Assessment of AquaCrop model in simulating maize canopy cover, soil-water, evapotranspiration, yield, and water productivity for different planting dates and densities under irrigated and rainfed conditions

Agricultural Water Management 224 (2019) 105753

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Assessment of AquaCrop model in simulating maize canopy cover, soilwater, evapotranspiration, yield, and water productivity for different planting dates and densities under irrigated and rainfed conditions⋆ Rupinder Sandhu, Suat Irmak

T

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University of Nebraska-Lincoln, Lincoln, NE, USA

A R T I C LE I N FO

A B S T R A C T

Keywords: Model performance Available soil-water Water use efficiency Water stress Deficit irrigation Subsurface drip irrigation

A well-tested crop model can be an important tool in assessment of different crop management scenarios to improve crop yield and water productivity. In this study, the FAO AquaCrop model was evaluated for predicting maize (Zea mays L.) canopy cover (CC), available soil-water (ASW), grain yield, crop evapotranspiration (ETc), and water use efficiency (WUE) when grown at three different planting dates and densities under rainfed and subsurface drip-irrigated conditions in Nebraska, USA. The model adequately simulated CC in 2011 with root mean squared error (RMSE) and model efficiency (EF) in the range of 5.3 to 12.7% and 0.42 to 0.94, respectively; while in drier 2012, CC was substantially under-predicted with higher RMSE of 24.4%. ASW was consistently underestimated in both years with negative EF value for most treatments in 2011, while in 2012, this trend was more pronounced in irrigated treatments, especially during mid-season. ETc estimates were marginal with prediction error of up to 15% in 2011 and 18% in 2012, and overall RMSE of 35 mm for both years. Simulated grain yield was acceptable in 2011 with deviations from measured data up to 8% while in 2012, the deviations were much higher up to 40% for rainfed treatments with overestimations in both years. The errors in ETc and yield simulations are considered high, especially when considering the model application in waterlimiting or/and rainfed conditions, and low-yielding areas in which these errors can be a substantial portion of the total yields produced and in seasonal total ETc from production fields and can cause challenges for growers and decision-makers. The overestimation of yield and underestimation of ETc resulted in overestimation of WUE, producing inconsistent estimates in both years. Detailed analyses of model performance and potential reasons for the discrepancies and areas that require improvements in the model are presented.

1. Introduction Sustainable utilization of resources in agriculture and narrowing yield gaps for all crops play a key role in providing sufficient food for rapidly increasing world population (Tittonell and Giller, 2013). Low and non-uniformly distributed rainfall as well as water limitation and crisis scenarios limiting further expansion of irrigation pose a major challenge to improve/increase crop production. Such challenges in agriculture can be addressed through proper agronomic management strategies, including effective soil, crop, and irrigation management; appropriate planting population density and planting dates; selection of improved hybrids; and crop protection to maximize crop productivity. These are especially necessary practices to enhance productivity and efficiency of production of major agronomic row crops grown

worldwide, including maize. Maize is one of the most important and widely distributed cereal crops in the world, accounting for around 1.05 billion metric tons of global production. About one third of the global maize production occurs in the U.S.A., making it the largest maize producer in the world. Nebraska is one of the leading maize cultivation states in the U.S.A., accounting for more than 36 million hectares harvested in 2017 and a total crop production of 42.7 million metric tons (United States Department of Agriculture-National Agricultural Statistics Service (USDA-NASS), 2017). However, this productivity is highly influenced by the local crop, soil, and irrigation management practices and the maize productivity can have significant impact on food supply and demands. In addition, high growing season and interannual variability in rainfall amounts and distribution, especially in semi-arid conditions of Nebraska, further necessitates the adoption of

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The trade names or commercial products are provided solely for the information of the reader and do not constitute a recommendation for use by the authors or their institutions. ⁎ Corresponding author. E-mail address: [email protected] (S. Irmak). https://doi.org/10.1016/j.agwat.2019.105753 Received 5 February 2019; Received in revised form 7 June 2019; Accepted 14 June 2019 Available online 23 August 2019 0378-3774/ © 2019 Elsevier B.V. All rights reserved.

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year, respectively, for each latitude range as reported by Long et al. (2017). Thus, selection of appropriate planting dates is also one of the important factors to ensure maximum productivity. The aforementioned results indicate the necessity of evaluation and determination of local optimal planting dates and densities under rainfed as well as irrigated conditions for maximizing utilization of available resources, improve crop yield and productivity. However, it is not feasible to evaluate combination of large number of crop management options in field conditions for many different regions and environmental conditions due to time, finance, and resource constraints. In this context, a well-tested, calibrated, and validated crop model can serve as an important tool to evaluate maize response to the factors influencing yield and water use efficiency. In some cases, crop models have proven to be useful in simulating some of the crop management scenarios using fewer resources. Heng et al. (2009) concluded that the FAO AquaCrop model is very useful for the design and evaluation of water management options and for studying the effect of location, soil type, and sowing date under rainfed and irrigated crop production. Although numerous crop models of varying complexities have been developed, including Crop-Syst (Stöckle et al., 2003), EPIC (Cavero et al., 2000; Ko et al., 2009), DSSAT (Jones et al., 2003; Hoogenboom et al., 2017), WOFOST (Boogaard et al., 1998) and STICS (Brisson et al., 1998,2002), most of these models are quite demanding, sophisticated and require highly detailed input parameters which limit their applicability. Moreover, unavailability of open-access source code and lack of proper user support documentation in some cases inhibits the use of model by people other than the model developers. In this context, a recently developed AquaCrop model (Raes et al., 2009; Steduto et al., 2009) has been suggested to be a user-friendly model that uses relatively small number of input parameters that are explicit and mostly intuitive as compared with other crop models and attempts to balance simplicity, accuracy, and robustness. The model is transparent in formulation, well supported with comprehensive documentation for end users and easy to use. Besides, the model performance has been successfully evaluated for several crops, including maize (Hsiao et al., 2009), soybean (Paredes et al., 2015), wheat (Stricevic et al., 2011), barley (Araya et al., 2010), and sunflower (Stricevic et al., 2011). However, implementation of any model for practical purposes require evaluation, calibration, and validation using field measurements from different climatic regions under different water management conditions to ensure accuracy and understand potential limitations of the model. Thus, if AquaCrop is to be adopted by the end users, it must be able to simulate crop yield and yield-influencing factors reasonably well, which requires site-specific and careful testing, calibration, and validation of model. The overall goal of this study was to explore the applicability of AquaCrop model in investigating optimum site-specific maize management strategies to maximize crop yield and productivity. To the best of our knowledge, this research is the first and foremost resource to evaluate AquaCrop for semi-arid conditions of Nebraska in the context of model performance relative to different planting densities and dates under varying water management scenarios using data from extensive and carefully conducted multi-year field research. Due to its inter-annual variable climatic conditions such as rainfall patterns and amounts, air temperatures, and evaporative demand, Nebraska is an ideal research location to test different crop management strategies. Many crop research experiments have been and are being continuously conducted at South Central Agricultural Laboratory (SCAL) near Clay center, Nebraska since 2003 by the senior author (S. Irmak). Therefore, availability of abundant and extensive long-term field-measured datasets for almost all crop variables under different irrigation methods (center pivot, subsurface drip, and gravity) under different crop, water, and soil (tillage) management practices are very valuable and unique in regards to comprehensive assessment of model performance at this location. This study is unprecedented in its inclusion of all field-measured crop parameters, including canopy cover, total soil-water in the root-

appropriate crop production techniques to optimize yield. Maize yield is influenced by several factors such as weather conditions, hybrid, soil conditions, weed pressure, diseases, nutrient deficiency, irrigation, and crop management practices. Crop yield is highly influenced by water availability, which is also affected by inter-annual variability in rainfall patterns, thus requiring timely irrigation practices. Such variability in rainfall becomes a deciding factor in estimating the appropriate dates of planting to prevent water stress. Besides, in the past few decades, availability of improved crop hybrids with higher tolerance to increased planting population densities has improved crop yields consistently (Duvick, 2005). Therefore, it is important to investigate and determine optimum planting population densities, planting dates and irrigation amounts and their interactions to maximize grain yield and crop water productivity. Plant population density (PPD) is one of the major factors impacting crop yield and water use. Increase in PPD usually results in more grain due to early canopy closure that maximizes light interception and decreases soil surface evaporation (Westgate et al., 1997). However, when PPD is above certain level, the grain yield tends to decline because of limited availability of intercepted radiation, nutrients and water under intense interplant competition (Griesh and Yakout, 2001). Required optimum PPDs vary widely from region to region, depending on environmental conditions and hybrids. For instance, higher PPDs may be adopted for high rainfall or irrigation conditions while low PPDs are usually recommended for rainfed or dryland conditions. In the central US Corn Belt, average maize PPD has increased from 30,000 plants per hectare (pph) in the 1930s to 80,000 plants ha–1 or greater at present (Duvick et al., 2004). This is because of the adaption of newer hybrids to high planting densities and increased stress resistance for higher yield production. Li et al. (2015) studied the impact of PPDs, ranging from 15,000 to 180,000 pph, under no water stress on grain yield and reported that grain yield was significantly impacted by PPD ranges of ≤47,000 pph, 47,000–83,000 pph and ≥107,000 pph, while PPD of 83,000–107,500 pph was grain yield stable range. Teasdale (1998) reported increase in grain yield when PPD increased from 64,000 to 90,000 pph in one year and linear decline of yield in dry year in Maryland. Cox and Cherney (2001) reported similar maize silage yield for PPDs of 80,000 to 116,000 pph in Aurora, New York. El-Hendawy et al. (2008) studied maize yield response to three PPDs (48,000 pph, 71,000 pph and 95,000 pph) under three irrigation rates, including full irrigation treatment (FIT), 80% of FIT and 60% of FIT. They reported highest grain yield for PPD of 48,000 pph under full irrigation for dripirrigated maize in sandy soil and concluded that yield increased with increasing irrigation rates and decreasing PPD. Irmak and Djaman (2016) reported that there is a certain optimum combination of PPD and planting date to achieve maximum yield that also depends on the climatic conditions in a given year when investigating maize yield response to three PPDs (59,300, 74,100, and 88,900 pph) and three planting dates under subsurface drip irrigation and rainfed conditions in Nebraska, USA. This implies that the planting date is an important management factor affecting maize yield. In the context of climate change, selection of optimum planting date window based on the onset of the rainy season is critical to improving crop grain yield and productivity. The planting dates have been progressing towards earlier dates, contributing to the increased maize yield in different USA growing regions (Bruns and Abbas, 2006). Delayed planting after late April in mid-South (Bruns and Abbas, 2006) and after June in southeast may result in significant yield reductions and more frequent infestation of pests and diseases (Swanson and Wilhelm, 1996). However, later planting dates can produce greater yield than earlier planting in dryland conditions. Planting maize before or after the optimum date was reported to result in reduced leaf area index, total dry matter production, and grain yield (Swanson and Wilhelm, 1996). Planting date of high-yielding USA maize regions for latitude groups between 25 and 30; 30 and 35; 35 and 40; 40 and 45; and 45 and 50 °N was 42 and 88; 89 and 106; 107 and 118; 119 and 128; and 129 and 135 day of 2

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Fig. 1. (a) Annual cumulative precipitation and grass-reference evapotranspiration (ETo) and (b) monthly total precipitation in 2011 and 2012 growing seasons.

