rop and livestock insurance feasibility study: final · pdf filerop and livestock insurance...

Crop and Livestock

Insurance Feasibility

Study: Final Report

Conducted by Syngenta Foundation

for Access to Finance Rwanda and

MINAGRI

July 2012

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Contents

0. Acknowledgements ............................................................................................................................. 3

1. Executive Summary ............................................................................................................................. 4

2. Goals of the Study ............................................................................................................................... 9

3. Methodology ..................................................................................................................................... 10

4. Data infrastructure and meta-data map .......................................................................................... 14

5. Analysis of Crop Value Chains ........................................................................................................... 17

1. Maize ............................................................................................................................................. 18

2. Livestock ........................................................................................................................................ 23

3. Potatoes ........................................................................................................................................ 30

5. Coffee ............................................................................................................................................ 42

6. Rice ................................................................................................................................................ 51

7. Beans ............................................................................................................................................. 55

8. Wheat ............................................................................................................................................ 59

9. Cassava .......................................................................................................................................... 63

10. Sorghum ...................................................................................................................................... 67

6. Regulatory Environment ................................................................................................................... 69

7. Sources and Stakeholders consulted ................................................................................................ 71

Annex 1: Crop Tables ............................................................................................................................ 74

Annex A: Maize ................................................................................................................................. 75

Annex B: Livestock ............................................................................................................................ 77

Annex C: Potatoes ............................................................................................................................. 79

Annex D: Tea ..................................................................................................................................... 82

Annex E: Coffee ................................................................................................................................. 85

Annex F: Rice ..................................................................................................................................... 89

Annex G: Beans ................................................................................................................................. 92

Annex H: Wheat ................................................................................................................................ 95

Annex I: Cassava ................................................................................................................................ 97

Annex J: Sorghum.............................................................................................................................. 99

Annex 2 : List of tea cooperatives ....................................................................................................... 101

Annex 3: Weather Station Investment and Operational Cost ............................................................ 102

Annex 4: Meta-data Map .................................................................................................................... 104

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0. Acknowledgements

The Ministry of Agriculture and Animal Resources was instrumental in carrying out the feasibility

study, especially in developing the report and providing valuable contacts throughout the Rwandan

agricultural sector. Special thanks go to the MINAGRI Permanent Secretary and Director of Planning

who provided guidance and support, with Gilbert Kayitare, Alistair Sussock, and Barbara Mbabazi

giving insightful input from study design to conclusion.

Access to Finance Rwanda (AFR) funding made the study possible. Livingstone Nshemereirwe

provided active support through connecting the consultancy team with relevant stakeholders and

offering feedback on methodology and reports.

The real value and validity of this study comes from the direct feedback and discussions with many

farmers, cooperatives, banks, NGOs, insurers, ministries, companies and development partners

throughout Rwanda. It is our hope that this feasibility study can serve as a foundation and gateway

for the continued development of agricultural insurance in Rwanda.

The feasibility study was run by a team from the Syngenta Foundation for Sustainable Agriculture’s

Kilimo Salama project, managed by Rose Goslinga. The professional team included Wairimu Muthike,

Laura Johnson, and Olive Ashimwe, with contributions from many other members of the Syngenta

Foundation. Their commitment and professionalism is much appreciated.

Eric Rwigamba

Acting Technical Director

Access to Finance Rwanda

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1. Executive Summary

Goal of the study

In Rwanda’s Vision 2020, the Government of Rwanda outlines its goal for the agriculture sector to

become a professional sector, wherein 50% of the land is cultivated using improved practises such as

the use of mineral fertilizers. To reach this level the sector requires investment by its stakeholders,

but foremost it requires investment from Rwandan farmers.

Investing in agriculture is unfortunately not as easy as investing in brick and mortar. Whether

agricultural production fails or succeeds does not only depend on a farmers’ agricultural knowhow, it

also depends on the climatic and environmental conditions, which are generally beyond the

grower’s control.

Access to Finance Rwanda (AFR), in collaboration with the Ministry of Agriculture and Animal

Resources (MINAGRI) contracted the Syngenta Foundation for Sustainable Agriculture (SFSA) to

undertake a feasibility study to investigate the potential of developing crop and livestock insurance

in Rwanda. The goals of this study can be summarised by the following questions:

1) What risks and value chains can be insured?

2) Can insurance products be attractive and affordable to farmers?

3) Can crop and livestock insurance products be financially attractive to the private sector?

Methodology

Recognizing the diversity of the agriculture and livestock sector, the SFSA team conducting this study

investigated the commercial viability of 10 agricultural value chains. Analysis was done on maize,

wheat, sorghum, beans, Irish potatoes, coffee, tea, rice, cassava, and dairy livestock.

The study was conducted in three phases; the first consisting of data collection (reports and previous

studies) per crop, the second included discussions with sector stakeholders and field visits to verify

the data, and the third consisted of collecting weather and yield data to develop a meta-map of data

available for index insurance purposes.

For a product to be commercial viable, three key elements have to be in place:

1. The risks identified by farmers need to be insurable

2. For the risks identified, data needs to be available

3. The premium volume after 3 years and margin from this premium volume needs to be

sufficiently large enough to cover the operational cost of the product

The last factor is key for insurance companies to remain interested in the product beyond the initial

pilot stage. The premium volume and margin were key determinants to classify pilots into high,

medium, or low potential.

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Data infrastructure and meta-data map

Insurance relies on statistics, be they yield, mortality, or weather data. For this study a review of

available statistics was done to determine the availability and quality of data. A map was

constructed showing the available weather stations in Rwanda, included in Annex 4.

Area yield data is available for most crops at a district level since the restructuring of districts in

2006. The data is collected on a seasonal basis by MINAGRI in conjunction with the National Institute

for Statistics (NISR). While the sample size is not very large, it gives a basis for an area yield product

The National Agricultural Development Board (NAEB) collects yield data on a yearly (monthly for

some crops) basis for export crops.

Daily weather data is predominantly recorded through 329 manual stations operated by the Rwanda

Meteorological Agency (RMA), 261 of which are currently operational. Most stations that started

recording before 1990 have a gap between 1990 and 2009, though reinsurers confirmed that if

stations have collected 2-3 years of data after this gap, they could be used for index insurance.

For index insurance purposes, fully automated weather stations (AWS) are required and will need to

be installed. AWS frequently transmit data to a central server where it is be fully accessible. Given

Rwanda’s topography and climate variability, a

dense network of stations would be required.

Calculations estimate 131 stations would be

needed to cover the 52% of Rwanda that is arable

and supports crop production. The investment and

installation cost of such a project would be around

700,000 USD. Annual operational and maintenance

costs would be 160,000 USD, including

depreciation and replacement, or 1,200 USD per

year per station.

Livestock insurance requires other types of data.

Through the Bill and Melinda Gates Foundation

(BMGF)-supported East African Dairy Development

(EADD) project, mortality and related disease

prevalence data was collected in 2009. Additional

surveys are planned by EADD and are necessary to

allow insurers to understand the frequency of risks.

General findings for crop and livestock insurance

From the study and field visits, three factors

emerged as obstacles to developing viable crop and

livestock insurance:

1. Lack of distribution channels in the food crop sector

2. Dominance of management-related risk over production risk in the cash crop sector

Interviewed cooperative dairy farmer

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3. Lack of data in the livestock sector

In parts of the food crop sector, it is apparent that the lack of professionally organised farmer groups

would limit the distribution of insurance. While the risk of erratic rainfall is clear to farmers, and the

demand for insurance is there, the number of effective aggregated distribution channels (like

agribusinesses, microcredit institutions, and professional farmer cooperatives) through which

premiums could be paid is limited, but not non-existent. The alternative, where insurance companies

sell insurance “door to door,” is not attractive as this would require a very intensive and costly

financial education effort. Premium volumes and margins would similarly not be attractive to

insurers for some of the food crops researched.

For cash crops, the

obstacle is of a

different nature. While

the sector is organised

with clear markets,

some of the risks

identified by farmers

are often more related

to management rather

than production. Risks

such as timely access to

inputs or fluctuating

market prices were

ranked higher than

climatic risks. Finding a

relevant risk is the key

challenge for developing effective insurance products for cash crops. However, there are cash crops

facing insurable climatic issues that farmers need protection from.

For the crop sector, data on yield and weather is generally available; however for livestock, data

proved an obstacle. The only livestock data useful for insurance purposes was collected by EADD in

2009. More mortality surveys can be carried out in target areas, and additional efforts are needed to

estimate historical the frequency of catastrophic risks, such as widespread disease outbreaks, that

farmers would want to insure against.

Considering the above obstacles, there were four value chains identified where pilots would be

attractive for both farmers and insurers. There were also three value chains classified as “medium”

potential and three as “low” potential. These value chains are considered in detail below and in

Section 4 of this report. It should be noted that this classification does not make any judgement

about the economic viability of the crop itself, but simply makes a statement about its “insurability”.

The estimations of the projected premium volumes and margins are conservative, the focus being on

the sum insured for only the inputs, rather than projecting to insure farmers’ harvests. This is based

on experiences in other countries where farmers found “harvest insurance” difficult to afford in

Green coffee drying in Eastern Rwanda

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many cases. Only existing distribution organisations were considered in projections, while much

more potential would exist were there to be growth within current organizations and new

organizations formed in the coming years. Note that all insurance premium rates given in this study

are only estimates and are likely to change slightly based on the region. The given rates are

representative, based on the team’s experiences in Kenya.

In total, estimates show that for the 10 value chains forming the core of agricultural production in

Rwanda, the premium potential after 3 years would be 1.47 bln RWF or 2.4 mln USD, insuring over

300,000 farmers after 3 years, protecting 33 bln RWF or 55 mln USD in agricultural investment.

The total premium volume from the four high potential value chains after 3 years is 1.2 bln RWF or

1.9 mln USD, insuring over 115,000 farmers after 3 years, protecting 29 bln RWF in agricultural

investment. The total premium volume from the four medium potential value chains after 3 years is

153 mln RWF or 253,00 USD, insuring over 100,000 farmers after 3 years, protecting 1.8 bln RWF in

agricultural investment. Tables 1 and 2 provide details on the viability and projections for each of the

10 studied value chains.

Table 1: Total Market Potential

Total Market Potential

High potential Medium Low Total Total USD

Number of Farmers

115,939 93,186 102,985 312,109

Ag Investment Secured RWF

29,844,942,400 1,769,853,000 1,724,390,950 33,339,186,350 54,924,524

Premiums RWF 1,181,109,872 146,132,890 137,886,280 1,465,129,042 2,413,722

Margin RWF 236,221,974 34,882,143 27,577,256 345,721,373 569,557

Table 2: Specific Crop Potentials

Specific Crop Potentials

High Potential

Maize Livestock Potatoes Tea

Number of Farmers

75'000 28'877 2'500 9'562


2'940'000'000 23'785'902'400 1'400'000'000 1'719'040'000

Premiums RWF 235'200'000 713'577'072 112'000'000 120'332'800

Margin RWF 47'040'000 142'715'414 22'400'000 24'066'560

Medium Potential Low Potential

Coffee Rice Beans Wheat Cassava Sorghum

Number of Farmers

19'336 6'200 67,650 2'881 104 50'000


649'950'000 239'100'000 880,803,000 144'035'000 6'499'950 786'928'000

Premiums RWF 56'545'650 19'123'000 70,464,240 11'522'800 455'000 62'954'240

Margin RWF 16'963'695 3'825'600 14,092,848 2'304'560 91'000 12'590'848

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Regulation and Enabling Environment

In terms of insurance regulation, the following suggestions are proposed to create a regulatory

environment where crop and livestock insurance will grow. These suggestions are built on

experiences in the Indian market where such markets were also implemented:

1. Incentive/require credit to be extended to rural areas so a market for agricultural loans

would be created. A 10% quota similar to that of India’s is advised.

2. Create a requirement that agricultural credit needs to be insured. This way a market for

agricultural insurance is created.

3. Rather than subsidize the insurance premiums, remove the Value Added Tax (VAT) of 18%

from any insurance products supporting the rural sector, as this will instantly make all

products 18% more affordable.

Development partners can also support insurance companies and specialized agriculture insurance

service providers through a targeted grant program allowing private sector players to develop and

test products that reach the most farmers. In the absence of such funding, purely private sector

players are unlikely to develop products as the development cost and uptake time form a high

barrier to entry.

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2. Goals of the Study

In order to reach the goal of a more professional Rwanda agricultural sector, investment by

stakeholders and Rwandan farmers is required along with innovative solutions to encourage and

protect increased farmer investment. The success of agricultural production does not only depend

on a farmers’ agricultural knowhow, it also on the climatic and environmental conditions, which are

generally beyond the grower’s control. This is where the development of agricultural insurance

becomes important.

Access to Finance Rwanda (AFR) with the Ministry of Agriculture and Animal Resources (MINAGRI)

contracted the Syngenta Foundation for Sustainable Agriculture (SFSA) to undertake a feasibility

study investigating the potential of developing crop and livestock insurance in Rwanda. The goals of

this study were:

1. Investigate to what extent risks can be insured through formal risk transfer systems such as

index insurance and indemnity-based insurance.

2. Investigate to what extent these insurance products would be attractive and affordable to

farmers.

3. Investigate whether the insurance products proposed would be financially viable for the

private sector stakeholders involved.

4. Propose and outline pilots that could be financially viable for insurers, and relevant to and

affordable for farmers.

The expectation is that through this study the market for agricultural insurance is quantified. Based

on these estimates, insurance companies could become interested in expanding their market

penetration beyond the current urban concentration, as they now have an informed starting point

to venture into the sector.

Table 3: Selection Vision 2020 Indicators

Selected Vision 2020 Indicators 2000 2010 2020

Agricultural GDP Growth (%) 9 8 6

Agriculture as part of GDP % 45 47 33

Land under modernized agriculture (%) 3 20 50

Fertilizer application in kg/ha/year 0.5 8 15

Soil erosion protection (%) of total land 20 80 90

Source: MINAGRI, 2012

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3. Methodology

The study aims to estimate the potential for developing commercially viable crop and livestock

insurance in Rwanda. Recognizing the diversity of the agriculture and livestock sector, the team

conducting the study investigated the commercial viability of 10 value chains, establishing for each

whether it would be beneficial for the farmers and stakeholders in the sector to insure the crop, and

if it would be financially attractive for the insurer. Analysis was done on maize, livestock, Irish

potatoes, tea, coffee, rice, beans, wheat, cassava, and sorghum.

The study was conducted in three phases; the first consisting of data analysis per crop, the second

included discussions with sector stakeholders and field visits to verify the data, and the third

consisted of collecting weather and yield data to develop a meta-map of data available for index

insurance purposes. These three phases resulted in the identification of sectors where insurance

could be provided sustainably and of potential pilots where the insurance sector could start insuring

crops. Illustration 1 provides an overview of the methodology.

Phase 1: Data collection

In the first phase the team investigated the number of farmers growing each crop, hectares grown,

value of inputs used per hectare, yield, and value of the harvest. Where sources were contradictory,

estimates were formulated. A full overview of the data collected per crop can be found in parts A-J

of Annex 1.

Data was collected data from Rwanda’s MINAGRI, its agencies, and its development partners’

projects, Rwanda’s Financial Institutions and Development Banks, the National Institute of Statistics

of Rwanda, and the International Livestock Research Centre (ILRI). Additional information was

gathered from several crop specific studies and interviews with agribusinesses and agriculture NGOs.

A full list of the organisations interviewed and resources consulted is found in section 6.

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Phase 2: Field visits

In the second phase the collected data was verified

by field visits and discussions with farmers,

cooperatives, and other stakeholders. As data on

commercial profitability can change quickly over

time, field and stakeholder interviews generally

provide a good sounding board for testing market

research data, especially when assessing the actual

risks and input use.

The crop tables in Annex 1 often cite a source and

discussion from which the data was collected, but

data is only included if it can be verified using

other credible sources.

Phase 3: Weather and yield data collection

In the third phase of the study data was collected

to prepare a map that shows data availability.

Traditional crop insurance based on farm visits and

assessments has not been viable in most

developing countries and faces difficulties with moral hazard and adverse selection. Index insurance,

either through area yield or weather indices, provides an alternative. Since it employs an objective

third party data source, index insurance does not have the same drawbacks as traditional insurance.

Index based products cover specific risks and therefore need to “fit” the crop insured. For instance, if

the crop does not suffer from drought risk, drought index insurance is not a relevant product and

farmers will not likely to be willing to pay for the cover.

A meta-data map shows where each kind of data is available, be it weather data that can be used for

weather index products or yield data that can be used for area yield index products. Since

sufficiently long and complete data series are needed for index insurance to be priced, a meta-data

map should also reflect this information.

Once a meta-data map is produced it needs to be continuously updated by stakeholders who find

new data sources or who are implementing new data collection methods. A specific example is when

a new weather station is installed-- this should be added to the meta-data map. A meta-data map

therefore is useful in showing where and what type of insurance can be implemented and areas that

lack data.

Phase 4: Identification of commercially viable products and pilots

After considering the data collected in the first three phases, value chains were identified where

crop or livestock insurance could be distributed. For a product to be commercial viable, three key

elements should be present:

SFSA team member interviewing a wheat farmer

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1. The risk identified by farmers needs to be insurable

2. For the risk identified, the appropriate data needs to be available

3. The premium volume and margin from this premium volume after 3 years needs to be

sufficiently large enough to cover the operational costs of implementing and administering

the product

For the risk to be insurable it must fit with an insurance product available in the market. Table 4

provides an overview of the products available in the market and the risks they can cover.

Table 4: Available Agriculture Insurance Products

Product Type of farmer Risks covered Crops Data needed

Multi-peril, visit based

Large and Livestock farmer

All named risks, including hail

All crops Yield data from individual farms

Weather index

Small and Large Drought,

Excess rain, Weather-related disease

Maize, Beans, Sorghum, Wheat, Potatoes , Coffee

Weather data (min of 15 years)

Satellite based index

Small and Large

Drought, Excess rain,

Weather-related disease, Flooding

All above + Horticulture and Irrigated crops

Satellite data (min of 15 years)

Area yield index

Small and Large All catastrophic risks, no

hail All above + Coffee

and Tea Yield data

(min of 15 years)

The third factor listed is key for insurance companies to remain interested in the product beyond the

initial pilot stage. There are two factors that influence this: operational cost and potential premium

volume.

Operational cost is determined by the distribution channel used for the product. Crops that are

grown by farmers in groups, for well-established agribusinesses, or for which financing is available,

have a lower operational cost to distribute. Experience shows that the operational cost for

distributing products to farmers organised in groups is 15% to 20% of the premium, while the

operational cost for distributing products to individual farmers is 30% to 40% of the premium.

Potential premium volume is determined by the market potential, in particular how many farmers

grow the crop, but also to what extent growers invest in inputs for the crop. Coupled together with

how the farmers are organised, this determines a realistic future premium volume.

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Table 5: Data collected

Item Phase I Phase II Phase III

Number of Growers in RW X

Hectares under crop X

Annual production MT X

Producer price MT/USD) X

Average yield MT/Ha X

Total Countrywide Harvest Value USD X

Production areas X

Target areas for Pilot

X

Operational partners/potential aggregators in Pilot

X

Risks identified by farmers/ aggregators X

Type of data source most relevant

X

Availability of data source (nr of years and areas)

X

Planting time

X

Harvest time

X

Any Inputs used farmers in area? If so which? X

Number of crop cycles per year X

Value of Inputs per acre USD X

Estimated insurance premium rate

X

Estimated potential market penetration X

Reasonable estimated market penetration of total X

Potential loan size in pilot USD X

Premium rate estimate

X

Value of pilot in premiums (USD)

X

Assumed penetration after 3 years

X

Potential number of farmers after 3 years

X

Potential market size loans year 3 USD

X

Potential market size premiums year 3 Inputs only USD

X

Potential market size premiums year 3 Input + Output USD

X

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4. Data infrastructure and meta-data map

Insurance relies on statistics - be they yield, mortality, or weather data. For this study a review of

available statistics was done to determine which data was available, where was it recorded, and if

there were any gaps in the data. With this information a map was constructed showing the available

weather stations in Rwanda. This map is available in hard and soft copy through Google Earth, with

an example given in Annex 4. With such a map, stakeholders in the sector can quickly assess the

viability of index insurance for any given client who expresses an interest in insurance.

Area yield data

Area yield data consists of yield per hectare and hectares cultivated, which should be consistently

collected each season.

