An Analysis of the Impacts of Climate Change on Crop Yields and

Zerihun G. Kelbore

GSSS, University of Trento

International Conference on Climate Change Impacts and

Adaptation for Food and Environmental Security

SEARCA, College, Los Banos, Laguna, Philippines

November 2012

International Conference on Climate Change Im

pacts and Adaptation for Food and Environm

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Introduction

Climate of Ethiopia: An Overview

Data and Methodology

Results

Simulation : Impacts of Future Climate Change on Crop

Yields and Yield Variability

Conclusion



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Kiremt/Meher refers to the long and heavy rainy season

which lasts from June to September; correspond to the main

meher growing season.

Belg is a short and moderate rainy season that lasts from

February to May.

accounts for only 5 to 10 percent of the total annual grainproduction of the country; the remaining share goes to Meher

season harvest



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IPCC forecasts change in climate

warming world (1.8 to 4.00C )

Increases in CO2 levels

Precipitation patterns would change

Sea levels would rise

Frequency of extreme weather events would increase

… and etc



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Consequences

Agricultural production will be substantially affected, but impacts unevenly distributed across regions (low latitude and developing countries severely hit)

Global agricultural productions may decline by 16% by 2080, in developing countries by around 20% (Cline, 2007)



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AFRICA

Considered the most disproportionately affected region in the world, with low levels of irrigation (only 6 %, while in Asia 38%)

Share of Agriculture in GDP is high

Particularly, in Sub-Saharan Africa it provides 70% of employment and contributes 30% to GDP



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ETHIOPIA

Agriculture

Provides employment to more than 80% of the population

Contributes around 43% to overall GDP

90% of export earnings, 70% of raw materials to secondary

activities (MOFED, 2009/10)

But largely dependent on rains, only about 5% of the

cultivated land irrigated?

66% produced consumed, and only about 16 % marketed

(CSA, 2010)



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The risk of climate extremes increase as very few farmers are

using irrigation

Drought risk could severely affect farmers` livelihood as it

may reduce farm production up to 90% (WB, 2003)

Farmers have traditional coping mechanisms, but fail in times

of drought

Other risk management options include, lower value, lower

yield, and lower risk cultivation; perpetuating poverty

(Dercon, 2005)



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How average crop yields (maize, teff, and wheat) their variance responded to changes in weather?

How future climate changes will affect crop yield and its variability in Ethiopia?



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Diverse topography Has 11 climatic zones broadly categorized as

dry climate, tropical rainy climate and temperate rainy climate (NMSA, 1996)

Different atmospheric circulation patterns resulted in determine rainfall patterns across the country (SEJ, ITCZ, etc)

Two important rainy periods: Kiremt (June to September), and Belg (February to May)



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Data

Crop Yield Data for Maize, Teff, and Wheat

Agricultural Sample Surveys since 1979/80 to 2008/09

(Meher season post-harvest )

Included 14 Zones from three regions (Amhara(5),

Oromia (6), and SNNP (3))



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National Average Crop Yield

1979/80 to 2008/09

Average Crop Yield

Crops 1979/80 2008/09 %change

Teff 9.5 12.2 28.42

Wheat 17.34 22.24 28.3

Maize 11.09 17.46 57.44



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Summary of Regional Rainfall Data A time series rainfall data for 14

weather stations for the period

1979/80 to 2008/09

Kiremt/Meher and Belg season

rainfalls

Obtained from Meteorological

Services Agency of Ethiopia

AMHARA

Variable Mean Std. Dev. Observations

kiremt 798.83 309.91 N = 145

belg 233.11 171.20 n = 5

T = 29

OROMIA

kiremt 655.51 459.60 N = 174

belg 347.28 236.53 n = 6

T = 29

SNNPR

kiremt 432.78 202.32 N = 87

belg 323.71 113.66 n = 3

T = 29



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Methodology

The Just and Pope (1978) Stochastic Production

Function

Decomposes production function into theoutput level and variability of thatoutput level

5.0;; itititit ZhXfY

;itXf

5.0; itit Zh

termstochastic is it 2

itV and 0 itE



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Panel Unit Root

Fisher Type Panel Unit Root Test (Maddala and Wu, 1999)

With Dickey-Fuller statistics of AR(1)

We reject the null at 0.01 significance level.

i oneleast at for ,0:H ; i allfor ,0:H

N allfor T as freedom of degrees 2N as ddistribute is

ln2

a0

i

2

1

withP

pPN

i

N allfor T as freedom of degrees 2N as ddistribute is i

2 withP



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P

(drift, lag (1), demeaned, N=14)

Crops

Teff 145.32*

Wheat 138.72*

Maize 149.90*

Rainfall

Kiremt 258.66*

Belg 180.36*

Annual 193.71*

Table 2. Fisher Type Unit Root Test Results

*Significant at 1% with | 2(28)=48.28



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In addition, ADF tests of individual unit roots were conducted and

• 71% of the units for teff

• 50% of the units for wheat

• 57% of the units for maize

• 71% of the units for kiremt

• 86 % of the units for belg, found stationary



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T

i

n

i

T

i

n

i it

itit

itXh

XfYXhNL

1 1 1

2

;

