sustainability
Article
Accounting for Weather Variability in Farm Management
Resource Allocation in Northern Ghana: An Integrated
Modeling Approach
Opeyemi Obafemi Adelesi 1 , Yean-Uk Kim 1, Heidi Webber 1,*, Peter Zander 1 , Johannes Schuler 1 ,
Seyed-Ali Hosseini-Yekani 1 , Dilys Sefakor MacCarthy 2 , Alhassan Lansah Abdulai 3, Karin van der Wiel 4,
Pierre C. Sibiry Traore 5,6 and Samuel Godfried Kwasi Adiku 7
1 Leibniz Centre for Agricultural Landscape Research (ZALF), 15374 Müncheberg, Germany;
adelesi.opeyemi_obafemi@zalf.de (O.O.A.)
2 Soil and Irrigation Research Centre, School of Agriculture, University of Ghana, Accra P.O. Box LG 68, Ghana
3 CSIR-Savanna Agricultural Research Institute, Tamale P.O. Box TL 52, Ghana
4 Royal Netherlands Meteorological Institute, 3731 GA De Bilt, The Netherlands
5 ICRISAT-Senegal, Dakar P.O. Box 24365, Senegal
6 Manobi Africa PLC, Dakar P.O. Box 25026, Senegal
7 Department of Soil Science, University of Ghana, Accra P.O. Box LG 245, Ghana
* Correspondence: webber@zalf.de
Abstract: Smallholder farmers in Northern Ghana face challenges due to weather variability and
market volatility, hindering their ability to invest in sustainable intensification options. Modeling
can help understand the relationships between productivity, environmental, and economical aspects,
but few models have explored the effects of weather variability on crop management and resource
allocation. This study introduces an integrated modeling approach to optimize resource allocation
Citation: Adelesi, O.O.; Kim, Y.-U.; for smallholder mixed crop and livestock farming systems in Northern Ghana. The model combines
Webber, H.; Zander, P.; Schuler, J.; a process-based crop model, farm simulation model, and annual optimization model. Crop model
Hosseini-Yekani, S.-A.; MacCarthy, simulations are driven by a large ensemble of weather time series for two scenarios: good and bad
D.S.; Abdulai, A.L.; van der Wiel, K.; weather. The model accounts for the effects of climate risks on farm management decisions, which can
Traore, P.C.S.; et al. Accounting for
help in supporting investments in sustainable intensification practices, thereby bringing smallholder
Weather Variability in Farm
farmers out of poverty traps. The model was simulated for three different farm types represented in
Management Resource Allocation in
Northern Ghana: An Integrated the region. The results suggest that farmers could increase their income by allocating more than 80%
Modeling Approach. Sustainability of their land to cash crops such as rice, groundnut, and soybeans. The optimized cropping patterns
2023, 15, 7386. https://doi.org/ have an over 50% probability of increasing farm income, particularly under bad weather scenarios,
10.3390/su15097386 compared with current cropping systems.
Academic Editors: Vasileios
Keywords: bio-economic farm model; integrated model; weather risk; mixed cropping system;
Tzanakakis and Theocharis
CLEM; Northern Ghana; SIMPLACE
Chatzistathis
Received: 21 February 2023
Revised: 16 April 2023
Accepted: 18 April 2023 1. Introduction
Published: 28 April 2023 There is considerable pressure on smallholder farmers in sub-Saharan Africa to in-
crease production as a means to improve their income and food security, as well as to
supply food to larger and growing urban markets [1–4]. With land expansion posing an un-
Copyright: © 2023 by the authors. acceptable pressure on biodiversity, sustainable intensification of agriculture is considered
Licensee MDPI, Basel, Switzerland. to be an increasingly appropriate approach. This implies intensifying agricultural produc-
This article is an open access article tion [2,5] while ensuring environmental protection, maintaining soil fertility and soil carbon,
distributed under the terms and preserving biodiversity, and building the climatic resilience of farming systems [1,2,6].
conditions of the Creative Commons Despite the considerable effort to promote the sustainable intensification of small-
Attribution (CC BY) license (https:// holder farming in semi-arid West Africa [7], success remains limited. Farmers are chal-
creativecommons.org/licenses/by/ lenged by many constraints related to poor soil fertility, weak market infrastructure, lack
4.0/). of access to credit facilities, fluctuating prices, and high weather variability [8], which has
Sustainability 2023, 15, 7386. https://doi.org/10.3390/su15097386 https://www.mdpi.com/journal/sustainability
Sustainability 2023, 15, 7386 2 of 21
made farming very risky [3,9–15]. While farmers face many sources of risk, including price
volatility and market shocks, illness of family members and livestock, and pest and disease
outbreaks [15], the risks related to (extreme) weather such as heat and drought remain sig-
nificant because of their frequency and magnitude being projected to increase with climate
change [16–18]. Together, the wide range of risks and uncertainties faced by farmers in the
region are key reasons for them not investing in improved farming practices [12].
Mixed crop—livestock farmers in Northern Ghana have adopted several management
strategies on their farms to minimize the impact of these risks. Farmers may choose more
drought-resistant crops and varieties, change planting dates, and/or plant a mixture of
crops and varieties [19]. However, the benefits of these risk-reducing practices are unlikely
to be sufficient in years with severe yield failures due to drought [20,21] or pests, which is
the reason behind many of the current efforts to promote insurance products. Many farmers
in the greater West African Sudan Savannah already use livestock as informal insurance
in the case of insufficient income from cropping. In addition to providing traction and
food, animals can be sold in years with crop yield failure to overcome cash shortages [22].
As such, crop residues are often preferably used as fodder instead of being incorporated
into soils to improve soil fertility [20,23,24]. On the other hand, formal insurance products
remain unattractive to many farmers for a number of reasons [25], including that their cost
in good years may preclude other investments. Comprehensively considered, managing
risk in farming will likely need elements of risk reduction, risk transfer, savings and smart
risk-taking, among other factors [26], and the strategy depends on the specifics of the farms,
agro-ecology, and institutional and market factors. In the context of an increasingly variable
and extreme climate, it is important to better assess which combination of risk management
options best serves the livelihood objectives of smallholder farmers.
Given the host of the soil—crop—weather—animal—market complexity of a farm-
ing system, system analysis tools are helpful for complementing experimentation and for
exploring a wide range of options and conditions. System modeling that integrates biophys-
ical and economic models is one option for investigating complex systems by capturing
crop and livestock production, soil fertility-related processes, on-farm and off-farm labor
availability, and livestock feeds in addition to socio-economic behavior of farmers [4,27].
Such models can help to better understand farmers’ behavior and the effects on resource
allocation [28]. They can also allow for the evaluation of likely outcomes of proposed
interventions and for the identification of interactions among components across scales
and levels [29]. This is evident in models such as the one described by [29] and often
include linear programming, system dynamics, and agent-based modeling. Several models
could also be combined in order to explain the interactions among complex socio-economic,
bio-physical, and socio-ecological processes, which are often referred to as bio-economic
farm models [29–32].