The seasonal crop assessment conducted by MINAGRI and NISR collects data for a range of crops

(see Table 6). The data is available on a district basis, however the statistical sample size is stronger

on a provincial and national basis. This reflects its primary purpose; the data is used for estimations

of agricultural growth and GDP. Since it is used for these important calculations, its availability is

ensured and is publicized every year in September (B season) and March (A season).

Area yield data for export crops is collected by the National Agricultural Export Board (NAEB). For

tea, each factory records area grown and production on a monthly basis. Coffee production data is

recorded by the exporters marketing the coffee, though consistent area data is not collected.

Table 6: Crop data collected for MINAGRI crop assessment

Sorghum Peas Groundnuts

Maize Vegetables Soya

Wheat Yam & Taro Banana

Rice Cassava Irish Potato

Beans Fruits Sweet Potato

Source: MINAGRI

Weather data

Through 329 ground observation weather stations a variety of climatological data has been collected

in Rwanda. Of these ground observation stations, 261 stations have been confirmed to be

operational. 257 stations are manual, while 4 stations are automatic. There are 9 locations where

agro-meteorological measurements are made - i.e. data on temperature, wind speed,

evapotranspiration, and soil sensors - rather than only rainfall readings. There are 72 stations that

have rainfall and temperature readings.

For stations with up-to-date information further analysis was done. More than 105 stations have less

than 5 years of data, while 15 stations have between 5 and 15 years of data, and 30 stations have

more than 15 years of data.

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Since 2008 and 2009, a number of efforts have started to rehabilitate the system. The Food and

Agricultural Organisation of the United Nations (FAO) installed 88 rain gauges across the country in

2009. However, as these stations were manual and there was no support for district officials to make

the readings and to transmit of the data, many of them are in disrepair. The Rwanda Meteorological

Agency has since made a significant effort to improve data collection and availability. It is upgrading

its database and software to access the data, and plans to install 12 automatic synoptic stations

before the start of 2013. The agency is further looking to develop a data policy to regulate the

sector.

While the data gap between 1990 and 2008 is significant, the quality of data up to 1990 is good

enough to “bridge” this gap. Discussions with one of the main reinsurers, Swiss Re, confirmed this

conclusion. However, it will be key that from now on the data is recorded systematically to avoid

further gaps.

For insurance purposes, fully automated weather stations (AWS) will need to be installed to limit the

time it takes to transmit data and to make it accessible. This will in turn limit the gaps in the data.

Given Rwanda’s topography and climate variability, a dense network of stations would be required.

Taking into account that 52% of Rwandan land is arable (World Bank, 2010), experience and

calculations show that 131 stations would be needed to cover the country’s arable areas. See Annex

3 for the detailed calculation.

According the team’s experience in Kenya, the investment and installation cost of such a project

would be around 700,000 USD. Annual operational costs of the network come to 160,000 USD,

including depreciation and replacement cost, or 1,200 USD per year per station. Annex 3 has a more

detailed cost breakdown. From this calculation, it is clear that such a network could not be operated

from insurance income alone, and should only be installed if other services, such as weather

forecasts or weather based agricultural advisory services, can be implemented.

Since insurance companies alone cannot pay for such a network, it makes sense for the Rwanda

Meteorological Agency to install weather stations with support from development partners. To

justify the investment, organizations would need to develop other products or services using data

from the weather station network. A data pricing plan or cost sharing plan would support network

maintenance.

Satellite Data

Alternative to ground information, satellite data is available and can insure farmers against risks

such as drought, excess rain, flooding, temperature fluctuations, and weather-related disease. There

are thousands of satellite based products that can be developed for a number of data sources. Those

produced in the United States by government departments and agencies are free, while those

produced by European governments are charged for. Determining which source and product makes

most sense requires investigation and an upfront investment by the insurer.

Rainfall estimation products use the presence and temperature of high clouds to estimate rainfall.

One such product is ARC (African Rainfall Climatology) and has data available since 1983. It produces

a daily estimate of rainfall for a 10km square area. It can be obtained for free through the National

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Oceanic and Atmospheric Administration’s (NOAA) Climate Prediction Centre (CPC). ARC has been

used to insure over 20,000 farmers in Ethiopia through a partnership between Oxfam and the

Columbia Earth Institute. An alternative to ARC is TAMSAT (Tropical Applications of Meteorology

using Satellite data and ground based observations), providing rainfall data in 10 day intervals from

the National Aeronautics and Space Administration (NASA).

Alternative to rainfall-based satellite products, vegetation-based products can give an indication of

drought, disease, or other events that cause lower crop yields. Vegetation-based products measure

the greenness of the area as a proxy for plant photosynthesis and health. It should be noted that

vegetation-based products cannot always measure losses well since green crops may not be fruit

bearing crops. Furthermore, as vegetation-based products look into greenness, the presence of

green trees in combination with a larger pixel size may mean that in an area where there are many

green trees, their presence cancels out the impact of drought on the crop.

With this caveat in mind, Normalized Difference Vegetation Indexes (NDVIs) can be used and such

products are available at various levels of granularity. For example, the NDVI/AVHRR (Advanced Very

High Resolution Radiometer) takes measurements on an 8 km spatial resolution and provides data

every 10 days at a 4 km resolution since 1981. EVI/MODIS (Enhanced Vegetative Index derived from

MODerate resolution Imaging Spectroradiometer) provides good data for areas with very high

vegetation density and could be relevant for Rwanda.

Livestock data

To develop livestock insurance products, data on animal mortality and cause of death has to be

collected. The International Livestock Research Institute (ILRI) collected data on animal health and

mortality as part of the wider East African Dairy Development (EADD) project. However, since this

project was only implemented in Eastern Rwanda, it may be difficult to extrapolate to a national

level.

Additional surveys are planned to measure the effectiveness of the EADD project and add to the

data. These surveys and their outcomes will further inform on the livestock sector and will allow

insurers to understand the frequency at which risks occur. It would be beneficial to insurers if data is

collected for the whole country.

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5. Analysis of Crop Value Chains

For the study, 10 crop value chains were analyzed for commercial potential and feasibility for

insurance, and then grouped by potential. The following sections include the findings, with extensive

supporting tables in Annex 1.

High Potential Value Chains

1. Maize

2. Livestock

3. Potatoes

4. Tea

Medium Potential Value Chains

5. Coffee

6. Rice

7. Beans

Low Potential Value Chains

8. Wheat

9. Cassava

10. Sorghum

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1. Maize Maize is a widely cultivated staple food crop in Rwanda. In recent years it has increased in yield and

area under cultivation. From 2010A to 2011A land area allocated to maize went up by 22%, from

123,338 Ha to 150,407 Ha (MINAGRI, 2011). Currently 17% of arable land is under maize cultivation.

The Third Integrated Household Living Conditions Survey (EICV3) reported that an increasing

proportion of households are cultivating maize. Rwanda’s earnings in 2011A from maize was 200

mln USD, with 525,679T (STARTS, 2011) produced and retailed at 240 RWF per kg. Maize is the 4th

largest food crop for Rwandan farmers in terms of annual production in 2011 in metric tons.

Maize farmers organized into cooperatives have better access to agronomic training and can market

their products in bulk. An insurance product for maize could assist farmers in accessing input loans

from financial institutions by reducing the risk of non-repayment because of a poor growing season.

For successful implementation of an insurance product for maize, farmer groups would need to be

vetted for strong management and financial accountability.

A fertilizer-input insurance pilot with 25,000 input-linked farmers cultivating 23,300 Ha would have

premium volumes of 78,400,000 RWF or 129,160 USD. In a period of three years with 75,000 input-

linked farmers cultivating 70,000 Ha the premium volume with an input cover could be 235,200,000

RWF or 387,479 USD. For program’s sustainability, Kilimo Salama would expect an operational fee of

20% of the premium, which for the sector after three years would amount to 46,040,000 RWF or

77,992 USD annually.

Sector Organization

Smallholder maize farmers in Rwanda are organized into cooperatives. Through their cooperatives,

farmers can access loans and agronomical training, purchase inputs, and market their products in

bulk as a group. Some visited groups have opted to consolidate their land and farm communally,

while others have individual farms in addition to a consolidated piece of land.

A maize cooperative in Eastern Rwanda

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Sector Investment

In Rwandan maize production use of improved seeds, pesticides, and fungicides is still low. While

organic or manure fertilizers are commonly used, only 7% of farmers report using chemical fertilizers

alone or in mix with organic fertilizers, and 3% of farmers use pesticides (MINAGRI, 2011). The

Ministry of Agriculture has put more efforts into increasing crop productivity by subsidizing

improved seeds and chemical fertilizers through the Crop Intensification Program (CIP) (MINAGRI,

2011). Farmers invest mainly in traditional seed, agricultural equipment, and agricultural manpower

(NAS 2008, 2010). Of all the expenditures in agricultural inputs, the cost of the agricultural

manpower is the most expensive (NAS 2008, 2010).

Table 7: Maize fertilizer cost

Item Quantity/Ha Cost per unit (RWF) Total (RWF)

DAP (Kg) 100 300 30,000

Urea (Kg) 50 240 12,000

Total

42,000

(ENAS, 2012)

Sector Risks

Erratic rains and drought were identified as

the second and third main risks after the

availability of drying houses to the sector.

Most recently, in 2011, maize planted late in

season A (late October - early November)

was hit by drought leading to a 12% decline

in maize yield. The decrease in yield reached

26% in South, 19% in West, 4% in North, and

5% in East (MINAGRI, 2011). Since both

weather and yield data is available, an

insurance product for erratic rain and

drought risks can be developed and priced.

Table 8: Risks for maize growers

Risk and description Resulting loss

Lack of drying houses, airy structure that allows grain to lower moisture content

Farmers are forced to dry in the farms where rains and pests cause post-harvest losses of 10%.

Drought Low Cumulative rainfall. Drought at grain filling causes yield losses of 30-40% depending on the severity.

Erratic rains Rains disappear after planting leading to

Drying house for a professional maize cooperative in Eastern Rwanda, financed

through a bank loan.

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replanting. Observed in 2010 A, 2007 A

Prices, costs of production (especially labor) are relatively high

Compromises in the inputs used and product quality

Input availability, fertilizer, certified seed, and crop protection products

Delay in planting and low subsequent productivity

Data availability

A good source of area yield data comes from the Ministry of Agriculture and Animal Resources

(MINAGRI), in collaboration with the Rwanda Agriculture Board (RAB), the National Institute of

Statistics of Rwanda (NISR), and World Food Program (WFP) who organize a crop assessment survey

for each agricultural season (MINAGRI, 2011). Weather data is also available from the Rwanda

Meteorological Department.

Potential products

The relevant products ranked in order of preference are shown in Table 9.

Table 9: Potential insurance products for maize

Product Risk covered Comments

Area yield index

All risks that cause lower yields

Use the crop assessment survey data Farmers need to be in organized groups Requires insured group to have high penetration in the district, at least 30%.

Satellite index

Drought, Excess and Erratic Rain

Requires robust satellite product, and will require ground proofing. Cost to insure all risks at the same time will be costly, therefore a maximum of two risks will have to be selected.

Weather index

Drought, Excess and Erratic Rain

Requires investment in automated weather stations Due to the hilly nature of the country this would be expensive and there would be high potential for basis risk. Data cleaning and gap filling would also be required.

Conclusion Maize

The Table 10 provides a schematic overview of the opportunity in maize using cooperatives as

distribution channel for insurance. The overall detailed table showing all indicators collected is

provided in Annex 1A.

Table 10: Summary overview of maize

Viability Comment

Are the risks insurable?

Yes Drought, erratic rainfall

No Access to inputs, Management and Market prices

Is data available? Yes

Area yield data by season and year Weather data available from Rwanda Meteo. Satellite data available

What is the premium volume after 3 years?

235,200,000RWF 387,479 USD

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(from input insurance)

What is the operational cost/ Premium margin to distribute the product after 3 years?

20% per year 46,040,000 RWF

77,992 USD

How many farmers reached after 3 years?

75,000 farmers

Ag investment insured after 3 years?

2,940,000,000 RWF

4,843,493 USD (inputs only)

Commercially viable? High Potential Pilot with 1 district with 25,000 farmers in year 1 and cover 75,000 farmers in 6 districts by year 3.

Public Investments needed? In AWS, in case of weather index In Satellite data, in case of satellite index

Pilot and immediate potential

Kirehe district where Enterprise

Nkubili and Sons (ENAS) and the

Kirehe Watershed Management

Program (KWAMP) work would

be a strategic location to pilot

maize insurance. ENAS is a

private sector investor that

distributes government

subsidized fertilizer to farmers

on credit. Repayment of the

credit has been poor because

farmers think of it as

government grants. Additionally

maize production in Kirehe faces

periodic rainfall deficits further

affecting repayment. Because

ENAS is lending to farmers at its own risk, non-repayment adversely affects the success of operations

in the area (CIP, 2010).

From preliminary discussion with ENAS, Kilimo Salama would consider a partnership to deliver

financial education and insurance which could mitigate the risks caused by drought and assist in

raising repayment levels.

Table 11: Maize Pilot Potential

Year 1 Year 2

Distributor Enterprise Nkubili and Sons

(ENAS) and KWAMP Enterprise Nkubili and Sons

(ENAS)and KWAMP

Number of farmers reached 25,000 50,000

Acreage (Ha) 23,333 46,667

Quality maize sold to the World Food Programme’s P4P from a maize cooperative.

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Who pays for the premiums? Farmers pay through the cooperatives that can finance the premiums and deduct from harvest.

What is insured? 980,000,000 RWF

1,614,498 USD [Input Cost]

1,960,000,000 RWF 3,228,995 USD

Annual Premium volume at 8% 78,400,000 RWF

129,160 USD 156,800,000 RWF

258,320 USD

Annual Premium margin at 20% 15,680,000 RWF

25,832 USD 31,360,000 RWF

51,664 USD

Risks Insured Drought, Erratic/Excess rains Drought, Erratic/Excess

rains, Flooding

Required pilot partners and roles

Cooperative: insured on behalf of farmers Broker/Insurance company: develops the product Insurance company/reinsurer: carries the risk Data provider: area yield, satellite or weather data KWAMP - Education and Technical assistance Enterprise Nkubili and Sons (ENAS) – Timely Input Supply WFP – Buyers

Public Investment needed Depending on product: AWS weather stations + Maintenance = 21,000 USD Satellite Data: 15,000 USD

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2. Livestock Dairy livestock is an increasingly important sector in the agricultural sector of Rwanda, now with

over 1.4 mln dairy cows managed by 720,000+ dairy farmers (MINAGRI Livestock Populations 2011;

NISR, 2012). The government has made dairy a priority area, as is clear by the number of projects

running nationally to improve the number and quality of dairy cows available, and consequently the

amount of milk available to the Rwandan people. Several initiatives focus on transitioning the dairy

breeds from local (producing about 10 litres per day) to imported or exotic breeds like Friesians and

Jerseys that produce 16 to 25 litres of quality milk per day. These exotic cows, and their crosses with

local cows, are on average worth 520,000 RWF per head, with some worth several million RWF.

There is a large amount of value stored in these cows that farmers are very keen to protect.

Dairy insurance products developed for Rwanda would likely be individual accidental death covers.

Farmers would insure individual cows against the most common diseases, rare catastrophes, and

accidents (struck by lightning, poisoning, etc). The product pricing would be based on cow mortality

rates in the specific areas. To lower these rates, all insurance policies would be offered with an

animal care package (Table 11) of routine, but critical, measures to prevent the most common

diseases.

There are several organizations working with a large number of dairy farmers that provide dairy

cows, organization, training expertise, loans, or a market for milk. These are potential partners or

aggregators for the dairy livestock insurance product.

Potential partners:

1. One Acre Fund is an NGO working with a large number of crop and livestock farmers. One Acre

Fund is interested in training farmers on dairy production and care and could serve as a means to

group farmers and implement the animal care package and insurance at scale. One Acre Fund would

Dairy farmer with his exotic cow from the ‘One Cow per Poor Family’ program

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give loans to dairy farmers on a yearly basis, and the insurance and care package could become part

of these loans.

2. East Africa Dairy Development (EADD), a project of Heifer International, works with over 24,000

farmers who own 100,000 dairy cows in Rwanda. They currently work with 6 chilling plants and help

farmers improve dairy production, market access, and knowledge application through extension

trainings. EADD could serve as a key stakeholder in introducing the insurance product to the farmers

and cooperatives they work with.

3. One Cow Per Poor Family is a project launched by the Rwandan government in 2006 in an attempt

to improve rural nutrition. The program has expanded rapidly and has distributed a total of 113,579

cows to poor families (MINAGRI Annual Report 2010-2011). Being a major source of quality cows for

new dairy farmers, the project could be a strong partner for the insurance project by making

insurance and livestock care packages a requirement when a farmer receives a dairy cow. The care

package and training would be especially beneficial to new farmers who may be less experienced

with dairy production and care.

4. Dairy Chilling Plants are where farmers, grouped into cooperatives, deliver their milk. Farmers are

already organized around these chilling plants and have a steady income stream from milk deliveries

that could be used as collateral to access credit and to prepay the premium and purchase the animal

care package. Existing chilling plant extension staff could assist with farmer insurance education and

with ensuring implementation of the care package.

The main risks that farmers cited as

causing death or decreased milk

production were diseases (Tick-borne,

Foot and Mouth, Lumpy Skin, Tsetse fly-

borne) and the absence of vaccines that

the government was supposed to supply.

Other stresses to dairy production

included poor cooperative management

and the volatility in milk prices. The

disease risks can be covered by an

accidental death insurance policy that

would also cover other accidents (cow

struck by lightning, or falling in a ditch)

beyond the farmer’s control. This would

be a different kind of product than that

which is offered to crop farmers through

indexes. Farmers would insure individual

cows and in the case of death a trained

project veterinarian would evaluate the claim.

For the initial pilot, One Acre Fund is a recommended partner, since they would bundle the

insurance and care packages into their loans and could supply animal health training through their

existing field staff. The product would be offered to 1,000 farmers, each insuring one cow worth an

average of 520,000 RWF. At 3% premium, each farmer would pay 15,600 RWF per year for the

Animal health shop at a milk collection center in Eastern Rwanda

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insurance and 17,552 RWF per cow for the care package, for a total investment of 33,152 RWF or 55

USD. The pilot would lay the foundation for a commercially viable product, with premium the first

year coming to 21 mln RWF.

Table 11: Proposed Dairy Care Package

Draft Dairy Care Package and Indicative Pricing

Product Cost Per Treatment (RWF) Annual Cost

(RWF) Annual Cost

(USD)

Deworming (once every 3 months)

Albenol, Nilzan or others 800 3,200 5.27

Tick Control - Acaricides (spraying twice a week)

Deltox or others 138 14,352 23.64

Vaccines (yearly or half-yearly) – subject to MINAGRI feedback

Foot and Mouth Disease

Free from government, though challenges with availability and delivery would need to be addressed

East Coast Fever (new, limited availability)

Anthrax

Anaplasmosis (optional)

Lumpy Skin

Total Cost 17,552 28.92

Sector Organisation

The vast majority of the 720,000+ dairy farmers in Rwanda own between 2-3 cows, are grouped into

cooperatives, and deliver milk to collection centers or chilling plants. The sector has had increased

government and development focus in the past 6 years. While milk is also consumed by the family, a

healthy exotic dairy cow can produce 16-22 litres per day and the excess is delivered to a center for

consumption elsewhere in the country or for further processing. There are several large

organizations, mentioned above as potential partners, who work with groups of farmers and help

them organize into cooperatives. This trend is likely to continue as more farmers take up dairy cows

and become more serious about quality milk

production.

There are larger-scale professional dairy

farmers mostly operating in the east where

they can graze large herds of 50-100+ animals.

Some of these farmers have advanced animal

care practices and very low mortality rates and

others suffer from frequent disease issues and

loss of cows. These farmers also vary in terms

of the breeds they manage, some with herds of

pure Friesians to others with herds of

predominately local and exotic crosses. Kirebe Milk Collection Center

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Sector Investment

From discussions with Banque Rwandaise de Développement (BRD), there is investment and credit

available to the dairy sector to both cooperatives for the building of chilling plants and collection

centers, as well as to large professional dairy farmers for the purchase of exotic cows. There have

however, been high default rates in the past years as a result of the price of milk fluctuating and

falling. The bank has also had issues with poor management at cooperatives and cooperatives

dissolving without repaying their loans. Even in this environment, BRD says they will continue

making loans to the dairy sector.

