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رحمانی, اصغر, رستمی نیا, محمود, صیدی, نسیبه, موسوی, سید روح الله. (1405). مدل‌سازی مکانی و پیش‌بینی شاخص حاصلخیزی خاک در دو منطقه خشک و نیمه‌خشک استان ایلام. سامانه مدیریت نشریات علمی, 40(1), 1-21. doi: 10.22092/ijsr.2026.371527.803
اصغر رحمانی; محمود رستمی نیا; نسیبه صیدی; سید روح الله موسوی. "مدل‌سازی مکانی و پیش‌بینی شاخص حاصلخیزی خاک در دو منطقه خشک و نیمه‌خشک استان ایلام". سامانه مدیریت نشریات علمی, 40, 1, 1405, 1-21. doi: 10.22092/ijsr.2026.371527.803
رحمانی, اصغر, رستمی نیا, محمود, صیدی, نسیبه, موسوی, سید روح الله. (1405). 'مدل‌سازی مکانی و پیش‌بینی شاخص حاصلخیزی خاک در دو منطقه خشک و نیمه‌خشک استان ایلام', سامانه مدیریت نشریات علمی, 40(1), pp. 1-21. doi: 10.22092/ijsr.2026.371527.803
رحمانی, اصغر, رستمی نیا, محمود, صیدی, نسیبه, موسوی, سید روح الله. مدل‌سازی مکانی و پیش‌بینی شاخص حاصلخیزی خاک در دو منطقه خشک و نیمه‌خشک استان ایلام. سامانه مدیریت نشریات علمی, 1405; 40(1): 1-21. doi: 10.22092/ijsr.2026.371527.803

مدل‌سازی مکانی و پیش‌بینی شاخص حاصلخیزی خاک در دو منطقه خشک و نیمه‌خشک استان ایلام

پژوهش های خاک
مقاله 2، دوره 40، شماره 1، خرداد 1405، صفحه 1-21    اصل مقاله (2 M)
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.22092/ijsr.2026.371527.803
نویسندگان
اصغر رحمانی1؛ محمود رستمی نیا* 2؛ نسیبه صیدی2؛ سید روح الله موسوی3
1مؤسسه تحقیقات خاک و آب، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران.
2گروه آب و خاک، دانشکده کشاورزی، دانشگاه ایلام، ایلام، ایران.
3گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه تهران، کرج، ایران.
چکیده
استفاده از رویکردهای معمول شناسایی حاصلخیزی خاک در مناطق با وسعت زیاد، فعالیتی زمان‌بر و پرهزینه است. از این رو پژوهش حاضر با هدف مدل‌سازی و پیش‌بینی پراکنش مکانی شاخص حاصلخیزی خاک (SFI) در دو منطقه خشک (دهلران) و نیمه‌خشک (بدره) در استان ایلام، با استفاده از مدل‌های جنگل تصادفی (RF)، کوبیست (CB) و کریجینگ معمولی (OK) انجام شد. در مجموع 204 نمونه خاک از افق سطحی دو منطقه به روش ابرمکعب لاتین برداشته و ویژگی‌های فیزیکی و شیمیایی آن‌ها اندازه‌گیری شد. متغیرهای محیطی شامل شاخص‌های توپوگرافی، سنجش از دور و اقلیمی (میانگین بارش و دمای سالانه) به‌عنوان ورودی مدل‌ها استفاده شد. انتخاب متغیرهای مؤثر به دو روش «شاخص تورم واریانس« (VIF) و الگوریتم «باروتا« (Boruta) انجام شد. در نهایت، 9 متغیر در منطقه دهلران و 12 متغیر در منطقه ولیعصر بدره انتخاب شد. نتایج نشان داد مدل RF با مقادیر R² برابر 0/79 و 0/60 و RMSE برابر0/64 و 0/69 به‌ترتیب در اراضی دهلران و بدره، دقت بالاتری نسبت به دو مدل دیگر دارد. نقشه‌های پهنه‌بندی نشان داد که بیش از ۷۰ درصد اراضی هر دو منطقه در کلاس‌های حاصلخیزی زیاد و بسیار زیاد قرار دارند. بررسی اهمیت متغیرها بیانگر نقش کلیدی میانگین دمای سالانه (MAT) و بارش (MAP) در تغییرپذیری SFI در هر دو منطقه مطالعاتی است. هم‌چنین، شاخص‌های سنجش از دور در دهلران و عوامل توپوگرافی در بدره تأثیر بیشتری داشتند. به طور کلی، ترکیب الگوریتم RF با داده‌های اقلیمی، توپوگرافی و سنجش از دور، ابزاری کارآمد برای تولید نقشه‌های دقیق حاصلخیزی خاک فراهم می‌آورد.
