Environmental Policy Integrated Climate Research Articles

Evaluating the utility of weather generators in crop simulation models for in-season yield forecasting

CONTEXTCrop yield forecasting is crucial for ensuring food security and adapting to the impacts of climate change, as it provides early insights into potential harvest outcomes and helps farmers and policymakers make informed decisions in the face of changing environmental conditions. The accuracy of the crop model–based yield forecasting frameworks is affected by the uncertainty in future weather data, which is often substituted with synthetic weather realizations generated by stochastic weather generators. OBJECTIVEThis study aims to assess the performance of three recent stochastic weather generators—Global Weather Generator (GWGEN), WeatherGEN, and R Multi-Sites Autoregressive Weather GENerator (RMAWGEN) — in producing synthetic weather realizations that accurately represent regional climate variations and their impact on winter wheat yield forecasting. METHODSWe utilized historical weather data from Daymet, an interpolation of daily meteorological observations that produces gridded datasets with a spatial resolution of 1 km. This data was used both as an input for the weather generators and for evaluating the performance of the generated weather realizations. Furthermore, the weather realizations generated by these weather generators across multiple winter wheat field sites in Kansas were employed in the calibrated Environmental Policy Integrated Climate (EPIC) crop model to assess the potential impact of variations in weather generators on the accuracy of crop yield forecasts. RESULTS AND CONCLUSIONSRMAWGEN and WeatherGEN excelled in accurately simulating rainy days and precipitation amounts, with WeatherGEN particularly effective in wet months and RMAWGEN performing best in dry months, showcased their proficiency in diverse weather conditions. RMAWGEN consistently showed lowest error across all variables, including precipitation, solar radiation, and both maximum and minimum temperatures. Except for GWGEN, both RMAWGEN and WeatherGEN demonstrate good agreement with Daymet in replicating spatial variability patterns. RMAWGEN notably outperformed other weather generators, particularly during the forecasting period. Consequently, it showed superior capabilities in forecasting crop yields closely matching the simulated results with Daymet data. SIGNIFICANCEThe findings of this study are crucial for selecting accurate weather data estimates for crop yield forecasting. Utilizing alternative sources such as ensembles of multiple weather generators or outputs from sub-seasonal multi-model forecast systems may further enhance the accuracy of crop yield forecasts.

Mid-season nitrogen management for winter wheat under price and weather uncertainty

ContextIn-season nitrogen (N) management tools are essential for optimizing N application rates, maximizing farmers’ economic returns and minimizing adverse environmental impacts. The primary limitation to developing such tools is the risk associated with uncertainties in weather forecasts and crop price projections required to estimate yields and returns for different N rates. Therefore, characterizing the risk associated with these uncertainties is crucial for determining optimum N rates in-season. ObjectiveThis study investigated the N application decision-making process for farmers, accounting for risks associated with weather and crop price uncertainties through crop modeling and economic analysis. MethodsWe used field trial data for winter wheat in Kansas to examine how optimal nitrogen rates and economic returns vary over sites, years, and differing farmers’ risk attitudes. First, the Environmental Policy Integrated Climate (EPIC) agroecosystem model was used to simulate the distribution of final yields under different N applications during early spring. Then, an autoregressive moving average (ARMA) model estimated the wheat price distribution at harvest based on historical prices. Finally, optimal N application rates for farmers with different risk appetites were estimated using two risk decision models: the constant-absolute-risk-averse (CARA) expected utility model, which treats upside (higher-than-expected returns) and downside (lower-than-expected returns) deviations equally, and the invariant-preference, generalized-deviation (IPGD) model, which focuses on downside risk. ResultsWe found that optimal N rates vary greatly between sites and years, as well as across farmers with different risk preferences. Due to the positive skewness of economic return distribution, farmers tend to apply lower N rates when considering downside risk. On average, the optimal N rate for farmers with a CARA coefficient of 0.002 is 77 kg/ha in the CARA model and 67 kg/ha in the IPGD model. Compared to the outcome of risk-neutral N usage, risk-averse N usage for a farmer with a CARA coefficient of 0.008 could reduce the uncertainty (standard deviation) of return by 6.2 %, on average, while the expected return decreased by only 1.2 %. ConclusionsBy lowering the N rate, risk-averse farmers would reduce the uncertainty of returns and incur a minor return loss, suggesting the possibility of improving agricultural resilience while also improving N use efficiency. Our analysis also underscores the importance of yearly site-specific N management, given the substantial variation in optimal rates across years and locations. SignificanceThis study provides the foundation for an N application decision framework that considers both weather and price uncertainty. The analysis also demonstrates the potential co-benefit of enhancing agriculture’s climate and market resilience while potentially lowering N losses.

