Modeling Zucchini squash irrigation requirements in the Syrian Akkar region using the FAO56 dual-Kc approach

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Modeling Zucchini squash irrigation requirements in the Syrian Akkar region using the FAO56 dual-Kc approach Hanaa Darouicha,*, Razan Karfoulb, Haitham Eidb, Tiago B. Ramosc, Nisreen Baddourb, Ali Moustafab, Mahmoud I. Assaadb a Centro de Investigação em Agronomia, Alimentos, Ambiente e Paisagem (LEAF), Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017, Lisboa, Portugal b General Commission for Scientific Agriculture Research (GCSAR), Hejaz Station, Damascus, Syria c Centro de Ciência e Tecnologia do Ambiente e do Mar (MARETEC), Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001, Lisboa, Portugal

A R T I C LE I N FO

A B S T R A C T

Keywords: Evapotranspiration SIMDualKc Soil water balance Water productivity

The coastal area of the Syrian Akkar region is particularly suitable for intensive vegetable production, and Zucchini squash (Curcubita pepo L.) is one of the most profitable crops grown there. Local agricultural policies have been especially concerned at improving irrigation water management in the region by rationalizing water and nutrient use, modernizing irrigation methods, and minimizing the associated environmental risks. The objective of this study was to compute the soil water balance in Zucchini squash plots irrigated with furrow and drip methods using the SIMDualKc model during four growing seasons (2012–2015). The furrow plots (treatment T0) represented the traditional irrigation practices in the study area while the drip plots (treatments T1 and T2) followed alternative irrigation schemes. The SIMDualKc model was able to simulate soil water contents measured in the field plots, returning root mean square error values lower than 0.002 m3 m−3 and modelling efficiencies ranging from 0.166 to 0.732. The basal (non-stressed) crop coefficients (Kcb) varied from 0.18 to 0.22, 0.85 to 0.98, and 0.56 to 0.71 during the initial, mid-season, and end-season stages, respectively, with the actual values (Kcb act) often matching the potential ones. The evaporation coefficients (Ke) varied according to irrigation schedules and methods during the early crop stages, but were minimized with full canopy development. Drip T1 presented always the highest water productivity (WP) indicators due to the higher frequency of irrigation events, with less water applied per event. In contrast, furrow T0 resulted in substantial percolation losses, which affected yields and WP. Hence, model results evidenced the benefits of precise irrigation for Zucchini squash production in the Syrian Akkar region, further enhancing the need for sustainable water management practices in local production systems.

1. Introduction The cultivated land in the fertile Syrian coastal area has been steadily expanding over the years due to several irrigation projects that improved water availability in the region (Chard, 1981; Amery, 2002). Irrigation now covers 29 % (65,500 ha) of the cultivated coastal land (226,000 ha; 3.7 % of the Syrian cultivated land), with surface systems being traditionally adopted (CBS, 2017). While these systems can be regarded as very efficient if land is precisely levelled and water is applied uniformly (Pereira et al., 2002; Darouich et al., 2014), the characteristics of small size local farming systems (1.3 ha in average) have limited its correct management, resulting in low water use efficiencies. As a result, modern methods such as drip irrigation have been slowly

⁎

replacing the traditional systems, covering now close to 43 % (29,000 ha) of the irrigated area (CBS, 2017). Drip irrigation methods, by delivering small and precise water amounts with relative high frequency, are especially suited to field and greenhouse-grown vegetables, which together with citrus are the main intensive irrigated farming systems in the Syrian coastal area (Wattenbach, 2006). The most prominent and profitable vegetables are tomato (Solanum lycopersicum L.), cucumber (Cucumis sativus L.), squash (Cucurbita pepo L.), and eggplant (Solanum melongena L.). However, the estimated production area is associated with a certain uncertainly as these plants are often included in double cropping systems or partial cultivated under young new tree orchards. Despite the small size of local farming systems, vegetables

Corresponding author at: LEAF, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017, Lisboa, Portugal. E-mail address: [email protected] (H. Darouich).

https://doi.org/10.1016/j.agwat.2019.105927 Received 21 July 2019; Received in revised form 7 November 2019; Accepted 12 November 2019 0378-3774/ © 2019 Elsevier B.V. All rights reserved.

Please cite this article as: Hanaa Darouich, et al., Agricultural Water Management, https://doi.org/10.1016/j.agwat.2019.105927

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Fig. 1. Location of the Zahid experimental station, Akkar plain region, Syria.

adopt the FAO56 single crop coefficient (Kc) approach (Allen et al., 1998), which provides acceptable estimates of crop evapotranspiration (ETc) rates. On the other hand, the FAO56 dual Kc approach, which is less applied, allows for the separate estimation of crop transpiration and soil evaporation rates through the partitioning of ETc (Allen et al., 1998). This approach is also usually considered more precise when estimating irrigation needs on a daily basis, particularly when dealing with the impacts of soil wetting by rain or irrigation in a partially covered soil, or when high frequency events take place (Allen et al., 2005). The dual Kc approach has been implemented in the SIMDualKc model (Rosa et al., 2012a), which considers also crop (density, height, ground cover), irrigation (methods and scheduling), and management (active ground cover, mulching, intercropping) factors that may further affect ETc. The model has been applied with success for wheat, maize, tomato, pepper, and soybean in Northern China (Zhao et al., 2013; Zhang et al., 2013, 2018; Wei et al., 2014; Qiu et al., 2015); maize in southern Brazil (Martins et al., 2013); wheat in northern Syria (Rosa et al., 2012b); cotton in Uzbekistan (Rosa et al., 2012b); potato in southern Italy (Paredes et al., 2018); and maize, peach, and olive in southern Portugal (Paço et al., 2012, 2019; Rosa et al., 2016). As a result, the SIMDualKc model may well constitute an important reference tool for improving irrigation water management in vegetable crops grown in the Syrian coastal area. However, model applications focused on vegetable production have been quite limited so far and additional research is required. Hence, the objectives of this study are: (i) to simulate soil water contents in Zucchini squash (Cucurbita pepo L.) plots irrigated with furrow and drip methods using the SIMDualKc model during four growing seasons (2012–2015); (ii) to estimate the daily ETc rates and

production contributes to the high share of very profitable crops grown in the region, taking advantage of market opportunities with the proximity of highly-dense urban areas as well as very favorable environmental and climate conditions. The major production risks are associated with the price return of perishable crops (Wattenbach, 2006), while the greatest environmental risks are the over-fertilization of crops and consequent pollution of groundwater supplies with high concentrations of nitrate (Halwani et al., 1999; Abou Zakhem and Hafez, 2007; Kattaa et al., 2010). Improving land and water productivity in the Syrian coastal agricultural area have thus become a top priority for the environmental protection of groundwater and downstream water bodies (Abou Zakhem and Hafez, 2007; Kattaa et al., 2010; Thomas et al., 2003). Several measures were implemented in the region as well as in other agricultural areas in Syria with the aim of rationalizing agricultural water use, improving irrigation systems performance, increasing farmers income, modernizing irrigation methods and techniques (e.g. by converting the traditional surface systems to drip), and improving fertigation techniques (Varela-Ortega and Sagardoy, 2003; Darouich et al., 2012; Fader et al., 2016; Mourad and Alshihabi, 2016; Abou Zakhem et al., 2019). This means that crop water requirements need to be properly assessed as well as the most suitable irrigation schedules (irrigation timing, length, and quantity) need to be accurately defined for improving irrigation water use, maximizing yields, and minimizing the associated environmental risks. Modeling can play here a fundamental role in complying with such objectives. Calibrated crop water simulation models are now seen as suitable tools for computing crop water requirements and irrigation scheduling (Raes, 2002; Pereira et al., 2003; Domínguez et al., 2011; Rosa et al., 2012a; Vanuytrecht et al., 2014). Most of these models 2

