Scientia Agricultura Sinica ›› 2020, Vol. 53 ›› Issue (18): 3679-3692.doi: 10.3864/j.issn.0578-1752.2020.18.005

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Using Canopy Time-Series Vegetation Index to Predict Yield of Winter Wheat

XIANG FangLin(),LI XinGe,MA JiFeng,LIU XiaoJun,TIAN YongChao,ZHU Yan,CAO WeiXing,CAO Qiang()   

  1. Nanjing Agricultural University/National Engineering and Technology Center for Information Agriculture/Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs/Engineering and Research Center for Smart Agriculture, Ministry of Education/Jiangsu Key Laboratory for Information Agriculture, Nanjing 210095
  • Received:2019-12-09 Accepted:2020-03-12 Online:2020-09-16 Published:2020-09-25
  • Contact: Qiang CAO E-mail:2017101003@njau.edu.cn;qiangcao@njau.edu.cn

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Abstract:

【Objective】The research elaborated the dynamic change trend of canopy time series vegetation index in winter wheat. The yield prediction model was constructed based on time series vegetation index in wheat, which provided effective technical support for obtaining crop yield information timely and accurately. 【Method】 From 2017 to 2019, the field experiments involving different nitrogen (N) rates and varieties were conducted in Ten Thousand Acres Grain Industrial Park located in Xinghua, Jiangsu province. The normalized difference red edge (NDRE) and normalized difference vegetation index (NDVI) were obtained from the active canopy sensor RapidSCAN CS-45. The curve of time-series vegetation index was fitted based on the double logistic function, and the characteristic parameters of curve were extracted. The correlation between each characteristic parameters and yield were analyzed. The yield estimation models were verified with independent test data. 【Result】 The results of the study indicated that the relationship between NDRE and yield was performed well at booting and heading stage, and R2 of them was 0.86 and 0.85, respectively. The results of multiple stepwise linear regression showed that the yield prediction models could be improved using NDRE of two or more growth stages, compared with using single growth stage information. The first and second selected periods were jointing and booting stage, respectively. Based on the relative NDRE (RNDRE) and relative NDVI (RNDVI) of the whole growth period, the time-series curve was constructed and the yield prediction models were developed using the characteristic parameters of the curve. The maximum value, accumulative value and growth rate of RNDRE and RNDVI time series curve had a good relationship with the yield. The yield prediction models based on the maximum and accumulative values of RNDRE performed satisfactorily with validation using independent data, the R2 was greater than 0.80, and the relative root mean square error and relative error were less than 10%. The validation effect was better than NDRE-based prediction model with the single-period or multi-period, which was better than NDVI-based yield predicted model. 【Conclusion】 The maximum and the accumulative RNDRE extracted from the canopy time series vegetation index had a good potential to estimate the yield, which provided technical support for real-time and accurate yield prediction in the field.

Key words: winter wheat, canopy sensor, time series vegetation index, yield, prediction model

Fig. 1

Map of experiment 1"

Table 1

Coefficients of determination (R2) for the relationship between vegetation indices (NDRE and NDVI) and yield at different growth stages across varieties and years"

植被指数
Vegetation index		 品种
Variety		 样本
Sample		 拔节期
Jointing stage		 孕穗期
Booting stage		 抽穗期
Heading stage		 开花期
Flowering stage		 灌浆期
Filling stage
NDRE 镇麦12 Zhenmai 12 30 0.66 0.89 0.88 0.86 0.89
扬麦23 Yangmai 23 30 0.46 0.83 0.82 0.75 0.84
宁麦13 Ningmai 13 30 0.72 0.92 0.92 0.91 0.91
所有品种 All 90 0.60 0.86 0.85 0.81 0.81
NDVI 镇麦12 Zhenmai 12 30 0.62 0.79 0.81 0.84 0.83
扬麦23 Yangmai 23 30 0.54 0.79 0.75 0.77 0.75
宁麦13 Ningmai 13 30 0.81 0.86 0.81 0.84 0.85
所有品种 All 90 0.63 0.79 0.77 0.80 0.76

Table 2

Stepwise multiple linear regression models based on vegetation indices for estimating winter wheat yield at different growth stages"

