M

MACHINE VISION

See Visible and Thermal Images for Fruit Detection

MACHINE VISION IN AGRICULTURE

John BillingsleyUniversity of Southern Queensland, Toowoomba, QLD,Australia

DefinitionMachine vision: Visual data that can be processed bya computer, including monochrome, color and infra redimaging, line or spot perception of brightness and color,time-varying optical signals.Agriculture: Horticulture, arboriculture and other croppingmethods, harvesting, post production inspection andprocessing, livestock breeding, preparation and slaughter.

IntroductionThe combination of low-cost computing power and applica-tions that target its use for entertainment has led to a readilyavailable platform for analyzing vision signals in a varietyof ways. Applications are many and various, but some ofthe most potentially significant ones are found in agriculture.

Sorting by colorFor some decades, a simplified form of machine vision hasbeen used for sorting produce (Tao et al., 1995). Tomatoesmay be picked green for ripening in the shed. When theircolor is changing, they ride a conveyor that has pocketsfor individual fruit. These holders can be tripped bya computer signal, to eject the tomato at one of many pack-ing stations corresponding to the degree of ripeness, asdefined by color.

Avery similar system is used for grading apples, as seenin Figures 1 and 2.

Fragments of nut shell can be detected when pecan nutsare shelled, once again by detection of the color. Kernelsfall through the inspection area at a speed of around onemeter per second. In an early system, light that wasreflected from the kernel was split between two photocellsthat detected the intensity of different wavelengths. Theratio enabled color to discriminate between nut and shell.More recent versions incorporate laser scanning. As thenut falls further, a jet of air is switched to deflect anydetected shell into a separate bin.

Detection of weedsColor discrimination can also be used in the field to dis-criminate between plants and weeds for the applicationof selective spraying (Åstrand and Baerveldt, 2002; Zhanget al., 2008). It is possible for the color channels of the cam-era to include infra-red wavelengths, something that can beachieved by removing the infra-red blocking filter froma simple webcam. Figure 3 is a low-resolution frame-grabshowing one stage in the detection of a grass-like weed,“panic,” during trials in sugar cane. The computer hasmarked pixels determined to be “weed” in yellow.

Quality assessmentTrue machine vision concerns shape information. Oneexample is the assessment of fodder quality. From a web-cam image of a sample handful of hay, the stem-widthscan be measured and a histogram displayed. Color can alsobe determined by comparison of the video signal againstthat from the image of a calibration card. This makes anobjective standard possible, to achieve agreement betweenvendor and purchaser (Dunn and Billingsley, 2007).

Shape information can also be used to monitor thegrowth of a crop, measuring stem length between nodesautomatically (McCarthy et al., 2008).

Jan Gliński, Józef Horabik & Jerzy Lipiec (eds.), Encyclopedia of Agrophysics, DOI 10.1007/978-90-481-3585-1,# Springer Science+Business Media B.V. 2011

Livestock identificationA more detailed version of shape information, in the formof an s-psi profile, has been used to discriminate betweenanimals of different species passing a checkpoint whilethey approach a watering place. Now the image representsthe profile of the animal as seen against a background.A blue background simplifies discrimination for produc-ing a silhouette. From this a boundary can be found byedge-tracing, which yields a sequence of incremental

Machine Vision in Agriculture, Figure 1 Apples moving towards washing and grading.

Machine Vision in Agriculture, Figure 2 A conveyorautomatically ejects each apple at the correct station.

Machine Vision in Agriculture, Figure 3 A frame-grab from thecomputer-processing of image data to locate weeds.

434 MACHINE VISION IN AGRICULTURE

vectors that form a “Freeman chain.” These in turn delivera sequence of tangent angles, to be plotted against the dis-tance travelled around the circumference of the silhouette,as seen in Figure 4.

From the original megabyte image, some 256 bytes ofdata now define the shape of the animal in view, for corre-lating against a set of templates to separate sheep fromgoats or camels from cattle. A gate is then driven toexclude or to draft the animals. This work is being refinedto assess the condition of cattle as they pass a point where

their RFID tag can be read, to monitor their progresstowards “harvesting.”

PickingMany attempts have been made over the years to usemachine vision to detect and locate fruit for automaticpicking. Although positive research results have beenreported, operational speeds are generally low and therehas been very limited commercial success. It has beensuggested that the growing availability of broadband com-munication can make it possible for “armchair pickers” totele-operate picking machinery, with the aid of real-timeimaging.

Machine guidanceVision guidance has been applied with great technical suc-cess to tractors (Billingsley and Schoenfisch, 1996).Within each received image, the software locates the rowsof crop and assesses the control action needed to bring thevehicle back on course. The algorithms were developed inthe early 1990s, when computing power was much lessplentiful. The result is a robust strategy that can time-sharewith GPS analysis and other signal processing.

Within the field of view, “keyholes” are defined thatwill each contain the image of a single row. Within eachkeyhole, plant images in the form of green pixels areregarded as data points through which to constructa regression line. For the next image, the keyholes aremoved to be centered on these regression lines. The setof two or three keyholes move at once to indicate thevanishing point, from which the heading error and the lat-eral displacement can be deduced. These signals then givea steering command to bring the tractor back on track, to

Machine Vision in Agriculture, Figure 4 S-psi plot of the outline of a steer.

Machine Vision in Agriculture, Figure 5 Frame-grab from thevideo image that is steering a tractor at speed.

MACHINE VISION IN AGRICULTURE 435

an accuracy of a centimeter or two. A frame-grab from thesteering software is shown in Figure 5.

The method is greatly superior to the use of GPS, satel-lite navigation, since an operation such as cultivating willcut the ground at a point relative to the actual location ofthe plants, not relative to where the plants are supposedto have been planted.

It is unfortunate that marketing of the system startedwhen enthusiasm for GPS was reaching fever pitch andsuccess has passed it by. Nevertheless research on visionguidance is widespread (Gottschalk et al., 2008).

ConclusionsAs sensors and computing power become ever cheaper,the opportunity for farm robotics increases. It would beunwise to make a large machine autonomous, not leastfor insurance against the damage it might cause. Smallrobot machines, however, can already be equipped withvision, navigation, detection and communication systemsat a very modest price. We may very soon see teams of“Autonomous Robot Farmhands” at work in the field.

BibliographyÅstrand, B., and Baerveldt, A.-J., 2002. An agricultural mobile

robot with vision-based perception for mechanical weed control.Journal Autonomous Robots, 13(1), 21–35.

Billingsley, J., and Schoenfisch, M., 1996. The successful develop-ment of a vision guidance system for agriculture. Computers andElectronics in Agriculture, 16(2), 147–163.

Dunn, M., and Billingsley, J., 2007. The use of machine vision forassessment of fodder quality. In Proceedings of the 14th Interna-tional Conference on Mechatronics and Machine Vision inPractice, Xiamen, PRC, 3–5 December 2007, pub IEE ISBN1-4224-1357-5, pp. 179–184.

Gottschalk, R., Burgos-Artizzu, X. P., Ribeiro, A., Pajares, G., andSainchez-Miralles, A., 2008. Real-time image processing forthe guidance of a small agricultural field inspection vehicle. InProceedings Mechatronics and Machine Vision in Practice,2008. pp. 493–498

McCarthy, C. L., Hancock, N. H., and Raine, S. R., 2008. On-the-gomachine vision sensing of cotton plant geometric parameters:first results. In Billingsley, J., and Bradbeer, R. S. (eds.),Mechatronics and Machine Vision in Practice. New York:Springer-Verlag, pp. 305–312.

Tao, Y., Heinemann, P. H., Varghese, Z., Morrow, C. T., andSommer, H. J., III, 1995. Machine vision for color inspectionof potatoes and apples. Transactions of the ASAE, 38(5),1555–1561.

Zhang, Z., Kodagoda, S., Ruiz, D., Katupitiya, J., andDissanayake, G.,2008. Classification of bidens in wheat farms. In Proceedings ofMechatronics and Machine Vision in Practice, 2008, pp. 505–510.

MACROPORE FLOW

SynonymsFunnel flow; Preferential flow

See Bypass Flow in Soil

MAGNETIC PROPERTIES OF SOILS

Andrey AlekseevLaboratory Geochemistry and Soil Mineralogy, Instituteof Physicochemical and Biological Problems of SoilScience, Russian Academy of Sciences, Pushchino,Moscow Region, Russia

SynonymsEnvironmental magnetism; Soil magnetism

DefinitionMagnetic properties of soils are dominantly controlled bythe presence, volumetric abundance, and oxidation state ofiron in soils. Different types of Fe oxides, Fe–Ti oxides,and Fe sulfides are the predominant causes of magneticsoil characteristics. The concentration of magnetic Feoxides in soils is affected by the parent material and soil-forming factors and processes.

IntroductionMagnetism is a fundamental property of all natural mate-rials. The most important kinds of magnetic properties arethose called diamagnetism, paramagnetism, ferromagne-tism, ferrimagnetism, and superparamagnetism. The mag-netic properties of soils are a subject of investigationstarted first more than 50 years ago (Le Borgne, 1955).Magnetism of soils have traditionally been investigated inthe environmental science and geophysics communities toindicate soil development, paleosols and climate change,pollution, and as tools for archaeological mapping andprospecting (Thompson and Oldfield, 1986; Maher andThompson, 1999; Evans and Heller, 2003; Maher, 2008).

Soil magneticsThe magnetic characteristics of soil and sediments reflectthe amount and quality of ferruginous minerals they con-tain and are connected with their content, mineralogy,and the grain size. The presence of Fe oxides in differentforms and quantities is the predominant cause of the mag-netic properties of soils. Iron oxide minerals can be bothpedogenic (product of soil formation) and lithogenic(unweathered minerals from the parent material) in origin.Iron is the most common element in the crust of the earth.Iron is not only essential to plant development, but it alsoparticipates in the formation of complexes of clay andorganic matter, which in turn influence soil structure andfertility. Iron-containing minerals can be found in igneous,metamorphic, and sedimentary rocks. As a rule, clayminerals possess paramagnetic properties. The most wide-spread minerals of sedimentary rocks and soils quartz,carbonates, feldspars are diamagnetic or weak paramag-netic and also do not bring the appreciable contributionto the magnetic behavior of soils. Hydrated Fe oxides likegoethite, which is the most abundant Fe oxide in soilsaround the world, ferrihydrite, and lepidocrocite play

436 MACROPORE FLOW

a minor role in determining the magnetic character ofsoils. The concentration of (magnetic) Fe oxides in soilsis affected by the parent material, soil age, soil-formingprocesses, biological activity, and soil temperature (Singeret al., 1996). In soils, primary ferromagnetic minerals ofdetrital origin derive from the disintegration of the bed-rock and they reflect its mineralogy. Secondary mineralsare formed through complex chemical and biological pro-cesses, which also depend on climate and the soil pH,humidity, and organic matter content. These processesoperate not only on primary ferromagnetic minerals, butalso on the elementary iron contained in many silicates.Depending on the parent material, the physicochemicalconditions and the pedogenetic processes, goethite, hema-tite, maghemite, or magnetite can be formed. Mineralogi-cally, by soil magnetism point of view, the most importantingredients are magnetite (Fe3O4), maghemite (g-Fe2O3),and hematite (a-Fe2O3). The main properties and pathwayof formation of these minerals are discussed in manybooks and review papers (Thompson and Oldfield, 1986;Cornell and Schwertmann, 2003; Mullins, 1977;Schwertmann, 1988). The nature, content, and grain sizeof each magnetic phase reflect the physicochemical condi-tions of the soil. For example, magnetic susceptibilityenhancement in topsoil is found in most temperate soils(Le Borgne, 1955; Babanin, 1973; Maher, 1986; Alekseevet al., 1988), except in acidic, podzolic, and waterloggedconditions (Maher, 1998); it can be related to soil condi-tions at the surface. Magnetic minerals, which occur invery small concentration in most soils, are as fingerprintsof pedological processes. At present, several theories areput forward to explain the concentration and distributionsof the ferrimagnetic minerals in soils: burning, biotictransformation of hydrous ferric oxides; abiotic transfor-mation of hydrous ferric oxides; residual primary min-erals; magnetotactic bacteria; anaerobic dissimilatorybacteria; and atmospheric contamination and pollution.Environmental magnetic studies have revealed thata range of ferrimagnetic minerals can be formed at Earthsurface temperatures and pressures within soils and sedi-ments, rather than merely “inherited” from disintegrationand weathering of magnetic mineral-bearing igneousrocks. Notably, trace concentrations of nanoscale magne-tite can be precipitated in situ in the soil matrix ofwell-drained, generally oxidizing, near-neutral soils(Maher, 2008). The generally accepted view is that mostsoils produce secondary nanoscale iron oxides magne-tite/maghemite in surface horizons (Maher 1998;Alekseev et al., 2003; Maher et al., 2003; Blundell et al.,2009). The magnetic research techniques are expresswhich allow producing the mass analysis in comparisonwith other methods. Magnetic methods have many advan-tages over other techniques: nearly all rocks and soilscontain magnetic iron oxides/sulfides, sample preparationand measurement is quick, easy and generally nondestruc-tive, and the methods are sensitive to both concentrationand grain-size – particularly for ultrafine grains, whichcan be difficult to detect by other means.

Magnetic parameter and instrumentationTo describe magnetic properties of soil, different types ofmagnetization are commonly used.

Magnetic susceptibility – when a low-intensity mag-netic field is applied to a material, the net magneticmoment (magnetization, M) is proportional to the appliedfield strength (H). Therefore, the low-field magnetic sus-ceptibility, which is defined asM/H and expressed per unitvolume (k) or per unit mass (w), is a material-specificproperty. Magnetic susceptibility (w) reflects the total con-centration of ferrimagnetic or total concentration of para-magnetic minerals and antiferromagnetic with lowcontent of ferromagnetics. A magnetic susceptibility (w)is one of most simply obtained magnetization characteris-tics and rather large set of the data on the application ofthis parameter for the problems of soil science exists.

Remanent magnetization occurs within ferromagneticand ferromagnetic minerals and exists in the absence ofan applied field.

Viscous remanent magnetization refers to the delay ofthe secondary magnetic field relative to the primary mag-netic field and has been linked to the presence of superpar-amagnetic grains of iron oxides.

Four laboratory instruments make up the basic require-ments for magnetic characterization of environmentalsamples and soils: a susceptibility bridge (preferably dualfrequency); a magnetometer; magnetizing coils; and ademagnetizer. These magnetic techniques are nondestruc-tive and sensitive to trace amounts of magnetic minerals(Thompson and Oldfield, 1986; Maher and Thompson,1999; Maher, 2008).

The magnetic properties of soils typically have beenstudied using the following equipment as example: –mag-netic susceptibility (w) – MS 2 Bartington or KappameterKT-5-9 (field measurements), Kappabridges (2–4) (labo-ratory measurements); frequency – dependent magneticsusceptibility (wfd) – MS 2 Bartington; curves of satura-tion magnetizations (IRM) in magnetic fields withstrength up to 1–4 T – Molspin magnetometer andMolspin pulse magnetizer; anhysteretic remanent magne-tization (ARM) – complex of the equipment includedMolspin demagnetizer and Molspin magnetometer; com-plete magnetization curves (hysteresis curve) – vibratingsample magnetometer-VSM Molspin (Table 1).

Soil magnetism applicationsPresent instruments and methods enable very sensitivedetermination of low concentrations of strong ferrimag-netic minerals in soils. Possible mechanisms of magneticenhancement of soils due to increased concentrations ofsecondary ferrimagnetic minerals are discussed above.Herewith, we will outline some examples of applicationof magnetic study of the soils. Magnetic properties mea-surements of soils are mostly used for three purposes: toread the climatic signal recorded by palaeosols, to identifypollution in soils, and as tools for archaeological mappingand prospecting.

MAGNETIC PROPERTIES OF SOILS 437

As example, recently, a quantitative, soil magnetism-based climofunction has been established for the area ofthe Russian steppe (Maher et al., 2002; Maher et al.,2003). A similar correlation between rainfall and magneticsusceptibility was previously obtained for the Chinese LoessPlateau and explained as a result of pedogenic formation ofmagnetite and maghemite via oxidation/reduction processesthrough soil wetness events (Maher and Thompson, 1999).

The pedogenic magnetic response of these well-drained, near-neutral, Russian steppe soils appearsstrongly correlated with that of the similarly well-drainedand buffered modern soils across the Chinese Loess Pla-teau (and across thewider Northern Hemisphere temperatezone). Such correlation suggests that the rainfall compo-nent of the climate system is a key influence on soil mag-netic properties in both these regions. This direct couplingof the soil magnetism of modern soils with present-day cli-mate substantiates the use of magnetic climofunctions tomake quantitative estimates of past rainfall variations fromthe magnetic properties of buried palaeosols for both theRussian steppe and the Chinese Loess Plateau.

Applying a soil magnetism climofunction, calculatedfrom a modern-day soil training set, to each set of buriedsoils enables quantitative estimation of precipitation ateach time step when soil burial occurred as for Holocenepaleosols or loess-paleosols sequences of Pleistocene(Alekseeva et al., 2007).

Atmospherically deposited ferrimagnetic particles ofanthropogenic origin also contribute a great deal to theconcentration-dependent magnetic properties of top soils,such as low-field magnetic susceptibility. The highest con-centration of anthropogenic ferrimagnetic particles is usu-ally found in humic layers (e.g., Strzyszcz et al., 1996).Practically all industrial fly ashes contain a significantfraction of ferromagnetic particles, the most importantsources being fly ashes produced during combustion offossil fuel (Hanesch and Scholger, 2002; Kapička et al.2003). Other sources, such as iron and steel works, cement

works; public boilers and road traffic also contribute tocontamination by anthropogenic ferrimagnetics (Helleret al., 1998; Scholger, 1998; Hoffmann et al., 1999). Incontrast to particles of pedogenic origin, anthropogenicferrimagnetics are characterized by specific morphologyand distinct magnetic properties. They are often observedin the form of spherules, with the magnetic phase fre-quently sintered on aluminum silicates or amorphoussilica. Prevailing ferrimagnetic phases are Fe oxides,namely magnetite and maghemite, with Fe ions very oftensubstituted by other cations (Strzyszcz et al., 1996). Appli-cation of the comparatively simple technique of measuringmagnetic susceptibility enables delineation of areas withconcentrations of deposited anthropogenic ferrimagneticssignificantly above background values. Magnetic map-ping thus represents a rapid, sensitive, and cheap tool fortargeting the areas of interest. These studies showed thatin polluted areas, the magnetic susceptibility of surfacesoil layers is considerably higher. Recently, rock-magneticmethods have been applied tomodern soils in several envi-ronmental studies (for an overview see, e.g., Petrovskýand Ellwood, 1999). Measurements of low-field magneticsusceptibility of surface soils have been applied recentlyaround local pollution sources and at a larger, regionalscale, areas in Poland and Great Britain and Austria havebeen investigated (Strzyszcz et al., 1996; Heller et al.,1998; Hanesch and Scholger, 2002; Magiera et al., 2006 ).

SummaryIn a course of soil formation, the change of magnetic prop-erties of soils in comparison with parent material takesplace. The amplitude of these changes depends on the fac-tors of soil formation. Generally, the conditions of trans-formation of iron-containing minerals result inenhancement of the contents of ferric oxides in soils.The analysis of magnetic properties of zonal soils showsthat the behavior of soil magnetics in profile reflectsgenetic properties of soils at the soil type level and

Magnetic Properties of Soils, Table 1 Magnetic parameters and their interpretation (Thompson and Oldfield, 1986; Maher andThompson, 1999; Evans and Heller, 2003; Maher, 2008)

w (10�8 m3 kg�1) Magnetic susceptibility (w or wlf ) – total concentration of ferrimagnetic or total concentration of paramagnetic mineralsand antiferromagnetic by low content of ferrimagnetic

wfd %- Frequency-dependent magnetic susceptibility. Is calculated on a difference of measurements at different frequencies(for MS2 460 wlf and 4,600 Hz whf accordingly): (wfdÞ% ¼ ðwlf � whf Þ=wlf � 100

Reflects the presence of ultrafine ferrimagnetic grains. Is especially sensitive to the size of particles in an interval0.015–0.025 mm

wARM(10�8m3kg�1)

Susceptibility of anhysteretic remanent magnetization (ARM). Maximum intensity of the alternating field used in theinstrument Molspin demagnetizer for magnetization �100 mT, with step of decrease of a magnetic field for eachcycle 0.016 mT, strength of the constant biasing field�0.08 mT. It is high sensitive to ferrimagnetic with the size ofparticles in an interval 0.02–0.4 mm. Reflects the presence of fine-grained magnetite (stable single domain grains)

wARM/SIRM(m A�1)

The ratio is sensitive to grain-size changes of ferrimagnetics. For superparamagnetic particles the significances in aninterval �0.5 to 1.5 are characteristic, for stable single domain particles 1.8–2.0

IRM100mТ/SIRM Allows evaluating the contents of ferrimagnets (magnetite, maghemite). As the majority of ferrimagnetic is fullysaturated in fields of 100 mТ

SIRM-IRM300mТ Can be used for approximating the total concentration of high coercitivity minerals (hematite + goethite)Remanence acquired in a field of 1T is referred as SIRM (SIRM = IRM1,000mТ.). It is necessary to note that the fullsaturation for antiferromagnetic phases can be reached at fields strength above 4 Т

438 MAGNETIC PROPERTIES OF SOILS

connected with distribution and state of iron minerals insoils and landscapes. Atmospherically deposited ferri-magnetic particles of anthropogenic origin also contributea great deal to the concentration-dependent magneticproperties of top soils. Magnetic properties measurementsof soils are mostly used for three purposes: to read the cli-matic signal recorded by palaeosols, to identify pollutionin soils, and as tools for archaeological mapping andprospecting. The definition of soil magnetism as a geneticparameter, which is able together with other soil propertiesto be used for diagnostics of soil looks to be useful andimportant. Magnetic methods have many advantages overother techniques: sample preparation and measurement isquick, easy and generally nondestructive, and the methodsare sensitive to both concentration and grain-size – partic-ularly for ultrafine grains, which can be difficult to detectby other means. Hence, magnetic analyses of soils providean additional, sensitive window on soil iron.

BibliographyAlekseev, A. O., Kovalevskaya, I. S., Morgun, E. G., and

Samoylova, E. M., 1988. Magnetic susceptibility of soils ina catena. Soviet Soil Science (Pochvovedenie), 8, 27–35.

Alekseev, A. O., Alekseeva, T. V., and Maher, B. A., 2003. Mag-netic properties and mineralogy of iron compounds in steppesoils. Eurasian Soil Science, 36, 59–70.

Alekseeva, T., Alekseev, A., Maher, B. A., and Demkin, V., 2007.Late Holocene climate reconstructions for the Russian steppe,based on mineralogical and magnetic properties of buriedpalaeosols. Palaeogeography, Palaeoclimatology,Palaeoecology, 249, 103–127.

Babanin, V. F., 1973. The use of magnetic susceptibility in identify-ing forms of iron in soils. Soviet Soil Science (Pochvovedenie), 5,487–493.

Blundell, A., Dearing, J. A., Boyle, J. F., and Hannam, J. A., 2009.Controlling factors for the spatial variability of soil magneticsusceptibility across England and Wales. Earth-ScienceReviews, 95, 158–188.

Cornell, R. M., and Schwertmann, U., 2003. The Iron Oxides.Weinheim: Wiley-VCH.

Evans,M. E., and Heller, F., 2003.Environmental Magnetism: Princi-ples and Applications of Enviromagnetics. San Diego: Academic.

Hanesch, M., and Scholger, R., 2002.Mapping of heavy metal load-ings in soils by means of magnetic susceptibility measurements.Environmental Geology, 42, 857–870.

Heller, F., Strzyszcz, Z., and Magiera, T., 1998. Magnetic record ofindustrial pollution in forest soils of Upper Silesia, Poland. Jour-nal of geophysical research, 103, 17767–17774.

Hoffmann, V., Knab, M., and Appel, E., 1999. Magnetic suscepti-bility mapping of roadside pollution. Journal of GeochemicalExploration, 66, 313–326.

Kapička, A., Jordanova, N., Petrovský, E., and Podrázský, V., 2003.Magnetic study of weakly contaminated forest soils. Water, Airand Soil Pollution, 148, 31–44.

Le Borgne, E., 1955. Susceptibilité magnetique anormale du solsuperficiel. Annales de Geophysique, 11, 399–419.

Magiera, T., Stryszcz, Z., Kapička, A., and Petrovský, E., 2006.Discrimination of lithogenic and anthropogenic influences ontopsoil magnetic susceptibility in Central Europe. Geoderma,130, 299–311.

Maher, B. A., 1986. Characterisation of soils by mineral magneticmeasurements. Physics of the Earth and Planetary Interiors,42, 76–92.

Maher, B. A., 1998. Magnetic properties of modern soils and Qua-ternary loessic paleosols: palaeoclimatic implications.Palaeogeography, Palaeoclimatology, Palaeoecology, 137,25–54.

Maher, B. A., 2008. Environmental magnetism and climate change.Contemporary Physics, 48, 247–274.

Maher, B. A., and Thompson, R. (eds.), 1999.Quaternary Climates,Environments and Magnetism. Cambridge: Cambridge Univer-sity Press.

Maher, B. A., Alekseev, A., and Alekseeva, T., 2002. Variation ofsoil magnetism across the Russian steppe: its significance foruse of soil magnetism as a paleorainfall proxy. Quaternary Sci-ence Reviews, 21, 1571–1576.

Maher, B. A., Alekseev, A., and Alekseeva, T., 2003.Magnetic min-eralogy of soils across the Russian steppe: climatic dependenceof pedogenic magnetite formation. Palaeogeography, Palaeocli-matology, Palaeoecology, 201, 321–341.

Mullins, C. E., 1977. Magnetic susceptibility of the soil and its sig-nificance in soil science: a review. Journal of Soil Science, 28,223–246.

Petrovský, E., and Ellwood, B. B., 1999. Magnetic monitoring ofair-land and water-pollution. In Maher, B. A., and Thompson,R. (eds.), Quaternary Climates, Environments and Magnetism.Cambridge: Cambridge University Press.

Scholger, R., 1998. Heavy metal pollution monitoring by magneticsusceptibility measurements applies to sediments of the riverMur (Styria, Austria). European Journal of Environmental andEngineering Geophysics, 3, 25–37.

Schwertmann, U., 1988. Occurrence and formation of iron oxides invarious pedoenvironments. In Stuki, J. W., Goodman, B. A., andSchwertmann, U. (eds.), Iron Oxides in Soils and Clay Minerals.Dordrecht: Reidel.

Singer, M. J., Verosub, K. L., Fine, P., and TenPas, J., 1996.A conceptual model for the enhancement of magnetic suscepti-bility in soils. Quaternary International Journal, 34–36,2443–2458.

Strzyszcz, Z., Magiera, T., and Heller, F., 1996. The influence ofindustrial immisions on the magnetic susceptibility of soils inUpper Silesia. Studia Geophysica et Geodaetica, 40, 276–286.

Thompson, R., and Oldfield, F., 1986. Environmental Magnetism.London: Allen and Unwin.

Cross-referencesClay Minerals and Organo-Mineral AssociatesClimate Change: Environmental EffectsDatabases of Soil Physical and Hydraulic PropertiesPhysical Properties for Soil ClassificationMapping of Soil Physical PropertiesMineral–Organic–Microbial InteractionsNanomaterials in Soil and Food AnalysisOxidation–Reduction Reactions in the EnvironmentParent Material and Soil Physical PropertiesPhysical (Mechanical) Weathering of Soil Parent MaterialWildfires, Impact on Soil Physical Properties

MAGNETIC RESONANCE IMAGING IN SOIL SCIENCE

Andreas PohlmeierICG-4, Research Centre, Jülich, Germany

DefinitionMagnetic resonance imaging (MRI) or magnetic resonancetomography (MRT) is a noninvasive, three-dimensional

MAGNETIC RESONANCE IMAGING IN SOIL SCIENCE 439

(3D) imaging technique formonitoringwater content, waterfluxes, and tracer transport in porous media. It uses theeffect of nuclear magnetic resonance (NMR) of certainatomic nuclei, mostly 1H in H2O, which is modulated bythe chemical and physical environment inside the porousmedium. The methods yield finally 2D (slices) or 3D (vol-ume graphics) images of the medium under investigation.

BasicsMagnetic resonance imaging is based upon the physicaleffect of nuclear magnetic resonance (NMR) of spin bear-ing atomic nuclei (Callaghan, 1991; Blümich, 2000). Themost important NMR active nuclei in soil science applica-tions are 1H, 13C, 19F, 31P, and 23Na, of which mostly 1Hwith a spin quantum number of I =½ (e.g., in H2O) is usedfor imaging purposes leading to a nuclear magneticmoment m. If placed in an external magnetic field B0pointing into z-direction of a Cartesian coordinate system,(The direction of the external magnetic field B0 is mostlydetermined by convention as “z”) the nuclear magneticmoment precesses around the axis of B0 with the Larmorfrequency u0:

u0 ¼ o0=2p ¼ �gjB0j=2p (1)

The parameter g, a proportionality constant termed asgyromagnetic ratio, is a basic property of the respectivenucleus. For protons its value is gH = 2.68 � 108 T�1 s�1,leading to typical values of u0 = 300 MHz at |B0| = 7 T instationary superconducting magnets, and u0 = 4.3 MHz at|B0| = 0.1 T in mobile low-field relaxometers and scanners.Due to the quantum mechanical nature of the nuclear spinfor proton magnetic moments, only two states are allowedin an external magnetic field: parallel (↑) and antiparallelorientation (#). The energy gap between these two statesisDE = hu0, where h is Planck’s constant and the two statesare populated according to Boltzmann’s law: n↑/n# = exp(�DE/kT ). In practice, it is more convenient not to regardsingle spins but ensembles of spins, which may be treatedsemi-classically. If an ensemble is sufficiently large, the x-and y-components of the nuclear magnetic momentsprecessing around the z-axis cancel mutually, so only thez-component persists. Since the lower energy state isslightly higher populated than the higher state, a macro-scopic magnetic moment M0 pointing into z-direction isobservable. Now, by absorption of electromagnetic radia-tion matching exactly the Larmor frequency, spins mayswap from parallel to antiparallel orientation. Regardingthe ensemble, this irradiation by a sufficiently long pulse,termed as 90� pulse, leads to a rotation of M0 into the xy-plane, where it precesses again with the Larmor frequencyu0 around the z-axis. Subsequently, the excess energy ofthe ensemble is lost by two relaxation mechanisms:

Firstly, the coherence of the magnetic moments in thexy-plane decays with the transverse relaxation time T2,see Equation 2 and radiation is emitted, which is detect-able by an external receiver coil. This is the free inductiondecay (FID).

Mxy ¼ M0exp �t=T2ð Þ; (2)

It is composed of two contributions: coherence loss(dephasing) in static inhomogeneities, which is reversible,and irreversible loss due to stochastic motions. The revers-ible contribution of dephasing can be restored by the cre-ation of a spin echo by means of the application ofa 180� pulse after a period of tE/2 (cf. Equation 5) afterthe 90� pulse. What remains is the irreversible part. Fordetails, see textbooks (Callaghan, 1991; Blümich, 2000).

Secondly, the thermal equilibrium is restored, that is,magnetization in the z-direction is reformed again. Thisis called longitudinal relaxation characterized by therelaxation time T1:

Mz ¼ M0ð1� exp �t=T1ð ÞÞ (3)

Purewater possesses relaxation times of about 3 s in highfield, but the environment, in which the interesting watermolecules are located, reduces T1 and T2 considerably.Decisive factors are (1) pore size, (2) pore filling factor,(3) pore geometry, (4) chemical nature of pore walls,(5) dissolved paramagnetic substances and, in case of T2,(6) diffusive motion in internal magnetic field gradients.By the latter effect T2 can get much faster than one millisec-ond, which has implications on imaging capabilities, seebelow. The investigation of relaxation times is the basis ofrelaxometric exploration of pore space in geological mate-rials (Dunn et al., 2002), see also the article Proton NMRRelaxometry in Soil Science, Nr of G. Schaumann in theencyclopedia.

ImagingMagnetic resonance imaging (MRI) is the extension ofNMR by adding spatial resolution to the observed NMRsignal. In the following a basic spin echo imaging methodis presented exemplarily (see Figure 1). According toEquation 1, the precession frequency is proportional tothe magnetic field B0. If an additional spatially variablemagnetic field (a gradient Gslice) is added to B0 duringexcitation only one slice is excited according toEquation 4:

u0 ¼ �gjB0 þ Gslice � zj=2p: (4)

All subsequent detectable signals originate onlyfrom this slice leading to the family of the so-calledmulti-slice imaging sequences, see Figure 1. The nextdimension (here: x) is addressed by application ofa further gradient Gread (orthogonal to Gslice) during thedetection of the signal that encodes the frequency of thereceived signal with respect to the x-direction. The thirddimension is finally encoded by the phase-selectiveGphase, orthogonal to the other two gradients. After itsapplication, the phase of the signal is turned with respectto a reference, and the phase shift is proportional to theposition on the y-axis. The real-space image is finallyobtained by two-dimensional Fourier transformation.The signal intensity is given by

440 MAGNETIC RESONANCE IMAGING IN SOIL SCIENCE

SðxyzÞ / r0ðxyzÞ 1� expð�tR=T1ðxyzÞÞð Þ� expð�tE=T2ðxyzÞÞ; (5)

where r0 is the spin density, tR is the repetition timebetween successive excitation pulses, and tE is the echotime, that is, twice the period between 90� and 180� pulse,T1 and T2 are the longitudinal and transverse relaxationtimes, respectively. Note that r0 as well as T1 and T2depend on space. The sequence in Figure 1 is only an

example of a basic, but still widespread MRI pulsesequence (Spin echo multi slice sequence, also termed asspin warp sequence). Besides this, many partly very spe-cialized sequences exist, for example, rapid imaging,relaxometric imaging, and motion sensitive imaging(Callaghan, 1991; Blümich, 2000).

HardwareSchematically, the necessary hardware consists of follow-ing components (Figure 2): (1) a cylindrical magnet witha bore, in which the gradient system (2) and the transmit-ter–receiver coil (3) is placed. Stationary magnets aremostly superconductors, where permanent current flowsin a liquid helium cooled coil, which creates a mainmagnetic field B0 pointing along the axis of the cylinder(z-direction). The gradient system consists of three addi-tional coils that create orthogonal magnetic field gradientsin the x-, y-, and z-directions. These gradients are operatedby the spectrometer (4), which creates and controls thepulse sequences via gradient amplifiers (5). The excitationof the spin system and the monitoring of the transmittedsignals are performed by the rf-coil (3), which transmitsand receives rf-pulses. This is also controlled by the spec-trometer (4) and the rf amplifier (6) In order to be able toturn the magnetization M0 into the xy-plane the directionof the magnetic field B1 of the rf-pulses must be orthogo-nal to B0. This is performed in conventionalsuperconducting scanners by a so-called birdcage-rf coil.For novel low-field scanners, which are composed ofHalbach-rings of permanent magnets (Raich and Blümler,2004), B0 points orthogonal to the main axis, and the rfcoil can be a simple solenoid coil.

ApplicationsThe application of MRI for soil systems started in the1980s. A very early example was the unilateral imagingof water content in a natural soil on the field scale, wherean electromagnet was positioned on a sledge and pulledacross a field by a tractor (Paetzold et al., 1985). The signalcreated by a radiofrequency irradiation pulse was detectedand water content was derived from the signal intensity.Most recently, the imaging of water on the field scale drawsagain attraction by the further development of magneticresonance sounding (NMRS) or surface NMR (Royand Lubczynski, 2005; Mohnke and Yaramanci, 2008;Yaramanci et al., 2008). Originally, this method has beendeveloped for detection of aquifers in geological formationslike karsts. But with the advancing technology, especiallywith respect to noise compensation, the method might getavailable for the investigation of soils with much less watercontent and much faster relaxation times (Hertrich et al.,2007).

