Root Zone Water Quality Model Research Articles

Simulating hydrologic cycle and crop production in a subsurface drained and sub-irrigated field in Southern Quebec using RZWQM2

Agricultural system models are promising tools in evaluating the agro-environmental effects of water management practices. However, very few models have been tested using a comprehensive hydrologic data set. The present study’s objective was to evaluate the hydrologic component of RZWQM2 (Root Zone Water Quality Model) using a comprehensive hydrological dataset including subsurface tile drainage, subirrigation, soil water content, sap flow and crop growth data such as leaf area index, crop yield and crop growth stages. Drawing on 2008 and 2009 data from a farm site in Southern Quebec, the RZWQM2 model showed accurate simulation in soil water content, sap flow, growth stage, leaf area index, and crop yield. While mean values for growing season tile flow under both free drainage (FD) and controlled drainage with subirrigation (CD-SI) were reasonably accurate, winter tile flow was significantly overestimated, indicating RZWQM2’s reliability to be compromised by its imperfect winter drainage process. Accordingly, a Kalman filter technique was applied to enhance model reliability and reduce predictive uncertainties. A novel RZWQM2 model equipped with a Kalman filter algorithm adequately simulated, in both calibration and validation phases, the hydrology and corn growth which occurred under both FD and CD-SI systems at the selected field site. Simulation results suggest that RZWQM2 model can be used for water management under subsurface drained and irrigated field and the Kalman filter technique significantly improved the accuracy of RZWQM2 model in simulating winter drainage in cold areas.

Effects of Subsurface Drainage Systems on Water and Nitrogen Footprints Simulated with RZWQM2

Abstract. Developing drainage water management (DWM) systems in the Midwest to reduce nitrogen (N) transport to the northern Gulf of Mexico hypoxic zone requires understanding of the long-term performance of these systems. Few studies have evaluated long-term impacts of DWM, and the simulation of controlled drainage (CD) with the Root Zone Water Quality Model (RZWQM) is limited, while shallow drainage (SD) has not been examined. We tested RZWQM using nine years (2007-2015) of field data from southeast Iowa for CD, SD, conventional drainage (DD), and undrained (ND) systems and simulated the long-term (1971-2015) impacts. RZWQM accurately simulated N loss in subsurface drainage, and the simulations agreed with field data that CD and SD substantially reduced N loss to drainage. As indicated by the field data, the SD N concentration was predicted to be greater than DD and CD, likely due to reduced time of travel to shallower drains. The long-term simulations show that CD and SD reduced annual N lost via tile drainage by 26% and 40%, respectively. Annual reductions in N lost via tile drainage ranged from 28% in the driest years to 22% in the wettest years for CD and from 56% in the driest years to 35% in the wettest years for SD. Considering spring N loading for the purpose of addressing hypoxia in the Gulf of Mexico, CD was found to be less effective than SD, and in many years CD exported more N in the spring than DD. Spring N loading (April through June) was indicated by the EPA Science Advisory Board to have the greatest impact on hypoxia in the northern Gulf of Mexico. Therefore, improvement of CD systems within the months of April through June to reduce N loss via drainage across the upper Midwest landscape may be required. Limited research in the upper Midwest has addressed spring N loading under controlled drainage systems (CD). This research will help model developers, model users, and agricultural scientists more clearly understand N transport under different systems, including CD, SD, and ND, which will aid in developing the design and management of drainage systems to reduce N transport from tile-drained agriculture to surface waters. Keywords: Agricultural simulation model, Drainage water management, Nonpoint-source pollution, Northern Gulf of Mexico hypoxic zone, Nutrient reduction, Subsurface drainage.