There are examples of modeling approaches that are intended to capture the effects
of weather, price, and production risks on different farm household components, such as
consumption [4,8,10]. However, to the best of our knowledge, none have yet included
feedback on crop and soil management due to altered farm-level decisions. This is con-
sidered to be crucial in assessing how weather (and other) risks affect crop management
and the suitability of different risk management strategies. It is particularly important
when considering the implications for longer-term sustainability outcomes with respect
to environmental variables (soil organic carbon and biodiversity). Furthermore, while
the impact of shocks and risk management options can be evaluated through scenario
analyses [33], models that can simultaneously evaluate farmers’ response to weather shocks
and the subsequent impacts on farm assets such as livestock or natural capital are still
lacking. Furthermore, previous studies have focused on optimizing production in the face
of risks over a typical planning period, with few attempts having been made to account for
the effects of shocks on farm trajectories [34].
In this context, the objective in this study was to develop an integrated modeling
approach that captures the probability of changes in assets, incomes, or resource allocation
Sustainability 2023, 15, 7386 3 of 21
in response to degrees of weather variability and price volatility for smallholder mixed
farming systems in Northern Region, Ghana. To this end, we combined a crop model,
farm simulation model, and optimization model to assess the effects of weather- and
market-related variability in the optimization of resource allocation. Two weather scenarios
(relatively good and bad) constructed from large ensemble climate modeling were used to
force the integrated model.
2. Materials and Methods
2.1. Study Area
The study was based on data from the Northern Region, Ghana, which is located in the
Guinea Savannah ecological zone. Up until a recent re-demarcation into new administrative
regions, the zone comprised three main regions, namely the Northern Region, Upper West
Region and Upper East Region. The Northern Region has a population of approximately
2.5 million inhabitants and has a large rural farming population [35]. It is characterized by
a single rainy season occurring between May and October, with an annual rainfall between
750 mm and 1050 mm and an annual mean temperature between 22.4 ◦C and 33.9 ◦C. There
is typically a prolonged dry season between November and March/April, and the impacts
of climate change (frequent floods, drought, and bush fires) are already pronounced in the
region [3,36,37]. The main food crops grown in the Northern Region include maize, rice,
sorghum, millet, cowpea, and groundnut. Cash crops such as soybean, tomato, onion, and
leafy vegetables are grown in the area, while livestock reared in the area include cattle and
small ruminants such as goats and sheep [38].
2.2. Sampling Technique and Data Collection
An initial household survey of 700 households was carried out during the 2020 agri-
cultural season across the Upper West Region, Upper East Region, and Northern Region.
The data were filtered for the Northern Region households only, which reduced the dataset
to 378 households located in the Tolon, Savelugu, and Mion districts. The data were used to
develop a farm typology based on socio-economic characteristics such as age, sex, and farm
resource endowments (mainly land and herd size). The data were clustered into three farm
types with different resource endowments (described in the following section). A second
in-depth survey was carried out before the 2022 planting season to obtain detailed crop
and livestock production data, which were used in the modeling exercise. Respondents
were randomly selected, resulting in a total of 45 households distributed equally between
our three farm types. Data were obtained with the aid of structured questionnaires added
to the JotBi app developed by CGIAR CASCAID [39]. Historical crop price distributions
were obtained from the Ghanaian Ministry of Food and Agriculture reports [40,41] and
adjusted for inflation using the producer’s price index data obtained from [42].
2.3. Formation of Farm Typology
The selection of variables for the typology was performed based on the literature,
expert opinion, context, and local context [43–45]. Ten variables (Table 1) that best classify
the households based on income and resource endowments in the dataset were used
to cluster the farmers. The diversity among farmers was defined based on household
characteristics and resource endowment. The dataset was checked for missing data and
outliers, and these were controlled for through imputation and list-wise deletion techniques
as proposed by [43,46]. Of the 378 households, 340 were retained for analysis, and the farm
households were clustered into 3 farm types, comprising 61 low-resource-endowed farms,
181 medium-resource-endowed farms, and 98 high-resource-endowed farms, respectively.
The farm household typology was constructed using a principal component analysis (PCA)
on the 340 households with a factor analysis on mixed data (FAMD) analysis. The “FAMD”
function within the “FactoMineR” package in R software (Version 4.0.2.) was used [47,48]
as it is best suited for both continuous and categorical variables [49]; we also used the
package to carry out hierarchical clustering to obtain the different typologies [43].
Sustainability 2023, 15, 7386 4 of 21
Table 1. Summary of variables used for typologies.
Variable Description Unit
Age Age of the household head years
Cash at hand Cash at hand at the beginning of the season GHS
Sex Sex of the household head -
Household size Number of individuals in the household -
Herd size Total herd size -
Input costs Total cost of production inputs GHS/year
Land size Total land size ha
Main crop Main crop cultivated by farmers -
Non-farm income Annual household off farm income GHS/year
Total annual income Total annual household income GHS/year
GHS is the Ghanaian Cedi, which is the official Ghanaian currency.
2.4. Meteorological Forcing Data
To assess the model’s response to weather variability and risk, large ensemble climate
modeling data were used. Large ensemble climate modeling is a technique that produces
many different weather realizations for a given period and state of the climate, effectively
sampling the whole distribution of possible weather [50,51]. This means that we can
directly assess the risks of extreme events by running the simulated weather data through
the integrated bio-economic model.
The large ensemble dataset used here contains 2000 years of present-day weather
conditions, which were generated using EC-Earth global climate model data [52]. EC-Earth
is an Earth system model representing atmosphere, ocean, land, and ice conditions. The
ensemble consists of 400 members, each consisting of a 5-year simulation of a period
representing the present-day global climate (as observed in 2011–2015). Details on the large
ensemble experimental set-up can be found in [50]. The data were previously used for
crop modeling studies in [8,53–55], and the data were extracted for the grid point that was
nearest to the study site, Tamale (09◦ N and 00◦ W).
The modeled temperature and precipitation data were bias-corrected using station
data from Nyankpala. For temperature, the daily maximum, minimum, and annual cycle
were computed using a harmonic function [56]; the simulated data were then corrected by
the difference between the harmonic based on the observed data and the harmonic based
on the simulated data.
µ(t) = a K+ ∑k 1 bksin(kωt) + ckcos(kωt) (1)=
where
µ = Temperature (C);
t = Day- of- year (1–366);
K = Order of harmonic function determined using BIC (K = 4);
ω = 2π/365.25.
a, bk, and ck are coefficients of the harmonic function.
The precipitation was corrected on a monthly basis using a method from [54]. First,
the number of precipitation days of EC-Earth data was corrected to the observed number to
solve for the drizzle effect. The precipitation values of the days with precipitation amounts
falling below a threshold were set to 0. The threshold was determined by matching the
EC-Earth precipitation days to the observed precipitation days (≥0.1 mm d−1). After
correcting the drizzle bias, the EC-Earth monthly precipitation amount was corrected by a
multiplicative factor to match the observed monthly precipitation amount.
Sustainability 2023, 15, 7386 5 of 22 
 
to solve for the drizzle effect. The precipitation values of the days with precipitation 
amounts falling below a threshold were set to 0. The threshold was determined by match-
ing the EC-Earth precipitation days to the observed precipitation days (≥0.1 mm d−1). After 
correcting the drizzle bias, the EC-Earth monthly precipitation amount was corrected by 
a multiplicative factor to match the observed monthly precipitation amount. 
