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    20 September 2020, Volume 41 Issue 09
     Impact of Climate Change on Layout of Double Cropping Rice in Guangxi
    HUANG Wei, WU Xuan-ke, LIU Yong-yu, HE Yan, AN Jia-jun
    2020, 41(09):  539-551.  doi:10.3969/j.issn.1000-6362.2020.09.001
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     Climate change has become an indisputable fact. According to the fifth report of the Intergovernmental Panel on Climate Change (IPCC), the global average temperature has increased by 0.85 ℃ in the past 100 years. Warming as the main feature of climate change has had a series of important impacts on cropping system and quality layout. Its concrete performance is as follows: crop planting boundaries moved to north and expanded to high altitude, crop variety layout and crop planting structure changed obviously, and multi-cropping indices increased significantly. Relevant researches revealed that annual and seasonal average temperature showed a roughly upward trend in paddy region of Guangxi. In addition, growth period and agricultural climate resources allocation pattern changed obviously in these areas. Layout of double cropping rice is sensitive to the change of agricultural resources. However, the impacts of climate change on the planting layout of double cropping rice in Guangxi was seldom discussed. In order to reveal the change characteristics of layout of double cropping rice under the background of climate change, meteorological data from Guangxi Meteorological Bureau and geographic data from National Geomatics Center of China were utilized in the study. Four climatic factors were selected as the key factors which affected the layout of rice planting .They were safe days of growth period of double cropping rice, active integrated temperature (≥10℃) during safe growth period, total sunshine hours during the period of daily average temperature above 10℃ in a year and annual average temperature respectively. In this paper they were abbreviated to D, AT, SH and T. Based on the daily temperature and sunshine hours of 91 meteorological stations in Guangxi from 1960 to 2019, changing trend of four key climatic factors were analyzed. The study period was divided into two phases, namely first 30 years (P1:1960-1989) and last 30 years (P2:1990-2019). The equal weight principle was used to evaluate and score each key climatic factor in the two phases. According to the total scores of four key climatic factors, double cropping rice planting zones were divided into four climatic suitable regions in Guangxi, namely regions of single cropping and ratooning rice (S+R), regions of early and medium maturing double cropping rice (E+M), regions of medium and late maturing double cropping rice (M+L) and regions of late maturing double cropping rice (L+L). Different combination patterns of double cropping rice mentioned above were determined by their demands for agricultural climatic resources. Generally speaking, demand of single cropping and ratooning rice for heat resources was small. It mainly distributed in the high altitude areas of north and west of Guangxi, while the late maturing double cropping rice distributed in south areas involved Youjiang river valley of Guangxi because of the great demand for heat resources and longer safe growth season. Early and middle maturing double cropping rice and middle and late maturing double cropping rice mainly distributed in the central areas of Guangxi. Their demands for agricultural climatic resources were between S+R and L+L. In the end, the spatial distribution characteristics and changing rules of different combination patterns of double cropping rice were analyzed in two phases. The results revealed that safe days of growth period of double cropping rice increased significantly in the north and west of Guangxi. Active integrated temperature(≥10℃) and annual average temperature increased significantly, while the total sunshine hours during the period of daily average temperature above 10℃ in a year decreased significantly in most study areas. The heat resources increased towards to the north and high altitude, while sunshine hours decreased from the north to the south. Comparing with the first 30 years (P1:1960-1989), climatic suitable areas of single cropping and ratooning rice and medium and late maturing double cropping rice increased by 2.1 and 4.2 percent point, while those of early and middle maturing double cropping rice and late maturing double cropping rice decreased by 3.6 and 2.7 percent point in the last 30 years (P2:1990-2019). The significantly changing areas mainly distributed in Yangshuo and Lipu regions of Guilin, the surrounding regions of Hezhou, Mashan and Longan regions of Nanning. The purpose of this research is to provide a scientific reference to make full use of the sunlight and heat resources under background of climate change and to optimize planting layout of double cropping rice in Guangxi.
