Climate Impacts on Agriculture: Implications for Forage and Rangeland Production

Projections of temperature and precipitation patterns across the United States during the next 50 yr anticipate a 1.5 to 2°C warming and a slight increase in precipitation as a result of global climate change. There have been relatively few studies of climate change effects on pasture and rangeland (grazingland) species compared to those on crop species, despite the economic and ecological importance of the former. Here we review the literature on responses of pastureland and rangeland species to rising atmospheric CO2 and climate change (temperature and precipitation) and discuss plant and management factors likely to influence pastureland and rangeland responses to change (e.g., community composition, plant competition, perennial growth habit, seasonal productivity, and management methods). Overall, the response of pastureland and rangeland species to increased [CO2] is consistent with the general responses of C3 and C4 vegetation, although exceptions exist. Both pastureland and rangeland species may experience accelerated metabolism and advanced development with rising temperature, often resulting in a longer growing season. However, soil resources will often constrain temperature effects. In general, it is expected that increases in [CO2] and precipitation will enhance rangeland net primary production (NPP) whereas increased air temperatures will either increase or decrease NPP. Much of the uncertainty in predicting how pastureland and rangeland species will respond to climate change is due to uncertainty in future projections of precipitation, both globally and regionally. This review reveals the need for comprehensive studies of climate change impacts on pastureland and rangeland ecosystems that include an assessment of the mediating effects of grazing regimes and mutualistic relationships (e.g., plant roots-nematodes; N-fixing organisms) as well as changes in water, carbon, and nutrient cycling

Climate Impacts on Agriculture: Implications for Crop Production

Changes in temperature, CO2, and precipitation under the scenarios of climate change for the next 30 yr present a challenge to crop production. This review focuses on the impact of temperature, CO2, and ozone on agronomic crops and the implications for crop production. Understanding these implications for agricultural crops is critical for developing cropping systems resilient to stresses induced by climate change. There is variation among crops in their response to CO2, temperature, and precipitation changes and, with the regional differences in predicted climate, a situation is created in which the responses will be further complicated. For example, the temperature effects on soybean [Glycine max (L.) Merr.] could potentially cause yield reductions of 2.4% in the South but an increase of 1.7% in the Midwest. The frequency of years when temperatures exceed thresholds for damage during critical growth stages is likely to increase for some crops and regions. The increase in CO2 contributes significantly to enhanced plant growth and improved water use efficiency (WUE); however, there may be a downscaling of these positive impacts due to higher temperatures plants will experience during their growth cycle. A challenge is to understand the interactions of the changing climatic parameters because of the interactions among temperature, CO2, and precipitation on plant growth and development and also on the biotic stresses of weeds, insects, and diseases. Agronomists will have to consider the variations in temperature and precipitation as part of the production system if they are to ensure the food security required by an ever increasing population

4EHP and GIGYF1/2 mediate translation-coupled messenger RNA decay

Current models of mRNA turnover indicate that cytoplasmic degradation is coupled with translation. However, our understanding of the molecular events that coordinate ribosome transit with the mRNA decay machinery is still limited. Here, we show that 4EHP-GIGYF1/2 complexes trigger co-translational mRNA decay. Human cells lacking these proteins accumulate mRNAs with prominent ribosome pausing. They include, among others, transcripts encoding secretory and membrane-bound proteins or tubulin subunits. In addition, 4EHP-GIGYF1/2 complexes fail to reduce mRNA levels in the absence of ribosome stalling or upon disruption of their interaction with the cap structure, DDX6, and ZNF598. We further find that co-translational binding of GIGYF1/2 to the mRNA marks transcripts with perturbed elongation to decay. Our studies reveal how a repressor complex linked to neurological disorders minimizes the protein output of a subset of mRNAs

Regional scale cropland carbon budgets: Evaluating a geospatial agricultural modeling system using inventory data

Accurate quantification and clear understanding of regional scale cropland carbon (C) cycling is critical for designing effective policies and management practices that can contribute toward stabilizing atmospheric CO2 concentrations. However, extrapolating site-scale observations to regional scales represents a major challenge confronting the agricultural modeling community. This study introduces a novel geospatial agricultural modeling system (GAMS) exploring the integration of the mechanistic Environmental Policy Integrated Climate model, spatially-resolved data, surveyed management data, and supercomputing functions for cropland C budgets estimates. This modeling system creates spatiallyexplicit modeling units at a spatial resolution consistent with remotely-sensed crop identification and assigns cropping systems to each of them by geo-referencing surveyed crop management information at the county or state level. A parallel computing algorithm was also developed to facilitate the computationally intensive model runs and output post-processing and visualization. We evaluated GAMS against National Agricultural Statistics Service (NASS) reported crop yields and inventory estimated county-scale cropland C budgets averaged over 2000e2008. We observed good overall agreement, with spatial correlation of 0.89, 0.90, 0.41, and 0.87, for crop yields, Net Primary Production (NPP), Soil Organic C (SOC) change, and Net Ecosystem Exchange (NEE), respectively. However, we also detected notable differences in the magnitude of NPP and NEE, as well as in the spatial pattern of SOC change. By performing crop-specific annual comparisons, we discuss possible explanations for the discrepancies between GAMS and the inventory method, such as data requirements, representation of agroecosystem processes, completeness and accuracy of crop management data, and accuracy of crop area representation. Based on these analyses, we further discuss strategies to improve GAMS by updating input data and by designing more efficient parallel computing capability to quantitatively assess errors associated with the simulation of C budget components. The modularized design of the GAMS makes it flexible to be updated and adapted for different agricultural models so long as they require similar input data, and to be linked with socio-economic models to understand the effectiveness and implications of diverse C management practices and policies

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

[No abstract available

Framing sustainability in a telecoupled world.