Sustainability 2023, 15, 7386 2.5. Modeling Approach 5 of 21
2.5.1. Model Approach Overview 
An integrated bio-economic model was developed to simulate outcomes of annual 
2.5. Modeling Approach
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lows1: . SIMPLACE: simulates crop grain and biomass yield in response to weather, soil, and
management. These simulations are passed to CLEM annually and to the optimization
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cashT oaenxdp lhoreertdh esiinzte;g rated model response under different degrees of weather variability,
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wCeLatEhMer .c onditions. This was performed by classifying climate ensemble members to either
the good or bad weather scenario based on the simulated grain yields for each ensemble
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sembmlee mbeemrs.bTehre. B30ehcaiguhseest tahned g3r0alionw yesiet lmde laenvreell avtaivreieysie aldmmoenmgb tehrse wcreorepg tryouppeesd (ein.gto., rice yield 
above 3000 kg ha−1 vs. soybean yield around 1000 kg ha−1), the relative yield was calculated 
for each crop type (i.e., the yield of a given year divided by the average yield in all 2000 
Sustainability 2023, 15, 7386 6 of 22 
 
Sustainability 2023, 15, 7386 years). Thereafter, the mean relative yield was calculated across crop types for6 oefa2c1h en-
semble member (5-year simulation), and these mean yields were used to classify the en-
semble members. The 30 highest and 30 lowest mean relative yield members were 
thgeroguopoedda inndtob tahde wgoeoadth aenrdsc beanda rwioesaathnedr wsceerneairnicolsu adnedd winetrhee insicmluudleadti oinn ,thase sshimowulnatiinon, as 
Fsihguowren2 ,into Fgiegnuerrea t2e, dtois gtienncetrwateea tdhiesrtirnecstp wonesaetshienr trheespinotnesgersa tiend tmheo dinetle. grated model. 
 
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Sustainability 2023, 15, 7386 7 of 21
2.5.2. Crop Model
The crop model simulates crop growth in response to the weather information in-
putted and key management choices/options [20,59,60]. These simulations are important
to/for other models like the optimization model because crop yield will determine the
household income and amount of food available to smallholder households in most cases,
thereby affecting many household decisions. The SIMPLACE crop modeling framework
(see SIMPLACE website) provided both the CLEM and optimization model a simulation
of crop grain, biomass yield and nitrogen content of crops (Table 2). The above-ground
growth crop module used in SIMPLACE is Lintul-5 [61], which was for this study and
coupled with a modified version of Slim Water [62], various FAO-56 based modules for
evapotranspiration [63], and a heat stress module [64] driven by simulated crop canopy
temperature [65]. The soil inputs were generated based on the literature [66]. Crop parame-
ters calibrated from previous studies were used [57], and crop management activities were
set according to the farmers’ management practices as obtained through interviews.
Table 2. Variable inputs-outputs among models.
Base Year (Year 1) Subsequent Years (Year 2–5)
Type of Model
Variable Input Variable Output Variable Input Variable Output
• Weather and soil • Crop yield • Weather and soil • Crop yield
Crop Model condition distributions condition distributions
• Farm management • Crop biomass • Farm management • Crop biomass
• Crop yield • Crop yield
• Crop biomass • Net farm income • Crop biomass
CLEM model • Initial production • Herd size • Farm production
• Net farm income
activities activities:
• Herd size
crop choice
• Crop yield • Farm endowments
distribution • Farm production • Resource shortages • Farm production
Optimization model • Crop biomass activities:crop choice • Cash at hand
activities:
• Farm endowments • Herd size crop choice
Maize crop yields were simulated at three levels of nitrogen fertilizer application
(i.e., maize with low, medium, and high fertilizer application rates), referred to as maize-
low, maize-medium and maize-high, respectively (see the procedure for classifying fertilizer
application rates in File S1-Supplementary Materials), because maize production in the
study area is constrained by fertilizer application and intensity [67]. Maize-low applied
17.5 kg N/ha of fertilizer, maize-medium applied 49.4 kg N/ha of fertilizer and maize-high
applied 114 kg N/ha of fertilizer. In addition, soybean, rice, and groundnut were applied
with 17.4 kg N/ha, 49 kg N/ha and 4 kg N/ha of fertilizer respectively. The simulated
grain yields from the SIMPLACE model were corrected using the survey yield data and
multiplicative factors for each crop type to capture the yield-reducing factors that cannot
be simulated by SIMPLACE, e.g., lack of seeds, labor, herbicides, pesticides, etc.
2.5.3. The Crop Livestock Enterprise Model (CLEM)
The Crop Livestock Enterprise Model (CLEM) explores farm management outcomes
that are subject to the available resources using the data obtained from the biophysical
crop-soil model. The CLEM model is a whole farm enterprise model developed by the
Commonwealth Scientific and Industrial Research Organization (CSIRO— see the CLEM
website). The model combines all farm resources (i.e., labor, capital, land, equipment etc.)
with farm management activities (i.e., ploughing, weeding, fertilizer application, household
consumption, etc.) and provides the related balances such as net income, food in storage,
etc., on a monthly basis [58]. The resources added to the model include land, finances, labor
(which include both household and hired labor), animal feed store, grazing feed store, rumi-
Sustainability 2023, 15, 7386 8 of 21
nants, and non-ruminant animal herds. Table 2 shows all data inputs and outputs from the
CLEM model and the other models. We added all farm management activities carried out
by the farmers to the model, ranging from crop management to livestock management, all
on-farm and off-farm labor activities, and income and expenditures, including remittances
and household consumption. All of these farm resources and management activities were
added for each farm type in CLEM, and the annual results include stocks of all resources
and farm endowments in the simulation timeframe. Livestock are sold in the CLEM model
only to meet household consumption in extreme cases where the households do not have
enough money for consumption. This is performed because livestock usually serve as a
store of wealth rather than serving as an item for regular trade [22] as such, households
will only sell their livestock in dire situations (see File S2 in Supplementary Materials for
all assumptions made in the CLEM model).
