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Journal of Integrative Agriculture  2023, Vol. 22 Issue (10): 2993-3005    DOI: 10.1016/j.jia.2023.02.003
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Modelling the crop yield gap with a remote sensing-based process model: A case study of winter wheat in the North China Plain

YANG Xu1, 2, ZHANG Jia-hua1, 2#, YANG Shan-shan1, WANG Jing-wen2, BAI Yun1, ZHANG Sha1#

1 Research Center for Remote Sensing and Digital Earth, College of Computer Science and Technology, Qingdao University, Qingdao 266071, P.R.China
2 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, P.R.China

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摘要  

理解作物产量差(YG)的空间分布对提高作物产量至关重要。目前的研究通常集中在站点尺度上,当扩展到域尺度上可能会导致相当大的不确定性。为了解决这一问题,本研究采用基于改进北方生态系统生产力模拟器(BEPS遥感驱动过程冬小麦作物产量模型(PRYM-Wheat),模拟了2015-2019年华北平原冬小麦的产量差。通过统计产量数据进行产量验证,表明PRYM-Wheat模型在模拟冬小麦实际产量(Ya)方面具有良好的性能。研究区Ya的分布差异较大,由东南向西北呈下降趋势。遥感估算结果表明,研究区域的平均YG6400.6 kg ha-1。江苏省YG产量最大,为7307.4 kg ha-1。安徽YG最小,为5842.1 kg ha-1。通过分析YG对环境因素的响应,发现YG与降水之间没有明显的相关性,而YG与累积温度之间存在较弱的负相关关系此外,YG与海拔升高呈正相关。总的来说,研究作物产量差YG)可以为今后提高作物产量提供方向。



Abstract  

Understanding the spatial distribution of the crop yield gap (YG) is essential for improving crop yields.  Recent studies have typically focused on the site scale, which may lead to considerable uncertainties when scaled to the regional scale.  To mitigate this issue, this study used a process-based and remote sensing driven crop yield model for winter wheat (PRYM-Wheat), which was derived from the boreal ecosystem productivity simulator (BEPS), to simulate the YG of winter wheat in the North China Plain from 2015 to 2019.  Yield validation based on statistical yield data revealed good performance of the PRYM-Wheat Model in simulating winter wheat actual yield (Ya).  The distribution of Ya across the North China Plain showed great heterogeneity, decreasing from southeast to northwest.  The remote sensing-estimated results show that the average YG of the study area was 6 400.6 kg ha–1.  The YG of Jiangsu Province was the largest, at 7 307.4 kg ha–1, while the YG of Anhui Province was the smallest, at 5 842.1 kg ha–1.  An analysis of the responses of YG to environmental factors showed no obvious correlation between YG and precipitation, but there was a weak negative correlation between YG and accumulated temperature.  In addition, the YG was positively correlated with elevation.  In general, studying the specific features of the YG can provide directions for increasing crop yields in the future

Keywords:  remote sensing       PRYM-Wheat model        yield gap        environmental factors        North China Plain  
Received: 28 September 2022   Accepted: 23 December 2022
Fund: 

This work was jointly supported by the Shandong Key Research and Development Project, China (2018GN C110025), the National Natural Science Foundation of China (41871253), the Central Guiding Local Science and Technology Development Fund of Shandong — Yellow River Basin Collaborative Science and Technology Innovation Special Project, China (YDZX2023019), the Natural Science Foundation of Shandong Province, China (ZR2020QD016), and the “Taishan Scholar” Project of Shandong Province, China (TSXZ201712).  

About author:  #Correspondence ZHANG Jia-hua, E-mail: zhangjh@radi.ac.cn; ZHANG Sha, E-mail: zhangsha@qdu.edu.cn

Cite this article: 

YANG Xu, ZHANG Jia-hua, YANG Shan-shan, WANG Jing-wen, BAI Yun, ZHANG Sha. 2023. Modelling the crop yield gap with a remote sensing-based process model: A case study of winter wheat in the North China Plain. Journal of Integrative Agriculture, 22(10): 2993-3005.