Harvesting Fertilized Rye Cover Crop: Simulated Revenue, Net Energy, and Drainage Nitrogen Loss

Core Ideas Fertilizing winter rye increased estimated revenue and harvestable biomass. Fertilizing winter rye increased net energy production. Harvesting fertilized winter rye reduced simulated drainage N loss. Rye revenue in response to fertilizer rate plateaued at approximately 120 kg N ha−1. Field studies are needed to evaluate fertilized/harvested rye cover crop. Harvesting fertilized rye (Secale cereale L.) cover crop has been suggested as a method to increase producer revenue and biofuel feedstock production, but drainage N loss impacts are currently unknown. Using the tested Root Zone Water Quality Model (RZWQM) across several N rates, spring application of 120 kg N ha‐1 prior to winter rye harvest reduced drainage N loss by 54% compared with no cover crop and by 18% compared with planted rye that was neither fertilized nor harvested. Estimates of producer revenue and net energy were also positive, with 8.3 Mg ha‐1 of harvested rye biomass. If confirmed by field studies, these results suggest that double‐cropping fertilized rye is a promising strategy to increase producer revenue, increase net energy production, and reduce drainage N loss.

Modeling Evapotranspiration and Crop Growth of Irrigated and Non-Irrigated Corn in the Texas High Plains Using RZWQM

Abstract. Accurate quantification and management of crop evapotranspiration (ET) are critical to optimizing crop water productivity for both dryland and irrigated agriculture, especially in the semiarid regions of the world. In this study, four weighing lysimeters in Bushland, Texas, were planted to maize in 1994 with two fully irrigated and two non-irrigated for measuring crop ET. The Root Zone Water Quality Model (RZWQM2) was used to evaluate soil water balance and crop production with potential evapotranspiration (PET) estimated from either the Shuttleworth-Wallace method (PTSW) or the ASCE standardized alfalfa reference ET multiplied by crop coefficients (PTASCE). As a result, two water stress factors were defined from actual transpiration (AT) and were tested in the model against the lysimeter data, i.e., AT/PTSW and AT/PTASCE. For both water stress factors, the simulated daily ET values were reasonably close to the measured values, with underestimated ET during mid-growing stage in both non-irrigated lysimeters. Root mean squared deviations (RMSDs) and relative RMSDs (RMSD/observed mean) values for leaf area index, biomass, soil water content, and daily ET were within simulation errors reported earlier in the literature. For example, the RMSDs of simulated daily ET were less than 1.52 mm for all irrigated and non-irrigated lysimeters. Overall, ET was simulated within 3% of the measured data for both fully irrigated lysimeters and undersimulated by less than 11% using both stress factors for the non-irrigated lysimeters. Our results suggest that both methods are promising for simulating crop production and ET under irrigated conditions, but the methods need to be improved for dryland and non-irrigated conditions. Keywords: ET, RZWQM modeling, Stress factor, Weighing lysimeter.

Development of an irrigation scheduling software based on model predicted crop water stress

Modern irrigation scheduling methods are generally based on sensor-monitored soil moisture regimes rather than crop water stress. Crop water stress is difficult to measure in real-time, but can be computed using agricultural system models. In this study, an irrigation scheduling method and its facilitate software based on RZWQM2 model (Root Zone Water Quality Model) predicted crop water stress were developed and evaluated. The timing of irrigation was based on the occurrence of model-simulated water stress, while the depth of irrigation was based on the fraction of the soil moisture deficit (K) needed to replenish the soil water content (θ) at any given time to field capacity (θfc), i.e., from θt0 to θt0+K(θfc-θt0). The predicted water stress for different K values was tested based on RZWQM2 scenarios calibrated against data collected in a drip-irrigated corn (Zea mays L.) field near Greeley, Colorado, USA between 2008 and 2010, and in a sprinkler irrigated soybean [Glycine max (L.) Merr.] field in Noxubee, Mississippi, USA in 2014. For the Colorado site, the simulated full irrigation (K =1) using this newly developed water stress-based irrigation approach saved 30.5%, 17.3% and 7.1% in total irrigation depth in successive years, whereas higher frequency with 60–90% of full irrigation at each event (0.6 ≤ K ≤ 0.9) provided water savings of as much as 35%, 30%, and 16%, respectively. The water stress-based irrigation scheme showed that crop yield was not affected, with a negligible change about 0.03–3.81% decrease. These water savings were a result of the water stress-based irrigation regime maintaining sufficient water to meet crop root water uptake requirements without constantly fully rehydrating the soil, thereby minimizing evaporation from the soil surface and soil water storage after grain filling. For the Mississippi site, this newly developed water stress-based irrigation software could improve crop yield by 291 kg ha−1 though consume 3.43 cm more water than field irrigation regime. Similarly, high frequency irrigation (lower K) under water stress-based regime resulted in higher water use efficiency. This study suggested that the water stress-based irrigation scheme could save water use and maintain crop yield in semi-arid region, while in humid region it could increase crop yield while consume more water. Further work is needed to install this system in an irrigated field and test its performance under different climate and soil conditions.

