Abstract
Multi-criteria decision-making (MCDM) is an important means for evaluating resources and environment, and sensitivity analysis can enhance understand the robustness of evaluation results. Spatial visualization has been used in sensitivity analysis of MCDM, but the sensitivity results are still generally summarized by presenting traditional statistical measurements that omit the spatial information. To address this issue, this paper proposed a novel spatially measurement approach of sensitivity analysis by introducing the spatial barycenter model (SBM), which overcame the limitations of existing statistical methods and provided the spatial directivity of uncertainty for the MCDM results. According to our proposed method and its application in farmland quality evaluation (FQE) in an arid area of China, the mean of the absolute average change rate (MACR) and the SBM were applied to test the sensitivity of farmland quality to different evaluation factors from both numerical and spatial perspectives. From the numerical perspective, the soil organic matter and irrigation capacity were the most sensitive factors determined by the MACR. From the spatial perspective, the ≥10 °C accumulated temperature (AT) and precipitation were the most sensitive factors measured by the SBM. Based on the SBM, the spatial configuration of farmland quality index was most sensitive to increase of AT in a northwesterly direction. Calculating the SBM is computationally inexpensive and provides a straightforward indication of spatial direction for the changes of FQE results with changes of parameters. This means it can provide improved understandings and new insights into the comprehensive measurement of sensitivity analysis and agricultural production layout.
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The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
References
Balta MO, Yenil HU (2019) Multi criteria decision making methods for urban greenway: the case of Aksaray, Turkey. Land Use Policy 89:104224. https://doi.org/10.1016/j.landusepol.2019.104224
Berezowski T, Nossent J, Chormanski J, Batelaan O (2015) Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model. Hydrol Earth Syst Sc 19:1887–1904. https://doi.org/10.5194/hess-19-1887-2015
Borgonovo E, Plischke E (2016) Sensitivity analysis: a review of recent advances. Eur J Oper Res 248:869–887. https://doi.org/10.1016/j.ejor.2015.06.032
Butler J, Jia JM, Dyer J (1997) Simulation techniques for the sensitivity analysis of multi-criteria decision models. Eur J Oper Res 103:531–546. https://doi.org/10.1016/S0377-2217(96)00307-4
Chang JJ, Zhu JX, Xu L, Su HX, Gao Y, Cai XL, Peng T, Wen XF, Zhang JJ, He NP (2018) Rational land-use types in the karst regions of China: insights from soil organic matter composition and stability. Catena 160:345–353. https://doi.org/10.1016/j.catena.2017.09.029
Chen Q, Yang LE, Luo J, Liu FG, Zhang YL, Zhou Q, Guo R, Gu XJ (2021) The 300 years cropland changes reflecting climate impacts and social resilience at the Yellow River–Huangshui River Valley, China. Environ Res Lett 16:065006. https://doi.org/10.1088/1748-9326/abe82a
Chen Y, Yu J, Khan S (2010) Spatial sensitivity analysis of multi-criteria weights in GIS-based land suitability evaluation. Environ Modell Softw 25:1582–1591. https://doi.org/10.1016/j.envsoft.2010.06.001
Cornwell E, Sposito V, Faggian R (2021) Agricultural adaptation mainstreaming and its study through a systemic adaptation assessment framework: a sub-alpine case-study. J Rural Stud 84:22–30. https://doi.org/10.1016/j.jrurstud.2021.03.010
Curiel-Esparza J, Gonzalez-Utrillas N, Canto-Perello J, Martin-Utrillas M (2015) Integrating climate change criteria in reforestation projects using a hybrid decision-support system. Environ Res Lett 10:094022. https://doi.org/10.1088/1748-9326/10/9/094022
Dell'Oca A, Riva M, Guadagnini A (2020) Global sensitivity analysis for multiple interpretive models with uncertain parameters. Water Resour Res 56:e2019WR025754.. https://doi.org/10.1029/2019wr025754
Deng XZ, Gibson J, Wang P (2017) Management of trade-offs between cultivated land conversions and land productivity in Shandong Province. J Clean Prod 142:767–774. https://doi.org/10.1016/j.jclepro.2016.04.050