کلیدواژه‌ها
درون‌یابی مکانی؛ شاخص حاصلخیزی خاک؛ متغیرهای کمکی؛ نقشه‌برداری رقومی خاک؛ یادگیری ماشین
عنوان مقاله [English]
Spatial Modeling and Prediction of the Soil Fertility Index in Two Arid and Semi-Arid Regions of Ilam Province, Iran
نویسندگان [English]
Asghar Rahmani1؛ Mahmood Rostaminya2؛ Nasibeh Sayedi2؛ Seyed Roohollah Mousavi3
1Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.
2Water and Soil Department, Agriculture Faculty, Ilam University, Ilam, Iran.
3Department of Soil Science, University of Tehran, Karaj, Iran.
چکیده [English]
Background and Objectives: Soil fertility is one of the most critical factors determining the sustainability of agricultural ecosystems and ensuring food security. Declining soil fertility directly affects crop productivity and, consequently, food security. Understanding the spatial variability of fertility distribution patterns is essential for efficient soil management. In recent years, the integration of advanced machine learning algorithms with geostatistical methods has provided powerful tools for modeling and predicting soil fertility indicators such as the Soil Fertility Index (SFI). In arid and semi-arid regions, water scarcity, soil salinity, and climatic variability are major challenges for sustainable agricultural production. Therefore, spatial modeling of soil fertility and identification of its driving factors can serve as a scientific basis for regional land-use planning and resource management. The present study aimed to model and predict the spatial distribution of SFI in two arid and semi-arid regions, Miameh–Dehloran and Valiasr–Badreh (Ilam Province, western Iran), using Random Forest (RF) and Cubist (CB) machine learning algorithms, and to compare their performance with the conventional Ordinary Kriging (OK) method. Ultimately, this research seeks to develop a region-based spatial model for SFI prediction in the agricultural lands of the Zagros region in western Iran.
 

 



Materials and Methods: Soil sampling was conducted in the Miameh–Dehloran and Valiasr–Badreh areas using the conditioned Latin hypercube sampling (cLHS) method. A total of 133 and 71 surface soil samples were collected from the respective regions. The samples were analyzed for physical, chemical, and biological properties to calculate the Soil Fertility Index (SFI). Auxiliary environmental variables, including topographic parameters derived from the Digital Elevation Model (DEM) and remote sensing (RS) indices, were used as predictors. The most relevant variables were selected using the Variance Inflation Factor (VIF) and Boruta algorithms, resulting in 9 and 12 selected predictors for the Miameh–Dehloran and Valiasr–Badreh sites, respectively. Additionally, two climatic variables—mean annual precipitation (MAP) and mean annual temperature (MAT)—were included based on expert judgment. The RF, CB, and OK models were trained and validated, and their predictive performances were evaluated using the coefficient of determination (R²) and root mean square error (RMSE). Spatial prediction maps of SFI were generated in ArcGIS based on the best-performing model.
