Treated wastewater irrigation: Soil variables and grapefruit tree performance

Agricultural Water Management 204 (2018) 126–137

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Treated wastewater irrigation: Soil variables and grapefruit tree performance

T

⁎

Indira Paudela,b, Asher Bar-Tala, , Guy J. Levya, Nativ Rotbarta, Jhonathan E. Ephrathc, Shabtai Cohena a

Institute of Soil, Water and Environmental Sciences, ARO, Volcani Center, Rishon LeZion, Israel Department of Soil and Water Sciences, The Hebrew University of Jerusalem, The Robert H. Smith Faculty of Food Agriculture and Environments, Israel c French Associates Institute for Agriculture and Biotechnology of Drylands, Blaustein Institutes for Desert Research, Ben Gurion University of the Negev, Israel b

A R T I C LE I N FO

A B S T R A C T

Keywords: Treated waste water Sodium adsorption ratio Exchangeable sodium percentage Aggregate stability Citrus Ion toxicity

Soil degradation and declining tree performance following long term irrigation with treated wastewater (TWW) have been reported recently in orchards grown on clay soils. In an attempt to reverse this situation our research objectives were to quantify the effects of replacing TWW irrigation with fresh water (FW) on water uptake, water and mineral status, growth and yield of citrus trees in relation to soil physical and chemical properties. A field experiment was carried out in a commercial grapefruit orchard in a clay soil with a history of TWW irrigation. Changing irrigation water quality from TWW to FW significantly decreased soil solution electrical conductivity (EC), Na and Cl concentration, sodium adsorption ratio (SAR), exchangeable sodium percentage (ESP) and improved aggregate stability (AS) of the soil. The concentrations of Na and Cl in leaves and roots were lower in FW-irrigated trees than in TWW-irrigated ones. Fruit yield, shoot and root growth, leaf area, water status and water uptake were all significantly and favorably affected by replacing TWW with FW. Although fruit yield increased by replacing TWW with FW irrigation, it was not significantly associated with any single or group of the studied soil attributes. However, in a stepwise regression analysis a correlation was established between fruit yield and leaf Cl and soil AS. Our findings indicate that the negative effects of irrigation with TWW are (i) through damage to soil structure leading to reduced water uptake and (ii) via accumulation of Na and Cl in roots and leaves of grapefruits to toxic levels. The positive effects of alternating poor quality water (TWW) with water of high quality (FW) occur in a relatively short time span, i.e. several months to two years, thus promoting the viability of this management practice.

1. Introduction Irrigation with treated wastewater (TWW) is an attractive option for expanding agriculture when fresh water is scarce and/or limited, eg. in arid and semi-arid regions (Dobrowolski et al., 2008). Irrigation with TWW also contributes nutrients that partially replace fertilization (Fares and Alva, 1999; Hadas and Kislev, 2010). Many publications reported that irrigation with TWW had no detrimental effects on tree growth and productivity (Bielorai et al., 1978; Reboll et al., 2000; Parsons et al., 2001; Morgan et al., 2008). However, recent studies reported damage to plantations and gradual yield decreases (up to about 75%; Noshadi et al., 2013b) and recommended investigation prior to the further application of TWW in orchards (Noshadi et al.,

2013b; Assouline et al., 2015; Yang et al., 2010; Pedrero et al., 2013). The main causes proposed for the negative effects of TWW on soil and crops are (i) osmotic effects on the water potential of the soil and plants (Carden et al., 2003), (ii) toxic effects when ions, eg. Na (sodium), Cl (Chloride), and Boron (B), reach threshold levels (Aucejo et al., 1995; Noshadi et al., 2013a), and (iii) deterioration of the soil physical and hydraulic properties such as hydraulic conductivity and aggregate stability due to high sodium adsorption ratio [SAR] and exchangeable sodium percentage [ESP], and consequences in the rooting zone (Levy and Assouline, 2010; Noshadi et al., 2013b; Assouline et al., 2015; Assouline and Narkis, 2013; Schacht and Marschner, 2015; Bardhan et al., 2016). Long term irrigation with relatively saline TWW (threshold EC

Abbreviations: AS, aggregate stability; BOD, biochemical oxidation demand; COD, chemical oxidation demand; DOM, dissolved organic matter; EC, electrical conductivity; ESP, exchangeable sodium percentage; FW, fresh water; PC, principal component; PCCA, principal component and classification analysis; SAR, sodium adsorption ratio; TSS, total suspended solids; TWW, treated waste water; Ψstem, stem water potential ⁎ Corresponding author. E-mail address: [email protected] (A. Bar-Tal). https://doi.org/10.1016/j.agwat.2018.04.006 Received 29 October 2017; Received in revised form 19 March 2018; Accepted 7 April 2018 0378-3774/ © 2018 Elsevier B.V. All rights reserved.

