Crop insurance loss data can illuminate variations in agricultural impacts from exposure to weather and climate-driven events, and can improve our understanding of agricultural vulnerabilities. Here we perform a retrospective analysis of weather and climate-driven reasons for crop loss (i.e. cause of loss) obtained from the Risk Management Agency of the United States Department of Agriculture. The federal crop insurance program has insured over $440 billion in liabilities representing farmers' crops from 2001 to 2016. Specifically, we examine the top ten weather and climate-driven causes of loss from 2001 to 2016 across the nation comprising at least 83% of total indemnities (i.e. insurance payouts provided to farmers after crop loss events). First, we analyzed the relative fraction of indemnities by causes of loss, over different spatial and temporal resolutions. We found that drought and excess precipitation comprised the largest sources of crop loss across the nation. However, these causes varied strongly over space and time. We applied two additional normalization techniques to indemnities using (1) insurance premia and the gross domestic product implicit price deflator, and (2) liabilities to calculate the loss cost. We conducted trend analyses using the Mann-Kendall statistical test on loss cost over time. Differential trends and patterns in loss cost demonstrated the importance of spatio-temporal resolution in assessing causes of loss. The majority of monthly significant trends (p<0.05) showed increasing loss cost (i.e. increasing indemnities or decreasing liabilities) in response to weather events. Finally, we briefly discuss an online portal (AgRisk Viewer) to make these data accessible at multiple spatial scales and sub-annual time steps to support both research and outreach efforts promoting adaptation and resilience in agricultural systems.

As managers of agricultural and natural resources are confronted with uncertainties in global change impacts, the complexities associated with the interconnected cycling of nitrogen, carbon, and water present daunting management challenges. Existing models provide detailed information on specific sub-systems (e.g., land, air, water, and economics). An increasing awareness of the unintended consequences of management decisions resulting from interconnectedness of these sub-systems, however, necessitates coupled regional earth system models (EaSMs). Decision makers' needs and priorities can be integrated into the model design and development processes to enhance decision-making relevance and "usability" of EaSMs. BioEarth is a research initiative currently under development with a focus on the U.S. Pacific Northwest region that explores the coupling of multiple stand-alone EaSMs to generate usable information for resource decision-making. Direct engagement between model developers and non-academic stakeholders involved in resource and environmental management decisions throughout the model development process is a critical component of this effort. BioEarth utilizes a bottom-up approach for its land surface model that preserves fine spatialscale sensitivities and lateral hydrologic connectivity, which makes it unique among many regional EaSMs. This paper describes the BioEarth initiative and highlights opportunities and challenges associated with coupling multiple stand-alone models to generate usable information for agricultural and natural resource decision-making.

Abstract.Regional climate change impact (CCI) studies have widely involved downscaling and bias correcting (BC) global climate model (GCM)-projected climate for driving land surface models. However, BC may cause uncertainties in projecting hydrologic and biogeochemical responses to future climate due to the impaired spatiotemporal covariance of climate variables and a breakdown of physical conservation principles. Here we quantify the impact of BC on simulated climate-driven changes in water variables (evapotranspiration (ET), runoff, snow water equivalent (SWE), and water demand for irrigation), crop yield, biogenic volatile organic compounds (BVOC), nitric oxide (NO) emissions, and dissolved inorganic nitrogen (DIN) export over the Pacific Northwest (PNW) region. We also quantify the impacts on net primary production (NPP) over a small watershed in the region (HJ-Andrews). Simulation results from the coupled ECHAM5-MPI-OM model with A1B emission scenario were first dynamically downscaled to 12 km resolution with the WRF model. Then a quantile-mapping-based statistical downscaling model was used to downscale them into 1/16 • resolution daily climate data over historical and future periods. Two climate data series were generated, with bias correction (BC) and without bias correction (NBC). Impact models were then applied to estimate hydrologic and biogeochemical responses to both BC and NBC meteorological data sets. These impact models include a macroscale hydrologic model (VIC), a coupled cropping system model (VICCropSyst), an ecohydrological model (RHESSys), a biogenic emissions model (MEGAN), and a nutrient export model (Global-NEWS).Results demonstrate that the BC and NBC climate data provide consistent estimates of the climate-driven changes in water fluxes (ET, runoff, and water demand), VOCs (isoprene and monoterpenes) and NO emissions, mean crop yield, and river DIN export over the PNW domain. However, significant differences rise from projected SWE, crop yield from dry lands, and HJ-Andrews's ET between BC and NBC data. Even though BC post-processing has no significant impacts on most of the studied variables when taking PNW as a whole, their effects have large spatial variations and some local areas are substantially influenced. In addition, there are months during which BC and NBC post-processing produces significant differences in projected changes, such as summer runoff. Factor-controlled simulations indicate that BC post-processing of precipitation and temperature both substantially contribute to these differences at regional scales.We conclude that there are trade-offs between using BC climate data for offline CCI studies versus directly modeled climate data. These trade-offs should be considered when designing integrated modeling frameworks for specific applications; for example, BC may be more important when considering impacts on reservoir operations in mountainPublished by Copernicus Publications on behalf of the European Geosciences Union. M. Liu et al.: What is the importance of climate mod...

