Download Climate Change Impacts On Rainfed Corn Production In

University of Central Florida
Electronic Theses and Dissertations
Masters Thesis (Open Access)
Climate Change Impacts On Rainfed Corn
Production In Malawi
2013
Kondwani Msowoya
University of Central Florida
Find similar works at: http://stars.library.ucf.edu/etd
University of Central Florida Libraries http://library.ucf.edu
Part of the Engineering Commons, and the Water Resource Management Commons
STARS Citation
Msowoya, Kondwani, "Climate Change Impacts On Rainfed Corn Production In Malawi" (2013). Electronic Theses and Dissertations.
Paper 2771.
This Masters Thesis (Open Access) is brought to you for free and open access by STARS. It has been accepted for inclusion in Electronic Theses and
Dissertations by an authorized administrator of STARS. For more information, please contact [email protected].
CLIMATE CHANGE IMPACTS ON RAINFED CORN PRODUCTION IN MALAWI
by
KONDWANI MSOWOYA
Bachelor of Science in Civil Engineering, 2007
University of Malawi, the Polytechnic
A thesis submitted in partial fulfillment of the requirements
for the degree of Master of Science
in the Department of Civil, Environmental and Construction Engineering
in the College of Engineering and Computer Science
at the University of Central Florida,
Orlando Florida
Fall Term
2013
Major Professor: Kaveh Madani
ABSTRACT
Agriculture is the mainstay of the economy in Malawi and accounts for 40% of the Gross
Domestic Product (GDP) and 90% of the export revenues. Corn (maize) is the major cereal crop
grown as staple food under rainfed conditions, covers over 92% of the total agricultural area, and
contributes 54% of the caloric intake. Corn production is the principle occupation and major
source of income for over 85% of the total population in Malawi. Issues of hunger and food
insecurity for the entire nation are associated with corn scarcity and low production. Global
warming is expected to cause climate change in Malawi, including changes in temperature and
precipitation amounts and patterns. These climate changes are expected to affect corn production
in Malawi. This study evaluates the impacts of climate change on rainfed corn production in
Malawi. Lilongwe District, with about 1,045 square miles of agriculture area, has been selected
as a representative area. First, outputs of 15 General Circulation Models (GCMs) under different
emission scenarios are statistically downscaled. For this purpose, a weather generator (LARSWG) is calibrated and validated for the study area and daily precipitation as well as minimum
and maximum temperature are projected for 15 GCMs for three time horizons of 2020s, 2050s
and 2090s. Probability assessment of bounded range with known distributions is used to deal
with the uncertainties of GCMs’ outputs. These GCMs outputs are weighted by considering the
ability of each model to simulate historical records. AquaCrop, a new model developed by FAO
that simulates the crop yield response to water deficit conditions, is employed to assess potential
rainfed corn production in the study area with and without climate change. Study results indicate
an average temperature increase of 0.52 to 0.94oC, 1.26 to 2.20oC and 1.78 to 3.58oC in the nearterm (2020s), mid-term (2050s) and long-term (2090s) future, respectively. The expected
changes in precipitation during these periods are -17 to 11%, -26 to 0%, and -29 to -3%. Corn
ii
yields are expected to change by -8.11 to 0.53%, -7.25 to -14.33%, and -13.19 to -31.86%,
during the same time periods. The study concludes with suggestion of some adaptation strategies
that the Government of Malawi could consider to improve national food security under climate
change.
iii
First, this study is dedicated to my beloved mother Maggie and my father Wyson for making me
who I am today. I also dedicate this work to the love of my life Ruth and my daughter Vania for
their perpetual love, support and for believing in me.
I love you so much.
iv
ACKNOWLEDGMENTS
My sincere gratitude goes to my thesis advisor Dr. Kaveh Madani for his perpetual
support and for entrusting me with the responsibility to carry out this research. I am thankful to
my MS committee members Dr. Dingbao Wang and Dr. Petros Xanthopolous for their
constructive advice rendered during the course of the study. I also acknowledge profound
comments from Jeffrey Elledge, which contributed to the perfection of this work.
I am greatly indebted to Rahman Davtalab for his tireless support throughout the study.
He was always there for me whenever I needed his assistance. Thanks also to Andres Vargas,
Milad Hooshyer, Ahmed Hamed, Marcial Mota, Tyler Pierce, Mousa Maimoun, Juan Gonzalez,
Amir Zarvichi, Saeed Hadian, Chris Rowney, and all HEESA members at UCF for their precious
comments in preparation of this work.
Many thanks go to my beautiful and wonderful friend and wife Ruth, for her
encouragement and support throughout the course of my studies.
Special recognition and appreciation to Mr. Adams Chavula, the Principal
Agrometeorologist in the Department of Climate Change and Meteorological Services in
Malawi, for providing me with all the required historical observed weather data.
My sincere gratitude also goes to my financiers, the Fulbright Scholarship Board for
according me the opportunity to advance my knowledge and skills.
Family and friends too numerous to mention from back home in Malawi, UK, US,
Australia, Netherlands, Japan and all parts of the world, I say thank you for being there for me.
I love you all, May the Almighty God always bless you!
v
TABLE OF CONTENTS
LIST OF TABLES ......................................................................................................................... ix
LIST OF FIGURES ........................................................................................................................ x
CHAPTER ONE: INTRODUCTION ............................................................................................. 1
1.1 Background ........................................................................................................................... 1
1.2 Problem Statement and Study Justification .......................................................................... 4
1.3 Research Objectives .............................................................................................................. 6
1.4 Method Summary.................................................................................................................. 6
CHAPTER TWO: LITERATURE REVIEW ................................................................................. 8
2.1 Potential Climate Change Impacts on Crop Production ....................................................... 8
2.2 Models Used to Predict Climate Change (GCMs) .............................................................. 11
2.3 Climate Change Emission Scenarios .................................................................................. 13
2.4 Selection of Baseline Period ............................................................................................... 15
2.5 Downscaling of GCM Outputs ........................................................................................... 16
2.6 Crop Model ......................................................................................................................... 19
CHAPTER THREE: BRIEF OVERVIEW OF THE STUDY AREA.......................................... 22
CHAPTER FOUR: RESEARCH METHODOLOGY.................................................................. 26
4.1 Introduction ......................................................................................................................... 26
4.2 Long Ashton Research Station Weather Generator (LARS-WG) ...................................... 28
4.2.1 Input data for LARS-WG............................................................................................. 28
4.2.2 Outline of the Process of Stochastic Weather Generation ........................................... 29
4.2.2.1 Calibration of the Model ....................................................................................... 29
4.2.2.2 Model Validation .................................................................................................. 31
4.2.2.3 Generation of Synthetic Weather Data ................................................................. 32
4.3 GCM’s Emission Scenarios ................................................................................................ 34
4.4 Uncertainty Analysis of GCMs ........................................................................................... 36
4.4.1 Step 1: Weighting of GCMs ........................................................................................ 36
4.4.2 Step 2: Generation of Probability Distribution Functions (PDFs) ............................... 37
4.4.3 Step 3: Generation of Cumulative Distribution Functions (CDFs) ............................. 38
4.5 Crop Yield Estimation with AquaCrop............................................................................... 38
4.5.1 Introduction .................................................................................................................. 38
vi
4.5.2 Description of the Model ............................................................................................. 39
4.5.2.1 The Atmosphere: Required Input Climate Data ................................................... 41
4.5.2.2 The Crop ............................................................................................................... 42
4.5.2.3 The Soil ................................................................................................................. 43
4.5.2.4 The Management .................................................................................................. 44
4.5.3 Algorithm and Calculation Procedure of AquaCrop ................................................... 44
4.5.3.1 Drainage ................................................................................................................ 45
4.5.3.2 Surface Runoff and Infiltration ............................................................................. 46
4.5.3.3 Soil Evaporation.................................................................................................... 47
4.5.3.4 Crop Transpiration ................................................................................................ 48
4.5.3.5 Biomass Production .............................................................................................. 49
4.5.3.6 Response of Harvest Index (HI) to Water Stress .................................................. 50
4.5.4 Calibration of AquaCrop.............................................................................................. 51
4.5.5 Validation of AquaCrop ............................................................................................... 52
CHAPTER FIVE: RESULTS AND DISCUSSION ..................................................................... 55
5.1 Introduction ......................................................................................................................... 55
5.2 Estimation of Future Climate Variables ............................................................................. 55
5.2.1 Calibration and Validation of LARS-WG ................................................................... 55
5.2.2 Estimating Future Climate Variables ........................................................................... 58
5.2.3 Uncertainty Analysis of GCMs .................................................................................... 62
4.2.3.1 Weighting of GCMs .............................................................................................. 62
4.2.3.2 Probability Distribution Functions (PDFs) ........................................................... 62
4.2.3.3 Cumulative Distribution Functions (CDFs) .......................................................... 66
4.2.3.4 Generation of Probability Percentiles ................................................................... 69
5.3 Corn Yield Production with Aquacrop ............................................................................... 76
5.3.1 Model Calibration ........................................................................................................ 76
5.3.2 Model Validation ......................................................................................................... 77
5.3.3 Simulating Current and Future Corn Yields with Climate Change ............................. 78
5.3.4 Correlation Analysis between Corn Yields and Rainfall and Temperature Changes .. 81
CHAPTER SIX: CONCLUSIONS AND RECOMMENDATIONS ........................................... 84
6.1 Study Results ...................................................................................................................... 84
vii
6.2 Suggestions to Climate Change Adaptation Strategies ....................................................... 86
6.3 Study Limitations ................................................................................................................ 88
6.4 Future Work ........................................................................................................................ 89
6.4.1 Introduce Supplementary Irrigation Corn Farming ..................................................... 89
6.4.2 Improving the Water Retaining Capabilities of the Soils ............................................ 88
6.4.3 Intra Annual and Inter Seasonal Variations ................................................................. 89
6.4.4 Using the Up to Date Emission Scenarios (AR5) ........................................................ 89
REFERENCES ............................................................................................................................. 90
viii
LIST OF TABLES
Table 1. A Comparison between NWP Models and GCMs ........................................................ 12
Table 2. The GCMs used in this study.......................................................................................... 12
Table 3. Driving forces for emission scenarios for 2020, 2050 and 2100 timelines .................... 14
Table 4. Characteristics of the major emission scenarios ............................................................. 14
Table 5. Description of the two emission scenarios used in this study......................................... 15
Table 6. Comparative summary of the relative merits and demerits of statistical and dynamical
downscaling techniques ................................................................................................. 18
Table 7. Summary of crop models used in climate change impact studies .................................. 20
Table 8. CO2 concentrations in parts per million (ppm) for the four main climate scenarios
(SRES) ........................................................................................................................... 35
Table 9. Input crop parameters used in calibration of AquaCrop ................................................. 52
Table 10. Statistical comparison of synthetic and observed weather data using Q test ................ 56
Table 11. Calculated weight of each GCM in simulating future rainfall...................................... 63
Table 12. Calculated weight of each GCM in simulating future temperature .............................. 63
Table 13. Summary of mean annual and overall ranges of estimated climate variables in each
time period, at different scenarios and probability percentiles ...................................... 70
Table 14. AquaCrop validation results including prediction error statistics for corn yields ........ 78
Table 15. Corn production (tons/hectare) at different probability percentiles.............................. 79
Table 16. Summary of values of correlation coefficient for rainfall and maximum temperature 83
ix
LIST OF FIGURES
Figure 1. Malawi’s agricultural value-added and total GDP (Billion USD), 1970-2008 ............... 2
Figure 2. Corn production and price in Malawi from 1980-2012 .................................................. 4
Figure 3. Major emission scenarios .............................................................................................. 15
Figures 4a and 4b. Map of Malawi and location and soil types in Lilongwe District. ................. 22
Figure 5. Corn production and area under cultivation in the Lilongwe District (2004-2013) ...... 24
Figure 6. Topography of Lilongwe District ................................................................................. 24
Figure 7. Land use and vegetative cover in the Lilongwe District ............................................... 25
Figure 8. Methods that have been used in this study .................................................................... 27
Figure 9. Main components of soil-plant-atmosphere continuum in AquaCrop .......................... 40
Figure 10. Plot of P-values for X2 and t tests for rainfall and temperature................................... 57
Figure 11: Box plots showing the simulated mean monthly rainfall for 15 GCMs under scenarios
A and B for three different time steps. ........................................................................ 59
Figure 12. Box plots showing the simulated mean monthly minimum temperature for 15 GCMs
under scenarios A and B for three different time periods. .......................................... 60
Figure 13. Box plots showing the simulated mean monthly maximum temperature for 15 GCMs
under scenarios A and B for three different time steps.. ............................................. 61
Figure 14. Sample Discrete PDFs outlining the relationship between weights of 15 GCMs and
monthly changes in relative rainfall.. .......................................................................... 64
Figure 15. Sample Discrete PDFs outlining the relationship between weights of 15 GCMs and
monthly changes in minimum temperature................................................................. 65
Figure 16. Sample Discrete PDFs outlining the relationship between weights of 15 GCMs and
monthly changes in maximum temperature.. .............................................................. 66
Figure 17. Sample CDFs for rainfall based on PDFs in Figure 14. .............................................. 67
Figure 18. Sample CDFs for minimum temperature based on PDFs in Figure 15. ...................... 68
Figure 19. Sample CDFs for maximum temperature based on PDFs in Figure 16. ..................... 69
Figure 20. The estimated future changes in relative rainfall at three probability percentiles. The
horizontal dash-dot line indicates no changes in rainfall.. .......................................... 71
Figure 21. Estimated future changes in minimum temperature at three probability percentiles.. 72
Figure 22. Estimated future changes in maximum temperature at three probability percentiles . 73
Figure 23. Plots of changes in rainfall, minimum temperature and maximum temperature under
each scenario and risk level ........................................................................................ 75
Figure 24. Results of AquaCrop validation using the 2000-2004 simulated and recorded corn
yields ........................................................................................................................... 77
Figure 25. Changes in corn production for 2015-2035 relative to baseline period of 1971-2000.80
Figure 26. Change in corn production for 2046-2065 relative to baseline period of 1971-2000. 80
Figure 27. Change in corn production for 2080-2099 relative to baseline period of 1971-2000. 80
Figure 28. Correlation analysis between change in corn yields and change in precipitation and
maximum temperature respectively ............................................................................ 82
x
LIST OF ABBREVIATIONS
B
:
Biomass
CC
:
Canopy Cover
CDFs
:
Cumulative Distribution Functions
CPT
:
Climate Predictability Tool
CSI
:
Critical Success Index
Dr
:
Root zone depletion
DSSAT
:
Decision Support System for Agro-technology Transfer
ETo
:
Reference evapotranspiration
FAO
:
Food and Agriculture Organization of the United Nations
FAOSTAT
:
Food and Agriculture Organization Statistics
GCM
:
Global Circulation Model
GCM
:
Global Circulation Models
GDD
:
Growing Degree Day
GDP
:
Gross Domestic Product
GoM
:
Government of Malawi
HI
:
Harvest Index
IPCC
:
Intergovernmental Panel for Climate Change
JICA
:
Japan International Cooperation Agency
Kcb
:
Crop transpiration coefficient
Ke
:
Crop evaporation
Kr
:
Reduction coefficient
Ks
:
Stress coefficient
KS
:
Kolmogorov-Smirnov
KW
:
Kruskal- Wallis
LAI
:
Leaf Area Index
Lars-WG
:
Long Ashton Research Station Weather Generator
LEPS
:
Linear Error in Probability Space
MAE
:
Mean Absolute Error
MAGM
:
Master of Science in Agricultural Meteorology
MAPE
:
Mean Absolute Percentage Error
xi
MK
:
Malawi Kwacha
NSO
:
National Statistics office
NWP
:
Numerical Weather Prediction
PDFs
:
Probability Distribution Functions
RMSE
:
Root Mean Square Error
SEPLD
:
Social Economic Plan for Lilongwe District
SRA
:
Scenario A
SRES
:
Special Report on Emission Scenarios
UDS
:
United States Dollar
USDA
:
US Department of Agriculture
USGCRP
:
United States Global Change Research Program
xii
CHAPTER ONE: INTRODUCTION
1.1 Background
Agriculture is the mainstay of economy in Malawi. It employs 85% of the labor force,
standing for 40% of the Gross Domestic Product (GDP) and 90% of the foreign exchange
earnings (Msukwa, 1994; GoM, 2007b; Mucavele, 2009). With a subtropical climate, Malawi
relies on rainfall for its agricultural production. Since time immemorial, corn has been the main
staple food crop for the entire nation. Of the 9.4 million hectares of total land under cultivation
between 1960 and 2000, only 7,404 hectares of this land was irrigated, leaving 99.99% of the
total agricultural land for rainfed farming. Corn covers more than 92% of the total agricultural
area (GOM, 2007b) and contributes over 54% of the caloric intake in Malawi (NSO, 2005). Most
importantly, agriculture is the principle occupation for over 85% of the total population in
Malawi (Kachule, 1994; GoM 1995). Other food crops grown on a smaller scale include cassava,
potatoes, rice, coffee, tea, sorghum, beans, Irish potatoes, bananas, groundnuts and sugarcane.
Figure 1 shows the contribution of agriculture to the overall GDP for the past four decades,
clearly indicating that the rate of economic growth in Malawi is greatly influenced by agriculture
(Tchale, 2009).
Over the past few decades, corn production has been on the decline due to erratic rainfall
and temperature changes, with rains either falling for shorter periods and in smaller amounts than
usual or due to serious extreme events like droughts and flash floods. Heavy droughts
experienced in 1991-92, 1994-95 and 2004-2005, were followed by flash floods in the
subsequent years. During these periods, corn production significantly declined leaving a wide
margin between production and consumer satisfaction (GoM, 2007b). Since 2005, corn
production has fluctuated from one year to another, with an average production of 0.7-1.5 tons
1
per hectare (t/ha). Corn production below 1.5 t/ha indicates famine for that particular period
(JICA, 2008). Previous reports in Malawi indicate that low corn production in some parts of the
nation due to low rainfall has resulted in farmers living on an empty stomach for as long as five
months until the next growing season (Kanyama-Phiri et al., 1994). Hunger crisis experienced in
the aforementioned periods led to declarations of national state of emergency (GoM, 2012).
National GDP (Billion USD)
14
GDP (Billion USD)
Agriculture Value Added (Billion USD)
12
10
8
6
4
2
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
1978
1976
1974
1972
1970
0
Year
Figure 1. Malawi’s agricultural value-added and total GDP (Billion USD), 1970-2008 (Reproduced from
Tchale, 2009).
Previous research on climate change impacts on crop production in different parts of the
world agrees with the assertion that climate change has major implications for agricultural
production in general and for corn yield in particular. In the United States and other developed
nations, extensive studies on the impacts of climate change on agricultural production have been
carried out (Cai et al., 2009; Adams, 1989; Rosenzweig, 1989; Mendelson et al., 1994). There
has been relatively little research in developing nations although recently, a few papers have
been published (Schmidhuber and Tubiello, 2007; Ringler et al., 2010; Gohari et al., 2013). Yet,
2
developing countries are the ones which could suffer more from the effects of climate change,
due to their limited economic capability and adaptive capacity. Being one of the least developed
countries, Malawi’s corn production can be affected by climate change impacts. Nevertheless,
little is known about such impacts to date.
Population increase in the region has been blamed as a contributing factor to low crop
production. This is because population increase has resulted in loss of agricultural land to
settlement, continuous farming and deforestation, eventually leading to soil erosion and
infertility. In consequence, the market value of agricultural grains has increased dramatically
(Ng’ong’ola et al., 1992).
Figure 2 shows the changes in corn production and average price in Malawi for the 19802011 period. It is clear from this figure that corn production has indeed not been stable; however,
corn prices have been steadily on the rise over the past decades. In 1980’s, an average national
growth rate of 3% in corn production was observed, followed by a growth rate decline of 2
percent per annum from 1990 to 1994. Heavy drought periods experienced from 1991 to 1992,
exacerbated corn production during this period and during the following years. A series of
positive and negative growth rates were experienced between 1994 and 2004, resulting in a tip
over of 2.2% per annum in 2004 (Chirwa et al., 2006). Steady increase in corn production was
realized from 2005-2008 when the Government of Malawi (GoM) introduced its subsidy
program giving farmers the opportunity to buy farm inputs like fertilizer and seeds, at subsidized
rates. However, low rainfall amounts and poor rainfall distribution in most parts of the country
have since resulted in low corn yields.
