Evaluation of Aquacrop model for a potato crop under different irrigation conditions

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Evaluation of Aquacrop model for a potato crop under different irrigation conditions F. Montoya, D. Camargo, J.F. Ortega, J.I. Córcoles ∗ , A. Domínguez Universidad de Castilla-La Mancha (UCLM), Centro Regional de Estudios del Agua (CREA), Ctra. de Las Pe˜ nas, km 3.2, 02071 Albacete, Spain

a r t i c l e

i n f o

Article history: Received 9 March 2015 Received in revised form 5 October 2015 Accepted 18 October 2015 Available online xxx Keywords: Water productivity Solanum tuberosum (L.) Water deficit Biomass Evapotranspiration

a b s t r a c t The processes of calibration and validation of the Aquacrop model for the simulation of the growth and development of a potato crop (Agria cultivar) that was irrigated with a center pivot system are described in this study. The field experiments were conducted during 2011 (calibration) and 2012 (validation) in a semiarid region in southeastern Spain. The potatoes were irrigated with four treatments (120%, 100%, 80%, and 60% of the water requirement). The Aquacrop model was suitable for the simulation of the growth and development of potatoes in the climatic conditions of the study area. The canopy cover, total dry matter, dry matter of tubers and evapotranspiration were the primary variables analyzed. An acceptable goodness of fit was found between observed and simulated values. So, statistical indicators such as the Willmott index of agreement (d) and the coefficient of determination (R2 ) showed good values (d and R2 > 0.90) for the primary variables analyzed. Both the model and the observed data found that 80% and 60% of the water requirements were the treatments that were most efficient in the use of water. A high temperature stress coefficient affecting the harvest index is recommended to be incorporated in the model for avoiding overestimations of yield. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Potatoes (Solanum sp.) are the fifth most important crop worldwide, after sugar cane, maize, rice, and wheat (FAOSTAT, 2013). In the Castilla-La Mancha region (Spain), 3898 ha are cultivated, which represent 4.1% of the national agricultural production (MAGRAMA, 2013). The scarce availability of water resources requires development of new strategies to save water and energy, most of which should focus on improving water use efficiency (Shahnazari et al., 2007). Deficit irrigation (DI) is a way to economically optimize the use of water (English et al., 1990; Fereres and Soriano, 2007; Domínguez et al., 2012). However, this approach may have a strong effect on potato crops, with reductions in crop yield and quality of tubers (Shock et al., 1998; Fabeiro et al., 2001; Kashyap and Panda, 2003; Onder et al., 2005; Vos and Haverkort, 2007). Crop models based on different irrigation strategies and for specific edaphoclimatic conditions can be used to predict crop yields (Pereira et al., 2002). Non-specific crop models as CropSyst (Stöckle et al., 2003) or STICS (Brisson et al., 2003), as well as

potato simulator models as Substor-POTATO (Griffin et al., 1993) and LINTUL-POTATO (Kooman and Haverkort, 1995), require the calibration of a great number of parameters for the simulation process. Moreover, the scientific approach of these models does not recommend their use by farmers and technicians. The Aquacrop model attempts to balance accuracy, simplicity and robustness to be useful for technicians and managers of irrigable areas (Raes et al., 2009; Steduto et al., 2009). This model analyzes crop growth and development (canopy cover and accumulated biomass), soil water content and evapotranspiration from herbaceous crops. However, due to its simplicity the model is not able to simulate the soil erosion, the emission of CO2 to atmosphere, or the balance of nutritional elements. The Aquacrop model was used to simulate the growth of several crops, including cotton (Farahani et al., 2009), maize (Hsiao et al., 2009; Katerji et al., 2013; Paredes et al., 2014), wheat (Andarzian et al., 2011), barley (Araya et al., 2010), quinoa (Geerts et al., 2009), sunflower (Todorovic et al., 2009), tomato (Katerji et al., 2013), and potato (García-Vila and Fereres, 2012). The Aquacrop manual defines a set of calibrated values for the most relevant crops. For the potato crop, Raes et al. (2012) stated that the goodness of fit for the calibrated parameters values was low. Other researchers (Domínguez et al., 2011; García-Vila and Fereres, 2012) analyzed potato crop yield simulations, but the model

∗ Corresponding author. Fax: +34 967 599 269. E-mail address: [email protected] (J.I. Córcoles). http://dx.doi.org/10.1016/j.agwat.2015.10.019 0378-3774/© 2015 Elsevier B.V. All rights reserved.

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Table 1 Climatic conditions during the 2011 and 2012 experimental years. Year

Month

Average Tmax (◦ C)

Average Tmin (◦ C)

Average Rad (MJ m−2 day−1 )

Accumulated ET0 (mm)

Accumulated P (mm)

2011

March April May June July August March April May June July August

12.4 20.7 23.8 29.0 31.6 32.8 16.0 16.5 25.1 31.2 32.6 34.6

2.1 6.0 9.0 12.6 14.6 15.4 -0.9 4.8 8.7 13.2 14.6 15.6

12.6 19.1 23.0 27.0 26.0 23.4 17.0 17.4 23.1 25.0 26.6 22.8

63.4 101.9 111.2 185.3 207.4 197.3 86.0 99.2 156.1 201.3 218.9 201.5

21.0 45.3 40.7 19.8 4.0 8.2 32.0 37.1 16.1 35.5 0.5 0.0

2012

Tmax : maximum daily temperature; Tmin : minimum daily temperature; Rad: daily sun radiation; ET0 : FAO-Penman Monteith reference evapotranspiration; P: rainfall. Table 2 Soil physical properties of the experimental plots (2011 and 2012). Cropping season

Depth (m)

Texture

Bulk density (g m−3 )

2011

0.00–0.22 0.22–0.43 0.43–0.70 0.00–0.25 0.25–0.42 0.42–0.70 0.70–0.95

FA FA AF FAA FAA F FL

1.45 1.48 1.68 1.40 1.40 1.35 1.37

2012

Water content FC (vol.%)

WP (vol.%)

Sat (vol.%)

30.2 30.2 17.3 29.4 29.4 22.8 30.0

15.0 15.0 6.7 12.1 12.1 9.0 12.5

44.9 44.5 35.7 46.4 46.9 47.2 47.8

Ksat (mm day−1 )

CN

191.1 215.1 1846.4 137.1 117.6 142.5 264.1

85

FC: field capacity; WP: wilting point; Sat: saturation; Ksat : saturated hydraulic conductivity; CN: runoff curve number; FA: clay loam; AF: sandy loam; FAA: sandy clay loam; F: loam; FL: silt loam.

