有机培肥与耕作方式对稻麦轮作土壤团聚体和有机碳组分的影响

为了明确不同外源有机物和耕作方式对土壤地力培育的影响,以水稻-小麦轮作系统为对象,通过2个年度(2016—2018年)大田试验研究了外源有机物(秸秆和有机肥)和耕作方式及其交互作用[稻麦秸秆还田配合旋耕(SR),稻麦秸秆还田配合翻耕(SP),秸秆不还田、增施有机肥配合旋耕(MR),秸秆不还田、增施有机肥配合翻耕(MP),秸秆不还田、不施用有机肥、旋耕深度15 cm(CKR)]对土壤团聚体和有机碳组成的短期影响。结果表明:SR处理能够降低水稻季土壤容重并增加总孔隙度。相比CKR,小麦季SR处理显著增加0.05mm水稳性团聚体含量,增加量为7.2%。此外,外源有机物和耕作对土壤有机碳活性组分具有显著影响。其中,易氧化有机碳(EOC)主要受耕作与有机物交互作用影响,酸水解有机碳(LPIc和LPII_c)主要受耕作措施的影响, SR处理的土壤EOC和LPI_c含量比CKR提高0.3～2.6 g·kg~(-1)。颗粒有机碳(POC)主要受外源有机物的影响,并且秸秆还田处理POC平均含量高于增施有机肥处理,增加量为0.75g·kg~(-1)。短期内,外源有机物和耕作及其交互作用对稳定性有机碳(黑碳和矿物结合态有机碳)的影响较小。综上,秸秆还田配合旋耕有助于提高土壤水稳性团聚体和活性有机碳的含量(EOC、LPI_c和POC)。     

典型喀斯特洼地植被恢复过程中土壤碳氮储量动态及其对极端内涝灾害的响应

西南喀斯特地区是我国主要的生态脆弱区之一,石漠化严重,旱涝灾害频发。植被恢复是提升脆弱生态系统土壤碳氮固持的有效方式,但该区不同植被恢复方式土壤碳氮动态监测的研究还很缺乏。本研究以典型喀斯特峰丛洼地为对象,选取人工林、牧草地、人工林+牧草地、撂荒地自然恢复4种最主要的植被恢复方式为研究对象,以耕地作为对照,对比分析退耕前(2004年)、退耕10年(2014年)和13年后(2017年)土壤碳氮储量动态变化特征。其中2004—2014年研究区未发生极端内涝灾害, 2014—2017年连续发生2次极端内涝灾害事件。研究结果表明,退耕10年后, 4种恢复方式下土壤有机碳(SOC)储量均显著增加,但退耕13年后,除撂荒地SOC持续增加外,其他3种恢复方式下SOC表现出下降趋势。植被恢复后土壤全氮(TN)储量提升相对缓慢,退耕10年仅牧草地显著增加,退耕13年后人工林+牧草和撂荒地TN增加,且撂荒地在退耕后呈持续增加趋势。相关性分析结果表明,土壤交换性Ca~(2+)与SOC、TN均呈显著正相关关系,且与2014年相比, 2017年不同植物恢复方式下土壤交换性Ca~(2+)均显著下降,这可能与研究区2015年和2016年连续内涝灾害有关。以上结果说明,不同恢复方式均能显著提升喀斯特地区土壤碳氮固持,并以自然恢复最佳,其生态系统能有效抵御极端气候灾害带来的负面影响。     

