Two experiments were conducted to in- vestigate the effects of net energy （NE） level on the performance and carcass traits of finishing pigs fed low crude protein （CP） diets supplemented with crystalline amino aci...Two experiments were conducted to in- vestigate the effects of net energy （NE） level on the performance and carcass traits of finishing pigs fed low crude protein （CP） diets supplemented with crystalline amino acids （CAA）. A total of 216 （Exp. 1） and 360 （Exp. 2 ） barrows （ Yorkshire × Landrace× Duroc ） were allotted to one of six treatments （n =6）. The experimental diets were based on corn and soybean meal, and consisted of a high-CP diet that contained approximately 16% CP and 2.50 Mcal/kg of NE as well as five low-CP diets in which the CP level of the diet was reduced by approximately four percentage units but was supplemented with crystalline lysine, methionine, threonine, and tryptophan. The low CP diets provided 2.64, 2.58, 2.50, 2.42, or 2.36 Mcal/kg in Exp. 1 as well as 2.45, 2.40, 2.35, 2.30, or 2.25 Mcai/kg in Exp.2. In Exp. 1, a linear （P=0.03） increase in weight gain was observed with decreasing NE level while the ratio of gain to feed was unaffect- ed （P 〉 0.05） by NE level. There was a linear in-crease （P = 0.01 ） in the percentage of fat-free lean and a linear decrease （ P = 0.03 ） in the percentage of total fat with decreasing NE levels. In Exp. 2, a sig- nificant quadratic （ P = 0.03 ） effect of NE level was observed for weight gain. The ratio of gain to feed demonstrated a significant （P 〈0.01 ） quadratic effect with pigs fed 2.35 and 2.40 Mcal/kg of NE, Pigs fed the diet containing 2.40 Mcal/kg of NE had the lowest percentage of total fat （36. 95% ） and the highest percentage of fat-free lean （49.36%）. The overall results of these experiments indicate that feed- ing either a surplus or a deficiency of NE is detrimen- tal to both pig performance and carcass composition when low CP diets supplemented with CAA are fed. Our results indicate that when the CP content of the diet is reduced by four percentage units and the diet is properly supplemented with CAA, maximum per- formance and carcass quality will be obtained if the diet provides approximately 2.42 Mcal/kg of NE.展开更多
Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiote...Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent.In this study,spatiotemporal prediction models for monthly Arctic SIC at 1-to 3-month leads are developed based on U-Net-an effective convolutional deep-learning approach.Based on explicit Arctic sea-ice-atmosphere interactions,11 variables associated with Arctic sea-ice variations are selected as predictors,including observed Arctic SIC,atmospheric,oceanic,and heat flux variables at 1-to 3-month leads.The prediction skills for the monthly Arctic SIC of the test set(from January 2018 to December 2022)are evaluated by examining the mean absolute error(MAE)and binary accuracy(BA).Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems(CFSv2 and NorCPM).By analyzing the relative importance of each predictor,the prediction accuracy relies more on the SIC at the 1-month lead,but on the surface net solar radiation flux at 2-to 3-month leads.However,dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes,especially in autumn.Therefore,the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.展开更多
基金supported by the National Natural Science Foundation of P.R.China (No.NSFC30525029)
文摘Two experiments were conducted to in- vestigate the effects of net energy （NE） level on the performance and carcass traits of finishing pigs fed low crude protein （CP） diets supplemented with crystalline amino acids （CAA）. A total of 216 （Exp. 1） and 360 （Exp. 2 ） barrows （ Yorkshire × Landrace× Duroc ） were allotted to one of six treatments （n =6）. The experimental diets were based on corn and soybean meal, and consisted of a high-CP diet that contained approximately 16% CP and 2.50 Mcal/kg of NE as well as five low-CP diets in which the CP level of the diet was reduced by approximately four percentage units but was supplemented with crystalline lysine, methionine, threonine, and tryptophan. The low CP diets provided 2.64, 2.58, 2.50, 2.42, or 2.36 Mcal/kg in Exp. 1 as well as 2.45, 2.40, 2.35, 2.30, or 2.25 Mcai/kg in Exp.2. In Exp. 1, a linear （P=0.03） increase in weight gain was observed with decreasing NE level while the ratio of gain to feed was unaffect- ed （P 〉 0.05） by NE level. There was a linear in-crease （P = 0.01 ） in the percentage of fat-free lean and a linear decrease （ P = 0.03 ） in the percentage of total fat with decreasing NE levels. In Exp. 2, a sig- nificant quadratic （ P = 0.03 ） effect of NE level was observed for weight gain. The ratio of gain to feed demonstrated a significant （P 〈0.01 ） quadratic effect with pigs fed 2.35 and 2.40 Mcal/kg of NE, Pigs fed the diet containing 2.40 Mcal/kg of NE had the lowest percentage of total fat （36. 95% ） and the highest percentage of fat-free lean （49.36%）. The overall results of these experiments indicate that feed- ing either a surplus or a deficiency of NE is detrimen- tal to both pig performance and carcass composition when low CP diets supplemented with CAA are fed. Our results indicate that when the CP content of the diet is reduced by four percentage units and the diet is properly supplemented with CAA, maximum per- formance and carcass quality will be obtained if the diet provides approximately 2.42 Mcal/kg of NE.
基金supported by the National Key Research and Development Program of China[grant number 2022YFE0106800]an Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number 311024001]+3 种基金a project supported by the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number SML2023SP209]a Research Council of Norway funded project(MAPARC)[grant number 328943]a Nansen Center´s basic institutional funding[grant number 342624]the high-performance computing support from the School of Atmospheric Science at Sun Yat-sen University。
文摘Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent.In this study,spatiotemporal prediction models for monthly Arctic SIC at 1-to 3-month leads are developed based on U-Net-an effective convolutional deep-learning approach.Based on explicit Arctic sea-ice-atmosphere interactions,11 variables associated with Arctic sea-ice variations are selected as predictors,including observed Arctic SIC,atmospheric,oceanic,and heat flux variables at 1-to 3-month leads.The prediction skills for the monthly Arctic SIC of the test set(from January 2018 to December 2022)are evaluated by examining the mean absolute error(MAE)and binary accuracy(BA).Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems(CFSv2 and NorCPM).By analyzing the relative importance of each predictor,the prediction accuracy relies more on the SIC at the 1-month lead,but on the surface net solar radiation flux at 2-to 3-month leads.However,dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes,especially in autumn.Therefore,the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.