2.5.4. Chance-Constrained Risk Optimization Model
A whole farm non-linear mathematical programming model (The model is written
in GAMS (General Algebraic Modeling System) language; full details are available from
the authors upon request) was developed with the final parameterization being based on
the farmers’ production activities in the region. To optimize risk in bio-economic farm
models, it is customary to introduce the fluctuations of crop contribution margins in the
model [24,30,68]. Here, the sources of uncertainty constituting risk were the effects of
weather scenarios on crop yields and the variation in herd size and cash at hand. To include
these risks in the optimization model, the chance-constrained risk optimization model
of [69,70] was used as:
Max : CE = E(GM)− RP (2)
where
CE = Certainty equivalence of farmer’s gross margin
E (GM) = Expected gross margin
RP = Farmer’s risk premium
subject to:
∑Jj 1 aijxj ≤ bi i = 1, . . . , n (3)=
where
aij = Coe f f icient in the ith constraint f or variable xj
xj = Level o f jth activity
bn(bm) = Endowment o f the nth input (mth “non certain” input)
[ ]
Prob ∑Jj 1 amjxj ≤ bm ≥ β m = 1, . . . , M (4)=
E GM ∑J
( )
( ) = j 1 E cmj xj (5)=
( ) where
anj amj = Technical coe f f icient matrix o f the nth input (mth “non certain” input) and the jth activity
( ) β = Con f idence level
E cmj = Expected contribution margin o f the jth activity
J J ( )RP = 0.5ρ ∑i 1 ∑j 1 V cmi, cmj x= = ixj (6)
Sustainability 2023, 15, 7386 9 of 21
where
( ) ρ = Farmer′s absolute risk aversion coe f f icient
V cmi, cmj = Variance covariance matrix o f the ith and the jth activity′s contribution margin (7)
xj ≥ 0 j = 1, . . . , m
Equation (2) indicates that the risk caused by the fluctuation of the contribution margin
of activities—which is caused by the variation in the yield or price—is included in the ob-
jective function of the model in the form of the farmer’s risk premium. However, according
to Equation (6), return fluctuations do not affect all farmers in the same way because they
have different risk attitudes, which is reflected in their risk aversion coefficients.
Another aspect of risk captured in this optimization is related to the uncertainty
in inputs such as cash at hand and herd size (see Equation (4)). It is assumed that the
farmer has certainty about the endowment of some inputs at the beginning of each year
(Equation (3)) but is uncertain about others (Equation (4)). These uncertain constraints are
specified to be met with a given probability (confidence level) [69,70].
These variables contain risks as their outcomes depend on a combination of several risky
factors. To include these risks in the right hand side of the optimization model, the means
and standard deviations were calculated from the distribution (i.e., cash at hand and herd
size) and included in the chance-constrained programming. Following [71], Equation (4) was
entered into the optimization model in the mathematical form of Equation (8):
∑Jj 1 amjxj ≤ E(bm)− σ
−0.5
= bm(1− β) (8)
where E(bm) and σbm are the expected value and standard deviation of mth non-certain
input, respectively.
Equation (6) includes a parameter for risk aversion to account for the risk behavior of
the farmers. As this parameter is subjective [72,73], the model was solved for different risk
aversion coefficients, each of the results were discussed with experts from the region, and
the most suitable and efficient production plan was chosen for the farmers [74]. Risk was in-
corporated into the objective function using a quadratic programming approach [72,75,76].
The standard deviation for the total gross margin was calculated from the variance-
covariance matrix of contribution margins for all production activities [68]. The household
utility was measured using the discretionary income, which is the income available for
household use after paying for all essential expenses, including household consumption,
clothing, school fees, etc. [68,77]. The farming household comprises the household head,
their spouses, children, or other people in the household that are able to earn any kind of
income (see File S3 in the Supplementary Materials for all assumptions and File S4 for the
constraints included in the optimization model).
All mathematical equations included in the optimization model are documented in
File S4 of the Supplementary Materials. For the chance-constrained programming, we used
the General Algebraic Modeling System (GAMS), version 31.2 with the solver DICOPT.
2.5.5. The Integrated Model-Model Coupling
The three models (crop model, farm optimization model, and CLEM model) were
recursively integrated on an annual basis. As a first step, the crop model simulations were
conducted for all crop and fertilization levels for all weather scenarios, scenario members,
and years. The simulation results for the crop grain and biomass yields were stored in a
database for access by the CLEM and optimization models.
Then, for each farm typology, the CLEM and optimization models were parameter-
ized with initial farm management activities from the survey. Next, starting with one
weather scenario, the simulation proceeded by using the CLEM model for the first year
of the weather ensembles, which resulted in a distribution of values for the herd size and
cash at hand. We ran CLEM for the first year using every ensemble member to obtain
30 independent outputs from the 30 ensemble members. Livestock were only considered
Sustainability 2023, 15, 7386 10 of 21
sold if there was not enough money to meet household expenditure, also considering
available credit.
The optimization model, in turn, simulates annual crop and land allocation, which
are updated in CLEM the following year (i.e., year 2), as shown in Figure 2 above. This
process is repeated for 5 years to obtain 30 different outputs from 30 ensemble members.
This simulation is also repeated for different risk aversion parameters in the optimization
model. This is then repeated for each farm type and weather scenario.
The model results at the end of the 5-year simulation show the responses of the differ-
ent farms to the various weather conditions, and we observed how the integrated model
captures shock, which in this case was due to weather variability. The same simulation
was also performed using CLEM without interacting with the optimization model. This
was conducted in order to compare the results of the current cropping pattern as simulated
by CLEM (which is comparable to many studies in the literature) with the results of the
integrated model (which provides a step forward in the possibility of making complete
assessments). The integrated model was developed in R software v4.3.0 [48].
3. Results
3.1. Farm Typology
The names given to the derived typology are: low-resource-endowed farms (LRE),
medium-resource-endowed (MRE), and high-resource-endowed farms (HRE). The LRE
farms, as shown in Table 3, are farms with predominantly female household heads and
have relatively smaller land and household sizes. The MRE farms are composed of pre-
dominantly middle-aged male household heads, with small household sizes and relatively
average land size, while the HRE farms are farms with male household heads, large
household sizes and relatively large farm sizes.
Table 3. Differences among the three farm types.
Unit LRE * MRE * HRE *
Adult in household 1 2 2
Children (between 6 and 18) 1 1 5
Children (less than 6) 0 0 2
Remittances GHS/year 300 338 843
Non-farm income GHS/year 567 1431 482
Income from livestock sales GHS/year 500 441 1014
Farm maintenance cost GHS/year 150 208 311
Energy spending cost GHS/year 100 83 170
Household living costs GHS/year 120 735 981
Cash at hand (beginning of the season) GHS/year 126 1331 2393
Average amount of loan GHS/year 47 1906 2536
Loan rate % per month 8 8 8
Input expenses (GHS) GHS/year 73 663 1757
Total land area (hectare) ha 0.9 4.0 6.9
Machinery rental cost (GHS) GHS/year 148 275 462
Cattle 12 6 6
Goat 2 9 8
Sheep 0 3 6
Poultry 14 19 18
Animal supplement costs (GHS) GHS/year 12 61 105
Veterinary visit cost GHS/year 0 10 25
* LRE represents low-resource-endowed, MRE represents medium-resourced-endowed and HRE represents
high-resource-endowed farms, respectively.
The household size has an impact on farm production as in many cases, they serve as
labor for farming activities [78]. On the one hand, this can imply a relatively cheaper source
of labor for the HRE farms compared with the LRE and MRE farms. On the other hand, a
large household size means that more people in the household have food requirements.
Sustainability 2023, 15, 7386 11 of 22 
 
Goat   2 9 8 
Sheep   0 3 6 
Poultry  14 19 18 
Animal supplement costs (GHS) GHS/year 12 61 105 
Veterinary visit cost GHS/year 0 10 25 
* LRE represents low-resource-endowed, MRE represents medium-resourced-endowed and HRE 
represents high-resource-endowed farms, respectively. 
