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

Soil Organic Carbon and Nitrogen Feedbacks on Crop Yields under Climate Change

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Group averaged absorption cross sections in the resonance region for some hafnium isotopes /

"Knolls Atomic Power Laboratory, Schenectady, New York, operated for the U.S. Atomic Energy Commission by the General Electric Company"--Cover."Contract No. W-31-109 Eng-52.""November 28, 1956.""Physics (TID-4500, 12th ed.).""KAPL-1635."Includes bibliographical references (p. 31).U.S. Atomic Energy Commission Contract ;Mode of access: Internet

Benchmark Data Set for Wheat Growth Models: Field Experiments and AgMIP Multi-Model Simulations.

The data set includes a current representative management treatment from detailed, quality-tested sentinel field experiments with wheat from four contrasting environments including Australia, The Netherlands, India and Argentina. Measurements include local daily climate data (solar radiation, maximum and minimum temperature, precipitation, surface wind, dew point temperature, relative humidity, and vapor pressure), soil characteristics, frequent growth, nitrogen in crop and soil, crop and soil water and yield components. Simulations include results from 27 wheat models and a sensitivity analysis with 26 models and 30 years (1981-2010) for each location, for elevated atmospheric CO2 and temperature changes, a heat stress sensitivity analysis at anthesis, and a sensitivity analysis with soil and crop management variations and a Global Climate Model end-century scenario

AgMIP Wheat Pilot Data 4 release

This dataset contains the underlaying data for the study: Benchmark data set for wheat growth models: field experiments and AgMIP multi-model simulations. Open Data Journal for Agricultural Research : ODjAR The data set includes a current representative management treatment from detailed, quality-tested sentinel field experiments with wheat from four contrasting environments including Australia, The Netherlands, India and Argentina. Measurements include local daily climate data (solar radiation, maximum and minimum temperature, precipitation, surface wind, dew point temperature, relative humidity, and vapor pressure), soil characteristics, frequent growth, nitrogen in crop and soil, crop and soil water and yield components. Simulations include results from 27 wheat models and a sensitivity analysis with 26 models and 30 years (1981-2010) for each location, for elevated atmospheric CO2 and temperature changes, a heat stress sensitivity analysis at anthesis, and a sensitivity analysis with soil and crop management variations and a Global Climate Model end-century scenario