Ecosystem respiration of old and young irrigated citrus orchards in a semiarid climate

Agricultural and Forest Meteorology 280 (2020) 107787

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Ecosystem respiration of old and young irrigated citrus orchards in a semiarid climate

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Bernardo Martin-Gorriza, María M. González-Reala, , Gregorio Egeab, Alain Baillea a Área de Ingeniería Agroforestal, Escuela Técnica Superior de Ingeniería Agronómica, Universidad Politécnica de Cartagena. Paseo Alfonso XIII, 48, 30203, Cartagena, Spain b Área de Ingeniería Agroforestal, Escuela Técnica Superior de Ingeniería Agronómica, Universidad de Sevilla. Ctra. Utrera km 1, 41013, Sevilla, Spain

A R T I C LE I N FO

A B S T R A C T

Keywords: Eddy covariance Soil chamber Net ecosystem production Aboveground respiration Belowground respiration Citrus sinensis

Both biotic and abiotic factors are involved in the seasonal variability of ecosystem respiration (Re) and its aboveground (Rag) and belowground (Rs) components. Knowledge of these factors is crutial to predict the respiration processes of structurally-distinct orchards under varying environmental conditions. This paper aims to characterize those factors in and across adult (AO) and young (YO) drip-irrigated citrus orchards over a 2-year period. Two methods for estimating Re were used and compared. In the first one (C-method) Re was calculated as the sum of Rag and Rs components, with each being estimated using organ-specific and soil respiration models previously validated and calibrated from chamber-based respiration and biometric canopy measurements. The second method was based on the determination of nighttime Re from data of early morning Net Ecosystem Exchange (NEE-method) provided by eddy covariance sensors located above the canopy. Estimates of Re by the two methods compared reasonably well. The C-method indicated that Rs was the predominant component of Re in both orchards, with a main peak in early spring (ratio Rs/Re ∼0.75 and 0.65 in AO and YO, respectively) during the period with no fruit load and minimum Rag. Data obtained with the NEE-method were used to test the performance of functional relationships between Re and abiotic factors (i.e., temperature through a Q10 function, and soil water content). It was found that a model only based on abiotic factors was unable to explain the differences in Re across sites; whereas accounting for canopy productivity and structure by introducing the leaf area index (LAI) as an additional driving variable notably increased the predictive power of the models in describing changes in Re, both seasonally and across sites. The study suggested that biotic factors explained a large part of the differences in Re between orchards, and that LAI could be an appropriate driving variable for predicting the impact of tree age and structural heterogeneity on Re.

1. Introduction Tree orchards have evolved considerably in the last decades in many countries of the world due to the application of new management practices (e.g. precision agriculture). Intensive orchard production applies heavy-duty agricultural technology, such as irrigation, fertilization, crown pruning and shaping, and soil management practices (mulching, control of weed flora, tillage, drainage and organic matter amendments) to improve the soil structural and textural properties. The high level of external inputs (water, fertilizers, organic matter) into the soil results in spatiotemporal patterns of ecosystem respiration (Re) and its components (González-Real et al., 2017, 2018), whose magnitude and variability are still poorly known. Whether Re in modern irrigated orchards is higher than in traditional ones, and whether it might counterweight the increase in C-capture associated to the increase in ⁎

gross primary production and productivity of modern orchards is still an unanswered question. It has recently been reported that the magnitude of the main CO2 fluxes in orchards was somewhat similar to that of temperate forest ecosystems (Zanotelli et al., 2013). However, differences in the components of Re between forest ecosystems and fruit tree orchards are to be expected due to the differences in the fate of the biomass produced (e.g. in the relative allocation of CO2 fixed to plant organs, specially to fruit production) (Zanotelli et al., 2013). Among Re components, soil respiration (Rs) represents the largest CO2 efflux to the atmosphere of terrestrial ecosystems (Raich and Schlesinger, 1992; Raich and Potter, 1995; Schlesinger and Andrews, 2000). A high number of studies have explored Rs in relation to environmental factors for a large range of woody ecosystems (Raich and Schlesinger, 1992) and there has recently been a growing

Corresponding author. E-mail address: [email protected] (M.M. González-Real).

https://doi.org/10.1016/j.agrformet.2019.107787 Received 12 March 2019; Received in revised form 30 September 2019; Accepted 1 October 2019 0168-1923/ © 2019 Elsevier B.V. All rights reserved.

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37°71′N, 0°98′W) (González-Real et al., 2018). The orchards were planted with adult (AO) and young (YO) sweet citrus trees (Citrus sinensis, L.) (32- and 12-year-old, respectively); their characteristics are given in Table 1. The inter- and intra-row spacing of trees (Table 1, Fig. 1) corresponded to a lower plantation density in AO than in YO (416.7 and 740.7 trees ha−1, respectively). The details of irrigation scheduling, water supply by means of an automatized drip-irrigation system, soil characteristics and management, tree pruning and crown thinning are described in GonzálezReal et al. (2018).

interest in characterizing Rs of agricultural systems, whether irrigated or not (Franck et al., 2011; Nardino et al., 2013; Zanotelli et al., 2013; González-Real et al., 2017, 2018). Despite these recent studies in orchards, it is difficult to characterize the magnitude and variability of Re components in such human-controlled ecosystems. The C balance of orchards has classically been assessed through surveys of tree dimensions and shape (dendrometry) and/or from the carbon stocks methods (Iglesias et al., 2013; Liguori et al., 2009). Only few studies has been based on measurements of CO2 Net Ecosystem Exchange (NEE) by eddy covariance techniques, mainly because of the small extension of orchards which hinders fulfilling the fetch requirements of flux tower techniques compared to forest ecosystems (Nardino et al., 2013). An alternative for determining Re is to use chamber-based respiration data, enabling the contribution to Re of aboveground (leaves, fruits and woody organs respiration) and belowground (soil respiration) components to be characterized (Goulden et al., 1996; Lavigne et al., 1997; Tang et al., 2008); however, this approach was also scarcely used in field-grown orchards. This work aimed to determine the magnitude and time-evolution of the components of Re in two citrus orchards over a 2-year period in southern Spain. The objectives were (1) to compare Re and its components estimated from two ground-based methods (chamber- and towerbased measurements) and (2) to understand seasonal variations in Re components. To fulfill these objectives, we analyzed the performance of empirical multivariate models to predict Re of structurally-distinct orchards (Valentini et al., 2000; Curiel-Yuste et al., 2004; Davidson et al., 2006). Knowledge of factors controlling the componenents of Re is an important step to understand the mechanisms involved in the respiration processes (Ryan and Law, 2005) and to get reliable predictions of Re under varying environmental conditions (Valentini et al., 2000; Curiel-Yuste et al., 2004; Davidson et al., 2006).

2.2. Stand structural attributes Twenty randomly distributed trees within the orchard area were used to determine, twice a month, the crown leaf area with a LAI-2200 Plant Canopy Analyzer (LI-COR, Inc., Lincoln, NE, USA). The crown leaf area and plant density (Table 1) were used to calculate the leaf area index (LAI m2 leaves m−2 ground). These data were used to interpolate the LAI values on a daily basis. In AO, most of the trees generally bear four first-order branches extending upward from a short trunk (Table 1) whereas in YO most bear three first-order branches. These branches bear second-, third- and higher-order branches, especially in AO. Three shoot flushes per year occur in orange trees, with a main flush in early spring followed by two minor flushes in summer and autumn only producing leafy shoots (Hall and Albrigo, 2007; Bevington and Castle, 1985). The flower branches, grown during the main shoot flush, are generally inserted in 1-year-old branches. Tree height, trunk height and diameter were determined in five representative trees per orchard (Table 1). Two trees per orchard were selected for measurements of branch length and diameter (d) and for the estimation of the total number of old and young branches. We randomly selected one first-order branch per tree bearing higher order branches for measurements of their length with a tapeline and their diameter with a caliper (or their circumference with a tapeline in old branches). The diameter (or circumference) was measured at the branch base (dbb) and at the branch edge. The total number of old branches per tree was estimated using the mean number of higher order branches existing on the selected first order branches and the number of first order branches per tree. The total number of young branches (1year-old) per tree was estimated in a similar way, by counting the