In the lab, MRI has also been applied for the investiga-tion of water content and dynamics in repacked naturalporous media and natural soil cores (Nestle et al., 2002).The general problem for its application in soils is theinherently rapid T2 relaxation times (Hall et al., 1997;

signal

Gread (x )

Gphase (y)

Gslice (z)

rf pulse

Time

0 TE

P90 P180

Magnetic Resonance Imaging in Soil Science, Figure 1 Basicspin echo imaging pulse sequence. P90 and P180 mean 90� and180� pulses of electromagnetic irradiation for excitation of thespin ensemble system and creation of the echo, respectively.The gradients in this example are applied in x-, y-, andz-directions, but can also be applied in other orders.

(5)

(4)

(1)

(2)

(3)

(6)

z

xy

Magnetic Resonance Imaging in Soil Science,Figure 2 Schematic magnetic resonance imaging (MRI) scanner.For details, see text.

MAGNETIC RESONANCE IMAGING IN SOIL SCIENCE 441

Votrubova et al., 2000), down to the sub-millisecondrange. Also, one must take into consideration that T2 fora given porous medium is not a constant but depends alsoon tE due to diffusional motion in internal gradients(Barrie, 2000; Dunn et al., 2002). The limiting value oftE is about 1.5 ms at present, so in the past many imagingstudies in natural soils failed. However, the group ofM. Cislerova was able to apply the method on infiltrationprocesses in natural soil cores (Cislerova et al., 1997;Votrubova et al., 2003), where the infiltrating waterfollowed preferential flow paths in a network ofmacropores, which are characterized by comparably longT2 relaxation times. Figure 3 shows an image of a centralvertical slice through a soil core after infiltration fromtop, which was obtained by a spin-echo sequence usingtE =5 ms. Clearly visible are the preferential flow paths.

Besides 1H2O imaging, also other nuclei can be used formonitoring soil processes. This method is quite rare, butpromising for the quantification of dual phase fluxes.Simpson et al. monitored water and fluorinated compounds(fluorinated benzene, NaF, trifluralin) in four natural soilcores. Since free water possesses longer relaxation times,and it is still visible when imaged at long tE, the authorsvaried tE in the range between 2.5 and 40 ms in order todifferentiate between bound water and free water. Thenthe displacement of water by hexafluorbenzene duringinfiltration from top was imaged by 19F MRI. This

fluorinated liquid has been chosen as model nonaqueousphase liquid (NAPL), because 19F possesses the samespin like water and a similar gyromagnetic ratio of gF =2.52 � 108 T�1 s�1.

A permanently challenging topic in soil science is theimaging of flow processes. MRI is principally very suit-able for such purposes, since it can monitor the motionof water and therefore flow velocities and diffusion coeffi-cients directly (Callaghan et al., 1988; Baumann et al.,2000; Scheenen et al., 2001). This is performed by theintroduction of additional motion encoding gradientsbefore the detection of echoes, which prolongates intervalbetween excitation pulse and detection. Therefore, thesemethods are restricted to the investigation of sedimentsand model porous media with sufficiently slow transverserelaxation times (Baumann et al., 2000; Herrmann et al.,2002b). For applications in soils, the first difficulty is theshort transverse relaxation time, which lets signals vanishafter some milliseconds. Secondly, flow processes in soilsare mostly slower than the few tenths of mm/s, which isthe present limit for flow imaging (Bendel, 2009).So, fluxes should be better visualized indirectly by motionof tracers. Popular tracers are paramagnetic ions like Cu2+,Ni2+, or Mn2+ (Greiner et al., 1997; Herrmann et al.,2002a; Oswald et al., 2007), and complexes likeGdDTPA2�

that are widely used in medicine (Hermann et al., 2008;Haber-Pohlmeier et al., 2009, 2010). The mode of actionis predominantly the reduction of the longitudinal relaxationtime T1. If one sets the experimental parameter TR inEquation 5 to a sufficiently small value, the signal intensityin regions without tracer is low, whereas signal intensitiesoriginating from regions with high tracer concentra-tions remain high. This technique has been successfullyapplied for quantifying flux processes in natural porousmedia and very recently for the first time in a natural soil(Haber-Pohlmeier et al., 2009, 2010).

As already mentioned, soils possess short relaxationtimes, which can reduce the intensities of echoes consider-ably or prevent the detection of echoes completely.A family ofMRI techniques based on single point imagingcan overcome this restriction by avoiding the creation ofechoes and probe directly the free induction decay, whichappears after any exciting pulse (Balcom et al., 1996).Their general drawbacks are long measuring times com-bined with low resolution. However, such methods areused for the investigation of water in porous rocks(Gingras et al., 2002; Chen et al., 2006) and root–soilsystems (Pohlmeier et al., 2008).

The final topic to be addressed here is the investigationof root–water–soil relations by MRI. MRI is especiallysuitable for this, since such interactions are sensitive forclassical invasive methods but possess huge importancefor the understanding of plant growth and stress tolerance.The earliest investigations range back to the 1980s(Bottomley et al., 1986; Bacic and Ratkovic, 1987; Brownet al., 1990; Chudek et al., 1997). For imaging of root sys-tems, the short transverse relaxation times of soil materialhelps, since roots possess relative long relaxation times, so

Flowartifact

Infiltrationhead

y (p

ixel

s)

Centrallineartifact

Blurringand shapedistortion

Supportplate 125

100

75

50

25

0

100

0 100000 200000 300000 400000 MRI intensity (a.u.)

x (pixels)887563503825

Magnetic Resonance Imaging in Soil Science,Figure 3 MR image of a soil core after infiltratation of waterfrom top. (Modified from Votrubova et al., 2003. Copyright[2003] American Geophysical Union, Reproduced/modified bypermission of American Geophysical Union.)

442 MAGNETIC RESONANCE IMAGING IN SOIL SCIENCE

by the choice of long echo times (tE in Equation 5) the sig-nal from the soil is completely faded out, and only the rootsystem appears in the images (MacFall and van As, 1996;Menzel et al., 2007; Pohlmeier et al., 2008). In contrast, ifone intends to measure water content in the vicinity ofroots one should use very short echo times or even employsingle point imaging techniques (Pohlmeier et al., 2007).Figure 4 shows an example of water content changes dur-ing a desiccation experiment over 6 days in a ricinus rootsystem grown in fine sand, where the water content wasdetermined by a multi-slice multi-echo pulse sequencewith quite short echo time. The resulting echo-trains arefitted by exponential functions and the water content wasobtained from the amplitude of these functions and cali-bration on reference tubes with known water content.The root system architecture was imaged by a fast spinecho method with longer echo time. The stronger deple-tion in the top layers of the soil is visible, whereas the bot-tom regions remain wetter.

Using MRI, several authors stated water depletionzones around roots (MacFall et al., 1990; Segal et al.,2008) while under very dry conditions also hints on oppo-site trend, that is, increased water contents around rootsare found (Carminati et al., 2010). This is still a topic of dis-cussion. Further necessary for the interpretation of sucheffects is the combination of noninvasive 3D images withmodel calculations based on soil physical principles(Javaux et al., 2008), see also the article Plant Soil Inter-actions, Modelling of M. Javaux in this encyclopedia.

Summary and outlookSummarizing, one can state that MRI is very suitable formonitoring processes in model and natural soils like watercontent changes, root system architecture, flow processes,

and tracer motion. One should always take into accountthat transverse relaxation times decrease with decreasingpore size and water content and increasing content of para-magnetic ions like Fe3+ and Mn2+. Thus, MRI is generallymore sensitive for water in macropores. Therefore, quanti-tative water content imaging should employ fast echotimes in combination with multi-echo sequences andextrapolation to zero time, or even ultrafast pulsesequences like SPRITE (Single Point Imaging with T1Enhancement [Balcom et al., 1996; Pohlmeier et al.,2007]). Such sequences abandon the creation of an echoand sample the FID directly on the expense of enhancedoverall measuring time. Flux processes can be visualizeddirectly in macropores or indirectly by the usage of con-trast agents (tracers).

BibliographyBacic, G., and Ratkovic, S., 1987. NMR Studies of Radial

Exchange and Distribution of Water in Maize Roots: The Rele-vance of Exchange Kinetics. Journal of Experimental Botany,38, 1284–1297.

Balcom, B. J., MacGregor, R. P., et al., 1996. Single-point rampedimaging with T-1 enhancement (SPRITE). Journal of MagneticResonance Series A, 123(1), 131–134.

Barrie, P. J., 2000. Characterization of porous media using NMRmethods. Annual Reports on NMR Spectroscopy, 41, 265–316.

Baumann, T., Petsch, R., et al., 2000. Direct 3-d measurement of theflow velocity in porous media using magnetic resonance tomog-raphy. Environmental Science & Technology, 34(19), 4242–4248.

Bendel, P., 2009. Quantification of slow flow using FAIR.MagneticResonance Imaging, 27(5), 587–593.

Blümich, B., 2000. NMR imaging of materials. Oxford: Clarendon.Bottomley, P. A., Rogers, H. H., et al., 1986. NMR Imaging Shows

Water Distribution and Transport in Plant-Root Systems Insitu.Proceedings of the National Academy of Sciences of the UnitedStates of America, 83(1), 87–89.

Brown, J. M., Kramer, P. J., et al., 1990. Use of Nuclear MagneticResonance Microscopy for Noninvasive Observations of Root-Soil Water Relations. Theoretical and Applied Climatology, 42,229–236.

Callaghan, P. T., 1991. Principles of Nuclear Magnetic ResonanceMicroscopy. Oxford: Oxford University Press.

Callaghan, P. T., Eccles, C. D., et al., 1988. NMR Microscopy ofdynamic displacements – k-space and q-space imaging. Journalof Physics, E21, 820–822.

Carminati, A., Moradi, A., et al., 2010. Dynamics of soil water con-tent in the rhizosphere. Plant and Soil, 332, 163.

Chen, Q., Rack, F., et al., 2006. Quantitative magnetic resonanceimaging methods for core analysis. New techniques in SedimentCora Analysis. London. Geological Society London, 267,193–207.

Chudek, J. A., Hunter, G., et al., 1997. An application of NMRmicroimaging to investigate nitrogen fixing root nodules. Mag-netic Resonance Imaging, 15, 361–368.

Cislerova, M., Votrubova, J., et al., 1997. Magnetic ResonanceImaging and Preferential Flow in Soils. Characterization andMeasurement of the Hydraulic Properties of UnsaturatedPorous Media.. Riverside: University of California.

Dunn, K. J., Bergmann, D. J., et al., 2002. Nuclear Magnetic Reso-nance, Petrophysical and Logging Applications. Amsterdam:Pergamon.

Gingras, M. K., MacMillan, B., et al., 2002. Visualizing the internalphysical characteristics of carbinate sediments with magnetic

Magnetic Resonance Imaging in Soil Science, Figure 4 Ricinusroot–soil system. Water content difference maps Dy betweenday-6 and day-1, overlaid by the root system. The long cylindersin the left part are reference tubes. (From Pohlmeier et al., 2010.)

MAGNETIC RESONANCE IMAGING IN SOIL SCIENCE 443

resonance imaging and petrography. Bulletin of Canadian Petro-leum Geology, 50(3), 363–369.

Greiner, A., Schreiber,W., et al., 1997.Magnetic resonance imagingof paramagnetic tracers in porous media: quantification of flowand transport parameters. Water Resources Research, 33(6),1461–1473.

Haber-Pohlmeier, S., Van Dusschoten, D., et al., 2009. Waterflowvisualized by tracer transport in root-soil-systems using MRI.Geophysical Research Abstracts, 11, EGU2009–8096.

Haber-Pohlmeier, S., Stapf, S., et al., 2010. Waterflow monitored bytracer transport in natural porous media using MRI. Vadose ZoneJournal, 9, 835–845.

Hall, L. D., Amin, M. H. G., et al., 1997. MR properties of water insaturated soils and resulting loss of MRI signal in water contentdetection at 2 tesla. Geoderma, 80(3–4), 431–448.

Hermann, P., Kotek, J., et al., 2008. Gadolinium (iii)complexes asMRI contrast agents: ligand design and properties of the com-plexes. Dalton Transactions, June 21, 3027–3047.

Herrmann, K. H., Pohlmeier, A., et al., 2002a. Three-dimensionalimaging of pore water diffusion and motion in porous mediaby nuclear magnetic resonance imaging. Journal of Hydrology,267(3–4), 244–257.

Herrmann, K. H., Pohlmeier, A., et al., 2002b. Three-dimensionalnickel ion transport through porous media using magnetic reso-nance Imaging. Journal of Environmental Quality, 31(2),506–514.

Hertrich, M., Braun, M., et al., 2007. Surface nuclear magnetic res-onance tomography. IEEE Transactions on Geoscience andRemote Sensing, 45, 3752–3759.

Javaux, M., Schröder, T., et al., 2008. Use of a Three-DimensionalDetailed Modeling Approach for Predicting Root Water Uptake.Vadose Zone Journal, 7(3), 1079–1088.

MacFall, J. S., and Van As, H., 1996. Magnetic resonance imagingof plants. In Shachar-Hill, Y., and Pfeffer, P. E. (eds.), NuclearMagnetic Resonance in Plant Biology. Rockville: The AmericanSociety of Plant Physiologists, pp. 33–76.

MacFall, J. S., Johnson, G. A., et al., 1990. Observation of a waterdepletion region surrounding loblooly pine roots by magneticresonance imaging. Proceedings of the National Academy of Sci-ences of the United States of America, 87, 1203–1207.

Menzel, M. I., Oros-Peusquens, A.M., et al., 2007. 1 H-NMR imag-ing and relaxation mapping – a tool to distinguish the geograph-ical origin of German white asparagus? Journal of PlantNutrition and Soil Science, 170, 24–38.

Mohnke, O., and Yaramanci, U., 2008. Pore size distributionsand hydraulic conductivities of rocks derived from MagneticResonance Sounding relaxation data using multi-exponentialdecay time inversion. Journal of Applied Geophysics, 66(3–4),73–81.

Nestle, N., Baumann, T., et al., 2002. Magnetic resonance imagingin environmental science. Environmental Science & Technology,36(7), 154A–160A.

Oswald, S. E., Spiegel, M. A., et al., 2007. Three-dimensionalsaltwater-freshwater fingering in porous media: contrast agentMRI as basis for numerical simulations. Magnetic ResonanceImaging, 25, 537–540.

Paetzold, R. F., Matzkanin, G. A., et al., 1985. Surface Soil-WaterContent Measurement Using Pulsed Nuclear Magnetic-Resonance Techniques. Soil Science Society of America Journal,49(3), 537–540.

Pohlmeier, A., Oros-Peusquens, A. M., et al., 2007. Investigation ofwater content and dynamics of a Ricinus root system in unsatu-rated sand by means of SPRITE and CISS: correlation of rootarchitecture and water content change. Magnetic ResonanceImaging, 25, 579–580.

Pohlmeier, A., Oros-Peusquens, A. M., et al., 2008. Changes in SoilWater Content Resulting from Ricinus Root Uptake Monitored

by Magnetic Resonance Imaging. Vadose Zone Journal, 7,1010–1017.

Pohlmeier, A., Vergeldt, F., et al., 2010. MRI in Soils: Determina-tion of water content changes due to root water uptake by meansof Multi-Slice-Multi-Echo sequence (MSME). The Open Mag-netic Resonance Journal, 3, 39–47.

Raich, H., and Blümler, P., 2004. Design and Construction ofa Dipolar Halbach Array with an Homogeneous Field from N *8 Identical Bar-Magnets - NMR-Mandhalas-. Conc. Magn.Reson. B Magn. Reson. Eng., 23B, 16–25.

Roy, J., and Lubczynski, M. W., 2005. MRS multi exponentialdecay analysis: aquifer pore size distribution and vadose zonecharacterization. Near Surface Geophysics, 3(4), 287–298.

Scheenen, T. W. J., Vergeldt, F. J., et al., 2001. Microscopic imagingof slow flow and diffusion: a pulsed field gradient stimulatedecho sequence combined with turbo spin echo imaging. Journalof Magnetic Resonance, 151(1), 94–100.

Segal, E., Kushnir, T., et al., 2008. Water uptake and the hydraulicsof the root hair rhizosphere.Vadose Zone Journal, 7, 1024–1037.

Votrubova, J., Sanda, M., et al., 2000. The relationships betweenMR parameters and the content of water in packed samples oftwo soils. Geoderma, 95(3–4), 267–282.

Votrubova, J., Cislerova, M., et al., 2003. Recurrent ponded infiltra-tion into structured soil: A magnetic resonance imaging study.Water Resources Research, 39(12), 1371.

Yaramanci, U., Legchenko, A., et al., 2008. Magnetic ResonanceSounding Special Issue of Journal of Applied Geophysics,2008. Journal of Applied Geophysics, 66(3–4), 71–72.

Cross-referencesAgrophysical Objects (Soils, Plants, Agricultural Products, andFoods)Agrophysics: Physics Applied to AgricultureImage Analysis in AgrophysicsInfiltration in SoilsMagnetic Properties of SoilsNondestructive Measurements in SoilNoninvasive Quantification of 3D Pore Space Structures in SoilsPlant Roots and Soil StructurePlant–Soil Interactions, ModelingPore Size DistributionProton Nuclear Magnetic Resonance (NMR) Relaxometry in SoilScienceRoot Water Uptake: Toward 3-D Functional ApproachesSoil Hydraulic Properties Affecting Root Water UptakeSoil–Plant–Atmosphere ContinuumSoil Water FlowSolute Transport in Soils

MAGNETIC TREATMENT OF IRRIGATION WATER,EFFECTS ON CROPS

Basant Maheshwari, Harsharn GrewalSchool of Natural Sciences, Hawkesbury Campus,Building H3, University ofWestern Sydney, Penrith SouthDC, NSW, Australia

DefinitionWater productivity: Crop yield per unit volume of waterused. Definitions of water productivity differ based on thecontext. For example, from the point of view of growing

444 MAGNETIC TREATMENT OF IRRIGATION WATER, EFFECTS ON CROPS

crops, obtaining more kilograms per unit of transpiration isthemain aspect productivity of water. In the case of regionalor catchment scale, the focus of water productivity is thevalue derived from the use of water for purposes such ascrops, forests, fisheries, ecosystems, and other uses.Electromagnetic field: A field of force associated witha moving electric charge equivalent to an electric fieldand a magnetic field at right angles to each other and tothe direction of propagation.Magnetic treatment: Exposure of material such as water toa magnetic field for a short duration (a few seconds) orlonger to possibly change some of its properties for bene-ficial effects.

IntroductionThe total volume of fresh water available is limited but thedemand for water is growing at a rapid pace. In this con-text there has been a growing interest to use water moreefficiently and effectively, increase reuse of effluent,develop ways to use lower quality water and improve theoverall productivity of water used for irrigation. Thismeans we need to develop ways that will increase the pro-ductivity and sustainability of water used for irrigation.One of the ways by which we can reduce the total waterused for irrigation is to employ practices that improvecrop yield per unit volume of water used (i.e., water pro-ductivity). There have been claims made that the magnetictreatment of irrigation water can improve water productiv-ity. If those claims are valid there is scope for magnetictreatment of water to save water and assist in coping withthe future water scarcity.

Effects of magnetic fieldThere is very little study reported, with valid scientificexperiments, on the effects of magnetic treatment of wateron crop yield and water productivity. However, there havebeen some closely related studies that report on the effectsof magnetic field on seed germination, plant physiology,and overall plant growth and, as such, those studies mayindirectly help to understand the role of magnetictreatment of irrigationwater and plant growth. For example,Lin and Yotvat (1990) reported an increase in water produc-tivity in both livestock and crop farming withmagnetically treated water. Some studies have shown thatthere is an increase in the number of flowers, earliness andtotal fruit yield of strawberry and tomatoes by usingmagnetic fields (Esitken and Turan, 2004; Danilov et al.,1994). An increase in nutrient uptake bymagnetic treatmentwas also observed in tomatoes by Duarte Diaz et al. (1997).

External electric and magnetic fields influence both theactivation of ions and polarization of dipoles in living cells(Johnson and Guy, 1972; Moon and Chung, 2000). Elec-tromagnetic fields (EMFs) can alter the plasma membranestructure and function (Paradisi et al., 1993; Blank, 1995).Goodman et al. (1983) reported an alteration of the level ofsome mRNA after exposure to EMFs. Amaya et al. (1996)and Podleśny et al. (2004) have shown that an optimal

external electromagnetic field accelerates the plantgrowth, especially seed germination percentage and speedof emergence.

Some studies focused on how static magnetic fieldaffect chlorophyll and phytohormone levels in someplants. Turker et al. (2007) observed that chlorophyll andphytohormone levels decreased when static magneticfield, parallel either to gravity force (field-down) or anti-parallel (field-up) was applied to maize plants. However,chlorophyll concentration increased in sunflowers byapplying magnetic field in either direction.

Magnetic fields can also influence the root growth ofsome plant species (Belyavskaya, 2001, 2004; Murajiet al., 1992, 1998; Turker et al., 2007). In the case of maize(Zea mays) the exposure of maize seedlings to 5 mT mag-netic fields at alternating frequencies of 40–160 Hzimproved root growth (Muraji et al., 1992). However,there was a reduction in primary root growth of maizeplants grown in a magnetic field alternating at 240–320 Hz. The highest growth rate of maize roots wasachieved in a magnetic field of 5 mTat 10 Hz. Turker et al.(2007) reported an inhibitory effect of static magnetic fieldon root dry weight of maize plants but there wasa beneficial effect of magnetic fields on root dry weightof sunflower plants.

Belyavskaya (2004) and Turker et al. (2007) reportedthat a weak magnetic field has an inhibitory effect on thegrowth of primary roots during early growth. The prolifer-ative activity and cell reproduction in meristem in plantroots are reduced in weak magnetic fields (Belyavskaya,2004). The cell reproductive cycle slows down due tothe expansion of the G1 phase in many plant species andthe G2 phase in flax and lentil roots. There was a decreasein the functional activity of genomes at early pre-replicateperiod in plant cells exposed to weak magnetic fields. Ingeneral, these studies conclude that weak magnetic fieldscause intensification of protein synthesis and disintegra-tion in plant roots.

Impact of heat stress at 40�C, 42�C, and 45�C for40 min in cress seedlings (Lepidium sativum) was reducedby exposing plants to extremely low-frequency (ELF)mag-netic field (50 Hz, 100 mT) (Ruzic and Jerman, 2002). Mag-netic fields act on the same cellular metabolic pathways astemperature stress and as such the study suggests that mag-netic fields act as a protective factor against heat stress.

Magnetic treatment and seed germinationMagnetic treatment of seed or water used for germinationcan influence germination and seedling emergence. Reinaet al. (2001) reported an increase in germination percent-ages of lettuce seeds by treating these with 10mTstationarymagnetic fields. They reported that magnetic fields resultedin an increase in water absorption rate of lettuce seeds andmay have contributed to increased germination percent-ages. Some studies reported that the magnetic exposure ofseeds, viz., cereals and beans, prior to sowing can improvegermination rate and early growth (Pittman 1963a, b;

MAGNETIC TREATMENT OF IRRIGATION WATER, EFFECTS ON CROPS 445

Pittman andAnstey, 1967). Similarly, the application of sta-tionary magnetic fields before sowing had a significantincrease in germination rates and seedling vigor in ground-nut, onion, and rice seeds (Vakharia et al., 1991; Alexanderand Doijode, 1995). The exposure of broad bean seeds byPodleśny et al. (2004) to variable magnetic strengths beforesowing showed some beneficial effects on seed germinationand emergence. In particular, they found that seedling emer-gence was more regular after the use of the magnetic treat-ment and occurred 2–3 days earlier in comparison toseedlings in the control treatment. In tomatoes, De Souzaet al. (2006) observed that the magnetically treated tomatoseeds improved the leaf area, leaf dry weight, and yield oftomato crop under field conditions.

The beneficial effects of magnetically treated irrigationwater have also been reported on germination percentagesof seeds. For example, an increase in germination of Pinustropicalis seeds from 43% in the control to 81%with mag-netically treated water was observed by Morejon et al.(2007). For tomatoes, pepper, cucumber and wheat seedsHilal and Hilal (2000) reported that germination and seed-ling emergence was improved when magnetically treatedwater and seeds were used. In particular, germination ofpepper seeds was higher with magnetically treated seedswhen compared with magnetically treated irrigation waterand cucumber seeds had the highest germination percent-age when both irrigation water and seeds were magneti-cally treated.

How do magnetic fields influence?The mechanisms that influence plant growth and seed ger-mination through magnetic treatment are not well under-stood. Some beneficial effects of the treatment could berelated to the “gas bubble-water interface” (Vallée et al.,2005). Furthermore, these effects may be related to mech-anisms such as intramolecular and intra-ionic interactions,effects of Lorentz forces, dissolution of contaminants andinterfacial effects (Baker and Simon, 1996). The changesin hydrogen bonding and increased mobility of Na+ andCl� ions with exposure of irrigation water to magneticfields may also play some role in the plant growth and seedgermination (Chang and Weng, 2008). It is also suggestedthat the magnetic treatment of water may result in changesin physical and chemical properties of water such ashydrogen bonding, polarity, surface tension, conductivity,pH, refractive index and solubility of salts (Smikhina,1981; Srebrenik et al., 1993; Amiri and Dadkhah, 2006;Otsuka and Ozeki, 2006; Chang and Weng, 2008).

Magnetic treatment and water productivityMaheshwari and Grewal (2009) investigated the effects ofmagnetically treated potable water, recycled water andsaline water on crop yields and water productivity undercontrolled environmental conditions in a glasshouse. Themain aim of the study was to examine the impact of mag-netic treatment of different water sources on water produc-tivity and yield of snow peas, celery, and peas.

The study has provided some preliminary results onhow the magnetic treatment influences the key parametersof (1) water – pH and EC; (2) crop – yield, water produc-tivity, total crop water use and crop nutrient composition;and (3) soil – pH, EC, and available N, P, and K. Statisticalanalysis of the data indicated that the effects of the mag-netic treatment varied with crop type and the source ofwater. There was no statistically significant effect of mag-netic treatment on the total water used by the crop duringthe growing season in any of the three crops. However,the magnetic treatment of water tends to increase (statisti-cally significant) crop yield (fresh weight) and water pro-ductivity (kg of fresh or dry produce per kL of water used)of celery and snow peas. On the other hand, the magnetictreatment had no significant effect on both crop yield andwater productivity for peas.

In general, the results obtained during this preliminarystudy on the use of magnetically treated water on celeryand snow peas are interesting but the effect of the mag-netic treatment on crop yield and water productivity wasvariable and occurred under some set of conditions andnot in others. Therefore, from the glasshouse experimentaldata, it is difficult to make any recommendation with cer-tainty as to the effectiveness of the magnetic treatmentunder field conditions.

SummaryThe past studies reveal that the magnetic field or treatmentcan affect plant growth and other related parameters. Sim-ilarly, the past studies have indicated that there are somebeneficial effects of magnetic treatment on seed germina-tion and seedling emergence. Nevertheless, we have noclear understanding yet as to the mechanisms behind theseeffects on plant growth, water productivity, and thechanges magnetic treatment brings about in nutritionalaspects of seed germination and seedling growth.

To assess the potential of themagnetic treatment for prac-tical applications, we need further testing under field condi-tions to clearly understand and demonstrate the beneficialeffects of the magnetically treated irrigation water for cropproduction under real-world situations. Further research isalso warranted to understand how the magnetic treatmentaffects crop and soil parameters and therefore soil, cropand water quality conditions under which the treatment willbe effective to provide water productivity gains.

BibliographyAlexander, M. P., and Doijode, S. D., 1995. Electromagnetic field:

a novel tool to increase germination and seedling vigour of con-served onion (Allium cepa L.) and rice (Oryza sativa L.) seedswith low viability. Plant Genetic Resources Newsletter, 104,1–5.

Amaya, J. M., Carbonell, M. V., Martinez, E., and Raya, A., 1996.Effects of stationary magnetic fields on germination and growthof seeds. Horticultural Abstracts, 68, 1363.

Amiri, M. C., and Dadkhah, A. A., 2006. On reduction in the sur-face tension of water due to magnetic treatment. Colloids andSurfaces, A: Physicochemical and Engineering Aspects, 278,252–255.

446 MAGNETIC TREATMENT OF IRRIGATION WATER, EFFECTS ON CROPS

Baker, J. S., and Simon, J. J., 1996. Magnetic amelioration of scaleformation. Water Research, 30, 247–260.

Belyavskaya, N. A., 2001. Ultrastructure and calcium balance inmeristem cells of pea roots exposed to extremely low magneticfields. Advances in Space Research, 28, 645–650.

Belyavskaya, N. A., 2004. Biological effects due to weak magneticfield on plants. Advances in Space Research, 34, 1566–1574.

Blank, M., 1995. Biological effects of environmental electromag-netic fields: molecular mechanisms. BioSystems, 35, 175–178.

Chang, K. T., and Weng, C. I., 2008. An investigation into structureof aqueous NaCl electrolyte solutions under magnetic fields.Computational Materials Science, 43, 1048–1055.

Danilov, V., Bas, T., Eltez, M., and Rizakulyeva, A., 1994. Artificialmagnetic field effects on yield and quality of tomatoes. ActaHorticulturae, 366, 279–285.

De Souza, A., Garci, D., Sueiro, L., Gilart, F., Porras, E., and Licea,L., 2006. Pre-sowing magnetic treatments of tomato seedsincrease the growth and yield of plants. Bioelectromagnetics,27, 247–257.

Duarte Diaz, C. E., Riquenes, J. A., Sotolongo, B., Portuondo,M. A., Quintana, E. O., and Perez, R., 1997. Effects of magnetictreatment of irrigation water on the tomato crop. HorticulturalAbstracts, 69, 494.

Esitken, A., and Turan, M., 2004. Alternating magnetic field effectson yield and plant nutrient element composition of strawberry(Fragaria x ananassa cv. camarosa). Acta AgriculturaeScandinavica. Section B: Soil and Plant Science, 54, 135–139.

Goodman, R., Basset, C. A., and Henderson, A., 1983. Pulsing elec-tromagnetic fields induce cellular transcription. Science, 220,1283–1285.

Hilal, M. H., and Hilal, M. M., 2000. Application of magnetic tech-nologies in desert agriculture. 1 – Seed germination and seedlingemergence of some crops in a saline calcareous soil. EgyptianJournal of Soil Science, 40, 413–422.

Johnson, C. C., and Guy, A. W., 1972. Non-ionizing electrostaticwave effects in biological materials and systems. Proceedingsof Institute of Electrical and Electronics Engineers, 60, 692–718.

Lin, I. J., and Yotvat, J., 1990. Exposure of irrigation and drinkingwater to a magnetic field with controlled power and direction.Journal of Magnetism and Magnetic Materials, 83, 525–526.

Maheshwari, B. L., and Grewal, H. S., 2009. Magnetic treatment ofirrigation water: its effects on vegetable crop yield and water pro-ductivity. Agricultural Water Management, 96, 1229–1236.

Moon, J., and Chung, H., 2000. Acceleration of germination oftomato seeds by applying AC electric and magnetic fields. Jour-nal of Electrostatics, 48, 103–114.

Morejon, L. P., Castro Palacio, J. C., Velazquez Abad, L. G., andGovea, A. P., 2007. Simulation of pinus tropicalis M. seeds bymagnetically treated water. International Agrophysics, 21,173–177.

Muraji, M., Nishimura, M., Tatebe, W., and Fujii, T., 1992. Effect ofalternating magnetic field on the growth of the primary root ofcorn. Institute of Electrical and Electronics Engineers Transac-tion of Magnetics, 28, 1996–2000.

Muraji, M., Asai, T., and Tatebe, W., 1998. Primary root growth rateof Zea mays seedlings grown in an alternating magnetic field ofdifferent frequencies. Biochemistry and Bioenergetics, 44,271–273.

Otsuka, I., and Ozeki, S., 2006. Does magnetic treatment of waterchange its properties? The Journal of Physical Chemistry, 110,1509–1512.

Paradisi, S., Donelli, G., Santini,M. T., Straface, E., andMalorni,W.,1993. A 50-Hz magnetic field induces structural and biophysicalchanges in membranes. Bioelectromagnetics, 14, 247–255.

Pittman, U. J., 1963a. Magnetism and plant growth. 1. Effect on ger-mination and early growth of cereal seeds. Canadian Journal ofPlant Science, 43, 512–518.

Pittman, U. J., 1963b. Magnetism and plant growth. III. Effect ongermination and early growth of corn and beans.Canadian Jour-nal of Plant Science, 45, 549–555.

Pittman, U. J., and Anstey, T. H., 1967. Magnetic treatment of seedorientation of a single harvest snap bean (Phaseolus vulgaris L).Proceedings of American Society of Horticultural Science, 91,310–314.

Podleśny, J., Pietruszewski, S., and Podleśna, A., 2004. Efficiencyof the magnetic treatment of broad bean seeds cultivated underexperimental plot conditions. International Agrophysics, 18,65–71.

Reina, F. G., Pascual, L. A., and Fundora, I. A., 2001. Influence ofa stationary magnetic field on water relations in lettuce seeds.Part II: experimental results. Bioelectromagnetics, 22, 596–602.

Ruzic, R., and Jerman, I., 2002. Weak magnetic field decreases heatstress in cress seedlings. Electromagnetic Biology and Medicine,21, 69–80.

Smikhina, L. P., 1981. Changes in refractive index of water on mag-netic treatment. Colloid Journal, 2, 401–404.

Srebrenik, S., Nadiv, S., and Lin, L. J., 1993. Magnetic treatment ofwater- a theoretical quantum model. Magnetic and ElectricalSeparation, 5, 71–91.

Turker, M., Temirci, C., Battal, P., and Erez, M. E., 2007. Theeffects of an artificial and static magnetic field on plant growth,chlorophyll and phytohormone levels in maize and sunflowerplants. Phyton- Annales Rei Botanicae, 46(2), 271–284.

Vakharia, D. N., Davariya, R. L., and Parameswaran, M., 1991.Influence of magnetic treatment on groundnut yield and yieldattributes. Indian Journal of Plant Physiology, 34, 131–136.

Vallée, P., Lafait, J., Mentré, P., Monod, M. O., and Thomas, Y.,2005. Effects of pulsed low frequency electromagnetic fieldson water using photoluminescence spectroscopy: role of bub-ble/water interface. The Journal of Chemical Physics, 122,114513–8.

Cross-referencesIrrigation with Treated Wastewater, Effects on Soil StructureMagnetic Properties of SoilsPhysics of Plant NutritionPlant Roots and Soil StructurePlant–Soil Interactions, ModelingPlant WellnessWater Use Efficiency in Agriculture: Opportunities for Improvement

MANAGEMENT EFFECTS ON SOIL PROPERTIESAND FUNCTIONS

Rainer HornInstitute for Plant Nutrition and Soil Science,Christian-Albrechts-University zu Kiel, Kiel,Germany

DefinitionThe introduction and application of agricultural and for-estry machinery often result in severe soil compactionand soil deformation with intense decrease in total porevolume, alterations in the pore-size distribution, and espe-cially in their functions concerning gas, water and heattransfer as well as altered accessibility of chemical adsorp-tion sites of clay minerals, organic substances, and furthervariable exchange places.

