The Global Gridded Crop Model Intercomparison (GGCMI) phase 1 dataset of the Agricultural Model Intercomparison and Improvement Project (AgMIP) provides an unprecedentedly large dataset of crop model simulations covering the global ice-free land surface. The dataset consists of annual data fields at a spatial resolution of 0.5 arc-degree longitude and latitude. Fourteen crop modeling groups provided output for up to 11 historical input datasets spanning 1901 to 2012, and for up to three different management harmonization levels. Each group submitted data for up to 15 different crops and for up to 14 output variables. All simulations were conducted for purely rainfed and near-perfectly irrigated conditions on all land areas irrespective of whether the crop or irrigation system is currently used there. With the publication of the GGCMI phase 1 dataset we aim to promote further analyses and understanding of crop model performance, potential relationships between productivity and environmental impacts, and insights on how to further improve global gridded crop model frameworks. We describe dataset characteristics and individual model setup narratives.

/pdf/the-global-gridded-crop-model-intercomparison-phase-1-1sm4ewck2e.pdf

The Global Gridded Crop Model Intercomparison phase 1 simulation dataset

Cropping intensity is one of the most important decisions made independently by farmers in Vietnam. It is a crucial variable of various economic and process-based models. Rice is grown under irrigated triple- and double-rice cropping systems and a rainfed single-rice cropping system in the Vietnamese Mekong Delta (VMD). These rice cropping systems are adopted according to the geographical location and water infrastructure. However, little work has been done to map triple-cropping of rice using Sentinel-1 along with the effects of water infrastructure on the rice cropping intensity decision. This study is focused on monitoring rice cropping patterns in the An Giang province of the VMD from March 2017 to March 2018. The fieldwork was carried out on the dates close to the Sentinel-1A acquisition. The results of dual-polarized (VV and VH) Sentinel-1A data show a strong correlation with the spatial patterns of various rice growth stages and their association with the water infrastructure. The VH backscatter (σ°) is strongly correlated with the three rice growth stages, especially the reproductive stage when the backscatter is less affected by soil moisture and water in the rice fields. In all three cropping patterns, σ°VV and σ°VH show the highest value in the maturity stage, often appearing 10 to 12 days before the harvesting of the rice. A rice cropping pattern map was generated using the Support Vector Machine (SVM) classification of Sentinel-1A data. The overall accuracy of the classification was 80.7% with a 0.78 Kappa coefficient. Therefore, Sentinel-1A can be used to understand rice phenological changes as well as rice cropping systems using radar backscattering.

/pdf/monitoring-and-mapping-of-rice-cropping-pattern-in-flooding-5369sage68.pdf

Monitoring and Mapping of Rice Cropping Pattern in Flooding Area in the Vietnamese Mekong Delta Using Sentinel-1A Data: A Case of An Giang Province

Ammonia surface flux is bidirectional; that is, net flux can be either upward or downward. In fertilized agricultural croplands and grasslands there is usually more emission than deposition especially in midday during warmer seasons. In North America, most of the ammonia emissions are from agriculture with a significant fraction of that coming from fertilizer. A new bidirectional ammonia flux modeling system has been developed in the Community Multiscale Air Quality (CMAQ) model, which has close linkages with the Environmental Policy Integrated Climate (EPIC) agricultural ecosystem model. Daily inputs from EPIC are used to calculate soil ammonia concentrations that are combined with air concentrations in CMAQ to calculate bidirectional surface flux. The model is evaluated against surface measurements of NH3 concentrations, NH4+ and SO42- aerosol concentrations, NH4+ wet deposition measurements, and satellite retrievals of NH3 concentrations. The evaluation shows significant improvement over the base model without bidirectional ammonia flux. Comparisons to monthly average satellite retrievals show similar spatial distribution with the highest ammonia concentrations in the Central Valley of California (CA), the Snake River valley in Idaho, and the western High Plains. In most areas the model underestimates, but in the Central Valley of CA, it generally overestimates ammonia concentration. Case study analyses indicate that modeled high fluxes of ammonia in CA are often caused by anomalous high soil ammonia loading from EPIC for particular crop types. While further improvements to parameterizations in EPIC and CMAQ are recommended, this system is a significant advance over previous ammonia bidirectional surface flux models.

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2019MS001728

New Bidirectional Ammonia Flux Model in an Air Quality Model Coupled With an Agricultural Model.

Efficiency and feasibility of Best Management Practices to reduce nutrient loads in an agricultural river basin

We review concepts that have been used to address uncertainty in integrated modeling. Although conceptual approaches to tackle uncertainty seem diverse, a synthesis reveals more similarities than differences in the concepts published, especially if one adopts a model application-oriented focus. Given our findings, we slightly adapt the uncertainty framework table (UFT) developed by Walker et al. (2003) and apply it for an existing integrated modeling framework for the bio-economy. The use of color coded UFTs with a graphical visualization of uncertainty propagation pathways has facilitated a better overview and communication of uncertainties and uncertainty propagation pathways among the authors. Systematic consideration of uncertainties in integrated modeling may allow for more trust in model results and, if simulations are conducted, for a better robustness check of policy interventions.

