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20 August 2016, Volume 37 Issue 04

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Characteristics of Temperature and Precipitation Change along Increasing Elevations in Different Agriculture Regions of Southwest China

TAO Jian, DONG Jian-xin, LIU Guang-liang, ZHANG Ge-li, ZHU Jun-tao, SONG Wen-jing, WANG Cheng-dong, CHEN Ai-guo, WANG Shu-sheng

2016, 37(04):  379-389.  doi:10.3969/j.issn.1000-6362.2016.04.001

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The meteorological station records during 1960-2013 were used to investigate temporal and spatial characteristics of temperature and precipitation change in different agriculture regions (i.e. the Tibetan Plateau agriculture region, the southwest China agriculture region, the south China agriculture region) of southwest China, and compare relative contributions of elevation factor and latitude factor by standardized regression coefficient with the purpose of exploring change features of the characteristics along increasing elevations. The results showed that, first, southwest China experienced a significantly warming and drying trend during 1960-2013, especially after 2000. The climate warming showed a significant trend, especially in the Tibetan Plateau agricultural region and the south China agricultural region. Specifically, the climate warming in the Tibetan Plateau agricultural region occurred earlier than other regions. Second, the study area underwent a significant climate drying trend, especially in the border of Yunan and Guizhou provinces. Third, the effect of the elevation factor was quantified to be more significant than the latitude factor by the standardized regression coefficient. Change trends of temperature and precipitation increased along increasing elevations meaning the positive correlation function of the elevation factor in the climate change process, which reveals higher climate sensitivity in higher elevation areas of southwest China. Along increasing elevations, a higher climate warming trend was caused by a decreasing trend of surface latent heat, and thereby brought about an increasing wetting trend due to a stronger evapotranspiration under the warming trend. The elevation-dependent change trend of temperature and precipitation indicated an enhanced climate fluctuation in higher elevations.

Comparison and Modification of Five Crop Reference Evapotranspiration Models for Qinhuai River Basin

QIN Meng-sheng, HAO Lu, SHI Ting-ting, SUN Lei, SUN Ge

2016, 37(04):  390-399.  doi:10.3969/j.issn.1000-6362.2016.04.002

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The daily reference crop evapotranspiration (ET0) was estimated using the FAO-56 Penman-Monteith and five other methods (Irmak-Allen, Makkink, Turc, Jensen-Haise and Hargreaves) and meteorological data from seven sites located in inside and surrounding areas of the Qinhuai River Basin for the period of 2000-2013.Taken the FAO-56 Penman-Montieth method as a reference, the original empirical coefficients of five other methods were calibrated. The results were analyzed with mean absolute error (MAE), mean relative error (MRE), correlation coefficient (r) and non-parametric Wilcoxon test at an annual and a monthly scale respectively. We aimed to obtain one method which requires less data with high accuracy for the Qinhuai River Basin. With daily results of five methods as independent variable and daily results of FAO-56 Penman-Montieth method as dependent variable, monthly linear regression equations were established. Monthly correction parameters could be found based on these equations. This research indicated that, Irmak-Allen, Makkink, Turc and Hargreaves methods were not applicable at an annual scale when the original empirical coefficients were used. At a monthly scale, when original empirical coefficients were used, large biases and significant differences were found for most of the methods in most months except May to August for Irmak-Allen method, September to November for Turc method, April and September to November for Hargreaves method. After model calibration, the Makkink method performed best followed by Turc method at annual scale. The MAE,MRE and r was 14.9mm·y-1,1.42% and 0.89 respectively. No significant difference existed between the results of Makkink and the FAO-56 Penman-Montieth method. In contrast, there were significant differences between the results of Irmak-Allen and FAO-56 Penman-Montieth method. The Hargreaves method was still not applicable due to a poor correlation. At a monthly scale, considering the accuracy of estimation, Makkink and Turc methods were used by collocation. Turc method was recommended to use from April to October, MAE and MRE was 2.1-6.1mm·mon-1 and 2.9%-4.3%, Makkink method was recommended to use in the period from January to May and November to December, MAE and MRE was 1.2-2.4mm·mon-1 and 3.2%-5.7%. No significant difference existed and coefficient of variation of monthly MRE was small in the two periods for each method. Considering continuity of time, Hargreaves method was recommended to use from January to December, MAE and MRE was 1.9-10.4mm·mon-1 and 4.0%-9.2%.No significant difference existed and coefficient of variation of monthly MRE was small from January to December for Hargreaves method.

