Performance of AquaCrop model in simulating maize growth, yield, and evapotranspiration under rainfed, limited and full irrigation

doi:10.1016/j.agwat.2019.105687

Agricultural Water Management

Volume 223, 20 August 2019, 105687

https://doi.org/10.1016/j.agwat.2019.105687 Get rights and content

Highlights

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AquaCrop simulations of maize growth, yield, ET_c were compared to measured data for six years.
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In all cases, the model estimates using default values resulted in unsatisfactory estimates.
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Calibration resulted in adequate simulation of CC.
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The model simulated grain yield quite well, except for rainfed treatments in most years.
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The model performance declined substantially in water stress, excess water, and high VPD conditions.

Abstract

The performance assessments of AquaCrop model using long-term field-measured data are rare. In this study, the model was evaluated relative to maize growth, yield, and water use parameters/variables under different water stress conditions over six years (2005–2010) in Nebraska, USA. The model was calibrated and validated for full irrigation treatment (FIT), limited irrigation (50, 60, and 75% of FIT), and rainfed. Model default parameters provided very poor estimates for mostly all variables. After calibration, the model adequately simulated daily canopy cover (CC) for both 2009 and 2010 (NRMSEs ≤ 15.6%), except for slight discrepancies in 2010. However, the model overestimated the final biomass in 2009 due to overestimation of biomass development in the late growth stages. The simulation of final biomass was better in 2010 (NRMSE = 5.3%) than in 2009 (NRMSE = 31%). The model simulated grain yield quite well (NRMSE of 7.7 and 12.1%, EF of 0.8 and 0.7) during both calibration and validation, respectively, except for rainfed treatments in most years. The model was not able to simulate total soil-water accurately in most cases, except for 2009 growing season. The crop evapotranspiration (ET_c) was simulated with good accuracy during 2007, 2009, and 2010 and the higher prediction error (up to 16.5%) was observed for dry years (2005 and 2006) and wet year (2008). The model performance declined substantially in conditions of water stress, excess water, and high evaporative demand. In conclusion, the AquaCrop simulated yield and ET_c (with slight underestimation) quite well in some cases, but encountered substantial difficulties in simulating biomass and soil-water, especially during years of low precipitation and high evaporative demand as well as in wet year. Further evaluation of the model is needed using field measured evaporation and transpiration data to determine the accuracy of ET_c partition by the model to better simulate soil-water and ET_c, which are critical for the estimation of in-season irrigation requirements, biomass production, and yield.

Introduction

Globally, agriculture accounts for 70–75% of total freshwater withdrawals. Food production has increased by more than 100% in the last 30 years and about 60% more food will be needed by year 2050 to meet the food, fiber, feed, and energy demands of the rapidly growing population. According to FAO projections, food production in irrigated land area will need to be increased by more than 50% by 2050, but only a 10% increase in water withdrawal by agriculture will be possible (FAO, 2017) based on current global water supply and demand analyses and projections. Therefore, increase in water and food demand with limited resources, especially water, necessitates improving the efficiency of water use in crop production, which has been and will continue to be a significant challenge for agriculture. Declining worldwide irrigation/water resources and uncertainty in precipitation due to its inter- and intra-annual variability has increased the risk of failure for crop production. Under such circumstances, deficit or limited irrigation can provide important strategic options for enhancing crop water use efficiency (Fereres and Soriano, 2007). Crop water use efficiency is defined as the ratio of economic yield of a crop or biomass produced per unit of crop evapotranspiration (ET_c) (Irmak, 2015a,b). Crop models that can accurately estimate various crop growth parameters, soil-water dynamics, crop water use, and expected yield under different irrigation levels can provide vital assistance to successfully implementing limited and full irrigation management practices.

