1. NATIONAL AGRICULTURE INSURANCE PROGRAM
OVERVIEW OF CROP INSURANCE PROGRAMME
Tom Dienya
Ministry of Agriculture and Irrigation

2. Kenya Size: 581,309 km2 (224,445 sq million)
Population : about 45 Million
Arid land- 58%
Semi Arid- 21%
Medium-high potential- 13%

3. Introduction Agricultural producers progressively facing more risks
1. Climate Change Vs rain-fed agric
Increasing unreliable rainfall, droughts, floods;
New diseases; new pests
2. Majority smallholder vulnerable; low resilience
High poverty
Difficulty in bouncing back after a shock
3. Low Access to credit
Demand for /collateral due to unpredictability of farming

4. Introduction Serious national impacts associated with Agric risks
Since 1970, Kenya has experienced a total of 41 major floods affecting 6.9 million people
Since 1970, 12 major drought events
Between 2008 and 2017: estimated KShs 699 billion in livestock losses and KShs 121 billion in crop losses
Over the past 12 years, the Government of Kenya has spent on average KShs 4.2 billion per year on disaster relief funding
In 2017: Ksh 16 Billion used; Maize floor subsidy: 9 Billion

5. Introduction Kenya’s Standard response mechanism not effective
Food aid/Cash Aid to affected population
Budget relocations
Post-drought livestock restocking
Key Problems:
Slow
Expensive
Disrupted budget; national plans
Face targeting challenges
Continuous food aid can reinforce external dependency

6. Agric Insurance as alternative
Overall: Purpose of Insurance is to Reduce impact of risks in Agric
Farmers’ compensated for losses associated with agric risks
Build resilience of poor farmers: ability to remain in production; bounce back after set- back
Increase farmers’ access to credit, and inputs
Improve agricultural productivity, transition from subsistence to commercial farming
Eliminate post disaster response challenges eg revised national funds, aid dependency, etc
Provide social protection to the poor
Options of Insurance
Conventional (individual) insurance
Government supported Insurance

7. Historical perspective
For several years MoA&I has been investigating options to support agriculture insurance in Kenya
In 2013, Agric Insurance formed part of Jubilee party Manifesto
: National Gov commissioned the World Bank to undertake comprehensive Agriculture Insurance Solutions Appraisal and Policy Review for Kenya.
World Bank tasked to explore the potential agric insurance and to develop large scale Public-Private Partnerships (PPPs) based crops and livestock insurance in Kenya.

8. Historical perspective
Tow main guiding documents developed by World Bank
Policy Note
Background Paper

9. World Bank Recommendations
Comprehensive agric insurance programme with livestock and crops component
Livestock Insurance: larger focus on the arid, pastoral regions where livestock is main source of livelihood
Livestock insurance uses vegetation/NDVI index
Crops insurance programme
Crop Insurance based on Area Yield Index Insurance

10. World Bank Recommendations
Public Private Participation model
Govt support: data management, farmers mobilization, capacity development; subsidies, policy and regulatory framework
Private Insurance companies: form pool; develop and market insurance products
Financial institutions: bundle credit lending to insurance
Dev Partners: provide other technical support
Typical examples:
India; Mongolia; Spain, United States
On a global basis, only 7% of transaction volume is purely private

11. Area Yield Index Insurance
Type of crop insurance selected for up-scaling and direct gov support
AYII- Insurance of farmers grouped in homogeneous geographical areas/units with near-similar yields of the insured crop
Historical yield of the crop in each identified homogeneous area form the index that is used for setting premium cost, development of insurance product and lose assessment
Under AYII farmers purchase insurance, pay premium individually. However, historical yield, lose assessment and payouts are determined by the data collected from the homogeneous areas

12. Area Yield Index Insurance
AYII Process
Identify the crop and counties to be covered
Create the homogeneous Unit Areas of Insurance (UAI)
Gather and clean historical data
Develop insurance product and set premium cost
Undertake farmers’ awareness creation
Undertake product underwriting (sale to farmers)
Undertake crop cutting at end of season to determine actual yields
Decide on payout or not
Conclude the season and prepare for next season

13. Selection of Crop Consideration: Practice by many farmers
Food security
Contribution to agricultural value addition
Availability of Data
Note:
1. Choice should be Demand Driven by counties
2. Private insurance allowed to insure other crops under traditional arrangements
Maize and wheat have been selected to start implementing agric insurance under a PPP approach.

14. Creating Unit Area of Insurance
Similar AEZ- soils, climate, elevation above seal level, vegetation, etc
Similar farming practices- land cultivation, husbandry practices, cultural practices
Following existing administrative boundaries
Confirmation by local extension officers, local leaders, farmers;
Existence of historical yield data (10-15 years)

15. Defining UAI Example: Narok County
In the hypothetical administrative area of “Narok South” as the UAI;
The average historical yield of insured crop is established.
But how homogeneous is Narok South Sub-county?.
Boundaries of a hypothetical
“Narok South” Sub-county

16. Definition of UAI….cont
How homogeneous is Narok South for a crop eg wheat?
Or would it be more appropriate to segment the area in smaller homogeneous sub-areas?
For ease of administration, the boundaries of UAIs to follow existing admin boundaries eg sub-location, Locations, wards
Boundaries of hypothetical sub-areas
Boundaries of hypothetical
“Narok South”

17. Gather and Clean Historical Yield Data
Identify Gov Source of data to lowest admin levels
Strive to get 10 years data or more
Check data quality; missing data; outliers
Draw trend; see whether yields have been steady, increasing or decreasing trend
Assess years of low yields and confirm with historical records of regional/national hazards/disasters
Undertake measures to ensure data quality and reliability

