Enhancing the Adaptive Capacity of Smallholders to Climate Variability through Response Farming Innovations | Natural Resource Management (Soil and Water Conservation)
Semiarid areas in the ECA region are characterised by high climatic variability. This is especially manifest in the erratic nature of seasonal rainfall with respect to onset, quantity, distribution and cessation. This constitutes a major constraint to decision making by rainfall dependent smallholder farmers, with respect to production management. This coupled with the fickle nature of the season hampers increased crop productivity leading to food insecurity. Climate change-induced variability has only made a bad situation worse by causing shifts in seasonality accompanied by new abiotic stresses. Most agricultural production systems in the ECA region are rain-fed and predominantly subsistence in nature, supported by poor management practices, hence is vulnerable to the impacts of climate variability. Often, the smallholder farmers are the ones mostly dependent on economic activities that are sensitive to the climate. Despite agriculture being the mainstay for nearly 70% of the region’s population, it continues to remain underdeveloped due to inadequate adoption of yield enhancing technologies in almost all countries in ECA.
Climate-responsive management techniques are likely to improve crop productivity in the region and would in turn broaden the scope of product range for rain fed agriculture as each tactical response would target specific enterprises, value chains crop varieties. The project which was implemented in seven ASARECA member countries - Kenya, Ethiopia, Madagascar, Sudan, Eritrea, South Sudan and DR Congo was implemented in three levels; testing of technologies and appropriate response options, validation of previously identified options and upscaling of proven technologies and corresponding response options. The implementation levels varied from one country to the other depending on previous research work conducted in those countries. This is a description of the upscaling level which was implemented in Kenya.
The use of climate information for farm level decision making has in recent years been adopted as a way to address the problem of climate variability and change. The seasonal rainfall forecast given by the national meteorological agencies has particularly gained prominence among livelihoods practitioners for purpose of informing pre-season activities such as choice of crops and seed varieties, determination of planting dates, livestock stocking and destocking, feed preparation among others. This type of information has however been available to few persons while many members of communities have not had access to such information. The main impediment being lack of access to relevant, understandable and timely information mainly due to lack of capacity and the analogue methods (print media and traditional radio and TV), used to disseminate such information. The development of seasonal forecast informed agro-advisory was earlier tested and validated in Kenya and has continued to give impetus and relevance to the seasonal forecast in the agriculture sector. Over the last few years, many stakeholders and institutions including the ASARECA have endeavoured to ensure availability of relevant, understandable and timely climate information for agriculture especially in the rainfed arid and semi arid regions. This has seen the increased need for the
seasonal climate information in the agriculture sector in Kenya, particularly for informing agro-activities during the main crop growing seasons in country. The response arming approach, which was developed through action based research and adaptation learning process has been identified as an appropriate methodology for linking seasonal climate information to local scale rainfed agriculture enterprises and other climate dependent environmental livelihoods.
Upscaling Approach for Response Farming
In Kenya, the usefulness of advisory had earlier been evaluated in previous project (Making the Best of Climate: Adapting Agriculture to Climate Variability), in a range of climatic conditions. This research work focused on scaling up of response farming approaches with an overall aim of developing and testing a system for timely generation and dissemination of agro-advisories to farmers and enhancing the capacity of farmers and extension agents in understanding and utilizing that information.
