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Title: Assessing Impact of Climate Variability and
Change on Maize Yield in Gamo Zone, Southern
Ethiopia: A Modelling Perspective.
Gizachew Kassa
Arba Minch Water Technology Institute, Arba Minch University, SNNPR
Manyazwal Getachew
Anirudh Bhowmick (  bhowmickanirudh@gmail.com )
Research Article
Keywords: Climate, CMIP5, RCPs, APSIM7.9, Maize Yield, Gamo Zone.
Posted Date: April 5th, 2023
DOI: https://doi.org/10.21203/rs.3.rs-2759924/v1
License:   This work is licensed under a Creative Commons Attribution 4.0 International License.
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Abstract
Computing seasonal anomalies and Mann-Kendal trend test combined with APSIM maize crop modeling,
the impact of climate variation and change on Maize crop production has been assessed; by comparing
the simulated result during baseline and 3 CMIP5 climate model projections of 2 Representative
Concentration Pathways (RCP 4.5&8.5) climate scenarios in Gamo Zone, Southern Ethiopia, case of 4
Woreda. Based on standardized precipitation index (SPI) analysis, a moderately wet and very wet climate
condition will predominate during the 2nd rainy season (Sept - Nov). While the main rainy season (March-
May), moderately dry and very dry climate conditions will predominate during the mid and end-term of the
21st century compared to the baseline period. Inconsistent decreasing and increasing temperatures and
rainfall trends in response to climate change have been detected. The APSIM7.9 crop model adequately
simulates the maize grain yield with a Root Mean Square Error value of (4.5 to 9.13 q/ha) across four
selected woreda. The maize yield potential variation of lowest, highest limits and median ranges up to
1.2, 30.5 and 16 q/ha with a coefficient variation of 2.4% on average in future three particular periods.
The median yield change showed a reduction up to 36.5% and 18.75% across all woreda during the mid
term (2041-2070) and end- term (2071-2100) as compared to the baseline period (1980-2005), due to the
reduction of precipitation in the main rainy season and annual total rainfall reduction; while it showed a
positive change up to 29.2% during the near-term (2010-2040) period of the 21st century in both RCP
scenarios.
1. Introduction
The primary manifestation of climate variability is seasonal precipitation fluctuation which has strong
links to the ENSO years (Troccoli, 2010). Mainly the high temporal and spatial variation of rainfall
reflected by dry spells and recurrent droughts and floods could be considered the most important factors
affecting agricultural productivity (Laux et al., 2010). And hence the within and between-season rainfall
variability is often taken as the reason for crop failure and food shortages (Kansiime et al., 2013). The
crop water availability is mostly related to the rainy season start date, cessation and length of the
growing season, which are always varied due to the seasonal variation of rainfall. Precipitation
seasonality affects crop production and the livelihoods of societies in regions of arid and semi-arid
climatology (Thornton et al., 2009).
Climate change could manifest itself primarily through significant changes in average temperature and
precipitation in a specified geographical location and time. And the likely impacts on the vulnerability of
agricultural production require better understanding so that the resilience to current climate variability
associated risk with longer-term climate change can be projected and appropriate decisions would be
taken to enhance crop's resilience where it is threatened or lost (Thornton et al., 2014). Climate variability
results in increasing food insecurity in the future, before considering how people deal with climate
variability and extremes, and how they may adapt in the coming decades (Thornton et al., 2014).
Changes in climatic conditions influence soil moisture availability, nutrients and water uptake by the
plant. Most of the Earth’s Climatic Zones undergo a significant contrast between summer and winter.

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Seasons are characterized by opposite mass or energy transfers that add up in an annual budget at the
end of the annual cycle. The climate is in equilibrium when annual budgets are null on average. The
mean climate state starts drifting if the annual budget resulting from seasonality is shifted under the
influence of external forcing (Carré & Cheddadi, 2017).
Climate seasonality may be defined differently depending on the climate variable. The temperature
annual cycle is generally close to sinusoidal and therefore well defined by annual minima and maxima.
Precipitation seasonality, in contrast, is correctly defined by the rainfall distribution throughout the year,
which requires an internal chronology with a monthly or lower resolution (Carré & Cheddadi, 2017).
The mean value of climate variables such as rainfall, temperature (maximum and minimum), potential
evapotranspiration, and solar radiation influences crop yields under rain-fed conditions in many parts of
the world (Adamgbe & Ujoh, 2013).
The livelihood of most rural people of Gamo Zone crop production is mostly dependent on rainfall
seasons, which is highly vulnerable to climate instability (Saguye, 2016). Historical crop production
failure and yield variability in the Zone are partly attributed to climate instability. Thus the study has
shown how the mean crop yield (maize) and its variability are affected by shifts in climate.
Crop models predict the response of crops to weather, soil, and management by simulating crop growth
and development of plant organs like leaves, roots, stems and grains. Thus, a crop growth simulation
model not only predicts the final state of total biomass or harvestable yield but also contains quantitative
information about major processes involved in plant growth and development (B. Y. Fosu-Mensah et al.,
2012). Process-based modeling approaches use the knowledge or understanding of the crop yield
formatting process through mathematical relations that are based on plant physiology, agro-climatic and
plant-soil-atmosphere interactions (physiological and biochemical processes) (Kpongor, 2007) and they
simulate crop development and growth (Waha et al., 2015). Hence, process-based models arise primarily
from the understanding of processes rather than from statistical relationships. They have the capability
in quantify potential yield gaps between prevailing management options and potential yields of different
crops (Hengsdijk & Langeveld, 2009). They provide a means of quantifying possible dynamics in crop
yield responses over a given time within a given location. In this regard, however, most agronomic
researchers are focused on results that are site and season specific statistical relationships of crops'
response to weather forcings in the study area. Process based crop growth and yield predictive crop
models are required in carrying out analysis of yield formation beyond agronomic research which are
capable of simulating both temporal and spatial dynamics of crop yields, since they explicitly consider
plant physiology, agro-climatic conditions, and biochemical processes (B. Y. Fosu-Mensah et al., 2012)
description. An internationally renowned and very sophisticated simulator of agricultural production
systems is produced by the APSIM ( agricultural production system) (Archontoulis et al., 2014; Gaydon et
al., 2017; D. Holzworth et al., 2006; D. P. Holzworth et al., 2014a; Keating et al., 2003a).The model has a
suite of modules that enable the simulation of systems that cover a range of plant, soil, climate and
management interactions at locations, and most of its modules were evaluated in various crop cultivars

