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Precision agriculture adoption and technical efficiency: An analysis of sugarcane farms in Brazil

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  • Carrer, Marcelo José
  • Filho, Hildo Meirelles de Souza
  • Vinholis, Marcela de Mello Brandão
  • Mozambani, Carlos Ivan

Abstract

Precision Agriculture Technologies (PATs) are at the core of the fourth revolution in farming technology, also called Agriculture 4.0. This study evaluates the determinants of PATs adoption and its impacts on technical efficiency (TE) and technology gap ratio (TGR) of sugarcane farms in the state of São Paulo, Brazil. A selectivity correction model for stochastic frontiers is combined with a metafrontier production function approach to estimate the role of a set of determinants of PATs adoption and its impacts on TE and TGR. In person interviews with 131 sugarcane farmers provided cross-sectional farm level data from the 2018/19 crop year. The estimates of a sample selection equation showed that farming size, farmer's schooling and technical assistance positively affect PATs adoption by sugarcane farmers. Estimates of stochastic production frontiers (SPFs) and metafrontier revealed that the average of the TE and TGR scores of adopters are higher than those of non-adopters. The managerial gaps (TE) between adopters and non-adopters are considerably wider than their technology gaps (TGR). The adoption of PATs subsidizes farmers decision-making process which increased the efficiency in inputs use, an important issue for economic and environmental sustainability in sugarcane farming.

Suggested Citation

  • Carrer, Marcelo José & Filho, Hildo Meirelles de Souza & Vinholis, Marcela de Mello Brandão & Mozambani, Carlos Ivan, 2022. "Precision agriculture adoption and technical efficiency: An analysis of sugarcane farms in Brazil," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    • Handle: RePEc:eee:tefoso:v:177:y:2022:i:c:s0040162522000427
    DOI: 10.1016/j.techfore.2022.121510
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    as
    1. Abdul-Rahaman, Awal & Abdulai, Awudu, 2018. "Do farmer groups impact on farm yield and efficiency of smallholder farmers? Evidence from rice farmers in northern Ghana," Food Policy, Elsevier, vol. 81(C), pages 95-105.
    2. Rask, Kevin, 1995. "The Structure of Technology in Brazilian Sugarcane Production, 1975-87: An Application of a Modified Symmetric Generalized McFadden Cost Function," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(3), pages 221-232, July-Sept.
    3. González-Flores, Mario & Bravo-Ureta, Boris E. & Solís, Daniel & Winters, Paul, 2014. "The impact of high value markets on smallholder productivity in the Ecuadorean Sierra: A Stochastic Production Frontier approach correcting for selectivity bias," Food Policy, Elsevier, vol. 44(C), pages 237-247.
    4. Cliff Huang & Tai-Hsin Huang & Nan-Hung Liu, 2014. "A new approach to estimating the metafrontier production function based on a stochastic frontier framework," Journal of Productivity Analysis, Springer, vol. 42(3), pages 241-254, December.
    5. Boris Bravo-Ureta & Daniel Solís & Víctor Moreira López & José Maripani & Abdourahmane Thiam & Teodoro Rivas, 2007. "Technical efficiency in farming: a meta-regression analysis," Journal of Productivity Analysis, Springer, vol. 27(1), pages 57-72, February.
    6. Ma, Wanglin & Renwick, Alan & Yuan, Peng & Ratna, Nazmun, 2018. "Agricultural cooperative membership and technical efficiency of apple farmers in China: An analysis accounting for selectivity bias," Food Policy, Elsevier, vol. 81(C), pages 122-132.
    7. Christopher O’Donnell & D. Rao & George Battese, 2008. "Metafrontier frameworks for the study of firm-level efficiencies and technology ratios," Empirical Economics, Springer, vol. 34(2), pages 231-255, March.
    8. Elizaphan J. O. Rao & Bernhard Brümmer & Matin Qaim, 2012. "Farmer Participation in Supermarket Channels, Production Technology, and Efficiency: The Case of Vegetables in Kenya," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 94(4), pages 891-912.
    9. Édson Luis Bolfe & Lúcio André de Castro Jorge & Ieda Del’Arco Sanches & Ariovaldo Luchiari Júnior & Cinthia Cabral da Costa & Daniel de Castro Victoria & Ricardo Yassushi Inamasu & Célia Regina Grego, 2020. "Precision and Digital Agriculture: Adoption of Technologies and Perception of Brazilian Farmers," Agriculture, MDPI, vol. 10(12), pages 1-16, December.
    10. Boris Bravo-Ureta & William Greene & Daniel Solís, 2012. "Technical efficiency analysis correcting for biases from observed and unobserved variables: an application to a natural resource management project," Empirical Economics, Springer, vol. 43(1), pages 55-72, August.
    11. Walton, Jonathan C. & Larson, James A. & Roberts, Roland K. & Lambert, Dayton M. & English, Burton C. & Larkin, Sherry L. & Marra, Michele C. & Martin, Steven W. & Paxton, Kenneth W. & Reeves, Jeanne , 2010. "Factors Influencing Farmer Adoption of Portable Computers for Site-Specific Management: A Case Study for Cotton Production," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 42(2), pages 193-209, May.
    12. Pivoto, Diesson & Barham, Bradford & Dabdab, Paulo & Zhang, Debin & Talamin, Edson, 2019. "Factors influencing the adoption of smart farming by Brazilian grain farmers," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 22(4), April.
    13. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    14. Subal Kumbhakar & Efthymios Tsionas & Timo Sipiläinen, 2009. "Joint estimation of technology choice and technical efficiency: an application to organic and conventional dairy farming," Journal of Productivity Analysis, Springer, vol. 31(3), pages 151-161, June.
    15. Barnes, A.P. & Soto, I. & Eory, V. & Beck, B. & Balafoutis, A. & Sánchez, B. & Vangeyte, J. & Fountas, S. & van der Wal, T. & Gómez-Barbero, M., 2019. "Exploring the adoption of precision agricultural technologies: A cross regional study of EU farmers," Land Use Policy, Elsevier, vol. 80(C), pages 163-174.
    16. D'Antoni, Jeremy M. & Mishra, Ashok K. & Powell, Rebekah R. & Martin, Steven W., 2012. "Farmers’ Perception of Precision Technology: The Case of Autosteer Adoption by Cotton Farmers," 2012 Annual Meeting, February 4-7, 2012, Birmingham, Alabama 119734, Southern Agricultural Economics Association.
    17. William Greene, 2010. "A stochastic frontier model with correction for sample selection," Journal of Productivity Analysis, Springer, vol. 34(1), pages 15-24, August.
    18. George Battese & D. Rao & Christopher O'Donnell, 2004. "A Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies," Journal of Productivity Analysis, Springer, vol. 21(1), pages 91-103, January.
    19. Zhu, Xiaoke & Hu, Ruifa & Zhang, Chao & Shi, Guanming, 2021. "Does Internet use improve technical efficiency? Evidence from apple production in China," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    20. Lambert, Dayton M. & Paudel, Krishna P. & Larson, James A., 2015. "Bundled Adoption of Precision Agriculture Technologies by Cotton Producers," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 40(2), pages 1-21, May.
    21. Renato Villano & Boris Bravo-Ureta & Daniel Solís & Euan Fleming, 2015. "Modern Rice Technologies and Productivity in the Philippines: Disentangling Technology from Managerial Gaps," Journal of Agricultural Economics, Wiley Blackwell, vol. 66(1), pages 129-154, February.
    22. repec:zwi:journl:v:43:y:2012:i:1:p:55-72 is not listed on IDEAS
    23. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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    Cited by:

