CLIMATE CHARACTERIZATION AND MODELING THE IMPACT
OF CLIMATE CHANGE ON PRODUCTION OF MAIZE (Zea mays L.)
UNDER DIFFERENT CONSERVATION TILLAGE PRACTICES IN
CENTRAL RIFT VALLEY OF ETHIOPIA
MSc THESIS
AGERE LUPI EDAO
JANUARY, 2015
HARAMAYA UNIVERSITY
i
Climate Characterization and Modeling the Impact of Climate Change on
Production of Maize (Zea mays L.) under Different Conservation Tillage
Practices in Central Rift Valley of Ethiopia
A Thesis Submitted to the School of Natural Resource Managements and
Environmental Sciences, School of Graduate Studies
HARAMAYA UNIVERSITY
In Partial Fulfillment of the Requirements for the Degree of
MASTER OF SCIENCE IN AGRICULTURE (SOIL SCIENCE)
By
Agere Lupi Edao
JANUARY, 2015
HARAMAYA UNIVERSITY
ii
SCHOOL OF GRADUATE STUDIES
HARAMAYA UNIVERSITY
As Thesis research advisors, we hereby certify that we have read and evaluated this Thesis
entitled: Climate Characterization and Modeling the Impact of Climate Change on
Production of Maize (Zea mays L.) under Different Conservation Tillage Practices in
Central Rift Valley of Ethiopia prepared under our guidance by Agere Lupi. We
recommend that it be submitted as fulfilling the Thesis requirement.
Kibebew Kibret (PhD)
Major Advisor
__________________
Signature
________________
Date
Girma Mamo (PhD)
Co-Advisor
__________________
Signature
________________
Date
As members of the Board of Examiners of the MSc Thesis Open Defense Examination, we
certify that we have read and evaluated the Thesis prepared by Agere Lupi Edao and
examined the candidate. We recommended that the Thesis be accepted as fulfilling the Thesis
requirement for the degree of Master of Science in Agriculture (Soil Science).
Lisenwork Nigatu (PhD)
Chairman
________________
Signature
_________________
Date
Muketer Mohamed (PhD)
Internal Examiner
_______________
Signature
________________
Date
Akililu Makesha (PhD)_
External Examiner
________________
Signature
________________
Date
Final approval and acceptance of the Thesis is contingent upon the submission of the final
copy of the Thesis to the Council of Graduate Studies (CGS) through the school Graduate
Committee (SGC) of the candidate’s major school
iii
DEDICATION
This Thesis is dedicated to my
Uncle Qumbi Leliso,Erba,
Father Korme Godet Jimma
and
mother Bulitu Leliso Erba
She was passed away at my childhood without seeing my fruitful life
iv
STATEMENT OF THE AUTHOR
First, I declare that this thesis is my bonafide work and that all sources of materials used for
the thesis have been duly acknowledged. This thesis has been submitted in partial fulfillment
of the requirements for an MSc degree in soil science at the Haramaya University and is
deposited at the University Library to be made available to borrowers under rules of the
Library. I solemnly declare that this thesis is not submitted to any other institution anywhere
for the award of any academic degree, diploma, or certificate.
Brief quotations from this thesis are allowable without special permission provided that
accurate acknowledgement of source is made. Requests for permission for extended quotation
from or reproduction of this manuscript in whole or in part may be granted by the head of the
major department or the Dean of the School of Graduate Studies when in his or her judgment
the proposed use of the material is in the interests of scholarship. In all other instances,
however, permission must be obtained from the author.
.
Name: Agere Lupi Edao
Signature: ______________
Place: Haramaya University, Haramaya
Date of Submission: ______________
v
BIOGRAPHICAL SKETCH
Agere Lupi was born on 22 May 1978 in Oromia region, East Shoa Administrative Zone,
Liben Chuquala woreda at the village of Liben Gadula. He attended his elementary school and
junior secondary school education from 1987-1989 at the Liben Gadula Elementary School
and 1990-1993 at Leteneal Colonel Dejene Sime Junior Secondary School, Adama. He then
attended his secondary school education from 1994-1997 at St. Joseph Senior Secondary
School (Adama).Soon after completing his secondary education, he joined the then Ambo
College of Agriculture, now Ambo University, in 1998 and graduated with Diploma in
General Agriculture in 1999. In May 2000, he was employed by Oromia Irrigation
Development Authority as an irrigation Agronomist at Fentale Woreda and served there until
March 2008. He was then transferred to Oromia Bureau of Agriculture at Lume Woreda in
East Shoa Zone where he served as an agronomist until June 2010. In July, 2008 he was
employed by the Ethiopian Institute of Agricultural Research (EIAR) as assistant researcher
position and served until October, 2013.
In 2005 he joined the Summer In-Service Program at Haramaya University graduated with a
Bachelor of Science degree in Plant Science in September 2009. He joined the School of
Graduate Studies at Haramaya University in October 2013 to pursue his MSc studies in Soil
Science.
vi
ACKNOWLEDGMENT
I would like to express my heartfelt thanks to my major advisor, Dr. Kibebew Kibret, for his
valuable advice, insight and guidance starting from proposal development to the completion
of the research work and also deserve special thanks for his field visit to the research site, in
his busy schedule and giving invaluable comments and directions to the field work and also
my co-advisor, Dr. Girma Mamo for his encouragement throughout the course of the study.
My special thanks also go to the Ethiopian Institute of Agricultural Research for granting me
study leave and covering all the costs of the research work through the ECAW project fund. I
am also very grateful to Melkassa Agricultural Research Center for providing land for
experimentations, Melkassa Agro Meteorology Research Process for providing me long-term
climate data.
I am also indebted to, Dr.Tolessa Debele, Dr.Habtamu Admasu Dr Nigussie Dechassa, Mr.
Fitih Ademe Mr. Mesfin Hundesa, Mr. Fikadu Getachew, Mr. Feyisa Soboke, and Mr.
Lagasse Teshome, and also thanks the staff of Meteorology Research Division staff at MARC
for their great support in providing all the necessary materials and constructive advice on
climate data analysis. Special thanks also go to Mr. Wondimagegn Kassa from Teppi
Agricultural Research center for sending my salary on time.
Last but not the least, I would like to extend my heartfelt thanks and appreciation to all my
family members Mss.Elisabeth Alemu (wife), Siyanet Agere (daughter), Rebira Agere (son),
Mr. Alemayehu Mitiku, Mss.Tadelech Feyisa, Mr. Guta Korme, Addis Alemu, Mr. Nathan
Alemu and Mss.Yeshihherg Alemu members for family for their generous assistance, moral
support and helpful encouragement during my graduate study with all their kindness and
affection.
First and foremost, I would like to extend my unshared thanks to the Almighty GOD for his
divine help and capacity given to me to realize my aspiration in all aspects of my life.
vii
ABBREVIATIONS AND ACRONYMS
AR4
APSRU
ATARC
CRV
CSA
CSM
CV
DOY
DSSAT
EOS
ETo
FAO
GCM
HadCM3
IMF
INSTAT
IPCC
ITCZ
LGP
MAKESEN
MARC
Me-2
Me-4
MM
MOA
NCEP
NM
NMSA
NRD
RWP
SD
SDSM
SOM
SOS
SRES
STRF
TAR
TMM
UNFCCC
USAID
Assessment Report 4
Agricultural Production System Research Unit
Adami Tulu Agricultural Research Center
Central Rift Valley
Central Statistics Agency
Crop Simulation Model
Coefficient of Variation
Days of the Year
Decision Support System for Agro-technology Transfer
End of the Season
Evapo-Transpiration
Food and Agriculture Organization
Global Circular Model
Hadley Couple Center Model
International Monetory Fund
Interactive Stastic
Intergovernmental Panel for Climate Change
Inter –Tropical Convergence Zone
Length of Growth Period
Mann-Kendall Sen
Melkassa Agricultural Research Center
Melkassa 2
Melkassa 4
Modified Moldboard
Ministry of Agriculture
National Centre for Environmental Prediction
Normal Meresha
National Meteorology Service Agencey
Number of Rainy Days
Ripper-Wing Plows
Standard Deviation
Stastically dowanscaling Model
Soil Organic Matter
Start of the Season
Special Report on Emission Scenarios
Seasonal Total Rainfall
Third Assessment Reports
Twice Modified Moldboard
United Nation Framework Convention Climate Change
United States Agency for International Development
viii
TABLE OF CONTENTS
DEDICATION
STATEMENT OF THE AUTHOR
BIOGRAPHICAL SKETCH
ACKNOWLEDGMENT
ABBREVIATIONS AND ACRONYMS
LIST OF TABLES
LIST OF FIGURES
LIST OF APPENDIX TABLES
ABSTRACT
1 INTRODUCTION
2 LITERATURE REVIEW
2.1. Climate Characterization and Agriculture
2.1.1.Start and end rainy season and length of growing periods
2.1.2.Seasonal rainfall
2.1.3.Probability of dry spell
2.1.4.Seasonal rainfall and annual temperature trend analysis using mann-kendall
and sen’s test
2.2. Impact of Climate Change on Crop Production
2.2.1. Average temperature creases
2.2.2. Change in rainfall amount and pattern
2.3. Climate of Ethiopia and Its Local Classifications
2.4. Impact of Climate Change on Crop Production in Ethiopia
2.5.Impact of Climate Change on Maize Production in Central Rift Valley of Ethiopia
2.6. Adaptation Measures on Maize Production in Central Rift Valley of Ethiopia
2.7. Conservation Tillage and Climate Change
2.8. Climate Change Mitigation Using Improved Tillage Practice
2.9 .Climate Change and Decision Supporting Tools
2.9.1. Downscaling global circulation model output
2.9.2. Hadley center couple version 3
2.9.3 .Use of statically downscaling model for climate scenarios analysis
2 9.4. Crop simulation model
2.9.5. DSSAT model and climate change
3 MATERIALS AND METHOD
3.1.Description of the Study Areas
3.2.Characterzing the Climate of the Mieso, Melkassa and Adami Tulu Areas
3.2.1.Meteorological data source and quality assessment
3.2.2.Analysis of the start, end and length of growing season
3.2.3.Analysis of probability dry spell occurrence
3.2.4.Rainfall and temperature trends analysis
3.3.DSSAT Model Calibration for Maize
3.4 Soil and Crop data Required for .DSSAT Model Calibration
3.4.1. Soil data
ix
iv
v
vi
vii
viii
xi
xii
xiii
xiv
1
4
4
4
4
5
6
7
7
7
8
9
9
10
12
13
14
14
14
15
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19
19
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21
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25
26
26
4
5
6
7
3.4.2. Crop data
3.5. Future Climate Scenarios Analysis
3.6.Analysis of Impact of Climate Change and Adaptation Options on Maize Production
at Melkassa
RESULTS AND DISCUSSIONS
4.1.Climate Characterization of the Growing Seasons
4.1.1. Start of the season (SOS) and End of season (EOS)
4.1.2 .Length of growing season (LGS) and number of rainy days
4.1.3. Seasonal total rainfall throughout the growing periods
4.1.4. probability of dry spells Length Occurrence
4.2 Trend of Rainfall at Growing Periods for Mieso, Melkassa and Adami Tulu Stations
4.3.Trends of Annual Maximum and Minimum Temperature for Mieso, Melkassa and
Adami Tulu Stations
4.4.DSSAT Model Calibrated Result for Maize
4.5.Dowanscaled Baseline and Projection of future scenarios
4.5.1 Downscaled baseline scenarios
4.5.1.1.Dowanscaled rainfall
4.5.1.2.Dowanscaled maximum temperature
4.5.1.3.Dowanscaled minimum temperature
4.5.2 Projected scenarios of 2011-2099 for Melkassa
4.5.2.1 projected change in rainfall
4.5.2.2 projected change in maximum temperature
4.5.2.3 projected change in minimum temperature
4.6.Response of maize yield to impact of projected climate change scenarios under
different tillage practice
4.6.1.Impact of projected climate on Me-2 under different tillage practice
4.6.2.Impact of projected climate on Me-4 under different tillage practice
4.7.Adaptation Options Potential in Maize Production Under Central Rift Valley of
Ethiopia
4.7.1 Changing tillage practice without changing maize variety
4.7.2.Changing maize variety without changing tillage practice
SUMMARY AND CONCLUSION
REFERENCE
APPENDICES TABLES AND FIGURES
x
27
28
30
32
32
32
33
36
36
39
39
40
43
43
43
44
45
46
46
47
48
49
51
52
53
54
57
58
62
73
LIST OF TABLES
Tables
page
1 Description of environmental and agronomic requirements for, and characteristics of
Melkassa 2 and 4 maize varieties used for this study
25
2
List of predictor variables that gave better correlation results at p< 0.05.
30
3
Descriptive statistics of important rainfall characterstices for Mieso, Melkassa and
Adami Tulu weather stations
35
4
Trend of season rainfall (mm) at Mieso Melkassa and Adami Tulu’s
39
5
Trend of annual, maximum and minimum temperature (oC) at Mieso, Melkassa and
Adami Tulu
40
Percent change in average maize grain yield from baseline (1977-2013) without and
with change maize variety
55
Percent change in maize grain yield without changing tillage practice and with
change maize varieties from base period average yield (1977-2013)
57
6
7
xi
LIST OF FIGURES
Figures
page
1 Location of the study areas
20
2 Probability of dry spells longer than 5,7, 10, and 15 days at Melkassa starting from
April first respectively
37
3 Probability of dry spells longer than 5,7, 10, and 15 days at Adami Tulu starting from
April first respectively
37
4 Probability of dry spells longer than 5,7, 10, and 15 days at Mieso starting from April
first respectively
38
5 Relationship between observed and simulated yield (Kg/ha )(A), maturity days(B) and
flowering days (C) for melkassa-2 grown under Melkassa climate
42
6 Relationship between observed and simulated yield in kg/ha (A), maturity days(B) and
Days of flowering (C) for Melkassa-4 grown under Melkassa climate
43
7 Observed and simulated average seasonal precipitation of Melkassa for the base period
(1977-2013)
44
8 Observed and simulated pattern of monthly rainfall at Melkassa for the base period
(1977-2013).
44
9 Pattern of observed and simulated mean daily maximum temperature for the base
period (1977-2013)
45
10 Pattern of observed and simulated mean daily minimum temperature for the base
period (1977-2013).