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2 dS m−1) leads to changes in the chemical properties of soil and increasing concentrations of ions in the soil solution, especially in clay soils (Lado et al., 2012). This increases the osmotic potential of the TWW, the soil water potential and the osmotic gradient between the soil solution and the plant, reducing water uptake by plants and root activity (Assouline et al., 2015). The main toxic effects stem from high concentrations of the most common ions, Na and Cl, and from some trace ions like B (Ben-Hur, 2004). For many years B was a major threat to citrus orchards irrigated with TWW in Israel, but in the past decade new regulations have led to a large reduction in the median B concentration in TWW from 0.4 to 0.17 mg l−1 (Tarchitzky et al., 2004). The effects of salinity and high SAR on the physical properties of the soil are amplified in fine-textured soils due to the adsorption of Na to clay minerals leading to clay swelling, particle dispersion, altered soil structure, water retention, and clogging of water conducting pores (Levy and Assouline, 2010; Assouline et al., 2015). Recently it was suggested that during irrigation with TWW, the effect of the presence of dissolved organic matter in the TWW was equivalent to an increase in SAR of two to three units (Suarez and Gonzalez-Rubio, 2017). Several studies further suggested that long term irrigation with TWW affected structural porosity by modification of the composition of the dissolved organic matter (Bardhan et al., 2016) and/or by clogging of soil pores by dispersed organic particles (Levy et al., 1999). These processes may reduce the hydraulic conductivity (Assouline and Narkis, 2013; Lado et al., 2012, Assouline et al., 2015; Schacht and Marschner, 2015; Suarez and Gonzalez-Rubio, 2017), aeration (Assouline et al., 2015), root growth, plant water uptake and performance of plants in clay soils (Bravdo et al., 1992; Li et al., 2006). Although many studies have shown the advantages (Maurer et al., 1995; Pedrero et al., 2015; Parsons et al., 2001; Zekri and Koo, 1993) and disadvantages (Araújo et al., 2001; Bielorai et al., 1978; Morgan et al., 2008; Parsons et al., 2001; Pedrero et al., 2010; Pedrero et al., 2013; Zekri and Koo, 1993) of using TWW for citrus orchard irrigation, few studies focused on the effects of winter rainfall on soil recovery (Assouline and Narkis, 2013; Bhardwaj et al., 2007). We are not aware of previous studies of soil-tree response to irrigation with FW following long term irrigation with TWW, except for an observation that reported positive influences of replacing TWW with FW in citrus orchards (Azenkot et al., 2005). We hypothesized that damage to the soil-tree system following long term TWW irrigation is reversible, and the questions are to what extent and how fast the soil-tree system will recover following replacement of TWW with FW. We propose to test our hypothesis in the same orchard where a preliminary study compared soil properties in irrigated rows with those in the unirrigated area between rows (Bardhan et al., 2016). They reported a significant reduction in the saturated and unsaturated (at low metric water potential) hydraulic conductivity of the TWW-irrigated soil. The research objectives were to quantify the effects of replacing TWW irrigation with FW on water uptake, plant water and mineral status, growth and yield of citrus trees in relation to soil physical and chemical properties.

Fig. 1. Crop reference evapotranspiration (ET0, solid line), rainfall (Vertical bars) and irrigation (dotted line) in mm. Values are monthly average data from September 2012 to June 2015.

carbonate content was considerable, about 10%, leading to pH above 7.0 and probably a high buffer capacity for pH. The CEC was high, in agreement with the clay content. ESP was high, probably due to the long term irrigation with TWW containing relatively high SAR. The orchard had a history of irrigation with treated wastewater from 1986 until 1991, followed by 10 years of experiments with two water sources, FW and TWW (Lado et al., 2012), which led to significant yield decline and severe damage of trees including mortality of a few trees. From 2001 to 2004 different management treatments or remediation of the orchard were examined (Azenkot et al., 2005). The trees were uprooted and rain-fed annual crops were grown during the next 4 years, and then the current orchard was planted in 2007 and irrigated with TWW. Each tree row was irrigated with a set of two drip line laterals, located along both sides of the trees, 0.5 m apart; each lateral consisted of a set of drippers with emitter discharge of 1.6 L h−1 (Amnon Drip, Naan Dan Jain, Israel), 0.5 m apart. The experimental orchard was divided into six blocks, each with one plot for each treatment in a random arrangement. Each plot was 3 rows of 5 trees, the central 3 trees in the median row were monitored as treatment trees, and 12 were border trees. Treatments were secondary treated domestic TWW and fresh water (FW) and the differential irrigation treatments started in May 2013, after the end of the rainfall season (Fig. 1). Meteorological data were collected from an automatic weather station (Campbell Scientific Inc., Logan UT) sited in the experimental field. Air temperature (Tair), solar radiation (Rs), vapor pressure deficit (VPD), relative humidity (RH) and wind speed were measured (Fig. 1). Irrigation doses were scheduled on the basis of weekly ETc estimated as crop reference evapotranspiration (ETo), calculated using the Penman–Monteith equation (Allen et al., 1998). The total annual dose was 550–650 mm. Treatments and yield monitoring lasted four years and other detailed measurements were made during 2 years after starting irrigation with FW.

2. Materials and methods 2.1. Experimental site, plant material and design

2.2. Chemical characterization of irrigation water A field study was conducted in a commercial fruit bearing orchard of Ruby Red grapefruit (Citrus paradisi Macf.) on C. volcameriana rootstock planted at 5 × 4 m spacing at Kibbutz Mizra in the Izra’el Valley, Israel (32°40′N 35°37′E 83 m asl). Climate is typical Mediterranean with a long dry season requiring irrigation, and a rainy period (with mean precipitation of 570 mm) in the winter (November-March) (Fig. 1). Soil was clay Chromic Haploxerert. Soil properties, determined at the beginning of the experiment in 2012 before starting the FW irrigation treatment, are listed in Table 1. The clay size particle fraction was high, 690 g kg−1, whereas the sand fraction was much lower, 160 g kg−1. The

Water samples were taken from irrigation drippers and from main taps bimonthly during the irrigation season. Samples were analyzed for pH (pH meter), NH4-N and NO3-N with an auto analyzer (QUICKCHEM, Lachat Instruments, Milwaukee, WIS, USA), Na and K with a flame photometer (M410, Sherwood, Scientific Ltd, Cambridge-CB1), Ca and Mg concentrations were determined with an atomic absorption spectrometer (AA 800, Perkin Elmer, Norwalk, CT, USA) and the concentrations of Fe and B were determined by inductively coupled plasma (ICP-ICAP 6500 DUO Thermo, England). Electrical conductivity (EC) 127

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Table 1 Selected properties of the upper 0.6 m of the studied soil (standard error in parentheses). Taxonomy

Chromic Haploxerert †

Particle-size distribution Sand g kg−1

Silt

160(10)

150(30)

CaCO3

OM†

pH

EC

B

CEC

ESP

dS m−1

mg kg−1

cmolc kg−1

%

0.69(0.05)

0.8(0.1)

44.2(4.3)

8.42(0.90)

Clay g kg−1 690(20)

103(50)

1.5(0.15)

7.2(0.1)

Organic matter.

