Thermal data to monitor crop-water status in irrigated Mediterranean viticulture

Agricultural Water Management 176 (2016) 80–90

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Thermal data to monitor crop-water status in irrigated Mediterranean viticulture I.F. García-Tejero a,b,∗,1 , J.M. Costa a,c,∗,1 , R. Egipto c , V.H. Durán-Zuazo b , R.S.N. Lima d,e , C.M. Lopes c , M.M. Chaves a,c a

Plant Molecular Physiology Laboratory, ITQB, Universidade Nova de Lisboa, Oeiras, Portugal IFAPA Centro ‘Las Torres-Tomejil’, Ctra. Sevilla-Cazalla, km. 12,2. 41,200. Alcalá del Río, Sevilla, Spain c LEAF, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, Lisboa, Portugal d Departamento de Biologia, Escola de Ciências e Tecnologia, Instituto de Ciências Agrárias e Ambientais Mediterrânicas (ICAAM), Universidade de Évora Apartado 94, 7002-554 Évora, Portugal e Universidade Federal de Sergipe, Cidade Universitária Prof. José Aloísio de Campos. Av. Marechal Rondon, s/n Jardim Rosa Elze, CEP 49100-000 São Cristóvão/SE, Brazil b

a r t i c l e

i n f o

Article history: Received 19 September 2015 Received in revised form 5 May 2016 Accepted 7 May 2016 Keywords: Precision viticulture Grapevine water relations Leaf-gas exchange Thermography Thermal index and Canopy temperature

a b s t r a c t Canopy temperature (TC ) can be a robust indicator of grapevine water status. However, the assessment of TC by thermography in field conditions requires optimization, especially under variable environmental conditions (daily and seasonal) just like it occurs in Mediterranean areas. Besides, simplicity and robustness should be the basis of a wider use of thermography in field conditions. Therefore the comparison of simpler and more complex approaches in terms of thermal indicators is wise to test. Our major aims were: i) to assess the performance of four common thermal indicators (canopy temperature − TC , Crop Water Stress Index − CWSI, index of relative stomatal conductance − IG , and the difference between TC and the surrounding air − Tcanopy-air ) to support irrigation decisions ii) to optimize the timing of thermal measurements for different genotypes and iii) to obtain mathematical functions to estimate leaf gas exchange parameters on the basis of thermal data. The trial was conducted in the summer of 2013, in south Portugal. Two V. vinifera red varieties, Touriga Nacional and Aragonez (syn. Tempranillo) were tested. Vines were subjected to two irrigation regimes: i) sustained-deficit irrigation (based on the farm’s schedule − control) and ii) regulated-deficit irrigation (∼50% of the control). We found that measurements done between 11:00 and 17:00 h provide the most significant correlations between TC , CWSI and IG and leaf stomatal conductance and net photosynthesis for both genotypes. Different linear mathematical functions were obtained to estimate leaf gas exchange based on the best performing thermal indicators under field conditions. Our results emphasize the value of TC as a relevant explanatory variable of vine’s physiological status, in spite of being a simpler and non-normalized thermal indicator. The potential relevance of TC for grapevine modelling and phenotyping is also discussed. © 2016 Elsevier B.V. All rights reserved.

1. Introduction Water is the major limiting natural resource for crop production in irrigated agriculture, especially in semi-arid areas of European Mediterranean countries (EEA, 2012; Costa et al., 2007; GarcíaTejero et al., 2014). Nevertheless, the area of intensive irrigated

∗ Corresponding authors at: Plant Molecular Physiology Laboratory, ITQB, Universidade Nova de Lisboa, Oeiras, Portugal. E-mail addresses: [email protected] (I.F. García-Tejero), [email protected] (J.M. Costa). 1 These authors had an equal contribution. http://dx.doi.org/10.1016/j.agwat.2016.05.008 0378-3774/© 2016 Elsevier B.V. All rights reserved.

agriculture (e.g. viticulture), has been increasing in Southern European countries, namely in Portugal and Spain (Costa et al., 2016; ESYRCE, 2012). Studies on more precise irrigation in viticulture are needed to promote water savings under unfavourable environments and specific terroirs (Möller et al., 2007; Chaves et al., 2010; Costa et al., 2016). Previous literature has shown variations in the water use and leaf gas exchange traits between different grapevine genotypes subjected to drought or deficit irrigation (Costa et al., 2012; Tomás et al., 2014; Bota et al., 2016) and seasonal variations in the leaf water use efficiency due to changing environmental conditions and leaf aging are also reported (Escalona et al., 2003; Williams 2012; Tramontini et al., 2014; Medrano et al., 2015; Bellvert et al., 2015).

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Fig. 1. Diurnal and seasonal time course of the climatic conditions along the trial and for four different days of the year (DOY): DOY 171, 20 June, DOY 198, 17 July; DOY 220, 8 August and DOY 235, 21 August 2013.

Punctual measurements are often used to monitor crop water status in field conditions. Among them, pre-dawn and stem water potential are reliable indicators of plant-water status and are used to support irrigation scheduling (Naor, 2000; Lampinen et al., 2001; Jones, 2004a; García-Tejero et al., 2011a). Moreover, in species with strict stomatal regulation such as grapevine (Chaves et al., 2010), stomatal conductance to water vapour (gs ) can be a robust indicator of water stress (Chaves et al., 2010). Jones (2004a), for example, argued that gs would be a better indicator of plant response to soil water deficit than leaf water potential because reductions in gs may occur before any measurable change in plant water status. However, punctual measurements of gs cost time and labour, and

require high number of replicates for a sound assessment, which makes such method less suitable for open field applications (Jones, 1999; Costa et al., 2010, 2013). In grapevine, different varieties may require different irrigation strategies due to differences in the regulation of leaf-gas exchange and plant water relations (Escalona et al., 2003; Chaves et al., 2010; Costa et al., 2012; Tomás et al., 2014; Bota et al., 2016). This can result in variable leaf/canopy temperature phenotype (Costa et al., 2012). As a consequence, more precise monitoring of grapevine water status on the basis of thermal data is required, especially when dealing with different genotypes in the same vineyard.