Interactions between distant places are increasingly widespread and influential, often leading to unexpected outcomes with profound implications for sustainability. Numerous sustainability studies have been conducted within a particular place with little attention to the impacts of distant interactions on sustainability in multiple places. although distant forces have been studied, they are usually treated as exogenous variables and feedbacks have rarely been considered. To understand and integrate various distant interactions better, we propose an integrated framework based on telecoupling, an umbrella concept that refers to socioeconomic and environmental interactions over distances. The concept of telecoupling is a logical extension of research on coupled human and natural systems, in which interactions occur within particular geographic locations. The telecoupling framework contains five major interrelated components, i.e., coupled human and natural systems, flows, agents, causes, and effects. We illustrate the framework using two examples of distant interactions associated with trade of agricultural commodities and invasive species, highlight the implications of the framework, and discuss research needs and approaches to move research on telecouplings forward. The framework can help to analyze system components and their interrelationships, identify research gaps, detect hidden costs and untapped benefits, provide a useful means to incorporate feedbacks as well as trade-offs and synergies across multiple systems (sending, receiving, and spillover systems), and improve the understanding of distant interactions and the effectiveness of policies for socioeconomic and environmental sustainability from local to global levels

Simulating Potential Switchgrass Production in the United States

Using results from field trials of switchgrass (Panicum virgatum L.) in the United States, the EPIC (Environmental Policy Integrated Climate) process-level agroecosystem model was calibrated, validated, and applied to simulate potential productivity of switchgrass for use as a biofuel feedstock. The model was calibrated with a regional study of 10-yr switchgrass field trials and subsequently tested against a separate compiled dataset of field trials from across the eastern half of the country. An application of the model in a national database using 8-digit watersheds as the primary modeling unit produces 30-yr average switchgrass yield estimates that can be aggregated to 18 major watersheds. The model projects average annual switchgrass productivity of greater than 7 Mg ha-1 in the Upper Mississippi, Lower Mississippi, and Ohio watersheds. The major factors limiting simulated production vary by region; low precipitation is the primary limiting factor across the western half of the country, while moderately acidic soils limit yields on lands east of the Mississippi River. Average projected switchgrass production on all crop land in the continental US is 5.6 Mg ha-1. At this level of productivity, 28.6 million hectares of crop land would be required to produce the 16 billion gallons of cellulosic ethanol called for by 2022 in the 2007 Energy Independence and Security Act. The model described here can be applied as a tool to inform the land-use and environmental consequences of switchgrass production

Uncertainties in global crop model frameworks: effects of cultivar distribution, crop management and soil handling on crop yield estimates

Global gridded crop models (GGCMs) combine field-scale agronomic models or sets of plant growth algorithms with gridded spatial input data to estimate spatially explicit crop yields 40 and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different bio-physical models, setups, and input data. While algorithms have been in the focus of recent GGCM comparisons, this study investigates differences in maize and wheat 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 45 Intercomparison (GGCMI) project. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, geographic distribution of cultivars, and selection of subroutines e.g. for the estimation of potential evapotranspiration or soil erosion. The analyses reveal long-term trends and inter-annual yield variability in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. Absolute yield levels as well depend not only on nutrient supply but 50 also on the parameterization and distribution of crop cultivars. All GGCMs show an intermediate performance in reproducing reported absolute yield levels or inter-annual dynamics. Our findings suggest that studies focusing on the evaluation of differences in bio-physical routines may require further harmonization of input data and management assumptions in order to eliminate background noise resulting from differences in model setups. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions 55 in setups appears the best solution for bracketing such uncertainties as long as comprehensive global datasets taking into account regional differences in crop management, cultivar distributions and coefficients for parameterizing agro-environmental processes are lacking. Finally, we recommend improvements in the documentation of setups and input data of GGCMs in order to allow for sound interpretability, comparability and reproducibility of published results

Substantial Differences in Crop Yield Sensitivities Between Models Call for Functionality‐Based Model Evaluation

Crop models are often used to project future crop yield under climate and global change and typically show a broad range of outcomes. To understand differences in modeled responses, we analyzed modeled crop yield response types using impact response surfaces along four drivers of crop yield: carbon dioxide (C), temperature (T), water (W), and nitrogen (N). Crop yield response types help to understand differences in simulated responses per driver and their combinations rather than aggregated changes in yields as the result of simultaneous changes in various drivers. We find that models' sensitivities to the individual drivers are substantially different and often more different across models than across regions. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response types across models and their configurations suggests that models need to undergo further scrutiny. We suggest establishing standards in model evaluation based on emergent functionality not only against historical yield observations but also against dedicated experiments across different drivers to analyze emergent functional patterns of crop models