The nitrogen (N) dilution curve is a useful tool for farmers to assess the effectiveness of fertilizer application. The N dilution curve describes the decrease in plant N as biomass increases. This concept has not yet been tested for its applicability and robustness under different cutting regimes in grasslands. We conducted a principal components analysis on biomass yield and N concentration data to discern relationships among experimental, climatic, and management factors. Next, two N dilution curve parameters were calibrated for different cutting frequencies. We compared N uptake using four different methods utilizing calibrated N dilution curves, a reference curve, and different cutting regimes representing different physiological ages of the crops at cutting. Our results show that excluding cutting frequency information overestimates values of N uptake. Calibration of the N dilution curve according to cutting frequency improves N uptake estimates relative to observed values. Therefore, N uptake is better estimated using both the N dilution curve and the cutting regime information.

Climate change is increasing mean and extreme temperatures in the Southwestern United States, leading to a suite of changes affecting agricultural production. These include changes in water, soils, pathogens, weeds, and pests comprising the production environment. The aim of this synthesis is to describe the anticipated leading agricultural pressures and adaptive responses, many of which are near-term actions with longer-term consequences. In the semiarid Southwestern United States, climate change is expected to increase water scarcity. Surface water shortage is the leading reason for recent diminished crop yields in the Southwest. Drought and lack of water represent the leading regional weather-related cause of crop loss from 1989 to 2017. Thus, water scarcity has been and will continue to be a critical factor leading to regional crop vulnerability. Soils, pathogens, weeds, and insects are components of the agricultural production environment and are directly influenced by near-term weather and long-term climate conditions. Field crops, vegetable crops, and perennial crops have unique production requirements and diverse management options, many already used in farm management, to cope with production environment changes to build climate resilience. Farmers and ranchers continuously respond to changing conditions on a near-term basis. Long-term planning and novel adaptation measures implemented may now build nimble and responsive systems and communities able to cope with future conditions. While decision-support tools and resources are providing increasingly sophisticated approaches to cope with production in the 21st century, we strive to keep pace with the cascading barrage of inter-connected agricultural challenges.

There is increasing enthusiasm around the concept of soil health, and as a result, new public and private initiatives are being developed to increase soil health-related practices on working lands in the United States. In addition, billions of U.S. public dollars are dedicated annually toward soil conservation programs, and yet, it is not well quantified how investment in conservation programs improve soil health and, more broadly, environmental health. The Environmental Quality Incentives Program (EQIP) is one of the major U.S. public conservation programs administered on privately managed lands for which public data are available. In this research, we developed a multi-dimensional classification system to evaluate over 300 EQIP practices to identify to what extent practices have the potential to improve different aspects of soil and environmental health. Using available descriptions and expert opinion, these practices were evaluated with a classification system based on the practice's potential to exhibit the following environmental health outcomes: (i) principles of soil health to reduce soil disturbance and increase agrobiodiversity; (ii) a transition to ecologically-based management to conserve soil, water, energy and biological resources; and (iii) adaptive strategy to confer agroecosystem resilience. Further, we analyzed nearly $7 billion U.S. dollars of financial assistance dedicated to these practices from 2009 through 2018 to explore the potential of these investments to generate environmental health outcomes. We identified nine practices that fit the highest level of potential environmental health outcomes in our classification systems. These included wetlands and agroforestry related practices, demonstrating that ecologically complex practices can provide the broadest benefits to environmental health. Practices with the greatest potential to improve environmental health in our classification system represent 2–27% of annual EQIP funding between 2009 and 2018. In fiscal year 2018, these practices represented between $13 and 121 million, which represented ~0.08% of total annual USDA expenditures. These classifications and the subsequent funding analysis provide evidence that there is tremendous untapped potential for conservation programs to confer greater environmental health in U.S. agriculture. This analysis provides a new framework for assessing conservation investments as a driver for transformative agricultural change.

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Assessing regional-scale vulnerability of agricultural systems to climate change and variability is vital in securing food and fiber systems, as well as sustaining rural livelihoods. Farmers, ranchers, and forest landowners rely on science-based, decision-relevant, and localized information to maintain production, ecological viability, and economic returns. This paper synthesizes the collection of research on the future of agricultural production in the Southwestern United States. A variety of assessment methods indicate the diverse impacts and risks across the Southwest, often related to water availability, which drives adaptive measures in this region. Sector-or species-specific adaptive measures have long been practiced in this region and will continue to be necessary to support agricultural production as a regional enterprise. Diversification of crop selection and income source imparts climate resilience. Building upon biophysical vulnerability through incorporating social and economic factors is critical to future adaptation planning efforts. The persistence and adaptive capacity of agriculture in the waterClimatic Change (2018) 148:339-353 https://doi