The document discusses a study on the impact of climate change on teff production in Southeast Tigray, Ethiopia. It finds that climate factors, particularly temperature and rainfall, significantly influence net teff revenues. Increasing temperature was found to reduce teff revenues, while increasing rainfall was found to increase teff revenues. Adaptation to climate change was also found to play a key role in net revenues. The study utilized a Ricardian model and data from 210 farming households across three agro-ecological zones to analyze how various factors, including climate, socioeconomic characteristics, and adaptations strategies, influence teff production and revenues.

1. The Impact of Climate Change on Teff Production in Southeast Tigray, Ethiopia
AJAERD
The Impact of Climate Change on Teff Production in
Southeast Tigray, Ethiopia
Felix Moses Tembo
Department of Land Management, Faculty of Environmental Sciences, Mzuzu University, Malawi
Email: tembofelix@yahoo.com
The paper reports results of a study on investigating impacts of climate change on teff (Eragrostis
tef) production in three agro-ecological zones (highlands, midlands and lowlands) of Endamehoni
and Raya Azebo weredas of Tigray. The impact of climate change on teff farming was estimated
taking into account farm households’ characteristics, socio-economic, climate, adaptations,
production factors and agro-ecological settings in a low-income developing country. Ricardian
model was used to analyze data obtained from teff farming households. From the fourteen
predictor variables fitted in the model, six variables e.g. climate factors, adaptation strategies,
production factors, weather and climate information, socio-economic factors and agro-ecology
were found to have significance influence on net revenues with model coefficients at p=0.05 and
less. Climate factors (temperature and rainfall) and adaptation to climate change were found to
play key roles on net revenues. Increasing (decreasing) temperature reduces (increases) teff
revenues. Therefore, policies of government on adaptation ought to be given enough attention to
reduce vulnerability and improve food security among teff farming communities in rural areas.
Key words: climate change, perception, teff, net revenue, Ricardian model
INTRODUCTION
Climate change refers to changes in the climate noticed by
changes in variability of certain properties (e.g.
temperature, rainfall) that persist over years (Sumelius et
al. 2009). According to Raisanen and Tuomenvirta (2008)
changes in climate arise as a consequence of natural
variability as well as human activity. Climate change is a
real global challenge (Yumbya et al. 2014) and its impacts
have totally changed agriculture status of the world
(Gebretsadik 2012). Climate change triggers high
temperatures and reduced rainfall resulting into decreased
crop yields affecting particularly African countries (Di Falco
et al. 2011; Yesuf et al. 2008).
Ethiopia covers about 112.3 million hectares of land and
16.4 million is suitable for crop production (Deressa et al.
2009). Ethiopia is particularly vulnerable to climate change
because its economy is largely built on weather sensitive
agriculture production (IPCC 2007; Mendelsohn 2000).
Agriculture is a source of livelihood to majority of Ethiopian
population where small-scale subsistence farming is
predominant (Di Falco et al. 2011). It accounts for about
45% of the GDP and 85% of total employment but
extremely vulnerable to climate change (Yumbya et al.
2014). An existing atlas on poverty and climate
vulnerability in Africa (Yesuf et al. 2008) locates Ethiopia
as one of the most vulnerable countries. It is shown by
periodical extreme events in form of frequent droughts
(1965, 1974, 1983, 1984, 1987, 1990, 1991, 1999, 2000,
2002 and 2011). Therefore, annual rainfall and other
climatic factors have critical influence on teff (Eragrostis
tef) yields, one of the major food crops for Ethiopia.
The impact of climate change is mainly the cause for food
insecurity particularly rural areas of Ethiopia (Gebretsadik
2012). According to FAO (2010), the rising temperature
and variability in rainfall pattern have direct impact on crop
production and food security. For example, Tigray is highly
vulnerable to climate related events such as droughts. The
historic droughts of 1990; 1991; 1999; 2000; 2011; 2014
(Yumbya et al. 2014) particularly resulted in reduced crop
yields. Reports from IBC (2012) and FAO (2010) indicate
repeated failures in teff production due to localized
droughts associated with erratic rainfall. As farmers
depend on teff produce, they expect food and stable
Journal of Agricultural Economics and Rural Development
Vol. 4(1), pp. 389-396, March, 2018. © www.premierpublishers.org, ISSN: 2167-0477
Analysis

2. The Impact of Climate Change on Teff Production in Southeast Tigray, Ethiopia
Felix M. Tembo 390
income from the crop to sustain their livelihoods. However,
any shock on the sector results in food insecurity and
broader economic consequences for rural farming
community.
