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- P-ISSN2765-2203
- E-ISSN2765-2211
- KCI Candidate
Abstract
Harmful pet insects, if not controlled, can negatively affect people, plants and their surrounding environment. In Vietnam, all crops are regularly impacted by pest insects. In serious cases, crops can be totally destroyed by insect pests. Harmful insects that damage crops often grow fast and increase rapidly. Therefore, research on insects is crucial for managing pests, protecting crops, and forecasting pest situation in the following years. This study aimed to collect data regarding changes of pests on rice and corn as two main crops in four provinces in Red River Delta of Vietnam, including Thai Binh, Nam Dinh, Ha Nam, and Hung Yen, from 2018 to 2022. Primary data were collected from reports of government agencies and official statistics. Based on these data, this study evaluated changes of pest insects in five years, discussed reasons for such changes and response methods, and forecasted pest’s behavior in the following years. Significant findings of this study include the fact that Vietnam has to face many difficulties to develop its agricultural sector. For insect management, an essential action is to do ground surveys to gather all related data including weather data, pesticide data, crop yield, and product quality. This information is meaningful for finding out causes of changes, understanding relationships between insects and surrounding factors, and predicting the situation in the following years.
Abstract
Ecological monitoring provides indispensable data for biodiversity conservation and sustainable resource management. However, the complexity and variability inherent in ecological monitoring data necessitate robust verification processes to ensure data integrity. This study employed Benford's Law, a statistical principle traditionally used in fields such as finance and health sciences, to evaluate the authenticity of ecological monitoring data related to the abundance of migratory bird species across various locations in South Korea. Benford's Law anticipates a specific logarithmic distribution of leading digits in naturally occurring numerical datasets. Our investigation involved two stages of analysis: a first-order analysis considering the leading digit and a second-order analysis examining the first two digits of bird population counts. While the first-order analysis displayed moderate conformity to Benford's Law that suggested overall data integrity, the second-order analysis revealed more pronounced deviations, indicating potential inconsistencies or inaccuracies in certain subsets of the data. Although our data did not perfectly align with Benford's Law, these deviations underscore the complex nature of ecological research, which is influenced by a multitude of environmental, methodological, and human factors.