炭添加物對基于粒徑研究砂性基質(zhì)飽與導(dǎo)水率影響

1、炭添加物對基于粒徑研究砂性基質(zhì)飽與導(dǎo)水率影響摘要：泥炭/砂混合物是一種廣泛應(yīng)用于高爾夫果嶺坪床，運動場草坪,藝盆栽，及濾水系統(tǒng)中的基質(zhì)。而，對于泥炭/砂混合基質(zhì)水力特性的直接測量耗時較長。 試驗的目的為：（1）測試符合美國高爾夫協(xié)會（usga）推薦 標(biāo)準(zhǔn)的砂與高比例泥炭混合基質(zhì)的飽和導(dǎo)水率（ksat）是否 為層流并遵循達西法則。（2）研究泥炭類別和有機物含量 對之前建立的預(yù)測飽和導(dǎo)水率的多重線性回歸模型的準(zhǔn)確 性的影響。試驗采用3種泥炭類別即木本泥炭移（peat,inc. minnesota, usa), 苔聲泥炭 (sun gro horticulture,maryland, usa）和葦苔

2、泥炭 （dakota peat, north dakota,usa）,并分別按照泥炭占 0%, 0. 2%, 0. 4%, 0. 8%, 1.6%, 4%,8%和10%的重量配比與純砂混合。試驗評估的模型如下：關(guān)鍵詞：高爾夫球場；果嶺；根層基質(zhì)；導(dǎo)水率；土壤 分析中圖分類號：g 849.3； s 151.9文獻標(biāo)識碼：a文章編號：1009-5500 (2013) 05-0066-06收稿日期：2013-08-15； 修回日期:2013-10-09作者簡介：王一淳（1983-）,女，黑龍江省佳木斯市人。e-mail： yichun. wanglive. cnestimating water co

3、nductivity of sand-based rootzonematerials from particle size distribution：effects of peat amendmentswang yi-chun, li de-ying(department of plant sciences, north dakota state university, fargo, nd 58108, usa)abstract： sand and peat mixtures are widely used in constmcted root zones of golf course put

4、ting greens and sports fields , containerized horticulture , and water "iteringsystems. direct measurement ofhydraulic proper ties often is time consuming. one of the objectives of this study was to test if saturated water flow is laminar and obeys darcy" s law when a large amount of peat

5、is mixed with sand that conform to the usga specifications. another objective was to evaluate a previously developed multipie linear regression ( mlr ) model for predictingsaturated water conductivity (ksat) as affected by peat types and organic matter ( 0m ) content woody sphagnum peat (peat, inc.

6、minnesota, usa), sphagnum peat moss (sun gro horticulture, maryland, usa), and reed sedge peat(dakota peat, north dakota, usa) were mixed with sand at 0%, 0. 2%, 0. 4%, 0. 8%, 1.6%, 4%, 8%, and 10%(w/w)in the final mixtures the model tested was loglo (ksat) 二5. 3407-0.5286p b-1. 2846cp-0.0442c+0. 06

7、12 © 5-0 6095 e 95 , with p b as bulk density(g/cm3), cp as capillary porosity (%), c as silt content (%), and 巾 5,巾 10,巾 16，巾 84，巾 95 values from the particle size distribution curve representing grain size in phi ( © ) unit briefly, ©x二-log (2, d), with x representing the percentage

8、 of sand mass smaller than d in size in a traditional particle size distribution curve. resuits showed that darcy? s law prevailed at hydraulic pressure gradients up to 3. resuits also showed that, with exception of less humified sphagnum peat moss at >4%, the model provided fair predictions of k

9、sat (r2 =0. 74) for 0m content up to 10%.key words： golf course； putting greens； root zone medium ； water conductivity ； soil analysis introductionsand and peat mixtures are widely used in construeted root zones of golf course putting greens and sports fields (li et al, 2000), containerized horticul

10、tural (heiskanen and rikala, 1998), and water filtering systems (tao et al, 2009) water holdingand water conductivity of such mixtures are very important properties in their application because t hese properties dictate irrigation , drainage , solute movement, and soil aeratiori. the united states g

