A GIS-Driven, Machine Learning-Enhanced Framework for Adaptive Land Bonitation

Bogdan Văduva (), Anca Avram, Oliviu Matei, Laura Andreica and Teodor Rusu
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Bogdan Văduva: Department of Electrical Engineering, Electronics and Computers, Technical University of Cluj-Napoca, Str. Victor Babes nr. 62/A, 430083 Baia Mare, Romania
Anca Avram: Department of Electrical Engineering, Electronics and Computers, Technical University of Cluj-Napoca, Str. Victor Babes nr. 62/A, 430083 Baia Mare, Romania
Oliviu Matei: Department of Electrical Engineering, Electronics and Computers, Technical University of Cluj-Napoca, Str. Victor Babes nr. 62/A, 430083 Baia Mare, Romania
Laura Andreica: Department of Electrical Engineering, Electronics and Computers, Technical University of Cluj-Napoca, Str. Victor Babes nr. 62/A, 430083 Baia Mare, Romania
Teodor Rusu: Department of Technical Sciences and Soil Sciences, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, Calea Mănăștur 3-5, 400372 Cluj-Napoca, Romania

Agriculture, 2025, vol. 15, issue 16, 1-23

Abstract: Land bonitation, or land rating, is a core instrument in agricultural policy used to evaluate land productivity based on environmental and climatic indicators. However, conventional Bonitation Coefficient (BC) methods are often rigid, require complete indicator sets, and lack mechanisms for handling missing or forecasted data—limiting their applicability under data scarcity and climate variability. This paper proposes a GIS-integrated, modular framework that couples classical BC computation with machine learning-based temporal forecasting and spatial generalization. Specifically, we apply deep learning models (LSTM, GRU, and CNN) to predict monthly precipitation—one of the 17 indicators in the Romanian BC formula—using over 61 years of data. The forecasts are spatially interpolated using Voronoi tessellation and then incorporated into the bonitation process via an adaptive logic that accommodates both complete and incomplete datasets. Results show that the ensemble forecast model outperforms individual predictors, achieving an R 2 of up to 0.648 and an RMSE of 18.8 mm, compared to LSTM ( R 2 = 0.59 ), GRU ( R 2 = 0.61 ), and CNN ( R 2 = 0.57 ). While the case study focuses on precipitation, the framework is generalizable to other BC indicators and regions. This integration of forecasting, spatial generalization, and classical land evaluation addresses key limitations of existing bonitation methods and lays the groundwork for scalable, AI-enhanced land assessment systems. The forecasting module supports BC computation by supplying missing climate indicators, reinforcing that the primary aim remains adaptive land bonitation.

Keywords: G.I.S.; crop yield; land bonitation; machine learning; voronoi tessellation (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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