The quantitative impact of different leaf temperature determination on computed values of stomatal conductance and internal CO2 concentrations

Agricultural and Forest Meteorology 279 (2019) 107700

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The quantitative impact of different leaf temperature determination on computed values of stomatal conductance and internal CO2 concentrations Lingling Zhanga, Sheng Zhanga,b, a b

T

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College of Forestry, Northwest A&F University, Yangling, Shaanxi, 712100, China College of Life Science and Technology, Xinjiang University, Urumchi, 830046, China

A R T I C LE I N FO

A B S T R A C T

Keywords: Infra-red temperature sensor Intercellular CO2 concentration Leaf temperature LI-COR 6400 Stomatal conductance Photosynthesis

The LI-COR 6400 portable photosynthesis system is the most widely used and cited instrument for measuring leaf gas exchange in plant physiology. The focus of this paper is to evaluate three ways of computing/measuring leaf temperature: the LI-COR energy budget computation, the LI-COR leaf thermocouple (T/C), and an infra-red (IR) sensor chip (MLX90615) incorporated into the LI-6400 leaf chamber. The IR-sensor was calibrated against known targets, and then used to cross check the other two methods. The results showed that the IR-sensor was superior to the LI-COR thermocouple and agreed closer with the energy budget values of leaf temperature. The magnitudes of the errors were quantified in terms of °C difference in leaf temperature measured by T/C (as control) and IR sensor versus percentage difference in stomatal conductance (gs) and intercellular CO2 concentration (Ci) computed from leaf temperature. The temperature difference (IR versus other methods) in the range of -1.89 to 1.58 °C resulted in relative errors in gs from 14.7% to -18.0% and Ci from 16.5% to -7.8% of the values reported by the standard LI-6400 calculations depending on species and temperature. Reasons are given for trusting the IR-sensor over the other methods and it is suggested that the next generation LI-COR photosynthesis system include an IR-sensor.

1. Introduction The purpose of this paper is to compare three methods of leaf temperature, TL, measurement when used simultaneously in one of the popular portable photosynthesis systems, i.e., the LI-6400 (LI-COR, Lincoln, Nebraska, USA). Many manufactures of portable photosynthesis systems offer more than one type of leaf temperature assessment. For example, model CIRAS-3 (PP System, Boston, USA) offers an IRsensor or contact thermistor or energy budget and the model LI-6400 offers a contact thermocouple or energy budget. But to our knowledge, a study directed at determining which system of TL assessment is optimum is lacking or out of date. In our opinion, the way to compare two different TL measurements is best done by monitoring both outputs simultaneously while measuring a leaf within a cuvette. Simultaneous measurements require the custom modification of hardware and/or software in a portable photosynthesis system. Users of portable photosynthesis systems need a sound scientific basis to decide if one wants to use the energy budget method versus the contact sensor method or the non-contact IR method. Therefore, it is necessary to review why measuring TL accurately is important to help the users of LI-6400 with a better operation of experiments.

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Temperature influences almost every aspect of plant growth (Mahan and Yeater, 2008), and it has been used as an essential parameter for calculation of stomatal conductance (gs), intercellular CO2 concentration (Ci) (Morrow and Slatyer, 1971; Ball, 1987), evaporation (Xu and Singh, 2001), and prediction of crop yields and evapotranspiration (Irmak et al., 2000; Ajayi and Olufayo, 2004). Stomatal conductance is an important indicator in the study of photosynthetic rate (Yoshie, 1986) and the response of leaves to light (Boccalandro et al., 2012) and water (Djebbar et al., 2012). Furthermore, intercellular CO2 concentration was a significant value when studying drought stress in plants (Brodribb, 1996). Therefore, the accuracy of leaf temperature measurements will affect the value of the other temperature-based parameters and more importantly, the studies based on these parameters. There are two methods commonly used to detect the surface temperature of plant materials: contact sensors using thermocouples or thermistors and non-contact infrared thermometers. Thermocouple/ thermistor based temperature measurement is systematically underestimated because of the air temperature gradients near the leaf surface (Meyer et al., 1985; McDermitt, 1990; Rochette et al., 1990). The complexity of the ‘plant-atmosphere interaction’ system will influence

Corresponding author at: College of Forestry, Northwest A&F University, Yangling, Shaanxi, 712100, China. E-mail addresses: [email protected] (L. Zhang), [email protected] (S. Zhang).

https://doi.org/10.1016/j.agrformet.2019.107700 Received 15 February 2019; Received in revised form 1 August 2019; Accepted 4 August 2019 0168-1923/ © 2019 Elsevier B.V. All rights reserved.

Agricultural and Forest Meteorology 279 (2019) 107700

L. Zhang and S. Zhang

Nomenclature TairTC Concept symbols that stand for the “idea” of the parameter measured TLEB Ci gs Ta TIR TL TS σ

ε

Intercellular CO2 concentration, CO2 ppm inside the leaf air spaces Stomatal conductance (computed) The air temperature inside the LI-6400 cuvette The target temperature given by IR sensor The leaf temperature The surface temperature of the IR sensor The constant of proportionality that the total radiant heat energy emitted from a surface proportional to the fourth power of its absolute temperature in Stephan-Boltzmann law statements The ratio of the thermal radiation from a surface to the radiation from an ideal black surface at the same temperature as given by the Stephan–Boltzmann law

TLIR TLTC ΔT ΔTε=1

Symbols for computed parameters depending on method of temperature measurement Ci(LEB) Ci(LIR) Ci(LTC) gs(LEB) gs(LIR) gs(LTC)

Symbols for measured parameters by method used Tair(IRGA) Air temperature measured inside the LI-6400 infrared gas analyzer (IRGA) taken as a proxy for the air temperature

Ciusing TLEB Ci using TLIR Ciusing TLTC gs using TLEB gs using TLIR gs using TLTC

implimented in the LI-6400 firmware, it is important to note that Tw is assumed equal to Ta as measured with the LI-6400 leaf thermocouple removed from the leaf surface to measure air temperature in the cuvette chamber. Using Eq. (1), it is possible to compute theoretical values of TL versus Photosynthetically Active Radiation (PAR) at Ta equals to 25 °C. For that some typical values of λE are either constant with PAR or declining linearly with PAR. This also provides a sensitivity analysis to examine the relative magnitude of terms in the numerator and denominator of Eq. (1). These are shown in Fig. 1. In the LI-6400 software, it is assumed that the wall temperature in the LI-6400 cuvette equals to the air temperature at all PAR values. And the LI-COR leaf temperature therocouple has to be moved a few mm away from the lower leaf surface to measure the air temperature in the cuvette. This moved themocouple provides an alternative estimate of air temperature in addition to the air temperature measured in the water vapour IRGA sensor. It will also be shown in this paper that the LI-6400 IRGA air temperature (Tair(IRGA)) can differ from the air temperarature measured in the cuvette a few mm below the leaf surface (TairTC). At saturating light, the numerator of Eq. (1) is dominated by PAR with the first term largest at 220 W m−2, λE is second largest at 88.2 W m−2, and the middle term is

