Linking crop yield to seasonal climate variations in Gamo ...

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Linking crop yield to seasonal climate variations in Gamo Highlands, Ethiopia

Analysis using an Ecophysiological (GECROS) and the Weather Research and Forecasting (WRF) Models

WAGENINGEN UR

August 27, 2014

Thomas Torora Minda

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Thesis Supervisors

Dr. J (Jordi) Vilà-Guerau de Arellano

Associate professor of boundary layer meteorology

Meteorology and Air Quality Group

E-mail: [email protected]

Wageningen University and Research Center

The Netherlands

Dr. Ir. MK (Michiel) van der Molen

Assistant professor of land-use change and mesoscale meteorology

Meteorology and Air Quality Group

E-mail: [email protected]

Wageningen University and Research Center

The Netherlands

Linking Seasonal Climate Variations to Crop Yield in Gamo Highlands, Ethiopia

Analysis using an Ecophysiological (GECROS) and the Weather Research and Forecasting

(WRF) Models

TT Minda

Thesis, Wageningen University and Research center

E-mail: [email protected]; [email protected]

mailto:[email protected]




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WAGENINGEN UR

Linking crop yield to seasonal climate variations

in Gamo Highlands, Ethiopia

Analysis using an Ecophysiological (GECROS) and the Weather Research

and Forecasting (WRF) Models

A Minor-thesis for the partial fulfillment of the degree of master in Climate Studies

(MCL), specialization: Atmospheric Chemistry and Air Quality

August 27, 2014

Thomas Torora Minda

Thesis Supervisors

Dr. J (Jordi) Vilà-Guerau de Arellano

Dr. Ir. MK (Michiel) van der Molen

Meteorology and Air Quality (MAQ)

Wageningen, the Netherlands

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Dedication

May God Bless the Dutch and its land

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ACKNOWLEDGMENTS

I have always impressed with perfect supervision of professors of the meteorology and air

quality (MAQ) chair group, Wageningen University and Research Center (WUR). As usual, I

express my heartfelt thanks to Dr. Jordi Vilà and Dr.Ir. Michiel van der Molen.

The high temporal (10 years) and spatial (2-by-2 km2) resolution of WRF model runs conducted

in the National Center for Atmospheric research (NCAR), Boulder, CO, USA. I express my

wholehearted appreciations and thanks to Dr. Pedro A. Jimenez, project scientist at the Research

Application Laboratory, NCAR.

At the beginning of this thesis work, I had limited knowledge in crop science and modelling.

Thanks to Marie Combe (PhD candidate) for her immense support in GECROS model

understanding and python scripting. I’m also so thankful to Prof P.C. Struik (professor of crop

physiology) for his fundamental comments and ideas for future directions.

From the beginning of my study in WUR, his regular advisory services and encouragements

helped me a lot. You are right person at right position. I want to express my gratitude to Dr. Ir.

Rudi Roijackers!

You both shared not only your offices, but your seats too. I have no words to express your lovely

helps during this work! Thank you Syioum Gizachew and Teklu Teshome, college of Medicine,

Arba Minch University.

The deans of Arba Minch College of Heath Sciences, Yosph Sonko, Ashenafi Abosa, and

Mesirach Hailu allowed me to work in the college offices. I express my thanks to you all! The

medical laboratory staffs have shared their offices and seats. I have special thanks to Hallelujah

Getachew, Temesgen Eticha, Zeleke Gizachew, Abate Atimut, Shemlis Sheferaw, Bereket

Workalemaw, and Derib Alemu.

My study in such globally leading education institute, WUR, is sponsored by the Netherlands

Organization for International Cooperation in Higher Education (nuffic). The last two years were

one of the critical transition states in my life! I have no word to express my deep feeling to the

organization and the Dutch people and their land! I have shared your good thoughts and

prosperity!

Thank you God, the Holy Spirit, the Creator, Owner, and Sustainer of the Universe. These all are

because of you. Thank you so much.

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ABSTRACT

Climate change posed huge lose in crop productivity, mainly in the developing world. This study aimed at

identifying drought seasons/years; best potato (Solanum tuberosum L.) sowing dates; and locations for

optimum yield in the Gamo highlands (locations around Arba Minch town), Ethiopia. High temporal and

spatial resolutions were applied for weather and crop yield modelling. The Weather Research and

Forecasting (WRF) model was used to simulate the seasonal inter-climatology. Hourly outputs of 10

years (2001 – 2010) model run were made. The inner most model domain (d04) was centered at Arba

Minch and mainly covered (resolution of 2x2 for 84x84 km2 area) the Gamo highlands. Series of 48 hours

runs were conducted, in which the first 24 hours (the previous day) taken as model spin-up period. Daily

weather outputs of (maximum and minimum temperatures, rainfall, wind speed, the incoming shortwave

radiation, and vapor pressure deficit), radiation budget, and the surface energy balance were extracted.

The Standardized Precipitation Index (SPI), anomalies in temperature, radiation, and surface energy

balances were considered to identify drought during potato sowing season (Belg, which is from February

to May). An ecophysiological (GECROS) model was implemented to reproduce productivity. The WRF

model outputs were considered as input for GECROS. A 10-by-10 km2 resolution was modelled in the

WRF’s model domain. Model sensitivity experiments of sowing dates (Jan 15, Feb 01, Feb 15, and Mar

01); climate change assumptions (increased temperatures, and rainfall); and crop management option

(application of fertilizer) were considered. The WRF model overestimated rainfall, wind speed, and

underestimated temperatures. However, the model was capable to reproduce the drought year 2009 in

Arba Minch and its vicinity. The GECROS productivity maps showed that areas with altitude ≥ 2000m

above sea level identified as climatologically potential potato growing locations in the Gamo highlands.

Early potato sowing, before the start of the Belg, was preferred as it gives relatively higher yield and less

exposed to plant diseases. Crop-management sensitivity experiment showed that application of fertilizers

was the best way to robust productivity. We suggest further model experiments for optimum fertilizer rate

application. Model experiments in climate change assumptions showed decline in productivity for

highlands, and sever decline for the lowlands. The yield declined is mainly explained by decreasing the

number of harvest days.

Key words: Potato (Solanum tuberosum L.), WRF, SPI, GECROS, yield, climate change.

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TABLE OF CONTENTS

ACKNOWLEDGMENTS .................................................................................................... v

ABSTRACT ........................................................................................................................ vi

LIST OF FIGURES ............................................................................................................. ix

LIST OF TABLES................................................................................................................ x

LIST OF APPENDICES ..................................................................................................... xi

LIST OF ABBREVIATIONS AND ACRONYMS ...........................................................xii

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

1.1 Objectives ....................................................................................................................... 2

1.2 Research Questions ......................................................................................................... 2

1.3 Hypothesis....................................................................................................................... 3

1.4 Research Approach ......................................................................................................... 3

2 METHODS ................................................................................................................... 4

2.1 The Study Area ............................................................................................................... 4

2.2 The Ethiopian Climate and Climatic Zones .................................................................... 6

2.3 Agro-meteorological Models: WRF and GECROS ........................................................ 8

2.3.1 WRF Model setup ................................................................................................................. 9

2.3.2 The GECROS Model .......................................................................................................... 10

2.4 Design of Model Experiments ...................................................................................... 12

2.5 Data Analysis ................................................................................................................ 13

2.5.1 Data Analysis: Meteorological ............................................................................................ 13

2.5.2 Data Analysis: Crop yield ................................................................................................... 14

2.5.3 The Principal Component Analysis: Weather Vs Yield...................................................... 14

3 RESULTS ................................................................................................................... 15

3.1 WRF Model Outputs ..................................................................................................... 15

3.1.1 Model Performance ............................................................................................................. 15

3.1.2 Seasonal-Inter-Climatology: Meteorological Extremes ...................................................... 16

3.2 GECROS Model Outputs .............................................................................................. 20

3.2.1 Weather and Crop Productivitity: The Principal Component Analysis (PCA) ................... 20

3.2.2 GECROS Model Performance ............................................................................................ 21

3.2.3 GECROS Modelled Potato Fresh Matter Yield: Arba Minch and Chencha Stations ......... 22

viii

3.2.4 Productivity and Related terms Maps: The Gamo Highlands ............................................. 27

4 DISCUSSIONS ........................................................................................................... 33

5 CONCLUSIONS ......................................................................................................... 36

REFERENCES ................................................................................................................... 37

APPENDICES .................................................................................................................... 42

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LIST OF FIGURES Figure 1: Potato Production Areas and Average Yields in Ethiopia. Adapted from (IPC, 2009, Hirpa et al.,

2010). ............................................................................................................................................................ 1

Figure 2: Steps followed to achieve thesis objectives ................................................................................... 3

Figure 3: Map of the mother WRF model domain. The domain centered in Ethiopia and covers the

neighboring countries and Northwest part of the Indian Ocean. The ‘yellow’, ‘white’, and ‘green’

colored subplots show the nested domains ‘d02’, ‘d03’, and ‘d04’, respectively. The detailed topographic

and Woreda map of ‘d04’ shown below. ...................................................................................................... 4

Figure 4: The topographic and Woreda map on the WRF model domain 04. The inner nested domain

centered at Arba Minch (A.M.) town covers 82km in x and y directions. The model resolution is

2kmx2km. ..................................................................................................................................................... 5

Figure 5: WRF Model Land Use Map of Model Domain 04. The inner nested domain centered at Arba

Minch (A.M.) town covers 82km in x and y directions. The model resolution is 2kmx2km. ...................... 6

Figure 6: The Monthly Mean Climate of Arba Minch (from the year 1987 to 2013) and Chencha (from

1957 to 2012). Daily weather data is obtained from Arba Minch Weather station. ..................................... 8

Figure 7: Weather-yield forecasting outline applied in this report. Variables such as: humidity (obtained

from daily rainfall), max and min temperatures, global radiation, wind speed, day length (calculated from

latitude), and soil category. These WRF variables were implemented as in input for the crop model,

GECROS. In addition, crop management options, atmospheric CO2 (380 ppm), etc. were considered as

GECROS inputs. ........................................................................................................................................... 9

Figure 8: Relational diagram of the GECROS growth simulation model using standard symbols (boxes-

state variables, valves-rate variables, ellipses/circles-intermediate variables, small crossed circles-

environmental variables, full-line arrows-material flows, and dashed-line arrows-information flows). Note

that: model inputs in blue color were inputs supplied for the model experiments. The reader is referred

(Xinyou and Van Laar, 2005) for further explanation. ............................................................................... 11

Figure 9: Climatology for maximum and minimum temperatures, rainfall, and wind speed for observed

(Arba Minch) and WRF model data. Stations: Arba Minch (a) and Chencha (b). The dash line in a plot

shows 39 years (1974-2013) of observations of the climate variable. Observation data obtained from Arba

Minch weather station. ................................................................................................................................ 16

Figure 10: Ten years WRF outputs of seasonal (Belg and Kiremt) anomalies from seasonal climatology:

rainfall (top), maximum (middle), and minimum (bottom) temperatures for Arba Minch (a), and Chencha

(b) stations. .................................................................................................................................................. 19

Figure 11: WRF modelled anomalies from climatology for radiation (Qn) and surface energy (GS, SH, LH)

budgets for Arba Minch (a) and Chencha (b) stations. ............................................................................... 20

Figure 12: The observed and GECROS modelled potato productivity (qt ha-1

) for Chencha Woreda during

the years 2001 to 2010. Potato sowing date in GECROS was Feb 01 during the Belg season. Observation

data was missing for the years: 2001, 2002, 2004, and 2005 (GGAD, 2008). ........................................... 22

Figure 13: GECROS Modelled Potato Sowing date Sensitivity Experiments Fresh-Matter Yield difference

from control (Feb01) for Arba Minch (above) and Chencha (below). Sowing date Sensitivity Experiments

were conducted on dates: Jan 15, Feb 15, and Mar 01. Note that: (1) the Sowing season was Belg, except

experiment Jan 15; (2) Fertilizer was not applied in the model. ................................................................. 25

Figure 14: GECROS Modelled Potato Sensitivity Experiments (in WRF and GECROS) Fresh-Matter

Yield difference from control (Feb01) for Arba Minch (above) and Chencha (below). Note that: (1) For

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all experiments, the Sowing date was Feb 01 during the Belg season; (2) Fertilizer was not applied in the

model, except the fertilized experiment (pink colored plot on the right side of this figure). ...................... 26

Figure 15: GECROS Modelled Potato Fresh Matter Yield (qt ha-1

) for the Gamo Highlands from 2001 to

2010. The Model resolution is 10kmx10km for 82kmx82km area. The potato was sowed on Feb 01

during the Belg season. ............................................................................................................................... 28

Figure 16: GECROS Modelled Potato Yield Map for the Gamo Highlands for Sensitivity Experiments:

Control (Feb 01), sowing dates: Jan 15, Feb 15, and March 01. The Model resolution is 10kmx10km for

82kmx82km area. All the Experiments were conducted during the Belg season, except experiment Jan 15.