Valuable climate and agriculture nowledge and technology data and information was compiled and systematically organized to create an information base. A database of local climate and corresponding agro-technology data sets as required for characterizing and quantifying impacts and potential responses to climate variability on agricultural systems was developed. The database was developed to be interactive and contain local scale location specific knowledge on local climate, value chain and relevant agricultural technologies which have been developed to address specific impacts of climate variability and extreme events. The local scale datasets was specific for locations (ward, locations, sub-locations or village), for each of the locations in Kitui County. The climate datasets were organized to accommodate scenarios based on the three normative rainfall categories of (Above normal, near normal and below normal), often used to describe the seasonal forecast issued by the Kenya Meteorological Service. The scenarios in the database further were arranged to capture the two main rainfall seasons in Kenya, long rains, March, April, May and short rains, October, November, and December. The local scale seasonal forecast for each season, form one component of the database. The climate component of the database contain two sections, the normative sections containing information on historical averages and trends and the second section containing future climate information. The section of future climate information was updated on a seasonal basis, based on the seasonal rainfall forecast issued by KMS. The section with future climate information contain datasets with expected onset date of seasonal rainfall , normative indication of expected seasonal rainfall, warning of extreme events, drought, floods, storm s, ground frost, high temperatures etc. , distribution (spatial/temporal ) of seasonal rainfall poor/good, cessation date of seasonal rainfall. The agriculture component of the database is organised according to the types of adaptive technologies existing. Guided by available literature the relevant agriculture technologies and recommended actionable options of climate sensitive agriculture practices was identified for inclusion into the database. The identification of the technologies was done through literature review, collaborated with expert opinion from scientists and practitioners.
These agriculture technologies and recommended actionable options of climate sensitive agriculture practices was identified at local scales (ward, location, sub-location or village – lowest administrative area in Kenya) and corresponding to respective agro-climatic zone. The agriculture technologies and recommended actionable options of climate sensitive agriculture practices further categorised into respective climate sensitivity in accordance with the norm ative seasonal rainfall forecast categories of above normal, normal and below normal rainfall.
The development of a database of relevant location specific agriculture technologies and recommended actionable options of climate sensitive agriculture practices was done in a participatory process comprising stakeholders from across value chains and relevant insti tutions. The development of components of the datasets of technologies and recommended actionable options of climate sensitive agriculture practices of agriculture activities was guided by the normative rainfall and agro-ecological zonings. These options consider general impacts of season climate on agriculture for specific locations in each sub-county/ward. The datasets were categorised to include specific scenarios and technologies for land preparation (tillage types, water conservation, terrace types etc);, specific scenarios and technologies for crops management, (crop types and varieties: cereals- Maize, sorghum, millet - planting date, pulses- beans, pegion peas green grams cow-peas, dolichos etc);, specific scenarios and technologies for soil fertility (fertilizer application, farm yard manure application etc); specific scenarios and technologies for water harvesting (runoff
harvesting, insitu water harvesting). The database was developed such that the agriculture technologies and recommended actionable options of climate sensitive agriculture practices would easily be matched in an interactive setting with corresponding climate type and scenario in the climate component of the database. This categorization of technologies enables the system to match the forecast seasonal rainfall potential with corresponding response options of agriculture technologies and recommended actionable options of climate sensitive agriculture practices and thereby develop corresponding location specific seasonal agroadvisories. This arrangement of the data base ensures corresponding agriculture technologies designed for specific climates are fitted accordingly in the database such that locally relevant agro-technologies are
matched with respective climate scenario. The information in the database represents climate response options for the forecast rainfall following the two rainfall seasons (March- May and Oct-Dec) in Kenya. When fully developed, the database makes a vital tool for automation of the seasonal scenario and agroadvisory development process.
The project developed an IT-based system with the aim of improving the efficiency of preparation and dissemination of location specific weather based agro-advisory. The IT system is computer based software which enables the effective and systematic interaction of various components of the database described above. This automated interaction is used to match the seasonal climate forecast data with corresponding agriculture technologies and recommended actionable options of climate sensitive agriculture practices, built in the database.
Every growing season, the seasonal forecasts issued at national level by the KMS was downscaled to local scales before the seasonal forecast data is entered into the database. The automated system match the corresponding agriculture technologies and recommended actionable options of climate sensitive agriculture practices, first interpreted for the local scales (sub-locations). The automated IT system was used to integrate respective local scale seasonal forecast and the corresponding local scale agriculture technologies and develop a Weather Based Agro-Advisory for the respective season either March April May (long rains) and Oct Nov Dec season (short rains) for the local area(sub-location). The system therefore provide the process of effective automated devel opment of the Weather Based Agro-Advisory and provide timely and cost effective availability of actionable options for use at farm level during the respective season.
