The use of geographic information system and 1860s cadastral data to model agricultural suitability before heavy mechanization. A case study from Malta

Abstract

The present study seeks to understand the determinants of land agricultural suitability in Malta before heavy mechanization. A GIS-based Logistic Regression model is built on the basis of the data from mid-1800s cadastral maps (cabreo). This is the first time that such data are being used for the purpose of building a predictive model. The maps record the agricultural quality of parcels (ranging from good to lowest), which is represented by different colours. The study treats the agricultural quality as a depended variable with two levels: optimal (corresponding to the good class) vs. non-optimal quality (mediocre, bad, low, and lowest classes). Seventeen predictors are isolated on the basis of literature review and data availability. Logistic Regression is used to isolate the predictors that can be considered determinants of the agricultural quality. Our model has an optimal discriminatory power (AUC: 0.92). The positive effect on land agricultural quality of the following predictors is considered and discussed: sine of the aspect (odds ratio 1.42), coast distance (2.46), Brown Rendzinas (2.31), Carbonate Raw (2.62) and Xerorendzinas (9.23) soils, distance to minor roads (4.88). Predictors resulting having a negative effect are: terrain elevation (0.96), slope (0.97), distance to the nearest geological fault lines (0.09), Terra Rossa soil (0.46), distance to secondary roads (0.19) and footpaths (0.41). The model isolates a host of topographic and cultural variables, the latter related to human mobility and landscape accessibility, which differentially contributed to the agricultural suitability, providing the bases for the creation of the fragmented and extremely variegated agricultural landscape that is the hallmark of the Maltese Islands. Our findings are also useful to suggest new questions that may be posed to the more meagre evidence from earlier periods.