Despite increased frequency and intensity of climate risks
in the region empirical studies on climate change impacts
are lacking. With climate change a real global challenge it
is important to investigate these climate risks at household
level. Investigating farmers’ perceptions and impacts of
climate change are essential because they may give
directions for more suitable adaptation strategies for the
region. Local farmers’ perceptions on climate change
govern their responses to localized climate risks on their
livelihoods (Deressa et al. 2014). Therefore, indigenous
knowledge coupled with scientific scenario is worthwhile to
design policy directions for future coping strategies.
Farming communities in the region have experienced
repeated food insecurity and received grain assistance
due to droughts. Food assistance is a common
phenomenon in fighting food shortages. For instance,
Ethiopia needed almost 897,000 metric tons of food
assistance during the 2000/2001 drought period and
almost 455 million US Dollar worth of food assistance in
2008/2009 (GOE 2009). Drought and food shortages
therefore, necessitated conducting household level
empirical study to make use of indigenous knowledge and
experience in designing feasible coping strategies more
appropriate for local farmers.
The main objective of the study was to analyze impacts of
climate change on teff production. In theory there exists a
relationship between various factors that are assumed to
influence impacts of climate change on agriculture and
their directions. For example, perceptions of farm
household on climate change arise from multiple
directions. This is because farm household’s level of
awareness on climate change and its intensity provide
lessons on how farmers perceive change in the climate. It
is hypothesized therefore, that socio-economic factors,
climate factors, adaptation strategies, production factors
and agro-ecological settings influence teff production and
revenues. Linear regression therefore was used to access
the impact of climate change on teff revenues. The paper
therefore focused on farmers’ perceptions of the long-term
changes in temperature and rainfall and impacts of climate
change on teff revenues.
Several studies carried out globally e.g. United States of
America, France, China, India, Brazil, Cameroon, South
Africa, Zimbabwe and Kenya used Ricardian model to
estimate impacts of climate change on agriculture. For
example, Mendelsohn et al. (1994) estimated the
economic impact of climate change on land values. It used
the Ricardian model to calculate the economic impact of
climate change on agriculture. Cross sectional data of
almost 3,000 counties in United States of America was
used. The results indicated that seasonal temperature in
all seasons excluding autumn increased farm revenues.
Similarly, Passel et al. (2012) employed a continental
scale Ricardian analysis to estimate the impact of climate
change on European agriculture. The analysis was carried
out using data on climate, soil, socio-economic
characteristics from 37,000 farms across Europe. The
findings indicate that land values are sensitive to climate.
Increase/decrease in rainfall increases/reduces farm
revenue by 3% per centiliter of rainfall. Additionally,
Bayecha (2013) analyzed the economic impacts of climate
change on teff production in central Ethiopia. It used data
from 150 teff farmers in Lume and Gimbichu districts of
Ethiopia. It analyzed farmers’ perceptions on long-term
change in climate (temperature and rainfall) and
adaptation strategies on teff production. The study
revealed that temperature had negative impact on net teff
revenue while rainfall had positive impact on net teff
revenue.
METHODOLOGY
Study area
Tigray is located in northern part of Ethiopia (Figure 1) with
an altitude ranging from 400 to 4,000 m above mean sea
level. It lies between latitudes 12° 15’ N and 14° 57’ N and
longitudes 36° 27’ E and 39° 59’ E. It covers an area of
about 53,000 km2 (CSA 2015). In 2008 the population was
estimated to be 4.3 million with an annual growth rate of
2.5%, predominantly rural and engaged in rain-fed
agriculture (Abraha et al. 2012). Administratively, Raya
Azebo is subdivided into 18 kebeles at an altitude ranging
from 930 to 2,300 m above mean sea level with 90% of the
weredas described as midland and 10% lowland (Tesfay
et al. 2014). Endamehoni is described as mostly highlands
with an altitude of 2,432 m above mean sea level at
Maichew. The climate is generally semi-arid with two types
of rainfall patterns (mono and bimodal). Annual rainfall
ranges from 800-1,000 mm per year dropping to 400 mm
(Edwards et al. 2006). Farming system is predominantly
mixed with livestock keeping. Major crops grown in the
area include sorghum, teff, barley and maize (Tesfay et al.
2014). Some major challenges facing the area range from
flooding, soil erosion, drought and erratic rainfall.
