EVALUATION OF ON-FARM SOIL FERTILITY RESEARCH IN THE RAINFED LOWLAND RICE FIELDS OF SUKUMALAND, TANZANIA
Bibliographic record
Abstract
The first ever on-farm soil fertility research in the rainfed lowland rice (Oryza sativa) fields of Sukumaland, in northwest Tanzania, was carried out between 1990 and 1996 in response to farmers' complaints about declining rice yields. From diagnosis to extension, the research approach followed that of the International Maize and Wheat Improvement Center (CIMMYT). In 1990/91, rice yields in the Maswa district increased sharply when nitrogen at a rate of 30 kg ha−1 in the form of urea was broadcast in flooded rice fields at tillering. Similar research was subsequently conducted in other parts of Sukumaland to evaluate this type of low-dose nitrogen application under varying circumstances. In 1995/96, higher doses of nitrogen (60 and 120 kg ha−1) and a high dose of phosphorus (17.5 kg ha−1) were applied for comparison in Sengerema district. Between 1990 and 1996, the average increase in rice yield from the application of 30 kg N ha−1 varied between 463 and 986 kg ha−1. In 1995/96, the same application of N was more economical than both 60 and 120 kg N ha−1, and no phosphorus deficiency was found. The deteriorating ratio between the price of rice at the farm gate and that of urea, however, threatens the adoption of this technology by farmers. Adaptability analysis showed that the relatively small differences in response per field (environment) in all years did not justify a need for multiple different extension messages. Until more detailed recommendations can be made, therefore, a single dose of 30 kg N ha−1, in the form of urea, applied to rice at tillering is recommended for the whole of Sukumaland to reverse the decline in yields. Further on-farm research should concentrate on improving the efficiency of nitrogen fertilization and on determining the optimum rates of other major nutrients to refine this initial recommendation.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".