设施蔬菜种植对土壤有机碳的影响研究

近年来,为满足市场对新鲜果蔬日益增长的需求,我国设施蔬菜种植面积快速增长。设施种植具有环境封闭、高水肥投入和高产出等特点,这种高强度的土地利用影响土壤有机碳的稳定性,进而影响我国农业固碳政策,对我国实施碳达峰与碳中和战略带来巨大挑战。文章通过文献综述,探讨我国设施蔬菜种植的发展,及其对土壤有机碳含量和组分的影响,并提出开展生命周期评价、建立长期定位监测点、构建和优化针对设施蔬菜种植的土壤有机碳模型等建议,以期完善设施蔬菜种植对土壤有机碳的影响研究。

Dynamics models of soil organic carbon

As the largest pool of terrestrial organic carbon, soils interact strongly with atmosphere composition, climate, and land change. Soil organic carbon dynamics in ecosystem plays a great role in global carbon cycle and global change. With development of mathematical models that simulate changes in soil organic carbon, there have been considerable advances in understanding soil organic carbon dynamics. This paper mainly reviewed the composition of soil organic matter and its influenced factors, and recommended some soil organic matter models worldwide. Based on the analyses of the developed results at home and abroad, it is suggested that future soil organic matter models should be developed toward based-process models, and not always empirical ones. The models are able to reveal their interaction between soil carbon systems, climate and land cover by technique and methods of GIS (Geographical Information System) and RS (Remote Sensing). These models should be developed at a global scale, in dynamically describing the spatial and temporal changes of soil organic matter cycle. Meanwhile, the further researches on models should be strengthen for providing theory basis and foundation in making policy of green house gas emission in China.

Equality Testing for Soil Grid Unit Resolutions to Polygon Unit Scales with DNDC Modeling of Regional SOC Pools

Matching soil grid unit resolutions with polygon unit map scales is important to minimize the uncertainty of regional soil organic carbon(SOC) pool simulation due to their strong influences on the modeling.A series of soil grid units at varying cell sizes was derived from soil polygon units at six map scales,namely,1:50 000(C5),1:200 000(D2),1:500 000(P5),1:1 000 000(N1),1:4 000 000(N4) and 1:14 000 000(N14),in the Taihu Region of China.Both soil unit formats were used for regional SOC pool simulation with a De Nitrification-DeC omposition(DNDC) process-based model,which spans the time period from 1982 to 2000 at the six map scales.Four indices,namely,soil type number(STN),area(AREA),average SOC density(ASOCD) and total SOC stocks(SOCS) of surface paddy soils that were simulated by the DNDC,were distinguished from all these soil polygon and grid units.Subjecting to the four index values(IV) from the parent polygon units,the variations in an index value(VIV,%) from the grid units were used to assess its dataset accuracy and redundancy,which reflects the uncertainty in the simulation of SOC pools.Optimal soil grid unit resolutions were generated and suggested for the DNDC simulation of regional SOC pools,matching their respective soil polygon unit map scales.With these optimal raster resolutions,the soil grid units datasets can have the same accuracy as their parent polygon units datasets without any redundancy,when VIV < 1% was assumed to be a criterion for all four indices.A quadratic curve regression model,namely,y = – 0.80 × 10^(–6)x^2 + 0.0228 x + 0.0211(R^2 = 0.9994,P < 0.05),and a power function model R? = 10.394?^(0.2153)(R^2 = 0.9759,P < 0.05) were revealed,which describe the relationship between the optimal soil grid unit resolution(y,km) and soil polygon unit map scale(1:10 000x),the ratio(R?,%) of the optimal soil grid size to average polygon patch size(?,km^2) and the ?,with the highest R^2 among different mathematical regressions,respectively.This knowledge may facilitate the grid partitioning of regions during the investigation and simulation of SOC pool dynamics at a certain map scale,and be referenced to other landscape polygon patches' mesh partition.

Factors Acquisition and Content Estimation of Farmland Soil Organic Carbon Based upon Internet of Things

Aiming at the shortage of sufficient continuous parameters for using models to estimate farmland soil organic carbon(SOC) content, an acquisition method of factors influencing farmland SOC and an estimation method of farmland SOC content with Internet of Things(IOT) are proposed in this paper. The IOT sensing device and transmission network were established in a wheat demonstration base in Yanzhou Distict of Jining City, Shandong Province, China to acquire data in real time. Using real-time data and statistics data, the dynamic changes of SOC content between October 2012 and June 2015 was simulated in the experimental area with SOC dynamic simulation model. In order to verify the estimation results, potassium dichromate external heating method was applied for measuring the SOC content. The results show that: 1) The estimated value matches the measured value in the lab very well. So the method is feasible in this paper. 2) There is a clear dynamic variation in the SOC content at 0.2 m soil depth in different growing periods of wheat. The content reached the highest level during the sowing period, and is lowest in the flowering period. 3) The SOC content at 0.2 m soil depth varies in accordance with the amount of returned straw. The larger the amount of returned straw is, the higher the SOC content.

