Syntheses of the Current Model Applications for ...

Published December 5, 2014

Syntheses of the Current Model Applications for Managing Water and Needs for Experimental Data and Model Improvements to Enhance these Applications L.R. Ahuja,* Liwang Ma, Robert J. Lascano, S.A. Saseendran, Q.X. Fang, David C. Nielsen, Enli Wang, and Paul D. Colaizzi

Abstract This volume of the Advances in Agricultural Systems Modeling series presents 14 different case studies of model applications to help make the best use of limited water in agriculture. These examples show that models have tremendous potential and value in enhancing site-specific water management for different soils and climates, and evaluating cropping system sustainability over the longer term, when model results are integrated with the available field measurements in experimental studies. Here we summarize applications of 11 system models commonly reported in the literature for agricultural water management along with those presented in this volume. These 11 models vary greatly in simulating agricultural system components for water balance related processes (the differences in crop growth and N balance processes were even greater among models), which need to be kept in mind when reading about the applications of each model. A sensor-based automated irrigation scheduling system is presented. Finally, we summarize further needs for experimental data and model improvements to enhance future water management applications. Abbreviations: ABA, abscisic acid; AgMIP, Agricultural Model Inter-Comparison and Improvement Project; APEX, Agricultural Policy Environmental eXtender model; APSIM, Agricultural Production Systems Simulator; CGMS, Crop Growth Monitoring System; CSM, cropping system model; CWPF, crop water production function; DSS, decision support system; DSSAT, Decision Support System for Agrotechnology Transfer; EPIC, Environmental Policy-Integrated Climate; ET, evapotranspiration; IRT, infrared thermometer; NCP, North China Plain; PALMS, Precision Agricultural Landscape Modeling System; PSA, plant–soil–atmosphere; RZWQM2, Root Zone Water Quality Model; STICS, Simulateur mulTIdisciplinaire pour les Cultures Standard; SWAP, Soil Water Atmosphere Plant; TSEB, two-source energy balance; WOFOST, WOrld FOod STudies; WUE, water use efficiency. L.R. Ahuja ([email protected]), Liwang Ma ([email protected]), S.A. Saseendran (Saseendran. [email protected]), USDA-ARS, Agricultural Systems Research Unit, 2150 Centre Ave., Bldg. D, Ste. 200 Fort Collins, CO 80526. *Corresponding author. Q.X. Fang, Agronomy College, Qingdao Agricultural University, Changcheng Rd. 700, Chengyang District, Qingdao, Shandong, China, 266108 ([email protected]). Robert J. Lascano, USDA-ARS, Wind Erosion and Water Conservation Research Unit, Cropping Systems Research Laboratory, 3810 4th St., Lubbock, TX 79415 ([email protected]). David C. Nielsen, USDA-ARS, Central Plains Resources Management Research Unit, 40335 County Rd. GG, Akron, CO, 80720-0400 ([email protected]). Enli Wang, CSIRO Land and Water, Christian Laboratory, Clunies Ross St., Black Mountain ACT 2601, Australia ([email protected]). Paul D. Colaizzi, USDA-ARS, Conservation and Production Research Lab., P.O. Drawer 10, 2300 Experiment Station Rd., Bushland, TX 79012-0010 ([email protected]). doi:10.2134/advagricsystmodel5.c15 Copyright © 2014. ASA, CSSA, SSSA, 5585 Guilford Rd., Madison, WI 53711-5801, USA. Practical Applications of Agricultural System Models to Optimize the Use of Limited Water Lajpat R. Ahuja, Liwang Ma, and Robert J. Lascano, Editors Advances in Agricultural Systems Modeling, Volume 5. Lajpat R. Ahuja, Series Editor

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A

s we review the case studies presented in this volume, see Table 15–1 for

a summary of how the 11 commonly used models differ in simulating

agricultural system components.

THE AUSTRALIAN APSIM MODEL

The Agricultural Production Systems Simulator (APSIM) modeling framework (Keating et al., 2003; Wang et al., 2002) was developed to simulate the biophysical processes in farming systems as they interact with environmental changes and management intervention to determine the productivity and environmental impact of farming systems. APSIM can simulate more than 30 crops (including pasture and tree species) and provides great flexibility for the user to specify complex crop rotations and management regimes. The model has been widely applied in both Australia and China to assist in the development of management strategies for increasing water and nutrient use efficiencies. Extensive validation of the APSIM model against experimental data was done by Chen et al. (2010a, b), Zhang et al. (2012), and J. Wang et al. (2011) for exploring the productivity and water use of the wheat (Triticum aestivum L.)–maize (Zea mays L.) double cropping system under irrigation management options in the North China Plain (NCP), where the groundwater table is rapidly declining. APSIM modeling using 40 yr of historical climate data at Luancheng, NCP showed that the current irrigation practice is often managed to maximize crop yield, and a reduction in irrigation amount would lead to significantly increased water use efficiency (WUE) (Chen et al., 2010a,b). Due to the interannual climate variation, 140 to 420 mm of irrigation water for wheat and 0 to 170 mm for maize would be needed to meet the crop water demand, which would cause the groundwater to decline at a rate about

