Demographic profile of banana farmers in the municipalities of Allacapan, Lasam, Gattaran and Baggao in the Province of Cagayan, Philippines

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Research Paper 15/05/2023
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Demographic profile of banana farmers in the municipalities of Allacapan, Lasam, Gattaran and Baggao in the Province of Cagayan, Philippines

Gonzales Angelina T., Diosa G. Alasaas
J. Bio. Env. Sci.22( 5), 69-75, May 2023.
Certificate: JBES 2023 [Generate Certificate]

Abstract

The study aimed to determine the socio-demographic profile of banana farmers in Allacapan, Lasam, Gattaran and Baggao, Cagayan; (2) identify the banana information strategies, technical assistance and support services offered to the respondents; (3) determine the knowledge and skills; and (4) the extent of improvement of the socio-economic status of the farmers. Banana Farmers in Allacapan, Lasam, Gattaran and Baggao, Cagayan Socio-Demographic Profile of the Respondents. The NAMRIA form was used to gather data to the banana farmers thru an individual interview from October to December 2020. The gathered data from the respondents were analyzed using the frequency counts, weighted means, and percentages. The geo-referenced locations of the banana farms were analyzed using the QGIS software. Results revealed that for the yield, majority of the banana growers in Allacapan had a yearly harvest ranging from 1000-5000 kilograms which means that low yield was obtained by the farmers. The GPS recordings of the banana farms were collected from March to May 2021. Based on the results of the study, the actual field visits and tracking of the banana samples using the geo-tracker application was a reliable technique. Moreover, training on banana production shall be included also to create awareness on the recommended practices for banana production.

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Aspinall R. 1992. Described an inductive modelling procedure based on Bayes’ theorem for analysis of pattern in spatial data. International Journal of Geographical Information Science 6, 105-121.

Diekmann, M. Puuter CAJ. 1966. FAO/IPGI. Technical Guidelines for the Safe Movement of Germplasm No. 151: Musa (2nd Edn), Food and Agriculture Organization of the United Nations/International Plant Genetics Resources Institute. Rome.

Gonzales AT. 2018. Cacao mapping using Geographic Information System (GIS) in the Province of Cagayan, Philippines. Journal of Biodiversity and Environmental Sciences (JBES) ISSN: 2220-6663 (Print) 2222-3045 (Online) Vol. 13, No. 2, p. 381-386, 2018. www.innspub.net

Guisan A, Theurillat JP, Kienast F. 1998. Predicting the Potential Distribution of Plant Species in an Alpine Environment. Journal of Vegetation Science 9, 65-74.

Hirzel AH, Hausser J, Chessel D, Perrin N. 2002. Ecological-niche factor analysis: how to compute habitat suitability maps without absence data? Ecology 83, 2027-2036.

Leathwick JR, Rowe D, Richardson J, Elith J, Hastie T. 2005. Using multivariate adaptive regression splines to predict the distributions of New Zealand’s freshwater diadromous fish. Freshwater Biology 50, 2034-2052.

Pitt JPW, Worner SP, Suarez AV. 2009. Predicting Argentine ant spread over the heterogeneous landscape using a spatially explicit stochastic model. Ecological Applications 19, 1176-1186. 10.

Rafoss T. 2003. Spatial stochastic simulation offers potential as a quantitative method for pest risk analysis. Risk Analysis 23, 651-661.

Yemshanov D, Koch FH,mcKenney DW, Downingmc, Sapio F. 2009. Mapping Invasive Species Risks with Stochastic Models: A Cross-Border United States- Canada Application for Sirex noctilio Fabricius. Risk Analysis 29, 868-864.