How Anticipatory Action Transformed Drought Response in Southern Africa
From Early Warning to Early Action: How Anticipatory Action Transformed Drought Response in Southern Africa
Introduction: A Region on the Edge of Crisis
During the 2023–2024 El Niño season, Southern Africa faced a severe drought risk. Even before the shock, 2.3 million people were already experiencing food insecurity across the region. The El Niño event exacerbated this crisis by delaying the onset of the main rainfall season (November to April) and causing below-average rainfall across multiple countries. This shortfall in precipitation coincided with the lean season, disrupting the planting period and ultimately leading to reduced crop yields.
The core challenge:
To mitigate the impacts of predicted below-average rainfall, the challenge was not only to respond — but to respond early enough. Information often does not reach — or is not understood by — those who need it most, particularly vulnerable and remote communities.
To address this, WFP activated its Anticipatory Action (AA) framework, releasing pre-arranged funding and implementing early interventions across four countries: Lesotho, Madagascar, Mozambique, and Zimbabwe. In July 2023, these countries reached their triggers for moderate and severe drought ahead of the rainy season, unlocking around US$14 million in pre-arranged AA finance.
What is Anticipatory Action?
Anticipatory Action aims to prevent or reduce humanitarian impacts by taking proactive measures before anticipated hazards threaten lives and livelihoods. AA is implemented during a window of opportunity between the issuance of a forecast and the expected onset of the climate shock. The approach relies on agreed plans that outline necessary activities, reliable early warning information, and pre-arranged financing, which is released predictably and rapidly when an agreed trigger point is reached.
The Intervention: Reaching the Last Mile
As part of the AA activations, WFP supported a range of actions across the four countries, including the development and dissemination of Last-Mile Early Warning Messages (LMEWM) and climate information services to over 1.2 million people. LMEWM involves the timely dissemination of weather alerts and advisories to communities, particularly in vulnerable areas, to enable them to act ahead of the disaster.
These messages translate complex climate forecasts into simple, actionable advice, delivered through locally relevant channels including SMS and WhatsApp groups, community radio broadcasts, public meetings and local leaders, and agricultural extension officers. Messages were also translated into local languages, ensuring accessibility and cultural relevance.
Complementary Anticipatory Actions
LMEWM were not standalone. They were integrated with other anticipatory actions, creating a holistic system where information and resources worked together:
The M&E System: Measuring What Matters
One of the strengths of this case is the presence of a structured Monitoring & Evaluation system. The evaluation approach included endline assessments, surveys across communities, and Key Informant Interviews (KIIs) with stakeholders including AA focal points from WFP Country Offices, National Meteorological and Hydrological Services (NMHS), Ministries of Agriculture, National Disaster Risk Management agencies, and partner organizations.
Key Results
The control group (non-beneficiaries) also showed strong results: 60.3% reached, 83.5% on time, 70.3% understood, and 63.5% applied the information. This demonstrates the broad reach and effectiveness of LMEWM even beyond targeted AA beneficiaries.
From Information to Action: Real Impact on the Ground
The true value of M&E lies not in data collection but in behavioral change and outcomes. This case provides strong evidence of both. Farmers who received and followed LMEWM:
- Adopted water harvesting techniques
- Planted drought-tolerant crops as recommended
- Used shade nets to protect crops
- Engaged in more frequent discussions on weather and preparedness at the community level
A critical shift
While farmers experienced that wide maize fields dried out, they could still harvest from drought-tolerant crops planted as recommended in the LMEWM. This demonstrates the shift from reactive crisis response to proactive resilience building.
As one key informant in Zimbabwe noted: "If we continue to invest in early warning messaging, we will actually save the assets and livelihoods of people."
Building Blocks for Success: Best Practices and Lessons Learned
Capacity strengthening of National Meteorological and Hydrological Services (NMHS)
WFP supported training in trigger development and monitoring, downscaling of forecasts, and establishment of climate databases. Mozambique transitioned from the absence of a drought Early Warning System to establishing a comprehensive climate database. Lesotho received a high-performance computing system for improved forecast accuracy.
Training Agricultural Extension Officers (AEOs) through PICSA
Participatory Integrated Climate Services for Agriculture (PICSA) trained AEOs to interpret seasonal climate forecasts, assess climate risks, and integrate climate services into agricultural practices. AEOs trained well in advance were more efficient in outreach compared to those trained just before activations.
Technical Working Groups (TWGs) for collaboration
TWGs comprising NMHS, relevant ministries, and implementing partners were established to develop triggers and thresholds, define roles and responsibilities, and coordinate implementation. In Mozambique, stakeholders noted: "This time there was less duplication of efforts because we identified the responsibilities of each actor, and it all worked smoothly."
