Validation of the AquaCrop model for irrigated rice production under varied water regimes in Bangladesh

Agricultural Water Management 159 (2015) 331–340

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Validation of the AquaCrop model for irrigated rice production under varied water regimes in Bangladesh M. Maniruzzaman a,∗ , M.S.U. Talukder b , M.H. Khan c , J.C. Biswas d , A. Nemes e a

Irrigation and Water Management Division, Bangladesh Rice Research Institute, Gazipur, 1701, Bangladesh Sylhet Agricultural University, Sylhet 3100, Bangladesh Department of Irrigation and Water Management, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh d Soil Science Division, Bangladesh Rice Research Institute, Gazipur, 1701, Bangladesh e Norwegian Institute for Bioeconomy Research, P.O. Box. 115, 1431 Ås, Norway b c

a r t i c l e

i n f o

Article history: Received 15 August 2014 Received in revised form 19 June 2015 Accepted 22 June 2015 Keywords: Crop model Dry season Water use efficiency Soil water balance Crop yield Nash–Sutcliffe efficiency

a b s t r a c t Crop growth simulation models of varying complexity have been developed to predict the effects of soil, water, nutrients and climate on biomass and grain yields and water use efficiency of different crops. In this study, the AquaCrop model was calibrated and validated for rice crop growth modeling under different irrigation water regimes at the Bangladesh Rice Research Institute, Gazipur, Bangladesh during the 2008–09 and 2009–10 winter (dry) seasons. Three irrigation water regimes were examined: irrigation with continuous standing water (CSW), and irrigation at 3 or 5 days after water disappearance (3 or 5 DAWD) from the field as potential water saving adaptations. Model performance was evaluated in terms of prediction error (Pe ), coefficient of determination (R2 ), the normalized root mean squared error (NRSME), the Nash–Sutcliffe model efficiency coefficient (EF) and Willmott’s index of agreement (d). The model calibration yielded 0.94 < R2 < 0.99, 14.5 < NRMSE < 21.6, 0.83 < EF < 0.95 and 0.97 < d < 0.99 in simulating canopy cover (CC) percentage and above-ground biomass. Model validation yielded 0.98 < R2 < 0.99, 8.6 < NRMSE < 12.9, 0.94 < EF < 0.97 and d = 0.99 in simulating CC percentage and above-ground biomass. In calibration and validation, respectively, the prediction errors for grain yield varied from 5.55 to 7.70% and 8.22 to 11.54%, and for biomass production from 2.62 to 5.19% and 7.95 to 11.15%, indicating good model performances. Based on crop yield, water use and its use efficiency, the IR69515-KKN-4-UBN-42-1-1 genotype showed better productivity in the dry season under the 3 DAWD irrigation water regime compared to the other examined treatments, which was shown by both the experimental data and the model simulations using FAO recommended conservative model parameters. The FAO AquaCrop model was able to predict rice growth and yield with acceptable accuracy under different water regimes, making this model a suitable candidate to facilitate local scenario studies related to irrigation scheduling, yield prediction or studies related to climate change and adaptation. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Rice is one of the most widely grown crops in the world and is used as staple food by more than 3.5 billion people. Over 90% of the world’s rice is produced and consumed in Asia (IRRI, 2012). While the population of Asian countries is growing steadily, land and fresh water availability for rice production is shrinking, raising concerns about food security, and potentially on a longer term, political stability. Although fresh water availability for agriculture is declining in many Asian countries (Postal, 1997), its demand for rice is increasing (Pingali et al., 1997). Approximately 50% of the

∗ Corresponding author. E-mail address: [email protected] (A. Nemes). http://dx.doi.org/10.1016/j.agwat.2015.06.022 0378-3774/© 2015 Elsevier B.V. All rights reserved.

fresh water is used for rice production in Asia (Guerra et al., 1998). In Bangladesh, water demand by house-holds, agriculture as well as industry continually increases (Bindraban, 2001). As a result, regulation of water allocation is one of the burning issues for policy makers. With recent advances in computing facilities and model development, simulation of the anticipated impact of introducing new crop varieties, evaluating the effects of alternative management decisions or other natural or human-induced changes is seen as a cost effective approach. However, simulation models need to be calibrated and parameterized to local conditions before they can be used as predictive tools. Crop growth models in general contain a set of equations that estimate the production rate of biomass from the captured resources such as carbon dioxide, solar radiation, and water (Azam et al., 1994; Steduto, 2003). The water-