45
11 Projected mean monthly precipitation pattern at Melkassa compared to the base period
under A2a (a) and B2a (b) scenarios.
47
12 Change in monthly maximum temperature in the future (2011-2099) from the base
(1977- 2013) period under A2a (a) and B2a (b) scenarios.
48
13 Change in average monthly minimum temperature in the future (2011-2099) from the
base (1977- 2013) period under A2a (a) and B2a scenarios.
49
14 Change of maize yield in A2a and B2a scenarios
50
15 The Probability of exceedance in yield of Melkassa 2 (A-twice modified mold boar.Bmodifed mold board-ripper wing plows-normal mersha) using different tillage practice
in relative to base period (1977-2013) and future (2020s, 2050s and 2080s) within A2a
and B2a emission scenarios at Melkassa station
51
16 The Probability of exceedance in yield of Melkassa 4 (A-twice modified mold boar.Bmodifed mold board-ripper wing plows-normal mersha) under different tillage practice
in relative to the base period (1977-2013) and future (2020s, 2050s and 2080s) within
A2a and B2a emission scenarios at Melkassa station
53
xii
LIST OF TABLES IN THE APPENDIX
Appendix Tables
1
page
Comparison of observed against simulated parameters for Me-2 under Melkassa
climate
73
Comparison of observed against simulated parameters for Me-4 under Melkassa
climate
73
3
Projected temperature and rainfall change under different scenarios
74
4
Projected change in monthly rainfall at Melkassa
74
5
Projected change in monthly minimum temperature at Melkassa
75
6
Projected change in monthly maximum temperature at Melkassa
75
2
xiii
CLIMATE CHARACTERIZATION AND MODELING THE IMPACT OF CLIMATE
CHANGE ON PRODUCTION OF MAIZE (zea Mays L.) UNDER DIFFERENT
CONSERVATION TILLAGE PRACTICES IN CENTRAL RIFT VALLEY OF
ETHIOPIA
ABSTRACTS
Climate and agriculture are highly interrelated in Ethiopia, where the driver of the economy
is agriculture. There is a need to characterize and asses the impacts and explore the
adaptation options for changing climate in the Central Rift Valley (CRV) of Ethiopia. The
experiment that involves the Maize (Me-2 and Me-4) crop was conducted at Melkassa
Agricultural Research Center (MARC) using split plot design, to model impact of climate
change on maize (Me-2 and Me-4) production, and assesses the effects of twice modified
moldboard, one time modified moldboard, Ripper-wing plows and normal mersha
conservation tillage practices on production of maize under projected climate change. In
order to characterize the climate of Mieso, Melkassa, and Adami Tulu located in CRV of
Ethiopia daily climate data were obtained from MARC for Mieso, Melkassa and Ademi Tulu
and used for characterization using INSTAT V3.37. The HadCM3 model predictors and
previous maize (Me-2 and Me-4) yield data were obtained from the National Centre for
Environmental Prediction (NCEP) and MARC, respectively. Climate change scenarios for
rainfall, minimum and maximum temperatures were developed for the period 2011-2099 from
HadCM3 A2a and B2a SRES emission scenarios using SDSM v4.2.9, the input of which was
used for impact and adaptation options study using DSSAT v4.5. The mean start, end of
season and length of growing season (LGS) are found to be May 26, September 14, and 99
days at Mieso site; May 27, October1st and 97 days at Melkassa site; May 26, September 11,
and 109 days in Adami Tulu site. The rainy days ranged from 92-165, 92-147, and 92-110
days in Mieso, Melkassa and Adami Tulu, respectively. The seasonal mean rainfall at Mieso,
Melkassa and Adami Tulu is 438, 577.7 and 430.3 mm. The probability of occurrence of dry
spell lengths of 5, 7, 10, and 15 days reaches minimum value during the peak rainy months
and then starts to increase after the end of the rains. The Man-Kendall and Sen.’s slope
estimations indicated increasing seasonal rainfall and maximum and minimum temperatures
during 2020, 2050, and 2080s. Under changing future climate, Melkassa-2 will perform
better, while Melkassa-4 will be affected negatively during 2020, 2050, and 2080s under A2a
and B2a emission scenarios. Combined use of modified moldboard tillage with Melkassa-2
maize variety will give better yield as compared to the normal Maresha yield in 2020, 2050,
and 2080s under A2a and B2a emission scenarios. Hence, developing new varieties and use
of appropriate soil moisture conserving tillage practices will enhance adaptation capacity of
farmers under changing climate in CRV of Ethiopia.
xiv
1. INTRODUCTION
Agriculture is highly dependent on local natural resources including climatic conditions and
therefore climate and agriculture are highly interrelated. Global warming is projected to
increase as could be best explained by increase in concentration of CO2 and rising
temperature, as well as increased variation in rainfall, pattern. The rising temperature might
enhance the heat load on phonological stages of crops. Eventhough the change in climatic
variables can have some beneficial effect for some crop in some place on the world, the
overall impact tends to move in opposite direction. Accordingly, the challenges of the change
in frequency and severity of drought and floods can be controlled and their effects
significantly reduced. (en.org/wiki/Agriculture in climate. April, 2013).
Accounting for 52% of the 90% of the total export earnings and employing about 85% of the
labor force, agriculture in Ethiopian economy’s what petroleum is for developed countries.
(USIDS, 2007: IMF, 2002; World Bank, 2002; CSA, 1999). However, Ethiopian agriculture
is dominated by smallholder farmers with an average per capita land holding of less than a
hectare. The production system mainly depends on rain fed system and low input including
fertilizer and pesticides usage.
United Nation Framework Convention on Climate Change (2001) reported that there is a
declining trend in annual rainfall over Northern and increasing trends over the central parts of
Ethiopia. Global Circulation Model outputs (GCMs) for the year 2030 shows an increasing
temperature by 1oC and decreases in rainfall up to 2% (IPCC, 2003). According to this
projection, climate change will enhance the frequency of extreme events; making agricultural
sectors vulnerable, thus resulting in poor harvest and /or total crop failure. Consequently this
will be a complex challenge to feed the ever growing human population.Viccet (2004)
reported that Ethiopia ranks as seventh most vulnerable country in Africa to the impact of
climate change. Grey and Saddoff (2005,2006) and World Bank (2005) reported the existence
of strong link between Ethiopian economy and climate performance, however over a third of
its growth potentials cost the country due to climate driven risks and is likely to reduce this
potentials by 38% and increase poverty by 25% over 12 year periods .Overall, crop
1
productions are predicted to further dwindle lagging very much behind population growth and
increasing food insecurity at house hold and national level and thus, perpetuation of incessant
poverty The production agricultural sustainability driven by climatic resources in agrarian
Ethiopia is at risk being comprised (Habtamu,et al,2012).
In context, maize (Zea, mays.L), a tropical climate crop is widely grown in the study area
covering about 40% of the total maize production in Ethiopia where it contributes less than
20% to the total annual production.( Mandefro et al.,2002), In Ethiopia, maize covers the
largest cultivated land as compared to other cereals, pulses, and oils crop, with average annual
production of 6 million tones. Out of the estimated total cultivated land (2.05million ha), it
covered 11.32% in 2010/2011 and 6.36% in 2011/12 (CSA, 2012,). From the figure one may
note that, although the percentage of land under maize production gradually decreases, the
total area still continued to increase as a result of more and more new land is being
encroached and put under cultivation each year. In more details, maize is grown chiefly
between elevation of l500 and 2200 meters above sea level and requires large amounts of
rainfall to ensure good harvests. It is particularly important in southwest Ethiopia, being the
highest producer (Dereje and Eshetu, 2010).
Reportedly, maize yield has been in a declining trend under the intensively eroded and
degraded soils (Tolessa and Tesfa, 2011). The highly pulverized soil condition with, erosive
and erodible rainfall which predisposes the loss of topsoil and water losses, results in a highly
degraded soil with very low productivity.
For Ethiopia, conservation tillage system that involves contour plowing and sub soiling has
been developed using a modified Maresha Plow, and increases infiltration by disrupting plow
pans (Busscher et al., 2002). Also, Maresha Modified sub soilers have been found to
effectively disrupt the plow pan resulting in increased soil water availability (Temesgen et al.,
2009; McHugh et al., 2007).
This study was therefore designed to study Climate characterization and the impact of climate
change on maize production in the Central Rift Valley of Ethiopia and the effects of soil
2
moisture conservation practices in mitigating the effects of climate change on maize
production. The Central Rift Valley in Ethiopia constitutes the heart and corridor that extends
from the Afar Triangle in the north to the Chew Bahir in southern Ethiopia (FAO, 1984).
Therefore, this research result presents the contribution of improved soil water management
practices in maize farming, which is one of the best adaptation measures to the changing
climate under specific locality of the Central Rift Valley of Ethiopia. The overall objectives of
this research were:
To characterize the climate of the Central Rift Valley of Ethiopia using historical
ground observation weather data of Mieso, Melkassa and Adami Tulu.

To model impact of climate change on maize production in the Central Rift Valley
of Ethiopia.

To assess the effect of different conservation tillage practices as an adaptation
measures on production of different maize varieties under projected climate
change.
3
2. LITERATURE REIVEW
2.1. Climate Characterization and Agriculture
2.1.1. Start and end of rainy season and length of growing periods
The start of the rainy season both for Belg (the shorter) and Kiremt (the main growing season)
can be identified based on simple soil water balance model (FAO,1978).Using penman
equation for calculating evapo-transpiration a start of period can be obtained when a dekadal
(ten day) rainfall amounts is equal or greater than half of the reference evapo-transpiration
during the begging of the rainy season similarily,end of rainy season is obtained when a
dekadal rainfall amount is less than half of the corresponding reference evapo-transpiration at
the end of rainy season .Then after, the length of growing season (LGS) may be defined by
counting the number of days between the start and end of the growing season plus the period
required to evapo-transpire the 100 mm moisture stored in the soil during rainy season
(Fitsum, 2009; Mersh, 2003; FAO, 1978). According to Mupangwa et al. (2013) length of
growing season is calculated by subtracting the date of the beginning from the date of ending
of the growing season.
The minimum daily rainfall threshold of 25 mm can indicate the termination of the growing
season (Zargin, 1987). Others estimated the start and end of the rainy season without
evapotranspiration input data (Feyera, 2013; Edoga, 2007; Messay, 2006; Stern et al., 2006;
Girma, 2005). They applied different criteria in setting an onset and end date of rainy season
for different crops exhibiting different maturity. Girma (2005) and Raman (1974) adopted 20
mm of total rainfall received over three consecutive days that were not followed by greater
than 10 days of dry spell length within 30 days from planting days. A period of 30 days is
average length for the initial growth stage of most crops and this criterion is useful in
practically mitigating the seedling establishment related to rainfall risk (Girma, 2005; Allen et
al., 1998).
2.1.2. Seasonal (Kirmet) rainfall
According to the report of Krauer (1998) reports, the kiremet rainfall contributes for 50 to
90% of the annual rainfall over major rainfall areas of the country and responsible for 85 to
95% of the production of food crops of Ethiopia. It is relatively stable when compared to the
4
Belg season rainfall. However, irregularity and deficiency of the rainfall of this season affect
the food production of the country (NAPA, 2007; NMA, 1996a).
The Inter-tropical Convergence Zone (ITCZ) is located north of Ethiopia and pronounced
cyclic cell along the ITCZ are over between June and September in North Africa and the
Arabian Peninsula. The rest of the country comes under the influence of the Atlantic
equatorial weserlies and Southerly winds from the equatorial Indian Ocean. The southwestern
equatorial westlies ascend over the south west highlands and produce the many rainy season
over most parts of the highland of Ethiopia. During Kiremt season the maximum rainfall
occurs over the southern highlands (600-1200 mm) while the rest of the regions get lesser
amount (Messay, 2006)
2.1.3 Probability of dry spell occurrence
The dry spell becomes critical in rain-fed agriculture, particularly for the establishment of
seedling or so after planting. In general, a dry spell of any length could occur at any stage of
crop growth; however, it is potentially detrimental if it coincides with the most sensitive
stages such as flowering and grain filling (Stern et al., 1982). Estimation of the probability of
a given amount of rainfall and dry spell length is extremity important for agriculture planning.
In a given crop growing season, decisions are made according to determined probability of
receiving certain amount of rainfall or not. This type of calculation is called analysis of initial
probabilities. Similarily,working for the probability of having a rain in the next day if we had
rain this day and the probability of getting rain next day given past day is dry are termed as
conditional probability. The above probabilities can be performed by Markov chain model
probability analysis.
According to Stern et al. (2006) the degree of wetness could be defined in terms of any
amount of rainfall and the choice of any threshold values depends on the purpose of which the
different probabilities may be used. Under the initial probability the percentage probability of
the day being wet or dry can be obtained while in the case of conditional probability, it is
possible to estimate the percentage of one or more wet or dry being followed by one or more
wet or dry days (Tilahun, 2000)
5
2.1.4. Rainfall and temperature trend analysis using mann-kendall and sen’s test
Global climate has changed significantly in the last hundred years. Global mean surface air
temperature has increased by 0.74 °C during the last century (IPCC 2007). Increasing
temperature, snow cover retreat and changing patterns of precipitation, are among the many
consequences which are attributed to climate change. Trend detection in temperature and
rainfall time series is one of the interesting research areas in climatology. It is noted that
rainfall and temperature changes are not globally uniform. Regional variations can be much
larger, and considerable spatial and temporal variations may exist between climatically
different regions (Yue and Hashino, 2003)
As several researchers cited, the Mann-Kendall and Sen’s (MAKESEN) slope estimators test
are used to identify trends in the hydro-meteorological and climatology variables due to
climate change (Timo et al., 2002) in different parts of the world. Trend analysis and change
point detection in temperature and rainfall series have been investigated by many researchers
throughout the world (Buhairi, 2010; Croitoru et al., 2012; Karpouzos et al., 2010; Sun et al.,
2010; Smadi , 2006). The Mann–Kendall test is a non-parametric approach, widely applied in
various trend detection studies (Alexander and Arblaster, 2009; Kizza et al., 2009; Karaburun
et al., 2011). As mentioned in Arndt et al. (2009) the method is best to detect trend because it
is not affected by outliers and missing data. The non-parametric Mann-Kendall test is used to
determine whether there is a positive or negative trend in data with their statistical
significance.