The household size has an impact on farm production as in many cases, they serve 
as labor for farming activities [78]. On the one hand, this can imply a relatively cheaper 
source of labor for the HRE farms compared with the LRE and MRE farms. On the other 
hand, a large household size means that more people in the household have food require-
Sustainability 2023, 15, 7386 11 of 21
ments. The positive correlation between household size and farm size found in this study 
is in agreement with the real farms in the study area, as highlighted by [79]. 
The positive correlation between household size and farm size found in this study is in
3a.g2r. eCemroepn Yt iwelidth the real farms in the study area, as highlighted by [79].
Figure 3 shows the distribution of crop yields in the bad and good weather scenarios 
a3s.2 s.iCmrouplaYtieedld by SIMPLACE. As expected, the yield from maize with a low fertilizer rate 
was tFhigeu lroew3 eshsto wams tohnegd iastlrli bmuatiiozneo ffecrrtoilpizyeierl drastien tchreobpasd, aanndd gtohoed ywieeladth werassc ennoarti ossoa ds ifferent 
usnimduelra ttehde btwy SoI MwPeaLtAhCerE .scAesneaxrpioecst ebde,ctahuesyei eitl disf rloimmimteadi zme owrieth bay lnowutrfeiertnilti zdeerfircaiteenwcays than by 
ctlhime laotwe.e sRtiacme iosn ag halilgmh-ayizieeldfeirntigli zceror pra itne  ctrhoep as,raena dasth iet pyireoldduwcaess naot so different und−1erthe two weather scenarios because it is limited more by nutrient deficiebnocuytt h40an00b ykgcl ihmaat eo.n aver-
aRgiece inis gaohoidgh w-yeiaetldhienrg. Fcroorp thine tcheereaarelsa, avsairtiapbroildituyc eins  athboeu yti4e0ld0s0 wkgerhea −g1enoenraavlleyr ahgieghiner in the 
bgaodo dyewaerast hcoerm. Fpoarrethde wceirthea tlsh,ev garoioabdi lyiteyairns tahnedy iienlcdrsewaseerde gweintehr athllye hamighoeurnitn othf efebratidlizer ap-
pyleiaerds. compared with the good years and increased with the amount of fertilizer applied.
 
FFiigguurree 33.. DDiissttrribibuutitoionno focfr corpoypi eyldiesldfosr ftohre tthweo twweoa twheeratshceenr asrcioesna(groioosd (agnododba adn),dr ebdabda)r, sreshdo bwars show 
tthhee ddiissttrriibbuuttiioonni nin‘ b‘abda’dw’ weaethaethreern seenmsebmlebmlee mmbeemrsb, eanrsd, balnude bbalurse sbhaorws sthheodwis tthrieb udtiisotnriibnu‘tgiooond i’n ‘good’ 
wweeaatthheerr eennsseemmbblelem memembebrse.rTs.h Tehbeo xbpolxoptslointds iicnadteic2a5tteh 2to5t7h5 ttho p7e5rtche npteilrecse. nTthileebsl.a Tchked botlsacrekp dreostesn rtepresent 
oouuttlliieerrss.. TThhee fifigguurereis iso botbatinaeindebda sbeaosne othne tahvee raavgeeryaigeeld yoief ltdh eo3f 0thene s3e0m ebnlesemmemblbee mrs.embers. 
3.3. Economic Analysis
3.3. ETchoneommoicd eAlendalfyasrims income comprises both on-farm and off-farm income, which in-
cludeTshoef fmeroindgeleadbo frasremrv incecsomtoeo tchoemr pfarrimseesr sboanthd osenl-lfinagrmfa ramndp oroffd-ufacrtsma minocnogmoet,h werhich in-
cilnucdomese osffouerrciensg.  Mlaabiozer issemrvaiicnelsy tcou lotitvhaeter dfaarsmaeforso dancrdo pseilnlitnhge rfeagrimon p, wroidthurcetlsa taivmeloynlgow other in-
cgormoses msoaurgrciness.( TMabalieze4 )isp emr ahiencltyar ceu, eltsipveactieadll yaws ah efnoohdo ucsreohpo lidn ltahbeo rrecgoisotsna, rwe iatchc oruelnatteidvely low 
gforor.sRs imce,aorgnitnhse (oTtahberleh 4an) dp,eirs htheecmtaorest, eecsopneocmiailclayl wcrohpenin htoheusreeghioolnd,  wlahbiochr ecxopsltasi nasrew ahcycounted 
ftohre. cRriocpe,h oasn athreel aottihveelry hhaignhdg, irso stshme margoisnt compared with the other crops (Table 4). Table 4was calculated based on the current produ ectcioonnoamctiivciatli ecsroofpt hine tfharem resginiotnh,e wsthuidcyha erxepa.lains why 
the crop has a relatively high gross margin compared with the other crops (Table 4). Table 
43 .w4.aOs pctaimlcaullLaatned Ablalosceadti oonn the current production activities of the farms in the study area.  
The optimal cropping pattern for each farm type under both weather scenarios is
presented in Figure 4. For the current cropping activity, both the LRE and MRE farms
cultivated a high share of maize-low (28% and 33% of their land area, respectively), which
is expected due to the high cost of fertilizers in the study area [80]. The HRE farms, on the
other hand, were able to invest in fertilizers to cultivate a high share of maize-medium
because they could afford it. However, under the bad weather scenario, all farm types
allocated their land to maize-low only, and the proportion of land allocation to maize
considerably declined to 5% and 1% for the LRE and MRE farms, respectively. This
occurred as a result of it becoming expensive to invest in fertilizers. Although many studies
have highlighted that maize yield can be increased through increasing fertilizer application
Sustainability 2023, 15, 7386 12 of 21
rates [81,82], Ref. [83] noted that water availability is another major limiting factor of maize
yield. In addition, the total land area cultivated by the MRE and HRE farms became much
smaller, reducing from 4 ha and 5 ha to 1 ha and 2 ha, respectively, as a result of the poor
yield under the bad weather scenario. Farmers were better off allocating a large share of
their land to groundnut and soybean under the bad weather scenario. As the weather
scenario changed from bad to good, the land share for cash crops increased for all farm
types. In addition, under the good weather scenario, the need to diversify the crop choice
reduced, and this is evident with the cropping patterns for all the farm types, where over
90% of land area was allocated to rice for LRE farms and more than 80% was allocated to
rice and groundnut for MRE farms.
Table 4. Average contribution margin per crop per farm (in GHS/ha) based on current
production data.