不同温度下TMV侵染枯斑三生烟的LncRNA差异表达

【目的】 筛选不同温度下烟草花叶病毒（Tobacco mosaic virus,TMV）侵染后枯斑三生烟（Nicotiana tabacum var. Samsun NN）差异表达的长链非编码RNA（long non-coding RNA,lncRNA）,研究lncRNA在枯斑三生烟抗性反应中的作用。【方法】 N基因的温度敏感性使枯斑三生烟在25℃时具备对TMV的抗性、在31℃抗性丧失,在这两个温度条件下对枯斑三生烟接种TMV和磷酸盐缓冲盐水（phosphate buffered saline,PBS）,48 h后提取系统叶总RNA,构建链特异性文库后进行深度测序。对测序结果进行过滤后利用HTSeq将有效数据与近缘品种TN90（N. tabacum var. TN90）基因组比对,筛选得到lncRNA后利用FPKM法估计lncRNA的表达水平。通过edgeR筛选差异表达lncRNA（differentially expressed lncRNA,DElncRNA）,并利用qRT-PCR技术对这一结果进行验证。通过共定位及共表达分析预测DElncRNA的靶基因,通过参考基因组注释、GO和KEGG富集分析研究靶基因的功能。【结果】 4个处理共12个样本经lncRNA-seq各测得约8 000万条clean reads,共获得4 737条已知lncRNA、40 169条新lncRNA。其中64个lncRNA在不同温度条件下TMV侵染后存在差异表达,qRT-PCR测定结果显示这些lncRNA的测序正确率在80%左右,表明本研究所得测序数据具备较高的可信度。对DElncRNA进行靶基因预测,发现一些基因同时被25℃下调和31℃上调的DElncRNA靶向。靶基因注释功能丰富,主要参与植物抗病、激素和代谢等生理过程。部分可能与激素通路相关的lncRNA,在25℃下TMV侵染时呈现下调趋势,而在31℃下TMV侵染则呈现上调趋势。GO富集分析显示靶基因主要参与构成膜、囊泡等组分,具备钙、钾离子通道抑制剂活性等分子功能,使相应离子得以转运引发随后的反应,同时也参与发病、抗原加工和呈现、细胞分裂素代谢等生理过程。KEGG分析发现靶基因显著富集在植物激素信号转导通路,25℃下调和31℃上调的DElncRNA靶基因同时富集在激素信号传导、ABC运输蛋白、苯丙烷类生物合成等通路。【结论】 不同温度（25℃和31℃）条件下TMV侵染枯斑三生烟后,长链非编码RNA差异表达,DElncRNA通过作用于激素信号传导、物质转运等过程参与寄主系统获得性抗性反应。研究结果可为揭示植物系统获得性抗性中lncRNA的调控功能以及新型抗病毒技术开发提供依据。     

施氮量对农田土壤有机氮组分及酶活性的影响

【目的】探讨不同施氮量条件下土壤氮素转化酶活性和有机氮组分含量的变化规律,并分析氮素转化酶活性与各有机氮组分之间的关系,为陇中黄土高原旱作农业区合理制定施肥量和施肥方案提供参考依据。【方法】基于设置在陇中黄土高原定西市李家堡镇麻子川村的不同施氮量（0（CK）、52.5（N1）、105（N2）、157.5（N3）、210（N4）kg N·hm^-2）春小麦长期定位试验,收获后使用Bremner法测定0—40 cm土层中有机氮组分含量,以及4种氮素转化相关酶的活性。【结果】土壤有机氮组分分配比例顺序为氨基酸态氮>酸解铵态氮>酸解未知态氮>氨基糖态氮,同一土层随着施氮量的增加土壤有机碳、全氮、酸解总氮、氨基酸态氮、酸解铵态氮和脲酶活性、蛋白酶活性均呈先增大后降低的趋势,除全氮外其余都在N2处理时最大,全氮含量在N3处理时达到最大;同一处理不同土层间均随土层加深而降低。冗余分析结果表明,全氮含量和蛋白酶活性是影响陇中黄土高原农田有机氮组分分布与转化的关键因子;碳氮比与所有有机氮组分均呈负相关,蛋白酶、有机碳和脲酶与氨基酸态氮呈极显著正相关。【结论】综合而言,N2处理土壤供氮潜力最高,全氮和蛋白酶是影响该区春小麦土壤有机氮组分转化的关键因子。氮肥合理施用能明显提高土壤有机氮含量,不同施氮量条件下土壤有机氮组分变化差异明显,改变了氮素相关转化酶的活性。     

Soil carbon increases with long-term cattle stocking in northern temperate grasslands