MANAGEMENT EFFECTS ON SOIL PROPERTIES AND FUNCTIONS 447

IntroductionSoils as three-phase systems fulfill not only the archivefunction (historical memory of former properties, cli-mates, management practices, land use, etc.), they areessentially needed for plant growth and yield, as reservoirfor microbes and they are also responsible for filtering andbuffering of soil water in order to get clean drinking- andgroundwater, they contribute to the transformation underin situ conditions and serve as the resource for raw mate-rial. In the following, the process of soil deformation willbe shortly summarized in order to derive the interactionsbetween soil structure and chemical as well as physicaland biological properties, which are affected by soil defor-mation. Finally, some conclusions for a sustainable landuse and soil management will be given.

Soils: their functions in view of the existing soilprotection laws or recommendationsSoils as nonrenewable goods have to be protected andshould be only used according to their properties. TheEuropean Soil Charta 1972 of the European Assemblywas the first, which did underline and support this ideaand which in 1998 became the nucleus for the GermanSoil Protection Law. This law was the first and until nowthe only one in Europe while, e.g., the European SoilFramework Directive (2006) is still under a very contro-versial debate. However, there is an urgent need to specifymore in detail the requests and limitations but also toquantify the limits of an unprevented or unhindered landuse. It has to be pointed out that for a sustainable soil landuse their physical, chemical, and biological soil functionsmust be related to the intensity and function values andhave to include the dependent recommendations in orderto prevent any irreversible soil degradation.

If we consider the properties for arable horticulture,landscape planning, and in forestry soils, they all requireprimarily a sufficiently rigid pore system, which guaran-tees the water, gas and heat exchange, nutrient transportand adsorption as well as an optimal rootability, whichalso includes a sufficient microbial activity and composi-tion in order to also decompose the plant debris. All theserequests must be included in a quantitative way in the Pro-tection law but there is still an urgent need to specify these“optimal” properties. However, it is very difficult for theland user to forecast and to relate their own farming man-agement practices to coming weather conditions, e.g., thestorage capacity for water and nutrients in the topsoil suf-ficient to grow a good crop yield during the following sea-son under dry conditions or are the soils permeableenough to drain off the access rain water and to reaeratethe root zone quick enough in order to avoid decliningredox potential values and the accumulation of anoxicgases in the pores. Thus, the major challenges areinterlinked between soil and weather conditions, whichrequest a more intense analysis of the effect of soil struc-ture on the nutrient, gas, and water fluxes in anthropogen-ically managed soils under various land uses.

Processes of aggregate formation and persistenceSoils containingmore than 12% clay (particle size< 2 mm)or even pure sandy soils with some salts tend to formaggregates. Usually the process occurs when soils dryand swell, and it is further enhanced by biological activity.Aggregates may show great variation in size from crumbs(diameter < 2 mm) to polyhedres or subangular blocks of0.005–0.02 m, or even to prisms or columns of more than0.1 m. During the first period of shrinkage, mineral parti-cles are pulled together by capillary forces, which increasethe number of points of contact and result in a higher bulkdensity. The initial aggregates always have rectangular-shaped edges because, under these conditions, stressrelease would occur perpendicular to the initial crack andstress would remain parallel to the crack (strain-inducedfracturing). However, due to the increased mechanicalstrength the particle mobility declines and results in theformation of nonrectangular shear plains as the followingcrack generation. They are created after repeated swellingand shrinking processes and result in fractures in whichthe value of the angle of internal friction determines thedeviation from 90� (Or and Ghezzehei, 2002). In newlyformed aggregates, the number of contact points dependson the range of water potential and on the distribution ofparticle sizes as well as on their mobility (i.e., state of dis-persion, flocculation, and cementation). Soil shrinkage,including crack formation, increases bulk density ofaggregates. The increase in bulk density with the initialwetting and drying of the soil permits the aggregates towithstand structural collapse. The increase of the strengthof single aggregates is further enhanced by a particlerearrangement, if the soil is nearly saturated with waterincreasing the mobility of clay particles due to dispersionand greater menisci forces of water (Horn and Dexter,1989). Subsequent drying causes enhanced adhesion bycapillary forces, which lead to greater cohesion as mineralparticles are brought into contact following theevaporatory losses of the capillary water. Thus, thestrength of the bigger, i.e., initial aggregates is increaseddue to a particle mobilization and results both in smallerand stronger aggregates with even a smaller aggregatebulk density. The strongest aggregate type under thisaspect is the spherical shape, which has reached the stageof the smallest free entropy. Therefore, aggregate strengthdepends on (1) capillary forces, (2) intensity of shrinkage(normal/residual), (3) number of swelling and shrinkagecycles, i.e., the shrinkage/swelling history, (4) mineralparticle mobility (i.e., rearrangement of particles toachieve arrangements of lowest free energy), and (5) bond-ing energy between particles in/or between aggregates orin the bulk soil. Generally, aggregates persist as long asthe soil strength (defined by the failure line ofMohr–Coulomb) is higher than the given load or shrink-age forces soils remain rigid. If however, additionalkinetic energy (which even is more efficient in combina-tion with accessible water) is applied, aggregate deteriora-tion and homogenization occur. Thus, a complete

448 MANAGEMENT EFFECTS ON SOIL PROPERTIES AND FUNCTIONS

homogenization of the soil structure due to shearing and/or puddling takes place if kneading (expressed as octahe-dral shear stresses and mean normal stresses) exceeds theaggregate and structure strength. After a mostly completehomogenization normal shrinkage, processes restart again(Horn et al., 1994). Consequently, a weaker soil structureand finally a pasty structure can be defined by very smallcohesion and angle of internal friction values (Horn,1976; Janssen et al., 2006). Thus, the determination of soiland/or aggregate strength has always to be subdivided into(1) mechanically, hydraulically, or chemically prestressedand (2) virgin conditions, which also ultimately affect thepredictability of physical properties. These general ideashave been described in greater detail by Horn andBaumgartl (2002), Groenevelt and Grant (2002), Grantet al. (2002), and Peng and Horn (2005).

In conclusion, it has to be stated, that aggregate forma-tions as well as changes in aggregate strength are directlyrelated also to tillage systems. Conventional tillageespecially of the A-horizon annually includes plowing,chiseling, and the seedbed preparation apart from multiplewheeling events depending on the crop managementrequirements, which ends in mostly homogenized struc-ture conditions annually. Thus, a more complete aggregateformation will not take place. Conservational tillagesystems on the other hand causes less disturbance andallow a more complete rearrangement of particles andstrengthening of the structure system if preserved through-out several years.

Soil structure and soil use: how to sustain therequired and natural soil and site-specificfunctionsDuring the last 4 decades, not only the mass of the agricul-tural and forestry machines but also the frequency ofwheeling has increased intensely and resulted ina compression and soil deformation status, which can besuboptimal concerning plant growth and crop yield as wellas the uncertainty of getting a predictable yield. At pre-sent, the maximum mass of machines exceeds by far60 Mg and therefore also enhance the probability of sub-soil compaction and long-term soil degradation. Not onlyin agriculture but also in forestry, such enlarged machinesare used for tree harvesting and clear cutting and results inan intense subsoil degradation due to shear and vibrationinduced soil deformation especially if the soil water con-tent in the subsoil is high and the internal soil strength verylow (Horn et al., 2007).

Soil processes like the formation of a platy structure,deterioration of a continuous pore system are thereforesigns of an intense soil degradation, which also coincideswith an increased anisotropy of pore functions and maycause an increased lateral soil movement (= soil water ero-sion). For more details, see Soane and van Ouwerkerk(1994), Pagliai and Jones (2002), Ehlers et al. (2003),Lipiec and Hatano (2003), and Horn et al. (2005, 2006).

Consequences of soil deformation on changesin soil functionsCation adsorption capacity, intensity, andaccessibilitySoil aggregates can be classified by a certain accessibilityof the exchange places for cations, which in comparisonwith that one of the homogenized material may beintensely reduced. We have to differentiate between thecapacity and intensity properties. In homogenized soils,capacity (= potential) and intensity (= actual) parametersare identical, which holds true especially for the seedbedwhere fertilized ions are mostly adsorbed at the exchangeplaces of the soil particles. For soil horizons witha prismatic structure, we can assume a reduction of up to15% from the potential properties, while soil horizonswith blocky or subangular blocky structure will even havea reduction of up to 50–80%.

In this context also soil texture and bulk density of thebulk soil and of the single aggregates further affect theseactual conditions. Soils with a crumbly structure haveagain an improved accessibility due to a macroscopichomogenization of the aggregates themselves by themicrobial activity as can be also derived from their particleand organic substance arrangement in the bulk soil. Thus,we can assume a nearly 90% accessibility of the cationexchange capacity values under these conditions. If, how-ever, soils have a platy structure, the intensity (= actual)properties are as small as 30% of the capacity values dueto the compressed pores in between singles particleswithin the plats and because of correspondingly retardedion mass flow and diffusion within the plats. Thus, platsprovide the greatest differences between the capacity andintensity properties and therefore also the most reducedaccessibility of the exchange places. These differences canalso be derived from the relationship between the exchange-able cations and the actual hydraulic conductivity, whichis a measure of the effect of preferential flow processes.Compared to the theoretical CEC of 140 mmol kg�1 soil,the actual values were the smaller the higher was thehydraulic conductivity (Figure 1). The drier the soil thesmaller the water fluxes and the more retarded are theexchange processes the higher is the actual value but it willnot reach the maximum values under the dominatinghydraulic site properties (Figure 2).

Thus, neither the fertilized nutrients nor the natural ionswithin the soil can be fully accounted for a good yield, ifthe aeration, the accessibility, and nutrient availabilityare not provided.

Soil water and gasAn increased soil volume coincides with a reduced porevolume with the dominance of finer pores and less coarserones. Thus, the air capacity is reduced with increasing soildeformation but the changes in the pores, which containthe water available for plants depends on the appliedstresses as well as the texture and the bulk density.

MANAGEMENT EFFECTS ON SOIL PROPERTIES AND FUNCTIONS 449

The hydraulic conductivity as a water available for plantsdepends tensorial function in principle decreases withapplied stresses by many orders of magnitude while theunsaturated hydraulic conductivity is even increased ata given matric potential value (Figure 3).

Even more important is the increased relevance of theanisotropy of hydraulic functions. Mualem (1984), Tigges(2000), Doerner (2005), and Doerner and Horn (2009)documented the increasing effect of stress-and shear-affected horizontal anisotropy of the hydraulic and gaspermeability, which coincides not only with a retardedgas exchange and an increased proportion of, e.g., CO2

in the pores but which will also result in an enhanced lat-eral water flow and soil erosion. Figure 4 documents thestress-and strain-induced changes in hydraulic propertiesand functions.

Effect of soil management on gas compositionEach reduction in conducting coarse pores also result notonly in a prevented CO2 exchange to the atmosphere anda prevented O2 flux in the soil but will also lead to anoxicconditions in the soil, which will also affect the quality andquantity of the emitted gas (Glinski and Stepniewski,1985).

120

100

80

60

40

20

00.1 1 10 100

1 + a + kb

1,000

Hydraulic conductivity (cm/day)

Exc

hang

eabl

e ca

tions

(m

mol

c/kg

soi

l)

PercolationVolume Measurement Fit r 2 p

90 ml110 ml130 ml170 ml

0.8610.847

0.8510.974

<0.0001<0.0001<0.0001<0.0001

Model: S(k ) =SGBL

Management Effects on Soil Properties and Functions, Figure 1 Effect of soil structure on chemical exchange processes (takenfrom Hartmann et al., 1998).

Homogenized

Aggregated

160

140

120

100

80

60

40

20

0

Bas

e ca

tions

(m

mol

c/kg

soi

l)

0 hPa

0 hPa

–30 h

Pa

–60 h

Pa

–100

hPa

Management Effects on Soil Properties and Functions, Figure 2 Effect of predrying and of aggregation on the base cationadsorption in comparison to the homogenized conditions (Bt-horizon of a Luvisol derived from loess, blocky structure).

450 MANAGEMENT EFFECTS ON SOIL PROPERTIES AND FUNCTIONS

Aeration and gas exchange in soils always depend onthe microbial composition and activity, which dependingon the pH value and water saturation can lead to an enor-mous alteration of the gas composition, microbial activityand composition, and redox potential values. As an exam-ple of possible stress effects on redox potential changes independence of the internal soil strength (expressed as

precompression stress) Figure 5 informs stress-dependentEh value changes in an Ah horizon of a Cambisol. It canbe seen that stressing with up to 35 kPa followed bya stress release (0 kPa) at both depths always resulted ina complete recovery. If exceeding the internal soil strengthof approximately 42 kPa by a further increase to 47 kPaa significant and irreversible decline was detected withno recovery after stress release. Thus, all chemical ele-ments, which undergo a valence change, depending onthe actual Eh value at the given pH may be also mobilizedand translocated to the more aerated soil volumes.Amongst the Eh-sensitive elements are under moreun-aerated soil conditions, e.g., from the completelyaerated condition: NO3

– (to N2O or at last NH4+ ) or

CO2 (to CH4 in case of complete anoxic conditions)as well as Fe (3+ to 2+) or Mn (4+ to 2+ ). Thus, also thechemical compositions of the gas composition in the soilor that of the seepage water are affected.

Ball et al. (2000), e.g., described the effect of N2Oemission due to wheeling throughout the year and alsoincluded N-fertilization. In compacted soils, N2O emis-sion was significantly increased after fertilization andespecially if in addition rainfall had re-saturated the soil.Under zero compaction, no intense changes were to beseen, which can be explained by a higher water infiltrationrate and a quicker reaeration of the coarse and gas andwater-conducting pores. If we further consider the com-paction dependent reduction of CH4 consumption in soils,which is approximately 50%, we can also calculate theCO2 equivalents from CH4 consumption are texturedependent (Teepe et al., 2004): 47 kg ha�1 a (silty clayloam), 157 (silt), and 249 (sandy loam). Assuming that

–3

–4

–5

–6

–7

–8

–9

–10

–11

–120 –20 –40 –60 –80 –120–100

Matric potential (kPa)

0 kPa20 kPa50 kPa150 kPa400 kPa

log

Hyd

raul

ic c

ondu

ctiv

ity (

m/s

)

Management Effects on Soil Properties and Functions,Figure 3 Stress-dependent change of hydraulic properties:hydraulic conductivity as a function of matric potential.

Unsaturated hydr.conductivityincreaseddue to decline in pore diameter atdefined matric potential

Water fluxreducedaccording topore diameterdecrease

Rearrangementof particles and/or poretortuosity effects

Anisotropyof porefunctionsKhor>Kvert

Shear

Original CompactionCompaction

Settlement and shear

1 2 3

4 5

K= r2/8h

Management Effects on Soil Properties and Functions, Figure 4 Stress and strain effects on hydraulic properties.

MANAGEMENT EFFECTS ON SOIL PROPERTIES AND FUNCTIONS 451

only 6% of the area was compacted, the reduction of theCH4 consumption in terms of CO2 equivalents resultedin 4, 11, and 13 kg CO2 ha

�1 a�1. In forest soils, the spatialpattern of the depth dependent CO2 concentration revealsa significant increase directly below the wheel track,which fades off to the side and to the depth (Figure 6).

Based on the dataset of Stepniewska et al. (1997), a soiltexture-dependent resistivity values for a given redoxpotential value of 300 mV could be used to quantifythe resistance against Eh changes. If 300 mV irrespectiveof the stress applied at a given texture is still availableafter 5 days, we could classify the soil as rigid. If, on theother hand, within less than 3 days the critical value is fur-ther declined a more severe stress effect has to beconsidered.

Soil deformation and thermal propertiesDenser, less aerated, andmore water saturated soils are notonly less aerated but they are mostly cold due to the higherspecific heat capacity and thermal conductivity.

Additionally, radiation effects are reduced during coldnights but as a negative effect also the often expectedfreezing effect for structure reamelioration is furthermorereduced.

Under European climatic conditions, such soil amelio-ration is mostly restricted to the plowed A horizon butdoes not deeper down. Even if the subsoil gets frozen,the ice lense formation gain would primarily createa horizontal platy structure due to water volume expan-sion during freezing. Furthermore, we have to evaluatethe orientation of newly formed pores, which as soon asthey are mostly horizontally oriented would collapseduring the following restressing of the topsoil duringtrafficking.

Soil deformation and microbial activitySoil compaction causes a reduced accessibility for soilmicrobes, which may be partly not negative because thedamages caused by ingestion through collemboles andmol-lusk are reduced. The activity of earthworms (penetrationand formation of new burrows) depends on the internal soilstrength. They can apply up to 200 kPa lateral pressure inorder to translocate soil particles to the side and even100 kPa in radial direction. The less intense the tillage treat-ments the higher are the number of earthworms. Under con-servation tillage, more than 100 individuals were countedper meter square, while less than ten were available at thesame time under conventional tillage in a luvisol derivedfrom loess. However, even if we consider these 100 individ-uals/m² their activity results only in the realeviation of 30Mg/ha *a (for year), which is an enormous amount but lessthan 1% of the strain-affected soil compaction. McKenzieet al. (2009) documented that earthworms like Lumbricusand Allolobophora species can withstand high externalstresses, which is not the fact for mesofauna: acari andcollembolae, which will be squeezed under the wheelseven within the top 30-cm apart from the fact that soil com-paction and deformation result in a retarded aeration andaccumulation of CO2 or even CH4 in soil pores and hindersthe normal population growth.

450

400

350

300

250

200

150

100

50

00 0 35 470 012

Mechanical stress (kPa)

8 cm depth 12 cm depth

Red

ox p

oten

tial (

mV

)

Management Effects on Soil Properties and Functions,Figure 5 Redox potential values as a function of applied cyclicmechanical stress. Cambisol Ah horizon, pH: 5.2, �60 hPa (datataken from Horn, 1985).

0.0 1.0 2.0 3.0 4.0 5.0

10

10

20

30

0

Dep

th [c

m]

CO2 (Vol %)

TrackTrack edge

InterspaceControl

Management Effects on Soil Properties and Functions,Figure 6 CO2-concentration in the soil air at different depthsand distance from the wheel track due to the application ofa chain harvester (45 Mg) in the Black Forest. The wheeling wascarried out in 2000 (taken with courtesy from J. Schaffer, 2005).

452 MANAGEMENT EFFECTS ON SOIL PROPERTIES AND FUNCTIONS

Consequences of soil deformation on furtherenvironmental processesThe effects of soil compaction on plant growth are wide-spread. They depend not only on the intensity of soil com-paction but also on the plant properties themselves.

The closer to the soil surface and the thicker thecompacted layer is, the more pronounced negative effecton plant growth and yield. It is essential to analyzewhether or not the compacted soil layer is completelyimpermeable or there are still a few coarse pores, whichcan help to reach the deeper and less dense soil layersand to overcome such limitations in the topsoil. Oxygendeficiency results in a reduced root growth, increasedCO2 and ethylene concentration and the formation ofshort-chained fatty acids in the soil. Because the stresspropagation with depth in always three-dimensional, wealso have to analyze the effects in deeper depth becausecompacted areas at depth will be mostly surrounded byroots but not penetrated and cause a reduced availabilityof nutrients and also of oxygen at these depths.

Consequences of soil deformation on soil erosionIt is obvious that stress application always results in the for-mation of a platy structure and horizontally oriented coarsepores, which increase the horizontal hydraulic conductivityand result in an increased water and solute transport.Among others Fleige (2000) proofed that the soil loss dueto water erosion in the slightly sloppy glacial till region ofSchleswig Holstein increased ten times compared to noncompacted sites and also resulted in a downhill mass trans-port of more than 35 Mg ha�1 a�1 and the formation ofa colluvisol in the flatter region (onsite damage comparedto the even worse offsite effects).

Some remarks on crop yieldFrom the agricultural point of view, crop yield and the netbenefit are the main criterion. Thus, yield decline of up to35% in the first year after soil compaction are to be consid-ered as more than critical. But even if these reductions areonly rarely to be seen the uncertainty of getting sufficientyield continuously increased over the years. At present,more than 32 Mio ha of arable soils are irreversiblydegraded even only in Europe due to soil compaction theeffect is even continuously increasing in recent years. Theyield decline due to soil compaction for sugar beets can bederived from the smaller beet weight per ha also becausethe destruction of the beets during harvest is increased.

Van den Akker et al. (1999) mentioned that a singlewheeling with a 38 Mg sugar beet harvester results in anannual yield loss of 0.5%, which sums up to approxi-mately 100 Mio € a�1 loss calculated on the basis of500,000 ha in Europe.

If the machine mass of the sugar beet harvestersincreases with 5 Mg/10 years (as it was the fact in thepast), we have to even expect higher yield losses in thefuture. Concerning the yield losses of corn in the USA

we have to consider approximately 6%/year due to soilcompaction while corresponding losses of grain in the for-mer USSR results in 7–8% of the total yield; sugar beetsshowed a decline by 3% while corn yield was loweredby 4% due to soil compaction effects. Finally, in Germanythe often predicted wheat yield increase due to the benefi-cial climate, soil and genetic properties of the seeds underthe improved CO2 concentration in the atmosphere and thehigher temperature only resulted in 0.1 Mg ha�1a�1 inSchleswig Holstein while in Bavaria the yield remainedonly constant irrespective of all these improvementsbecause the proportion of the headland increased becauseof the larger machines, which require more space for turn-ing round in comparison to the total field area.

Effect of soil structure degradation on thelandscape hydrologyDeep drainage of rainwater requires permeable soils witha continuous and large pore system. As soon as the arablelandscapes remain compacted with a reduced waterinfiltrability (can be reduced up to three orders ofmagnitude)and storage capacity. Very pronounced are these effects dur-ing and after heavy rainstorm events and can result in anincreased surface water runoff also in combination witha pronounced nutrient and heavy metal leaching. If duringa rain storm most of the pores become water saturated butonly the coarse pores (which due to soil compaction arereduced in diameter and total amount) are still aerated butadditional rainwater will completely saturate the soil vol-ume, then the storage capacity is quickly filled and all theadditional rainwater will result in surface water runoff.

If we further assume that the natural saturated hydraulicconductivity of uncompacted soils in the loess area withapproximately 40% pore volume reaches 2.7 cm day�1

on average we can assume a corresponding water infiltra-tion of 2.7 cm day�1 at a hydraulic gradient of 1. Ifdue to soil compaction the hydraulic conductivity isreduced to 1 cm day�1 but the rainfall is the sameas before, 1.7 cm day�1 of water contribute to the surfacewater volume. If we now multiply this amount with thetotal landscape area, the small value of 17 l m�2 result in17,000 m3 km–2. Thus, the high floods in riverbeds likethe Rhine or Elbe in Germany can be easily also relatedto the negative effects of subsoil compaction. Soil slakingdue to the kinetic energy input of the water droplets, whichis the more pronounced, the higher is the initial water sat-uration in combination with the increased shear forces ofthe water suspensions result in an even higher soil deterio-ration and soil loss (for more detailed information see Vander Ploeg and Schweigert, 2001 and Gieska et al., 2003).

Protection measuresThe worldwide identical tendency of increased machinemasses at an identical contact area pressure, which isfar beyond the acceptable values for a sustainable landmanagement prevent a natural reamelioration of the

MANAGEMENT EFFECTS ON SOIL PROPERTIES AND FUNCTIONS 453

precompressed (= overcompacted) soils by increasing thepore volume and improved pore continuity. In order to gainbetter and safer growth conditions, these precompressed(= overcompacted) and deformed soils must be improvedby more site-adjusted soil management systems (conserva-tion instead of conventional tillage and a more intenseshrinkage due to water uptake by roots followed bya corresponding increased soil shrinkage and stabilization.The consequent application of traffic lanes for all field oper-ations would result in normal compacted seedbeds inbetween (like in horticulture) with highest yields, the conse-quent application of machines, which are light and havea contact area pressure smaller than the precompressionstress over depth throughout the years/decades will cer-tainly result in a newly arranged and stronger pore system,which guarantees a better aeration and quicker water infil-tration linked with a better rootability.

In the past, numerous approaches tried to improve theeffectivity of the machines and to reduce the soil deforma-tion risk but all attempts like reduced tire inflation pres-sure, belts, larger tires were not as successful as expectedbecause the main problem remains the still increasingmachine mass and/or the frequency of wheeling through-out the year. The final concluding picture, which under-lines the multidisciplinary effects of soil deformation onsite properties is shown in Figure 7.

SummaryThe rigidity of soils depends primarily on the internal par-ticle arrangement and thus on the rigidity of the soil struc-ture, which guarantee gas, water, heat, and nutrienttransport and exchange processes. If, however, the exter-nal stresses applied exceed the internal soil strength, notonly the pore system collapse and will be diminished involume and size but also the pore continuity and the acces-sibility of surfaces (e.g., for cation exchange) are nega-tively affected. Consequently, not only the plant growth,yield, and the predictability of them become moreundefined but also the emission of green house gasesincreases or flooding events will occur more often.

BibliographyBall, B. C., Horgan, G. W., and Parker, J. P., 2000. Short range spa-

tial variation of nitrous oxide fluxes in relation to compactionand straw residue. European Journal of Soil Science, 51,607–616.

Doerner, J., 2005. Anisotropie von Bodenstrukturen undPorenfunktionen in Böden und deren Auswirkung auf Transport-prozesse im gesättigten und ungesättigten Zustand. PhD thesis,H. 68, ISBN: 0933-680X.

Doerner, J., and Horn, R., 2009. Direction-dependent behavior ofhydraulic and mechanical properties in structured soils underconventional and conservation tillage. Soil and Tillage Research,102, 225–233.

Management Effects on Soil Properties and Functions, Figure 7 Summary of the effects of stress application on soil properties andfunctions (translated and slightly modified from van der Ploeg et al., 2006).

454 MANAGEMENT EFFECTS ON SOIL PROPERTIES AND FUNCTIONS

European Soil Framework Directive (2006) Proposal for a directiveof the European parliament and of the council establishinga framework for the protection of soil and amending directive.2004/35/EC, p. 30.

Ehlers, W., Schmidtke, K., and Rauber, R., 2003. Änderung derDichte undGefügefunktion südniedersächsischer Lössböden unterAckernutzung. Landnutzung und Landentwicklung, 44, 9–18.

Fleige, H., 2000. Ökonomische und ökologische Bewertung derBodenerosion am Beispiel einer JungmoränenlandschaftOstholsteins. PhD thesis, Schriftenreihe des Instituts fürPflanzenernährung und Bodenkunde, H. 53, ISBN: 0933-680X.

Gieska, M., van der Ploeg, R. R., Schweigert, P., and Pinter, N.,2003. Physikalische Bodendegradierung in der HildesheimerBörde und das Bundes-Bodenschutzgesetz. Berichte überLandwirtschaft, 81(4), 485–511.

Glinski, J., and Stepniewski, W., 1985. Soil Aeration and Its Rolefor Plants. Bocca Raton: CRC Press. 287 pp.

Grant, C. D., Groenevelt, P. H., and Bolt, G. H., 2002. On hydrostat-ics andmatristatics of swelling soils. In Raats, P. A. C. et al. (eds.),Environmental Mechanics: Water, Mass, and Energy Transfer inthe Biosphere. Geophysical Monograph 129, AGU, pp. 95–105.

Groenevelt, P. H., and Grant, C. D., 2002. Curvature of shrinkagelines in relation to the consistency and structure ofa Norwegian clay soil. Geoderma, 106, 235–247.

Hartmann, A., Gräsle, W., and Horn, R., 1998. Cation exchangeprocesses in structured soils at hydraulic properties. Soil andTillage Research, 47, 67–73.

Horn, R., 1976. Festigkeitsänderungen infolge von Aggregierung-sprozessen eines mesozoischen Tones. Diss. TU Hannover.

Horn, R., 1985. Die Bedeutung der Trittverdichtung durch Tiere aufphysikalische. Eigenschaften alpiner Böden.Z.f. Kulturtechniku.Flurbereinigung 26, 42–51.

Horn, R., and Dexter, A. R., 1989. Dynamics of soil aggregation inan irrigated desert loess. Soil and Tillage Research, 13, 253–266.

Horn, R., Taubner, H., Wuttke, M., and Baumgartl, T., 1994. Soilphysical properties and processes related to soil structure. Soiland Tillage Research, 30(Special Issue), 187–216.

Horn, R., Van Den Akker, J. J. H., and Arvidsson, J. (eds.), 2000.Subsoil compaction: distribution, processes and consequences.Advances in GeoEcology, 32, 480S, Reiskirchen.

Horn, R., and Baumgartl, T., 2002. Dynamic properties of soils. InWarrick, A. W. (ed.), Soil Physics Companion. Boca Raton:CRC, pp. 17–48.

Horn, R., Fleige, H., Peth, S., and Peng, X. H. (eds.), 2006. Soilmanagement for sustainability. Advances in GeoEcology, 38,497 pp. ISBN: 3-923381-52-2.

Horn, R., Fleige, H., Richter, F.-H., Czyz, E. A., Dexter, A., Diaz-Pereira, E., Dumitru, E., Enache, R., Rajkai, K., De La Rosa, D.,and Simota, C., 2005. SIDASS project part 5: prediction ofmechanical strength of arable soils and its effects on physicalproperties at various map scales. Soil and Tillage Research, 82,47–56.

Horn, R., Vossbrink, J., Peth, S., and Becker, S., 2007. Impact ofmodern forest vehicles on soil physical properties. Forest Ecol-ogy and Management, 248, 56–63.

Janssen, I., Peng, X., and Horn, R., 2006, Physical soil properties ofpaddy fields as a function of cultivation history and texture.Advances in GeoEcology, 38, 446–455, ISBN: 3-923381-52.

Lipiec, J., and Hatano, R., 2003. Quantification of compactioneffects on soil physical properties and crop growth. Geoderma,116, 107–136.

McKenzie, B. M., Kühner, S., MacKenzie, K., Peth, S., and Horn,R., 2009. Soil compaction by uniaxial loading and the survivalof the earthworm Aporrectodea caliginosa. Soil and TillageResearch, 104, 320–323.

Mualem, Y., 1984. Anisotropy of unsaturated soils. Soil ScienceSociety of America Journal, 48, 505–509.

Or, D., and Ghezzehei, T. A., 2002. Modeling post-tillage soil struc-tural dynamics: a review. Soil and Tillage Research, 64, 41–59.

Pagliai, M., and Jones, R. (eds.), 2002. Sustainable Land Manage-ment – Environmental Protection – A Soil Physical Approach.Advances in GeoEcology, 35, Catena, Reiskirchen. ISBN:3-923381-48-4.

Peng, X., and Horn, R., 2005. Modelling soil shrinkage curve acrossa wide range of soil types. Soil Science Society of America Jour-nal, 69, 584–592.

Schäffer, J., 2005. Bodenverformung und Wurzelraum. In Teuffelvon, K., Baumgarten, M., Hanewinkel, M., Konold, W.,Spiecker, H., Sauter, H.-U., von Wilpert, K. (Hrsg.) (eds.),Waldumbau für eine zukunftsorientierte Waldwirtschaft.Springer, Berlin, 422 S, pp. 345–361, ISBN: 3-540-23980-4.

Soane, B., and van Ouwerkerk, C. (eds.), 1994. Soil Compaction.Elsevier Verlag, Amsterdam, ISBN: 0-444-88286-3.

Stepniewska, Z., Stępniewski, W., Gliński, J., and Ostrowski, J.,1997. Atlas of Redox Properties of Arable Soils of Poland.Lublin-Falenty: Institute of Agrophysics PAS and Institute ofLand Reclamation and Grassland Farming.

Teepe, R., Brumme, R., Beese, F., and Ludwig, B., 2004. Nitrousoxide emission and methane consumption following compactionof forest soils. Soil Science Society of America Journal, 68,605–611.

Tigges, U., 2000. Untersuchungen zum mehrdimensionalenWassertransport unter besonderer Berücksichtigung derAnisotropie der hydraulischen Leitfähigkeit. PHD thesis,Schriftenreihe des Instituts für Pflanzenernährung undBodenkunde, H. 56, ISBN: 0933-680X.

Van der Ploeg, R. R., and Schweigert, P., 2001. ReduzierteBodenbearbeitung im Ackerbau – eine Chance fürWasserwirtschaft, Umwelt und Landwirtschaft.Wasserwirtschaft, 91, 9.

van der Ploeg, R. R., Ehlers, W., and Horn, R., 2006. Schwerlast aufdem Acker. Spektrum der Wissenschaft. August 2006, 80–88.

van den Akker, J. J. H., Arvidsson, J., and Horn, R., (eds.), 1999.Experiences with the impact and prevention in the EuropeanCommunity. Report 168, ISSN: 0927–4499.

Cross-referencesAgrophysical Objects (Soils, Plants, Agricultural Products, andFoods)Crop Responses to Soil Physical ConditionsEarthworms as Ecosystem EngineersPhysical Degradation of Soils, Risks and Threats

MAPPING OF SOIL PHYSICAL PROPERTIES

Jan Gliński1, Janusz Ostrowski21Institute of Agrophysics, Polish Academy of Sciences,Lublin, Poland2Institute of Technology and Life Sciences, Raszyn, Poland

DefinitionA map is a visual reflection of an area drawn to differentscales. To illustrate special purposes thematic maps aredesigned.

IntroductionSoil physical properties show great variability as wellin vertical as horizontal scale. Their significance for

MAPPING OF SOIL PHYSICAL PROPERTIES 455

agricultural and environmental purposes requires maps(Várallyay, 1989; Várallyay, 1994; Stępniewska et al.,1997; Ostrowski et al., 1998; Wösten, 2000; Walczaket al., 2002a; Duffera et al., 2007; Iqbal et al., 2005;Leenhardt et al., 2006; Santra et al., 2008).

One can distinguish two groups of soil physical proper-ties which are mapped:

� Current such as: water content, soil temperature� Not significantly dependent on time such as soil min-

eral composition, texture, specific surface area

Maps can reflect:

� Physical short-lasting properties such as soil moisture,soil temperature.

� Physical long-lasting processes such as soil erosion andsoil desertification. These maps are continuouslyrepeated.

� Soil physical properties based on their spatialvariability.

A variety of different techniques are used for construc-tion of maps of soil physical properties. They are basedon: delineation of soil units, which represent areas ofdetermined classes of soil physical property units, remotesensing, digital format, and spatial interpretation of point-based measurements of soil physical properties. The con-version of point data into continuous raster maps allowsthe survey data to be incorporated into computer and moreeasily used for decisionmakers (Taylor and Minasny,2006). Maps can be prepared on the basis of existing dataor on data coming from analyses of field soil samplesderived from representative places (areas) appointed inadvance. Most maps of soil physical properties concerntop soil layers, but for modeling of environmental pro-cesses mapping of deeper soil layers is also made(Stępniewska et al., 1997; Stawiński et al., 2000; Walczaket al., 2002a; Walczak et al., 2002b). Within soil taxo-nomic unit areas of some soil maps, the extent and distri-bution of physical properties such as texture andhydrology are distinguished (Białousz et al., 2005).Now, the general procedure applied in the preparation soilmaps is based on the estimation semivariogram parame-ters of soil properties using geostatistical tools and furtherapplying them to drawing maps using ordinary kriging(Usowicz et al., 1996; Santra et al., 2008).

Computer maps of physical properties of soilsWösten (2000) elaborated the map of total available waterfor plants on a European scale. In his work he based on thedatabase of HYdraulic PRoperties of European Soils(HYPRES) which contained information on about 4,500soil horizons representing 95 different soil types of 12European countries. Computerized manipulation of thisdatabase and its derived pedotransfer functions in combi-nation with the 1:1,000,000 scale Soil Map allowed forthe generation of the spatial distribution of soil wateravailability within Europe.