     Regulating Effect of Air Humidity on Tomato Growth and Root Exudates during Flowering Period under High Temperature Condition
    XU Chao,YANG Zai-qiang,WANG Ming-tian,HAN Wei,WEI Ting-ting
    2020, 41(09):  552-563.  doi:10.3969/j.issn.1000-6362.2020.09.002
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     Increasing air humidity to relieve high temperature stress is one of the common measures in greenhouse management. To study the regulation mechanism of air relative humidity on tomato growth and disaster recovery in high temperature conditions, the tomato variety "Jinguan 5" was used as the experimental material. This experiment was carried out at the Agricultural Experimental Station of Nanjing University of Information Science and Technology from September 2018 to January 2019. Two dynamic temperature (T) levels (daily maximum temperature/daily minimum temperature such as 32℃/22℃ and 38℃/28℃), three relative humidity (RH) levels (50%±5pp, 70%±5pp and 90%±5pp) and four stress days (3d, 6d, 9d and 12d) were set, and 25℃/15℃ and 50%±5pp were taken as control (CK). The net photosynthetic rate (Pn), total dry weigh t(Wtotal), root dry weight (Wroot), root activity (Rv), and the type and concentration of low molecular weight organic acids(LMWOAs) in root soil were measured at 0, 7, 14, 21 and 28d after treatment. The results showed that: (1) Pn and RV decreased, but root shoot ratio and LMWOAs increased significantly under high temperature, and each index value under 32℃ treatment was significantly higher than 38℃, RH increased to 70%, Pn, Wroot and root shoot ratio increased significantly. (2) At flowering stage, tomato mainly secreted oxalic acid and succinic acid. At high temperature, oxalic acid, malic acid, lactic acid, acetic acid and propionic acid increased, while tartaric acid and formic acid decreased. (3) T and RH were negatively correlated with LMWOAs, and Pn, Wroot, Wtotal and Rv were significantly positively correlated with LMWOAs. (4) Increasing RH to 70% at high temperature condition could increase Pn and Wroot and promote the root system to secrete LMWOAs, which was conducive to the recovery and growth of tomato after disaster. However, when RH reached to 90%, Pn and Wroot would be reduced, which will aggravate the damage of high temperature to plants and is not conducive to the recovery after disaster. Therefore, about 70% air humidity can alleviate the high temperature disaster to a certain extent.
     Determination of the Optimal Harvest Period for the Grape Variety Cabernet Sauvignon in Gravel Vineyard at the Eastern Foothills of Helan Mountain
    CHEN Ren-wei,ZHANG Xiao-yu,YANG Yu,WANG Jing,ZHANG Ya-hong,HU Hong-yuan,DING Yong-ping
    2020, 41(09):  564-574.  doi:10.3969/j.issn.1000-6362.2020.09.003
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     The vineyards are developing very fast in recently year at the eastern foothills of Helan mountain in Ningxia Hui Autonomous Region of Northwest China as the wine industry is invested by the incentive of the local policy.‘Cabernet Sauvignon’is the dominated grape variety for the wine industry with the cultivation extension in the past years. The wine quality is subject to the mature degree of the grape, therefore, the harvest dates are of very importance in order to make high quality wine. The best grape harvested in the optimal period is helpful for the formation of the characteristics of chateau. A case study for identifying the optimal harvest period of grape was carried out from August 21 to October 9, 2018. This experiment was conducted in gravel vineyard and the grape was harvested in different dates and then the contents of reducing sugar, the titratable acid, the soluble solids, the total phenols, the tannins and anthocyanins, pH, the sugar-acid ratio and the solid-acid ratio in grape fruits were measured in the laboratory thereafter. The K-means clustering analysis and the principal component analysis were applied to comprehensively evaluate the quality indices of the grape fruit, analyze the changes of the fruit quality index content of ‘Cabernet Sauvignon’ grape against the different harvest date, and then to conclude the best harvest period of the grape variety ‘Cabernet Sauvignon’ in gravel vineyard at the eastern foothills of Helan Mountain, Ningxia. The results showed that every quality index of the grape fruits basically meets the requirements of high-quality wine brewing if the grape is harvested September 10 afterwards although these indices varied to some extent with the harvest date. The results also showed that the harvest date has strong impact on the grape quality. After the K-means clustering analysis, two harvest periods were recommended, the early one is from August 21 to September 7, and the later one is from September 10 to October 9 in which period the harvested grape may be used to make the high quality wine. The principal component analysis was applied further to evaluate the grape quality harvested in the later period. The results showed that the overall scores of the grapes harvested during the period from September 28 to October 9 were all greater than zero, and the highest score of 1.84 was on September 28. This study concludes that in the case of the meteorological conditions in 2018 the optimal harvest period for the grape Cabernet Sauvignon in gravel vineyard at the eastern foothills of Helan Mountain in Ningxia Hui Autonomous Region was from September 10 to October 9, in which the best harvest date was September 28.
