Effects of Water Source and Technology on Energy Use and Environmental Impacts of Rice Production in Northern Iran-Reference-Cited by-同舟云学术

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Effects of Water Source and Technology on Energy Use and Environmental Impacts of Rice Production in Northern Iran

Published:2022-11-29 Issue:12 Volume:58 Page:
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resources Research

Author:

Jamali Mohsen¹^ORCID,Bakhshandeh Esmaeil²^ORCID,Emadi Mostafa³^ORCID

Affiliation:

1. Department of Agroecology Environmental Sciences Research Institute Shahid Beheshti University Tehran Iran

2. Genetics and Agricultural Biotechnology Institute of Tabarestan & Sari Agricultural Sciences and Natural Resources University Sari Iran

3. Department of Soil Sciences College of Crop Sciences Sari Agricultural Sciences and Natural Resources University Sari Iran

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021WR031546

Reference84 articles.

1. Agricultural carbon footprint is farm specific: Case study of two organic farms

2. Energy use pattern in rice production: A case study from Mazandaran province, Iran

3. Greenhouse gas implications of novel and conventional rice production technologies in the Eastern-Gangetic plains

4. Study and determination of energy consumption to produce conventional rice of the Guilan province

5. Comparison of GHG emissions and farmers’ profit of large-scale and individual farming in rice production across four regions of Thailand

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1. Performance of Neural Networks in the Prediction of Nitrogen Nutrition in Strawberry Plants;AgriEngineering;2024-06-18

2. Remote sensing-based paddy yield estimation using physical and FCNN deep learning models in Gilan province, Iran;Remote Sensing Applications: Society and Environment;2024-04

3. Environmental Impacts of Rice Intensification Using High-Yielding Varieties: Evidence from Mazandaran, Iran;Sustainability;2024-03-20

4. Development of machine learning models for estimating wheat biophysical variables using satellite-based vegetation indices;Advances in Space Research;2024-01

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