1.5 m yr−1, when other sources of groundwater recharge were not considered. This estimated rate of groundwater decline approximates the measured decline since the 1990s. If >180 mm irrigation water were available, partitioning it to wheat and maize would lead to higher total yield than applying it only to wheat. Modeling also showed an overall 4 to 6% increase in grain yield and improved WUE resulted from adoption of the “double delay” management option, that is, delayed sowing of winter wheat and harvesting of maize, due to the increase in temperature before winter, in the last three decades (X.C. Wang et al., 2011).

401

NRCS curve number

Soil Conservation Service (SCS) curve number

Penman–Monteith equation

Priestley-Taylor method and Penman–Monteith method

Penman–Monteith equation

Tipping bucket casModified Penman equation or Priestley- cade approach Taylor equation

Tipping bucket, casPriestley–Taylor method and Penman cade approach method

CropSyst

Daisy

DSSAT

EPIC

Actual evaporation Soil hydraulic properties

Richards’ equation

Potential Exfiltration rate based on Darcy’s law on the surface or energy available for evaporation

Two-stage evapo- Upper drain limit and lower ration curve limit of plant available water, Ksat Two-stage evapo- Soil type, bulk density, field ration curve capacity and witling point

0–1 factor; potential root water uptake/potential transpiration 0–1 factor; actual water uptake/ potential plant transpiration

Empirical equation based on soil water content and root length density Empirical equation based on potential transpiration, root depth, soil water content and an empirical water extraction distribution parameter.

When ponding Steady-state radial flow to single root exceeds surface depression storage

0–1 factor; actual evapotranspiration/ potential evapotranspiration

van Genuchten retention curve, Brooks–Corey, Campbell, Mualem equation for hydraulic conductivity

Two-stage evapo- Field capacity and wilting ration curve point, saturated hydraulic conductivity (Ksat), Campbell equation

0–1 factor; actual transpiration/ potential transpiration

NRCS curve number

Tipping bucket cascade approach or Richards’ equation

Calculated from potential difference between soil water and plant xylem and soil water or root conductance

Two-stage evapo- Field capacity and wilting ration curve point, saturated hydraulic conductivity (Ksat)

Maximum soil water extrac- 0–1 factor; on soil tion multiplied by soil water water depletion stress

NRCS curve number

0–1 factor; actual wa- Two-stage evapo- Upper drain limit and lower limit of plant available ter supply/potential ration curve water, Ksat plant water demand (calculated from RUE for the day).

Plant water stress

Empirical soil drainage ability function

A function of total soil water above lower limit of plant available water

NRCS curve number

AquaCrop

Tipping bucket cascade approach or Richards’ equation

Modified Priestley– Taylor method or Penman–Monteith

Actual water uptake

Runoff

APSIM

Water movement in soil

Potential evapotranspiration

Model name

Table 15–1. Water balance simulated in the eleven models.

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Potential evapotranspiration

Penman–Monteith equation, Priestley– Taylor equation

Shuttleworth–Wallace

Penman method for soil evaporation, adaptation of the Shuttleworth and Wallace method for crop potential transpiration

Penman–Monteith equation

Penman method

Model name

HERMES

RZWQM2

STICS

SWAP

WOFOST

Tipping bucket approach

Richards’ equation

Soil type, initial and maximum surface storage, saturated soil hydraulic conductivity 0–1 factor; actual transpiration/potential transpiration Potential transpiration as influenced by water shortage and oxygen reduction

Potential evaporation rate and days since last rainfall

Mualem–van Genuchten Darcy’s law calculates maximum relations, with a modification near saturation. evaporation rate for wet soil, which is further limited for drying soil (Black et al., 1969; Boesten and Stroosnijder, 1986). Feddes et al. (1978) function between 0 and 1.

As a function of maximum Rainfall rate exceeds the in- root water extraction rate filtration rate, or when the water table reached the soil surface.

Exceed maximum surface storage

Two stage evapo- Soil residual soil water, soil ration curve water content at wilting point and soil water content at field capacity)

0–1 factor; actual transpiration/potential transpiration

Analogy with a Empirical equation based reservoir on potential transpiration and soil water content

Brooks–Corey soil water retention curves; Mualem equation for unsaturated hydraulic conductivity

Determined by solving the Richards’ equation with either constant flux or constant head upper boundary condition

Tipping bucket approach

Soil hydraulic properties

Capacity parameters deEmpirical equation based on soil rived from soil texture and distance from groundwater water content

Actual evaporation

0–1 factor; actual transpiration/potential transpiration or potential root water uptake/potential transpiration as in DSSAT

Water in exNimah-Hanks equation or cess of Ksat and empirical equation from DSSAT macropore flow if exists