the accuracy of temperature measurements. Many researchers have studied the ‘plant-atmosphere interaction’ system since the concept of ‘boundary layer’ was first put forward in physics and fluid mechanics (Blasius, 1907). Van Driest and Blumer (1963) studied heat exchange across boundary layers and the impact of wind speed profiles were studied by Grace and Wilson (1976) and Parlange et al. (1971). The boundary layer resistance was calculated based on the energy balance. A wet filter paper was exposed within a leaf cuvette at a certain temperature range of 15–35 °C to construct the relationship between boundary layer resistance and cuvette relative humidity (Parkinson, 1985). Brenner and Jarvis (1995) presented a field method for continuous estimation of boundary layer conductance by measuring the temperature difference between a millet leaf and an identical replica heated with an electric current. To the above two methods can be added a third indirect approach, which is the use of energy budget calculations to compute the likely leaf temperature based on all sources of heat transfer: light energy flux (short wave and long wave), latent heat flux by water evaporation and heat diffusion through air. Rochette et al. (1990) derived a computational algorithm for the LI6200 (Eq. 17-9 in the LI-6400 manual version 6) to compute leaf temperature from energy budget considerations without the need of thermocouples in direct contact with leaves in the LI-6200 assimilation chamber. Therefore, the temperature difference between leaf and air in the LI-6400 chamber can be estimated by:

TL − Ta =

inside the leaf cuvette Air temperature measured by the LI-6400 thermocouple when removed from the leaf surface Leaf temperature computed when energy budget calculations are turn on Leaf temperature measured with the MLX90615 IR-sensor Leaf temperature measured with the LI-6400 thermocouple TLIR minus other measure of T (read captions and legends carefully) Temperature differences between Ts and TIR assuming ε equals 1

aRq + 2εσ (Tw4 − Ta4 ) − λE 2cp gbh + 8εσTa3

(1)

−2 -1

where Rq (μmol m s ), photosynthetically active radition, a equals 0.16, the coefficient factor from Rq to net short-wave radiation (Rn) expressed in W m−2 (LI-COR 6400 Manual version 6 table 17-1 for the 6400-02B LED light source), λ equals 4.41 × 104 J mol-1, the latent heat of vaporization of water; E is transpiration rate (mmol H2O m−2 s1 ); ε equals 0.95 (K/K), the emissivity of the leaf, while the emissivity refers to the effectiveness of the surface (leaf, in this case) in emitting energy as thermal radiation (electromagnetic radiation with wavelength depending on the temperature) (Chung, 2016); σ equals 5.67 × 10-8 W m−2 K-4, the Stephan-Boltzmann constant; gbh equals 1.2 mol m -2s-1, the boundary layer conductance of one side of the leaf for water vapor; cp equals 28 J mol-1 K-1, the specific heat of air; Tw (K) and Ta (K) equal the chamber wall and air temperature, respectively. As

Fig. 1. Y-axis = the measured difference between leaf temperature measured using IR sensor (TL) and air temperature (Ta) versus X-axis photosynthetically active radiation (PAR). The ΔT assumes λE is constant at 88.2 W m−2 and ΔT* assumes λE falls linearly from 88.2 to 44.1 W m−2 as a function of PAR. 2

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smallest at 11.5 W m−2. In the demoninator the left term (boundary layer term) dominates the right term by 73.2 versus 11.4 W m-2 K-1. In recent years many advances have been made with non-contact measurement of leaf temperature using infrared thermometers (IR). The most outstanding advantage of IR temperature sensing over contact thermocouple/thermistor measurement is that it avoids any influence of air temperature gradients near the leaf surface (Mahan and Yeater, 2008). Handheld (temperature guns) or stationary infrared thermometers have been used often in agricultural research to remotely measure the surface temperatures for crop status monitoring (O’Shaughnessy et al., 2011) and to schedule irrigation based on plant feedback (Jones, 2004; Peters and Evett, 2007). There are many advantages of infrared thermometers including no physical contact, realtime and automatic collection of data, economy of cost and simple operation. Therefore, it is both economical and convenient to use infrared thermometers in agricultural production and research (Mahan and Yeater, 2008). All portable photosynthetic systems seamlessly calculate a large number of parameters including those that are temperature dependent. The task of altering calculations is now simplified because some manufacturers provide Microsoft Excel spreadsheets with all raw data recorded and all equations for computation of derived parameters embedded in the spreadsheet cells. Those who are interested about why gs and Ci depend on TL may consult the equations in the Supplemental Theory.

Analyze the influences of IR-sensor on temperature-based parameters. Three approaches were involved in investigating 3 objectives: the calibration of IR-sensor under controlled temperature, the accurate comparisons of the IR-sensor to the other two ways of the temperature measurement/computation and the assessment of how the magnitude of errors in the measurement of TL influence the computation of gs and Ci. 2. Materials and methods We did measurements on several crop and tree species (Zea mays L., Helianthus annuus L., Ginkgo biloba L., Prunus persica (L.) Batsch, Vigna unguiculata (L.) walp., Koelreuteria paniculata Laxm., Schefflera octophylla (Lour.) Harms), and excised leaves of 11 species collected on the campus of Northwest A&F University in Yangling, Shaanxi, China (34°15′N, 108°4′ Elevation of 457 m) which were: Prunus serrulata Lindl., Cotinus coggygria Scop., Prunus persica (L.) Batsch, Prunus persica f. atropurpurea, Catalpa bungei C. A. Mey, Firmiana simplex (L.) W. F. Wight, Ginkgo biloba L., Liriodendron Chinense (Hemsl.) Sarg., Populus tomentosa Carr., Syringa reticulata var. mandshurica, Ligustrum lucidum Ait. 2.1. Temperatures All species were measured at the ambient air temperature of the plants in the range of 25 to 36 °C. The best results usually arise when the temperature of the cuvette is adjusted to match the ambient air temperature of the plant. For example, if the cuvette temperature is higher than what the IRGAs used to measure water mole fraction then excessive evaporation from the leaf at the higher cuvette temperature can cause water vapor condensing conditions in the IRGAs compromising the accuracy of E measurement. Hence lab measurements were done at 25 °C for about half of the samples (grown in pots) and in the field on warm summer days with ambient air temperatures from 30 to 36 °C for the remaining samples. An IR temperature sensor (part number MLX90615, Melexis, Ypres, Belgium) was built into a base plate together with the standard LI-6400 thermocouple that could be substituted for the base plate of the LI-6400 cuvette. Details of fabrication are given in Supplemental Methods Section together with some preliminary experiments done to confirm that the LI-6400 light source did not interfere with IR temperature

