تخمین مکانی ماده آلی خاک با داده‌های نامطمئن و کمکی خاکی و روش آنتروپی حداکثر اریب

نوع مقاله : مقاله پژوهشی

نویسندگان

1 پیداش رده بندی و تخمین مکانی

2 دانشگاه مراغه

چکیده

ماده آلی خاک یکی از مهمترین فاکتورهای کیفیت خاک می­باشد و آگاهی از وضعیت آن در خاک، از مهمترین اقدامات در جهت مدیریت منابع اراضی است. با توجه به هزینه و زمان زیادی که در جهت پایش ماده آلی خاک نیاز است، هر روشی که بتواند با حداقل نمونه استفاده از هر نوع داده­های خاکی، بهترین نقشه­های ماده آلی را تولید کند، قدمی در راستای کشاورزی پایدار خواهد بود. هدف از انجام این تحقیق، تخمین مکانی ماده آلی خاک با استفاده از اندازه­گیری­های با دقت پایین ماده آلی، داده­های کمکی خاکی و روش آنتروپی حداکثر اریب (BME) بود. نمونه­برداری از 122 نقطه و از عمق 20-0 سانتی­متری از دشت بناب و میاندوآب صورت گرفت. سپس، ماده آلی خاک به روش والکلی و بلاک و نیز به شکل تسهیل شده آن، تعیین گردید. همچنین، برخی از پارامترهای خاکی از جمله بافت، پایداری خاکدانه­ها و آهک نیز در نمونه­ها اندازه­گیری شدند. تخمین مکانی ماده آلی با داده­های نامطمئن، مدل خطی توسعه یافته با استفاده از داده­های کمکی خاکی و روش BME انجام شد. براساس نتایج، بالاترین R، کمترین RMSE و nRMSE با مقادیر به ترتیب 97/0،  07/0 درصد و 06/0 متعلق به تخمین مکانی ماده آلی با داده­های نامطمئن ماده آلی با درنظر گرفتن خطا بود. همچنین استفاده از مدل خطی توسعه یافته با داده­های کمکی خاکی و وارد کردن خطای این داده­ها در معادلات تخمین، منجر به بهبود تخمین نسبت به زمانی گردید که از خطا استفاده نشده بود (R، RMSE و nRMSE به ترتیب از 65/0، 58/0 و 55/0 به 85/0، 31/0 و 29/0 بهبود یافت). با توجه به نتایج حاصل از این تحقیق، روش BME امکان استفاده از طیف وسیعی از اطلاعات خاکی را در تخمین فراهم می­آورد و با  ادغام خطای ناشی از استفاده از داده­های نامطمئن در تخمین، منجر به بهبود تخمین مکانی ماده آلی گردید.

کلیدواژه‌ها

عنوان مقاله [English]

Spatial Prediction of Soil Organic Matter with Soft and Axillary Data Using Bayesian Maximum Entropy Method

نویسندگان [English]

  • nikou Hamzehpour 1
  • sara mola ali abasiyan 2
1 هیئت غلمی
2
چکیده [English]

Soil organic matter (SOM) is one of the important soil quality factors and knowledge of its condition in soil, is one of the most important steps in the management of land resources and controlling soil losses. As SOM monitoring is an expensive and time-consuming task, any method which can produce high quality maps of SOM with available axillary soil data and less samples, would be a step forward in reaching the goals of sustainable agriculture. The aim of this research is to predict SOM using soft data, auxiliary data and Bayesian maximum entropy method (BME). Soil samples were gathered from the Bonab-Miandoab plain, and almost 122 samples were collected from 0-20 cm depth of surface soil. SOM and some other soil properties including soil texture, aggregate stability, and calcium carbonate equivalent were measured. Later spatial prediction of SOM was done using SOM soft data, auxiliary data and generalized linear model (GLM) using BME method. Results showed that the highest R, lowest RMSE and nRMSE with values of 0.97, 0.07 and 0.12 respectively, belonged to spatial prediction of SOM with soft data and error. Results also revealed that the developed GLM model with calculated error, resulted in better R, RMSE and nRMSE in comparison to predictions with GLM model without error (R, RMSE and nRMSE improved from 0.65, 0.58 and 0.55 to 0.85, 0.31 and 0.29 respectively). As a conclusion, BME method has provided the possibility of merging error resulted from the use of soft data, in spatial prediction equations and through that, has helped to improve spatial prediction of SOM.

