Abstract
This research aimed to evaluate the quality of soils for rapeseed crop production by Boolean and fuzzy-analytical hierarchy process (FAHP) approach in northwest of Iran. To this purpose, the physical, chemical, and topography quality indicators of land were selected based on agricultural considerations that were obtained from 83 fields. The spatial distribution of soil quality indicators was prepared using inverse distance weighting (IDW) technique. Also, validation of the developed model was performed using composite operator. The results showed that physical and chemical properties were key deciding parameters for the evaluation of soil quality. In the developed models, clay, sand, silt, soil organic matter, pH, calcium carbonate equivalent, electrical conductivity, and elevation were selected as modeling parameters. AHP technique showed that soil texture and elevation had the strongest and weakest influences on rapeseed yield, respectively. By dividing lands into four suitability categories, FAHP could more easily classify lands into soil quality classes where 36.3% of the study area was permanently unsuitable, 39.7% was marginally suitable, 22.6% was moderately suitable, and 1.4% was suitable. The comparison results of soil quality and rapeseed yield map by composite operator showed that FAHP with 77% agreement provided better results than Boolean approach with 39% agreement. Finally, this research will provide a reasonable record in ensuring crop yield security, agronomic use and management of rapeseed as well as increasing crop income. Hence, FAHP was introduced as an efficient approach.
Similar content being viewed by others
Data availability
This manuscript has no associated data; however, some data will be made available on reasonable request.
References
Aparicio, V., & Costa, J. L. (2007). Soil quality indicators under continuous cropping systems in the Argentinean Pampas. Soil and Tillage Research, 96(1-2), 155–165. https://doi.org/10.1016/j.still.2007.05.006
Baalousha, H. M., Tawabini, B., & Seers, T. D. (2021). Fuzzy or non-fuzzy? A comparison between fuzzy logic-based vulnerability mapping and DRASTIC approach using a numerical model. A case study from Qatar. Water, 13(9), 1288. https://doi.org/10.3390/w13091288
Bariklo, A., Alamdari, P., Moravej, K., & Servati, M. (2022). Application of land properties in estimation of wheat production by FAO and gene expression programming (GEP) models. Arabian Journal of Geosciences, 15(7), 1–13. https://doi.org/10.1007/s12517-022-09868-9
Bünemann, E. K., Bongiorno, G., Bai, Z., Creamer, R. E., De Deyn, G., de Goede, R., Fleskens, L., Geissen, V., Kuyper, T. W., Mäder, P., & Pulleman, M. (2018). Soil quality–A critical review. Soil Biology and Biochemistry, 120, 105–125. https://doi.org/10.1016/j.soilbio.2018.01.030
Burrough, P. A., MacMillan, R. A., & Van Deursen, W. (1992). Fuzzy classification methods for determining land suitability from soil profile observations and topography. Journal of Soil Science, 43(2), 193–210. https://doi.org/10.1111/j.1365-2389.1992.tb00129.x
Cao, Y., Carver, S., & Yang, R. (2019). Mapping wilderness in China: Comparing and integrating Boolean and WLC approaches. Landscape and Urban Planning, 192, 103636. https://doi.org/10.1016/j.landurbplan.2019.103636
Cécillon, L., Barthès, B. G., Gomez, C., Ertlen, D., Génot, V., Hedde, M., Stevens, A., & Brun, J. J. (2009). Assessment and monitoring of soil quality using near-infrared reflectance spectroscopy (NIRS). European Journal of Soil Science, 60(5), 770–784. https://doi.org/10.1111/j.1365-2389.2009.01178.x
Davis, L. (1987). Genetic algorithms and simulated annealing an overview, Genetic algorithms and simulated annealing (1st ed.). Pitman Publishing.