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driven crop growth models assume a linear relationship between biomass growth rate and transpiration through a water productivity parameter (Tanner and Sinclair, 1983; Steduto and Albrizio, 2005). The water-driven growth concept is used e.g., in CropSyst and AquaCrop models (Steduto et al., 2009; Raes et al., 2009a). One of the major advantages of the water-driven models over radiation-driven models is the opportunity to normalize the water productivity (WP*) parameter for climate and thus these models have a wide applicability in different locations under varying climatic and spatio-temporal settings (Steduto and Albrizio, 2005; Steduto et al., 2007). Most simulation models require a large number of input parameters as well as advanced modeling skills and experience for their calibration and subsequent operation. Certain models are also cultivar specific and are not easily amenable for more generic use. In this context, the FAO AquaCrop model (Steduto et al., 2009; Raes et al., 2009a) is seen to be rather user friendly. The AquaCrop model has been parameterized and tested for a number of crops grown worldwide (Hsiao et al., 2009; Araya et al., 2010a; Stricevic et al., 2011). They reported that the maximum prediction error for maize was 3.6%, and for sugar beet 12.2%, and they concluded that the AquaCrop model could be used in impartial decision-making and in the selection of crops to be given irrigation priority in areas where water resources are limited. Raes et al. (2010) provided an assessment of the degree to which AquaCrop has been tested for various crops. Rice had been among the least tested crops at the time; and literature on the AquaCrop model being tested in rice cultivars is still rather limited, even including conference papers and/or university theses. In a conference paper, Saadati et al. (2011) reported on calibrating and evaluating AquaCrop for a lowland local rice cultivar, using data from an experiment with five irrigation treatments over two years, in a semi-arid environment in Iran. Lin et al. (2012) has evaluated the performance of AquaCrop under moist, subtropical climate in a small agricultural catchment near Yingtan, Jiangxi Province in China considering a matrix of two irrigation treatments and two different cropping histories. In his thesis, Nikolaus (2013) evaluated the performance of AquaCrop as a rice yield prediction tool in a tropical inland valley in Benin, under various cultivation systems representing ponding and non-ponding irrigation systems, and the option of using soil fertilization. Shrestha et al. (2013) calibrated and validated the AquaCrop model to local field data of multiple cereal crops – among them rice – cultivated in the lowland (Terai) area of Nepal as part of a study that aimed at assessing attainable field management strategies under potentially limited water and fertilizer availability. Katambara et al. (2013) used Aquacrop to evaluate the effect of different plant/hill spacing on rice production in an SRI system in Tanzania. Bhattacharya and Panda (2013) report on an Aquacrop-based evaluation of rice yields in multiple experiments across different years with differing weather conditions in West Bengal, India. The cited studies reported varied degrees of success in simulating canopy cover (CC), biomass and/or grain yield production. It can be stated in general that the performance metrics reported for the simulation of rice growth were not worse than those reported by other studies on simulating the growth and production of other crops by Aquacrop. However, we have to note that e.g., the studies of Bhattacharya and Panda (2013) and Katambara et al. (2013) provide little or no information on the experimental sites and the sample/data collection, and therefore, the quality of the observed yield data cannot be reliably evaluated. The study of Lin et al. (2012) report seemingly decent performance metrics, but the trends identified between observed and simulated yields (Figure 8 therein) require attention. The reader can identify that the time-series of available biomass and CC data (especially the latter) is rather short, and there is no field CC information available in the later crop development stages. Hence, the model’s calibration may

not have been optimal. Therefore, the list of studies that provide suitable information to evaluate model performance for simulating rice production is even shorter than it appears, especially in the form of peer-reviewed journal articles. We therefore recognized that further testing of the AquaCrop model in rice cultivars is desirable, and that it has not yet been tested under the conditions in Bangladesh. With the vision of using AquaCrop for local scale decision making for field level water management, this study was undertaken to examine the capabilities of the model and its FAO-recommended parameterization to describe rice growth, biomass production and grain yield in alternative irrigation scenarios during two water-limited seasons in Bangladesh. 2. Materials and methods 2.1. Model description In order to simulate crop growth and yield, the AquaCrop model accounts for soil physical and hydraulic processes, atmospheric conditions (rainfall, temperature, evapo-transpiration, ET and carbon dioxide concentration), crop physiological and productivity parameters (phenology, crop cover, rooting depth, biomass production and harvestable yield) and field management components (irrigation, fertilizer and field agronomic practices) (Raes et al., 2009a; Steduto et al., 2009). AquaCrop simulates the water balance components of soil evaporation and crop transpiration separately based on the daily canopy cover and soil drying, using the daily ETo calculated from weather data (Raes et al., 2012). The crop responds to water stress through four stress coefficients (leaf expansion, stomata closure, canopy senescence, and change in harvest index, HI). Using the crop water productivity parameter, AquaCrop calculates daily aboveground biomass production (Hsiao et al., 2009; Steduto et al., 2009). The normalized crop water productivity (WP*) is considered to be nearly constant for a given climate and crop. The WP* for rice is set between 15 and 20 gm−2 (Raes et al., 2009b) and yield is obtained by multiplying biomass by the crop specific HI. The internal adjustment of HI in relation to available water depends on timing, severity and duration of water stress (Hsiao et al., 2009; Raes et al., 2009a; Steduto et al., 2009). The HI is adjusted in response to five water stress coefficients, namely: the coefficients for inhibition of leaf growth, for inhibition of stomata, for reduction in green canopy duration due to senescence, for reduction in biomass due to pre-anthesis stress and for pollination failure (Raes et al., 2009a; Steduto et al., 2009). The HI for rice is typically found between 35 and 50 percent, depending on genotype and stress factors. The parameters that determine the development of canopy cover (CC) are the canopy growth coefficient (CGC), the canopy decline coefficient (CDC), the initial CC (CCo), maximum CC, days to recovery after transplanting, and the start of canopy senescence. The CGC controls the rate at which the canopy expands and the CDC controls how fast the canopy dies off after the start of canopy senescence. The AquaCrop model contains several user-specified options to simulate irrigation practices, including timing, amounts to be applied, and a selection of irrigation methods. All the threshold and sensitivity parameters were used in the model according to Steduto et al., (2009). A summary of the adjusted crop parameters used is presented in the Results section (Table 3). 2.2. Site description, experimental setup and procedures Crop trials were conducted on a 0.5 ha field of the research farm of the Bangladesh Rice Research Institute (BRRI) in Gazipur, Bangladesh (23◦ 45 N latitude, 90◦ 22’ E longitude, 8.4 m AMSL) during the dry (Boro) season of 2008–09 and 2009–10. Three irri-

