







































Global Sustainability Research                                                    ISSN: 2833-986X                                                 
https://doi.org/10.56556/gssr.v3i2.960 

                                                                  

 

Global Scientific Research              36 
 

 

Analysis of the volatility of the price of cassava in Cameroon: implications 

for food security 

 
Joseph Serge Mbarga Evouna1*, Borice Augustin Ngounou2 

 
1University of Buea, Cameroon, Department of Agricultural Economics and Agribusiness, Cameroon 
2University of Dschang, Cameroon, Research Laboratory in Fundamental and Applied Economics, Cameroon 
 
Corresponding Author: Joseph Serge Mbarga Evouna. Email: mbaserges18@gmail.com 
Received: 25 May, 2024, Accepted: 22 June, 2024, Published: 29 June, 2024 

 

Abstract   

With a contribution of up to 71% to CEMAC production, Cameroon is one of the major producers of cassava and 

taro, accounting for more than 83% of root and tuber production. It is the 11th largest producer in the world and 

the 4th in Africa. Cassava is a vital food source for over 500 million people, ranking as the third-largest source 

of calories in the tropics after rice and maize. Its importance as a source of income for the majority of poor rural 

farmers in Cameroon cannot be overstated. However, there has been a persistent increase in the prices of cassava 

and other food commodities in Cameroon. This study was designed to investigate the determinants of cassava 

price volatility in Cameroon over the period 1994-2022. The TAR-MTAR method was employed in this study. 

Our results showed that cassava prices increased significantly by an average of 46% annually, with a volatility 

level of 30.8% annually and 177.8% over the entire period (1994-2022). This indicates that cassava prices have 

been rising rapidly and unpredictably, which can have various implications for consumers, farmers, and the 

economy as a whole. The research demonstrated that cassava price volatility occurred at the beginning, middle, 

and end of the year due to factors such as climate change, cassava yield, and interest rates. It has been suggested 

that the government should implement a mapping policy and selling models to ensure a stable supply of cassava. 

 

Keywords: Volatility; Cassava; Food security; Cameroon 

 

Introduction 

Agricultural commodities, especially food, are essential to meet national food needs and rural livelihoods (OECD-

FAO, 2024; Lestari et al., 2022). The availability of these commodities is necessary to ensure the daily food 

consumption of society and to ensure a consistent food supply (Shen et al., 2024). However, agricultural 

commodities often experience price fluctuations in their development Agricultural commodities often experience 

price fluctuations in their development (Antwi et al., 2021; Kumari et al., 2019; Nigatu & Adjemian, 2020; 

Nugroho et al., 2018; Sativa et al., 2017; Lestari et al., 2022; Smith, Johnson, & Lee, 2024). Food security is 

influenced by the transmission and fluctuations in food prices, and the latter has long been a recurring problem in 

many African countries (Chitondo et al., 2024; Onumah et al., 2022; Hamilton et al., 2020; HLPE, 2011; Olila et 

al., 2016; Onyuma et al., 2006; Sousa, 2017). 

Previous studies (Temple, 2006; Nzossie, 2013; Kane et al., 2015) reported alarming increases and volatility in 

staple food prices in Cameroon, coupled with inadequate market price transmission, that plunged millions of 



Global Sustainability Research 

Global Scientific Research              37 
 

people into food insecurity, worsening the living conditions of many people (Akpan and Udoh, 2009).Food price 

volatility poses a significant threat to agriculture, especially in developing countries (Subervie, 2007; OECD, 

2011). This volatility can stem from various factors such as inadequate transport infrastructure, communication 

services, government intervention mechanisms, the complexity of marketing channels, and contractual 

agreements among economic actors (Meyer and von Cramon-Taubadel, 2004). These imperfections are 

exacerbated by irregularities in the number and transparency of market participants, which alter market structures 

and subsequently affect price determination. Agricultural markets often deviate from the conditions of pure and 

perfect competition (Guerrien, 2006), leading to non-reciprocal relationships in commodity price movements 

across different stages of the marketing chain.Understanding the level of market integration within Cameroon, 

shaped by its unique geographical characteristics, is crucial. Equally important is discerning the factors that 

influence why some regions exhibit high spatial integration while others show weak or no integration at all 

(Gonzalo et al., 2012). 

Furthermore, weak integration implies limited domestic supply responses to increasing commodity prices. A non-

integrated market operates blindly, with producers unable to discern highly valued global market trends, 

potentially leading to suboptimal decision-making and inefficiencies (Gonzalo et al., 2012). Vavra and Goodwin 

(2005) observed that the speed of market adjustments to shocks hinges on the actions of market agents—such as 

wholesalers, distributors, processors, and retailers—who facilitate transactions across market levels. In an 

unintegrated market, incomplete price information may distort production decisions. Market price instability can 

profoundly impact food security, particularly affecting the access of poor households to food in the short term 

and influencing producers' incentives to invest and enhance production in the long term (Galtier, 2009).In 

Cameroon in particular, fluctuations in these prices, which exacerbate situations of food insecurity for the poorest 

households, have raised debate about the role of agriculture in this country, food security, and even food self-

sufficiency insofar as the country is heavily dependent on food imports (Minkoua, 2018; Kane, 2018). The 

formulation of policies to stabilise food markets is a key issue for long-term agricultural development through the 

control of price movements, which are recognised in the literature as affecting farmers' technological investments. 

Implementing these policies requires prior identification of the various factors that influence food prices. Food 

price volatility is a major agricultural phenomenon, particularly in developing countries, given that agriculture is 

the main source of income for populations (Prakassh, 2011). Agricultural price volatility is a phenomenon that 

has often occurred in the past but has never reached current figures (Lanfranchi et al., 2019). Today, this condition 

has become a structural feature of global agricultural markets. According to Ceballos et al. (2017), the problem 

of agricultural price instability can be attributed to one main factor linked to the evolving dynamics of world 

markets. This price phenomenon has received considerable attention in the economic literature (e.g., Lloyd, 2017; 

Assefa et al., 2015; Frey & Manera, 2007; Meyer & Cramon-Taubadel, 2004), which examines the links between 

prices at all stages of the agricultural market.  

Agricultural price volatility is a phenomenon that has often occurred in the past but has never reached current 

figures (Lanfranchi et al., 2019; Pan& Zheng, 2023). Today, this condition has become a structural feature of 

global agricultural markets. According to Ceballos et al. (2017), the problem of agricultural price instability can 

be attributed to one main factor linked to the evolving dynamics of world markets. This price phenomenon has 

received considerable attention in the economic literature (e.g., Lloyd, 2017; Assefa et al., 2015; Frey & Manera, 

2007; Meyer & Cramon-Taubadel, 2004), which examines the links between prices at all stages of the agricultural 

market.  

 This study also contributes to the existing literature on the transmission of agricultural commodity price volatility 

in developing countries. In particular, volatility transmission models may vary from one country to another due 

to differences in institutional and economic development. While much of the current literature has focused on 



Global Sustainability Research 

Global Scientific Research              38 
 

developed countries capable of large-scale production through advanced technologies and efficient business 

operations, such as Germany (Assefa et al., 2017) and the United States (Buguk et al., 2003), few studies have 

focused on developing countries with the characteristics of a decentralised, smallholder economy. Our results 

provide new Cameroonian evidence for this strand of the empirical literature. Furthermore, we contribute to the 

literature on the dynamics of price volatility in agricultural commodity markets in Cameroon (e.g Njoda & Nkot, 

2017; Minkoua et al, 2018; Kane et al, 2019;).  

Literature review 

Conceptual framework 

The schema depicts that whether or not markets are integrated depends on several factors. These factors are at the 

heart of the search for better prescription in order to improve the efficiency of the markets. 

 

 

 

 

 

 

 

 

  

 

 

 

 

 

 

 

Figure 1: Conceptual Framework 

Source: author’s conceptualization, 2022. 

