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ARS Home » Pacific West Area » Maricopa, Arizona » U.S. Arid Land Agricultural Research Center » Water Management and Conservation Research » Research » Publications at this Location » Publication #387422

Research Project: Advancing Water Management and Conservation in Irrigated Arid Lands

Location: Water Management and Conservation Research

Title: Cotton response to CO2, water, nitrogen and plant density - A repository of FACE, AgIIS and FISE experiment data

Author
item Kimball, Bruce
item Thorp, Kelly
item BARNES, EDWARD - Cotton, Inc
item CHOI, CHRISTOPHER - University Of Wisconsin
item CLARKE, THOMAS - Retired ARS Employee
item Colaizzi, Paul
item FITZGERALD, GLENN - Agriculture Victoria
item HABERLAND, JULIO - Universidad De Chile
item HENDREY, GEORGE - Queens College
item Hunsaker, Douglas - Doug
item KOSTRZEWSKI, MICHEAL - Pima Research
item LAMORTE, ROBERT - US Department Of Agriculture (USDA)
item LEAVITT, STEVEN - University Of Arizona
item LEWIN, KEITH - Retired Non ARS Employee
item MAUNEY, JACKSON - Retired Non ARS Employee
item NAGY, JOHN - Retired Non ARS Employee
item PINTER, PAUL - Retired Non ARS Employee
item WALLER, PETER - University Of Arizona

Submitted to: Open Data Journal for Agricultural Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/29/2022
Publication Date: 12/28/2022
Citation: Kimball, B.A., Thorp, K.R., Barnes, E.M., Choi, C.Y., Clarke, T.R., Colaizzi, P.D., Fitzgerald, G.J., Haberland, J.A., Hendrey, G., Hunsaker, D.J., Kostrzewski, M.A., Lamorte, R.L., Leavitt, S.W., Lewin, K., Mauney, J.R., Nagy, J., Pinter, P.J., Waller, P.M. 2022. Cotton response to CO2, water, nitrogen and plant density - A repository of FACE, AgIIS and FISE experiment data. Open Data Journal for Agricultural Research. 8:1-5. https://doi.org/10.18174/odjar.v8i0.18152.
DOI: https://doi.org/10.18174/odjar.v8i0.18152

Interpretive Summary: ARS researchers from Phoenix and Maricopa, AZ; the University of Arizona, Tucson and Maricopa, Brookhaven National Laboratory, and other locations conducted three sets of experiments on cotton. The first (1989-1991) was a FACE (Free-Air CO2 Enrichment) at ample and limited supplies of water. The second in 1999 was an Agricultural Irrigation Imaging System (AgIIS, pronounced ag-eyes) whereby a cart with remote-sensing instruments ran back and forth on a track on top of a linear-move irrigation system which supplied ample and limited supplies of water and nitrogen fertilizer. The third in 2002-2003 was a FAO-56 Irrigation Scheduling Experiment (FISE) with varying irrigation scheduling method, planting density, and nitrogen fertilizer. Data from these experiments are ideal for validating cotton growth models which can be a management aid for today’s farmers and can provide predictions about the likely effects of global change on future cotton productivity. Therefore, this manuscript, along with eight associated data files, have been assembled for publication in an “open” data journal so that anyone in the future will have easy access to this valuable dataset. The dataset is comprehensive and includes management, soils, weather, physiology, phenology, growth, yield, and water use data. Using carbon isotopic tracing, carbon flows were measured from the air to the plants to sequestration in the soil. This research will benefit all consumers of cotton fiber garments, as well as cotton seed oil products.

Technical Abstract: Several cotton experiments have been conducted at the University of Arizona’s Maricopa Agricultural Center from which datasets have been obtained documenting cotton responses to elevated CO2 concentrations, water supply, nitrogen fertilizer, and planting density. In particular, these experiments included FACE (free-air CO2 enrichment; CO2, water; 10 treatment-years), AgIIS (Agricultural Irrigation Imaging System, pronounced Ag Eyes; nitrogen fertilizer, water supply; 4 treatment-years), and FISE (FAO-56 Irrigation Scheduling Experiments; irrigation scheduling method, planting density, nitrogen fertilizer; 24 treatment-years). Besides achieving the experimental objectives of determining cotton’s response to the several variables, as well as testing remote sensing techniques, the comprehensive datasets are suitable for validating plant growth models because they include weather, soils, management, growth, yield and other data.