Table of Content

20 October 2020, Volume 41 Issue 10

Previous Issue    Next Issue

Cover Download

Article Download

Comparison of Phenological Models of Robinia pseudoacacia (L.) in the Warm- temperate Region of Eastern China

YU Pei-yang, TONG Xiao-juan, LI Jun, ZHANG Jing-ru, LIU Pei-rong

2020, 41(10):  609-621.  doi:10.3969/j.issn.1000-6362.2020.10.001

Asbtract ( 695 )   PDF (1005KB) ( 404 )  

Related Articles | Metrics

Vegetation phenology is a powerful indicator of the response of terrestrial ecosystems to climate change and plays an important role in water, carbon exchange and energy balance. Under the background of global warming, the changes of vegetation phenology and simulating the phenological phase of plants have been paid much attention. Up to date, the performance of most phenolgy models on spring phenolgy is better, but the simulated autumn phenology has been less accurate. In this study, meteorological data and surface phenological data (leaf bud opening date, first leaf date, first flowering date and leaf coloring date) at 10 sites were selected as the input of the SW, Unichill and DNGDD models, which were used to simulate the phenological phase of Robinia pseudoacacia(L.). The objectives were to examine the performace of SW, Unichill and DNGDD models simulating spring and autumn phenology, and give a guide on optimum the parameters of phenology models. The odd-year data of the sites were used as the internal test, and the even-year data were used as the cross test. The simulation values of three models were compared with the observed ones for analysis to find the best one to predict vegetation phenology. The simulated annealing algorithm was applied to optimum the parameters of SW, Unichill and DNGDD models. The simulated spring and autumn phenology was compared with the measured one, and evaluate the modelling to select the best one to predict vegetation phenology.The results showed that the leaf bud opening, the first leaf, the first flowering dates of Robinia pseudoacacia (L.) were significantly negatively correlated with average temperature in the same period. Average temperature was the main factor affecting phenology. The phenological period in spring had the strongest correlation with mean air temperature, but not significant correlation with mean daily minimum temperature and mean daily maximum temperature. Therefore, compared with Unichill and DNGDD models, SW performed better in simulating spring phenology. The variance interpretation (R2) of cross test of SW model simulating the leaf bud opening, the first leaf and the first flowering dates were 0.807, 0.876 and 0.907 separately and the root mean square error (RMSE) 6.0, 4.6 and 4.4 days. Mean temperature in the daytime and night mean temperature in autumn had different influences on phenology. There was a large deviation when leaf coloring date was simulated only using average temperature. Average temperature was replaced by daily minmum and maximum temperature in DNGDD model. DNGDD model performed well in comparison with SW model when the leaf coloring date in autumn was simulated, and R2 of cross check was 0.580. Therefore, SW model is the best one to simulat the spring phenological phases, while DNGDD model perforemed well in simulating the autumn phenological phase.

Research on the Simulation Model of Tomato Development Period in Solar Greenhouse Based on Clock Model Method

WEN Yong-jing, LI Chun, DONG Chao-yang, CHENG Chen, LIU Shu-mei, GONG Zhi-hong, LI Zheng-fa, FENG Li-ping

2020, 41(10):  622-631.  doi:10.3969/j.issn.1000-6362.2020.10.002

Asbtract ( 379 )   PDF (491KB) ( 503 )  