Yam Production-Related Agro-climatological Risks and Yam Yield Modeling in Côte d’Ivoire: A Review

In this paper, we present a review of the agro-climatological-related risk of yam production and models developed for yam yield prediction in Côte d’Ivoire. Four official national platforms (Ministry of Agriculture and Rural Development (MINADER), National Center for Agricultural Research (CNRA), National Agency for Rural Development Support (ANADER), Airport, Aeronautical and Meteorological Exploitation and Development Company (SODEXAM)) and six scientific search engines were investigated in this study including Theses.fr, African Journal Online, Science Direct, Google Scholar, WorldCat and Semantic Scholar. Using the boolean parameters “AND”, “OR” and “()” to facilitate and direct our search, we were able to define four key phrases comprising the topic words that were used in the search. Exclusion and inclusion criteria for the selection of documents were also defined in advance, as well as the criteria for reviewing and extracting information from selected documents. The results showed that no work in the field of agro-climatological risks related to yam production and yam yield modeling in Côte d’Ivoire was available on these online research platforms at the time of this literature review. However, other studies similar to the scope of this review on yam exist in several West African countries, particularly Ghana, Benin and Nigeria, and also in the Caribbean. These studies use simulation models such as the Approach for Land Use Sustainability (SALUS) model, the Environmental Policy Integrated Climate (EPIC) model and the Cropping Systems Simulation (CROPSYST) model for growth, yield modeling and the influence of climatic parameters on yam. In addition to these models, artificial intelligence through machine learning models was also seen in this review as an excellent tool for yield prediction for several crops including yams.

Combined effects of climate change and agricultural intensification on soil erosion in uphill shifting cultivation in Northeast India

AbstractShifting cultivation will face increasing pressure from erosion‐related land degradation caused by rising cultivation intensities and climate change. However, empirical knowledge about future trends of soil erosion and thus land degradation in shifting cultivation systems is limited. We use the Environmental Policy Integrated Climate (EPIC) model to first explore the combined effects of climate change and agricultural intensification on soil erosion of uphill shifting cultivation systems, using six surveyed soil profiles. We assess interactions between climate change, the length of the fallow period, and slope inclinations for a near (2021–2050) and far (2071–2100) future period, considering three climate scenarios, five climate models, fallow periods between one and 20 years, and slopes between five and 70% steepness. Our results show a significant nonlinear relationship between global warming and erosion. Until the end of the century, erosion is estimated to increase by a factor of 1.2, 2.2, and 3.1 under the SSP126, SSP370, and SSP585 scenarios, respectively, compared with the historical baseline (1985–2014). Combined effects from climate change, fallow length, and slope inclination indicate that steep slopes require longer fallow periods, with an increase of slope from 5% to 10% multiplying the required fallow length by a mean factor of 2.5, and that fallow periods will need to be extended under higher global warming if erosion rates are to remain at current levels. These findings are novel as they link climate change effects on shifting cultivation systems to different slopes and fallow regimes, making an important contribution to understanding future erosion dynamics of traditional smallholder production systems in mountainous terrain, with relevant implications for policies on agricultural intensification.