2. Materials and methods 2.1. Experimental orchards The characterization of ecosystem respiration (Re) was conducted during the same period (from February 2010 to January 2012) and in the same orchards and experimental units (Fig. 1) as those previously used for the characterization and modeling of soil respiration under a semiarid Mediterranean climate (Murcia Region, southeast Spain,

Fig. 1. Schematic layout of the sampling units in adult (AO) and young (YO) citrus trees. Boxes represent the position of the collars at the sampling points (S1–S9) distributed along three transects parallel to the drip-line: transect DL (points S1–S3) was close to the drip line; transect HW (points S4–S6) was half-way between the drip line and the middle of the alley and transect MA (points S7–S9) corresponded to the middle of the alley. Tree trunks and collars are not drawn to scale. H: 6.0 m and 4.5 m in AO and YO, respectively; L: 2.0 and 1.5 m in AO and YO, respectively; h1: 1.80 m and 1.35 m in AO and YO, respectively and h2: 1.2 m and 0.9 m in AO and YO, respectively. dm = crown mean diameter (∼4.8 m and ∼3.0 in AO and YO, respectively). 2

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Table 1 Characteristics of adult (AO) and young (YO) sweet citrus trees (Citrus sinensis, L.) during the period 2010 and 2011, along with row-spacing and orchard area.

Cultivar Rootstock Age of trees (years) Plant density (pd, trees m−2 ground) Inter- x intra-row spacing (m) Orchard area (ha) Tree height (m) Mean trunk diameter (m)* Leaf area index (LAI, m2 leaf m−2 ground) Canopy cover fraction Total branch surface (St, m2 tree−1) Mean number of fruits per tree at harvest, Nf

AO

YO

cv. ‘Navelate’ C. reshni Hort. ex Tanaka 32 0.04167 6.0 × 4.0 16 3.0–3.8 0.18 ± 0.01 3.1–4.0 0.70–0.76 9.3 ± 2.0 265 ± 52

cv. ‘Navel-Powel’ C. sinensis L. Osbeck × Poncirus trifoliata L. 12 0.07074 4.5 × 3.0 37 2.0–2.5 0.10 ± 0.01 2.2–3.0 0.42–0.50 7.0 ± 1.5 165 ± 25

⁎ Measured at maximum trunk height (0.25 ± 0.03 m and 0.35 ± 0.04 m in AO and YO, respectively). Estimated aboveground fresh biomass in autumn from pruned branches of different diameter: ∼89 and ∼46 kg tree−1 in AO and YO, respectively.

branches (diameter within 5.5–9.5 cm), Rb was determined using a closed gas exchange system (CIRAS 2®) with its soil chamber attachment (SRC-1) (González-Real et al., 2018). We implemented the method described by Xu et al. (2000) using four PVC tubes (one tube per tree) with a short length (3 cm) and diameter (3.4 cm) horizontally mounted on the branches at 1.3–1.6 m above the soil. For proper connection between the PVC tube and the chamber, a sleeve (diameters 3.5 and 10.1 cm) was placed between the tube and a collar previously connected to the chamber (Xu et al., 2000). In young branches (i.e. 1year-old branches, diameter <1 cm) the measurements of Rb were performed during the same period following Egea et al. (2014) with a branch bag (capacity ∼25 L) open gas exchange system. The young branches were previously defoliated and the branch thorns and the fruits were removed. The values given by the CIRAS2 system of CO2 efflux were reported to branch surface using an allometric equation relating Sb to mean branch diameter (Section 2.5.1). As for Rl, we assumed that Rb follows a temperature-dependent function, where branch temperature was approximated by air temperature. Rb at orchard scale was calculated as described in Section 2.5.1.

number of young branches extending upward from an adult branch and the number of adult branches per tree bearing young branches. To calculate the branch surface area (Sb) we assumed that the old branches formed a truncated cone shape; whilst the young branches were assumed to form a cylindrical shape. Sb was estimated using an allometric Sb vs. dbb relationship, pooling the data of adult and young branches of AO and YO. The values of Sb were used to calculate the total surface area per tree (St) as the sum of the corresponding Sb of young and old branches. St was upscaled to orchard scale using the plant density. 2.3. Respiration of aboveground organs 2.3.1. Leaf respiration Dark leaf respiration rate (Rl) was measured in the morning both in darkness and in light in AO trees using a portable gas exchange system (CIRAS2, PP Systems, Hitchin, Hertfordshire, UK) and a leaf chamber model PLC-6 (U) (PP Systems). Measurements of Rl under dark conditions (Photosynthetic Active Radiation, PAR = 0 J m−2 s−1) were carried out at ambient CO2 concentration (Ca = 380 µmol mol−1) following 20 min of dark adaptation. Rl was measured at different leaf temperatures (Tl) in autumn and winter (Tl within 5–30 °C) and in summer (Tl within 22–46 °C). Fully expanded leaves were randomly selected from two tress, including the periphery (112 outer canopy leaves) and inside (88 inner canopy leaves) the tree crown. The percentage of outer and inner canopy leaves was set at 80% and 20%, respectively, for AO; and 70% and 30%, respectively, for YO. These values were obtained by running a light absorption model adapted from the model of Boote and Loomis (1991). The two types of leaves were selected to overcome the complexity in determining the effective leaf area of different leaf classes (leaves of the current and previous year's growth and leaves of flower and leafy shoots) (Section 2.2) in evergreen trees with more than one shoot flush per year. Rl has long been observed to be inhibited in the light (Brooks and Farquhar, 1985), although the inhibition does seem to be species-specific (Sun et al., 2014). In our study, Rl in the light was measured by the Laisk method (Laisk, 1977) under three levels of PAR (12, 13 and 72 J m−2 s−1) at Tl = 25 °C and Ca within the range 5–150 µmol mol−1. A total of 10 and 12 values of Rl (inner and outer canopy leaves, respectively) were estimated by this method from measurements carried out in late summer and in winter. Rl was assumed to follow a leaf temperature-dependent Q10 function, where leaf temperature was approximated by air temperature (Ta) measured at flux towers (Section 2.6.2).

2.3.3. Fruit respiration Fruit respiration rate (Rf) was measured in the laboratory in fruits detached from the plant at different stages of development (Bain, 1958). Tood et al. (1961) reported that, compared to attached fruits, no drastic changes occurred when Rf was measured in fruits recently detached from the plant. Additionally, fruit fresh (FMf) and dry biomass and fruit diameter were determined at regular intervals in fruits randomly collected in AO and YO trees (five fruits per tree and three trees per orchard). Rf was measured at near 25 °C under dark conditions with a plastic bag (∼5 L capacity) open system (PLC-I, PP systems) connected to a portable infrared gas analyzer (CIRAS2, PP systems) as described in Egea et al. (2014). The respiration rates given by the system per square meter of a previously fixed surface were reported to the total fruit fresh weight enclosed in the plastic bag. Ta and PAR (<3 J m−2 s−1) were continuously recorded by the CIRAS2. Rf per unit of fruit fresh biomass was assumed to follow a temperature-dependent Q10 function, with fruit temperature approximated by air temperature. Rf at orchard scale was calculated as described in Section 2.5.1. 2.4. Soil-related measurements 2.4.1. Sampling protocol Measurements of soil respiration (Rs, g CO2 m−2 ground h−1), soil temperature (Ts, °C) and volumetric soil moisture (θs, m3 m−3) were conducted on two experimental sampling units (Fig. 1) as described in González-Real et al. (2018). Briefly, nine stainless steel rings (diameter 10.5 cm, height 5 cm) per experimental unit were inserted 3 cm into the

2.3.2. Branch respiration Branch respiration rate (Rb) was measured in the morning, in summer and in autumn at Ta within 15–30 °C, in AO trees selected for measurements of branch length and diameter (Section 2.2). In old 3

