Keoagile et al. (2026) Assessing Crop Yield Variability Using Meteorological Drought Indices for Agricultural Drought Monitoring in Botswana

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Identification

• Journal: Climate
• Year: 2026
• Date: 2026-03-25
• Authors: Kgomotso Happy Keoagile, Modise Wiston, Nicholas Mbangiwa
• DOI: 10.3390/cli14040077

Research Groups

Not explicitly stated in the provided text.

Short Summary

This study assesses drought impact on Botswana's agricultural sector by evaluating the predictive power of various drought indices on crop yields and integrating local knowledge, revealing the sector's high vulnerability and the need for integrated early warning systems.

Objective

• To assess the impact of drought on Botswana’s agricultural sector using various drought indices and statistical models, and to evaluate their ability to predict crop yields.

Study Configuration

• Spatial Scale: 25 km (for CHIRPS and CHIRTS data)
• Temporal Scale: Seasonal/annual time scales (1, 3, 6, and 12 months) for drought indices.

Methodology and Data

• Models used: Univariate and multivariate statistical models (linking drought indices and Percentage Area Affected by drought to crop yields).
• Data sources:
  • Climate Hazards Center Infrared Precipitation with Station (CHIRPS) rainfall data.
  • Climate Hazards Center Infrared Temperature with Station (CHIRTS) temperature data.
  • National crop yield data for Botswana.
  • Questionnaire data (to capture local knowledge and perceptions of drought).

Main Results

• Variability in most crops (sunflower, maize, sorghum, and pulses) was best explained by the Standardized Precipitation Evapotranspiration Index (SPEI) at a 6-month scale (SPEI-6), particularly within Percentage Area Affected (PAA) multivariate models.
• Sunflower showed the highest explanatory power (R² = 0.48), followed by maize (R² = 0.43).
• Millet variability was best explained by the Standardized Precipitation Index (SPI) at a 3-month scale (SPI-3), though with a lower R² (0.26).
• Negative coefficients across most crops indicated that drought impacts resulted in low yields.
• Local knowledge confirmed drought as a major perceived indicator of climate change, with perceived effects including yield decline, crop damage, and increased crop pests and diseases.

Contributions

• Provides a comprehensive assessment of drought impacts on Botswana's agricultural sector by integrating multiple drought indices, statistical modeling, and local knowledge.
• Identifies specific drought indices and model configurations (SPEI-6 under PAA multivariate models) as the most effective predictors for crop yield variability in the region.
• Quantifies the vulnerability of key crops to drought in Botswana, offering insights for targeted agricultural and water management strategies.
• Underscores the critical need for integrated drought early warning systems and adaptive agricultural policies to enhance resilience in a climate-vulnerable semi-arid region.

Funding

Not explicitly stated in the provided text.

Citation

@article{Keoagile2026Assessing,
  author = {Keoagile, Kgomotso Happy and Wiston, Modise and Mbangiwa, Nicholas},
  title = {Assessing Crop Yield Variability Using Meteorological Drought Indices for Agricultural Drought Monitoring in Botswana},
  journal = {Climate},
  year = {2026},
  doi = {10.3390/cli14040077},
  url = {https://doi.org/10.3390/cli14040077}
}