Climate change impacts on rainfed corn production in Malawi have not been adequately
investigated to date. Given the significance of this staple crop for the nation’s labor force and
3
GDP, this study intends to bridge the knowledge gap on the effects of climate change on rainfed
corn production in Malawi.
3.5
0.4
Production
Price
0.35
Million Tons
3
0.3
2.5
0.25
2
0.2
1.5
0.15
1
0.1
0.5
0
1980
0.05
Price in thousand USD per Ton
4
0
1985
1990
1995
Year
2000
2005
2010
Figure 2. Corn production and price in Malawi from 1980-2012 (Chirwa et al., 2006; GoM, 2013)
1.2 Problem Statement and Study Justification
The fourth report by the Intergovernmental Panel on Climate Change (IPCC) indicates
that on average, global temperature has increased by 0.74oC (0.56 to 0.92oC) from 1906-2005,
and a warming of 0.2oC per decade is estimated for the next 2 decades (SRES, 2000;
IPCC, 2007). Extensive studies have shown that the changes in global temperature will have a
significant effect on agricultural production (Thornton et al., 2006; Mendelsohn, 2008; USGCRP,
2009; Lee et al., 2012). These studies have helped to comprehend and appreciate the extent by
which crops would be vulnerable to varying climatic conditions. For instance, studies have
investigated the effects of climate change on crop yield variability (Carlson et al., 1996; Phillips
et al., 1998; Podestá et al., 1999; Rubas et al., 2006) and crop yield (Kaiser et al., 1993;
Rosenzweig and Parry 1994; Riha et al., 1996; Reilly et al., 2003; Parry et al., 2004). In
4
summary, these studies have provided evidence that climate change will have impacts on
agricultural production.
Poor spatial rainfall distribution and pattern often results in crop production losses
(Rockström, 2000). Shortage of corn production in Malawi results in significant rise in market
prices of corn (GoM, 2006). This has a direct impact on the local population of which 85% have
an income of less than $1 per day. A market information report on food crops by the Malawian
Ministry of Food Security (2006) indicates that in 2005 and 2006, the average national corn
prices were $0.14/kg and $0.37/kg, respectively, indicating an increase of 160% just in a year.
This rise was as a result of the drought experienced within this period. However, as of June 2013,
the national average corn price was $0.53/kg, representing an increase of approximately 276%
from 2005 (GoM, 2006; 2013). This phenomenon simply shows that despite an increase in
production (about 150% increase in corn production), corn prices have also significantly
increased, perhaps due to the increased population and demand.
Amongst all sectors (energy, agriculture, water, fisheries, livestock et cetera), agriculture
is the most sensitive and vulnerable to climate change (IPCC, 1990). The second IPCC report
(1996) predicted that tropical and subtropical regions would experience higher losses in crop
production, while temperate climates might gain in productivity with climate warming. Issues of
hunger and famine in Malawi are associated with low corn production as a result of poor rainfall.
It is apparent therefore, that as a subtropical nation that wholly relies on rainfed farming,
Malawi’s food production could be greatly impacted on by changes in climate. Therefore,
studying the potential impacts of climate change on rainfed corn production in Malawi would be
necessary to take timely mitigating actions to minimize the undesirable impacts.
5
1.3 Research Objectives
The main objective of this study is to evaluate the effects of climate change on rainfed
corn production in Malawi. Specifically, the study aims to achieve the following objectives:
i. Quantifying future changes in temperature and precipitation relative to the baseline
period of 1971-2000;
ii. Estimating potential current and future corn production in the study area in various future
time periods under different climate change scenarios; and
iii. Recommending some adaptation strategies to minimize the undesirable impacts of
climate change on food production in Malawi.
1.4 Method Summary
Lilongwe District, the largest corn growing/producing District in Malawi, has been
selected as representative study area to estimate the potential effects of climate change on corn
production in the nation. Relative to the baseline period 1971-2000 for which historical climate
data were available, future changes in climate variables in 2015-2035 (2020s), 2046-2065
(2050s) and 2080-2099 (2090s) are estimated using General Circulation Models (GCMs).
Following the method proposed by Gohari et al. (2013) to cope with uncertainties of GCMs
projections, a probability analysis method with bounded distribution functions is used, which
results in generation of mean monthly PDFs and CDFs from which risk levels at 25%, 50%, 75%
are extracted. The LARS-WG stochastic weather generator (Semenov and Barrow, 2010) is used
to downscale GCMs outputs to local area. Statistical indicators are used to investigate the
significance and reliability of LARS-WG to predict future climate data, based on historical
climate data. ETo Calculator (Allen et al., 1998), a FAO simulation model, is used to estimate
evapotranspiration based on Penman-Monteith algorithm. AquaCrop (Steduto et al., 2009), a
6
new model developed by FAO, is used to estimate crop production under different climate
scenarios, with and without climate change. The study concludes with suggestion of some
adaptation strategies that the Government of Malawi could consider to improve national food
security associated with climate change.
The rest of this thesis is structured as follows: Chapter 2 reviews literature on the
potential impacts of climate change on crop production and provides basic information on
climate change models and scenarios as well as the downscaling technique used in this study.
This chapter also provides background information on the available crop yield simulation
models. Chapter 3 provides a brief background of the study area while Chapter 4 outlines the
overall methodology used in order to meet the study objectives. Chapter 5 presents and discusses
the results and major findings of the study. Chapter 6 suggests some adaptation techniques to
local decision makers to minimize the cost of climate change for Malawi, outlines the limitations
of the study and concludes the study.
7
CHAPTER TWO: LITERATURE REVIEW
2.1 Potential Climate Change Impacts on Crop Production
There is overwhelming evidence that the earth’s planet is warming as a result of
greenhouse gasses (IPCC, 2007). Burning of fossil fuels and changes in land use are the major
sources of greenhouse gasses; and if nothing is done to curb these practices, the concentrations of
greenhouse gasses in the atmosphere will grow substantially by the next century (IPCC, 2007;
Mendelsohn, 2008). Climate change is expected to cause various effects; however, the largest
impact of global climate change will be on agriculture (Nordhaus, 1991; Cline, 2007).
The United States Global Change Research Program (USGCRP, 2009) report indicates
that despite an increase in crop yields as a result of technological improvements, extreme
weather changes have negatively affected production in some years. The report argues that
warmer temperatures not only shorten growth periods, they also lead to lower crop yields.
Shorter growth periods may be appropriate in areas with soils of low moisture content; however
this leads to accelerated growth in some varieties of crops like corn by shortening period of seed
germination, crop growth and maturity. Ultimately, accelerated growth of crops results to a
significant decrease in crop yields for a given amount of land. Tubiello et al., (2002) agrees with
the notion that climate change greatly affects agricultural crop production, albeit the magnitude
will vary from a place to another and from one crop to another due to different natural and
anthropogenic factors that contribute to climate change.
Several studies indicate that increase in temperature negatively affects crop growth and
hence crop yields, however, the impact varies between crops and across regions since different
crops respond differently to climate variability (Thornton et al., 2006; Mendelsohn, 2008;
USGCRP, 2009; Lee et al., 2012). Mendelshon (2008) reviewed different studies that measure
8
the magnitude of the impact of warming on farms in developing countries. His review affirmed
the hypothesis that “tropical and subtropical agriculture in developing countries is more sensitive
to climate than temperate agriculture’’. His study concluded that even marginal changes in global
warming will negatively affect crop growth and hence production in Africa and Latin America.
Ringler et al. (2010) assessed Climate Change Impacts on food security in Sub-Saharan Africa
using a comprehensive scenario which is based on ensembles of 17 GCMs. The study results
depicted high temperatures and mixed changes in rainfall by the year 2050. The study also found
that climate change would result in a decrease in crop yields excepting for millet and sorghum;
with wheat exhibiting the largest decrease of -22% by year 2050. The study also determined that
with climate change, prices of corn, rice and wheat would increase by 4%, 7% and 15%
respectively by 2050 relative to the baseline period. Other predicted 2050 impacts by 2050
include: changes in crop production and increase in growth area, less affordability of food due to
price increase, reduction in calorie intake and increased childhood malnutrition for the SubSaharan region
Kimball (1983) conducted a study in the U.S. and discovered that increased amounts of
carbon dioxide (CO2) can increase crop yields. However, this can be counteracted by a rise in
temperature, leading to a reduction in crop yield overall. Cai et al. (2009) assessed climate
change impacts on rainfed corn production in central Illinois, U.S. Study results indicate that
under rainfed conditions, corn yields will decline by 23 to 34% in 2055 in central Illinois. In
addition, if no adaptation measures are implemented, the study estimated a probability of 32 to
70% of not meeting 50% of the potential yield by the year 2055.
You et al. (2009) carried out research in China and found out that an increase in
temperature of 1 °C during the growing period may lead to wheat production reduction of 3–10%
9
and a decrease in winter wheat production of about 5%-35% (Ozdogan, 2011). Tao and Zhang
(2010) determined that with higher temperatures, corn yields are likely to decrease by 2.4-45.6%
in northern China plain. An average decrease in crop production of 15% may be incurred with
higher temperatures and evapotranspiration rates despite constant precipitation, as a result of
shorter crop growth periods which negatively affects time to crop maturity (Schlenker and
Lobell, 2010). A similar study, conducted in northwestern Turkey, indicates that winter wheat
yields may decline by more than 20% due to climate change causing shorter growth periods and
reduced precipitation (Ozdogan, 2011). Similar studies within the same region determined that
higher temperatures have a positive effect on crop yield. For instance, in northern China plain,
corn, rice, potato, and winter wheat yields could increases with increasing temperature and
precipitation under climate change (Chaves et al., 2009).
A recent study evaluated the agricultural responses to climate change in Iran’s ZayandehRud River Basin for a period of 2015-2035. The study considered four crops; wheat, barley, rice
and corn, and determined that for an average monthly temperature change of 1.1oC to 1.5oC and
precipitation decrease of 11% to 31% within the basin, crop production would decrease as
follows: 2.5% to 20.7% for wheat, 1.4% to 17.2% for barley, 2.1% to 9.5% for rice, and 5.7% to
19.1% for corn (Gohari et al., 2013).
Schmidhuber and Tubiello (2007) reviewed the potential impacts of climate change on
global food security. They determined that climate change will adversely impact all four aspects
of food security namely: availability, stability, utilization and access. However, the effects will
differ across regions and nations depending on the socio-economic policies embraced by each
region or nation in response to climate change impacts. The study further states that climate
change will exacerbate the dependence on imports by developing nations, with Sub-Saharan
10
Africa being more vulnerable compared to South Asia which will be affected but to a lesser
extent. The paper concludes that climate change will mostly affect poor people.
2.2 Models Used to Predict Climate Change (GCMs)
General Circulation Models (GCMs) are “computer based version of earth’s system that
mathematically simulates the climate system and the interaction between the system
components” (Hassan, 2012). They simulate historical, present and future climate scenarios
taking into account the level of greenhouse gases and aerosols under different future projections.
The process is achieved by dividing the oceans and atmosphere into a horizontal grid with a
horizontal resolution of 2o- 48o with 10-20 layers aligned vertically. This enables predictions of
climate change for the next 100 years using a coarse grid scale (IPCC, 2001 and Dibike and
Coulibaly, 2005). In general, most GCMs are capable of simulating global and continental scale
processes in detail and provide a reliable representation of the average planetary climate (Hassan,
2012).
GCMs use the same equations of motion as a numerical weather prediction (NWP)
model. The major difference is that NWP models are used to simulate future weather conditions
for a short and medium period of time (say 1-3 days and 4-10 days, respectively). In contrast,
GCMs predict weather conditions for much longer periods (multiple decades), besides providing
statistical interpretation of the results (e.g. means, medians, variance and standard deviations)
(Geerts and Linacre, 1998). NWP models and GCMs are compared in Table 1, while Table 2
lists the 15 GCMs used in this study.
11
Table 1. A Comparison between NWP Models and GCMs (Geerts & Linacre, 1998)
CONTRASTS
Goal
Spatial coverage
Temporal range
Spatial resolution
Relevance of initial conditions
Relevance of clouds, radiation
Surface (land, ice, ocean)
Relevance of ocean dynamics
Relevance of model stability
Time dimension
SIMILARITIES
Physics
NWP
to predict weather
regional or global
days
variable (20-100 km)
high
low
low
low
low
essential
Method
Finite difference expression of continuous equations, or spectral
representation; run prognostically
Variables and motion of the atmosphere in 3 dimensions
Controlled by spatial resolution (CFL condition)
Output
Maximum time step
GCM
to predict climate
global
years
usually coarse
low
high
high
high
high
ignored
Use equations of motion (plus radioactive transfer equations, water
conservation equations.)
Table 2. The GCMs used in this study (Semenov, 1992; 2012)
Center
Beijing Climate Centre
Canadian Centre for Climate Modeling and
Analysis
Centre National de Recherches
Australia's Commonwealth Scientific and
Industrial Research Organization
Institute of Atmospheric Physics
Goddard Institute for Space Studies
Geophysical Fluid Dynamics Laboratory
Hadley Centre for Climate Prediction and
Research/UK Met. Office
Hadley Centre for Climate Prediction and
Research/UK Met. Office
Institute for Numerical Mathematics
Institute Pierre Simon Laplace
Meteorological Research Institute, Japan
Max-Planck Institute for Meteorology
National Centre for Atmospheric Research
National Centre for Atmospheric Research
Center
Acronym
BCC
China
Global Climate
Model
BCC-CM1
Grid
Resolution
1.9o x 1.9o
CCCma
Canada
CGCM3 (T47)
2.8o x 2.8o
CNRM
France
CNRM-CM3
1.9o x 1.9o
CSIRO
Australia
CSIRO-MK3.0
1.9o x 1.9o
LASG
GISS
GFDL
China
USA
USA
2.8o x 2.8o
3o x 4o
2.0o x 2.5o
UKMO
UK
FGOALS-g1.0
GISS-AOM
GFDL-CM2.1
HadCM3
UKMO
UK
HadGEM1
1.3o x 1.9o
INM
IPSL
NIES
MPI-M
NCAR
Russia
France
Japan
Germany
USA
INM-CM3.0
IPSL-CM4
MIROC3.2 (hires)
ECHAM5-OM
CCSM3
4o x 5o
2.5o x3.75o
1.1o x 1.1o
1.9o x 1.9o
1.4o x 1.4o
NCAR
USA
PCM
2.8o x 2.8o
12
Country
2.5o x3.75o
2.3 Climate Change Emission Scenarios
Climate models project future climate based on different emission scenarios, each based
on a specific set of assumptions (Shaka, 2008). These assumptions include, future trends in
energy demand, emitted greenhouse gases, changes in land use and behavior of climate system
over a long period of time, etc. (Houghton et al., 2001).
Figure 3 presents a schematic representation of the four major emission scenarios as per
the Special Report on Emission Scenarios (SRES) (IPCC, 2007). These scenarios (A1, A2, B1
and B2) make different assumptions about future population increases, economic and
technological developments, energy and land use, and the global approaches to sustainability
which differently affects emissions of greenhouse gasses. In general, scenario A prioritizes
economic development, whereas scenario B is more concerned with environmental
sustainability. Numbers “1” and “2” represent different technological advancements within
storylines, with “1” representing a much faster and diverse development and “2” representing a
slower and regional development (IPCC, 2007). To put it another away, a future that is more
concerned with addressing global problems and sustainable environment is represented by
scenario B1. On the other hand, a future in which nations prioritize their regional development,
leading to unsustainable and unequal economic growth is represented by scenario A2 (Anderson
et al., 2008).
Table 3 shows a summary of the driving forces for the 2020, 2050 and 2100 timelines
leading to the development of A1, A2, B1 and B2 scenarios. Table 4 gives a brief description of
the major characteristics of each scenario.
13
Table 3. Driving forces for emission scenarios for 2020, 2050 and 2100 timelines (Reproduced from
SRES)
Scenario Group
A1
A2
B1
B2
Population (billion)(1990=5.3)
2020
7.6
8.2
7.6
7.6
2050
8.7
11.3
8.7
9.3
2100
7
15.1
7
10.4
12
World GDP (10 USD/year (1990=21)
2020
57
41
53
51
2050
187
82
136
110
2100
555
243
326
235
Per capita income ratio: developed countries and economies in transition (Kyoto Treaty Annex
1) to developed countries (Kyoto Treaty non-Annex 1) (1990=16.1)
2020
6.2
9.4
8.4
7.7
2050
2.8
6.6
3.6
4
2100
1.6
4.2
1.8
3
Table 4. Characteristics of the major emission scenarios (IPCC SRES, 2000)
Scenario Group
Population Growth
GDP Growth
Energy Use
Land Use Change
Oil/Gas Resource
Availability
Technological
Change
Change Favoring
A1B
Low
Very High
Very High
Low
Medium
A2
Low
Very High
High
Low
Medium
B1
Low
High
Low
High
Low
B2
Medium
Medium
Medium
Medium
Medium
Rapid
Rapid
Medium
Medium
Balanced
Non-Fossil Fuel
Efficiency and
Dematerialization
"dynamics as
usual"
Two commonly used emission scenarios, i.e. SRA1 and SRB1, are considered here in the
analysis of the impacts of future climate change on corn yield. For simplicity, these two
scenarios are referred to as SRA and SRB respectively. Table 5 provides a description of these
two scenarios.
14
Figure 3. Major emission scenarios (Reproduced from Anderson et al., 2008)
Table 5. Description of the two emission scenarios used in this study (IPCC- 4th Assessment Report)
Scenario
SRA1
Description
A future world affected by a rapid economic growth, global population that peaks in
mid-century and declines thereafter, and rapid introduction of new and more efficient
technologies, with the development balanced across energy sources
SRB1
A convergent world with the same global population as in the A1 storyline but with
rapid changes in economic structures toward a service and information economy,
with reductions in material intensity, and the introduction of clean and resourceefficient technologies
2.4 Selection of Baseline Period
The baseline period is the reference period on which calculation of future climate
changes is based. Definition of the baseline period is important in order to select the observed
climate dataset that combines with climate change information to generate climate change
projections (Houghton et al., 2001). Carter et al. (1994), Mohammed (2009) and Hassan (2012)
outline four criteria that are commonly used in selection of the baseline period. First, the
baseline period must truly represent the current or recent averages of climate conditions within
15
the area. Second, the baseline period must be sufficiently long and cover a wide range of climate
variations, including extreme weather conditions. Third, the suitable baseline period is the one
for which the major climatic data like rainfall (precipitation), temperature, sunshine and relative
humidity are readily available, easily accessible and adequately distributed over space. Fourth,
the baseline period should have high quality climate data (with few missing data, if any).
Based on the aforementioned selection criteria, a baseline period of 30 years from 19712000 was selected for the purpose of this study.
2.5 Downscaling GCMs Outputs
Global Circulation Models (GCMs) have been developed to predict average large-scale
phenomenon of atmospheric weather (Holton, 1992). GCMs are quite capable of simulating
global climatic data at continental and hemispherical scales, but have limited capacity in
simulating local weather features and dynamics at sub-grid scale (Wigley et al., 1990; Carter et
al., 1994). Outputs from climate models cannot be used directly in climate studies at local level
due to the existence of a mismatch in spatial resolution between GCM outputs and regional
hydrological/statistical models, in addition to the large coarse resolution of GCMs, compared to
hydrologic/statistical models that use very fine spatial resolutions (Dibike and Coulibaly, 2005).
Even if future GCMs runs on high resolution, there will still be a need to downscale the outputs
so that they match the local sites before being further used in climate change studies (DOE,
1996).
Downscaling therefore converts the coarse spatial resolution of any GCM outputs to a
fine spatial resolution by generating local data from GCM outputs. This is done by linking global
scale predictions to regional dynamics in order to generate climate variables specific to a
particular region (Flint and Allan, 2012). Downscaling can be done using different approaches,
16
including: (i) nesting a regional climate model into an existing GCM, (ii) statistical regressions
and (iii) using stochastic weather generators.
