Factors influencing digital technology investments on French farms

Maha Ben Jaballah (), Aude Ridier and Karine Daniel ()
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Maha Ben Jaballah: LARESS - Unité de Recherche Sciences Sociales (LARESS) - ESA - Ecole Supérieure des Agricultures
Karine Daniel: LARESS - Unité de Recherche Sciences Sociales (LARESS) - ESA - Ecole Supérieure des Agricultures

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Abstract: Pressure is mounting to enhance farm productivity, sustainability, and decision-making efficiency, which has bolstered interest in digital technologies. These technologies may provide substantial benefits, such as improved resource use, traceability, and task automation, but tool integration remains highly variable across farms. This study explored digital technology investment patterns in France, defining investment levels as low, medium, or high based on technology cost, functionality, and complexity. The degree of digital technologies adoption by the country's farms has been highly heterogenous, with marked differences tied to production-system-specific investment levels. To this end, we explored the key factors associated with digital technology investment across and within production systems by carrying out binary and multinomial logistic regression on paired data for 6,152 farms obtained from the 2020 Agricultural Census and 2019 Farm Accountancy Data Network survey. We found that digital technology investment was more likely when farm managers had a higher level of agricultural education or were married, farms were larger, had a corporate legal status, and participate in farmer groups, or there was greater environmental commitment. In contrast, investment was less likely when farm managers were older or more experienced. Investment level varied across production systems. Farms with bovine livestock were more likely to make high-level investments in digital technologies. Vineyards, especially large vineyards, were overall less likely to invest in digital technologies but, when they did, investment levels were low to medium. These findings underscore the need for targeted, sector-specific policy instruments to promote a more inclusive and effective digital transition in agriculture.

Keywords: Farm types; Regression analysis; Investment cost analysis; Determinants; Innovation (search for similar items in EconPapers)
Date: 2026-09-25
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Published in Precision Agriculture, 2026, 27 (5), pp.149. ⟨10.1007/s11119-026-10429-3⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05773300

DOI: 10.1007/s11119-026-10429-3

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