Table 2 Composition of the applied fresh water (FW) and treated wastewater (TWW) (mean + one standard deviation) throughout the experiment (2013–2015). Year Water quality

2013 FW

2014

2015

2013 TWW

2014

2015

TSS (mg l−1) COD (mg l−1) BOD (mg l−1) FEC.Colliform pH EC(dS m−1) Cl (mg l−1) Na (mg l−1) Ca++ (mg l−1) Mg++ (mg l−1) SAR (mmole l−1) N-No3 (mg l−1) N-NH4 (mg l−1) TN (mg l−1) P (mg l−1) K+ (mg l−1) Fe+++ (mg l−1) B(mg l−1)

7.3 ± 3 12 ± 4 5.6 ± 1.2 54 ± 5 7.65 ± 0.24 1.05 ± 0.05 223 ± 15 122 ± 6 45 ± 2.9 23 ± 0.9 3.8 ± 0.6 7 ± 0.52 38.76 ± 0.03 45.0 ± 0.002 8.8 ± 0.01 38 ± 5 0.25 ± 010 0.10 ± 0.002

3.57 ± 1.3 N.D N.D 54 ± 5 8.05 ± 0.24 1.15 ± 0.05 49 ± 15 121 ± 12 45.1 ± 2.9 23.1 ± 0.9 3.65 ± 0.6 7.75 ± 0.52 43.09 ± 3 51 ± 3.2 8.8 ± 0.1 41.9 ± 5 0.25 ± 010 0.1 ± 0.002

7.3 ± 3 12.4 ± 4 5.6 ± 1.2 23.1 ± 0.9 7.25 ± 0.2 1.2 ± 0.05 57 ± 15 86 ± 6 40 ± 3 22 ± 2 2.7 ± 0.6 5 ± 0.5 45 ± 5 51 ± 4 6.5 ± 0. 4 41 ± 5 0.25 ± 010 0.1 ± 0.002

29.9 ± 5 100 ± 12 15.6 ± 3 800 ± 55 8.2 ± 0.15 2.3 ± 0.25 343 ± 35 202 ± 16 55 ± 1.94 23 ± 1.94 5.8 ± 0.6 5.8 ± 1.59 39.0 ± 0.93 45.9 ± 5 9.3 ± 3 38.8 ± 2 0.8 ± 0.01 0.08 ± 0.002

45.9 ± 12.3 81.5 ± 21 17.93 ± 1.99 800 ± 55 8.2 ± 0.15 2.3 ± 0.25 343 ± 35 326 ± 8 83 ± 1.94 33 ± 1.94 7.8 ± 0.6 6 ± 1.59 59 ± 0.93 67 ± 5 6.3 ± 1.31 39.0 ± 5 0.8 ± 0.01 0.08 ± 0.002

29.9 ± 5 100 ± 12 15.6 ± 3 840 ± 1.94 8.65 ± 0.1 2.2 ± 0.25 280 ± 35 359 ± 16 50 ± 5 44 ± 5 8.9 ± 0.6 8±2 52 ± 3 58 ± 6 7.5 ± 1 42 ± 4 0.8 ± 0.01 0.08 ± 0.002

BOD − Biochemical oxygen demand, COD − Chemical oxygen demand, ND − Not determined, SAR − Sodium adsorption ratio, TN − total nitrogen, TSS − Total suspended solids.

by ammonium acetate extraction (Thomas, 1982). ESP was then calculated as the ratio of the latter to the former. Aggregate stability was determined using a modification of the laser diffraction (LD) technique, commonly used for particle size distribution (e.g., Eshel et al., 2004; Bardhan et al., 2016). A Malvern Mastersize 2000 (Malvern Instruments Ltd., Southborough, MA, USA) with a HeNe laser beam (633 nm wavelength) was used to measure particles in the range of 0.02–2000 μm. Calculation of the size distribution of the measured particles was with Malvern software V5.0, based on theory developed by Mie in 1908 (Wriedt, 2009). Individual soil samples (0.2–0.4 g) were transferred to the fluid module containing deionized water and equipped with a mechanical stirrer. The variation in the amount of soil transferred to the fluid module depended on the need to satisfy the light transmittance requirements of the laser analyzer (∼10–20% of light obscuration). The suspension was then subjected to 10 consecutive 1-min runs using stirring and pump speeds at levels that prevent, on the one hand, particles from settling out of the suspension and, on the other hand, the creation of air bubbles. Aggregate stability at the end of every 1 min run was estimated from the volume percent of the median size particle (d[0.5]); the greater the volume percent of the median aggregate, the more stable the aggregates.

was measured using an electrical conductivity meter (EC meter, Crison Instruments, S.A., Barcelona, Spain). Variables that are indicators of organic matter, chemical oxidation demnad (COD) and biochemical oxidation demand (BOD) were determined by a commercial service laboratory. Total suspended solids (TSS) were determined from turbidity measurements with a Dinko-D-110 (Dinko Instruments S.A., Barcelona, Spain) turbidity meter. The composition of the water of the TWW and FW treatments in all irrigation seasons are given in Table 2. 2.3. Soil sample analysis Soil samples were collected at the end of the irrigation seasons, 27 October 2013 and 6 October 2014. One set of soil samples per plot (four soil samples per irrigation treatment) was collected with a soil auger down to 150 cm soil depth (in spreads of 30 cm) inside the tree row and 30 cm away from the emitter. Soil samples were dried in a forced-air oven at 60 °C until the soil was dry and crushed to pass a 2-mm sieve. Mechanical composition of the soil sample was measured using a hydrometer (Gee and Bauder, 1986), available NO3–N and NH4–N concentrations were determined by soil extraction with 1 mol l−1 KCl (Keeney and Nelson, 1982), and available P by soil extraction with 0.5 mol l−1 NaHCO3 (Olsen and Sommers, 1982). The extracted NO3–N, NH4–N, and Olsen P were measured calorimetrically using the auto-analyzer described above. Soil solutions extracted with double-deionized water (soil:water ratio of 1:5) were shaken for 1 h, centrifuged for 5 min at 4000–4500 rpm and then filtered. The following variables were determined in the soil solution extract using the same equipment described above: EC, pH, K, Na, Ca, Mg and Cl. Manitol extraction was used for boron extraction (Lado et al., 2012). Cation exchange capacity (CEC) was measured with sodium acetate extraction (Rhoades, 1982) and exchangeable sodium

