Evaluation of an on-the-go technology for soil pH mapping

Since conventional sampling and laboratory soil analysis do not provide a cost effective capability for obtaining geo-referenced measurements with adequate frequency, different on-the-go sensing techniques have been attempted. One such recently commercialized sensing system combines mapping of soil electrical conductivity and pH. The concept of direct measurement of soil pH has allowed for a substantial increase in measurement density. In this publication, soil pH maps, developed using on-the-go technology and obtained for eight production fields in six US states, were compared with corresponding maps derived from grid sampling. It was shown that with certain field conditions, on-the-go mapping can significantly increase the accuracy of soil pH maps and therefore increase the potential profitability of variable rate liming. However, in many instances, these on-the-go measurements need to be calibrated to account for a field-specific bias. After calibration, the overall error estimate for soil pH maps produced using on-the-go measurements was less than 0.3 pH, while non-calibrated on-the-go and conventional field average and grid-sampling maps produced errors greater than 0.4 pH.     

Field-scale validation of an automated soil nitrate extraction and measurement system

One of the many gaps that needs to be solved by precision agriculture technologies is the availability of an economic, automated, on-the-go mapping system that can be used to obtain intensive and accurate ‘real-time’ data on the levels of nitrate nitrogen (NO₃–N) in the soil. A soil nitrate mapping system (SNMS) has been developed to provide a way to collect such data. This study was done to provide extensive field-scale validation testing of the system’s nitrate extraction and measurement sub-unit (NEMS) in two crop (wheat and carrot) production systems. Field conditions included conventional tillage (CT) versus no tillage (NT), inorganic versus organic fertilizer application, four soil groups and three points in time throughout the season. Detailed data analysis showed that: (i) the level of agreement, as measured by root mean squared error (RMSE), mean absolute error (MAE) and coefficient of efficiency (CE), between NEMS soil NO₃–N and standard laboratory soil NO₃–N measurements was excellent; (ii) at the field-scale, there was little practical difference when using either integer or real number data processing; (iii) regression equations can be used to enable field measurements of soil NO₃–N using the NEMS to be obtained with laboratory accuracy; (iv) future designs of the SNMS’s control system can continue to use cheaper integer chip technology for processing the nitrate ion-selective electrode (NO₃ ⁻–ISE) readings; and (v) future designs of the SNMS would not need a soil moisture sensor, ultimately saving on manufacturing costs of a more simple system.     

Textural analysis of soil images to quantify and characterize the spatial variation of soil properties using a real-time soil sensor

The primary aim of this work was to predict soil moisture content and soil organic matter using soil image texture statistics. Co-occurrence method texture statistics were used to characterize Andisol soils to extend the possibility of using RGB color space in representing composite soil color. Four co-occurrence method textural features; angular second moment (ASM), contrast (CON), correlation (COR) and inverse difference moment (IDM) calculated from generalized matrix for image texture representation were used to describe soil moisture content variation under laboratory conditions. It was found that CON and COR had negative responses to moisture content (MC) and ASM had positive response to MC. The same were also observed in direct captured field soil images in terms of textural indices against MC and soil organic matter (SOM). The correlations were significant for ASM and COR in fertilizer and combined (fertilizer-manure) plots and insignificant in manure plots. To relate sub-surface image textural indices and soil properties for individual years, stepwise multiple linear regression (SMLR) and supervised feed-forward neural networks (NN) were investigated in an attempt to provide minimal prediction errors. The improvements achieved by NN with minimal prediction errors were better than SMLR in different years. It was assumed that several years of data sets with a much larger number of observations could be used to differentiate fundamental soil properties.     