.................................................................................................................................................................... 29

Figure 17: GECROS Model Sensitivity Experiments: Potato Fresh-Matter Yield Anomaly from control

experiment (sowed on Feb 01) for the Gamo Highlands. The Sensitivity Experiments: Rainfall (10%

added), TMax + 50C, TMin + 5

0C, and Fertilized Agriculture. The Model resolution is 10kmx10km for

82kmx82km area. All the Experiments were conducted during the Belg season (sowing date: date Feb

01). .............................................................................................................................................................. 31

Figure 18: GECROS Model Sensitivity Experiments: Number of taken for Harvest Anomaly from control

experiment (sowed on Feb 01) for the Gamo Highlands. The Sensitivity Experiments: Rainfall (10%

added), TMax + 50C, TMin + 5

0C, and Fertilized Agriculture. The Model resolution is 10kmx10km for

82kmx82km area. All the Experiments were conducted on date Feb 01 (during the Belg season). ........... 32

LIST OF TABLES Table 1: The Ethiopian Agricultural and Climatic Seasons Classifications. ................................................ 7

Table 2: WRF Model Version 3.1.1 physics configurations ....................................................................... 10

Table 3: Models Experimental Design ........................................................................................................ 13

Table 4: Classification of the Standard Precipitation Index (SPI): The SPI thresholds and categories. ..... 14

Table 5: Statistical WRF model Validation ................................................................................................ 15

Table 6: SPI for Arba Minch Station. The seasonal classification is based on the drought severity

classification for sowing periods. For the classification, the main sowing season, Belg, was mainly

focused. ....................................................................................................................................................... 17

Table 7: SPI for the Chencha station. The seasonal classification is based on the drought severity

classification for sowing periods. For the classification, the main sowing season, Belg, was mainly

focused. ....................................................................................................................................................... 18

Table 8: The Principal Components (PC) for the weather variables that explain the modelled potato fresh-

matter yield. The meteorological variables considered were Chencha station for the year 2009. .............. 21

Table 9: GECROS modelled fresh-matter potato yield validation for Chencha Woreda from 2001 to 2010.

.................................................................................................................................................................... 21

Table 10: Statistical summary of GECROS modelled potato fresh-matter yield (qt ha-1

) for Arba Minch

town from 2001-2010. The sensitivity experiments: Sowing date experiments (Jan-15, Feb-01 [control],

Feb-15, and Mar-01), Weather experiments (TMax+50C, TMin+5

0C, and RF+0.1RF), and experiment

with fertilizer application. ........................................................................................................................... 23


) for Chencha from

2001-2010. The sensitivity experiments: Sowing date experiments (Jan-15, Feb-01 [control], Feb-15, and

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Mar-01), Weather experiments (TMax+50C, TMin+5

0C, and RF+0.1RF), and experiment with fertilizer

application. .................................................................................................................................................. 23

Table 12: GECROS modelled potato fresh-matter yield statistical Summary for the year 2009, the Gamo

Highlands. The sensitivity experiments: Sowing date experiments (Jan-15, Feb-01 [cont.], Feb-15, and


0C, and RF+0.1RF), and fertilized agriculture. ...... 30

LIST OF APPENDICES Appendix 1: WRF Model Version 3.1.1: namelist.input and namelist.wps parameters ............................ 42

Appendix 2: Crop parameterization: Specific genotype ............................................................................. 42

Appendix 3: Management Options ............................................................................................................. 43

Appendix 4: Model Constants .................................................................................................................... 43

Appendix 5: Crop Specific Parameters ....................................................................................................... 43

Appendix 6: Genotype Specific Parameters ............................................................................................... 44

Appendix 7: Soil Model Parameterization .................................................................................................. 44

Appendix 8: Atmospheric Parameters ........................................................................................................ 45

Appendix 9: User-defined irrigation and fertilizer applications ................................................................. 45

Appendix 10: GECROS modelled potato yield map: Sensitivity experiment, sowing date 15 Jan ............ 46

Appendix 11: GECROS modelled potato yield map: Sensitivity experiment, sowing date 15 Feb ........... 47

Appendix 12: GECROS modelled potato yield map: Sensitivity experiment, sowing date 01 Mar .......... 49

Appendix 13: GECROS modelled potato yield map: Sensitivity experiment, TMax + 50C ...................... 51

Appendix 14: GECROS modelled potato yield map: Sensitivity experiment, TMin + 50C ....................... 52

Appendix 15: GECROS modelled potato yield map: Sensitivity experiment, RF + 0.1RF ....................... 54

Appendix 16: GECROS modelled potato yield map: Sensitivity experiment, Fertilized Agriculture ........ 55

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LIST OF ABBREVIATIONS AND ACRONYMS

ASL Above Sea Level

Bega October to January

Belg February to May

CSA Central Statistical Agency (of Ethiopia) DAP Di-Ammonium Phosphate (fertilizer)

ECMWF European Center for Medium-Range Weather Forecasts Re-

Analysis ENSO El Niño Southern Oscillation

GDP Gross Domestic Product

GECROS Genotype-by-Environment interaction on CROp growth

Simulator model

Gn Surface energy balance

IOD Indian Ocean Dipole

ITCZ Inter-tropical Convergence Zone

Kiremt June to September

MAQ Meteorology and Air Quality

MBE Mean Bias Error

Meher Agricultural seasonal naming for Kiremt climatic season NCAR National Center for Atmospheric research

NDMC U.S National Drought Mitigation Center

NMA National Meteorological Agency (of Ethiopia) PBL Planetary Boundary Layer

PCA Principal Component Analysis

PCi Principal Component i (i = 1, 2, or 3)

Qn Net radiation budget

qt Quintal (a mass weighs 100 kg) RCP8.5 Representative Concentration Pathways 8.5

RMSE Root Mean Square Error

SNNPRs Southern Nations, Nationalities, People’s Regional State SPI Standardized Precipitation Index

SSA Sub-Saharan Africa

WRF Weather Research and Forecasting

WUR Wageningen University and Research Center YSU Yonsei University scheme

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1 INTRODUCTION

Potato (Solanum tuberosum L.) is one of the fastest growing food crops in Sub Saharan Africa

(SSA). Its total production in some SSA countries more than doubled during the last fifteen years

(Haverkort et al., 2012, Tesfaye, 2010). In Ethiopia, potato is one of the major root and tuber

crops. The crop is grown on nearly 164,000 hectare (ha), and cultivated by 1.3 million

smallholder farmers (Figure 1) (Tufa, 2013, IPC, 2009, Haverkort et al., 2012). Agriculture

accounts 45% and 80% for Gross Domestic Product (GDP), and employment, respectively.

Potato can grow in highlands from 1500 to 3200 m Above Sea Level (ASL) with annual rainfall

between 600 to 1200 mm. This condition comprises about 90% of the Ethiopian population lives

and 70% of the total arable land of the country (Tufa, 2013). In the Southern Nations,

Nationalities, and Peoples Regional state (SNNPRs), Ethiopia, potato production area has

reached 8,978 ha during the main harvesting season (Mazengia et al., 2013). The Gamo

highlands, in the Gamo Gofa zone administrative zone, are one of the potential potato growing

areas. The highlands are the main focus of this study.

Figure 1: Potato Production Areas and Average Yields in Ethiopia. Adapted from (IPC, 2009, Hirpa et al.,

2010).

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Potato is mainly growing in two seasons in Ethiopia. Belg (February to May), and Meher (June

to October) agricultural seasons (Tufa, 2013, Mazengia et al., 2013). In Chencha, one of the

Woredas in the Gamo highlands, potato is sown during Belg (February), and Meher (mid-August

to mid-September) (Mazengia et al., 2013). There is more potato production during Meher than

Belg, as the former season with more disease pressure (Tufa, 2013, Haverkort et al., 2012). Of

the total cultivated land under potato, nearly 77% is cultivated during Belg season (Tufa, 2013).

Crop dynamics strongly depend on the weather conditions. These conditions are highly

dependent on the year climatology and spatial variability of meteorological factors. So, it is

interesting to investigate how weather modifications lead to changes in the crop yield. This

research will investigate the crop dynamics in the Gamo highlands, Ethiopia during the period

2001-2010. The methodology is based 10 year period that has been produced by the Weather

Research Forecasting (WRF) model employing a high resolution (2-km-spatial-resolution)

simulation. We use this high resolution due to the complex topography of the region under study

(Jiménez et al., 2010, Jiménez and Dudhia, 2012). The meteorological output of the model

would be used as an input for the Genotype-by-Environment interaction on CROp growth

Simulator (GECROS) model to estimate the crop yield (Yin and Struik, 2010, Xinyou and Van

Laar, 2005). GECROS is a generic ecophysiological model that forecasts crop growth and

development as affected by genetic characteristics, climatic, and edaphic environmental variables

(Khan et al., 2013). A combination of an atmospheric (e.g., WRF, etc.) and a crop (e.g.,

GECROS, etc.) models has been used in other regions (Combe et al., 2014, Warrach-Sagi et al.,

2011, Challinor et al., 2003, Challinor et al., 2005), perhaps not in Ethiopia. Furthermore, we

develop a high (10x10 km2) resolution crop yield and related variables map for the area under

study. This study is aimed at identify potential sowing areas and best date for planting potato

from weather and seasonal climate perspectives in the Gamo highlands.

1.1 Objectives

The general objective of the study was to identify potential sowing areas and best date for

planting potato from weather and seasonal climate perspectives. The general objective is

classified into the following specific objectives.

(1) To identify meteorological extreme (drought/wet) seasons/years for the period 2001-2010.

(2) To predict crop yield using the GECROS model using the meteorological variables

calculated from WRF.

(3) To generate potato yield map for the Gamo highlands, Ethiopia.

(4) To conduct model (in WRF and GECROS) sensitivity experiments.

