Socioeconomic Impacts of Mesoscale Weather Systems and Regional Scale Climate Variations
NOAA/CPO – Climate Information for Agricultural Management in the Southern Great Plains
Funding Type: CIMMS Task III (Program Manager – Nancy Beller-Simms)
Objectives
Identify the potential to use substantial geographical separation
of production areas as a risk management tool. Machinery and land contractual
arrangements for production areas separated by substantial geographical
distance will be the specific foci examined in pursuit of this general
objective. Assessing the importance of climate variability within this
risk management context also will be a major consideration.
Accomplishments
The maturation of crops from planting to harvest is dependent primarily
on accumulated heat and a sufficient water supply, because plants grow
in a stepwise manner which is strongly influenced by ambient temperature.
Even the time between seed planting and emergence is very specific to the
amount of accumulated heat given a constant planting depth, sufficient
moisture, and non-freezing temperatures. Thus the use of calendar days
to predict the timing of important agricultural windows is largely inaccurate
and potentially costly, especially for large commercial farms, where such
timing inaccuracies can have a negative impact on the bottom line. Since
a deficient water supply can at least partially be compensated via irrigation
practices, accumulated heat is the most important factor in predicting
agricultural windows, as well as determining ideal planting and harvesting
dates to maximize crop yields. In the present study, Pacific and Atlantic
Ocean sea surface temperature patterns are used to form seasonal predictions
of accumulated heat, precipitation, first freeze date, and drought stress,
which can be used by farmers to estimate plant emergence, flowering, harvesting,
and even crop yield.
Traditional growing degree days (GDDs) are used here as a measure of accumulated heat, similar to the heating degree days and cooling degree days of energy consumption studies, but tailored for specific crop analysis. GDDs are calculated by subtracting 10oC from the daily mean temperature, after setting daily minimum temperatures below 10oC equal to 10oC and daily maximum temperatures above 30oC equal to 30oC, and then totaling the daily values over a desired time period (Cox 2006). This assumes no appreciable plant growth for an ambient temperature below 10oC and above 30oC (Cox 2006). Generally, a higher number of GDDs results in faster plant emergence, and an earlier ideal harvesting date given a sufficient water supply (Cox 2006). For example, most corn hybrids require around 1300 GDDs from planting date to maturity, and an ideal hybrid must be selected prior to planting to optimize corn yield at harvest (Cox 2006). Growing season (date of mean last freeze in the spring to mean first freeze in the fall) and monthly mean GDD totals for the Eastern U. S. are presented in the figure below.
The accurate prediction of GDD anomalies during important agriculture windows can help farmers substantially in contractual arrangements involving the sharing of equipment and personnel between farms of different geographical location. Accurate predictions of GDDs can also be very helpful in the management of fertilizer and insecticide application, since the maturation of insects and fertilizer nitrification/mineralization are also dependent on cumulative heat (Griffin and Honeycutt 2000). In the present study, the linear and non-linear relationships between GDDs and other important climate variables and several Pacific and Atlantic Ocean sea surface temperature patterns are presented (Atlantic Ocean patterns yet to be determined), and objective physical reasoning for these relationships will soon be explored. The Pacific Ocean patterns include El Niño/Southern Oscillation (ENSO), the North Pacific Oscillation (NPO), and the Pacific Decadal Oscillation (PDO), and were determined from an S-mode Principal Components Analysis (PCA) of seasonal sea surface temperatures of different time periods. The score time series from the PCA will be used to determine the linear and non-linear teleconnections with climate variables important to the agribusiness sector.
This project is ongoing.
Growing season and monthly mean GDD totals for the eastern U.S. calculated from a 1949-2000 mean.