2.4. Leaf and root mineral analysis At the end of the summer, leaves and roots were sampled for chemical analyses. Samples were rinsed twice in tap water, once in HCl solution (1 mL l−1), and finally in double-distilled water. Samples were oven dried (65 °C), ground in a mill, weighed into 100 mg portions, and digested with 5 mL of nitric acid (65%) and a few drops of perchloric acid (60%) at 130 °C. Na and K were analyzed with a flame photometer as above. Boron concentrations were measured using inductively coupled plasma-atomic, described above. Cl ions were extracted 128

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Fig. 2. The effect of irrigation water quality on EC, Cl, Na, Ca, and Mg in the soil profile at the end of the first and second years, 27 October 2013 and 6 October 2014. Bars indicate two standard deviations. Table 3 Mean electrical conductivity (EC dS m−1), Na, Cl, B and Sodium absorption ratio (SAR) measured in water:soil extract (5:1) of field soil samples from October 2013 and 2014, and mean exchangeable sodium percentage (ESP) and aggregate stability (AS) measured in October 2014. variable

EC dS m-1

Na meq l−1

Cl mg kg−1

B mg kg−1

SAR meq l−1

ESP meq 100 g−1

AS

FW TWW Variable Water quality Year Block Water quality *Year

0.8 ± 0.1 1.2 ± 0.02 Probability of F 0.06 0.1 0.08 0.33

3 ± 0.3 3.7 ± 0.3

255 ± 67 401 ± 32

1.30 ± 0.01 1.25 ± 0.01

3.55 ± 0.2 4.90 ± 0.3

2.8 ± 0.5 5.9 ± 0.6

55 ± 1.3 50 ± 0.9

0.0003 0.2 0.45 0.3

0.01 0.9 0.25 0.02

0.02 0.6 0.007 0.1

0.04 0.007 0.6 0.91

0.025 – 0.0035 –

0.002 – 0.03 –

df 1 1 5 3

FW = Fresh water, TWW = Treated waste water. Data for the upper 60 cm of soil. Bold values indicate significant effect of water quality (FW vs TWW), block, year or interaction of water quality with year by three-way ANOVA (WQ, Year, Block) analysis.

129

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Fig. 3. Soil profile SAR and ESP as affected by irrigation water quality at the end of 2 years of irrigation with TWW and Fresh water; 6 October 2014. Bars indicate two standard deviations.

Stems were marked about 10 cm above the graft union in the beginning of the first season (May 2013). Trunk diameter at the mark was measured at the end of each irrigation season (September–October) using a digital caliper.

independently from the dry material using double distilled water and filtering, and measured using a chloridometer (chloride analyzer 926, Corning, Medfield, MA, USA). 2.5. Plant water uptake; sap flow

2.8. Yield and quality

Sap flow was monitored continuously with 2-cm long thermal dissipation probes in three trees in each plot, i.e. in 32 trees, using discontinuous and continuous heating methods alternated every other day. Thermal dissipation sensors were manufactured in our laboratory and connected to a multiplexer (Campbell Scientific, Model AM16/32) and a data-logger (CR1000). For details of construction, calibration and corrections for 2-cm long thermal dissipation the radial distribution of sap flux density and radial depths, see Paudel et al. (2015).

Fruit set and fruit load were determined from four secondary branches in the 2 central trees for each replicate (8 trees per treatment) from bloom to harvest. The branches were facing in the four directions and had basal diameters between 2 and 3 cm. Fruit set was calculated as the percentage of fruits with respect to the total number of flowers. Yield was assessed in 3 trees per plot, (2 × 6). Determinations included number of fruits per tree, total fresh weight and fruit distribution in commercial diameters (UNECE2009). Fruit quality was assessed each year for 20 randomly collected fruits per replication. Measurements included peel thickness, fruit weight (g/ fruit) and juice volume. Fifty milliliter of juice per fruit was used to assess internal fruit quality, including titratable acidity (TA), pH and soluble solid content. Titratable acidity was determined by titrating 10 mL of juice with 0.1 mol l−1 NaOH to pH 8.1 using an automatic titration system (AOAC 1984). pH was measured with a pH meter (Crison 507, Crison Instruments, S.A., Barcelona, Spain) and retractable soluble solid content (SSC, 0Brix) with a handheld refractometer (Atago N1, Tokyo, Japan).

2.6. Stem water potentials Midday stem water potential (Ψstem) was measured every fortnight at solar noon (12:00 h GMT) using a pressure chamber (model 3000; ARIMAD, M.R.C. ltd., Holon, Israel). Two mature, fully expanded leaves from the canopy and close to the trunk were taken for each replicate per treatment. The leaves were enclosed in polyethylene bags covered with aluminum foil at least 1 h before the measurement (McCutchan and Shackel, 1992). 2.7. Plant vegetative growth

2.9. Statistical design and analysis Root growth minirhizotron measurements; Root development with respect to soil depth and time was monitored with minirhizotrons (BTC100X, Bartz Technology Co., Santa Barbara, CA, USA). Clear acrylic tubes (three per tree at distances of 50, 100, and 150 cm from the trunk and three trees per plot with two replication plots) were installed along the dripper line adjacent to rows in January 2013. Tubes were 1 m long with an inside diameter of 5.8 cm and an outside diameter of 6.1 cm. Tubes were sealed at the bottom end with rubber stoppers. Tubes were installed vertically, i.e. perpendicular to the soil surface. Measurements were made every month during the irrigation period, and data (total number of roots and root length) were analyzed using Rootfly Software Version 2.0.2. Shoot length was measured every two weeks to one month. Three newly grown shoots were marked in each tree in April/May and circumferences measured with a tape during the irrigation period. Leaf area was measured each year in August and September. About ten leaves were sampled randomly from each tree of the plot from four blocks every tree and scanned (HP ScanJet 3970). Images were analyzed using ImageJ software.

