1. Effect of Climate Change on NDVI and Crop Yield Association for Index Insurance
Askar Choudhury, James Jones, John Kostelnick, Frank Danquah,
Raquiba (Lena) Choudhury, Aslihan Spaulding
Illinois State University, USA
Presented at the Western Risk and Insurance Association Annual Meeting
Maui, Hawaii
January 5, 2016

2. Objectives:
Establish associations between index and crop yield through associative modeling taking into account variations in climatic factors.
Purpose:
Develop index insurance using NDVI

3. Need for Protecting Farmers
Drought can destroy crops
Climate change is making droughts more frequent
Farmers have difficulty getting loans without insurance

4. How Do We Help Farmers?
Index Insurance may be a viable alternative to traditional insurance, specifically for developing countries

5. Index Insurance Payout occurs when pre-specified trigger is activated
Implementation is faster and simpler
Reduces administrative and other costs.
Reduces moral hazard associated with traditional insurance

6. Data: 2011 and 2012
Crop (Corn) yield (bushels per acre) data were obtained from Soy Capital in McLean county, Illinois
Normalized Difference Vegetation Index (NDVI) data were obtained using MODIS satellite system
2011: Normal year for crop yield (average yield year)
2012: Drought year for crop yield (poor yield year)

7. Fields selected from McLean County

8. What is NDVI?
Normalized Difference Vegetation Index (NDVI) is an index of plant “greenness”. It measures the density of green vegetation.
Calculated from the red and near infrared (NIR) bands of remotely sensed satellite imagery
Useful to study vegetation dynamics or plant phenological changes over time.

9. NDVI derived from MODIS satellite imagery for two different dates

10. Table1: Correlation of NDVI and Yield
Year
NDVI May_9
NDVI May_25
NDVI Jun_10
NDVI Jun_26
NDVI Jul_12
NDVI Jul_28
NDVI Aug_13
NDVI Aug_29
2011
0.2610
0.5419
0.5326
0.6739
2012

11. Table2: Average NDVI and Yield
Year
NDVI May_9
NDVI May_25
NDVI Jun_10
NDVI Jun_26
NDVI Jul_12
NDVI Jul_28
NDVI Aug_13
NDVI Aug_29
Yield
2011
3387.5
6222.9
182.95
2012
7601.7
116.62

12. NDVI and Climate factor
We investigated further to see if broader climatic factor such as, Sea Surface Temperature (SST) shows any lag effect on NDVI

13. SST, Precipitation, and NDVI
Sea surface temperature (SST) variations is likely associated with deviations in precipitation
One of the major weather factor that have impact on NDVI is precipitation
Therefore, variations in NDVI is likely due to global atmospheric and sea surface temperature (SST) change

14. Fig1: SST Plot: February – May

15. Fig2: SST Plot: May - August

16. Fig3: Average NDVI Plot: May – August

17. Climate Change
One of the environmental anomalies that influence SST variations is El Niño
El Niño is a naturally occurring phenomenon that influences SST and the wind pattern

18. Climate Change
El Niño episodes varies in their intensities and are labeled as normal/moderate and extreme/super
During normal El Niño episode, the usual average rise of SST of the tropical Pacific Ocean is 0.5 °C
During an extreme El Niño episode, the average rise of SST is 0.9 °C

19. Climate Change Monthly Mean SST During a Normal El Niño Year
Monthly Mean SST During a Super El Niño Year

20. High and Dry: How the 2012 Drought Affected Illinois Agriculture
Transportation was jeopardized when Mississippi River dropped significantly
Livestock producers were impacted

21. Associative model
We develop associative model for index insurance using NDVI and incorporate differential climate effect.

22. TABLE 3: Model’s results of NDVI on Yield
Variable DF
Parameter Estimate
Standard Error
t Value
Pr > |t|
Intercept 1
-11.60
<.0001
Climate-Effect
(year-dummy)
-7.58
May_25
5.24
Jun_10
3.68
0.0004
Jul_12
3.07
0.0028
Aug_13
6.98
R-Square
0.8899

23. Insurance Payment Structure

24. CONCLUSIONS:
We observe that climate change impacts NDVI and thus crop yield
Proper modeling of association of crop yield can provide feasible index insurance

25. Thank You!