 

The heed to these strategies depends on a number of factors affect the market integration system. These include 

institutional, environmental and socio-economic factors (Figure 1). Institutional factors include government 

policies, lack of information, lack of basic infrastructure such as all-weather roads, storage facilities, transport, 

lack of credit, etc. Most assessments of constraints for farmers indicate that the lack of rural transport is a major 

obstacle for farmers seeking to market their surplus crops. Limited local transport means not only increased costs 

but also that traders are less willing to travel, market information is more limited and farmers' choice of marketing 

Institutional Factors 

 

➢ Government policies  

➢ Transaction cost 

➢ Infrastructure 

➢ Lack of information 

➢ Lack of credit 

 

Socio-economic Factors 

 

➢ Population 

Growth   

➢ Income 

➢ Supply 

➢ Technology  

 

Environmental Factors 

 

➢ Climate Change 

➢ Saisonality 

➢ Agricultural 

Production 

 

 

PRICE VOLATILITY 

 

➢ Functioning markets 

➢ Availability of food 

➢ Price stability 

➢ Supply undisrupted 

 



Global Sustainability Research 

Global Scientific Research              39 
 

channels is very limited, which greatly reduces their ability to sell their produce, which greatly reduces their 

bargaining power. While socio-economic factors include: Population growth, income, supply and technology. 

Finally, environmental factors include: Climate change, seasonality, and agricultural production. 

Therefore, when two markets are integrated due to functioning markets, Availability of food, Price stability, 

supply undisrupted, it means that government policies need to be set up to improve transport and communication 

infrastructure, and cassava price information needs to be adequate for market participants, which leads to 

decisions that contribute to efficient outcomes or efficient markets. Improving transaction costs can increase the 

participation of market agents and improve the flow of cassava from surplus to deficit areas.  

To achieve this objective, the government may resort to price controls, which can be seen as a price guarantee 

policy. In one case, the government may set an artificially high price (floor price) to ensure a higher income for 

producers than in the case of a free market. In another case, the government may impose artificially low prices 

(price ceiling) to ensure that consumers receive a lower income than in the case of a free market and thus increase 

their purchasing power. The alternative to price control policies is the "price band" (Holt and Aradhyula, 1990). 

In the latter case, the government only intervenes if prices fluctuate outside a defined price band. Thus, to stabilise 

prices when they fluctuate outside the band, the government can use, among other things: imports, exports and 

stock changes (De Janvry et al., 1995). Indeed, such measures do not allow prices to fully play their role as a 

'signal' to actors; a signal that allows economic agents to efficiently use their scarce resources (Petkantchin, 2006). 

Thus, instead of stabilising prices in agricultural markets, these measures can act as distortions of the market and 

make it more volatile, mainly due to overproduction in one case, and in the other, it can discourage investment, 

innovation and production. On the other hand, other economic policy measures, such as taxes and subsidies, 

market-oriented government programmes, and loan ratios have been mentioned as determining food price 

volatility. 

Methodology 

Source of Data  

 

Cameroon is located between Latitude 2° N to 13° N: Longitude 8° 25° E and 16° 20° E in the Central African 

sub region. It opens to the Atlantic Ocean in the West with a total coastline of 402 km. It is bounded to the west 

by Nigeria, North-east by Chad, South by Gabon, DR Congo, and Equatorial Guinea and to the East by Central 

African Republic. It has a total surface area of 475 650 km 475,650 km which is distributed into five 

agroecological zones in ten geographical regions (MINFOF, 2018) (MINFOF, 2018). It is composed of 10 regions 

with five major agroecological zones Most notably the Sudano-Sahelian, High guinea savannah, western 

highland, Monomodal Humid Rainforest, and the Bimodal Rainforest (Fig. 1). This agroecological diversity 

permits the conditions necessary for the growth of most crops which characterize other African nations hence the 

name "Africa in miniature" (MINEPDED, 2017). This By this natural virtue over other African nations, makes 

Cameroon has become the breadbasket of Central Africa and supplies Gabon, Central African Republic, 

Equatorial Guinea, and Tchad as well as neighboring Nigeria to the west. 

 Our study is conducted in the West, Northwest (Western highlands), and the Littoral and South-West 

(Monomodal Humid Rainforest), the Centre, East and South (Bimodal Humid Rainforest) regions of Cameroon 

(MINADER, 2010; MINRESI, 2007). This three agroecology are chosen because they have a characteristic 

tropical climate of two seasons (a rainy season and a dry season) which permits the cultivation of cassava (Molua 

and Lambi, 2015). Apart from the favorable climate for cassava production, these areas also harbor the largest 

markets for retail and wholesale of cassava produced (Yaoundé, Douala and Bafoussam) and its derivatives. 



Global Sustainability Research 

Global Scientific Research              40 
 

Furthermore, the “main” markets of these areas are interconnected by accessible roads. Thus, making it ideal for 

our analysis.   

 

Figure 2: Map of Cameroon showing selected study area and location of markets 

Source: (Constructed from Atlas-forest dataset Cameroon, 2023) 

 

It covered traders who sells and buy cassava in the study area. Secondary data used in this study was obtained 

from the National Institute of Statistics (NIS) are used to analyze the determinants of food price volatility in 

Cameroon. We consider the period from January 1994 to December 2022 and was divided into three strata 

(Regions). Secondary data from the National Institute of Statistics (NIS) were used to examine the possible 

threshold effects on price transmission two threshold co-integration models, namely the threshold autoregressive 

(TAR) model and the momentum-threshold autoregressive (M-TAR) model are used. (Enders and Siklos,2001) 

established these threshold co-integration tests where negative and positive deviations from the long-run 

equilibrium are not adjusted in the same way, that is, there is asymmetry in long-run adjustment to the equilibrium 

(Ndoricimpa and Achandi, 2014). The lag of the variables is used in TAR model, whereas previous period’s 

changes are preferred in M-TAR model as a threshold variable. 

To model the possibility that the short-run dynamic relationship acts in diverse ways depending on the magnitude 

of deviation from the equilibrium, threshold co-integration is used. The TAR model captures asymmetrically 

“deep” movements in the series, while the M-TAR model captures asymmetrically sharp or “steep” movements. 



Global Sustainability Research 

Global Scientific Research              41 
 

(Enders and Siklos, 2001) proposed the following steps to test for threshold co-integration using TAR and M-

TAR models. In the first step, the following long-run equilibrium relationship is estimated: 

 

P1
t = β0 +βP2

t +µt (1) 

where, P1
t and P2

t are the price of rice in two markets within a pair, say, farm and wholesale price or retail and 

wholesale price, respectively. µ is the disturbance term. Then the following equation is estimated using Ordinary 

Least Squares (OLS): 

∆µt = Itρ1µt−1 +(1 − It)ρ2µt−1 +Σik=1 β∆µt−i +εt                                (2) 

Where, µt is the residual series from Equation (7), k is the lag length and It is the Heaviside indicator 

Function such that: 

( 1 if µ t−1 ≥λ 

 

It = For TAR model (8) 

 

0 if µt−1 <λ 

And 

It  For M-TAR model (3) 

The lagged dependent variable values are added in order to ensure that the residuals are white noise. The lag 

lengths are selected using AIC and SBIC. 

Finally, TAR and M-TAR co-integration and adjustment process are specified as: 

( ρ µ− +ε if µ− ≥λ) 

The number of lags k to include in the TAR and M- TAR models were also selected by using TAR and M-TAR 

models. The optimal threshold value λ minimizing the residuals sums of squares was estimated using (Chan’s, 

1993) method. Given the alternative models, model selection procedures such as the AIC and SBIC provides a 

basis for choosing between TAR and M-TAR.  A model with the lowest AIC and SBIC should be preferred 

(Acquah, 2012). 

