Introduction

Climate change today is resulting in increased climate variability (IPCC 2007). The impacts of increased climate variability are pronounced, especially in least developed countries (LDCs), where most agricultural production systems are rain-fed and where people have few resources to cope with and adapt to these changes. Climate variability and droughts are already important stress factors in Africa, where rural households have been adapting to such shocks for decades (Mortimore and Adams 2001; Mertz et al. 2009a), and in very dry regions, they have even moved ‘beyond climate’ dependence in their adaptation and coping strategies (Nielsen and Reenberg 2010). Nonetheless, rural households are likely to experience greater uncertainty in their rural production, and the negative shocks and trends from increased climate variability will affect rural livelihoods, thus exposing rural household welfare to greater levels of risk (Cooper et al. 2008; Paxson 1993; Udry 1994; Alderman 1996; Lim and Townsend 1998; Deaton 1991; Alderman and Paxson 1994; Fafchamps and Lund 2003; Kazianga and Udry 2006). Consumption smoothing is also more difficult with repeated shocks than with a single shock (Alderman et al. 2006). Repeated over longer periods, existing coping strategies can degenerate the asset base and even amplify households’ vulnerability to climate change impacts. Consequently, household welfare is most likely to be depreciated.

The climate change debate has stimulated an increasing interest in measuring and analyzing human vulnerability (Eakin and Luers 2006; Vincent 2007; Mertz et al. 2009a). Vulnerability to climate change impacts is the degree to which a system is susceptible and unable to cope with the adverse effects of climate change (IPCC 2007; Adger 2006). The key parameters of vulnerability are the stress to which a system is exposed, its sensitivity and its adaptive capacity. Thus, the vulnerability of a household will determine its ability to respond to and recover from the shocks.

When a large negative shock occurs, the usual household activities may not yield sufficient income. Studies have reported high-income variability related to risks of various forms associated with fluctuations in crop yields (Townsend 1994; Kinsey et al. 1998). If all the households in a community, district or region are affected, local income-earning activities are unlikely to be available or sufficient. In this case, relying on the support of family members or others may not be possible unless they have migrated and can contribute with remittances. In such a situation, formal or informal insurance transfers (credit or insurance) from outside the community are necessary, while inter-temporal transfers (e.g. the depletion of individual or community-level savings) are also possible. Besides seeking assistance, households may also pursue other activities as part of their coping strategies. Many examples, including temporary migration to find jobs, longer workdays, collecting wild foods and collecting forest products for sale are reported (e.g. Thornton et al. 2007; Davies 1996; De Waal 1987).

Keeping livestock as an asset to cope with shocks is another common strategy as a flexible and mobile resource with lower dependence on climate factors (Mertz et al. 2010), and it may solve short-term problems. The returns can also be negative, however, as a severe drought may cause destocking due to livestock death or low fertility (Dercon 2002). The consequence may be the loss of some or even all livestock just when it is needed as part of a self-insurance scheme.

The overall conclusion of these studies is that most households succeed in protecting their short-term consumption from the full effects of income shocks, but that in the long term, these shocks have consequences for low-income households, which are forced to, for example, reduce their investment in children’s health and schooling, or sell productive assets in order to maintain consumption. These consequences raise an argument for targeting policies to increase such vulnerable households’ ability to resist and respond to these shocks. To target policies, it is necessary to identify, and differ between, the conditions that make households vulnerable, besides being poor. Even though that difference in access to basic resources such as water, infrastructure and public facilities is recognized as major contributors to differences in household welfare, there exists little work to explore the degree of variation from one locality to another. While this type of work has been limited mainly by lack of adequate data and resources, studies by Merrey et al. (2005), Kristjanson et al. (2005) and Okwi et al. (2007) suggest that policies need to target local specificities to be effective. Building on their work and recognizing that vulnerability of a society to climate variability is influenced by its development path, the physical exposures, the distribution of resources and the institutional setting, this paper aims to explore local patterns of climate variability and potential impacts from this on local communities. The recognition of variation in local factors may feed into both local and national policy making aimed at reducing poverty and improving the ability to respond to climate variability and associated shocks.

