作者机构：
[Zhao H.; 王艺哲; 张含丰; 赵杭; 胡旺] College of Resources and Environment/Hunan Provincial Key Laboratory of Farmland Pollution Control and Agricultural Resources Use, Hunan Agricultural University, Changsha, 410128, China;[周旋] Institute of Soil and Fertilizer, Hunan Academy of Agricultural Sciences, Changsha, 410125, China
通讯机构：
[Zhang, Y.-P.] C;College of Resources and Environment/Hunan Provincial Key Laboratory of Farmland Pollution Control and Agricultural Resources Use, Hunan Agricultural University, Changsha, China
关键词：
生物炭;南荻;水稻;氨挥发
摘要：
在洞庭湖区农田施用秸秆生物炭不仅能实现秸秆资源化利用,还可降低环境污染压力。本研究于2020年采用水稻盆栽试验,研究了不同南荻秸秆生物炭施用量对土壤氨挥发速率、累积氨挥发量、表面水pH值和NH_4~+-N浓度的影响。供试土壤为第四纪红土发育的红黄泥和花岗岩发育的麻砂泥水稻土,设置6个南荻秸秆生物炭添加处理,即分别以土柱0~ 20 cm土壤重量的0%、1%、2%、4%、6%和8%比例添加生物炭,每盆施用复合肥200 kg N·hm~(-2)。结果表明:施用生物炭导致两种土壤之间或不同生物炭处理之间的氨挥发速率和累积量均存在显著差异。麻砂泥施用生物炭处理在施肥后第2天出现氨挥发峰值,且较不施生物炭处理峰值降低了23.6%~53.4%;红黄泥氨挥发峰值出现在施肥后第7 ~ 13天,且其峰值随着生物炭添加量的增加而升高。整体上,麻砂泥土壤的氨挥发速率均高于红黄泥。麻砂泥土壤<4%生物炭添加量能抑制土壤氨挥发速率及累积量,其中以2%处理降幅最大(46.9%), 但生物炭添加对水稻生长前期表面水pH值的影响不显著;红黄泥土壤随着南荻生物炭用量的增加,表面水中pH值和NH_4~+-N浓度增加,导致氨挥发速率及累积量增幅达1.3~10.5倍。回归分析显示,生物炭添加量是影响两种土壤氨挥发的关键因素。Elovich方程能较好地拟合两种土壤的氨挥发累积量随时间的变化动态,各施炭处理的相关系数均达极显著水平。总体上,对于偏中性的麻砂泥土壤,施用一定量的南荻生物炭对氨排放有一定的抑制作用,而对于酸性的红黄泥土壤,增施南荻生物炭会通过提高表面水的pH值和NH_4~+-N浓度促进氨挥发,因此针对不同类型土壤施用南荻秸秆生物炭应注意选择适宜用量,以降低氮素损失。
作者机构：
[谢桂先; 彭建伟; 田昌; 荣湘民; 黄思怡] National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, College of Resources and Environment, Hunan Agricultural University, Changsha, 410128, China;Rural Energy and Environment Agency, Ministry of Agriculture and Rural Affairs, Beijing, 100125, China;[周旋] Institute of Soil and Fertilizer, Hunan Academy of Agricultural Sciences, Changsha, 410125, China;[王英姿] College of Horticulture, Hunan Agricultural University, Changsha, 410128, China;[徐泽] Agricultural and Rural Bureau of Changsha County, Changsha, 410100, China
通讯机构：
[Wang, Y.-Z.] C;College of Horticulture, China
摘要：
Heavy metal pollution affects soil ecological function. Biochar and compost can effectively remediate heavy metals and increase soil nutrients. The effects and mechanisms of biochar and compost amendments on soil nitrogen cycle function in heavy-metal contaminated soils are not fully understood. This study examined how biochar, compost, and their integrated use affected ammonia-oxidizing microorganisms in heavy metal polluted soil. Quantitative PCR was used to determine the abundance of ammonia-oxidizing archaea (AOA) and bacteria (AOB). Ammonia monooxygenase (AMO) activity was evaluated by the enzyme--linked immunosorbent assay. Results showed that compost rather than biochar improved nitrogen conversion in soil. Biochar, compost, or their integrated application significantly reduced the effective Zn and Cd speciation. Adding compost obviously increased As and Cu effective speciation, bacterial 16S rRNA abundance, and AMO activity. AOB, stimulated by compost addition, was significantly more abundant than AOA throughout remediation. Correlation analysis showed that AOB abundance positively correlated with NO3--N (r = 0.830, P < 0.01), and that AMO activity had significant correlation with EC (r = -0.908, P < 0.01) and water-soluble carbon (r = -0.868, P < 0.01). Those seem to be the most vital factors affecting AOB community and their function in heavy metal-polluted soil remediated by biochar and compost. (C) 2020 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.