Empirical equation depend- 0–1 factor; actual transpiration/ ing on soil water content potential transpiraand root length density tion

N/A

Plant water stress

Actual water uptake

Runoff

Green-Ampt for infiltration and Richards’ Equation for redistribution

Modified capacitybased approach including capillary rise

Water movement in soil

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In Australia, APSIM-based modeling has been extensively used to investigate options for improving crop management and WUE in the dryland farming systems, including crop or cultivar choice, pre-crop (crop sequence, weed control, residue management) and in-crop (sowing time, plant density, N management) management options (Kirkegaard et al., 2014), to effective use of seasonal climate forecasts combined with on-farm decision making (Wang et al., 2008a, 2009a,b). The model is also made available to growers and consultants through a simplified web interface known as Yield Prophet (Birchip Cropping Group) (Hochman et al., 2009b), enabling them to track their crop growth and soil conditions in real time. APSIM-based modeling has also played an essential role in benchmarking WUE and defining the attainable yield targets and yield gaps of various crops across climatic zones in Australia grain regions (Hochman et al., 2009a; Wang et al., 2009b). This has enabled the separation of management impacts from climate impacts and the identification of constraints that limit crop yield and WUE (Hochman et al., 2009a, 2012). Combining observational data from 334 wheat fields and APSIM modeling, Hochman et al. (2009b) showed that further improvement in WUE (up to 21.4 kg grain ha−1 mm−1) might be achieved by optimized plant density together with early sowing and higher N input. Kirkegaard and Hunt (2010), using modeling, further demonstrated the relative impacts and interactions of a range of pre-crop and in-crop management decisions on water productivity and showed how a novel genetic trait (long coleoptiles) that enable deeper sowing, could interact with different management options to increase the water-limited yield of wheat from 1.6 to 4.5 t ha−1. Subsequently, Hunt and Kirkegaard (2011) and Hunt et al. (2013) showed that better summer fallow management (weed control and residue management) alone could lead to increased wheat yield and WUE. In a recent WUE initiative that challenged researchers and growers to increase WUE of grain-based production systems by 10% in 5 yr, APSIM-based modeling provided calculations of both a priori outcomes that were tested experimentally and extrapolation of results across sites, seasons, and up to the whole-farm scale (Kirkegaard et al., 2014). It was demonstrated through both experiments and modeling that water productivity at paddock scale can be improved by better summer weed control (37–140%), inclusion of break crops (16–83%), earlier sowing of appropriate varieties (21–33%), and matching N supply to soil type (91% on deep sands). Capturing synergies from combinations of pre- and in-crop management could increase wheat yield at the farm scale by 11 to 47% (Kirkegaard et al., 2014). Lyon et al. (2003) applied the APSIM model to determine optimum corn plant populations and initial soil water contents at planting and recommended a plant population of 3 plants m−2, with an initial soil water of 240 mm in the root zone to reduce production risk under semiarid western Nebraska conditions.

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Conversely, Balwinder-Singh et al. (2011) found that the Australian APSIM wheat model failed to simulate wheat yield under deficit irrigation after calibrating for full irrigation treatments in central Punjab, India, which indicated that further improvements are needed in modeling of plant responses to water need. In this volume, Ollenburger and Snapp (2014) used APSIM to evaluate several options for sustainable intensifications of maize-based smallholder cropping at two sites in Malawi under changing climate scenarios and fertility levels. The options included intercropping, rotations of maize with pigeonpea [Cajanus cajan (L.) Huth], and continuous maize crop. The study showed that the tradeoffs among N and water limitations influenced yield and crop failure risk among sole crops, rotations, and intercrop systems.

THE FAO AQUACROP WATER MANAGEMENT MODEL

The Food and Agriculture Organization (FAO) of United Nations developed the

AquaCrop model to simulate attainable yields of herbaceous crops in response to water (Steduto et al., 2009). The model is based on the concept that for a given crop species, biomass production per unit of transpiration is constant when normalized for evaporative demand and CO2 concentration of the atmosphere. AquaCrop calculates daily first the green canopy cover from planting density and a canopy growth coefficient, bypassing leaf area index. Transpiration and soil water evaporation are simulated separately based on the fractional canopy cover and reference evapotranspiration (ET). Yield as fruit, grain, or root product is calculated from biomass and the harvest index. In the model water stress can inhibit canopy expansion and canopy transpiration, accelerate canopy senescence, and alter harvest index. Each of the effects has its own stress function with its own sensitivity threshold based on the fractional depletion of available soil water in the root zone. Water balance in the root profile is determined by a drainage function, root absorption, and soil water evaporation. AquaCrop has fewer parameters than most crop specific models, and many of its parameters are conservative. Once calibrated with extensive experimental data, the conservative parameters are applicable to diverse soils, seasons, and climates without local calibration. The model is particularly suited to assess crop production where water is the key limiting factor and is especially useful to devise water management strategies, such as timing of deficit irrigation and optimal planting time for rainfed cropping. The simplicity of the model does not permit simulation of mineral nutrient cycles, but soil fertility levels can be accommodated by calibration with local biomass data of a well-fertilized and a nutrient-deficient treatment. Low and high air temperature damage on pollination and harvestable yield are considered, as is cold inhibition of biomass production. Since its release in 2009, AquaCrop

