简介：土壤体积密度的测量为理解土壤的物理、化学、生物的性质是重要的。精确、快速的土壤体积密度测量技术在农业试验性的研究起一个重要作用。这评论是存在测量方法的一篇全面摘要并且为未来开发评估他们的优点，劣势，错误的潜在的来源，和方向。这些技术能广泛地作为直接、间接的方法被分类。直接方法包括核心，土块，和挖掘采样，而间接方法包括放射和回归途径。核心方法最广泛地被使用，但是为采样使用多重土壤深度耗时、困难。coring柱体的尺寸使用了，操作员经验，采样深度，并且在原处显著地玷污潮湿内容影响它的精确性。土块方法是合适的因为有重黏土的使用，和它的精确性依赖于设备刻度，弄干时间，和操作员经验，但是这个过程复杂、耗时。挖掘技术最通常被用来评估森林土壤的体积密度，但是把他们不能与大毛孔在土壤被使用，他们的测量精确性被土壤质地和分析的类型强烈影响的主要限制选择。间接方法看起来比直接途径有更大的精确性，但是有更高的费用，是更复杂的，并且要求更大的操作员经验。一如此的途径使用gamma放射，和它的精确性被土壤深度强烈影响。当他们能做间接大小，回归方法是节俭的，但是这些取决于土壤质地和有机物内容和地理、气候的性质的好、优秀数据。另外，它的精确性象大多数另外的途径一样，与采样深度减少。

简介：InordertoprovideascientificbasisforriceyieldestimationandimprovetheacouracyofyieldestimationinZhejiangProvince,Regionalizationindicesforriceyieldestimationbyremoesensing(RS)intheprovinceweredeterminedbyconsjderingthespecialfeaturesofyieldestimationbyRS,andbasedonanalysisofthenaturalconditionsofZhejiangProvince,Theindicesdeterminedincludedricecroppingsystem,agroclimate,landform,surfacefeaturesturctureandriceyieldlevel,wherericeplantingsystemwasconsideredasthemianone,ThenreionalizationfroriceyieldestimationbyRSwascompletedbyspatialneighboringanalysiswiththeGeographicalInfromationSystem(GIS)technologycombinedwithusigoftreealgorithmTheprovincewasdividedintotworegions,i.e.,thesingle-croppingriceregionwhichwassubdividedinto3regionsincludingthoseinmountainsofnorthewstZhejiang,waternetworkareaofnorthZhejiangandmountainsofsothZhejiang,anddouble-croppingriceregionwhichwassudividedinto5regionsincludingthoseonplainofnorthZhejiang,coastalplainsandhillsofsoutheastZhejiang,Jin-QuBasinofmiddleZhejiang,hillsofeastZhejiang,andhillsandmountainsofnorthewatZhejiang,thisregionaliztiontookthecountybordersastheregionboundaries,kepttheregionsconnectiveandmadetheadministrativregionsintegrityand,then,couldmeettherequirementsofriceyieldestimationbyRS,showingthattheresultswerequitesatisfying.

简介：Basedontheconceptionoffluidmechanics,thepaperpropoundsamodelformonthlypotentialevapotranspirationETi(mm):ETi=22di(1.6+Ui^1/2)woi(1-hi)/Pi^1/2(273.2+ti)^1/4whereiisthenumberofamonth,Pithemeanmonthlyatmosphericpressure(hPa),tithemeanmonthlyairtemperature(℃),dithenumberofdaysinthemonth,Uithemeanmonthlywindvelocitymeasuredatheight10-12m(m/s),woithesaturatedwatervapourpressureatti(mmHg,1mmHg=133.322Pa),andhithemeanmonthlyrelativehumidity.TheannualaridityKis:K=12∑i=1ETi/rawhereraisthemeanannualprecipitation(mm).Baseduponthedataof669sitesinChinaduring1951-1980,therelationsamongthesoilmoistureregime,thevegetationandthevalueofKmaybeillustratedasfollows:KSoilmoistureregimeVegetationK<1UdicFOrest1≤K<2semi-udicFroest,froest-steppeandsteppe2≤K<3.5Semi-aridicAridsteppe3.5≤K<11AridecDesert-steppeK≥11VeryaridicDesert.

