GC53G-1034: Quantifying the Impacts of Early and Late Growing Season Precipitation on Midwestern US Corn Production: A Downscaling and Modeling Approach 1 2 2,3 William J. Baule , Prakash Kumar Jha , Amor V.M. Ines 1. Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, MI U.S.A. 2. Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI U.S.A. 3. Department of Biosystems and Agricultural Engineering, East Lansing, MI U.S.A.
Introduction
Climate variability is a strong driver of crop production, influencing crop physiology and yields. Despite a strong link with the general climate of a region, intra-annual weather variability is a major component in the ultimate yield and quality of agricultural crops and in maintaining balance in the soilplant-atmospheric continuum; this is particularly true for prolonged dry spells/ droughts. During 2012, much of the major corn-growing area of the United States experienced a significant drought. Where by the late growing season 52% of corn crops in the United States were reported to be in “poor” or “very poor” condition (Fig 1).1 Ultimately, it has been estimated that ~4 billion bushels of potential corn yield were lost during the 2012 drought U.S. Drought Monitor
North Central
Fig 1. As of September 18th, 2012, 98.21% of the North Central United States experienced some form of drought. 55.08% was rated as severe or worse (Source: NDMC)
September 18, 2012
(Released Thursday, Sep. 20, 2012) Valid 8 a.m. EDT
Intensity:
Using 2012 as a case study, we attempt to quantify and explore the effects of management through planting date and intra-season precipitation variabilpb o n m l ad e d c c b a bk a ity/accumulation during a drought situation through a combination of non-parametric climate downscaling (modified FResampler2) and process-based crop simulations (DSSAT3) at seven locations across the Midwestern United States (Fig 2). Our two primary questions are: 1. How would corn yields and associated parameters respond to different realizations of precipitation based on a dry (D), near-normal (N), and above normal (A) seasonal climate forecast (SCF) with respect to precipitation in the early growing season (April, May, June) and late growing season (July, August, September) in 2012? Is there utility to producers for management? D0 Abnormally Dry
D1 Moderate Drought D2 Severe Drought
D3 Extreme Drought
D4 Exceptional Drought
The Drought Monitor focuses on broad-scale conditions. Local conditions may vary. See accompanying text summary for forecast statements.
Author:
David Simeral Western Regional Climate Center
http://droughtmonitor.unl.edu/
Fig 3. A diagram of the modified FResampler workflow. The years from 1981-2010 were used as the sample pool for the weather series. Adapted from: Capa-Morocho et al. 20164
Table 1. General management options used in DSSAT for simulating corn production
DSSAT3 was then used to simulate corn production under the downscaled weather scenarios. Soil types, corn cultivar, and fertilizer form/application were adjusted for individual locations and regional practices. Three different planting dates were simulated: Early (May 1st), Middle (May 15th), & Late (June 1st).
Results
Fig 5. Scatter plots showing the relationship of harvested yield (y-axis) to growing season precipitation (x-axis) for late-season downscaling with a May 1st planting date for Belle Plaine, IA (top) and Urbana, OH (bottom) for dry (left), normal (middle), and wet (right). Least squares regression line and coefficient of determination are shown on each plot.
Conclusions
2. Does precipitation scenario or planting date have a larger effect on corn yields, using 2012 as a base year scenario? Fig 2. Seven location across the Midwestern United States where climate data for 2012 were downscaled and crop simulations were conducted using the output. The numbers on the map correspond to the following locations: 1. Aberdeen, SD (AB), 2. Belle Plaine, IA (BE), 3. Fon du Lac, WI (FO), 4. Bay City, MI (BA), 5. Dixon, IL (DI), 6. Columbus, IN (CO), 7. Urbana, OH (UR).
a
Methods
A modified version of FResampler2 (Fig 3) was used to downscale early and late season weather series for three precipitation scenarios, in the form of SCFs for precipitation: • Dry: 60% Below Normal, 30 Near Normal, 10% Above Normal • Normal: 33% Below Normal, 34% Near Normal, 33% Above Normal • Wet : 10% Below Normal, 30% Near Normal, 60% Above Normal The generated weather series from FResampler for daily rainfall, maximum/ minimum temperature, and solar radiation replace the observed 2012 weather during the target season. 100 realizations for each scenario were generated for both the early and late growing seasons for a total of 600 realizations.
Fig 4. Boxplots showing distributions of growing season precipitation from DSSAT simulations for each category of precipitation realizations (D = Dry, N = Normal, W = Wet) using the three different planting dates (5/1 = May 1st, 5/15 = May 15th, 6/1 = June 1st) for the late-season downscaling. Results from early-season downscaling show a similar spatial and temporal pattern. Black dot shows the observed growing season precipitation from 2012 at each location.
b
Fig 3. Boxplots showing distributions of harvested yields from DSSAT simulations for each category of precipitation realizations (D = Dry, N = Normal, W = Wet) using the three different planting dates (5/1 = May 1st, 5/15 = May 15th, 6/1 = June 1st) for early season downscaling (a) and late season downscaling (b). Black dot shows the harvested yields from DSSAT using the observed weather from 2012.
• Growing season precipitation is a key driver of crop yield in the Midwest, with higher precipitation accumulations generally resulting in higher simulated corn yields. Particularly in the western portions of the region. • Timing of precipitation events, planting date, and other factors such as: fertilizer form/application, and soil type all contribute substantially to corn yield at these locations. • FResampler provides a convenient tool for downscaling precipitation, temperature, and solar radiation based on historical data and seasonal climate forecast probabilities. • Early season downscaling allows for identification of best management strategies based on information that would be available to a producer at the beginning of a growing season, before the crop has been planted and fertilizers applied, in the case of a late season drought. • Late season downscaling, where the early growing season is known, allows us to evaluate which management strategies provide the best outcomes given an uncertain late growing season in a hindcast context References
1. Rippey, B.R., 2015. The US drought of 2012. Weather and Climate Extremes, 10, pp.57-64.; 2. Han, E. and Ines, A.V., 2017. Downscaling probabilistic seasonal climate forecasts for decision support in agriculture: A comparison of parametric and non-parametric approach. Climate Risk Management, 18, pp.51-65.; 3. Jones, J.W., Hoogenboom, G., Porter, C.H., Boote, K.J., Batchelor, W.D., Hunt, L.A., Wilkens, P.W., Singh, U., Gijsman, A.J. and Ritchie, J.T., 2003. The DSSAT cropping system model. European journal of agronomy, 18(3-4), pp.235-265. 4. CapaMorocho, M., Ines, A.V., Baethgen, W.E., Rodríguez-Fonseca, B., Han, E. and Ruiz-Ramos, M., 2016. Crop yield outlooks in the Iberian Peninsula: Connecting seasonal climate forecasts with crop simulation models. Agricultural systems, 149, pp.75-87.
For more information please contact: William Baule (
[email protected]), Prakash Jha (
[email protected]) or Amor V.M. Ines (
[email protected])