M. Maniruzzaman et al. / Agricultural Water Management 159 (2015) 331–340 Table 1 Physical and chemical properties of the soil at the experimental field. Property

Value

Soil depth [cm] Sand [%] Silt [%] Clay [%] Soil texture Bulk density, Bd [g cm−3 ] pH(1:2.5) Organic matter [%] Total N [%] Available P [ppm] Exchangeable K [meq 100 gm−1 soil] Available Zn [ppm] Available S [ppm]

0–45 5.56 48.96 45.48 Silty clay 1.54 6.63 1.59 0.97 9.40 0.26 1.43 26.63

cover more than 90% of the ET comes from transpiration. These water balance components were compared with model simulated water balance components. The difference in storage was calculated as the difference in field water levels at transplanting and at maturity, plus the difference in water contents in the root zone at transplanting and at physiological maturity stages. Leaf area was measured in five crop-growth stages for the estimation of canopy development. In each growth stage, green leaves were separated and leaf area was measured by removing 8 hills per plot (4 + 4 hills from opposite corners of the plot), in order to obtain leaf area index (LAI). LAI was in turn used to calculate crop CC, which was compared with model simulated CC. Lacking a widely accepted relationship for rice, we used the relationship between LAI and CC established for maize by Hsiao et al., 2009 and Heng et al., 2009 as CC = 1.005[1 − exp(−0.6 × LAI) ]1.2

gation treatments: continuous standing water (CSW), and surface irrigation applied after 3 days or 5 days of standing water disappearance (3DAWD or 5DAWD) were assigned in a randomized complete block design with four replications. A deep tube well supplied irrigation water. Unit plots (5 × 6 m) were separated by 20 cm wide compacted earth bunds. Polyethylene sheeting was embedded in the middle of the bunds down to 45 cm depth, in order to prevent losses of water to lateral water flow. Forty-fiveday old seedlings of the IR69515-KKN-4-2-1-1 genotype were transplanted – at the density of 3 seedlings per hill – on January 17 and 14, respectively, in 2009 and 2010 at 20 × 20 cm spacing and harvest took place in late April in both years. Fertilizers at 140:30:75:18:5 kg ha−1 of NPKSZn were used as recommended by BRRI, in the form of urea, triple super phosphate, potassium chloride, gypsum and zinc sulfate. The amounts refer to the elemental form of each nutrient. All fertilizers were applied as basal fertilizers, except nitrogen. The urea was applied in three equal splits at 15–20, 30–35 and 40–45 days after transplanting (DAT). Physical and chemical properties of the soil at the experimental plots are presented in Table 1. Water balance within the field was calculated according to Cabangon et al. (2004): I + R = ET + SP + D + S

333

(1)

where I is irrigation, R is rainfall, ET is evapo-transpiration, SP is seepage and percolation, D is over-bund drainage (all in mm d−1 ) and S is the difference in soil water storage (mm). The inflow and outflow rates are summed up from transplanting to physiological maturity on a seasonal basis. Irrigation, rainfall and drainage were directly measured, and over-bund drainage was deemed negligible, given the experimental setup. Measured amounts of irrigation water were applied directly to the plots. Water balance components like irrigation and rainfall were directly measured by a flow meter and a rain gauge respectively. The ET and percolation data were determined by the simultaneous use of closed-bottom and open-bottom lysimeters placed in the field. The dimensions of the squared lysimeter were 0.5 m × 0.5 m × 0.5. The conditions of rice cultivation, fertilization and water scheme inside the lysimeters were made similar to field conditions. Forty centimeters of soil inside the lysimeter was supplied from a corresponding paddy plot and adjusted to the level of the fields. Four rice hills were planted within a lysimeter corresponding to density of 25 hills m−2 , which was the same as in other parts of the crop field. Water depth inside the lysimeters was kept to about 5–7 cm, similar to field conditions. The daily change in water levels in the lysimeters was measured using PVC-pipe piezometers and manual recording, and observed daily ET was recorded, similarly to the approach by Vu et al. (2005). Evaporation and transpiration were separated from the adjusted ETc according to Allen et al. (1998). They considered that at sowing nearly 100% of the ET comes from evaporation, while at full crop

