https://orcid.org/0009-0008-1272-5566
Figures
Abstract
This study empirically investigates the short- and long-term impacts of climatic and non-climatic factors on wheat yield in Somalia from 1986 to 2019, a region facing severe food insecurity exacerbated by climate variability and prolonged conflict. Utilizing the Autoregressive Distributed Lag (ARDL) modeling approach, robust for small sample sizes and mixed orders of integration, we establish a significant long-run cointegrating relationship. Long-run analysis indicates a positive, albeit modest, impact of CO2 emissions on wheat yield, consistent with the CO2 fertilization effect, yet caution is warranted given the concurrent negative effects of other climatic stressors. Crucially, short-run results reveal that both increasing temperature and rainfall significantly reduce wheat yield, suggesting that erratic precipitation and extreme heat events pose immediate threats to production. Interestingly, political instability showed a counter-intuitive positive short-run coefficient, highlighting the complex and possibly localized dynamics of conflict on agriculture in Somalia that demand further nuanced investigation. This finding departs from conventional literature emphasizing conflict’s detrimental role in food systems. The study’s novel contribution lies in providing the first comprehensive quantitative analysis for Somalia, integrating a broad spectrum of drivers within a dynamic framework. This offers critical empirical evidence for policymakers and international partners, underscoring the urgent need for climate-smart agricultural adaptations, improved water management, and sustained peacebuilding efforts to build a resilient and food-secure future for Somalia.
Citation: Osman MA, Yousuf AM, Hussein MA, Awale MH, Muse AH (2026) Modeling short- and long-term climatic and non-climatic drivers of wheat yield in Somalia (1986–2019): Evidence from an ARDL approach. PLOS Clim 5(7): e0000804. https://doi.org/10.1371/journal.pclm.0000804
Editor: Benjamin Sultan, IRD: Institut de recherche pour le developpement, FRANCE
Received: December 1, 2025; Accepted: June 1, 2026; Published: July 14, 2026
Copyright: © 2026 Osman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Availability of data materials The datasets analyzed during the current study are publicly available in the WDI database, accessible via the World Bank website at https://databank.worldbank.org/source/world-development-indicators.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Abbreviations: ADF, augmented dickey-fuller; AIC, akaike information criterion; ARDL, autoregressive distributed lag; CO2, carbon dioxide; CUSUM, cumulative sum; DV, dependent variable; ECT, error correction term; ECM, error correction model; PP, phillips-perron; VAR, vector autoregression; WDI, World Development Indicators
Introduction
Global food security remains one of the most pressing challenges of the 21st century, fundamentally linked to sustainable development, economic stability, and human well-being [1]. Agriculture is the cornerstone of the global food system and the primary source of livelihood for billions, particularly in developing nations where it is a critical engine for economic growth [2]. Wheat (Triticum aestivum L.) stands out as a crop of paramount importance within this system. As a primary staple food, it provides approximately 20% of the total calories and protein consumed by the global population, making its consistent production vital for nutritional security and socio-economic stability [3]. The global wheat market is vast and interconnected, meaning that production shocks in one region can have cascading effects on food availability and prices worldwide [4].
However, agricultural systems globally, and wheat production specifically, face an unprecedented threat from climate change [5]. Rising global temperatures, alterations in precipitation patterns, and the increasing frequency of extreme weather events like droughts and floods are creating significant production risks [6]. For wheat, climatic variables are critical determinants of yield. Elevated temperatures, especially during the grain-filling stage, can severely reduce crop productivity and quality [7], while water scarcity from inadequate or erratic rainfall is a primary limiting factor in many of the world’s breadbaskets [8]. Conversely, while rising atmospheric carbon dioxide (CO2) concentrations can have a fertilization effect on C3 plants like wheat, this benefit is often negated by the simultaneous negative impacts of heat and water stress [9].
