Contract Farming in Tanzania: Experiences from Tobacco and Sunflower
Bibliographic record
Abstract
Contract farming, where farmers sign contracts that commit them to growing specific crops or products and selling them to specific purchasers, is an important part of agriculture in developed countries. They are found in some situations in developing countries, when ‘outgrowers’ contract to supply an estate or company, which runs a processing operation such as a tea or sugar factory, or when exporters are committed to supply high-value crops for export, such as cut flowers or spices. Their relative importance is likely to continue to increase. Farmers also sign contracts when they accept inputs on credit. The contracts specify that they will sell their crops to a cooperative or trading organization that has arranged credit, which will deduct the costs of the inputs supplied from the money paid to the farmers for their crops. This chapter investigates some of the issues that have arisen when contract farming has been attempted in Tanzania. It draws on data from two research studies, which sampled farmers who were growing flue-cured tobacco, where contracts have been institutionalized for more than fifty years, and sunflower where contracts were introduced but did not survive as a significant component of the marketing system. From these studies and desk research, the chapter draws conclusions about what is needed to sustain and further develop contract farming in Tanzania. Background Eaton and Shepherd start their widely quoted briefing note by pointing out that contracts are not new and cover many situations. Thus any system of share-cropping, in which a landlord is entitled to a share of the harvest, implies a contract. T. J. Byres shows that the Greeks had systems of share-cropping more than 2,500 years ago (Byres 1983; 3). Some of the most exploitative share-cropping was in the Southern states of the USA in the last half of the nineteenth century. More recently, in Africa, farmers, recruited in the 1950s to the Gezira irrigation scheme on the Nile in the Sudan, signed contracts that required them to grow cotton and sell it to the scheme. The World Bank-funded schemes to support farmers growing a number of crops in Tanzania in the 1970s and 1980s included credit supported by contracts. Contracts are also fundamental to agri-business, when processing companies contract with large farms, but also when large farms or marketing agents contract with smaller outgrowers (Watts 1994: 26–8; Eaton & Shepherd 2001: 1–2).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".