植被指数
Vegetation index		 回归方程
Regression equation		 决定系数
R2		 相对均方根误差
RRMSE (%)		 相对误差
RE (%)		 赤池信息准则
AIC
NDRE y= -2.16x1+16.83x2+9.15x3-4.40x4-2.35x5-0.75 0.86 8.98 9.82 -97.95
y= -2.85x1+17.69x2+5.67x3-3.23x5-0.90 0.86 8.99 9.84 -99.74
y= -2.54x1+21.94x2-2.14x5-0.81 0.86 9.02 9.82 -101.37
y= -2.53x1+19.90x2-0.75 0.86 9.02 9.84 -103.01
NDVI y=0.14x1+11.23x2-13.22x3+23.12x4-3.57x5-8.31 0.81 10.41 11.70 -71.45
y=11.25x2-13.36x3+23.27x4-3.38x5-8.36 0.81 10.40 11.76 -73.44
y=12.21x2-14.79x3+19.45x4-7.45 0.81 10.43 11.62 -74.96

Fig. 2

Dynamic changes of NDRE based on AGDD with different nitrogen rates The red dots indicate the tests at the greening stage, jointing stage, booting stage, heading stage, flowering stage, filling stage and maturity stage. A: Zhenmai12; B: Yangmai23; C: Ningmai13; D: Zhenmai12; E: Yangmai23; F: Ningmai13. The same as below"

Fig. 3

Dynamic changes of NDVI based on AGDD with different nitrogen rates"

Fig. 4

The dynamic changes of time-series curve of RNDRE (A) and RNDVI (B) with different nitrogen rates"

Table 3

The fitting parameters of time-series curve of RNDRE and RNDVI based on RAGDD with different nitrogen rates"

植被指数 Vegetation index 氮处理
N treatment		 曲线参数 Parameters of curves 决定系数
R2		 均方根误差
RMSE
A1 A2 a b c d
RNDRE N0 0.655 0.506 7.10 0.21 11.84 0.84 0.78 0.066
N1 0.876 0.714 8.97 0.27 14.25 0.85 0.91 0.063
N2 0.969 0.791 11.42 0.28 15.04 0.88 0.96 0.047
N3 0.988 0.786 12.74 0.28 15.51 0.90 0.97 0.043
N4 0.995 0.788 12.70 0.28 15.10 0.90 0.97 0.043
RNDVI N0 0.837 0.668 5.85 0.18 14.29 0.86 0.79 0.082
N1 0.956 0.785 8.80 0.23 15.70 0.88 0.91 0.062
N2 0.987 0.776 11.59 0.23 16.60 0.92 0.94 0.053
N3 0.997 0.760 12.29 0.24 16.22 0.93 0.96 0.044
N4 0.996 0.754 12.46 0.24 15.84 0.93 0.95 0.045

Table 4

The characteristic parameters of time-series curve of RNDRE and RNDVI with different nitrogen levels"

氮处理
N treatment		 RNDRE RNDVI
最大值
Maximum		 累积值
Accumulative value		 增长速率
Increase rate		 下降速率
Decrease rate		 最大值
Maximum		 累积值
Accumulative value		 增长速率
Increase rate		 下降速率
Decrease rate
N0 0.582 0.349 0.721 1.187 0.750 0.456 0.772 1.693
N1 0.809 0.464 1.245 1.841 0.911 0.558 1.186 2.091
N2 0.931 0.550 1.598 2.037 0.968 0.623 1.396 2.039
N3 0.963 0.580 1.731 2.028 0.981 0.637 1.452 1.983
N4 0.968 0.583 1.737 2.018 0.980 0.639 1.488 1.885

Table 5

The characteristic parameters of time-series RNDRE and RNDVI curve based prediction models of winter wheat yield"

