Research in Ecology

Field Application of SeMNPV (Spodoptera exigua Multiple Nucleopolyhedrovirus) for Controlling Spodoptera exigua on Shallot and Scallion in the Mekong Delta, Vietnam

Xuan Trinh Thi — 2025-12-30

Shallot and scallion are among the essential horticultural crops in the Mekong Delta of Vietnam, and their yields and quality are affected by the beet armyworm, Spodoptera exigua. To control this pest, farmers have used a large number of insecticides, which has led to resistance due to continuous use and environmental harm, thereby increasing interest in biological methods, including the Spodoptera exigua multiple nucleopolyhedrovirus (SeMNPV). The study aims to assess the efficacy of the liquid and powder formulation of SeMNPV against Spodoptera exigua under the actual agricultural conditions of Soc Trang (Shallot) and Vinh Long provinces (Scallion). The experimental arrangement used four treatments: SeMNPV liquid, SeMNPV powder, Radiant 60SC (a chemical insecticide), and the control. Some of the factors evaluated were larval density, level of leaf damage, yield, and natural enemies attack. The SeMNPV powder had the least damage, only 0.97 per cent (damage of plant), and a yield of 28.59 t/ha, and was almost equally good as the Radiant 60SC, which produced a damage of 2.53 per cent (damage of plant) and a yield of 29.41 t/ha. SeMNPV treatments significantly reduced larval numbers (down to fewer than 10 larvae/m² by day 40) and enhanced natural enemies, parasitoids, and microsporidia. The untreated one had the highest damage (8.03%), while the yield was lowest at 19.61 t/ha. Therefore, SeMNPV in powder form has the potential to control S. exigua infestations and to encourage biological control, making it a favourable, environmentally friendly addition to synthetic insecticides in the cultivation of shallot and scallion.

The Habitat Ecology Species of (Pteropus vampyrus) and (Pteropus lylei) Natural Exploitation as Food Sources in Tra Vinh, Vietnam

Le Thanh Quang — 2025-12-26

Bat population appears is maintained as the last habitat of these species in the La Ca Pagoda (old) area of the Khmer ethnic people in Tra Vinh. Methods are interview, count individuals in the habitat, species, population, determine the type of plants, the height of the living bats; results of the current status of species, quantity, distribution, residence and reproduction of species; impacts on 2 species of horse bats related to the conservation of horse bats; compared to previous by the Ehb function, with the parameters Hchb (Horseback bat community) the existence of the community compared to the previous time, at least 1 breeding season; Hhhb (Horse bat habitat) on living conditions; Hfhb (Horse bat food) on food sources for the community to live; Hphb (Horse bat protection) on conditions to protect the community; evaluated by score (0–1); the results the species Pteropus vampyrus decreased by 79.51%, the species Pteropus lylei increased by 6.8 times. The total number is 1239 individuals, including 1072 THB and 168 LHB. Both of these species have been recorded in previous surveys. The number of individuals counted was larger than the previous survey (1239 individuals compared to 957 individuals); the number of LHB in this survey was much smaller than the previous count (168 individuals compared to 820 individuals); the number of THB was 1072 individuals compared to 137 individuals). Using a function-based assessment method with ecologically relevant parameters can objectively assess the distribution and status of threatened populations to effectively protect their survival.

Greenhouse Effect Evaluation: Giga Chat Optimization Algorithm (GCOA)

Alexey Mikhaylov — 2025-12-30

The algorithm is designed to solve the global problem of multi-objective optimization with constraints in the context of greenhouse gas assessment and mitigation. Artificial intelligence provides unique opportunities for analyzing large amounts of data and identifying hidden relationships between various factors affecting emissions. The use of AI makes it possible to develop effective emission reduction strategies, predict the consequences of various scenarios, and evaluate the effectiveness of decisions made. Machine learning algorithms are capable of modeling complex systems such as energy infrastructure, transportation, and industry to determine the best ways to minimize emissions. The greenhouse effect and related climate change pose one of the most serious threats to our future. Innovative approaches and modern technologies are needed to effectively combat these problems. Government intelligence, in particular, Giga Chat, offers a variety of services for analysts, forecasting, and user support. Their use can significantly accelerate the transition to sustainable development and achieve the goals of the Paris Agreement to limit global temperature growth to 1.5 ℃. However, realizing the potential of AI requires careful preparation and consideration of many factors, including data quality, ethics, and technical aspects. Only through the joint efforts of scientists, politicians, and society will we be able to overcome the challenge of climate change and build a future that is safe for future generations.