Growing conditions influence non-destructive estimation of chlorophyll in leaves of Valeriana jatamansi

• Regression models were calibrated for non-destructive estimation of leaf Chlorophyll.
• Second-degree polynomial regression model was best fitting.
• Influences of leaf morphology and growing conditions compel to develop circumstances-specific models.

Leaf chlorophyll (Chl) content is an indicator of physiological conditions and nutritional status of a plant. However, the conventional methods for estimating the Chl content in leaves are analytical based destructive and time consuming, and these methods do not allow to study on the same sample over time. Valeriana jatamansi, a perennial medicinal herb, is now endangered and at the edge of becoming extinct in India. It is therefore a pressing need to conserve and maintain this species in their natural habitat. One experiment was conducted to ascertain whether the CCM-200, a hand-held Chl meter, could be effectively used to estimate Chl content of V. Jatamansi in a non-destructive manner. The regression equations between CCM-200 reading and actual chlorophyll values have been developed for three accessions of V. jatamansi grown under two different conditions. In the present data-sets, second-degree polynomial regression model has been proven to be the best model with higher R2 (R2 = 0.852–0.964, P ≤ 0.01) and lower ACI values (94.47–171.78) for all the cases (except plastic film greenhouse grown accession-3). For accession-5 and accession-7, the model predicted values of total Chl were very close to conventionally measured values with an RMSE < 3.50 μg cm−2. However, the calibrated models were circumstances-specific.

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