1.2 Research Questions

(1) What is the capability of WRF model in reproducing key meteorological variables?

(2) Does the GECROS model identify potential potato cropping locations and calendar for the

Gamo highlands?

(3) What is the impact of changing climate scenarios for crop yield

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1.3 Hypothesis

The hypothesis of the research is formulated as: extreme (drought/wet) meteorological

conditions cause decline in potato yield.

1.4 Research Approach

To achieve the thesis objectives, the following approach has followed. Ten-years WRF model

run was made. From the model output, atmospheric, surface energy variables, and soil

parameters were extracted. The validated WRF model outputs were taken as input for crop

model, GECROS. A number of model experiments (basic and sensitivity) were designed for both

models. Finally, from crop yield a reasonable potato sowing date and cropping place were

identified. The thesis approach is explained as follows (Figure 2).

Figure 2: Steps followed to achieve thesis objectives

WRF

(10 yrs model run)

GECROS

Yield, validation

RB, SEB

(Global rad, etc.)

MET Obs

(Temp, RF, etc.)

MET

(RF, temp, etc.)

Soil

(Types, parameter)

WRF

(model validation & Expts )

GECROS

Model Experiments

Yield Obs

(Potato yield)

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2 METHODS

2.1 The Study Area

The Gamo highlands are geographically located in Ethiopia, the Southern Nations Nationalities

and People’s Regional state (SNNPRs), Gamo Gofa Zone on the Western shores of the Rift

Valley lakes Abaya and Chamo (Figure 3-4). Part of the Gughe mountain range, the Gamo

highlands run northeast to southwest are nearly 100 km long and 30 km wide (060

02-27’N and

370 10-37’E), and reach above 4,000 meters above sea level. The terrain is characterized by

slopping and rugged in the north and plain land in the southern section (Samberg et al., 2010,

Bayu, 2012, Samberg et al., 2013). The location is home for nearly 1 million Gamo people,

speaking an Omotic language. Dry evergreen mountain forest, mixed deciduous woodlands, and

alpine grasslands are the native vegetations in the area (Samberg et al., 2013). Agricultural

production in the Gamo highlands is almost entirely composed of subsistence farms of overall

less than 0.25 hectare per household (Bayu, 2012, Samberg et al., 2013). The high elevations,

rugged terrain, high population densities, and absecence of all-weather roads have precluded the

establishment of large-scale or commerical agriculture. The traditional Gamo homestead is

ringed by the household’s enset plantation, vegetables, root crops, a mixture of tree, and

agroforestry trees (Samberg et al., 2013).

Figure 3: Map of the mother WRF model domain. The domain centered in Ethiopia and covers the

neighboring countries and Northwest part of the Indian Ocean. The ‘yellow’, ‘white’, and ‘green’ colored

subplots show the nested domains ‘d02’, ‘d03’, and ‘d04’, respectively. The detailed topographic and Woreda

map of ‘d04’ shown below.

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The Gamo Gofa zone (which contains the Gamo highlands) has a total area of 12,581.4 square

kilometer and consists of 15 Woredas namely: Arba Minch Zuria, Mirab Abaya, Kucha, Kamba,

Dita, Zala, Melokoza, Chencha, Dara Malo, Bonke, Uba Debretsehay, Boreda, Geze Gofa, Oyda,

and Danba Gofa (CSA, 2007). For this report, some of the Woredas of the zone were not

included, while part of Derashe and Amaro Woredas included from the Segen

peoples’adminsitrative zone (Figure 4).

Figure 4: The topographic and Woreda map on the WRF model domain 04. The inner nested domain

centered at Arba Minch (A.M.) town covers 82km in x and y directions. The model resolution is 2kmx2km.

The WRF model land use map indicated that Lake Abaya and Chamo are the major land

use/covers. The land use/cover is in the order: Lakes > Cropland/woodland/mixed dry

land/irrigated cropland and pasture > evergreen broadleaf forest > grass land/shrub land (Figure

5).

6

Figure 5: WRF Model Land Use Map of Model Domain 04. The inner nested domain centered at Arba Minch

(A.M.) town covers 82km in x and y directions. The model resolution is 2kmx2km.

2.2 The Ethiopian Climate and Climatic Zones

Ethiopia is geographically located between 30 25’ to 18

0 North, and 33

0 to 48

0 East, in the Horn

of Africa. The Indian and Atlantic oceans are the sources of moisture for nearly all rainy seasons.

During the months of February to May (Belg season), Southeasterly winds transport moisture

from the Indian Ocean to the most areas of the country. During the months of June to September

(Kirmet season), southwesterly (from Atlantic Ocean), and southeasterly (from Indian Ocean)

winds transport moisture to the country. This depicts the main rainy season in most of the

country. During this season, moisture gradually transports into the country, as the Inter-Tropical

Convergence Zone (ITCZ) north-south movement with the equatorial trough (Debele, 2009,

Minda, 2014, NMA, 2011, Degefu, 1987). Overall, rainfall decreases as one move from South to

North. But, the rainfall trend is highly influenced by the topography of the country.

Unlike the two seasons in the most tropical regions, Ethiopia has three seasons as result of the

terrain characteristics and geographical location. According to the National Meteorological

Agency (NMA), Ethiopia has three seasons namely: Belg (small rainy season in most of the

country), Kiremt (main rainy season in most of the country), and Bega (dry season) (Degefu,

1987, NMA, 2011) (Table 1). The Southern (which includes the Gamo highlands) and

7

Southwestern parts follow a bimodal rainfall distribution during March to May and September to

October.

Belg is the period from February to May (Figure 6). It denotes the major rainy season for

Southern sector while it is the sort rainy season over northern Ethiopia. The season’s rain is

generated by the development of high over Arabians Sea, generation and propagation of frontal

system, and the North-South prolonged trough coupled with easterly wave develop along the

Horn of Africa (Debele, 2009). Belg rain is significant from agricultural and hydrologic

perspectives. Belg rain accounts for nearly 5 to 15% of the national food crop. It is the main

season for potato cropping in Southern Ethiopia. Hence, this study is focused in the Belg season.

A delay in the Belg rain usually results in the death of thousands of animals (Degefu, 1987).

Agriculturally, the Belg named as Belg cropping.

Kiremt is the period from June to September (Figure 6). Kiremt is the main rainy season in most

of the country and mostly characterized as the South-West monsoon-type. The season is mainly

modulated by: synoptic-scale systems; the presence of El Niño Southern Oscillation (ENSO)

events, along with the formation of the Indian Ocean Dipole (IOD); and the ITCZ, which plays a

great role in controlling the spatial and temporal rainfall coverage throughout the year (Korecha

et al., 2010). Nearly 85 to 95% of the national food crop produced during Kiremt season

(Degefu, 1987, NMA, 2011). Agriculturally, the Kiremt named as Meher cropping.

Bega is season is the period from October to January. The characterized by dry/cool condition

dominated due to the dry air mass origins that prevail over North, East, and central Ethiopia. In

South, Southeast, and Southwest get the second short rain during September to October (Debele,

2009) (Figure 6). Agriculturally, Bega is characterized as a harvest season (Table 1).

The maximum temperature, minimum temperature, wind speed, relative humidity, sunshine

hours, and the total yearly rainfall climatology of Arba Minch are 30.5 0C, 17.5

0C, 0.65 ms

-1,

57%, 7.6 hr, and 898 mm, respectively. Of the 898 mm annual rainfall, 44%, 28%, and 28%

rains during Belg, Kiremt, and Bega seasons, respectively (Minda, 2009). On the other hand,

Chencha gets 1409 mm total annual rainfall, of which 36 %, 35%, and 30% rains during Belg,

Kiremt, and Bega seasons, respectively.

Table 1: The Ethiopian Agricultural and Climatic Seasons Classifications.

Seasons Classification

Months Climatic Agricultural Rainfall Observations

Feb, Mar, Apr, May Belg Belg Main rains

Jun, Jul, Aug, Sep Kiremt Meher Small rains

Oct, Nov, Dec, Jan Bega Harvest period Dry season

8

Figure 6: The Monthly Mean Climate of Arba Minch (from the year 1987 to 2013) and Chencha (from 1957

to 2012). Daily weather data is obtained from Arba Minch Weather station.

2.3 Agro-meteorological Models: WRF and GECROS

For the modelling work, we used a state-of-the-art, high temporal and spatial resolution, flexible

weather model coupled to an advanced crop yield model sensitive to extreme weather situations.

The Weather Research and Forecasting (WRF), version 3.1, model was used for weather

forecasting (Peckham et al., 2012). Temporal resolution of one hour weather forecast for 10

years period was implemented on 2km-by-2km spatial resolution (model domain 04) (Figure 3-

4). GECROS, relatively new crop systems dynamics model was implemented to model crop

Belg Belg

Belg

Belg

Kiremt

Kiremt

9

yield and related outputs (Yin and Struik, 2010, Xinyou and Van Laar, 2005). WRF outputs such

as meteorological variables, soil category, and latitude (to calculate day-length) were applied as

input to the GECROS crop model (Figure 7).

Figure 7: Weather-yield forecasting outline applied in this report. Variables such as: humidity (obtained

from daily rainfall), max and min temperatures, global radiation, wind speed, day length (calculated from

latitude), and soil category. These WRF variables were implemented as in input for the crop model,

GECROS. In addition, crop management options, atmospheric CO2 (380 ppm), etc. were considered as

GECROS inputs.

2.3.1 WRF Model setup

The Weather Research and Forecasting/Chemistry ( ) model is a numerical weather

prediction and an atmospheric simulation system designed for both research and operational

applications (Skamarock et al., 2005). It is the state-of-art that gives a forecast of atmospheric

chemistry beyond what is used by the today’s existing meteorological forecast models (Peckham

et al., 2012).

WRF is a fully compressible, Euler non-hydrostatic (with a hydrostatic option) model. Its

vertical coordinate is a terrain-following hydrostatic sigma (σ) pressure coordinate. This can

possibly make it suitable to study the meteorological dynamics of mountainous landscapes such

as Ethiopia. WRF uses higher order numerics: Runge-Kutta 2nd

and 3rd

order advection schemes

in both horizontal and vertical directions. It uses a time-split small step for acoustic and gravity-

wave modes. The dynamics part conserves scalar variables (Steeneveld et al., 2013, Torres,

2013, Minda, 2014).

Ten years (2001 to 2010) WRF run was made. Model initialization (boundary and initial

conditions) was obtained from the European Center for Medium-Range Weather Forecasts

(ECMWF) Re-Analysis (Jiménez et al., 2011, Wanjun, 2013, Jiménez et al., 2010). Series of 48

hours runs were made in which the first 24 hours were considered as model spin-up period. WRF

WRF

Rainfall

Temperature (max & min)

Global radiation

Wind Speed

Day length (latitude)

Soil catagory Crop Management

(irrigation, fertilizer)

Atmospheric variable

(CO2)

GECROS Crop Yield

10

model was initialized as cold start at 0000 UTC of each day; updating boundary conditions every

six hours; and recording data every hour (Jiménez et al., 2010).

To understand transport from larger area, the mother domain (d01) was designed to include

Ethiopia, the neighboring countries, and part of the Indian Ocean. The inner nested domain, d04,

was centered Arba Minch and with very high model resolution (2kmx2km). The model’s

parameters (the dynamical core, physics configuration, etc.) discussed (Table 2), a detailed one

in (Appendix 1).