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Canopy temperature Touriga Nacional

(◦ C) Aragonez

Stomatal conductance to water vapour(mol H2 O m−2 s−1 )

Net photosynthesis rate (␮mol CO2 m−2 s−1 )

Touriga Nacional

Touriga Nacional

Aragonez

Aragonez

DOY 171

Time 8h 11 h 14 h 17 h 20 h

SDI 23.5 (0.1) 23.3 (0.7) 25.1 (0.3) 30.1 (0.2) 23.2 (1.1)

RDI 22.4 (0.9) 24.0 (0.4) 25.1 (0.5) 30.1 (0.8) 22.5 (0.2)

SDI 21.9 (0.6) 23.4 (0.2) 25.4 (0.4) 28.4 (1.5) 22.8 (0.7)

RDI 21.9 (0.4) 23.9 (0.2) 25.3 (0.1) 29.1 (0.5) 22.3 (0.4)

SDI 0.29 (0.02) 0.26 (0.02) 0.16 (0.01) 0.19 (0.01) 0.09 (0.02)

RDI 0.33 (0.01) 0.27 (0.01) 0.21 (0.01) 0.18 (0.02) 0.10 (0.02)

SDI 0.28 (0.03) 0.28 (0.01) 0.19 (0.02) 0.24 (0.01) 0.05 (0.02)

RDI 0.28 (0.01) 0.29 (0.02) 0.19 (0.02) 0.18 (0.01) 0.03 (0.01)

SDI 16.3 (0.9) 16.3 (1.7) 13.2 (1.2) 13.7 (0.7) 5.9 (2.5)

RDI 16.2 (0.6) 17.2 (1.1) 14.5 (0.7) 13.5 (0.6) 6.7 (1.4)

SDI 15.9 (1.0) 16.4 (0.2) 14.4 (0.9) 15.6 (1.9) 3.8 (2.2)

RDI 16.4 (0.6) 16.4 (0.4) 14.1 (0.4) 13.6 (0.6) 2.1 (0.5)

198

8h 11 h 14 h 17 h 20 h

28.3 (1.3) 32.1 (0.8) 34.7 (0.2) 35.1 (0.3) 29.1 (0.8)

28.0 (1.0) 33.8 (0.9) 35.9 (0.3) 37.8 (0.4) 28.7 (0.3)

27.5 (0.5) 31.2 (0.7) 33.2 (0.9) 33.8 (1.2) 28.8 (0.7)

29.5 (0.8) 32.5 (0.8) 35.5 (0.3) 36.6 (0.3) 28.5 (0.2)

0.25 (0.02) 0.17 (0.02) 0.14 (0.01) 0.11 (0.01) 0.04 (0.02)

0.23 (0.02) 0.16 (0.02) 0.07 (0.01) 0.09 (0.01) 0.04 (0.01)

0.30 (0.02) 0.25 (0.05) 0.14 (0.02) 0.14 (0.04) 0.02 (0.01)

0.25 (0.01) 0.16 (0.03) 0.07 (0.01) 0.08 (0.01) 0.01 (0.00)

15.8 (0.5) 12.1 (0.8) 10.6 (0.9) 8.6 (0.9) 2.5 (2.0)

16.1 (0.3) 12.0 (1.4) 6.4 (0.3) 7.2 (0.6) 2.6 (0.9)

18.3 (0.4) 15.4 (1.4) 9.5 (1.0) 8.9 (1.3) 0.9 (1.7)

16.1 (0.8) 11.4 (1.7) 6.6 (0.8) 6.8 (0.6) 0.4 (0.4)

220

8h 11 h 14 h 17 h 20 h

23.8 (1.1) 28.7 (0.8) 31.5 (0.5) 33.6 (0.3) 27.3 (1.4)

23.9 (0.9) 27.7 (0.5) 31.0 (0.2) 35.2 (0.6) 27.4 (0.7)

24.0 (0.7) 27.9 (0.4) 30.9 (0.7) 34.4 (1.3) 26.1 (1.6)

24.6 (0.1) 27.8 (0.5) 32.0 (0.1) 34.4 (0.4) 26.3 (0.6)

0.20 (0.01) 0.17 (0.01) 0.13 (0.02) 0.10 (0.01) 0.05 (0.01)

0.20 (0.02) 0.18 (0.02) 0.11 (0.01) 0.09 (0.01) 0.08 (0.01)

0.24 (0.02) 0.21 (0.03) 0.14 (0.02) 0.13 (0.03) 0.06 (0.02)

0.19 (0.01) 0.16 (0.02) 0.17 (0.01) 0.08 (0.01) 0.03 (0.01)

14.7 (0.6) 12.0 (0.2) 9.7 (1.4) 8.4 (0.5) 2.5 (1.5)

15.5 (0.4) 14.8 (0.9) 8.4 (0.6) 8.6 (0.6) 4.8 (1.2)

16.7 (0.7) 14.9 (0.7) 10.6 (0.6) 10.1 (1.4) 2.7 (2.0)

15.2 (0.4) 13.0 (1.0) 9.8 (0.6) 7.7 (0.6) 1.8 (0.6)

235

8h 11 h 14 h 17 h 20 h

26.2 (1.1) 31.1 (0.3) 34.4 (0.4) 35.0 (0.6) 31.2 (0.4)

27.6 (0.4) 33.2 (0.2) 36.1 (0.2) 36.2 (0.3) 30.8 (0.2)

25.8 (0.5) 31.7 (0.6) 35.1 (0.3) 34.9 (0.9) 31.1 (0.3)

27.0 (0.1) 33.6 (0.2) 36.4 (0.4) 35.7 (0.2) 31.4 (0.1)

0.25 (0.03) 0.27 (0.03) 0.16 (0.03) 0.15 (0.02) 0.04 (0.02)