The first method of downscaling entails identifying a specific area and applying some
driving factors from a GCM to the regional climate models. This method is also referred to as
dynamic downscaling because regional climate models are dynamic models like GCMs, albeit
they exist in three layers. The first layer operates through a GCM; the second layer uses the local
data for the specified area; and the third layer mathematically generates results based on the
previous two layers. The output is moderately local data driven by both global models and
regional specifics. The complexity of the models used makes the process challenging and less
attractive. The main advantage of this technique is that it better simulates extreme climate events
like floods and droughts and climate anomalies at regional scale (Fowler et al., 2007; Harun et
al., 2008).
Statistical regression involves using different regression methods to link local climate
data to specific drivers in GCMs. First, a relationship between large scale variables (driving
factors of GCMs) and climate variables at local scale is established. The established relationship
is then used to predict future climate variables under various situations as stipulated by GCMs. In
other words, regression models entail establishing “linear or nonlinear relationships between sub
grid scale (e.g. single-site) parameters and coarse-resolution (grid-scale) predictor variables”
(Hassan, 2012).
Stochastic weather generators (WGs) are also statistically driven. WGs generate
statistical linkages within climate variables in order to simulate present and future weather
conditions of a specified station or area, using the historical observed climate variables of that
station/area. The generated data are expected to have similar statistical properties with historical
17
observed data and the output data are complete records of daily time series unlike observed
records which may have some missing data (Kevin et al., 2005). WGs provide a wide range of
statistical interpretation of the observed and simulated daily weather variables for a particular
site (Harun et al., 2008). The WGs are said to underestimate inter-annual variability (Buishand,
1978; Johnson et al., 1996; Chenet al., 2009). Table 6 provides a general comparison of the
dynamic and statistical downscaling methods.
Table 6. Comparative summary of the relative merits and demerits of statistical and dynamical
downscaling techniques (Wilby and Wigley, 1999; Hassan, 2012)
Statistical Downscaling
Dynamic Downscaling
Comparatively cheap and
computationally efficient
Produces responses based on physically
consistent processes
Produces finer resolution
Information from GCM-scale output that
can resolve atmospheric processes on a
smaller scale
Can provide point-scale
Climatic variables from GCMscale output
Advantages
Disadvantages
Can be used to derive variables
not available from RCMs
Easily transferable to other
regions
Based on standard and accepted
statistical procedures
Able to directly incorporate
observations into method
Require long and reliable
observed historical data series for
calibration
Dependent upon choice of
predictors
Non-stationarity in the predictorpredict and relationship
Climate system feedbacks not
included
Dependent on GCM boundary
forcing; affected by biases in
underlying GCM
Domain size, climatic region and
season affects downscaling skill
18
Computationally intensive
Limited number of scenario ensembles
available
Strongly dependent on GCM boundary
forcing
This study employs the Long Ashton Research Station Weather Generator (LARS-WG),
(Semenov and Barrow, 2002: 2010) a well-known weather generator that can estimate current
and future climate variables for a specific area. LARS-WG is preferred for its ability to fill in
missing data and interpolate/extrapolate data to a defined period of time using statistical
properties of the historical observed data (Dibike and Coulibaly, 2005). LARS-WG has been
tested in varying climatic conditions and has demonstrated a good performance in reproducing
various weather statistics including extreme weather events in most situations (Semenov et al.,
1998, Semenov 2008a). Results from the Lars-WG are in the form of daily time-series climate
variables like rainfall (precipitation), maximum and minimum temperature, and solar radiation
(Semenov and Barrow, 2002; Shaka, 2008).
2.6 Crop Model
Several studies have been conducted to evaluate the impacts of climate change on crop
production using crop simulation models. Examples of crop models that have been frequently
applied thus far include: CERES-Wheat (Crop Environment Resource Synthesis) (Godwin et al.,
1989; Jones et al., 2003), DSSAT (Hoogenboom et al., 1994) SWAP (soil–water–atmosphere–
plant model) (van Dam et al., 1997), CERES-Maize (corn) (Jones & Kiniry, 1986), Crop Syst
(Stöckle, 2003), CropWat (Allen et at., (1998), and InFoCrop (Singh et al., 2005a). Of these
models, three are said to have a strong emphasis on interactions between crop, water and climate
change, namely (i) CropWat, (ii) AquaCrop and (iii) SWAP (Aerts and Droogers, 2004). These
three models have been widely used and tested in different climate change impact studies
worldwide, have a user-friendly interface, and are openly accessible. Table 7 gives a summary of
different crop models that have been used in previous climate change impacts assessment
studies.
19
Table 7. Summary of crop models used in climate change impact studies (Kang et al., 2009)
Crop model
CERES-Maize
CERES-Wheat
Crop Syst
Ceres Rice
SWAP
InFocrop
IBSNAT-ICASA
GLAM
GLYCIM
SWAT
AquaCrop
Target crop
Maize
Maize
Maize
Maize
Wheat
Wheat
Wheat
Rice
Rice
Rice
Rice and Wheat
Cereal/soybean
Peanut
Soybean
Maize
Teff
Cotton
Maize
Area of Study
Dry matter
Sustainable production
Planting date and different
weather
Precise and deficit irrigation
CO2 levels
CO2 levels
Rainfall and warming
CO2 levels
CO2 levels
Elevated CO2 and temperature
Climate change
Climate Change
Climate uncertainty
Rainfall & CO2 concentration
Climate vulnerability
Yield response to water
Deficit irrigation optimization
Yield response to water
References
Cuculeannu et al. (2002)
Walker et al. (2006)
Tojo et al. (2007)
Popova et al. 2005)
Eitzinger et al. (2003)
Luo et al. (2003)
Anwar et al. (2007)
Yao et al. (2007)
Droogers et al. (2004)
Krishman et al. (2007)
Aggerwal et al. (2006)
Parry et al. (1999)
Challinor (2008)
Xie et al. (2004)
Reddy et al. (2000)
Araya et al. (2010)
Garcia Villa et al. (2009)
Zinyengere et al. (2011)
In this study, the FAO (Food and Agriculture Organization) AquaCrop Version 4
(Steduto et al., 2009) is used to estimate corn production in the study area under different climate
scenarios. AquaCrop provides an improved and powerful approach for assessing crop yield
response to water deficit. This model has been developed and recommended by the FAO for
several reasons: requires less climate data than most crop models, has a user-friendly interface,
has a strong focus on relationships amongst climate change, carbon dioxide, water and crop
yields, and it has a large number of users globally (Steduto et al., 2009). AquaCrop is free
software and easily accessible at http://www.fao.org/nr/water/aquacrop.html.
Numerous validation tests for AquaCrop have been carried out in different parts of the
world. These tests have approved the reliability of AquaCrop when the input information is
20
clear-cut (Mainuddin et al., 2010; Todorovic et al., 2009; Heng et al., 2009; Farahani et al.,
2009).
21
CHAPTER THREE: BRIEF OVERVIEW OF THE STUDY AREA
Lilongwe District contains the capital city of Malawi. It is located at the center of Malawi
at latitude and longitude 13.50 south and 33.420 east respectively, and at an elevation of 1133m
above sea level. Figure 4a shows the map of Malawi and Figure 4b shows the location and types
of soil in the Lilongwe District. Note that sandy loam soil is the prevalent soil type in the area.
a
b
Figures 4a and 4b. Map of Malawi and location and soil types in Lilongwe District (SEPLD, 2006).
The 2008 population and housing census revealed that the Lilongwe District has a total
population of 1.9 million people, of which 35% live in urban areas whilst 65% persons live in
rural areas. With the intercensal growth rates of 4.3 and 3.1 for Lilongwe’s urban and rural areas,
respectively, the current (2013) total population for the District is estimated at 3.2 million people
(NSO, 2008). During which
The Lilongwe District has a humid subtropical climate that is relatively dry and strongly
seasonal. The warm and wet season is from November to April, during which the District
22
receives over 95% of the total annual rainfall. The annual average amount of rainfall for
Lilongwe is 900 mm, with the month of February receiving the highest monthly rainfall amount
of about 211 mm. A cool dry winter stretches from May to August. During this period, mean
temperature ranges from 17 oC to 27 oC, and minimum temperatures range from -3 oC to 10 oC,
with the coldest temperatures in the months of June and July. A hot and dry season runs from
September to October, when temperatures go as high as 36 oC and humidity ranges from 50% to
80%. The month of July is normally very dry (Malawian Department of Climate Change
and Meteorological Services, 2013)
Like in all parts of the nation, corn is by far the most important staple food for the people
of the Lilongwe District. Average per capita corn consumption is approximately 133 kg per
month. Corn also accounts for about 74% of the caloric intake per households and is cultivated
by 97% of farmers. Corn is considered to be a source of life and wealth by smallholder farmers
and occupies 54% of the land cultivated by small scale farmers (Peter and Harrera, 1989; IFPRI,
2002; NSO, 2005; FAO 2009a). It is therefore not surprising that the focus on food security in
Malawi and in the District is on corn production.
Figure 5 shows trends in corn production and the area under cultivation in the Lilongwe
District during the 2004 to 2013 period. This figure shows that corn yields steadily increased
from the 2004 growing season followed by a decline between the 2006 to 2008 growing seasons.
Like in many parts of the nation, the decline is attributed to drought conditions that the nation
experienced during these seasons. With the introduction of subsidized fertilizers, corn yields
increased after the 2007 growing season. However, even with fertilizer subsidy, corn yields have
not been as high as it was expected, despite continuous expansion in the cultivated area. Low
rainfall amounts and shorter rainfall periods are considered to be the factors contributing to the
23
decline in corn yields (GoM, 2013). Figure 6 shows the topography of the Lilongwe District and
Figure 7 shows land use and vegetative cover in the Lilongwe District. Note the coverage of
rainfed farming in the District in Figure 7.
3.5
250
200
Yield (Tons)
2.5
150
2.0
1.5
Yield
100
Area
1.0
50
0.5
0.0
2002
2004
2006
2008
Year
2010
2012
0
2014
Figure 5. Corn production and area under cultivation in the Lilongwe District (2004-2013)
Figure 6. Topography of Lilongwe District (SEPLD, 2006)
24
Area (1000 Hectares)
3.0
Figure 7. Land use and vegetative cover in the Lilongwe District (SEPLD, 2006)
25
CHAPTER FOUR: RESEARCH METHODOLOGY
4.1 Introduction
This chapter first outlines the Long Ashton Research Station Weather Generator (LARSWG) model, a statistical model for downscaling climate variables. A description of model
calibration and validation to determine its potential to simulate future climate data is then
illustrated. Fifteen (15) GCMs each under scenarios A and B are also discussed. Second, due to
high uncertainties associated with GCMs, the chapter provides a description of probability
analysis for dealing with uncertainty of the models using weighting method. This process leads
to generation of mean monthly probability distribution functions (PDFs) and cumulative
distribution functions (CDFs), under each scenario for each time period. Consequently, climate
variables at different probability percentiles (25%, 50% and 75%) are estimated. Third, we
describe how AquapCrop, a crop simulation model estimates potential present and future corn
production using climate variables generated in the previous section. Figure 8 is a step by step
description of the methods used in this work.
The historical observed metrological data used in this study was obtained from the
Department of Climate Change and Meteorological Services in the Ministry of Natural
Resources, Energy and Environment in Malawi. The data is from the Chitedze Research Station,
the main meteorological station in Lilongwe District. The following climate data was obtained
for a baseline period of 30 years (1971-2000):
(i)
Daily minimum and maximum temperature,
(ii)
Daily precipitation (rainfall)
(iii)
Mean daily relative humidity
(iv)
Daily hours of sunshine and
26
(v)
Daily wind speed
Figure 8. Methods that have been used in this study
GCMs simulated data for mean monthly precipitation, minimum and maximum
temperature, relative humidity and wind speed for the baseline period (1971–2000), were
extracted from the Canadian Climate Change Scenario Network Site. The site is accessible at:
(http://www.cccsn.ec.gc.ca/?page=dd-gcm ). The monthly differences between observed and
GCMs simulated climate data for the baseline period of 1971-2000, were calculated and used for
analysis in subsequent chapters.
Three time periods are considered for future climate projections: the near future (20152035), the medium future (2046-2099) and the long term future (2080-2099); abbreviated to
2020s, 2050s and 2090s respectively. The estimated climate data in each of the three time
27
periods can give an idea of future changes in precipitation and temperature, and with the use of
crop models, future changes in food production can be estimated.
4.2 Long Ashton Research Station Weather Generator (LARS-WG)
This study utilizes Long Ashton Research Station Weather Generator (LARS-WG
Version 5.0) to simulate future weather data for Malawi, with Lilongwe District as a case study.
LARS-WG is implemented in C++ with full Windows interface and is freely accessible at
http://www.rothamsted.ac.uk/mas-models/larswg.php. The model simulates weather data at a
single site under current and future conditions (Racsko et al., 1991; Semenov et al., 1998;
Semenov and Brooks, 1999). LARS-WG Version 5 also includes fifteen (15) General Climate
Models (GCMs) which have been used in the IPCC 4th Assessment Report (2007). Each of the
models comprises at least three of the following scenarios: SRA1B, SRA2 and SRB1, for three
time periods (2020s, 2055s and 2090s). The simulated data from the model are in form of daily
time-series for the following climate variables: maximum and minimum temperature (°C),
precipitation (mm) and solar radiation in Mega joule per square meter day (MJm-2day-1)
(Semenov and Barrow, 2002). Based on the model developer (Semenov and Barrow, 2002:
2010), a brief description of the model procedure in simulating weather data is provided in the
subsequent section.
4.2.1 Input Data for LARS-WG
The following weather data (in time series) are required as inputs to the model;
(i) Minimum temperature (°C)
(ii) Maximum temperature (°C)
(iii)Precipitation (rainfall) (mm)
(iv) Solar radiation (MJm-2day-1)
28
In the absence of solar radiation, the model accommodates the use of sunshine hours.
LARS-WG automatically converts the sunshine hours to solar radiation using an algorithm
which was described by Rietveld (1978).
The Model will either work with precipitation
(rainfall) only, or precipitation and any combination of the aforementioned climate variables.
Other important input information to be specified are the name of the station from which
historical observed data was obtained and the location of the weather station in form of latitude,
longitude and altitude.
The ability of LARS-WG to simulate reliable data depends on the availability of
observed data. The model simulates future weather data based on as little as a single year of
observed weather data. However, the more observed daily weather data used, the closer to a true
value are the simulated results. Semenov and Barrow (2002) recommend the use of daily weather
data of at least between 20-30 years for better results. Weather data for long periods are
significant in a way that they capture some of the less frequent events like droughts and floods.
In this study, daily historical observed weather data for a period from 1971-2000 was used as
baseline data.
4.2.2 Outline of the Process of Stochastic Weather Generation
According to Semenov and Barrow (2002), the process of generating synthetic weather data can
be grouped into three distinct steps:
(i) model calibration, (ii) model validation and (iii)
generation of synthetic weather data. Each of these processes is described herein.
4.2.2.1 Calibration of the Model
Calibration is the first step that is executed by the model in order to generate synthetic
weather data. Calibration of LARS-WG is carried out by a function on the main menu called
“Site Analysis”. The process is done so as to determine statistical characteristics and site
29
parameters of the observed weather data. In our study, site analysis was performed on observed
data for a period of 30 years (1971-2000). Once the program encounters “illegal data” during
execution, an “error” is displayed. “Illegal” data includes the value of minimum temperature
being greater than maximum temperature and non-negative precipitation values.
Examples of statistical characteristics of weather data in the output file include the
following;
(i) Empirical distribution characteristics for the length of wet and dry series of days in the
observed data,
(ii) The mean and standard deviation, by month, of wet and dry series length,
(iii) Rainfall distribution, maximum, minimum, number of observed years (months or days),
mean and standard deviation.
(iv) Maximum, minimum, number of observed years (months or days), mean and standard
deviation of temperature.
(v) Periods of cool and warm weather
(vi) Maximum, minimum, number of observed years (months or days), mean and standard
deviation of solar radiation.
Examples of weather generated site parameters in the output file include the
following;
(i) Histogram intervals and frequency of events in the intervals for precipitation amount by
month from January to December.
(ii) Fourier coefficients for means and standard deviations of minimum and maximum
temperature on wet and dry days
30
(iii) Average autocorrelation value for minimum and maximum temperature and solar
radiation.
(iv) Solar radiation amount (MJm-2day-1) on wet and dry days by month from January to
December.
4.2.2.2 Model Validation
Once LARS-WG has been calibrated, its ability to simulate future weather data in the
representative study site is assessed. Validation is a process that is used to determine how well a
model can simulate potential future climate variables. The process involves comparing and
analyzing the statistical characteristics of the observed and synthetic weather data in order to
determine the existence of any statistically-significant differences between them. Validation of
the model can be conducted in two different ways: (i) using the GENERATOR option to
synthesize daily weather data based on the information in the site parameter files and then
undertake comparisons between the observed and synthetic data ‘off-line’, or (ii) using the Q-test
option that executes statistical comparisons between climate parameters derived from observed
weather data and synthetic weather data generated using LARS-WG. The Q-test achieves this
using two options: “Test” and “Compare”, both of which compares the probability distributions
for the synthetic and observed data using the Chi-square goodness-of-fit test and the means and
standard deviations using the t and F tests (Semonov and Barrow, 2002).
Using the “Test” option, the user generates synthetic data for any number of years; 300
years is set as the default value. The generated synthetic data are then analyzed and parameter
files produced. The parameter files contain information about probability distribution, mean and
standard deviations of the synthetic data.
Results from “Compare option” compares the
statistical characteristics of the observed data with those of simulated data generated from the
31
parameter files calculated from the observed data. The output parameters from this function are
(i) the degrees of freedom, Chi-squared values and probabilities for wet and dry series and
monthly distribution of precipitation, and (ii) observed/weather generated mean and standard
deviation, t- and F- values and probabilities for maximum and minimum temperature, rainfall
and solar radiation both in daily and monthly formats (Semonov and Barrow, 2002). In this
study, the Q-test was used in validation of the model due to its simplicity in data interpretation.
4.2.2.3 Generation of Synthetic Weather Data
Once LARS-WG has been calibrated (Site Analysis) and the performance of the weather
generator has been verified (Q-test), the “Generator” option then generates synthetic weather
data. Synthetic weather data generated from this option have the same characteristics as observed
weather data. This option also enables one to generate synthetic weather data analogous to a
scenario of climate change. The following output data is obtained from this function;
(i) Relative change in monthly mean rainfall
(ii) Relative change in duration of wet and dry spell
(iii) Absolute change in monthly mean minimum temperature
(iv) Absolute change in monthly mean maximum temperature
(v) Relative changes in daily temperature variability
(vi) Absolute change in monthly mean radiation
To approximate the probability distributions of wet and dry series, and variables like
daily precipitation, maximum and minimum temperature and solar radiation, LARS-WG uses a
semi-empirical distribution (SED) also known as probability distribution function (PDF). Due to
an increase in number of intervals of this version of LARS-WG (from 10 in the previous version
to 23), there is greater probability of obtaining output data that is accurate. For each climatic
32
variable v, there exists a value for climate variable vi that corresponds to probability of
occurrence pi, and can be estimated as follows (Semenov and Barrow, 2010);
where P ( ) signifies the probability based on observed climate variable denoted as vobs. Each
climate variable has 2 fixed values of probability, po and pn, where po = 0 and pn = 1, and
corresponding values of vo = min {vobs} while vn = max {vobs}. To precisely estimate the extreme
values of climate variable, extremely low and high values of the variable are assigned a
probability value pi that is close to 0 and 1 respectively. The remaining values are evenly
distributed on a probability scale.
LARS-WG version 5 enables one to easily calculate the SED for climate variables. Some
climate variables are believed to follow an annual circle. For instance, higher values of variables
like temperature and radiation are reported to be higher in summer and lower in winter
particularly in the Northern Hemisphere. A smooth seasonal curve for daily and maximum or
minimum temperature and radiation is produced by calculating the SED. To estimate the SED
for a given day, the model interpolates SEDs between two months. For instance, if we want to
calculate SED, denoted Dk for any day within the year denoted k. If the middle day of the month
m is denoted km, and assuming [k-km] <=15. Distributions for 3 months denoted Mm-1, Dm and
Dm+1 are used to calculate Dk. To preserve the universality, a reasonable assumption that km-1<
k< = km is made. The resulting distribution Dk, for a day k, is a weighted sum of 2 distributions
namely Dm-1 and Dm and is written as:
(
where
)
N (m) is the number of days in a month m. α is meant to
reduce the difference between mean value Em of distribution Dm, and the mean value Ēm, for the
33
month m, calculated from values generated from interpolated distributions Dd for each day of the
month. Ēm is thus given in equation 3 below.