(2)

and assumed sufficient similarity between the two crops. Dry biomass of the above ground plant at each crop growth stage was also obtained by weighing the total biomass of the samples collected for LAI determination, after keeping them in the oven for 48 h at 65 ◦ C. Grain yield was estimated from 5 m2 areas of each plot, excluding border areas. Collected samples were threshed, cleaned and dried to determine grain yield, which was then converted to kg per hectare yield at 14 percent moisture content (Gomez, 1972). We note that we did not identify a problem with missing or dead hills during sampling, and neither did we witness any notable effects by pests and diseases. 2.3. Climatic data and input Weather data required by the AquaCrop model are daily values of maximum and minimum air temperature, reference crop evapo-transpiration (ETo ), rainfall and mean annual carbon dioxide (CO2 ) concentration. The ETo was estimated using the ETo calculator distributed with the model by using daily maximum and minimum temperatures, wind speed at 2 m above ground-surface, sunshine hours, solar radiation, and maximum and minimum relative humidity (RH). The observed climatic parameters were collected from BRRI’s own weather station on site, and the Gazipur weather station of the Bangladesh Metrological Department. The temperature and ETo patterns during the experimental periods in 2008–2009 and 2009–2010 are shown in Fig. 1. 2.4. Soil data and inputs Soils information required by AquaCrop are the number of soil layers that differ in their characteristics, soil texture, volumetric water content at saturation, field capacity, permanent wilting point and saturated hydraulic conductivity. In this study, one homogeneous soil layer with silty-clay texture was used with a depth of 0.45 m. While using a deeper soil matrix in the simulations is usually desirable, this choice was made to match the depth at which field water balance components could feasibly be measured (discussed earlier) in order to be able to compare measured and simulated soil water balance components. Since measured soil hydraulic data were not available for this field soil, the pedotransfer functions built into the model were used to generate such information. The experimental site did not contain any impervious or restrictive soil layers to obstruct root growth. 2.5. Crop input data and parameters The date of transplanting, the number and spacing of seedlings, maximum CC (CCx ), duration of flowering, maturity and senescence dates were recorded and used as model input parameters. The canopy decline coefficient, crop coefficient for transpiration at full

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Fig. 1. Temperature and ETo patterns during the experimental periods at BRRI farm, Gazipur in (a) 2008–09 and (b) 2009–10.

CC, soil water depletion thresholds for the inhibition of leaf growth and stomatal conductance, and the acceleration of canopy senescence were used as recommended for the rice crop in the model (Raes et at., 2012). These parameters were presumed to be applicable to a wide range of conditions and not be specific for a given crop cultivar (Heng et al., 2009). 2.6. Irrigation management In CSW, water was ponded on the soil surface continuously, while in AWD treatments, ponded water was allowed to deplete to a certain degree. The irrigation schedule that is outlined in Fig. 2 was directly inputted into the model by specifying the dates and depths of irrigation, and specifying basin irrigation as the applicable irrigation method. We note that the seemingly accelerated water draw-down in the piezometers after standing water on the soil surface disappears is likely due to the combined effect of the expected hydraulic properties of the matrix of a silty-clay soil prepared for paddy and capillarity: ‘easily available’ water in any meso- and macropores and as well as in cracks that formulate as the soil dries draws down first in response to atmospheric demand. This is not equilibrated quickly enough from the soil matrix due to its strong water retention capacity and the expectedly very small unsaturated hydraulic conductivity – resulting in quicker visible draw-down of water in the piezometer than in the surrounding soil matrix. Field management was set-up to resemble the expectedly good (non-stressing) fertility levels from the described fertilization, and to account for the presence of basin-end bunds at the height of 25 cm. Due to the combined effect of surrounding bunds, controlled irrigation, and very low amounts of rainfall during the growing period, surface runoff was negligible. 2.7. Model calibration and validation AquaCrop version 4.0 was used in GDD (Growing degree days) mode to account for the thermal effect on the modeled dry season irrigated rice (Boro rice), which was transplanted in the winter

Fig. 2. Patterns of rainfall, irrigation and field water levels under the different water regimes during the 2008–09 and 2009–10 experimental seasons. RF = rainfall, CSW = continuous standing water, DAWD = days after water disappearance, DAT = days after transplanting.

and harvested in the summer. The model was calibrated against observed CC, above ground biomass and grain yield from the field experiment during 2008–2009. We elected to run the model separately for each replicate since there was a slight variation in the amount of applied irrigation water (as reflected in Table 2), as well as the day at which maturity was reached, CC and biomass production. Subsequently, the predicted output values were statistically compared with observed biomass and yield data obtained from the experimental plots under three different water regimes. The difference between model predicted and observed data was minimized by using a trial and error approach in which one specific input variable was chosen as the reference variable at a time and adjusting only those parameters that influenced the reference variable the most. The procedure was repeated until the closest match between observed and model simulated values was found for all treatments. The water balance components predicted by the calibrated model were also compared with field observed values. The calibrated model was subsequently validated against the experimental results from the 2009–2010 season using the same model parameterization and rice genotype. 2.8. Model evaluation criteria The goodness of fit between simulated and observed values was corroborated by using various prediction error statistics. Model performance was evaluated in terms of prediction error (Pe ), coefficient of determination (R2 ), the normalized root mean square error (NRSME), the Nash–Sutcliffe model efficiency coefficient (EF) and Willmott’s index of agreement (d) (Raes et al., 2012; Abedinpour et al., 2012; Whitmore, 1991; Krause et al., 2005; Shrestha et al., 2013). The coefficient of determination (R2 ) ranges from 0 to 1 and values greater than 0.5 are considered acceptable in watershed sim-

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Table 2 Irrigation water depth, rainfall amount, above ground biomass, grain yield, WUE and HI under different water treatments during the 2008–09 and 2009–10 experimental periods. The standard deviation of observations is given in brackets where applicable. Treatment

Irrigation water applied (mm)

Calibration period 2008–09 CSW 749 (7.79) 593 3 DAWD (13.11) 519 5 DAWD (10.23) Validation period 2009–10 843 CSW (9.59) 647 3 DAWD (6.48) 596 5 DAWD (11.20)