Some cooperatives also offer credit to their farmers for the purchase of animal health inputs and

occasionally for cows. This credit is based on how much milk the farmer consistently delivers to the

collection center. Over time, the loan is repaid by deducting small portions from the payments made

for daily milk deliveries. If the insurance product is offered through cooperatives or chilling plants,

the insurance premium and care package could be repaid using a similar mechanism.

Sector Risks

The following risks were identified by dairy farmers, cooperatives and other stakeholders in the

sector. Table 12 ranks risk in order of importance from the most important to the least important.

Table 12: Risks for Dairy Livestock


Poor cooperative management

Cooperatives and milk collection centers can be

prone to management issues, leading to loan

defaults, farmers not being paid for milk in a timely

manner, and lack of support for purchasing inputs

and accessing credit from the cooperative.

Milk price volatility

The price of milk fluctuates throughout the year and

throughout different areas of the country. The rapid

introduction of exotic dairy cows into Rwanda has,

according to some, decreased the price of milk

because of increased supply and decrease demand

as more families have milk produced at home. Lower

milk prices can affect farmer/cooperative ability to

repay loans and purchase care inputs. This is also a

disincentives to invest in inputs as market prices are

A free-range herd of local and exotic crosses in far Eastern Rwanda

http://www.brd.com.rw/

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unknown and can shift.

Diseases (Tick-borne, Foot and Mouth,

Lumpky Skin, Tsetse fly-borne)

Diseases are the main causes of dairy cow mortality.

Many are preventable with proper animal care and

product use – issues that would be addressed with

the combination insurance and care package. There

are foundational animal care practices in Rwanda,

but there is much room for improvement.

Access to vaccines

Currently, the government supplies vaccines to dairy

farmers free of charge, but the supply is not

dependable and often cows go unvaccinated. This

increases the prevalence of preventable diseases,

causing decreased milk production or death.

Access to quality animal health inputs

Products to prevent major diseases are not always

available or farmers lack credit to purchase them.

This is being addressed through credit from

cooperatives. Farmers should also know the ideal

combination of inputs and method to rotate the

active ingredients to maximize effectiveness.

Data availability

There is limited dairy cow mortality data available for individual cooperatives and areas, though with

further meetings with cooperatives and partners more may emerge. ILRI and EADD have been

conducting mortality data studies with EADD cooperatives around Rwanda that can be broadened to

include new pilot and target areas. As long as these studies are being conducted, the current supply

of data is not a long-term issue for product development.

Potential products

The relevant products are ranked in order of preference are shown in table 13.

Table 13: Potential insurance products for dairy livestock


Mortality

data

Named risks –

diseases and

accidental death

Requires current and historical data on cattle mortality

including causes of death, year, and severity of

catastrophic events. This can be collected by objective

third-parties (ILRI) where adequate data does not

currently exist.

Conclusion Dairy Livestock

There is strong potential to offer a commercially viable dairy livestock insurance product in Rwanda

coupled with an animal care package to ensure improved animal health care and reduced mortality

rates. There are several possible aggregators and partners and strong government support for such a

product. A pilot should be planned and launched in the next 6 months.

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Table 14: Summary Overview Dairy Livestock

Viability Comments

Are the risks insurable? Yes Disease and accidental death risks can be insured

Is data available? Yes EADD and ILRI have data for some cooperatives, more can be collected via third-party implemented surveys

What is the premium volume after 3 years (RWF)?

713,577,072 (value of animals)

= premium rate * cow value

What is the operational cost/premium margin to distribute the product?

20% per year 20% * 713 mln RWF = 143 mln RWF or 235k USD

How many farmers and cows reached? 28,877 farmers,

45,742 cows

Agricultural value insured? 24 bln RWF/ 39 mln USD

Commercially viable? Yes Farmers organized, aggregators available, and increased interest in the dairy sector

Public Investments needed? Conducting mortality data surveys, improved distribution of vaccines by government


Initial discussions have been held with the management of One Acre Fund (Tubura) and they are

keen to investigate the potential for an accidental death dairy livestock insurance product. The

insurance would be coupled with a best-practice animal care input package and dairy health

practices trainings to further reduce mortality rates-- elements that fit very well into One Acre

Fund’s current model.

The current projections for the pilot are to start with 1,000 of their farmers in the first year and

steadily grow, bringing in other aggregators as the product evolves and becomes more technically

sound. To set up the pilot and gather information on animal care product availability, pricing, use,

and distribution, key stakeholders like MINAGRI animal resources department and ILRI will be

consulted. Additionally the Kilimo Salama team will work to establish the necessary expertise in

Rwanda.

Table 15: Dairy Livestock One Acre Fund Pilot

Year 1 Year 2

Partner/Aggregator One Acre Fund One Acre Fund


Cows insured 1,000 10,500

Who pays for the premiums? Cost of the premium and animal care package are included in the loan farmers take from One Acre Fund and repay over the year.

Estimated premium per average cow insured (RWF)

15,600 15,600

Estimated premium per average cow insured (USD)

26 26

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Cow value insured (RWF) 520,000,000 5,460,000,000

Annual Premium volume at 4% (RWF) 20,800,000 218,400,000

Annual Premium margin at 20% (RWF) 4,160,000 43,680,000

Annual Premium margin at 20% (USD) 6,853 71,960

Risks Insured Named risks - disease and accidently death

Named risks - disease and accidently death


- One Acre Fund (aggregator): makes loans to farmers to cover premiums and animal care package cost, provides livestock care trainings to farmers, facilitates claim evaluation

- Broker/Insurance company: develops the product, has ground staff for sales, training, claims evaluation

- Insurance company/reinsurer: carries the risk

- Data provider: mortality data for specific insured areas

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3. Potatoes Irish potatoes in Rwanda are mainly produced in the North, which has favorable ecological and

climatic conditions. Nyabihu, Rubavu and Musanze have volcanic ash soil and the high altitude

necessary for potato production (International Potato Centre, 2002). Potatoes are grown year round

and are harvested three times a year (IMBARAGA association, 2012; Crop Assessment 2011A Season,

2011). Countrywide, 91,996 ha of Irish potatoes are cultivated, representing 10% of all land under

cultivation. The national average yield is 12.02 T/ha (Crop Assessment 2011A Season, 2011).

The Government has been encouraging potato production through improved seed availability,

promotion of organic and chemical fertilizer use, encouragement of crop protection, post-harvest

management, processing, and organization of potato markets (National Agricultural Policy, 2004).

Irish potatoes are highly responsive to fertilizer application and have become an important source of

income due to high consumption levels (National Agricultural Policy, 2004).

An insurance product would best be channelled through cooperatives. In a pilot insuring the value of

inputs, the premium volume would be 11.2 mln RWF or 18,451 USD in the first year, while after 3

years the premium volume would be 112 mln RWF or 184,514 USD, covering 2,500 farmers in

cooperatives. Given the effectiveness of the aggregators, the operational cost would be 20% of the

premium for the product, amounting to 22.4 mln RWF or 36,903 USD annually.

Sector Organisation

Rwanda’s potato sector has both small and large farmers operating on plots ranging from 0.5

hectares to 100 hectares. Farmers with over 1 hectare of Irish potatoes are considered large farmers

and operate in cooperatives working on consolidated lands. Small or subsistence farmers operate

under an association or farmer group’s umbrella (Enterprise Network Mapping Study in Rwanda,

2009).

Sector Investment

Table 16 lists the inputs for one hectare of potatoes (Urugaga IMBARAGA Musanze, 2012). It is

estimated that 80% of potato farmers in cooperatives adopt such a package (KOABIKI Cooperative,

2012).

Healthy potato plants in Northern Rwanda

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Table 16: Potato Inputs

Production Cost Item Quantity/Ha Cost per unit

(RWF) Total (RWF)

Seed (Kg) 2000 300 600,000

Transport of Manure (T) 2000 5 10,000

Inorganic Manure (Kg) 300 380 114,000

Dithane (Kg) 25 2,000 50,000

Suprmethrine (L) 2 4,000 8,000

Ridomil (Kg) 1 14,000 14,000

Total 796,000

Sector Risks

In table 17 the risks identified by stakeholders in the sector are ranked in order of importance from

the most important to the least important. The estimated losses due to the described risk are

derived from discussions with Rwandan farmers during field visits in June 2012 and experiences in

Kenya by the consultancy team.

Table 17: Risks for potato growers


Access to Inputs, quality seed and fertilizer

Limited supply of good quality seed leads to use of inappropriate seed varieties and lower yields

Post-harvest management, maintenance of quality as produce is stored and moved from the farm to the market

High potato quantitative and qualitative crop loss. Only an average of 30% of the produce is transported to bigger markets in Kigali that would fetch a higher price (165-175 RFW). 70% is sold locally at lower prices (120-130 RWF).

Disease, fungal, bacterial, or viral attacks on a crop

Potato is a high-risk/high-return crop, heavily dependent on weather and particularly vulnerable to late blight disease

Climate, uncertain and erratic rainfall

Affects germination, tuber formation and flowering stages. Sporadic rain observed in 2011A, excess rain observed in 2012A

Partial and Sporadic Information, market price and quantity information

The actors along the value chain often do not know each other and do no cooperatively work together

Data availability

The Ministry of Agriculture and Animal Resources in collaboration with the Rwanda Agriculture

Board (RAB), the National Institute of Statistics of Rwanda, and the World Food Program organize a

crop assessment survey each agricultural season (Source; Crop Assessment 2011a Season: Crop

Area, Yield and Production, MINAGRI, 2011). Yield data would be most relevant for potato insurance

since the highest insurable risk of potatoes relates to disease. Rainfall and satellite data to cover for

erratic rainfall risks is available, as per the map in Annex 4.

Potential products


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Table 18: Potential insurance products for Irish Potatoes


Area yield index

All risks causing lower yield.

Use the crop assessment survey data. Farmers need to be into organized groups Requires insured group to have high percentage of penetration in the district, at least 30%.

Satellite index

Excess and Erratic Rain


Weather index

Drought, Excess and Erratic Rain, Weather related disease (Blight)

Requires investment in automated weather stations Due to the hilly nature of the country this would be expensive. Data cleaning and gap filling would also be required.

Conclusion Potato

The Table 19 provides a schematic overview of the opportunity in potato using cooperatives as

distribution channel for insurance. The overall detailed table showing all indicators collected is

provided in Annex 1C.

Table 19: Summary overview for potato

Viability Comment

Are the risks insurable?

Yes Drought, erratic rainfall, and weather related disease

No Access to inputs, management, and markets


Area yield data by season and year. Weather data available from Rwanda Meteo. Satellite data available.


112,000,000 RWF 184,514 USD

(Inputs)


20% per year 22,400,000RWF 36,903 USD


2,500 farmers

Ag investment insured after 3 years? 1.4 bln RWF 2.3 mln USD

(Inputs)

Commercially viable? High Potential Requires 1 out of every 5 cooperatives to take up the product.

Public Investments needed? In AWS, in case of weather index. Procurement of satellite data, in case of satellite index.

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Rwanda has 50 potato cooperatives with an average

membership of 500 farmers, each producing on 0.7

ha (IMBARAGA Musanze, 2012). Insurance would be

piloted with cooperatives working with a strong

aggregator that would collect premiums from its

members.

National farmers union IMBARAGA would be the key

partner in identifying cooperatives to be recruited

for the program past the pilot phase, and in

providing farmer education in insurance. Table 20

shows an overview of the pilot potential.

Table 20: Potato Pilot Potential

Year 1 Year 2

Distributor – Cooperatives/associations

COAMU and COIMV

IMPUYAKI, COAMU, COAMV, COOABIKI, CAMR, KOMUBI, COOHAMU, COAEBU, CAMS, COTEMU, COIMU, KMTB, COABI, COJYIMU

Number of farmers reached 250 1250

Acreage (Ha) 175 875

Who pays for the premiums? Farmers pay through the cooperatives that can finance the premiums and deduct from harvest.

What is insured? 140 mln RWF 230,643 USD

(Input)

700 mln RWF 1.1 mln USD

(Input)

Annual Premium volume at 8% 11.2 mln RWF

18,451 USD 56 mln RWF 92,257 USD

Annual Premium margin at 20% 2.2 mln RWF

3,690 USD 11.2mln RWF 18,451 USD

Risks Insured Excess Rainfall Weather related disease – Blight

Excess Rainfall Weather related disease – Blight


- Cooperative: insured on behalf of farmers - Broker/Insurance company: develops the product - Insurance company/reinsurer: carries the risk - Data provider: area yield, satellite or weather data - RAB - Inputs supply - National farmers union IMBARAGA - technical assistance

on technology transfer, training

Public Investment needed Depending on product: 3-5 AWS weather stations + Maintenance = 21,000 USD Satellite Data: 15,000 USD

Cooperative farmer with a potato plant suffering from Late Blight

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4. Tea

Tea continues to be one of top exports of Rwanda. Close to 40,000 farmers grow the crop, on over

13,000 hectares (MINAGRI website). Annual earnings from the sector average 55 mln USD with an

average of over 21,000 MT of made tea being produced. Investment in the sector is growing

following the privatization of tea factories. The sector is roughly divided into three organizational

structures: cooperatives cultivating 20% of area, industrial estates on 32% of area, and smallholder

tea growers, the villageois, making up the remaining 48% of area cultivated.

The cooperative and industrial estates are professionally run, with total investment in inputs

annually at 4.2 mln USD or 2.7 bln RWF. For smallholders, the villageois, it is estimated that the

annual investment per Ha is 30% less than for the cooperatives and estates, as they use less fertilizer

but more labor (Rwanda Mountain Tea, 2012).

The cooperatives and estates experience similar risks and it would therefore be advisable to develop

an insurance product for them jointly to maximize the economies of scale. The premium volume in

the first year of the pilot would be 22.4mln RWF or 36,000 USD, while after 3 years the premium

volume could be expected to grow to 120mln RWF or close to 200,000 USD, covering 50% of the

estates and 3 out of 4 cooperatives. Given the effectiveness of the aggregators, an operational cost

of 20% would be expected, which for the sector after three years would mount to 24mln RWF or

40,000 USD annually.

Drought, frost, and flooding were identified as the main risks to the sector. Both weather and yield

data is available, which means that a product could be developed and priced.

The key risk would be the variation is weather risks across the country. Since factories are spread

across the country, products will have to be tailored to some extent to local risk profiles. If replicated

well, the product could become profitable.

Tea fields producing for Mulindi Tea Factory

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4.1 Smallholder Cooperative Tea

Sector Organization – Cooperative Tea

20% of Rwanda’s tea growers are organised into cooperatives and associations. The members of

these organisations own the land jointly and employ laborers for farm activities such as pruning and

weeding. These should not be confused with cooperatives for the villageois, where the farmers are

organised in cooperatives, but each own their land.

The price of tea is set at a national level, and in each area one tea factory will form the main market

for the cooperative. Since there is a national price for green leaf tea, there are no incentives for side

selling by the farmers. In addition, green leaf tea has to be transported to the factories within a short

time frame, which strengthens the relationship between the farmer and his nearby processing

facility.

After harvesting, the cooperative sets aside a part of its income for labour, transport, fertilizer,

drainage, and management of the cooperative for the next season. While there is no formal

insurance available, some cooperatives will self-insure themselves from risks such as drought and

frost.

Of the 40,000 smallholders growing tea, there are close to 12,000 smallholders organised into 4

COOPTHEs. A full list, including the 15 other cooperatives that organise the villageois, is located in

Annex 2. The COOPTHEs currently cultivate on 2,747 ha of tea.

Sector Investment – Cooperative

Tea

For a single cooperative, a hectare of

tea will require inputs worth 400,000

RWF or 670 USD mainly consisting of

labor costs for weeding and pruning

and the cost of fertilizer (BRD, 2012;

PDCRE, 2012), For all cooperatives,

required inputs total 1.1 bln RWF or

1.8 mln USD. The use of inputs is

widely adopted in the sector and is

supported by the aggregated

purchase of inputs through

cooperatives.

The value of the green leaf harvest is

700,000 RWF per hectare per year or

1150 USD per hectare, which translates into is 1.9 bln RWF or 3.1 mln USD per hectare per year

country wide for the associations and cooperatives.

Entry to NAEB’s Mulindi Tea Factory that the interviewed COOPTE delivers to

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Sector Risks – Cooperative Tea

Table 21 ranks the risks identified by stakeholders (cooperatives, factories and individual farmers)

from most to least. The estimate losses due to the described risk are derived from cooperative

records in Rwanda and from experiences in Kenya by the consultancy team.

Table 21: Risks for cooperative tea growers


Drought, and erratic rain which comes at the wrong time of the season, i.e. rains in the dry season or dry periods in the rainy season.

Drought affects the crops on the hillsides. 30% of yield lost historically.

Frost, particularly during the dry season, or when plants are water stressed. High temperatures during the day of 26-28 degrees followed by low temperatures at night of below 5 degrees.

Leaves scorch, after which they fall of the tree. This requires farmers to prune the trees and will halt production for 3 months, leading up to losses of up to 60% of yield in Rwanda (COOPTHE, 2012). Losses affecting over 400ha have been recorded (Rwanda Mountain Tea, 2012).

Flooding, high water levels away soil and tea trees in the valleys

Reduces production for about 2 months. And 10-15% of yield lost historically in Rwanda (COOPTHE, 2012).

Pests: Red Mites, a pest that occurs during dry periods and attacks the leaves

Leaves shed and result in reduced yields. Red mite reported up to 30% loss in Rwanda (Rwanda Mountain Tea, 2012)

Disease: wood rot/hypoxylon, a fungal disease transmitted through pruning causes root rot

Roots rot, branches break, losses up to 30% (Infonet, 2012)

Data availability

Yield data in kg of green tea is available on a monthly basis per cooperative and per factory through

NAEB. Each factory furthermore has a weather station, which has been recording rainfall and

temperature data.

Historical production data for COOPTE, displayed in the director’s office

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Potential products

The relevant products ranked in order of preference are shown in table 22.

Table 22: Potential insurance products for cooperative tea


Area yield index

All risks that cause lower yield.

Using NAEB’s area yield data available by factory. Reinsurers would require the data to be aggregated by area. Key issue may be that ALL the farmers providing the data are the ones that are insured, leading to moral hazard on the side of the insured.

Satellite index

Drought, Frost, Flooding, Weather related diseases


Weather index

Drought, excess rain.

Using tea factory weather data. Requires investment in automated weather stations

Conclusion Cooperative Tea

Cooperative tea can be insured, and through its organized nature the premiums can be attractive.

The Table 23 provides a schematic overview of the opportunity in tea using cooperatives as

distribution channel for insurance.

However, since only 20% of farmers are in cooperatives, the growth potential is limited. The overall

detailed table showing all indicators collected is provided in Annex 1D.

Table 23: Summary overview for Cooperative Tea

Viability Comments

Are the risks insurable? Yes Drought, Frost, Flooding, Weather related disease


Area yield data by month and year at NAEB by factory Weather data available by factory. Satellite data available


60mln RwF/ 101,000USD

(Inputs)

What is the operational cost/ Premium margin to distribute the product?

20% per year 20%*33 mln RWF= 12.3 mln RWF or 20,274 USD


9,555 farmers

Ag investment insured after 3 years? 880 mln RWF/ 1.4 mln USD

(Inputs)

Commercially viable? Yes Feasible with 3 out of 4 cooperatives taking up the product.

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Expansion potential beyond these 3 is there, but would require more training of these cooperatives.

Public Investments needed? Training in financial literacy of cooperatives In AWS in case of weather index In Satellite data in case of satellite index


During the field work, Mulindi tea factory was visited. While in discussion with the Coopthe

cooperative leadership and members it became clear that the cooperative currently self-insures its

members for flood, drought, and frost risk by spreading any individual losses over the whole

cooperative membership.

They expressed an interest in insuring this risk and given their structured nature, a pilot with Mulindi

tea factory could be a good starting point to insure the other cooperatives in the country that

experience these same risks. Their neighboring cooperative Cotevemu would be the first point of

expansion if a pilot were successful. Table 24 shows an overview of the pilot potential.

Table 24: Smallholder Tea Cooperative Pilot

Year 1 Year 2

Distributor Coopthe Coopthe and Cotevemu


Acreage 500 1691

Who pays for the premiums? Farmers pay through the cooperatives that can finance

the premiums and deduct from harvest.

