ArticlePloS one2025
Enhancing food security sustainability through digital information extension services in rural Uganda: Maize postharvest evidence-based strategies.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPostharvest losses caused by poor drying and storage practices lead to maize waste, reduced food availability, unsafe food due to aflatoxin contamination, and income loss for farmers. This exacerbates food insecurity and threatens the livelihoods of rural communities. Leveraging digital solutions to provide quality maize postharvest handling information is critical to mitigating these challenges.
objectiveThis study examined maize postharvest handling extension information services provided by rural agricultural extension officers in selected districts of Uganda, focusing on how evidence-based practices supported by information sources, expert knowledge, and ICT infrastructure ensure the quality and relevance of the information delivered.
methodsA convergent parallel mixed-methods design was employed. Data were collected from 312 rural smallholder maize farmers, 22 extension officers, and four information officers. Qualitative data were analyzed thematically using ATLAS.ti version 24, quantitative data were analyzed in SPSS version 29 to generate descriptive statistics and conduct Pearson's chi-square tests.
findingsAgriculture extension officers applied digital evidence-based information practices to improve maize postharvest handling advisory services. The study reported that the most frequently used practices were asking (identifying farmers' information needs), acquiring (gathering relevant information), appraising (evaluating information quality), and applying (guiding farmers' decisions). Aggregation (organizing information for accessibility) and assessment (evaluating the effectiveness of applied information) were less practiced. The study suggests a significant relationship between evidence application and maize postharvest handling practices (p = 0.002). No significant relationship between asking farmers' information needs and maize postharvest handling practices (p = 0.887). The study identified inadequate updated sources of information, insufficient skills in evidence-based practices, and information communication technologies.
conclusionThis study suggests that decision-makers update the evidence source and develop continuous professional development (CPD) training programs for rural agricultural extension officers to equip them with knowledge and skills in maize postharvest handling, evidence-based information practices, and new developments in ICTs.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.