Agricultural breadbaskets shift poleward given adaptive farmer behavior under climate change.

Modern food production is spatially concentrated in global "breadbaskets." A major unresolved question is whether these peak production regions will shift poleward as the climate warms, allowing some recovery of potential climate-related losses. While agricultural impacts studies to date have focused on currently cultivated land, the Global Gridded Crop Model Intercomparison Project (GGCMI) Phase 2 experiment allows us to assess changes in both yields and the location of peak productivity regions under warming. We examine crop responses under projected end of century warming using seven process-based models simulating five major crops (maize, rice, soybeans, and spring and winter wheat) with a variety of adaptation strategies. We find that in no-adaptation cases, when planting date and cultivar choices are held fixed, regions of peak production remain stationary and yield losses can be severe, since growing seasons contract strongly with warming. When adaptations in management practices are allowed (cultivars that retain growing season length under warming and modified planting dates), peak productivity zones shift poleward and yield losses are largely recovered. While most growing-zone shifts are ultimately limited by geography, breadbaskets studied here move poleward over 600 km on average by end of the century under RCP 8.5. These results suggest that agricultural impacts assessments can be strongly biased if restricted in spatial area or in the scope of adaptive behavior considered. Accurate evaluation of food security under climate change requires global modeling and careful treatment of adaptation strategies.

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.

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.

Underestimation of N2 O emissions in a comparison of the DayCent, DNDC, and EPIC models.

Process-based models are increasingly used to study agroecosystem interactions and N2 O emissions from agricultural fields. The widespread use of these models to conduct research and inform policy benefits from periodic model comparisons that assess the state of agroecosystem modeling and indicate areas for model improvement. This work provides an evaluation of simulated N2 O flux from three process-based models: DayCent, DNDC, and EPIC. The models were calibrated and validated using data collected from two research sites over five years that represent cropping systems and nitrogen fertilizer management strategies common to dairy cropping systems. We also evaluated the use of a multi-model ensemble strategy, which inconsistently outperformed individual model estimations. Regression analysis indicated a cross-model bias to underestimate high magnitude daily and cumulative N2 O flux. Model estimations of observed soil temperature and water content did not sufficiently explain model underestimations, and we found significant variation in model estimates of heterotrophic respiration, denitrification, soil NH4+ , and soil NO3- , which may indicate that additional types of observed data are required to evaluate model performance and possible biases. Our results suggest a bias in the model estimation of N2 O flux from agroecosystems that limits the extension of models beyond calibration and as instruments of policy development. This highlights a growing need for the modeling and measurement communities to collaborate in the collection and analysis of the data necessary to improve models and coordinate future development.