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studies (Archontoulis et al., 2014; Gaydon et al., 2017; D. Holzworth et al., 2006; D. P. Holzworth et al.,
2014b) and has been used for many impact studies of climate dynamics on crops, and yield predictions
(B. Y. Fosu-Mensah et al., 2019; Kosamkar & Kulkarni, 2019; Mohammed et al., 2022; Muller & Martre,
2019; Ngoune Tandzi & Mutengwa, 2019a; SK, 2021). The use of climate models combined with crop
models for simulation to study future climate impact on maize crop growth, development and yield is
essential for long-term planning in household food security and developing mitigation and adaptation
options (Stevens & Madani, 2016).
Crop simulation models estimate different yield levels, depending on the assumptions and modelling
approach. Hence, crop models typically in daily time-step and sufficient details of physiological principles
are used to predict long-term yield potential (Charles et al., 2017). Yield potential is the biophysical yield
obtained with a sufficient amount of water to prevent scarcity, and suitable temperature climatology
which determines the cropped length of the growing season, and the optimum amount of solar radiation
during the growing season (Van Ittersum et al., 2013). It is obtained from non-irrigated crops or rain-fed
crops, which are exposed to limited water availability conditions depending on the quantity or the timing
of rainfall, and the soil's capacity to store water (Gajić et al., 2018). APSIM is more suitable to estimate
both yield potential and water-limited yield; that yield is also limited by water supply, and hence
influenced by soil water holding capacity and root depth, and field topography. According to (Lamsal et
al., 2018), the principle of crop modelling, this yield measure is pertinent to benchmark rain-fed crops and
is typically parameterized against the real crop phenotype at a given agro-climate region.
This research has been focused to assess the potential impact of projected climate variability and
climate change in a modelling approach, using three CMIP5 coupled global climate models output of two
plausible climate scenarios (RCP4.5 &8.5)) and maize crop modelling (APSIM7.9) at different selected
Woreda, which insights to devise adaptation options on small spatial scales where farmers operate
maize farming activity in the Zone as (Zinyengere et al., 2014) description. This maize crop production
modelling and yield simulation at different Woreda sites has been tried to analyse temporal maize yield
gaps of median yield values in response to climate forcing during baseline and future particular periods,
which allows for the identification of constraints, trade-offs and opportunities for improvements in
prevailing climate conditions as (Guilpart et al., 2017) research work. Based on this predicted yield
variation and long-term change, farmers will be prepared for forthcoming seasonal climate conditions by
improving crop management options and making an effort to devise the best adaptation to seasonal
climate fluctuation.
2. Materials And Methods
2.1. General methodology
Using observed climate and site specific maize crop characteristics and soil data at different selected
Woreda, Agricultural Productions Systems Simulator (APSIM) maize crop modelling process has been
carried out and simulate maize grain yield; and identifying the sensitive parameters of physical and

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chemical characteristics of the soil, and various amounts of climate variables. Then, simulated maize
grain yield using observed weather data input (2013–2017) has been compared with the corresponding
year's actual measured maize grain yield to check the performance of APSIM7.9 model in predicting the
maize yield potential under prospected climate scenarios. The next step was simulating maize yield for
baseline and future particular periods using 3 CMIP5 model outputs of 2 plausible climate scenarios
(RCP4.5 & RCP8.5) and evaluating the temporal yield gaps in different climate models and scenarios at
different Woreda so as to assess the impact of future climate instability on maize crop production in the
Zone as shown in (Fig. 1).
2.2. Description of the Study Area
The study area shows the specific place where the study has been carried out; it has been conducted in
Gamo Zone, Southern Ethiopia. It is geographically located between 5° to 8°N and 35° to 38o
E and
between 517 m and 4207 m above sea level. For this study, maize crop modelling was conducted at four
selected Woreda (Arbaminch Zuria, M/abaya, Kucha & Dramalo) based on the agro-climatic suitability of
major cereal crops study (Teyso & Anjulo, 2016) .
Climatology
The Gamo Zone experiences humid tropical climatology with slight daily and seasonal variations of
temperature. The weather condition of this area becomes hot during the winter season as compared with
the other seasons. The Zone has a bimodal rainfall climatology with a low precipitation season (Sept to
Nove), and the main precipitation occurrence (March to May).
Soil
Soils are the basic constraints in building blocks of livelihoods in agrarian society (Amejo, 2018). The soil
features classes are similar in most Woreda which consists of sandy clay loam and sandy loam and has
a deep profile of up to 180cm depth in different land use systems (Zebire et al., 2019). For this study, the
soil types by their texture are clay loam, loam, loam sandy and sandy clay loam which were identified by
soil expert sample collection during field observation. These soil types for each selected Woreda were
used for setting soil modules in APSIM7.9 model calibration with the identified GPS-coordinates for
modeling maize production and grain yield simulation in response to site specific climate conditions.

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Table 1
Soil parameters utilized during calibration under rainfed conditions in the study area.
Depth
(cm)
BD
(g/cc)
LL
(mm/mm)
DUL
(mm/mm)
SAT
(mm/mm)
PAWC
(mm)
KL
(/day)
XF
(0–1)
0–10 1.42 0.17 0.3 0.9 13.1 0.08 0.7
10–30 1.38 0.18 0.31 0.41 26.0 0.07 1.0
30–60 1.56 0.19 0.31 0.42 36.0 0.06 0.8
60–90 1.44 0.22 0.32 0.43 30.0 0.07 1.0
90–120 1.61 0.25 0.32 0.44 20.1 0.06 1.0
120–150 1.52 0.27 0.32 0.44 15.0 0.05 0.9
150–180 1.73 0.29 0.33 0.44 10.5 0.04 0.9
BD is bulk density, SAT is saturation water content, DUL is drained upper limit, LL is lower limit/wilting
point, (both KL & XF are root growth parameters under the soil).
The moisture properties of the soils are described by the soil water characteristics (DUL, LL, SAT) used for
APSIM7.9 model calibration (Table 1) with actual maize crop management options for simulations of
maize production using the actual site specific weather data. KL is a fraction of plant available water
(PAW) able to be extracted/per day from a particular soil layer. The KL factor is derived empirically and
incorporates soil and plant characteristics that restrict the rate of water uptake (Dalgliesh et al., 2016).
The plant accessible water capacity (PAWC) is impacted by the presence of sub-soil limitations, through a
decrease in the capacity of the crop to draw water from the soil profile. This is reflected in an increase in
the crop lower limit (CLL%) which reduces PAWC. The root exploration factor (XF) is used in APSIM to
slow down the advance of the root exploration front (Dalgliesh et al., 2016).
2.3. Data types and sources
Observed daily weather data (precipitation, maximum and minimum temperature and Solar radiation)
were collected from the study area existing meteorological stations of the Ethiopian Meteorological
Institute (EMI) from (1988–2017). Evapotranspiration for observed and future was estimated using
(Hargreaves & Samani, 1985) ETo calculation method for APSIM model input.
Three GCMs/CMIP5 models of re-grided climate data were downloaded via CMIP5 Earth grid Federation
(ESGF) node (https://esgf-node.llnl.gov/search/cmip5) and extracted to each selected sites weather
station in daily time step (Table 2).

