The Impact of Climate Change on the Agricultural Sector in SADC Countries

Abstract

Agriculture is a key sector for economic growth, food security, and rural livelihoods within the member nations of the Southern African Development Community (SADC). However, the agricultural systems throughout the SADC regions face serious threats from climate change, which is seen through temperature rises, irregular rainfall patterns, and the rising frequency of droughts. The study examines the impacts of climate change on agricultural productivity in four SADC countries: South Africa, Zambia, Zimbabwe, and Malawi. It also assesses the impact of institutional structures, policy initiatives, and technological advancements in enhancing agricultural resilience to climate change. The Panel Autoregressive Distributed Lag (PARDL) model was employed to assess short and long run impact of climate change on agricultural productivity. The findings reveal that precipitation significantly increases agricultural productivity in the long run, but not in the short run. In addition, governance inefficiencies, which are measured by control of corruption index have negative long run impacts on agricultural productivity. The estimated speed of adjustment (ECT: −0.9557) demonstrated a strong long run equilibrium relationship, indicating that agricultural productivity converges to its long run trend regardless of short run fluctuations. In conclusion, the findings of this study provide essential knowledge to assist policymakers, researchers, and development agencies in the creation of evidence-based policies aimed at improving agricultural resilience to climate change across SADC member countries.

1. Introduction

Agriculture is a key sector for economic growth, food security, and rural livelihoods within the member nations of the Southern African Development Community (SADC) [1]. Although agriculture contributes differently to the GDP across the region, but it remains the main source of income for millions of people who earn their living mainly through farming as the main means of survival across rural areas. However, the agricultural sector faces major difficulties because of urbanization along with environmental changes leading to temperature rises, unstable rain patterns, and more frequent droughts in the SADC region. South Africa is one of the few SADC countries with the infrastructure to support extensive irrigation, while many other members still rely on rain-fed agriculture, which leaves them extremely sensitive to changes in the climate [2,3].

An extensive body of research has been explored, focusing on the relationship between climate change and agricultural productivity in Southern Africa. A study about agricultural output changes in Eastern and Southern Africa through analysis of temperature and precipitation levels [4]. Author [5] examined how climate change impacts the water supply patterns along with food security within the SADC member nations. Author [6] studied weaknesses in regional food delivery systems, while author [7] investigated development responses to sudden climate events and proposed adaptation plans. These studies demonstrate rising climate change threats yet fail to expose how specific countries in the region approach adaptation challenges together with their policy implementation for climate change adaptation.

Based on the adaptive resilience theoretical framework by author [8], this study suggests that both ecological and institutional capacities affect the degree to which agricultural systems can endure climate-related shocks. By incorporating climatic, economic, and institutional factors, the study promotes a comprehensive understanding of agricultural vulnerability and resilience in the SADC area. Focused on South Africa, Zambia, Zimbabwe, and Malawi, the study utilizes a panel ARDL model to evaluate the short and long term effects of temperature and rainfall on agricultural output while considering institutional quality through factors such as control of corruption and government effectiveness. These nations were chosen because of the consistent panel data accessible, the importance of agriculture in their economies, and the variety in institutional responses to climate change. The evaluation also involves a comparative analysis of adaptation strategies in these nations. The findings aim to guide policymakers and development stakeholders by identifying successful governance and adaptation strategies to boost agricultural resilience amid climate fluctuations throughout the SADC region.

1.1. Innovative Contribution of the Study

Although many studies have investigated the impacts of climate change on agricultural productivity, the majority focus on national-level assessments, failing to account for variations between countries in institutional reactions, agro-ecological systems, and the ability to adapt. This study presents multiple original contributions. Firstly, it utilizes a comparative cross-national strategy within the Southern African Development Community (SADC), assessing the impact of climate change on agricultural productivity in South Africa, Zambia, Zimbabwe, and Malawi through a common empirical model. Secondly, it utilizes the Panel Autoregressive Distributed Lag (PARDL) model, a dynamic panel approach that allows for the estimation of both short- and long run effects, thus addressing methodological constraints found in previous research that depended on static or time-series techniques. Third, incorporating institutional governance factors, particularly control of corruption and government effectiveness, together with climatic and input variables provides a comprehensive theoretical framework that combines ecological, economic, and institutional aspects of resilience. These advancements offer new empirical and policy-related insights to the climate-agriculture literature, especially regarding sub-Saharan Africa.

1.2. Research Objectives

The study is guided by the following objectives:

• To evaluate the short and long run impacts of climate factors (namely, temperature and precipitation) on agricultural productivity in the selected SADC nations.
• To examine how institutional quality, measured by control of corruption and government effectiveness, influences agricultural resilience and affects the impact of climate variability on productivity.

1.3. Research Questions

The study addresses the following research questions:

• What are the short and long run impacts of climate factors (namely, temperature and precipitation) on agricultural productivity in the SADC region?
• How the quality of institutional governance affects the resilience of agriculture to climate inconsistency?
• Do cross-national variances in institutional quality amongst SADC nations shape the effectiveness of adaptation approaches?

1.4. Hypotheses

The empirical study tests the hypotheses below:

H1. 

Higher precipitation levels rise agricultural productivity in the long run.

H2. 

Rising temperatures decrease agricultural productivity, especially in rain-fed systems.

2. Literature Review

2.1. Climate Change and Agricultural Productivity in SADC Countries

The impact of climate change on agricultural productivity has been extensively documented, with results showing distinct patterns based on geographical regions and farming systems and agricultural sub-sectors. The Southern African Development Community (SADC) region experiences major food security and rural livelihood challenges as a result of its changing climates, which include temperatures rises, unpredictable rainfalls, and severe weather events [1]. With the use of existing empirical research, this section explores how climate change affects agricultural production of crops and livestock, and it includes detailed analysis of crucial adaptation methods together with research methodology found in previously published studies.