For the examination of exposure to climate variability and influence on households’ livelihoods, there is a growing literature that contrasts local perceptions and scientific records on climate data in Africa. Ovuka and Lindqvist (2000) found that Kenyan farmer perceptions of decreasing trend in rainfall correspond with recorded monthly and annual rainfall data. This result is further verified by studies in South Africa (Thomas et al. 2007) and Burkina Faso (West et al. 2008). Results from Senegal (Mertz et al. 2009b) also point in the direction of corroboration between actual and perceived evidence on rainfall. As a contrast to these studies, Meze-Hausken (2004) found no evidence of correlation between peoples perceptions and recorded rainfall data from Ethiopia. Building on this literature, this paper analyzes households’ self-reported harvest shocks against recorded rainfall data.

Not only the amount but also the timing of rainfall is essential to ensure stable crop development and secure the efficiency of farm inputs (Antle et al. 2004; Porter and Semenov 2005). Rosenzweig et al. (2002) computed that, under scenarios of increasingly heavy precipitation, production losses due to excessive soil moisture have the potential to double by 2030. In scenarios with higher rainfall intensity, Nearing et al. (2004) projected increased risks of soil erosion. Cooper et al. (2008) simulated crop productivity in maize for an area in Kenya under increased climate variability with an increase in the total amount of rainfall. The results showed considerable yield variations, with contrasting patterns of seasonal rainfall distribution. Probabilistic projections for major crops based on multiple global circulation models, however, did not project significant crop losses in 2030 in East Africa with the exception of cowpea (Lobell et al. 2008). Another study using a single climate model with a multiple scenario assessment shows yield increases in cassava, sorghum, rice and millet in East Africa and decreases in wheat and maize yields by 2030, the latter mainly in Tanzania (Liu et al. 2008). There are relatively high discrepancies in the results of these two studies, which demonstrate the difficulties involved in predicting future crop yields and the extent to which harvest shocks will become more frequent in the future. There is, however, agreement that variability in climate in general and rainfall in particular are important sources of production and price risks which can lead to income fluctuations for rural households. The focus has mainly been on inter-temporal changes within a given location or region and the local (spatial) variability of such climate factors has often been neglected.

Consequently, the objective of this paper is to examine to what extent rural households experience shocks to income arising from rainfall variability and to explore how rainfall patterns vary from one locality to another. In addition, the paper looks into the local patterns of how households cope after a shock and link the spatial distribution of coping strategies with environmental conditions. This will provide some basic background that could benefit policy-makers in targeting interventions with regard to rural extension services, etc. The paper is organized as follows. Section “The local context” presents the local context of the research area. This includes an introduction of local conditions at district level. Section “Data and methodology” introduces the applied data and methodology, and Sect. “Results and Discussion” analyses the results and provides a discussion of these. Finally, Sect. “Conclusions” concludes.

The local context

The Kagera region is the remotest region from the administrative center in Tanzania of Dar es Salaam situated in the northwestern part of Tanzania by the shores of Lake Victoria and borders Rwanda, Burundi and Uganda (Fig. 1). The infrastructure in Kagera is characterized by poor roads compounding the isolation further. Kagera has a varied topography, with tropical vegetation, including forests and wide-open grasslands. Large parts of the region are characterized by low soil fertility, and—mainly in areas near and along the lake shores—soil erosion on sloping land is another problem (URT 2003). Nonetheless, the region produced between 1996/1995 and 2000/2001 an annual surplus of 681,000 tons of starch food (URT 2003). It is, however, the region in Tanzania with the lowest per capita GDP, and 29% of all households in Kagera live below the basic needs poverty line (URT 2002b).

