Irrigation scheduling from stem diameter variations: A review

Agricultural and Forest Meteorology 150 (2010) 135–151

Contents lists available at ScienceDirect

Agricultural and Forest Meteorology journal homepage: www.elsevier.com/locate/agrformet

Review

Irrigation scheduling from stem diameter variations: A review J.E. Ferna´ndez *, M.V. Cuevas Instituto de Recursos Naturales y Agrobiologı´a (IRNAS-CSIC), Avenida de Reina Mercedes, No. 10, 41012 Seville, Spain

A R T I C L E I N F O

A B S T R A C T

Article history: Received 12 June 2009 Received in revised form 26 October 2009 Accepted 9 November 2009

Precise irrigation is essential in arid and semi-arid areas where water is scarce. This has impelled the scientific community to develop new technologies for scheduling irrigation. Of these, the ones relying on plant-based water-stress indicators have been found to have the greatest potential. Thus, measurements of stem water content, canopy temperature, sap flow, and stem diameter variation (SDV), among other variables, have proved useful not only for research purposes, but also for precise irrigation scheduling in commercial orchards. In this work we focus on the use of SDV records for irrigation scheduling. Of those mentioned above, this is the water-stress indicator that has received most attention from the scientific community, in terms of its potential for irrigating commercial orchards. Apart from being capable of an early detection of water stress, even if this is mild, SDV can be continuously and automatically recorded. This is a clear advantage over conventional indicators such as stem water potential (C stem). Among the SDV-derived indices that are useful for scheduling irrigation, the maximum daily shrinkage (MDS) and stem growth rate (SGR) are the most widely used. For young trees, and in periods of rapid stem growth, SGR could be a better indicator than MDS. In periods of negligible growth, however, SGR cannot be used as an indicator of plant water stress. Considerable differences in both MDS and SGR as a function of crop load have been reported for some species. It has been found, that SDV outputs are affected by seasonal growth patterns, crop load, plant age and size, and other factors, apart from water stress. Thus, expert interpretation of SDV records is required before using them for scheduling irrigation, which limits their potential for automating the calculation of the irrigation dose. For some species, the MDS vs C stem relationships show diurnal hysteresis and seasonal changes. Some relationships also shown an increase of MDS as the plant water potential fell to a certain value, after which MDS decreases as the plant water potential became more negative. This has been reported for peach, lemon, grapevine and olive, among other species. Although SDV-derived indices show a high plant-to-plant variability, in most cases the signal intensity is high enough to achieve an acceptable sensitivity, which, for peach, lemon and pepper has been found to greater than that of C stem and leaf conductance (gl). In plum, apple and grapevine, however, C stem is more sensitive than MDS and SGR. In any case, the usefulness of an SDV-derived index for irrigation scheduling must be evaluated for the orchard conditions. In this work we describe the qualities that must be considered in such evaluation. One of them, the signal intensity, is being successfully used to schedule low-frequency irrigation in orchards of a variety of species, for both fulland deficit-irrigation treatments. When combined with aerial or satellite imaging, SDV measurements are useful for scheduling irrigation in large orchards with high crop-water-stress variability. ß 2009 Elsevier B.V. All rights reserved.

Keywords: Maximum daily shrinkage Trunk growth rate Dendrometer LVDT sensor Water-stress indicator Sap flow Transpiration

Contents 1. 2.

3.

Towards precise irrigation scheduling . . . . . . . . . . . . . . . . . . . Stem diameter variations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Fundamentals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. SDV recording . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3. SDV-derived indices for irrigation scheduling . . . . . . . . Relationships between SDV and other water-stress indicators

* Corresponding author. Tel.: +34 954 62 47 11; fax: +34 954 62 40 02. E-mail address: [email protected] (J.E. Ferna´ndez). 0168-1923/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.agrformet.2009.11.006

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

136 137 137 138 139 139

136

4.

5.

6.

J.E. Ferna´ndez, M.V. Cuevas / Agricultural and Forest Meteorology 150 (2010) 135–151

3.1. SDV and water potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. SDV and sap flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Comparison of sensitivity of SDV-derived indices with that of other water-stress indicators Difficulties for interpreting SDV records. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Degree of water stress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Seasonal growth patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. Crop load . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4. Plant age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5. Crop management. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Irrigation scheduling from SDV records . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1. Evaluating the usefulness of the SDV-derived indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.1. Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.2. Signal strength. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.3. Sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.4. Earliness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.5. Reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.6. Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2. Methods to schedule irrigation from SDV records . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1. Using absolute values of SDV-derived indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2. The signal-intensity approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.3. Combining SDV with SF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3. Development of commercial tools for irrigating from SDV records . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

List of symbols and abbreviations A net CO2 assimilation rate, net photosynthesis rate ANOVA analysis of variance CG cumulative growth CV coefficient of variation vapour pressure deficit of the air Da DG daily growth DR daily recovery plant transpiration Ep ET evapotranspiration crop evapotranspiration ETc potential evapotranspiration ETo leaf conductance gl stomatal conductance gs ID irrigation dose LVDT linear variable differential transformer, linear variable displacement transducer md mean daily MDS maximum daily shrinkage MDSref reference MDS MNSD minimum stem diameter mx daily maximum MXAWCF maximum daily available soil water content fluctuations MXSD maximum stem diameter n number of replicates N number of days PAR photosynthetically active radiation PRI photochemical reflectance index coefficient of determination r2, R2 global solar radiation Rs RDI regulated deficit irrigation SDV stem diameter variation SF sap flow SGR stem growth rate Ta air temperature

Tl vs XDV

uv Cleaf Cpd CS Csoil Cstem CX

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . .

139 140 141 142 142 142 142 143 143 143 143 144 144 144 146 147 147 147 147 147 148 148 148 149 149

leaf temperature versus xylem diameter variation volumetric soil water content leaf water potential predawn leaf water potential water potential of storage tissues soil water potential midday stem water potential xylem water potential

1. Towards precise irrigation scheduling Fruit tree orchards are common in arid and semi-arid areas where water for irrigation is scarce. This, together with an increasing world population that has to be fed and with other water-using sectors competing for the limited water resources, makes the use of precise irrigation techniques in those orchards unavoidable. The response of the scientific community to this challenge has been to invest a substantial amount of research in the development of deficit irrigation approaches (Goodwin and Boland, 2002; Ferna´ndez et al., 2006a; Naor, 2006) and of new irrigation technologies based on more-precise, user-friendly water-stress indicators. Some can be continuously and automatically recorded, having a great potential for irrigation scheduling (Jones, 2004). Combined with airborne imagery, soil properties mapping, and other methods to define water restriction zones within the orchard, these plant-based indicators can be useful for scheduling irrigation even in large, heterogeneous orchards (Acevedo-Opazo et al., 2008; Ramos et al., 2009). Today, in fact, fruit tree orchards and vineyards are being irrigated based on changes in the stem diameter (Doltra et al., 2007; Velez et al., 2007) or from sap-flow records (Green et al., 2006; Ferna´ndez et al., 2008a,b). The aim of this work was to review the use of changes in the stem diameter for precise irrigation scheduling.

J.E. Ferna´ndez, M.V. Cuevas / Agricultural and Forest Meteorology 150 (2010) 135–151

2. Stem diameter variations 2.1. Fundamentals There is a substantial amount of literature on the fundamentals of stem diameter variations (SDV). When transpiration (Ep) begins early in the morning, a tension is created in the xylem from the evaporative surface of the leaves to every organ of the plant. Part of the water stored in the plant tissues during the night is then lost, allowing the plant to respond rapidly to changes in atmospheric demand, without the need to rely on water uptake by the roots, which starts later (Hinckley and Bruckerhoff, 1975). This affects every water-storing organ of the plant, so diurnal diameter changes occur in all parts of the plant, including the stem, branches, roots, leaves, and fruits (Ueda and Shibata, 2001; Sevanto ˇ erma´k et al., 2007). For large plants such as trees, the et al., 2002; C water stored within the trunk may contribute substantially to their Ep (Lassoie, 1973; Hinckley and Bruckerhoff, 1975; Herzog et al., ˇ erma´k et al., 2007). Water from the phloem and related 1995; C tissues (cambium and green tissues of the bark, made up predominantly of parenchyma cells), as well as from the living ˇ erma´k and tissues of the outer xylem (Brough et al., 1986; C Nadezhdina, 1998; Zweifel et al., 2000), is withdrawn and lost by Ep, so that the trunk diameter decreases. Changes in the water content of extensible tissues of the stem are readily reversible, causing diurnal SDV. Such variations are driven by changing water potential in xylem (Dobbs and Scott, 1971; Molz and Klepper, 1973; Jarvis, 1975). Water can also be withdrawn from the inner, woody tissues of the xylem, but then cavitation rather than ˇ erma´k shrinkage occurs, as this tissue is less elastic (Jarvis, 1975; C and Nadezhdina, 1998; Scholz et al., 2008). Irvine and Grace (1997) reported that more than 90% of SDV recorded in 41-year-old Scots pine trees occurred in phloem tissues, and Scholz et al. (2008) reported that the denser sapwood tissues of several species of savanna trees exhibited smaller changes in cross-sectional area per unit change in water potential compared with the living tissues located between the cambium and the cork (outer parenchyma). In other cases, however, sapwood dimensional changes have been reported to contribute greatly to the diametral change of the whole stem. Thus, Sevanto et al. (2002) reported that the amplitude of the xylem diameter variation (XDV) in Scots pine was about 30–50% that of SDV. Sevanto et al. (2003a) measured both XDV and SDV at different heights in large Scots pine and beech trees. In the Scots pine tree, the amplitude of XDV was 50–90 mm (depending on the measurement height) for the xylem and 150 mm for the whole stem. For the beech tree, values were 20 mm for the xylem and 30 mm for the whole stem. Differences between species were attributed to different wood elasticity. Three different patterns of SDV during the growing season have been described for various tree species (Hinckley and Bruckerhoff, 1975; Lassoie, 1979; Antonova et al., 1995). First, with low evaporative demand and high soil-water availability, stem diameter increased from one morning to the next, with, during the day, often either no decrease or just a reduction in the rate of diameter increase; this pattern corresponded to periods of active growth. Second, with high evaporative demand and high soilwater availability, stem diameter increased from one morning to the next, but an appreciable decrease could take place during the day; this pattern was characterised by a mixture of growth and tissue rehydration. Third, during periods of high evaporative demand and low soil-water availability, there was a reduction in stem diameter, with only a partial recovery during the night. This pattern was due solely to changes in hydration. In a comprehensive ˇ erma´k et al. (2007) recorded significant seasonal changes paper, C in stored water in large Douglas-fir trees, and compared the diurnal and seasonal dynamics with those of sap flow and stem volume.

137

This allowed them to highlight relevant features related to the role of stored water in the tree water balance. Biophysical properties of stem water storage tissue were studied by Scholz et al. (2008), and the effect of stored water on Ep of large trees was modelled by Verbeeck et al. (2007a,b) and De Pauw et al. (2008a), among others. Models also help to understand the contribution of cavitation to Ep and xylem water potential, CX (Ho¨ltta¨ et al., 2009). All these and many other papers have clarified aspects of the tree hydraulic capacitance, of relevance in understanding the nature and dynamics of SDV. Nevertheless, major aspects such as how cavitated xylem elements are refilled under tension remain unexplained (Phillips et al., 2009). The work by Herzog et al. (1995) illustrates the diurnal dynamics of SDV, together with those of the sap flow (SF) through the stem and the simultaneous exchange of water between the xylem and the phloem and related tissues. They reported five distinct phases in the diurnal courses of SF and SDV, as shown in Fig. 1A. Phase I describes the stem’s rehydration at night, when the lowest SF rates are recorded. The internal stores replenish, and the stem radius increases. Phase II describes the lag between the increase in flow soon after dawn and the shrinking of the stem. Phase III is the period during which the flow through the stem increases to its daily maximum, and the stem shrinks rapidly. Phase IV describes the lag between the maximum in the flow and the minimum in the

Fig. 1. Daily dynamics of sap flow and stem radius recorded by the authors on a bright day of high evaporative demand in the summer of 2006, in a 37-year-old olive tree (Olea europaea L.) (A). They are explained by considering five different phases (after Herzog et al., 1995): Phase I is the nocturnal period, in which there is a recharge of the storage water; sap flow is measurable for a certain time after sunset, then becomes negligible. Phase II is the lag between the rise in flow soon after sunrise and the maximum stem diameter recorded during the day. Phase III is the period of fast shrinkage of the stem until maximum sap-flow values occur. Phase IV is the lag between the maximum flow and the minimum stem diameter recorded during the day. Phase V is the period in which the stem swells and sap flow again becomes negligible. The idealised evolution of storage water is shown in Graph B (after Verbeeck et al., 2007a). Graph C illustrates the axial flow of sap in the stem and the exchange of water between the xylem and the storage tissues (cambium, phloem, living tissues of the bark) for the phases shown in Graph A; also shown are the corresponding variations in the water potential of the xylem (CX) as compared with that of the storage tissues (CS) (after Herzog et al., 1995).

