Abstract:
Rubber (Hevea brasiliensis) is a major plantation crop in Sri Lanka, yet the smallholder productivity
has dropped due to increasing climate variability and soil erosion. The farmers typically
do not have access to place-specific, timely agricultural guidance and hence adopt inappropriate
adaptation strategies. The present study aimed to conceptualise and pilot test a digital
platform, Rubber Farmer Assistant, for providing science-based, real-time advice for enhancing
resilience and productivity. Data collection was informed by the Technology Acceptance
Model (TAM) and the Diffusion of Innovations Theory. A mixed methods design was used,
combining quantitative (structured, closed-ended questionnaire) and qualitative (open-ended
responses) approaches. Quantitative data were collected from 32 smallholder farmers, and usercentred
design was informed by qualitative responses derived from open-ended questions. The
tool was pre-tested for validity (Cronbach’s α =0.84). Quantitative data were analysed through
descriptive statistics using Python. Supporting secondary data from the Rubber Research Institute
of Sri Lanka and other institutions in the country assisted in building a robust scientific
basis.A central component of the platform is the Farm Health Score Calculator, which provides
site-specific recommendations derived from weighted indicators like soil colour, drainage, ideal
rainfall (1,500–3,000 mm), and temperature (20–35◦C), with corresponding weights of 26.7%,
26.7%, 26.7%, and 20%. The application has a modular design with provisions for soil and
climate analysis, agricultural advice, and expert contact support. Participatory design and iterative
farmer testing enhanced usability. Results showed that 81.3% of the respondents had
frequent climate problems, and 71.9% had soil erosion. Adoption was enthusiastic, with 93.8%
interested in using the platform and 74.2% giving the highest rating to expected improvement
(mean = 4.6, SD = 0.72). A chi-square test confirmed a positive correlation between climate
impact and adoption of the platform (χ2 = 12.34, p < 0.05). This study confirms that digital
agriculture technologies have the potential to bridge information gaps, enable sustainable agriculture,
and scale across similar agro-ecological zones.