Anhui Provincial Bureau of Statistics. 2019. Anhui Statistical Yearbook. China Statistics Press, Beijing. (in Chinese)

Bai Y, Zhang J H, Zhang S, Upama A K, Yao F M, Tertsea I. 2017. Using recipitation, 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 Systems9, 168–192.

Burke M, Lobell D B. 2017. Satellite-based assessment of yield variation and its determinants in smallholder African systems. Proceedings of the National Academy of Sciences of the United States of America114, 2189–2194.

Cao D, Zhang J, Han J, Zhang T, Yang S, Wang J, Prodhan F A, Yao F. 2022. Projected increases in global terrestrial net primary productivity loss caused by drought under climate change. Earth’s Future10, e2022EF00268.

Collatz G J, Ball J T, Grivet C, Berry J A. 1991. Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: A model that includes a laminar boundary layer. Agricultural and Forest Meteorology54, 107–136.

Curnel Y, de Wit A J W, Duveiller G, Defourny P. 2011. Potential performances of remotely sensed LAI assimilation in WOFOST model based on an OSS experiment. Agricultural and Forest Meteorology151, 1843–1855.

Egli D B. 2008. Comparison of corn and soybean yields in the United States: Historical trends and future prospects. Agronomy Journal100, S79–S88.

Evans L T, Fisher R A. 1999. Yield potential: Its definition, measurement, and significance. Crop Science39, 1544–1551.

Fang Q, Wang H G, Ma B W, Li D X, Li R Q, Li Y M. 2015. Effect of density and nitrogen application on population quality and yield formation in ultra-high yielding winter wheat. Journal of Triticeae Crops35, 364–371. (in Chinese)

Franch B, Vermote E F, Skakun S, Roger J C, Becker-Reshef I, Murphy E, Justice C. 2019. Remote sensing based yield monitoring: Application to winter wheat in United States and Ukraine. International Journal of Applied Earth Observation & Geoinformation76, 112–127.

Guan K, Berry J A, Zhang Y, Joiner J, Guanter L, Badgley G, Lobell D B. 2016. Improving the monitoring of crop productivity using space borne solar-induced fluorescence. Global Change Biology22, 716–726.

Hebei Provincial Bureau of Statistics. 2019. Hebei Statistical Yearbook. China Statistics Press, Beijing. (in Chinese)

Henan Provincial Bureau of Statistics. 2019. Henan Statistical Yearbook. China Statistics Press, Beijing. (in Chinese)

Huang J, Tian L, Liang S, Ma H, Becker-Reshef I, Huang Y, Su W, Zhang X, Zhu D, Wu W. 2015. 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 Meteorology204, 106–121.

Javed T, Zhang J H, Bhattarai N, Zhang S, Rashid S, Bai Y, Ahmad S, Henchir M, Kamran M. 2021. Drought characterization across agricultural regions of China using standardized precipitation and vegetation water supply indices. Journal of Cleaner Production313, 127866.

Jiang M N, Liu C S, Chen M S. 2017. Quantifying potential yield and water-limited yield of summer maize in the North China Plain. In: Remote Sensing and Modeling of Ecosystems for Sustainability XIV. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, San Diego, California, United States.

Jiangsu Provincial Bureau of Statistics. 2019. Jiangsu Statistical Yearbook. China Statistics Press, Beijing. (in Chinese)

Ju W, Gao P, Zhou Y, Chen J M, Chen S, Li X. 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 Sensing31, 1573–1587.

Klijn F, Haes H A U D. 1994. A hierarchical approach to ecosystems and its implications for ecological land classification. Landscape Ecology9, 89–104.

Liu J, Chen J M, Cihlar J. 2003. Mapping evapotranspiration based on remote sensing: An application to Canada’s landmass. Water Resources Research39, 1189.

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.