On the smaller, regional (country) scale application, theconstruction of maps of field water capacity, water con-ductivity, specific surface area, redox properties, and alsopotential denitrification in soils is presented on the exam-ple of Poland. These maps were prepared in cooperationbetween two institutes: the Institute of Agrophysics ofthe Polish Academy of Sciences in Lublin and the Instituteof Grassland Farming and Land Reclamation in Falenty.

Construction of mapsPolish soils (mostly mineral arable soils) are very muchdifferentiated in terms of their origin, connected withdifferent parent rocks (mostly sands, loams and silts ofglacial and post-glacial formations), location in the land-scape (lowlands, uplands, mountains), and influences ofclimate which has a transition character between the mar-itime and continental. On the surface of the land, theyform a mosaic of various soil taxonomic units which arecartographically presented on maps of different scales.According to the FAO classification they are Rendzinas,Phaeozems, Cambisols, Luvisols, Podzols, Fluvisols,Gleysols, and Histosols (Białousz et al., 2005). The prep-aration of maps of physical properties variability and dif-ferentiation of Polish arable soils was based on dataderived from the existing Bank of Soil Samples (Glińskiet al., 1992) stored in the Institute of Agrophysics inLublin. The samples represent 29 soil units of similarproperties concerning homogeneous areas (Table 1), dis-tinguished on the basis of works of the Institute of SoilScience and Plant Cultivation in Puławy (Witek, 1974).

The area of soil units ranges from 380 to 40,980 km2

in terms of their appearance in Poland. Each of the unit isrepresented by a number of soil profiles in proportionsappropriate to its area. Soil samples with disturbed andundisturbed structures were taken from topsoils (0–20 cm),upper subsoils (20–40 cm), with predominance of themineralization processes of the organic matter containedin it, and lower subsoils (below 40 cm) with natural fea-tures of the mineral soil substrate. This way a collectionwas created of soil samples coming from 1,000 soil pro-files localized throughout the country, representative ofthe variability and differentiation of the soil cover. Soilsamples collected in the Bank served for the acquisitionof point data from measurements and standardized stud-ies on physical properties of soils which were stored inthe Soil–Cartographic Database (Ostrowski et al., 1998)and then computer processed to generate color maps of:field water capacity, water hydraulic conductivity, spe-cific surface area, redox properties, and potential deni-trification in soils. The maps were obtained throughstatistical analysis of a set of soil units and of individualmeasurements of random soil samples. They were plot-ted using computer technology by combining the resultsof measurements with the content of the arable landmap on a scale of 1:1,000,000 and digitally recordedin the Soil–Cartographic Database. The base consistsof a set of files containing the contents of the soilmap and the software necessary for the storing and

456 MAPPING OF SOIL PHYSICAL PROPERTIES

creating of various sorts of derivative maps relating tothe soil cover (Walczak et al., 2002a). The basic file con-tains a map of soil units distribution. The main function ofthe processing system is grouping the soils into appropriateclasses and then applying an identical color to the contoursof all soils belonging to this class. As a result, a color topicalmap showing class distribution is created. In the mathemat-ical approach, the solution consists in a topological connec-tion of soil contours into one topical contour according tothe assumed function of subordination. When working outthe topical maps, a preliminary assumption is that a soilmap will be introduced to the computer for the automaticgeneration of topical data by the input of transformationtables of soil units into topical units (expressed for givenphysical soil property values). The basis for the generationof a computer map image is a procedure based on analgorithm:

< Jg1 2�On; Jg2 2 On . . . . . . ; Jgm 2 On >2 TJn;

where TJn – the nth topical unit, On – the nth soil evalua-tion, Jg1,......, Jgm – soil units belonging to the n evaluation.

Due to the character of the generalization of results, itwas established that maps would be generated on the scaleof 1:2,500,000, so that they can be sized to an A-3 format,ensuring appropriate clarity and legibility of the spatialstructure of soil feature carted. It was assumed that soil

contours with a determined property would be signaledwith a coloring into which other forms of land use wouldbe incorporated, i.e., grassland, larger housing areas(towns), forests, and inland water reservoirs. Since the dis-tribution of the above elements, supplementing the con-tent of the map gives sufficient information on thespatial location on the country scale, no topographic con-tent was introduced into the maps of physical soil proper-ties such as roads, river nets, names of towns, so as not todisrupt legibility and clarity of their content or diminishtheir informative value. The contours shown in the colormaps group mean physical properties values within theclasses of the property determined preserving also individ-ual deviations resulting from detailed studies.

Maps of hydro-physical properties of soilsThe hydro-physical properties of soils play a significantrole both in agriculture, through biomass production, andin natural environment through water quality. Theirknowledge is necessary for the interpretation and forecast-ing of nearly all physical, chemical, and biological pro-cesses which occur in soil environment. Two mainhydro-physical properties of soils weremapped, e.g., thoseconcerning field water capacity (Walczak et al., 2002b)and water conductivity (Walczak et al., 2002a). Using thestandard method (pressure chamber),the relation between

Mapping of Soil Physical Properties, Table 1 Soil units, their area in Poland, and number of profiles representing particular units

No. of soil unit Soil unit Area (km2) No. of soil profiles

1 Rendzic Leptosols – pure 1,900 82 Rendzic Leptosols – mixed 450 83 Haplic Phaeozems 2,360 104 Haplic Luvisols and Dystric Cambisols – loose sands 40,980 1695 Haplic Luvisols and Dystric Cambisols – light loamy sands 1,630 186 Haplic Luvisols and Eutric Cambisols – loamy sands 6,050 107 Eutric Cambisols – loamy sands over loams 267a Haplic Podsols – loamy sands 18,580 (7 + 7a) 478 Eutric Cambisols – light loams 348a Haplic Podzols – light loams 18,970 (8 + 8a) 439 Eutric Cambisols – medium loams 279a Haplic Podzols – medium loams 9,370 (9 + 9a) 1310 Eutric Cambisols and Haplic Luvisols – heavy loams 1,210 911 Eutric Cambisols and Haplic Luvisols – loams 5,700 1112 Haplic Luvisols and Distric Cambisols – gravels 880 2313 Eutric Cambisols – hydrogenic silts 813a Haplic Podzols – hydrogenic silts 7,980 (13 + 13a) 1314 Haplic Luvisols and Eutric Cambisols – loess 10,560 2315 Haplic Luvisols and Eutric Cambisols – clays 500 916 Haplic Luvisols and Eutric Cambisols – loams and skeleton loams 1,680 1017 Haplic Luvisols and Eutric Cambisols – loams 1,920 3418 Haplic Luvisols and Eutric Cambisols – clays 380 719 Haplic Luvisols and Eutric Cambisols – silt 2,010 1320 Eutric Fluvisols – loams and silts 5,050 1021 Distric Fluvisols – sands 2,110 1322 Eutric Fluvisols – light silty loam 700 723 Mollic Gleysols – dev. from loams and silts 6,600 3124 Mollic Gleysols – dev. from sands 3,940 2725 Terric Histosols 11,400 11

Total 162,320 672

MAPPING OF SOIL PHYSICAL PROPERTIES 457

soil water potential and water content within the rangefrom 0.1 kJ m�3 (pF 0) to 1,500 kJ m�3 (pF4.2) was deter-mined in soil samples representing the plough layers, sub-soils, and deeper layers. The results obtained weresubordinated to ten 5% intervals from <5 to 45–50%water capacity (cm cm�3) and used to construct color mapsfor topsoils, upper subsoils, and lower subsoils. An exam-ple of one of such maps is shown in Figure 1.

Water conductivity coefficient k was determined instandard 100-cm3 cylinders filled with undisturbed soilin which holes were drilled at heights of 1, 2.5, and 4 cmfrom the bottom and TDR humidity measuring probestogether with micro-tensiometers measuring soil waterpotential were installed in the holes. The soil samples weresaturated with water till full water capacity was reachedand then left under cover for 24 h in order to reacha state of thermodynamic equilibrium. Afterward, thesamples were uncovered and their humidity level and soilwater potentials (at 0.1, 16, and 100 kJ m�3) were moni-tored during evaporation. The TDR gauges were linkedto a PC which made automatic measurements possible,and the values of humidity and water potential taken wererecorded on the computer. These measurements were usedto calculate the coefficient of water conductivity k. Itsvalues were divided into three intervals:

� For 0.1 kJ m�3– above 0.5 cm day�1

� For 16 kJ m�3 – from 0.01 to 0.5 cm day�1

� For 100 kJ m�3 – below 0.01 cm day�1

and were then related to generalized soil units on colormaps for topsoils, upper subsoils, and lower subsoils.

Maps of soil specific surface areaKnowledge of the specific surface area of soils permitsjoint interpretation of clay and humus content and compo-sition in soil phenomena and processes affecting produc-tive properties of soils and natural environment. Thisknowledge and cartography of this feature alsomakes pos-sible better creation of spatial hydro-physical and fertilityprognostic models of soil processes. Four color mapsof total and external soil surface area expressed inm2 g�1 of soil were elaborated for the topsoils and uppersubsoils (Stawiński et al., 2000). Soils for mapping weregrouped in categories, e.g., eight categories of totalspecific surface area for topsoils: <12, 12–16, 16–20,20–30, 30–50, 50–70, 70–90, and >90 m2 g�1.

Maps of soil redox propertiesRedox status of soils is a base for understanding many soilproperties such as composition of soil solution, soil reaction,availability of water and nutrients for plants, gases emissionto the atmosphere, stability of metal-organic compounds,electrokinetic soil properties, biological activity, and others.Redox potential (Eh) is a measure of soil aeration status.When analyzing soil properties under oxygen deficit dueto an excessive soil moisture, a new term – redox resistance

of soil was found purposeful to introduce (Gliński andStępniewska, 1986). Its measure is a time (in days) afterwhich, in a fixed conditions of moisture (full saturation withwater) and temperature, soil Eh drops below 300 or 400mV.These values, named t300 and t400 indicators, correspondwith the reduction of iron and manganese in soil at 300mVand nitrate decomposition at 400 mV. For surface layersof Polish soils, at a temperature of +20oC, values of t300range from 0.2 to 4.4 days and t400 from 0 to 1.7 days. Fordeeper layers they are higher. The color maps, in the amountof 33, were gathered in the Atlas of the Redox Properties ofArable Soils in Poland (Stępniewska et al., 1997).

These maps show:

� Soil redox resistance indicators t300 and t400 of ploughlayers, subsoils and deeper layers at the temperaturesof 4�C, 10�C, 15�C, and 20�C for t300, and 4�C, 7�C,and 20�C for t400

� Standardized redox potential (Eh at pH 7) at two tem-peratures 4�C and 20�C

The maps may be used for:

� Evaluation of hazards and agricultural damage (e.g.,crop yield losses) connected with temporal water satu-ration of soil.

� Estimation of ecological damage connected with nitro-gen losses due to denitrification and the emission ofnitrous oxide (strong greenhouse gas) to theatmosphere.

� Prediction of negative ecological and agriculturaleffects of climate change.

� Using t300 and t400 values for land reclamation designand as an environmental parameter for hydrologicalcalculations (admission time of soil waterlogging).

Map of potential denitrification in soilsDenitrification of nitrates (V ) is the main mechanismresponsible for N2O production in ecosystems under hyp-oxic conditions. To show its importance the term potentialdenitrification (PD) was proposed as an important featurein agriculture (efficiency of nitrate fertilization) and natu-ral environment (greenhouse gas emission). The formulaused for the calculation of PD (kg ha�1 day�1 N–N2O)is based on the knowledge of soil redox resistance indica-tors t300 and t400, nitrogen contentCNO3�N (mg (kg soil�1)),soil layer thickness H (m), and soil bulk density d(Mg m�3):

PD ¼ 10HdCNO3�N=t300 � t400:

The analyses carried out on about 700 samplesrepresenting topsoils gathered in the Bank of Soil Samplesallowed to calculate PD for soil units of the entire territoryof Poland. On the basis of homogenous values of potentialdenitrification six groups of PDwere distinguished (<7.5,7.5–15, 15–30, 30–50, 50–80, and>80) and presented inthe form of a map (Gliński et al., 2000).

458 MAPPING OF SOIL PHYSICAL PROPERTIES

0–5 5–10 10–15 15–20 20–25

25–30 30–35 35–40 40–45 45–50

Other features

Grass-lands

Mineral arable soils of Polandsubsoil W |% cm3 cm−3|

Water ForestsBuilt-upareas

Mapping of Soil Physical Properties, Figure 1 Map of water retention at field water capacity (pF 2.2) of Polish soils. (From Walczaket al., 2002b.)

MAPPING OF SOIL PHYSICAL PROPERTIES 459

ConclusionsMaps constructed on local (plot, field) and regional scaleshowing distribution of soil physical properties have adefinite area of interest, e.g., for irrigation planning,soil water management, land reclamation, precisionagriculture (precision farming, precision tillage), droughtmitigation, and efficient use of fertilizers. They are alsoessential for modeling various current and predicted soilphysical properties (processes) and are helpful for deci-sions on application of these management practices todefined soil areas.

BibliographyBiałousz, S., Marcinek, J., Stuczyński, T., and Turski, R., 2005. Soil

survey, soil monitoring and soil databases in Poland. EuropeanSoil Bureau – Research Report No. 9 ESB-RR9, pp. 263–273.

Duffera, M., White, J., and Weisz, R., 2007. Spatial variability ofSoutheastern U.S. Coastal Plain soil physical properties: impli-cations for site-specific management.Geoderma, 137, 327–339.

Gliński, J., and Stępniewska, Z., 1986. An evaluation of soil resis-tance to reduction processes. Polish Journal of Soil Science,19, 15–19.

Gliński, J., Ostrowski, J., Stępniewska, Z., and Stępniewski, W.,1992. Soil samples bank representing mineral soils of Poland(in Polish). Problemy Agrofizyki (Series Monographs), 66, 4–57.

Gliński, J., Stępniewska, Z., Stępniewski, W., Ostrowski, J., andSzmagara, A., 2000. A contribution to the assessment of poten-tial denitrification in arable mineral soils of Poland. Journal ofWater and Land Development, 4, 175–183.

Iqbal, J., Thomasson, J. A., Jenkins, J. N., Owens, P. R., andWhisler, F. D., 2005. Spatial variability analysis of soil physicalproperties of alluvial soils. Soil Science Society of America Jour-nal, 69, 1338–1350.

Leenhardt, D., Voltz, M., Bornand, M., andWebster, R., 2006. Eval-uating soil maps for prediction of soil water properties. Euro-pean Journal of Soil Science, 45, 293–301.

Ostrowski, J., Stępniewska, Z., Stępniewski, W., and Gliński, J.,1998. Computer maps of the redox properties of arable soils inPoland. Journal of Water and Land Development, 2, 19–29.

Santra, P., Chopra, U. K., and Chakraborty, D., 2008. Spatial vari-ability of soil properties and its application in predicting surfacemap of hydraulic parameters in an agricultural farm. CurrentScience, 95, 937–945.

Stawiński, J., Gliński, J., Ostrowski, J., Stępniewska, Z.,Sokołowska, Z., Bowanko, G., Józefaciuk, G., Księz.opolska, A.,and Matyka-Sarzyńska, D., 2000. Spatial characterization ofspecific surface area of Arable soils in Poland (in Polish). ActaAgrophysica (Series Monographs), 33, 1–52.

Stępniewska, Z., Stępniewski, W., Gliński, J., and Ostrowski, J.,1997. Atlas of the Redox Properties of Arable Soils of Poland.Lublin: IA PAN Lublin – IMUZ Falenty.

Taylor, J. A., and Minasny, B., 2006. A protocol for convertingqualitative point soil pit survey data into continuous soil propertymaps. Australian Journal of Soil Research, 44(5), 543–550.

Usowicz, B., Kossowski, J., and Baranowski, P., 1996. Spatialvariability of soil thermal properties in cultivated fields. SoilTillage Research, 39, 85–100.

Várallyay, Gy., 1989. Mapping of hydrophysical properties andmoisture regime of soils. Agrokémia és Talajtan, 38, 800–817.

Várallyay, Gy., 1994. Soil databases, soil mapping, soil informationand soil monitoring systems in Hungary. FAO/ECE InternationalWorkshop on Harmonization of Soil Conservation MonitoringSystems (Budapest, 14–17 September 1993). Budapest:RISSAC, pp. 107–124.

Walczak, R., Ostrowski, J., Witkowska-Walczak, B., andSławiński, C., 2002a. Spatial characteristics of water conductiv-ity in the surface level of Polish arable soils. InternationalAgrophysics, 16, 239–247.

Walczak, R., Ostrowski, J., Witkowska-Walczak, B., andSławiński, C., 2002b. Spatial characteristic of hydro-physicalproperties in arable mineral soils in Poland as illustrated bythe field water capacity (FWC). International Agrophysics,16, 151–159.

Walczak, R., Ostrowski, J., Witkowska-Walczak, B., andSławiński, C., 2002c. Hydrophysical Characteristics of MineralArable Soils of Poland (in Polish with English summary). ActaAgrophysica (Series Monographs), 79, 5–98.

Witek, T., 1974. An agricultural productive space in numbers (inPolish). Puławy: Institute of Soil Science and Plant Cultivation.

Wösten, J. H. M., 2000. The HYPRES database of hydraulic prop-erties of European soils. Advances in GeoEcology, 32, 135–143.

Cross-referencesRemote Sensing of Soils and Plants ImagerySpatial Variability of Soil Physical Properties

MATRIC POTENTIAL

See Physical Dimensions and Units Use in Agriculture;Water Use Efficiency in Agriculture: Opportunities forImprovement

MATRIX OF THE SOIL

The assemblage and arrangement of the solid phase (min-eral and organic components) constituting the body of thesoil, and the interstices (pores) contained within it.

BibliographyIntroduction to Environmental Soil Physics (First Edition) 2003

Elsevier Inc. Daniel Hillel (ed.) http://www.sciencedirect.com/science/book/9780123486554

MEASUREMENTS NON-DESTRUCTIVE

See Nondestructive Measurements in Fruits; Nondestruc-tive Measurements in Soil

MECHANICAL IMPACTS AT HARVEST AND AFTERHARVEST TECHNOLOGIES

Zbigniew Ślipek, Jarosław FrączekDepartment of Mechanical Engineering and Agrophysics,University of Agriculture, Kraków, Poland

DefinitionMechanical impact. A complex of physical phenomenathat takes place at impact of two or more bodies.

460 MATRIC POTENTIAL

Harvest technology. A sequence of activities and opera-tions occurring one after the other, performed by machineor carried out manually in order to gather the ripened cropfrom the field.After harvest technology. Operations regarding the han-dling of harvested crop, including transportation, sorting,cleaning, drying or cooling, storing, and packaging.

IntroductionEach technological system of harvest and post-harvesthandling requires the use of machines whose operatingelements bring about plant material load, thus permittingthe end product to be satisfactory both in terms of quantityand quality (Figure 1). On the one handmechanical impactis required in order to permit the machine to functionaccording to its purpose, but on the other hand this oftencauses undesired effects (loss). The desire to increase

machine output is connected with the ever more aggres-sive impact of machines on material, which usually leadsto greater damage being caused to crops.

There are twoways inwhich the rational use of machinesfor harvest and post-harvest handling may take place:

� By appropriate selection and by steering the operatingparameters

� By selecting the best time for performing the technolog-ical process

TheoryMachine–plant interaction that takes place during thepost-harvest collection and handling process is primarilyof an impact nature. Concerning the physical descriptionof interaction that occurs in such situations, it is necessaryto take into account the mechanics of impact and its impli-cation to plant materials (Mohsenin, 1986).

PLANT MACHINE

Harvest technology Post-harvest technology

HARVESTEDCROP

SYSTEM

MACHINEAGROCULTURAL

PRODUCTSCropping date Quantity

Machine operatingparameters

Quality

QuantityQuality

Crop structuresMoisture content

Physical properties Machine operatingparameters

HARD

SOFTMECHANICAL

IMPACTS

INTERMEDIATE

NEGATIVE POSITIVE

E

F

F

E

C

T

S

FINAL CROPLOSSES

EK

n

EK

EK

n

n

Mechanical Impacts at Harvest and After Harvest Technologies, Figure 1 Machine–plant material system relations.

MECHANICAL IMPACTS AT HARVEST AND AFTER HARVEST TECHNOLOGIES 461

In order to indicate the presence of forces during impact itis necessary to consider themechanical properties of both theimpacting object and the object being impacted. A typicalcharacteristic of impact is the occurrence of so-called instan-taneous forces, which act very briefly (Dt ! 0) andattain high values in comparison to other forces that occur.The measure for instantaneous forces is the impact pulse:

S ¼Zt0þDt

t0

PðtÞdt (1)

where S – impact pulse t0 – moment of occurrence of theinstantaneous force Dt – time of operation of the instanta-neous force P(t) – instantaneous force.

The course of impact may be divided into two phases:compression and restitution (Gilardi and Sharf, 2002).The first phase, which involves a sudden increase in force,starts as soon as the bodies encounter each other at t0 pointand end at tm point when distortion attains maximum value(Figure 2a). The relative velocity of both bodies is thenequal to zero. In the second phase instantaneous forcedecreases. The shape of the P(t) curve depends on:

� The elastic–plastic properties and dimensions of theimpacting bodies

� Body surface shape (particularly at the point of contact)� The direction of the velocity vector� Impact energy value� Freedom of distortion

For plant materials this curve is determined empirically.S1 and S2, impact pulse values, respectively in phases oneand two of impact, serve the purpose of determining therestitution coefficient:

e ¼ S1S2

(2)

It defines the proportion between elastic and plastic dis-tortion. If e = 1 we have to do with ideally elastic impact;however for e = 0 impact is ideally plastic. For true plantmaterials “e” belongs to the (0; 1) range and to a very largedegree depends on the moisture content of the material (thelower the moisture content the greater the restitution coeffi-cient). It is determined when a body impacts a motionlessobstacle (Figure 2b). In such cases normal velocity isdefined: initial vin and final vfin, while the restitution coeffi-cient is calculated as the ratio of both velocities.

Total distortion e that takes place during impact is thesum of elastic distortion es and plastic distortion ep. Themagnitude of these distortions depends on anatomicaland morphological structure and the physical propertiesof plant material. The anisotropicity of these types ofmaterials hinders any clear definition of mechanical andendurance parameters, which are very strongly correlatedwith moisture, which has a decisive influence on elasticand plastic properties (and therefore on the type of distor-tion). Plant materials with large water content are domi-nated by plastic traits, while dry material contains elasticor brittle characteristics. This is reflected in the effects ofimpact. Concerning materials with large water content,the energy of elastic distortions constitutes only a smallpart of overall distortion energy. During harvest andpost-harvest handling one may distinguish the followingtypes of load (Figure 1):

� Hard – these are manifest when the initial value of Einimpact kinetic energy is dissipated by the impactingbody (e.g., the fall of an apple, tomato, or melon ontothe hard surface of a crate leads only to the distortionof the impacting fruit).

� Soft – this is the kind of impact in which distortion ofthe impacted body is considerably greater than distor-tion of the impacting body (e.g., the impact of a beateragainst threshed material leads only to the distortionof the impacted plant mass).

� Intermediate – this is the simultaneous distortion of theimpacting body and of the impacted body (e.g., the fallof a single piece of fruit onto a pile of fruit kept in a con-tainer leads to all of the impacted bodies being distorted).

In terms of agrophysics the negative effect of impact isplastic distortion, which leads to a permanent deterioration

P

S1

a

b

S2

t0 + Δt

t0 + Δt

e

e

<

∈(0;1)

1

0

t0

t0

–

tm

nin

–nin

–nin

nfin

–

–

nfin–

nfin–

=

=

=

e 0=

=

0n

tm

t

Mechanical Impacts at Harvest and After HarvestTechnologies, Figure 2 Body impact against a motionlessobstacle (a) change of instantaneous force; (b) types of impacts.

462 MECHANICAL IMPACTS AT HARVEST AND AFTER HARVEST TECHNOLOGIES

in the quality of the biological material. The followingshould be distinguished:

� Local distortion – these cover a relatively small areathat focuses immediately on the point of impact andleads to different types of micro-damage (abrasion, skinpuncture, etc.). These distortions are the result ofinstantaneous force and depend on local plant materialsusceptibility and the shape of the surface at the pointof contact.

� Global distortion – these appear in the form of distur-bance waves (distortions, stress) covering the entire vol-ume of the impacted body. Under such circumstancespermanent distortions appear on a macro scale (grainfragmentation, tomato cracking, apple crushing etc.).

It must be noted that surface and internal stress causedduring impact are far greater at the point of contact thanbeyond that point.

Harvest and after harvest processes – agrophysicalaspectsGrain cropsMost frequently the harvesting of these plants takes placein the form of a single phase. The technological processinvolves the cutting of plants, transport, threshing, separa-tion and cleaning, and the gathering of grain (seed) ina container. In combine-harvesters, the most intensiveeffect on plant material takes place in the threshing unitwhich, depending on the construction, threshes by strik-ing, wiping, and vibrating the threshed material.Threshing drum operations considerably distort plantmass (e.g., cereals) by soft impacts, which, depending oninstantaneous contact conditions (moisture content ingrain, grain orientation in terms of the working element,collision velocity, and friction) could lead to total grainfragmentation or to a variety of damage to the grain.Low intensity threshing which is favorable in terms ofmechanical damage (low drum revolutions, large operat-ing slot between the drum and the threshing floor) may,however, cause grain loss through incomplete grainthreshing.

Threshing output and effectiveness depends on:

� The properties of the threshed material (type and vari-ety, material threshing capacity, moisture content, theelastic properties of stalks or culms, the ration of grainto straw, and others)

� Technical conditions (the type of threshing unit, regu-lating parameters, and others)

� Threshing unit feed (the volume of feed, evenness, thearranging of plants in the threshing layer, and others)

The operating parameters of modern combine-harvesters may be steered electronically, on the basis ofinformation on the exit and entry points of harvested mate-rial, supplied online. The implementation of information

used for steering purposes is the subject of research.The gauging of the mechanical properties of plant mate-rial, including susceptibility to mechanical impact, is car-ried out at laboratory workstations, which permit theapplication of precise loads under determined and con-trolled conditions (Kang et al., 1995).

Transport, cleaning, and drying take place after theharvesting of grain. During these processes the materialis subject to a variety of additional mechanical impacts.The danger of damaging the grain during these processesis, admittedly, far smaller; however, mechanical loadmay overlap and the damage previously caused to grainmay become worse (Frączek and Ślipek, 1999). This leadsto a reduction in raw material quality during storage(infestation by microorganisms, the settling of pests,increased respiration intensity) and processing. Mechani-cal damage to grains to be used as seeds (in particulardamage to the germ) may reduce their ability to germinateand emerge.

Fruits and vegetablesThe market of today requires that fruits and vegetables, inparticular those items intended to be eaten directly, be ofhigh quality – this includes the right shape, color and,above all, the absence of any damage. The collection ofthese products with the use of machines, therefore,requires that extreme care be exerted as a result of loadimpact. The technological collection of fruit is based onthe mechanical effects of vibrating or rotating parts.

Mechanical collection from trees usually takes place bymeans of vibration (e.g., Mateev and Kostadinov, 2004).The fruit falls on special screens which are spread outbeneath the crowns of trees or (when using simplifiedtechnologies) directly onto the ground from where it isgathered and loaded onto containers. These types of tech-nologies are primarily used when fruit is collected forprocessing.

Fruits such as raspberries, currants, gooseberries, andstrawberries are collected with the use of harvesters onlyfor processing purposes. Collection for consumption pur-poses still takes place by hand. That does notmean that withthis technology the fruit does not come in direct contactwithmechanical elements. During collection various types ofdevices are used in order to facilitate manual collectionand increase output (conveyors, elevators). In recent times,the use of autonomous robots for collecting fruit consider-ably decreases load impact, which is the reason why fruitgets damaged. Further development of these robots willundoubtedly help reduce the damage caused to fruit, partic-ularly if collected for direct consumption purposes.

During the mechanical collection of fruit it is alsoimportant to make sure that machines operate under condi-tions not permitting any considerable damage to occur tothe parent plant because of the need for yielding in the nextvegetation season.

MECHANICAL IMPACTS AT HARVEST AND AFTER HARVEST TECHNOLOGIES 463

The main purpose of post-harvest technologies is toincrease the added value of the product. Various techno-logical lines perform the selection, cleaning, washing, san-itation, classification, weighing, and packing of products.While these technologies are in operation, mechanicalinfluence, admittedly, takes place at low mechanicalspeed, but still the fruits and vegetables are exposed tothe danger of damage and bruising (e.g., Ragni andBerardinelli, 2001). When products are subject to gravita-tional transport or change the direction in which theymove they are particularly prone to damage.

The technologies for collecting vegetables usuallyinvolve separating the usable part growing above theground (e.g., cabbage, cucumber, tomato), extracting theroot (e.g., carrot, parsley) or tubers (potatoes), separation,gathering the yield, and transporting it. Most frequentlymachines damage the skin and bruise the harvestableproducts. During post-harvest handling the products arecalibrated, washed, weighed, and packed. Multiple impactevents and associated damage result later in greater respi-ration intensity and loss of storage life.

ConclusionOriginally, research in agrophysics concentrated onassessing various mechanical traits of agricultural plantsand was based primarily on destructive methods. In recentyears a range of nondestructive methods of assessing thephysical condition of plant material has been elaborated(e.g., Ortiz et al., 2001). Thesemethods include image anal-ysis (size, shape, weight, surface morphology, skin color),electrical and optical properties, vibration and acoustic testsand nuclear magnetic resonance techniques. Destructivemethods that offer direct information about the mechanicalproperties of fruits and vegetables (mechanical impact tests)are used less and less frequentlywhen assessing the suitabil-ity of plants for collection. These tests, however, are usedfor verifying the methods of direct assessment, outliningthe mechanical characteristics of materials for the purposeof designing machines or for modelling work processes.

Post-harvest collection and handling machines whosedesigns are based on comprehensive scientific know-how, should meet the requirements on high quality andsafety of agricultural products. The implementation ofmechatronic systems permits, to an increasingly largedegree, the subtle control and steering of technologicalprocesses. Comprehensive operations aimed at improvingthe quality of agricultural products require thoroughknowledge of machine and plant systems. It is particularlyimportant to be aware of the physical parameters of plantmaterial, which are quickly and easily gauged in thedynamic technological processes.

BibliographyFrączek, J., and Ślipek, Z., 1999. Fatigue strength of wheat grains.

The analysis of grain deformation at multiple loads. Part 1. Inter-national Agrophysics, 13, 93–97.

Gilardi, G., and Sharf, I., 2002. Literature survey of contact dynam-ics modelling.Mechanism andMachine Theory, 37, 1213–1239.

Kang, Y. S., Spillman, C. K., Steele, J. L., and Chung, D. S., 1995.Mechanical properties of wheat. Transactions of the AmericanSociety of Agricultural Engineers, 38, 573–578.

Mateev, L. M., and Kostadinov, G. D., 2004. Probabilistic model offruit removal during vibratory morello harvesting. BiosystemsEngineering, 87, 425–435.

Mohsenin, N. N., 1986. Physical Properties of Plant and AnimalMaterials, 2nd edn. New York: Gordon and Breach Science.

Ortiz, C., Barreiro, P., Correa, E. F., Riquelme, F., and Ruiz-Altisent, M., 2001. Non-destructive identification of woollypeaches using impact response and near-infrared spectroscopy.Journal of Agricultural Engineering Research, 78, 281–289.

Ragni, L., and Berardinelli, A., 2001. Mechanical behaviour ofapples, and damage during sorting and packaging. Journal ofAgricultural Engineering Research, 78, 273–279.

Cross-referencesAgrophysical Properties and ProcessesCrop Yield Losses Reduction at Harvest, from Research toAdoptionNondestructive Measurements in Fruits

MECHANICAL RESILIENCE OF DEGRADED SOILS

Heiner FleigeInstitute for Plant Nutrition and Soil Science, Christian-Albrechts-University zu Kiel, Kiel, Germany

Mechanical resilience of soils can be defined by the valueof the precompression stress (Pc), which depends espe-cially on soil texture, soil structure, and water content.Pc is derived from confined compression tests onundisturbed, aggregated, and unsaturated soils. It isassumed that at stresses lower than the Pc, an elastic stresspath exists, resulting in no extra soil deformation and thus,no change in the pore system and its function. Stressesbeyond the Pc induce changes of pore functions and porecontinuity because of plastic soil deformation (= degradedsoil). Consequently, not only water infiltration will bereduced but it also causes reduced gas, and water fluxes,as well as water logging, interflow, runoff, and soil ero-sion. The prediction of the Pc and its effects on physicalproperties by pedotransfer functions as well as Pc-mapson different scales (e.g., Europe, Germany, farm scale)are demonstrated in Horn et al. (2005) and Horn andFleige (2009).

BibliographyHorn, R., Fleige, H., Richter, F.-H., Czyz

., E. A., Dexter, A., Diaz-

Pereira, E., Dumitru, E., Enarche, R., Mayol, F., Rajkai, K.,De la Rosa, D., and Simota, C., 2005. Prediction of mechanicalstrength of arable soils and its effects on physical properties atvarious map scales. Soil and Tillage Research, 82, 47–56.

Horn, R., and Fleige, H., 2009. Risk assessment of subsoil compac-tion for arable soils in Northwest Germany at farm scale. Soil andTillage Research, 102, 201–208.

464 MECHANICAL RESILIENCE OF DEGRADED SOILS

MEMBRANES, ROLE IN WATER TRANSPORT IN THESOIL–ROOT–XYLEM SYSTEM

Marian KargolIndependent Department of Environment Protection andModelling, The Jan Kochanowski University ofHumanities and Sciences, Kielce, Poland

IntroductionThis article discusses agrobiophysical aspects of soil waterintake by a plant root and water transport along its radialroute. In other words, this article presents certain interpre-tative ideas explaining the mechanisms of water transportin the soil–root–xylem system. In this context, a certainamount of basic information on membranes (cell mem-branes in particular) and permeability of various sub-stances across them shall be presented first. Next, majormembrane theories on the processes of water intake fromthe soil and its two-way transport across the root (fromthe soil to the xylem of the vascular cylinder, and in theopposite direction) shall be discussed. The issue of rootpressure generation shall also be investigated (Fiscus,1975, 1986; Ginsburg, 1971; Kargol, 1994, 1995, 2007;Pitman, 1982; Steudle et al., 1987; Taura et al., 1987).

Membranes and mathematical formalisms of theirsubstance transportPorous membranesIn general, membranes can be divided into artificial andbiological. The former include, for example, cellophane,nephrophan, or collodion membranes. The latter includecell and cellular organelle membranes, as well as tissuemembranes.

In research practice, we mostly deal with porous mem-branes. This also pertains to cell membranes. Porousmembranes can be divided into two groups, that is, homo-geneous and heterogeneous. A membrane is homoge-neous if its pores do not differ from one another incross-section radius dimensions. If a membrane’s poresdo differ from one another in their cross-section radiusdimensions, it is to be treated as heterogeneous. Processesof permeability across a variety of membranes have beeninvestigated mainly with the use of the Kedem &Katchalsky (KK) formalism equations (Katchalsky andCurran, 1965). In recent years, equations of the mechanis-tic formalism (MF) have been developed (Kargol, 2002,2006, 2007; Kargol and Kargol, 2003, 2006).