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soil at fixed locations (points S1 to S9) on three transects within the footprint of the eddy covariance measurements. The rings were distributed (i) along the drip-line (points S1-S3, transect DL, hereafter), (ii) between the drip-line and the middle of the alley (points S4-S6, transect HW, hereafter) and (iii) in the middle of the alley (points S7-S9, transect MA, hereafter) (Fig. 1). Measurements of Rs, Ts and θs were replicated three times per sampling point (27 measurements of each variable per experimental unit) and the mean of the three replicates of Rs, Ts and θs was considered as representative of each variable at the sampling point.

i=2

R bO = St dp ∑ R bi − 18Q10,bi (Ta − 18)/10 f St,i

where Rbi−18 is the branch reference respiration (i = 1: young branches, d < 1 cm; i = 2: adult branches, d within 2–10 cm), Q10,bi as in Eq. (1) and fSt,i is the fraction of St of the corresponding branch type. St (Table 1) is the sum of young and old branch surface area (Sb), with the latter being deduced for each type of branch from measurements of dbb:

Sb = 407.3d1.377 bb

RfO = (Rf − 18Q10,f ((Ta − 18)/10) ) 10−3 FMf Nf dp

Rf − 18 = aRf FMf −bRf

(4b)

In Eq. (4b), FMf was calculated as a function of days after bloom (DAB) using an exponential fit:

FMf = a fmexp(−bfmexp(−c fm DAB))

(4c)

where afm, bfm and cfm are best-fit parameters of Eq. (4c) to the experimental data. Total aboveground respiration. At orchard scale, total aboveground respiration (Rag, g CO2 m−2 ground h−1) was calculated as:

R ag = RlO + R bO + RfO

(1)

(5)

2.5.2. Belowground respiration The values of the soil variables (Rs, Ts and θs) at the sampling unit scale were obtained as follows (González-Real et al., 2018):

where the index j respectively refers to leaf (l), branch (b) and fruit (f) (see Section 2.3). Rj was reported to either organ surface (Rl and Rb) or organ fresh weight (Rf). Q10,j is the ratio between Rj at a given temperature and Rj at a temperature 10 °C lower, and Rj−18 is the reference respiration at Ta = 18 °C, considered to be constant for leaves, branches and aboveground respiration. For fruits, Rf−18 was assumed to follow a decreasing power function of fruit fresh biomass (see Eq. (4b)). Leaf respiration: Using the mean value of LAI measured in 20 tree samples per orchard as representative of the whole orchard, the values of Rl measured in darkness (g CO2 m−2 leaf h−1, Eq. (1)) were reported at orchard scale (RlO, CO2 m−2 ground h−1) by:

(6)

X = XDL fDL + XHW fHW + XMA fMA

where XDL, XHW and XMA give the values of a given soil variable averaged over the transects DL, HW and MA, respectively, whereas fDL, fHW and fMA give the ratio between the corresponding transect area and the sampling unit area (Fig. 1). In Eq. (6), fDL, fHW and fMA were fixed at 0.4, 0.3 and 0.3, respectively, based on an estimation of the dimensions of the wetted area induced by drip-irrigation with respect to the total sampled area. The impact of Ts and θs on soil respiration was previously described by González-Real et al. (2018), in drip-irrigated citrus, using a bivariate model through the product of two independent functions of soil temperature (f(Ts)) and moisture (g(θs)) (Kutsch and Kappen, 1997), modulated by the reference soil respiration without restrictions due to soil moisture (Rs−18):

i=2 i=1

(4a)

with Rf−18 as in Eq. (1), Q10,f fixed at 1.9 (Mishra and Gamage, 2007) and FMf in g of fresh biomass per fruit. Nf is the total number of fruits per tree (Table 1). From visual observations, it was assumed that 20% of fruits were progressively lost (fallen fruits) from fruit set to fruit harvest. From our measurements of FMf and Rf along the fruit growth cycle, we found that Rf−18 (Eq. (4a)) was appropriately estimated using a power function of FMf (pooled data of AO and YO) (Table 2):

2.5.1. Aboveground respiration and its components The respiration rate (Rj) of an aboveground organ j was assumed to follow a temperature-dependent Q10 function:

∑ Rli−18 Q10,li(Ta −18)/10fLAI,i

2

with dbb within 0.5–14 cm, Sb in cm and dbb in cm (R = 0.97). Trunk respiration was neglected due to the small height of the trunks (Table 1). Fruit respiration. Rf per unit of fruit fresh biomass (g CO2 kg−1 h−1, Eq. (1)) was calculated at orchard scale (RfO, g CO2 m−2 ground h−1) from:

2.5. Estimation of above- and belowground respiration at orchard scale

RlO = LAI

(3b) 2

2.4.2. Soil respiration, soil temperature and soil water content Rs, Ts and θs were measured simultaneously every two weeks between 9:00 h and 11:00 h (universal time coordinated, UTC) at all the sampling points (S1-S9) for 26 days in 2010 and 28 days in 2011. The spatial average of Rs within this time interval was representative of that measured at daytime scale in citrus orchards (González-Real et al., 2018) and in some forest ecosystems (Xu and Qi, 2001). In both orchards, the weeds were prevented by application of herbicides. Prior to Rs measurements, litter fall (mostly fallen leaves) was removed from the collars. Ts was measured in the 5–10 cm soil layer with a portable Pt-100 sensor (Selvise Pro-TT, JRI, France); whereas θs was measured at 5 cm depth using a TDR (Time Domain Reflectometry) soil moisture sensor (Theta Probe, model ML2-X, Delta-T Devices Ltd., Cambridge, UK). During a great part of the experimental period, fortnightly measurements of Ts and θs were complemented and compared with continuous measurements of both variables that were recorded in the middle of the transects DL, HW and MA (Fig. 1) at 10 cm depth. These measurements were respectively recorded with a temperature sensor (Hydra Probe II, Stevens Vitel Inc., VA, USA) and a TDR soil moisture sensor connected to an automatic datalogger CR1000 (Campbell Scientific, Logan, USA) and the values were integrated over a period of 1 h.

Rj = Rj − 18 Q10,j((Ta − 18)/10)

(3a)

i=1

(2)

where Rli−18 is Rl for a given leaf type i (i = 1 for inner canopy leaves, i = 2 for outer leaves), Q10,li as in Eq. (1) and fLAI,i is the fraction of LAI of the ith foliage type. Branch respiration. As for Rl, the mean value of branch respiration for the sampled trees was assumed to be representative of the whole orchard and Rb (g CO2 m−2 branch h−1, Eq. (1)) was upscaled to orchard scale (RbO, g CO2 m−2 ground h−1) using the total branch surface area (St, m2 tree−1) and plant density (dp, trees m−2 ground) (Table 1), from:

−1

(ΔS(Ts + 273.16) − ΔEd) ⎞ ⎤ R s = R s − 18 Q10 ((Ts − 18)/10) ⎡1 + EXP ⎛ ⎢ R(Ts + 273.16) ⎝ ⎠⎥ ⎣ ⎦ ⎜

⎟

(−a sθs Ln(θs /bs))

(7) −1

−1

where R = ideal gas constant (0.008314 kJ mol K ), ΔS = entropy (taken equal to 0.705 kJ mol−1), ΔEd = energy of deactivation (kJ mol−1) and as and bs are the best-fit parameters to experimental 4

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Table 2 Parameter values ( ± SE) of leaf, branch and fruit respiration and aboveground respiration (Eq. (1)). For both leaves and branches, the reference respiration (Rj-18) in g CO2 m−2 organ h−1. For fruits, Rj-18 in g CO2 kg−1 fruits h−1 with fruit fresh biomass (FMf, in g per fruit) estimated as a function of days after bloom (DAB) (Eq. (4c)). For aboveground respiration, Rj-18 in CO2 m−2 ground h−1. R2 = coefficient of determination, RMSE = root mean square error. d = branch diameter. Orchard

Rj = Rj-18Q10,j((Ta-18)/10)

Aboveground respiration and its components Rj-18

Outer canopy leaves (DA) Inner canopy leaves (DA) Outer canopy leaves (LI) AO Inner canopy leaves (LI) AO &YO Fruits AO Branches (d < 1 cm) AO Branches (d: 2–10 cm) AO Aboveground respiration YO Aboveground respiration Fruit fresh biomass (Eq. (4c)) AO FMf − AO=211.4 exp(−6.70 exp(−0.0138DAB)) YO FMf − YO = 288.4 exp(−6.91 exp(−0.0146 DAB)) AO

0.102 0.043 0.094 0.046 0.557 0.093 0.587 0.264 0.260

Q10,j ± 0.010 ± 0.006 ± 0.03 ± 0.03 FMf−0.494 ± 0.005 ± 0.040 ± 0.010 ± 0.010

1.75 ± 1.86 ± 1.75* 1.86* 1.90* 2.00* 2.00 ± 2.13 ± 2.25 ±

0.05 0.02

0.30 0.01 0.02

RMSE

R2

0.054 0.050 – – 0.030 – 0.180 0.016 0.030

0.81 0.70 – – 0.97 – 0.68 0.98 0.95

9.0 14.0

0.98 0.98

Leaf respiration in the dark (DA) and in the light (LI). ⁎ Fixed value.