Related Articles | Metrics

Tomato is one of the most important vegetables grown in China and around the world. In the facility production of tomato, not only the genetic characteristics of varieties and the management of water and fertilizer, but also the meteorological conditions such as temperature and light are important factors affecting the high yield and quality growth and development of tomato. In the actual production, the favorable meteorological conditions are often obtained by adjusting the temperature and light, so as to achieve the purpose of efficient production. Based on the clock model, this study attributed the influencing factors of each development stage to the air temperature and sunshine hours in the greenhouse, summarized the temperature of three basis points in each development stage of tomato, and constructed the development process simulation model of "Rijk Zwaan882" and "Provence" two common greenhouse tomato varieties in the north of China. The experiment was conducted in solar greenhouse (116.97°E, 39.43°N, altitude 8m) of Tianjin Agricultural Science and Technology Innovation Base from 2013 to 2015. According to the characteristics of light and temperature response of tomato growth and development in solar greenhouse, the different development stages and development phases of tomato were characterized by the mathematical index expression in clock model, and the development stage of tomato was indexed. Then, the model parameters of each development stage were solved, and the initial values of the model parameters, such as the basic development coefficient, the genetic parameters of temperature response characteristics and the genetic parameters of light response characteristics, were obtained, and the model was statistically tested and adjusted. The error between the simulated value and the measured value was minimized, and the final value of the model parameters was obtained. A simulation model of tomato development periods in greenhouse were established based on the clock model. The results showed that, firstly, the regression estimated root mean square error (RMSE) between the simulated values and the actual observed values in the five tomato development stages of the model were 8.3, 14.4, 16.3, 23.7 and 28.1 days, respectively. The standard root mean square error (NRMSE) of regression estimation were 20.78%, 20.18%, 20.21%, 17.35% and 14.86%, respectively, indicating that the simulation effect of this model was good. Secondly, the clock model simulation results was compared with the method of growing degree days (GDD) model simulation results. the RMSE of the tomato in each development stage of the clock model of the simulated values and the measured values was in 8.3−28.1 days, NRMSE was in 14.86%−20.78%, and the RMSE of the tomato in each development stage of GDD model of the simulated values and measured values was in 5.9−33.1 days, NRMSE was in 15.09%−34.38%. It was showed that the clock model could accurately predict development of greenhouse tomato development periods. In general, it was helpful to provide guidance for greenhouse tomato planting users to determine planting time, marketing time, management and control, so as to improve the economic benefits of tomato planting.

Effect of Photoperiod on Fluorescence Characteristics of Photosynthetic System of Fresh-cut Chrysanthemum Leaves under High Temperature

LU Si-yu, YANG Zai-qiang, ZHANG Yuan-da, ZHENG Han, YANG Li

2020, 41(10):  632-643.  doi:10.3969/j.issn.1000-6362.2020.10.003

Asbtract ( 342 )   PDF (4433KB) ( 567 )  