Results and discussions 

Findings show that prices are very unstable (coefficient of variation greater than 15%) within the study period. 

The fluctuations calculated from the coefficient of variation show that the price of cassava varies by 51.5% above 

or below its average value in the West Region. 

 

∆µt = 1 t 1 t t 1 ρ2µt−1 +εt if µt−1 <λ For TAR model and; (4) 



Global Sustainability Research 

Global Scientific Research              42 
 

 

Figure 3: Evolution of cassava prices on the West, Centre and Littoral markets 

Source: NSI/ Author (2022) 

 

Figure 3 below shows that, in general, cassava prices on the studied markets are on an upward trend, with the 

price of cassava on the Littoral remaining the highest at an average of 130.5 CFAF/kg, while the lowest price is 

observed on the West market with an average price of 64.9 CFAF/kg. A comparison of prices on the three markets 

from January 2000 to December 2012 shows that the price on the western market is characterized by little 

fluctuation, while the prices of manioc on the central and coastal markets are characterized by strong fluctuations. 

This irregularity could be explained by seasonality. From January 2012 to December 2022, a comparison of prices 

on the three markets shows that the price of cassava in the Littoral and Centre markets remains relatively constant, 

while the price in the West Region market is characterized by strong fluctuations. The implication is that the West 

Region remains the area where the supply of cassava is cheapest;  

Table 1: Coefficients of variation for cassava prices. 

Below summarizes the calculation of coefficients of variation from January 2012 to December 2022 for cassava 

prices. 

Region Mean Standard deviation CV (%) 

West 64.9 33.4 51.5 

Centre 110.5 24.0 21.7 

Littoral 130.5 22.0 16.9 

Source: SNI/ Author (2022) 

Generally, prices are very unstable (coefficient of variation greater than 15%) over the study period. The 

fluctuations calculated from the coefficient of variation show that the price of cassava varies by almost 51.5% 

above or below its average value in the West Region. 

0

20

40

60

80

100

120

140

160

180

1
/1

/2
0

0
0

1
2

/1
/2

0
0

0

1
1

/1
/2

0
0

1

1
0

/1
/2

0
0

2

9
/1

/2
0

0
3

8
/1

/2
0

0
4

7
/1

/2
0

0
5

6
/1

/2
0

0
6

5
/1

/2
0

0
7

4
/1

/2
0

0
8

3
/1

/2
0

0
9

2
/1

/2
0

1
0

1
/1

/2
0

1
1

1
2

/1
/2

0
1

1

1
1

/1
/2

0
1

2

1
0

/1
/2

0
1

3

9
/1

/2
0

1
4

8
/1

/2
0

1
5

7
/1

/2
0

1
6

6
/1

/2
0

1
7

5
/1

/2
0

1
8

4
/1

/2
0

1
9

3
/1

/2
0

2
0

P
ri

x/
K

g

Mois

price_West price_centre price_littoral



Global Sustainability Research 

Global Scientific Research              43 
 

The price of cassava is therefore very unstable in the West Region. On the Central market, it fluctuates by almost 

21.7% above or below its average value. In the Littoral market, it fluctuates around its average value by almost 

16.9%. The price of cassava is therefore less volatile in the Littoral region than in the other two regions. 

Table 2: Matrix of price correlation coefficients 

Correlation Price_Centre Price Littoral Price_West  

Price_Centre 1 0.88 0.88 

Price_Littoral 0.88 1 0.75 

Price_West 0.88 0.75 1 

Source: SNI/ Author (2022) 

From the table 2, findings show that the price series in the Centre Region is strongly (r = 0.88) positively correlated 

with the price of the Littoral. In general, this shows that prices are relatively correlated in the study period. 

Study of the seasonality of variables 

The Kruskal-Wallis test, applied to the three nominal price series, gave the following results: KW= 25.03 (P-

Value= 0.9%) for the cassava price series in the Centre and a KW= 9.28 (P-Value= 59.6%) statistic in the Littoral 

and finally a DW = 38.12 (p-value =0.007%) statistic for the West series. 

Thus, at the 10% threshold, the price of cassava in the Littoral shows no seasonality, whereas the price of cassava 

in the Centre and West are seasonal. However, the cassava price series in the Centre and West Regions have been 

corrected for seasonal variations. 

 

Study of the stationarity of variables 

In order to study cointegration, a necessary condition is that the variables must be integrated of the same order. 

This is why we devote this section to the study of the stationarity of the variables. In this work, the Philips-Perron 

(PP) Augmented Dickey and Fuller (ADF) unit root test and the Kwiatkowski-Phillips-Schmidt Shin (KPSS) 

stationarity test without trend or constant are performed on all the variables in the study.  

Table 3: Unit root test on the different variables 

 Level Tests  First Difference  

Variables ADF PP KPSS ADF PP KPSS 

PRICE_CENTRE 

 

-1.94 (0.94) 

-1.94 

(0.97) 

1.97 

(0.46) -1.94 (0) *  -1.94 (0) * 0.08 (0.46) 

PRICE_LITTORAL -1.94 (0.98) 

-1.94 

(0.99)  -1.94(0) * -1.94(0) * 0.25 (0.46) 

PRICE_WEST -1.94(0.54) 

-1.94 

(0.74)  -1.94(0) *  -1.94(0) * 0.04 (0.46) 

Source : Our calculations (2022)on Eviews 10.0   

Note:  * indicates that the test allows us to conclude that the series is stationary. 

 

The values in brackets represent the p-values of the test at the 5% threshold. 



Global Sustainability Research 

Global Scientific Research              44 
 

After analysing the stationarity of the variables, we are interested in studying a possible long-term relationship 

between prices in the different regions. The results of these tests are reported in Table 3. The study of the 

stationarity of the variables shows that all the variables are stationary in first difference. Indeed, the two tests 

(ADF, PP, KPSS) lead to the conclusion that the price of cassava in the Littoral, Centre and West regions admit 

a unit root in level but are all stationary in first difference. Thus, they are all integrated of order 1 at the 5% 

threshold. In conclusion, the variables are considered to be integrated of order 1. 

 

 Pairwise cointegration results 

 

 Engle & Granger's two-step test (1987) 

 

➢ Step 1 [Long-term relationship]. 

A stationary linear combination of the variables is sought by estimating each of the following long-term 

relationships and the one with a stationary residual is selected. 

Long-term relationships of the Granger cointegration test between the different regions. 

➢ Step 2 [Test for stationarity of estimated residuals]. 

 

Table 4: Results of the stationarity test of the estimation residual of the long-term relationship 

 Constant Centre 

Region 

Price 

Littoral 

Region price 

 West 

Region 

Price 

ADF 

Model residual (1) -0.89 (0) * - 1.14 (0) - -6.35 (-3.37) ** 

Model residual (2) 

 

3.17 (0) * - - 0.37(0)  -4.84 (-3.37) ** 

Model residual (3) 3.86 (0) * - - 0.24(0) -5.65 (-3.37) ** 

 

Note: * indicates that the test leads to the conclusion that the series is stationary. The values in brackets represent 

the p-values of the test at the 5% threshold.** Value read from Mackinnon's (1993) table. 

Source: Our calculations (2022) 

These results show that the residual series of the long-term model is stationary. These two steps finally indicate 

that cassava prices are linearly cointegrated such that there is a restoring force tending to bring the Centre cassava 

price back to the long-term equilibrium in response to variations in the Littoral cassava price. 

There is therefore a linear long-term relationship between the different cassava price series. In particular, the price 

of cassava in the Littoral is explained by the price of cassava in the West Region in the long term. 

Once the long-term relationship has been estimated, stationarity tests have been carried out on the residual. In this 

case the Dickey and Fuller (1979) table is no longer valid, the Mackinnon (1993) table is used. The results show 

that the residuals are stationary, so the variables are cointegrated. 

