Agroecosystem Model Research Articles

Tackling climate risk to sustainably intensify smallholder maize farming systems in southern Africa

Sustainable intensification (SI) of low input farming systems is promoted as a strategy to improve smallholder farmer food security in southern Africa. Using the Limpopo province South Africa as a case study (four villages across a climate gradient), we combined survey data (140 households) and quantitative agronomic observations to understand climate-induced limitations for SI of maize-based smallholder systems. Insights were used to benchmark the agroecosystem model Agricultural Production System sIMulator, which was setup to ex ante evaluate technology packages (TPs) over 21-seasons (1998–2019): TP0 status quo (no input, broadcast sowing), TP1 fertiliser (micro dosing), TP2 planting density (recommended), TP3 weeding (all removed), TP4 irrigation, TP5 planting date (early, recommended), and TP6 all combined (TPs 1–5). An additional TP7 (forecasting) investigated varying planting density and fertiliser in line with weather forecasts. Input intensity levels were low and villages expressed similar challenges to climate risk adaptation, with strategies mostly limited to adjusted planting dates and densities, with less than 2% of farmers having access to water for irrigation. Simulations showed that combining all management interventions would be expected to lead to the highest mean maize grain yields (3200 kg ha−1 across villages) and the lowest harvest failure risk compared to individual interventions. Likewise, simulations suggested that irrigation alone would not result in yield gains and simple agronomic adjustments in line with weather forecasts indicated that farmers could expect to turn rainfall variability into an opportunity well worth taking advantage of. Our study emphasises the need for a cropping systems approach that addresses multiple crop stresses simultaneously.

Integrated Modeling of U.S. Agricultural Soil Emissions of Reactive Nitrogen and Associated Impacts on Air Pollution, Health, and Climate.

Agricultural soils are leading sources of reactive nitrogen (Nr) species including nitrogen oxides (NOx), ammonia (NH3), and nitrous oxide (N2O). The propensity of NOx and NH3 to generate ozone and fine particulate matter and associated impacts on health are highly variable, whereas the climate impacts of long-lived N2O are independent of emission timing and location. However, these impacts have rarely been compared on a spatially resolved monetized basis. In this study, we update the nitrogen scheme in an agroecosystem model to simulate the Nr emissions from fertilized soils across the contiguous United States. We then apply a reduced-form air pollution health effect model to assess air quality impacts from NOx and NH3 and a social cost of N2O to assess the climate impacts. Assuming an $8.2 million value of a statistical life and a $13,100/ton social cost of N2O, the air quality impacts are a factor of ∼7 to 15 times as large as the climate impacts in heavily populated coastal regions, whereas the ratios are closer to 2.5 in sparsely populated regions. Our results show that air pollution, health, and climate should be considered jointly in future assessments of how farming practices affect Nr emissions.

Comparison of simulated nitrogen management strategies using DRAINMOD-DSSAT and RZWQM2

Agroecosystem models provide valuable insights into agricultural management decisions and can serve as a useful tool to quantify the effects of management practices under varying conditions where field experimentations are impractical. We evaluated a newly integrated comprehensive model, DRAINMOD-DSSAT, for hydrology, nitrogen (N) dynamics, and crop yield using eight years (1993–2000) of measured data from a no-tilled subsurface-drained, corn-soybean agricultural system near Danville, Illinois. The model satisfactorily predicted drainage flow and NO3-N losses with Nash-Sutcliffe efficiency (NSE), the ratio of the root mean squared error to the standard deviation (RSR), and percent bias (PBIAS) of 0.68 and 0.60, 0.56 and 0.63, and − 11.6% and − 2.2%, respectively, and crop yield with nRMSE, PBIAS and index of agreement (d) of 8.4%, − 0.6%, and 1, respectively. For the same experimental dataset, the performance comparison of DRAINMOD-DSSAT to Root Zone Water Quality Model (RZWQM2) demonstrated that the two models were most different in their simulation of N loss through seepage and denitrification. Further, we used DRAINMOD-DSSAT to simulate the effects of management practices (N application rates and timings) on NO3-N losses and crop yield and compared them with RZWQM2 simulated results. The results showed that both models provided the same conclusion on the effects of N management strategy on NO3-N losses and crop yield, but they differ in quantity. This study supports being cautious in using only one model to conclude the quantification of the effectiveness of particular agricultural management practices.