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has received worldwide attention, being evaluated and applied to a variety of crops, especially in developing countries. The resulting many publications generally show that the model simulated biomass production and yield with reasonable to good fidelity, although under- or overestimations did occur, especially when water was seriously limiting. Because the simulated results were compared with measured data that are commonly not that definitive or are limited in scope, a more systematic and critical in-depth evaluation, based on extensive experimental data, is needed. The model is frequently lauded for its limited number of parameters and low input requirements, simplicity, and ease of use and has been incorporated into water management economics models (Garcia-Vila and Fereres, 2012; Donati et al., 2013) and scaled up for regional and watershed applications (Lorite et al., 2013).

THE CROPSYST MODEL

CropSyst is a process-based cropping systems model designed to be simple and user friendly for a wide range of user communities across agriculture and related scientific disciplines (Stöckle et al., 2003). The model simulates the impacts of cropping system management (e.g., cultivar selection, crop rotation, irrigation, N fertilization, salinity, and tillage operations) on productivity and the soil–water–air environment. The generic crop growth routine available within

CropSyst allows simulation of various crops of interest to the user. It has already been parameterized to simulate several crops, trees, and cropping systems (e.g., Stöckle et al., 1994, 2003; Stöckle and Debaeke, 1997; Giardini et al., 1998; Pannkuk et al., 1998; Stöckle and Nelson, 1999; Confalonieri and Bechini, 2004; Sommer et al., 2012; Fumagalli et al., 2013). CropSyst has been used for simulations of crop rotations and nutrient management across the world. Pannkuk et al. (1998) established the ability of CropSyst in modeling spring and winter wheat yields and evapotranspiration in a wheat–fallow rotation in eastern Washington region for different tillage and residue management practices. Bellochi et al. (2002) used CropSyst to simulate corn biomass, N uptake, and two soil variables (water and NO3–N) in response to different types of tillage, N fertilization, and ground cover at Pisa, central Italy. They reported reasonable simulations of biomass, water content, and canopy area index; simulations of N dynamics needed improvement. Confalonieri and Bechini (2004) parameterized CropSyst to simulate semiperennial alfalfa (Medicago sativa L.) and found that the simulations were within 3 to 6% of the observed values across 3 yr. Confalonieri and Bocchi (2005) simulated flooded rice (Oryza sativa L.) in northern Italy within 20 to 22% of measured values. Diaz-Ambrona et al. (2005) simulated impacts of crop rotations, including fallow–wheat–pea (Pisum

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sativum L.) and mustard (Brassica juncea L.)–wheat–pea, and management on the water balance of farming systems in the semiarid region of southeastern Australia, where deep drainage beyond the root zone and rising water tables contribute to salinization of soils and water streams. In a 5-yr experiment, the simulations of yield were close to measured yield for wheat, mustard, and field pea, with reasonable estimates of crop biomass, phenological development, soil water content, and water use. More recently, Sommer et al. (2008) applied CropSyst for simulating cotton (Gossypium hirsutum L.) production in the Khorezm region of Uzbekistan, with acceptable results for biomass. CropSyst has also been applied extensively for developing decision support system (DSS) tools for soil–water–nutrient management in agriculture. Badini and Dioni (2001) combined CropSyst with spatial weather data in a GIS software and developed a decision support tool to determine soil suitability for crops, screen existing technologies, and build recommendation packages for a farming scenario. Dalla Marta et al. (2011) applied CropSyst for analyzing energy and water use related to the cultivation of energy crops in the Tuscany region of Italy. Long-term (50 yr) climate data from 19 locations were used in the simulations of crop production, water requirement, and other management practices to compute costs of energy crop cultivation. Belhouchette et al. (2012) linked CropSyst to an economic optimization model and built a DSS tool for assessing the sustainability of irrigated farming systems in a Tunisian region. In the DSS, CropSyst was used to build a database to determine the relationships between agricultural management practices, crop yields, and environmental effects (salt accumulation in soil and leaching of nitrates) in a context of high climatic variability. The database was then fed into a recursive stochastic model set for a 10-yr plan that allowed analyzing the effects of cropping patterns on farm income, salt accumulation, and nitrate leaching. Lehmann and Finger (2014) developed a bio-economic model linking the CropSyst to an economic decision model at field scale. They then used this model with a genetic algorithm to optimize irrigation management decisions. Marsal and Stöckle (2012) developed a DSS for scheduling irrigation in a pear (Pyrus communis L.) orchard by parameterizing the generic crop growth model in CropSyst to forecast plant water potential in trees for irrigation management. Fumagalli et al. (2013) evaluated the N leaching associated with the application of sewage sludge in rice and corn crops in the Lombardy region of northern Italy using the CropSyst model. They detected a significant effect of the sludge type and application timing on annual N leached in rice (22–154 kg N ha−1). For corn, the application of sludge in the late fall period resulted in greater N leaching (61 kg N ha−1) and lower yields compared to late winter fertilization and

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associated risks in potato (Solanum tuberosum L.) production in the Broye catchment in Switzerland.

