Scholarlink

<p>Somalia’s reliance on rain-fed agriculture makes its food security vulnerable to climate change. Despite this exposure, limited quantitative evidence exists modeling the combined impacts of climatic drivers and agricultural inputs on cereal production. This study assesses the short- and long-term determinants of Somalia’s cereal output, examining temperature, rainfall, CO₂ emissions, and fertilizer consumption. We employed an Autoregressive Distributed Lag (ARDL) framework, suited for small samples and mixed-order integrated variables, using annual time-series data from 1980 to 2019 from Our World in Data. This dataset, comprising 40 observations, is subject to inherent data constraints typical of time-series analyses in data-scarce regions, potentially limiting generalizability. The methodology was validated by cointegration bounds testing, confirming a stable long-run equilibrium, and the model’s robustness was verified through diagnostic and stability tests, including CUSUM and CUSUMSQ. Results reveal a significant negative long-run impact from temperature: a 1&#xa0;°C rise in mean temperature is associated with a 29.76% decrease in cereal production. Conversely, fertilizer use is a positive driver, with a 1% increase linked to a 2.94% long-run gain. The model’s error-correction coefficient (− 0.988) indicates rapid adjustment, reflecting Somalia’s subsistence agriculture where 98.8% of short-run disequilibrium is corrected within a year. These findings identify rising temperatures as the principal threat to food security while highlighting fertilizer access as critical for building resilience. The study provides policy directives, emphasizing the need to promote heat-tolerant crops, invest in water management infrastructure, and improve fertilizer supply chains. By delivering Somalia-specific elasticities, this research contributes actionable evidence for climate-resilient agricultural planning in a data-scarce region.</p> Graphical Abstract <p>This visual summary depicts the quantitative framework and principal findings of our study on Somali cereal yields. The logical flow moves from left to right, illustrating the full research process.&#xa0;The workflow begins&#xa0;with national-level data for Somalia (1980–2019), representing key variables including temperature, rainfall, and fertilizer use.&#xa0;This national dataset for Somalia (1980–2019), with variables like temperature, rainfall, and fertilizer use, undergoes econometric analysis. Using the Autoregressive Distributed Lag (ARDL) model—crucial given data scarcity—the analysis captures short- and long-term effects influencing cereal production. The results section of the graphic&#xa0;visualizes the critical, divergent impacts revealed by the model. A rise in mean temperature is shown to cause a severe 29.8% long-run decline in cereal yield, highlighting a major climate threat. In contrast, a 1% increase in fertilizer use provides a crucial productivity boost, enhancing yields by 2.9%. The description also notes rainfall’s positive short-term effect.&#xa0;The graphic concludes with a clear policy takeaway, visually supported by icons, that synthesizes these findings: to build resilience, Somalia must prioritize investment in heat-tolerant crops, improve water management, and secure farmer access to essential inputs. By translating complex econometric results into an accessible visual narrative, this abstract provides policymakers with an urgent, evidence-based tool for enhancing food security in a climate-vulnerable nation.</p> <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Harvests Under Heat: Quantifying the Long-Run Impacts of Climate and Fertilizers on Somalia Cereal Yields: an ARDL analysis

  • 期刊论文

  • 发表日期：

    2025年12月12日

查看全文
  • Mohamed Abdi Abdullahi,
  • Abdisalam Hassan Muse,
  • Yahye Hassan Muse,
  • Mukhtar Abdi Hassan,
  • Saralees Nadarajah

摘要

Somalia’s reliance on rain-fed agriculture makes its food security vulnerable to climate change. Despite this exposure, limited quantitative evidence exists modeling the combined impacts of climatic drivers and agricultural inputs on cereal production. This study assesses the short- and long-term determinants of Somalia’s cereal output, examining temperature, rainfall, CO₂ emissions, and fertilizer consumption. We employed an Autoregressive Distributed Lag (ARDL) framework, suited for small samples and mixed-order integrated variables, using annual time-series data from 1980 to 2019 from Our World in Data. This dataset, comprising 40 observations, is subject to inherent data constraints typical of time-series analyses in data-scarce regions, potentially limiting generalizability. The methodology was validated by cointegration bounds testing, confirming a stable long-run equilibrium, and the model’s robustness was verified through diagnostic and stability tests, including CUSUM and CUSUMSQ. Results reveal a significant negative long-run impact from temperature: a 1 °C rise in mean temperature is associated with a 29.76% decrease in cereal production. Conversely, fertilizer use is a positive driver, with a 1% increase linked to a 2.94% long-run gain. The model’s error-correction coefficient (− 0.988) indicates rapid adjustment, reflecting Somalia’s subsistence agriculture where 98.8% of short-run disequilibrium is corrected within a year. These findings identify rising temperatures as the principal threat to food security while highlighting fertilizer access as critical for building resilience. The study provides policy directives, emphasizing the need to promote heat-tolerant crops, invest in water management infrastructure, and improve fertilizer supply chains. By delivering Somalia-specific elasticities, this research contributes actionable evidence for climate-resilient agricultural planning in a data-scarce region.

Graphical Abstract

This visual summary depicts the quantitative framework and principal findings of our study on Somali cereal yields. The logical flow moves from left to right, illustrating the full research process. The workflow begins with national-level data for Somalia (1980–2019), representing key variables including temperature, rainfall, and fertilizer use. This national dataset for Somalia (1980–2019), with variables like temperature, rainfall, and fertilizer use, undergoes econometric analysis. Using the Autoregressive Distributed Lag (ARDL) model—crucial given data scarcity—the analysis captures short- and long-term effects influencing cereal production. The results section of the graphic visualizes the critical, divergent impacts revealed by the model. A rise in mean temperature is shown to cause a severe 29.8% long-run decline in cereal yield, highlighting a major climate threat. In contrast, a 1% increase in fertilizer use provides a crucial productivity boost, enhancing yields by 2.9%. The description also notes rainfall’s positive short-term effect. The graphic concludes with a clear policy takeaway, visually supported by icons, that synthesizes these findings: to build resilience, Somalia must prioritize investment in heat-tolerant crops, improve water management, and secure farmer access to essential inputs. By translating complex econometric results into an accessible visual narrative, this abstract provides policymakers with an urgent, evidence-based tool for enhancing food security in a climate-vulnerable nation.