Physiological Maturity in Wheat Based on Kernel Water and Dry Matter

• Published in: Agronomy journal Vol. 92; no. 5; pp. 895 - 901
• Main Authors: Calderini, Daniel F., Abeledo, Leonor G., Slafer, Gustavo A.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Madison American Society of Agronomy 01-09-2000
• Subjects: Agronomy. Soil science and plant productions; Biological and medical sciences; Economic plant physiology; Fructification, ripening. Postharvest physiology; Fundamental and applied biological sciences. Psychology; Growth and development; Monocotyledones; Chemical analysis; Grains; Harvesting date; Dry matter; Model study; Cereal crop; Ripening; Gramineae; Regression model; Angiospermae; Water content; Decision aid; Spermatophyta; Physiology; Developmental stage; Triticum aestivum; Cultivar
• ISSN: 0002-1962; 1435-0645
• DOI: 10.2134/agronj2000.925895x

Estimation of the time of physiological maturity could be beneficial to avoid yield penalties due to lodging, sprouting, hail, and other harvest risks. The aim of our study was to evaluate a simple empirical relationship (model) to determine physiological maturity by simultaneously analyzing the water and dry matter dynamics of wheat kernels. An experiment was conducted in which two cultivars with different kernel mass potential were sown on four dates. Fresh and dry kernel mass from different positions in the spike were measured twice weekly. To validate the regression model, measured and calculated data from different cultivars, growing seasons, and kernel positions were compared. A negative linear relationship between kernel dry matter (relative to final kernel mass) and kernel water concentration was determined. This showed that in wheat, physiological maturity is reached at 37% of kernel water concentration. Validation of the regression model was done using data from field experiments in Argentina and Mexico, and from controlled‐conditions experiments reported in the literature. The regression model successfully simulated results from field experiments ( r=0.98;P<0.001 .) In addition, data from controlled‐conditions experiments showed the same negative linear relationship between relative kernel dry matter and kernel water concentration ((slope=−0.4)), and the model achieved a good fit for measured data ((r=0.96;P <0.001)). This regression model is proposed for use by farmers and crop managers, who can simply measure grain humidity with grain moisture meters.