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Irrigation in drip T1 aimed to fulfil crop water needs and maximize water productivity indicators (section 2.3). Irrigation in drip T2 aimed to produce percolation losses by exceeding the available soil water storage capacity. Irrigation depth and frequency constituted thus the major variables between treatments. These plots included 3 laterals placed along the 3 plant rows. In-line drippers were spaced 0.5 m, with a discharge rate of 8 L h−1 at 100 kPa. Irrigation in drip T1 was triggered when soil water depletion reached 25%–42% of the readily available water (RAW) depending on the crop stage. Water was then applied to fulfill 25 % and 50 % of RAW for the initial and remaining crop stages, respectively. Thus, drip T1 irrigation registered a relatively high frequency (3–6 days), and small irrigation depths per event (11–18 mm) (Table 3). Irrigation in drip T2 was triggered when soil water depletion reached 34%–65% of RAW, also depending on the crop stage. Water was applied to fulfill 70 % and 120 % of RAW for the initial and remaining crop stages, respectively. So, irrigation frequency in drip T2 was slightly lower (5–12 days) than in the previous treatment, with higher amounts applied per event (27–45 mm) (Table 3). Every year, the Zucchini squash, which variety is also referred to as Cousa squash, was planted in a nursery between January 10th and 20th, and then transplanted to the experimental site in March. The dates of the crop growth stages in each season are documented in Table 2. Manure (30 Mg ha−1) was applied once before plantation. Besides that, fertilization was applied in three batches along the growing season, with N, P, and K units summing 120, 60, and 90 kg ha−1. Weed control was performed manually. Soil water contents were measured using a neutron probe, with access tubes installed in the middle of the second row of each plot, in the four replicates, until a depth of 0.45 m. Crop height (h) and mean diameter were also monitored using a tape during each stage of the different growing seasons. The fraction of ground cover (fc) was then estimated from the crop’s mean diameter (assuming a round shape of the plant’s canopy) and plant’s spacing. Fruit were collected in all replicates at the end of each growing season, and the fresh weight was recorded.

respective crop coefficients for Zucchini squash grown in the Akkar plain (Syria) using the dual Kc approach; and (iii) to analyze the soil water balance from a water saving and economic perspective. Results of this study will help to improve irrigation water use efficiency in the Syrian agricultural coastal area, contributing to a better assessment of vegetables irrigation needs in the region. 2. Material and methods 2.1. Field experiment 2.1.1. Site description The field experiment was carried out at the Zahid research center (34°41′37″N, 35°59′16″E, 12 m a.s.l.), in the western part of the Akkar plain, Tartus governorate, Syria, from April 2012 to June 2015 (Fig. 1). The Akkar Plain is located on a narrow coastal area of fertile land between the cities of Trípoli (Lebanon) and Tartus (Syria). Several rivers cross the region, including the Al-Abrash River, limiting the territory to the north; the Al-Janoubi River and its tributaries Kalife and Abo Falat, along the Syrian-Lebanon border; the Bared River, limiting the territory to the south; and the Ostouene River, situated in between the previous two rivers (Thomas et al., 2003). Several wells provide for additional water resources, which are used for irrigation of 6400 ha of agricultural land in Latakia and 11,700 ha in Tartus (CBS, 2017). The groundwater table depth varies between 10 and 20 m (Abou Zakhem and Hafez, 2007). The climate in the region is classified as Hot-summer Mediterranean climate (Csa). The mean annual surface air temperature is 19.3 °C, with the mean daily temperatures at the coolest (January) and warmest (August) months reaching 11.5 and 27.0 °C, respectively. The mean annual precipitation is 930 mm, occurring mostly between October and May, while the mean annual reference evapotranspiration is 1300 mm. Fig. 2 presents the meteorological conditions observed at the local weather station during the four experimental seasons. The available information includes daily values of maximum and minimum surface air temperatures (Tmin and Tmax, ºC), wind speed (u2, m s−1), minimum relative humidity (RHmin, %), sunshine hours (Isun, h), and rainfall (P, mm). The soil in the study area was classified as a Vertisol (IUSS Working Group, 2014), with the main soil physical and chemical properties presented in Table 1.

2.2. Modeling approach 2.2.1. Model description The SIMDualKc model (Rosa et al., 2012a) computes the soil water balance at the plot scale on a daily basis as follows:

2.1.2. Experimental design and treatments The experiment involved irrigation of Zucchini squash (Cucurbita pepo L. cv. Magda Hybrid Squash) with different methods (furrow and drip) and schedules during four growing seasons (2012–2015). The experimental area (35 m × 20.5 m) was relatively flat, with 0.005 % and 0.002 % slope in the west and south directions, respectively. A Randomized Complete Block Design was established, with three treatments (furrow T0; drip T1; drip T2) and four replicates per treatment (Fig. 3). Each plot size (replicate) was 2.25 m × 15 m and included three cultivated rows with 0.75 m spacing between rows and 0.40 m between plants (about 33,333 plants ha−1). Non-irrigated areas distanced each plot by 1 m. The irrigation period lasted from plant transplantation to harvest (Table 2). The water was conveyed from a well to the field by a PVC mainline and distributed to three polyethylene manifold pipes (Fig. 3). A drainage network was set at 1.25–1.75 m depth, with drain pipes distancing 15–25 m. The furrow T0 treatment represented the traditional irrigation scheme followed by farmers in the region. The furrow basin (3 furrows per replicate distanced 0.75 m between each other) was supplied by one pipe with a discharge of approximately 5 m3 h−1. Irrigation was applied with an elapsed time of 0.5 h, i.e. the average time for water to reach the end of the furrow basin. Table 3 presents the furrow irrigation amounts, which were applied every 6–30 days depending on weather conditions. The drip T1 and T2 treatments considered different schedules.

Dr,i = Dr,i−1 − (P− RO)i − Ii − CRi + ETc act,i + DPi

(1)

in which Dr is the root zone depletion (mm), P is the rainfall (mm), RO is the runoff (mm), I is the net irrigation depth (mm), CR is the capillary rise from the groundwater table (mm), ETc act is the actual crop evapotranspiration (mm), and DP is the deep percolation from the root zone (mm), all referring to day i or day i-1. Crop evapotranspiration is computed according to the FAO56 dual Kc approach (Allen et al., 1998, 2005), thus estimating separately the components related to crop transpiration (Tc, mm) and soil evaporation (Es, mm):

Tc = K cb ETo

(2)

Es = K e ETo

(3)

where Kcb is the potential basal crop coefficient, Ke is the evaporation coefficient, and ETo is the reference evapotranspiration (mm). Actual transpiration values (Tc act, mm) are obtained by introducing a dimensionless stress coefficient (Ks) into Eq. (2) to account for the effect of soil water depletion on Tc values:

Tc act = K s Tc = K s K cb ETo = K cb act ETo

(4)

where Kcb act is the actual basal crop coefficient, and Ks (with varying values from 0 to 1) is defined as follows: 3

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Fig. 2. Daily maximum (Tmax, °C) and minimum (Tmin, °C) air temperatures, minimum relative humidity (RHmin, %), number of sunshine hours (Isun, h), wind speed (WS, m s−1), precipitation (mm), and reference evapotranspiration (ETo, mm).

