Interactions of Mean Climate Change and Climate Variability on Food Security Extremes

Recognizing that climate change will affect agricultural systems both through mean changes and through shifts in climate variability and associated extreme events, we present preliminary analyses of climate impacts from a network of 1137 crop modeling sites contributed to the AgMIP Coordinated Climate-Crop Modeling Project (C3MP). At each site sensitivity tests were run according to a common protocol, which enables the fitting of crop model emulators across a range of carbon dioxide, temperature, and water (CTW) changes. C3MP can elucidate several aspects of these changes and quantify crop responses across a wide diversity of farming systems. Here we test the hypothesis that climate change and variability interact in three main ways. First, mean climate changes can affect yields across an entire time period. Second, extreme events (when they do occur) may be more sensitive to climate changes than a year with normal climate. Third, mean climate changes can alter the likelihood of climate extremes, leading to more frequent seasons with anomalies outside of the expected conditions for which management was designed. In this way, shifts in climate variability can result in an increase or reduction of mean yield, as extreme climate events tend to have lower yield than years with normal climate.C3MP maize simulations across 126 farms reveal a clear indication and quantification (as response functions) of mean climate impacts on mean yield and clearly show that mean climate changes will directly affect the variability of yield. Yield reductions from increased climate variability are not as clear as crop models tend to be less sensitive to dangers on the cool and wet extremes of climate variability, likely underestimating losses from water-logging, floods, and frosts

Soil sorptivity enhancement with crop residue accumulation in semiarid dryland no-till agroecosystems

Water capture and precipitation use efficiency are of great importance in dryland cropping systems because the world\u27s dependence on food produced in dryland areas continues to increase. Growing season evapotranspiration potential greatly exceeds growing season precipitation rates in dryland areas, creating a water deficit for crops. Management practices that positively impact soil physical properties increase the potential for soils to capture water. One way to assess the ability of soils to capture water is through the measurement of sorptivity. Sorptivity is defined as the cumulative infiltration proportionality constant and is governed by surface soil physical properties such as texture, degree of aggregation and aggregate stability. A study was conducted to determine how crop residue accumulation after 12 years of no-till management affects surface soil sorptivity under semi-arid dryland conditions and how sorptivity is related to surface soil physical properties known to be related to crop residue accumulation. Surface soil sorptivity, bulk density, porosity (total and effective) and aggregation measurements were made across cropping systems and soil positions representing a wide gradient of crop residue accumulation at 3 sites in eastern Colorado. Results show that increasing crop residue accumulation will have the indirect effect of increased sorptivity via improvements in soil aggregation, bulk density, and porosity that are conducive to water infiltration. Management practices that result in greater amounts of crop residue returned to the soil system lead to beneficial soil physical properties that increase water sorptivity, greatly reducing the potential for runoff and erosion, and thereby increase the precipitation use efficiency of the system

Simulation of maize evapotranspiration: An inter-comparison among 29 maize models

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Prediction of Evapotranspiration and Yields of Maize: An Inter-comparison among 29 Maize Models

An important aspect that determines the ability of crop growth models to predict growth and yield is their ability to predict the rate of water consumption or evapotranspiration (ET) of the crop, especially for rain-fed crops. If, for example, the predicted ET rate is too high, the simulated crop may exhaust its soil water supply before the next rain event, thereby causing growth and yield predictions that are too low. In a prior inter-comparison among maize growth models, ET predictions varied widely, but no observations of actual ET were available for comparison. Therefore, another study has been initiated under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). This time observations of ET using the eddy covariance technique from an 8-year-long experiment conducted at Ames, IA are being used as the standard. Simulation results from 29 models have been completed. In the first “blind” phase for which only weather, soils, and management information were furnished to the modelers, estimates of seasonal ET varied from about 200 to about 700 mm. A detailed statistical analysis of the daily ET data from 2011, a “typical” rainfall year, showed that, as expected, the median of all the models was more accurate across several criteria (correlation, root mean square error, average difference, regression slope) than any particular model. However, some individual models were better than the median for a particular criteria. Predictions improved somewhat in later stages when the modelers were provided additional leaf area and growth information that allowed them to “calibrate” some of the parameters in their models to account for varietal characteristics, etc