Related Articles | Metrics

Chrysanthemum is a typical short−day plant, which blossoms only when the sunshine length is shorter than the critical day length, and the critical day length is 12 h·d−1. In order to meet the market demand and promote the chrysanthemum to bloom in the long sunshine season, black shading materials are often used to shorten the day. There were many studies on short−day treatment to control the flowering period of chrysanthemum at home and abroad, but they failed to solve the problem of high temperature obstacle of willow buds in chrysanthemum during shading in summer. The appearance of willow buds indicates that chrysanthemum is still in the vegetative growth stage, and the process of flower bud differentiation is hindered. Continuous high temperature environment is an important factor leading to the emergence of willow buds in chrysanthemum. In this experiment, chrysanthemum variety "Hongmian" was used as the test material. The photoperiod experiment was carried out at high temperature of (32±2)℃/(22±2)℃(day/night), and the photoperiod duration was set as 7h/17h(Ph7), 8h/16h(Ph8) , 9h/15h(Ph9), 10h/14h(Ph10) and 11h/13h(Ph11), respectively with 13h/11h(CK) as control. The experiment began on July 20, 2019, and ended on August 25, 2019 when willow buds appeared in chrysanthemum seedlings. The photosynthetic structure of chrysanthemum leaves is very sensitive to adversity, which is the primary site of adversity damage. The light response curve, photosynthetic pigment content (including chlorophyll a, chlorophyll b, carotenoid and chlorophyll total) and rapid fluorescence induction kinetics curves of leaves were measured and analyzed before the formation of chrysanthemum willow buds. The curves of chlorophyll fluorescence kinetics OJ, OI, OK and IP phases were standardized as relatively variable fluorescence W, WOJ=(Ft−F0)/(FJ−F0), WOI=(Ft−F0)/(FI−F0), WOK=(Ft−F0)/ (FK−F0), WIP= (Ft−FI)/(FP−FI), and the fluorescence differential kinetics ΔW was calculated, ΔW=W−Wref, where Wref is the relatively variable fluorescence at the corresponding time of CK. That is, ΔWOJ=WOJ−Wref, ΔWOI=WOI−Wref, ΔWOK=WOK−Wref, ΔWIP=WIP−Wref, in order to understand the absorption and utilization of light energy by different photoperiod systems of chrysanthemum at high temperature in the process of photoreaction. By analyzing photosynthetic rate and the operation of photosynthetic electron transfer chain in photosynthetic structure (PSⅡ and PSⅠ), it is expected to provide scientific reference for the diagnosis and analysis of leaf photosynthesis and the study of photosynthetic performance when chrysanthemum is unable to differentiate normally. The results showed that: (1) the content of photosynthetic pigment was the lowest at ph7 and Ph8, and the reduction of NADP+ in the reaction center of photosystem II(PS II), the oxygen-releasing complex and the terminal electron acceptor bank of photosystem I(PS I) was slightly eased in the middle of the 26−day experiment, but it was inhibited at other times, and the photosynthetic capacity was the worst correspondingly. (2)The abnormal differentiation of willow bud inflorescence occurred at Ph10. The photosynthetic potential of Ph10 is great, but the oxygen-releasing complex of PSII is always inactive, and the photosynthesis changes with the strength and decline of the energy connection between PSII photosynthetic units. (3)The photosynthetic pigment of Ph11 leaves is the maximum after CK, and its photosynthetic performance is relatively stable. The continuously enhanced PSI and PSII activities make photoelectrons transfer normally under the premise of inactivation of oxygen-releasing complex. The photosynthetic system of chrysanthemum leaves treated with Ph7 and Ph8 was the most seriously damaged at high temperature. The photosynthetic system of chrysanthemum leaves treated with Ph10 was more sensitive, and the photosynthetic system of chrysanthemum leaves treated with Ph11 had stronger stress resistance.

Effect of High Temperature in Seedling Stage on Phenological Stage of Strawberry and its Simulation

XU Chao, WANG Ming-tian, YANG Zai-qiang , HAN Wei, ZHENG Sheng-hua

2020, 41(10):  644-654.  doi:10.3969/j.issn.1000-6362.2020.10.004

Asbtract ( 471 )   PDF (608KB) ( 698 )  

Related Articles | Metrics

High temperature is one of the common agricultural meteorological disasters, affecting the growth and development of crops. In order to study the effect of high temperature at the seedling stage on the phenology of strawberry in greenhouses, the strawberry variety " Benihoppe " was taken as the experimental material, and different high temperatures (32℃/22℃, 35℃/25℃, 38℃/28℃, and 41℃/31℃) and different stress days (2d, 5d, 8d and 11d) were performed on the strawberry seedlings in greenhouses in 2018 and 2019, and then transplanted to Venlo glass greenhouse for normal cultivation experiment. The data of 2018 quantitatively were used to analyze the effects of high temperature and stress days on the phenology of strawberries in greenhouses, and constructed three models for the effects of high temperature on the growth period of strawberries, including the PDT model, the TEP model, and the GDD model. The experimental data in 2019 were fitted to verify the established model. The results showed that mild (32°C for 2 to 11 days) and moderate high temperature (35°C for 2 to 8 days) at the seedling stage helped strawberries to early entry to the flowering stage, fruit setting stage and picking period, while severe (38°C for 2 to 5 days) and very severe (38°C 8 to 11 days and 41°C for 2 to 11 days) high temperature delayed the time for strawberries to enter the phenology mentioned above. Compared with the TEP model and GDD model, the high temperature impact model based on the PDT was more accurate and had the smallest error. The coefficient of determination(R2) between the simulated values and the measured values of flowering stage, fruit setting stage and harvesting stage were 0.84, 0.82 and 0.97, respectively, the root mean square error(RMSE) were 1.39d, 1.50d and 1.56d, respectively, the relative error(RE) were 2.27%, 2.23% and 1.57%, respectively. Therefore, in the greenhouse strawberry planting process, it is recommended to use the PDT model to predict the start and end times of the strawberry flowering stage, fruit setting stage and harvesting stage.