2. Materials and methods

zone, agronomic practices, irrigation date and amounts, crop evapotranspiration, grain yield, and water use efficiency, which in many cases is mostly lacking in other research reports because availability of such extensive datasets on all crop variables is a very challenging task, though it is highly important and desirable in modelling approaches. This research will provide fair and trustworthy information about model performance relative to each of the above-mentioned crop parameter, evaluate the extent of the model’s applicability for Nebraska conditions, assess model’s performance and potential limitations, and provide suggestions for improvements in the model, which will enhance the prediction accuracy. The hypothesis of this study was that AquaCrop can be employed in selecting adaptive crop management practices to improve maize yield and productivity in semi-arid conditions of Nebraska with sufficient accuracy. Sandhu and Irmak (2019) evaluated the AquaCrop model using six years (2005–2010) of field-measured maize data in which they suggested that default parameters provide highly inaccurate results and a set of calibrated parameters were developed that improved the model performance as compared with the default parameters after appropriate calibration and validation. Therefore, the specific objective of this study was to investigate the performance of AquaCrop model for different management practices relative to three planting densities ranging from low to high and three planting dates (from early to late dates) under irrigated and rainfed conditions. This information will shed light on the extent of applicability of the model for Nebraska conditions and aid crop growers, researchers, policy makers and extension professionals in assessing and reducing risks of crop failure, yield loss and achieve maximum profits by adopting appropriate crop and in-season water management strategies selected in advance based on modelling.

2.1. Research site and experimental design The extensive field data used in this study were obtained from experiments conducted by the senior author at the University of Nebraska-Lincoln, South Central Agricultural Laboratory (SCAL) (40°34′N and 98°8′W at an elevation of 552 m above mean sea level), near Clay Center, Nebraska during growing seasons of 2011 and 2012. The experiment details and procedures were outlined by Irmak and Djaman (2016) and are briefly discussed here. The research site lies in the transition zone between sub-humid and semi-arid climate zones with the mean annual precipitation of 731 mm, mean annual temperature of 10.3 °C and mean annual snowfall of 381 mm (U.S. Climate Data, 2018). Significant annual and growing season variability of precipitation in both magnitude and timing requires supplemental irrigation, especially for a high-water user crops like maize. The major soil type at the site is a Hastings silt loam, which is a well-drained upland soil (fine, montmorillonitic, mesic Udic Argiustoll) with particle size distribution of 15% sand, 65% silt, and 20% clay, with 2.5% organic matter content in the topsoil (Irmak, 2010). The soil at the site has field capacity of 0.34 m3 m−3, permanent wilting point of 0.14 m3 m−3, saturation point of 0.53 m3 m−3, saturated hydraulic conductivity (Ksat) of 430 mm day−1 and the effective rooting depth of maize at the site is 1.50 m. The experiment was laid out in a field covering an area of 13.5 ha and irrigated using subsurface drip irrigation (SDI) system that was installed in 2004. Maize hybrid Mycogen 2D770 was planted on three dates (May 4, 16, and 23) and was harvested on November 3 in the 2011 growing season. In 2012, similar maize hybrid Mycogen 2V707 was planted on April 24, May 8, and May 17 and was harvested on October 9. Besides three planting dates, which were referred to as first planting (FP), second planting (SP), and third planting (TP), three plant population densities (P) of 59,300, 74,100, and 88,900 plants per hectare (pph) referred to as P1, P2, and P3 were evaluated during both 3

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(mm) from the beginning to the end of the growing season. Surface runoff from each treatment was quantified using the United States Department of Agriculture-Natural Resources Conservation Service curve number method (United States Department of Agriculture-Soil Conservation Service (USDA-NRCS), 1985). Based on the experimental site’s hydrologic soil group, land use, treatment, and other condition, the curve numbers of 75 was used. Deep percolation was calculated using a daily water balance computer program as mentioned in Djaman and Irmak (2012). Maize was harvested using a plot combine with an eight-row head to measure grain yield per plot and was adjusted to the 15.5% grain moisture content. WUE was calculated to determine maize water productivity response to treatments using the following equation:

growing seasons. All treatments were further investigated under irrigated as well as rainfed settings. Therefore, a total of 18 treatments were studied in this experiment using a completely randomized block design with four replications. Maize was planted at soil depth of 0.05 m in the east-west direction with plant row spacing of 0.76 m in both seasons. The plots were fertilized uniformly, and all herbicide, fungicide and insecticides were applied uniformly when needed (Irmak and Djaman, 2016). Irrigation timing and amounts varied due to differences in PPD and planting dates during both years. 2.2. Weather trends at research site The weather data including rainfall, maximum and minimum air temperature, maximum and minimum relative humidity, solar radiation and wind speed in both growing seasons were measured using a Bowen ratio energy balance system (BREBS) installed in the research field as part of the Nebraska Water and Energy Flux Measurement, Modeling, and Research Network (NEBFLUX); Irmak, 2010). The overall climate trends during 2011 and 2012 growing seasons for precipitation and grass-reference evapotranspiration (ETo) are presented in Fig. 1. The 2011 growing season received greater amount of precipitation (349 mm) than 2012 growing season (285 mm). There was a relatively uniform distribution of precipitation events in 2011 throughout the growing season, except for few dry periods. May was the wettest month with the same precipitation amount of about 120 mm in both years, which coincided with early vegetative growth stages of maize, indicating that there was enough soil moisture at the start of growing season for emergence and the soil-water was uniformly distributed in the experimental field. Only very small amounts of precipitation were observed during July and August, corresponding to water intensive water stress-sensitive stages of maize (silking and grain filling) and these effects were more pronounced in 2012 growing season. The maximum air temperatures (Tmax) were greater in 2012 than in 2011. The mean air temperatures (Tmean) during early growth stages in May and June were greater in 2012 by 2.7 and 1.5 °C, respectively, as compared with 2011. Similarly, vapor pressure deficits were substantially higher in 2012 than 2011, indicating very high atmospheric evaporative demands in 2012 and the mean relative humidity was higher in 2011 than in 2012.

WUE = Y/ETc where, WUE is expressed in kg m is grain yield (g m-2).

on a unit water volume basis and Y

2.4. AquaCrop model description AquaCrop is suggested as a user friendly and practitioner-oriented type of model which consists of four sub-models: (1) the atmosphere sub-model consisting of rainfall, ETo, and CO2 concentration; (2) Crop sub-model involving crop growth, development, senescence, and yield; (3) Management sub-model that includes irrigation and field management practices; (4) Soil sub-model that handles soil-water balance. With a daily time step, the model simulates successively the four major processes of crop production, including crop development, transpiration (Tr), above ground biomass production, and yield. First, AquaCrop simulates crop growth, development, and senescence using green canopy cover (CC). The CC is then used with ETo and coefficient for transpiration (KcTr) to calculate transpiration. Similarly, soil evaporation coefficient is used with CC and ETo to calculate soil evaporation. Daily transpiration (Tri) is translated into a proportional amount of daily aboveground biomass produced (Bi) through biomass water productivity of crop normalized for evaporative demand and atmospheric CO2 (WP*) as:

Tr Bi = WP * ⎛⎜ i ⎞⎟ ⎝ ETo, i ⎠

(3)

The model simulates soil-water balance by keeping track of incoming (rainfall, irrigation and capillary rise) and outgoing (R, ETc, and D) water fluxes at the boundaries of the root-zone. The effects of water stress on the crop are expressed through four stress response coefficients that are functions of fractional depletion of total available soilwater in the crop root-zone. These coefficients include canopy expansion, stomatal conductance, canopy senescence, and harvest index (HI), with their own level of sensitivity to water stress. Crop yield is calculated by multiplying the simulated above ground biomass with the reference HI, which is continuously adjusted during yield formation based on its response to water and/or temperature stresses. Additional underlying concepts, principles, and assumptions of the model are described in detail by Steduto et al. (2009); Raes et al. (2009), and Hsiao et al. (2009).

