1
Dew is a Major Factor Affecting the Vegetation Water Use Efficiency
2
rather than a Source of Water In the Eastern Mediterranean.
3
Jiftah Ben-Asher1 and Pinhas Alpert2 and Arie Ben-Zvi3.
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1. Jacob Blaustein Institute for Desert Research Ben Gurion University of the Negev,
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Israel 2. Department of Geophysics and Planetary Sciences, Tel Aviv University, Israel.
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3 Hydrologic service Israel .
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Correspondence and requests for materials should be addressed to J. Ben-Asher
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(benasher@bgumail.bgu.ac.il).
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1
Abstract
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The purpose here is to re-examine the ecological importance of dew in arid and semi arid
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regions with a focus over the eastern Mediterranean. This re-evaluation is of particular
14
importance under the controversial perspective that dew is insufficient as a source of water to
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plants but it is sufficient to promote the spread of plant diseases . Adana, Turkey was selected
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as an appropriate semi arid test ground with well documented meteorological data and a newly
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developed photosynthesis (Pn) and transipiration rate (Tr) monitor (PTM) which was used to
18
detect the response of Tr and Pn to the presence of dew on the leaves. A convolution
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theoretical model was used to simulate “no dew” days and simultaneously PTM measurements
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were used to obtain actual situations with “dew”. Contrary to expectations we detected
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separated, early peaks of Pn and late peaks of Tr . It led to average ratio of about 2:1 units of
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water use efficiency (WUE) for "dew" affected versus "no dew" conditions. The integrated
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effect over a day showed that dew improved WUE by 20-40%. The impressive performance of
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the dew affected WUE was explained by a synergy between a) low transpiration during dew
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affected morning hours and b) high CO2 gradient toward the canopy. The first resulted from
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dew formation that created humid environment at the near vicinity of the leaf followed by low
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leaf to air vapor pressure deficit ( VPD). It minimized transpiration while maintaining optimal
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stomatal conductance. The second resulted from night respiration that induced high CO2
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gradient from the air toward the canopy. This synergy resulted in an intensive carbon intake
30
at a low water cost and explained the ecological importance of dew .
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Introduction
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The controversial perspective: dew as a major ecological factor.
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Modern science has shown that the amount of water from dew for plant water budgets is
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negligible, but that it is sufficient to promote the spread of plant disease. It is for this reason
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2
that dew has been recently described mainly as a source of inspiration for poets rather than as
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something beneficial for plants. In contrast, in ancient time dew was believed to be a source of
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great blessing. Pioneering dew research in the late 1950's, early 1960's emphasized the
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inspiration of dew upon poets as opposed to its impact on plant water status [Monteith, 1963;
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Montieth, 1957]. These quantitative studies criticized the descriptive studies on dew with
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sentences including: "every poet who has sung the beauties of Nature has added his tribute to
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the sparkling dew drops…and ecological investigations of dew have strayed into description
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which belong to poetry". The new studies found that for purely physical reasons dew is
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unlikely to support the water budget of plants since the ratio of potential condensation to
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potential evaporation is roughly 1:7 in humid climate and 1:14 in arid climate. This was
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further supported by measurements of dewfall in the Negev desert of Israel showing that
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maximum total dewfalls do not exceed 0.2 mm per day in any case, whereas actual evaporation
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of about 5-6 mm per day is not uncommon. Other reports counted 100-200 dew nights which
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amounted to 50-100 mm per year[Zangvil 1996; Goldreish 2003; Jacobs, et al. 2006]. Today,
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the role of dew cited is mostly negative. Plant pathologists emphasize the negative role played
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by dew in the promotion of plant diseases [ Santos et al 2008; Jacobs et al. 2006]. The entry
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in Encyclopedia Britannica reads :”From the biological viewpoint, the usefulness of dew is
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doubtful, as dew may stimulate the growth of fungi harmful to plants [Encyclopedia Britannica
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2007]. Another research states, "Dew plays an important role in the propagation of certain
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plant pathogens [Huschke 1991]." Amazingly nothing is mentioned about the benefit of dew to
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the plant water regime.
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On these grounds it has been argued that the contribution of dew to the overall vegetation
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water regime is negligible. Current dew research is dominated by phytopathologists who are
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concerned with the calculation of leaf wetness duration that is related to plant disease
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occurrence [Sentelhas et al 2008] and plant protection against the negative role played by the
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dew in the promotion of plant diseases.
