Understanding the Impact of Drought on Crop Yield in South and North Carolina

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Transcription:

Understanding the Impact of Drought on Crop Yield in South and North Carolina Junyu Lu, Gregory J. Carbone Department of Geography, University of South Carolina, Columbia

Introduction Social& economical Drought Impact Environmental Ecological

Drought Classification Meteorological Drought Absence or reduction of precipitation over a region, precipitation are used commonly as primary indicator Agricultural drought Occurs at a critical time during the growing season resulting in declining soil moisture and crop failure Drought Classification Socio-economic drought Associated deficits of water resources systems leading to failure to meet the demand of some economic goods and social needs Hydrological drought Precipitation deficits over a prolonged period that affect surface or subsurface water supply Heim 2002, Mishra and Singh 2010

Data Source Long-term county-level agriculture statistics were obtained from USDA s National Agricultural Statistics Service (NASS) (http://www.nass.usda.gov/). County-level drought indices (Monthly PDSI, Palmer Z-index, 3- Month SPI, 6-Month SPI, 9-Month SPI) are obtained from Dynamic Drought Index Tool (DDIT) For Basins In North and South Carolina (https://www.dnr.sc.gov/drought/).

Drought Indices Monthly PDSI (Palmer Drought Severity Index) Based on the supply-and-demand concept of the water balance equation by using long term historical precipitation, temperature data and also Available Water Content (AWC) of the soil. Palmer Z-index Palmer Z-index can be expressed as the "Moisture Anomaly Index." Each monthly Z value is a measure of the departure from normal of the moisture climate for that month. SPI (Standardized Precipitation Index) SPI is based only on precipitation data. It is standardized into different time scale and can be comparable over space and time.

State Crop Yield Trend

County Corn Yield Trend Beaufort, NC Robeson, NC Orangeburg, SC Clarendon, SC

Research Question? How can we separate irregular roughness caused by weather related factor from long-term trend caused by science advancement and technology development? What are the drought impacts on crop yield?

Trend Simulation Method: Simple Linear Regression Model Simple Linear Regression Model y = β 0 + β 1 x + ε Where β 0, β 1 are unknown constants x represents time (year) y represents crop yield ε represents random effects

Trend Simulation Method: Moving Average Model Moving average smoothing can be used to smooth out the irregular roughness and high-frequency variation. Centered Moving Average: use values both before and after the current time Half a decade (5 years) and One decade (10 years) 5 Years Centered Moving Average Model mx t = 1 5 x t-2 + 1 5 x t-1 + 1 5 x t + 1 5 x t+1 + 1 5 x t+2 10 Years Centered Moving Average Model mx t = 1 4 20 x + 1 t-5 å 10 x t+ j + 1 20 x t+5 j=-4

Simple linear regression model VS Centered moving average model Corn Yield in Beaufort, NC Simple 10 5 Year Linear Moving Regression Average Model Model

Decomposition Model Additive Decomposition Model: xt Trend Weather related factor Random Multiplicative Decomposition Model: xt Trend * Weather related factor * Random

Robeson, NC Original Data Additive decomposition Multiplicative decomposition Linear Regression Model 5 Year centered Moving Average Model 10 Year Centered Moving Average Model

Moving Average Model vs Simple Linear Regression Model

Drought Year vs No Drought Year

Drought Year vs No Drought Year

Results Centered moving average (CMA) model performed better than linear regression model to simulate the long-term trends of the crop yield. The multiplicative decomposition model did better at separating out high frequency variation than the additive decomposition model. Detrended corn yield showed a higher level of correlation with 3- Month SPI in June, July and August than the other months and detrended soybean yield showed a higher level of correlation with 3-Month SPI in July, August and September.

Discussion In this research, we are focused on drought impact on crop yield. In the future, we need to incorporate other extreme weather impacts, such as flood, heat wave and tropical storm.

Thank you! Questions or comments?