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Regional Sustainability >

2025 , Vol. 6 >Issue 2: 100215

DOI: https://doi.org/10.1016/j.regsus.2025.100215

  • Ivette Gnitedem KEUBENG , a, * ,
  • George Achu MULUH a ,
  • Vatis Christian KEMEZANG b, c
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收稿日期: 2024-10-14

  修回日期: 2025-01-30

  录用日期: 2025-03-30

  网络出版日期: 2025-05-21

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Controlling agricultural product price volatility: An empirical analysis from Cameroon

  • Ivette Gnitedem KEUBENG , a, * ,
  • George Achu MULUH a ,
  • Vatis Christian KEMEZANG b, c
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  • aFaculty of Economics and Management, University of Dschang, Dschang, 96, Cameroon
  • bFaculty of Economics and Applied Management, University of Douala, Douala, 2701, Cameroon
  • cLaboratory of Quantitative Methods, Economics and Applied Management, University of Douala, Douala, 2701, Cameroon
*E-mail address: (Ivette Gnitedem KEUBENG).

Received date: 2024-10-14

  Revised date: 2025-01-30

  Accepted date: 2025-03-30

  Online published: 2025-05-21

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2666-660X/© 2025 Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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Ivette Gnitedem KEUBENG , George Achu MULUH , Vatis Christian KEMEZANG . [J]. Regional Sustainability, 2025 , 6(2) : 100215 . DOI: 10.1016/j.regsus.2025.100215

Abstract

Motivated by a significant impact of price volatility on food security and economic stability in Cameroon, this study aims to understand the factors influencing agricultural product price volatility (APPV) and formulate effective policies for mitigating its negative impact and promoting sustainable economic growth. Specifically, this research used the autoregressive distributed lag-error correction model (ARDL-ECM) to analyse the impact of agricultural productivity, agricultural product imports, population, temperature variation, gross domestic product (GDP) per capita, and government expenditure on APPV based on the annual data from 2000 to 2021. The ARDL-ECM estimation results revealed that agricultural productivity (β=4.901), agricultural product imports (β=1.012), population (β=13.635), and GDP per capita (β=2.794) were positively related to APPV, while temperature variation (β= -0.990) and government expenditure (β= -8.585) were negatively related to APPV in the long term. However, temperature variation had a positive relationship with APPV in the short term. Moreover, the Granger causality test showed that there were bidirectional causality of APPV with agricultural productivity and agricultural product imports, and unidirectional causality of APPV with population, temperature variation, GDP per capita, and government expenditure. The findings highlight the importance of public policies in stabilizing agricultural product prices by investing in agricultural research, improving access to agricultural inputs, strengthening farmer capacities, implementing climate adaptation measures, and enhancing rural infrastructure. These policies can reduce APPV, improve food security, and promote inclusive economic growth in Cameroon.

Key words： Agricultural product price volatility (APPV); Autoregressive distributed lag-error correction model (ARDL-ECM); Food security; Agricultural productivity; Climate change