∑
⁄
α is selected to satisfy the following conditions;
|
|
where Av is an absolute acceptable error for variable v1 and Rv is a relative error as a proportion
to Ēm. Having calculated Ēm, from equations (1) through (4), equation (4) can be rearranged as
follows;
|
|
where ∆m- = Em- Em-1 and ∆m+ = Em+1-Em and α is limited as follows: 1 ≤ α ≤ 4. When Em is a
local minimum or local maximum, meaning both ∆m- and ∆m+ have different signs, we use an
unmodified distribution Dm, for all days k, where |
| ≤ 15 (Semenov and Barrow, 2010).
4.3 GCM’s Emission Scenarios
In chapter 2, we saw that GCMs are significant in estimation of future climate data. To
estimate future climate data, GCMs require emission scenarios. Each scenario is created based
on assumption of future concentration of greenhouse gasses in the atmosphere, particularly
carbon dioxide (CO2). By selecting a model and a scenario, LARS-WG generates daily climate
variables for the site under study. The magnitude of the output variables depends on the selected
model-scenario. Output data from this process includes daily maximum and minimum
temperature (oC), daily rainfall (mm) and daily radiation (MJm-2day-1).
In this work, we use the outputs from 15 GCMs under two emission scenarios: SRA1 and
SRB1, as recommended by IPCC Special Report on Emission Scenarios (SRES) (Houghton et
al., 2001). Under SRA1, SRES assumes a future world of very rapid economic growth, global
34
population that peaks in mid-century and declines thereafter, and rapid introduction of new and
more technologies. B1 scenario assumes a convergent world with the same global population as
in A1 storyline, but with rapid changes in economic structure towards a service and information
economy, and reduction in materials intensity and the introduction of clean and resources
efficient technologies (IPCC-SRES, 2007).
Table 8 shows the key assumptions of the SRES emissions scenarios and corresponding
increases in CO2 concentrations for three different time periods.
Table 8. CO2 concentrations in parts per million (ppm) for the four main climate scenarios (SRES). CO2
for base line scenario (1960-1990) is 334ppm (Nakicenovic and Swart, 2000)
CO2 concentration, ppm
Scenario
Key assumptions
A1B “the rich
word”
A world characterized by very rapid economic
growth (3%/yr), low population growth (0.27%/yr)
and rapid introduction of new and efficient
technologies. There is global economic and cultural
convergence and capacity building.
Cultural identities separate different regions
making the world more heterogeneous and less
cooperative. Emphasis is put on family values,
local traditions and high population growth (0.83%
per year) with little emphasis on economic growth
(1.65% per year) and material wealth.
Rapid
change
in
economic
structure,
dematerialization, improved equity more global
concern regarding environmental and social
sustainability, with introduction of clean
technologies. Global population reaches 7 billion
A heterogeneous society with emphasis on local
solutions to economic, social and environmental
sustainability as opposed to global solutions. High
priority is given to human welfare, equality and
environmental protection.
A2 “The
separated
world”
B1 “The
sustainable
world”
B2 “the world
of
technological
inequalities
35
20152035
20462065
20812100
418
541
674
414
545
754
410
492
538
406
486
581
4.4 Uncertainty Analysis of GCMs
We have discussed thus far, that due to uncertainties associated with GCMs, it is
necessary that the uncertainties are dealt with accordingly. This procedure is significant because
the uncertainties affect the credibility of the outputs, and the subsequent impact assessment
studies. Outputs from GCMs are often an extreme range of low and high values of climate
variables, rendering the results impracticable for further assessment studies (Reilly et al., 2003;
Cai et al., 2009). Three major factors contribute to uncertainty of GCMs: (i) differences in
emissions scenarios derived from projections of economic activity, population growth, and
technological development; (ii) sensitivity of global climate to greenhouse gas forcing; (iii)
regional variability, which occurs between models of different regional responses (RRs) and
within models of chaotic behaviors and modes of climate variability (Huntingford et al., 2005;
Cai et al., 2009).
Numerous methods for dealing with uncertainties of GCMs have been proposed, some of
which include (i) using a central prediction with error bars, (ii) expressing the results as a central
prediction, (iii) using a bounded range with known probability distribution and (iv) by using a
bounded range with larger range of unknown probabilities (OECD, 2003; Gohari et al., 2013). In
our study, we use a bounded range with known probability distribution (Gohari et al., 2013) to
deal with the uncertainties of fifteen (15) GCMs because it is the most used technique. This
technique requires the execution of three steps which are described below.
4.4.1 Step 1: Weighting of GCMs
The first step of this technique involves weighting each of the 15 GCMs used in the study
based on the Mean Observed Temperature-Precipitation (MOTP) method (Equation 6) (MassahBavani and Morid, 2005; Gohari et al., 2013). In order to weight each GCM, the ability of the
36
model to project weather data is considered. In other words, the method considers the monthly
average difference between observed and simulated climate variables (precipitation and
minimum and maximum temperature).
⁄|
( ⁄|
|
|)
where Wij is the weight of GCM j in month i; and |
| is the absolute difference between the
average precipitation (rainfall) or temperature between observed value and the value simulated
by GCM j in month i.
4.4.2 Step 2: Generation of Probability Distribution Functions (PDFs)
This step entails generation of PDFs of changes in climate variables based on the
calculated weights. The PDFs outline the relationship between the weight of each GCM and the
average changes in monthly precipitation, minimum temperature and maximum temperature.
With 15 GCMs and 2 emission scenarios used in this work, 30 PDFs are thus constructed for
each month. The generated discrete PDFs of the main variables are ultimately converted to
cumulative probability functions (CDFs). Several studies have identified the use of Gamma
distribution function as an important tool for analysis of climate data (Ines and Hansen, 2006;
Block et al., 2009; Piani et al., 2010; Gohari e al., 2013). Based on similar studies that were
carried by Pindyck (2012), Teutschbein and Seibert (2012) and Gohari et al. (2013), the Gamma
function has been selected for generation of cumulative distribution functions as follows;
where α and β are shape and scale parameters of the Gamma distribution function respectively, -x
37
is the climate variable (temperature or precipitation), and is the incomplete Gamma function as
given in Equation (8).
∫
By changing values of α and β, we obtain the best fit based on maximum likelihood
model. The summation of squared error (Equation 9) has been used to show how best the
Gamma function fits the data.
∑
where yi is the data point; ¥i is the estimation of Gamma function and n is the number of data
points. For this study, n = 15.
4.4.3 Step 3: Generation of Cumulative Distribution Functions (CDFs)
In this step, the PDFs generated in the previous step are converted to CDFs for each of
the 12 months (January-December). Next, values of climate change variables at three probability
percentiles are extracted from the generated CDFs at the following risk levels: 25th, 50th, and 75th
probability percentile. The calculated climate variables at three different percentiles are then
used as input data for modeling of potential corn production with AquaCrop. The 25th probability
percentile indicates a scenario of high changes in precipitation and low temperature changes. The
75th probability percentile represents a scenario with low changes in precipitation but high
temperature changes. The 50th probability percentile is the median probability percentile for both
precipitation and temperature.
4.5 Crop Yield Estimation with AquaCrop
4.5.1 Introduction
The complexities associated with quantification of crop responses to water deficit led to
the introduction of empirical production functions as a realistic approach to assessing crop
38
response to water availability (Steduto et al. 2009). In the FAO Irrigation and Drainage Paper
no. 33, Doorenbos and Kassam (1979), presents an empirical function approach for assessing the
yield response of tree crops and field crops using the following equation:
where Yx and Ya are maximum and actual yield, ETx and ETa represent maximum and actual
evapotranspiration and Ky is the proportionality constant between relative yield loss and relative
reduction in evapotranspiration. The FAO Paper number 33 was later revised to a framework
that differentiates field crops from tree crops. This led to the development of a new field crop
model called AquaCrop which evolved from Equation (10), and was designed for planning,
management and scenario simulations of field crops. The model’s balance between accuracy,
simplicity and robustness renders it preferable over other models (Steduto et al., 2009). In this
section of the study, we discuss the conceptual framework, design, structure and unique features
of AquaCrop version 4.0.
4.5.2 Description of the Model
As stated in 3.5.1 above, AquaCrop originated from an empirical function approach as
depicted in Equation (10) by separating actual evapotranspiration (ETa) into soil evaporation (Es)
and crop transpiration (Ta); and the final yield (Y) into biomass (B) and harvest index (HI). The
separation of ETa to Es and Ta is important particularly during incomplete ground cover where Es
is not applicable. On the other hand, separating Y into B and HI is necessary as it enables one to
distinguish the functional relationships that exist between B and environment and HI and
environment. These changes led to the development of a linear equation (Equation 11), that is
referred to as the “AquaCrop growth engine” (Steduto et al., 2009).
∑
39
where WP is the parameter for water productivity (kg of biomass per m2 per mm of water
transpired over the period when production of biomass occurred) and Ta is crop transpiration
(mm). Steduto et al. (2009) also explains that the transition from Equation (10) to Equation (11)
improved the robustness of the model and time scale. The former is used for long periods like
seasons normally in months, whilst the latter is used for daily time steps, a period that is essential
for assessing crop response to water deficits. Figure 9 is a flowchart showing the functional
relationships of the soil-plant-atmosphere continuum of AquaCrop.
Figure 9. Main components of soil-plant-atmosphere continuum in AquaCrop (Steduto et al., 2009)
As shown in Figure 9 above, the structure of AquaCrop encompasses a complex
relationship amongst soil, plant and atmospheric continuum. The model comprises the soil and
its water balance; the plant, which includes crop development, growth and yield processes; and
40
the atmosphere that includes temperature, rainfall, evaporative demand and carbon dioxide
concentration. AquaCrop also incorporates some management aspects including irrigation,
fertilizer application and control of field runoff through ridging and mulching (Steduto et al.,
2009). These relationships are discussed in more detail in the following sub-sections.
4.5.2.1 The Atmosphere: Required Input Climate Data
To run AquaCrop, five key meteorological input variables are required: (i) daily rainfall,
(ii) daily minimum air temperature (iii) daily maximum air temperature (iv) daily evaporative
demand of the atmosphere expressed as reference evapotranspiration (ETo), and (v) mean annual
carbon dioxide (CO2) concentration in the bulk atmosphere. The first four parameters are
obtained from a meteorological station; while the mean CO2 used is from Mauna Loa
observatory records in Hawaii (Raes et al., 2012). Apart from daily time series, the first four
parameters may also be provided at 10 day or monthly time scales. At run time, AquaCrop down
scales the 10 day or monthly time scales into daily values (Gommes, 1983).
In addition, AquaCrop does not include an algorithm for calculation of ETo, thus a
separate software, FAO ETo Calculator (version 3.2), is used to calculate ETo (Raes et al., 2008;
FAO, 2009). The procedure followed for ETo calculation is identical to that outlined in the FAO
Irrigation and Drainage Paper 56, using the FAO Penman-Monteith equation (Allen et al.,
1998) which is given by:
where
ETo
reference evapotranspiration (mm day-1)
Rn
net radiation at the crop surface (MJm-2day-1)
G
soil heat flux density (MJ m-2 day-1)
T
mean daily air temperature at 2 m height (oC)
41
u2
wind speed at 2 m height (ms-1)
es
saturation vapor pressure (kPa)
ea
actual vapor pressure (kPa)
es-ea
saturation vapor pressure deficit (kPa)
∆
slope vapor pressure curve (kPa oC-1)
γ
psychometric constant (kPa oC-1)
Figure 9 indicates that temperature influences crop phenology, rainfall and ETo
are significant inputs into the water balance model, whilst CO2 concentration of the bulk
atmosphere has an influence on water productivity (WP) and crop growth rate (Steduto et al.,
2009).
4.5.2.2 The Crop
Figure 9 outlines five major components associated with the crop system namely:
phenology, aerial canopy, rooting depth, biomass production and harvestable yield. As the crop
grows, its canopy expands and its roots are simultaneously deepened, thereby establishing main
development stages. The canopy represents the source of actual transpiration which is used to
determine the amount of biomass produced from the water productivity (WP). Using the harvest
index (HI), the actual harvestable part of biomass, the yield, is calculated using Equation (13)
(Steduto et al., 2009).
where, Y is the yield, B is biomass and HI is harvest index. The harvest index is essentially a
percentage of biomass that is translated into yield.
Steduto et al. (2009) states that canopy is the most significant component of AquaCrop,
because the amount of water transpired from the crop determines biomass production and hence
42
the yield. Its expansion is expressed as a percentage of green canopy ground-cover (CC), such
that where there is no water stress for the crop, canopy expansion from emergency to full
development follows exponential growth and exponential decay during the first and second
halves of full development respectively as shown in Equations 14 and 15.
where CC is canopy cover at time t, CCo is the canopy cover at t=0, CGC is the canopy growth
coefficient in fraction per day or per degree day, CCx is the maximum canopy cover and t is the
time in days or degree days. The growing degree days (GDD) which is the number of
temperature degrees that determine a proportional crop growth and development are calculated
using Equation 16:
where Tmax and Tmin are maximum and minimum temperatures and Tbase is the temperature at
which crop growth stops.
4.5.2.3 The Soil
AquaCrop allows configuration of up to 5 layers of soil of different texture in its profile.
All the soil texture classes found in USDA triangle are set as default in the model; however, the
user has an option to input their own values applicable to the soil under study. Each soil texture
class is associated with specific hydraulic properties, and the model estimates the hydraulic
characteristics of the soils entered by the user using pedotransfer functions. The user can also
specify values of hydraulic conductivity of the soil(s). The model requires the following
hydraulic characteristics: drainage coefficient (τ), the hydraulic conductivity at saturation (ksat),
43
the volumetric water content at saturation (θsat), field capacity (θFC), and wilting point (θWP)
(Steduto et al., 2009).
4.5.2.4 The Management
The management section of the model comprises two parts: (i) a more general field
management and (ii) a more specific water management. Field management considers practices
that could be applied to a farm with an aim of realizing the desired harvest/yield. Examples of
field management include using field surface practices like mulching to reduce soil evaporation
or using soil bunds to reduce surface runoff, both of which subsequently enhance infiltration.
Field management also involves managing the fertility levels of the soil. This option considers
three fertility levels namely: non-limiting, medium, and poor fertility. These fertility levels
affect the water productivity (WP), fraction of canopy growth coefficient (CGC), maximum
canopy cover (CCx), and rate of decline of canopy during senescence (Steduto et al., 2009).
The options considered in water management include either rainfed agriculture (with no
irrigation) or irrigation (in which case the user selects method of irrigation i.e sprinkler, drip,
surface or furrow). The irrigation part of the model is used to simulate the crop response under
supplemental or deficit irrigation.
4.5.3 Algorithm and Calculation Procedure of AquaCrop
The algorithm and calculation procedures of AquaCrop place more emphasis on the soil
water balance (Steduto et al., 2009). The model uses a set of equations to simulate soil water
movement and root water uptake. The equations describe the dependent variable θ (soil water
content), using dimensionless drainage coefficient τ, which is obtained from Ksat (hydraulic
conductivity at saturation) for vertical flow of water in the soil profile (Raes et al., 2006).
44
The soil profiles in AquaCrop are grouped into smaller fractions and this is done in order
to nearly accurately describe the movement, retention and water uptake of the soil during the
growing season. In total, the model comprises 12 soil compartments each of which measure
0.1m thick (Az). Depending on the crop and soil type, the size of each compartment is adjusted
to cover the entire root zone. Each of these soil compartments has different hydraulic
characteristics depending on the type of soil associated with a particular compartment (Steduto
et al., 2009).
In the following subsections, we discuss soil drainage, surface runoff and infiltration, soil
evaporation, crop transpiration and biomass production in context of AquaCrop model.
4.5.3.1 Drainage
Drainage of water in each soil compartment occurs when water content θ, is above field
capacity, FC. The amount of water that drains from compartment i, at time step ∆t, is expressed
by exponential drainage function, which considers water content θi and the drainage properties of
the soil (Equation 17) (Raes, 1982; Raes et al., 1988; 2006)
is the decrease in soil water content θ during ∆t (m3m-3d-1), τ is the drainage
where
coefficient, θsat is volumetric water content at saturation, and θFC is the field capacity. As the
value of τ becomes close to 1, a fully saturated soil will drain the fastest (approximately
between 1 to 2 days); conversely, smaller values of τ (τ <1) decreases water drainage capabilities
of the soil.
Numerous studies indicate that AquaCrop perfectly mimics the infiltration and drainage
patterns that occur in in situ field observations (Descheemaeker, 2006; Raes et al., 2006; Geerts
45
et al., 2008). To simulate drainage in a soil profile containing many and different compartments,
the drainage ability
of each compartment is considered. An assumption is made that the
total drainage occurring in soil compartments at the top, will pass through lower compartments
so long the compartments at the bottom have greater or equal drainage characteristics with
compartments at the top. If a compartment at the bottom has less drainage capabilities than the
one at the top, excess water is store within the compartment thereby increasing its water content
and subsequently its drainage ability.
4.5.3.2 Surface Runoff and Infiltration
To simulate surface run off, AquaCrop employs the Curve Number Method proposed by
USDA (1964), Rallison (1980) and Steenhuis et al. (1995). The Curve Number, abbreviated as
CNII, is adjusted according to the wetness of the top layer of the soil. Smedema and Rycroft
(1983) uses some derived relationships for curve number values for “antecedent moisture class”
(AMC) I (dry), II and III (wet), in which a soil depth of about 0.3m is considered for determining
AMC. For simplicity, surface runoff resulting from crop irrigation is considered to be zero. If the
surface runoff through irrigation is significant, the model requires that the user inputs the net
water application.
On the other hand, water infiltration (through irrigation or rainfall) into the soil
compartments is limited by the saturated hydraulic conductivity (Ksat) of the soil in the top layer.
Depending on the drainage characteristics of the soil compartments, storage of water occurs
simultaneously with drainage, from top to bottom compartments (Steduto et al., 2009).
46
4.5.3.3 Soil Evaporation
In AquaCrop, soil evaporation occurs in two stages: an energy limiting stage (Stage I)
and a falling rate stage (Stage II) (Philip, 1957; Ritchie, 1972). In stage I, the readily evaporable
water (REW) is estimated using Equation 18.
is the soil water content (m3m-3) when air
where θFC is water content at field capacity,
dried, and
is the thickness (m) of the evaporating surface. The U value of Ritchie (1972)
that binds the relationship between REW and cummulative evaporation for Stage I, was
determined by assuming that the value of
point θPWP, and
equal half the value of permanent wilting
equals 40mm. Thus evaporation is limited to the energy limiting stage, so
long REW remains in surface layer, and the rate of evaporation is the maximum rate, Ex.
Howerer, when REW is depleted in Stage I, then evaporation occurs in the falling rate stage
(Stage II). This results in flow of water from bottom to surface layer of the soil (Ritche, 1972,
Raes, 2009). Soil evaporation for both energy limiting and falling rate stages is determined using
Equation 19.
where, Ex is the maximum rate of evaporation,
is evaporation coefficient for fully wet
and bare soil surface (default value set at 1.10), ETo is reference evapotranspiration, and Kr is the
dimensionless evaporation reduction coefficient. For stage I, Kr is set at 1, but decreases to
values lower than 1 as evaporation switches from stage I to stage II. As soil water content
diminishes, so does its hydraulic conductivity. To account for the decline in hydraulic
conductivity, the model uses an exponential function as shown in Equation 20 (Allen et al.,
1998).
47
where fK is the decline factor, and Wrel is the relative water content of the soil layer. Ritchie
(1972) discovered that when fK = 4, there exists a “good fit between the square root of time
approach and the soil water content approach used by AquaCrop in the simulation of stage II
evaporation”. Kr varies sturdily with Wrel and is estimated using Equation 20 above.