138

J.E. Ferna´ndez, M.V. Cuevas / Agricultural and Forest Meteorology 150 (2010) 135–151

radius at its reversal point in the afternoon. Finally, Phase V is characterised by a decrease in the flow and an increase in the stem radius. This period may last for a few hours after sunset, if the tree has transpired a lot during the day. The daily evolution of storage water shown in Fig. 1B does not correspond to actual values, but represents the idealised evolution that can be expected on a warm sunny day (after Verbeeck et al., 2007a). Fig. 1C shows the expected exchange of water between the xylem and the phloem and related storage tissues for each of the five phases described in Fig. 1A (after Herzog et al., 1995). During Phases III and IV, water is withdrawn from the phloem and related tissues, due to a decrease in CX; consequently, the stem radius decreases. Water is withdrawn from the storage tissues most readily during Phase III, after the nocturnal rehydration in Phases I and II. During Phase IV, withdrawal of water becomes progressively restrained. At the beginning of Phase V, the exchange of water changes direction, due to CX becoming greater than the water potential in the phloem and related tissues. The nocturnal rehydration starts in this phase, and continues throughout Phases I and II; simultaneously, the stem diameter increases. In addition to reversible changes in tissue hydration, SDV depends on the following factors (Daudet et al., 2005): irreversible radial growth, reversible shrinking and swelling in relation to changing levels of hydration and thermal expansion (Kozlowski, 1971; Klepper et al., 1971; McBurney and Costigan, 1984; Ame´glio and Cruiziat, 1992; Simonneau et al., 1993; Zweifel et al., 2000; Cochard et al., 2001), and contraction and expansion of dead conducting elements due to the increase and relaxation of internal tensions (Irvine and Grace, 1997; Offenthaler et al., 2001; Sevanto et al., 2002). A tide effect related to the lunar rhythm has also been reported by Zu¨rcher et al. (1998), although Vesala et al. (2000) disagreed. In this last work, the authors acknowledge circadian rhythms associated with cell shrinking and swelling, but suggest that they are associated with variations in the osmotic strength of living cells, rather than with a gravitational signal. The potential of SDV as a water-stress indicator relies on the fact that the diameter variations due to the shrinking and swelling from the changing levels of hydration greatly exceed those resulting from daily growth of tissues or direct temperature variations (Kozlowski, 1972). Even so, the correct interpretation of SDV records for irrigation scheduling requires a clear distinction between the five components mentioned above. The theory of radial water movement back and forth between xylem and phloem has led to the use of diffusion-type kinetics for the propagation of water content variation in isotropic and homogeneous tissues. This is the case of the diffusion models of Molz et al. (1973) and Parlange et al. (1975). These models, however, do not take into consideration the water storage capacity in cells, and those by Molz and Klepper (1972), Parlange et al. (1975), and Panterne et al. (1998) do not consider seasonal changes in the relationship between SDV-derived indices and the plant water potential. Other models, such as that by Ge´nard et al. (2001), do take into account the water storage capacity of extensible living tissues, while Daudet et al. (2005) included the effects of the carbon status, in addition to those of water limitation, in the analysis of radial growth. The inclusion of growth is essential if good simulation results are to be obtained, as demonstrated by Steppe et al. (2006). They developed a flow-and-storage model which includes radial stem growth based on Lockhart’s equation (1965) for irreversible cell expansion. 2.2. SDV recording The first stem growth readings for scientific purposes were low-frequency, low-precision records using tapes or callipers

(Marsham, 1759; Mohl, 1844). The first highly sensitive dendrometers and dendrographs were developed in the late 1800s (Bo¨hmerle, 1883; Friedrich, 1890). Friedrich (1905) was able to record growth electrically, but the technique did not become popular until it was simplified by modern technology. Radial changes were monitored by Phipps and Gilbert (1960) with a linear motion potentiometer, and by Impens and Schalck (1965) with a variable differential transformer. Kozlowski and Winget (1964) used the Fritts dendrograph (Fritts and Fritts, 1955), and Holmes and Shim (1968) used the dial-gauge dendrometer described by Byram and Doolittle (1950). The paper by Breitsprecher and Hughes (1975) includes a detailed list of references on stem diameter measurements carried out from the beginning of the 20th century, with radial (measuring growth along a single radius), diametral (two opposing radii), and circumferential (many radii of a plant conductive organ) dendrometers. Radial dendrometers are ideal for monitoring the activity of smaller portions of the cambial cylinder, while diametral and circumferential devices provide a larger sample that can be more indicative of total stem growth. Recording SDV for assessing water stress in plants increased considerably when technology enabled precise measurements (Fritts, 1961; Kozlowski and Winget, 1964; Holmes and Shim, 1968), originating the so-called micromorphometric method (Huguet, 1985), defined as the accurate measurement and continuous recording of the diameter of stems, fruits, and roots. From the beginning of the 1990s, most authors working on irrigation scheduling have used linear variable differential transformers (LVDT), also called linear variable displacement transducers. The LVDT-type sensors are robust and of high precision, close to 1 mm. In many cases, however, the resolution cannot be expected to be greater than about 10 mm, due to errors associated to calibration, voltage recording, temperature changes, etc. (Intrigliolo and Castel, 2004). Ueda et al. (1996) reported advantages (small size, light weight, low price, ease of use, and reliability) of the strain-gauge method over the LVDT-type sensor in estimating diurnal changes in stem and branch diameters of a large tree. The method was also used by Ueda and Shibata (2001). Depending on the dendrometer model and the purpose of the measurement, nails or screws may have to be driven into the tree, either to support the instrument or to serve as a fixed reference against which growth is measured. This disturbance frequently evokes abnormal growth acceleration in the vicinity of the injury (Breitsprecher and Hughes, 1975). For this reason, the dendrometer holder is usually attached tightly to the stem by elastic straps, which do not affect growth. Holders are usually of aluminium and INVAR, an alloy containing 64% Fe and 35% Ni, with a thermal expansion around 1.7  106 8C1 (Li et al., 1989; Doltra et al., 2007). The dendrometers must be calibrated prior to installation. This can be done using a precision micrometer (Intrigliolo and Castel, 2004) or metal plates of known thickness (Sevanto et al., 2005). In fruit trees and other plants of large stem diameter, dendrometers are installed on the side of the stem opposite to the sun’s trajectory, to minimise negative effects of heating by direct solar radiation. They must be at a certain distance from the ground to avoid interference from growing weeds, and as far as possible from any scars, such as those caused by grafts, pruning, and tillage operations. The outer, dead tissues of the bark must be removed before installation, allowing the contact point of the sensor to rest directly on the living tissues of the bark. The dendrometer must always be in contact with the surface, for which a spring or glue can be used (Ueda and Shibata, 2001, used cyanoacrylate glue), and the whole area must be covered with insulating materials (rubber foam, vaseline, silicon resin, etc.) and reflective thermoprotecting foil to minimise both heating by direct solar radiation and the

J.E. Ferna´ndez, M.V. Cuevas / Agricultural and Forest Meteorology 150 (2010) 135–151

139

Table 1 Advantages and limitations of using SDV-derived indices for irrigation scheduling. See ‘‘List of symbols and abbreviations’’ for the meaning of the variables shown. Advantages

Limitations

They integrate the plant response to water conditions both in the soil and in the atmosphere.

SDV records are influenced by plant size, phenological stage, fruit load, and other factors (Section 4), which must be taken into account when using them for irrigation scheduling. This curtails the potential of SDV records for automatic irrigation. The hygroscopic nature of stem surfaces leads to useless SDV records on foggy and rainy days. The direct impact of rain drops on the sensors produces noisy records (errors are minimised by removing the dead tissues of the bark before setting the contact point of the dendrometer, and by sheltering the sensors). Traffic in the orchard, rain drops, birds, insects, and any other cause of physical contact with the dendrometers may yield noisy records (once again, proper sheltering is required to prevent those errors). Temperature fluctuations induce changes in stem diameter, due to thermal expansion of the tissues (errors are minimised by placing the dendrometer on the shaded side of the stem, and by covering it with isolating materials). When considering CG or SGR records for a large number of days, errors might occur because these SDVderived indices are affected not only by the plant water status, but also by damage from pests and pathogens, nutritional stress, leaf senescence, increasing xylem cavitation, and other processes. In fast-growing plants, dendrometers may have to be repositioned several times during the growing season. The potential of SDV-derived indices for scheduling irrigation in heterogeneous orchards may be limited by the high tree-to-tree variability (this can be avoided by combining SDV measurements with methods to define water restriction zones within the orchard).

Non-destructive, continuous measurements.

Data recording and data transmission can be automated.

Sensors are reasonably reliable and robust, easy to install, and relatively inexpensive to buy, operate, and maintain. Early detection of water stress, even when this is mild.

impact of rain drops. These and other recommendations are given in Table 1. Once installed, the dendrometer is connected to a data logger programmed to automatically scan the sensor outputs every few seconds and store average values every few minutes. The data logger is usually provided with a system for data transmission to the user’s computer. Depending on local conditions, and on the precision required in the measurements, it may be advisable to record also the temperature fluctuations of both the holder and the wood close to the dendrometer, to assess the effect of the thermal expansions on the recorded diameter changes. Corrections for the effect of temperature on the holder are essential when this is made of materials such as steel, with a noticeable thermal expansion (Irvine and Grace, 1997). Sevanto et al. (2005) considered the effect of stem temperature, and Scholz et al. (2008) include an example of how to obtain temperature-corrected diameter measurements.

3. Relationships between SDV and other water-stress indicators 3.1. SDV and water potential Values of Cstem are widely used to assess the plant water status, because of their reliability, low variability, and relatively good prediction of yield response to water stress (Shackel et al., 1997; Naor, 2006). In fact, Cstem is considered to be more sensitive than other indicators such as stomatal conductance (gs) and net CO2 assimilation rate (A), at least for moderate water deficits (Goldhamer et al., 1999; Moriana and Fereres, 2002). Their measurements cannot, however, be easily automated. This explains the interest

2.3. SDV-derived indices for irrigation scheduling The indices that are useful for irrigation scheduling and can be calculated from SDV records are depicted in Fig. 2. Details on the meaning of SDV-derived indices are given by Goldhamer and Fereres (2001) and Gallardo et al. (2006a), among others. The maximum daily shrinkage (MDS) and stem growth rate (SGR) are, in that order, the most widely used SDV-derived indices. The potential and limitations of these and other SDV-derived indices are given in Sections 3 and 4. Some authors consider three distinct phases within a typical daily SDV cycle on summer days (Downes et al., 1999; Deslauriers et al., 2003), clearly identifiable in Fig. 2: the shrinkage phase, defined as the period during which the stem radius decreases, usually from an early morning maximum; the recovery phase, defined as the portion of the cycle during which the stem radius increases until it reaches the value recorded at that morning maximum; and the increment phase, defined as the period during which the stem radius continues increasing, until the beginning of the shrinkage phase of the next diurnal cycle. Some days, typically when the plant is under severe water stress, the stem does not undergo an increment phase, and daily growth (DG) values may even be negative. For some purposes, it may be advisable to convert the SDV data to areas and normalise by the initial area in order to obtain comparable, size-independent measurements of daily dimensional changes (Scholz et al., 2008).

Fig. 2. SDV-derived indices. The plotted data were recorded by the authors with an LVDT sensor installed in the trunk of a 37-year-old ‘Manzanilla de Sevilla’ olive tree. DOY = day of year.

140

J.E. Ferna´ndez, M.V. Cuevas / Agricultural and Forest Meteorology 150 (2010) 135–151

shown by many authors for relating SDV-derived indices to Cstem. There is evidence showing the potential advantages—apart from the ease of data recording and transmission—of SDV records over those of plant water potential for irrigation scheduling. Goldhamer et al. (1999) demonstrated that, in 8-year-old peach trees, SDV detected stress earlier than Cstem, and that the signal strength (Section 5.1.2) of trunk MDS for detecting water deficit was greater than that of Cstem. This last result agrees with findings of Goldhamer and Fereres (2001) in well-irrigated almond trees. Moriana and Fereres (2002) found that SGR, MXSD (maximum stem diameter), and MNSD (minimum stem diameter) were more useful than Cstem for an early detection of water stress in young olive trees. However, they did not find differences in MDS between stressed and control trees. These results contrast with those obtained by Intrigliolo and Castel (2004, 2006a) in young plum trees, who found that Cstem and predawn leaf water potential (Cpd) were better plant water-stress indicators than MDS and SGR. Furthermore, Intrigliolo and Castel (2006b) found that MDS was more variable than Cstem for young plum trees, so that more determinations of MDS than of Cstem were needed to estimate plant water status with similar precision. Nevertheless, today many consider MDS as a water-stress indicator as useful as Cstem for ˜ o et al., 2009a,b,c). irrigation purposes (De Swaef et al., 2009; Ortun Under non-limiting soil-water conditions, an increase in the evaporative demand of the atmosphere induces a decrease in Cstem, which increases the soil-plant water potential gradient and, as a consequence, Ep increases, turning into an increase in MDS ˜ o et al., 2006a). For steady-state environmental conditions, (Ortun therefore, SDV and Cleaf should be closely related, but under nonsteady-state conditions typical for the field environment, diurnal changes in stem diameter may lag changes in Cleaf by minutes or hours (Scholz et al., 2008). In fact, diurnal hysteresis between these two variables has been reported by many authors (Klepper et al., 1971; Parlange et al., 1975; Garnier and Berger, 1986; Ge´nard et al., 2001). Doltra et al. (2007) found a marked diurnal hysteresis between SDV and Cstem in 10-year-old apple trees, being SDV values lower in the afternoon than in the morning, for the same value of Cstem. They noted that the observed hysteresis range was higher than those observed in other woody species such as peach (Cohen et al., 2001), citrus (Ginestar and Castel, 1996), and pine (Parlange et al., 1975; Wronski et al., 1985), and suggested that this might indicate that the shrinking tissues of the apple trunk could show lower hydraulic conductivities. It seems, however, that Doltra et al. (2007) did not consider the possible effect of tree size on such comparison. Changing relationships between MDS and plant water status during growing seasons have also been reported for several species. Early work with SDV measurements already showed that, at the beginning of a drought cycle, MDS increases relative to that of the control but, if the drought cycle is long enough, MDS values can become smaller than those of the control (Klepper et al., 1973; Hinckley and Bruckerhoff, 1975). This may be partly due to the swelling of the stem being reduced during severe drought because the store tissues cannot be recharged during the night. Other causes such as a reduction in the radial hydraulic conductivity of bark tissues, and changes in temperature, leaf area, senescence, and increasing xylem cavitation as the season was ending, which could have reduced the contribution of the xylem to total shrinkage, were suggested by Ge´nard et al. (2001) and Fereres and Goldhamer (2003). The work of Ge´nard et al. (2001) showed that trunk shrinkage was highly sensitive to changes in tissue elastic modulus. Therefore, lower MDS for a given Cstem value may be associated with an increase in the proportion of older, less elastic tissues. The low reliability of some relationships between MDS and plant water status caused Intrigliolo and Castel (2004) to recommend testing them before their use for schedule irrigation.