Table 2: WRF Model Version 3.1.1 physics configurations

Physics configuration Scheme specifications

Microphysics option WRF Single-Moment Six-class (WSM6) scheme (Hong and

Lim, 2006)

Long wave radiation option rrtm scheme (Mlawer et al., 1997)

Shortwave radiation option Dudhia scheme (Dudhia, 1989)

Surface-layer option Monin-Obukhov scheme (Monin and Obukhov, 1954)

Land-surface option Thermal diffusion scheme (Dudhia, 1996)

Planetary boundary-layer (PBL) option YSU scheme (Hong et al., 2006)

Cumulus option Kain-Fritsch scheme (Kain, 2004), and no Cu physics

2.3.2 The GECROS Model

2.3.2.1 Model Overview

GECROS is a relatively new, crop systems dynamics model developed by (Yin and Struik,

2010). Genotype-by-Environment interaction on CROp growth Simulator (GECROS) model was

used for examining responses of biomass and protein production in arable crops to both

environmental and genotypic characteristics (Xinyou and Van Laar, 2005). The model uses new

algorisms to summarize current knowledge of individual physiological processes, their

interactions and feedback mechanisms. GECROS maintains balance between model structure,

high computational efficiency, and accurate model output. The major features of the model are:

(1) direct coupling of components for leaf nitrogen, stomatal conductance, photosynthesis,

transpiration, and senescence; (2) carbon-nitrogen interaction to determine root-shoot

partitioning and sink strengths to determine within-shoot partitioning; (3) simulating seed protein

production in relation to crop nitrogen budget; (4) applicable to the world’s most important

agricultural crops (e.g., Wheat, Barley, Rice, Maize, Soybean, Sugar beet, and Potato) and any

production situations free of weeds, pests, and diseases. The conceptual flow chart for the

GECROS model is given in (Figure 8). The model’s source code is programmed in FORTRAN

Simulation Translator (Xinyou and Van Laar, 2005).

11

Figure 8: Relational diagram of the GECROS growth simulation model using standard symbols (boxes-state

variables, valves-rate variables, ellipses/circles-intermediate variables, small crossed circles-environmental

variables, full-line arrows-material flows, and dashed-line arrows-information flows). Note that: model inputs

in blue color were inputs supplied for the model experiments. The reader is referred (Xinyou and Van Laar,

2005) for further explanation.

2.3.2.2 GECROS Model Setup: input Parameters

GECROS model runs with time step of one day. Initial values of all the state variables (boxes in

Figure 8) have to be set. The required daily meteorological model inputs are: maximum and

minimum air temperatures (0C), global radiation (kJ.m

-2.day

-1), vapour pressure (kilo Pascal),

precipitation (mm.day-1

), and mean wind speed (m.s-1

). Latitude of the location is needed to

calculate the day length. In addition, crop management options such as daily water supply

(irrigation) and nitrogen supply (fertilization) for crop uptake can be estimated; once the growth

model is coupled with a process-based soil model. This coupled model can then be used for

examining crop production in response to physical environment conditions, edaphic variables

(soil characteristics), and crop management options (amount and timing of irrigation,

fertilization, crop population density, and timing of sowing) designed (Xinyou and Van Laar,

2005). The GECROS model input parameters implemented in this work were given (Appendix 2

to Appendix 9).

12

2.4 Design of Model Experiments

Control and sensitivity experiments were designed for both GECROS and WRF (Table 3). The

crop yield experiments were done to search for ideal potato sowing date mainly during drought

meteorological season. The dates were selected from potato sowing calendar in Gamo highlands

area. Currently, the calendar in use is from January 09 to March 08 during the Belg harvest

season. Off course, potato has harvested in both Belg (starts nearly mid-January to February) and

Meher (starts mid-August) harvest seasons (AMRDO, 2005). For this work, the Belg has been

selected as it is the main season for potato cropping in Gamo highlands. Belg season is the main

potato production season due to the low potato disease (for example, the late blight infection),

and favorable market conditions (Haverkort et al., 2012). Sowing date experiments were selected

from January 15 to March 01, in two weeks interval, for a drought Belg season in 2004 (Table 3).

The sowing date, February 01, was selected for control model run, as the month is recommended

as Belg sowing date in Chencha Woreda (Mazengia et al., 2013).

Precipitation, maximum and minimum temperatures model sensitivity experiments were

designed in WRF (Table 3). The Intercontinental Panel for Climate Change (IPCC) recent report

on climate scenarios showed that enhanced rainfall during the short rainy season (Belg) for East

Africa as a result of either anomalous low-level easterly flow of moist air into the African

continent, or a weakening of the low-level westerly flow over the northern Indian Ocean that

transports moisture away from the continent (Stocker et al., 2013). The 10% additional rainfall

experiment was designed in order to consider the precipitation future scenario for East Africa.

Representative Concentration Pathways 8.5 (RCP8.5) temperature scenario indicate that the

global mean surface temperatures for the end of the 21st century (2081-2100) relative to 1986-

2005 will likely be in the ranges 2.6 to 4.8 0C (Stocker et al., 2013). The temperature model

sensitivity experiments were designed so as to integrate an increased surface temperature due to

global warming. The designed model experiments were shown (Table 3). From the 2kmx2km

WRF model output, a high resolution (10kmx10km), yield and related terms maps were

generated.

For crop yield and related terms mapping, specific year (for example 2004, a drought year) might

be selected. However, for station-wise (for Arba Minch and Chencha) yield output, all the years

were considered. Station, Arba Minch, is selected due to its rich climate data mainly for

validating WRF’s outputs. But, potato doesn’t grow in Arba Minch in reality. Chencha is one of

the potential potato growing areas in the Gamo highlands. We specifically focused to study the

seasonal climate variations and yield outputs of the two contrasting areas climatologically.

13

Table 3: Models Experimental Design

Changes in the Models

Run Model Run WRF GECROS Assumption/s

1 Control

(Feb 01)

Default model outputs: MET

radiation, surface variables,

and soil types. Most of the

outputs were validated with

observations.

The model run was conducted using

the WRF inputs. Model parameters

were calibrated based on literature,

yield observations, and land

management options (fertilizer use,

irrigation, etc.) in Arba Minch and

Chencha.

The main Potato sowing

season, Belg, was selected.

The Belg sowing calendar is

from 09 Jan to 08 Mar. Feb 01

was taken as control

experiment.

2 Jan 15 Default model outputs Sowing date was Jan 15 Identify the optimal sowing

date climatologically

3 Feb 15 Default model outputs Sowing date was Feb 15 Identify the optimal sowing


4 Mar 01 Default model outputs Sowing date was Mar 01 Identify the optimal sowing


5 Tmax+50C Maximum temperature +

50C

Default An enhanced warming due to

increased greenhouse effect

6 Tmin+50C Minimum temperature + 5

0C Default An enhanced warming due to

increased GH effect

7 RF+0.1RF Precipitation + 0.1x

Precipitation

Default 10% increased precipitation

scenario due to enhanced GH

effect

8 Fertilized Default model outputs 45 kg/ha of ammonium-N (4.5

gN.m-2

.day-1

on the sowing date)

and 100 kg/ha of nitrate-N (5 gN.m-

2.day

-1 on the sowing and after the

45th

from the sowing date).

Recommended fertilizer rate

for potato sowing (Tufa, 2013,

Hirpa et al., 2012).

2.5 Data Analysis

2.5.1 Data Analysis: Meteorological

Two weather stations were selected for validating WRF outputs. Arba Minch weather station is

one of the 24 synoptic stations in Ethiopia with international code of 63500 (NMA, 2013,

NCAR/RAP, 2013, Minda, 2014). Climate data (maximum and minimum temperatures, rainfall,

wind, relative humidity, sunshine hours) were obtained from 1987 to 2013 (Figure 6). Chencha

weather station is categorized as ordinary station by the National Meteorological Agency

(NMA), Ethiopia*. The data obtained was rainfall, and much of the data was missing. The WRF

model outputs were validated statistically (Section 3.1).

WRF’s weather outputs were used to identify extreme weather events. Belg and Kiremt seasons

were selected. These seasons selected since they are Belg and Meher potato cropping. Drought

years/seasons were given due attention as rain fed agriculture is sensitive to extreme weather

events such as drought. A number of statistical techniques, atmospheric and surface indicators,

*: http://www.ethiomet.gov.et/stations/regional_information/4#Ordinary (accessed on 26 June 2014)

http://www.ethiomet.gov.et/stations/regional_information/4#Ordinary

14

were implemented to identify meteorological drought from model outputs. The atmospheric

indicators are rainfall and maximum temperature. The Standardized Precipitation Index (SPI)

was applied Equation (2.1). SPI has gained wider acceptance in the detection and estimation of

the magnitude, intensity, and spatial extent of drought (Patel et al., 2007, Raja et al., 2014,

McKee et al., 1993, Guttman, 1999). The SPI thresholds and categories discussed (Table 4). The

maximum temperature was used to identify heat waves (Fouillet et al., 2006). The positive

extreme deviations from the mean, for sensible and latent heat fluxes, were considered as surface

drought conditions.

(2.1)

Where SPIij is the SPI of the ith

month at jth

time scale, Xij is the total precipitation for the ith

month at jth

time scale, and µij and σij are long-term mean and standard deviation, respectively,

associated with the ith

month at jth

time scale.

Table 4: Classification of the Standard Precipitation Index (SPI): The SPI thresholds and categories.

SPI Threshold Category

SPI ≤ -2 Extremely dry

-1.9 ≤ SPI ≤ -1.6 Severely dry

-1.5 ≤ SPI ≤ -1.3 Moderately dry

-1.2 ≤ SPI ≤ -0.5 Dry

-0.4 ≤ SPI ≤ 0.99 Near normal

1.0 ≤ SPI ≤ 1.49 Wet

1.5 ≤ SPI ≤ 1.99 Moderately wet

SPI ≥ 2.0 Extremely wet

2.5.2 Data Analysis: Crop yield

Potato yield and related terms (e.g., number of days taken for harvest) were modelled for Arba

Minch and Chencha stations. The results expected to indicate the suitable sowing date mainly

during drought years. High resolution (10km x 10km) yield maps were plotted for the Gamo

Highlands. The outputs were used to identify the potential potato cropping locations,

climatologically. The model sensitivity experiments designed were tabulated (Table 3).

2.5.3 The Principal Component Analysis: Weather Vs Yield

The Principal Component Analysis (PCA) is a mathematical technique that transforms a number

of (possibly) correlated variables into a (smaller) number of uncorrelated variables called

principal components (PCs) (Pires et al., 2008b, Pires et al., 2008a). PCA makes PCs, which are

uncorrelated, and orthogonal to each other. The first PC (PC1) explains the largest fraction of the

original data variability, and each succeeding component accounts for as much of the remaining

variability as possible (Minda, 2009, Minda, 2014). We have applied the PCA to categorize

weather variables with similar impact on crop yield.

15

3 RESULTS

3.1 WRF Model Outputs

3.1.1 Model Performance

Meteorological variables such as: maximum and minimum temperatures, rainfall, and wind

speed extracted from 10 years WRF model outputs. The outputs validated. Statistical techniques

such as: the Mean Bias Error ( ), the Root Mean Square Error ( ), and the coefficient

of correlation (r) used for model validation (Willmott, 1982, Ott and Longnecker, 2001) as

explained in Equations (3.1) to (3.3) and (Figure 9).