植被指数
Vegetation index		 特征参数
Characteristic parameter		 单产预测模型
Yield prediction model		 R2 RRMSE
(%)		 RE
(%)		 AIC
RNDRE 最大值 Maximum y = 8.72x - 1.37 0.80 10.2 12.9 62.29
累积值 Accumulative value y = 14.39x - 1.22 0.82 9.9 12.9 60.41
增长速率 Increase rate y = 3.32x + 1.38 0.82 9.9 12.8 60.35
下降速率 Decrease rate y = 3.73x - 0.75 0.73 12.0 14.3 71.98
RNDVI 最大值 Maximum y = 14.24x - 7.03 0.77 11.0 13.4 66.48
累积值 Accumulative value y = 18.24x - 4.58 0.80 10.2 12.9 62.18
增长速率 Increase rate y = 4.82x - 0.02 0.81 10.2 12.8 61.84
下降速率 Decrease rate y = 5.04x - 3.72 0.22 20.3 25.6 103.41

Table 6

Validation results of yield prediction model based on NDRE and NDVI in winter wheat"

植被指数
Vegetation index		 模型
Model		 单产预测模型
Yield prediction model		 R2 RRMSE
(%)		 RE
(%)
RNDRE 单生育时期线性回归单产模型
Yield model based on linear regression in single growth stage		 拔节期 Jointing y=12.47x+1.67 0.59 24.3 23.5
孕穗期 Booting y =17.34x-0.65 0.79 18.7 18.2
抽穗期 Heading y =17.46x-0.73 0.77 17.4 16.6
开花期 Flowering y =17.97x-0.87 0.79 17.7 16.7
灌浆期 Filling y =17.46x+0.13 0.71 15.5 14.4
多元逐步线性回归模型
Yield model based on stepwise multiple linear regression		 y= -2.16x1+16.83x2+9.15x3-4.40x4-2.35x5-0.75 0.81 17.6 17.3
y= -2.85x1+17.69x2+5.67x3-3.23x5-0.90 0.80 17.8 17.6
y= -2.54x1+21.94x2-2.14x5-0.81 0.78 18.3 18.2
y= -2.53x1+19.90x2-0.75 0.79 17.7 17.4
RNDVI 单生育时期线性回归单产模型
Yield model based on linear regression in single growth stage		 拔节期 Jointing y = 8.8027x - 0.4221 0.56 14.2 17.2
孕穗期 Booting y = 11.822x - 3.4517 0.73 13.6 14.4
抽穗期 Heading y = 12.877x - 4.3125 0.68 16.1 17.4
开花期 Flowering y = 17.707x - 8.1348 0.73 17.5 19.5
灌浆期 Filling y = 13.383x - 3.9326 0.75 16.4 15.3
多元逐步线性回归模型
Yield model based on stepwise multiple linear regression		 y=0.14x1+11.23x2-13.22x3+23.12x4-3.57x5-8.31 0.69 16.6 19.6
y=11.25x2-13.36x3+23.27x4-3.38x5-8.36 0.69 16.7 19.7
y=12.21x2-14.79x3+19.45x4-7.45 0.68 16.3 18.2

Table 7

Validation results of evaluation with winter wheat yield prediction models based on the characteristic parameters of time-series RNDRE and RNDVI curve"

特征参数
Characteristic parameter		 RNDRE RNDVI
R2 RRMSE (%) RE (%) R2 RRMSE (%) RE (%)
最大值 Maximum 0.81 8.0 8.4 0.65 12.1 15.1
累积值 Accumulative value 0.80 9.6 9.9 0.72 16.9 15.9
增长速率 Increase rate 0.36 19.8 20.8 0.01 78.1 90.8
下降速率 Decrease rate 0.44 24.3 23.7 0.07 46.0 44.8

Fig. 5

Validation results of evaluation with winter wheat yield prediction models based on the characteristic parameters of time-series RNDRE (A: Maximum; B: Accumulative value) and RNDVI (C: Maximum; D: Accumulative value) curve "

