DMMMSU External Publications

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Browsing DMMMSU External Publications by Subject "Agricultural technology"

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    Elevating sericulture:
    (Journal of Biodiversity and Environmental Sciences, 2025-02) Apilado, Leonora P.; Hufalar, Julius Peter M.; Nillo, Rofolfo G.
    The Don Mariano Marcos Memorial State University Sericulture Research and Development Institute (DMMMSU-SRDI) in Bacnotan, La Union, Philippines, advocates the application of optimal sericulture techniques at extension sites to boost the income of seri-farmers, particularly those from marginal sectors. A prime example is the Sericulture Demonstration Project Farm (Seri-Farm) in Barangay Lioac Sur, Naguilian, La Union, which has successfully operated for over a decade (2011–2022) and remains active today. The farm serves as a dynamic showcase of innovative sericulture technologies, promoting sustainable agriculture, rural livelihood improvement, and economic development. Seri-farmers diversified their activities, engaging in grain and vegetable farming, animal husbandry, sugarcane cultivation, and vinegar/winemaking, with sericulture as a complementary endeavor. The initiative provided employment opportunities for out-of-school youth and community workers while catering to both domestic and international silk markets. The farm’s medium-textured soil, high nutrient content, and favorable weather during most rearing seasons supported high-value cocoon production, though larval performance was affected in 2017 by disease and climate change. Over its 11-year span, the Seri-Farm achieved significant results, completing 31 rearing cycles and producing 392.7 kg of fresh cocoons, generating a total income of PhP94,935.00. Additionally, allied products such as cuttings, saplings, seeds, novelty items, fruit beverages, vinegar, wine, and compost contributed PhP176,215.00, underscoring the economic potential of integrated sericulture farming systems.
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    Factors influencing adoption of selected peanut protection and production technologies in Northern Luzon, Philippines
    (Technology in Society, 2018-11) Launio, Cheryll C.; Luis, Janet S.; Angeles, Yolanda B.
    The peanut is the world's second most important legume, and peanut and peanut-based products have a large potential in the Philippines and other tropical developing countries both in terms of import substitution and export promotion. This study used survey data to describe the socioeconomic and technological level of peanut farmers in major peanut-producing areas in the country, and determine the adoption factors and constraints for selected technologies. A total of 296 farmers in 10 provinces were interviewed in 2015. The binary logit model was applied to explore the farm-level factors affecting the adoption of selected technologies (seed inoculant, seed treatment, chemical fertilizer and pesticides) for peanut farming. Results indicate that decision factors vary per technology but relatively consistent drivers to technology adoption are farmer gender, attendance to seminar or training in peanut production, availability of irrigation source, and membership in organization. Household size was an adoption constraint in select technologies. Technology uptake was also influenced by regional differences. Implications are increased provision of seminars and trainings for peanut farmers; facilitation of farmers' organizations; support by local government units (LGUs) in technology promotion, and ensuring adequate irrigation. Informing decision makers on the factors and constraints to adoption of these technologies will largely make for evidence-based policymaking. The adoption of the selected technologies covered in this study will not only improve farmers' yield and income, but may eventually contribute to lower production cost per unit and lower prices in the long run, which has implications to the whole value-chain of peanut and peanut-based products.
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    Image-based Mangifera indica pathogen recognition using artificial intelligence
    (Association for Computing Machinery (ACM), 26-08-2024) Baculo, Maria Jeseca C.; Rivera, Nema Rose D.; Cuison, Floribeth P.
    Mangifera Indica, holds significant global export value. This study focuses on implementing object detection frameworks to identify five surface defects in this mango variety, which is crucial for maintaining its export quality. The methodology involves training four object detection frameworks. Results show that the modified region extraction technique, which uses adaptive binarization and morphological operations catered to detect mango surface defects, with EfficientNet as the base learner, demonstrated improved accuracy with a mean Average Precision (mAP) of 0.842 at an Intersection over the Union (IoU) threshold of 0.75.