In terms of meeting global food demand, maize (Zea mays L.) plays an important role as a grain crop, accounting for nearly 30% of the total global grain production and is used in numerous industrial applications, including human food and fiber needs, animal feed, and energy production. It is a major irrigated and rainfed crop globally and in the U.S., including in Midwestern states. Maize yield is affected by several factors such as planting date, type of hybrid, planting density, weather conditions, soil fertility, management strategies, and soil-water availability. Water stress is one of the most important factors affecting maize yield and it is very sensitive to water stress, especially during tasseling-silking and grain fill periods, resulting in significant yield reduction (Otegui et al., 1995). Several studies have reported the impacts of water deficits on maize to vary depending on the development growth stage at which the stress occurs. Ghooshchi et al. (2008) reported that water deficit before silking, during silking, and during grain fill growth stage decreased yield by 12.5, 42.0, and 22.5%, respectively. Irmak (2015a, b) reported that no significant grain yield difference was observed between full irrigation treatment (FIT) and 75% FIT, indicating limited irrigation as viable practice for increasing crop water productivity in south central Nebraska conditions. This is in agreement with findings by Stewart et al. (1977) and Doorenbos and Kassam (1979).

Field experiments are critically important and effective ways to investigate management options to improve yield under limited water availability and provide understanding of plant-soil-water-atmosphere interactions and their impacts on grain yield and other important plant phenological and physiological responses. Data and information generated from such field experiments are critical in development, calibration, and validation of simulation models that are designed to study the behavior of a system. These models are considered as a possibility to investigate a wide range of management strategies with reduced efforts and low cost and can provide invaluable information and data for scenarios for which conducting fields experiments may not be even possible. Various crop models of different complexity have been developed such as 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 sophisticated and require substantial input parameters/variables and may not always be feasible or amenable for general use.

The simulation of crop development and growth parameters is based on complex interactions between climate, crop, and soil parameters and management practices. Majority of the models require highly detailed input data and information about crop growth that might not be available for most locations. Moreover, the applicability of most of the models is also limited by the unavailability of open-access source code. However, a recently developed FAO AquaCrop model (Raes et al., 2009; Steduto et al., 2009) is a user-friendly model that attempts to balance simplicity, accuracy, and robustness. The AquaCrop model simulates water-limited yield under different management conditions using smaller number of explicit and mostly-intuitive parameters as compared with other crop models as it has been suggested that most of the parameters are conservative and do not change with location (Raes et al., 2009; Steduto et al., 2009, 2012). The AquaCrop has been successfully applied to multiple crops across wide range of climatic and agronomic conditions. In addition, the open source version of AquaCrop (AquaCrop-OS) is also available that can be run in multiple programming languages and operating systems (Foster et al., 2016). The model performance has been evaluated for several crops, including maize, soybean (Paredes et al., 2015), wheat (Andarzian et al., 2011), barley (Araya et al., 2010), potato (Razzaghi et al., 2017), and sunflower (Stricevic et al., 2011). Hsiao et al. (2009) parameterized and tested the model for maize using datasets from 6 years of field experiments at Davis, CA, USA that included varying plant densities, planting dates, hybrids, and atmospheric evaporative demand under different irrigation amounts. They discussed the usefulness and limitations of AquaCrop model in simulating canopy cover (CC), biomass, water content, and water use efficiency. A set of conservative parameters were developed for maize in the aforementioned study and they were validated in a later study by Heng et al. (2009) for locations with diverse environments. They reported that model performance was more satisfactory under non-water stress and mild stress treatments as compared with severe water stress treatments. Similar finding was reported by Abedinpour et al. (2012), indicating that AquaCrop model predicted maize yield more accurately for full irrigation and 75% of the field capacity (FC) treatments as compared with rainfed and 50% of FC treatment when assessing maize response under varying irrigation and nitrogen regimes in a semi-arid region. They reported that the prediction error in simulation of grain yield and biomass under all treatments ranged from 1.35 to 9.9% and from 0.84 to 17.5%, respectively. Mebane et al. (2013) evaluated the model performance in simulating grain yield, biomass, and soil-water content of rainfed maize in Pennsylvania, U.S.A. and reported accurate simulation of progression of cumulative biomass and grain yield with time with index of agreement (d) ranging from 0.96 to 0.99. In contrast, Katerji et al. (2013) highlighted the limitations of AquaCrop model in simulating the effects of water stress on maize yield, biomass, and ET_c. They reported that the model overestimated maize yield and biomass and underestimated ET_c, resulting in overestimation of water use efficiency. Paredes et al. (2014) suggested that when appropriate parametrization is adopted, AquaCrop can simulate maize biomass and yield under deficit conditions adequately. Aforementioned findings indicate the necessity to appropriately calibrate and validate the model for specific regions to improve the model performance in estimating crop yield, water use, and crop growth parameters and evaluate limited irrigation strategies to develop effective management strategies.