The agreement between MDS and Cstem can improve significantly when data of different phenological stages are considered separately. Fereres and Goldhamer (2003) found, in 4-year-old almond trees, a weak relationship between MDS and Cstem (r2 = 0.26 on a seasonal basis), with a marked hysteresis as the season progressed. Correlations were, however, significantly stronger for the early (June, r2 = 0.59) and late (October–November, r2 = 0.49) periods. Intrigliolo and Castel (2004) found a poor correlation between MDS and the soil water potential (Csoil) in 5year-old plum trees when considering the data for the whole season (r2 = 0.52), but it was good for the early post-harvest (r2 = 0.72) and better for the fruit growth period (r2 = 0.89). Intrigliolo and Castel (2006b) also found, in 6–8-year-old plum trees, that the relationship between MDS and Cstem improved when the data of the two mentioned periods were considered separately. A better understanding of the relationships between SDVderived indices and plant water potential can be achieved through modelling. So et al. (1979) tested, for cotton and soybean, a method for deriving Cleaf values from SDV records, and used a model to correct for diurnal time lags. Archer et al. (2001) proposed a model to derive CX from SDV values, which they calibrated for walnut. Steppe et al. (2006, 2008a) demonstrated that the use of mathematical modelling in combination with continuous plantbased measurements allowed accurate predictions of Cstem under conditions of low water stress (Cstem > 1.0 MPa), although the model failed for more-stressing conditions. Steppe et al. (2008a) mentioned that such failure can be avoided by using continuous measurements of Csoil as an additional input variable to the model, as demonstrated by De Pauw et al. (2008b). 3.2. SDV and sap flow Another plant-based indicator with a great potential for irrigation scheduling is SF. As in the case of SDV, advances in electronics and data transmission, together with improved scientific and practical knowledge, have increased the suitability of SF methods for irrigation scheduling. If properly calibrated, they can be used for accurate in situ determinations of plant water consumption of fruit tree species requiring irrigation (Green et al., 2003; Ferna´ndez et al., 2006b). Ferna´ndez et al. (2008a) evaluated different options for irrigation scheduling in grape vineyards and in olive, apple, and Asian pear tree orchards. Their results lend support to the use of in situ SF measurements, together with modelled Ep rates used as a reference, to schedule irrigation. The relationship between SDV and SF records in large trees has been analysed by Herzog et al. (1995), Ueda and Shibata (2001), ˇ erma´k et al. (2007), and Sevanto et al. (2008), among others. It has C to be taken into account that the two plant-based indicators are ˇ erma´k and Nadezhrelated to different physiological processes. C dina (1998) had already pointed out that, under high evaporation demand, water is extracted from all stem tissues, even, under longterm drought, from the inner sapwood. In contrast, dendrometer records reflect extraction of water from the outermost part of the last annual ring, phloem, and outer parenchyma. This means that only part of the water extracted from xylem is associated with ˇ erma´k et al. (2007) related the volume changes of the tissues. C diurnal and seasonal dynamics of storage water to SF and SDV in large Douglas-fir trees. They reported large amounts of free water, mostly in the stem sapwood, which contribute substantially to daily Ep. Related information was provided by Sevanto et al. (2001, 2003a), who compared xylem and whole-stem diameter variations with the stand transpiration estimated using the eddy-covariance technique. Sevanto et al. (2008) outlined that, since water movement inside the xylem is caused by changes in the water tension, the SF rate should be directly proportional to the water

J.E. Ferna´ndez, M.V. Cuevas / Agricultural and Forest Meteorology 150 (2010) 135–151

tension gradient and, therefore, also linearly linked to XDV. However, it has been seen that, even for small trees, SF is buffered by storage (Zweifel et al., 2001), particularly in the early morning and late afternoon (Caspari et al., 1993; Moreno et al., 1996; Ferna´ndez et al., 2008a). The mechanistic model developed by Pera¨ma¨ki et al. (2001, 2005), based on the linear relationship between XDV and SF rates, could be used to calculate Ep rates from XDV, but it requires inputs which are difficult to measure. The fact that SDV depends mainly on the water stored in the phloem and related tissues, as well as in the outer xylem, while SF depends more strongly on the level of hydration of the whole sapwood, explains peculiarities in the behaviour of each indicator. Cohen et al. (2001) found, in peach trees, that the relationship between SF and SDV showed a hysteresis loop whose shape depended on the stress level: there was more trunk shrinkage in the afternoon than in the morning for the same SF rate, the effect being greater in stressed trees. They concluded that SF would have faster responses than SDV. Escalona et al. (2002) found a close relationship (R2 = 0.67) between daily SF and DG in grapevines. As reported by Scholz et al. (2008), the time lag between the onset of Ep and the initiation of SF or stem shrinkage at the base of the tree has been attributed to the time required for the capacitive release of water from stem water storage compartments (Schulze et al., 1985; Steinberg et al., 1990; Goldstein et al., 1998; Pera¨ma¨ki et al., ˜ o et al. (2006a) recorded, in 242001). At the seasonal level, Ortun year-old lemon trees, high and constant SF values in a period when MDS decreased progressively. While the relationship between MDS and ETo was linear, that of SF and ETo was curvilinear, i.e. at high values of ETo, SF decreased, but not MDS. The decrease of daily SF under high atmospheric demand could have been associated to active stomatal regulation of transpiration through the phenomenon of stomatal oscillations (Buckley, 2005), as observed by Dzikiti et al. (2007) in orange trees. For the linear MDS vs ETo relationship, ˜ o et al. (2006a) suggested that this was probably due to water Ortun recruitment from additional stem tissue capacitances when evaporative demand increased and Cstem fell below a threshold. Conejero et al. (2007a) found, in young peach trees, that for a given air vapour pressure deficit (Da) the corresponding SF values were higher in the June to mid-August period than from midAugust to October, and suggested that this could have been due to decreases in Ep as a result of the ontogenic changes that occur in leaves, including changes with leaf maturation in hormonal balance, membrane permeability, and cell wall strength, as well as differences in stomatal physiology, leaf surface wax distribution, and cuticular water loss rates of new and old leaves (Syvertsen et al., 1981; Sola´rova´ and Pospı´sˇilova´, 1983). Other factors affecting the relationship between SF and SDV are changes in the volume of the rhizosphere wetted by irrigation or in the groundwater table (Intrigliolo and Castel, 2006b; Lubczynski, 2009), as well as the plant carbon balance, which seems to affect SDV more closely than SF (Sevanto et al., 2003b; Daudet et al., 2005; Flore and Layne, 1997). In any case, these papers, as well as those by Steppe et al. ˇ erma´k et al. (2007) and Sevanto et al. (2008), (2006, 2008a,b), C show that water-flow dynamics within a plant can be better understood by combining information from SF and SDV. 3.3. Comparison of sensitivity of SDV-derived indices with that of other water-stress indicators Several authors have compared the sensitivity (Section 5.1.3) of different water-stress indicators, a crucial quality in determining the usefulness of any indicator for irrigation scheduling. Goldhamer et al. (1999) found the following sensitivity ranking for 8year-old peach trees growing in a lysimeter: MXAWCF > MNSD > MDS > MXSD > Cstem = A = Cpd = Cleaf, MXAWCF being the maximum daily available soil-water content fluctuations. For similar

141

trees growing in the field, the ranking was MXAWCF > MNSD > MDS > Cstem = Cleaf = MXSD = Cpd > A. As the upper soil layers were depleted in the days following the onset of deficit irrigation, the MXAWCF shifted to deeper soil layers and its magnitude decreased substantially as soil water extraction took place over a much larger soil depth. Thus, MXAWCF was a relatively poor stress indicator after soil water extraction moved to lower parts of the profile. Intrigliolo and Castel (2006a) found, in plum, the following degree of correlation between plant and soil-water status indicators and fruit weight at harvest: Cstem  Cpd  gs > MDS > SGR > Csoil. Considering that gs was much more variable than Cpd and Cstem, and despite the operational advantages of LVDT for continuous monitoring of plant water status, they concluded that Cstem and Cpd could be the best plant water-stress indicators in plum. Gallardo et al. (2006a) observed, for potted pepper plants in a greenhouse, that the signal values (defined in Section 5.2) were greater for MDS than for Cstem but, because of the lower noise values of Cstem, the sensitivity index (the signal/noise ratio) was much higher for Cstem than for MDS. Doltra et al. (2007) found, in 10-year-old apple trees, that Cstem had the best sensitivity index, followed by Cpd, MDS, and gs. Remorini and Massai (2003) found that the diurnal dynamics of SF rates in 4-year-old peach trees were associated to the irrigation regime: in stressed trees, SF rates reached a peak in the early morning and decreased thereafter, indicating stomatal closure; irrigated trees, however, maintained high water consumption even in the middle of the day. When the water stress increased, SF was more sensitive than SDV, because MDS values in water-stressed trees were higher than in irrigated trees but became lower under severe water stress. A few days after a recovery irrigation, previously stressed trees and control trees showed similar Cleaf and Cstem, but SF remained impaired. The authors reported that when water stress develops over a prolonged period of time, water flow in stressed trees may remain permanently impaired, so that recovery is incomplete. They suggested that this could be due to the high sensitivity of peach to cavitation, which drastically reduces hydraulic conductivity of the xylem and, consequently, impedes plant recovery even after many days of irrigation. Despite significant differences in SDV and SF between the two water treatments, the authors concluded that the sensitivity of the indicators was SDV > SF rate > SF cumulated = Cpd = Cstem > mid˜ o et al. midday Cleaf > Tl, Tl being the leaf temperature. Ortun (2004a) found, in 2-year-old lemon trees in pots, that SF and MDS had an earlier response and were more sensitive than MXSD and MNSD, and that SF was more reliable than MDS for indicating the soil-water status. However, in two companion experiments with ˜ o et al. (2004b, 2005) reported that the same plant material, Ortun MNSD and MDS were the most-sensitive indicators in young lemon trees, followed by Cpd and leaf conductance (gl). Later, in two ˜o works with 24-year-old lemon trees under field conditions, Ortun et al. (2006a,b) analysed the relationships between Cstem, SF, and MDS, and main driving variables of Ep. They reported that SF was more closely related to potential evapotranspiration (ETo), global solar radiation (Rs), Da, and air temperature (Ta) than were MDS and Cstem. SF and Cstem were more closely correlated with ETo, while ˜ o et al. MDS showed the best correlation with mean daily Ta. Ortun (2006a) suggested that the control of MDS by Cstem could be ˜ o et al. (2006b) found that MDS mediated through SF. Ortun increased in response to water stress until a certain level but, when Cstem fell below 1.8 MPa, the MDS signal intensity (Section 5.1.2) decreased. However, Cstem and SF signal intensities increased ˜ o et al. (2007) progressively during the water stress period. Ortun evaluated the sensitivity and reliability of different water status indicators in potted 2-year-old lemon trees subjected to flooding conditions and a recovery period. They reported that the MDS signal intensity was more suitable than SF, Cpd, Cleaf, and Cstem for

142

J.E. Ferna´ndez, M.V. Cuevas / Agricultural and Forest Meteorology 150 (2010) 135–151

use in automatic irrigation. Conejero et al. (2007a,b) compared the sensitivity of SF and MDS in 4–5-year-old peach trees, and concluded that an irrigation schedule driven by MDS signal intensity is more consistent and more sensitive to changes in peach tree water status than one driven by SF signal intensity. However, Intrigliolo and Castel (2007a) reported that, for normally fruited, moderately irrigated grapevines, SDV appears to be an unsuitable tool for continuously monitoring water status in grapevines, particularly after veraison, because of its high variability and, particularly for SGR, a strong dependence on the year. These results illustrate the complexity of deciding which plant-based waterstress indicator is most sensitive and, in short, most useful, for scheduling irrigation. An additional complication is that SDV records are affected by seasonal growth patterns, crop load, plant age, and other factors detailed in the next section. 4. Difficulties for interpreting SDV records Interpreting SDV records for irrigation scheduling is, in most cases, far from straightforward. In fact, expert supervision is usually required when using SDV records for irrigation scheduling. This is one limitation of SDV-derived indices for automating irrigation; another is that SDV does not depend solely on water stress, as we have just mentioned. Below, we detail to what extent plant characteristics and crop management affect the reliability of SDV records. Additional causes are mentioned in Table 1. 4.1. Degree of water stress It can be deduced from Section 2.1 that the phloem and related tissues, as well as the outer xylem, act as capacitors in the plant conductive system, accumulating and storing water during periods of low evaporative demand (e.g. at night) and releasing it to the plant flow stream during periods of high evaporative demand. This has been corroborated by Tyree and Ewers (1991) and Sevanto et al. (2002), among others. However, if plant water stress surpasses a threshold value, there might be a depletion of the water reservoir of the phloem tissues and then an end of water recruitment (Zweifel et al., 2000). This could explain results showing an increase of MDS as the plant water potential fell to a certain value, after which MDS decreased as the plant water potential became more negative. This has been reported for peach (Cohen et al., 2001; Remorini and Massai, ˜ o et al., 2004b), grapevine (Intrigliolo and 2003), lemon (Ortun Castel, 2007a), and olive (Moriana et al., 2000), among other species. It seems that there is no fixed threshold value of plant water potential from which MDS begins to decrease, since results from the mentioned papers show significant differences even for the same species. This is despite some agreement, such as that of Cohen et al. (2001), who found, in 8-year-old peach trees, increasing MDS values for Cpd records above a threshold ˜ o et al. (2004b), who found a value of 1.0 MPa, and Ortun similar Cpd threshold value in 2-year-old lemon trees in pots. ˜ o et al. (2006b) found, in 24-year-old lemon However, Ortun trees, an increase in MDS with decreasing values of Cstem, until 1.8 MPa. For more-severe water stress, MDS decreased. This change in the slope of the SDV-derived index vs plant water potential has not been found in other cases (Marsal et al., 2002; Fereres and Goldhamer, 2003; Intrigliolo and Castel, 2006a). The level of plant water stress reached, and the tissues’ ability to retain water against a water potential gradient, may bring about such effects. In plant tissues with low resistance to water flow and high hydraulic capacitance, as in grapevines (Zimmermann and Milburn, 1982), water can be extracted more easily from the phloem tissues, leading to a quicker end of water storage.

