SF on Farm Decision Kenya

8/12/2019 SF on Farm Decision Kenya

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TITLE: Effects of Seasonal Forecasts on Farm Decision Making for Small Holder

Farming systems in Semi Arid Parts of Kenya

Authors *W N Githungoa

, R G Kinuthiab

, K Kizitoc

, KPC Raod

AFFILIATION INSTITUTIONSKenya Meteorological Department

aEmail: [email protected]

University of Nairobibemail: [email protected];

Kenya Agricultural Research Institutec email: [email protected]

International Centre for Research in Semi Arid Tropics ICRISATd

email: [email protected]

ABSTRACT

Impacts of adverse effects of climate change to farming communities can be reduced by

availing climate forecasts to farmers to enable making of informed farming decision and

adaptation. The needs and demand for climate information vary according to the production

systems and hence the usability of forecasts depends on the characteristics of the farmers and

their geographical location. Meteorological services in the Greater Horn of Africa (GHA)

region issue seasonal climate forecasts on regular basis as part of their operations. The lack of

a comprehensive profile of users of the forecast has left a gap between the information that isuseful to farmers and what is actually provided for public consumption. Difficulties in

interpreting and applying the forecasts include, mismatch between the variables in the

forecasts and the operational needs of farmers and simple lack of comprehending what is

entailed in the forecasts. Downscaling seasonal forecasts to local scales, promises to deliver

benefits of seasonal forecasts for smallholder farmers in the semi arid, districts of Kitui,

Mwingi and Mutomo in Kenya for improved on-farm decision-making.

Key Words: climate, downscale, information, farming, decision-making

1 INTRODUCTION

Seasonal climate forecasting can increase preparedness and lead to better social, economic

and environmental outcomes within agricultural production systems,. The production of rain-

fed crops in semi-arid tropics exhibits large variation in response to the variability in seasonal

rainfall. There are several farm-level decisions such as the choice of cropping pattern,

whether to invest in fertilizers, pesticides, the choice of the period for planting, plant

population density for which the appropriate choice (associated with maximum production or

minimum risk) depends upon the nature of the rainfall variability or the prediction of climatic

variables for a specific year. The purpose of this paper is to describe the framework used bya team of researchers implementing the research project Managing risk, reducing


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vulnerability and enhancing agricultural productivity under a changing climate to

assess the potential in the use of downscaled climate forecasts in agricultural decision-making

and to summarize what was learned in the research process. Climate forecasting is one of

many risk management tools that play an important role in agriculture decision-making.

Agrawala et al. (2001) has decried the fact that only a few examples of seasonal climate

forecasts are being used successfully by vulnerable groups, despite international efforts toimprove societal responses. OBrien et al. (2000) has indicated that one of the primary

reasons for this scenario is that forecast information does not specifically target vulnerable

groups, which results in poor availability of information and therefore a barrier to ease of

access to the information. The forecast information is often not tailored to suit target farmers

in content and delivery style, which means that they may end up not being able to access it

even if it was availed to them (Patt, 2001; Broad et al., 2002). In the Greater Horn of Africa

(GHA), region, seasonal climate forecasts are more widely available at present than they

were a decade ago. Dialogue between producers of information, researchers and different

categories of decision makers has been enhanced by outreach activities through the GHA

Climate Outlook Forums (GHACOF) and the Kenya Meteorological Departments awareness

and sensitization services. However, gaps still exist between information provided andspecific information desired by decision makers. The challenge eliminating this gap rests

with both the providers of information, who do not always understand the users needs, and

users of the seasonal predictions, whose capacity for interpretation and comprehension of

the forecasts is low. Despite the availability of relatively reliable forecasts from the Kenya

Meteorological Department, farmers seldom use this information for farm level decision-

making (Hansen, 2002; Hammer et al., 2001,). This is mainly due to lack of adaptability of

the information to the local needs and difficult in accessing the information on time and in a

format that farmers can easily understand. This gap is mainly caused by lack of capacity for

interpretation and use of the forecasts (Hansen et al, 2009). Providing location specific and

easy to understand climate forecasts can therefore enhance farmers capacity to use climate

forecasts to manage risk. Downscaled and location-specific forecasts may assist small holder

farmers in taking proper decisions at the farm level.