Improving the simulation of permanent grasslands across Germany by using multi-objective uncertainty-based calibration of plant-water dynamics

The dynamics of grassland ecosystems are highly complex due to multifaceted interactions among their soil, water, and vegetation components. Precise simulations of grassland productivity therefore rely on accurately estimating a variety of parameters that characterize different processes of these systems. This study applied three calibration schemes – a Single-Objective (SO-SUFI2), a Multi-Objective Pareto (MO-Pareto), and, a novel Uncertainty-Based Multi-Objective (MO-SUFI2) – to estimate the parameters of MONICA (Model for Nitrogen and Carbon Simulation) agro-ecosystem model in grassland ecosystems across Germany. The MO-Pareto model is based on a traditional Pareto optimality concept, while the MO-SUFI2 optimizes multiple target variables considering their level of prediction uncertainty. We used measurements of leaf area index, aboveground biomass, and soil moisture from experimental data at five sites with different intensities of cutting regimes (from two to five cutting events per season) to evaluate model performance. Both MO-Pareto and MO-SUFI2 outperformed SO-SUFI2 during calibration and validation. The comparison of the two MO approaches shows that they do not necessarily conflict with each other, but MO-SUFI2 provides complementary information for better estimations of model parameter uncertainty. We used the obtained parameter ranges to simulate grassland productivity across Germany under different cutting regimes and quantified the uncertainty associated with estimated productivity across regions. The results showed higher uncertainty in intensively managed grasslands compared to extensively managed grasslands, partially due to a lack of high-resolution input information concerning cutting dates. Furthermore, the additional information on the quantified uncertainty provided by our proposed MO-SUFI2 method adds deeper insights on confidence levels of estimated productivity. Benefiting from additional management data collected at high resolution and ground measurements on the composition of grassland species mixtures appear to be promising solutions to reduce uncertainty and increase model reliability.

Climate change impact on wheat and maize growth in Ethiopia: A multi-model uncertainty analysis.

Ethiopia’s economy is dominated by agriculture which is mainly rain-fed and subsistence. Climate change is expected to have an adverse impact particularly on crop production. Previous studies have shown large discrepancies in the magnitude and sometimes in the direction of the impact on crop production. We assessed the impact of climate change on growth and yield of maize and wheat in Ethiopia using a multi-crop model ensemble. The multi-model ensemble (n = 48) was set up using the agroecosystem modelling framework Expert-N. The framework is modular which facilitates combining different submodels for plant growth and soil processes. The multi-model ensemble was driven by climate change projections representing the mid of the century (2021–2050) from ten contrasting climate models downscaled to finer resolution. The contributions of different sources of uncertainty in crop yield prediction were quantified. The sensitivity of crop yield to elevated CO2, increased temperature, changes in precipitations and N fertilizer were also assessed. Our results indicate that grain yields were very sensitive to changes in [CO2], temperature and N fertilizer amounts where the responses were higher for wheat than maize. The response to change in precipitation was weak, which we attribute to the high water holding capacity of the soils due to high organic carbon contents at the study sites. This may provide the sufficient buffering capacity for extended time periods with low amounts of precipitation. Under the changing climate, wheat productivity will be a major challenge with a 36 to 40% reduction in grain yield by 2050 while the impact on maize was modest. A major part of the uncertainty in the projected impact could be attributed to differences in the crop growth models. A considerable fraction of the uncertainty could also be traced back to different soil water dynamics modeling approaches in the model ensemble, which is often ignored. Uncertainties varied among the studied crop species and cultivars as well. The study highlights significant impacts of climate change on wheat yield in Ethiopia whereby differences in crop growth models causes the large part of the uncertainties.