 

The threshold cointegration test 

The estimation of a TAR model, from the residual from the above long-run relationship, yielded the following 

results: TAR model of the threshold cointegration test (Enders&Siklos, 1998). 

 



Global Sustainability Research 

Global Scientific Research              45 
 

Table 5: TAR model estimation with endogenous determination of the city pair threshold 

Centre and Littoral Regions TAR MTAR 

Threshold τ -0.04 -0.005 

𝜌1 0.83 (0) * -0.16 (0) 

𝜌2 0.48 -0.56 

Tmax 15.9 -0.55 (0) * 

Φ (𝜌1 = 𝜌2 = 0) 148.3 (0) * 29.47 (0) * 

 W (𝜌1 = 𝜌2) 15.7 (0) * 17.18 (0) * 

𝐷𝑊 2.03 2.05 

Number of delays 1 1 

Centre and West Regions   

Threshold τ 0.06 0.03 

𝜌1 0.99 (0) * -0.31 (0) 

𝜌2 0.81 -0.11 

Tmax 21.4 -3.17 

Φ (𝜌1 = 𝜌2 = 0) 394.8 (0) * 15.7 (0) * 

 W (𝜌1 = 𝜌2) 7.5 (0) * 7.15 (0) * 

𝐷𝑊 2.09 1.95 

Number of delays 1 1 

Littoral and West Regions   

Seuil τ 0.08 0.001 

𝜌1 0.57 (0) * -0.26 (0) 

𝜌2 0.84 (0) -0.18 (0) 

Tmax 19.53 -3.45 

Φ (𝜌1 = 𝜌2 = 0) 218.4 (0) * 16.8 (0) * 

 W (𝜌1 = 𝜌2) 9.9 (0) * 1.23 (0.26) * 

𝐷𝑊 2.12 2.05 

Number of delays 1 1 

Source: Author's calculation (2022) 

Note: * denotes that the coefficients are significant at the 5% level. Values in brackets denote the p-value 

associated with the coefficients. 

 

Once the long-term relationship has been estimated, stationarity tests have been carried out on the residual. In this 

case the Dickey and Fuller (1979) table is no longer valid, the Mackinnon (1993) table is used. The results show 

that the residuals are stationary, so the variables are cointegrated. 

Markets in the Centre and Littoral 

Overall, the long-run analysis shows that the changes made on the Littoral markets are fully transmitted to the 

Centre market.  Indeed, the elasticity of the Centre price with respect to the Littoral price is greater than 1 for all 

relationships. A 1% increase in Littoral prices in the long run leads to a 114% increase in the price of cassava in 

the Centre. Elasticities greater than 1 indicate that Littoral prices are not fully transmitted to Centre prices. Once 

the long-term relationship was estimated, stationarity tests were carried out on the residual. 

 



Global Sustainability Research 

Global Scientific Research              46 
 

Markets in the Centre and West  

The long-run analysis shows that changes in the West markets are not fully transmitted to the Centre market.  

Indeed, the elasticity of the Centre price with respect to the West price is less than 1 for all relationships. A 1% 

increase in West prices in the long run leads to a 37% increase in the price of cassava in the Centre. Elasticities 

below 1 indicate that West prices are not fully transmitted to Centre prices. 

Markets in the Littoral and West  

Table 5 shows that changes in the West Region market are not fully transmitted to the Littoral market.  Indeed, 

the elasticity of the Littoral price with respect to the West Region price is less than 1 for all relationships. 1% 

increase in West Region ern prices in the long run leads to a 24% increase in the price of cassava in the Littoral 

Region. Elasticities below 1 indicate that West Region prices are not fully transmitted to Littoral prices 

Seasonality tests  

 

The summary statistics for cassava price series are presented in Table 6. The overall mean and standard deviation 

of cassava product yields do not show any clear results in terms of superiority of cassava price volatility between 

the Centre, Littoral and West markets. 

Table 6: Descriptive Statistics of some covariates  

  N Minimum Maximum Mean SD 

CENTRE  

rainfall_yde 27 1206,30 1836,70 1444,88 141,57 

temp_yde 27 23,90 26,53 25,39 0,76 

Yield_yde 23 398,65 2236997,01 958615,60 808582,74 

inflation_yde 23 79,32 106,85 93,08 8,49 

price_yde 27 43,00 149,42 97,96 32,05 

LITTORAL 

rainfall_dla 27 2813,90 4706,49 4056,13 524,15 

temp_dla 27 27,00 28,30 27,60 0,35 

Yield_dla 23 195,45 597229,35 257811,07 215132,22 

inflation_dla 23 86,45 116,46 101,45 9,26 

price_dla 27 72,20 164,15 118,82 27,81 

WEST 

rainfall_baf 27 1313,70 1988,60 1807,01 143,90 

temp_baf 27 19,30 24,03 22,03 1,31 

Yield_baf 23 32,37 238073,02 103270,92 85682,52 

inflation_baf 23 160,93 216,80 188,85 17,23 

price_baf 27 1,00 136,79 53,76 41,74 

Source: NIS, 2022 

The results and the conclusion of seasonality tests are presented in table 6. We used two tests: the F-test and the 

Q2 parameter test. We use seasonally adjusted data for econometric models when the two tests have suggested 

the existence of seasonality. However, when the results have pointed out the evidence of a non-stable seasonality 



Global Sustainability Research 

Global Scientific Research              47 
 

and lead to contradictory conclusions (When the F test suggest the presence of seasonality, but the Q2 parameter 

suggest the rejection of the hypothesis of seasonality), I use non-seasonally adjusted data. 

 

Determinants of price volatility  

 

From what appears in Table 7, we can see that the model is globally significant for modelling price volatility. The 

variables that explain this volatility are yield, inflation, interest, temperature and climate. The determinants of 

price volatility of cassava are presented in table 48 below. All the variables are significant and affect market 

efficiency in one way or the other, either positively or negatively. To begin with,  

• Yield 

The coefficient on the yield variable is positive and significant at the 5% level. This result indicates that a seller's 

returns increase the probability that cassava prices are volatile at the 5% level. Marginal effects inform us that 

sellers' returns contribute 25% to the probability that cassava prices are volatile.  

• Interest  

The coefficient of the variable Interest is negative and significant at the 5% level. This result indicates that interest 

reduces the probability that cassava prices are volatile. The marginal effects inform us that interest contributes 

negatively to 57% of the probability of price volatility. 

• Climate 

The coefficient on the Climate variable is positive and significant at the 5% level. This result indicates that climate 

change increases the probability that cassava prices will be volatile. The marginal effects inform us that the stock 

variation contributes to 17% of the probability of cassava price volatility. 

Table 7: Drivers of price volatility  

Variables  Coefficients  p value  dY/dx 

 

Yield 

  

0.25**  

  

0.009  

  

0.25  

Inflation -0.076  0.259  -0.076  

Interest 0.082** 0.000  0.055  

Temperature -0.68 0.311  -0.685  

Climate 0.17 0.000 0.171  

Constant  0.265*  0.070  0.000  

Sigma  0.115*  0.005    

Prob>chi2  0.0000      

LR chi2 (5)   44.05      

Source: Author’s computation based on survey data, 2022; Notes: ***, **, * represent significance at the 1%, 

5% and 10% respectively 

From the analysis of these results, we observe that cassava prices in the Centre, West and Littoral regions in the 

over the period 1994-2022. 



Global Sustainability Research 

Global Scientific Research              48 
 

 

Figure 4: Trend Total prices  

Source: NSI/ Author, 2022 

Figure 4 exhibit a trend in yearly total prices of cassava if there is growth, it will be evident that the highest 

cassava price was relatively at the beginning and end of the year.  