Rainfall (mm)

Grain yield (t ha−1 )

WUE (kg ha−1 mm−1 )

Biomass (t ha−1 )

HI (%)

104

5.700 (0.05)

13.571 (0.43)

42 (1.35)

104

5.905 (0.28)

14.048 (0.29)

42 (1.41)

104

5.407 (0.08)

6.68 (0.06) 8.46 (0.05) 8.68 (0.08)

13.183 (0.49)

41 (1.41)

33

5.596 (0.07)

13.324 (0.23)

42 (1.13)

33

5.740 (0.31)

13.349 (0.66)

43 (1.02)

33

5.185 (0.11)

6.39 (0.08) 8.44 (0.07) 8.24 (0.09)

12.646 (0.62)

41 (1.31)

CSW = continuous standing water, 3 DAWD = 3 days after water disappearance, 5 DAWD = 5 days after water disappearance.

Table 3 Crop parameters used in the model for calibration. Description

Maximum canopy growth (CCx ) Maximum root length Recovery time after transplanting Time from transplanting to start flowering Time from transplanting to start senescence Time from transplanting to maturity Length of flowering stage

Value

Unit

CSW

3 DAWD

5 DAWD

93 0.45 6 46 71 100 10

97 0.45 6 44 68 98 10

92 0.45 6 42 67 96 10

% m days days days days days

CSW = continuous standing water, 3 DAWD = 3 days after water disappearance, 5 DAWD = 5 days after water disappearance.

ulations (Moriasi et al., 2007). In case of NRMSE, simulation results can be considered excellent if NRMSE is smaller than 10%, good if it is between 10 and 20%, fair if it is between 20 and 30% and poor if it is larger than 30% (Raes et al., 2012). The value of EF ranges from minus infinity to 1 in which 1 indicates a perfect match between the model and the observations, while 0 EF means that model predictions are as accurate as represented by the average of the observed data. A negative EF indicates that the mean of the observations is a better predictor than the model. The value of d ranges between 0 and 1, where 0 indicates no agreement and 1 indicates a perfect agreement between the predicted and observed data. 3. Results and discussion 3.1. Field experiment results Field experimental results of applied irrigation water, rainfall, grain yield, above ground biomass, water use efficiency (WUE) and HI under different water regimes are shown in Table 2 for both the calibration season (2008–09) and the validation season (2009–10). The lowest grain yield and biomass observed were 5.185 and 12.646 t ha−1 under the 5 DAWD treatment during 2009–10, and the highest were 5.900 and 14.048 t ha−1 under the 3 DAWD treatment during 2008–09, respectively. The WUE ranged between 6.39 kg ha−1 mm−1 (CSW, 2009–10) and 8.68 kg ha−1 mm−1 (5 DAWD, 2008–09). Approximately 21–23% irrigation water was saved without yield reduction in the 3 DAWD treatment; whereas the amount of water saved was 29–31% in the 5 DAWD treatment, although with a significant reduction of grain yield compared to the CSW and 3 DAWD treatments. The experimental results indicate that the 3 DAWD option of irrigation scheduling was the best

among the examined three options for achieving greater grain yield for the amount of water used, i.e., for the best WUE. Although application of the 5 DAWD irrigation regime saved more water, it caused some apparent stress to the crop and reduced both biomass and the grain yield. In our study, the WUE of the 5 DAWD irrigation scheduling option is at par with that of the 3 DAWD option, but there is a greater risk of crop stress with the 5 DAWD option depending on water availability and prevailing ET. More studies are necessary however, – including studies on additional soil types and under varied ET and water regimes – to allow widely founded conclusions about the degrees of risk and differences in efficiency between DAWD irrigation scheduling options. At the same time, while saving irrigation water is more than a factor of economy, for a farmer, the costs and benefits from saving water vs. risking yield reduction will be strongly driven by the market and any applicable policies. There is also generally little emphasis given to other potential impacts of changing irrigation patterns. Changing waterlogging conditions will, for example, have an impact on greenhouse gas emissions (Pandey et al., 2014) or on the amount of water recharge into deeper layers through percolation. Latter can have a significant long-term impact on ground-water reserves in a given area, so recharge rates need to be studied and accounted for when planning long term changes in irrigation schemes. 3.2. Crop parameterization The calibrated values of the crop growth coefficient (CGC) varied with different water regimes, whereas the canopy decline coefficient (CDC) for different water regimes remained the same. The recovery days were similar for all the irrigation regimes but the

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Table 4 Indicators of goodness-of-fit in estimating canopy cover (CC) and biomass for model calibration in 2008–09 and validation in 2009–10. Mean values are presented for four simulation runs, with the range given in brackets. Indicators

Crop metrics and irrigation schemes Biomass (t ha−1 )

Canopy cover (%) CSW Calibration period 2008–09 0.96(0.93–0.99) R2 NRMSE 16.0(14.0–17.2) EF 0.91(0.86–0.94) 0.98(0.95–1.00) d Validation period 2009-10 2 0.98(0.96–0.99) R 11.9(10.0–13.6) NRMSE EF 0.95(0.90–0.98) d 0.99(0.97–1.00)

3 DAWD

5 DAWD

CSW

3 DAWD

5 DAWD

0.94 (0.92–0.96) 21.6 (19.9–24.4) 0.83 (0.79–0.86) 0.97 (0.94–0.99)