Data sources, sampling and analysis
Both quantitative and qualitative data were used. The data
was mainly obtained from a survey conducted at the three
villages using a structured questionnaire. Meteorological
data (temperature and rainfall) was sourced from National
Meteorological Agency of Tigray.

3. The Impact of Climate Change on Teff Production in Southeast Tigray, Ethiopia
J. Agric. Econ. Rural Devel. 391
Figure 1: Map of Ethiopia showing location of Tigray and study area.
In designing the survey, a stratified sampling technique
with simple random was employed. At first, two out of 35
weredas in the region were purposively selected because
they were teff growing areas but also vulnerable to climate
change. The two weredas were Endamehoni (highland)
and Raya Azebo (midland and lowland). Secondly, three
villages were purposively selected from the highland,
midland and lowland representing one village at each
agro-ecological zone. . Thirdly, the sampled households
were randomly selected from the identified villages. A
sample of 210 households were selected from the three
agro-ecological zones comprising of 70 farmers from the
highlands, 70 farmers from midlands and 70 farmers from
lowlands.
A table was used to describe farmers’ long-term
perceptions about temperature and rainfall variability.
Graphs were used to show long term (30 years) trends in
temperature and rainfall variability while estimated
coefficients of net revenues drawn from the regression
model were show in a table.
The econometric model
Most empirical studies on climate change impacts on
agriculture employ the Ricardian approach. There are
other models also used e.g. Computable General
Equilibrium (CGE), Agronomic-Simulation and Production
Function Analysis. CGE model is commonly used for
quantifying costs and benefits on how economic activities
affect the environment and vice versa (Van Ierland, 1999).
Agronomic-Simulation model emphasizes the dynamic
physiological process of plant growth and seed formation
(Maganga and Malakini 2015). Production Function
Analysis calculates environmental damage (Rosenzweig
and Iglesias 1994). Having explored the three models, it
was noted that they have limitations. CGE models are
extremely aggregated while agronomic models are weak
to capture adaptation strategies. Production Function
Analysis also has been criticized for having inherent
biases and tends to overestimate climate change damage
on agriculture because of failure to take into account
immeasurable variability of substitutions that may displace
outdated activities for climate change. The Ricardian
approach, therefore, has been commended because it
takes into account all shortfalls inherent in other models.
The approach has been proved to be appropriate because
it corrects for biases that are found in other approaches
like the production function analysis (Mendelsohn et al.
2000). It takes into account the direct impacts of climate
on crop yields as well as the indirect substitution of diverse
inputs and different activities introduced and possible
adaptations by computing farm revenues.
Theoretically, it is assumed that changes in farm output in
terms of products’ value (quantity of farm produce) is
shown in farmland value (Bello and Maman 2015).
Farmland net productivity is shown in net revenues.
Previous literature indicate that farmland’s value is found
to be sensitive to seasonal climatic conditions of rainfall
and temperature (Mendelsohn and Dinar 2003;
Mendelsohn et al. 2000). Ricardian was named after
David Ricardo (1772-1823) because of his original opinion
that land value is reflected in net productivity. Net revenue
(𝑅) reflects net productivity and the principle follows the
equation: 𝑅 = ∑ 𝑃𝑖 𝑄𝑖(𝑋, 𝐹, 𝑍, 𝐺) − ∑ 𝑃𝑥 𝑋 (1)
Where 𝑃𝑖 the market price of yield 𝑖, 𝑄𝑖 the output of yield
𝑖, 𝑋 the vector of farm inputs, 𝐹 the vector of climate

4. The Impact of Climate Change on Teff Production in Southeast Tigray, Ethiopia
Felix M. Tembo 392
variables, 𝑍 the vector of physical variables (e.g. agro-
ecology), 𝐺 the vector of socio-economic variables (e.g.