H2O gas analyzer located at the top of the towers measured CO2, heat and water flux densities as the covariance of the vertical wind velocity (w) with CO2, air temperature and water vapor density fluctuations. The sampling frequency was 10 Hz for the eddy covariance and 10 s for the meteorological instrumentation. All data were stored as 30-min averages on Campbell CR3000 data loggers. Calculation of the CO2 fluxes was performed in situ by the LI-COR software. The eddy covariance data were processed with temperature and relative humidity measurements used to correct for oxygen and density effects on the CO2 fluxes (Webb–Pearman–Leuning—WPL correction; Webb et al., 1980) and integrated over a 30-min period. A standard 2-D coordinate transformation was applied (Kaimal and Finnigan, 1994) which assumes that advection of scalars can be neglected. The CO2 flux densities measured by the eddy covariance system were assumed representative of the rate of net ecosystem exchange (neglecting any minor CO2 flux storage in the layer of air below the eddy covariance system). We first selected days with high quality data obtained during the two years of measurements, with no or very few missing data in the daytime, therefore, requiring no gap filling or only simple interpolation for gap-filling the NEE data set. Second, for each day of this high quality data set, half-hourly data of NEE under well mixed conditions (friction velocity: u* > 0.2 m s−1 in AO, and >0.15 m s−1 in YO) and low PAR (≤100 J m−2 s−1) (see below) were selected. On an annual scale, the number of days fulfilling these requirements, in 2010 and 2011, was higher in AO (115 and 190 days, respectively) than in YO (92 and 174 days, respectively). For the same days, the values of daily net ecosystem production (NEP24 = -NEE24) were obtained by summing the halfhourly positive values of NEP data during daytime and subtracting from this sum the value of Ren calculated as described below. At low PAR, the relationship between photosynthesis and PAR could be assumed to be linear (Kirschbaum and Farquhar, 1987) and we applied this assumption to NEE (=-NEP, net ecosystem production) (Baldocchi, 2003):

data. In Eq. (7), f(Ts) was assumed to follow a Q10 function modulated by a temperature-constrained equation (the third term in Eq. (7)) accounting for the departure of Rs from a Q10 function observed at high Ts (Collatz et al., 1991). g(θs), the fourth term in Eq. (7), was assumed to follow a convex parabolic-like shape (Wang et al., 2015). 2.6. Estimating ecosystem respiration Re was estimated using two independent calculation schemes in what refers to the input data: (i) the chamber-method (C-method), based on chamber measurements of Re components (see above Sections 2.3–2.5) and (ii) the NEE-method, based on measurements of net ecosystem exchange (NEE) by eddy covariance flux towers. Note that the latter implicitly includes the contribution of litter (e.g. remnants from crown pruning, crown thinning, fallen leaves and fruits) whereas the former does not. 2.6.1. C-method In the C-method, Rag and Rs were calculated by running the corresponding respiration models (Eqs. (5) and (7), respectively) at hourly intervals with the required input data (Ts, Ta and θs) measured continuously in the orchards over the 2-year period of observation. By integrating the data of the night period, we obtained the nighttime component of daily Re for each day of the 2-year period:

R en = R ag,n + R s,n

(8)

where Rag,n and Rs,n were nighttime aboveground and soil respiration, respectively. In the same way, the daytime component (Red) was calculated as the sum of the corresponding diurnal components (Rag,d and Rs,d). The daily estimates of Re (24 h) were obtained from:

R e24 = R ag,n + R ag,d + R s,n + R s,d

(9)

− NEE = α PAR + m

Hereafter, the C-method estimates of Re and its components Ren and Red are noted as Re,C, Ren,C and Red,C, respectively.

(10)

where the slope α represents a proxy for the apparent canopy lightutilization efficiency, and the offset m is considered as the average Re for the previous night, hereafter noted Ren,off. The reasons for applying Eq. (10) were to avoid the use of unrealistic nighttime NEE data (Goulden et al., 1996) and to remove the bias introduced by photosynthetic limitations that modified the curvilinear light-response of photosynthesis (Lasslop et al., 2010) even at moderate values of PAR as observed in citrus (Jifon and Syvertsen, 2003). Only morning data were used to fit Eq. (10) because lower NEE values occurred in the afternoon than in the morning for

2.6.2. NEE-method The two orchards were equipped in summer 2009 with eddy covariance towers to continuously monitor net ecosystem exchange. At the top of the towers (∼5.5 m), air temperature and relative humidity were measured with Vaisala HMP45C probes (Vaisala, Finland) and solar radiation was measured with solarimeters LI-200SZ (LICOR, Lincoln, NE, USA). A Campbell Scientific Inc. (Logan, UT, USA) 3-D sonic anemometer and a LICOR Inc. (Lincoln, NE, USA) LI7500 infrared CO2/ 5

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Fig. 2. (a)–(b) Temporal trends of air temperature (Ta) and vapor pressure deficit (VPD) (mean daily values), (c)–(d) solar global radiation (S) and daily values of precipitation (PP) in 2010 ((a) and (c)) and 2011 ((b) and (d)). Adult (AO) and young (YO) orange trees.

R e − 18 = aLAI/(1 + bLAI exp(−cLAI LAI))

similar conditions of PAR (Lasslop et al., 2010).

where aLAI, bLAI and cLAI are the best-fit parameters to the experimental data. Re predicted by Eq. (12) is referred to as ReNEE to differentiate it from Re provided by the chamber-method (Re,C, Section 2.6.1). We assumed that Eq. (12), fitted with the nighttime data set, was also valid for the daytime period (Reichstein et al., 2005; Barba et al., 2018). The diurnal values of Re (Red,NEE) were therefore calculated using the same set of equations. Finally, daily Re from the NEE method was obtained as:

2.7. Model of ecosystem respiration A temperature-based model was fitted to the data set of Ren,off using an Arrhenius type equation of air temperature (f(Ta)) (Lloyd and Taylor, 1994) currently used for estimating the temperature sensitivity of Re (Reichstein et al., 2005):

Re = R e − 18 EXP[Eo ((1/((273.16 + 18) − To)) − (1/((Ta + 273.16) − To)))]

R e24,NEE = R en,NEE + R ed,NEE

(11) where Re−18 is the reference Re at Ta = 18 °C. f(Ta) is the second term of Eq. (11). To (K) was fixed to 227 K (−46 °C). Eo (K) gives the Re sensitivity to temperature and is linked to the activation energy of Re (Lloyd and Taylor, 1994). In a second step, nighttime Re was estimated from a bivariate model, similar to that used for soil respiration, integrating a function of soil moisture (g(θs)) in Eq. (11) through the final formulation:

(14)

2.8. Data analysis Parameterization of the respiration models was performed by least square curve fitting and non-linear programming. The annual variation in the measured variables was characterized using the standard deviation (SD). The coefficient of determination (R2) and the root mean square error of the estimate (RMSE) were used as a measure of the accuracy of the regressions performed. For the model of ecosystem respiration, the model efficiency (ME, varying from -∞ to 1, Janssen and Heuberger, 1995), which gives a measure of the degree of agreement between two data sets, was also included. ME = 1 indicates a perfect agreement of all data pairs, ME = 0 indicates that the model accuracy is as accurate as the mean value of observed data, ME < 0 indicates that the observed mean value is a better predictor than the model. The analysis was carried out using statistical software (Statgraphics Plus for Windows Version 5.1).

