Data-driven Livestock Manure Allocation: Integrating Soil Analysis and Nutrient Requirements with Machine Learning

Dilek Selçuk; Ünal Kızıl

doi:10.29133/yyutbd.1817079

Data-driven Livestock Manure Allocation: Integrating Soil Analysis and Nutrient Requirements with Machine Learning

Abstract

Efficient livestock manure management has become increasingly critical as farm intensification generates large nutrient surpluses that may threaten soil and water quality if not properly allocated. Addressing and implementing existing animal waste through appropriate planning is one of the major environmental problems in our country. In this study, a hybrid model integrating machine learning algorithms (MLA) and the multi-criteria decision-making (MCDM) approach was developed. The study was conducted across 18 fields at Aşkın Dairy Farm, located in Dümrek Village, central Çanakkale province. Gradient Boosting (GB), Random Forest (RF), Support Vector Regression (SVR), and K-Nearest Neighbors (KNN) algorithms were used in the study. Furthermore, the MCDM model enabled prioritization of fields by considering criteria such as distance from barns to fields, plant nitrogen requirements, and analysis results. The results revealed that the GB algorithm was the most successful, with the lowest Root Mean Square Error (RMSE= 0.0057) and the highest R² (0.99). The findings demonstrate that tree-based methods, in particular, are more effective for manure distribution. Furthermore, it was determined that the 1,454 tons of annual manure produced on the farm meets the needs of six fields, while the remaining fields require chemical fertilizer support.

Keywords

Supporting Institution

Çanakkale Onsekiz Mart University, Scientific Research Projects Coordination Unit (BAP)

Project Number

FYL-2024-4948

Ethical Statement

Ethical approval is not required for this study because no direct measurements were made on animals.

Thanks

We would like to thank Barış Aşkın, the owner of Aşkın Farm, for allowing us to use his farm as material.

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Details

Primary Language

English

Subjects

Agricultural Structures , Agricultural Management of Nutrients

Journal Section

Research Article

Authors

Dilek Selçuk
0009-0005-4039-4504
Türkiye

Ünal Kızıl ^*
0000-0002-8512-3899
Türkiye

Publication Date

April 16, 2026

Submission Date

November 4, 2025

Acceptance Date

March 16, 2026

Published in Issue

Year 2026 Number: 1

DOI

https://doi.org/10.29133/yyutbd.1817079

IZ

https://izlik.org/JA42GW89UU

Cite

RIS / Bibtex

APA

Selçuk, D., & Kızıl, Ü. (2026). Data-driven Livestock Manure Allocation: Integrating Soil Analysis and Nutrient Requirements with Machine Learning. Yuzuncu Yıl University Journal of Agricultural Sciences, 1, 1817079. https://doi.org/10.29133/yyutbd.1817079