2.2. Climate Change and Crop Productivity

Empirical studies indicate that the effects of temperature and rainfall fluctuations on agricultural production are considerably influenced by three factors: geographic location, crop choice, and adaptive capability. For example, areas at high altitudes might gain advantages from minor warming owing to longer growing periods, while lowland regions endure heat stress and losses from evapotranspiration [9]. Likewise, crops such as sorghum and millet show greater resilience to heat and drought than maize or wheat, resulting in varied reactions to comparable climatic stressors [10]. Adaptive abilities such as access to irrigation, early warning systems, and extension services enable farmers to lessen negative climatic impacts, especially in areas with institutional backing and climate-smart farming methods [8].

Rainfall pattern variability, including dry seasons and heavy rainstorms, has been proven to be essential for agricultural output determination. According to author [11], agricultural production in Uganda experiences significant changes when long term rainfall and temperature averages deviate. Findings were that wheat and sorghum yields in Ethiopia were strongly affected by rainfall variations between years and across seasons, with regression analysis predicting more than 80% of yield fluctuations [12]. Similarly, author [13] showed rainfall variations to cause annual yield shifts in crops, yet rising temperature remains as a primary negative factor in staple crop production.

Furthermore, studies indicate that carbon emissions have been linked to a decrease in agricultural productivity. Author [14] found that fluctuations in temperature alongside rainfall and carbon emissions extensively influenced crop and livestock performance in Nigeria. The long term negative effects of climate change endanger food security, which requires specific adaptive measures to strengthen the agricultural sector’s ability to withstand challenges.

2.3. Mechanisms of Climate Impact on Agriculture

Climate change influences agricultural output through various direct and indirect processes. Higher temperatures boost evapotranspiration, leading to a quicker loss of soil moisture and causing heat stress in plants, especially during crucial growth stages such as flowering and grain filling [15]. Variations in rainfall patterns decrease water supply for rain-fed farming, disturb planting schedules, and increase vulnerability to both droughts and floods, all of which lower yields [16]. Moreover, variations in temperature and humidity conditions encourage the growth of pests and diseases, which further reduces output and increases input expenses. These biophysical disturbances are worsened by institutional limitations, such as restricted access to extension services, inadequate infrastructure, and low adaptive capacity, which hinder prompt and efficient responses.

2.4. Climate Change and Livestock Production

Livestock production extensively reacts to climate conditions because it depends on both ambient temperatures and water resources. The findings confirm that climate change produces harmful impacts on livestock productivity as well as revenues and sustainability. Author [17] found that Kenya faces negative consequences for net livestock financial outcomes as a result of erratic temperature changes and rainfall patterns. The research determined extension services together with farmer training combined with credit access served as vital factors to fight against climate-related farming losses.

The studies indicate that carbon emissions lead to a decrease in agricultural productivity. Author [14] showed that temperature, alongside rainfall and carbon emissions, had an extensive influence on the performance of Nigerian crops and livestock. The agricultural sector requires precise adaptation strategies due to long-lasting climate change threats to ensure its resilience.

2.5. Adaptation Mechanisms in Agricultural Systems

Adaptation strategies are essential for minimizing the negative impacts of climate variability on agricultural output, especially in rain-fed systems dominant throughout SADC nations. These strategies, such as expanding irrigation, adopting drought-resistant crop varieties, implementing climate-smart agricultural practices, and obtaining timely weather updates, boost resilience and lessen vulnerability to climate disturbances. Empirical research highlights the significance of institutional backing, such as agricultural extension services, credit access, and sound governance, in promoting successful adaptation [18,19]. Considering that the empirical model in this research includes indicators of institutional governance (control of corruption, government effectiveness), this section offers a conceptual foundation for comprehending how governance influences adaptation capacity within the agricultural sector.

A study from practical studies shows that adaptation strategies can greatly improve the agricultural sector’s ability to endure climate-related challenges. For example, Author [18] discovered a U-shaped relationship between precipitation variability and agricultural production in Uganda, emphasizing that properly designed adaptation strategies can reduce early productivity declines. Their study highlights the essential function of extension services in assisting farmers in coping with climate shocks and reducing yield fluctuations.

Several research efforts have investigated the efficiency of public policy measures aimed at mitigating the negative impacts of climate change. Author [20] discovered that extreme temperatures led to greater yield losses compared to rainfall fluctuations, indicating a requirement for heat-adaptive farming methods. Author [19] proposed that a comprehensive set of adaptation measures, including improved irrigation, climate-smart agricultural practices, and wider distribution of agro-meteorological data, are crucial parts of national strategies for agricultural resilience.

Other empirical studies highlight the stabilizing function of irrigation systems in mitigating climatic risks. Author [21] showed that the application of irrigation in the Great Plains of the U.S. successfully reduced yield fluctuations caused by unpredictable weather patterns. Even though the research originates from a high-income setting, its results are particularly significant for rain-fed agricultural systems in the SADC area, where irrigation is still not well-developed but can have a considerable effect when implemented thoughtfully.

2.6. Research Gaps and Justification for the Current Study

Although a significant amount of research has recorded the impacts of climate change on agriculture output in multiple African nations, there are still several gaps that exist. Firstly, many empirical studies utilize country-specific methods, which leads to a lack of comparative regional analysis that reflects the differences in institutional capacity, agroecological traits, and adaptive responses across countries in the Southern African Development Community (SADC) region.

Secondly, the use of advanced panel econometric methods is quite limited, especially the Panel Autoregressive Distributed Lag (PARDL) and Nonlinear ARDL (NARDL) models, which can differentiate between the short run and long run impacts of climate factors (such as temperature and rainfall) on agricultural production, all while considering asymmetries and dynamic relationships. Third, although existing studies have recognized the importance of adaptation strategies such as irrigation and extension services, few have quantitatively assessed their effectiveness in a comprehensive model that integrates climatic, institutional, and economic factors.

This study tackles these gaps through a multi-country econometric analysis involving four SADC nations, namely: South Africa, Zambia, Zimbabwe, and Malawi, which were chosen based on data accessibility, the significance of agriculture in their economies, and varied institutional responses to climate change. Utilizing the panel ARDL method, this research identifies short run variations and long run patterns while investigating the varying effects of adaptation strategies. By doing this, this study offers new empirical insights into how combined policy and technological strategies can reduce climate-related risks and improve agricultural resilience throughout the SADC region.