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Contrary to the above we found that dew formation serves as an integral part in the general
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strategy of vegetation water economy in the arid and semi arid zones. The significant
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contribution of dew to the plant water economy will be explained and demonstrated hereafter.
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Materials and Methods
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A field study was carried out in the Chukurova basin near Adana Turkey (~ 37° 01' N,
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35° 21' E; elevation 10-150 m). This site was selected as an appropriate test ground after
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thorough analysis of meteorological data [Kimura et al. 2007; Kitoh 2007] from 1994 to 2003
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showed that every summer, throughout 330±70 hours the dew point temperature was larger or
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equal to the air temperature, and the relative humidity during these hours varied between 99
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and 100%, thus satisfying the necessary conditions for dew formation. The basin is situated in
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the Mediterranean region surrounded by the Taurus Mountains. The Mediterranean climate is
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hot and dry in summers and mild and rainy in winters.
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The experimental approach
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Meteorological measurements: Over a period of 5 days, we measured and calculated the
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relevant meteorological variables including:PIR-1, a Photosynthesis Radiation Sensor which
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measured photosynthesis photon flux desity (PPFD). The ATH measured ambient temperature
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and relative humidity from which Vapor Pressure Deficit ( VPD) and Dew Point Temperature
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(DPT) were determined using one of the dew point calculators
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(http://www.decatur.de/javascript/dew/index.html) when relative humidity (RH) was at its
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maximum. The measured DPT and associated climatic conditions are given in table 1
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Table 1
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. Within the Chukurova basin maximal relative humidity (RH), ranged between 93 and 99%
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and on the Taurus Mountains it was about 10% less. Minimal ambient temperature varied
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4
between 21 and 23°C and minimal leaf temperarures varied between 19 and 21°C. Dew was
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formed when DPT was lower than leaf temperature. In the early morning hours canopy
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temperature was below the DPT of the surrounding air and leaves were covered with dew in
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four nights on three crops. Dew formation was not detected when leaf temperature was slightly
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higher than the DPT.
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Plant physiological measurements: The measurements of photosynthesis and transpiration
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were taken with PTM-48. Photosynthesis and Transpiration Monitor[ Bio Instruments 2007].
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The combined measurements of leaf temperature that was measured with LT sensors.and RH
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enabled the monitor's software to determine the leaf to air VPD. Photosynthesis and
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transpiration data from the leaf chambers as well as data from the additional sensors were
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automatically recorded every 30 min around-the-clock. Considering Fick's first law the leaf
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conductance was obtained from the ratio: gl = TR/ VPD (TR units) .With (gl) leaf hydraulic
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conductance which is a coefficient of proportionality between transpiration flux density
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(converted to H2O volumes area-1 time-1) and its driving force VPD in terms of partial vapor
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pressure[Alessio et al.2004].
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The PTM : This is a new portable device equipped with a four-channel automated system for
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monitoring CO2 exchange and transpiration of leaves. Each of the four chambers is a self-
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clamping leaf chambers, which operate one-by-one in such a manner that one of the leaf
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chambers is closed at a time while all others remain open. Thus, most of time, the sample
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leaves are not disturbed. The CO2 exchange is determined by decrement of CO2 concentration
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at the outlet of the leaf chamber, which is compared with the concentration of incoming
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ambient air . Transpiration rate is determined in much the same way using the absolute
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concentration of water vapor in the air. To shorten the measurement cycle, the absolute
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humidity is computed during a transient period between 20th and 30th second after closing the
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chamber. The calculation algorithm takes into account the rising humidity inside the chamber
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5
and, hence, allows determining the initial transpiration rate at the ambient air humidity. Note
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that the PTM provides unique capability of continuous measurement of undisturbed gas
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exchanged of a single leaf . Other available devices for measurements of leaf gas exchans are
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capable of manual sampling and hence require special efforts and arrangements to observe the
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early morning dew formation ( as opposed to monitoring ) whereas the PTM provides
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automated long-term monitoring of fluxes at the leaf levels. Additional details on the PTM and
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the organization of the records were given by Ben Asher et al.( 2008). It should be mentioned
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here that eddy covariance is another useful method for simultaneous monitoring of PN and TR
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(Mildenberger et al. 2009) but unlike the ordinary diffusion theory the diffusion coefficients of
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H2O and CO2 are the same. In the eddy covariance theory a small packet of air moves more or
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less as a unit carrying with it all the H2O and the CO2 molecules that it contains and hence
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separation between PN and TR under "dew" and "no dew" conditions are not possible.