Dynamic Change of Evapotranspiration and Influenced Factors in the Spring Maize Field in Northeast China

GUO Chun-ming, REN Jing-quan, ZHANG Tie-lin, YU Hai

2016, 37(04):  400-407.  doi:10.3969/j.issn.1000-6362.2016.04.003

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Using the observed data of large-scale weighing lysimeter, the distribution characteristics and influenced factors of evapotranspiration(ET_c) in the spring maize field were analyzed. The results showed that the total ET_c was 362.3mm and the mean diurnal evapotranspiration was 2.6mm·d^-1 during the whole growth period. From the distribution of the growth period, ET_c was low during the sowing to seven leaves stage, which the mean diurnal evapotranspiration was 1.4mm·d^-1 and accounted for 11.7% of the whole growth period. It rose rapidly since the seven leaves stage, reached the maximum from big flare to heading stage(4.3mm·d^-1). The ET_c from heading to milk maturity was 97.2mm and the percentage was 26.8% of the whole growth period. From the distribution of the month, the ET_c from July to August was 207.0mm which accounted for 54.5% from May to September, but the ET_c of May, June and September were low, and the percentage was 11.6%, 19.6% and 14.3%, respectively. From hourly change of the evapotranspiration, it could be graphed as a line with a single-peak which occurred around noon with lower evapotranspiration observed in the morning and evening. The ET_c increased significant linearly with increases in leaf area index, solar radiation, 5cm soil temperature, mean air temperature, maximum air temperature and minimum air temperature, while it responded to changes in relative humidity and vapor deficit in a quadratic curve manner with a pattern of first increased and then decreased. Leaf area index was the major biological factors and the 5cm soil temperature and solar radiation were the major environmental factors for evapotranspiration.

Estimation of Chlorophyll-a Concentration in Taihu Lake by Using Back Propagation (BP) Neural Network Forecast Model

WANG Xue-lian, SONG Yu-zhi, KONG Fan-fan, WANG Yu-jia

2016, 37(04):  408-414.  doi:10.3969/j.issn.1000-6362.2016.04.004

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To estimate chlorophyll-a concentration in the centre area of Taihu Lake, back propagation (BP) neural network forecast model was constructed based on principal component analysis according to conventional water quality monitoring data and meteorological data in Taihu from 2001 to 2006 and the sensitivity analysis of model was performed. The results showed that in the centre area of Taihu Lake, estimated value of chlorophyll-a concentration according to BP neural network forecast model had a better fit with the measured data of chlorophyll-a concentration. Through sensitivity analysis of established estimation model, it was found that temperature and dissolved oxygen were highly related with the chlorophyll-a concentrations. At the same time, chlorophyll-a concentrations in different areas of Taihu Lake (Meiliang Bay, Gonghu Bay, Zhushan Bay and East Taihu) were estimated by using BP neural network forecast model, close agreement was observed between estimated and the measured data of chlorophyll-a concentration. In general, BP neural network forecast model could be used to estimate and predict the chlorophyll-a concentration of the whole lake in Taihu.

Comparison of Machine Learning Algorithms and Hargreaves Model for Reference Evapotranspiration Estimation in Sichuan Basin

FENG Yu, CUI Ning-bo, GONG Dao-zhi

2016, 37(04):  415-421.  doi:10.3969/j.issn.1000-6362.2016.04.005

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Reference evapotranspiration (ET0) is an essential component of agricultural water management, accurate estimation of ET0 is vital in irrigation scheduling. This study investigated the applicability of two machine learning algorithms, the generalized regression neural networks (GRNN) and wavelet neural networks (WNN), in modeling ET0 only with temperature data at Suining meteorological station, central Sichuan basin. The performances of GRNN and WNN models were compared with the empirical Hargreaves (HS1) and two calibrated Hargreaves (HS2, HS3) models. From the results, the root mean square error (RMSE), model efficiency (Ens) and coefficient of determination(R2) were 0.395mm×d-1, 0.924 and 0.902 for GRNN model, 0.401mm×d-1, 0.911 and 0.901 for WNN model, respectively. The performances of GRNN and WNN model were much better than HS1, HS2 and HS3 model. A further performances evaluation of GRNN and WNN model was conducted, which manifested the better applicability of GRNN and WNN models in western and eastern Sichuan basin.