2.7. Literature Summary

This summary of literature emphasizes important empirical results, theoretical perspectives, and unsolved gaps pertinent to the ongoing study. Empirical research demonstrates that climate change produces major negative impacts on agricultural productivity, mainly affecting systems that rely on rainfall for irrigation. The critical elements that determine agricultural output are temperature and rainfall variation, and they demonstrate more vulnerability to crop production when temperatures rise. Strategies that focus on irrigation together with extension services and climate-smart agricultural practices demonstrate the potential to reduce negative impacts from climate change. In addition, econometric research should address existing knowledge gaps that focus on climate change effects throughout SADC countries.

3. Data and Methods

The study employs a panel dataset that includes South Africa, Zambia, Zimbabwe, and Malawi from 1992 to 2022. The dependent variable, agricultural output, is represented by the Crop Production Index (CrPI), whereas climate factors consist of yearly average temperature and overall precipitation. Institutional quality is assessed through the World Bank’s indicators for control of corruption (CoC) and government effectiveness (GE). All variables are obtained from the World Bank’s World Development Indicators and Climate Knowledge Portal.

Before estimation, panel unit root tests (ADF-Fisher, PP-Fisher, and IPS) are conducted to assess the stationarity properties of the variables. It is confirmed that the variables are integrated at I (0) or I (1), permitting the use of a Panel Autoregressive Distributed Lag (PARDL) model, which is inconsistent with author [22]. The model specifications incorporate country-fixed effects and permit variations in short-term dynamics while imposing common long term coefficients.

The econometric analysis unfolds in two stages. Firstly, the long run relations between variables are assessed using the PARDL framework. Secondly, a model for error correction (ECM) is derived to reflect short-term dynamics and adjustment speed. Diagnostic tests for serial correlation, heteroscedasticity, and the normality of residuals are conducted to guarantee model robustness. All estimations are carried out utilizing EViews 12.

3.1. Study Area

Figure 1 presents the Map of the Southern African Development Community (SADC) region, with the four countries analyzed in this study: South Africa, Zambia, Zimbabwe, and Malawi.

3.2. Data Description

This study employs a balanced panel dataset consisting of 4 countries namely, South Africa, Zambia, Zimbabwe, and Malawi, analyzed over the period from 1992 to 2022, yielding 30 annual observations for each country. This results in a total of 120 panel observations (4 nations × 30 years). The panel is balanced, providing consistent time coverage throughout all cross-sectional units. The countries chosen were selected due to data accessibility, the significance of agriculture to their economies, and their changing levels of exposure and institutional reactions to climate variability.

The dependent variable used in this study is agricultural productivity, represented by the Crop Production Index (CrPI). This index evaluates the annual amount of agricultural production for each nation in comparison to a baseline timeframe. It includes key agricultural products believed vital for food security and economic importance, with values modified based on average global prices. The CrPI is derived from the World Bank’s World Development Indicators (WDI) and is shown as an index with a base year of 2015 = 100. This standardized measurement allows for consistent comparisons of crop production across countries over time.

The independent variables include climate indicators, specifically average annual temperature and overall precipitation, along with agricultural inputs and governance indicators that are recognized to affect production efficiency and adaptive capacity. Temperature and precipitation information is obtained from the World Bank Climate Knowledge Portal, whereas governance indicators are sourced from the Worldwide Governance Indicators (WGI). Data on fertilizer usage, indicating the intensity of agricultural inputs, is obtained from the WDI.

Variable Description and Justification

Table 1. Detailed description of each variable, including its abbreviation, definition, and data source.

Variable	Abbreviation	Details	Source
Agricultural Productivity (Crop Production Index)	${CrPI}_t$	The Crop Production Index (CrPI) represents an agricultural output measure that derived its base period values from 2014 to 2016 with a value of 100.	World Bank
Air Temperature (Temp)	${TEMP}_t$	Mean surface temperature measures the effects of climate heat stress on agricultural production levels in degrees Celsius (°C).	World Bank (Climate Knowledge Portal)
Precipitation (Prep)	${PREP}_t$	Observed precipitation annually (mm), influencing soil moisture and water availability for crops.	World Bank (Climate Knowledge Portal)
Fertilizer Consumption (FC)	${FC}_t$	Percentage of fertilizer production used in agriculture, showing inputs in agricultural input.	World Bank
Control of Corruption (CoC)	${CoC}_t$	An effective measure of governance indicator of anti-corruption and institutional integrity.	World Bank
Government Effectiveness (GE)	${GE}_t$	A measure of the quality and efficiency of public services, policy implementation, and institutional capacity.	World Bank

Source: Author’s own computations.

presents a detailed description of each variable, including its abbreviation, definition, and data source.

3.3. Model Specification

Model specification entails determining the optimal framework of an empirical model to demonstrate theoretical economic relations. This study investigates the impact of climatic elements (temperature, rainfall), agricultural resources (fertilizer application), and institutional standards (corruption control, government efficiency) on agricultural productivity in chosen SADC countries.

Taking into account the dynamic features of climate-agriculture relationships, the study employs a Panel Autoregressive Distributed Lag (PARDL) model, enabling the concurrent estimation of short run and long run relations [22,23]. The panel ARDL approach accounts for cross-sectional differences and simulates the adjustment mechanisms toward equilibrium after short-term deviations. This modelling methodology aligns with established practices in empirical studies on climate change and agricultural productivity [24].

The use of the panel ARDL model in this study is based on several essential assumptions. Firstly, the model requires underlying variables that are either stationary at level, I (0), or become stationary following first differencing, I (1), but none must be integrated of order two, I (2). Therefore, panel unit root tests were performed to verify that all variables meet this requirement [22]. Secondly, the model postulates the existence of a stable long run cointegration relation among the variables, validating the use of an error correction formulation. Thirdly, the model accommodates variation in short run dynamics and intercepts among countries while enforcing stability in long run coefficients.

Moreover, with a modest small sample size (four countries over 30 years), the panel ARDL method is especially appropriate, as it effectively handles small to medium-sized panels. However, possible risks encompass the existence of cross-sectional dependence or structural breaks, which, if substantial, may skew standard errors and influence the reliability of inferences. To reduce these risks, suitable post-estimation diagnostic test was carried out, and robustness checks were executed to confirm the results’ validity.