0.25 (0.02) 0.13 (0.01) 0.09 (0.01) 0.09 (0.01) 0.03 (0.01)

0.27 (0.02) 0.20 (0.06) 0.15 (0.02) 0.12 (0.01) 0.02 (0.01)

0.18 (0.03) 0.16 (0.03) 0.10 (0.01) 0.07 (0.01) 0.01 (0.01)

16.1 (0.8) 14.3 (0.9) 10.7 (1.4) 8.6 (1.0) 0.5 (0.9)

15.1 (0.5) 10.0 (1.0) 6.2 (0.2) 6.0 (0.6) 0.7 (1.1)

16.0 (0.9) 13.4 (1.5) 8.6 (0.8) 8.3 (0.4) 0.7 (1.1)

13.5 (1.3) 10.8 (1.1) 7.9 (1.2) 5.0 (1.0) 0.2 (0.7)

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Table 1 Diurnal variation of canopy temperature (◦ C), stomatal conductance to water vapour (mol H2 O m−2 s−1 ), and net photosynthesis rate (␮mol CO2 m−2 s−1 ) during the four sampling days of the year (DOY), measured for V. vinifera plants cvs. Touriga Nacional and Aragonez (syn. Tempranillo) subjected to sustained deficit irrigation (SDI) and regulated deficit irrigation (RDI). Canopy temperature was measured with a ThermaCam FLIR SC660 (Flir Systems, USA) and leaf gas exchange parameters were determined by using a portable leaf gas exchange meter Licor-6400 (Licor, USA) equipped with a transparent leaf chamber. Values are means SE in brackets (n = 4).

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Thermal imaging emerged as a non-invasive and robust technique to assess crop-water status and irrigation scheduling in grapevine but is still not widely used in modern orchards or vineyards (Jones and Leinonen, 2003; Grant et al., 2006, 2007; Jones and Grant, 2016). Water stress induces stomatal closure, which limits transpiration and reduces evaporative cooling, resulting in higher leaf temperature (Tleaf ) and/or canopy temperature (TC ) (Jones, 1999, 2004b; Jones et al., 2009). Tleaf or TC can thus provide information to estimate stomatal conductance to water vapour and plant-water status (Jones 2004b; Berni et al., 2009a,b; Jones et al., 2009). However, the relationships between Tleaf or TC and leaf physiological parameters are not always straightforward, in particular under variable meteorology (Tair , amount/angle of incident radiation, wind speed, vapour pressure deficit) (Jones, 2004b; Jones et al., 2009). Simplicity and robustness should be the basis to promote the use of thermography in field conditions (Fuentes et al., 2014). Therefore, the comparison of simpler or more complex approaches in terms of thermal indicators is wise to test. In order to make thermal imaging a more robust tool for field applications, different thermal indices were developed to normalize absolute values of Tleaf or TC (Jones et al., 1997, 2002; Maes and Steppe, 2012). These thermal indices are based on the estimation of artificial temperature references (See Eqs. (2) and (3)). The reference Twet works as a proxy of the minimal Tleaf measured in plants under optimal watering conditions and maximum transpiration. The reference Tdry is a proxy of the maximum Tleaf /TC due to maximum stomatal closure under severe water stress. The crop-water stress index (CWSI) and the index of relative stomatal conductance (IG ) or the difference between TC and Tair (Tcanopy-air ) are the most often used and were successfully tested in different crops, including grapevine (Jones 1999; Jones et al., 2002; Grant et al., 2007; Costa et al., 2012; Bellvert et al., 2014). However, these indices require previous testing and validation for ground based and airbone thermal sensing. Previous works tested different strategies to optimize thermography use in the field by developing robust protocols based on thermal indices (Jones et al., 2002, 2009; Grant et al., 2006; Möller et al., 2007; Zia et al., 2009; Baluja et al., 2012; Bellvert et al., 2014; Pou et al., 2014). However, some questions remain though. For instance, to find which is the most practical but still robust thermal index that can be a proxy of plant physiological traits (e.g. leaf gas-exchange behaviour), and secondly, how would the variety/genotype influence thermal indices performance and time of measurements? The aim of the present work was to evaluate the performance under field conditions of different thermal indicators (TC , CWSI, IG and Tcanopy-air ) to assess the water status of two red V. vinifera varieties [Aragonez (syn. Tempranillo), and Touriga Nacional], based on the relationships between these indicators and leaf stomatal conductance and net photosynthesis. In parallel, we aim to obtain the most representative relationships between the four thermal indicators and eco-physiological parameters.

2. Material and methods 2.1. Location, plant material and growing conditions The trial was conducted in the 2013 in a commercial vineyard (Herdade do Esporão), Reguengos de Monsaraz, southern Portugal (38◦ 23 55.00 N, 7◦ 32 46.00 W). Eleven year-old red grapevines [Vitis vinifera L. cvs. Aragonez (syn. Tempranillo) and Touriga Nacional] grafted onto 1103 Paulsen rootstock were studied. Vines were spaced 1.5 m within and 3 m between rows, on N-S orientation. Vines were spur-pruned on a bilateral Royat Cordon

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system and trained in a vertical shoot-positioning system with a pair of movable wires. All vines were uniformly pruned with 15–16 nodes per vine. Soil texture is a sandy-loam to silty-clay-loam, with a pH of 7.0–7.6, a low content in organic matter (10.5 g kg−1 ) and high P2 O5 and K2 O values (110 and 173 mg kg−1 , respectively). The two varieties were planted side by side in two adjacent plots with the same experimental design: randomized complete block, with four blocks and two irrigation treatments: Sustained Deficit Irrigation − SDI, irrigated with ∼30% of ETC , and a Regulated Deficit Irrigation (RDI, ∼15% of ETC ). The elemental plot comprised three adjacent rows (two buffer rows and a central one for data collection). Measurements were done in two blocks, using two vines per block. Water was applied using a single pipe of drip emitter (1/m) with a flow rate of 2.2 L h−1 . Crop-water requirements were weekly calculated according to Allen et al. (1998), using the reference evapotranspiration values obtained from an automatic weather station located in the experimental orchard, and considering a crop coefficient (Kc) equal to 0.7. Irrigation water was applied 1–2 times per week, starting at berry touch (berries beginning to touch − stage 77 of the BBCHscale for grapes). The crop evapotranspiration (ETc ) and rainfall accumulated during the irrigation period were 429 and 9.4 mm respectively. The SDI treatment received a total of 111 mm (∼30% ETc during the irrigation period) in 16 irrigation events, and the RDI treatment (late deficit at the ripening period), received 53 mm (∼15% ETc during the irrigation period) in 8 irrigation events.