    1. Konstantina Ragazou & Alexandros Garefalakis & Eleni Zafeiriou & Ioannis Passas, 2022. "Agriculture 5.0: A New Strategic Management Mode for a Cut Cost and an Energy Efficient Agriculture Sector," Energies, MDPI, vol. 15(9), pages 1-17, April.
    2. Wanglin Ma & Sanghyun Hong & W. Robert Reed & Jianhua Duan & Phong Luu, 2023. "Yield effects of agricultural cooperative membership in developing countries: A meta‐analysis," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 94(3), pages 761-780, September.
    3. Uztürk, Deniz & Büyüközkan, Gülçin, 2022. "Smart Agriculture Technology Evaluation: A Linguistic-based MCDM Methodology," Agri-Tech Economics Papers 337128, Harper Adams University, Land, Farm & Agribusiness Management Department.
    4. Uztürk, Deniz & Büyüközkan, Gülçin, 2022. "Smart Agriculture Technology Evaluation: A Linguistic-based MCDM Methodology," Land, Farm & Agribusiness Management Department 337128, Harper Adams University, Land, Farm & Agribusiness Management Department.
    5. Wenyuan Hua & Zhihan Chen & Liangguo Luo, 2022. "The Effect of the Major-Grain-Producing-Areas Oriented Policy on Crop Production: Evidence from China," Land, MDPI, vol. 11(9), pages 1-28, August.
    6. Stefania Troiano & Matteo Carzedda & Francesco Marangon, 2023. "Better richer than environmentally friendly? Describing preferences toward and factors affecting precision agriculture adoption in Italy," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 11(1), pages 1-15, December.

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