In general, using the panel ARDL approach enhances the dependability and strength of the model’s results by effectively reflecting the dynamic interactions among climatic elements, institutional quality, and agricultural output in the SADC area. Finally, the full econometric framework, which covers the autoregressive and distributed lag elements, is detailed in Section 3.4.3 (panel ARDL Subsection) Equation (4).

3.4. Analytical Technique

The Panel Autoregressive Distributed Lag (PARDL) approach is a suitable econometric tool for assessing both long and short run impact between climate change and agricultural productivity. The study uses the PARDL framework to allow for different orders of integration and capture dynamic interactions among variables. However, it does not apply to non-stationary variables integrated at the I (2) order of integration [23].

3.4.1. Descriptive Statistics

Descriptive analysis consists of outlining the key features of the dataset before performing econometric estimation. This study incorporates measures of central tendency (average), dispersion (standard deviation), and range (minimum and maximum) for every variable [25,26]. These statistics offer an initial insight into the distribution and variability of crucial indicators such as temperature, precipitation, fertilizer usage, governance factors, and the Crop Production Index (CrPI) across the chosen SADC nations during the study timeframe (1992–2022).

Descriptive statistics play a crucial role in detecting outliers, assessing data normality, and understanding the magnitude and comparability of variables before conducting econometric modeling. They also indicate the suitability of particular econometric models (e.g., panel fixed effects, ARDL), considering the cross-sectional and temporal variation in the data.

Although descriptive statistics are typically included in the methodology chapter,

Table 2. Descriptive statistics for the key variables.

Statistic	CRPI	PREP	TEMP	LCOC
Mean	94.4827	804.164	21.2957	1.04121
Median	98.7500	878.920	22.0300	1.14613
Maximum	141.610	1400.73	23.4200	1.20412
Minimum	32.3400	380.520	17.3600	0.60206
Std. Dev.	27.9165	262.301	1.77763	0.17809
Skewness	−0.49716	−0.10227	−1.07863	−1.40062
Kurtosis	2.40880	1.74949	2.46016	3.56987
Jarque-Bera	5.18543	6.22176	19.1628	31.6651
Probability	0.07482	0.04456	0.00007	0.00000
Sum	8786.890	74787.3	1980.50	96.8329
Sum Sq. Dev.	71698.60	6329760	290.7183	2.917804
Observations	93	93	93	93

Source: Author’s own computations using EViews 12 Software.

in Section 4 in the Findings and Discussions displays and explains the summary statistics utilized in the analysis.

3.4.2. Panel Unit Root Test

The presence of a unit root in economic time series suggests non-stationarity, the main focus in econometrics, which leads to spurious regression outcomes if addressed incorrectly. Standard unit root tests, such as the Augmented Dickey-Fuller (ADF) test, Phillips-Perron (PP) Fisher, Im, Pesaran, and Shin (IPS), are frequently used to explore stationarity in panel data [27,28].

Econometric modelling requires unit root detection to validate its results, specifically when estimating long run relationships between variables. Conducting stationarity tests through proper testing techniques reduces the risk of spurious regressions and ensures that data structures can be handled properly with the PARDL model [29].

The study utilizes the ADF, PP, and IPS test to check variable stationarity to avoid spurious regression.

• Augmented Dickey-Fuller Test

The ADF test is used to examine if a unit root is present in an autoregressive model [22].

Econometrically, the ADF test can be expressed as follows:

$$∆Y_t=\alpha +{\gamma Y}_{t-1}+{\sum }_{i=1}^p{\beta }_i∆Y_{t-1}+{\epsilon }_t$$

(1)

where:

$∆Y_t=Y_t-Y_{t-1}$ is the differenced value of the series to remove trends,

${\alpha }_i$ is a constant term0,

$\gamma$ is the coefficient of the lagged dependent variable ${(Y}_{t-1})$,

${\beta }_i$ tests for the presence of a unit root in each individual unit $(∆Y_{t-1})$,

$p$ is the number of lags included in the model, ${\epsilon }_t$ is a white-noise error term.

The null hypothesis of a unit root is: $H_o: \gamma =0$, indicating the presence of the unit root, while the alternative hypothesis $H_1:\gamma <0$ suggesting stationarity [30].

• Phillips-Perron (PP) Fisher

The PP Fisher test functions as an alternative to ADF and IPS tests when conducting panel data unit root tests. The PP Fisher test is advantageous in addressing heteroskedasticity together with serial correlation, which is usually used in economic time series. To determine whether a unit root exists across all units, the Phillips-Perron test is applied individually for each cross-sectional unit in the panel, and the results are then combined [31]. The Phillips-Perron test general form is as follows:

$$∆Y_{it}={\alpha }_i+{{\beta }_{i}Y}_{i,t-1}+{\sum }_{j=1}^p{\gamma }_{ij}∆Y_{i,t-j}+{\epsilon }_{it}$$

(2)

$Y_{it}$ is the value of the $i$ cross-sectional unit at time $t,$

${\alpha }_i$ is the intercept for the $i-th$ unit,

${\beta }_i$ tests for the presence of a unit root in each individual unit,

${\gamma }_{ij}$ are the coefficients of the differenced terms.

The null hypothesis of a unit root is: $H_o$: each cross-sectional unit has a unit root, indicating the presence of the unit root, while the alternative hypothesis $H_1:$ atleast some cross-sections are stationary [31].

• Im, Pesaran, and Shin (IPS)

The IPS test analyzes panel data stationarity by performing an average of unit root tests across cross-sections. It allows for different unit root processes within various cross-sectional units by extending the Augmented Dickey-Fuller (ADF) test to a panel framework [31]. The IPS test inherits its formula from:

$$∆Y_{it}={\alpha }_i+{{\gamma }_{i}Y}_{i,t-1}+{\sum }_{j=1}^p{\beta }_{ij}∆Y_{i,t-j}+{\epsilon }_{it}$$

(3)

$Y_{it}$ is the value of the $i$ cross-sectional unit at time $t,$

${\alpha }_i$ is the individual-specific intercept (fixed effects),

${\beta }_i$ is the coefficient for the differenced lag terms,

${\gamma }_{ij}$ is the coefficient for the lagged dependent variable ${(Y}_{it-1})$,

The null hypothesis of a unit root is: $H_o: {\gamma }_i=0$ (no stationarity across all cross-sections), indicating the presence of the unit root, while the alternative hypothesis $H_1:{\gamma }_i<0$ (at least some cross-sections are stationary) suggesting stationarity [31].

