A Comparative Analysis of Machine Learning and Pedotransfer Functions Under Varying Data Availability in Two Greek Regions

Panagiotis Tziachris (), Panagiota Louka, Eirini Metaxa, Miltiadis Iatrou and Konstantinos Tsiouplakis
Additional contact information
Panagiotis Tziachris: Soil and Water Resources Institute, Hellenic Agricultural Organization (ELGO)—“DEMETER”, 570 01 Thessaloniki, Greece
Panagiota Louka: NEUROPUBLIC SA, 6 Methonis St., 185 45 Piraeus, Greece
Eirini Metaxa: Soil and Water Resources Institute, Hellenic Agricultural Organization (ELGO)—“DEMETER”, 570 01 Thessaloniki, Greece
Miltiadis Iatrou: Soil and Water Resources Institute, Hellenic Agricultural Organization (ELGO)—“DEMETER”, 570 01 Thessaloniki, Greece
Konstantinos Tsiouplakis: Soil and Water Resources Institute, Hellenic Agricultural Organization (ELGO)—“DEMETER”, 570 01 Thessaloniki, Greece

Agriculture, 2025, vol. 15, issue 11, 1-19

Abstract: The current study evaluates the performance of pedotransfer functions (PTFs) and machine learning (ML) algorithms in predicting the soil bulk density (BD) across two distinct regions in Greece—Kozani and Veroia—using both limited and extended sets of soil parameters. The results reveal significant regional differences in prediction accuracy. In the full dataset scenario, Veroia consistently exhibits superior predictive performance across all models (PDF RMSE: 0.104, ML RMSE: 0.095) compared to Kozani (PDF RMSE: 0.133, ML RMSE: 0.122). Generally, ML models outperform PTFs in terms of the RMSE and MAE in both regions with the full dataset. However, PTFs occasionally demonstrate higher R 2 values (Veroia PTF R 2 : 0.35 vs. ML R 2 : 0.28), suggesting a better explanation of the overall variance despite larger errors. Notably, the effectiveness of ML appears to be affected by the availability of data. In Kozani, when restricted to basic soil properties, ML’s performance (RMSE: 0.129, R 2 : 0.16) becomes similar to that of PTFs (RMSE: 0.133, R 2 : 0.16). However, incorporating the full dataset substantially enhances ML’s predictive power (RMSE: 0.122, R 2 : 0.26). Conversely, in Veroia, the inclusion of more variables paradoxically results in a slight decline in ML performance (ML_min RMSE: 0.093, R 2 : 0.31 vs. ML RMSE: 0.095, R 2 : 0.28). These contrasting results emphasize the need for context-specific modeling strategies, careful feature selection, and caution against the assumption that more data or complexity inherently improves the predictive performance.

Keywords: soil bulk density; soil parameters; machine learning; pedotransfer functions; soil organic carbon (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
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/11/1134/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/11/1134/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:11:p:1134-:d:1663565

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().