Small area methods for agricultural data: A two-part geoadditive model to estimate the agrarian region level means of the grapevines production in Tuscany

Small area estimation (SAE) based on linear mixed models can be inefficient to deal with semicontinuous variables that have a portion of values equal to zero and a continuous, often skewed, distribution among the remaining values. Moreover when small domains of study are geographical areas an adequate use of geographic information and geographical modeling can provides more accurate estimates for small area parameters. In this paper we suggest a two-part geoadditive small area model that can deal with these issues, frequently addressed in many fields of applied research, among which the agricultural one. The performance of the suggested model is then illustrated by an application to agricultural data: the model is used to estimate the per farm average grapevine production in Tuscany at Agrarian Region level.