4.3 Description of the study area .1 Tanzania: An Overview .1 Tanzania: An Overview
4.3.5 Data collection
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Figure 4. 6: Overview on data collection
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Recurrence of extreme weather events like droughts and floods, their severity and related impacts
Qualitative and Quantitative:
Questionnaire, FGD and Interview
Emergence of new weeds and crop diseases and pests, their perception as to why these have emerged in recent years etc.
Qualitative: FGD
Economic factors
Financial capital and income
The role of financial capital asset as a motivating factor for decision making to change farming practices
Qualitative and Quantitative:
Questionnaire
Average annual household income in
Tanzanian Shillings (TZS) and whether this motivates making changes in farming practices
Quantitative: Questionnaire
Access to
markets,
infrastructure and communication
Market access and its role to the changes Qualitative and Quantitative:
Questionnaire, Focus Group Discussion and Interview Availability of infrastructures,
communication system and their role in motivating changes to farming practices
Qualitative and Quantitative:
Questionnaire, FGD and Interview
Increasing demands of agricultural produce and quest for personal/household own development as motivation for changing farming practices
Qualitative and Quantitative:
Questionnaire, FGD and Interview
Social Education, knowledge and skills
Formal and informal education institutions in the area and/or nearby, what and how they do contribute to the decisions by households to change farming practices
Qualitative and Quantitative:
Questionnaire
Visits of extension officers with their contribution to the changes
Quantitative: Questionnaire, FGD and interview
Various other sources of agricultural knowledge and skills as well as how such sources are accessed by the farmers, and how the acquired knowledge contributes to changes
Qualitative and Quantitative:
Questionnaire, FGD
Information and awareness
Learning visits by the farmers, feedback from researchers, co-operative societies and farmers associations or social groups (if any) and their contribution to the changes by those farmers
Qualitative and Quantitative:
Questionnaire, FGD and interview
Cultural dimensions
Social norms Any social norms that influence your decision to change farming practices, such as food preferences and prohibition of farming some crops in this area
Qualitative: Questionnaire, FGD
Traditions and beliefs
Traditional groups such as council of elders which do influence/dictate changes in the farming practices for any purpose
Qualitative and Quantitative:
Questionnaire, FGD Local techniques and technologies for
weather forecasting/prediction and their influence in changing farming practices over time
Qualitative: Questionnaire, FGD and interview
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hic changes
Population growth
Increase in the number of household members as a factor for decision to change farming practices
Qualitative: FGD
Psychologi cal:
Individual character
Whether the changes are a result of individual character and experience to try to improve one’s agricultural production or address problems such as crop failure etc.
Qualitative and Quantitative:
Questionnaire, FGD
Influence of neighbours and significant others
If the changes are influenced by others and how
Qualitative and Quantitative:
Questionnaire Governanc
e
Leadership The influence of leadership at all levels in farmers decisions to change farming practices
Qualitative: Questionnaire, FGD and interview
Policy decisions If the changes are response to government policy decisions
Qualitative and Quantitative:
Questionnaire, FGD and interview
Political decisions and influences
If political decisions have a role to play in the changes by the farmers
Qualitative and Quantitative:
Questionnaire, FGD and interview
Technolog y
Various technologies that have constantly been motivating farmers to change farming practices and whether they have improved their production, their
sustainability etc.
Qualitative and Quantitative:
Questionnaire, FGD and interview
Meteorolo gical Data
Climatic conditions
Rainfall and temperature trends and conditions in the area over 30 years ago to check if there have been changes and correlate with the perceptions of the local communities on the state of their local climate as well as the changes in the farming practices happening or recently happened in the area
Quantitative: TMA Meteorological records
4.3.5.1 Primary data Household Questionnaire
A questionnaire having both close-ended and open-ended questions was administered to 189 households aimed at obtaining information on, among others, important demographic and socio-economic information from the identified households; farmers’ perceptions on the local climate status; any changes in their farming practices overtime; and explanation of those changes in terms of specific types of crops and crop varieties as well as factors that motivate farmers’ decisions to undertake such changes farming practices. Other aspects included socio-economic implications of the changes at both household and community levels; and long-term policy and strategic interventions for enhanced resilience, which farmers believe they could be able to support them to not only adapt but also enhance their resilience to climate change and variability in future. Figure 4.7 is an illustration of the data collection exercise using questionnaire.
To capture the data from farmers’ perceptions, possible changes in the climate relevant to smallholder farmers’ context were listed. Then farmers were asked to select only possible changes in the local climate they believed that they had been experiencing according to their
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best level of knowledge and experience in the area. The baseline was a minimum of 30 years.
