Table of Content

    20 September 2021, Volume 42 Issue 09
    Effects of Elevated Atmospheric CO2 Concentration and Nitrogen Application on Mass Fractions of Carbon and Nitrogen Assimilates in Functional Leaves of Maize after Flowering
    LI Ming, LI Ying-chun, HAN Xue, NIU Xiao-guang, MA Fen, WEI Na, HE Yu-tong, GUO Li-ping
    2021, 42(09):  715-728.  doi:10.3969/j.issn.1000-6362.2021.09.001
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    In order to provide the theoretical support for the change of maize physiological processes under global climate change based on the experimental data and provide the evidence for parameter adjustment for crop models, the effects of elevated atmospheric CO2 concentration (eCO2) and its interaction with nitrogen application on the dynamic of concentrations of carbon and nitrogen assimilates and the yield of maize, one of the C4 crops, were studied based on the free air CO2 enrichment (FACE) system. The summer maize variety ZHENGDAN 958 was planted to conduct the field experiments. Four treatments were set under two atmospheric CO2 concentrations, ambient CO2 concentration (aCO2, 400μmol·mol−1) and elevated CO2 concentration(eCO2, 550μmol·mol−1) at two nitrogen levels including zero nitrogen (ZN) and conventional nitrogen (CN, 180 kg N·ha−1) The maize yield and some other physiological parameters were measured including the concentration and dynamic of carbon assimilates (ie. soluble sugar, starch and total carbon), nitrogen assimilates (ie. nitrate, free amino acid, soluble protein, insoluble nitrogen compounds including cell wall-N and thylakoid-N, and total nitrogen), and C/N ratio since flowering of summer maize in the functional leaves. Results showed that: (1) The above-ground biomass and yield of summer maize did not show significant increase under eCO2 possibly due to unexpected disturbance from insect pests at this study. (2) Under eCO2, the concentration of carbon assimilates, including soluble sugar and starch, showed significantly (P<0.05) increase in the functional leaves after flowering. For the nitrogen assimilates, the concentration of some assimilates with simple components, including nitrate, free amino acids, soluble protein, as well as total N and C/N ratio showed increase to some extent (P>0.05); while the concentration of structural N components, including thylakoid-N and cell wall-N, were decreased significantly at later period after flowing, implying the synthesis of structural N components were impacted under eCO2. (3) Under N application, both the carbon assimilates (ie. soluble sugar and starch at most stages) and major nitrogen assimilates were increased significantly in the functional leaves after flowering, as well as the biomass and yield of summer maize. While the concentration of total C did not show significant change in the functional leaves. (4) N application under eCO2 could promote the concentration of simple fractions of carbon assimilate (ie. soluble sugar and starch at most stages) and initial nitrogen assimilates (ie. Nitrate, free amino acids and soluble protein) in the functional leaves of maize, as well as total C, above-ground biomass and yield. Therefore, under the future climate change characterized by increased atmospheric CO2 concentration, appropriate N management and regulation in physiological processes would be helpful in promoting the carbon and nitrogen assimilation and beneficial to the high yield and good quality of maize.
