Segmenting PGS smallholders for assessing the digital adoption: Scaling climate-smart agricultural waste management in Eastern Thailand

IF 6.2 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Agricultural Systems Pub Date : 2026-03-01 Epub Date: 2025-12-03 DOI:10.1016/j.agsy.2025.104594

Eain Dray Aung , Nophea Sasaki , Takuji W. Tsusaka

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引用次数: 0

Abstract

CONTEXT

Adoption of climate-smart agricultural technologies in low- and middle-income countries remains uneven despite the rapid spread of mobile-based solutions. In Thailand, open-field burning of agricultural residues continues to drive greenhouse gas emissions, air pollution, and rural health risks. Digital innovations such as KaseChar—a prototype mobile application—offer potential to promote sustainable residue management; however, smallholder readiness and adoption barriers remain poorly understood.

OBJECTIVE

This study aims to analyze smallholder farmers' behavioral readiness to adopt KaseChar by integrating the Technology Acceptance Model (TAM) with behavioral segmentation to identify heterogeneous adoption profiles and their determinants.

METHODS

Data were collected from 150 Participatory Guarantee System (PGS) farmers in Chachoengsao Province, Thailand, using household surveys, key informant interviews, and focus group discussions. K-means clustering based on TAM constructs (perceived usefulness, perceived ease of use, behavioral intention), socio-demographic traits, and digital access variables identified farmer segments. Probit regression models assessed financial, demographic, and institutional drivers of adoption likelihood.

RESULTS AND CONCLUSIONS

Four behavioral segments emerged—Digitally Confident Adopters (28 %), Skeptical Traditionalists (22 %), Cost-Conscious Opportunists (25 %), and Socially Influenced Learners (25 %). Higher waste management costs significantly increased adoption probability, while older age reduced it. Social influence, peer networks, and access to training enhanced adoption intentions, whereas high data costs and low digital literacy constrained uptake. These results suggest that one-size-fits-all digital strategies risk excluding vulnerable groups; instead, precision interventions tailored to behavioral segments can promote inclusive digital transitions.

SIGNIFICANCE

By combining behavioral segmentation with TAM, this study advances understanding of smallholder heterogeneity in digital agriculture adoption. Findings from KaseChar demonstrate how differentiated extension strategies, economic incentives, and peer-led training can strengthen climate-smart transitions and accelerate progress toward Thailand's Bio-Circular-Green (BCG) economy goals.

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对PGS小农进行细分以评估数字化应用：在泰国东部扩大气候智能型农业废物管理

尽管基于移动的解决方案迅速普及，但在低收入和中等收入国家，气候智能型农业技术的采用情况仍然不均衡。在泰国，露天焚烧农业残留物继续造成温室气体排放、空气污染和农村健康风险。kasechar（一个原型移动应用程序）等数字创新提供了促进可持续残留物管理的潜力；然而，小农的准备程度和采用障碍仍然知之甚少。目的本研究旨在通过将技术接受模型（TAM）与行为分割相结合，分析小农采用KaseChar的行为准备情况，以识别异质采用概况及其决定因素。方法采用入户调查、关键信息人访谈和焦点小组讨论等方法，对泰国查清绍省150名参与保障制度农户进行数据收集。基于TAM结构（感知有用性、感知易用性、行为意图）、社会人口统计学特征和数字访问变量的K-means聚类确定了农民群体。概率回归模型评估了采用可能性的财务、人口和制度驱动因素。结果和结论：出现了四个行为部分：数字自信的采用者（28%），怀疑的传统主义者（22%），成本意识的机会主义者（25%）和受社会影响的学习者（25%）。较高的废物管理成本大大增加了采用的可能性，而年龄的增加则降低了采用的可能性。社会影响、同伴网络和获得培训的机会增强了采用意愿，而高昂的数据成本和较低的数字素养限制了采用。这些结果表明，一刀切的数字战略可能会将弱势群体排除在外；相反，针对行为细分量身定制的精准干预措施可以促进包容性数字化转型。通过将行为分割与TAM相结合，本研究促进了对小农数字农业采用异质性的理解。KaseChar的研究结果表明，差异化的推广战略、经济激励和同行主导的培训可以加强气候智能型转型，加快泰国实现生物循环绿色（BCG）经济目标的进程。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Agricultural Systems 农林科学-农业综合

CiteScore

13.30

自引率

7.60%

发文量

174

审稿时长

30 days

期刊介绍： Agricultural Systems is an international journal that deals with interactions - among the components of agricultural systems, among hierarchical levels of agricultural systems, between agricultural and other land use systems, and between agricultural systems and their natural, social and economic environments. The scope includes the development and application of systems analysis methodologies in the following areas: Systems approaches in the sustainable intensification of agriculture; pathways for sustainable intensification; crop-livestock integration; farm-level resource allocation; quantification of benefits and trade-offs at farm to landscape levels; integrative, participatory and dynamic modelling approaches for qualitative and quantitative assessments of agricultural systems and decision making; The interactions between agricultural and non-agricultural landscapes; the multiple services of agricultural systems; food security and the environment; Global change and adaptation science; transformational adaptations as driven by changes in climate, policy, values and attitudes influencing the design of farming systems; Development and application of farming systems design tools and methods for impact, scenario and case study analysis; managing the complexities of dynamic agricultural systems; innovation systems and multi stakeholder arrangements that support or promote change and (or) inform policy decisions.