2.3. Field research data The data collected in the field included weather data, leaf area index (LAI), soil-water content (SWC) and grain yield and other variables that were derived from field measured data, including crop evapotranspiration (ETc), ETo, and crop water use efficiency (WUE) (Irmak and Djaman, 2016). LAI was measured every ten days from each treatment for both growing seasons using a leaf canopy analyzer (LiCor-2200, LI-COR Biosciences, Lincoln, Nebraska, USA.). The volumetric soil-water content was measured using a model 4302 neutron attenuation probe (Troxler Electronics Laboratories, Inc., NC, USA) at 0.30, 0.60, 0.90, 1.20, 1.50, and 1.80 m soil depths twice a week throughout both growing seasons in two replications of each treatment. The neutron probe access tubes were installed between two plants in the middle plant row. The neutron probe measurements were used for analyzing soil-water content dynamics and determine ETc using a soilwater balance approach. Seasonal ETc was calculated using a general soil-water balance approach: ETc = P + I + U – R – D ± ΔSWS

(2) −3

2.5. AquaCrop input data and calibration AquaCrop model tends to maintain balance between accuracy, simplicity, and robustness that uses a moderate number of input parameters. These input parameters are specified in climate, crop, soil, and management files and can be modified by the user. These are separated into two distinct categories: (1) conservative parameters that do not change with time, management practices or location and are provided as default values in the model; (2) non-conservative parameters, also known as user/cultivar specific parameters, that can vary from year to year, with location or between various crop varieties and should be

(1)

where, ETc is crop evapotranspiration (mm), P is precipitation (mm), I is irrigation water applied (mm), U is upward soil-water flux (mm; assumed zero because the groundwater depth is approx. 33 m below the surface), R is run-off (mm), D is deep percolation from the crop rootzone (mm), and ΔSWS = change in soil-water storage in the soil profile 4

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observed in the field or provided by the user. The conservative parameters include canopy growth (CGC) and canopy decline coefficient (CDC); crop coefficient for transpiration (Tr); normalized water productivity (WP*); soil-water depletion thresholds for the inhibition leaf growth, stomatal conductance, and acceleration of canopy senescence; reference harvest index (HIo);and coefficients for adjusting harvest index (HI) in relation to inhibition of leaf growth and of stomatal conductance. Besides conservative parameters, the user-specific information include weather and soil information, plant density and irrigation, as well as the specifics of the selected crop cultivar. The climate data required by the model consists of five daily weather input variables: minimum and maximum air temperatures, ETo, precipitation, and the mean annual CO2 concentration in the atmosphere. In this study, the first four weather variables for 2011 and 2012 were obtained from the BREBS flux tower that was installed in the experimental field as discussed earlier. The CO2 concentration is the annual mean value that was measured by the Mauna Loa Observatory in Hawaii and the model has inbuilt files for past and current CO2 concentration values while the user can enter future year’s CO2 values in the model. The values for non-conservative parameters in the crop tab such as plant density, time of sowing, maximum root depth, etc. were obtained from Irmak and Djaman (2016) based on the field measurements while the values for conservative parameters were obtained from Sandhu and Irmak (2019) that were calibrated using extensive field datasets from Irmak (2015a, b) for maize growing season of 2009 and validated for 2005, 2006, 2007, 2008, and 2010 growing seasons. Extensive calibration procedures were applied to have a robust and fair calibration of the model to obtain justifiable validations. It should be noted that, in most cases, it is neither necessary nor possible to measure all input parameters/variables in the field continuously to calibrate and/or validate the model. However, the performance of the model in terms of simulating some of the critical singular processes (i.e., transpiration) can be assessed though modularity structure of the model by simulating the parameters/variables that strongly influence those critical processes (i.e., transpiration). For example, in this study, the performance of AquaCrop model in simulating transpiration was assessed in terms of model’s performance in estimating CC development and biomass production. The calibration procedure used in Sandhu and Irmak (2019) was performed using the guidelines outlined in the AquaCrop reference manual and FAO Irrigation and Drainage Paper No. 66, Crop Yield Response to Water (Steduto et al., 2012) and is briefly discussed here. The crop phenology is primarily guided by the type of cultivar and air temperature (Raes et al., 2009). Therefore, growing degree day (GDD) mode was used to run the model and crop stage lengths were adjusted based on field observations. The AquaCrop windows version 6.0 was used and field measured data of 2009 growing season was used for calibration. First, the canopy cover (CC) curve parameters were quantified iteratively using trial and error approach until the simulated values were close to the field-observed values. The crop growth stages including time to emergence, maximum CC, start of canopy senescence, and maturity were adjusted based on field-measured data. Next, the calibration of parameters affecting soil-water dynamics was performed followed by calibration of biomass and yield by adjusting the water productivity and the transpiration crop coefficient. Then, the final yield was simulated by adjustment of harvest index to match with measured yields. The calibrated parameters obtained by Sandhu and Irmak (2019) as well as the model default parameters were used in this study to test the performance of model under different planting densities and dates under both rainfed and irrigated conditions during 2011 and 2012 growing seasons. The calibrated model by Sandhu and Irmak (2019) indicated good agreement between simulated and field measured CC (R2 > 0.86, EF > 0.83 and NRMSE ≤ 7.3%), soil water content in the root zone (R2 > 0.88, EF > 0.61 and NRMSE ≤ 5.2%), crop evapotranspiration (R2 = 0.97, EF = 0.80 and NRMSE = 3.2%) and final grain yield (R2 = 0.99, EF = 0.80 and NRMSE = 7.7%). The NRMSE indicates the normalized

root mean square error values. The model performance varied during validation from year to year such that the performance declined in conditions of very high water stress (dry 2005 & 2006 years) but was acceptable during normal years (having precipitation close to long term average). The present study is continuation of model validation and evaluation for different planting densities and dates under both irrigated and rainfed conditions. The other input data for the model included management parameters such as field and irrigation information, soil data including field capacity, permanent wilting point, saturation point, and saturated hydraulic conductivity and groundwater table depth that were obtained from the field measurements (Irmak and Djaman, 2016) in 2011 and 2012 growing seasons. The soil properties such as curve number and saturated hydraulic conductivity were further fine-tuned during calibration and determined iteratively against field-measured soil-water content and ETc by Sandhu and Irmak (2019). Similarly, stress factors affecting canopy expansion, stomatal closure, and crop senescence were adjusted to obtain close match between simulated and observed values under conditions of no water stress as well as under rainfed conditions. The HI value was adjusted for different planting densities in this study. Some studies have reported that HI increases with increase in PPD and decreases after the optimal PPD is reached (DeLougherty and Crookston, 1979; Rahmati, 2009). Therefore, adopting same value of HI for all PPDs might lead to error(s) in estimation of crop yield. In this study, default HI value of 48 was used for lowest PPD of 59,300 pph (P1). For P2 and P3, HI of 54 was adopted based on the value reported by Djaman et al. (2013). Input data for simulation tabs included simulation period, initial conditions, and field data information. Simulation period was based on the planting and harvesting dates of the crop in each year. For initial conditions, the soil profile was considered to be near soil field capacity. One of the reasons for this assumption is that there is an ample rainfall and snow event before the start of maize growing season in the experimental region that usually recharges the soil-water to near field capacity. However, to confirm this assumption, the simulations started from the day of high rainfall event and continued towards the maize planting date in each year that resulted in soil-water content to be at or very close to field capacity. The measured soil-water content throughout each growing season was converted to available soil-water (ASW) to be inputted into the field data tab. The field-measured LAI was used to estimate green canopy cover, following the procedure outlined in Hsiao et al. (2009). 2.6. Model evaluation As discussed earlier, the model calibration parameters already suggested by Sandhu and Irmak (2019) were used to run the model and are presented in Table 1. The model was evaluated for 2011 and 2012 growing season for its performance in simulating CC, ASW, yield, ETc, and WUE under different planting dates and PPDs. The output of AquaCrop model in comparison with the field measurements was assessed using both qualitative and quantitative approaches where the qualitative approach included graphical interpretation of results to evaluate the trends in simulated and field-measured data and the quantitative approach consisted of using statistical indicators which included prediction (simulation) error or percent deviation (Pe) of simulations from measured data, root mean squared error (RMSE), NashSutcliffe model efficiency coefficient (EF), coefficient of determination (R2) and coefficient of residual mass (CRM):

Pe =

(Si − Mi ) × 100 Mi

(4)

n

RMSE =

∑i = 1 (Si − Mi )2 n

(5)

∑i = 1 (Si − Mi )2 n ¯ )2 ∑ (Mi − M

(6)

n

EF = 1 −

i=1

5

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2011 and second and third planting dates in P2 and P3 plant densities. The most common trend of deviations between simulated and observed values was during earlier canopy closure phase and accelerated senescence periods/stages with simulated CC resulting in shorter growing season. One of the reasons for this disagreement is that maize phenology responds differently under different environmental conditions. Latitude is a major factor that significantly affects temperature, sunshine hours, and duration of crop growth such that maize GDDs increases with increase in latitudes (Liu and Xie, 2013). Therefore, the canopy growth parameters need to be adjusted considerably for good estimation of CC development, because default parameters used in the model were obtained using data measured at Davis, CA, USA, which is at much lower latitude than Nebraska. Paredes et al. (2014) reported similar results for CC curve when using default parameters, indicating tendency for underestimation of observed CC values with high RMSEs (> 16.6%) and low to medium model efficiency (EF ranging from 0.18 to 0.71). On the contrary, the model showed better prediction of CC for both growing seasons when calibrated parameters were used, although there were small deviations. In 2011, the model was able to capture the trend of CC development well under both irrigated and rainfed treatments as indicated by high R2 values (0.66 ≤ R2≤0.98), good EF (0.42 ≤ EF≤0.94), and low estimation errors (5.30%≤RMSE≤12.70%) for most of the treatments. Under first planting (FP), there was a good agreement between simulated and measured CC for all planting densities throughout the growing season under both irrigated and rainfed treatments as indicated by good R2≥0.66, EF ≥ 0.66, and low RMSEs≤8.8%. The small deviations between simulated and observed CC, especially during mid-season stages, might be the result of sampling errors. Under second planting (SP), the agreement between simulated and observed CC was good, except for P1 and P3 that had low EF values of 0.42 and 0.46 and high RMSE of 11.7 and 11.2%, respectively, under irrigated treatments. However, high R2≥0.92, high EF ≥ 0.84 and low RMSEs≤7.7% for all other treatments, including for both rainfed and irrigated treatments, indicated good model performance. One of the discrepancies observed was a slight deviation for CC at the start of the growing season [33 days after planting (DAP)] where model-simulated CC was lower than the observed values, which was more profound in P1 and P3 planting densities. Similarly, for third planting (TP) date, simulated CC was lower than observed values until 39 DAP. Moreover, at the later growth stages close to 88 DAP, the model simulated CC was higher than the observed CC values and this trend was consistent in all PPDs under both irrigated and rainfed treatments. In general, R2≥0.86, EF ≥ 0.54, and RMSEs≤12.7% indicated good model performance in simulation of CC for most of the treatments. Comparatively, in 2012, the model performance declined in terms of CC simulations for all treatments. Substantial deviations between simulated and measured CC was consistently observed during midseason stage until senescence in all treatments and slight differences existed during early growth stage for FP and SP dates. In general, CC simulations indicated better performance under irrigated treatments as compared with the rainfed conditions. Under FP, model overestimated CC until 56 DAP and substantially underestimated CC development after 64 DAP in both rainfed and irrigated treatments as depicted by marginal R2≤0.69, low EF ≤ 0.47, and high RMSEs that ranged from 16.4% to 24.4%, thus, indicating poor model performance. Relatively, for SP, the model predictions for canopy development improved with R2≥0.81, EF ≥ 0.61, and RMSEs in the range of 14.1 to 18.2%, although some of the differences similar to those observed during FP were evident in these treatments as well. However, for TP, the model simulated the canopy progression during early vegetative stages quite precisely as indicated by high R2≥0.94, moderate EF ≥ 0.53, and RMSEs in the range of 11.7 to 19.8%, but the disparities during mid-season and later growth stages were, again, prevalent in all treatments. Overall, the model substantially underestimated the canopy development during

