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Bio-Inspired Hybridization of Artificial Neural Networks, An Application for Mapping the Spatial Distribution of Soil Texture Fractions

Here, series of hybridized artificial neural network models with bio-inspired metaheuristic optimization algorithms such as a genetic algorithm, particle swarm optimization, bat, and monarch butterfly optimization algorithms, were built for predicting particle size fractions.

Optimization of Rain Gauge Networks for Arid Regions Based on Remote Sensing Data

The current research introduces a new integrated methodology for targeting the upgrading and improvement of coarse rain gauge networks in arid regions. The findings of the present research support the impression that remote sensing data is an excellent option for places with no or few rain gauges, as it enables the gathering of more frequent records at a higher resolution.

Rain Gauge Networks Using Remote Sensing

The current research introduces a new integrated methodology for targeting the upgrading and improvement of coarse rain gauge networks in arid regions. The findings of the present research support the impression that remote sensing data is an excellent option for places with no or few rain gauges, as it enables the gathering of more frequent records at a higher resolution.

Assessing the Influence of Soil Quality on Rainfed Wheat Yield

The study region’s soil quality is mostly poor and moderate, therefore, more attempts should be made to apply organic inputs like farm yard manure, compost or green manure and potassium fertilizer in rainfed wheat fields. Generally, the soils of the Inceptisol order exhibited greater soil quality and rainfed wheat yield than soils of the Entisol order.

The Effect of Soil Quality on Wheat Yield

The study region’s soil quality is mostly poor and moderate, therefore, more attempts should be made to apply organic inputs like farm yard manure, compost or green manure and potassium fertilizer in rainfed wheat fields. Generally, the soils of the Inceptisol order exhibited greater soil quality and rainfed wheat yield than soils of the Entisol order.

Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models

Land suitability assessment is essential for increasing production and planning a sustainable agricultural system, but such information is commonly scarce in the semi-arid regions of Iran. Therefore, our aim is to assess land suitability for two main crops (i.e., rain-fed wheat and barley) based on the Food and Agriculture Organization “land suitability assessment framework” for agricultural land in Kurdistan province, Iran.

Land Suitability Assessment Using Machine Learning

Land suitability assessment is essential for increasing production and planning a sustainable agricultural system, but such information is commonly scarce in the semi-arid regions of Iran. Therefore, our aim is to assess land suitability for two main crops (i.e., rain-fed wheat and barley) based on the Food and Agriculture Organization “land suitability assessment framework” for agricultural land in Kurdistan province, Iran.

Impacts of vegetation anomalies and agricultural drought on wind erosion over Iran from 2000 to 2018

In this work, seasonal variations of vegetation cover and wind erosion activity level over Iran for a long-term period from 2000 to 2018 were investigated using the NDVIA and DCA, respectively. The change trends and the rate of changes in the study variables were determined by the Mann-Kendall test and sen's slop estimator.

Impacts of Vegetation Anomalies on Wind Erosion

In this work, seasonal variations of vegetation cover and wind erosion activity level over Iran for a long-term period from 2000 to 2018 were investigated using the NDVIA and DCA, respectively. The change trends and the rate of changes in the study variables were determined by the Mann-Kendall test and sen's slop estimator.

Conventional and Digital Soil Mapping in Iran

This review has identified research gaps that need filling. In Iran, coherent national scale DSM with consistent methodology is needed to update legacy soil maps, and to apply DSM in forestlands, hillslopes, deserts, and mountainous regions which have largely been ignored in recent DSM studies.