 
Results: The results indicated that the Random Forest model outperformed both the Cubist and Ordinary Kriging models in predicting SFI values. The R² of RF was 0.79 for Miameh–Dehloran and 0.60 for Valiasr–Badreh, while the RMSE values were 0.64 and 0.69, respectively. These results demonstrate the superior ability of RF in capturing nonlinear relationships between soil fertility and environmental covariates. According to the spatial distribution maps, approximately 74.14% of the Miameh–Dehloran and 77.33% of the Valiasr–Badreh areas fell within the “very high” (F1) and “high” (F2) fertility classes, indicating considerable potential for agricultural productivity. The climatic variables MAT and MAP were identified as the most influential predictors of SFI. In Miameh–Dehloran, remote sensing indices—especially vegetation and spectral reflectance indicators—played a major role, whereas in Valiasr–Badreh, topographic parameters such as elevation, slope, and aspect were more dominant. These spatial differences reflect the contrasting climatic and geomorphological conditions of the two regions. Combining RF with RS and topographic data significantly improved prediction accuracy and enabled the generation of high-resolution fertility maps suitable for precision agriculture applications.
 

 



Conclusion: The findings highlight the potential of the Random Forest algorithm as a robust and reliable approach for spatial modeling of soil fertility in arid and semi-arid environments. RF effectively captured complex interactions among climatic, topographic, and spectral variables, leading to accurate and detailed SFI prediction maps. The study confirmed that climatic variables, particularly temperature and precipitation, play a decisive role in determining the spatial variability of soil fertility. The resulting maps can serve as valuable tools for agricultural planning, selection of suitable crop types, and sustainable management of soil and water resources. Overall, the proposed modeling framework provides an efficient strategy for optimizing land potential, improving crop yields—especially for wheat—and contributing to food security and sustainable agricultural development in the drylands of western Iran.

 
کلیدواژه‌ها [English]
Digital soil mapping, Soil fertility index, Auxiliary variables, Machine learning, spatial interpoltion
مراجع
  1. Adhikari, K., Hartemink, A. E., Minasny, B., Bou Kheir, R., Greve, M. B. and Greve, M. H. 2014. Digital Mapping of Soil Organic Carbon Contents and Stocks in Denmark*. PLOS ONE, 9(8): e105519. DOI: 10.1371/journal.pone.0105519 ([PMC][2]).
  2. Akinwande ,  Dikko  H.,  and  Samson  A.  2015. Variance Inflation  Factor:  As  a  condition  for  the  inclusion  of suppressor variable(s) in regression analysis. Open Journal of Statistics 5: 754-767.
  3. Amirian Chekan, A., Taghizadeh Mehrjerdi, R., Sarmadian, F. and Heidary, A. 2017. Three-dimensional mapping of soil texture using spline depth functions and artificial neural networks. Iranian Journal of Soil and Water Research, 48(1), 113-123.
  4. Ayoubi, S., Sadeghi, N., Afshar, F.A., Abdi, M.R., Zeraatpisheh, M. and Rodrigo-Comino, J. 2021. Impacts of oak deforestation and rainfed cultivation on soil redistribution processes across hillslopes using 137 Cs techniques. For. Ecosyst. 8, 32.
  5. Azizi, K., Garosi, Y., Ayoubi, S. and Tajik, S. 2023. Integration of Sentinel-1/2 and topographic attributes to predict the spatial distribution of soil texture fractions in some agricultural soils of western Iran. Soil and Tillage Research, 229, 105681. https://doi.org/10.1016/j.still.2023.105681.
  6. Bannai, M. H.1998. Soil moisture and temperature map. Soil and Water Research Institute of Iran.
  7. Bakhshandeh, E., Hossieni, M., Zeraatpisheh, M. and Francaviglia, R. 2019. Land use change effects on soil quality and biological fertility: a case study in northern Iran. Eur. J. Soil Biol. 95, 103119. https://doi.org/10.1016/j.ejsobi.2019.103119.
  8. Charman, P.E.V. and Roper, M.M. 2007. Soil Organic Matter. In: Charman, P.E.V. and Murphy, B.W., Eds., Soils—Their Properties and Management, 3rd Edition, Oxford University Press, Melbourne, 276-285.
