Mapping and characterization of stakeholders in the fodder value chain in Southern Rangelands of Kenya: Understanding their roles, interactions and influences
Bibliographic record
Abstract
Livestock production is the most important economic activity in Kenya. Land use practice in arid and semi-arid lands (ASALs) of Kenya is most affected by droughts, which lead to pasture scarcity. Fodder production has been adopted by communities in ASALs to enhance feed availability and generating alternative source of livelihood. However, the scale of fodder production remains low, with poor markets being key challenge in realizing the economic benefits of fodder production. This study was conducted in Makueni and Kajiado Counties to understand institutional structures and stakeholders together with their roles and influences on fodder production and marketing. The two counties were purposively selected as they were some of the target areas where fodder production practices and technologies had been promoted. Data was collected through NetMap tool for identifying and mapping actors, their roles, influence and interactions along the value chain. NetMaps were subjected to quantitative analysis using Organizational Risk Analysis tool in Social Network Analysis to map, as well as measure relationships and influences of stakeholders and examine their relative positions. Results revealed that various stakeholders play multiple roles along the chain, which determine their position and influence in decision-making. Most influential actors included Kenya Agricultural and Livestock Research Organization (KALRO), NGOs and CBOs – they determine access to information, technical and funding resources and markets; thus, key in upscaling, out-scaling and commercialization of fodder production in the study areas. Working in groups enabled producers to easily access technical support, information and interact with other stakeholders in the chain. Efforts for enhancing fodder production should provide more technical support and capacity building to CBOs due to their centrality and strategic position in the network which make them the main stakeholders.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".