Not AvailableQuantification of plants biophysical variable, economic yield and oil content of
oilseed Brassica is important to know the potential impact of in-season weather
variability. Agroclimatic models may be used to predict the plants' response and
adaptability in the soil-plants-atmospheric systems and thereby screening various
mitigation options to combat impinging climate change. In this study, some
important biophysical indicators viz., leaf area index (LAI), dry biomass,
economic seed yield and oil content of Indian mustard have been predicted using
thermal unit based regression models following field experimentations carried out
in two consecutive winter seasons of 2005-06 and 2006-07 on a sandy clay loam
soil of IARI research farm, New Delhi. Linear and non-linear regression models
were developed in which thermal indices viz., Growing Degree Days (GDD),
Heliothermal Unit (HTU) and Photothermal Unit (PTU) have been used as
independent variables. These thermal units were cumulated up to maximum leaf
area index and dry biomass and 50% physiological maturity. Models developed
from pooled data showed statistically significant and positive correlations existed
between biophysical variables with thermal units.GDDand PTU based regression
models may be recommended for predicting leaf area index (LAI = 0.008 ×GDD-
3.54; R = 0.78 * * and LAI = 0.0007 × PTU - 3.31; R = 0.75 * *) and dry biomass
production (Dry biomass = 1.89 × GDD - 1060.3; R = 0.87 * * and Dry biomass =
0.15 ×PTU- 794.02;R = 0.85 * *).HTUbased regression models were found to be
better predictor only when accumulated values of the index exceeded 1000 Cd
hours (LAI = 0.0005 × HTU + 0.69; R = 0.31 and Dry biomass = 0.11 × HTU +
202.81; R = 0.51). The generated agroclimatic models may be complementary to
decision support systems for predicting biophysical parameters under semi-arid
subtropical environment using daily information on critical weather parameters.Not Availabl