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Soils variables : Only three (out of the four) leaf chambers were use to monitor leaf gas
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exchange. The forth chamber was used to monitor soil respiration . Three soil moisture Sensors
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(TDR= Time Domain Reflectometry ) were used to determine the water content as a
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suplamental data. These supporting sensors were used to compare soil water contents and soil
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respiration of the irrigated crops and the non irrigated natural vegetation in order to
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characterize the differences between the two habitats. .
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Crops and Mediterranean vegetation: The field crops were: Cotton (Gossypium
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hirsutum) , Soybean (Glycine max), and Maize (Zea mays) , One orchard plantation Lemon
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(Citrus limon) was studied and a groupe of natural Mediterranean vegetation spicies: Pine
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( Pinus pinea), Pistachio (Pistacia terebinthus) and Oak ( Phyllyrea media . This group was
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located on the slope of the Taurus mountins elvated some 150 m. above sea level.
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Measurements conducted from June 16 to June 22 2003 and a summary of important data is
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given in table 1 .
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Table 1.
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Each day another crop was equipped with the measuring system for at least 24 hours with the
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hope to detect dew formation. In the nights of June16-19 the dew point temperature was
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higher than the leaf temperature and dew was detected on the leaves. In the nights of 20 and 21
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the dew point temperature was smaller than the leaf temperature and dew was not formed.
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Data analysis :First we should emphasize that there is low probability to find a day in which
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two fields of the same crop and environmental conditions differ from one another only by the
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presence of dew . Thus, simple comparison between crops response to "dew" and "no dew"
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conditions is impossible "de facto". In order to overcome this problem we characterized leaves
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properties by two empirical dimensionless parameters: " unit photosynthesis" and "unit
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transpiration". It was assumed that CO2 uptake is accompanied by water efflux through the
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stomata and both are generated only by PPFD. Consequently, proportions of transpiration
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and photosynthesis change diurnally in concert with PPFD. Another generality that will be
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discussed in more detailes in the theory section, is that unlike dew affected leaves in dry leaves
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the two processes are linked linearly to PPFD. Thus, to separate “dew” from “no-dew”
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conditions we developed a convolution model that was based on empirical data. solved it in
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Excel (Olsthoorn 2008) and represented the “no dew” situation during the daylight hours. .
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Statistical considerations:
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The diurnal evolution of transpiration and photosynthesis of the different plants was unified by
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presenting the measured values in terms relative to the maximum fluxes. We presented the
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data in relative term because it allows to combine many values and processes in a concise
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manner and representation of a large group in this way is easier to comprehend than a large
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mass of actual data . Furthermore, the diurnal evolution of gas exchange can be extracted for
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each crop from the product of maximum fluxes and the relative value at any given time. .
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The maximum fluxes of cotton corn and soybean are given in table 2.
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Table 2
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The dimensionless data are thus an average of actual values from three dew affected plants
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with three replications such that each data point represent a population of 9 plants. The
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standard deviation (s.d.) of ambient CO2 was determined with four probes. Average s.d. of
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the normalized values was 0.13 for both photosynthesis and transpiration.
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Results and discussion
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Experimental observations
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The results in Fig. 1A display the lag time between peak Pn and peak Tr of the dew affected
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crops ( 3 crops with 3 replications n=9, 1 standard deviation, s.d. = 0.13). From Fig.1A ,
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maximum photosynthetic rates were measured several hours earlier than maximum Tr rate.
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Fig 1B shows the same trend with actual fluxes of H2O and CO2 for cotton leaves
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Fig. 1
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8
1.2
DIMENSIONLESS FLUX
1.0
0.8
0.6
0.4
0.2
PHOTOSYNTHESIS (mol m-2s-1)
-0.2
20
6
Photosynthesis
Transpiration
5
15
4
10
3
5
2
0
1
-5
TRANSPIRATION (mmol m-2s-1)
0.0
0
0
5
10
15
20
25
TIME OF THE DAY
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Fig. 1. The effect of dew formation on the diurnal course of Pn and Tr. (A) Normalized values
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for the three dew affected plants: Cotton, Maize and Soybean . (B) Example of actual data for
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cotton. Pn scale is given on the left ordinate. Tr scale is given on the right ordinate. The double
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arrow line indicates 9:00am at which the largest difference and ratio between Pn and Tr was
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obtained. It thus indicates the time of maximum WUE.