  9. Hag Husein, H., Lucke, B., Bäumler, R. and Sahwan, W. 2021. A contribution to soil fertility assessment for arid and semi-arid lands. Soil Systems, 5(3), p.42.
  10. Hengl, T., Mendes de Jesus, J., Heuvelink, G. B., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A. and Kempen, B. 2017. SoilGrids250m: Global gridded soil information based on machine learning. PLoS one, 12(2), e0169748.
  11. Hobley, E., Wilson, B., Wilkie, A., Gray, J. and Koen, T. 2015. Drivers of soil organic carbon storage and vertical distribution in Eastern Australia. Plant and Soil, 390(1), 111-127.
  12. Jahandideh mahjan abadi, V. A., karami, A., mousavi, S. R. and Asadi Rahmani, H. 2017. Evaluation of Spatial Variability of Biological Indicators of Soil Quality in Wheat Farms of Pasargad Plain. Water and Soil, 31(4), 1247-1259. doi: 10.22067/jsw. v31i4.60472
  13. Jenny, H. 1994. Factors of soil formation: a system of quantitative pedology. Courier Corporation.
  14. Karimi, H., Fathizad, H., Taze, M. 2014. Investigation different geostatistical algorithms for zoning annual rainfall in Ilam province. App. Res. J. Geogr.Sci. 14(35).
  15. Lagacherie, P. 2008. Digital soil mapping: a state of the art. Digital soil mapping with limited data, 3-14.
  16. Lamichhane, S., Kumar, L. and Wilson, B. 2019. Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review. Geoderma, 352, 395-413.
  17. Ma, Z., Shi, Z., Zhou, Y., Xu, J., Yu, W. and Yang, Y. 2017. A spatial data mining algorithm for downscaling TMPA 3B43 V7 data over the Qinghai–Tibet Plateau with the effects of systematic anomalies removed. Remote Sensing of Environment, 200, 378-395.
  18. Meymand, F. E., Ramezanpour, H., Yaghmaeian, N. and Eftekhari, K. 2023. Spatial modeling of a soil fertility index using digital soil mapping: A case study from the Honam watershed, Iran. Desert, 28(2). Doi: 22059/jdesert.2023.95754.
  19. Matinfar, H. R., Maghsodi, Z., Mousavi, S. R. and Rahmani, A. 2021. Evaluation and Prediction of Topsoil organic carbon using Machine learning and hybrid models at a Field-scale. catena, 202, 105258.
  20. McBratney, A. B., Santos, M. M. and Minasny, B. 2003. On digital soil mapping. Geoderma, 117(1-2), 3-52.
  21. Minasny, B., McBratney, A. B., Malone, B. P. and Wheeler, I. 2013. Digital mapping of soil carbon. Advances in agronomy, 118, 1-47.
  22. Mousavi, S. R., Sarmadian, F., Omid, M. and Bogaert, P. 2022. Application of machine learning models in spatial estimation of soil phosphorus and potassium in some parts of Abyek Plain. Iranian Journal of Soil Research, 35(4), 397-411.
  23. Moshiri, F. 2013. Guidelines for integrated management of and wheat plant nutrition, soil and water research institute (In Persian).
  24. Mousavi, S. R., Sarmadian, F., Omid, M. and Bogaert, P. 2022. Three-dimensional mapping of soil organic carbon using soil and environmental covariates in an arid and semi-arid region of Iran. Measurement, 201, 111706.
  25. Nabavi, S. F., Yaghmaeian Mahabadi, N. and MahmoudSoltani, S. 2021. Assessment of Soil Fertility Using Fuzzy Membership Functions and AHP in Paddy Fields (Case Study: Research Fields Goldasht, Amol). Iranian Journal of Soil and Water Research, 52(1), 109-122. doi: 10.22059/ijswr.2020.308462.668707.