Ks =

TAW − Dr,i TAW − RAW

relative humidity (RHmin) differs from 45 %, and/or when the average wind speed differs from 2 m s−1 (Allen et al., 1998). The Kcb values are further adjusted to crop characteristics (density and height) using a density coefficient (Kd), which accounts for the increase in Kcb with the increase in the amount of vegetation (Allen and Pereira, 2009). Soil evaporation is limited by the amount of energy available at the soil surface in conjunction with the energy consumed by transpiration, and by water availability in the surface soil layer (Allen et al., 1998, 2005). Es is thus maximum during the early crop stages when the topsoil is wetted by rain or irrigation and energy is largely available at the soil surface. Contrarily, Es is minimum when the crop fully shadows the soil, limiting the energy available for evaporation, and/or when the surface soil layer is dry. Accordingly, the evaporation coefficient (Ke) in Eq. (3) is computed as follows (Allen et al., 2005):

(5)

in which TAW and RAW are respectively the total and readily available soil water relative to the rooting depth (mm). These are given by:

TAW= 1000 Z r (θFC − θ WP )

(6)

RAW= p TAW

(7)

where θFC and θWP are the soil water contents at field capacity and the wilting point (m3 m−3), respectively, Zr is the root depth (m), and p is the depletion fraction for no stress. When soil depletion exceeds the depletion fraction for no stress, i.e., the soil water content drops below RAW, Tc values are reduced due to water stress (Ks < 1.0); otherwise, Ks = 1.0 and no water stress occurs. Additionally, the model internally corrects the Kcb values to local climatic conditions when the minimum Table 1 Main soil properties of the Zahid experimental station. Depth (cm)

0-15 15-30 30-45 45-60 60-75 75-90

Soil texture (%)

Bulk Density (g cm−3)

Sand (2.00.05 mm)

Silt (0.050.002 mm)

Clay (< 0.002 mm)

20 20 24 22 22 22

28 30 30 28 28 28

52 50 46 50 50 50

1.24 1.25 1.30 1.43 1.41 1.40

Organic Matter (%)

2.41 2.23 1.49 – – –

4

Water content

Total Available Water (mm)

Soil Saturation (vol. %)

Field Capacity (vol. %)

Wilting Point (vol. %)

52.0 52.0 51.0 47.0 48.0 49.0

44.8 44.4 42.8 40.9 41.3 41.4

23.0 24.2 24.2 29.8 29.2 29.0

32.8 30.3 27.9 16.6 18.2 18.5

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Fig. 3. Schematic layout of the experimental plots. Table 2 Zucchini squash growth stages, and respective crop height (h), root depth (Zr) and fraction of ground cover (fc). Year

2012 2013 2014 2015 h (m) Zr (m) fc (-)

Crop growth stages

Kr =

Rapid growth

Mid-season

Harvest

01 April 10 March 1 March 1 March 0.20 0.45 0.05

11 April 21 March 10 March 10 March 0.20 0.45 0.40

06 May 01 April 01 April 05 April 0.55 0.45 0.75

01 June 11 June 21 May 01 June 0.55 0.45 0.99

61 93 81 92 – – –

K e = Kr (K c max − K cb min) ≤ f ew K c max

(10)

where TEW is the maximum depth of water that can be evaporated from the evaporation soil layer when it has been completely wetted (mm), REW is the depth of water that can be easily evaporated without water availability restrictions (mm), and De is the evaporation layer depletion at the end of day i−1 (mm). De is computed through a daily water balance of the evaporation soil layer, with the evaporation decreasing as the evaporable soil water decreases in the evaporation soil layer beyond REW. Finally, for the remaining parameters in Eq. (1): DP is computed using the parametric time decay function proposed by Liu et al. (2006), which relates the soil water storage above field capacity with the draining time until θFC is reached:

Total days

Initial

TEW − De,i−1 forDe,i-l >REW TEW − REW

(8)

Wa = aD t bD in which Kr is the evaporation reduction coefficient with varying values from 0 to 1, Kc max is the maximum value of Kc (i.e., Kcb + Ke) following a rain or irrigation event, and few is the fraction of the soil that is both exposed to radiation and wetted by rain or irrigation. The Kr is calculated using the two-stage drying cycle approach where the first stage is the energy limited stage, and the second is the water limited stage (Ritchie, 1972; Allen et al., 1998, 2005):

Kr = 1 forDe,i-1≤REW

(11)

where Wa is the actual soil water storage in the root zone (mm), aD is the soil water storage comprised between saturation and field capacity (mm), bD is an empirical dimensionless parameter, and t is the time after an irrigation or rain that produces a storage above field capacity (days); RO is estimated using the curve number approach proposed by the USDA-SCS (1972); CR is not considered in this study as the experimental area was limited downwards by a drainage system.

(9)

2.2.2. Model setup The following data were used as inputs to the SIMDualKc model: (i)

Table 3 Irrigation treatments. Year

2012 2013 2014 2015

Furrow T0

Drip T1

Drip T2

Nr. events

Depth per event (mm)

Total (mm)

Nr. events

Depth per event (mm)

Total (mm)

Nr. events

Depth per event (mm)

Total (mm)

7 6 7 10

74 74 74 74

518 444 518 740

15 17 18 21

11 11 11 11

273 275 273 321

11 9 11 16

28 30 28 27

402 351 417 612

-

18 17 17 17

5

-

44 44 45 44

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determination (R2), the root mean square error (RMSE), the ratio of the RMSE to the standard deviation of observed data (NRMSE), the percent bias of estimation (PBIAS), and the modeling efficiency (NSE), respectively given as (Loague and Green, 1991; Legates and McCabe, 1999; Moriasi et al., 2007):

Table 4 Default and calibrated model parameters. Parameters

Default values

Calibrated values

Kcb ini Kcb mid Kcb end p ini p mid p end TEW (mm) REW (mm) Ze (m) aD (mm) bD (-) CN (-)

0.15 0.90 0.70 0.50 0.50 0.50 31 8 0.10 397 −0.0173 85

0.20 – 0.30 0.90 – 1.00 0.61 – 0.74 0.60 0.50 0.50 31 8 0.10 400 −0.02 85

n

b0 =

R2 =

∑i= 1 Oi Pi n

∑i= 1 O2i

(12)

2 n ¯ )(Pi − P¯) ∑i= 1 (Oi − O ⎫ ⎧ n n 0.5 0.5 ¯ )2] [∑ (Pi − P¯)2] ⎬ ⎨ [∑ (Oi − O i= 1 ⎭ ⎩ i= 1

(13)

n

∑i= 1 (Oi − Pi )2

RMSE =

Kcb, basal crop coefficient for the initial (Kcb ini), mid-season (Kcb mid), and end season (Kcb end) stages; p, depletion fraction during the initial (pini), mid-season (pmid), and end season (pend) stages; TEW, total evaporable water; REW, readily evaporable water; Ze, depth of the soil evaporation layer; aD and bD, parameters of the deep percolation; CN, curve number.

