意大利葡萄种植者采用精准农业的异质行为：来自潜在阶层分析的见解

IF 7.6 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Precision Agriculture Pub Date : 2026-09-10 DOI:10.1007/s11119-026-10448-0

Adriano Biondo,Antonino Galati,Francesco Caracciolo

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引用次数: 0

摘要

精准农业技术包括卫星数据、无人机、田间机器人和自动导航，作为提高葡萄种植可持续性、竞争力和环境效率的工具，正日益得到推广。然而，意大利葡萄酒种植者采用这些做法表现出显著的异质性，受到结构和行为因素的综合影响。本研究旨在根据意大利葡萄酒种植者采用pat的意图来确定不同的群体，并研究这些意图与四种行为结构之间的关系，这四种行为结构捕获了认知、社会和风险相关的影响。方法本研究通过对272名意大利葡萄种植者的潜在类广义结构方程模型进行估计，整合了基于剖面的视角。类别成员模型基于多方面的概念化，包括风险感知、风险容忍度、感知易用性和主观规范，特定技术的五年意图被纳入特定类别的probit截点。结果出现了三种截然不同的特征：风险敏感的怀疑论者、选择性实用主义者和以可用性为导向的高采用者，他们在不同技术之间的采用模式截然不同。参与农村发展计划是成为倾向较高的潜在阶层，即选择性实用主义和面向可用性的高采用者的最强有力的预测指标。此外，对pat的熟悉程度、男性性别和参与财团与高倾向阶层呈正相关，而年龄呈弱负相关。这些结果为特定人群的推广和政策设计提供了信息，为谨慎的种植者提供了优先的风险缓解试验，为选择的剖面提供了有针对性的信息服务，并为高倾向的人提供了综合的pat捆绑包。

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Heterogeneous behaviours towards precision agriculture adoption among Italian winegrowers: insights from latent class analysis

Abstract Purpose Precision Agriculture technologies including satellite data, drones, field robots and automatic guidance, are increasingly promoted as tools to improve sustainability, competitiveness, and environmental efficiency in viticulture. However, the adoption of these practices among Italian winegrowers exhibits significant heterogeneity, influenced by a combination of structural and behavioural factors. This study aims to identify distinct groups of Italian winegrowers based on their intentions to adopt PATs, examining how these intentions relate to four behavioural constructs that capture cognitive, social, and risk-related influences. Method The present study integrates a profile-based perspective by estimating a latent-class generalised structural equation model on 272 Italian winegrowers. The model of class membership is predicated upon a multifaceted conceptualisation encompassing risk perception, risk tolerance, perceived ease of use, and subjective norms and the technology-specific five-year intentions are incorporated as class-specific probit intercepts. Results Three qualitatively distinct profiles emerge: risk-sensitive sceptics, selective pragmatists and usability-oriented high adopters with sharply different adoption patterns across technologies. Participation in rural development schemes is the strongest predictor of membership in the higher-propensity latent classes, namely the selective-pragmatist and usability-oriented high-adopter profiles. In addition, familiarity with PATs, male gender and participation in consortium are positively associated with the higher-propensity class, while age shows a weak negative correlation. Conclusion These results inform the design of extension and policy for specific segments, prioritising risk-mitigation trials for cautious growers, targeted information services for select profiles, and integrated PATs bundles for those with a high propensity.

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来源期刊

Precision Agriculture 农林科学-农业综合

CiteScore

12.30

自引率

8.10%

发文量

103

审稿时长

>24 weeks

期刊介绍： Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.