滴灌追氮管理对宿根蔗田土壤氮组分及N2O排放的影响

【目的】研究滴灌追氮管理对宿根蔗田土壤氮组分和N₂O排放的影响，揭示影响土壤N₂O通量的土壤因子。【方法】以二代宿根蔗Saccharum officinarum为研究对象，在移动防雨棚内进行2个滴灌灌水量[田间持水量的70%～80%(W0.8)和田间持水量的80%～90%(W0.9)]及3种滴灌追氮比例(等氮量250 kg·hm^-2，其中，N0为用作追肥的氮肥全部施用到土壤中，N5为50%土施追氮、50%用滴灌系统施用，N7为30%土施追氮、70%滴灌追氮)的田间试验。在甘蔗生长的各个时期测定蔗田土壤N₂O通量、pH和氮组分含量，并分析土壤N₂O通量与土壤pH和氮组分含量的关系。【结果】土壤N₂O通量在施用氮肥和灌水后2 d较高，其中，分蘖后期和成熟期W0.9N5处理的土壤N₂O通量显著低于其他处理。W0.9条件下，分蘖后期N5处理的土壤N₂O累积排放量分别比N0和N7低47.3%和11.8%，伸长后期N5处理的...     

Combined use of hyperspectral VNIR reflectance spectroscopy and kriging to predict soil variables spatially

Hyperspectral visible near infrared reflectance spectroscopy (VNIRRS) and geostatistical methods are considered for precision soil mapping. This study evaluated whether VNIR or geostatistics, or their combined use, could provide efficient approaches for assessing the soil spatially and associated reductions in sample size using soil samples from a 32 ha area (800 × 400 m) in northern Turkey. Soil variables considered were CaCO₃, organic matter, clay, sand and silt contents, pH, electrical conductivity, cation exchange capacity (CEC) and exchangeable cations (Ca, Mg, Na and K). Cross-validation was used to compare the two approaches using all grid data (n = 512), systematic selections of 13, 25 and 50% of the data and random selections of 13 and 25% for calibration; the remaining data were used for validation. Partial least squares regression (PLSR) analysis was used for calibrating soil properties from first derivative VNIR reflectance spectra (VNIRRS), whereas ordinary-, co- and regression-kriging were used for spatial prediction. The VNIRRS-PLSR method provided better prediction results than ordinary kriging for soil organic matter, clay and sand contents, (R ² values of 0.56–0.73, 0.79–0.85, 0.65–0.79, respectively) and smaller root mean squared errors of prediction (values of 2.7–4.1, 37.4–43, 46.9–61, respectively). The EC, pH, Na, K and silt content were predicted poorly by both approaches because either the variables showed little variation or the data were not spatially correlated. Overall, the prediction accuracy of VNIRRS-PLSR was not affected by sample size as much as it was for ordinary kriging. Cokriging (COK) and regression kriging (RK) were applied to a combination of values predicted by VNIR reflectance spectroscopy and measured in the laboratory to improve the accuracy of prediction of the soil properties. The results showed that both COK and RK with VNIRRS estimates improved the predictions of soil variables compared to VNIRRS and OK. The combined use of VNIRRS and multivariate geostatistics results in better spatial prediction of soil properties and enables a reduction in sampling and laboratory analyses.     

Evaluation of soil flame disinfestation(SFD) for controlling weeds,nematodes and fungi

Soil flame disinfestation(SFD) is a form of physical disinfestation that can be used both in greenhouses and on field crops. Its use for soil disinfestation in different crop growing conditions makes it increasingly attractive for controlling soilborne pathogens and weeds. But little is known about the effect on weeds and soilbrone diseases. This study reports on greenhouses and field crops in China that determined the efficacy of SFD to control weeds, nematodes and fungi. It also determined the impact of SFD on the soil physical and chemical properties(water content, bulk density, NO_3~–-N content, NH_4~+-N content, conductivity and organic matter) in three field trials. A second generation SFD machine was used in these trials. SFD treatment significantly reduced weeds(87.8%) and root-knot nematodes(Meloidogyne incognita)(98.1%). Plant height and crop yield was significantly increased with SFD treatment. NO_3~–-N and NH_4~+-N increased after the SFD treatment, and there was also an increase in soil conductivity. Water content, bulk density and organic matter decreased significantly in the soil after the SFD treatment compared to the control. Soil flame disinfestation is a potential technique for controlling weeds and diseases in greenhouses or in fields. SFD is a non-chemical, safe, environmentally-friendly soil disinfection method.     

