Modeling Cotton Lint Yield and Water Use Efficiency Responses to Irrigation Scheduling Using Cotton2K-Reference-Cited by-同舟云学术

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Modeling Cotton Lint Yield and Water Use Efficiency Responses to Irrigation Scheduling Using Cotton2K

Published:2016-07 Issue:4 Volume:108 Page:1614-1623
ISSN:0002-1962
Container-title:Agronomy Journal
language:en
Short-container-title:Agron.j.

Author:

Attia Ahmed¹²,Rajan Nithya³,Nair Shyam S.⁴,DeLaune Paul B.²,Xue Qingwu⁵,Ibrahim Amir M. H.³,Hays Dirk B.³

Affiliation:

1. Agronomy Dep. Zagazig University SharqiaEG44519

2. Texas A&M AgriLife Research and Extension Center VernonTX76384

3. Dep. of Soil and Crop Sciences Texas A&M Univ. College StationTX77843

4. Dep. of Agricultural Sciences and Engineering Technology Sam Houston State University HuntsvilleTX77341

5. Texas A&M AgriLife Research and Extension Center AmarilloTX79106

Funder

Cotton Incorporated

Publisher

Wiley

Subject

Agronomy and Crop Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.2134/agronj2015.0437

Reference44 articles.

1. Yield, Quality, and Spectral Reflectance Responses of Cotton under Subsurface Drip Irrigation

2. Application of DSSAT-CERES-Wheat model to simulate winter wheat response to irrigation management in the Texas High Plains

3. Growth Dynamics of the Cotton Plant during Water‐Deficit Stress

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1. Progression of drip irrigation and fertigation in cotton across the globe and its future perspectives for sustainable agriculture: an overview;Applied Water Science;2023-08-25

2. Evaluation of growth-stage-based variable deficit irrigation strategies for cotton production in the Texas High Plains;Agricultural Water Management;2023-04

3. Potato productivity in response to furrow irrigation practices, rabbit manure rates, and potassium fertilizer levels;Egyptian Journal of Soil Science;2022-12-01

4. Coupling Process-Based Models and Machine Learning Algorithms for Predicting Yield and Evapotranspiration of Maize in Arid Environments;Water;2022-11-12

5. Simulating productivity of dryland cotton using APSIM, climate scenario analysis, and remote sensing;Agricultural and Forest Meteorology;2022-10

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