Fig. 1
figure 1

Districts in Kagera region and the distribution of population density, agricultural potential and market access. The population data are based on the Tanzania Population and Housing Census (National Census Bureau of Tanzania 2002) and depict population density for 2002 by fourth-level administrative unit (wards). To estimate market accessibility, locations are classified according to distance to road, road quality and distance to nearest town. It is assumed that all areas are equally accessible outside roads, all transportation on roads is assumed to be by motor vehicle, and transportation outside roads is assumed to be walking. The analysis of market accessibility is based on data from Africover available at http://www.africover.org. Agricultural potential is based on average annual rainfall and soil quality. Rainfall data are available from the KHDS data set, and soil quality is based on data from International Livestock research Institute (ILRI) available at http://www.ilri.org/

Full size image

The rainfall pattern in Kagera is bimodal, with annual rainfall ranging from 1,000 to 2,000 mm. The rainy seasons are defined as March–May (long rainy season) and October–December (short rainy season). In the long rainy season, the data used in this paper show that Kagera receives on average 41% of total annual rainfall, while the short rainy season receives on average 32%. The remaining 27% falls mainly in January and February. Table 1 presents a summary of the recorded rainfall data for Kagera as a total mean and for the respective districts. The data reveal large differences in variability in between the districts. Karagwe in the north experiences large variability from year to year, with a 1,600 mm disparity, while Ngara in the south, on the contrary, receives significantly less average rainfall, but with less variability both within rain seasons and between years.

Table 1 Recorded rainfall data in Kagera Region and districts from 1980 to 2003
Full size table

The population of Kagera is approximately two million people (according to the 2002 Census), and the population density varies between the six districts of Biharamulo, Bukoba Rural, Bukoba Urban, Karagwe, Muleba and Ngara (Fig. 1; Table 2). Given that urban and rural households have different vulnerabilities and livelihood strategies, Bukoba Urban is excluded from the analysis in this paper where the focus is on rural areas.

Table 2 Characteristics of rural Kagera
Full size table

In some parts of Kagera, land ownership is customary. For example, the land belongs to a clan and clan members approve any agreement related to selling of land. The livelihoods of the rural population are primarily based on a range of rain-fed annual crops such as maize, sorghum and tobacco in the south and bananas and coffee in the north. Mainly traditional cultivation methods and tools are used, and capital investments are minimal, with land and labor as the principal factors of production. These aspects render agricultural production in the region, and thus rural livelihoods, highly dependent on the agricultural potential of land resources, water availability, the availability of land and access to local markets. All these factors vary a great deal between the districts, with Ngara having the lowest degree of population density, agricultural potential and market access (Fig. 1).

Drawing on information from Kessy (2004), De Weerdt (2008) and EDI (2004), the districts in rural Kagera are characterized by intra-district heterogeneity in livelihood vulnerabilities and opportunities. The slopes in the northern district, Karagwe, make transportation of agricultural products to the local markets burdensome, but roads leading into Uganda and market centers are relatively good. Therefore, the population in Karagwe has the potential to benefit economically from its geographic location. It is also the most highly populated district together with Bukoba Rural. After agriculture, the major income-generating activity in Karagwe is livestock keeping. Households in Bukoba Rural also gain from access to markets in Uganda and base their livelihoods mainly on banana and coffee growing. Because of the location next to Lake Victoria, households in Bukoba Rural have access to fishing, which is the second most important income-generating activity in the district after agriculture. Likewise, households in Muleba districts also gain from fishing opportunities, as a supplement to agriculture, and have relatively good market access to the center in Bukoba Urban. At the same time, population density is quite high and contributes to existence of local markets and trade.

Biharamulo is the poorest district in Kagera and is located in the southern part of the region. Soil quality is poor, and rainfall is well below the Kagera average. As a contrast to households in the northern districts, who base their livelihoods to a large extent on banana/coffee-farming systems, households in Biharamulo rely generally on sorghum/coffee/cotton-farming systems. Tobacco production is increasing after a collapse of coffee and cotton markets. Ngara is, according to the KHDS data, the second poorest district in the region, and it is the least populated district. It has little rainfall and low soil quality. These factors render agricultural production difficult in Ngara, but the district has the advantage of relatively good market access, not least to markets in Rwanda and Burundi. A livelihood system based on banana and coffee farming together with fishing activities enables households to sustain income and food security even during years with large anomalies in weather patterns. In districts where households mainly rely on agriculture for income and food intake, households would be expected to be more vulnerable to sudden or unexpected shocks to income and crops. Such heterogeneities surely exist not only between districts but also between households within each district.

