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Assessment of the FAO AquaCrop model in the simulation of rainfed and supplementally irrigated maize, sugar beet and sunflower
Agricultural Water Management 98 (2011) 1615–1621
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Assessment of the FAO AquaCrop model in the simulation of rainfed and supplementally irrigated maize, sugar beet and sunflower Ruzica Stricevic a,∗ , Marija Cosic a , Nevenka Djurovic a , Borivoj Pejic b , Livija Maksimovic c a b c
University of Belgrade, Faculty of Agriculture, Nemanjina 6, 11080 Zemun, Serbia University of Novi Sad, Faculty of Agriculture, Trg Dositeja Obradovica 8, 21000 Novi Sad, Serbia Institute of Field Crops and Vegetables, Maksima Gorkog 30, 21000 Novi Sad, Serbia
a r t i c l e
i n f o
Article history: Received 18 October 2010 Accepted 25 May 2011 Available online 6 July 2011 Key words: AquaCrop Model Simulations Maize Sugar beet Sunflower
a b s t r a c t Farming in Serbia is traditionally rainfed. Analyses show that drought events of varying severity are frequent in this region, although there is no specific pattern. There is a distinct need for an objective assessment of the impact of drought on strategic field crops, to solve the dilemma whether irrigation is required or not. For this reason, and based on available field data, the FAO AquaCrop water driven model was selected to simulate yield and irrigation water use efficiency (IWUE) for three major field crops (maize, sunflower, and sugar beet), under two scenarios: (1) natural water supply and adequate supply of nutrients, and (2) supplementary irrigation and adequate supply of nutrients. The experiments presented here were conducted between 2000 and 2007 in northern Serbia, where chernozem soil is prevalent. Data of 2003 cropping seasons were used for local calibration, whereas the remaining years for validation. Results were such that local calibration resulted in very minor changes of AquaCrop coefficients (e.g., maize basal crop coefficient, sunflower harvest index, etc.). Simulated maize yield levels exhibited the greatest departure from measured data under irrigation conditions (−3.6 and 3.3% during an extremely dry and an extremely wet year, respectively). Simulated sunflower yield levels varied by less than 10% in 8 out of 10 comparisons. The most extreme variation was noted during the extremely wet year. The difference between simulated and measured values in the case of sugar beet was from −10.2 to 12.2%. Large differences were noted only in two or three cases, under extreme climatic conditions. Statistical indicators – root mean square error (RMSE) and index of agreement (d) – for all three crops suggested that the model can be used to highly reliably assess yield and IWUE. This conclusion was derived based on low values of RMSE and high values of d (in the case of maize and sugar beet 0.999 for both yield and IWUE, and in the case of sunflower 0.999 for yield and 0.884 for IWUE). It is noteworthy that under wet conditions, the model suggested that sunflower and sugar beet do not require irrigation, as confirmed by experimental research. These data are significant because they show that the AquaCrop model can be used in impartial decision-making and in the selection of crops to be given irrigation priority in areas where water resources are limited. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Soil characteristics and climatic conditions in northern Serbia are extremely favorable for field crop farming. Plant nutrition is not a limiting factor for high yield since there is vast experience in fertilization, fertility control, and plant protection. Water is the only limiting factor. Field crops can be rainfed because there are frequent years with average precipitation levels during the crop cycle of 300–400 mm. These levels, along with stored soil moisture, pro-
∗ Corresponding author. Tel.: +381 11 615315; fax: +381 11 2193 659. E-mail addresses: [email protected] (R. Stricevic), [email protected] (M. Cosic), [email protected] (N. Djurovic), [email protected] (B. Pejic), [email protected] (L. Maksimovic). 0378-3774/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.agwat.2011.05.011
vide sufficient amounts of water for achieving high yield. However, yield of maize, sunflower, and sugar beet cultivated on more than 50% of Serbia’s arable land, is reduced by as much as 60% during dry years (Pejic et al., 2009; Maksimovic and Dragovic, 2004; Matovic et al., 2002). Previous investigations have revealed that during a series of 56 consecutive years, there were 14 drought events and three years of excessive moisture, but most years were “normal” although they exhibited significant departures from average values. The severity and occurrence of drought events in this region are difficult to predict accurately (Stricevic et al., 2011). In a market-driven agriculture such as Serbia’s, drought events and extended periods of excessive moisture directly affect field crop farming, as well as animal husbandry, and ultimately impact the food industry. This leads to changes in agricultural product supply and demand in the marketplace, and to price fluctuations.
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Numerous organizations (ministries, insurance companies, trading companies) are keen to find out how weather conditions will affect yield, in order to prepare and respond appropriately. Yield forecasts have generally been based on experience and subjective assessments, which are certainly unreliable. Today, it is possible to apply numerous advanced techniques and methods to predict yield, or to predict the impact of drought on yield. Wu and Wilhite (2004) used the Standardized Precipitation Index (SPI) and the Crop-Specific Drought Index (CSDI) to assess the risk of drought for soy and maize in Nebraska, USA. Using canonical discriminant and classificatory discriminant analyses, Wu and Wilhite generated high-risk and low-risk data sets based on the properties of variables derived from SPI and CSDI for soy and maize. Ji and Peters (2003) studied the impact of drought on vegetation in the US Great Plains and concluded that the SPI method, over a period of three months, best described the severity of drought and its impact on vegetation but that, applying remote detection, it was better to assess the entire vegetation season. Park et al. (2005) applied three adaptive techniques: (i) general linear models (GLMs), (ii) artificial neural networks (ANNs), and regression trees (RTs) to predict crop yield under varying soil and land management conditions. Quiring and Papakryiakou (2003) compared four indices (Palmer Drought Severity Index, Palmer’s Z-index, Standardized Precipitation Index, and NOAA Drought Index) to forecast Canada Western Red Spring wheat yield in the Canadian Prairies. Their research showed that these indices were the best drought indicators for the Canadian Prairies, but that there were significant yield forecast variations which required prior evaluation of these methods before any one of them was used in practice. Crop growth models have been commonly used to simulate biomass and yield of various plant species. Gerik et al. (1988) successfully applied SORKAM, a dynamic crop growth model, to simulate ratoon grain sorghum in several climatic regions of Texas. WOFOST, SWAP, and MARS models were developed at Wageningen, in the Netherlands (Boogaard et al., 1998; Van Dam et al., 2008), which turned out to be very reliable for sunflower biomass and yield simulations under Mediterranean conditions (Todorovic et al., 2009). Yu et al. (2006) simulated maize and wheat yield under large-scale farming conditions using the Root Zone Water Quality Model (RZWQM) or the CERES model, over a relatively long time period. They concluded that the model, previously calibrated per local soil and climate conditions of the Northern China Plain, allowed for successful maize and wheat yield simulations for various fertilization and irrigation scenarios, with relatively small errors. Based on their research, Ma et al. (2006) arrived at similar conclusions and confirmed that there was no statistically significant difference between the data derived by means of these models. They recommended that both models be used for different soil, water, and farming conditions. To simulate maize growth and grain yield, Zand-Parsa et al. (2006) developed a Maize Simulation Model (MSM). This model was validated for the Far area in Iran, using data from two years of research of maize growth for different fertilization and irrigation levels. The model proved to be very reliable for the estimation of maize yield. Given that water is a common limiting factor for high yield throughout the world, FAO recently developed a crop growth model – AquaCrop. This model targets a broad base of users (farmers, consultants, water managers, and policymakers) who require information about biomass and yield production of crops farmed under different water and nutrient availability conditions. The model is relatively easy to use and the 33 types of required input data related to climate, soil, agricultural techniques and crop characteristics can be readily derived from experimental research. Steduto et al. (2009) and Raes et al. (2009) provide a detailed description and present the architecture of the model. Following local calibration and validation, the model can safely be used to simulate crop production
in practice (Hsiao et al., 2009; Heng et al., 2009). Since water is a common limiting factor in Serbia, the FAO AquaCrop – version 3.0 water driven model was selected to simulate yield of the three major field crops: maize, sunflower, and sugar beet under two scenarios: (1) natural water supply and adequate supply of nutrients, and (2) supplementary irrigation and adequate supply of nutrients. Since it is often debated whether irrigation of field crops is justifiable in Serbia, this paper will assess whether the model can be used to determine Irrigation Water Use Efficiency (IWUE) and to develop irrigation strategies. 2. Materials and methods 2.1. Experimental data Experiments were conducted at an experimental site near Rimski Sancevi (45◦ 20 N latitude; 19◦ 51 E longitude, 84 m above sea level), belonging to the Institute of Field Crops and Vegetables. Maize experiments were conducted from 2000 to 2006, sunflower experiments from 2000 to 2005, and sugar beet experiments from 2000 to 2007, under both rainfed and irrigation conditions. The soil at the experimental site is comprised of very deep, wellstructured clayey chernozem, with a deep mollic horizon. Water characteristics and physical properties of the soil are good. Its total porosity is 54.9 and 48.8%, field capacity (upper limit) is 29.4 and 35.8%, the wilting point is 12.3 and 15.1% (all by volume), and the bulk density is 1.13 and 1.35 g cm−3 for 0–0.3 m and 0.3–0.75 m layers, respectively. Chemical characteristics are extremely favorable; pH levels vary from neutral to slightly alkaline. The content of humus is 3.5–5.5%, of total nitrogen 0.18–0.25%, of P2 O5 0.17–0.18%, and of K2 O about 0.73%. Mineral fertilizers were added during the experiment, as required, to maintain soil fertility. The climate of the experimental field is characterized as continental. The average precipitation level during the studied period of 56 years is 614 mm (maximum of 998 mm and minimum of 288 mm). During the growing cycle from April to September, the average precipitation sum is 358 mm (maximum of 745 mm and minimum of 148 mm). Fig. 1 shows average precipitation levels and average air temperatures by month, indicating crop water deficit during the summer months. The average reference evapotranspiration rate during the warmest months (July and August) is up to 5 mm day−1 ; days, generally windy, when it reaches 6 mm day−1 are rare. Irrigation is not required during certain years because precipitation sums during the growing cycle, along with the water stored within the soil, are sufficient to ensure high yield.