1.1. The size of thermocouples can compromise TL measurements Fig. 2 shows a photograph of the LI-6400 thermocouple next to a leaf surface with the boundary layer drawn to scale on the photograph. The boundary layer thickness was computed from data supplied in the LI-6400 manual and by applying Fick’s law and the ideal gas law. The defect with the T/C sensor is that it is made of very thick wire (about 76 μm) compared to the leaf thickness (185 μm). The soldered T/C juction (about 620 μm diameter) has high thermal conductivity compared to the leaf and makes poor thermal contact with the leaf. In this photo the T/C was removed from the LI-COR cuvette and was oriented so that there were two points of contact with the leaf surface, but two point contact cannot be guaranteed during actual measurements so single-point contact is more likely in routine use. It can be seen that (1) the thermocouple nearly spans the boundary layer thickness, (2) the physical contact of the thermocouple is limited to quite a small surface area of contact on the leaf, and (3) the temperature gradient expected in the boundary layer spans the entire thickness of the thermocouple. Therefore it is expected that the T/C will read a temperature intermediate between the leaf temperatue and the air temperature as shown qualitatively in Fig. 3. Deviations between leaf and air temperature will be driven by energy absorbed by the leaf (mostly PAR). In Fig. 2, the leaf T/C measures a temperature intermediate between the leaf and air. Therefore, in Fig. 3, we predict that the TLIR (leaf temperature measured with the IR-sensor)-Tair(IRGA) will have a greater slope and intersect the TLTC (leaf temperature measured with the LI-6400 thermocouple)-Tair(IRGA). However, TLIR - Tair(IRGA) and TLEB (computed by the energy budget method)-Tair(IRGA) would increase with the same slope. Any errors in the computation or measurement of leaf temperature will influence the temperature-based parameters such as stomatal conductance, intercellular CO2 concentration and CO2 response curve accordingly. Rather than the operation of IR sensors, TL measurements using radiometers require the knowledge of the fundamental principles of radiant energy, the effects of target emissivity and the absorption and reflection of radiation from the surrounding environment on the radiometric detector. The objectives of this study were: 1. Test the accuracy of IR-sensor for temperature measurement; 2. Compare the IR senor values of leaf temperature to the energy budget calculations in Eq. (1) and (3).

Fig. 2. T/C sensor with a rather thick leaf (185 μm). The white transparent rectangle refers to boundary layer (about 700 μm). In this photo the T/C was removed from the LI-COR cuvette and was oriented so that there were two points of contact with the leaf surface. 3

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In Fig. 5, two plots could be made: (1) TL equals ΔTε=1+ Ts vs Tbath which is the expected leaf temperature in the thermally protected environment of Fig. 4, and the plot should have slope very near 1 but the slope would not reflect values of ε because only the ΔT portion is influenced by ε; (2) A more representative graph of the errors would result from a plot of ΔTε=1 versus Tbath-Ts, which would have a slope of 1-ε and relatively large deviations of measured ΔTε=1 from the best fit line. As expected, ΔTε=1 consistently underestimated (Tbath – Ts) as the presumed (TL – Ts), which equals to ΔTtrue. In which ΔTtrue is the temperature difference between leaf temperature and ambient temperature of IR sensor. A plot of ΔTε=1 vs ΔTtrue always had a slightly negative slope which we interpreted as the value of (1-ε) of the target leaf. The MLX90615 IR-sensor has a rather wide field of view, FOV, (90 degrees angle) beyond the sensitive surface. So experiments were designed to keep all leaves close enough such that the diameter of the FOV was less than half the leaf width and the IR-sensor was positioned near the leaf midrib for floating leaf experiments and about 1 cm diameter inside the LI-6400 cuvette. The repeatability of ΔTIR measurements was equated to the rootmean-square error of the deviation of the points about the linear regression of ΔTε=1 versus ΔTtrue. If ΔTε=1 equals to mΔTtrue + b is the best fit line with slope m and intercept b then the RMSerror is given by:

Fig. 3. This shows the likely qualitative relationship between ΔT vs photosynthetically active radiation (PAR), solid line is TLIR-Tair(IRGA) and dash line is TLTC-Tair(IRGA). In which, TLIR stands for leaf temperature measured by IR sensor, Tair(IRGA) stands for air temperature measured by the LI-6400 at the IRGA and TLTC stands for leaf temperature measured by the LI-6400 T/C.

sensing.

RMSerror =

mean∑ (ΔTε = 1 − ΔTtrue )2

(2)

2.2. Accuracy and repeatability evaluation 2.4. A-Ci and light response curves and mode of temperature measurements To evaluate the accuracy of the IR-sensor, two methods were used: 1. Calibration of IR against a target with controlled temperature; 2. Comparisons of the quantitative impact that two different ways of temperature measurement had on the values of the temperature-based parameters (gs and Ci).

The accuracy of temperature measurement influences the computed value of two temperature-based parameters, gs and Ci. During a light response curve measurement, PAR radiation is varied while simultaneously measuring all the parameters logged by the LI-6400 (see theory above). Using the dual sensor plate, we logged temperature with the LICOR T/C and the IR-sensor simultaneously using a synchronized timebase while using standard LI-6400 protocol to measure and log both ACi curves and light response curves. A-Ci curves were plotted as

2.3. Determination of leaf ε and SD of ΔT measurements The calibration of the IR-sensor was accomplished by pointing the sensor towards target excised leaves floating on the surface of a constant temperature bath (Neslab RTE-7D, Thermo Scientific, Newington) which can control temperature to ± 0.02 °C. The temperature of the water bath agreed with several mercury thermometers graduated in tenths of a degree to within 0.1 °C. Fig. 4 shows a drawing of the experimental setup. The MLX90615 chip contains an IR sensor, a thermal sensor to measure the “surface temperature, Ts” of the IR sensor. The IR sensor consists of a series of thermocouples (a thermopile) with the cold junctions placed behind a thick chip substrate and hot junctions placed over the thin IR sensitive membrane. The IR sensor at first has a temperature difference with the object (leaf in this case), while there will be some thermal energy emitted by the leaf which is given by εσTL4, where ε is the emissivity of the leaf, σ is the Stefan Boltzmann constant, and TL is the Kelvin temperature of the leaf. There will be a balance of IR radiation absorbed on the membrane versus the IR emitted by the membrane heats (or cools). During that, the thermopile output signal in volts (VIR) which will be converted to digital values ΔT (the differences between object temperature and Ts). Also Ts has a voltage signal that is linear function of Ts. And at last, TL is computed from ΔT + Ts. To compute the correct temperature rise, ΔT, the MX90615 needs to know ε of the leaf object. According to the LI-6400 manual (page 17-3), the emissivity of leaves in the energy budget calculation is assigned a value of 0.95. Therefore, if the MX90615 is set to expect an ε equals 1 then the TL which is ΔTε=1+Ts will be underestimated progressively relative to the actual TL as ΔT rises. We recorded values of Ts and TIR given by the IR sensor and calculated ΔTε=1 (temperature differences between Ts and TIR assuming ε equals 1). So if plot ΔTε=1 versus the actual ΔT (in this case, Tbath-Ts), the slope of the plot will equal (1-ε). This principle was used to calibrate the value of ε of several leaf species.