The impact of these climatic stressors is not uniform; it is significantly mediated by a complex web of non-climatic factors, including socio-economic conditions, institutional capacity, and political stability [10]. Economic development, often measured by Gross Domestic Product (GDP), influences a nation’s capacity to invest in agricultural research, infrastructure, and adaptive technologies [11]. Access to critical inputs such as fertilizers is a direct driver of yield, yet its availability can be constrained by economic and institutional failures [12]. In many developing nations, foreign aid plays a crucial role in supporting agricultural development, though its effectiveness remains a subject of debate, particularly in fragile contexts [13]. Perhaps most critically, political instability and conflict can completely dismantle food systems, disrupt markets, displace farming populations, and undermine any potential for climate adaptation, creating a vicious cycle of poverty and food insecurity [14].
Nowhere are these intertwined challenges more acute than in Somalia. Located in the arid and semi-arid lands of the Horn of Africa, Somalia is exceptionally vulnerable to climate variability and change [8]. The country’s agricultural sector, which is the backbone of its economy, is predominantly rain-fed and highly susceptible to recurrent droughts and unpredictable rainfall [1]. This climatic vulnerability has been profoundly exacerbated by decades of state collapse, civil conflict, and political fragmentation, which have decimated infrastructure, eroded institutional capacity, and crippled the country’s ability to respond to shocks [15]. Consequently, Somalia faces one of the world’s most severe and protracted humanitarian crises, with millions experiencing chronic food insecurity [16].
Despite the critical need for evidence-based policymaking to enhance resilience in Somalia, there remains a significant gap in the literature. While many studies have analyzed the impact of climate change on agriculture in other regions [17], few have provided a robust, quantitative analysis for Somalia that integrates both climatic and the overriding non-climatic drivers of crop yield. This study seeks to fill this gap by employing the Autoregressive Distributed Lag (ARDL) modeling approach. The ARDL framework is particularly well-suited for this analysis due to its robustness in small sample sizes—a common constraint with data from fragile states—and its ability to handle variables with mixed orders of integration without pre-testing [18].
Therefore, the primary objective of this study is to empirically model the short- and long-term effects of key climatic variables (temperature, rainfall, CO2 emissions) and critical non-climatic variables (political instability, GDP, foreign aid, fertilizer use, and land area) on wheat yield in Somalia for the period 1986–2019. By providing a holistic and dynamic analysis, this research aims to contribute vital empirical evidence for policymakers and international partners working to build a more resilient and food-secure future for Somalia.
Data and methodology
This section outlines the data sources, model specification, and the econometric procedures employed to analyze the short- and long-term impacts of climatic and non-climatic factors on wheat yield in Somalia.
Data description
This study employs annual time-series data for Somalia covering 1986–2019, comprising 34 observations. The dependent variable is wheat yield (tonnes per hectare). Independent variables include climatic factors—average annual temperature (°C) and rainfall (mm)—and non-climatic factors such as CO₂ emissions (kt), political instability (binary: 1 = instability, 0 = stable), gross domestic product (constant USD), foreign aid received (constant USD), fertilizer consumption per capita (kg), and land area under wheat cultivation (hectares). To reduce heteroskedasticity and improve normality, all continuous variables were transformed into natural logarithms, except the binary political instability variable (Table 1).
The Fig 1 illustrates the long-term trajectory of wheat yield in Somalia from 1986 to 2019 (in logarithmic form). Wheat yield remained relatively low and stable during the late 1980s and 1990s, followed by moderate fluctuations in the early 2000s. A notable upward trend emerges after 2010, indicating substantial improvement in wheat productivity in the later years of the study period. The overall pattern suggests gradual yield enhancement despite periods of volatility, likely reflecting changes in climatic conditions, agricultural practices, and socio-economic factors.
Estimation procedure (econometric modeling and specification)
Econometric model specification: The ARDL approach.
To investigate the dynamic relationship between the variables, this study employs the Autoregressive Distributed Lag (ARDL) bounds testing approach to cointegration, developed by [19]. The ARDL model is chosen for several key reasons:
- It is effective and provides robust results for small sample sizes.