Cost-Benefit Table with Survey Data Maize-Low Maize-Medium Maize-High Soybean Upland Rice Groundnut
Tillage 1.5 3.4 5.1 2.1 3.6 4.1
Fertilization 6.0 20.1 20.7 5.1 4.9 0.2
Sowing 13.4 36.1 43.7 20.9 7.9 35.1
Weeding 13.7 39.2 56.6 25.0 52.3 39.3
Harvesting 16.4 50.5 58.9 40.5 55.9 54.6
Threshing 4.3 5.9 21.2 8.4 5.7 12.3
Total 55.3 155.2 206.2 102.0 130.4 145.6
Tillage 88.3 146.7 254.8 151.5 377.4 267.5
Fertilizer + service 210.4 1234.6 2165.6 209.6 680.1 34.5
Seed + service 19.4 15.5 57.2 128.5 111.6 113.1
Input cost (cedi per ha) Herbicide + service 77.2 121.1 398.5 110.4 185.8 157.8
Harvesting 13.8 27.3 70.4 28.2 26.8 40.5
Threshing 15.2 6.5 55.4 27.2 20.8 44.1
Total 424.3 1551.7 3001.9 655.3 1402.4 657.5
Total variable cost (cedi per ha) 1530.5 4654.7 7126.5 2696.3 4011.2 3570.4
Average yield (kg per ha) 660.6 2162.2 3294.7 1600.9 4229.0 3037.3
Crop price (cedi/kg) 1.7 1.7 1.7 1.8 1.5 1.7
Total revenue (cedi per ha) 1101.0 3603.6 5491.2 2935.0 6343.5 5062.2
Gross contribution (cedi per ha) 676.7 2051.9 2489.3 2279.6 4941.1 4404.7
Contribution margin (cedi per ha) −429.5 −1051.1 −1635.3 238.7 2332.4 1491.9
These were obtained from the survey and they are used to parameterize the model.
3.5. Effects on Incomes and Assets
The probabilities of the increases in assets after five simulation years are shown in
Figure 5 for the integrated model with different values of the risk aversion coefficient. The
results show that in good weather scenarios and with a low risk aversion coefficient, the
probability that farmers would see their income increase over the 5-year simulation period
was more than 60%. This is shown in Figure 5A–C, where the risk aversion coefficients used
were 0, 0.0001, and 0.001, respectively. This means that farmers’ assets will increase and they
will be in a better position to cope with shocks if their management decisions are influenced
by a model that takes into account the full situation. However, the probability of increasing
income was higher under the good weather scenarios; in the bad weather scenarios, these
probabilities did not fall below 50% for all farm types. If the farmers continue with their
current management practices, they are not able to adjust in response to shocks or other
factors because they are not well informed to react to the shocks they may encounter; as a
result, they have a lower probability of increasing their income in both the bad and good
weather scenarios. In addition, as the risk aversion coefficient increased (Figure 5E–G),
there was a much lower probability that the farmers’ income would increase after 5 years.
This is expected as at a high risk aversion, the farmers’ likelihood of perceiving a greater
Sustainability 2023, 15, 7386 13 of 21
Sustainability 2023, 15, 7386 13 of 22 
 
probability of losses increases [84], and they tend to avoid risky investments, leading to
lower incomes [85].
  
Figure 4. Cropping pattern in the different weather scenarios. The middle row shows the optimal
Figure 4. Cropping pacrttoeprpnin ginp atthteer ndinifftheerebnadt weeaathtehresrc esncaerino,atrhieotso.p Trohwe smhoiwdsdtlhee rcourwre nsthdoiswtrisb utthioen ,oapntdimtheal 
cropping pattern in thbeo tbtoamdr owwesahtohwesr tshceednisatriibou,t itohnei ntothpe groowd wsheaothwers stchenea rciuo.rTrehne tfi rdstisctorluibmuntiroepnr,e saentds  tthhee 
bottom row shows thleo wd-irsetsroiubrcuet-ieondno wine dthfaer mgso, tohde  swecoenadthcoelru mscnerneparreisoen. tTs thhee mfierdsitu mco-rleusmounrc er-eenpdroewseednftasr mths,e 
low-resource-endoweadn dfatrhme tsh,i rtdhceo sluemconnredp rceoseluntms tnhe rheipghr-erseseonutrsc et-hened moweeddiufamrm-sr.eTshoeuyreclleo-wencodloorwweitdh fstarripmess, 
and the third column rinepthreedsoennuttsp tlhoter ehpirgehse-nrtesstoheulracned-ealnlodcaotwionedto fmaramizesw. Tithhelo wyeflelrotiwliz ecrorlaoters ,wthiethy esltlorwipwesit hin 
dots shows land allocation to maize-medium, the plain yellow color shows the land allocation to
the donut plot represemnatisz et-hheig lha,nthde garleloencacotiloorns htoow ms tahiezlea nwdiatlhlo lcoatwio nfetortsiolyizbeearn rsa, tthees,b tluheec yoleolrloshwow ws iththe  ldanodts 
shows land allocationa tlolo cmatiaoinzteo-gmroeudnidunmut,, atnhdet hpelaliginht yoeralnlogews hcoowlos trh sehlaonwd asl ltohcaet iloanntodr aiclel.ocation to maize-
high, the green color shows the land allocation to soybeans, the blue color shows the land allocation 
to groundnut, and the lighFto or rbaonthgteh sehinotwegsr attheed lmanodde al lalnodcaCtLioEnM t,om raijcoer.i ty of the livestock were sold at the
beginning of the simulation to enable the farm households to meet their consumption
needs. In the subsequent years, few livestock were sold. This was expected because during
3.5. Effects on Incometsh eadnadta Acoslsletcsti on process and subsequent discussions with experts from the study area, it
The probabilitiweass oobf stehrvee dinthcartefaarsmeesr siuns uaasllsyertusn out of cash after planting and before harvest. Duringthese periods, they meet their needs  bayfbteorrr ofiwvien gs. iTmheumlaotdioelnth yereeaforrse saorled mshoostwofnth ien 
Figure 5 for the integlivreastteodck matothdeeble wginitnhin gdiofff tehreesnimt uvlatliuoensto ocfo tvheret hrieshko uasveehrosldios’nm cinoiemffiumciexnpte.n Tsehs.e 
results show that inT ghios oisdre wfleectaedthinerF isgcueren6a,rwiohesr eaanlldo fwthiethfa ram lhoowus erhisolkd sahvaevresaiboonu tcao5e0ffi% pcrioebnatb,i ltithye 
probability that farmofearssm waloleurlhde rsdeseiz tehaetitrh einecnodmofe5 iyneacrrse. ase over the 5-year simulation period 
was more than 60%. This is shown in Figure 5A–C, where the risk aversion coefficients 
used were 0, 0.0001, and 0.001, respectively. This means that farmers’ assets will increase 
and they will be in a better position to cope with shocks if their management decisions are 
influenced by a model that takes into account the full situation. However, the probability 
of increasing income was higher under the good weather scenarios; in the bad weather 
scenarios, these probabilities did not fall below 50% for all farm types. If the farmers con-
tinue with their current management practices, they are not able to adjust in response to 
shocks or other factors because they are not well informed to react to the shocks they may 
encounter; as a result, they have a lower probability of increasing their income in both the 
bad and good weather scenarios. In addition, as the risk aversion coefficient increased 
(Figure 5E–G), there was a much lower probability that the farmers’ income would in-
crease after 5 years. This is expected as at a high risk aversion, the farmers’ likelihood of 
perceiving a greater probability of losses increases [84], and they tend to avoid risky in-
vestments, leading to lower incomes [85]. 