Fig. 4. A diagram of the experimental setup for calibration of the IR-sensor and determination of the value of emissivity, ε, of the leaf as ‘seen’ by the IR-sensor based on the sensors sensitivity to a portion of the IR spectrum. 4

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Fig. 5. Two plotted cases of the 'calibration' of the IR-sensor. A: IR-temperature output (TIR) versus bath temperature (Tbath). And B: The difference between TIR and Tbath versus the difference between Tbath and the sensor body temperature of the IR sensor (Ts).

The statistical analysis of all the data like those in Fig. 5 for all species in Table 1 revealed that the mean difference ± SD between the TL and TIR was 0.14 ± 0.09 °C without correction for ε and the mean was 0.10 °C after correction for the average ε in Table 1 for the calibrated temperature range of 21–29 °C. Not all MLX90615 chips used in our IR-sensor may be that good so a calibration is advisable before using a specific chip in a portable photosynthesis system.

photosynthetic rate on the Y-axis and intercellular CO2 concentration (Ci) on the X, while light response curves were photosynthetic rate on the Y-axis and PAR on the X. The LI-6400 produces Microsoft Excel files with computational equations embedded in the tables of computed values. These files were then uploaded into a microcomputer and the T/ C determined TL values were replaced with IR-sensor recorded values which made the Excel sheet automatically recalculate all parameters that are a function of temperature including Wi (internal mole fraction of water in the leaf), gs, and Ci. By this method we were able to evaluate the impact of different modes of recorded TL on the dependent variable of interest (gs and Ci). Light curves were measured automatically using the light curve auto program in LI-6400 portable photosynthesis system (PAR set 1500, 1200, 1000, 800, 600, 400, 200, 100, 50, 20, 0 μmol m−2 s-1, flow rate set 500 μmol s-1 and concentration of CO2 set 400 μmol CO2 mol-1, Tblock 25℃). Robinia and Populus in pots and 6 species (Zea mays, prunus persica, Ginko biloba, Helianthus annuus, vigna unguiculata, Koelreuteria paniculata) in the field were measured. A-Ci curves were measured automatically using the A-Ci curve auto program in LI-6400 portable photosynthesis system (concentration of CO2 was set 400, 300, 200, 100, 50, 400, 400, 600, 799, 999, 1200 μmol CO2 mol−1 and flow rate set 500 μmol s−1, Tblock 25℃). Nine curves were measured on Schefflera octophylla (suffruticosa plant) in the lab and 3 curves on 3 species including herbaceous plant as Helianthus annuus and woody plant as Ginko biloba (gymnosperm) and Robinia pseucdoacacia (angiosperm) in the field. In addition, we showed examples of some of the curves in Supplementary figures produced for the different plants like herbaceous plant as Zea mays, woody plant as Prunus persica (angiosperm) in the field. The above measurements were repeated for most of species listed above in the two measuring modes provided by the LI-6400, i.e., using the leaf T/C to measure leaf temperature (TLTC) or using the energy budget calculation to compute leaf temperature (TLEB) while simultaneously logging the values of leaf temperature reported by the IR-sensor (TLIR). By comparing these three estimates of leaf temperature (TL), some conclusions can be drawn about which one may be more correct.

3.2. Light response curves without activation of the energy budget option In all light response curves, E (transpiration rate) was a non-linear function of PAR. A typical plot of E and ΔT which is leaf temperature measured with the IR-sensor and the LI-COR T/C minus air temperature (Tair(IRGA)) as a function of PAR is shown in Fig. 6. In Fig. 6 it can be seen that the IR-air temperature was slightly nonlinear at the lowers E values whereas in Fig. 1 the theoretical curve based on LI-6400 energy budget equations was linear both when E was constant vs PAR or when it changes linearly with PAR. The non-linear behavior of the IR-air temperature could be duplicated in the energy budget calculations if E is non-linear (data not shown). Note that the leaf T/C changes were smaller and show no sign of non-linearity. This suggested that the leaf T/C values were dominated by the air temperature in the leaf cuvette. More examples were plotted in Fig. 7 and supplemental Fig. S1 except that now the right Y-axis was focused on gs, which is computed from various estimates of TL. Two different values of gs resulted Table 1 Table of the slope of the plot ΔTε=1 (the reported temperature differences, TIRTs) with ε = 1 (ε is the ratio of the thermal radiation) versus the actual ΔT (Tbath-Ts), computed leaf emissivity, and the root-mean square (RMS) errors observed in 10 experiments.

3. Results 3.1. Calibration of the IR-sensor and leaf emissivity (ε) The plot in Fig. 5 gives 2 examples of 10 leaves measured which shows the cases of IR-sensor calibration. The RMS error of the regressions in Fig. 5B gives a measure of the SD of the repeatability of ΔT measurements of the IR-sensor; in the cases shown, the RMS errors were 0.031 and 0.013 °C. The statistics for 10 experiments on 9 species are shown in Table 1. The mean ε of leaves and range in Table 1 is in the range expected for crop and tree leaves. The RMS error indicates that the repeatability of measurements is ± 0.04 °C for all species.

Species

Slope

Emissivity

RMS error (℃)

Ligustrum lucidum Ait Syringa reticulata var. mandshurica Populus tomentosa Carr. Ginkgo biloba L. Firmiana simplex (L.) W.F.Wight Catalpa bungei C.A.Mey Prunus persica f. atropurpurea Prunus persica (L.) Batsch Cotinus coggygria Scop. (red) Cotinus coggygria Scop. (green)a

−0.0417 −0.0394 −0.0458 −0.0726 −0.0634 −0.0595 −0.0522 −0.0644 −0.0526 −0.0501 Mean sd min max

0.958 0.961 0.954 0.927 0.937 0.941 0.948 0.936 0.947 0.950 0.946 0.011 0.927 0.961

0.022 0.027 0.031 0.035 0.043 0.023 0.040 0.040 0.013 0.019 0.029 0.010 0.013 0.043

a Cotinus leaves are red when immature and green when mature yet the emissivity values are similar.