THE DANISH DAISY MODEL

The Daisy model has been widely used in Europe for water and N management for groundwater quality. It was developed in Denmark by Soren Hansen (Hansen et al., 1991). In an early application of the model, Svendsen et al. (1995) found that spatial variability in irrigation amount across the field contributed to the lack of model responses to irrigation. In a study in NCP, Krobel et al. (2010) found out that simulated soil water contents did not correctly respond to irrigation events unless on-site measured soil properties were used in the Daisy model. They also showed that crop parameters had at least the same sensitivity in simulating soil water dynamics as soil parameters. A unique approach in the Daisy model is its simulation of abscisic acid (ABA) in roots for better simulation of plant water stress (Plauborg et al., 2010), although the inclusion of ABA in the model did not affect simulated yield and water use significantly in their experiments. Kloss et al. (2012) developed a crop water production function (CWPF) for corn in Montpellier, France based on stochastically generated weather data (17 yr) using the Daisy model and found that simulated yield was affected mainly by temperature and radiation under full irrigation, but by interval of irrigation events at deficit irrigation. With the higher probability of dry seasons under the projected climate change for 2080, the Daisy model simulated reduced yield by

15%. Styczen et al. (2010) developed a plant–soil–atmosphere (PSA) management model based on Daisy to schedule irrigation from simulated soil water content, crop type, crop growth stage, and irrigation method and to schedule fertigation based on simulated plant and soil N concentrations. The threshold values to initiate and end an irrigation event were determined by experimental observations and expert opinions. An interesting feature of this PSA system was its linkage to water supply and its utilization for risk assessment when wastewater was applied (i.e., E. coli). In another study, Walser et al. (2011) found that the Daisy model was not adequate in simulating crop yield under severe water stress conditions. They further used an irrigation optimization algorithm in conjunction with the Daisy model to obtain optimal irrigation schedules for given irrigation amounts available for a crop season. The optimized irrigation schedule could increase production by as much as 41%.

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THE DSSAT CROP SYSTEMS MODEL

The Decision Support System for Agrotechnology Transfer (DSSAT) is a suite of process oriented cropping system models (CSM) integrated into a single software package to enable the seamless application of crop simulation in research and decision-making (Jones et al., 2003). The computer source codes of all CSMs are modular (for easy replacement or addition of modules) and incorporate more than 28 crops as modules using common soil and weather modules. The CSM models of different crops were derived mainly from their earlier versions of CROPGRO and CERES family of crop growth models (Jones et al., 2003). The DSSAT crop models, especially CROPGRO and CERES, have been widely tested and applied in many research areas and production environments across the world (Jones et al., 2003). The CROPGRO is a process-oriented model for grain legumes, based on the SOYGRO, PNUTGRO, and BEANGRO models that consider detailed crop carbon, soil water, and soil N processes. CROPGRO evaluations and applications include work by Heinemann et al. (1999) for optimizing irrigation; Asare et al. (1996), Garrison et al. (1999), and Kizaso and Ritchie (1997) for N balance and the effects of soil water excess effects; Pang et al. (1998) for determining chemical leaching potential; Piper et al. (1998) for cultivar performance; Batchelor et al. (1994), Sau et al. (1999), and Alagarswamy et al. (2000) for calculating crop development and seed yield; and Soler and Hoogenboom (2007) for determining irrigation scheduling in peanut (Arachis hypogaea L.) and cotton. More recently, de Oliveira et al. (2012) tested CROPGRO-dry bean model for simulations of various dry bean cultivars in Brazil and obtained reasonable match with the experiments. Similarly, Lomeling et al. (2014) simulated rainfed cowpea [Vigna unguiculata (L.) Walp.] phenology in Sudan using the CROPGRO-cowpea model with reasonable accuracies for applications. Ruiz-Nogueira et al. (2001) used the CROPGRO-soybean [Glycine max (L.) Merr.] model to establish best sowing windows for rainfed soybean cultivars at three locations in northwestern Spain. Jagtap and Jones (2002) combined GIS software with the CROPGRO-soybean model and developed a DSS for regional yield assessment. Using the cassava (Manihot esculenta Crantz) simulation model available within the DSSAT, Sarawat et al. (2004) developed a DSS for helping farmers and producers make better decisions by integrating information into a more useable form, altering production systems, enhancing management skills, and reducing costs of production. Development of DSS tool for soybean management for optimizing production in Thailand was reported by Banterng et al. (2010). Cammarano et al. (2012) developed optimum irrigation practices for water management in cotton cropping systems for maximizing economic returns in Australia.