N loss to drain flow and N2O emissions from a corn-soybean rotation with winter rye

Anthropogenic perturbation of the global nitrogen cycle and its effects on the environment such as hypoxia in coastal regions and increased N2O emissions is of increasing, multi-disciplinary, worldwide concern, and agricultural production is a major contributor. Only limited studies, however, have simultaneously investigated NO3− losses to subsurface drain flow and N2O emissions under corn-soybean production. We used the Root Zone Water Quality Model (RZWQM) to evaluate NO3− losses to drain flow and N2O emissions in a corn-soybean system with a winter rye cover crop (CC) in central Iowa over a nine year period. The observed and simulated average drain flow N concentration reductions from CC were 60% and 54% compared to the no cover crop system (NCC). Average annual April through October cumulative observed and simulated N2O emissions (2004–2010) were 6.7 and 6.0kgN2O-Nha−1yr−1 for NCC, and 6.2 and 7.2kgNha−1 for CC. In contrast to previous research, monthly N2O emissions were generally greatest when N loss to leaching were greatest, mostly because relatively high rainfall occurred during the months fertilizer was applied. N2O emission factors of 0.032 and 0.041 were estimated for NCC and CC using the tested model, which are similar to field results in the region. A local sensitivity analysis suggests that lower soil field capacity affects RZWQM simulations, which includes increased drain flow nitrate concentrations, increased N mineralization, and reduced soil water content. The results suggest that 1) RZWQM is a promising tool to estimate N2O emissions from subsurface drained corn-soybean rotations and to estimate the relative effects of a winter rye cover crop over a nine year period on nitrate loss to drain flow and 2) soil field capacity is an important parameter to model N mineralization and N loss to drain flow.

Numerical simulation of agricultural sediment and pesticide runoff: RZWQM and PRZM comparison

AbstractAgricultural sediment and pesticide runoff is a widespread ecological and human health concern. Numerical simulation models, such as Root Zone Water Quality Model (RZWQM) and Pesticide Root Zone Model (PRZM), have been increasingly used to quantify off‐site agricultural pollutant movement. However, RZWQM has been criticized for its inability to simulate sedimentation processes. The recent incorporation of the sedimentation module of Groundwater Loading Effects of Agricultural Management Systems has enabled RZWQM to simulate sediment and sediment‐associated pesticides. This study compares the sediment and pesticide transport simulation performance of the newly released RZWQM and PRZM using runoff data from 2 alfalfa fields in Davis, California. A composite metric (based on coefficient of determination, Nash–Sutcliffe efficiency, index of agreement, and percent bias) was developed and employed to ensure robust, comprehensive assessment of model performance. Results showed that surface water runoff was predicted reasonably well (absolute percent bias <31%) by RZWQM and PRZM after adjusting important hydrologic parameters. Even after calibration, underestimation bias (−89% ≤ PBIAS ≤ −36%) for sediment yield was observed in both models. This might be attributed to PRZM's incorrect distribution of input water and uncertainty in RZWQM's runoff erosivity coefficient. Moreover, the underestimation of sediment might be less if the origin of measured sediment was considered. Chlorpyrifos losses were simulated with reasonable accuracy especially for Field A (absolute PBIAS ≤ 22%), whereas diuron losses were underestimated to a great extent (−98% ≤ PBIAS ≤ −65%) in both models. This could be attributed to the underprediction of herbicide concentration in the top soil due to the limitations of the instantaneous equilibrium sorption model as well as the high runoff potential of herbicide formulated as water‐dispersible granules. RZWQM and PRZM partitioned pesticides into the water and sediment phases similarly. According to model predictions, the majority of pesticide loads were carried via the water phase. On the basis of this study, both RZWQM and PRZM performed well in predicting runoff that carried highly adsorptive pesticides on an event basis, although the more physically based RZWQM is recommended when field‐measured soil hydraulic properties are available.