- It can be applied regardless of whether the variables are integrated of order zero, I (0), order one, I [1], or a mix of both.
- It allows for the simultaneous estimation of both short-run and long-run coefficients in a single equation.
The ARDL model is specified as an error correction model (ECM) as follows:
Where:
Δ represents the first difference operator.
lnYt is the natural log of wheat yield at time t.
lnXₖ represents the vector of explanatory variables (climatic and non-climatic).
P and q are the optimal lag lengths for the dependent and independent variables, respectively.
β coefficients represent the short-run dynamics.
δ coefficients represent the long-run relationship.
εt is the white noise error term.
Estimation procedures
The analysis involves a multi-step procedure to ensure the validity and robustness of the model. These include
Unit root tests:.
Before applying the ARDL model, the stationarity properties of all variables were examined using the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests. This step is crucial to confirm that no variable is integrated of order two, I [2], which would violate a core assumption of the ARDL bounds testing approach.
Optimal lag selection:.
The selection of the appropriate lag length is critical for the ARDL model. The optimal lag for the model was determined using the Akaike Information Criterion (AIC) based on an underlying Vector Autoregression (VAR) model, as it is known to perform well in small samples.
Cointegration analysis:.
The ARDL bounds test was performed to determine if a long-run equilibrium relationship exists among the variables. The test involves calculating an F-statistic from the model and comparing it against two sets of critical values: a lower bound assuming all variables are I (0) and an upper bound assuming all variables are I [1].
- If the F-statistic exceeds the upper bound, the null hypothesis of no cointegration is rejected, indicating a long-run relationship.
- If the F-statistic is below the lower bound, the null hypothesis cannot be rejected.
- If the F-statistic falls between the bounds, the result is inconclusive.
Post-estimation diagnostic and stability tests:.
Following the ARDL model estimation, several diagnostic tests were conducted to validate the model’s reliability. These included tests for serial correlation (Breusch-Godfrey LM test), heteroskedasticity (Breusch-Pagan test), and normality of the residuals (Jarque-Bera test). Furthermore, the stability of the long-run and short-run coefficients was examined using the Cumulative Sum (CUSUM) test.
Empirical results and discussion
This section presents the empirical findings of the study, from descriptive statistics and preliminary tests to the final ARDL model estimates.
Descriptive statistics and correlation
Table 2 provides the descriptive statistics for the variables used in the model (in their logarithmic form). The mean wheat yield (ln) is -0.973, while temperature and rainfall show relatively low standard deviations, indicating less variability over the period. The Jarque-Bera test for normality suggests that most variables, including wheat yield, temperature, and rainfall, are normally distributed at a 5% significance level.
The correlation matrix in Table 3 reveals preliminary relationships. Wheat yield has a strong positive correlation with temperature (0.623) and GDP (0.681), and a very strong negative correlation with fertilizer use (-0.879) and land area (-0.885). These strong correlations highlight the potential for multicollinearity, reinforcing the suitability of the ARDL model, which is robust to such issues.
Structural break unit root test
The results of the ADF and PP unit root tests are summarized in Table 4. At their levels, variables such as wheat yield, CO2 emissions, GDP, and fertilizer use are non-stationary. However, both tests confirm that all variables become stationary after the first differencing, I [1]. As the variables are a mixture of I (0) and I [1] and none are I [2], the ARDL model is the appropriate estimation technique.
ARDL model results (lag selection criteria)
The optimal lag length for the ARDL model was determined as ARDL (4,3,4,4,4,4) based on the AIC. The results of the bounds test for cointegration are presented in Table 4. The calculated F-statistic of 27.294 is well above the upper critical bound at the 1% significance level (4.68), leading to the rejection of the null hypothesis of no cointegration. This confirms a stable long-run relationship between wheat yield and the selected climatic and non-climatic variables (Table 5).
Long-run and short-run estimation of parameters
Table 6 presents the estimated long-run and short-run coefficients from the ARDL model.