2 OBJECTIVES

The broad objective of the project was to develop an effective method for communicatingseasonal climate forecast information to small scale farmers.

Specific objectives include:

(i) To enhance farmers knowledge on available climate information and their ability to

anticipate extreme climate events;

(ii)

To enhance farmers capacity for observing climatic parameters and their use in

guiding farm activities; and

(iii) To build farmers capacity to interpret probabilistic seasonal forecasts for purposes

of on-farm decision making.

3 LITERATURE REVIEW


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3.1 Traditional Strategies to Manage Climatic Risk

From historic time, to date, the climatic considerations have always remained key to the

settlement patterns and development trend of humankind. Throughout human history,

societies have expected seasonal changes similar to the local historical averages and a certain

amount of variation around these averages, but, despite their efforts to forecast thesevariations, they have not typically counted on much skill in predicting them. Thus, they have

conditioned themselves to expect climatic surprises and only hope to to deal with the impacts

reactively. The newly developed scientific skill in climate forecasting may fundamentally

change the ways social systems cope with or react to climatic variation by reducing the

magnitude or frequency of climatic surprises through provision of forecast information that

enable adequate lead time to prepare for climatic events.

Climate variability has a major influence on agricultural production in smallholder farming

systems of Kenya. Most communities in arid and semi arid parts of Kenya are predominantly

agro-pastoralists and climatic variability has a direct bearing on their livelihood support

opportunities with rainfall being the most important climate parameter. Over the years,smallholder farmers in Kenya had their climate monitoring methods, which were passed on

from generation to another generation Nyakwada et al., (2003). The indicators used for

prediction included the environment around them such as plants, birds, animals, insects, stars,

the moon, winds, clouds, lightning patterns and heat and humidity. Indigenous knowledge

and tradition still play a role in climate risk management in Kenya. Farmers in Ukambani

community (Kitui, Mwingi, Machakos, and Makueni) of Kenya, where the project is being

implemented, have indicated up to twenty (20) rainfall indicators used at different stages of

any given rainfall season. Detailed explanations of various seasonal indicators among various

Kenyan communities are well documented by Nyakwada et al., (2003).

3.2 Seasonal Climate Forecasts

Growing understanding of ocean-atmosphere interactions and advances in modeling the

global climate systems now provide a usable degree of predictability of climate several

months in advance for many parts of the world (Goddard et al. 2001). Combined with the

ability to systematically quantify agricultural management responses via simulation analysis,

the enhanced understanding and modeling of climate issues offers an opportunity to improve

climate risk management (Meinke and Stone 2004). Integrating seasonal climate forecasting

with agricultural system analysis can increase its effectiveness (Hammer et al. 2000; Meinke

et al. 2001). The recent improvement of seasonal climate forecasts has meant that forecasts

on how much rain to expect over the season, and predictions of seasonal variability of rainfallare now widely available. The seasonal forecast is based on the fact that lower-boundary

forcing, measured by sea surface temperatures, drives future atmospheric perturbations

(Murphy et al. 2001). These boundary conditions evolve slowly and so enable predictions of

rainfall to be made (Palmer and Anderson 1994).

Seasonal forecasts are probabilistic Fig 1 and rainfall is often forecasted as the chance of

being above normal, below normal or near normal. The normal amount of rainfall is

the middle third (tercile) of the average rainfall for the past number of years of rainfall data

used to develop the forecast. The forecast is usually issued for a period of one to three months

and suggests the probability of a given amount of rainfall expected over that period. The

forecast gives an indication of date of onset and cessation of rain. The forecast however doesnot give much weight to the temporal distribution, such that if the amount of rainfall forecast


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were to fall over a few days, the seasonal forecast would still be correct but the impact could

be catastrophic (Agrawala et al. 2001). It is this probabilistic nature that needs to be given

attention in dissemination of forecast information so that farmers do not assume it to be

deterministic.