;;ln2ln*

2

1ln

Provides a maximum likelihood estimates of parameters and δ

•Panel Data Model

•Fixed or Random Effects Model

•Hausman Test rejects the null hypothesis that the panel unit specific

errors (ui) are not correlated with the regressors; and the random effects

model with a maximum likelihood option is estimated

•



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An increase in kiremt season rainfall increases crop yields in

SNNP region

It has a relative decreasing effect on teff and wheat yields in

Amhara and Oromia regions

An increase in belg rain increases maize yield in Oromia

region, reduces teff and wheat yield in SNNP region

Change in crop production technology increases mean crop

yields across regions at an increasing rate



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An increase in kiremt rainfall decreases variability of teff and

wheat yields in SNNP, and maize yield in Amhara region

An increasing belg rainfall decreases variability of average teff

yield in SNNP region, and average maize yield in Oromia

region

Technical change decreases variability of all crops across

regions



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Table 3: Estimate Coefficients from Mean Crop Yield Regressions

Teff se Wheat se Maize Se

Kiremt 0.1436*** (0.0751) 0.1480*** (0.0810) -0.0159 (0.0517)

Belg -0.0327 (0.0292) -0.0227 (0.0293) 0.1050*** (0.0618)

D1_kiremt -0.1495*** (0.0875) -0.1743*** (0.0941) -0.0476 (0.0818)

D2_kiremt -0.1452*** (0.0809) -0.2239** (0.0888)

Trend -0.0143*** (0.0074) -0.0148** (0.0073) 0.0017 (0.0094)

Trend^2 0.0007* (0.0002) 0.0011* (0.0002) 0.0005*** (0.0003)

D3_kiremt 0.0198 (0.1255)

D1_belg -0.0413 (0.0859)

D3_belg -0.1329 (0.1128)

D1 1.0562** (0.5326) 1.0474*** (0.5750) 0.3940 (0.8080)

D2 1.0616** (0.4831) 1.4419* (0.5334)

D3 0.5191 (0.9681)

Intercept 1.3620 (0.4623) 1.5484* (0.5044) 2.1258* (0.5106)

N 359 352 359

Ha 7.85 (0.3460) 3.13 (0.6797) 8.37 (0.3983)

Standard errors in parentheses * **p<0.10 ** p<0.05 * p<0.01



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Teff SE Wheat SE Maize SE

Kiremt -0.936** (0.434) -0.578*** (0.307) 0.226 (0.155)

Belg -0.939*** (0.553) 0.226 (0.234) -0.328 (0.303)

D1_kiremt 0.716 (0.546) 1.101* (0.425) -0.689* (0.167)

D2_kiremt 0.781*** (0.457) 0.868** (0.373)

D1_belg 0.952 (0.632) 0.276 (0.408)

D2_belg 1.213*** (0.627)

D3_kiremt 0.410** (0.170)

D3_belg -0.0905 (0.664)

Trend -0.00667 (0.0135) -0.0166 (0.0191) -0.0901* (0.0121)

D1 -10.18** (4.618) -7.535* (2.591) 2.813 (2.800)

D2 -11.34* (3.801) -5.736** (2.284)

D3 -2.228 (4.044)

Intercept 7.041** (3.180) -1.330 (2.340) -2.055 (2.250)

N 359 352 359

Ha 10.12 (0.1820) 4.12 (0.6605) 2.90 (0.8943)

Standard errors in parentheses ***<0.10 **p<0.05 *p<0.01

Table 4. Estimated Coefficients from the Variance Function Regression



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Employed coefficients estimated using the historical data

Simulate the projected % change of average crop yield and its

variability using climate projections from three Atmosphere

Ocean General Circulation Model (AOGCMS) including

CGCM2, PCM, and HadCM3 for the years 2050 and 2100

A2 and B2 Emission Scenarios A2 :heterogeneous world, slowly converging birth rates, economic growth is regional,

slow technological change

B2 : emphasis on local solutions to environmental, economic and social problems,

population growth slower than A2, intermediate economic development; slow

technological change



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Table 7. % Change in Mean Crop Yields at Regional and National Level Due to Change in

Climate

2050 2100

Region Teff Wheat Maize Teff Wheat Maize

Oromia -3.58 -7.26 -1.51 -1.62 -9.59 0.18

Amhara -1.93 -0.19 47.86 -0.66 -12.30 -0.21

SNNPR 1.89 -17.04 -3.09 -0.28 -17.23 -8.19

National -2.43 -6.21 10.84 -1.09 -11.03 -1.14



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Table 9. % Change in standard deviation of Yields at Regional and National Level

2050 2100

Regions Teff Wheat Maize Teff Wheat Maize

ORO -2.49 1.20 39.76 -1.84 11.91 1.34

AMH -0.01 3.09 52.06 0.17 31.20 3.33

SNNPR 16.23 8.66 43.15 60.14 15.40 7.55

National 0.03 2.40 43.39 3.74 17.52 2.75



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Impacts vary across different crops and regions

Maize is more promising than Teff and Wheat for 2050

The decline in crop yields may continue pushing prices up

Adaptation to Climate change strategies needs to take into

account regional realities and possible changes in cropping

patterns



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Results may change :

when temperature variable is considered

Emission scenarios change

if downscaled climate change projections are used



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Documents

An Analysis of the Impacts of Climate Change on Crop Yields and