2.2. Measurements Climate data (Tair , relative humidity, wind speed and solar radiation) were hourly collected, using an automatic weather station (iMetos 1, Pessl Instruments GmbH, Werksweg, Austria), located in the vineyard and close to the experimental plot (about 900 m). Vine water status was assessed along the trial by measuring leaf water potential at pre-dawn (pd ) with a Scholander pressure chamber (Soil Moisture Equipment Corp., Sta. Barbara, CA, USA). A total of 4 leaves per irrigation treatment and genotype were used. Canopy temperature (TC ) and leaf gas-exchange traits were measured along the day (8:00 h, 11:00 h, 14:00 h, 17:00 h, and 20:00 h) and along the growing season: i) 20 June (Day of the year − DOY 171, majority of berries touching), ii) 17 July (DOY 198, beginning of ripening: berries begin to develop variety-specific colour), iii) 8 August (DOY 220, mid-ripening) and iv) 21 August (DOY 235, full ripening, and 1 and 2 weeks before the commercial harvest of Aragonez and Touriga Nacional respectively). TC was measured by thermal imaging. Measurements were made periodically along the day with a thermal camera (Flir SC660, Flir Systems, USA, 7–13 ␮m, 640 × 480 pixels), with an emissivity (␧) set at 0.96. Each pixel corresponds to an effective temperature reading (Jones, 2004b). The imager was placed perpendicularly to the canopy, at about 2 m from the sunlit side. One image was made per vine, using two plants per variety, treatment, and replication (n = 4). Background temperature was determined by measuring the temperature of a crumpled sheet of aluminium foil placed near the leaves of interest using ␧ = 1 (Jones et al., 2002). Thermal images were analysed by using the ‘Flir QuickReport’ software (Flir Systems, USA). TC was estimated on the basis of the average temperature of 2–3 selected sunlit regions of interest (ROI) in each image. This avoided selection of pixels of shaded spots and/or of stem woody material. A total of 4 thermal images were taken per variety and irrigation treatment at each time of observation. The absolute TC values were normalized by calculating the thermal

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Fig. 2. False coloured infrared thermal images taken along the season at different days of the year (DOY) and time of the day. A) 20 June (DOY 171), B) 17 July (DOY 198), C) 8 August (DOY 220) and D) 21 August (DOY 235) measured for vines of the variety Touriga Nacional (TN) and Aragonez (syn. Tempranillo) (AR) subjected to regulated deficit irrigation (RDI) or to sustained deficit irrigation (SDI). Thermal images were done with a thermal camera (Flir SC660, Flir Systems, USA, 7–13 ␮m, 640 × 480 pixels) with emissivity set at 0.96. Images were taken at about 2 m distance from the sunlit side of the canopy.

indices CWSI, IG , and Tcanopy-air as follows (Jones, 2004b; Maes and Steppe, 2012): Tcanopy-air = TC −Tair CWSI = IG =

(Tc − Twet ) (Tdry − Twet )

(Tdry − Tc ) (Tc − Twet )

(1) (2)

(3)

Where Tdry and Twet are reference temperature values for a leaf with fully closed stomata and for a fully transpiring leaf, respectively; TC is the canopy temperature and Tair the temperature of the surrounding air. On each monitored vine, two fully sunlight exposed mature leaves were selected to obtain reference values of Tdry and Twet . One leaf was covered on both sides with vaseline 20–30 min before images were taken, whereas the other leaf was sprayed with water 15–20 s prior to thermal image acquisition. Individual leaf gas-exchange measurements (stomatal conductance to water vapour – gs , and net CO2 assimilation – An ) were made by using a portable infrared gas-exchange system (LI-6400; LiCor Inc., Lincoln, Nebraska, USA), equipped with a 6 cm2 transparent leaf chamber, with an air-flow rate set at 500 ␮mol s−1 . These measurements were taken in two fully expanded and sunlit leaves per tested plant, using a total of 4 plants per irrigation treatment and variety. 2.3. Statistical analysis For each measurement day, an exploratory and descriptive analysis was made of all physiological measurements (gs , An and TC ) and a Levene’s test was applied to test the variance homogeneity for the studied variables. To determine the effects of the measurement

time, irrigation treatment and cultivar a three way ANOVA was applied using the statistical software SPSS (SPSS Inc., 15.0 Statistical package; Chicago, IL, USA). To evaluate the relationships between variables, a linear correlation analysis between the thermal indicators (TC , Tcanopy-air , CWSI and IG ) and the crop physiological variables (gs , An ) was done for each time of observation (hour) and variety (n = 32). The obtained correlation coefficients were used to support the decision on the best time of day to carry out TC readings for both varieties as well ass to assess robustness of the above thermal indicators as proxies of physiological meaningful traits (gs and An ). Using the pooled data corresponding to the time periods during which thermal measurements showed the highest linear correlation coefficients, (between 11:00 and 17:00 h), a regression analysis between TC , Tcanopy-air , CWSI and IG (independent variables) and leaf gas-exchange parameters (dependent variables) was performed. Finally, the obtained linear regressions for each genotype were compared (slope and intercept) using a covariance analysis at a confidence level of 95%.

