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Published online 3 April 2009
Published in Agron J 101:556-563 (2009)
DOI: 10.2134/agronj2008.0137x
© 2009 American Society of Agronomy
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MODELING

Photothermal Quotient Specifications to Improve Wheat Cultivar Yield Component Models

Lawton Lanier Nalleya,*, Andrew P. Barkleyb and Ken Sayrec

a Dep. of Agricultural Economics and Agribusiness, Univ. of Arkansas, Agricultural Building Room 217, Fayetteville, AR 72701
b Dep. of Agricultural Economics, Kansas State Univ., 217 Waters Hall, Manhattan, KS 66506
c CIMMYT, P.O. Box 6-641, Mexico D.F. 06600, Mexico

* Corresponding author (llnalley{at}uark.edu).

Previous research has demonstrated the importance and statistical significance of the photothermal quotient (PTQ) to predict and explain wheat (Triticum aestivum L.) yields. The objective of this study is to respecify PTQ to enhance the explanatory power of statistical models used to explain grains per square meter (GM–2), and increase understanding of weather's impact on yields. The primary objective is to identify and quantify potential gains from including separate components of solar radiation (Solar) and temperature (Temp) in place of PTQ (Solar/Temp) to improve wheat yield model explanatory ability. The study also determines the optimal time interval that defines PTQ by varying the number of days before and after 50% anthesis. Using wheat test plot data from Centro Internacional de Mejoramiento de Maíz y Trigo (CIMMYT) in Mexico's Yaqui Valley, a multivariate regression-based stochastic model of wheat yields was used to estimate the impact of altering PTQ definition and specification. Results support previous research: the maximum F-test value of 66.02 and adjusted R2 value of 0.446 were obtained for 31 d before to 1 d after 50% anthesis. Interpretation and analysis were also enhanced by disaggregating PTQ into separate variables Solar and Temp. A 1 MJ m–2 d–1 increase in Solar increased GM–2 by 1.25%, whereas a 1°C increase in Temp decreased GM–2 by 2.8%. This difference in yield responsiveness to weather components results in greater statistical significance, explanatory power, and interpretation of GM–2 models. Future research that builds on these results will better explain, predict, and forecast crop yields.

Abbreviations: BedsMinus, raised planting beds without fungicide application • BedsPlus, raised planting beds with fungicide application • Bread, bread wheat • CIMMYT, Centro Internacional de Mejoramiento de Maíz y Trigo • Durum, durum wheat • GM–2, grains per square meter • Melgas, traditional planting technique in Mexico's Yaqui Valley • Nets, raised planting beds with fungicide application and nets • PTQ, photothermal quotient • PTQ-D, disaggregated photothermal quotient • RLYR, the year a cultivar was released to the public • Solar, solar radiation exposure • Temp, average daily temperature • Triticale, Triticale wheat

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Received for publication October 15, 2008.





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