Table 1 Default and calibrated maize parameters of AquaCrop model used in this study. Parameter

Default

Calibrated

Base temperature (°C) Cut off temperature (°C) Canopy cover per seedling (cm2 plant−1) Maximum rooting depth (m) Crop coefficient for transpiration (Kcb) Normalized crop water productivity (g m2) Canopy expansion stress coefficient (Pupper) Canopy expansion stress coefficient (Plower) Canopy expansion curve shape Stomatal conductance threshold (Pupper) Stomatal closure shape factor Canopy senescence stress coefficient (Pupper) Canopy senescence shape factor Aeration stress coefficient (% vol saturation) Canopy decline coefficient (% GDD−1) Reference harvest index (%) Crop growth stages (GDD) Time from sowing to emergence Time from sowing to max canopy cover Time from sowing to senescence Time from sowing to maturity Time from sowing to flowering Length of flowering stage

8 30 6.5 2.3 1.05 33.7 0.14 0.72 2.9 0.69 6 0.69 2.7 5 1.06 48 – 80 705 1400 1700 880 180

8 30 6.5 1.5 1.05 31.7 0.14 0.72 2.9 0.4 6 0.45 2.7 5 0.768 54 – 85 716 1603 1750 885 190

n

CRM =

n

∑i = 1 Mi − ∑i = 1 Si n ∑i = 1

Mi

(7)

where, Mi and Si (i = 1, 2, …, n) represent measured and simulated ¯ represent mean value. Pe was used to assess values, respectively, and M deviation between observed and simulated variables relative to the observed values with Pe approaching zero indicating better model performance. RMSE measures the magnitude of difference between simulated and observed values and ranges from 0 to positive infinity with 0 indicating perfect and infinity indicating poor model performance. EF determines the relative magnitude of the residual variance as compared with the variance of observations indicating how well the observed vs. simulated data fits the 1:1 line (Nash and Sutcliffe, 1970; Moriasi et al., 2007). EF ranges from minus infinity to 1 where 1 indicates perfect match between the model and the field-measured data. The R2 and EF were used to quantify the predictive power of the model while RMSE indicates the error in model prediction. The CRM value indicates the tendency of model to over- or under-predict the measurements where positive CRM values indicate underestimation and negative values indicate overestimation (Hassanli and Ebrahimian, 2016). The paired ttest was also conducted for alpha value of 0.05 for statistical comparisons of simulated and field-measured variables. 3. Results and discussion 3.1. Canopy cover (CC) Canopy cover (CC) is a crucial component of water-driven AquaCrop model and accurate estimation of this variable is essential for the model to produce good/acceptable estimates of ETc, biomass, and, hence, grain yield. In this study, CC parameters that have been already calibrated by Sandhu and Irmak (2019) for the same study site were used to simulate the CC progression during 2011 and 2012 growing seasons and the comparisons of resulting CC curves with the measured CC for all treatments are shown in Figs. 2–5. The goodness of fit indicators relative to CC curves obtained using calibrated parameters are given in Table 2. Simulation results using default parameters (without calibration) indicated poor agreement (data not shown) between measured and simulated CC with high RMSE values (11 to 35% in 2011 and 18 to 42% in 2012) and very low EF values that were mostly negative in all treatments, except for third planting date in all plant densities in 6

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Fig. 2. Simulated and measured canopy cover (CC) for all irrigated treatments in 2011 growing season. Vertical bars indicate standard deviation. Some of the standard deviation values are too small to be visible on some of the data points.

transpiration, biomass, soil-water, and yield are strictly tied to CC curve (calculated from LAI), which could provide misleading results in some cases. It was interesting and important to note that the field-measured LAI in much drier 2012 growing season was substantially greater than that in 2011 while the model simulations indicated opposite. This indicates that the field-measured results could differ substantially from the model or theoretical expectations. Second, the model simulates

mid-season and late-season growth stages, i.e., 60, 50, and 52 DAP for FP, SP, and TP dates, respectively. Some of the reasons that could contribute towards discrepancies between model-simulated and fieldmeasured CC data are discussed here. First, the field data indicated that, statistically, planting date or density did not have significant effect (P > 0.05) on LAI in any treatment or year (Irmak and Djaman, 2016). But, in AquaCrop, all major components, including evaporation,

Fig. 3. Simulated and measured canopy cover (CC) for all rainfed treatments in 2011 growing season. Vertical bars indicate standard deviation. Some of the standard deviation values are too small to be visible on some of the data points. 7

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Fig. 4. Simulated and measured canopy cover (CC) for all irrigated treatments in 2012 growing season. Vertical bars indicate standard deviation. Some of the standard deviation values are too small to be visible on some of the data points.

LAI and CC (Nielsen et al., 2012). Third, previously reported literature has established that the model performance declines in conditions of water stress (Katerji et al., 2013; Hsiao et al., 2009; Sandhu and Irmak, 2019). The year 2011 was wetter than 2012 with relatively more uniform distribution of precipitation throughout the growing season, although both years were drier than the long-term average (Irmak and Djaman, 2016) and 2012 was one of the driest and hottest years in Nebraska’s recorded climate history. This justifies the better model

green CC instead of LAI to describe crop growth and development that is calculated by a relationship developed by Hsiao et al. (2009) to obtain CC from LAI to be used in the model comparisons. However, some studies have reported that this relationship between LAI and CC works when LAI < 2 m2 m–2 and for higher LAI values the model predicts lower CC. Since CC is the first step in calibration of model, it becomes very important that field-observed LAI is precisely converted to CC through development of accurate and site-specific relationship between

Fig. 5. Simulated and measured canopy cover (CC) for all rainfed treatments in 2012 growing season. Vertical bars indicate standard deviation. Some of the standard deviation values are too small to be visible on some of the data points. 8

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careful and precise calibration of CC curve parameters to obtain accurate/acceptable simulation results.

Table 2 Goodness of fit indicatorsb relative to model prediction of canopy cover and available soil-water (ASW) for 2011 and 2012 growing seasons for all treatments. FP, SP, and TP: First, second and third planting, respectively; P1, P2, and P3: 59,300 plants per hectare (pph), 74,100 pph, and 88,900 pph, respectively. RMSE: root mean squared error; EF: Nash-Sutcliffe model efficiency coefficient. Year

2011

2012

Treatmenta

Irrigated FPP1 FPP2 FPP3 SPP1 SPP2 SPP3 TPP1 TPP2 TPP3 Rainfed FPP1 FPP2 FPP3 SPP1 SPP2 SPP3 TPP1 TPP2 TPP3 Irrigated FPP1 FPP2 FPP3 SPP1 SPP2 SPP3 TPP1 TPP2 TPP3 Rainfed FPP1 FPP2 FPP3 SPP1 SPP2 SPP3 TPP1 TPP2 TPP3

Canopy Cover (CC, %)

Available soil-water (ASW, mm)

R2

RMSE (%)

EF

R2

RMSE (mm)