In the agricultural sector, forecasts have provided information for agricultural decisionsrelating to dryland farming, irrigated farming and livestock management (Marshall et al.

1996; Hammer et al., 2000). The types of decisions making that seasonal forecasts can

support include both operational short-term decisions, and tactical and strategic longer-term

decisions. A strategic decision for smallholder farmers might be to maximize total crop yield.

The accompanying operational decision might involve deciding what variety of crop to plant

in order to achieve maximum yields. A forecast for below-normal rainfall could encourage

drought-resistant seeds to be planted instead of long-maturing varieties that require more

moisture and might fail completely without adequate moisture. If the forecast provides better

than best-guess information about the rainfall in the succeeding season, it allows better

decision making and maximization of conditions (Walker et al. 2001). The opportunity to

manage variation is a strength of seasonal forecasts that is just as important as decreasingnegative impacts of climatic variability. The most useful forecast information, according to

the farmers, are the early warning on anticipated poor season, the commencement of the

season and adequacy of anticipated rains (Phillips et al, 2001). It is probable for people

living in low rainfall zones that seasonal forecasts for wetter years are of greater value than

warnings of a poor season (Phillips, 1998). Above all the forecast should be stated and

presented in a language and in terms that the target end users understand Unganai (2000)

3.3 Current State of Seasonal Climate Forecasts in Kenya

The seasonal forecast issued by the Kenya Meteorological Department is based on the

consensus seasonal rainfall forecast developed by ICPAC. The forecast is based on

considerations of the prevailing atmospheric-oceanic conditions and an ensemble of seasonal

climate forecasts generated by leading global climate prediction centres; European Centre for

Medium Range Forecasts (ECMWF), UK Meteorological Office (UKMO), International

Research Institute (IRI), and the regional IGAD Climate Prediction and Applications Centre

(ICPAC). This consensus forecast is produced as part of the GHACOF process, and is based

on discussions by many interested parties. Consensus on the long-term prospects of each

rainfall season is established through regional climate outlook fora. The fora attracts climate

experts from global and regional climate prediction centers. The seasonal climate forecasts

are based on empirical diagnostic analyses with Global Sea Surface Temperature (SST)anomaly patterns, the sate of El-nino Southerly Oscillation (ENSO), North Atlantic

Oscillation (NAO), Indian Ocean Dipole (IOD) and upper level stratospheric winds such as

the Madden Julian Oscillation (MJO), being the main predictors. The forecasts are issued in

terciles i.e. below normal, normal and above normal with the probability of rainfall being in

each of three categories as the forecast. These forecasts are are normally issued for relatively

large homogeneous rainfall regions (over 9000km2) extending over three month periods. The

probabilities indicated are weights of expected climate categories of above normal, normal

and below normal.

The definition of the boundaries between the forecast regions is a process of this discussion.

The forecast is composed of categorical information of rainfall performance during theseason for large regions (the least is over 9000km2). This is indicated in terms of above-


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normal, normal and below -normal categories. The categories are further described by

weights indicating the probability of occurrence of each category of forecast. This

probabilistic categorization is what users find difficult to interpret and understand. Down-

scaling the seasonal forecast is the process of interpreting the indicated probabilities into

categories of rainfall amount which users could find easier to comprehend or understand.

Current seasonal climate forecasts do not provide adequate information required for farm

level decision making. For example theres no information about the intra-seasonal character

of the rainfall season i.e. the extent of wet/dry periods within the season. The demarcation of

areas is often difficult for users located on the border line of the forecast spatial coverage

since the forecast changes drastically each time. Besides, There is limited understanding of

climate forecasting science among agricultural practitioners and extension agents and this

makes it difficult for smallholder farmers to interpret and use forecasts on their own. A

typical example of a seasonal forecast is shown in the figure 1. This covers the period

between October and December 2009. The national weather forecasts in Kenya are

disseminated through radio/TV, printed press, and the internet. Experience has shown that

radio broadcasts are the most efficient means of communicating climate forecasts to ruralcommunities in the country, while the Internet is least efficient method of climate forecast

dissemination.