Fig. 1. Mean monthly air temperature and precipitation sum.
R. Stricevic et al. / Agricultural Water Management 98 (2011) 1615–1621
Conversely, when precipitation is insufficient, supplementary irrigation is often used. Years during which irrigation is required over the entire growing cycle are rare. Climate input data for the simulations were obtained from a first-order weather station which was located within the experimental field. Weather data: daily precipitation sums, daily low and high air temperatures, minimum and maximum relative air humidity, wind speed, and insolation, were obtained from a local weather station at Rimski Sancevi, to calculate reference evapotranspiration rates applying the FAO Penman-Monteith method (Allen et al., 1998). The experiments were set up using a split-plot block system. Sowing was conducted according to the local practices and all required agritechnical measures, typical of local conditions, were undertaken. Maize hybrid NS-640, Lara sugar beets and sunflower hybrid NS-H-111 were grown under two scenarios: rainfed and irrigated. Irrigation norms were determined based on daily evapotranspiration calculations and soil moisture measurements. The lower limit for soil dryness was 50% of the total available amount of water. The soil was generally irrigated to field capacity, such that the crops enjoyed suitable conditions for realizing their genetic potential. The crop was harvested immediately after maturity, such that harvest and maturity dates coincide. Table 1 shows sowing, emergence, flowering and harvesting dates and plant density for maize, sugar beet and sunflower as well as total precipitation levels during the growing cycle and, for irrigated crops, total amounts of water for the growing cycle. The above table clearly shows that 2000 was an exceptionally dry year, with a precipitation sum lower than average by a factor of 2.5. On the other hand, 2001 was distinctly wet and the precipitation sum exhibited a significant departure from average levels. Weather conditions during the remaining years were deemed to be normal. 2.2. Model parameters and input data AquaCrop version 3.0 offers files which contain parameters suitable for the simulation of maize, sugar beet and sunflower. Some of these parameters are not universal and need to be adjusted to local conditions and the studied cultivars. Crop development data were real data, derived from field investigations, while other data were adjusted and calibrated. All input parameters were locally calibrated for the studied scenarios of irrigated maize, sunflower and sugar beet yield over the year of 2003, while the model was validated for all scenarios without irrigation and scenarios with irrigation for the remaining years. 2003 was a moderately dry year and, as such, deemed suitable for the adjustment of all model parameters. The effect of soil fertility on yield was not addressed since sufficient amounts of mineral matter were added under all scenarios, to ensure the realization of full genetic potential. Other significant input parameters are presented in Table 2, which clearly shows that default values were generally used for water stress of all three crops. Even though this parameter is known to be conservative and applicable under all conditions (Hsiao et al., 2009), a slightly lower value (kcb = 1.01), relative to the default value (kcb = 1.03), was selected for this study. Biomass data for the growing season were not available, such that a reliable calibration could not be performed by varying biomass-related parameters. A similar reduction in the kcb value was noted for sunflower and sugar beet yield. In the case of sunflower yield, the difference between default values of the harvest index (35%) and the proposed harvest index (26%) is attributable to the characteristics of the NS hybrid (Joksimovic et al., 2001). Initial soil moisture under all scenarios was generally equal to field capacity. This is not rare, because in the studied climatic region, groundwater levels are maintained and sufficient amounts of precipitation accumulated to wet the soil to field capacity. Dur-
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ing the dry year 2000, the soil was irrigated to field capacity in all sunflower and maize scenarios. 2.3. Data analysis IWUE was calculated based on the difference in yield between the irrigated and rainfed crop and the overall irrigation norm, which can be expressed as: IWUEm =
Yim − Yrm , I
IWUEs =
Yis − Yrs I
(1)
where Yim = measured yield of irrigated crop, Yrm = measured yield of rainfed crop, Yis = simulated yield of irrigated crop, Yrs = simulated yield of rainfed crop, and I = total amount of water added by irrigation. Three statistical methods were used to analyze and compare yield data derived from field investigations and from simulations. The first was the root mean square error (RMSE) method:
⎡ ⎤ n 1 RMSE = ⎣ (Si − Mi )2 ⎦ n
(2)
i=1
where Si and Mi = simulated and measured values, respectively, and n = number of observations. The unit of RMSE is the same for both variables, and the model’s fit improves when RMSE tends toward zero. The index of agreement (d) was calculated using the Willmott (1982) equation:
n
d=1−
n i=1
i=1
(Si − Mi )2
¯ + |Mi − M|) ¯ (|Si − M|
2
(3)
¯ = average values of measured data. The index of agreewhere M ment is a descriptor and its values range from 0 to 1. The model simulates the studied parameter better as the value approaches 1. 3. Results and discussion Simulation results for maize, sunflower and sugar beet yield, using calibration data sets, are presented in Table 3. Calibration results show an extremely good match between measured values and those simulated by the model. The only exception was sugar beet yield, where the difference was 16.5% but only under rainfed conditions. The parameters obtained from model calibrations were used for model validation. Measured and simulated results for validated data sets of rainfed and irrigated maize, sunflower and sugar beet yield are presented in Table 4. The largest differences between measured and simulated values for irrigated maize yield were noted for 2000 and 2001, but are virtually insignificant (−3.6 and 3.4%). All other deviations were smaller. The year 2000 was extremely dry and maize was under stress throughout the vegetation period, while 2001 was extremely wet. Heng et al. (2009) reported much greater deviations under severe stress conditions for maize. In general, deviations of rainfed maize yield were small (<2%), attesting to the fact that the parameter values proposed in the default file for stress conditions are applicable to continental climates as well. Using similar parameter values, Hsiao et al. (2009) reported much greater deviations from measured values (from −17.1 to +23.8%), but they used different cultivars from the present study. Results of simulated sunflower yield levels varied by less than 10% in 8 out of 10 comparisons. The greatest deviations were noted in 2001; during that year, the differences between measured and simulated values ranged between 17.6% (rainfed) and 14.2% (irrigated). The over-estimated values were a result of the fact that this was an extremely wet year, which did not favor sunflower due to
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Table 1 Plant density, sowing/emergence/flowering/harvesting dates, precipitation sums for the vegetation period (P), total irrigation norms (I) for irrigated crops. Year 2000 2001 2002 2003 2004 2005 2006 2000 2001 2002 2003 2004 2005 2000 2001 2002 2003 2004 2005 2006 2007
Plant density (plant ha−1 ) Maize 65,000 65,000 65,000 63,500 50,000 50,000 58,000 Sunflower 50,750 50,500 44,356 48,000 48,666 43,583 Sugar beet 90,000 76,834 66,740 77,000 80,312 74,917 70,000 80,000
Sowing
Emergence
Flowering
Harvesting
P (mm)
I (mm)
18April 06 April 16 April 22 April 20 April 11 April 22 April
28 April 30 April 29 April 1 May 10 May 1 May 1 May
11 July 10 July 1 July 1 July 5 July 2 July 2 July
20 September 28 September 8 October 14 October 8 October 17 October 20 October
125.5 742 274.1 276.6 359.4 532.5 370
180 60 120 200 105 60 180
19 April 6 April 22 April 22 April 20 April 13 April
28 April 30 April 4 May 5 May 30 April 30 April
03–12 June 04–15 June 11–18 June 12–20 June 14–21 June 14–21 June
30 August 5 September 11 September 11 September 7 September 2 September
122.9 638.8 210.3 218.2 316.1 455.3
180 100 240 165 105 120
25 March 5 April 27 March 27 March 29 March 31 March 3 April 3 April
11 April 20 April 16 April 24 April 8 April 18 April 20 April 16 April
– – – – – – – –
23 October 9 October 9 October 16 October 20 October 20 October 8 October 15 October
155.6 699.6 235.1 236.1 459.1 533.7 415.7 388.3
390 150 350 210 120 105 180 85
Table 2 Input parameters for maize, sunflower and sugar beet yield simulations that differ from default parameters. Parameter
Value Maize Default
Base temperature, ◦ C Cut off temperature, ◦ C Initial canopy cover (CCo), % Canopy decline (CDC), % per day Canopy expansion (CGC), % per day Maximum canopy cover (CCx), % Crop coefficient (kcb ) at CC = 100% Maximum effective rooting depth, m Water productivity (WP), g m−2 Adjusted harvest index Harvest index (Hlo), % Water stress Leaf growth threshold (pupper ) Stomatal stress coefficient curve shape Early canopy senescence (pupper ) Senescence curve shape Adjustment HIo to water stress Before flowering During flowering (pupper ) During yield formation HI increased by inhibition of leaf growth HI reduced by inhibition of stomata
8
Sunflower Default
Calibrated
10
4 40 0.29 13.6 21.8 98 1.1
10 30 0.25 15 10.1 90 1.0
18 10.8 35
26 16.1 26
0.49
0.33
16.3 96 1.03
10 90 1.01
33.7
33
7 3
Sugar beet
Calibrated
– 8
its sensitivity to excessive moisture and plant diseases prompted by humid conditions. AquaCrop predicts achievable sugar beet yield via sugar digestion. Based on experimental data pertaining to sugar beet root yield and sugar digestion by year of our research, sugar yield levels achieved during the experiment were comparable to those predicted by the model. The differences between simulated
0.15 2.5 0.7 2.5
0.22 2.7 0.8 2.7
5 0.85
8 0.8
3
8
Default
0.1 13.6 98 1.15 1.0
Calibrated
0.45 10 87 1.1 2.2
and measured values ranged between −10.2 and 12.2%. Underestimated yield levels were derived for an extremely dry year, under irrigation conditions, while over-estimated values were obtained for rainfed conditions in 2002 and 2003. Given that the model performed so well simulating yield under extremely dry conditions in the studied area (0.22%), further adjustments of parameter values did not improve results.
