NATIONAL AGRICULTURE INSURANCE PROGRAM OVERVIEW OF CROP INSURANCE PROGRAMME Tom Dienya Ministry of Agriculture and Irrigation tm.dienya@gmail.com

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

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

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

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

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

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 2013- 205: 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.

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

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

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

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

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

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.

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)

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

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”

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

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 2010- probably outlier. What is the problem in 2013? Yields trend- general decline, but picked up from 2010. Why? Overall; by data trend, how risky is Kanduyi (Determines Insurance cover level and premium cost)

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 acre @price 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 90- 60%) Cost of insurance (premium) or cost of product = small % of sum assured; usually between 4-8%

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

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

AYII- Determination of Payouts

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

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

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

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

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

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

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

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

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

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

The End…… THANK YOU