[1] 谭昌伟, 杜颖, 童璐, 周健, 罗明, 颜伟伟, 陈菲. 基于开花期卫星遥感数据的大田小麦估产方法比较. 中国农业科学, 2017,50(16):3101-3109.
doi: 10.3864/j.issn.0578-1752.2017.16.005
TAN C W, DU Y, TONG L, ZHOU J, LUO M, YAN W W, CHEN F. Comparison of the methods for predicting wheat yield based on satellite remote sensing data at anthesis. Scientia Agricultura Sinica, 2017,50(16):3101-3109. (in Chinese)
doi: 10.3864/j.issn.0578-1752.2017.16.005
[2] 侯学会, 牛铮, 高帅, 黄妮. 基于SPOT-VGT NDVI时间序列的农牧交错带植被物候监测. 农业工程学报, 2013,29(1):142-150.
HOU X H, NIU Z, GAO S, HUANG N. Monitoring vegetation phenology in farming-pastoral zone using SPOT-VGT NDVI data. Transactions of the Chinese Society of Agricultural Engineering, 2013,29(1):142-150. (in Chinese)
[3] 冯美臣, 杨武德, 张东彦, 曹亮亮, 王慧芳, 王芊. 基于TM和MODIS数据的水旱地冬小麦面积提取和长势监测. 农业工程学报, 2009,25(3):103-109.
FENG M C, YANG W D, ZHANG D Y, CAO L L, WANG H F, WANG Q. Monitoring planting area and growth situation of irrigation-land and dry-land winter wheat based on TM and MODIS data. Transactions of the Chinese Society of Agricultural Engineering, 2009,25(3):103-109. (in Chinese)
[4] 任建强, 陈仲新, 周清波, 刘佳, 唐华俊. MODIS植被指数的美国玉米单产遥感估测. 遥感学报, 2015,19(4):568-577.
REN J Q, CHEN Z X, ZHOU Q B, LIU J, TANG H J. MODIS vegetation index data used for estimating corn yield in USA. Journal of Remote Sensing, 2015,19(4):568-577. (in Chinese)
[5] 苏腾飞, 刘全明, 苏秀川. 基于多种植被指数时间序列与机器学习的作物遥感分类研究. 江苏农业科学, 2017,45(16):219-224.
SU T F, LIU Q M, SU X C. Remote sensing classification of crops based on time series of multiple vegetation indices and machine learning. Jiangsu Agricultural Sciences, 2017,45(16):219-224. (in Chinese)
[6] WARDLOW B D, EGBERT S L, KASTENS J H. Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. central great plains. Remote Sensing of Environment, 2007,108(3):290-310.
[7] ZHENG H B, CHENG T, YAO X, DENG X Q, TIAN Y C, CAO W X, ZHU Y. Detection of rice phenology through time series analysis of ground-based spectral index data. Field Crops Research, 2016,198:131-139.
[8] PAN Y Z, LI L, ZHANG J S, LIANG S L, ZHU X F, SULLA-MENASHE D. Winter wheat area estimation from MODIS-EVI time series data using the crop proportion phenology index. Remote Sensing of Environment, 2012,119:232-242.
doi: 10.1016/j.rse.2011.10.011
[9] 陈鹏飞, 杨飞, 杜佳. 基于环境减灾卫星时序归一化植被指数的冬小麦单产估测. 农业工程学报, 2013,29(11):124-131.
CHEN P F, YANG F, DU J. Yield forecasting for winter wheat using time series NDVI from HJ satellite. Transactions of the Chinese Society of Agricultural Engineering, 2013,29(11):124-131. (in Chinese)
[10] ANTHONY N R, ANATOLY G, YI P, ELIZABETH W S, BRYAN L, TIMOTHY A. Continuous monitoring of crop reflectance, vegetation fraction, and identification of developmental stages using a four band radiometer. Agronomy Journal, 2013,105(6):1769.
doi: 10.2134/agronj2013.0242
[11] LIU X J, FERGUSON R, ZHENG H B, CAO Q, TIAN Y C, CAO W X, ZHU Y. Using an active-optical sensor to develop an optimal NDVI dynamic model for high-yield rice production (Yangtze, China). Sensors, 2017,17(4):672.
[12] MAGNEY T S, EITEL J U H, HUGGINS D R, VIERLING L A. Proximal NDVI derived phenology improves in-season predictions of wheat quantity and quality. Agricultural and Forest Meteorology, 2016,217:46-60.
doi: 10.1016/j.agrformet.2015.11.009
