Climate Variability and GDP Structural Risk in Rainfed Agriculture and Livestock System: Evidence from Sudan
Abstract
This study investigates how climate variability shapes GDP structural risk in Sudan's agricultural systems, with a particular focus on rainfed agriculture and livestock-based production systems. Unlike irrigated agriculture, both systems operate under climate-dependent conditions, making them highly sensitive to rainfall variability, temperature extremes, and seasonal uncertainty. The analysis integrates agro-climatic conditions, probability distribution functions, and spatial econometric techniques to examine how environmental variability translates into sectoral economic outcomes.
The empirical results reveal a clear structural divergence between rainfed crop and livestock systems. In rainfed agricultural zones such as Gedaref, characterized by rainfall variability ranging from 300 to 800 mm and growing seasons of 57 to 117 days, crop production exhibits relatively stable but low GDP contributions, constrained by seasonal dependence and limited productivity. In contrast, livestock systems in Darfur and Kordofan operate under lower and more variable rainfall conditions (150–530 mm) and grazing-based production, leading to higher average GDP contributions but with substantial volatility. Probability distribution analysis confirms this distinction: crop GDP is characterized by narrow and approximately symmetric distributions, whereas livestock GDP displays wide, positively skewed, and heavy-tailed distributions, reflecting significant exposure to extreme outcomes and systemic risk. Spatial econometric results further indicate significant clustering of vulnerability and resilience across regions, suggesting that climate-induced risks are geographically concentrated and subject to spillover effects.
The key contribution of this study is the development of a unified analytical framework that combines climate variability, distribution-based risk analysis, and spatial econometrics to quantify structural GDP risk in agricultural systems. The findings demonstrate that rainfed and livestock systems embody a fundamental trade-off between stability and volatility, providing new empirical insights into how climate-dependent production systems transmit environmental shocks into macroeconomic instability. These results offer a strong basis for designing targeted, climate-informed policies to enhance resilience in rainfed agricultural economies.
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Introduction
This study explicitly focuses on rainfed agricultural systems, which dominate Sudan's agricultural landscape and are more vulnerable to climate variability than irrigated systems. Climate change poses a major threat to agricultural production, rural livelihoods, and macroeconomic stability in climate-sensitive economies. In Sudan, this challenge is especially important because both crop production and livestock systems are strongly exposed to temperature stress, rainfall variability, and drought conditions (Kheiry Ishag, June 2026). Agriculture remains central to livelihoods and economic performance, while livestock constitutes a major productive asset and income source in many regions of the country (Osama M. A., 2021; Nibras Hussein M., 2026; M. Melissa Rojas et al., 2017; Kheiry Ishag, April 2026).
Evidence from Gedaref State—the largest rainfed agricultural region in Sudan—shows that annual maximum temperature increased by 0.03°C per year, annual minimum temperature increased by 0.05°C per year, and annual rainfall showed no significant overall trend, although rainfall variability and rainy-season length significantly affected crop productivity. The study further reports that temperature variables were negatively associated with the yields of sorghum, sesame, cotton, millet, and sunflower, while rainfall and rainy-season length positively influenced several crops, especially sorghum, sesame, and sunflower (Osama M.A., 2021).
The Sudan livestock strategy reports a national herd of 111.8 million heads, a contribution of about 25% to GDP, and livelihood dependence of approximately 40%. It also reports that livestock feed is based primarily on natural grazing (55%), followed by crop residues (25%), both of which are directly sensitive to rainfall and drought. The strategy explicitly identifies climate change, droughts, and desertification reducing grazing land availability as major sector threats (Nibras Hussein M., 2026).
Most previous studies focus on crop impacts or livestock structure separately and often rely on linear econometric approaches. However, climate shocks are inherently asymmetric: a drought shock may be more damaging than an equal-sized rainfall gain is beneficial, and warming may impose stronger losses than cooling provides benefits. This justifies the use of nonlinear methods such as NARDL. It also motivates a spatial perspective, because neighboring provinces/states may share climate exposure, market access, and ecological interactions. Spatial econometric research explicitly argues that agriculture and climate outcomes often exhibit spatial dependence and spillover effects, and that ignoring them can bias empirical inference (Amouzay H. and El-Ghini Ahmed, 2025; Yun, Seong D and Benjamin M Gramig, 2022; Zouabi, Oussama and Nicolas Peridy, 2015; Desmet and Rossi-Hansberg, 2024).