4 MATERIALS AND METHODS

4.1 Farmers Climate Information Requirements

Assessments based on surveys and focus group discussions on stakeholder climate

information needs was carried out in the Kitui, Mwingi and Mutomo districts of semi aridEastern Kenya to investigate the utilization of climate information in agricultural

management. The assessments were done to determine the way the terminology used in

information dissemination and precision of the information influence decision-making. The

following were identified as information requirements for decision making in on-farm

agricultural management: Onset date for the main rains, quality of the rainy season or total

amount of rainfall, cessation date for the main rains, temporal and spatial distribution for the

main rains, timing and frequency of rainy and dry periods or wet and dry spells and

agronomic recommendations in terms of which crop varieties to grow.

4.2 Downscaling: Quantifying Probabilistic Seasonal Forecasts of Estimated RainfallAmounts to Local Scales

Statistical downscaling involves the application of relationships identified in the observed

climate, between the large and smaller-scale, to climate model output. It assumes that the

relationships between predictors (large-scale variables) and predictands (small-scale surface

variables) do not vary under climate change conditions. The process required to adapt model

outputs to end-user demands is complex. The FACT-FIT agro-climatological toolkit was used

for down-scaling the seasonal forecast issued by the Kenya Meteorological Department. The

FACT-FIT agro-climatological toolkit utilizes a technique based on Monte Carlo simulation

for adjusting existing mean climatologic statistical parameters of rainfall to match forecast

information. The resultant new parameters define the probability of events for the forecastinterval and are used to quantitatively perform adjustments to describe climatologically


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derived probability distribution of rainfall in proportion to the forecast probabilities Barnston

et al., (2000). This technique, termed the Forecast Interpretation Tool (FIT), estimates new

distribution parameters defining the probability distribution for the forecast interval. This

distribution can be used to predict the likelihood of specific events during the forecast

interval at specific stations or for rainfall fields so long as there is a reasonable climatological

distribution. Details of the methodology and approach for seasonal estimation of rainfall aredocument by Husak et al (2010). The toolkit allows the users to generate probability maps,

indicating locations that are likely or unlikely to attain critical rainfall amounts (thresholds).

Fig 2 shows downscaled rainfall amounts for Kitui, Mwingi and Mutomo districts in Eastern

Kenya for the March to May season in the year 2009. The threshold is set based on long term

climate averages of the relevant location or specific crop water requirements. The maps

generated are used to determine geographical areas that are at risk of experiencing significant

rainfall amount deficit or surplus or increased likelihood of drought or flood occurrence

depending on the areas specific climatic vulnerability. Farmers are more comfortable

interpreting indicative rainfall amounts rather than probabilistic categories of forecast.

Applying this process to the seasonal forecasts for two seasons each year in March April May

(MAM) and October November December (OND) seasons for the years 2008 and 2009,helped to develop downscaled climate information for Kitui, Mwingi and Mutomo

districts.Tables 1 and 2 show the respective seasonal forecast and corresponding rainfall

amounts in the three districts. The downscaled seasonal forecast and identified suitable

localized agriculture practices for the season are compiled into an agro-advisory bulletin for

each location.

4.3 Integrating Seasonal Forecasting In On Farm Decision Making

The project prepared the participation of farmers in two phases: The first socialisation

phase began in late 2007 and focused on increasing farmer knowledge on climate and theuse of seasonal forecast information to develop a cropping strategy. Farmer group leaders

were trained on interpretation of probabilistic seasonal forecasts with a special ttention being

given to leaders of farmer groups who would be expected to train other farmers, especially in

geographic locations with low literacy levels. The second phase covered the year 2008 and

2009 and it mainly focused on operation and capacity-building for farmer groups to integrate

climate and forecast information into their farming activities. In order to prepare for the

implementation of the two phases, agricultural extension agents were trained by staff from

Department of Meteorology to act as intermediaries/trainers. Topics covered in the training

for the extension staff included:

1)

Elements of weather and climate including the difference between weather andclimate

2) Rain formation processes

3) Understanding terminologies used in seasonal forecasting

4) Understanding probability concepts

5) Use of climate forecast information in planting strategies

6)

Quantifying the economic benefits of using climate forecast information

Farmers in the project research area grow a mixture of fast maturing maize, pigeon pea, green

grams, sorghum and millet as their staple crops. The community members already have

access to the seasonal rainfall forecasts developed at the bi-annual Greater Horn of Africa

Climate Outlook Forum (GHACOF). The GHACOF forecasts are downscaled to nationallevel and disseminated, by the Kenya Meteorological Department through radio and print


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media, with the former being the preferred mode of dissemination. The forecasts contain

categorical probabilities of rainfall estimates for the two crop growing seasons in the study

area: long rains season (MarchMay- MAM) and short rains season (OctoberDecember-

OND). These categorical estimates are in the form of probabilities for rainfall totals falling in

the ranges of below normal (a range defined by the 10 driest of the past 30 seasons), normal,

or above normal (a range defined by the 10 wettest of the past 30 seasons). Beginning inFebruary 2008, a series of seasonal participatory climate forecast workshops in each project

site were held, designed to assist the group of farmers in each study site to better understand

the forecast and to be able to apply it to their farm management decisions. The workshops

took place in a location chosen by the farmers but always were held in the homes each of the

farmers on rotation basis. Often the meeting lasted 2 -3 hours, and was conducted in English

and Kiswahili and interpreted into the local language. The workshops followed a common

format, designed to assist farmers in applying the forecast information:

They were were asked to comment on the previous seasons rainfall data, and whether it

was in tandem with their recollection of the forecast.

The forecast performance was measured against rain-gauge records on farmers fields andcompared with farmers own historical knowledge of local rainfall quantities and

estimates of actual ranges of rainfall.

Farmers were then asked to comment on the success of their management practices in the

past year, given the rainfall received.

Farmers were asked to offer their insights into the coming years rainfall, based on their

interpretation of local traditional rainfall indicators.

The forecast for the coming season was explained to farmers, in terms of the probabilities

for below-, about-, and above-normal rainfall levels.

The forecast was downscaled, using the FACT-FIT agro-climatological toolkit.

The information used to generate the forecast was explained in simple terms and

questions were encouraged, including a discussion on El Nino phenomenon.

And finally, a discussion was facilitated between farmers and the local agricultural

extension service officers on the appropriate farm management practices for the coming

year, taking into account the forecast, the local indicators and available planting seed.

4.4 Traditional practice and Prudent Preparation and Decisions of a Growing Season in

Kitui, Mwingi and Mutomo Districts

Farmers decisions for various farm operations in the three study districts is so much

influenced by the conventional local practices in the region for each of the two seasons - long

rains season or the short rains season. There exists knowledge on potential actions/responsesandapproaches for each of the two seasons among the locals in the study area

4.4.1 Traditional Allocation of Resources in Different Seasons

Farming Practices

During the March, April May (MAM) season, farmers concentrate on drought resistant and

fast-maturing crops such as green grams, maize and also tend to increase the herd size

livestock. The acreage under crop is low compared to the short rains season. Onsets of the

rains determine the crop species and variety to be sowed, while planting depths are also

determined by the amount of rainfall received before planting.


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During the October December season (OND), the most reliable rain season, there is

increase in acreage and planting of late maturing crops and agro-forestry species. The

converse is true. Should the farmers anticipate bad season, based on traditional indicators,

they plant less acreage and resort to fast-maturing crops like in MAM season. Crops for this

season include maize, pigeon peas, beans, dollicos, cotton, pumpkins, and establishment of

pastures restocking of livestock and spraying against ticks.

