SPATIAL AND TEMPORAL VARIABILITY OF COFFEE (Coffea

arebica L.) WATER AND IRRIGATION REQUIREMENT MAPPING

FOR JIMMA ZONE

MSC THESIS

MINDA TADESSE BEDANE

HAWASSA UNIVERSITY, HAWASSA, ETHIOPIA

MARCH, 2019

SPATIAL AND TEMPORAL VARIABILITY OF COFFEE (Coffea

arebica L.) WATER AND IRRIGATION REQUIREMENT MAPPING

FOR JIMMA ZONE

MINDA TADESSE BEDANE

A THESIS SUBMITTED TO SCHOOL OF WATER RESOURCE ENGINEERING

HAWASSA UNIVERSITY

HAWASSA, ETHIOPIA

IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE

DEGREE OF MASTER OF SCIENCE IN WATER RESOURCES

ENGINEERING AND MANAGEMENT

MARCH, 2019

SCHOOL OF GRADUATE STUDIES

HAWASSA UNIVERSITY

ADVISORS’ APPROVAL SHEET

(Submission Sheet-1)

This is to certify that the thesis entitled “Spatial and Temporal Variability of Coffee

(Coffea arebica L.) Water and Irrigation Requirement Mapping for Jimma Zone”

submitted in partial fulfillment of the requirements for the degree of Master's with

specialization in water resource engineering and management, the Graduate Program

of the Department/School of bio-system and environmental engineering, and has been

carried out by Minda Tadesse Bedane, Id. No WREMR/018/09, under my/our

supervision. Therefore, I/we recommend that the student has fulfilled the requirements and

hence hereby can submit the thesis to the department.

Abraham Woldemichael (PhD.) _______________ ______________ Name of Major Advisor Signature Date

Tilahun Hordofa (PhD.) __________________ ________________ Name of Co-Advisor Signature Date

SCHOOL OF GRADUATE STUDIES

HAWASSA UNIVERSITY

EXAMINERS' APPROVAL SHEET-1

(Submission Sheet-2)

We, the undersigned, members of the Board of Examiners of the final open defense by

Minda Tadesse have read and evaluated his/her thesis entitled “Spatial and Temporal

Variability of Coffee (Coffea arebica L.) Water and Irrigation Requirement Mapping for

Jimma Zone”, and examined the candidate. This is, therefore, to certify that the thesis has

been accepted in partial fulfillment of the requirements for the degree.

__________________________ __________________ _______________ Name of the Chairperson Signature Date

__________________________ __________________ _______________ Name of Major Advisor Signature Date

__________________________ __________________ _______________ Name of Internal Examiner Signature Date

__________________________ __________________ _______________ Name of External Examiner Signature Date

__________________________ __________________ _______________ SGS Approval Signature Date

Final approval and acceptance of the thesis is contingent upon the submission of the final

copy of the thesis to the School of Graduate Studies (SGS) through the

Department/School Graduate committee (DGC/SGC) of the candidate's department.

Stamp of SGS

Date: ___________________

HAWASSA UNIVERSITY

SCHOOL OF GRADUATE STUDIES

EXAMINERS' APPROVAL SHEET-2

(Submission Sheet-3)

As members of the Board of Examiners of the final Master's degree open defense, we

certify that we have read and evaluated the thesis prepared by Minda Tadesse

Bedane under the title “Spatial and Temporal Variability of Coffee (Coffea arebica

L.) Water and Irrigation Requirement Mapping for Jimma Zone”, and recommend

that it be accepted as fulfilling the thesis requirement for the degree of Master's of

Science in Bio-system and Environmental Engineering with Specialization in Water

Resource Engineering and Management.

__________________________ __________________ _______________ Name of the Chairperson Signature Date

__________________________ __________________ _______________ Name of Internal Examiner Signature Date

__________________________ __________________ _______________ Name of 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 SGS through the DGC/SGC of the candidate's

department/School.

Thesis approved by

_______________________ _________________ ________________

DGC/SGC Signature Date Certification of the final Thesis

I hereby certify that all the corrections and recommendation suggested by the

Board of Examiners are incorporated into the final Thesis entitled “Spatial and

Temporal Variability of Coffee (Coffea arebica L.) Water and Irrigation Requirement

Mapping for Jimma Zone” by Minda Tadesse.

______________________ __________________ _________________ Name of the Designate Signature Date

Stamp of SGS

Date: ___________________

i

DEDICATION

I dedicate this thesis manuscript to my daughters Merry Minda, Sifan Minda, my mother

Ms. Warkitu Tadesse, my wife Ms. Mekdas Belay and my friend Mohammed Jihad.

ii

STATEMENT OF THE AUTHOR

The author declares that this thesis work is his bona fide work and that all sources of

materials used for this thesis have been dully acknowledged. This thesis has been

submitted in partial fulfillment of the requirements for an advanced M.Sc. Degree at the

Hawassa University and is deposited at the University Library to be made available to

borrowers under rules of the Library. The author declares 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 allowed without special permission provided that

accurate acknowledgement of source is made. Requests for permission for extended

quotation or reproduction of this manuscript in whole or in part may be granted by

the Head of the Department of Bio-system and Environmental Engineering or the

Dean of School of Graduate Studies when in his or her judgment the proposed use

of the material is in the interest of scholarship. In all other instances, however,

permissions must be obtained from the author.

Name: Minda Tadesse Bedane Signature: ________________

Place: Hawassa University, Hawassa

Date of submission: _March, 2019_________________

iii

ACKNOWLEDGEMENT

I am greatly thankful to the Ethiopian Institute Agricultural Research (EIAR) for

permitting me to join the School of Graduate Studies at Hawassa University to

pursue my master study in Water Resource Engineering and Management.

I want to express my sincere gratitude to my advisors, Dr Abraham Woldemichael and Dr

Tilahun Hordofa, for their intellectual stimulation, professional guidance,

encouragement, and inspiring during the project work and valuable criticism of the

thesis. I am really grateful for their friendship approach and for providing me a range of

possibilities to broaden my knowledge especially during the fieldwork at Jimma/JARC/

and thesis write-up.

I would like to extend my appreciation and sincere thanks to Mr. Robel Admasu and

Jimma Research Center staff in general for their guidance, providing me facilities

whenever required, and their cooperativeness during the author conducting research at

research center.

I also like to express my deepest gratitude to my family’s W/ro Mekdas Belay, my

Children: Merry Minda and Sifan Minda, Mr. Rata Zawde and Mr. Mohammed Jihad and

his families for their moral support and hospitality.

Above all, I would like to thank the Almighty and Merciful God, for providing him all the

patience and endurance during his study.

iv

LIST OF ABBRIVIATIONS AND ACRONYMS

CRO Coffee Research Organization

CSA Central Statistical Authority

CWR Crop water requirement

DEM Digital elevation model

DW Dry weight

ECFF Environment and Coffee Forest Forum

ENMA Ethiopian National meteorological agency

ETc Crop Evapotranspiration

EIAR Ethiopian Institute of agricultural research

ETo Reference Evapotranspiration

FC Field Capacity

FOA Food and Agricultural Organization

FW Fresh weight

IR Irrigation requirement

JARC Jimma agricultural research center

JZIA Jimma Zone Irrigation Authority

Kc Crop Coefficient

KS Kolmogorov-Smirnov

RH Relative humidity

RMSE Root mean sum square error

SDD Spatial Dependency Degree

SNHT Standard normal homogeneity test

SSH Sunshine hours

Tmax Maximum Temperature

Tmin Minimum temperature

WS Wind speed

v

TABLE OF CONTENTS

CONTENTS Page

DEDICATION ................................................................................................................... i

STATEMENT OF THE AUTHOR.................................................................................... ii

ACKNOWLEDGEMENT ................................................................................................ iii

LIST OF ABBRIVIATIONS AND ACRONYMS ............................................................ iv

TABLE OF CONTENTS .................................................................................................. v

LIST OF TABLES .......................................................................................................... vii

LIST OF FIGURES ........................................................................................................ viii

LIST OF APPENDIX ....................................................................................................... ix

ABSTRACT ...................................................................................................................... x

1. INTRODUCTION ..................................................................................................... 1

1.1 Background ......................................................................................................... 1

1.2 Statement of the Problem .................................................................................... 3

1.3 Objectives ........................................................................................................... 3

1.3.1 General objective ......................................................................................... 3

1.3.2 Specific objectives ....................................................................................... 3

2. LITERATURE REVIEW .......................................................................................... 4

2.1 Importance of Coffee in Ethiopia......................................................................... 4

2.2 Coffee Growing Climate and Season ................................................................... 5

2.3 Coffee Crop Water Requirement ......................................................................... 6

2.4 Coffee Crop Coefficients ..................................................................................... 7

2.5 Reference Evapotranspiration .............................................................................. 8

2.6 Coffee Irrigation Requirement ............................................................................. 9

2.7 The FAO CROPWAT Model .............................................................................. 9

2.8 Geo-statistical Interpolation and Mapping ......................................................... 11

3. MATERIALS AND METHODOLOGY .................................................................. 13

3.1 Description of Study Area ................................................................................. 13

3.1.1 Location ..................................................................................................... 13

3.1.2 Climate ...................................................................................................... 13

3.1.3 Crop productions ........................................................................................ 14

3.2 Selection of Meteorological Station ................................................................... 14

vi

TABLE OF CONTENTS (CONTINUED)

3.3 Data collection and measurement ...................................................................... 15

3.3.1 Meteorological data .................................................................................... 15

3.3.2 Crop data ................................................................................................... 18

3.4 Estimation of reference evapotranspiration ........................................................ 22

3.5 Estimation of crop water and irrigation requirement .......................................... 23

3.6 Statistical Analysis ............................................................................................ 23

3.7 Mapping of ETo, Coffee ETc and IR ................................................................. 24

4. R1ESULTS AND DISCUSSION............................................................................. 25

4.1 Climate data consistency test ............................................................................. 25

4.2 Coffee Crop Coefficient .................................................................................... 27

4.3 Reference Evapotranspiration ............................................................................ 29

4.3.1 Validation of CROPWAT 8 model ............................................................. 29

4.3.2 Explanatory analysis .................................................................................. 29

4.3.3 Annual and Monthly spatial distribution of reference evapotranspiration .... 30

4.3.4 Cross-validations of Model ........................................................................ 33

4.4 Coffee Crop water Requirement ........................................................................ 37

4.4.1 Explanatory analysis .................................................................................. 37

4.4.2 Annual and monthly spatial distribution of coffee ETc ............................... 37

4.4.3 Semi-Variogram models and parameters .................................................... 39

4.5 Cross-validations of kriging interpolations ........................................................ 40

4.6 Coffee Irrigation Requirement ........................................................................... 45

5. SUMMARY, CONCLUSION AND RECOMMENDATIONS ................................ 51

5.1 Summary and Conclusions ................................................................................ 51

5.2 Recommendations ............................................................................................. 52

6. REFERENCES ........................................................................................................ 54

APPENDICES ................................................................................................................ 65

BIOGRAPHICAL SKETCH ........................................................................................... 73

vii

LIST OF TABLES

Table 1. Water balance and climate adjusted coffee Kc for initial, mid and late stage at

JARC station ..................................................................................... 28

Table 2. NDVI and Kc value for model development during 2017/18 years and Kc NDVI

.......................................................................................................... 28

Table 3. Coffee crop Kc and length of development stage................................................ 29

Table 4. ETo estimated from full, exported and CROPWAT estimated data at Jimma

station for 2015 ................................................................................. 33

Table 5: Statistic analysis of monthly and annual ETo for Jimma Zone ........................... 33

Table 6. Pearson Correlations of monthly ETo with metrological parameters .................. 34

Table 7. RMSE between modeled monthly ETo and observed ETo values ....................... 34

Table 8. Descriptive statistics of monthly and annual Coffee ETc for Jimma Zone .......... 40

Table 9. RMSE for monthly and annual coffee ETc for ordinary and IDW ...................... 41

Table 10. Parameters of the variogram models obtained for monthly and annual values of

coffee ETc in Jimma Zone ................................................................. 42

Table 11. Statistics analysis of monthly and annual coffee IR for Jimma Zone ................ 48

viii

LIST OF FIGURES

Figure 1.The location Map of the study area .................................................................... 13

Figure 2. Meteorological stations (A) and selected stations by Thiessen polygons (B) ..... 15

Figure 3. Classification of climate region using dendrogram ............................................ 18

Figure 4. Sketch of experimental field and sampling method ........................................... 20

Figure 5. SNHT Test results of mean annual Tmin for Jimma station 1985-2016 ............. 17

Figure 6. Double mass of Jimma station of January Tmin 1985-2016 .............................. 17

Figure 7. Corrected minimum temperature of Shebe station by double mass curve .......... 25

Figure 8. Double mass curve of annual mean rainfall of meteorological stations .............. 26

Figure 9. Mean monthly ETo for Jimma zone .................................................................. 35

Figure 10 Histogram (A) and Boxplot (B) for annual ETo ............................................... 35

Figure 11. ETo homogeneity test for August at Jimma stations in rainbow software ........ 35

Figure 12. ETo kriging maps of annual and monthly ETo for Jimma zone ....................... 36

Figure 13: Histogram (A) and box-plot (B) for annual coffee ETc (mm/ year) ................. 42

Figure 15. Semi-variogram and fitted model for mean annual coffee ETc in Jimma ......... 43

Figure 14: Histogram (A) and box-plot (B) for annual coffee ETc (mm/ year) ................. 43

Figure 16. ETc kriging maps of monthly coffee crop ETc for Jimma zone ....................... 44

Figure 17. Monthly coffee ETc, IR and effective rain fall for Jimma zone ....................... 49

Figure 18. IR kriging maps of Monthly and annual coffee IR for Jimma zone .................. 50

ix

LIST OF APPENDIX

Appendix Table 1. Monthly Tmax data of Jimma stations prepared for MICE imputation 65

Appendix Table 2. Descripitve statistics of climate dataset for different stations .............. 66

Appendix Table 3. Areal mean climate dataset estimated by Thiessen polygon method for

Jimma zone ........................................................................................ 67

Appendix Table 4. KS test values calculated for 80% non- exceedance probability for

monthly ETo (1985 to 2016) for each station...................................... 68

Appendix Table 5. ETo (mm/day) values of the different stations .................................... 69

Appendix Table 6. Coffee ETc (mm) estimated for different stations ............................... 70

Appendix Table 7. coffee irrigation requirement (mm) estimated for different stations .... 71

x

ABSTRACT

Coffee is one of the dominant cash crops grown in Ethiopia, particularly in Jimma zone. Absence

of rainfall during four to six months of the year affects coffee production. Study was undertaken in

Jimma zone, Oromia regional state Southwest of Ethiopia, with the objective of evaluating and

characterizing the water requirement of coffee (ETc) and mapping for planning and management

options for Jimma Zone. The study was undertaken at 22 sites of which 14 being from Jimma zone

and the remaining eight sites were from around the vicinity of study area. The reference

evapotranspiration (ETo) was estimated using Penman–Monteith method, with monthly datasets of

22 stations for the period of 31 years. An adjustment was made to Kc values to account for

deviations from standard conditions. The coffee ETc and irrigation requirement (IR) were

determined using CROPWAT 8.0 software. The results of ETo, ETc and IR were further analyzed

by means of geo-statistical tools in R software using gstat package. Maps were created using

ordinary kriging combined with linear regression method. The results have shown that the ETo,

coffee ETc and IR have shown moderate to greater variability, i.e the CV for annual ETc and IR

ranged from 5.5 to 31.8%, respectively. The high annual and monthly coffee ETc values were

observed around the extreme eastern tip, extreme southern tip and north western tip. In contrary

low ETc values were observed in southern and western regions. April (131.1 mm) and November

(80.9 mm), respectively, presented the highest and the smallest coffee ETc. The high annual coffee

IRs locations include: northern and eastern parts whereas the low in western and southern parts.