Considering the limitations and the need for better parameterization of the model referred in the aforementioned literature, the current study was conducted to calibrate and validate the AquaCrop model for maize grown in a transition zone between sub-humid and semiarid environment of Nebraska. There is no or very limited literature available for use of AquaCrop in Nebraska or greater Midwestern corn belt region of the U.S.A. and most of the available studies for maize consider validation using short-term field data, mostly 2 or 3 growing seasons (or less) in which the long-term inter-annual variability in environmental conditions affecting maize response to water and other variables may not be considered. Therefore, to the best of our knowledge, the present study is the first long-term validation work of AquaCrop model for maize in Nebraska and Midwestern region. The specific objective of this study was to calibrate the AquaCrop model for estimating maize growth and development (including CC), response to water, soil-water content/total soil-water, ET_c, biomass, and yield and to validate the model using long-term field-observed datasets and evaluate the performance and accuracy of the model in estimating the aforementioned variables under full and various levels of limited irrigation and rainfed production.

Section snippets

Site description

Field-measured datasets from long-term and extensive research conducted and published by Irmak (2015a,b) were used as the primary data source for this study. As described in Irmak (2015a,b), the experimental data used in this study were obtained from a long-term research project conducted from 2005 to 2010 growing seasons 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,

Weather conditions

Daily seasonal precipitation and irrigation amounts in the FIT treatments for each growing season are presented in Fig. 1. The cumulative growing season precipitation was 306, 388, 588, 692, 487, and 566 mm in 2005, 2006, 2007, 2008, 2009, and 2010 growing seasons, respectively. The seasonal precipitation varied not only in total amounts between six seasons, but also within each growing season. The timing of precipitation plays an important role in crop development as some crop stages are more

Conclusions

Six years (2005–2010) of data collected through extensive field experiments were used to calibrate and validate AquaCrop model in a transition zone between semi-arid and sub-humid region in south central Nebraska. Crop growth parameters; including CC, in-season biomass development, final biomass; water use parameters, including SWC (TSW) and ET_c, and final grain yields were estimated and compared with the field-observed data after careful parameterization of the model. Although all the

Acknowledgements

This study is based upon work that is supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, Dr. Suat Irmak’s Hatch Project, under the Project Number NEB-21-155.

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Performance of AquaCrop model in simulating maize growth, yield, and evapotranspiration under rainfed, limited and full irrigation☆

Highlights

Abstract

Introduction

Section snippets

Site description

Weather conditions

Conclusions

Acknowledgements

Agric. Water Manage

Agric. Water Manage.

Agric. Water Manage.

Agric. Water Manage.

Fields Crops Res.

Eur. J. Agron.

Agric. Water Manage.

Agric. Water Manage.

J. Hydrol.

Field Crops Res.

Agric. Water Manage

Agric. Water Manage.

Agric. Syst.

Agric. Water Manage.

Eur. J. Agron.

Agric. Water Manage.

Modeling maize yield and soil water content with AquaCrop under full and deficit irrigation management

Water Resour. Manage.

Crop Evapotranspiration—Guidelines for Computing Crop Water Requirements

Evaluating optimum limited irrigation strategies for corn production in the Ogallala Aquafer region

J. Irrig. and Drain. Eng., ASCE

User’s Guide for the WOFOST 7.1 Crop Growth Simulation Model and WOFOST Control Center 1.5. Technical Document 52

STICS: A generic model for the simulation of crops and their water and nitrogen balances. I. Theory and parametrization applied to wheat and corn

Agronomy

STICS: a generic model for simulating crops and their water and nitrogen balances. II. Model validation for wheat and maize

Agronomy