Sensitivity of the Terrestrial Ecosystem to Precipitation and Temperature Variability over China

In this study, the sensitivities of net primary production(NPP), soil carbon, and vegetation carbon to precipitation and temperature variability over China are discussed using the state-of-the-art Lund-Potsdam-Jena dynamic global vegetation model(LPJ DGVM). The impacts of the sensitivities to precipitation variability and temperature variability on NPP, soil carbon, and vegetation carbon are discussed. It is shown that increasing precipitation variability, representing the frequency of extreme precipitation events, leads to losses in NPP, soil carbon, and vegetation carbon over most of China, especially in North and Northeast China where the dominant plant functional types(i.e., those with the largest simulated areal cover) are grass and boreal needle-leaved forest. The responses of NPP, soil carbon, and vegetation carbon to decreasing precipitation variability are opposite to the responses to increasing precipitation variability. The variations in NPP, soil carbon, and vegetation carbon in response to increasing and decreasing precipitation variability show a nonlinear asymmetry. Increasing precipitation variability results in notable interannual variation of NPP. The sensitivities of NPP, soil carbon, and vegetation carbon to temperature variability, whether negative or positive, meaning frequent hot and cold days, are slight. The present study suggests, based on the LPJ model, that precipitation variability has a more severe impact than temperature variability on NPP, soil carbon, and vegetation carbon.

Cultivation Influences on Soil Organic Carbon Associated with Texture in Seasonally Frozen Zones

[Objective] This study aimed to examine indicative roles of texture representing soil organic carbon presence and variability subsequent to cultivation under cold temperate climates with seasonal freeze-thaw events. [Method] Three chronosequences were selected for paired comparisons. Soil samples were collected at six depths with a 10 cm increment. Analysis of variance with general linear model and regression was performed for statistical analysis. [Result] In seasonally frozen soils where fragmentation of macroaggregates was stimulated, soil organic carbon level was positively associated with clay + silt proportion due to a wider textural range, better than sole clay content. Exponential function better fitted the experimental data to present progressively increased effectiveness of clay + silt content in maintaining carbon. Clay content explained 12%-41% and 14%-43% of variation via linear and exponential functions, respectively. Accordingly, clay + silt content explained 47%-65% and 46%-70%. [Conclusion] Texture reflected soil organic carbon occurrence as consequences of reclamation. For seasonally frozen soils with wider textural ranges, it is robust to adapt clay + silt content as dependent variable and exponential function. The generated algorithms provided an available pathway to estimate soil organic carbon losses following cultivation and to evaluate soil fertility.

Spatially distributed modelling and mapping of soil organic carbon and total nitrogen stocks in the Eastern Mau Forest Reserve,Kenya

Detailed knowledge about the estimates and spatial patterns of soil organic carbon(SOC) and total nitrogen(TN) stocks is fundamental for sustainable land management and climate change mitigation.This study aimed at:(1) mapping the spatial patterns,and(2) quantifying SOC and TN stocks to 30 cm depth in the Eastern Mau Forest Reserve using field,remote sensing,geographical information systems(GIS),and statistical modelling approaches.This is a critical ecosystem offering essential services,but its sustainability is threatened by deforestation and degradation.Results revealed that elevation,silt content,TN concentration,and Landsat 8 Operational Land Imager band 11 explained 72% of the variability in SOC stocks,while the same factors(except silt content) explained 71% of the variability in TN stocks.The results further showed that soil properties,particularly TN and SOC concentrations,were more important than that other environmental factors in controlling the observed patterns of SOC and TN stocks,respectively.Forests stored the highest amounts of SOC and TN(3.78 Tg C and 0.38 Tg N) followed by croplands(2.46 Tg C and 0.25 Tg N) and grasslands(0.57 Tg C and 0.06 Tg N).Overall,the Eastern Mau Forest Reserve stored approximately 6.81 Tg C and 0.69 Tg N.The highest estimates of SOC and TN stocks(hotspots) occurred on the western and northwestern parts where forests dominated,while the lowest estimates(coldspots) occurred on the eastern side where croplands had been established.Therefore,the hotspots need policies that promote conservation,while the coldspots need those that support accumulation of SOC and TN stocks.