1. Introduction

Agricultural product price volatility (APPV) has become a major concern in the current global economy, particularly in developing countries in Africa. APPV has an adverse impact on food security, economic stability, and the well-being of rural populations (Ali and Bayale, 2024; Chonya, 2024). According to the World Bank (2024), African countries are particularly vulnerable to APPV because they rely on agriculture for job creation and income generation. APPV can also discourage investment in the agricultural sector, which can hinder economic growth and rural development (FAO, 2014). Furthermore, APPV can impact international trade exchanges, thereby affecting the trade balance and foreign exchange reserves of countries (Aizenman et al., 2024).
Cameroonian government has implemented various initiatives to promote agricultural development and stabilize agricultural product prices. One key initiative is the Family Agriculture Development Plan that aims to enhance the productivity and income of smallholder farmers by providing access to improved seeds, fertilizers, and modern farming techniques (MINEPAT Cameroon, 2022). This plan includes training programs, subsidies for agricultural inputs, and the establishment of farmer cooperatives to facilitate market access. Although the plan has helped increase agricultural productivity and reduce rural poverty, challenges remain in reaching all targeted smallholder farmers (World Bank, 2011). Another significant strategy is the Agricultural Growth and Transformation Strategy that seeks to transform the agricultural sector by promoting value addition, improving infrastructure, and enhancing market access (MINEPAT Cameroon, 2022). This strategy focuses on developing agro-industrial zones, improving storage and transportation infrastructure, and fostering public-private partnerships in the agricultural sector, which has increased investments and improved market access for smallholder farmers, contributing to economic growth and food security (Lescuyer et al., 2020).
To mitigate the impact of APPV, governments have implemented price stabilization measures, including the establishment of strategic reserves for key agricultural commodities and price support programs (World Bank, 2011). These measures help reduce APPV and ensure food security, although they face challenges related to financing and implementation (Gozgor, 2019). In addition, governments have invested in climate adaptation policies to increase the resilience of the agricultural sector to climate change. These policies promote climate-smart agricultural practices, such as the development of drought-resistant seeds, the improved irrigation systems, and climate information services for smallholder farmers (Bellemare, 2015). These initiatives have helped smallholder farmers adapt to climate change and reduce the impact of climate change-related shocks on agricultural production (Erdogan et al., 2024).
As illustrated in Figure 1, agricultural product prices in Cameroon have experienced an upward trend during 2000-2021, with significant annual fluctuations. This volatility has a negative impact on agricultural production, food security, and the economic stability of the country (Kane, 2018). Cameroon serves as a representative case study for examining the factors influencing APPV in developing countries for several reasons. Firstly, Cameroonian reliance on agriculture makes it as a typical example facing the challenges of APPV (Pemi et al., 2024). Secondly, the policy environment in Cameroon provides a comprehensive framework for analyzing the effectiveness of agricultural development policies (Kane, 2018). Thirdly, the country’s diverse climatic zones and vulnerability to climate change make it a representative case for analysing the impact of climatic factors on APPV (Ali and Bayale, 2024). Finally, as a member of the Central African Economic and Monetary Community, Cameroonian experiences and policies have significant implications in sub-Saharan Africa, making it a relevant case study for understanding agricultural development for the region (Aizenman et al., 2024).
Fig. 1. Evolution of agricultural product prices in Cameroon during 1991-2023.
Emergence by 2035 is a key objective of Cameroonian National Development Strategy (NDS30), aiming to structurally transform the economy, with agriculture playing a crucial role. Agriculture employs 70.000% of the active population and contributes 22.000% of gross domestic product (GDP), and its low productivity limits economic growth and poverty reduction (Lescuyer et al., 2020; Pemi et al., 2024). It is essential to boost exports, farmer incomes, and rural economic growth by increasing productivity and production in high-potential sectors like cocoa, coffee, cotton, and palm oil (Lescuyer et al., 2020). Additionally, understanding and reducing APPV is vital for stabilizing prices, encouraging investments, and strengthening economic actors’ confidence in the agricultural sector. In the context of the mid-term evaluation of emergence by 2035, it is also crucial to understand the factors influencing APPV and implement strategies to reduce it. According to the NDS30, increasing agricultural productivity is a key pillar of the structural transformation of Cameroonian economy (MINEPAT Cameroon, 2022). However, this transformation cannot be achieved without stabilizing agricultural product prices, which will encourage investments in the sector and boost the confidence of economic actors.
Recent studies have highlighted the negative impact of APPV on economic integration, particularly in middle- and high-income countries (Gozgor, 2019). While many studies have focused on identifying the causes of APPV, such as supply shocks (Buguk et al., 2003; Bellemare et al., 2013; Hendricks et al., 2018), underinvestment in the agricultural sector (Headey and Fan, 2008), financial speculation (Cafiero et al., 2011), and increased demand from emerging markets (Tothova, 2011; Mugera, 2015; Tadasse et al., 2016; Laborde et al., 2020; Mustafa et al., 2024), few studies have examined the consequences of APPV (Barrett, 2013; Kalkuhl et al., 2016; Torero, 2016; Shittu et al., 2017; Darpeix, 2019). APPV might have significant repercussions for various actors in the food value chain, including producers, intermediaries, and consumers (Mustafa et al., 2024). Accounting can play an important role in managing APPV by providing real-time information on the internal and external factors influencing prices (Oriekhoe et al., 2024). Finally, APPV has a significant impact on food security, particularly in developing countries (Adekunle et al., 2024).
Innovative approaches in addressing agricultural and economic challenges have been highlighted in recent studies. For instance, He et al. (2025) demonstrated how digitalization affects carbon emissions in animal husbandry, showing that digital development provides new solutions for emission reduction and is essential for promoting sustainable development. Similarly, Kitole et al. (2024) explored the impact of digitalization on agricultural transformation in Tanzania, emphasizing how access to credit, extension services, and government support can drive the adoption of digital technologies among smallholder farmers, thereby enhancing their welfare and economic prospects. Guo (2023) examined the impact of a multi-goal policy on agricultural land efficiency using a quasi-natural experiment based on the natural resource conservation and intensification pilot scheme. Their findings showed that the multi-goal land use policy reduces agricultural land efficiency and has a differential impact on subsector land, highlighting the need to clarify the impacts of such policies on various sectors.
While recent studies have examined the impact of APPV on the economy, consumers, and producers, there are still gaps in understanding the underlying factors of this volatility, particularly in developing countries (Subervie, 2008; Anderson, 2013; Minot, 2014; Wossen et al., 2018; Ali and Bayale, 2024; Chonya, 2024; Osei et al., 2024). Most studies have focused on the impact of APPV on food security and poverty, neglecting other important dimensions, such as implications for the food supply chain and economic governance (Dev and Rao, 2010; von Braun and Tadesse, 2012; Alexandri, 2016; Mayer, 2016; Ben Abdallah et al., 2021; Mustafa et al., 2024).
Therefore, this paper aims to answer the following questions: (i) what are the key driving factors of APPV in Cameroon? (ii) how do these driving factors interact, and what is their relative impact on APPV? and (iii) which policy measures can be implemented to stabilize agricultural product prices and enhance food security? This study used the autoregressive distributed lag-error correction model (ARDL-ECM) to provide empirical evidence on the driving factors of APPV based on the annual data from 2000 to 2021. Additionally, this study contributes to the literature by examining the impact of government expenditure on APPV, an aspect largely neglected in previous studies. It evaluated whether public spending has a stabilizing or destabilizing impact on agricultural product prices. The innovation of this paper lies in its comprehensive analysis of the multifaceted driving factors of APPV in Cameroon. By integrating economic, climatic, and governmental factors, this study provides a holistic understanding of the dynamics influencing APPV. Moreover, the use of advanced econometric models, such as the ARDL-ECM and the Granger causality test, ensures robust and reliable findings. This innovative approach not only fills the gaps in the existing literature, but also offers practical insights for policymakers to design effective strategies for stabilizing agricultural product prices and promoting sustainable economic growth.

