Please wait a minute...
Journal of Integrative Agriculture  2022, Vol. 21 Issue (6): 1786-1789    DOI: 10.1016/S2095-3119(21)63713-9
Special Issue: 农业生态环境-遥感合辑Agro-ecosystem & Environment—Romote sensing
Agro-ecosystem & Environment Advanced Online Publication | Current Issue | Archive | Adv Search |
From statistics to grids: A two-level model to simulate crop pattern dynamics
XIA Tian1, 2, WU Wen-bin2, ZHOU Qing-bo3, Peter H. VERBURG4, YANG Peng2, HU Qiong1, YE Li-ming5, ZHU Xiao-juan6
1 Key Laboratory for Geographical Process Analysis & Simulation, Hubei Province/College of Urban & Environmental Sci. Central China Normal University, Wuhan, Hubei 430079, P.R.China
2 Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China
3 Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China
4 Institute for Environmental Studies, VU University Amsterdam, Amsterdam 1085, The Netherlands
5 Department of Geology, Ghent University, Ghent 9000, Belgium
6 Commercial and Economic Law School, China University of Political Science and Law, Beijing 100088, P.R.China
Download:  PDF in ScienceDirect  
Export:  BibTeX | EndNote (RIS)      
摘要  

本研究提出一种统计数据空间化的方法构建多时像农作物种植格局空间数据集来解决数据缺失的问题。该方法采用两层嵌套结构实现土地利用层和农作物层模拟,其中第一层模拟的耕地数据用于控制第二层农作物种植格局空间模拟范围。第二层农作物层采用空间迭代的方法按分配规则进行农作物面积统计数据空间化,最终实现农作物空间格局动态模拟。该模型在中国黑龙江省地区进行2000-2019年农作物空间格局模拟,结果表明模型模拟精度较高,能够实现长时间序列的农作物种植面积统计数据空间化应用,未来该模型能广泛应用于农业土地系统各方面研究及生产应用




Abstract  Crop planting patterns are an important component of agricultural land systems.  These patterns have been significantly changed due to the combined impacts of climatic changes and socioeconomic developments.  However, the extent of these changes and their possible impacts on the environment, terrestrial landscapes and rural livelihoods are largely unknown due to the lack of spatially explicit datasets including crop planting patterns.  To fill this gap, this study proposes a new method for spatializing statistical data to generate multitemporal crop planting pattern datasets.  This method features a two-level model that combines a land-use simulation and a crop pattern simulation.  The output of the first level is the spatial distribution of the cropland, which is then used as the input for the second level, which allocates crop censuses to individual gridded cells according to certain rules.  The method was tested using data from 2000 to 2019 from Heilongjiang Province, China, and was validated using remote sensing images.  The results show that this method has high accuracy for crop area spatialization.  Spatial crop pattern datasets over a given time period can be important supplementary information for remote sensing and thus support a wide range of application in agricultural land systems.
Keywords:  crop planting pattern        spatialization        simulation        spatiotemporal change        remote sensing  
Received: 05 February 2021   Accepted: 16 April 2021
Fund: The research described in this paper is supported and financed by the National key Research and Development Program of China (2019YFA0607400), and the Fundamental Research Funds for the Central Universities, China (CCNU19TS045). 
About author:  XIA Tian, E-mail: xiatian@ccnu.edu.cn; Correspondence WU Wen-bin, Tel: +86-10-82105070, E-mail: wuwenbin@caas.cn; ZHU Xiao-juan, Mobile: +86-13581895899, E-mail: xiaojuanzh@cupl.edu.cn

Cite this article: 

XIA Tian, WU Wen-bin, ZHOU Qing-bo, Peter H. VERBURG, YANG Peng, HU Qiong, YE Li-ming, ZHU Xiao-juan. 2022. From statistics to grids: A two-level model to simulate crop pattern dynamics. Journal of Integrative Agriculture, 21(6): 1786-1789.

