Evaluating Urban Sewage Sludge Distribution on Agricultural Land Using Interpolation and Machine Learning Techniques

Andrea Lazzari, Simone Giovinazzo, Giovanni Cabassi, Massimo Brambilla, Carlo Bisaglia and Elio Romano ()
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Andrea Lazzari: Council for Agricultural Research and Economics (CREA), Research Centre Animal Production and Aquaculture, Via Antonio Lombardo 11, 26900 Lodi, Italy
Simone Giovinazzo: Council for Agricultural Research and Economics (CREA), Research Centre for Engineering and Agro-Food Processing, Via Milano 43, 24047 Treviglio, Italy
Giovanni Cabassi: Council for Agricultural Research and Economics (CREA), Research Centre Animal Production and Aquaculture, Via Antonio Lombardo 11, 26900 Lodi, Italy
Massimo Brambilla: Council for Agricultural Research and Economics (CREA), Research Centre for Engineering and Agro-Food Processing, Via Milano 43, 24047 Treviglio, Italy
Carlo Bisaglia: Council for Agricultural Research and Economics (CREA), Research Centre for Engineering and Agro-Food Processing, Via Milano 43, 24047 Treviglio, Italy
Elio Romano: Council for Agricultural Research and Economics (CREA), Research Centre for Engineering and Agro-Food Processing, Via Milano 43, 24047 Treviglio, Italy

Agriculture, 2025, vol. 15, issue 2, 1-13

Abstract: The European Union promotes the development of a sustainable approach to solid waste management and disposal. Sewage sludge (SWS) is a good example of this economic model because it has fertilizing and soil-conditioning characteristics. This study employed a conventional manure spreader to evaluate the distribution of SWS on agricultural land. Various interpolation methods and machine learning models were employed to analyze the spatial distribution patterns of the sludge. Data were collected from 15 sampling trays across a controlled field during three separate trials. Statistical analysis using ANOVA highlighted significant variations in sludge quantities along the longitudinal axis but not along the latitudinal one. Interpolation methods, such as spline, cubic spline, and inverse distance weighting (IDW) were used to model the distribution, while machine learning models (k-nearest neighbors, random forest, neural networks) classified spatial patterns. Different performance metrics were calculated for each model. Among the interpolation methods, the IDW model combined with neural networks achieved the highest accuracy, with an MCC of 0.9820. The results highlight the potential for integrating advanced techniques into precision agriculture, improving application efficiency and reducing environmental impact. This approach provides a solid basis for optimizing the operation of agricultural machinery and supporting sustainable waste management practices.

Keywords: precision agriculture; manure spreader; soil improvement; ANOVA; big data; spatial analysis (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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