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179
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Separated, early peaks of Pn and late peaks of Tr are contrary to expectations because the
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pathway for diffusion of CO2 into leaves is similar to the pathway for diffusion of H2O out of
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leaves. Both are strongly linked to stomatal conductance and solar radiation. Thus the two
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processes are expected to be in the same phase. [Collatz et al.1991;Fritschen 1973; Slatyer
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1960;de Wit 1978; Pollard and Thompson 1995, Nobel 2005].
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In Fig 1 however, Pn in the early morning hours was weakly linked to Tr or not linked at all.
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We argue here, and later demonstrate it experimentally that, thanks to the dew, this weak
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linkage is an inherent part of a strategy aimed at maximizing WUE which is most important in
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habitats where water supply is limited. The presence of dew on the leaves creates temporary
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humid conditions in the leaf-air boundaries , followed by a reduction of the driving force for Tr
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thus increasing stomata aperture [Jarvis et al. 1999; Monteith 1995; Mott and Parkhurst
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1991]. The biophysical basis for the results is shown in Fig 2 that presents the stomatal
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conductance resulting in response to low Tr in the morning (Fig 1). Concurrently , high Tr at
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noon time resulted in low canopy conductance. This result was explained theoretically by a
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reanalysis of the stomata response (Monteith 1995). The conclusion of this reanalysis was that
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stomatal conductance and the equivalent canopy conductance (g) respond to the rate of Tr
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rather than to humidity per se. In general, dg/dTr is negative. That is when dew covers the
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leaves the hydraulic conductivity of the canopy, is high and at noon when Tr is at its peak the
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hydraulic conductivity is low.
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Fig. 2
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10
1.0
A
0.8
CANOPY CONDUCTANCE
(Dimless)
0.6
0.4
0.2
0.0
Dew affected conductance Y=0.154X-0.6; r2=0.88
Unaffected conductance ; Y=0.135X-0.6 ;r2=0.88 2=0.92
-0.2
4
5
6
7
8
9
10
TIME OF THE DAY
60
B
50
(mm hr-1)
40
30
20
10
0
0
5
10
15
20
25
TIME OF THE DAY
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Fig.2 The effect of dew formation on the course of canopy conductance. A) Zoom on the early
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morning average of normalized canopy conductance of “dew” “no dew” plants (1s.d.=0.36).
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B) Example of actual data and the standard deviation for cotton. The solid straight lines show
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the linear correlations of “dew”and “no-dew” conductance as a function of time from 4 to
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9:30am.
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In Fig. 2A it is most important to note that canopy conductance during the most effective
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photosynthetic hours, with dew in the morning, was about 2.5 times larger than that in the
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afternoon, when peak Tr occurs. At 9:00am it was about 1.5 times larger than the plants
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without dew. It resulted from the presence of dew on the leaves which caused a temporary
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reduction in VPD associated with relatively high stomatal conductance. Fig. 2B is the actual
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canopy conductance of cotton. As an example of its diurnal cycle it shows typical bimodal
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midday depression in the cotton field. The main peak in the morning is affected by the dew.
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Then it displays the midday depression and a second small peak in the afternoon that is
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unaffected by the dew.
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In terms of the molecular diffusion equations(Fick's first law ), any increase in canopy
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conductance will cause a linear increase in the assimilation rate of CO2 . Thanks to the dew, Tr
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will not increase in proportion because, stomata are open early in the morning when water
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vapor gradient is minimal and the evaporative ability of the atmosphere is also small. Moreover,
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as shown in Fig.3, night respiration increased, dramatically , CO2 concentration in the air
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Fig 3
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550
1=Mature corn
2=Young cotton and soy bean
3. Natural Mediterranean vegetation
CO2(ppm)
500
450
1
400
350
2
3
300
0
5
10
TIME
15
20
Fig.3 The effect of night respiration on the diurnal course of CO2 concentration in the air.
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Lines are the moving average of four sampling channels. An example of actual data and their
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standard deviation of the average is shown for the mature corn.