3.4.3. Panel Auto-Regressive-Distributed-Lags

The Panel Auto-Regressive Distributed Lags (PARDL) model is used in the study to estimate both the long and short run relationships. The estimation begins with the long run model, followed by the short run model [23]. The general representation of the long run model is as follows:

$$\begin{gathered} {CrPI}_t={\sum }_{j=1}^p{\beta }_{ij}{CrPI}_{i,t-j}+{\sum }_{j=0}^q{\theta }_{ij}{TEMP}_{i,t-j}+{\sum }_{j=0}^q{\omega }_{ij}{PREP}_{i,t-j}+ \\ {\sum }_{j=0}^q{\delta }_{ij}{FC}_{i,t-j}+{\sum }_{j=0}^q{\sigma }_{ij}{CoC}_{i,t-j}+{\sum }_{j=0}^q{\partial }_{ij}{GE}_{i,t-j}+{\mu }_{it} \end{gathered}$$

(4)

where:

$Cr{PI}_{it}$ = Crop Production Index as a proxy of Agriculture productivity

${TEMP}_{it}$ = Air Temperature

${PREP}_{it}$ = Precipitation

${FC}_{it}$ = Fertilizer Consumption

${CoC}_{it}$ = Control of Corruption

${GE}_{it}$ = Government Effectiveness

${\mu }_{it}$ = is the error term

$i$ = 1, 2 and 3, 4 (South Africa, Zambia, Zimbabwe, and Malawi)

$t$ = 1992, 1993–2022 (time dimension covering 30 years)

This long run model facilitates the empirical evaluation of the links between climate factors, governance indicators, agricultural inputs, and farming productivity in certain SADC nations. ${\beta }_{ij}, {\theta }_{ij},{\omega }_{ij}, {\delta }_{ij}$,${\sigma }_{ij}, \mathrm{a}\mathrm{n}\mathrm{d} {\partial }_{IJ}$ represent long run coefficients of the independent variable.

3.4.4. Panel Error Correction Model (PECM)

After estimating the long run relationship, the short run dynamics are represented through a Panel Error Correction Model (PECM). The PECM framework considers short term deviations from the long term equilibrium and measures the rate of return to that equilibrium [23].

$$\begin{gathered} ∆{CrPI}_t={\beta }_i{CrPI}_{i,t-1}+{\sum }_{j=1}^{P-1}{\phi }_{IJ}∆{CrPI}_{i,t-j}+{\sum }_{j=1}^{q-1}{\rho }_{IJ}∆{TEMP}_{i,t-j}+{\sum }_{j=1}^{q-1}{\gamma }_{IJ}∆{PREP}_{i,t-j}+ \\ {\sum }_{j=0}^{q-1}{\theta }_{ij}{∆FC}_{i,t-j}+{\sum }_{j=0}^{q-1}{\omega }_{ij}{∆CoC}_{i,t-j}+{\sum }_{j=0}^{q-1}{\delta }_{ij}{∆GE}_{i,t-j}+{\epsilon }_{it} \end{gathered}$$

(5)

where:

• $∆ =$ denotes the first difference operator
• ${\beta }_i$ is the error correction term (ECT) which represents the speed of adjustment toward long run equilibrium.
• A significant and negative $\beta i$ confirms the presence of cointegration, indicating that short-term deviations from equilibrium are corrected over time. If $\beta i$ = 0 then there is no cointegration of variables.
• The coefficients ${{\varnothing }_{ij}, \rho }_{ij},{\theta }_{ij},{\omega }_{ij}, {\delta }_{ij}$ and ${\gamma }_{ij}$ Denotes short run coefficients.
• ${\epsilon }_{ij}$ is the error term, independently distributed across cross-sections and time.

The short run model therefore reflects the immediate effects of climate and governance factors on agricultural output, while the error correction term guarantees that any divergences from the long term trend are suitably corrected.

3.5. Post Estimation Tests (Diagnostic Tests)

• Normality Test

Normality testing is a vital post-estimation diagnostic that checks if the residuals from the regression model follow an approximately normal distribution, an important assumption for making valid inferences in small to moderate sample research. In a Panel Autoregressive Distributed Lag (PARDL) model, maintaining the normality of residuals improves the reliability of the estimates for both long run and short run parameters.

The Jarque-Bera (JB) test is used to determine normality by analyzing the skewness and kurtosis of the distribution of residuals. The JB test’s null hypothesis declares that the residuals follow a normal distribution. The null is rejected when the probability value (p-value) is lower than the standard significance level (usually 0.05), suggesting evidence contrary to normality [22].

The following is the test statistic:

$$\mathit{JB}\mathit{=}n \mathit{[}{\displaystyle \frac{S^2}{6}}\mathit{+}{\displaystyle \frac{(K-{3)}^2}{24}}\mathit{]}$$

(6)

where:

n = sample size,

S = skewness coefficient

K = Kurtosis coefficient.

S = 0 and K = 3 for a normally distributed variable.

Although other normality tests such as the Shapiro-Wilk test [32] and the Anderson-Darling test [33] are available, the Jarque-Bera test is commonly favoured in large-sample contexts and regression analyses because of its asymptotic characteristics. In the context of panel data, the Central Limit Theorem (CLT) implies that slight departures from normality are unlikely to significantly impact the efficiency of estimators, particularly when dealing with larger panels. However, verifying residual normality is essential for the strength and reliability of hypothesis testing and the creation of confidence intervals. In this research, the outcomes of the Jarque-Bera test showed that the residuals are nearly normally distributed, thus supporting the statistical validity of the panel ARDL regression estimates.

