A review of some aspects of Uganda’s crop agriculture: Challenges and opportunities for diversified sector output and food security
Bibliographic record
Abstract
Despite Uganda being largely agricultural, with the climate and soils suitable for the production of a wide range of crops, average total factor productivity growth in agriculture has been negative for the last two decades.National agricultural output has grown at only 2% per annum over the last five years, compared to 3.3% per annum growth in Uganda's population over the same period.As such, food insecurity and poverty remain widespread and the prevalence of national food imports has increased in the last decade.Many crops have in the past been introduced into the country, evaluated for their performance and suitability to local production zones, and many reached a stage of adoption by Uganda's farming households.However, due to several intrinsic and extrinsic factors, including policy changes, planting of some previously highly regarded crops has either declined or been forgotten altogether.In certain instances, some other crops have remained in isolated spots on farmers' fields or parklands lacking organized production and marketing.We review some aspects of Uganda's crop sector and the nature of past and present government agricultural policies, highlighting issues pertaining to some of the would-be important and yet seemingly neglected crops with the aim of bringing them into the limelight for further consideration by research and policymakers.We also make recommendations for the revitalization of reviewed crops in order to contribute to sustainable food security, diversified agriculture sector output and expansion of the national export base.Further, amidst global climatic changes, increased crop diversification and selection of crops most suited for different zones has been fronted as a rational and cost-effective method for building resilience into agricultural systems.Lessons and recommendations are scalable to other countries, with similar farming systems.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".