Grassland management aimed at enhancing carbon (C) in soil is an important tool in mitigation of rising atmospheric CO₂, yet little is known of how grassland soil C changes with livestock stocking rate (SR). We relate soil organic and inorganic C mass (t ha⁻¹ to 60 cm depth) with cattle stocking over periods of 7–27 year for 32 paddocks distributed across nine community pastures in the mixed-grass prairie of Saskatchewan, Canada. Initial analysis comparing Akaike information criterion models showed that cattle SR explained a greater proportion of variance in soil C, particularly soil organic C, than rainfall. Soil organic C mass increased with cattle SR (R² = .293; p = .001), even when the latter was normalized to account for differences in vegetation composition and growing conditions among pastures. Normalized SR varied from 0.49 to 2.30 times recommended levels, over which SOC increased from 24.7 to 57.4 t ha⁻¹. Increases in soil organic C under greater stocking coincided with increased abundance of introduced vegetation, particularly the rhizomatous grass Poa pratensis. Inorganic soil C accounted for 34.6% of total soil C, being particularly large below 30 cm soil depth, but did not vary with stocking rate. These findings indicate that both organic and inorganic C are important pools of C in northern temperate grassland soils, with soil organic C positively associated with long-term cattle SR. Further studies are recommended to understand more fully the mechanisms regulating grazing impacts on soil C mass in northern temperate grasslands.     

Biochar addition mitigates nitrogen loss induced by straw incorporation and nitrogen fertilizer application

Biochar has been shown to be potentially beneficial for enhancing yields and soil properties, and diminishing nitrogen (N) losses. However, it remains unclear how biochar regulates soil carbon (C) and N to mitigate N losses induced by straw mixing with N fertilizer in dryland soils. Therefore, we investigated the effects of straw mixing (S₁), S₁ with biochar (SB) and no straw inputs (S₀), and routine urea application rates (N₁) and 70% of routine rates (N_0.7) on yields and N losses, and identify the relationship between N losses and soil C and N compounds. Results showed that N_0.7 and N₁ were suitable for the maize and wheat seasons, respectively, contributing to mitigating N losses without reducing crop yields. Moreover, in the maize season, N_0.7-SB significantly mitigated the straw-induced NH₃-N and N₂O-N emissions by 106% and 81%, respectively. In the wheat season, N₁-SB reduced the straw-induced NH₃-N and N₂O-N emissions by 35% and 66%, respectively. In addition, N_0.7-SB sharply reduced soil inorganic N (SIN) storage in the maize season. Furthermore, the NH₃-N and N₂O-N emission rates were negatively correlated with dissolved organic carbon/SIN content (0–20 cm) (DOC/SIN_0-20). N losses (N₂O-N and NH₃-N emissions and SIN storage) were positively correlated with SIN_0-20, but negatively correlated with soil organic carbon / SIN_0-20 (SOC/ SIN_0-20). This study provides further evidence that biochar with an appropriate N application rate decreased SIN_0-20 and increased DOC/SIN_0-20, thus reducing SIN storage and the straw-induced gaseous N emissions without decreasing crop yields.     

沉积物中生物活性磷赋存形态对耗氧有机物的响应

[目的] 本研究为验证耗氧有机物影响沉积物中生物活性磷的赋存形态的假说。[方法]以葡萄糖代替耗氧有机物，将沉积物暴露于耗氧有机物30天。[结果表明] 不充气时，葡萄糖浓度0、2、4、8 mg/L下的表层沉积物Ex-P含量为0.02±0.01、0.02±0.01、0.03±0.01、0.02±0.01 μmol/g；充气时，相应葡萄糖浓度下的表层沉积物Ex-P含量为0.03±0.00、0.03±0.01、0.03±0.01和0.06±0.02μmol/g，葡萄糖浓度8mg/L下的Ex-P含量高于葡萄糖浓度0mg/L下的（P< 5%）。不充气时，葡萄糖浓度0、2、4和8mg/L 下的表层沉积物IP含量为0.96±0.42、0.74±0.29、0.97±0.78和0.88±0.22μmol/g；充气时，相应葡萄糖浓度下的表层沉积物IP含量为0.37±0.10、0.46±0.16、0.41±0.06和0.69±0.05μmol/g，葡萄糖浓度8mg/L下的IP含量高于葡萄糖浓度0mg/L下的（P< 5%）。[结论]耗氧有机物对沉积物中生物活性磷有双重阈值作用。     