S-map parameters for APSIM

Agroecosystem models have become an important tool for impact assessment studies, and their results are often used for management and policy decisions. Soil information is a key input for these models, yet site-specific soil property data are often not available, and soil databases are increasingly being used to provide input parameters. For New Zealand, the digital spatial soil information system S-map provides geospatial data on a range of soil characteristics, including estimates of soil water properties. We describe a protocol for how properties from S-map can be used as input parameters for the APSIM (Agricultural Production Systems sIMulator) framework. Finally, we investigate how changes in the physical description of soil layers, and soil organic matter pools, affect the various outputs of APSIM.•This paper presents a description of how information from S-map, a digital soil map of New Zealand, can be used for building a soil description for APSIM.•A sensitivity analysis shows the effect of soil layering and the set-up setup, size, and distribution of SOM pools on model outputs, including plant growth and N leaching

Internal flow of nutrients in organic farming systems in tidal swamp

Sustainable nutrient management is critical to the success of an agroecosystem. The potential of nutrient flow to agroecosystems in tidal swamps can be observed in several of soil and water management. Tidal swamp land management based on surjan and tabukan system are often used to farm several sub-agricultures, namely rice fields, ponds, and citrus plantations. This study aimed to determine N nutrients’ input, output, and internal flow in organic farming systems. This study is limited to observing the internal components. The integrated organic farming system in tidal swampland, usually used for orange in the raised beds and rice in the sunken beds/wetland sections or fishponds, demontrated the potential for inorganic N-flow between farming sub-systems and between components within the system. Scenarios for the use of rice straw for external production sub-systems such as for citrus plantations and composting for external purposes need consideration. This potential can be a sub-component of nutrient input for other sub-systems or the next planting season. The simulation of the integrated rice-citrus-fishpond agroecosystem model showed a positive nutrient balance, especially N nutrients. This model needs to be further developed.

Simulating no-tillage effects on crop yield and greenhouse gas emissions in Kentucky corn and soybean cropping systems: 1980–2018

CONTEXTNo-tillage (NT) is a conservation practice that aims to minimize soil disturbance and improve crop production. However, NT effects on crop production remain controversial due to the spatial heterogeneity of climate and soil conditions. Some studies argued that NT might offset its greenhouse gas (GHG) mitigation potential in agriculture by promoting soil N2O emissions. OBJECTIVEThis study used a process-based agroecosystem model (DLEM-Ag) along with spatially explicit environmental datasets to quantify the long-term effects of NT on crop yield and GHG emissions in corn and soybean cropping systems in the state of Kentucky (USA) from 1980 to 2018. METHODSThe DLEM-Ag was used to quantify the long-term effects of NT on crop yield and GHG emissions in corn and soybean cropping systems in the state of Kentucky. Three spatiotemporal tillage scenarios, i.e., historical varying tillage, consistent conventional tillage (CT), and consistent NT, were adopted to simulate changes in crop yield and GHG emissions. RESULTS AND CONCLUSIONSOverall, our results showed that NT could reduce soil CO2 (−1.6% for corn and −4.53% for soybean) and N2O emissions (−10.5% for corn and −19.6% for soybean) in Kentucky, as compared to CT, although corn and soybean yields with NT were not significantly different from those with CT. Our further analysis suggested that air temperature and soil clay content were the two main factors influencing NT advantages in reducing GHG emissions. The increased temperature decreased the benefits of mitigating GHG emissions, while high clay content soils had less N2O emission under NT. SIGNIFICANCEThis study represents one of few attempts to quantify the effects of NT on crop yield and soil GHG emissions at the regional scale using an agroecosystem modeling approach. The findings from this study provide insights into how NT can enhance agroecosystem production stability and support climate change mitigation. This information may be used by the scientific community and policymakers working on practical technologies to mitigate climate change from agriculture.