What is insured RWF/per Ha 400'000

(Inputs)

400'000

(Inputs)

Annual Premium volume at 7% RWF 14'000'000 47'348'000

Annual Premium margin at 20% RWF 2'800'000 9'469'600

Risks Insured Frost Frost


- Cooperative: insured on behalf of farmers

- Broker/Insurance company: develops the product

- Insurance company/reinsurer: carries the risk

- Data provider: satellite or weather data

- NAEB: yield data provider

Public Investment needed

Depending on product:

3-5 AWS weather stations + Maintenance = 21,000 USD

Satellite Data: 15,000 USD

4.2 Industrial Tea

Sector Organization – Industrial Tea

32% of Rwanda’s tea is grown on industrial estates. These estates are now all owned by local and

international investment groups who market their tea directly at the Mombasa auction.

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Of the 13,000 hectares of tea grown in Rwanda, 4,200Ha is cultivated in industrial blocks, each

having an average size of 300 hectares. The current 11 factories are Mata, Ncyli, Kitabi, Rubaya,

Nyabihu, Pfunda, Gisovu and Sorwathe.

Sector Investment – Industrial Tea

A single hectare of tea requires inputs worth 400,000 RWF or 670 USD, mainly consisting of labour

cost for weeding and pruning as well as cost of fertilizer (BRD, 2012; PDCRE, 2012). These inputs are

worth 1.68 bln RWF or 2.76 mln USD for the entire industrial sector. The use of inputs is widely

adopted by the industrial growers to maximize their investment.

The value of the green leaf harvest is 850,000 RWF or 1400 USD per hectare, which translates into

3.5 bln RWF or 5.8mln USD per year countrywide for the all the industrial estates.

Sector Risks – Industrial Tea

The risks identified by industrial growers were the same as those identified in the cooperative

sector.

Data availability

Yield data in kg of green tea is available on a monthly basis per factory through NAEB. Each factory

furthermore has a weather station, which has been recording rainfall and temperature data.

Potential products

Since the risks and the source of the data are the same, the relevant products for the industrial

sector are the same as for the cooperative sector.

Conclusion Industrial Tea

Industrial estates can be insured. Because of their professional nature and similarity to the

cooperative sector, insurance companies can capitalize on economies of scale to insure both types of

clients. The Table 25 provides a schematic overview of the opportunity in tea using estates as a

distribution channel for insurance.

However, since only 32% of tea is grown on estates and land sizes are unlikely to increase, the

growth potential is limited. The overall detailed table showing all indicators collected is provided in

Annex 1D.

Table 25: Summary overview for Industrial Tea

Yes/No Comment

Are the risks insurable? Yes Drought, Frost, Flooding, Weather related disease


Area yield data by month and year at NAEB Weather data available by factory. Satellite data available


42mln RWF/ 69,000USD

(Inputs)

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How many farmers reached? 7 estates

Ag investment insured? 8840 mln RWF/

1.3 mln USD (Inputs)

Commercially viable? Yes Particularly, in conjunction with cooperative insurance

Public Investments needed? In AWS in case of weather index In Satellite data in case of satellite index


Rwanda Mountain Tea is one

of the premier tea holding

companies and currently

manages two estates, Rubaya

and Mata. In discussions with

their management it was

established that the company

is very interested in insuring

itself from frost risk. While

frost occurs regularly, the

extent varies widely,

sometimes affecting 20ha,

other times affecting 400ha

overnight.

Rwanda Mountain Tea

estimates that in 2008 they lost

3 months-worth of tea, valued at nearly 200,000 USD or 120mln RWF due to frost overnight in Mata

estate. Their factory’s weather station has recorded min and max temperatures for sufficient periods

and could act as the historical dataset on which the index could be based.

While Rwanda Mountain Tea is interested in taking up an insurance product their main concerns are

the following 3 factors: 1) the insurers ability to make payouts, 2) the cost of the premium, and 3)

the speed of payment. Given that an index product could be reinsured and could pay out quickly,

such a product could be built relatively quickly and profitably.

Further discussions in the sector confirmed that not only does Mata face this issue, but so do Kitabi,

Nshili, and Mulindi. Other estates experience losses due to frost, but to a lesser extent. This would

allow for the product to expand and reach a large scale within 3 years.

Healthy tea plants

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Table 26: Industrial Tea Estate Pilot

Year 1 Year 2

Distributor Rwanda Mountain Tea 1

factory Rwanda Mountain Tea,

2 factories


Ha 300 600

Who pays for the premiums? Tea company/ Estate

What is insured? 400'000/ha

(Inputs) 400'000/ha

(Inputs)

Annual Premium volume at 7% in RWF 8'400'000 16,800,000

Annual Premium margin at 20% in RWF 1'680'000 3'360'000

Risks Insured Frost Frost


- Estate: insured - Broker/Insurance company: develops the

product - Insurance company/reinsurer: carries the risk - Data provider: satellite or weather data - NAEB: yield data provider

Public Investment needed Depending on product: 3-5 AWS + Maintenance = 21,000 USD Satellite Data: 15,000 USD

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5. Coffee The coffee sector has grown rapidly in recent

years, overtaking tea as Rwanda’s top

agricultural export earner. Coffee is grown

widely across the country and is typically a

smallholder crop. Close to 390,000 farmers

have coffee trees, on an area of over 28,826

hectares (NAEB, 2009). Annual earnings from

the sector were 73.9 mln USD in 2011 (BNR,

2012) with over 16,000 MT of coffee produced

(MINAGRI, 2011).

The sector is divided into three organisational

structures: cooperatives cultivating 47% of

area, large scale professional farmers on 1%,

and smallholders making up the remaining

52%. While the large scale producers are

professionals, many cooperatives have

received support from the government,

development partners, and local and

international NGOs to improve production

levels.

There are great improvements in yield to be realised through improved practises. For example,

farmers in Kenya record an average of 4-5 kgs of coffee per tree. Such yields have also been

recorded in Rwanda, but the average for professionally run cooperatives is 2kgs per tree. While this

may sound low, the average smallholder is likely to get even lower yields of about 0.2kgs per tree,

since these farmers use very little inputs to support production.

The price of coffee depends on the world market price and varies based on quality. Various levels of

certification can result in much higher prices per kg, increasing from 5 to 8 USD per kg (PRICE, 2012).

Current investment by professional farmers and cooperatives ranges from 233,000 RWF (BRD, 2012)

to 352,000 RWF per hectare (Technoserve, 2012). It is estimated that only 10% of cooperatives do

the full range of good agricultural practises and investments (PRICE, 2012). With 15,000 hectares

under professional production, the total investment in the sector is 744 mln RWF or 1.2 mln USD. If

all smallholder farmers used such levels of inputs, sector investment would add another 8.5 bln RWF

or 13.8 mln USD. Yields could increase to 2 kg per tree, resulting in 144,000 MT, an 800% growth

over the current 16,000MT.

Since coffee is grown countrywide, different areas experience different risks. In interviews with

stakeholders the mains risks identified were not production, but management related. While erratic

and excess rainfall causes production losses, the timely availability of fertilizers and access to credit

are greater stumbling blocks. Financial institutions like BRD and Opportunity Bank have entered this

Coffee factory worker spreading green coffee to dry evenly

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space, with BRD providing over 8 bln RWF in loans to cooperatives and private enterprises to

construct infrastructure such as wet mills (BRD, 2012).

Since area data for coffee is limited, an insurance product would only be able to insure against

weather risks. Discussions with cooperatives showed that weather risks are not the main cause of

concern for coffee farmers. If cooperatives and private professionals were to insure against

production risks caused by weather, a product would be most sustainable if it could cater for both

the cooperatives and the large-scale growers.

Since production is spread across the country, products would be tailored to local risk profiles. If

products can be replicated across regions, they could be sustainable.

5.1 Smallholder Cooperative Coffee

Sector Organization – Cooperative Coffee

20% of Rwanda’s coffee growers are organised into cooperatives and associations (NAEB, 2009).

Such organisations are necessary as farmers cannot sell coffee directly to the open market, but have

to sell to recognized coffee exporters. Through coffee exporters farmers can access markets,

however only few exporters provide inputs on credit.

Of the 390,000

smallholders growing

coffee there are close to

78,000 smallholders

organised into 180

cooperatives (NAEB,

2009; PDCRE, 2012). The

average number of trees

these farmers have is

around 200, covering 0.1

hectares (Technoserve,

2012). The total area

grown by farmers

affiliated with

cooperatives is

estimated to be 13,500

hectares, where each

cooperative works with

farmers cultivating 75 hectares in total.

Sector Investment – Cooperative Coffee

A hectare of coffee will require inputs worth 352,000 RWF or 580 USD, mainly consisting of fertilizers

and mulching (Technoserve 2012), which is worth 4.7 bln RWF or 7.8 mln USD for the entire

cooperative sector. The use of inputs is however not widely adopted and therefore for our analysis

we will assume that currently only 10% of cooperatives use the full range of inputs.

Bags of dried green coffee ready to be collected for further processing

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At an average yield of 2 kgs per tree in the cooperative sector, the value of the harvest is 1.1 mln

RWF or 1800 USD per year, which translates into 14.9 bln RWF or 24.7mln USD per year countrywide

for all of the associations and cooperatives.

Sector Risks – Cooperative Coffee

In table 26 the risks in the sector are ranked in order of importance from the most important to the

least important. The estimated losses due to the described risk are based on records from

stakeholders in the sector from Rwanda interviews.

Table 26: Risks for cooperative coffee growers


Access to inputs, in specific fertilizers.

Without fertilizers, yields will remain far below 2 kgs per tree, and remain at 0.2 kgs per tree

Prices, fluctuations in world coffee prices

World market prices were high in 2011 spurring increased production in 2012. This rapid increase in supply could have a downward shock of up to 40% in coffee prices, making profitable production in Rwanda more difficult (PRICE, 2012).

Drought, erratic rains during flowering

Reduces number of berries by an estimated 50% in similar varieties grown in Kenya (Tropical Farm Management, 2012)

Excess Rain, rains during wood formation and at mid-season

Causes the crop to produce fewer flowers, and less berries in similar varieties grown in Kenya. At wood formation this can lead 20% loss in yield and at mid-season this can lead to a 50% loss (Tropical Farm Management, 2012). This was confirmed by Rwandan farmers.

Lack of capacity at washing stations, during peaks in harvest season

Causes the quality of the berries to deteriorate, leading to lower prices.

Data availability

Production data in kg of coffee is available on a seasonal basis from NAEB, however as the area

cultivated can change drastically between years, it is difficult to use for insurance indexes. Weather

data is less available in western Rwanda, and since this is the area where 32% of coffee is grown this

is likely to add an additional cost for weather station based insurance. Most appropriate and cost

effective would therefore be satellite-based products.

Potential products


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Table 27: Potential insurance products for cooperative coffee


Satellite index

Drought, Excess Rain, Weather related diseases


Weather index

Drought excess rain

Using any available weather data. Requires investment in automated weather stations. Premium may be high in some areas lacking historical data.

Non insurance products

Price risk would not be insured through an insurance product; rather farmers would need to be able

to access a price hedge through an international commodity exchange. Such products would only be

interesting for very large coffee buyers as the high fixed transaction costs would mean they are not

attractive for individual cooperatives. Developing such products is however outside the scope of this

feasibility study.

Conclusion Cooperative Coffee

Coffee cooperatives can be insured and there is a huge potential for growth in the sector. However

management risks pose a challenge to profitability in the sector, and only organisations that have

regular access to inputs will be willing to pay for insurance. The Table 28 provides a schematic

overview of the opportunity in coffee using cooperatives as a distribution channel for insurance.

However, since only 20% of farmers are in cooperatives, the growth potential is limited and will

require additional investment in the sector. The overall detailed table showing all indicators

collected is provided in Annex 1E.

Coffee trees in Kirehe

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Table 28: Summary overview for cooperative coffee

Viability Comments

Are the risks insurable through insurance products?

No

Access to inputs Price Risk not through insurance products – price hedge

Yes Drought Excess Rainfall

Is data available?

No Area data not regular/reliable Weather data not widely available in some areas

Yes Weather data available in some areas Satellite data available


41.3 mln RwF/ 68,123 USD

(inputs)



How many farmers reached after 3 years? 19,285 farmers

Ag investment insured after 3 years? 475 mln RWF/ 783,131 USD

(inputs)

Commercially viable? Maybe

Feasible with only 10% of 180 cooperatives taking up the product. If these cooperatives can be reached cost effectively it is possible.

Public Investments needed? Strengthening of cooperatives to access inputs In AWS in case of weather index In Satellite data in case of satellite index


During the field work,

cooperatives trained by

Technoserve and an IFAD

supported project in Kirehe,

KWAMP, were visited and

discussions were held with the

BRD. BRD currently lends 8 bln

RWF annually to 25 cooperatives.

Adding insurance to these input

loans would protect this large

portfolio and allow for it to grow

further without exposing the

financier to risk. For an insurer, Ripening coffee cherries

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this product would further provide an excellent starting point as these cooperatives have access to

credit and therefore improved inputs. Table 29 shows an overview of the pilot potential.

Table 29: Small Holder Coffee Cooperative Pilot

Year 1 Year 2

Distributor BRD to 2 cooperatives BRD to 6 cooperatives


Acreage 150 450

Who pays for the premiums? BRD pre-finances the premiums while farmers pay through the cooperatives that access the BRD loans. BRD deducts the premiums at harvest time.

What is insured RWF/per Ha 352,000 (inputs)

352,000 (inputs)

Annual Premium volume at 8.7% in RWF

4,500,000 13,780,800

Annual Premium margin at 30% in RWF 1,300,000 4,100,000

Risks Insured Drought

Excess Rain Drought

Excess Rain


- BRD: insured on behalf of cooperatives - Cooperative: beneficiary of insurance - Broker/Insurance company: develops the product - Insurance company/reinsurer: carries the risk - Data provider: satellite data


Depending on product: - 5-10 AWS weather stations + Maintenance = 42,000

USD (only to be installed if cooperatives can also be insured in the same area

- Satellite Data: 15,000 USD

5.2 Large scale professional coffee growers

Sector Organization –

Professional Coffee

Only 1% of Rwanda’s coffee is

grown by large scale farmers.

These large scale farms are an

average of 15 hectares. As a

comparison, such professional

estates in Kenya start at 80

hectares. According to industry

experts there are about 100

such farmers in Rwanda

(PDCRE, 2012). These farmers

are professional and make

good profits. Their high level of

education means that they

would understand the benefits

Green coffee drying

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of insurance quickly and could be interested in insuring their investment.

However for insurance purposes this market may not be cost effective. The high professionalism of

these farmers would require personalised services, since they would not accept an off-the-shelf

product that does not perfectly fit their needs. However, due to their small size and number the

market will be too small for specifically tailored products. Unless the number of such farmers

increases rapidly, it will be difficult to insure these farms. The high land pressure in Rwanda means

the number of such farmers is unlikely to rapidly increase.

Sector Investment – Professional Coffee

BRD estimates that professional growers invest in inputs worth 233,000 RWF or 380 USD per

hectare, mainly consisting of fertilizers, mulching materials, and agrochemicals (BRD, 2012), worth a

total of 350 mln RWF or 575,000 USD per year for all growers countrywide. The use of inputs is

widely adopted by the professional growers to maximize their investment.

The value of the coffee harvest is 15.1 mln RWF or 25,000 USD per Ha per year, which translates into

is 22 bln RWF or 37mln USD per year countrywide for the professional farmers. The high value of

their harvest reflects the higher yield at 2 kgs of coffee per tree. These numbers show that

particularly when prices for coffee are high, these farms can be highly profitable.

Sector Risks – Professional Coffee

The risks identified by professional growers are outlined in table 30. Note that the higher level of

these farmers means they have a lower management risk, but remain with production risks such as

price and weather.

Table 30: risks to professional coffee growers


Prices, fluctuations in world coffee prices

World market prices were high in 2011 spurring

increased production in 2012. This rapid

increase in supply could have a downward

shock of up to 40% in coffee prices, making

profitable production in Rwanda more difficult

(PRICE, 2012).

Drought, Erratic rains during flowering

Reduces number of berries by an estimated

50% in similar varieties grown in Kenya

(Tropical Farm Management, 2012) and

confirmed by Rwandan farmers

Excess Rain, Rains during wood formation and

at mid-season

Causes the crop to produce fewer flowers, and

less berries in similar varieties grown in Kenya.

At wood formation this can lead 20% loss in

yield and at mid-season this can lead to a 50%

loss (Tropical Farm Management, 2012).

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Non insurance products

Non insurance products are the same as for the cooperative coffee farmers. Due to the professional

nature of these farmers, a price hedge through an international commodity exchange could be

interesting for them. Developing such a product is however outside the scope of this feasibility

study.

Data availability

Data availability is the same as for the cooperative coffee farmers.

Potential products

Since the risks and the source of the data are the same, the relevant products for the professional

farmers are the same as for the cooperative sector.

Conclusion Professional Coffee Farmers

Professional farmers can be insured, but their relatively small hectarage and total number limit their

potential. Offering insurance to them would only make sense in the long run if they can be offered in

areas where cooperatives are also active to take advantage of economies of scale.

The Table 31 provides a schematic overview of the opportunity in coffee using professional growers

as a distribution channel for insurance. This assumes 50% of professional farmers would take up the

product after 3 years. The operational margin would be set at 30% as the products would need to be

tailored. The overall detailed table showing all indicators collected is provided in Annex 1E.

Table 31 Summary overview for Professional Coffee growers

Viability Comment

Are the risks insurable through insurance products?

No Access to inputs Price Risk through non insurance products

Yes Drought Excess Rainfall

Is data available?

No

Though NAEM collects area yield data, the abrupt changes in area cultivated/harvested between years makes it difficult to develop a viable product.

Yes Weather data available in some areas Satellite data available


15.2 mln RwF/ 25,000 USD


30% per year 30%*15.2 mln RWF= 4.5 mln RWF or 7,500 USD

How many farmers reached after 3 years? 50 farmers

Ag investment insured after 3 years? 174.7 mln RWF/

287,000 USD (inputs)

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Commercially viable? Maybe Only if the products can be offered close to a cooperative interested in insurance.



Professional growers also access loans through BRD’s agricultural loans facility, which provides the

most developed and cost efficient avenue to offer insurance. This will be particularly cost efficient if

BRD is also used to offer insurance to coffee cooperatives. A pilot with five professional growers

would make for a good starting point. In year 2, this number should triple for the product to be

scalable and reach 50% of such growers by year 3.

Table 32: Professional Coffee Growers Estate Pilot

Year 1 Year 2

Distributor BRD BRD


Hectares 75 225

Who pays for the premiums? BRD pre-finances the premiums on behalf of the farmers who access the BRD loans. BRD deducts the premiums at harvest time.

What is insured? 233'000/ha

(inputs) 233'000/ha

(inputs)

Annual Premium volume at 8.7% in RWF

1'520'325 4,560,975

Annual Premium margin at 30% in RWF 456,098 1'368'293

Risks Insured Drought Excess Rain

Drought Excess Rain


- BRD: insured on behalf of farmers - Professional Farmers: beneficiaries - Broker/Insurance company: develops the product - Insurance company/reinsurer: carries the risk - Data provider: satellite or weather data


Depending on product and area: 5-10 AWS weather stations + Maintenance = 42,000 USD

(only to be installed if cooperatives can also be insured in the same area

Satellite Data: 15,000 USD

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Smallholder rice farmers preparing rice fields

6. Rice Rice is grown in 12,000 ha in government reclaimed marshlands and lowlands in Rwanda (National

Agricultural Policy, 2004). The areas allocated to rice production are 1% of the total land cultivated in

the South, 1% in the West, 2% in the East, and 1% in Kigali. The eastern province shows higher

productivity attributable to the larger land allocation for rice production in Gatsibo, Nyagatare,

Bugesera, and Kirehe.

Government policy on reducing imports has resulted in an increase of rice production (Rwanda

Agricultural Survey, 2006; National Institute of Statistics of Rwanda Ministry of Finance and

Economic Planning, 2007). This has also lead to locally produced rice becoming a strongly

commercialized crop with 47% of the produce sold to markets (Third Integrated Household Living

Conditions Survey).

The crop is cultivated by about 62,000 smallholder farmers operating under 55 cooperatives, each

farmer with an average 0.2 hectares (Rwanda IFAD - System of Rice Intensification Flyer, 2009). Due

to the organizational structure of the sector an insurance pilot targeting cooperatives would be most

efficient. Such a product would have a premium volume of 1,912,800 RWF or 3,151 USD at a pilot

stage, while after 3 years the premium volume for an input cover would grow to 19,123,000 RWF or

31,512 USD, covering 6,200 farmers clustered in cooperatives. Given the effectiveness of

aggregators, an operational cost of 20% on the input cover would be expected, which for the sector

after three years would amount to 3,825,600 RWF or 6,302 USD annually.