Mapping soil erodibility over India

Soil erosion is a major environmental problem worldwide, and almost half of India’s total geographical area is susceptible to it. The Revised Universal Soil Loss Equation (RUSLE) has been widely used globally to estimate soil erosion, and the Soil erodibility factor, denoted by the K-factor, is an essential component of RUSLE. Although previous studies have assessed soil erodibility in India, they have been limited to small scales such as watersheds or districts. A national-scale assessment of soil erodibility doesn’t exist and is critical to developing a systematic understanding of soil erosion over India. In this study, we estimated soil erodibility factors over India using RUSLE Nomograph and Environmental Policy Integrated Climate (EPIC) model approaches at a high resolution of 250 m. Our results showed that the K-factor estimated using the Nomograph approach was more accurate than the observed soil erodibility factors. Additionally, we developed erodibility indices such as CR (Clay Ratio), MCR (Modified Clay Ratio), and CLOM (Critical Level of Organic Matter) to assess their sensitivity with respect to soil erodibility factors. Finally, we created a susceptibility to erosion map over India using CLOM index classification. The national average soil erodibility factor for India is estimated to be 0.028 t-ha-h/ha/MJ/mm. Histosols soil type is the least susceptible to erosion, while the Xerosols soil type is most susceptible among the prevalent soil classes in India. This is the first national-scale mapping of soil erodibility over India, providing an essential asset for soil conservation and erosion management planning by experts.

Coupling of SWAT and EPIC Models to Investigate the Mutual Feedback Relationship between Vegetation and Soil Erosion, a Case Study in the Huangfuchuan Watershed, China

Identifying the feedback relationship between soil erosion and vegetation growth would contribute to sustainable watershed management. In order to study the long-term interaction between soil erosion and vegetation change, a comprehensive modeling framework was proposed by combining the Soil and Water Assessment Tool (SWAT) and the Environmental Policy Integrated Climate (EPIC) model. The Huangfuchuan Watershed was taken as an example area due to serious erosion and large-scale conversion of farmland to forest. Based on long-term variation analyses from 1956 to 2020, the effect of land cover change on runoff and sediment discharge was quantified using SWAT to create scenario simulations, and then environmental stresses factors (i.e., soil water content, nitrogen, and phosphorus contents) output by SWAT were input into EPIC to evaluate effects of soil erosion on potential biomass of vegetation. Results showed that the annual runoff reduction was 32.5 million m3 and the annual sediment reduction was 15 million t during the past 65 years. The scenario we created using the SWAT simulation showed that both forest and grassland reduced water yield, while bare land increased water yield by 10%. In addition, grassland and forest reduced soil erosion by 20% and 18%, respectively, while bare land increased sand production by 210%. The EPIC model results exhibited a negative correlation between the potential for vegetation biomass and erosion intensity. The average annual potential biomass of forest and grass under micro-erosion was 585.7 kg/ha and 485.9 kg/ha, respectively, and was 297.9 kg/ha and 154.6 kg/ha, respectively, under the extremely strong erosion. The results of this study add to the body of information regarding how soil erosion and vegetation biomass interact with each other. The proposed coupled SWAT-EPIC strategy may provide a way for further investigating the quantitative relationship between soil erosion and vegetation cover.

Soil organic carbon and soil erodibility response to various land-use changes in northern Thailand

Conversion from forestland to agricultural land can influence soil organic carbon (SOC) stocks and soil erodibility. Therefore, the objectives of this study were to assess the SOC stocks and soil erodibility changes relative to land-use changes in the Mae Chaem Basin, Chiang Mai Province, Thailand. Seven adjacent fields were selected and studied during two periods of land-use change: 1) conversion of natural forest to maize fields and 2) replacement of maize with other crops (pumpkin, upland rice, integrated farming, and mango) and grassland. These adjacent fields were located on hillslopes with elevations and slopes ranging from 600 to 800 m a.s.l. and 9 to 25 %, respectively. Soil samples were collected at depths of 0–100 cm from five pits in each field. SOC stocks were estimated using depth- and mass-based approaches. Soil erodibility was calculated based on the Environmental Policy Integrated Climate (EPIC) model equation. The results showed that the natural forest contained 174.4 Mg C ha−1 of SOC stock at 0–100 cm soil depths. After conversion to maize fields, the SOC stock (0–100 cm) was reduced to 96.1 (44.9 % lost) and 82.7 (52.5 % lost) Mg C ha−1 according to the depth-based and mass-based approaches, respectively. Replacement of maize fields by grassland and pumpkin tended to increase the SOC stocks, whereas they decreased in mango, integrated farming, and upland rice due to the loss of organic matter. This study detected that silt concentration has a highly positive correlation, whereas SOC has a strongly negative correlation with soil erodibility. Grassland had the lowest soil erodibility compared to the other croplands due to lower silt concentration and higher organic carbon. This study indicated that land-use types and conversion time influenced SOC stocks, with variations between soil depths.