∑

(3.1)

√[ ∑

]

(3.2)

∑

√∑ ∑

(3.3)

Where N, Oi, Mi, , were number of observations, observed, modelled, mean of the

observed, mean of the modelled variables, respectively.

Table 5: Statistical WRF model Validation

Model Validation: Statistical terms

Met Variable r MBE RMSE

Max Temp 0.50 -3.92 3.95

Min Temp 0.43 -0.13 0.47

Wind speed 0.19 0.20 0.23

RF 0.48 267.20 373.97 Note that r is unitless and all the rest statistical terms take the unit of the respective variable.

The coefficient of correlation (r) showed that model outputs were not well correlated with

observations. The observed precipitation at Chencha weather station was best correlated (r =

0.72) with WRF prediction. On the other hand, wind speed at Arba Minch was least represented

(r = 0.19). The MBE and RMSE statistical analysis showed that WRF well represented

temperatures and wind speed observations. The WRF model overestimated rainfall (Table 5).

There might be some data quality problems in rainfall observations. Figure 9 showed plots of

max and min temperatures, wind speed, and precipitation for Arba Minch and Chencha weather

stations. The figure indicated that the year 2009 categorized as climatologically outlier for both

Arba Minch and Chencha stations. During this year, both observations and model analysis

16

showed maximum and minimum temperatures higher than the climatology. Moreover, the

rainfall showed less than the normal during the year. However, in this work, our main focus was

the seasonal variabilities, mainly the Belg season. This is selected as it is potato sowing period in

the Gamo highlands. Detailed seasonal-climatology analysis will be discussed in Section (3.1.2).

(a) (b)

Figure 9: Climatology for maximum and minimum temperatures, rainfall, and wind speed for observed

(Arba Minch) and WRF model data. Stations: Arba Minch (a) and Chencha (b). The dash line in a plot shows

39 years (1974-2013) of observations of the climate variable. Observation data obtained from Arba Minch

weather station.

3.1.2 Seasonal-Inter-Climatology: Meteorological Extremes

We have discused seasonal-inter-climatology analysis (10 years WRF model output). This data

set is vital in our areas in which weather observations are so limited or missing. From the 10

years model output, seasonal meteorological extremes were identified. These extreme events are

droughts, heat waves, wet, and normal seasons during Belg. Our main interst were drought/heat

wave periods in the given season. We identified such seasons as they severely affect agriculture,

and hence needed to optimize yield.

17

The U.S National Drought Mitigation Center (NDMC, 2014) defines drought as a normal,

recurrent of climate. The NDMC further explains drought in two definition domains: conceptual

and operational. The conceptual definition states that: drought is a protracted period of deficient

precipitation resulting in extensive damage to crops, resulting in loss of yield. The operational

definition is based on the onset, severity, and end of droughts. It is based on mathematical

indices as calculated from his historical climate average (often based on a 30-year period of

record) (NDMC, 2014). Tsegaye Tadesse, the NDMC’s climatologist, defines droughts and

floods as droughts and floods are mainly caused by large-scale ocean-atmosphere-land

circulation patterns (Smith, 2014). (NDMC, 2014) and (Van der Molen et al., 2011) categorized

drought definition in terms of approaches to measuring drought: agricultural (affecting yield),

meteorological (precipitation), hydrological (run-off, water levels), ecological (ecosystem

functioning), and socioeconomic (demand and supply).

In this study we classified drought based on meteorological variables (precipitation, minimum,

and maximum temperatures), radiation (net radiation) and surface energy (sensible, latent,

ground heat fluxes) balance analysis. The seasonal, Standard Precipitation Index (SPI) was

applied to identify seasonal drought. Moreover, positive (relatively higher values) anomalies

from climatology for minimum and maximum temperatures, sensible and latent heat fluxes

considered as indicator of drought.

Based on the SPI concept discussed (Section 2.5.1), and the SPI threshold (Table 4), seasonal

inter-climate variations were identified (Table 6-7). The SPI analysis showed that the Belg of

2004 and 2009 years categorized as dry years for both Arba Minch and Chencha stations. Belg of

the years 2003 and 2006 were moderately wet for Arba Minch. Similarly, the year 2010

categorized as extremely wet for the highland station, Chencha. The year 2002 was near normal

for both stations. We classified 2009, and 2002 as typical drought, and normal years,

respectively. The year 2009 was mainly selected for yield mapping comparisons for different

model sensitivity experiments.

Table 6: SPI for Arba Minch Station. The seasonal classification is based on the drought severity

classification for sowing periods. For the classification, the main sowing season, Belg, was mainly focused.

SPI seasons

Drought severity classification

Year Belg Kiremt Bega Belg Kiremt (Meher) Bega (Harvest season)

2001 -0.75 -0.44 1.92 Dry Near normal Moderately wet

2002 0.02 -0.84 1.13 Near normal Dry Wet

2003 1.97 1.12 0.09 Moderately wet Wet Near normal

2004 -0.88 -1.04 0.58 Dry† Dry Near normal

2005 -0.58 -0.67 -0.93 Dry Dry Dry

2006 1.72 0.01 0.54 Moderately wet Near normal Near normal

2007 -0.32 2.26 -1.55 Near normal Extremely wet Severely dry

† : The SPI threshold values were given in Table 4.

18

SPI seasons



2008 -0.83 0.11 -0.43 Dry Near normal Near normal

2009 -0.73 -0.98 -0.83 Dry Dry Dry

2010 0.38 0.47 -0.52 Near normal Near normal Dry

Table 7: SPI for the Chencha station. The seasonal classification is based on the drought severity

classification for sowing periods. For the classification, the main sowing season, Belg, was mainly focused.

SPI seasons



2001 0.62 1.34 1.46 Near normal Wet Wet

2002 0.33 -0.69 0.32 Near normal Dry Near normal

2003 0.10 0.01 0.12 Near normal Near normal Near normal

2004 -0.80 -0.99 0.28 Dry‡ Dry Near normal

2005 -0.14 -0.82 -0.82 Near normal Dry Dry

2006 0.68 -0.74 0.57 Near normal Dry Near normal

2007 -0.91 2.04 -1.87 Dry Extremely wet Severely dry

2008 -1.17 0.84 -1.16 Dry Near normal Dry

2009 -0.98 -0.79 1.34 Dry Dry Wet

2010 2.29 -0.21 -0.23 Extremely wet Near normal Near normal

Extremely high surface temperatures are associated with droughts/heat waves (Black et al., 2004,

Barriopedro et al., 2011, Semenza et al., 1996, Jiménez et al., 2011). The droughts affect crop

yield. Seasonal maximum and minimum temperature anomalies were considered. The year 2009

was showed higher temperatures for Belg and Kiremt seasons for both Arba Minch and Chencha

weather stations. The rainfall anomaly for Belg and Kiremt seasons were typically showed lower

than the climatology for the year (Figure 10). These seasons, during 2009, were identified as dry

using the SPI threshold values (Table 6-8).

‡ : The SPI threshold values were given in Table 4.

19

(a) (b)

Figure 10: Ten years WRF outputs of seasonal (Belg and Kiremt) anomalies from seasonal climatology:

rainfall (top), maximum (middle), and minimum (bottom) temperatures for Arba Minch (a), and Chencha (b)

stations.

We considered sensible and latent heat fluxes as surface energy indictors of drought. The net

radiation flux (Qn) absorbed at the surface is given in Equation (3.4) (Wallace and Hobbs, 2006).

(3.4)

Where Sin, and Lin be the fluxes of downward shortwave and longwave solar radiation that reach

the Earth’s surface, respectively. On the other hand, Sout, and Lout are surface-reflected, and

surface emitted radiation fluxes of shortwave, and longwave, respectively. Equation (3.4) is re-

written and the net radiation flux was calculated as explained in (Su, 1999):

(3.5)

Where α is the albedo, ε is the surface emissivity, σ is the Stefan-Boltzmann constant (5.67x10-8

Wm-2

K-4

), Tsk is the surface skin temperature. The Sin, Lin, and Tsk were extracted from WRF

outputs as plotted in (Figure 11).

20

The surface energy is calculated as formulated in Equation (3.6) (Su, 1999, Wallace and Hobbs,

2006):

(3.6)

Where LH is the latent heat flux, SH is the sensible heat fluxes, and GS (positive downward, away

from the surface) is the conduction of heat down into the ground. The sensible and latent heat

fluxes at the surface were extracted from WRF outputs as plotted (Figure 11).

The Belg season anomalies from climatology for the radiation and surface heat fluxes budget

showed the following facts. The year 2010 an outlier for both Arba Minch and Chencha stations.

The same is true for sensible and latent heat fluxes (Figure 11). This also agrees with SPI

analysis (mainly for Chencha station) discussed (Table 7). However, the Belg of the year 2009

was not an outlier as indicated in SPI thresholds. The year’s Belg season was almost near normal

in terms of radiation and surface energy fluxes. On the other hand, the year 2001 for Arba Minch

could be taken as an outlier. Similarly, the years 2007 and 2008 for Chencha station.

(a) (b)

Figure 11: WRF modelled anomalies from climatology for radiation (Qn) and surface energy (GS, SH, LH)

budgets for Arba Minch (a) and Chencha (b) stations.

3.2 GECROS Model Outputs

3.2.1 Weather and Crop Productivitity: The Principal Component Analysis (PCA)

The Principal Component Analysis (PCA) statistical method was applied to explain the modelled

potato fresh-matter yield in terms of weather variables. The first three principal components

explained 90% of the variance. The analysis described nearly half (45%) of the variance (PC1)

explained by incoming solar radiation, minimum temperature, rainfall, and wind speed. The

21

second principal component (PC2) showed that more than one-third (35%) of the variability in

yield was explained by maximum temperature and vapor pressure deficit. In addition to the PC1,

wind speed explained nearly 10% of the yield variability (PC3) (Table 8). The PCA was applied

Chencha station for the year 2009.

Table 8: The Principal Components (PC) for the weather variables that explain the modelled potato fresh-

matter yield. The meteorological variables considered were Chencha station for the year 2009.

Principal Component (PC)

Weather variable PC1 PC2 PC3

Radiation -0.81 0.47 -0.01

TMAX -0.08 0.95 -0.08

TMIN 0.83 0.35 0.30

DVP 0.42 0.88 0.06

Wind -0.73 -0.01 0.68

RAIN 0.78 -0.28 0.26

Eigenvalue 2.67 2.11 0.63

Variability (%) 44.53 35.18 10.45

Cumulative Variance (%) 44.53 79.71 90.15

Note that Bolded numbers indicate the most influencing values of the corresponding PC

3.2.2 GECROS Model Performance

The GECROS modelled potato productivity was validated with the observation in Chencha

Woreda (Figure 12). The model validation statistical terms, the Mean Bias Error ( ), the

Root Mean Square Error ( ), and the coefficient of correlation (r) were applied as explained

in Equations (3.1) to (3.3).

Table 9: GECROS modelled fresh-matter potato yield validation for Chencha Woreda from 2001 to 2010.

GECROS Model Validation: Statistical terms

Station r MBE RMSE

Chencha 0.65 24.6 26.4

The GECROS model yield output indicated that coefficient of correlation (r) is satisfactory.

However, the model was overestimated yield (Table 9). This might be due to several reasons.

