Forecasting Cotton Yield in the Southeastern United States using Coupled Global Circulation Models-Reference-Cited by-同舟云学术

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Forecasting Cotton Yield in the Southeastern United States using Coupled Global Circulation Models

Published:2010-01 Issue:1 Volume:102 Page:187-196
ISSN:0002-1962
Container-title:Agronomy Journal
language:en
Short-container-title:Agron.j.

Author:

Baigorria Guillermo A.¹,Chelliah Muthuvel²,Mo Kingtse C.²,Romero Consuelo C.¹,Jones James W.¹,O'Brien James J.³,Higgins R. Wayne²

Affiliation:

1. Agricultural & Biological Engineering Dep. Univ. of Florida, Frazier Rogers Hall Gainesville FL 32611

2. Climate Prediction Center NCEP/NWS/NOAA. 5200 Auth Rd Camp Springs MD 20746

3. Center for Ocean‐Atmospheric Prediction Studies Florida State Univ. 2035 E. Paul Dirac Dr. Tallahassee FL 32306

Funder

National Oceanic and Atmospheric Administration- Climate Project Office- Climate Test Bed

Southeast Climate Consortium

Publisher

Wiley

Subject

Agronomy and Crop Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.2134/agronj2009.0201

Reference37 articles.

1. Assessing Predictability of Cotton Yields in the Southeastern United States Based on Regional Atmospheric Circulation and Surface Temperatures

2. Understanding rainfall spatial variability in southeast USA at different timescales

3. Potential predictability of crop yield using an ensemble climate forecast by a regional circulation model

4. Assessing uncertainties in crop model simulations using daily bias-corrected Regional Circulation Model outputs

5. A Degeneracy in Cross-Validated Skill in Regression-based Forecasts

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2. Free weather forecast and open-source crop modeling for scientific irrigation scheduling: proof of concept;Irrigation Science;2023-08-22

3. Using Artificial Neural Network (ANN) for Short-Range Prediction of Cotton Yield in Data-Scarce Regions;Agronomy;2022-03-29

4. Scientific Irrigation Scheduling by Utilizing Weather Api and Open Source Modeling in Texas High Plains and Rio Grande Basin;SSRN Electronic Journal;2022

5. Mid-season empirical cotton yield forecasts at fine resolutions using large yield mapping datasets and diverse spatial covariates;Agricultural Systems;2020-09

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