Time series model for food price volatility 

To analyze the determinants of food price volatility in Cameroon, we have applied time series econometric models 

as suggested earlier. Then, we have successfully estimated the ARMA and GARCH model. First, the results of 

standard unit root tests without and with structural break (ADF and PP) suggest that all the return price series are 

stationary (see table 1.8 in the appendix). In addition, the Box and Jenkins approach has been used to determine 

the appropriate ARMA structure of each return price series. Also, for each ARMA model, a Breusch-Godfrey 

Serial Correlation LM Test has been applied and an ARCH LM test has been used to test for ARCH effect 

Table 6: The Stationarity - Augmented Dickey-Fuller Test 

Table 8 shows the result of stationarity test using ADF method. 

Variable  Dickey-Fuller Augmented Test  

Level test First Difference Test 

Total Price  1.88 (0.9859) -16.65264 (0.0000) 

Source: NSI/ Author, 2022 

The result indicates that none of the series is stationary. Consequently, the levels of the series will generate 

spurious results if used for estimation. The results of the test indicate that the real price series for cassava globally 

are seasonal (indicating the effect of the period) at a significance level of 5%. Consequently, the levels of the 

series will generate spurious results if used for estimation.  

 

20

40

60

80

100

120

140

160

94 96 98 00 02 04 06 08 10 12 14 16 18 20 22

TOTALPRICE_F



Global Sustainability Research 

Global Scientific Research              49 
 

 

Figure 5:  Returns Total Price 

Source: NSI/ Author, 2022 

 

Graph shows the Variance is non-constant. The result indicates that returns total price of figure 5 exhibit a positive 

and negative trend and shows that the cassava price in the three regions are volatile. In addition, visual inspection 

of NSI returns over the period 1994-2022, shown in Figure 5, reveals that changes in volatility over time tend to 

cluster financial returns, which is also an indicator of long-term memory. In other words, large changes tend to 

be followed by other large changes, and vice versa, small changes are also followed by other small changes. 

Case of the Centre 

 

Table 9: Different ARMA model estimated - Centre region 

Table 9 shows the Different ARMA model estimated to determine a parsimonious result  

Estimation of AR(1) model 

Variable Coefficient T-statistic P-value 

Constante 0.004051 1.629179 0.1042 

AR(1) 0.296276 1.320480 0.1875 

MA(1) -0.453142 -2.165231 0.0310 

SIGMASQ 0.002920 30.57256 0.0000 

t-value 3.183072 (0.024023)   

R-squared 0.026267   

AIC -2.976001   

Source: NSI/ Author, 2022 

 

-.3

-.2

-.1

.0

.1

.2

.3

.4

94 96 98 00 02 04 06 08 10 12 14 16 18 20 22

RETURNSTOTALPRICE_F



Global Sustainability Research 

Global Scientific Research              50 
 

Estimation of AR (2) model 

Variable Coefficient T-statistic P-value 

Constante 0.004058 1.622644 0.1056 

AR(1) -0.153269 -3.956025 0.0001 

MA(2) -0.094088 -2.550553 0.0112 

SIGMASQ 0.002917 31.43821 0.0000 

t-value 3.302474 (0. .020484)   

R-squared 0.026267   

AIC -2.976975   

 

Estimation of AR(3) model 

Variable Coefficient T-statistic P-value 

Constante 0.004035 1.545840 0.1230 

AR(3) -0.009256 -0.236241 0.8134 

MA(1) -0.164158 -4.569258 0.0000 

SIGMASQ 0.002929 31.30939 0.0000 

t-value 2.773586 (0.041377)   

R-squared 0.022965   

AIC -2.972632   

Source: NSI/ Author, 2022 

We choose ARMA(1 2) since both ar(1) and ma(2) coefficients are significant 

Table 9 represents the Augmented Dickey Fuller (ADF) test to examine the unit roots in 

return series. The main result based on this test is that; ADF is statistically significant at 1% 

level. This indicates to reject null hypothesis and accept that the returns are stationery; hence, 

it is mean reverting. That all confirms the non-existence of autocorrelation. 

Table 7: Estimated ARCH/GARCH Model 

Variable Coefficient T-statistic P-value 

C 0.002408 3.763262 0.0002 

Residu^2(-1) 0.086584 1.634157 0.1031 

Residu^2(-2) 0.074639 1.408578 0.1598 

t-value 2.564759 (0.078371)   

R-squared 0.014323   

AIC -6.100315   

Source: NSI/ Author, 2022 

There is no ARCH effect since the residual term and the F-test are not significant. Hence, the overall result does 

not require volatility modeling since there is no volatility at the end of the forecasted period. 

 

 

 



Global Sustainability Research 

Global Scientific Research              51 
 

The case of the Littoral 

 

Table 8: Different ARMA model estimated_ Littoral region 

Table 11 shows the Different ARMA model estimated to determine a parsimonious result  

Estimation of AR(1) model 

Variable Coefficient T-statistic P-value 

Constante 0.003568 1.308532 0.1915 

AR(1) 0.429074 4.274186 0.0000 

MA(1) -0.681973 -8.140619 0.0000 

SIGMASQ 0.007508 31.58446 0.0000 

t-value 9.050770 (0.000009)   

R-squared 0.071237   

AIC -1.987797   

Source: NSI/ Author, 2022 

Estimation of AR(2) model 

Variable Coefficient T-statistic P-value 

Constante 0.003449 1.056366 0.2915 

AR(1) -0.270578 -8.542321 0.0000 

MA(2) -0.149229 -3.243118 0.0013 

SIGMASQ 0.007529 28.34785 0.0000 

t-value 9.050770 (0.000009)   

R-squared 0.068718   

AIC -2.028551   

Source: NSI/ Author, 2022 

 

Table 11 shows the Series is stationary and the result of stationarity test using ADF method. The result indicates 

that none of the series is stationary. We choose ARMA(1 1) since both ar(1) and ma(1) coefficient are more 

significant. 

Both AR (1) is close to significance and MA(1) coefficient is significant which represent  the Mean Equation and 

the ARCH i.e RESID(-1)^2 and GARCH coefficients which represent the VARIANCE EQUATION are 

significant as well. RESID(-1)^2 + GARCH = 0.747 approximately 0.75 which is closer to 1, implying that the 

persistence of volatility is high. The result shows that cassava price is volatile. The summation of the coefficients 

of the ARCH (0.145997) and GARCH (0.595997) is very close to one, and this shows that price of cassava 

continue to be volatile and it is in line with a priori expectation. Observations show that inflations in Cameroon 

fluctuate and affect household spending patterns. F test shows no Heteroscedasticity implying no ARCH effect 

again. Hence model is robust. 

There are no more lags for the Autocorrelation and Partial correlation functions and the probability values are 

greater than 0.05. Again the model is good. So the GARCH (1 1) model satisfies the model specification. 

 

 

 



Global Sustainability Research 

Global Scientific Research              52 
 

The case of the West 

 

Table 19: Different ARMA model estimated  

Table 12 shows the Different ARMA model estimated to determine a parsimonious result  

Estimation of AR(1) model 

Variable Coefficient T-statistic P-value 

Constante 0.013358 3.215642 0.0014 

AR(1) 0.449879 1.762539 0.0788 

MA(1) -0.527957 -2.150203 0.0322 

SIGMASQ 0.006837 36.56307 0.0000 

t-value 0.861104 (0.0461437)   

R-squared 0.007245   

AIC -2.125225   

Source: NSI/ Author 

Estimation of AR (2) model 

Variable Coefficient T-statistic P-value 

Constante 0.013259 3.164055 0.0017 

AR(2) -0.629740 -6.739161 0.0000 

MA(2) 0.447714 4.113170 0.0000 

SIGMASQ 0.006606 34.23517 0.0000 

t-value 5.016618 (0.002034)   

R-squared 0.040780   

AIC -2.125225   

Source: NSI/ Author, 2022 

We choose ARMA (2 2) since both ar(2) and ma(2) coefficient are significant 

We choose ARCH (2) because the residual is significant and the F-test is significant as well. 