First, the average farm-land owned by individual farmer is less than half a hectare. This makes

yield estimation much challenging. Second, a single productivity for Woreda level cannot be

valid as the agro-ecological zone varies significantly. Third, the productivity estimation

conducted by the Woreda Agricultural office was not scientifically standardized. Forth, the entire

data management system in Woreda agricultural office is so poor. The time-series plot of the

observed and modelled potato fresh-matter yield was presented (Figure 12).

22

Figure 12: The observed and GECROS modelled potato productivity (qt ha-1

) for Chencha Woreda during

the years 2001 to 2010. Potato sowing date in GECROS was Feb 01 during the Belg season. Observation data

was missing for the years: 2001, 2002, 2004, and 2005 (GGAD, 2008)§.

Having validated GECROS model, we have conducted a number of model (both WRF and

GECROS) experiments (control and sensitivity analysis). The control experiment was named as

Feb01, which has taken the potato sowing date in GECROS. The other sowing date sensitivity

experiments were: Jan15, Feb15, and Mar01. The model experiments in WRF were: TMax +

50C, TMin + 5

0C, and RF + 0.1RF. The last experiment in crop management was: Fertilized. The

GECROS model analysis was conducted for point locations (Arba Minch and Chencha stations);

and areal (yield mapping for the Gamo Highlands). In reality, potato doesn’t grow in lowlands

(such as Arba Minch). We have interested to reveal climatological factors behind the fact.

Details of the model experiments design were discussed in Section (2.4).

3.2.3 GECROS Modelled Potato Fresh Matter Yield: Arba Minch and Chencha Stations

Comparing sowing date model experiments, the highest (11.7% more than the Cont. experiment)

yield was obtained for Feb 15 sowing during the Belg season. This period is nearly the start of

Belg rain (Figure 6). Both early and late sowing caused decline in productivity in Arba Minch.

Five 0C additional in maximum and minimum temperatures showed promising increase in yield.

However, an increase in rainfall did not showed decline in yield. Overall, for Arba Minch,

climate change assumptions are benefitting. A shift in crop management, addition of Nitrogen

fertilizers, showed the highest potato yield (284% additional yield to the control experiment)

(Table 10).

§ : Potato observation data was obtained from the Gamo Gofa Zone Agriculture Department

23


) for Arba Minch town

from 2001-2010. The sensitivity experiments: Sowing date experiments (Jan-15, Feb-01 [control], Feb-15, and



application.

GECROS Model Experiments productivity (qt ha-1

) from 2001-2010: Arba Minch Station

Summary

Stat

Jan-15 Feb-01

(Cont.)

Feb-15 Mar-01 TMax+50C TMin+5

0C RF+0.1RF Fertilized

Minimum 95.1 92.7 102.1 93.6 110.1 100.1 93.9 336.7

Maximum 111.8 122.1 126.3 145.9 166.4 120.7 123.4 439.0

Mean 101.9 104.8 111.5 113.8 131.8 109.8 104.9 384.5

SD 5.4 7.5 7.8 14.8 15.8 5.9 7.7 28.6

% increase

from Cont.

-3.2 0.0 11.7 -4.7 48.9 10.5 0.1 284.8

The Statistical summary of GECROS model output indicated that early (either before or at the

Belg rain begins) sowing was the best for potato cropping (Table 11). A slight increased yield

observed for Mar 01 sowing date is not advantageous as its high risk for plant diseases. Increase

in maximum temperature decreased yield. However, increases in minimum and rainfall were

slightly increased productivity. A shift from organic farming was increased productivity by

394% additional as compared to the control experiment. Yield in Arba Minch was strongly

variable as compared to Chencha. Application of fertilizer was the key crop management option

for improved productivity. Yield maps below give a better spatial outlook for productivity in the

Gamo highlands.


) for Chencha from

2001-2010. The sensitivity experiments: Sowing date experiments (Jan-15, Feb-01 [control], Feb-15, and Mar-

01), Weather experiments (TMax+50C, TMin+5


application.

GECROS Model Experiments productivity (qt ha-1

) from 2001-2010: Chencha Station

Summary

Stat

Jan-15 Feb-01

(Cont.)

Feb-15 Mar-01 TMax+50C TMin+5

0C RF+0.1RF Fertilized

Minimum 95.1 93.0 88.7 86.9 93.4 98.4 93.6 332.1

Maximum 112.6 114.7 103.3 107.1 108.9 115.8 112.2 568.8

Mean 102.4 103.2 96.6 98.1 100.9 106.3 103.4 486.6

SD 5.4 5.4 5.1 6.4 4.5 4.6 4.8 62.6

% increase

from Cont.

-0.7 0.0 -3.7 2.3 -2.9 4.2 1.6 394.1

Time series plots (anomalies from Feb 01 sowing date experiment) of sowing date sensitivity

experiments (Figure 13); and model experiments in weather, and crop management (Figure 14)

were further explained statistical summary in (Table 10) and (Table 11). The sowing date model

experiments plots below showed similar variabilities for both stations: decline in productivity

24

during the wet seasons/years (2001, 2006, and 2010); an increased productivity during 2009. An

enormous decline mainly in 2006 might be related to the abrupt change in incoming radiations

(much higher longwave than shortwave) (Figure 11). This reasoning goes to the years 2001 and

2010. Perhaps, other meteorological factors, mainly increased rainfall may contribute.

The main differences between the sowing date sensitivity experiments: delayed sowing (mid

Belg) in Arba Minch and early sowing (before the start of Belg) increased potato productivity;

the yield variabilities among model experiments were so higher in Arba Minch than Chencha

station.

Model sensitivity experiments (Figure 13) in Weather variables and crop-management showed

the following facts. The plots were anomalies from Feb 01 sowing date experiment (control run).

GECROS sensitivity experiments (increased in maximum and minimum temperatures, and

rainfall) showed a decline in potato productivity (as compared to control run) for Arba Minch

station. The variabilities among model experiments were high as compared to Chencha station.

The effects of increases in weather variables were negligible as compared to control experiment

for Chencha station. Comparing experiments, an increased in maximum, and minimum

temperatures caused a decline (anomaly below zero), and increase in productivity, respectively.

Application of fertilizer tremendously, more than any other factor, increased productivity in both

Arba Minch and Chencha stations. The decline in yield in 2001, 2003, and 2010 might be due to

amplified rainfall and possibly vapor pressure deficit as shown in PCA (Table 8). Comparing

Arba Minch and Chencha stations, the productivity of former was less predictable. This might be

an indicator that describes potato doesn’t grow in Arba Minch. Overall, fertilizer application is

the key crop-management tool to robust potato productivity in Gamo highlands.

25

Figure 13: GECROS Modelled Potato Sowing date Sensitivity Experiments Fresh-Matter Yield difference

from control (Feb01) for Arba Minch (above) and Chencha (below). Sowing date Sensitivity Experiments

were conducted on dates: Jan 15, Feb 15, and Mar 01. Note that: (1) the Sowing season was Belg, except

experiment Jan 15; (2) Fertilizer was not applied in the model.

26

Figure 14: GECROS Modelled Potato Sensitivity Experiments (in WRF and GECROS) Fresh-Matter Yield

difference from control (Feb01) for Arba Minch (above) and Chencha (below). Note that: (1) For all

experiments, the Sowing date was Feb 01 during the Belg season; (2) Fertilizer was not applied in the model,

except the fertilized experiment (pink colored plot on the right side of this figure).

27

3.2.4 Productivity and Related terms Maps: The Gamo Highlands

3.2.4.1 Potato Fresh Matter Yield Map: Feb 01 Sowing date (control run)

Potato fresh-matter yield was modelled for the Gamo highlands. The sowing date was Feb 01

(control run) at the start of Belg season. The design of model experiments was tabulated (Table

3). The control model run annual maps (Feb 01 sowing date experiment) showed the following

facts. The highlands Woredas: north-west part of Arba Minch Zuria, Chencha, south of Boreda,

Dita, centeral and north of Bonke, east of Kamba, South-east of Daramalo, the northweastern

part of Mirab Abaya, Eastern tip of Kucha are potato sowing areas in Gamo highlands. These

regions are high altitude areas (Figure 4). Terrain-deriven climate is the reason for the yield

variablity. We focused on the highlands as they are areas of interst. The main decline in

productivity was observed for the wet year 2010. However, an increased yield was modelled for

the drought years 2004, and 2009. This outcome was aginst our research hypothsis formulated in

Section (1.3). More explanation will come in the next chapter.

28

Figure 15: GECROS Modelled Potato Fresh Matter Yield (qt ha-1

) for the Gamo Highlands from 2001 to

2010. The Model resolution is 10kmx10km for 82kmx82km area. The potato was sowed on Feb 01 during the

Belg season.

29

3.2.4.2 GECROS Model Expreiments: Potato fresh-matter yield map, model comparisons

between sowing dates, for the year 2009

Four sowing date model sensitivity experiments were conducted for the Gamo highlands. The

experiments were name as: Jan 15, Feb 01 (Cont.), Feb 15, and Mar 01. The model analysis

indicated that late sowing (March 01, during the Belg season) was most advantageous for the

Gamo highlands (Figure 16). The statistical summary further explained the result (Table 12).

High productivities in lowland areas for Jan 15, Feb 01, and Mar 01 sowing dates were not areas

of interest, as the highlands are potato growing areas.

Figure 16: GECROS Modelled Potato Yield Map for the Gamo Highlands for Sensitivity Experiments:

Control (Feb 01), sowing dates: Jan 15, Feb 15, and March 01. The Model resolution is 10kmx10km for

82kmx82km area. All the Experiments were conducted during the Belg season, except experiment Jan 15.

3.2.4.3 Weather variables and fertilizer application model Experiments: Anomaly from the

Control run, for the year 2009

Model sensitivity experiments for increased in maximum and minimum temperatures, and

rainfall were conducted. Potato fresh-matter yield anomalies from the control run (sowing date

Feb 01 experiment) revealed the following facts. Increased in rainfall model sensitivity

experiment showed decline in productivity for highlands. Increases in temperatures exhibited

30

slight decline in productivity for highlands, and sever decline for the lowlands. Nitrogen

fertilizers applications greatly improved production and productivity, except in the North-

western tip of the model domain (Table 12) and (Figure 17). In general, climate change

assumptions for the Gamo highlands caused decline in potato productivity.

Table 12: GECROS modelled potato fresh-matter yield statistical Summary for the year 2009, the Gamo

Highlands. The sensitivity experiments: Sowing date experiments (Jan-15, Feb-01 [cont.], Feb-15, and Mar-

01), Weather experiments (TMax+50C, TMin+5

0C, and RF+0.1RF), and fertilized agriculture.

GECROS Sensitivity Experiments Fresh-Matter Yield for 2009: The Gamo Highlands

Summary

Statistics

Jan-15 Feb-01

(Cont.)

Feb-15 Mar-01 RF+0.1RF TMax+50C TMin+5

0C Fertilized

Minimum 11.1 9.9 11.1 11.5 9.9 11.2 9.9 9.6

Maximum 124.7 139.2 138.7 172.7 142.2 141.3 119.4 538.1

Mean 103.4 98.8 99.7 107.7 98.8 96.4 99.1 350.1

SD 12.6 12.7 16.8 21.9 12.8 28.4 22.0 102.6

%increase

from Cont.