Sector Organization

Rice is cultivated by low-input and low-risk smallholder farmers clustered in cooperatives. The

cooperatives source an agronomist to advise on planting, growing techniques and how to deal with

disease, and purchase all inputs on behalf of the farmers. Farmers pay for the agronomist’s services

and the inputs with a portion of their harvest.

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Sector Investment

In Rwanda the Government regulates the prices of fertilizer, assisting cooperatives to source inputs.

(Ntende Cooperative, 2012)

Table 33: Rice Inputs

Input Used/Ha Quantity/Ha Cost per unit Total

Seed (Kg), RAB Varieties; Watt and Yunyin

20 400 8,000

NPK (Kg) 250 380 95,000

Urea (Kg) 175 470 82,250

Cypermetrine (L), Pesticide - Rice Fly 1 8,000 8,000

Benonyl (Kg), Disease Control 1 6,000 6,000

Total 199,250

Sector Risks

In table 34 the risks in the sector are ranked in order of importance from the most important to the

least important. The estimated losses due to the described risk are based on discussions during field

visits with Rwandan farmers in June 2012, records from stakeholders in the sector, and the team’s

experience from other countries.

Table 34: Risks for cooperative rice growers


Access to seeds – Government has a program for reclamation of marshland, which has led to a significant increase in the area available for rice cultivation.

This has resulted in an increased demand for rice seeds. To cover the seed shortages, farmers are using seeds of poor performing rice varieties available to them. The untimely access to good quality seeds has also been reported to limit farmers’ ability to start the season on time and obtain the high yields possible with the System of Rice Intensification (SRI).

Floods and Irrigation water availability - equitable distribution of water is a major constraint during the dry season.

There were floods in 2011 Season A and 2012 Season B. Floods cause a maximum loss of 25% (Ntende Rice Cooperative Field Visit, 2012). Drought can cause 15-50% losses and are experienced in un-reclaimed (less productive) marshlands which have no dams and would be more expensive to insure.

Access to Inputs and Markets - joint purchase of inputs and produce marketing of is difficult to organize

Producers have had to market the rest individually, realizing relatively low prices

Disease - Fungal, bacterial or viral

Uncontrolled Pyriculariose causes the loss of 30% of rice production. (Ntende Cooperative, 2012)

Farmer agronomy knowledge Poor agricultural practice

Data availability

The MINAGRI, in collaboration with RAB, the National Institute of Statistics of Rwanda (NISR), and

WFP regularly organizes a crop assessment survey for each agricultural season. This yield data would

be most relevant for insurance to cover against disease risks.

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Potential products


Table 35: Potential insurance products for rice


Area yield index


Use the crop assessment survey data. Farmers need to be in organized groups. Yield data from the Cooperatives.

Satellite index Drought and Flooding

Requires robust satellite product ground proofing. Cost to insure all risks at the same time will be costly, therefore a maximum of two risks will have to be selected.

Conclusion Cooperative Rice

Table 36 provides a overview of

the opportunity in rice using

cooperatives as a distribution

channel for insurance. The

overall detailed table showing

all indicators collected is

provided in Annex 1F.

Table 36: Summary overview for Rice

Viability Comment

Are the risks insurable? Yes Flooding

No Access to inputs and Management

Is data available? Yes Area yield data by season and year and Satellite data


19,123,000 RWF 31,512 USD

(Input)


20% per year 3,825,600 RWF 6,302 USD

Farmers reached after 3 years? 6,200 farmers

Ag investment insured? 239,1000 RWF 393,904 USD

(Input)

Commercially viable? Medium Present

Potential

Requires 6,200 farmers clustered in 3-4 cooperatives with an average membership of 2000 farmers to take up the product.

Rice field of reclaimed marshland protected by a

government-built dam.

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For productive reclaimed areas, management risks rank higher than weather risks.

Public Investments needed? In timely area yield data collection


An insurance pilot in rice would need to be targeted at cooperatives that have a large membership.

This is because farmers cultivate smaller land sizes of approximately 40m by 50m. Cooperatives

would be identified with the help of Union of Rice Cooperatives of Rwanda (UCORIRWA). Groups

with an onsite agronomist and good working relations with RSSP would be ideal to act as model

examples, post-pilot. Groups that have also had training by PAPSTA would be best placed to take

part in the pilot.

Table 37: Rice Pilot Potential

Year 1 Year 2

Distributor NTENDE NTENDE, UCONGI and

UCORIBU

Number of farmers reached 620 3,100

Acreage (Ha) 120 600

Who pays for the premiums? Farmers pay through the cooperatives that can finance

the premiums and deduct from harvest.

What is insured? 23,910,000 RWF

39,390 USD (Input)

119,550,000 RWF 196,952 USD

(Input)

Annual Premium volume at 8% in RWF 1,912,800 RWF

3,151 USD 9,564,000 RWF

15,756 USD

Annual Premium margin at 20% in RWF 382,560 RWF

630 USD 1,912,800 RWF

3,151 USD

Risks Insured Flooding Flooding


Cooperative: insured on behalf of farmers Broker/Insurance company: develops the product Insurance company/reinsurer: carries the risk MINAGRI: area yield data MINAGRI, Union of Rice Cooperatives of Rwanda (UCORIRWA), Support Project for the Strategic Plan for the Transformation of Agriculture (PAPSTA) Traders

Public Investment needed Depending on product: AWS weather stations + Maintenance = 21,000 USD Satellite Data: 15,000 USD

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7. Beans Beans are one of the main staple food crops for

Rwandans. EICV3 estimated that 90.2% of

households grow beans (NISR, 2012). The over 1.5

mln farming households in Rwanda, produce an

annual 330,000MT of beans, largely for household

consumption. An average farmer owning 0.72Ha

will grow beans on 20% of their land, or 0.14Ha

(NISR, 2012). The total acreage under beans is

estimated to be 320,000Ha annually (MINAGRI,

2011). At an average price of 250 RWF per kg, the

annual value of beans at 82.5bln RWF illustrates it

is one of main food crops.

Yields in the sector are relatively low at 1T per ha.

The use of fertilizer and growing of climbing beans

instead of bush beans could double the yield to 2T

per ha (One Acre Fund, 2012). While there are

some cooperatives organized to grow beans,

farmers tend to sell any of their surplus beans

directly to on-farm gate buyers or local markets.

Farming cooperatives growing beans have been

aided by efforts of ACDI VOCA’s programs supported by USAID and IFDC. Other institutions such as

One Acre Fund have promoted the use of more inputs in bean cultivation through providing

agricultural credit.

To reach the higher yields, an investment of 13,000 RWF or 20 USD per 0.14Ha is required (One Acre

Fund, 2012). This translates to 93,000 RWF/Ha or 153 USD/Ha. This investment would be used for

fertilizer, seed, and labor costs. Currently the average investment in inputs for farmers growing

beans is mainly seed at 21,000 RWF/Ha or 35 USD/Ha.

Drought, excess rain, pests, and disease all affect the bean yields. Both weather and yield data is

available, which means that an insurance product could be developed and priced. The lack of

organization in the sector however, would make it difficult to distribute an insurance product that

would need to be channeled through an aggregator that encourages farmers to invest in their crop

and grow a surplus. Assuming that such an organization would reach 5% of all Rwandan farmers

after 3 years, this would reach 75,000 farmers, insuring 976mln RWF or 1.6mln USD. After 3 years a

premium volume of 78mln RWF would be reached, close to 129,000 USD. Depending on the

effectiveness of the aggregators an operational cost of 20% annually would be expected, which for

the sector after three years would mount to 15.6mln RWF or nearly 26,000 USD annually. The key

challenge to insuring this crop would be the availability of an effective distribution channel.

Sector Organization

Where beans are cultivated, they are intercropped with maize (Enterprise Network Mapping Study

In Rwanda, 2009). There are 151 registered maize cooperatives which is an accurate indication of the

Beans in Northern Rwanda

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cooperatives specialized in marketing beans. However, only 5% of farmers are members of such

cooperatives. Some of these cooperatives have been supported by ACDI/VOCA through USAID to

increase agricultural production and expand the use of agricultural inputs and services in order to

help increase farmer incomes.

Sector Investment

The investment in beans by farmers outside those cooperatives is minimal, as most farmers use

manure rather than mineral fertilizers. From experiences in Kenya, if farmers do not invest in their

crops, they will not insure it. Therefore unless a well organized structure is available through which

farmers can invest in beans, an insurance product will not be viable.

A hectare of beans will require inputs worth 90,000 RWF or 150 USD, consisting mainly of fertilizers,

labor, and sticks for climbing beans. Climbing beans can yield a harvest of 2T per Ha, while bush

beans will provide a maximum of 1.5T per Ha. The value of the bean harvest is 250,000 RWF per Ha

or 420 USD per Ha, which translates into 80 bln RWF or 134 mln USD per year countrywide for all

farmers. However, as most of this is consumed by the grower, this income is never monetized.

Sector Risks

In table 38 the risks identified by stakeholders in the sector are ranked in order of importance from

the most important to the least important. The estimated losses due to the described risk are

derived from farmer records, experiences in Kenya of the consultancy team, and interviews with

Rwandan farmers.

Table 38: Risks for bean growers


Erratic rainfall - Drought and heavy rains which come at the wrong time of the season, i.e. rains in the dry season or dry periods in the rainy season

Drought can lower yields by up to 50%. Excessive rains can cause beans to rot or lower yield by up to 40%.

Access to labor - As a seasonal crop, lack of labor for planting and weeding is prevalent

Lack of labor affects good agricultural practices like timely planting, weeding, and application of fertilizer.

Pests, Disease – Beans have many insect and disease pressures, both in the field and in storage. Some diseases are seed borne and are easily transmitted through infected seeds. The major insects and diseases are beans flys and bean stem maggots, Angula leaf spots, bean anthracnose, common blight, halo blight, and bean common mosaic virus.

These diseases are seed born and are managed through clean seed or treated seed

Pricing – Cost of production and labour are high compared to market prices

Compromises in the input use and end product quantity and quality

Data availability

Yield data in is available from MINAGRI on a seasonal basis per district as part of the crop

assessment data. A weather index through satellite data would also be viable and would allow for

smaller groups of farmers to be insured against specific risks.

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Potential products


Table 39: Potential insurance products for beans


Area yield index


Would require the insured to have a high penetration at district or provincial level.

Satellite index

Drought, Excess Rain, Weather related diseases

Requires robust satellite product and ground proofing. Cost to insure all risks at the same time will be costly, therefore a maximum of two risks will have to be selected.

Weather index

Drought, Excess rain.

Using existing weather station data. Requires investment in automated weather stations and will attract a premium in pricing.

Non-insured risks

The diseases and pests of beans are manageable

through good agricultural practices. Insurance

would therefore not be the most cost effective

option since good management practices will

lead to higher yields, which is preferred by

farmers over any insurance compensation.

Conclusion Beans

While the risks that beans face can be insured,

the lack of organization in the sector means that

it is unlikely to attract commercial insurers. Table

40 provides a schematic overview of the

opportunity in beans using cooperatives and

those enterprises and NGOs that provide credit

to the sector as a distribution channel for

insurance. The overall detailed table showing all

indicators collected is provided in Annex 1G.

Table 40: Summary overview beans

Viability Comment

Are the risks insurable? Yes

Drought, Excess Rain, Weather related disease

No Pests and disease


Seasonal area yield data by district Weather data available as per meta data map Satellite data available

What is the premium volume after 3 years? 59mln RwF/ 97,200USD

Bean and potato farmer in Northern Rwanda

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20% 20%*59 mln RWF= 11.8 mln RWF or 19,440 USD

How many farmers reached after 3 years? 75,000 farmers

Ag investment insured after 3 years? 980 mln RWF/ 1.6 mln USD

Commercially viable? Maybe

The proposed sum insured should be achieved through a small number of cooperatives or organizations.

Public Investments needed? Training in financial literacy of cooperatives In AWS in case of weather index In Satellite data in case of satellite index


One Acre Fund currently insures its bean farmers in Huye and Karongi using its own yield data

through an area yield index product. Such a product could be expanded to cover more farmers in

Rwanda in more districts. The area yield index covers farmers for a range of risks that cause yield

fluctuations. A weather index product could also be used in areas where there is a dearth of area

yield data because market penetration of One Acre Fund is not high enough at the district level.

One Acre Fund expressed an interest in insuring this risk given their undiversified nature and since

they would like to protect their outstanding loans. As One Acre Fund expands to other districts they

are interested in expanding this cover to protect their clients, particularly in more drought prone

areas.

Table 41: Beans Pilot

Year 1 Year 2

Distributor One Acre Fund One Acre Fund


Acreage 873 2619

Who pays for the premiums? Farmers pay through One Acre Fund , which can finance the premiums and deduct from loan repayment.

What is insured RWF/per Ha 92'000 90'000

Annual Premium volume at 6% in RWF 4'700'000 14'300'000

Annual Premium margin at 20% in RWF 953,840 2'800'000

Risks Insured All yield risks All yield risks


One Acre Fund: insured on behalf of farmers Broker/Insurance company: develops the product Insurance company/reinsurer: carries the risk Data provider: satellite or weather data MINAGRI: yield data provider


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8. Wheat Wheat is considered a cash crop in the Rwandan market,

grown on 34,000 hectares during Seasons A and B (Crop

Assessment 2011 and Crop Assessment 2012A). Its

production has grown over the past 5 years. Since wheat

must be processed to be consumed, it must be delivered to

production plants to be milled and is not easily consumed by

the household. Currently the market is made of small and

medium holder farmers, producing on a range of .25 and 2

hectares. The input supply chain for quality seed and the

market for farmers’ production have become less stable

over the last year, causing difficulties for farmers.

Observed wheat yields have risen rapidly from about 0.5

tons per hectare in 2007 at the inception of the CIP

program, to 1.3 in 2009 (CIP Strategies 2011). The growth

has continued and average yields were over 2 tons per

hectare in 2011 season B. Much of these gains can be

attributed to increased use of subsidized fertilizer and the

over 1000 MT of improved seed imported by CIP (MINAGRI

Annual Report 2010-2011). However, recently the sector has

faced difficulties, particularly due to the lack of available improved seed. At harvest, farmers also

faced the challenge of decreased demand from buyers, leaving farmers with excess production.

Access to markets, drought, excess and erratic rain, lack of capital, wheat rust disease, and post-

harvest storage were identified as the main risks and challenges faced by wheat farmers. While

there is limited yield data available due to the short time frame that wheat has been cultivated on a

wide scale in Rwanda, the weather-related and disease risks could be insured with a conventional

area yield or weather index product. However, at the present time the market size is not large

enough in terms of the value of agricultural inputs used, and the scale at which wheat is being

produced to justify developing such a product.

Wheat cooperative member in Northern Rwanda

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If a product were to be developed and piloted with the Inganzo Cooperative the premium on inputs

would be 80,000 RWF or 132 USD per hectare. After three years if 2,881 farmers were reached, the

value of the premium would be 11.5 mln RWF or 18,900 USD. However, given the disorganized

structure of the many of the cooperatives and the lack of a strong aggregator to pre-pay the

premiums, it would be difficult to achieve this level of adoption of insurance. It is advised that an

insurance product for wheat only be launched when the wheat industry in Rwanda grows and

matures, with more steady demand for farmers’ production and a more stable value chain.

Sector Organization

The estimated 57,000 farmers growing wheat in Rwanda are organized into cooperatives or

associations and sell their produce to milling companies. These cooperatives could be linked to

processing companies for insurance administration, but recent farmer interviews indicated that

many milling companies have either decreased their demand or left the market. In a discussion with

AZAM, company representatives indicated that they have not been procuring wheat domestically in

Rwanda. Likewise, a cooperative explained that AZAM was not buying from them due to the

inadequate quality of wheat they were producing. For the sector to stabilize and regain the size it

had in 2011, there need to be more clear buyers of wheat offering competitive and predicable

market prices.

Sector Investment

To be grown properly, wheat requires fertilization and good quality seed. Physical input costs should

come to 100,000 RWF per hectare. These costs are mainly for a mix of Urea, DAP, or NKP, in addition

to manure to feed the crop.

Individual wheat farmers have little access to credit to purchase quality inputs. Farmers interviewed

discussed difficulties getting loans, and BRD indicated that it currently does not loan to wheat

farmers. BRD’s only experience with loans for wheat was for a processing plant that went bankrupt.

Wheat fields in Northern Rwanda with volcanoes in the background.

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Sector Risks

The following risks were identified by stakeholders in the sector. In table 42 risks are ranked in order

of importance from the most important to the least.

Table 42: Risks for cooperative wheat

Risk Resulting loss

Access to markets

Buyers are leaving the market, leaving farmers with their harvests and nowhere to sell. This is a strong disincentive to invest and grow wheat next season as their market is not assured.

Access to inputs

Accessing quality improved seed was difficult in the 2011 seasons, causing planting delays resulting in a 19% decline in yield and farmers planting wheat on 64% less area.

Drought, excess rain, erratic rain

Wheat is severely affected by droughts during the growing season and excess rain at harvest. It is also impacted by flooding or lodging during the season.

Lack of capital Farmers cannot access loans to purchase inputs.

Diseases (Wheat Rust)

Wheat Rust reduces yields and stresses plants. It becomes more prevalent with excessive rain.

Post-harvest storage

Without adequate structures to dry and store the harvested wheat, losses result from excess moisture and pests, as well as a decrease in quality.

Data availability

The Ministry of Agriculture and Animal

Resources (MINAGRI), in collaboration

with Rwanda Agriculture Board (RAB),

the National Institute of Statistics of

Rwanda (NISR), and World Food

Program (WFP) organize a crop

assessment survey for each agricultural

season. This survey could be used for

an area yield index product.

Potential products

The relevant products ranked in order

of preference are shown in table 43.

Table 43: Potential insurance products for cooperative wheat


Area yield index

All risks causing lower yields

Could use the crop assessment survey data, though there are not enough years of data. Farmers need to be in organized groups Requires insured group to have high % penetration in the district, at least 30%. Wheat production is not wide spread enough to meet this mark, however.

Wheat Rust on Rwandan wheat

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Satellite index

Drought, Excess Rain


Weather index

Drought, Excess Rain

Requires investment in automated weather stations Due to the hilly nature of the country this would be expensive and high potential for basis risk. Data cleaning and gap filling would also be required.

Uninsurable risk

The key risk for wheat currently is access to markets and timely inputs, which are both management

risks rather than production risks. These risks cannot be insured using conventional products.

Instead, they require improvements in the market, rather than insurance.

Conclusion Wheat

At the present time, due to the instability in the wheat sector and the most important risks being

uninsurable, an insurance pilot for wheat is not recommended. If in the future the market grows,

AZAM or another processor emerges as a strong aggregator, and there is more stable access to

quality seed and fertilizer, the viability of an insurance product can be investigated at that time.

Table 44: Summary Overview Wheat

Viability Comment

Are the risks insurable? Limited Drought/excess rain/Rust, but not the other top risks

Is data available? Limited Area yield data is available. Weather data scattered. Satellite data available.


11,400,000


20% 20%* 11.5 mln RWF = 2.3 mln RwF or 3,800 USD

How many farmers reached? 2,881 farmers

Ag input value insured? 144 mln RWF/

237 k USD (inputs)

Commercially viable? Not yet Market size is small, but with strong aggregators and larger volumes, there is potential



At this time an insurance pilot for wheat is not recommended. While there the potential to insure

wheat in the future, the current market size would not support an insurance product. Between the

drastic fluctuations in area under cultivation, the buyers of wheat showing instability, and the

volatility of the market for wheat, it is a less attractive crop for farmers to invest in.

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9. Cassava

Cassava is an important food security crop in Rwanda, with 52% of agricultural households growing

cassava on 199,224 hectares during Seasons A and B (EICV3 and Crop Assessment 2011). The vast

majority of the crop is consumed by the household or sold at the local market; there is little

processing of cassava apart from what is done at the new Kinazi processing company. The sector is

almost completely made up of individual farmers, with only one cassava-focused cooperative

identified.

Productivity of cassava has risen rapidly from about 5 tons per hectare in 2007 at the inception of

the Crop Intensification Program (CIP), to 11 in 2009 (CIP Strategies 2011). The growth has continued

and average yields are now over 13 tons per hectare in 2012 season A. Much of these gains can be

attributed to the increased use of improved cassava cuttings, distributed through CIP by the Rwanda

Agricultural Board (RAB).