Drought Index Insurance of Maize in Water Critical Period Based on CERES-Maize Model: A Case Study of Changwu, Shaanxi

YANG Xiao-juan, ZHANG Ren-he, LU Hai-dong, XUE Ji-quan, LIU Yuan, YAO Ning, LUAN Qing-zu, BAI Wei, LIANG Wei, LIU Bu-chun

2020, 41(10):  655-667.  doi:10.3969/j.issn.1000-6362.2020.10.005

Asbtract ( 475 )   PDF (869KB) ( 601 )  

Related Articles | Metrics

The policy-based agricultural insurance of maize in Shaanxi province was impeded due to its moral hazard, adverse selection and high management cost. Weather index agricultural insurance takes specific meteorological index as trigger which can avoid the defects of the traditional agricultural insurance, and is one of the effective solutions to the current predicament of agricultural insurance. Changwu county located in Shaanxi, an important maize production region, drought stress in the water critical period is the main limiting factor that inhibits maize growth and yield. Therefore, constructing drought stress model for maize in its water critical period and studying the drought index insurance are of great significance for designing the weather index insurance and solving the dilemma of current traditional agricultural insurance. To isolate the influence of a single meteorological factor at a specific crop growth stage, a field experiment of rain-fed maize was conducted in Dryland Agriculture Experiment Station of Northwest A&F University in Changwu from 2011 to 2013.The field experimental data of weather variables, soil, management practices and maize growth and development, were used to calibrate and validate CERES-Maize model. The accumulated precipitation from June 21 to August 31 in Changwu was defined as the drought index (DI) of maize during the water critical period. The DI in 2013 was treated with ±20, ±40, ±60, ±80, ±100, ±120, ±140, ±160, ±180, ±200, ±220 and ±240mm and then distributed daily to simulate the maize yield using CERES-Maize model. The water treatment corresponding to the maximum simulated yield was set as the critical point, and the water treatment less than the critical point was set as the drought treatment. The drought stress model was constructed based on the data of drought treatment and the corresponding simulated yield, in combination with the disaster grade of yield reduction rate, the drought levels and the corresponding drought index thresholds were determined. The optimal distribution model of drought index was selected through EasyFit software using the meteorological data of Changwu from 1990 to 2019, and the occurrence probability of different drought levels in the water critical period of maize were estimated by the selected model. The drought index insurance rate of maize in water critical period was determined by ratemaking method based on the occurrence probability of each drought grade and the corresponding yield reduction rate. The compensation scheme of drought index insurance was designed using the projection pursuit regression method. The results showed that the average absolute relative error (ARE) and relative root mean square error (RRMSE) of CERES-Maize simulation were less than 10%, which was in line with the requirements of crop model simulation accuracy. A linear relationship was showed between maize drought index (DI) during the water critical period and the simulated yield loss（y, %）, that was y=-0.55DI+107.17. The Log-logistic model performed best for the drought index distribution, and the Anderson-Darling (AD) test value was only 0.20. The occurrence probability of light, moderate, severe and excessive drought was 9.75%, 5.90%, 3.71% and 3.50%, respectively. The premium rate of drought index insurance of maize in water critical period was 5.6%. The compensation will start when DI is ≤185 and be graded as the maize under drought stress.

