Agriculture Staitistics
BIOMETRICS – Vol. II - Statistical Methodology in Agriculture and Horticulture - A. Mead
STATISTICAL METHODOLOGY IN AGRICULTURE AND
HORTICULTURE
A. Mead
Warwick HRI, University of Warwick, U.K
Keywords: Variability, experimental design, analysis of variance (ANOVA), regression, generalized linear model (GLM), analysis of deviance, restricted maximum likelihood (REML), spatial data, precision agriculture, on-farm experimentation.
Contents
U
SA NE
M SC
PL O
E –
C EO
H
AP LS
TE S
R
S
1. Introduction
2. Current methodology
2.1. Experimental Design
2.2. Analysis of Variance
2.3. Regression Analysis
2.3.1. Linear Regression
2.3.2. Non-linear Regression
2.4. Generalized Linear Models (GLMs)
2.5. Residual or Restricted Maximum Likelihood (REML)
3. Future developments
3.1. Analysis of Spatial Data
3.2. Precision Agriculture
3.3. On-farm Experimentation
Glossary
Bibliography
Biographical Sketch
Summary
Many modern statistical techniques were first developed for use in agricultural research, and many basic statistical tools are still important for such research. Good experimental design, following the basic principles of replication, blocking and randomization, allows the control of anticipated environmental variation and the estimation of treatment effects in the presence of such variation. Analysis of variance provides a wide-ranging approach to the analysis of data from designed experiments, aiding the interpretation of the results of complex experiments. Regression analysis can be used to explore the relationships between a quantitative response variable and one or more quantitative explanatory variables. Linear regression techniques primarily provide an exploratory approach, whilst non-linear regression techniques allow the modeling of responses using biologically realistic relationships. Generalized linear models (GLMs) provide an important tool for working with the non-Normally distributed data that is common in the crop protection experimentation that frequently
STATISTICAL METHODOLOGY IN AGRICULTURE AND
HORTICULTURE
A. Mead
Warwick HRI, University of Warwick, U.K
Keywords: Variability, experimental design, analysis of variance (ANOVA), regression, generalized linear model (GLM), analysis of deviance, restricted maximum likelihood (REML), spatial data, precision agriculture, on-farm experimentation.
Contents
U
SA NE
M SC
PL O
E –
C EO
H
AP LS
TE S
R
S
1. Introduction
2. Current methodology
2.1. Experimental Design
2.2. Analysis of Variance
2.3. Regression Analysis
2.3.1. Linear Regression
2.3.2. Non-linear Regression
2.4. Generalized Linear Models (GLMs)
2.5. Residual or Restricted Maximum Likelihood (REML)
3. Future developments
3.1. Analysis of Spatial Data
3.2. Precision Agriculture
3.3. On-farm Experimentation
Glossary
Bibliography
Biographical Sketch
Summary
Many modern statistical techniques were first developed for use in agricultural research, and many basic statistical tools are still important for such research. Good experimental design, following the basic principles of replication, blocking and randomization, allows the control of anticipated environmental variation and the estimation of treatment effects in the presence of such variation. Analysis of variance provides a wide-ranging approach to the analysis of data from designed experiments, aiding the interpretation of the results of complex experiments. Regression analysis can be used to explore the relationships between a quantitative response variable and one or more quantitative explanatory variables. Linear regression techniques primarily provide an exploratory approach, whilst non-linear regression techniques allow the modeling of responses using biologically realistic relationships. Generalized linear models (GLMs) provide an important tool for working with the non-Normally distributed data that is common in the crop protection experimentation that frequently
Bibliography: Cochran, W.G. & Cox, G.M. (1957). Experimental Designs (Second edition), 611 pp. New York: Wiley. Dobson, A.J. (2001). An Introduction to Generalised Linear Models (Second edition), 240 pp. London: Chapman & Hall Draper, N.R. & Smith H. (1998). Applied Regression Analysis (Third edition), 736 pp. New York: Wiley. Mead, R. (1988). The Design of Experiments: Statistical Principles for Practical Application, 620 pp. Biometry (including a dissertation on "Multidimensional Scaling and its application in Sensory Analysis") at the University of Reading (1987)