Diagnosing Web Data of ICTs to Provide Focused Assistance in Agricultural Adoptions

• URL: http://arxiv.org/abs/2111.00052v1
• Date: Fri, 29 Oct 2021 19:24:58 GMT
• Title: Diagnosing Web Data of ICTs to Provide Focused Assistance in Agricultural Adoptions
• Authors: Ashwin Singh, Mallika Subramanian, Anmol Agarwal, Pratyush Priyadarshi, Shrey Gupta, Kiran Garimella, Sanjeev Kumar, Ritesh Kumar, Lokesh Garg, Erica Arya, Ponnurangam Kumaraguru
• Abstract summary: We focus on the web infrastructure of one such ICT - Digital Green that started in 2008. Our research finds that farmers with higher adoption rates adopt videos of shorter duration and belong to smaller villages. We model the adoption of practices from a video as a prediction problem to identify and assist farmers who might face challenges in adoption in each of the five states.
• Score: 9.621466132073175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The past decade has witnessed a rapid increase in technology ownership across rural areas of India, signifying the potential for ICT initiatives to empower rural households. In our work, we focus on the web infrastructure of one such ICT - Digital Green that started in 2008. Following a participatory approach for content production, Digital Green disseminates instructional agricultural videos to smallholder farmers via human mediators to improve the adoption of farming practices. Their web-based data tracker, CoCo, captures data related to these processes, storing the attendance and adoption logs of over 2.3 million farmers across three continents and twelve countries. Using this data, we model the components of the Digital Green ecosystem involving the past attendance-adoption behaviours of farmers, the content of the videos screened to them and their demographic features across five states in India. We use statistical tests to identify different factors which distinguish farmers with higher adoption rates to understand why they adopt more than others. Our research finds that farmers with higher adoption rates adopt videos of shorter duration and belong to smaller villages. The co-attendance and co-adoption networks of farmers indicate that they greatly benefit from past adopters of a video from their village and group when it comes to adopting practices from the same video. Following our analysis, we model the adoption of practices from a video as a prediction problem to identify and assist farmers who might face challenges in adoption in each of the five states. We experiment with different model architectures and achieve macro-f1 scores ranging from 79% to 89% using a Random Forest classifier. Finally, we measure the importance of different features using SHAP values and provide implications for improving the adoption rates of nearly a million farmers across five states in India.

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