通讯机构：
[Xin Wu] C;College of Resources and Environment, Hunan Agricultural University, Changsha, Hunan 410128, China<&wdkj&>Key Laboratory of Agro-ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, The Chinese Academy of Sciences, National Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and Poultry Production Hunan Provincial Engineering Research Center for Healthy Livestock and Poultry Production, Changsha, Hunan 410125, China<&wdkj&>Institute of Biological Resources, Jiangxi Academy of Sciences, Nanchang 330096, China
摘要：
This research aimed to study whether Enteromorpha polysaccharide-zinc (EP-Zn) can act as an alternative to antibiotics in weaned piglet feeds. Two hundred and twenty-four weaned piglets from 14 pens were randomly assigned into 1 of 2 groups according to their body weight and litter size (7 pens/group). The piglets in the antibiotics group were fed with olaquindox at 400 mg/kg and enduracidin at 800 mg/kg basal diet, and piglets in the EP-Zn group were fed with EP-Zn at 800 mg/kg basal diet. One piglet per pen was selected to collect samples after 14 d of feeding. Results showed that EP-Zn supplementation significantly increased the plasma anti-oxidants level compared with the antibiotics group. However, a nonsignificant difference was observed in growth performance between treatment groups. Additionally, the intestinal tight junction (TJ) protein expression and the histopathologic evaluation data showed that EP-Zn contributed to improving intestinal development. Further, piglets in the EP-Zn group had a lower level of intestinal inflammation-related cytokines including IL-6 (P < 0.001), IL-8 (P < 0.05), IL-12 (P < 0.05) and tumor necrosis factor-alpha (TNF-alpha) (P < 0.001), and showed an inhibition of the phosphorylation nuclear transcription factor-kappa B (peNFekB) (P < 0.05) and total NF-kB (P < 0.001) level in the jejunal mucosa. Taken together, it is supposed that EP-Zn, to some extent, would be a potent alternative to prophylactic antibiotics in improving the health status of weaned piglets. (C) 2021 Chinese Association of Animal Science and Veterinary Medicine. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.
作者机构：
[Shen Lu-ming; He Shao-fang] Hunan Agr Univ, Coll Informat & Intelligence, Changsha 410128, Peoples R China.;[Xie Hong-xia] Hunan Agr Univ, Coll Resources & Environm, Changsha 410128, Peoples R China.
通讯机构：
[Xie, H.-X.] C;College of Resources & Environment, China
关键词：
有机质;高光谱;生成式对抗网络;交叉验证岭回归;BP神经网络
摘要：
已有的土壤有机质含量估测模型大多以光谱特征波段、 线性和非线性模型为基础, 较少考虑通过拓展样本数据建模集来提高模型的估测能力。 为进一步提高土壤有机质高光谱反演模型估测精度, 提出利用生成式对抗网络(GAN)合成伪高光谱数据和有机质含量的动态估测模型。 选取湖南省长沙市及周边区域的水稻田为研究对象, 采集土样和实测高光谱数据(350~2 500 nm), 室内化学测定有机质含量。 以高光谱数据和有机质含量为基础, 利用生成式对抗网络生成等量新数据, 结合原始数据建模集组成增强建模集。 在GAN正式训练中, 每轮训练完成后, 设置4个观测点(对应增强建模集中含50, 100, 150和239个生成样本), 动态构建交叉验证岭回归(RCV)、 偏最小二乘回归(PLSR)和BP神经网络(BPNN)土壤有机质含量估测模型(分别简称GAN-RCV, GAN-PLSR和GAN-BPNN), 并在相同测试集上实施模型评估。 实验结果表明: (1)原始数据建模集上拟合的估测模型中, 交叉验证岭回归表现最佳, 决定系数(R2)和均方根误差(RMSE)分别为0.831 1和0.189 6; (2)GAN的150轮正式训练中, 增强建模集上动态构建的GAN-RCV, GAN-PLSR和GAN-BPNN模型性能显著提高, 具体表现为: GAN-RCV的R2取得最大值0.890 9(RMSE 0.153 7)、 最小值0.850 5 (RMSE 0.18)与平均值0.868 7(RMSE 0.168 6), 最大R2比建模集上拟合的RCV提高了7.2%(RMSE降低了18.9%), GAN-PLSR获得R2最大值0.855 4(RMSE 0.176 9)、 最小值0.727 0 (RMSE 0.243 2)与平均值0.780 1 (RMSE 0.217 7), 最大R2比建模集上拟合的PLSR提高了20.6%(RMSE降低了29.5%), GAN-BPNN表现最佳, R2取得最大值0.905 2(RMSE 0.143 3)、 最小值0.801 7(RMSE 0.207 3)与平均值0.868 1(RMSE 0.168 6), 最大R2比建模集上拟合的BPNN提高了30.8%(RMSE降低了44.5%); (3)随着增强建模集中生成样本数量增加, 模型精度提升效果呈先升后降趋势, 4个观测点中第3个观测点的模型性能提升最显著。 