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Table 2
Selected CMIP5/ GCMs and Scenarios for this study
CMIP5
Models
Model description
references
Periods
(time slices)
Climate scenarios
(RCPs)
MRI-CGCM3
HadGEM2-ES
GFDL-ESM2
(Yukimoto et al., 2012)
(Cd. Jones et al., 2011)
(Dunne et al., 2012)
(1980–2005) baseline
(2010–2100) future
RCP4.5
RCP8.5
MRI-CGCM3: Coupled Global Climate Model of the Meteorological Research Institute version3
HadGEM2-ES: The Hadley Centre Global Environmental Model, version 2, Earth System GFDL's ESM2:
Geophysical Fluid Dynamics Laboratory Global coupled climate-carbon Earth System Models
version2 and RCP: Representative concentration pathways
Crop data such as maize cultivar, sowing date and yield, soil textures and actual maize crop management
options (planting dates & plant density, fertilizer) were collected at selected Woreda agricultural offices
and field observation for APSIM model calibration and simulation configuration. The current and
historical maize variety is BH-144 in all selected Woreda in the Zone, which is a short rainfall season
maize variety.
2.4. Climate Data Bias-correction
In order to reduce the discrepancy between the historical observed and CMIP5 model outputs to
reproduce future weather elements in daily time steps, bias correction was applied using CMhyd tool
(Rathjens et al., 2016), distribution mapping (DM) multiplicative and additive for precipitation and
temperature respectively against the site existed nearest weather stations. The climate model outputs
performance has been checked to reduce the uncertainty of the projected climate conditions and to use
climate data inputs for crop models (Waha et al., 2015). Hence, the CMhyd tool gives the degree of
agreement or performance measures of all parameter results by plotting observed, historically corrected
and corrected scenarios for comparison like Mean monthly precipitation, Monthly standard deviation,
coefficient of variation, 90th percentile, wet day probability and precipitation intensity were used to check
the performance of the bias-corrected climate data. These methods were used because of their best
performance than others by applying all bias-correction methods in the tool and comparing their result in
correcting the biasness. The corrected GCMs climate data has been used for APSIM crop model input for
future maize crop system simulation and impact (Dubey et al., 2021; Mendez et al., 2020; Nyunt et al.,
2013; Tumsa, 2022) and others.
2.5. Analysis of Climate Variability
Climate variability could be explained in many manifestations of climate parameters indices and
objectives of interest. The projected long-term climate variability has been analysed using SPI
considering two wet seasons' rainfall deviation in each future particular period under different models
and prospected scenarios in the selected Woreda, since internal climate variability is attributed to short

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rain fluctuations (Herrero et al., 2010). For this study, climate variability has been treated as the
perspective of two wet seasons rainfall anomaly using standardized precipitation index,SPI (Wolyn &
Mckee, 1994) for considering the agricultural drought in response to soil moisture to meet the need of
crop production at some particular periods prospected scenarios and also to consider wet and dry
periods, which also affect agricultural production.
2.6. Analysis of Climate Change
To estimate the long-term changing in climate in the study area, Man-Kendal trend test of precipitation
and temperatures has been computed using XLSTAT software., since the purpose of trend analysis is to
determine if the values of a series of data have a general increase or decrease with time. This can also
enable the identification of the changes in the value from the long term period of the climate by
comparing the trend of the baseline with the future period value numerically. Man-Kendall trend test has
been formulated by (Mann, 1945) as non-parametric test for trend detection and the test statistics
distribution had been given by (Kendall, 1975) for testing non-linear trends and the turning point. It is non-
parametric test since it can avoid the problem caused by data skew (Smith, 2000) and also no ground for
statistical distribution (i.e. normal distribution). The magnitude of the trend is predicted by the Sen’s Slope
estimator test (Sen, 1968), which is applied where the trend is assumed to be linear, indicating that
quantification of changes per unit of time, and it has been used by many climate change researchers
(Caporali et al., 2021; Getachew, 2017; Hussain et al., 2015; Mohammed Junaid & Santhanakrishnan, n.d.;
Sharma et al., 2016; Sonali & Kumar, 2013).
2.7. APSIM Maize Crop Modeling
The Maize (Zea Mays L.) crop modeling has been undertaken using the agricultural production systems
simulator (APSIM) model version7.9 and was created in response to the need to improve planning and
forecasting for crop production under different climatic, soil and management conditions in the rural
properties (Keating et al., 2003a). The Soil-Wat module is a cascading water balance model that owes
much to its precursors in CERES and the algorithms for redistribution of water through soil profile have
been inherited from the CERES family of models (Probert et al., 1998). The most popular maize model in
the world, Crop-Environment Resource Synthesis Maize (CERES-Maize), is still the mother seed for other
maize models, such as APSIM (Keating et al., 2003b). it models maize (Zea mays L) growth, yield and
soil water content under the prevailing weather conditions as part of the study (Song & Jin, 2020). It is the
most comprehensive model of maize (Zea mays. L) (C. A. Jones et al., 1983).
The maize module in APSIM mimics the growth of a maize crop in a daily time step (on an area basis
rather than a plant-by-plant basis) (Keating et al., 2003c). Maize growth, development and yield are
simulated in response to climate variables (temperatures, precipitation, evapotranspiration and solar
radiation from the meteorological module), soil water supply (from the SoilWat module) and soil nitrogen
(from the SoilN module). The maize module provides a response on its soil water and nitrogen uptake to
the SoilWat and SoilN modules on a daily basis for resetting these systems (Sheng et al., 2019). The
SoilWat module receives data on crop cover in order to calculate runoff and evaporation rates. When the

