Integrating numerical methods and machine learning to optimize agricultural land use

Aug 4, 2026

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Optimizing agricultural land use becomes increasingly critical as food demand rises while natural resources remain limited. This study proposes a combined methodology using numerical methods and machine learning for analyzing and forecasting land-use optimality, aiming to support rational and sustainable management. Principal component analysis and factor analysis reduce dimensionality and extract key latent factors, while the simplex method allocates crop resources under constraints. LightGBM, XGBoost, and SVM predict optimal land use and yield, with LightGBM reaching 99.98% accuracy and offering decision-support value.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Abstract

Introduction

Motivation: food security and resource constraints

Numerical methods for optimization and data analysis

Machine learning for prediction and decision automation

Integrated approach (PCA + FA + Simplex + ML)

Integrating numerical methods and machine learning to optimize agricultural land use Assemgul Tynykulova1, Ayagoz Mukhanova1, Ainagul Mukhomedyarova2, Zhanar Alimova3, Nurbolat Tasbolatuly4, Ulmeken Smailova5, Mira Kaldarova6, Marat Tynykulov7 1Department of Information Systems, L. N. Gumilyov Eurasian National University, Astana, Republic of Kazakhstan 2Institute of Agricultural Technology, West Kazakhstan Agrarian-Technical University, Uralsk, Republic of Kazakhstan 3Faculty of Computer Science, Toraighyrov University, Pavlodar, Republic of Kazakhstan 4Higher School of Information Technology and Engineering, Astana International University, Astana, Republic of Kazakhstan 5Center of Excellence of Autonomous Educational Organization Nazarbayev Intellectual Schools, Astana, Republic of Kazakhstan 6Higher School of Information Technology and Engineering, Astana International University, Astana, Republic of Kazakhstan 7Department of Biotechnology and Microbiology, Faculty of Natural Sciences, L. N. Gumilyov Eurasian National University, Astana, Republic of Kazakhstan Article Info ABSTRACT Article history: Received Apr 16, 2024 Revised Jun 8, 2024 Accepted Jun 16, 2024 Keywords: Dimensionality reduction Factor analysis Feature selection Machine learning models Numerical methods Principal component analysis Corresponding Author: In the current context, optimizing the utilization of agricultural land resources is increasingly vital for production intensification. This study presents a methodological approach employing numerical methods and machine learning algorithms to analyze and forecast land use optimality. The objective is to develop effective models and tools facilitating rational and sustainable agricultural land resource management, ultimately enhancing productivity and economic efficiency. The research employs data dimensionality reduction techniques such as principal component analysis and factor analysis (FA) to extract key factors from multidimensional land data. The simplex method is utilized to optimize resource allocation among crops while considering constraints. Machine learning algorithms including extreme gradient boosting (XGBoost), support vector machine (SVM), and light gradient boosting machine (LightGBM) are employed to predict optimal land use and yield with high accuracy and efficiency. Analysis reveals significant differences in model performance, with LightGBM achieving the highest accuracy of 99.98%, followed by XGBoost at 95.99%, and SVM at 43.65% . These findings underscore the importance of selecting appropriate algorithms for agronomic data tasks. The study's outcomes offer valuable insights for formulating agricultural practice recommendations and land management strategies, integrable into decision support systems for the agricultural sector, thereby enhancing productivity and production efficiency. This is an open access article under the CC BY-SA license. Ayagoz Mukhanova Department of Information Systems, L. N. Gumilyov Eurasian National University 010000 Astana, Republic of Kazakhstan Email: [ayagoz.mukhanova.83@mail.ru](ayagoz.mukhanova.83@mail.ru) 1. INTRODUCTION In the modern world, where the planet's population is constantly growing and natural resources are limited, the task of ensuring food security is becoming increasingly urgent. One of the key directions for solving this problem is the optimization of agricultural land use, which involves efficiently using land resources [1]–[3] to increase productivity [4] while simultaneously reducing the negative impact on the environment. Advances in information technology and machine learning are opening up new opportunities for agriculture, making it possible to analyze large volumes of data to optimize land use decisions. Numerical methods such as principal component analysis (PCA), factor analysis (FA), and simplex method are traditionally used to solve various optimization and data analysis problems. They

FAQs

What goal does the study pursue in agricultural land use optimization?

To develop effective models and tools that analyze and forecast land-use optimality, enabling rational and sustainable management and improving productivity and economic efficiency.

How are key factors extracted from multidimensional land data?

The research applies dimensionality reduction techniques, including principal component analysis and factor analysis, to identify key variables and consolidate latent factors affecting agricultural productivity.

Which machine learning models are used to predict optimal land use and yield, and how do they perform?

LightGBM, XGBoost, and SVM are employed. LightGBM achieves the highest reported accuracy (99.98%), followed by XGBoost (95.99%), while SVM shows lower accuracy (43.65%).

Aug 4, 2026

Upload by Hazel