Modeling hourly subsurface drainage using steady-state and transient methods

Computer models have been frequently used to simulate the hydrologic and environmental processes in subsurface-drained cropland. The widely-tested steady-state Hooghoudt (ssH) equation, implemented in the Root Zone Water Quality Model (RZWQM2, version 2.94.00), serves in simulating subsurface drainage. However, transient methods such as the integrated Hooghoudt (inH) and van Schilfgaarde (vanS) equations have seldom been implemented in models. In the present study, RZWQM2’s hydrologic component was modified to initiate the soil water redistribution process when rainfall occurred. The three drainage equations (ssH, inH and vanS) were tested in each of two versions of RZWQM2 (original and modified). Field data from Iowa (2007–2008) and Ontario (2009–2010) were used to evaluate different model version×equation combinations’ simulation accuracy at both daily and hourly scales, evaluated using the percent of bias (PBIAS), Nash-Sutcliffe efficiency coefficient (NSE), and the Index of Agreement (IoA). On a daily scale and across equations, for the Iowa data the original model (PBIAS⩽14.96,NSE⩾0.40,⩾0.69) was outperformed by the modified model (PBIAS⩽6.48,NSE⩾0.70,IoA⩾0.76). Similarly, for the Ontario data, the original model (PBIAS⩽8.87,NSE⩾0.19,IoA⩾0.65) was outperformed by the modified model (PBIAS⩽3.59,NSE⩾0.31,IoA⩾0.67). However, based on a parity of PBIAS, NSE and IoA values, hourly scale tile drainage computed using the modified model equipped with transient equations did not improve model performance compared with the original ssH equation.

Winter rye as a cover crop reduces nitrate loss to subsurface drainage as simulated by HERMES

HERMES is a widely used agricultural system model; however, it has never been tested for simulating N loss to subsurface drainage. Here, we integrated a simple drain flow component into HERMES. We then compared the predictions to four years of data (2002–2005) from central Iowa fields in corn-oybean with winter rye as a cover crop (CC) and without winter rye (NCC). We also compared the HERMES predictions to the more complex Root Zone Water Quality Model (RZWQM) predictions for the same dataset. The average annual observed and simulated N loss to drain flow were 43.8 and 44.4kgN/ha (NCC) and 17.6 and 18.9kgN/ha (CC). The slightly over predicted N loss for CC was because of over predicted nitrate concentration, which may be partly caused by slightly under predicted average annual rye shoot N (observed and simulated values were 47.8 and 46.0kgN/ha). Also, recent research from the site suggests that the soil field capacity may be greater in CC while we used the same soil parameters for both treatments. A local sensitivity analysis suggests that increased field capacity affects HERMES simulations, which includes reduced drain flow nitrate concentrations, increased denitrification, and reduced drain flow volume. HERMES-simulated cumulative monthly drain flow and annual drain flow were reasonable compared to field data and HERMES performance was comparable to other published drainage model tests. Unlike the RZWQM simulations, however, the modified HERMES did not accurately simulate the year to year variability in nitrate concentration difference between NCC and CC, possibly due in part to the lack of partial mixing and displacement of the soil solution. The results suggest that 1) the relatively simple model HERMES is a promising tool to estimate annual N loss to drain flow under corn-soybean rotations with winter rye as a cover crop and 2) soil field capacity is a critical parameter to investigate to more thoroughly understand and appropriately model denitrification and N losses to subsurface drainage.

