Trends in Crop Response in Long-Term Field Experiment | Statistical Modeling

Rajarathinam Arunachalam

ISBN 10: 3846529338 / ISBN 13: 9783846529331

Published by LAP LAMBERT Academic Publishing, 2011

About this Item

Trends in Crop Response in Long-Term Field Experiment | Statistical Modeling | Rajarathinam Arunachalam | Taschenbuch | 156 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783846529331 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

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Bibliographic Details

• Title: Trends in Crop Response in Long-Term Field Experiment | Statistical Modeling
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2011
• Language: English
• ISBN 10: 3846529338
• ISBN 13: 9783846529331
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 10D millimeters 220 millimeters width by 150 millimeters height by 10 millimeters depth
• Weight: 250 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

In long-term experiments on fertilizer nutrients and manures, the original data are of zig-zag in nature and there is a need to apply trend-fitting techniques to develop smooth trends to enable interpretation of the data in a better way. Very few attempts have been made in this direction. Present investigation on two long-term experiments was undertaken (i) to fit the trends in crop yields due to various treatments and (ii) to study the characteristics and to compare the trends for assessing the crop response to manure and fertilizer nutrients. The treatment-wise yield data for two crop sequences viz., groundnut-wheat-fodder sorghum at Junagadh and paddy-wheat-greengram at Navsari centers of the then Gujarat Agricultural University for 14 to 18 years were used. Two different approaches viz., parametric and non-parametric regression methods were employed to fit the trend on yield due to different treatments over the years. In parametric regression approach, polynomial and Auto Regressive Integrated Moving Average (ARIMA) methods were employed. Eight different polynomial models with varying degrees were tried using the moving average values. Among the eight polynomial m

About the Author

Dr.A.Rajarathinam is a Professor of Statistics and currently Head of the Department of Statistics at Manonmaniam Sundaranar University, Tamil Nadu, India. He previously taught at Anand Agricultural University, Anand, Gujarat, India in the area of Agricultural Statistics.He received the Ph.D. degree in Agricultural Statistics.