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227
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(For example within the corn field, from 320 to 460 ppm). During these hours the CO2
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gradient is at its maximum. Thus, with high potential for CO2 intake and low potential for Tr
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each unit of fixed CO2 costs less water. The difference between Mediterranean vegetation and
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cultivated crops was studied by Evrendilek et al. [2008] who concluded that net ecosystem
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emission (NEE) increased as Pn decreased, and soil respiratory loss of CO2 to the atmosphere
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increased. Our measurements indicated that soil respiration was the dominant contributor to
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NEE. Soil respiration of the Mediterranean vegetation was only about 20 µmol m-2 s-1 while
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that of the cultivated crop reached 40 µmol m-2 s-1. Thus, the natural vegetation suppressed
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the contribution of Mediterranean ecosystems to NEE as shown in Fig 3.
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Modeling the "no dew " conditions.
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I. Biophysical consideration:
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The usual case which causes a loss of water from the leaf during daylight hours (from late
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morning to early afternoon) requires the vapor pressure of the leaf to be greater than is in the
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surrounding air and the stomata is opened. However in the early morning hours when vapor
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pressure in the air is greater than that in the leaf then a net diffusion of water vapor occurs
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toward the leaf. This can increase the vapor pressure at the leaf surface to saturation value and
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form dew or even intake of water into the leaf. Stomata control the exit of water vapor from
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leaves and the entry of CO2 into them. Upon illumination stomata is open through a well
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known (Nobel 2005 ) set of biophysical process. In the usual case above, stomata opening
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leading to the CO2 intake that is necessary for photosynthesis, result in an inevitable loss of
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water for which the driving force is the availability of irradiative energy. Our results (Fig 4 )
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corroborated with many others ( for example Mildenberger et al. 2009) and showed that
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during a "no dew" testing day the transpiration and photosynthesis started at sunrise and both
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reached the sinusoidal peak shortly after the peak of the PPFD.
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Fig 4
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13
2000
PPFD(mol m-2 s-1)
6
Photosynthesis
Transpiration
PPFD
PPFD
5
1500
4
1000
3
2
500
1
0
0
0
4
8
12
16
20
PHOTOSYNTHESIS (mol m-2 s-1)
-2 -1
TRANSPIRATION
(mmol m s )
253
24
TIME OF THE DAY
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Fig 4. The course of PPFD transpiration and photosynthesis in Lemon. Measurements were
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taken under "no dew " conditions to demonstrate the signal (PPFD)that results in a
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simultaneous and linear response in gas exchange ( CO2 and H2O) as opposed to the above
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(Fig.1 ) "dew" conditions. .
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In figure 4 photosynthesis and transpiration responded linearly to changes in PPFD with a
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correlation coefficient r2=0.72 and 0.86 for transpiration and photosynthesis respectively. The
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linearity of "no dew" behavior suggest that the growth rate of CO2 fixation by the leaves and
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transpiration from the leaves is large when PPFD is large at least over a significant part of its
262
range . Moreover this linearity means that the flow of the CO2 and H2O is independent of one
263
another. The importance of this last consequence is that each of the two components is
264
affected by its own concentration gradient. For example the concentration and the gradient of
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CO2 and hence the photosynthetic flux is not affected by the dew. Thus, when modeling it as a
266
14
function of PPFD the model is a "no dew" model. One way to describe such behavior is with
267
the convolution type of expression that will be given in the mathematical consideration.
268
II. Mathematical consideration:
269
Convolution is a form of superposition that efficiently deals with input varying arbitrarily in
270
time or space. PPFD in this case is the energy input that varies with time. Even though
271
convolution is well-known since the 19th century, to our knowledge the method was not used
272
extensively or not used at all in micrometeorology. It works whenever superposition is
273
applicable, that is, for linear systems. As shown above in "no dew " systems photosynthesis
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and transpiration are the impulses that responded linearly to the pulse of energy input (PPFD).
275
This linearity implies that the response of photosynthesis and transpiration to PPFD is
276
unique and it has a unique "unit step" response. This unit step response contains all the
277
dynamic information of the system but dew is not included because, as was previously stated
278
the calculated photosynthetic flux is affected linearly by PPFD and "dew" do not take part in
279
the calculated process. For this reason we provided the convolution model as an indication for
280
"no dew " conditions to compare with the actual measurements during the "dew" hours when
281
dew is effective.
282
The development of the convolution model was based on actual sets of continuous
283
measurements of photosynthesis and transpiration and PPFD as a driving force. We extracted
284
"unit steps" response which were marked : UTr for unit transpiration and UPn for unit
285
photosynthesis using the practice of deconvolution
286
A simple way to express the convolution that was carried out in the study is in a matrix form:
287
[Tr] = [PPFD][UTr]
[1a]
288
[Pn] = [PPFD][UPn]
[1b]
289
Where UTr and UPn are matrices of dimensionless units of Pn and Tr that are produced by
290
one unit of PPFD.