Apparent electrical conductivity in dry versus wet soil conditions in a shallow soil

The general objective of this study was to evaluate the stability of patterns of apparent soil electrical conductivity (EC_a) in dry versus wet soil conditions in a shallow soil typically used for pastures in Mediterranean conditions of the southern region of Portugal. A 6 ha experimental field of permanent bio-diverse pasture was divided into 76 squares of 28 × 28 m. The soil electrical conductivity was measured using a Dualem 1S sensor under dry conditions (June 2007) and under wet conditions during the rainy season (March 2010). Soil samples, geo-referenced with GPS, were collected in a depth range of 0–0.30 m. The soil was characterized in terms of bedrock depth, moisture content, texture, pH, organic matter content, and macronutrients (nitrogen, phosphorus, and potassium). Pasture samples, also geo-referenced with GPS, were collected to measure the pasture dry matter yield. The statistical analysis of apparent electrical conductivity between dry and wet soil conditions resulted in a linear significant correlation coefficient (R = 0.88). The results also showed a significant correlation between apparent electrical conductivity and the relative field elevation (R = ?0.64 and R = ?0.66), the pasture dry matter yield (R = 0.42 and R = 0.48), the bedrock depth (R = 0.40 and R = 0.27), the pH (R = 0.50 and R = 0.49), the silt (R = 0.27 and R = 0.38) and soil moisture content (R = 0.48 and R = 0.45), in dry and wet conditions, respectively. A multi-variate regression was carried out using the following soil parameters that showed significant correlation with EC_a and that did not present multi-collinearity: pH, bedrock depth, silt and moisture content. The results showed, in dry and wet conditions, that the analysis was significant (R = 0.75 and R = 0.84, respectively). Overall, these results indicate the temporal stability of EC_a patterns under different soil moisture contents, which is relevant with respect to the time when a field should be surveyed and is important for using the electrical conductivity sensor, as a decision support tool for management zones in precision agriculture.     

博斯腾湖人工和天然芦苇湿地土壤呼吸动态变化规律及其影响因素

为研究干旱区淡水湖泊湿地土壤呼吸作用的动态变化及其影响因素,以博斯腾湖人工和天然芦苇(Phragmites australis)湿地为样地,于2014年11月—2015年9月,利用LI-840A土壤碳通量自动测定仪对土壤呼吸的日变化和季节动态进行了监测。结果表明:人工、天然芦苇湿地土壤呼吸速率的日变化表现为明显的单峰曲线,且呈现出一定的波动性;两种芦苇湿地土壤呼吸速率均具有明显的季节变化特征,生长旺盛期(7—8月)的土壤呼吸作用最强,越冬期(11月)土壤呼吸作用最弱;博斯腾湖芦苇湿地土壤呼吸速率为正值,且人工芦苇湿地土壤呼吸速率高于天然芦苇湿地;5 cm深土壤温度、近地表温度和空气相对湿度是土壤呼吸速率的主要影响因素;人工、天然芦苇湿地土壤呼吸速率均与土壤p H和盐分呈负相关,而与土壤有机碳呈正相关。     

A Model for Agro-Economic Analysis of Soil pH Mapping

Core soil sampling followed by laboratory analysis is the traditional method used to map soil pH prior to variable rate application (VRA) of lime on cropland. A recently developed automated soil sampling system capable of measuring soil pH on-the-go has significantly increased sampling resolution. However, adoption of such systems must be justified economically. This paper presents a method for assessing the economic benefit from automated mapping of soil pH prior to variable rate lime application. In this work, geostatistical, agronomic, and economic methods were used to generate a comprehensive numerical model for quantitative assessment of the net return over cost of liming for different lime management strategies. The strategies included: automated pH mapping, manual grid soil sampling, and whole field sampling used in combination with either variable or fixed rate liming. The model was demonstrated using a simulated field with known average pH and semivariogram model. The analysis showed the largest benefit ($6.13ha^–1year^–1) from using VRA with automated soil pH mapping versus VRA based on 1ha (2.5acres) manual grid point sampling for the selected simulated field conditions. A sensitivity analysis demonstrated that for a wide range of field conditions and crop prices, VRA plus automated mapping promises higher relative benefits than VRA based on either manual grid point or grid cell sampling.     