2. Materials and methods

2.1. Data sources

Table 1 provides a detailed explanation and the sources of selected data during 2000-2021. APPV was chosen as the dependent variable, while agricultural productivity, agricultural product imports, population, temperature variation, GDP per capita, and government expenditure were the independent variables.
Table 1 Description of the variables.
Variable Abbreviation Unit Period Source
Agricultural product price volatility APPV - 2000-2021 FAOSTAT (2024)
Agricultural productivity AP - 2000-2021 USDA ERS (2023)
Agricultural product imports API 109 USD 2000-2021 FAOSTAT (2024)
Population POP 107 persons 2000-2021 WDI (2024)
Temperature variation TV °C 2000-2021 FAOSTAT (2024)
Gross domestic product (GDP) per capita GDP USD/person 2000-2021 WDI (2024)
Government expenditure GOVT 109 USD 2000-2021 WDI (2024)

Note: -, dimensionless. FAOSTAT, Food and Agricultural Organization of the United Nations; USDA ERS, Economic Research Service of the United States Department of Agriculture; WDI, World Development Indicators.

2.2. Theoretical methodology

This study chose the ARDL-ECM and the Granger causality test for several reasons. Firstly, the ARDL-ECM is flexible and suitable for small samples, handling the variables that are stationary at the level (I(0)), at the first difference (I(1)), or a combination of both. This eliminates the need to pretest the order of integration, simplifying the modeling process and avoiding specification errors. Secondly, the ARDL-ECM can also estimate the short- and long-term dynamics in a single equation, providing a comprehensive understanding of factors influencing APPV. Moreover, the Granger causality test was adopted to explore the direction of causality between the variables. Unlike traditional tests, the Granger causality test does not require pretesting for unit roots and cointegration, which can be error-prone. Based on the vector autoregression model, the Granger causality test considers dynamic causality and detects the short- and long-term relationship of the variables. Increasing the order of the vector autoregression model to match the maximum order of integration helps ensure the reliability of the results.

2.2.1. Model identification process

The model identification process involves two steps. First, this study conducted the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests to check for the stationarity of the variables. The results indicated that some variables were stationary at I(0), while others were stationary at I(1). Next, we applied the bound cointegration test of the ARDL-ECM proposed by Pesaran et al. (2001) to examine the long-term cointegration between the variables. The test results indicated the presence of cointegration among the variables.
Following this, this study selected the optimal lag length of the ARDL-ECM. The chosen lag length ensures that the model captures the dynamic relationship between the variables without overfitting. Subsequently, we calculated the ARDL-ECM results using the selected lag length and checked for the significance of the coefficients. The error correction term was used to capture the speed of adjustment towards the long-term equilibrium. Finally, we performed diagnostic tests including Breusch-Godfrey Lagrange Multiplier test, White test, Lagrange Multiplier test for Autoregressive Conditional Heteroscedasticity (ARCH), Ramsey Regression Equation Specification Error Test (RESET), Jarque-Bera test, and Durbin-Watson statistic, to check for serial correlation, homoscedasticity, ARCH effects, the omitted variables, the normality of residuals, and autocorrelation, respectively. The results confirmed the robustness of the ARDL-ECM.

2.2.2. Model specification

To analyze the factors influencing APPV in Cameroon, this study established a model based on supply and demand theory. According to this theory, the interaction between supply and demand can determine the price. We assumed that APPV is influenced by agricultural productivity, agricultural product imports, population, temperature variation, GDP per capita, and government expenditure. An increase in agricultural productivity or agricultural product imports can boost supply and reduce APPV, while a decrease in agricultural productivity or agricultural product imports can have the opposite impact. A growing population can increase demand and APPV. Additionally, temperature variation, GDP per capita, and government expenditure can also affect both supply and demand, thus influencing APPV. The theoretical framework was used to explore the dynamic interaction of these driving factors in Cameroon, referring to the methodologies of Kornher and Kalkuhl (2013), Minot (2014), and Bellemare (2015). The model is organized as follows:
APPVt=f(APt, APIt, POPt, TVt, GDPt, GOVTt),
where APPV is the agricultural product price volatility; t is the year (a); f is the function; AP is the agricultural productivity; API is the agricultural product imports (109 USD); POP is the population (107 persons); TV is the temperature variation (°C); GDP is the GDP per capita (USD/person); and GOVT is the government expenditure (109 USD).
To avoid multicollinearity, we took the natural logarithm of the variables as follows:
lnAPPVt=α+β1lnAPt+β2lnAPIt+β3lnPOPt+β4lnTVt+β5lnGDPt+β6lnGOVTt+εt,
where α is the intercept; β1, β2, β3, β4, β5, and β6 are the corresponding coefficients of the independent variables; and ε denotes the unbiased and normally distributed error term.

