Agronomic Challenges and Opportunities for Smallholder Terrace Agriculture in Developing Countries
Bibliographic record
Abstract
Improving land productivity is essential to meet increasing food and forage demands in hillside and mountain communities. Tens of millions of smallholder terrace farmers in Asia, Africa, and Latin America who earn $1-2 per day do not have access to peer-reviewed knowledge of best agronomic practices, though they have considerable traditional ecological knowledge. Terrace farmers also lack access to affordable farm tools and inputs required to increase crop yields. The objectives of this review are to highlight the agronomic challenges of terrace farming, and offer innovative, low-cost solutions to intensify terrace agriculture while improving local livelihoods. The article focuses on smallholder farmers in developing nations, with particular reference to Nepal. The challenges of terrace agriculture in these regions include lack of quality land area for agriculture, erosion and loss of soil fertility, low yield, poor access to agricultural inputs and services, lack of mechanization, labor shortages, poverty, and illiteracy. Agronomic strategies that could help address these concerns include intensification of terraces using agro-ecological approaches along with introduction of light-weight, low-cost, and purchasable tools and affordable inputs that enhance productivity and reduce female drudgery. To package, deliver, and share these technologies with remote hillside communities, effective scaling up models are required. One opportunity to enable distribution of these products could be to "piggy-back" onto pre-existing snackfood/cigarette/alcohol distribution networks that are prevalent even in the most remote mountainous regions of the world. Such strategies, practices, and tools could be supported by formalized government policies dedicated to the well-being of terrace farmers and ecosystems, to maintain resiliency at a time of alarming climate change. We hope this review will inform governments, non-governmental organizations, and the private sector to draw attention to this neglected and vulnerable agro-ecosystem in developing countries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".