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maize crop is harvested, root and leftover residues are "transferred" from maize to the residue and SoilN
modules, respectively. A SoilWat module uses distinct algorithms for saturated or unsaturated flow to
determine the redistribution of water and solutes throughout the given soil profile (Mthandi et al., 2014). It
integrates other modules such that APSIM can accurately predict maize development, yield, and
evapotranspiration (https://www.apsim.info/documentation).The SoilWater module is also in charge of
transferring heat, solutes, and water across different parts of the system. In order to simulate the impact
of water stress on various plant growth processes, soil water deficit factors are calculated.
In order to simulate the effects of water stress on various plant growth processes, the soil water deficit
parameters are determined (Saseendran et al., 2015). Four plant growth processes—photosynthesis
(photo), phenology (pheno), and leaf-expansion (expansion)—each with a different sensitivity to water
stress—are represented by three water deficit variables that are calculated (Saseendran et al., 2008). By
dividing the actual soil water supply, lower limit (sw - ll) by the potential soil water supply, upper limit, to
determine the water availability ratio (dul - ll). This ratio has been utilized for relation illustration to derive
the stress factors of photosynthesis and leaf expansion. A factor of 0 is complete stress and 1 is no
stress.
2.7.1. Crop Management for Simulation Configuration
It has been used “Sow using a variable rule with intercropping” management option since it is a highly
generalized manager template during simulation which encompasses the issues like sowing windows,
soil & rainfall, plant density, fertilization at sowing, manure applications and tillage operations. It has
been used in “Reset Water, Nitrogen and Surface OM” management to gauge the impact of rainfall
variability alone on crop N response, and also to eliminate variations in starting conditions (soil water &
N) and crop management (sowing date, plant density, cultivar) for the simulation. It has been also re-set
soil-water and Nitrogen at sowing each season so that carry-over effects of water and nitrogen or
rundown in soil organic content are not influencing the results. Harvesting rules for intercropping,
removing residue on affixed day and output at harvesting for intercropping were also used for crop
management during simulation.
2.7.2. Simulation Data Requirements
Crop models commonly used to simulate yield potential and water-limited yield require a minimum data
set of daily weather variables like precipitation, min-max temperatures, incident solar radiation,
evapotranspiration and some measure of humidity, i.e. relative humidity, actual vapour pressure, dew
point temperature (Sadras et al., 2015a). APSIM simulation has been configured by specifying the
modules to be used in the simulation and the data sets required by those modules. APSIM modules
typically require initialization data and temporal data as the simulation proceeds. Initialization data is
usually categorized into generic data (which defines the module for all simulations) and simulation-
specific parameter data were site, cultivar and management characteristics. Soil characteristics for soil
modules, climate readings for meteorological modules, soil surface characteristics, and surface residue
definition are typical site-specific features (Keating et al., 2003b). Management is specified using simple

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language to define a set of rules, calculations and messages to modules that are used during the
simulation (Keating et al., 2003b). Data is currently stored in keyword free format grouped into sections
stored in text files. APISM-SoilN2 (SoilN2) module uses the annual average ambient air temperature and
annual amplitude in mean monthly air temperature (TAV and AMP) to account for the site's daily soil
temperature. These values are calculated from site temperatures and read by SoilN2, which used to
describe soil water use efficiency for each sites.
2.7.3. Sensitivity Analysis
Model parameterization involves modifying sensitive input parameters, within an acceptable range in an
attempt to match model output to measured data based on a predefined objective function.
Sensitivity analysis is done on a model to determine how sensitive the output of the model is to changes
in the input parameters in order to understand the behavior of the model. It helps to determine, in order of
priority, the parameters that show the most influence on the output variability (Lenhart et al., 2002). And
hence, soilN2 (soil nitrogen), SoilN (soil phosphorus), and SoilWat (soil water), modules were linked with
APSIM 7.9 maize crop modeling parameterization. The soil water characteristics were adjusted for
various soil water contents, such as lower limit (ll-15), drained upper limit (dul-ll) and saturated (sat)
volumetric water contents (Table 1). Hence, the APSIM model sensibility has been conducted by changing
the parameter value in each module within the maize module calibrated range to see what effect this
parameter has on the result compared with the initial simulation.
This was calculated using a linked multiple simulation technique because linked simulations have the
advantage of allowing changes to one component to affect all linked components, and the "climate
control" component allows changes to rainfall and temperature by a fixed percentage and constant
amount, respectively. This allows looking at various changes in precipitation, temperatures and the
resulting crop yield. The daily weather data were the vital input parameters that all processes are derived
by its variables.
2.7.4. APSIM Model Simulation Evaluation
All crop simulation models require adequate calibration, testing and validation against measured field
data to ensure that the simulation results are reasonable (Charles et al., 2017). The APSIM model
performance in simulating maize production and grain yield has been evaluated in different agro-
ecologies of Ethiopia (Araya et al., 2015). In order to ensure that the model simulations were compared to
the agronomic reality in this study area sites, the maize grain yield simulated (2013–2017) under
farmland actual observed climate variables and soil chemical-physical characteristics compared with the
correspondence years actual measured BH-144 maize grain yield to check the APSIM7.9 model
performance in predicting the future maize yield potential (expected yield) under projected climate
scenarios at different Woreda in the Zone. The model performance in predicting the future maize grain
yield potential was evaluated using the square of the correlation coefficient (R2
), Nash-Sutcliffe efficiency

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(Nash & Sutcliffe, 1970), root mean square error (RMSE) and modified coefficient of efficiency (E1) as (B.
Fosu-Mensah, 2013).
2.7.5. Simulation Under Projected Climate
To explore the effects of projected climate variability and climate change on the Maize cropping system,
it has been setting up future long-term multiple season simulations pertaining to the current farm
management practices, crop characteristics and soil water contents during calibration in each selected
Woreda. However, different site weather data inputs based on the three GCMs/CMIP5 climate models and
2 RCP scenarios (RCP4.5 and 8.5) datasets. Hence, it has been used “prenewmet” object in the "Climate
Control" component from the Meteorological module of APSIM7.9 model standard toolbox to adjust daily
temperatures, rainfall, evapotranspiration and solar radiation up or down according to the plausible
climate scenarios. And also the “ini” object from climate control allows for avoiding the default “no co2
response” Parameterisation to “co2 response” for each simulation period in response to anticipated
climate conditions.
The impact of projected climate conditions on maize production has been evaluated using the output
from APSIM7.9 maize crop modeling running with baseline and future scenarios generated from 3CMIP5
models as (Ruiz-Ramos & Mínguez, 2010) research method. Hence, the impact was assessed by taking
the temporal yield gaps of lower, median & upper limits of simulated yield in response to projected
climate variability, and percent change of the median yield in response to long-term climate change under
various GCMs and scenarios for each selected Woreda.
3. Result And Discussion
3.1. Seasonal Precipitation Anomaly
Based on the SPI analysis of two rainy seasons (MAM and SON) under MRI-CGCM3 climate model
precipitation projection, near normal and moderately wet and very wet climate conditions will dominate
during each particular period of the 21st century relative to baseline (Table 3). The GFDL-ESM2M global
climate model prediction showed a nearly normal and moderately wet and very wet climate condition
during the spring season (MAM) and 2nd rainy season (SON) in both RCP scenarios each selected
Woreda and particular periods. While the HadGEM2-ES climate model 3 months computed SPI values
revealed that, near normal dry (MAM) and very wet (SON) climate conditions, with the exceptional
prediction of the very dry season (MAM) during the end-term of the 21st century.