Apart from perceptions on changes in the local climate, the study was of interested also to enquire on farmers’ views regarding their prediction on the state of climate in future. Farmers were asked to predict how the immediate future climate would be, approximately the next five to ten years, whether they had the feeling that it would slowly be going back to normal or the situation would continue to be worse as time goes on.
Regarding changes in the farming practices and factors influencing these changes, two initial lists, one for the possible changes and the other for possible factors motivating changes in the farming practices. The possible factors were obtained from similar studies (for example, Acquah, 2011; Gbetibouo, 2009; Mertz et al., 2008; Belaineh Legesse, 2013) and researcher’s experience in the smallholder farmers’ Tanzanian context as well as the research area. In addition, smallholder farmers had a room to propose any other changes and factors which seemed to have been left out from the lists. After a pretest and initial results, the lists were reduced. Possible changes, which had a score below 10 percent from both the pretest and the data collection exercises were identified as insignificant and rejected, hence dropped. On the other hand, reduction of the factors considered both rejection of some possible factors at the stage of pretest before data collection (please refer to Section 4.3.6); percentage score of each of the factors (those with less than 1% overall score were considered rejected and removed from the list); and results from the qualitative data on the same.
Thus in the final lists, there were eight possible changes and seven possible factors. The final list of changes in the farming practices contained: shift to higher yielding crops/varieties;
introduce new crops/varieties; shift to shorter cycle crops/varieties; stop cultivating some crops/varieties; shift to crops that command good market prices; shift to drought resistant crops/varieties; intensify irrigation; and diversify household income sources. The seven factors forming the final list were: negative effects of climate change and variability; financial capital;
income needs; good markets; high living costs and demands for personal and household needs;
household size; and influence of others, for example, neighbors.
To obtain the data on factors motivating such changes in the farming practices, a table containing both possible changes in the farming practices and possible motivating factors was developed. In the first row, it contained a list of fourteen possible factors. In addition, possible changes in the farming practices that have already been discussed in the previous paragraphs were also listed in the first column of the same table (Appendix 1, Question 6.1 is an illustration). This was meant to allow smallholder farmers to identify changes in the farming practices that they had been making and correlate them with possible factors that motivated them to make decision(s) to undertake those changes. Then, they were able to check the boxes where possible changes and possible factors cross each other. Each of the respondents was allowed to tick multiple factors against multiple changes and vice versa. In addition, a follow-up two columns table (the first with containing similar changes in the farming practices and the second with blank spaces) was prepared to allow farmers to provide details of the changes made in the farming practices including types of crops and crop varieties as well as other details on alternative income generating activities and the like.
For the immediate future coping/adaptation options and long-term policy and strategic interventions, two separate lists were made. The first list contained a table with potential immediate future coping/adaptation options, while the second contained a list of potential long-term strategic and policy adaptation interventions. The items making the lists were drawn from both literature (for example, Hassan and Nhemachena, 2008; Middison, 2006; Panda, et. al.,
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2013; Gbetibouo, 2009; Mertz, et. al., 2008; Deressa, et. al., 2008; Below, et. al., 2010) and researcher’s local experiences. The lists covered various thematic areas such as water resource management, sustainable natural resource use and management, adaptation technologies, research and development as well as information management. Farmers were then asked to identify those, according to their knowledge and experiences, they can form the best possible options to support them in both enhancing their adaptive capacity and long-term resilience to adverse impacts of climate change and variability.
As for the social economic implications, ten possible implications were listed and respondents were asked to identify those they thought were correct according to their knowledge and experiences. Respondents were also asked to indicate whether the implication(s) they identified were experienced only at household level, at community level or both.
Figure 4.7: Questionnaire data collection in Manga Mikocheni and Mkundi villages
Semi structured and non-structured Interviews and Focus Group Discussion
A checklist of questions was used to undertake interviews to 17 interviewees from institutions specified in the list of the sample (Table 4.4 and Figure 4.6). The aim was to obtain technical information on all issues raised by the farmers and validate farmers’ information through a technical and experts’ window. In addition, a few selected elders from the villages were also interviewed with a similar aim of cross checking the information obtained through the questionnaire in a historical perspective.
In this study, 25 individual smallholder farmers (forming four groups, one for each village) from randomly selected households were asked to discuss issues related to historical background of the villages and changes in the local climate that have been experienced over time. Other aspects included decision-making to change farming practices, timing and reasons for such decisions as well as projections of future climate impacts and their adaptation interventions. For both interviews and FGD, the responses were directly recorded and later on, transcribed into text for analysis.