Figure 3 Sample of weather based agro-advisory message sent on SMS
A computer based tool was developed to enable effective dissemination of the developed agro-advisories.
For purpose of wide coverage and upscaling of information, the dissemination was done on SMS and email
to all persons and stakeholders whose mobile phones and email contacts were registered with the
automated system. The dissemination utilizes a computer based internet (multimedia) communication
system. The advisory messages were sent to recipients as text messages. The system utilize a multimedia
approach which enables the system to overcome the challenge of limited characters (140 number of
characters allowable in text messages in mobile phone), associated with dissemination of text messages on
The enabling of automation of the dissemination process of agro-advisory involves first selection of users
of the agro-advisory from all locations of Kitui County. The persons chosen include community leaders,
chiefs, county/sub-county/ward/village administrators, heads of NGO and CBO, agriculture extension from
all locations in the county and leaders of farmer groups. The leaders were registered with the automated
system such that the system would identify them with their locations institutions and their mobile phone
numbers. At least 3500 persons from each of the 230 sub-locations/villages, within the 40 wards and 8 subcounties of Kitui County, benefitted by receiving the information. These linkages act as intermediaries for
dissemination of the information to the communities. The linkage when completed provides a network of
at least 15 persons in each village who have access to the automated dissemination of SMS based seasonal
climate information and agro-advisory. This approach enabled community level outreach and access of the
climate information in Kitui county. Over 200,000 households in Kitui County received appropriate
seasonal climate information and appropriate local level agro-advisory to match the season. Most of the
beneficiaries (> 70%) of climate information were women, youth and persons with disabilities. To a big
extent, this information enabled over 450,000 farmers in Kitui County to prepare their farms and invested
accordingly in readiness for Oct-Dec 2013 rainfall season. Despite the low amounts of rainfall recorded,
farmers were able to achieve sufficient food security levels in that season all attribute to the wise decisions
they made informed by the seasonal forecasts and agro-advisories they received on their phones.
Quote from Beneficiaries (Mary Mueni Kyome Village Kitui County) ‘This season,I received the forecast on my phone. Use of climate information has eradicated poverty from my house. It has not only saved me from perpetual dependence on food relief but has also given me a stable source of income with which I am able to pay school fees for my children.
Use of climate information in managing my farm has eradicated poverty from my house. It has not only saved me from perpetual dependence on food relief but has also given
me a stable source of income with which I am able to pay school fees for my children.
Insitu water harvesting decisions in the semi arid
regions which require seasonal climate information
The project beneficiaries included different gender categories (men, women, youths, persons with disability
and elderly persons). Specifically, the project improved the food security, income and livelihoods lives of
over 3500 persons (1800 women, 900 men, 600 youth, 200 persons with disabilities and 35 elderly
persons) who all were community leaders and received information directly on the phones or emails and
also acted as intermediaries to pass on information to others. The project also benefited over 500,000
persons mainly women and youth who received information from the intermediaries in all 230 villages of
- A farmer having prior information on rainfall potential through forecasting helps to optimize farm level plans or otherwise, in case rainfall arrival is early or late respectively, This also helps to maximize benefits from good rains, and stabilize yield in case season turns bad.
- The automated weather based agro-advisory is effective when information is sent to farmers on their mobile phones. The approach needs to be improved for replication in other arid and semi arid regions.
1. Limited capacity among stakeholders to translate forecast into advisories that can easily be taken up by farmers.
2. The seasonal dependence nature of project activities did not allow for complete seasonal cycle of researchable concepts.
1. Response farming is not a new concept, but it’s a kind of reimagining farming with a new level of thinking and ‘reengineering of the already existing farm management practices differently.
2. The automated weather based agro-advisory information sent to farmers needs to be backed up by intensive capacity development among farmers and support stakeholders.
3. The database used to develop the automated weather based agro-advisory needs to be up-dated regularly to incorporate new and upcoming technologies and also include all other data types including crop and livestock relevant technologies.