De Corato, U. (2020). Agricultural waste recycling in horticultural intensive farming systems by on-farm composting and compost-based tea application improves soil quality and plant health: A review under the perspective of a circular economy. Science of the Total Environment, 738, 139840. https://doi.org/10.1016/j.scitotenv.2020.139840
De Gruijter, J. J., Walvoor, D. J. J., & Bragato, G. (2011). Application of fuzzy logic to Boolean models for digital soil assessment. Geoderma, 166(1), 15–33. https://doi.org/10.1016/j.geoderma.2011.06.003
De Laurentiis, V., Secchi, M., Bos, U., Horn, R., Laurent, A., & Sala, S. (2019). Soil quality index: Exploring options for a comprehensive assessment of land use impacts in LCA. Journal of Cleaner Production, 215, 63–74. https://doi.org/10.1016/j.jclepro.2018.12.238
Doran, J. W., & Parkin, T. B. (1994). Defining and assessing soil quality. Defining Soil Quality for a Sustainable Environment, 35, 1–21. https://doi.org/10.2136/sssaspecpub35.c1
Dos Santos, W. P., Silva, M. L. N., Avanzi, J. C., Acuña-Guzman, S. F., Cândido, B. M., Cirillo, M. Â., & Curi, N. (2021). Soil quality assessment using erosion-sensitive indices and fuzzy membership under different cropping systems on a Ferralsol in Brazil. Geoderma Regional, 25, e00385. https://doi.org/10.1016/j.geodrs.2021.e00385
Eko Saputro, T., & Daneshvar Rouyendegh, B. A. (2016). Hybrid approach for selecting material handling equipment in a warehouse. International Journal of Management Science and Engineering Management, 11(1), 34–48. https://doi.org/10.1080/17509653.2015.1042535
Elaalem, M., Comber, A., & Fisher, P. (2011). A comparison of fuzzy AHP and ideal point methods for evaluating land suitability. Transactions in GIS, 15(3), 329–346. https://doi.org/10.1111/j.1467-9671.2011.01260.x
El-saatty, T. L. (1980). The analytic hierarchy processes: Planning, Priority Setting, Resource Allocation. New York: McGraw-Hill International Book Company.
Fierer, N. (2017). Embracing the unknown: Disentangling the complexities of the soil microbiome. Nature Reviews. Microbiology, 15(10), 579–590. https://doi.org/10.1038/nrmicro.2017.87
Friedt, W., Tu, J., & Fu, T. (2018). Academic and economic importance of Brassica napus rapeseed. In The Brassica napus genome. Springer. https://doi.org/10.1007/978-3-319-43694-4_1
Garbuzov, M., Couvillon, M. J., Schürch, R., & Ratnieks, F. L. (2015). Honey bee dance decoding and pollen-load analysis show limited foraging on spring-flowering oilseed rape, a potential source of neonicotinoid contamination. Agriculture, Ecosystems & Environment, 203, 62–68. https://doi.org/10.1016/j.agee.2014.12.009
Geng, S., Li, W., Kang, T., Shi, P., & Zhu, W. (2021). An integrated index based on climatic constraints and soil quality to simulate vegetation productivity patterns. Ecological Indicators, 129, 108015. https://doi.org/10.1016/j.ecolind.2021.108015
Gigović, L., Drobnjak, S., & Pamučar, D. (2019). The application of the hybrid GIS spatial multi-criteria decision analysis best–worst methodology for landslide susceptibility mapping. ISPRS International Journal of Geo-Information, 8(2), 79. https://doi.org/10.3390/ijgi8020079
Greer, K., Martins, C., White, M., & Pittelkow, C. M. (2020). Assessment of high-input soybean management in the US Midwest: Balancing crop production with environmental performance. Agriculture, Ecosystems & Environment, 292, 106811. https://doi.org/10.1016/j.agee.2019.106811
Hariri, A. (1997). Geological sheet 1:100000 Bukan. Created by Geological Survey of Iran
Hoseini, Y. (2019). Use fuzzy interface systems to optimize land suitability evaluation for surface and trickle irrigation. Information Processing in Agriculture (IPA), 6(1), 11–19. https://doi.org/10.1016/j.inpa.2018.09.003
Hüllermeier, E. (2011). Fuzzy sets in machine learning and data mining. Applied Soft Computing, 11(2), 1493–1505. https://doi.org/10.1016/j.asoc.2008.01.004
Ismail, S. M., Said, L. A., Radwan, A. G., Madian, A. H., & Abu-ElYazeed, M. F. (2020). A novel image encryption system merging fractional-order edge detection and generalized chaotic maps. Signal Processing, 167, 107280. https://doi.org/10.1016/j.sigpro.2019.107280
Kaufmann, M., Tobias, S., & Schulin, R. (2009). Quality evaluation of restored soils with a fuzzy logic expert system. Geoderma, 151(3-4), 290–302. https://doi.org/10.1016/j.geoderma.2009.04.018
Keshavarzi, A., Tuffour, H. O., Bagherzadeh, A., Tattrah, L. P., Kumar, V., Gholizadeh, A., & Rodrigo-Comino, J. (2020). Using fuzzy-AHP and parametric technique to assess soil fertility status in northeast of Iran. Journal of Mountain Science, 17(4), 931–948. https://doi.org/10.1007/s11629-019-5666-6
Kumar, N., Singh, S. K., Mishra, V. N., Reddy, G. O., & Bajpai, R. K. (2017). Soil quality ranking of a small sample size using AHP. Journal of Soil and Water Conservation (JSWC), 16(4), 339–346. https://doi.org/10.5958/2455-7145.2017.00050.9
Li, X. M., Min, M., & Tan, C. F. (2005). The functional assessment of agricultural ecosystems in Hubei Province, China. Ecological Modelling, 187(2-3), 352–360. https://doi.org/10.1016/j.ecolmodel.2004.09.006
Malczewski, J. (1999). GIS and multicriteria decision analysis (p. 408). John Wiley & Sons.