0.96(0.95–0.97) 15.5(14.6–16.9) 0.91(0.87–0.93) 0.98(0.95–1.00)

0.99(0.98–1.00) 14.5(13.4–15.9) 0.95(0.92–0.98) 0.99(0.98–1.00)

0.99(0.98–1.00) 14.6(12.8–15.9) 0.95(0.91–0.98) 0.99(0.98–1.00)

0.98(0.96–0.99) 17.4(15.6–18.3) 0.94(0.91–0.97) 0.98(0.96–1.00)

0.99 (0.98–1.00) 8.6 (7.6–10.3) 0.97 (0.95–0.99) 0.99 (0.98–1.00)

0.98(0.97–0.99) 12.9(10.7–14.3) 0.94(0.92–0.97) 0.99(0.97–1.00)

0.99(0.98–1.00) 11.8(10.0–13.8) 0.97(0.93–1.00) 0.99(0.98–1.00)

0.99(0.98–1.00) 12.4(10.6–14.7) 0.96(0.94–0.98) 0.99(0.98–1.00)

0.99(0.97–1.00) 11.2(10.3–12.8) 0.97(0.95–0.99) 0.99(0.97–1.00)

CSW = continuous standing water, 3 DAWD = 3 days after water disappearance, 5 DAWD = 5 days after water disappearance.

days to reach maximum CC, senescence and maturity varied slightly among different water regimes (Table 3). The reproductive growth period consists of the flowering and the yield formation stages. These are controlled in the model by the days to flowering (42–46 DAT), flowering period (10 days) and days of initiation of yield. The maximum CC was reached by the middle of the flowering stage. The largest maximum CC value of 97% was found in the 3 DAWD irrigation option, and hence applied to the model parameterization. The effective rooting depth was set at 0.45 m for all treatments, with the maximum rooting depth reached during the flowering stage. The value of 19.0 g m−2 was used for WP*, which is within the range (15–20 g m−2 ) suggested by Raes et al. (2009b) for C3 crops. The harvestable yield produced by the crop was the product of the simulated biomass and the final HIo , after any correction for the effects of stress factors. We used the default reference HIo (43%) for rice in the model setup, despite finding variation in the experimentally observed HI (varied between 41 and 43%), which variation may have been a result of random in-field variation. All other crop parameters in model calibration were set to the FAO recommended values. 3.3. Model calibration results Indicators of goodness-of-fit of the calibrated model to the field observations are presented in Table 4, and a comparison of the observed and model predicted biomass and grain yields is shown in Table 5. The model – using FAO recommended conservative parameters and locally adapted calibration parameters – performed equally well in simulating rice growth patterns under the different irrigation regimes. While patterns were well simulated, we observed a slight, but systematic looking overestimation in biomass production (2.62–5.19%) and grain yield (5.55–7.7%) relative to the experimentally observed means. In most cases, the simulated values fell outside the standard deviation observed from four replicated plots. The obtained average goodness-of-fit metrics for modeling CC and biomass production respectively are: 0.94 < R2 and 0.98 < R2 ; 15.5 < NRMSE < 21.6 and 14.5 < NRMSE < 17.4; 0.83 < EF and 0.94 < EF; 0.97 < d and 0.98 < d, under the three irrigation water regimes. These metrics indicate good to excellent performance by the calibrated model, a performance that is as good, or often better, than that of most cited Aquacrop applications (e.g., Du et al., 2011; Shrestha et al., 2013; Iqbal et al., 2014; Araya et al., 2010a). The observed slight overestimations can still support the good statistical metrics presented in Table 4. Fig. 3 shows that biomass estimates were mostly outside the standard deviation of observations; biomass was consistently overestimated during the simulated period under all examined water regimes. This indicates

that the source of deviation is in the initial phases – whether that originates from the experimental data, the model, or its parameterization. Grain yields have shown a more positive bias than biomass estimates did, which may be a slight indication that the reference HIo value to be used should potentially be lower than 0.43, i.e., towards our experimental observations communicated above. However, changing the default reference HIo would need stronger experimental and statistical support, and small errors in field sample collection cannot be completely ruled out either. 3.4. Model validation results The model was validated against field data collected in the 2009–10 dry season, using the parameterization calibrated on data from 2008 to 09. The 2009–10 dry crop growing season was drier and warmer than the previous (calibration) season. The combined effect of larger atmospheric demand is reflected in the ET0 trend in Fig. 1, and thus larger amounts of irrigation water were required to fulfill the prescribed irrigation regimes (c.f. Table 2). The measured and simulated CC and above ground biomass for the validation season are shown in Fig. 4, goodnessof-fit indicators of model validation are presented in Table 4, and a comparison of the observed and model predicted biomass and grain yields is shown in Table 5. The indicative goodness-of-fit metrics for the validation portion of our study – for CC and biomass, respectively, – are: 0.98 < R2 and 0.99 < R2 ; 8.6 < NRMSE < 12.9 and 11.2 < NRMSE < 12.4; 0.94 < EF and 0.96 < EF; d = 0.99 and d = 0.99 across the three irrigation water regimes, with no noticeable pattern among them. The AquaCrop model showed robust performance in the validation. Similarly to the calibration results, estimates of biomass and grain yield were somewhat, but systematically over the observed values; resulting in 7.95–11.15% and 8.22–11.54% differences respectively across the different irrigation regimes. This pattern follows what was observed during calibration, although there is no noticeable difference between the degree of overestimation of biomass and grain yields. The observed differences are acceptably small, and support the good statistical metrics presented in Table 4. The obtained statistical metrics meet the range of those reported in previously published studies that modeled rice growth, as well as other cereals (e.g., Hsiao et al., 2009; Araya et al., 2010a,b; Iqbal et al., 2014; Mkhabela and Bullock, 2012) using Aquacrop. Saadati et al. (2011) reported validation season prediction errors of simulated biomass and yield between −10.0–9.4% and −19.0–0.2%, respectively, for rice growth simulation, and the mean of the root mean square errors (RMSE) in grain yield over the simulated irrigation treatments was 0.7 t ha−1 in their validation season. Nikolaus (2013) reported high R2 values (0.90–0.93), and ME val-