Coupling the Dynamic Tillering Model to Rice Growth Model ORYZA2000 to Simulate Rice Tillering

YANG Shen-bin, CHEN De, WANG Meng-meng, HUANG Wei, JIANG Xiao-dong

2016, 37(04):  422-430.  doi:10.3969/j.issn.1000-6362.2016.04.006

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This paper takes the simulation of rice tillering as an example to examine the possibility of extending the functions of ORYZA2000 by coupling rice tillering models. To achieve this, the Dynamic Tillering Model was selected and then coupled with ORYZA2000 using one-way coupling method. With this method, the Dynamic Tillering Model accepts daily outputs from ORYZA2000 without any feedback from the tillering model. Here, ORY-TIL is used to represent the coupled model for easy description. In order to examine the reliability and accuracy of the ORY-TIL model, tillering observations from rice seeding experiments for two rice varieties (i.e. Liangyoupeijiu and Yangdao 6) in 2012 were used. With the data, key parameters in ORY-TIL model were first calibrated, and then the model was used to simulate the rice tillering for the second seeding and the forth seeding experiments for evaluation. The results showed that the coupled model was not only able to simulate rice development, above-ground biomass and yield, but also was able to simulate the dynamic of rice tillering with acceptable accuracy. The correlation coefficients between simulated and measured rice development, above-ground biomass and tillering were all above 0.95, significant at the 0.01 probability level, for both Yangdao 6 and Liangyoupeijiu. Meanwhile, the root mean square error for the simulated tillering were 24.3till·m^-2 and 34.9till·m^-2 for Yangdao 6 and Liangyoupeijiu respectively. Large discrepancy between simulate and measured rice tillering was in the decreasing phase of the tillering. In conclusion, the ORY-TIL model with one-way coupling scheme shows a good performance in the simulation of rice tillering, which can be taken as a practical reference for extension of rice growth models.

Relative Sensitivity of Main Growth Durations to Temperature for Winter Wheat in North China Plain

GAO Jing, Wu Ding-rong, WANG Pei-juan,CHEN Jing-hua, YAN Feng, ZHAO Yu-fei, WANG Jia-qiang

2016, 37(04):  431-436.  doi:10.3969/j.issn.1000-6362.2016.04.007

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Under the condition of climate change, crop growth duration changed substantially. As a result, sensitivity of crop growth duration to temperature becomes an important part of the research of climate change impact on agriculture. Aiming to overcome shortcoming of the existing research about sensitivity, this paper put forward the concept of relative sensitivity and applied in North China Plain (NCP) to analyze the spatial-temporal characteristics of winter wheat. Using winter wheat phenology observation and daily-average temperature data from 65 agricultural meteorological observation stations in the NCP during 1980-2012, the changes of growth duration and average temperature during 4 major growth periods were calculated, including sowing to the start of overwintering (S-O), the start of overwintering to turning green (O-T), turning green to hearing (T-H) and heading to maturity (H-M). Based on the calculation, relative sensitivity of growth duration to temperature was calculated. Linear regression and GIS spatial interpolation were used to explain the results. Results showed that: (1) average temperature increased during all four growth period. Growth duration varied substantially spatially and temporally; (2) For the period S-O, sensitivity varied from -0.113 to 0.029℃-1, with an average of -0.040℃-1. Sensitivity is fairly stable during this period. Generally, middle plain is more sensitive than the north and south plain; (3) For the period O-T, sensitivity varied from -0.081 to 0.091℃-1, with an average of 0.013℃-1. Sensitivity is not stable for most stations and varied much among different stations; (4) For the period T-H, sensitivity varied from -0.112 to -0.035℃-1, with an average of -0.074℃-1. Value of sensitivity has no obvious regional distribution, but the stability of sensitivity is extremely high; (5) For the period H-M, sensitivity varied from -0.114 to 0.014℃-1, with an average of -0.042℃-1. The south plain is more sensitivity than the north. Stability of sensitivity is very high during this period. Temperature sensitivity is varied substantially among different developmental stages, and also varied significantly in different region. This study promotes understanding of regional crop response to climate change and varieties shift, and provides scientific basis for simulating phenology and hence yield response to future climate change.

Effects of Exogenous Salicylic Acid on Photosynthetic Characteristics of Peanut Leaves under Elevated UV-B Radiation