Moreno, J. F. S. (2007). Applicability of knowledge based and fuzzy theory oriented approaches to land suitability for upland rice and rubber, as compared to the farmers’ perception: A case study of Lao PDR. ITC, UK: Master of Science, University of Southampton.
Nazari, H., Mohammadkhani, N., & Servati, M. (2023). Saffron yield estimation by adaptive neural-fuzzy inference system and particle swarm optimization (ANFIS-SCM-PSO) hybrid model. Archives of Agronomy and Soil Science, 69(3), 461–475. https://doi.org/10.1080/03650340.2021.2004588
Pontius, R. G., & Cheuk, M. L. (2006). A generalized cross-tabulation matrix to compare soft-classified maps at multiple resolutions. International Journal of Geographical Information Science, 20(1), 1–30. https://doi.org/10.1080/13658810500391024
Pradhan, B., Sezer, E. A., Gokceoglu, C., & Buchroithner, M. F. (2010). Landslide susceptibility mapping by neuro-fuzzy approach in a landslide-prone area (Cameron Highlands, Malaysia). IEEE Transactions on Geoscience and Remote Sensing, 48(12), 4164–4177. https://doi.org/10.1109/TGRS.2010.2050328
Rizzo, R., Medeiros, L. G., de Mello, D. C., Marques, K. P., de Souza Mendes, W., Silvero, N. E. Q., Dotto, A. C., Bonfatti, B. R., & Dematte, J. A. (2020). Multi-temporal bare surface image associated with transfer functions to support soil classification and mapping in southeastern Brazil. Geoderma, 361, 114018. https://doi.org/10.1016/j.geoderma.2019.114018
Sarkar, D., & Haldar, A. (2005). Physical and chemical methods in soil analysis: Fundamental concepts of analytical chemistry and instrumental technique. New Delhi: New age International.
Sekovski, I., Del Río, L., & Armaroli, C. (2020). Development of a coastal vulnerability index using analytical hierarchy process and application to Ravenna province (Italy). Ocean and Coastal Management, 183, 104982. https://doi.org/10.1016/j.ocecoaman.2019.104982
Sharma, K. L., Mandal, U. K., Srinivas, K., Vittal, K. P. R., Mandal, B., Grace, J. K., & Ramesh, V. (2005). Long-term soil management effects on crop yields and soil quality in a dryland Alfisol. Soil and Tillage Research, 83(2), 246–259. https://doi.org/10.1016/j.still.2004.08.002
Škapa, S., & Vochozka, M. (2019). Waste energy recovery improves price competitiveness of artificial forage from rapeseed straw. Clean Technologies and Environmental Policy, 21(5), 1165–1171. https://doi.org/10.1007/s10098-019-01697-x
Souza, F. B. D., Souza, É. D. J. C. D., Garcia, M. C. D. M., & Madeira, K. (2018). A fuzzy logic-based expert system for substrate selection for soil construction in land reclamation. REM – International. Engineering Journal, 71, 553–559. https://doi.org/10.1590/0370-44672017710155
Sridhar, P., & Ganapuram, S. (2021). Morphometric analysis using fuzzy analytical hierarchy process (FAHP) and geographic information systems (GIS) for the prioritization of watersheds. Arabian Journal of Geosciences, 14(4), 1–29. https://doi.org/10.1007/s12517-021-06539-z
Stankovic, R. S., & Astola, J. (2011). From Boolean logic to switching circuits and automata. In Towards modern information technology: Springer Press.