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Using the CERES-Wheat and CERES-Maize models, Yang et al. (2006) investigated agricultural water use and its impact on groundwater depletion in the piedmont region of the NCP and established a strong correlation between the two. Based on the correlation, the authors concluded that sustainable groundwater depletion is possible if better water-saving technologies are applied. In Northwest China, S. Wang et al. (2012) reported that less than 50% of flood irrigation water was used in plant transpiration. Using the CERES-Maize model, they showed that reducing or skipping irrigation events at certain growth stages increased irrigation WUE by as much as 66% in the system. In this volume, Mercau and Otegui (2014) used the CERES-Maize model with 41 yr of historical weather data to evaluate the combined effects of late sowing, variable soil water at planting, and variable rates of N on the interannual variation in yield of a corn hybrid in a single- and wheat–maize double-crop systems at four locations in Argentina. The study defined the conditions under which late sowing was successful and increased yield stability. Soil water at sowing time was the major factor that defined the success. Soler et al. (2007) applied CSM-CERES-Maize model for planting window determination and yield forecasting for corn grown off-season in a subtropical environment in Piracicaba, SP, Brazil. Using the developed DSS, yield forecasting was possible 45 d before the harvest date for all four maize hybrids investigated. He et al. (2012) used the CERES-Maize model and developed irrigation and N best management practices for sweet corn production on sandy soils in Florida. Iyanda et al. (2014) developed a DSS tool for identifying potential areas for corn production in Nigeria. Thorp et al. (2008) developed a DSS tool called “Apollo” to help researchers in using DSSAT crop models to analyze precision farming and applications. Apollo has the capability to use various crop models in DSSAT and manage outputs for spatially variable land and management.

THE EPIC MODEL AND ITS MODIFICATIONS

The EPIC (Environmental Policy-Integrated Climate) model was first developed to simulate soil erosion effect on soil productivity (formerly known as the Erosion Productivity Impact Calculator, Williams et al., 1989) and then was extended to simulate agricultural management effects on crop production and soil and water resources (Williams et al., 1996). After that, the model was further extended to the whole farm and small watershed levels, and called APEX (Agricultural Policy Environmental eXtender model) (Williams and Izaurralde, 2006), to evaluate various land management strategies considering sustainability, erosion (wind, sheet,

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and channel), economics, water supply and quality, soil quality, plant competition, weather and pests. Management capabilities of APEX include irrigation, drainage, furrow diking, buffer strips, terraces, waterways, fertilization, manure management, lagoons, reservoirs, crop rotation and selection, pesticide application, grazing, and tillage. Besides these farm management functions, APEX can be used in evaluating the effects of global CO2 and climate change, designing environmentally safe and economical landfill sites, designing biomass production systems for energy, and other spin-off applications. The model operates on a daily time step (some processes are simulated with hourly or less time steps) and is capable of simulating hundreds of years if necessary. Farms may be subdivided into fields, soil types, landscape positions, or any other desirable configuration. The model has been widely calibrated and tested under different climate and soil conditions (X.C. Wang et al., 2011, 2012) and used to manage irrigation water for better crop yield and WUE at different spatial and temporal levels in different countries, such as the United States, China, France, and Italy. At the field level, an early study by Cabelguenne et al. (1995) used EPIC model to maximize maize yield with limited irrigation level of 1000 m3 ha−1 based on a 20-yr simulation in the Toulouse Auzeville region of France. Later, the EPIC model was modified as EPIC-PHASE to incorporate the varying effect of water stress at different growth stages on the harvest index (Cabelguenne and Debaeke, 1996). Cabelguenne et al. (1997) used the EPIC-PHASE model to evaluate the real-time irrigation based on model calculations every 5 d. They found that the difference between actual and forecasted weather data led to different irrigation management, but using the measured weather data calculated the measured yield and reduced the risk of overirrigation and N leaching. Santos et al. (2000) used EPICPHASE for exploring other irrigation strategies. In Georgia, USA, irrigation applications for the major crops were evaluated using EPIC model (Guerra et al., 2004, 2005). They found that the simulated irrigation requirement agreed well with farmer-applied irrigation amount for cotton and peanut, but more water was applied by farmers for maize than the simulated irrigation requirements in the region. The EPIC model was also demonstrated as a decision support tool to evaluate the full and deficit irrigation management for cotton and maize in southern Texas (Ko et al., 2009) and to explore irrigation schedules for optimal sunflower (Helianthus annuus L.) yield in Italy (Rinaldi, 2001). Recently, the model was applied for simulating crop yield and soil water dynamics for better crop irrigation management in the semiarid region of China (Wang and Li, 2010; X.C. Wang et al., 2011, 2014; Zhao et al., 2013).