16

19

15

16

16

16

17

18

18

18

19

19

19

20

20 16 15

20

21

28

16 16 15 16

16 16 16 16

16 17 16

17

17 17

17

17 17

17 18

18

18

18 18

18 18

18 18

18 18

19 21

22

19 19

19 20 20

17 19

20 19 19 20

20

20 20 20 20

20

20 20

21 21

21 22 21 20

21 19

21 21 22

26

25 22

21 21

23 22 22 22

22 22

23 23 23 22 23 23 22

23 23 22

23 22

23 25

25 33

% of CWR T1

60

T2

80

T3

100

T4

120

14 14 14 14 14 14

14

Spa

parameters were not calibrated by experimental field tests specifically designed to calibrate the model. The main aim of this paper was to use field data to calibrate and validate the Aquacrop model to simulate the growth and development of a potato crop (cv. Agria) in semiarid conditions. The secondary aim was to improve the reliability of the parameters included in the Aquacrop manual, so the model could be used as a decision support system tool for potato growers and managers of irrigable areas.

n1

2. Materials and methods n Spa Spa n3 Spa n4 Spa n5

A field experiment was conducted during the 2011 and 2012 cropping seasons on a commercial potato farm in Aguas Nuevas (Albacete, Spain; 38◦ 56 N, 1◦ 53 W, 695 m.a.s.l.). The study area has a warm Mediterranean climate (Papadakis, 1966). The highest temperature (Tmax ) is reached in summer (33 ◦ C), with high seasonal variability in average temperatures (3.8 ◦ C in January and 24.4 ◦ C in July). The accumulated rainfall (P) is 360 mm year−1 (in spring and winter) and the accumulated annual reference evapotranspiration (ET0 ) is around 1300 mm year−1 . For both cropping seasons, the climatic data were obtained from a weather station close to the experimental plots (300 m) (Table 1). The daily ET0 was computed using the FAO-Penman-Monteith equation (Allen et al., 1998). The summers were the hottest season, with the highest temperatures reached in July and August (35.1 ◦ C and 36.2 ◦ C, respectively in 2011; 37.1 ◦ C and 41 ◦ C, respectively in 2012) and with more days with high temperatures (>35 ◦ C) in 2012 than 2011 (19 and 7 days, respectively). The accumulated rainfall during the crop cycles was 160 mm (2011) and 130 mm (2012), with 50% of the rainfall during the crop stages of sowing and flowering. The soil was classified as a torriorthent (USDA-NRCS, 2006), with medium depth that ranged from 40 cm to 55 cm. The soil physical properties, including bulk density, texture, and field capacity and

2

2.1. Location of the experimental design

Fig. 1. Experimental quarter section of the center pivot.

wilting points, were obtained in the laboratory (Table 2). The saturation and saturated hydraulic conductivity were determined using empirical equations (Saxton et al., 1986), whereas the runoff curve number (CN) was determined according to NCRS (2004). For the most superficial horizons, the soil water retention curves did not show differences (Table 2) because of the similar texture of the soils. The horizons located below the surface layer had a high C/N ratio (22.0) and a low content of organic matter (0.40–0.60%).

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100

100

a

b 80

Canopy cover (%)

Canopy cove r (% )

80

60

40

60

40

20

20

0

0

100

100

c

d

DDP

DDP

80

Canopy cover (%)

80

Canopy cove r (% )

3

60

40

20

60

40

20

0

0

0

50

100

150

0

200

50

Days aer planng

Observed 201 1

100

150

20 0

Days aer planng

Observed 201 2

Simulated 201 1

Simulated 2012

Fig. 2. Vertical bars: standard deviation of the average data. Progression of the simulated and the observed canopy cover during 2011 and 2012 ((a): 120% treatment; (b): 100% treatment; (c): 80% treatment; and (d): 60% treatment).

2.2. Crop management In both cropping seasons, the potato cultivar Agria was grown as one of the most used varieties in the area, which are destined for processing into French fries. The crop cycle was from March to August. The tubers were directly sown on March 9, 2011, and March 16, 2012. In 2011 and 2012 cropping seasons, the harvest was conducted when tubers reached the maturity, which occurred 152 and 173 days after planting, respectively. In 2012, the longer crop cycle was because of the high temperatures registered for a prolonged period. The plant density was 5.9 plants m−2 in 2011 and 5.7 plants m−2 in 2012. All other cultivation techniques followed the traditional farming practices for the area (De Juan et al., 2003) to maximize crop yield and quality. 2.3. Experimental design The experimental plot was a potato crop that covered 4.9 ha of a total of 18.4 ha, within a central pivot irrigation system. The pivot system had a total length of 238 m and a system capacity of 1.3 l s−1 ha−1 . Rotating Spray Plate Sprinklers (RotatorTM , Nelson Irrigation Co., Walla Walla, WA, USA) were installed at a height of 1.4 m on all spans with 1.5 m between sprinklers. The sprinklers had pressure regulators with output pressure set to 140 kPa and a 9 m width of the spray pattern. The experimental design had four irrigation treatments, which were a percent (120%, 100%, 80% and 60%) of the water requirement of the crop computed with FAO methodology (Allen et al., 1998). Each irrigation treatment had three replicates (Fig. 1). The experimental plots were distributed at random in spans 3 and 4 of the irrigation system and were distributed along a fully overlapped

Table 3 Irrigation frequency for each irrigation treatment and percent of the crop water requirement (CWR). Irrigation treatment

Frequencya (dimensionless)

% CWR (%)

T1 T2 T3 T4

6 of 6 5 of 6 4 of 6 3 of 6

120 100 80 60

a During six consecutive irrigation events, the irrigation dose was the same. The dose of the next six irrigations or the interval between irrigation events was modified according with the phenological stage of the crop and the climatic conditions. So, after 5 irrigation events, T2 received the irrigation requirements for full irrigation in a certain period of time. In the same period, T1 received one more irrigation event (+20%), T3 one less irrigation event (−20%), and T4 two less irrigation events (−40%).

strip with a width of 6 m on sections of circular crowns. Each span was equipped with electric valves that were controlled by one irrigation programmer. The experimental plots were 10 m long and 6 m wide (60 m2 ). The irrigation frequency was based on groups of six irrigation events, which applied the same dose in a given period of time. The sample irrigation frequency for the four treatments is presented in Table 3. 2.4. Irrigation management. Control of soil water The schedule for irrigation was determined using the simplified water balance in the root zone, according to FAO 56 methodology (Allen et al., 1998). The crop coefficients (Kc) used for the reference irrigation treatment (100% of Crop Water Requirements, CWR) were based on FAO 56 (Allen et al., 1998), which were different according to the phenological stage of the potato (0.5 at onset of growth; 1.15 at tuber formation; and 0.75 before ripening).