Canopy cover impacts on the magnitude of evaporation. For a soil that is covered by crop
canopy, the rate of evaporation (Ex) in the energy limiting stage (stage I) is estimated using
Equation 21.
where CC* is the portion of soil surface covered and adjusted for micro-advective effects, and is
estimated using Equation 22 (Adams et al., 1976; Villalobos and Fereres (1990).
As canopy cover (CC) senesces (grows old) due to phenology or water stress as a result
of Ex, Ex is estimated using Equation 23.
(
)
where fcc is the adjustment factor expressing the sheltering effect of the dead canopy cover, and
CCtop is the canopy cover before the senescence. At maximum canopy cover, CCtop = CCx, the
maximum canopy cover.
4.5.3.4 Crop Transpiration
For a well-watered field, crop transpiration is proportional to the canopy cover and is
estimated using Equation 24.
48
where CC* is the adjusted canopy cover as in Equation 22, and Kctr,x is the coefficient for
maximum crop transpiration. After maximum canopy cover (CCx) has been attained, the crop
begins to age slowly, consequently transpiration declines. To account for this phenomenon, an
adjustment factor fage is multiplied to
, leading to the adjusted coefficient of transpiration
. As senescence continues, crop photosynthesis and transpiration remarkably drops, and
another adjustment factor that accounts for scenescence fsen, is applied to
(fsen =1 at start
of scenescence (when CC=CCx) and 0 where no canopy exists). These relatioships are shown in
Equations 25 to 27.
where
(
)
and,
where α is a program parameter and is used to decrease (α<1) or increase (α>1) the reduction
efficiency of transpiration and photosynthesis of the aging canopy (Raes et al., 2009).
4.5.3.5 Biomass Production
According to Steduto et al. (2009), biomass (B) above the ground is derived from
transpiration through crop water productivity (WP) normalized for reference evapotranspiration
(ETo) and carbon dioxide (CO2). Considering effects of low temperatures, chemical properties of
harvestable parts of the plant, and modifications of dimensionless factors, the daily above ground
biomass (m) is estimated using Equation 28.
(
)
49
where Ksb is the stress coefficient accounting for effects of low temperature on biomass
production, fWP is the adjustment factor (fWP ≤1) accounting for differences in chemical
composition of biomass and harvestable parts, Tr and ETo are crop transpiration and reference
evapotranspiration respectively.
4.5.3.6 Response of Harvest Index (HI) to Water Stress
As discussed in 3.5.2.2, final crop yield (Y) is the product of final biomass (B) and
harvest index (HI). Steduto et al. (2009) outlines the effects of water stress on HI. As flowering
or formation of tuber commences, HI is programmed to increase until plant maturity. The value
of HI at plant maturity under no stress conditions is referred to as reference harvest index (HIo)
for a specific crop type. The impact of water stress on HI depends on its severity and time of
occurrence, with the reproductive stage being the most crucial phase. However, if crop growth
continues, water stress may lead to a reduction in leaf growth thereby increasing the HI.
Equation 29 indicates that the rate of increase (dHI/dt) of harvest index (HI) is expedited when
the maximum canopy cover is reached and the value of Ksexp is less than 1.
(
where, (
)
(
)
) is the rate of increase of HI for non stress conditions,
is the adjustable crop
parameter, and Ksexp,t is the adjustment factor for the competition between vegetative
and reproductive growth after flowering begins at time t. Even though growth of canopy cover is
complete, Equation 29 is still applicable so long as crop growth continues.
Crop response to water stress includes reduction in leaf growth and closure of stomata.
This slows down the increase in HI whose rate of increase is described in Equation 30.
√
(
)
(
)
50
where b is the adjustable crop parameter, and
is the adjustment factor for stress that leads
to stomata closure and reduction in photosynthesis.
When water stress is so severe to allow pollination to occur, harvest index is represented
by the relationship shown in Equation 31 below.
[∑(
where,
)]
is the
(31)
adjusted for the reduction in pollination caused by stress, Kspol is the
water stress coefficient for pollination on a given day, F is the fraction of the total number of
potentially successful flowers going through anthesis on that day, and
is a factor allowing for
the effects of excessive sinks (Raes et al., 2009).
4.5.4 Calibration of AquaCrop
Calibration of AuaCrop was achieved by comparing the recorded/actual corn production
(as provided by the Department of Agriculture in Ministry of Agriculture and Food Security in
Malawi) with the corn yield simulated by the model. Input data detailing various crop parameters
used in the model were obtained from Chitedze research station. By comparing the actual and
simulated corn yields, we were able to adjust crop parameters, field management and soil
characteristics through trial and error, until a closest match between recorded and simulated corn
yield was achieved. Data (recorded corn yields, rainfall, minimum and maximum temperature,
wind speed at 2m above ground, mean relative humidity and daily number number of sunshine
hours) for Chitedze station for year 2005 was used to calibrated the model.
Table 9 presents some of the most important crop input parameters that were used in
calibration of the model (Abedinpour et al., 2012).
51
Table 9. Input crop parameters used in calibration of AquaCrop
Description of parameter
Base temperature
Cut-off temperature
Canopy growth coefficient (CGC)
Canopy decline coefficient (CDC) at
senescence
Leaf growth threshold (Pupper)
Value used
10
40
20.9
0.948
0.16
Leaf growth threshold (Plower)
Leaf growth stress coefficient curve
shape
Expansion stress coefficient (Pupper)
Expansion stress coefficient (PLower)
Expansion stress coefficient curve shape
Stomatal conductance threshold (Pupper)
Stomatal stress coefficient curve shape
Senescence stress coefficient curve shape
Senescence stress coefficient (Pupper)
0.81
2.8
Coefficient, inhibition of leaf growth on
HI
8.0
Coefficient, inhabitation of stomata on HI
4.5
Maximum basal crop coefficient (Kcb)
Time from sowing to emergence
Time to maximum canopy cover
Time from sowing to start flowering
Time from sowing to start senescence
Time from sowing to maturity
Length of the flowering stage
Length of building up HI
1.25
8
52
70
98
127
10
52
0.18
0.63
1.2
0.65
1.9
1.6
0.46
Unit of parameter
o
C
o
C
% day−1
% day−1
% of TAW (fraction of total
available water (TAW))
% of TAW
Unit less (moderately convex
curve)
% of TAW
% of TAW
% of TAW
Unit less
Unit less (high convex curve)
Unit less (moderately convex curve)
Unit less (Initiation of canopy
senescence)
Unit less (HI increased by
inhibition of leaf growth at
anthesis)
Unit less (HI increased by
inhibition of stomata at anthesis)
Unit less
Days
Days
Days
Days
Days
Days
Days
4.5.5 Validation of AquaCrop
Having calibrated AquaCrop, it was significant that the model be validated in order to
evalute its performance in simulating crop yields. Model validation is important in order to
determine if the model has the ability to replicate the data, to analyse the effectiveness of model
calibration and compare synthetic data with those done in previous studies (Krause et al., 2005).
52
Loague and Green (1991) indicates that there are numerous statistical indicators for evaluating
perfomance of AquaCrop, nonetheless, Willmott (1984) argues that each of the statisital
indicators have their own weaknesses and strengths. To effectively evaluate the performance of
the model, the use of ensemble statistical indicators is appropriate (Willmott, 1984). In our
analysis of performance of AquaCrop, the following list of statistical indicators were used:
prediction error (Pe), coefficient of determination (R2), mean absolute error (MAE), root mean
square error (RMSE) and model efficiency (E). E and R2 indicate the predictive power of the
model whilst Pe, MAE and RMSE is used to signify the amount of error associated with the
model prediction (Abedinpour et al., 2012).
Calibrated crop paramaters, management practices and soil types and conditions
remained constant. Corn yields for years 2000 to 2004 were used to compare with those
simulated by the model within the same time period.
Equations 32-36 show the statistical relationships that were used to evaluate the
performance of the model based on its predictive prowess and amount of error associated with it
(Nash and Sutcliffe, 1970). Equation 32 is a relationship used to estimate, R2, which is the
squared values of Pearson colleration coefficient that ilustrates squared ratio between covariance
and the multiplied standard deviations of the observed and estimated corn yields.
∑
[
√∑
̅
̅
̅
∑
̅
]
53
∑
∑
(
)
where, Si and Oi are synthetic and actual (observed) production, ̅ i is the mean value of Oi and
N is the number of observtions.
√
∑
√∑
The model is said to perform better when values of E and R2 approaches one and when
values of Pe, MAE and RMSE approaches zero (Moriasi et al., 2007).
54
CHAPTER FIVE: RESULTS AND DISCUSSION
5.1 Introduction
This chapter presents the results of the study in order to meet the stated objectives. First,
the results of the estimated future climate variables using GCMs, and statistical downscaling of
the outputs using LARS-WG, are presented. Results from statistical comparison between
historically observed and simulated weather data and those derived from model calibration are
also presented. Third, outcomes of uncertainty analysis of GCMs in climate projections are
discussed. Ultimately, we discuss the calibration and validation procedure of the crop model
(AquaCrop) and present the results of future simulated corn yields relative to the baseline period.
5.2 Estimation of Future Climate Variables
5.2.1 Calibration and Validation of LARS-WG
The Q test function was used to determine the ability of Lars-WG to rationally estimate
future climate variables. This was achieved using two statistical tests; chi-square test (X2) and ttest. The chi-square test was used to determine the existence of any significant difference
between the expected and observed frequencies in the meteorological data. Additionally, a t-test
was used to check the existence of any reliable difference between the means of the estimated
and observed data sets. Large X2 and t values indicates the existence of real difference between
observed and estimate climate variables. Conversely, smaller X2 and t values indicate that there
is less difference between observed and estimated data sets.
Each X2 and t value has a
corresponding p-value, which is the probability that the pattern of data in the sample could be
produced by random variables. A p-value of 0.05 simply means there is a probability of 5% that
there is no difference between observed and estimate data. P-values below the set significance
55
level indicate that the simulated climate variables are far from the true climate values. For the
purpose of this study, a p-value was set at 0.05 which is commonly used in climate studies.
Table 10 shows the results of the p-values for both X2 and t-tests for rainfall, minimum
and maximum temperature from January to December. Figure 10 plots the monthly p-values of
X2 and t-tests for each of the three variables shown in Table 10. The results indicate that p-values
in all months for both rainfall and temperature are larger than the selected significance level of
0.05. Thus the model can be said to satisfactorily simulate future climate data.
Table 10. Statistical comparison of synthetic and observed weather data using Q test
(Significant p-value = 0.05)
Rainfall
Month
Minimum Temperature
Maximum Temperature
X2
pvalue
t
pvalue
X2
pvalue
t
pvalue
X2
pvalue
t
pvalue
Jan
0.06
1.00
0.30
0.77
0.05
1.00
0.05
0.96
0.05
1.00
0.75
0.46
Feb
0.07
1.00
0.48
0.63
0.05
1.00
0.14
0.97
0.05
1.00
0.26
0.79
Mar
0.06
1.00
0.68
0.50
0.16
0.91
2.21
0.08
0.05
1.00
1.25
0.22
Apr
0.08
1.00
0.25
0.81
0.11
1.00
1.60
0.11
0.11
1.00
0.39
0.70
May
0.22
0.57
0.26
0.80
0.05
1.00
0.43
0.67
0.16
0.91
2.04
0.75
Jun
0.31
0.19
0.33
0.74
0.11
1.00
0.86
0.39
0.11
1.00
0.28
0.78
Jul
0.22
0.60
0.25
0.80
0.05
1.00
0.02
0.98
0.05
1.00
0.94
0.35
Aug
0.39
0.14
0.96
0.34
0.11
1.00
1.31
0.19
0.05
1.00
0.07
0.95
Sep
0.13
0.98
0.13
0.90
0.05
1.00
0.09
0.93
0.11
1.00
2.48
0.36
Oct
0.07
1.00
0.13
0.90
0.11
1.00
1.31
0.19
0.11
1.00
1.78
0.18
Nov
0.06
1.00
1.15
0.25
0.16
0.91
1.85
0.07
0.05
1.00
0.44
0.66
Dec
0.06
1.00
0.39
0.70
0.11
1.00
0.34
0.74
0.05
1.00
0.22
0.83
56
1.2
chi square
t-tes
sig value
P-value (Rainfall)
1
0.8
0.6
0.4
0.2
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
1.2
P-value (max temp)
chi square
t-tes
sig value
1
0.8
0.6
0.4
0.2
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
1.2
P-value (Min temp)
chi square
t-tes
sig value
1
0.8
0.6
0.4
0.2
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Figure 10. Plot of P-values for X2 and t tests for rainfall and temperature
57
5.2.2 Estimating Future Climate Variables
Fifteen (15) GCMs were used to project future rainfall and temperature under two
emission scenarios (A and B), in three different time horizons (2020s, 2050s and 2090s) relative
to a baseline period of 1971-2000. To avoid a mismatch between global and local climate data
due to low resolution of GCMs, outputs of GCMs were downscaled using Lars-WG, a stochastic
weather generator. Figures 11 to 13 show the simulated mean monthly values of rainfall,
minimum and maximum temperature in each time horizon, under each scenario.
In general, Figures 11 to 13 indicate a wide range in synthetic climate variables as
predicted by each of the 15 GCMs in each emission scenario. For instance, consider the
estimated rainfall amount in the month of January estimated under emission scenario B in 2020s
(SRB-2020s). Simulated rainfall amount by all GCMs for this month ranges approximately
between 210-270mm. This range in magnitude of output data simply confirms the notion that
output weather variables from GCMs are associated with uncertainties. This phenomenon recurs
in the rest of the months, scenarios and in all time periods both in simulated rainfall and
minimum and maximum temperature data. It is therefore significant that such uncertainties are
accounted for before outputs of GCMs are used in climate change assessment studies.
58
250
300
50%
75%
Observed
SRA-2020's
200
150
100
50
Simulated rainfall (mm)
Simulated rainfall (mm)
300
0
200
150
100
50
0
Month
Month
300
50%
75%
Observed
SRA-2050's
250
200
150
100
50
Simulated rainfall (mm)
Simulated rainfall (mm)
300
0
50%
75%
Observed
SRB-2050's
250
200
150
100
50
0
Month
Month
300
50%
75%
Observed
SRA-2090's
250
200
150
100
50
Simulated rainfall (mm)
300
Simulated rainfall (mm)
50%
75%
Observed
SRB-2020's
250
0
50%
75%
Observed
SRB-2090's
250
200
150
100
50
0
Month
Month
Figure 11: Box plots showing the simulated mean monthly rainfall for 15 GCMs under scenarios A and B
for three different time steps. The box plots indicate the lower (50%) and upper (75%) quartiles, the line
between them is the median, and the lower and upper whiskers represent the mean ± standard deviation.
The dotted black line shows the historical observed mean monthly rainfall.
59
25
50%
75%
Observed
SRA-2020's
20
Simulated min temp (oC)
Simulated min temp(oC)
25
15
10
20
15
10
5
5
Month
Month
25
50%
75%
Observed
SRA-2050's
20
Simulated min temp (oC)
Simulated min temp (oC)
25
15
10
5
50%
75%
Observed
SRB-2050's
20
15
10
5
Month
Month
25
25
SRA-2090's
50%
75%
Observed
20
Simulated min temp (oC)
Simulated min temp (oC)
50%
75%
Observed
SRB-2020's
15
10
50%
75%
Observed
SRB-2090's
20
15
10
5
5
Month
Month
Figure 12. Box plots showing the simulated mean monthly minimum temperature for 15 GCMs under
scenarios A and B for three different time periods. The box plots indicate the lower (50%) and upper
quartiles (75%), the line between them is the median, and the lower and upper whiskers represent the
mean ± standard deviation. The dotted black line shows the historical observed mean monthly minimum
temperature.
60
35
50%
75%
Observed
Simulated maxi temp (oC)
Simulated max temp (oC)
35
SRA-2020's
30
25
20
25
20
35
Simulated max temp (oC)
Simulated max temp (oC)
50%
75%
Observed
Month
SRA-2050's
30
25
20
SRB-2050's
25
20
50%
75%
Observed
Month
35
SRA-2090's
Simulated max temp (oC)
Simulated max temp (oC)
50%
75%
Observed
30
Month
35
SRB-2020's
30
Month
35
50%
75%
Observed
30
25
20
50%
75%
Observed
SRB-2090's
30
25
20
Month
Month
Figure 13. Box plots showing the simulated mean monthly maximum temperature for 15 GCMs under
scenarios A and B for three different time steps. The box plots indicate the lower (50%) and upper
quartiles (75%), the line between them is the median, and the lower and upper whiskers represent the
mean ± standard deviation. The dotted black line shows the historical observed mean monthly maximum
temperature.
61
5.2.3 Uncertainty Analysis of GCMs
As indicated in 4.2.2, high uncertainties associated with GCM’s output greatly affect the
confidence levels of the results of any climate assessment studies. Several methods have been
proposed for dealing with uncertainties of GCMs. This study employs the use of probability
assessment of bounded range with known probability distribution in order to deal with
uncertainties of the 15 GCMs. This process first involves determination of individual weights of
each model generation of probability distribution functions (PDFs) and construction of
cumulative distribution functions (CDFs) based on the generated PDFs.
5.2.3.1 Weighting of GCMs
Each GCM is weighted according to its potential to simulate climate variables. Table 11
shows the weight of each GCM in simulating future changes rainfall and Table 12 shows the
weight of each GCM in simulating future changes temperature in each month.
5.2.3.2 Probability Distribution Functions (PDFs)
This is the second process of uncertainty analysis of GCMs. It involves generating
monthly PDFs of rainfall and minimum and maximum temperature. Figures 14-16 show sample
monthly discrete PDFs for the 15 GMs under scenarios A (SRA) and B (SRB).
62
Table 11. Calculated weight of each GCM in simulating future rainfall
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
BCM2
CGMR
CNCM3
CSMK3
FGOALS
GFCM21
GIAOM
HADCM3
HADGEM
INCM3
IPCM4
MIHR
MPEH5
NCCCSM
NCPCM
0.04
0.00
0.01
0.00
0.01
0.04
0.01
0.05
0.08
0.03
0.00
0.01
0.05
0.01
0.09
0.15
0.03
0.02
0.00
0.03
0.03
0.05
0.27
0.04
0.03
0.00
0.03
0.01
0.02
0.12
0.05
0.11
0.03
0.02
0.01
0.01
0.06
0.01
0.03
0.04
0.06
0.08
0.01
0.05
0.06
0.07
0.02
0.02
0.01
0.00
0.13
0.33
0.03
0.03
0.00
0.06
0.03
0.07
0.02
0.00
0.01
0.00
0.03
0.01
0.04
0.04
0.01
0.11
0.43
0.03
0.02
0.00
0.05
0.03
0.42
0.01
0.01
0.01
0.00
0.02
0.01
0.01
0.02
0.14
0.28
0.03
0.02
0.14
0.15
0.06
0.01
0.08
0.03
0.10
0.32
0.01
0.02
0.00
0.03
0.07
0.05
0.06
0.01
0.03
0.02
0.03
0.08
0.01
0.15
0.00
0.07
0.05
0.12
0.07
0.01
0.05
0.05
0.06
0.02
0.03
0.12
0.84
0.05
0.06
0.30
0.06
0.01
0.09
0.13
0.04
0.01
0.01
0.06
0.01
0.03
0.01
0.01
0.01
0.00
0.01
0.01
0.06
0.14
0.03
0.04
0.01
0.03
0.05
0.04
0.32
0.86
0.23
0.05
0.02
0.02
0.51
0.05
0.05
0.02
0.03
0.11
0.06
0.01
0.05
0.03
0.04
0.03
0.01
0.03
0.01
0.02
0.03
0.01
0.01
0.00
0.02
0.02
0.39
0.03
0.16
Table 12. Calculated weight of each GCM in simulating future temperature
BCM2
CGMR
CNCM3
CSMK3
FGOALS
GFCM21
GIAOM
HADCM3
HADGEM
INCM3
IPCM4
MIHR
MPEH5
NCCCSM
NCPCM
Jan
0.04
0.01
0.06
0.20
0.01
0.01
0.09
0.01
0.05
0.00
0.00
0.01
0.01
0.02
0.49
Feb
Mar
Apr
0.17
0.03
0.08
0.02
0.01
0.03
0.07
0.30
0.06
0.13
0.04
0.23
0.04
0.02
0.09
0.03
0.00
0.03
0.08
0.02
0.13
0.03
0.01
0.03
0.24
0.01
0.08
0.02
0.01
0.05
0.01
0.00
0.01
0.02
0.01
0.07
0.03
0.01
0.02
0.06
0.02
0.04
0.04
0.52
0.04
May
Jun
0.05
0.09
0.03
0.02
0.04
0.02
0.13
0.11
0.08
0.04
0.05
0.33
0.06
0.03
0.08
0.03
0.22
0.13
0.03
0.02
0.02
0.02
0.12
0.11
0.03
0.02
0.04
0.02
0.02
0.02
July
Aug
0.06
0.03
0.02
0.01
0.02
0.02
0.09
0.67
0.04
0.01
0.07
0.02
0.03
0.01
0.02
0.01
0.16
0.04
0.02
0.01
0.03
0.01
0.37
0.13
0.02
0.01
0.03
0.01
0.02
0.02
Sep
Oct
0.10
0.06
0.15
0.06
0.06
0.04
0.05
0.04
0.02
0.02
0.05
0.05
0.03
0.05
0.02
0.02
0.07
0.12
0.11
0.18
0.03
0.03
0.08
0.12
0.03
0.02
0.04
0.04
0.17
0.14
Nov
0.13
0.03
0.07
0.04
0.01
0.03
0.12
0.01
0.10
0.24
0.01
0.11
0.02
0.03
0.05
Dec
0.12
0.04
0.06
0.06
0.01
0.04
0.24
0.01
0.08
0.02
0.01
0.02
0.01
0.15
0.14
63
0.30
0.25
Weights of GCMs
Weights of GCMs
0.30
2020s-SRA
0.20
0.15
0.10
0.05
2050s-SRA
0.20
0.15
0.10
0.05
-
0.9
0.95
1
1.05
Relative rainfall change
0.8
1.1
0.9
1
1.1
Relative rainfall change
1.2
1.00
1.00
2050s-SRB
0.80
Weights of GCMs
Weights of GCMs
0.25
0.60
0.40
0.20
2090s-SRB
0.80
0.60
0.40
0.20
-
0.8
0.9
1
1.1
Relative rainfall change
0.8
1.2
0.9
1
1.1
Relative rainfall change
1.2
Figure 14. Sample Discrete PDFs outlining the relationship between weights of 15 GCMs and monthly
changes in relative rainfall. The top two graphs indicate relative rainfall changes in January for scenario A
for 2020s and 2050s. The bottom 2 graphs indicate relative rainfall changes in February for scenario A for
2050s and 2090s.