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The EPIC model has been used for estimating regional crop water requirements (Wriedt et al., 2009) and regional crop yield and water productivity (Liu et al., 2007a,b; Jiang et al., 2015; Van der Velde et al., 2010). Liu (2009) developed a GIS-based EPIC model (GEPIC) to study crop–water relations on large scales with high spatial resolution and applied it to estimate wheat yield and crop water productivity at a regional level (NCP) (Liu et al., 2007a) and global scale (Liu et al., 2007b). They found higher crop water productivity for irrigation wheat than rainfed wheat in both regional (North China Plain) and global scales, and the high global variability in crop water productivity suggested that global water use could be reduced through food trade among countries. In the NCP, two irrigation scenarios of reducing current irrigation amount by 20% and replacing irrigated wheat by rainfed wheat were compared. The simulation results showed similar effect on wheat yield between the two scenarios, suggesting that it is better to change irrigated wheat to rainfed wheat with less reduction in wheat yield under the same amount of water saving. The EPIC model has also been used to explore irrigation strategies for climate variability and climate change (Dono et al., 2013a,b; Izaurralde et al., 2003; Chavas et al., 2009; Rinaldi and De Luca, 2012). For example, Dono et al. (2013b) used EPIC to evaluate the availability of irrigation water and the irrigation requirement of maize as influenced by short-term climate variability in the central Mediterranean basin, Italy. They found that the primary factor that influenced economic return was the reduced stability in the future irrigation water supply, which will likely result in higher groundwater extraction and lower demand of labor. In another study, using the GIS-based EPIC model, Dominguez-Faus et al. (2013) estimated the climate change effect on water requirement for irrigated corn ethanol production in the United States and found that irrigation rates would increase by 9% while corn yields decreased by 7% even when the projected increased irrigation requirements were met.

THE GERMAN HERMES MODEL

HERMES is a model developed in Germany by Kersebaum (1995). Although it

was developed mainly for N management, it has been used for irrigation management as well. After calibrating the HERMES model for four N treatments at three farmers’ sites for winter wheat and summer maize rotation in NCP from 2009 to 2011, Michalczyk et al. (2014) applied the model to develop best water and N management scenarios for the region. They found that N rate could be reduced to 17% and irrigation water to 72% of farmer’s practice without significantly affecting crop yields. As a result, N leaching was reduced to 1.8% of current level beyond the 0.9-m soil profile and 0.9% of current level beyond the

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2.0-m soil profile. Using 25 yr (1987–1991) of weather data from the semiarid Saskatchewan, Canada, Kersebaum et al. (2008) found the model responded well to weather variability in simulating wheat yield and biomass, although soil water was not well simulated during winter because freezing and thawing were not considered in HERMES. Long-term crop rotations with various arable crops and permanent grassland under rainfed and irrigated conditions were simulated with HERMES for 13 sites in Czech Republic (Hlavinka et al., 2014), showing a good performance in comparison with observed crop yields and biomass production. Response of crop models on heat and water stress after flowering of winter wheat and maize was compared for seven models (DSSAT, EPIC, WOFOST, AQUACROP, FASSET, HERMES, and CROPSYST) by Eitzinger et al. (2013). While HERMES and AQUACROP showed relative moderate responses to drought conditions, WOFOST exhibited the highest sensitivity. Within the model intercomparison of Palosuo et al. (2011) the performance of eight crop models were investigated among other sites for a rainfed and irrigated plot in Germany. The highest response to the different water supplies was simulated by DAISY and HERMES. While HERMES underestimated crop biomass, which was consistent with a slight underestimation of soil water content during summer drought, the DAISY model overestimated crop growth, due to an overestimation of soil moisture. DSSAT distinctly underestimated crop biomass, although the soil moisture was significantly overestimated. Using a modified version of HERMES (MONICA), Nendel et al. (2014) found that irrigation would reduce the risk of drought in part of Germany, especially for maize, under the projected climate conditions of 2070. However, irrigation amount depended on crop type, other management practices (i.e., crop rotation), and location. Gandorfer and Kersebaum (2009) used the HERMES model to assess the economic effect of irrigation on wheat production under climate change scenarios for three sites in Bavaria. Although wheat yield risk was reduced in nearly all realizations, the financial investment to establish new irrigation equipment was only beneficial for the driest scenario.

THE RZWQM2 MODEL

The Root Zone Water Quality Model (RZWQM2) project was initiated in the early

1990s, and the model has been used widely worldwide for water management (Ahuja et al., 2000; Ma et al., 2007). In the U.S. Great Plains, Saseendran et al. (2010) demonstrated that the calibrated RZWQM2 model could be used to identify new crop rotations to better cope with the highly variable precipitation conditions of the semiarid Great Plains without conducting time-consuming field experiments. Saseendran et al. (2013), also using RZWQM2, evaluated long-term yields and net returns of three grain crops—corn, canola [Brassica juncea (L.) Czern. subsp.