HAN Yan, HAN Chen-guang, CUI Rong-hua, NING Shu-nan

2016, 37(04):  437-444.  doi:10.3969/j.issn.1000-6362.2016.04.008

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A field experiment was conducted to investigate the effects of exogenous salicylic acid on diurnal variations of the net photosynthetic rate (Pn), transpiration rate (Tr), stomata conductance (Gs), intercellular CO2 concentration(Ci), and leaf water use efficiency (WUE) of peanuts leaves at acicula forming stage under the enhanced UV-B with a portable photosynthesis system (LI-6400, USA). Two plots, i.e., the natural light plot and UV-B radiation enhancement plot (E, increase the amount equivalent to local 4-5 months), was set, both with two small plots be sprayed respectively with distilled water (S0) and salicylic acid (SA) for 3 days in a fixed period from the acicula forming stage (July 25). The results showed that, compared with CK, the Pn, Tr, Gs, and WUE of peanuts leaves under S0 treatment with 20% enhanced UV-B radiation decreased 35.7%, 25.0%, 25.0% and 10.0%, and then those of SA treatment decreased 30.4%, 17.9%, 35.3% and 19.4%, respectively. The results indicated that the net photosynthetic rate, transpiration rate, stomata conductance, and water use efficiency of peanuts leaves could be reduced under enhanced UV-B radiation, and the salicylic acid could relieve the inhibitory effect of UV-B radiation on net photosynthetic rate. However, it could not relieve the inhibitory effect of UV-B radiation on the transpiration rate, stomata conductance, and water use efficiency of peanuts leaves.

Spatial and Temporal Variation of Net Primary Productivity and Its Relationship with Climate Factors in the Chinese Loess Plateau

SHI Xiao-liang, YANG Zhi-yong, WANG Xin-shuang?, GAO Jun, HU Yan

2016, 37(04):  445-453.  doi:10.3969/j.issn.1000-6362.2016.04.009

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Based on AVHRR GIMMS NDVI and MODIS NDVI data, spatial-temporal variations of the net primary productivity (NPP) in the Chinese Loess Plateau from 1982 to 2014 was simulated using CASA model. Besides, the relationship between NPP and climate factors was analyzed at grid scale. The results showed that average annual NPP from 1982 to 2014 was 254.0gC?m-2 in the Chinese Loess Plateau and the NPP has increased over the past three decades. The average annual NPP of deciduous broadleaf forest was 513.0gC?m-2, which was the highest for different vegetation types, followed by evergreen coniferous forest, meadow, cropland, shrub and steppe. The NPP differs greatly between different vegetation types. There was significant spatial difference of vegetation NPP, and the NPP of the south parts of the study area was higher than that of the north part. Since 1999, the government of China began to carry out the police that is “replace agriculture with forestation and conserve forest” in the study area. Before the project of returning farmland to forestland or grassland (1982-1998), there was no significant change of vegetation NPP in most regions of the study area. However, since the implementation of large-scale vegetation construction from 1999, the annual mean NPP of the study area has increased significantly at a rate of 5.38gC?m-2. About 66.6% of the study area showed an increasing NPP trend, especially in northern Shaanxi Plateau, Taihang-Lvliang mountains, where significant increase in vegetation NPP has been seen. The green for grain project has greatly improved the vegetation state. The NPP had a significant positive correlation with the precipitation, but it had no obvious relationship with temperature. Therefore, precipitation is the main factor driving vegetation NPP change.

Effects of High Temperature Hours and Thermal Accumulated Temperature on Seed Setting Rate of Super Hybrid Rice

YU Sha, LU Kui-dong, XIE Bai-cheng, HU Xue-yuan, HUANG Wan-hua

2016, 37(04):  454-463.  doi:10.3969/j.issn.1000-6362.2016.04.010

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An experimental rice field for different stage sowing was conducted in 2012 and 2013 respectively, taking super hybrid rice Jinxin203 as material. Based on yields data and observed meteorological data, the effect of high temperature hours and day thermal accumulated temperature after heading stage on rice setting percentage and empty grain percentage was discussed. The results showed that the main reason to low setting percentage of rice was undergoing high temperature from milky stage to mature stage, but high temperature and empty grain percentage had not obvious correlation. There was negative correlation between setting percentage and high temperature hours, daily thermal accumulate temperature, and hours thermal accumulate temperature, but there was positive correlation between empty grain percentage and high temperature hours, daily thermal accumulate temperature, and hours thermal accumulate temperature. The three factors existed threshold values to rice setting percentage. The threshold values of high temperature hours from milky stage to mature stage were 44.4h, 32.6h, 22.6h, 15.0h and 6.0h, respectively. The threshold values of daily thermal accumulate temperature from heading to mature stage was 18.6℃·d, and 12.8℃·d from milky stage to mature stage. The threshold values of hours thermal accumulate temperature from heading to mature stage was 44.9℃·d, and 53.2℃·d from milky stage to mature stage. When temperature was lower than household value, the rice setting percentage decreased with high temperature accumulated. When high temperature reached the household value, the setting percentage had not changed further. The result indicated that high temperature from milky stage to mature stage was the key factor to lead the setting percentage decreasing.