64
0.60
2020s-SRA
0.50
Weights of GCMs
Weights of GCMs
0.60
0.40
0.30
0.20
0.10
0.40
0.30
0.20
0.10
-
0.4
0.9
Min temperature change (oC)
1.4
0.4
2.4
4.4
Min temperature change (oC)
6.4
0.6
0.6
2020s-SRB
0.5
Weights of GCMs
Weights of GCMs
2090s-SRA
0.50
0.4
0.3
0.2
0.4
0.3
0.2
0.1
0.1
0
0
0.4
0.9
1.4
1.9
Min temperature change (oC)
2050s-SRB
0.5
0.4
2.4
0.6
0.8
1
Min temperature change (oC)
1.2
Figure 15. Sample Discrete PDFs outlining the relationship between weights of 15 GCMs and monthly
changes in minimum temperature. The top two graphs indicate temperature changes in March for scenario
A for 2020s and 2090s. The bottom 2 graphs indicate temperature changes in April for scenario B for
2020s and 2050s.
65
0.3
2090s-SRA
2050s-SRA
Weights of GCMs
Weights of GCMs
0.3
0.2
0.1
0.1
0
0
0.4
1.4
2.4
Max temperature change (oC)
0.4
3.4
2.4
4.4
Max temperature change (oC)
6.4
0.4
0.4
2020s-SRB
0.3
Weights of GCMs
Weights of GCMs
0.2
0.2
0.1
0
2050s-SRB
0.3
0.2
0.1
0
0
0.25
0.5
0.75
1
Max temperature change (oC)
1.25
0
0.5
1
1.5
2
2.5
Max temperature change (oC)
3
Figure 16. Sample Discrete PDFs outlining the relationship between weights of 15 GCMs and monthly
changes in maximum temperature. The top two graphs indicate temperature changes in April for scenario
A for 2050s and 2090s. The bottom 2 graphs indicate temperature changes in April for scenario B for
2020s and 2050s.
5.2.3.3 Cumulative Distribution Functions (CDFs)
The generated PDFs were converted to CDFs using the gamma distribution function
whose parameters alpha (α) and beta (β) were determined from a simple code written in Matlab
programming language. We then extracted climate variables at 25%, 50% and 75% probability
percentiles from the developed CDFs. Climate values at these three percentiles were then used as
input data for corn yield impact assessment with AquaCrop.
66
Figures 17-19 indicate sample monthly CDFs from the generated PDFs, for two scenarios
at three different time steps (2020s, 2050, and 2090s).
1
1
2050s SRA
Exceedence Probability
Exceedence Probability
2020s SRA
0.8
0.6
0.4
0.2
0
0.6
0.4
0.2
0
0.9
1
0.95
1
1.05
Relative rainfall change
1.1
0.7
0.8 0.9 1 1.1 1.2
Relative rainfall change
1
2050s SRB
2090s SRB
Exceedence probability
Exceedence probability
0.8
0.8
0.6
0.4
0.2
0.8
0.6
0.4
0.2
0
0
0.8
0.9
1
1.1
Relative rainfall change
1.2
0.8
Figure 17. Sample CDFs for rainfall based on PDFs in Figure 14.
67
0.9
1
1.1
Relative rainfall change
1.2
1
1
2090s SRA
Exceedence probability
Exceedence probability
2050s SRA
0.8
0.6
0.4
0.2
0
0.8
0.6
0.4
0.2
0
0
0.5 1 1.5 2 2.5
Change in Tmin(oC)
3
0
6
1
1
2090s SRB
Exceedence probability
2020s SRB
Exceedence probability
1
2
3
4
5
Change in Tmin(oC)
0.8
0.6
0.4
0.2
0.8
0.6
0.4
0.2
0
0
0.5
1
1.5
2
Change in Tmin (0C)
2.5
0
2
4
6
Change in Tmin (oC)
Figure 18. Sample CDFs for minimum temperature based on PDFs in Figure 15.
68
8
1
2050s SRA
0.8
Exceedence probability
Exceedence probability
1
0.6
0.4
0.2
0
0
0.5
1 1.5 2 2.5 3
Chang in Tmax (oC)
2090s SRA
0.8
0.6
0.4
0.2
0
3.5
0
1
6
1
2020s SRB
2050s SRB
0.8
Exceedence probability
Exceedence probability
1
2
3
4
5
Change in Tmax (oC)
0.6
0.4
0.2
0
0.8
0.6
0.4
0.2
0
0
0.5
1
Change in Tmax (oC)
1.5
0
1
2
3
Change in Tmax (OC)
4
Figure 19. Sample CDFs for maximum temperature based on PDFs in Figure 16.
5.2.3.4 Generation of Probability Percentiles
The magnitude of the expected changes in rainfall, minimum and maximum temperature
at three different probability percentiles (25%, 50% and 75%), were determined from the
synthetic CDFs in 4.2.2.3 for both scenarios (SRA and SRB) and in three time steps (2020s,
2050s and 2090s). The results are plotted as shown in Figures 20-22 below.
Figure 20 shows the expected changes in future rainfall amounts under two scenarios.
The simulated results depict that higher rainfall changes are expected under scenario A than
69
scenario B in all three time periods. The expected changes in monthly rainfall for each time
period varies between risk levels. Results indicate increase in rainfall in some months and
decrease in rainfall in same months at different risk levels. For example, rainfall is predicted to
decrease between may and August ( cool and dry winter), excepting for SRA in 2020s where it is
expected to increase. On average, annual rainfall amounts are predicted to decrease by 2% to
17%, 0% to 26% and 3% to 29% in 2020s, 2050s and 2090s respectively. Average annual and
overall ranges of estimated rainfall in each time period, under each scenario and risk level, are
shown in Table 13.
Table 13. Summary of mean annual and overall ranges of estimated climate variables in each time period,
at different scenarios and probability percentiles
Climate Variable
Scenario
Percentile
25
A
50
Rainfall
Change (%)
25
B
50
75
75
2020s
2050s
2090s
11
-5
-6
-1
-16
-19
-11
-26
-29
-2
0
-3
-9
-12
-17
-17
-23
-29
-17
-26
-29
11
0
-3
Tmin
Change (oC)
2020s
2050s
2090s
0.65
1.59
2.64
0.79
1.86
3.03
0.94
2.15
3.46
0.59
1.31
1.84
0.72
1.61
2.32
0.88
1.94
2.87
0.59
1.31
1.84
0.94
2.15
3.46
Tmax
Change (oC)
2020s
2050s
2090s
0.58
1.56
2.53
0.75
1.86
3.02
0.94
2.2
3.58
0.52
1.26
1.78
0.68
1.61
2.3
0.88
2.01
2.87
0.52
1.26
1.78
0.94
2.2
3.58
70
Range
1.8
2020s-SRA
2
25% probability percentile
50% probability percentile
75% probability percentile
Relative precipitation change
Relative precipitation change
2
1.6
1.4
1.2
1
0.8
0.6
0.4
1.8
2020s-SRB
1.6
1.4
1.2
1
0.8
0.6
0.4
Month
1.4
2050s-SRA
1.2
25% probability percentile
50% probability percentile
75% probability percentile
Relative precipitation change
Relative precipitation change
Month
1.4
1
0.8
0.6
0.4
0.2
1.2
25% probability percentile
50% probability percentile
75% probability percentile
0.8
0.6
0.4
0.2
Month
1.4
1.4
2090s-SRA
1.2
Relative precipitation change
Relative precipitation change
2050s-SRB
1
Month
1
0.8
0.6
0.4
0.2
25% probability percentile
50% probability percentile
75% probability percentile
25% probability percentile
50% probability percentile
75% probability percentile
0
1.2
2090s-SRB
1
0.8
0.6
0.4
0.2
25% probability percentile
50% probability percentile
75% probability percentile
0
Month
Month
Figure 20. The estimated future changes in relative rainfall at three probability percentiles. The horizontal
dash-dot line indicates no changes in rainfall. The top two plots indicate relative rainfall changes for the
71
near future (2020s), the middle plots indicate relative rainfall changes for the middle period (2050s) and
the bottom plots indicate the relative rainfall changes in the long term period (2090s), all under SRA and
SRB.
1.6
1.4
Change in min temp (oC)
Change in min temp (oC)
1.6
25% probability percentile
50 probability percentile
75 probability percentile
2020s-SRA
1.2
1
0.8
0.6
0.4
1.4
2020s-SRB
1.2
1
0.8
0.6
0.4
Month
Month
4.5
3.5
2050s-SRA
25% probability percentile
50% probability percentile
75% probability percentile
Changes in min temp (oC)
Changes in min temp (oC)
4.5
4
3
2.5
2
1.5
4
2050s-SRB
3.5
3
2.5
2
1
Month
Month
4.5
25% probability percentile
50% probability percentile
75% probability percentile
4.5
2090s-SRA
Change in min temp (oC)
Change in min temp (oC)
25% probability percentile
50% probability percentile
75% probability percentile
1.5
1
4
25% probability percentile
50% probability percentile
75% probability percentile
3.5
3
2.5
2
4
2090s-SRB
25% probability percentile
50% probability percentile
75% probability percentile
3.5
3
2.5
2
1.5
1
Month
Month
Figure 21. Estimated future changes in minimum temperature at three probability percentiles. The top two
plots indicate minimum temperature changes for the near future (2020s), the middle plots indicate
72
1.4
25% probability percentile
50% probability percentile
75% probability percentile
2020s-SRA
1.2
1
0.8
0.6
Change in max temp (oC)
Change in max temp (oC)
minimum temperature changes for the middle period (2050s) and the bottom plots indicate the minimum
temperature changes in the long term period (2090s), under SRA and SRB.
0.4
1.4
2020s-SRB
1.2
25% probability percentile
50% probability percentile
75% probability percentile
1
0.8
0.6
0.4
0.2
Month
Month
2050s-SRA
2.7
25% probability percentile
50% probability percentile
75% probability percentile
Change in max temp(oC)
Change in max temp (oC)
3.2
3.2
2.2
1.7
1.2
0.7
0.2
2.7
2050s-SRB
2.2
1.7
1.2
0.7
0.2
Month
Month
2090s-SRA
25% Probability percentile
50% probability percentile
75% probability percentile
Change in max temp (oC)
Change in max temp (oC)
5.5
5
4.5
4
3.5
3
2.5
2
1.5
1
25% probability percentile
50% probability percentile
75% probability percentile
5.5
5
4.5
4
3.5
3
2.5
2
1.5
1
2090s-SRB
25% probability percentile
50% probability percentile
75% probability percentile
Month
Month
Figure 22. Estimated future changes in maximum temperature at three probability percentiles. The top
two plots indicate maximum temperature changes for the near future (2020s), the middle plots indicate
maximum temperature changes for the middle period (2050s) and the bottom plots indicate the maximum
temperature changes in the long term period (2090s), all under SRA and SRB.
73
Figures 21 and 22 are plots of minimum and maximum temperatures for scenarios A and
B under three percentiles at three time periods. Overall, temperature is expected to increase in all
cases, with higher temperature changes expected under scenario A than in scenario B. Absolute
values for changes in minimum temperature for 2020s, 2050s and 2080s are 0.59oC to 0.94oC,
1.31oC to 2.15oC and 1.84oC to 3.46oC respectively. Similarly, changes in maximum temperature
for the same time periods were estimated as follows; 0.59oC to 0.94oC, 1.31oC to 2.15oC and
1.84oC to 3.46oC respectively. A summary of average changes in minimum and maximum
temperature for each scenario and time period is shown in Table 13.
Figure 23 shows plots of percent changes in rainfall and absolute changes in minimum
and maximum temperature under each scenario and risk level.
74
Change in Rainfall (%)
15
10
5
0
-5
-10
-15
-20
-25
-30
-35
2020s
A25
A50
2050s
A75
B25
2090s
B50
B75
Change in min temp (oC)
Scenario Percentile
4
2020s
3.5
2050s
2090s
3
2.5
2
1.5
1
0.5
0
A25
A50
A75
B25
B50
B75
Scenario percentile
Change in max temp (oC)
4
2020s
3.5
2050s
2090s
3
2.5
2
1.5
1
0.5
0
A25
A50
A75
B25
B50
B75
Scenario percentile
Figure 23. Plots of changes in rainfall, minimum temperature and maximum temperature under each
scenario and risk level
75
Simulated rainfall and temperature results compare well with those obtained in the
previous studies within the same area. Saka et al. (2003) used four GCMs (CSIRO, ECHAM,
CGCMI and HadCM2) to estimate future changes in weather data for years 2020, 2075 and 2100
using baseline weather data of 1961-1990. They estimated that mean temperature will increarse
by 1oC by the year 2020, 2oC by the year 2075 and 4oC by the year 2100. These estimates are
consistent with results obtained in this study which are summarised in Table 13. In terms of
rainfall, they also discovered that its pattern was not so certain with increase and decrease in
different months in a year. However, they predicted a decrease in annual rainfall amounts
ranging from 2% to 8% relative to the baseline period which is less than the values obtained in
this study.
Simulated weather data for Malawi reflects what scholars have defined as an
‘Aridification Scenario’, or the ‘dry’ and ‘core’ scenarios, meaning a reduction in rainfall
amount and an increase in temperatures (Hulme, 2000; Saka et al., 2003).
5.3 Corn Yield Estimation with Aquacrop
5.3.1 Model Calibration
Calibration of AquaCrop was carried out in order to set a benchmark for future
simulation of corn yields at various probability percentiles and different time periods. By
inputting the 2005 weather data into the model, the model grossly overestimated corn yields
when compared with recorded data. It was therefore essential that adjustment of important and
sensitive parameters in the model be carried out. By adjusting crop, management and soil
properties in the main menu of AquaCrop, we were able to derive an output yield close in value
to the recorded yield in 2005. Table 9 in 4.5.4 shows the final calibrated values of crop
parameters which were vital in determining the final Biomass, Harvest Index and hence Yield of
76
corn using AquaCrop.
Before calibrating the model, first we estimated the potential
evapotranspiration (ETo) using the FAO ETo Calculator which uses the Penman-Monteith
equation. Recorded data for corn production in year 2005 for Lilongwe District was .92 tons per
hectare. After varying the model parameters, the closest simulated production was 0.926 tons per
hectare. Once model calibration was complete, the model was then validated to determine its
potential to simulate corn yields.
5.3.2 Model Validation
The FAO AquaCrop model was validated in order to determine its potential to simulate
future corn yields at various probability percentiles and time periods. Historical recorded corn
yields for the District from 2000-2004 were used to compare with those simulated by AquaCrop.
Figure 24 shows a plot of the results obtained from the model validation versus the recorded
yields for the case study.
Simulated Yield (ton/ha)
2
1.8
Yield = 0.9012x + 0.1505
R² = 0.911
1.6
1.4
1.2
Corn Yield
1
Linear (Corn Yield)
0.8
0.8
1
1.2
1.4
1.6
1.8
2
Observed Yield (ton/ha)
Figure 24. Results of AquaCrop validation using the 2000-2004 simulated and recorded corn yields
From Figure 24, a coefficient of determination (R2) of 0.911 was obtained.
Other
statistical parameters like prediction error (Pe), model efficiency (E), root mean square error
77
(RMSE) and mean absolute error (MAE) were estimated using Equations 33-36. Table 14
presents a summary of the results of the statistical analysis used in validation of AquaCrop.
Table 14. AquaCrop validation results including prediction error statistics for corn yields
Year
Yields (ton/ha)
Pe
E
Observed
Simulated
(±%)
2000
1.87
1.92
2.67
2001
1.62
1.46
9.88
2002
1.83
1.81
1.09
2003
1.04
1.12
7.69
2004
1.38
1.42
2.75
R2
RMSE
MAE
(ton/ha)
0.92
0.91
0.09
0.00
Note that the minimum and maximum prediction error (Pe) for corn yields for five years
growing seasons (2000-2004) ranges from 1.09 to 9.88%, and RMSE and MAE values were 0.09
and 0.00 respectively. The three statistical parameters are either equal or close to zero, an
indication of the suitability of the model to simulate future corn yields. Estimated values of
model efficiency (E) and coefficient of determination (R2) of 0.92 and 0.91 respectively are both
close to 1, which further increases confidence in the model’s potential to simulate future corn
yields.
The validation results indicate that the model can be used to simulate future corn yields
within acceptable deviations from the true values. The values of simulated corn yields exhibited
very little departure from the historical harvested yields and this was substantiated with the
statistical analysis that was carried out in the process of model validation.
5.3.3 Simulating Current and Future Corn Yields with Climate Change
Simulating future potential rainfed corn yields with climate change is the main focus of
this study. To achieve this objective, we used the calibrated and validated crop model
(AquaCrop) to estimate both average historical corn yields for the baseline period and future
78
potential corn production at various probability percentiles. Climate data (rainfall, minimum and
maximum temperature, sunshine hours, relative humidity) for the 30 years baseline period (19712000) were broken down into monthly averages in each of the 12 months. The results from the
model run indicated an average baseline period corn production of 3.41 ton/ha. This value is
considered as corn production without climate change. Average values of future potential corn
yields at various probability percentiles were calculated in a similar manner. Table 15
summarizes the simulated corn yields as a percentage, relative to the baseline period.
Table 15. Corn production (tons/hectare) at different probability percentiles
Period
Percentile
2015-2035
(2020s)
2046-2065
(2050s)
2080-2099
(2090s)
25
50
75
25
50
75
25
50
75
Production change (%)
Scenario A
Scenario B
-6.58
-4.70
-5.14
-8.19
-9.84
-14.33
-13.19
-14.86
-16.24
0.53
-8.11
-4.85
-7.25
-11.72
-13.54
-14.01
-15.39
-31.86
Range (%)
0.53
-8.11
-7.25
-14.33
-13.19
-31.86
Figures 25-27 show the changes in corn production relative to the baseline period and are
generated from the results shown in Table 15. In general, the results suggest a decrease in corn
yields for all the three time steps, scenarios and percentiles excepting for 2015-2035 period
where a positive increase in corn production of 0.53% is expected under scenario B at 25%
probability percentile. Note that SRA25 means change in corn production for scenario A at 25%
probability percentile.