This study therefore develops an integrated framework with four layers: it develops a unified analytical framework integrating climate, rainfed crops, livestock, and GDP systems; it captures asymmetric climate effects using a nonlinear SAR and SDM model approach; it identifies structural transmission differences in risk between rainfed and livestock systems; and it incorporates spatial dependence to explain regional clustering and risk spillovers.
The paper's central logic is that national GDP effects are transmitted through both a crop-shock channel in Gedaref and a livestock-grazing channel in Darfur/Kordofan (Osama M. A., 2021; Nibras, 2026). Recent evidence suggests that agricultural resilience is shaped not only by climate variability but also by livestock system structure and feed demand pressures. In Sudan, regions with high livestock concentration face greater vulnerability due to dependence on natural grazing systems. This study integrates these factors into a composite resilience index and examines spatial interdependence across regions.
Conclusion
This paper presents an integrated framework for analyzing climate impacts on Sudan's crop systems, livestock systems, and macroeconomic vulnerability. Gedaref provides explicit evidence of crop–climate sensitivity, while Darfur–Kordofan represents the country's major grazing and livestock vulnerability corridor. The proposed PARI and SAR framework extends the study toward a provincial resilience perspective, although full statistical estimation will require additional province/state-by-year data and a spatial weights matrix.
Rainfed agriculture provides relatively stable but low GDP contributions, while livestock systems generate higher output but exhibit significant volatility due to dependence on grazing and rainfall. This structural divergence explains the observed differences in GDP risk profiles.
The findings confirm that climate effects are asymmetric, with drought shocks dominating economic outcomes, and spatially interconnected, with risk clustering across regions. The evidence from probability distribution analysis demonstrates that while crop systems provide stability with moderate risk, livestock systems introduce significant volatility and tail risk, reinforcing their role as a major source of climate-driven economic instability.
The spatial analysis (SAR and SDM) indicates that agricultural resilience and vulnerability are spatially clustered, with low-resilience regions concentrated in drought-prone livestock systems and high-resilience regions in irrigated central areas. The SDM results further suggest that climate shocks and feed pressure transmit across regions through shared grazing systems, animal mobility, and economic linkages, confirming that agricultural resilience in Sudan is spatially interconnected rather than isolated at the regional level.
The PARI and FPI analyses reveal strong regional disparities in resilience. Darfur exhibits the lowest resilience (PARI = 0.10), reflecting high exposure to climate variability and strong dependence on grazing systems. Kordofan shows medium-low resilience (0.37), while Gedaref demonstrates medium-high resilience (0.67). The Central Region shows the highest resilience (0.93), reflecting irrigation infrastructure and higher adaptive capacity.
The results demonstrate that climate vulnerability in Sudan is not only environmental but also structural and systemic, embedded in production systems and amplified through spatial spillovers. The overall message is that Sudan's climate vulnerability is nonlinear, spatially differentiated, and system-wide. Modernization therefore needs to be integrated, regionally targeted, and climate-smart, linking crop science, grazing systems, feed infrastructure, veterinary systems, market access, and spatial resilience planning.
References
[1] Abdelgawwad, N. A., & Kamal, A. L. M. (2023). Contributions of investment and employment to the agricultural GDP growth in Egypt: An ARDL approach. Economies, *11*(8), 215. https://doi.org/10.3390/economies11080215
[2] Banerjee, A., Dolado, J. J., Galbraith, J. W., & Hendry, D. F. (1993). Cointegration, error correction, and the econometric analysis of non-stationary data. Oxford University Press.
[3] Ahmed, N., Xinagyu, G., Alnafissa, M., et al. (2025). Linear and non-linear impact of key agricultural components on greenhouse gas emissions. Scientific Reports, *15*, 5314. https://doi.org/10.1038/s41598-025-88159-1
[4] Al-Musawi, Z. K., Vona, V., & Kulmány, I. M. (2025). Utilizing different crop rotation systems for agricultural and environmental sustainability: A review. Agronomy, *15*(8), 1966. https://doi.org/10.3390/agronomy15081966
[5] Alexis Kangatlam, & Moudjare Helgat Bybert. (2025). Climate change and food security in sub-Saharan Africa. International Journal of Economics and Management Sciences, *4*(1), 64-99.
