A total of 225, 3 weeks old weaned pigs of the Landrace × Yorkshire × Duroc breeds with initial average body weight (BW) of 6.25±0.6 kg were randomly assigned to 3 different treatments based on their BW, with each having 5 replicates. Each replicate contained 15 pigs, resulting in 75 pigs per treatment group. The treatment included a basal diet (CON), CON + allicin and cinnamaldehyde mixture 500 ppm (ALI), CON + Bacillus-based probiotics 500 ppm (PRO) tested for 42 days in a 3-phase feeding program (0–14 as phase 1, days 15–28 as phase 2, and days 29–42 as phase 3). Result shows final BW (6.3%) and average daily gain (9.0%) in the overall phase were higher (p<0.05) in PRO compared with CON. At d 14, the level of Escherichia coli was lower (p<0.05) in ALI (12.0%) and PRO (13.2%) over CON. At days 28 (14.6%) and 42 (12.8%), the level of Escherichia coli was lower (p<0.05) in PRO compared with CON. The level of tumor necrosis factor-α was lower (p<0.05) in PRO over CON (15.0%). Superoxide dismutase (9.2%) and immunoglobulin A (19.4%) were higher (p<0.05) in PRO over CON. We concluded that dietary PRO at 500 ppm showed better performance in piglets by enhancing their growth performance and health.

Rye, whole-crop barley and Italian Ryegrass are major winter forage species in Korea, and yield monitoring of winter forage species is important to improve forage productivity by precision management of forage. Forage monitoring using Unmanned Aerial Vehicle (UAV) has offered cost effective and real-time applications for site-specific data collection. To monitor forage crop by multispectral camera with UAV, we tested four types of vegetation index (Normalized Difference Vegetation Index; NDVI, Green Normalized Difference Vegetation Index; GNDVI, Normalized Green Red Difference Index; NGRDI and Normalized Difference Red Edge Index; NDREI). Field measurements were conducted on paddy field at Naju City, Jeollanam-do, Korea between February to April 2019. Aerial photos were obtained by an UAV system and NDVI, GNDVI, NGRDI and NDREI were calculated from aerial photos. About rye, whole-crop barley and Italian Ryegrass, regression analysis showed that the correlation coefficients between dry matter and NDVI were 0.91∼0.92, GNDVI were 0.92∼0.94, NGRDI were 0.71∼0.85 and NDREI were 0.84∼0.91. Therefore, GNDVI were the best effective vegetation index to predict dry matter of rye, wholecrop barley and Italian Ryegrass by UAV system.