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Accurate simulation of plant growth depends not only on plant parameters, but also on soil parameters. Although there is uncertainty in measured soil parameters and root distributions, their effects on simulated plant growth have been much less studied. This study evaluates the simulated responses of six crops, wheat (Triticum aestivum L.), maize (Zea mays L.), barley (Hordeum vulgare L.), soybean (Glycine max L. Merr.), peanut (Arachis hypogaea L.), and chickpea (Cicer arietinum L.), under various water and N management to different methods of estimating soil hydraulic properties and soil root growth factor (SRGF) in root zone water quality model (RZWQM2) that contains the decision support system for agrotechnology transfer (DSSAT) Version 4.0 plant growth models. The two methods of obtaining the soil water retention curve (SWRC) in RZWQM2 were based on (i) known soil water contents at both 33 and 1500 kPa suctions, or (ii) soil water content at 33 kPa only. The two methods of estimating saturated hydraulic conductivity (Ksat) were (i) soil texture class based average Ksat or (ii) Ksat calculated from effective porosity (difference between soil water contents at saturation and at 33 kPa). For the six crops, simulation results showed that the soil water balance was affected more by Ksat than by SWRC, whereas the simulated crop growth was affected by both Ksat and SWRC. Small variations in the SRGF did not affect soil and crop simulations, and SRGF could be estimated with a simple exponential equation.

Results with an empirically based one-parameter model showed that the pore-size distribution index (lambda) described in the Brooks and Corey formulation of soil hydraulic properties can scale the soil-water retention curves below the air-entry pressure head (psi(b)) values across dissimilar soils. It is shown here that psi(b) and saturated hydraulic conductivity (K(s)) are also strongly related to lambda, and thus all hydraulic parameters may be estimated from lambda. The major objective here was to examine how these relationships to lambda lead to relationships for infiltration and soil water contents during redistribution across soil textural classes. The Root Zone Water Quality Model simulated infiltration for four rainfall intensities and two initial pressure head conditions and redistribution for four initial wetting depths and two initial pressure head conditions in 11 textural class mean soils. All infiltration results across textural classes were scaled quite well by using the lambda-derived normalization variables based on the dimensional analysis of the Green-Ampt model. Thus, if infiltration for one soil (lambda) is known, infiltration for other soils (lambdas) can be estimated. Additionally, we present infiltration, as well as redistribution, as explicit functions of lambda. These functions can be used to approximately estimate infiltration and soil water contents across soil types for other soils and conditions by interpolation. This study enhances our understanding of the soil-water relationships among soil textural classes, and hopefully, provides a basis of further studies under field conditions for (i) estimating spatial variability of soil water for site-specific management and (ii) for scaling up results in modeling from plots to fields to watersheds.

Agricultural systems models are essential tools to assess potential climate change (CC) impacts on crop production and help guide policy decisions. In this study, impacts of projected CC on dryland crop rotations of wheat-fallow (WF), wheat-corn-fallow (WCF), and wheat-corn-millet (WCM) in the U.S. Central Great Plains (Akron, Colorado) were simulated using the CERES V4.0 crop modules in RZWQM2. The CC scenarios for CO 2 , temperature and precipitation were based on a synthesis of Intergovernmental Panel on Climate Change (IPCC 2007 ) projections for Colorado. The CC for years 2025, 2050, 2075, and 2100 (CC projection years) were super-imposed on measured baseline climate data for 15–17 years collected during the long-term WF and WCF (1992–2008), and WCM (1994–2008) experiments at the location to provide inter-annual variability. For all the CC projection years, a decline in simulated wheat yield and an increase in actual transpiration were observed, but compared to the baseline these changes were not significant ( p  > 0.05) in all cases but one. However, corn and proso millet yields in all rotations and projection years declined significantly ( p  < 0.05), which resulted in decreased transpiration. Overall, the projected negative effects of rising temperatures on crop production dominated over any positive impacts of atmospheric CO 2 increases in these dryland cropping systems. Simulated adaptation via changes in planting dates did not mitigate the yield losses of the crops significantly. However, the no-tillage maintained higher wheat yields than the conventional tillage in the WF rotation to year 2075. Possible effects of historical CO 2 increases during the past century (from 300 to 380 ppm) on crop yields were also simulated using 96 years of measured climate data (1912–2008) at the location. On average the CO 2 increase enhanced wheat yields by about 30%, and millet yields by about 17%, with no significant changes in corn yields.