In the case of Long-Run Interpretation: The results indicate that in the long run, CO2 emissions have a positive and significant impact on wheat yield at the 10% level. A 1% increase in CO2 emissions is associated with a 0.201% increase in wheat yield. Temperature and rainfall show negative coefficients, while GDP has a positive coefficient, though they are not statistically significant in this small sample. For the Short-Run Interpretation: the findings show short run, temperature, rainfall, and political instability have a significant impact on wheat yield. An increase in temperature and rainfall negatively affects yield, while political instability shows a positive coefficient, which may be counter-intuitive and requires further contextual analysis. Error Correction Term (ECT): The coefficient of the error correction term, ECT (-1), is -1.512. While this is statistically significant, its value is outside the expected range of -1–0. This suggests a very rapid, possibly oscillating, adjustment back to the long-run equilibrium following a shock. The model indicates that any deviation from the long-run path is more than fully corrected in the subsequent year.
Model diagnostic and stability checks
The results of the post-estimation diagnostic tests are presented in Table 7. The model successfully passes the tests for heteroskedasticity and normality of residuals. However, the Breusch-Godfrey test indicates the presence of serial correlation, which should be noted as a limitation of the current model specification.
The stability of the model was confirmed using CUSUM. As shown in Fig 2, the plots of both statistics remain within the 5% critical bounds, indicating that the coefficients of the ARDL model are stable and reliable over the study period.
Overall, the climatic variables display distinct long-term patterns. Temperature (Fig 3A) shows a gradual and persistent upward trend, reflecting increasing warming over the study period, while rainfall (Fig 3B) remains highly variable with alternating wet and dry years and no clear directional change. CO₂ emissions (Fig 3C) increase steadily, consistent with expanding economic activity. Among the non-climatic factors, political instability and GDP (Fig 3D) exhibit notable fluctuations and gradual improvement over time, respectively. Foreign aid, fertilizer use, and land area under wheat cultivation (Fig 3E) show irregular patterns, with periodic spikes in aid, low and unstable fertilizer use, and fluctuating cultivated area, suggesting inconsistent production incentives and vulnerability to climatic shocks. Overall, these trends highlight the complex interplay of climatic and socio-economic factors influencing wheat production in Somalia during 1986–2019 (Fig 3).
(A) Temperature, (B) Rainfall, (C) CO₂ emissions, (D) Political instability and GDP, and (E) Foreign aid, fertilizer use, and land area under wheat cultivation.
Conclusion
This study embarked on a critical investigation into the short- and long-term dynamics of climatic and non-climatic factors influencing wheat yield in Somalia from 1986 to 2019, employing the Autoregressive Distributed Lag (ARDL) modeling approach. Our findings confirm the existence of a stable long-run cointegrating relationship between wheat yield and the selected variables, underscoring the complex, multi-faceted nature of agricultural productivity in a fragile, climate-vulnerable context [15].
In the long run, our analysis revealed that CO2 emissions exert a statistically significant positive influence on wheat yield. This finding aligns with the global literature suggesting a potential CO2 fertilization effect on C3 plants like wheat [9]. However, it is crucial to interpret this in the context of other climate variables, as the benefits of elevated CO2 are often negated by the detrimental impacts of heat and water stress [9], which our short-run results highlight. While temperature and rainfall showed negative coefficients in the long run, their lack of statistical significance in this specific ARDL long-run model, unlike some global studies [8], may be attributable to the inherent variability and localized impacts within Somalia, or perhaps the dominance of other factors in shaping long-term equilibrium. Similarly, GDP showed a positive but not significant long-run coefficient, contrasting with broader assertions about economic development’s role in agricultural investment [11], which could again point to the unique challenges of a conflict-affected economy [14].
The short-run dynamics, however, painted a more immediate and starker picture of vulnerability. We found that increases in both temperature and rainfall significantly negatively affect wheat yield in the short term. The negative impact of rising temperatures on crop productivity is well-documented globally, particularly during critical growth stages [7]. The negative effect of rainfall, seemingly counter-intuitive for a rain-fed economy, could reflect the impact of erratic rainfall patterns, flash floods, or excessive precipitation during critical periods, which are increasingly associated with extreme weather events under climate change [6]. This contrasts with the general assumption that more rain is always beneficial, suggesting the need for nuanced understanding of rainfall variability and intensity rather than just total volume.
