Assessing Spatial Variability of Soil Properties in Mediterranean Smallholder Farming Systems

• Published in: Land (Basel) 2022-04, Vol.11 (4), p.557
• Main Authors: Kosma, Chariklia, Triantafyllidis, Vassilios, Zotos, Anastasios, Pittaras, Antonios, Kouneli, Varvara, Karydogianni, Stella, Mavroeidis, Antonios, Kakabouki, Ioanna, Beslemes, Dimitrios, Tigka, Evangelia L., Roussis, Ioannis, Bilalis, Dimitrios
• Format: Article
• Language: English
• Subjects: Agricultural land; Agricultural management; Agricultural practices; Agricultural production; Agriculture; Best management practices; Clustering; Corn; Crop fields; Crops; Cultivation; Data collection; Decision analysis; Decision making; Decision support systems; Empirical analysis; Factor analysis; Farmers; Farming systems; Farms; Indicators; Land use; Precipitation; Principal components analysis; Small farms; smallholder farming systems; Soil fertility; soil indicators; Soil management; Soil properties; Soil testing; spatial soil variability; Sustainable practices; Variability
• ISSN: 2073-445X; 2073-445X
• DOI: 10.3390/land11040557

Smallholder farming systems are typical of the European Mediterranean region. Small farms of less than 2 hectares cover approximately 15% of cropland in the southern EU and only 5% across the EU. The greater variability of cultivated species per unit of cropland (ha), the different approaches, and empirical application of cultivation practices by smallholder farmers increase the spatial variability of soil properties. Therefore, a decision support tool for effective management practices was formed based on a soil indicators set, which is sensitive to changes under agricultural management practices and different LUs. The data for this task were collected from 364 crop fields. The data were clustered and correlated based on (a) the existing soil units (SU): Fluvisols, Cambisols, Luvisols, and Calcisols, and (b) the LU: pastureland, annual, and permanent crops. Principal component analysis (PCA) identified up to seven main components that can better explain soil variability properties. The results indicated that the selected soil indicators can explain only 70.98% of soil variability. Clustering the parameters based on LU and SU can explain up to 80% and 82% of soil properties’ variability, respectively. Factor analysis could function as a decision support tool for soil fertility management by farmers or policy makers, who aim to achieve higher yields, promote sustainable practices, maintaining, at the same time, a low cost of cultivation.