In Tanzania, there are a number of key institutional structures and policy instruments in place at municipal level, including municipal land-use planning integrating perspectives on exposure to natural hazards, risk and emergency plans, and climate and energy plans. The proposed projects under the National Adaptation Programme of Action (NAPA) in Tanzania is planned to be implemented with high level ministries, such as Ministry of Agriculture and Food Security, but in cooperation with Local Government authority, Tanzania Meteorology Authority, local communities and NGOs. This may provide a basis for an increase in capacity at local scales in order to cope with climate change as well as it could increase formal responsibilities for land-use and other planning. Local level vulnerability assessments could in this way be a key instrument for municipalities and may pave the way for integrating climate change perspectives in local governance. However, at this stage, the NAPA assessment of vulnerabilities and adaptation strategies focus on country level factors and therefore may miss out on local complexity and lead to too generalized conclusions.

Data and methodology

The analyses of household shocks and coping strategies are based on household survey data from the Kagera Health and Development Survey (KHDS) 2004. The KHDS data and documents are available at http://www.worldbank.org/lsms/. The KHDS was originally adapted from the World Bank’s Living Standards Measurement Study (LSMS) questionnaires and consisted of 912 households interviewed in three waves from 1991 to 1994, in nearly 50 communities. The 2004 wave used in this paper consists of over 2,700 households from the baseline of 912 that were re-contacted because people had moved out of their original household. One of the main purposes of the KHDS was originally to collect data that can be used to analyze the impact of HIV/AIDS, which has been a major problem in Kagera, with prevalence rates of 24% during the 1980s (Beegle et al. 2006). One concern that arises using this data set is that the survey design originally included an over-sampling of households considered to be at risk based on the following criteria: those with a sick adult and/or those households that had experienced an adult death in the past 2 years at the time the first household was listed (about 6 months before the first survey round). It is possible that households that have experienced a death in earlier years respond differently to shocks. However, the 2004 wave used in the analysis of this paper was collected 13 years after the first wave, and therefore, the household may have overcome the difficulties caused by a death or illness. Nonetheless, this must be acknowledged as a limitation of the analysis.

A second concern is that the analysis uses the section of the KHDS based on households’ perceptions of shocks to income due to weather-related harvest failures. Using self-reported shocks may contain some weaknesses, as self-reported shocks are by definition subjective and dependent on the perceptions of the individual included in the sample. This implies that one individual might report an occurrence of harvest failure, while another household in the same community, or even in the same household, might perceive the situation differently and thus not report any occurrence of harvest failure. The question on self-reported shocks in the KHDS questionnaire asks what type the respective years were. It is therefore not possible to draw conclusions regarding the length of the hardship. And even though five opportunities for answers were provided regarding whether they had a good or bad year, the affirmative answers may still cover heterogeneous responses (Tesliuc and Lindert 2004). For example, some households in the same area may have lost most of their harvest and thereby a large potential income share due to heavy rains during or after the harvest season because they had only limited storage capacity. On the other hand, other households in the same area could also have reported the shock, but did not experience as large a negative impact to income and experienced less loss than the other households as they were able to store their harvest properly, away from the rains. These types of differences in responses cannot be extracted from the survey and will be a weakness that must be acknowledged in the analysis.

The analysis in this paper was mainly carried out at district level, this being justified by the differences in rainfall patterns within the region and because rural households rely heavily on agricultural production, which is closely linked to rainfall quantity and distribution. The KHDS data refer to administrative districts before 2000 (after which Bukoba Rural and Biharamulo were subdivided).

