Water use of a super high-density olive orchard submitted to regulated deficit irrigation in Mediterranean environment over three contrasted years

Abstract

The measurement of transpiration at the field level is a challenging topic in crop water use research, particularly for orchards. The super high-density olive orchard system is in great expansion all over the world, so these investigations are necessary to assess the trees water use under different irrigation techniques. Here, transpiration at plant and stand scales was measured using the sap flow thermal dissipation method, in an olive orchard (cv. “Arbosana”) subjected to standard (SI) and regulated deficit irrigation (RDI) with a withholding irrigation period under Mediterranean climate (southern Italy). The measurement method was used after specific calibration and correction for wound effect, azimuthal and gradient errors. Water use efficiency (WUE) and water productivity were determined over three complete growth seasons (2019–2022). The seasons were submitted to highly contrasted weathers. Measurements of stem water potential and stomatal conductance showed that the RDI trees were under mild-moderate water stress only during the withholding irrigation period, otherwise the two treatments were under the same good water conditions. Following these small water differences between treatments, results showed that seasonal transpiration (E_p) was not significantly different in the two treatments in all seasons (249 and 267 mm, 249 and 262 mm, 231 and 202 mm for SI and RDI in the three seasons, respectively) and that WUE was greater in RDI treatment without any impact on yield. The main conclusion is that, when the available water in the soil is limited, olive trees decrease transpiration under any atmospheric conditions, but when the water in the soil is amply available, high atmospheric demand conditions lead to a decrease in tree transpiration.

Appendices

Appendix 1: Specific calibration of TDM and the correction factors

The mass flux density (J_i, gm⁻² s⁻¹) was determined by the relation (Granier 1985; Lu et al. 2003):

$$J_{i} = aK^{b} ,$$

(10)

with a and b determined by specific local calibration. According to Alarcón et al. (2005), McCulloh et al. (2007) and Zhou et al. (2017), the calibration to find the specific coefficients a and b in the Eq. (10) was carried out on three 5-year-old trees of the investigated variety (Arbosana) cultivated in pots placed in plastic cylindric pots. The calibration curve is obtained plotting the J_s0 of Eq. (4) vs the K values, obtaining for a and b the values (Supplementary Material IV):

$$J_{i} = 140K^{1.118} .$$

(11)

To have suitable sap flow measurements for correct plant water consumption, the mass flux density measured in each sampled tree was corrected for (i) the effect of wounds; (ii) the effect of azimuthal variations; (iii) the effect of gradient (inside the sapwood from the youngest to oldest channels). For each of the above-mentioned three corrections, the coefficients were calculated in the period June–August 2022, after the experiment (Ferrara et al. 2023).

A coefficient C_w was determined to correct the mass flux density for the wound effects (Wiedemann et al. 2016) as:

$$J_{i} = 140\left( {C_{{\text{w}}} K} \right)^{1.118} .$$

(12)

For this aim, a couple of TDM probes (20 mm), one heated and one unheated, were installed in parallel to the already measuring probes (installed in the first part of January 2021) in two trees, at the same height and 20 mm from the already installed ones, in the period 23 June–23 August 2022. Hence, since the wound effect is due to the probe insertion in the trunk and then, affects the measured ΔT, correction is limited to the K parameter.

The azimuthal variations of mass flux density were analysed in two sampled trees by calculating a correction coefficient C_a, as reported in Shinohara et al. (2013); therefore, the mass flux density is now:

$$J_{i} = C_{{\text{a}}} a\left( {C_{{\text{w}}} K} \right)^{b} .$$

(13)

The azimuthal variations of sap flux density were analysed in two sampled trees, by adding two couples of TDM probes (20 mm) at 120° and 240°, in the period 23 June–23 August 2022. For considering this azimuth effect we followed Shinohara et al. (2013), to extrapolate sap flux density in the north direction, using the north sensor as a reference to the three integrated directions (averaged over the three directions). Hence, the correction coefficient is calculated as the ratio of the mean sap flux density in the three directions to the sap flux density in the north direction.

Finally, to account for a radial gradient in sap flux density, the sapwood depth of each sampled tree was divided in a set of 20 mm increments and a Gaussian function was applied to estimate the sap flux density in each increment as suggested by Pataki et al. (2011) for angiosperms and verified in the field:

$$J_{{{\text{s}}i}} = 1.033J_{i}\ {\text{exp}}\,\left[ { - 0.5\left( {\frac{x - 0.09963}{{0.4263}}} \right)^{2} } \right],$$

(14)

where J_si is the sap flux density in each increment i, x is the normalized depth of each sapwood increment (0 ≤ x < 1) and J_i is calculated using Eq. (6).

Here, to test the used function (Eq. 14) over the active sapwood (Rana et al. 2020), two new set of TDM probe were installed in parallel to the already measuring probes in two sampled trees, at the same height and 20 mm from the other ones, in the period 23 June–23 August 2022. In this case, beyond the 20 mm probes, commercial sap flow probes of 10 mm length (SFS2 Type M, UP, Steinfurt, Germany) were installed. From the sap flow density measured from the couple of probes the J_i values in the layer 10–20 mm of the sapwood depth was derived according to Iida and Tanaka (2010) as follows:

$$J_{{{\text{s}} 10 - 20}} = \frac{{J_{{{\text{s}} 0 - 20}} - \alpha J_{{{\text{s}} 0 - 10}} }}{\beta },$$

(15)

where α and β are the proportions of the sapwood area from depths of 0–10 mm to that of 0–20 mm, and from depths of 10–20 mm to that of 0–20 mm, respectively.

Finally, the whole tree transpiration of each sampled tree (E_p, gs⁻¹) was determined as:

$$E_{{\text{p}}} = \mathop \sum \limits_{i = 0}^{m} J_{{{\text{s}}i}} {\text{SWA}}_{i} ,$$

(16)

where m is the number of 20-mm increments in sapwood depth, J_si is the sap flux density determined by Eq. (14) and SWA_i is the sapwood area at each depth increase i.

Appendix 2: the potential transpiration

Potential transpiration (E_p0) for this olive orchard is calculated by the Penman–Monteith model under this form:

$$E_{{{\text{p0}}}} = \frac{1}{\lambda }\frac{{\varepsilon r_{{\text{a}}} A + \rho c_{{\text{p}}} {\text{VPD/}}\gamma }}{{\left( {1 + \varepsilon } \right)r_{{\text{a}}} + r_{{\text{c, min}}} }},$$

(17)

with λ (MJ kg⁻¹) being the latent heat of water vaporization, r_a the aerodynamic resistance (s m⁻¹), r_c,min is the minimum canopy resistance (s m⁻¹), fixed to 65 sm⁻¹ (Villalobos et al. 2000), ρc_p the volumetric heat capacity of air (J m⁻³ K⁻¹), A is the energy available to the canopy (W m⁻²), VPD is the vapor pressure deficit of the air (kPa), γ is the psychrometric constant (kPa K⁻¹) and ε = Δ/γ is the rate of change of latent heat content with sensible heat content of saturated air (kPa K⁻¹). Since the use of Eq. (17) implies already a simplified representation of the behaviour of the real canopy, i.e. the adoption of a “big leaf” model (i.e., Monteith 1965; Sellers et al. 1996), here we accurately determined all terms to reduce the inaccuracies in the calculation of E_p0.

Since E_p0 should be referred to the canopy site, and the weather variables were measured in a separate meteorological station, Rana and Katerji (2008) was followed; details on the approach can also be found in Rana et al. (2023). After a local calibration of 12 months (January–December 2021), G was considered as a constant at the daily scale and equal to 0.09 R_n.