[13] SHAVER T M, KHOSLA R, WESTFALL D G. Evaluation of two ground-based active crop canopy sensors in maize: growth stage, row spacing, and sensor movement speed. Soil Science Society of America Journal, 2010,74(6):2101-2108.
[14] TANAKA S, KAWAMURA K, MAKI M, MURAMOTO Y, YOSHIDA K, AKIYAMA T. Spectral Index for quantifying leaf area index of winter wheat by field hyperspectral measurements: A case study in Gifu prefecture, central Japan. Remote Sensing, 2015,7(5):5329-5346.
[15] THOMPSON L J, FERGUSON R B, KITCHEN N, FRAZEN D W, MAMO M, YANG H, SCHEPERS J S. Model and sensor-based recommendation approaches for in-season nitrogen management in corn. Agronomy Journal, 2015,107(6):2020-2030.
[16] 王仁红, 宋晓宇, 李振海, 杨贵军, 郭文善, 谭昌伟, 陈立平. 基于高光谱的冬小麦氮素营养指数估测. 农业工程学报, 2014,30(19):191-198.
WANG R H, SONG X Y, LI Z H, YANG G J, GUO W S, TAN C W, CHEN L P. Estimation of winter wheat nitrogen nutrition index using hyperspectral remote sensing. Transactions of the Chinese Society of Agricultural Engineering, 2014,30(19):191-198. (in Chinese)
[17] ZHOU X, ZHENG H B, XU X Q, HE J Y, GE X K, YAO X, CHENG T, ZHU Y, CAO W X, TIAN Y C. Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 2017,130:246-255.
[18] LU J J, MIAO Y X, SHI W, LI J X, YUAN F. Evaluating different approaches to non-destructive nitrogen status diagnosis of rice using portable RapidSCAN active canopy sensor. Scientific Reports, 2017,7(1):14073.
doi: 10.1038/s41598-017-14597-1 pmid: 29074943
[19] LI S Y, DING X Z, KUANG Q L, ATA-UI-KARIM S T, CHENG T, LIU X J, TIAN Y C, ZHU Y, CAO W X, CAO Q. Potential of UAV-based active sensing for monitoring rice leaf nitrogen status. Frontiers in Plant Science, 2018,9:1834.
doi: 10.3389/fpls.2018.01834 pmid: 30619407
[20] BONFIL D J. Wheat phenomics in the field by RapidScan NDVI vs. NDRE. Israel Journal of Plant Sciences, 2016,64:41-45.
[21] RUSSELLE M P, WILHELM W W, OLSON R A, POWER J F. Growth analysis based on degree days. Crop Science, 1984,24:28-32.
doi: 10.2135/cropsci1984.0011183X002400010007x
[22] FRANCH B, VERMOTE E F, BECKER-RESHEF I, CLAVERIE M, HUANG J, ZHANG J, JUSTICE C, SOBRINO J A. Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR growing degree day information. Remote Sensing of Environment, 2015,161:131-148.
doi: 10.1016/j.rse.2015.02.014
[23] MCMASTER G S, SMIKA D E. Estimation and evaluation of winter wheat phenology in the central great plains. Agricultural and Forest Meteorology, 1988,43(1):1-18.
[24] WANG X X, WANG Q J, FAN J, SU L J, SHEN X L. Logistic model analysis of winter wheat growth on China's loess plateau. Canadian Journal of Plant Science, 2014,94(8):1471-1479.
doi: 10.4141/CJPS2013-293
[25] FISCHER A. A model for the seasonal variations of vegetation indices in coarse resolution data and its inversion to extract crop parameters. Remote Sensing of Environment, 1994,48(2):220-230.
doi: 10.1016/0034-4257(94)90143-0
[26] SHMUELI G. To explain or to predict? Statistical Science, 2010,25(3):289-310.
doi: 10.1214/10-STS330
[27] AKAIKE H. A new look at the statistical model identification. IEEE Transactions on Automatic Control, 1974,19(6):716-723.
[28] BOZDOGAN H. Model selection and Akaike's information criterion (AIC): the general theory and its analytical extensions. Psychometrika, 1987,52(3):345-370.
[29] MKHABELA M S, BULLOCK P, RAJ S, WANG S, YANG Y. Crop yield forecasting on the Canadian prairies using MODIS NDVI data. Agricultural and Forest Meteorology, 2011,151(3):385-393.