EF

0.88 0.77 0.83 0.94 0.98 0.92 0.92 0.96 0.86

5.60 6.40 5.30 11.70 6.60 11.20 10.20 8.20 12.50

0.78 0.73 0.75 0.42 0.89 0.46 0.81 0.88 0.69

0.64 0.56 0.32 0.83 0.76 0.64 0.72 0.81 0.77

78.00 69.50 75.90 53.80 56.10 58.10 43.90 46.10 50.50

−1.30 −2.20 −2.67 0.08 0.10 −0.27 0.30 0.55 0.28

0.74 0.77 0.66 0.98 0.96 0.92 0.96 0.94 0.94

8.50 6.70 8.80 7.70 5.30 6.70 10.50 7.90 12.70

0.68 0.77 0.66 0.84 0.94 0.89 0.77 0.90 0.54

0.72 0.69 0.76 0.79 0.79 0.77 0.76 0.83 0.79

79.40 87.30 80.90 62.90 66.50 87.40 69.70 58.20 65.50

−1.39 −1.39 −0.96 −0.16 −0.07 −1.15 −0.43 0.13 −0.16

0.46 0.31 0.69 0.90 0.86 0.90 0.98 1.00 0.98

17.10 20.60 16.40 14.10 16.90 15.20 11.70 13.00 17.80

0.38 0.23 0.47 0.80 0.68 0.76 0.88 0.82 0.66

0.88 0.85 0.40 0.90 0.83 0.79 0.81 0.46 0.90

36.70 44.60 47.30 31.20 43.80 52.00 37.50 47.00 33.50

0.43 0.51 −0.59 0.68 0.58 0.37 0.42 0.41 0.77

0.38 0.29 0.18 0.81 0.83 0.81 0.98 0.98 0.94

18.10 19.60 24.40 17.60 17.40 18.20 17.50 17.60 19.80

0.30 0.24 0.17 0.64 0.64 0.61 0.69 0.61 0.53

0.96 0.96 0.92 0.96 0.96 0.94 0.96 0.94 0.94

23.00 46.40 46.70 25.50 46.70 49.40 30.50 49.30 46.30

0.88 0.39 0.30 0.88 0.43 0.50 0.87 0.64 0.73

3.2. Available soil-water (ASW) The neutron probe-measured ASW in the maize root-zone along with the simulated ASW from AquaCrop is presented in Figs. 6–9 when using calibrated parameters. When default parameters were used, the model-simulated ASW with very low accuracy as indicated by high RMSEs, ranging from 43.4 to 108 mm in 2011 and from 21 to 84 mm in 2012 as well as very low EF values in the range of -4.36 to 0.38 in 2011 and -3.06 to 0.92 in 2012 (Table 2). Only 3 treatments (FPP1, SPP1, and TPP1) under rainfed conditions were simulated accurately out of a total of 36 cases inclusive of both 2011 and 2012 treatments. Paredes et al. (2014) also reported very low model performance in simulating ASW when using default parameters. They reported RMSE values, ranging from 16 to 36 mm, that are similar to or lower than those obtained in this study and negative EF values, ranging from -8.4 to 0.0, that are similar to the findings in this study. These results indicate the need for additional adjustments in model parameters and thus, the necessity of carefully calibrated parameters using measured soil-water data. Field observations showed that treatments affected soil-water uptake in both seasons and soil-water depletion was higher in magnitude in 2012 as compared with 2011 due to drier conditions (Irmak and Djaman, 2016). They reported that the maximum soil-water depletion occurred in the topsoil layer (0-0.30 m) and minimum occurred in the deepest layers; however, there was decreasing trend in the soil-water uptake in that layer, suggesting water uptake also occurred in deeper layers. The decreasing trend in soil-water towards the later growing season was captured quite well by the AquaCrop model as well. It was observed that the model tends to simulate the soil-water depletion trend well in a number of treatments, but not the magnitude. Moreover, higher soil-water depletion in drier 2012 was also captured well by the model as observed in the rainfed treatments soil-water data in 2012. However, in most of the treatments, a clear and consistent trend of underestimation of ASW was evident in both years, especially in the middle part of the growing season and overestimation (mostly in the late season) in some of the treatments in 2012. The reason for underestimation could be related to underestimation of CC as discussed in the previous section, leading to inaccurate partitioning between transpiration and evaporation by the model such that the model might be simulating smaller transpiration and higher evaporative losses. This indicates that despite adopting a careful parameterization and calibration, the AquaCrop model did not simulate ASW properly. Hsiao et al. (2009) reported that the model simulated decline in ASW less than the measured values, resulting in overestimation of ASW by a significant amount (about 80 mm) for maize grown in California, Davis, USA. The goodness of fit (between measured and simulated soil-water data) indicators presented in Table 2 show that the model performance was not satisfactory in 2011 (0.32 < R2 < 0.83, 43.9 mm < RMSE < 87.4 mm, -2.67 < EF < 0.55). The model performance declined for rainfed treatments as evident from larger RMSEs and lower EF values as compared with irrigated treatments. Only the simulated ASW for TP P2 treatment could be considered acceptable as compared with the rest of the treatments in 2011. In 2012, the model performance improved as compared with 2011 as indicated by R2 values, ranging from 0.40 to 0.96, RMSEs varying from 23 to 52 mm and EF ranging from -0.59 to 0.88. In general, the model performance was acceptable for irrigated treatments, including FPP2, SPP1, SPP2, and TPP3 and rainfed treatments, including FPP1, SPP1, TPP1, TPP2, and TPP3. Also, the model tends to overestimate ASW during late season in some of the treatments, including irrigated treatments (TPP2 and TPP3) and rainfed treatments (FPP1, SPP1, and TPP1). Overall, the model was unable to accurately simulate the mid-season ASW in all treatments during both 2011 and 2012 growing seasons and the trend of underestimation of

a FP, SP and TP: First, second and third planting, respectively; P1, P2 and P3: 59,300, 74,100 and 88,900 plants per hectare (pph), respectively. b R2: coefficient of determination; RMSE: root mean squared error; EF: NashSutcliffe model efficiency coefficient.

performance for simulation of CC in 2011 as opposed to 2012. Katerji et al., 2013 reported that in stressed treatments, the model-simulated CC did not match observed CC after 60 DAP and it systematically underestimated the maximum CC values. Similar trend was evitable in this current study for the 2012 growing season. Lastly, the differences in CC can also be partially attributed to the sampling and measurement errors in LAI as discussed earlier and/or to the increased variation in LAI in the field due to impact of extreme hot and dry conditions. The RMSE values obtained in this study for 2011 were similar to those reported by Hsiao et al. (2009) with low RMSE values, ranging from 4.8 to 13.6% while the RMSEs for 2012 were comparatively higher. Heng et al. (2009) reported larger RMSEs for rainfed maize in the range of 7.2 to 34.5% that are similar or larger than those obtained in this current study for drier 2012 growing season. EF values obtained in the present study were close to or smaller than those reported by Heng et al. (2009) that were in the range of 0.81 to 0.99 for irrigated treatments and -2.01 for rainfed treatments. These results clearly exhibit the importance of 9

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Fig. 6. Simulated and measured available soil-water (ASW) in the maize root zone (0–1.5 m) for all irrigated treatments in 2011 growing season.

Fig. 7. Simulated and measured available soil-water (ASW) in the maize root zone (0–1.5 m) for all rainfed treatments in 2011 growing season.

tendency of overestimation in topsoil and underestimation in deeper soil layers. The poor estimates of ASW can also be caused by inaccuracy in estimation of transpiration and evaporation, pertaining to use of low or less precise transpiration and evaporation coefficients. Paredes et al. (2014) reported that AquaCrop tends to overestimate transpiration and underestimate evaporation, resulting in bias in ASW simulations. They

simulated ASW was prevalent in most of the treatments and underestimation in few of them during late season. Similarly, Nyakudya and Stroosnijder (2014) reported that model performance for ASW was low as indicated by large RMSE and EF values close to zero. Farahani et al. (2009) reported that the simulation errors in ASW were non-uniformly distributed throughout the soil profile with a 10

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Fig. 8. Simulated and measured available soil water (ASW) in the maize root zone (0–1.5 m) for all irrigated treatments in 2012 growing season.

Fig. 9. Simulated and measured available soil-water (ASW) in the maize root zone (0–1.5 m) for all rainfed treatments in 2012 growing season.

3.3. Crop evapotranspiration

recommended that the proportionality between transpiration crop coefficient and CC curve should be revised since the CC curve is not impacted by water stress throughout the season, but only during the vegetative stage, which is not the case with crop coefficient.

Accurate simulation of ETc is critical to prove the model applicability for irrigation aid and water management under different conditions, especially in conditions of water stress. The comparisons between simulated and field-measured ETc for all treatments are presented in Figs. 10 and 11 and Table 4. In general, a t-test comparison 11

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Fig. 10. Simulated and measured crop evapotranspiration (ETc) for all irrigated and rainfed treatments under first (FP), second (SP,) and third (TP) planting dates in 2011 and 2012 growing season.

treatment to 563 mm for irrigated TPP2 with seasonal average of 516 mm. During model simulations, ETc ranged from 475 mm for rainfed TPP1, TPP2, and TPP3 treatments to 550 mm for irrigated FPP3 treatment with overall prediction error in the range of 1.3% (6.5 mm) to 8.6% (47 mm) and 11 out of 18 cases had Pe < 5%, except for TPP2 that had a value of 14.8% (84 mm). In general, the positive CRM value of 0.024 indicated the tendency of model to under-predict ETc for 2011 growing season. This trend of underestimation of ETc was consistently observed for SP and TP under both rainfed and irrigated conditions; however, for FP, the simulated values were higher than the measured values. The range of prediction error reported in this study is similar to the range of 1.3 to 8.4% reported by Heng et al. (2009) and 0.9 to 8.2% reported by Raja et al. (2018), but less than the range of 5 to 37% for maize grown in Mediterranean region under different levels of plant water stress reported by Katerji et al., 2013 and 6.5 to 22.2% in tropical

of the measured and simulated ETc values was not significant (P > 0.05) for both years; however, detailed results of the model simulations are presented here. When using default parameters, overall, the model estimates of ETc were unacceptable due to very low R2 of 0.25, very high RMSE of 48 mm, very low EF of 0.05, and high prediction error, ranging from 0.4 to 33%, except for few treatments. The CRM value of -0.007 in 2011 and -0.083 in 2012 indicated that the model slightly overestimated seasonal ETc and the trend of over-prediction was more pronounced in drier 2012 growing season. Paredes et al. (2014) reported underestimation of ETc when default parameters were used, but they obtained higher EF values in the range of 0.47 to 0.87 and low RMSEs in range of 5 to 9.7 mm. They also mentioned that the model performance was decreased in case of water stressed experiments. In 2011, field-measured ETc ranged from 479 mm for rainfed SPP3

Fig. 11. Simulated and measured crop evapotranspiration (ET) for all irrigated and rainfed treatments under first (P1), second (P2), and third (P3) planting densities in 2011 and 2012 growing season. 12

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underestimation of ETc by the model (Ran et al., 2017; Greaves and Wang, 2016; Oiganji et al., 2016; and Katerji et al., 2013). Katerji et al., 2013 reported that the model systematically underestimates seasonal ETc and concluded that the low deviation between simulated and measured seasonal ETc indicated that the periods during which model overestimated daily ETc were balanced by the periods when the model underestimated daily ETc. Heng et al. (2009) validated the model for daily ETc calculations and found that the model predicted very high ETc peaks at the start of irrigation treatments due to high values of air temperature and wind speed input data. Perhaps, it might be possible that the model over-predicts ETc values under conditions of high evaporative demands. Paredes et al. (2014) also tested AquaCrop for seasonal ETc and reported that AquaCrop was able to simulate ETc with acceptable accuracy (0.74 < R2 < 0.89, 5.1 mm < RMSE < 6.7 mm, 0.70 < EF < 0.87), and no trends of over- or under-estimation of ETc were detected. The discrepancies between simulated and measured ETc values can also be attributed to larger dependency of transpiration crop coefficient (KcTr) on CC curve due to which impact of water stress on transpiration is minimized, suggesting the need to revise KcTr and CC curve proportionality (Paredes et al., 2014; Katerji et al., 2013). Differently, Ran et al. (2017) supported the ETc calculation approach based on CC utilized in the AquaCrop model and concluded that the ETc values simulated by the model were very close to the field-measured values. Therefore, there is a need of more detailed examination of ETc and its partitioning to evaporation and transpiration to determine the true reasons for such inconsistency. In addition, Greaves and Wang (2016) highlighted that the bias between simulated and measured ETc values might result from excessive drainage simulated by the model and spatial variability of the soil in the field.