[6] Amede, T., Konde, A. A., Muhinda, J. J., & Bigirwa, G. (2023). Sustainable farming in practice: Building resilient and profitable smallholder agricultural systems in sub-Saharan Africa. Sustainability, *15*(7), 5731. https://doi.org/10.3390/su15075731
[7] Amouzay, H., & El-Ghini, A. (2025). A systematic review of key spatial econometric models for assessing climate change impacts on agriculture. The Review of Regional Studies, *55*(1). https://doi.org/10.52324/001c.136990
[8] Chen, X., Wang, H., Zhu, X., & Zhang, X. (2025). Spatial-temporal characteristics of agricultural economic resilience and spatial spillover effects of driving factors: Evidence from provincial panel data in China. Frontiers in Environmental Science, *13*, 1437018. https://doi.org/10.3389/fenvs.2025.1437018
[9] de Sousa, K., Sparks, A., Ashmall, W., van Etten, J., & Solberg, S. (2020). chirps: API Client for the CHIRPS Precipitation Data in R. Journal of Open Source Software. https://doi.org/10.21105/joss.02419
[10] Desmet, K., & Rossi-Hansberg, E. (2024). Climate change economics over time and space. Annual Review of Economics, *16*.
https://doi.org/10.1146/annurev-economics-031620-031234
[11] Ebtihag Hashim Mohammed Ealgzoli. (2023). Measuring the impact of livestock export on GDP during the period 2010-2020 in Sudan. International Journal on Humanities and Social Sciences, *52*. https://doi.org/10.33193/IJoHSS.52.2023.652
[12] FAO. (2016). Resilience Index Measurement and Analysis II (RIMA II). https://www.fao.org/publications
[13] IFPRI. (2024). Sustainable livestock development in Sudan: Challenges, opportunities, and policy priorities (Sudan Strategy Support Program Working Paper 19). H. Alfadul, K. Siddig, M. Ahmed, H. Abushama, & O. K. Kirui.
[14] Kheiry Ishag. (2026a). Sudan Gezira Scheme Institutions Performance Index (IPI): Integration of physical and functional modernizations framework. Sustainable Agriculture Research, *15*(1), 1. https://doi.org/10.5539/sar.v15n1p1
[15] Kheiry Ishag. (2026b). Gezira Scheme production system vulnerability assessment resilience interventions policy and econometric framework. Sustainable Agriculture Research, *15*(1), 44. https://doi.org/10.5539/sar.v15n1p44
[16] Kheiry Ishag. (2026c). Agricultural output and value retention economic integration in Sudan: Real activity ARDL evidence and structural transformation framework (1979–2024). IOSR Journal of Economics and Finance, *17*(3), 66-81.
https://doi.org/10.9790/5933-1703036681
[17] Rojas-Downing, M. M., Nejadhashemi, A. P., Harrigan, T., & Woznicki, S. A. (2017). Climate change and livestock: Impacts, adaptation, and mitigation. Climate Risk Management, *16*, 145-163. https://doi.org/10.1016/j.crm.2017.02.001
[18] Nibras Hussein Mohammed. (2026). Sudan livestock development strategy 2026-2031 [Report].
[19] Omokpariola, D. O., Agbanu-Kumordzi, C., Samuel, T., et al. (2025). Climate change, crop yield, and food security in Sub-Saharan Africa. Discover Sustainability, *6*, 678. https://doi.org/10.1007/s43621-025-01580-4
[20] Osman, M. A. A., Onono, J. O., Olaka, L. A., Elhag, M. M., & Abdel-Rahman, E. M. (2021). Climate variability and change affect crops yield under rainfed conditions: A case study in Gedaref State, Sudan. Agronomy, *11*, 1680.
https://doi.org/10.3390/agronomy11091680
[21] Semosa, P. D. (2025). The impact of climate change on the agricultural sector in SADC countries. Sustainability, *17*, 5177.
https://doi.org/10.3390/su17115177
[22] Siddig, K., Stepanyan, D., Wiebelt, M., Grethe, H., & Zhu, T. (2020). Climate change and agriculture in the Sudan: Impact pathways beyond changes in mean rainfall and temperature. Ecological Economics, *169*, 106566.
https://doi.org/10.1016/j.ecolecon.2019.106566
[23] Ye, Y., Zou, P., Zhang, W., Liu, X., Liu, B., & Kang, X. (2022). Spatial–temporal evolution characteristics of agricultural economic resilience: Evidence from Jiangxi Province, China. Agronomy, *12*(12), 3144. https://doi.org/10.3390/agronomy12123144
[24] Yun, S. D., & Gramig, B. M. (2022). Spatial panel models of crop yield response to weather: Econometric specification strategies and prediction performance. Journal of Agricultural and Applied Economics, *54*(1), 53-71. https://doi.org/10.1017/aae.2021.24
[25] Zouabi, O., & Peridy, N. (2015). Direct and indirect effects of climate on agriculture: An application of a spatial panel data analysis to Tunisia. Climatic Change, *133*(2), 301-320. https://doi.org/10.1007/s10584-015-1458-3.