The experimental design was randomized blocks. Analyses of Variance (ANOVA) were carried out with JMP 12.0 software (SAS Institute), and ANOVA was used to obtain an F value for significance in two or three-way linear models, with water quality, block and year and their interactions. Mean separations were performed on the basis of the Tukey-Kramer honestly significant difference (HSD) test at p = .05. A standardized principal component and classification analysis (PCCA) (Statistica, StatSoft, Inc, (2004), Dell, USA) was used to identify possible relationships between our tested soil indices and the tree parameters. The PCCA searches for linear combinations of variables and identifies a group of principal components (PCs) that account for maximum variance shared by the variables of the data set under study (Jolliffe, 2002). A decision whether a PC is worth considering for further analysis depends on the level of the eigenvalue associated with it; we examined only PCs with an eigenvalue > 1.0 as proposed by Brejda et al. (2000). Further classification of the tested variables is carried out by using factor loadings. The factor loadings are equivalent to the correlations between the variables and the components (since the 130

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3. Results 3.1. Irrigation water quality The most significant water quality parameters for TWW and FW are shown in Table 1. Differences between the two irrigation water sources were significant during the whole experiment. High values of organic matter (COD and BOD) and total suspended solids (TSS) were found in the secondary TWW. Salinity was higher in the TWW (EC ≈ 2.3) than in FW (EC ≈ 1.1 dS m−1). The main ions that contributed to the higher EC in TWW were Cl− and Na+, whereas the differences in Ca and Mg were smaller and inconsistent (Table 1). The higher Na concentration in TWW relative to FW without a parallel increase in Ca and Mg elevated the SAR level from a mean value of 3.4 in the FW to about 6.8 in TWW. In addition, TWW had high concentrations of the major nutrients, N (mainly as NH4+ and some as organic nitrogen), P and K and similar to the FW after injection of liquid fertilizers. The overall quantity of N, P and K supplied with the TWW met the citrus fertilization requirements. Boron concentrations were similar in both types of water and it’s concentration was well below 0.4 mg l−1, the threshold value for B toxicity for citrus (Grieve et al., 2012; Mass, 1990). Heavy metal concentrations were below thresholds in both sources of water (not shown). 3.2. Soil chemical and physical properties The effects of the irrigation water type on EC, pH, Cl, cations (Na, Ca and Mg) and the SAR in the solution of the soil profile are presented in Fig. 2 and Table 3. The pH, Cl and Na were higher in the top layers of the TWW than in the FW treatment and the differences between TWW and FW increased from the end of the first year (2013) to the end of the second year (2014) and decreased with depth. EC, Ca and Mg of the soil solution did not differ significantly between treatments. A trend of lower values of Ca and Mg concentrations in the TWW at 30 cm was found at the end of the second year of irrigation; consequently the SAR in TWW was significantly higher in FW from the upper soil down to 120 cm; the SAR in TWW and FW was in the range of 4.5-5.0 and 2.84.5, Respectively. EC and concentrations of Cl, Ca and Mg decreased from the upper layer downward to 120–150 cm depth, whereas the trend in Na concentration was in the opposite direction. Accumulation of salts in the upper soil is result of selective uptake of ions by the trees and the opposite distribution of Na is due to the higher preference of the cation exchange complex to Ca, Mg and K over Na. In the second year of the experiment the concentrations of dissolved organic carbon (DOC) and inorganic carbon (IC) in the top layers for TWW were significantly higher than those for FW. B concentrations in the soil profile were relatively high, despite the fact that B concentration in both water types was very low, < 0.1 mg l−1. The high B concentration in the soil is probably residual from a prior period when TWW contained more B, i.e. 0.5–1.0 mg l−1. B in the first year was similar in the two treatments and a reduction was found in the second year; the gap between the first and second year was larger in the FW than the TWW, probably due to displacement of sorbed B by higher concentrations of bicarbonates and orthophosphate in the TWW. However, in both water treatments the available B values were very close to each other and well above critical toxicity levels of 0.4 mg l−1 for citrus (Grieve et al., 2012; Mass, 1990). Two other soil properties, ESP, and AS (known to be affected by ESP), were measured only at the end of the irrigation season of the second year, 2014. The mean ESP of the top 0–60 cm depth was much higher for TWW than FW (Fig. 3, Table 3), while the AS at this soil depth was higher for FW (Fig. 4, Table 3). The largest difference in the ESP between TWW and FW was at 30–60 cm. A similar observation was noted by Levy et al. (2014a). AS at 0–30, 30–60 and 60–90 cm decreased with shaking time as expected. In all the measured depths and through the process of shaking, higher AS values were obtained from the soil irrigated with FW than with TWW (Fig. 4, Table 3).

Fig. 4. Aggregate stability (AS), expressed as median aggregate size (D[0.5]), as a function of leaching time with deionized water of samples from the soil profile as affected by irrigation water quality at the end of 2 years of irrigation with TWW and FW, October 2014. Bars indicate two standard deviations.

current analysis is based on the correlation matrix). Within a given PC, presence of variables having a factor loading close to ǀ1ǀ suggests that these variables share a large proportion of the variance represented by the PC, and hence the existence of some relationships among them. Only variables associated with a factor loading having an absolute value > 0.8 were considered for further analysis of their relations. The stepwise regression procedure of JMP 12.0 was used to obtain the best linear equation for the quantitative effects of the measured independent soil factors (EC, Na, Cl, SAR, ESP, AS of the 0–60 cm depth, 6 October 2014) on tree dependent variables (Fruits yield, trunk growth, shoot branches and root length growth).

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Fig. 5. Leaf (A, B and C) and root (D, E and F) mineral (Na, Cl, and B) composition after summer irrigation in 2013 and 2014. FW = Fresh water, TWW = Treated waste water. Different letters indicate significant differences between water qualities for each plant organ according to the Tukey-Kramer HSD test at p = .05. Bars indicate two standard deviations.

