Background and Aim: Gastrointestinal (GI) nematode infection remains an important problem in livestock, particularly cattle. The infection may lead to serious health complications and affect animal products. The objective of this study was to investigate GI nematode infection and its associated risk factors in dairy and beef cattle farmed in Pak Chong District of Nakhon Ratchasima Province, northeast Thailand. Materials and Methods: Fecal specimens were collected from 101 dairy cattle and 100 beef cattle. Formalin-ethyl acetate concentration techniques were used to process the samples and the samples were observed under a light microscope. Samples were subjected to molecular identification of specific genera using conventional polymerase chain reaction and DNA sequencing. Results: The overall prevalence of GI nematode infection was 33.3%. The strongyle nematode was the most significant GI nematode in this area with a prevalence of 28.4%. The prevalence of strongyle nematodes was 58.0% in beef cattle and only 7.9% in dairy cattle. Trichuris spp. was another nematode found in both types of cattle with an overall prevalence of 5.0% with 9.0% in beef cattle and 1.0% in dairy cattle. The results of the epidemiological study indicate that the age of cattle, food, water sources, farming system, and housing floor are the most important risk factors. Among the strongyle nematodes, Ostertagia spp. was the most prevalent (82.0%), followed by Haemonchus spp. (62.3%) and Trichostrongylus spp. (8.2%), respectively. Conclusion: Infection with GI nematodes still exists in this area, particularly in beef cattle. Our reported data may benefit local parasitic control policies in the future. Keywords: beef cattle, dairy cattle, gastrointestinal nematode, strongyle nematodes.

High-resolution images captured by Unmanned Aerial Vehicles (UAVs) play a vital role in precision agriculture, particularly in evaluating crop health and detecting weeds. However, the detailed pixel information in these images makes classification a time-consuming and resource-intensive process. Despite these challenges, UAV imagery is increasingly utilized for various agricultural classification tasks. This study introduces an automatic classification method designed to streamline the process, specifically targeting cassava plants, weeds, and soil classification. The approach combines K-means unsupervised classification with spectral trend-based labeling, significantly reducing the need for manual intervention. The method ensures reliable and accurate classification results by leveraging color indices derived from RGB data and applying mean-shift filtering parameters. Key findings reveal that the combination of the blue (B) channel, Visible Atmospherically Resistant Index (VARI), and color index (CI) with filtering parameters, including a spatial radius (sp) = 5 and a color radius (sr) = 10, effectively differentiates soil from vegetation. Notably, using the green (G) channel, excess red (ExR), and excess green (ExG) with filtering parameters (sp = 10, sr = 20) successfully distinguishes cassava from weeds. The classification maps generated by this method achieved high kappa coefficients of 0.96, with accuracy levels comparable to supervised methods like Random Forest classification. This technique offers significant reductions in processing time compared to traditional methods and does not require training data, making it adaptable to different cassava fields captured by various UAV-mounted optical sensors. Ultimately, the proposed classification process minimizes manual intervention by incorporating efficient pre-processing steps into the classification workflow, making it a valuable tool for precision agriculture.

This study investigates the distribution of the Lymnaea (Radix) spp. in Pak Chong district, Nakhon Ratchasima province, northeast Thailand, where a vast cattle farming area is located and has a high prevalence of Fasciola spp. in the cattle. By random selection, 1,414 snails were collected from the natural and man-made ponds. The snails were recorded for morphology and processed for DNA isolation. The snail species were investigated by conventional PCR using a 16S rDNA-specific primer. The result demonstrated that all collected snails were R. (L.) rubiginosa. Moreover, the infection of Fasciola gigantica in the snails was investigated by PCR using a cytochrome c oxidase I (COX1)-specific primer. The results illustrated that the overall prevalence was 22.5% (318/1414), with the highest prevalence in the Nong Sa Rai subdistrict at 73.6% (81/110), which is the highest prevalence of Fasciola gigantica in the snail host that has ever been reported. The lowest prevalence existed in the Pong Ta Long subdistrict at 3.7% (4/109). Our results corresponded to the previous report on the Fasciola spp. infection in the cattle from this area, and the geographical analysis revealed that the most suspected factor would be the earth dam located in these subdistricts, where many animals live freely during the day. Our findings could be helpful for further parasite control and could trigger the study of the biology and associated factors in the future.

1. Mangrove forests serve an essential function in reducing carbon emissions, thereby supporting the national climate policy objectives. In Southeast Asia (SEA), mangrove loss averaged 16,387 hecta...

Heat hotspots cause environmental and health problems to residents in urban areas. Green space has been used to reduce the temperature in urban areas. However, the size and location of the green space that can reduce the temperature in those areas are challenging. This study aims to identify the heat hotspot of an urban area and estimate the green space proportion to reduce the heat hotspot. Therefore, the Split Window method (SW) was initially employed to calculate the Land Surface Temperature (LST) data from the Landsat series in the summertime of 2014, 2016, and 2018. The LST data 2018 were used in heat hotspot investigation using Moran's I and Getis-Ord Gi*. The results show the clustering patterns of LST occurring in barren lands, racetracks, and built-up areas in Buriram Municipality. Then, the monthly regression modeling between the green space proportions and LST was analyzed and applied to the hotspot areas. The green space proportions were represented by estimating in regression models showing the ratio of green space and decreasing temperature in hotspot areas. As a result, the green space proportion around 45% of the area is suggested to mitigate the heat hotspot. The explored green space proportion was applied to the 2014 and 2016 data to assess the feasibility of hotspot mitigation. This research presents a simplified technic that will enable urban planners to estimate the green space proportion to reduce the heat hotspots effectively.