Anderson W, You L Z, Wood S, Wood-Sichra U, Wu W B. 2015. An analysis of methodological and spatial differences in global cropping systems models and maps. Global Ecology and Biogeography, 24, 180–191.
Betts R A, Golding N, Gonzalez P, Gornall J, Kahana R, Kay G, Mitchell L, Wiltshire A. 2015. Climate and land use change impacts on global terrestrial ecosystems and river flows in the HadGEM2-ES Earth system model using the representative concentration pathways. Biogeosciences, 12, 1317–1338.
Chen C Q, Qian C R, Deng A X, Zhang W J. 2012. Progressive and active adaptations of cropping system to climate change in Northeast China. European Journal of Agronomy, 38, 94–103.
Chen X W, Fan R Q, Shi X H, Liang A Z, Zhang X P, Jia S X. 2013. Spatial variation of penetration resistance and water content as affected by tillage and crop rotation in a black soil in Northeast China. Acta Agriculturae Scandinavica (Section B: Soil & Plant Science), 63, 740–747.
Costanza R. 1989. Model goodness of fit: A multiple resolution procedure. Ecological Modelling, 47, 199–215.
Kuenzer C, Knauer K. 2013. Remote sensing of rice crop areas. International Journal of Remote Sensing, 34, 2101–2139.
Eitelberg D A, Van Vliet J, Verburg P H. 2015. A review of global potentially available cropland estimates and their consequences for model-based assessments. Global Change Biology, 21, 1236–1248.
Foley J A. 2005. Global consequences of land use. Science, 309, 570–574.
Gao J, Liu Y S. 2011. Climate warming and land use change in Heilongjiang Province, Northeast China. Applied Geography, 31, 476–482.
Heistermann M, Müller C, Ronneberger K. 2006. Land in sight? Achievements, deficits and potentials of continental to global scale land-use modeling. Agriculture, Ecosystems & Environment, 114, 141–158.
Li X C, Liu X P, Yu L. 2014. A systematic sensitivity analysis of constrained cellular automata model for urban growth simulation based on different transition rules. International Journal of Geographical Information Science, 28, 1317–1335.
Li Z G, Tang H J, Yang P, Wu W B, Chen Z X, Zhou Q B, Zhang L, Zou J Q. 2012. Spatio-temporal responses of cropland phenophases to climate change in Northeast China. Journal of Geographical Sciences, 22, 29–45.
Melo de Oliveira Santos C L, Camargo Lamparelli R A, Dantas Araujo Figueiredo G K, Dupuy S, Boury J, dos Santos Luciano A C, Torres R D S, le Maire G. 2019. Classification of crops, pastures, and tree plantations along the season with multi-sensor image time series in a subtropical agricultural region. Remote Sensing, 11, 334.
Mirkatouli J, Hosseini A, Neshat A. 2015. Analysis of land use and land cover spatial pattern based on Markov chains modelling. City, Territory and Architecture, 2, 1–9.
Neumann K, Stehfest E, Verburg P H, Siebert S, Müller C, Veldkamp T. 2011. Exploring global irrigation patterns: A multilevel modelling approach. Agricultural Systems, 104, 703–713.
Nielsen J Ø, De Bremond A, Roy Chowdhury R, Friis C, Metternicht G, Meyfroidt P, Munroe D, Pascual U, Thomson A. 2019. Toward a normative land systems science. Current Opinion in Environmental Sustainability, 38, 1–6.
Noszczyk T. 2019. A review of approaches to land use changes modeling. Human and Ecological Risk Assessment: An International Journal, 25, 1377–1405.
Olofsson P, Foody G M, Herold M, Stehman S V, Woodcock C E, Wulder M A. 2014. Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42–57.
Pilehforooshha P, Karimi M, Taleai M. 2014. A GIS-based agricultural land-use allocation model coupling increase and decrease in land demand. Agricultural Systems, 130, 116–125.
Pontius R G, Schneider L C. 2001. Land-cover change model validation by ROC method for the Ipswich watershed, Massachusetts, USA. Agriculture, Ecosystems & Environment, 85, 239–248.
Qureshi M R N, Singh R K, Hasan M A. 2018. Decision support model to select crop pattern for sustainable agricultural practices using fuzzy MCDM. Environment Development & Sustainability, 20, 641–659.
Rounsevell M D A, Pedroli B, Erb K H, Gramberger M, Busck A G, Haberl H, Kristensen K, Kuemmerle T, Lavorel S, Lindner M, Hermann L C, Metzer M J, David M R, Alexander P, Marta P S, Reenberg A, Vadineanu A, Verburg P H, Wolfslehner B. 2012. Challenges for land system science. Land Use Policy, 29, 899–910.
Song Q, Zhou Q B, Wu W B, Hu Q, Lu M, Liu S B. 2017. Mapping regional cropping patterns by using GF-1 WFV sensor data. Journal of Integrative Agriculture, 16, 337–347.
Temme A J A M, Verburg P H. 2011. Mapping and modelling of changes in agricultural intensity in Europe. Agriculture, Ecosystems & Environment, 140, 46–56.
Verburg P H, Alexander P, Evans T, Magliocca N R, Malek Z, Rounsevell M D A, van Vliet J. 2019. Beyond land cover change: Towards a new generation of land use models. Current Opinion in Environmental Sustainability, 38, 77–85.
Verburg P H, Rounsevell M D A, Veldkamp A. 2006. Scenario-based studies of future land use in Europe. Agriculture, Ecosystems & Environment, 114, 1–6.
