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Record W4200457842 · doi:10.3389/fsufs.2021.611089

Farmers' Sources and Varieties of Cassava Planting Materials in Coastal Kenya

2021· article· en· W4200457842 on OpenAlexfundno aff
Patrick Clay Kidasi, Dora Kilalo Chao, Elias Otieno Obudho, A. W. Mwang’ombe

Bibliographic record

VenueFrontiers in Sustainable Food Systems · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCassava research and cyanide
Canadian institutionsnot available
FundersMastercard Foundation
KeywordsSowingCropScarcityAgricultureAgricultural scienceGeographyProductivityAgroforestryLivestockDescriptive statisticsProduction (economics)AgronomyBiologyForestryMathematicsEconomicsEconomic growth

Abstract

fetched live from OpenAlex

Cassava ( Manihot esculenta Crantz) grows in diverse agro-ecological zones. In Kenya, it is widely cultivated in Western and Coastal regions. It is mainly grown for food and nutrition security and excess roots are sold to generate income for the farm households. Its productivity per unit of the land area is high compared to maize and wheat which are staple crops in the country. However, scarcity of cassava planting materials and pests and diseases limit production in these regions. This study aimed at revealing the sources and varieties of cassava planting materials used by farmers and other farmers' practices in coastal Kenya. Four focus group discussions (FGD) and a survey were conducted in 2018, using a semi-structured questionnaire targeting 250 farmers. The data collected on the sources of planting materials, preferred varieties and the practices employed by the farmers in cassava production, was analyzed using descriptive statistics. Majority of farmers (83%) interviewed indicated that they recycled planting materials from the previous crop while some 67% respondents obtained the planting material from their neighbors. Kenya Agricultural and Livestock Research Organization's (KALRO) and the local markets were reported as sources of planting materials by 11 and 5% farmer respondents, respectively. The only formal seed source reported was KALRO. The rest, own seed, neighbors, and the local markets, were informal seed sources. Piecemeal harvesting practiced by 98% of the farmers favored planting material recycling. Farmers dedicated a small proportion of their land (mean 0.2 ha) to cassava production as reported by 60 and 72.8% of farmers from Taita Taveta and Kilifi, respectively. Slightly above half (56%) of the farmers in Kilifi dedicated slightly more land to cassava, planting between 100 and 4,000 cuttings (2.5% of an acre up to a full acre) compared to 87% farmers from Taita Taveta who planted 100 cuttings or less (which is about 2.5% of an acre or less). A majority (81.1%) of farmers in Taita Taveta planted local cassava varieties compared to Kilifi's 57.8%. Slightly above half of the farmer respondents reported Kibandameno as the preferred variety followed by Tajirika as the second preferred variety as reported by 18% farmers. Kibandameno was preferred for its sweet taste by 75.6% farmers while Tajirika was preferred by 52.4% farmers because of the high yielding capacity. Nearly all farmer respondents, in Taita Taveta County obtained the planting material from informal seed sources, except a negligible number, who reported buying their planting material from KALRO, a formal seed source, far from their locality. Farmers sourcing cuttings from a formal seed source such as those from Kilifi County were more likely to use a tractor for land preparation compared to those who sourced planting materials informally who more likely had scarce knowledge on cassava production and the value of cassava. Therefore, interventions to establish a sustainable healthy cassava planting materials seed system are needed to address the systemic constraint and help develop a viable cassava value chain.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.819
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.208
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2021
Admission routes1
Has abstractyes

Explore more

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