4.3.5.2 Secondary data
Alongside perceptions from the questionnaire, interviews and FGDs, daily rainfall records were collected from three stations, which are under the Tanzania Meteorological Agency (TMA).
These are Suji Mission, whose code number is 9437004 and its elevation is 1560 MASL (Figure 4.8); Buiko Hydromet with code number 9438009 and elevation of 536 MASL; and
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Same Meteorological Station code numbered 9437003 at an elevation of 860 MASL (Notter, 2010). In addition, temperature data from Same Meteorological station was obtained. As alluded earlier, use of rainfall and temperature data was intended to ascertain the perceptions of smallholder farmers; local leaders; village, district and PBWB experts on the state of the local climate, which were expressed in the questionnaire, interviews and FGD. In other words, it was intended to gauge the perceptions against actual climate data obtained from the relevant and mandated authority in Tanzania and see how these two sets of data tally in terms of results.
While this was a relevant and approach, two challenges need to be stated at the outset. One is that of all the four villages in which the socio-economic data were collected (through questionnaire, interviews and FGD); only one village (Mtae) had a rainfall station. However, rainfall data from this station could not be used in the research due to major data gaps (740 days, almost 2 years missing). This means that rainfall data used are from stations located in different areas outside the four villages but within the research area except for Same Meteorological Station, whose inclusion was due to the fact that it is the only main station with both temperature and rainfall data. It is important also to state that Buiko Hydromet had 2.6 percent of data gaps. However, this was found to be manageable, hence its data were used in the study. Details on how the gaps were handled during analysis are explained in the next sub-sections. The two other stations, Same Meteorological Station and Suji Mission rainfall station, had no gaps thereby making them perfect benchmarks for the state of climate in the area especially because one (Same) is located in the lowlands while Suji Mission is located in the highlands. The other challenge was lack of temperature data for the rest of the stations except Same Meteorological Station. This compelled the researcher to rely only on data from Same Meteorological Station (for temperature). Please see the details in Table 4.5.
Table 4.5. Details of the weather stations in which rainfall data were collected Station Lat. Long. Approx Distance
apart
Data Years No. of Years Gap (Rainfall) Rainfall Temp Rainfall Temp (%) Buiko
Hydrom et Station
-4.650 38.050 1.5km for Manga Mikocheni and about 15km for Mkundi village
1962-2012 - 51 - 2.6
Same Met Station
-4.083 37.733 Between 40-70km from the closest to the furthest village
1962-2012 1970-2013
51 44 0
Suji Mission Rainfall Station
-4.371 37.900 About 8km to Kambeni
1977-2012 - 36 - 0
Source: Notter, (2010); field data and observation
Same Meteorological Station is located in the north eastern part of Tanzania (Tumbo et al., 2010). Same town lies on the foothills of the South Western Pare Mountains and is characterised by semi-arid climate with highly variable and unreliable rainfall (Tumbo et al., 2010). Suji Mission Station is located in the highlands, South Pare Mountains at an elevation of 1560MASL. The location of this station in terms of elevation and being on the windward side of South Pare Mountain gives it an advantage of recording high rainfall as compared to Same Meteorological Station, which has a history of recording low rainfall because it is on the leeward side of South Pare Mountains. Rainfall data for Buiko Hydromet station in this study represent Manga Mikocheni and Mkundi villages. The two villages, Buiko and Manga
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Mikocheni, are about 0.5 km apart while Buiko and Mkundi are about 25 km apart. This station is located in the lowlands along Dar es Salaam-Arusha Road. Being in the lowlands, it is a good representative of the two villages because they are in almost similar elevation and share a number of characteristics such as the type of crops grown and climatic conditions as well as the average amount of rainfall they ought to receive, according to the interview data.
Figure 4.8: Rainfall data collection at Suji Mission Rainfall Station
4.3.5.3 Stakeholders’ validation workshop and field excursion
As part of Clim-A-Net Project implementation, a workshop was held in Lushoto, Tanzania on 2nd,followed by a field excursion on 3rd of April 2014 (Figures 4.9a and 4.9b illustrate). This workshop was attended by stakeholders from Same District Council, Lushoto District Council, Pangani Water Basin Office and the Mamba Myamba Ginger Cooperative Society alongside researchers, professors and Clim-A-Net project coordinating team. In addition, a field excursion was undertaken to selected areas and villages within the research area including Manga Mikocheni village. These were important in not only for the team to have opportunity to observe but also allowing stakeholders to validate the data collected from smallholder farmers in the villages, local leaders, experts and elders.
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