Search published articles


Showing 2 results for Rostaminia
Qualitative, Quantitative and Economic Land Suitability Evaluation for Wheat, Maize and Sesame Production in Mehran Plain, Ilam Province
A. Jalalian, M. Rostaminia, S.h. Ayoubi, A.m. Amini,
Volume 11, Issue 42 (winter 2008)
Abstract

  Extension of cultivation areas becomes gradually impossible due to ever-increasing population growth and urban area development in Iran. Therefore, it is very important to use the existing cultivated lands more efficiently. Land suitability evaluation makes the sustainable use of the lands feasible. The objective of this study was qualitative, quantitative and economic suitability evaluation of irrigated croplands for wheat, maize and sesame in Mehran plain, Ilam Province. Soil survey in the field, laboratory analysis of the soil samples, qualitative, quantitative and economic evaluation were different successive stages of this research. In qualitative evaluation, climatic, topographic and soil suitability classes were determined according to the degree of the matching with plant requirements, by parametric (square root) method. Quantitative and economic evaluations were done based on observed, potential and marginal yield analysis. Results of the qualitative land evaluation showed that most of the land units were classified moderately suitable for given crops because of soil limitations. Qualitatively, most of the land units were classified in the same classes as, or in lower classes than quantitative suitability classes for wheat and maize production, due to high management level at the farms. Whereas quantitative classes of sesame were determined lower than qualitative classes induced by low management level for this crop. Economic land suitability classification showed that the wheat production was the most economic land utilization type. Results of the economic assessment suggested that the cultivation of wheat in rotation with sesame would produce the most income for different units and could be increased in future using improvement in management level in the study area for sesame cultivation.


Digital Mapping of Soil Family Class Using the Machine Learning Approach (A Case Study: Semi-Arid lands in the West of IRAN)
Z. Maghsodi, M. Rostaminia, M. Faramarzi, A Keshavarzi, A. Rahmani, S. R. Mousavi,
Volume 24, Issue 2 (Summer 2020)
Abstract
Digital soil mapping plays an important role in upgrading the knowledge of soil survey in line with the advances in the spatial data of infrastructure development. The main aim of this study was to provide a digital map of the soil family classes using the random forest (RF) models and boosting regression tree (BRT) in a semi-arid region of Ilam province. Environmental covariates were extracted from a digital elevation model with 30 m spatial resolution, using the SAGAGIS7.3 software. In this study area, 46 soil profiles were dug and sampled; after physico-chemical analysis, the soils were classified based on key to soil taxonomy (2014). In the studied area, three orders were recognized: Mollisols, Inceptisols, and Entisols. Based on the results of the environmental covariate data mining with variance inflation factor (VIF), some parameters including DEM, standard height and terrain ruggedness index were the most important variables. The best spatial prediction of soil classes belonged to Fine, carbonatic, thermic, Typic Haploxerolls. Also, the results showed that RF and BRT models had an overall accuracy and of 0.80, 0.64 and Kappa index 0.70, 0.55, respectively. Therefore, the RF method could serve as a reliable and accurate method to provide a reasonable prediction with a low sampling density.

Page 1 from 1     

© 2024 CC BY-NC 4.0 | JWSS - Isfahan University of Technology

Designed & Developed by : Yektaweb