79
Change in Production (%)
2.00
0.00
-2.00
-4.00
-6.00
-8.00
-10.00
% Change
SRA25
-6.58
SRB25
0.53
SNRA50
-4.70
SNRB50
-8.11
SNRA75
-5.14
SNRB75
-4.85
Change in Production (%)
Figure 25. Change in corn production for 2015-2035 relative to baseline period of 1971-2000.
0.00
-2.00
-4.00
-6.00
-8.00
-10.00
-12.00
-14.00
-16.00
% change
SRA25
-8.19
SRB25
-7.25
SNRA50
-9.84
SNRB50
-11.72
SNRA75
-14.33
SNRB75
-13.54
Change in Production (%)
Figure 26. Change in corn production for 2046-2065 relative to baseline period of 1971-2000.
0.00
-5.00
-10.00
-15.00
-20.00
-25.00
-30.00
-35.00
% change
SRA25
-13.19
SRB25
-14.01
SNRA50
-14.86
SNRB50
-15.39
SNRA75
-16.24
SNRB75
-32.67
Figure 27. Change in corn production for 2080-2099 relative to baseline period of 1971-2000.
80
The expected percentage changes in corn yields for 2015-2035, 2046-2065, and 20802099 are 0.53% to -8.11%, -0.73% to -14.33%, and -13.19% to -31.86% respectively.
Saka et al. (2003) used CERES Maize to simulate corn yield for a 1961-1990 baseline
period within the same area and discovered that there was a strong correlation between observed
and simulated corn yields for the same period (with simulated yields being slightly higher than
observed production). They then concluded that for future periods, corn yields would also be
higher relative to the baseline period. However, this assumption appears to be ambiguous
because first, in their study, they directly used outputs of GCMs into the simulation model
without downscaling them. We discussed earlier on that due to low resolution of GCMs, their
outputs need to be downscaled before carrying out further climate change assessment studies to
avoid a mismatch between global and local climate data. Second, we also discussed that owing to
high uncertainty levels associated with GCMs, it is necessary that an analysis to account for the
uncertainties of the models is carried out. This procedure was also not done in their study. Third,
the fact that historical recorded corn yields matches with historical simulated yields does not
guarantee future recurrence of similar phenomenon. The 1961-1990 baseline period used in their
study may be considered as pre-change period of climate change, therefore, production in the
post-change period (2020, 2075, 2210), where changes in atmospheric greenhouse gasses is
considered, must be estimated before major conclusions are drawn.
5.3.4 Correlation Analysis between Corn Yields and Rainfall and Temperature Changes
A correlation analysis was carried out to examine the existence of a linear association
between changes in predicted corn yields and changes in rainfall and temperature, as shown in
figure 28 (a and b) below. A correlation coefficient is determined by taking the square root of the
coefficient of determination (R2), with the sign of the correlation coefficient being the same as
81
the sign of the coefficient of x in the equation. Values of correlation coefficient range between -1
and +1, with each of the extremes indicating that the two variables are perfectly related in a
negative and positive linear sense, respectively. A correlation coefficient of 0 indicates that there
is no linear relationship between the two variables.
5
y = 0.0079x - 4.7687
R² = 0.0007
0
∆ Corn Yields (%)
-5
y = 0.2678x - 7.1527
R² = 0.8886
-10
-15
2020s
-20
2050s
y = 0.4077x - 10.593
R² = 0.4046
-25
-30
2090s
a
-35
-40
-30
-20
-10
∆P (%)
0
10
20
5
∆ Corn Yields (%)
2020s
y = -5.6145x - 0.7362
R² = 0.1006
0
-5
2050s
2090s
y = -7.4392x + 2.2061
R² = 0.7781
-10
-15
-20
y = -2.7287x - 10.279
R² = 0.0577
-25
-30
b
-35
0
0.5
1
1.5
2
2.5
o
∆Tmax ( C)
3
3.5
4
-
Figure 28. Correlation analysis between change in corn yields and change in precipitation and maximum
temperature respectively
82
Figure 28a shows a positive linear relationship between changes in corn yields and
rainfall changes for 2050s and 2090s, and no linear relationship in 2020s. The figure shows that a
decrease in precipitation results in decrease in corn yields. Negative values of correlation
coefficient in figure 28b indicate that an increase in temperature results in decrease in corn
yields. Correlation coefficients results in each time period are summarized in Table 16. In
general, excepting for 2020s, absolute values of correlation coefficients associated with changes
in corn yields and rainfall changes are higher than those for changes in corn yields and maximum
temperature changes. This signifies that future corn yields could be more sensitive to rainfall
changes compared with changes in temperature.
Table 16. Summary of values of correlation coefficient for rainfall and maximum temperature
Correlation coefficients
Period
Rainfall
Tmax
2020s
0.03
(-) 0.32
2050s
0.94
(-) 0.88
2090s
0.64
(-) 0.24
83
CHAPTER SIX: CONCLUSIONS AND RECOMMENDATIONS
6.1 Study Results
The main objective of this study was to evaluate climate change impacts on rainfed corn
production in Malawi. 15 GCMs were used to estimate future climate variables in three different
time periods (2020s, 2050s and 2090s) using two scenarios (SRA and SRB). Historical weather
data (1971-2000) for Chitedze Meteorological Station in Lilongwe District was utilized for
future climate projections under climate change. Lilongwe District was selected as a
representative site of the study.
Owing to low resolution of GCMs that result in a mismatch between simulated global and
local data, it was significant to downscale the outputs of GCMs (rainfall and temperature data).
Downscaling of GCMs simulated weather data was achieved using LARS-WG model, a
stochastic weather generator that has been extensively used in climate change studies. The model
was calibrated and validated in order to determine its potential to estimate significant and reliable
future climate variables. High p-values of chi-square and t tests signified high similarity and
reliability between observed and simulated climate data.
GCMs are also associated with high uncertainty levels which negatively impact on the
credibility of the output data. This study employed the use of probability analysis in which a
bounded range with known probability distribution was used to account for the uncertainties of
fifteen (15) GCMs. The procedure involved (i) weighting of GCMs, (ii) generating monthly
probability distribution functions (PDFs) and (iii) constructing monthly cumulative probability
functions (CDFs) from which weather data at three risk levels (25%, 50% and 75%) in three time
periods and two scenarios, were extracted and used as input data for the crop model.
84
AquaCrop Version 4, a new model developed by FAO that simulates the yield response
to water deficit conditions, was used to assess potential corn production under rainfed farming
with and without climate change. The model was first calibrated and validated using historical
data to determine its reliability to simulate corn yields and statistical indicators like R2, E,
RMSE, MAE and Pe were used to assess the suitability of the model to simulate potential corn
yields. AquaCrop does not include an algorithm for calculating ETo, therefore, the FAO ETo
Calculator was used to estimate evapotranspiration for potential crop production in AquaCrop.
Results indicate mean annual rainfall changes of 11 to -17%, 0 to -26% and -3 to -29% in
2020s, 2050s and 2090s respectively. However, mean monthly rainfall patterns depicted both
increase and decrease behavior in different months and time period. An overall increase in mean
monthly temperature in all three time periods was observed. Average minimum temperature
changes for 2020s, 2050s and 2090s were 0.59oC to 0.94oC, 1.31oC to 2.15oC and 1.84oC to
3.46oC respectively. Similarly, changes in maximum temperature for the same time steps were as
follows; 0.52oC to 0.94oC, 1.26oC to 2.20oC and 1.78oC to 3.58oC respectively. Simulated
temperature results compare well with the 2007 report of the Intergovernmental Panel on
Climate Change (IPCC) which indicates that in sub-Saharan Africa, temperatures will rise by
over 3 °C in 21st century. Results of previous study by Saka et al. (2003) in the same region also
concurred with the findings of this study, particularly those of temperature changes.
The results from the crop model show an overall decrease in corn yields in all three time
periods. The expected range of changes in corn production for 2020s, 2050s, and 2090s are as
follows; 0.53% to -0.81%, -0.73% to -14.33%, and -13.19% to -31.86% respectively.
85
6.2 Suggestions to Climate Change Adaptation Strategies
We discussed earlier on that in the past, Malawi has experienced some extreme and
devastating weather events like droughts and floods that caused severe food shortage in the
country. The results of this study suggest that if no adaptation measures are implemented,
climate change will exacerbate food shortage in Malawi. Recent news from Malawi’s Ministry of
Agriculture indicates that in the 2013/2014 financial year, over 2 million people in Malawi are
reported to be food insecure due to poor harvest in the just ended growing season (GoM, 2013).
In view of the aforementioned, this paper suggests some adaptation strategies that the
Government of Malawi could consider to ensure food security under climate change.
Rockstrom (2003) argues that water harvesting could be an answer to future droughts and
may supplement the existing rainfed farming. Water harvesting may be a viable solution for
Malawi. Rainwater can be harvested and stored during the rainy season and could be used to
irrigate crops during dry season. This may allow for multiple harvests within a year thereby
increasing the likelihood of food security for the nation. In addition, Malawi may need to
consider introducing or intensifying irrigation farming by introducing user friendly irrigation
technologies which could be easily operated by subsistence farmers who are generally illiterate
and have little technical know-how. This will not only improve corn production, but also
increase the labor force, subsequently boosting economic status of farmers and of the nation as a
whole
In his article titled “Malawians rethink maize as climate dries”, Sanje (2013) conducted a
survey which revealed that due to climate change, some parts of Malawi barely receive rainfall
for a period of 120 days, the average time to maturity for local varieties of corn. This leads to
accelerated crop growth and hence negatively impacts production. As an adaptation strategy, the
86
study recommends the combined farming of local and hybrid varieties of corn which could
increase corn yields. Hybrid varieties mature between 60 to 80 days after planting, on the other
hand, local varieties take more time to mature, but have developed resilience to climate changes
including dry spells because they have been grown in this area since time immemorial. Therefore
by planting different varieties of corn, there is a high probability of realizing a good harvest.
Tchale (2009) discovered that on average, the soils in Malawi lose 40, 6.6 and 32.2 kg
per hectare per year of nitrogen (N), phosphorus (P) and potassium (K) respectively, mainly due
to runoff and soil erosion. Natural soil conservation methods like mulching and zero-tilling are
some of the practices that could conserve nutrients like carbon into the soil thereby maintaining
its fertility. Natural methods like mulches are said to retain soil moisture and moderate soil
temperatures, consequently subduing the spread of diseases and pests (Dea and Scoones, 2003).
AGRHYMET (2004) outlines a list of indigenous adaptation strategies that have been adopted in
some parts of Africa for preventing nutrient loss, namely: “controlled bush clearing; using tall
grasses such as Andropogon gayanus for fixing soil surface nutrients washed away by runoff;
erosion-control bunding to reduce significantly the effects of runoff; restoring lands by using
green manure; constructing stone dykes; managing low-lying lands and protecting river banks.”
These indigenous adaptation strategies may also be considered in Malawi.
Crop diversification may also help to deal with food insecurity under climate change. We
also discussed earlier on that corn farming is the primary focus food crop in Malawi. However,
with climate change, it may be important to consider other food crops like potatoes, rice, and
cassava. Food crops like cassava do not require as much water as corn, have better resistance to
droughts, and are less fertilizer dependent. Crop diversification may not only ensure food
security but also reduce fertilizer costs thereby uplifting the economic welfare of the farmers.
87
Another adaptation strategy under consideration is biotechnology, defined by the online
free dictionary as a “technique in which living organisms or parts of organisms are altered to
make or modify agricultural products, to improve crops, or develop microbes for specific uses in
agricultural processes”. ECA (2002) believes that biotechnology research could also help African
nations particularly those in Sub-Saharan Africa to effectively deal with climate change effects
on agriculture like pests, insects and droughts. By manipulating the genetic composition of seed
crops, new varieties of crops are likely to evolve that will be pest, drought and insect resistant.
6.3 Study Limitations
Like any other work, this study also has some limitations which may, to a certain extent,
affect the overall results. Human error is one of the crucial areas which may affect the results.
Climate change studies involve dealing with a huge amount of data and human error may occur
at two stages: data processing and in-situ recordings. However, human error is minimized during
observations by taking multiple readings and recording an average value of the climate variables.
Second, the study estimates corn production based on monthly average values of a long period of
time (20 year in each time period). Long term averages do not account for intra-annual weather
variability which may be critical in assessing climate change impacts on crop production. Third,
AquaCrop, a crop simulation model used in this study, does not consider the effect of pests and
weeds on corn yields. Pests and weeds play a significant role in total biomass production and
hence yield. However, the assumption is that proper field management practices are employed
which retards the growth of weeds and outbreak of pests. Fourth, average values of some soil
properties like hydraulic conductivity were used in the model; in practice, these values may vary
immensely from one point to another. Model efficiency assumptions associated with emission
scenarios are other important areas that may limit study results.
88
6.4 Future Work
6.4.1 Introduce Supplemental Irrigation Corn Farming
The study results have estimated a decrease in rainfed corn production in each of the
three time periods. By supplementing rainfed farming with irrigation, the crop model will run on
combined rainfed and irrigation scenario and estimated yields compared with the results obtained
in this study.
6.4.2 Improving the Water Retaining Capabilities of the Soils
We discussed in 6.2, that the soils in Malawi lose substantial amounts of nitrogen (N),
phosphorus (P) and potassium (K), mainly due to runoff and soil erosion. Excessive loss of
nutrients and water may limit crop growth and hence affect crop yield. Management practices
like mulching and erecting soil bunds may to be considered while running the model. The effect
of improving the water retaining capabilities through the above mentioned management practices
may be quantified by the crop model by comparing the yields of a well-managed field with those
of normal managed soils (business as usual).
6.4.3 Intra Annual and Inter Seasonal Variations
Future work shall also consider estimating corn yields taking into consideration both intra
annual and seasonal variations of climate change.
6.4.4 Using the Up to Date Emission Scenarios (AR5)
Estimations of future weather data in this report were based on the assumptions of
emission scenarios as outlined in the Fourth IPCC Report (AR4). However, IPCC has just
released the fifth assessment report (AR5) which is considered to provide a comprehensive
assessment of the physical science basis of climate change. Future work may therefore consider
incorporating these up to date assumptions in climate change studies.
89
REFERENCES
Abedinpoura M., Sarangib A., Rajputb T.B.S., Singhb M., Pathakc H., and Ahmad T. 2012.
Performance evaluation of AquaCrop model for maize crop in a semi-arid environment.
Agricultural Water Management 110 (2012) 55– 66
Adams J.E., Arkin G.F., and Ritchie J.T. 1976. Influence of row spacing and straw mulch on first
stage drying. Soil Sci. Soc. Am.J. 40:436-442.
Adams R.M. 1989. Global climate change and agriculture: An economic perspective. American
Journal of Agricultural Economics 71(5):1272-1279.
Aerts, J. Droogers, P. (Eds.) 2004. Climate Change in Contrasting River Basins: Adaptation
Strategies for Water, Food, and Environment. CABI Books, Londen, p. 288.
Aggarwal PK, Kalra N, Chander S. 2006. InfoCrop: A dynamic simulation model for the
assessment of crop yields, losses due to pests, and environmental impact of agroecosystems in tropical environments. I. Model description. Agric Syst; 89:1–25
AGRHYMET. 2004. Rapport synthèse de l’enquête générale sur les itinéraires d’adaptation des
populations locales à la variabilité et aux changements climatiques conduite sur les projets
pilotes/ par AGRHYMET et l’UQAM, par Hubert N’Djafa Ouaga, 13 pp
Allen R., Pereira L.S., Raes D., and Smith M. 1998. Crop evapotranspiraron-Guidelines for
computing crop water requirements. Irrigation and Drainage Paper No. 56. FAO, Rome.
Anderson E.R., Cherrington., E.A., Flores A.I., Perez J.B., Carrillo R., and Sempris E. 2008.
Potential Impacts of Climate Change on Biodiversity in Central America, Mexico, and the
Dominican Republic, CATHALAC / USAID. Panama City, Panama. 105 pp
Assessment: MAGICC and SCENGEN Version Barrow,E.M., 2.4 Workbook, Climate
Research Unit, Norwich, U.K.
90
Barron J. 2004. Dry spell mitigation to upgrade semi-arid rainfed agriculture: water harvesting
and soil nutrient management for smallholder maize cultivation in Machakos, Kenya. Ph.D.
Thesis. Stockholm University, Sweden.
Barron J., Rockctröm, J., Gichuki, F., Hatibu, N. 2003. Dry spell analysis and maize yields for
two semi-arid locations in East Africa. Agric. For. Meteorol. 117, 23–37.
Buishand T.A. 1978. Some remarks on the use of daily rainfall models. Journal of
Hydrology 36, 295–308.
Cai X, Wang D., Lauren R. 2009. Impact of Climate Change onCrop Yield: A Case Study of
Rainfed Corn in Central Illinois, Journal of Applied Meteorology and Climatology. Vol.
48:1868-1881.
Carlson R.E., Todey D.P., Taylor S.E. 1996. Midwestern corn yields and weather in relation to
extremes of the southern oscillation. J Prod Agric 9:347–352
Carter T. R., Parry M. L., Harasawa., H., and Nishioka S. 1994. IPCC technical guidelines for
assessing climate change impacts and adaptations. London,United Kingdom/Tsukuba,
Japan : University College/Centre for Global Environmental Research.
Challinor A.J., Wheeler T.R. 2008. Crop yield reduction in the tropics under climate change:
processes and uncertainties. Agric Forest Meteorol; 148:343–56
Chen J., Zhang X.C., Liu W.Z., and Li Z., 2009. Evaluating and extending CLIGEN
precipitation generation for the Loess Plateau of China. Journal of the American
Water Resources Association 45 (2), 378–396
Chirwa W.E., Kydd J., and Dorward A. 2006. Future Scenarios for Agriculture in Malawi:
Challenges and Dilemmas, Future Agricultures
91
Cline W. 2007. Global Warming and Agriculture (Washington, DC: Peterson Institute for
International Economics.
Cuculeanu V, Tuinea P, and Balteanu D. 2002. Climate change impacts in Romania:
vulnerability and adaptation options. Geol J; 57:203–9.
Dea D. and I. Scoones, 2003. Networks of knowledge: how farmers and scientists understand
soils and their fertility: a case study from Ethiopia. Oxford Development Studies, 31, 461478.
Department of the Environment (DOE). 1996. Review of the potential effects of climate
change in the United Kingdom. London: HMSO.
Descheemaeker, K. 2006. Pedological and hydrological effects of vegetation restoration in
enclosures established on degraded hill slopes in the highlands of Northern Ethiopia. Ph.D.
diss., Dissertationes de Agricultura no. 725. K.U. Leuven Univ., Leuven, Belgium.
Downscaling Climate Data, http://www.climate-decisions.org
Droogers P., van Dam J., and Hoogeveen J. 2004. Adaptation strategies to climate changes to
sustain food security. In: Aerts J, Droogers P, editors. Climate change in contrasting river
basins: adaptation strategies for water, food and environment. The Netherlands: CABI
Publishing; p. 49–73.
ECA (Economic Commission for Africa). 2002. Harnessing technologies for sustainable
development. Economic Commission for Africa Policy Research Report, Addis Ababa, 178
pp.
Eitzinger J., Stastna M., and Zalud Z. 2003. A simulation study of the effect of soil water balance
and water stress on winter wheat production under different climate change scenarios.
Agric Water Manage; 61:195–217.
92
FAO. 2007b. Adaptation to Climate Change in Agriculture, Forestry, and Fisheries: perspective,
framework and priorities.FAO, Rome.
FAO. 2008a. The state of world fisheries and aquaculture. FAO, Rome
FAO. 2009. ETo Calculator: Land and Water Digital Media Series No. 36.FAO, Rome.
Farahani, H. J., Izzi, G. and Oweis, T.y. 2009: Parameterization and Evaluation of the AquaCrop
Model for Full and Deficit Irrigated Cotton. Agronomy Journal 101: 469-476.