Climate data induced uncertainties in simulated carbon fluxes under corn and soybean systems

CONTEXTNet carbon balance on croplands depends on numerous factors (e.g., crop type, soil, climate) and their interactions. Agroecosystem models are generally used to assess cropland carbon fluxes because of their ability to capture the complex interactive effects of factors influencing carbon balance. For regional carbon flux simulations, generally gridded climate data sets are used because they offer data for each grid cell of the region of interest. However, studies consistently report uncertainties in climate datasets, which affect the accuracy of carbon flux simulations. OBJECTIVEThe objectives were to 1) determine the uncertainties in daily weather variables of commonly used high resolution gridded climate datasets in the U.S (NARR, NLDAS, Prism and Daymet); 2) estimate their impact on the accuracy of simulated Net Ecosystem Exchange (NEE) under irrigated and non-irrigated corn and soybeans using the Environmental Policy Integrated Climate (EPIC) agroecosystem model, and 3) understand the relative sensitivity of the NEE to various climate variables. METHODSThe observational data at four flux tower cropland sites in the U.S Midwest region were used to quantify the uncertainties in the gridded weather datasets, and EPIC simulations were performed at each flux tower site using each gridded climate dataset. Further, sensitivity analysis using Extended Fourier Amplitude Sensitivity Test (EFAST) was conducted. RESULTS AND CONCLUSIONSResults suggest that daily weather variables in all gridded climate datasets display some degree of bias, leading to considerable uncertainty in simulated NEE. The gridded climate datasets produced based on interpolation techniques (i.e. Daymet and Prism) were shown to have less uncertainties, and resulted in NEE estimates with relatively higher accuracy, likely due to their higher spatial resolution and higher dependency on meteorological station observations. The Mean Absolute Percentage Errors (MAPE) values of average growing season NEE estimates for Dayment, Prism, NLDAS and NARR include 22.53%, 23.45%, 62.52% and 66.18%, respectively. The NEE under irrigation (MAPE = 53.15%) tends to be more sensitive to uncertainties compared to the fluxes under non-irrigation (MAPE = 34.19%). Further, this study highlights that NEE responds differently to the individual climate variables and management. Under irrigation management, NEE are more sensitive to temperature. Conversely, under non-irrigation, precipitation is the most dominant factor influencing NEE uncertainty. SIGNIFICANCEThese findings demonstrate that careful consideration is necessary when selecting climate data to mitigate uncertainties in simulated NEE. Further, alternative approaches such as integration of remote sensing data products may help reduce the models' dependency on climate datasets and improve the accuracy in the simulated CO2 fluxes.

Bayesian calibration and uncertainty analysis of an agroecosystem model under different N management practices

Process-based agroecosystem model provides a powerful tool to evaluate the effects of various field management practices on soil water balance, nitrogen (N) fates, and crop growth for cropland ecosystems. However, parameter calibration of those models is usually time-consuming and uncertain due to the involvement of complex soil and plant growth processes in models. In this study, the Different Evolution Adaptive Metropolis (DREAM) algorithm was successfully used to estimate model parameters and quantify uncertainties of the soil Water Heat Carbon Nitrogen Simulator (WHCNS) model. The calibration efficiency was further improved by identifying the sensitive parameters using global sensitivity analysis. The simulated soil water content, crop N uptake, and yield were in agreement with the measured values, with indices of NRMSE, IA, and NSE ranging from 6.4% to 22.1%, from 0.59 to 0.98, and from 0.43 to 0.92, respectively. The simulation of soil nitrate concentration showed a relatively large NRMSE that varied from 35.9% to 50.2% but was still within the acceptance range. This study suggested that the two-step method of global sensitivity analysis and DREAM algorithm is a robust way to estimate model parameters and quantify uncertainties of agroecosystem models.