Mann-Kendall and Sen.’s (MAKESEN) slope estimators performs two types of statistical
analyses. First the presence of a monotonic increasing or decreasing trend was tested with the
non-parametric Mann-Kendall test and secondly the slope of a linear trend was estimated with
the non-parametric Sen.’s method (Gilbert 1987). As mentioned in Timo et.al.(2002) Sen.’s
slope estimator is not affected from gross data error or outliers, it is used to estimate the slope
for Mann-Kendall test even with missing data.
6
2.2. Impacts of Climate Change on Crop Production
2.2.1. Average temperature increases
Rising in mean, maximum and minimum temperature are projected for most regions of the
world as result of climate change. It is expected that countries in low latitude (Tropical and
sub-tropical) regions ,where water availability is low ,would generally be at risk of decreased
crop yield at even 1 to 2 oC of warming (FAO, 2008b ; Parry et al., 2007).This is as result of
increased evapotranspiration and accompanying soil water deficits (Bals et al., 2008).Thus,
the phenomenon would result in some of agricultural lands in Sub Saharan Africa
(SSA),which is located in tropics, becoming unsuitable for cropping and some grass lands
becoming unsuitable for pasture (Bals et al., 2008).This would result in crop yield reduction
the regions .The extents of this declines in yields is still unknown, but some analysis suggests
that it would be sever (Bals.et al., 2008).
Increasing temperatures resulted in reduction in crop yields by affecting a range of
physiological, biochemical and molecular processes. Sensitivity to supra optimum
temperatures and mechanisms of tolerance depend on the severity, timing and duration of heat
stress together with the developmental stage of the plant. The most significant factors related
with yield lessening under heat stress are increased sterility, shortened life cycle, reduced light
interception and the perturbation of carbon assimilation processes (photosynthesis,
transpiration, and respiration (Reynolds et al., 2011).
2.2.2. Change in rainfall amount and patterns
It is expected that , the temperate regions could become wetter and dry areas in the tropics
could become drier (FAO, 2008b).The intensity of rainfall storms could increase with rainfall
and becoming more variable and unpridictable.The change in rainfall can affect soil erosions
rate and soil moisture, both of which are important for crop productions..SSA would
experience decreased precipitations which according to Parry et al. (2007) is about 20%.Thus
,increases in temperature along with reduced precipitations will likely result in loss of arable
lands ,increases aridity, increases salinity and ground water depletions (Bals et al., 2008)
shortage of water could limits window of opportunities to maintain or extended the cultivated
7
agricultural lands through the use of irrigation FAO (2008) opined that reduction invaluably
good quality water for crop at certain times of the year will negatively affect food
supplies.SSA depends on rain fed agriculture and other deformations of the rainfall patter
would limits crop productions and this would bring untold, physical and scio-economic
hardships for the farmers
2.3.
Climate of Ethiopia and Its Local Classification
Ethiopia’s seasonal climate is mainly described by the seasonal migration of the Inter-tropical
Convergence Zone (ITCZ) and the underlying atmospheric circulation as well as by the local
topography. It has a diverse climate, ranging from semi-arid desert in the low lands to humid
and temperate type in the southwest (IFPRI, 2011; MOA, 2000). In Ethiopia the local climate
classification is based on elevation and temperature. In other words, based up on the elevation
for any area, there is an associated decline in mean annual temperature. This is a way to
understanding and identifying traditional climate zone of a given area. The three traditional
climate zones of Ethiopia are: Kola ( less than 1800 m a.s.l and mean annual temperature 2028 oC),Woina dega (between 1500 m and 1900 m a.s.l and mean annual temperature 16-20
o
C), and Dega (greater than 1900 m a.sl and mean annual temperature 6-16 oC) ( IFPRI, 2011;
MOA, 2000).
The climate of Ethiopia ranges from humid to semi-arid with abundant and scarce soil/air
moisture. Moreover, an extreme variation in rainfall occurs from season to season and yearto-year. This inter-seasonal and inter-annual rainfall variability imposes severe impacts on
agriculture, water resources, and other socio-economic activities of the country (Gebrhiwot,
2010). Data until 1970s show that and parts of central, eastern, and northeastern Ethiopia is
positive, especially for July and August which are the peak months of the Kiremt season.
According to Bewket( 2009) later shows a negative anomaly for most of the years throughout
the season The average mean annual minimum temperatures from 40 stations and for the
period 1951-2006 show that there has been a warming trend over the last 55 years throughout
the country, increasing by about 0.37 °C every decade. The trend analysis shows that annual
rainfall remained more or less constant when averaged over the whole country (NAPA,
2007).Mean annual temperature distribution over the country varies from about 10 C over
8
the highlands of northwest, central and southeast to about 35 C over north-eastern lowlands
(NAPA, 2007).
2.4.. Impacts of Climate Change on Crop Production in Ethiopia
The impacts of rainfall on crop production in Ethiopia are closely related to its total seasonal
rainfall, and dry/wet spells. Thus, declines in seasonal rainfall total and dry/wet spells are
more critical and determinant of crop growth and final yield. This is because unusual rainfall
amounts and distributions usually lead to poor harvest and/or completes crop failures at the
end of the season (Tesfaye and Assefa, 2010).
According to Nigist, (2009) crop failures were frequent in recent times in semi-arid regions of
Ethiopia due to climate change. Climate change has posed complex challenges to Ethiopian
agriculture through increased frequency of drought events as well as unpredictable rains that
fall in a shorter but more intense incident (Arndt et al., 2009). Hence, given a crowd of stress
factors in semi-arid regions in particular, stresses on crop yields and environmental system
beyond recovery. For this reason, the impacts of climate change on agriculture may bring
additional burdon to the development challenges of ensuring food security and reducing
poverty (Peter et al., 2003)
.
2.4.
Impact of Climate Change on Maize Production in Central Rift Valley of
Ethiopia
Maize has been one of the C4 plants for which climate change impact assessments have been
carried out .Climate change is also likely to lead to an increase in temperature and decreases
in rainfall (Jone et al., 2012). Maximum temperature is projected to increase by an average of
2.6 °C across maize mega environments in Sub-Saharan Africa (Cairns et al., 2012). While a
few degrees increase in temperature is likely to increase crop yields in temperate areas, in
many tropical areas even minimal increases in temperature may be detrimental to food
production.
The climate change impact on agricultural production in the tropics and subtropics will be
greatest, particularly in Africa vulnerable due to the range of projected impacts, multiple
9
stresses and low adaptive capacity. Compared to the situation without climate change,
Climate change is projected to reduce maize production globally by 3 to 10% by 2050
(Rosegrant et al., 2009).According to the current reports more than 20,000 past trial of maize
yields in Africa over an eight-year period exhibited maize yields were reduced by 1 and 1.7%
for every degree day above 30°C under optimal and drought conditions respectively (Lobell et
al., 2011).
According to Jones and Thornton (2003) report, due to increased temperatures and reduced
rainfall, crop yields in Africa may fall by 10 to 20% by 2050. However, this figure masks
variation. In report of Thornton et al. (2009) in some areas crop reductions will be greater
(northern Uganda, southern Sudan, and the semi-arid areas of Kenya, Ethiopia and Tanzania)
while in other areas crops yields may increase (southern Ethiopia highlands, central and
western highlands of Kenya and the Great Lakes Region)
In Ethiopia the impacts of rainfall on crop production are closely related to its total seasonal
rainfall amount or its intra-seasonal distribution (Bewket, 2009). Therefore, the annual or
seasonal rainfall, decline in the peak and retreat of rainfall are more critical and detrimental to
crop germination and yield. Poor harvest and/or complete crop failures at the end of the
seasons are due to infrequent rainfall amounts and pattern (Tesfaye and Assefa, 2010).
According to Girma et al. (2011) and Efrem and Girma (2009) a rising of temperature on
maize production in the CRV of Ethiopia remains uncertain and there could be a risk of
significance yield losses. Thus, the impact of climate change on maize production is highly
expected in CRV Ethiopia.
2.6. Adaptation Measures on Maize Production in Central Rift Valley of Ethiopia
To reduce the negative impact of climate change identification of adaptation as one of the
policy options on maize production (Adger et al., 2003: Kurukulasuriya and Mendelsohn,
2006a). Adaptation to climate change refers to adjustment in natural or human systems in
response to actual or expected climatic stimuli or their effects, which moderates harm or
exploits beneficial opportunities (IPCC, 2001).Common adaptation methods in agriculture
10
include: use of new crop varieties and management’s that are more suited to drier conditions,
irrigation, crop diversification, tillage practice and changing planting dates (Nhemachena and
Hassan, 2007: Kurukulasuriya and Mendelsohn, 2006;)
Through breeding approaches it is possible to develop climate-adapted varieties by the
drought proven breeding methodologies in managed stress screening has resulted in
significant grain yield increases under drought stress in conventional breeding for tropical
maize production (Bänziger et al., 2006). The development of molecular breeding technology
and phenotyping offers new high-throughput approaches to developing varieties for future
climates (Cabrera-Bosquet et al., 2012). Furthermore, novel alleles associated with drought,
heat and water logging tolerance, and stress combinations have also been identified using the
latest advances in whole genome sequencing (Ortiz et al., 2009) that can be used for breeding
new high yielding Together these developments should speed up the development of climate
adapted maize varieties.
Climate change will be especially detrimental to crop production in cropping systems where
soils have degraded to an extent that they no longer provide sufficient buffer (for example,
adequate water holding capacity) in contradiction of drought and heat stress. These affects
will be most severe if irrigation is not available to compensate for decreased rainfall or to
mitigate the effects of higher temperature. Improving genetic adaptation to heat or drought
stress alone will not address these problems; there is also a need for harmonizing agronomic
interventions, like cropping system, date of planting ,tillage practice and soil and water
conservation (Hobbs and Govaerts, 2010). To be realized the benefits from investment in
genetic technology if crops are grown in well-managed soils that maximize expression of
genetic potential and buffer the crop against climate change.
In Ethiopia, the risks of change in climate patterns that smallholders face is believed to be due
to low adaptive capacity and limited adaptation options of the agricultural sector (Yesuf et
al.,2008).Climate change and adaptation option has not yet mainstreamed in national research
system and development effort and local adaptive response to climate variability and are not
well documented in Ethiopia (Bewket, 2012; Belay et al., 2014) In order to mitigate the
11
impacts of rainfall variability on crop production, Walker
and Mamo (2007) reported
‘decision support tool’ for prediction of rainfall as the best possible alternative planting for a
given homogenous rainfall zone of Central Rift Valley of Ethiopia
2.7. Conservations Tillage and Climate Change
Among different operations, soil tillage, due to its influence on physical, chemical, and
biological properties of the soil environment, is considered among the most important factors
in agriculture that would affect crop yield (Keshavarzpour and Rashidi, 2008). Conservation
tillage has greater impacts on erosion rates than on runoff and infiltration (Leys et al., 2010).
Soil tillage has a major influence on the water intake, storage, evaporation and absorption of
water from the soil by plant roots, biological activity, and organic matter break down, which
influence the soil aeration, soil moisture and soil temperature. Kovac and Zak (1999) found
that the changes in soil physical properties were influenced by different tillage treatments but
the changes were small and insignificant. Some authors pointed out that the tillage treatments
affected the soil physical properties, especially, when the same tillage system has been
practiced for a longer time (Jordhal and Karlen, 1993).
Primary tillage is generally found to be necessary for creating a favorable root proliferation
zone, enhancing water percolation and increasing porosity of the soil. The experiment
conducted on plough type and tillage frequency for the production of maize in the dry land
areas of Ethiopia showed 75, 43 and 25% increase in grain yield of maize with the use of erf
and mofer attached moldboard ploughs over the traditional plough when ploughing, once,
twice and thrice, respectively (Melesse et al., 2001).
According to Endeshaw et al. (2009), farmers pointed out that the use of the new plough has
achieved complete plugging in one pass thereby reducing tillage passes by 50%, hence
farmers could save time to dwell on other activities. In addition, it resulted in improved tillage
and seedbed preparation; and therefore increased water infiltration and timeliness in land
preparation and weeding, reduced drudgery and savings in labor and time compared to the
traditional plough. Combination study of planting density of three maize verities (Melkassa-1,
ACV6 and A511) and tillage methods for moisture conservations at Melkassa and Mieso
12
demonstrated the importance of tie-ridging for improved maize productivity and thus
suggested that it could be used in other moisture stress areas having small rain fall Pattern in
the country (Tesfa et al.,2011).
2.8. Climate Change Mitigation Using Improved Tillage Practice
Greenhouse gases (GHG) emitted into the atmosphere through agriculture activities and have
been reported to be contributing to an increase in global mean temperature of approximately
0.74°C (1.33°F) over the past century (IPCC 2007).sequestration
activities
enhance
and
preserve carbon sinks and include any practices that store carbon through crop-land
management “best practices”, such as no-till agriculture, or slow the amount of stored carbon
released into the atmosphere through burning, tillage, and soil erosion. Sequestered carbon is
stored in soils, resulting in increases in soil organic carbon (SOC). Soil carbon sequestration is
estimated to account for 89 percent of the technical mitigation potential in agriculture,
compared to 11 percent for emissions abatement (Smith et al., 2007a).
The technical potential of global cropland soils to sequester carbon through a combination of
these techniques has been estimated at 0.75 to 1 Gt/year total (Lal and Bruce, 1999). One
technique emphasized in the literature for as having a high mitigation potential is no-till
agriculture. Estimates indicate that tillage reductions on global cropland could provide a full
“wedge” of emissions reductions up to 25 Gt over the next 50 years (Pacala and Socolow,
2004).However, have noted that tillage reductions may not be feasible in all soil types (Baker
,2007) argue that improper sampling techniques, together with modern, gas based
measurements, cast doubt on previous findings of positive carbon offsets through tillage
reductions. Tillage practice on sandy loam and loam soil type in the dry land, CRV of
Ethiopia, markedly improved organic matter content, N concentrations, and soil moisture
content (Worku et al., 2006).