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22

22

a

18 16 14 12

10 8 6 4

18 16

14 12

10 8

6 4

2

2 0

0

22

22

c

d

20 Total dry maer (x10 3 kg ha-1 )

20 Tot al dry m at t e r (x1 0 3 kg ha-1 )

b

20

Total dry maer (x10 3 kg ha-1 )

Tot al dry m at t e r (x10 3 kg ha-1 )

20

18 16 14

12 10 8 6 4

18 16

14 12 10

8 6

4

2

2

0

0

0

50

10 0

15 0

200

0

50

Days aer planng

Observed 201 1

10 0

15 0

20 0

Days aer planng

Observed 201 2

Simulated 201 1

Simulated 2012

Fig. 3. Vertical bars: standard deviation of the repetitions. Progression of the simulated and the observed total dry matter during 2011 and 2012 ((a): 120% treatment; (b): 100% treatment; (c): 80% treatment; and (d): 60% treatment). Table 4 Average accumulated water received for each irrigation treatment during 2011 and 2012 cropping seasons. Irrigation treatment

120% 100% 80% 60% p-Value

2011

2012

Total water received (mm)

CV (%)

Ratio

Total water received (mm)

CV (%)

Ratio

700.3a 598.2b 483.0c 396.0d

1.8 1.8 3.3 4.6 –

1.00 0.84 0.69 0.56 –

892.3a 791.1b 679.3c 550.7d

1.9 2.0 1.6 1.5 –

1.00 0.88 0.76 0.61 –

*

*

CV: coefficient of variation of the three replicates per irrigation treatment. * p < 0.01, ANOVA (Duncan test). Table 5 Phenological stage and thermal time. 2011

2012

Date

DAP

Phenological stage (BBCH scale)

Thermal timea (◦ C)

Date

DAP

Phenological stage (BBCH scale)

Thermal timea (◦ C)

9th March

0

0

16th March

0

37

332

3rd May

8

5th May

57

553

28th May

73

29th June

112

1468

1st July

107

Planting (07) Emergence (09) Onset of flowering and Tuber formation (40–51) End of flowering (639)

0

15th April

Planting (07) Emergence (09) Onset of flowering and Tuber formation (40–51) End of flowering-Onset of senescence (639–91)

7th August

144

2075

5th September

173

Onset of senescence (91) Harvest (49–95)

8th August

152

Harvest (49–95)

2324

372 735

1391

2855

DAP: Days after planting. a McMaster and Wilhelm (1997).

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During the 2011 and 2012 seasons, 46 and 61 irrigations were applied to the 120% irrigation treatment plots, respectively, which resulted in accumulated irrigation water of approximately 700.3 and 892.3 mm, respectively (Table 4). Approximately half of the irrigation events were evaluated with Merriam and Keller (1978) methodology, with the aim to control the total amount of water received for each irrigation treatment. The uniformity coefficient ranged from 88 to 92% (2011) and from 81 to 93% (2012), with an average accumulated coefficient of uniformity of 91% for both cropping seasons. To guarantee the germination and the establishment of the crop, all the irrigation treatments received the same amount of water until the plants reached the stage with nine unfolded (>4 cm) leaves on the main stem, at approximately 75 and 67 days after planting in 2011 and 2012, respectively. Thus, the total accumulated irrigation water received per irrigation treatment was significantly different, and the ratio between the 120% treatment and the actual water received was slightly different for each irrigation treatment (Table 4). The soil moisture contents were measured during the cropping seasons with sensors based on frequency domain reflectometry technology (EnviroScanTM , SentekPty Ltd., South Australia), which were calibrated in advance (Camargo et al., 2012). The sensors were placed to a depth of 0.4 m (0.1 m intervals). The soil water potential was measured using WatermarkTM (Irrometer Corp., Riverside, CA, USA) sensors placed at 0.2 m, 0.3 m and 0.4 m depths. These sensors were used to locate the zero flux plane (ZFP) (Khalil et al., 2003; Jiménez et al., 2010) at a depth of 0.3 m. According to the readings of both sensors, only the 120% irrigation treatment showed percolation below the ZFP. Moreover, no capillary rise was detected. The surface runoff was not included because the design, management and the slope of the plots (less than 0.5%) eliminated this effect. 2.5. Crop growth and development The crop growth and development data were used to calibrate and validate the Aquacrop model. The potato crop was monitored once per week to determine the growth stage of the crop using the Biologische Bundesanstalt, Bundessortenamt and CHemical industry scale (BBCH) (Hack et al., 1993). Thus, the duration of each phenological stage was determined based on thermal time (McMaster and Wilhelm, 1997), with 2 ◦ C as the base temperature (Tb) and 26 ◦ C as the upper temperature (Tu) (Griffin et al., 1993; Kooman and Haverkort, 1995; Steduto et al., 2012) (Table 5). During crop development in both cropping seasons, the duration of the phenological stages was not different among irrigation treatments. According to the BBCH scale, of the potato growth stages, flowering and formation of tubers occur at the same time. However, in the 2012 cropping season, these two stages were not simultaneous; crop leaf senescence occurred at 684 growing-degree-days (GDD), after the end of flowering. The dry matter content, leaf area index, absorbed photosynthetically active radiation, and canopy cover were determined every 15 days for each irrigation treatment and replicate. In the 2011 and 2012 cropping seasons, the crop was sampled 8 and 9 times, respectively, between crop establishment and harvest. At each sample, two plants were selected from each experimental plot, and the dry matter content of leaves, aerial and underground stems, roots and tubers was determined. The leaf area was determined with an automated infrared imaging system LI-COR-3100C (LI-COR Inc., Lincoln, NE, USA). A balance was conducted to compute the absorbed photosynthetically active radiation using the measurements of the SunScanTM canopy analysis system (Delta-T Devices Ltd., Cambridge, UK) (Varlet-Grancher et al., 1989). The canopy cover (CC) was measured with a microdrone md-400 (Microdrones

5

Inc., Kreuztal, Germany), a vertical takeoff and landing (VTOL) quadracopter aircraft. This device had a digital camera (PENTAXTM , Golden, CO, USA) with an integrated zoom lens and a resolution of 12 MPx. The flight altitude was approximately 40 m. The CC was computed using LAIC software (Córcoles et al., 2013; Ballesteros et al., 2014). With this tool, the pixels were grouped into vegetation (green, aboveground parts of the crop) and nonvegetation portions, i.e., the soil, shadows, and stones. When the ripening of tubers stage was reached, the experimental plots were harvested. The crop yield was determined for each irrigation treatment after manual harvest of 18 m2 from the center of the experimental plot (60 m2 ). Hence, for each experimental plot, the crop yield, dry matter content of potato tubers (DMtub), total dry matter content (TDM), and the harvest index were determined. 2.6. Crop evapotranspiration The actual crop evapotranspiration (ET) was computed with two methods. The first method was based on the simplified water balance using the EnviroScanTM (WBSET ) sensors, which monitored the changes in volumetric water content to the ZFP depth. Thus, for each irrigation treatment, the ET was computed for the days in between two consecutive irrigation events during the crop cycle. The ET was computed as follows: ET = I + Pe − S