 

Table 110: GARCH result in the volatility of cassava 

Variable Coefficient T-statistic P-value 

Constante 0.011408 1.201368 0.2296 

AR(1) -0.476581 -5.259695 0.0000 

MA(5) 0.367306 3.121450 0.0018 

VARIANCE EQUATION 

Constante 0.003655 1.435872 0.1510 

Residu (-1)^2 0.150000 1.237744 0.2158 

GARCH(-1) 0.600000 2.253127 0.0243 

R-squared 0.019909   

AIC -2.301007   

Source: NSI/ Author, 2022 



Global Sustainability Research 

Global Scientific Research              53 
 

The table above presents the results of the heteroscedasticity test of the residuals. The analysis of this table shows 

that the residuals are homoscedastic (P=0.0000). 

RESID(-1)^2 + GARCH = 0.747 approximately 0.75 which is closer to 1, implying that the persistence of volatility 

is high. The GARCH(1,1) model as modelled is significant overall. There is therefore conditional 

heteroscedasticity in the error term. The Chi2 distribution and the critical probability associated with this 

distribution of the GARCH(1,1) regression show that the model as specified is globally significant at the 5% 

threshold for the price of cassava as indicated in Table 14 above. 

Both AR(2) and MA(2) coefficients which represent  the Mean Equation are significant only in ARCH(1 2) i.e 

RESID(-1)^2 and RESID(-2)^2 coefficients which represent the variance equation are significant as well.  

RESID(-1)^2 + RESID(-2)^2  = 0.2 which is not closer to 1, implying that the persistence of volatility is very low. 

RESID(-1)^2 + GARCH = 0.2, is very close to one, and this shows that the average price of the selected major 

food items will continue to be high will continue to be volatile. The result shows that cassava price is volatile. 

The summation of the coefficients of the ARCH (0.150000) and GARCH (0.050000) is very close to one, and 

this shows that price of cassava continue to be volatile and it is in line with a priori expectation. Observations 

show that inflations in Cameroon fluctuate and affect household spending patterns. 

F test shows no Heteroscedasticity implying no ARCH effect again. Hence model is robust.  

The table above presents the results of the heteroscedasticity test of the residuals. The analysis of this table shows 

that the residuals are homoscedastic (P=0.0000). 

 

Discussion 

The results of the Augmented Dickey-Fuller test for cassava prices are individually significant at the 5% level. 

The model estimation process consisted of two steps: a stationary test and the determination of the ARCH model. 

Stationary tests were used to assess trends (Erkekoglu, Garang and Deng, 2020; Şahinli, 2020). Data can be 

stationary if the process does not change over time. The Augmented Dickey-Fuller (ADF) test was used to perform 

the stationary test. The ADF test was used to determine whether the data analysed contained a unit root. If the p-

value was > 0.05, then H0 was accepted and the cassava price data were not stationary. However, if the p-value 

was < 0.05, H0 was rejected and the cassava price data were stationary. The result of the ARCH/GARCH analysis 

indicated that the persistence of volatility is greater in cassava prices. he model used to detect heteroskedasticity 

can be determined by examining the significance of the probability values of the F-stat and chi-squared at the 5% 

significance level (Das, Paul, Bhar and Paul, 2020; Deb, 2021; Lakshmanasamy, 2021; Lestar et al., 2022). The 

Arch/Garch result also implies that higher cassava price volatility could adversely affect households by causing 

hunger or severe food insecurity, leading to malnutrition and riots. 

Specifically, in the West region, the conditional variance coefficient is positive and individually significant at 5%. 

This indicates that the F-test does not show heteroskedasticity, which means that there is no ARCH effect. This is 

indicated by the F-statistic and chi-square probability values of 0.0000 < 0.05. The model is therefore analysed 

in more detail using ARCH-GARCH analysis (Jordaan et al., 2007; Manogna & Mishra, 2020). The results show 

the heteroscedasticity test for the residuals; the sum of the ARCH and GARCH effects (0.997) indicates that 

cassava prices are highly volatile; 

In the Littoral region, the F-test shows no heteroscedasticity, which means that there is no ARCH effect. The 

model is therefore robust. There are no more lags in the autocorrelation and partial correlation functions, and the 

probability values are greater than 0.05. The GARCH (1 1) model therefore satisfies the model specification. The 

value of the ARCH coefficient illustrates the high volatility of manioc prices. The closer the value of the ARCH 

coefficient (1.0) is to zero, the lower the volatility (Monk et al., 2010; Thiyagarajan et al., 2015). The estimated 

model yielded an ARCH coefficient of 0.277477, implying that the volatility of cassava in the coastal region from 



Global Sustainability Research 

Global Scientific Research              54 
 

1994 to 2004 is relatively low and close to zero. Volatility is a measure of price fluctuations or expected price 

movements over time (Barbaglia, Croux and Wilms, 2020; Onour and Sergi, 2011; Manogna and Mishra, 2020; 

Thiyagarajan et al., 2015; Lestar et al., 2022). Volatility also refers to unexpected price fluctuations, but this has 

yet to be determined. Some measures of volatility and risk assessment can be based on deviation, standard 

deviation and coefficient of variation. 

In the Centre region, the conditional variance coefficient is positive and individually significant at 5%. This 

indicates that there is no ARCH effect, as the residual term and the F-test are not significant. The constant term is 

insignificant. The coefficient on the first lagged value of the price of cassava is significant at 5%. Thus, the price 

of cassava today is determined by the price of cassava in the immediate past. The significance of the ARCH and 

GARCH terms indicates that the volatility of prices in a given month depends on the volatility of prices in the 

previous month and that the variance of the current price depends on the variances of past prices. The coefficients 

imply that when price peaks occur, the process is very slow to reverse. The volatility of cassava prices can be 

measured using the conditional deviation norm, which was the root of ARCH-GARCH (1.0). The price of cassava 

in the Centre Region was not very volatile between 2012 and 2020. However, in certain months of 2008 and 2012, 

relatively large fluctuations occurred several times in January, June and October. The high volatility of cassava 

prices between 2008 and 2014 was due to the rise in commodity prices and the global financial crisis, which 

generally made sellers reluctant to unload their produce due to the risk of damage and high loss rates.  

The findings of the model show that inflation and climate have positive and significant effects on the price of 

cassava. This means that a percentage change in inflation is expected to increase the price of cassava by 0.79 % 

and 1% increase in the exchange rate is expected to increase cassava price by 1.02%. This calls for effective 

management of these macroeconomic variables to provide continuous stable environment against price 

fluctuation. Moreover, variables such as yield and temperature have positive relationship with the price of cassava 

while interest rate and rainfall have negative relationship with cassava price though they are not significant. 

The result is consistent with an empirical work by Gilbert (1989) which indicated that inflation level and its 

variability are major factors that influence food price volatility and can greatly affect the investors including 

farmers. This assertion was also stated by IMF (2008) which also showed that fluctuations in inflation and 

exchange rate are condiments for output price volatility. 

The positive relationship between the price of cassava and the quantity supplied (cassava yield) is consisted with 

the economic theory which states a positive relationship between the price of a commodity and its supply. This 

may explain why high prices persisted in Cameroon even after the 2008/9 food price crisis. Indeed, the volatility 

of cassava prices is a problem in Cameroon and several other countries (Devi, Srikala, & Ananda, 2015; Kane et 

al., 2018). The high volatility of cassava prices requires government intervention to stabilise them (Kane et al, 

2018). This can serve as a reference for the government to develop policies to stabilise cassava prices. 

Conclusion  

The results highlight the following main points concerning price fluctuations for the cassava studied. Firstly, they 

confirm that cassava prices are more unstable than those of their presumed imported substitutes, which are 

storable and/or processed products. Secondly, a multiplicity of factors determines price fluctuations, such as 

inflation, interest, temperature and climate. Flows between regions, and probably the associated cropping systems 

that predominate in agroforestry, help to mitigate the impact of climatic hazards on consumer prices. Furthermore, 

the aim of reducing the uncertainties of domestic food markets could lead to renewed investment in local food 

chains, whose competitiveness would contribute to development mechanisms (poverty reduction, job creation, 

etc.). 

