Performance of WOFOST Model for Simulating Maize Growth, Leaf Area Index, Biomass, Grain Yield, Yield Gap, and Soil Water under Irrigation and Rainfed Conditions-Reference-Cited by-同舟云学术

Performance of WOFOST Model for Simulating Maize Growth, Leaf Area Index, Biomass, Grain Yield, Yield Gap, and Soil Water under Irrigation and Rainfed Conditions

Published:2022-02 Issue:2 Volume:148 Page:
ISSN:0733-9437
Container-title:Journal of Irrigation and Drainage Engineering
language:en
Short-container-title:J. Irrig. Drain Eng.

Author:

Amiri Ebrahim¹^ORCID,Irmak Suat²,Yaghouti Hadis³

Affiliation:

1. Professor, Dept. of Water Engineering, Lahijan Branch, Islamic Azad Univ., Lahijan 4416939515, Iran (corresponding author). ORCID: .

2. Professor and Department Head, Dept. Agricultural and Biological Engineering, The Pennsylvania State (Penn State) Univ., University Park, PA 16802.

3. Graduate Research Assistant, Dept. of Soil, Science and Research Branch, Islamic Azad Univ., Tehran 1477893955, Iran.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Agricultural and Biological Sciences (miscellaneous),Water Science and Technology,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29IR.1943-4774.0001644

Reference37 articles.

1. Assessing the Performance of the WOFOST Model in Simulating Jujube Fruit Tree Growth under Different Irrigation Regimes

2. Improving Jujube Fruit Tree Yield Estimation at the Field Scale by Assimilating a Single Landsat Remotely-Sensed LAI into the WOFOST Model

3. Planting Date, Hybrid Maturity, and Weather Effects on Maize Yield and Crop Stage

4. Review of yield gap explaining factors and opportunities for alternative data collection approaches

5. A regional implementation of WOFOST for calculating yield gaps of autumn-sown wheat across the European Union

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3. Growth Indexes and Yield Prediction of Summer Maize in China Based on Supervised Machine Learning Method;Agronomy;2022-12-30