3. Results 3.1. Climate conditions and vine water status Fig. 1 shows the diurnal variation of solar radiation, Tair , air relative humidity (RH), and air vapour-pressure deficit (VPD) for the four observation dates. The most stressful environmental conditions occurred between 14:00 and 17:00 h. Solar radiation was similar along the trial period except under the cloudy conditions of 21 August (235 DOY). However, this did not reduce Tair and the highest Tair values (up to 35 ◦ C) occurred between 12:00 to 17:00 h, reaching a peak of 39 ◦ C at 15:00 h. Tair values were lowest in early season (20 June, 171 DOY), ranging from 16 ◦ C at 8:00 h to 28 ◦ C at 17:00 h. On 17 July (198 DOY) and 8 August (220 DOY) Tair values

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were similar, ranging from 22 to 35 ◦ C, and from 20 to 33 ◦ C, respectively. RH showed an inverse trend to Tair , with the lowest values occurring on 21 August, and the highest ones on 20 June. The RH measured on 17 July and 8 August was similar for both dates. The highest VPD values occurred on 21 August (reaching values up to 4.0 kPa between 10:00 and 18:00 h), almost 3-fold higher than the minimum values registered on 20 June. Thus, during the trial, three distinct atmospheric situations were experienced by the crop: i) no-stress (DOY 171, 20 June), with low Tair and low VPD; ii) severe stress (DOY 235, 21 August), with very high VPD and Tair ; and iii) moderate to severe stress due to high Tair and VPD (DOY 198 and 220; 17 July and 8 August). Crop-water status during the experimental period was assessed by measuring leaf water potential at pre-dawn (pd ) at each monitoring date (Fig. 3). On DOY 171 we found no differences in pd between treatments evidencing the absence of water stress at the starting phase of the trial. In general, the pd decreased along the season for all treatments (Fig. 3). Aragonez vines experienced a more pronounced decrease and had lower pd than Touriga Nacional, especially under RDI conditions and under specific climatic conditions (DOY 198 and 235). The range of pd values observed along the trial (−0.2 < pd < −0.5 MPa) shows that the imposed degree of water stress was within the range of what is considered mild-to moderate water stress. 3.2. Canopy temperature and individual leaf gas exchange Table 1 shows the diurnal variation of TC and leaf-gas exchange parameters for each irrigation treatment and variety. On DOY 171 significant differences were found between Aragonez-SDI and remaining treatments. At 17:00 h TC values of Aragonez-SDI were about 3 ◦ C lower than the remaining treatments (Table 1, Fig. 2A). On DOY 198, TC increased along the day with a maximum at 17:00 h being the highest TC observed for Touriga Nacional-RDI, (Table 1, Fig. 2B). At DOY 220, (Table 1, Fig. 2C), the maximum TC was detected only at 17:00 h but no significant differences existed between RDI and SDI vines (Table 2). Finally, at DOY 235, the highest TC occurred again in the afternoon (14:00 and 17:00 h) and also in RDI vines (Table 1, Fig. 2D). The trend observed for TC is in line with the variation found for gs. Stomatal conductance to water vapour was higher in SDI vines (Table 1) and the Aragonez-SDI had the highest gs in agreement with the lowest TC values (Table 1). At DOY 171, the highest values of gs were observed in the morning (8:00 h and 11:00 h) for both varieties (Table 1). At DOY 198 the SDI treatment presented significantly higher gs and Aragonez vines showed the highest gs along the day (Table 1). On DOY 220, the variation pattern of gs was similar to the one observed on DOY 198, which can be explained by similar climate conditions. Plants of Aragonez-SDI had the highest gs while Touriga Nacional-RDI showed the lowest. At the end of the season (DOY 235), and under the most severe drought conditions (lowest pd of the trial) (Fig. 3), SDI vines had larger gs than RDI on both varieties. Concerning leaf net photosynthesis (Table 1), the main differences were related to time of measurements (Table 2). The highest An was measured during the first measurements of the day (at 8:00 and 11:00 h) which paralleled the larger stomatal conductance. Irrigation strategy also influenced significantly the photosynthetic rates of vines, and at DOYs 198, 220 and 235 we observed the highest rates of photosynthesis in SDI vines. In turn, most pronounced differences between irrigation treatments for the observed leaf gas exchange traits were found at 14:00 h (on DOY 198), and at 11:00, 14:00 and 17:00 h (on DOY 235). In fact, these two DOYs were characterized by the most stressful climatic conditions. Overall the highest variability in the studied parameters was associated with two major variables: first with the time of measure-

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Fig. 3. Leaf-water potential at pre-dawn (␺pd, MPa) measured along the season, on 20 June (DOY 171), 17 July (DOY 198), 8 August (DOY 220) and 21 August (DOY 235) for vines of the varieties Touriga Nacional (TN) and Aragonez (syn Tempranillo) (AR) subjected to regulated deficit irrigation (RDI) or sustained deficit irrigation (SDI). Values are means ± SE (n = 4 leaves).