(1)

where ET is the actual evapotranspiration (mm); I is the net irrigation (mm); Pe is the effective rainfall (mm); and S is the variation in the soil water content (mm). The second method was based on the Bowen Ratio station (BRET ) (Campbell Scientific Ltd., Leicestershire, UK). In both cropping seasons, the device was located at the center of the second pivot span (Fig. 1), which was in the 120% irrigation treatment. This location was used because it was necessary to guarantee minimum fetch conditions (100:1) (Brutsaert, 1982; Payero et al., 2003; Allen et al., 2011). The Bowen station was installed four times: after planting, at flowering, at maximum crop growth, and at senescence. With both methodologies, notably, the maximum rootextracted water approached 10.0 mm day−1 , which was obtained in the stage of maximum crop growth with the maximum vegetative cover. 2.7. AquaCrop model AquaCrop is a model of crop water productivity that simulates the yield response of herbaceous crops to water (Steduto et al., 2012). The model simulates the biomass progression using the soil water and salt balance, certain atmospheric parameters (temperature, rainfall, ET0 , and atmospheric CO2 concentration), and the characteristics (i.e., water productivity, thermal time, crop coefficient. . .) and management (i.e. irrigation schedule, type of irrigation system, soil fertility. . .) of the crop. The effect of extreme temperatures affecting the crop phenology and biomass accumulation, as well as the harvest index (HI) of grain crops, is also considered. The soil water balance in combination with some key crop parameters (canopy development ability, stomatal control of transpiration, canopy senescence, and HI) allow to simulate the effect of water stress on the development of the crop (Steduto et al., 2009). The model describes two types of crop parameters: conservative and non-conservative. The conservative parameters are crop specific but do not change materially with time, management practices, geographic location or climate. They are also assumed not to change with cultivars unless shown otherwise (Hsiao et al., 2009; Raes et al., 2009; Steduto et al., 2009). The water balance and the

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16

a

14

Tuber dry maer (x10 3 kg ha-1 )

Tube r dry m at t e r (x1 0 3 kg ha-1 )

16

12 10 8 6 4 2 0

12 10 8 6 4 2 0

50

0

100

150

200

0

16

50

100

150

200

50

10 0 Days aer planng

150

200

16

c

14

Tuber dry maer (x10 3 kg ha-1 )

Tube r dry m at t e r (x1 0 3 kg ha-1 )

b

14

12

10 8 6 4 2 0

d

14 12

10 8 6 4 2 0

0

50

100 Days aer planng

150

Observed 201 1

200

0

Observed 201 2

Simulated 201 1

Simulated 2012

Fig. 4. Vertical bars: standard deviation of the repetitions. Progression of the simulated and the observed tuber dry matter during 2011 and 2012 ((a): 120% treatment; (b): 100% treatment; (c): 80% treatment; and (d): 60% treatment).

10

y = 1.06x - 0.12 R² = 0.99

5

0

0

DMtub simulated (x10 3 kg ha-1)

15

5 10 DMtub observed (x10 3 kg ha -1)

y = 1.25x - 0.26 R² = 0.97

0

y = 0.97x - 0.14 R² = 0.99

5

0

15

10

0

b

10

0

15

c

5

DMtub simulated (x103 kg ha-1)

15

a

DMtub simulated (x10 3 kg ha -1)

DMtub simulate d (x103 kg ha-1)

15

5 10 DMtub observed (x10 3 kg ha -1)

15

5 10 DMtub observed (x10 3 kg ha-1)

15

d

10

5

0

y = 1.14x - 0.22 R² = 0.97

0

5 10 DMtub observed (x10 3 kg ha-1)

15

Fig. 5. Progression of the simulated and the observed tuber dry matter during 2011 and 2012 with HI = 0.73 ((a): 120% treatment; (b): 100% treatment; (c): 80% treatment; and (d): 60% treatment).

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Table 6 Statistical comparison between observed and simulated canopy cover values for the two experimental years. The slope, intercept, and R2 are from the linear regression between simulated vs. observed values. Treatment

120% 100% 80% 60%

n

2011

3 3 3 3

2012

RMSE

d

Slope

Intercept

R2

RMSE

d

Slope

Intercept

R2

9.85 7.46 6.19 6.17

0.94 0.97 0.97 0.98

0.93 1.01 1.21 1.14

6.17 −1.64 −19.84 −14.19

0.80 0.88 0.97 0.95

5.04 7.82 8.19 12.67

0.99 0.99 0.98 0.96

1.14 1.12 1.08 1.22

−11.22 −12.09 1.04 −4.79

0.99 0.97 0.96 0.94

n: Number of repetitions; RMSE: root mean square error (%); d: Willmott’s index of agreement (dimensionless).

160

a

Soil water content (mm)

Soil water content (mm)

120 100 80 60 40 20 120 72

92

112

132

Soil Water content (mm)

Soil water content (mm)

100 80 60 40

92

112

132

152

80 60 40 92

112

132

152

172

92

112

132

152

172

92

112

132 DAP

152

172

112

132

152

172

d

140 120 100 80 60 40

e

160

Soil water content (mm)

Soil water content (mm)

120 72 100 80 60 40

72

92

112

132

152

Soil water content (mm)

g 100 80 60 40 20

72

f

140 120 100 80 60 40 20 160 72

20

Soil water content (mm)

100

20

20

120

120

20 160 72

152

c

b

140

h

140 120 100 80 60 40 20

72

92

112

132

152

72

92

Days aer planng AquaCrop

Days aer planng EnviroScan

FC

PWP

Fig. 6. FC: field capacity; PWP: permanent wilting point. Progression of the simulated and the measured soil moisture content at 0.3 m depth in 2011 ((a): 120% treatment; (c): 100% treatment; (e): 80% treatment; and (g): 60% treatment) and 2012 ((b): 120% treatment; (d): 100% treatment; (f): 80% treatment; and (h): 60% treatment).

separation of evapotranspiration into evaporation and crop transpiration were calculated daily (Raes et al., 2009). With the daily transpiration value, the crop growth equation proposed by Steduto et al. (2009) was used, which included the WP* factor. This parameter increases the robustness of the model, because it remains

constant for a wide range of climatic conditions and crop management strategies (Steduto et al., 2007).