Sys, C., Van Ranst, E., & Debaveye, J. (1991). Land evaluation. Part 1: Principles in land evaluation and crop production calculations. Agriculture Publications, No. 7. Brussels: General Administration for Development Cooperation.
Thapa, R. B., & Murayama, Y. (2008). Land evaluation for peri-urban agriculture using analytical hierarchical process and geographic information system techniques: A case study of Hanoi. Land Use Policy, 25(2), 225–239. https://doi.org/10.1016/j.landusepol.2007.06.004
Tercan, E., & Dereli, M. A. (2020). Development of a land suitability model for citrus cultivation using GIS and multi-criteria assessment techniques in Antalya province of Turkey. Ecological Indicators, 117, 106549. https://doi.org/10.1016/j.ecolind.2020.106549
Urbina-Salazar, D., Vaudour, E., Baghdadi, N., Ceschia, E., Richer-De-Forges, A. C., Lehmann, S., & Arrouays, D. (2021). Using Sentinel-2 images for soil organic carbon content mapping in croplands of southwestern France. The usefulness of Sentinel-1/2 derived moisture maps and mismatches between Sentinel images and sampling dates. Remote Sensing, 13(24), 5115. https://doi.org/10.3390/rs13245115
Vahidi, M. J., Zahan, M. H. S., Atajan, F. A., & Parsa, Z. (2022). The effect of biochars produced from barberry and jujube on erosion, nutrient, and properties of soil in laboratory conditions. Soil and Tillage Research, 219, 105345. https://doi.org/10.1016/j.still.2022.105345
Vahidi, M. J., Behdani, M. A., Servati, M., & Naderi, M. (2023). Fuzzy-based models’ performance on qualitative and quantitative land suitability evaluation for cotton cultivation in Sarayan County, South Khorasan Province, Iran. Environmental Monitoring and Assessment, 195(4), 488. https://doi.org/10.1007/s10661-023-11109-9
Wahba, M., Fawkia, L. A. B. İ. B., & Zaghloul, A. (2019). Management of calcareous soils in arid region. International Journal of Environmental Pollution and Environmental Modelling, 2(5), 248–258.
Wu, C., Liu, Q., Ma, G., Liu, G., Yu, F., Huang, C., Zhao, Z., & Liang, L. (2019a). A study of the spatial difference of the soil quality of the Mun River basin during the rainy season. Sustainability, 11(12), 3423. https://doi.org/10.3390/su11123423
Wu, C., Liu, G., Huang, C., & Liu, Q. (2019b). Soil quality assessment in Yellow River Delta: Establishing a minimum data set and fuzzy logic model. Geoderma, 334, 82–89. https://doi.org/10.1016/j.geoderma.2018.07.045
Ying, X., Zeng, G. M., Chen, G. Q., Tang, L., Wang, K. L., & Huang, D. Y. (2007). Combining AHP with GIS in synthetic evaluation of eco-environment quality—A case study of Hunan Province, China. Ecological Modelling, 209(2-4), 97–109. https://doi.org/10.1016/j.ecolmodel.2007.06.007
Yue-Ju, X. U. E., Shu-Guang, L. I. U., Yue-Ming, H. U., & Jing-Feng, Y. A. N. G. (2010). Soil quality assessment using weighted fuzzy association rules. Pedosphere, 20(3), 334–341. https://doi.org/10.1016/S1002-0160(10)60022-7
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.
Zhong, B., Xia, L., & Su, S. (2022). Effects of programming tools with different degrees of embodiment on learning Boolean operations. Education and Information Technologies, 1-21. https://doi.org/10.1007/s10639-021-10884-7
Author information
Authors and Affiliations
Contributions
The experiments and modeling were designed and performed by MS and NM. Data analysis and writing of the original draft were undertaken by HN. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Ethics approval
All authors have read the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors and are aware that no changes can be made to authorship once the paper is submitted.
Competing interests
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Nazari, H., Mohammadkhani, N. & Servati, M. Performance of soil quality indicators in estimation and distribution of rapeseed yield. Environ Monit Assess 195, 1529 (2023). https://doi.org/10.1007/s10661-023-12164-y
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10661-023-12164-y