2.2.3. Autoregressive distributed lag-error correction model (ARDL-ECM)

To examine the long-term relationship between the variables, this study applied the bound cointegration test of the ARDL-ECM initially proposed by Pesaran et al. (1999, 2001). Modifications were implemented to eliminate residual serial correlation and account for endogenous variables.
A multivariate method was used to explore the relationship of APPV with agricultural productivity, agricultural product imports, population, temperature variation, GDP per capita, and government expenditure, respectively. The bound cointegration test of the ARDL-ECM can evaluate the presence or absence of cointegration, generating the short- and long-term dynamics. The bound cointegration test of the ARDL-ECM offers numerous advantages. Specifically, it simplifies application and interpretation, provides precise and reliable estimation of the long-term characteristics, and eliminates endogeneity and autocorrelation issues. Additionally, it allows for the simultaneous evaluation of the short- and long-term correlations, corrects structural breaks, and removes outlier data points. The ARDL-ECM distinguishes the long-term relationship from the short-term dynamics, which is valuable for examining economic issues. The generic ARDL model can be written as follows:
Δ ln APPV t = α + ∑ i = 1 p β 1 Δ ln AP t − i + ∑ i = 1 q β 2 Δ ln API t − i + ∑ i = 1 r β 3 Δ ln POP t − i + ∑ i = 1 s β 4 Δ ln TV t − i + ∑ i = 1 u β 5 Δ ln GDP t − i + ∑ i = 1 v β 6 Δ ln GOVT t − i + ε t
where p, q, r, s, u, and v represent the total number of lags for agricultural productivity, agricultural product imports, population, temperature variation, GDP per capita, and government expenditure; and i is the the number of lag. The model represented by Equation 3 is an example of a particular type of error correction model (ECM), where the coefficients are not restricted in any way. This behavior is considered a “preestablished ECM” as indicated by Pesaran et al. (2001).
Given the previously described equation, the following null and alternative hypotheses were considered. Null hypothesis (H0): there is no cointegration relationship between the variables, meaning that the variables do not share a common long-term trend and do not revert to a long-term equilibrium after a shock; and alternative hypothesis (H1): there exists a cointegration relationship between the variables, meaning that the variables share a common long-term trend and revert to a long-term equilibrium after a shock.
F-statistic was used to establish whether the variables have a long-term relationship. As the distribution of F-statistic is unusual, there are no specific critical values of the statistic for any pair of the variables at I(0) and I(1). To address this, Pesaran et al. (2001) determined the critical values of the asymptotic distribution of F-statistic, proposing lower and upper bounds on the critical values for various situations. Given relatively small sample in this study, we compared the generated F-statistic values with the bounds in the statistical significance tables provided by Narayan (2005), which are more suitable for smaller samples.
The lower and upper bounds established by Pesaran et al. (2001) were used to evaluate F-statistic. We assumed that all variables are integrated at I(0) to determine the lower bound and at I(1) to determine the upper bound. This means that I(0) and I(1) represent the orders of integration used to identify the critical values for the bound test, rather than the critical values themselves. If F-statistic value is below the lower bound, cointegration does not exist. If it is above the upper bound, cointegration is present in the model. If F-statistic value falls between the lower and upper bounds, the test is inconclusive. For t-statistic, if the value exceeds the upper bound, it indicates a long-term relationship between the variables. If it is below the lower bound, the data are nonstationary. We applied the regular ECM to estimate both the short-term dynamics and the long-term equilibrium adjustments between the variables:
Δ ln APPV t = α + ∑ i = 1 p β 1 Δ ln AP t − i + ∑ i = 1 q β 2 Δ ln API t − i + ∑ i = 1 r β 3 Δ ln POP t − i + ∑ i = 1 s β 4 Δ ln TV t − i + ∑ i = 1 u β 5 Δ ln GDP t − i + ∑ i = 1 v β 6 Δ ln GOVT t − i + μ ECT t − i + ε t
where ECT is the error correction term and μ is the coefficient of error correction term, indicating how rapidly the system adjusts to short-term disruptions and returns to its long-term equilibrium. The ECM incorporates both short-term and long-term dynamics, ensuring that long-term equilibrium information is preserved (Akono and Kemezang, 2024).

2.2.4. Granger causality test

A linear Granger causality test was performed to analyze the temporal and causal relationship of APPV with agricultural productivity, agricultural product imports, population, temperature variation, GDP per capita, and government expenditure. Assuming that xt and yt are stationary, they can be used in a bivariate vector autoregression model. The formulas are as follows:
x t = ρ 1 + ∑ i = 1 k γ i x t − i + ∑ i = 1 k δ i y t − i + ε 1 t ,
y t = ρ 2 + ∑ i = 1 k τ i x t − i + ∑ i = 1 k φ i y t − i + ε 2 t ,
where xt represents the first variable at time t; yt represents the second variable at time t; ρ1 and ρ2 are the intercept terms; k represents the lag order, which is the number of past observations included in the model; γi and τi are the coefficients for the lagged values of xt-i; δi and φi are the coefficients for the lagged values of yt-i; and ε1t and ε2t are the error terms, representing the unexplained variation in xt and yt.
Accordingly, two hypotheses were proposed. H01: δ1=δ2=•••=δk=0. This hypothesis states that xt does not the Granger cause of yt. If this hypothesis cannot be rejected, it implies that the past value of xt does not help to predict information about yt, but only the information contained in past values of yt. H02: τ1=τ2=•••=τk=0. This hypothesis states that yt does not the Granger cause of xt. If this hypothesis cannot be rejected, it implies that the past value of yt does not help predict information about xt, but only the information contained in past values of xt.