Uncertainty Concepts for Integrated Modeling - Review and Application for Identifying Uncertainties and Uncertainty Propagation Pathways

Will soil organic carbon (SOC) and yields increase for conservation management systems in tropical zones in response to the next 100 years? To answer the question, the Environmental Policy Integrated Climate (EPIC) model was used to study the effects of climate change, cropping systems, conservation agriculture (CA) and conservation tillage management practices on SOC and crop productivity in Kampong Cham, Cambodia. The EPIC model was successfully calibrated and validated for crop yields, biomass, SOC and nitrogen based on field data from a five-year field experiment. Historical weather (1994–2013) was used for baseline assessment versus mid-century (2046–2064) and late-century (2081–2100) climate projections generated by the Geophysical Fluids Dynamics Laboratory (GFDL) CM2.1 global climate model. The simulated results showed that upland rice yield would increase the most under the B1 scenario in mid-century for all treatments, followed by soybean and maize. Cassava yield only increased under CA treatment when cultivated as a continuous primary crop. Carbon sequestration was more sensitive to cropping systems and crop rotation than climate change. The results indicated that the rotated CA primary crop (maize) systems should be prioritized for SOC sequestration as well as for increasing crop productivity. In addition, rice systems may increase SOC compared to soybean and cassava.

Evaluation of Long-Term SOC and Crop Productivity within Conservation Systems Using GFDL CM2.1 and EPIC

We propose VSHC for calculating soil water percolation in continuous models. VSHC relates permeability to daily hydraulic properties to estimate hydraulic conductivity. VSHC performed well at field scale and improved estimates of peak streamflows.

The Variable Saturation Hydraulic Conductivity Method for Improving Soil Water Content Simulation in EPIC and APEX Models

The Conservation Effects Assessment Project (CEAP) was established to develop a scientific understanding and methodology for estimating environmental benefits and effects of conservation practices on agricultural landscapes at watershed, regional, and national scales. CEAP9s goal is to improve the effectiveness of conservation practices and programs by quantifying conservation effects and providing the science and education needed to enrich conservation planning, implementation, management decisions, and policy. Field observations and computer-based simulation of the effects of agricultural conservation are important components of CEAP. This research supports the CEAP effort by utilizing well-documented field data in conjunction with the Agricultural Policy/Environmental eXtender (APEX) model to quantify long-term conservation effects. In this study, field data collected at a research site located near Tifton, Georgia, were used to evaluate APEX and to quantify long-term benefits of implementing conservation tillage in the Atlantic Coastal Plain region of the United States. The objectives of this research were to (1) quantify differences in crop yield, hydrology, and sediment transport for conventional and conservation tillage systems in the Atlantic Coastal Plain; (2) develop a calibrated APEX simulation for these tillage systems; and (3) quantify the performance of the APEX model with respect to crop yields, hydrology, and sediment yield. Fourteen years of crop yield, surface runoff, subsurface flow, and sediment transport data were quantified comparing conventional tillage (CT) to strip tillage (ST). No treatment differences were found for either cotton (Gossypium hirsutum L.) or peanut (Arachis hypogea L.) yields. Surface runoff from the CT was found to be 1.7 times that of the ST, while subsurface flow from the ST was found to be 1.7 times that of CT. Total water loss (surface and subsurface) was nearly equivalent for the two systems: 30% of annual rainfall. Satisfactory model performance was found for APEX surface runoff simulations. Mixed results were found for model performance of crop yield while model simulations of subsurface flow and sediment yield were less than satisfactory. The APEX modeling framework provides a useful tool for assessing crop yield and hydrologic differences between tillage management systems in the Atlantic Coastal Plain. Additional refinement of modeling approaches may be necessary to adequately represent subsurface flow and sediment transport in these same systems.

https://www.jswconline.org/content/jswc/75/3/400.full.pdf

Conservation tillage effects in the Atlantic Coastal Plain: An APEX examination

Development of an EPIC parallel computing framework to facilitate regional/global gridded crop modeling with multiple scenarios: A case study of the United States

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Evaluation of Long-Term SOC and Crop Productivity within Conservation Systems Using GFDL CM2.1 and EPIC

The Variable Saturation Hydraulic Conductivity Method for Improving Soil Water Content Simulation in EPIC and APEX Models

Conservation tillage effects in the Atlantic Coastal Plain: An APEX examination

Development of an EPIC parallel computing framework to facilitate regional/global gridded crop modeling with multiple scenarios: A case study of the United States