    Based on the Phenological Model to Study the Possible Changes of Apple Flowering Dates under Future Climate Scenarios in Shaanxi Province
    WANG Run-hong , RU Xiao-ya , JIANG Teng-cong , WANG Jin-hong , WANG Zhao , SU Bao-feng , ZHANG Dong, YU Qiang , FENG Hao , HE Jian-qiang
    2021, 42(09):  729-745.  doi:10.3969/j.issn.1000-6362.2021.09.002
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    Shaanxi has large apple cultivation areas and high apple yields, but its yields are susceptible to late frost. The occurrence of freezing damage is closely related to the apple flowering date and the time of late frost. Therefore, accurate prediction of apple flowering date and research on the temporal and spatial changes of apple flowering date is of great significance to the disaster prevention and mitigation of apple production. In this study, the phenological models (e.g. Spring warming model, Sequential model, Overlap model, and Parallel model) were used to study the variations of apple flowering date (including both first flowering date and end flowering date) in Shaanxi Province under the background of climate change. Firstly, four phenological models were selected to evaluate the results of model simulation on apple flowering date in Shaanxi apple producing areas, and the optimal flowering prediction models in the study area needed to be screened out. Then, based on the selected optimal model, the apple flowering date of each representative station (e.g. Luochuan, Baishui, Fengxiang, and Changwu) during the historical period (1980−2019) was simulated. Finally, based on the future meteorological datasets generated by 33 Global Climate Models (GCMs), the selected model was used to simulate the apple flowering date at each representative station from 2021 to 2100 under the two scenarios of RCP4.5 and RCP8.5, and the temporal and spatial variations of flowering date were analyzed. The results showed that the Sequential model was the optimal model to simulate the apple flowering dates in the Eastern and Western area of Weibei, while the Parallel model was the optimal model for Yan'an and the Western area of Guanzhong. From 1980 to 2019, the first flowering date of representative stations was advanced by 3.4−4.7d·10y−1, and the end flowering date of representative stations was advanced 3.3−4.6d·10y−1. The apple flowering date in the study area was gradually delayed from southeast to northwest, and the average annual flowering duration was about 10−11 days. Under the RCP4.5 scenario, the advanced rate of first and end flowering date was 0.7−0.9d·10y−1 and 0.6−0.8d·10y−1 at representative stations from 2021 to 2100. Compared with 1980−2019, the average first and end flowering date for 2021 to 2060 were advanced 0−4.4 days and 0−5.0 days at representative stations, and the average first and end flowering date for 2061 to 2100 were advanced 3.4−7.6 days and 2.6−8.2 days at representative stations. Under the RCP8.5 scenario, the advanced rate of first and end flowering date were 1.3−1.8d·10y−1 and 1.3−1.6d·10y−1 at representative stations from 2021 to 2100. Compared with 1980−2019, the average first and end flowering date for 2021 to 2060 were advanced 1.3−5.9 days and 1.0−6.1 days at representative stations, and the average first and end flowering date for 2061 to 2100 were advanced 6.7−12.4 days and 6.2−12.3 days at representative stations. Under future climatic conditions, the spatial distribution of apple flowering date was basically the same as the historical period, but the duration of flowering date was slightly shortened. For the first time, this study combined the flowering date prediction model with future climate datasets to study the apple flowering date variations in Shaanxi apple producing areas, and it will provide some theoretical basis for coping with the freezing damage in flowering dates caused by climate change in Shaanxi apple producing area.
    Assessment of Coupling Degree between Water Requirement of Main Cereal Crops and Precipitation in Growing Season in Liaoning Province
    ZHANG Feng-yi , CHI Dao-cai, CHEN Tao-tao
    2021, 42(09):  746-760.  doi:10.3969/j.issn.1000-6362.2021.09.003
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    The purpose of this research is to provide a basis for the efficient utilization of water resources and the promotion of agricultural water suitable development in Liaoning province. Based on the daily meteorological data of Liaoning province from 1957 to 2017 and crop coefficients in the study area, the water demand rules of major grain crops (spring maize, soybean and rice) and the degree of water demand satisfied by precipitation were calculated and analyzed based on the SIMETAW model. The results showed that: (1) the annual mean water requirements of spring maize, soybean and rice in the whole growing season in Liaoning were 511.8mm, 509.4mm and 605.1mm, respectively, showing an insignificant decreasing trend. (2) The coupling degree of water demand and precipitation in the whole growing season of spring maize and soybean were 0.821 and 0.814, respectively, that is to say, precipitation satisfied 82.1% and 81.4% water demand respectively, and the remaining 17.9% and 18.6% still needed irrigation or supplementary irrigation before sowing. Especially in the western region, the guaranteed rate of coupling degree was only 28.2% and 21.1% when the coupling degree was greater than 0.8. The coupling degree of rice in the whole growing season was 0.464, the guaranteed rate of coupling degree greater than 0.4 was only 69.1% in the whole province, and the guaranteed rate was as low as 36.8% in the western region. (3) In the four subregions of Liaoning, the coupling degree of the three crops was the largest in the eastern region, followed by the central, southern and western regions. The coupling degree of the three crops in each growth stage was the highest in the middle growth stage, followed by the rapid growth stage, and the lowest in the early and mature stages. In recent years, the coupling degree of spring maize and soybean at the early growth stage increased significantly, while the coupling degree of the three crops at the mature stage showed a significant downward trend. The occurrence of spring drought and autumn drought should be paid attention to and irrigation should be supplemented in time for the main grain crops in Liaoning province. Under the current precipitation conditions, it was the most suitable for planting spring maize in Liaoning province, especially in the western region where water resources were scarce, and soybean was most suitable for planting in the eastern and central areas of Liaoning province, while rice was most suitable for planting in the eastern and southern areas of Liaoning province.