Soil/water conservation

During OND season the main soil and water conservation activity is rehabilitation of water

harvesting facilities, while during MAM season farmers in Kitui and Mutomo districts

concentrate in establishment and construction of water harvesting structures. In Mwingi

district, soil/water conservation establishment is done in June-early October. In some areas,

soil/water conservation works is done in Jan-Feb

Nutrient management

During June July August September (JJAS) season application of manure in preparation for

OND season is the main nutrient management activity. Use of inorganic fertilizers is

practiced during actual planting in OND season.

In general, the traditional practices used as the baseline measurement for prudent decisions by

farmers and which were considered to be highly influenced by climate information as

analyzed in this paper include: (i) Choice of crop and variety, where farmers would be

expected to invest in crops which optimize output in good seasons or minimize risk in low

rainfall seasons (ii) Level of investment in land, where farmers are expected to

increase/decrease land under crop during wet/dry forecast (iii) Investment in farm input,

where farmers make choice concerning farm iputs that are sensitive to climate informationsuch as seed species and variety and soil and water management practices. The observed

trend is increase in use of chemical fertilizer during wet season and decrease of the same

during anticipated dry season and increase in use of manure during anticipated dry season and

decrease in use of the same during anticipated wet season, and (iv) Acqusition of Credit,

where farmers would be expected to go for financing of farm inputs during good seasons.

Only one group (Kaveta FFS) had an internal credit facility mechanism which according to

group by-laws was only accessed for farm related activities, and the same was monitored.

5 RESULTS & DISCUSSIONS

The use of forecasts was associated with varied decisions made by individual farmers results

of which have been analyzed below for each season.

5.1 March-April-May season 2008

This was the first season of the project to tests farmers capability and acceptance of use of

season forecast information in on-farm decision making. Indications show that only 12% of

the farmers in Kitui and 8% in Mutomo used the decisions suggested in the agro-

meteorological bulletin developed by the research team. The season forecast indicated normalto below-normal rainfall, yet several farmers, did not show signs of utilizing drought resilient


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crops or varieties. Fig 3 shows the land utilization for the various seasons, low land

utilization was recorded during the March May 2008.

5.2 October-November-December season (OND) 2008

This was the second season of trials of the integration of seasonal forecasts in on-farm

decision making. Forecast indication showed normal to above normal rainfall. Farmers were

enthusiastic and went out and increased land under crop and invested in seeds and fertilizer

Fig 4 shows the mount of fertilizer used by farmers in Kitui district, while Fig 5shows the

amount of fertilizer used by farmers in Mutomo. The season was however disappointing as it

turned out that the actual temporal distribution of rain was bad and the season was

characterized by long dry spells which caused water stress on the crops resulting in crop

failure.

5.3 March-April-May season (MAM) 2009

The MAM 2009 season was the third season of the project in the field. The meteorologist had

a hard time explaining the outcome of the previous season, and also gaining confidence of

farmers given the failed forecast of OND 2008. However this was one of the seasons which

had greater success in risk management actions among farmers. Forecast indications of

below-normal rainfall were well interpreted in the downscaled rainfall amounts via the

FACT-FIT toolkit. It was clear among the participating farmers that the season was going to

be dry and probably a severe drought. Farmers responded positively to the forecast

information by restraining investments, and went for low risk investments such as less land

on crop, use of farm yard manure in place of chemical fertilizer. Farmers in Mutomoincreased the amount of land on millet and sorghum which are more drought tolerant crops.

5.4 October-November-December season 2009

The OND 2009 season was greatly pronounced over the media as an El Nino year. Farmers

eagerly awaited the seasonal forecast information from the meteorological department to

enable them confirm their expectations. The forecast indication of near-normal to above-

normal rainfall for all the locations of the study sites was well received by the farmers.

Farmers reacted positively to forecast and invested greatly in land, labour and farm inputs.

Conspicuous during this season was the heavy investments in chemical fertilizer, unlike in all

the previous seasons.

Farmers expected huge gains from their farm activities. Most farmers went for greater

investments in form of high yielding crop varieties and hybrid seeds. There was a problem

however in the distribution of rain in Mwingi and Mutomo districts, with long dry spells in

between rainfall episodes causing water stress on crops.