Liu Y, Wang E L, Yang X G, Wang J. 2010. Contributions of climatic and crop varietal changes to crop production in the North China Plain, since 1980s. Global Change Biology16, 2287–2299.

Liu Z J, Yang X G, Hubbard K G, Lin X M. 2012. Maize potential and yield gaps in the changing climate of northeast China. Global Change Biology18, 3441–3454.

Liu Z Z. 2016. Remote sensing research of small regional yield estimation for winter wheat based on improved CASA model. MSc thesis, Henan University, China. (in Chinese)

Lobell D B, Cassman K G, Field C B. 2009. Crop yield gaps: Their importance, magnitudes, and causes. Annual Review of Environment & Resources34, 179–204.

Ma J H, Liu Y, Yang X G, Wang W F, Xue C Y. 2010. Characteristics of climate resources under global climate change in the North China Plain. Acta Ecologica Sinica30, 3818–3827. (in Chinese)

Mahadevan P, Wofsy S C, Matross D M, Xiao X M, 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 Cycles22, 521–539.

Meng Q F, Sun Q P, Chen X P, Cui Z L, Yue S C, Zhang F S, Romheld V. 2012. Alternative cropping systems for sustainable water and nitrogen use in the North China Plain. Agriculture Ecosystems & Environment146, 93–102.

National Bureau of Statistics. 2019. China Statistical Yearbook. China Statistics Press, Beijing. (in Chinese)

Neumann K, Verburg P H, Stehfest E, Muller C. 2010. The yield gap of global grain production: A spatial analysis. Agricultural Systems103, 316–326.

Pan X H, Deng Q H. 2007. Progress of research on crop harvest index. Acta Agriculturae Universitis Jiangxiensis29, 1–5. (in Chinese)

Potter C S, Randerson J T, Field C B, Matson P A, Vitousek P M, Mooney H A, Klooster S A. 1993. Terrestrial ecosystem production: A process model based on global satellite and surface data. Global Biogeochemical Cycles7, 811–841.

Schwalbert R A, Amado T, Corassa G, Pott L P, Prasad P V V, Ciampitti I A. 2020. Satellite-based soybean yield forecast: integrating machine learning and weather data for improving crop yield prediction in southern Brazil. Agricultural and Forest Meteorology284, 107886.

Shandong Provincial Bureau of Statistics. 2019. Shandong Statistical Yearbook. China Statistics Press, Beijing. (in Chinese)

Stuart A M, Pame A R P, Silva J V, Dikitanan R C, Rutsaert P, Malabayabas A J B, Lampayan R M, Radanielson A M, Singleton G R. 2016. Yield gaps in rice-based farming systems: insights from local studies and prospects for future analysis. Field Crops Research194, 43–56.

Tian H R, Wang P X, Tansey K, Han D, Zhang J Q, Zhang S Y, Li H M. 2021. A deep learning framework under attention mechanism for wheat yield estimation using remotely sensed indices in the Guanzhong Plain, PR China. International Journal of Applied Earth Observation and Geoinformation102, 102375.

Tilman D, Balzer C, Hill J, Befort B L. 2011. Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences of the United States of America108, 20260–20264.

Tittonell P, Giller K E. 2013. When yield gaps are poverty traps: The paradigm of ecological intensification in African smallholder agriculture. Field Crops Research143, 76–90.

Wang J, Lopez-Lozano R. Weis. M, Buis S, Li W, Liu S. Baret F, Zhang J. 2022. Crop speciffc inversion of PROSAIL to retrieve green area index (GAI) from several decametric satellites using a Bayesian framework. Remote Sensing of Environment278, 113085.

Wang J W, Zhang J H, Bai Y, Zhang S, Yang S S, Yao F M. 2019. Integrating remote sensing-based process model with environmental zonation scheme to estimate rice yield gap in Northeast China. Field Crops Research246, 107682.

Wang P J, Sun R, Zhang J H, Zhou Y Y, Xie D H, Zhu Q J. 2011. Yield estimation of winter wheat in the North China Plain using the remote-sensing-photosynthesis-yield estimation for crops (RS-P-YEC) model. International Journal of Remote Sensing32, 6335–6348.