Comparing Simulated Nitrate-Nitrogen Concentration In Subsurface Drainage Using Drainmod-N II and RZWQM2

This study used a 16-year (1989–2004) field experiment conducted in Iowa to compare the performance of DRAINMOD-N II and Root Zone Water Quality Model2 (RZWQM2) in predicting nitrate-nitrogen (NO3-N) concentration and losses in a subsurface drained field at nine nitrogen application rates (0–252 kg N ha−1). Results showed that the performance of both models can ‘satisfactorily’ predict NO3-N concentration. The Nash–Sutcliffe efficiency (NSE), ratio of the root mean squared error to the standard deviation (RSR), and percent bias (PBIAS) for DRAINMOD-N II were 0.87, 0.36, and −2%, respectively, in simulating NO3-N concentration; and 0.84, 0.39, and −9%, respectively, in simulating NO3-N loss. By contrast, NSE, RSR, and PBIAS for RZWQM2 were 0.76, 0.49, and −3%, respectively, in simulating NO3-N concentration; and 0.64, 0.6, and −16%, respectively, in simulating NO3-N loss. This study indicated that both models can be used to simulate NO3-N loss and concentration in drainage flow at different N application rates. However, DRAINMOD-N II demonstrated slightly better performance than RZWQM2. In terms of the maximum contaminant level, the threshold N rates predicted by RZWQM2 and DRAINMOD-N II did not differ significantly from those in observations and can be used for agricultural production. This study highlights the advantages of DRAINMOD-N II in assessing the effects of nitrogen management practices. Copyright © 2017 John Wiley & Sons, Ltd.

Analysis of Parameter Sensitivity and Identifiability of Root Zone Water Quality Model (RZWQM) for Dryland Sugarbeet Modeling

Abstract. Sugarbeet is being considered as one of the most viable feedstock alternatives to corn for biofuel production since herbicide-resistant energy beets were deregulated by the USDA in 2012. Growing sugarbeets for biofuel production may have significant impacts on soil health and water quality in the north-central regions of the U.S., where 50% of the nation’s total sugarbeets were produced in 2015. Almost all the current sugarbeet models simulate only plant growth and yield but have no capability to simulate the effects of sugarbeet production on soil and water quality. The Root Zone Water Quality Model (RZWQM) is a widely used model that simulates crop yield, water flow, and transport of salts and nitrogen in crop fields. RZWQM is currently linked to 23 specific crop models in the Decision Support System for Agrotechnology Transfer (DSSAT) version 4.0, not including a sugarbeet model. In this study, the Crop and Environment REsource Synthesis (CERES) in RZWQM was adapted for sugarbeet simulation to model the soil and water quality impact of sugarbeet for biofuel production. The Beet model was then evaluated against dryland sugarbeet production at the Carrington Research and Extension Station (North Dakota) in 2014 and 2015. The PEST (Parameter ESTimation) tool in RZWQM was used for parameter estimation and sensitivity and identifiability analysis. The model did reasonably well in both 2014 (d-statistic = 0.709 to 0.992; rRMSE = 0.066 to 1.211) and 2015 (d-statistic = 0.733 to 0.990; rRMSE = 0.043 to 0.930) in terms of simulating leaf area index, top weight, root weight, soil water content, and soil nitrates. Under dry conditions, the most sensitive soil parameters were soil bulk densities and saturated hydraulic conductivities in different layers. Identifiability analysis also showed that three to five model parameters may be identifiable by calibration datasets. RZWQM enhanced with a sugarbeet module and its parameter analysis can be used for water use optimization under dryland conditions. Keywords: Biofuels, CERES, DSSAT, RZWQM, Sugarbeet.