B = WP ∗ × ˙

 Tr  ET

(2)

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Fig. 7. Simulated, observed (Bowen ratio device), and calculated (simplified water balance using EnviroScanTM sensor readings) evapotranspiration in 2011 ((a): 120% treatment; (c): 100% treatment; (e): 80% treatment; and (g): 60% treatment) and 2012 ((b): 120% treatment; (d): 100% treatment; (f): 80% treatment; and (h): 60% treatment).

where B is the biomass (g MS m−2 ); WP* is the normalized water productivity (g m−2 ); Tr is the crop transpiration (mm); and ET0 is the reference evapotranspiration (mm). The crop yield was determined by the combination of the simulated aboveground biomass (Eq. (2)) and the Reference Harvest Index (HI0 ). The HI0 is a nonconservative parameter that varies depending on the deficit of irrigation water experienced by the crop, which depends on the crop stage and the severity of the stress (Steduto et al., 2009). So, Raes et al. (2012) established values that depended on the crop cultivar, which ranged from 0.70 to 0.85 (Struik, 2007). Regarding the Agria cultivar, the HI0 during the 2011 cropping season ranged from 0.80 to 0.87. 2.8. Model calibration and validation The calibration of the model was conducted through an iterative process that introduced the data values that best simulated the primary crop growth parameters (canopy cover, CC; total dry matter content, TDM; evapotranspiration, ET; total dry matter content at harvest and crop yield; and water movement in soil, Ws ). The values used were obtained from several sources: experimental data from

the 2011 cropping season, data obtained from other researchers (Domínguez et al., 2011; García-Vila and Fereres, 2012), and data from the reference manual for Aquacrop (Raes et al., 2012). The validation of the model was conducted with experimental data from the 2012 cropping season. Additionally, calibrated for the 2011 cropping season, the conservative and nonconservative parameters, which depend on the crop cultivar, were regarded as constant. The other nonconservative parameters were modified according to field measurements. The conservative parameters that were calibrated using the field sample data were the crop growth (CGC) and crop senescence (CDC) coefficients, in addition to the water productivity (WP*). These parameters were calibrated for the reference irrigation treatment (100%) in 2011. To compute the CGC and CDC, the equations proposed by Raes et al. (2012) and data such as the maximum CC (CCx) or initial CC (CC0 ) were used. Thus, the CGC and CDC were computed with a nonlinear resolution (Microsoft Excel 2010), which was used to obtain the optimum goodness of fit between the measured and the simulated CC for the 100% irrigation treatment in 2011. To determine the WP*, the period of time for the crop to reach maximum canopy cover (approximately 96%) was used. For

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this case, all the water was consumed through transpiration processes without evaporation, and this parameter was determined for a period of 52 days (from day 75 to 126 after planting). The water stress coefficients were obtained according to Raes et al. (2012). To evaluate the goodness of fit between measured and simulated data, the root mean square error (RMSE), the Willmott index of agreement “d” (Willmott, 1982), the coefficient of determination (R2 ) and the coefficients of the regression curve were used. The model was well calibrated and validated when the measured and simulated data for CC, TDM, Ws , and ET reached “d” values higher than 0.9, RMSE values close to 0, and R2 values near 1 (Benli et al., 2007; Todorovic et al., 2009; Araya et al., 2010; Raes et al., 2012). Moreover, total biomass production and crop yield simulations were acceptable when the differences between measured and simulated data were ±10% (Farahani et al., 2009) and when the percentage of simulated data for each parameter that satisfied this requirement was equal to or higher than 70% (Domínguez et al., 2012). The standard deviation is a measure of the dispersion of a set of data from its mean. In consequence, simulated values within the standard deviation of observed data is another way to validate the accuracy of the calibration (Ginoux et al., 2004; Chiti et al., 2010).

  n 1 RMSE =  (Si − Oi )2 n

(3)

9

sunflowers is longer than that in maize (860 ◦ C for potato and 800–1000 ◦ C for sunflower compared with 350–450 ◦ C for maize). In the simulations of 2011, the CCx value (96%) was reached in all the treatments, except in the treatment with the largest deficit, which reached a slightly lower value than the maximum CC (between 93% and 95%; Fig. 2d). In the validation year, the model overestimated the observed CC in the 80% and 60% irrigation treatments by 12% and 13%, respectively. Nevertheless, the simulated CC was within the standard deviation of the observed data (Fig. 2c and d). The Willmott index of agreement (“d”) and the calculated RMSE for CC had excellent goodness of fit for most of the treatments, with strong linear relationships and high coefficients of determination (Table 6). The d values were higher than 0.90, and the RMSEs were below 10%, except for the 60% treatment (2012), which was caused by the overestimation of CC during the CCx stage (Table 6). Similar results were found by Farahani et al. (2009) and Hsiao et al. (2009) for a maize crop. The coefficients of determination and the slopes were close to 1, whereas the intercept coefficients moved away from the reference value (0) because of the lack of fit between the simulated and observed values at the beginning and at the end of the simulation of the CC progression. 3.2. Progression of total and tubers dry matter

i=1

where the RMSE is the normalized root mean square error; Si is the simulated value; Oi is the measured value; and n is the number of measurements. d=1−

ni=1 (Si − Oi )2 ni=1 (|Si − MO| + |Oi − MO|)2

(4)

where d is the Willmott index of agreement; Si is the simulated value; Oi is the measured value; MO is the average value of “n” measured values; and n is the number of measurements. 3. Results and discussion 3.1. Canopy cover In both the processes of calibration (2011) and validation (2012), there was good fit between the simulated and observed CC values (Fig. 2). However, in 2012, significant differences among treatments were not found with the simulated CC data as occurred with the field data. Additionally, in the 60% treatment, significant differences were found approximately 100 days after planting (DAP) in the year of the validation. Generally, the standard deviation of the observed CC data during the maximum CC stage (CCx) was lower than the standard deviation calculated for the stages of vegetative development and senescence (Fig. 2). This result was explained by the variability in the leaf architecture of the plants used to monitor the CC. Thus, in a suitable way, the model simulated the progression of the average observed CC data for the entire growing cycle. Similar conclusions were reached by Hsiao et al. (2009), Araya et al. (2010) and García-Vila and Fereres (2012) in simulations of maize, barley, and potato, respectively. The CGC and CDC coefficients were calibrated as 1.49% GDD−1 and 0.38% GDD−1 , respectively. These values are similar to those recommended by Raes et al. (2012) for a potato crop (CGC: 1.70–2.00% GDD−1 ; CDC: 0.20% GDD−1 ). Compared with other crops, the CGC was similar to that of maize (1.20–1.30% GDD−1 ) and sunflower (1.50% GDD−1 ) (Farahani et al., 2009; Todorovic et al., 2009; Paredes et al., 2014), whereas the CDC was lower than that of maize and was similar to that of sunflower (0.60% GDD−1 ; Raes et al., 2012) because the stage of senescence in potatoes and