291
15
Equation [1] is a discrete convolution equation and following the biophysical considerations , it
292
allows the computation of Tr and Pn without dew. Given PPFD, UTr and UPn the unknown
293
unit transpiration and photosynthesis (UTr and UPn) were determined by the reverse process
294
(deconvolution), from the ratios: [Pn]/[PPFD] and [Tr]/[PPDF].
295
This was needed in order to derive continuous processes of transpiration and
296
photosynthesis that are strongly affected by PPFD and less or not affected by dew formation.
297
We now have two expressions for WUE:
298
WUEWD=Pn(t)/Tr(t)
[ 2a]
299
WUEWOD = [PPFD][UPn]/ [PPFD][UTr]
[2b]
300
where the subscripts of the WUE are with dew (WD) and without dew (WOD)
301
III Using measured and convoluted WUE for comparison of "dew" and "no dew"
302
conditions
303
The results of calculated WUE are displayed in Fig.5.
304
Fig.5
305
306
16
0.014
0.010
0.010
0.008
0.008
0.006
0.006
0.004
0.004
0.002
0.002
0.000
0.014
Maize WD
Maize WOD
0.012
0.010
0.008
0.006
0.004
0.002
0.000
0.014
0.000
0.014
Pistacio WD
Pistacio WOD
0.012
0.010
0.008
0.006
0.004
0.002
0.000
0.014
Cotton WD
Cotton WOD
0.012
Lemon WD
Lemon WOD
0.012
WATER USE EFFICIENCY (mol/mmol)
WATER USE EFFICIENCY (mol/mmol)
0.014
Soybean WD
Soybean WOD
0.012
Oak WD
Oak WOD
0.012
0.010
0.010
0.008
0.008
0.006
0.006
0.004
0.004
0.002
0.002
0.000
0.000
6
8
10
12
14
DAY LIGHT TIME
16
18
6
8
10
12
14
16
18
307
DAY LIGHT TIME
Fig.5 The course of WUE during daylight hours. The “dew” affected lines (marked with WD )
308
are results of measurements while the “no dew ” lines (marked with WOD) resulted from the
309
convolution model. Note that measurements of WUE on the right hand side of Fig.5 were taken
310
under “no dew” conditions as specified in table1, but are included in the text as “dew” in order
311
to make a consistent distinction between measured and convoluted lines.
312
On the left side of Fig.5, WUE that was affected by dew and obtained from the actual
313
measurements ( Eq.2a) is compared to the ratio that was convoluted ( Eq.2b )and linked to
314
PPDF with a weak or no affect of dew . Clearly until late morning WUE with “dew” based on
315
actual data ranged between 0.010 and 0.014 WUE units while WUE in the afternoon it was
316
about one order of magnitude smaller and ranged between 0.002 and 0.004 . It can also be seen
317
that the afternoon values of WUEWD were very close to the low WUEWOD of the simulated “no
318
dew” conditions. On the right hand side of Fig. 5 the actual measurements of WUEWD (Eq.
319
17
2a) were practically the same as the modeled value (WUEWOD in Eq. 2b) throughout the entire
320
day and they ranged between 0.0 and 0.004. This result emphasizes the situation when the
321
major factor affecting WUE is solar radiation ( in its PPFD form ) and not the presence of
322
dew. Here both the measured and the simulated WUE with “no dew “ conditions are lower than
323
that on the left hand side when dew was a dominant factor affecting WUE.
324
The strength of this convolution analysis is in its ability to approximate properly what would
325
be the WUE under "no dew " conditions. It was formulated in order to demonstrate the
326
difference between WUEWD and WUEWOD and as such it strengthen the experimental data
327
obtained from a large group of plants and crops which suggested that the presence of dew
328
improved water use efficiency.
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Concluding comments and related issues.