environment under deficit irrigation management as reported by Greaves and Wang (2016). Although the prediction error in this study was lower than some of the above-mentioned literature, the ETc showed overall high RMSE value of 31 mm, very low R2 of 0.003, and very low EF of -1.93, indicating that model estimates of seasonal ETc as compared to field-measured values were very poor for 2011 growing season. These results were comparable to those shown by Araya et al. (2017) for a semi-arid climate in which they reported R2 of 0.42 and high normalized RMSE of 13.8%, indicating low model performance for ETc simulations. Contrarily, Ran et al. (2017) and Oiganji et al. (2016) evaluated the performance of the AquaCrop model in simulation of ETc and found close agreement between measured and simulated values. In general, the model predicted ETc fairly well for first and second planting under all PPDs with prediction error less than or equal to 5%. Raja et al. (2018) studied the model performance for maize PPD of 83,333 pph under three different planting dates of 28 April, 10 May, and 25 May and reported that prediction error of measured and simulated ETc was in the range of 0.88 to 8.18% with overall RMSE value of 17 mm and R2 of 0.71, indicating acceptable model accuracy. They mentioned that the results demonstrated moderately insufficient accuracy with delayed planting dates in the initial growth stages and suggested adjustment in soil evaporation coefficient (Ke), especially in the initial growth stages to improve model performance. In drier 2012, the field measured ETc was lower than those observed in 2011, ranging from 355 mm for rainfed SPP2 to 577 mm for irrigated FPP3 with seasonal average of 465 mm. The pooled data indicated greater increase of ETc by 26 mm for every 10-day delay in planting date (Irmak and Djaman, 2016). However, during simulation, ETc ranged from 415 mm for TPP3 (similar ETc of 417 for TPP1 and TPP2) to 510 mm for irrigated FPP3. Similar to the results of 2011, the AquaCrop model underestimated ETc in 2012 as indicated by the positive CRM value of 0.019, but, with much higher prediction error in the range of 1% (4.7 mm) to 18% (64 mm). Generally, the model simulations of ETc for second and third planting under irrigated conditions and third planting under rainfed conditions for all PPDs performed best among all treatments. The model statistics indicated R2 of 0.61, RMSE of 38.4 mm, and EF value of 0.50 for 2012. Araya et al. (2017) also obtained similar RMSE values in the range of 7 to 18% for maize in a semi-arid environment in Kansas. Similarly, Greaves and Wang (2016) reported RMSE values in the range of 9.5 to 18.4% for two out of three experiments conducted in Taiwan. In 2012, for all treatments, the model exhibited very poor to moderate accuracy in simulation of ETc as indicated by R2 of 0.61, RMSE of 38 mm, and EF of 0.54. These results indicated that model performance for ETc was better for drier 2012 as compared with 2011 growing season, although some values of RMSEs in 2012 were much greater than 2011 (Table 3). Therefore, the model performance did not decline for the water stress environments in this study conditions as opposed to the common conclusion of decrease in model accuracy with increase in water stress as reported by Greaves and Wang (2016). In both years, the model-simulated and field-measured ETc values differed as much as 47 to 64 mm. This is a high prediction error range in model simulations, especially when considering the model application in water-limiting conditions and in areas that have water allocation in which even a small amount of under- or overestimation can cause substantial challenges for growers, managers, and other agricultural and water management professionals and decisionmakers. One of the causes of errors in simulated ETc is the model incompetency in simulation of ASW. Precise simulation of ASW or soil water-content in the root-zone is an indication of good estimation of ETc in the soil-water balance component of the AquaCrop model (Geerts et al., 2009). As discussed in the previous section, the model performance in terms of simulation of ASW was unacceptable such that the model severely underestimated the ASW in most cases. This could have resulted in underestimation of ETc values simulated by the model. Our results are similar to those reported in some other studies mentioning

3.4. Grain yield The results of model performance pertaining to grain yield are presented in Figs. 12 and 13 and goodness of fit parameters are presented in Table 3. Model simulations with default parameters and variables showed that model was inadequate in predicting grain yield during both years (RMSE = 1.8 ton ha−1 and EF = 0.41), which evidence the necessity of using appropriately calibrated crop parameters. AquaCrop model predictions for grain yield satisfactorily agreed with the measured data when calibrated parameters were used, which is indicated by strong R2 of 0.86, RMSE of 0.97 ton ha−1, and EF of 0.83 for all yield (pooled) data. The RMSEs of grain yield simulation during 2011 and 2012 under all treatments (pooled data) were 0.48 and 1.28 ton ha−1, respectively. Likewise, EF values of 0.47 and 0.61 were observed for 2011 and 2012 growing seasons, respectively. Lowest RMSEs and highest EF values were obtained under irrigated treatments during both years, indicating that model performance declined in water stress conditions and this is also evident from higher RMSEs in drier 2012 growing season. These indices of agreement between simulated and measured data were similar to those reported by Heng et al. (2009) who reported RMSEs in the range of 0.65 to 1.57 ton ha−1 and EF values varying from -0.47 for rainfed treatment to 0.97 for irrigated treatments, concluding that the model was considered unsatisfactory in prediction of maize yield under water stress conditions. On a treatment-average basis, irrigated yields were greater than rainfed yields by 0.33 ton ha−1 in 2011 and by 3.4 ton ha-1 in drier 2012. A t-test comparison between simulated and measured grain yields indicated that the model estimates were very close to the field data (P > 0.05) during both years, except for rainfed treatments in 2012 year (P < 0.05). Similarly, the simulated yields were tested among different planting dates, PPDs and irrigation treatments. Irrespective of irrigation treatments and planting dates, the model-simulated yields were significantly different (P < 0.05) between P1 and P2; and P3 and P4, while P2 and P3 were not different in both years (P > 0.05). When compared among different planting dates, significant differences were 13

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Table 3 Goodness of fit indicators relative to model prediction of crop evapotranspiration (ETc), grain yield and water use efficiency (WUE) for 2011 and 2012 growing season under all treatments. Pe: percent deviation of simulated maize ETc values from measured values; Meas: field measured maize ETc; Sim: simulated maize ETc using calibrated parameters of AquaCrop model; FP, SP and TP: First, second, and third planting, respectively; P1, P2, and P3: 59,300 plants per hectare (pph), 74,100 pph, and 88,900 pph, respectively. Trtb

Crop ET 2011 Meas

Crop ET 2012 Sim mm

__________________________

Irrigated FPP1 FPP2 FPP3 SPP1 SPP2 SPP3 TPP1 TPP2 TPP3 Rainfed FPP1 FPP2 FPP3 SPP1 SPP2 SPP3 TPP1 TPP2 TPP3

Default

Sim-Meas

____________________

Pesim _______

%

Pedefault

Meas

_______

_____________________

Sim Default mm _______________________

Sim-Meas

Pesim ________

Pedefault %

________

503.00 522.00 541.00 515.00 507.00 526.00 539.00 563.00 522.00

522.60 534.90 550.30 505.50 498.90 508.20 492.40 479.50 486.60

549.40 559.30 571.00 501.30 526.60 528.30 478.00 491.30 493.40

19.60 12.90 9.30 −9.50 −8.10 −17.80 −46.60 −83.50 −35.40

3.90 2.47 1.72 1.84 1.60 3.38 8.65 14.83 6.78

9.22 7.15 5.55 2.66 3.87 0.44 11.32 12.74 5.48

470.00 554.00 577.00 487.00 467.00 496.00 479.00 518.00 486.00

486.00 475.60 509.60 475.60 452.50 473.20 496.80 502.90 467.40

537.00 537.50 560.90 495.00 499.70 506.70 508.00 528.50 517.70

16.00 −78.40 −67.40 −11.40 −14.50 −22.80 17.80 −15.10 −18.60

3.40 14.15 11.68 2.34 3.10 4.60 3.72 2.92 3.83

14.26 2.98 2.79 1.64 7.00 2.16 6.05 2.03 6.52

496.00 502.00 511.00 504.00 512.00 479.00 507.00 517.00 514.00

522.60 522.80 522.80 497.50 495.60 495.80 474.70 475.10 475.50

549.40 560.30 561.30 501.90 514.90 516.70 469.80 482.80 484.70

26.60 20.80 11.80 −6.50 −16.40 16.80 −32.30 −41.90 −38.50

5.36 4.14 2.31 1.29 3.20 3.51 6.37 8.10 7.49

10.77 11.61 9.84 0.42 0.57 7.87 7.34 6.62 5.70

501.00 419.00 448.00 463.00 355.00 367.00 443.00 394.00 437.00

453.20 453.00 452.70 418.80 418.50 418.70 417.20 416.90 414.50

511.40 511.40 511.90 468.90 471.60 471.00 473.10 473.80 473.60

−47.80 34.00 4.70 −44.20 63.50 51.70 −25.80 22.90 −22.50

9.54 8.11 1.05 9.55 17.89 14.09 5.82 5.81 5.15

2.08 22.05 14.26 1.27 32.85 28.34 6.79 20.25 8.38

0.1–12.1% for irrigated treatments in both years. The prediction error for simulated yields was less than 10% for 30 out of total 36 cases. In 2011, it was observed that the minimum error (0.4%) in grain yield prediction was obtained in irrigated SPP2 and rainfed SPP3, while the maximum was obtained for FPP1 in both irrigated (6.5%) and rainfed treatments (8%). In 2012, irrigated FPP1 had minimum error of 0.1% while rainfed SPP2 had maximum error of 40.1%. Similar prediction error statistics for grain yield were reported by Abedinpour et al. (2012) (0.84–16%) for semi-arid region of Northern India, Araya et al. (2017) (1–12%) in a semi-arid climate of Kansas, Hsiao et al. (2009) (1–23.8%) in California, Heng et al. (2009) (2–12.4%) for Texas, and Katerji et al., 2013 (4.2–13.3%) under contrasting water stress conditions in a Mediterranean region. The RMSEs obtained in this study (0.36 ≤ RMSE≤1.61 ton ha−1) were comparable to those reported in the literature for maize grain yield. For instance, RMSEs of 0.71 for calibration and 1.77 Mg ha−1 for validation under full and deficit irrigation in semi-arid climate of Iran (Ahmadi et al., 2015), 0.65–1.57 ton ha−1 under varying water stress levels for rainy weather and sandy soil in Florida (Heng et al., 2009), 1.73 ton ha−1 under deficit and full irrigation in a Mediterranean

observed only between SP and TP; and FP and TP in 2011 while other treatments did not exhibit any significant differences due to planting dates. Besides, simulated irrigated yields were significantly greater (P < 0.05) than rainfed yields in both years. Most of the simulated results were consistent with the field-measured data, indicating that AquaCrop can be employed to differentiate crop yield among varying planting dates, PPDs and irrigation treatments based on the yield predictions. However, it is important to note that the model’s yield simulation error range (RMSEs ranging from 0.65 to 1.57 ton ha−1) can still be considered high, especially in irrigated, but low yield productivity areas where 1.57 ton ha−1 can be a significant portion of the final yield produced. Also, when the model is used to predict rainfed yields, this performance error can also constitute a high percentage of total yields produced under non-irrigation conditions in which yields are usually much lower than irrigated conditions. All these important points justify the need for improvement in AquaCrop for yield predictions with different planting densities and dates. The comparison of measured and simulated rainfed grain yield showed prediction error in the range of 0.4–8.0% in 2011 and 0.1–40.1% in 2012 (Table 5). The range of prediction error was