Re = R e − 18 EXP[Eo ((1/((273.16 + 18) − To)) − (1/((Ta + 273.16) − To)))] (−aNEEθs Ln(θs /bNEE))

(13)

(12)

where g(θs) (third term of Eq. (12)) is a function similar to that used for soil respiration (fourth term of Eq. (7)) and aNEE and bNEE are the bestfit parameters to the experimental data. Eqs. (11) and (12) were first fitted assuming Re−18 as constant along the growing cycle and, in a second step, as a logistic function of LAI by substituting the values of Re−18 by: 6

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respectively). Similarly, the annual mean of θs24 was lower in 2011 (0.29 ± 0.04 and 0.20 ± 0.07 m3 m−3 in AO and YO, respectively) than in 2010 (0.32 ± 0.04 and 0.23 ± 0.03 m3 m−3 in AO and YO, respectively). In Fig. 3a–d, the daytime values of Ts (Tsd) and θs (θsd) corresponded to the days where Rs was measured (Fig. 6a–d). The leaf area index varied within a relatively narrow range (3.1–4.0 m2 m−2 in AO and 2.2–3.0 m2 m−2 in YO, Table 1). The maximum values were reached in May-June after the new leaves flush in March and their consecutive growth. The minimum values were reached after the severe pruning in autumn (González-Real et al., 2018, their Fig. 2). Fruit fresh biomass (FMf) was ∼28% higher in YO than in AO at the end of the rapid fruit growth period (stage II) (Bain, 1958) and at the end of fruit maturation (stage III, harvest), when FMf reached 194 ± 10 g in AO and 260 ± 12 g in YO (Fig. 4a). Fruit load at orchard scale (FL, kg fruit fresh biomass m−2 ground) was at its minimum in April in both years reaching 2.05 ± 0.2 and 3.15 ± 0.3 kg m−2 at harvest in AO and YO, respectively (Fig. 4b).

3. Results 3.1. Seasonal trends 3.1.1. Abiotic and biotic factors Solar global radiation (S) and the daily mean values of air temperature (Ta) and air vapor pressure deficit (VPD) (Fig. 2a–b) were similar in both orchards over the 2-year period. The maximum of daily mean Ta was reached in August in both years and both orchards (near 28 °C), whereas the corresponding minimum was reached in January in 2010 and 2011 (near 4.5° and 3.3 °C, respectively) (Fig. 2a). In AO and YO, S reached its maximum in June in both years (near 30 MJ m−2 day−1), with mean values of 26.5 ± 1.0 and 8.6 ± 1.1 MJ m−2 day−1 in summer and winter, respectively (Fig. 2b). Unlike Ta and S, the maximum values of VPD were higher in 2011 (4.1 and 4.7 kPa in AO and YO, respectively) than in 2010 (3.2 and 4.0 kPa in AO and YO, respectively). The main difference between years was in the total amount of rainfall (313 mm y−1 in 2010 against 205 mm y−1 in 2011). A severe drought episode occurred from June to November 2011 (cumulated rainfall = 25 mm), inducing a rainfall deficit of 150 mm with respect to the normal (Fig. 2b). Daily mean soil temperature (Ts24) reached its maximum during the drought period of 2011 (30.4 °C in YO against 27.8 °C in AO) (Fig. 3a–d). The minimum was reached in January–February in both orchards (within 7.5–8.1 °C in both years). The annual mean of Ts24 was 15.4 ± 5.0 °C in AO and 18.7 ± 6.4 °C in YO during the first year and increased ∼2.2 °C in both orchards in the second (drought) year. The daily values of soil water content (θs24) peaked in January–February (Fig. 3a–d), with mean values in AO in 2010 (0.39 ± 0.03 m3 m−3) and in 2011 (0.34 ± 0.04 m3 m−3) higher (0.12 and 0.06 m3 m−3, respectively) than in YO. The minimum of θs24 was lower in 2011 than in 2010 in both AO (0.230 ± 0.04 and 0.290 ± 0.03 m3 m−3, respectively) and YO (0.136 ± 0.02 and 0.202 ± 0.03 m3 m−3,

3.1.2. Aboveground respiration and its components The Q10 values of outer and inner canopy dark-adapted leaves were rather similar (1.75 ± 0.05 and 1.86 ± 0.02, respectively, Table 2), whereas Rl−18 was higher in the former (0.102 ± 0.010 and 0.043 ± 0.006 g CO2 m−2 leaf h−1, respectively). Rl exhibited similar values in darkness and in light when the former was extrapolated at 25 °C using the Q10 given in Table 2. Thus, the values of Q10 and Rl−18 obtained for the nighttime period (Table 2) were extrapolated to daytime to calculate daily Rl at orchard scale (RlO) (Eq. (2)). RlO increased sharply after the main leaf flush until the summer and decreased from this period onwards (Fig. 5a–b) following pruning and decreasing temperature (Fig. 2a). RlO peaked in July–August in AO and YO (mean 5.40 ± 0.30 and 4.40 ± 0.30 g CO2 m−2 day−1, respectively) and reached its minimum in December-January (mean 1.59 ± 0.26 and 1.20 ± 0.25 g CO2 m−2 day−1 in AO and YO, respectively). Fig. 3. Temporal trends of daytime (Tsd) and daily (Ts24) soil temperature and daytime (θsd) and daily (θs24) soil water content in ((a)–(b)) adult (AO) and ((c)–(d)) young (YO) citrus trees over the 2-year observation period. Tsd and θsd: spatially-averaged values recorded in the sampling points of the transects (DL, HW and MA, Fig. 1) in the 5–10 cm soil layer and at 5 cm depth, respectively. Ts24 and θs24: mean values of Ts recorded in the middle of the three transects at 10 cm depth. Vertical bars give the standard deviation of the mean.

7

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Fig. 4. Values (a) of fruit fresh biomass (FMf) in 2010 and 2011 as a function of days after bloom (DAB) and (b) mean values over the 2-year period of fruit load (FL) in adult (AO) and young (YO) citrus trees. Vertical dotted lines in (a) separate the stages of fruit development. In (a) the points are experimental values and the lines correspond to the exponential equation (Eq. (4c)) with the parameter values given in Table 2. In (b) DAB = 0 corresponds to 20 March (mean value of blooming stage for the two orchards). Vertical bars in (a) and (b) give the standard deviation of the mean.

2.76 ± 0.28 g m−2 ground day−1 in AO and YO, respectively) until reaching its minimum in April (mean 0.44 ± 0.10 1 g m−2 ground day−1, in AO). Rag exhibited rather similar values in AO and YO (Fig. 5a–b) (P > 0.05), with AO showing higher RlO rates (mean 34%) and lower RbO (mean 8%) and RfO (mean 14%) rates than YO. From the values of Rag provided by Eq. (5), it was checked that Rag could be well described, as its components, by a Q10 function of Ta (Table 2). In this case, Rag−18 was similar in AO and YO (mean 0.262 ± 0.01 g CO2 m−2 h−1) due to higher rates of RfO in YO that practically compensated for the higher values of RlO in the former. Similar Q10 was also found in both orchards (2.1 ± 0.01 and 2.2 ± 0.02 in AO and YO, respectively). It should be noted that the daytime values of Rag (in g CO2 m−2 ground h−1) were found highly correlated with NEPd (P < 0.05) across orchards through the following relationship, which also accounts for changes in LAI:

The mean value of Rb−18 reported to branch surface was approximately six times higher in 1-year-old branches (0.583 ± 0.040 g m−2 branch h−1) than in old ones (0.093 ± 0.005 g m−2 branch h−1) (Table 2). Rb of young branches at orchard scale (RbO) was calculated from Eqs. 3a-b using the Q10 found for the old ones (Table 2). RbO reached rather similar values in AO and in YO (Fig. 5a–b), with the latter having a higher plant density and a branch surface per tree 23% lower than in AO (9.3 ± 2.0 branch tree−1) (Table 1). Similarly to leaf respiration, RbO peaked in July–August in both years (mean 2.40 ± 0.09 and 2.30 ± 0.09 g CO2 m−2 day−1 in AO and YO, respectively) and reached its minimum in December-January (∼0.69 ± 0.062 g CO2 m−2 day−1). Fruit reference respiration (Rf−18) was well described by a decreasing power function of FMf (Eq. (4b), Table 2). The values of Rf at orchard scale (RfO) were greater in YO than in AO (Fig. 5a–b), with the former having higher values of FMf (Fig. 4a) and plant density (Table 1). RfO peaked in August (mean 2.26 ± 0.23 and

Fig. 5. Temporal trends of foliar (RlO), branch (RbO) and fruit (RfO) respiration rate and total aboveground respiration rate (Rag) at orchard scale in (a) adult (AO) and (b) young (YO) orange trees. RlO, RbO, RfO and Rag estimated from Eqs. (2), (3a)–(3b), (4a)–(4c) and (5), respectively. In (a)–(b) mean values of Rj for the period 2010–2011. Vertical bars correspond to the maximum fortnight standard deviation. 8

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Table 3 Estimates ( ± SE) of the parameters of the bivariate model for soil respiration at orchard scale (Rs, Eqs. (6)–(7)). Rs-18 = soil reference respiration (g CO2 m−2 ground h−1), f(Ts) = the temperature response function and g(θs) = the soil moisture response function, ΔEd = energy of activation (kJ mol−1), as and bs = the best-fit parameters to experimental data. RMSE = root mean square error (g CO2 m−2 ground h−1). f(Ts)

g(θs)

Orchard

Rs-18

Q10

ΔEd

as

bs

RMSE

R2

AO YO

0.590 ± 0.02 0.300 ± 0.01

1.59 ± 0.07 1.94 ± 0.12

214.9 ± 7 218.1 ± 7

5.28 ± 0.1 3.10 ± 0.2

0.72 ± 0.03 0.98 ± 0.12

0.060 0.056

0.76 0.82

R ag,d = (0.745/(1 + 513.07exp(− 1.43LAI)))exp(NEPd /2.12)

ME = 0.82) (Table 4). The assumption of a constant Re−18 led to higher values of the temperature sensitivity of Re (i.e. Eo = 175 K, Table 4, equivalent to Q10 = ∼1.6) than when it was assumed to be dependent on LAI (i.e. Eo = 125 K, Table 4, equivalent to Q10 = ∼1.4).

(15)

with R2 = 0.68, and RMSE = 0.08 g CO2 m−2 ground h−1. 3.1.3. Belowground respiration The bivariate Ts–θs model, previously evaluated in González Real et al. (2018), was used in this study as it explained a higher level of the variance of Rs (R2 = 0.76 and 0.82 in AO and YO, respectively, Table 3) than the temperature-based model (R2 = 0.71 in both orchards, results not shown). The reference soil respiration Rs−18 was found largely higher for AO than for YO (0.590 ± 0.02 and 0.300 ± 0.01 g CO2 m−2 ground h−1, respectively), whereas the values of Q10 and ΔEd were both higher in YO (1.94 ± 0.12 and 218.1 ± 7 kJ mol−1, respectively) than in AO (1.59 ± 0.07 and 214.9 ± 7 kJ mol−1, respectively) (Table 3). This indicated that the maximum of the temperatureconstrained function was reached in AO at Ts values lower (26 °C) than in YO (32 °C). Similarly, the maximum of the function g(θs) was reached in AO at lower θs values than in YO (within 0.27 ± 0.019 and 0.35 ± 0.04 m3 m−3, respectively). Daytime Rs (Rsd) reached its minimum in January in both years (3.4 ± 0.8 and 1.9 ± 0.4 g CO2 m−2 ground day−1 in AO and YO, respectively) (Fig. 6a–d). In both orchards, Rsd peaked earlier than Tsd (Fig. 3a–d). Rsd experienced a drastic reduction in YO (42%) during the drought episode of 2011 (Section 3.1.1.) compared to AO (21%). In AO, the peak values of Rsd were of similar magnitude in both years (∼11.0 g CO2 m−2 ground day−1); whereas in YO they were much lower in the dry than in the normal year (6.8 and 8.4 g CO2 m−2 ground day−1, respectively). The estimated (Rsd,est) and observed (Rsd,obs) values of Rsd in AO (Fig. 6a–b) and in YO (Fig. 6c–d) provided a linear relationship with a slope value close to unity (within 0.90–0.95 and 0.83–0.90, respectively) and a small offset. In AO and YO, nighttime estimates of Rs (Rsn,est) reached their minimum in January–February in both years (mean 4.2 ± 0.7 and 2.2 ± 0.2 g CO2 m−2 ground night−1, respectively) and the peak values in July–August (7.0 ± 0.35 and 4.1 ± 0.3 g CO2 m−2 ground day−1, respectively).

3.2.2. Estimates of daily Re The daily values of Re (Re24) were reconstituted throughout the experimental period by running (i) the model of Re (Re24,NEE, Eq. (14)) and (ii) the set of equations based on chamber measurements (Re24,C, Eq. (9)). The Re24 values provided by the two methods were in reasonable agreement, showing a strong linear relationship (R2 ∼0.93, mean RMSE = 1.1 g m−2 day−1) (Fig. 8a–b) similar to that found for the comparison of nighttime Re (Fig. 7c and f). 3.3. Seasonal partitioning of daily Re The components of Re24 were obtained for the entire experimental period by applying the different sub-models constitutive of the Cmethod at daily scale (Eq. (9)). The partitioning of Re24 exhibited important temporal variations as illustrated in Fig. 9a–b for the year 2011. The main component was soil respiration, with the ratio Rs24/Re24 peaking in April (within 0.68–0.75) when the contribution of the aboveground components reached its minimum. Following this peak, the contribution of soil respiration to Re experienced a sharp decline reaching its minimum in August (0.58 in AO and 0.44 in YO). This downward trend was concomitant with a rapid increase in leaf respiration (RlO,24/Re24 up to 0.23–0.25) after the new leaves flush and in fruit respiration (RfO,24/Re24 up to 0.10–0.16) during stage II of maximum fruit growth rate (Fig. 4a). Note that the contribution of RfO,24 to Re24 peaked later compared to that of both RbO,24 (up to 0.10–0.22 in summer) and RlO,24. The balance over the 2-year period indicated that Re was 31% higher in AO than in YO (1792 ± 113 and 1236 ± 60 g C m−2 year−1, respectively) (Table 5) and that Rs represented the highest contribution to Re (67% and 56% in AO and YO, respectively) (Table 5). Among the components of Rag, the contribution of leaf respiration to Re was predominant, followed by that of branch and fruit respiration (up to 20%, 11% and 13%, respectively in YO). The differences in Rag between orchards were moderate, with 8% higher values in AO than in YO. RlO was 23% higher in AO (323 ± 32 g C m−2 ground year−1) with a higher LAI than in YO (Table 1); whereas RfO was 24% higher in YO (155 ± 30 g C m−2 ground year−1) because of a higher plant density and fruit load than in AO (Table 1, Fig. 4b).

3.2. Estimates of ecosystem respiration Nighttime estimates of Ren obtained by the NEE- (Eq. (10)) and the C-method agreed reasonably well in seasonal trend as well as in the magnitude of Ren for the two years in AO and YO (Fig. 7a, b, d and e, respectively). The linear Ren,off vs Ren,C relationship provided a slope near unity, a small offset (Fig. 7c and f) and a mean RMSE of 0.85 g CO2 m−2 ground h−1.