3. Results and discussion

3.1. Descriptive analysis of the variables

Table 2 shows that APPV had high variability with the mean of 1.085, whereas agricultural productivity had low variability with the mean of 88.727 during 2000-2021. The mean of agricultural product imports was 0.853×109 USD with moderate variability. Temperature variation had moderate variability with the mean of 0.872. The GDP per capita had low variability with the mean of 1311.289 USD/person. The population had moderate variability with the mean of 2.050×107 persons. Finally, government expenditure had a mean of 3.090×109 USD with moderate variability as well.
Table 2 Descriptive statistics of the variables.
Variable Mean Standard deviation Minimum Maximum
APPV 1.085 0.489 0.220 2.600
AP 88.727 11.163 70.006 104.652
API (×109 USD) 0.853 0.183 0.562 1.186
TV (°C) 0.872 0.283 0.230 1.290
GDP (USD/person) 1311.289 97.242 1137.625 1454.564
POP (×107 persons) 2.050 0.380 1.510 2.720
GOVT (×109 USD) 3.090 1.010 1.620 4.580
The correlation matrix presented in Table 3 shows that APPV was positively correlated with agricultural productivity (β=0.461), agricultural product imports (β=0.256), population (β=0.419), GDP per capita (β=0.359), and government expenditure (β=0.396). These positive correlations can be explained by the theory of supply and demand (Hemathilake and Gunathilake, 2022). An increase in agricultural productivity led to the increase of supply, which can initially stabilize prices. However, if demand remains inelastic, fluctuations in product supply can lead to APPV. Similarly, an increase in agricultural product imports can influence APPV, making it more sensitive to global price fluctuations.
Table 3 Pearson correlation coefficients of the variables.
lnAPPV lnAP lnAPI lnPOP lnTV lnGDP lnGOVT
lnAPPV 1.000
lnAP 0.461** 1.000
(0.031)
lnAPI 0.256 0.652*** 1.000
(0.250) (0.001)
lnPOP 0.419* 0.874*** 0.415* 1.000
(0.052) (0.000) (0.055)
lnTV -0.101 0.396* 0.169 0.528** 1.000
(0.656) (0.068) (0.452) (0.012)
lnGDP 0.359 0.736*** 0.388* 0.769*** 0.608*** 1.000
(0.101) (0.000) (0.074) (0.000) (0.003)
lnGOVT 0.396* 0.720*** 0.519** 0.686*** 0.481** 0.755*** 1.000
(0.068) (0.000) (0.013) (0.000) (0.023) (0.000)

Note: The values in parentheses represent the P-values associated with the correlation coefficients. *, significance at the 10% level; **, significance at the 5% level; ***, significance at the 1% level.

On the other hand, temperature variation was negatively correlated with APPV (β= -0.101). The negative relationship can be justified by the adaptation mechanisms of smallholder farmers and market operators in response to climate change. Smallholder farmers may adopt climate-resilient agricultural practices, such as using drought-resistant seeds or improving irrigation systems, which can stabilize agricultural production and agricultural product prices. Additionally, public policies for storage and strategic reserves can also play a role in stabilizing prices during the period of climate change (Turton and Barreto, 2006). Moreover, the significance of the correlations between different variables varied.

3.2. Stationarity test of the variables

The ADF and PP tests were used for stationarity test for the variables at I(0) and I(1). The critical values for both tests were based on the 5% significance level. The results of the stationarity tests presented in Table 4 indicate that APPV and temperature variation were stationary at I(0), whereas agricultural productivity, agricultural product imports, GDP per capita, government expenditure, and population were stationary at I(1). Therefore, the variables can be used in the cointegration test and the ARDL-ECM.
Table 4 Results of Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests for stationarity.
Variable ADF unit root test PP unit root test Stationary
I(0) I(1) I(0) I(1)
lnAPPV -3.199** - -3.190** - I(0)
(0.020) - (0.021) -
lnAP -0.825 -3.496*** -0.838 -3.473*** I(1)
(0.812) (0.008) (0.808) (0.009)
lnAPI -2.182 -3.989*** -1.755 -3.578*** I(1)
(0.213) (0.002) (0.403) (0.006)
lnTV -4.799*** - -2.651* -5.473*** I(0)
(0.000) - (0.083) (0.000)
lnGDP -1.873 -3.123** -1.737 -3.098** I(1)
(0.345) (0.025) (0.412) (0.027)
lnGOVT -1.907 -3.681*** -1.806 -3.741*** I(1)
(0.329) (0.004) (0.378) (0.004)
lnPOP -1.130 -3.958*** 0.954 -3.714*** I(1)
(0.703) (0.002) (0.994) (0.004)

Note: -, no value. The values in parentheses represent the P-values associated with the test statistics. I(0), at the level; I(1), at the first difference. *, significance at the 10% level; **, significance at the 5% level; ***, significance at the 1% level.

3.3. Bounds test for cointegration of the variables

Table 5 shows that F-statistic value was 6.644 and t-statistic value was -6.527. These values were compared to the critical values provided by Kripfganz and Schneider (2020) to determine if there was a long-term cointegration between the variables. The results suggested that there was a long-term relationship between the variables, which was significant at the 10% and 5% levels. These findings aligned with the results of the ARDL-ECM, which accommodates differences in the order of integration between the variables and effectively estimates their long-term cointegration.
Table 5 Bounds test results for cointegration.
Critical value P-value
10% significance level 5% significance level 1% significance level
Lower bound Upper bound Lower bound Upper bound Lower bound Upper bound Lower bound Upper bound
F-value 2.746 4.237 3.462 5.249 5.411 7.972 0.004 0.021
t-value -2.523 -3.963 -2.956 -4.527 -3.900 -5.770 0.000 0.004
Decision Reject Reject Inconclusive -
F-statistic 6.644
t-statistic -6.527

Note: -, no decision.

