Direct2Farm proves the case for mobile-based agro-advisory services in India
Bibliographic record
Abstract
Smallholder farmers in India are largely dependent on other farmers as their main source of agricultural information, with government extension services geographically limited and agricultural dealers not always trusted to provide impartial advice.Between January and July 2015, 400,000 farmers were registered to receive seasonal, crop/livestock-specific information on their mobile phones via CABI's Direct2Farm (D2F) SMS and voice message service.The service was free and provided targeted, personalized messages based on users' location, language and farming practices, including choice of crops and livestock.Around 10 million voice messages and 5 million text messages were sent by the D2F service during 2015.Although a significant proportion of these failed to reach their intended targets, around 40% of the initial 400,000 registered farmers became active users of the service, regarding D2F as a valuable and credible source of agricultural information.Around 90% of active users reported sharing the information received with other farmers and recommending the service to them.Over 70% reported taking action on the basis of the information received, including having their soil tested, planning their activities on the basis of weather predictions and purchasing recommended agricultural products.72% of farmers reported increased productivity and a reduction in crop losses as a result of acting on the information. Direct2Farm proves the case for mobile-based agro-advisory services in India Key highlights• Between January and July 2015, 400,000 farmers in six Indian states were registered to receive free agricultural information in the form of SMS and voice messages via CABI's D2F service.• Information delivery, which took place between January 2015 and June 2016, was highly targeted, based on farmers' location, language, choice of crops, livestock and farming practices.• Between September and November 2015, the D2F service carried out 714 information campaigns coinciding with critical periods in the farming calendar, comprising around 10 million voice messages.• Around 90% of D2F users reported being highly likely to share the messages they received and to recommend the service to others.Of those who shared information, 86% said that the person they shared it with had also reported it as useful.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".