distance to market) and 𝑃𝑥 the vector of input prices
(Mendelson et al. 1994). The farmer is presumed to
choose X to maximize net revenues given the physical
characteristics of farm and market price. This model is a
reduced form that examines how different explanatory
variables F, Z, and G affect net revenues. Normal
Ricardian model depends on the following quadratic
formula:
𝑅 = 𝐵0 + 𝐵1𝐹 + 𝐵2𝐹2 + 𝐵3𝑍 + 𝐵4𝐺 + 𝜀 (2)
Where, 𝜀 is an error term. It presents both linear and
quadratic regression for temperature and rainfall. The
expected directional impact of single climate variable on
net revenue estimated at the mean is:
𝐸 [
𝑑𝑅
𝑑𝑓𝑖
] = 𝐵1, 𝑖 + 2 ∗ 𝑏2, 𝑖 ∗ 𝐸[𝑓𝑖] (3)
The quadratic term reveals the nonlinear shape of net
revenue of climate reaction function (Equation 2). Net
revenue function indicates U-shaped when quadratic
equation is positive and hill-shaped when quadric equation
is negative. The relationship between seasonal climate
variables is difficult to predict and may contain a mixture of
positive and negative coefficients. The change in annual
benefits (∆𝑈), a resultant from climate change from C0 to
C1 can be defined as:
∆𝑈 = 𝑅(𝐶1) − 𝑅(𝐶0) (4)
If climate change increases net revenue it will be useful
and if climate change reduces net revenue it will be
harmful. Observations across different climates of the
season can reveal sensitivity of climate on crop yields. This
study, therefore, used linear regression to estimate teff net
revenue to impacts of climate change.
Independent variables for regression model
Table 1 shows summary of independent variables in
assessing impact of climate change on teff net revenue.
Teff revenues are assumed to be a function of farm
household characteristics (e.g. age, education),
institutional support (e.g. farm to farm extension, formal
extension services, access to formal credit), adaptation
strategies, climate factors (e.g. temperature, rainfall),
production factors (e.g. land size, seed, fertilizer, labor)
and agro-ecological settings (e.g. highland, midland,
lowland).
Table 1: Definition of explanatory variables
Variable Value Type Exp. sign
Access to climate information 1=yes, 0=no Dummy +/-
Access to formal extension services 1=yes, 0=no Dummy +/-
Access to farm-to-farm extension 1=yes, 0=no Dummy +/-
Access to adaptation strategy 1=yes, 0=no Dummy +/-
Age of household head Years Continuous +
Education status of household head School attended (years) Continuous +
Total number of livestock Number Continuous +
Size of land holding Hectare Continuous +
Amount of seeds used Kg/ha Continuous +
Amount of Fertilizer used Kg/ha Continuous +
Labor–individuals engaged in farm Number Continuous +
Distance to market Kilometer Continuous +
Rainfall Millimeter Continuous +
Temperature Degrees Celsius(°C) Continuous -
RESULTS AND DISCUSSION
Farmers perceptions to climate change
The results indicate 90% of farmers perceive increasing
temperature, 5% decreasing temperature and 4% perceive
no change as shown in Table 2. Similarly, 92% of farmers
perceive decreasing rainfall, 3% increasing rainfall and 5%
no change. In the past thirty years, increasing temperature
and decreasing rainfall are leading perceptions among teff
farmers in the study area.
Table 2: Farmers perceptions of long term temperature
and rainfall changes (N=200).
Climate variables Respondents Response (%)
Temperature increase 180 90
Temperature decrease 8 4
No change in temperature 12 6
Rainfall increase 6 3
Rainfall decrease 184 92
No change in rainfall 10 5

5. The Impact of Climate Change on Teff Production in Southeast Tigray, Ethiopia
J. Agric. Econ. Rural Devel. 393
Figure 2: Maichew Meteorological Station average temperature.
Data Source: Government of Ethiopia (2016).
Figure 3. Maichew Meteorological Station annual rainfall.
Data Source: Government of Ethiopia (2016).
Temperature variability
Yearly average temperature shows an increasing trend of
0.1°C from 1985 to 2012 as indicated in Figure 2. Minimum
and maximum ideal temperature limit for teff growth was
found to be 13.2°C and 25.2°C respectively (Yumbya et al.
2014). But currently, temperatures are within the ideal limit
for proper teff growth. Should the rising trend in
temperature continues, it is likely to reach a critical limit for
proper teff growth before 2050. This increasing trend in
temperature will have negative consequences for teff
growth and will result in reduced yields contributing to food
insecurity.
Rainfall variability
Annual amounts of rainfall have shown decreasing trend
(0.03 mm) from 1976 to 2012 as shown in Figure 3.
Yumbya et al. (2014) noted that good amount of rainfall
distribution for teff growth ranges from 600-1900 mm.
Rainfall conditions for proper teff growth are ideal currently
though affected by droughts (e.g. 1990, 1999, 2000,
2014). If annual rainfall continues decreasing, teff growth
will reach a critical limit before 2040. Decreasing annual
rainfall affects teff production negatively.