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Table 3
SPI value ranges of seasonal rainfall projected under different models and
scenarios.
Time slice MRI-CGCM3 GFDL-ESM2 HadGEM2-ES
Baseline
RCP4.5_NT
RCP4.5_MT
RCP4.5_ET
RCP8.5_NT
RCP8.5_MT
RCP8.5_ET
MAM SON MAM SON MAM SON
0.5–0.7 0.9 − 1.14
0.4–0.8 0.78–1.13
0.01–0.5 1.1–1.15
0.3–0.6 1.13–1.15
0.4–0.64 1.1–1.15
0.3–0.7 1.1–1.15
0.2–0.7 1.0–1.15
0.2–1.1 0.9–1.1
-0.5–0.1 -0.1–1.0
-0.3–0.0 0.5–1.1
-0.5–0.3 0.7–1.1
-0.4–0.1 0.13–1.0
-0.3–0.12 0.4–1.1
-0.3–0.1 -0.9–1.15
-0.3–0.4 -0.8–0.4
-1.15–1.0 -0.4–0.8
1.0–1.15 -0.8–0.4
-1.13–0.7 -0.3–1.15
-1.0–1.0 -0.6–0.4
1.0–1.15 -0.8–1.15
-1.15–1.0 -0.8–1.15
MAM = March-April- May, SON = September-October-November, NT = Near-term (2010–2040), MT = Mid-
term (2041–2070), ET = End-term (2071–2100), Baseline (1980–2005).
3.2. Precipitation and Temperature Trend
The total annual rainfall Man-Kendal trend test statistics indicated that there is an insignificant upward
increasing trend during the near-term period at each Woreda projected under MRI-CGCM3 model; a
decreasing trend during the mid-term under both scenarios. While a significantly increasing annual total
precipitation trend with an annual rate of 18.9 mm/yr under RCP8.5 scenarios during the end-term of the
21st century. While it has an insignificant decreasing rate in total annual precipitation 3.3 mm/yr & 6.4
mm/yr during the near & end-terms of the century under RCP4.5 scenarios.The global model GFDL-
ESM2M projection showed inconsistent precipitation trends rather it fluctuates in each particular period
under both prospected scenarios in each selected Woreda. The HadGEM2-ES model prediction agrees
with the MRI-CGCM3 model projection in most results. The result reveals the precipitation annual total
rainfall increasing rate is 16, 19.4 & 13.8 mm/yr during the near, mid & end-term of the century in both
scenarios at Arbaminch Zuria Woreda. The annual total rainfall decreasing rate of 25.3, 16.4, 14.5, 26.3
mm/yr under RCP4.5 and 19.3, 12.3, 10.4, 20.5 mm/yr under RCP8.5 at Arbaminch, M/abaya, Dramalo &
Kucha Woreda respectively during the mid-term of the century (Table 4).
Based on Mann-Kendal trend test result, the average rate of increasing temperature was estimated to be
0.0975 oC, 3.8 oC, 1.74 oC/30 yrs under RCP4.5 scenarios; while it has to be 0.51 oC, 2.04 oC & 1.32 oC/30
years under RCP8.5 scenarios during the near, mid and end-terms of the 21st century respectively. While
the average rate of decreasing tendency has to be 0.1 oC, 0.23 oC/30 yrs) during the mid-term under
RCP4.5 and end-term under RCP8.5 scenarios (Table 5).

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The daytime air temperature annual trend of Sen’s slope estimator implied that there is an insignificant
increasing trend in all three selected CMIP5 temperature projections and both GHGs concentration
scenarios. With the exception, an insignificant decreasing trend projected by MRI-CGCM3 model during
the end-term of the century at Arbaminch Zuria Woreda, and during the near & mid-terms by GFDL-
ESM2M model RCP4.5 scenarios at Mirab Abaya & Dramalo Woreda; and also has decreasing trend
during the end-term by HadGEM2-ES model projection RCP4.5 scenarios in all 3 Woreda (Table 5).
However, the prospected temperature condition during each particular period's temperature trend
detection indicated, there is no significant upward increase and a downward decreasing trend in most
Woreda at 0.05 significant level through the 21st century in response to long-term change in climate in
the study area.
As the computed P-value (P) is greater than the significance level (α = 0.05), there is no significant trend in
the series while it is less than the significance level alpha, there is a significant trend in the series. SS
(Sen’s Slope) is the change (mm /annual), and it tells the trend magnitude per annual. a positive value
indicates the trend is an increase while a negative value is a decreasing trend (Table 4). Slope (Sen’s
slope) is the change (oC /annual), and it tells the trend magnitude per annual. a positive value indicates
an increasing trend and a negative value is decreasing trend (Table 5).

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Table 4
Precipitation Mann-Kendal trend test statistics (P-value & Sen’s slope) MRI-CGCM3
Selected
woreda
MK test Baseline RCP4.5 RCP8.5
NT MT ET NT MT ET
Arbaminch
zurya
P-value
SS
1.0 0.32 0.22 0.72 0.43 4.4 1.04 -4.1 0.16 0.27 0.04 7.6 -6.9
18.9
Dramalo P-value
SS
1.0 0.12 0.15 1.0 0.67 4.5 0.12 -2.4 0.5 0.08 0.038 3.7 -6.6
11.14
M/abaya P-value
SS
0.54 4.4 0.47 0.52 0.5 -4.6 -6.5 1.13 0.54 0.18 0.036 2.7 -4.3
13.1
Kucha P-value
SS
0.9 0.79 0.13 0.94 0.67 5.9 -0.8 -2.9 0.4 0.16 0.03 4.5 -9.3
22.3
GFDLESM2M
Arbaminch
zurya
P-value
SS
0.3 -12.1 0.39 0.91 0.45 -7.5 -1.3 -3.9 0.18 0.65 0.2 -11.1 2.2
-9.78
Dramalo P-value
SS
-0.095
-11.2
0.9 0.64 0.91 -1.1 3.2 1.7 0.18 0.65 0.2 -9.6 8.6 -5.9
M/abaya P-value
SS
0.88 4.8 0.39 0.24 0.94 -4.6 -6.5 1.13 0.54 0.9 0.65 -3.8 -1.2 3.8
Kucha P-value
SS
0.41
-13.7
1.0 0.6 0.9 0.006 2.9 1.13 0.64 0.83 0.8 -3.13 -1.4
-1.75
HadGEM2-ES
Arbaminch
zurya
P-value
SS
0.3 5.4 0.000 0.0 0.0001 15.98 -25.3
19.4
0.004 0.0 0.001 9.7 -19.3
13.8
Dramalo P-value
SS
0.01 10.8 0.001 0.001 0.001 8.6 -16.4
9.3
0.1 0.002 0.04 3.9 -12.5
4.9
M/abaya P-value
SS
0.16 4.8 0.0001 0.001 0.0001 10.3
-14.5 11.4
0.003 0.001 0.0 7.01
-10.3 9.1
Kucha P-value
SS
0.07 11.9 0.0001 0.001 0.0 12.8 -26.3
16.3
0.029 0.001 0.03 7.3
-20.5 7.2