RMSE

NRMSE=

n

∑i= 1 (Oi − Pi )2

PBIAS= 100

daily values of Tmin and Tmax (ºC), u2 (m s−1), RHmin (%), Isun (h), and P (mm) measured in a local weather station during the 2012–2015 growing seasons (Fig. 2); (ii) the particle size distribution (%) measured with a hydrometer according to the USDA classification limits (Soil Survey Staff, 2011); (iii) the θFC (m3 m-3) and θWP (m3 m-3) values measured on undisturbed soil samples of 100 cm3 collected in different soil layers using the pressure plate apparatus (Dane and Hopmans, 2002) (Table 1); (iv) the depth of the evaporation layer (Ze, 0.1 m), and respective values of TEW (31 mm) and REW (8 mm) estimated according to the soil texture and water holding characteristics of the soil evaporation layer (Allen et al., 2005) (Table 4); (v) the initial (measured) soil water depletion in both the root zone (0–30 % of TAW) and the evaporation soil layer (0–30 % of TEW); (vi) the observed dates defining the different crop stages during the 2012–2015 growing seasons (Table 2); (vii) the default basal crop coefficients (Kcb) for the initial, mid-season, and end-season stages (Table 4; Allen et al., 1998); (viii) the default soil water depletion fraction for no stress (p) for the same crop stages (Table 4 ; Allen et al., 1998); (ix) the measured root depth (Zr = 0.45 m) and plant height (h, m), and the estimated fraction of ground cover (fc), also for all crop growth stages (Table 2); (x) the crop row orientation (east-west) and width (0.4 m); (xi) the multiplier value (ML = 1.7) on the effective fraction of soil covered or shaded by vegetation (fc eff) to account for the effect of canopy density on Tc values (Allen and Pereira, 2009); (xii) the default deep percolation parameters (aD =400 mm, bD = -0.020) relative to the parametric equation of Liu et al. (2006) (Table 4); (xiii) the curve number (CN = 85) corresponding to a fine textured soil covered with vegetables (Rosa et al., 2012a); (xiv) the data referring to irrigation dates and applied depths; and (xv) the measured fraction of the soil surface wetted by irrigation (fw = 0.40 for drip and fw = 1.00 for furrow irrigation).

(14)

n−1

n ∑i= 1

× 100 (15)

(Oi − Pi) n

∑i= 1 Oi

(16)

n

NSE= 1 −

∑i= 1 (Oi − Pi )2 n ¯ )2 ∑ (Oi − O i= 1

(17)

where Oi and Pi are respectively the observed and model predicted ¯ and P¯ are the respective mean values, and n is the values at time i, O number of observations. b0 values close to 1 indicate that the predicted values are statistically close to the observed ones. R2 values close to 1 indicate that the model explains well the variance of observations. RMSE and NRMSE values close to zero indicate small estimation errors and good model predictions (Legates and McCabe, 1999). PBIAS values close to zero indicate that model simulations are accurate, while positive or negative values indicate under- or over-estimation bias, respectively. NSE values close to 1 indicate that the residuals’ variance is much smaller than the observed data variance, hence the model predictions are good. On the contrary, if NSE is less than zero the modelpredicted values are worse than simply using the observed mean (Nash and Sutcliffe, 1970). 2.3. Water productivity indicators The experimental results were further assessed using the water productivity indicators described in Pereira et al. (2009, 2012). These include the water productivity relative to the total water used (WPWU, kg m−3), the irrigation water applied (WPirrig, kg m−3), the consumptive use (WPET, kg m−3), and transpiration (WPT, kg m−3), respectively given as:

2.2.3. Model calibration and validation The SIMDualKc model was calibrated during the 2012 growing season and validated during the following years (2013–2015). Calibrated procedures consisted of adjusting model parameters one at a time until deviations between measured and simulated soil water contents were minimized (Pereira et al., 2015). A trial and error procedure was adopted. The default Kcb and p values relative to the initial, midseason, and end-season crop stages were first adjusted. Then, the deep percolation parameters aD and bD were modified. Finally, the Kcb and p values relative to the different crop stages were again adjusted until errors were minimized. The soil evaporation parameters were found to provide reasonable results and were not modified. The model performance was evaluated using various goodness-of-fit indicators, including the regression coefficient (b0), the coefficient of

WPWU =

Ya TWU

(18)

WPirrig =

Ya IWU

(19)

WPET =

Ya ETc act

(20)

WPT =

Ya Tc act

(21)

where Ya is the actual (measured) yield (kg ha−1), TWU is the total water used given by the sum of effective P, I, and the variation of the soil water storage (ΔSW; m3 ha−1), IWU is the irrigation water use given by I (m3 ha−1), ETc act is the estimated actual evapotranspiration (m3 ha−1), and Tc act is again the estimated actual crop transpiration (m3 ha−1). The economic water productivity (EWP, $ m−3) and the economic water productivity ratio (EWPR) were also computed as follows 6

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Fig. 4. Measured and simulated soil water contents in the root zone (0.45 m) in furrow (T0) and drip (T1, T2) irrigation treatments during the 2012–2015 growing seasons.

remaining close to field capacity due to the higher irrigation frequency (15–21 events) and lower depths (11−18 mm) applied per event (Table 3). Finally, measured and simulated soil water contents in drip T2 were in-between the previous values due to the adopted irrigation schedule (Table 3). The SIMDualKc model was able to reproduce field measurements reasonably well during the 2012 growing season (Table 5), returning higher R2 values for drip T1 and T2 (R2 = 0.542-0.669) than for furrow T0 (R2 = 0.335). The error of the estimate was quite small, resulting in RMSE values lower than 0.002 mm for all treatments. However, the NRMSE value revealed the lower accuracy of model simulations for furrow T0 (NRMSE = 25.1 %). Both, the b0 and PBIAS values indicated a small under prediction of observations by the model in furrow T0 and drip T1, while observations were slightly overpredicted in drip T2. Finally, the NSE value was found to be again lower for furrow T0 (NSE = 0.166) and higher for drip T2 (NSE = 0.662). The parameters calibrated during the 2012 growing season (Table 4) were validated during the following years (2013–2015), with the goodness-of-fit indicators generally improving when compared with the earlier year (Table 5). The R2 values varied from 0.554 to 0.810, which confirmed that the model could explain most of the variability of the observed data in the different treatments. The error of the estimate was again quite small, with the RMSE values matching those obtained during calibration, but this time also the normalized indicator (NRMSE) resulted in values always lower than 13.3 %. The b0 and PBIAS values showed a tendency for the predicted values to be very close to observations, with small exceptions (e.g., drip T2 during the 2013 growing season). Also, the NSE values resulted in relatively high values ranging from 0.467 to 0.732, which indicate that the residual variance was much smaller than the measured data variance. Overall, the general good agreement between simulated and

(Pereira et al., 2009, 2012):

EWP=

Value(Ya) TWU

EWPR=

Value(Ya) Cost(TWU)

(22)