Equations of the KK formalismTransport equations of the KK formalism have beenderived on the grounds of linear thermodynamics of irre-versible processes, basing on a model of a membranetreated as a “black box.”A practical version of these equa-tions may be thus written:

Jv ¼ LpDP � LpsDP; (1)

js ¼ oDPþ ð1� sÞcsJv; (2)

where Jv and js are density of the solution volume flow (v)and density of the solute flow (s); DP=P2–P1 is themechanical pressure difference between P2 and P1;DP=RT(C2�C1) is osmotic pressure difference; Lp, sand o are coefficients (of filtration, reflection, and perme-ability); �cs = 0.5 (C1 +C2) is the mean concentration ofconcentrations C1 and C2; R is the gas constant; T istemperature.

Transport parameters occurring therein are given by theformulas

Lp¼ JVDP

� �DP¼0

; (3)

s ¼ DPDP

� �Jv¼0

; (4)

o ¼ jSDP

� �Jv¼0

: (5)

The above notation of Equations 1 is adequate to themembrane system as schematically depicted in Figure 1.In this system, the membrane M separates the compart-ments A and B, filled with solutions, which satisfy theinequality C1<C2. They are under mechanical pressures,which satisfy the relation P1<P2. Generally speaking, theKK equations are rather difficult in terms of interpretation.This pertains particularly to the parameter o, whichappears in Equation 2. This results from the fact that thisparameter, as defined by the Formula 5, is determined atJv = 0, that is, at simultaneous existence on the membraneof two stimuli that satisfy the relation: |DP| = |–sDP|.

This means that, in the case of membranes for which sis found in the interval 0<s< 1, the transport of a givensolute across the membrane is generated by two stimuli.Hence it follows that o is not only the coefficient of diffu-sive solute permeability. The terms oDP and (1�s)�csJvalso pose problems for interpretation. According to this

m m

Kp

C2 >

P2 >

Jv

A BJv

C1

P1

js

M

ΔP = P2–P1 = ρgh

Membranes, Role in Water Transport in the Soil–Root–XylemSystem, Figure 1 Model membrane system with stirrers m,m(description in text).

MEMBRANES, ROLE IN WATER TRANSPORT IN THE SOIL–ROOT–XYLEM SYSTEM 465

formalism, membranes can be divided into semipermeable(at s= 1), selective (when 0<s <1), and nonselective(if s= 0).

Equations of the mechanistic formalism (MF)In research work, we mainly deal with porous membranes.This also pertains, as we now know, to cell membranes.Consequently, in recent years, we have developed the so-called mechanistic formalism for membrane substancetransport (Kargol, 2002, 2006, 2007; Kargol and Kargol,2003, 2006). The equations of this formalism take thefollowing forms:

Jvm ¼ LpDP � LpsDP; (6)

jsm ¼ odDPþ ð1� sÞ�csLpDP; (7)

where (analogous to the KK equations), JvM is the solutionvolume flow and jsM is the solute flow. These flows arefunctions of the pressure difference DP and the pressuredifference DP.

Individual terms of these equations signify filtration,osmosis, diffusion, and convection. The equations havebeen derived on the basis of a model of the membrane sys-tem as shown in Figure 2. In this system, the heteroge-neous porous membrane M contains the statisticalnumber of cylindrical pores permeable to water. Themem-brane separates the compartments A and B, which containsolutions of the concentrations C1 and C2 under mechani-cal pressures P1 andP2.

In a real membrane, pores are arranged randomly. Inorder to facilitate the present considerations, it has beenassumed that pores of that membrane are arrangedaccording to their dimensions in one direction, from thepores with the smallest radiuses (rmin

1 ), to the largest ones(rmax

N ). With reference to this membrane, it is possible to

find such a solute (s) with such a molecule radius rs thatthe following relation will be fulfilled:

rmin1 ¼ rw < r2 . . . < rs < . . . < rmax

N ;

where rw is the radius of the solvent (water) molecules.Under the circumstances, the membrane M may bedivided into Part (a), which contains a certain number naof semipermeable pores (with the reflection coefficientsa = 1 and filtration coefficient Lpa), and Part (b), whichcontains nb =N–na of permeable pores. This part of themembrane has the filtration coefficient Lpb and reflectioncoefficient sb = 0. From the above, it follows thata single pore of the membrane may take values of thereflection coefficient sp amounting to either 0 or 1(Kargol, 2002, 2006; Kargol and Kargol, 2003a, b, 2006).

Having analyzed the membrane system as shown inFigure 2, it is easy to understand that Jva = Jvwa is the sol-vent (water, w) volume flow, while Jvb is the solution vol-ume flow, which permeates across Part (b) of themembrane. The flow Jvb has two components, that is, Jvwb(water volume flux) and Jvsb (solute volume flux). Theseflows, in turn, also have two components each, thatis, JDP

vwband JDP

vwb, and Jvsd and Jvsk (which are solvent (w)

and solute (s) volume flows respectively). All these flowsare generated by the stimuli DP and DP respectively. Itmust be noted that in the light of this membrane model,the transport situation appears to be absolutely clear interms of interpretation. On this basis, it also makes senseto say that the mechanistic formalism for membrane trans-port (MF) offers a more thorough investigation tool thanthe KK formalism.

On the equivalence of the equations of the KKformalism and the MF formalismTransport equations of the mechanistic formalism (MF)may in general be treated as alternative to the equationsof the KK formalism. This view results mainly from thefact that the equations for the solute flow of these formal-isms, that is, Equations 2 and 7, display certain differ-ences. Yet thanks to our introduction of the so-calledtransport parameter correlations between Lp, s and o(KK), and Lp, s and od (MF), it is easy to observe thatthese (KK and MF) equations are mutually equivalent(Kargol, 2007; Kargol and Kargol, 2006). The relationsare given by the formulas

o ¼ ð1� s2Þ�csLp; (8)

od ¼ ð1� sÞ�csLp: (9)

On the basis of the above, the following relation isobtained: o=od (1 +s).

Thanks to these relations, it becomes possible to use –for further and more thorough investigations – theenormous experimental potential (related to substancepermeation across cell membranes), which has so far beenobtained on the basis of the transport equations of the KKformalism.

JvMKp

ΔP = P2 – P1 = ρgh

r1>rw(a), naA B

(b), nb

C2>

P2> m

Jva = Jvwa

rsJvb = Jvwb + Jvsb

Jvwb > 0ΔP

Jvwb = Jvwb + JvwbΔP ΔΠ

Jvsb = Jvsd + JvskΔPΔΠ

Jvwb < 0ΔΠ

Jvsd ΔΠ

JvskΔP

r maxN

C1

P1mM

Membranes, Role in Water Transport in the Soil–Root–XylemSystem, Figure 2 Membrane system (M, membrane; A and B,compartments; m, m, stirrers).

466 MEMBRANES, ROLE IN WATER TRANSPORT IN THE SOIL–ROOT–XYLEM SYSTEM

Cell membranes as porous structuresIn general, it may be stated that in research practice wemainly deal with porous membranes. As is becomingincreasingly well known, cell membranes are porous,too. Suffice it to say that cell membranes have variouspores (channels), through which in a controlled mannervarious substances, including water can penetrate (Kargol,2007; Stryer, 2000). Speaking about these pores, we alsomean water channels, formed by certain transport proteins(called aquaporins). The porous structure of the cell mem-brane is also formed by ion channels through which (whenopen) not only ions but also water can pass. These chan-nels have hydrophilic internal walls and are filled withwater. Pores formed by some antibiotics, located in thelipid bilayer of the cell membrane, may also be permeableto water. Moreover, certain pores can occur directly in thelipid bilayer of cell membranes. All these pores vary fromone to another in terms of their cross-section radius dimen-sions. They fulfill various specialized transport functionspertaining to permeability of water and various solutesthrough them.

Membrane ideas for explanation of watertransport processes across the rootWe shall now consider some basic issues related toagrobiophysical aspects of water intake from the soil byplants, and water transport across the root (Fiscus, 1975,1986; Ginsburg, 1971; Kargol, 1994, 1995, 2007; Pitman,1982; Steudle et al., 1987; Taura et al., 1987). The discus-sion of these issues will be preceded by an outline of theroot structure.

Anatomical structure of the rootIn the soil, the root must have particular contact with thewater found therein. It is also important for the contact tooccur mainly in the trichome zone of the root. This zone isthe most capable of water intake.With a view to facilitatingour discussion of the mechanisms of root water intake (aswell as water movement to the xylem of the vascular cylin-der), let us look at its main structural features. They havebeen shown schematically in Figure 3. The protoplasts of

primary bark cells are, as we know, joined with one anotherby means of cytoplasmic bridges (plasmodesmata). Withthe help of these bridges, they are also linked – on the out-side – with epidermis cell protoplasts, and on the inside –with endodermis cell protoplasts. The protoplasts of theparenchyma cells of the vascular cylinder are similarlyjoined to the endodermis. Thus, the root symplast spreadsfrom the epidermis to the xylem, forming a cytoplasmaticcontinuum. This continuum provides a symplastic waterroute. It is illustrated by Line (Sy).

Cell walls and intra-cell voids form the so-calledapoplastic water route. This route is shown by the brokenline (Ap) in Figure 3. Even though it stretches from the epi-dermis to the xylem, it still has in the endodermis layera significant barrier (in connection with the existence ofthe Casparian band therein). In general, in the root radialwater route, three barriers can be distinguished. They havebeen marked with Figures 1, 2, and 3.

The Fiscus single-membrane theory of watertransport across the rootAscribing to barriers 1, 2, and 3 (cf. Figure 3) the status ofmembranes, several membrane models have been devel-oped to emulate the root radial water route. The simplestof these is the single-membrane model, as proposed byFiscus (Fiscus, 1975, 1986). In this model, shown inFigure 4, all the three above mentioned barriers have beenreplaced with one membrane M with the filtration coeffi-cient Lpr, the reflection coefficient sr and the permeabilitycoefficient or. The membrane separates two solutions ofthe concentrations Cso and Csx, that is, the soil solutionand the exudate (the xylem solution). These remain undermechanical pressures Po and Px respectively. Basing onthat model, it has been possible (on the basis of the KKequations) to determine experimentally the values of all

w

w

Ap

Sy

1 2 3

Phloem

Xylem

Stele

Co

Casparian bandEndodermisEpidermisCortex

Membranes, Role in Water Transport in the Soil–Root–XylemSystem, Figure 3 Fragment of root cross section, trichomezone (Sy, symplastic route; Ap, apoplastic route; w,w, trichomes;1, 2 and 3, barriers).

M

CsoCsx

PxPo

Root

Water

Soil

Xylem

ExudateΔPh = ρgh

Floem

JvF

Jvr

jsr

jsA

Membranes, Role in Water Transport in the Soil–Root–XylemSystem, Figure 4 Single-membrane model of root radialroute (M is the membrane; Csx, Cso are concentrations; Px, Po andDPh are mechanical pressures; Jvr and jsr are passive flows; jAsis the active solute flow; Jvf is the phloem volume flow; r isdensity; g is gravitational acceleration; h is height).

MEMBRANES, ROLE IN WATER TRANSPORT IN THE SOIL–ROOT–XYLEM SYSTEM 467

the three transport parameters (Lpr, sr, and or), for roots ofvarious crop plants and for various solutes. Due to thedetermination of these parameters, it has been possible todescribe transport properties (of the model root) by meansof the KK equations thus written

Jvr ¼ LprDP � LprsrDP

¼ Lpr Px � Poð Þ � LprsrRT Cx � Coð Þ; (10)

Jsr ¼ orDPþ ð1� srÞ�csJvr¼ orRT Csx � Coð Þ þ ð1� srÞ�csJvr;

(11)

where Jv is the volume flow; js is the solute flow; DP andDP are (mechanical and osmotic) pressure differences;�cs = 0,5(Cx +Co) is the mean concentration (of the concen-trations Csx and Cso); Px and Po are mechanical pressuresin the xylem and the soil; R and T are the gas constantand temperature.

According to the model described with Equations 13and 14, the flows Jvr and jsr are determined (in their valueand direction (sense)) by the values of pressure differencesDP and DP. This model appears to be rather far removedfrom the biological reality. Nonetheless, it has playeda particularly important role in experimental andtheoretical investigations pertaining to determination oftransport properties of the root radial transport route ina variety of crop plants (Fiscus, 1975, 1986; Steudleet al., 1987; Kargol, 1994, 2007). Moreover, it explainsto a large extent the agrobiophysical water transportmechanisms across the root as well as generation of theso-called root pressure. This pressure is expressed by theformula DPZ=rgh, where r is density, g is gravitationalacceleration; h is the height to which the root can pumpwater under that pressure.

According to this model, root pressure is osmosisdriven, as the experimentally determined coefficient srpertaining to the membrane M, which emulates the rootradial water route takes the value of sr> 0. On the basisof this model (Fiscus, 1975, 1986) as well as Steudle andothers (Steudle et al., 1987), experimentally determinedthe parameters Lpr, sr, and or for many crop plant roots.

The Ginsburg double-membrane model, whichexplains radial water transport in the rootAnother model, which emulates the radial water route isGinsburg’s double-membrane model shown in Figure 5(Ginsburg, 1971). While constructing it, Ginsburgassumed that, in the radial segment of the root symplasticroute, water encountered two barriers (1 and 2: cf.Figure 3). He gave these barriers the status of membranesMo and Mi, and ascribed filtration coefficients (Lpo andLpi), as well as reflection coefficients (so and sand) tothem respectively. In this model, the membrane Mo sepa-rates solutions of the concentrations Cso, and Cs, whilethe membrane Mi – solutions of the concentrations Csand Csi, where Cso, Cs and Csi are the concentrations ofthe solution in the soil, between the membranes and in

the apoplast of the vascular cylinder. With the use of theKedem–Katchalsky equation for the flow Jv (i.e., Equa-tion 1), Ginsburg demonstrated that transport propertiesof his model are thus formulated:

Jvr ¼ � LRTðsi � soÞCs þ LRTðssiCi � siCsoÞ� L Pi � Poð Þ;

(12)

where Jvr is the volume flow; Pi and Po are mechanicalpressures in the soil and in the apoplast of the vascular cyl-inder; and L =LpoLpi(Lpo + Lpi)

�1.From an analysis of this equation, it follows that –

depending on the value of the concentration Cs (whichmay be regulated with the active flow jAs of the solute(s)) – the flow Jvr may occur in accordance with the con-centration gradient (at Cs0<Csi), under iso-osmotic con-ditions (at Cs0 =Csi), as well as against the concentrationgradient (at Cs0>Csi). These conclusions, if referred towater transport across the root, are the main researchachievement resulting from the Ginsburg model.

It must be added here that many membrane modelshave been developed to emulate the root radial waterroute, including multi-membrane models (Pitman, 1982;Taura et al., 1987; Kargol, 1995, 2007).

Single-membrane theory of two-way watertransport along the root radial routeWe shall presently consider the single-membranemodel ofthe root radial water route, which we have developed(Kargol, 2007) on the basis of the MF formalism. Sche-matically, it has been presented in Figure 6. In this model,the assumption is that the membraneM is a heterogeneousporous structure, which emulates (in a manner analogousto the Fiscus model) the entire radial water route inthe root. It contains the statistical number of N pores,which are permeable to water. Individual pores vary inareas (A) of their cross sections and are arranged randomly(arbitrarily). To facilitate our considerations, the poreshave been arranged in one direction, from the smallestA1, to the largest Amax

N . With reference to this membrane,it is possible to select such a solute (s), with the molecule

1 2

Mo Mi

PiCsPCsi

Jvr

Lpo Lpi

Po Cso

Vacuoleσo σi

jsA

Membranes, Role in Water Transport in the Soil–Root–XylemSystem, Figure 5 Two-membrane Ginsburg model(description in text).

468 MEMBRANES, ROLE IN WATER TRANSPORT IN THE SOIL–ROOT–XYLEM SYSTEM

cross-section area As, that the following relation will besatisfied: A1<A2< . . .<As< . . . < Amax

N .Under the circumstances, it is justified to divide the

membrane M into Part (a) that contains na semipermeablepores and Part (b) that contains nb permeable pores. Theseparts are to be ascribed the reflection coefficients sa = 1and sb = 0 respectively. This membrane (according toFigure 6) separates the soil water with the solute (s) con-centration Cso and the xylem solution (the exudate) withthe solute concentration Csx. Due to the occurrence ofactive transport jAs of the solute (s) in the root, it maybe assumed that on the membraneM there occurs the pres-sure difference DC=Csx�Cso. Thus, there also occurs theosmotic pressure difference DP =RTDC=RT(Csx�Cso).As a consequence, within the pores na, the osmotic volumeflow will be generated Jva = Jvwa (which is in fact a waterflow). This means, in turn, that the model root under con-sideration is able to generate root pressure DPZ=rgh. Inconnection with the existence of this pressure, on themembrane at issue there also occurs, apart from the pres-sure difference DP, the mechanical pressure differenceDP=Px–Po =DPh. In the presented situation (accordingto the MF formalism), it may be written that [7]

Jvwa ¼ LpaDP � LpaDP

¼ Lpa Px � Poð Þ � LpaRT Csx � Csoð Þ; (13)

where Lpa is the filtration coefficient of semipermeablepores na. Because Lpa =Lprsr, the above equation, whichdescribes the osmotic transport of water takes the follow-ing form:

Jvwa ¼ LprsrðDP � DPÞ; (14)

where Lpr is the filtration coefficient for the entire mem-brane (all N pores), and sr is the reflection coefficient ofthe membrane M.

In connection with the occurrence on the membraneM of the pressure difference DP, within its pores nb the vol-ume flow Jvb will be generated and given by the formula

Jvb ¼ LpbDP ¼ ð1� srÞLprDP; (15)

where Lpb = (1–s)Lpr is the filtration coefficient of thepores nb. While analyzing Figure 5, it is easy to see that

Jvb ¼ Jvwb þ Jvsb ¼ JDPvwbþ JDPvwb þ JDPvsb þ JDPvsb ;

where Jvwb and Jvsb are the volume flows of water (w) andthe solute (s); and JDPvwb and J

DPvsb are volume flows of water

and the solute, driven by the pressure difference DP; whileJDPvwb and JDPvsb are volume flows of water and the solute,driven by the pressure difference DP. The flow JDPvwb maybe expressed by the following formula (Kargol, 2007):

JDPvwb ¼ ð1� srÞð1� �cs �VsÞLprDP� ð1� srÞLprDP;

(16)

where �cs is the mean concentration of the concentrationsCsx and Cso; and �Vs is the molar volume of the substance(s). Equation 14 and the Formula 16 are the sought expres-sions that pertain to water transport along the root radialroute. This transport is realized simultaneously in twoopposite directions. Equation 14 formulates osmotic trans-port of water from the soil to the xylem of the vascular cyl-inder, and Equation 16 depicts water transport in theopposite direction. In order to clarify this, Plots 1 and 2of these equations have been provided. They have beenpresented and explained in Figure 7. Plot 3, in turn, illus-trates the relation of the net flow Jvw, given by the formula:

Jvw ¼ Jvwa þ JDPvwb ¼ LprDP � LprsrDP: (17)

From the discussion of the latter relation(Equation 17), two main conclusions follow. If DP = 0,

Cso

Po

Root

Water

Jva = Jvwa

Jvb = Jvwb + Jvsb

SoilM

Xylem

Exudate

Csx

PX

a

Al

ΔPh = ρgh

na

nb

As

b

Phloem

ANmax

jsA

Membranes, Role in Water Transport in the Soil–Root–XylemSystem, Figure 6 Single-membrane root model in which themembrane M is a heterogeneous porous structure (Csi, Cso areconcentrations; Pi, Po are mechanical pressures; Jva, Jvb arevolume flows; na and nb are numbers of semipermeable andpermeable pores; jAs is the active flow of the substance (s)).

0

1

2

3

ΔP = σΔΠ ΔP = ΔΠ ΔP

Jvwa, Jvwb, Jvww

Membranes, Role in Water Transport in the Soil–Root–XylemSystem, Figure 7 Plots of relations of flows Jvwa = f(DP) andJDPvwb = f(DP), obtained on the basis of Equation 13 – Plot 1,and Equation 16 – Plot 2. Plot 3 depicts the relation Jvw = Jvwa+JDPvwb= f(DP).

MEMBRANES, ROLE IN WATER TRANSPORT IN THE SOIL–ROOT–XYLEM SYSTEM 469

then Jvwa+JDPvwb = �LprsrDP. If DP = |�sDP|, thenJvwa+JDPvwb = 0, which means that the amounts of waterabsorbed or removed by the root are the same.

Conclusions� The above discussion of water transport across the plant

root has demonstrated that the root is capable of gener-ating root pressure. Under the influence of this pressure,water may be pumped through the stem xylem up toa certain height.

� The discussion has also demonstrated that the proposedsingle-membrane model of the root radial route(containing a heterogeneous porous membrane) dis-plays the properties of simultaneous water transport inopposite directions (to the xylem of the vascular cylin-der and out of the root – to the soil). This original inves-tigation result is particularly important from theagrophysical viewpoint, particularly in terms of plantsmaintaining homeostasis.

� Due to this two-way water transport, the plant can alsoobtain necessary nutrients from the soil as well assimultaneously removing into the soil both water andsuperfluous (and frequently harmful) products ofmetabolism occurring in root cells. These unneededproducts are food for many bacteria, which subsist inthe direct vicinity of the root. Their superfluous metab-olism products, in turn, may provide the plant with nec-essary nutrients.

BibliographyFiscus, E. L., 1975. The interaction between osmotic- and pressure-

induced water flow in plant roots. Plant Physiology, 55,917–922.

Fiscus, E. L., 1986. Diurnal changes in volume and solute transportcoefficient of Phaseolus roots. Plant Physiology, 80, 752–759.

Ginsburg, H., 1971. Model for iso-osmotic water flow in plant root.Journal of Theoretical Biology, 32, 917–922.

Kargol, M., 1994. Energetic aspect of radial water transport acrossthe bean root. Acta Physiologiae Plantarum, 16, 45–52.

Kargol, M., 1995. Introduction to Biophysics. Ed. WSP.pp.189–215.

Kargol,M., 2002.Mechanistic approach tomembranemass transportprocesses. Cellular & Molecular Biology Letters, 7, 983–993.

Kargol, M., 2006. Physical Foundations of Membrane Transport ofNon-electrolytes. Poland: Copyright and printed by PPU“Ekspres Druk” Kielce, pp. 25–31.

Kargol, M., 2007. Mass Transport Processes in Membranes andTheir Biophysical Implications. Poland: WSTKT Kielce,pp. 19–144.

Kargol, M., and Kargol, A., 2003a. Mechanistic formalism formembrane transport generated by Osmotic and mechanical pres-sure. General Physiology and Biophysics, 22, 51–68.

Kargol, M., and Kargol, A., 2003b. Mechanistic equations for mem-brane substance transport and their identity with Kedem-Katchalsky equations. Biophysical Chemistry, 103, 117–127.

Kargol, M., and Kargol, A., 2006. Investigation of reverse osmosison the basis of the Kedem- Katchalsky equations and mechanis-tic transport equations. Desalination, 190, 267–276.

Katchalsky, A., and Curran, P. F., 1965. Nonequilibrium Thermody-namics in Biophysics. Cambridge, MA: Harvard UniversityPress, pp. 113–132.

Pitman, G.M., 1982. Transport across plant roots.Quarterly Reviewof Biophysics, 15, 481–553.

Steudle, E., Oren, R., and Schulze, E. D., 1987. Water transport inmaize roots. Plant Physiology, 84, 1220–1232.

Stryer, L., 2000. Biochemistry. Poland: PWN Warszawa,pp. 280–384.

Taura, T., Furumoto, M., and Katou, K., 1987. A model for watertransport in stele of plants roots. Protoplasma, 140, 123–132.

Cross-referencesCoupled Heat and Water Transfer in SoilPlant Roots and Soil StructureRoot Water Uptake: Toward 3-D Functional ApproachesSoil Hydraulic Properties Affecting Root Water UptakeSoil–Plant–Atmosphere ContinuumStomatal Conductance, Photosynthesis, and Transpiration, ModelingWater Budget in SoilWater Effects on Physical Properties of Raw Materials and FoodsWater Reservoirs, Effects on Soil and GroundwaterWater Uptake and Transports in Plants Over Long Distances

MICROBES AND SOIL STRUCTURE

Vadakattu GuptaEntomology, CSIRO, Glen Osmond, SA, Australia

DefinitionMicrobes – Single and multicelled microorganisms thatare microscopic in size (<100 mm in body width) includ-ing bacteria, fungi, algae, and protozoa.Soil structure – The arrangement of primary soil particlesand the pore spaces between them.

Soil particles are arranged together to form aggregateswhich are held together by organic matter and microbialagents (Tisdall and Oades, 1978). Aggregates that are sta-ble to wetting are a key component of good soil structureas they help to maintain soil porosity and aeration at opti-mal levels for soil biota and plants.

Soil microorganisms play an important role in the forma-tion and stabilization of aggregates. Bacteria and fungi pro-duce a variety ofmucilaginous polysaccharides, by utilizingthe more easily available C substrates from fresh residuesand roots, which act like glue and help them attach to clays,sands, and organic materials, resulting in the formation ofnew aggregates. Fungal hyphal networks facilitate the for-mation of macroaggregates (250–2,000 mm) throughenmeshment of soil particles with organic debris (Guptaand Germida, 1988), whereas, decomposing plant residuesand microbial debris encrusted with soil particles, by muci-lages, form the core of microaggregates (20–250 mm) (Sixet al., 2004). Both mycorrhizal and saprophytic fungi con-tribute to aggregate formation and stabilization. The influ-ence of mucigels through their binding capacity is mostlyseen at a scale <50 mm within the aggregates. As thedecomposition of organic material progresses, microbialmetabolites further permeate the surrounding mineral crust,increasing the interparticle cohesion and promoting the sta-bility of aggregates. The production of hydrophobic

470 MICROBES AND SOIL STRUCTURE

400

μM20

KV

2210

52

400

μM10

μm

29.

9 kv

20 K

V90

121

s

100

μM20

KV

2011

72

40 μ

M20

KV

2811

22

4 μM

20 K

V95

142

s1.

55E

315

38/2

4G

UP

TA

a de

f

bc

G

MicrobesandSoilStructure,Figure

1Scan

ningelectronmicrographsofsoilag

gregates,thebackb

oneofsoilstructure:aggregatesofferadiversearrayofmicrositesfor

microbialcolonizationovershortdistances.Stab

lemacroag

gregates(a,b)arecomposedofdecomposingcropresidues(particulate

organ

icmatter),individualprimary

particles,microag

gregates(c),poresofvaryingsizes,an

dbiota

remnan

ts.Fungalhyp

halnetw

orks(thread

-likestructures)holdsoilparticlesan

dmicro

aggregatesonto

the

surfaceofcropresiduesas

partoftheform

ationofmacroag

gregates(d).(e)Bacteriaan

dfungicolonizethesurfacesan

dsm

allp

oresofmacroag

gregates(e).Organ

iccompounds(glues,G)producedbybacteria(f)an

dfungih

elp

bindtheprimaryparticlesan

dplantdebristo

form

stab

leag

gregates.

MICROBES AND SOIL STRUCTURE 471

substances by microorganisms increases the repellency ofaggregates, which decreases the rate of soil wetting andinfluences aggregate stability. The relative importance offungi vs. bacteria for aggregation is dependent on soil tex-ture through its influence on porosity characteristics andnutrient availability (Figure 1).

Good soil structure provides an array of niches, i.e., interms of redox potential and substrate availability, whichcan house diverse microbial communities. The distribu-tion of bacterial and fungal communities and their func-tion varies between different aggregate size classes(Gupta and Germida, 1988). Macroaggregates generallycontain more labile organic matter with rapid turnovertimes and a greater proportion of fungal biomass. Mycelialnetworks of fungi help them exploit the heterogeneous soilmatrix where carbon and nutrient resources are spatiallyseparated (1) over extended distances at microbial scaleand (2) in unconnected pore networks.

The coexistence of diverse bacterial communitieswithin 1-cm3 area reflects the high degree of spatial het-erogeneity in soil microniches. Bacteria often reside inpores and inner surfaces of aggregates as microcoloniesof 2–16 bacteria each, and extensive colonization isrestricted to microsites with higher C availability, e.g., rhi-zosphere and outer surfaces of freshly formed macroag-gregates (Foster, 1988). Location of different phenotypicand functional groups of bacteria varies in different partsof aggregates, e.g., denitrifying bacteria and diazotrophsare generally located within inner parts of aggregates(Hattori, 1988; Mummy et al., 2006). However, locationof aggregates in relation to roots, organic residues, andmacropores is more important for determining the micro-bial community composition and their activity. Bacteriapresent within inner portions of larger aggregates,microaggregates, and micropores are protected from des-iccation and grazing by protozoan predators. The turnoverof these organisms is generally low compared to thoselocated in macropores. Disturbance affects aggregation,aeration, and accessibility of substrates, thereby influenc-ing diversity and activity of microbes in soil.

BibliographyFoster, R. C., 1988. Microenvironments of soil microorganisms.

Biology and Fertility of Soils, 6, 189–203.Gupta, V. V. S. R., and Germida, J. J., 1988. Distribution of micro-

bial biomass and its activity in different soil aggregate size clas-ses as affected by cultivation. Soil Biology and Biochemistry, 20,777–787.

Hattori, T., 1988. Soil aggregates as microhabitats for microorgan-isms. Report of the Institute of Agricultural Research, TohokuUniversity, Vol. 37, pp. 23–36.

Mummy, D., Holben, W., Six, J., and Stahl, P., 2006. Spatial strati-fication of soil bacterial populations in aggregates of diversesoils. Microbial Ecology, 51, 404–411.

Six, J., Bossuyt, H., Degryze, S., and Denef, K., 2004. A history ofresearch on the link betweenmicroaggregates, soil biota, and soilorganic matter dynamics. Soil and Tillage Research, 79, 7–31.

Tisdall, J. M., and Oades, J. M., 1978. Organic matter and water-stable aggregates in soils. Journal of Soil Science, 33, 141–163.

Cross-referencesEarthworms as Ecosystem EngineersMicrobes, Habitat Space, and Transport in SoilMineral–Organic–Microbial InteractionsPlant Roots and Soil StructureSoil Aggregates, Structure, and StabilitySoil Biota, Impact on Physical PropertiesTillage, Impacts on Soil and Environment

MICROBES, HABITAT SPACE, AND TRANSPORTIN SOIL

Karl RitzNatural Resources Department, National Soil ResourcesInstitute, School of Applied Sciences, CranfieldUniversity, Cranfield, UK

DefinitionMicrobes. Life forms that are nominally microscopic (notdiscernable with the unaided human eye) and typicallycomprised of one cell (unicellular).Habitat space. Ecological or environmental volume,encompassing the physical, chemical, and biological envi-ronment that is inhabited by particular organisms,populations and communities.Transport. Movement of entities (e.g., gases, liquids, sol-utes, particulates, organisms) from one region of a definedspace to another.

Microbial life in soilThe living fraction of soils collectively known as the bio-mass, typically constitutes only a small percent of the totalorganic matter, but it is of great significance with regardto soil functioning (Paul, 2007). The biota play fundamentalroles in delivering the majority of the ecosystem goods andservices that soils provide, ranging from soil formation, car-bon and nutrient cycling, modulating structural dynamics,regulating biotic populations, and providing biodiversityand genetic resources (Bardgett, 2005; Kibblewhite et al.,2008). Whilst the body size range of soil organisms spansseveral orders-of-magnitude from centimeters to microme-ters (mm, 10�6m), themajority of the soil biomass is alwaysmicrobial, represented predominantly by bacteria, archaea,and fungi, plus to a lesser extent, protozoa. (Algal biomassin soils is generally present but relatively insignificant,except in some wetland rice systems in the tropics, and isthen predominantly confined to the surface, since there isno light beyond a few millimeter depth in most soils, hencephototrophic organisms do not prevail beyond this zone.However, a small biomass does not necessarily mean algaeare functionally unimportant in soil systems.) Multicellularorganisms such as nematodes and the other soil fauna arenot deemed microbial and hence are outside the scope ofthis entry. The soil microbial biomass typically rangesbetween different ecosystems from tens to thousands of

472 MICROBES, HABITAT SPACE, AND TRANSPORT IN SOIL

mg C g�1 soil, depending upon the system and a wide vari-ety of factors. However, there is generally a positive rela-tionship between the concentration of organic matter insoil and microbial biomass, since carbonaceous materialrepresents the primary energy source for the majority ofthe soil biota. Soil biodiversity is generally greater than inany other ecological compartment, particularly withinmicrobial groups.

The soil habitatThe solid phases of soil are comprised of a diverse mixtureof inorganic (“mineral” materials derived from the parentgeology or glacial deposition) and organic (containing car-bon) materials, made up of living matter (biota) ornonliving matter) components (Brady and Weil, 2002).Soils are produced by gradual processes of biogeochemi-cal transformation, including “weathering,” a range of ero-sive chemical, physical, and biological mechanisms thatproduce a population of mineral particles of varying sizes.The small (nanometer), medium and large (millimeter)components are classified as the clay, silt, and sand frac-tions respectively. These mineral constituents eventuallycombine with organic components, predominantly origi-nating from green plants, to form relatively thin “topsoils.”The inorganic and organic constituents of soil are arrangedin space to form the so-called soil pore network. The ori-gins of soil pore networks are that the fundamental sand-silt-clay components, mediated by organic materials,aggregate to form larger units (Tisdall and Oades, 1983).Generally, the forces binding such aggregates togetherare greater at smaller-size scales, and hence there tendsto be a greater stability of soil structure at these smallerscales. These small units then aggregate further to createlarger structures, with an associated hierarchy of scaleand structural stability. Since the units are nonuniform,their packing creates a porous matrix, and since the con-stituents carry such a wide size range, the porous networkis heterogeneous across a concomitantly wide range ofscales. It is the pore network that comprises the physical,and primary, habitat for all soil organisms, representinga form of “inner space” in which the entirety of below-ground life inhabits (Figure 1). The nature of the pore net-work imparts a structural organization to soil communitiesand strongly influences the way they function and interact(Young and Ritz, 2005).

How soil structure affects microbial functionThe exceptional heterogeneity of the soil pore networkimparts some significant properties to the soil system fromthe perspective of the microbiota. First, it provides a hugesurface area for potential colonization. Many soil bacteriaare adapted to adhere to surfaces and occur as colonies onthe surface of pore walls. Filamentous fungi are also welladapted to grow though soil pore networks by the processof hyphal elongation and branching, but also require somedirect contact with substrates in order to absorb water andnutrients released by the action of the extracellular

enzymes they produce. It is hypothesized that the extremestructural heterogeneity of soils is one basis for the excep-tional diversity found in the soil microbiota, since itprovides an extreme range of spatially isolated microhab-itats which can lead to adaptive radiation (Zhou et al.,2002). Second, it governs the distribution and availabilityof nutrient resources to the biota. Unlike in aquatic sys-tems, where most constituents are relatively well mixedby virtue of existing in a fluid matrix, the physical struc-ture of soils essentially discretizes nutrient resources intospatially distinct patches, many of which will then bephysically protected from being accessed by organisms.This can occur as a result of coating with soil minerals,or by being located in soil pores smaller than the physicalsize of potential consumer organisms. Such physical pro-tection mechanisms can equally apply to dead organicmatter, or potential prey in the case of active predatorssuch as protozoa.

The connectivity and tortuosity of the pore networkgoverns the movement of gases, liquids, and associatedsolutes, as well as particulates and organisms, throughthe matrix. Sessile organisms such as attached bacterialcolonies rely upon the delivery of substrates to the colony,usually in water phases, as well as oxygen for aerobicrespiration, via such pathways. The foraging distancestaken by motile organisms when searching for substrateor prey – and hence the amount of energy consumed –willalso be affected by path lengths for movement, which arerelated to these properties. There is a strong interactionbetween the pore network, water, and microbial activity,fundamentally linked to the relationships between thematric potential of a soil and the associated distributionof water between differently sized pores. Bacteria and pro-tozoa require water films to move in, and their passagewill be curtailed where there is no continuity in such fea-tures. Fungi are less constrained since hyphae can growextended distances through air-filled pores.