Sustainability 2S0u2s3ta, i1n5a,b 7il3it8y6 2023, 15, 7386 14 of 22 14 of 21
 
 
Figure 5. DisFtrigibuurteio5n.  oDfi sptrroibuabtiiolintyo tfhparto fbarambielirtsy’ itnhcaotmfaer mweilrls i’nicnrceoamsee owveirll 5i nyceraerass, easo sviemru5layteeadr sb,ya s simulated
the integratebdy mthoedienl.t eTghreaste dprmoobdabeill.itTiehse sweeprer oobbatbaiilniteides bwy ecroemopbatariinged thbey acvoemrapgaer iingcotmhee aivne trhaeg e income in
first 3 years tohfe thfier sstim3 yuelaatrisono ftoth tehsei mlasutl a2t iyoenartso othf ethlea sstim2 uyelaatrisono.f (tAhe)-Ssiimuullaattiioonn .re(Asu)-ltS iwmiuthla rtiisokn result with
aversion coerffiisckieanvte 0r;s i(oBn)-croisekf fiacvieernstio0n;  (cBo)e-rffiisckieanvte =r s0io.0n00c1o;e (fCfic)-ireinstk =av0e.0r0si0o1n;  (cCo)e-ffirisckieanvt e=r s0i.o0n0; c(oDe)f-ficient = 0.00;
Sustainability 2023, 15, r7i3s8k6  aversion( Dco)e-rffiisckieanvte =rs 0io.0n1;c (oEe)f-fircisiekn atv=er0s.i0o1n;  (cEo)e-ffirisckieanvte =r s0i.o1n; (cFo)-erfifiscki eanvter=si0o.1n; c(oFe)-ffirisckieanvt e=r 1si,1o (5nG oc)f- o2e2f ficient = 1,
 risk aversion coefficient = 4. 
(G)-risk aversion coefficient = 4.
For both the integrated model and CLEM, majority of the livestock were sold at the 
beginning of the simulation to enable the farm households to meet their consumption 
needs. In the subsequent years, few livestock were sold. This was expected because during 
the data collection process and subsequent discussions with experts from the study area, 
it was observed that farmers usually run out of cash after planting and before harvest. 
During these periods, they meet their needs by borrowing. The model therefore sold most 
of the livestock at the beginning of the simulation to cover the households’ minimum ex-
penses. This is reflected in Figure 6, where all of the farm households have about a 50% 
probability of a smaller herd size at the end of 5 years. 
  
FFiigguurere 6.6 P. rPorboabbailbitiyli toyf osmf samllearl lheerrhd esridzes aizt ethaet etnhde oefn fidvoef yfievares.y Reaesrus.ltRs ewseurlet sowbtaeirneeodb btayi cnoemdpbayr-comparing
itnhge thaev eavraegraeghe ehredrds iszizeea afftteerr aaccccoouunntitningg fofro trhteh fiersfitr isntitiinali tsiaalless a(alet sth(ea tbethgeinbneinggin onf itnheg soimf tuhlaetsioimn)u lation) to
to run the model to the average herd size at the end of the simulation. (A)—presents the result from 
trhuen inthteegmratoedde ml toodtehl,e wavheilrea (gBe)—heprrdesseinzets atthteh reeesunldt forfomth eCsLimEMu,l awtihoinch.  (iAs )b—asperde soenn tthset hceurrreesnutl t from the
cirnotpepgirnagte pdattmeorndse ol,f wthhei flear(mBe)—rs. presents the result from CLEM, which is based on the current cropping
patterns of the farmers.
4. Discussion 
4.1. Relevance of the Integrated Model 
Given increasingly variable and extreme weather conditions and other shocks (mar-
kets, disease, war), supporting the sustainable intensification of farming systems will re-
quire assessing how resource allocation decisions are altered and affected by shocks [86] 
and what the implications are for longer-term developments of assets, including natural 
capital [87]. Indeed, it is well known that many intensification options and an improved 
agronomy are associated with larger potential losses during bad weather years [12,21] (see 
Figure 4). The correct mix of risk reduction, risk transfer, and enabling prudent invest-
ments to cope with agronomic risks will differ based on the farm [9,37,88], agro-ecology, 
market, and institutional context [89]. It is therefore important to develop risk assessment 
frameworks to understand the appropriate risk management pathways to achieve sus-
tainable intensification. The integrated model presented here offers a novel approach to 
make such assessments by considering how negative weather could affect production 
and, in turn, future investments, which was accomplished by combining farm-level opti-
mization and simulation with process-based crop simulation modeling. Like many other 
bio-economic farm optimization approaches, our optimization model contains the strong 
assumption that farmers allocate resources and make production decisions to maximize 
their gross margins as modulated by their risk aversion characteristics. Through linking 
our optimization model with an annual simulation model (CLEM) that accounts for 
monthly resource flows, our annual optimization approach explicitly accounts for how 
bad weather affecting crop yields in the previous season limits cash availability and may 
alter subsequent cropping system management decisions. Many models have studied the 
effects of weather and other production risks on different household or farm components 
such as household production, land degradation [68,90], the farm production system [91], 
and prices and subsidies [92]. However, unique in our approach is the desire to drive the 
integrated model with a large ensemble of possible weather realizations (for both good 
Sustainability 2023, 15, 7386 15 of 21
4. Discussion
4.1. Relevance of the Integrated Model
Given increasingly variable and extreme weather conditions and other shocks (mar-
kets, disease, war), supporting the sustainable intensification of farming systems will
require assessing how resource allocation decisions are altered and affected by shocks [86]
and what the implications are for longer-term developments of assets, including natural
capital [87]. Indeed, it is well known that many intensification options and an improved
agronomy are associated with larger potential losses during bad weather years [12,21] (see
Figure 4). The correct mix of risk reduction, risk transfer, and enabling prudent investments
to cope with agronomic risks will differ based on the farm [9,37,88], agro-ecology, market,
and institutional context [89]. It is therefore important to develop risk assessment frame-
works to understand the appropriate risk management pathways to achieve sustainable
intensification. The integrated model presented here offers a novel approach to make such
assessments by considering how negative weather could affect production and, in turn,
future investments, which was accomplished by combining farm-level optimization and
simulation with process-based crop simulation modeling. Like many other bio-economic
farm optimization approaches, our optimization model contains the strong assumption that
farmers allocate resources and make production decisions to maximize their gross margins
as modulated by their risk aversion characteristics. Through linking our optimization
model with an annual simulation model (CLEM) that accounts for monthly resource flows,
our annual optimization approach explicitly accounts for how bad weather affecting crop
yields in the previous season limits cash availability and may alter subsequent cropping
system management decisions. Many models have studied the effects of weather and other
production risks on different household or farm components such as household production,
land degradation [68,90], the farm production system [91], and prices and subsidies [92].
However, unique in our approach is the desire to drive the integrated model with a large
ensemble of possible weather realizations (for both good and bad weather scenarios) to
assess the probability of a given outcome. With the probabilistic approach, we allow for the
assessment of how likely outcomes (changes in incomes, assets, and cropping patterns) are,
and this is understood as a good basis for supporting change at local levels [93].