By these studies finding, the following recommendations were made: 



Global Sustainability Research 

Global Scientific Research              55 
 

1. Marketers should organize themselves into cooperatives to enable them reap the benefits of economy of scale 

in areas of product transportation and storage. This would also help them benefit from credit facilities from 

agricultural and commercial banks and other micro credit financial institutions; 

2. Means of transport should also be put in place thanks to the efforts of cooperatives to link farms to the market 

in order to reduce marketing costs and increase profits for traders. 

3. There should also be the erection of market stalls, stores, and reduction in market taxes so as to improve the 

marketing of cassava in the study area. 

 

Declaration  

Acknowledgment: I would like to express my deepest gratitude to all those who have provided invaluable support 

and guidance throughout the process of conducting this research. 

 

Funding: The authors declare that they have not received any funding for the production of this paper 

Conflict of interest: The authors declare that they have no known competing financial interests or personal 

relationships that could have appeared to influence the work reported in this paper 

Ethics approval/declarations:  Not applicable 

Consent to participate: Not applicable 

Consent for publication: Not applicable 

Data availability: “The authors declare that they Availability of data and material” 

 

Authors contribution: MS contributed to the design, to the mobilisation of the database, the estimation, the 

introduction and the interpretation of the document. ME helped to complete the work, interpret the results and 

proofread the document as a whole. MM helped to complete the work, interpret the results and proofread the 

document as a whole. NB helped design the methodology and interpret the results 

 

References 

 

Antwi, E., Gyamfi, E. N., Kyei, K., Gill, R., & Adam, A. M. (2021). Determinants of Commodity Futures Prices: 

Decomposition Approach. Mathematical Problems in Engineering, 2021. 

https://doi.org/10.1155/2021/6032325 

Abdullah et Hossain M. R. (2013). New Cooperative Marketing Strategy for Agricultural Products in Bangladesh. 

World Review of Business Research, vol 3, pp 130-14 

Adenegan Kemisola et al., (2013). Determinants of Market Orientation among Smallholders Cassava Farmers in 

Nigeria. Global Journals Inc. (USA) .  

Agbebi F.O., Fagbote T. A. (2012). The role of middlemen in fish marketing in Igbokoda fish market, Ondo-state, 

south western Nigeria. The Journal of International Development and Sustainability, pp 880-888  

Anwarudin, M. J., Sayekti, A. L., Marendra, A. K., & Yusdar Hilman, D. (2015). Dinamika Produksi dan 

Volatilitas Harga Cabai: Antisipasi Strategi dan Kebijakan Pengembangan. Pengembangan Inovasi 

Pertanian, 6, 33–42  

Asgharpur, H., Vafaei, E., & Abdolmaleki, H. (2017). The Asymmetric Exchange Rate Pass-Through to Import 

Price Index : The Case Study of Iran. Iranian Journal of Economic Studies, 6(1), 47–64. 

https://doi.org/10.22099/ijes.2018.19977.1243 



Global Sustainability Research 

Global Scientific Research              56 
 

Babihuga, R., & Gelos, G. (2015). Commodity Prices : Their Impact on Inflation in Uruguay Commodity Prices 

: Their Impact on Inflation in Uruguay (Issue December) 

Barbaglia, L., Croux, C., & Wilms, I. (2020). Volatility Spillovers in Commodity Markets: A Large T-Vector 

Autoregressive Approach. Energy Economics, 85. https://doi.org/10.1016/j.eneco.2019.104555.  

Barrett, C. B. ( 2001).Measuring Integration and Efficiency in International Agricultural Markets” Review of 

Agricultural Economics. Oxford University Press on behalf of Agricultural & Applied Economics 

Association, 23:19-32  

Borkowski, B., Krawiec, M., Karwański, M., Szczesny, W., & Shachmurove, Y. (2021). Modeling Garch 

Processes in Base Metals Returns using Panel Data. Resources Policy, 74. 

https://doi.org/10.1016/j.resourpol.2021.102411. 

Charnes, A. C. ( 1978). Measuring the efficiency of decision making Units. European Journal of Operations 

Research, 2, 429-444  

Chitondo Lufeyo et al., (2024). Ensuring National Food Security in Southern Africa: Challenges and Solutions 

March 2024 DOI: 10.5281/zenodo.10785042   

Carolina, R. A., Mulatsih, S., & Anggraeni, L. (2016). Analisis Volatilitas Harga dan Integrasi Pasar Kedelai 

Indonesia dengan Pasar Kedelai Dunia. Jurnal Agro Ekonomi, 34(1), 47–66  

Das, T., Paul, R. K., Bhar, L. M., & Paul, A. K. (2020). Application of Machine Learning Techniques with 

GARCH Model for Forecasting Volatility in Agricultural Commodity Prices. Journal of The Indian Society 

of Agricultural Statistics, 74(3), 187–194.  

Deb, P. (2021). Fish Price Volatility Dynamics in Bangladesh Selected Paper prepared for presentation at the 2021 

Agricultural & Applied Economics Association Annual Meeting , Austin , TX , August 1 – August 3 Fish 

Price Volatility Dynamics in Bangladesh. 2021 Agricultural & Applied Economics Association Annual 

Meeting, 1–25. 

Devi, I. B., Srikala, M., & Ananda, T. (2015). Price volatility in major chilli markets of India. Indian Journal of 

Economics and Development, 3(3), 194–198  

Djomo, C.R.F; Ukpe, H.U; Ngo, V, N; Mohamadou, S; Adedze, M; Pemunta, V. (2021). Perceived Effects of 

Climate Change on Profit Efficiency among Small Scale Chili Pepper Marketers in Benue State, Nigeria. 

GeoJournal, 86, 1849–1862. https://doi.org/10.1007/s10708-020-10163-x  

Fameliti, S. P., & Skintzi, V. D. (2022). Statistical and Economic Performance of Combination Methods for 

Forecasting Crude Oil Price Volatility. Applied Economics, 54, 3031–3054. 

https://doi.org/10.1080/00036846.2021.2001425. 

F.G.M., B. H. (2005). Analyse et identification des acteurs de la filière racine et tubercule aux activités de 

production-transformation-commercialisation dans la ville de Douala. 20 p. . 

Farrell, M. (1957). Measurement of Production Efficiency. Journal of Royal Statistical Society, 120, 253-281  

Fufa, D. D., & Zeleke, B. L. (2018). Forecasting the Volatility of Ethiopian Birr/Euro Exchange Rate Using Garch-

Type Models. Annals of Data Science, 5, 529–547. https://doi.org/10.1007/s40745-018-0151-6  

Huffaker, R., Canavari, M., & Muñoz-Carpena, R. (2018). Distinguishing between Endogenous and Exogenous 

Price Volatility in Food Security Assessment: An Empirical Nonlinear Dynamics Approach. Agricultural 

Systems, 160, 98-109. https://doi.org/10.1016/j.agsy.2016.09.019.   

Huchet-Bourdon, M. (2011). Agricultural Commodity Price Volatility: An Overview. OECD Food, Agriculture 

and Fisheries Papers, No.52. Paris: OECD Publishing. https://doi.org/10.1787/5kg0t00nrthc-en.  

Jannah, M., Sadik, K., & Afendi, F. M. (2021). Study of Forecasting Method for Agricultural Products Using 

Hybrid ANN-GARCH Approach. Journal of Physics: Conference Series, 1863. 

https://doi.org/10.1088/1742-6596/1863/1/012052. 