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Table 5 Calibration and validation results of simulating biomass and grain yield production under different water regimes during 2008–09 and 2009–10. Standard deviations are given in brackets, Pe is calculated on the mean values. Treatment

Biomass (t ha−1 ) Observed

Calibration period 2008–09 13.571 (0.43) CSW 14.048 (0.29) 3 DAWD 5 DAWD 13.183 (0.49) Validation period 2009–10 13.324 (0.23) CSW 13.349 (0.66) 3 DAWD 12.646 (0.62) 5 DAWD

Yield (t ha−1 ) Simulated

Pe (±%)

Observed

Simulated

Pe (±%)

14.276 (0.54) 14.466 (0.22) 13.528 (0.15)

5.19 2.98 2.62

5.700 (0.05) 5.905 (0.28) 5.407 (0.08)

6.139 (0.20) 6.233 (0.33) 5.818 (0.14)

7.70 5.55 7.59

14.383 (0.61) 14.838 (0.30) 13.794 (0.28)

7.95 11.15 9.08

5.596 (0.07) 5.740 (0.31) 5.185 (0.11)

6.056 (0.29) 6.403 (0.34) 5.672 (0.07)

8.22 11.54 9.39

CSW = continuous standing water, 3 DAWD = 3 days after water disappearance, 5 DAWD = 5 days after water disappearance.

ues of 0.88–0.95 t ha−1 and 0.87–0.96 t ha−1 in simulating biomass production and grain yield in their different treatments. Nikolaus (2013) also noted a slight but systematic overestimation of biomass amount for the fertilized, ponded treatments by up to 10%, although they also noted uncertainties about the field biomass and grain yield data collection. Shrestha et al. (2013) reported grain yield simulation statistics of 0.20, 0.94 and 0.92 in relative RMSE (in t ha−1 ), R2 and Nash-Sutcliffe efficiency (EF) for their simulations. Their study also noted the potential effects of imperfect manual harvesting in leaving some of the biomass on the ground inadvertently, thereby reducing the ‘observed’ total biomass. Despite every effort to prevent that, our study could also have been affected by imperfections in the field assessment of biomass and final grain yield. Recurring reports on slight overestimations in biomass or yield may strengthen the suspicion that imperfect field data collection may indeed be at least partly behind the overestimations.

The effects of small errors at the plot scale can become noticeably significant when scaled up to production scales. Several authors reported much greater deviations between observed and modeled yields under severe water stress or rainfed conditions, compared to well watered treatments for winter wheat, maize, teff and canola crops simulated by AquaCrop (e.g., Iqbal et al., 2014; Heng et al., 2009,b; Araya et al., 2010a,b; Zeleke et al., 2011; Abedinpour et al., 2012). While our experiments did not present severe water stress conditions on the crop, we did not see any gradient in model performance with the degree of water stress. 3.5. Water balance components Simulating the water balance correctly may lend greater confidence in simulated crop growth and yield indicators; hence we

Fig. 3. Simulated and measured canopy cover (CC) and biomass for the calibration period (2008–09) under three water regimes: CSW (plots a,b), 3 DAWD (plots c,d) and 5 DAWD (plots e,f). Error bars indicate the standard deviation across replicated measurements. The simulated values depict one representative model run.

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Fig. 4. Simulated and measured canopy cover (CC) and biomass for the validation period (2009–10) under three water regimes: CSW (plots a,b), 3 DAWD (plots c,d) and 5 DAWD (plots e,f). Error bars indicate the standard deviation across replicated measurements. The simulated values depict one representative model run.

present field observed and simulated water balance components in Table 6. Rainfall amounted to 33 mm in the 2008–09 season and 104 mm in the 2009–10 season. The amount of irrigation water necessary to meet the irrigation treatment requirements varied from 519 to 843 mm on average, showing the expected pattern of CSW > 3DAWD > 5DAWD within each experimental year, and notable pair-wise differences between the wetter and the drier year. Most simulated components were simulated with a deviation smaller than 10% – in both the calibration and validation periods – and the greatest deviation was found to be 15.34% (validation year, 5DAWD, percolation). We interpret these as indications of generally good performance by the model and its parameterization, especially since locally measured soil hydraulic properties were not available. Measured evaporation shows a declining trend with less applied water. While the prediction errors were not large, the

trend was not returned perfectly by the model. Trends in the measured (and calculated) transpiration rates could not be identified, whereas the model indicated lesser transpiration in the 5DAWD system than in the CSW and 3DAWD system. We note that the allocation of evaporated and transpired amounts of water relies on assumptions in both the model and in the field measurements, and they interact and dynamically change over the growing season. This was taken into account to the extent possible, cited in the methodology section, but their perfect evaluation in field conditions is difficult. That considered, we believe that the model performed well, and it would be speculative to claim whether the measurements or the model deviate more from the ‘true values’. Differences and trends in measured percolation rates reflected the total water demand by the different irrigation treatments well. Some differences are seen in the simulated percolation rates and