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Table 5
Average temperature Mann-Kendal (MK) trend test statistics (P-value & Sen’s slope). MRI-CGCM3
Selected
woreda
MK Baseline RCP4.5 RCP8.5
NT MT ET NT MT ET
Arbaminch
zurya
P-value
SS
0.71
0.006
0.68 0.022 0.24 0.003 0.4
0.018
0.15 0.0 0.8 0.0023 0.085
-0.003
Dramalo P-value
SS
0.66
0.009
0.68 0.022 0.24 0.003 0.037
0.18
0.145 0.0 0.86 0.023 0 .085
0.003
M/abaya P-value
SS
0.86
0.003
0.61 0.005 0.256 0.004
0.036 0.016
0.119 0.0 0.47 0.02 0.072
0.01
Kucha P-value
SS
0.71
0.006
0.7 0.02 0.24 0.003 0.004
0.02
0.15 0.0 0.9 0.023 0.1 -0.003
GFDLESM2M
Arbaminch
zurya
P-value
SS
0.002
0.05
0.66 0.166 0.36 0.006 0.02
0.011
0.127 0.013 0.001 0.025
0.024 0.042
Dramalo P-value
SS
0.001
0.041
0.8 0.4 0.77 0.005 -0.007
-0.004
0.25 0.045 0.0 0.02 0.017
0.047
M/abaya P-value
SS
0.003
0.048
0.7 0.19 0.5 -0.003 -0.015
-0.008
0.6 0.01 0.02 0.007 0.014
0.039
Kucha P-value
SS
0.001
0.04
0.78 0.4 0.9 0.005 -0.007
0.001
0.25 0.045 0.0 0.002 0.017
0.048
HadGEM2-ES
Arbaminch
zurya
P-value
SS
0.071
0.03
0.0001 0.052 0.6 0.049
0.027 -0.01
0.0 0.0001 0.0001 0.06 0.048
0.057
Dramalo P-value
SS
0.1 0.03 0.0001 0.007 0.61 0.049
0.033 -0.009
0.001 0.0001 0.0 0.052 0.053
0.054
M/abaya P-value
SS
0.095
0.031
0.0001 0.007 0.6 0.043
0.033 -0.009
0.001 0.0001 0.0 0.051 0.053
0.054
Kucha P-value
SS
0.12
0.03
0.0001 0.006 1.0 0.043
0.033 0.0002
0.001 0.0001 0.0001 0.052
0.053 0.06
3.3. Maize Crop Modeling Results
3.3.1. Model Sensitivity and Parameterization
Sensitive parameters in each APSIM module were identified using observed climate variables in maize
crop simulation. The soil water parameters were more sensitive in altering the simulation result. Plant
available water capacity (PAWC), soil water supply (LL) and soil water potential/drained upper limit (DUL)
were more sensitive (Table 1) in response to maize crop water stress during photosynthesis and leaf area

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expansion in altering simulation results.The model sensitivity analysis indicated that maize grain yield
probability is due to various amounts of climate variable inputs. The plotting showed the maize yield
probability and variation in response to changes in various amount of weather variables in a specified
site's actual crop management options. This plotting implied that changes in different amounts of
climate variables could have a significant effect on maize yield simulations, especially since maize yield
is more sensitive to changes in precipitation than maximum and minimum temperatures. The blue line
(Maize_50N_5yrs) is the normal value of weather data inputs with 50 kg fertilization application, while
others are the changes from the normal value which showed the yield change in reference with the
normal condition probability exceedance simulating simultaneously (Fig. 2, probability Exceedence).
Runoff, evaporation and drainage are affected by weather and soil water storage capacity. The storage of
soil water suddenly increases due to rainfall occurrence and becomes declines due to evaporation and
drainage loss. Since the distribution of daily precipitation amounts enables us to see the storage of soil
water as a water balance (Fig. 2 / Soil_water storage), which is influenced by the soil type.
The box plotting implied, the lower, upper limit and median variation of Maize yield as the perspective of
extractable soil water (ESW) in response to various amounts of actual historical observed climate
variables during simulation (Fig. 2 / ESW vs Maize yield). There may be a consensus in literature on how
much climate variables caused a certain amount of yield changes however, this process-based Maize
crop modeling deploys the yield variation for various amounts of climate variable inputs to evaluate the
future maize yield response to projected climate scenarios.
3.3.2. APSIM Maize Yield Simulation Evaluation
The plotting (Fig. 3) indicated that the simulated yield is fitted with the real agronomic maize grain yield
to predict the future Maize grain yield under various CMIP5 models climate scenarios. The Maize grain
yield simulated using historical observed weather data well agrees with the correspondence years
measured yield at different selected Woreda (Table 6). The computed RMSE has a moderate value (4.5 to
13.2 q/ha) across the selected Woreda, in which there is no wide range of deviation from the historically
measured maize grain yield.
Table 6
APSIM model Maize yield simulation performance evaluation at each woreda.
Evaluating
statistics
Selected
woreda
R2
NSE
RMSE
E1
Arbaminch M/Abaya Kucha Dramalo
0.72 0.71 10.4 0.13 0.87 0.54 4.5 0.28 0.73 0.83 8.4 0.64 0.72 0.86 13.2 0.56
3.3.3. Projected Maize Yield Potential

