Agricultural research is most oftenly based on observational studies and experimentation resulting in multi-response variables. The selection of appropriate variety to grow; amount and types of fertilizers, insecticides and pesticides to apply; the irrigation system to use; the plant sowing technology to apply and to assess the soil fertility through chemical analysis of macro and micro nutrients available in the soil are the major areas of interest for the researcher to work on for the improvement of the agricultural productivity in terms of quality and quantity. The role of Statistics in planning agricultural research, designing experiments, data collection, analysis, modeling and interpretation of agricultural results is very well established. The basic principles and theoretical development of experimental designs pioneered by R. A. Fisher are the result of collaborative work of agricultural scientists and statisticians. In the process of experimentation and observational studies,...
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References and Further Reading
Ali A (1983) Interpretation of multivariate data: Comparison of several methods of interpreting multivariate data from a series of nutritional experiments, University of Sussex, Unpublished PhD thesis
Ali A, Clarke GM, Trustrum K (1985) Principal component analysis applied to some data from fruit nutrition experiments. The Statistician 34:365–370
Ali A, Clarke GM, Trustrum K (1986) Log-linear response functions and their use to model data from plant nutrition experiments. J Sci Food & Agric 37:1165–1177
Hotelling H (1936) Relation between two sets of variates. Biometrika 28:321–377
Jolliffe IT (2002) Principal component analysis, 2nd edn. Springer, USA
Mardia KV, Kent JT, Bibi JM (1979) Multivariate analysis. Academic, London
McCullagh P, Nelder JA (1989) Generalized linear models, 2nd edn. Chapman and Hall, London
Mead R, Pike DJ (1975) A review of response surface methodology from a biometric viewpoint. Biometrics 31(4):803–851
Nelder JA (1966) Inverse polynomials, a useful group of multifactor response functions. Biometrics 22:128–141
Nelder JA (1977) A reformation of linear models (with discussion), J R Stat Soc A140:48–76
Nelder JA, Wedderburn WM (1972) Generalized linear models. J R Stat Soc (General) A135(3):370–384
Pike DJ (1977) Inverse polynomials: A study of parameter estimation procedures and comparison of the performance of several experimental design criteria. University of Reading, Unpublished PhD thesis
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Ali, A. (2011). Analysis of Multivariate Agricultural Data. In: Lovric, M. (eds) International Encyclopedia of Statistical Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04898-2_116
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