Automatic Identification Technology of Lycium barbarum Flowering Period and Fruit Ripening Period Based on Faster R-CNN

ZHU Yong-ning, ZHOU Wang, YANG Yang, LI Jian-ping, LI , Wan-chun, JIN Hong-wei, FANG Feng

2020, 41(10):  668-677.  doi:10.3969/j.issn.1000-6362.2020.10.006

Asbtract ( 361 )   PDF (896KB) ( 552 )  

Related Articles | Metrics

From 2018 to 2019, 16 sets of Lycium barbarum farmland monitoring systems had been built in Ningxia. Each system took 10 images every day, and over 30,000 images of the growth of Lycium barbarum trees were taken in two years. To study the recognition technology of the flowering period and fruit ripening period of Lycium barbarum based on these images, three methods were used in this paper to judge the developmental stage of Lycium barbarum. The first one was the field observation method. In this method, two fields where the real-life monitoring system was installed were selected, and the Lycium barbarum trees in the two fields were manually observed once in every two days during the growing season. The Lycium barbarum trees selected by manual observation should be consistent with the ones photographed by the farmland monitoring systems. The second method was expert visual judgment, in which 5 experienced experts were invited to judge all the images. The judgment standard was as follows. If there were 5 features in a certain developmental period in an image, it was considered that this Lycium barbarum tree had reached the universal period of this developmental period. If 5 out of 10 images on a certain day reached the universal period of this developmental period, it was considered that the Lycium barbarum population in the filed had entered this developmental period. Based on the opinions of the experts, the result of the expert visual judgment was given. The third method is the automatic recognition method. In this method, more than 3000 images with characteristics of Lycium barbarum flowering and fruit ripening were screened out from all the images. Removed the images with lens fouling or unsatisfactory field of view, and finally, the number of remaining image samples was 1210. To avoid the phenomenon of underfitting or overfitting due to too few or too many images of a certain category involved in training, rotation, cropping and flipping were used for data enhancement. The data enhanced samples were divided according to the format of the PASCAL VOC2007 data set. Finally, a total of 7260 experimental samples were obtained, including 5808 images in the training set and 1452 images in the test set. According to the significant image characteristics of Lycium barbarum in the flowering and fruit ripening periods, the labelImg label tool was used to label all the flowers and fruits in the image samples, marking 12100 ‘flower’ labels and 11602 ‘fruit’ labels. Then, faster region-based convolutional neural network (Faster R-CNN) was utilized to train and classify the selected images, and to construct the algorithm for identifying the flowering period and fruit ripening period of Lycium barbarum. In the constructed algorithm, the judgment standard was the same as that in the second method, and the time series judgment was introduced when judging the different stages of flowering or fruit ripening. Taking AP and mAP as the evaluation indicators of the automatic recognition model, the results showed that the mAP value could reach 0.74 on the test set when the important hyperparameters batch size and the number of iterations in the network structure were set to be 64 and 20000 respectively, which outperforms other hyperparameters setting. Comparing the results of the three methods, it could be found that the difference between the automatic recognition results and the field observation records during the same period was 0-12 days. The main reason for the difference was that the observation objects and standards of the two methods were inconsistent. The observation object of the automatic recognition method was a two-dimensional image, and it could not be judged when the feature was occluded. The object of field observation is the Lycium barbarum tree, which is not affected by occlusion. Besides, the standards of these two methods were different. The standard of the automatic recognition method was based on the number of feature points observed in the image, while the field observation method was based on the ratio of the observed feature points to the expected feature points of the Lycium barbarum tree that could not be obtained in the automatic recognition method. The difference between the two methods could not be eliminated fundamentally, so it was difficult to optimize the automatic recognition algorithm using the results of the field observations method. The comparison results also showed that the difference between the automatic recognition results and the expert visual judgments was within 2-5d. The judgment objects and standards of these two methods were consistent, so the results were highly comparable. The results of expert visual judgment could be used as the verification standard to optimize and adjust the automatic recognition method.