降雨对草地土壤呼吸季节变异性的影响

利用土壤碳通量自动观测系统(LI-8150)对呼伦贝尔草原在自然降雨条件下的土壤呼吸作用进行了野外定位连续观测,研究结果表明:降雨对土壤呼吸作用存在激发效应和抑制效应,降雨发生后1-2 h内土壤呼吸速率可增加约1倍,当单次或者连续降雨累积量大于7-8 mm,或土壤含水量大于29%-30%时,降雨对土壤呼吸会产生明显的抑制作用。土壤呼吸的激发效应往往体现在次日,表现为次日平均土壤呼吸速率的显著升高;而抑制效应则在当日即可体现出来,表现为观测当日平均土壤呼吸速率的明显下降。土壤呼吸季节变异性与降雨频率和降雨强度密切相关,在降雨量一定的情况下,较低的降雨频率和较高的降雨强度会增加土壤呼吸的变异性。呼伦贝尔草甸草原而言,在生长季土壤平均含水量为16.5%时,土壤呼吸的温度敏感性值(Q₁₀)为2.12;而平均土壤含水量为26%时,Q₁₀值为2.82,明显高于前者,土壤含水量与Q₁₀之间存在正相关关系。降雨导致土壤呼吸的激发效应和抑制效应交替发生,使草地土壤呼吸的季节变异性增加,降雨格局变化必然会对草地碳循环和碳通量特征产生深刻影响。     

Monitoring of soil organic carbon over 10 years in a Mediterranean silvo-pastoral system: potential evaluation for differential management

Soil organic carbon (SOC) plays a vital role in determining the susceptibility to land degradation. The recommended procedure for the recovery of the characteristic poor soils of the Southern region of Portugal is the installation of grazed permanent pastures and increase of soil fertility. The objectives of this study were: (i) to identify the spatial and temporal patterns of soil nutrients at four points in time over a 10-year period in a perennial pastureland; (ii) to test new tools for survey of the spatial variability of soil nutrients; (iii) to evaluate the potential for differential organic management. A 6 ha permanent bio-diverse pasture field, grazed by sheep and improved by annual application of super phosphate fertilizer, was installed on a shallow soil in Mediterranean conditions. Spatial variability and temporal stability of topsoil macronutrients (phosphorus, nitrogen and potassium), SOC and pH were measured. The results indicate that SOC and pH have great potential for implementing differential management. In the case of SOC, the management classes map shows that over 80 % of the area has temporal stability, while more than 50 % of the area has low levels of SOC (<10 g kg^?1), justifying the potential for differential application of C-rich organic soil amendments. The geospatial measurements of apparent soil electrical conductivity (EC_a) and NDVI index showed significant correlation between these parameters and soil properties, revealing the potential of these tools for producing detailed soil maps, decisive for understanding the changes in soil properties under sustainable management systems.     

Spatial variability and temporal stability of apparent soil electrical conductivity in a Mediterranean pasture

The general objectives of this study were to evaluate (i) the specificity of the spatial and temporal dynamics of apparent soil electrical conductivity (EC_a) measured by a electromagnetic induction (EMI) sensor, over 7 years, in variable conditions (of soil moisture content (SMC), soil vegetation cover and grazing management) and, consequently, (ii) the potential for implementing site-specific management (SSM). The DUALEM 1S sensor was used to measure the EC_a in a 6 ha pasture experimental field four times between June 2007 and February of 2013. Soil spatial variability was characterized by 76 samples, geo-referenced with the global positioning system (GPS). The soil was characterized in terms of texture, moisture content, pH, organic matter content, nitrogen, phosphorus and potassium. This study shows a significant temporal stability of the EC_a patterns under several conditions, behavior that is an excellent indicator of reliability of this tool to survey spatial soil variability and to delineate potential site-specific management zones (SSMZ). Significant correlations were obtained in this work between the EC_a and relative field elevation, pH, silt and soil moisture content. These results open perspectives for using the EMI sensor as an indicator of SMC in irrigation management and of needs of limestone correction in Mediterranean pastures. However, it is interesting to extend the findings to other types of soil to verify the origin of the lack of correlation between the EC_a data measured by DUALEM sensor and properties such as the clay, organic matter or phosphorus soil content, fundamental parameters for establishment of pasture SSM projects.     