充分的实验表明: 基于GAN动态构建的有机质含量估测模型显著改善了模型预测性能。 依据测试集上的评估结果, 可择优使用最佳模型进行后续土壤有机质含量估测。 In the previous study of the estimation model of soil organic matter content, most models were based on the feature bands, linear and non-linear empirical models rarely explored the ability promotion using an extended modeling dataset. To further improve the performance of the estimation model, it proposed a dynamic estimation model of soil organic matter content using generative adversarial networks (GAN) to generate the pseudo hyperspectral and organic matter content. Paddy soil samples and hyperspectral data (350~2 500 nm) were collected from Changsha and its surrounding areas of Hunan Province, and the organic matter content was monitored chemically. Based on these data, equivalent new samples were generated by GAN and combined with the modeling set to form anenhanced modeling set. After completing each epochformal training of GAN, the prediction models of soil organic matter content were dynamically constructed using cross-validation ridge regression (RCV), partial least squares regression(PLSR) and BP neural network (BPNN) on four observation points (corresponding 50, 100, 150 and 239 generated samples in enhanced modeling set) (the abbreviation of models were GAN-RCV, GAN-PLSR and GAN-BPNN). The experimental results showed that: (1) Among the estimation models fitted on modeling set of the origin data, RCV was the best-performing model, whose determination coefficient (R2) and root square error (RMSE) were 0.831 1 and 0.189 6; (2) In the 150 epochs formal training of GAN, the performance of GAN-RCV, GAN-PLSR and GAN-BPNN dynamically constructed on the enhanced modeling set were significantly improved, specific performances: R2 of GAN-RCV obtained the maximum 0.890 9 (RMSE 0.153 7), minimum 0.850 5 (RMSE 0.18) and mean 0.868 7 (RMSE 0.168 6), the maximum R2 increased by 7.2% (RMSE decreased by 18.9%) compared with RCV fitted on the modeling dataset, R2 of GAN-PLSR had the maximum 0.855 4 (RMSE 0.176 9), minimum 0.727 0 (RMSE 0.243 2) and mean 0.780 1 (RMSE 0.217 7), the maximum R2 increased by 20.6% (RMSE decreased by 29.5%) than PLSR constructed on the modeling dataset, GAN-BPNN performed best, whose R2 had the maximum 0.905 2(RMSE 0.143 3), minimum 0.801 7(RMSE 0.207 3) and mean 0.868 1(RMSE 0.168 6), the maximum R2 increased by 30.8%(RMSE decreased by 44.5%) comparing BPNN fitted on the modeling set; (3) With the increase of the number of generated samples in the enhanced modeling dataset, the improvement effect of model accuracy showed a trend of increasing first and then decreasing, and among the four observation points, the model constructed on the third had the most significant performance improvement. Sufficient experiments showed that the dynamic estimation model based on GAN improved the performance effectively. According to the evaluation results on the test set, the optimum model could be used to predict the soil organic matter content in the follow-up application.
