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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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    2. Osrof, Hazem Yusuf & Tan, Cheng Ling & Angappa, Gunasekaran & Yeo, Sook Fern & Tan, Kim Hua, 2023. "Adoption of smart farming technologies in field operations: A systematic review and future research agenda," Technology in Society, Elsevier, vol. 75(C).
    3. 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.
    4. 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.
    5. 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.
    6. Hanani AR, Nuhfil & Asmara, Rosihan & Fahriyah, Fahriyah, 2023. "Technology gap ratio decomposition in sugarcane farming in Indonesia," Asian Journal of Agriculture and Rural Development, Asian Economic and Social Society (AESS), vol. 13(01), January.
    7. 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.
    8. 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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