11、olf association (usga) recommends tests of particle size distribution, water retention, capillary porosity (cp) at 30 cm water suction, om, and ksat, and evaluations of shape/roimdness of sand particles ( usga green section staff, 1993) direct measurement of hydraulic properties is time consuming .a

12、ccording to a survey conducted by the proficiency test program , the confidence interval for particle size analysis is +/t0 to +/- 35%, and that for saturated water conduetivity is +/- 20% using the usga specified procedures (miller and amacher, 2001) . inconsistency of these test resuits between an

13、d within the laboratories may cause inconvenience in bidding and contract during the construction and management of a golf coursepredicting saturated water conductivity from some basic and more accurate analysis is an incentive driven alternate approach.many models for estimation of hydraulic proper

14、 ties do not include om content as a predictor (hazen, 1893) when om is considered, very often it is treated as clay-sized particles (ar ya a nd paris, 1981 ； carman, 1956； childs and collis-george, 1950； fair and ha/tch, 1933； millington and quirk, 1959)however, peat and other orga nic mat erials u

15、sed in sand root zone mixtures are fibrous rather than layer-silicates. predicting water conductivity from basic soil properties using multivariate analyses and mlr have been attempted (brakensiek, et al. , 1984； puckett, et al. , 1985； campbell, 1985； saxton et a1. , 1986； vereecken, et al.， 1990 ；

16、 jabro, 1992； sperry and peirce , 1995 ) . model evaluations also have been conducted by many authors (tietje and hennings， 1996； zhang et al. , 2000) .there have been no thorough model comparisons for sand-predominant soils that are used for sports fields. li et al. (2008) developed a step-wise mlr

17、 model to predict ksatof sand-based root zone materials from known physical properties including bulk density，capillary porosity, clay content, and particle size distribution. the 292 samples were collected from commercial laboratories representing 200 locations from over 40 states in america and tw

18、o provinces of canada, with peat content ranging from 0 to 1.2%(w/w)(li et al. , 2008) .the model is：loglo (ksat)=5.3407-0. 5286p b-1. 2846cp-0.0442c+0. 0612 «5-0. 609510+0. 085<b95lwhere p b is bulk density (g/cm3), cp is capillary porosity (%), c is s訂t content, and ©5,10, ©95val

19、ues from the particle size distribution curve for grain size in phi (e) unit. notice that 0m is not included in this mode 1. this may be because of the low content (0% to 1. 2%) in common sand/peat mixtures of golf course putting green root zones, or collinearity between those predicting variablesor

20、ganic matter tends to accumulate as the sand-based root zones age (mcclellan et al. , 2007； wang et al., 2013)horticultural container mixes and water filtering sys tems use 0m in high percentages .therefore, a robust model is needed for predictingwaterconductivityundert hese conditions .taylor et al

21、. (1997) reported that water inf訂tration rate was as high as 1. 03 m/h for fine sand with up to 2. 98% reed sedge pea t by weight .one of the objectives of this study was to evaluate adequacy of the mlr model developed by li et al. ( 2008 ) for predicting water conductivity based on particle size an

22、alysis of sand-based root zone materials with different peats and a wide range of 0m content. another objective was to test if the saturated water flow is laminar and obeys darcy， s law in a porous medium of sand that conform to the usga specifications in mixture with a wide range of peat type and r

23、atio.materials and methodssamples and physical propertieswoody sphagnum peat (peat, incminnesota, usa), sphagnum peat moss (sun gro horticulture, maryland, usa), and reed sedge peat (dakota peat， minnesota， usa ) were thoroughly mixed with sand that has a particle size distribution conform to the us

24、ga specifications. weight percentages of peat in the final mixture were 0%, 0. 2%, 0.4%, 0. 8%, 1. 6%, 4%, 8%, and 10%.the mixtures were packed into brass cylinders (6 cm diam 5 4 cm i. d. ) using a compactor equipped with a 1. 36 kg hammer.compaction was kept consistent by 5 drops of the hammer fro