Simulating the Impacts of Climate Change on Maize Yields Using EPIC: A Case Study in the Eastern Cape Province of South Africa

Climate change has been projected to impact negatively on African agricultural systems. However, there is still an insufficient understanding of the possible effects of climate change on crop yields in Africa. In this study, a previously calibrated Environmental Policy Integrated Climate (EPIC) model was used to assess the effects of future climate change on maize (Zea mays L.) yield in the Eastern Cape Province of South Africa. The study aimed to compare maize yields obtained from EPIC simulations using baseline (1980–2010) weather data with maize yields obtained from EPIC using statistically downscaled future climate data sets for two future periods (mid-century (2040–2069) and late century (2070–2099)). We used three general circulation models (GCMs): BCC-CSM1.1, GFDL-ESM2M and MIROC-ES under two Representative Concentration Pathways (RCPs), RCP 4.5 and RCP 8.5, to drive the future maize yield simulations. Simulation results showed that for all three GCMs and for both future periods, a decrease in maize production was projected. Maize yield was projected to decrease by as much as 23.8% for MIROC, RCP 8.5, (2070–2099). The temperature was projected to rise by over 50% in winter under RCP 8.5 for both future periods. For both future scenarios, rainfall was projected to decrease in the summer months while increasing in the winter months. Overall, this study provides preliminary evidence that local farmers and the Eastern Cape government can utilise to develop local climate change adaptation strategies.

Crop calendar optimization for climate change adaptation in rice-based multiple cropping systems of India and Bangladesh

Adjusting crop calendars may present an effective adaptation measure to avoid crop yield loss and reduce water use in a changing climate. In order to better understand potentials and limitations of adjusting crop calendars for climate change adaptation of tropical multi-cropping systems with short fallow periods, we used a regionally calibrated Environmental Policy Integrated Climate (EPIC) agronomic model to estimate annual caloric yield and blue water requirement (BWR) of irrigated double-rice and rice-wheat cropping systems in India and Bangladesh. We adjusted crop calendars by (a) single-objective optimization to maximize annual caloric yield and (b) multi-objective optimization to minimize BWR under current and future climate scenarios, focusing on climatic drivers of optimal growing seasons. While the short time intervals between harvest of kharif crops and (trans-)planting of rabi crops limit the space for planting date shift in the study area, our results indicate that crop calendar adjustment has great potential to reverse yield loss induced by temperature rise and decrease BWR by utilizing monsoon precipitation. The study indicates a trend towards earlier planting of rabi wheat to mitigate heat stress during the reproductive stage. Moreover, earlier planting of kharif rice can help to utilize monsoon precipitation, avoid cold stress of kharif rice during anthesis, and allow for early wheat sowing during the historic period. By the 2080s, the increase of heat stress in summer and the decrease of cold stress in winter seems to allow more flexibility for late rice in kharif season, but a conflict between later planting for yield improvement and earlier planting for blue water saving is expected in kharif rice on the Indo-Gangetic plain of India and Bangladesh. Therefore, the trade-off between yield improvement and irrigation water use needs to be carefully considered to promote adaptive adjustment of crop calendars under climate change.

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.

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.