4.7 0.0 0.9 9.0 0.0 -2.4 0.3 254.4

31

Figure 17: GECROS Model Sensitivity Experiments: Potato Fresh-Matter Yield Anomaly from control

experiment (sowed on Feb 01) for the Gamo Highlands. The Sensitivity Experiments: Rainfall (10% added),

TMax + 50C, TMin + 5

0C, and Fertilized Agriculture. The Model resolution is 10kmx10km for 82kmx82km

area. All the Experiments were conducted during the Belg season (sowing date: date Feb 01).

3.2.4.4 Modelled Potato Number of Days taken to Harvest (Anomaly): for the year 2009, and

sowing date Feb 01 sowing date sensitivity experiments

Number of harvest days (days from sowing to harvest) are good indicators of potato productivity.

Overall, the lowlands required less harvest days as compared to the highlands. This might be a

reason for potato to be not produced in the lowlands. The number of harvest day’s anomaly plots

was showed that increased rainfall, maximum, and minimum temperatures mainly caused decline

in number of harvest days mainly for the highlands. On the other hand, application of fertilizer

application increased the number of harvest days for the highlands, but decline for the lowlands

(Figure 18).

32

Figure 18: GECROS Model Sensitivity Experiments: Number of taken for Harvest Anomaly from control

experiment (sowed on Feb 01) for the Gamo Highlands. The Sensitivity Experiments: Rainfall (10% added),

TMax + 50C, TMin + 5

0C, and Fertilized Agriculture. The Model resolution is 10kmx10km for 82kmx82km

area. All the Experiments were conducted on date Feb 01 (during the Belg season).

33

4 DISCUSSIONS

We implemented the Weather Research and Forecasting (WRF) model to reproduce weather,

atmospheric and surface energy budget, and soil types. The model run was conducted for ten 10

years: 2001 – 2010. Four model domains (d01 – d04) were implemented. The inner, nested

domain (d04) was centered at Arba Minch, Ethiopia. The domains resolution was 2-by-2 km2

resolution, which covered 84-by-84 km2. WRF initialization was obtained from the ECMWF re-

analysis (Jiménez et al., 2011, Wanjun, 2013, Jiménez et al., 2010). Series of 48 hours runs were

conducted, in which the first 24 hours (the previous day) taken as model spin-up period. The

model’s physics was configures as: microphysics option – the WRF Single-Moment Six Class

scheme (Hong and Lim, 2006), longwave radiation option – rrtm scheme (Mlawer et al., 1997),

shortwave radiation option – Dudhia scheme (Dudhia, 1989), surface-layer option – the Monin-

Obukhov scheme (Monin and Obukhov, 1954), land surface option – Thermal diffusion scheme

(Dudhia, 1996), and the PBL option – Yonsei University scheme (YSU) (Hong et al., 2006).

We used the WRF model outputs to simulate potato yield in the Gamo highlands, Ethiopia. An

ecophysiological model was applied. The Genotype-by-Environment interaction on CROp

growth Simulator (GECROS) model was applied to simulate potato yield and related variables.

The model runs with time-step of a day. The daily meteorological model inputs are: maximum

and minimum air temperatures (0C), global radiation (kJ.m

-2.day

-1), vapour pressure (kilo

Pascal), precipitation (mm.day-1

), and mean wind speed (m.s-1

) (Xinyou and Van Laar, 2005).

These model inputs were generated from the 10 years WRF run outputs.

The statistical model validation showed that WRF underestimated maximum and minimum

temperatures, overestimated rainfall, and wind speed in both Arba Minch and Chencha stations.

A similar study in Arba Minch (Minda, 2014) showed that WRF mainly underestimate

temperature. This might be related to terrain-smoothening capability in the model for the

complex topography of the region. The mean sea-level height in the model is higher than the

stations’ height. The overestimated wind speed was also related to this reasoning. Overall, in the

study (Tie et al., 2007), WRF underestimated temperature. (Jiménez and Dudhia, 2011)

explained that WRF presented a high wind speed bias over unresolved topographic features (for

example, plains and valleys).

WRF was capable to reproduce the drought year 2009, in most of the year’s season in Arba

Minch and the vicinity. The inter-seasonal-climatological 10 years model analysis showed that

the year was typical meteorological drought/heat wave period in Arba Minch and Chencha. We

implemented the Standardized Precipitation Index (SPI) (Patel et al., 2007, Raja et al., 2014,

McKee et al., 1993); deviation of maximum temperature from the climatology to identify

droughts (Fouillet et al., 2006); and anomalies from surface energy (sensible and latent heat

fluxes) terms form climatology. Higher anomalies than the mean climatology for latent and

sensible heat fluxes considered as drought indicators. (Vautard et al., 2007) described soil

moisture deficit enhances local heat fluxes. This situation, relatively high sensible heat fluxes

34

from climatology, considered as indicator of drought. Weather analysis by the National

Meteorological Agency (NMA) and a study showed that Arba Minch and its vicinity got rainfall

less than 50% of the climatology (NMA, 2009, Minda, 2009). We mainly focused one of the

drought years during the period 2001 to 2010, for GECROS yield simulation. It was 2009.

Weather and crop are highly correlated, in particular at critical phenological stages of a plant. It

is the weather rather than climate prediction that is required for crop-yield modelling purposes

(Challinor et al., 2003). From the GECROS model output, the mean incoming solar radiation

(kJ.m-2

.day-1

); maximum and minimum temperatures (0C) positively correlated. These variables

categorized in first Principal Component (PC1) of the Principal Component Analysis (PCA),

which mainly explained the modelled yield. On the other hand, these variables are uncorrelated

with both mean rainfall (mm.day-1

) and vapor pressure deficit (kPa). However, with the addition

of these and the aforementioned variables explained 65% of the modelled yield. Wind speed

acted differently that it was nearly uncorrelated with other variables. It explained nearly quarter

of the modelled potato yield (PC2) (Section 3.2.1).

The GECROS modelled fresh-matter potato productivity for Chencha was nearly 100, and 500 qt

ha-1

for unfertilized, and fertilized agricultural systems, respectively. The observational data

estimated by the Gamo Gofa Zone Agricultural Office in the range 50 to 90 qt ha-1

(GGAD,

2008). Farmers productivity estimate studied by (Mazengia et al., 2013) showed that the

productivity was nearly form 10 to 25 qt ha-1

. Both the observed and farmer estimates were less

than the modelled and national average productivity. Studies showed that the average potato

productivity of Ethiopia is nearly 80 to 100 qt ha-1

(Haverkort et al., 2012, Tufa, 2013, Addisu

and Habtamu, 2013). The average productivity obtained for the southern Ethiopia estimated to be

70 to 80 qt ha-1

(Hirpa et al., 2010).

In Ethiopia, the area of land under potato is only 2.3% of the total land area potentially suitable

for potato farming (Tufa, 2013). In the country, potato grows in four main areas: the eastern, the

central, the northwestern, and southern (Hirpa et al., 2010, IPC, 2009). These areas cover 83% of

potato growing farmers (Hirpa et al., 2010). The authors mapped potato growing areas and

productivity (Figure 1). The major potato growing zones in the southern Ethiopia are: Gamo

Gofa, Gurage, Hadiya, Kambata, Wolaita, Sidama, and Silte (Tufa, 2013, Hirpa et al., 2010). In

the Gamo Gofa zone; Dita, Boreda, Uba Debre Tsehay, Zalla, Oyda, Arba Minch Zuria, Kamba,

Geze Gofa, Daramalo, Chencha, Melo Goza, and Mirab Abaya Woredas are identified as potato

growing areas (GGAD, 2008). The SNNPRs agricultural research institute identified that

altitudes start between 1,600 to 2,400m are suitable for potato cropping (Tesfaye, 2011). The

GECROS productivity maps showed that areas ≥ 2000m above sea level (ASL) identified as

climatologically potential potato growing locations in the Gamo highlands. With this model

output, we identified Dita, Chencha, centeral and north Bonke, south of Boreda, east of Kamba,

northwest of Mirab Abaya north-west of Arba Minch Zuria, South-east of Daramalo, and Eastern

tip of Kucha are in decreasing order of productivity (Figure 15).

35

In SNNPRs regions potato grows in both Belg (February to May) and Meher (June to October)

seasons (Hirpa et al., 2010). In Chencha, one of the Gamo highland regions, potato is grown

during the Belg and Meher seasons. The crop more grows during the Belg than Meher (Mazengia

et al., 2013). This is due to the Belg is with less crop disease incidence as compared to Meher

season, for local potato varieties (Tufa, 2013, Haverkort et al., 2012). For this productivity

modelling task the Belg season was selected. From the four sowing date model experiments (Jan

15, Feb 01, Feb 15, and Mar 01) during Belg season, the highest yield obtained for the Mar 01

sowing date, 107qt ha-1

. The next highest productivity modelled for the Jan 15 sowing date, 103

qt ha-1

(Table 12). However, early sowing, before the start of Belg, can be considered as best

calendar for potato cropping in the Gamo highlands. This crop calendar is selected as it gives

high potential yield, and relatively least exposure to plant diseases, as compared to other planting

days.

Model sensitivity experiments were conducted for changing climate scenarios (increased rainfall,

and maximum and minimum temperatures), changes in crop management (application of

fertilizer). Of all model experiments, addition of fertilizer gave the highest yield (Figure 17,

Appendix 16). The recommended fertilizer application rate is 165 kg urea and 195 kg DAP per

hectare (Tufa, 2013, Hirpa et al., 2012). We applied 45 kg/ha of ammonium-N (4.5 gN.m-2

.day-1

on the first day) and 100 kg/ha of nitrate-N (5 gN.m-2

.day-1

on the sowing and after the 45th

day

from the sowing date). Application of fertilizers was the best way to robust productivity.

Moreover, the yield contrast between the highlands and lowlands was so high during fertilizer

application. On the other hand, increased rainfall model sensitivity experiment showed decline in

productivity for highlands. Increases in temperatures exhibited slight decline in productivity for

highlands, and sever decline for the lowlands. The climate change assumptions showed decline

in productivity mainly by decreasing the number of harvest days (Figure 18). The impact was so

high for the highlands, the potato growing areas.

Average of a single seasonal climate variable (for example, rainfall) often poorly correlates with

crop yield. Crop production is a function of dynamic, nonlinear interactions between weather,

soil water and nutrient, physiology of the plant and crop-management practices (Baethgen et al.,

2008). The yield decline in wet year, 2010 cannot be explained in terms of rainfall. An enormous

outlier in radiation and surface energy balance might explain the productivity decline (Figure

11). The hypothesis we designed in Section (1.3) failed to some degree. The yield in drought

Belg season during the year 2009 did not show least productivity as compared to other years

(Figure 15). This may need to define the term drought from all crop-relevant climate variables

for example radiation budget, surface energy balance, etc.

36

5 CONCLUSIONS

1 WRF model overestimated rainfall, wind speed, and underestimated temperature. However,

the model was capable to reproduce the drought year 2009, in most of the year’s season in

Arba Minch and the vicinity.

2 The GECROS productivity maps showed that areas ≥ 2000m above sea level (ASL)

identified as climatologically potential potato growing locations in the Gamo highlands. With

this model output, we identified Dita, Chencha, centeral and north Bonke, south of Boreda,

east of Kamba, northwest of Mirab Abaya north-west of Arba Minch Zuria, South-east of

Daramalo, and Eastern tip of Kucha are in decreasing order of productivity in the model

domain.

3 Early potato sowing, before the start of the Belg, was preferred as it gives relatively higher

yield less exposed to plant diseases.

