Physiological maturity in wheat based on kernel water and dry matter.

By Daniele Calderini, L. Abeledo, G. Slafer. 2000

92 citations recorded by the source corpus.

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.

Subfields: Agronomy and Crop Science, Plant Science

View Paper: https://www.semanticscholar.org/paper/7245a931d6e3f65486d02c3041e12461e6eeec73

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Agronomy and Crop Science

  1. M. E. Otegui
  2. L. Borrás
  3. G. Slafer
  4. R. A. Fischer
  5. V. Sadras
  6. G. Maddonni
  7. D. Miralles
  8. R. Savin

In Plant Science

  1. M. E. Otegui
  2. L. Borrás
  3. G. Slafer
  4. R. A. Fischer
  5. V. Sadras
  6. G. Maddonni
  7. D. Miralles
  8. R. Savin

Citations in the corpus