4. Findings and Discussion

This section outlines and explains the empirical findings derived from the panel ARDL estimations. The results are structured into short run and long run assessments to highlight the dynamic impacts of climate factors (temperature and precipitation) and institutional factors (government effectiveness, control of corruption) on agricultural productivity.

The discussion combines empirical results with current theoretical and empirical literature, offering contextual insights within the distinct agricultural, climatic, and institutional settings of the SADC region. Diagnostic assessments, such as normality and cointegration evaluations, are also mentioned to confirm the reliability of the econometric models. Finally, in the following section, the key findings inform the resulting policy implications.

4.1. Descriptive Statistics

Table 2 presents the descriptive statistics for the key variables, including the Climate-Related Productivity Index (CRPI), precipitation (PREP), temperature (TEMP), and Log of control of corruption (LCOC).

The CRPI reveals significant variations between SADC nations, with a mean of 94.48 and 27.92 standard deviations, indicating differences in climate-related agricultural productivity. PREP also shows significant variations. The average rainfall measurement is 804.16 mm, and the standard deviation reaches 262.30 mm, indicating seasonal and annual precipitation pattern changes. The results align with the findings of authors [11] as well as [12], who established rainfall variability as a fundamental factor affecting agricultural yields in Uganda and Ethiopia, respectively.

The variable TEMP shows smaller dispersion compared to PREP, with a standard deviation of 1.77°. However, its skewness of −1.08 and kurtosis of 2.46 are non-normally distributed temperature series. Empirical evidence, as shown in the study by author [13] shows that rising temperatures negatively affect staple crop yields in agricultural systems, reinforcing concerns over fluctuations of the temperature in SADC agriculture.

The LCOC shows a skewness of −1.40 indicating an uneven distribution of governance efficiency across SADC nations. Overall, the descriptive statistics show substantial differences among SADC countries in terms of governance and climate-related variables. According to author [26], the descriptive statistical measures such as skewness and kurtosis offer crucial insights into data distribution and help identify structural differences within datasets. Furthermore econometric analysis is necessary to evaluate the effect of the differences on resilience and agricultural productivity.

4.2. Preliminary Tests

The first subsection of the econometric analysis commences by performing panel unit root tests through the combination of Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP Fisher) and Im-Pesaran-Shin methods to check the stationarity of data. Conducting these tests is necessary because this prevents spurious regressions between variables intended for further analysis.

Following the stationarity test, the Panel Autoregressive Distributed Lag (PARDL) model is implemented to allow testing of both the long and short run relationships between the studied variables. The method provides key benefits by addressing endogeneity issues of the data.

Next will be a detailed result analysis and discussions based on agricultural productivity effects of climate change, and finally the conclusion.

4.2.1. Panel Unit Root Test

The ADF-Fisher Chi-square, PP-Fisher Chi-square, and IM-Pesaran-Shin tests were applied to perform panel unit root tests for the model to ensure robustness.

Table 3. Panel unit root test results for all key variables.

Variables	Model Specifications	ADF-Fisher Chi Square		PP-Fisher Chi Square		IM-Pesaran-Shin
		Level	First Difference	Level	First Difference	Level	First Difference	Order of Integration
CRPI	Intercept	0.5320	0.0000	0.1182	0.0000	0.9561	0.0000	I (1)
LCOC	Intercept and trend	0.0023 **		0.0000 **		0.0008 **		I (0)
PREP	Intercept and trend	0.0000 **		0.0000 **		0.0001 **		I (0)
LNFC	Intercept and trend	0.9762	0.0083	0.9448	0.0000	0.9789	0.0072	I (1)
TEMP	Intercept and trend	0.0001 **		0.0000 **		0.0000 **		I (0)

Source: Author’s own computations using EViews 12 Software. Note: ** means the rejection of the null hypothesis at 1% and 5%.

presents results that verify that variables are either stationary at level I (0) or first difference I (1) thereby allowing panel ARDL analysis for both long run and short run examinations.

These findings align empirical research by author [17,34], which found both short and long run that effects of climate variable and agricultural productivity.

4.2.2. Panel Auto Regression Distributed Lags Model Estimates

Table 4. Long run PARDL estimates and ECM short run dynamic PARDL estimation.

Model: ARDL (1, 1, 1, 1)
Long run Equation	Coefficient	Standard Error	T-Statistics	Probability
PREP	0.017607	0.008070	2.181775	0.0327
TEMP	−3.358244	3.271122	−1.0266334	0.3083
LCOC	−73.40682	10.82063	−6.783970	0.0000
Short run Equation
COINTEQ01	−0.955701	0.233128	−4.099466	0.0000
D(PREP)	−0.004445	0.005900	−0.753358	0.4539
D(TEMP)	0.408412	2.389093	−0.170949	0.8648
D(LCOC)	6.471118	51.50629	0.125637	0.9004
C	166.9159	58.20971	2.867492	0.0055
@TREND	2.871124	0.728732	3.939890	0.0002
Log likelihood	−246.2508

Source: Author’s own computations using EViews 12 Software.

presents the Long run PARDL estimates and ECM short run dynamic PARDL estimation that captures the relationship between Climate Change on the Agricultural Sector in SADC Countries.

• Long Run Analysis

The long run estimation findings show that precipitation (PREP) has a statistically significant and positive effect on agricultural productivity (p < 0.05). This result aligns with the works of authors [12,35], who contend that consistent rainfall trends are crucial for agricultural performance in developing nations. In terms of Southern African Development Community (SADC), total seasonal rainfall contributes to groundwater recharge, sustains vital soil moisture levels, and boosts rotational cropping practice—mechanisms that collectively improve agricultural productivity. Considering that a significant part of agriculture in the SADC area relies on rainfall and lacks access to established irrigation systems, this finding highlights the importance of consistent rainfall as a long term factor influencing food security and rural livelihoods.

On the other hand, the variable Log of control of corruption (LCOC) shows a significant negative relationship with agricultural productivity (p < 0.01). This result aligns with authors [14,36], which shows that governance shortcomings, especially corruption, undermine the effectiveness of agricultural policies, misappropriate resources meant for rural infrastructure (such as irrigation and transport systems), and hinder the execution of climate adaptation strategies. As time progresses, poor institutional quality increases vulnerability to climate risks, lowers private investment in agriculture, and lessens the adaptive abilities of smallholder farmers, ultimately weakening the resilience of the entire sector.