Vulnerability Assessment of Maize Yield Affected by Precipitation Fluctuations: A Northeastern United States Case Study

Crop yields are threatened by global climate change. Maize has high water requirements, and precipitation fluctuations can impact its yield. In this study, we used the Environmental Policy Integrated Climate (EPIC) model to simulate maize yields in eight northeastern U.S. states. We used precipitation fluctuations and the coefficient of variation (CV) of yield as indicators to construct a vulnerability curve for the CV of yield and precipitation fluctuations. We then evaluated the vulnerability of maize yields under precipitation fluctuations in the region. We obtained the following results: (1) the fitted vulnerability curves were classified into three categories (positive slope, negative slope, and insignificant fit), of which the first category accounted for about 92.7%, indicating that the CV of maize yield was positively correlated with precipitation fluctuations in most parts of the study area; and (2) the CV of maize yield under 11 precipitation fluctuation scenarios was mapped to express the CV at the spatial level, and the maize yield in Connecticut and Maryland proved to be the most sensitive to precipitation fluctuations. This study provided a theoretical and experimental basis for the prevention of maize yield risk under fluctuating precipitation conditions.

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.

Contrasting contributions of five factors to wheat yield growth in China by process-based and statistical models

China’s wheat growth from 1981 to 2015 was faster than most countries, characterized by notable linear growth and more than 100 % rise in yield. Understanding the reasons for such fast growth is essential for stakeholders in developing countries to gain insight and be able to establish efficient yield-increasing strategies for the future. Here, using a statistical function and a process-based crop model, we dissected, quantified and compared the contributions of five causal factors to China’s national wheat yield growth. For past yield growth from 1981 to 2015, the Environmental Policy Integrated Climate (EPIC) model estimated that five drivers have contributed to national wheat yield growth by 57.4 % (fertilizer), 37.9 % (cultivar), 7.9 % (irrigation), -1.0 % (area), and -2.2 % (climate), while the Cobb-Douglas (C–D) production function estimated contributions of 79.8 %, 16.1 %, 8.0 %, -2.6 %, and -1.2 %, respectively. Furthermore, we conducted a temporal two-paired comparison of the yield contributions estimated by the two methods and explored the reasons for such difference. Results indicated that the distinct nature of the two methods and inconsistency of the input data resulted in the divergent estimations in amount and temporal scale. The way to decrease the uncertainty of each factor’s contribution in each of the methods was to maintain consistency in input data, clearly understand the methods’ assumptions, and include as many interactions as possible. In addition, utilization of an ensemble of multiple models was able to achieve a more robust assessment, especially with the inclusion of both statistical and process-based models. This study has for the first time systematically compared the applicability of the two methods in evaluating the contribution of the five drivers to yield, and provided instructions for improvements. Future researchers should pay attention to developing a consistent manner of integrating multiple approaches, which have the potential partly to offset the weakness of individual methods and thus lead to better estimations.

Improving the simulation of soil temperature within the EPIC model

Soil temperature is a key driver of several physical, chemical, and biological processes. The Environmental Policy Integrated Climate (EPIC) is a comprehensive ecosystem model that simulates soil temperature dynamics using a cosine function approach driven by daily air temperature and average annual soil temperature at damping depth, which may erroneously predict lower soil temperatures in winter. A new cosine model and a pseudo-heat-transfer model were therefore developed and implemented for simulating soil temperature. The two methods were evaluated by comparing simulated daily soil temperatures with observed data at 24 study sites. Results showed that the two new methods had similar performance and the better statistical results obtained with these new methods demonstrated the ability to better predict the soil temperature for a wide range of pedoclimatic conditions, land management, and land uses. The main reason for the improved performance was due to a better prediction of soil temperature during the winter period.

