Based on the data of weather variables and plant traits collected from field experiments with 7 genotypes of sunflower conducted in 6 kharif seasons during 1994 to 1999 in a rainfed alfisol, an attempt has been made in this paper to predict plant growth and identify weather variables which significantly influence the plant growth as measured by different plant traits. Regression diagnostics like predictability (R2) and prediction error () have been derived to assess the influences of rainfall, sunshine hours maximum and minimum temperature, relative humidity and vapor pressure deficit at 7 AM and 2 PM on leaf nitrogen, leaf area, leaf weight, leaf number stomatal conductance, photosynthesis, stem weight and stem nitrogen 30, 45 and 60 days after sowing. Positive and significant influence of (i) sunshine hours, minimum temperature, relative humidity and vapor pressure deficit at 7 AM on stomatal conductance, (ii) vapor pressure deficit at 2 PM on photosynthesis, and negative and significant influence of (i) relative humidity and vapor pressure deficit at 7 AM and 2 PM on leaf N, (ii) minimum temperature and vapor pressure deficit at 7 AM on leaf number, and (iii) minimum temperature, relative humidity and vapor pressure deficit at 7 AM on stem N were observed. Based on a graphical plot of 64 combinations of predictability (R2) and error in prediction () of plant traits through weather variables, the influences have been categorized into 4 groups viz. high R2 and low (Group I), high R2 and high (Group II), low R2 and high (Group III) and low R2 and low (Group IV). Based on the regression diagnostics of 64 pairs of weather and plant variables, 30 were in Group III 27 in Group I, 5 in Group II and 2 in Group IV. All plant traits were significantly predictable with minimal error through relative humidity at 7 AM (except leaf area) followed by sunshine hours (except leaf N and photosynthesis), relative humidity and vapor pressure deficit at 2 PM (except leaf number, stem N and stomatal conductance) in Group I. Significant influence of rainfall on leaf and stem weight and vapor pressure deficit at 7 AM on stem N and stomatal conductance were also observed in the study. The influences of weather variables on the remaining plant traits were found to be either non-significant or they occurred with a higher prediction error in the 6 years of study.

estimates of correlation; regression analysis; plant traits; weather variables; predictability; error in prediction