The average observed WP* value for the 100% treatment was 17.7 g m−2 with a standard deviation of 1.6 g m−2 . After the process of calibration, the WP* was estimated as 19.0 g m−2 , and this value was within the range recommended by Raes et al. (2012) for C3 crops (15–20 g m−2 ) and the confidence interval determined with the field data. As simulated by the model, the values of TDM were suitable during the vegetative development and ripening stages (Fig. 3). However, during the formation of tubers (between 80 and 120 DAP in 2011 and between 100 and 150 DAP in 2012; Fig. 3), the model underestimated the TDM by approximately 10–15% when compared with the field data. The generalized underestimation of TDM during the entire growing cycle was also observed by other authors for maize (Hsiao et al., 2009) and barley (Araya et al., 2010). Notably, during the validation year, the model overestimated the field data by approximately 14% and 5% for the 80% and 60% treatments, respectively. The high temperatures that occurred during 2012, in conjunction with the water deficit conditions, caused a drop in the translocation rate of dry matter to the tubers of these treatments (Figs. 3c and d) (Kooman et al., 1996). The accumulated dry matter in tubers (DMtub) showed a good fit in both the calibration and validation processes (Fig. 4). The reference harvest index (HI0 ) was calibrated as 0.82, which was within the range proposed by Raes et al. (2012) for potato (0.70–0.85). In the validation year, the model overestimated DMtub in all the treatments, because the HI value was not properly simulated with Aquacrop (average observed HI = 0.74 vs. average simulated HI = 0.81; Fig. 4c and d). Between the beginning of flowering and the beginning of senescence (Table 5), the standard deviation of the observed TDM (Fig. 3) and DMtub (Fig. 4) was higher than between beginning of senescence and tuber ripening stages. The results obtained during the initial stages of growth might be explained by the difference in rates of translocation of photoassimilates to tubers, which stabilize after the beginning of senescence (Kooman et al., 1996). Previous studies with Aquacrop for the simulation of crop growth did not analyze the progression of yield (Farahani et al., 2009; García-Vila et al., 2009; Geerts et al., 2009; Hsiao et al., 2009; Todorovic et al., 2009; Araya et al., 2010; Andarzian et al., 2011; Katerji et al., 2013; Paredes et al., 2014). For the potato crop, the

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Table 7 Statistical comparison between simulated and observed changes in total dry matter (TDM) and tuber dry matter (DMtub) for the two experimental years. The slope, intercept, and R2 are from the linear regression between simulated vs. observed values. Treatment

n

TDM

2011

3 3 3 3 3 3 3 3

120% 100% 80% 60% DMtub 120% 100% 80% 60%

2012

RMSE

d

Slope

Intercept

R2

RMSE

d

Slope

Intercept

R2

1.19 1.32 0.87 1.71 1.38 2.48 1.05 2.02

0.99 0.99 0.99 0.97 0.98 0.95 0.99 0.94

0.92 0.93 0.95 0.93 1.02 0.95 1.06 0.91

0.30 −0.15 0.61 0.46 −1.22 −1.56 −0.59 −1.10

0.97 0.98 0.97 0.93 0.98 0.91 0.96 0.92

1.03 2.44 1.55 0.50 1.21 0.75 2.03 1.35

1.00 0.99 0.99 1.00 1.00 1.00 0.98 0.99

0.98 0.88 1.23 1.06 1.20 1.09 1.40 1.29

−0.33 −0.81 −0.86 −0.21 −0.15 −0.17 −0.31 −0.26

0.99 0.98 0.97 0.99 0.99 0.99 0.97 0.96

n: Number of repetitions; RMSE: root mean square error (×103 kg ha−1 ); d: Willmott’s agreement index (dimensionless).

Table 8 Observed and simulated biomass, crop yield, and harvest index for the calibration and validation of the model. Treat.

Calibration120 100 80 60 p-Value – Validation 120 100 80 60 – p-Value

n

3 3 3 3 – 3 3 3 3 –

Biomass (× 103 kg ha−1 )

Yield (× 103 kg ha−1 ) +

Harvest index +

++

++

Obs.

Sim.

Dev.(%)

Obs.

Sim .

Dev .(%)

Sim .

Dev.

16.40a 16.88a 16.92a 14.51b

16.92a 16.85a 15.58b 13.60c

14.17a 14.29a 13.30a 11.59b

14.27a 14.20a 12.93b 10.88c

*

*

*

*

17.38a 19.26a 13.67b 12.25b

18.11a 17.50a 16.22b 13.10c

13.05a 13.67a 10.46b 8.90b

14.97a 14.44a 13.06b 10.06c

*

*

*

0.68 −0.58 −2.81 −6.17 – 14.73 5.61 24.81 13.03 –

– – – – – 13.33a 12.85a 11.63b 8.96c

*

3.21 −0.16 −7.95 −6.29 – 4.19 −9.11 18.73 6.94 –

– – – – – 2.14 −5.98 11.11 0.62 –

*

(%)

Obs.

Sim+ .

Dev+ .(%)

Sim++ .

Dev++ .(%)

0.87a 0.85a 0.80b 0.80b

0.84a 0.84a 0.83a 0.80b

*

*

0.76 0.71 0.77 0.73 ns

0.83a 0.82a 0.80b 0.77c

−2.65 0.12 3.73 0.11 – 8.76 16.10 5.00 5.61 –

– – – – – 0.74a 0.73a 0.72a 0.68b

– – – – – −3.17 3.35 −6.53 −5.98 –

*

*

Treat: treatment; n: number of repetitions; Obs: observed value; Sim: simulated value; Dev: deviation between simulated and observed values; ns: Not significant; +: Harvest index = 0.82; ++: Harvest index = 0.73.

evaluation of the progression of the harvestable portion is basic to improve the quality of the calibration process, because several factors related to water deficit stress affect the daily calculation of the HI (Raes et al., 2012). The errors in the estimation of the field TDM and DMtub data were low, ranging from 0.50 to 2.48 Tm ha−1 in both years (Table 7), which was between 4% and 17% of the observed data. Larger errors occurred in the 80% and 60% treatments in the validation year (Table 7). The d index was higher than 0.90 for both variables in all the treatments (Table 7). The RMSE and d index values were similar to those obtained by other authors in simulations of the biomass progression of maize (Heng et al., 2009; Hsiao et al., 2009; GarcíaVila and Fereres, 2012; Katerji et al., 2013), wheat (Andarzian et al., 2011), barley (Araya et al., 2010), sunflower (Todorovic et al., 2009; García-Vila and Fereres, 2012), cotton (Farahani et al., 2009; GarcíaVila et al., 2009; García-Vila and Fereres, 2012), tomato (Katerji et al., 2013) and potato (García-Vila and Fereres, 2012). The slopes of the regression lines and the coefficients of determination were close to 1, and the intercept coefficients tended to 0. 3.3. Total biomass and yield, and harvest index For the calibration year, the simulated values of total biomass and crop yield at harvest were similar to those observed from the field, with differences within ±10% for all treatments (Table 8). In the validation year, the biomass of the 80% treatment and the crop yield of three of the four treatments were overestimated by the model (Table 8). The rates of error between the observed and simulated yields were higher than 10% and were caused by a poor simulation of the final HI. The HI values obtained in the field tests were similar to those obtained by other authors (Kooman et al., 1996; Struik, 2011; Quiroz, 2012; Raes et al., 2012). However, the HI values obtained during the validation season were approximately 10% lower than