Testing the EPIC Richards submodel for simulating soil water dynamics under different bottom boundary conditions

AbstractMost biogeochemical models simulate water dynamics using the tipping bucket approach, which has been often found to be too simplistic to represent vadose zone dynamics adequately under shallow groundwater conditions. Recently, a solution to the Richards equation using the Mualem–van Genuchten model (Rich‐vGM) has been added into the EPIC (Environmental Policy Integrated Climate) model to address this shortfall. Its performance was tested using lysimeters operating under free drainage (FD) and at a shallow water table (60‐ [WT60] and 120‐cm depth [WT120]). Model accuracy was also compared with the upgraded tipping bucket‐based method implemented into EPIC (the variable saturation hydraulic conductivity method [VSHC]). Soil water content (SWC) data were split into calibration and validation subsets. Model evaluation also included annual evapotranspiration (ET), percolation (PRK), and upward water movements to assess underlying soil water balance factors. The submodels provided accurate and similar results upon comparison with SWC measures under FD (Nash–Sutcliffe coefficient [NSE] = 0.26 and 0.61 using VSHC and Rich‐vGM, respectively). The Rich‐vGM model accurately reproduced observed SWC and ET (e.g., NSE = 0.70 and percentage bias [PBIAS] = −3.7% for WT120, respectively) although it slightly overestimated PRK (PBIAS = 47.8%, on average). Instead, VSHC proved unable to correctly simulate shallow groundwater conditions (e.g., NSE = −1.85 for WT60 SWC). Under shallow groundwater conditions, the Rich‐vGM method is recommended, despite the additional data required and the need to define the bottom boundary conditions according to water table fluctuations. In conclusion, the Richards solver introduced and tested in EPIC improved the model's ability to represent complex biophysical and biogeochemical processes in terrestrial ecosystems associated with the hydrological balance.

Evaluation of climate change impacts and effectiveness of adaptation options on nitrate loss, microbial respiration, and soil organic carbon in the Southeastern USA

CONTEXTClimate change presents an agricultural challenge in the Southeastern USA with implications for maintaining environmental quality. OBJECTIVEThe objective of this study was to assess climate change impacts and adaptation practices (biochar and irrigation) simulated with the Environmental Policy Integrated Climate (EPIC) model on nitrate-N (NO3-N) losses, microbial respiration (MR) and soil organic carbon (SOC) in the Southeastern USA. METHODSThe EPIC model was used to assess the impacts of climate change and adaptations on NO3-N losses in leachate and runoff from the soil profile (0–100 cm), loss of soil C via MR (0–100 cm), and impacts on SOC stocks (0–10 cm) for representative farms growing C3 and C4 crops within ten Southeastern USA states. The adaptations explored were annual biochar applications and irrigation. Historical baseline (1979–2009) and future (2041–2070) climate scenarios were used for simulations with CO2 concentrations of 360 ppm and 500 ppm, respectively. Four regional climate models (RCMs), nested within global climate models (GCMs) for their boundary conditions, simulated changes in air temperatures, precipitation, and solar radiation. RESULTS AND CONCLUSIONSClimate change increased simulated NO3-N losses in leaching and runoff by 40–80%, compared to historical baseline scenarios that was attributed to overall increased annual precipitation under three of the four RCM_GCM models. For the C4 crops, NO3-N leaching and runoff losses were 16–47% and 31–45% lower than for the C3 crops, respectively. Biochar applications reduced NO3-N leaching in the West region during 2066–2070 under the RCM3_CGCM3 model. The differences in MR between the C4 and C3 crops ranged from 3 to 75%. SOC increased under C4 crops and when biochar was applied. We concluded that inclusion of C4 crops in crop rotations and the applications of biochar under wetter climate scenarios may be a promising adaptation strategy to reduce NO3-N losses and increase SOC content in the soils of the Southeastern USA. SIGNIFICANCEThis study represents one of the first attempts to assess the effectiveness of climate change adaptations such as the agricultural use of biochar and irrigation. The findings from this study strongly contribute to our understanding of potential climate change impacts on a region's agriculture and resulting environmental footprint. This information may be used by the scientific community along with decision and policy makers working on conceptual and practical technologies to mitigate the impacts of climate change on agriculture and the environment.

