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Technical Efficiency and its Determinants in Tomato Production in Karnataka, India: Data Envelopment Analysis (DEA) Approach

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  • Murthy, D. Sreenivasa
  • Sudha, M.
  • Hegde, M.R.
  • Dakshinamoorthy, V.

Abstract

Low productivity in agriculture is mainly due to the inability of the farmers to exploit the available technologies fully, resulting in lower efficiencies of production. The present study has estimated the technical and scale efficiencies of tomato-producing farms in Karnataka, considering different production levels and has identified the determining factors of their technical efficiency. The study is based on the data collected from the major tomato-producing regions of Karnataka, viz. Kolar and Bangalore rural districts of Karnataka, under three-production situations, viz. small, medium and large farms. Data Envelopment analysis (DEA) and log linear regression models have been used for estimating the technical efficiency and its determining factors, respectively. The study has indicated that most of the farms irrespective of size of holding have shown technical inefficiency problems. The medium farmers have been observed with best measures of technical efficiency, which has been explained by factors such as the land and labour productivity and education. Though medium farmers have been found efficient, with higher yields, it is the small farmers who have emerged as price-efficient producers in terms of lower cost on production (Rs 1.72/kg compared to Rs 2.01 in medium farms and Rs 1.85 in large farms) and higher unit profit. Most of the farms have been observed to have potential to expand production and productivity, increasing technical efficiency as majority have been performing with increasing returns to scale.

Suggested Citation

  • Murthy, D. Sreenivasa & Sudha, M. & Hegde, M.R. & Dakshinamoorthy, V., 2009. "Technical Efficiency and its Determinants in Tomato Production in Karnataka, India: Data Envelopment Analysis (DEA) Approach," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 22(2), July.
  • Handle: RePEc:ags:aerrae:57399
    DOI: 10.22004/ag.econ.57399
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    2. Lukáš Čechura & Zdeňka Žáková Kroupová & Antonella Samoggia, 2021. "Drivers of Productivity Change in the Italian Tomato Food Value Chain," Agriculture, MDPI, vol. 11(10), pages 1-17, October.
    3. Muhammad Aamir, Shahzad, 2017. "Price Forecasting Model for Perishable Commodities: A Case of Tomatoes in Punjab, Pakistan," MPRA Paper 81531, University Library of Munich, Germany, revised 01 Aug 2017.
    4. Anirban Nandy & Piyush Kumar Singh & Alok Kumar Singh, 2021. "Systematic Review and Meta- regression Analysis of Technical Efficiency of Agricultural Production Systems," Global Business Review, International Management Institute, vol. 22(2), pages 396-421, April.
    5. Ashok K. Mishra & Mike G. Tsionas, 2020. "A Minimax Regret Approach to Decision Making Under Uncertainty," Journal of Agricultural Economics, Wiley Blackwell, vol. 71(3), pages 698-718, September.
    6. Rudra Bahadur SHRESTHA & Wen-Chi HUANG & Shriniwas GAUTAM & Thomas Gordon JOHNSON, 2016. "Efficiency of small scale vegetable farms: policy implications for the rural poverty reduction in Nepal," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 62(4), pages 181-195.

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