(23)

where Value(Ya) is the value of the achieved yield ($), and Cost(TWU) is the cost of the irrigation water ($). The EWPR shows the impacts of water prices on the economic return of irrigation. The yield value was here set to 0.35 $ kg−1 (MAAR, 2017), corresponding to an inflation of the 2010 price multiplied by 10 as a result of speculation with the ongoing war in Syria. The price of the irrigation water was set to 0.055 $ m-3 (MAAR, 2017), increasing only 2.5 times as the product is supported/supplied by the government. 3. Results and discussion 3.1. Soil water contents Fig. 4 shows the daily averages of the soil water contents measured in the root zone layer of each experimental treatment during the 2012, 2013, 2014, and 2015 growing seasons, and compares these values with the SIMDualKc simulations. Irrigation events and amounts are also given in the same figure. The furrow T0 treatment registered less irrigation events (6–10 events) and larger irrigation depths (74 mm) per event (Table 3). Measured and simulated soil water content values showed there the largest variations amongst treatments, reaching values close to saturation immediately after irrigation events and then decreasing to values below field capacity due to water redistribution, soil evaporation, and crop transpiration. Drip T1 exhibited a contrasting behavior, with measured and simulated soil water contents 7

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Table 5 Goodness-of-fit indicators relative to calibration (2012) and validation (2013–2015) of soil water content simulations. Year

Irrigation method

b0 (-)

R2 (-)

RMSE (m3 m−3)

NRMSE (%)

PBIAS (%)

NSE (-)

2012

Furrow T0 Drip T1 Drip T2 Furrow T0 Drip T1 Drip T2 Furrow T0 Drip T1 Drip T2 Furrow T0 Drip T1 Drip T2

0.988 0.985 1.004 1.016 0.994 1.023 1.016 1.002 0.998 0.993 1.001 1.002

0.335 0.542 0.669 0.696 0.678 0.810 0.765 0.554 0.617 0.611 0.749 0.608

0.000 0.001 0.002 0.002 0.000 0.001 0.002 0.001 0.002 0.002 0.000 0.002

25.1 13.9 9.4 10.6 8.1 7.5 7.2 11.4 10.1 10.3 5.6 13.3

1.157 1.399 −0.684 −1.643 0.593 −2.453 −1.872 −0.228 0.038 0.452 −0.164 −0.449

0.166 0.443 0.662 0.590 0.646 0.720 0.732 0.516 0.608 0.601 0.730 0.467

2013

2014

2015

b, regression coefficient; R2, coefficient of determination; RMSE, root mean square error; NRMSE, ratio of the RMSE to the standard deviation of observed data; PBIAS, percent bias; NSE, model efficiency.

during 2012, 2013, and 2014 for furrow T0, and during 2012 for drip T1 (Fig. 5). In furrow T0, the longest water stress period was registered during the mid-season stage of 2013, with the Kcb values being reduced several times to a minimum of 0.54, equaling again the potential values with the increase of soil water contents after the following irrigation or rain events. The Ke values showed a decreasing trend from sowing to the beginning of the mid-season stage (Fig. 5), reaching then values close to zero with full canopy development when less energy was available at the soil surface for evaporation. Nonetheless, the Ke values were subjected to large variability dictated by the occurrence of rainfall or irrigation events. This was particularly notorious during the 2012 growing season when the mid-season stage was reached at a later date compared with other seasons. Finally, the Kc act curve naturally described the same trends observed for Kcb act and Ke. Yet, Fig. 5 clearly shows that the drop of Ke values during the crop development stages were somehow compensated by the increase of the Kcb act values, maintaining thus the Kc act values relatively high. Note that the Kc act curve for the Syrian Akkar Plain found some correspondence with the one presented by Mohammad (2004) for summer squash grown in Irbid, Jordan. This author reported Kc values of 0.71 and 0.60 for the mid-season and end-season stages, respectively, which were lower than the ones found in this study but comparable nevertheless. However, for the initial season stage, the Kc value reported there (Kc = 0.24) was considerably lower, approaching only the Kcb values found in this study (i.e., without considering the evaporation component). Likewise, Amer (2011) reported Kc act values for summer squash grown in Menofia, Egypt, which were close to the ones found in this study. This authors reported Kc act values ranging from 0.42-0.44 and 0.69-0.71 for drip and furrow irrigation during the initial crop stage, respectively. During the mid-season stage, the corresponding values increased to 1.01–1.03 and 1.06–1.10, falling then to 0.61-0.66 and 0.65-0.71 during the end-season stage. On the other hand, Rouphael and Colla (2005) referred higher Kc values ranging from 0.10 to 1.15, but for summer squash grown in greenhouse conditions in Viterbo, Italy.

measured data during the four seasons indicated that the model input parameters in Table 4 were adequately fitted for simulating soil water dynamics in all experimental fields. Yet, some exceptions were noticed with the NSE values remaining lower than 0.6 in furrow T0 during the 2012 and 2013 seasons, drip T1 during 2012 and 2014, and drip T2 during 2015 (Table 5). Ramos et al. (2012) discussed possible sources of error for explaining the deviations between measured and simulated soil water contents found in their study, which included field measurement, model inputs and model structure errors. Some of those causes can here also be considered, namely those related to the quality of the measurement dataset, particularly the representativeness of spatially averaged soil water contents measured over a certain soil volume with the neutron probe for describing the distribution of water under highly spatially non-uniform irrigation systems (drip and furrow); the depth at which those measurements were taken, likely misrepresenting smaller irrigation events such as in drip T1; and the number of measurements taken per growing season, with smaller datasets (e.g., 2012) possibly emphasizing the weight of existing outliers. Nonetheless, most of the goodness-of-fit indicators were within the range of values reported in other successful SIMDualKc applications for vegetables (Zhang et al., 2018; Patil and Tiwari, 2019) as well as other crops (Zhao et al., 2013; Wu et al., 2015; Rosa et al., 2016; Paredes et al., 2018), thus confirming the reliability of model simulations. 3.2. Potential and actual crop coefficients Fig. 5 presents the seasonal variation of the potential non-stressed basal crop coefficients (Kcb), the actual basal crop coefficients (Kcb act), the soil evaporation coefficients (Ke), and the actual crop coefficients (Kc act = Kcb act + Ke) computed by SIMDualKc for Zucchini squash during the 2012, 2013, 2014, and 2015 growing seasons. Also included are the rainfall and irrigation events. The Kcb values for the different crop stages were first defined as in Table 4, and then further adjusted to local weather conditions, crop density, and crop height following Allen and Pereira (2009). During the 2012 growing season, the Kcb values for the initial, mid-season, and end-season crop stages were set to 0.18, 0.85, and 0.60, respectively. During the following years, the Kcb values presented small variations due to climate variability but also to the dates of the crop stages that varied along the different seasons (Table 2). During the 2013 season, the Kcb values reached 0.18, 0.89, and 0.68 during the initial, mid-season, and end-season crop stages, respectively. In 2014, those values matched 0.22, 0.94, and 0.56, while in 2015 the corresponding values were 0.18, 0.98, and 0.71. As such, the Kcb values found for zucchini squash grown in the Syrian Akkar Plain were in accordance with Allen et al. (1998), who provided reference values of 0.15, 0.90, and 0.70 for the same crop stages. The Kcb act values reflected some water stress along the growing seasons, with a departure from the potential Kcb values being noticed