     Applicability Evaluation of TRMM 3B43 Precipitation Data for Downscaling in Yunnan Province
    YU Yuan-he, WANG Jin-liang
    2020, 41(09):  575-586.  doi:10.3969/j.issn.1000-6362.2020.09.004
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      Precipitation exerts an important role in the exchange of matter and energy in the global water cycle, affecting soil moisture, vegetation growth, and surface runoff. By employing existing station data, the spatial distribution of precipitation obtained by the meteorological observation method was obtained by interpolation. However, the interpolation results of a small number of meteorological stations are challenging to accurately analyze the spatial variation characteristics of actual precipitation. Launched on 28 November 1997, the Tropical Rainfall Measuring Mission (TRMM) was jointly developed by the United States National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA), thus providing long time series and covering most regions of the world with precipitation rate data. Nevertheless, TRMM was obtained by indirect precipitation measurement. Meanwhile, there were related errors and uncertainties. As a result, TRMM accuracy evaluation was the primary work of regional precipitation research. The terrain of Yunnan Province is complex, and the altitude difference is large. In addition, it is affected by the southwest monsoon and southeast monsoon. Complex factors such as the uneven distribution of precipitation may affect the detection capabilities of TRMM satellites. However, the current evaluation of the applicability of TRMM data in Yunnan Province is only a simple analysis of the coefficient of determination. Besides, the research on the factors affecting the accuracy of TRMM precipitation data and errors such as deviation rate is still lacking. In the present study, the accuracy of TRMM 3B43 precipitation data was evaluated in Yunnan, where the terrain was complex, aiming to provide reliable precipitation product data for regional precipitation research and hydrological forecasting. Monthly precipitation from 25 meteorological stations in Yunnan province from 2009 to 2018 provided by the China Meteorological Data Service Center was used to analyze the characteristics of TRMM 3B43 precipitation data. The correlation coefficient(R), BIAS, root mean square error (RMSE) and mean absolute error (MAE) were used to analyze the applicability between TRMM 3B43 monthly precipitation data and meteorological station data. Then, the relationship between the TRMM 3B43 precipitation data corresponding to each station and the elevation, slope, and aspect was discussed in this study. Finally, the data was downscaled to the seasonal and annual scales. At the same time, its applicability was evaluated. Some results the current study showed that: (1) the mean difference between TRMM 3B43 precipitation data and measured precipitation data was small, especially at Luxi station, where the difference was only 0.02mm. The TRMM 3B43 precipitation data was basically in consistence with the measured precipitation data, showing that there were more in the west and south and less in the east and north. It was roughly distributed step by step with the altitude. However, the difference in the spatial distribution of precipitation caused by the difference in altitude and latitude of each station also revealed the difference between TRMM 3B43 and the measured precipitation. Therefore, it is of much necessity to evaluate its applicability before using TRMM data.(2) With an R as high as 0.9392, BIAS close to zero, RMSE as low as 32.9776mm, and MAE as low as 20.5730mm, TRMM 3B43 displayed an extremely significant correlation between monthly precipitation and measured precipitation. In the range of TRMM 3B43 monthly precipitation less than 250mm, the accuracy of the fitting was relatively higher. The TRMM 3B43 precipitation data exceeded 0.735 at 25 stations, which passed the significance test at the 0.01 level, and the deviation and error of most stations were small with high overall accuracy. However, due to the different geographical locations of the stations, the deviation and error of the data presented certain differences. (3) The accuracy of TRMM 3B43 data was more affected by the slope than the altitude and aspect. The correlation coefficients of slope and R, RMSE and MAE were 0.8167, 0.7071 and 0.6865 respectively, showing strong quadratic function relationship characteristics. Except for the TRMM 3B43 precipitation data at Gongshan station and Weixi station, the accuracy of most stations at different altitudes, slopes and aspect was higher, having stronger applicability. Particularly, the data applicability was stronger for sites located at an altitude of 1000-2000m, slope less than 4°, and slope direction of 160°-240°. (4) The correlation coefficient of TRMM 3B43 data after time downscaling was slightly reduced. The error was slightly larger, especially in the winter and the annual scale slightly remained less suitable. With the largest error in summer, the RMSE and MAE of the TRMM 3B43 precipitation in Yunnan Province and the measured precipitation in each season were both less than 97mm and 78mm, respectively. The transmissibility of errors caused the RMSE and MAE of the annual scale TRMM data to become larger, and the applicability was the worst compared to other time scales. Therefore, the TRMM 3B43 monthly precipitation data had high accuracy in Yunnan region, which could thus provide effective supplement to the surface precipitation data.
