The goal of this study was to estimate the emissions of greenhouse gases (GHG), namely methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2) from poultry and pig production in South Korea over the last 10 years (2005 through 2014). The calculations of GHG emissions were based on Intergovernmental Panel on Climate Change (IPCC) guidelines. Over the study period, the CH4 emission from manure management decreased in layer chickens, nursery to finishing pigs and gestating to lactating sows, but there was a gradual increase in CH4 emission from broiler chickens and male breeding pigs. Both sows and nursery to finishing pigs were associated with greater emissions from enteric fermentation than the boars, especially in 2009. Layer chickens produced lower direct and indirect N2O emissions from 2009 to 2014, whereas the average direct and indirect N2O emissions from manure management for broiler chickens were 12.48 and 4.93 Gg CO2-eq/yr, respectively. Annual direct and indirect N2O emissions for broiler chickens tended to decrease in 2014. Average CO2 emission from direct on-farm energy uses for broiler and layer chickens were 46.62 and 136.56 Gg CO2-eq/yr, respectively. For pig sectors, the N2O emission from direct and indirect sources gradually increased, but they decreased for breeding pigs. Carbon dioxide emission from direct on-farm energy uses reached a maximum of 53.93 Gg CO2-eq/yr in 2009, but this total gradually declined in 2010 and 2011. For boars, the greatest CO2 emission occurred in 2012 and was 9.44 Gg CO2-eq/yr. Indirect N2O emission was the largest component of GHG emissions in broilers. In layer chickens, the largest contributing factor to GHG emissions was CO2 from direct on-farm energy uses. For pig production, the largest component of GHG emissions was CH4 from manure management, followed by CO2 emission from direct on-farm energy use and CH4 enteric fermentation emission, which accounted for 8.47, 2.85, and 2.82 Gg-CO2/yr, respectively. The greatest GHG emission intensity occurred in female breeding sows relative to boars. Overall, it is an important issue for the poultry and pig industry of South Korea to reduce GHG emissions with the effective approaches for the sustainability of agricultural practices.

This paper proposes a novel method that uses stator current signals to detect motor faults under operational speed and load torque conditions. Previous studies on motor current signature analysis (MCSA) have been devoted to developing methods to detect faults in non-stationary conditions; however, they have limitations. Conventional methods require much domain knowledge or parameter selection for signal decomposition, and are applicable under limited variable conditions. Thus, this paper proposes a new feature, drive-tolerant current residual variance (DTCRV), for fault detection. This new approach requires no domain knowledge and is applicable under varying speed and load torque conditions. In the proposed method, first, the envelope of the current signal is calculated to extract its modulation. Second, the drive-related signal, which greatly varies based on speed and load torque conditions, is extracted from the enveloped current signal. Third, the drive-tolerant current residual (DTCR) is calculated; the DTCR is defined as the subtraction of the drive-related signal from the enveloped current signal. Finally, the new health feature is calculated as the variance of the DTCR. To demonstrate the proposed method, experimental studies were conducted under several operating conditions (i.e., different speed profiles and load torque levels) with two fault modes: 1) a stator inter-turn short and 2) misalignment. Results confirm the ability of DTCRV to promptly and accurately detect faults in a variety of conditions; in contrast, conventional methods are greatly affected by the operating conditions.