the seedling growth level of winter wheat before winter significantly affected post winter growth rate. The average growth rate of wheat from turning green to heading followed a parabolic function with the change of aboveground biomass per unit area before winter. Therefore, the initial value of aboveground biomass at the beginning of turning green needed in the crop models can be estimated according to the meteorological conditions in the overwintering period. At the same time, it is necessary and feasible to use the seedling growth of winter wheat before winter to correct the simulation of post winter biomass.

Mechanism Analysis on Photosynthetic Attenuation in Cucumber Leaves under Low Temperature and Weak Light Condition

ZHANG Yao, YANG Zai-qiang, JIANG Yu-han, SU Ze-yang, XU Ruo-han, LONG Yu-yun

2022, 43(04):  285-294.  doi:10.3969/j.issn.1000-6362.2022.04.004

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To study the mechanism of photosynthetic attenuation in cucumber seedling leaves under low temperature and weak light conditions, using cucumber variety 'Jinyou 101' as experimental material, a two-factor environmental control experiment of low temperature and weak light was carried out by using artificial climate boxes in Nanjing University of Information Science and Technology from 2020 to 2021. The daytime/night temperature was set at 13℃/3℃, 16℃/6℃, 19℃/9℃ and 12℃/22℃, and the photosynthetically active radiation (PAR) was set at 200, 400μmol·m−2·s−1. There were eight treatments, each of which lasted for 3, 6, 9 and 12 days, respectively. The treatment with light and temperature of 28℃/18℃ and 800μmol·m−2·s−1 was used as control (CK). The chloroplast pigment content, photosynthetic parameters, and kinetic parameters of rapid chlorophyll fluorescence induction in cucumber leaves at the seedling stage were measured under different treatments. The results showed that: (1) the chloroplast pigment content of cucumber leaves decreased with the decrease of temperature and PAR, and decreased with the prolongation of treatment time. (2) Under different treatments, the maximum photosynthetic rate (Pmax), light saturation point (LSP), stomatal conductance (Gs), and transpiration rate (Tr) decreased with the increase of stress degree and time, and the light compensation point (LCP) increased with the increase of stress degree and time. The results showed that the deeper the degree of low temperature and weak light stress and the longer the stress time, the weaker the photosynthesis of cucumber leaves. (3) With the deepening of stress and the prolongation of stress time, the JIP phase of the fast fluorescence induction kinetic curve (OJIP curve) of cucumber leaves decreased gradually, the parameters of energy distribution ratio (φPo, ψEo, and φEo) decreased gradually, and the light energy absorbed (ABS/RC) and trapped (TRo/RC) per unit active reaction center gradually increased, but the light energy used for electron transport (ETo/RC) still decreased. This study confirmed that low temperature and weak light treatment reduced the stomatal conductance of cucumber leaves and hindered gas exchange; the antenna pigment content decreased, and the light energy absorbed and captured by the leaves decreased; the performance of PSII decreased, and the energy used for photochemical reactions decreased; therefore, the photosynthesis of the leaves decreased.

Forecast Model of Apple First Flowering Date Based on the Coupling of Daily Air Temperature Characteristic Values and Chill/Heat Accumulation Model