Verburg P H, Soepboer W, Veldkamp A, Limpiada R, Espaldon V, Mastura S S A. 2002. Modeling the spatial dynamics of regional land use: The CLUE-S model. Environmental Management, 30, 391–405.
Voinov A, Costanza R, Wainger L, Boumans R, Villa F, Maxwell T, Voinov H. 1999. Patuxent landscape model: Integrated ecological economic modeling of a watershed. Environmental Modelling & Software, 14, 473–491.
Waiyasusri K, Yumuang S, Chotpantarat S. 2016. Monitoring and predicting land use changes in the Huai Thap Salao Watershed area, Uthaithani Province, Thailand, using the CLUE-S model. Environmental Earth Sciences, 75, 1–16.
Wu W B, Yu Q Y, Verburg H P, You L Z, Yang P, Tang H J. 2014. How could agricultural land systems contribute to raise food production under global change? Journal of Integrative Agriculture, 13, 1432–1442.
Xia T, Wu W B, Zhou Q B, Verburg P H, Yu Q Y, Yang P, Ye L M. 2016. Model-based analysis of spatio-temporal changes in land use in Northeast China. Journal of Geographical Sciences, 26, 171–87.
Xia T, Wu W B, Zhou Q B, Yu Q Y, Verburg P H, Yang P, Lu Z J, Tang H J. 2014. Spatio-temporal changes in the rice planting area and their relationship to climate change in Northeast China: A model-based analysis. Journal of Integrative Agriculture, 13, 1575–1585.
Yao Y M, Ye L M, Tang H J, Tang P Q, Wang D Y, Si H Q, Hu W J, Van Ranst E. 2015. Cropland soil organic matter content change in Northeast China, 1985–2005. Open Geosciences, 7, 234–243.
Ye L M, Malingreau J P, Tang H J, Ranst E V. 2016. The breakfast imperative: The changing context of global food security. Journal of Integrative Agriculture, 15, 1179–1185.
Ye L M, Xiong W, Li Z G, Yang P, Yang P, Wu W B, Yang G X, Fu Y J, Zou J Q, Chen Z X, Van Ranst E, Tang H J. 2013. Climate change impact on China food security in 2050. Agronomy for Sustainable Development, 33, 363–374.
Ye L M, Yang J, Verdoodt A, Moussadek R, Van Ranst E. 2010. China’s food security threatened by soil degradation and biofuels production. In: The Proceedings of the 19th World Congress of Soil Science: Soil Solutions for a Changing World. 1–6 August, 2010. Brisbane, Australia.
Yu Q Y, Hu Q, van Vliet J, Verburg P H, Wu W B. 2018. Globeland30 shows little cropland area loss but greater fragmentation in China. International Journal of Applied Earth Observation and Geoinformation, 66, 37–45.
Yu Q Y, Wu W B, Liu Z H, Verburg P H, Xia T, Yang P, Lu Z J, You L Z, Tang H J. 2014. Interpretation of climate change and agricultural adaptations by local household farmers: A case study at Bin County, Northeast China. Journal of Integrative Agriculture, 7, 1599–1608.
Zhang L P, Zhang S W, Zhou Z M, Hou S, Huang Y F, Cao W D. 2016. Spatial distribution prediction and benefits assessment of green manure in the Pinggu District, Beijing, based on the CLUE-S model. Journal of Integrative Agriculture, 15, 465–474.
Zhang S L, Zhang X Y, Huffman T, Liu X B, Yang J Y. 2011. Influence of topography and land management on soil nutrients variability in Northeast China. Nutrient Cycling in Agroecosystems, 89, 427–438.
Zhou Y, Li X H, Liu Y S. 2020. Land use change and driving factors in rural China during the period 1995–2015. Land Use Policy, 99, 105048.
[1] ZHANG Sha, YANG Shan-shan, WANG Jing-wen, WU Xi-fang, Malak HENCHIRI, Tehseen JAVED, ZHANG Jia-hua, BAI Yun. 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[J]. >Journal of Integrative Agriculture, 2023, 22(9): 2865-2881.
[2] Jae-Hyun RYU, Dohyeok OH, Jaeil CHO. Simple method for extracting the seasonal signals of photochemical reflectance index and normalized difference vegetation index measured using a spectral reflectance sensor[J]. >Journal of Integrative Agriculture, 2021, 20(7): 1969-1986.
[3] WU Cheng-yong, CAO Guang-chao, CHEN Ke-long, E Chong-yi, MAO Ya-hui, ZHAO Shuang-kai, WANG Qi, SU Xiao-yi, WEI Ya-lan. Remotely sensed estimation and mapping of soil moisture by eliminating the effect of vegetation cover[J]. >Journal of Integrative Agriculture, 2019, 18(2): 316-327.
[4] ZHOU Tian-mei, WU Zhen, WANG Ya-chen, SU Xiao-jun, QIN Chao-xuan, HUO He-qiang, JIANG Fang-ling . Modelling seedling development using thermal effectiveness and photosynthetically active radiation[J]. >Journal of Integrative Agriculture, 2019, 18(11): 2521-2533.
[5] Yanbo Huang, CHEN Zhong-xin, YU Tao, HUANG Xiang-zhi, GU Xing-fa. Agricultural remote sensing big data: Management and applications[J]. >Journal of Integrative Agriculture, 2018, 17(09): 1915-1931.
[6] HUANG Qing, WANG Li-min, CHEN Zhong-xin, LIU Hang. Effects of meteorological factors on different grades of winter wheat growth in the Huang-Huai-Hai Plain, China[J]. >Journal of Integrative Agriculture, 2016, 15(11): 2647-2657.
[7] HE Ying-bin, CAI Wei-min. Linking a farmer crop selection model (FCS) with an agronomic model (EPIC) to simulate cropping pattern in Northeast China[J]. >Journal of Integrative Agriculture, 2016, 15(10): 2417-2425.
No Suggested Reading articles found!