13
2.9. Climate Change and Decision Supporting Techeniques
2.9.1. Downscaling global circulation model outputs
Global Circulation Models (GCM) are mathematical representations of atmosphere; ocean, ice
cap, and land surface processes based on physical laws and physically based empirical
relationships. Such models have been used to examine the impact of increased greenhouse gas
concentrations on future climate. GCMs estimate changes for dozens of meteorological
variables for grid boxes that are typically 250 kms in width and 600 kms in length. Their
resolution is therefore quite coarse (IPCC AR4,2007).The most advanced GCMs couple
atmosphere and ocean models and are referred to as coupled ocean atmosphere GCMs Gates
et al. (1996) for an evaluation of coupled GCMs. However, the results are made available to
the general scientific community and have so far been used for studies of climate change and
its impacts on natural, social, and economic systems (IPCC AR4, 2007).
The GCM results will be first statistically downscaled so as to increase the spatial resolution,
thus making the data more appropriate for a regional assessment. These GCM simulations
were run at IS92a (or similar) emission scenario and were made within the frame of the
Coupled Model Inter comparison Project (Covey et al., 2003).Availability of a relatively high
number of Global Circulation Models (GCMs) in combination with a variety of emission
scenarios, which are based on various assumptions on future socio economic development of
human society and environment (Houghton et al., 2001), results in a wide spectrum of
possible climate change scenarios that differ significantly in some key parameters
2.9. 2.Hadley centre coupled model version 3 (HadCM3)
HadCM3 is a coupled atmosphere-ocean GCM (AOGCM) developed at the Hadley Centre
and it was developed from the earlier HadCM2 model and is part of their “Unified Model”
family of weather and climate models. HadCM3 is composed of two components: an
atmospheric model (HadAM3) and an ocean model, which includes a sea ice model
(HadOM3). Various improvements were applied to the 19 level atmosphere model with a
horizontal resolution of 2.750x 3.750and the 20 level ocean model with a horizontal resolution
of 1.250x 1.250and as a result the model requires no artificial flux adjustments to prevent
excessive climate drift. The atmosphere and ocean exchange information once per day, heat
14
and water fluxes being conserved exactly. Momentum fluxes are interpolated between
atmosphere and ocean grids so are not conserved precisely, but this non-conservation is not
thought to have a significant effect. The HadCM3 model was used by the Hadley Centre to
provide input for the IPCC Third (TAR) and Fourth Assessment Reports (AR4) and used by
Climate Prediction.Net also HadCM3 is a very powerful tool: in principle all the different
aspects of the simulations can be controlled and studied.
2.9.3. Use of statistical downscaling for climate scenarios analysis
The computationally inexpensive and site specific future climate information that can reveal
the future likelihood are critical issues in order to reduce the negative impacts of climate
change on agriculture. Global Circulation Models (GCM), built based on various assumptions
of greenhouse gas concentrations could, therefore, provide a reliable future climate scenario.
The most widely used GCM scenario for agricultural and other decision applications is
doubling of the concentration of atmospheric carbon dioxide (Wilby et al., 2004).The
information obtained from GCMs could then be taken into a method for obtaining high
resolution climate or climate change information from a relatively coarse resolution global
climate models through statistical climate downscaling processes typically with a resolution
range of 150-300 km by 150-300 km (Wilby and Dawson, 2007;Wilby et al., 2004).
In statistical downscaling technique, regional or local climate information is drived first by
developing a statistical model which relates large scale climate variables or “predictors” to
regional and local variables or predictand (Fealy and Sweeney, 2007). Large scale predictor
variables are then extracted from GCM output and used to drive the statistical model that
generate local scale climate for future time period (Swansburg et al.,2004). Although mean
temperature and precipitation (seasonal, monthly, or daily) are the most commonly used local
predictand, statistical downscaling has also been applied to generate local scenarios of cloud
cover, daily temperature range, extreme temperatures, relative humidity, sunshine duration,
snow cover duration, and sea-level anomalies (Kattsov and Kallen, 2010).
On the other hand, optimal choice of predictors could depend upon the predictand themselves.
For instance, for downscaling local temperature, large scale fields of geopotential height or air
15
temperature might be used. For precipitation, large scale fields such as mean sea level
pressure, geopotential height; absolute or specific humidity and divergence are all predictor
candidates (Kattsov and Kallen, 2010). In general the selected predictors should either in
isolation or combined be able to account for most of the observed variations in the predictand
(Kattsov and Kallen, 2010).
Global Circulation models on which downscaling is based as well as downscaling techniques
themselves are continuously improving thus ; allowing for a cyclical approach to review and
evaluate adaptation plans each time an improved projection is released for a particular local
activities (Corell and Carter, 2007). Therefore, mitigation and adaptation strategies will
evolve in planning communities’ activities at all levels of governments, industry and business,
and other formal and informal institutions of a society for managing the projected risks of
climate change as obtained from statistical downscaling (Droogers and Aerts, 2005;Wilby et
al., 2004).
The outputs of the Hadley Centre Coupled Model (HadCM3) A2a and B2a scenarios are the
most commonly used GCM for downscaling future climate of an area. For instance, Zeray et
al. (2006) in his study used HadCM3 GCM model outputs established on the A2 and B2
SRES emission scenarios to downscale climate change scenarios of Lake Ziway of Ethiopia.
In addition to climate scenario analysis, the outputs of HadCM3 A2a and B2a scenarios could
also be used for climate change impact analysis using crop modeling (Tachieobeng et al.,
2010).
2.9.4. Crop simulation model
Crop simulation models are defined as computer programs that simulate of crop growth by
numerical integration of constituent processes with (Matthews et al., 2002). More specifically,
it is a computer program describing the process based dynamics of the growth of a crop (e.g.
rice, wheat, maize, groundnut, tea, etc.) in relation to the environment, operating on a timestep an order of magnitude below the length of growing season, and with the capacity to
output variables describing the state of crop at different points in time (e.g. biomass per unit
area, yield, etc) (Matthews and Stephens, 2002).These crop models copycat crop growth and
16
developments for a given set of inputs or information of soil, weather, and crop specific
model parameters.
In agricultural production system, crop simulation models are normally used to assess impact
of projected climate change. This has been proven by several scientists (Carbone et al., 2003;
Chipansi et al., 2003; Chalinor et al., 2004). Crop simulation models have been an effective
and extensive tool in studying plant and climate relationship or climate impact studies. Jones
et al. (2003) used CERES-Rice to assess the effect of climate change on rice production. On
the other hand, Tsvetsinskaya et al. (2003) used DSSAT crop models to determine the effect
of spatial scale of climate change scenarios on the crop production in the Southeastern United
States. Shufen et al. (2011) modeled the potential corn production in China under two climate
change scenarios using CERES-Maize.
2.9.5. DSSAT model and impact of climate change
Decision Support System for Agro technology Transfer (DSSAT) is a software package that
integrates the effect of crop phenotype, soil, weather, and crop management system through a
database system and allows users to simulate experiments on desktop computers in a minute,
which would take significant quantity of time to conduct (ICASA, 2005). According to Jones
et al. (2003), DSSAT enables the user to study the “what if” results of different management
option and strategies through its different independent programs that operate together. These
programs include crop simulation models and databases that describe weather, soil,
experiment condition and measurements, and genotype information. The software also
enables users to prepare inputs for each of the programs and compare simulation results with
observation, giving users confidence in the models or determine possible modification to
achieve improve accuracy. In addition, DSSAT programs allow users to assess risk associated
with different crop production strategies through its multi-year simulation option. Conversely,
DSSAT also has a built-in function to specify changes in weather variables without directly
modifying the original weather file which suits it for climate change impact studies. In recent
updates of the software it can also directly read historical atmospheric carbon dioxide data
from Mauna Loa, Hawaii (Hoogenboom et al., 2010).
17
According to Jone et al. (2003), DSSAT consists of different crop models such as CERESMaize for corn. Recently, DSSAT crop models have been cited by UNFCC (UNFCC, 2008)
as a tool which can be combined or integrated into other tools or methods to evaluate impacts,
vulnerability, and adaptation to climate change
18
3.
3.1.
MATERIALS AND METHODS
Descriptions of the Study Areas
The experiment that involved the maize crop was conducted at Melkassa Agricultural
Research Center (MARC). The Center is located at 8° 24 'N latitude and 39° 12'E longitude
and at an altitude of 1550 meter above sea level (m.a.s.l.) within 15 km south-east of Adama
Town and 115 km from Addis Ababa, in semi-arid region of the Central Rift Valley (CRV) of
Ethiopia. The site receives a mean annual rainfall of 826.5 mm and the maximum and
minimum annual mean temperatures are 28.5 and 13.8 °C(1977-2013), respectively.
According to Ministry of Agriculture (MOA, 2000) the agro-ecology of the area is
characterized under sub moist, mountain and plateau, tepid to cool based on the growing
season, temperature and altitude of the area. The soil type at the study site is a well-drained
silty clay loam soil largely developed from volcanic parent material. Crops grown in the area
include maize (Zea mays L.), sorghum (Sorghum bicolor), teff (Eragrostis teff), and other
cereals, pulses, and oil crops (MARC, 1998).
The second study site Adami Tulu Agricultural Research Center (ATARC) located at 160 km
to south east of Addis Ababa with geographical location of 7° 52´N latitude and 38° 43´ E
longitude and with altitude of 1640 m. a.s.l. The mean annual rain fall and maximum and
minimum temperatures of the areas last 39 years are 798.53 mm and 26.8 °C and 14.5 °C
(1973-2012) respectively .The third study site Mieso located to the east of Addis Ababa at
about of 300 km with).the geographical location of 8o 48’N latitude and 40o 9’ E longitudes
and found at an altitude that range from 1470 m a s l. The mean annual temperature minimum
14.7 oC, maximum temperature 39.4 oC and the mean rainfall is 465.7 mm, respectively
(Shinji et al., 2009
19
Figure 1; Location map of the study areas
3.2.
Characterization of Climate of Mieso, Melkassa and Adami Tulu Areas
3.2.1. Meteorological data sources and quality assessment
The past and current climate of the three study areas found in the Central Rift valley (CRV) of
Ethiopia was characterized using ground observation data that were recorded at MARC
(1977-2013), ATARC (1973-2012), and Mieso (1973-2012) meteorological stations The
whole dataset did not have more than 10% missing values. The characterization focused on
determination of dates of the start and end of the season, length of the growing season, dry
spell and number of rainy days using procedures described by Stern et al. (1982). INSTAT
software v3.36 was used for analysis of the daily rainfall data. The data series was also
examined for homogeneity using the cumulative deviation method and no heterogeneity was
detected. Some missing and the outlier data were estimated using INSTAT+ v3.37 first order
Markov-chain simulation model Stern and Knock, 2006). The main reason for choosing this
model to fill the missing daily rainfall, minimum and maximum temperature data is that it
does not overstate the result and gives a more accurate model to each of the study areas as has
been explained by NMSA (1996b).
20
3.2.2. Determining the start, end and length of the growing season
The beginning of the rainy season can be defined as the first occurrence of at least 20 mm
rainfall totaled over 3 consecutive days (Stern et al., 1982). This potential start can be a false
start if an event, dry spell, occurs afterwards, as a dry spell of 9 days in the next 21 days. This
paper also adopted this approach and the earliest start of season (SOS) was defined as the first
occasion when the rainfall accumulated within a 3-day period was 20 mm. Since the study
areas exhibit a mono modal rainfall pattern (long rains during April–September), April 1st was
taken as the earliest possible planting date for the study area.. Accordingly, the potential
starting date of the growing season was defined as the first occasion from April 1st that has at
least 20 mm rainfall within a 3-day period. An experimental evidence revealed the choice of
50% ETo as the threshold for water availability, for the crop water stress becomes severe
when the available water drops below half of the crop water demand (<0.5 ETo) (Doorenbos
and Kassam, 1979). Hence, the minimum required rainfall amount of a particular date of onset
should be at least half of the amount of evapotranspiration (ETo) of that particular date.
The end of the season (EOS) was determined from rainfall-reference evapotranspiration
relationship. The end of the season is the end of rainy season plus the time required to
evapotranspire 100 mm of stored soil (Vertisols) water (Feyera, 2013; Girma, 2005; Stern et
al., 1982; Kassam et al., 1978;). There was humid period, when ETo was less than the rainfall
at the study areas. So, surplus stored soil water was available to continue the growing season
beyond the end of the growing season or end of rains? The rainy season was assumed to end
after 1st September when 5-day cumulative rainfall was less than 0.5 of the ETo. At the EOS
the reference evapotranspiration was 5.5 mm day-1, 5.5 mm day-1 and 5.6 mm day-1 at Mieso,
Melkassa and Adami Tulu, respectively. Therefore, the end of the growing season was
extended by 18 days (100 mm/5.5 mm day-1), 18 days (100 mm/5.5 mm day-1) and 18 days
(100 mm/5.6 mm day-1) at Mieso, Melkassa and Adami Tulu respectively.
Length of the growing season (LGS) is a key factor in deciding on the maturity of cultivars to
be grown in dissimilar rainfall regimes (Stewart, 1989). Therefore, LGS was considered as the
period from the start of the rain to the cessation of the growing season. It was calculated by
21
subtracting the date of the beginning of the rainy season from the date of end of the growing
season (Mupangwa et al., 2013).
3.2.3. Analysis of probability of occurrence of dry spells,
For each meteorological station (Mieso, Melkassa and Adami Tulu) the daily rainfall data
were fitted to a simple Markov chain model. The chance of rain was assessed both when the
previous day was dry, i.e. the chance that a dry spell would continue, and also when the
previous day was rainy, i.e. the chance that a rainy spell would continue, which is known as a
Markov chain (Stern and Cooper, 2011; Stern et al., 2006). The probability of dry spell
lengths of 5, 7, 10 and 15 days during the growing season were determined from the Markov
chain model to obtain an overview of dry spell risks during the crop growing season and
provide a viable decision aid to various practitioners. Dry spells lengths of 5 to 15 days were
selected in order to accommodate both drought sensitive and drought tolerant cultivars during
the growing season. The following expressions were used in Markov chains analysis of dry
spell in the study areas (Reddy et al., 2008):
Fd
n
Fw
Pw 
n
F
Pww  ww
Fw
Pd 
P dd 
(3.1)
(3.2)
(3.3)
F dd
Fw
(3.4)
Pwd  1  Pdd
Pdw  1  Pww
(3.5)
(3.6)
where Pd is the probability of day being dry and Fd is number of dry day, Pw is the probability
of day being wet, Fw is the number of wet days and n is the number of observation, P ww is the
probability of wet day followed by another wet days, Fww is the number of wet days followed
by other wet day, Pdd is the probability of dry day followed by another dry day, and Fdd is
number of dry day followed by another dry day during the growing season.