کلیدواژه‌ها [English]

  • Auxiliary data
  • error
  • modified Walkley and Black method
  • soil texture

مراجع

Reference
Acosta JA., Faz A., Jansen B., Kalbitz K., and Martinez-Martinez S. 2011. Assessment of salinity status in intensively cultivated soils under semiarid climate, Murcia, SE Spain. Journal of Arid Environments, 75: 1056-1066.
Bogaert P., and D,Or D. 2002. Estimating soil properties from thematic soil maps: The Bayesian Maximum Entropy. Soil Science Society of American Journal, 66: 1492-1500.
Bruun TB., Elberling B., Neergaard A., and Magid AJ. 2015. Organic carbon dynamics in different soil types after conversion of forest to agriculture. Land Degradation and Development, 26: 272-283.
Brus DJ., Bogaert P., and Heuvelink GBM. 2008. Bayesian maximum entropy prediction of soil categories using a traditional soil map as soft information. European Journal of Soil Science, 59(2): 166-177.
Christakos G. 1990. A Bayesian / maximum –entropy view to the spatial estimation problem. Mathmatical Geological, 22(7): 763-777.
Christakos G. 2000. Modern spatiotemporal geostatistics. Oxford University Press, New York. 312pp.
Christakos G. 2002. On the assimilation of uncertain physical knowledge bases: Bayesian and non-Bayesian techniques. Advances in Water Resources, 25(8-12): 1257-1274.
Douaik A., Van Meirvenne M., and Toth T. 2004. Spatio-temporal kriging of soil salinity rescaled from bulk soil electrical conductivity. In: Sanchez Vila X., Carrera J. and Gomez-Hernandez J. (Ed.), GeoEnv IV: Geostatistics for Environmental Applications. Netherlands: Kluwer Academic Publishers.
Dono G., Cortignani R., Dell D., Deligios P., Doro L., Lacetera N., Mula L., Pasqui M., Quaresima S., Vitali A., and Roggero PP. 2016. Winners and losers from climate change in agriculture: insights from case study in Mediterranean basin. Agricultural systems, 147: 65-75.
Forth H. 1990. Fundamentals of soil science. 8th Ed. New York: Wiley. ISBN: 0-471-52279-1.
Giordano R., Liersch S., Vurro M., and Hirsch D. 2010. Integrating local and technical knowledge to support soil salinity monitorinf in the Amudarya river basin. Journal of Environmental Management, 91: 1718-1729.
Gee GW., and Bauder JW. 1986. Particle size analysis. In: Klute A. (Ed.), Methods of Soil Analysis. Part I. Physical and Mineralogical Methods, 2nd ed., Agronomy Monograph. No: 9. ASA and SSSA. Madison, WI.
Hamzehpour N., and Bogaert P. 2017. Improved spatiotemporal monitoring of soil salinity using filtered kriging with measurement errors: an application to the West Urmia Lake, Iran. Geoderma, 275: 22-33.
Hamzehpour N., and Eghbal MK. 2016. Spatiotemporal prediction of soil salinity boundary using kriging with measurement errors. Journal of Ecology, Environment and Conservation, 22(3): 57-67.
Hamzehpour N., Eghbal MK., Bogaert P., Toomanian N., and Oskoui RS. 2013. Spatial prediction of soil salinity using kriging with measurement errors and probabilistic soft data. Arid Land Research and Management, 27 (2): 128-139.
Heuvelink GBM. 1998. Error Propagation in Environmental Modeling with GIS, London: Taylor and Francis.
Jaynes ET. 1982. On the rationale of Maximum-Entropy methods. Proceeding of the IEEE, 70: 939-952.
Jegajeevagan K., Sleutel S., Ameloot N., Kader MA., and De Neve S. 2013. Organic matter fractions and N mineralization invegetable-cropped sandy soils. Soil Use and Management, 29(3): 333-343.
Kempen B., Heuvelink GBM., Brus DJ., and Stoorvogel JJ. 2010. Pedometric mapping of soil organic matter using a soil map with quantified uncertainty. Europian Journal of Soil Science, 61: 333-347.
Li Y., Shi Z., and Li F. 2007. Delineation of site-specific management zones based on temporal and spatial variability of soil electrical conductivity. Pedosphere, 17 (2): 156–164.
Liu Sh., An N., Yang J., Dong Sh., Wang C., and Yin Y. 2017. Prediction of soil organic matter variability associated with different land use types in mountainous landscape in southwestern Yunnan province, China. Catena, 133: 137-144.
Marchant P., Villanneaue J., Arrouays D., Sabyn P. A., and Rawlins BG. 2015. Quantifying and mapping topsoil inorganic carbon concentrations and stocks: approaches tested in France. Soil Use and Management, 31: 29-38.
