Economic Analysis of Grain Product Metrics

The study aims to analyse the key factors affecting grain production in Kazakhstan to develop recommendations for improving the efficiency and sustainability of the agricultural sector. Statistical methods and econometric modelling techniques were used, including the least squares method with heteroscedasticity and autocorrelation robust errors and autoregression with external factors for time series analysis. These methods were used to estimate the impact of various internal and external factors on the gross grain harvest. The analysis demonstrated that grain yields depend on a variety of factors, such as innovations in agricultural technology, climatic conditions and economic policy. The identified factors were grouped with measurable indicators for each, which became the basis for building models. The study determined that the autoregressive model is more suitable for describing the impact on the dependent variable – grain harvest. The most influential indicators are yields and research and development costs. The results of the study can be used to adjust agricultural policy and strategies for agricultural development in Kazakhstan. Proposals for optimising land use and integrating modern agricultural technologies will increase productivity and reduce the impact of negative factors.

Authors

Institutions

Publication Details

Journal
Agris on-line Papers in Economics and Informatics
Published
2025-06-29
DOI
https://doi.org/10.7160/aol.2025.170208
Primary Topic
Agricultural Economics and Policy
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Economic Analysis of Grain Product Metrics

Valentin Uteulin, Gulnar Lukhmanova, Olessya Lemechshenko, Kulzhamal Bleutayeva et al.
Agris on-line Papers in Economics and Informatics
Agricultural Economics and Policy
article

Economic Analysis of Grain Product Metrics

Valentin Uteulin, Gulnar Lukhmanova, Olessya Lemechshenko, Kulzhamal Bleutayeva, Baglan Murzabekova
article en

Abstract

The study aims to analyse the key factors affecting grain production in Kazakhstan to develop recommendations for improving the efficiency and sustainability of the agricultural sector. Statistical methods and econometric modelling techniques were used, including the least squares method with heteroscedasticity and autocorrelation robust errors and autoregression with external factors for time series analysis. These methods were used to estimate the impact of various internal and external factors on the gross grain harvest. The analysis demonstrated that grain yields depend on a variety of factors, such as innovations in agricultural technology, climatic conditions and economic policy. The identified factors were grouped with measurable indicators for each, which became the basis for building models. The study determined that the autoregressive model is more suitable for describing the impact on the dependent variable – grain harvest. The most influential indicators are yields and research and development costs. The results of the study can be used to adjust agricultural policy and strategies for agricultural development in Kazakhstan. Proposals for optimising land use and integrating modern agricultural technologies will increase productivity and reduce the impact of negative factors.

Agris on-line Papers in Economics and InformaticsVol. 17(2)
Almaty Technological University (KZ), Kostanay State University A Baitursynov (KZ), Zhetysu University named after Ilyas Zhansugurov (KZ)
Openalex Percentile: Top 77%
Agricultural Economics and Policy
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.