Monthly rainfall data were obtained from the KHDS. The data cover the period from 1980 to 2004 collected from a total of 21 rainfall stations distributed between the districts in the region. The stations are spatially distributed with 2 stations in Biharamulo, 7 in Bukoba Rural, 5 in Karagwe, 4 in Muleba and 2 in Ngara. Comparison with longer time series of rainfall and reports from FAO indicates well coherence with the KHDS rainfall data. The year 2004 lacks a number of observations from Biharamulo, Ngara and Muleba. In addition, Biharamulo lacks three observations from 1999 and one each from 1993, 1997 and 2000. Bukoba Rural lacks one observation each from 2001 and 2004. Based on this, year 2004 was removed from the analysis, while the remaining missing observations were accounted for during the analyses by omitting these months for the respective districts. Temperature is also important for agricultural production, but mainly for long-term changes in agricultural potential (Lobell et al. 2008; Liu et al. 2008). Therefore, temperature is omitted in this paper, the focus being on short-term shocks.

Results and discussion

Rainfall variability and shocks to household income

The shocks that households in Kagera have reported are primarily related to death, harvest failure and illness (Table 3). This paper is limited to examining shocks defined as low income due to poor harvests of agricultural crops caused by unfavorable weather conditions, since these can be expected to be linked more or less directly to climate variability. This type of shock will hereafter be referred to as a harvest shock.

Table 3 Distribution of shocks reported by households in Kagera 1994–2003
Full size table

Shocks related to death, illness, low crop prices and lost wage employment can also be linked to climate variability, but the links are less direct and difficult to examine. Examples include increases in the incidence of malaria, which are major contributors to illness and deaths and have been attributed to increased climate variability in several studies (WHO 2003; Zhou et al. 2004; Wandiga et al. 2006; Abeku et al. 2003). Crop prices and employment opportunities are, among other things, influenced by agricultural productivity. Prices are affected by supply (crop yields and market access), while demands for labor are affected by seasonal fluctuations.

Overall, the distribution of harvest shocks in Kagera (Table 4) during the 10-year recall period gives the impression that 2000 and 2003 were the years with the least favorable weather conditions for producing crops. The years 2000 and 2003 dominate, with almost 50% of the total number of recorded harvest shocks.

Table 4 District harvest shock distribution
Full size table

The district-level analysis shows a different tendency, and the frequency of shocks is unevenly distributed between the districts. It appears that the distribution of reported harvest shocks is very much district-specific, with considerable variations between both years and districts (Table 4). Three districts in particular, Bukoba Rural, Karagwe and Ngara have high frequencies of reported harvest shocks. There may be a wide number of reasons for this, such as farm mismanagement, pest attacks or even illnesses and deaths in the family. Nonetheless, if Table 4 is compared with Fig. 2, it appears that the distribution of households reporting shocks seems to be associated with variability in rainfall. Figure 2 shows rainfall anomalies on an annual and seasonal basis. The Figure also reflects the distribution of shocks presented in Table 4. Assuming that there is a correlation between the frequency of households reporting harvest shocks and rainfall patterns, harvest shocks seem to be associated with variability in terms of excessive rains in a season, dry spells and cumulated patterns, the latter meaning that excessive rains in 1 year can result in households reporting harvest shocks in the forthcoming year.

Fig. 2
figure 2

Rainfall anomalies calculated based on deviation from the medium-term district mean rainfall records (1980–2003) received in any given year. The lines are associated with the secondary x-axis and show the frequency of households for each district who reported harvest shocks in respective years. Source: Own calculations based on KHDS

Full size image

The rainfall data show large disparities in rainfall patterns between the districts. Overall, households reporting harvest-related income shocks are over-represented in districts with relatively large annual and seasonal variability: Karagwe, Bukoba Rural, Ngara and Muleba. In the district with relatively stable rainfall patterns, Biharamulo, the frequency of households reporting shocks is low. The trends in rainfall patterns based on the data from 1980 to 2004 point towards a shift in onset of rainfall and general patterns. With the exception of Biharamulo, where rainfall has remained almost stable, all districts have experienced an increase in amount of rainfall. The period outside the rainy seasons from June to September remains relatively dry, while, except for Biharamulo, the January–February rainfall trend shows a large increase especially in January rainfall. Especially, Bukoba Rural has an increasing trend in amount of rainfall for the two rainy seasons as well as the January–February rains.