Modeling N Concentration and Uptake for Maize Hybrids under Growth Stage-Based Deficit Irrigations

Abstract. Current maize hybrids have lower critical aboveground biomass nitrogen (N) concentration (TCNP) and grain N concentration (GNC) compared to older hybrids, but few crop models have incorporated this trend. The objective of this study was to evaluate alternative algorithms for calculating TCNP (biomass-based method) and GNC (grain N demand based on N dilution curve) for predicting crop N concentration and N uptake for a current maize hybrid in the CERES-Maize model as implemented in the Root Zone Water Quality Model (RZWQM). Experimental data were obtained from a field study on maize irrigated to meet various percentages (40% to 100%) of evapotranspiration demand at both vegetative and reproductive stages in 2012 and 2013 in Greeley, Colorado. The original RZWQM showed little response of aboveground N concentration (AGBNC) to the irrigation treatments and overpredicted GNC in both years. As a result, crop N uptake was generally overpredicted, with root mean square error (RMSE) values of 28 to 60 kg N ha-1 for the two years. Adjusted coefficients in the original TCNP and GNC algorithms (RZWQM_ADJ) effectively reduced the overpredicted GNC but with less improvement in response to the irrigation treatments in 2013 compared with the original RZWQM simulations. The RZWQM with modified TCNP and GNC algorithms simulated lower GNC and AGBNC than the original version, significantly improved the responses to the irrigation treatments, and captured the variations in measured GNC among seasons. The corresponding crop N uptake simulations improved more in 2012 than in 2013, with lower RMSE values of 16 to 32 kg N ha-1 than the original and RZWQM_ADJ versions. The better-predicted grain N uptake by the alternative algorithms could be helpful to making better crop N management decisions under different deficit irrigation conditions. Keywords: CERES-Maize, Crop N concentration, Crop N demand, Crop N uptake, Deficit irrigation, Maize hybrid, RZWQM.

Optimizing Et-Based Irrigation Scheduling for Wheat and Maize with Water Constraints

Abstract. Deficit irrigation has been shown to increase crop water use efficiency (WUE) under certain conditions, even though the yield is slightly reduced. In this study, the Root Zone Water Quality Model (RZWQM) was first calibrated with measured data from a large weighing lysimeter from 1998 to 2003 at the Yucheng Experimental Station in the North China Plain for daily evapotranspiration (ET), soil water storage (0-120 cm), leaf area index (LAI), aboveground biomass, and grain yield. The calibrated model was then used to explore crop responses to ET-based irrigation management using weather data from 1958 to 2015 and identify the most suitable ET-based irrigation schedules for the area. Irrigation amount was determined by constraining irrigation to a percentage of potential crop ET (40%, 60%, 80%, and 100% ETc) at the various growth stages of wheat [planting to before winter dormancy (P-D), green up to booting (G-B), booting to flowering (B-F), and flowering to maturity (F-M)] and of maize [planting to silking (P-S) and silking to maturity (S-M)], subject to seasonal water availability limits of 100/50, 200/100, 300/150, and 400/200 mm and no water limit for wheat/maize seasons, respectively. In general, wheat was more responsive to irrigation than maize, while greater influence of weather variation was simulated on maize than on wheat. For wheat with seasonal water limits, the highest average WUE was simulated with the highest targeted ETc levels at both the G-B and B-F stages and lower targeted ETc levels at the P-D and F-M stages. However, the highest average grain yield was simulated with the highest targeted ETc levels at all four growth stages for no water limit and the 400 mm water limit, or at both the G-B and B-F stages for the 300 and 200 mm water limits. For maize, lower targeted ETc levels after silking did not significantly affect maize production due to the high season rainfall, but irrigation of 60% ETc before silking was recommended. These results could be used as guidelines for precision irrigation along with real-time weather information. Keywords: Deficit irrigation, Evapotranspiration, Growth stage, RZWQM, Water use efficiency, Wheat and maize.