4. Discussion 3.2.1. Model parameterization of ecosystem respiration The temperature-based model of Re (Eq. (11)) was found underperforming (R2 = 0.57, RMSE = 0.14 g CO2 m−2 h−1 and MR = 0.37, Table 4) when compared to the bivariate model (Eq. (12)) (R2 = 0.68; RMSE = 0.13 g CO2 m−2 h−1, ME = 0.54, Table 4), both parameterized with a constant Re−18. Assuming an explicit dependence of Re−18 on the temporal variations of LAI across sites, (Eq. (13)) improved the prediction of Re with both the temperature-based model (R2 = 0.82 and RMSE = 0.10 g CO2 m−2 day−1, ME = 0.80) and the bivariate model (R2 = 0.84; RMSE = 0.08 g CO2 m − 2 h − 1,

4.1. Above- and belowground respiration and their driving factors 4.1.1. Aboveground respiration Air temperature was found to be the main factor driving the response of aboveground respiration in both orchards over the experimental period (R2 within 0.68–0.98) (Table 2); an expected result, since the components of Rag (leaf, branch and fruit respiration) were assumed to depend on Ta. The temporal variability in daily Rag can also be described by an exponential relationship seasonally and across the 9

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Fig. 6. Temporal trends of estimated (Rsd,est, Eqs. (6)–(7) and observed (Rsd,obs) daytime soil respiration at orchard scale throughout 2010 and 2011 in ((a)–(b)) adult (AO) and ((c)–(d)) young (YO) citrus orchards. PP = daily precipitation. Vertical bars give the standard deviation of the mean.

4.1.2. Belowground respiration As previously reported (González-Real et al., 2018), the Rs model integrating Ts and θs as driving variables (Eq. (7)) led to relatively high values of the explained variance in AO and YO (R2 = 0.82 and 0.79 respectively). It has been observed that models integrating soil moisture give better predictions of Re than did models only based on temperature (Sharkhuu et al., 2016). Previous studies reported that root respiration decreased at high temperature in ‘Concord’ vines (Huang et al., 2005) and in Agave deserti (Palta and Novel, 1989) and that microbial activity was negatively affected by high soil temperature (Agren et al., 1991). We found that the sensitivity of Rs to temperature (characterized through Q10 and the energy of deactivation, ΔEd) decreased at high Ts (see below), suggesting that the impact of global warming on citrus soil respiration might be lower than expected. To the best of our knowledge there is little information regarding

orchards by using daily net carbon assimilation (NEPd) and LAI as driving variables (Section 3.1.2), suggesting that aboveground respiration might be partly controlled by assimilate supply. Leaf respiration (RlO) was the main aboveground CO2 efflux (54% and 40% of Rag in AO and YO respectively, on an annual scale), followed by branch (24% and 35%, respectively) and fruit (22% and 25%, respectively) respiration. Annual RlO represented approximately 18% and 20% of Re in AO and YO, respectively. Overall, our results indicated that the contribution of Rag to Re (33–44%) in citrus orchards was within the range of previous published data in forest ecosystems (Goulden et al., 1996; Tang et al., 2008). The main difference was that the allocation of biomass to fruit production in citrus led to a high contribution of fruit respiration to Rag to fulfill the energy requirements for the maintenance of existing fruit biomass and for fruit growth (biosynthesis of new biomass) (Amthor, 1984).

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Fig. 7. Temporal trends of nighttime ecosystem respiration (Ren) estimated from eddy covariance (Ren,off, Eqs. (12)–(14)) and chamber-based measurements (Ren,C, Eqs. (5)–(7) and (9)), and of the difference ΔRe (=Ren,C-Ren,off) in ((a)–(d)) adult (AO) and ((b)–(e)) young (YO) citrus trees. (c) and (f) relationships between Ren,off and Ren,C with pooled data of AO and YO. In (c) and (f): dotted lines and continuous lines correspond, respectively, to the lines y = x and to the relationships given in the figures.

the values of ΔEd associated with soil respiration, probably because the temperature sensitivity is currently described using a decreasing Q10 at high temperature (Atkin et al., 2000). Our estimates of ΔEd (215–222 kJ mol−1) were within the range of data published for other physiological processes (Niinemets et al., 1999; Egea et al., 2011). ΔEd was higher in YO than in AO (Table 3), suggesting that the temperature sensitivity of Rs was higher in YO, with a larger proportion of unrooted soil than in AO and therefore a higher relative contribution of heterotrophic respiration (González-Real et al., 2017). In AO, with adult trees, the value of the optimum Ts (function f(Ts) in Eq. (7)) required to reach the maximum of Rs (26 ± 1 °C) was within the range considered as optimal for root growth in citrus (25–28 °C) (Bevington and Castle, 1985). In YO, with young trees, this optimum was found much higher (32 ± 1 °C) suggesting that the roots of YO might have acclimated to prevailing conditions of high soil temperature, as previously

observed in citrus (Buwalda et al., 1992; Bryla et al., 2001). Soil moisture is a key determinant of Re in irrigated orchards. Soil water content is affecting soil respiration (Liu et al., 2002) through its impact on the respiration drivers (substrate availability and substrate turnover) (Bryla et al., 2001). In our experimental conditions, drip-irrigation induced locally high gradients of root colonization affecting Rs and its partitioning between autotrophic and heterotrophic respiration. In the wet strips with moderate Ts, soil temperature is predominant in controlling Rs; whereas in the dry ones the interation between high Ts and low soil water content, Rs is mostly driven by soil moisture (González-Real et al., 2017). The relatively high values of the variance of soil respiration explained by abiotic factors in the bivariate model (R2 = 0.76 and 0.82 for AO and YO respectively, Table 3) underlined their importance in driving the seasonal changes of Rs (González-Real et al., 2018). Briefly,

Table 4 Values ( ± SE) of reference ecosystem respiration (Re-18 in g CO2 m−2 ground h−1) estimated as a constant value and as a function of LAI Eq. (13)) and of the temperature (f(Ta): Eo (K)) and soil moisture (g(θs): aNEE and bNEE) parameters response functions of Re (Eqs. (11)–(12)) based on data of eddy covariance. R2 = coefficient of determination. RMSE = root mean square error (g CO2 m−2 ground h−1), ME = model efficiency. Pooled data of adult and young orange trees in 2010 and 2011. Number of days of observation = 474. (*) fixed value. Re-18 = f(LAI)

g(θs)

f(Ta)

Eq.

Re-18 Constant

aLAI

bLAI

cLAI

aNEE

bNEE

Eo

11 12 11 and 13 12 and 13

0.650 ± 0.010 0.641 ± 0.070 _ _

_ _ 1.88 ± 0.84 2.16 ± 0.59

_ _ 16.40 ± 3.86 12.44 ± 1.22

_ _ 0.652 ± 0.16 0.735 ± 0.17

_ 3.28 ± 0.50 _ 2.98 ± 0.13

_ 0.88 ± 0.36 _ 0.61 ± 0.02

175 175 125 125

11

± 11 (*) ± 7 (*)

RMSE

ME

R2

0.16 0.13 0.10 0.08

0.37 0.54 0.80 0.82

0.57 0.68 0.82 0.84

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Fig. 8. Comparison of daily ecosystem respiration (Re) predicted with the Re-model built from the NEE data set (Re24,NEE, Eqs. (12)–(14)) and with the C-method (Re24,C, Eqs. (5)–(7) and (9)). Pooled data of adult (AO) and young (YO) citrus trees in (a) 2010 and (b) 2011. In (a) and (b): dotted lines and continuous lines correspond, respectively, to the lines y = x and the relationships given in the figures.