Co-development and translation of LMEWM
Messages were translated into local languages: Sesotho in Lesotho, Mahafaly and Bara in Madagascar, Shangana in Mozambique, and Shona in Zimbabwe. This enhanced accessibility, comprehension, and use of LMEWM across linguistic communities.
Diversified dissemination channels
Channels included SMS and WhatsApp, community radio, public gatherings, agricultural extension officers, social media (Facebook, YouTube), and innovative methods like puppet shows in Madagascar for regions with high illiteracy rates.
Building trust through accuracy and integration of local knowledge
Trust is built through consistent and accurate LMEWM delivery over time. Communities prefer a combination of Indigenous Knowledge Systems (IKS) and scientific forecasts for reliable climate information. Zimbabwe and Lesotho are exploring ways to integrate local knowledge into forecasts to improve prediction and foster stronger community trust.
The Remaining Gap: Where Traditional M&E Falls Short
Despite its success, the case also highlights important limitations that traditional M&E systems face:
Most insights come from endline evaluations — after the intervention, limiting real-time adaptive management.
Messages are broad, not tailored to individual households or specific risk levels.
Limited ability to track who actually reads messages, who acts on them, and what works best in real time.
Even with data, decisions are not always fast enough, targeted enough, or adaptive enough.
The Opportunity: AI in Monitoring and Evaluation
Artificial Intelligence does not replace M&E — it enhances it. The case study reveals several opportunities where AI could further transform anticipatory action:
Key Lessons for M&E Professionals
Information is not enough
Impact comes from understanding plus action. The case demonstrates that even with broad reach, effectiveness depends on comprehension, trust, and the ability to act on advisories.
Timing matters more than precision
Early action often matters more than perfect data. Pre-arranged financing and trigger mechanisms enable rapid response when forecasts indicate risk.
Behavior change is the ultimate indicator
Not outputs (messages sent) but decisions and actions (farmers changing practices) determine success. M&E must measure adoption and behavioral outcomes.
Trust is built over time
Consistent and accurate delivery of LMEWM builds community trust. Combining scientific forecasts with Indigenous Knowledge Systems enhances credibility and uptake.
Recommendations Moving Forward
The case study concludes with several key recommendations for future anticipatory action and M&E systems:
- Continue to invest in NMHS capacity strengthening, ensuring training is conducted well in advance of activations
- Establish long-term partnerships early to foster effective collaboration and avoid last-minute logistical challenges
- Establish feedback mechanisms to allow for continuous adjustments and refinements, ensuring LMEWM remain responsive to evolving community needs
- Promote regular delivery of climate services beyond activation periods to maintain a heightened state of readiness and build trust
- Diversify communication channels while exploring new methods to amplify language and improve accessibility
- Establish clear structures and defined roles within co-development processes, ensuring efforts are properly decentralized and tailored to local needs
- Foster inclusive community engagement and implement gender- and disability-transformative strategies
Frequently Asked Questions
What is the difference between Early Warning Messages and Last-Mile Early Warning Messages?
Early Warning Messages are alerts designed to inform communities and decision-makers about potential hazards. Last-Mile Early Warning Messages specifically target the delivery of these alerts to vulnerable communities and individuals, using localized channels such as public meetings, community radio, mobile phones, or local leaders to reach those most at risk.
How were messages tailored to local contexts?
Messages were translated into local languages (Sesotho, Mahafaly, Bara, Shangana, Shona), co-developed with local stakeholders including Agricultural Extension Officers and community representatives, and tailored at district and village levels to ensure contextual relevance and understandability.
What role did Indigenous Knowledge Systems play?
Studies found that users generally do not trust IKS or scientific forecasts in isolation — they prefer a combination of both for reliable climate information. Zimbabwe and Lesotho are exploring ways to integrate local knowledge into forecasts to improve prediction and foster stronger community trust and engagement.
How can AI enhance anticipatory action M&E systems?
AI can enable predictive modeling of household-level vulnerability, personalized messaging tailored to individual farmers and crop types, real-time data collection and feedback loops, and decision-support dashboards for resource allocation and risk identification — transforming M&E from a retrospective function into a real-time decision engine.
A Shift from Reactive to Proactive Humanitarian Response
The WFP case study demonstrates that early warning systems work, communities respond when information is clear and actionable, and anticipatory action can significantly reduce the impact of crises. The collective efforts during the 2023/2024 El Niño activation have laid a strong foundation for enhancing the effectiveness of LMEWM across Southern Africa.
Strengthening early warning systems to enable action before disasters not only saves more lives and livelihoods, but it is also a more efficient, cost-effective, and dignified approach to humanitarian response.
The next frontier is not better reporting — it is better decision-making. AI offers the opportunity to transform M&E from a retrospective function into a real-time decision engine. The question is no longer "Do we have data?" but "Are we using data fast and intelligently enough to change outcomes?"
Related Resources