简介：为简洁的原因，最通常使用的水文学模型基于土壤保存服务曲线数字(SCS-CN)当模特儿，它可能是为中国的黄土高原上的流量的评价的一种好选择；然而，高空间的异质，主要在土壤类型由碎裂的地形和变化引起了，可以限制它的适用性到这个区域。因此，到一个小分水岭的SCS-CN模型的适用性，高原上的Liudaogou，被评估，在模型的最适当的起始的抽象比率(Ia/S)价值被反的方法确定。结果证明标准SCS-CN模型对在Liudaogou分水岭的流量的评价适用，模型表演根据相对错误和Nash-Sutcliffe效率的价值是可接受的。因为，为分水岭的最适当的Ia/S值是0.22与这修改Ia/S值，模型性能稍微被改进。当一个重降雨事件(50.1公里)没被考虑时，模型表演不对Ia/S价值的修正敏感，它暗示模型用标准Ia/S价值，因为很少超过50公里的单个降雨事件发生在那个区域，能为Liudaogou分水岭被推荐。流量数量由SCS-CN模型为Liudaogou分水岭预言了，用修改Ia/S价值，当降雨价值比50公里低时，与增加降雨逐渐地增加了，而当降雨超过了50公里时，预言的数量很快增加了。这些调查结果可能在在中国的黄土高原上解决严肃的土壤和水损失的问题是有用的。

简介：PotexperimentswerecarriedouttoestimateN2fixationbyvetch,milkvetch,sicklealfalfaandbroadbeaninpurestandusinga^15N-labelledsoil.Winterwheatwasusedasthenon-fixingcontrol.The15N-labelledsoilusedwaspreparedbygrowingcorn-wheat-cornsuccessivelyonanearlyorganic-matter-freeXiashuloesssupplementedwithadequateamountsof(15NH4)2SO4,P,Kandmicronutrients,thenincorporatingthese15N-labelledplantmaterialsintothesoilaftereachhavest,andallowingtheplantmaterialstobedecomposedaerobicallyfor410dafterincorporationoftheplantmaterialofthethirecrop.The15Nenrichmentofwheatplant-Nvariedslightlywithorgans,withamaximumdifferenceof9.8%,Basedon15NenrichmentofsoilNinferredfromthemeanvalueofthe15Nenrichmentindifferentorgansofwheat79%-91%oftotalNinthetopsand67%-74%oftotalNintherootsoflegumesstudiedwerederivedfromatmosphere.EstimatebyisotopedilutionmethodwasingoodagreementwiththatbytheconventionaldifferencemethodprovidedvaluesobtainedbythelatterwerecorrectedforseedN,andalsowiththatfromthemeasurementofNaccumulatedinthetopsofthelegumes.

简介：Theradialbasisfunction(RBF)emergedasavariantofartificialneuralnetwork.Generalizedregressionneuralnetwork(GRNN)isonetypeofRBF,anditsprincipaladvantagesarethatitcanquicklylearnandrapidlyconvergetotheoptimalregressionsurfacewithlargenumberofdatasets.Hyperspectralreffectance(350to2500nm)datawererecordedattwodifferentricesitesintwoexperimentfieldswithtwocultivars,threenitrogentreatmentsandoneplantdensity(45plantsm-2).Stepwisemultivariableregressionmodel(SMR)andRBFwereusedtocomparetheirpredictabilityfortheleafareaindex(LAI)andgreenleafchlorophylldensity(GLCD)ofricebasedonreffectance(R)anditsthreedifferenttransformations,thefirstderivativereffectance(D1),thesecondderivativereffectance(D2)andthelog-transformedre?ectance(LOG).GRNNbasedonD1wasthebestmodelforthepredictionofriceLAIandGLCD.TherelationshipsbetweendifferenttransformationsofreffectanceandriceparameterscouldbefurtherimprovedwhenRBFwasemployed.Owingtoitsstrongcapacityfornonlinearmappingandgoodrobustness,GRNNcouldmaximizethesensitivitytochlorophyllcontentusingD1.ItisconcludedthatRBFmayprovideausefulexploratoryandpredictivetoolfortheestimationofricebiophysicalparameters.