2005). The temperature sensitivity of Rs decreased at high temperature, suggesting that Rs is likely to be affected by limitations of substrate availability. It has been reported that conditions of high temperature may impose substrate limitations on the microbial population (Goulden et al., 1996; Kutsch and Kappen, 1997; Burton et al., 1998). These conditions are known to reduce carbon assimilation in citrus (Jifon and Syvertsen, 2003), likely affecting the supply of carbohydrates to the roots as a source for respiration.

the higher values of Rs found in AO were mainly due to two-fold higher values of Rs−18 than in YO. The magnitude of the difference in Rs−18 between orchards compensated for a lower temperature sensitivity of Rs in AO than in YO (Q10 = 1.59 and 1.94, respectively). The strong difference in Rs−18 between orchards might be ascribed to a higher LAI in AO which favored higher substrate availability for roots and thus the construction of a larger root system, as previously observed in the same orchards (González-Real et al., 2017). In forest ecosystems, Reichstein et al. (2003) found a strong correlation between maximum leaf area (a site-specific variable) and reference respiration. However, the differences in Rs between AO and YO could partly be linked to the type of rootstock (Keutgen and Huysamer, 1998). This result confirmed that any abiotic factor affecting either the photosynthetic activity or the supply of carbohydrates to the roots will affect soil respiration, as observed in natural ecosystems (Kuzyakov and Cheng, 2001; Irvine et al.,

4.2. Ecosystem respiration 4.2.1. Nighttime and daily estimates of Re We found that there was no systematic error or bias between paired nighttime estimates of Re (Ren,off and Ren,C). The temperature sensitivity

Fig. 9. Monthly trend of daily ecosystem respiration (Re) partitioning into aboveground respiration (Rag/Re) and its components (leaf, RlO/Re, branch, RbO/Re, and fruit, RfO/Re) and belowground respiration (Rs/Re) at orchard scale in (a) adult (AO) and (b) young (YO) citrus trees. 12

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Table 5 Mean annual values (g C m−2 ground year−1) of soil respiration (Rs) and aboveground respiration (Rag, Eq. (5)) and their components at orchard scale (leaf, RlO, branch, RbO, and fruit, RfO, respiration) in adult (AO) and young (YO) orange trees. In brackets: the relative contribution of the respiratory fluxes to ecosystem respiration (Re, Eq. (9)). Rs calculated from Eqs. (6)–(7) with parameters values given in Table 3. Mean values for 2010 and 2011. Orchards

Rs

Rag

RlO

RbO

RfO

Re

AO

1200 ± 69 (67%) 694 ± 30 (56%)

592 ± 50 (33%) 542 ± 35 (44%)

323 ± 32 (18%) 248 ± 13 (20%)

144 ± 10 (8%) 139 ± 12 (11%)

125 ± 15 (7%) 155 ± 30 (13%)

1792 ± 113 (100%) 1236 ± 60 (100%)

YO

found for Re in citrus orchards (Eo ∼125 K, Table 4) was within the range of values published for forest ecosystems (Migliavacca et al., 2011). In previous studies, Ren,off was found to be lower than Ren,C (Lavigne et al., 1997; Tang et al., 2008). We found the same result for most of the 2-year observation period, except for some short specific periods of the year (Fig. 7a, b, d and e). The paired values of daily Re calculated as specified in Section 3.2.2 (i.e. Re24,NEE, Eqs. (12)–(14) and Re24,C, Eq. (9)) were significantly related. However, contrary to what was observed at nighttime scale, the linear Re24,NEE vs Re24,C relationship exhibited a relatively high offset (3.5 g CO2 m−2 day−1) and the slope deviated from unity (0.75–0.76). There is a large body of work dealing with the uncertainties in Re estimates from the two methods (Goulden et al., 1996; Baldocchi et al., 1997; Lavigne et al., 1997; Lasslop et al., 2010). In our study, the impact of uncertainty in LAI on Re (Eq. (13)) might be important, because an error in the former led to a similar error in Re. It is likely that possible errors in sampling the spatial distribution of Rs, Ts and soil moisture; in sampling the temporal variations of Rs and LAI (low frequency of measurements); in upscaling the fluxes to the orchard level or in the threshold value used for filtering the friction velocity are at the root of the discrepancies of Re24 between methods. Globally, the values found for Re agreed well with those reported for olive orchards (Nardino et al., 2013), but were higher than those observed by Zanotelli et al. (2013) for apple orchards. In our experimental conditions, Rs (1200 ± 69 and 694 ± 30 g C m−2 year−1, in AO and YO, respectively) exhibited a high contribution to Re in both AO (∼68%) and YO (∼56%) (Table 5), confirming previous observations in forest ecosystems (Epron et al., 1999; Matteucci et al., 2015; Brændholt et al., 2018). The lowest values of the ratio Rs/Re occurred in summer (0.48 and 0.54 in AO and YO, respectively) as observed in forest ecosystems (Brændholt et al., 2018). The highest ratio was found in springtime in YO, and in spring and autumn in AO (Fig. 9a–b).

in LAI (∼30% higher in AO than in YO, respectively). This stressed the importance of biotic factors (tree age and structural heterogeneity) in driving the differences in Re between orchards; whereas abiotic factors were mainly involved in controlling the time pattern of Re. 4.2.3. Seasonal partitioning of Re The seasonal trend of Re partitioning between aboveground and belowground components seemed to be independent of tree age and structure (Fig. 9a–b). The Rs/Re ratio peaked (within 0.70–0.80) concomitantly with a secondary peak of root growth (Bevington and Castle, 1985) and prior to the spring flush of vegetative shoots and leafy inflorescences (Bevington and Castle, 1985; Hall and Albrigo, 2007) giving rise to an increase in LAI (González-Real et al., 2018, their Fig. 2). During this period, leaf and fruit respiration were both at their minimum (Fig. 5a–b) and the demand for assimilates by the generative organs was low (Fig. 4b). A secondary peak in Rs/Re was observed in autumn during the main peak of root growth (Bevington and Castle, 1985) competing with the presence of fruits at stage III when the fruit growth rate began to decrease (Fig. 4a). High values of root respiration when the fruit demand for growth decreased as it reached maturity were also reported in V. vinifera by Franck et al. (2011). The minimum of Rs/Re occurred in summer (∼0.60 in AO and ∼0.50 in YO) when both leaf and fruit respiration were at their maximum coinciding with peak values of LAI and new leaves and shoots expanding from the second flush in summer. It is known that citrus roots exhibit a low growth rate in summer (Bevington and Castle, 1985), indicating that during this period the process of root respiration mainly satisfied the demand in energy required by maintenance of existing root biomass (Amthor, 1984) and ion absorption (Bouma et al., 1996). We concluded that the seasonal changes in the contribution of above- and belowground components to Re were subjected to the priority of generative (fruit sink-strength) and vegetative organs in attracting assimilates (Minchin and Thorpe, 1996). 5. Conclusions

4.2.2. Processes and factors affecting Re We found that estimation of Re only based on abiotic factors as driving variables (temperature and soil water content) Eqs. (11)–((12) was not really satisfactory (ME = 0.54, R2 = 0.68; RMSE = 0.13 g CO2 m−2 h−1, Table 4). A better description of the variability in Re (seasonal and across sites) was obtained through an explicit dependence of Re−18 on LAI (Eq. (13)), added as a driving variable. This assumption not only improved the predictive performance of the model (R2 = 0.86, ME = 0.84) compared to a constant Re−18, but also reduced the sensitivity of Re to both temperature and soil moisture (Table 4). This finding indicated that the dependence on temperature of Re may mask the impact of prevailing conditions of temperature and soil moisture on assimilates supply (through LAI). Even when drip-irrigated, citrus orchards presented soil moisture patterns that might affect Re through the impact of moisture heterogeneity on Rs particularly in the young orchard which showed the highest variability in θs (GonzálezReal et al., 2017 and 2018). Our results indicated that tree age and structure strongly affected Re of citrus orchards and hence net ecosystem production as observed in natural ecosystems (Tang et al., 2008). First, the differences in Re between the orchards (Table 5) seemed to be linked to similar differences

This work contributes to a better understanding of the factors controlling Re in irrigated citrus orchards. First, it reveals that biotic factors (tree age and structural heterogeneity) are predominant in driving the differences in Re between orchards; whereas abiotic factors mainly control the seasonal pattern of Re in orchards Second, it provides further insight into the magnitude and partitioning of Re in such agricultural ecosystems. The seasonal trend of Re partitioning between aboveground and belowground components appears to be independent of tree age and structure, and mainly driven by tree phenology and the capacity of generative (fruit sink-strength) and vegetative organs in attracting assimilates as a source for respiration. Citrus orchards allocate a high fraction of assimilates to fruit production, which leads to a non-negligible contribution of fruit respiration to aboveground respiration. The large differences in Re−18 found between the old and young orchard also suggest that stand age is a key factor in determining the long-term evolution of Re, mainly through changes in the belowground components. In particular, the Rs/Re ratio increases substantially with age because old stands have a higher cost of maintenance respiration 13

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due to their larger root system and greater area of roots colonization than young stands.

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