Geo-CropSim: A Geo-spatial crop simulation modeling framework for regional scale crop yield and water use assessment

Remote sensing derived datasets (e.g. Leaf Area Index (LAI)) are increasingly being used in process based cropping system models to improve the prediction skill of the simulations when implementing operationally at regional scale. However, challenges such as inadequate quality of the available remote sensing data products and high reliance of models on climate variables and their uncertainties still exist. To address these challenges, we developed Geo-CropSim, a spatial modeling framework to use high quality remote sensing products in the Environmental Policy Integrated Climate (EPIC) agroecosystem model to regulate simulated processes and improve predictions of crop yield and evapotranspiration. Geo-CropSim comprises three main features 1) pixel level model initialization using crop emergence dates; 2) ability of the EPIC model to read in the PROSAIL (i.e. combined PROSPECT leaf optical properties model and SAIL canopy bidirectional reflectance model) inversion-based crop type LAI; and 3) a stress adjustment function to regulate simulated stress using LAI anomalies. To understand its performance, we implemented it over the State of Nebraska to estimate corn (Zea mays L.) and soybean (Glycine max [Merr.]) yields and evapotranspiration (ET) for 2012 (drought year) and 2015 (normal year) at 500-m resolution. Results showed that emergence dates and seasonal LAI captured spatial and temporal differences in crop progression (e.g. delayed planting in 2015) and growth (e.g. declined LAI in 2012) driven by regional differences in crop management and weather conditions very well. These differences were reflected in Geo-CropSim yield estimates, and showed improved spatial and temporal details compared over those from EPIC simulations obtained without using remote sensing derived emergence and LAI. Results revealed that Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) of Geo-CropSim yield estimates, computed based on USDA-NASS reported yields, were 18.85% and 1.22 Mg ha−1 for corn, and 17.90% and 0.46 Mg ha−1 for soybeans, respectively, which are substantially lower than those of original EPIC estimates (MAPE = 33.74% and RMSE = 2.18 Mg ha−1 for corn; and MAPE = 40.71% and RMSE = 0.98 Mg ha−1 for soybeans). Further, Geo-CropSim was able to capture ET and transpiration dynamics reasonably well (e.g. 10–12 % lower values for soybeans compared to corn values), and showed good agreement with flux measurements (i.e. R2 values of 0.63 and 0.72, RMSE values of 29.88 and 33.41 mm, and MAPE values of 5.0% and 6.8% for corn and soybean, respectively). Overall, this study demonstrated that Geo-CropSim has considerable potential to serve as a reliable operational tool to assess crop yields and water use under various cropping systems and to help in regional yield monitoring and water resource management.

Assessing the impacts of cover crops on maize and soybean yield in the U.S. Midwestern agroecosystems

• Non-legume cover crops cause 3.9 % yield penalty for maize, no penalty for soybean. • Legume cover crops have no significant impacts on maize and soybean yield. • Pathways for yield penalty are N and O 2 deficiency, water stress and cooler soil. • Optimizing cover crop growth windows could maximize the benefit from cover crops. • The ecosys model is an effective tool to quantify cover crop impacts on crop yield. Growing cover crops is one of the most promising conservation practices with multiple benefits. However, the impacts of cover crops on the productivity of the maize-soybean [ Zea mays L. - Glycine max (L.) Merr.] rotation system in the U.S. Midwest still have large uncertainties based on results obtained from field experiments, specifically across different soil properties, climate conditions, and land management practices. Process-based models fully validated with data from field experiments across these diverse conditions provide an effective tool to quantify cover crop impacts on cash crop productivity and optimize the management of cover crops accordingly. In this study, we aim to answer the following questions: (1) What are the impacts of cover crops on cash crop yield in the U.S. Midwest agroecosystems? (2) What are the mechanistic pathways of cover crop to affect cash crop yield (e.g. through influencing soil water, nitrogen and O 2 dynamics)? (3) What management practices can be used to mitigate the negative impacts on cash crop yield caused by cover crops? To address these questions, we calibrated and validated a sophisticated process-based agroecosystem model, ecosys , using field experimental data from 2013 to 2018 across Illinois, and then used ecosys to assess the impacts of winter cover crops on maize and soybean yield under different management practices. Our study revealed the following findings: (1) planting non-legume cover crops can cause 3.9 ± 3% yield reduction for maize and no significant impacts on soybean yield, while planting legume cover crops has no significant impact on the yield of either maize or soybean; (2) the maize yield reduction caused by planting cover crops can be mainly explained by nitrogen deficiency induced by increased immobilization, water competition in dry areas, cooler soil surface and oxygen competition; (3) later termination of non-legume cover crops before maize can result in larger maize yield loss due to intensive competition for resources (e.g. water and nitrogen), and the impacts of non-legume cover crops on maize yield reduction can be minimized by optimizing cover crop termination time in the spring. Overall, in the U.S. Midwestern maize-soybean rotation system, we found that although non-legume cover crops cause yield reduction for maize through resource competition, this yield reduction can be minimized through management practices, such as controlling termination time of cover crop and proper fertilizer management.