25、m a height of 305 mm (usga green section staff, 1993) .there are three replicates for each mixture forming a total of 66 mixture samples.organic matter content was tested by the loss on ignition method .particle size distribution for sand fractions was analyzed with the dry sieve method, and the cla

26、y frac tion was analyzed with the pipe tte met hod (usga green section staff, 1993) .total porosity was calculated from：x=1- p b p ss- p b p ppwhere p b is bulk density； p s is the sand particle density； p p is the peat particle density； s and p are sand and peat content in the mixture by weight , r

27、espectively. particle density of sand was measured with pycnometers.capillary porosity was calculated from the total porosity minus the volumetric water content of the samples at -30 cm suction head. sand grain shape and roundness was visually evaluated and assigned a descriptive category. saturated

28、 water conduetivity was measured by a cons tant head met hod following klute(1986).particle size distribution curves weredeveloped based on the cumulative percentage weight versus particle diameters in d units .the 2 units are related to nominal diameter d (mm) via：statisticsregression variables in

29、the mlr model to predict ksat include om content, cp, clay content，silt content， and <1)5, <1)10, 4)16, <1)50, <1)84, (1)95 values of particle size distribution curve .saturated water conductivity is of lognormal distribution, with the logarithmic mean and the logarithmic standard deviat

30、ion. therefore, ksat data were transformed using loglo function before statistical analysisstepwise regression with forward, backward, and stepwise methods was used in the procreg procedure of sas (9.1) package (sas institute inc,cary, nc, usa) . the stepwise regression process was also compared wit

31、h the robustreg procedure for outlier and leverage identification.results and discussionfrom the 1 : 1 line of measured ksat values plotted against the predicted values using model (arga. and paris, 1984), it can be seen that the previous linear regression model (li et al. , 2008) is not adequate in

32、 predicting the ksat values of the samples that had a wide range of peat types and content due to a low r2 of 0. 56 (fig la) . residues after the model fit were correlated with selected variables as：loglo (ksat)二0. 4081+0. 6403cp-0. 06940m (2)this means tha/t the variability may change with 0m types

33、 and content. a close observation of data points revealed that all outliers are sphagnum peat moss mixtures with 0m at more than 4% (fig. la). the predicted and measured 1 : 1 line improved to r2 of 0. 74 (fig lb) after the removal of 6 sphagnum peat moss data points .sphagnum peat moss is the least

34、 decomposed 0m of three peats, which has the highest 0m content and lowest bulk density (bk) (table 1) .after the removal of those sphagnum peat moss data points，the model provided fairly adequate prediction of ksat values of 60 samples that had 0m content up to 10%.organic matter content is not inc

35、luded in the mlr model by li et al. (2008) this is probably because 0m is correlated with bk, cp of the root zone mixtures. when combining the data from this study with that from li et al. (2008) (table 2), a new stepfig. 1 comparison of the predicted saturated hydraulic conductivity based on a mult

36、i pie linear model (li et ol. , 2008) with measured value a) outlier data points of sphagnum peat moss indicated in shadeb) outlier data points of sphagnum peat moss are removed, wise mlr model was developed as follows：loglo(ksat)=4. 961-0. 807 p b-1. 178cp-0. 0370m-0. 0109d5-0.2019 4)84+0. 180 4>

37、;95(3)again, this new model is not adequate in predicting ksat , with an r2 of 0.46 , which is relatively low .however, it was shown that with the inclusion of om , clay content was dropped as one of the predictors further analysis showed a significant correlation among 0m, cp, bk, and clay (table 3

38、), indicating that cp and bk may be more powerful predictors in the mode1.table 1 physical proper ties of peats and sand used in the studytable 2 descriptive statistics for the data set from 66 sand/peat mixture samplestable 3 pearson correlation coefficients(n=348, p4%, the model provided fair pred

39、ictions of ksat (r2 = 0. 74) for 0m content up to 10%.fig .2 saturated hydraulic conductivity of sand/peat mixtures tested at different hydraulic pressure gradients. woody sphagnum peat，sphagnum peat moss, and reed sedge peat were mixed with sand at 0%, 0. 2%, 0. 4%, 0. 8%, 1. 6%, 4%, 8%, and 10% (w

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