4.2. Seasonal growth patterns Changes in stem growth rate depend on a variety of factors, including phenological stage, fruit development, and environmental conditions. Mature tree trunk growth slows as the season progresses and may be followed by a stoppage or a shrinking trend during some fruit growth phases or under high evaporative demand, even under non-limiting soil-water conditions. Differences in the growth pattern due to the tree’s age may markedly affect the potential of an SDV-derived index for irrigation scheduling. For young trees, and in periods of rapid stem growth, SGR could be a better indicator than MDS. This is because MDS, for those trees and conditions, could be affected more by growth than by the level of water stress. Results bearing this out have been observed in young peach (Goldhamer and Fereres, 2001), olive ˜ o et al., 2004b), and (Moriana and Fereres, 2002), lemon (Ortun almond (Nortes et al., 2005) trees. On the other hand, in periods of negligible growth, SGR cannot be used as an indicator of plant water stress. This has been found, for instance, in periods of active fruit development in grapevine plants (Intrigliolo and Castel, 2007a) and in plum (Intrigliolo and Castel, 2007b) and olive trees (Pe´rez-Lo´pez et al., 2008). For a period of low or negligible trunk growth rates in 8-year-old peach trees, Goldhamer et al. (1999) reported that the most-sensitive indices were MNSD, MXSD, and MDS, in that order. In the case of 2-year-old olive trees under intensive cultivation, Moriana and Fereres (2002) observed that the fast trunk growth rates displaced both the MXSD and the MNSD values upwards, causing the MDS of deficit trees to be similar to that of the control. Also in young trees (2-year-old lemon trees in ˜o pots), but during a slow trunk diameter growth period, Ortun et al. (2004b) found a lack of MNSD and MXSD response to water deficit, which contrasted with a clear response of the two indices when the trunk grew faster. The authors agreed with Goldhamer and Fereres (2001) that neither of the mentioned indices has much significance when the trunk is not growing. They also reported that, in a period of rapid trunk growth, differences in MDS between treatments decreased, affecting the usefulness of this SDV-derived index as a water-deficit indicator. Because the seasonal growth patterns of any plant also depend on the crop load, this is an additional factor that must be taken into account when analysing the SDV records, as detailed below. 4.3. Crop load SDV records are greatly affected by the presence, and amount, of fruits. Moriana et al. (2003) detected large differences in the seasonal pattern of trunk growth in 18-year-old olive trees, depending on crop load. This species shows a marked alternate bearing. Trees with heavy fruit load (‘on’ year) exhibited the mostactive trunk growth until some four weeks after full bloom, and grew very slowly for the rest of the season, while trees with a light crop load (‘off’ year) grew steadily throughout the season at an increasing rate. Moriana and Fereres (2004) compared seasonal variation in MDS between one ‘off’ year and one ‘on’ year, and found higher MDS for a given evaporative demand in the ‘on’ year compared with the ‘off’ year. Intrigliolo and Castel (2004) calculated MDS and SGR from SDV records in 5-year-old plum trees. During most of the fruit growth period, when SGR was minimum, MDS was higher in the less-irrigated treatment than in the control, and correlated well with Cstem (r2 = 0.89). After harvest, however, when SGR was higher, this correlation decreased as the season progressed (r2 = 0.73  0.52), as did the slope between MDS and Cstem, suggesting tissue elasticity changes. Later in the season, SGR was better related to plant water status. The authors pointed out that their observations indicate some of the difficulties in obtaining reference values useful for irrigation

J.E. Ferna´ndez, M.V. Cuevas / Agricultural and Forest Meteorology 150 (2010) 135–151

scheduling based exclusively on plant water status measurements. They stated that the low SGR values during the fruit growth period were likely due to the fruit-to-vegetative growth competition as fruits are strong sinks and have priority for assimilates (Grossman and De Jong, 1994; Flore and Layne, 1997). Intrigliolo and Castel (2004, 2006b) reported lower MDS for a given Cstem after harvest than before harvest. They suggested that this could be associated with an increase in the proportion of older, less elastic tissues, as well as to seasonal changes in the osmolarity of phloem tissues, caused by shifts in whole-plant sink-source relationships. The phloem tissues in the Prunus species can be expected to have a lower osmotic potential after harvest, because of the absence of the fruit sink (Loescher et al., 1990; Flore and Layne, 1997). Therefore, the water potential gradient between the xylem and the phloem should be smaller after harvest, which explains the lower MDS values. Intrigliolo and Castel (2007b) observed, also in plum trees, considerable differences in both MDS and SGR as a function of crop load: high crop load increased MDS by 34% and decreased SGR by 48%. Crop load, however, did not affect the plant water status. The different responses of MDS and Cstem to crop load were a consequence of a higher MDS for a given Cstem in the high-cropping trees than in the low-cropping trees. In a work with 14–15-year-old grapevines, Intrigliolo and Castel (2007a) observed that SGR ceased when berries became the dominant sink, independently of water status. The effect of crop load on SGR was also analysed by Pe´rez-Lo´pez et al. (2008) in 5–6-year-old olive trees. They observed that both the seasonal SGR evolution and its dependence on Ta varied depending on crop load: at the beginning of the season, SGR was closely related to Ta; later in the season, with the presence of developing fruit, SGR showed a different performance, not associated to water stress. They found a coincidence in the timing of endocarp expansion and a decrease in SGR, and reported that this behaviour could be used in certain deficit-irrigation strategies, as an accurate marker for indicating the beginning of the phases most drought-resistant in the olive growing season. This and other evidence (Sevanto et al., 2003b; Daudet et al., 2005) suggests that not only the plant water status, but also its carbon status, should be considered when interpreting SDV records, as already mentioned. 4.4. Plant age Different SDV responses may be obtained for the same species, depending on plant age. Goldhamer and Fereres (2001) and Moriana and Fereres (2002) reported (for peach and olive trees, respectively) that in young, well-watered trees, trunk growth throughout the season is reflected in MXSD and MNSD records, making these parameters potentially useful indicators for irrigation scheduling, while MDS may become a better indicator for irrigation scheduling when trunk growth slows as the tree matures. It seems, therefore, from this and previously mentioned evidence, that different SDV-derived indices may be needed when scheduling the irrigation of young compared with mature trees. In tomato plants grown in a greenhouse, Gallardo et al. (2006b) found strong linear relationships between MDS and Cleaf for each drying cycle. The slopes of these relationships, however, differed with crop age, indicating that there was no constant relationship between MDS and Cleaf over the whole season. For non-mature trees, an increase in age means an increase in size. It has been seen that phloem thickness is a function of tree size, so absolute trunk shrinkage rates are influenced by tree size (Naor and Cohen, 2003). The effect of tree size on the MDS vs Cstem relationships was investigated by Intrigliolo and Castel (2006a) in 6–8-year-old plum trees. Their results show that for tree trunk diameters ranging between 8 and 13 cm, MDS increased 13% for each centimetre of increase in

143

trunk diameter, as a result of the thicker phloem tissues of the larger trees. 4.5. Crop management Intrigliolo and Castel (2004) compared their findings on SDVderived indices and other water-stress indicators in plum trees with those of Goldhamer and Fereres (2001) in almond trees. MDS signal values found by Goldhamer and Fereres (2001) were higher than those by Intrigliolo and Castel (2004). The latter reported that these differences could have been due to differences in the irrigation system, since they drip-irrigated the trees, while Goldhamer and Fereres (2001) used microsprinklers that wetted the whole orchard floor. Gallardo et al. (2006a) carried out a study with melon and pepper growing in a greenhouse. In potted pepper plants, the signal values were greater for MDS than for Cstem but, because of the lower noise of Cstem, the signal/noise ratio was much higher for Cstem than for MDS. For soil-grown pepper plants, there were no detectable responses to soil drying in the SDVderived indices. The authors suggested that the small rooting volume of pots favour these responses. 5. Irrigation scheduling from SDV records The use of SDV records for irrigation scheduling was first proposed by Hendrickson and Veihmeyer (1941). A device which used the relationship between SDV and the water status of fruit trees to control irrigation was patented by the French Institut National de la Recherche Agronomique (INRA) in 1984 (Huguet et al., 1992). Huguet (1985), Li et al. (1989), Schoch et al. (1989), Li and Huguet (1990), Pelloux et al. (1990), and Huguet et al. (1992) used SDV records for irrigation scheduling. Garnier and Berger (1986) concluded that it was possible to use SDV to automate irrigation of peach trees, although Huguet et al. (1992) concluded that further studies were needed to understand how to use MDS and SGR values to achieve an objective irrigation scheduling approach. Pelloux et al. (1990) described the Pepista system, which can be used to control irrigation from SDV records. An example is that of Bussi et al. (1999), who used the Pepista system to control irrigation in a 10-year-old peach orchard. Among the first works to evaluate the sensitivity of several soil- and plant-based waterstress indicators, including SDV-derived indices, are those by Goldhamer et al. (1999) and Cohen et al. (2001). The fundamentals of scheduling irrigation from SDV measurements reported by Goldhamer and Fereres (2001) have been followed by most authors who have worked on this subject since then. Most of those exercises are about evaluating the suitability of SDV records for scheduling irrigation. In some others, irrigation in orchards of different fruit tree species has actually been scheduled from SDV records. Details are given below. For simplicity, Sections 5.1 and 5.2 will refer to trees and to MDS values, the most widely used SDV-derived index for scheduling irrigation. The concepts shown, however, are applicable to any other plant and SDV-derived index, with certain exceptions, commented on below. 5.1. Evaluating the usefulness of the SDV-derived indices We will now address the characteristics that must be analysed when evaluating the suitability of any indicator for irrigation scheduling, including any SDV-derived index. It has to be taken into account that the results of such evaluation may depend on the phenological stage and the level and history of the water stress ˜ o et al., 2004b). It is advisable, therefore, suffered by the crop (Ortun to evaluate the suitability of any water-stress indicator during the phenological periods particularly sensitive to water stress. For stone fruit tree species, Intrigliolo and Castel (2006a) recommend

144

J.E. Ferna´ndez, M.V. Cuevas / Agricultural and Forest Meteorology 150 (2010) 135–151

doing the evaluation during the last phase of fruit growth, when avoidance of even a mild plant water deficit is important to optimise crop yield. 5.1.1. Variability One of the limitations of using SDV records for irrigation scheduling comes from a high tree-to-tree variability. This is quantified by the CV value. A high variability, or noise, means that a large number of trees must be instrumented to reduce the uncertainty to reasonable levels. The variability of a particular water-stress indicator may increase with the water stress (Naor and Cohen, 2003). In any case, the variability must be considered relative to the signal strength. Naor et al. (2006) analysed the variability of Cleaf, Csoil, Cstem, and MDS, in apple, nectarine, and pear orchards. The plant waterstress indicators were less variable than Csoil. The authors stated that, since most of the daily SDV occurs in the living parts of the bark, MDS differences between trees may be due to differences between trees in the width of the living tissues of their bark. Furthermore, the MDS lags behind Cstem, indicating that there is an appreciable resistance to water flow between the bark and the trunk xylem. Therefore, the variable resistance to water flow (between the bark and the trunk xylem) of different trees could result in a higher variability of the MDS. Naor et al. (2006) concluded that for apple, nectarine, and peach, around 17 SDV measurements are needed to obtain a similar level of error to that from 5 Cstem values. This is in agreement with results from Gallardo et al. (2006b), who recorded Cleaf, Cstem, MDS, and SGR in tomato plants grown under greenhouse conditions. They found that SDV-derived indices consistently had much higher noise than Cleaf and Cstem: the CV values were 50–140% for SGR and 40–60% for MDS, compared with 5–12% for Cleaf and Cstem. The high treeto-tree variability normally found for plant-based indicators may call for a high number of instrumented trees, which can make the outlay and operational costs unaffordable in most commercial orchards (Naor and Cohen, 2003; Naor et al., 2006). This limitation may be overcome by combining the plant-based methods with other methods useful for detecting areas of similar characteristics within heterogeneous orchards (Section 5.3). 5.1.2. Signal strength As already stated, absolute values of any plant-based waterstress indicator mean little if they are not considered relative to similar measurements made in plants under non-limiting soilwater conditions—i.e. to a reference value. The signal strength, or signal intensity, also called signal value or just signal, of an SDVderived index is defined as actual MDS Signal intensity ¼ reference MDS The actual MDS is that derived from the SDV records made in representative trees of the orchard, usually under deficit irrigation. A high signal value means that the SDV-derived index responds intensively even to mild water deficits. If the signal strength is sufficient, the noise caused by a high tree-to-tree variability may not be as critical. Several approaches to determine the reference values of MDS (MDSref) have been described. One of them is based on keeping trees in the orchard under non-limiting soil-water conditions, i.e. reference trees. This can be achieved easily by modifying either the number of emitters per tree or the discharge rate, to supply 100– 150% of the crop evapotranspiration (ETc). Disadvantages of this approach, as pointed out by Goldhamer and Fereres (2001), are that nitrogen leakage can become excessive and, in poorly drained soils, anoxia may affect root function. They warn of the need for frequent inspection of the reference trees, to ensure they remain

representative of those in the orchard. This approach was used by Moriana and Fereres (2002), Moriana et al. (2003), Nortes et al. ˜ o et al. (2006a), among others. (2005) and Ortun A second approach to determine reference values of MDS is that based on the use of reference equations, also known as baseline relationships. These are regression equations between the chosen SDV-derived index, often MDS or SGR, calculated from SDV records made in trees under non-limiting soil-water conditions, and a meteorological variable closely related to plant water stress and easy to measure. Maximum or average daily values of Ta and Da are widely used, as well as daily ETo and Rs values. Reference equations can be obtained from measurements made in the same orchard, either in weeks before the irrigation season or in the previous year. The MDSref values are then calculated during the irrigation season by inputting actual values of the related variable into the regression equation. Since Goldhamer and Fereres (2001) proposed the method, many authors have derived reference equations for different crops (Table 2). The user must be aware that changes may occur in the slope of the regression line during the season, due to a variety of factors affecting the SDV-derived index. This implies that a reference equation obtained under certain conditions should not be applied in subsequent years if tree conditions change, and it is certainly risky to use it in a different orchard. However, this depends on the species. Thus, Intrigliolo and Castel (2007b) observed, in fully irrigated plum trees, that the relationship between MDS and evaporative demand increased with crop load, indicating that different reference equations must be used to adjust for tree crop load when using MDS to determine plant water ˜ o et al. (2009c), however, status and irrigation requirements. Ortun found no effect of crop load on MDS values measured in mature lemon trees, and concluded that reference equations between MDS and ETo, Da, and Ta can be obtained by pooling data across several seasons. An additional approach to derive reference values of MDS and Cstem is described by De Swaef et al. (2009). Instead of focusing solely on non-limiting soil-water conditions, the authors defined reference values for the two water-stress indicators based on measured plant responses to water deficits. Basically, they related SF (first converted into the amount of energy that was dissipated through leaf transpiration and then normalised by Rs), maximum A (measured at 1800 mmol PAR m2 s1), and DG values to MDS and Cstem by non-linear regression, pooling data from both a control and a stressed young apple tree. Additional information on this approach can be found in the papers by De Pauw et al. (2008b) and Steppe et al. (2008a). 5.1.3. Sensitivity This quality is defined as the signal:noise ratio: Sensitivity ¼

signal intensity CV

The sensitivity integrates both the signal strength and the variability, and is highly informative for assessing the usefulness of any plant-based water-stress indicator (Table 3). An alternative to the above equation is to assess the sensitivity with a daily ANOVA between control and deficit-irrigated plants, as Gallardo et al. (2006b) did for tomato plants growing under greenhouse conditions. Numerous studies have reported that SDV-derived indices have higher sensitivity values than Cleaf and Cstem, due to their much larger signal intensities, despite a larger relative variability (Goldhamer et al., 1999; Goldhamer and Fereres, 2001; Moriana and Fereres, 2002; Remorini and Massai, 2003). Intrigliolo and Castel (2004), however, observed the contrary when analysing SDV records from 5-year-old plum trees growing in the field. They found a higher sensitivity for Cstem than for MDS because of the

J.E. Ferna´ndez, M.V. Cuevas / Agricultural and Forest Meteorology 150 (2010) 135–151

145

Table 2 Reference equations obtained in grapevines and fruit trees of various species. For simplicity, coefficients of the curvilinear relationships are not shown. Missing slope and intercept values mean either they were not given by the authors or represent a non-significant relationship. See ‘‘List of symbols and abbreviations’’ for the meaning of the coefficients and variables shown. Crop

Period

Related variables

r2 or R2

6-year-old almond trees.