doi: 10.1016/j.agrformet.2010.11.012
[30] 王长耀, 林文鹏. 基于MODIS EVI的冬小麦单产遥感预测研究. 农业工程学报, 2005,21(10):90-94.
WANG C Y, LIN W P. Winter wheat yield estimation based on MODIS EVI. Transactions of the Chinese Society of Agricultural Engineering, 2005,21(10):90-94. (in Chinese)
[31] HASSAN M A, YANG M J, RASHEED A, YANG G J, REYNOLDS M, XIA X C, XIAO Y G, HE Z H. A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. Plant Science, 2019,282:95-103.
doi: 10.1016/j.plantsci.2018.10.022 pmid: 31003615
[32] KANKE Y TUBAÑA B, DALEN M, HARRELL D. Evaluation of red and red-edge reflectance-based vegetation indices for rice biomass and grain yield prediction models in paddy fields. Precision Agriculture, 2016,17(5):507-530.
[33] DEMPEWOLF J, ADUSEI B, BECKER R I, HANSEN M, POTAPOV P, KHAN A, BARKER B. Wheat yield forecasting for Punjab province from vegetation index time series and historic crop statistics. Remote Sensing, 2014,6:9653-9675.
doi: 10.3390/rs6109653
[34] MENG L H, ZHANG X L, LIU H J, GUO D, YAN Y, QIN L L, PAN Y. Estimation of cotton yield using the reconstructed time-series vegetation index of Landsat Data. Canadian Journal of Remote Sensing, 2017,43(3):244-255.
doi: 10.1080/07038992.2017.1317206
[35] ZHANG K, GE X K, SHEN P C, LI W Y, LIU X J, CAO Q, ZHU Y, CAO W X, TIAN Y C. Predicting rice grain yield based on dynamic changes in vegetation indexes during early to mid-growth stages. Remote Sensing, 2019,11(4):387.
[36] WARDLOW B D, EGBERT S L. Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. central great plains. Remote Sensing of Environment, 2008,112(3):1096-1116.
doi: 10.1016/j.rse.2007.07.019
[37] CHU L, LIU Q S, HUANG C, LIU G H. Monitoring of winter wheat distribution and phenological phases based on MODIS time-series: A case study in the Yellow River Delta, China. Journal of Integrative Agriculture, 2016,15(10):2403-2416.
doi: 10.1016/S2095-3119(15)61319-3
[38] JOHNSON D M. An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States. Remote Sensing of Environment, 2014,141:116-128.
doi: 10.1016/j.rse.2013.10.027
[39] HUANG J X, TIAN L Y, LIANG S L, MA H Y, BECKER-RESHEF I, HUANG Y B, SU W, ZHANG X D, ZHU D H, WU W B. Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model. Agricultural and Forest Meteorology, 2015,204:106-121.
doi: 10.1016/j.agrformet.2015.02.001
[40] MA H Y, HUANG J X, ZHU D H, LIU J M, SU W, ZHANG C, FAN J L. Estimating regional winter wheat yield by assimilation of time series of HJ-1 CCD NDVI into WOFOST-ACRM model with Ensemble Kalman filter. Mathematical and Computer Modelling, 2013,58(3/4):759-770.
doi: 10.1016/j.mcm.2012.12.028
[41] TAO J B, WU W B, ZHOU Y, WANG Y, JIANG Y. Mapping winter wheat using phenological feature of peak before winter on the north China plain based on time-series MODIS data. Journal of Integrative Agriculture, 2017,16(2):348-359.
doi: 10.1016/S2095-3119(15)61304-1
[42] DUVEILLER G, LOPEZ-LOZANO R, CESCATTI A. Exploiting the multi-angularity of the MODIS temporal signal to identify spatially homogeneous vegetation cover: A demonstration for agricultural monitoring applications. Remote Sensing of Environment, 2015,166:61-77.
doi: 10.1016/j.rse.2015.06.001
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