those obtained in the calibration season (Table 8). Hanks (1983) stated that HI is sensitive to large changes in the weather. Temperature and photoperiod are the main weather factors affecting the rate of accumulation of biomass, partitioning of biomass to leaves, stems, roots, stolons and tubers (Struik and Ewing, 1995). Many experiments stated that high temperatures reduce the harvest index (Wheeler et al., 1986; Wolf et al., 1990; Van Dam et al., 1995); at early stages they cause delay or even inhibit tuber initiation (Struik and Ewing, 1995); and they extend the potato crop cycle, mainly when they occur between tuber initiation and the end of leaf growth (Kooman et al., 1996; Fleisher and Timlim, 2006; Struik, 2007). Dwelle et al. (1981) concluded that the optimum temperature for the photosynthesis process is below 25 ◦ C. Optimum temperatures for both total dry matter yield and tuber dry matter yield is 20 ◦ C at 12 h of photoperiod (Wheeler et al., 1986), while optimum temperature to starch synthesis is 21.5 ◦ C (Mohabir and John, 1988). Kooman and Haverkort (1995) determined that minimum and maximum temperatures for a proper development of this crop are between 0 and 7 ◦ C and 30–35 ◦ C, respectively. Therefore, the high temperatures that occurred during the tuber formation stage of the validation season were the cause of these differences. In 2011, the 80% and 60% treatments were the most efficient in terms of water use, which presented significant differences with the other two treatments (Table 9). These treatments did not show differences between observed and simulated values. With regards to year 2012, not significant differences were found among treatments. The high temperatures in 2012 decreased the WUE results. The Aquacrop model does not include a high temperature stress factor to decrease the daily accumulation of DMtub and the final crop yield, which caused the differences observed during the simulations (Table 8). Thus, the simulations of the four treatments in the validation year were repeated with HI0 = 0.73 instead of 0.82 (average HI for the 100% treatment in 2012 and 2011, respectively; Table 8); these simulations improved the DMtub values (Fig. 5),

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Table 9 Observed and simulated water use efficiency (WUE). WUE (kg DMT m−3 ) Treatment

Observed 2011

Simulated 2011

Observed 2012

Simulated 2012

120% 100% 80% 60% p-Value <0.05*

2.02a 2.37b 2.84c 2.85c

1.99a 2.25b 2.46c 2.49c

**

**

1.74 2.15 1.74 1.87 ns

1.77 1.90 2.00 1.92 ns

DMT: total dry matter content of potato; ns: not significant. * ANOVA with Duncan’s test. ** Significant. Table 10 Root mean square error (RMSE) and aggregation index (d) for the progression during 2012 of the simulated tuber dry matter (DMtub), with the harvest index = 0.73. Treatment

n

120% 100% 80% 60%

DMtub

Validation

3 3 3 3

RMSE (× 103 kg ha−1 )

d (dimensionless)

0.52 0.50 1.38 0.87

1.00 1.00 0.99 1.00

Table 11 Statistical comparison between simulated and observed final biomass, yield, and harvest index in 2011 and 2012. The slope, intercept, and R2 are from the linear regression between simulated vs. observed values. n

Calibration

Validation

RMSE

d

Slope

Intercept

2

R

B Y

4 4

0.85 0.41a

0.87 0.97

1.16 1.27

−3.02 −3.90

0.72 0.99

HI

4

0.02

0.82

0.48

0.43

0.59

a

RMSE a

1.65 1.76a 0.72b 0.07 0.04b

d

Slope

Intercept

R2

0.88 0.83 0.96b 0.37 0.36b

0.58 0.92 0.82b 0.16 0.14b

7.06 2.53 2.25b 0.69 0.61b

0.73 0.87 0.87b 0.02 0.02b

n: Number of repetitions; RMSE: Root mean square error; B: biomass; Y: yield; HI: harvest index. a × 103 kg ha−1 . b Values with HI = 0.73. Table 12 Root mean square error (RMSE) and aggregation index (d) for crop evapotranspiration in 2011 and 2012. Treatment

n

Calibration

Validation −1

RMSE (mm day 120% 100% 80% 60%

3 3 3 3

1.17 0.98 1.02 0.82

)

d

RMSE (mm day−1 )

d

0.89 0.91 0.91 0.91

0.80 0.72 1.39 1.09

0.95 0.97 0.87 0.91

n: Number of repetitions; d: Willmott’s agreement index (dimensionless).

the final crop yield, and the final HI. The differences between the observed and simulated yields were within ±10% in three of the four treatments (Table 8). The RMSE, d (Table 10), and R2 values (Fig. 5) all indicated high correlation between the simulated and the observed data. The biomass, final crop yield and HI were statistically analyzed (Table 11), and the results were consistent with the values published by other authors (Hsiao et al., 2009; Todorovic et al., 2009; Araya et al., 2010; García-Vila and Fereres, 2012). The use of the HI = 0.73 considerably improved the significance of the results (Table 11). 3.4. Soil moisture The change in soil moisture simulated by the Aquacrop model was similar to the EnviroScanTM sensor measurements up to a

depth of 0.3 m (PFC; Fig. 6). Each figure corresponds to one of the monitored plots per treatment. As expected, the monitored and simulated soil moisture contents of the 120% and 100% treatments (Fig. 6a–d) were higher than those of the 80% and 60% treatments (Fig. 6e–h). In both seasons, the soil moisture content of the deficit treatments ranged between permanent wilting point (PWP) and field capacity (FC). Nevertheless, the soil moisture content of the 120% treatment was close to or over the FC line (Fig. 6a and b), which caused percolation. The 100% treatment was near the FC line (Fig. 6c and d). The deficit treatments showed a drop in the soil moisture content with time (Fig. 6e–h). In 2011 for ten days, EnviroScanTM sensors registered values below the theoretical PWP, whereas the model simulated values close to the PWP (Fig. 6g). According to the growth rate of the crop in the 60% treatment, the readings of the sensors were incorrect because the crop did not dry out, which can be explained because the sensor readings were outside of the calibration range. According to the changes in soil moisture of the 100% treatment (Fig. 6c and d), the reference crop coefficient used for the irrigation scheduling was correct because this treatment received sufficient water to reach the potential crop yield. All treatments had R2 values between 0.45 and 0.74, which indicated that the simulations were similar to the soil moisture progressions read by the sensors. In both years, the lowest correlations occurred during the first growing period (Fig. 6). These results are different from those reported by Farahani et al. (2009) and Paredes et al. (2014) who found that overestimations were higher when the soil moisture content was lower. This effect was not clearly

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Table 13 Parameter values for the simulation of a potato crop (cv. Agria) using the Aquacrop model. Parameter

Value

Source

Crop Phenology Time to emergence, GDDNC Time to maximum effective rooting depth, GDDNC Time to start tuber formation, GDDNC Time to start senescence, GDDNC Time to harvest, GDDNC Time to maximum canopy cover, GDDC Time for tuber formation, GDDC