A particularly noteworthy, albeit counter-intuitive, short-run finding was the positive coefficient of political instability on wheat yield. This dissimilarity with established literature, which widely posits political instability and conflict as devastating to food systems [15], requires careful contextualization. It could potentially reflect short-term surges in local production during specific periods of relative calm or localized interventions, or perhaps a complex reporting dynamic during periods of instability where data might be less reliable or focus on areas with minimal conflict. Alternatively, it could signify a misidentification or proxy issue within the binary variable for political instability, suggesting a need for more granular or nuanced measures in future research. The robust and significant error correction term, while outside the conventional range, points to a rapid, if potentially oscillating, adjustment mechanism back to the long-run equilibrium following short-term shocks, highlighting the inherent resilience or rapid re-adaptation capabilities, however challenging, within the Somali agricultural system.
This study makes several novel contributions to the existing literature, particularly by addressing the significant gap in quantitative analysis for Somalia [17]. By integrating both climatic and a comprehensive set of non-climatic factors (political instability, GDP, foreign aid, fertilizer use, and land area) within a single ARDL framework, we provide a more holistic understanding of wheat yield determinants in a particularly fragile and data-scarce region. The ARDL model’s suitability for small sample sizes [18] enabled us to generate robust empirical evidence for Somalia, a nation severely impacted by climate variability and decades of conflict [1]. This dissimilarity from many broader regional or global studies, which often overlook the profound influence of non-climatic, socio-political fragility on agricultural outcomes, underscores the unique challenges and priorities for food security in contexts like Somalia. Our findings, therefore, offer vital empirical evidence for policymakers and international partners striving to enhance resilience and food security in Somalia.
Recommendations and policy implications
The empirical findings from this study provide critical insights for developing targeted and effective policies to bolster wheat yield and enhance food security in Somalia, a nation acutely vulnerable to both climatic and non-climatic shocks [1].
- 1. Prioritize Climate Change Adaptation for Rainfall Variability and Temperature Management:
Implication: The significant negative short-run impact of both temperature and rainfall on wheat yield necessitates urgent climate adaptation strategies. The negative rainfall effect, deviating from expected positive benefits in rain-fed agriculture, strongly suggests that erratic and extreme precipitation events (floods, droughts) are more damaging than total rainfall volume. This corroborates global trends of extreme weather impacts on crop production [6].
Recommendation: Policymakers should invest in climate-smart agriculture. This includes promoting drought-resistant wheat varieties, developing efficient water harvesting and irrigation systems (e.g., small-scale dams, cisterns, drip irrigation) to manage both scarcity and excess rainfall, and implementing early warning systems for extreme weather events. Research into heat-tolerant wheat strains suitable for Somalia’s specific conditions is also vital, given the upward trend in temperature [7].
- 2. Strategic Management of CO2 Benefits and Associated Risks:
Implication: While CO2 emissions showed a positive long-run impact on wheat yield, this ‘fertilization effect’ is a complex phenomenon often overshadowed by negative climatic impacts [9].
Recommendation: Policymakers should not view rising CO2 as a net positive without addressing other climatic stressors. Instead, focus should be on creating optimal growing conditions to potentially harness CO2 benefits while rigorously mitigating temperature and water stress through improved agronomic practices and stress-tolerant crops.
- 3. Address the Nuances of Political Instability and its Agricultural Impact:
Implication: The counter-intuitive positive short-run coefficient for political instability highlights the need for a deeper, more granular understanding of conflict’s impact. While globally conflict is catastrophic for food systems [14], localized or short-term dynamics might exist.