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Maize yield potential (expected yield) simulated under prospected climate conditions (Fig. 4) and
probability of exceedance (Fig. 7) for 3 periods of time slices of the 21st century, and the baseline (1980–
2005) and to evaluate the climate impact based on this simulation model output.
3.3.4. Prospected Maize Yield Variation
The long-term maize yield temporal variability as the perspective of the prospected climate variability
simulated under various GCMs/CMIP5 climate model projections of two RCPs climate scenarios has
been assessed using the lower, median and upper values for baseline and 3 future particular periods as
shown in (Fig. 5). Hence the plotting showed the 5th, 25th, 75th, 95th percentiles and medians to illustrate
the baseline and projected Maize grain yield potential variability in response to climate variations during
each particular period across different locations.
The maize grain yield simulated during the baseline period climate inputs on average at four selected
Woreda lowest, median and highest values are 5.5, 17.3 & 32.4 Q/ha with a coefficient of variation
(2.75%).
Maize grain yield simulated under MRI-CGCM3 climate model input, Arbaminch Zuria Woreda, the lowest
& highest values of projected maize grain yield potential ranges from 3.91 and 20.63 Q/ha with a median
value of 12.9 Q/ha and coefficient of variation of (3.9%). At M/abaya, it ranges between 2.6 and 31.9
Q/ha and has a median value of 18.6 Q/ha with a coefficient of variation (3.4%). At Dramalo Woreda, the
lowest, median & highest values of grain yield potential have 1.32,16.6 and 39.0 Q/ha with a coefficient
of variation of (1.3%). While at Kucha Woreda, the lowest, and highest and median values are 0.32, 37.3 &
18.3 Q/ha respectively with a coefficient of variation (2%).
The GFDL-ESM2 climate model simulation of potential maize yield at Arbaminch Zuria Woreda, lowest,
and highest & median values are 1.8, 29.6 & 21.9 Q/ha, with a coefficient of variation of (3.4%); At
M/abaya, the lowest, highest and median values are 0.3, 19.3 & 12.8 Q/ha respectively with a coefficient
of variation (2.7%); At Dramalo Woreda, it has values 1.5, 26.8 & 21.8 Q/ha with a coefficient of variation
(2.3%); at Kucha Woreda, it has lowest, highest & median values of 0.6, 23.9 & 16.2 Q/ha respectively with
a coefficient of variation (1.9%) under both RCPs scenarios (Fig. 5).
Under HadGEM2-ES model future maize grain yield potential simulation lowest, highest and median
values at Arbaminch Zuria Woreda range 0.3,29.0 & 9.25 Q/ha respectively and the coefficient of
variation is (1.8%); while at M/abaya, it ranges 0.4,34.8 & 9.0 Q/ha with a coefficient of variation (1.2%);
at Dramalo, it ranges 0.1,41.1 & 15.2 Q/ha; and at Woreda Kucha, lowest, highest and median values
showed 0.6, 35.2 & 17.9 Q/ha with a coefficient of variation (2.8%).
3.3.5. Projected Maize Yield Potential Changes
Median of the predicted maize grain yield potential simulated under climate change in different particular
periods and various locations has been arranged in (Table 7), which would help to reduce risk of seasonal

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variation, allowing famers to be prepared for best options in taking advantage of good seasons as
(Ngoune Tandzi & Mutengwa, 2019b) description. This median value of the modelled yield potential
simulated for baseline and future 3 particular periods climate constraints encompasses best
management practices and provides a more relevant benchmark for improvement as (Sadras et al.,
2015b) yield gap analysis, and hence the impact of projected change in climate has been evaluated at
each selected woreda (Fig. 6).
Table 7
Median of maize yield potential (Q/ha) simulated under 3CMIP5 models (future 30-year time slice
compared to 30-year baseline time series).
Time
slice
MRI-CGCM3 GFDL-ESM2 HadGEM2-ES
A M D K A M D K A M D K
Baseline
RCP4.5_NT
RCP8.5_NT
RCP4.5_MT
RCP8.5_MT
RCP4.5_ET
RCP8.5_ET
15.6 23.1 21.4 27.5 15.2
27.3 24.0 25.9 17.2 25.5
26.8 27.1 17.9 22.8 24.5
24.1 15.9 21.4 22.2 23.2
15.5 24.5 22.1 24.7 13.6
19.5 17.9 19.1
13.8 13.5 15.1 13.8 21.8
5.8 14.1 12.8 19.8 10.4
17.4 15.7 19.4 17.8 17.7
13.5 16.9 16.0 15.5 12.7
11.6 16.4 14.7 12.2 11.4
13.8 11.5 10.4
19.5 9.5 9.7 17.6 19.3
12.9 14.0 15.4 18.3 11.6
14.7 27.3 8.0 8.4 7.0 7.0
9.7 7.4 4.9 7.9 19.8 4.2
12.8 25.8 13.4 11.4 13.7
20.7
A = Arbaminch, M = M/abaya, D = Dramalo, K = Kucha: are selected woreda; Near-term, NT (2010–2040),
Mid-term, MT(2041–2070), End-term, ET (2071–2100).
Based on the long-term temporal maize grain yield potential gap analysis, a positive change up to 12.9%
& 15.1% under RCP4.5 & RCP8.5 scenarios respectively across 3 Woreda on average during the near-term
period; except at Kucha Woreda which has a negative change up to 3.6% during this period under MRI-
CGCM3 climate model projection.
During the mid-term period, the median yield change has a positive change of up to 14.6% & 2.75% under
RCP4.5 & RCP8.5 scenarios across 3 Woreda, while it has a negative change of up to 13.9% under both
RCP climate scenarios at Kucha Woreda, and has a negative change of up to 18.75% and a positive
change of 4.6% during the end-term period under RCP8.5 & RCP4.5 maize grain yield potential simulation
at 3 Woreda as compared to the baseline yield simulation, except at Kucha which has a decline up to 10%
during this period in both scenarios (Fig. 6).
The GFDL-ESM2 global climate model climate projection inputs of maize grain yield potential simulation
median change up to -23.7% on average at M/abaya & Dramalo Woreda; and a positive change up to 41%
at Arbaminch & Kucha Woreda under RCP4.5 & RCP8.5 during the near -term period relative to baseline.
While during the mid-term period a positive change up to 22.5% & 10.9% at 3 Woreda, except Kucha,