Growing season Growing season

MDS vs ETo MDS vs md Da

0.46 0.63

0.036 0.067

4-year-old almond trees. Irrigation above ETc.

Growing Growing Growing Growing Growing

MDS MDS MDS MDS MDS

0.64 0.58 0.59 0.54 0.63

0.054 0.034 0.008 0.007

4-year-old almond trees. Irrigation at 120% ETc.

Growing season

MDS vs ETo

0.48

5-year-old plum trees.

Fruit growth Early post-harvest Late post-harvest

MDS vs Cstem MDS vs Cstem MDS vs Cstem

0.89 0.72 0.52

5/7-year-old plum trees. Fully irrigation.

Fruit growth Post-harvest Fruit growth Post-harvest Fruit growth Post-harvest Fruit growth Post-harvest

MDS MDS MDS MDS MDS MDS MDS MDS

Tomato. Fully-irrigated plus non-irrigated plants.

Winter Spring Summer

Pepper. Irrigation at 100% ETc.

Growing Growing Growing Growing Growing

Fitting model

Reference

0.127 0.012

Linear Linear

Goldhamer and Fereres (2001)

0.019 0.025 0.112 0.143

Linear Linear Linear Linear Exponential

Fereres and Goldhamer (2003)

25.2

Linear

Nortes et al. (2005)

325.68 224.02 130.96

98.7 75.9 10.8

Linear Linear Linear

Intrigliolo and Castel (2004)

0.73 0.24 0.71 0.32 0.72 0.54 0.90 0.61

79.1 181.3 46.9 31.6 37.0 80.4 40.9

Exponential Linear Linear Linear Linear Linear Linear Linear

Intrigliolo and Castel (2006b)

25.51 15.8 9.9 123.8 92.2 315.3 188.4

MDS vs Cleaf MDS vs Cleaf MDS vs Cleaf

0.60 0.57 0.83

83.9 59.3 185.2

Linear Linear Linear

Gallardo et al. (2006b)

season season season season season

MDS MDS MDS MDS MDS

vs vs vs vs vs

mx Da md Da Rs ETo md Ta

0.63 0.44 0.48 0.59 0.05

1.98 6.2 2.62 2.17

32.1 62.0 2.95 6.09

Linear Linear Linear Linear Linear

Gallardo et al. (2006a)

Melon. Irrigation at 100% ETc.

Growing season

MDS MDS MDS MDS MDS

vs vs vs vs vs

mx Da md Da Rs ETo md Ta

0.57 0.50 0.64 0.73 0.28

22.87 51.47 2.21 10.26 3.84

7.84 9.01 6.83 4.17 36.76

Linear Linear Linear Linear Linear

2-year-old lemon trees. Irrigated above ETc.

Mid July–mid September Mid July–mid September

MDS vs Cstem MDS vs ETo

0.51 0.60

0.04

0.19

24-year-old lemon trees. Irrigated above ETc.

Growing season

MDS MDS MDS MDS MDS MDS

vs vs vs vs vs vs

ETo md Rs mx Da md Da mx Ta md Ta

0.66 0.47 0.53 0.56 0.75 0.76

0.05 0.001 0.08 0.15 0.02 0.02

18/19-year-old mandarin trees. Irrigation at 100–120% ETc.

Growing Growing Growing Growing

MDS MDS MDS MDS

vs vs vs vs

md Da md Ta md Rs ETo

0.30 0.40 0.52 0.49

37-year-old olive trees. Irrigation above ETc.

Early June–mid September

MDS MDS MDS MDS MDS

vs vs vs vs vs

md Ta mx Ta md Da mx Da ETo

0.68 0.79 0.69 0.82 0.55

5-year-old olive trees. Fruiting year. Irrigated at 100% ETc.

Growing season Growing season

SGR vs md Ta SGR vs mx Ta

0.77 0.75

4-year-old peach trees. Irrigated at 128% ETc.

July August–October July August–October Growing season Growing season

MDS MDS MDS MDS MDS MDS

0.71 0.53 0.79 0.72 0.49 0.51

season season season season season

season season season season

vs vs vs vs vs

vs vs vs vs vs vs vs vs

vs vs vs vs vs vs

md Da mx Da md Ta mx Ta ETo

ETo ETo md md md md

Ta Ta Da Da

Cstem Cstem

md md mx mx md mx

Ta Ta Ta Ta Da Da

Slope

22.7

Intercept

22.2 18.5 47.5

Peak function Linear

˜ o et al. (2006a) Ortun

0.06 0.06 0.09 0.07 0.13 0.07

Linear Linear Linear Linear Linear Linear

˜ o et al. (2006b) Ortun

133.35 14.62 1.15 53.93

180.73 17.92 112.99 151.58

Linear Linear Linear Linear

Velez et al. (2007)

0.05 0.04 0.16 0.13 0.11

0.79 0.78 0.03 0.07 0.14

Linear Linear Linear Linear Linear

Moreno et al. (2006)

Peak function Peak function

Pe´rez-Lo´pez et al. (2008)

Linear Linear Linear Linear Linear Linear

Conejero et al. (2007a)

0.07 0.02 0.05 0.02 0.16 0.09

1.46 0.08 1.07 0.19 0.10 0.12

J.E. Ferna´ndez, M.V. Cuevas / Agricultural and Forest Meteorology 150 (2010) 135–151

146

Table 3 Signal intensity (Signal, %), coefficient of variation (CV, %), and sensitivity values (defined in Section 4.2) of different water-stress indicators and SDV-derived indices used in various herbaceous and woody crops. See ‘‘List of symbols and abbreviations’’ for the meaning of column headings. Crop

Period

6-year-old almond (n = 4)

Summer, low stress Summer, moderate stress

uv

2-year-old, pot-grown lemon trees in greenhouse (n = 4)

Signal CV Sensitivity

24-year-old lemon trees (n = 4)

Signal CV Sensitivity

5-year-old plum

Fruit growth

(n = 6)

Soil-grown autumn and spring tomato crops in greenhouse (n = 4)

Cleaf

Cstem

155

MDS

SGR

195 14 13.93

330 16 20.6

˜ o et al. (2004a) Ortun

119 7 17.1

123 5 22.4

110 7 15.1

˜ o et al. (2006a) Ortun

118

147

125

7 16.0

17 8.4

25 4.6

Post-harvest

Signal CV Sensitivity

245 50 4.9

120 8 15.0

136 21 6.5

216 26 8.3

Winter cycle (autumn crop)

Signal

121

146

CV Sensitivity

5 24.2

6 20.2

39 3.7

140 –

Summer cycle (spring crop)

Signal CV Sensitivity

153 11 13.9

158 12 13.2

211 37 5.7

– 112 –

142 3 47.3

249 28 8.9

127 10 12.7

131 12 10.9

Soil-grown melon in greenhouse (n = 4)

Signal CV Sensitivity

117 8 14.6

Intrigliolo and Castel (2004)

–

Gallardo et al. (2006b)

Gallardo et al. (2006a)

Before veraison

Signal (%) CV (%) Sensitivity

13 33 0.39

127 14 9.07

After veraison

Signal (%) CV (%) Sensitivity

17 40 0.42

63 9 7.0

Deficit period

Signal CV Sensitivity

115 15 7.52

104 28 3.68

Recovery period

Signal CV Sensitivity

98 5 17.82

116 45 2.60

much lower CV of Cstem. Moreover, Intrigliolo and Castel (2007a) found that the sensitivity of MDS and SGR values calculated from SDV records in 14–15-year-old grapevine plants was lower than that of Cleaf and Cstem determinations. Similarly, Gallardo et al. (2006b) found, in tomato plants under greenhouse conditions, that average sensitivity values of Cleaf and Cstem were notably higher than for MDS. This was so despite the higher average signal values of MDS because of the much higher CV values of the MDS records. These conflicting results on the sensitivity of different indicators may be partly due to the relative size of the signal and noise components, and to the fact that various factors affect the potential of SDV-derived indices to detect plant water stress (Sections 3.1 and 4). Sensitivity can be calculated if the SDV-derived index is based on absolute data, e.g. MDS and SGR, but not when values are relative, such as those of MXSD and MNSD. These latter two SDVderived indices are usually calculated following the normalisation

Reference Goldhamer and Fereres (2004)

43 3.6

121

SF

– 12.4 – – 10.5 –

CV Sensitivity

Signal CV Sensitivity

4-year-old peach trees (n = 4)

Cpd

– 2.9 – – 3.2 –

Signal

Pot-grown pepper in greenhouse (n = 3)

14/15-year-old grapevine (n = 6)

Csoil

Signal CV Sensitivity Signal CV Sensitivity

12 9 1.33

9 26 0.35

48 35 1.37

29 9 3.2

14 32 0.44

– 37 –

Intrigliolo and Castel (2007a)

Conejero et al. (2007a)

of the values of stem diameter to zero at the beginning of the studied period. When SGR values are close to zero in the control plants and negative values are recorded in deficit plants, the above equation gives erroneous values. In such cases, absolute differences between the values for control and deficit plants can be compared, for each of the SDV-derived indices, by performing an ANOVA, as mentioned above. 5.1.4. Earliness The SDV-derived index responds early to water stress if high signal intensity values are recorded soon after soil water becomes limiting. Again, an ANOVA analysis may be useful for evaluating the earliness of the response to the developing water stress, since it indicates the appearance of significant differences between values in the chosen SDV-derived index between deficit and reference plants. This procedure was followed by Gallardo et al. (2006a) with SDV records in pepper and melon plants.

J.E. Ferna´ndez, M.V. Cuevas / Agricultural and Forest Meteorology 150 (2010) 135–151

Early detection of water stress is particularly useful when irrigating young orchards, as well as when applying an RDI strategy (Nortes et al., 2005). In the first case, the avoidance of even mild water stress is important if the trees are to grow and become mature as fast as possible. In the second case, the precise determination of periods for reducing or withholding irrigation is essential to prevent the crop suffering undesired water stress. 5.1.5. Reliability A system, design, or algorithm is reliable if it is able to perform its required functions under stated conditions for a specified period of time. Standard equipment to record SDV is highly reliable, being able to work under field conditions for the whole irrigation season, which in arid and semi-arid areas last for several months. The ˜ o et al. (2004a) when concept of reliability was also used by Ortun evaluating the usefulness of SF and SDV-derived indices as plant water status indicators. 5.1.6. Robustness A system, design, or algorithm is robust if it is able to cope well with variations in its operating environment with minimal damage, alteration, or loss of functionality. The robustness of standard equipment to record SDV is limited by the dendrometers being affected by physical contact (traffic in the orchard, insects, rain drops, etc.), variations of the stem temperature, and high air humidity affecting the swelling and shrinking of the cortical tissues, among other factors detailed in Table 1. Intrigliolo and Castel (2004) speak of the robustness of the relationship between SDV records and any other indicator of plant water status, such as Cstem. 5.2. Methods to schedule irrigation from SDV records 5.2.1. Using absolute values of SDV-derived indices Li et al. (1989) scheduled irrigation in a peach orchard with a Pepista system to which LVDT sensors were connected. Irrigation started when an MDS value of at least 70 mm was registered. They concluded that this irrigation protocol satisfied the tree’s water needs, saved water, and improved crop performance, as compared with another treatment in which irrigation was controlled from tensiometer measurements. Bussi et al. (1999) used a similar approach, also in a mature peach orchard, to schedule irrigation, but added a new requirement for the onset of irrigation: water was applied when at least three sensors of the four installed registered a DG close to zero and an MDS of 70 mm. Their conclusions were in agreement with those of Li et al. (1989): the micromorphometric method appeared well-suited for the optimisation of irrigation scheduling in peach orchards, and yielded better results than the tensiometric method. Later, Besset et al. (2001) irrigated potted peach trees, also with a Pepista system, to maintain MDS values between 100 and 200 mm (‘‘control’’), 200 and 400 mm (‘‘light water stress’’), and 400 and 500 mm (‘‘high water stress’’). They reported clear differences in Cstem and A, as well as in yield and fruit quality, between treatments. These are some of the very few cases in which absolute values of SDV-derived indices have been used to schedule irrigation. As pointed out by many authors (Fereres and Goldhamer, 2003; Intrigliolo and Castel, 2006b; Velez et al., 2007), single measurements of any plant-based water indicator depend largely on the meteorological conditions. To be evaluated, therefore, they must be compared to reference values obtained from similar plants growing under non-limiting soilwater conditions. 5.2.2. The signal-intensity approach Because of the coupling between plant water relationships and evaporative demand mentioned above, Goldhamer and

