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Title: Projecting corn and soybeans yields under climate change in a Corn Belt watershed

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Resource Type:
Journal Article: Publisher's Accepted Manuscript
Journal Name:
Agricultural Systems
Additional Journal Information:
Journal Volume: 152; Journal Issue: C; Related Information: CHORUS Timestamp: 2017-12-20 22:49:53; Journal ID: ISSN 0308-521X
Country of Publication:
United Kingdom

Citation Formats

Bhattarai, Mukesh Dev, Secchi, Silvia, and Schoof, Justin. Projecting corn and soybeans yields under climate change in a Corn Belt watershed. United Kingdom: N. p., 2017. Web. doi:10.1016/j.agsy.2016.12.013.
Bhattarai, Mukesh Dev, Secchi, Silvia, & Schoof, Justin. Projecting corn and soybeans yields under climate change in a Corn Belt watershed. United Kingdom. doi:10.1016/j.agsy.2016.12.013.
Bhattarai, Mukesh Dev, Secchi, Silvia, and Schoof, Justin. Wed . "Projecting corn and soybeans yields under climate change in a Corn Belt watershed". United Kingdom. doi:10.1016/j.agsy.2016.12.013.
title = {Projecting corn and soybeans yields under climate change in a Corn Belt watershed},
author = {Bhattarai, Mukesh Dev and Secchi, Silvia and Schoof, Justin},
abstractNote = {},
doi = {10.1016/j.agsy.2016.12.013},
journal = {Agricultural Systems},
number = C,
volume = 152,
place = {United Kingdom},
year = {Wed Mar 01 00:00:00 EST 2017},
month = {Wed Mar 01 00:00:00 EST 2017}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1016/j.agsy.2016.12.013

Citation Metrics:
Cited by: 4works
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  • Cited by 4
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  • 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)more » 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 firstly dynamically downscaled to 12 km resolutions with WRF model. Then a quantile mapping based statistical downscaling model was used to downscale them into 1/16th degree resolution daily climate data over historical and future periods. Two series climate data were generated according to the option of bias-correction (i.e. 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 datasets. These im20 pact models include a macro-scale hydrologic model (VIC), a coupled cropping system model (VIC-CropSyst), an ecohydrologic 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 region scales. We conclude that there are trade-offs between using BC climate data for offline CCI studies vs. direct modeled climate data. These trade-offs should be considered when designing integrated modeling frameworks for specific applications; e.g., BC may be more important when considering impacts on reservoir operations in mountainous watersheds than when investigating impacts on biogenic emissions and air quality (where VOCs are a primary indicator).« less
  • Anticipating the impacts of climate change on crop yields is critical for assessing future food security. Process-based crop simulation models are the most commonly used tools in such assessments1,2. Analysis of uncertainties in future greenhouse gas emissions and their impacts on future climate change has been increasingly described in the literature3,4 while assessments of the uncertainty in crop responses to climate change are very rare. Systematic and objective comparisons across impact studies is difficult, and thus has not been fully realized5. Here we present the largest coordinated and standardized crop model intercomparison for climate change impacts on wheat production tomore » date. We found that several individual crop models are able to reproduce measured grain yields under current diverse environments, particularly if sufficient details are provided to execute them. However, simulated climate change impacts can vary across models due to differences in model structures and algorithms. The crop-model component of uncertainty in climate change impact assessments was considerably larger than the climate-model component from Global Climate Models (GCMs). Model responses to high temperatures and temperature-by-CO2 interactions are identified as major sources of simulated impact uncertainties. Significant reductions in impact uncertainties through model improvements in these areas and improved quantification of uncertainty through multi-model ensembles are urgently needed for a more reliable translation of climate change scenarios into agricultural impacts in order to develop adaptation strategies and aid policymaking.« less
  • The effect of climate change on quality and quantity of runoff from a northern, agricultural watershed was simulated using the Soil and Water Assessment Tool, 1996 Version (SWAT96). SWAT`s snow evaporation submodel was modified. SWAT was calibrated using water quality and quantity data measured in the Cottonwood River near New ULM, MN. The standard errors after calibration were 3.31 mm, 157 kg/d, 752 kg/d, 3744 kg/d, and 85 t/d for mean monthly streamflow, P yield, ammonia (NH{sub 3})/organic N yield, nitrate (NO{sub 3}) yield, and sediment yield, respectively. The standard error for monthly streamflow was 9.62 mm. SWAT96 was thenmore » used to simulate the effect on the Cottonwood River watershed of a 2xCO{sub 2} climate scenario, obtained from the Canadian Climate Center`s global circulation model. Assuming land cover and land management remained constant, SWAT96 projected a decrease in mean annual streamflow, P yield, NH{sub 3}/organic N yield, NO{sub 3}/nitrate (NO{sub 2}) yield, and sediment yield. Mean monthly values changed significantly for many months of the year under the 2xCO{sub 2} climate scenario. The standard errors in SWATs baseline simulations, however, were too high for the simulated climate change effects to be measurable for NO{sub 3}/NO{sub 2} and sediment yields. The model assumptions and calibration methods used to obtain the accuracy required for simulating the effects of climate change lead to the conclusions that land use/land cover and land management practices are likely to have a greater impact on water quality than climate change and that SWAT must be calibrated to be used for climate change analysis.« less