     Information Diffusion Model of Banana Yield Estimation Based on Vegetation Growth
    CAI Da-xin,LIU Shao-jun,CHEN Hui-lin,TIAN Guang-hui
    2020, 41(09):  587-596.  doi:10.3969/j.issn.1000-6362.2020.09.005
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     Banana is an important tropical fruit in Hainan. Limited by meteorological disasters and the level of production technology, banana production has weak stability and large inter-annual fluctuations in Hainan. Remote sensing yield estimation is currently one of the most widely used crop yield estimation methods, especially suitable for large-area, uniformly distributed planting types. Therefore, carrying out research on banana yield estimation by remote sensing and accurately grasping the change trend of yield is of great significance to the planning and stable development of the banana planting industry. Based on Landsat-8 and MODIS data, the object-oriented method was first used to extract the spatial distribution of banana growing areas in Hainan Island, and then the Mahalanobis distance method based on the time series vegetation index was used for the second extraction, and finally the results of the two extractions were spatially overlaid. The method of field verification at random selected points was used to evaluate the classification accuracy. Aiming at the problem of the small number of regional yield estimation samples, the MODIS data from 2014 to 2015 and the banana regional yield data in 2015 were collected. The growth of banana throughout the growth period was used as an input variable to establish an information diffusion model for regional yield estimation. NDVI data was calculated by daily MODIS images, and synthesized to monthly data. The monthly composite values of NDVI was summed to obtain the cumulative value of NDVI throughout the growth period. Using the obtained vector files of banana planting areas, the cumulative NDVI values of bananas were extracted in 18 counties during the whole growth period. As the input and output variables of the information diffusion model, the cumulative value of NDVI and yield data were logarithmically normalized. The normal diffusion function was used to diffuse the sample information into the whole field, and an information matrix composed of information increments was established. Then the fuzzy relationship between the cumulative value of NDVI and the yield was obtained from the information matrix, that was the fuzzy relationship matrix. Finally, through the fuzzy approximate reasoning method, the simulated yield was obtained from the input samples. The cross-validation method was used to evaluate the accuracy of production estimation, and at the same time, the accuracy of the production estimation model for the simulation of production change trends was evaluated. Three kinds of information diffusion estimation schemes were constructed by combining multiple growth stages: scheme I was the NDVI cumulative value modeling scheme for the whole growth period; scheme Ⅱ was the scheme of joint input of vegetative growth stage and reproductive growth stage; scheme Ⅲ was the scheme of joint input during the budding stage and fruit development stage. The estimation accuracy of each plan was compared at last. The results showed that: (1) the comprehensive classification method using object-oriented and Mahalanobis distance had a higher accuracy. The total classification accuracy and Kappa coefficient were 82.5% and 0.7338 respectively, and the result of the consistency test was better. (2) The information diffusion model based on banana growth during the whole growth period had high yield estimation accuracy, with an average relative error of 26.0%, a coefficient of determination of 0.9216, and good explanatory power and stability; the accuracy of the estimation of inter-annual yield change trends reached 83.3%. (3) The univariate information diffusion estimation scheme based on the entire growth period had the highest accuracy, and the average relative error was reduced by 2.9 and 10.4 percentage point respectively compared with the other two multivariate modeling schemes. Based on the above results, it could be found that a fuzzy relationship was established with the information diffusion method between banana growth and yield by constructing a fuzzy set. The model was estimated with high accuracy, good explanatory ability and stability, and the accuracy rate for predicting the inter-annual yield change trend was also high, which could meet actual business needs. Through the establishment of a phase-by-growth yield estimation program to compare the impact of each developmental stage on the yield estimation accuracy, it was found that the effect of information diffusion model with whole growth