Table 4 AquaCrop model-simulated and field-measured results of maize evapotranspiration (ETc) in 2011 and 2012 growing seasons for all treatments. RMSE: root mean squared error; EF: Nash-Sutcliffe model efficiency coefficient. Mode of model parameters

Year/data

Goodness of fit indicators Grain yield (ton ha−1)

ETc (mm)

Using default parameters Using default parameters Using calibrated parameters

2011 2012 All Data 2011 Irrigated 2011 Rainfed 2011 (All) 2012 Irrigated 2012 Rainfed 2012 (All) All Data

CWUE (kg m−3)

R2

RMSE

EF

R2

RMSE

EF

R2

RMSE

EF

0.03 0.63 0.25 0.05 0.11 0.00 0.58 0.24 0.61 0.54

39.67 54.78 47.83 35.76 26.15 31.32 37.56 39.18 38.38 35.03

−3.71 0.05 0.05 −3.10 −4.70 −1.93 −0.07 0.21 0.54 0.49

0.69 0.77 0.84 0.84 0.54 0.71 0.72 0.16 0.73 0.86

0.91 2.37 1.80 0.36 0.58 0.48 0.83 1.61 1.28 0.97

−0.88 −0.32 0.41 0.81 −1.55 0.47 0.66 −2.61 0.61 0.83

0.14 0.55 0.44 0.10 0.48 0.03 0.30 0.28 0.49 0.52

0.21 0.40 0.32 0.32 0.32 0.32 0.25 0.36 0.31 0.31

−1.30 0.18 0.30 −4.51 −5.91 −4.72 0.00 −1.52 −0.05 0.14

14

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Fig. 12. Simulated and measured crop yield (ton ha−1) for all irrigated and rainfed treatments under first (FP), second (SP), and third (TP) planting dates in 2011 and 2012 growing season.

climate of Portugal (Paredes et al., 2014) and 0.74–1.25 ton ha−1 for rainfed maize in Pennsylvania (Mebane et al., 2013) have been reported. The EF values reported in this study (0.83 for pooled data, 0.47 in 2011 and 0.61 in 2012, and negative values for rainfed treatments) were similar to those reported by Paredes et al. (2014) (0.82 for calibration) and Heng et al. (2009) (-0.47 for rainfed to 0.97 for irrigated), but smaller than those reported by Abedinpour et al. (2012) (0.99 for calibration and 0.98 for validation). The CRM value for grain yield was calculated to determine the model tendency to over or under-predict simulated values. The CRM value for 2011 was 0.01, indicating minimal underestimation of yield, while in 2012, the CRM value of -0.05 indicated slight overestimation and the pooled data for both years had CRM value of -0.01. Overall, the simulated yields were in line with the measured values. The CRM value of -0.15 was obtained in rainfed 2012 treatments, indicating over-prediction of yield was more

pronounced in rainfed treatments. Salemi et al. (2011) also reported CRM values of -0.02 for biomass yield of silage maize grown in arid region, supporting model’s tendency to over predict yield. In general, the results of this study highlighted two major discrepancies in simulation of grain yield. First, the model is unable to predict grain yield under water stress conditions such as in rainfed treatments. Second, the model tends to overestimate grain yield, especially in dry weather conditions. This outcome was also justified by several other researchers, including Katerji et al., 2013 and Heng et al. (2009) and they mentioned that the model’s incapability of simulating accelerated senescence of maize canopy under water stress which affected grain yield. Hsiao et al. (2009) discussed that the likely reason for such behavior is that while the simulation of senescence is a complex process, the simplification procedure used in AquaCrop represents extreme simplification of the reality. It should be noted that for a water-

Fig. 13. Simulated and measured crop yield (ton ha−1) for all irrigated and rainfed treatments under first (P1), second (P2), and third (P3) planting densities in 2011 and 2012 growing season. 15

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Table 5 AquaCrop model-simulated and field-measured results of maize yield in 2011 and 2012 growing seasons for all treatments. Pe: percent deviation of simulated maize ETc values from measured values; Meas: field-measured maize evapotranspiration (ETc); Sim: simulated maize ETc using calibrated parameters of AquaCrop model; FP, SP, and TP: First, second and third planting, respectively; P1, P2, and P3: 59,300 plants per hectare (pph), 74,100 pph, and 88,900 pph, respectively. Trtb

Grain yield 2011 Meas _____________________

Irrigated FPP1 FPP2 FPP3 SPP1 SPP2 SPP3 TPP1 TPP2 TPP3 Rainfed FPP1 FPP2 FPP3 SPP1 SPP2 SPP3 TPP1 TPP2 TPP3

Sim ton ha−1

Grain yield 2012 Default

Sim-Meas

_____________________

Pesim _______

%

Pedefault _______

Meas ___________________

Sim ton ha−1

Default

Sim-Meas

___________________

Pesim _______

%

Pedefault _______

12.15 13.83 14.34 11.98 13.13 13.75 12.24 13.61 13.96

11.36 13.54 13.89 12.13 13.08 13.62 12.71 13.69 14.04

11.87 12.46 12.55 11.85 12.41 12.49 11.87 12.41 12.49

−0.79 −0.29 −0.45 0.15 −0.05 −0.13 0.47 0.08 0.08

6.53 2.08 3.16 1.21 0.38 0.95 3.86 0.55 0.60

2.32 9.88 12.47 1.08 5.49 9.16 3.03 8.85 10.53

9.36 9.63 13.07 11.39 9.86 11.77 11.60 13.69 10.74

9.37 10.20 11.49 10.63 10.96 11.68 11.18 12.72 11.41

11.38 11.38 12.16 11.83 12.17 12.36 11.98 12.52 12.47

0.01 0.57 −1.58 −0.76 1.10 −0.09 −0.42 −0.97 0.67

0.14 5.91 12.12 6.65 11.11 0.76 3.64 7.06 6.21

21.54 18.14 7.00 3.88 23.41 5.00 3.25 8.53 16.11

12.34 13.00 13.58 12.50 12.85 12.88 12.67 12.88 13.31

11.36 12.82 12.85 11.77 12.92 12.93 11.96 13.48 13.51

11.87 12.46 12.55 11.85 12.41 12.49 11.87 12.41 12.49

−0.98 −0.18 −0.73 −0.73 0.07 0.05 −0.72 0.60 0.20

7.97 1.40 5.35 5.85 0.57 0.40 5.64 4.66 1.51

3.82 4.12 7.57 5.19 3.43 3.03 6.31 3.68 6.16

8.32 6.98 7.52 7.97 6.75 8.66 8.40 6.72 9.23

8.30 9.34 9.29 8.40 9.46 9.41 8.21 9.26 9.14

10.57 10.56 10.55 10.83 10.98 11.00 10.68 10.78 10.82

−0.02 2.36 1.77 0.43 2.71 0.75 −0.19 2.54 −0.09

0.29 33.75 23.56 5.38 40.10 8.66 2.23 37.80 0.99

27.04 51.29 40.33 35.91 62.73 27.03 27.18 60.34 17.17

parameters (R2 = 0.52, RMSE = 0.31 kg m−3, EF = 0.14). The CRM values of -0.07 and -0.11 for 2011 and 2012 growing seasons, respectively, indicated that model overestimated WUE in both seasons. During simulation of WUE, the difference between field-measured and modelsimulated WUE data was also assessed on the basis of prediction error, which ranged from 1.5 to 23.6% in 2011 and from 1.7 to 32.2% in 2012. The highest error values were more frequent in rainfed treatment in 2012, probably due to high water stress level experienced by the crop. The t-test performed on pooled data for both years also confirmed that there were significant (P < 0.05) differences between field-measured and simulated WUE values. However, when the treatments were analyzed individually for each year, it was observed that model-simulated results were close to field data for P1 in both years and P3 in 2012 (P > 0.05). Best simulated results for WUE were obtained for FP and SP in 2011 and for SP and TP in 2012. Clear trends were absent between irrigated and rainfed treatments in any of the years, but 2011 results showed better simulation of WUE under rainfed treatments whereas in 2012, model performance was better for irrigated treatments. The bias in the ETc values simulated by the model in both years could have impacted WUE simulations. Similar to the results in this study, Abedinpour et al. (2012) reported that model prediction error in simulating WUE varied from 2.35% to 27.5% for different irrigation and nitrogen levels. However, they obtained lower RMSE of 0.12 kg m−3 and EF in the range of 0.66 to 0.74, indicating acceptable model performance in simulating WUE. In contrast, Heng et al. (2009) tested AquaCrop for simulation of WUE and reported smaller deviations in the range of 1.3 to 15.3% and small standard error of 0.17 kg m-3 in Texas. Katerji et al., 2013 also reported unsatisfactory performance of the model for WUE and mentioned that the model tends to overestimate WUE in some treatments and underestimate in others. They mentioned that the model overestimated the inhibition of the canopy expansion affecting CC and simulating low ETc and finally inhibit yield, which consequently affect WUE. Likewise, Greaves and Wang (2016) assessed AquaCrop model for simulating maize WUE under deficit irrigation conditions in a tropical environment and estimated large WUE deviations from measured values in the range of 6.0% to 32.2% due to some significant mismatch between simulated and measured ETc. They also indicated that the deviations between WUE values were not a function of the level of plant water