Fereres, E. 2010. Crop Yield Response to water
Firmino G. Mucavele. 2009. True Contribution of Agriculture to Economic Growth and Poverty
Reduction: Malawi, Mozambique and Zambia Synthesis Report; Food, Agriculture and
Natural Resources Policy Network (FANRPAN)
Fowler H. J., Blenkinsop S., and Tebaldi C. 2007. Linking climate change modelling to impacts
studies: Recent advances in downscaling techniques for hydrological modelling. Int J
Climatol, 27, 1547–1578.
Geerts S. and Lunacre E. 1998. What are General circulation models? (http://wwwdas.uwyo.edu/~geerts/cwx/notes/chap12/nwp_gcm.html)
Geerts S., D. Racs M. Garcia Cardenas O. Condori J. Mamani R. Miranda J. Cusicanqui C.
Taboada E., Yucra and J. Vacher. 2008. Could deficit irrigation be a sustainable practice
for quinoa (Chenopodium quinoa Willd.) in the Southern Bolivian Altiplano? Agric. Water
Manage. 95:909-917.
Godwin D.C., Ritchie J.T., Singh U., Hunt, L. 1989. A User’s Guide to CERES WheatV2.10. International Fertilizer Development Center, Muscle Shoals, AL.
93
Gohari A., Eslamian S., Abedi-Koupaei J., Bavani A.M., Wang D. and Madani K. 2013. Climate
change impacts on crop production in Iran's Zayandeh-Rud River Basin. Science of the
Total Environment, 442: 405–419.
GoM (Government of Malawi). 2001. State of Environment Report for Malawi 2001, Lilongwe:
Ministry of Natural Resources and Environmental Affairs.
GoM (Government of Malawi). 2007b. Economic Report, Ministry of Economic Planning and
Development, Lilongwe, Malawi.
GoM (Government of Malawi). 2013. Department of Climate Change and Meteorological
Services,
Ministry
of
Environment
and
Climate
Management.
Accessible
at
http://www.metmalawi.com/.
GoM (Government of Malawi). 2013. Ministry of Agriculture and Food Security,
Lilongwe, Malawi
GoM (Government of the Republic of Malawi). 2005/2006. Ministry of Agriculture and Food
Security, Impact of Green Maize Consumption on Potential Production (Lilongwe and
Dowa Case Study)
Gommes R.A. 1983. Pocket computers in agrometeorology, Plant production and Protection
Paper n. 45. FAO, Rome, Italy, 140 p
Harun S. 1999. Forecasting and Simulation of Net Inflows for Reservoir Operation and
Management. Universiti Teknologi Malaysia: Ph.D Thesis.
Hassan Z.B. 2012. Climate Change Impact on Precipitation and Streamflow in a Humid Tropical
Watershed
Heng L.k., Hsiao,T.C., Evett, S., Howell, T. and Steduto P. 2009. Validating the FAO AquaCrop
Model for Irrigated and Water Deficient Field Maize. Agronomy Journal 101: 488-498.
94
Holton J. R. 1992. An introduction to dynamic meteorology, 3rd Edition. SanDiego, CA:
Academic.
Hoogenboom G., Jones, J.W., Wilkens, P.W., Batchelor, W.D.,Bowen, W.T., Hunt, L.A.,
Pickering, N.B., Singh, U., Godwin,D.C., Bear, B., Boote, K.J., Ritchie, J.T., White,
J.W., 1994. Crop Models, DSSAT Version 3.0. International Benchmark Sites
Network for Agrotechnology Transfer, University ofHawaii, Honolulu, 692 pp
Houghton J. T., Ding. 2000. Climate change 2001: the scientific basis:contribution of working
group I to the third assessment report of the intergovernmental panel on climate change.
Cambridge: Cambridge University Press.
Hulme. 2000. Using a Climate Scenario Generator for Vulnerability and Adaptation
Hunink J.E., and Droogers P. 2010. Climate Change Impact Assessment on Crop Production in
Albania, World Bank Study on Reducing Vulnerability to Climate Change in Europe and
Central Asia (ECA) Agricultural Systems
Huntingford C., C. H. Lambert, J. H. C. Gash, C. M. Taylor, and Challinor A. J. 2005. Aspects
of climate change prediction relevant to crop productivity. Philos. Trans. Roy. Soc.
London,360B, 1999–2009, doi:10.1098/rstb.2005.1748.
in central-eastern Argentina and El Niño-Southern Oscillation. J Appl Meterol 38:1488–
1498
IPCC (Intergovernmental Panel on Climate Change). 2007. Climate Change: Impacts,
Adaptation and Vulnerability, Contribution of Working Group II to the Fourth Assessment
Report of the Intergovernmental Panel on Climate Change (Cambridge Univ Press,
Cambridge, UK), in press.
95
IPCC SRES. 2000. Nebojsa Nakicenovic and Rob Swart (Eds.), Cambridge University Press,
UK. Pp 570.
IPCC. 2001. Impacts, Adaptation and Vulnerability, Contribution of Working Group II to the
Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge,
UK: Cambridge University Press.
Jaehyuk Lee J., Nadolnyak D., and Hartarska V. 2012. Impact of Climate Change on
Agricultural Production in Asian Countries: Evidence from Panel Study. Department of
Agricultural Economics and Rural Sociology, 202 Comer Hall, Auburn University,
Auburn, AL 36849-5406
Japan Association for International Collaboration of Agriculture and Forestry (JAICAF). 2008.
The Maize in Zambia and Malawi.
Johnson G.L., Hanson C.L., Hardegree S.P., Ballard E.B. 1996. Stochastic weather
simulation: overview and analysis of two commonly used models. Journal of Applied
Meteorology 35 (10), 1878–1896.
Jones C.A. and Kiniry J.R. 1986. CERES-Maize model: A simulation model of maize
growth and development. Texas A & M University Press, 194 pp.
Jones J.W., G. Hoogenboom C.H. Porter, K.J. Boote, W.D. Batchelor, L.A.Hunt, P.W.
Wilkens, U. Singh, A.J. Gijsman, and J.T. Ritchie. 2003. The DSSAT cropping
system model. Eur. J. Agron. 18:235–265.
Kachule R.N. 1994. Economic analysis of beans production on northern Malawi: Implications
for agricultural policy. Ms Theisis. Bunda College, Lilongwe Malawi
96
Kaiser H.M, Riha S.J, Wilks DS, Rossiter D.G, Sampath R. 1993. A farm-level analysis of
economic and agronomic impacts of gradual climate warming. AmJ Agric Econ 75(2):387–
398
Kang., Khan S., Ma X. 2009. Climate change impacts on crop yield, crop water productivity and
food security-A review. Progress in Natural Science, Pp 1665–1674.
Kanyama-Phiri, GY., Kamangira J.B., and Wellard K. 1994. The farmer Participatory
Agroforestry Research project. Summary of preliminary findings and proposal for the next
phase of research. Lilongwe, Malawi.
Kevin M., Ramesh R., John O., Imran A., Bahram and G. 2005. Evaluation of
weathergenerator ClimGen for southern Ontario. Canadian Water Resources Journal
30(4), 315–330.
Kimball B.A. 1983. Carbon Dioxide and Agricultural Yield: An Assemblage and Analysis of
430 Prior Observations. Agron. J. 75:779-788
Krishnan P., Swain D.K, and Bhaskar B.C. 2007. Impact of elevated CO2 and temperature on
rice yield and methods of adaptation as evaluated by crop simulation studies. Agric Ecosyst
Environ;122:233–42.
Luo Q, Williams M, and Bellotti W, 2003. Quantitative and visual assessments of climate change
impacts on South Australian wheat production. Agric Syst; 77:173–86.
Mainuddin M., Hoanh C.T., Jirayoot K., Halls A.S., Kirby M., Lacombe G. and Srinetr V. 2010.
Adaptation options to reduce the vulnerability of mekong water resources, food security
and the environment to impacts of development and climate change. CSIRO: Water for a
Healthy Country National Research Flagship. 152 pp.
97
Malawi Government (GoM). 1995. Economic Report, 1995, Ministry of Economic Planning and
Development. Zomba Malawi.
McMichael A, Woodruff R, Whetton P, Hennessy K, Nicholls N, Hales S, Woodward A,
Kjellstrom T. 2003. Human Health and Climate Change in Oceania: Risk Assessment,
2002 (Department of Health and Aging, Canberra, Australia).
McMichael A.J., R.E. Woodruff and Hales S. 2006. Climate change and human health: present
and future risks. Lancet, 367, 859-869.
Mendelsohn R. 2008. The Impact of Climate Change on Agriculture in Developing Countries,
Journal of Natural Resources Policy Research, 1:1, 5-19
Mendelson R., Dinar A., and Dalfelt A. 2000. Climate change impacts on African agriculture.
http://www.worldbank.org/wbi/sdclimate/pdf.
Mohammed Y. 2009. Climate change impact assessment on soil water availability and crop yield
in Anjeni Watershed Blue Nile Basin. Arba Minch University: Master Thesis
Moriasi D. N., Arnold, J. G., Liew M. W. V., Bingner R. L., Harmel R. D., and Veith T. L. 2007.
Model evaluation guidelines for systematic quantification of accuracy in watershed
simulations. Transactions of The ASABE 50, 885–900.
Nash J.E., Sutcliffe J.V. 1970. River flow forecasting through conceptual models. I. A discussion
of principles. Journal of Hydrology 10, 282–29
Ng'ong'ola D.N., Mtimuni, B.M., Kanyama-Phiri, G., Nothale, D.W. and Wiyo, K.A. 1992.
Socio-economic forecasts for land use programmes in Malawi, in Kwapata, M. 1993, Ibid
Nordhaus William D. 1991. To slow or not to slow: The economics of the greenhouse
effect, The Economic Journal, 101, pp. 920–937.
NSO (National Statistics Office). 2008. Population and Housing Census. Final Report, Zomba
98
OECD. 2003. Special Issue on Climate Change: Climate Change Policies: Recent development
and long term issues. Paris: Organization for Economic Cooperation and Development
(OECD) publications.
Owens T., Hoddinot J., and Kinsey B. 2003. Ex-ante actions and ex-post public responses to
drought shocks: evidence and simulations from Zimbabwe. World Dev., 31, 1239-1255
Ozdogan M. 2011. Modeling the impacts of climate change on wheat yields in northwestern
Turkey. Agr Ecosyst Environ; 141:1-12
Parry M., Rosenzweig C., Iglesias A. 1999. Climate change and world food security: a new
assessment. Global Environ Change: S51–67.
Parry M.L., Rosenzweig C., Iglesias A., Livermore M., and Fischer G. 2004. Effects of climate
change on global food production under SRES emissions and socio-economic scenarios.
Global Environ. Change. 14, 53–67
Paul W.G. 2012. Biomass Supply from Corn Residues: Estimates and Critical Review of
Procedures, Department of Economics, Iowa State University
Phillips JG, Cane M.A, Rosenzweig C. 1998. ENSO seasonal rainfall patterns and simulated
maize yield variability in Zimbabwe. Agric For Meteorol 90:39–50
Podestá G.P., Messina C.D.,Grondona M.O, Magrin G.O. 1999. Associations between grain crop
yields
Popova Z, Kercheva M. 2005. CERES model application for increasing preparedness to climate
variability in agricultural planning-risk analyses. Phys Chem Earth; 30:117–24.
Racksko P., Szeidl L., and Semenov M. 1991. A serial approach to local stochastic weather
models. Ecological Modeling, 57, 27–41.
99
Raes D, Steduto P, Hsiao T. C., and Fereres E. 2012. FAO, Reference Manual for AquaCrop
Version 4, Land and Water Division Rome, Italy
Raes D. 1982. A summary simulation model of the water budget of a cropped soil: Ph.D. diss.
Dissertationes de Agricultura no. 122. K. U. Leuven Univ., Leuven, Belgium.
Raes D., Geerts S., Kipkorir E., Wellens J., and Sahli A. 2006. Simulation of yield decline
as a result of water stress with a robust soil water balance model. Agric. Water
Manage. 81:335-357.
Raes, D., H. Lemmens P., Van Aelst M., Vanden Bulcke, and Smith M. 1988. IRSISIrrigationScheduling Information System. Volume 1. Software Reference Manual.
K.U. Leuven, Dep. Land Management, Reference Manual 3. Leuven, Belgium.
Rallison R.E. 1980. Origin and evolution of the SCS runoff equation. p.912924. In Proc.
Symp. on Watershed Management. ASCE, New York.
Reddy V.R, Pachepsky Y.A.2000. Predicting crop yields under climate change conditions
from monthly GCM weather projections. Environ Modell Software;15:79–86.
Riha S.J, Wilks D.S, Simoens P. 1996. Impact of temperature and precipitation variability
on crop model predictions. Clim Change 32(3):293–311
Ringler C., Zhu, T., Cai X., Koo J., and Wang,D. 2010. Climate Change Impacts on Food
Security in Sub -Saharan Africa. IPFPRI.
Rockström J. 2000. Water resources management in smallholder farms in Easternand Southern
Africa: an overview. Phys. Chem. Earth (B) 25 (3), 275–283.
Rockström J. 2003. Resilience building and water demand management for drought
mitigation. Phys. Chem. Earth, 28, 869-877
100
Rosenzweig C, Parry ML. 1994. Potential impact of climate change on world food supply.
Nature 367:133–138
Rosenzweig C. 1989. Global climate change: Predictions and observations. American Journal of
Agricultural Economics 71(5):1265-1271.
Rowlinson, P. 2008. Adapting Livestock Production Systems to Climate Change – Temperate
Zones. Livestock andGlobal Change conference proceeding. May 2008, Tunisia.
Rubas D.J., Hill H.S.J., Mjelde J.W. 2006. Economics and climate applications: exploring
the frontier. ClimRes 33:43–54
Saka A.R, Phiri I.M.G, Kamdonyo D.R, and Kumwenda J.D.T. 2003. The Potential Impact
of Climate Change on the Agiculture Sector in Malawi. Ministry of Natural Resources
and Environmental Affairs, Agriculture and Livelihood
Sanje K. 2013. Malawians rethink maize as climate dries, Thomson Reuters Foundation.
http://www.trust.org/item/?map=malawians-rethink-maize-planting-as-climate-dries/
Schmidhuber J. and Tubiello F.N. 2005. Global food security under climate change. vol.
104. pp; 19703–19708.
Semenov M. A., and Barrow E. M. 2002. LARS-WG: a stochastic weathergenerator for use
in climate impact studies (Version 3.0). User Manual.
Semenov M. A., and Brooks R. J. 1999. Spatial interpolation of the LARS-WG stochastic
weather generator in Great Britain. Climate Research, 11, 137-148.
Seo S., and Mendelsohn R. 2007. An Analysis of Livestock Choice: adapting to climate
change in Latin American farms. World Bank Policy Research Working Paper No.
4164.
101
SEPLD. 2006. Social Economic Profile for Lilongwe District (Including District
Development Planning Framework
Shaka, A. K. 2008. Assessment of climate change impacts on the hydrology of Gilgel
Abbay Catchment in Lake Tana Basin, Ethiopia Enschede, Netherlands. The
International Institute for Geo-information Science and Earth Observation: Master
Thesis.
Shimpei M. 1991. A guide to Ecological Agriculture in the tropics, Lessons from Nature
Singh J.P., Govindakrishnan P.M., Lal SS and Aggarwal P.K. 2005a. Increasing the
efficiency of Agronomy
Singh R, Hales S, deWet N, Raj R, Hearnden M, Weinstein P. 2001. Environ Health
Perspect 109:55–59.
Steduto P.,T., C. Hsiao., D. Raes, and E. Fereres. 2009. AquaCrop-The FAO crop model to
simulate yield response to water: I. Concepts and underlying principles. Agron. J.
101:426-437 (this issue).
Steenhuis T.S., M. Winchell, J., Rossing J.A., Zollweg, and M. F. Walter. 1995. SCS
Runoff equation revisited for variable-source runoff areas. J. Irrig. Drain. Eng.
121:234-238.
Stöckle C.O., Donatelli, M., Nelson, R. 2003. CropSyst, a cropping systems simulation
model. Eur. J. Agron. 18 (3/4), 289–307 (Second special issue “2nd International
Symposium on Modeling Cropping Systems, Florence, Italy”).
Stroosnijder L. 2007. Rainfall and land degradation. In: Sivakumar, M.V.K., Ndiang’ui,N.
(Eds.), Climate and Land Degradation. Springer, pp. 167–195
102
Tao F, Zhang Z. 2010. Adaptation of maize production to climate change in North China
Plain: quantify the relative contributions of adaptation options. Eur J Agron; 33:103–
16
Tao F, Zhang Z. 2010. Adaptation of maize production to climate change in North China
Plain: quantify the relative contributions of adaptation options. Eur J Agron, 33:103–
16
Tchale H. 2009. The Efficiency of Smallholder Agriculture in Malawi; World Bank:
Lilongwe, Malawi.
Thornton P.K., Jones P.G., Owiyo T, Kruska R.L., Herrero M., Kristjanson P., Notenbaert
A., Bekele N and Omolo A, with contributions from Orindi V, Otiende B, Ochieng
A, Bhadwal S, Anantram K, Nair S, Kumar V and Kulkar U. 2006: Mapping
Climate Vulnerability and Poverty in Africa. Report to the Department for
International Development, ILRI, Nairobi. p.171.
Todorovic M., Albrizio,R., Zivotic, L., Abi Saab, M.T., Stockle, C. and Steduto, P. 2009.
Assessment of AquaCrop, CropSyst, and WOFOST Models in the Simulation of
Sunflower Growth under Different Water Regimes. Agronomy Journal 101: 509521.
Tojo Soler C.M, Sentelhas P.C, Hoogenboom G. 2007. Application of the CSM-CERESMaize model for planting date evaluation and yield forecasting for maize grown offseason in a subtropical environment. Eur J Agron; 27:165–77.
Tubiello F.N, Rosenzweig C, Goldberg R. A., Jagtap S., Jones J. W. 2002. Effects of
climate change on US crop production: simulation results using two different GCM
scenarios. Part I: Wheat, potato, maize, and citrus
103
United States Environmental Protection Agency (EPA),
http://www.epa.gov/climatechange/basics/
USDA. 1964. Estimation of direct runoff from storm rainfall. p. 1-24. In National
Engineering Handbook. Soil Conservation Service, USDA, Washington, DC.
USGCRP. 2009. Global Climate Change Impacts in the United States. Karl, T.R., J.M.
Melillo, and T.C. Peterson (eds.). United States Global Change Research Program.
Cambridge University Press, New York, NY, USA
van Dam, J.C., Huygen, J., Wesseling, J.G., Feddes, R.A., Kabat,P., van Walsum, P.E.V.,
Groenendijk, P., van Diepen, C.A. 1997. Theory of SWAP Version 2.0, Report #71.
DepartmentWater Resources, Wageningen Agricultural University, 167 pp
Vasilev V. 2003. Epidemiol Infect 132:51–56
Villalobos F.J., and E. Fereres. 1990. Evaporation measurements beneath corn, cotton, and
sunflower canopies. Agron. J. 82:1153-115
Walker N.J., Schulze R.E. 2006. An assessment of sustainable maize production under
different management and climate scenarios for smallholder agro-ecosystems in
KwaZulu-Natal, South Africa. Phys Chem Earth; 31:995–1002.
Willmott, C. J. 1984. On the evaluation of model performance in physical geography.
InSpatial Statistics and Models, Gaile GL, Willmott CJ (eds). D. Reidel: Boston.
443–460
World Fish Center. 2007. Fisheries and aquaculture can provide solutions to cope with
climate change. Issues brief, 1701.
Xie H, Eheart JW. 2004. Assessing vulnerability of water resources to climate change in
midwest. Copyright ASCE; 1–10.
104
Xu C. Y. 1999. From GCMs to River Flow: A Review of Downscaling Methods and
Hydrologic Modelling Approaches. Progress in Physical Geography, 23(2), 229–
249.
Yao F, Xu Y and Lin El. 2007. Assessing the impacts of climate change on rice yields in the
main rice areas of China. ClimChange; 80:395–409.
You L, Rosegrant MW, Wood S, Sun D. 2009. Impact of growing season temperature on
wheat productivity in China. Agric for Meteorol; 149:1009–14.
105