The temperature coefficient (TEMP) is negative but insignificant (p = 0.3083), indicating that over time, temperature fluctuations do not have a separate impact on agricultural productivity in the SADC region. This could be due to the occurrence of drought-resistant crops in the area, such as sorghum, millet, and heat-adapted maize hybrids, which are better suited to moderate temperature rises than those grown in temperate regions. Additionally, long term adaptation methods such as modified planting times, utilizing enhanced seed types, and diversifying agricultural systems seem to reduce the potentially negative impacts of increasing temperatures. The findings of authors [37] support this, showing that adaptation behaviors moderate the dynamics between climate and agriculture.

These long term findings emphasize the importance of consistent rainfall patterns and strong institutional governance in maintaining agricultural productivity in SADC nations. The comparatively muted influence of temperature, when looked at in conjunction with regional adaptive measures, further reinforces the idea that internal adaptation can alleviate certain adverse effects of climate variability.

• Short Run Analysis

The estimated Error Correction Term (ECT) is −0.9557 and statistical significance (p < 0.01), suggesting a strong preference towards long run equilibrium. Approximately 95.57% of the deviation from equilibrium is corrected each year, indicating a speedy adjustment rate and validating the existence of a stable long term relationship.

In the short run dynamic estimates, the coefficients for precipitation (PREP), temperature (TEMP), and institutional quality (LCOC) are not statistically significant. This implies that climate and institutional factors do not have immediate impacts on agricultural productivity, aligning with the delayed response processes present in ecological and agronomic systems. According to authors [2,5], the agricultural calendar in SADC nations aligns with seasonal rainfall, and the positive impacts of precipitation become evident only after soil moisture has been absorbed, germination delays, and the maturation processes of crops have taken place.

The area’s distinct climatic fluctuations featuring intraseasonal rainfall inconsistency, repeated drought occurrences, and spatial diversity further reduce the probability that individual rainfall events will affect productivity in the short term. Authors [12] highlight that it is not merely the occurrence of rainfall, but its reliability throughout various crop growth phases that influences productivity results.

Moreover, the short run insignificance also highlights the constrained absorptive and adaptive abilities of numerous smallholder farmers in SADC. The lack of irrigation technologies, insufficient spread of early warning systems, and poorly developed extension services hinder farmers’ capacity to react effectively to short-term climate changes. According to author [36], both institutional and community-level structural difficulties delay timely reactions to changes in the environment. As a result, in the absence of quick response systems, favourable short-term rain may not result in significant productivity improvements until the structural obstacles are resolved.

This aligns with results from authors [38,39], who claim that climate impacts on agriculture are influenced by ecological inactivity, costs of behavioural adjustments, and infrastructural constraints. Therefore, the empirical data highlights the need for thorough, proactive adaptation strategies that go beyond seasonal fluctuations and concentrate on enhancing systemic resilience.

• Post-Estimation Test: Normality Test

Figure 2 presents histogram and normal distribution curve of model residuals.

The Jarque-Bera test was employed to assess the normality of the residuals, resulting in a test statistic of 0.54 and a corresponding p-value of 0.99. Since this p-value is greater than the typical 5% significance level, the null hypothesis of normally distributed residuals cannot be rejected. The skewness of −0.03 and kurtosis of 2.94, with both values near the anticipated outcome for normality. As stated by authors [22,40], normally distributed residuals confirm the validity of classical linear regression assumptions, thereby supporting the statistical reliability of the model’s estimates and inferences.

• Conclusions

The empirical findings confirm that agricultural productivity in the SADC area is closely linked with both climatic and institutional factors. Although long term rainfall patterns are crucial for maintaining productivity in rain-fed agricultural systems, the quality of institutions, particularly the lack of corruption, significantly influences climate resilience and the effectiveness of infrastructure. While temperature may not have a direct impact over an extended period, this seems to illustrate the moderating role of internal adaptation strategies used at the farm level.

In the short run, the restricted reaction to climatic and governance shocks highlights the structural inflexibility and adaptation lags present in the existing agricultural systems. These results collectively highlight the significance of incorporating climate-resilient infrastructure, efficient governance reforms, and focused assistance for smallholder farmers in national and regional agricultural strategies. Based on empirical research such as that conducted by authors [35,36,37], this study promotes shifting from reactive adaptation planning to anticipatory approaches to secure food and support rural development in the face of increasing climate variability.

5. Climate Resilience Policy Implications: Challenges and Strategic Recommendations for the SADC Region

The findings from the econometric analysis offer crucial insights into the relationship between climate variables and agricultural productivity in the SADC region. Agricultural production, proxied by Crop Production Index (CrPI) shows direct relationships with precipitation levels (PREP) and the control of corruption indicator (LCOC), which represents governance quality. The results are aligned with the authors [35,36,37], which reveal both PREP and LCOC impact agricultural productivity. Furthermore, studies by [39] along with [41] emphasized the extended effects of climate change on agricultural yield, therefore highlighting the importance of developing adaptable strategies. Based on the findings of the study, numerous policy recommendations emerge:

5.1. Strengthening Governance Institutions

The statistical analysis highlights the crucial negative relationship between the control of corruption index and agricultural productivity, highlighting the need for institutional reforms. Transparent governance and efficient resource management are critical for implementing effective climate adaptation measures. Studies by author [36] support this by indicating that weak institutions reduce agricultural efficiency. To address this, policymakers should focus on improving land administration systems, enhancing transparency in agricultural funding, and strengthening anti-corruption techniques. There is to ensure that resources reach farmers and climate adaptation initiatives are effectively implemented.

The negative long-term relation between the control of corruption index and agricultural productivity highlights the need for institutional changes. Transparent governance and effective resource management are essential for executing climate adaptation strategies.

Challenges:

• Political opposition to anti-corruption reforms.
• Weak institutional capability at the local level.

Measures to counteract:

• Make independent anti-corruption agencies for agricultural areas.
• Employ digital platforms (such as blockchain) to enhance clarity in the allocation of subsidies.
• Put resources into decentralized governance and community agricultural organizations.