Table 3 Simulation results for calibration data sets of maize, sunflower and sugar beet yield (irrigated and rainfed treatments in 2003), and deviation from measured values. Year
Maize Sunflower Sugar beet
Rainfed
Irrigated
Measured (Mg ha−1 )
Simulated (Mg ha−1 )
Deviation (%)
Measured (Mg ha−1 )
Simulated (Mg ha−1 )
Deviation (%)
9.6 3.2 8.7
9.7 3.3 10.1
0.4 5.0 16.5
13.5 3.3 12.5
13.6 3.3 12.2
0.9 0.3 −1.8
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Table 4 Measured vs. simulated results for validated data sets of rainfed and irrigated maize, sunflower and sugar beet yield. Year
Rainfed Measured (Mg ha−1 )
2000 2001 2002 2004 2005 2006 2000 2001 2002 2004 2005 2000 2001 2002 2004 2005 2006 2007
Maize 8.0 9.6 11.2 10.5 13.8 13.9 Sunflower 4.5 (3.0) 4.5 4.4 2.9 Sugar beets 10.9 11.5 8.5 13.4 14.4 13.1 14.3
Irrigated Simulated (Mg ha−1 )
Deviation (%)
Measured (Mg ha−1 )
Simulated (Mg ha−1 )
Deviation (%)
8.0 9.7 11.2 10.5 13.6 14.0
−0.2 0.7 −0.3 0.3 −1.0 0.4
13.4 10.8 13.6 13.0 14.2 14.8
13.0 11.1 13.7 12.9 14.6 15.0
−3.6 3.4 0.6 −0.2 2.8 1.4
4.2 3.6 4.8 4.4 2.8
−6.4 17.6 5.9 0.4 −4.5
5.5 (3.1) 5.1 4.0 1.8
5.1 3.6 4.8 4.4 1.9
−7.0 14.2 −4.3 10.1 6.6
10.9 11.4 9.5 13.3 14.4 13.1 14.3
0.2 −1.3 12.2 −0.8 −0.3 −0.3 −0.0
19.8 11.4 11.2 15.5 14.0 13.5 16.0
17.8 11.3 12.4 15.5 14.1 13.4 16.2
−10.2 −0.2 10.5 −0.2 0.8 −0.3 1.3
Note: The values given in parentheses represent yields reduced by crop diseases; they are not included in the overall assessment.
Figs. 2–4 show simulated and measured maize, sunflower and sugar beet yield levels, respectively. The graphics reflect only model validation data. It is important to note that trendline gradients of irrigated and rainfed crops virtually coincide. The straight-line equation and the coefficient of determination (R2 ) for maize show that the model simulated grain yield with a high degree of reliability. Trendline gradients deviate from the desirable straight line (x = y) for sugar beet slightly more than for sunflower, and vice versa in the case of the coefficient of determination. This is as expected, since the studied sunflower hybrid, on the one hand,
tolerates drought and, on the other hand, drought events do not occur regularly in the studied region. Simulated yield values for the year 2001 were excluded; they were not suitable for a model confidence assessment because the yields measured were affected by crop diseases. Dry periods have been recorded at different stages of development, in varying degrees. Wet years are also frequent, and they occasionally cause a reduction in yield due to disease. The model reflected IWUE rather well for all three crops (Table 5). In the case of maize, the poorest simulation results were obtained for the year 2005. During that year, there was sufficient precipitation to support high yield and the model simulated rain-
Fig. 2. Measured vs. simulated yield of maize.
Fig. 4. Measured vs. simulated yield of sugar beet.
Table 5 Irrigation water use efficiency for measured and simulated maize, sunflower and sugar beet for validation data set. Year
2000 2001 2002 2004 2005 2006 2007 Fig. 3. Measured vs. simulated yield of sunflower.
Maize
Sunflower
IWUEm
IWUEs
3.01 1.93 1.99 2.34 0.77 0.50
2.45 2.02 1.76 1.80 1.43 0.56
IWUEm 0.56 0.09 0.23 −0.37 (−0.98)
Sugar beet IWUEs
IWUEm
IWUEs
0.5 0.00 0.03 0.00 0.00
2.28 −0.10 0.79 1.79 −0.37 0.20 1.97
1.74 0.00 0.81 1.73 0.00 0.16 2.22
Note: The values given in parentheses represent yields reduced by crop diseases; they are not included in the overall assessment.
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Table 6 Seasonal water balance of rainfed and irrigated maize, sunflower and sugar beet, shown via input components (P – precipitation, I – irrigation, Ws – stored soil moisture), and output components (Dperc. – deep percolation and ETa – actual evapotranspiration). Year
Rainfed P (mm) Maize 125.5 742.0 274.1 276.6 359.4 532.5 370.0 Sunflower 122.9 638.8 210.3 218.2 316.1 455.3 Sugar beet 155.6 699.6 235.1 236.1 459.1 533.7 415.7 388.3
2000 2001 2002 2003 2004 2005 2006 2000 2001 2002 2003 2004 2005 2000 2001 2002 2003 2004 2005 2006 2007
Irrigated Ws (mm)
Dperc. (mm)
ETa (mm)
P (mm)
I (mm)
Ws (mm)
Dperc. (mm)
ETa (mm)
309.5 0 197.3 189.9 170.2 42.8 179.1
0 197.6 0 0 0 115.2 0
435.0 544.4 471.0 466.5 529.6 460.0 549.1
125.5 742.0 274.1 276.6 359.4 532.5 370.0
180 60 120 200 105 60 180
241.3 0 78.7 93.6 80.8 12.0 96.8
0 257.6 0 0 0 122.5 102
546.8 544.4 472.8 570.2 545.2 482.0 544.8
234.3 0.0 217.4 270.7 196.6 48.0
0 152.1 0 0 0 0
432.6 486.7 427.7 488.9 512.7 503.3
122.9 638.8 210.3 218.2 316.1 455.3
180 100 240 165 105 120
221.8 0 32.76 125.5 90.1 0
0 221 0 0 0 65.7
524.7 516.0 513.8 508.7 511.2 509.6
304 0 277.7 269.1 131.8 52.9 253.1 157.0
0 55 0 0 0 0 0 0
459.6 644.6 512.8 505.2 590.9 586.6 668.8 545.3
155.6 699.6 235.1 236.1 459.1 533.7 415.7 388.3
390 150 350 210 120 105 180 85
221.4 0 95.5 140.7 16.90 33.3 145.5 166.6
0 127.5 0 0 0 0 0 0
767.0 722.1 680.6 586.8 596.0 672.0 741.3 639.6
fed yield excellently, but did not simulate irrigated yield as well as it did rainfed yield, resulting in an over-estimation of IWUE. Both IWUEm and IWUEs values suggested that sunflower, under continental climate conditions, did not consume water efficiently (yield did not change or even declined). The only exception was the extremely dry year of 2002, during which irrigation water was used efficiently, albeit to a much lesser extent compared to that for maize or sugar beets. Contrary to sunflower, sugar beet used both precipitation and water added by irrigation efficiently. It is noteworthy that for wet conditions the model indicated that irrigation of sunflower and sugar beet was not required (IWUE = 0.0), which was confirmed by field experiments. The data are significant in that they show that AquaCrop model can be used to make impartial decisions about crops which should be given irrigation priority when water resources are limited. These features of the model were used by Geerts et al. (2010) to schedule quinoa irrigation, and Araya et al. (2010) to select the optimal date of harvesting of barley in Ethiopia. Wherever water resources are sparse, there is a need to increase maize and wheat WUE. In China, for instance, a model has been proposed for increasing maize and wheat IWUE which can be used to recommend management practices that will maximize IWUE (Hongzhan et al., 2009). To assess the relevance of this research to water management, a water balance analysis was prepared for irrigated and rainfed crops. The results are shown in Table 6. Water balance input components included precipitation (P), irrigation (I), and stored soil moisture (Ws), accumulated during the winter (the crop can draw some 300 mm from a depth of up to two meters, a considerable source of water in the region). The land is flat; there is no surface runoff but there is deep percolation. Looking at the water balance of rainfed crops, it is apparent that during most years the main sources of
water were precipitation and stored soil moisture used for evapotranspiration. Deep percolation was recorded only in 2001 (and in the case of maize, in 2005 as well). It was also rare for irrigated crops, generally occurring in the spring, while the crop is still underdeveloped or is at the end of its growing cycle, when water demand is low. The need for irrigation was noted during the summer and that is why sugar beet and maize used irrigation water efficiently. During wet and moderately dry years, actual evapotranspiration was very similar for both irrigated and rainfed crops, which was especially true of maize and sunflower, but not as much for sugar beet where irrigation was generally justified except during wet years. Our data clearly show that irrigation under the studied climatic and soil conditions was only supplemental to precipitation and that it was difficult to establish alternative irrigation schedules because irrigation treatments resulted in post-harvest soil moisture levels which were favorable for soil cultivation and sowing of winter crops. RMSE’s and indices of agreement for maize, sunflower and sugar beet are shown in Table 7 for validation data set. It is apparent that simulated and measured data agree rather well in all three cases. When comparing these three crops, one should keep in mind that the highest simulation accuracy was achieved for sugar beet, followed by maize and then sunflower. Although sunflower simulation data were somewhat poorer than those for the other crops, statistical indicators show that AquaCrop simulated sunflower yield better under continental than Mediterranean conditions. Based on our research, we derived lower RMSE values and a higher index of agreement compared to those derived under Mediterranean conditions by Todorovic et al. (2009), which is likely a result of the difference between experimental data: longer period of observation and fewer irrigation treatments (in
