Determinants of sustainability of greenhouse farming technology among farmers in Kakamega county, Kenya
Notice bibliographique
Résumé
The sustainability of greenhouse farming has been a major concern worldwide as \ncountries like Canada have emphasized on the use of integrated pest management \nstrategies rather than the use of pesticides, other countries like Netherlands have looked \nat how technical knowhow of their farmers can be improved, how efficient use of water \ncan be achieved and how energy can be used more efficiently. In Kenya according to \nKARI (Kenya Agricultural Research Institute) sustainability is still faced by many \nchallenges including lack of technical back up on the innovation. In Kakamega County \n30% of farmers own greenhouses but after a period of 2 to 3 years only 5% of them still \nown these greenhouses despite the fact that a comparison done worldwide through \nliterature review shows that the uptake of this technology is increasing. The purpose of \nthe study was to investigate the determinants of sustainability in greenhouse farming \ntechnology amongst farmers in Kakamega County, Kenya. This study was guided by the \nfollowing objectives: To determine how integrated pests and disease management \ninfluence the sustainability of greenhouse technology in Kakamega County, To assess \nthe extent to which utilization of energy influences the sustainability of greenhouse \nfarming technology among farmers in Kakamega County, to examine how modern \nirrigation influences the sustainability of greenhouse farming technology and, to establish \nthe level at which technical training influence the sustainability of greenhouse technology \namong farmers in Kakamega County. Descriptive survey design was used. The sampling \nframe of 202 was provided by the County Director of agriculture Kakamega, where a \nsample size of 132 farmers was identified using the Krejcie and Morgan (1970) formula \nfor determining the sample size. The study used questionnaire to collect data. Pilot testing \nwas used as an important step in making the instrument reliable for the purpose of the \nstudy. The Cronbach‟s coefficient for determination of reliability of data collection \ninstruments was calculated as 0.769. Both descriptive and inferential statistics were used \nto analyze data. The study established that there was a significant positive correlation \nbetween the results seen with the integrated pest and disease management system and the \nsustainability of greenhouse farming technology in Kakamega County (N=127; r=0.47; \np˂0.01). A significant positive correlation was also noted on whether or not a farmer had \nchallenges with the integrated pest and disease management system and the sustainability \nof greenhouse farming in Kakamega County (N=127;r=0.57; p˂0.05). The analysis \nestablished that there was a significant positive correlation between the utilization of \nrenewable energy and sustainability of greenhouse farming technology in Kakamega \nCounty (N=127;r=0.32; p˂0.05).There was a significant positive correlation between the \nuse of modern irrigation systems and the sustainability of greenhouse farming technology \nin Kakamega County (N=127;r=0.29; p˂0.05).There was also a significant strong \npositive correlation between technical training of farmers and sustainability of \ngreenhouse farming technology in Kakamega County (N=127;r=0.61; p˂0.05).The study \nrecommends sensitization and strengthening on: the concept of integrated pest and \ndisease management system in greenhouse farming technology, benefits of utilizing the \nrenewable energy sources as a way of further reducing the cost of fuel used in the \ngreenhouse farming technology and, adoption of modern irrigation system to enhance the sustainability of greenhouse farming technology.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,003 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».