Access to markets, drought, price fluctuations, and access to working capital were identified as the

main risks and challenges faced by farmers in the sector. While there is limited weather and yield

data is available, the majority of the risks are uninsurable with conventional products. This

compounded by the unorganized nature of the sector, low input use, and predominately local

consumption make cassava a low potential and low priority crop for which to develop agricultural

insurance products.

If a product was to be developed and piloted with the one cooperative, Rahango Cooperative, the

full premium on inputs would be 455,000 RWF or 750 USD for the cooperative’s 150 hectares under

production. After three years if the cooperative’s 104 farmers took a harvest cover, the premium

would be 2.8 mln RWF or 4,600 USD. These amounts are not feasible to support the development of

an insurance product. If in the future Kinazi processing company grows to a point where they are

procuring large volumes and values of cassava in the local Rwandan market, then the insurance

potential could be re-evaluated.

Sector Organization

The vast majority of the estimated 780,000 Rwandan farmers growing cassava are unorganized

around the crop, with only one cassava-focused cooperative found. This cooperative of 104 farmers

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was formed, and initially linked to the Kinazi cassava processing plant, with the expectation that

Kinazi would purchase their production. As of July 2012, the plant is not purchasing dried cassava

from the farmers.

Cassava is predominately locally consumed, with the price determined at the local market level.

Farmers have been experiencing wide price fluctuations, between 80-150 RWF per kg of dried

cassava. There are no bulk buyers of cassava identified in the Rwandan market, and this absence of

aggregators would make providing insurance on cassava more difficult.

Sector Investment

Cassava is often treated as a low input crop, with a high-yielding cooperative investing just 43,333

RWF or 71 USD per hectare, mainly for the cost of improving cuttings (often bought once every three

years) and locally procured manure fertilizers. In fertile areas very little fertilizer is added.

Individual farmers have little access to credit to purchase quality inputs, though BRD does lend to 20

large scale cassava producers (growing 10+ hectares) to purchase NPK and improved cuttings. There

are very few such producers and

the vast majority of the sector

consists of farmers with .1-.3

hectares under cassava

production.

Sector Risks

The following risks were identified

by stakeholders in the sector. In

table 45 risks are ranked in order

of importance from the most

important to the least important,

with estimate losses due to the

described risk.

Table 45: Risks for cooperative cassava


Access to markets

Kinazi did not buy production as expected, less income for investment in crop next year and less incentive to grow cassava

Drought, prolonged periods of very little to no rain

While cassava is drought tolerant, during extreme periods of no rain, 40% losses in yield and delayed maturity can occur.

Price Fluctuations

Disincentives to invest in inputs as market prices are unknown and can shift.

Access to capital Most farmers cannot access loans to purchase inputs.

Diseases (cassava mosaic disease, bacterial blight)

Not often experienced, when they do occur ISRA brings new, clean cuttings so farmers can replant. Ruhango Cooperative has not experienced disease outbreaks in recent years.

Cassava infected with Cassava Mosaic Disease (CMD).

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Data availability

The Ministry of Agriculture and Animal Resources (MINAGRI), in collaboration with RAB, the National

Institute of Statistics of Rwanda (NISR), and World Food Program (WFP) organize a crop assessment

survey for each agricultural season. This survey could be used for an area yield index product.

Potential products


Table 46: Potential insurance products for cooperative cassava


Area yield index

All risks causing lower yields

Use the crop assessment survey data Farmers need to be in organized groups – which in the case of cassava they are not Requires insured group to have high % penetration in the district, at least 30%.

Satellite index

Drought Requires robust satellite product and ground proofing. Cost to insure all risks at the same time will be costly, therefore a maximum of two risks will have to be selected.

Weather index

Drought

Requires investment in automated weather stations Due to the hilly nature of the country this would be expensive and high potential for basis risk. Data cleaning and gap filling would also be required.

Conclusion Cassava

Due to the unorganized nature of farmers and production in the cassava sector, lack of cooperatives,

low investment in inputs, cassava’s nature as a locally consumed and not cash crop, and the key risks

being uninsurable, an insurance pilot is not recommended for cassava.

Table 47: Summary Overview Cassava

Viability Comment

Are the risks insurable? Limited Drought, but not the other top risks

Is data available? Limited

Area yield data only for large areas. Weather data scattered. Satellite data available.


455,000


20% 20%* 455k RWF= 91,000 RwF or 150 USD

How many farmers reached? 104 farmers

Ag input value insured? 6.5 mln RWF/

11 k USD

Commercially viable? No Without aggregators or a wide cooperative presence it is not scalable

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A pilot at this point is not recommended as the current insurance market potential is low and many

of the key risks faced by farmers are uninsurable (access to markets, price fluctuations, and access to

capital). The cassava cooperative met with is one of very few cassava cooperatives in the country. It

was organized to supply Kinazi, a new cassava processing company. Unfortunately at the time of this

feasibility study, Kinazi was not purchasing dried cassava from the cooperative and could therefore

not serve as an aggregator for the insurance product. In the future if Kinazi is operational and

purchases cassava on a wide scale, the insurance potential can be re-evaluated.

Table 48: Cassava Cooperative Pilot

Year 1 Year 2

Distributor Ruhango Cooperative Ruhango Cooperative


Hectarage 150 150

Who pays for the premiums?

Farmers pay through the cooperatives, but it is unlikely the cooperative could pre-finance premiums. This upfront cost would be difficult for farmers to bear, making success and strong adoption unlikely.

What is insured RWF/per Ha (inputs) 43,333 43,333

Annual Premium volume at 7% in RWF

455,000 455,000

Annual Premium margin at 20% in RWF

91,000 91,000

Risks Insured Drought Drought


Cooperative: insured on behalf of farmers (would not be feasible due to the nature of the cooperative) Broker/Insurance company: develops the product Insurance company/reinsurer: carries the risk Data provider: satellite or weather data


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10. Sorghum Sorghum is a drought resistant crop and in Rwanda

there is low input investment with minimal usage of

inorganic fertilizer and pesticides by farmers (Crop

Assessment 2011a Season). This makes the crop a low

risk - low investment product. High production areas

are found in the Southern and Eastern provinces

which produce 55% of the total sorghum in Rwanda.

Farmer field visits in Gatsibo in the Eastern province

showed that Sorghum growers are predominantly

individual growers and are not clustered in any form

of farmer cooperatives.

In Rwanda the area under sorghum production has

decreased in the last 10 years (FAOSTAT, 2011). This

is partially attributed to the fact that sorghum has low

yields per hectare compared to other cereal crops

and was not included in the Government supported

Crop Intensification Program. The crop is however still

strongly commercialized with 32% of the produce sold

to markets (EICV3). Annual earnings from the sector

were over 85 million USD in 2011 Season A where

161,229 T of sorghum was produced and sold at 227

RWF/Kg (Rwanda Statistical Year Book, 2011).

From experiences in Kenya, for insurance products to be viable for insurers and attractive to

farmers, there must be some investment made by the farmer in the crop. This investment then

becomes the sum Insured. Since sorghum is a low risk, low input crop, the investment farmers make

is minimal. In discussions with Rwandan farmers during field visits in June 2012 the maximum

investment a farmer would make is 40,000 RWF per Ha, or 66 USD, mainly for seed.

Since the sum insured is so minimal, the likelihood of farmers wanting to insure their crop is also

unlikely. The lack of organizational structures in the sector means that there are no effective

channels through which farmers could be insured cost effectively and products would have to be

sold individually to farmers. These two factors combined mean that an insurance product would

have poor adoption potential and is not advised at this time. If the market were to change, as it did

in Kenya, where a beer brewery started buying sorghum for its low end beers, a distribution channel

would emerge and an insurance product could become viable.

Rwandan sorghum field

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Table 49: Summary overview Sorghum

Viability Comment

Are the risks insurable? No Drought resistant low risk, low input crop

Is data available? Yes Area yield data by season and year and Weather data

Commercially viable? Minimal Potential

Due to minimal investment there is low likelihood of farmers wanting to insure their crop. The lack of organizational structures in the sorghum sector means that there are no effective channels through which farmers could be insured cost effectively and products would have to be sold individually to farmers

Public Investments needed?

Building up of markets and aggregators

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6. Regulatory Environment

The insurance industry in Rwanda falls in the Non-Bank Financial Services Sector and is licensed and

regulated by the Central Bank (BNR). It constitutes of insurance companies and intermediaries

(Brokers, Agents, and Loss Adjustors). The legal framework governing insurance services in Rwanda

includes the Law Governing Insurance Business in Rwanda of 2008 and various regulations issued by

the regulator (Central Bank).

There are 5 insurance companies currently licensed in Rwanda, 6 insurance brokers, and 90 agents.

The regulatory climate uses international best practices, putting the highest burden in terms of

capital requirements and reporting on insurance companies, and has recently requested that these

insurance companies separate their life and non-life businesses. The capital and reporting

requirements for brokers are also demanding, in line with the advisory role they are expected to

fulfil to the client. Agents attract the lightest level of regulation as they are expected to be regulated

by the insurance company that has contracted their services.

Currently one insurance company provides livestock insurance, SONARWA. Another company,

SORAS provides weather index insurance and area yield index insurance through pilot schemes in

cooperation with the Ministry of Agriculture and two specialized micro insurance ventures,

MicroEnsure and Syngenta Foundation’s Kilimo Salama. SONARWA’s livestock product was

developed with support of its Nigerian agricultural team through its parent company. This

emphasizes that the existing insurance sector players are not developing new products, but instead

that outsiders with specialized agricultural experience are developing products to fit the Rwandan

market. Both index insurance ventures are supported through grant funds of the World Bank’s

private sector arm, the International Finance Corporation (IFC) as without this support they could

not cover the initial costs to develop and tailor products for the local market.

In discussions with the sector stakeholders the regulatory environment was described generally as

enabling, however one factor was mentioned that limited the growth of the sector; the affordability

of products. The experience in India in this case can be informative: the Indian government has

made it compulsory for financial institutions to extend at least 10% of their credit in rural areas. This

has meant that all large financial institutions now have a significant investment in the rural areas.

These financial institutions have tried to innovate in these areas to ensure these products are also

profitable.

In addition to this measure, the Indian government has made it compulsory for any agricultural loan

to be insured, so as to protect the farmer from building up debts in years where harvests fail. These

two measures combined have led to a large and ready market for crop insurance. To cover the cost

of insurance, the Indian government has furthermore subsidized the cost of insurance which means

that a farmer currently pays 1-2% of the cost rather than the effective 8% premium. While this is

expensive for the Indian taxpayer, it is a measure that is common in the rest of the world and is

accepted under the World Trade Organisation’s measures to support the agricultural sector.

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Since the Rwandan government is already involved in supporting the agricultural sector through

various subsidies, it could be advised to look into cost effective ways of promoting the sector. Three

measures are proposed in particular:

1) Regulate the amount of credit extended to the rural areas. This way a market for agricultural

loans will be created. A 10% rural quota similar to that of India’s.

2) Require that agricultural credit be insured. This way a market for agricultural insurance is

created.

3) Finally, rather than subsidizing the insurance premiums, remove the Value Added Tax (VAT)

of 18% from any insurance products supporting the rural sector, as this will instantly make

all products 18% more affordable.

Development partners can also support insurance companies and specialized agriculture insurance

service providers through a targeted grant program allowing private sector players to develop and

test products that reach significant numbers of farmers. In the absence of such funding, purely

private sector players are unlikely to develop products as the development cost and uptake time

form a high barrier to entry.

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7. Sources and Stakeholders consulted

BNR, 2012, Annual Report 2011, via website available:

http://www.bnr.rw/docs/publicnotices/BNR%20ANNUAL%20REPORT%202011%20FINAL.pdf

BRD, 2012, Discussion with Emmanuel Karuranga, Senior Director Credit Administration and Internal

BRD Documentation

Care International, 2009, Enterprise Network Mapping Study in Rwanda

Care International, 2012, Enterprise Network Mapping Study in Rwanda, Esoko, 2012,

www.esoko.gov.rw/

COACMU Potato Cooperative, 2012, field visit and interview

COOPTHE, 2012, Discussion with Leonidas Munyaneza President and Manager of COOPTHE

Economics development and poverty reduction strategy (EDPRS), 2006, Agric Data File Nov 28 2006

Enterprise Nkubili and Sons (ENAS), 2012, discussion and visit to site

FAOSTAT database, 2011, Rwanda production statistics

Heifer International/East Africa Dairy Development (EADD), 2012, Interview with Country Director

and Project Manger

IFAD, 2009, System of Rice Intensification Flyer Rwanda,

http://Ciifad.Cornell.Edu/Sri/Countries/Rwanda/Rwandaifadsriflyerjan09.Pdf

IFAD, 2010, Crop Intensification Program (2008-2009) Evaluation Report

IFAD, 2012, Discussion with Project Coordinator Janvier Gasasira

IMBARAGA Potato Association, 2012, a national farmers union in Musanze, field visit and interview

Inganzo Wheat Cooperative, 2012, field visit and interview

International Potato Center (CIP), 2002, Charles Crissman, A Proposal for a Rwanda Potato Sector

Development Program Agricultural Policy Development Project Research Report No. 17

Infonet, 2012, http://www.infonet-biovision.org/default/ct/141/crops

Gatsibo bean farmer, 2012, field visit and interview

Global Partnership Initiative for Plant Breeding Capacity Building, 2007, Plant Breeding and Related

Biotechnology Capacity Rwanda

http://www.bnr.rw/docs/publicnotices/BNR%20ANNUAL%20REPORT%202011%20FINAL.pdf

http://www.esoko.gov.rw/

http://www.infonet-biovision.org/default/ct/141/crops

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Kinazi Cassava Plant, 2012, discussion with Ratagungira Nicolas the sales manager

KOABIKI Maize Cooperative, 2012, field visit and interview

MINAGRI, 2011, Crop Assessment 2011A Season: Crop Area, Yield and Production

MINAGRI, 2011, Annual Report FY 2010/2011, Government of Rwanda

MINAGRI, 2011, Livestock Populations 2011 Report supplied by Dr. Rutagwenda

MINAGRI, 2012, meeting with Dr. Rutagwenda on dairy livestock

MINAGRI website, 2012, http://www.minagri.gov.rw/index.php?option=com_content&view=

article&id=177%3Athe-tea-industry-in-rwanda&catid=117%3Atea-factory-&lang=en

Mulindi Tea Factory, 2012, Discussion with Leonidas Munyaneza President and Manager of

COOPTHE

NAEB, 2012, Discussion with Corneille Ntakirutimana Deputy Director General Production and Chain

NAEB, 2009, Coffee Census,

www.naeb.gov.rw/index.php?option=com_content&view=article&id=49&Itemid=79

National Institute of Statistics of Rwanda Ministry of Finance and Economic Planning, 2007,

Rwanda Agricultural Survey 2006

National Institute of Statistics of Rwanda, 2010, National Agricultural Survey 2008,

http://Amis.Minagri.Gov.Rw/Sites/Default/Files/User/Gor_-_Agricultural_Policy.Pdf

NISR, 2012, The Third Integrated Household Living Conditions Survey (EICV3), Main indicators report,

Government of Rwanda

Ntende Rice Cooperative, 2012, field visit and interview

One Acre Fund, 2012, Discussion with Justin Mullikin and internal yield statistics One Acre Fund

Rwanda for bush beans and climbing beans.

PDCRE, 2012, Discussion with Project Coordinator Alfred Mutebwa

PRICE, 2012, Discussion with Project Coordinator Alfred Mutebwa

Ruhango Cassava Cooperative, 2012, field visit and interview

Rwanda Mountain Tea, 2012, Discussion with Antony Butera, Managing Director Mata and Rubaya

Tea factories

http://www.minagri.gov.rw/index.php?option=com_content&view=%20article&id=177%3Athe-tea-industry-in-rwanda&catid=117%3Atea-factory-&lang=en

http://www.minagri.gov.rw/index.php?option=com_content&view=%20article&id=177%3Athe-tea-industry-in-rwanda&catid=117%3Atea-factory-&lang=en

http://www.naeb.gov.rw/index.php?option=com_content&view=article&id=49&Itemid=79

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Rwanda Statistics, 2011, Rwanda Statistical Year Book

Syngenta Foundation, 2012, Kilimo Salama Long Rains Report

Technoserve, 2012, Discussion with Napur Parikh, Coffee Initiative Program Manager and field visits

to Kamonyi districts.

Tropical Farm Management, 2012, Discussions with Managing Director Jeremy Hulme and

Smallholder manager William Muthui.

Uriganeza Maize Cooperative, 2012, field visit and interview

World Bank 2012, http://www.tradingeconomics.com/rwanda/arable-land-percent-of-land-area-

wb-data.html

World Food Program, 2009, Comprehensive Food Security and Vulnerability Analysis and Nutrition

Survey, VAM Food Security Analysis, Rwanda

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Annex 1: Crop Tables A. Maize

B. Livestock

C. Potatoes

D. TEa

E. Coffee

F. Rice

G. Beans

H. Wheat

I. Cassava

J. Sorghum

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Annex A: Maize

Maize Part A: Market size estimates

Parameters Maize Data sources/Comments

Average Number of farmers under ENTERPRISE NKUBILI AND SONS (ENAS)

75,000

ENTERPRISE NKUBILI AND SONS (ENAS) Meeting July

2012

Acreage under crop (Ha) 70,000

Number of Operational Districts 6

Average number of Ha per farmer 0.93

Producer price (RWF/kg) 240

Average Yield (kg/Ha) 3,500

ENTERPRISE NKUBILI AND SONS (ENAS) Initiative Harvest Value (RWF)

245,000,000 Farmers sale to traders and

to WFP

ENTERPRISE NKUBILI AND SONS (ENAS) Initiative Harvest Value (USD)

403,624

Production areas Nyagatare, Gatsibo, Kayonza, Kirehe, Ngoma, Rwamagana

Target areas for Pilot Kirehe Kirehe has 25,000 farmers,

technical support from KWAMP


ENTERPRISE NKUBILI AND SONS (ENAS), KWAMP, WFP, Traders

(MINIMEX)

Risks identified by farmers/aggregators Drought and Erratic rains affects

repayment

Type of data source most relevant Weather data and Satellite data To monitor the effect of the

weather on the inputs supplied to farmer


Rusumo-BGM - 21yrs, Kirehe-Bukora - 6yrs, Kirehe - 4yrs,

Mpanga - 4yrs, Bukora, Nyamugali - 1yr, Musaza - 1yr,

Nyarubuye - 2yrs

Rwanda Meteorology Map

Start of season Season A (Sept), Season B (Mar)

Harvesting time Season A (Feb), Season B (Jul)

Inputs DAP and Urea ENTERPRISE NKUBILI AND

SONS (ENAS) supply fertilizer

Number of crop cycles per year 2 Crop Assessment 2011A

Season

Maize Part B: Investment estimates

Value of Inputs per ha (RWF) 42,000 ENAS

Estimated insurance premium rate 8.00% Based on risks insured

Estimated insurance premium per Ha (RWF) 3,360

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Maize Part C: Market penetration estimates

Estimated market penetration after 2 years in percentage

66% Based on 75,000 farmers

Estimated market penetration after 2 years in Farmers

50,000 = 75k* 66%

Estimated market penetration after 2 years in Ha

46,667 =total hectares *

penetration

Estimated market value after 2 years in Harvest (RWF)

39,200,000,000 =total value * penetration

Estimated market value after 2 years in Harvest (USD)

64,579,901


75,000 100% of farmers


70,000 100% of hectares

Estimated market value after 3 years in Harvest (RWF)

58,800,000,000 100% of value

Estimated market value after 3 years in Harvest (USD)

96,869,852

Maize Part D: Pilot estimates

Number of farmers in Pilot 25,000 25% of total farmers

Number of Ha in Pilot 23,333 25% of land

Potential loan size in pilot per Ha (RWF) 42,000 Based on ENAS inputs

Potential loan size in pilot per farmer (RWF) 39,200 =loan * average ha per

farmer

Total Potential loan size in pilot (RWF) 980,000,000 = loan/ha * pilot ha

Total Potential loan size in pilot (USD) 1,614,498

Value of pilot in premiums (RWF) 78,400,000 8% premium

Value of pilot in premiums (USD) 129,160

Margin on premium inputs pilot (RWF) 15,680,000 20% Operational margin

Margin on premium inputs pilot (USD) 25,832

Potential input loans year 2 (RWF) 1,960,000,000 Based on expansion

estimates

Potential premiums year 2 (RWF) 156,800,000

Potential premiums year 2 (USD) 258,320

Margin on premium inputs year 2 (RWF) 62,720,000

Margin on premium inputs year 2 (USD) 103,328

Potential input loans year 3 (RWF) 2,940,000,000

Potential premiums year 3 (RWF) 235,200,000

Potential premiums year 3 (USD) 387,479



exchange rate USD RWF 607

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Annex B: Livestock Livestock Part A: Market size estimates