Long-term simulation of growth stage-based irrigation scheduling in maize under various water constraints in Colorado, USA

Due to varying crop responses to water stress at different growth stages, scheduling irrigation is a challenge for farmers, especially when water availability varies on a monthly, seasonal and yearly basis. The objective of this study was to optimize irrigation between the vegetative (V) and reproductive (R) phases of maize under different available water levels in Colorado. Long-term (1992–2013) scenarios simulated with the calibrated Root Zone Water Quality Model were designed to meet 40%–100% of crop evapotranspiration (ET) requirements at V and R phases, subject to seasonal water availabilities (300, 400, 500 mm, and no water limit), with and without monthly limits (total of 112 scenarios). The most suitable irrigation between V and R phases of maize was identified as 60/100, 80/100, and 100/100 of crop ET requirement for the 300, 400, 500 mm water available, respectively, based on the simulations from 1992 to 2013. When a monthly water limit was imposed, the corresponding suitable irrigation targets between V and R stages were 60/100, 100/100, and 100/100 of crop ET requirement for the above three seasonal water availabilities, respectively. Irrigation targets for producing higher crop yield with reduced risk of poor yield were discussed for projected five-year water availabilities.

Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization

Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so as to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and NO3-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.

Modeling yield and biomass responses of maize cultivars to climate change under full and deficit irrigation

With as much as 4.8°C increase in air temperature by end of 21st century, new crop cultivars are needed for adapting to the new climate. The objective of this study was to identify maize (Zea mays L.) cultivar parameters that maintain yield under projected climate for late in the 21st century under full and deficit irrigation in a semi-arid region. The Root Zone Water Quality Model (RZWQM2) was calibrated with four years of maize data from northeastern Colorado, USA, under various irrigation conditions and was then used to simulate climate change effects on maize production with current management practices. Results showed that projected climate change decreased yield by 21% and biomass by 7% late in the 21st century (2070–2091) under full irrigation, compared to yield in the current climate (1992–2013). Under deficit irrigation, the corresponding reductions were 14% and 3%, respectively. Using the cultivar parameters calibrated with RZWQM2 for southern Colorado condition did not show yield decrease under future climate, but it simulated much lower yield under current climate in northeastern Colorado. A cultivar from the DSSAT (Decision Support Systems for Agrotechnology Transfer) crop database (GL 482) produced similar yield to experimental data under current climate and increased yield by 4% at full irrigation under future climate in northeastern Colorado. Using Latin Hypercube Sampling (LHS), we also identified 70 cultivars with longer maturity duration (between silking and physiological maturity) and higher grain filling rate for mitigating climate change effects on maize production. These two identified traits can guide plant breeders in developing cultivars for the future.

Climate-Optimized Planting Windows for Cotton in the Lower Mississippi Delta Region

Unique, variable summer climate of the lower Mississippi (MS) Delta region poses a critical challenge to cotton producers in deciding when to plant for optimized production. Traditional 2–4 year agronomic field trials conducted in this area fail to capture the effects of long-term climate variabilities in the location for developing reliable planting windows for producers. Our objective was to integrate a four-year planting-date field experiment conducted at Stoneville, MS during 2005–2008 with long-term climate data in an agricultural system model and develop optimum planting windows for cotton under both irrigated and rainfed conditions. Weather data collected at this location from 1960 to 2015 and the CSM-CROPGRO-Cotton v4.6 model within the Root Zone Water Quality Model (RZWQM2) were used. The cotton model was able to simulate both the variable planting date and variable water regimes reasonably well: relative errors of seed cotton yield, aboveground biomass, and leaf area index (LAI) were 14%, 12%, and 21% under rainfed conditions and 8%, 16%, and 15% under irrigated conditions, respectively. Planting windows under both rainfed and irrigated conditions extended from mid-March to mid-June: windows from mid-March to the last week of May under rainfed conditions, and from the last week of April to the end of May under irrigated conditions were better suited for optimum yield returns. Within these windows, rainfed cotton tends to lose yield from later plantings, but irrigated cotton benefits; however, irrigation requirements increase as the planting windows advance in time. Irrigated cotton produced about 1000 kg·ha−1 seed cotton more than rainfed cotton, with irrigation water requirements averaging 15 cm per season. Under rainfed conditions, there is a 5%, 14%, and 27% chance that the seed cotton production is below 1000, 1500, and 2000 kg·ha−1, respectively. Information developed in this paper can help MS farmers in decision support for cotton planting.