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juncea], and proso millet (Panicum miliaceum L. subsp. miliaceum)—and two forage crops—foxtail millet [Setaria italica (L.) P. Beauv. subsp. italica] and spring triticale (´ Triticosecale rimpaui Wittm.) at Akron, CO and Sidney, NE in the United States at various soil water contents at planting, and assessed these crops’ potential use in increasing dryland cropping intensity within a wheat–summer crop–fallow rotation. Based on initial soil water content at planting, they proposed to select a summer crop in this rotation that has the probability of giving the highest net return. These results were used to develop an Excel (Microsoft) spreadsheet tool for the two locations (Fig. 15–1) to assess production risk. Later Nielsen et al. (2012) simulated canola yields at nine central Great Plains locations based on initial soil water content at planting, with 16 yr of weather records, and developed an Excel spreadsheet decision support tool (Fig. 15–2) that provides farmers with estimates of the probability of attaining or exceeding specified canola yields. In a more recent study, Saseendran et al. (2014) developed long-term CWPFs for corn yield at different levels of irrigation in Colorado for planning limited or deficit irrigation. They also found that the CWPFs were independent of soil type and location when expressed as relative yield versus relative ET or relative plant water supply. In this volume, Fang et al. (2014a) used the RZWQM2 model to successfully reproduce the measured data of soil water content, ET, and corn yield for 4 yr at

Fig. 15–1. Excel spreadsheet decision support tool for summer crop selection (canola, triticale [shown], millet, corn) based on simulated yields generated with weather data from 1948 to 2008 and four initial soil water contents at planting at Sidney, NE and Akron, CO. The simulated yields were generated using RZWQM2 and were published in Saseendran et al. (2013). Unpublished spreadsheet tool prepared by the coauthor David Nielsen, USDA-ARS, Akron, CO.

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several irrigation levels in Colorado. The calibrated and tested model was then used to explore various combinations of water stress levels (created by different levels of ET-based irrigation, accounting for rainfall) applied at each of the vegetative and reproductive growth stages of corn over long-term local weather conditions, with additional constraints applying to the total seasonal water supply available. At high water supply and irrigation levels, the best grain yield and WUE were obtained with irrigations to meet 80 to 100% of the potential crop ET. At low water supply levels, best yield results were attained with irrigations to meet 60% of the potential ET demand during the vegetative stage and 100% during the reproductive stage. The model can be used to develop such locationspecific recommendations for other locations in the world if some local data are available to determine the specific soil and cultivar parameters. Saseendran et al. (2014, this volume) extended the case study of Fang et al. (2014a) to evaluate the long-term effects of irrigation interval, soil water content at

Fig. 15–2. Excel spreadsheet decision support tool for canola production at nine locations in the Great Plains based simulated yields generated with weather data from 1993 to 2008 and four initial soil water soil water contents using RZWQM2 simulation results as reported by Nielsen et al. (2012). Unpublished spreadsheet tool prepared by David Nielsen, USDA-ARS, Akron, CO.

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planting, and N applications rates on corn yield at various levels of ET-based irrigation kept uniform during the entire growth period. More frequent irrigation intervals and higher initial soil water increased grain yield and WUE, especially at limited irrigation levels meeting less than 100% of crop potential ET. The N rate had a significant effect on production and water use and N use efficiencies. They developed long-term, site-specific recommendations for N rates for different levels of irrigation. In China, Fang et al. (2010) applied the RZWQM2 model with long-term weather data (1961–1999) to investigate various irrigation strategies for the wheat– maize double cropping system in the NCP. The study showed that the preseason irrigation for wheat commonly practiced by local farmers should be postponed to the most sensitive growth stage (stem extension) for higher yield and WUE in the area. Preseason irrigation for maize was needed in 40% of the years. With different amounts of limited irrigation available (100, 150, 200, or 250 mm yr−1), 80% of the water allocated to the critical wheat growth stages and 20% applied at maize planting achieved the highest WUE and the least water drainage over the long term for the two crops. In 2013, Fang et al. (2013) extended RZWQM2 applications to 15 locations in the NCP once the model was calibrated for local county level wheat and corn yield. They found that when N and/or irrigation inputs were reduced to 40 to 80% of their current levels, N leaching generally was reduced considerably without compromising crop yield greatly. Matching N input with crop requirements under limited water conditions helped achieve lower N leaching and lower soil N accumulation. Based on the long-term simulation results and water resource availability in the region, it is recommended to irrigate at 60 to 80% of the current water levels and fertilize only at 40 to 60% of the current N rate to minimizing N leaching without compromising crop yield. In southern Portugal, Cameira et al. (1998) applied RZWQM to irrigated corn under different N management practices (fertigation vs. broadcast) and found that the model simulated yield was within 5% of experimental values. They also obtained good simulations of soil water and soil N in the growing seasons with