As oxygen diffuses about 10,000 times more slowly inwater than in air, water-filled pores – or narrow necks tolarger pores – effectively act as valves, preventing the pas-sage of oxygen and hence its availability to organisms foraerobic respiration. In these circumstances, many so-called facultative microbes can switch their metabolismto alternative biochemical pathways involving anaerobicprocesses.

How microbes affect soil structureWhilst soil structure strongly affects the distribution andfunctioning of microbes and microbial communities, themicrobiota also play important roles in soil structuraldynamics (Brussaard and Kooistra, 1993). Microbes cre-ate soil structure by a number of direct and indirect pro-cesses, including (1) moving and aligning primaryparticles along cell or hyphal surfaces; (2) adhering parti-cles together by the action of adhesives involved in colonycohesion, and other exudates, such as extracellular poly-saccharides (EPS); (3) enmeshment and binding of

MICROBES, HABITAT SPACE, AND TRANSPORT IN SOIL 473

aggregates by fungal hyphae and actinomycete filaments,and associated mycelia; and (4) coating pore walls withhydrophobic compounds, particularly by fungi which pro-duce such polymers to insulate their mycelia, which havea relatively large surface area:volume ratio.

Soil structure is also destroyed by the action ofmicrobes, since much of the organic material whichserves to bind soil particles together is also potentiallyenergy-containing substrate which microbes will

decompose if they can gain access to it. This is the reasonwhy frequent soil disturbance, such as where repeatedtillage is applied to soils, typically leads to a degradationof soil structure and a loss of soil C. Undisturbed soilshave a high proportion of physically protected organicmatter in them. When these are disturbed, such stabilizingmaterial becomes available to microbial assimilation,is decomposed, and a proportion of the C is lost asrespired CO2.

Microbes, Habitat Space, and Transport in Soil, Figure 1 Soil microbes in the soil habitat, visualized in thin sections of undisturbedsoil, prepared appropriately to preserve biological tissue (cf. Nunan et al., 2001). (a) Bacterial cells in worm cast. (b) Bacterial colonyembedded in soil matrix. (c) Mycelia of Rhizoctonia solani in sterilized soil. Note preferential growth in larger pores and apparentlack of penetration of denser masses of soil. (d) Unidentified mycelium in field soil, with abundant sporangia in main region ofpore. (e) Naked amoeba passing through narrow pore in arable soil. (f ) Testate amoebae accumulating near pore wall of arable soil.Scale bar ca. 25 mm.

474 MICROBES, HABITAT SPACE, AND TRANSPORT IN SOIL

Microbes and transport phenomenaTransport processes in soils are fundamentally affected by(1) the nature of thematerial being transported– gas, liquid,solute, colloid, particle, or organism; (2) the porosity, con-nectivity, and tortuosity of the pore network; (3) theexchange properties of the soil, and particularly their distri-bution in pore surfaces; (4) the distribution of water withinthe pore network; (5) interactions with the soil biota.

Microbial effects upon transport processes are gener-ally indirect. Via their involvement with a plethora of ele-mental cycles, microbes transform the constitution andnature of compounds such that their propensity to betransported can be markedly altered, for example, by min-eralizing elements from organic forms, or by altering theredox states of compounds, which can have profoundeffects upon transportability. Soil structure is affected bymicrobial actions as summarized above – and this in turnaffects the moisture release curve, and hence distributionof water.

In relation to direct effects, bacterial movement throughthe soil matrix will result in a concomitant transport of ele-ments bound in the bacterial cells, but given that themajority of bacteria in soils are sessile, this is not consid-ered to be of great significance. Whilst motile, and henceprone to movement through the soil matrix, protozoan bio-mass is generally insufficient to likely influence nutrienttransport phenomena directly. Protozoa can carry viablebacteria in their cells, including pathogenic forms, andhence may play a role in dispersal of such cells withinsoils. Fungi, however, have a much greater direct influ-ence upon transport processes (Ritz, 2006). This arisesas a result of the mycelium which is essentially aconnected and integrated network, which is spatially iso-lated from the soil matrix. Materials can be translocatedwithin mycelia by both active and passive processes, andare to some extent under the control of the fungal organ-ism. Some primary nutrient elements which appearparticularly prone to transport by filamentous fungi areC, N, P, K, S. Other elements, including some heavymetals and radionuclides, have been reported to bedirectly translocated by fungi. Of particular significanceis the transport of P by mycorrhizal fungi to their hostplants (Smith and Read, 2008).

ConclusionsMicrobial activity creates soil structure which in turnaffects the actual and potential function of such organisms.One way of conceptualizing the complex interplaybetween soil microbes – indeed, the entire soil biota – isthat of “soil architecture,” which encompasses the notionof communities living in an appropriately physicallystructured environment, which allows, but modulates,a wide range of direct and indirect interactions whichdeliver the range of functions that lead to an integratedand sustainable system. An inappropriately structured soil(typically, one which has a low porosity) will be detrimen-tal to the biota since there will be a compromised habitat,

manifest as inadequate space and a constrained potentialfor the dynamics of gases, liquids, solutes, and organisms.Without such dynamics, soil functioning will then inevita-bly also be impaired.

BibliographyBardgett, R. D., 2005. The Biology of Soil: A Community and

Ecosystem Approach. New York: Oxford University Press.Brady, N. C., and Weil, R. R., 2002. The Nature and Properties of

Soils. Upper Saddle River: Prentice-Hall.Brussaard, L., and Kooistra, M. J., 1993. Soil Structure/Soil Biota

Interrelationships. Amsterdam: Elsevier.Kibblewhite, M. G., Ritz, K., and Swift, M. J., 2008. Soil health in

agricultural systems. Philosophical Transactions of the RoyalSociety London B, 363, 685–701.

Nunan, N., Ritz, K., Crabb, D., Harris, K., Wu, K., Crawford, J. W.,and Young, I. M., 2001. Quantification of the in situ distributionof soil bacteria by large-scale imaging of thin-sections ofundisturbed soil. FEMS Microbiology Ecology, 37, 67–77.

Paul, E. A., 2007. Soil Microbiology, Ecology and Biochemistry.Oxford: Academic.

Ritz, K., 2006. Fungal roles in transport processes in soils. In Gadd,G. M. (ed.), Fungi in Biogeochemical Cycles. Cambridge:Cambridge University Press, pp. 51–73.

Smith, S. E., and Read, D. J., 2008. Mycorrhizal Symbiosis. Thirdedition. London: Academic.

Tisdall, J. M., and Oades, J. M., 1983. Organic matter and water-stable aggregates in soils. Journal of Soil Science, 33, 141–163.

Young, I. M., and Ritz, K., 2005. The habitat of soil microbes. InBardgett, R. D., Usher, M. B., and Hopkins, D.W. (eds.), Biolog-ical Diversity and Function in Soils. Cambridge: CambridgeUniversity Press, pp. 31–43.

Zhou, J. Z., Xia, B. C., Treves, D. S., Wu, L. Y., Marsh, T. L.,O’Neill, R. V., Palumbo, A. V., and Tiedje, J. M., 2002. Spatialand resource factors influencing high microbial diversity in soil.Applied and Environmental Microbiology, 68, 326–334.

Cross-referencesBiofilms in SoilEarthworms as Ecosystem EngineersMicrobes and Soil StructureSoil Biota, Impact on Physical Properties

MICROCOSM

A little world; a world in miniature (opposed to macro-cosm). Term often used in soil experiments.

BibliographyKurt-Karakus, P., and Kevin C. Jones. 2006. Microcosm studies on

the air–soil exchange of hexachlorobenzene and polychlorinatedbiphenyls. Journal of Environmental Monitoring, 8:1227–1234.

MICRO-IRRIGATION

A water management irrigation technique using a micro-sprinkler or drip irrigation system to minimize waterrunoff.

MICRO-IRRIGATION 475

Cross-referencesWater Use Efficiency in Agriculture: Opportunities for Improvement

MICROSTRUCTURE OF PLANT TISSUE

Krystyna KonstankiewiczInstitute of Agrophysics, Polish Academy of Sciences,Lublin, Poland

SynonymsMicrostructure; Structure

DefinitionThe microstructure of plant tissue is the geometricarrangement of cells as the fundamental elements of thatstructure. The packing of cells is not tight – between cells,surrounded by stiff call walls, there are intercellularspaces. The system of plant microstructure is a three-phaseone – the interior of the cells and of the intercellular spacesis filled with liquid and gas, while the cell walls are thesolid phase. The shape and size of cells are determinedby the cell walls which, due to their physical properties,play the role of “skeleton” of the whole object and affectits properties.The microstructure of plant tissue is its fundamental mate-rial feature and determines its other properties – chemical,physical, and biological.

IntroductionThe latest trends in research of plant tissues relate to theidentification and description of the basic features of thematerial – on the microscale, and then to the search fortheir relations with the properties of whole objects, includ-ing their quality features (Mebatsion et al., 2008). One ofthe fundamental physical properties that characterize thematerial under study is its structure, which determines allof its other properties, for example, physical, chemical,and biological (Aguilera and Stanley, 1990). Materialstudies have a long tradition, supported by practical expe-rience resulting from the production of a variety of mate-rials (Kunzek et al., 1999). Based on the quantitativedescription of the structure of a material studied one canmake comparisons between objects or record changestaking place within a single object as a result of, forexample, a technological process, storage, or from non-homogeneity of the material (Wilkinson et al., 2000).Knowledge on the effect of structure on the properties ofa material, in turn, can be utilized for controlling the tech-nological processes so as to obtain materials with desiredproperties as well as for designing totally new materials(Bourne, 2002).

The fundamental structural element of plant tissue isthe cell and that is the element to whose size relations ofother properties of the material studied are sought(Konstankiewicz and Zdunek, 2005) (Figure 1).

Many years of research in this field, including also myown, indicate that the cellular structure is a characteristicfeature of plant tissues, and every conclusion may onlybe based on current results of quantitative analysis ofstructural parameters, especially those relating to cell size(Konstankiewicz et al., 2002; Zdunek and Umeda, 2005).

Microscope observations of cellular structuresof plantsMicroscope observations are the source of information oncellular structures. Commonly available and continuallyimproved microscope methods permit the observation ofcell structures at various rates of magnification, and digitaltechniques of recording the images obtained permit analy-sis of the information they contain. The broad array ofmicroscopes of various types – optical, confocal, electron(scanning, transmission), acoustic, X-ray, atom force,Raman – with a variety of additional accessories and soft-ware, used for routine observations with very good resultsfor various materials, provides a set of excellent researchtools also with relation to plant tissues (Sargent, 1988;Pawley, 1989; Hemminga, 1992; Blonk and van Aalst,1993; Kaláb et al., 1995; Davies and Harris, 2003;Konstankiewicz and Zdunek, 2005; Figure 2).

A separate group is constituted by methods of imagingof large sample areas –macroscopes, thanks to which it ispossible to assess the spatial organization of structural ele-ments, arrangement of cells, and to study identified direc-tions and large zones of structural changes resulting fromprocesses under study (Zdunek et al., 2007; Devauxet al., 2008; Figure 3).

The choice of suitable equipment is not easy, yet it fre-quently determines the successful solution of problems

Microstructure of Plant Tissue, Figure 1 Microstructure ofpotato parenchyma – preparation of sample: 1 mm sliced witha razor blade and washed with tap water. Image obtained withthe Tandem Scanning Reflected Light Microscope (TSRLM),0.47 mm � 0.65 mm.

476 MICROSTRUCTURE OF PLANT TISSUE

posed, and may also save time and cost. The objective ofthe research, that is, focusing on the significant elementsof the structure and elimination of useless details whileensuring methodological correctness, is of fundamentalimportance. The location of specimen taking and thechoice of the plane of observation are other significant ele-ments. Whereas, the application of preliminary prepara-tion of specimens, from simple slicing to the fixing ofstructure, may introduce disturbances in the structuresunder observation that have to be taken into account insubsequent examinations and in the formulation of con-clusions (Sun and Li, 2003; Delgado and Rubiolo, 2005;Spector and Goldman, 2006; Otero et al., 2009).

In the studies on the physical properties of plant tissues,methods are sought for the obtainment of such images of

the structure that will provide distinct representation ofcell walls that determine the overall dimensions of the cells,permit the measurement of those dimensions and then theircorrelation with other features of the tissue under study(De Smedt et al., 1998; Fornal et al., 1999; Konstankiewiczand Zdunek, 2001; Hepworth and Bruce, 2004).

For an image of a structure to be suitable for computeranalysis, it must be of high quality, that is, primarily witha very good level of contrast. The structural elements ofinterest should be clearly defined, countable, and measur-able. This is a difficult task in the case of plant tissues thathave a low level of coloring, sometimes are even transpar-ent, and additionally, due to their high content of water,tend to dry quickly and get deformed in the course ofobservation.

a b

Microstructure of Plant Tissue, Figure 2 Images of microstructure obtained with the Confocal Scanning Laser Microscope (CSLM),each one 1.4 � 1.4 mm (a) apple and (b) potato parenchyma, sample after preparation procedure.

a b

Microstructure of Plant Tissue, Figure 3 Microstructure of carrot (a) and potato parenchyma (b) sample in natural state. Imageobtained with the macroscope, 6 mm � 4.5 mm.

MICROSTRUCTURE OF PLANT TISSUE 477

The quantitative description of a cellular structurerequires the development of research methodology suit-able for a given object. Even with well-developed proce-dures, the experience and knowledge of the observer areindispensable for correct interpretation of informationcontained in the images analyzed (Zdunek et al., 2004;Gancarz et al., 2007).

Image analysis of plant tissue microstructureGood-quality microscope images of the structure of planttissue can be subjected to detailed analysis leading toquantitative characterization of the object studied. Com-puter image analysis used for the purpose has been knownsince the beginning of the 1960s, and has undergonea violent development in the period of common availabil-ity of digital cameras and of various types (Serra, 1982;Serra, 1988; Kaláb et al., 1995; Abbott, 1999; Wojnaret al., 2002). Computers, cameras, and software are neces-sary equipment of every microscope set. The use of soft-ware for image analysis may appear to be a simple task,especially when there is easy access to professional offersand an abundance of promising results concerning othermaterials. It turns out, however, that the task isa complex one and requires good experience as well asknowledge not only about the object of study itself but alsoabout the methods of image taking, acquisition,processing, and analysis. In spite of the continual advancesof specialist knowledge in the field of image analysis, andthe resultant occasional problems with its understanding,its possession at a basic level is a requirement for everyuser in order to avoid errors that may bring irreversibleeffects at the final stage of formulation of conclusions.

Computer image analysis is the most helpful when weneed to make multiple computations or measurements,or when comparing images with one another. Such prob-lems are encountered not only in research, but also in diag-nostics or in quality control. These tasks require numerousreplications and, most importantly, objectivity of theresults obtained. For images with sufficiently good qual-ity, it is possible to perform automatic analysis with thehelp of professional software (Bourne, 1981; Devauxet al., 2005; Cybulska et al., 2008). In practice, however,application of automated image analysis is not always pos-sible even with good quality of the images, and very oftenimage evaluation and manual corrections by an experi-enced observer are a necessity.

In a flat (2D) image, the size of a cell is defined by mea-sured parameters: surface area, perimeter, diameters, andchords. However, the expected size of a cell as a spatial(3D) object should be its volume and shape and the dimen-sions of such a solid. Identification of a spatial structuralelement permits the modeling of the structure of the wholeobject.

The latest solutions propose highly specialized tech-niques of computer tomography for mathematic modelingof 3-D cell structures of plant tissues. Such models open

up new possibilities in the range of simulation studies onthe transport of liquids and gases through the system ofcell walls and intercellular spaces and on the processesof deformation under mechanical effects (Lee and Ghosh,1999;Maire et al., 2003; Tijskens et al., 2003; Kuroki et al.,2004; Mebatsion et al., 2006; Mebatsion et al., 2009).

Stereological methods in studies onmicrostructure of plant tissueThe need for practical application of structural studieswas at the root of the appearance of quantitative methodsfor structure description – the stereological methods(Underwood, 1970). The notable advance of theoreticalstereology in recent years is related, among other things,with the development of microstructure observation tech-niques and precision sampling methods (Kurzydłowskiand Ralph, 1995). It is the method of selection of objectsto be studied that largely determines the systematic errorof measurement. One of the fundamental errors is to con-duct estimations of structure on the basis of a single crosssection of a sample or a single selected area of the speci-men. That is the erroneous assumption of homogeneityand isotropy of a medium (Ryś, 1995). Plant tissues havea nonhomogeneous and unstable cellular structure, withfrequent discontinuities of notable dimensions (Hamanand Konstankiewicz, 2000; Konstankiewicz et al., 2002).The morphology of their structure is a resultant of manyfactors – cultivar, object size and shape, type of tissue,method of cultivation, time of harvest, postharvestprocessing, and many others. Information on the heteroge-neity of structure is obtained on the basis of microscopeobservation of natural or fixed specimens (Figure 4).

This permits the selection of characteristic areas inthe section of the object – heterogeneity of position, aswell as finding whether there occurs spatial orientationof elements of the structure – anisotropic heterogeneity(Gao and Pitt, 1991). Such a preliminary assessment deter-mines the choice of the place and direction of taking spec-imens, as well as of their number, and in consequencedetermines the correctness of the final result. It is alsoimportant whether the results are to describe a specificarea of the object studied or the object as a whole. Largeareas of uniform structure permit the taking of specimenswith sizes sufficient not only for microscope observationbut also for other determinations, for example, of mechan-ical properties (Zdunek et al., 2004). This is important inthe study of relations between the geometric parametersof the structure and other properties of plant tissue. In caseof doubt as to the homogeneity of studied areas withinthe object, for example, the type of tissue or anisotropyfeatures, specimens should be taken from various zones –close to the surface, from the center, and also from inter-mediate areas, varying the directions of specimen taking.

Based on the analysis of large sets of microscopeimages, it is possible to determine the minimumnumber of cells representative for the whole population

478 MICROSTRUCTURE OF PLANT TISSUE

(Maliński et al., 2000). As an example, for the paren-chyma tissue of potato tuber the minimum numbero cells cannot be less than 300, with high repeatability ofresults for several independent observers. Properly chosennumber of cells under analysis permits the determinationof not only the mean values of the geometric parametersof the cell but also their distributions within the objectstudied (Maliński et al., 1991; Figure 5).

Determination of parameters describing a cellular struc-ture requires the development of suitable researchmethod-ology for a given object.

Due to the extensive range of variability of plant cellsizes, a microscope image may contain an insufficientnumber of whole objects. In such situations, it may be nec-essary to merge images so that the side of the resultantimage be severalfold larger than the mean cell diameter.

The procedures relating to the choice of object, speci-men, and area in the specimen for microstructure observa-tion, have a significant effect on the final result anddetermine the weight of the relationships obtained.

a

c

b

Microstructure of Plant Tissue, Figure 4 Cellular structure of apple (a) potato – cells with starch (b) and onion (c), respectively.Images obtained with optical microscope, samples after preparation, each one 1 mm � 0.8 mm.

Microstructure of Plant Tissue, Figure 5 Microstructure ofpotato parenchyma. Example for an image obtained bycomposing 16 images – TSRLM, 1.5 � 1.2 mm.

MICROSTRUCTURE OF PLANT TISSUE 479

SummaryStructure is an important material feature of plant tissue.Investigation of the microstructure of plant tissue is diffi-cult due to the large biological diversity of the materialand to the necessity of simultaneous consideration ofthe effect of a large number of factors resulting from thecomplex conditions of cultivation and storage and fromthe increasingly demanding technological processes.Such complex materials, with the rapid development ofmeasurement techniques –microscopes, image analysis –require particularly precise selection of proper measure-ment methods, preceded with fundamental studies. Inmost cases, there is no possibility of adaptation of mea-surement systems commonly used in studies on othermaterials, and the new research methods being developedrequire constant improvement due to the rapid develop-ment of measurement techniques and to the changing cul-tivar requirements related with the destined application ofagricultural raw materials and products, and with theincreasing expectations of the consumers. The utilizationof knowledge on plant tissue structure in the process ofplant production is the best method of obtaining, ina reproducible manner, materials with expected proper-ties, and of rapid estimation of the quality of plant mate-rials and products.

All the methods for the investigation of the structure,together with other properties, of plant tissues have poten-tial for development, both due to the originality of meth-odological solutions and to the considerable potential offuture practical applications.

BibliographyAbbott, J. A., 1999. Quality measurements of fruits and vegetables.

Postharvest Biology and Technology, 15, 201–225.Aguilera, J. M., and Stanley, D. W., 1990. Microstructural Princi-

ples of Food Processing and Engineering. London: ElsevierApplied Science.

Blonk, J. C. G., and van Aalst, H., 1993. Confocal scanning lightmicroscopy in food research. Food Research International, 26,297–311.

Bourne, M., 1981. Physical properties and structure of horticulturalcrops. In Peleg, M., and Bagley, E. G. (eds.), Physical Propertiesof Foods. Westport: AVI, pp. 207–228.

Bourne, M. C., 2002. Food Texture and Viscosity: Concept andMeasurement. London: Academic.

Cybulska, J., Zdunek, A., Konskyy, R., and Konstankiewicz, K.,2008. Image analysis of apple tissue cells after mechanical defor-mation. Acta Agrophysica, 11(1), 57–69.

Davies, L. M., and Harris, P. J., 2003. Atomic force microscopy ofmicrofibrils in primary cell walls. Planta, 217, 283–289.

De Smedt, V., Pauwels, E., De Baerdemaeker, J., and Nicolaï, B.M.,1998. Microscopic observation of mealiness in apples:a quantitative approach. Postharvest Biology and Technology,14, 151–158.

Delgado, A. E., and Rubiolo, A. C., 2005. Microstructural changesin strawberry after freezing and thawing processes. Lebensmittel– Wissenschaft und Technologie, 38, 135–142.

Devaux, M.-F., Barakat, A., Robert, P., Bouchet, B., Guillon, F.,Navez, B., and Lahaye, M., 2005. Mechanical breakdown and

cell wall structure of mealy tomato pericarp tissue. PostharvestBiology and Technology, 37(3), 209–221.

Devaux,M.-F., Bouchet, B., Legland, D., Guillon, F., and Lahaye,M.,2008. Macro-vision and grey level granulometry for quantificationof tomato pericarp structure. Postharvest Biology and Technology,47, 199–206.

Fornal, J., Jeliński, T., Sadowska, J., and Quattrucci, E., 1999. Com-parison of endosperm microstructure of wheat and durum whestusing digital image analysis. International Agrophysiscs, 13,215–220.

Gancarz, M., Konstankiewicz, K., Pawlak, K., and Zdunek, A.,2007. Analysis of plant tissue images obtained by confocal tan-dem scanning reflected light microscope. InternationalAgrophysics, 21(1), 49–53.

Gao, Q., and Pitt, R. E., 1991. Mechanics of parenchyma tissuebased on cell orientation and microstructure. Transactions ofthe American Society of Agricultural Engineers, 34, 232–238.

Haman, J., and Konstankiewicz, K., 2000. Destruction processes inthe cellular medium of a plant – theoretical approach. Interna-tional Agrophysics, 14, 37–42.

Hemminga, M. A., 1992. Introduction to NMR. Trends in Food Sci-ence and Technology, 3, 179–186.

Hepworth, D. G., and Bruce, D. M., 2004. Relationships betweenprimary plant cell wall architecture and mechanical propertiesfor onion bulb scale epidermal cells. Journal of Texture Studies,35(6), 586–602.

Kaláb, M., Allan-Wojtas, P., and Miller, S. S., 1995. Microscopyand other imaging techniques in food structure analysis. Trendsin Food Science and Technology, 6, 177–186.

Konstankiewicz, K., Czachor, H., Gancarz, M., Król, A., Pawlak,K., and Zdunek, A., 2002. Cell structural parameters of potatotuber tissue. International Agrophysics, 16(2), 119–127.

Konstankiewicz, K., and Zdunek, A., 2001. Influence of turgor andcell size on the cracking of potato tissue. InternationalAgrophysics, 15(1), 27–30.

Konstankiewicz, K., and Zdunek, A., 2005.Micro-structure Analy-sis of Plant Tissues. Lublin: Centre of Excellence for AppliedPhysics in Sustainable Agriculture Agrophysics.

Kunzek, H., Kabbert, R., and Gloyna, D., 1999. Aspects of materialscience in food processing: changes in plant cell walls of fruitsand vegetables. Zeitschrift Lebensmittel UndersuchungForschung, A, 208, 233–250.

Kuroki, S., Oshita, S., Sotome, I., Kawagoe, Y., and Seo, Y., 2004.Visualisation of 3-D network of gas-filled intercellular spaces incucumber fruit after harvest. Postharvest Biology and Technol-ogy, 33, 255–262.

Kurzydłowski, K. J., and Ralph, B., 1995. The QuantitativeDescription of the Microstructure of Materials. London:CRC Press.

Lee, K., and Ghosh, S., 1999. A microstructure based numericalmethod for constitutive modelling of composite and porousmaterials. Material Science and Engineering, A, 272, 120–133.

Maire, E., Fazekas, A., Salvo, L., Dendievel, R., Youssef, S.,Cloetens, P., and Letang, J. M., 2003. X-ray tomography appliedto the characterization of cellular materials. Related finite ele-ment medeling problems. Composites Science and Technology,63, 2431–2443.

Maliński, M., Cwajna, J., and Chrapoński, J., 1991. Grain size dis-tribution. Acta Stereologica, 10, 73–83.

Maliński, M., Cwajna, J., andMokry,W., 2000. Procedure for deter-mination minimal number of measurements and for testingresults repeatability in quantitative evaluation of materialsmicrostructure. In Proceedings of Sixth International Confer-ence, Stereology and Image Analysis in Material Science,STERMAT 2000, September 20–23, Cracow, pp. 259–264.

480 MICROSTRUCTURE OF PLANT TISSUE

Mebatsion, H. G., Verboven, P., Ho, Q. T., Mendoza, F., Verlinden,B. E., Nguyen, T. A., and Nicolaï, B. M., 2006. Modelling fruitmicrostructure using novel ellipse tessellation algorithm. CMES– Computer Modelling in Engineering and Sciences, 14(1),1–14.

Mebatsion, H. G., Verboven, P., Ho, Q. T., Verlinden, B. E., andNicolaï, B. M., 2008. Modeling fruit (micro)structure, why andhow? Trends in Food Science and Technology, 19, 59–66.

Mebatsion, H. G., Verboven, P., Melese, E. A., Billen, J., Ho, Q. T.,and Nicolaï, B.M., 2009. A novel method for 3-Dmicrostructuremodeling of pome fruit tissue using synchrotron radiationtomography images. Journal of Food Engineering, 93,141–148.

Otero, L., Martino, M., Zaritzky, N., Solas, M., and Sanz, P. D.,2009. Preservation of microstructure in peach and mango duringhigh-pressure-shift frizing. Journal of Food Science, 65,466–470.

Pawley, J., 1989. The Handbook of Biological Confocal Micros-copy. Madison: Electron Microscopy Society of America.

Ryś, J., 1995. Stereology of Materials. Cracow: Fotobit-Design (inPolish).

Sargent, J. A., 1988. The application of cold stage scanning electronmicroscopy to food research. Food Microstructure, 7, 123–135.

Serra, J., 1982. Image Analysis and Mathematical Morphology.London: Academic.

Serra, J., 1988. Image analysis and mathematical morphology, Vol.2: Theoretical Advances. London: Academic.

Spector, D. L., and Goldman, R. D., 2006. BasicMethods inMicros-copy. Protocols and Concepts from Cells. A Laboratory Manual.New York: Cold Spring Harbor Laboratory Press.

Sun, D. W., and Li, B., 2003. Microstructiral change of potato tis-sues frozen by ultrasound-assisted immersion freezing. Journalof Food Engineering, 57, 337–342.

Tijskens, E., Ramon, H., and De Baerdemaeker, J., 2003. Discreteelement modelling for process simulation in agriculture. Journalof Sound and Vibration, 266, 493–514.

Underwood, E. E., 1970. Quantitative Stereology. Massachusetts:Addison Wesley.

Wilkinson, C., Dijksterhuis, G. B., and Minekus, M., 2000. Fromfood structure to texture. Trends in Food Science and Technol-ogy, 11, 442–450.

Wojnar, L., Kurzydłowski, K. J., and Szala, J., 2002. Practice ofImage Analysis. Polish Society of Stereology: Cracov (inPolish).

Zdunek, A., Konskyy, R., Cybulska, J., Konstankiewicz, K., andUmeda, M., 2007. Visual textureanalysis for cell size measure-ments from confocal images. Acta Agrophysica, 21(4), 409–415.

Zdunek, A., and Umeda, M., 2005. Influence of cell size and cellwall volume friction on failure properties of potato and carrot tis-sue. Journal of Texture Studies, 36, 25–43.

Zdunek, A., Umeda, M., and Konstankiewicz, K., 2004. Method ofparenchyma cells parametrisation using fluorescence imagesobtained by confocal scanning laser microscope. ElectronicJournal of Polish Agricultural Universities, AgricultureEngineering, http://www.ejpau.media.pl. 7, 1.

Cross-referencesAgrophysics: Physics Applied to AgriculturePhysical Phenomena and Properties Important for Storage ofAgricultural ProductsPlant BiomechanicsQuality of Agricultural Products in Relation to Physical ConditionsRheology in Agricultural Products and FoodsShrinkage and Swelling Phenomena in Agricultural Products

MINERAL FERTILIZERS

See Fertilizers (Mineral, Organic), Effect on Soil PhysicalProperties

MINERALISATION

Conversion to a mineral substance. In soil science the con-version by microbes of organic matter into simple inor-ganic compounds.

Cross-referencesMineral–Organic–Microbial Interactions

MINERAL–ORGANIC–MICROBIAL INTERACTIONS

Małgorzata Dąbek-Szreniawska1, Eugene Balashov21Institute of Agrophysics PAN, Polish Academy ofSciences, Lublin, Poland2Agrophysical Research Institute RAAS, RussianAcademy of Agricultural Sciences, St. Petersburg, Russia

IntroductionSoil minerals, organic matter, and microorganisms areinteracting with each other. These interactions play animportant role in creating soil structure, controlling bio-geochemical processes, and turnover of organic and inor-ganic substances in soil.

A great role of clay minerals in the formation of com-plexes with soil microorganisms and organic substancesof microbial origin and new quantitative approaches andhypothesis to the studies of interactions between mineralsoil solid phase and microorganisms are pointed out.

Minerals, organic matter, andmicroorganisms have sig-nificant higher-order interactions in terrestrial ecosystem.These reactions exert strong control over soil physical,chemical, and biological processes. Soil mineral surfacesplay a vital role in catalysis of abiotic formation of humicsubstances. Soil minerals, especially noncrystalline min-erals, have the ability to chemically stabilize soil organicmatter. Organic substances also act as binding agents topromote and stabilize aggregation. There are distinct inter-active mechanisms between soil minerals, organic matter,and microbes, which should have a direct influence on thestability and cycling of C, N, P, and S (Huang et al., 2005).

Emerson (1959) presented a physical aspect of themicrostructure of a soil aggregate. Assuming that a soilaggregate is composed mainly of clay and quartz, theseparticles may be linked directly by organic matter orthrough two or more domains which are themselves linkedby organic matter.

MINERAL–ORGANIC–MICROBIAL INTERACTIONS 481

A mineral soil solid phase more or less easily adsorbsbacteria on the surface. Negatively charged adsorbentsrepel rather than attract bacteria of negative charge. Anattention of scientists has been drawn to the fact that thetype of microorganisms is more significant than the typeof adsorbents. For instance, gram-positive, immobilemicroorganisms are characterized by the highest absorb-ing affinity to soil particles.

A particle-size fractionation of soils is commonly usedto quantify the content of soil organic matter andmicrobialbiomass in different particle-size fractions.

A great role of clay minerals is known in the formationof complexes with soil microorganisms and organic sub-stances of microbial origin (Chenu, 1993; Golchin et al.,1995; Schnitzer et al., 1988). The formation of complexesbetween extracellular polysaccharides and montmorillon-ite decreases the rate of microbial decomposition of thesepolysaccharides and other labile organic matter. There-fore, clay soils have a greater capacity for the protectionof biomass of microorganisms within the soil matrix thansand soils (Hassink et al., 1993).

The role of interactions of soil minerals with soilmicroorganisms and their metabolites in the formation ofsoil aggregates was being studied for a long-term period(Harris et al., 1966; Bossuyt et al., 2001; Six et al.,2004). An ability of fungi and bacteria to participate inaggregate formation is dependent on soil texture, its com-position, and availability of C and N. Compared to bacte-ria, fungi play a dominant role especially in water-stablemacroaggregation of light-textured soils with a poorfertility (Guggenberger et al., 1999).

Hattori and Hattori (1976) observed the physical envi-ronment in soil microbiology and undertook an attemptto extend the principles of microbiology to soil microor-ganisms. A formation of complexes was studied betweenEscherichia coli cells and loam soil particles of differentmineralogical origin. Pyrophyllite and kaolinite com-plexes being combined with E. coli cells, showed an adhe-sion to loam particles and formed microaggregates. The“cell–loam particle” complexes treated with sodium ionswere stable in acidic medium and unstable in alkalinemedium. The loam particles treated with Cu++, Co++, orFe ++ demonstrated stronger adhesion with bacterial cellsthan loam ones treated with Na+, and the complexes werestable in alkaline medium but labile in acidic medium(Hattori, 1970).

Three hypotheses have been proposed concerningmechanisms of the adsorption of the mineral’s moleculeson the surface of bacterial cells (Marshall, 1971):

(a) Adsorption of the molecules could proceed throughon the surfaces of bacteria and mineral particles(“face-to-face adsorption”).

(b) Adsorption of the molecules could proceed throughon their edges (“edge adsorption”).

(c) Adsorption could result from both mentioned mecha-nisms (“mixed adsorption”).

Adsorption of sodium bentonite was studied on Bacil-lus subtilis cells (Lahav, 1962). The effect of sodium ben-tonite (molecules smaller than the bacterial cells) on theelectrophoretic mobility of Bacillus subtilis was testedin solutions of sodium chloride and of sodium phosphatewith different concentrations of hydrogen ions and ionicforce. It was noted that the adsorption of sodium benton-ite on bacteria was a reversible process. Bacterial popula-tion consisted of two types of microorganisms. Theaddition of bentonite affected the electrophoretic mobil-ity of one bacterial type, while the other one was notaffected. The first type represented bacteria-adsorbingbentonite, whereas the second one included bacteria notadsorbing bentonite on their surface. Moreover, the pres-ence of both bacterial types was found only in thepresence of bentonite. The effect of bentonite on bacterialmobility depended on pH, ionic force, and contents ofelectrolytes. The lower were the pH values and the higherwas the ionic force, the more effective was the effect ofbentonite.

Among different types of bacteria, for instance, theinfluence of the bacterial biomass of Arthrobacter sp. onthe formation and stabilization of water-stable macroag-gregates in the soil was studied after the addition of plantresidues, mineral fertilizers (NPK, Ca), and bentonite(Dąbek-Szreniawska, 1974). The introduction of the bac-terial biomass of Arthrobacter sp. resulted in an increasedamount of water-stable macroaggregates (>250 mm). Thehighest increase in the water-stable aggregation wasinduced by a combined influence of bacterial biomassand plant residues during a 3-month incubation. Applica-tion of bentonite generally increased the water stabilityof aggregates. Application of mineral NPK fertilizerscaused a decrease in the water stability of aggregates asa result of changes in the activity and the composition ofsoil microbial community.