Computer models have been widely used to evaluate the impact of agronomic management on nitrogen (N) dynamics in subsurface drained fields. However, they have not been evaluated as to their ability to capture the variability of nitrate‐nitrogen (NO3–N) concentration in subsurface drainage at a wide range of N application rates due to possible errors in the simulation of other system components. The objective of this study was to evaluate the performance of Root Zone Water Quality Model2 (RZWQM2) in simulating the response of NO3–N concentration in subsurface drainage to N application rate. A 16‐yr field study conducted in Iowa at nine N rates (0–252 kg N ha−1) from 1989 to 2004 was used to evaluate the model, based on a previous calibration with data from 2005 to 2009 at this site. The results showed that the RZWQM2 model performed “satisfactorily” in simulating the response of NO3–N concentration in subsurface drainage to N fertilizer rate with 0.76, 0.49, and −3% for the Nash‐Sutcliffe efficiency, the ratio of the root mean square error to the standard deviation, and percent bias, respectively. The simulation also identified that the N application rate required to achieve the maximum contaminant level for the annual average NO3–N concentration was similar to field‐observed data. This study supports the use of RZWQM2 to predict NO3–N concentration in subsurface drainage at various N application rates once it is calibrated for the local condition.

Anthropogenic activities continue to emit potential greenhouse gases (GHG) into the atmosphere leading to a warmer climate over the earth. Predicting the impacts of climate change (CC) on food and fiber production systems in the future is essential for devising adaptations to sustain production and environmental quality. We used the CSM-CROPGRO-cotton v4.6 module within the RZWQM2 model for predicting the possible impacts of CC on cotton (Gossypium hirsutum) production systems in the lower Mississippi Delta (MS Delta) region of the USA. The CC scenarios were based on an ensemble of climate projections of multiple GCMs (Global Climate Models/General Circulation Models) for climate change under the CMIP5 (Climate Model Inter-comparison and Improvement Program 5) program, that were bias-corrected and spatially downscaled (BCSD) at Stoneville location in the MS Delta for the years 2050 and 2080. Four Representative Concentration Pathways (RCP) drove these CC projections: 2.6, 4.5, 6.0, and 8.5 (these numbers refer to radiative forcing levels in the atmosphere of 2.6, 4.5, 6.0, and 8.5 W·m−2), representing the increasing levels of the greenhouse gas (GHG) emission scenarios for the future, as used in the Intergovernmental Panel on Climate Change-Fifth Assessment Report (IPCC-AR5). The cotton model within RZWQM2, calibrated and validated for simulating cotton production at Stoneville, was used for simulating production under these CC scenarios. Under irrigated conditions, cotton yields increased significantly under the CC scenarios driven by the low to moderate emission levels of RCP 2.6, 4.5, and 6.0 in years 2050 and 2080, but under the highest emission scenario of RCP 8.5, the cotton yield increased in 2050 but declined significantly in year 2080. Under rainfed conditions, the yield declined in both 2050 and 2080 under all four RCP scenarios; however, the yield still increased when enough rainfall was received to meet the water requirements of the crop (in about 25% of the cases). As an adaptation measure, planting cotton six weeks earlier than the normal (historical average) planting date, in general, was found to boost irrigated cotton yields and compensate for the lost yields in all the CC scenarios. This early planting strategy only partially compensated for the rainfed cotton yield losses under all the CC scenarios, however, supplemental irrigations up to 10 cm compensated for all the yield losses.

Correct simulation of overwinter condition is important for the growth of winter crops and for initial growth of spring crops. The objective of this study was to investigate overwinter soil water and temperature dynamics with the simultaneous heat and water (SHAW) model and with its linkage to the root zone water quality model (RZWQM), a hybrid model of RZWQM and SHAW (RZ-SHAW) in a Siberian wildrye grassland under two irrigation treatments (non-irrigation and pre-winter irrigation) in two seasons (2005–2006 and 2006–2007). Experimental results showed that pre-winter irrigation considerably increased soil water content for the top 60-cm soil profile in the following spring, but had little effect on soil temperature. Both SHAW and RZ-SHAW simulated these irrigation effects equally well, which demonstrated a correct linkage between RZWQM and SHAW. Across the treatments and years, the average root mean square deviation (RMSD) for simulated total soil water content (liquid plus frozen) was 0.031 m3 m−3 for both RZ-SHAW and SHAW models, and that for liquid water content alone was 0.028 m3 m−3 for both models. Both models provided better simulation of total and liquid soil water contents under non-irrigation condition than under pre-winter irrigation conditions. On average, RZ-SHAW simulated soil temperature slightly better with an average RMSD of 1.4°C compared to that of 1.8°C by SHAW. Both RZ-SHAW and SHAW simulated the soil freezing process well, but were less accurate in simulating the soil thawing processes, where further improvements are desirable. These simulation results show that the SHAW model is correctly implemented in RZWQM (RZ-SHAW), which adds the capability of RZWQM in simulating overwinter soil conditions that are critical for winter crops.