The highest IR values was predicted in February (77.7mm) whereas no IRs from June to August.

The coffee ETc for Jimma zone is a good indicator and benefits to the coffee growers to help in

planning the water resources of the area for best agricultural water management practices.

Key words: Coffee, ETc, irrigation requirement, ETo

1

1. INTRODUCTION

1.1 Background

Water is one of the most important inputs essential for the crop production because plants

require water for growth and tissue expansion. Plants need water continuously during their

life and in huge quantities (Nidhi et al, 2012). However, over 90% of the water required by

plants is lost through transpiration (Morison, 2008). Thus, the evapotranspiration is an

important hydrological process (Lu et al., 2005). Hence improved water management in

agriculture depends on reliable estimates of water use by plants (Pereira, 2011).

The first step in estimation of crop water requirement is determination of evaporative

demand of atmosphere, which is expressed as the reference evapotranspiration (ASCE,

2005). Its accurate estimates were important in irrigation management. However, direct

measurement is both inconvenient and expensive. For this reason, many methods have

been developed for estimating ETo from climate data (Doorenbos and Pruitt, 1977; Allen

et al., 1998). However, despite difference in their climate data requirement, most of the

methods lack required accuracy in estimating the value of ETo for any particular area. But,

Penman Monteith method provides more accurate estimation of ETo (Allen et al., 1998).

The crop coefficients are the 2nd important parameter that plays an essential role in

estimating the crop ETc in general (Pereira et al., 1999) particularly for coffee. Allen et al.

(1998) suggested Kc values for many crops including coffee under different climatic

conditions which are commonly used in places where the local data is not available.

However, there is a need for local calibration of the Kc under given climatic conditions

(Kashyap and Panda, 2001). Thus, the coffee Kc obtained from FAO should be adjusted to

local condition especially for study areas by combining NDVI and soil water balance since

both are related to fractional ground cover (Rouse et al., 1974; Peirera et al., 1957).

Crop water requirement (ETc) is the amount of water required to compensate the

evapotranspiration loss from the cropped field during a specified period of time

(Todorovic, 2005). The requirement is ether applied by rainfall or irrigation. The major

environmental factors affecting ETc are climate and water which limiting plant growth

(Sijali, 2001). ETc can be either directly measured or in-directly estimated. Direct

measurement is remains challenging, because it is expensive and time consuming (Amatya

et al., 2016) particularly for developing country like Ethiopia. Among in-direct methods,

2

ETc estimated by combining ETo and Kc, is the widely used method. Like annual crops,

several authors directly measure coffee ETc using Bowen ratio-energy balance technique

(Gutierrez and Meinzer, 1994; Fabio et al., 2005). But it is impractical for large areas, to

resolve this problem, coffee ETc estimates from ETo and Kc is among best alternatives at

stations (Lemos Filho et al., 2010). Accurate estimates of ETc for coffee are crucial

because too little water is substantially reducing growth without wilting (Pereira, 1994).

Irrigation plays important roles in agriculture production (Sax, 2000). Adequate and timely

irrigation leads to high yield of crops in general particularly for coffee. It raises the

productivity of land through increase of crop productivity per hectare (Ali, 2010). The

productivity of coffee can be enhanced with irrigation. Coffee irrigation is a promising

technique that may provide both yield increase and expansion of coffee plantations in areas

considered unsuitable due to the occurrence of water shortage (Silva et al., 2008). Brazilian

can obtain a potential yield of 6 to7 ton ha-1 under irrigation (Naan, 2009). Even in

Ethiopia, Tesfaye et al. (2013) found in a coffee growing area in Jimma zone that the

increase in coffee yield by about 24% from fully irrigated compared to rain-fed coffee

trees. However, the Ethiopian coffee production systems particularly for Jimma zone are

remained traditional till now, where coffee productivity is severely affected by water

shortage especially during critical stage of flowering and fruit set.

In recent year, drought severity happed frequently in Jimma zone. For example, in 2016/17

during dry years, hundreds hectares of coffee plantation were devastated due to the lack of

supplemental irrigation (ECFF, 2017). This is happed because of farmer practiced poor

irrigation management; they just traditionally wait for rain. One of the main problems of

the irrigator is not know the amount of water that has to be applied to the field to meet the

water needs of the crops. In addition, most farmers in study area have not an appropriate

plan for irrigation (JZIA, 2017).

As Jimma is a large zone also in practice estimating of ETo and ETc at every spatial

location is difficult from sparsely distributed data. Geo-statistical interpolation methods

such as ordinary kriging are a promising tool to overcome this problem. It generates both

spatial autocorrelation and prediction map depending up on semi-variogram and distance

(Li and Heap, 2008). However, ordinary kriging method is not capable of using secondary

information as predictor. Thus, combined method of ordinary kriging with linear

3

regression helps to solve this problem (Knotters et al., 1995). For areas like Jimma, which

is characterized by diverse topography and limited number of stations, using auxiliary

information such as elevation plays significant role in improving interpolation quality.

1.2 Statement of the Problem

Coffee plays a significant role in the economy of Ethiopia, contributing over 60% of

the nation’s foreign exchange earnings, 30% of the government's direct revenue, 8%

output o f the agricultural sector and 4% of the gross domestic (Girma, 2011). Coffee

is the dominant cash crop cultivated in Jimma zone, covering an area of about 17.8% and

contributing 19.5% to the countries' national coffee product. In spite of the importance of

the crop in the country’s economy, it is characterized by low productivity, which is about

76.92% yield reduction as compared to potential yield recorded at JARC station (CSA,

2016; Tadesse, 2017). The low yield may due to traditional production technologies.

Seasonal water stress and recurrent drought are among the major factors which account for

low yields of the crop. In addition, the coffee production systems in the zone are largely

rain-fed and thus, their success is sensitive to climate variability.

The temporal and spatial distribution of rainfall throughout the year is un-evenly

distributed in Jimma zone. Over 71.1% of rainfall is received during the rainy summer

season (June to September) while the rest 28.9 % received during eight months (October to

May). This means atmospheric water demand is greater than rainfall during dry spells.

To achieve effective planning on irrigation water management, accurate information is

needed for crop water requirement. The availability of spatial and temporal coffee ETc and

IR information is limited in study area and yet not estimated. In addition, the shortfalls of

data at a regional scale and for irrigation project design engineers.

1.3 Objectives

1.3.1 General objective

The general objectives of this study were to analyze and characterize the water requirement

of coffee crops and mapping for planning and management options for Jimma zone.

1.3.2 Specific objectives

To assess the variability of water requirement for coffee crop in Jimma zone,

throughout the year

To map spatial and temporal ETc and IR requirement of coffee in Jimma zone.

4

2. LITERATURE REVIEW

2.1 Importance of Coffee in Ethiopia

Coffee production is important to the Ethiopian economy with about 15 million people

directly or indirectly deriving their livelihoods from coffee. Coffee is also a major

Ethiopian export commodity generating about 25% of Ethiopia’s total export earnings.

Ethiopia is the fifth largest producer of coffee in World, Almost two percent of the world’s

coffee comes from Ethiopia (Tefera and Tefera, 2013; Chauhan et al., 2015 ).

Coffee Arabica is dominant cash crops produced in Jimma zone. Arabica coffee is native

to southern Ethiopia and was cultivated in the Gibe valley for many centuries. Southwest

coffee growing areas include Jimma, Limmu, and Mizan Tafari, because these areas enjoy

the best soil and climatic combinations (Yonas, 2002). Coffee production systems in

Ethiopia are generally categorized into four areas i.e. forest coffee, semi-forest coffee,

garden coffee, and plantation coffee. Forest coffee is a wild coffee grown under the shade

of natural forest trees. Semi-forest coffee farming is a system where farmers thin and select

forest trees to let sufficient sunlight to the coffee trees and to provide adequate shade.

Garden coffee is normally found in the vicinity of a farmer’s residence with other crops.

Plantation coffee is planted by the government or private investors for export purposes

(Tefera and Tefera, 2013).

In Ethiopia, flower and fruit development are phased to maximize the likelihood that the

fruit will expand during the rains and after a flush of new leaves. Hence floral initiation

occurs during the cool, dry winter period; the flowers then remain dormant during the dry

season and blossom after the first showers that invariably precede the main rains. The

`pinhead fruits remain dormant before expanding after the beginning of the rains by which

time the new flush of leaves, triggered by the same `blossom' showers, have expanded.

According to (Wintgens, 2009) reports the coffee dry periods were determined when the

monthly rainfall amount was less than approximately 60 mm. The Flowering is assumed

to start one week after the first heavy rainfall following the dry season (DaMatta et al.,

2007). Intense rainfall throughout the year (without dry seasons) can lead to scattered

harvests and low yield (Cannell, 1985).

5

2.2 Coffee Growing Climate and Season

The coffee growth in Ethiopia particularly for Jimma zone is significantly affected by

environmental conditions. Coffee Arabica can successfully provide yield within narrow

range of environmental conditions. The temperature, rain fall and variation of season of

the year are the major factor governing the coffee growth and yields. Lack of rain fall and

increase of temperature can aggravate the soil moisture depletion and coffee getting

stressed. The variation of temperature through the year is due to annual weather patterns

or seasonality (ECFF, 2017).

Coffee Arabica is successfully grown in the altitude range of 1000-1500m, optimum

temperature ranges from 18 to 25°C. Require fertile soil with optimum pH range from 5.4

to 6.0, annual rainfall ranges from 1200 to 2000 mm is required and especially during

flowering and maturity, but regulated moisture deficit during dormancy period.

Flowering is initiated after the first rains showers during dry season probably in January,

February or early March and maturity of cherries requires a dry period that can be up to 5

months (Kuit et al., 2004). The climate of Ethiopia’s coffee lands is tropical, but due to

the large central highland area (mostly above 1000 m) much of the country can be

classified as cool-tropical. The seasons are largely defined by rainfall. There are three

main seasons: Bega, Belg, and Kiremt (Robel, 2012).

Bega (October to January/February) the long dry season, this is when coffee harvesting

and processing takes place. The severity of the main dry season depends largely on

location but also on other physical characteristics, including altitude, slope and slope

aspect. There may be slight to moderate rainfall or barely any at all. Belg (February to

May): the first rains before the main wet season, which may either represent the early part

of the wet season, or a distinct short wet season followed by a short dry period before the

main rains. This is the main period for coffee flowering, fruit initiation and early

development. Kiremt (June to September): the main wet season. This is the period for

final coffee fruit development and ripening. Around the end of September there is a sharp

decline in rainfall. Throughout the coffee lands including Jimma zone, temperatures are

generally higher during the dry season and lower in the wet seasons (Robel, 2012; ECFF,

2017). Thus, the productions of coffee in Jimma zone are greatly dependent on climate

variability especially rainfall and temperatures.

6

2.3 Coffee Crop Water Requirement

Crop water requirements are defined here as "the depth of water needed to meet the water

loss through evapotranspiration (ETc) of a disease-free crop, growing in large fields

under non-restricting soil conditions including soil water and fertility and achieving full

production potential under the given growing environment, Crop water requirement refers

to the amount of water that needs to be supplied (Doorenbos and Pruit, 1977).

ETc can also be derived from meteorological and crop data by means of the Penman-

Monteith equation. The Penman-Monteith method is used for the estimation of

atmospheric water demand or ETo (Allen et al., 1998). The accuracy of determination of

ETc will be largely dependent on the type of the climatic data available and the accuracy

of the method chosen to estimate the evapotranspiration (Nuha and Henery, 2000).

There are various methods to calculate ETc of coffee crop. These are direct and indirect

methods. The indirect methods involve calculating ETc from meteorological data, soil

water balance, and pan evaporation method (Blore 1966; Pereira, 1957; Wallis, 1963).

Allen et al. (1998) developed general guideline that used to calculate ETo from

meteorological data using Penman-Monteith equation and finally multiply it by crop

coefficient to obtain crop water requirement.

Lemos Filho et al. (2010) analyzed ETc for coffee grown at Minas Gerais State, Brazil

from 17 years (1961-1978) meteorological data. They reported annual coffee ETc values

varied from 798 to 1510 mm/year. However, they did not adjust coffee Kc obtained from

FAO. But this method is relatively simple and cost effective because coffee ETc is easily

calculated at already established meteorological stations. That is whys this method was

selected to analyze coffee ETc and IR in Jimma zone.

Silva et al. (2009) measured coffee ETc using soil water balance with neutron probe; the

water balance was performed at 14 days interval for about 2 years (720 day) in Brazil.

They reported coffee ETc varied from 1.83 to 6.4 mm /day with average of 4.01 mm/day.

The direct method such as Bowen ratio-energy balance technique, which directly

measure ETc of coffee as evaporative heat flux (Gutierrez and Meinzer, 1994).

However, this method requires advanced material and skilled expertise; besides this it’s

7

impractical to measure directly coffee ETc especially for large areas like Jimma zone

where duplicated experiment is needed that represents the whole area.

2.4 Coffee Crop Coefficients

The crop coefficient (Kc) is a factor indicating the ratio of ETc to the ETo. There are

various factors that determining Kc are crop type, climate, soil evaporation and crop

growth stage. The variability of actual Kc to tabulated data further increases when

regarding not only climatic differences but also variation in soil type, seeds and

management practices (Neugebauer, 2013).

There are different methods to calculate coffee Kc. These methods include water balance,

NDVI and Bowen ratio-energy techniques. Silva et al. (2006) measured coffee Kc using

water balance method combined with ETo. Gutierrez and Meinzer (1994) directly

measured coffee Kc with help of Bowen ratio-energy method in combination with local

ETo. However, Almhab (2011) derived the coffee Kc from satellite images; normalized

difference vegetation index was compared with FAO Kc to determine actual Kc at local

level. Blore (1966) attempted to measure the coffee Kc of weed clean coffee by

measuring soil water contents and combine with local climate data.

The most commonly used and standard coffee Kc value is the one that published on FAO

irrigation and drainage paper 56. The tabulated Kc values presented for coffee are in the

range of 0.9-0.95 for a clean weeded crop, and 1.05-1.10 for a crop with weeds. These

values are for well managed crops, 2-3 m tall, grown in a sub-humid climate (Allen et al.,

1998). However, the coffee Kc should be adjusted to the local conditions.

Depletion of soil-water content between p and the permanent wilting point will result in a

proportional reduction of actual evapotranspiration (FAO, 2012). The coffee water

extraction rate depends on amount of water available in the soil, soil depth and rooting

depth. Maximum rooting depth is defined as the deepest depth attained by a crop for any

specific soil situation (Johnson, 2007). The maximum rooting depth for coffee ranges from

0.9-1.5 m depth (Allen et al., 1998). Root concentration of coffee is in the 30-60 cm depth

(CRO, 2006). According to Pereira (1957) the most active portion of coffee root depth is 1

m where coffee gets most of their water. Water at depth greater this may be lost to deep

percolation.

8

2.5 Reference Evapotranspiration

The reference surface is a hypothetical grass reference crop with an assumed crop height

of 0.12 m, a fixed surface resistance of 70 s m-1and an albedo of 0.23. The reference

surface closely resembles an extensive surface of green, well-watered grass of uniform

height, actively growing and completely shading the ground (Allen et al, 1998).