6. The Impact of Climate Change on Teff Production in Southeast Tigray, Ethiopia
Felix M. Tembo 394
Table 3: Estimated regression coefficients of net teff revenue
Variable Coef. Std.Err. t P>|t| [95% Conf. Interval]
Age 0.001 .0013557 0.74 0.460 -.0016708 .0036797
Education -0.005 .0060655 -0.80 0.426 -.0168113 .0071269
Rainfall 0.529 .1676664 3.16 0.000* .1982657 .8599546
Average temp. -0.617 .2216918 -2.78 0.006* -1.054747 -.1798154
Adaptation 1.022 .2794522 3.66 0.000* .4702346 1.573124
Farm-to-farm -0.155 .1632815 -0.95 0.343 -.4774341 .1669744
Formal ext. 0.019 .0691758 0.28 0.779 -.1170694 .1559404
Climate info. 0.211 .1098528 -1.92 0.047* -.4274729 .0060732
Market distance 0.007 .0100222 0.69 0.488 -.0128174 .0267365
Land size 0.261 .1101811 2.37 0.045* .0437495 .4785914
Seed 0.313 .0880712 3.55 0.000* .1392864 .4866976
Fertilizer 0.261 .1085698 2.40 0.017* .0462749 .4747416
Labor 0.441 .2519131 1.75 0.040* -.0565388 .9376267
Livestock 0.260 .1088982 2.39 0.018* .0450261 .4748051
Highland 0.253 .0901512 2.81 0.006* .0751319 .4307364
Midland 0.149 .0856049 1.75 0.082* -.0190012 .3186595
_cons 0.315 .164137 1.92 0.005 -.0091054 .6386794
Number of obs = 200 F (20,179) = 4.56 Prob > F = 0.0000 R-squared = 0.3376 Adj R-squared = 0.2636 Root MSE =
.21208. (*significance at 5% and less). Source: Author’s own calculations.
Impact of climate change on net revenue
Table 3 presents a summary of regression model in
assessing the impact of climate change on teff revenues.
Teff revenue is assumed to be a function of household
head characteristics, formal and non-formal institution
support, adaptation strategies, climate factors, weather
and climate information, production factors and agro-
ecology.
Climate factors: Results of the study reveal that climate
factors (temperature: p=0.006 and rainfall: p=0.000) are
statistically significant with negative and positive influence
respectively on net revenue. The results suggest that
increase in temperature reduces net revenue and increase
in rainfall increases net revenue, keeping other factors
constant. A study by Kurukulasuriya Mendelsohn (2008)
found that African farms are very sensitive to climate
particularly temperature. The study further observed that
farm revenues are poorer in places of higher temperatures
than lower temperatures. Similarly, a study by Deressa
(2007) on ‘Measuring The Economic Impact of Climate
Change on Ethiopian Agriculture’ observed that increasing
temperature by (2.5-5) °C affects net revenue per hectare
slightly and reducing rainfall by (7-14) % also reduces net
revenue per hectare. It is assumed that increasing
temperature and decreasing rainfall is damaging to crop
production like teff. Should the trend in climate variability
continues, it will only increase food insecurity to already
vulnerable farmers.
Climate adaptation: The results found that climate
adaptation (p=000) was statistically significant with
positive impact to net revenue. The results suggest that
farm households who practiced adaptation strategies to
impacts of climate change had increased teff revenues,
keeping other factors constant. Those farm households
who practiced adaptation strategies had higher farm
revenue than those that did not. It is assumed that farm
diversification generates extra income to farm households.
Based on marginal effect, Yesuf et al. (2008) estimated
that farm households that took climate change adaptation
tended to produce 95-300 kg more food per hectare than
those that did not. He noted that farm households that
engaged in adaptation measures reduced the effect of
climate change by almost 10-29%. This implies that the
effect of climate change is reduced if farm households
engage in adaptation strategies by increasing farm
revenues. The results are also consistent with the
argument in literature (Yesuf et al. 2008; Di Falco et al.
2011) that climate change adaptation partly compensates
the impacts of climate change on agriculture production.
Production factors (inputs): The results show all
conventional inputs (seed p=000; fertilizer p=0.017; labor
p=0.040) displaying signs consistent with predictions of
economic theory and statistically significant with positive
influence to farm revenue. The results suggest that farm
households who used more seeds, more fertilizers and
more farm labor had increased net revenue, keeping other
factors constant. Naturally, it is expected that use of more
seeds, more fertilizers and more labor tend to increase
farm revenue. Therefore, only those farm households that
used more seeds, more fertilizer and additional farm labor
realized increased net revenues.