Unfortunately, the number of observations with regard to households reporting shocks is too limited to allow a statistical analysis of the relationship between shocks and rainfall patterns.

One conclusion, which can be drawn from this, is that large local differences exist between the districts. Rainfall variability and shock distribution both vary, and aggregated data for Kagera do not provide valuable information at the regional level for policy-makers.

Household coping strategies

Over time, rural households develop a range of coping strategies as a buffer against uncertainties in their rural production induced by annual variations in rainfall combined with socio-economic drivers of change (Cooper et al. 2008). These coping strategies spread risk and aim to reduce the negative impacts on household welfare from income shocks due to harvest failures. Coping strategies may be preventive strategies such as altering planting dates, introducing other crops and making investments of water equipment, or may be in-season adjustments in the form of management responses. Lastly, they may be reactive strategies used after the negative impacts or the so-called shock due to harvest failure. The latter most often include consumption smoothing, the sale of assets such as livestock, remittances from family members outside the household and income from casual employment (Niimi et al. 2009; Kinsey et al. 1998; Kochar 1995; Rahmato 1991; De Waal 1987). It has also been argued that coping strategies for small rural households vary both between households and over time according to preferences, objectives and the capacity to change. The capacity to change includes financial and technological issues as well as the willingness to change traditional thinking.

Based on qualitative data, Kessy (2004) identified a number of strategies among households in rural Kagera for coping with a sudden or anticipated shock. Among the most important strategies were depletion of assets, namely selling livestock, especially goats and agricultural produce. Also, mortgaging land was mentioned as a coping strategy, whereas selling land is very rare, partly due to the local land tenure as described in Sect. “The local context”. The majority of households in Kagera do not have many assets to deplete and depend on their land to counter shocks. For example, a respondent in Kessy (2004) experienced a severe storm that destroyed all his banana plants. As a coping strategy, he planted short-term crops such as sweet potatoes and beans to take them through while waiting for the bananas to mature. Consequently, the dependency of land makes mortgaging of land problematic and may push the household further down the poverty ladder. Lack of assets further contributes to the vulnerability of poor households since it is not possible to borrow money without collateral.

For an expected shock or when the households have a longer period to react, cattle is sold a priori in the nearby cattle market where the seller gets a better price compared to selling to a rich man in the village. In such a situation, access to markets is of great importance. When given more time to react households would seek employment, make local brew or borrow money from relatives and friends. However, in case of a community-wide shock, households would borrow money or seek remittances from households in nearby villages. The importance of remittances from children residing elsewhere has increased the interest among households in Kagera to invest in children’s schooling as an insurance for parents’ old age. The majority of households also referred to “do nothing” in case of a community-wide shock. Only a few households were found to accumulate savings. Banks are absent, and keeping too much cash in the household is perceived as dangerous because of the risks of theft and fire. Lastly, assistance from government or non-governmental organizations was mentioned as inevitable during community-wide shocks. Lundberg et al. (2005) found evidence that the poorer households in Kagera mainly rely on private remittance relative to the better off households who have better access to more formal credit institutions. Such evidence highlights the importance of improving access to formalized credit due to the risk of hardship not only affecting one village but several neighboring villages that face the same climate conditions.

In the KHDS questionnaire, households were asked how they coped after a harvest shock. The responses are shown in Table 5 and correspond well with the strategies identified in Kessy (2004). In four of the six districts, reduced consumption is the most important coping strategy. Reduced consumption is most likely associated with the strategy of “do nothing” as mentioned by Kessy (2004). In the two remaining districts, Biharamulo and Muleba, casual employment is the most important strategy. In the four districts where reduced consumption is the most important strategy, casual employment is the second most reported strategy. Support from others, or remittances, shows up as an important coping strategy in most districts. The introduction of alternative crops also plays an important role and must be considered a preventive strategy, one that assumes unfavorable production conditions for the next season. Looking into the data, again there are large disparities between the districts. Karagwe and Bukoba Rural, which are the main producers of banana and coffee, have the lowest percentages of households reporting the introduction of new crops as a coping strategy. In these districts, 50% of nutrition intake comes from bananas (Gallez et al. 2004), while coffee production is a major cash crop contributing substantially to rural incomes (URT 2003). Households in Kagera that produce bananas and coffee are less likely to diversify their farming activities and more prone to fall into poverty (De Weerdt 2008). In the southern districts, where production mainly relies on annual rain-fed crops such as maize, sorghum and tobacco, which are easier to substitute than coffee and banana, there seems to be a tendency for households to be more likely to introduce new crops as a response to harvest failure.