4.2. Evaluation of Land Allocation Outputs
Considering the high cost of fertilizers in the study area, it is uneconomical to use
fertilizers during extreme weather cases due to the high-cost-and low-benefits of fertilizers
during these periods [80,94]. This is reflected in the crop choices in the integrated model,
which tends towards the cultivation of crops with low fertilizer rates in the bad weather
scenario, as the weather is most likely the yield limiting factor in this case and not the soil
fertility [83,95]. For farm households to cope with adverse weather conditions, the model
shows that one should allocate a higher proportion of land to groundnut and soybeans. In
favorable weather years, i.e., good weather scenarios, it becomes economical to allocate
most of the land area to rice and buy food crops such as maize from the market to feed the
households; this is particularly the case for LRE farms with land areas of less than 1 ha.
These findings are also supported by the profitability analysis report of rice by [96], which
indicated that rice is very profitable in normal weather. In addition, [97] concluded that rice
yield and profitability significantly increase in good weather scenarios. One main reason
why the model is able to allocate a large share of land to economic crops is because we did
not include land allocation constraints for food crops. This decision was made because
many times the model prioritizes food crops, which are less economical, thereby reducing
farmers’ income. Instead, the model accounted for consumption costs, which could either
come from the farmers’ own production or from the market.
4.3. Assessing the Probability of Outcomes
Ref. [98] highlighted that after several bad weather years, many farmers experience
different kinds of difficulties, including the need for external support to have food. How-
Sustainability 2023, 15, 7386 16 of 21
ever, the farming households in our integrated model were able to improve their incomes
at the end of 5 years, even in bad weather scenarios. This is possible because the farming
decisions are informed by taking weather, farm assets, household consumption and other
important farmers’ conditions into consideration. In addition, the higher the risk aversion
coefficient used for the simulations, the more likely that the farmers’ income was reduced
at the end of 5 years, because smaller land areas are allocated to crops, the farmers are
more likely to diversify their cropping activity and the farmers tend to cultivate more food
crops (see Table S2 in the Supplementary Materials). This is the case with several studies
that assume that farmers are highly risk-averse as they choose farm plans that provide
a satisfactory level of security (e.g., growing larger shares of food crops in the case of
smallholder farmers), even if it requires them to sacrifice income on average [33,74,75].
The farmers’ herd size, particularly the large ruminants, are a major indication of
the farmers’ wealth, and they will only sell such livestock as a last resort. This means
that even if a farm household has small cash at hand, livestock size is another factor
in determining the farmers’ wealth [4,22,99]. For the integrated model and the current
management systems simulated by the CLEM model, the farmers are able to meet their
household expenditure after the first year without selling livestock, thereby preserving
their wealth [100,101]. However, farmers sold livestock according to their household size at
the beginning of the simulation because they do not have enough resources for all activities.
Selling livestock can be prevented if more flexible-term, low-interest rate loans are available
to the farmers.
4.4. Inclusion of Risk
The risk level is affected by the degree of the variances and the relationship of the
covariance of the contribution margin of different enterprises (e.g., different crops on the
field) in combination with each other [102]. An advantage of the integrated model over the
CLEM model is that the integrated model considers several risk elements. This shows in
many ways how combining activities can help to reduce the farmers’ risk [76] and that the
cropping activities and subsequent income of the farmers are the results that are obtained
after considering the risk elements and risk behaviors of farmers. The chance-constrained
programming includes the introduction of a confidence level that artificially lowers the
probability of resource availability to a certain limit, which allows for the decision-maker
to have a certain level of probability that this limit will be met [74]; in other words, it
is the level of certainty that the decision-maker has that the constraint will be met. For
the chance-constrained model, we applied a 95% confidence level. This implies that the
constraints of the model will be satisfied with a probability of 95%.
4.5. Study Limitations
One of the major limitations of this study is the assumption of constant costs. We
adopted this approach because the weather data used are simulated data and not actual
observed historical data. Ideally, adding yearly variation to input costs would better justify
the results of this study. In addition, we did not account for payment for household labor.
As this is an ideal situation for economic studies, in this case, the household expenditure
and consumption were accounted for, but paying the household labor the prevailing wage
rate would make most of the farmers go bankrupt in many cases.
Another limitation of this study is that we did not consider long-term crop rotation
effects such as soil organic carbon (SOC) on soil characteristics. We neglected the rotation
effects because the simulations in this study were carried out over a relatively short-
term period (5 years). In a future study, it would be interesting to see the long-term
trajectory of SOC under different crop rotations and include such changes in the integrated
model to optimize cropping systems, considering not only economic aspects but also
environmental aspects.
Sustainability 2023, 15, 7386 17 of 21
5. Conclusions
This study presented an integrated bio-economic model that simulates the impact of
weather on farm management. The main focus of this paper was to compare the results
of the integrated model with simulations from a household model (CLEM). The model
offers a novel approach for risk assessment frameworks, which can help to understand risk
management strategies and pathways and achieve sustainable intensification. Additionally,
the model helps farmers to adapt to climate change by making informed management
decisions with the long-term effect of increasing their income. By using a large ensemble
climate forcing dataset, the model is able to assess the probabilities of outcomes. The
conclusion from this study is that the integrated model provides more support for small-
holder farmers under different weather conditions as the farm-level resource allocations are
informed by environmental conditions, resource availability, and farmers’ risk perceptions.
An interesting finding from this study is that farmers are advised to allocate a large portion
of their land to cultivate rice crops, particularly in scenarios of good weather. By doing
so, they have enough money to meet their household expenditure until the following
harvest seasons. With the results of this study, farmers can therefore shift their focus from
cultivating a large proportion of food crops to more market-oriented crops as they become
better off, and they are thus able to cope much better under bad weather scenarios.
To better understand what kind of management options can support farmers’ invest-
ments in sustainable intensification options and bring them out of the poverty trap, it
is desirable to carry out a future study that can test different risk management options
using our integrated modeling framework. This can be accomplished by testing different
risk management strategies, such as insurance options, in combination with sustainable
intensification options, such as residue management. Such a study can reliably inform
farmers of the best risk management options for them under different weather scenarios.
Supplementary Materials: The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/su15097386/s1, File S1: Calculations for classifying maize crop
with varying fertilizer application intensity; File S2: CLEM model assumptions; File S3: Farm
optimization model assumptions; File S4: Optimization model constraints; Table S1: Description
of mathematical symbols used in the optimization model; Table S2: Scenario analysis of cropping
activity under changing risk aversion coefficient.
Author Contributions: Conceptualization, O.O.A., H.W., P.Z. and J.S.; methodology, O.O.A., Y.-U.K.,
H.W., P.Z., J.S., S.-A.H.-Y., D.S.M., A.L.A., K.v.d.W., P.C.S.T. and S.G.K.A.; software, O.O.A. and
Y.-U.K.; validation, H.W., P.Z., J.S., S.-A.H.-Y., D.S.M., A.L.A., K.v.d.W., P.C.S.T. and S.G.K.A.; data
curation, O.O.A. and Y.-U.K.; writing—original draft preparation, O.O.A.; writing—review and
editing, O.O.A.; visualization, O.O.A.; supervision, H.W.; project administration, H.W.; funding
acquisition, H.W. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
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