ments (and related environmental conditions) and secondly the irrigation strategy adopted (Table 2). Despite punctual differences between varieties no clear distinct pattern for TC and for leaf gas exchange was found between Touriga Nacional and Aragonez. This can be related with the mild water stress conditions experienced by vines during the trial (−0.2 < pd < −0.5 MPa) (Fig. 3). 3.3. Relationships between thermal indicators and individual leaf gas-exchange In order to evaluate the relationships between thermal data and leaf gas-exchange traits (gs , An ), a correlation analysis was performed separately for each time of measurement and variety (Table 3). While no significant (p < 0.05) correlations were found at beginning (8:00) and at the end of the day (20:00 h), we found significant correlations between thermal data and leaf gas-exchange parameters for the remaining hours. The correlation coefficients were negative for TC , Tcanopy-air and CWSI; and positive for IG (Table 3). The most robust relationships were found for the set of measurements taken at 11:00, 14:00 and 17:00 h in both varieties, being the highest correlation coefficients found for the relationships between TC , CWSI and IG with An and gs . In turn, the index Tcanopy-air was found to be correlated significantly with gs at 11:00 h (only for Aragonez) and at 14:00 h (for both varieties) (Table 3). Considering that the best relationships for CWSI and IG were obtained between 11:00 and 14:00, the pooled data corresponding to the measurements done within this time interval was used in a linear regression analysis performed for each variety. The relationships between CWSI and gs in Aragonez and Touriga presented significant determination coefficients (r2 = 0.72 and 0.61, respectively, Fig. 4). A similar behaviour was observed for the relationship CWSI vs An (Fig. 4), with r2 values of 0.66 and 0.56 for Aragonez and Touriga Nacional, respectively. The regression analysis between IG and gas exchange parameters presented also significant determination coefficients which were higher for the IG vs. gs relationship (r2 = 0.77 and 0.76 in Aragonez and Touriga, respectively) than for the IG vs. An (r2 = 0.62 and 0.64 in Aragonez and Touriga, respectively) (Fig. 4). Once selected the different relationships for each cultivar, we have compared the differences between the slopes and the intercept of the obtained regression lines by ANCOVA. Because we found no effect of the variety either on the slopes and the intercepts a single function was built with the pooled data for 11–14 h, from the two varieties (Fig. 4).

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Table 2 Multifactorial ANOVA for each studied parameter (stomatal conductance to water vapour, gs ; net photosynthesis, An ; and canopy temperature, TC ) for each day of observation (DOY) and for the overall data set.The * and ** indicate statistically significant differences at P < 0.05 and 0.01 respectively; ns, not significant. Variables

Interactions

DOY 171

Parameters gs An TC TC

Hour (H) ** ** **

Irrigation (I) * ns ns

Variety (V) ns ns **

HxI ns ns ns

HxV ns ns *

VxI * ns ns

HxIxV ns ns ns

198

gs An TC

** ** **

** ** **

ns ns ns

ns ns *

ns ns ns

ns ns ns

ns ns ns

220

gs An TC

** ** **

** ** ns

ns ns ns

ns ns ns

ns ns ns

** ** ns

ns ns ns

235

gs An TC

** ** **

** ** **

* ns ns

ns ns *

* ns ns

ns ns ns

* ns ns

Global

gs An TC

** ** **

** ** **

ns ns ns

ns ns ns

ns ns ns

* ns ns

ns ns ns

Table 3 Pearson´ıs correlation coefficients between thermal indicators (TC , Tcanopy-air , CWSI and IG ) and leaf gas exchange traits (stomatal conductance to water vapour − gs and net photosynthesis − An ) measured for V. vinifera Touriga Nacional (TN) and Aragonez (syn. Tempranillo) (AR) grapevines. Physiologicaltrait

gs

– An

TC

Tcanopy-air

CWSI

IG

Hour

AR

TN

AR

TN

AR

TN

AR

TN

8h 11 h 14 h 17 h 20 h

−0.05 −0.51* −0.76** −0.85** 0.24

−0.02 −0.53* −0.69** −0.72** −0.36

0.23 −0.44* −0.39* −0.33 −0.07

0.22 −0.20 −0.39* 0.00 −0.18

−0.18 −0.82** −0.84** −0.84** −0.11

−0.17 −0.82** −0.86** −0.86** 0.17

0.06 0.81** 0.82** 0.39 0.01

−0.20 0.81** 0.82** 0.09 −0.04

8h 11 h 14 h 17 h 20 h

0.06 −0.54* −0.85** −0.88** −0.09

0.09 −0.65* −0.77** −0.82** −0.29

0.33 0.31 −0.14 0.08 0.08

0.25 0.04 −0.29 0.30 −0.07

−0.05 −0.8** −0.82** −0.5* 0.05

0.07 −0.76** −0.69* −0.5* 0.21

−0.06 0.66* 0.77** 0.32 −0.12

0.00 0.82** 0.66* 0.23 −0.09

TC , canopy temperature; CWSI, Crop Water Stress Index; IG , index ofrelative stomatal conductance; Tcanopy-air , the difference between canopy and air temperature; gs , stomatal conductance to water vapour; An , net photosynthesis. * and ** indicates significant correlation coefficients at p < 0.05 and 0.01 levels respectively (n = 32).

Regarding the regression analysis between TC and leaf gas exchange traits, a similar statistical procedure was used. The pooled data relative to the 14:00 and 17:00 h time period was used for a linear regression analysis for each variety (Fig. 4). Significant determination coefficients were obtained for TC vs An in Touriga Nacional (r2 = 0.58) and Aragonez (r2 = 0.72) and for TC vs gs in Touriga Nacional (r2 = 0.48) and Aragonez (r2 = 0.60). In this case, the ANCOVA analysis showed a significant effect of the variety on the slopes and intercepts, and separated relationships were retained for each variety. 4. Discussion Previous literature showed that thermal imaging can be used to estimate grapevine water status under field conditions (Jones et al., 2002; Möller et al., 2007; Costa et al., 2012; Grant, 2012). Nevertheless, rigorous management of deficit irrigation under variable climate conditions requires identification of the most adequate and robust thermal indicators as well as the best time of the day to perform infrared imaging measurements. Our results show that the best moment to monitor vine’s water status of the two varieties occurs between 14 and 17 h. Indeed, this is the moment of the day when the most significant differences in terms of TC , the thermal indices and leaf physiological traits (gs and An ) are verified between vines subjected to RDI and SDI regimes (Table 1). The fact that the highest Tair and air evaporative demand are verified between 11:00 h and 17:00 h (Fig. 1)