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which has a negative change of 2.17% & 28.95 respectively under RCP4.5 & RCP8.5 relative to the
baseline. While during the end-term period, the median yield change has a negative change up to 10.2 &
21.8% under RCP4.5& RCP8.5 scenarios across 3 Woreda, except at M/abaya which has a positive
change up to 11.8% during this period and both scenarios as compared to the baseline yield simulation.
The HadGEM2-ES global model climate input APSIM7.9 maize yield potential simulation median yield
change analysis showed a positive change up to 40.1% & 43% under RCP4.5 & RCP8.5 during the near-
term period, except Arbaminch which has a negative change of 5%. During the mid-term period, the
median has a negative change up to 41.7% on average across 4 Woreda under both scenarios. While
during the end-term period a positive change of up to 26.7% under RCP4.5 on average across 3 Woreda,
except M/abaya which has a negative change of 55.7%, and under RCP8.5, it showed a positive change
up to 26% on average at 3 Woreda except at Arbaminch which has a negative change up to 31.3% as
compared to the baseline period yield simulation.
The HadGEM2-ES and MRI-CGCM3 global models agree in predicting the future maize grain yield
potential and have a negative change during the mid and end-terms, which is consistent with the
projected seasonal rainfall reduction during the main rainy season (MAM) and the decreasing trend in
mean annual rainfall during these periods in response to the projected climate change; and has a
relatively positive change during the near-terms of the 21st century as compared to the baseline.
This research result implied that climate variability and change will have a particularly threatening effect
on maize crops at Kucha Woreda relative to others during the medium-term period scenarios (2041–
2070). While Dramalo woreda has a relatively less effect due to the seasonality of climate change
(Fig. 6).
The simulated yield median percent change under GFDL-ESM2 agrees with the other two models during
the near and end-terms, while it is not consistent during the mid-term period which has a positive change
across 3 Woreda, except Kucha which has a negative change under all 3 CMIP5 models. This difference
might be due to the model's structure and their initial and boundary conditions during climate modelling.
HadGEM2-ES Maize yield reduction at M/abaya Woreda during the mid and end terms in both scenarios
is due to the probability of drought years predicted by the model precipitation SPI value which showed
moderate dry years (2035–2040) (McKee et al., 1993) agricultural drought analysis indexes. The median
yield change of the simulated maize grain yield potential under RCP8.5 has a more positive change than
under RCP4.5 climate scenario in different models and various Woreda, which is comprehensive with a
relative precipitation enhancement during the near and end-terms under this high CO2 concentration
scenario RCP8.5 in each selected CMIP5 models.
4. Conclusions
This study tried to strive to evaluate the impact of climate variability and change on maize crop
production by setting up APSIM7.9 maize crop modeling in conjunction with 3CMIP5 climate models

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climate forcing scenarios (RCP4.5&8.5) across four Woreda in Gamo Zone, Southern Ethiopia.
Based on two wet season rainfall anomaly analyses (SPI), climate variability response of high and very
high rainfall occurrences during 2nd rainy season (SON) in future periods relative to baseline. While the
main rainy season (MAM) relatively moderate dry and very dry climate conditions will predominate during
the mid and end-terms of the 21st century relative to the baseline period. However, inconsistent upward
increasing and a downward decreasing trend in annual total rainfall in future particular climatological
periods in the Zone has been detected. This signifies to conclude that a short-season precipitation
enhancement becomes more prevalent than else season's intermittent rainfall occurrence relative to
history in the study area. The Man-Kendal trend detection of temperature revealed, there is no significant
upward increasing and downward decreasing trend during the long-term periods. Climate variability
becomes more plausible than the long-term significant climate change in the study area.
The APSIM Maize crop modeling showed a very good performance in predicting the future maize grain
yield potential in response to projected climate scenarios. The model adequately simulates the final grain
yield with RMSE value of 4.5 to 9.13 q/ha compared with the measured yield on average across the
selected 4 woreda.
The simulation result revealed that there is an increment in the change in long-term mean yield and
variability relative to baseline, which provides insights into climate variability and change impacts
adaptation options, and is relevant for current decision-making about climate adaption policies and
measures as the perspective of food security in the study area. The maize yield variability is driven by
seasonal rainfall deviation than temperature departure.
Based on the simulated maize grain yield potential, the median yield decreases up to 36.5% and 18.75%
on average across all Woreda during the mid-term and end-terms due to the reduction of precipitation
during spring (main rainy season) and relative annual total rainfall reduction and high temperature as
compared to the baseline period; while it showed a positive change up to 29.2% during the near-term of
the 21st century in both RCP scenarios. Climate variability and change has a significant negative impact
during the mid-term (2041–2070) compared to the baseline and a slight effect during the end-terms
(2071–2100) of the 21st century across most Woreda, which emphasizes decisions in taking appropriate
adaptation measures.
Based on the seasonal rainfall analysis, the maize crop production becomes improved during the 2nd
rainy season (SON) as compared to the major rainy season (MAM), which has more positive rainfall
anomaly relative to the baseline period. This crop modelling work becomes more comprehensive for best
decision-making with crop sowing date scenarios for sowing guidelines by analyzing the season onset,
cessation and length of rainfall occurrence based on the projected rainfall scenario derived from various
climate models.
Declarations

/33
Ethics Approval Not applicable.
Consent to Participate Not applicable.
Consent for Pulication Not applicable.
Competing Interests The authors declare no competing interests.
Funding This project was not funded but supported by institutes for data request.
Carriedout:Under the supervision of Arbaminch University, College of Natural Science research center,
Abaya compus, Ethiopia.
Author Contribution: Gizachew Kassa conceptualized, research, supervision, original draught preparation,
data curation, methodology, software, original data preparation and writing the original manuscript.
Manyazwal Getachew: carried out the methodology, validation, supervision, writing-reviewing, and editing
tasks, as well as the examination of the software and the findings analysis and discussion sections.
Anirudh Bhowmick: The corresponding author checks the review, prepares the text, and makes
adjustments, draft check and repreparation of manuscript and corrections.
Note: the final manuscript was read and approved by all writers. We also reaffirm that we all approved of
the order in which the authors are listed in the manuscript. We recognise that the corresponding author
serves as the exclusive point of contact for editorial matters (including Editorial Manager and direct
communications with the office). He is in charge of informing the other authors of his progress, their
submission of corrections, and his final acceptance of the proofs. We certify that the associated author
may access the email address we gave as being current and accurate.
Availability of Data and Material Data is available upon request to the authors.
Code Availability Codes are available upon request to the authors.
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Figures

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Figure 1
Schematic showing flow of the methods designed for this study.

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Figure 2
Maize yield probability, Soil water storage vs daily rainfall and yield vs Extracted Soil water (ESW) in
response to various amounts of climate variables.

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Figure 3
The correlation of actual and simulated Maize grain yield in (kg/ha) at selected Woreda (2013-2017).

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Figure 4
Predicted Maize yield potential (kg/ha) comparison under various climate models baseline and
scenarios.

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Figure 5
Variations of simulated maize yield potential under MRI-CGCM3 model.

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Figure 6
Maize yield median changes simulated using CMIP5 (future 30-year time slice relative to 30-year
baseline) imposed on baseline time series.

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Figure 7
Probability Exceedance of Maize Yield Potential Simulated under the three selected CMIP5/GCMs (MRI-
CGCM3, HadGEM2-ES, GFDL-ESM2M) climate models.

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