Wang P J, Xie D H, Zhang J H, Sun R, Chen S H, Zhu Q J. 2009. Application of BEPS model in estimating winter wheat yield in North China Plain. Transactions of the Chinese Society of Agricultural Engineering25, 148–153. (in Chinese)

Wang Y L, Xu X G, Huang L S, Yang G J, Fan L L, Wei P F, Chen G. 2019. An improved CASA model for estimating winter wheat yield from remote sensing images. Remote Sensing11, 1088.

Wang X, Zhang J, Xun L, Wang J, Wu Z, Henchiri M, Zhang S, Zhang S, Bai Y, Yang S, Li S, Yu X. 2022. Evaluating the effectiveness of machine learning and deep learning models combined time-series satellite data for multiple crop types classification over a large-scale region. Remote Sensing14, 2341.

Wang Z J, Guo T C, Zhu Y J, Wang J H, Zhao M. 2003. Study on the characteristics of canopy light radiation in super high yield wheat. Acta Botanica Boreali-Occidentalia Sinica23, 1657–1662. (in Chinese)

Wart J V, Kersebaum K C, Peng S B, Milner M, Cassman K G. 2013. Estimating crop yield potential at regional to national scales. Field Crops Research143, 34–43.

Wu X, Yang W, Wang C, Shen Y, Kondoh A. 2019. Interactions among the phenological events of winter wheat in the North China Plain-based on field data and improved MODIS estimation. Remote Sensing11, 2976.

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 Research87, 131–154.

Yang S, Zhang J,Wang J, Zhang S, Bai Y, Shi S, Cao D. 2022. Spatiotemporal variations of water productivity for cropland and driving factors over China during 2001–2015.Agricultural Water Management262, 107328.

Yang X G, Liu Z J. 2014. Advances in research on crop yield gaps. Scientia Agricultura Sinica47, 2731–2741. (in Chinese)

Yao F M, Li Q Y, Zeng R Y, Shi S Q. 2021. Effects of different agricultural treatments on arrowing winter wheat yield gap and nitrogen use efficiency in China. Journal of Integrative Agriculture20, 383–394.

Yao F M, Tang Y J, Wang P J, Zhang J H. 2015. Estimation of maize yield by using a process-based model and remote sensing data in the Northeast China Plain. Physics and Chemistry of the Earth Parts A/B/C87, 142–152.

Yuan W, Chen Y, Xia J, Dong W, Magliulo V, Moors E, Olesen J E, Zhang H. 2016. Estimating crop yield using a satellite-based light use efficiency model. Ecological Indicators Integrating Monitoring Assessment & Management60, 702–709.

Zhang Q, Zhang L, Evers J, Van der Werf W, Zhang W, Duan L. 2014. Maize yield and quality in response to plant density and application of a novel plant growth regulator. Field Crops Research164, 82–89.

Zhang S. 2018. Study of the winter wheat yield and efficiency gaps in HuangHuai-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, Bai Y, Zhang J H. 2021a. Remote sensing-based quantification of the summer maize yield gap induced by suboptimum sowing dates over North China Plain. Remote Sensing13, 3582.

Zhang S, Bai Y, Zhang J H, Shahzad A. 2021b. 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 Agriculture20, 408–423.

Zhang X, Qin W, Chen S, Shao L, Sun H. 2017. Responses of yield and WUE of winter wheat to water stress during the past three decades - A case study in the North China Plain. Agricultural Water Management179, 47–54.

Zhang X, Zhang Q. 2016. Monitoring interannual variation in global crop yield using long-term AVHRR and MODIS observations. ISPRS Journal of Photogrammetry and Remote Sensing114, 191–205.

Zhao J. 2015. Study on maize yield potential and spatial enhancement under climate change. Ph D thesis, China Agricultural University, China. (in Chinese)

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