Showing papers by "Indian Agricultural Statistics Research Institute published in 1996"

Use of spectral data in Markov chain model for crop yield forecasting

Abstract: Crop yield is mainly dependent on weather, soil and technological inputs. Yield forecasting models have been developed mainly using multiple regression techniques based on biometrical characters of the plants and/or weather parameters. Matiset al. (1985) proposed another approach of crop yield modelling using Markov Chain theory based on biometrical characters. The integration of remote sensing with other technologies has provided an immense scope to improve upon the existing crop yield models. In the present study, multi date spectral data during crop growth period was used in Markov Chain Model to forecast wheat yield. The results indicate that the use of spectral data near the maximum vegetative growth of wheat crop improves the efficiency and reliability of yield forecast about a month before its actual harvest.

Quantitative Structure−Activity Relationship Studies of O,O-Bisaryl Alkyl Phosphonate Fungicides by Hansch Approach and Principal Component Analysis

Abstract: Quantitative structure−activity relationship studies of O,O-bisaryl alkyl phosphonate fungicides were conducted by the Hansch approach. The variation in fungicidal activity among members of the series against all the four test fungi depended mainly on changes in hydrophobicity of the phenyl substituents and molar refractivity of the alkyl substituent. Molar refractivity of the substituents at the para position of the phenyl ring and electronic factors also played significant roles in two cases each. Principal component analysis of the fungicidal activity data revealed that the first principal component had 79.2% of the information content. Physicochemical interpretation of the scores corresponding to the first principal component gave results consistent with those obtained by Hansch analysis. It appears that the fungicidal molecule interacts with the bioreceptor through the two para positions of the phenyl rings and the alkyl substituent, and that the steric bulk of the substituents play a dominant role d...

A-efficient block designs for comparing two disjoint sets of treatments

Abstract: This article studies an experimental setting in which it is desired to compare v1 test treatments with v2 sun control treatments via bk experimental units arranged in b blocks of size k each. Under this setting some sufficient conditions are obtained to establish the A-optimality of designs for making test treatments -control treatments comparisons. Some general methods of construction of variance balanced designs for making test treatments-control treatments comparisons are given. The A-efficiencies of the designs constructed is investigated. A table of some designs for two control treatments alongwith their A-efficiencies is prepared.

Trend analysis in all-India marine products export using statistical modelling techniques

Abstract: Various statistical modelling techniques, viz. Polynomial function fitting approach, Non-linear mechanistic growth modelling approach and ARIMA time-series methodology are employed for describing the all-India marine products export during 1960-'61 to 1994-'95. The underlying assumptions are examined by statistically sound procedures. Attempts are made to develop the most suitable statistical model. The identified model is then used to forecast the marine products export for three more years

Forecast of Sugarcane Yield Using Eye Estimate Alongwith Plant Characters

Abstract: The present study proposes to include farmers' appraisal (eye estimate) as one of the variables in the models alongwith plant characters. This will reduce the number of plant character in the model, thus reducing the cost on data collection. This has been demonstrated through regression and probability models.

On the Estimation of Intra‐Sire Regression Heritability Subject to Linear Constraint

Abstract: The derivation of the restricted intra-sire regression heritability estimator is provided. Procedures for obtaining a stable estimate of residual error variance σ 2 are outlined. A small illustration based on live data is given.