5

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Fig. 6. Typical temperatures differences reported during light-response curves measured in the LI-6400. Instead of reporting A (assimilation rate) versus PAR, we focus on the values of E and ΔT which is the measured leaf temperature (by IR sensor or LI-6400 T/C) minus the air temperature (Tair(IRGA)) measured by the LI-6400 at the IRGA. Two measures of leaf temperature were used: (1) the LI-6400 leaf T/C (TLTC). (2) the IR-sensor (TLIR); ΔTLIR = TLIR-Tair(IRGA) and ΔTLTC = TLTC -Tair(IRGA). NOTE that during the LI-6400 protocol the air temperature in the IRGA sensor is assumed to equal the air temperature in the leaf cuvette. Also difference between ΔTLTC and ΔTLIR also equal to that between TLIR and TLEB because Tair(IRGA) is the same.

depending on which value was used for TL, i.e., TLIR or TLTC, the IR senor or thermocouple values, respectively.

Fig. 7. Measured differences for leaf and air temperature determined by the IRsensor and the LI-6400 relative to gs values. In which, TLIR-Tair(IRGA) (+) and TLTC-Tair(IRGA) (×), TLIR stands for leaf temperature measured by IR sensor, Tair(IRGA) stands for air temperature measured by the LI-6400 at the IRGA and TLTC stands for leaf temperature measured by the LI-6400 T/C. gs stands for stomatal conductance and gs(LIR), gs(LTC) stands for gs computed by TLIR, TLTC separately.

3.3. Light response curves with activation of the energy budget option Typical light response curves (response of gs vs PAR) with the activation of the energy budget option are shown in Fig. 8 and supplemental Fig. S2. During this kind of measurement, the leaf T/C was moved a few mm below the leaf to get a more accurate measure of air temperature below the leaf within the cuvette. Two differences should be noted between Figs. 7 and 8. The gs values reported follow different functional trends because now the LI-6400 used the TLEB value to compute gs rather than the TLTC value. Secondly the trends of ΔTEB equals to TLEB- Tair(IRGA) in which Tair(IRGA) is air temperature measured inside the LI-6400 IRGA and ΔTIR equals to TLIR- Tair(IRGA) did not intersect each other at low PAR (300–600 μmol m−2 s-1) as they did in Fig. 7. When the energy budget option was activated, the values of ΔTEB and ΔTIR tended to parallel each other differing by an approximately constant value (within experimental error). The parallel differences ranged between 0.1 to 0.6 °C depending on species and when the differences were consistently different by about 0.1 °C, they were not significantly different. The time for full response of gs to PAR probably required more time than the time between changes in PAR in these experiments. Hence the reported gs values probably were not the stable values that might be reached after a long period of constant PAR. The purpose of these experiments (Figs. 7 vs 8) was to emphasize the difference in leaf temperature depending on how it was measured (TLTC vs TLIR) or how it was computed (TLEB vs TLIR). In both Figs. 7 and 8, the leaf temperatures reached stable values quickly, regardless of how the values were measured or computed (data not shown).

energy budget option was activated because then the leaf T/C was lowered a few mm below the leaf to measure the air temperature in the cuvette while the LI-6400 still logs the air temperature inside the IRGA sensor. During normal operation the IRGA air temperature was used as a proxy for the air temperature in the cuvette. The data in Fig. 9 shows that the proxy temperature was not a good estimate of real air temperature a few mm below the lower side of the leaf. The reason for these differences was not investigated but it was likely that differing energy budgets (driven by PAR) generated different thermal environments in the cuvette versus the IRGA which was fairly remote from the leaf cuvette. Differentials vary among species is caused by different transpiration rate which will influence the thermal environments. In some species of plants transpiration lowers the temperature of leaves on hot days several degrees below that of the surrounding air (Fuller, 1955). During the experiment, the mean transpiration rate from high to low are Prunus persica (2.71 mmol H2O m−2 s-1), Heianthus annuus (2.61 mmol H2O m−2 s-1), Ginko biloba (1.64 mmol H2O m−2 s-1) and Zea mays (1.15 mmol H2O m−2 s-1) which almost has the similar order of temperature difference variation. 3.5. Impact of ΔT on Ci and gs during light response curves and A-Ci curve measurements

3.4. Air temperature measurement comparisons

Figs. 7 and 8 showed the dependence of gs and ΔT on PAR, where ΔT

Additional temperature comparisons were made possible when the 6

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errors in computed values of gs or Ci as a function of error in TL measurement. The computed values of both gs and Ci depend on the method used to measure the value of TL. The impact on the method of temperature measurement on computed values is easily evaluated by cutting and pasting different TL values into the LI-6400 Excel worksheet. Hence another easy way of evaluating the impact of the method of measuring TL is to compute the relative error in gs or Ci based on the method. The reference methods will always be TLTC or TLEB (the LI-6400 methods) and the comparison method was TLIR. The relative error resulting can then be defined as:

Δgs gs

=

gs, ref − gs (LIR) gs, ref

(3a)

or

Ci, ref − Ci,(LIR) ΔCi = Ci Ci, ref

(3b)

Table 2(A) listed the relative error of Ci and gs caused by temperature differences during A-Ci curves for 5 species while Table 2(B) are that during light curves. The relative error of Ci and gs varies among different species. For A-Ci curves the relative error could be 4.37% to 3.57% in gs and 4.31% to −1.15% in Ci with temperature difference range from −0.34 to −0.27℃. While for light response curves the relative error could be as huge as 14.71% to −18.00% in gs and 16.53% to -7.83% in Ci with temperature difference range from 1.58 to −1.89℃. The accurate of temperature measurement should be noticed during both light curves and A-Ci curves for that a small range of temperature difference could cause huge errors of gs and Ci, especially during A-Ci curves when Ci is the main parameter that the researchers focused on. Values of Eq. (3a), (3b) were plotted in Fig. 10A for Robinia data collected during a light response curve and in Fig. 10B for Schefflera collected during an A-Ci curve measurement. The X-axis values in Fig. 10A are ΔT equals to TLTC – TLIR. And they go from negative at high light to positive at low light as can be seen from inspection of similar light response curves in Fig. 7. The X-axis during the A-Ci curve measurement has the same meaning in Fig. 10B as 10A, but during the A-Ci curve PAR is constant. To understand why TLIR and TLTC are changing we have to appeal to the energy balance formula (Eq. (1)). During the A-Ci measurement the external concentration of CO2 is changing from low to high values to produce changes in Ci, but this also causes a decrease in gs. Hence we would expect λE in Eq. (1) to decrease as external CO2 increases because of the decrease in gs. The decrease in λE will cause an increase in TL if air temperature and aRq equals to 0.16 PAR are constant. More examples are shown in Fig. S3. The ‘error’ in Ci does not actually change the shape of the A-Ci curve, i.e., the curves are identical regardless of which estimate of leaf temperature is used (TLTC or TLIR data not shown). While this result is superficially counter intuitive, it is easily explained by remembering that the Y-axis of an A-Ci curve is the net assimilation rate and the value of net assimilation is approximately independent of TL over the narrow temperature ranges in Fig. 10B. All points of Ci(LIR) and Ci(LTC) fall on the same curve but at different positions in the Y-axis domain. See more curves in Fig. S4.