3.3. Elemental contents in leaves and roots

3.5. Plant vegetative growth

During the experimental period, concentrations of macro- and micro-nutrients (N, P, K, Ca, Mg, Fe and Mn) in the leaves were measured in all treatments and found to be sufficient with no significant effect of water quality (Table A1, Appendix in Supplementary material), whereas FW significantly reduced Na and Cl in leaves and roots relative to TWW irrigation (Fig. 5 and Table A1, Appendix in Supplementary material). Leaf and root B concentrations were not affected by water quality, in agreement with the low B concentrations in both TWW and FW. In general, leaf B was relatively high but below the critical value of 200 mg kg−1 for B toxicity in citrus (Storey and Walker, 1998).

Every year shoot and root length increased with time from the beginning to the end of the irrigation season and this increase was significantly higher for FW than TWW (Fig. 7). In general leaf area and trunk diameter for FW were higher than for TWW (Fig. 8A), although differences in trunk diameter growth were not significant (Fig. 8B). A significant increase in leaf area of 22% for FW was found in the second growing season (Fig. 8A). 3.6. Yield and fruit quality Several yield parameters (fruit set, fruit diameter, crop load, fruit weight and yield) measured during harvest in the fall after three irrigation periods are presented in Table 4. Overall fruit yield was higher for FW than TWW (Table 4 and Fig. 9). Treatment effects on fruit yield were not significant in the first two years, although fruit diameter and fruit weight were significantly affected (Table 4) and a significant effect on fruit yield was obtained from the third year onwards (Fig. 9). Fruit quality parameters like total soluble solids, titrateable acid content and maturity index (MI) were not affected by water quality, but peel thickness (PT) was in all three growing seasons (Table A2, Appendix in Supplementary material).

3.4. Stem water potential and water uptake Sap flow and mid-day stem water potential (Ψstem) were monitored from the beginning of the experiments (Fig. 6). Both were generally higher for FW than TWW. Significant differences in sap flow were observed after four months of irrigation, with small differences between treatments in winter (rainfall season without irrigation) and larger differences in summer (dry season with irrigation) (Fig. 6A). Similar effects of irrigation with FW versus TWW on mid-day stem water potential (Ψstem) were observed (Fig. 6B) and the effects of season on Ψstem were similar to those for sap flow.

4. Discussion The results show that irrigation with TWW influenced the whole 132

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Fig. 6. Plant water uptake (sap flow) (A), and mid-day stem water potential (B) in 2013 and 2014. FW = Fresh water, TWW = Treated waste water. Bars indicate two standard deviations.

be a result of impaired water transport and availabilty. Negative effects on soil physical properties could cause problems for sap flow in trees growing in different soils (Li et al., 2006). Several studies have shown that irrigation with TWW can damage soil structure and reduce porosity and hydraulic conductivity, mainly because of higher SAR in the TWW compared with FW (Assouline and Narkis, 2013; Bardhan et al., 2016; Levy et al., 1999; Levy and Assouline, 2010; Schacht and Marschner, 2015); thus, reduced plant performance and yield could be related to soil degradation (Assouline and Narkis, 2013; Assouline et al., 2015). In our study the SAR was determined in a relatively high water to soil ratio (5:1), hence its values (4.9 and 3.5 (meq l−1).5 for TWW and FW, respectively) were lower than those expected from the conventional water paste method. The ECe can be estimated using the function suggested by Slavich and Petterson (1993):

citrus orchard system from the soil through the root system, trunk, leaf growth and up to fruit yield. Use of the whole system approach allows comprehensive insight of the properties of TWW that negatively influence citrus trees physiology and yield directly and indirectly, as well as TWW effects on soil properties. We found negative responses of vegetative growth and fruit yield of citrus trees grown in clay soil in response to irrigation with TWW (Table 4 and Fig. 9). The negative effects of TWW irrigation in clay soil are in agreement with our hypothesis, as well as previous studies that reported reductions of fruit yield and quality of various orchard indices in response to irrigation with TWW in clayey soils, and decline and economic losses in commercial orchards related to TWW irrigation (Assouline and Narkis, 2013; Assouline et al., 2015; Azenkot et al., 2005; Pedrero et al., 2010, 2013). The fruit yield reduction in clayey soils was mainly due to a significant decrease in fruit set and a minor decrease in the fruit number, as found for saline TWW irrigated lemons (Garcı́a-Sánchez et al., 2002), although a decline in fresh fruit weight was also found (Table 4). Higher fruit yields for FW after three irrigation seasons followed significant early influences on sap flow, stem water potential and vegetative growth (Figs. 6–8). TWW significantly reduced leaf area, shoot and root growth rates on heavy clay soil (Figs. 6 and 7). Although other studies showed negative effects of irrigation with TWW on canopy growth (Morgan et al., 2008) no previous studies reported the influence of TWW on root growth, except our earlier report (Paudel et al., 2016b). The finding that Ψstem was lower for TWW is similar to previous studies (Gibberd et al., 2003; Paranychianakis et al., 2004; Walker et al., 2001). We previously reported lower leaf gas-exchange and higher photorespiration for TWW in a clay soil (Paudel et al., 2016a). The effects of water quality on plant water uptake and status could

ECe = EC1:5 (2.46 + 3.03/θsg)

(1)

For a clay soil the factor is 6.25 and the calculated ECe values in the field (based on Table 3) were 5.0 and 7.5 dS m−1 for the FW and TWW, respectively. Indeed, the ESP of the TWW irrigated clay soil reached a high value of ≈7.0 (Fig. 3). This high ESP was probably the main reason for the lower AS in the field TWW-irrigated soil (Fig. 4). The higher SAR and ESP and the lower AS in the field samples for TWW soil (Table 3, Figs. 3 and 4) suggest higher sensitivity of the TWW soil to clay swelling, poor structural stability, and reduced hydraulic conductivity (Bardhan et al., 2016; Bhardwaj et al., 2007). However, it has also been proposed that the degradation in soil phsical and hydraulic properties following irrigation with TWW can also be associated with the dissoved organic matter (DOM) added to the soil by TWW, which can alter the characteristics of the soil DOM and consequently 133