New quantitative approaches to the studies of interac-tions between mineral soil solid phase and microorgan-isms give an opportunity to get more detailedinformation about structure and function of soil microbialcommunity in micro- and macroaggregates (Väisänenet al., 2005).

ConclusionsThe distinct interactive mechanisms between soil min-erals, especially clay minerals, which form complexeswith microorganisms and organic substances are con-firmed. They affect the stability and cycling of soil chem-ical components and have a positive effect on soilmicrostructure and aggregation.

BibliographyBossuyt, H., Denef, K., Six, J., Frey, S. D., Merckx, R., and

Paustian, K., 2001. Influence of microbial population on aggre-gate stability. Applied Soil Ecology, 16, 195–208.

482 MINERAL–ORGANIC–MICROBIAL INTERACTIONS

Chenu, C., 1993. Clay- and sand-polysaccharide associations asmodels for the interface between microorganisms and soil:water-related properties and microstructure. Geoderma, 56,143–146.

Dąbek-Szreniawska, M., 1974. The influence of Arthrobacter sp.on the water stability of soil aggregates. Polish Journal of SoilScience, 7, 169–179.

Emerson, W.W., 1959. The structure of soil crumbs. Journal of SoilScience, 10, 235–244.

Golchin, A., Oades, J. M., Skjemstad, J. O., and Clarke, P., 1995.Structural and dynamical properties of soil organic matter asreflected by 13C natural abundance, pyrolisis mass spectroscopyand solid-state 13C NMR spectroscopy in density fractions of anoxisol under forest and pasture. Australian Journal of SoilResearch, 33, 59–76.

Guggenberger, G., Elliott, E. T., Frey, S. D., Six, J., and Paustian,K., 1999. Microbial contributions to the aggregation ofa cultivated soil amended with starch. Soil Biology and Biochem-istry, 31, 407–419.

Harris, R. F., Chesters, G., and Allen, O. N., 1966. Dynamics of soilaggregation. Advances in Agronomy, 18, 107–169.

Hassink, J., Bouwman, L. A., Zwart, K. B., Bloem, J., andBrussaard, L., 1993. Relationships between soil texture, physicalprotection of organic matter, soil biota and C andN mineralization in grassland soils. Geoderma, 57, 105–128.

Hattori, T., 1970. Adhesion between cells of E. coli and clay parti-cles. Journal of General and Applied Microbiology, 16,351–359.

Hattori, T., and Hattori, R., 1976. The physical environment in soilmicrobiology: an attempt to extent principles of microbiology tosoil microorganisms. CRC Critical Reviews in Microbiology, 4,423–461.

Huang, P.-M., Wang, M.-K., and Chiu, Ch-Y, 2005. Soil mineral-organic matter-microbe interactions: Impact on biogeochemicalprocesses and biodiversity in soils. Pedobiologia, 49, 609–635.

Lahav, N., 1962. Adsorption of sodium bentonite particles on Bacil-lus subtilis. Plant and Soil, 17, 191–208.

Marshall, K. C., 1971. Sorptive interactions between soil particlesand microorganisms. Soil Biochemistry, 2, 409–445.

Schnitzer, M., Ripmeester, J. A., and Kodama, H., 1988. Character-ization of the organic matter associated with a soil clay. SoilScience, 145, 448–454.

Six, J., Bossuyt, H., De Gryze, S., and Denef, K., 2004. A history ofresearch on the link between (micro)aggregates, soil biota, andsoil organic matter dynamics. Soil and Tillage Research, 79,7–31.

Väisänen, R. K., Roberts, M. S., Garland, J. L., Frey, S. D., andDawson, L. A., 2005. Physiological and molecular characterisa-tion of microbial communities associated with different water-stable aggregate size classes. Soil Biology and Biochemistry,37, 2007–2016.

Cross-referencesClay Minerals and Organo-Mineral AssociatesMicrobes and Soil StructureOrganic Matter, Effects on Soil Physical Properties and ProcessesSoil Aggregates, Structure, and Stability

MIRE

See Peats and Peatlands, Physical Properties

MODELING THE THERMAL CONDUCTIVITYOF FROZEN FOODS

Vlodek R. Tarnawski1, Wey H. Leong2,Toshihiko Momose31Division of Engineering, Saint Mary’s University,Halifax, NS, Canada2Department of Mechanical and Industrial Engineering,Ryerson University, Toronto, ON, Canada3Bavarian Environment Agency, Hof/Saale, Germany

DefinitionThermal conductivity of foods. The bulk property of foodproducts describing their ability to conduct heat resultingfrom a gradient of temperature. It is measured in Wattsper Kelvin per meter (W K�1 m�1).Frozen foods. Foods with their aqueous solutionsconverted into ice and then stored at a temperature belowfreezing point.Thermal conductivity models. Equations describing thematerial ability to conduct heat.

IntroductionA large variety of foods (meat, fish, poultry, fruits, vegeta-bles, bakery, dairy foods, etc.) require refrigeration (chill-ing and/or freezing) to maintain their high quality,edibility, and nutritive values. Solid foods are capillary,porous, colloidal materials consisting of water, solid con-stituents (e.g., carbohydrates, fats, proteins, vitamins, orminerals), and in some instances, air (bakery products,ice cream, cheese, etc.). The majority of fresh naturalfoods (meat, fish, fruits, vegetables) are fully saturatedwith aqueous solution (solid food components dissolvedin water). During a freezing process, food water isconverted into ice and the solution concentrationincreases; consequently, the food initial freezing point(Tf) decreases below 0�C. Good knowledge of frozen foodthermophysical properties, particularly thermal conduc-tivity (l), is required in the design of freezing/thawingequipment and analysis of chilling, freezing, storage, andthawing processes. This property describes food’s abilityto distribute heat strictly by a conduction mode. It is verydifficult to measure and also to estimate as it is influencedby numerous parameters such as initial water content, tem-perature (T ), density, food composition, anisotropy, icefraction, measuring techniques, etc. Available experimen-tal data usually do not cover a full T range encounteredduring freezing, storing, and thawing processes; often ldata are contradictory and incomplete (missingmeasurement details, T, composition data, a sample prep-aration procedure, and uncertainty analysis). Therefore,there is a strong demand for the use of l models in com-puter analysis of food freezing, storing, and thawingprocesses.

MODELING THE THERMAL CONDUCTIVITY OF FROZEN FOODS 483

Thermal conductivity of food componentsWater and ice are two main components of frozen foods.Thermal conductivity of water (lw) is approximately dou-ble that of other solid constituents, while with respect toice it is approximately four times smaller. There isa weak lw dependence on T. On the contrary to water,the thermal conductivity of ice (lice) increases consider-ably with decreasing T. As a result, ice has a dominanteffect on the frozen food’s ability to conduct heat. Severalpredictive equations, lice(T ), lice in W/m K and T in �C,have been published over the last 30 years (Sakazumeand Seki, 1978; Yen, 1981; Choi and Okos, 1986; U.S.Army Corps of Engineers 1996).

lChoi�Okos ¼ 2:2196� 0:0062489 � T þ 0:00010154 � T2

lSakazume�Seki ¼ 2:2156� 0:0100456 � T þ 0:00003445 � T2

lYen ¼ 6:727 � exp �0:0041 � ðT þ 273:15Þ½ �lUS�Army ¼ 2:21� 0:011 � T

(1)

Fukusako (1990) reviewed available lice(T ) data andfitting correlations and concluded that variations amongthe reported data were caused mainly by differences insample purity, sample preparation, methods of measure-ment, and reproducibility of experimental data. The differ-ence in predicted l is on average below 0.05 W/m K(Figure 1). The predictive lice equation by Choi and Okos(1986) is commonly used in food engineering for model-ing l of frozen foods within a range of T from �40�C to0�C; lice increases by about 18.6% as Tdecreases from�1�C to �40�C. Therefore, lice dependence on T maystrongly influence the l of lean frozen foods that usuallycontain a large amount of ice. Thermal conductivities ofremaining basic food components (including water) are

also estimated from equations given by Choi and Okos(1986) and in comparison to ice, l shows a weak depen-dence on T.

lwater ¼ 0:57109� 0:0017625 � T þ 0:00000607036 � T2

lprotein ¼ 0:17881� 0:0011958 � T þ 0:0000027178 � T2

lfat ¼ 0:18071� 0:00027604 � T þ 0:00000017749 � T2

lash ¼ 0:32962� 0:0014011 � T þ 0:0000029069 � T2

lcarbohydrates ¼ 0:2014� 0:0013874 � T þ 0:0000043312 � T2

lfiber ¼ 0:18331� 0:0012497 � T þ 0:0000031683 � T2

(2)

Thermal conductivity of frozen foodsTwo measuring techniques of l are commonly used forfrozen foods, namely: steady and transient state. Thesteady-state technique (a guarded hot plate) requiresa long time to reach the equilibrium T of tested foodsand therefore it is prone to moisture migration and spoil-age. The transient state technique offers a smaller size ofequipment, much shorter measuring times, smaller foodsamples and consequently, it is currently a preferablechoice for l measurement. Thermal conductivity probes,however, lack thorough verification at T < Tf and latentheat of ice may considerably alter measured l (Wangand Kolbe, 1990). Besides, thermal conductivity measure-ments are based on a highly simplified assumption thatfoods are homogeneous and isotropic materials. In fact,fibrous foods usually conduct different amount of heat ina perpendicular and parallel direction to fibers; this differ-ence could be up to about 15% for frozen beef (Heldman,2003). As a result, experimental data gathered may be bur-dened with errors of unknown magnitude. Fat has a very

2.70

2.65

2.60

2.55

2.50

2.45

2.40

2.35

2.30

2.20

2.15

2.25

–40 –35 –30 –25 –20 –15 –10 –5 0Temperature (�C)

Choi-Okos (1986)Yen (1981)

US-Army (1996)Sakazume-seki (1978)

The

rmal

con

duct

ivity

(W

/mK

)

Modeling the Thermal Conductivity of Frozen Foods, Figure 1 Thermal conductivity of ice as a function of temperature.

484 MODELING THE THERMAL CONDUCTIVITY OF FROZEN FOODS

low l (0.2–0.5 W/m K) and therefore, its increasing con-tent has a decreasing effect on the frozen food’s l. Themajority of frozen foods show a strong l dependence ondecreasing T. Figure 2 displays experimental l dataof selected foods, by Pham and Willix (1989) andWillix et al. (1998), undergoing freezing from �1�Cto �40�C; within this T range l increases by a factorof 2–3.

Predicting ice fraction in frozen foodsThorough knowledge of ice fraction in frozen foods is cru-cial for obtaining good l estimates. One of the most fre-quently used mice models was proposed by Schwartzberg(1976).

mice ¼ mw � mbð Þ � 1� TfT

� �; (3)

where mw = mass fraction of water at Tf in �C, mb = massfraction of bounded water, and T = temperature of frozenfood in �C. For example, for meat products, Tf � �0:9�Cand mb ¼ 0:4 � mprotein (Pham and Willix, 1989).

The above equation gives good estimates for highlydiluted solutions, i.e., Tf is close to 0�C.

Golovkin and Tchizov (1951) provided an empiricalrelation for ice fraction that follows closely experimentalmice data of a variety of foods such as meat, fish, milk,eggs, fruits, and vegetables. Their experimental mice, from�1�C to �65�C, was refitted and coefficients of the orig-inal empirical relation were slightly adjusted.

mice ¼ 1:133

1þ 0:339log 1þ T�Tfj j½ �

r2 ¼ 0:996� �

: (4)

The initial freezing point is a key parameter in theabove Equations 3 and 4; its value, however, is difficultto predict or measure. In general, Tf for fish, meats, fruits,and vegetables varies from �2�C to �0.5�C; a summaryof Tf predictive models for a variety of foods waspublished by Sun (2006). A comprehensive summary ofmice modeling approaches was given by Fikiin (1998)and Rahman (2001).

Models predicting thermal conductivityof frozen foodsFor modeling purposes, solid foods are often subdividedinto two groups, namely: fully saturated with water (fish,meat, fruits, vegetables, etc.) and partly saturated withwater (bread, butter, ice cream, etc.), i.e., food pores arefilled with water and/or moist air, respectively. Measuringl of porous saturated/unsaturated materials undergoingfreezing is difficult, error prone, time consuming, labori-ous, and equipment expensive. Therefore, several predic-tive models of l for frozen fully saturated poroussystems (e.g., food and soils) were proposed in the past.In the majority of cases, these models are based ona system composition data and l of their components.The models for saturated solid foods consider that heat isbeing distributed by conduction only, while for unsatu-rated foods more complex modeling attempts are requiredto take into account simultaneous phenomena of waterevaporation, condensation, and ice formation. A concisereview of selective predictive models is given below.

Maxwell–Eucken (Maxwell–E) modelMaxwell (1873) published a model for predicting electricalconductivity of randomly distributed and noninteracting

Lamb leg: muscles parallel to heat flowLean beefMozzarella cheese

Trevally filletsVanilla ice cream

Lamb leg: muscles perpendicular to heat flow

1.80

1.60

1.40

1.20

1.00

0.80

0.60

0.40

0.20

0.00–40 –35 –30 –25 –20 –15 –10 –5 0

Temperature (�C)

The

rmal

con

duct

ivity

(W

/mK

)

Modeling the Thermal Conductivity of Frozen Foods, Figure 2 Thermal conductivity of frozen foods vs. temperature.

MODELING THE THERMAL CONDUCTIVITY OF FROZEN FOODS 485

homogeneously dispersed spheres (d) in a homogeneouscontinuous medium (con); and distances between sphereswere assumed to be considerably larger than spheres’ radii.Eucken (1932) extended thismodel to conduction heat flowin porous media and provided the following weighted aver-age relation.

l ¼ kcon � ycon � lcon þ kd � yd � ldkcon � ycon þ kd � yd ; (5)

kd ¼ 13

Xx¼a;b;c

1þ ðd� 1Þ � gx½ ��1 d ¼ ldlcon

; (6)

where kcon ¼ 1, y are respective volume fractions, gx isa shape factor for each particle’s dimension in the dis-persed phase. For solid spherical particles, gx ¼ 1 3= isapplied to all three dimensions.

Levy modelLevy (1981) modified the Maxwell–Emodel by replacingthe volume fraction of a dispersed phase, yd, by a newfunction Fthat eliminates a dilemma of phase designation.

F ¼ 1s� 12þ yd � 1

2

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi2s� 1þ 2 � yd

� �2

� 8 � yds

s

s ¼ ðd� 1Þ2ðdþ 1Þ2 þ 0:5 � d:

(7)

In reality, porous media such as foods, however, are notnecessarily composed of solid spheres and they may notbe isolated from each other.

deVries modelFurther modification of the Maxwell–E model was doneby deVries (1963), who replaced a spherical shape of solidgrains by rotated oblate ellipsoids dispersed in the contin-uous medium (water, air, ice), generating the followingrelation.

l ¼kcon � ycon � lcon þ

PNj¼1

kj � yj � lj

kcon � ycon þPNj¼1

kj � yj; (8)

where N is the number of solid components; each solidgrain component ( j) has the same weighting factor kjand the same thermal conductivity lj.

kj ¼ 13

Xx¼a;b;c

1þ ljlcon

� 1

� �� gx

� ��1

; (9)

where gx is a solid particle ellipsoidal shape factor for eachof three ellipsoid axes (a, b, c); ga þ gb þ gc ¼ 1,a ¼ b ¼ p � c, where p is a shape value; thus, ga ¼ gb,while gc ¼ 1� 2 � ga.

The models byMaxwell–E, Levy (Equations 5–7), anddeVries (Equations 8–9) are at first sight, identical inform, but in fact a different shape of solid particles (spherevs. rotated ellipsoid) is used in these models. The shapevalue for spheres is known ( p ¼ 1), while for rotatedellipsoid the p is usually obtained by fitting the model toexperimental data. Consequently, the model by deVriesis rarely used in food engineering.

Mascheroni modelMascheroni et al. (1977) assumed that solid frozen meatsare made of partially dehydrated fibers ( f ), surroundedby ice and/or water randomly dispersed in a continuousmatrix of the remaining food tissue. The first version ofthe model considers that heat flow is perpendicular (?)to meat fibers:

l? ¼ lice � lf � 1� xiceð Þxice � lf þ lice � 1� xiceð Þ þ lice � xice; (10)

where xice ¼ 1� ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1� yice

p, yice ¼ mice � rb

rice, rb ¼ food

bulk density, rice ¼ density of ice.The second model option assumes that heat flow is

parallel (//) to the meat fibers:

l== ¼ lice � xice þ 1� xiceð Þ�

lice � x2ice þ lf � 1� xiceð Þ2þ4 � xice � 1� xiceð Þ1lfþ 1

lice

" #:

(11)

Gori modelGori (1983) assumed that frozen meats (also used for fro-zen soils) are composed of cubic cells, each havinga concentrically located cubicle, representing all solidfood components (s), surrounded with the continuousmedium (unfrozen water [w], ice [i], or a mixture of both[wi]). The first version of the model considers the horizon-tal parallel isotherms in the cubic cell.

1l¼ b� 1lwi �bþ

b

lwi � b2 � 1� �þ ls

b¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffi

11� ywi

3

r(12)

The second version of the model takes into accounta vertical parallel heat flux in the cubic cell.

l ¼ 1b�ðb�1Þ

lwiþ b

ls

þ lwi � b2 � 1

b2: (13)

Effective medium theory (EMT) modelThe EMT model was adapted to predicting l of vegetablefoods by Mattea et al. (1986).

Xn1

yj �lj � llj þ 2l

¼ 0: (14)

486 MODELING THE THERMAL CONDUCTIVITY OF FROZEN FOODS

For frozen foods, three phases were considered:

l1 ¼ lw y1 ¼ yw l2 ¼ lice y2 ¼ yicel3 ¼ ls y3 ¼ 1� yw � yice

Modified resistor series modelCleland and Valentas (1997) proposed a modified resistorseries model for calculating l of frozen foods, which mayalso contain bounded water (b).

yw þ yb þ ysl

¼ yw þ yblw

þ ysPnj¼1

yjys� lj

: (15)

Geometric mean model (GMM)The GMMwas successfully used in predicting l of frozensoils by Johansen (1975).

l ¼ lyss � lyiceice � lyww : (16)

Parallel model (//)It assumes parallel configuration of the system compo-nents in the direction of heat flow.

l== ¼Pn1yi � ljPn1yj

: (17)

Series model (S)It assumes series configuration of the system componentsin the direction of heat flow.

lS ¼

Pnj¼1

yi

Pnj¼1

yjlj

: (18)

Pham (1990) carried out a comprehensive analysis ofseven physical predictive models and one set of empiricalequations for l of frozen foods and concluded that themodel by Levy (1981) was the most accurate followedby the effective medium theory (EMT) model and theMaxwell–Eucken model. Tarnawski et al. (2005) ana-lyzed and compared eight models (deVries, Levy,Mascheroni, Maxwell–Eucken, Gori, geometric mean,effective medium theory, modified resistor) and their 54versions, developed for frozen foods and soils, against ldata of 13 meat products (Pham and Willix, 1989) in Tranging from �1�C to �40�C. The thermal conductivityof ice and other food components were assumed to be Tindependent; the fraction of ice was modeled usinga relation given by Schwartzberg (1976). The models by

deVries, Levy, and Mascheroni produced results veryclose to experimental data. In general, relatively small dif-ferences in predictions were observed, i.e., Relative RootMean Squared-Error (RRMSE) was ranging from 5% to7%; it was probably due to meat anisotropy, constant lice,varying composition, measuring errors, and limitation ofpredictive models. A similar analysis carried out byTarnawski et al. (2007) for T-dependent lice showed thatthe models by Mascheroni et al. (1977) are marginallybetter than the other remaining models. Carson (2006)published a comprehensive review of models applied tounfrozen and frozen foods. Wang et al. (2006) verified31 predictive models by comparing them with l conduc-tivity data for 22 meat and seafood products (Willixet al., 1998; Pham and Willix, 1989), with T ranging from�40�C to +40�C. The results obtained showed that thebest 15 models had RRMSE varying from 11.5% to16.4%. van der Sman (2008) modeled thermophysicalproperties of frozen meat products using their compositiondata, l(T ) for water and ice, with the ice fraction estimatedby a relation given by Schwartzberg (1976). The l modelby Torquato and Sen (1990) was extended to ternary sys-tems (frozen meats) by modeling two existing phases(unfrozen water and muscle fiber) as one continuousmedium and handling ice crystals as the dispersed phase.The model introduces a factor dependent on the structureand orientation of protein fibers. This modeling attemptwas compared with experimental l data by Pham andWillix (1989) and Willix et al. (1998), and predictionswith an error of 10% were reported. In the past, compre-hensive reviews on modeling l of foods were also givenby Sweat (1985) and Sanz et al. (1989).

In terms of frozen unsaturated food products (internalpores filled in with moist air), l modeling is much moreintricate as porosity of foods and filling mediums (water,air) have to be considered. Cogné et al. (2003) measuredl of ice creams using a hot-wire probe technique.The measured l was affected by T and the ice cream’sapparent density. For l modeling, ice cream was consid-ered as a heterogeneous and multiphase system composedof a frozen concentrated continuous liquid phase matrix(carbohydrates, proteins, lipids, and unfrozen water) filledin with ice and air. The dispersed phase (liquid water andsolid components) l was estimated by a parallel modeland then together with lice(T) was applied to de Vriesmodel for obtaining l of a standard mix at different T;then, this l with a value of lair was applied to Maxwell–Eucken model to obtain the overall l. Jury et al. (2007)carried out l measurements of bread with T ranging from�20�C to 80�C. Bread was assumed to be composed ofdry fraction (proteins, carbohydrates, lipids, mineral salts,etc.) and pores containing moist air, both surrounded bywater.When this system is exposed toT< Tf, very complexsimultaneous heat and moisture transfer phenomena (evap-oration–condensation effects, ice formation, etc.) takeplace (Hamdami et al., 2004). Consequently, measuringand estimating effective l for unfrozen and frozen breadsstill remains a very complex issue (Hamdami et al., 2003).

MODELING THE THERMAL CONDUCTIVITY OF FROZEN FOODS 487

Conclusions and recommendationsAccurate l predictions for frozen foods are difficult due toa large number of modeling parameters involved, such asinitial water content, temperature, density, food structure,anisotropy, initial freezing point, rate of ice formation,temperature-dependent thermal conductivity of ice, etc.Frozen foods are often considered as a mixture of homo-geneous solid components being dispersed in continuousphase (ice, water, or water+ice). There are, however, stillsome dilemmas with a proper selection of a continuousmedium and therefore, further studies are needed.A choice of water+ice as the continuous phase is promis-ing as it improves l predictions and eliminates discontinu-ity at the initial freezing point Tf, but nevertheless itrequires further comprehensive testing. Temperature-dependent lice has a strong influence on predicted l of fro-zen lean meats; but, there are some unanswered concernsregarding the equations currently used for lice estimation,as lice also depends on ice structure and a freezing processfor both water solution and pure water. Taking intoaccount these issues and the enormous complexity offoods and the freezing process, developing an accuratemechanistic l model for frozen foods is rather unrealistic.Review of frozen food literature reveals that Levy modeland Mascheroni’s equations produce very close results toexperimental data (within approximately 10% error). Themodel by Mascheroni, however, is simpler in form,accounts for food fiber orientation, does not require anyfitting, and it produces close predictions for low-fat meats.For high-fat meats, Levy’s model gives better l estimates,but the model structure is too complex and therefore it isnot easy to use. Highly complex predictive models are dif-ficult to handle when applied to food products of uncertainfood structure, composition, and unknown thermal con-ductivity, because numerous rough assumptions have tobe made. Besides, it is still unclear what the real impactof highly accurate l predictions will have on the analysisof freezing processes and designing freezing/thawingequipment, due to a variety of foods having very diversephysical properties. It appears that, the demand for sophis-ticated and difficult to use l models may not be fully jus-tified and more attention should be paid to the use ofsimple models (e.g., geometric mean, series–parallel,etc.) applied to a wide variety of foods.

BibliographyCarson, J. K., 2006. Review of effective thermal conductivity

models for foods. International Journal of Refrigeration,29(6), 958–967.

Choi, Y., and Okos, M. R., 1986. Effects of temperature and compo-sition on the thermal properties of foods. In Le Maguer, M., andJelen, P. (eds.), Food Engineering and Processes Applications,1. Amsterdam: Elsevier Applied Science, pp. 93–101.

Cleland, D. J., and Valentas, K. J., 1997. Prediction of freezing timeand design of food freezers. In Singh, R. P., and Valentas, K. J.(eds.), 1997. CRC Press: Handbook of Food Engineering Prac-tice, pp. 71–124.

Cogné, C., Andrieu, J., Laurent, P., Besson, A., and Nocquet, J.,2003. Experimental data and modeling of thermal properties ofice creams. Journal of Food Engineering, 66(4), 469–475.

DeVries, D. A., 1963. Thermal properties of soils. In van Wijk,W. R. (ed.), Physics of Plant Environment. New York: Wiley,pp. 210–235.

Eucken, A., 1932. Thermal conductivity of ceramics refractorymaterials. Forsch. Geb. Ing., B-3, Forschungsheft, 53, 6–21.

Fikiin, K. A., 1998. Ice content prediction methods during foodfreezing: a survey of the eastern European literature. Journal ofFood Engineering, 38, 331–339.

Fukusako, F., 1990. Thermophysical properties of ice, snow, andsea ice. International Journal of Thermophysics, 11(2),353–372.

Golovkin, N. A., and Tchizov, G. B., 1951. Refrigeration technol-ogy of food products (in Russian). Pischepromizdat, 1951, 232.

Gori, F., 1983. A theoretical model for predicting the effective ther-mal conductivity of unsaturated frozen soils. In Proceedings ofthe Fourth International Conference on Permafrost, Fairbanks(Alaska). Washington: National Academy Press, pp. 363–368.

Hamdami, N., Monteau, J.-Y., and Le Bail, A., 2003. Effective ther-mal conductivity of a high porosity model at above and sub-freezing. International Journal of Refrigeration, 7, 809–816.

Hamdami, N., Monteau, J.-Y., and Le Bail, A., 2004.Thermophysical properties evolution of French partly bakedbread during freezing. Food Research International, 37, 703–713.

Heldman, D. R., 2003. Encyclopedia of Agricultural, Food, andBiological Engineering. Marcel Dekker.

Johansen, O., 1975. Thermal conductivity of soils. PhD Thesis,University of Trondheim, Norway, CRRELTranslation 637.

Jury, V., Monteau, J.-Y., Comiti, J., and Le-Bail, A., 2007. Determi-nation and prediction of thermal conductivity of frozen partbaked bread during thawing and baking. Food Research Interna-tional, 40, 874–882.

Levy, F. L., 1981. A modified Maxwell–Eucken equation for calcu-lating the thermal conductivity of two-component solutions ormixtures. International Journal of Refrigeration, 4, 223–225.

Mascheroni, R. H., Ottino, J., andCalvelo, A., 1977. Amodel for thethermal conductivity of frozen meat.Meat Science, 1, 235–243.

Mattea, M., Urbicain, M. J., and Rotstein, E., 1986. Prediction ofthermal conductivity of vegetable foods by the effective mediumtheory. Journal of Food Science, 51(1), 113–115.

Maxwell, J. C., 1873. A Treatise on Electricity and Magnetism, 1stedn. Oxford: The Calerdon Press, Vol. 1, p. 440.

Pham, Q. T., 1990. Prediction of thermal conductivity of meats andother animal products. In Spiess, W. E. L., and Schubert, H.(eds.), Engineering and Food, vol. 1st edn. London: ElsevierApplied Science, pp. 408–423.

Pham, Q. T., and Willix, J., 1989. Thermal conductivity of freshlamb meat, offal and fat in the range –40 to + 30�C: measure-ments & correlations. Journal of Food Science, 54(3), 508–515.

Rahman, M. S., 2001. Thermophysical properties of foods. InD-Wen Sun. (ed.), Advances in Food Refrigeration. LeatherheadPublishing: Surrey.

Sakazume, S., and Seki, N., 1978. Thermal properties of iceand snow at low temperature region. Trans. JSME, 44(382),2059–2069.

Sanz, P. D., Alonso, M. D., and Mascheroni, R. H., 1989.Equations for the prediction of thermophysical properties ofmeat products. Latin American Applied Research, 19, 155–163.

Schwartzberg, H. G., 1976. Effective heat capacity for the freezingand thawing of food. Journal of Food Science, 41, 152–156.

Sun, Da-Wen., 2006. Handbook of Frozen Food Processing andPackaging. Boca Raton: Taylor & Francis.

488 MODELING THE THERMAL CONDUCTIVITY OF FROZEN FOODS

Sweat, V. E., 1985. Thermal conductivity of food: present state ofthe data. Trans. American society of heating refrigerating andairconditioningEngineers, 19(part B), 299–311.

Tarnawski, V. R., Cleland, D. J., Corasaniti, S., Gori, F., andMascheroni, R. H., 2005. Extension of soil thermal conductivitymodels to frozen meats with low and high fat content. Interna-tional Journal of Refrigeration, 28, 840–850.

Tarnawski, V. R., Bovesecchi, G., Coppa P., and Leong, W. H.,2007. Modeling thermal conductivity of frozen foods with tem-perature dependent thermal conductivity of ice. The22ndInternational Congress of Refrigeration, August 21–26,2007, Beijing, China.

Torquato, S., and Sen, A. K., 1990. Conductivity tensor of aniso-tropic composite media from microstructure. Journal of AppliedPhysics, 67(3), 1145–1155.

U.S. Army Corps of Engineers, 1996. Engineering & Design –IceEngineering: Manual No. 1110-2-1162, Washington D.C. p. 2.

van der Sman, R. G. M., 2008. Prediction of enthalpy and thermalconductivity of frozen meat and fish products from compositiondata. Journal of Food Engineering, 84, 400–412.

Wang, D., and Kolbe, E., 1990. Thermal conductivity of surimi-measurement and modeling. Journal of Food Science, 50(5),1458–1462.

Wang, J. F., Carson, J. K., Cleland, D. J., and North, M. F., 2006.A comparison of effective thermal conductivity models forfrozen foods, in Innovative Equipment and Systems for Comfortand Food Preservation, Proceedings, Auckland 2006,pp. 336–343.

Willix, J., Lovatt, S. J., and Amos, N. D., 1998. Additional thermalconductivity values of foods measured by a guarded hot plate.Journal of Food Engineering, 37, 159–174.

Yen, Y. C., 1981. Review of thermal properties of snow, ice and seaice. CRREL Report, 81(10), 1–27.

Cross-referencesDatabases on Physical Properties of Plants and AgriculturalProducts

MODULUS OF ELASTICITY (YOUNG’S MODULUS)

The ratio of normal stress to corresponding strain belowthe proportional limit of a material (e.g., plant stalks androots).

MODULUS OF RIGIDITY

The ratio of shear stress to the shear strain below the pro-portional limit of a material displacement per unit samplelength.

MOHRE CIRCLE OF STRESS

A graphical representation of the components of stress act-ing across the various planes at a given point, drawn withreference to axes of normal stress and shear stress.

BibliographyGlossary of Soil Science Terms. Soil Science Society of America.

2010. https://www.soils.org/publications/soils-glossary

Cross-referencesFriction Phenomena in Soils

MONITORING PHYSICAL CONDITIONS INAGRICULTURE AND ENVIRONMENT

Wojciech M. SkieruchaDepartment of Metrology and Modeling of AgrophysicalProcesses, Institute of Agrophysics, Polish Academy ofSciences, Lublin, Poland

DefinitionMonitoring physical conditions in agriculture and envi-ronment is a collection and processing of data that allowto determine temporal and spatial variation of these condi-tions to make proper decisions in the future for the benefitof sustainable food production.

IntroductionAgriculture has both positive and negative influence onenvironment. Its primary function is meeting the growingdemand for food. Agriculture creates habitats not only forhumans but also for wildlife and plays an important rolein sequestering carbon, managing watersheds, and pre-serving biodiversity. But agriculture degrades naturalresources causing soil erosion, introducing unrecoverablehydrological changes, contributing in groundwater deple-tion, agrochemical pollution, loss of biodiversity, reducingcarbon sequestration from deforestation, and carbon diox-ide emissions from forest fires (Doran, 2002).

Physical conditions in agriculture and environment canbe defined as physical properties and processes involvedin mutual relation between the processes of food and fiberproduction and the impact of these processes on naturalagro-environment. They include topography, surfacewater and groundwater distributions, heat-temperaturedistributions, wind direction changes, and intensity.

The measures of physical conditions in agriculture andenvironment should include a minimum number of mea-surable physical indicators, that can provide sufficientdata not only for decision making in the production offood and fiber but also in ecosystem function and themaintenance of local, regional, and global environmentalquality.

Agricultural sustainability and environmental qualityare determined by the health of soil, which is a thin layerof the earth surface forming the interface between agricul-ture and environment. Soil quality, which can be described

MONITORING PHYSICAL CONDITIONS IN AGRICULTURE AND ENVIRONMENT 489

by the combination of its physical, chemical, and biologi-cal properties and contamination by inorganic and organicchemicals is critical to environmental sustainability(Arshad and Martin, 2002). The physical properties ofsoils play a major role in controlling mass and energytransport in the subsurface environment.

The basic physical soil quality indicators recommendedor used by soil researchers are as follows (Schoenholtzet al., 2000): texture (influence retention and transport ofwater and nutrients), soil depth and topsoil depth (majorfactor in total nutrient, water, and oxygen availability),bulk density (decides about root growth, rate of watermovement, soil volume expression), soil water content,soil temperature, available water holding capacity(informs about plant available water, erosivity), soilroughness (erosivity, soil tilth), saturated hydraulic con-ductivity (water and air balance, hydrology regulation),resistance to penetration (root growth), porosity (water/air balance, water retention, root growth), and aggregatestability and size distribution (root growth, air/waterbalance).

The need for monitoring physical conditions in agricul-ture and environment is increasing, because of increasingpressure on natural resources, sustainability, and exhaus-tion of nonrenewable resources. Advances in sensors,computers, and communication devices result in greatamounts of temporal and spatial information that shouldbe processed in real-time (or near real-time) to produceunambiguous information for the decision-making stage.

Robust, low-cost, and preferably real-time sensing sys-tems are needed for monitoring physical conditions inagriculture and environment. Commercial products havebecome available for some sensor types. Others are cur-rently under development, especially in the field of preci-sion agriculture.

Selection of the monitoring methodMonitoring is a series of measurements (or observations)done in a planned fashion in order to assemble a morecomplete view of existing conditions through time. Singleobservations or surveys are made at a point in time torecord the current condition. Monitoring, in a formalsense, is a series of surveys.

The goals, applied sensors, data collection strategies,and methods of analysis used in monitoring must bedefined in detail and in advance before taking further stepsthat sometimes require formal, technical, and financialarrangements. These elements and their relationshipdepend on the temporal and dimensional scale of plannedobservations. There are technical means to measureminute quantities of chemicals in the environment. Atthe other end of the dimensional scale, there are space-based satellite sensors routinely scanning and mappingthe earth surface several times a day.

The choice of mass and volume of the sampled mate-rial, size of measurement probes, and temporal and spatial

locations should be chosen for assuring the measured sam-ple to be representative of the target component. This isespecially important for soil because it is not homoge-neous both in space and in time.