147

Fereres (2001) proposed what we will refer to here as the signal-intensity approach. Prior to the beginning of the irrigation season, when the orchard soil is under non-limiting soil-water conditions (all the instrumented trees must be generously irrigated in case the soil profile has not been fully recharged by rainfall), SDV records are taken to calculate the so-called reference signal (Signalref): average treatment MDS Signalref ¼ average reference MDS The treatment MDS is the one derived from SDV records in representative trees that later, during the irrigation season, will be under the irrigation treatment imposed in the orchard, normally a deficit-irrigation treatment. MDSref is determined from SDV records in reference trees or is estimated from a reference equation. In most cases, Signalref values are close to 1, although they may differ notably from that value even if the characteristics of the treatment and reference trees are apparently similar (see comments below on the work by Velez et al., 2007). To impose the desired water treatment during the irrigation season, Signalref is multiplied by a threshold value, which yields the target signal (Signaltarget): Signaltarget ¼ Signalref  threshold value The threshold value determines the level of deficit irrigation. When there is no irrigation-related stress, the threshold value is 1, while increasing values impose increasing stress levels. The threshold value must be determined from previous observations in the orchard, or obtained from the literature. During the irrigation season, SDV records are continuously taken in the same trees earlier used to determine the Signalref values. From those SDV records, we derive the actual signal values (Signalactual), which must be kept as close as possible to the Signaltarget value by frequently adjusting the irrigation dose (ID). Most authors compared Signalactual to Signaltarget every 2–3 days, and varied ID by 10–20% if the two values differed: If Signalactual > Signaltarget, ID is raised by 10%. A greater percentage, e.g. 20%, has sometimes been used, especially early in the season, due to uncertainty in determining the initial application amounts. This would allow faster convergence of the applied water amounts and tree water requirements. If Signalactual < Signaltarget, ID is lowered by 10–20%. This approach has been tested in orchards of various fruit tree species. Goldhamer and Fereres (2004) were the first to use it, in a 6-year-old almond orchard. They used a reference equation between MDS and mean daily Da to calculate Signalactual. They considered that a threshold value of 1.75 would result in mild stress with little effect on production, and that a threshold value of 2.75 would cause a more severe stress. The goal of the experiment was to have the Signalactual values range around the Signaltarget values calculated for threshold values of 1.75 and 2.75, by adjusting ID by 10% every 3–5 days. Conejero et al. (2007b) irrigated 5-year-old peach trees following an irrigation scheduling approach based on maintaining Signaltarget = Signalref, i.e. the threshold value was 1. They took SF and SDV records, and calculated the Signalactual values from the SF signal intensity and from the MDS signal intensity. The ID value was decreased by 10% when SF or MDS signal intensity on at least 2–3 consecutive days was at or below unity, and it was increased by 10% when the signal intensity on at least 2–3 consecutive days exceeded unity. Results indicated that the irrigation schedule driven by MDS signal intensity was more consistent and more sensitive to changes in peach tree water status than that driven by SF signal intensity. Garcı´a-Orellana et al. (2007)

148

J.E. Ferna´ndez, M.V. Cuevas / Agricultural and Forest Meteorology 150 (2010) 135–151

followed the same approach as Conejero et al. (2007b) to irrigate 31year-old lemon trees, but they used the MDS signal intensity only, and fixed two levels of water stress by using threshold values of 1.25 for treatment T1 and 1.35 for treatment T2. Treatment T0 was that of reference trees irrigated to 140% ETc throughout the season. For periods of very low water demand, Signalactual values were frequently below Signaltarget and presented fluctuations greater than desired. The authors concluded that it is feasible to schedule irrigation in lemon trees using only MDS values, but for periods of very low evaporative demand it may be necessary to use higher MDS signalintensity threshold values and/or lower irrigation frequency. Ortun˜o et al. (2009a) used the same irrigation approach as Garcı´a-Orellana et al. (2007) to estimate water requirements in an orchard of 33-yearold lemon trees. The work by Ortun˜o et al. (2009b) also complements that by Garcı´a-Orellana et al. (2007), with an analysis of the production response to different irrigation strategies based on the MDS signal intensity. Velez et al. (2007) used the signal intensity approach to irrigate 18–19-year-old ‘Clementina de nules’ citrus trees for two consecutive years (2003 and 2004). They used reference trees to calculate Signalactual, irrigated at 115% ETc in 2003 and at 110% ETc in 2004. Signalref was determined from MDS values calculated from SDV records taken in the orchard before deficit irrigation was applied, resulting in 0.74 in 2003 and 0.79 in 2004. They then used a threshold value of 1.25 to calculate Signaltarget, which previous results obtained in the same orchard showed enough for the desired deficit irrigation treatment. To keep Signalactual  Signaltarget, ID was varied by 10– 20% with respect to the control about every seven days. With this irrigation approach, they achieved water savings of 18% (2003) and 12% (2004) for the period after the June fruit drop to October. 5.2.3. Combining SDV with SF The usefulness of combining SDV and SF measurements to understand better how plants use water is discussed in Section 3.2. Such combination has proved useful for irrigation scheduling. Thus, Steppe et al. (2008a) have recently developed the STACI (Software Tool for Automatic Control of Irrigation), which combines online measurements of SDV and SF to estimate the amounts and frequency of irrigation. The method was tested in a pilot-scale setup with young apple trees (about 2.5 cm stem diameter) grown in containers, under conditions of low atmospheric demand. The model was calibrated daily, using a moving window of 4 days of past data of SDV. This proved crucial, as the model was no longer valid after some 4 days, due to the temporal variability of some of the model parameters. According to the authors, their work shows that a mechanistic model can be used for irrigation scheduling, thanks to progress made in mathematical modelling and computer technology. They claimed, however, that the model should be validated under a wider range of evaporative conditions and during the development of greater drought stress.

continuous data collection and transmission. The sensor outputs must be easy to interpret, and suitable for use in automatic irrigation control. Visual readouts are welcome. The information provided by the indicator should be easily integrated in methods to define water restriction zones within the orchard, since this will guarantee suitability in heterogeneous orchards. With all these characteristics, the indicator and related equipment would be a user-friendly tool for maximum profit with minimum water use. SDV records and related equipment present most of the requirements specified above. In fact, some irrigation tools based on SDV records have been developed and tested in commercial orchards, and some others are in progress. We have already mentioned the Pepista system, marketed by the French company Agro-Technologie (www.agro-technologies.com). Other companies, such as the Spanish Verdtech (www.verdtech.es) and the Israeli Phytech (www.phytech.com), have developed automatic monitoring systems of key variables in the orchard, including SDV. In both cases, the goal is to obtain continuous records of soil, plant, and weather variables, which are provided in a user-friendly format for an early detection of water stress and a more rational irrigation scheduling. Verdtech has promoted and participated in several research projects, together with national and international research institutions, generating a substantial amount of information on the commercial use of dendrometry. Most of that information has been published in project reports (Loveys et al., 2005; Ferna´ndez and Cuevas, 2008; Ferna´ndez et al., 2009; Royo et al., 2009). In addition, research teams of public organisms, such as that of the Irrigation Department of the Centro de Edafologı´a y Biologı´a Aplicada del Segura (CEBAS, Spanish National Research Council), are developing, together with private companies, irrigation tools and strategies based on SDV measurements, among other variables, aimed specifically at commercial use. When combined with aerial or satellite imaging, irrigation tools based on SDV records can be useful for precise irrigation in large orchards with high crop-water-stress variability. Thus, Sua´rez et al. (2008) assessed the relationship between the canopy photochemical reflectance index (PRI) with water stress at the canopy level, in an olive orchard. Airborne PRI demonstrated sensitivity to diurnal changes in physiological indicators of water stress, such as canopy temperature minus air temperature, gs and Cstem, measured in the field at each time of image acquisition. This approach of combining field measurements with airborne images was also used by ZarcoTejada et al. (2009) in olive, peach and orange orchards, to detect variability in fluorescence emission as a function of stress status. Berni et al. (2009) obtained low cost remote sensing products from an unmanned helicopter, which successfully combined spatial resolution and quick turnaround times, suitable for a number of applications, including precision farming or irrigation scheduling. It seems, therefore, that the future of combining airborne imagery with automated records of plant water stress, such as those provided by SDV measurements, is promising.

5.3. Development of commercial tools for irrigating from SDV records 6. Conclusions From what is said above, and from what some authors ˜ o et al., 2004b; (Goldhamer and Fereres, 2001, 2004; Ortun Gallardo et al., 2006a; Naor, 2006) have reported, the characteristics of a good indicator for irrigation scheduling can be summarised as follows. The variable sensed by the indicator must be closely related to horticultural parameters of economic importance, such as crop yield and fruit quality. The indicator must respond quickly (earliness) and markedly (intensity) even to mild water deficits. It must be highly sensitive, thanks to low variability between sensors and high signal intensity—the latter may make the indicator acceptable, even if the variability is high. The sensors must be reliable and robust, inexpensive, and easy to install, operate, and maintain; they must allow automated and

SDV measurements have great potential for scheduling irrigation in commercial orchards of grapevines and fruit trees, although several factors can easily affect the usefulness of the SDV-derived indices when the method is not properly used. First, robust and reliable LVDT sensors and related equipment for data collection and transmission, suitable for operating in the field for the whole irrigation season, are available on the market. In most cases, however, little care is taken to prevent errors deriving from poor thermal isolation of the sensors or from casual contact with workers, rain drops, and small animals, which may cause unacceptable noise. Therefore, more-effective shelters for the sensors seem to be required, together with additional records of

J.E. Ferna´ndez, M.V. Cuevas / Agricultural and Forest Meteorology 150 (2010) 135–151

the stem temperature close to the sensor location, especially when used in southern latitudes and in crops with open canopies. Although SDV-derived indices show a high plant-to-plant variability, in most cases the signal intensity is high enough to achieve an acceptable sensitivity. Care must be taken, however, to choose the SDV-derived index most appropriate for crop conditions, and to interpret the recorded values correctly. Expert supervision of the SDV outputs is usually required, as they are affected by a variety of factors, apart from water stress. Various approaches using SDV records to schedule irrigation have been proposed and tested for herbaceous and woody crops. Results show that the signalintensity approach, normally with MDS values, can be used successfully to schedule low-frequency irrigation in orchards of a variety of species, for both full- and deficit-irrigation treatments. However, the suitability of the method for automatic irrigation scheduling is limited by the effect of crop factors other than water stress on SDV records. The capacity of the method to inform on how the plant uses water and to schedule irrigation is usually improved when SDV measurements are combined with those of other plant water-stress indicators such as SF. Moreover, when combined with airborne imagery, soil properties mapping, and other methods to define water restriction zones within the orchard, SDV measurements are useful for scheduling irrigation even in large, heterogeneous orchards. Acknowledgements Drs. A. Moriana, D. Intrigliolo, and A. Torrecillas clarified some of our doubts on how to handle SDV records, and sent us valuable comments and suggestions. Dr. A. Diaz-Espejo helped us with ecophysiological concepts, and Dr. E. Chaco´n with mathematical aspects. The experiments made by the authors were funded by the Consejerı´a de Innovacio´n, Ciencia y Empresas of the Junta de Andalucı´a (research projects C03-056 and ECOSAT), and by the EU research project ref. STREP 023120. We thank the Consejo Superior de Investigaciones Cientı´ficas (CSIC) and the Instituto de Recursos Naturales y Agrobiologı´a de Sevilla (IRNAS), for providing us with the facilities required for the literature review. References Acevedo-Opazo, C., Tisseyre, B., Guillaume, S., Ojeda, H., 2008. The potential of high spatial resolution information to define within-vineyard zones related to vine water status. Precision Agriculture 9, 285–302. Ame´glio, T., Cruiziat, P., 1992. Daily variations of stem and branch diameter: schort overview from a developed example. In: Karalis, T.K. (Ed.), Mechanics of Swelling. NATO ASI Series, vol. H. 64. Springer–Verlag, Berlin, pp. 193–204. Antonova, G.F., Cherkashin, V.P., Stasova, V.V., Varaksina, T.N., 1995. Daily dynamics in xylem cell radial growth of Scots pine (Pinus sylvestris L.). Trees 10, 24–30. Archer, P., Cohen, M., Ame´glio, T., Valancogne, C., Anto´n, A., 2001. Trunk diameter variations in relation to walnut water potential. Acta Horticulturae 562, 47–53. Berni, J.A.J., Zarco-Tejada, P., Sua´rez, L., Fereres, E., 2009. Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Transactions on Geoscience and Remote Sensing 47, 722– 738. Besset, J., Ge´nard, M., Girard, T., Serra, V., Bussi, C., 2001. Effect of water stress applied during the final stage of rapid growth on peach trees (cv. Big-Top). Scientia Horticulturae 91, 289–303. Bo¨hmerle, K., 1883. Die Pfister’sche Zuwachsuhr. Zentralblatt fu¨r das gesamte Forstwesen 9, 83–93. Breitsprecher, A., Hughes, W., 1975. A recording dendrometer for humid environments. Biotropica 7 (2), 90–99. Brough, D.W., Jones, H.G., Grace, J., 1986. Diurnal changes in water content of the stems of apple trees, as influenced by irrigation. Plant Cell and Environment 9, 1–7. Buckley, T.N., 2005. The control of stomata by water balance. New Phytologist 168, 275–292. Bussi, C., Huguet, J.G., Besset, J., Girard, T., 1999. Irrigation scheduling of an early maturing peach cultivar using tensiometers and diurnal changes in stem diameter. Fruits 54, 57–66. Byram, G.M., Doolittle, W.T., 1950. A year of growth for a short leaf pine. Ecology 31, 27–35. Caspari, H.W., Green, S.R., Edwards, W.R.N., 1993. Transpiration of well-watered and water-stressed Asian pear trees as determined by lysimetry, heat-pulse,