  26. Nariyanti, S., Herawati, A., Herdiansyah, G., Irianto, H., Riptanti, E. W. and Qonita, A. 2022. Soil fertility index based on altitude: A comprehensive assessment for the cassava development area in Indonesia. Annals of Agricultural Sciences, 67(2), 158–
  27. Naorem, A., Jayaraman, S., Dang, Y. P., Dalal, R. C., Sinha, N. K., Rao, C. S. and Patra, A. K. 2023. Soil constraints in an arid environment—challenges, prospects, and implications. Agronomy, 13(1), 220.
  28. Nussbaum, M., Spiess, K., Baltensweiler, A., Grob, U., Keller, A., Greiner, L., Schaepman, M.E. and Papritz, A. 2018. Evaluation of digital soil mapping approaches with large sets of environmental covariates. Soil 4, 1–22. https://doi.org/10.5194/soil-4-1-2018.
  29. Pal, S. S. and Shurpali, N. J. 2006. Variation in soil organic carbon as influenced by climate under different cropping systems in India. JOURNAL-INDIAN SOCIETY OF SOIL SCIENCE, 54(3), 294.
  30. Parsaie, F., Farrokhian Firouzi, A., Mousavi, S. R., Rahmani, A., Sedri, M. H. and Homaee, M. 2021. Large-scale digital mapping of topsoil total nitrogen using machine learning models and associated uncertainty map. Environmental Monitoring and Assessment, 193(4), 162.
  31. Peng, Y., Liu, Z., Lin, C., Hu, Y., Zhao, L., Zou, R. and Mao, X. 2022. A New Method for Estimating Soil Fertility Using Extreme Gradient Boosting and a Backpropagation Neural Network. Remote Sensing, 14(14), 3311.
  32. Rahmani, A., Sarmadian, F. and Arefi, H. 2022. Digital Mapping of Top-soil Thickness and Associated Uncertainty Using Machine Learning Approach in Some Part of Arid and Semi-Arid Lands of Qazvin Plain. Iranian Journal of Soil and Water Research, 53(3), 585-602. doi: 10.22059/ijswr.2022.338007.669195
  33. Rossel, R. V. and A. B. McBratney. 2008. Diffuse reflectance spectroscopy as a tool for digital soil mapping. In: 165-172. In: Hartemink, A. (Eds.), Digital Soil Mapping with Limited Data, Springer
  34. Rostaminia, M., Rahmani, A., Mousavi, S. R., Taghizadeh-Mehrjardi, R. and Maghsodi, Z. 2021. Spatial prediction of soil organic carbon stocks in an arid rangeland using machine learning algorithms. Environmental Monitoring and Assessment, 193(12), 815.
  35. Shahbazi, K. & Besharati, H. (2013). Overview of agricultural soil fertility status of Iran. *Land Management Journal*, 1(1): 1–15.
  36. Shahbazi, K.; Marzi, M.; Mohammadi, M. H.; Asadi, H.; Fathi-Gardelidani, A.; Hashemi-Nasab-Zavareh, K. S.; Tolouei, R.; Beheshti, M.; Avizhgan, I.; Cheraghi, M. (Ed: Mohammadi, Z.). 2024 (1403). Soil Analysis Methods: Sampling, Chemical and Physical Procedures. Soil & Water Research Institute (SWRI), Karaj, Iran. ISBN: 978-622-6705-36-3. 1,074 pp.
  37. Shahriari, M., Delbari, M., Afrasiab, P. and Pahlavan-Rad, M.R. 2019. Predicting regional spatial distribution of soil texture in floodplains using remote sensing data: A case of southeastern Iran. Catena, 182, 104149. https://doi.org/10.1016/j.catena.2019.104149.
