engleski

Predictions of crop agronomic performance based on chlorophyll a fluorescence measurements

Modelling of crop growth often takes into account any physiological or environmental variable explaining a considerable portion of variance. The major constraint in such models is finding the reliable and stable predictors of crop performance. Measurements of chlorophyll a fluorescence (ChlF) at a given time point present all the physiological states the plant has been through along with indication of the current photosynthetic performance. ChlF has been shown to respond quickly to many abiotic and biotic stresses in many species. The objective of our study was to evaluate the predictive ability of agronomic performance using ChlF measurements in maize and barley breeding trials, and to analyze the variance structure of the obtained predictions. The ChlF in maize was measured on the ear leaf of 216 hybrids in anthesis in eight environments in Croatia and Turkey. Measurements of 36 barley cultivars were obtained from two environments in Croatia sown in two planting densities making totally four environment-density combinations. Partial least squares (PLS) regression models were set with grain yield and grain harvest moisture as responses, and 118 measured ChlF transients as explanatory variables. After cross-validation, the validated models explained 34.5% variance for maize yield, 82.2% for maize grain moisture, 73.2% for barley yield, and 65.5% for barley grain moisture. Most interestingly, genetic components of variance of both predicted traits in maize have shrunken to zero, while the same components have increased in barley predictions of both yield and grain moisture. Properties of ChlF based predictions in other crop species are yet to be elucidated, while the use of such predictions in maize and barley will be discussed.

maize ; barley ; chlorophyll a fluorescence ; predictions

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