Linking multi-media modeling with machine learning to assess and predict lake chlorophyll a concentrations

Eutrophication and excessive algal growth pose a threat on aquatic organisms and the health of the public, environment, and the economy. Understanding what drives excessive algal growth can inform mitigation measures and aid in advance planning to minimize impacts. We demonstrate how simulated data from weather, hydrological, and agroecosystem numerical prediction models can be combined with machine learning (ML) to assess and predict chlorophyll a (chl a) concentrations, a proxy for lake eutrophication and algal biomass. The study area is Lake Erie for a 16-year period, 2002–2017. A total of 20 environmental variables from linked and coupled physical models are used as input features to train the ML model with chl a observations from 16 measuring stations. Included are meteorological variables from the Weather Research and Forecasting (WRF) model, hydrological variables from the Variable Infiltration Capacity (VIC) model, and agricultural management practice variables from the Environmental Policy Integrated Climate (EPIC) agroecosystem model. The consolidation of these variables is conducive to a successful prediction of chl a. Aside from the synergistic effects that weather, hydrology, and fertilizers have on eutrophication and excessive algal growth, we found that the application of different forms of both P and N fertilizers are highly ranked for the prediction of chl a concentration. The developed ML model successfully predicts chl a with a coefficient of determination of 0.81, bias of −0.12 μg/l and RMSE of 4.97 μg/l. The developed ML-based modeling approach can be used for impact assessment of agriculture practices in a changing climate that affect chl a concentrations in Lake Erie.

Modelling Agroforestry’s Contributions to People—A Review of Available Models

Climate change, increasing environmental pollution, continuous loss of biodiversity, and a growing human population with increasing food demand, threaten the functioning of agro-ecosystems and their contribution to people and society. Agroforestry systems promise a number of benefits to enhance nature’s contributions to people. There are a wide range of agroforestry systems implemented representing different levels of establishment across the globe. This range and the long time periods for the establishment of these systems make empirical assessments of impacts on ecosystem functions difficult. In this study we investigate how simulation models can help to assess and predict the role of agroforestry in nature’s contributions. The review of existing models to simulate agroforestry systems reveals that most models predict mainly biomass production and yield. Regulating ecosystem services are mostly considered as a means for the assessment of yield only. Generic agroecosystem models with agroforestry extensions provide a broader scope, but the interaction between trees and crops is often addressed in a simplistic way. The application of existing models for agroforestry systems is particularly hindered by issues related to code structure, licences or availability. Therefore, we call for a community effort to connect existing agroforestry models with ecosystem effect models towards an open-source, multi-effect agroforestry modelling framework.

Modelling of soil depth and hydraulic properties at regional level using environmental covariates- A case study in India

Soil hydraulic properties are important for agroecosystem modelling, irrigation scheduling, drought risk assessment, and preparation of proper land use planning. Field capacity (FC) and permanent wilting point (PWP) are the two vital soil hydraulic properties that determine the availability of water for plant growth. In the present study, we evaluated four data mining algorithms for mapping of field capacity and permanent wilting point along with soil depth over 16.2 M ha of Andhra Pradesh state, South India. Class pedo-transfer function was developed based on the soil texture classes (760 observations) and the profile total field capacity (TFC) and total profile permanent wilting point (TPWP) were computed for 1 m soil depth. Multi-linear Regression (MLR), Cubist, Support Vector Machine (SVM), and Random Forest (RF) models were evaluated using 2267 soil profile datasets collected over the study area. Along with Landsat-8 data and climatic datasets, terrain attributes such as plan curvature, profile curvature, topographic wetness index (TWI), topographic position index (TPI), Multi-resolution Index of Valley Bottom Flatness (MrVBF), and Multi-resolution Ridge Top Flatness (MrRTF) were used as environmental covariates. The models were calibrated using 80% of total field observations and validated using 20% of observations. The validation results showed that RF outperformed all other models for the prediction of both soil depth and profile TFC and TPWP. RF explained 39% of the variation of total field capacity and permanent wilting point with RMSE of 133 mm and 89 mm, respectively. The highest mean TFC and TPWP were found in Vertisols (256 and 154 mm, respectively) and Inceptisols (216 and 126, mm respectively). The high-resolution (250 m) maps of hydraulic properties and soil depth are useful for land use planning and different crop modelling studies.

