HUDUDLARARO TEXNOLOGIK NOMUTANOSIBLIKLARNI HISOBGA OLGAN HOLDA QISHLOQ XOʻJALIGI ISHLAB CHIQARISHINING TEXNOLOGIK CHEGARAVIY SAMARADORLIGINI BAHOLASH METODOLOGIYASINI TAKOMILLASHTIRISH

Affiliation
Namangan State Technical University image/svg+xml

Abstract

The article examines the methodological foundations for assessing the technological frontier efficiency of agricultural production under interregional technological heterogeneity. It substantiates the integration of the metafrontier approach and stochastic frontier analysis models that account for regional differences in production technologies, resource endowments and technological capabilities. A methodological solution is proposed to separate technical inefficiency from technology gaps, determine the technology gap ratio and comparatively assess the production capacities of regions. The approach serves to scientifically substantiate regional priorities for raising agricultural production efficiency. The study found that in 2016-2024 the nominal volume of agricultural output in the country grew 3.81-fold, Namangan region’s share reached 7.39 percent in 2024, and in 2025 real growth rates across the region’s districts ranged from 101.4 to 107.3 percent. A classification dividing regions into four strategic groups by technical efficiency and technology gap ratio was developed.

Keywords

regional economy, agricultural production, technological heterogeneity, metafrontier, stochastic frontier analysis, technical efficiency, technology gap ratio, regional imbalance, production frontier, resource productivity


References

  1. Aigner, D., Lovell, C. A. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21- 37. https://doi.org/10.1016/0304-4076(77)90052-5
  2. Battese, G. E., Rao, D. S. P., & O’Donnell, C. J. (2004). A metafrontier production function for estimation of technical efficiencies and technology gaps for firms operating under different technologies. Journal of Productivity Analysis, 21(1), 91-103. https://doi.org/10.1023/B:PROD.0000012454.06094.29
  3. Food and Agriculture Organization of the United Nations. (2025). The state of food and agriculture 2025: Addressing land degradation across landholding scales. FAO. https://doi.org/10.4060/cd7067en
  4. Greene, W. H. (2005). Reconsidering heterogeneity in panel data estimators of the stochastic frontier model. Journal of Econometrics, 126(2), 269-303. https://doi.org/10.1016/j.jeconom.2004.05.003
  5. Hayami, Y., & Ruttan, V. W. (1970). Agricultural productivity differences among countries. American Economic Review, 60(5), 895-911. https://ideas.repec.org/a/aea/aecrev/v60y1970i5p895-911.html
  6. Huang, C. J., Huang, T. H., & Liu, N. H. (2014). A new approach to estimating the metafrontier production function based on a stochastic frontier framework. Journal of Productivity Analysis, 42(3), 241-254. https://doi.org/10.1007/s11123-014-0402-2
  7. Kumbhakar, S. C., Wang, H. J., & Horncastle, A. P. (2015). A practitioner’s guide to stochastic frontier analysis using Stata. Cambridge University Press. https://doi.org/10.1017/CBO9781139342070
  8. Meeusen, W., & van den Broeck, J. (1977). Efficiency estimation from Cobb- Douglas production functions with composed error. International Economic Review, 18(2), 435-444. https://doi.org/10.2307/2525757
  9. O’Donnell, C. J., Rao, D. S. P., & Battese, G. E. (2008). Metafrontier frameworks for the study of firm-level efficiencies and technology ratios. Empirical Economics, 34(2), 231-255. https://doi.org/10.1007/s00181-007-0119-4
  10. Organisation for Economic Co-operation and Development. (2025a). Agricultural policy monitoring and evaluation 2025: Making the most of the trade and environment nexus in agriculture. OECD Publishing. https://doi.org/10.1787/a80ac398-en
  11. Organisation for Economic Co-operation and Development. (2025b). Developments in agricultural policies and support by country. In Agricultural policy monitoring and evaluation 2025: Making the most of the trade and environment nexus in agriculture. OECD Publishing. https://doi.org/10.1787/a80ac398-en
  12. Wang, H. J., & Ho, C. W. (2010). Estimating fixed-effect panel stochastic frontier models by model transformation. Journal of Econometrics, 157(2), 286-296. https://doi.org/10.1016/j.jeconom.2009.12.006
  13. Oʻzbekiston Respublikasi Prezidentining 2025-yil 12-avgustdagi PF-130-son “Qishloq xoʻjaligi sohasiga zamonaviy texnologiyalarni joriy etishni yanada jadallashtirish orqali samaradorlikni oshirish chora-tadbirlari toʻgʻrisida”gi Farmoni. https://lex.uz/docs/-7694880
  14. Oʻzbekiston Respublikasi Prezidentining 2026-yil 24-apreldagi PF-68-son “Qishloq xoʻjaligi ishlab chiqaruvchilarining yerdan foydalanishdagi mustaqilligini taʼminlashga qaratilgan navbatdagi chora-tadbirlar toʻgʻrisida”gi Farmoni. https://lex.uz/docs/-8167799

Downloads

Download data is not yet available.