driven models, such as AquaCrop, yield performance depends on precise simulation of ETc and soil-water dynamics. Errors in simulation of evaporation and transpiration might lead to inaccuracy in estimation of yield. Also, the yield overestimation by the model is likely related to this insufficiency in partition of ETc into E and T (Paredes et al., 2014). Moreover, the results of our study indicate that it is very important to use appropriate HI value that can vary for different crop varieties, planting densities, weather conditions, geographical location, and other factors. For instance, in this study, HI value of 54 for P2 and P3 from field measurements was used in simulation of yield as opposed to the default value of 48, which significantly improved yield simulations while the default value was used for lowest PPD (P1) due to unavailability of HI data. Nevertheless, the pooled data results show moderate to good adequacy of AquaCrop model for estimating maize yield, except for water stress conditions. 3.5. Water use efficiency Improvement in crop WUE (aka crop water productivity, CWP) is critical to improving agricultural productivity under scenarios of limited water resources, increasing food demand, and climate change. Estimation of WUE through field experiments is not always feasible to test wide range of field management combinations, including planting dates and densities, irrigation amounts and timings, fertilizer applications, etc., but accurate simulation models can be beneficial to fill these gaps. Most studies (Ahmadi et al., 2015; Ran et al., 2017; Hassanli and Ebrahimian, 2016; Mebane et al., 2013) conducted in past using AquaCrop model for maize did not assess its performance for simulation of WUE. In this study, the field-measured WUE was compared with the model-simulated values and the results are presented in Figs. 14 and 15, while tabulated results and statistical indicators to assess model performance efficiency are presented in Tables 6 and 3. The field-measured WUE ranged from 2.3 to 2.8 kg m−3 in 2011 and from 1.7 to 2.6 kg m−3 in 2012. The simulation results in our study indicated WUE in the range of 2.3 to 3.0 kg m−3 in 2011 and 1.9 to 2.6 kg m−3 in 2012, which closely mimicked the range of field data. Although, the range was close, the model-simulated values indicated bias in model performance for simulating WUE when using default (R2 = 0.44, RMSE = 0.32 kg m−3, EF = 0.30) and calibrated 16

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Fig. 14. Simulated and measured water use efficiency (WUE) for all irrigated and rainfed treatments under first (FP), second (SP), and third (TP) planting dates in 2011 and 2012 growing season.

4. Conclusions

stress as mentioned in some other research reports. The large deviation (7.2 to 43.7%) and relatively low R2 of 0.43 for regression analysis between simulated and field-measured WUE was also reported by Todorovic and Albrizio (2009) in simulating sunflower growth under different water regimes. In an effort to explore how different models perform in simulating WUE of several crops, including cotton, maize, quinoa, and sunflower, Evett and Tolk (2009) concluded that the simulation models estimated WUE adequately under well-watered conditions, but not under conditions of water stress. This tendency of model limits its applicability for exploration of deficit irrigation or rainfed scenarios. The results of this study and the above-discussed literature indicate that the model needs improvements, especially for ETc calculation that has a substantial impact on WUE under both irrigated and water stress conditions.

The AquaCrop model was tested using extensive field-measured data from 2011 and 2012 growing seasons for simulating maize CC, ASW, ETc, grain yield, and WUE in Nebraska, USA. Several treatment combinations, including three planting dates and three PPDs were evaluated under rainfed and irrigated conditions. CC was simulated satisfactorily in 2011 with RMSE and EF in the range of 5.3 to 12.7% and 0.42 to 0.94, respectively, except for irrigated SPP1 and SPP3 treatments. However, in dry 2012, the model performance declined and higher RMSEs in range of 11.7 to 24.4% were obtained due to substantial underestimation of CC during mid-season and late crop growth stages. The ASW exhibited opposite trend to CC, indicating better model performance in dry year in 2012 (0.40 ≤ R2≤0.96, -0.59≤ EF ≤ 0.88 and 23 ≤ RMSE≤52 mm) than in 2011 growing season (0.32 ≤ R2≤0.83, -2.67 ≤ EF≤0.55 and 44 mm ≤ RMSE≤87 mm) for

Fig. 15. Simulated and measured water use efficiency (WUE) for all irrigated and rainfed treatments under first (P1), second (P2), and third (P3) planting densities in 2011 and 2012 growing season. 17

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Table 6 AquaCrop model-simulated and field-measured results of maize water use efficiency (WUE) in 2011 and 2012 growing seasons for all treatments. Pe: percent deviation of simulated maize ETc values from measured values; Meas: field-measured maize evapotranspiration (ETc); Sim: simulated maize ETc using calibrated parameters of AquaCrop model; FP, SP, and TP: First, second and third planting, respectively; P1, P2, and P3: 59,300 plants per hectare (pph), 74,100 pph, and 88,900 pph, respectively. Trtb

WUE 2011 Meas _____________________

Irrigated FPP1 FPP2 FPP3 SPP1 SPP2 SPP3 TPP1 TPP2 TPP3 Rainfed FPP1 FPP2 FPP3 SPP1 SPP2 SPP3 TPP1 TPP2 TPP3

WUE 2012 Sim kg ha−1

Default

Sim-Meas

_____________________

Pesim ________

Pedefault %

________

Meas ___________________

Sim kg ha−1

Default

Sim-Meas

___________________

Pesim ________

Pedefault %

________

2.42 2.65 2.65 2.33 2.59 2.61 2.27 2.42 2.55

2.32 2.69 2.73 2.55 2.79 2.85 2.70 2.99 3.02

2.39 2.46 2.47 2.55 2.57 2.58 2.68 2.73 2.73

−0.10 0.04 0.08 0.22 0.20 0.24 0.43 0.57 0.47

4.13 1.51 3.02 9.44 7.72 9.20 18.94 23.55 18.43

1.24 7.17 6.79 9.44 0.77 1.15 18.06 12.81 7.06

1.99 1.74 2.26 2.34 2.11 2.37 2.42 2.64 2.21

2.04 2.26 2.35 2.30 2.49 2.54 2.28 2.56 2.47

2.29 2.29 2.35 2.48 2.53 2.54 2.42 2.46 2.48

0.05 0.52 0.09 −0.04 0.38 0.17 −0.14 −0.08 0.26

2.51 29.89 3.98 1.71 18.01 7.17 5.79 3.03 11.76

15.08 31.61 3.98 5.98 19.91 7.17 0.00 6.82 12.22

2.81 2.46 2.54 2.69 2.44 2.68 2.54 2.45 2.51

2.32 2.62 2.63 2.52 2.78 2.78 2.63 2.97 2.97

2.39 2.46 2.47 2.55 2.59 2.60 2.69 2.74 2.75

−0.49 0.16 0.09 −0.17 0.34 0.10 0.09 0.52 0.46

17.44 6.50 3.54 6.32 13.93 3.73 3.54 21.22 18.33

14.95 0.00 2.76 5.20 6.15 2.99 5.91 11.84 9.56

1.66 1.67 1.68 1.72 1.90 2.36 1.90 1.71 2.11

1.92 2.16 2.15 2.07 2.34 2.32 2.00 2.26 2.24

2.21 2.21 2.20 2.39 2.42 2.43 2.31 2.33 2.34

0.26 0.49 0.47 0.35 0.44 −0.04 0.10 0.55 0.13

15.66 29.34 27.98 20.35 23.16 1.69 5.26 32.16 6.16

33.13 32.34 30.95 38.95 27.37 2.97 21.58 36.26 10.90

CRM values were -0.07 and -0.11 for 2011 and 2012 growing seasons, respectively, indicated that model overestimated WUE in both seasons. The results indicate that the model requires improvements, especially for ETc estimations that have a substantial impact on WUE. Overall, the AquaCrop model should be modified to improve simulation of soil-water content, which in turn affects other related variables such as ETc and WUE. Besides, the observed bias in simulated values is most likely due to insufficient parameterization of crop parameters in the later growth stages in the model that is highly impacted by crop senescence stress coefficients. Moreover, it is important to use appropriate HI values to obtain accurate simulation of grain yield. Therefore, incorporation of the above-mentioned improvements in the model can make it a reliable tool for studying wide range of crops’ response to water under various management practices.

most of the treatments. A common trend of underestimation of simulated values was observed in both years during mid-season and late growth stages and this effect was more pronounced in 2011. The best predictions of ASW in 2012 were obtained for PPD of 59,300 pph under all three planting dates and also for third planting date under all three PPDs under rainfed conditions, indicating better model performance for ASW under water stress conditions. Owing to the underestimation of ASW, the model-simulated ETc values were, in general, lower than the field-measured data in both years (CRM of 0.024 in 2011 and 0.019 in 2012). In 2011, the overall prediction error was in the range of 1.3% (6.5 mm) to 14.8% (44 mm) with 11 out of 18 cases having Pe < 5% with a high RMSE value of 31 mm, very low R2 of 0.003, and very low EF of -1.93, indicating substantial bias in model estimates. In 2012, the model statistics indicated R2 of 0.61, RMSE of 38.4 mm, and EF value of 0.50 with prediction error in the range of 1 to 18%, indicating insufficient accuracy. However, the simulated ETc values were statistically similar (P > 0.05) to the measured values despite of the aforementioned discrepancies. In terms of grain yield predictions, the model estimates of yield were satisfactory, except for some rainfed yields in both years with pooled data showing R2, RMSE, CRM, and EF values of 0.86, 0.97 ton ha−1, -0.01, and 0.83 ton ha−1, respectively (P > 0.05), in both years, except for rainfed treatments in 2012 (P < 0.05). Overall, differences between simulated and measured yield ranged from 0.4 to 8% in 2011 and 0.1 to 40.1% in 2012. It is important to note that the model’s yield simulation error range (RMSEs ranging from 0.65 to 1.57 ton ha−1) can still be considered high, especially in irrigated, but low yield productivity areas where 1.57 ton ha−1 can be a significant portion of the final yield produced. Also, when the model is used to predict rainfed yields, this performance error can also constitute a high percentage of total yield produced under non-irrigation conditions in which yields are usually much lower than irrigated conditions. Nevertheless, the pooled data results show moderate accuracy of AquaCrop model for estimating maize yield, except for water stress conditions, in which yield simulations were inaccurate. The model-simulated values indicated bias in model performance for simulating WUE when using default (R2 = 0.44, RMSE = 0.32 kg m-3, EF = 0.30) and calibrated parameters (R2 = 0.52, RMSE = 0.31 kg m-3, EF = 0.14) (P < 0.05 for both years) and the

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