149

and estimated by a Penman–Monteith model. Agricultural and Forest Meteorology 67, 13–27. ˇ erma´k, J., Kucˇera, J., Baurerle, W.L., Phillip, N., Hinckley, M., 2007. Tree water C storage and its diurnal dynamics related to sap flow and changes in stem volume in old-growth Douglas-fir trees. Tree Physiology 27, 181–198. ˇ erma´k, J., Nadezhdina, N., 1998. Sapwood as the scaling parameter—defining C according to xylem water content or radial pattern of sap flow? Annales des Sciences Forestie`res 55, 509–521. Cochard, H., Forestier, S., Ame´glio, T., 2001. A new validation of the Scholander pressure chamber technique based on stem diameter variations. Journal of Experimental Botany 52, 1361–1365. Cohen, M., Goldhamer, D.A., Fereres, E., Girona, J., Mata, M., 2001. Assessment of peach tree responses to irrigation water deficits by continuous monitoring of trunk diameter changes. Journal of Horticultural Science and Biotechnology 76 (1), 55–60. Conejero, W., Alarco´n, J.J., Garcı´a-Orellana, Y., Abrisqueta, J.M., Torrecillas, A., 2007a. Daily sap flow and maximum daily trunk shrinkage measurements for diagnosing water stress in early maturing peach trees during the post-harvest period. Tree Physiology 27, 81–88. Conejero, W., Alarco´n, J.J., Garcı´a-Orellana, Y., Nicola´s, E., Torrecillas, A., 2007b. Evaluation of sap flow and trunk diameter sensors for irrigation scheduling in early maturing peach trees. Tree Physiology 27, 1753–1759. Daudet, F.A., Ame´glio, T., Cochard, H., Archilla, O., Lacointe, A., 2005. Experimental analysis of the role of water and carbon in tree stem diameter variations. Journal of Experimental Botany 56, 135–144. De Pauw, D.J.W., Steppe, K., De Baets, B., 2008a. Unravelling the output uncertainty of a tree water flow and storage model using several global sensitivity analysis methods. Biosystems Engineering 101, 87–99. De Pauw, D.J.W., Steppe, K., De Baets, B., 2008b. Identifiability analysis and improvement of a tree water flow and storage model. Mathematical Biosciences 211, 314–332. De Swaef, T., Steppe, K., Lemeur, R., 2009. Determining reference values for stem water potential and maximum daily trunk shrinkage in young apple trees based on plant responses to water deficit. Agricultural Water Management 96, 541– 550. Deslauriers, A., Morin, H., Urbinati, C., Carrer, M., 2003. Daily weather response of balsam fir (Abies balsamea (L.) Mill.) stem radius increment from dendrometer analysis in the boreal forests of Que´bec (Canada). Trees 17, 477–484. Dzikiti, S., Steppe, K., Lemeur, R., Milford, J.R., 2007. Whole-tree level water balance and its implications on stomatal oscillations in orange trees [Citrus sinensis (L.) Osbeck] under natural climatic conditions. Journal of Experimental Botany 58, 1893–1901. Dobbs, R.C., Scott, D.R.M., 1971. Distribution of diurnal fluctuations in stem circumference of Douglas-fir. Canadian Journal of Forest Research 1, 80–83. Doltra, J., Oncins, J.A., Bonanay, J., Cohen, M., 2007. Evaluation of plant-based water status indicators in mature apple trees under field conditions. Irrigation Science 25, 351–359. Downes, G., Beadle, C., Worledge, D., 1999. Daily stem growth patterns in irrigated Eucalyptus globulus and E. nitens in relation to climate. Trees 14, 102–111. Escalona, J., Flexas, J., Medrano, H., 2002. Drought effects on water flow, photosynthesis and growth of potted grapevines. Vitis 41 (2), 57–62. Fereres, E., Goldhamer, D.A., 2003. Suitability of stem diameter variations and water potential as indicators for irrigation scheduling of almond trees. Journal of Horticultural Science and Biotechnology 78 (2), 139–144. Ferna´ndez, J.E., Cuevas, M.V., 2008. Ensayo de riego en olivo realizado por el IRNAS para la empresa Verdtech Nuevo Campo S.A. Informe de Seguimiento de los trabajos realizados en 2007 por el IRNAS, 100 pp. Ferna´ndez, J.E., Cuevas, M.V., A´lvarez, R.J., 2009. Integracio´n pra´ctica de nuevos me´todos de diagno´stico en olivo realizado por el IRNAS para la empresa Verdtech Nuevo Campo S.A. Informe de Seguimiento de los trabajos realizados en 2008 por el IRNAS, 148 pp. Ferna´ndez, J.E., Diaz-Espejo, A., Infante, J.M., Dura´n, P., Palomo, M.J., Chamorro, V., Giro´n, I.F., Villagarcı´a, L., 2006a. Water relations and gas exchange in olive trees under regulated deficit irrigation and partial rootzone drying. Plant and Soil 284, 273–291. Ferna´ndez, J.E., Dura´n, P.J., Palomo, M.J., Dı´az-Espejo, A., Chamorro, V., Giro´n, I.F., 2006b. Calibration of sap flow measurements by the compensation heat-pulse method in olive, plum and orange trees: relations with xylem anatomy. Tree Physiology 26, 719–728. Ferna´ndez, J.E., Green, S.R., Caspari, H.W., Diaz-Espejo, A., Cuevas, M.V., 2008a. The use of sap flow measurements for scheduling irrigation in olive, apple and Asian pear trees and in grapevines. Plant and Soil 305, 91–104. ˜ o, J.C., Diaz-Espejo, A., Muriel, J.L., Cuevas, M.V., Ferna´ndez, J.E., Romero, R., Montan Moreno, F., Giro´n, I.F., Palomo, M.J., 2008b. Design and testing of an automatic irrigation controller for fruit tree orchards, based on sap flow measurements. Australian Journal of Agricultural Research 59, 589–598. Flore, J.A., Layne, D.R., 1997. Prunus. In: Zamski, E., Schaffer, A.A. (Eds.), Photoassimilate Distribution in Plants and Crops: Sink-source Relationships. Dekker, New York. Friedrich, J., 1905. Zuwachsautograph. Zentralblatt fu¨r das Gesamte Forstwesen 31, 456–461. Friedrich, J., 1890. Zuwachsmesser. Zentralblatt fu¨r das Gesamte Forstwesen 16, 174–179. Fritts, D.C., 1961. An evaluation of three techniques for measuring radial tree growth. Bulletin of the Ecologycal Society of America 42, 54–55.

150

J.E. Ferna´ndez, M.V. Cuevas / Agricultural and Forest Meteorology 150 (2010) 135–151

Fritts, H.C., Fritts, E.C., 1955. A new dendrograph for recording radial changes of a tree. Forest Science 1, 271–276. Gallardo, M., Thompson, R.B., Valdez, L.C., Ferna´ndez, M.D., 2006a. Response of stem diameter variations to water stress in greenhouse-grown vegetable crops. Journal of Horticultural Science and Biotechnology 81 (3), 483–495. Gallardo, M., Thompson, R.B., Valdez, L.C., Ferna´ndez, M.D., 2006b. Use of stem diameter variations to detect plant water stress in tomato. Irrigation Science 24, 241–255. ˜ o, M.F., Garcı´a-Orellana, Y., Ruiz-Sa´nchez, M.C., Alarco´n, J.J., Conejero, W., Ortun Nicola´s, E., Torrecillas, A., 2007. Preliminary assessment of the feasibility of using maximum daily trunk shrinkage for irrigation scheduling in lemon trees. Agricultural Water Management 89, 167–171. Garnier, E., Berger, A., 1986. Effect of water stress on stem diameter changes of peach trees growing in the field. Journal of Applied Ecology 23, 193–209. Ge´nard, M., Fishman, S., Vercambre, G., Huguet, J.G., Bussi, C., Besset, J., Habib, R., 2001. A biophysical analysis of stem and root diameter variations in woody plants. Plant Physiology 126, 188–202. Ginestar, C., Castel, J.R., 1996. Use of stem dendrometers as indicators of water stress in drip-irrigated citrus trees. Acta Horticulturae 421, 209–219. Goldhamer, D.A., Fereres, E., 2001. Irrigation scheduling protocols using continuously recorded trunk diameter measurements. Irrigation Science 20, 115– 125. Goldhamer, D.A., Fereres, E., 2004. Irrigation scheduling of almond trees with trunk diameter sensors. Irrigation Science 23, 11–19. Goldhamer, D.A., Fereres, E., Mata, M., Girona, J., Cohen, M., 1999. Sensitivity of continuous and discrete plant and soil water status monitoring in peach trees subjected to deficit irrigation. Journal of the American Society for Horticultural Science 124 (4), 437–444. Goldstein, G., Andrade, J.L., Meinzer, F.C., Holbrook, N.M., Cavalier, J., Jackson, P., Celis, A., 1998. Stem water storage and diurnal patterns of water use in tropical forest canopy trees. Plant, Cell and Environment 21, 397–406. Goodwin, I., Boland, A.-M., 2002. Scheduling deficit irrigation of fruit trees for optimizing water use efficiency. In: Deficit Irrigation Practices, Water Reports, vol. 22, FAO, 109 pp. Green, S.R., Clothier, B.E., Jardine, B., 2003. Theory and practical application of heatpulse to measure sap flow. Agronomy Journal 95, 1371–1379. Green, S.R., Kirkham, M.B., Clothier, B.E., 2006. Root uptake and transpiration: from measurements and models to sustainable irrigation. Agricultural Water Management 86, 165–176. Grossman, Y.L., De Jong, T.M., 1994. PEACH: a simulation model of reproductive and vegetative growth in peach trees. Tree Physiology 14, 329–345. Hendrickson, A.H., Veihmeyer, F.J., 1941. Some factors affecting the rate of growth of pears. American Society for Horticultural Science Proceedings 39, 1–7. Herzog, K.M., Ha¨sler, R., Thum, R., 1995. Diurnal changes in the radius of a subalpine Norway spruce stem: their relation to the sap flow and their use to estimate transpiration. Trees 10, 94–101. Hinckley, T.M., Bruckerhoff, D.N., 1975. The effect of drought on water relations and stem shrinkage of Quercus alba. Canadian Journal of Botany 53, 62–72. Holmes, J.W., Shim, S.Y., 1968. Diurnal changes in stem diameter of Canary Island pine trees caused by soil water stress and varying microclimate. Journal of Experimental Botany 19, 219–232. Ho¨ltta¨, T., Cochard, H., Nikinmaa, E., Mencuccini, M., 2009. Capacitive effect of cavitation in xylem conduits: results from a dynamic model. Plant, Cell and Environment 32, 10–21. Huguet, J.G., 1985. Appre´ciation de l’e´tat hydrique d’une plante a` partir des variations microme`triques de la dimension des fruits ou des tiges au cours de la journe´e. Agronomie 5, 733–741. Huguet, J.G., Li, S.H., Lorendeau, J.Y., Pelloux, G., 1992. Specific micromorphometric reactions of fruit trees to water stress and irrigation scheduling automation. Journal of Horticultural Science 67 (5), 631–640. Impens, I.I., Schalck, J.M., 1965. A very sensitive electronic dendrograph for recording radial changes of a tree. Ecology 46, 183–184. Intrigliolo, D.S., Castel, J.R., 2004. Continuous measurement of plant and soil water status for irrigation scheduling in plum. Irrigation Science 23, 93–102. Intrigliolo, D.S., Castel, J.R., 2006a. Performance of various water stress indicators for prediction of fruit size response to deficit irrigation in plum. Agricultural Water Management 83, 173–180. Intrigliolo, D.S., Castel, J.R., 2006b. Usefulness of diurnal trunk shrinkage as a water stress indicator in plum trees. Tree Physiology 26, 303–311. Intrigliolo, D.S., Castel, J.R., 2007a. Evaluation of grapevine water status from trunk diameter variations. Irrigation Science 26, 49–59. Intrigliolo, D.S., Castel, J.R., 2007b. Crop load affects maximum daily trunk shrinkage of plum trees. Tree Physiology 27, 89–96. Irvine, J., Grace, J., 1997. Continuous measurements of water tensions in the xylem of trees based on the elastic properties of wood. Planta 202, 455–461. Jarvis, P.G., 1975. Water transfer in plants. In: de Vries, D.A., Afgan, N.H. (Eds.), Heat and Mass Transfer in the Biosphere. Part I. Transfer Processes in the Plant Environment. John Wiley & Sons, New York, pp. 369–394. Jones, H.G., 2004. Irrigation scheduling: advantages and pitfalls of plant-based methods. Journal of Experimental Botany 407, 2427–2436. Klepper, B., Browning, V.D., Taylor, H.M., 1971. Stem diameter in relation to plant water status. Plant Physiology 48, 683–685. Klepper, B., Taylor, H.M., Huck, M.G., Fiscus, E.I., 1973. Water relations and growth of cotton in drying soil. Agronomy Journal 65, 307–310. Kozlowski, T.T., 1971. Growth and Development of Trees, vol. II. Academic Press, New York.

Kozlowski, T.T., 1972. Shrinking and swelling of plant tissues. In: Kozlowski, T.T. (Ed.), Water Deficits and Plant Growth, vol. III. Academic Press, New York, pp. 1–64. Kozlowski, T.T., Winget, C.H., 1964. Diurnal and seasonal variations in radii of tree stems. Ecology 45, 149–155. Lassoie, J.P., 1973. Diurnal dimensional fluctuations in a Douglas-fir stem in response to tree water status. Forest Science 19, 251–255. Lassoie, J.P., 1979. Stem dimensional fluctuations in Douglas-fir stem in response to tree water status. Forest Science 25, 132–144. Li, S.H., Huguet, J.G., 1990. Controlling water status of plants and scheduling irrigation by the micromorphometric method for fruit trees. Acta Horticulturae 278, 333–342. Li, S.H., Huguet, J.G., Bussi, C., 1989. Irrigation scheduling in mature tree peach orchard using tensiometers and dendrometers. Irrigation and Drainage Systems 3, 1–12. Lockhart, J.A., 1965. An analysis of irreversible plant cell elongation. Journal of Theoretical Biology 8, 689–696. Loescher, W.H., McCamant, T., Keller, J.D., 1990. Carbohydrate reserves, translocation, and storage in woody plant roots. HortScience 25, 274–281. Loveys, B., McCarthy, M., Jones, H.G., Theobold, J., Skinner, A., 2005. When to water? Assessment of plant-based measurements to indicate irrigation requirements. Final Report to Grape and Wine Research & Development Corporation, project No. CSP02/02, 111 pp. Lubczynski, M.W., 2009. The hydrogeological role of trees in water-limited environments. Hydrogeology Journal 17, 247–259. Marsal, J., Gelly, M., Mata, M., Arbones, A., Rufat, J., Girona, J., 2002. Phenology and drought affects the relationship between daily trunk shrinkage and midday stem water potential of peach trees. Journal of Horticultural Science and Biotechnology 77, 411–417. Marsham, R., 1759. Observations on the growth of trees. Philosophical Transactions of the Royal Society 51, 7–12. McBurney, T., Costigan, P.A., 1984. The relationship between stem diameter and water potentials in stems of young cabbage plants. Journal of Experimental Botany 35, 1787–1793. Mohl, H., 1844. Ueber die Abha¨ngigkeit des Wachsthums der dicotylen Ba¨ume in die Dicke von der Physiologischen Tha¨cigkeit dei Bla¨tter. Botanische Zeitung 2 (8992), 113–116. Molz, F.J., Klepper, B., 1972. Radial propagation of water potential in stems. Agronomy Journal 64, 469–473. Molz, F.J., Klepper, B., 1973. On the mechanism of water-stress-induced stem deformation. Agronomy Journal 65, 304–306. Molz, F.J., Klepper, B., Browning, V.D., 1973. Radial diffusion of tree energy in stem phloem: an experimental study. Agronomy Journal 65, 219–222. Moreno, F., Conejero, W., Martı´n-Palomo, M.J., Giro´n, I.F., Torrecillas, A., 2006. Maximum daily trunk shrinkage reference values for irrigation scheduling in olive trees. Agricultural Water Management 84, 290–294. Moreno, F., Ferna´ndez, J.E., Clothier, B.E., Green, S.R., 1996. Transpiration and root water uptake by olive trees. Plant and Soil 184, 85–96. Moriana, A., Fereres, E., 2002. Plant indicators for scheduling irrigation of young olive trees. Irrigation Science 21, 83–90. Moriana, A., Fereres, E., 2004. Establishing reference values of trunk diameter fluctuations and stem water potential for irrigation scheduling of olive trees. Acta Horticulturae 664, 407–412. Moriana, A., Fereres, E., Orgaz, F., Castro, J., Humanes, M.D., Pastor, M., 2000. The relations between trunk diameter fluctuations and tree water status in olive tree (Olea europaea L.). Acta Horticulturae 537, 293–297. Moriana, A., Orgaz, F., Pastor, M., Fereres, E., 2003. Yield responses of a mature olive orchard to water deficits. Journal of the American Society for Horticultural Science 128 (3), 425–431. Naor, A., 2006. Irrigation scheduling and evaluation of tree water status in deciduous orchards. Horticultural Reviews 32, 111–165. Naor, A., Cohen, S., 2003. Sensitivity and variability of maximum trunk shrinkage, midday stem water potential and transpiration rate in response to withholding irrigation from field-grown apple trees. HortScience 38, 547–551. Naor, A., Gal, Y., Peres, M., 2006. The inherent variability of water stress indicators in apple, nectarine and pear orchards, and the validity of a leaf-selection procedure for water potential measurements. Irrigation Science 24, 129– 135. Nortes, P.A., Pe´rez-Pastor, A., Egea, G., Conejero, W., Domingo, R., 2005. Comparison of changes in stem diameter and water potential values for detecting water stress in yound almond trees. Agricultural Water Management 77, 296–307. Offenthaler, I., Hietz, P., Richer, H., 2001. Wood diameter indicates diurnal and longterm patterns of xylem water potential in Norway spruce. Trees 15, 215–221. ˜ o, M.F., Alarco´n, J.J., Nicola´s, E., Torrecillas, A., 2004a. Comparison of continuOrtun ously recorded plant-based water stress indicators for young lemon trees. Plant and Soil 267, 263–270. ˜ o, M.F., Alarco´n, J.J., Nicola´s, E., Torrecillas, A., 2004b. Interpreting trunk Ortun diameter changes in young lemon trees under deficit irrigation. Plant Science 167, 275–280. ˜ o, M.F., Alarco´n, J.J., Nicola´s, E., Torrecillas, A., 2005. Sap flow and trunk Ortun diameter fluctuations of young lemon trees under water stress and rewatering. Environmental and Experimental Botany 54, 155–162. ˜ o, M.F., Alarco´n, J.J., Nicola´s, E., Torrecillas, A., 2007. Water status indicators of Ortun lemon trees in response to flooding and recovery. Biologia Plantarum 51 (2), 292–296.