Table 5 Households’ main coping strategies for harvest shocks in rural Kagera 1994–2003
Full size table

The available data do not permit us to identify the new crop varieties that are being introduced. However, evidence from the literature suggests that uncertainty of rainfall patterns generally causes households to behave differently from how they would have done if the weather was known (Phillips et al. 2000; Mendelsohn et al. 2007). This uncertainty generally leads households to choose crops that will resist larger weather extremes and are tolerant of weather variations, but most often their yields are lower, and farmers invest in a lower level of inputs than the optimal, due to the risk of losing the investment. Lack of communication of seasonal forecasts and inadequate extension services in Kagera further contribute to inefficient agricultural practices. Kessy (2004) notes that despite the presence of agricultural extension officers, information on good crop husbandry are generally not extended to a majority of farmers. Previous efforts from Africa have shown that access to climate information can be effective and contribute to reduce vulnerability of rural livelihoods to climate variability (Patt and Gwata 2002; Patt et al. 2007; Ziervogel 2004). Communication of seasonal forecasts should be addressed in any regional or district policy guidance, especially with a view towards mid-season review. An extension officer can also draw attention to the role of the following year’s soil and crop management subsequent to an abnormally wet or dry year.

As a consequence of the different household responses between districts, the need to target policies to the local conditions that households are facing is emphasized. In areas where households are likely to introduce new crops, improved information on future seasonal patterns and supervision of suitable crops with expected seasonal rainfall patterns could turn out to be beneficial. In areas where households are less likely to introduce other crops, policies on the management of current crops could prove valuable. In both situations, shorter-term seasonal weather forecasting would reduce the uncertainty of weather patterns and hence the risk of less beneficial decision making. It has been demonstrated that farmers in both East and West Africa see opportunities to benefit from seasonal weather forecasts (Rao and Okwach 2005; Roncoli et al. 2009).

Conclusions

The analysis of reported harvest shocks and rainfall data indicated coherence between the number of shocks and anomalies in rainfall patterns with relatively large annual and seasonal variability both in terms of modest and excessive rains. Furthermore, the results of the analysis of harvest shocks showed large discrepancies between districts in the distribution of shocks. The rainfall data further revealed large local divergences in rainfall patterns, with dissimilarities in both magnitude and seasonal or annual variability between districts. Districts with more rainfall and large seasonal variability experienced more shocks than districts with less rainfall and less seasonal variability. The conclusion of this analysis is that the timing of rainfall appears to affect the distribution of harvest shocks more than the magnitude of annual precipitation. However, this could not be statistically proven due to data limitations. A change in timing of rainfall would nonetheless encourage mid-season planning strategies to take advantage of the increasing trend in rainfall outside regular rainfall seasons. A change in timing of rainfall together with extreme rainfall events, including both drought and excessive rains, also emphasize the increasing need for multi-year planning.

The local differences between the districts are further emphasized in the analysis of coping strategies that households follow in response to harvest shocks. While the results for the region as a whole do not reveal great differences in applied strategies, district-level data demonstrate how local strategies differ between localities.

Breaking out of a low productivity–low income trap, which can be intensified by increased climate variability and inadequate coping strategies, requires a locally targeted approach to enable households to access fully and benefit from policies such as promoting improved technology, efficient markets and supportive strategies. The returns on policies and development initiatives in one locality will depend upon the respective challenges and conditions that households and institutions confront. For example, initiatives on rainfall harvesting for irrigation in crop production during dry seasons can be expected to result in a much higher return if the intervention is fitted to approximate local rainfall distribution.