22
3.2.4. Seasonal rainfall and annual temperature trend analysis
Trends of annual minimum and maximum temperature and seasonal rainfall for the study
areas were assessed using the Mann–Kendall trend test and Sen’s slope estimator. The Mann–
Kendall test is a non-parametric approach, widely applied in various trend detection studies
(Karaburun et al., 2011; Alexander and Arblaster, 2009). Statistical analyses and other
computations were performed with INSTATv3.37 statistical software (Stern et al., 2006).
Accordingly, the test of the null hypothesis H0 (no trend) states that the data (x1, x2,........,xn) is
a sample of n independent and identically distributed random variables. The alternative
hypothesis H1 (there is trend) of a two-sided test, on the other hand, states that the distribution
of Xk and Xj is not identical for all k, j ≤ n with k ≠ j. The test statistic S is computed from
equations 3.7 and 3.8:
n 1
S 
n
 sgn (
j
 k )
(3.7)
k 1 j  k 1
where Xj and Xk are the annual values in years j and k, j> k, respectively, and
 1, if ( j  k )  0

sgn(   sgn( j  k )  0, if ( j  k )  0
 1, if ( j  k )  0

(3.8)
For n larger than 10, the standard normal Z test statistic was used and computed from
equation 3.9 as:
 s 1
 var(s )     s  1


Z  0          s  0
 s 1

     s  1)
 var(s )
(3.9)
The level of significance α= 0.05 (95% confidence interval) was applied for each analyzed
seasonal rainfall, and annual maximum and minimum temperature trend analysis for the three
study areas.
23
The magnitude of trend was predicted by the Sen’s estimator. As National Non-point Source
Monitoring Program stated (2011) on monotonic trend analysis, the null hypothesis of no
trend is rejected when S and τ are significantly different from zero. If a significant trend is
found, the rate of change can be calculated using the Sen’s Slope estimator.
Ti 
j  k
  for.i  1,2,3        N
jk
(3.10)
Where xj and xk are considered as data value at time j and k (j>k) correspondingly.
The median of these N values of (Ti) is represented as Sen’s estimator of slope given by
 T N  1 ................. N  o d d
 2
Qi   

1
  T N  T N  2  ....... N  e v e n
 2  2
2

Sen.’s estimator is computed as Q
med=T
(3.11)
(N+1)/2
if N appears odd, and it is considered as
Qmed=[TN/2+T(N+2)/2]/2 if N appears even. At the end, Q med is computed by a two sided test at
100 (1-α)% confidence interval and then a true slope was obtained by the non-parametric test.
Positive value of Qi indicates an upward or increasing trend and a negative value of Qi gives a
downward or decreasing trend in the time series.
3.3. DSSAT Model Calibration for Maize
DSSAT 4.5 crop modeling software was used to simulate yield of two maize varieties (Me-2
and Me-4) grown at Melkassa station for preiod 2002-2011 and 2006-2012, respectively.
Some of the important characteristics and requirements of these maize varieties used is
presented in Table 1. To run DSSAT 4.5, historical daily meteorological time series, like
maximum and minimum temperature, rainfall and solar radiation data were stacked using
Excel 2007, while DSSAT utilities were used to choose relevant cultivar for calibration and
validation.
Specific cultivar coefficients for the genotypes used in this experiment were not available in
the list of genotypes in the DSSAT model, therefore, evaluation was done using basic
information for the cultivar coefficients provided within the DSSAT software. Soon after
24
calibration, the cultivar coefficients were adjusted using the GLUE interface on DSSAT v4.5
using the previous data for both varieties.
Finally, the Maize .xml file containing these varieties was browsed to DSSAT 4.5. Queries on
tillage, sowing date, planting density, fertilizer and harvesting rules were also completed and
fitted to the model. Days to flowering, days to maturity and yield (kg/ha) were simulated.
While running the model, varied soil parameters and genetic coefficients were adjusted
iteratively until the convincing result was obtained and the output/simulated/ results were
exported to Excel 2007 to check the fitness of the model with the observed data based on their
R2 value by drawing the trend line., besides descriptive statistics like mean, SD and CV were
computed using equation indicated below.
n
  (
i
1
SD 
CV 
2
)
i
(3.13)
n
SD
x100

(3.14)
Table 1. Description of environmental and agronomic requirements for, and characteristics of
Melkassa 2 and 4 maize varieties used for this study
Agronomic and morphologic characters
Altitude (masl)
RF the ( mm)
Seed rate (kgha-1)
Planting date(DOY)
Fertilizer rate(kgha-1)
Day to anthesis (no.days)
Day to silking (no.days)
Day to maturity(no.days)
1000 seed weight(g)
Ear height (cm)
Plant height (cm)
Seed color
Pollen color
Grain texture
Kernel row arrangement
Source MARC 2013, low land maize research program
25
Me-2
1200-1700
600-800
25-30
122-153
46
64
65
68
130
360-410
80-90
170-190
White
Yellow
Semi-dent
Straight
Me-4
1000-1600
500-700
25
SOS
46
64
53
55
105
350-400
60-75
140-165
White
White
Semi dent
Straight
3.4. Soil and Crop Data Required for Calibration of the DSSAT Model
3.4.1. Soil data
Two composite soil samples were taken from the experimental field by driving a soil auger
into the soil at different soil depths (0-30 and 30-60 cm) before sowing. The collected samples
were prepared for further analysis of selected soil chemical and physical properties. The
results of this analysis were used to characterize the experimental soil. To collect soil data that
was used as an input for calibration of the DSSA model, soil pit of 180 cm depth was dug at
the experimental site. Samples were collected from each genetic horizon. The profile was
described and the soil classified following the FAO (2006) guidelines. The collected samples
were air dried, ground and passed through a 2-mm diameter sieve.
Determination of soil particle size distribution was done by the hydrometer method as
described in Okalebo et al. (2002). Bulk density was analyzed from undisturbed soil collected
by core sampler as described in Sahlemedhin and Taye (2000).
The Drained Upper Limit (DUL) was determined using the method described in APSRU
(1999). An area of 3 m by 3 m was watered until saturated using drip irrigation and the bank
of the pond was lined in order to limit lateral water movement and evaporation with plastic
sheet. An access tube was installed at the center of the pond. The pond was then allowed to
drain the water until drainage ceases. Portable neutron probe was used to read the moisture
content every 15 minutes until further downward drainage ceased, which took 48-72 hours.
Core sampler was used to take soil sample from the watered areas up to the depth of 60 cm at
an interval of 15 cm for determination of soil bulk density. The soil moisture content of the
top 15 cm that was determined using the gravimetric method was converted into volumetric
moisture content using bulk density as follows (equation 3.10):
DULi  Wix
BD
DW
(3.15)
where DUL is drained upper limit expressed in volume percentage, w is gravimetric water
content expressed in mass percentage, BD is dry bulk density of the soil (g/cm3), DW is
density of water (equal to 1 g/cm3) and i represents each layer.
26
Crop lower limit (CLL) was determined using the method described in APSRU (1999). A
small plot near the planted area was selected for Melkassa-2 and Melkassa-4 varieties. The
plot with plants in it, at their flowering stage, was covered with plastic sheet until the plants
wilted permanently. Soil samples were collected at 15 cm intervals to a depth of 60 cm every
7 or 15 days for determination of moisture content. Similar procedure was used to convert the
gravimetric moisture content into volumetric water content. The water crop lower limit was
estimated using Equation 3.11 as:
CLL  Wx
BD
DW
(3.16)
where CLL is drained upper limit expressed in volume percentage, w is gravimetric water
content expressed in mass percentage, BD is dry bulk density of the soil (g/cm 3), DW is
density of water (equal to 1 g/cm3) and i represents each layer.
The pH of the soils was measured using a pH meter with combined electrode in supernatant
suspension of 1:2.5 soil water ratio as described by Carter (1993). The organic carbon content
(OC) was determined using the Walkley and Black (1934) method as described in
Sahlemedhin and Taye (2000). The total N was determined by the Kjeldahl method using
micro-Kjeldahl distillation unit and Kjeldahl digestion stands (Jackson, 1958) as described in
Sahlemedhin and Taye (2000). The available phosphorus was measured following the Olsen
method as described by Olsen et al. (1954) using sodium bicarbonate (0.5MNaHCO3) as
extractant. Cation exchange capacity (CEC) was determined using the ammonium acetate (pH
7) method as described by Chapman (1965
3.4.2. Crop data
Crop and crop related data collected for calibration purpose include gross plot area per
replication, rows per plot, plot length (m), plot spacing, plot lay out, harvest area (m 2), harvest
method, yield (kg/ha), and yield components. As initial conditions soil moisture content and
initial N content were determined.
Data collected on related agronomic management include date of planting, date of emergence,
plant distribution in a row, plant population at seeding and emergence, row spacing and
planting depth. The rate and time of fertilizer application of the fertilizers used, type, date and
depth of tillage practice, methods and, date of harvest were also recorded. Weather data (on
27
daily basis); rainfall, solar radiations, minimum and maximum temperature were collected and
used for impact and adaptation options study.
.
3.5. Future climate scenario analysis
The raw data were downscaled from the website of Canadian Environment for data
distribution center (http://www.cics.uvic.ca/scenarios/index.cgi?Scenarios). Projected changes
in rainfall and temperature were analyzed based on global circulation models (GCMs) and
two IPCC emission scenarios, A2 and B2. The A2 represents one of the high emission
scenarios, while B2 belongs to the low emission variants (Nakicenovic and Swart, 2000).
The GCMs HadCM3 was used and the results were used to assess the potential changes in
regional climate from 2020 (2011-2040), 2050 (2041-2070) and 2080 (2071-2099)s and
compare to baseline periods of 1961-2001. The results from the GCM were first statistically
downscaled using SDSM so as to increase the spatial resolution, thus making the data more
appropriate for a regional impact assessment using DSSAT(4.5V) CSM ,that will influence
maize production in the CRV of Ethiopia.
Method of downscaling;
After calibrating the crop simulation model of DSSAT for the two maize varieties, the next
step was developing climate change scenarios to assess the future impacts of these climate
variables on production of these maize varieties (Me-2 and Me-4) in Melkassa areas. SDSM
Version 4.2.9 was adopted for spatial downscaling of daily rainfall, minimum and maximum
temperature from GCM predictors to the scale of the study area (Wilby and Dawson, 2007).
For the purpose of the analysis, data was downloaded from HadCM3 global model grid box
between 8.12 0N and 39.400E.
To identify large downscaling predictor variable(s) that indicate significant correlation at p <
0.05 significance level, screening of potential downscaling predictor variables was done for
observed Melkassa station time series (1977-2013) of rainfall and minimum and maximum
temperatures (Table 2). Using the relationship of linear regression developed between the
predictor variables selected and the local station data, the SDSM was calibrated using twenty
28
years (1977-1997) observed station climate data and daily observed (standardized) gridded
data (1961-2000). Before further analysis, the model was validated using the remaining
station data from 1998 to 2013 so as to make series adjustment.
Finally, downscaling of daily rainfall and minimum and maximum temperature scenarios
were done. For this finding, the HadCM3 A2a and B2a were the two daily GCM derived
predictor variables used for scenario generation. The A2a and B2a are story line scenarios
developed by IPCC SRES. The A2a scenario describes a highly heterogeneous future world
with regionally concerned economies (high rate of population growth, increased energy use,
land-use changes and slow technological change). Similarly, B2a is regionally concerned with
but with a general evolution towards environmental protection and social equity (lower rate of
population growth, a smaller increase in GDP but more diverse technological changes and
slower land use changes).
The Hadley Centre Coupled Model (HadCM3) scenario generation operation produces
ensembles of daily weather series under future forcing (H3A2a 1961-2099 and H3B2a 19612099) with two (A2 and B2) emission scenarios relative to the 1961-1990 normal periods. As
a final product of downscaling twenty ensembles of daily climate data were generated for this
study. The downscaled future daily ensembles of climate data were then used to examine
monthly patterns and general trend of annual rainfall, average annual minimum and maximum
temperatures of the study area for base period and future (2011-2099) periods by averaging
the ten independent ensemble data. Furthermore, climate change scenarios were projected for
the periods 2020, 2050 and 2080.
Predictor selection.
The empirical relationships for identification between gridded predictors (NCEP-reanalysis)
and predictand variables (minimum temperature, maximum temperature, and rainfall)
composed from stations is central to all statistical downscaling methods. This engrosses
identifying the proper downscaling variables that have strong correlation with the predictand
variable. Accordingly, the type and notations of large scale GCM predictor variables, which
gave better correlation with Melkassa station measured daily precipitation, daily minimum
29
and maximum temperature at 5% significant level, are shown in Table 2. The results indicate
that the downscaled temperature correlations have good agreement with observed data of the
study area while precipitation was too difficult to know the major predictors at a stations
Table 2.list of predictor variables that gave better correlation results at p< 0.05.
Station
Melkassa
predictand
Predictors(NCEP reanalysis)
Notations
Partial r2
Maximum
temperature
Mean sea level pressure
Nceppslpaf.dat
0.240
850hap zonal velocity
Ncepp8_uaf.dat
0.164
minimum
temperature
500hap geopotential height
Ncepp500paf.dat
0.344
Surface zonal velocity
Ncepp_uaf.dat
0.380
Mean temperature at 2m
Nceptempaf.dat
0.199
Surface zonal velocity
Ncep_uaf.dat
0.197
Relativity humidity at r 500hap
Ncepr500af.dat
0.133
Rainfall
The partial correlation coefficient(r) shows the explanatory power that is specific to each
predictor that is significant at 5% significance level. As indicated in the partial correlation
coefficients on mean sea level pressure followed by 850hPa zonal velocity has strong
correlation with maximum temperature whereas surface zonal velocity and 500hap
geopotential height has strongest correlation with minimum temperature and mean
temperature at 2 m and surface zonal velocity followed by relative humidity at r500 hap is
strongly correlated with local precipitation relative to the other predictors (Table 2).
2.6.