Fig. 8. Plot of the difference between leaf and air temperature as measured by the IR-sensor (+) and that computed by the energy budget method (×) during the measurement of light response curves for A: Prunus persica; and B: Zea Mays. Also shown are the values of stomatal conductance, gs, computed from each temperature (TLIR and TLEB). ΔTLIR=TLIR-Tair(IRGA) and ΔTLEB=TLEB-Tair(IRGA). Additional plots for 2 more species are available in Supplemental Fig. S2.

4. Discussion Fig. 9. Differences in air temperature between the air below the leaf in the cuvette (TairTC) and the air temperature recorded in the IRGA (Tair(IRGA)) during the energy budget measurements.

One would hope that the observation made in results and discussion below would apply generically to all portable photosynthetic systems. In addition to the two manufactures referenced in the Introduction there are also systems available from CID Bio-Sciences (Camas, WA, USA), ADC Bio-Sciences (Hoddesdon, England) and Walz GmbH (Heinz, Germany). Superficially from photographs at the manufacturers’ websites the cuvettes look quite similar. But the authors are reluctant to

equals to TLIR minus TLTC or TLEB, and these figures showed how light energy (PAR) causes the different values of TLIR, TLTC or TLEB and its influence on calculated gs. But these figures do not directly give relative 7

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Table 2 Impact of leaf temperature measurement differences on Ci and gs during light response curves and A-Ci curve measurements on 5 species. A: A-Ci curves Species

Robinia pseudoacaciaa Schefflera octophyllaa Ginko bilobaa Prunus persicab Zea maysb

Range of temperature differences (℃)

Relative error gs

Ci

0.14˜0.23 −0.56˜0.27 −0.34˜-0.27 −0.15˜0.3 0.07˜0.27

−4.44%˜-7.65% 4.73%˜-2.45% 4.37%˜3.57% 2.08%˜-3.88% 4.41%˜1.27%

−0.35%˜-1.41% 3.08%˜-1.04% 4.31%˜-1.15% 1.11%˜-0.37% 0.47%˜-0.92%

B: Light curves Species

Robinia pseudoacaciaa Schefflera octophyllaa Zea maysa Ginko bilobab Prunus persicab a b

Range of temperature differences (℃)

Relative error

1.58˜-1.89 0.57˜-1.00 0.39˜-1.41 0.52˜-0.01 0.39˜-0.49

gs

Ci

14.71%˜-18.0% 12.79%˜-7.7% 21.06%˜-6.24% 0.09%˜-2.48% 10.91%˜-5.32%

16.53%˜-7.83% 3.23%˜-1.36% 26.31%˜-1.67% 0.28%˜-6.04% 6.55%˜-1.52%

The range of temperature difference (IR versus TC). Means the range of temperature difference (IR versus Energy budget).

guarantee that the results from our LI-6400 quantitatively reflect the results to be expected from the other companies’ instruments. In leaf chamber, the leaf would be 0.984 cm above IR sensor with the FOV of about 3.04 cm2 which has a wider range of measurement compared to that of the T/C (contact surface area of only two points).The leaf area in leaf chamber is 6 cm2 which means that the IR sensor can measure 50.67% of leaf compared to the poor contact of T/ C. Therefore, it is fair to say that IR sensor has a better thermos-contact than T/C which can be an advantage of producing more accurate temperature measurement. The emissivity of crops in this study were from 0.927 to 0.961, which is similar to the emissivity of beans (0.957) given by Fuchs and Tanner (1966) or within the range 0.9-0.98 according to Idso et al. (1969). While some of literatures shows that the emissivity ranged from 0.957 to 0.978 (Fuchs and Tanner, 1966; López et al., 2012; Rubio et al., 1997), it may because that a complex vegetation surface have an emissivity greater than that of individual components because multiple reflection is expected to take place among plant components (Chen and Zhang, 1989). Fig. 10 gave the relative errors in the estimation of gs and Ci in Robinia and Schefflera which depended on the method of measuring TL. Below we will discuss which methods give the most accurate estimates of gs and Ci but for now it is instructive to examine more examples which only required a replot of Figs. 7 and 8. All the data in Figs. 7 and 8 are just parametric plots of data in Fig. 10, where the common parameter is the PAR and the parametric variables are TLIR, TLTC, TLEB, gs(LIR), gs(LTC), gs(LEB), Ci(LIR), Ci(LTC), and Ci(LEB). So at common PAR values, all that is required is to compute ΔT and Δgs/gs and ΔCi/Ci as defined in Eqs. (3a) and (3b). Before examining more examples of data like Fig. 10, the reader needs to understand a fundamental concept. The linear relationship in Fig. 6 through 8 demonstrated a positive correlation between PAR and ΔT. In contrast a good correlation between the axes in Fig. 10 through 12 proved that difference measures of temperature (ΔT) measured different things. Hence a poor correlation proved that TLIR or TLEB did not influence by the same variant as TLTC; see below. Figs. 11 and S4 was derived from a replot of Figs. 7 and S1 where TLTC was the reference temperature. Figs. 12 and S5 was a replot of Figs. 8 and S2 where TLEB was the reference temperature (Eq. (3)). The plots in Fig. 11 demonstrated that the leaf thermocouple (TLTC) measured a different value than the IR-sensor (TLIR). At high PAR, ΔT was negative and at low PAR ΔT was positive exactly as predicted by

Fig. 10. These plots show the influence of temperature differences on stomatal conductance based on data from 4 leaves pooled together. A is light response curve on Robinia; B is from A-Ci curves on Schefflera). ΔT = TLTC - TLIR, where TLTC means the temperature measured by LI-6400 portable photosynthesis T/C, and TLIR means the temperature measured by IR-sensor. Δgs/gs = [gs(LTC)gs(LIR)]/gs(LTC), where gs(LTC) means stomatal conductance calculated based on TLTC and gs(LIR) means stomatal conductance calculated based on TLIR. ΔCi/Ci = [Ci(LTC)-Ci(LIR)]/Ci(LTC).

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Fig. 12. These plots are similar to those in Fig. 11 except that the energy budget option was turned on during measurements of light response curves (replotted from Fig. 8) Right ordinate ΔCi/Ci = [Ci(LEB)-Ci(LIR)]/Ci(LEB); Left ordinate Δgs/gs = [gs(LEB)-gs(LIR)]/gs(LEB) and abscissa is ΔT = TLIR-TLEB.