Various methods were developed to estimate ETo, amongst the FAO four methods are

the commonly utilized methods namely Blaney Criddle , radiation, pan evaporation and

Penman methods .However, these method have their own shortcoming, their prediction

value are normally deviated from actual values (Doorobos and prutt 1977). The selection

of each methods are greatly depends on availability of climatic data and the level of

accuracy required. The inputs required for estimation of ETo are climate, altitude and

cultural conditions and related data. Some of the methods require limited input, where as

others require the extensive measured data, such T-mean, T-max, T-min, radiation,

relative humidity, wind speed at 2 m, sunshine hours, and different constants values.

Blaney-Criddle method uses mean temperature (T) and percentage (p) of total annual

daylight hours occurring during the period being considered (Blaney and Criddle, 1950 ;

Allen et al., 1998; Doorenbos and Pruit, 1977).

Radiation method uses measured air temperature and sunshine, cloudiness or radiation to

estimate ETo, however this method has its own shortcoming. Modified Penman utilizes

measured energy balance (radiation) and aerodynamic including wind speed and

humidity to estimate ETo, the shortcoming of this method is the requirement of local

calibration of wind function to obtain best result. In Pan Evaporation, ETo is the product

of both evaporation from pan surface and pan coefficients. The measurement of ETo are

primary affected by different factors such as radiation, wind, temperature and humidity.

The pan transfers heats to waters and facilitates evaporation process, and winds takes

evaporated water, subjected to local microclimate and produce more error when it is not

properly installed. The mentioned four methods are subjected short coming and deviation

from normal value. To overcome such defect of four methods, experts and researchers

were developed more accurate method called Penman Montieth (Allen et al, 1998).

9

2.6 Coffee Irrigation Requirement

Irrigation water requirements are the quantity of irrigation water in addition to precipitation

required to produce the desired crop yield. The amount and timing of precipitation strongly

influence irrigation water requirements (SCS, 1993). Irrigation aims to replace the water

used by the crops or lost by evaporation or drainage through the soil with objects of

producing the optimum yield and to apply water efficiently (Luke, 2006).

Mitchell (1988) summarized the merit of irrigating coffee as follows as:

Reduces drought period in coffee;

Provides a reliable insurance cover against crop failure for the coming year;

Increases the beneficial microbial content of the soil;

Increases the organic matter decomposition in soil in-situ because of the

prevailing high temperatures;

Enables uniform fruit set and uniform berry size; and

Improves the nutrient uptake.

Fernandes et al. (2009) concluded that, the productivity of drip irrigated coffee plant is

95% higher than non-irrigated ones. This suggests that coffee irrigation can significantly

increase the productivity of coffee crops. According Tesfaye et al. (2008) study

conducted in Jimma zone indicated that, supplemental irrigation is required as the annual

rainfall is less than the seasonal coffee ETc and its distribution is usually erratic.

2.7 The FAO CROPWAT Model

The FAO CROPWAT is a computer program that can calculate ETo, ETc and IR from

climatic and crop data. The program is interactive in nature. In addition, the program

allows the development of irrigation schedules for different management conditions and

the estimation of scheme water supply for varying cropping patterns. The CROPWAT

model is based on a water balance model where the soil moisture status is determined on

a daily basis from calculated evapotranspiration and inputs of rainfall and irrigation uses

the sole recommended FAO Penman-Monteith method for estimating ETo (FAO, 2009).

The climatic data required for input into CROPWAT are temperature, relative humidity,

wind speed and sunshine hours for the calculation of ETo. It has also four different

10

methods to calculate effective rainfall (FAO, 2009). Through the input of crop data

(growth stages, Kc value, root zone depth and allowable soil moisture depletion factor),

the programme calculates the ETc on a decade (10-day) basis. The decade values of ETc

summed up to obtain monthly values (Savva and Frenken, 2002).

Several researchers have been used the CROPWAT model for analysis of ETo, ETc and

irrigation requirements of crops including coffee during different growth stages

(dormancy, bud initiation, flowering, fruit set and maturity) (Naik et al., 2015; Surendran

et al., 2015; Lemos Filho et al., 2010).

Wubengeda et al. (2014) calculated ETo by CROPWAT 8.0 model using Penman -

Monteith equation for Sub-humid areas of Arsi zone, Ethiopia at two meteorological

stations. They reported monthly ETo values ranged from 3 to 5mm/day.

Fitsum (2017) analyzed temporal variability and trend of ETo in Sinana District, south

eastern Ethiopia. This study, considered Tmax, Tmin, RH, wind speed and sunshine hours

data with respect to station’s locations to calculate monthly ETo values for sub-humid

areas. The calculation of ETo was performed in CROPWAT 8.0 model using Penman –

Monteith equations. He reported ETo values varied from 3.22 to 4.34 mm/day.

Patel et al. (2017) carried out similar work on ETo estimation using CROPWAT model at

Ludhiana district (Punjab), north India. In this study, ETo was calculated using Penman -

Monteith equations from climate data record from 1970-2012. They detected that Penman-

Montieth method is the best method to estimate ETo. They reported average ETo values of

3.65 mm/day.

Mahtsente (2017) studied water demand analysis and irrigation requirement for major

crops at Holetta catchment, Awash Sub-basin, Ethiopia. She determined ETo, ETc and IR

for major crops for humid areas using CROPWAT 8 model based up on penman Monteith

method. The reported ETo values ranged from 3 to 5 mm/day.

Trivedi et al. (2018) estimated ETc of crop using CROPWAT 8.0 model for Shipra River

Basin in Madhya Pradesh, India. They estimated ETc of Soybean and other major crop

from long-tem meteorological data including rainfall, Tmax, Tmin, relative humidity, wind

speed and sunshine hour of stations as input data in CROPWAT Model 8.0. ETo was

calculated using Penman Monteith method. They reported average annual ETo values of

11

4.14 mm/day. However, the problem of this study was, they used average long-term ETo

values instead of 80% probability of non-exceedance ETo values to calculate ETc.

Doorenbos and Pruitt (1977) recommended 75 to 80% probability of non-exceedance ETo

values for calculation of the crop ETc and IR especially for design purpose.

2.8 Geo-statistical Interpolation and Mapping

The potential application of geostatistical interpolation in water resource and in particular

in irrigation management has been widely acknowledged by several authors and many

interpolation methods such as ordinary kriging, simple kriging and universal kriging for

derive climate related parameters such as temperature, rainfall, ETo and crop water

requirement had been successfully tested in different locations ((Fonteh and Podmore,

1994; Smith et al., 2007; Lemos Filho et al., 2010; Agnew and Palutikof, 2000).

However, the real applications of geostatistical interpolation method in irrigation

management are few in Ethiopia particularly for study areas.

Geo-statistical interpolation such as ordinary kriging play an important role in preparing

regional maps from sparsely distributed data; because it is not always possible to sample

the entire study area. Thus, unknown values must be estimated from data collected from

particular location that can be sampled (Burrough and McDonnell, 1998; Sahoo, 2012).

Several authors tested the performance of ordinary kriging methods for analyze spatial

variability of precipitation, evapotranspiration, and temperature. Cross-validation along

with applicable conditions of different models is applied to obtain the best-fit

interpolation model (Mardikis et al., 2005; Yang et al., 2011). However, ordinary kriging

does not take into account secondary information which has strong relationship with

primary variable (Li and Heap, 2008). For large areas like Jimma zone that consists of

diverse topography it is difficult to accurately estimate ETo and ETc without considering

topographic effect.

Hence, to improve interpolation results, combined utilization of ordinary kriging with

linear regression helps to overcome these problems. The auxiliary variables that affect the

spatial distribution of ETo and ETc at regional scale particularly for study area are many

uncertain factors, mainly elevation. Several authors used multiple regression model

12

combined with interpolation method, they obtained better results (Agnew and Palutikof,

2000; Martinez, 1996).

Agnew and Palutikof (2000) used multiple regression models that were refined by kriging

of the residuals to develop seasonal maps of temperature and precipitation in the

Mediterranean basin using latitude, elevation and distance from the sea as predictor and

they obtained better estimation results of ETo and precipitation.

Smith (2014) compared estimation methods of ETo at un-sampled locations. He

compared different interpolation methods in interpolating climate parameter and ETo.

Among them, Kriging with covariates of elevation and temperature had resulted in

smaller residuals.

Martinez (1996) carried out works on multivariate geo-statistical analysis of

evapotranspiration and precipitation in mountainous terrain. The ETo and precipitation

data was extended to 5-km grid for cokriging purpose. He pointed out that Co-kriging

improved ETo estimation uncertainty by about 11.5% as compared to ordinary kriging.

The assumption of co-kriging and combined use of ordinary kriging with linear

regression is more or less similar. Thus combined utilization of ordinary kriging with

linear regression play important role in minimizing ETo and ETc estimation error

particularly for topographically diverse area like Jimma zone.

Lemos Filho et al. (2010) analyzed spatial distribution map of coffee ETc using 45

meteorological stations data using ordinary kriging in R with GeoR package and obtained

good results. However, they didn’t take into account the topographic effect in

interpolation process. But, for study area it is important to consider topographic effect in

interpolation process to obtain best results because elevations have strong correlation

with ETo and ETc. For interpolation purpose, the computer program like R-software can

have ability to generate spatial surface prediction map using ordinary kriging method

combined regression method (Team, 2013)

13

3. MATERIALS AND METHODOLOGY

3.1 Description of Study Area

3.1.1 Location

The study was conducted in one of coffee growing areas of Jimma zone, Oromia region

state, south west of Ethiopia. It is geographically located approximately between 7° 5'

38"N - 8° 54' 19" N latitude and 34° 49' 34" E - 38° 39' 17" E longitude with maximum

and minimum altitude of 3231 and 859 m above sea level, respectively. Due to altitudinal

differences, the zone has great physiographic diversity. The zone has a total area of about

19,293.5 km2. It has 20 districts and one administrative town with a total of 545 kebeles

(lowest administrative units) of which 515 of them are rural and 30 are urban. According

to CSA (2013) reports, the total projected population of the zone for 2017 is 3,209,127.

Figure 1.The location Map of the study area

3.1.2 Climate

The agro-climate condition of Jimma zone has been classified under sub-humid agro-

ecological zone. From long-term (1985-2016) records of meteorological data of 20

stations, the areal mean climate time series for the study area were estimated using the

Thiessen polygon method procedures (Fiedler, 2003).

The average areal annual rainfall was 1728.7 mm, about 71.5% of the annual total

amount of rainfall occurs between May and September and the rest 28.5%

14

from October to April, with a peak in the months of July and August. The mean

areal minimum and maximum monthly rainfall values were 31.2 and 280 mm occurring

during January and August, respectively. The average areal monthly Tmin varied from

10.7 to 12.9℃, with the annual mean of 11.9℃. The average areal monthly Tmax of the

zone ranged from 23.0 to 28.2℃ with the annual mean of 25.8℃, for March and

July, respectively. The average RH is varied from 55.5 to 82.3. The average mean

sunshine hour durations of zone varied from 3.56 to 8.16 hours (Appendix Table 3).

3.1.3 Crop productions

Farmers grow crops twice a year, one during the rainy season (June-September) with

rainfall, the other during dry season (December-May) using irrigations. The farmers grow

different crops in the study area. The major annual crops grown include teff, maize,

sorghum, wheat, and barley during rainy season; and tomato, onion, carrot, cabbage and

pepper during off-season. Farmers also produce stimulant perennial cash crops like coffee

and Khat. Of these, major crops coffee takes the lion shares. Coffee is mainly produced

under rain-fed conditions.

3.2 Selection of Meteorological Station

The Thiessen polygon method (Fiedler, 2003) was used to select spatially representative

stations and clipped to study area’s boundary in ArcGIS 10.1. Accordingly, 20 stations

were clipped to study area as of polygon. It includes Chida, Gojeb, Shebe, Dedo, Jimma,

Yebu, Bedelle, Arjo, Ejajji, Atinago, Limmu-genet, Natri, Sokoru, Yaya, Wolkite,

Botorbacho, Gatira, Chira, Agaro and Asendabo. Additionally 2 stations were added to

increase the extent of data to cover the entire area of zone; it includes Gore and Bonga.

Hence a total 22 stations were used for this study, fourteen being in Jimma zone and

remaining eight from around the vicinity of study area (Figure 2).

15

Figure 2. Meteorological stations (A) and selected stations by Thiessen polygons (B)

3.3 Data collection and measurement

3.3.1 Meteorological data

The daily data for the period 1985 through 2016 (31 years) were collected from ENMA.

These collected climate data series includes maximum temperature, minimum

temperature, and wind speed at 2 m height, relative humidity, sunshine hours and rainfall.

Climate data processing: The climate time series at stations is require careful analysis for

their missing value, validity and homogeneity test before use. The monthly climate data

were tabulated in “Table” for each month starting from 1985 to 2016.

Filling missed climate time series: These were entered in R-software with MICE package

(Van Buuren and Groothuis-Oudshoorn, 2011). The monthly missed climatic variables

including rain fall were entered from the same months in the given time interval (1985-

2016) using multivariate imputation and chained equations method.

Homogeneity test of time series: Standard normal homogeneity test (SNHT) method

proposed according to Alexandersson (1986) was used for detecting inhomogeneity of

annual mean or total climate time series of Tmin, Tmax, RH, WS, sunshine hours and

rainfall data from 1985-2016 at gauging stations . SNHT performed using XLSTAT 2017

software (Addinsoft, 2017). Change in average or a presence of trend was detected.

The breaks in a certain year were corrected by double mass slopes with help of

Hydrognomon software (Kozanis et al., 2010). In this method a group of 5 to 10

neighboring stations are chosen in the vicinity of doubtful station. The mean annual time

A B

16

series values are serially arranged in reverse chronological order to determine relative

consistency.

From figure 3, it was observed that the slight shift of mean in annual minimum temperature

from 11.24 to 11.96℃ around 1990, two means were detected one 11.24℃ and the other

11.96℃, about the deviation of 0.72℃ from the 1st mean. Which showed the data before

1990 was inconsistent and corrected by double mass curve of Figure 4. Similar to this one,

for the other twenty-one stations, the consistency of their climate time series (Tmin, Tmax,

WS at 2 m, RH, SH, and rainfall) were checked using similar procedure.

The double mass curve of Tmin of annual for Jimma station was presented as an example

in figure 4. From figure 4, a slight break in the slope of the double mass curve of Tmin

was observed around 1990; the software corrected Tmin data of Jimma station as follows:

Ratio of the consistent period (2016-1990 or left line slope = �2 =

∑ ���� ���� ,�������

∑ ���� ���� ,�������

) was (Jimma station cumulative / other stations cumulative).

Ratio of the in-consistent period (1989-1985 or right line slope = �1 =

∑ ���� ���� ,������� �∑ ���� ���� ,���

����

∑ ���� ���� ,������� �∑ ���� ���� ,� ��

����

) was (Jimma Station cumulative / other stations

cumulative).

Therefore, the correction factor was (consistent period ratio / inconsistent period

ratio) �� ,��

��=

�.���

�.��= 1.12, and then Tmin data from 1989 to 1985 was:

���� ���� � ���

��= ���� ���� � × 1.12 (Figure 4).

Finally, the corrected annual Tmin of Jimma station was presented as of Figure 7

17

Figure 3. SNHT Test results of mean annual Tmin for Jimma station 1985-2016

Figure 4. Double mass of Jimma station of January Tmin 1985-2016

Importing monthly Wind speed, relative humidity and sunshine hours: Agglomerative

hierarchical clustering classification method (Everitt et al., 2001) was used to classify

meteorological stations into similar climate regime with an input of parameter latitude,

longitude, altitude, Tmin, Tmax, and precipitation. The similarity between meteorological

10.5

11

11.5

12

12.5

13

1980 1985 1990 1995 2000 2005 2010 2015 2020

An

nu

al

mea

n m

inim

um

tem

per

atu

re

Year

mean mu1 = 11.238 mu2 = 11.957

18

stations was calculated using Ward's method (Ward, 1963). The Euclidian distance

was used to determine the distance between stations as dendrogram height.