332 967 553 1468 2324 967 1748

m m m m m m m

Crop growth and development Plant density, plants m−2NC Depth of sowing, mNC Initial canopy cover, %NC Maximum effective rooting depth, mNC Shape factor describing root zone expansionC Maximum canopy cover, % C Base temperature, ◦ C C Upper temperature, ◦ C C Canopy size of transplanted seedling, cm2 plant−1C Canopy growth coefficient, % ◦ C−1 day−1C Canopy decline coefficient, % ◦ C−1 day−1C Water productivity, g m−2 C Maximum root extraction, mm day−1C Water extraction pattern, % C Crop transpiration coefficient C Canopy shelter in late season, % C

5.8 0.20 0.59 0.40 1.5 96.0 2.0 26.0 10 1.49 0.38 19.0 10 40-30-20-10 1.15 60

m m e m b1 m b2 b2 e cv cv cv m b1 b3 b1

Yield formation Reference harvest index, % NC Possible increase of HI caused by water stress before starting yield formation, % C Positive impact of restricted vegetative growth during yield formation on HI C Negative impact of stomata closure during yield formation on HI C Allowable maximum increase of specified HI, % C

82.0 3 None 10 5

cv e b1 e b1

0.20 0.60 0.55 0.70 3 3 3

b1 b1 b1 b1 b1 b1 b1

7

b1

Soil water stress Upper threshold for canopy expansion C Lower threshold for canopy expansion C Upper threshold for stomata closure C Upper threshold for early canopy senescence C Shape factor for canopy expansion C Shape factor for stomata closure C Shape factor for early canopy senescence C Air Temperature Stress Minimum growing degrees required for full biomass production, ◦ C day−1C

C: conservative; NC: nonconservative; cv: calibrated and validated using field data; b: obtained from the bibliography; e: estimated from field data; m: measured in the experimental plots; GDD: growing-degree-days (◦ C). 1 Steduto et al. (2012) 2 Griffin et al. (1993); Kooman and Haverkort, (1995); and Steduto et al. (2012) 3 Allen et al. (1998)

observed by these authors when the soil moisture content was close to FC. In the studies of Raes et al. (2012) and Paredes et al. (2014), the goodness of fit was high between observed and simulated soil moisture progressions and depended on suitable parameterization of the CC factor.

3.5. Crop evapotranspiration The comparisons among the actual crop evapotranspiration (ET) values simulated by the model and those obtained by the Bowen ratio device (BRET ) and the simplified water balance calculated through the EnviroScanTM sensor measurements (WBSET ) showed moderate correlations (R2 between 0.60 and 0.89; Fig. 7). Generally, the model underestimated the measured ET (Fig. 7) and produced higher correlation coefficients for BRET (Fig. 7a and b) than for WBSET (Fig. 7c–h). Katerji et al. (2013) observed a systematic underestimation of ET when simulating maize and tomato crop growth and concluded that when the water deficit was high the underestimation was also high. By contrast, García-Vila et al. (2009) simulated a cotton crop

and found a better correlation between observed and simulated ET values for the treatments with lower ET values. The simulations had a higher dispersion of the data from a 1:1 relationship when the water deficit increased (Fig. 7e–h). The differences might be caused by the time required for the complete infiltration and redistribution of the water in the soil. The EnviroScanTM sensors progressively responded to the supply of water, whereas the Aquacrop model considered the total infiltration and redistribution of the irrigation/rainfall water supplied during the day. The ET simulations had a suitable aggregation with the calculated ET values obtained from the EnviroscanTM readings (Table 12). The RMSE in the estimation of the ET was approximately 1.0 mm day−1 , and similar results were obtained by Heng et al. (2009) who found RMSE values between 1.58 and 2.85 mm day−1 for a maize crop cultivated with different irrigation strategies.

3.6. Parameterization of a potato crop After the calibration and validation of the model, the values assigned to the parameters required for the simulation of a potato

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crop (cv. Agria) under the semiarid conditions of the study area are presented in Table 13. The conservative values should be the same for any potato cultivar cultivated in any other area in the world (Hsiao, 2012). Moreover, nonconservative values should be used with caution under different climatic conditions and/or cultivars. 4. Conclusions The Aquacrop model (4.0 version) was calibrated and validated for a potato crop grown in the Mediterranean and semiarid conditions of southeast Spain. The results of the model for progression of canopy cover, biomass, crop yield, soil water content, and crop evapotranspiration showed high goodness of fit between simulated and observed values. For a suitable simulation of a potato crop, the primary conservative parameters to be calibrated from the field data are the canopy growth coefficient (CGC), canopy decline coefficient (CDC), and water productivity (WP*). The remaining conservative parameter values were similar to those provided in the manual for the model. The model accurately simulated biomass at harvest in both experimental years (difference between simulated and observed data was within ±10%). However, the simulated yield was not accurate for the validation year because the harvest index (HI) values used in the model were higher than those obtained under field conditions. Thus, the model should incorporate a temperature stress coefficient that affects the HI during bulb formation when the crop is affected by high temperatures. The robustness of the model and of the conservative parameters allowed the model to simulate the potato crop response in two years with significantly different weather. Higher yields were obtained in the 100% and 80% treatments compared with other treatments. Irrigation moisture at depths greater than in these treatments did not significantly increase biomass and yield. Additionally, irrigation treatments with less than 80% recommended moisture had large declines in yield. This study increased the goodness of fit for the parameters published in the Aquacrop manual for potato and will allow future users to improve the simulation of the effects of different deficit irrigation strategies for this crop. Acknowledgements This paper was developed within the framework of the project “Riego por aspersión: aplicación de agua, agronomía y flujos de retorno (AGL2010-21681-C03-02)” that was funded by the Spanish Ministry of Science and Innovation (MICINN). The authors also wish to thank the farmers and technicians of the Agroenvironment Educational Center for their support (Aguas Nuevas, Albacete). References Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop Evapotranspiration-guidelines for Computing Crop Water Requirements-FAO Irrigation and Drainage Paper 56, 300. FAO, Rome, pp. 6541. Allen, R.G., Pereira, L.S., Howell, T.A., Jensen, M.E., 2011. Evapotranspiration information reporting: II. Recommended documentation. Agric. Water Manage. 98, 921–929. Andarzian, B., Bannayan, M., Steduto, P., Mazraeh, H., Barati, M.E., Barati, M.A., Rahnama, A., 2011. Validation and testing of the AquaCrop model under full and deficit irrigated wheat production in Iran. Agric. Water Manage. 100, 1–8. Araya, A., Habtu, S., Hadgu, K.M., Kebede, A., Dejene, T., 2010. Test of AquaCrop model in simulating biomass and yield of water deficient and irrigated barley (Hordeum vulgare). Agric. Water Manage. 97, 1838–1846. Ballesteros, R., Ortega, J.F., Hernández, D., Moreno, M.A., 2014. Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part I: description of image acquisition and processing. Precis. Agric., 1–14. Benli, B., Pala, M., Stockle, C., Oweis, T., 2007. Assessment of winter wheat production under early sowing with supplemental irrigation in a cold highland

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