Econometric analysis of grain yields (using the example of the Republic of Azerbaijan)
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2025.16.2.01Keywords:
Productivity, Crops, Econometric analysis, Agriculture, Statistical modeling.Dimensions Badge
Issue
Section
License
Copyright (c) 2025 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This article is an econometric analysis of the influence of factors affecting the yield of grain crops in the Republic of Azerbaijan. In the course of the work, the dependence of yield on climatic, economic and agrotechnical factors was assessed based on correlation and regression analysis. The results of statistical modeling were formed, which allows identifying the most important determinants, such as the number of meteorological workers, the level of mechanization of production at the enterprise, the average annual number of crops, the use of mineral fertilizers, and government funding. The data obtained can be used to develop detailed recommendations for increasing the efficiency of grain crop production, as well as developing forecast models to improve planning in agriculture.Abstract
How to Cite
Downloads
Similar Articles
- Aditi Sahariya, Chellapilla Bharadwaj, Iwuala Emmanuel, Afroz Alam, Phytochemical Profiling and GCMS Analysis of Two Different Varieties of Barley (Hordeum vulgare L.) Under Fluoride Stress , The Scientific Temper: Vol. 12 No. 1&2 (2021): The Scientific Temper
- Ashish Nagila, Abhishek K Mishra, The effectiveness of machine learning and image processing in detecting plant leaf disease , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- S. Nagarani, Amalraj P., Lakshay Phor, Nishank S. Pimple, Banashree Sen, Ramaprasad Maiti, Vikas S. Jadhav, Innovative technological advancements in solving real quadratic equations: Pioneering the frontier of mathematical innovation , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Rajesh Kumar Singh, Genetic Variability in Aromatic Rice , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
- Lakshmi Priya, Anil Vasoya, C. Boopathi, Muthukumar Marappan, Evaluating dynamics, security, and performance metrics for smart manufacturing , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Dr. Mohini Darji, Dr. Yashesh Darji, Dr. Rajdipsinh Vaghela, Dr. Bhaumik Machhi, Nikunj Bhavsar, Arpit Bhatt, Hardik Parmar, Deep Learning Approaches for Regional Rainfall Time Series Prediction Using ERA5 Dataset , The Scientific Temper: Vol. 17 No. 04 (2026): The Scientific Temper
- M. Yamunadevi, P. Ponmuthuramalingam, A review and analysis of deep learning methods for stock market prediction with variety of indicators , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- Medha, Improvising the Mind: Metacognitive Skill Formation Through Musical Practice Among Youth , The Scientific Temper: Vol. 17 No. 01 (2026): The Scientific Temper
- Surendra Singh Bisht, Saurabh Charaya, Rachna Mehta, A Comparative and Hybrid Machine Learning Framework for IoT-Based Predictive Maintenance of Rotating Machinery , The Scientific Temper: Vol. 17 No. 02 (2026): The Scientific Temper
- Tara K. Sharma, Problems and prospects of tourism financing in Sikkim , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
<< < 12 13 14 15 16 17 18 19 20 21 > >>
You may also start an advanced similarity search for this article.

