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Journal of Integrative Agriculture  2024, Vol. 23 Issue (4): 1381-1392    DOI: 10.1016/j.jia.2023.09.030
Agro-ecosystem & Environment Advanced Online Publication | Current Issue | Archive | Adv Search |

Improved simulation of winter wheat yield in North China Plain by using PRYM-Wheat integrated dry matter distribution coefficient

Xuan Li1, Shaowen Wang1, Yifan Chen1, Danwen Zhang1, Shanshan Yang1, Jingwen Wang2, Jiahua Zhang1, Yun Bai3, Sha Zhang1#

1 Research Center for Remote Sensing and Digital Earth, College of Computer Science and Technology, Qingdao University, Qingdao      266071, China

2 Center for Geospatial Information, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055,      China

3 School of Geographical Sciences, Hebei Normal University, Shijiazhuang 050024, China

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摘要  

准确估算区域尺度冬小麦产量对我国粮食安全和供需平衡预警具有重要意义。目前,大多数遥感过程模型使用生物量×收获指数(HI的方法估算区域尺度冬小麦产量。然而,收获指数的时空差异是造成区域尺度冬小麦产量估算误差的主要原因之一,干物质分配系数(Fr)能够动态反映冬小麦生育期内干物质分配和积累的情况。本研究将站点尺度的冬小麦各器官Fr耦合到冬小麦产量估算的遥感过程模型(PRYM-Wheat提高华北平原区域尺度冬小麦产量的估算精度。利用改进后的PRYM-Wheat模型(PRYM-Wheat-Fr)估算冬小麦产量并与统计产量进行精度比较。三年(2000-2002)平均产量结果表明,利用PRYM-Wheat-Fr估算冬小麦产量与统计产量比较的R²=0.55RMSE=0.94t ha-1;基于HIPRYM-Wheat模型(PRYM-Wheat-HI)估算冬小麦产量与统计产量比较的R²=0.30RMSE=1.62t ha-1PRYM-Wheat-Fr模型比PRYM-Wheat-HI模型估算冬小麦产量的R²提高了0.25RMSE降低了0.68t ha-1。同时,2013-2015年的验证结果也表明,PRYM-Wheat-Fr的冬小麦产量估算精度优于PRYM-Wheat-HI的冬小麦产量估算精度。所以,PRYM-Wheat-Fr模型能够更好地估算区域尺度冬小麦产量,是估算区域尺度冬小麦产量的有效工具。



Abstract  

The accurate simulation of regional-scale winter wheat yield is important for national food security and the balance of grain supply and demand in China.  Presently, most remote sensing process models use the “biomass×harvest index (HI)” method to simulate regional-scale winter wheat yield.  However, spatiotemporal differences in HI contribute to inaccuracies in yield simulation at the regional scale.  Time-series dry matter partition coefficients (Fr) can dynamically reflect the dry matter partition of winter wheat.  In this study, Fr equations were fitted for each organ of winter wheat using site-scale data.  These equations were then coupled into a process-based and remote sensing-driven crop yield model for wheat (PRYM-Wheat) to improve the regional simulation of winter wheat yield over the North China Plain (NCP).  The improved PRYM-Wheat model integrated with the fitted Fr equations (PRYM-Wheat-Fr) was validated using data obtained from provincial yearbooks.  A 3-year (2000–2002) averaged validation showed that PRYM-Wheat-Fr had a higher coefficient of determination (R²=0.55) and lower root mean square error (RMSE=0.94 t ha–1) than PRYM-Wheat with a stable HI (abbreviated as PRYM-Wheat-HI), which had R² and RMSE values of 0.30 and 1.62 t ha–1, respectively.  The PRYM-Wheat-Fr model also performed better than PRYM-Wheat-HI for simulating yield in verification years (2013–2015).  In conclusion, the PRYM-Wheat-Fr model exhibited a better accuracy than the original PRYM-Wheat model, making it a useful tool for the simulation of regional winter wheat yield.

Keywords:  dry matter partition       remote sensing model       winter wheat yield       North China Plain   
Received: 24 May 2023   Accepted: 21 August 2023
Fund: This work was supported by the National Natural Science Foundation of China (42101382 and 42201407), and the Shandong Provincial Natural Science Foundation, China (ZR2020QD016 and ZR2022QD120).
About author:  Xuan Li, E-mail: lixuan_qdu@163.com; #Correspondence Sha Zhang, Mobile: +86-15932673234, E-mail: ZhangSha@qdu.edu.cn

Cite this article: 

Xuan Li, Shaowen Wang, Yifan Chen, Danwen Zhang, Shanshan Yang, Jingwen Wang, Jiahua Zhang, Yun Bai, Sha Zhang. 2024.

Improved simulation of winter wheat yield in North China Plain by using PRYM-Wheat integrated dry matter distribution coefficient . Journal of Integrative Agriculture, 23(4): 1381-1392.