Marlet S., Bouksila F., and Bahri A. 2009. Water and salt balance at irrigation scheme scale: A comprehensive approach for salinity assessment in a Saharan oasis. Agricultural Water Management, 96: 1311-1322.
Meersmans J., Martin MP., Ridder FD., Lacarce E., Wetterlind J., Baets S. D., Bas CL, Louis BP., Orton TG., Bispo A., and Arrouays D. 2012. A novel soil organic C model using climate, soil type and management data at national scale in France. Agronomy and Sustainable Developments, 32: 873-888.
Mirzaee S., Ghorbani-Dashtaki S., Mohammadi J., Asadi H., and Asadzadeh F. 2016. Spatial variability of soil organic matter using remote sensing data. Catena, 145: 118-127.
Nelson RE. 1982. Carbonate and Gypsum. P. 181-196. In: Page A.L. (Ed.), Methods of Soil Analysis. Part 2. Chemical and Microbiological Properties, 2nd Edition, Agronomy Monograph. No: 9. ASA and SSSA. Madison, WI.
Novara A., Gristina L., Sala G., Galati A., Crescimanno M., Cerda A., Badalamenti E., La Mantia T. 2017. Agricultural land abandonment in Mediterranean environment provides ecosystem services via soil carbon sequestration. Science of Total Environment, 574: 420-429.
Saia., S, Benitez E., Garcia-Garrido JM., Settanni L., Amato G., Giambalvo D. 2014. The effect of arbuscular mycorrhizal fungi on total plant nitrogen uptake and nitrogen recovery from soil organic material. Journal of Agricultural Science, 152: 370-378.
Schillaci C., Acutis M., Lombardo L., Lipani A., Fantappie M., Marker M., and Saia S. 2017. Spatio-temporal topsoil organic carbon mapping of a semi-arid Mediterranean region: the role of land use, soil texture, topographic indices and the influence of the remote sensing data to modeling. Science of the Total Environment, 601-602: 821-832.
Serre ML., and Christakos G. 1999. Modern geostatistics: computational BME in the light of uncertain physical knowledge-the Equus Beds study. Stochastic Environmental ResearchandRisk Assessment, 13: 1-26.
Shanon, CE. 1948. A mathematical theory of communication. Bell System Technical Journal, 27: 379-423.
Stevens F., Bogaert P., and Wesemael BV. 2015. Detecting and quantifying field-related spatial variation of soil organic carbon using mixed-effect models and airborne imagery. Geoderma, 259-260: 93-103.
Stevens A., Miralles I., van Wesemael B. 2012. Soil organic carbon predictions by air-borne imaging spectroscopy: comparing cross-validation and validation. Soil Science Society of Americal Journal, 76: 2174-2183.
Sulivan DG., Shaw JN., and Rickman D. 2005. IKONOS imagery to estimate surface soil property variability in two Alabama physioraphies. Soil Science Society of American Journal, 170: 954-968.
Triantafilis J., Odeh IOA., and McBratney AB. 2001. Five geostatistical models to predict soil salinity from electromagnetic induction data across irrigated cotton. Soil Science Society of American Journal, 65: 869-878.
Walkley A., and Blackm IA. 1934. An examination of the Degtjareff method for determining organic carbon in soils: Effect of variations in digestion conditions and of inorganic soil constituents. Soil Science, 63: 251-263.
Wang T., Kang F., Cheng X., Han H., Bai Y., and Ma J. 2017. Spatial variability of organic carbon and total nitrogen in the soils of a subalpine forested catchment at Mt. Taiyue, China. Catena, 155: 41-52.
Wu C., Wu J., Luo Y., Zhang L., and DeGloria SD. 2009. Spatial prediction of soil organic matter content using cokriging with remotely sensed data. Soil Science Society of American Journal, 73: 1202-1208.
Zaouche M., Bel L., and Vaudour E. 2017. Geostatistical mapping of topsoil organic carbon and uncertainty assessment in Western Paris croplands (France). Geoderma Regional, 10: 126-137.
Zhang S., Huang Y., Shen C., Ye H., and Du Y. 2012. Spatial prediction of soil organic matter using terrain indices and categorical variables as auxiliary information. Geoderma, 171-172: 35-43.
Zhang R., Shouse P., and Yates S. 1997. Use of pseudo-crossvariograms and cokriging to improve estimates of soil solute concentrations. Soil Science Society of American Journal, 61: 1342-1347.

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  • تاریخ دریافت: 19 اردیبهشت 1397
  • تاریخ بازنگری: 16 بهمن 1398
  • تاریخ پذیرش: 24 مهر 1397

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