4 Application of fertilizers was the best way to robust productivity. Further model experiments

are suggested for optimum use of fertilizer rate.

5 Model experiments in climate change assumptions overall showed decline in productivity for

highlands, and sever decline for the lowlands. The yield declined is mainly explained by

decreasing the number of harvest days.

6 The crop modelling using GECROS was conducted by assuming a general potato genotype.

However, a specific genotype with known model input parameters (e.g., thermal time),

phenological characteristics (flowering or maturity durations, etc.,) morphological

characteristics (e.g., plant height, seed size under optimum conditions), may greatly improve

the performance of the model.

7 We applied soil type outputs from WRF. However, the model’s output was poor. It gave only

two types soils in the model domain. We suggest high resolution soil data input much

improves our work.

8 Coupling WRF model with GECROS much improves productivity modelling. The coupled

model setup may facilitate to study high spatial and temporal resolutions.

37

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APPENDICES

Appendix 1: WRF Model Version 3.1.1: namelist.input and namelist.wps parameters

Parameter Parameter value/option selected

(d01, d02, d03, d04)

WRF dynamical core ARW

Run start period 2001-01-01_00:00:00

Run end period 2010-12-31_00:00:00

Time step (s) 300 s

Model spin-up period 1 day (from 2 days run, the 1st day was for

model spin-up)

End index of a domain W-E; staggered dimension, #grid points 55, 49, 49,43

End index of a domain S-N; staggered dimension, #grid points 55, 49, 49,43

End index of a domain bottom-top; staggered dimension, #

eta_levels

36, 36, 36, 36

Pressure levels 36

Number of soil levels 4

Grid spacing (dx or dy) 54kmx54km, 18kmx18km, 6kmx6km,

2kmx2km

i_parent_start 1, 20, 16, 21

j_parent_start 1, 20, 18, 15

Feedback 1

Physics configuration

Microphysics option 6, 6, 6, 6 (WSM 6-class graupel scheme)

Long wave radiation option 1, 1, 1, 1 (rrtm scheme)

Shortwave radiation option 1, 1, 1, 1 (Dudhia scheme)

Minutes between rad physics 30, 30, 30, 30

Surface-layer option 1, 1, 1, 1 (Monin-Obukhov scheme)

Land-surface option 1, 1, 1, 1 (thermal diffusion scheme)

Boundary-layer option 1, 1, 1, 1 (YSU scheme)

Cumulus option 1, 1, 1, 0 (Kain-Fritsch scheme, and no Cu

physics)

Surface urban physics option 0, 0, 0 , 0(no surface urban physics option)

Diffusion option 0, 0, 0, 0

Diffusion factor 0.12, 0.12, 0.12, 0.12

Hydrostatic option .true., .true., .true., .true.,

Moisture advection option 1, 1, 1, 1,

Scalar advection option 1, 1, 1, 1,

Appendix 2: Crop parameterization: Specific genotype

Parameter Symbol Value Source Remark

Short/long day crop SLP -1 Long day crop

(In)determinate crop DETER -1 Indeterminate crop

C3/C4 crop C3C4 1 C3 crop

Lodging LODGE -1 No lodging

Legume LEGUME -1 No legume

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Appendix 3: Management Options

Management option Symbol Unit Value Source Remark

Plant density NPL plant.m-2

4.4 (Addisu and Habtamu, 2013)

N supply NSWI - 1 N supply is calculated

with fertilizer

Water supply WSWI - 1 No water supply

Appendix 4: Model Constants

Constant Symbol Unit Value Source

Convexity for light response of electron transport

(J2) in photosynthesis THETA - 0.7 (Xinyou and Van Laar, 2005)

Inclination of sun angle for computing DDLP INSP [Degree] -2 (Xinyou and Van Laar, 2005)

Initial fraction of C in the shoot FCRSH [gC.gC-1

] 0.5 (Xinyou and Van Laar, 2005)

Initial fraction of N in the shoots FNRSH - 0.62 (Xinyou and Van Laar, 2005)

Critical root weight density WRB [g.m-2cm-1[ 0.25 (Xinyou and Van Laar, 2005)

Carbon cost of symbiotic nitrogen fixation CCFIX [gC.gN-1] 6 (Xinyou and Van Laar, 2005)

Appendix 5: Crop Specific Parameters

Parameter Symbol Unit Value Source

Growth efficiency for vegetative organs YGV [gC/gC] 0.81 (Xinyou and Van Laar, 2005)

Carbon fraction in the vegetative organs CFV [gC/gC] 0.48 (Xinyou and Van Laar, 2005)

Efficiency of germination EG [g.g-1


Fraction of fat in the storage organs FFAT - 0 (Xinyou and Van Laar, 2005)

Fraction of lignin in the storage organs FLIG - 0.03 (Xinyou and Van Laar, 2005)

Fraction of organic acids in the storage organs FOAC - 0.05 (Xinyou and Van Laar, 2005)

Fraction of minerals in the storage organs FMIN - 0.05 (Xinyou and Van Laar, 2005)

Base temperature for phenology development TBD [0C] 0 (Xinyou and Van Laar, 2005)

Optimal temperature for phenology development TOD [0C] 25 (Xinyou and Van Laar, 2005)

Ceiling temperature for phenology development TCD [0C] 37 (Xinyou and Van Laar, 2005)

Curvature for temperature response TSEN [0C] 1 (Xinyou and Van Laar, 2005)

Leaf width LWIDTH [m] 0.025 (Xinyou and Van Laar, 2005)

Stem dry weight per unit of plant height CDMHT [g.m-2

.m-1

] 170 (Xinyou and Van Laar, 2005)

Maximum rooting depth RDMX [cm] 100 (Xinyou and Van Laar, 2005)

Minimum specific leaf N content for

photosynthesis

SLNMIN [gN.m-2

leaf] 0.35 (Xinyou and Van Laar, 2005)

Initial N concentration in living leaves LNCI [gN/g] 0.05 (Xinyou and Van Laar, 2005)

Minimum N concentration in the roots RNCMIN [gN.g-1] 0.005 (Xinyou and Van Laar, 2005)

N concentration in the stems STEMNC [gNg-1


44


Specific leaf area constant SLAO [m2 leafg

-1] 0.033 (Xinyou and Van Laar, 2005)

Energy of activation for JMAX EAJMAX [Jmol-1

] 70890 (Xinyou and Van Laar, 2005)

Fraction of sigmoid curve in flexion in entire

Plant height growth period

PMEH - 0.6 (Xinyou and Van Laar, 2005)

Fraction of sigmoid curve in flexion in entire

seed growth period

PMES - 0.5 (Xinyou and Van Laar, 2005)

Proportion of seed N that comes from

nonstructural N in vegetative organs

accumulated before end of seed-number

determining period

PNPRE - 0.95 (Xinyou and Van Laar, 2005)

Slope of linear relationship between JMAX and

leaf nitrogen

XJN [µmol e-.s-

1.gN-1]

120 (Xinyou and Van Laar, 2005)

Slope of linear relationship between VCMX and

leaf nitrogen

XVN [µmol e-.s-

1.gN-1]

60 (Xinyou and Van Laar, 2005)

Appendix 6: Genotype Specific Parameters


Development stage at start of photosensitive period SPSP - 0 (Xinyou and Van Laar, 2005)

Development stage at end of photosensitive period EPSP - 0.7 (Xinyou and Van Laar, 2005)

Development stage at end of seed-number

determining Period for indeterminate crops

ESDI - 1.1 (Xinyou and Van Laar, 2005)

Maximum crop nitrogen uptake NUPTX [gN.m-2

.d-1


Standard seed nitrogen concentration SEEDNC [gN.g-1

] 0.0159 Sinclair and de Wit 1975

Maximum plant height HTMX [m] 0.99 (Addisu and Habtamu, 2013)

Leaf angle (from horizontal) BLD [degree] 50

Seed weight SEEDW [g-1

] 50 Ewing and Struik 1992

Minimal thermal days for vegetative phase MTDV [day] 15 Khan 2012

Minimal thermal days for reproductive phase MTDR [day] 50 Khan 2012

Photoperiod sensitivity of phenological

development

PSEN [h-1

] 0

Development stage when transition from CB to CX

is fastest

TM - 1.5

Factor for initial N concentration of seed fill CX - 1

Factor for final N concentration of seed fill CB - 1

Appendix 7: Soil Model Parameterization


Fraction of dead leaf N incorporated into soil litter PNLS - 1

Percentage of clay in the soil CLAY [%] 7

Soil water content at maximum holding capacity WCMAX [m3m

-3] 0.439 WRF

Soil water content at field capacity WCFC [m3m

-3] 0.329 WRF

Minimum soil water content WCMIN [m3m

-3] 0.066 WRF

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Standard value of decomposition rate constant for resistant RPMRO [yr-1

] 0.3

Standard value of decomposition rate constant for

decomposable plant material

DPMRO [yr-1

] 10

Decomposition rate constant for humidified organic matter

in the soil

HUMR [yr-1

] 0.02

Decomposition rate constant for microbial in the soil BIOR [yr-1

] 0.66

Ratio DPM/RPM of added plant material DRPM 1.44

Residual ammonium-N in the soil RA [gNm-2

] 1

Fraction of *initial microbial biomass in the soil in the

initial total soil organic carbon (Stocker et al.)

FBIOC 0.03

Initial soil microbial biomass humified soil organic matter BHC gCm-2

] 3500

Total organic C in the soil TOC [gCm-2

] 7193

Residual nitrate-N in the soil RN [gNm-2

] 1

Multiplication factor for initial soil water status MULTF 1

Time constant for soil temperature dynamics TCT [day] 4

Soil resistance for water vapor transfer, equivalent to leaf

stomata resistance

RSS [sm-1

] 80

Thickness of upper evaporative soil layer SD1 [cm] 5

Time constant for some soil dynamic processes TCP [day] 1

Appendix 8: Atmospheric Parameters


Factor for change in vapor pressure, for sensitivity

analysis COEFV - 1

Factor for change in radiation, for sensitivity analysis COEFR - 1

Factor for change in temperature, for sensitivity

analysis COEFT - 0

Ambient CO2 concentration CO2A - 380

Appendix 9: User-defined irrigation and fertilizer applications

Management option Symbol Unit Amount

applied

Remark

Irrigation (water added) IRiA [mmd-1

] 0 Based on observation

ammonium-N added in the 1st fertilizer application (1st

day)

FNA1 [gNm-2

d-1

] 0/10 Past/Based on current

observation

nitrate-N added in the 1st fertilizer application (5th day) FNN1 [gNm-2

d-1


observation

nitrate-N added in the 1st fertilizer application (45th day) FNN5 [gN.m-2

d-1


observation

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Appendix 10: GECROS modelled potato yield map: Sensitivity experiment, sowing date 15 Jan

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Appendix 11: GECROS modelled potato yield map: Sensitivity experiment, sowing date 15 Feb

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Appendix 12: GECROS modelled potato yield map: Sensitivity experiment, sowing date 01 Mar

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Appendix 13: GECROS modelled potato yield map: Sensitivity experiment, TMax + 50C

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Appendix 14: GECROS modelled potato yield map: Sensitivity experiment, TMin + 50C

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Appendix 15: GECROS modelled potato yield map: Sensitivity experiment, RF + 0.1RF

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Appendix 16: GECROS modelled potato yield map: Sensitivity experiment, Fertilized Agriculture

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