INNOVATIVE TECHNOLOGIES AND INVESTMENT EFFICIENCY IN COTTON-TEXTILE CLUSTERS: OPTIMIZING COSTS THROUGH INDUSTRY 4.0 SOLUTIONS.
Keywords:
Paxta yetishtirish, xarajatlarni optimallashtirish, regressiya tahlili, qishloq xo‘jaligi investitsiyalari, tomchilatib sug‘orish, klaster mablag‘lari, mavsumiy ish o‘rinlari, quyosh energiyasi, barqaror qishloq xo‘jaligi.Abstract
The cotton production process plays a crucial role in the agricultural sector,
requiring efficient resource utilization and cost optimization to ensure economic and
environmental sustainability. This study examines the impact of various factors on the production
costs of cotton clusters located in oasis regions of Uzbekistan. Using a regression model, we
analyze the influence of total land area, number of farms, share of drip irrigation systems, yield,
availability of irrigation and reclamation facilities, agricultural investments, cluster funding, bank
loans, seasonal employment, and solar energy usage on production costs. The findings provide
valuable insights for improving cluster operations, modernizing agricultural technologies, and
promoting sustainable farming practices. The results can serve as a scientific and practical basis
for enhancing economic efficiency and environmental sustainability in cotton production.
References
Cronbach L.J. Coefficient Alpha and the Internal Structure of Tests // Psychometrika. – 1951. – Vol. 16, No. 3. – P. 297–334.
Wooldridge J.M. Introductory Econometrics: A Modern Approach. – Cengage Learning, 2020.
Hayashi F. Econometrics. – Princeton: Princeton University Press, 2000.
Greene W.H. Econometric Analysis. – Pearson, 2018.
Ruan Y. Exploring Multiple Regression Models: Key Concepts and Applications // Science and Technology of Engineering, Chemistry and Environmental Protection. – 2024. – Vol. 1, No. 7. DOI: 10.61173/yjpt3s59.
Janković S. The Multivariate Statistical Analysis – Multiple Linear Regression // International Journal on Biomedicine and Healthcare. – 2022. – Vol. 10, No. 4. – P. 173–175. DOI: 10.5455/ijbh.2022.10.173-175.
Mignon V. The Multiple Regression Model // Classroom Companion Economics. – 2024. – P. 105–170. DOI: 10.1007/978-3-031-52535-3_3.
Breusch T.S., Pagan A.R. A Simple Test for Heteroscedasticity and Random Coefficient Variation // Econometrica. – 1979. – Vol. 47, No. 5. – P. 1287–1294.
Bera J., Jarque C. Efficient Tests for Normality, Homoscedasticity and Serial Independence of Regression Residuals // Economics Letters. – 1980. – Vol. 6, No. 3. – P. 255–259.
Gujarati D.N., Porter D.C. Basic Econometrics. – McGraw-Hill, 2009.
Kutner M.H., Nachtsheim C.J., Neter J. Applied Linear Statistical Models. – McGraw-Hill, 2005.
James G., Witten D., Hastie T., Tibshirani R. An Introduction to Statistical Learning: With Applications in R. – Springer, 2013.
Jiang J. Multiple Linear Regression // Applied Medical Statistics. – 2021. – P. 345–367. DOI: 10.1002/9781119716822.ch15.