土壤pH对茶园土壤有效氟含量的影响

以茶园土壤为对象,通过室内模拟培养和田间改良试验,研究了茶园土壤pH对土壤中水溶态氟和交换态氟含量的影响.结果表明,在模拟培养下,当土壤pH由4.0降至3.5时,土壤水溶态氟含量迅速升高,当pH值在4.0~5.0之间时,水溶态氟含量较为稳定,之后随着土壤pH升高水溶态氟含量增加;交换态氟含量的变化趋势与之大致相反,且pH对20~40 cm土层土壤有效氟的影响比0~20 cm土层显著,对水溶态氟的影响比交换态显著,在强酸性土壤条件下对有效氟含量的影响比酸性条件下显著.田间试验结果与之基本一致,以pH 5.0为基点,随土壤pH值的上升或下降,两层土壤水溶态氟含量均增加,其中pH值降低对土壤水溶态氟的影响更大.     

不同土地利用方式土下岩溶溶蚀速率及影响因素

以重庆中梁山为例,通过野外埋放标准溶蚀试片、土壤CO2收集装置和进行亮蓝染色示踪试验,测试不同土地利用方式下不同土层碳酸盐岩溶蚀速率、土壤CO2浓度、土壤溶解性有机碳(DOC)含量及土壤含水量、土壤pH值和孔L隙度等性质,探讨不同土地利用方式土下溶蚀速率差异及其影响因素.结果表明不同土地利用方式对土壤理化性质产生影响,形成特定的岩溶微环境,进而影响土下不同层次的岩溶作用:土壤CO2浓度是影响林地和草地旱季土下溶蚀速率的重要驱动力;土壤含水量和供水能力是影响旱季不同土地利用方式溶蚀速率的关键因素;菜地产生的酸性物质较多,土壤pH值最低,其平均溶蚀速率高于林地;土壤DOC随水下渗迁移性强,是林地土下50 cm处溶蚀速率高于土下20 cm处的原因之一.该文为西南岩溶区土下岩溶机理、岩溶碳汇提供理论依据和数据参考.     

秸秆还田深度对春玉米农田土壤有机碳、氮含量和土壤酶活性的影响

为研究秸秆还田旋耕深度对土壤理化性质和酶活性的影响,明确不同秸秆还田深度条件下土壤理化性质与酶活性的关系,在3年（2016—2018年）田间微区定位试验条件下,研究秸秆旋耕还田10 cm（S₁D₁）、20 cm（S₁D₂）、30 cm（S₁D₃）和秸秆移除旋耕10 cm（S₂D₁）、20 cm（S₂D₂）、30 cm（S₂D₃） 6个处理对东北春玉米农田土壤理化性质和酶活性的影响。结果表明：旋耕深度（D）及其与秸秆处理（S）交互作用（S×D）显著影响土壤有机碳（SOC）含量,0~20 cm土层S₁D₁、S₁D₂处理SOC含量较S₁D₃处理高1.2%~16.0%,而20~40 cm土层S₂D₃处理SOC含量最高。旋耕深度、秸秆处理及两者交互作用对土壤硝态氮（NO₃^--N）和铵态氮（NH₄⁺-N）含量、蔗糖酶和过氧化氢酶活性影响显著。在0~40 cm土层,D₁、D₂旋耕深度下秸秆还田处理NO₃^--N含量比秸秆移除处理平均提高46.9%和34.9%,NH₄⁺-N含量平均降低31.6%和4.4%。在各旋耕深度下,S₁处理0~20 cm土层蔗糖酶和脲酶活性高于S₂处理,20~30 cm土层过氧化氢酶活性低于S₂处理。相关性分析表明,SOC、土壤全氮（TN）与NO₃^--N、NH₄⁺-N含量和蔗糖酶活性呈显著正相关,与pH、土壤含水量（SWC）呈显著负相关。主成分分析表明,与S₁D₁相比,S₁D₂对0~20土层蔗糖酶、脲酶、过氧化氢酶活性和0~40 cm土层SOC、TN含量影响更明显。综上所述,秸秆旋耕还田20 cm可改善0~40 cm土层养分水平,提高土壤酶活性,推荐为东北春玉米产区农田土壤培肥的合理秸秆还田方式。     