J.E. Ferna´ndez, M.V. Cuevas / Agricultural and Forest Meteorology 150 (2010) 135–151 ˜ o, M.F., Brito, J.J., Conejero, W., Garcı´a-Orellana, Y., Torrecillas, A., 2009a. Using Ortun continuously recorded trunk diameter fluctuations for estimating water requirements of lemon trees. Irrigation Science 27, 271–276. ˜ o, M.F., Brito, J.J., Garcı´a-Orellana, Y., Conejero, W., Torrecillas, A., 2009c. Ortun Maximum daily trunk shrinkage and stem water potential reference equations for irrigation scheduling of lemon trees. Irrigation Science 27, 121–127. ˜ o, M.F., Garcı´a-Orellana, Y., Conejero, W., Pe´rez-Sarmiento, F., Torrecillas, A., Ortun 2009b. Assessment of maximum daily trunk shrinkage signal intensity threshold values for deficit irrigation in lemon trees. Agricultural Water Management 96, 80–86. ˜ o, M.F., Garcı´a-Orellana, Y., Conejero, W., Ruiz-Sa´nchez, M.C., Alarco´n, J.J., Ortun Torrecillas, A., 2006b. Stem and leaf water potentials, gas exchange, sap flow, and trunk diameter fluctuations for detecting water stress in lemon trees. Trees 20, 1–8. ˜ o, M.F., Garcı´a-Orellana, Y., Conejero, W., Ruiz-Sa´nchez, M.C., Mounzer, O., Ortun Alarco´n, J.J., Torrecillas, A., 2006a. Relationships between climatic variables and sap flow, stem water potential and maximum daily trunk shrinkage in lemon trees. Plant and Soil 279, 229–242. Panterne, P., Burger, J., Cruziat, P., 1998. A model of the variation of water potential and diameter within a woody axis cross-section under transpiration conditions. Trees 12, 293–301. Parlange, J.-Y., Turner, N.C., Waggoner, P.E., 1975. Water uptake, diameter change, and nonlinear diffusion in tree stems. Plant Physiology 55, 247–250. Pelloux, G., Lorendeau, J.Y., Huguet, J.G., 1990. Pepista: translation of plants behaviour by the measurement of diameters of stem or fruits as a self-adjusted method for irrigation scheduling. In: Proceedings of the 3rd International Congress for Computer Technology, May 1990, Frankfurt-sur-le-Main, BadSoden, Germany, pp. 229–235. Pera¨ma¨ki, M., Nikinmaa, E., Sevanto, S., Ilvesniemi, H., Siivola, E., Hari, P., Vesala, T., 2001. Tree stem diameter variations and transpiration in Scots pine: an analysis using a dynamic sap flow model. Tree Physiology 21, 889–897. Pera¨ma¨ki, M., Vesala, T., Nikinmaa, E., 2005. Modeling the dynamics of pressure propagation and diameter variation in tree sapwood. Tree Physiology 25, 1091– 1099. Pe´rez-Lo´pez, D., Moriana, A., Rapoport, H., Olmedilla, M., Ribas, F., 2008. New approach for using trunk growth rate and endocarp development in the irrigation scheduling of young olive orchards. Scientia Horticulturae 115, 244–251. Phillips, N.G., Scholz, F.G., Bucci, S.J., Goldstein, G., Meinzer, F.C., 2009. Using branch and basal trunk sap flow measurements to estimate whole-plant water capacitance: comment on Burgess and Dawson (2008). Plant and Soil 315, 315–324. Phipps, R.L., Gilbert, G.E., 1960. An electric dendrograph. Ecology 41, 389–390. Ramos, J.G., Cratchley, C.R., Kay, J.A., Casterad, M.A., Martı´nez-Cob, A., Domı´nguez, R., 2009. Evaluation of satellite evapotranspiration estimates using groundmeteorological data available for the Flumen District into the Ebro Valley of N.E. Spain. Agricultural Water Management 96, 638–652. Remorini, D., Massai, R., 2003. Comparison of water status indicators for young peach trees. Irrigation Science 22, 39–46. Royo, J.B., Santesteban, L.G., Miranda, C., 2009. Verificacio´n y testaje de relaciones entre lecturas continuas de planta-clima-suelo y medidas fisiolo´gicas puntuales en planta con los nuevos sensores Plantsens de Verdtech con su nueva elec˜ o 2008 por la tro´nica. Informe-memoria sobre las actividades realizadas en el an UPNA, 31 pp. Schoch, P.G., L’Hoˆtel, J.C., Dauple, P., Conus, P., Fabre, M.J., 1989. Microvariations de diame`tre de tige pour le pilotage de l’irrigation. Agronomie 9, 137–142. Scholz, F.G., Bucci, S.J., Goldstein, G., Meinzer, F.C., Franco, A.C., Miralles-Wilhelm, F., 2008. Temporal dynamics of stem expansion and contraction in savanna trees: withdrawal and recharge of stored water. Tree Physiology 28, 469–480. ˇ erma´k, J., Matyssek, R., Penka, M., Zimmermann, M., Vasicek, R., Schulze, E.D., C Gries, W., Kucˇera, J., 1985. Canopy transpiration and water fluxes in the xylem of the trunk of Larix and Picea trees—a comparison of xylem flow, porometer and cuvette measurements. Oecologia 66, 475–486. Sevanto, S., Ho¨ltta¨, T., Hirsikko, A., Vesala, T., Nikinmaa, E., 2005. Determination of thermal expansion of green wood and the accuracy of tree stem diameter variation measurements. Boreal Environment Research 10, 437–445. Sevanto, S., Mikkelsen, T.N., Pilegaard, K., Vesala, T., 2003a. Comparison of tree stem diameter variations in beech (Fagus sylvatica L.) in Sorø Denmark and in Scots pine (Pinus sylvestris L.) in Hyytia¨la¨, Finland. Boreal Environment Research 8, 457–464. Sevanto, S., Nikinmaa, E., Riikonen, A., Daley, M., Pettijohn, J.C., Mikkelsen, T.N., Phillips, N., Holbrook, N.M., 2008. Linking xylem diameter variations with sap flow measurements. Plant and Soil 305, 77–90. Sevanto, S., Vesala, T., Pera¨ma¨ki, M., Nikinmaa, E., 2002. Time lags for xylem and stem diameter variations in a Scots pine tree. Plant Cell and Environment 25, 1071–1077.

151

Sevanto, S., Vesala, T., Pera¨ma¨ki, M., Nikinmaa, E., 2003b. Sugar transport together with environmental conditions controls time lags between xylem and stem diameter changes. Plant Cell and Environment 26, 1257–1265. Sevanto, S., Vesala, T., Pera¨ma¨ki, M., Pumpanen, J., Ilvesniemi, H., Nikinmaa, E., 2001. Xylem diameter changes as an indicator of stand-level evapo-transpiration. Boreal Environment Research 6, 45–52. Shackel, K.A., Ahmadi, H., Biasi, W., Buchner, R., Goldhamer, D., Gurusinghe, S., Hasey, J., Kester, D., Krueger, B., Lampinen, B., McGourty, G., Micke, W., Mitcham, E., Olson, B., Pelletrau, K., Philips, H., Ramos, D., Schwankl, L., Sibbett, S., Snyder, R., Southwick, S., Stevenson, M., Thorpe, M., Weinbaum, S., Yeager, J., 1997. Plant water status as an index of irrigation need in deciduous fruit trees. HortTechnology 7 (1), 23–29. Simonneau, T., Habib, R., Goutouly, J.P., Huguet, J.G., 1993. Diurnal changes in stem diameter depend upon variations in water content: direct evidence in peach trees. Journal of Experimental Botany 260, 615–621. So, H.B., Reicosky, D.C., Taylor, H.M., 1979. Utility of stem diameter changes as predictors of plant canopy water potential. Agronomy Journal 71, 707–713. Sola´rova´, J., Pospı´sˇilova´, J., 1983. Photosynthetic characteristics during ontogenesis of leaves. 8. Stomatal diffusive conductance and stomata reactivity. Photosynthetica 17, 101–151. Steinberg, S.L., McFarland, M.J., Worthington, J.W., 1990. comparison of trunk and branch sap flow with canopy transpiration in pecan. Journal of Experimental Botany 41, 653–659. Steppe, K., De Pauw, D.J.W., Lemeur, R., 2008a. A step towards new irrigation scheduling strategies using plant-based measurements and mathematical modelling. Irrigation Science 26, 505–517. Steppe, K., De Pauw, D.J.W., Lemeur, R., 2008b. Validation of a dynamic stem diameter variation model and the resulting seasonal changes in calibrated parameter values. Ecological Modelling 218, 247–259. Steppe, K., De Pauw, D.J.W., Lemeur, R., Vanrolleghem, P.A., 2006. A mathematical model linking tree sap flow dynamics to daily stem diameter fluctuations and radial stem growth. Tree Physiology 26, 257–273. Sua´rez, L., Zarco-Tejada, P.J., Sepulcre-Canto´, G., Pe´rez-Priego, O., Miller, J.R., Jime´˜ oz, J.C., Sobrino, J., 2008. Assessing canopy PRI for water stress detecnez-Mun tion with diurnal airborne imagery. Remote Sensing of Environment 112, 560– 575. Syvertsen, J.P., Smith, M.L., Allen, J.C., 1981. Growth rate and water relations of citrus leaf flushes. Annals of Botany 47, 97–105. Tyree, M.T., Ewers, F.W., 1991. The hydraulic architecture of trees and other woody plants. New Phytologist 119, 345–360. Ueda, M., Shibata, E., 2001. Diurnal changes in branch diameter as indicator of water status of Hinoki cypress, Chamaecyparis obtuse. Trees 15, 315–318. Ueda, M., Yoshikawa, K., Okitu, J., 1996. Measurement of diurnal changes in stem and branch diameter using strain gauges. Journal of Forest Research 1, 139– 142. Velez, J.E., Intrigliolo, D.S., Castel, J.R., 2007. Scheduling deficit irrigation of citrus trees with maximum daily trunk shrinkage. Agricultural Water Management 90, 197–204. Verbeeck, H., Steppe, K., Nadezhdina, N., Op De Beeck, M., Deckmyn, G., Meiresonne, ˇ erma´k, J., Ceulemans, R., Janssens, I.A., 2007a. Model analysis of L., Lemeur, R., C the effects of atmospheric drivers on storage water use in Scots pine. Biogeosciences 4, 657–671. Verbeeck, H., Steppe, K., Nadezhdina, N., Op De Beeck, M., Deckmyn, G., Meiresonne, ˇ erma´k, J., Ceulemans, R., Janssens, I.A., 2007b. Stored water use L., Lemeur, R., C and transpiration in Scots pine: a modeling analysis with ANAFORE. Tree Physiology 27, 1671–1685. Vesala, T., Sevanto, S., Paatero, P., Nikinmaa, E., Pera¨ma¨ki, M., Ala-Nissila¨, T., Ka¨a¨ria¨inen, J., Virtanen, H., Irvine, J., Grace, J., 2000. Do tree stems shrink and swell with the tides? Tree Physiology 20, 633–635. Wronski, E.B., Holmes, J.W., Turner, N.C., 1985. Phase and amplitude relations between transpiration, water potential and stem shrinkage. Plant, Cell and Environment 8, 613–622. Zarco-Tejada, P.J., Berni, J.A.J., Sua´rez, L., Sepulcre-Canto´, G., Morales, F., Miller, J.R., 2009. Imaging chlorophyll fluorescence with an airborne narrow-band multispectral camera for vegetation stress detection. Remote Sensing of Environment 113, 1262–1275. Zimmermann, M.H., Milburn, J.A., 1982. Transport and storage of water. In: Lange, O.L., Nobel, P.S., Osmond, C.B., Ziegler, H. (Eds.), Physiological Plant Ecology, vol. II. Springer, Berlin, pp. 135–151. Zu¨rcher, E., Cantiani, M.-G., Sorbetti-Guerri, F., Michel, D., 1998. Tree stem diameters fluctuate with tide. Nature 392, 665–666. Zweifel, R., Item, H., Ha¨sler, R., 2000. Stem radius changes and their relation to stored water in stems of young Norway spruce trees. Trees 15, 50–75. Zweifel, R., Item, H., Ha¨sler, R., 2001. Link between diurnal stem radius changes and tree water relations. Tree Physiology 21, 869–877.