Bocci, Chiara

Monica, Pratesi

Nicola, Salvati

Petrucci, Alessandra

English

Florence Research

Agricultural Use of Tuscan Landscape: Small Area Estimation and Sub-Regional Mapping Uso agricolo del territorio in Toscana: stima per piccole aree e mappatura a livello sub-regionale   Chiara Bocci, Alessandra Petrucci Dipartimento di Statistica “G. Parenti,  Università degli Studi di Firenze, viale Morgagni, 59 – 50134 Firenze bocci@ds.unifi.it alex@ds.unifi.it  Monica Pratesi, Nicola Salvati Dipartimento di Statistica e Matematica Applicata all’Economia,  Università degli Studi di Pisa, via Ridolfi, 10 – 56124 Pisa m.pratesi@ec.unipi.it salvati@ec.unipi.it    Riassunto: Gli attuali disegni di campionamento utilizzati in ambito agricolo sono stati progettati per avere stime a livello regionale. Per ottenere stime per domini sub-regionali si rende quindi necessario applicare metodologie specifiche, basate su modelli che utilizzano informazioni ausiliarie relative anche alle aree “vicine”. Tali stimatori indiretti considerano, oltre alla classica variabilità spiegata dalle variabili ausiliarie del modello, anche quella indotta da specifici effetti di area. Seguendo tale approccio, il lavoro descrive l’applicazione, sia a dati simulati che a dati reali (SPA), di uno stimatore EBLUP combinato con un modello a effetti casuali di area spazialmente correlati. I risultati ottenuti permettono la mappatura dell’uso agricolo del territorio regionale con attenzione a domini geografici più fini di quelli attualmente disponibili.  Keywords: Small area estimation, Spatial correlation, Spatial EBLUP, Simultaneous autoregressive model.   1. Introduction  In the context of Italian agricultural surveys the term “small area” generally refers to a local geographical area, such as province or municipality. Predicting average or total crop area for municipalities has not often been attempted, both due to a lack of available survey data and the interest focused on Italian provinces (Benedetti et al., 2004). When traditional municipality-specific direct estimator does not provide adequate precision it is possible to employ indirect estimators that “borrow strength” from related areas. The indirect estimators can incorporate specific random area effects that account for between areas variation beyond what is explained by auxiliary variables included in the model. Traditionally the random area effects are considered independent, but in practice, basically in most of the applications on environmental data, it should be more reasonable to assume that the random area effects between the neighbouring areas (for instance the neighbourhood could be defined by a contiguity criterion) are correlated and the correlation decays to zero as distance increases.  – 177 –The aim of this work is to estimate the average production of olives in each Local Economy System (LES) of the Tuscany, using the EBLUP estimator under a model with spatially correlated errors (Salvati, 2004). The LES are aggregations of municipalities but they are different from provinces. The paper is organized as follows: section 2 recalls the Spatial EBLUP procedure. Section 3 discusses the results of the application of Spatial EBLUP to the estimation of the average production of olives at LES level and reports some final remarks.   2. Spatial area level random effect models  Letϑ  be the parameter of inferential interest (small area total iy , small area mean iy  with i = 1…m) and assume that the direct estimator ϑˆ  is available and design unbiased  e+= ϑϑˆ                                                                                                              (1)  with e independent sampling error with mean 0 and variance ϕ . The spatial dependence among small areas is introduced specifying a linear mixed model with spatially correlated random effects for the ϑ  parameter:   ZvXβ +=ϑ                                                                                                        (2)  where X is the matrix of the area specific auxiliary covariates ),...,,( 21 ipiii xxx=x , β  is the regression parameters vector 1×p , Z is a matrix of known positive constants, v is the second order variation. The deviations from the fixed part of the model Xβ  are the result of an autoregressive process with parameter ρ  (spatial autoregressive coefficient) and proximity matrix W (Cressie,1993; Anselin, 1992):   uWIvuWvv 1)( −−=⇒+= ρρ                                                                       (3)  where u  is a vector of independent error terms with zero mean and constant variance 2uσ  and I is mm×  identity matrix. Combining (1) and (2), with e independent of v, the model with spatially correlated errors is:  .)