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The performance of a series of Complete and Incomplete Neighbour Balanced Block Designs for Auto Regressive (AR), Moving Average (MA) and Nearest Neighbour (NN) error correlation structure is studied when generalized least squares estimation is used. We have compared the efficiency of AR(2), MA(2) and NN correlation structures. It is observed that efficiency for direct as well as neighbour effects is high, in case of complete block designs for NN correlation structure. In case of incomplete block designs MA(2) correlation structure turns out to be more efficient as compared to others models with   2 1 ,  in the interval 0.1 to 0.4. It is therefore concluded that when block sizes are large and neighbouring plots are highly correlated, generalized least squares for estimation of direct and neighbour effects can be used. The gain in efficiency of NNBD and NNBIBD over regular block design is high under MA(2) models for direct and neighbour effects of treatments.

Efficiency of Nearest Neighbour Balanced Block Designs for Second Order Correlated Error Structure