PREDICTION MODELS FOR NON-DESTRUCTIVE ESTIMATION OF TOTAL CHLOROPHYLL CONTENT IN SUGARCANE

Abstract

Total chlorophyll content of sugarcane is an important indicator of plant health, directly correlated to the photosynthetic potential of the crop. With recent technological advancements, portable chlorophyll meters have largely replaced biochemical chlorophyll estimation, requiring laborious extraction procedure with solvents like acetone and dimethyl sulphoxide. Chlorophyll meters determine only ‘greenness’ index, which has to be converted into scientifically standard units in order to make the data comprehensive. Prediction models for inter-conversion of chlorophyll units are available for crops like rice, wheat, sorghum, barley, maize, etc., but not for sugarcane till date. In the present study, total chlorophyll content was recorded in diverse sugarcane germplasm and commercial hybrids using both non-destructive and destructive sampling methods. A strong positive correlation was observed between meter readings (SPAD and CCI) with total chlorophyll content estimated using 80% acetone (r = 0.800 and 0.793) and dimethyl sulphoxide (r = 0.915 and 0.868). Regression models for the best fit curve between meter reading and extracted chlorophyll values of the tested sugarcane germplasm and hybrids were non-linear, polynomial equations of the second order. The model developed was validated in an independent experiment wherein sugarcane variety Co 86032 was subjected to increasing nitrogen levels. Highly significant linear regression was found between observed and predicted values of all estimates of total chlorophyll content with almost negligible prediction error. Thus, the model calibrated and validated for sugarcane germplasm and commercial hybrids would be a small yet significant step towards aiding high-throughput phenotyping in sugarcane thereby accelerating crop improvement programmes.