RZWQM2 simulated management practices to mitigate climate change impacts on nitrogen losses and corn production

Gaseous and aqueous nitrogen (N) losses from corn (maize) production systems are concerns under projected climate change. In the present study, the Root Zone Water Quality Model (RZWQM2) was used to test the ability of agricultural management practices (N application rate, corn cultivar, planting date, tillage and controlled drainage) to mitigate future climate change effects on N losses and corn yield in a subsurface drained field in Iowa, USA. Under a future downscaled climate scenario, the simulated non-constant N2O emission factor (EF), yield-scaled global warming potential and N loss through drainage increased with increasing fertilization above an optimal N rate of 120 kg N ha−1. This rate represents the optimal tradeoff point between environmental issues and economic returns. While yields of the cultivar IB1068 DEKALB declined with climate change, yields of cultivar, IB 0090 GL 482 in the future climate were greater than historical yields of IB 1068 DEKALB.

Impact assessment of climate change and later-maturing cultivars on winter wheat growth and soil water deficit on the Loess Plateau of China

The use of adapted crop cultivars is an effective approach to cope with climate change. The objective of this study was to evaluate the impacts of climate change and the use of later-maturing cultivars on winter wheat yields and soil water deficit in dryland farming on the Loess Plateau of China. The later-maturing cultivars of winter wheat (Triticum aestivum L.) were generated by increasing the growing degree day requirement according to Root Zone Water Quality Model 2. The results showed that the wheat yields and water use efficiency were significantly improved by the later-maturing cultivars and fluctuated along with precipitation trends. The downward trend of wheat growth duration due to climate warming conditions was effectively counteracted and even reversed by introduction of the later-maturing cultivars. Increased precipitation during the longer growth period of the later-maturing cultivars partly compensated for the wheat evapotranspiration, so that the soil water deficit level was maintained or even reduced. The results of this study contribute to gaining improved understanding of the effective phenotypic characteristics of dryland winter wheat to cope with climate change using model-based assessments.

Simulating Maize Production, Water and Surface Energy Balance, Canopy Temperature, and Water Stress under Full and Deficit Irrigation

Abstract. Surface energy balance is critical to the understanding of crop evapotranspiration (ET) requirements and crop water stresses. The objective of this study was to evaluate the simulation of crop growth, water and surface energy balance components, canopy temperature, and water stress under fully irrigated and deficit-irrigated corn in eastern Colorado using a hybrid version of the Root Zone Water Quality Model (RZWQM) and the Simultaneous Heat and Water model (SHAW) (RZ-SHAW). The field experiment was conducted in 2010 under both full and deficit irrigation conditions with energy balance measured using the Bowen ratio method. The model simulated grain yield satisfactorily, with an error of less than 5%. Leaf area index, daily ET, soil water content, canopy temperature, and energy balance components, including net radiation (R n ), latent heat (LE), sensible heat (H), and ground heat flux (G), were simulated well, with coefficients of determination (R 2 ) ≥ 0.64 and model efficiencies (ME) ≥ 0.57 for both full and deficit irrigation fields. The RZ-SHAW model accurately predicted the responses of crop growth and ET to water stress, and the simulated water stress was in good agreement with measured elevated canopy temperature with deficit irrigation after silking. However, the model performance was not acceptable in predicting plant height for water-stressed corn, and the simulated increase in canopy temperature under deficit irrigation was about 30% of the observed temperature increase. This study suggests that while the RZ-SHAW model can be used to evaluate the response of corn yield and water and energy balances to water stress, the simulation of the effects of water deficit on plant height and canopy temperature need further improvement.