Fan MM, Lal R, Zhang H, Margenot AJ, Wu JT, Wu PB, Zhang LM, Yao JT, Chen FR, Gao C (2020) Variability and determinants of soil organic matter under different land uses and soil types in eastern China. Soil Till Res 198:104544. https://doi.org/10.1016/j.still.2019.104544
Feyissa AH, Gernaey KV, Adler-Nissen J (2012) Uncertainty and sensitivity analysis: Mathematical model of coupled heat and mass transfer for a contact baking process. J Food Eng 109:281–290. https://doi.org/10.1016/j.jfoodeng.2011.09.012
Hyde KM, Maier HR, Colby CB (2005) A distance-based uncertainty analysis approach to multi-criteria decision analysis for water resource decision making. J Environ Manage 77:278–290. https://doi.org/10.1016/j.jenvman.2005.06.011
Jin HF, Shi DM, Lou YB, Zhang JL, Ye Q, Jiang N (2021) Evaluation of the quality of cultivated-layer soil based on different degrees of erosion in sloping farmland with purple soil in China. Catena 198:105048. https://doi.org/10.1016/j.catena.2020.105048
Koo HM, Chen M, Jakeman AJ, Zhang FY (2020) A global sensitivity analysis approach for identifying critical sources of uncertainty in non -identifiable, spatially distributed environmental models: a holistic analysis applied to SWAT for input datasets and model parameters. Environ Modell Softw 127:104676. https://doi.org/10.1016/j.envsoft.2020.104676
Li HW, Chen YN (2020) Assessing potential land suitable for surface irrigation using groundwater data and multi-criteria evaluation in Xinjiang inland river basin. Comput Electron Agr 168:105079. https://doi.org/10.1016/j.compag.2019.105079
Li ZH, Jin XL, Liu HL, Xu XG, Wang JH (2019) Global sensitivity analysis of wheat grain yield and quality and the related process variables from the DSSAT-CERES model based on the extended Fourier Amplitude Sensitivity Test method. J Integr Agr 18:1547–1561. https://doi.org/10.1016/S2095-3119(18)62046-5
Ligmann-Zielinska A, Jankowski P (2014) Spatially-explicit integrated uncertainty and sensitivity analysis of criteria weights in multicriteria land suitability evaluation. Environ Modell Softw 57:235–247. https://doi.org/10.1016/j.envsoft.2014.03.007
Ling H, Okada K (2007) An efficient earth mover’s distance algorithm for robust histogram comparison. Ieee T Pattern Anal 29:840–853. https://doi.org/10.1109/tpami.2007.1058
Liu XQ, Liu YS, Liu ZJ, Chen ZF (2021) Impacts of climatic warming on cropping system borders of China and potential adaptation strategies for regional agriculture development. Sci Total Environ 755:142415. https://doi.org/10.1016/j.scitotenv.2020.142415
Luo XL, Bai XY, Tan Q, Ran C, Chen H, Xi HP, Chen F, Wu LH, Li CJ, Zhang SR, Zhong X, Tian S (2022) Particulate organic carbon exports from the terrestrial biosphere controlled by erosion. Catena 209:105815. https://doi.org/10.1016/j.catena.2021.105815
Ma J, Yan GX, Li HY, Guo SH (2016) Sensitivity and uncertainty analysis for Abreu & Johnson numerical vapor intrusion model. J Hazard Mater 304:522–531. https://doi.org/10.1016/j.jhazmat.2015.11.005
Malczewski J (2006) GIS-based multicriteria decision analysis: a survey of the literature. Int J Geogr Inf Sci 20:703–726. https://doi.org/10.1080/13658810600661508
Moradi S, Yousefi H, Noorollahi Y, Rosso D (2020) Multi -criteria decision support system for wind farm site selection and sensitivity analysis: case study of Alborz Province, Iran. Energy Strateg Rev 29:100478. https://doi.org/10.1016/j.esr.2020.100478
Namiotko V, Galnaityte A, Krisciukaitiene I, Balezentis T (2022) Assessment of agri-environmental situation in selected EU countries: a multi-criteria decision-making approach for sustainable agricultural development. Environ Sci Pollut R 29:25556–25567. https://doi.org/10.1007/s11356-021-17655-4
Pathak R, Sahany S, Mishra SK (2020) Uncertainty quantification based cloud parameterization sensitivity analysis in the NCAR community atmosphere model. Sci Rep-Uk 10:17499. https://doi.org/10.1038/s41598-020-74441-x
Paul M, Negahban-Azar M, Shirmohammadi A, Montas H (2020) Assessment of agricultural land suitability for irrigation with reclaimed water using geospatial multi-criteria decision analysis. Agr Water Manage 231:105987. https://doi.org/10.1016/j.agwat.2019.105987