Recommendation: Future research and policy should aim for more nuanced indicators of political stability/instability. Policy interventions should focus on peacebuilding initiatives and localized governance strengthening that directly protect agricultural assets, supply chains, and farmer livelihoods, even during broader periods of instability. This may involve micro-level support for farmers in relatively stable pockets or establishing “safe corridors” for agricultural trade.
- 4. Strengthen Economic Foundations and Agricultural Input Access:
Implication: Although GDP and foreign aid did not show statistically significant long-run impacts in this specific model, their established importance in agricultural development globally [13] cannot be ignored, especially in a developing nation like Somalia where agriculture is the economic backbone [2]. The low and unstable fertilizer use (Fig 3) points to a critical input deficiency.
Recommendation: Long-term economic development strategies must prioritize agricultural sector growth. This includes facilitating access to affordable agricultural inputs like fertilizers, improved seeds, and modern farming equipment. Efforts to attract and effectively utilize foreign aid for agricultural infrastructure, research, and farmer training programs are essential, ensuring aid is channeled transparently and efficiently to build sustainable capacity.
- 5. Foster Institutional Capacity for Data-Driven Policy:
Implication: The use of ARDL, while robust for small samples [18], underscores the existing data limitations in fragile states. Reliable, consistent data are fundamental for robust analysis and evidence-based policymaking.
Recommendation: Invest in strengthening national statistical capacities within Somalia, particularly in agricultural and climatic data collection. This includes improving monitoring stations, establishing robust data reporting mechanisms from local to national levels, and promoting research collaborations to enhance the availability and quality of data for future studies and policy formulation.
By adopting these multi-faceted policy approaches, Somalia can better navigate the intertwined challenges of climate change and socio-political fragility, moving towards a more resilient and food-secure future for its population, in line with global food security objectives [1].
Strengths and limitations of the study
This study presents several methodological and empirical strengths that contribute significantly to the literature on agricultural economics and climate resilience in fragile states. First, the primary strength lies in the application of the Autoregressive Distributed Lag (ARDL) bounds testing approach. Given the data constraints inherent to Somalia—a context marked by limited historical records due to prolonged conflict—the ARDL framework proved robust for the relatively small sample size (34 observations) and effectively handled variables with mixed orders of integration (I (0) and I [1]). Second, unlike many prior studies that focus exclusively on climatic variables, this research adopts a holistic approach by integrating critical non-climatic drivers, such as political instability, foreign aid, and economic indicators. This comprehensive specification allows for a more realistic modeling of the complex determinants of wheat yield in a conflict-affected economy. Third, the study fills a significant empirical gap by providing one of the first quantitative assessments of the CO₂ fertilization effect versus climatic stressors specific to the Somali context, offering evidence-based insights for policymakers where qualitative assessments previously dominated.
Despite these contributions, the study is subject to certain limitations that warrant caution in the interpretation of results. First, the use of annual time-series data from 1986 to 2019 relies on secondary sources (World Development Indicators). While the best available, data reliability during the peak years of the Somali civil war (early 1990s) is a challenge common to all research in this region. Second, the operationalization of political instability as a binary variable (0 = stable, 1 = instability) is a relatively coarse measure. It fails to capture the intensity, geographic spread, or specific nature of the conflict, which may explain the counter-intuitive positive short-run coefficient observed. Third, while the model passed tests for normality and heteroskedasticity, the Breusch–Godfrey test indicated the presence of serial correlation, suggesting that some dynamic structures or omitted variables (such as pest outbreaks or regional rainfall distribution) might not be fully captured. Finally, the use of aggregate national data masks regional heterogeneity; as Somalia has distinct agro-ecological zones, the impact of rainfall and temperature likely varies significantly between the riverine agricultural belts and the arid pastoral regions. Future research could address these limitations by employing panel data across regions or utilizing high-resolution geospatial climate data.
Acknowledgments
The authors extend their sincere gratitude to Amoud University for providing the necessary resources and an enabling environment to conduct this research. We also thank the World Bank for making the World Development Indicators (WDI) data publicly accessible, which was crucial for this study.
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