    Evolution of the Frost Hazards Based on Gridded Meteorological Data across China in 1961−2018
    BAI Lei , ZHANG Fan, WEN Yuan-qiao , SHI Chun-xiang , WU Jing , SHANG Ming , ZHU Zhi , MENG Jun-yao
    2021, 42(09):  761-774.  doi:10.3969/j.issn.1000-6362.2021.09.004
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    Long term gridded meteorological data in 1961-2018 was used in this study instead of in situ observation in previous studies. The spatial and temporal patterns of frost identification thresholds, frost occurrence frequency and frost intensity in the mainland China were investigated through trend analysis and cluster analysis, in order to use climate resources under climate change and reduce the losses caused by frost disasters to agricultural production. The main results were as follows, (1) for screen threshold for frost hazards, the frost date information including first frost date, last frost date and frost free days was detected with detailed geographically spatial pattern and reasonable spatial feature in the threshold of daily 2m minimum air temperature of 0℃ than the ones detected by the threshold of daily 2m minimum air temperature of 2℃.(2)In 1961−2018, first frost date in most China was generally delayed (1−3d·10y−1), and last frost date is advanced. Both of them induced the frost-free days’ increase (1−3d·10y−1). (3)The annual frequency of different frosts intensities had a weakly increased in northern China and obviously decreased in southern China. And the annual frequency of spring frost in different intensities was higher than that of autumn frost in different intensities. (4)The frost hazard’s regionalization clustered by frost date and frequency of frost intensity had five sub-regions, which were subtropical zone, warm tropical zone, middle temperate zone, cold temperate zone, and Qinghai-Tibet Plateau zone. In these sub-regions, the Qinghai-Tibet plateau zone had the most dramatic variation in frost dates. Generally, the warming trend in China had a significant weakening effect on the occurrences of frost damage in time, space and intensity.
    A Comprehensive Drought Evaluation Model in Beijing-Tianjin-Hebei Region Based on Deep Learning Algorithm
    HU Xiao-feng, WANG Dong-li, ZHAO An-zhou, LIU Xian-feng, WANG Jin-jie
    2021, 42(09):  775-787.  doi:10.3969/j.issn.1000-6362.2021.09.005
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    Drought is the most common natural hazard in the Beijing-Tianjin-Hebei region. Timely and accurate drought evaluation is crucial for socio-economic development and agricultural production. Unilateral factors such as vegetation or precipitation are usually only considered in current drought assessment, which have some limitations in actual drought evaluation. In this study, multiple drought-causing factors such as precipitation, temperature, soil and terrain were considered comprehensively. Land surface temperature (LST), normalized difference vegetation index (NDVI), precipitation and other multi-source data from 2007 to 2017 were used to construct a comprehensive drought evaluation model with standardized precipitation evapotranspiration index (SPEI) as the target value under Tensorflow frame in the Beijing-Tianjin-Hebei region, Chinese main grain production base. Determination coefficient (R2) and root mean square error (RMSE) were used to evaluate model accuracy. The station standardized precipitation index (SPI), soil relative moisture data and meteorological disaster data for the Beijing-Tianjin-Hebei region in 2016 were used to validate the accuracy of model in time and space. The results showed that model in training and test sets had different performance in various months, with R2 both greater than 0.5 and RMSE less than 0.55. Comprehensive drought evaluation model had the best performance in November. Comprehensive drought index (CDI) output from the model was close to SPI and SPEI at Miyun station, and the change trend was basically consistent. The correlation coefficients between the CDI of the model and SPI, relative soil moisture at a 10cm depth were greater than 0.7 and 0.4 respectively. Both of them passed 0.01 significance level test. Spatially, compared with SPEI, the results of drought events in Beijing-Tianjin-Hebei region from March to July in 2016 calculated by CDI were more consistent with actual situation, which indicated that the comprehensive evaluation model was applicable for drought monitoring in Beijing-Tianjin-Hebei region. 