Fig. 11. These plots show the influences of temperature differences on Intercellular CO2 concentration (Ci) and stomatal conductance (gs). Data for the two species were replotted from Fig. 7. Right ordinate ΔCi/Ci = [Ci(LTC)-Ci(LIR)]/ Ci(LTC); Left ordinate Δgs/gs = [gs(LTC)-gs(LIR)]/gs(LTC) and abscissa is ΔT = TLIRTLTC.

incoming and outgoing fluxes of energy shown in Eq. (1). The main incoming source of energy was from the absorption of PAR (aRq) and the main outgoing source of energy loss was the latent heat of evaporation, λE. A minor source of energy gain or loss was due to the sensible heat diffusion between the TL and Ta whenever the two values differ but the difference can be positive or negative depending on the relative values of PAR absorption (aRq) and λE. The cross over point when TLIR equals TLTC did not occur precisely when TL equals Ta in Fig. 7 (data not shown) but the deviations could be explained by the expected measuring resolution of temperature ( ± 0.05 °C) and by the fact that air temperature in the cuvette did not equal the air temperature in the IRGA (Fig. 9). The reader needs to remember that when the LI-COR T/C was used to measure leaf temperature instead of the temperature sensor in the IRGA the LI-6400 uses as a proxy for Ta. However the deviations between true Ta and the proxy temperature become evident when measuring parameters with the activation of energy budget option in the LI-6400 system (Fig. 9). The accuracy by leaf temperature measurement was of great importance in the estimation of the parameters based on the temperature values (gs and Ci). According to the results, even a small amount of error made by temperature measurement lead to significant differences in the temperature-based parameters. The weight of the evidence was in favor of the hypothesis that TL could be measured more accurately by using an IR-sensor by the LI-6400 T/C sensor. It can be seen that the errors in the measurement of gs from 14.71% to -18.00% and for Ci ranged from 16.53% to -7.83% for Ci. (Table 2, Fig. 10A and 11). Therefore, the accuracy of temperature measurement was quite important for getting nonbiased parameters. As expected, the TLEB computed during energy budget computations paralleled the TLIR measured simultaneously. Since these lines did not

the energy budget equation (Eq. 1). The time for thermal equilibrium in the energy budget (Eq. 1) ought to be much faster than the time required for stomata to respond to changes in the level of light. This difference in tempo explains why points were curved upwards for ΔCi/ Ci as PAR dropped below the photosynthetic compensation point (net assimilation A equals 0) the decline in PAR closed stomata but the respiration drove Ci up. Fig. 12 applied to the case where the energy budget calculation of TLEB was turned on and used as the reference value. The range ΔT on the X-axis was much reduced from 0.55 °C (highest to lowest) for corn and peach in Fig. 12 versus 1.8 °C in Fig. S4 for corn and peach. The Y-axis values were also more scattered which confirmed that TLEB and TLIR measured fundamentally the same energy budget processes. However the small correlations that remained in some plots demonstrated a systematic difference between the two measures of TL. The thermocouple in LI-6400 portable photosynthesis system needs physical contact with leaf to detect the temperature, but the area of contact is quite small and the size of the T/C junction is about the same as the thickness of the leaf boundary layer (Fig. 2). Our hypothesis (Fig. 3) was that the slope of TLTC versus PAR would be smaller than the range of TLIR versus PAR and that the values should cross over at the point at which real leaf temperature (TL) reaches the air temperature (Ta). The data supported the hypothesis. The reader can further observe that the Y-axis ranges for relative error of gs and Ci in Fig. 12 were correspondingly smaller than that in Fig. 11. At this point we cannot prove that TLEB values are less accurate than TLIR values but our hypothesis is that the TLIR values might be more accurate as explained below. The ultimate TL is influenced by an energy balance of all the 9

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IR-sensor. The IR-sensor, like contact thermocouple/thermistors, work reliably only with large leaves that cover the whole cuvette surface. Hence the energy budget option is the only reasonable choice for many small-leaved angiosperms, grasses and conifers.

tend to cross over each other (Fig. 8) as PAR changes, one can conclude that this signifies that they both responded to the energy budget environment of the leaf. The fact that the values TLEB and TLIR differ a little (0.1 to 0.6 °C) deserves some consideration because the choice of which value to use for subsequent computations will influence the computed values of gs and Ci in Figs. 10–12. The values for any one species could differ for at least two reasons: (1) There could be something wrong with the approximations used in the energy budget equation (Eq. (1)). (2) Or there could be some systematic error in the Ts value used by the IRsensor to compute surface temperature; Ts is the temperature of the lower side of IR sensitive membrane (Eq. 3). Or a combination of (1) and (2) might explain the differences between TLIR and TLEB. We reject option (2) because the error in Ts should be the same for every species but the difference between TLIR and TLEB varies between species (Figs. 8 and 12). We also think that the TLIR values were more reliable than the TLEB values because of the water bath experiments in which the IR temperature was positioned vertically less than 2 cm above the abaxial side of the leaf while leaves floated in a controlled temperature bath (Figs. 4, 5 and Table 1). In these experiments the leaf temperature was likely to be equal to the bath temperature and by changing the bath temperature we could change the difference between leaf temperature and Ts in the sensor. During these experiments the TLIR agreed to Tbath within 0.14 °C when ε was set to 1 in the IR-sensor when the bath minus Ta values differed from -1 to +2 °C which equals to the range inside the LI-6400 measuring environment and the total Ta range was 21 to 29 °C. See the calibration curves shown in Fig. 5. In the experiments presented above we did not use the calibration corrections in Table 1, because they were rather small and variable between species specific corrections for specific species would be cumbersome and would not be done by most users of the LI-6400. Hence we opted to omit these corrections in this study but such corrections might be valuable in the future. More work is advisable before one can reliably confirm that TLIR is more precise than TLEB. Future studies may address this question adequately to make a final decision. While we are convinced that the MLX90615 IR-sensor is preferable to the LI-6400 T/C sensor for measurement of leaf temperature we are aware that few users would be capable of fabricating their own IRsensor system. Also the present use of the MLX90615 IR-sensor is cumbersome because it is not fully integrated into the LI-6400 LPL software; hence an independent computer was needed to log IR temperatures. We used Arduino-based hardware and software for our IRsensor because it was all in the public domain without serious restrictions of patents or intellectual property rights. Hence we will share our software and hardware design with any individual scientist or manufacturer of photosynthesis systems who wish to adopt the technology. The sharing of information will be free of any payments for intellectual property rights. All inquiries for technical details can be emailed to the corresponding author.