From figure 5, it was seen that the 22 meteorological stations considered for this study

were grouped into three broad clusters, when we cut the dendrograms at the threshold of

10. Namely, cluster 1: Agaro, Atinago, Bonga, Chida, Gojeb, Jimma, Limmu-genet, Natri,

and Shebe; cluster 2: Arjo, Bedelle, Chira, Dedo, Gatira and Yebu; and cluster 3:

Asendabo, Botorbacho, Ejajji, Sokoru, Wolkite and Yaya. First individual cluster average

was calculated. Therefore, these climate time series data were exported from their

respective cluster.

Figure 5. Classification of climate region using dendrogram

3.3.2 Crop data

The collected crop data were includes planting date, harvesting date, dormancy period,

flowering date, pinhead date, rapid pinhead expansion, maturity date, Kc values, and

growth stages, rooting depth, critical depletion and crop height.

Coffee crop coefficient (Kc): The crop coefficient value obtained from published FAO

56 paper was adjusted to local conditions (Allen et al., 1998).

19

Adjustment of Kc initial: In this study, the Kc initial was derived both from water

balance and satellite images of the same coffee field (About 6 ha) found at JARC station.

A water balance was estimated from five years old coffee stands (74110 coffee variety).

The spacing between rows and trees are 2 and 1.5 m, respectively. One plot of about 40

× 40 m was selected from center of coffee field for water balance establishment. Three

coffee trees were sampled diagonally from selected plot for soil moisture sampling and

plant height measurement; all measurement was performed on these selected trees. Soil

water balance was performed taking soil samples for water content down to the 1 m depth

below tree canopies and about 20 cm from the tree trunk. Prior to weighing and oven

drying, the soil sample taken from 1m depth was thoroughly mixed. Finally, soil samples

were oven dried for 24 hours at 105 ℃. The gravimetric soil moisture content was

determined by using equation (3.8).

MC (mm )= 1000 ∗ �(�� ��� )

��� ∗ �� ∗ �� 3.8

where, FW is fresh weight (gm), DW is dry weight (gm), BD is bulk density (gm cm-3),

MC is moisture content and Zr is root depth and is 1m, most feeder roots of coffee founds

up to 1m depth, beyond this depth water is lost as deepercolation. Once soil moisture

content of a period was determined, change in soil water content ∆S of a root zone of a

coffee crop is equal to the difference between the amount of water added to the root zone,

Qi, and the amount of water withdrawn from it, Qo (Hillel, 1998) in ten day interval was

expressed by equation (3.9).

∆S = �� − �� 3.9

Where, ∆S is Change in soil water content (mm), �� is moisture content after ten days

mm, and �� is moisture content at start time (mm).

Bulk density: The soil sample taken by core sampler was oven dried at 105 ℃ until

constant weight attained. According Miller and Donahue (1990) Soil bulk density is:

p� =� �

�� 3.10

where: �� is soil bulk-density (g/cm3), Wd is weight of dry soil (g), and �� is volume

of core sampler (cm3).

20

Coffee ETc was determined using Equation (3.11) from measurement of gravimetric soil

sampling in ten-day interval during the initial season. Deepercolation and runoff values

were estimated by AquaCrop (Steduto et al., 2009).

ETc = � + � − DP − RO ± ∆� 3.11

where, P is rainfall (mm), DP is deep percolation (mm), RO is runoff (mm) and ∆� is

change in soil moisture (mm).

Figure 6. Sketch of experimental field and sampling method

Finally, the initial Kc value was determined from calculated ETc and ETo during

respective days. The daily ETo was calculated using standard method with decade

climatic data of JARC stations, situated about 212 m away from measuring site.

Kc�������=���

��� 3.12

The calculation was done for each interval separately and finally the Kc values of three

intervals were used for Kc-NDVI modeling.

To obtain more accurate information, the Kc initial was also estimated by satellite image

of landsat 8 with 30 m × 30 m pixel size for field under study. Four sky clear images

were downloaded from (http://earthexplorer.usgs.gov). The dates were 03 December

2017, 19 January 2018, 12 February 2018 and 12 June 2018. The downloaded images

were clipped to coffee field boundary. Prior calculation of NDVI the red and near-

inferred bands were converted into TOA. The NDVI was calculated using Rouse et al.

(1974) method.

NDVI =�����

���� � 3.13

21

where, NIR is near-infrared band, R is red band and NDVI is normalized difference

vegetation index.

The Kc initial was modeled using NDVI, climate corrected Kc FAO, and field measured

Kc data following (Kamble et al., 2013) methods using linear regression equation. The

Equation 3.15 was derived using Kc of water balance and climate adjusted Kc FAO with

its corresponding values of NDVI close to the water balance plot in December, January,

February and June.

Kc = 1.4402x − 0.14 3.15

After determining the Kc for all pixels within an image the Kc for a field can be

determined by calculating the arithmetic mean of all pixels within its boundary:

��,����� =�

�∑ ��,�

��� � 3.16

where, Kc field is field the arithmetic mean Kc for a crop field, Kc, iis the crop coefficient

for a crop image pixel and n is the number of pixels within a field. Finally, the arithmetic

mean of field Kc value derived from NDVI was used as input for initial stage in

CROPWAT 8.0.

Adjustment of Kc Midseason and late season: For specific adjustment in climates

where RHmin differs from 45% or where u� is larger or smaller than 2.0 m/s, the Kc mid

and late values (Allen et.al., 1998) were adjusted by equation 3.17.

Kc��� �� ����= Kc�����+ ⌊0.04(u� − 2)− 0.004(RH ��� − 45⌋��

��

�.�

3.17

where, Kc (tab) is the value of Kc taken from Table 12 of Allen et al. (1998), u� is the

long-term mean value for daily wind speed at 2 m during the mid-season or late season

stages (m/s), RH ��� - mean value of daily minimum relative humidity during mid-season

and late season growth stage, h is the mean plant height during both season.

The long-term daily RH and wind speed were collected from JARC stations during

respective growth stage of coffee crops. The adjusted Kc mid-season and late season

values were finally used for CROPWAT 8.0 input. However, Kc adjustment using water

22

balance is impractical during these periods due to progressive rainfall. Three plants were

diagonally selected; their heights were measured using tape and finally, mean value used.

Coffee growth stage: The development stage length data were collected from JARC

field observation report (Yilma et al., 1997) and interviewing coffee agronomy

researchers. Having these data the lengths of coffee development stage were determined

based on coffee phonological stages. Coffee was harvested from October to December.

3.4 Estimation of Reference Evapotranspiration

The CROPWAT 8.0 models based on Penman -Monteith equation was used for estimation

of ETo for each meteorological stations: The ETo was calculated using equation 3.3:

ET� =�.��� ∆(� ����)� �

���

�� �����(�����)

∆� �(�� �.����) (3.3)

where, ET� ∶ Reference evapo transpiration [mm day��], R� ∶Net radation at crop

surface [JM m �� day�� ], G: soil heat flux [JM m �� day�� ], T: mean daily temperature

[℃ ], ��:Wind speed at 2m height [m s��], (�� − ��): Saturation vapor pressure [kPa],

∆: Slope vapor pressure curve [kpa ℃��], � : Psychrometric constant [kpa ℃��]

The monthly values of climate parameters T max (℃), T min (℃), RH (%), wind speed (m

s-1) at 2 m height and sunshine hours of each year was fed into a computer-based

CROPWAT 8.0 for windows. The monthly ETo values for each year from the long-term

meteorological data were obtained from CROPWAT 8.0 (FAO, 2009).

The accuracy of ETo estimation through CROPWAT 8.0 from limited climatic data was

evaluated with that of full climatic data. The accuracy of exported data from neighboring

stations was also accessed with that of ETo estimated from full set of climatic data.

The monthly long-term from 1985 to 2016 (31 years) ETo data obtained were entered and

fitted to different standard frequency distribution models using a computer-based routine,

Rainbow software (Raes et al., 2006). The distribution that best fitted the data was used to

work out ETo occurrence at 80 percent probability of non-exceedance level. Kolmogorov-

Smirnov (KS) test was used to test monthly ETo (185-2016) normality for individual

23

station separately in rainbow software. Homogeneity test of monthly ETo was performed

based on the cumulative deviations from the mean (Buishand, 1982) in rainbow software.

Once the normality and homogeneity of ETo data was checked for individual station, the

ETo at 80 percent probability of non-exceedance was taken out for all stations. Then the

extracted regional monthly ETo values was again subjected to normality test using

Shapiro-Wilk test.

3.5 Estimation of crop water and irrigation requirement

Having values of Kc and ETo at 80 percent probability of non-exceedance, a ETc was

calculated according to Allen et al. (1998) using equation 3.4.

ETc = ETo x Kc 3.4

where, ETo is ETo (mm/day), ETc is crop water requirement (mm/day) and Kc is crop

coefficients given as fraction.

The irrigation requirements was estimated as difference between crop water

requirement and effective rainfall. It was calculated using equation (3.5):

�� = ��� − �� 3.5

where, IR is irrigation requirement (mm) and Pe is effective rain fall (mm)

The effective rainfall was calculated considering losses and dependable rainfall. This

formula may be used for design purposes where 80% probability of exceedance is required.

The effective rainfall was computed following procedure given in FAO (2009) from:

P��� = 0.6 ∗ P − 10 for Pmonth <= 70 �� (3.6)

P��� = 0.8 ∗ P − 24 for Pmonth > 70 �� (3.7)

where, Peff is effective rainfall (mm) and P is monthly rainfall (mm).

3.6 Statistical Analysis

Having ETo, coffee ETc and irrigation requirement calculated at station level, exploratory

data analysis were performed using R (version 3.4.1) software. The distributions of data

were analyzed using classical statistics. To protect unwanted data outliers that affect the

24

descriptive statistics and characterization of spatial variation, histograms and Box-plots

were used for each parameter that requires inspections.

3.7 Mapping of ETo, Coffee ETc and IR

To improve the accuracy of estimation, a linear regression equation was developed

between observed data and station’s elevation. Using the linear regression equations the

residuals of ETo and ETc were estimated as the difference between observed and predicted

values at gage locations. Then the residuals of ETo and ETc value were interpolated using

ordinary Kriging over the entire region. Using DEM and regression equation, station based

data was further extended to 5 km spatial grids. The experimental semi-variogram was

calculated and variogram models were fitted for extended dataset of ETo and ETc. Next

ETo and ETc for each month starting from January to December were interpolated to

1km×1km spatial resolution by ordinary kriging using gstat package (Graler et al., 2016) in

R software (Team, 2013).

The final estimation of ETo and ETc at any location were obtained by:

Final prediction = residual interpolated map + regression kriged map

However, the IR was directly interpolated without considering elevation effects, because

weak correlation were observed between stations’ elevation and IR during dry season

(Table, 12).

The performances of the each fitted model prediction at un-sampled location were checked

by RMSE. The measured data at 22 stations were used to cross-validation by living one out

method; the model with smallest values of RMSE was selected for final interpolation

(Pebesma and Bivand, 2005)

RMSE = �∑ (�����)��

���

� 3.8

where RMSE is root mean square error, Oi is obseverd value, Pi is predicted value, n is

number of samples

25

4. RESULTS AND DISCUSSION

4.1 Climate data consistency test

The annual mean rainfall of all the stations were found to be consistent for the period of

1985 to 2016 with strong determination coefficient of R2 value greater than 0.9983. The

graphs of the cumulative annual rain fall of tests stations versus the cumulative rainfall of

the other station were found to be a straight line or perfectly fitted (Figure 8)

From figure 7 it was observed that the annual minimum mean temperature of Jimma

station was homogeneous.

Figure 7. Corrected minimum temperature of Jimma station by double mass curve

26

Figure 8. Double mass curve of annual mean rainfall of meteorological stations

27

4.2 Coffee Crop Coefficient

From three water balances during three months during initial stage, mean Kc values ranged

from 0.54 to 0.88, with the average of 0.70 at JARC station (Table 1). The mean Kc-

NDVI varied between 0.61 and 0.84, with the mean of 0.69 (Table 2) during initial stage.

The climate adjusted Kc for both mid-season and late-season stage was found to be 0.99.

For all growth stages, the variability of Kc value was large, ranging from 0.54 to 0.99

during initial and mid-season, respectively (Table 3). The Kc value during initial stage was

much low as compared to midseason stage due to water stress as the result of limited

precipitation during the dormancy period. During initial stage, Coffee plants are often

intentionally water stressed to reduce vegetation growth and to encourage development of

berries, under these condition, Kc values from FAO should be reduced (Allen et al., 1998).

Thus, the coffee Kc values were more probably related to seasonal rainfall fluctuation and

canopy cover.

Furthermore, the estimated Kc was observed to be high between wet months of March and

October during pinhead expansion and fruit set; and low during dry month of the

December to 1st week of February, corresponding to dormancy period.

The Kc was linearly decreased from December to 1st week of February as soil moisture

getting low due to limited rainfall and coffee plants were under water deficit, consequently,

losing leaves. Due to water stress and climatic condition adjusted Kc values were lower

than reported Kc for coffee that ranges between 1.05 to maximum 1.10 under shade

condition (Doorenbos and Pruitt, 1977). However, this result was found within reasonable

coffee Kc ranges suggested by different authors. Marin et al. (2016) reported that the

coffee Kc ranges between 0.6 (dry period) and 1.9 (wet period), with a mean of 0.99, when

average ETo is 3.2 mm day-1 in Brazil. Carr (2001) found that the coffee Kc values range

from 0.7 to 0.8. In a similar way the result was consistent with Oliveira et al. (2003) who

reported that Kc values varied from 0.7 to 1.5 using water balance method in Minas Gerais,

Brazil. Silva et al. (2009) reported that Kc varies between 0.75 and 1.43 at Piracicaba,

Brazil. Also Marin et al. (2005) presented coffee Kc values varies from 0.6 to 1.6 at Brazil.

The coffee Kc values was fluctuated with season, the lower Kc value was recorded during

dry season than wet season, which is consistent with Gutierrez and Meinzer (1994) who

reported much lower Kc during dry season than wet-season, suggesting that a seasonal

28

reduction in water use by coffee may have occurred independently of seasonal variation in

evaporative demand. Coffee stomata have been exhibited a strong closing response to

reduced atmospheric humidity (Fanjul et al., 1985).

Table 1. Water balance and climate adjusted coffee Kc for initial, mid and late stage at JARC station

Month DAP P (mm)

DP (mm) RO (mm) ∆S (mm) ETo mm/ day

ETc mm/dec

Kc

11-Dec 1-10

20-Dec 0.00 0.00 0.00 -29.75 3.40 29.75 0.88

9-Jan

19 -Jan 31-40 11.50 2.40 0.00 -11.9 3.37 22.8 0.68

29-Jan

8-Feb 51-60 10.20 1.10 0.00 -9.95 3.51 19.05 0.54

Kc initial mean 0.70

Long-term mean monthly climate, PH and climate adjusted Kc during mid-season and late season

Mid-season Late-season

Variables Apr May Jun Jul Aug Sep Mean October

RHmin 50.90 56.7 63.81 70.40 70.36 65.91 63.01 61.00 WS (m/s) 1.01 1.02 1.01 1.10 1.03 1.00 1.03 1.02

PH 2.81 2.85 2.87 2.93 2.96 2.98 2.93 3.12

Kc adjusted 0.99 0.99 Qo = previous soil moisture content, Qi= current soil moisture content, P= rainfall, DP=deep percolation, RO= run-off, ∆S= change in soil moisture, DAP= days after harvesting, PH (mm) = plant height, WS= wind speed (m/s), RHmin = minimum relative humidity (%).

