• Journal of Animal Environmental Science
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• v.17 no.3
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• pp.171-180
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• 2011

The researchers have tried to reduce ruminal methane gas ($CH_4$) and to convert it into beneficial nutrient for several decades. This study was conducted to screen the methane-reducing vegetables among lettuce, hot pepper, spring onion, onion, turmeric, sesame leaf, garlic, radish sprout, leek and ginger nutritiously on the in vitro ruminal fermentation. The heat-treated vegetables at the 10% of substrate (timothy) were used to reduce methane production on the in vitro anaerobic experiment of 0, 6, 12, 24 and 48 h incubation time. Total gas production, pH, ammonia, $H_2$, $CO_2$, $CH_4$, and volatile fatty acid (VFA) were measured as indicators of in vitro fermentation product containing methane gas. All treatments except garlic showed a tendency to increase in total gas production. The result of ammonia showed that garlic and hot pepper affected rumen bacteria concerned protein metabolism and that lettuce and spring onion increased ammonia production. Garlic decreased $CH_4$ production in inverse proportion to $H_2$. Lettuce, spring onion, onion, garlic, radish sprout, leek and ginger increased propionate of VFA. Garlic balanced the ruminal fermentation in the pH, $H_2$, $CH_4$, acetate and propionate. This results showed that methane production at in vitro study was inhibited by heat-treated garlic supplementation. In conclusion, this study suggests that ruminal fermentation covering methane production might be controled by proper vegetables.

• Journal of Korea Water Resources Association
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• v.36 no.6
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• pp.1047-1058
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• 2003

Seasonal occurrence of high ammonia nitrogen(NH3-N) concentrations has hampered chemical treatment processes of a water plant that intakes water at Buyeo site of Geum river. Thus it is often needed to quantify the effect of Daecheong Dam ouflow on the mitigation of $NH_3$-N contamination. In this study, multiple regression models were developed for forecasting daily $NH_3$-N concentrations using 8 years of water quality and dam outflow data, and verified with another 2 years of data set. During model development, the coefficients of determination($R^2$) and model efficiency($E_{m}$) were greater than 0.95. The verification results were also satisfactory although those statistical indices were slightly reduced to 0.84∼0.94 and 0.77∼0.93, respectively. The validated model was applied to assess the effect of different amounts of dam outflow on the reduction of $NH_3$-N concentrations in 2002. The NH3-N concentrations dropped by 0.332∼0.583 mg/L on average during January∼March as outflow increases from 5 to 50cms, and was most significant on February. The results of this research show that the multiple regression approach has potential for efficient cause and effect analysis between dam outflow and downstream water quality.