Inventory and GIS Mapping of the Three Existing Coffee Types at Cagayan State University Lal-lo Old Coffee Valena Plantation
Paper Details
Inventory and GIS Mapping of the Three Existing Coffee Types at Cagayan State University Lal-lo Old Coffee Valena Plantation
Abstract
The study was conducted at Cagayan State University Lal-Lo Campus from November 2, 2022 to February 18, 2023, focusing on the inventory and geo-tagging of coffee trees in an old plantation. Utilizing GPS devices and smartphones, researchers identified coffee types based on their morphological characteristics. The findings indicated that the plantation spans 11.4 hectares and contains a total of 3,801 coffee trees across caves 1 and 2. Among these, Robusta coffee trees showed a high percentage of non-bearing trees at 86.33% (2,489 trees), while only 13.66% (394 trees) were bearing fruit. Similarly, Liberica coffee trees were predominantly non-productive, with 94.46% (819 trees) not bearing fruit, compared to 5.54% (48 trees) that were productive. For Excelsa coffee trees, 58.82% (30 trees) were unproductive, while 41.17% (21 trees) bore berries.Data collected were geo-tagged using Google Earth applications, creating a map illustrating the distribution of the three coffee types. Factors contributing to the lack of berries included tree age, overgrowth of secondary vegetation, wildling proliferation, and insufficient water supply during dry seasons. The morphological analysis confirmed the presence of Robusta, Liberica, and Excelsa coffee types in the plantation.The study emphasizes the significance of inventory and geo-tagging for students and faculty engaged in coffee production research at CSU Lal-Lo. It recommends rehabilitating old coffee plants to enhance berry quality and suggests further studies on sustainable coffee management at the old coffee plantation at the valena site for regional sustainability in the coffee industry.
Banluewong P, Suwanwerakamtorn R. 2013. A Geographic Information System (GIS)-based analysis to predict the suitability of rubber plantation by Extreme Learning Machine and decision tree. Asian Conference on Remote Sensing 2013 5, 4249–4256 p).
Belal AA, EL-Ramady H, Jalhoum M, Gad A, Mohamed ES. 2021. Precision Farming Technologies to Increase Soil and Crop Productivity. In: Abu-hashim, M., Khebour Allouche, F., Negm, A. (eds) Agro-Environmental Sustainability in MENA Regions. Springer Water. Springer, Cham. https://doi.org/10.1007/978-3-030-78574-1_6
Fernandez ML, Balatico FVM, Clemente RA, Julian LJ, Pattung BC. 2022. Morphological Characterization and Identification of Existing Coffee Types at CSU Lal-lo Valena Site; International Journal of Biosciences 21(6), p 53-58.
Lagman Ma, Carmen Ablan. 2023. Variability in Philippine Coffea liberica provides Insights into Development Amidst a Changing Climate. Https://doi.org/10.3390/icc2023-14852
Sheth R, Brahmbhatt K. 2021. Geo-tagging of agricultural products using mobile application in remote areas. Reliability: Theory and Applications 16, p 369–375.
Swain KC, Singha C. 2018. Mapping of agriculture farms using GPS and GIS technique for precision farming. International Journal of Agricultural Engineering 11(2), 269-275 Copyright@2018:Hind Agri-Horticultural Society. Https://doi.org/10.15740/HAS/IJAE/11.2/269-275.
Talosig EE, Adriatico C, Yap FRP. 2019. Profiling and geo-tagging of Rubber tree plantation through geographic information system. Open Access Library Journal, 6(e), 5460 https://doi.org/10.4236/oalib.1105460
Tridawati E. 2020. Mapping the distribution of coffee plantations from multi resolution, multi-temporal, and multi-sensor data using a random forest algorithm. https://doi.org/10.3390/rs12233933
Udarno ML, Setiyono RT. 2015. Excelsa Coffee Performance of Meranti Islands District, Riau. Pros Sem Nas Masy Biodiv Indon 1, p 543-547.
Wang N, Jassogne L, Van Asten PJA, Mukasa D, Wanyama I, Kagezi G, Gille KE. 2015. Evaluating coffee yield gaps and important biotic, abiotic, and management factors limiting coffee production in Uganda. European Journal of Agronomy 63, p.1-11.
Yilma A, Aman M, Mekonnen N. 2020. Evaluation of Coffee Tree Productive Center Performance to Cycle Change. International Journal of Research Studies in Science, Engineering and Technology 7(10), p 11-17.
Roje Marie A. Clemente, Dr. Maribel L. Fernandez, Nenette T. Columna, MS Minalyn Ancheta, Angelo Pattung, 2025. Inventory and GIS Mapping of the Three Existing Coffee Types at Cagayan State University Lal-lo Old Coffee Valena Plantation. Int. J. Biosci., 26(3), 7-16.
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