(ˆ 1  euWIZXβ +−+= −ρϑ                                                                               (4)  The error terms v and e have respectively mm×  covariance matrices:  ( )( )[ ] 12 −−−= Tu WIWIG ρρσ  and )( idiag ϕ=R                                (5)  Under the model, the Spatial Best Linear Unbiased Predictor (Spatial BLUP) estimator of iϑ  is:  – 178 – ( )( )[ ]( )( )[ ]{ } )ˆˆ()(ˆ),(~112122βXZWIWIZZWIWIbβx−−−+××−−+=−−−ϑρρσϕρρσρσϑTTuiTTuTiiuSidiag                                    (6)  where ϑˆ(ˆ 111 −−−= VXX)VXβ TT  and Tib  is a m×1  vector 0)0,1,(0,0 ……  with value 1 in the i-th position. The estimator ),(~ 2 ρσϑ uSi  depends on the unknown variance components 2uσ  and ρ . Replacing the parameters with asymptotically consistent estimators ρσ ˆ,ˆ 2u , a two stage estimator )ˆ,ˆ(~ 2 ρσϑ uSi  is obtained and it is called Spatial EBLUP. The estimators can be obtained iteratively using the “Nelder-Mead” algorithm (Nelder and Mead, 1965) and the “scoring” algorithm in sequence. The Mean Squared Error and its estimation are not reported, more details are in Salvati (2004).   3. Results and final remarks  The Tuscany region is divided in 42 LES. The objective of inference is the average production of olives ( y=ϑ ) for each of the 42 small areas (LES). Auxiliary data at the LES level were available for this study. The major determinants of the average crop area are utilized: the surface area for production of olives (ha).  Hence, the Spatial EBLUP method is implemented to this data to estimate the average production of olives in each of the 42 small area within the study region using a SAR spatial model. The neighbourhood structure W is defined as follows: spatial weight, ijw  , is 1 if area i shares an edge with area j and 0 otherwise. For an easier interpretation, the general spatial weight matrix is defined in row standardized form, in which the row elements sum to one. Sampling variances, iϕ  are estimated smoothing the sampling error associated to the population level estimator (Rao, 1998). The estimated variance iϕˆ  is then treated as a proxy to iϕ . As result the )]ˆ,ˆ,ˆ(~[ 2 iuSimse ϕρσϑ  is greater than )],ˆ,ˆ(~[ 2 iuSimse ϕρσϑ . The value of the estimated spatial autoregressive coefficient ρˆ  is 0.859 ( 113.0.. =es ) with ML procedure and suggests small spatial relationship. Figure 1 displays the map of the Tuscany with the Spatial EBLUP estimates for the average production of olives per LES. The Spatial EBLUP method provides estimates with smaller average estimated standard errors than the EBLUP estimators (Table 1). Table 1 shows also the average estimated of MSE and its decomposition in g1, due to the random effects, g2, which accounts for the variability in the estimator βˆ , g3 due to estimate ρ  and 2uσ . To assess the accuracy of the Spatial EBLUP estimator a simulation study was carried out. The results (not reported here for lack of space) show a slight improvement of the MSE estimates (Pratesi and Salvati, 2005).   – 179 –Figure 1: The average production of olives  estimated for the 42 LES of Tuscany.      Table 1: Average Estimated Standard Errors (A.E.Se.) of  EBLUP and Spatial EBLUP estimators.   Estimator A.E.Se. A.E.MSE A.E.(g1) A.E.(g2) A.E.(g3) Spatial EBLUP 1.14  1.61 1.43 0.65 0.03 EBLUP 1.34 2.15 0.90 0.57 0.06  In conclusion, considering the case study, the use of Spatial EBLUP methodology, which takes into account the SAR spatial model in the small area estimation, reduces the confidence interval.   References  Anselin, L. (1992) Spatial Econometrics: Method and Models,  Kluwer Academic Publishers, Boston. Benedetti, R., Espa, G., Piersimoni, F. (2004): Esperienze di Stime provinciali indirette di variabili aziendali. Contributo al Convegno ISPA 2004, Università di Cassino. Cressie, N. (1993) Statistics for Spatial Data, John Wiley & Sons, New York. Pratesi, M., Salvati, N. (2005) Small Area Estimation: the EBLUP estimator with autoregressive random area effects, Working Paper 261, Dipatimento di Statistica e Matemtica Applicata all’Economia, Università di Pisa. Rao, J.N.K. (2003) Small Area Estimation, John Wiley & Sons, New York. Salvati, N. (2004) La correlazione spaziale nella stima per piccole aree: metodi proposti e casi di studio, Tesi di Dottorato in Statistica Applicata, Dipartimento di Statistica “G. Parenti”, Università degli Studi di Firenze. – 180 –

Agricultural use of Tuscan landscape: Small area estimation and sub-regional mapping

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Agricultural use of Tuscan landscape: Small area estimation and sub-regional mapping

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