3.3. Soil water balance Table 6 presents the soil water balance computed by SIMDualKc for Zucchini squash grown in different experimental treatments during the 2012, 2013, 2014, and 2015 growing seasons. Tc values varied from 193 to 281 mm. The lowest value was obtained in 2012, when the crop cycle lasted only 61 days (Table 2). During the following seasons, Tc values were more equal (239−281 mm) as well as the duration of the crop cycles (81–93 days), approaching also the values reported by Amer (2011) (Tp from 312 to 373 mm for a crop cycle of 93 days in Menofia, Egypt). 8

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Fig. 5. Seasonal variation of Kcb, Ke, Kcb act and Kc respective data on irrigation and precipitation.

act

in furrow (T0) and drip (T1, T2) irrigation treatments during the 2012–2015 growing seasons including the

resulted in considerable losses of water through deep percolation (Table 6). In furrow T0, DP cumulative values ranged from 287 mm to 551 mm, i.e., 50%–64% of the total water applied by both irrigation and rainfall. However, as shown in Fig. 6, most of the percolated water (78%–92%) was actually originated from irrigation (for 2013–2015 since in 2012 no noticeable rainfall events occurred). These results showed the inefficiency of the irrigation schedule representing the traditional furrow scheme used in the Syrian Akkar Plain, resulting also in substantial yield losses (see section 3.4). Drip T2 also produced very high DP cumulative values, ranging from 190 to 412 mm (i.e., 1.0–1.5 times higher than the corresponding Tc values). Here, most of the

As the Zucchini squash rooting depth is relatively shallow (0.45 m), irrigation schedules were always defined to maintain soil water contents at high levels and avoid the detrimental effects of water deficit on crop yields and fruit quality (number, diameter, length, and shape of fruits) (Richard et al., 2002; Ertek et al., 2004). Nonetheless, as described above, the Kcb act values occasionally departed from the potential values in furrow T0 and drip T1, when soil water depletion increased above RAW. However, water stress levels were never very extensive and for that reason Tc act values were only reduced up to 4 % when compared with potential values. On the other hand, maintaining soil water contents at high levels

Table 6 Components of the soil water balance. Year

2012

2013

2014

2015

Irrigation method

Furrow T0 Drip T1 Drip T2 Furrow T0 Drip T1 Drip T2 Furrow T0 Drip T1 Drip T2 Furrow T0 Drip T1 Drip T2

Input

Output

I (mm)

Net P (mm)

ΔSW (mm)

Tc (mm)

Tc

518 273 402 444 275 351 518 273 417 740 321 612

2 2 2 158 158 158 80 80 81 121 121 124

−3 18 18 −4 14 −5 25 18 18 14 5 −2

193 193 193 273 273 273 239 239 239 281 281 281

190 192 193 262 273 273 238 239 239 281 281 281

act

(mm)

Es 41 43 42 35 33 30 38 38 38 45 44 43

act

(mm)

DP (mm)

RO (mm)

287 60 190 302 141 201 349 95 241 551 124 412

0 0 0 22 22 22 19 19 18 8 8 5

I, irrigation; P, precipitation; ΔSW, soil water storage variation; Tc, potential transpiration; Tc act, actual transpiration; Es act, actual evaporation; DP, deep percolation; RO, runoff. 9

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Fig. 6. Irrigation, precipitation and deep percolation in furrow (T0) and drip (T1, T2) irrigation treatments during the 2012–2015 growing seasons.

Menofia, Egypt, with yields (25.0 to 45.7 Mg ha−1) depending on the seasonal irrigation depth and irrigation method. There, the highest yields were obtained for drip irrigation with a seasonal depth close to 300 mm. Also, Ertek et al. (2004) reported Zucchini squash yields from 22.4–44.7 Mg ha−1 in Turkey, finding a positive linear relation between irrigation water, plant water consumption, fruit traits, and yield. Zotarelli et al. (2008) further presented marketable yields from 20 to 43 Mg ha−1 for Zucchini squash grown in Florida, USA. However, drip T1 yields were still below those reported by Paris et al. (1986) for goldy courgette (Curcubita pepo L. cv. Goldy), who obtained, for the same plant density, marketable yields from 43.5–51.3 Mg ha−1 in two outdoor locations. On the other hand, drip T1 yields were much higher than those reported by Al-Omran et al. (2005) for Saudi Arabia (9.9028.30 Mg ha−1), as well as by El-Mageed and Semida (2015) for Cairo, Egypt (7.5-14.0 Mg ha−1). In both these locations, growth conditions were affected by salinity. The furrow T0 treatment returned the lowest marketable yields, with the average of 18.5 Mg ha−1 (range from 11.7–21.3 Mg ha−1) approaching the national average (17 Mg ha−1) in Syria (CBS, 2017). Drip T2 resulted in slightly higher yields than in furrow T0, with the average reaching 24.5 Mg ha−1 (range from 16.1–32.4 Mg ha−1). The explanation for the lower yields in these two experimental treatments should be associated with the substantial percolation amounts estimated by SIMDualKc during the four growing seasons (Fig. 6). The percolated water likely ended up promoting leaching of nutrients from the root zone layers, reducing their availability when compared with the drip T1 treatment. For example, Ramos et al. (2012) found that the movement of nitrogen (N) out of the root zone in irrigated sorghum was dependent on the amount of water flowing through the root zone, the amount of N applied, the form of N in the fertilizer, and the timing and

percolated water was again from irrigation (58%–86%). These results thus show that despite the introduction of a more modern irrigation method, water losses can still be substantial if the system is poorly managed (Darouich et al., 2014). Finally, drip T1 resulted in lower DP cumulative values (60 mm–141 mm) due to the higher frequency of irrigation events and lower depths applied per event. Contrarily to the previous treatments, most of percolated values were found after rainfall events, with water losses from irrigation totalizing only 29%–48% of the total DP values (Fig. 6). In all experimental treatments, cumulative values of soil evaporation were relatively low, summing less than 45 mm per season. This was mainly due to the short duration of crop seasons (less than 3 months) and the relatively fast and total coverage (fc = 0.99) of the soil surface by the plant canopy, which limited the available energy for evaporation. 3.4. Yields and water productivity indicators Table 7 presents the ANOVA table with the analysis of variance for the effects of the different treatments and growing seasons on Zucchini squash yields. Statistics showed a significant difference in the average between treatments and between years, while for the interaction between both factors only a significant value was recorded at the 99 % level of confidence. This indicated that yield responded to the irrigation treatments as well as to season variability. As demonstrated by the statistical analysis, yields varied significantly between treatments, with drip T1 always returning the highest marketable yields. In this treatment, Zucchini squash yields averaged 31.4 Mg ha−1, ranging from 20.6 Mg ha−1 in 2014 to 38.9 Mg ha−1 in 2013 (Table 8). Amer (2011) reported comparable yields for 10

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Table 7 Analysis of variance of measured yields and water productivity indicators in different experimental plots during four growing seasons. Source of variation Yield Seasons Treatments Seasons x Treatments Experimental Error WPWU Seasons Treatments Seasons x Treatments Experimental Error WPirrig Seasons Treatments Seasons x Treatments Experimental Error WPET Seasons Treatments Seasons x Treatments Experimental Error WPT Seasons Treatments Seasons x Treatments Experimental Error EWP Seasons Treatments Seasons x Treatments Experimental Error EWPR Seasons Treatments Seasons x Treatments Experimental Error

Degree of freedom

Sum of squares

Mean square

F.

