• Asian-Australasian Journal of Animal Sciences
• /
• v.17 no.11
• /
• pp.1615-1634
• /
• 2004

A review was undertaken to obtain information on the sustainability of pig free-range production systems including the management, performance and health of pigs in the system. Modern outdoor rearing systems requires simple portable and flexible housing with low cost fencing. Local pig breeds and outdoor-adapted breeds for certain environment are generally more suitable for free-range systems. Free-range farms should be located in a low rainfall area and paddocks should be relatively flat, with light topsoil overlying free-draining subsoil with the absence of sharp stones that can cause foot damage. Huts or shelters are crucial for protecting pigs from direct sun burn and heat stress, especially when shade from trees and other facilities is not available. Pigs commonly graze on strip pastures and are rotated between paddocks. The zones of thermal comfort for the sow and piglet differ markedly; between 12-22$^{\circ}C$ for the sow and 30-37$^{\circ}C$ for piglets. Offering wallows for free-range pigs meets their behavioural requirements, and also overcomes the effects of high ambient temperatures on feed intake. Pigs can increase their evaporative heat loss via an increase in the proportion of wet skin by using a wallow, or through water drips and spray. Mud from wallows can also coat the skin of pigs, preventing sunburn. Under grazing conditions, it is difficult to control the fibre intake of pigs although a high energy, low fibre diet can be used. In some countries outdoor sows are fitted with nose rings to prevent them from uprooting the grass. This reduces nutrient leaching of the land due to less rooting. In general, free-range pigs have a higher mortality compared to intensively housed pigs. Many factors can contribute to the death of the piglet including crushing, disease, heat stress and poor nutrition. With successful management, free-range pigs can have similar production to door pigs, although the growth rate of the litters is affected by season. Piglets grow quicker indoors during the cold season compared to outdoor systems. Pigs reared outdoors show calmer behaviour. Aggressive interactions during feeding are lower compared to indoor pigs while outdoor sows are more active than indoor sows. Outdoor pigs have a higher parasite burden, which increases the nutrient requirement for maintenance and reduces their feed utilization efficiency. Parasite infections in free-range pigs also risks the image of free-range pork as a clean and safe product. Diseases can be controlled to a certain degree by grazing management. Frequent rotation is required although most farmers are keeping their pigs for a longer period before rotating. The concept of using pasture species to minimise nematode infections in grazing pigs looks promising. Plants that can be grown locally and used as part of the normal feeding regime are most likely to be acceptable to farmers, particularly organic farmers. However, one of the key concerns from the public for free-range pig production system is the impact on the environment. In the past, the pigs were held in the same paddock at a high stocking rate, which resulted in damage to the vegetation, nutrient loading in the soil, nitrate leaching and gas emission. To avoid this, outdoor pigs should be integrated in the cropping pasture system, the stock should be mobile and stocking rate related to the amount of feed given to the animals.

• Journal of the Korean association of regional geographers
• /
• v.9 no.3
• /
• pp.373-384
• /
• 2003

This study analyses the soil loss due to cropland increase in the Hoeryeung area of northeast Korea, using Landsat images of 1987 TM and 2001 ETM, together with DTED, soil and geological maps, and rainfall data of 20 years. Items of land cover and land use were categorized as cropland, settlement, forest, river zone, and sand deposit by supervised classification with spectral bands 1, 2 and 3. RUSLE model is used for estimation of soil loss, and AML language for calculation of soil loss volumes. Fourier transformation method is used for unification of the geographical grids between Landsat images and DTED. GTD was selected from 1:50,000 topographic map. Main sources of soil losses over 100 ton/year may be the river zone and settlement in the both times of 1987 and 2001, but the image of the 2001 shows that sources areas have developed up to the higher mountain slopes. In the cropland average, increases of hight and gradient are 24m and $0.8^{\circ}$ from 1987 to 2001. In the case of new developed cropland, average increases are 75m and $2.5^{\circ}$, and highest soil loss has occurred at the elevation between 300 and 500m. The soil loss 57 ton of 1987 year increased 85 ton of 2001 year. Soil loss is highest in $30{\sim}50^{\circ}$ slope zones in both years, but in 2001 year, soil loss increased under $30^{\circ}$ zones. The size of area over 200 ton/year, indicating higher risk of landslides, have increased from $28.6km^2$ of 1987 year to $48.8km^2$ of 2001 year.

• Korean Journal of Remote Sensing
• /
• v.39 no.2
• /
• pp.193-205
• /
• 2023

A large amount of floating debris from land-based sources during heavy rainfall has negative social, economic, and environmental impacts, but there is a lack of monitoring systems for floating debris accumulation areas and amounts. With the recent development of artificial intelligence technology, there is a need to quickly and efficiently study large areas of water systems using drone imagery and deep learning-based object detection models. In this study, we acquired various images as well as drone images and trained with You Only Look Once (YOLO)v5s and the recently developed YOLO7 and YOLOv8s to compare the performance of each model to propose an efficient detection technique for land-based floating debris. The qualitative performance evaluation of each model showed that all three models are good at detecting floating debris under normal circumstances, but the YOLOv8s model missed or duplicated objects when the image was overexposed or the water surface was highly reflective of sunlight. The quantitative performance evaluation showed that YOLOv7 had the best performance with a mean Average Precision (intersection over union, IoU 0.5) of 0.940, which was better than YOLOv5s (0.922) and YOLOv8s (0.922). As a result of generating distortion in the color and high-frequency components to compare the performance of models according to data quality, the performance degradation of the YOLOv8s model was the most obvious, and the YOLOv7 model showed the lowest performance degradation. This study confirms that the YOLOv7 model is more robust than the YOLOv5s and YOLOv8s models in detecting land-based floating debris. The deep learning-based floating debris detection technique proposed in this study can identify the spatial distribution of floating debris by category, which can contribute to the planning of future cleanup work.