Table 2. NDVI and Kc value for model development during 2017/18 years and Kc NDVI

Image acquired date

Pixel NDVI

Kc measured at that pixel

Kc NDVI

Coffee field canopy characteristic

3-Dec 0.697 0.88 0.84 Moderate canopy cover 19-Jan 0.58 0.68 0.62 sign of leaf wilt for coffee and shed trees 12-Feb 0.46 0.54 0.61 leaf fall for coffee and shed trees, weeds between rows dried

9-Jun 0.79 0.99* Full effective canopy cover

Kc Mean initial stage 0.69 ‘*’ climate adjusted FAO Kc value for mid-season

29

Table 3. Summary of coffee Kc and length of development stage

Development stage

FAO Kc

Kc adjusted

Development stage

Root depth

(m)

P Description

Initial season 1.05 0.69 96 0.9 0.40 Dormancy period, from harvesting to flowering. From 01 November to 04 February

Development

>>

>>

66

>>

0.40

From flowering to fast pinhead expansion. From 05 February to 11 Apri1

Mid-season 1.10 0.99 168 1.5 0.40

From fast pinhead expansion to maturity. April,12 to September, 26

Late-season 1.10 0.99 35 1.5 0.40 From maturity to harvesting, from 27 September to 31 October.

Total 365 Plant height (average) 3.25 m

4.3 Reference Evapotranspiration

4.3.1 Validation of CROPWAT 8 model

The results revealed that the ETo estimated from exported data from similar climate

regime have good agreement with that of ETo estimated from full set of climatic data at

Jimma stations. The root mean square error (RMSE) value was 0.14 mm day-1. It was also

exhibited by strong determination coefficient of R2 value of 0.88. While, the ETo estimated

from limited data have lower accuracy to estimate ETo, because when compared to ETo

estimated with full set of climatic data have RMSE value of 1.24 mm day-1 and R2 value of

0.6. The result show that the monthly ETo estimated from exported data were better than

ETo estimated from limited data (Table 2).

4.3.2 Explanatory analysis

Homogeneity test of ETo: From figure 11, it can be clearly seen that deviations line were

not crossed horizontal lines of probabilities, the monthly ETo values of August for Jimma

station was homogenous. Similar to Jimma station, the monthly ETo values for 22 stations

are homogenous. When the deviation crosses one of the horizontal lines the homogeneity

of the data set is rejected with respectively 90, 95 and 99% probability (Dirk, 2013).

30

Normality test: The Kolmogorov-Smirnov values of long-term monthly and annual ETo

value for each station considered varied from 0.06 to 0.18, which showed that the data was

normally distributed, because estimated KS values was greater than significance level α at

p = 0.05. The distribution accepted with the significance level of 0.05 probabilities

(Appendix Table 4). With a significance level of α = 0.05, if KS ≥ 0.05, the data can be

deemed to be in normal distribution (Molin and Abdi, 1998).

Having the normal distributions of long term ETo at each stations. The regional estimated

annual and monthly P-value of Shapiro-Wilk test varied from 0.11 to 0.95, these values

was found within normal ranges. Therefore, regional monthly and annual ETo values were

normally distributed (Table 5). In Shapiro-Wilk test, when the P-values is less than or

equal to 0.05, the data does not fit the normal distribution with confidence level of 95%

(Royston, 1982). Furthermore, the box-plot revealed there was no outlier both for mean

annual and monthly ETo values (Figure 10 B). The histogram reveals some data dispersion

around the mean value and its frequency distribution (Figure 10 A). The data presented

with acceptable CV, however, relatively high CV was recorded during summer season due

to rainfall variation while low during dry season (Table 5).

4.3.3 Annual and monthly spatial distribution of reference evapotranspiration

Annual ETo values varied from 3.53 to 4.20 mm day-1 and averaged 3.84 mm day-1

when all 22 weather stations were combined (Table 5). Figure 12 M clearly indicates

that the maximum annual ETo value were located in southern tip (Shebe-Sombo, Dedo and

Omonada), northeastern (Botor-Tollay) and eastern tip (Sokoru) regions. Of the locations,

highest annual ETo value (4.5 mm day -1) was found in extreme eastern tip. The minimum

annual ETo value in Jimma zone were located in the western highlands (Gera, Setama,

Sigmo and Gatira), southern (Dedo and Seka-Chekorsa) and northeastern regions. From

these areas, lowest ETo value (3.2 mm day-1) was located in southeast region. The lowest

ETo had a reduction of 25.1 % as compared highest ETo predicted in extreme eastern tip.

In general, ETo value was smaller in highland areas because it is associated with colder

climate and higher precipitation. However, ETo value was higher in low altitude areas,

hence it is associated with warmer climates. Thus, the spatial variability of predicted ETo

value caused by varied elevation. The decrease of ETo with altitude is in accordance with

the decrease of air temperature with altitude. Because an altitudes have significant strong

31

negative correlation with annual ETo in Jimma zone (Table 6). The lower ETo in the

highland areas may contributed from altitudinal difference, the highland areas

characterized by high altitudes as compared to lowland areas. This means the greater

altitude of an area is the smaller temperatures, as altitudes increase the temperature

decreases because the temperature is the major climatic factor, which affects ETo (Allen et

al., 1998). With increasing elevation, the air temperature decreases and RH increase; thus,

the ETo generally decreases (Um et al., 2017). ETo will vary with altitude, every 1000 m

rise in altitude will result in 10% ETo reductions (Doorenbos and Pruitt, 1977)

The monthly ETo spatial distribution presented in Figure 12 has shown some variations

through the year. The spatial distribution was more or less similar for monthly and annual

ETo values. The monthly ETo increases from the southern highland areas towards the

extreme southern tip lowlands and again increase from eastern highland to extreme eastern

tip lowland areas along Gibe river basin, whereas ETo had decreasing trend from central

region to western highland regions of Jimma zone (Figure 12 A-L).

There were a significant (P<0.05) temporal variations among monthly ETo values (Table

5). The graph shows a decrease from March to July and increase from December to March

(Figure 9). The high ETo values were predicted from January to May and the values

ranged from 3.94 to 4.97 mm day-1, corresponding to dry season. Of all months, the

highest ETo value was predicted in March and the value varied from 4.12 to 4.97 mm day-1

(Table 5). The highest ETo value in March might due to warmer air and lower humidity.

During dry season, high amount of energy was available when temperatures are higher.

ETo values during summer season (July to September) was greatly very low and the values

ranged from 2.24 to 3.99 mm day-1 with the mean of 3.18 mm day-1. Summer season is a

main rainy season in Jimma zone. Of these months, the smallest ETo value was predicted

during the rainy month of July and the values varied from 2.34 to 3.43 mm day-1 (Table 5).

ETo during July had a reduction of 35.9% as compared to march. This low ETo value

during rainy season may due to lower air temperature and higher humidity as the result of

progressed rain fall. According to Singo et al.(2016) who reported that the

evapotranspiration rates tended to drop to low levels when the air around the plants

was too humid during rainy season.

32

The temporal variation of ETo was observed due to changes in solar radiations as a result

of a change in the season, statistically a significant strong correlations were observed

between monthly ETo values and climatic parameters at p<0.05 (Table 6). Low ETo

values were obtained during the summer when it was cool, cloudy and humid, while the

high during winter when it was hot, sunny and dry, (Table 5). The monthly ETo values

varies with same pattern with maximum air temperature and sunshine hours, but varies in

opposite direction with rainfall and relative humidity. Sunshine hour duration had strong

positive significant correlations with monthly ETo of November, December, January,

April, and May with the correlation values ranged from 0.79 to 0.89 (Table 6). Usually the

rises of temperature are happen, due to clear sky during dry season, and consequently

increasing ETo.

Maximum air temperatures had positively strong significant correlation (0.94***) with

monthly ETo values. Implying seasonal ETo values was mainly affected by maximum air

temperature (Table 6), as air temperature increases ETo will increase. This phenomenon

was confirmed by Rasul and Farooqi (1993) stated that an increase in temperature will

directly influence the evapotranspiration rate of atmosphere. The rainfall was negatively

correlated with ETo especially during the rainy month of July (-0.50), i.e. as rainfall

increases ETo become decreased (Table 6). Summer rainfall plays a vital role in reducing

the evaporative demand to some extent (Naheed and Rasul, 2010). Relative humidity had

strong negative correlation with monthly ETo values, Thus the relative humidity was the

dominant factor that determine the temporal fluctuation of monthly ETo values. In

equatorial countries like Ethiopia, the difference in ETo is due to cloud cover, temperature

and relative humidity (Adem et al., 2017).

This result was in agreements with Savva and Frenken (2002) who reported that ETo value

of 4.2 mm day-1 for April at Jimma zone. Similar finding was reported by De Bruin et al.

(2010) who reported that ETo values varied from 3 to 5 mm day-1 for February at Jimma

zone areas. Also similar results of monthly ETo values ranged from 4.01 to 5.24 mm day -1

were reported by Fitsume et al. (2017) at Akaki areas. They found out that lower ETo

values during peak rainy season and maximum during a dry season.

33

4.3.4 Cross-validations of Models

Among the fitted models, the exponential model has superior performance in producing

lower RMSE in seven situations including March, April, May, June, August, November,

and annual. The spherical model produced smaller RSME during the month of February,

October and December, whereas, the gaussian model produced lower RMSE during the

months of January, July and September (Table 7).

Table 4. ETo value estimated from full, exported and CROPWAT estimated data at Jimma station for 2015

Month CROPWAT (full data) exported data CROPWAT estimated

January 3.52 3.66 5.37

February 4.15 4.25 5.88

March 4.47 4.65 6.02

April 4.35 4.52 5.44

May 4.29 4.05 5.08

June 3.46 3.45 4.65

July 2.71 3.07 4.06

August 3.18 3.27 4.48

September 3.78 3.62 4.78

October 3.88 3.81 4.73

November 3.75 3.75 4.44

December 3.74 3.78 4.93

Annual 3.77 3.82 4.99

RMSE 0.14 1.26

Adjusted R² 0.88 0.60

Table 5: Statistic analysis of monthly and annual ETo for Jimma zone

Station ETo Model predicted ETo

Period Mean (mm)

Max

(mm) Min

(mm) SD CV (%) Shapiro-Wilk P-value

Min (mm)

mean

(mm) max

(mm) January 3.91de 4.23 3.70 0.15 3.84 0.51 3.49 3.90 4.22 February 4.43b 4.79 4.10 0.18 4.18 0.95 3.94 4.42 4.79 March 4.61a 4.99 4.20 0.20 4.44 0.77 4.12 4.60 4.97 April 4.47b 4.89 4.11 0.20 4.50 0.92 4.17 4.46 4.67 May 4.12c 4.47 3.66 0.19 4.62 0.91 3.63 4.10 4.48 June 3.59g 3.94 2.98 0.26 7.22 0.33 2.90 3.57 4.10 July 2.97j 3.25 2.57 0.19 6.53 0.21 2.34 2.95 3.43 August 3.04i 3.34 2.46 0.23 7.64 0.12 2.24 3.02 3.63 September 3.42h 3.70 2.88 0.23 6.82 0.11 2.64 3.40 3.99 October 3.95d 4.40 3.39 0.23 5.80 0.75 3.37 3.93 4.37 November 3.85e 4.30 3.43 0.23 5.89 0.92 3.16 3.84 4.36 December 3.78f 4.14 3.46 0.20 5.29 0.54 3.13 3.76 4.26 Annual mean 3.84 4.20 3.53 0.19 5.05 0.85 3.17 3.77 4.23 LSD ( 0.05) 0.06

34

Means followed by the same letters in a column are not significantly different from each other at a 5% probability level, CV= coefficient of variation, SD=standard deviation

Table 6. Pearson Correlations of monthly ETo with metrological parameters

Monthly Metrological parameters

Period Altitude Tmin Tmax RH Sunshine Rainfall

spatial correlations

January -0.69** 0.43* 0.69** -0.49 0.88* -0.11

February -0.64** 0.47* 0.65** -0.64 0.83* -0.07

March -0.58** 0.38 0.61* -0.72 0.86* -0.21

April -0.34 0.26 0.41 -0.43 0.89* -0.43

May -0.62** 0.39 0.57* 0.36 0.83* -0.32

June -0.65** 0.29 0.66** -0.69 0.52 -0.42

July -0.78*** 0.24 0.77*** -0.69 0.54 -0.41

August -0.84*** 0.33 0.8*** -0.72 0.39 -0.50*

September -0.81*** 0.39 0.76*** -0.64 0.44 -0.34

October -0.61** 0.34 0.59** -0.44 0.68 -0.22

November -0.75** 0.37 0.76*** -0.70 0.79* -0.33

December -0.79*** 0.43* 0.77*** -0.62 0.83* -0.12

Mean -0.78** 0.42* 0.75*** -0.52 0.79* -0.44*

Temporal correlation - 0.10 0.94*** -0.86** 0.81** -0.66* *, ** and *** = significant at 0.05, 0.01 and < 0.0001 level, respectively, WS= wind speed, RH=relative humidity

Table 7. RMSE value between modeled monthly ETo and observed ETo values

Ordinary kriging models Month Spherical Exponential Gaussian January 0.16 0.16 0.15* February 0.18* 0.19 0.22 March 0.22 0.19* 0.20 April 0.23 0.20* 0.28 May 0.20 0.19* 0.23 June 0.26 0.21* 0.22 July 0.22 0.17 0.16* August 0.24 0.22* 0.23 September 0.25 0.25 0.24* October 0.24* 0.24 0.24 November 0.23 0.22* 0.24 December 0.21* 0.21 0.22 Annual 0.19 0.18* 0.20 RMSE=Root mean square error, “*” = is selected model for interpolation.

35

Figure 9. Mean monthly ETo for Jimma zone

Figure 10 Histogram (A) and Boxplot (B) for annual ETo

Figure 11. ETo homogeneity test for August at Jimma stations in rainbow software

B A

36

Figure 12. ETo kriging maps of annual and monthly ETo for Jimma zone

37

4.4 Coffee Water Requirement

4.4.1 Explanatory analysis

The Shapiro-Wilk test results indicated that the monthly and annual coffee ETc was

normally distributed. Thus the calculated P-values ranged from 0.12 - 0.98, which was

found within acceptable ranges, which is suitable for statistical analysis without needing to

be processed (Table 8). If the p-value is greater than the chosen alpha level (0.05), the data

tested come from normally distributed populations (Royston, 1982). The histogram shapes

of annual coffee ETc looked like bell-shaped with the highest point in the middle or

symmetrical slopes in either side, which showed that the data are normally distributed

(Figure 13 A). Similar behavior is observed for monthly coffee ETc. The box-plot of mean

annual coffee ETc showed that there were no data outliers, similar patterns were

detected for mean monthly coffee ETc (Figure 13 B). This condition plays a significant

role in variogram analysis and kriging interpolations (Graler et al., 2016).

Coefficient of variation was found to be the greatest from June to September, and ranged

from 6.61 to 7.25 %, probably due spatial variation of rainfall in study areas. However,

relatively low CV (5.43 % on average) during the rest months (Table 8).