The investigation of the agricultural and environmentalphysical conditions sometimes requires physical removalof samples from the environment, for example, collectingsoil cores in the vadose zone disrupt soil profiles and cancreate preferential flow paths. Measurements involvingunrecoverable destruction of the measured medium arecalled destructive. Nondestructive measurements or mon-itoring is becoming increasingly important with the tech-nological miniaturization as well as the development ofsensors, electronics and telecommunication. The exampleof nondestructive monitoring is by means of satellite-based sensors to measure topography, plant cover, or top-soil temperature.

SelectivityThe key feature of the applied measurement method is itsselectivity, that is, the lack of sensitivity of the conversionfunction (calibration) on the influence from the factorsother than the measured one. Proper selectivity liberatesthe user from frequent, specific for each soil, in situcalibrations.

The solution to the problem of electric measurementof the physical quantity in selective way is to findsuch an electric property of the medium conditioning it,which is specific to the considered medium. For example,the specific property of molecular oxygen in electrolyte(“soil water”) is small activation energy of electrode reac-tion of its reduction. It can be concluded that electric mea-surement of oxygenation may be based on the ammetricmeasurement of the current of electrode reaction of oxy-gen molecules reduction (Malicki and Bieganowski,1999).

Concerning the problem of electric soil moisture mea-surement, the medium conditioning moisture is waterand its specific property is the polar structure of watermolecules. Polarity of water molecules is the reason thatdielectric permittivity (dielectric constant) of water ismuch higher than permittivity of soil solid phase (the rel-ative dielectric constant of water in the electric field of fre-quency below 18 GHz and at room temperature is about81, while the relative dielectric constant of solid phase is4–5 at the same conditions). The dielectric constant of soilstrongly depends on its water content, therefore it may beconcluded that electricmeasurement of soilmoisture shouldbe based on the measurement of its dielectric constant.

Similarly, concerning the issue of electric measurementof soil salinity, the media conditioning salinity are saltspresent in soils and the specific property is their ionicform. The ability to transport electric charge by the ionsin “soil water” allows the soil to conduct electric current.Therefore, the electric measurement of soil salinity shouldbe based on the measurement of its electric conductivity.

490 MONITORING PHYSICAL CONDITIONS IN AGRICULTURE AND ENVIRONMENT

Status of water as significant issue of agrophysicsIncreasing demands of water management result in thecontinuous development of its tools. One of the mostimportant, besides the simulation models of water bal-ance, is monitoring of water status in porous materialsdefined as a space-temporal recording of the water proper-ties that stimulate the phenomena and processes observedin the soil–plant–atmosphere system.

Concerning agrophysics, water status in porous mate-rials is the issue of first priority, because each phenomenonor process examined in its scope depends on water status.

Monitoring of water status is accomplished using digi-tal systems. The digital data acquisition systems react onlyon electric signals and the applied sensors must convertthe measured value into the proportional electric signal.

Water status of soil, as a porous material should beexpressed by minimum five variables: amount of waterin the soil (i.e., soil moisture), soil potential, salinity, oxy-genation, and temperature (Malicki, 1999).

The most difficult are the electric measurement of soilwater potential and soil water content (soil moisture);therefore, they are the subject of permanent research. It isassumed here, that the method successfully verified forsoils will be also applicable for other porous agriculturalmaterials because their structure is not as complex as soil.

Monitoring physical conditions in agriculture and envi-ronment is done by sampling, automatic ground measure-ment systems or remote sensing with the use of planes orsatellites.

SamplingThere are several soil basic physical properties that cannotbe measured automatically and the necessary informationabout their variability in time and/or space is gathered bymeans of traditional sampling. Soil mechanical properties,that is, soil texture and soil density or porosity, which isfunctionally dependent on soil density, do not change intime in uncultivated areas because they present the effectof long-term geological processes.

Sampling strategy in agro-environmental monitoringdepends on the objectives, which include increase agricul-ture productivity (yield indicators), optimize water use inirrigation systems, or increase carbon sequestration. Also,it depends on observation scale from the geographical oradministrative point of view: an experimental plot, field,landscape, watershed, farm, district, or whole country.Spatial and temporal variability must also be taken intoaccount in constructing time schedule and sampling loca-tions to be representative.

Sampling usually affects the environment, that is, thistype of measurement maybe destructive to the testedobject or its immediate vicinity. Drilling a deep well to col-lect groundwater does not affect the groundwater environ-ment but the geological profile is irreversibly damaged.Soil cores collected in the vadose zone disrupt soil profilesand can create preferential flow paths.

Ground monitoring systems with automated dataacquisition and processingThe technological progress in material science, electronics,telecommunication, and informatics effects in the develop-ment of new sensing devices that can be adopted in exam-ining objects of agricultural and environmental studies.They include Time Domain Reflectometry (TDR) andFrequency Domain Reflectometry (FDR) probes for thesimultaneous measurement of soil moisture, electrical con-ductivity, and temperature (Skierucha et al., 2006). Theresensing devices include sensors and transducers, wherethe former detects the signal or stimulus and the latterconverts input energy of one form into output energy ofanother form. An example of the sensor is thermistor givingthe change of resistance as the function of temperature. Thissensor associated with electrical circuitry forms an instru-ment, also called a transducer, that converts thermal energyinto electrical energy.

Another important element of a ground monitoring sys-tem is a data acquisition and processing unit, which mon-itors the output signal of the transducer and processes theresulting data into a form that can be understood by theend user. The basic features of this unit include user-friendly interface for the operator, large storage memory,physical communication interfaces preferably with serialtransmission from the instrument to the operator’s note-book. Telemetry with the application of wireless networksis becoming popular especially for distant ground moni-toring systems (Wang et al., 2006).

Monitoring stations must meet strong requirementsconcerning power consumption. The hardware designersshould use low-power electronic circuits and apply sleepmode operations whenever possible. Also, charging theinternal battery is accomplished with a solar panel.

Remote sensingRemote sensing in agriculture and environment is theacquisition of biological and geochemical informationabout the condition and state of the land surface by sensorsthat are not in direct physical contact with it. The transmis-sion of this information is in the form of electromagneticwaves reflected from the land surface either in passivemode – when the source of energy is the sun and/or theEarth, or in active mode – when the source energy is arti-ficially generated. The analyzed signal reflected from theland surface is composed with different wavelengths overthe electromagnetic spectrum (Artiola et al., 2004a).Today a large number of satellite sensors observe the Earthat wavelengths ranging from visible to microwave, at spa-tial resolutions ranging from submeters to kilometers andtemporal frequencies ranging from minutes to weeks ormonths (Rosenqvist et al., 2003).

The remote sensed data provide information about eco-system stability, land degradation and desertification(Artiola et al., 2004b), carbon cycling (Rosenqvist et al.,2003), erosion and sediment yield, soil moisture

MONITORING PHYSICAL CONDITIONS IN AGRICULTURE AND ENVIRONMENT 491

(Wigneron et al., 1998; Kerr, 2007), and plant and weedscover (Thorp and Tian, 2004).

ConclusionMonitoring physical conditions in agriculture and envi-ronment can be done in various temporal and dimensionalscales and with the application of numerous instrumentsand methods reflecting the current development of tech-nology. The received and processed data increase ourknowledge for the benefit of social, political, and eco-nomic sustainable development as well as for better under-standing the nature.

BibliographyArshad, A. A., and Martin, S., 2002. Identifying limits for soil qual-

ity indicators in agro-ecosystems. Agriculture, Ecosystems andEnvironment, 88, 152–160.

Artiola, J., Pepper, I. L., and Brusseau, M. L., 2004a. Monitoringand characterization of the environment (Chapter 11). In Envi-ronmental Monitoring and Characterization. Burlington:Elsevier, pp. 1–9.

Artiola, J., Pepper, I. L., and Brusseau, M. L., 2004b. Remote sens-ing for environmental monitoring (Chapter 11). In Environmen-tal Monitoring and Characterization. Burlington: Elsevier,pp. 183–206.

Doran, J. W., 2002. Soil health and global sustainability: translatingscience into practice. Agriculture, Ecosystems and Environment,88, 119–127.

Kerr, Y. H., 2007. Soil moisture from space: where are we? Hydro-geology Journal, 15, 117–120.

Malicki, M. A., 1999. Methodical aspects of water status monitor-ing in selected bio-materials. Acta Agrophysica (Series Mono-graphs), 19 (in Polish).

Malicki, M. A., and Bieganowski, A., 1999. Chronovoltammetricdetermination of oxygen flux density in the soil. InternationalAgrophysics, 13(3), 273–281.

Rosenqvist, Å., Milne, A., Lucas, R., Imhoff, M., and Dobson, C.,2003. A review of remote sensing technology in support of theKyoto Protocol. Environmental Science & Policy, 6, 441–455.

Schoenholtz, S. H., Van Miegroet, H., and Burger, J. A., 2000.A review of chemical and physical properties as indicators of for-est soil quality: challenges and opportunities. Forest Ecologyand Management, 138, 335–356.

Skierucha, W., Wilczek, A., and Walczak, R. T., 2006. Recent soft-ware improvements in moisture (TDR method), matric pressure,electrical conductivity and temperature meters of porous media.International Agrophysics, 20, 229–235.

Thorp, K. R., and Tian, L. F., 2004. A review on remote sensing ofweeds in agriculture. Precision Agriculture, 5, 477–508.

Wang, N., Zhang, N., andWang, M., 2006. Wireless sensors in agri-culture and food industry – recent development and future per-spective. Computers and Electronics in Agriculture, 50, 1–14.

Wigneron, J. P., Schmugge, T., Chanzy, A., Calvet, J. C., andKerr, Y.,1998. Use of passive microwave remote sensing to monitor soilmoisture. Agronomie, 18, 27–43.

Cross-referencesClimate Change: Environmental EffectsNondestructive Measurements in SoilOnline Measurement of Selected Soil Physical PropertiesRemote Sensing of Soils and Plants ImagerySpatial Variability of Soil Physical Properties

MUALEM EQUATION

Model for predicting hydraulic conductivity of unsatu-rated porous media.

BibliographyMualem, Y., 1976. A newmodel for predicting hydraulic conductiv-

ity of unsaturated porous media. Water Resources Research,12:513–522.

Cross-referencesWater Budget in Soil

MULCHING

See Water Use Efficiency in Agriculture: Opportunitiesfor Improvement

MULCHING, EFFECTS ON SOIL PHYSICALPROPERTIES

Antonio Jordán1, Lorena M. Zavala1,Miriam Muñoz-Rojas21MED_Soil Research Group, Departamento deCristalografía, Mineralogía y Química Agrícola,Universidad de Sevilla, Sevilla, Spain2Instituto de Recursos Naturales y Agrobiología de Sevilla(CSIC), Sevilla, Spain

DefinitionMulch. Mulch is any material, other than soil, placed orleft at the soil surface for soil and water management.Mulching. In agriculture and gardening, mulching is thepractice of leaving crop residues or other materials onthe soil surface for soil and water conservation and keep-ing favorable and stable environments for plant growth.

IntroductionMulching is a form of conservation tillage consisting ofleaving a layer of crop residues (CR) or other materialson the soil surface. Mulch helps to preserve high and sus-tainable yields by increasing the soil organic matter(SOM) content and therefore improving soil physicalquality. Mulch tilling is also a form of minimum tillageand a cost-efficient alternative for high-yield conservationagricultural practice.

Leaving CR or other substances on the soil surface isa traditional practice for protecting soil from erosion andenhancing fertility (Lal and Stewart, 1995). It has beenreported that conventional agricultural practices, based

492 MUALEM EQUATION

on intensive fertilization and chemical amendments, oftenlead to degradation processes, such as erosion (see entryTillage Erosion), acidification, and the emission of green-house gases (see entry Greenhouse Gases Sink in Soils).Current global problems such as population growth,greenhouse effect, malnutrition, water quality, reductionof agricultural land, and soil degradation (see entryDesertification: Indicators and Thresholds) require theimplementation of conservation tillage practices toaddress the problem of sustainability, food security, andenvironmental quality.

Materials used as mulchAwide variety of materials can be used as mulch. Most ofmulches are organic materials (e.g., CR, litter, straw,leaves, or weed biomass), but other inorganic or indus-try-derived materials can be also used (plastic film,gravels, or geotextiles). The election depends in bothavailability and objectives.

Organic mulches must be weed free, easy to apply, andreadily available by farmers. The mulch decompositiontime can vary greatly. Depending on the amount and typeof mulch, varying quantities of nutrients and organic mat-ter enter the soil during the decomposition process. Inagriculture, the most commonly used organic mulchesare crop/plant residues, produced on-site or off-site andleft on the soil surface after cropping (e.g., wheat straw).A large quantity of residue is produced annually, so thatit constitutes a renewable and easily available resource.Other mulches used in agriculture and gardening are woodchips, pine bark, and pine needles. Some wastes, such asshredded or composted clipped grass, litter, and smallbranches can also be used.

Inorganic materials such as geotextiles can be used asmulch. Some of the advantages of using geotextiles isthe prevention of weed growth (at least in a great propor-tion), and the normal aeration and water exchange. Rockfragments and gravels can be used as inorganic mulchmaterials. They show low decomposition rates and donot require annual replacement, but cannot be suitablefor all crops. Plastic films help control most weeds andcontribute to water conservation. Plastic films are usedpredominantly in extensive crop areas, but show someproblems: interruption of air, water, and nutrients flowbetween the topsoil and the atmosphere, as well as otherenvironmental problems such as disposal of plasticmaterials.

Effects on soil physical propertiesSoil structure and aggregate stabilitySoil structure is extremely important for the maintenanceof soil quality and productivity. Aggregate stability (AS)affects root density and elongation (see entry RootResponses to Soil Physical Limitations), air and waterflow and erosion (Amézketa, 1999). Some of the main fac-tors affecting soil aggregation are SOM content (see entry

Soil Aggregates, Structure, and Stability), texture andmoisture content, but external factors as crop type, tillagepractices, or microfauna are also important. Long-termtillage affects AS (Angers et al., 1993; Unger et al.,1998; Álvaro-Fuentes et al., 2008), as tillage destroysaggregates leading to a decrease in aggregate size and poreclogging by fine particles. It has been reported that decom-position rates of SOM are lower with minimum tillage andresidue retention, and consequently it increases over time(Loch and Coughlan, 1984; Dalal, 1989).

AS is determined by the cohesive forces between parti-cles. Therefore, it can be used as an index of structure andphysical soil stability. Soil texture, clay mineralogy, cat-ions, and the quantity and quality of SOM are key factorscontrolling aggregation. Plant roots (see entry Plant Rootsand Soil Structure), microorganisms (especially fungi),and organic substances are also involved in the formationof aggregates and AS. AS may vary seasonally (Hillel,1998) or during tilling. After mulching, increased SOMcontent contributes to enhance aggregation, as it has beenreported under a diversity of climate areas (Mulumba andLal, 2008; Jordán et al., 2010) even in the short term(Hermawan and Bomke, 1997).

Inorganic mulches (e.g., plastic film) show limited orno effect on soil structure. Zhang et al. (2008), for exam-ple, reported that under no tillage, the increase in SOMcontent and AS in soils under straw cover was higher thanunder plastic film; in this case, soil quality under plasticmulch was similar or even lower than in non-coveredsoils. In contrast, geotextiles may increase SOM content,improving topsoil structure and AS (Bhattacharyya et al.,2010).

Bulk density and porosityThe effects of CR on soil bulk density (BD; see entryBulk Density of Soils and Impact on their Hydraulic Prop-erties) are highly variable. Although high BD has beenobserved under mulch relative to conventional tillage(Bottenberg et al., 1999), decreased bulk densities havealso been reported by Oliveira and Merwin (2001) andGhuman and Sur (2001). In other cases, there is no rela-tionship between mulch rate and BD. This variabilitymay be due to differences in management practices, soiltype, and the type of mulch material used in the experi-ments. However, Mulumba and Lal (2008) found thatBD increased for mulching rates between 0 and 5 Mgha�1 wheat straw mulch, but strongly decreased for higherrates.

Pores of different size (see entry Pore Size Distribu-tion), shape, and continuity are created by abiotic andbiotic factors (Kay and VandenBygaart, 2002). Mulumbaand Lal (2008) found that total porosity increased signifi-cantly with increase in mulch rate after an 11-year treat-ment in the USA. Increased porosity due to mulchapplication has been also reported after shorter periods(Oliveira and Merwin, 2001; Jordán et al., 2010).

MULCHING, EFFECTS ON SOIL PHYSICAL PROPERTIES 493

Penetration resistanceMany studies have reported greater penetration resistancein soils under no-tillage practices than in other conven-tionally tilled soils in the upper centimeters, and, gener-ally, penetration resistance is higher under reducedtillage systems with residue cover. Mulch application hasa significant effect on penetration resistance but only atcertain stages of the crop production. After an experimentwith Polish Luvisols under reduced and no-tillage prac-tices, penetration resistance increased in the growing sea-son, causing reduced plant growth and crop yield (Pabinet al., 2003). In this case, straw mulch did not counteractthe negative changes in the parameters of the soil strength.According to Bielders et al. (2002), differences in penetra-tion resistance after different treatments are mostly due todifferences in intrinsic soil properties (e.g., cohesion, BD).

Crusting and sealingPlant residues on the soil surface protect it against crusting(Sumner and Stewart, 1992), improving AS and infiltra-tion rates (see entry Soil Surface Sealing and Crusting).Le Bissonnais and Arrouays (1997) observed that increas-ing SOM content decreased soil surface sealing. Aftera study in western Niger, Bielders et al. (2002) observedlow permeability erosion crusts and discontinuous struc-tural crusts with partially exposed clay skins in soils underconventional tillage, in contrast to mulched soils. As it hasbeen reported in stone-covered soils (Martínez-Zavala andJordán, 2008; Zavala et al., 2010), gravel mulch helps toavoid soil sealing and crusting (Poesen and Lavee, 1994).

The use of plastic filmmulches has spread considerablyas a way to reverse the low crop yields (e.g., Zhang andMa, 1994), increasing the risk of crust formation (Liet al., 2005).

Soil temperatureTemperature affects the rate of soil biological and chemi-cal processes (see entry Temperature Effects in Soil ).The amount of energy entering the soil depends stronglyon soil color, aspect, and the vegetative cover. CR on thesoil surface can affect or completely modify the soil tem-perature regime by reducing the amount of energy enter-ing the soil by the interception of radiation, shading thesoil surface, and buffering temperature variations.

Soil temperature range is usually narrower in mulchedthan unmulched soils. Wheat straw has a higher albedoand lower thermal conductivity than bare soil, and there-fore it reduces the input of solar energy (Horton et al.,1996). On the other hand, during colder periods, wheatstraw mulch on the soil surface insulates it from the colderatmosphere (Zhang et al., 2009).

Results after application of inorganic mulches varydepending on the mulch material. After field experimentsin the UK, Cook et al. (2006) demonstrated that soil tem-perature reduced with higher mulching rates. In contrast,the use of inorganic mulches can increase soil tempera-ture. Nachtergaele et al. (1998) reported that gravel mulch

increased soil temperature and decreased evaporation (seeentry Evapotranspiration) in vineyard soils in Switzer-land. Organic geotextiles can also attenuate extreme tem-perature fluctuations, reducing water loss throughevaporation. Inorganic materials such as plastic mulchesare often used to increase soil temperature in horticulture,leading to high yields. Apart from other environmentalproblems, intensive use of plastic mulches for increasingsoil temperature shows some limitations. Enhanced min-eralization rates can lead to exhaustion of SOM, affectinglong-term soil physical and chemical fertility (Li et al.,2004).

Soil waterMulching has a great impact on soil water and surfacewater. Mulching decreases runoff by improving infiltra-tion rate and increases water storage capacity by improv-ing retention (see entry Field Water Capacity). Inaddition, reduced evaporation rates help to extend theperiod of time during which soil remains moist.

Mulching improves considerably soil water character-istics, although different results have been reported.Organic mulches on the soil surface induce optimal soilconditions for plant growth, enhancing soil water reten-tion and availability, and increasing macroporosity(Martens and Frankenberger, 1992). Much research hasshown that use of mulch can increase infiltration anddecrease evaporation, resulting in more water stored andreduced runoff rates (e.g., Smika and Unger, 1986).

Wheat straw mulch is considered the best way ofimproving water retention in the soil and reducing soilevaporation. High available water capacities have beenreported under high mulching rates and reduced or no tillpractices. Mulumba and Lal (2008) and Jordán et al.(2010) found that even low mulch rates have a strongimpact on the available water content. Contrasting datahave been reported by Głąb and Kulig (2008), who foundno effect in available water content after applying mulchand different tillage systems. Results can also varybetween the upper and lower layers of the soil profile.

Soil erosion riskMany researchers have reported low or negligible soillosses in mulched soils in comparison with conventionalsoil tillage (see entry Water Erosion: Environmentaland Economical Hazard ). The hydrological/erosionalresponse of mulched soils depends largely on themulching rates applied during the crop period. It has beenreported that the erosive consequences of moderate stormsin the Mediterranean area could be strongly reduced byusing just 5 Mg ha�1 year�1 mulching rates (Jordán et al.,2010). A mulch layer increases the roughness and theinterception of raindrops, delaying runoff flow and favor-ing infiltration (García-Orenes et al., 2009). Low erosiveresponses of mulched soils have been reported fromdiverse climate areas of the world. In contrast, Jin et al.(2009) suggested that the relation between mulching rate

494 MULCHING, EFFECTS ON SOIL PHYSICAL PROPERTIES

and interrill soil detachment is not unique and can varydepending on rainfall intensity. Increasing cover ratesreduce infiltration and lead to an increasing net flux,which becomes deeper and faster in its concentrated flowpart. In this case, ponding is faster and deeper, thus thewater column pressure is greater and thereafter infiltrationtakes place more quickly and penetrates more deeply.However, this response can be overridden under moderaterainfall intensity by a dense soil cover or thick mulchlayers.

Plastic mulches substantially accelerate runoff genera-tion in slopes (Wan and El-Swaify, 1999). According toBhattacharyya et al. (2010), the use of geotextiles is aneffective soil conservation practice, but its efficiencydecreases in large areas. Despite synthetic geotextiles dom-inating the commercial market, geotextiles constructedfrom organic materials are highly effective in erosioncontrol and vegetation establishment (Ogbobe et al.,1998) and can be an ecological alternative for farmers(Giménez-Morera et al., 2010). Anyway, experimentalstudies under natural and simulated rainfall have demon-strated that cotton geotextiles reduce soil losses butincrease water losses, probably due to water repellency ofcotton. Although soil erosion can be severely reduced bygeotextiles at the pedon or meter scale, surface runoff mayresult in high erosion rates at slope and watershed scalesas more runoff will be available (Giménez-Moreraet al., 2010).

SummaryDuring the last decades, conservation tillage techniqueshave displaced conventional tillage in many areas of theworld. The use of CR left on the soil surface improves soilquality and productivity through favorable effects on soilphysical properties. Mulch farming is a form of conserva-tion tillage that preserves soil quality and the environment.Mulch affects soil physical properties by improving SOMcontent, increasing soil porosity and AS. Indirectly,mulching also regulates soil temperature, and increaseswater retention capacity.

An organic mulch layer serves as a protecting layeragainst rainfall-induced soil erosion by reducing dropimpacts and modifying the hydrological response of theexposed surface. CR and other organic mulches interceptrainfall and contribute to decrease runoff rates andenhance infiltration, protecting soil from erosion. Inor-ganic mulches as geotextiles, gravels, or plastic filmsshow a range of erosional responses to rainfall.

BibliographyÁlvaro-Fuentes, J., Arrúe, J. L., Gracia, R., and López, M. V., 2008.

Tillage and cropping intensification effects on soil aggregation:temporal dynamics and controlling factors under semiarid condi-tions. Geoderma, 145, 390–396.

Amézketa, E., 1999. Soil aggregate stability: a review. Journal ofSustainable Agriculture, 14, 83–151.

Angers, D. A., Samson, N., and Légère, A., 1993. Early changes inwater-stable aggregation induced by rotation and tillage in a soil

under barley production. Canadian Journal of Soil Science, 73,51–59.

Bhattacharyya, R., Smets, T., Fullen, M. A., Poesen, J., and Booth,C. A., 2010. Effectiveness of geotextiles in reducing runoff andsoil loss: a synthesis. Catena, 81, 184–195.

Bielders, C. L., Michels, K., and Bationo, A., 2002. On-farm evalu-ation of ridging and residue management options in a Sahelianmillet-cowpea intercrop. 1. Soil quality changes. Soil Use andManagement, 18, 216–222.

Bottenberg, H., Masiunas, J., and Eastman, C., 1999. Strip tillagereduces yield loss of snapbean planted in rye mulch.HortTechnology, 9, 235–240.

Cook, H. F., Valdes Gerardo, S. B., and Lee, H. C., 2006. Mulcheffects on rainfall interception, soil physical characteristics andtemperature under Zea mays L. Soil and Tillage Research, 91,227–235.

Dalal, R. C., 1989. Long-term effects of no-tillage, crop residue, andnitrogen application on properties of a Vertisol. Soil ScienceSociety of America Journal, 53, 1511–1515.

García-Orenes, F., Cerdà, A., Mataix-Solera, J., Guerrero, C., Bodí,M. B., Arcenegui, V., Zornoza, R., and Sempere, J. G., 2009.Effects of agricultural management on surface soil propertiesand soil-water losses in eastern Spain. Soil and Tillage Research,106, 117–123.

Ghuman, B. S., and Sur, H. S., 2001. Tillage and residue manage-ment effects on soil properties and yields of rainfed maize andwheat in a subhumid subtropical climate. Soil and TillageResearch, 58, 1–10.

Giménez-Morera, A., Ruiz Sinoga, J. D., and Cerdà, A., 2010. Theimpact of cotton geotextiles on soil and water losses from Med-iterranean rainfed agricultural land. Land Degradation andDevelopment, 21, 210–217.

Głąb, T., and Kulig, B., 2008. Effect of mulch and tillage system onsoil porosity under wheat (Triticum aestivum). Soil and TillageResearch, 99, 169–178.

Hermawan, B., and Bomke, A. A., 1997. Effects of winter covercrops and successive spring tillage on soil aggregation. Soiland Tillage Research, 44, 109–120.

Hillel, D., 1998. Environmental Soil Physics. San Diego: Academic.Horton, R., Bristow, K. L., Kluitenberg, G. J., and Sauer, T. J., 1996.

Crop residue effects on surface radiation and energy balance –review. Theoretical and Applied Climatology, 54, 27–37.

Jin, K., Cornelis, W. M., Gabriels, D., Baert, M., Wu, H. J.,Schiettecatte, W., Cai, D. X., De Neve, S., Jin, J. Y., Hartmann, R.,and Hofman, G., 2009. Residue cover and rainfall intensity effectson runoff soil organic carbon losses. Catena, 78, 81–86.

Jordán, A., Zavala, L. M., and Gil, J., 2010. Effects of mulching onsoil physical properties and runoff under semi-arid conditions insouthern Spain. Catena, 81, 77–85.

Kay, B. D., and VandenBygaart, A. J., 2002. Conservation tillageand depth stratification of porosity and soil organic matter. Soiland Tillage Research, 66, 107–118.

Lal, R., and Stewart, B. A., 1995. Soil Management – ExperimentalBasis for Sustainability and Environmental Quality. Boca Raton:Lewis. Advances in Soil Science.

Le Bissonnais, Y., and Arrouays, D., 1997. Aggregate stability andassessment of soil crustability and erodibility. II. Application tohumic loamy soils with various organic carbon contents. Euro-pean Journal of Soil Science, 48, 39–48.

Li, F. M., Wang, J., Xu, J. Z., and Xu, H. L., 2004. Productivity andsoil response to plastic film mulching durations for spring wheaton entisols in the semiarid Loess Plateau of China. Soil and Till-age Research, 78, 9–20.

Li, L. L., Huang, G. B., Zhang, R. Z., Jin, X. J., Li, G. D., andChan, K. Y., 2005. Effects of conservation tillage on soilwater regimes in rainfed areas. Acta Ecologica Sinica, 25,2326–2332.

MULCHING, EFFECTS ON SOIL PHYSICAL PROPERTIES 495

Loch, R. J., and Coughlan, K. J., 1984. Effects of zero tillage andstubble retention on some properties of a cracking clay. Austra-lian Journal of Soil Research, 22, 91–98.

Martens, D. A., and Frankenberger, W. T., 1992. Modification ofinfiltration rates in an organic-amended irrigated soil. AgronomyJournal, 84, 707–717.

Martínez-Zavala, L., and Jordán, A., 2008. Effect of rock fragmentcover on interrill soil erosion from bare soils in Western Andalu-sia, Spain. Soil Use and Management, 24, 108–117.

Mulumba, L. N., and Lal, R., 2008. Mulching effects on selectedsoil physical properties. Soil and Tillage Research, 98, 106–111.

Nachtergaele, J., Poesen, J., and van Wesemael, B., 1998. Gravelmulching in vineyards of southern Switzerland. Soil and TillageResearch, 46, 51–59.

Ogbobe, O., Essien, K. S., and Adebayo, A., 1998. A study of bio-degradable geotextiles used for erosion control. GeosyntheticsInternational, 5, 545–553.

Oliveira, M. T., andMerwin, I. A., 2001. Soil physical conditions ina New York orchard after eight years under differentgroundcover management systems. Plant and Soil, 234,233–237.

Pabin, J., Lipiec, J., Włodek, S., and Biskupski, A., 2003. Effect ofdifferent tillage systems and straw management on some physi-cal properties of soil and on the yield of winter rye in monocul-ture. International Agrophysics, 17, 175–181.

Poesen, J. W. A., and Lavee, H., 1994. Rock fragments in top soils:significance and processes. Catena, 23, 1–28.

Singh, B., Chanasyk, D. S., McGill, W. B., and Nyborg, M. P. K.,1994. Residue and tillage management effects on soil propertiesof a typic cryoboroll under continuous barley. Soil and TillageResearch, 32, 117–133.

Smika, D. E., and Unger, P. W., 1986. Effect of surface residues onsoil water storage. Advanced Soil Science, 5, 111–138.

Sumner, M. E., and Stewart, B. A., 1992. Soil Crusting – Chemicaland Physical Processes. Boca Raton: Lewis. Advances in SoilScience.

Unger, P. W., Jones, O. R., McClenagan, J. D., and Stewart, B. A.,1998. Aggregation of soil cropped to dryland wheat and grainsorghum. Soil Science Society of America Journal, 62,1659–1666.

Wan, Y., and El-Swaify, S. A., 1999. Runoff and soil erosion asaffected by plastic mulch in a Hawaiian pineapple field. Soiland Tillage Research, 52, 29–35.

Zavala, L. M., Jordán, A., Bellinfante, N., and Gil, J., 2010. Rela-tionships between rock fragment cover and soil hydrologicalresponse in a Mediterranean environment. Soil Science andPlant Nutrition, 56, 95–104.

Zhang, S. F., and Ma, T. L., 1994. Yield components and cultivationtechnology of corn with high grain yield through plastic filmcover in West Yellow River area. Gansu Agricultural Scienceand Technology, 1, 16–17.

Zhang, Z., Zhang, S., Yang, J., and Zhang, J., 2008. Yield, grainquality and water use efficiency of rice under non-floodedmulching cultivation. Field Crops Research, 108, 71–81.

Zhang, S., Lövdahl, L., Grip, H., Tong, Y., Yang, X., and Wang, Q.,2009. Effects of mulching and catch cropping on soil tempera-ture, soil moisture and wheat yield on the Loess Plateau ofChina. Soil and Tillage Research, 102, 78–86.

Cross-referencesBulk Density of Soils and Impact on their Hydraulic PropertiesDesertification: Indicators and ThresholdsEvapotranspirationField Water CapacityGreenhouse Gases Sink in Soils

Plant Roots and Soil StructurePore Size DistributionRoot Responses to Soil Physical LimitationsSoil Aggregates, Structure, and StabilitySoil Surface Sealing and CrustingTemperature Effects in SoilTillage ErosionWater Erosion: Environmental and Economical Hazard

MUNSELL COLOR SYSTEM

A color designation system that specifies the relativedegrees of the three simple variables of color: hue, value,and chroma.

Cross-referencesColor in Food EvaluationColor Indices, Relationship with Soil Characteristics

MYCORRHIZAL SYMBIOSIS AND OSMOTIC STRESS

Juan M. Ruiz-LozanoDepartamento de Microbiología del Suelo y SistemasSimbióticos, Estación Experimental del Zaidín (CSIC),Granada, Andalucía, Spain

DefinitionThe term Mycorrhiza comes from the Greek words“mycos,” meaning fungus and “rhiza,” meaning root andapplies to a mutualystic symbiosis between roots of mosthigher plants and a group of soil fungi belonging to thephyla Glomeromycota, Basidiomycota or Ascomycota.By this mutualystic association, the plant receives soilnutrients (especially phosphorus) and water, while thefungus receives a protected ecological niche and plant-derived carbon compounds for its nutrition (Varma, 2008).

The term Osmotic Stress refers to all the environmentalconditions that induce a water deficit in the plant tissues,limiting plant growth and development. It generallyincludes drought, cold and salinity, which directlydecreases the plant water content due to an also low soilwater content (drought) or which difficult the right uptakeof water from soil due to the diminution of soil waterpotential (cold and salinity).

Eco-physiological studies investigating the role of themycorrhizal symbiosis against osmotic stresses have dem-onstrated that the symbiosis often results in altered rates ofwater movement into, through, and out of the host plants,with consequent effects on tissue hydration and plantphysiology (Augé, 2001). Thus, it is accepted that themycorrhizal symbiosis protects host plants against the det-rimental effects of water deficit, and that this protectionresults from a combination of physical, nutritional and

496 MUNSELL COLOR SYSTEM

cellular effects. Several mechanisms have been proposedto explain these effects. The most important are: directuptake and transfer of water through the fungal hyphae tothe host plant, better osmotic adjustment of host plants,enhancement of plant gas exchange through hormonalchanges, and protection against the oxidative damage gen-erated by drought. Modulation by the mycorrhizal symbio-sis of plant genes involved in the response of plants towater deficit have also been described, with special empha-sis on the role of aquaporins (Ruiz-Lozano et al., 2006).Results obtained so far show that the mycorrhizal symbio-sis up- or down-regulates aquaporin genes depending onthe own intrinsic properties of the osmotic stress. In anycase, the induction or inhibition of particular aquaporinsby mycorrhizal symbiosis results in a better regulation ofplant water status and contribute to the global plant resis-tance to the stressful conditions, as evidenced by the better

growth andwater status of mycorrhizal plants under condi-tions of water deficit (Ruiz-Lozano and Aroca, 2009).

BibliographyAugé, R. M., 2001. Water relations, drought and vesicular-

arbuscular mycorrhizal symbiosis. Mycorrhiza, 11, 3–42.Ruiz-Lozano, J. M., and Aroca, R., 2009. Modulation of aquaporin

genes by the arbuscular mycorrhizal symbiosis in relation toosmotic stress tolerance. In Sechback, J., and Grube, M. (eds.),Symbiosis and Stress. Germany: Springer. ISBN 978-90-481-9448-3 in press.

Ruiz-Lozano, J. M., Porcel, R., and Aroca, R., 2006. Does theenhanced tolerance of arbuscular mycorrhizal plants to waterdeficit involve modulation of drought-induced plant genes?The New Phytologist, 171, 693–698.

Varma, A., 2008.Mycorrhiza. State of the Art, Genetics and Molec-ular Biology, Eco-Function, Biotechnology, Eco-Physiology,Structure and Systematics, 3rd edn. Berlin: Springer.

MYCORRHIZAL SYMBIOSIS AND OSMOTIC STRESS 497

http://www.springer.com/978-90-481-3584-4