Table 7 RMSE and index of agreement for measured and simulated maize, sunflower and sugar beet yield, and IWUE based on validation data sets. Variables
Maize
−1
Yield (Mg ha IWUE
)
Sunflower
Sugar beet
RMSE
d
RMSE (Mg ha−1 )
d
RMSE (Mg ha−1 )
d
0.140 0.2193
0.9998 0.9949
0.0767 0.5492
0.9998 0.8843
0.0278 0.0418
0.9999 0.9999
R. Stricevic et al. / Agricultural Water Management 98 (2011) 1615–1621
our case). The index of agreement for all crops and all treatments approached 1, which is another indicator that AquaCrop was able to simulate yield of the studied crops with a high degree of reliability. Low RMSE values and high index of agreement values with respect to IWUE for all three crops attest to the reliability of the model. The model predicted sugar beet IWUE exceptionally accurately, followed by maize and then sunflower. Having compared simulated maize, sunflower and sugar beet yield derived by means of the AquaCrop model with simulated yield derived by other models, as well as deviations and statistical indicators, we can safely say that AquaCrop simulates yield reliably and that it can be used by farmers, planners and other stakeholders to predict the impact of drought and excessive moisture. 4. Conclusion The maize, sunflower and sugar beet yield and irrigation water use efficiency simulation data we derived and analyzed suggest that the AquaCrop model can be used with a high degree of reliability in practical management, strategic planning of the use of water resources for irrigation, or estimation of yield with regard to climate change. Input data can readily be obtained from the field and the model is relatively easy to use. In the majority of cases, the parameters it suggests are applicable in different climatic regions. This fact is important because the model can be used even if limited input data are available. Although numerous other models have produced good crop yield simulation results, compared to them, this model is simpler, requires fewer input data, is generally available, and is highly reliable for the simulations of biomass, yield, and water demand. As such, it is recommended for applications under different climatic conditions. Acknowledgments The authors express their gratitude to the editors and reviewers, whose comments and suggestions were extremely valuable and helped improve this paper. The paper was prepared within the scope of the TR 37005 Project funded by the Serbian Ministry of Education and Science. References Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop Evapotranspiration. Guidelines for computing crop water requirements. Irrigation and drainage paper, No. 56. FAO, Rome. 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. Boogaard, H.L., van Diepen, C.A., Rotter, R.P., Cabrera, J.M.C.A., van Laar, H.H., 1998. User’s Guide for the WOFOST 7.1 Crop Growth Simulation Model and WOFOST Control Center 1.5. Technical Document 52. DLO-Winand Staring Centre, Wageningen.
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Geerts, S., Raes, D., Garcia, M., 2010. Using AquaCrop to derive deficit irrigation schedules. Agric. Water Manage. 98, 213–216. Gerik, T.J., Rosenthal, W.D., Duncan, R.R., 1988. Simulating grain yield and plant development of ratoon grain sorghum over diverse environments. Field Crop Res. 19 (1), 63–74. Heng, L.K., Hsiao, T.C., Evett, S., Howell, T., Steduto, P., 2009. Validating the FAO AquaCrop model for irrigated and water deficient field maize. Agronomy J. 101 (3), 488–498. Hsiao, T.C., Heng, L., Steduto, P., Rojas-Lara, B., Raes, D., Fereres, E., 2009. AquaCrop—The FAO crop model to simulate yield response to water. III. Parameterization and testing for maize. Agronomy J. 101 (3), 448–459. Hongzhan, L., Liang, W., Wang, G., Connor, D.J., Rimmington, G.M., 2009. A simulation model-assisted study of water and nitrogen dynamics and their effects on crop performance in the wheat–maize system: (II) Model calibration, evaluation and simulated experimentation. Front. Agric. China 3 (2), 109–121. Ji, L., Peters, A.J., 2003. Assessing vegetation response to drought in the Northern Great Plains using vegetation and drought indices. Remote Sensing Environ. 87 (1), 85–98. Joksimovic, J., Atlagic, J., Skoric, D., Dusanic, N., 2001. Inter-dependency of Yield Components and Harvest Index for Maize (in Serbian), vol. 35. Collection of Works of the Institute of Field Crops and Vegetables, Novi Sad, pp. 243–249. Ma, L., Hoogenboom, G., Ahuja, L.R., Ascough, I.I.J.C., Saseendran, S.A., 2006. Evaluation of the RZWQM-CERES-maize hybrid model for maize production. Agric. Syst. 87, 274–295. Maksimovic, L., Dragovic, S., 2004. Water requirements of field crops and effects of irrigation. Soil Plant 53 (2), 85–92, Belgrade. Matovic, G., Milivojevic, J., Bosnjakovic, G., Denic, M., 2002. Effects of irrigation scheduling variants of chernozem soil planted with sugar beet on root and sugar yield. Plant Soil 51 (2), 97–106, Belgrade. Park, S.J., Hwang, C.S., Vlek, P.L.G., 2005. Comparison of adaptive techniques to predict crop yield response under varying soil and land management conditions. Agric. Syst. 85, 59–81. Pejic, B., Bosnjak, Dj., Mackic, K., Stricevic, R., Simic, D., Drvar, A., 2009. Sensitivity of Maize (Zea mays L.) to Soil Moisture Deficit During Certain Vegetation Subperiods (in Serbian), vol. 1. Chronicle of Scientific Works, University of Novi Sad, Faculty of Agriculture, pp. 155–166. Quiring, S.M., Papakryiakou, T.N., 2003. An evaluation of agricultural drought indices for the Canadian Prairies. Agric. Forest Meteorol. 118, 49–62. Raes, D., Steduto, P., Hsiao, T.C., Fereres, E., 2009. AquaCrop—The FAO crop model to simulate yield response to water. II. Main algorithms and software description. Agronomy J. 101 (3), 438–477. Steduto, P., Hsiao, T.C., Raes, D., Fereres, E., 2009. AquaCrop—The FAO crop model to simulate yield response to water. I. Concepts and underlying principles. Agronomy J. 101 (3), 426–437. Stricevic, R., Djurovic, N., Djurovic, Z., 2011. Drought classification in Northern Serbia based on SPI and statistical pattern recognition. Meteorol. Appl. 18, 60–69, doi:10.1002/met207. Todorovic, M., Albrizio, R., Zivotic, Lj., Abi Saab, M.T., Stöckle, C., Steduto, P., 2009. Assessment of AquaCrop CropSyst, and WOFOST models in the simulation of sunflower growth under different water regimes. Agronomy J. 101 (3), 509–521. Van Dam, J.C., Groenendijk, P., Hendriks, R.F.A., Kroes, J.G., 2008. Advances of modeling water flow in variably saturated soils with SWAP. Vadose Zone J. 7 (May (2)), 2008. Willmott, C.J., 1982. Some comments on the evaluation of model performance. Bull. Am. Meteorol. Soc. 63, 1309–1313. Wu, H., Wilhite, D.A., 2004. An operational agricultural drought risk assessment model for Nebraska, USA. Nat. Hazard. 33, 1–21. Yu, Q., Saseendran, S.A., Ma, L., Flerchinger, G.N., Green, T.R., Ahuja, L.R., 2006. Modeling a wheat–maize double cropping system in China using two plant growth modules in RZWQM. Agric. Syst. 89, 457–477. Zand-Parsa, S.H., Sepaskhah, A.R., Ronaghi, A., 2006. Development and evaluation of integrated water and nitrogen model for maize. Agric. Water Manage. 81, 227–256.