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Fig. 7. Shoot length growth (A), and root length growth (B) in 2013 and 2014. FW = Fresh water, TWW = Treated waste water. Bars indicate two standard deviations.

salt accumulation in the root zone of orchards or field crops irrigated with secondary TWW in a sandy soil, but this process was less effective in a clay soil (Ben-Hur, 2004; Lado et al., 2012). The high concentrations of Na and Cl in the solution of the soils irrigated with TWW could exceed toxic levels, induce toxic stress in the trees, and in turn could result in impaired plant growth and yield (Pedrero et al., 2013). The concentrations of Na and Cl observed in the TWW exceeded the thresholds for detrimental effects in citrus; Na > 115 mg l−1 (Bernstein et al., 1956), Cl > 238 mg l−1 (Ayers and Westcot, 1985), whereas B was much lower than the threshold value, B > 0.4 mg l−1 (Grieve et al., 2012). High concentrations of salts may induce nutrient deficiency through ion uptake competition. However, in the present research the concentrations of all nutrients in the leaves were within the optimum ranges recommended by the Israeli field service and Legaz et al. (1995). On the other hand, Na concentrations in the roots and leaves and Cl concentrations in the roots were significantly higher in the TWW irrigated trees. In general, leaf Cl concentrations were much lower than the critical level for citrus, 7 mg g−1, whereas the highest leaf Na concentrations under the TWW treatment were above the critical level for citrus, 2.3 mg g−1 (Raveh, 2012; Storey and Walker, 1998). Na concentrations in plant organs (root and leaf) were significantly correlated with those in the soil solution (result not

negatively affect soil HC (Levy et al., 2014b). Recently, Suarez and Gonzalez-Rubio (2017) also suggetsed that the presence of DOM in TWW is equivalent to an increase of two to three units in soil solution SAR. Degradation of soil physical and hydraulic properties following irrigation with TWW, may lead to reduced water uptake and lower plant water potential, especially in a clay soil (Assouline and Narkis, 2013; Assouline et al., 2015; Schacht and Marschner, 2015). Nevertheless, other findings support alternative mechanisms that could explain the reduction in tree performance and yield, i.e. lower soil water potential and specific toxicity of Na and Cl. Water quality can affect soil water potential directly via salt load (Paranychianakis et al., 2004; Nicolás et al., 2016; Pérez-Pastor et al., 2014) and indirectly by inefficent salt leaching out of the rizosphere caused by soil structural damage, reduction of AS and hydraulic conductivity (Bhardwaj et al., 2007; Russo et al., 2015). In our study, salt accumulation in the soil was well above the optimum threshold of ECe = 1.4 dS m−1 proposed for citrus (Maas, 1993; Raveh, 2012), where ECe is the EC measured in the saturated soil extract. In fact, Ayers and Westcot (1985) indicated that ECe values higher than 1.5 dS m−1 might lead to grapefruit yield losses of up to 25%. Previous studies reported that in regions with > 500 mm of annual rainfall, the precipitation is sufficient to prevent long-term 134

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Fig. 9. Fruit yield during the 2012–2015 period of field grown grapefruit trees irrigated with freshwater (FW) and treated wastewater (TWW). * indicates significant differences of TWW from FW for each year at P = .05 (Student's ttest). Bars indicate two standard deviations. Table 5 PCCA results: eigenvalues, percent of total variance and loadings of the PC’s for the variables tested.

Eigenvalue % of total variance Variables tested Yield/tree Trunk growth Shoot growth Root length Sap flow Na −leaves Cl − leaves Na − roots Cl − roots EC Na Cl SAR ESP Aggregate stability

Fig. 8. Leaf area of fully mature leaves (A and B), and trunk diameter growth (C and D) after summer irrigation in 2013 and 2014. FW = Fresh water and TWW = Treated waste water. * indicates significant differences from FW according to Tukey-Kramer HSD test at p = .05. Bars indicate two standard deviations.

shown). In citrus trees, it is known that salinity alters leaf mineral composition, but until now experiments with TWW irrigation in citrus have not reported leaf Na toxicity (Pedrero et al., 2013). The high concentration of Na in the roots might be related to the morphological alteration of the roots and their reduced hydraulic conductivity reported previously (Paudel et al., 2016a). Boron concentrations in the TWW in this study were not higher than in the FW due to Israeli legislation from 1999 banning B in detergents. Tarchitzky et al. (2004) reported that the mdian B concentration in treated TWW, as reported by the Ministry of Agriculture and Rural Development, decreased from 0.41 to 0.17 mg l−1 between the years 2000 and 2004. Therefore, in this study B in the trees was residual from long term irrigation with TWW containing high B concentrations of 0.5–1.0 mg l−1 (Azenkot et al., 2005; Lado et al., 2012) prior to the legislation. The high concentration of B in our clayey soil is due to the high B adsorption capacity and strong affinity to the clayey soil (Lado et al., 2012; Yermiyahu et al., 2010) and very slow process of B leaching (Yermiyahu et al., 2010). Consequently, although in the current experiments the TWW had very low concentrations of B, those in the

PC1

PC2

8.275 55.169 Factor loadings −0.468 −0.58 −0.807 −0.980 −0.676 0.883 0.964 0.968 0.987 0.329 0.546 0.303 0.872 0.194 −0.862

1.933 12.886 0.462 −0.024 −0.199 0.020 0.437 0.207 −0.181 −0.118 −0.055 0.849 0.527 −0.614 0.032 −0.028 −0.131

leaves were above the threshold of 100 mg kg−1 (Legaz et al., 1995), independent of the irrigation treatment. PCCA analysis was conducted to quantify the effects and interactions of the treatments on soil and tree variables. Four PC’s with eigenvalues > 1 were identified which cumulatively explained ≥85% of the total variance in the data set, with the first PC (PC1) explaining a high proportion (55.2%). Because factor loadings of PC3 and PC4 were all below |0.8|, only those for PC1 and PC2 are presented (Table 5). Examination of the high loadings (> |0.8|) under PC1 revealed a clear inverse association between AS and SAR as well as between AS, SAR and a few tree parameters (shoot and root growth and Na and Cl concentrations in leaves and roots). AS was directly associated with shoot