Soil electrical conductivity as a function of soil water content and implications for soil mapping

Apparent soil electrical conductivity (EC_a) has shown promise as a soil survey tool in the Midwestern United States, with a share of this interest coming from the precision agriculture community. To fully utilize the potential of EC_a to map soils, a better understanding of temporal changes in EC_a is needed. Therefore, this study was undertaken to compare temporal changes in soil EC_a between different soils, to investigate the influence of changes in soil water content on soil EC_a, and to explore the impacts these EC_a changes might have on soil mapping applications. To this end, a 90 m long transect was established. Soil EC_a readings were taken in the vertical and horizontal dipoles at five points once every one to two weeks from June until October in 1999 and 2000. At the same time, soil samples were collected to a depth of 0.9 m for volumetric soil water content analysis. Soil EC_a readings were compared to soil water content. At four of the five sites linear regression analysis yielded r ² values of 0.70 or higher. Regression line slopes tended to be greater in lower landscape positions indicating greater EC_a changes with a given change in soil water content. Two of the soils had an EC_a relationship that changed as the soils became dry. This is an item of concern if EC_a is to be used in soil mapping. Results indicated that soil water content has a strong influence on the EC_a of these soils, and that EC_a has its greatest potential to differentiate between soils when the soils are moist. Soil water content is an important variable to know when conducting EC_a surveys and should be recorded as a part of any report on EC_a studies.     

Determining the within-field yield variability from seasonally changing soil conditions

Crop yield variations are strongly influenced by the spatial and temporal availabilities of water and nitrogen in the soil during the crop growth season. To estimate the quantities and distributions of water and nitrogen within a given soil, process-oriented soil models have often been used. These models require detailed information about the soil characteristics and profile architecture (e.g., soil depth, clay content, bulk density, field capacity and wilting point), but high resolution information about these soil properties, both vertically and laterally, is difficult to obtain through conventional approaches. However, on-the-go electrical resistivity tomography (ERT) measurements of the soil and data inversion tools have recently improved the lateral resolutions of the vertically distributed measurable information. Using these techniques, nearly 19,000 virtual soil profiles with defined layer depths were successfully created for a 30 ha silty cropped soil over loamy and sandy substrates in Central Germany, which were used to initialise the CArbon and Nitrogen DYnamics (CANDY) model. The soil clay content was derived from the electrical resistivity (ER) and the collected soil samples using a simple linear regression approach (the mean R² of clay = 0.39). The additional required structural and hydrological properties were derived from pedotransfer functions. The modelling results, derived soil texture distributions and original ER data were compared with the spatial winter wheat yield distribution in a relatively dry year using regression and boundary line analysis. The yield variation was best explained by the simulated soil water content (R² = 0.18) during the grain filling and was additionally validated by the measured soil water content with a root mean square error (RMSE) of 7.5 Vol%.     