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Assessment of the FAO AquaCrop model in the simulation of rainfed and supplementally irrigated maize, sugar beet and sunflower Ruzica Stricevic a,∗ , Marija Cosic a , Nevenka Djurovic a , Borivoj Pejic b , Livija Maksimovic c a b c
University of Belgrade, Faculty of Agriculture, Nemanjina 6, 11080 Zemun, Serbia University of Novi Sad, Faculty of Agriculture, Trg Dositeja Obradovica 8, 21000 Novi Sad, Serbia Institute of Field Crops and Vegetables, Maksima Gorkog 30, 21000 Novi Sad, Serbia
a r t i c l e
i n f o
Article history: Received 18 October 2010 Accepted 25 May 2011 Available online 6 July 2011 Key words: AquaCrop Model Simulations Maize Sugar beet Sunflower
a b s t r a c t Farming in Serbia is traditionally rainfed. Analyses show that drought events of varying severity are frequent in this region, although there is no specific pattern. There is a distinct need for an objective assessment of the impact of drought on strategic field crops, to solve the dilemma whether irrigation is required or not. For this reason, and based on available field data, the FAO AquaCrop water driven model was selected to simulate yield and irrigation water use efficiency (IWUE) for three major field crops (maize, sunflower, and sugar beet), under two scenarios: (1) natural water supply and adequate supply of nutrients, and (2) supplementary irrigation and adequate supply of nutrients. The experiments presented here were conducted between 2000 and 2007 in northern Serbia, where chernozem soil is prevalent. Data of 2003 cropping seasons were used for local calibration, whereas the remaining years for validation. Results were such that local calibration resulted in very minor changes of AquaCrop coefficients (e.g., maize basal crop coefficient, sunflower harvest index, etc.). Simulated maize yield levels exhibited the greatest departure from measured data under irrigation conditions (−3.6 and 3.3% during an extremely dry and an extremely wet year, respectively). Simulated sunflower yield levels varied by less than 10% in 8 out of 10 comparisons. The most extreme variation was noted during the extremely wet year. The difference between simulated and measured values in the case of sugar beet was from −10.2 to 12.2%. Large differences were noted only in two or three cases, under extreme climatic conditions. Statistical indicators – root mean square error (RMSE) and index of agreement (d) – for all three crops suggested that the model can be used to highly reliably assess yield and IWUE. This conclusion was derived based on low values of RMSE and high values of d (in the case of maize and sugar beet 0.999 for both yield and IWUE, and in the case of sunflower 0.999 for yield and 0.884 for IWUE). It is noteworthy that under wet conditions, the model suggested that sunflower and sugar beet do not require irrigation, as confirmed by experimental research. These data are significant because they show that the AquaCrop model can be used in impartial decision-making and in the selection of crops to be given irrigation priority in areas where water resources are limited. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Soil characteristics and climatic conditions in northern Serbia are extremely favorable for field crop farming. Plant nutrition is not a limiting factor for high yield since there is vast experience in fertilization, fertility control, and plant protection. Water is the only limiting factor. Field crops can be rainfed because there are frequent years with average precipitation levels during the crop cycle of 300–400 mm. These levels, along with stored soil moisture, pro-
∗ Corresponding author. Tel.: +381 11 615315; fax: +381 11 2193 659. E-mail addresses: [email protected] (R. Stricevic), [email protected] (M. Cosic), [email protected] (N. Djurovic), [email protected] (B. Pejic), [email protected] (L. Maksimovic). 0378-3774/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.agwat.2011.05.011
vide sufficient amounts of water for achieving high yield. However, yield of maize, sunflower, and sugar beet cultivated on more than 50% of Serbia’s arable land, is reduced by as much as 60% during dry years (Pejic et al., 2009; Maksimovic and Dragovic, 2004; Matovic et al., 2002). Previous investigations have revealed that during a series of 56 consecutive years, there were 14 drought events and three years of excessive moisture, but most years were “normal” although they exhibited significant departures from average values. The severity and occurrence of drought events in this region are difficult to predict accurately (Stricevic et al., 2011). In a market-driven agriculture such as Serbia’s, drought events and extended periods of excessive moisture directly affect field crop farming, as well as animal husbandry, and ultimately impact the food industry. This leads to changes in agricultural product supply and demand in the marketplace, and to price fluctuations.
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Numerous organizations (ministries, insurance companies, trading companies) are keen to find out how weather conditions will affect yield, in order to prepare and respond appropriately. Yield forecasts have generally been based on experience and subjective assessments, which are certainly unreliable. Today, it is possible to apply numerous advanced techniques and methods to predict yield, or to predict the impact of drought on yield. Wu and Wilhite (2004) used the Standardized Precipitation Index (SPI) and the Crop-Specific Drought Index (CSDI) to assess the risk of drought for soy and maize in Nebraska, USA. Using canonical discriminant and classificatory discriminant analyses, Wu and Wilhite generated high-risk and low-risk data sets based on the properties of variables derived from SPI and CSDI for soy and maize. Ji and Peters (2003) studied the impact of drought on vegetation in the US Great Plains and concluded that the SPI method, over a period of three months, best described the severity of drought and its impact on vegetation but that, applying remote detection, it was better to assess the entire vegetation season. Park et al. (2005) applied three adaptive techniques: (i) general linear models (GLMs), (ii) artificial neural networks (ANNs), and regression trees (RTs) to predict crop yield under varying soil and land management conditions. Quiring and Papakryiakou (2003) compared four indices (Palmer Drought Severity Index, Palmer’s Z-index, Standardized Precipitation Index, and NOAA Drought Index) to forecast Canada Western Red Spring wheat yield in the Canadian Prairies. Their research showed that these indices were the best drought indicators for the Canadian Prairies, but that there were significant yield forecast variations which required prior evaluation of these methods before any one of them was used in practice. Crop growth models have been commonly used to simulate biomass and yield of various plant species. Gerik et al. (1988) successfully applied SORKAM, a dynamic crop growth model, to simulate ratoon grain sorghum in several climatic regions of Texas. WOFOST, SWAP, and MARS models were developed at Wageningen, in the Netherlands (Boogaard et al., 1998; Van Dam et al., 2008), which turned out to be very reliable for sunflower biomass and yield simulations under Mediterranean conditions (Todorovic et al., 2009). Yu et al. (2006) simulated maize and wheat yield under large-scale farming conditions using the Root Zone Water Quality Model (RZWQM) or the CERES model, over a relatively long time period. They concluded that the model, previously calibrated per local soil and climate conditions of the Northern China Plain, allowed for successful maize and wheat yield simulations for various fertilization and irrigation scenarios, with relatively small errors. Based on their research, Ma et al. (2006) arrived at similar conclusions and confirmed that there was no statistically significant difference between the data derived by means of these models. They recommended that both models be used for different soil, water, and farming conditions. To simulate maize growth and grain yield, Zand-Parsa et al. (2006) developed a Maize Simulation Model (MSM). This model was validated for the Far area in Iran, using data from two years of research of maize growth for different fertilization and irrigation levels. The model proved to be very reliable for the estimation of maize yield. Given that water is a common limiting factor for high yield throughout the world, FAO recently developed a crop growth model – AquaCrop. This model targets a broad base of users (farmers, consultants, water managers, and policymakers) who require information about biomass and yield production of crops farmed under different water and nutrient availability conditions. The model is relatively easy to use and the 33 types of required input data related to climate, soil, agricultural techniques and crop characteristics can be readily derived from experimental research. Steduto et al. (2009) and Raes et al. (2009) provide a detailed description and present the architecture of the model. Following local calibration and validation, the model can safely be used to simulate crop production
in practice (Hsiao et al., 2009; Heng et al., 2009). Since water is a common limiting factor in Serbia, the FAO AquaCrop – version 3.0 water driven model was selected to simulate yield of the three major field crops: maize, sunflower, and sugar beet under two scenarios: (1) natural water supply and adequate supply of nutrients, and (2) supplementary irrigation and adequate supply of nutrients. Since it is often debated whether irrigation of field crops is justifiable in Serbia, this paper will assess whether the model can be used to determine Irrigation Water Use Efficiency (IWUE) and to develop irrigation strategies. 2. Materials and methods 2.1. Experimental data Experiments were conducted at an experimental site near Rimski Sancevi (45◦ 20 N latitude; 19◦ 51 E longitude, 84 m above sea level), belonging to the Institute of Field Crops and Vegetables. Maize experiments were conducted from 2000 to 2006, sunflower experiments from 2000 to 2005, and sugar beet experiments from 2000 to 2007, under both rainfed and irrigation conditions. The soil at the experimental site is comprised of very deep, wellstructured clayey chernozem, with a deep mollic horizon. Water characteristics and physical properties of the soil are good. Its total porosity is 54.9 and 48.8%, field capacity (upper limit) is 29.4 and 35.8%, the wilting point is 12.3 and 15.1% (all by volume), and the bulk density is 1.13 and 1.35 g cm−3 for 0–0.3 m and 0.3–0.75 m layers, respectively. Chemical characteristics are extremely favorable; pH levels vary from neutral to slightly alkaline. The content of humus is 3.5–5.5%, of total nitrogen 0.18–0.25%, of P2 O5 0.17–0.18%, and of K2 O about 0.73%. Mineral fertilizers were added during the experiment, as required, to maintain soil fertility. The climate of the experimental field is characterized as continental. The average precipitation level during the studied period of 56 years is 614 mm (maximum of 998 mm and minimum of 288 mm). During the growing cycle from April to September, the average precipitation sum is 358 mm (maximum of 745 mm and minimum of 148 mm). Fig. 1 shows average precipitation levels and average air temperatures by month, indicating crop water deficit during the summer months. The average reference evapotranspiration rate during the warmest months (July and August) is up to 5 mm day−1 ; days, generally windy, when it reaches 6 mm day−1 are rare. Irrigation is not required during certain years because precipitation sums during the growing cycle, along with the water stored within the soil, are sufficient to ensure high yield.