5.2. Enhancing Climate Risk Management

Government authorities need to establish climate risk management measures because of the direct relationship between precipitation variability and agricultural output. Authors [12,35] highlighted that unpredictability of rainfall acts as a key factor that shapes agricultural productivity levels in developing nations. To mitigate these risks, the government should improve water resource management, establish early warning systems for floods and droughts, and promote climate-resilient farming practices such as agroforestry and precision farming. All are necessary for reducing the negative effects of climate variability. Author [42] highlights effective climate risk management strategies with weather forecasting and agricultural insurance programs decrease the agricultural losses from extreme weather events.

Considering the significant influence of precipitation variability, governments need to implement effective climate risk management systems, which should include early warning systems, sustainable farming practices, and water resource planning.

Challenges:

• Higher costs for establishing meteorological infrastructure.
• Restricted technical knowledge among farmers.

Measures to counteract:

• Make regional collaborations to exchange weather information systems (cost-sharing approach).
• Offer community-oriented training sessions on understanding weather forecasts and implementing precautionary actions.

5.3. Implementing Long Term Adaptation Strategies

The impact of climate factors on agricultural systems demonstrates over long time horizons, which need long term adaptation strategies. Authors [37] emphasized that sustainable productivity in agriculture requires policy implementations that address climate risks across long term periods. To achieve this, the government should endorse research for drought-resistant crop changes as well as promote agroforestry practices and support eco-friendly land cultivation practices through financial incentives. Author [35] highlighted that precise agricultural techniques and mold-resistant seed technology help reduce climate risk factors.

The study highlights the slow, cumulative effects of climate factors, requiring ongoing adaptation approaches, such as drought-resistant plants and sustainable land management.

Challenges:

• Delayed investment returns hinder political prioritization.
• Farmers’ hesitance to embrace new technologies.

Measures to counteract:

• Provide initial financial incentives (such as subsidies and guaranteed purchase programs) to encourage adoption.
• Funding collaborations between public and private sectors to speed up advancements in agricultural technologies that are resilient to climate change.

5.4. Supporting Smallholder Farmers

Most agricultural workers in SADC nations comprise smallholder farmers whose climate-vulnerable situation makes them highly vulnerable to climate variation. On the contrary, author [35,37] argues that small-scale farmers need institutional support to construct resilience against climate vulnerabilities. To address this, the government should pass laws that make it easier for individuals to obtain affordable credit, offer options for climate insurance, and increase agricultural extension programs that support climate-resilient farming practices. Moreover, climate information along with training in adaptive farming practices, will help increase the resilience of smallholder farmers and improve overall food security and agricultural output.

Smallholder farmers are particularly vulnerable to climate fluctuations and require specific institutional assistance.

Challenges:

• Higher credit risk prevents banks from providing loans to small-scale farmers.
• Low insurance uptake caused by cost concerns.

Measures to counteract:

• Establish government-guaranteed agricultural loans and credit assurance programs.
• Create insurance products based on indices with subsidized premiums for climate-related risks.
• Enhance agricultural extension support with climate-smart farming practices.

5.5. Strengthening Regional Cooperation

Multiple SADC member states must collaborate to solve the regional climate problems across their member countries. Author [36] emphasized that climate risks require regional organizations to work together, which requires coordinating efforts for efficient mitigating and adaptive strategies. Governments should enhance joint efforts between countries regarding climate research partnerships together with resource exchanges and policy adjustments to boost agricultural sustainability. Strengthened collaborations between organizations across borders for water resource management and infrastructure development and regional climate adaptation measures lead to better climate response measures.

Cross-border climate challenges require collaborative efforts among SADC member countries to oversee common resources and farming systems.

Challenges:

• Diverse national interests and policy objectives.
• Fragile frameworks for cross-border governance.

Measures to counteract:

• Formulate official regional agreements regarding transboundary water management.
• Launch climate adaptation knowledge-sharing platforms across SADC.
• Facilitate collaborative investment initiatives in climate-resilient infrastructure at the regional level.

5.6. Investing in Climate-Resilient Infrastructure

The rising frequency of extreme weather patterns becomes a substantial threat to agricultural output levels. How investments made in infrastructure for irrigation and storage facilities and transportation systems help reduce the effects of climate change on agricultural systems [12]. Expanding the irrigation system reduces reliance on unpredictable clouds, while improving transportation and storage systems will help reduce post-harvest losses and improve market accessibility.

Investments in infrastructure such as irrigation systems, storage units, and transportation networks are essential for minimizing agricultural vulnerability.

Challenges:

• Financial restrictions due to conflicting budgetary demands.
• Sustaining operations and maintenance of new infrastructure.

Measures to counteract:

• Utilize public-private partnerships (PPPs) to attract private sector investment.
• Establish specific maintenance funds funded by agricultural taxes or user fees.
• Focus on small-scale irrigation and storage systems managed by the community to improve sustainability.

6. Conclusions

The findings of this study highlight that agricultural productivity in the SADC area is significantly affected by climate variability, the quality of governance, and the ability to adapt both institutionally and technologically. The empirical evidence shows that rainfall has a notable positive impact on agricultural production in the long term, whereas institutional flaws, especially corruption, reduce resilience and productivity throughout the area.

These dynamics highlight the need for thorough policy approaches to improve agricultural sustainability. In particular, the area needs solid strategies aimed at enhancing governance institutions, advancing climate risk management systems, investing in infrastructure resilient to climate change, and offering specific assistance to smallholder farmers, who form the foundation of agricultural production.

Additionally, tackling the identified challenges such as funding limitations, political opposition, and obstacles to adoption will require practical actions, including public-private collaborations, regional cooperative frameworks, and financial tools such as credit guarantees and index-based insurance programs.

In alignment with the findings of [35,36,37], this study confirms that effective agricultural policy frameworks in the SADC region need to concurrently promote climate adaptation, enhance institutional capacity, and ensure food security. Future studies should investigate the diverse effects of climate-smart practices across agro-ecological areas in the region and evaluate the scalability of new adaptation technologies in different institutional frameworks.