Abstract
This study aimed to predict the spatial distribution of cattle dung based on Bayesian estimation with a generalized linear mixed model (GLMM) and an intrinsic conditional autoregressive (CAR) model using herbage green biomass (GBM) and distance from a water trough (Dw) in a rectangular slope-grazed pasture (0.85 ha) in Hokkaido, Japan. After a 4-day grazing trial (June 14–18, 2010) with 20 Japanese Black cattle, we set 10 × 10 m grid cells (total of 85 cells) in the paddock and counted the number of dung deposits (Nd) in each cell. The 95% posterior probability intervals indicated that a greater Nd was distributed in areas with a higher GBM and those located closer to the water trough. Lower value of deviance information criterion (DIC) was obtained in CAR model (DIC = 291.5) than in GLMM (DIC = 502.6) to be suggested as a better model.
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