Global irrigation contribution to wheat and maize yield.

Irrigation is the largest sector of human water use and an important option for increasing crop production and reducing drought impacts. However, the potential for irrigation to contribute to global crop yields remains uncertain. Here, we quantify this contribution for wheat and maize at global scale by developing a Bayesian framework integrating empirical estimates and gridded global crop models on new maps of the relative difference between attainable rainfed and irrigated yield (ΔY). At global scale, ΔY is 34 ± 9% for wheat and 22 ± 13% for maize, with large spatial differences driven more by patterns of precipitation than that of evaporative demand. Comparing irrigation demands with renewable water supply, we find 30-47% of contemporary rainfed agriculture of wheat and maize cannot achieve yield gap closure utilizing current river discharge, unless more water diversion projects are set in place, putting into question the potential of irrigation to mitigate climate change impacts.

Carbon-Negative Biofuel Production.

Achievement of the 1.5 °C limit for global temperature increase relies on the large-scale deployment of carbon dioxide removal (CDR) technologies. In this article, we explore two CDR technologies: soil carbon sequestration (SCS), and carbon capture and storage (CCS) integrated with cellulosic biofuel production. These CDR technologies are applied as part of decentralized biorefinery systems processing corn stover and unfertilized switchgrass grown in riparian zones in the Midwestern United States. Cover crops grown on corn-producing lands are chosen from the SCS approach, and biogenic CO2 in biorefineries is captured, transported by pipeline, and injected into saline aquifers. The decentralized biorefinery system using SCS, CCS, or both can produce carbon-negative cellulosic biofuels (≤-22.2 gCO2 MJ-1). Meanwhile, biofuel selling prices increase by 15-45% due to CDR costs. Economic incentives (e.g., cover crop incentives and/or a CO2 tax credit) can mitigate price increases caused by CDR technologies. A combination of different CDR technologies in decentralized biorefinery systems is the most efficient method for greenhouse gas (GHG) mitigation, and its total GHG mitigation potential in the Midwest is 0.16 GtCO2 year-1.

Sustainable feedstock for bioethanol production: Impact of spatial resolution on the design of a sustainable biomass supply-chain.

This study assesses the role of spatial-resolution and spatial-variations in environmental impacts estimation and decision-making for corn-stover harvesting to produce biofuels. Geospatial corn-stover yields and environmental impacts [global warming potential (GWP), eutrophication, and soil-loss] dataset for two study areas in Wisconsin and Michigan were generated through Environmental Policy Integrated Climate (EPIC) model and aggregated at different spatial-resolutions (i.e., 100; 1000; 10,000 ha). For each spatial-resolution, decision-making was accomplished using an optimization routine to minimize different environmental impacts associated with harvesting stover to meet varied biomass demands. The results of the study showed that selective harvesting at higher-resolution (or lower-aggregation level) can result in significantly lower environmental impacts, especially at low stover demand levels. Additionally, the increased spatial resolution had more impact in minimizing the environmental impacts of corn stover harvest under a more variable landscape such as terrains and its influences are more pronounced for soil-loss and eutrophication potential compared to GWP.

Parameterization-induced uncertainties and impacts of crop management harmonization in a global gridded crop model ensemble.