有机肥部分替代化肥对甘蓝生长、品质及土壤状况的影响

为探明化肥的减施并增施有机肥对甘蓝生长的影响,改善因施用过量化肥导致的蔬菜品质下降、肥料利用率降低和土地盐碱化等问题。本试验在冬闲田种植条件下,以鸡心甘蓝‘探春’为试验材料,采用田间小区（100%化肥、75%化肥+25%有机肥、50%化肥+50%有机肥）试验方法研究化肥减量及有机肥增施对甘蓝生长、品质以及土壤状况的影响。结果表明,化肥减量同时增施一定量有机肥对甘蓝产量和生物量无显著影响;随着有机肥施用量的增加,甘蓝横径有显著增加;随着有机肥施用比重的增加,硝酸盐含量逐渐从26.3 mg/kg降至17.7 mg/kg,亚硝酸盐含量从0.86 mg/kg降至0.60 mg/kg;同时土壤放线菌数量显著提高至300.99×10⁴CFU/g,土壤真菌数量显著降低至149.99×10³CFU/g;减施化肥显著提高了土壤pH（土壤pH提高至7.03）,土壤有机质含量无显著变化。可见,有机肥部分替代化肥可满足甘蓝生长过程中对肥料的需求,T50处理的甘蓝能够在化肥减量50%的情况下保持产量与品质不下降,同时提高土壤状况与放线菌数量,是相同条件下冬闲田甘蓝栽培一种稳产合理的施肥模式。     

Variation in glomalin in soil profiles and its association with climatic conditions,shelterbelt characteristics,and soil properties in poplar shelterbelts of Northeast China

Glomalin-related soil protein(GRSP)sequesters large amounts of carbon and plays important roles in maintaining terrestrial soil ecosystem functions and ecological restoration;however,little is known about GRSP variation in 1-m soil profiles and its association with stand characteristics,soil properties,and climatic conditions,hindering GRSP-related degraded soil improvement and GRSP evaluation.In this study,we sampled soils from 1-m profiles from poplar(Populus spp.)shelterbelts in Northeast China.GRSP contents were 1.8–2.0 times higher in the upper 40 cm soil layers than at 40–100 cm.GRSP-related soil organic carbon(SOC)sequestration in deeper soil layers was*1.2 times higher than in surface layers.The amounts of GRSP-related nutrients were similar throughout the soil profile.A redundancy analysis showed that in both surface and deeper layers,soil properties(pH,electrical conductivity,water,SOC,and soil nutrients)explained the majority of the GRSP variation(59.5–84.2%);the second-most-important factor in GRSP regulation was climatic conditions(temperature,precipitation,and altitude),while specific shelterbelt characteristics had negligible effects(<5%).Soil depth and climate indirectly affected GRSP features via soil properties,as manifested by structural equation model analysis.Our findings demonstrate that GRSP is important for carbon storage in deep soils,regardless of shelterbelt characteristics.Future glomalin assessments should consider these vertical patterns and possible regulating mechanisms that are related to soil properties and climatic changes.     

Research of dissolved oxygen prediction in recirculating aquaculture systems based on deep belief network

Recirculating aquaculture has received more and more attention because of its high efficiency of treatment and recycling of aquaculture wastewater. The content of dissolved oxygen is an important indicator of control in recirculating aquaculture, its content and dynamic changes have great impact on the healthy growth of fish. However, changes of dissolved oxygen content are affected by many factors, and there is an obvious time lag between control regulation and effects of dissolved oxygen. To ensure the aquaculture production safety, it is necessary to predict the dissolved oxygen content in advance. The prediction model based on deep belief network has been proposed in this paper to realize the dissolved oxygen content prediction. A variational mode decomposition (VMD) data processing method has been adopted to evaluate the original data space, it takes the data which has been decomposed by the VMD as the input of deep belief network (DBN) to realize the prediction. The VMD method can effectively separate and denoise the raw data, highlight the relations among data features, and effectively improve the quality of the neural network input. The proposed model can quickly and accurately predict the dissolved oxygen content in time series, and the prediction performance meets the needs of actual production. When compared with bagging, AdaBoost, decision tree and convolutional neural network, the VMD-DBN model produces higher prediction accuracy and stability.