3.4. Impact of driving factors on agricultural product price volatility (APPV)

Table 6 reveals a robust fit of the ARDL-ECM, with a determination coefficient (R2) of 0.831. The error correction term was significantly negative, indicating that APPV adjusted toward its long-term equilibrium level. Firstly, the coefficient between APPV and agricultural productivity was 4.901, indicating that a 1.000% increase in agricultural productivity can lead to a 4.901% increase in APPV in the long term, with other factors holding constant. This positive relationship suggests that higher agricultural productivity, while increasing supply, does not necessarily stabilize prices. This can be attributed to the inelastic demand for agricultural products, where consumers do not significantly change their consumption in response to price changes (Wang et al., 2018). Additionally, market imperfections and speculative activities may contribute to this volatility. The theory of supply and demand dynamics explains this phenomenon: an increase in supply without a corresponding increase in demand can lead to price fluctuations as the market adjusts to the new equilibrium. This finding aligned with Bellemare (2015), who highlighted that increased agricultural productivity does not always lead to price stabilization due to demand inelasticity and market imperfections. Similarly, Kornher and Kalkuhl (2013) noted that supply-side improvements could paradoxically increase price volatility in certain contexts. The implication here is that policies aimed at increasing agricultural productivity should be accompanied by measures to improve market efficiency and reduce speculative activities. This could involve investments in market infrastructure, regulatory frameworks to curb speculation, and programs to enhance market transparency (Timmer, 2014).
Table 6 Autoregressive distributed lag-error correction model (ARDL-ECM) estimation results for the variables.
Variable Coefficient Standard error t-statistic P-value
Long-term relationship lnAPPV 4.901*** 1.242 3.940 0.002
lnAPI 1.012* 0.474 2.130 0.054
lnPOP 13.635*** 2.886 4.720 0.000
lnTV -0.990*** 0.235 -4.210 0.001
lnGDP 2.794 3.545 0.790 0.446
lnGOVT -8.585*** 1.795 -4.780 0.000
Short-term relationship Dl.(lnTV) 0.519 0.315 1.650 0.125
Error correction term -1.490*** 0.228 -6.530 0.000
Constant -146.483 36.453 -4.020 0.002
R2 0.831

Note: Dl.(lnTV) is the first difference of lnTV. R2, determination coefficient; *, significance at the 10% level; ***, significance at the 1% level.

Moreover, the coefficient between APPV and agricultural product imports was 1.012, indicating that a 1.000% increase in agricultural product imports can lead to a 1.012% increase in APPV in the long term. This positive relationship suggested that domestic prices that are dependent on international markets are vulnerable to global price fluctuations. Imperfections in domestic markets, such as inadequate infrastructure and the presence of monopolies, can exacerbate this volatility. The theory of international trade explains this relationship: global price shocks can be transmitted to domestic markets through import channels, leading to price volatility (Gilbert and Morgan, 2010). Similarly, Onumah et al. (2022) found that rice price volatility in Ghana was influenced by both domestic and international market factors. Minot (2014) highlighted the role of imports in transmitting global price shocks to domestic markets. The implication is that policies should aim to reduce dependence on agricultural product imports and improve domestic market infrastructure. This could involve promoting local production through subsidies, improving storage and transportation infrastructure, and implementing trade policies that protect domestic markets from excessive volatility.
The coefficient between APPV and population was 13.635, indicating that a 1.000% increase in population can lead to a 13.635% increase in APPV in the long term. This positive relationship suggested that the growth of population increases the demand for agricultural products, making prices more volatile in response to supply and demand shocks. The theory of demand-side economics can explain this relationship: increase in population leads to higher demand, which can cause price volatility if supply does not keep pace (Easterly and Levine, 2003). Similarly, Johnson (2011) reported that population growth can lead to increased demand and price volatility. Gozgor (2019) highlighted the impact of population growth on APPV. The implication is that policies should focus on managing demand-side factors and improving supply chain efficiency to mitigate the impact of population growth on APPV. This could involve investment in agricultural technology to increase yield, improve supply chain logistics, and implement demand management strategies such as price controls and subsidies.
The coefficient between APPV and temperature variation was -0.990, indicating that a 1.000% increase in temperature variation can lead to a 0.990% decrease in APPV in the long term. This negative relationship suggested that temperature variation has a stabilizing impact on APPV, likely due to adaptation strategies adopted by smallholder farmers and market operators. The theory of climate economics explains this relationship: adaptation strategies can mitigate the impact of climatic shocks on agricultural product prices, leading to price stabilization. Similarly, Bellemare (2015) found that climatic shocks can influence food price volatility, with adaptation strategies playing a crucial role in mitigating the impact. However, Erdogan et al. (2024) suggested that warmer temperature can lead to higher food prices, highlighting the context-specific nature of the relationship. This implies that policies should support adaptation strategies and improve climate information systems to mitigate the impact of temperature variation on APPV. This could involve investment in climate-resilient agriculture, improvement in weather forecasting systems, and implementation of climate-smart agricultural practices.
The coefficient between APPV and GDP per capita was 2.794, indicating a positive but non-significant relationship with APPV. This suggested that economic growth may influence APPV, but the relationship is not statistically significant. The theory of economic growth explains this relationship—economic growth can lead to increased demand for agricultural products, which can cause price volatility if supply does not keep up. Moreover, Darpeix (2019) noted that the relationship between economic growth and APPV is complex and context-specific. Aizenman et al. (2024) highlighted the multifaceted nature of this relationship. The implication is that policies should consider the broader economic context when addressing APPV. This could involve macroeconomic policies to stabilize economic growth, investments in agricultural infrastructure to increase supply, and demand management strategies to mitigate the impact of economic growth on APPV.
Finally, the negative coefficient of -8.585 for APPV and government expenditure indicated that an increase in government expenditure can lead to a decrease in APPV in the long term. This suggests that public policies can play a stabilizing role in agricultural markets by supporting production, improving infrastructure, and implementing risk management programs. The theory of public economics explains this relationship—government expenditure can stabilize prices by addressing market failures and providing public goods. This finding is supported by Purokayo and Umaru (2012), who suggested that targeted public policies can help mitigate APPV. Ali and Bayale (2024) highlighted the role of public investment in stabilizing agricultural product prices and improving food security. The implication is that increased government expenditure on agriculture can help stabilize prices and promote sustainable economic growth. This could involve investments in agricultural research and development, improving access to agricultural inputs, and increase in farmer capacities.