Table 6 Observed and simulated water balance components under different water regimes during 2008–09 and 2009–10. Standard deviations are given in brackets, Pe is calculated on the mean values. Treatment

Irrigation

Rainfall

Calibration period 2008–09 749 (7.79) 104 CSW 593 (13.11) 104 3 DAWD 519 (10.23) 104 5 DAWD Validation period 2009–10 843 (9.59) 33 CSW 3 DAWD 647 (6.48) 33 596 (11.20) 33 5 DAWD

Evaporation

Transpiration

Percolation

Storage

Obs.

Sim.

Pe (%)

Obs.

Sim.

Pe (%)

Obs.

Sim.

Pe (%)

Obs.

Sim.

84 (4.79) 78 (5.89) 72 (4.79)

80 (5.26) 69 (4.96) 67 (6.41)

−4.76 −11.54 −6.94

282 (9.11) 281 (9.01) 280 (9.74)

283 (8.47) 285 (10.15) 264 (9.42)

0.35 1.42 −5.71

468 (8.50) 405 (7.48) 309 (7.87)

533 (7.63) 389 (14.93) 321 (8.04)

13.89 -3.95 3.88

19 (3.74) −67 (5.44) −38 (3.30)

−43 (4.50) −46 (5.35) −29 (3.74)

96 (4.50) 85 (4.11) 79 (4.50)

85 (4.37) 72 (6.05) 81 (4.55)

−11.46 −15.29 2.53

261 (9.00) 260 (8.18) 259 (6.48)

280 (8.64) 286 (8.96) 261 (6.47)

7.28 10.00 0.77

509 (8.38) 376 (6.34) 339 (8.38)

522 (10.53) 328 (7.68) 287 (8.29)

2.55 −12.77 −15.34

10 (2.16) −41 (4.99) −48 (3.74)

−11 (2.94) −6 (1.71) 0 (3.16)

CSW = continuous standing water, 3 DAWD = 3 days after water disappearance, 5 DAWD = 5 days after water disappearance.

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amounts of water stored in the considered profile compared to the measurements. However, since those are sensitive to the hydraulic properties of the soil (i.e., water retention, hydraulic conductivity), and we only had pedotransfer-based values to parameterize the model, we would not interpret any such patterns beyond noting the generally acceptable degree of deviations, and noting that the model seemingly simulated more percolation and lesser  storage than measured under CSW, but vice versa in the two AWD irrigation treatments. 4. Summary and conclusions The AquaCrop model was calibrated and validated using experimental data of dry season (Boro) rice during the 2008–09 and 2009–10 growing seasons and the IR69515-KKN-4-UBN-4-2-1-1 genotype in transplanted conditions to predict canopy cover (CC), biomass and grain yield under three irrigation water regimes. Based on indicators of evaluation for CC, biomass and grain yield production and the assessment of the required irrigation water amounts, the 3 DAWD irrigation regime appears to be the most efficient water-saving option of the examined three options for rice production during the dry season in the examined conditions in Bangladesh. Comparable WUE was obtained in the 5 DAWD irrigation pattern that used less water, but at the cost of notably reduced grain yields. Hence, the balance between applied irrigation amounts and grain yield will likely be influenced by the availability of water and market conditions for the commodity. The Aquacrop model simulated the expected crop growth and water balance patterns with reasonable accuracy. The FAO-recommended parameterization of the model appears to work well, once the typical calibration parameters were calibrated to the locality and actual crop. We note that the often extremely good correlation metrics presented in the study are somewhat influenced/assisted by the relatively small number of observation points available over the season for which the comparison of observed and simulated values could be compared. Our study seemingly agreed with several cited studies that simulated rice production using AquaCrop in that the biomass production and grain yield was slightly but systematically overestimated. Since we cannot rule out imperfections in the collected field data, we cannot further conclude about any such deviation without better statistical support to our field data. Nevertheless, whether the deviation is due to any imperfection to a model process, or imperfections in the manual harvesting and/or field sample processing, or perhaps due to the often small scale experiments that are up-scaled with a margin of error, this trend may need particular and targeted attention. Such small overestimations, however, lose from their significance if the user is more focused on relative changes (i.e. due to changed climate, irrigation or management) than the absolute values. In our assessment, the obtained season-end metrics in terms of simulated water balance, as well as crop biomass and grain yield suggest great potential for the AquaCrop model to be reliably used in irrigation scheduling, yield prediction or potentially in climate related scenario studies in Bangladesh under different water regimes. Acknowledgements The authors acknowledge financial support by BRRI and IRRI for conducting this research. The authors also acknowledge the Ministry of Foreign Affairs of Norway and the Royal Norwegian Embassy, Dhaka for providing training in the use of the AquaCrop model. We personally thank Dr. Dirk Raes and Ms. Hanne van Gaelen for conducting the training.

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