Access to climate information: Access to information on
weather and climate is statistically significant (p=0.047)
with positive influence on net revenue. The results suggest
that access to weather and climate information increases
the probability of increasing net revenue, keeping other
factors constant. With access to information on weather
and climate farmers are equipped with knowledge and
skills on improve farming activities to optimize returns from
crop production. Farmers with information on weather can
plant seeds early to take advantage of first rains and avoid

7. The Impact of Climate Change on Teff Production in Southeast Tigray, Ethiopia
J. Agric. Econ. Rural Devel. 395
erratic and short rainy seasons. With access to extension
service, farmers are able to learn standard principles of
agriculture to optimize returns from farming.
Socio-economic variables: Results of the study found
that land size (p=0.045) and livestock (p=0.018)
statistically significant with positive influence on net
revenue. The results suggest therefore, that land size and
livestock keeping increases net revenue, keeping other
factors constant. Land constitutes the largest share of
agriculture assets in the study area as farmers’ livelihoods
are dependent on land. Normally, it is expected that bigger
land size and keeping a large herd of animals is associated
with wealth among farmers and tend to reduce
vulnerability to impacts of climate change. During periods
of crop failure farmers sell animals or a portion of land to
earn some income for livelihood.
Agro-ecology: The results found significant differences
across agro-ecological settings of the study area.
Highlands (p=0.006) and midlands (p=0.082) were found
to be statistically significant with positive influence to farm
revenue. The results suggest that farm households in the
highlands as well as the midlands have increased net
revenue, keeping other factors constant. The results as
compared to the lowlands imply that more revenue per
hectare is gained in the highlands, followed by the
midlands as observed by a stronger p-value of the
highlands than the midlands. A study by Kurukulasuriya
and Mendelsohn (2008) found that geographical locations
of higher temperatures (lowlands) get lower farm
revenues. Experience has shown that geographical
locations of higher temperature (lowlands) get lower farm
revenues than highlands. Applying these results to likely
future climates would indicate that lowland farms (dryland)
are exclusively more climate sensitive.
CONCLUSION
Based on descriptive analysis it can be stated that most
farm households are aware of the long-term changes in
temperature and rainfall. Meteorological data also indicate
increasing average temperature and decreasing amounts
of rainfall. The regression analysis show that climate
factors, adaptation strategies to climate change and agro-
ecology have strong influence on teff revenue. Increase
(decrease) in temperature reduces (increases) teff
revenue and increase (decrease) in rainfall increases
(reduces) teff revenue. Farm households that are engaged
in various adaptation strategies have an increased
probability of getting higher teff revenue than those that do
not. This means that farm households that practice
adaptation strategies experience reduced impacts.
Though rainfall distribution increases teff revenues, but too
much or too little reduces revenues indicating that climate
factors are the most important to teff production. The
results also indicate that farm households in highlands
obtain higher revenue per hectare than those in midlands
followed by lowlands. Climate factors and adaptation to
climate change appear to be strong determinants in
influencing teff revenues. Therefore, adaptation strategies
should be encouraged to assist teff farmers increase food
security and reduce vulnerability from impacts of climate
change.
ACKNOWLEDGEMENTS
This work is an extract from a thesis submitted in partial
fulfillment of the requirement for the Master of Science
Degree in Climate affairs at Mekelle University, Ethiopia
on 25th June, 2016. The author would like to thank Dr.
Tewodros Tadesse and Dr. Girmay Gebresamuel for their
advice and contribution towards this research work. Mr.
Frank Chirandu and all field officers engaged in the
research through participation in questionnaire adjustment
and final enumeration are also commended. The author
would also like to thank Dr. Mtafu Manda for taking his time
to review this paper and giving his constructive comments.
Thanks should also go to members of staff at Institute of
Climate and Society (ICS) for their support. Finally, my
sincere gratitude goes to Prof. Wales Singini, Dean,
Faculty of Environmental Sciences at Mzuzu University,
Intra-ACP – Mobility Project for Crop Scientists for Africa
Agriculture (CSAA) for sponsorship.
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Accepted 13 February 2018
Citation: Tembo MF (2018). The Impact of Climate
Change on Teff Production in Southeast Tigray, Ethiopia.
Journal of Agricultural Economics and Rural Development,
4(1): 389-396.
Copyright: © 2018 Felix Moses Tembo. This is an open-
access article distributed under the terms of the Creative
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