Fig. 1. Mean monthly air temperature and precipitation sum.
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Conversely, when precipitation is insufficient, supplementary irrigation is often used. Years during which irrigation is required over the entire growing cycle are rare. Climate input data for the simulations were obtained from a first-order weather station which was located within the experimental field. Weather data: daily precipitation sums, daily low and high air temperatures, minimum and maximum relative air humidity, wind speed, and insolation, were obtained from a local weather station at Rimski Sancevi, to calculate reference evapotranspiration rates applying the FAO Penman-Monteith method (Allen et al., 1998). The experiments were set up using a split-plot block system. Sowing was conducted according to the local practices and all required agritechnical measures, typical of local conditions, were undertaken. Maize hybrid NS-640, Lara sugar beets and sunflower hybrid NS-H-111 were grown under two scenarios: rainfed and irrigated. Irrigation norms were determined based on daily evapotranspiration calculations and soil moisture measurements. The lower limit for soil dryness was 50% of the total available amount of water. The soil was generally irrigated to field capacity, such that the crops enjoyed suitable conditions for realizing their genetic potential. The crop was harvested immediately after maturity, such that harvest and maturity dates coincide. Table 1 shows sowing, emergence, flowering and harvesting dates and plant density for maize, sugar beet and sunflower as well as total precipitation levels during the growing cycle and, for irrigated crops, total amounts of water for the growing cycle. The above table clearly shows that 2000 was an exceptionally dry year, with a precipitation sum lower than average by a factor of 2.5. On the other hand, 2001 was distinctly wet and the precipitation sum exhibited a significant departure from average levels. Weather conditions during the remaining years were deemed to be normal. 2.2. Model parameters and input data AquaCrop version 3.0 offers files which contain parameters suitable for the simulation of maize, sugar beet and sunflower. Some of these parameters are not universal and need to be adjusted to local conditions and the studied cultivars. Crop development data were real data, derived from field investigations, while other data were adjusted and calibrated. All input parameters were locally calibrated for the studied scenarios of irrigated maize, sunflower and sugar beet yield over the year of 2003, while the model was validated for all scenarios without irrigation and scenarios with irrigation for the remaining years. 2003 was a moderately dry year and, as such, deemed suitable for the adjustment of all model parameters. The effect of soil fertility on yield was not addressed since sufficient amounts of mineral matter were added under all scenarios, to ensure the realization of full genetic potential. Other significant input parameters are presented in Table 2, which clearly shows that default values were generally used for water stress of all three crops. Even though this parameter is known to be conservative and applicable under all conditions (Hsiao et al., 2009), a slightly lower value (kcb = 1.01), relative to the default value (kcb = 1.03), was selected for this study. Biomass data for the growing season were not available, such that a reliable calibration could not be performed by varying biomass-related parameters. A similar reduction in the kcb value was noted for sunflower and sugar beet yield. In the case of sunflower yield, the difference between default values of the harvest index (35%) and the proposed harvest index (26%) is attributable to the characteristics of the NS hybrid (Joksimovic et al., 2001). Initial soil moisture under all scenarios was generally equal to field capacity. This is not rare, because in the studied climatic region, groundwater levels are maintained and sufficient amounts of precipitation accumulated to wet the soil to field capacity. Dur-
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ing the dry year 2000, the soil was irrigated to field capacity in all sunflower and maize scenarios. 2.3. Data analysis IWUE was calculated based on the difference in yield between the irrigated and rainfed crop and the overall irrigation norm, which can be expressed as: IWUEm =
Yim − Yrm , I
IWUEs =
Yis − Yrs I
(1)
where Yim = measured yield of irrigated crop, Yrm = measured yield of rainfed crop, Yis = simulated yield of irrigated crop, Yrs = simulated yield of rainfed crop, and I = total amount of water added by irrigation. Three statistical methods were used to analyze and compare yield data derived from field investigations and from simulations. The first was the root mean square error (RMSE) method:
⎡ ⎤ n 1 RMSE = ⎣ (Si − Mi )2 ⎦ n
(2)
i=1
where Si and Mi = simulated and measured values, respectively, and n = number of observations. The unit of RMSE is the same for both variables, and the model’s fit improves when RMSE tends toward zero. The index of agreement (d) was calculated using the Willmott (1982) equation:
n
d=1−
n i=1
i=1
(Si − Mi )2
¯ + |Mi − M|) ¯ (|Si − M|
2
(3)
¯ = average values of measured data. The index of agreewhere M ment is a descriptor and its values range from 0 to 1. The model simulates the studied parameter better as the value approaches 1. 3. Results and discussion Simulation results for maize, sunflower and sugar beet yield, using calibration data sets, are presented in Table 3. Calibration results show an extremely good match between measured values and those simulated by the model. The only exception was sugar beet yield, where the difference was 16.5% but only under rainfed conditions. The parameters obtained from model calibrations were used for model validation. Measured and simulated results for validated data sets of rainfed and irrigated maize, sunflower and sugar beet yield are presented in Table 4. The largest differences between measured and simulated values for irrigated maize yield were noted for 2000 and 2001, but are virtually insignificant (−3.6 and 3.4%). All other deviations were smaller. The year 2000 was extremely dry and maize was under stress throughout the vegetation period, while 2001 was extremely wet. Heng et al. (2009) reported much greater deviations under severe stress conditions for maize. In general, deviations of rainfed maize yield were small (<2%), attesting to the fact that the parameter values proposed in the default file for stress conditions are applicable to continental climates as well. Using similar parameter values, Hsiao et al. (2009) reported much greater deviations from measured values (from −17.1 to +23.8%), but they used different cultivars from the present study. Results of simulated sunflower yield levels varied by less than 10% in 8 out of 10 comparisons. The greatest deviations were noted in 2001; during that year, the differences between measured and simulated values ranged between 17.6% (rainfed) and 14.2% (irrigated). The over-estimated values were a result of the fact that this was an extremely wet year, which did not favor sunflower due to
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Table 1 Plant density, sowing/emergence/flowering/harvesting dates, precipitation sums for the vegetation period (P), total irrigation norms (I) for irrigated crops. Year 2000 2001 2002 2003 2004 2005 2006 2000 2001 2002 2003 2004 2005 2000 2001 2002 2003 2004 2005 2006 2007
Plant density (plant ha−1 ) Maize 65,000 65,000 65,000 63,500 50,000 50,000 58,000 Sunflower 50,750 50,500 44,356 48,000 48,666 43,583 Sugar beet 90,000 76,834 66,740 77,000 80,312 74,917 70,000 80,000
Sowing
Emergence
Flowering
Harvesting
P (mm)
I (mm)
18April 06 April 16 April 22 April 20 April 11 April 22 April
28 April 30 April 29 April 1 May 10 May 1 May 1 May
11 July 10 July 1 July 1 July 5 July 2 July 2 July
20 September 28 September 8 October 14 October 8 October 17 October 20 October
125.5 742 274.1 276.6 359.4 532.5 370
180 60 120 200 105 60 180
19 April 6 April 22 April 22 April 20 April 13 April
28 April 30 April 4 May 5 May 30 April 30 April
03–12 June 04–15 June 11–18 June 12–20 June 14–21 June 14–21 June
30 August 5 September 11 September 11 September 7 September 2 September
122.9 638.8 210.3 218.2 316.1 455.3
180 100 240 165 105 120
25 March 5 April 27 March 27 March 29 March 31 March 3 April 3 April
11 April 20 April 16 April 24 April 8 April 18 April 20 April 16 April
– – – – – – – –
23 October 9 October 9 October 16 October 20 October 20 October 8 October 15 October
155.6 699.6 235.1 236.1 459.1 533.7 415.7 388.3
390 150 350 210 120 105 180 85
Table 2 Input parameters for maize, sunflower and sugar beet yield simulations that differ from default parameters. Parameter
Value Maize Default
Base temperature, ◦ C Cut off temperature, ◦ C Initial canopy cover (CCo), % Canopy decline (CDC), % per day Canopy expansion (CGC), % per day Maximum canopy cover (CCx), % Crop coefficient (kcb ) at CC = 100% Maximum effective rooting depth, m Water productivity (WP), g m−2 Adjusted harvest index Harvest index (Hlo), % Water stress Leaf growth threshold (pupper ) Stomatal stress coefficient curve shape Early canopy senescence (pupper ) Senescence curve shape Adjustment HIo to water stress Before flowering During flowering (pupper ) During yield formation HI increased by inhibition of leaf growth HI reduced by inhibition of stomata
8
Sunflower Default
Calibrated
10
4 40 0.29 13.6 21.8 98 1.1
10 30 0.25 15 10.1 90 1.0
18 10.8 35
26 16.1 26
0.49
0.33
16.3 96 1.03
10 90 1.01
33.7
33
7 3
Sugar beet
Calibrated
– 8
its sensitivity to excessive moisture and plant diseases prompted by humid conditions. AquaCrop predicts achievable sugar beet yield via sugar digestion. Based on experimental data pertaining to sugar beet root yield and sugar digestion by year of our research, sugar yield levels achieved during the experiment were comparable to those predicted by the model. The differences between simulated
0.15 2.5 0.7 2.5
0.22 2.7 0.8 2.7
5 0.85
8 0.8
3
8
Default
0.1 13.6 98 1.15 1.0
Calibrated
0.45 10 87 1.1 2.2
and measured values ranged between −10.2 and 12.2%. Underestimated yield levels were derived for an extremely dry year, under irrigation conditions, while over-estimated values were obtained for rainfed conditions in 2002 and 2003. Given that the model performed so well simulating yield under extremely dry conditions in the studied area (0.22%), further adjustments of parameter values did not improve results.