Credit is one aspect of agriculture which opens up financial risk. Fig 6 shows the credit

acquisition of farmers in Kitui. Many authors have regarded farmers as risk averse. Credit is

the most liquid investment a farmer may make, however despite the perceived risk, credit

acquisition among the farmers in Kitui have also been observed to follow a similar trend as

responce to climate information. Approximately 87% of the farmers in Kitui, acquired credit

during the OND 2009 season when the forecast indicated above normal rainfall influenced


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by El Nino phenomena. It is a big deviation when compared to the previous seasons when

only 24% acquired credit in OND 2008 when there was an indication of above-normal

rainfall. The fact that the above-normal forecast was associated with El Nino may have

motivated farmers to react positively.

6. CONCLUSIONS

Seasonal forecasts benefit farmers most when they give them the opportunity to take

advantage of good weather conditions. Downscaled seasonal forecasts can make the

difference between optimizing on climate opportunities and risk management in arid and

semi arid areas. Final evaluation of farmers confirmed long held opinion that integrating the

effectiveness of climate and forecast information into their cropping strategies had not been

effectively achieved. The key challenges identified were translating climate information into

user-friendly language for farmers and integrating this into effective adaptation to climate

change and variability. Utilization of land during the four seasons of the project had some

distinct impressions of forecast guided decision making. Forecast information had greaterinfluence on decisions of amount of land put on various crop types and seed varieties.

Seasonal forecasts benefit farmers most when it provides opportunity to take advantage of

climate conditions. Onset of rain and expected rainfall amounts is most important information

for arid and semi arid lands. Downscaling climate information may be expensive, but it

provides an informative approach for seasonal forecasts and farm-level decision making.

Capacity building among farmers and extension agents on interpretation and use of seasonal

forecast is necessary in order to reap the benefits of seasonal forecasts.

7. ACKNOWLEDGEMENT

The project Managing risk, reducing vulnerability and enhancing agricultural productivity

under a changing climate is supported by the Climate Change Adaptation in Africa (CCAA)

program, a joint initiative of Canadas International Development Research Centre (IDRC)and the United Kingdoms Department for International Development (DFID). The views

expressed in this paper are those of the authors and do not necessarily represent those of

DFID or IDRC.


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decisions; the case of smallholder farmers in Lesotho. The Geographical Journal, Vol.

170, No 1, pp 6-21 18 ibid


13/18

Table 1 Categorical Forecast and Corresponding Downscaled rainfall amounts for Kitui

SEASON Forecast

category

Category of

Estimated rainfall

Amounts for the

Season

Probability of

Occurrence of

indicated

amounts

Actual Rainfall

amounts received for

the Season

(as monitored by

farmers)

MAM

2008

normal to below

normal

240 -280mm 70% 80% 340.3

OND 2008 Near normal to

above normal

280 -300mm 40% - 50% 227

MAM

2009

Below normal 150 240mm 60% - 70% 203.6


above normal 260 -300

20% - 40% 596

Table 2: Categorical Forecast and corresponding downscaled rainfall Amounts for Mutomo

SEASON Forecast

category

Category of

Estimated rainfall

Amounts for the

Season

(downscaled by

FACT-FIT)

Probability of

Occurrence of

indicated

amounts

Actual Rainfall

amounts received

for the Season

(as monitored by

farmers)

MAM

2008

normal to

below normal

100 -200mm 10% -30% 140.3


above normal

260 -280mm 40% - 60% 341.4

MAM

2009

Below normal 200 -260mm 20% - 40% 70.7


above normal

240 -280mm 50% -60% 291.6


14/18

Figure 1 GHA Seasonal Climate Forecast MAM 2009


15/18

Figure 2 Downscaled seasonal forecast Kitui District MAM 2009


16/18

Figure 3 Land Under Crop in Mutomo


17/18

Figure 4 Amount of Fertilizer used by Farmers in Kitui

Figure 5 Amount of Fertilizer used by Farmers in Mutomo


18/18

Figure 6 Proportion of Farmers Seeking Local Credit in Kitui

Documents

SF on Farm Decision Kenya