    Multi-scale Evaluation and Validation of Soil Moisture Products of FY-3B/3C and CLDAS: A Case Study in Naqu Region of the Qinghai-Tibet Plateau
    WANG Zhuo-ying, LIU Yang-xiao-yue, YANG Ji, LIU Zhao-hua
    2021, 42(09):  788-804.  doi:10.3969/j.issn.1000-6362.2021.09.006
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    Soil moisture is an important variable for the surface hydrological cycle. High-precision soil moisture products, which have consistent time and space consistency, play a vital role in studying hydrological-driven climate and vegetation growth. In this paper, several soil moisture products were evaluated, including Fengyun satellite soil moisture products(FY-3B/3C) and Chinese Land Data Assimilation System Version 2.0(CLDAS-V2.0), aiming to clarify their accuracy characteristics of the time and space sequence. This study is expected to provide a reference for improving accuracy and inversion models of soil moisture products. The verification experiment was carried out in the Naqu region of the Qinghai-Tibet Plateau, based on the daily observed soil moisture data of 0-5cm, 0-10cm and 10-40cm at 1°×1°, 0.3°×0.3° and 0.1°×0.1° scale sparse observation networks. In comparison, the Global Land Data Assimilation System Vision 1.0(GLDAS-1) Noah soil moisture products were added to conduct the evaluate as a reference. The results showed that: (1) in terms of time sequence, CLDAS-V2.0 soil moisture products showed good temporal and spatial continuity. The null values of FY series soil moisture products were common existed, especially in freezing periods. FY and CLDAS-V2.0 soil moisture products usually overestimated the in-situ measurements, especially after the precipitation and in the vegetation growth period. (2) During the multi-scale evaluation, compared with the large-scale sparse observation station, the results were more stable and excellent on the small-scale sparse observation network. Correlation coefficients between soil moisture products and in-situ data at small scale sparse observation network were higher than those at large and middle scale sparse observation network, but Bias, RMSE and ubRMSD were lower. For example, correlation coefficient of CLDAS-V2.0 soil moisture product of 0-5cm at three(large, middle and small) scale sparse observation networks were 0.77, 0.78 and 0.79. (3) At multi-scale sparse observation networks, the results of validation were consistent. At 0−5cm, the quality of CLDAS-V2.0 soil moisture products was better than that of FY series soil moisture products on the whole. The daytime retrieved FY series soil moisture products were more accurate than the night observed data. At 0−10cm, GLDAS-1 Noah soil moisture products estimated closely soil moisture in Naqu region. However, R between GLDAS-1 Noah soil moisture products and in-situ data at multi-scale sparse observation networks were less than CLDAS-V2.0. At 10−40cm, CLDAS-V2.0 soil moisture products performed better than GLDAS-1 soil moisture products. The spatial statistical indices(Bias, RMSE and ubRMSD) of CLDAS-V2.0 and GLDAS-1 soil moisture products were closed, but correlation coefficients of CLDAS-V2.0 soil moisture products were much higher than those of GLDAS-1 soil moisture products. It showed that the quality of FY-3B/3C and CLDAS soil moisture products was relatively stable and reliable.