IFPRI, (2017). Why supporting Africa’s informal markets could mean better nutrition for poor city dwellers; 

"Informal Markets in Africa's Cities," and first appeared on the Malabo Montpellier Panel blog.. 



Global Sustainability Research 

Global Scientific Research              57 
 

Jones, W. (1972). The Structure of Staple Food Marketing in Nigeria as Revealed by Price Analysis. Food 

Research Institute Studies, 8(2): 95-124  

Kelechi Johnmary Ani, Vincent Okwudiba Anyika, Emmanuel Mutambara(2022). The impact of climate change 

on food and human security in Nigeria, International Journal of Climate Change Strategies and Management 

Vol. 14 No. 2, 2022, pp. 148-167  

Koffi-Tessio E.M., S. K. (2007). Structure, coût des transactions et spatiale des marchés des produits alimentaires 

au Togo. 

Koffi-Tessio E.M., T. K. ( 2000). Marges de commercialisation et l'équité du commerce des produits alimentaires 

au Togo. Lomé, Octobre 2002.  

Koffi-Tessio, E. M., Sedzro, K., Tossou, K. A., et Yovo, K. (2007). Structure, coûts des transactions et intégration 

spatiale des marches des produits alimentaires au Togo. 507–511  

Kornher, L., & Kalkuhl, M. (2013). Food price volatility in developing countries and its determinants. Quarterly 

Journal of International Agriculture, 52(4), 277–308. https://doi.org/10.22004/ag.econ.173649 

Kumari, R. V., Venkatesh, P., Ramakrishna, G., & Sreenivas, A. (2019). Agricultural market intelligence center, a 

case study of chilli crop price forecasting in Telangana. International Research Journal of Agricultural 

Economics and Statistics, 10(2), 257–261. https://doi.org/10.15740/has/irjaes/10.2/257-261 

Kuwornu, J. K. M., & Mensah-Bonsu, A. (2011). Analysis of Foodstuff Price Volatility in Ghana : Implications 

for Food Security. European Journal of Business and Management, 3(4), 100–118. 

Lanfranchi, M., Giannetto, C., Rotondo, F., Ivanova, M. & Dimitrova, V. (2019). Economic and social impacts of 

price volatility in the markets of agricultural products. Bulgarian Journal of Agricultural Science, 25 (6), 

1063–1068 

Lakshmanasamy, T. (2021). The Relationship Between Exchange Rate and Stock Market Volatilities in India : 

ARCH-GARCH Estimation of the Causal Effects. International Journal of Finance Research, 2(4), 245–

259. https://doi.org/10.47747/ijfr.v2i4.443 

Li, L. (2021). Risk of Investing in Volatility Products: A Regime-Switching Approach. Investment Analysts 

Journal, 50(1), 1–16.  

Madziwa, L., Pillalamarry, M., & Chatterjee, S. (2022). Gold Price Forecasting using Multivariate Stochastic 

Model. Resources Policy, 76.  

Magnus Benzie and Adam John, (2015 ). Reducing vulnerability to food price shocks in a changing climate, 

Stockholm Environment Institute 

Manogna, R.L., & Mishra, A. K. (2020). Price Discovery and Volatility Spillover: An Empirical Evidence from 

Spot and Futures Agricultural Commodity Markets in India. Journal of Agribusiness in Developing and 

Emerging Economies, 10(4), 447–473. https://doi.org/10.1108/JADEE-10-2019-0175. 

Mahyao, A., Germain. (2008). Etude de l’efficacité du système d’approvisionnement et de distribution des 

ignames précoces kponan à travers le circuit Bouna-Bondoukou-Abidjan en Côte d’Ivoire  

Monk, M. J., Jordaan, H., & Grové, B. (2010). Factors Affecting The Price Volatility of July Futures Contracts 

for White Maize in South Africa. Agrekon, 49(4), 446–458.  

Muflikh, Y. N., Smith, C., Brown, C., & Aziz, A. A. (2021). Analysing Price Volatility in Agricultural Value 

Chains using Systems Thinking: A Case Study of The Indonesian Chilli Value Chain. Agricultural Systems, 

192.  

Ntsama Mireille S. Etoundi. (2015). Le commerce agricole entre le Cameroun et les pays de la CEMAC. 

Economies et finances. Université d’Auvergne - Clermont-Ferrand I,. Français. 

Nugrahapsari, R. A., & Arsanti, I. W. (2018). Analisis Volatilitas Harga Cabai Keriting di Indonesia dengan 

Pendekatan ARCH GARCH. Jurnal Agro Ekonomi, 36(1), 25.  

Nugroho, A. D., Prasada, I. M. Y., Putri, S. K., Anggrasari, H., & Sari, P. N. (2018). Komparasi Usahatani Cabai 

Lahan Sawah Lereng Gunung Merapi dengan Lahan Pasir Pantai. AGRARIS: Journal of Agribusiness and 

Rural Development Research, 4(1).  



Global Sustainability Research 

Global Scientific Research              58 
 

Pan & Xuyun Zheng (2023), Price volatility transmission of perishable  agricultural products: evidence from 

China 

Putri, H., & Cahyani, P. C. (2016). Price Volatility of Main Food Commodity in Banyumas Regency Indonesia. 

International Journal on Advanced Science, Engineering and Information Technology, 6(3), 374–377. 

https://doi.org/10.18517/ijaseit.6.3.689 

Rostami et al., ( 2020). Ethics & Food: Food as a Strategic Commodity or a Natural Right of the Individual. 

International Journal of Ethics & Society (IJES)Journal homepage: www.ijethics.com? Vol. 2, No. 1. 

Shen Yifan, et al., ( 2024). Assessing the role of global food commodity prices in achieving the 2030 agenda for 

SDGs  

Sobti, N. (2020). Does Ban on Futures Trading (De)Stabilise Spot Volatility?: Evidence From Indian Agriculture 

Commodity Market. South Asian Journal of Business Studies, 9(2), 145–166. 

https://doi.org/10.1108/SAJBS-07-2018-0084  

Tewodaj Mogues. (2020). “Les marchés des denrées alimentaires en temps de COVID-19.” Issue Post, March 27, 

2020 (données du Département de l'agriculture des États-Unis. 

Valentine P. Nchinda, R. A. ( 2016). Performance of smallholder minisett seed yam farm enterprises inCameroon. 

African Journal of Agricultural and Resource Economics , Volume 11 Number 4 pages 277-291  

Von Braun, J., & Tadesse, G. (2012). Global Food Price Volatility and Spikes: An Overview of Costs, Causes, and 

Solutions. In ZEF- Discussion Papers on Development Policy (Issue 161).  

Wang, L., Ma, F., Liu, J., & Yang, L. (2019). Forecasting Stock Price Volatility: New Evidence from The GARCH-

MIDAS Model. International Journal of Forecasting, 36(2), 684-694.  

Wardhono, A., Indrawati, Y., Qori’ah, C. G., & Nasir, M. A. (2020). Institutional Arrangement for Food Price 

Stabilization and Market Distribution System: Study of Chili Commodity in Banyuwangi Regency. E3S 

Web of Conferences, 142.  

Webb, A. J., & Kosasih, I. A. (2011). Analysis of Price Volatility in the Indonesia Fresh Chili Market. Paper 

presented to the Annual Meeting of the International Agricultural Trade Research Consortium, December 

11-13, 2011. Retrieved from https://ei-ado.aciar.gov.au/sites/default/files/Webb 

Yip, P. S., Brooks, R., Do, H. X., & Nguyen, D. K. (2020). Dynamic Volatility Spillover Effects between Oil and 

Agricultural Products. International Review of Financial Analysis, 69. 

https://doi.org/10.1016/j.irfa.2020.101465  

Zheng Pan & Xuyun Zheng (2023) Price volatility transmission of perishable agricultural products: evidence from 

China, Economic Research-Ekonomska Istraživanja, 36:1, 2180058. 

 

 

 