  38. Song, X.-D., Brus, D. J., Liu, F., Li, D.-C., Zhao, Y.-G., Yang, J.-L. and Zhang, G.-L. 2017. Mapping soil organic carbon content by geographically weighted regression: A case study in the Heihe River Basin, China. Geoderma, 305, 314–327. [https://doi.org/10.1016/j.geoderma.2017.06.026] (https://doi.org/10.1016/j.geoderma.2017.06.026)
  39. Srisomkiew, S., Kawahigashi, M., Limtong, P. and Yuttum, O. 2022. Digital soil assessment of soil fertility for Thai jasmine rice in the Thung Kula Ronghai region, Thailand. Geoderma, 409, 115597.
  40. Sys, C., Van Ranst, E. and Dedaveye, J .1991. Land evaluation. Part II: methods in land evaluation. General administration for development
  41. Agriculture Publishing, No: 7, Brussels, Belgium
  42. Taghizadeh-Mehrjardi, R., Schmidt, K., Amirian-Chakan, A., Rentschler, T., Zeraatpisheh, M., Sarmadian, F. and Scholten, T. 2020. Improving the spatial prediction of soil organic carbon content in two contrasting climatic regions by stacking machine learning models and rescanning covariate space. Remote Sensing, 12(7), 1095.
  43. Tunçay, T., Kılıç, Ş., Dedeoğlu, M., Dengiz, O., Başkan, O. and Bayramin, I. 2021. Assessing soil fertility index based on remote sensing and gis techniques with field validation in a semiarid agricultural ecosystem. Journal of Arid Environments, 190, 104525. https://doi.org/10.1016/j.jaridenv.2021.104525.
  44. Vasu, D., Sahu, N. C. and Raghuwanshi, N. S. 2021. Modelling the spatial variability of soil micronutrients for site-specific nutrient management in a semi-arid tropical environment. *Modelling Earth Systems and Environment*, 7(4), 1797–1808. [https://doi.org/10.1007/s40808-021-01145-
  45. Wilding, L.P. 1985. Spatial variability: its documentation, accomodation and implication to soil surveys. In Soil spatial variability, Las Vegas NV, 166-194.
  46. Yang, R.M., Zhang, G.L., Liu, F., Lu, Y.Y., Yang, F., Yang, F., Yang, M., Zhao, Y.G. and Li, D.C. 2016. Comparison of boosted regression tree and random forest models for mapping topsoil organic 229 Agricultural Engineering (Scientific Journal of Agriculture), 48 (2), Summer, 2025 carbon concentration in an alpine ecosystem. Ecological Indicators, 60: 870-878. DOI: 10.1016/j.ecolind.2015.08.036.
  47. Zaheri Abdehvand, Z., Karimi, D., Rangzan, K. and Mousavi, S. R. 2024. Assessment of soil fertility and nutrient management strategies in calcareous soils of Khuzestan province: A case study using the Nutrient Index Value method. Environmental Monitoring and Assessment, 196(6), 503.
  48. Zeraatpisheh, M., Ayoubi, S., Sulieman, M. and Rodrigo-Comino, J. 2019. Determining the spatial distribution of soil properties using the environmental covariates and multivariate statistical analysis: a case study in semi-arid regions of Iran. J. Arid. Land 11, 551–566. https://doi.org/10.1007/s40333-019-0059-9.
  49. Zeraatpisheh, M., Jafari, A., Bodaghabadi, M.B., Ayoubi, S., Taghizadeh-Mehrjardi, R., Toomanian, N. and Kerry, R., Xu, M. 2020. Conventional and digital soil mapping in Iran: Past, present, and future. Catena, 188, 104424.
  50. Zhang, G. L. and Feng, L. I. U. 2017. Recent progress and future prospect of digital soil mapping: A review. Journal of integrative agriculture, 16(12), 2871-2885.
  51. Zhou, Y., Chen, S., Zhu, A.-X.-X., Hu, B., Shi, Z., and Li, Y. 2021. Revealing the scale- and location-specific controlling factors of soil organic carbon in Tibet. Geoderma 382, 114713. https://doi.org/10.1016/j.geoderma.2020.114713.
آمار
تعداد مشاهده مقاله: 213
تعداد دریافت فایل اصل مقاله: 193


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