P (> F)

3 2 6 36

1456 1825 218 419

485.5 912.6 36.3 11.6

41.7 78.4 3.1

< 0.0001*** < 0.0001*** 0.0145 *

3 2 6 36

42.9 253.8 11.0 17.2

14.3 126.9 1.8 0.5

29.9 265.6 3.8

< 0.0001*** < 0.0001*** 0.00469**

3 2 6 36

95.1 527.7 31.0 31.9

31.7 263.9 5.2 0.9

35.8 298.0 5.8

< 0.0001*** < 0.0001*** 0.00025***

3 2 6 36

109.7 215.9 17.3 49.6

36.6 108.0 2.9 1.4

26.6 78.4 2.1

< 0.0001*** < 0.0001*** 0.0786

3 2 6 36

163.1 287.4 21.6 67.1

54.4 143.7 3.6 1.9

29.2 77.1 1.9

< 0.0001*** < 0.0001*** 0.103

3 2 6 36

5.1 30.9 1.4 2.0

1.7 15.4 0.2 0.1

30.9 279.3 4.3

< 0.0001*** < 0.0001*** 0.00022***

3 2 6 36

3862 21364 1254 1268

1287 10682 209 35

35.5 303.3 5.9

< 0.0001*** < 0.0001*** 0.00022***

Significant levels: 0*** 0.001** 0.01*. WPWU, total water productivity; WPirrig, irrigation water productivity; WPET, consumptive use water productivity; WPT, transpiration water productivity; EWP, economic water productivity; EWPR, economic water productivity ratio.

2014 to 9.6 kg m−3 in 2013 (Table 8). When considering only the water productivity of irrigation water (WPirrig), given by the ratio between yield and the irrigation water applied (Pereira et al., 2012), the values were slightly higher than the previous ones, ranging now from 7.5 kg m−3 in 2014 to 14.1 kg m−3 in 2013. These values were comparable to Amer (2011), whose WPirrig value reached 14.6 kg m−3, and to Ertek et al. (2004), whose WPirri values ranged from 7.8–9.8 kg m−3. Similar conclusions were extracted for WPET and WPT. Once again, furrow T0 led to the lowest WPWU, WPirrig, WPET and WPT values, with these varying from 1.9 to 3.0 kg m−3, 2.3 to 3.8 kg m−3, 4.2 to 6.7 kg m−3, and 4.9 to 8.1 kg m−3, respectively (Table 8). Likewise, drip T2 indicators were quite low when compared with those in drip T1. These lower values can only be compared to Al-Omran et al. (2005) and El-Mageed and Semida (2015) despite obtained under saline conditions (1.37-3.64 kg m−3). Also Zotarelli et al. (2008) reported a wide variation of WPWU values, going from 4.3–57 kg m−3 due to irrigation treatments (schedules and depths applied) that varied from 66 to 482 mm of water applied. In the studied area, the WPWU values were thus much affected by the irrigation regime, with percolated water contributing substantially to such low values. Optimizing irrigation schedules seems thus essential for improving irrigation water efficiency in the Syrian Akkar Plain, minimizing losses while fulfilling crop water requirements. Drip T1 applied 38.1%–56.6% less water than furrow T0, allowing a higher irrigation frequency that made possible lower irrigation depths per event. The amount of percolation water was then reduced substantially, possibly minimizing also leaching and returning higher yields and higher WPWU, WPirrig, WPET and WPT values.

Table 8 Yield and water productivity indicators. Year

Irrigation method

Yield (Mg ha−1)

WPWU (kg m−3)

WPirrig (kg m−3)

WPET (kg m−3)

WPT (kg m−3)

EWP ($ m−3)

EWPR (-)

2012

Furrow T0 Drip T1 Drip T2 Furrow T0 Drip T1 Drip T2 Furrow T0 Drip T1 Drip T2 Furrow T0 Drip T1 Drip T2

15.48 27.96 22.87 16.67 38.89 26.85 11.67 20.57 16.05 21.30 38.03 32.41

3.0 9.6 5.4 2.8 8.7 5.3 1.9 5.5 3.1 2.4 8.5 4.4

3.0 10.2 5.7 3.8 14.1 7.7 2.3 7.5 3.8 2.9 11.8 5.3

6.7 11.9 9.7 5.6 12.6 8.8 4.2 7.4 5.8 6.5 11.7 10.0

8.1 14.6 11.8 6.3 14.2 9.8 4.9 8.6 6.7 7.6 13.5 11.5

1.0 3.3 1.9 1.0 3.0 1.9 0.7 1.9 1.1 0.9 3.0 1.5

19.0 65.2 36.2 23.9 90.0 48.7 14.3 48.0 24.5 18.3 75.3 33.7

2013

2014

2015

WPWU, total water productivity; WPirrig, irrigation water productivity; WPET, consumptive use water productivity; WPT, transpiration water productivity; EWP, economic water productivity; EWPR, economic water productivity ratio.

number of fertigation events. The same considerations should be assumed here. An alternative explanation could be associated with the higher irrigation amounts applied in these two treatments, with too much water aggravating root and stem rot diseases (Richard et al., 2002), reducing courgette yields. The water productivity of the water used (WPWU), given by the ratio between yield and the total water applied (Pereira et al., 2012), reached the highest values also in drip T1, which range was from 5.5 kg m−3 in 11

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Acknowledgments

Economically, drip T1 was also more benefic, with the economical water productivity (EWP) values, here computed as the ratio between yield value and total water applied (Pereira et al., 2012), varying from 1.9 to 3.3 $ m−3 (Table 8). For the furrow irrigation treatment, EWP values only reached 0.7–1.0 $ m−3 depending on the growing season. Nonetheless, this indicator cannot be considered here in absolute terms given the current fluctuation of prices with the ongoing war in Syria. Likewise, the economic water productivity ratio (EWPR), here computed as the ratio between yield value and water cost (Pereira et al., 2012), was higher for drip T1 (48.0–90.0) than in all other treatments (Table 8), showing the profitability of Zucchini squash production in the Syrian Akkar Plain region. These values were also much higher than those found for other strategic crops like wheat and cotton grown in semi-arid regions in Syria, which EWPR values were estimated to be 2.7 and 2.2, respectively (Darouich et al., 2014, 2017).