Quantifying carbon budget, crop yields and their responses to environmental variability using the ecosys model for U.S. Midwestern agroecosystems

As one of the major agricultural production areas in the world, the United States (U.S.) Midwest plays a vital role in the global food supply and agricultural ecosystem services. Although significant efforts have been made in modeling the carbon cycle dynamics over this area, large uncertainty still exists in the previous simulations in terms of reproducing individual components of the carbon cycle and their responses to environmental variability. Here we evaluated the performance of an advanced agroecosystem model, ecosys, in simulating carbon budgets over the U.S. Midwest, considering both the magnitude of carbon flux/yield and its response to environmental (climate and soil) variability. We conducted model simulations and evaluations at 7 cropland eddy-covariance sites as well as over 293 counties of Illinois, Indiana, and Iowa in the U.S. Midwest. The site-level simulations showed that ecosys captured both the magnitude and seasonal patterns of carbon fluxes (i.e., net ecosystem carbon exchange (NEE), ecosystem gross primary production (GPP), and ecosystem respiration (Reco)), leaf area index (LAI), and dynamic plant carbon allocation processes, with R2 equal to 0.92, 0.87, 0.87, and 0.78 for GPP, NEE, Reco, and LAI, respectively across all the sites compared with the observations. For regional scale simulations, ecosys reproduced the spatial distribution and interannual variability of corn and soybean yields with the constraints of observed yields and a new remotely sensed GPP product, with R2 of multi-year averaged simulated and observed yield equal 0.83 and 0.80 for corn and soybean, respectively. The simulated responses of carbon cycle dynamics to environmental variability were consistent with that from the empirical observations at both site and regional scales. Our results demonstrated the applicability of ecosys in simulating the carbon cycle and soil carbon dynamics of the U.S. Midwestern agroecosystems under different climate and soil conditions.

Planting miscanthus instead of row crops may increase the productivity and economic performance of farmed potholes

AbstractClimate change projections indicate that precipitation events in the central United States are expected to become more intense, more frequent in the spring, and less frequent in the summer. Such a precipitation shift could adversely impact crop yields, especially in subfield areas known as farmed potholes, which are highly susceptible to flooding and ponding, and crop death is more likely to occur, particularly early in the growing season. This suggests that planting alternative crops, such as more flood tolerant perennials, in these areas may be a more profitable option. Using observations of crop growth and yield along with ponding depth of a specific field and farmed pothole in the central United States, we developed a spatially explicit version of the agroecosystem model Agro‐IBIS to estimate water depth and crop yield. After evaluating the model, we conducted a case study for a specific farmed pothole with a range of future precipitation scenarios with Agro‐IBIS to simulate the effects of contemporary (2002–2016) and future precipitation on a conventional corn/soybean (Zea maysL. andGlycine maxMerr.) rotation and an alternative perennial miscanthus (Miscanthus × giganteusGreef et Deu.) cropping system. The depth and frequency of ponding increased under most future precipitation scenarios. The corn/soybean rotation had greater total loss (i.e., no yield) on average (>30%) for all scenarios in comparison to miscanthus (<10%). Under one future precipitation scenario with increased spring precipitation, both the corn/soybean rotation and miscanthus simulations showed an increase in yield. A simple budget analysis indicated that it is more profitable to plant miscanthus instead of corn or soybeans where yields in farmed potholes are consistently poor. Our findings show that potholes can be individually modeled, and their influence on yield can be quantified for use in future management decisions dictated by change in climate.