Climate Change Impact and Adaptation options Analysis on Maize Production
at Melkassa
DSSAT4.5 crop model was used to analyze the future potential climate change impact on
grain yield of Me-2 and Me-4 maize varieties under Melkassa climate. Split plot design was
used for arrangement of the treatments of twice modified moldboard,one time modified
moldboard,ripper-wing plows and normal mersha tillage practices,while GenStat software
was used for the ANOVA that was further used by DSSAT to address the adaptation options.
The input climate data for the impact analyses were the outputs of HadCM3 coupled
atmosphere-ocean GCM model for the A2a and B2a SRES emission scenarios which were
downscaled to the study site grid as described in the preceding section. Hence, the climate
30
change scenarios of precipitation, temperature and solar radiation were developed for future
three time horizons (2020s, 2050s, and 2080s).Then the downscaled climate scenarios were
applied to DSSAT 4.5 CSM to simulate future grain yield of both (Me-2 and Me-4) maize
verities based upon the experiment that has been conducted at MARC using different tillage
implements in 2013. Then to compare the yield within each scenario from ‘graph’ module of
DSSAT 4.5, a probability of exceedance chart that displays a single set of data against its
cumulative frequency was used. As a result, based on the output of the model, the plots for
pobablity exceedance to get exceeding a given quantity of yield in kg/ha in 2020s,2050 and
2080s under HadCMA2a and HadCM3B2a emission scenarios was drawn and copied to
microsoft office word 2007. Besides, the percentage change was calculated and descriptive
statstics was used to get insight to detail information. From the change based upon impact
(equ3.17)
result
using
altenetively
variety
and
tillage
practice
the
adaptation
measures(equ.3.18) were evaluated using the following relationships.
Y 
Ys  Yb
x100
Yb
(3.17)
where ∆Y=change of yields’= simulated yield Yb= baseline yield for Impact
Yst  Ysn
Y 
x100
Ysn
(3.18 )
where ∆Y=change of yields’,Yst= simulated yield using technology and Ysn=simulated yield
under locally used implements.
31
4. RESULTS AND DISCUSSION
4.1.
Climate Characteristics of the Growing Season
4.1.1. Start of the season (SOS) and end of the season (EOS)
Results of analysis of rainfall data at the three stations indicate that the growing season starts
on May 26 at Mieso and Adami Tulu areas, and on May 27 at Melkassa area with a
corresponding coefficient of variation of 31.3, 22.2, and 24.5% (Table 3). These dates
correspond to 148th and 149th days of the year (DOY) at Mieso and Adami Tulu, and
Melkassa areas, respectively. This indicates that the start of the growing season is late by a
day at Melkassa area compared to the other two areas in the Central Rift Valley of Ethiopia.
Nevertheless, the values of the coefficient of variation recorded at the three stations indicate
the existence of high variability particularly at Mieso area.
On the other hand, the end date of the season (EOS) falls on September 14 th at Mieso, October
1st at Melkassa, and September 11th at Adami Tulu areas with coefficient of variation of 5.7,
4.7, and 4.9%, respectively. The results indicate that the season comes to an end early at
Adami Tulu area, followed by Mieso, while it is longer at Melkassa. The end dates of the
season correspond to 259th, 275th, and 256th DOY for Mieso, Melkassa, and Adami Tulu
areas, respectively
.
The start of the season (SOS) rainfall in Mieso, Melkassa and Adama Tulu areas varied from
September 11th (257 DOY) to April 1st (92 DOY), July 21st (202 DOY) to April 1st (92 DOY)
and August 13th (227 DOY) to April 1st (92 DOY), respectively. The mean starting of
growing season in Mieso, Melkassa and Adami Tulu has very high standard deviation (SD) of
46, 33 and 36 days, respectively, indicating that the SOS is not stable because the recorded
standard deviations are out of the ranges suggested by Reddy (1990). The higher standard
deviation of the SOS suggests that pattern could not be understood and, thus, decision
pertaining to crop planting related activities will be with high risk.
.
The probability of occurrence of SOS once in four years (25% percentile) corresponds to 102,
126 and 113 DOY at Mieso, Melkassa and Adami Tulu areas, respectively. Whereas the
32
probability of occurrence of SOS twice in four years (50%ile) corresponds to 137, 152 and
155 and three times in four years also corresponds to 191, 177 and 180 DOY for Mieso,
Melkassa and Adami Tulu, respectively.
.
Therefore, earlier planting than on 102th, 126th and 113th DOY is possible in Mieso, Melkassa
and Adami Tulu once out of four years’ time. Also, earlier planting than 191 th (July 7), 177th
(June 24) and 180th (June 27) DOY is possible in three years every four years’ time
respectively. Moreover, the reliable planting date of maize and other cereal crops at and
around Mieso, Melkassa and Adami Tulu ranges between 137-155 DOY (May 15 to June 2).
The results of the rainfall analysis further indicate that there is a 25% chance once in four
years that the end of the season will fall on 245th and 269th DOY at and around Meiso and
Adami Tulu, and Melkassa areas, respectively. On the other hand, there is a 50% chance
(twice in 4 years) for the end of the season to be on 255th, 277th, and 250th DOY at and around
Mieso, Melkassa, and Adami Tulu areas, respectively. Also, the probability that the end of the
season can be on 271th, 285th, and 265th DOY for Mieso, Melkassa and AdamiTulu areas,
respectively, is three times in four years or 75%. At all the probability levels considered, the
end of the season is extended more at Melkassa compared to Mieso and Adami Tulu areas.
4.1.2. Length of growing season (LGS) and number of rainy days
Results of the analysis indicated that the length of growing season for maize production in the
main rainy season ranges from 12 to 192, 12 to 189 and 18 to 194 days at Mieso, Melkassa
and Adami Tulu areas, respectively, (Table 3) with corresponding coefficient of variation of
44.5, 25.6, and 37.5% (Table 3). The results indicate that the variability in the length of the
growing season, as indicated by the high CV, is very high at Meiso followed by Adami Tulu
and Melkassa areas
The results obtained send a clear message that the LGP at the three stations is highly variable
and, thus, needs a more cautions on planning and adoption of rainwater water harvesting
scheme, improving water use efficiency, selecting varieties that are drought tolerant or early
maturing, and using tillage practices that conserve soil moisture. If these are not put in place,
33
the sustainability of crop production and, thus, the efforts of ensuring food security in the
CRV of the country will be jeopardized.
Nevertheless, the results also confirm that maize varieties that require a LGP of up to 180
days can be produced with no risk in and around Mieso, Melkassa and Adami Tulu areas.
However, varieties with more than 180 days cycle cannot be produced in the study areas using
natural rainfall alone. On the other hand, growing maize varieties with 90 days cycle is also a
waste of the growing period, for these 90 days are too short and result in wastage of 90 days
of the growing period.
The mean number of rainy days at Mieso, Melkassa and Adami Tulu areas were 99, 97 and 97
with the CV of 14.9, 10.3 and 6%, respectively. The rainy days varied from 92 to 165, 92 to
147 and 92 to 110 at Mieso, Melkassa, and Adami Tulu, respectively. This indicates that there
is high variability in number of the rainy days in semi-arid CRV of Ethiopia with high risk for
successful crop production and livestock rearing due to the likely effect on pasture production
and water scarcity.
The early onset date suggests that crop cultivars of the longer maturity type could do better
with the late onset date (Stewart, 1988). The issue of LGS requires further due attention in
that one needs to know the type and level of risks of yield loss associated with cultivars of
different maturity categories, requiring different amounts of water during a sequence of
growth stages. It is only then that one can confidently pinpoint the most suitable maturity
cultivars to be planted in seasons with different onset date scenarios (Stewart, 1988).
According to Borrell et al. (2003) pointed out that, such weather information guided farming
can help in combining the genetic solutions into the management aspects,thus, providing
farmers with a range of viable options to combat drought.
34
Table 3.Descriptive statistics of important rainfall characterstices at Mieso, Melkassa and Adami Tulu weather stations
Minimum
Quartile 1
(25%ile)
SOS (DOY)
EOS (DOY)
LGP (No. of days)
NRD (day )
TSRF(mm)
92
245
12
92
137.2
102
245
67
92
302.38
SOS(DOY)
EOS (DOY)
LGS (No.of days)
NRD ( day)
TSRF (mm)
92
245
12
92
347.7
126
269
99
92
510.4
SOS (DOY)
EOS (DOY)
LGS (No.of days)
NRD (day)
TSRF(mm)
94
245
18
92
36.3
113
245
79
92
351.12
Seasonal rainfall
features
Quartile 2
(Median)
Quartile 3
(75%ile)
Mieso
137
191
255
271
108
155
92
99
416.15
596..8
Melkassa
152
177
277
285
130
155
84
99
593.7
648.2
Adami Tulu
155
180
250
265
102
140
92
99
412.75
505.55
Maximum
Mean
S.D (±)
C.V
(%)
257
292
192
165
716.20
148
259
111
99
438
46
14
49
14
168
31.3
5.7
44.5
14.9
38.4
202
306
189
147
754.8
149
275
126
97
577.5
33
15
32
10
106
22.2
4.7
25.6
10.3
18.4
227
290
194
110
710.5
148
256
109
96.
430.84
36
12
41
6
143
24.5
4.9
37.4
6.0
33.2
SOS = Start of season; DOY = day of the year; EOS = End of season; LGP = Length of growing period; NRD = Number of rainy days; TSRF = total seasonal
rainfall
35
4.1.3. Seasonal total rainfall throughout the growing period
The seasonal rainfall of the record period at Mieso ranged from a minimum of 137.2 mm to a
maximum of 716.2 mm with a mean value of 438 mm. This indicates the existence of high
variability in seasonal total rainfall as was also indicated by the high coefficient of variation
(38.4%) (Table 3). The total highest seasonal rainfall was recorded in the year 1987, while the
lowest was recorded the following year, 1988.
At Melkassa station, the seasonal rainfall varied from 754.8 mm recorded in the year 2005 to
347.7 mm recorded in the year 2002. The mean seasonal total rainfall, on the other hand, was
found to be 577.5 mm with a coefficient of variation of 18.4% (Table 3). This indicates the
existence of relatively low variability at Melkassa station as compared to the other two
stations. However, the fact that both the highest and lowest rainfall are recorded in recent
years indicates the recently growing high variability of rainfall in the area.
The variation in seasonal total rainfall, as obtained from this study, is high in which it varied
from 710.5 mm recorded in 1981 to 36.5 mm registered in 1987. The mean seasonal total
rainfall was found to be 430.84 mm with relatively high coefficient of variability (33.2%)
(Table 3), indicating the existence of high seasonal variability of rainfall at and around Adami
Tulu area.
4.1.4. Probability of dry spell length occurrence
After the 182, 162 and 142 DOY, the probability of getting dry spell length of 5, 7 and 10
days is less than 50% at Melkassa site and drops to below 20% at the beginning of the peak
period (June, July and August), which again gradually rises to 40% on 242, 252 and 262
DOY, respectively (Figure 2).
The probability of occurrence of a dry spell length of two weeks (15 days) during the growing
season is below 15% which indicates the existence of less risk for drought resistant crops
production (Mesey, 2006).
36
Figure 2.Probability of dry spells longer than 5, 7, 10, and 15 days at Melkassa site. Starting
from April first
The probability of getting dry spell length of 5, 7 and 10 days on 1 st dekadal of April in at
Adami Tulu is less than 96, 85 and 60%, respectively. On the other hand, the probability of
getting the same dry spell length on 1st dekadal of September falls to 80, 50 and 30%
respectively (Figure 3).
During the 2nd dekadal of July, the probability of occurrence of dry spell length of 5, 7 and 10
days drops to 55, 27 and 5%, respectively, which indicates the instability of the growing
season rainfall for sowing crops sensitive to water stress like maize,Teff and other cereals . It
shows that the chance of occurrence of dry spell length of more than 5, 7, and 10 days on 112
DOY is 40% which reduce to below 60, 40 and 20% at the end of June. This indicates that
planting maize before 181 DOY has the failure probability of 50% before establishment.
Figure 3.Probability of dry spells longer than 5,7, 10, and 15 days at Adami Tulu starting
from April first
37
At begging of the growing season on 1st April at Mieso area, the probability of getting dry
spell length of 5, 7, and 10 days is 97, 87 and 64% which gradually decreases to 87, 64, and
35% at the end of June, respectively. This implies that the risk of planting Maize before the
third dekadal of May is above 50% (Figure 4).
However, the probability of a week (7 day long) dry spell length is very low in peak periods.
After 1st dekadal of September, it increases histrionically from 64 to 96% in the 3rd dekadal of
September, respectively. The probability of two week (15 day long) dry spell occurrence is
found to be below 50% from April to end of September (Figure 4). This condition is
inappropriate for maturity and harvesting of crops. However, crops whose cycle extends to
October should be supplemented by irrigation. .
Figure 4.Probability of dry spells longer than 5, 7, 10, and 15 days at Mieso starting from
April first
Generally, for the study periods the probability of longer dry spells increases rapidly from
first dekadal of September on wards. To take risks of longer dry spells and decide to plant
during earliest months of the season, farmers should get access to irrigation and also other
mechanisms that minimize the loss of moisture from the farm land. Likewise, selection of
crop variety (drought escapers), cropping system, and tillage practices should be undertaken
in order to minimize losses as a result of the dry spells. In this manner, planting earlier than
the start of June is possible for the main rainy season at three sites. If a farmer cannot decide
to take risks of longer dry spells after planting (called risk averse), it means that he has to wait
until all the dry spell probabilities attain minimum values at the end of June or beginning of
July
38
4.2. Trend Analysis of Growing Seasonal of Rainfall under Mieso, Melkassa and Adami
Tulu conditions
The trend of rainfall during growing seasons at Mieso, Melkassa and Adami Tulu areas are
non significant in the past years. As indicated in Table 4, the result of Man-Kendall trend
estimation indicates that the growing season rainfall shows a positive trend at Mieso,
Melkassa and Adami Tulu, respectively. In which statistically non-significant, at Mieso
Melkassa and Adami Tulu. Simultaneously, the Sen.’s slope estimation also indicates that,
the trend of the rainfall of the growing season under Mieso, Melkassa and Adami Tulu
condition increases by 2.69, 2.52 and 0.13, respectively (Table 4).
Generally, the trend analysis result reveals that the growing season rainfall exhibited a slight
but statistically insignificant increase in the Central Rift Valley of the country. Belay et al.