Global gridded crop models (GGCMs) combine agronomic or plant growth models with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different biophysical models, setups, and input data. GGCM ensembles are frequently employed to bracket uncertainties in impact studies without investigating the causes of divergence in outputs. This study explores differences in maize yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison initiative. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, and selection of subroutines affecting crop yield estimates via cultivar distributions, soil attributes, and hydrology among others. The analyses reveal inter-annual yield variability and absolute yield levels in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. All GGCMs show an intermediate performance in reproducing reported yields with a higher skill if a static soil profile is assumed or sufficient plant nutrients are supplied. An in-depth comparison of setup domains for two EPIC-based GGCMs shows that GGCM performance and plant stress responses depend substantially on soil parameters and soil process parameterization, i.e. hydrology and nutrient turnover, indicating that these often neglected domains deserve more scrutiny. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for coping with uncertainties from lack of comprehensive global data on crop management, cultivar distributions and coefficients for agro-environmental processes. However, the underlying assumptions require systematic specifications to cover representative agricultural systems and environmental conditions. Furthermore, the interlinkage of parameter sensitivity from various domains such as soil parameters, nutrient turnover coefficients, and cultivar specifications highlights that global sensitivity analyses and calibration need to be performed in an integrated manner to avoid bias resulting from disregarded core model domains. Finally, relating evaluations of the EPIC-based GGCMs to a wider ensemble based on individual core models shows that structural differences outweigh in general differences in configurations of GGCMs based on the same model, and that the ensemble mean gains higher skill from the inclusion of structurally different GGCMs. Although the members of the wider ensemble herein do not consider crop-soil-management interactions, their sensitivity to nutrient supply indicates that findings for the EPIC-based sub-ensemble will likely become relevant for other GGCMs with the progressing inclusion of such processes.

The Global Gridded Crop Model Intercomparison phase 1 simulation dataset.

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.

The Renewable Fuel Standard May Limit Overall Greenhouse Gas Savings by Corn Stover-Based Cellulosic Biofuels in the U.S. Midwest: Effects of the Regulatory Approach on Projected Emissions.

The Renewable Fuel Standard (RFS) program specifies a greenhouse gas (GHG) reduction threshold for cellulosic biofuels, while the Low Carbon Fuel Standard (LCFS) program in California does not. Here, we investigate the effects of the GHG threshold under the RFS on projected GHG savings from two corn stover-based biofuel supply chain systems in the United States Midwest. The analysis is based on a techno-economic framework that minimizes ethanol selling price. The GHG threshold lowers the lifecycle GHG of ethanol: 34.39 ± 4.92 gCO2 MJ-1 in the RFS-compliant system and 46.30 ± 10.05 gCO2 MJ-1 in the non RFS-compliant system. However, hypothetical biorefinery systems complying with the RFS will not process the more GHG-intensive corn stover, and thus much less biofuel will be produced compared to the non RFS-compliant system. Thus, taken as a whole, the non RFS-compliant system would achieve more GHG savings than an RFS-compliant system: 10.7 TgCO2 year-1 in the non RFS-compliant system compared with 4.4 TgCO2 year-1 in the RFS-compliant system. These results suggest that the current RFS GHG reduction threshold may not be the most efficient way to carry out the purposes of the Energy Security and Independence Act in the corn stover-based biofuel system: relaxing the threshold could actually increase the overall GHG savings from corn stover-based biofuels. Therefore, the LCFS-type regulatory approach is recommended for the corn stover-based cellulosic biofuel system under the RFS program. In addition, our calculation of the GHG balance for stover-based biofuel accounts for SOC losses, while the current RFS estimates do not include effects on SOC.

Global patterns of crop yield stability under additional nutrient and water inputs.

Agricultural production must increase to feed a growing and wealthier population, as well as to satisfy increasing demands for biomaterials and biomass-based energy. At the same time, deforestation and land-use change need to be minimized in order to preserve biodiversity and maintain carbon stores in vegetation and soils. Consequently, agricultural land use needs to be intensified in order to increase food production per unit area of land. Here we use simulations of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 to assess implications of input-driven intensification (water, nutrients) on crop yield and yield stability, which is an important aspect in food security. We find region- and crop-specific responses for the simulated period 1980-2009 with broadly increasing yield variability under additional nitrogen inputs and stabilizing yields under additional water inputs (irrigation), reflecting current patterns of water and nutrient limitation. The different models of the GGCMI ensemble show similar response patterns, but model differences warrant further research on management assumptions, such as variety selection and soil management, and inputs as well as on model implementation of different soil and plant processes, such as on heat stress, and parameters. Higher variability in crop productivity under higher fertilizer input will require adequate buffer mechanisms in trade and distribution/storage networks to avoid food price volatility.