3.5. Diagnostic tests of the ARDL-ECM

From Table 7, we can see that the Breusch-Godfrey Lagrange Multiplier test had a statistic value of 2.158 and a P-value of 0.142, suggesting the absence of serial correlation. The White test for heteroscedasticity had a statistic of 21.000 and a P-value of 0.397, indicating that the null hypothesis of homoscedasticity cannot be rejected. The Lagrange Multiplier test for ARCH had a statistic value of 1.968 and a P-value of 0.161, suggesting that there was no ARCH effect. The RESET for the omitted variables had a statistic value of 6.850 with a P-value of 0.111, indicating that the model is well specified and does not suffer from the omitted variables. The Jarque-Bera test for the normality of residuals had a statistic value of 1.760 and a P-value of 0.415, suggesting that the residuals are normal. Finally, the Durbin-Watson statistic value was 2.289, indicating that there is no autocorrelation. Table 7 shows that the ARDL-ECM is applicable to this study. Cumulative sum plots confirm the stability of the model process during 2000-2021 (Fig. 2).
Table 7 Diagnostic test results of the ARDL-ECM.
Diagnostic test Statistic value P-value Decision
Breusch-Godfrey Lagrange Multiplier test 2.158 0.142 No serial correlation
White test 21.000 0.397 Homoscedasticity
Lagrange Multiplier test for ARCH 1.968 0.161 No ARCH effects
RESET 6.850 0.111 No the omitted variables
Jarque-Bera test 1.760 0.415 Normality
Durbin-Watson statistic 2.289 - No autocorrelation

Note: -, no P-value. ARCH, Autoregressive Conditional Heteroscedasticity; RESET, Ramsey Regression Equation Specification Error test.

Fig. 2. Cumulative sum of recursive residuals (a) and ordinary least squares (OLS) residuals of agricultural product price volatility (APPV) during 2000-2021. The gray area represents the 95% confidence interval around the null hypothesis of no structural break, while the red line represents the cumulative sum of residuals, indicating the stability of the model’s parameters.

3.6. Bidirectional and unidirectional causality between APPV and driving factors

The results of the Granger causality test, presented in Table 8, provide crucial insights into the causal relationship of APPV with agricultural productivity, agricultural product imports, population, temperature variation, GDP per capita, and government expenditure in Cameroon. The Granger causality test indicated a bidirectional causal relationship between agricultural productivity and APPV. This meant that changes in agricultural productivity can influence APPV, and conversely, the change of APPV can affect agricultural productivity. This bidirectional relationship suggests a feedback loop where agricultural productivity improvements can stabilize prices, but APPV can also affect smallholder farmers’ decisions and investments in productivity-enhancing technologies. Similarly, there was a bidirectional causal relationship between agricultural product imports and APPV. This implied that fluctuations in agricultural product imports can influence APPV, and vice versa. This relationship highlights the interdependence between domestic and international agricultural markets, suggesting that policies aimed at stabilizing domestic prices should consider the impact of international market dynamics.
Table 8 Results of the Granger causality test.
Variable Excluded variable Chi2 df P-value>Chi2
lnAPPV lnAP 6.588 2 0.037
lnAPI 9.863 2 0.007
lnPOP 526.230 1 0.000
lnTV 34.229 2 0.000
lnGDP 18.023 2 0.000
lnGOVT 2.642 2 0.267
lnAP lnAPPV 3.747 2 0.154
lnAPI 21.967 2 0.000
lnPOP 440.110 1 0.000
lnTV 2.485 2 0.289
lnGDP 1.220 2 0.543
lnGOVT 4.696 2 0.096
lnAPI lnAPPV 0.007 2 0.997
lnAP 9.828 2 0.007
lnPOP 356.450 1 0.000
lnTV 16.670 2 0.000
lnGDP 10.040 2 0.007
lnGOVT 3.594 2 0.166
lnPOP lnAPPV 0.599 2 0.741
lnAP 3.200 2 0.202
lnAPI 6.659 2 0.036
lnTV 6.619 2 0.037
lnGDP 13.391 2 0.001
lnGOVT 1.734 2 0.420
lnTV lnAPPV 24.776 2 0.000
lnAP 0.359 2 0.835
lnAPI 32.945 2 0.000
lnPOP 1983.300 1 0.000
lnGDP 26.117 2 0.000
lnGOVT 30.223 2 0.000
lnGDP lnAPPV 25.503 2 0.000
lnAP 43.133 2 0.000
lnAPI 31.420 2 0.000
lnPOP 952.450 1 0.000
lnTV 16.786 2 0.000
lnGOVT 38.956 2 0.000
lnGOVT lnAPPV 6.064 2 0.048
lnAP 128.400 2 0.000
lnAPI 158.050 2 0.000
lnPOP 699.120 1 0.000
lnTV 26.673 2 0.000
lnGDP 41.706 2 0.000

Note: df, degree of freedom. Chi2 statistic and associated P-value indicate whether there is a Granger causality between the variables. A low P-value (typically≤0.050) suggests that the null hypothesis of no Granger causality can be rejected, indicating that there is a causal relationship between the variables. The result of the Granger causality test indicates the direction of causality between the variables.