18. Gather and Clean Historical Yield Data
Case of Kanduyi- Bungoma County
LR crop yield higher than S.R crop, why?. Is there need for S.R crop insurance?
Years 2008, 2009 Low yields- PEV; Drought cases?
SR probably outlier. What is the problem in 2013?
Yields trend- general decline, but picked up from Why?
Overall; by data trend, how risky is Kanduyi (Determines Insurance cover level and premium cost)

19. Develop Insurance Product
Product= Sum assured (what farmers expect to get) PLUS Cost of insurance (premium) or what farmers pay for sum assured
Sum assured- based on average yield; eg 30 bags per of Ksh 2,500 = 45,000
To arrive at sum assured, establish historical Ave yield for each UAI and average price per Kg of the crop
Based on farming risks in the UAI (based on yield variations shown in historical data pluc acturual data analysis) insurance companies set percentage cover of the historical yields (usually between %)
Cost of insurance (premium) or cost of product = small % of sum assured; usually between 4-8%

20. Develop product Example: Kirinyaga County; Mwea West Sub-county
Maize Long Rains 2017
UAI NAME
Ward
Sub- location
Historical Yields (90 Kg bags/ acre)
Insurance Cover Level
Trigger Level (Bags/ Acre)
Ave Maize Price (Ksh/90 Kg)
Total Sum Assured (Ksh) per bag; and per acre
Premium (Ksh) cost
Subsidy GoK (Ksh)
Farmer Pay (Ksh)
UAI-1
Mukure
Kianjang’a
8.25
70%
5.8
2,400
13,920
835
418
UAI-2
Gitaku
UAI-3
Kiine
Kithumbu 8
5.6
13,440
806
403
UAI-4
Maitharui
UAI-5
Kariti
Sagana
9.5
6.7
16,080
965
482
UAI-6
Gacharu 10 7
16,800
1,008
504
UAI-7
Thigirichi

21. Yield assessment based on Crop Cutting
Payout Determination
Yield assessment based on Crop Cutting
Purchase of Crop Cutting tools: tape measure, weighing scales, moisture meters, Tablets/ smart phones
Training on crop cutting
Sampling of Farmers’ within UAI
Actual crop cutting;
Crop Cutting auditing by select team
Data analysis and actual yield determination;
Payout determination
Results announcement to farmers
Pay- two weeks after results announcement

22. AYII- Determination of Payouts

23. Advantages of AYII Multi-peril; covers several risks
Easy to understand and explain to farmers
Useful for covering large number of farmers
Minimal adverse selection or moral hazards: AYII does not depend on individuals; individuals' behavior does not affect the programme adversely
Cheap administrative costs for insurance companies
Cheaper premium compared to others

24. Disadvantages of AYII
Prone to Basis Risk-Defined as the potential paying farmer who does not deserve pay; or not paying one who deserves pay within the insured area.
Sources of Basis risk under AYII:
Localized perils (e.g. hail, or flooding by a nearby river), that do not impact on the UAI average yield; hence affected farmers lose out
Creation of Non homogeneous UAIs
Basis risk can be minimized by:
Proper identification of the homogeneous producing zones(UAIs)
Improve data accuracy and reliability
Develop “top-up” solutions to cater for specific localized perils

25. Initiatives to Improve AYII
State Department initiatives to improve on crop insurance programme:
Working with aggregators
Efforts to monitor insured crop
Development of ICT system

26. Working with Aggregators
Aggregators: systems/agencies that have farmers working in a group
Example: One Acre Fund; Cooperative society; Large farmer group
Identify crop used by group
Consider bundling of inputs with insurance
Work with officials to promote insurance up-take
ADVANTAGES:
Ease of selling insurance
Cover more farmers

27. Monitoring of Insured Crops
Effort by stakeholders to provide anxilliary data to support monitoring of insured crop and creation of UAIs
Stakeholders: Kenya Met Dpt; FEW NET; RCMRD; Climate Change Projects, NGOs
Activities
Purchase and distribution of automatic weather stations
Compilation of disaggregated rainfall data
Provision of satellite and remote sensing data eg NDVI, WRI, ET, etc
Provision of GIS related maps- soil types, vegetation, crop cover, etc to support creation of UAIs

28. USE OF ICT SYSTEM To Minimize manual data collection, reports,
Improve accuracy of data analysis
Improve timeliness of payouts

29. USE OF ICT SYSTEM FOR AYII
Use of mobile phone platform for data collection
Take photos and videos of crop conditions and crop cutting process
Use SMS to transmit data to processing software
Rapid Payments Through
M-Pesa

30. Achievements No of crops covered: Maize; moving to other crops soon
Year 1: Counties= 3; insured farmers= 900;
Year 2= 10 Counties covered; over 200,000 farmers
3) Working with One Acre Fund (9 counties) and WFP farmers in Kitui
Target farmers: 5 million by 2020
Estimated Gov annual budget: Ksh 300 Million; to scale to 2.2 Billion in next 4 years
No of insurance companies involved to date: 7; forming a pool
Development of ICT based data management system

31. Challenges
Obtaining accurate historical data for very small unit area of insurance
Determination of UAI in regions with high zonal variations
Simpler methods of sampling farmers
Crop cutting tedious and expensive
Limited awareness by target small holder farmers
History of mistrust of local insurance companies; culture of “No Insurance”
High cost of insurance in cases of catastrophic (large scale disasters)
Weak capacity of local insurance companies

32. Future perspectives Expand to more counties and crops
More involvement of counties- to select crops; do most of the work, contribute subsidy
Increased use of farmers’ aggregators
Bundling of insurance with input subsidies and farming loans
Promotion of Weather Index for Hort Crops- due to difficulties of crop cutting

33. The End……
THANK YOU