4.4.2 Annual and monthly spatial distribution of coffee water requirement

Spatially, the annual ETc predicted for coffee varied from 1048 to 1377 mm year-1 over the

entire study areas (Table 8). The spatial distributions of annual coffee ETc are more or less

similar with annual ETo values. The upper most annual coffee ETc value is with the

narrow range of 1300 to 1377 mm year-1 which predicted in extreme southern tip,

easternmost tip and extreme northeastern regions. Of the locations, the highest coffee ETc

value of 1377 mm year -1 was found in extreme eastern tip. The central region had medium

annual ETc values covering some parts of Serbo, Mana, Gomma, Limmu-genet, and

Asendabo Woredas (Figure 16). The least coffee ETc value was predicted in the western,

southern and northeastern regions, ranged from 1048 to 1150 mm year -1. Of locations, the

lowest ETc value of 1048 mm was predicted in southeastern part of region (Figure 16 M).

The apparent spatial variation of annual coffee ETc values might be due to ETo variation

as affected by elevation. The lower ETc values were predicted in highland areas, whereas

higher in lowland areas of Jimma zone. Generally, the possibility of an increasing in the

38

ETc values from the southern highland to extreme southern tip lowland and again increase

from eastern highland to extreme eastern lowland areas, whereas decrease from central

plain to western highland parts. The reason is that the effluence of altitudinal differences,

the highland parts of study area is characterized by higher elevation as compared to

lowland areas (Figure 16). This condition was justified by altitude was significantly

negatively strong correlated with ETo in the study area, as altitudes increase ETo will

decreases, consequently the ETc decreases (Table 6). Complimentary to this result, at

elevation above 2300 m, the annual evapotranspiration values have been gradually reduced

by the low atmospheric demand because of low temperatures as the elevation increases

(Kiptala et al., 2013). With increasing height, air temperature drops uniformly with altitude

at the rate of approximately 6.5 ℃ per 1000 meters (Jocik, 2004). An altitude is the major

factor which affect the ETc of the crops because it directly affects temperature. This was

also verified by Rasul and Farooqi (1993) who reported that increase in temperature will

directly influence the evapotranspiration rate and water requirement of crop.

The spatial distributions pattern of monthly coffee ETc was similar to that of annual coffee

ETc (Figure 16 A-L). Statically significant variation were observed among monthly ETc

values at P<0.05 (Table 8). The upper most range of monthly ETc value is within the

narrow range of 119.01 to 131.11 mm during the months of March, April, and May. Even

though statistically non-significant between them, the highest coffee ETc was observed in

April, during this month the coffee plants found at stage of fast pinhead expansions (Table

8). This highest ETc during April might be due availability of high evaporating energy,

optimum soil moisture and coffee is existed at full canopy cover and then it can able to

transpire at higher rate.

The least ETc value was recorded during the months of November, December, and

February, with the narrow ranges of 80.9- 91.84 mm, with the lowest in the November, but

non-significant between them, which corresponding to the dormancy period. The minimum

ETc value during these months might be due to limited soil water content and reduction of

transpiring areas as the result of leaf fall (Figure 16 K, L and A).

In general, ETc of coffee was lowest during the initial stage and highest during mid-season

stage. However, moderate during summer rainy season (June to September) due to lower

39

ETo. During summer rainy season the amount of energy available to make

evapotranspiration was quite low, consequently ETc values was suppressed.

This value was in agreement with Lemos Filho et al. (2010) who reported monthly coffee

ETc values for all months ranged from 37.63 to 152.79 mm, with annual ETc ranges from

798.25 to 1510.33 mm year-1 and mean of 1123.3 mm year-1. Pereira et al. (2011) who

reported that coffee ETc value ranges from 3.68 to 4.04 mm day-1 in Brazil, when ETo is

3.53 to 3.88 mm day-1. The current annual mean ETc value was consistent with Allen et

al. (1998) who reported that coffee ETc values ranged from 800 to 1200 mm year-1 for

sub-humid climates. Oliveira et al. (2003) reported coffee ETc value varied from 2.52 to

3.50 mm day-1 in Brazil. Gutierrez and Meinzer (1994) found that coffee ETc values vary

from 2.04 to 5.23 mm day-1. Also FAO (2018) recommended coffee ETc values varies

from 2.86 to 3.57 mm day-1 (20 to 25 mm per week) in Lao in Southeast Asia.

4.4.3 Semi-Variogram models and parameters

For all of variograms models fitted, the monthly and annual mean values of ETc revealed

moderate to strong spatial dependence degree, presenting SDD smaller than 27.1%. The

spatial SDD ranged from 0 to 27.1%. The strong spatial dependency was detected for all

months except for March in case of gaussian. The exponential model (0%) presented

strong spatial dependency degree, followed by spherical (2.2%) and gaussian (18.2%)

models. This means that the three models tested were characterized by strong spatial

dependency. The average SDD considered for all months varied from 0 to 18.23% (Table

10). Similar results were reported by Lemos Filho et al. (2010) who reported the spatial

dependency degree for monthly coffee ETc ranges from 0 to 30.42%, which exhibit

moderate to strong SDD. According to Cambardella et al. (1994), spatial dependency

degree has classified into: strong spatial dependency (SDD≤25%), moderate spatial

dependency (25%≤SDD≤75%), and weak (SDD≥75%).

In all months of the year; there is considerable variation of ranges, which varied from 9 to

30 km. The spherical model produced the range varied from 26 to 30 km, with a mean of

26.5 km. which means autocorrelation existed over quite a large range or sill will not reach

quickly. Whereas, exponential model produced ranges varied from 13.6 to 14.2 km, while

the gaussian model varied from 8.8 to 14.2 km, with the mean of 12.1 km. comparatively

spherical model produced the longer mean range, which is approximately 54.2 and 48.2%

40

of exponential and gaussian models, respectively; both exponential and gaussian models

produced approximately similar results (Table 10). The geographic diversity might cause

spatial heterogeneity of coffee ETc in Jimma zone within shorter range. Thus, the values

beyond this range distance are not spatially auto-correlated or no longer relationship.

4.4.4 Cross-validations of interpolation models

Among the fitted models, the exponential model was superior over other models followed

by gaussian model; however, the spherical model produced the best result only during the

month of March with lower RMSE (5.18). The exponential model produced the smallest

RMSE during February (3.64), May (5.94), June (7.04), September (6.46), October (6.60),

November (4.90) and annual (59.02). However, gaussian models produced the lowest

RMSE in January (3.46), April (5.19), July (4.77), August (6.01) and December (4.47)

(Table 9). There was strong determination coefficient R2 of 0.98 between exponential

model predicted annual ETc and observed ETc (Figure 14).

Table 8. Descriptive statistics of monthly and annual Coffee ETc for Jimma zone

Month

Station ETc Model predicted ETc

Mean SD CV Max Min Shapro-

wilk Min Mean Max mm mm % mm mm P-value mm mm mm

January 85.98efg 3.41 3.96 93.00 80.9 0.61 75.94 85.73 93.4 February 91.84def 3.76 4.10 99.30 85.2 0.94 81.7 91.59 99.31 March 124.26ab 5.37 4.32 134.50 113.5 0.86 111.4 123.9 133.7 April 131.11a 5.48 4.18 143.00 120.5 0.98 121.1 130.9 138.5 May 125.86ab 5.85 4.65 136.70 112.3 0.79 111.4 125.5 135.5 June 106.56c 7.21 6.77 116.50 89.5 0.19 86.56 106.1 121.3 July 93.29de 6.17 6.61 102.20 80.5 0.12 73.34 92.81 108.0 August 94.21de 6.83 7.25 103.30 77.4 0.20 70.78 93.65 111.5 Septembe 101.79cd 6.69 6.57 110.00 86.5 0.12 79.55 101.3 118.2 October 119.07b 6.72 5.64 132.40 103.1 0.81 101.5 118.6 132 November 80.92g 4.70 5.80 90.10 72.5 0.88 66.8 80.58 91.35 December 82.42fg 4.33 5.25 90.50 75.6 0.49 68.98 82.09 92.34 Annual 1237.22 60.1 4.85 1350.10 1129.4 0.57 1048 1233 1377 CV (%) 16.8 LSD@5% 10.2 Note, SD=standard deviation, CV= coefficient of variation, means with the same later are not significantly different from each other at 0.05.

41

Table 9. RMSE value for monthly and annual coffee ETc for ordinary models

Months Spherical Exponential Gaussian

January 3.60 3.59 3.46*

February 4.04 3.64* 3.68 March 5.18* 5.25 5.25

April 5.40 5.92 5.19*

May 6.13 5.94* 6.32

June 7.31 7.02* 7.47

July 5.41 5.49 4.77*

August 6.86 6.61 6.01* September 6.96 6.46* 7.14

October 6.82 6.60* 6.86

November 5.09 4.90* 5.16

December 4.74 4.47 4.47*

Annual 61.75 59.02* 63.29 *= selected model for interpolation, RMSE=root mean sum square of error

42

Figure 13: Histogram (A) and box-plot (B) for annual coffee ETc (mm/ year)

Table 10. Parameters of the variogram models obtained for monthly and annual values of coffee ETc in Jimma zone

Month model Partial sill Nugget SDD Range Model Partial sill Nugget SDD Range Model Partial sill Nugget SDD Range

Jan Exp 7.9 0.0 0.0 13.9 Sph 7.2 0.0 0.0 25.8 Gau 6.3 0.9 12.2 10.8

Feb Exp 8.1 0.0 0.0 13.8 Sph 7.3 0.1 1.5 26.1 Gau 6.3 1.1 15.1 12.0

Mar Exp 13.2 0.0 0.0 13.7 Sph 11.8 0.3 2.6 26.4 Gau 8.6 3.2 27.1 14.4

Apr Exp 8.2 0.0 0.0 13.6 Sph 7.1 0.4 5.8 27.5 Gau 6.4 1.1 14.9 10.3

May Exp 62.6 0.0 0.0 14.0 Sph 51.6 7.0 12.0 30.0 Gau 44.1 13.3 23.1 13.3

Jun Exp 31.5 0.0 0.0 14.1 Sph 28.5 0.2 0.6 26.1 Gau 23.1 5.3 18.5 13.1

Jul Exp 31.2 0.0 0.0 14.1 Sph 28.3 0.1 0.5 26.1 Gau 25.6 2.9 10.2 8.8

Aug Exp 43.0 0.0 0.0 14.2 Sph 39.0 0.1 0.3 26.1 Gau 33.6 5.3 13.7 11.8

Sep Exp 38.7 0.0 0.0 14.1 Sph 35.2 0.1 0.2 26.0 Gau 27.7 7.0 20.2 13.5

Oct Exp 24.3 0.0 0.0 14.1 Sph 21.9 0.3 1.3 26.4 Gau 19.5 2.6 12.0 10.5

Nov Exp 15.6 0.0 0.0 14.1 Sph 14.2 0.1 0.6 26.1 Gau 11.3 2.7 19.4 13.3

Dec Exp 14.2 0.0 0.0 13.9 Sph 12.8 0.1 1.0 26.0 Gau 10.1 2.7 21.0 13.5

Annual Exp 2798.0 0.0 0.0 14.1 Sph 2546.8 0.0 0.0 26.0 Gau 1995.7 514.6 20.5 13.5

Exp=exponential, Gau=Gaussian, Sph=Spherical,��� = (��/�� + �), SSD=spatial degree of dependency, Co= nugget, C= partial sill

A

B

43

Figure 14: Annual ETc validation using linear regression between actual and predicted data

y = 0.837x + 201.0R² = 0.986

1100

1150

1200

1250

1300

1350

1100 1150 1200 1250 1300 1350 1400

Pre

dic

ted

ETc

mm

Measured ETc mm

Predict…

Figure 15. Semi-variogram and fitted model for mean annual coffee ETc in Jimma

44

Figure 16. ETc kriging maps of monthly coffee crop ETc for Jimma zone

45

4.5 Coffee Irrigation Requirement

4.5.1 Explanatory analysis

The Shapiro-Wilk test result showed that monthly IR was normally distributed for January,

March, April, October, November, and December and annual with the p-values ranged

from 0.07 to 0.63 (Table 11). The higher CV of monthly IR was recorded for months of

May, June and September with the range of 167-268%, which is contributed by higher

spatial variability of rainfall in corresponding months. While monthly IR for other months

founds within acceptable CV range i.e. 13.23 to 62.7 %, and was considered to exhibit

moderate to higher heterogeneity (Table 11).

4.5.2 Spatial distributions of monthly and annual coffee Irrigation Requirement

The annual coffee IR for all stations ranged from 300.6 to 842.9 mm year-1. However the

predicted value ranged from 300.7 to 723.3 mm year-1 within outline of study areas (Table

12). The spatial distributions of annual coffee IR are heterogeneous in study area with CV

of 31.05 %, which is contributed by higher spatial variability of rainfall. The upper most

annual IR was predicted around regions of extreme eastern tip (Sokoru lowlands),

southeastern and northeastern regions, and the values ranged from 650 to 750 mm. In

contrast, the lower coffee IR, was predicted around locations of western (Sigmo, Gera,

Setama and Gatira), southeastern (Omonada) and southern (Dedo and Seka-Chekorsa),

with the values ranges from 300.7 to 450 mm year-1. However, the some regions of

Limmu-Kosa and Limmu-Seka experienced moderate coffee IR, with the values

approximately varied from 500 to 550 mm year-1 (Figure 18 H).

Spatially, the annual coffee IR was experienced increasing trends from western parts to the

eastern and again increases from southern to northern parts of the study area. The reason

of increment in both directions might be due to spatial variation of rainfall. The rain fall

had significant strong negative correlation with both latitude and longitude during dry

periods (Table 12). This means the amount of rain fall had significantly decreasing from

west to east and from south to north in study areas.

The spatial variations of monthly coffee IR were heterogamous with over all CV of 88.1%,

which is contributed by higher spatial variability of rainfall in corresponding months. The

spatial variation distributions of monthly IR for January, February, March and April have

46

indicated more or less similar distributions pattern. The high IR during these months was

indentified around region of eastern (Sokoru) and northern (Limmu-Kosa and Limmu-

Seka). Whereas the low coffee IR was predicted in western (Gera, Setama, Sigmo and

Gatira), southern (Seka-Chekorsa and Dedo) and eastern (around Natri) regions (Figure 18

A, B, C and D). The spatial distribution of coffee IR during May was similar with

September. During these months the least IR were predicted for western and southern

regions. However, the extreme eastern tips and southeastern region require high irrigation

relatively (Figure 18 E and F). The spatial distributions of coffee IR during October are

more or less similar with annual IR (Figure 18 G).

Statistically significant variations were observed among monthly coffee IR at p<0.05. The

upper most monthly value of coffee IR was predicted in November, December, January,

February and April, with range of 53.4 to 77.7 mm, and the highest predicted during

February (77.7 mm) (Table 12). This highest IR during period might be due to smaller

rainfall and high evaporative demand of atmosphere during these dry spells. The minimum

coffee IR was predicted from June to September (Figure 17) due to probability occurrence

of high rain fall during summer season (June to September). There is no irrigation

requirement during months of June, July and August in study areas, with mean values 0 to

1.7 mm, which approaches to ~ 0 mm; the ETc demand during these three months, were

completely satisfied by rain fall (Table 12).

In General, the coffee IR amount considerably decreased with increasing rate of

precipitation. Thus, IR has an inverse relationship with rainfall; it decreased with rise in

rainfall but increased with reduction fall of rainfall. The monthly IR exhibited the

decreasing trends from March to August. Thus, the main causes for temporal variation of

coffee IR might be due to fluctuation of rain fall and ETo through the year in Jimma zone.