Parameter Dairy Livestock

Data sources/Comments Local Exotic (50%+ cross)

Number of dairy-owning farmers 721,920 EICV3

Number large scale producers 1000 EADD Interview with Country Director and Project Manger,

June 2012 Cows per large scale producer 100

Number small scale producers 720,000 EICV3, MINAGRI, EADD

Cows per small scale producer 1.5 EADD, farmer interviews

Average value of cow (RWF) 250,000 700,000

MINAGRI - meeting with Dr. Rutagwenda June 2012

Total number each cows 914,842 228,711

Total value dairy cows (RWF) 228,710,600,000 160,097,420,000

Production areas Throughout whole country, higher

numbers in East and South Provinces MINAGRI Livestock

Populations 2011

Target areas for Pilot One Acre Fund One Acre Fund


One Acre Fund, Heifer International/EADD, Milk processors

Risks identified by farmers/ aggregators

Poor cooperative management

EADD, Kirehe Coop, other farmers

Milk price volatility

Tick-borne diseases, Foot and Mouth disease, Lumpky Skin disease, Tse-tse fly-

borne disease (limited areas in East)

Access to quality inputs

Access to vaccines from the government, no dependable supply

Data source most relevant Mortality data collected by ILRI for EADD EADD, ILRI

Availability of data source Bwisanga, Kabarore, Mbare, and

Nyagihanga compiled in 2012 EADD, ILRI

Input use

Vaccines (from government)

EADD, Kirehe Coop, other farmers

Acaricides (tick control spraying)

Deworming

Vitamins/mineral salts

Livestock Part B: Investment estimates

Value of care package inputs per cow (RWF)

17,552 EADD

Value of care package inputs national RWF

20,071,642,256 = care package value *

number of cows

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Value of care package inputs national USD

33,066,956

Value of cows in Rwanda (RWF) 388,808,020,000 = cow values * number

(per type)

Value of cows in Rwanda (USD) 640,540,395

Estimated insurance premium rate 3% Named risk cover –

diseases, accidently death

Estimated premium per average cow insured (RWF)

15,600 15,600

Estimated premium per average cow insured (USD)

26 26

Livestock Part C: Market penetration estimates

Estimated market penetration 4% Kenyan experience

Penetration after 3 years in farmers 28,877 = penetration * Number of

farmers

Penetration after 3 years in cows 45,742 = penetration * number of

cows

Cow value insured after 3 years RWF 23,785,902,400 = cows * average value

Cow value insured after 3 years USD 39,186,000

Livestock Part D: Pilot estimates

Number of farmers in Pilot 1,000 Farmers working with One

Acre Fund

Number of cows in Pilot 1,000 Each farmer insuring 1 cow

Potential cow value (RWF) 520,000,000 = number of cows *

weighted average value

Potential cow value (USD) 856,672

Value of pilot in premiums (RWF) 15,600,000 = premium rate * cow value


Livestock Part E: 3 Year Market Estimates

Number of farmers insured in year 3 28,877 One Acre Fund Farmers, EADD

Cooperatives

Cows insured after 3 years 45,742 = penetration * number of

cows

Cow value insured after 3 years RWF 23,785,902,400 = cows * average value

Cow value insured after 3 years USD 39,186,001

Premiums year 3 on cow value RWF 713,577,072 = premium rate * cow value

Premiums year 3 on cow value USD 1,175,580

Margin on premium RWF 142,715,414 = 20% * premium

Margin on premium USD 235,116

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Annex C: Potatoes Potato Part A: Market size estimates

Parameters Potato Sources of data

Farmers growing potatoes 185,000

A Proposal for a Rwanda Potato Sector Development Program

Agricultural Policy Development Project Research Report No. 17

Number of Potato Cooperative 50

National farmers union IMBARAGA -Musanze

Farmers per cooperative 500

Total Number of farmers in Potato Cooperatives

25,000


Acreage under crop (Ha) 91,996 Crop Assessment 2011a Season: Crop Area, Yield and Production

Annual production (kg) 2,171,517,000 Rwanda Statistical Year Book 2011

Producer price (RWF/kg) 130 Enterprise Network Mapping

Study In Rwanda, 2009. Koabiki Cooperative

Average yield (kg/ha) 23,604.47 Koabiki Cooperative - Cyuve

Sector Musanze

Countrywide harvest value (RWF) 282,297,210,000

Production areas Land allocated to Irish potato:

8% in South, 15% in West (Rubavu), 18% in North.

Crop Assessment 2011a Season: Crop Area, Yield and Production

Target areas for Pilot North (Musanze), West (Rubavu)


RAB, Individual Aggregators, Processing factories


Seed availability (few trusted multipliers), Climate (uncertain/

sporadic rainfall) COORPIFAMU - Rubavu

Type of data source most relevant Weather Data and Area Yield

Availability of data source 1989 - 2005 EDPRS Agric Data File Nov 28 2006

Start of season Season A (Sept - Jan), Season B

(Feb - Jul), Season C (June – Sept)

Harvesting time Season A (Feb), Season B (Aug),

Season C (Oct)

Input use Fertilizer, Insecticide, Pesticide,

Improved Seed, Labour

Number of crop cycles per year 3 Crop Assessment 2011a Season: Crop Area, Yield and Production

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Potato Part B: Investment estimates

Value of Inputs per ha (RWF) 800,000 Farmer feedback

Estimated insurance premium rate 8.00%

Estimated insurance premium per Ha (RWF) 64,000.00 = premium * inputs

Potato C: Market penetration estimates

Estimated potential penetration 10% Based on Kenya experience

Estimated penetration after 2 years in Farmers 1,250 = 5% penetration * farmers

in coops

Estimated penetration after 2 years in Ha 875 = 5% penetration * ha in

coops

Estimated value after 2 years in Harvest (RWF) 2,685,008,463 = estimated ha reached* price per kg * kg per ha

Estimated value after 2 years in Harvest (USD) 4,423,408

Estimated penetration after 3 years in Farmers 2,500 = 10% penetration *

farmers in coops

Estimated penetration after 3 years in Ha 1,750 = 10% penetration * ha in

coops

Estimated value after 3 years in Harvest (RWF) 5,370,016,925 = estimated ha reached* price per kg * kg per ha

Estimated value after 3 years in Harvest (USD) 8,846,815

Potato Part D: Pilot estimates

Number of farmers in Pilot 250 Working with one

cooperative

Number of ha in Pilot 175

Potential loan size in pilot per ha (RWF) 800,000

Potential loan size in pilot per farmer (RWF) 560,000 = investment per ha *

average ha per farmer

Total Potential loan size in pilot (RWF) 140,000,000 = loan * farmers insured

Total Potential loan size in pilot (USD) 230,643

Value of pilot in premiums (RWF) 11,200,000 = total loan * premium rate


Margin on premium inputs pilot (RWF) 2,240,000 = premium * 20% margin


Potential market size input loans year 2 (RWF) 700,000,000 = loan * farmers insured

Potential market size premiums year 2 (RWF) 56,000,000 = total loan * premium rate

Potential market size premiums year 2 (USD) 92,257

Margin on premium inputs year 2 (RWF) 11,200,000 = premium * 20% margin

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Potential market size input loans year 3 (RWF) 1,400,000,000 = loan * farmers insured

Potential market size premiums year 3 (RWF) 112,000,000 = total loan * premium rate

Potential market size premiums year 3 (USD) 184,514.00

Margin on premium inputs year 3 (RWF) 22,400,000.00 = premium * 20% margin


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Annex D: Tea

Tea Part A: Market size estimates

Tea national

Tea Largescale

Tea Smallscale

Data Sources/Comments

Number growers 40'000 14 11'943 For estates: Website MINAGRI

For cooperatives = hectares/average farm size

Average number of Ha per farmer

0.23 300 0.23

Hectares under crop ha 13'000 4'200 2'747

For cooperatives: Website MINAGRI

For estates = hectares * number of factories

Annual production (kg) made tea

21'500'000 7'224'000 4'724'840 = hectares *average yield

Producer price/kg/USD 2.59 2.59 2.59 MINAGRI 2010/2011 annual

report

Average Yield kg/Ha made tea

1'654 1'720 1'720 PRICE

Mutebwa

Average yield kg/Ha green leaf

6'781 8'500 7'000

For cooperatives and estates: Mulindi

For industrial: Rwanda Mountain Tea

For national = annual production* conversion rate

for green tea/ hectares

Price of green tea per KG RWF

100 100 100 per kg (Mulindi and Rwanda

Mountain Tea), 2012

Harvest value per Ha green leaf

1'117 1'400 1'153 = yield * price of green tea

Countrywide harvest value USD green tea

14'522'241 5'881'384 3'167'875 = yield * price of green tea*

total hectares

Countrywide Harvest Value USD made tea

63'900'000 18'710'160 12'237'336 BNR 2011

Production areas North, South,

West Province Website MINAGRI

Target areas for Pilot Rwanda

Mountain Tea Mulindi


BRD, Rwanda Mountain Tea, other

private investors

Wood Family Trust, BRD

cooperatives


Price risk Price risk Drought

PRICE, Mulindi, NAEB Coop

management Coop

management Flooding

Frost Frost Frost

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Drought Wood Rot

Pests, diseases Red mites


Area yield, Satellite, and Weather data

Availability of data source Weather and area yield available for many

cooperatives and large plantations for 15-20 years

Start of season October PRICE, Mulindi, NAEB

Harvesting time February PRICE, Mulindi, NAEB

Input use Plants (free), Fertilizers, Weeding, Pruning BRD

Crop cycles per year 1 1 1 PRICE, Mulindi, NAEB

Tea Part B: Investment estimates

Tea national

Tea Largescale

Tea Smallscale Data sources/Comments

Value of Inputs per ha (RWF)

400'000 400'000 400'000 PRICE, Mulindi, NAEB


7% 7% 7% Likely cost of cover for 2 risks

Value of inputs national RWF

5'200'000'000 1'680'000'000 1'098'800'000 = input cost per Ha * total Ha

Value of inputs national USD

8'566'722 2'767'710 1'810'214 = input cost per Ha * total Ha*

exchange rate

Part C: Market penetration estimates

Tea Largescale Tea Smallscale

Cooperatives and Estates

Comments, Sources and Calculations

Estimated potential market penetration

50% 80%

Estates - 50% uptake since some

risk is managed internally

Smallscale –most will take it as they are in

the same area and have this risk

Estimated penetration after 3 years in Farmers

7 9'555 9'562 = penetration *

Number of estates/farmers

Estimated penetration of total after 3 years in Ha

2'100 2'198 4'298 = penetration *

hectare

Estimated value of total after 3 years in Harvest USD

9'355'080 9'789'868 19'144'948 = hectares * price *

yield

Estimated value of total after 3 years in Harvest RWF

5'678'533'560 5'942'450'167 11'620'983'727

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Tea Part D: Pilot estimates

Tea national Tea Largescale Tea Smallscale

Data sources/Comments

Number of farmers in Pilot 1 2'174 2'175 = pilot ha / ha farm

size

Number of ha in Pilot 600 500 1'100 set at discretion

Potential input investment size in pilot per ha (RWF)

400'000 400'000

PRICE, NAEB

Potential input investment in pilot per farmer (RWF)

120'000'000 92'000 120'092'000 = inputs per ha *

average ha per estate/farmers

Potential input investment in pilot (RWF)

240'000'000 200'000'000 440'000'000 = input cost per acre

* pilot hectares

Potential input investment in pilot (USD)

395'387 329'489 724'876

Value of pilot in premiums (RWF)

16'800'000 14'000'000 30'800'000 = premium rate *

pilot loan size


27'677 23'064 50'741

Tea Part E: 3 Year Market Estimates

Tea national Tea Largescale Tea Smallscale


Number of farmers insured in year 3

7 9'555 9'562

Potential input investment year 3 (RWF)

840'000'000 879'040'000 1'719'040'000 = expected ha after

3 year * ha per hectare

Potential input investment year 3 (USD)

1'383'855 1'448'171 2'832'026

Potential premiums year 3 Inputs only (RWF)

58'800'000 61'532'800 120'332'800 = Potential market

size inputs * premium rate

Potential premiums year 3 Inputs only (USD)

96'870 101'372 198'242

Potential premiums year 3 Input and Output (RWF)

397'497'349 415'971'512 813'468'861 = Potential market

size output * premium rate

Potential premiums year 3 Input and Output (USD)

654'856 685'291 1'340'146

Margin on premium inputs (RWF)

11'760'000 12'306'560 24'066'560 = 20%* market size

premium inputs

Margin on premium harvest (RWF)

79'499'470 83'194'302 162'693'772 = 20%* market size

premium output

Margin on premium inputs (USD)

19'374 20'274 39'648

Margin on premium harvest (USD)

130'971 137'058 268'029

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Annex E: Coffee Coffee Part A: Market size estimates

Item Coffee National/

Total Coffee

largescale Coffee

cooperatives Data sources/Comments

Number of large scale growers/cooperatives

100 180 IFAD 2012, PRICE and

PDCRE project

Number of small scale growers in Rw

390'000

78'000

Coffee census 2009, NAEB, Technoserve

Coop Farmers: total farmers * 20% as per

Coffee census 2009

Total number of Trees for LS/Cooperatives

72'000'000

3'750'000

15'600'000

Coffee census 2009, NAEB Calc for professional

growers = Number of trees per Ha*

Total Ha Calc for cooperatives =

Number of cooperatives * Ha per coop *Number of

trees per Ha

Average number of trees per farmer per ha

183 2500 200

National: Coffee census NAEB 2009;

LS: PRICE 2012 Cooperatives:

Technoserve, 2012


0.07 15 0.07


Professional: PRICE 2012 Cooperatives:

Technoserve, 2012

Hectares under crop ha 28'826 1500 13500


Professional: PRICE 2012 Cooperatives = 75 ha on

average * Number of cooperatives

National Annual production (kg)

16'000'000

7'500'000

5'400'000

National: MINAGRI Annual Report 2011

Professional and Cooperative Farmer =

Number of trees per Ha * Average yield * Total

Acreage

Producer price/kg/USD 5.6 5 5 MINAGRI Annual Report

2011, IFAD 2012

Average Yield kg/Ha

555.05

5'000

400 = Ha/ National

production

Average Revenue per Ha RWF 1'886'741 15'175'000 6'070'000 = Yield per ha* price

Average Revenue per Ha USD

3'108

25'000

2'000 = Yield per ha* price *

exchange rate

Average yield per tree (kg) 0.22 2 2 Technoserve, 2012 and

IFAD, 2012

Countrywide Harvest Value USD

74'600'000 37'500'000 27'000'000 = Annual production/

Number of trees

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Countrywide Harvest Value RWF

45'282'200'000 22'762'500'000 16'389'000'000 = Annual production/

Number of trees * exchange rate

Production areas Country wide, all districts NAEB, 2009

Target areas for Pilot

Nyamasheke, Rusizi, Gakenke,

Kamonyi, Ngoma and Rutsiro, all

have between 4-6mln trees

Farmers accessing loans

through BRD ie. ENAS

Company

Abakunda Kawa- Rushashi Gakenke,

Coopakama Kibuye

KWAMP, 2012; BRD, 2012


BRD, Kivu coffee, Exporters

RWACOF and RCT

BRD client cooperatives:

Kivu Coffee etc.

BRD client cooperatives

PRICE and Technoserve

trained cooperatives

BRD, 2012; Technoserve, 2012; IFAD, 2012


Access to Fertilizers (1)

BRD, 2012; Technoserve, 2012; IFAD, 2012

Prices (2)

Erratic rainfall: Drought (3)

Erratic rainfall: Excess Rain (4)


Satellite, Rainfall data, World Coffee Market Prices Based on Kenyan

experience

Availability of data source See meta data map

Start of season October IFAD, 2012

Harvesting time March-May Technoserve, 2012

Any Inputs used farmers in area? If so which?

Manure 38%, Pesticides 52% Mulching 53%

Fertilizer 76% Manure 8%

Pesticides 52% Mulching 53%

NAEB, 2009, Coffee census

Number of crop cycles per year

1 1 1 BRD, 2012

Coffee Part B: Investment estimates

Coffee

National

Coffee Professional

Farmers

Coffee cooperative

Cooperatives and

Professionals Overall Sources

Value of Inputs per ha (RWF)

292'559 233000 352'000

National = average of cooperatives and

professional farmers Professional; BRD 2012

Cooperatives; Technoserve 2012


8.70% 8.70% 8.70% 8.70% Syngenta Foundation 2012

based on Kenyan experience

Value of inputs national RWF

8'433'319'167

349'500'000 4'752'000'00

0 5'101'500'000

= National hectares * Average value of inputs

Value of inputs national USD

13'893'442 575'783 7'828'666 8'404'448 = input cost per Ha * total

Ha* exchange rate

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Coffee Part C: Market penetration estimates

Coffee

National

Coffee Professional

Farmers

Coffee cooperatives

Cooperatives and

Professionals Data sources/Comments


30% 50% 10%

Syngenta Foundation 2012 based on Kenyan

experience


117'000 50 18 18'564 = % penetration* total

national number of farmers/cooperatives


8'648 750 1'350 2'100 = % penetration* total

acreage

Estimated market value after 3 years in Harvest USD

26'880'000 18'750'000 2'700'000 21'450'000 = Total acreage year 3 * producer price * average

yield per Ha

Estimated market value after 3 years in Harvest RWF

16'316'160'000

11'381'250'000

1'638'900'000

13'020'150'000 = Total acreage year 3 * producer price * average

yield per Ha * ex rate

Coffee Part D: Pilot estimates

Professional

Farmers Cooperatives

Cooperatives and Professionals

Overall Sources

Number of farmers in Pilot 5 2 2'148 set at discretion

Number of ha in Pilot 75 150 225 = number of farmers *

average acreage per farmer/cooperative

Potential loan size in pilot per ha (RWF)

233'000 352'000

National = average of cooperatives and

professional farmers Professional; BRD 2012

Cooperatives; Technoserve 2012

Potential loan size in pilot per farmer (RWF)

3'495'000 24'640 3'519'640 = value of inputs used

per ha* average ha

Total Potential loan size in pilot RWF)

17'475'000 52'800'000 70'275'000 = Ha in pilot * value of

inputs used per Ha

Total Potential loan size in pilot USD

28'789 86'985 115'774 = Ha in pilot * value of inputs used per Ha * ex

rate

Value of pilot in premiums (RWF)

1'520'325 4'593'600 6'113'925 = pilot loan size RWF*

premium rate


2'505 7'568 10'072 = pilot loan size RWF* premium rate * ex rate

Value of margin in pilot (RWF)

456'098 1'378'080 1'834'178 = value of pilot USD *

30%

Value of margin in pilot (USD) 751 2'270 3'022 = value of pilot RWF *

30%

Coffee Part E: 3 Year Market Estimates

Professional

Farmers Cooperatives

Cooperatives and Professionals



50 19'286 19'336 = Total Year 3

penetration Ha/ Average Ha + Number of

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professional farmers year 3

Potential market size input loans year 3 (RWF) per year

174'750'000 475'200'00

0 649'950'000

= Total Year 3 penetration Ha/ Average Ha * Value of inputs per

Ha * Crop cycles per year

Potential market size input loans year 3 (USD) per year

287'891 782'867 1'070'758

= Total Year 3 penetration Ha/ Average Ha * Value of inputs per

Ha * Crop cycles per year* exchange rate

Potential market size premiums year 3 Inputs only RWF per year

15'203'250 41'342'400 56'545'650 = market size inputs

year 3 * premium rate

Potential market size premiums year 3 Inputs only USD per year

25'047 68'109 93'156 = market size inputs

year 3 * premium rate * exchange rate

Potential market size premiums year 3 Input and Output ( RWF) per year

990'168'750 142'584'30

0 1'132'753'05

0 = market size harvest year 3 * premium rate

Potential market size premiums year 3 Input and Output ( USD) per year

1'631'250 234'900 1'866'150 = market size harvest

year 3* premium rate * exchange rate

Margin on premium inputs in RWF

4'560'975 12'402'720 16'963'695 = market size inputs

year 3* 30%

Margin on premium harvest in RWF

297'050'625 42'775'290 339'825'915 = market size harvest

year 3* 30%

Margin on premium inputs in USD

7'514 20'433 27'947 = market size inputs

year 3* 30%* exchange rate

Margin on premium harvest in USD

489'375 70'470 559'845 = market size harvest

year 3* 30%* exchange rate

Exchange rate USD to RWF 607

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Annex F: Rice

Rice Part A: Market size estimates

Parameter Rice Sources of data

Number of small scale growers in Rw

62,000 Rwanda IFAD - System Of Rice

Intensification Flyer


0.19

Hectares under crop 12,000 Crop Assessment 2011a Season

Annual production (kg) 80541000 Rwanda Statistical Yearbook

2011

Producer price (RWF/kg) 240 Ntende Cooperative Field Visit

2012

Average Yield (kg/Ha) 6711

Countrywide Harvest Value (RWF)

19,329,840,000

Production areas

Land allocated to Rice: 1% in South, 1% in West, 2% in East (Gatsibo, Nyagatare, Bugesera and Kirehe) and 1% Kigali. Rice

is grown in the government reclaimed marshlands

Crop Assessment 2011a Season: Crop Area, Yield and Production

Target areas for Pilot Gatsibo - Ntende Cooperative


Union of Rice Cooperatives of Rwanda, MINAGRI, Traders


Access to seed, Irrigation water and Marketing

Rwanda IFAD - System Of Rice Intensification Flyer

Floods

Diseases - Bacterial blight

Drought (Experienced in un-reclaimed marshlands with no dams)


Weather data and Area Yield

Availability of data source Gatsibo EDPRS Agric Data File, 2006

Start of season Season A (Sep-Jan), Season B (Feb-Jul)

Harvesting time Season A (Feb), Season B (Aug)

Inputs

Rice is low-input and low-risk for smallholder farmers, with limited

fertilizer. Improved seeds, insecticide, and fungicides are used.