(2014) reported that the Kiremt season rainfall, even if not statically significant, in the CRV
experienced in the past year decreasing trend but for the case of Melkassa and Adami Tulu
areas increasing trend.
Table 4. Trend of rainfall (mm) at Mieso Melkassa and Adami Tulu weather station
Station name
Sen.’s slope
(mm/year)
Mann-Kendall
tau
Mieso
Melkassa
Adami Tulu
2.69
2.52
0.13
0.85
1.41
0.373
Risk
(%)
Amount of STRF at SOS
34%
20%
53%
P -value
0.34
0.2
0.53
Confiden
ce
interval
-2.44 -6.9
-1.17-5.41
-2.75-5.22
4.3. Trend of Annual Maximum and Minimum Temperature of Mieso, Melkassa and
Adami Tulu stations
As shown in Table 5 below the result of Mann-Kendall trend estimation indicates that there
are positive trend in maximum temperature at there study sites ,while stastically significant at
Mieso but non-singinficant for Melkassa and Adami Tulu .In addition, the Sen.’s slope
estimator result shows that the maximum temperature over the areas has increased by 0.035,
0.018 and 0.095 0C/year at Mieso, Melkassa, and Adami Tulu, respectively.
39
In the same manner, the result of Mann-Kendall trend estimation shows that positive trend at
Mieso but negative trend at Melkassa and Ademi Tulu of the annual minimum temperature
and also non-significant. The Sen.’s slope estimation method proved that there was increasing
trend in annual minimum temperature by 0.009, 0.014 and 0.018 0C/year at Mieso, Melkassa
and Adami Tulu, respectively (Table 5).
.
From the foregoing, it can be inferred that the different trend analysis methods gave different
results regarding the trends of maximum and minimum temperature. The methods also gave
different results at the three stations for the same parameter. Therefore, this shows that there is
high variability of climate aspects in the semi-arid central rift valley of the country, which
signals the likely impact of this variability on crop production by increasing the probability of
occurrence of thermal drought particularly in the study areas. When this happens, the length
of the rainfed growing season will decrease and, thus, increase the risk of decreasing crop
yield.
Table 5.trend of annual, maximum and minimum temperature (oc) at Mieso, Melkassa and
Adami Tulu weather station
Station
Name
Sen’s slope0C/ Mannyear
Kendall
Risk (%)
p-value
Confidence
interval
0.00
0.6156
0.26-12.40
-0.04 - 0.025
Maximum Temperature
Mieso
Melkassa
0.035
0.018
Adami Tulu 0.095
5.11
1.63
0.0%
61.56%
3.25
16.4%
0.1641
Minimum Temperature
1.25
55.09%
0.055
Mieso
0.009
Melkassa
0.014
-1.6
7.9%
0.079
Adami Tulu 0.018
-0.9
58.8%
0.58
-0.117-0.66
-0.0100-0.072 - 0.004
-0.058-0.032
4.4. DSSAT Model Calibrated Results for Maize
Melkassa -2 maize variety
The DSSAT model was able to simulate most of the crop parameters with reasonable
accuracy. However, there were over and underestimation of parameters for some years
(Figure 5). Accordingly expect for the year 2009 the model overestimated the grain yield and
40
-
days to flowering of Melkassa-2 (Me-2) under Melkassa condition. Nevertheless, the variation
in observed yield and days to flowering were able to explain 97.9 and 71.27% the yield and
days to maturity simulated, respectively. . Similarly, the model overestimated the days to
maturity of all the years considered except the 2007 where it underestimated it. By and large,
the model was able to explain 67.97% of the variation in simulated days to flowering
From the 1:1 simulated vs observed plot, it can clearly be seen that there is almost no
deviation from the mean values for days to maturity, days to flowering and yield. This
indicates that the model simulated the actual days to maturity, days to flowering and yield
with high precision as is also indicated by the high R 2 values. Therefore, further study such as
sensitivity test and climate change impact analysis on Me-2 variety using the outputs of
DSSA is possible
.
41
Figure 5.Relationship between observed and simulated yield (Kg/ha )(A), maturity days(B)
and flowering days (C) for melkassa-2 under melkassa climate condition
Melkassa-4 maize veriety
Similar to the results obtained for Me-2, the DSSA model overestimated (2006 and 2009) the
grain yield of Me-4, while it underestimated the 2012 yield of this crop (Figure 6). However,
using variation in actual yield during the record periods was able to explain about 71.18% of
the variation in simulated yield indicating that there are other variables that need to be taken
into account for better agreement. The model also over- and underestimated the days to
flowering although the variation in actual days to flowering was able to explain about 87.94%
of the variation in simulated days to flowering. The results obtained in the simulation of the
days to maturity also involved under- and overestimations of the actual days to maturity.
However, the R2 value was high (89.14%).
From the simulation results on days to flowering, days to maturity, and grain yield, it can be
inferred that the DSSA model simulates these parameters with reasonable accuracy and, thus,
can be used for assessing the impacts of climate change on these crop parameters for
Melkassa area
42
Figure 6.Relationship between observed and simulated yield in kg/ha (A), maturity days(B)
and Days of flowering (C) for Melkassa-4 grown under Melkassa climate
4.5. Downscaling Baseline and Projection of Future Scenarios
4.5.1. Downscaled baseline scenarios for Melkassa
4.5.1.1. Downscaled rainfall
The simulated total rainfall by SDSM was found to be overestimated both seasonally and
annually (Figures 7 and 8). The model was able to simulate only the Bega season rainfall with
reasonable accuracy, whereas it did slightly well in estimating mean daily precipitation in
many months except for the months of September, October and December. Also, the GCM
outputs gave slightly satisfactory agreement with the observed rainfall data of Melkassa
condition,
.
43
In line with the results obtained in this study, the lack of replicating the extreme values was
also reported by Wilby et al. (2005) where they described it as “the model is less skilful at
replicating the frequency of events”. Nevertheless, the downscaled precipitation values
followed the same trend as the observed values expect for two months.
Figure 7.Observed and simulated average seasonal precipitation of Melkassa for the Base
Period (1977-2013)
Figure 8.observed and simulated pattern of monthly rainfall at Melkassa for the base period
(1977-2013).
4.5.1.2.
Downscaled maximum temperature
For the dry months of January, February, May and October, the SDSM slightly
underestimated the maximum temperature, while it overestimated it for the months of March,
April, August, September and November (Figure 9). The downscaled maximum temperature,
however, showed good agreement with observed values with its patterns and trend.
44
Figure 9. Pattern of observed and simulated mean daily maximum temperature for the base
period (1977-2013).
4.5.1.3.
Downscaled minimum temperature
The downscaled monthly minimum temperature for HadCM3A2a and HadCM3B2a for
Melkassa station was slightly underestimated for March, but slightly overestimated for
January, February, May, June, July, August, and December. For the month of September,
however, the model overestimated it for the B2a scenario as compared to the observed values
(Figure 10). For the remaining months, the model showed best performance in which the
minimum temperature
in oC
downscaled minimum temperature showed relatively good agreement with the observed data.
20
Observed
HadCM3A2a
HadCM3B2a
15
10
5
0
Jan
Feb
Mar
Ap
May
Jun
Jul
Aug
Sep
Oct
Nov Decm
Month
Figure 10.Pattern of observed and simulated mean daily minimum temperature for the base period
(1977-2013).
45
4.5.2. Projected scenarios of 2011-2099 for Melkassa
4.5.2.1. Projected changed in rainfalls
Projections made using the A2a and B2a scenarios showed different trends for the different
periods considered in this study. Accordingly, projections made using the A2a scenario
resulted in decreasing rainfall trend for 2050 and 2080 by 0.98 and 0.94 mm per year,
respectively, and decreasing trend for 2020 by 0.171 mm per year. On the other hand,
projection made using the B2a scenario predicted a decreasing rainfall trend during 2020,
2050, and 2080 by an amount that is equal to 0.98, 0.15, and 2.38 mm per year, respectively
(Figure 11).
Considering individual months, the 2020s period shows relatively wettest condition during
months from April to October under the A2a scenario, and March, April and December under
the B2a scenario. Except for the months of March, April and May, the wettest condition will
begin from January to December for B2a scenario, whereas for A2a scenario, except for the
months of February and May, it begins from January to August for the period 2050. For the
period 2080, the wettest condition will begin in the months of April, July, November and
December for A2a scenario, and January, February, March and July for B2a scenario (Figure
11).
The prediction also showed that the rainfall in the months of June, July and August (Kiremt)
will experience a decrease in amount under both scenarios during all the periods considered in
this study. On the other hand, the rainfall of the dry months will experience increase in total
amount for both scenarios during the three periods (Figure 11).
46
Figure 11.Projected mean monthly precipitation pattern at Melkassa compared to the base
period under A2a and B2a scenarios.
4.5.2.2. Projected change in maximum temperature
The highest monthly temperature during the 2020s, 2050s and 2080s will occur in January,
August, September, October, November and December under the A2a scenario. Nevertheless,
under B2a scenario, the highest monthly change in maximum temperature in projected climate
will occur during January, February, November and December. The monthly change in
minimum temperature follows the same increasing trend as for A2a scenario except the lower
increasing temperature values. On the other hand, the downscaled annual average maximum
temperature in 2020s shows an increasing trend by 0.036 oC/year and 0.0317 oC/year for A2a
and B2a emission scenarios, respectively. For 2050s, the increase will be 0.039 oC/year for
47
A2a scenario and 0.037 oC/year for B2a scenario. The annual average increment, 0.068
o
C/year for A2a and 0.0038 oC/year for B2a scenarios, will be in 2080s. In general, the future
will experience an increasing trend of maximum temperature in 2020, 2050 and 2080 within
the A2a and B2a scenarios (Figure 12).
Figure 12.Change in average monthly maximum temperature in the future (2011-2099) from
the base (1977- 2013) period under A2a (A) and B2a (B) scenarios.
4.5.2.3.Projected change in minimum temperature
Even though the future climate periods show similar trends in all the months, the highest
projected minimum temperature will occur during the months of January, February and April
for both scenarios in 2020, 2050 and 2080 periods. The downscaled minimum temperature in
2020s indicated that the minimum temperature will rise by 0.016 0C /year for A2a and 0.003
o
C/year B2 scenarios. For 2050s there will be a rising trend by 0.091 0C/year for A2a and
0.081 0C/year for B2a scenarios. But in case of 2080, there will be decreasing trend by
48
0.01oC/year for A2a and neither decreasing nor increasing trends for B2a scenarios (Figure
13).
In general, the future will experience an increasing trend of minimum temperature in 2020
and 2050 for both scenarios, while for 2080 there will be a decreasing trend under A2a
scenario only and neither decreasing nor rising trend under the B2a scenario..
change in minimum
temperture in oC
HadCM3B2a
2020
2050
2080
30
20
10
0
month
Figure 13. Change in average monthly minimum temperature in the future (2011-2099) from
the base (1977- 2013) period under A2a and B2a scenarios.
4.6. Response of Maize Yield to Impact of Projected Climate Change Scenarios under
Different Tillage Practice
During 2020, 2050 and 2080s and under A2a and B2a emission scenarios, the projected
climate change will have a positive impact on Me-2 maize variety and a negative impact on
Me -4 maize variety. The predicted change in percentage maize yield in the21st century (2020,
2050 and 2080) relative to the average yield of the base line years (1977-2013) under A2a and
49
B2a emission scenarios is due to the impact of projected climate change. The yield scenarios
show a general tendency of increasing future grain yields by 2.3% under A2a scenario,
whereas for B2a scenarios, a tendency of increasing by 2.9% is predicted for Me-2 maize
variety (Figure 14).
Figure 14 .change of maize yield under A2a and B2a scanerios under Melkassa condition
4.6.1. Impact of projected climate change on Melkassa -2 under different tillage practice
The projected climate change will have a positive impact on grain yield of Me-2 variety under
both emission scenarios (A2a and B2a) by 2020, 2050 and 2080 periods under different tillage
practices (Figure 15). The probability of risk of getting grain yield greater than 5538 kgha-1 in
2080s under A2a emission scenario, using modified moldboard tillage practice, is 70% and
above (Figure 15A), while using local Maresha, the risk of getting greater than 5032 kgha-1
grain yield is 70% and above (Figure 15D). This indicates, under future climate change, that
there is a better chance of obtaining higher grain yield using modified moldboard tillage as
compared to using the local Maresha. Modified moldboard tillage could, therefore, be taking
as one adaptation option under future climate change around Melkassa area.
50
Figure 15.The Probability of exceedance curve in yield of Me 2 using different tillage practice
(A-twice modified mold board,.B-modified mold board,C-ripper wing plow and D-local
mersha in relative to base period (1977-2013) and future (2020s, 2050s and 2080s) within
A2a and B2a emission scenarios at Melkassa station
The change in yield of Me-2 due to the use of twice moldboard ranged from 1.33-5.13%
under both A2a and B2a emission scenarios in 2020, 2050, and 2080s (Figure 15A), while the
use of one time modified moldboard will change the grain yield between 2.1 and 4.57% over
the base period under both scenarios and same periods (Figure 15B). On the other hand, when
Ripper-wing plow is used, the yield of this variety, as compared to the control, changes from
1.54 to 4.25% under both emission scenarios in 2020, 2050, and 2080s, whilst the use of
normal Marsha changes the yield by 1.79 to 4.96%.
51
4.6.2. Impact of projected climate change on Melkassa -4 under different tillage practice
The projected climate change will have a negative impact on the grain yield of Me-4 variety
with respect to the baseline yield of 1977-2013 under both scenarios (A2a and B2a) in 2020,
2050 and 2080s. The grain yield of Me -4 variety shows a declining trend under A2a and B2a
emission scenarios in 2020, 2050 and 2080s. According to Brassard and Singh (2008), higher
temperatures translate into faster crop development and earlier maturation which results in
lower crop yields because the plant intercepts less cumulative solar radiation before it reaches
maturity and harvest. Additionally, the projected increase in temperature may lead to
shortening of crop growing season where the worst situation will be with the highest
temperature increase in 2020, 2050 and 2080s.
.