LIU Miao, QIU Chun-xia, YANG Gui-jun, YANG Hao, CAI Shu-hong, ZHU Yao-hui

2022, 43(04):  295-307.  doi:10.3969/j.issn.1000-6362.2022.04.005

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Three major production areas of Fuji apple, Linyi (in Shanxi province), Luochuan (in Shaanxi province) and Qixia (in Shandong province) were selected as the study region, based on 1 km gridded meteorological data, actual first flowering date data, and Chilling Hour Model (CHM) and Growing Degree Hour (GDH) data of the survey sample points in 2019−2020, the optimal chill/heat requirement at the first flowering date of apple was obtained using the gridded search method. Then, the daily air temperature characteristic values (Tmax, Tmin and Tavg) were divided into seven daily air temperature characteristic factor combinations, including single factor, double factors and triple factors, and the random forest algorithm (RF) was used to establish three regional daily chill/heat accumulation models with different daily air temperature characteristic factor combinations to select the optimal daily air temperature characteristic factor. On the basis of which, forecasting model of apple first flowering date was established based on the optimal daily air temperature characteristic factor by using RF algorithm, and the accuracy of the forecasting model was evaluated by independent actual first flowering date data. The results showed that: (1) the optimal chill/heat requirement at the first flowering date for apple in three regions were 730CH and 7350GDH in Linyi, 345CH and 4950GDH in Luochuan, and 520CH and 4450GDH in Qixia. (2) Among the seven combinations of daily air temperature characteristics, the three regional daily chill/heat accumulation models with the combination of Tmax, Tmin and Tavg had high accuracy in estimating daily chill/heat accumulation, and the RMSE between the estimated daily chill accumulation and the daily chill accumulation obtained from the CHM model was 0.97−2.50CH, and the RMSE between the estimated daily heat accumulation and the daily heat accumulation obtained from the GDH model was 1.73−15.76GDH. (3) When the daily chill/heat accumulation was estimated by forecast model of apple first flowering date, the RMSE between the estimated daily chill accumulation and the daily chill accumulation based on the CHM model ranged from 1.08 to 1.14CH, and the RMSE between the estimated daily heat accumulation and the daily heat accumulation based on the GDH model ranged from 2.03 to 3.74GDH. When the model was used to forecast first flowering date of apple, R2 between the predicted and actual first flowering date was 0.92, and RMSE was 3.44d. The accuracy of the predicted first flowering date based on daily air temperature characteristic values was in overall agreement with that based on real hourly air temperature data, it indicated that the forecast model of apple flowering date established in this paper could effectively convert the input air temperature data from hourly scale to daily scale, which will have good application value and potential in the subsequent work on apple first flowering date forecasting.

Dynamic Diagnosis of Nitrogen Nutrition in Maize under Drip Irrigation Condition Based on UAV Image Parameters

ZHAI Yong-quan, WEI Xue, YUN Bin-yuan, MA Jian-zhen, JIA Biao

2022, 43(04):  308-320.  doi:10.3969/j.issn.1000-6362.2022.04.006

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The experiment on nitrogen fertilizer gradient of maize under drip irrigation in Ningxia Pingjibao farm was conducted from 2018 and 2019. The variety of Tianci 19 was used as an experimental material, six nitrogen level treatments were set, which was 0(N0), 90(N1), 180(N2), 270(N3), 360(N4) and 450(N5)kg(N)·ha⁻¹, respectively. P and K fertilizers was conventional fertilization treatment, which was 138kg·ha⁻¹ and 120kg·ha⁻¹. Using drip irrigation water and fertilizer integration technology, fertilizer was applied along with water. The proportion of fertilizer application in each growth stage was 10% in seedling stage, 45% in six leaves stage, 20% in silking stage and 25% in filling stage. Maize canopy images were obtained by UAV equipped with digital camera in the maize six leaves stage(V6), ten leaves stage(V10), twelve leaves stage(V12), silking stage(R1) and milk stage(R3), and used Matlab code developed by digital image recognition system to extract the corn canopy image the red light value R, green light value G and blue light value B. The correlation between 10 canopy image parameters and nitrogen nutrition indexes based on this calculation was studied, and the image color parameters with good stability and high sensitivity were screened out. The relationship model between diagnostic indices of maize nitrogen nutrition and image parameters was constructed and verified. In order to explore the feasibility of using UAV image to estimate the dynamic nitrogen nutrition of maize under drip irrigation in Ningxia Yellow River Irrigation area during the period of joining and milk ripening. The results showed that the ratio of green light to red light (G/R), standardized value of green light (NGI), and red-green-blue vegetation index (RGBVI) were highly correlated with plant nitrogen content and leaf nitrogen content and had small coefficient of variation, which could be used as the potential optimal color parameters for nitrogen nutrition diagnosis. The regression model of optimal color parameters with nitrogen content in plant and leaf was constructed, and the power function model could better predict nitrogen status of maize. The same nitrogen experiment in 2019 was used to verify the model. It was found that the R2 of measured and estimated value of NGI and plant nitrogen concentration and leaf nitrogen concentration were 0.738 and 0.689, respectively. The test indexes RMSE were 2.594 and 3.014, and nRMSE were 13.125% and 13.347%. The prediction accuracy and accuracy are higher than G/R and RGBVI. Therefore, NGI can be selected as the optimal parameter for dynamic diagnosis of nitrogen nutrition in drip irrigation maize at the stage of V6-R3 stage, and the correlation model between parameter NGI and plant nitrogen concentration (N_P=4.967×10⁶NGI^14.26) R² was 0.707. The R² of the model (N_L=1.707×10⁶NGI^12.88) was 0.654. The results showed that the application of UAV image technology could dynamically estimate the nitrogen nutrition status of maize during the period of V6-R3 stage in Ningxia Yellow River Irrigation area, and the nitrogen nutrition diagnostic model constructed could provide a theoretical basis for the precise allocation of nitrogen fertilizer for drip irrigation maize in Ningxia Yellow River irrigation area.