Qian FK, Chi YR, Lal R (2020) Spatiotemporal characteristics analysis of multifunctional cultivated land: a case-study in Shenyang, Northeast China. Land Degrad Dev 31:1812–1822. https://doi.org/10.1002/ldr.3576
Qian FK, Wang WW, Wang QB, Lal R (2021) Implementing land evaluation and site assessment (LESA system) in farmland protection: a case-study in northeastern China. Land Degrad Dev 32:2437–2452. https://doi.org/10.1002/ldr.3922
Qiu LF, Zhu JX, Pan Y, Hu W, Amable GS (2017) Multi-criteria land use suitability analysis for livestock development planning in Hangzhou metropolitan area, China. J Clean Prod 161:1011–1019. https://doi.org/10.1016/j.jclepro.2017.07.053
Rubner Y, Tomasi C, Guibas LJ (2000) The earth mover’s distance as a metric for image retrieval. Int J Comput Vision 40:99–121. https://doi.org/10.1023/A:1026543900054
Sahin F, Kara MK, Koc A, Sahin G (2020) Multi-criteria decision-making using GIS-AHP for air pollution problem in Igdir Province/Turkey. Environ Sci Pollut R 27:36215–36230. https://doi.org/10.1007/s11356-020-09710-3
Sironen S, Primmer E, Leskinen P, Similä J, Punttila P (2020) Context sensitive policy instruments: a multi-criteria decision analysis for safeguarding forest habitats in Southwestern Finland. Land Use Policy 92:104460. https://doi.org/10.1016/j.landusepol.2019.104460
Song FJ, Wang SJ, Bai XY, Wu LH, Wang JF, Li CJ, Chen H, Luo XL, Xi HP, Zhang SR, Luo GF, Yan MQ, Zhen QQ (2022) A new indicator for global food security assessment: harvested area rather than cropland area. Chinese Geogr Sci 32(02):204–217. https://doi.org/10.1007/s11769-022-1264-6
Song W, Liu ML (2017) Farmland conversion decreases regional and national land quality in China. Land Degrad Dev 28:459–471. https://doi.org/10.1002/ldr.2518
Wan XJ (2007) A novel document similarity measure based on earth mover’s distance. Inform Sciences 177:3718–3730. https://doi.org/10.1016/j.ins.2007.02.045
Xiao SA, Lu ZZ, Wang P (2018) Global sensitivity analysis based on distance correlation for structural systems with multivariate output. Eng Struct 167:74–83. https://doi.org/10.1016/j.engstruct.2018.04.027
Xu EQ, Zhang HQ (2013) Spatially-explicit sensitivity analysis for land suitability evaluation. Appl Geogr 45:1–9. https://doi.org/10.1016/j.apgeog.2013.08.005
Yang Z, Wang YF (2020) The cloud model based stochastic multi-criteria decision making technology for river health assessment under multiple uncertainties. J Hydrol 581:124437. https://doi.org/10.1016/j.jhydrol.2019.124437
Yuan XF, Shao YJ, Li YH, Liu YS, Wang YS, Wei XD, Wang XF, Zhao YH (2019) Cultivated land quality improvement to promote revitalization of sandy rural areas along the Great Wall in northern Shaanxi Province. J Rural Stud, China. https://doi.org/10.1016/j.jrurstud.2019.10.011
Zhang P, Deng XY, Long AH, Hai Y, Li Y, Wang H, Xu HL (2018) Impact of social factors in agricultural production on the crop water footprint in Xinjiang. China. Water-Sui 10:1145. https://doi.org/10.3390/w10091145
Zhu FL, Zhong PA, Cao Q, Chen J, Sun YM, Fu JS (2019) A stochastic multi-criteria decision making framework for robust water resources management under uncertainty. J Hydrol 576:287–298. https://doi.org/10.1016/j.jhydrol.2019.06.049
Zhu FL, Zhong PA, Sun YM (2018) Multi-criteria group decision making under uncertainty: application in reservoir flood control operation. Environ Modell Softw 100:236–251. https://doi.org/10.1016/j.envsoft.2017.11.032
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This research was jointly supported by the Second Tibetan Plateau Scientific Expedition and Research Program (STEP) (2019QZKK0603), National Natural Science Foundations of China (41601095), and Youth Innovation Promotion Association CAS (2021052).
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Dajing Li: conceptualization, methodology, data curation, writing—original draft preparation, investigation, writing. Erqi Xu: supervision and editing. Hongqi Zhang: reviewing and editing.
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Li, D., Zhang, H. & Xu, E. A spatial directivity–based sensitivity analysis to farmland quality evaluation in arid areas. Environ Sci Pollut Res 29, 66359–66372 (2022). https://doi.org/10.1007/s11356-022-20531-4
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DOI: https://doi.org/10.1007/s11356-022-20531-4