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Modern growers and ecologists with sharp senses to natural phenomena detected the blessings
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of dew. In biblical times also the dew blessing was considered to be similar or even to a larger
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extent as compared to rainfall but without proper scientific documentation (Monteith
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1957,1963). Our study with modern instrumentation provides for the first time, the
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experimental picture as to the interaction between dew and plant growth, illustrating how the
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dew plays an important role in biomass production at low water cost. Moreover, the
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consequences of this study are independent of the dispute regarding the role of dew as a water
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source. On one hand preliminary estimations based on global warming output suggest
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reductions in the dew frequencies with a potential significant effect on the WUE; a fact not
340
considered yet in the vast literature on global warming impacts. Prediction of regional warming
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[Kimura et al. 2007;Kitoh 2007] for the years 2070-2080 indicated a clear decline of relative
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humidity in the region from 100% down to 64±18% during the best hours for dew formation
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in the summer. If the predicted regional warming of the eastern Mediterranean becomes a
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reality, natural vegetation and agricultural crop will likely display reductions in WUE and
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increase in water demand in order to maintain the current biomass production. On the other
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18
hand elevated CO2 may result in significant increase of plant growth. Prediction of the impact
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of these two contradicting directions requires a more comprehensive model that is currently
348
under study and a different set of measurements. It can possibly be obtained by the
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approximated relationship (Kefi et al 2008) WUE ≈ Atmospheric CO2 concentration /VPD. As
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a first approximation our convolution model assumed that the major and the only driving force
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for Pn and Tr is the PPFD. Under this assumption, atmospheric CO2 concentration does not
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significantly affect the two functions. According to this approximation the response of the
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stomata to the environmental conditions in its near vicinity is hidden within the UPn which
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provides the photosynthetic rate per unit PPFD and the UTr which provides the transpiration
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rate per unit PPFD.
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1.
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AcknowledgementsThis study was funded by the Gerda Freiberg chair for Agricultural water
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management. BGU,,
grant from ministry of Science Israel and
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the Bundesministerium fuer Bildung und Forschung> (BMBF) ,The ICCAP project ( Impact of
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Climate Change on Agriculture Productivity of the Research Institute for Humanity and Nature
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(RIHN) in Kyoto Japan and TUBITAK , the ministry of science and technology Turkey.
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GLOWA Jordan River
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References
367
Alessio G.A., M. De Lillis, E. Brugnoli, and M. Lauteri )2004(, Water sources and water-use
368
efficiency in Mediterranean coastal dune vegetation , plant biol. 6: 350-357.
369
19
Ben-Asher J., A. Garcia y Garcia, and G. Hoogenboom(2008)Effect of high temperature on Pn and
370
Tr of sweet corn (Zea mays L. var. rugosa), Photosynthetica 46 : 595-603.
371
Bio Instruments. Daletown Company Ltd.Nicosia, P.O.Box: 28153, Post code 2091, Cyprus )
372
E-mail: info@daletown-phyto.com
373
Collatz G.J., J.T. Ball, C. Grive and J. A. Berry )1991(, Physiological and environmental
374
regulation of stomatal conductance, Pn and Tr: a model that includes a laminar boundary layer,
375
Agri. Forest Meteor. 54, 107-136.
376
Encyclopedya Britannica )2007(, Dew item in Encyclopedya Britannica.
377
Evrendilek, F., J. Ben-Asher, M. Aydın, and I. Çelik (2005), Spatial and temporal variations in
378
diurnal CO2 fluxes of different Mediterranean ecosystems in Turkey, J Environ Monitor, 7:
379
151-157.
380
Fritschen, L. J., and Doraiswamy, P. 1973, Dew: An Addition to the Hydrologic Balance of
381
Douglas Fir, Water Resour Res: 9, 891–894.
382
Goldreich,Y. )2003( The Climate of Israel , p. 133 Kluwer Academic Publishers Department
383
Dordrecht The Netherlands.
384
385
Huschke, R. E. (Ed.), )1991(, Dew item, Meteorological glossary p.88 6th edition 45 Beacon
386
St., Boston, MA 02108, 335 pp.
387
Jarvis A.J., P.C.Young , C.J.Taylor , W.J.Davies . )1999(, An analysis of the dynamic response
388
of stomatal conductance to a reduction in humidity over leaves of Cedrella odorata, Plant cell
389
environ: 913-924.
390
Jenkins, G.M., Watts, D.G., 1968. Spectral Analysis and its Applications. Holden-Day,
391
San Francisco, CA, USA. 525 p.
392
393
20
Kefi S.,M. Rietkerk, and G.G. Katul, 2008 Vegetation pattern shift as a result of rising
394
atmospheric CO2 in arid ecosystems Theor. Population Biol. 74:332-344
395
Kimura F.,S. Akimasa , J. Asanuma , A. Yatagai ( 2007), Downscaling of the global warming
396
projections to Turkey . http://www.chikyu.ac.jp/iccap/finalreport.htm.