Evaluating crop-soil-water dynamics in waterlogged areas using a coupled groundwater-agronomic model

Waterlogging on croplands has been a known problem for a long time, leading to adverse social, physical, economic and environmental issues. To better solve the problem, the complicated plant-soil-water dynamics system needs to be better understood. The challenge is to simulate the interactions between the components in the systems. There are models that simulate plant-soil-water system but either run the processes independently leading to inaccuracy or has high invasiveness of using integrated models. This paper presents a tightly coupled model, DayCent-MODFLOW, that links a 3D ground-water flow (MODFLOW) model and a 1D agroecosystem model (DayCent). DayCent is responsible for plant-soil-water dynamic in the root zone, whereas MODFLOW simulates head and groundwater flow in the saturated zone of the aquifer. DayCent passes deep percolation from the soil profile to the water table and, under conditions of waterlogging in which the water table is within the soil profile, DayCent soil hydrologic processes are constrained by the presence of the water table simulated by MODFLOW. The coupling is achieved by adopting a parallel inter-process communication technique MPI (Message Passing Interface). The model is applied to a waterlogged agricultural area (22 km2) in northern Colorado, USA and tested against groundwater head and rates of evapotranspiration (ET). The model runs in parallel with multiple processes on the largest AWS Linux server. Groundwater heads match measured heads to a reasonable degree, and ET rates match reference ET and are highly correlated with crop type. Results show the strong hydrologic interaction between the two models. Greenhouse gas emissions from soil (N2O and CH4) were also estimated by the model under the waterlogged conditions. Although the model can be used to simulate any plant-soil-aquifer system, no matter the depth of the water table, results from this study show that the model can be used to assess crop productivity, recharge, ET, and greenhouse gas emissions in areas of shallow groundwater.

Understanding the development of viticulture in Roman Gaul during and after the Roman climate optimum: The contribution of spatial analysis and agro-ecosystem modeling

Starting as early as the 6th century BCE in southern Gaul, viticulture extended after the Roman conquest as far as the south of England. This paper presents different types of multi-proxy spatial modeling incorporating climatic, environmental and geohistorical factors to analyze the causes of the expansion of vineyards outside the Mediterranean region in the 1st century CE. The effects of the Roman Climate Optimum on potential wine production were simulated through agrosystem modeling (Lund-Potsdam-Jena-managed-Land). We also produce a predictive model of areas potentially favorable to viticulture in the 1st c. CE, based on a series of geographical, archaeological and paleoclimatic criteria. The results highlight a strong impact of climate change on potential wine production as early as 1st c. BCE and favorable conditions for the development of viticulture as far as northern Gaul throughout the Roman period, especially on soils with southern exposures, along communication routes (main roads and rivers) and close to Roman urban centers. Necessary data to build the model are available at https://data.mendeley.com/datasets/j4gtjy9w87/3.

APEX-MODFLOW: A New integrated model to simulate hydrological processes in watershed systems

APEX (Agricultural Policy/Environmental eXtender) is an oft-used agroecosystem model but has limited use in groundwater-driven watersheds due to a simplistic representation of groundwater processes. This paper presents the linkage of APEX and the groundwater flow model MODFLOW into a single modeling code. The mapping of recharge, groundwater head, and groundwater-surface water interactions are handled internally via subroutines. The APEX-MODFLOW model is applied to three watersheds in the United States for testing code accuracy and hydrologic state variables and fluxes: the Animas River Watershed, Colorado and New Mexico (3543 km2); the Price River Watershed, Utah (4886 km2); and the Middle Bosque River Watershed, Texas (470 km2). Whereas the hydrology of the Animas River and Price River watersheds is driven by snowmelt and spring runoff, the hydrology of the Middle Bosque River Watershed is driven by summer thunderstorms. The model can be used for scenario analysis in groundwater-driven watersheds.