Monitoring on the Occurrence of Cnaphalocrocis medinalis Based on Multi-temporal HJ Satellite’s Remote Sensing Images

BAO Yun-xuan, LI Yu-ting, WANG Lin, GAO Wen-ting, ZHU Feng

2016, 37(04):  464-470.  doi:10.3969/j.issn.1000-6362.2016.04.011

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Two experimental rice fields (one was used as a reference and the other was for target analysis) were conducted in Tangzhuang, Gaoyou of Jiangsu Province in 2013, and the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Near-infrared Spectroscopy (NIR) were used to characterize the occurrence and evolution of C. medinalis, which were calculated from the satellite HJ-1A/1B retrieval data. A series of analyses were performed to disclose the relationship among these three indices and the occurrence frequency, severity, and evolution of C. medinalis. The results showed as follows: (1) The more the numbers of C. medinalis, the higher the changes of such characteristic parameters. (2) The positive correlations were found between the damage of C. medinalis and the discrepancy of characteristic parameters in these two experimental fields. (3) Quantitative correlation analyses showed that DNIR and folding leaf rate had a highly significant correlation (P<0.01), and DEVI and folding leaf rate had a significant correlation (P<0.05). While there was not significant between DNDVI and folding leaf rate. Therefore, it was feasible to using HJ satellite images to monitor and warn the outbreak and development of C. medinalis, which provided a new possible method to monitor dynamically the damage of C. medinalis.

Evaluation Index of Continuous Rain to Rape during Anthesis in Anhui Province

LIU Rui-na, YANG Tai-ming,CHEN Peng, WANG Xiao-dong

2016, 37(04):  471-478.  doi:10.3969/j.issn.1000-6362.2016.04.012

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Based on daily meteorological data of 44 observation stations and rape yield data from each county (city) from 1980 to 2014 in Anhui province, yield losses of rape and relevant characteristic quantities of continuous rain such as continuous rainy days, continuous precipitation and sunshine duration during anthesis were calculated by use of mathematical statistics. According to the principle that the factors with the greatest impact on the yield are induced and the correlations between the factors are lowest, the disaster causing factors were chosen and their critical values were determined. In the last, the grade indices of continuous rain disaster were established by K- means clustering analysis method and the index’s veracity was verified by means of the yield losses and investigated disaster date during 2010-2014.The results showed that the main influence factors of continuous rain disaster to rape during anthesis were continuous rainy days and continuous precipitation. The threshold values of continuous rainy days were 3 days and the threshold values of continuous precipitation were 20mm, 50mm and 70mm respectively in Jianghuai, Yanjiang and South Anhui. Based on this, the grade indices of continuous rain disaster was established respectively for Jianghuai, Yanjiang and south Anhui by using K- means clustering analysis method. The damage could be defined as slight when the yield losses was between 5%-10%，moderate when the yield losses was between 10%-20%, severe when the yield losses was between 20%-30% ,extra severe when the yield losses was more than 30%.The testing results indicated that the accurate rate of slight and moderate indices were higher（88%-100%）, while the accurate rate of severe and extra severe indices were relatively low（65%-70%）.

Accuracy Evaluation of Summer Maize Coverage and Leaf Area Index Inversion Based on Images Extraction Technology

LI Cui-na, ZHANG Xue-fen, YU Zheng-hong, WANG Xiu-fang

2016, 37(04):  479-491.  doi:10.3969/j.issn.1000-6362.2016.04.013

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How to extract accurately the crops from the complex field scene is the key to calculate crop coverage and invert LAI for crop segmentation methods. In this paper, the dynamic images of summer maize growth season in 2011-2012, in Zhengzhou,Taian and Gucheng, which were under the different light intensity and complex outdoor background with shadows, plants residues, were obtained through on-line, real-time automatic transmission device. Meanwhile, to overcome the error caused by image distortion, geometric correction of the raw images was needed, crop coverage extraction ability and leaf area index inversion performance of four popular crop extraction algorithms (ExG, ExGR, CIVE and AP-HI) were compared and evaluated. By comparison, the effective extraction method for canopy coverage and leaf area index of summer maize under complex environment was selected. On this basis, models of canopy coverage and leaf area index with canopy porosity method were established and verified with measured data. The results showed that the light intensity changes and complex field environment, which contained plant shadows and residues, had a significant impact on the accuracy of crop segmentation algorithm. And inversion model of AP-HI was superior to the other methods in both light adaptability and complex environment, the relative error compared with true image was less than 0.2 and higher than the current visual estimation accuracy. Leaf area index was estimated by four extraction algorithms in summer maize growth season in 2011 and 2012. Based on comparison of R and RMSR among models, high fitting models were selected. The optimal model for LAI was based on AP-HI extraction algorithm, which had the highest R (0.89-0.96) and the lowest RMSR (0.47-0.75). Considering the accuracy and stability of the model, inversion model of AP-HI based on the method of application had more advantages.