LEAF and MARETEC acknowledge the national funds from Fundação para a Ciência e Tecnologia (Projects UID/AGR/04129/2019 and UID/EEA/50009/2019). H. Darouich and T. B. Ramos were supported by contracts CEECIND/01153/2017 and CEECIND/01152/ 2017, respectively. References Abou Zakhem, B., Hafez, R., 2007. Environmental isotope study of seawater intrusion in the coastal aquifer (Syria). Environ. Geol. 51 (8), 1329–1339. Abou Zakhem, B., Al Ain, F., Hafez, R., 2019. Assessment of Field Water Budget Components for Increasing Water Productivity Under Drip Irrigation in Arid and Semi‐Arid Areas, Syria. Irrig. Drain. https://doi.org/10.1002/ird.2286. Allen, R.G., Pereira, L.S., 2009. Estimating crop coefficients from fraction of ground cover and height. Irrig. Sci. 28 (1), 17–34. Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop Evapotranspiration. Guidelines for Computing Crop Water Requirements. FAO Irrig. Drain Paper 56. FAO, Rome 300 pp. Allen, R.G., Pereira, L.S., Smith, M., Raes, D., Wright, J.L., 2005. FAO-56 dual crop coefficient method for estimating evaporation from soil and application extensions. J. Irrig. Drain. Eng. 131, 2–13. Al-Omran, A.M., Sheta, A.S., Falatah, A.M., Al-Harbi, A.R., 2005. Effect of drip irrigation on squash (Cucurbita pepo) yield and water-use efficiency in sandy calcareous soils amended with clay deposits. Agric. Water Manage. 73 (1), 43–55. Amer, K.H., 2011. Effect of irrigation method and quantity on squash yield and quality. Agric. Water Manage. 98 (8), 1197–1206. Amery, H.A., 2002. Irrigation Planning in Lebanon: Challenges and Opportunities. Water Resource Management, Traditional and Modern Irrigation Technologies in the Eastern Mediterranean. International Development and Research Center, Ottawa, pp. 111–123. CBS, 2017. Agriculture. Central Bureau of Statistics, Chapter 4. Tables 7, 10, 12, 16, 17, 18, Damascus, Syria. (Last Accessed 20.06.2019). http://cbssyr.sy/index-EN.htm. Chard, E.D., 1981. An Economic Analysis of the Akkar Plain Project. Utah State University, All Graduate Theses and Dissertations. Paper 4210. Dane, J.H., Hopmans, J.W., 2002. Pressure plate extractor. In: Dane, J.H., Topp, G.C. (Eds.), Methods of Soil Analysis, Part 4, Physical Methods. Soil Science Society of America Book Series. Soil Science Society of America, Madison, Wisconsin, pp. 688–690. Darouich, H., Cameira, R.M., Gonçalves, J.M., Paredes, P., Pereira, L.S., 2017. Comparing sprinkler and surface irrigation for wheat using multi-criteria analysis: water saving vs. Economic returns. Water 9 (1), 50. https://doi.org/10.3390/w9010050. Darouich, H., Gonçalves, J.M., Muga, A., Pereira, L.S., 2012. Water saving vs. Farm economics in cotton surface irrigation: an application of multicriteria analysis. Agric. Water Manage. 115, 223–231. Darouich, H., Pedras, C.M.G., Gonçalves, J.M., Pereira, L.S., 2014. Drip vs. Surface irrigation: a comparison focusing water saving and economic returns using multicriteria analysis applied to cotton. Biosyst. Eng. 122, 74–90. Domínguez, A., Tarjuelo, J.M., de Juan, J.A., López-Mat a, E., Breidy, J., Karam, F., 2011. Deficit irrigation under water stress and salinity conditions: the MOPECO-Salt Model Agric. Water Manage. 98, 1451–1461. El-Mageed, T.A.A., Semida, W.M., 2015. Effect of deficit irrigation and growing seasons on plant water status, fruit yield and water use efficiency of squash under saline soil. Sci. Hort. 186, 89–100. Ertek, A., Şensoy, S., Küçükyumuk, C., Gedik, I., 2004. Irrigation frequency and amount affect yield components of summer squash (Cucurbita pepo L.). Agric. Water Manage. 67 (1), 63–76. Fader, M., Shi, S., von Bloh, W., Bondeau, A., Cramer, W., 2016. Mediterranean irrigation under climate change: more efficient irrigation needed to compensate increases in irrigation water requirements. Hydrol. Earth Syst. Sci. Discuss. 20, 953–973. Halwani, J., Baroudi, B.O., Wartel, M., 1999. Nitrate contamination of the groundwater of the Akkar Plain in northern Lebanon. Sante 9 (4), 219–223. IUSS Working Group WRB, 2014. World Reference Base for Soil Resources 2014.International Soil Classification System for Naming Soils and Creating Legends for Soil Maps. World Soil Resources Reports No. 106. FAO, Rome. Kattaa, B., Al-Fares, W., Al Charideh, A.R., 2010. Groundwater vulnerability assessment for the Banyas Catchment of the Syrian coastal area using GIS and the RISKE method. J. Environ. Manage. 91, 1103–1110. Legates, D., McCabe, G., 1999. Evaluating the use of goodness of fit measures in hydrologic and hydroclimatic model validation. Water Resour. Res. 35, 233–241. Liu, Y., Pereira, L.S., Fernando, R.M., 2006. Fluxes through the bottom boundary of the root zone in silty soils: parametric approaches to estimate groundwater contribution and percolation. Agric. Water Manage. 84, 27–40. Loague, K., Green, R.F., 1991. Statistical and graphical methods for evaluating solute transport models: overview and application. J. Cont. Hydrol. 7, 183–196. MAAR, 2017. Land Use Balance, Department of Statistics and Planning. Ministry of Agriculture and Agrarian Reform, Damascus, Syria (in Arabic). Martins, J.D., Rodrigues, G.C., Paredes, P., Carlesso, R., Oliveira, Z.B., Knies, A.E., Petry, M.T., Pereira, L.S., 2013. Dual crop coefficients for maize in southern Brazil: model testing for sprinkler and drip irrigation and mulched soil. Biosyst. Eng. 115 (3), 291–310. Mohammad, M.J., 2004. Utilization of applied fertilizer nitrogen and irrigation water by

4. Conclusions The Akkar Plain in the Syrian coastal area offers great potential for intensive vegetable production. Agricultural policies implemented in the region have been aimed at improving irrigation water and nutrient use efficiency through the rationalization of irrigation and fertilization practices and modernization of irrigation systems and methods. The conversion of traditional surface systems to drip irrigation has been one of the main objectives of those policies as the latter system is usually considered as the most effective way to convey directly water and nutrients to plants, saving important amounts of water, and increasing yields. The modernization of irrigation methods in the Syrian Akkar Plain has provided the context for this research, which demonstrated the usefulness of the SIMDualKc model in computing Zucchini squash irrigation needs and comparing different management practices (irrigation methods and scheduling) during four growing seasons. The model was able to simulate soil water contents in different experimental plots, with the level of agreement between measured and simulated data returning RMSE values lower than 0.002 m3 m−3 and NSE values from 0.166 to 0.732 (NSEmean = 0.573). The drip T1 treatment, through the application of small irrigation depths with a relatively high frequency, was able to maintain soil water contents high while reducing percolation losses. On the other hand, the furrow T0 treatment, reproducing the traditional irrigation scheme, led to substantial percolation losses and reduced yields. Different water productivity indices were also computed, with all highlighting the advantages of drip T1 compared to furrow. Nevertheless, the importance of an adequate system management was also demonstrated, with the drip T2 treatment, which promoted also substantial percolation losses, leading to similar results as furrow T0. Hence, this study further showed that those agricultural policies aiming at converting traditional surface systems to modern drip irrigation methods need to be supported by proper agronomist services, capable of advising farmers in that transition, at risk of producing no effect on water savings. This study further presents Kcb values for the initial, mid-season, and end-season stages of Zucchini squash that should be considered when computing crop irrigation needs in the Syrian Akkar Plain. This is fundamental for the sustainability of vegetable production in the region, for improving land productivity and irrigation water use efficiency, and for minimizing the environmental problems typically associated with irrigation of intensive production systems.

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 12

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