RiceforAfrica.org

Number of crop cycles per year 2

Rice Part B: Investment estimates

Value of Inputs per ha (RWF) 199,250 Farmer/coop feedback


8.00% Based on risks covered

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Estimated insurance premium per Ha (RWF)

15,940

Rice Part C: Market penetration estimates

Estimated potential market penetration 10%


3,100


600

Estimated market value of total after 2 years in Harvest (RWF)

966,492,000

Estimated market value of total after 2 years in Harvest (USD)

1,592,244

Estimated market penetration of total after 3 years in Farmers

6,200

Estimated market penetration of total after 3 years in Ha

1,200


1,932,984,000


3,184,488

Rice Part D: Pilot estimates

Number of farmers in Pilot 620 Working with one coop

Number of Ha in Pilot 120

Potential loan size in pilot per ha (RWF) 199,250 Calculated inputs per ha


38,565

Total Potential loan size in pilot (RWF) 23,910,000 = loan * farmers

Total Potential loan size in pilot (USD) 39,390

Value of pilot in premiums (RWF) 1,912,800 = total loan * premium rate


Margin on premium inputs pilot (RWF) 4,782,000 = total premium * 20%

Margin on premium inputs pilot (USD) 630

Potential market size input loans year 2 (RWF)

119,550,000 Based on 5% penetration of total

farmers

Potential market size premiums year 2 (RWF)

9,564,000

Potential market size premiums year 2 (USD)

15,756




239,100,000 Based on 10% penetration of

total farmers

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19,128,000


31,512

Margin on premium inputs year 3 (RWF) 3,825,600.00



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Annex G: Beans Beans Part A: Market size estimates

Parameter Beans Sources of data

Total Number of Beans farmers 1,353,000 =Number of HH* % of households

growing beans

Number of Bean Cooperative 151

Based on number of maize cooperatives as they practise maize and bean

intercropping. Maize is cultivated in the whole country and is essentially

intercropped with beans. (Source; Enterprise Network Mapping Study In

Rwanda-Care International)

Average Number of farmers in Cooperatives in Rw

500 SFSA, Based on cooperatives in the field

Average Total Number of farmers in Bean Cooperatives in Rw

75,500 = number of cooperatives * number of

farmers per cooperative

Average number of Ha per farmer 0.14 SFSA, Based on cooperatives in the field

hectares under crop (Ha) 320,000 MINAGRI, 2011, Annual Report

Annual production (Kg) 330,000,000 NISR, 2012

Producer price (RWF/Kg) 250 Esoko, 2012

Average Yield (Kg/Ha 1,031.25 = average production/ hectares under

crop

Total Country wide Harvest Value (RWF)

82,500,000,000 = annual production * producer price

Production areas Rulindo, Ngororero, Karongi,

Ngoma and Rubavu Crop Assessment 2011a Season: Crop Area, Yield And Production, MINAGRI

Target areas for Pilot Karongi Presence of aggregator (One Acre Fund)


ACDI VOCA, MINAGRI (CIP), One Acre Fund


Drought, Availability of Labour,

Market Price Field Visit -Gatsibo, June 2012

Type of data source most relevant Weather data and Area yield


See meta data map Rwanda Meteo,

Start of season Season A (Sept). Season B

(Mar) SFSA, Based on cooperatives in the field

Harvesting time Season A (Feb). Season B (Jul) SFSA, Based on cooperatives in the field

Any Inputs used farmers in area? If so which?

Seeds and Fertilizers One Acre Fund


Beans Part B: Investment estimates

Value of Inputs per ha (RWF) 93,000.00 (One Acre Fund, 2012). Normal farmer

uses 21,000 RWF (35kg seed*600 RWF)

Estimated insurance premium rate 8.00% SFSA, Based on Kenyan experience

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7,440.00 = Value of inputs * Premium rate

Beans Part C: Market penetration estimates

Estimated potential market penetration Y3

5% SFSA, Based on Kenyan experience


33,825 = Potential market penetration * total

number of beans farmers*.5*market penetration year 3


4,736

= penetration Y2*average hectares per farmer


1,220,871,094

= Ha Y2* market price * average yield per Ha


2,011,320

= Ha Y2* market price * average yield per Ha * Exchange rate


67,650 = estimated % penetration Y3* Total

number of farmers


9,471 = number of farmers Y3* average

hectares per farmer


2,441,742,188 = Ha Y3 * average price * average yield


4,022,640 = market value in RWF * exchange rate

Beans Part D: Pilot estimates

Number of farmers in Pilot 13530 One Acre Fund, 2012

Number of ha in Pilot 1894.2 One Acre Fund, 2012

Potential loan size in pilot per ha (RWF)

93,000 One Acre Fund, 2012


13,020 = loan size * average hectares size

Total Potential loan size in pilot (RWF)

176,160,600 = loan size * number of Ha in pilot

Total Potential loan size in pilot (USD)

290,215 = loan size RWF * exchange rate

Value of pilot in premiums (RWF) 14,092,848 = pilot loan size * premium rate

Value of pilot in premiums (USD) 23,217 = pilot premium size RWF *

exchange rate

Margin on premium inputs pilot (RWF)

2,818,570 = premium pilot * 20%

Margin on premium inputs pilot (USD)

4,643 = margin RWF * exchange rate


440,401,500 = value of inputs * hectares Y2


35,232,120 = loans Y2 * premium rate

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58,043 = premium Y2 RWF* exchange rate

Margin on premium inputs year 2 (RWF)

7,046,424 = premium Y2 RWF * 20%

Margin on premium inputs year 2 (USD)

11,609 = margin Y2 RWF * exchange rate


880,803,000 = value of inputs * hectares Y3


70,464,240 = loans Y3 * premium rate


116,086.06 = premium Y3 RWF* exchange rate

Margin on premium inputs year 3 (RWF)

14,092,848.00 = premium Y3 RWF * 20%

Margin on premium inputs year 3 (USD)

23,217.21 = margin Y3 RWF * exchange rate

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Annex H: Wheat Wheat Part A: Market size estimates

Parameters Wheat Comments, Sources and Calculations

Number growers 57,000 Calculation from MINAGRI, Inganzo

Cooperative

Average number of Ha per farmer 0.5 Inganzo Cooperative, Agricultural

Show Contacts

Acreage under crop ha 33,858 MINAGRI 2010/2011 annual report,

Crop Assessment 2012A report

Annual production (MT) 66,636 Crop Assessment Survey 2011, Crop

Assessment Season 2012A

Producer price/kg/RWF 300 Inganzo Cooperative

Average Yield kg harvested/Ha 1,801 Crop Assessment Survey 2011, Crop


Total Country wide Harvest Value RWF

19,990,800,000 Calculation: Annual Production (kg) *

Producer price

Production areas Nyaruguru, Nyamagabe, Karongi,

Nyabihu, Burera, Gicumbi, Musanze, Kayonza

MINAGRI 2010/2011 annual report

Target areas for Pilot Musanze Inganzo Cooperative


Inganzo Cooperative, AZAM is not buying Rwandan wheat and other

processors are not buying Inganzo Cooperative


Lack of capital and Market Access

Inganzo Cooperative

Drought/erratic rain/excess rain

Wheat Rust (disease prevalent with excess rain)

Post-harvest storage

Type of data source most relevant Area yield, Satellite, Weather Data

Availability of data source Area yield data, 2008-2012 for

Inganzo Cooperative

Start of season A Mid Sept- mid Oct

Agricultural Show Farmer and RAB representative

Harvesting time season A Mid-January

Start of season B Mid Mar-5 April

Harvesting time season B End July-Mid Aug

Inputs used

Improved Seed

BRD, research, Inganzo Cooperative, Ag Show Contacts

Fertilizers (DAP or NPK, Urea, manure)

Fungicide

Number of crop cycles per year 2 0

Wheat Part B: Investment estimates

Value of Inputs per ha (RWF) 100,000 BRD, research, Inganzo Cooperative,

Ag Show Contacts

Estimated insurance premium 8% Likely cost of cover for 2 risks

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rate

Value of inputs national RWF 3,385,800,000 = input cost per Ha * total Ha

Value of inputs national USD 5,577,924 = input cost per Ha * total Ha*

exchange rate

Wheat Part C: Market penetration estimates


5%


2,881 = penetration * Number of farmers


900 = penetration * Hectarage

Wheat Part D: Pilot estimates

Number of farmers in Pilot 20 Farmers in Inganzo Cooperative

Number of ha in Pilot 10 Cultivation of Inganzo Cooperative


100,000 BRD, research, Inganzo Cooperative,

Ag Show Contacts


50,000 = input cost per acre * average

acreage per farmer

Total Potential input investment size in pilot RWF)

1,000,000 = input cost per acre * pilot acreage

Total Potential input investment size in pilot USD

1,647

Value of pilot in premiums (RWF) 80,000 = premium rate * pilot loan size

Value of pilot in premiums (USD) 132

Wheat Part E: 3 Year Market Estimates


2,881 Farmers in 20 cooperatives

Potential market size input investment year 3(RWF) per year


Potential market size input investment year 3(USD) per year

237,290

Potential market size premiums year 3 Inputs RWF per year

11,522,800 = premium rate * pilot loan size

Potential market size premiums year 3 Inputs USD per year

18,983.20

Margin on premium inputs in RWF

2,304,560

= 20%* market size premium inputs

Margin on premium inputs in USD 3,797


of 105

Annex I: Cassava Cassava Part A: Market size estimates

Parameters Cassava Comments, Sources and Calculations

Number growers

780,000

MINAGRI Agricultural Survey, EICV3: 1.5 million households with 52%

cultivating cassava

Average number of Ha per farmer 0.26 Crop Assessment Survey 2011

Acreage under crop ha 199,224 Crop Assessment Survey 2011

Annual production (MT)

2,604,389 Crop Assessment Survey 2011, Crop


Producer price/kg dry/RWF 40 Kinazi, Ruhango Cooperative

Average Yield kg harvested/Ha

13.07 Crop Assessment Survey 2011, Crop


Total Country wide Harvest Value RWF

34,725,186,667

Calculation: (Annual Production (kg) / 40 RWF per kg) / 3 kg harvested per 1

kg dry

Production areas

Nyanza, Gisagara, Huye, Nyamagabe, Ruhango, Muhanga,

Kamonyi,Karongi, Ngororero, Rusizi, Nyamasheke

BRD, Crop Assessment Season 2012A. Districts with 10%+ of agricultural land

under cassava

Target areas for Pilot Ruhango (not advised at present

time) BRD, Kinazi


Kinazi (not advised at present time)

BRD, Kinazi


Access to market – Kinazi not yet buying production

Kinazi, Ruhango Cooperative

Drought

Price Fluctuations

Access to capital

Diseases (cassava mosaic disease, bacterial blight)

Type of data source most relevant* Area yield, Satellite, and weather

data

Availability of data source Area yield data from MINAGRI

assessments

Start of season A March-May Kinazi, Ruhango Cooperative

Harvesting time season A Continual, mainly in dry seasons Kinazi, Ruhango Cooperative

Start of season B Oct-Dec Kinazi, Ruhango Cooperative

Harvesting time season B Continual, mainly in dry seasons Kinazi, Ruhango Cooperative

Inputs used Cuttings Kinazi, Ruhango Cooperative

Fertilizers (manure, limited NPK) Kinazi, Ruhango Cooperative

Number of crop cycles per year 2 Kinazi, Ruhango Cooperative

Cassava Part B: Investment estimates

Value of Inputs per ha (RWF) 43,333 Ruhango Cooperative

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Estimated insurance premium rate 7% Likely cost of cover for 2 risks

Value of inputs national RWF 8,633,040,000 = input cost per Ha * total Ha

Value of inputs national USD 14,222,471 = input cost per Ha * total Ha*

exchange rate

Cassava Part C: Market penetration estimates


5%


39,000 = penetration * Number of farmers


9,961 = penetration * Hectarage

Estimated market value of total after 3 years in Harvest USD

2,860,394 = hectarage * price * yield

Estimated market value of total after 3 years in Harvest RWF

1,736,259,333

Cassava Part D: Pilot estimates

Number of farmers in Pilot 104 Farmers in Ruhango Cooperative

Number of ha in Pilot 150 Cultivation of Ruhango Cooperative


43,333 Ruhango Cooperative


62,500 = input cost per acre * average

acreage per farmer

Total Potential input investment size in pilot (RWF)


Total Potential input investment size in pilot (USD)

10,708.40

Value of pilot in premiums (RWF) 455,000 = premium rate * pilot loan size

Value of pilot in premiums (USD) 749.59

Cassava Part E: 3 Year Market Estimates

Number of farmers insured in year 3 104 Farmers in Ruhango Cooperative

Potential market size input investment year 3 (RWF) per year


Potential market size input investment year 3(USD) per year

10,708

Potential market size premiums year 3 Inputs RWF per year

455,000 = premium rate * pilot loan size

Potential market size premiums year 3 Inputs USD per year

750

Margin on premium inputs in RWF 91,000 = 20%* market size premium inputs

Margin on premium inputs in USD 150

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Annex J: Sorghum Sorghum Part A: Market size estimates

Item Sorghum Sources of data

Number of small scale growers in Rw 500,000.00 RAB interview - Agric Show


Acreage under crop (Ha) 196732 Crop Assessment 2011a Season

Annual production (Kg) 227972000 Rwanda Statistical Year Book 2011

Producer price RWF/kg) 227 Rwanda Statistical Year Book 2011

Average Yield (Kg/Ha) 1,158.79

Total Country wide Harvest Value (RWF) 51,749,644,000

Production areas 3% in EasternProvince Crop Assessment 2011a Season

Target areas for Pilot East (Ngoma, Bugesera, Kirehe

District)


Not Identified

Risks identified by farmers/ aggregators Weevils, Diseases Interview - Post harvest stand RAB

Type of data source most relevant* Area Yield

Availability of data source 1989-2005 EDPRS Agric Data File Nov 28 2006

Start of season Season A (Nov).Season B (Jun)

Harvesting time Season A (Feb). Season B (Aug)

Inputs NPK, Urea, labour


Sorghum Part B: Investment estimates

Value of Inputs per ha (RWF) 40,000.00 RAB interview - Agric

Show

Estimated insurance premium rate 8.00%


3,200.00

Cassava Part C: Market penetration estimates

Estimated potential market penetration 10%


25,000


9,837


2,587,482,200


4,262,738


50,000

Estimated market penetration of total 19,673

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after 3 years in Ha


5,174,964,400


8,525,477

Sorghum Part D: Pilot estimates

Number of farmers in Pilot 5,000

Number of ha in Pilot 1,967

Potential loan size in pilot per ha (RWF) 78,692,800


15,739

Total Potential loan size in pilot RWF) 78,692,800

Total Potential loan size in pilot USD 129,642

Value of pilot in premiums (RWF) 6,295,424


Margin on premium inputs pilot (RWF) 1,259,085



393,464,000

Potential market size premiums year 2 Inputs only RWF

31,477,120


51,857




786,928,000

Potential market size premiums year 3 Inputs only RWF

62,954,240


103,714




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Annex 2 : List of tea cooperatives

1. COOPTHE Mulindi

2. COTHEVEM(Mulindi)

3. COOPTHE Shagasha

4. UMUCYAGI(Shagasha)

5. COOPTHE Gisakura

6. COOPTHE VIGI(Gisakura)

7. COOTHENYA(Mata)

8. KOBACYAMU(Kitabi)

9. KOTEGIM(Gisovu)

10. COOTHENK(Nshili-Kivu)

11. COOTRAGAGI(Rubaya)

12. COOPTHEGA(Nyabihu)

13. COOTP(Pfunda)

14. ASSOPTHE(Cyohoha-Rukeri)

15. KOTHEMUKI (Muganda-Kivu)

16. KATECOGRO(Karongi)

17. COTHEGA (Gatare)

18. COTHEGAB (Mushubi)

19. RUTHEGROC (Rutsiro)

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Annex 3: Weather Station Investment and Operational Cost

Estimation of number of weather stations (AWS) needed

Land size Rwanda

in km2 Arable land

Source of data

Arable land in km2

Area to be covered: 52.7% of Rwanda's land is arable

26'000.00 52.70% WB, 2010 13'702.00

Area covered by one weather station in average

radius (km) area

10 314.1

Weather stations needed (arable land / area covered per ws * correction for overlap, not arable land and topography)

#weather stations correction

factor total AWS

44 3

131

Investment cost in network

Item cost per unit units total USD

648'064

Stations, incl transport 4'000 131

523'477

Installation (based on 2010 data KS)

131

Fencing 25'000 131 3'271'729 40'079

Manpower 9'500 131 1'243'257 15'230

Transport to site 8'500 131 1'112'388 13'627

Spare parts Inventory (assumption) 2'000 10 20'000 25'652

Solar panels

10

sensor sets

10

RTUs

10

weather stations 4'000 5

20'000

Hosting costs (assumption)

10'000

Operation & Maintenance cost

Item Cost/

Station Interval

Cost/Y/ Station

# Stations

Cost Per Year Ksh

Cost Per Year €

Cost Per Year Usd

Caretaker (security, 1st level support)

24'000 1 24'000 131 3'140'86

0 38'476

Maintenance: Replacement of battery, cover seal and temp/RH sensor element

200 3 67 131

8'725 11'190

Maintenance Replacement of RG PCB and tipping spoon

100 5 20 131

2'617 3'357

Calibration of Global Radiation sensor

230 5 46 131

6'020 7'721

Extraordinary replacements

5'000

Data transmission 250 1

131 32'717

401

Cost Server hosting and support Adcon

180 1

131

23'556 30'213

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2nd level support (SLA or own employee)

1

15'000

Transport for service team (assumption)

10'000

Insurance cost (assumption: 2%)

4'000

Management cost (assumption)

10'000

Depreciation of AWS (5%/a)

200

131

26'174

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Annex 4: Meta-data Map

The Google Earth files and enlarged print outs are available upon request.

Back Cover Design, without header or footer

Rwanda contacts:

Access to Finance Rwanda (AFR)

Email [email protected]

[email protected]

Post P.O. Box 1599

Kigali, Rwanda

Ministry of Agriculture and Animal Resources (MINAGRI)

Website www.minagri.gov.rw


Phone +250 78 830 1498

Consultant contact:

Syngenta Foundation for Sustainable Agriculture (SFSA)

Kilimo Salama Project

Website www.syngentafoundation.org


Phone +254 70 301 8205

Rwanda Agricultural Insurance Feasibility Study

mailto:[email protected]

http://www.syngentafoundation.org/

mailto:[email protected]

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