The spatial variation of coffee IR in study area might be due to differences in climatic

parameters such as changes in temperature, solar radiation and percentage change in

effective rainfall (Nkomozepi and Chung, 2012).

During the months of November to end of December the coffee plants stay under

dormancy period or flower bud undergo dormant for about two months (November to end

of December), even though the requirement is there, the plants will remain un-irrigated for

about 60-70 days (Carr, 2013) to promote bud development, because continued rain or

47

irrigation would delay imposition of the drought (Astegiano et al., 1988). After optimum

impositions of soil moisture stress, the dormancy should be broken out by showers of rain

or irrigations, which is approximately around end of December, any delay rainfall during

this period, would results in prolonged dormancy period. If severely high air temperatures

are coupled with moisture stress, it lengthens the dormancy of some crops including coffee

(Allen et al., 1998). Much supply of water also resulted in delaying of coffee flowering.

Providing irrigations during dormancy periods will leads to physiological imbalance in the

flowering of coffee (Titus and Pereira, 2005). Similar recommendation was forwarded by

FAO (2018) coffee needs to be water-stressed for about four to eight weeks before

flowering to give a strong uniform flowering.

The winter dry period may extends from October to April/May in study area, and during

this period, coffee plants are subject to higher water deficit. High amount of leaves

dropped from coffee trees as physiological response. Winter irrigations (End of December

to 1st week of February) used to shorten dormancy periods, similar suggestions were

forwarded by Nkomozepi and Chung (2012), winter irrigations during December helps to

reduce drought period. Winter irrigation in study area during February and March will

helps to supplement showers rain fall deficit to obtain a complete blossom. Similar studies

conducted by Titus and Pereira (2005) indicated that winter irrigations helps to overcome

the inadequate natural blossom showers in the month of February and March essential for

flowering.

Tesfaye et al. (2013) conducted research at JARC; they clearly demonstrated that the

need of supplemental irrigation when ETc greater than seasonal rainfalls for coffee

during dry spells in study area, accordingly, supplemental full irrigation significantly

increased coffee yield, Supplemental full irrigation had 24 % yield advantage over rain

fed treatment. Eriyagama et al. (2014) mapped global coffee irrigation requirement, they

indicated positive coffee IR, which is varied from 0-200 and >200 mm year-1 for south

southwester Ethiopia (Jimma zone) and central Ethiopia, respectively. Peasley and Rolfe

(2003) who reported that 484 mm year -1 of coffee IR, at Newrybar site (Australia) for

low stressed coffee plants per annum. The values are almost consistent with my study.

48

Table 11. Statistics analysis of monthly and annual coffee IR for Jimma zone

Stations IR Model Predicted IR

MONTH Mean (mm)

SD (mm)

CV (%)

Max mm

Min mm

SW P-value

Min mm

Mean mm

Max mm

Jan 76.19a 10.08 13.23 92.1 57.2 0.53 57.2 74.8 86.9

Feb 77.70a 13.00 16.73 96.1 47.6 0.35 47.6 76.2 89.7

Mar 72.36ab 24.77 34.23 112.2 28.7 0.24 32.4 69.1 98.6

Apr 53.44b 29.67 55.52 112.5 4.5 0.63 4.5 49.7 94.2

May 13.48c 22.63 167.94 79.4 0.0 0.00 0.0 10.5 54.1

June 1.70c 4.55 268.62 14.9 0.0 0.00 0.0 1.1 13.0

Jul 0.00c 0.00 0.00 0.0 0.0 NA 0.0 0.0 0.0

Aug 0.00c 0.00 0.00 0.0 0.0 NA 0.0 0.0 0.0

Sep 5.93c 12.09 204.04 41.1 0.0 0.00 0.0 4.9 31.8

Oct 52.49b 32.92 62.71 116.7 7.5 0.10 16.6 50.5 99.0

Nov 60.98ab 15.67 25.70 90 38.3 0.25 41.4 67.0 82.1

Dec 70.18ab 13.44 19.16 90.4 43.7 0.33 43.7 75.3 83.5

Annual 484.45 150.48 31.06 842.9 300.6 0.07 300.7 466.2 723.3

CV 88.1

LSD@5% 21.1

SW= Shapiro-Wilk

Table 12. The Pearson correlation between Rainfall and geographic parameter

Month Longitude latitude Altitude

Jan -0.62** -0.78** -0.08

Feb -0.33 -0.83** -0.13

Mar -0.46* -0.73** 0.04

Apr -0.64** -0.69** 0.24

May -0.79** -0.16 0.53*

Jun -0.47* 0.40 0.67**

Jul -0.19 0.48* 0.68**

Aug -0.29 0.46* 0.73**

Sep -0.61** 0.16 0.53*

Oct -0.83** -0.22 0.23

Nov -0.77** -0.59** 0.15

Dec -0.63** -0.76** -0.03

Annual -0.70** -0.04 0.58** *, ** and *** = significant at 0.05, 0.01 and < 0.0001 level, respectively, WS= wind speed, RH=relative humidity

49

Figure 17. Monthly coffee ETc, IR and effective rain fall for Jimma zone

50

Figure 18. IR kriging maps of Monthly and annual coffee IR for Jimma zone

51

5. SUMMARY, CONCLUSION AND RECOMMENDATIONS

5.1 Summary and Conclusions

The coffee crop water requirements, as expressed by the crop evapotranspiration, vary

considerably spatially from location to location in study area. In this study, an

attempt was made to analyze spatial and temporal dynamics of coffee ETc in Jimma zone

of Oromia region. The aim was to estimate and to map coffee ETc and IR.

From the results maximum ETo was observed around the extreme southern tip and

extreme eastern tip region. Whereas smaller values were founds around western and

southern part. The temporal variation of ETo was significant, the highest ETo value was

recorded in March (4.61mm day-1) and the smallest in July (2.91 mm day-1).

The seasonal coffee Kc varied from 0.69 to 0.99 for Kc-NDVI and climate adjusted,

respectively. The lowest Kc values during initial stage whereas highest during midseason

stage. From monthly and seasonal maps of coffee ETc, the high annual coffee ETc were

observed in the extreme eastern, southern tip of Jimma zone, the values ranged from

1129.4 to 1350.1 mm year-1. In contrary the western and southern parts indicated by

small ETc requirement. The monthly coffee ETc has exhibited similar spatial distribution

with that of annual ETc. The monthly mean coffee ETc ranged from 80.9 (November) to

131.11 mm (April).

The maps of monthly and annual coffee IR exhibited that there were greater spatial

variability distributions in study areas, because the mean CV for all months and annual

IR varied from 31.1 to 86.8%. The higher coffee IR was found to be over the

northeastern, southeastern and central regions and some tip areas of the southwestern

area. Whereas the western and southern half region of study areas have been indicated by

smaller IR. Temporally, the high IR were occurred from January (76.19 mm) to April

(53.44), with the highest in the month of February (77.70 mm). In contrast, the coffee

plants did not need irrigation from June to September; thus the water requirements were

completely satisfied by rainfall. Naturally, coffee plants need dormancy period of about

28-70 days from November to end of December. Otherwise, a plant continues to grow in

vegetative as irrigation is continuous during dormancy period instead of flowering.

52

Therefore, even though the requirement is there, the coffee plants will not be irrigated

during the dormancy period.

Based on the results the following conclusion was drawn:

1. The high ETo was observed around areas of extreme southeastern tip and western

tip, while lower over areas of Western and half of the southern area.

2. Highest monthly ETo value was found during dry season of April

(4.61mm), while the lowest during wet rainy season of July (2.91 mm day-1).

3. Maximum monthly and annual coffee ETc was indicated in the regions of extreme

western, extreme southern tip and north western regions. In contrast, minimum

requirement exhibited around western and half of southern regions as well as

some pocket areas of Natri and Botorbacho areas. Temporally, minimum coffee

ETc recorded from November to February was due to critical soil moisture stress.

While greatest ETc value was seen from April to June, during midseason stage.

4. Highest irrigations requirement was observed during the month of February

(77.70 mm), while slightly nil from June to September.

5.2 Recommendations

In this study, Coffee ETc and IR were analyzed and mapped with the input of climate and

crop data. The Kc values were adjusted to local condition by water balance, satellite image

and long term climatic data at JARC station. Therefore, Further studies on predicting

coffee ETc at different locality using models and supplemented with replicated field

experiments (Bowen-ratio and lysimeter ) are essential to accurately estimate and map

coffee ETc in study area.

Based on the results the following recommendations were suggested:

1. Results of this study may literally help for designing purposes for engineers and

irrigation practitioners in the absence of data.

2. The results of this study will also help both experts and producers in order to

develop more efficient irrigation strategies in study areas.

3. Based upon long-term irrigation requirement estimation, coffee plants will

not require irrigation during June to August, since existing rainfall is adequate.

53

4. Due to limited rainfall during the extended dry period, the farmers are advised to

irrigate their coffee crop during dry months.

5. Generally it is recommended to create awareness for those who are participated in

coffee production systems, even though Jimma is situated in sub-humid areas it is

characterized by dry period for about 4-6 months, during these months severe soil

moisture deficit will need to compensate with artificial water application.

6. In view of the explicit distinction made by the geo-spatiality of the coffee crop

water requirement in Jimma zone, a correct understanding of the ETc value for

each locality will be most useful to coffee growers, where irrigation

management is concerned

54

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APPENDICES

Appendix Table 1. Monthly Tmax data of Jimma stations prepared for MICE imputation

year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1985 29.7 29.3 30.3 27.9 26.3 25.4 23.4 23.9 25.3 26.5 27.5 28.2

1986 29.5 29.3 28.9 27.6 27.8 24.8 24.6 25.4 25.3 26.8 28.8 28.3

1987 28.8 29.6 27.8 28.5 26.9 26.3 25.9 25.6 26.9 27.2 28.2 27.8

1988 28.1 28.2 30.3 28.9 28.1 25.8 23.3 24.3 24.7 26.0 27.1 27.8

1989 28.0 27.6 27.9 26.4 27.5 25.5 24.2 25.2 25.1 26.3 26.7 26.1

1990 26.9 26.4 27.6 27.7 27.3 25.5 24.4 24.8 25.3 27.0 27.5 27.9

1991 28.0 28.2 28.3 28.6 27.9 25.9 23.8 24.1 25.6 26.8 27.9 26.6

1992 27.4 27.8 29.6 28.9 28.2 26.8 24.5 24.1 26.1 26.3 27.0 27.9

1993 27.4 27.4 29.7 27.5 27.2 26.1 24.8 25.1 26.3 27.1 28.2 29.3

1994 30.2 30.9 29.9 28.8 27.0 25.3 23.4 24.0 25.5 28.0 28.0 29.1

1995 30.0 30.5 31.1 28.8 28.2 27.4 24.8 25.5 26.6 28.0 28.0 27.8

1996 27.0 29.7 28.9 27.8 27.3 25.2 24.5 24.9 26.1 27.5 27.8 27.3

1997 27.8 30.2 30.9 27.6 27.4 26.6 25.3 25.7 27.5 27.2 27.1 27.8

1998 28.1 29.2 29.6 31.0 28.8 27.6 24.9 25.5 26.4 27.1 28.1 29.2

1999 29.9 32.6 30.8 30.7 28.2 27.2 24.8 25.4 27.5 26.6 28.1 29.4

2000 30.8 32.5 33.2 29.7 28.4 26.8 25.7 25.3 27.2 27.1 28.1 28.7

2001 28.9 31.3 29.7 30.0 28.8 26.6 25.3 25.4 27.9 28.1 28.5 29.7

2002 28.6 31.2 29.9 30.6 29.2 26.1 25.8 25.4 26.8 27.7 28.2 27.6

2003 27.9 30.6 29.9 29.2 30.7 27.1 24.6 25.1 26.5 27.9 28.6 28.5

2004 29.7 30.0 30.3 29.1 29.1 25.9 25.0 25.3 26.3 27.0 28.0 27.8

2005 28.3 31.6 30.5 29.3 27.6 26.4 25.1 25.9 26.1 27.3 27.4 29.3

2006 30.2 31.2 29.9 28.7 28.3 27.2 25.5 24.6 25.9 27.6 27.7 27.5

2007 27.9 28.7 30.5 28.9 27.9 26.6 25.5 25.1 26.5 28.3 29.8 30.6

2008 31.3 31.3 32.5 30.1 28.3 27.1 24.8 25.6 25.8 26.8 26.6 28.0

2009 28.4 29.7 30.4 29.1 28.9 27.4 24.9 24.9 26.4 26.8 28.5 26.7

2010 28.3 28.5 29.0 29.3 27.6 26.7 25.0 24.9 25.9 27.8 28.3 27.2

2011 29.4 31.2 30.0 30.3 28.3 26.4 26.0 24.8 26.4 28.0 27.5 28.2

2012 30.3 31.6 31.5 29.5 29.0 26.3 24.9 24.9 25.8 27.8 27.9 28.1

2013 29.3 30.9 30.2 30.9 27.5 26.4 24.4 24.8 26.6 26.9 27.5 28.1

2014 28.7 30.2 30.4 NA NA 27.5 25.7 25.4 25.9 NA NA 28.0

2015 29.8 31.6 31.7 29.9 NA NA NA 26.6 27.6 NA 27.7 NA

2016 29.5 NA NA 28.5 NA NA NA 25.8 NA NA NA NA

NA is missed data

66

Appendix Table 2. Descriptive statistics of climate dataset for different stations

Stations

Tmin (oC) Tmax (oC) Rain fall (mm) RH (%) WS (m/s) SH (hrs)

mean Max Min Mean Max Min Sum Max Min mean Max Min mean Max Min Mean max min

Agaro 12.16 13.04 10.79 28.69 30.78 26.12 1621.0 244.9 29.7

Arjo 10.88 11.87 10.06 20.30 23.03 17.11 2364.2 417.6 20.0 73.08 89.16 55.09 1.53 1.89 1.12 6.48 8.41 2.94

Asendabo 12.49 14.24 9.42 27.15 29.56 23.79 1198.1 195.5 21.1

Atnago 13.96 14.94 13.09 27.81 30.51 24.89 1664.6 310.7 13.9

Bedelle 12.81 14.24 11.58 25.55 28.45 22.23 1886.0 346.9 14.5 73.07 86.97 59.54 1.08 1.29 0.94 6.98 8.74 3.47

Bonga 11.99 13.10 10.15 27.26 28.67 25.62 1603.4 200.5 42.1

Botorbacho 2.50 3.33 0.14 24.61 26.91 21.48 1487.1 299.8 15.8

Chida 15.04 15.79 14.45 26.73 29.41 22.99 1535.2 201.8 39.3

Chira 11.70 12.23 10.99 23.80 25.64 21.34 1890.7 262.3 50.8 65.04 81.35 48.84 1.00 1.09 0.89 6.26 8.09 3.27

Dedo 12.58 12.84 12.09 23.14 24.94 20.83 2047.8 333.7 37.4

Ejajji 13.91 15.75 11.41 28.06 31.46 23.71 1367.4 274.2 11.0 69.72 84.61 55.02

Gatira 11.53 12.15 11.00 22.15 24.44 19.74 2048.4 341.1 36.9 70.08 82.84 60.17 1.51 1.79 1.32 5.93 7.58 3.07

Gojeb 14.88 16.60 12.45 30.66 33.21 28.20 1668.6 257.8 40.2

Jimma 11.69 13.86 7.92 26.66 30.04 24.83 1541.2 224.6 34.2 73.53 83.00 64.39 0.81 0.97 0.71 6.45 8.05 3.73

Limu-genet 13.57 14.93 11.83 27.14 30.00 23.99 1831.31 318.3 23.1 66.39 82.17 49.05 0.96 1.15 0.84 7.10 8.99 3.88

