Andere Kunden interessierten sich auch für
![Some Statistical Models for Crop Yield Forecasting]( "Some Statistical Models for Crop Yield Forecasting")
Some Statistical Models for Crop Yield Forecasting32,99 €![Crop Yield Forecasting Using Discriminant Function Analysis]( "Crop Yield Forecasting Using Discriminant Function Analysis")
Crop Yield Forecasting Using Discriminant Function Analysis32,99 €![Statistical Study on Weather Based Crop Modelling & Yield Forecasting]( "Statistical Study on Weather Based Crop Modelling & Yield Forecasting")
Statistical Study on Weather Based Crop Modelling & Yield Forecasting29,99 €![Impact -Based Forecasting]( "Impact -Based Forecasting")
Impact -Based Forecasting26,99 €![Role of preharvest sprays of growth retardants on sweet potato]( "Role of preharvest sprays of growth retardants on sweet potato")
Role of preharvest sprays of growth retardants on sweet potato43,99 €![Time Series Modelling And Forecasting]( "Time Series Modelling And Forecasting")
Time Series Modelling And Forecasting36,99 €![Methodology of forecasting land fund diversification]( "Methodology of forecasting land fund diversification")
Methodology of forecasting land fund diversification18,99 €
Forecasting of preharvest apple yield is essential for timely management of preharvest and post harvest inputs and outputs. Apple yield is mainly affected by biometrical characters and weather parameters. In this book therefore, primary data were collected on the apple yield and biometrical characters of apple trees of five blocks of Kullu district of H.P. Maximum yield was observed in Nagar block & minimum in Banjar block during the study. Yield was found to be significantly and positively correlated with age, height, girth, diameter, canopy (N-S), canopy (E-W) and number of branches biometrical characters of apple tree. Whereas, forecasting model based on multiple regression analysis for Kullu district is Y=1.1272 + 0.9638X1 + 2.6485X2 + 3.5237X3 + 0.2138X4 + 0.4467X5 + 0.8395X6 + 1.3490X7 +1.0280X8 found to be fitted well based on R2, adjusted R2, SSE and RMSE in the data of biometrical characters of apple tree. The analysis concluded that tree age, tree height, girth, canopy spread, volume and number of branches are the important biometrical characters which effect apple yield. So, these characters are not to be neglected in an experiment dealing with apple yield forecasting.