As indicated in Figure 16A, the probability of exceedance curve shows that the risk of getting
grain yield greater than 2870 kgha-1 by using twice modified moldboard is 70% and above in
baseline year compared to the 2020, 2050 and 2080 period for the A2a and B2a emission
scenarios, whereas the probability of exceeding curve in Figure 16D indicates that the risk of
getting greater than 2223 kgha-1is 70% and above in 2020B2a when using normal Mersha
52
Figure 16.The Probability of exceedance in yield of Melkassa-4 (A B C D) under different
tillage practice (A-twice modified mold board, B-modified mold board, C-ripper wing plow,
and C-normal mersha) in relative to the base period (1977-2013) and future (2020s, 2050s and
2080s) within A2a and B2a emission scenarios at Melkassa station
On the other hand, the likely change in percentage of the yield within each scenario compared
to the baseline ranges from -3.13 to-17.44% for both A2a and B2a emission scenarios of
2020, 2050 and 2080s when using twice modified mold board. When using one time modified
moldboard plowing, the change in grain yield ranges from 0.67-5.94% in 2020, 2050 and
2080s for the A2a and B2a emission scenarios
.
Likewise, the likely percentage change of yield when using Ripper-wing plows, within each
scenario compared to the baseline, ranges from -2.7% to -7.44% for both emission scenarios
of 2020, 2050, and 2080s, whereas the change in yield as a result of the use of normal
Maresha ranges from -6.84 to -12.3%.
4.7 Adaptation Options Potential in Maize productions Under Central Rift Valley of
Ethiopia
As mentioned above, the response of simulated grain yield of maize to future climate change
indicates the impact of increasing temperature on maize yield. Hence, adaptation options
should address the impact of future extreme temperature events and its associated climate
risks such as increased evapotranspiration and reduced water availability to maize crop.
.
53
4.7.1. Changing tillage practice without changing maize varieties
Except in 2020 under A2a emission scenario, the use of twice modified moldboard tillage
practice is predicted to increase the yield of Melkassa-2 variety, while the yield of Melkassa-4
will increase except in 2020 under both emission scenarios. Furthermore, the use of Ripperwing plow is also expected to result in slight grain yield increase of Me-4 and decrease that of
Me-2 variety
A using modified moldboard tillage practice from the current tillage practice increases the
Me-2 variety grain yield by 3.4% under A2a scenarios and by 3.8% under B2a scenarios in
2020. Similarly, in 2050 under A2a and B2a scenarios, the grain yield of Me-2 increases by
3.9 and 3.3%, respectively. Additionally, the grain yield of Me-4 also is also likely to increase
by 4.4, 4.4, 4.5, 3.7 and 3.9% in period of 2020, 2050 and 2080 for the A2a and B2a emission
scenarios.
Additionally, the grain yield of Me-4 also is also likely to increase by 4.4%, 4.4%, 4.5%,
3.7% and 3.9% in period of 2020, 2050 and 2080 for the A2a and B2a emission scenarios, in
spite of this, during using twice modified moldboards the grain yield of Me-4 increase by
4.7% and 4.4% in period of 2020 and 2050 within A2a emission scenarios respectively
relative to the current local tillage practice.
The projected temperature is likely to aggravate the evapotranspiration, thus resulting in
reduction of moisture availably for maize crop. As indicated in Table 6, tillage using the
modified moldboard will likely increase the grain yield of Me-2 in the range of 3.2-3.9% in
2020, 2050 and 2080 in both scenarios (A2a and B2a) ,whereas Melkassa-4 variety’s yield
will increase in the range of 2.1-4.5% in 2020,2050 and 2080 in A2a and B2a emission
scenarios. Also, the use of twice modified moldboard increases the grain yield in 2020 and
2050 in A2a scenario even if it is less than the increment of grain yield by modified Maresha
(Table 6).
54
Table 6.Percent change in average maize grain yield from baseline (1977-2013) without and with change maize variety
Maize
variety
Percentage change of maize yield
Emission
Scenarios
changing in tillage practice
melkassa-2
melkassa-4
TMM
MM
RW
NM
2020
2050
2080
2020
2050
2080
2020
2050
2080
2020
2050
2080
A2a
-0.13
2.8
3
3.4
3.9
3.2
-0.6
-0.1
-0.9
2.9
1.7
2.9
B2a
3.7
2.1
3
3.8
3.3
3.2
-0.6
-4.8
-1
2.7
2.1
2.8
A2a
4.7
4.4
3
4.4
4.6
3.7
1.3
2.2
0.2
-12.1
-10.4
-6.8
B2a
-6.6
0.4
3.2
6.9
4.2
3.9
1.4
-1.8
0.4
-12.3
-7.1
-7.1
TMM=Twice modified mold board,, OMM=one time Modified mold board.RW=Ripper wing plow, NM=normal mersha
55
4.7.2. Changing the maize variety without changing tillage practice
.As indicated in Table 7, adjusting tillage practice for moisture retention to increase the water
use efficiency of maize crop is helpful in counterbalancing the adverse impacts of long term
increase in temperature under projected climate change. Increase in maize productivity
through manipulating existing genetic variations of cultivars is key adaptation strategy under
changing climate. For instance, the use of Melkassa-2 instead of Melkassa-4 maize variety is
expected to improve grain yield under the predicted climate change during all the periods
under both A2a and B2a emission scenarios. This highlights the importance of genetic
modification of the current varieties in improving adaptation to the impacts of climate change.
Table 7. Percent change in maize grain yield without changing tillage practice (normal
Maresha) and with change in maize varieties from base period average yield (1977-2013)
Melkassa-2
Emission scenario
Melkassa-4
2020
2050
2080
2020
2050
2080
A2a
2.9
1.7
2.9
-12.1
-10.3
-6.7
B2a
2.7
2.1
2.8
-12.3
-7.1
-7.1
Generally, under changing climate at Melkassa condition using different tillage practices for
maize production is central to adaption of future climate change.
57
5.
SUMMARY AND CONCLUSION
The Central Rift Valley (CRV) of Ethiopia is likely to be affected by climate change.
Identification of adaptation and mitigation strategies is imperative to overcome the impacts of
the change. In view of this, a study was conducted to characterize the climate and modeling
the impact of climate change on production of maize using different conservation tillage
practices in semi-arid CRV of Ethiopia. Climate of Mieso, Melkassa and Adami Tulu areas
was characterized for its relevant attributes using long-term data obtained from the respective
stations. Long-term climate data, large scale HadCM3 GCM predictors and maize yield data
were collected from MARC-maize research program and Agro meteorology research process
of MARC, and website of National Center for Environmental Prediction (NCEP),
respectively.
Among the rainfall parameters, date of start and end of the season, length of the growing
season were found to be the most variable climate-related events. Taking April 1st as a starting
date, the analysis showed that planting earlier, at three locations for maize, than April 24 (102
DOY), May 4 (126 DOY) and April 14 (113 DOY) are possible only once in every four
years’ time for Mieso, Melkassa and Adami Tulu, respectively. The earliest possible end of
the rains of the growing season is day 245 (September 1st) for Mieso and Adami Tulu, and
September 24 (269 DOY) at Melkassa. The latest one is 271 (September 28), 285 (October
10) and 265 DOY (September 20) at Mieso, Melkassa and Adami Tulu respectively. The
mean LGS of the three stations in the study area in number of days is 111, 126 and 109.
The maximum number of rainy days at Mieso, Melkassa and AdamiTulu varies from 92 to
165, 92 to 145, and 92 to 110, respectively. The seasonal mean rainfall at Mieso, Melkassa
and Adami Tulu is 438, 577.7 and 430.8 mm. The probability of occurrence of dry spell
lengths of 5, 7, 10, and 15 days, at all the three stations, decreases from a maximum around
April 1st to a minimum during the peak rainy months (June, July, and August) and then again
increases starting from middle of September to April 1st.
Sen.’s slope estimator indicates that the total seasonal rainfall during the growing season of
the areas increases by 2.517, 2.68 and 0.37 mm/year and also Mann-Kendall shows positive
58
trend. The linear regression model, on the other hand, shows increasing trends by 2.11, 2.21
and 1.22 mm/year for Melkassa, Mieso and Adami Tulu areas, respectively.
The result of the Man-Kendall trend estimation shows that there is positive trend of the
annual maximum temperature of the study areas and the result of Sen:s slop estimations also
indicates an increase by 0.035, 0.018 and 0.014 for Mieso, Melkassa and Adami Tulu,
respectively. The annual minimum temperature of the areas, as per Man-Kendall estimation,
shows positive trend for Mieso but negative trend for Melkassa and Adami Tulu and Sen.’s
slope shows that there was increasing trend by 0.009, 0.014 and 0.018 oC/year for Mieso,
Melkassa and Adami Tulu respectively.
The downscaling study showed that the SDSM was able to simulate all climate variables with
reasonable accuracy. The model overestimated the extreme values and keeps more or less the
average values of rainfall. However, the model simulated minimum and maximum
temperature with good accuracy indicating the potential use of the model for climate
prediction in the area with further calibration.
The output of GCM-HadCM3 downscaled by SDSM indicated that the projected rainfall at
Melkassa station shows a decreasing trend for A2a and B2a scenarios in periods of 2020,
2050 and 2080 except in 2020A2a. Whereas, the trend of projected maximum temperature
increases except for the 2080B2a emission scenarios and also the trend of projected minimum
temperature decreases in 2080A2a but increases for the A2a and B2a emission scenarios in
periods of 2020 and 2050.
From the DSSAT crop model output the projected change of climate on Me-2 maize variety
has positive impact expect for Me-4 maize variety. Therefore, under projected climate the
grain yield of Me-2 increase by 2.3 and 2.9% for A2a and B2a emission scenarios,
respectively, whereas for Me-4 maize variety under projected climate decreases by 5.85 and
3.08% for the A2a and B2a emission scenarios relative to the baseline grain yields under
different tillage practice.
59
From the findings of this study it can be inferred that the rainfall of the CRV is highly
variable resulting in unreliable planting dates and end of growing season. Under such
conditions, the risk that rainfed agriculture will fail is high. The use of improved tillage
practices and maize varieties could result in better grain yield under future climate conditions.
From the results of this study, the following can be recommended:

In order to adjust their farming practice, farmers need to use, under changing climate,
seasonal climate outlook information ‘for successful maize production.

To offset the adverse effects of increasing temperature, production of drought or heat
tolerant maize cultivars with optimum maturity periods are recommended.

Crop management practice like moisture retention practice that increase water use
efficiency should be used. Like Tillage practice

Future intervention need to be based on assessment of farmer’s adaptive recourse
toward integrating native knowledge with scientific innovations to adapt or mitigate
the impact of climate change.
60
6.
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7.
APPENDICES TABLES
7.1 .Appendix Table
Table 1.Comparison of observed against simulated parameters for Melkassa-2 under Melkassa
climate
year
Days of flowering
Days of maturity
Yield in kg/ha
OB
SI
OB
SI
OB
SI
2002
67
67
139
140
5434
5509
2003
70
69
140
140
5544
5606
2004
69
68
139
139
5374
5406
2005
68
68
139
140
6444
6446
2006
68
69
138
138
6563
6606
2007
69
69
139
138
5843
6002
2008
68
68
137
138
4174
4206
2009
68
67
137
137
5132
4874
2010
66
66
137
137
6249
6252
mean
68
68
138.3
138.56
5639.7
5656.3
SD
±1.17
±1.05
±1,22
±1.23
±744.5
±774.7
CV
1.7%
1.6%
0.8%
0.9%
13.2%
13.7%
Table 2 .comparison of observed against simulated parameters for melkassa-4 under melkassa
climate
Year
Days to flowering
Days to maturity
Yield in kg/ha
OB
SI
OB
SI
OB
SI
2006
58
60
109
110
4397
4410
2007
56
57
114
114
4572
4584
2008
60
63
110
110
4156
4173
2009
58
61
111
111
4185
4663
2010
58
59
113
112
4024
4075
2011
56
55
109
109
4067
4026
2012
61
64
110
111
4590
4156
2013
55
57
112
112
5210
5285
mean
57.75
51.5
111
111.1
4400
4397
SD
±2.059
±3.117
±1.852
±1.553
±392.8
±410.2
CV
3.6%
5.2%
1.7%
1.4%
8.9%
9.3%
73
Table 3. Projected temperature and precipitation change under different scenarios
2020
2050
2080
Tmax.HaCM3A2a
0.036
0.039
0.062
TminHadCM3A2a
0.016
0.009
-0.001
TmaxHadCM3B2a 0.031
0.0374
-0.0003
TminHadCM3B2a
0.003
-0.018
-0.0017
PreHadCM3A2a
0.954
0.955
-0.938
PrecHadCM3B2a
0.171
-0.152
-2.384
Table 4. Projected changes in monthly precipitations at Melkassa
B2a
month
2020
A2a
2050
2080
2020
2050
2080
Jan
1
5
8
2
14
4
Feb
3
7
11
2
5
2
Mar
5
3
3
4
7
3
Apr
7
3
4
15
16
14
May
3
4
6
13
0
1
Jun
3
1
1
9
3
6
Jul
1
7
3
11
10
1
Aug
2
4
2
5
12
5
sbtem
4
6
1
11
3
4
Oct
3
6
1
9
3
4
Nov
3
0
4
4
1
8
Dec
8
6
2
11
3
6
74
Table 5.projected changes in monthly minimum temperature at Melkassa
A2a
month
B2a
2020
2050
2080
2020
2050
2080
Jan
12
24
31
6
16
24
Feb
11
22
30
5
16
23
Mar
10
23
28
5
15
23
Apr
8
21
26
3
16
21
May
7
20
25
4
15
22
Jun
8
21
26
4
14
22
Jul
10
22
26
4
14
22
Aug
7
20
23
5
14
21
Sebte
7
20
22
4
13
20
Oct
5
20
23
4
14
20
Nov
7
21
24
3
13
21
Decm
7
19
25
5
15
20
Annual
8
21
26
4
14
21
Table 6. projected changes in monthly maximum temperature at melkassa
A2a
month
B2a
2020
2050
2080
2020
2050
2080
Jan
7
14
19
6
13
19
Feb
7
13
20
6
11
17
Mar
6
13
19
4
8
15
Apr
6
13
19
3
9
16
May
6
13
19
3
9
15
Jun
6
13
20
3
10
16
Jul
7
14
20
4
10
17
Aug
8
13
20
4
10
17
Sptem
8
13
19
4
10
16
Oct
8
14
20
4
10
18
Nov
7
13
20
7
13
20
Decm
8
14
20
7
13
19
Annual
7
13
20
5
11
18
75
76