MODEL FOR CORN KERNELS WEIGHT ESTIMATING BASED ON MATURE CORN EARS DIMENSIONAL PARAMETERS PUBLISHEDRobert Drienovsky, Adelina Anghel, Florin Sala firstname.lastname@example.org
By regression analysis in the present study were obtained models of grain weight prediction based on the dimensional parameters of mature ears of corn. There were randomly sampled 35 mature corn ears of variable sizes, the MAS59 hybrid, the FAO 500 Group. Were determined the parameters: corn ear length (L), base diameter (BD), middle diameter (MD) and tip diameter (TD) of corn ears, total weight of each corn ear (TW), grains weight (GW) and weight of corncobs (CCW). The length of the corn ears (L) recorded values between 12.6 - 20.3 ± 0.32 cm. The diameter at the base of the corn ears (BD) had values between 43.54 - 58.95 ± 0.57 mm; the diameter at the middle of corn ears (MD) had values between 45.34 - 55.78 ± 0.39 mm, and the diameter at the top of corn ears (TD) had values between 38.15 - 49.29 ± 0.41 mm. Total weight of each corn ears (TW) recorded values between 144.9 - 357.2 ± 9.76 g, and the weight of the grains varied between 128.7 - 300.3 ± 8.09 g. Corncobs weight recorded values between 16.2 - 59.9 ± 1.81 g. Given the high level of correlations identified between the studied biometric parameters and the grain weight, multiple linear regression analysis was used to test the possibility and safety of grain weight prediction, based on the biometric parameters of corn ears. A weight estimation equation of the grains was obtained based on all the parameters taken in the study, under conditions of R2 = 0.998, p << 0.001. From the regression analysis it was possible to obtain some functions of corn grains weight prediction based on each parameters, under conditions of R2 = 0.801 for parameter L, R2 = 0.811 for parameter BD, R2 = 0.590 for parameter MD, R2 = 0.345 for parameter TD, R2 = 0.993 for parameter TW, and R2 = 0.849 for parameter CcW, respectively.
correlations, grains weight, maize ears, prediction model, regression analysis
Biology applied in Agriculture