Table 4 Tree fruit set, yield and yield parameters in the experiment following FW and TWW irrigation. Fruit harvests of 2012–2017. Variable

Fruit set %

Fruit Diameter mm

Fruit weight g/fruit

Yield kg tree−1

Maturity Index

FW TWW ANOVA: F probability Variable Water quality Year Block Water quality*Year

7.3 ± 0.15 6.8 ± 0.18

106 ± 1.5 99 ± 2.0

309 ± 7 283 ± 8

105 ± 6 94 ± 4.5

6.6 ± 0.44 6.4 ± 0.20

0.24 0.1 0.12 0.78

0.001 0.007 0.99 0.27

0.003 0.0001 0.9 0.82

0.06 0.03 0.61 0.003

0.84 0.001 0.46 0.61

df 1 1 5

Bold values indicate significant effect of water quality (FW vs TWW), block, year or interaction of water quality with year by three-way ANOVA analysis (water quality, Year, Block). 135

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5. Conclusions

and root length and inversely with Na and Cl in leaves and roots (Table 5). The opposite applies to SAR. For PC2, only EC had a loading > 0.8 (Table 5), suggesting that PC2 is associated primarily soil solution salinity. The PCCA analysis (Table 5) highlighted the close association between soil and tree parameters. Root and shoot growth were directly related to AS, which indicates that AS could serve as an index for favorable soil conditions. Accumulation of Na and Cl in the roots and the leaves was directly associated with soil solution sodicity, which indicates a relative abandance of Na relative to Ca and Mg. The inverse relation between SAR and AS has been demonstrated in previous studies (e.g., Bhardwaj et al., 2007). The direct association between soil SAR and leaf and root Na probably results from increased Na uptake with increasing Na in the solution and the effect of major competing ions, Ca and Mg (Romero-Aranda et al., 1998). Concentration of Cl in the tree parts (leaves and roots) was associated with soil solution SAR and AS, but not with Cl concentration in the soil solution. These findings indicate that soil physical factors controlled Cl uptake, probably through combined uptake of Na with Cl, or through the effect of physical factors on the leaching of Cl. No attributes studied were closely associated with fruit yield even though it was increased by FW. Apparently more time would be needed for those associations to emerge. In order to quantify the relationships between tree variables and soil factors we conducted stepwise regressions for root growth, shoot growth, sap flow and fruit yield. Results showed that SAR explained 79.8% of the variance (p < 0.01) in root growth and AS increased r2 to 83.6% (p = .18). The obtained equation is: RootG = −0.185*SAR + 0.01*AS + 1.23

Treated wastewater is a valuable source for irrigation in semi-arid and arid regions, providing water and some nutrients (N, P, K) to crops. However, long term TWW irrigation in clay soil may have negative effects on trees performance and yield. The current study showed that the main negative effects of continuous irrigation of a clay soil with TWW, in comparison to FW, on the tree performance were associated with increased soil sodicity (SAR and ESP) and deterioration of soil structural stability as reflected by lower AS values. These findings support the hypothesis that the negative effect of irrigation with TWW is through damage to soil structure leading to reduced water uptake. An additional negative impact of irrigation with TWW is the accumulation of Na and Cl in roots and leaves of grapefruits to toxic levels. However, several physiological parameters, i.e. Ψstem, sap flow, leaf area, root and shoot length growth, and fruit yield, showed that replacing TWW with FW after many years of irrigation with TWW can reverse the negative effects on the trees. Our findings indicate that the positive effects of alternating poor quality water (TWW) with water of high quality (FW) occur in a relatively short time span, i.e. several months to two years, thus promoting the viability of this management practice. Acknowledgement The authors wish to thank Victor Lukyanov, Amit K Jaiswal, Dina Goldstein and Raneen Shawhana for technical assistance and Silvio Goldberg and Yaron Bertenstein from Kibbutz Mizra for maintenance of the orchard and assistance in conducting the field experiment. The project was funded by grant no. 301-0746-11 from the Chief Scientist of the Israeli Ministry of Agriculture and Rural Development. This financial support is gratefully acknowledged. This is contribution No. 60x/1x from the Agricultural Research Organization, Institute of Soil, Water and Environmental Sciences, Rishon LeZion, Israel.

(2)

For shoot growth AS explained 50.8% (p < 0.01). The obtained equation is: ShootG = 0.313*AS + 20.34

(3)

For sap flow the stepwise model included 3 soil variables (according to the order): Soil Cl, SAR and ESP explaining 26.9%, 45.9% and 57.1%, respectively. The p values were 0.044, 0.069 and 0.185, respectively. The obtained equation is:

Appendix A. Supplementary data

SapFlow = −0.019*Cl-2.209*SAR + 0.62*ESP + 48.6

References

Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.agwat.2018.04.006.

(4)

Stepwise regression for fruit yield as a function of soil variables gave low r2 (below 30%) and low significance for the soil variables. However, when the concentrations of Na and Cl in the leaves and roots were added a model with two variables was obtained:

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(5)Yield = −1.223*AS-29.687*LeafCl + 221.8 The first parameter, Leaf Cl, explained 27.9% of the variability and with the second parameter, AS, the model explained 48.4% of the variability with p values of 0.019 and 0.092, respectively. This equation indicates that at least part of the positive effect of replacing irrigation with TWW to FW on grapefruit yield is due to reduction of Cl concentration in the leaves, in support of the second mechanism described above. We are not familiar with previous studies of remediation of TWW damage by transition to FW. It might be expected that high ESP of the clay would prevent recovery of soil aggregate stability and hydraulic conductivity. Russo et al.’s (2015) simulations predicted that replacement of TWW with FW would reverse the negative effects of TWW irrigation on tree transpiration. The current study, together with our previous publications (Paudel et al., 2016a, 2016b) confirm the hypothesis (and simulation predictions) that replacing long-term (> 15 years) TWW irrigation with FW leads to recovery of most of soil and plant parameters.

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