Robust estimation of soil hydraulic parameters is essential for predicting soil water dynamics and related biogeochemical processes; however, uncertainties in estimated parameter values limit a model's ability for prediction and application. In this study, methods of global analysis (Latin hypercube sampling, LHS) and gradient-based optimization (PEST, parameter estimation software) were explored to calibrate soil hydraulic parameters in the Root Zone Water Quality Model (RZWQM2). Six methods of estimating Brooks–Corey parameters of the soil water retention curve and saturated hydraulic conductivity were evaluated to simulate daily soil water dynamics under fallow conditions in eastern Colorado. The calibrated soil hydraulic parameters showed similar trends with soil depth for the different estimation methods in RZWQM2 but resulted in large differences between simulated and observed soil water contents. The PEST optimization based on soil type values as initial estimates gave reasonable soil water responses but with some unrealistic soil hydraulic parameters and soil evaporation. Overall, errors in simulated soil water contents were reduced by using LHS to initialize and constrain the PEST parameter space, which also stabilized the cross-validation results. Calibration results using water content measurements at four depths (30, 60, 90, and 150 cm) were similar to results using 10 depths (30, 40, 50, 60, 70, 80, 90, 120, 150, and 170 cm). Calibrating soil hydraulic parameters remains challenging, but the combined calibration procedures (LHS + PEST) with cross-validation can reduce parameter uncertainties and improve model performance.

The semiarid U.S. Great Plains experiences a large variation in crop yields due to variability in rainfall, soil, and other factors. We analyzed (24‐yr) yields from a no‐till rotation of winter wheat (Triticum aestivum L.)–corn (Zea mays L.) or sorghum [Sorghum bicolor (L.) Moench]–fallow at three sites, each with three soil types along a catena in Colorado. We investigated: (i) effects of soil organic carbon (SOC), length of slope (SL), and other soil properties on yields; and (ii) degree in which variability in annual yields are explained with water variables of rainfall during the fallow, vegetative and reproductive stages, and soil water at planting. Wheat and corn/sorghum yields were strongly related to soil properties (R2 = 0.84; 0.97, respectively). Wheat yield regression had only SOC as a significant variable, whereas corn/sorghum had SOC and SL. Water factors explained 37 to 73% of annual variability in wheat yields at the site by soil level, 19 to 63% when pooled over soils within sites, and 35 to 40% pooled over all. For corn, the corresponding percentages were 16 to 64%, 26 to 57%, and 40 to 45%, respectively. Fallow rain made the highest contribution to R2 in wheat whereas reproductive rain did for corn/sorghum. We conclude SOC is a stand‐in for long‐term effects of water availability on production, but other natural factors greatly influence the annual yield variability. Combining mean yields determined from soil properties with CV of pooled annual yields, we can estimate mean and standard deviation of yields.

Agricultural system models are useful tools to synthesize field experimental data and to extrapolate results to longer periods of weather and to other cropping systems. The objectives of this study were: (i) to quantify the effects of crop management practices and tillage on soil water and spring wheat (Triticum aestivum L.) production in a continuous spring wheat system using the RZWQM2 model (coupled with CERES‐Wheat) under a dryland condition, and (ii) to extend the RZWQM2 model results to longer term weather conditions and propose alternate cropping systems and management practices. Measured soil water content, yield, and total aboveground biomass under different tillage and plant management practices were used to calibrate and evaluate the RZWQM2 model. The model showed no impacts of tillage but late planting greatly reduced grain yield and biomass, in agreement with observed differences among treatments. The hydrologic analysis under long‐term climate variability showed a large water deficit (32.3 cm) for spring wheat. Fallowing the cropland every other year conserved 4.2 cm of water for the following wheat year, of which only 1.7 cm water was taken up by wheat, resulting in a yield increase of 249 kg ha−1 (13.7%); however, the annualized mean yield decreased 782 kg ha−1 (43.1%) due to 1 yr of fallow. Other long‐term simulations showed that optimal planting dates ranged from 1 March to 10 April and the seeding rates with optimum economic return were 3.71 and 3.95 × 106 seeds ha−1 for conventional and ecological management treatments, respectively.

Synthesis and quantification of disciplinary knowledge at the whole system level, via the process models of agricultural systems, are critical to achieving improved and dynamic management and production systems that address the environmental concerns and global issues of the 21st century. Soil physicists have made significant contributions in this area in the past, and are uniquely capable of making the much-needed and exciting new contributions. Most of the exciting new research opportunities are trans-disciplinary, that is, lie on the interfacial boundaries of soil physics and other disciplines, especially in quantifying interactions among soil physical processes, plant and atmospheric processes, and agricultural management practices. Some important knowledge-gap and cutting-edge areas of such research are: (1) quantification and modeling the effects of various management practices (e.g., tillage, no-tillage, crop residues, and rooting patterns) on soil properties and soil-plant-atmosphere processes; (2) the dynamics of soil structure, especially soil cracks and biochannels, and their effects on surface runoff of water and mass, and preferential water and chemical transport to subsurface waters; (3) biophysics of changes in properties and processes at the soil-plant and plant-atmosphere interfaces; (4) modeling contributions of agricultural soils to climate change and effects of climate change on soil environment and agriculture; and (5) physical (cause-effect) quantification of spatial variability of soil properties and their outcomes, new methods of parameterizing a variable field for field-scale modeling, and new innovative methods of aggregating output results from plots to fields to larger scales. The current status of the various aspects of these research areas is reviewed briefly. The future challenges are identified that will require both experimental research and development of new concepts, theories, and models.