Author contributions LZ performed experiments and contributed to writing and editing. LZ and SZ did data analysis and SZ designed the experiment, provided theoretical input, contributed equally to writing, provided interpretation of results, and coordinated the fabrication of new components for the LI-6400. Acknowledgements This work was supported by the ‘The Recruitment program of Global Experts’ grant in China to Melvin T. Tyree (Z111021201). We thank Michael Fortney and Doug Gomez for electrical and mechanical assistance, respectively, in the fabrication of test sensors. Mr. Fortney wrote the program (sketch) used in our Arduino board. The sketch will be made available to anyone desiring it in keeping with the spirit of open-source hardware. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.agrformet.2019. 107700. References Ajayi, A.E., Olufayo, A.A., 2004. Evaluation of two temperature stress indices to estimate grain sorghum yield and evapotranspiration. Agron. J. 96, 1282–1287. Ball, J.T., 1987. Calculations Related to Gas Exchange, Stomatal Function. Stanford University Press, California. Blasius, H., 1907. Grenzschichten in Flüssigkeiten mit kleiner Reibung: Druck von BG. Teubner. Boccalandro, H.E., Giordano, C.V., Ploschuk, E.L., Piccoli, P.N., Bottini, R., Casal, J.J., 2012. Phototropins but not cryptochromes mediate the blue light-specific promotion of stomatal conductance, while both enhance photosynthesis and transpiration under full sunlight. Plant Physiol. 158, 1475–1484. Brenner, A., Jarvis, P., 1995. A heated leaf replica technique for determination of leaf boundary layer conductance in the field. Agric. For. Meteorol. 72, 261–275. Brodribb, T., 1996. Dynamics of changing intercellular CO2 concentration (Ci) during drought and determination of minimum functional ci. Plant Physiol. 111, 179–185. Chen, J.M., Zhang, R.H., 1989. Studies on the measurements of crop emissivity and sky temperature. Agric. For. Meteorol. 49 (1), 23–34. Chung, D.D.L., 2016. Carbon Composites: Composites with Carbon Fibers, Nanofibers, and Nanotubes. Butterworth-Heinemann, Amsterdam. Djebbar, R., Rzigui, T., Pétriacq, P., Mauve, C., Priault, P., Fresneau, C., De Paepe, M., Florez-Sarasa, I., Benhassaine-Kesri, G., Streb, P., 2012. Respiratory complex I deficiency induces drought tolerance by impacting leaf stomatal and hydraulic conductances. Planta 235, 603–614. Fuchs, M., Tanner, C.B., 1966. Infrared thermometry of vegetation. Agron. J. 58 (6), 597–601. Fuller, H.J., 1955. The Plant World. Henry Holt and Co, New York. Grace, J., Wilson, J., 1976. The boundary layer over a Populus leaf. J. Exp. Bot. 27, 231–241. Idso, S.B., Jackson, R.D., Ehrler, W.L., Mitchell, S.T., 1969. A method for determination of infrared emittance of leaves. Ecology 50 (5), 899–902. Irmak, S., Haman, D.Z., Bastug, R., 2000. Determination of crop water stress index for irrigation timing and yield estimation of corn. Agron. J. 92, 1221–1227. Jones, H.G., 2004. Irrigation scheduling: advantages and pitfalls of plant-based methods. J. Exp. Bot. 55, 2427–2436. López, A., Molina-Aiz, F.D., Valera, D.L., Peña, A., 2012. Determining the emissivity of the leaves of nine horticultural crops by means of infrared thermography. Sci. Hortic. 137, 49–58. Mahan, J.R., Yeater, K.M., 2008. Agricultural applications of a low-cost infrared thermometer. Comput. Electron. Agric. 64, 262–267. McDermitt, D., 1990. Sources of error in the estimation of stomatal conductance and transpiration from porometer data. HortScience 25, 1538–1548. Meyer, W.S., Reicosky, D.C., Schaefer, N.L., 1985. Errors in field measurement of leaf diffusive conductance associated with leaf temperature. Agric. For. Meteorol. 36, 55–64. Morrow, P.A., Slatyer, R.O., 1971. Leaf temperature effects on measurements of diffusive resistance to water vapor transfer. Plant Physiol. 47, 559–561.

5. Conclusions The MLX90615 IR-sensor exhibits high stability, fast response and excellent accuracy in the sheltered environment of the LI-6400 cuvette. The incorporation of an IR-sensor should be seriously considered in the next generation of the LI-COR portable photosynthesis system. Using a leaf T/C for measuring the correct air temperature adjacent to the leaf would further enhance the accurate computation of gs and Ci, but how much this might enhance gs and Ci awaits further study. We strongly recommend that users routinely use the energy budget option if a noncontact temperature sensor is not available in a manufacturer’s portable photosynthesis system. We suspect that few people know about or how to activate the energy budget option but it is likely to produce more accurate values of gs and Ci than obtained by using the TLTC values that are traditionally used by everyone. However, more work is needed to evaluate the merits of this recommendation to turn on the energy budget option during routine measurements compared to installing an 10

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measurements in LI-COR 6200 assimilation chamber using energy balance calculations. Agric. For. Meteorol. 53, 149–156. Rubio, E., Caselles, V., Badenas, C., 1997. Emissivity measurements of several soils and vegetation types in the 8–14, μm wave band: analysis of two field methods. Remote Sens. Environ. 59 (3), 490–521. Van Driest, E.R., Blumer, C.B., 1963. Boundary layer transition: freestream turbulence and pressure gradienteffects. AIAA J. 1 (6), 1303–1306. Xu, C.Y., Singh, V., 2001. Evaluation and generalization of temperature‐based methods for calculating evaporation. Hydrol. Process. 15, 305–319. Yoshie, F., 1986. Intercellular CO2 concentration and water-use efficiency of temperate plants with different life-forms and from different microhabitats. Oecologia 68, 370–374.

O’Shaughnessy, S.A., Hebel, M.A., Evett, S.R., Colaizzi, P.D., 2011. Evaluation of a wireless infrared thermometer with a narrow field of view. Comput. Electron. Agric. 76, 59–68. Parkinson, K., 1985. A simple method for determining the boundary layer resistance in leaf cuvettes. Plant Cell Environ. 8, 223–226. Parlange, J.-Y., Waggoner, P.E., Heichel, G.H., 1971. Boundary layer resistance and temperature distribution on still and flapping leaves I. Theory and laboratory experiments. Plant Physiol. 48, 437–442. Peters, R., Evett, S., 2007. Spatial and temporal analysis of crop conditions using multiple canopy temperature maps created with center-pivot-mounted infrared thermometers. Trans. ASABE 50, 919–927. Rochette, P., Pattey, E., Desjardins, R., Dwyer, L., 1990. Adjustment of leaf temperature

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