Amarasingha R P R K, Suriyagoda L D B, Marambe B, Gaydon D S, Galagedara L W, Punyawardena R, Silva G L L P, Nidumolu U, Howden M. 2015. Simulation of crop and water productivity for rice (Oryza sativa L.) using APSIM under diverse agro-climatic conditions and water management techniques in Sri Lanka. Agricultural Water Management, 160, 132–143.

Amthor J S. 1984. The role of maintenance respiration in plant growth. Plant, Cell and Environment, 7, 561–569.

Bai Y, Zhang J H, Zhang S, Koju U A, Yao F M, Igbawua T. 2017. Using precipitation, vertical root distribution, and satellite-retrieved vegetation information to parameterize water stress in a Penman-Monteith approach to evapotranspiration modeling under Mediterranean climate. Journal of Advances in Modeling Earth Systems, 9, 168–192.

Bilali A, Sun T, Wang J C, Zhang D, Zhang J F, Shi Y Q. 2019. Study on the accumulation and distribution model of winter wheat above ground dry matter under drip irrigation. Journal of China Agricultural University, 24, 33–43. (in Chinese)

Cao D, Feng J Z, Bai L Y, Xun L, Jing H T, Sun J K, Zhang J H. 2021. Delineating the rice crop activities in Northeast China through regional parametric synthesis using satellite remote sensing time-series data from 2000 to 2015. Journal of Integrative Agriculture, 20, 424–437.

Campoy J, Campos I, Villodre J, Bodas V, Osann A, Calera A. 2023. Remote sensing-based crop yield model at field and within-field scales in wheat and barley crops. European Journal of Agronomy, 143, 126720.

Chao Z H, Liu N, Zhang P D, Ying T Y, Song K H. 2019. Estimation methods developing with remote sensing information for energy crop biomass: A comparative review. Biomass and Bioenergy, 122, 414–425.

Chen J, Liu J, Cihlar J, Goulden M. 1999. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications. Ecological Modelling, 124, 99–119.

Chen R S, Ersi K, Yang J P, Lu S H, Zhao W Z. 2004. Validation of five global radiation models with measured daily data in China. Energy Conversion and Management, 45, 1759–1769.

Diepen C A, Wolf J, Keulen H, Rappoldt C. 1989. WOFOST: A simulation model of crop production. Soil Use and Management, 5, 16–24.

Fang Q, Zhang X Y, Chen S Y, Shao L W, Sun H Y. 2017. Selecting traits to increase winter wheat yield under climate change in the North China Plain. Field Crops Research, 207, 30–41.

Gilardelli C, Stella T, Confalonieri R, Ranghetti L, Campos-Taberner M, García-Har F J, Boschetti M. 2019. Downscaling rice yield simulation at sub-field scale using remotely sensed LAI data. European Journal of Agronomy, 103, 108–116.

Huang Y, Ryu Y, Jiang C Y, Kimm H, Kim S, Kang M, Shim K. 2018. BESS-Rice: A remote sensing derived and biophysical process-based rice productivity simulation model. Agricultural and Forest Meteorology, 256–257, 253–269.

Keating B A, Carberry P S, Hammer G L, Probert M E, Robertson M J, Holzworth D, Huth N I, Hargreaves J N G, Meinke H, Hochman Z, McLean G, Verburg K, Snow V, Dimes J P, Silburn M, Wang E, Brown S, Bristow K L, Asseng S, Chapman S, et al. 2003. An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy, 18, 267–288.

Ji X J, Yu Y Q, Zhang W, Yu W D. 2010. Spatial-temporal patterns of winter wheat harvest index in China in recent twenty years. Scientia Agricultura Sinica, 43, 3511–3519. (in Chinese)

Jones J W, Hoogenboom G, Porter C H, Boote K J, Batchelor W D, Hunt L A, Wilkens P W, Singh U, Gijsman A J, Ritchie J T. 2003. The DSSAT cropping system model. European Journal of Agronomy, 18, 235–265.

Ju W M, Gao P, Zhou Y L, Chen J M, Chen S, Li X F. 2010. Prediction of summer grain crop yield with a process-based ecosystem model and remote sensing data for the northern area of the Jiangsu Province, China. International Journal of Remote Sensing, 31, 1573–1587.

Li H, Tan F Y, Wang J L, Tan K Y, Xu Y, Tan Z W. 2016. Simulation on dry matter distribution coefficient for summer maize in North China. Crop Journal, 37, 335–342. (in Chinese)

Li J L, Guo Q L, Peng J Y. 2012. Remote sensing estimation model of winter wheat yield in Henan Province based on MODIS Data. Ecology and Environmental Sciences, 21, 1665–1669. (in Chinese)

Li K N, Yang X G, Liu Y, Xun X, Liu Z J, Wang J, Lü S, Wang E L. 2013. Distribution characteristics of winter wheat yield and its influenced factors in North China. Acta Agronomica Sinica, 38, 1483–1493. (in Chinese)

Liu J, Chen J M, Cihlar J, Park W M. 1997. A process-based boreal ecosystem productivity simulator using remote sensing inputs. Remote Sensing of Environment, 62, 158–175.

