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

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Hamzehpour Nikou; Mola Ali Abasiyan Sara

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: APPLIED SOIL RESEARCH Year:2020 | Volume:7 | Issue:4 Page(s): 18-34

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

291

Download:

Cites:

Information Journal Paper

Title

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

Author(s)

Hamzehpour Nikou | Mola Ali Abasiyan Sara | Issue Writer Certificate

Keywords

Auxiliary DataQ2

ErrorQ1

Modified Walkley and Black MethodQ1

Soil TextureQ2

Abstract

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...

Cites

No record.

References

No record.

Cite

APA: Copy

Hamzehpour, Nikou, & Mola Ali Abasiyan, Sara. (2020). Spatial Prediction of Soil Organic Matter with Soft and Axillary Data Using Bayesian Maximum Entropy Method. APPLIED SOIL RESEARCH, 7(4 ), 18-34. SID. https://sid.ir/paper/390894/en

Vancouver: Copy

Hamzehpour Nikou, Mola Ali Abasiyan Sara. Spatial Prediction of Soil Organic Matter with Soft and Axillary Data Using Bayesian Maximum Entropy Method. APPLIED SOIL RESEARCH[Internet]. 2020;7(4 ):18-34. Available from: https://sid.ir/paper/390894/en

IEEE: Copy

Nikou Hamzehpour, and Sara Mola Ali Abasiyan, “Spatial Prediction of Soil Organic Matter with Soft and Axillary Data Using Bayesian Maximum Entropy Method,” APPLIED SOIL RESEARCH, vol. 7, no. 4 , pp. 18–34, 2020, [Online]. Available: https://sid.ir/paper/390894/en

Related Journal Papers