can generate the largest differences in stomatal aperture as well as in leaf temperature. Moreover, more severe soil water deficit promotes TC differences between varieties and deficit irrigation treatments (Table 1, Fig. 3). Under less stressful climatic conditions (DOY 171), the differences in TC between irrigation treatments were only detected at 17:00 h. An identical pattern was found for the remaining days, in which the largest temperature differences (1.5–3.5 ◦ C) between irrigation treatments occurred when measurements were done at 11:00, 14:00 and 17:00 h. These results are in line with previous studies on grapevine subjected to deficit irrigation conditions and which report temperature differences of 2–8 ◦ C between plants subjected to different water stress levels and irrigation regimes (Costa et al., 2012; Grant et al., 2007; Möller et al., 2007). Additionally, other authors have shown that that the best moment of the day to do robust and physiologically sound temperature readings was at midday (Pou et al., 2014; Zia et al., 2009; Fuentes et al., 2012; Bellvert et al., 2015). Our findings show a high correlation between the thermal variables and leaf gas exchange parameters namely at 11:00 h, 14:00 h and 17:00 h (Table 3). The non-normalized thermal indicator TC , and the thermal indices CWSI and IG had the highest correlation coefficients with gs and An , whereas the index Tcanopy-air showed a significant determination coefficient only at 14:00 h, which translates some limitations of this last thermal index. The apparent low robustness of the index Tcanopy-air relatively to the other thermal indicators may relate to differences between the real microclimate data and the data provided by the meteorological station located at about

Table 4 Non exhaustive list of examples showing the different linear relationships between thermal indicators (TC , CWSI, IG ) and leaf stomatal conductance to water vapour (gs , mol H2 O m−2 s−1 ), measured for different genotypes, different exposition to sun light (shadow and sunlit), viewing sides of the canopy (lateral vs zenithal) and different measuring times and growing conditions (open field and greenhouse). Thermal indicator

Linear function (y = ax + b)

Genotype

Measurement details

Reference

TC

y = −0.01x + 0.46

Tempranillo

Costa et al. (2012)

TC

y = −0.02x + 0.96

Tempranillo

TC

y = −0.01x + 0.58

Trincadeira

TC

y = −0.03x + 32.7

Tempranillo

TC

y = −0.03x + 0.95

Tempranillo

TC

y = −0.01x + 0.69

Trincadeira

TC

y = −0.02x + 0.32

Tempranillo

TC

y = −0.03x + 0.36

Tempranillo

TC

y = −0.05x +0.39

Tempranillo

IG IG

y = 0.24x + 0.05 y = 0.01x − 0.01

Moscatel Tempranillo

IG

y = 0.11x − 0.04

Graciano

IG

y = 0.10x − 0.02

Graciano

IG

y = 0.08x + 0.09

Tempranillo

IG

y = 0.04x + 0.12

Trincadeira

IG

y = 0.125x

Chardonnay

CWSI

y = −0.42x + 0.30

Graciano

CWSI

y = −0.34x + 0.25

Graciano

CWSI

y = −0.27x +0.33

Tempranillo

CWSI

y = −0.13x + 0.25

Trincadeira

CWSI

y = −0.74x + 0.61

Merlot

CWSI

Y = −0.71x + 0.49

Chardonnay

CWSI

y = −x + 0.94 y = −1.4x + 0.69

Chardonnay, Pinot gris, Pinot noir, Merlot, Sauvignon blanc, Syrah, Chardonnay

9:00–11:00; lateral sunlit side, open-field 13:30–15:30 lateral sunlit side, open-field 9:00–11:00; lateral sunlit side, open-field zenithal side, open-field 10–12:00 & 14.00 zenithal side, open-field 13:30–15:30 lateral sunlit, open-field 08:00; lateral sunlit open-field 12:00; zenithal side; open-field 14:00; sunlit side; open-field 10:00; greenhouse 14:00; sunlit side; open-field 13:00; sunlit side; open-field 16:00; sunlit side; open-field 09:00–11:00; lateral sunlit; open-field 09:00–11:00; lateral sunlit; open-field 12:00–14:00; lateral shaded, open-field 13:00; lateral sunlit; open field 16:00; lateral sunlit; open field 09:00–11:00; lateral sunlit; open-field 09:00-11:00; lateral sunlit; open-field 11:30–13:00; lateral sunlit; open-field 12:00–14:00; lateral shaded; open-field 12:00–14:00; lateral shaded; open-field

Costa et al. (2012) Costa et al. (2012) Orbegozo (2011)

Costa et al. (2012) Grant et al. (2016) Grant et al. (2016) Grant et al. (2016) Grant et al. (2006) Grant et al. (2016) Pou et al. (2014) Pou et al. (2014) Costa et al. (2012) Costa et al. (2012)

Pou et al. (2014)

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Baluja et al. (2012)

Pou et al. (2014) Costa et al. (2012) Costa et al. (2012) Möller et al. (2007)

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Fig. 4. Correlations between leaf stomatal conductance to water vapour (gs ) and net photosynthesis (An ) with the Crop Water-Stress Index (CWSI) (A and B), the index relative stomatal conductance (IG ) (C and D), and the canopy temperature (TC ) (E and F) for the two studied genotypes (Aragonez syn. Tempranillo) ( ), and Touriga Nacional ( ). The linear regressions for CWSI and IG are based on the readings taken between 11:00 and 14:00 h whereas the regressions estimated for TC used readings carried out between 14:00 and 17:00 h. Grey points and discontinuous lines (Aragonez, syn. Tempranillo) and the black points and continuous lines (Touriga Nacional) correspond to the linear functions obtained for each cultivar. The general equation corresponds to the pooled data i.e. measurements done at 11:00 h and 14:00 h; (n = 128) for the case of the CWSI and IG .

