Electric Mobility in Agriculture: A Sustainable Solution for Farm-to-Market Transportation
Bibliographic record
Abstract
Abstract: This article presents a comprehensive critical analysis of the potential for electric mobility to serve as a sustainable solution for farm-to-market transportation in the agriculture sector. Drawing on a systematic review of secondary data, including academic literature, government and NGO reports, and documented case studies, the study evaluates the environmental, economic, and operational impacts of adopting electric vehicles (EVs) in agricultural logistics. The findings demonstrate that the transition to electric mobility, particularly when powered by renewable energy sources, can reduce greenhouse gas (GHG) emissions by up to 90% compared to conventional diesel vehicles. This substantial reduction is attributed to the elimination of tailpipe emissions and the use of clean energy for recharging, directly contributing to improved air quality and the health of rural communities. Economically, electric tractors and vehicles offer significant cost savings, with operational costs including fuel and maintenance reduced by 40% to 60%. Although the initial investment in electric vehicles is higher than traditional options, the lower running costs allow for a payback period of four to seven years, making EVs a cost-effective alternative over time, especially in regions with favourable electricity prices and high vehicle utilisation. Operationally, EVs have proven reliable for short- to medium distance farm-to-market transport, with pilot projects in countries such as India and Canada confirming their suitability for typical agricultural tasks. However, the widespread adoption of electric mobility in rural areas faces persistent barriers, notably high upfront costs and inadequate charging infrastructure. Many rural regions lack reliable electricity and charging stations, which limits scalability and practical implementation. To address these challenges, policy recommendations include targeted subsidies and incentives, investment in renewable energy-based charging infrastructure, and capacity building programs for farmers. These interventions are essential to unlock the full potential of electric mobility in agriculture, catalysing sustainability and resilience in food supply chains. Ultimately, the article argues that with appropriate policy support and infrastructure development, electric mobility can play a transformative role in decarbonising agricultural logistics and advancing broader climate action goals Keywords: Electric mobility, Agricultural transportation, Farm-to-market logistics, Sustainable agriculture, Electric vehicles (EVs), Renewable energy, Greenhouse gas emissions, Rural infrastructure, Cost-benefit analysis, Policy recommendations
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".