Natri 12.74 14.43 10.98 26.81 29.81 23.53 1583.17 265.7 21.3

Shebe 13.30 13.90 12.74 25.74 28.34 23.03 1600.41 235.1 40.4

Sokoru 13.34 14.46 12.16 26.13 28.59 23.06 1350.53 233.6 24.9 65.04 81.35 48.84 0.93 1.01 0.86 7.10 8.70 4.06

Wolkite 12.99 13.49 12.29 28.34 30.52 25.53 1133.3 233.8 7.5

Yaya 13.42 15.50 10.05 30.20 32.73 25.77 1025.05 219.2 11.5

Yebu 12.88 13.51 12.35 25.83 27.73 23.63 2067.74 324.0 42.5

67

Appendix Table 3. Areal mean climate dataset estimated by Thiessen polygon method for Jimma zone

Variables Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual Min Max

Rain fall (mm) 31.17 31.52 94.28 132.52 202.23 255.26 278.86 279.96 219.09 116.57 48.29 33.19 1728.67 31.17 279.96

Tmin (oC) 11.13 11.17 12.54 12.85 12.80 12.45 12.34 12.23 12.04 11.47 10.86 10.65 11.96 10.65 12.85

Tmax (oC) 27.19 27.25 28.21 27.34 26.32 24.66 23.03 23.21 24.28 25.44 26.20 26.52 25.84 23.03 28.21 RH (%) 61.97 56.56 57.36 63.60 73.71 78.87 83.47 84.09 81.50 71.90 66.32 62.21 70.13 84.09 56.56

WS (m/s) 1.07 1.19 1.27 1.30 1.26 1.15 1.09 1.07 1.05 1.01 0.96 0.97 1.11 1.30 0.96

sunshine hour (hrs) 7.97 8.03 7.50 7.23 6.89 5.69 3.56 3.61 5.06 7.69 8.16 7.96 6.61 8.16 3.56

Tmin is minimum temperature, Tmax is maximum temperature, RH is relative humidity, WS is wind speed

68

Appendix Table 4. KS test values calculated for 80% non- exceedance probability for monthly ETo (1985 to 2016) for each station

Stations Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec annual

Agaro 0.10 0.07 0.08 0.08 0.10 0.11 0.10 0.09 0.08 0.12 0.09 0.08 0.11

Arjo 0.15 0.11 0.11 0.12 0.13 0.14 0.18 0.08 0.13 0.18 0.09 0.12 0.10

Asendabo 0.07 0.07 0.12 0.06 0.14 0.11 0.14 0.12 0.11 0.08 0.10 0.15 0.08

Atinago 0.10 0.07 0.09 0.15 0.07 0.09 0.11 0.07 0.08 0.09 0.09 0.13 0.09

Bedelle 0.11 0.17 0.15 0.10 0.10 0.13 0.14 0.14 0.11 0.10 0.10 0.13 0.15

Bonga 0.08 0.08 0.10 0.10 0.06 0.07 0.11 0.08 0.10 0.07 0.08 0.12 0.14

Botorbacho 0.11 0.16 0.12 0.13 0.08 0.18 0.17 0.12 0.13 0.14 0.13 0.14 0.14

Chida 0.11 0.12 0.12 0.10 0.12 0.16 0.06 0.14 0.15 0.18 0.09 0.10 0.12

Chira 0.07 0.12 0.10 0.11 0.13 0.15 0.14 0.07 0.07 0.12 0.07 0.15 0.07

Dedo 0.09 0.13 0.09 0.08 0.14 0.12 0.12 0.10 0.14 0.18 0.15 0.06 0.11

Ejajji 0.10 0.15 0.12 0.07 0.11 0.13 0.15 0.14 0.10 0.12 0.12 0.09 0.14

Gatira 0.13 0.14 0.08 0.11 0.16 0.08 0.21 0.16 0.09 0.11 0.08 0.09 0.08

Gojeb 0.11 0.06 0.09 0.17 0.06 0.08 0.17 0.11 0.13 0.07 0.11 0.10 0.10

Gore 0.08 0.08 0.13 0.09 0.17 0.14 0.10 0.16 0.16 0.13 0.18 0.06 0.12

Jimma 0.17 0.08 0.06 0.11 0.12 0.06 0.08 0.08 0.12 0.16 0.09 0.12 0.10

Limmu-genet 0.06 0.15 0.10 0.09 0.18 0.14 0.19 0.15 0.14 0.12 0.08 0.06 0.06

Natri 0.11 0.09 0.14 0.15 0.12 0.09 0.13 0.16 0.11 0.14 0.14 0.11 0.12

Shebe 0.06 0.06 0.06 0.11 0.06 0.11 0.09 0.09 0.12 0.08 0.06 0.10 0.13

Sokoru 0.07 0.15 0.06 0.08 0.14 0.16 0.09 0.13 0.15 0.10 0.08 0.12 0.07

Wolkite 0.08 0.10 0.09 0.10 0.07 0.15 0.08 0.16 0.09 0.11 0.09 0.16 0.06

Yaya 0.08 0.13 0.09 0.11 0.08 0.12 0.17 0.11 0.08 0.07 0.12 0.14 0.13

Yebu 0.11 0.08 0.06 0.08 0.08 0.12 0.12 0.06 0.13 0.09 0.15 0.11 0.12

69

Appendix Table 5. Reference evapotranspiration (mm/day) values of the different stations

Stations

Months

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Agaro 3.95 4.45 4.66 4.47 4.31 3.80 3.16 3.30 3.70 4.01 3.96 3.87

Arjo 3.70 4.19 4.44 4.43 3.93 3.20 2.57 2.46 2.88 3.61 3.43 3.46 Asendabo 4.03 4.56 4.72 4.57 4.15 3.74 3.04 3.13 3.50 4.15 4.04 3.94

Atnago 3.91 4.46 4.70 4.50 4.24 3.94 3.11 3.25 3.65 3.96 3.90 3.81 Bedelle 3.89 4.39 4.66 4.72 4.15 3.35 2.79 2.85 3.44 4.02 3.83 3.77

Bonga 3.87 4.31 4.48 4.26 4.06 3.63 3.10 3.23 3.63 3.99 3.91 3.83 Botorbacho 3.75 4.17 4.38 4.30 3.95 3.51 2.97 2.98 3.28 3.82 3.71 3.61

Chida 3.90 4.40 4.40 4.30 4.00 3.50 2.97 3.12 3.48 4.10 3.94 3.91 Chira 3.78 4.10 4.20 4.11 3.83 3.35 2.77 2.80 3.10 3.67 3.57 3.61

Dedo 3.80 4.26 4.40 4.32 4.02 3.28 2.73 2.81 3.24 3.71 3.64 3.58 Ejajji 4.13 4.66 4.88 4.77 4.34 3.77 3.01 3.07 3.46 4.09 4.01 3.95

Gatira 3.70 4.22 4.39 4.24 3.91 3.36 2.71 2.78 3.17 3.82 3.57 3.49 Gojeb 4.08 4.57 4.83 4.52 4.30 3.81 3.19 3.34 3.69 4.09 4.08 4.07

Gore 3.74 4.36 4.52 4.42 3.66 2.98 2.62 2.67 2.98 3.39 3.49 3.48 Jimma 3.86 4.33 4.50 4.34 4.16 3.67 3.07 3.21 3.67 3.94 3.84 3.76

Limmu-genet 4.05 4.72 4.92 4.67 4.24 3.77 3.08 3.07 3.39 3.97 3.98 3.85 Natri 3.80 4.42 4.67 4.41 4.22 3.78 3.03 3.04 3.39 3.84 3.74 3.57

Shebe 3.84 4.34 4.52 4.21 4.02 3.55 2.99 3.22 3.53 3.87 3.79 3.76 Sokoru 4.02 4.56 4.71 4.58 4.19 3.64 3.01 3.06 3.43 4.07 4.01 3.91

Wolkite 4.14 4.65 4.82 4.70 4.29 3.93 3.22 3.26 3.59 4.34 4.24 4.09 Yaya 4.23 4.79 4.99 4.89 4.47 3.94 3.25 3.28 3.67 4.40 4.30 4.14

Yebu 3.95 4.47 4.56 4.51 4.11 3.45 2.86 2.90 3.32 3.95 3.82 3.70

70

Appendix Table 6. Coffee ETc (mm) estimated for different stations

Stations Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual

Agaro 87.7 92.5 125.3 131.8 131 112.4 99.6 102.3 109.6 121.9 83 84.5 1281.5

Arjo 80.9 87 119.8 128.9 119.6 95.4 80.5 77.4 86.5 110.1 72.5 75.6 1134.4 Asendabo 88.6 94.5 127.1 133.7 127.3 110.1 95.9 97.1 104.8 124.8 84.8 86 1274.9

Atnago 86 92.5 126.3 132.3 129.9 115.3 96.4 100.7 108.1 120.3 81.8 83.2 1272.7 Bedelle 85.6 91.2 125.9 136.9 126.4 100.3 87.9 90.4 101.4 120.7 80.8 82.3 1229.7

Bonga 85 89.4 120.4 125.6 123.8 107.5 97.3 100.1 107.6 121 82.2 83.4 1243.3 Botorbacho 82.2 86.7 118.1 125.8 120.8 103.9 93 92.5 98.3 115 77.9 79 1192.9

Chida 85.9 90.8 119.1 126.1 121.9 103.8 93.5 96.6 104.3 123.2 83.1 84.9 1233.1 Chira 82.5 85.2 113.5 120.5 116.7 99.2 87 86.9 93 110.4 75.3 78.7 1148.7

Dedo 83.1 88.3 118.7 126.6 121.7 98.1 85.8 87.6 96.4 112 76.4 78.4 1173.2 Ejajji 90.6 96.7 131.5 139.4 132.6 111.3 95.1 95.4 103.6 123.1 84.3 86.4 1289.9

Gatira 81.1 87.4 118.2 124.3 119.1 99.5 85.6 86.5 95 114.2 75.4 76.4 1162.7 Gojeb 89.8 95 129.4 133.4 130.9 112.7 100.5 103.3 109.8 124.2 85.7 88.4 1302.9

Gore 82.2 90.1 121.5 128 112.3 89.5 81.7 82.9 88.9 103.1 73 76.3 1129.4 Jimma 85.4 89.9 121.2 127.8 126.5 108.7 96.7 99.7 108.3 119.6 80.7 82.1 1246.7

Limmu-genet 88.3 97.5 132.2 136.9 129.9 111.2 96.8 95.4 101.6 120 83.3 84.2 1277.1 Natri 83.6 91.6 125.3 130.1 128.6 111.7 95.5 94.6 101 116 78.4 78.5 1234.6

Shebe 83.6 91.6 125.3 130.1 128.6 111.7 95.5 94.6 101 116 78.4 78.5 1234.6 Sokoru 88 94.3 127 134.1 127.9 107.9 94.7 95.1 102.8 122.6 84.1 85.3 1263.7

Wolkite 91.1 96.4 130 137.5 131.6 114.9 101.2 101.1 108 130.4 88.9 89.2 1320.2 Yaya 93 99.3 134.5 143 136.7 116.5 102.2 102 110 132.4 90.1 90.5 1350.1

Yebu 87.3 92.5 123.4 131.6 125.1 102.7 89.9 90.4 99.3 118.6 80.2 81.4 1222.5

71

Appendix Table 7. Coffee irrigation requirement (mm) estimated for different stations

Stations Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual

Agaro 79.7 82.2 76.2 64.5 6.2 0.0 0.0 0.0 0.0 27.4 64.8 75.6 476.7 Arjo 78.9 81.8 66.8 34.0 0.0 0.0 0.0 0.0 0.0 18.5 50.6 64.2 394.9

Asendabo 83.3 84.1 81.1 68.7 43.7 0.0 0.0 0.0 31.8 98.9 82.1 81.2 655.0 Atnago 85.9 84.7 87.8 71.1 6.9 0.0 0.0 0.0 0.0 51.0 68.5 80.4 536.3

Bedelle 85.4 86.9 94.4 79.8 1.7 0.0 0.0 0.0 0.0 31.1 70.6 76.5 526.4 Bonga 66.4 74.2 57.4 18.0 0.0 0.0 0.0 0.0 0.0 37.9 43.6 61.0 358.4

Botorbacho 81.4 81.7 83.8 75.1 4.2 0.0 0.0 0.0 9.4 90.1 67.3 78.8 571.8 Chida 72.2 61.1 28.7 6.7 0.0 14.8 0.0 0.0 4.6 41.6 63.7 50.7 344.1

Chira 57.2 63.9 43.0 19.0 0.0 0.0 0.0 0.0 0.0 16.6 42.7 58.1 300.6 Dedo 69.5 69.6 40.9 4.5 0.0 0.0 0.0 0.0 0.0 42.5 45.2 65.9 338.1

Ejajji 90.5 95.9 107.6 95.3 41.4 0.0 0.0 0.0 5.9 81.8 75.8 86.3 680.4 Gatira 65.6 74.8 77.1 28.0 0.0 0.0 0.0 0.0 0.0 21.7 48.3 64.2 379.6

Gojeb 70.6 76.9 69.5 51.1 2.8 0.0 0.0 0.0 0.0 56.6 60.4 74.2 462.2 Gore 74.5 84.4 89.8 66.8 0.0 0.0 0.0 0.0 0.0 7.5 38.3 69.3 430.6

Jimma 74.1 79.3 72.3 44.2 3.8 0.0 0.0 0.0 0.0 58.0 53.7 68.7 454.2 Limmu-genet 84.3 88.2 89.5 66.1 3.8 0.0 0.0 0.0 0.0 20.1 68.3 80.4 500.4

Natri 62.9 47.6 42.5 45.6 4.8 0.0 0.0 0.0 0.0 58.4 49.8 43.7 355.4 Shebe 62.9 47.6 42.5 45.6 4.8 0.0 0.0 0.0 0.0 58.4 49.8 43.7 355.4

Sokoru 81.5 85.8 92.5 69.0 34.0 0.0 0.0 0.0 6.7 86.7 79.1 80.1 615.4 Wolkite 90.3 85.5 101.7 90.8 59.0 7.6 0.0 0.0 30.9 116.7 87.5 89.1 759.2

Yaya 92.1 96.1 112.2 112.5 79.4 14.9 0.0 0.0 41.1 114.2 90.0 90.4 842.9

Yebu 66.9 77.0 34.7 19.2 0.0 0.0 0.0 0.0 0.0 19.0 41.4 61.5 319.8

72

Appendix Figure1. The DEM of study area

Appendix Figure 2.The point elevation extracted from DEM of study area for interpolation

73

BIOGRAPHICAL SKETCH

Minda Tadesse was born on September 07, 1987 in Dedo Wereda, Oromia region,

Ethiopia. He Attended his elementary school at Dedo Elementary School and Dedo high

school. He joined Alage Agricultural College in 2004 and graduated in 2006 with

Diploma in Natural Resource and immediately after his graduation, he was employed

by the Oromia Agricultural Bureau in Dedo wereda as supervisor and expert from 2007

to 2009 and after that he joined Jimma University in 2010 and graduated in 2012 with BSc

degree in Natural Resources Management soon after his graduation, he was employed by

the Ethiopia Institute of Agricultural Research (EIAR) at Jimma Agricultural

Research Centre and served as Assistant researcher I of Irrigation and Drainage researcher

from 2013 to 2016.

The author joined the School of Graduate Studies at Hawassa University 2016 to pursue

his MSc degree in Water Resources Engineering and Management in the School of

Bio-system and Environmental Engineering