900 m distance from the field trial. It is possible that the accuracy of this thermal index would increase if Tair values were gathered at the same time and closer to the canopy area imaged by the thermal camera. In this study we have shown that the four thermal indicators have advantages and disadvantages that must be accounted when using them for water-stress monitoring in field and support of irrigation strategies in the vineyard Regarding the simplicity and the time consuming aspects, the index Tcanopy-air would be recommendable because it is easy to calculate. Besides this thermal indicator was successfully used in ground and aerial thermal monitoring of woody crops such as olives (Berni et al., 2009a), citrus (García-Tejero et al., 2011b) almonds (García-Tejero et al., 2012, 2015) and grapevine (Baluja et al., 2012). These authors have found significant correlations between Tcanopy-air and gs and leaf water potential, when measurements were done at midday. In turn, Suárez et al. (2008) showed for olive trees that the diurnal variation of Tcanopy-air correlated significantly with the Photochemical Reflectance Index (PRI), a physiological reflectance index that indicates photosynthetic efficiency, attesting the physiological relevance of this “simple” index. In order to decide if Tcanopy-air can be considered as robust as the IG and CWSI, we should consider that both the CWSI and IG are based on TC readings and on artificial T references (Twet and Tdry ), which were directly collected near the zone of interest of the canopy of the monitored vines. This fact could justify the weaker relationships between leaf gas exchange parameters and

Tcanopy-air as compared to the relationships found for CWSI and IG. Nevertheless, determination of reference temperatures has also limitations that can limit robustness of the CWSI and IG , in particular under conditions of significant environmental variation and variable angle of observation in relation to the solar beam (Jones and Grant, 2016). An important aspect raised by our results is the possibility of using TC values as feasible indicator of vine’s water status and a basic parameter to have into account for water stress monitoring and irrigation management. This is corroborated by the Pearson’s correlation coefficients from the relationship between this indicator and gs or An . Indeed, significant relationships between TC and gs and An were obtained for both cultivars, using the data from measurements at 14:00 and 17:00 (Fig. 4) The simplicity of TC could favor its use as a preliminary stress indicator. However, TC is highly influenced by environmental conditions, and hence, it can have major limitations for remote sensing of crop water status especially under highly variable climate conditions (e.g. windy, slightly cloudy). On the contrary, CWSI and IG, which represent normalized values, may be more robust especially under more variable environmental conditions (e.g. radiation, wind, Tair ) along the day or in case there are limitations in obtaining a precise Tair near vine canopies. A final question was to assess to what extent grapevine genotype would be influencing robustness of the used thermal indices and related information on vine’s water status as this is not a fully covered aspect by literature. Different varieties may present differ-

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ent physiological response to water stress, expressed via different gs and transpiration rate and, consequently, different evaporative cooling and TC (Costa et al., 2012; Bota et al., 2016). Such differences in leaf gas exchange and TC will be reflected in the relationships between the thermal indices and plant water status and leaf gasexchange as described in previous literature (Costa et al., 2012; Bellvert et al., 2013, 2015). Since we have not observed a significant effect of the variety on the slopes and intercepts of the relationships between CWSI and IG indices and leaf gas exchange parameters, a single function can be used for the two genotypes. In fact, under mild water stress (predawn ranging between −0.2 and −0.5 MPa) the stomatal behaviour of the two varieties was similar, with no effect on the relationships between An or gs and TC (Tables 1 and 3) as well as on the thermal indices (Fig. 4). Although the linear regression models estimated in our study for Aragonez and Touriga are in line with previous findings for Mediterranean conditions (Table 4), literature also suggests some degree of variation between genotypes, the measuring conditions and the set up (Table 4). Finally, the obtained linear mathematical functions to predict vine’s leaf gas exchange on the basis of TC , CWSI and IG show the potential of this thermal indicators for modelling approaches in grapevine, namely to predict vine water status, growth and carbon balance along the day/season which is increasingly relevant in the context of precision viticulture as well as in the context of modern crop phenotyping and breeding (Greer and Weedon, 2012; Tuberosa, 2012; Klodt et al., 2015; Jones and Grant, 2016). 5. Conclusions Modern irrigated viticulture in the Mediterranean demands solutions to optimize crop monitoring and to improve water savings. In addition, simpler to operate but still robust methodology approaches should be the basis of a wider use of thermography in viticulture, Our results show that thermal imaging is a feasible tool to monitor remotely grapevines water status. Nonetheless, the decision on the most adequate thermal index to use and the best time of the day to perform measurements must be defined and have in consideration the combined effects of the genotype and the environment . Our results suggest that the best time to obtain robust and more physiologically meaningful thermal data to assess vine’s water status is between 11:00 h and 14:00 h (local time), independently of the variety. Regarding the robustness of the tested thermal indicators, our results suggest that CWSI and IG provide more meaningful information about the crop-water status than Tcanopy-air . Equally relevant from our findings is the fact that TC shows to be significantly correlated with gs and An , suggesting that it can be used as simpler parameter to support thermal remote sensing of water stress in grapevine. In spite of being a non-normalized thermal indicator, TC can work as an explanatory variable of vine’s eco-physiology with potential use for grapevine growth models. The usefulness of ground based TC data is also envisaged to support validation of aerial based data which is an increasingly important topic of thermal infrared remote sensing (Tang and Li, 2014). The absence of genotype effects on the relationships between CWSI and IG indices and gas exchange parameters allowed us to obtain a single function to monitor crop-water status independently of the variety. Nevertheless, the present results should be confirmed for more severe drought situations, where the effect of the genotype is expected to become more significant. Acknowledgements This research received funding from European Community’s Seventh Framework Programme (FP7/2007-2013) under the grant

89

agreement n◦ FP7-311775, Project INNOVINE. R. Egipto had a scholarship from INNOVINE and J.M. Costa had a scholarship from INNOVINE and FCT (SFRH/BPD/93334/2013), Portugal. I. GarcíaTejero had a contract co-financed by the European Social Fund Operational Programme (FSE) 2007–2013: “Andalucía is moving with Europe”. We also thank Herdade do Esporão (Reguengos de Monsaraz, Alentejo, PT) for the experimental vineyard facilities and all master students involved in the project. This work was supported by FCT, through R&D Unit, UID/Multi/04551/2013 (GreenIT) and the Cost Action FA1306 “The quest for tolerant varieties − Phenotyping at plant and cellular level” (EU Framework Programme Horizon 2020).

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