There was a unidirectional causal relationship between population and APPV. This indicates that an increase in population can lead to higher demand for agricultural products, which in turn increases APPV. However, changes in APPV had no significant impact on population. This finding underscores the importance of policies that address the supply side of the agricultural sector to meet the growing demand and stabilize prices. Additionally, there was a unidirectional causal relationship between temperature variation and APPV. This suggests that climate change, such as temperature variation, can influence APPV. However, changes in APPV have no significant impact on temperature variation. This relationship highlights the need for climate adaptation strategies to mitigate the impact of temperature variation on APPV.
The results of the Granger causality test also indicated a unidirectional causal relationship between GDP per capita and APPV. This implies that economic growth, as measured by GDP per capita, can influence APPV. Higher GDP per capita can lead to increased demand for agricultural products, thereby affecting agricultural product prices. However, changes in APPV have no significant impact on GDP per capita. This finding suggests that economic policies aimed at promoting growth should also consider their potential impact on agricultural price stability. Lastly, there was a unidirectional causal relationship between government expenditure and APPV, indicating that government spending on agriculture can influence APPV. Increased government expenditure can stabilize prices by supporting production, improving infrastructure, and implementing market interventions. However, changes in APPV have no significant impact on government expenditure. This finding underscores the role of public policies in stabilizing agricultural markets.
The Granger causality test revealed an overall causal relationship of APPV with agricultural productivity, agricultural product imports, population, temperature variation, GDP per capita, and government expenditure, which indicated that all driving factors have a significant impact on APPV in both the short and long term.

4. Conclusions and policy implications

This study aimed to understand the factors influencing APPV in Cameroon and design effective policies for mitigating its negative effects and promoting sustainable economic growth. Using the ARDL-ECM and annual data from 2000 to 2021, we examined the impacts of agricultural productivity, agricultural product imports, population, temperature variation, GDP per capita, and government expenditure on APPV. The key findings revealed that agricultural productivity, agricultural product imports, population, and GDP per capita are positively related to APPV in the long term, while temperature variation and government expenditure are negatively related to APPV.
Specifically, the long-term results indicated that a 1.000% increase in agricultural productivity will lead to a 4.901% increase in APPV, suggesting that higher agricultural productivity, while increasing supply, do not necessarily stabilize prices due to inelastic demand and market imperfections. Similarly, a 1.000% increase in agricultural product imports may result in a 1.012% increase in APPV, highlighting the influence of global price fluctuations on domestic markets. Population growth and GDP per capita also had a positive impact on APPV, with 1.000% increase in population and GDP per capita leading to 13.635% and 2.794% increase in APPV, respectively. Conversely, temperature variation had a stabilizing effect on APPV, with a 1.000% increase in temperature variation leading to a 0.990% decrease in APPV. This negative relationship is likely due to adaptation strategies adopted by smallholder farmers and market operators. Government expenditure also had a negative impact on APPV, indicating that increased public spending can stabilize agricultural product prices by supporting production and improving infrastructure. In the short term, temperature variation, particularly seasonal changes and extreme weather events, had a positive relationship with APPV.
To promote the structural transformation of the Cameroonian economy and the development of agricultural productivity, public policies should focus on specific and actionable strategies to reduce APPV. Firstly, public policies should encourage an increase in agricultural productivity by investing in agricultural research and development, improving access to agricultural inputs, and strengthening smallholder farmers’ capacities. This could help reduce the country’s dependence on agricultural product imports and stabilize agricultural product prices. However, it is crucial to address the challenges and risks associated with policy implementation. For instance, it is essential to ensure that research and development investments are effectively targeted and that smallholder farmers have access to the necessary training and resources to adopt new technologies. Coping strategies could include partnerships with international research institutions and the establishment of training programs for smallholder farmers. Secondly, public policies should aim to reduce the country’s dependence on agricultural product imports by encouraging local agricultural production. This could be achieved by implementing fiscal incentives for local producers and strengthening local supply chains. Challenges in this field might include competition from cheaper imported products and the need for significant infrastructure investments. Coping strategies could involve subsidies for local producers and investments in transportation and storage infrastructure to improve the efficiency of local supply chains. Thirdly, policies should consider the impact of temperature variation on APPV. Policymakers should therefore implement measures to adapt to climate change, such as the use of drought-resistant seeds and the improvement of irrigation systems. Initial costs of implementing climate-resilient practices are high. Coping strategies may involve government subsidies for climate-resilient technologies and the establishment of early warning systems for climatic events. Finally, public policies should aim to improve the living conditions of the rural population by increasing investments in rural infrastructures, such as roads, schools, and health centers. These measures could contribute to reducing rural poverty and improving food security. Coping strategies could involve public-private partnerships and community-based initiatives to leverage additional resources and ensure effective implementation
However, this study has certain limitations, including the limited availability of data and the lack of consideration of institutional, political, and other climate change-related factors. To deepen our understanding of the factors influencing APPV, future research could use high-frequency data, more advanced econometric methods, comparative studies, and examinations of the effects of government policies and external shocks. Studying the implications of APPV for food security and rural poverty is also important.

Authorship contribution statement

Ivette Gnitedem KEUBENG: conceptualization, formal analysis, investigation, and writing - original draft; George Achu MULUH: supervision, validation, and writing - review & editing; and Vatis Christian KEMEZANG: data curation, methodology, software, and writing - review & editing. All authors approved the manuscript.

Declaration of 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.

The authors would like to thank Dr. Gildas TIWANG, the editors, and the anonymous reviewers for their valuable comments and suggestions that greatly improved the manuscript.

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