林地覆盖对雷竹林土壤微生物特征及其与土壤养分制约性关系的影响

为了探讨林地覆盖雷竹林退化机理,给退化雷竹林恢复提供理论参考,对不同覆盖年限(CK、1、3 a 和6 a) 雷竹林土壤微生物区系组成和生物量碳(C_mic)、氮(N_mic)、磷(P_mic)等特征因子进行了测定,并分析了其与土壤养分的制约性关系。结果表明:(1) 雷竹林土壤微生物以细菌为主,真菌次之,放线菌最少,分别占土壤微生物总量的90.11%-98.03%、1.04%-9.22%和0.67%-1.37%。随覆盖年限增加,细菌、放线菌比率呈下降趋势,真菌比率呈上升趋势;土壤微生物总数、细菌和放线菌数量及C_mic、N_mic、P_mic均呈先升高后降低的变化趋势,试验雷竹林间差异极显著,真菌数量总体呈极显著升高趋势。(2)雷竹林土壤微生物特征因子与土壤有机质(SOM)、全氮(TN)、全磷(TP)、碱解氮(Available nitrogen, AN)和pH均呈显著或极显著相关,其中,CK和覆盖1 a、3 a雷竹林土壤微生物特征因子与土壤养分主要呈正相关,与pH呈负相关,而覆盖6 a雷竹林则相反。(3)不同覆盖年限雷竹林土壤养分与土壤微生物的制约性关系存在一定的差异,CK雷竹林土壤SOM、TN、AN、速效钾(AK)和pH主要影响土壤C_mic、N_mic和细菌,覆盖1 a雷竹林土壤SOM、TN、TP和AK主要影响土壤P_mic、放线菌和细菌,覆盖3 a雷竹林土壤SOM、TN、速效磷(AP)和AN主要影响土壤N_mic、放线菌和真菌,覆盖6 a雷竹林土壤SOM、TN和pH主要影响土壤N_mic、真菌。研究表明:长期覆盖雷竹林土壤细菌、放线菌数量与比例明显降低,真菌数量与比例明显提高,土壤养分与土壤微生物的制约性作用关系会发生较为明显变化,产生土壤障害,这是覆盖雷竹林退化的主要原因之一。     

人工油松林恢复过程中土壤理化性质及有机碳含量的变化特征

采用时空替代法,选取岷江上游大沟流域内不同恢复时期(12、18、25、35a)的人工油松林为研究对象,研究了植被恢复过程中土壤理化性质及有机碳含量的变化特征,同时探讨了它们之间的相互关系.研究结果表明沿恢复梯度,土壤质量得到了改善,主要表现为土壤粘粒含量、比表面积、有机质含量显著增加,土壤粉粒含量和pH值则显著下降.土壤有机质与土壤粘粒和比表面积呈显著正相关,与土壤容重呈显著负相关.此外,土壤有机碳含量沿恢复梯度显著增加,0-50 cm内土壤有机碳含量从5.59 kg/m2增加到12.64 kg/m2,土壤年平均固碳速率为0.31 kg/m2.     

蔗田滴灌施肥土壤甲烷排放通量与活性有机碳含量的关系

【目的】研究蔗田滴灌施肥对土壤活性有机碳含量和甲烷排放通量的影响,探讨蔗田滴灌施肥土壤甲烷排放通量与土壤活性有机碳含量之间的关系。【方法】2018年3—12月在南宁市灌溉试验站开展不同滴灌灌水、施肥的田间试验,试验设4种施肥水平:常规施肥(F100,N 250 kg·hm~(-2)、P_2O_5 150 kg·hm~(-2)、K_2O 200 kg·hm~(-2))、增量施肥1(F110,在F100基础上增加10%)、增量施肥2(F120,在F100基础上增加20%)和减量施肥(F90,在F100基础上减少10%),以及2种滴灌灌水水平:W180(180 m~3·hm~(-2))和W300(300 m~3·hm~(-2))。用常规法测定不同生育时期蔗田土壤甲烷排放通量和土壤活性有机碳含量,用Pearson法分析土壤甲烷排放通量与土壤活性有机碳含量的关系。【结果】在分蘖期,W300F120处理土壤可溶性有机碳(DOC)含量较W300F100提高了156%,而土壤CH_4排放通量较其他处理低。在成熟期,W300F120处理土壤DOC含量较W300F110增加了114%,微生物量碳(MBC)较W300F110增加了49.6%。蔗田土壤CH_4排放通量仅与土壤DOC含量呈显著正相关,相关系数为0.38。【结论】土壤DOC含量显著影响蔗田土壤甲烷排放通量。W300F120处理可以提高分蘖期和成熟期蔗田土壤可溶性有机碳含量、减少分蘖期蔗田土壤CH_4排放。