Table 3 Simulation results for calibration data sets of maize, sunflower and sugar beet yield (irrigated and rainfed treatments in 2003), and deviation from measured values. Year
Maize Sunflower Sugar beet
Rainfed
Irrigated
Measured (Mg ha−1 )
Simulated (Mg ha−1 )
Deviation (%)
Measured (Mg ha−1 )
Simulated (Mg ha−1 )
Deviation (%)
9.6 3.2 8.7
9.7 3.3 10.1
0.4 5.0 16.5
13.5 3.3 12.5
13.6 3.3 12.2
0.9 0.3 −1.8
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Table 4 Measured vs. simulated results for validated data sets of rainfed and irrigated maize, sunflower and sugar beet yield. Year
Rainfed Measured (Mg ha−1 )
2000 2001 2002 2004 2005 2006 2000 2001 2002 2004 2005 2000 2001 2002 2004 2005 2006 2007
Maize 8.0 9.6 11.2 10.5 13.8 13.9 Sunflower 4.5 (3.0) 4.5 4.4 2.9 Sugar beets 10.9 11.5 8.5 13.4 14.4 13.1 14.3
Irrigated Simulated (Mg ha−1 )
Deviation (%)
Measured (Mg ha−1 )
Simulated (Mg ha−1 )
Deviation (%)
8.0 9.7 11.2 10.5 13.6 14.0
−0.2 0.7 −0.3 0.3 −1.0 0.4
13.4 10.8 13.6 13.0 14.2 14.8
13.0 11.1 13.7 12.9 14.6 15.0
−3.6 3.4 0.6 −0.2 2.8 1.4
4.2 3.6 4.8 4.4 2.8
−6.4 17.6 5.9 0.4 −4.5
5.5 (3.1) 5.1 4.0 1.8
5.1 3.6 4.8 4.4 1.9
−7.0 14.2 −4.3 10.1 6.6
10.9 11.4 9.5 13.3 14.4 13.1 14.3
0.2 −1.3 12.2 −0.8 −0.3 −0.3 −0.0
19.8 11.4 11.2 15.5 14.0 13.5 16.0
17.8 11.3 12.4 15.5 14.1 13.4 16.2
−10.2 −0.2 10.5 −0.2 0.8 −0.3 1.3
Note: The values given in parentheses represent yields reduced by crop diseases; they are not included in the overall assessment.
Figs. 2–4 show simulated and measured maize, sunflower and sugar beet yield levels, respectively. The graphics reflect only model validation data. It is important to note that trendline gradients of irrigated and rainfed crops virtually coincide. The straight-line equation and the coefficient of determination (R2 ) for maize show that the model simulated grain yield with a high degree of reliability. Trendline gradients deviate from the desirable straight line (x = y) for sugar beet slightly more than for sunflower, and vice versa in the case of the coefficient of determination. This is as expected, since the studied sunflower hybrid, on the one hand,
tolerates drought and, on the other hand, drought events do not occur regularly in the studied region. Simulated yield values for the year 2001 were excluded; they were not suitable for a model confidence assessment because the yields measured were affected by crop diseases. Dry periods have been recorded at different stages of development, in varying degrees. Wet years are also frequent, and they occasionally cause a reduction in yield due to disease. The model reflected IWUE rather well for all three crops (Table 5). In the case of maize, the poorest simulation results were obtained for the year 2005. During that year, there was sufficient precipitation to support high yield and the model simulated rain-
Fig. 2. Measured vs. simulated yield of maize.
Fig. 4. Measured vs. simulated yield of sugar beet.
Table 5 Irrigation water use efficiency for measured and simulated maize, sunflower and sugar beet for validation data set. Year
2000 2001 2002 2004 2005 2006 2007 Fig. 3. Measured vs. simulated yield of sunflower.
Maize
Sunflower
IWUEm
IWUEs
3.01 1.93 1.99 2.34 0.77 0.50
2.45 2.02 1.76 1.80 1.43 0.56
IWUEm 0.56 0.09 0.23 −0.37 (−0.98)
Sugar beet IWUEs
IWUEm
IWUEs
0.5 0.00 0.03 0.00 0.00
2.28 −0.10 0.79 1.79 −0.37 0.20 1.97
1.74 0.00 0.81 1.73 0.00 0.16 2.22
Note: The values given in parentheses represent yields reduced by crop diseases; they are not included in the overall assessment.
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Table 6 Seasonal water balance of rainfed and irrigated maize, sunflower and sugar beet, shown via input components (P – precipitation, I – irrigation, Ws – stored soil moisture), and output components (Dperc. – deep percolation and ETa – actual evapotranspiration). Year
Rainfed P (mm) Maize 125.5 742.0 274.1 276.6 359.4 532.5 370.0 Sunflower 122.9 638.8 210.3 218.2 316.1 455.3 Sugar beet 155.6 699.6 235.1 236.1 459.1 533.7 415.7 388.3
2000 2001 2002 2003 2004 2005 2006 2000 2001 2002 2003 2004 2005 2000 2001 2002 2003 2004 2005 2006 2007
Irrigated Ws (mm)
Dperc. (mm)
ETa (mm)
P (mm)
I (mm)
Ws (mm)
Dperc. (mm)
ETa (mm)
309.5 0 197.3 189.9 170.2 42.8 179.1
0 197.6 0 0 0 115.2 0
435.0 544.4 471.0 466.5 529.6 460.0 549.1
125.5 742.0 274.1 276.6 359.4 532.5 370.0
180 60 120 200 105 60 180
241.3 0 78.7 93.6 80.8 12.0 96.8
0 257.6 0 0 0 122.5 102
546.8 544.4 472.8 570.2 545.2 482.0 544.8
234.3 0.0 217.4 270.7 196.6 48.0
0 152.1 0 0 0 0
432.6 486.7 427.7 488.9 512.7 503.3
122.9 638.8 210.3 218.2 316.1 455.3
180 100 240 165 105 120
221.8 0 32.76 125.5 90.1 0
0 221 0 0 0 65.7
524.7 516.0 513.8 508.7 511.2 509.6
304 0 277.7 269.1 131.8 52.9 253.1 157.0
0 55 0 0 0 0 0 0
459.6 644.6 512.8 505.2 590.9 586.6 668.8 545.3
155.6 699.6 235.1 236.1 459.1 533.7 415.7 388.3
390 150 350 210 120 105 180 85
221.4 0 95.5 140.7 16.90 33.3 145.5 166.6
0 127.5 0 0 0 0 0 0
767.0 722.1 680.6 586.8 596.0 672.0 741.3 639.6
fed yield excellently, but did not simulate irrigated yield as well as it did rainfed yield, resulting in an over-estimation of IWUE. Both IWUEm and IWUEs values suggested that sunflower, under continental climate conditions, did not consume water efficiently (yield did not change or even declined). The only exception was the extremely dry year of 2002, during which irrigation water was used efficiently, albeit to a much lesser extent compared to that for maize or sugar beets. Contrary to sunflower, sugar beet used both precipitation and water added by irrigation efficiently. It is noteworthy that for wet conditions the model indicated that irrigation of sunflower and sugar beet was not required (IWUE = 0.0), which was confirmed by field experiments. The data are significant in that they show that AquaCrop model can be used to make impartial decisions about crops which should be given irrigation priority when water resources are limited. These features of the model were used by Geerts et al. (2010) to schedule quinoa irrigation, and Araya et al. (2010) to select the optimal date of harvesting of barley in Ethiopia. Wherever water resources are sparse, there is a need to increase maize and wheat WUE. In China, for instance, a model has been proposed for increasing maize and wheat IWUE which can be used to recommend management practices that will maximize IWUE (Hongzhan et al., 2009). To assess the relevance of this research to water management, a water balance analysis was prepared for irrigated and rainfed crops. The results are shown in Table 6. Water balance input components included precipitation (P), irrigation (I), and stored soil moisture (Ws), accumulated during the winter (the crop can draw some 300 mm from a depth of up to two meters, a considerable source of water in the region). The land is flat; there is no surface runoff but there is deep percolation. Looking at the water balance of rainfed crops, it is apparent that during most years the main sources of
water were precipitation and stored soil moisture used for evapotranspiration. Deep percolation was recorded only in 2001 (and in the case of maize, in 2005 as well). It was also rare for irrigated crops, generally occurring in the spring, while the crop is still underdeveloped or is at the end of its growing cycle, when water demand is low. The need for irrigation was noted during the summer and that is why sugar beet and maize used irrigation water efficiently. During wet and moderately dry years, actual evapotranspiration was very similar for both irrigated and rainfed crops, which was especially true of maize and sunflower, but not as much for sugar beet where irrigation was generally justified except during wet years. Our data clearly show that irrigation under the studied climatic and soil conditions was only supplemental to precipitation and that it was difficult to establish alternative irrigation schedules because irrigation treatments resulted in post-harvest soil moisture levels which were favorable for soil cultivation and sowing of winter crops. RMSE’s and indices of agreement for maize, sunflower and sugar beet are shown in Table 7 for validation data set. It is apparent that simulated and measured data agree rather well in all three cases. When comparing these three crops, one should keep in mind that the highest simulation accuracy was achieved for sugar beet, followed by maize and then sunflower. Although sunflower simulation data were somewhat poorer than those for the other crops, statistical indicators show that AquaCrop simulated sunflower yield better under continental than Mediterranean conditions. Based on our research, we derived lower RMSE values and a higher index of agreement compared to those derived under Mediterranean conditions by Todorovic et al. (2009), which is likely a result of the difference between experimental data: longer period of observation and fewer irrigation treatments (in
