Spatial Modelling Techniques to Improve Data Analysis of Field Trials
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One of the major problem in the analysis of agricultural field trials is accounting for field variability. Lack of sound spatial model selection method hindered the use of appropriate spatial models to adequately account for field variability. Models currently in use for local and global variations are fraught with difficulties due to confounding problem between local and global variations. Strategies for monitoring field variability, methods of model selection and further development in spatial models are required to account for both local and global variation. Strategies that lead to an impr...
One of the major problem in the analysis of agricultural field trials is accounting for field variability. Lack of sound spatial model selection method hindered the use of appropriate spatial models to adequately account for field variability. Models currently in use for local and global variations are fraught with difficulties due to confounding problem between local and global variations. Strategies for monitoring field variability, methods of model selection and further development in spatial models are required to account for both local and global variation. Strategies that lead to an improved analysis of data from field trials are also necessary. Lessons learnt through the analysis of 513 large field trials from Ethiopia form the basis for development and extension of spatial models. Inefficiency of the conventional methods of analysis and possibility of improving both modelling and design is illustrated using results from the data. The book is useful for researchers, academicians and studnets in agriculture, biology, environment, natural resources.
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