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Information and communication technology-based service platform enabling the co-creation of agrometeorological services: A case study of the Laos Climate Services for Agriculture.
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2023-05-02
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95


Climate services for agriculture (agrometeorological services) are only possible through collaborative, interdisciplinary efforts. To this end, a national-scale service operated by centralized governments will mandate institutional coordination for data sharing, in addition to designated staff time and budget commitments for effective and sustainable operation; however, previous efforts have revealed that institutional coordination, particularly between meteorology and agriculture, represents the most difficult part preventing successful implementation in most countries. In this study, we examined the use of a top-down, information and communication technology (ICT)-based multidisciplinary platform for agrometeorological services for facilitating institutional coordination and ensuring project sustainability. The Strengthening Agro-climatic Monitoring and Information Systems (SAMIS) project, operational since 2018, focuses on building the adaptive capacity of agrometeorological services in Laos by combining the agricultural and meteorological sectors. The research here utilized the ICT-based service platform Laos Climate Services for Agriculture (LaCSA) to systematically engage relevant institutional partners in the co-creation process of agrometeorological services. The results showed that LaCSA enabled cocreation, including data sharing, collaborative tool use, institutional coordination, and policy support from multiple government entities across the agricultural and meteorological sectors. Key lessons and suggestions included co-training for both sectors, easy-to-use data management processes, early buy-in for policy makers, and creating standard operating procedures designating staff time and budgets. These results indicate that the coordinated operation of agrometeorological services through an ICT-based service platform can be successfully achieved in Laos as well as other developing countries, where institutional disconnects and human capital constraints hinder successful implementation of agrometeorological services.
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Optimizing Artificial Neural Network-Based Models to Predict Rice Blast Epidemics in Korea.
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To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of input data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combining two ANN models, the feed-forwa..
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Comparison of projected rice blast epidemics in the Korean Peninsula between the CMIP5 and CMIP6 scenarios.
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Abstract Recently, the International Panel for Climate Change released the 6th Coupled Model Intercomparison Project (CMIP6) climate change scenarios with shared socioeconomic pathways (SSPs). The SSP scenarios result in signifcant changes to climate variables in climate projections compared to th..