Ma Q R, Zhao H Q, Yang G X, Zhang Z H, Huang Y Q, Cui Z H. 2006. Analysis of dry matter accumulation and growth distribution law of winter wheat in Zhengzhou. Chinese Countryside Well-off Technology, 2006, 24–26. (in Chinese)

Mahadevan P, Wofsy S C, Matross D M, Xiao X, Dunn A L, Lin J C, Gerbig C, Munger J W, Chow V Y, Gottlieb E W. 2008. A satellite-based biosphere parameterization for net ecosystem CO2 exchange: Vegetation Photosynthesis and Respiration Model (VPRM). Global Biogeochemical Cycles, 22, 1–17.

McKinion J M, Baker D N, Whisler F D. 1989. Application of the GOSSYM/COMAX system to cotton crop management. Agricultural Systems, 31, 55–65.

Prasad N R, Patel N R, Danodia A, Manjunath K R. 2021. Comparative performance of semi-empirical based remote sensing and crop simulation model for cotton yield prediction. Modeling Earth Systems and Environment, 8, 1733–1747.

Qiao Y H, Yu Z R, Driessen P M. 2002. Quantification of dry matter accumulation and distribution among different organs of winter wheat. The Journal of Applied Ecology, 13, 543–546. (in Chinese)

Ren J Q, Chen Z X, Tang H J, Shi R X. 2006. Regional yield estimation for winter wheat based on net primary production model. Transactions of the Chinese Society of Agricultural Engineering, 22, 111–117. (in Chinese)

Ren S J, Guo B, Wu X, Zhang L G, Ji M, Wang J. 2021. Winter wheat planted area monitoring and yield modeling using MODIS data in the Huang-Huai-Hai Plain, China. Computers and Electronics in Agriculture, 182, 106049.

Su Y Z, Guo B, Zhou Z T, Zhong Y L, Min L L. 2020. Spatio-temporal variations in groundwater revealed by grace and its driving factors in the Huang-Huai-Hai Plain, China. Sensors (Basel), 20, 992.

Wang L, Zheng Y F, Yu Q, Wang E L. 2007. Applicability of agricultural production systems simulator (APSIM) in simulating the production and water use of wheat-maize continuous cropping system in North China Plain. Chinese Journal of Applied Ecology, 18, 2480–2486. (in Chinese)

Wart J V, Kersebaum K C, Peng S, Milner M, Cassman K G. 2013. Estimating crop yield potential at regional to national scales. Field Crops Research, 143, 34–43.

Wheeler T, von Braun J. 2013. Climate change impacts on global food security. Science, 341, 508–513.

Wu J J, Cheng G, Wang N, Shen H Z, Ma X Y. 2022. Spatiotemporal patterns of multiscale drought and its impact on winter wheat yield over North China Plain. Agronomy, 12, 1205–1209.

Xiao X, Zhang Q Y, Braswell B, Urbanski U, Boles S, Wofsy S, Moore III B, Ojima D. 2004. Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data. Remote Sensing of Environment, 91, 256–270.

Xie Y, Kiniry J R. 2002. A review on the development of crop modeling and its application. Acta Agronomica Sinica, 28, 190–195. (in Chinese)

Yang F, Yang J, Wang J, Zhu Y. 2014. Assessment and validation of MODIS and GEOV1 LAI with ground-measured data and an analysis of the effect of residential area in mixed pixel. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 763–774.

Yang H S, Dobermann A, Lindquist J L, Walters D T, Arkebauer T J, Cassman K G. 2004. Hybrid-maize - a maize simulation model that combines two crop modeling approaches. Field Crops Research, 87, 131–154.

Zhang S. 2018. Study of the winter wheat yield and efficiency gaps in Huang-Huai-Hai Plain based on remote sensing: Winter wheat area extraction, simulation using remote-sensed model and analysis of dominated factors. Ph D thesis, University of Chinese Academy of Sciences, China. (in Chinese)

Zhang S, Zhang J H, Bai Y, Koju U A, Igbawua T, Chang Q, Zhang D, Yao F M. 2018a. Evaluation and improvement of the daily boreal ecosystem productivity simulator in simulating gross primary productivity at 41 flux sites across Europe. Ecological Modelling, 368, 205–232.

Zhang S, Zhang J H, Bai Y, Yao F M. 2018b. Extracting winter wheat area in Huanghuaihai Plain using MODIS-EVI data and phenology difference avoiding threshold. Transactions of the Chinese Society of Agricultural Engineering, 34, 150–158. (in Chinese)

Zhang S, Bai Y, Zhang J H, Ali S. 2021. Developing a process-based and remote sensing driven crop yield model for maize (PRYM–Maize) and its validation over the Northeast China Plain. Journal of Integrative Agriculture, 20, 408–423.

Zhang S, Yang S S, Wang J W, Wu X F, Henchiri M, Javed T, Zhang J H, Bai Y. 2023. Integrating a novel irrigation approximation method with a process-based remote sensing model to estimate multi-years winter wheat yield over the North China Plain. Journal of Integrative Agriculture, 22, 2865–2881.

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