397
Kitoh, A. (2007), Future Climate Projections around Turkey by Global Climate Models.
398
http://www.chikyu.ac.jp/iccap/finalreport.htm.
399
Kozlowski T (2003), A comprehensive method of determining the soil unfrozen water curves 1.
400
Application of the term of convolution Cold Regions sci. tech.
401
36 : 71-79
. Mildenberger K., E. Beiderwieden , Y.-J. Hsia, and O. Klemm (2009) CO2 and water vapor
402
fluxes above a subtropical mountain cloud forest—The effect
403
of light conditions and fog. Agri. Forest Meteor 149:1730–1736
404
405
Montieth, J. L. (1957), Dew, Quarterly Journal of the Royal Meteorological Society, 83, 322-
406
341
407
Monteith, J. L. (1963), Dew Facts and Fallacies, in K.H.Rutter and F.H. Whitehead (eds.), The
408
Water Relations of Plants. Blackwell Scientific Publications, Oxford, pp. 37-56.
409
Monteith J.L. (1995),A reinterpretation of stomatal responses to humidity. Plant, Cell and
410
Environ. 18: 357-364.
411
Mott K.A. and D.F. Parkhurst (1991), Stomatal response to humidity in air and helox. Plant
412
Plant, Cell and Environ. 14,:509-515
413
Nobel, P.S.: (2005) .Physicochemical and Environmental Plant Physiology. 3rd Ed. –
414
Elsevier/Academic Press, Burlington
415
Olsthoorn T.N.(2008 )Do a Bit More with Convolution. Ground Wat. 46:13-22
416
21
Pollard D. and S.L.Thompson (1995), Use of a Land-Surface-Transfer Scheme (LSX) in a
417
Global Climate Model - the Response to Doubling Stomatal-Resistance. Glob. Planet. Change
418
10,129-161
419
Santos E.A., T.J. Gillespie and M.L. Gleason (2008): Suitability of relative humidity as an
420
estimator of leaf wetness duration. Agri. Forest Meteor. 148 , 392-400
421
Sentelhas P.C.and T.J. Gillespie (2008), Estimating hourly net radiation for leaf wetness
422
duration using the Penman-Monteith equation. Theor. Applied climate.: 91: 205-215.
423
Slatyer R.O., (1960), Absorbtion of water by plants.Botanical Review 26,331-393
424
de Wit C.T.,(1978), Simulation of assimilation , respiration and Tr of crops. Wageningen
425
Pudoc. 141pp Wageningen Netherlands
426
Zangvill, A., (1996), Six years of dew observations in the Negev Desert, Israel. J. Arid
427
Environments, 32, 361-371.
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List of Figures
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Fig. 1. The effect of dew formation on the diurnal course of Pn and Tr. (A) Normalized values
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for the three dew affected plants: Cotton, Maize and Soybean . (B) Example of actual data for
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cotton. Pn scale is given in the left ordinate. Tr scale is given in the right ordinate. The double
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arrows line indicates 9:00am at which the largest difference and ratio between Pn and Tr was
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obtained. It thus indicates the time of maximum WUE.
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Fig.2 The effect of dew formation on the course of canopy conductance. A) Zoom on the early
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morning average of normalized canopy conductance of “dew” “no dew” plants (1s.d.=0.36).
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B) Example of actual data and the standard deviation for cotton. The solid straight lines show
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the linear correlations of “dew”and “no-dew” conductance as a function of time from 4 to
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9:30am.
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Fig.3 The effect of night respiration on the diurnal course of CO2 concentration in the air. Lines
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are the moving average of four sampling channels. An example of actual data and their
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standard deviation of the average is shown for the mature corn.
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Fig 4. The course of PPFD transpiration and photosynthesis in Lemon. Measurements were
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taken under "no dew " conditions to demonstrate the signal (PPFD) that results in a
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simultaneous and linear response in gas exchange ( CO2 and H2O) as opposed to the above
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(Fig.1 ) "dew" conditions. .
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Fig. 5 The course of WUE during daylight hours. The “dew” affected lines (marked with
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WD ) are results of measurements while the “no dewlines ”(Marked with WOD) resulted from
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the convolution model. Note that measurements of WUE on the right hand side of Fig.4 were
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taken under “no dew” conditions and included in the text as “dew” just in order to make a
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consistent distinction between measured and convoluted lines.
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