Table 7 RMSE and index of agreement for measured and simulated maize, sunflower and sugar beet yield, and IWUE based on validation data sets. Variables
Maize
−1
Yield (Mg ha IWUE
)
Sunflower
Sugar beet
RMSE
d
RMSE (Mg ha−1 )
d
RMSE (Mg ha−1 )
d
0.140 0.2193
0.9998 0.9949
0.0767 0.5492
0.9998 0.8843
0.0278 0.0418
0.9999 0.9999
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our case). The index of agreement for all crops and all treatments approached 1, which is another indicator that AquaCrop was able to simulate yield of the studied crops with a high degree of reliability. Low RMSE values and high index of agreement values with respect to IWUE for all three crops attest to the reliability of the model. The model predicted sugar beet IWUE exceptionally accurately, followed by maize and then sunflower. Having compared simulated maize, sunflower and sugar beet yield derived by means of the AquaCrop model with simulated yield derived by other models, as well as deviations and statistical indicators, we can safely say that AquaCrop simulates yield reliably and that it can be used by farmers, planners and other stakeholders to predict the impact of drought and excessive moisture. 4. Conclusion The maize, sunflower and sugar beet yield and irrigation water use efficiency simulation data we derived and analyzed suggest that the AquaCrop model can be used with a high degree of reliability in practical management, strategic planning of the use of water resources for irrigation, or estimation of yield with regard to climate change. Input data can readily be obtained from the field and the model is relatively easy to use. In the majority of cases, the parameters it suggests are applicable in different climatic regions. This fact is important because the model can be used even if limited input data are available. Although numerous other models have produced good crop yield simulation results, compared to them, this model is simpler, requires fewer input data, is generally available, and is highly reliable for the simulations of biomass, yield, and water demand. As such, it is recommended for applications under different climatic conditions. Acknowledgments The authors express their gratitude to the editors and reviewers, whose comments and suggestions were extremely valuable and helped improve this paper. The paper was prepared within the scope of the TR 37005 Project funded by the Serbian Ministry of Education and Science. References Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop Evapotranspiration. Guidelines for computing crop water requirements. Irrigation and drainage paper, No. 56. FAO, Rome. 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. Boogaard, H.L., van Diepen, C.A., Rotter, R.P., Cabrera, J.M.C.A., van Laar, H.H., 1998. User’s Guide for the WOFOST 7.1 Crop Growth Simulation Model and WOFOST Control Center 1.5. Technical Document 52. DLO-Winand Staring Centre, Wageningen.
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Geerts, S., Raes, D., Garcia, M., 2010. Using AquaCrop to derive deficit irrigation schedules. Agric. Water Manage. 98, 213–216. Gerik, T.J., Rosenthal, W.D., Duncan, R.R., 1988. Simulating grain yield and plant development of ratoon grain sorghum over diverse environments. Field Crop Res. 19 (1), 63–74. Heng, L.K., Hsiao, T.C., Evett, S., Howell, T., Steduto, P., 2009. Validating the FAO AquaCrop model for irrigated and water deficient field maize. Agronomy J. 101 (3), 488–498. Hsiao, T.C., Heng, L., Steduto, P., Rojas-Lara, B., Raes, D., Fereres, E., 2009. AquaCrop—The FAO crop model to simulate yield response to water. III. Parameterization and testing for maize. Agronomy J. 101 (3), 448–459. Hongzhan, L., Liang, W., Wang, G., Connor, D.J., Rimmington, G.M., 2009. A simulation model-assisted study of water and nitrogen dynamics and their effects on crop performance in the wheat–maize system: (II) Model calibration, evaluation and simulated experimentation. Front. Agric. China 3 (2), 109–121. Ji, L., Peters, A.J., 2003. Assessing vegetation response to drought in the Northern Great Plains using vegetation and drought indices. Remote Sensing Environ. 87 (1), 85–98. Joksimovic, J., Atlagic, J., Skoric, D., Dusanic, N., 2001. Inter-dependency of Yield Components and Harvest Index for Maize (in Serbian), vol. 35. Collection of Works of the Institute of Field Crops and Vegetables, Novi Sad, pp. 243–249. Ma, L., Hoogenboom, G., Ahuja, L.R., Ascough, I.I.J.C., Saseendran, S.A., 2006. Evaluation of the RZWQM-CERES-maize hybrid model for maize production. Agric. Syst. 87, 274–295. Maksimovic, L., Dragovic, S., 2004. Water requirements of field crops and effects of irrigation. Soil Plant 53 (2), 85–92, Belgrade. Matovic, G., Milivojevic, J., Bosnjakovic, G., Denic, M., 2002. Effects of irrigation scheduling variants of chernozem soil planted with sugar beet on root and sugar yield. Plant Soil 51 (2), 97–106, Belgrade. Park, S.J., Hwang, C.S., Vlek, P.L.G., 2005. Comparison of adaptive techniques to predict crop yield response under varying soil and land management conditions. Agric. Syst. 85, 59–81. Pejic, B., Bosnjak, Dj., Mackic, K., Stricevic, R., Simic, D., Drvar, A., 2009. Sensitivity of Maize (Zea mays L.) to Soil Moisture Deficit During Certain Vegetation Subperiods (in Serbian), vol. 1. Chronicle of Scientific Works, University of Novi Sad, Faculty of Agriculture, pp. 155–166. Quiring, S.M., Papakryiakou, T.N., 2003. An evaluation of agricultural drought indices for the Canadian Prairies. Agric. Forest Meteorol. 118, 49–62. Raes, D., Steduto, P., Hsiao, T.C., Fereres, E., 2009. AquaCrop—The FAO crop model to simulate yield response to water. II. Main algorithms and software description. Agronomy J. 101 (3), 438–477. Steduto, P., Hsiao, T.C., Raes, D., Fereres, E., 2009. AquaCrop—The FAO crop model to simulate yield response to water. I. Concepts and underlying principles. Agronomy J. 101 (3), 426–437. Stricevic, R., Djurovic, N., Djurovic, Z., 2011. Drought classification in Northern Serbia based on SPI and statistical pattern recognition. Meteorol. Appl. 18, 60–69, doi:10.1002/met207. Todorovic, M., Albrizio, R., Zivotic, Lj., Abi Saab, M.T., Stöckle, C., Steduto, P., 2009. Assessment of AquaCrop CropSyst, and WOFOST models in the simulation of sunflower growth under different water regimes. Agronomy J. 101 (3), 509–521. Van Dam, J.C., Groenendijk, P., Hendriks, R.F.A., Kroes, J.G., 2008. Advances of modeling water flow in variably saturated soils with SWAP. Vadose Zone J. 7 (May (2)), 2008. Willmott, C.J., 1982. Some comments on the evaluation of model performance. Bull. Am. Meteorol. Soc. 63, 1309–1313. Wu, H., Wilhite, D.A., 2004. An operational agricultural drought risk assessment model for Nebraska, USA. Nat. Hazard. 33, 1–21. Yu, Q., Saseendran, S.A., Ma, L., Flerchinger, G.N., Green, T.R., Ahuja, L.R., 2006. Modeling a wheat–maize double cropping system in China using two plant growth modules in RZWQM. Agric. Syst. 89, 457–477. Zand-Parsa, S.H., Sepaskhah, A.R., Ronaghi, A., 2006. Development and evaluation of integrated water and nitrogen model for maize. Agric. Water Manage. 81, 227–256.