Using EPIC to simulate the effects of different irrigation and fertilizer levels on maize yield in the Eastern Cape, South Africa

Growing water scarcity and increasing Nitrogen (N) fertiliser prices in South Africa require more prudent N fertiliser and irrigation water use approaches. If the goal of sustainable agricultural intensification is to be realised, it is vital to develop location-specific agricultural land management strategies that promote increased crop productivity while minimising negative environmental impacts. In this study, a calibrated and validated Environmental Policy Integrated Climate (EPIC) model was used to simulate a range of N fertiliser and irrigation water levels on maize (Zea mays L.) yield in the Eastern Cape of South Africa. Results showed that a fertiliser and irrigation water management schedule combining approximately 200 kg N ha-1 and 580 mm irrigation water per maize growing season provided the highest average maize yield of 12.2 Mg ha-1 (an increase of +69% above farmers’ current maize yield levels). Nitrogen fertiliser application levels greater than 160 kg N ha-1 resulted in potential N fertiliser leaching losses of more than 35 kg N ha-1. The EPIC model can be considered a valuable tool to aid decision-makers in identifying optimal, site-specific irrigation water and N fertiliser application levels that contribute to increased maize crop productivity while maximising water use.

A semianalytical solution of the modified two‐dimensional diffusive root growth model

AbstractAccurate estimation of the temporal and spatial root water uptake patterns in root zone is needed for an improved understanding of water and chemical transport dynamics in vadose zone. Rooting system is of great importance to describe the plant root water uptake directly. In this study, the diffusive root growth model was coupled with the Environmental Policy Integrated Climate (EPIC) crop growth model through changing the boundary condition, and a new semianalytical solution of the modified root diffusive model was derived considering the dynamic root length density distribution simulated by the EPIC crop growth model. To test the modified root growth model, the field‐measured data of maize (Zea mays L.) and tomato (Solanum lycopersicum L.) root length density distribution were used for parameter optimization. A MATLAB program was developed by coupling the modified root diffusive model with the genetic algorithm to facilitate the parameters optimization. Results showed that the simulated root length density distribution at different times was in a good agreement with the observed values. The RMSE and bias values ranged from 0.22 to 0.25 cm cm–3 and from −3.0 to 24.5%, respectively. The modified diffusive root growth model can therefore be used to simulate the two‐dimensional root growth during the crop growing period.

Simulating alfalfa and pasture yields at regional and national scales in Canada from 1981 to 2019

CONTEXTThere has been a great deal of uncertainty in the reported alfalfa and pasture yields in the agricultural database as these commodities are typically used internally on farms and are not sold in the same way as other crops such as wheat, canola or corn. However, yields from alfalfa and pasture crops are important inputs (biological fixation) and outputs (feed) that can impact the estimates of agri-environmental indicators as the land area in Canada under these crops is very large. One of the indicators which uses this data is the residual soil nitrogen (RSN) which is the amount of inorganic N remaining in soils after harvest. High RSN levels can lead to impaired water quality (e.g., eutrophication) and global warming (N2O). Therefore, it is critical to obtain accurate alfalfa and pasture yield estimates. In previous versions of the Canadian Agricultural Nitrogen Budget Model (CANB), only provincial average yield values were used for alfalfa or pasture yields across all of the soils and landscapes. Soils, climates, and crop yields do vary within provinces, and a more accurate estimate of these yields over time and space is required. OBJECTIVEThe objective of this study was to collect recent published yield data and use the Environmental Policy Integrated Climate (EPIC) to estimate yields for the 2780 soil landscapes of Canada (SLC) polygons. METHODSThe EPIC model was used to calibrate and evaluate yields at field scale based on a total of 109, 59 and 47 treatments collected for alfalfa, improved and unimproved pasture yields, respectively, from Canadian publications. Due to satisfactory evaluation, we extended simulation to all SLC polygons based on SLC v3.2 soil and weather databases, representative crops as proxies for planting and harvesting date, and historical rates of fertilization. RESULTS AND CONCLUSIONSThe average simulated alfalfa, improved and unimproved pasture yields were 5.00, 3.85 and 1.22 Mg ha−1, respectively over the period from 1981 to 2019. Generally, the EPIC model showed reliable predictions for alfalfa and pasture yields under variable weather, soil, and management practices. SIGNIFICANCETherefore, it enabled us to provide improved estimates across soil landscapes polygons in Canada and thereby improve our agri-environmental indicator models.