period as the input variable was the best, which could take into account the impact of meteorological disasters and sensitive growth periods on yield. The accuracy of the yield estimation scheme II, which inputted both the vegetative growth period and the reproductive growth period, was higher than that of the program III, which only considered the reproductive growth period. The performance of information diffusion method with advantages in the processing of small sample data and ability to simulate nonlinear relationships was better. Applicability of yield estimation model based on full growth period in banana planting areas was judged satisfactory. The yield estimation model could be applied in the late stage of banana fruit expansion, and could also be used to carry out regular rolling forecasts after budding, to improve timeliness and provided scientific basis for agricultural departments and farmers to rationally arrange production and sales.
     Key Technologies of Monitoring High Temperature Stress to Rice by Portable UAV Multi Spectral Remote Sensing
    SHI Tao, YANG Tai-ming, HUANG Yong, Li Xiang, LIU Qi, YANG Yuan-jian
    2020, 41(09):  597-604.  doi:10.3969/j.issn.1000-6362.2020.09.006
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     Rice is an important cereal crop in the world and the most important food resource in China. Under the background of global warming, the degree and frequency of extreme high temperature heat waves are also increasing. From July to August every year, continuous high temperature weather with daily maximum temperature exceeding 35.0℃ often occurs in the middle and lower reaches of the Yangtze River affected by subtropical high. At this time, rice is in a sensitive period of growth and development, and continuous high temperature will seriously influences the physiological development of rice. Consequently, scientific and reasonable monitoring of the occurrence and development process of high temperature stress to rice is of great scientific significance and practical value for impact assessment of rice yield variation and agricultural production decisions in the context of global warming. At present, crop growth monitoring methods mainly rely on field investigation and satellite remote sensing. However, the traditional field investigation methods are time-consuming and labor-intensive, and the accuracy is also influenced by the subjective consciousness of investigators. In addition, satellite remote sensing also has some shortcomings, such as low spatial resolution, long transit period, cloud pollution and so on. So, the application technology research of real-time monitoring of crop growth using multi-spectral sensors carried by portable drones is an important supplement and improvement to the existing monitoring and investigation methods for high temperature stress to rice. In this paper, a remote sensing monitoring system for rice growth and data post-processing analysis and application methods were designed based on consumer-grade drones and portable multi-spectral sensors, and then Wuhu super rice production base in the middle and lower reaches of the Yangtze river was selected as the experimental area, and the continuous high temperature days from July 20 to August 9, 2019 was taken as the experimental period. Experimental results showed that there was a significant exponential relationship between the rice vegetation index and the leaf area index, with the correlation coefficient of 0.918, and then the inversion model of rice leaf area index was established. Finally, the discrimination conditions of leaf area index of rice under high temperature stress were further determined. The inversion model and discrimination conditions of leaf area index were used to extract and to analyze the spectral characteristics of rice under high temperature stress in the experimental area. During this continuous high temperature period, 15.3% of rice in the experimental area was damaged by the continuous high temperature stress, which is coincided with the reality from the field investigation conducted by agricultural department (i.e., the grain filling rate of rice was 82.2% in the experimental area). Compared with the traditional field survey and satellite remote sensing monitoring methods, the portable UAV multispectral remote sensing monitoring technology developed in this paper has advantages of high spatial resolution, real-time and large-scale monitoring and application of low cost, which is conducive to the popularization and promotion. It has a certain application prospect in the remote sensing monitoring of crop natural disasters.
     
    2020, 41(09):  605-607.  doi:10.3969/j.issn.1000-6362.2020.09.007
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