On the use of heterogenous and complex spatio-temporal data to produce reliable territorial agricultural statistics

Paolo Maranzano (Università degli Studi di Milano-Bicocca)

Thursday 23rd July 12:00-13:00
Maths 311B

Abstract

We present “Sequestering CARbon through Forests, AgriCulture, and land usE” (SCARFACE), a harmonized spatio-temporal dataset that integrates climate, air quality, airborne pollutant emissions, land cover, soil properties, agro-industry dynamics and socio-economic indicators, to jointly investigate interconnected processes linking agricultural systems, atmospheric dynamics, emissions, and socioeconomic conditions in the Po Valley, Northern Italy. The dataset adopts an annual panel structure from 2011 to 2024 defined over the 256 Agrarian Sub-Regions partitioning the Po Valley and comprises more than 2,700 indicators sourced from national and international public institutions. Heterogeneous data are harmonized within a processing workflow, tailored to the specific characteristics of each dataset, that guarantee spatial and temporal consistency of the output dataset. The dataset is designed for broad reuse across a range of analytical settings, including panel data analysis at moderate spatial and temporal resolutions, high-dimensional spatio-temporal modeling, spatial and spatio-temporal clustering, and policy-oriented applications. In particular, we present promising applications of area-level small area estimation (SAE) models aiming at producing reliable estimates of agronomic practices and the techno-productive structure of farms are derived from representative surveys, whose direct estimates may lack precision at fine spatial and temporal resolutions.

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