Methodology

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The research study needed two types of data. The first was the indigenous meteorological knowledge of farmers. In order to capture these data, a field survey was conducted using a questionnaire that interviewers used to ask farmers about their knowledge related to local meteorological issues. The second type of data consisted of historical meteorological data acquired from the weather stations within the survey areas.

Survey Sites

The geographical coordinates of the sites (Table 22.1) indicate that all the survey areas are located north of the equator, Masindi being the farthest from the equator (Fig. 22.1).

Field Surveys

Field surveys where conducted in Masindi, Wakiso, Jinja and Tororo districts. The choice of districts was mainly due to the presence of operational weather stations with long-term historical data namely Masindi, Namulonge, Jinja and Tororo respectively. Secondly the surrounding farming communities had some fair indigenous meteorological knowledge. An area collaborator working in the field of agriculture and residing in the districts was identified for each district. These collaborators had the role in assisting the research team in identifying research assistants and farmers to participate in the survey.

The research team developed a questionnaire as a survey instrument. The questionnaire was pre-tested in Jinja district after which corrections were made for the survey. A workshop of two days was conducted in Jinja for the research assistants who were identified to participate in the research study. The workshop was intended to enrich the research assistants with basic knowledge on both scientific and farmers' meteorological knowledge as well as reviewing the questionnaire.

The field surveys were conducted during the dry season months of January and February 2003. About 60 farmers in Masindi and Wakiso districts were interviewed. In Jinja and Tororo Districts 90 and 80 farmers, respectively were interviewed. The area collaborator for each place identified the participants with preference to elderly ones. Each survey took three days, and the interview was conducted in local languages. While interviewing the research assistant translated the questions in English into the local language. The responses in the local language where then translated to English. This was achieved through selecting research assistants who either worked or resided in the survey areas.

After the field surveys the answers in the questionnaires were computerized. Data entry operators were contracted to enter the data using Microsoft Excel software, following a designed format. The data were then coded for statistical analysis using the SPSS software. In order to harmonize the local words used in the survey areas with what was recorded in English, the survey areas where revisited to get further explanations from farmers. After the analysis hypothesis were derived from the findings related to how farmers use local atmospheric indicators to forecast the onset of the 1st wet seasonal rains.

Table 22.1. Geographical position of the survey sites

Station

Longitude (°E)

Latitude(°N)

Altitude (m)

Masindi

31.7

1.7

1 147

Namulonge

32.6

0.5

1 130

Jinja

33.2

0.5

1 175

Tororo

34.2

0.7

1 170

Fig. 22.1. Survey district sites are shown on the map of Uganda

Weather Station Data

In order to test the statistical validity of the knowledge and experience of how farmers use atmospheric indicators to forecast rains, records of local atmospheric conditions were needed. In this study data collected at the weather stations Masindi, Namulonge, Jinja, and Tororo belonging to the Uganda Department of Meteorology were used.

Daily weather data on precipitation, temperatures and winds were observed by meteorological observers at each of the stations and recorded on paper forms. The records are then sent to the headquarters of the meteorology department in Kampala where they are stored in the archive. In some stations a copy of the records is usually kept at the weather station.

The meteorological data set considered for this study included the period 1960 to 2003. Although some rainfall data were available in electronic medium, the majority of the data needed data entry, especially temperatures and winds. Daily temperatures, winds and rainfall data were entered using Excel spreadsheet software. Analysis of the computerized data revealed many gaps especially during the years 1975 to 1989. This was basically due to the number of civil wars which Uganda has gone through during those years. In light of the above problem, there was no continuous data set from 1960 to 2000.

Although the standard data set recommended for statistical analysis is 30 years (1960-2000), in this study a minimum of 10 years of recent continuous data set was considered. Therefore specific criteria were set as to how the limited data could be used for analysis. First the data set for analysis and development of a model should be for recent years from 1989 to 2003, since this should fairly reflect the recent climate. Due to the nonstationarity of meteorological observations, Nicholls (1984) highlights the need to use recent data to derive forecast equations. Secondly in order to get significant relationships, high cut off values of correlation of r = ±0.63 (P < 0.05) and r = ±0.76 (P < 0.01) were set as shown in Table 22.2.

Using excel software, the daily rainfall data for each station was smoothed using Pascal's 5-point coefficient weights. Based on the criteria in Table 22.3, the INSTAT software was used to determine the different historical onset dates for the first rains. The daily maximum temperatures for each station were smoothed using a 5-point coefficient weights. The smoothed data were then processed into 5-day average tem-

Table 22.2. Significant correlations values

Data set

P = 0.05

P = 0.001

30

0.360

0.460

25

0.390

0.500

20

0.440

0.570

15

0.510

0.640

10

0.630

0.760

Table 22.3. Criteria for determining the date onset of rains

Criteria

Option 1

Option 2

Earliest start date3

45

45

Threshold for rain (mm)b

2.45

4.95

Rain daysc

5

5

Total rainfall (mm)d

20

20

Days with rainfall6

3

3

a The date when the first rains may set in early. b Minimum amount of rain to be considered a rain day. c Number of consecutive days considered for analysis.

Minimum expected rainfall amounts in five consecutive days. e Minimum number of rainy day out of five consecutive days.

a The date when the first rains may set in early. b Minimum amount of rain to be considered a rain day. c Number of consecutive days considered for analysis.

Minimum expected rainfall amounts in five consecutive days. e Minimum number of rainy day out of five consecutive days.

perature values. The arrays of onset dates were then correlated with the arrays of maximum and minimum temperature average values. Since the objective of the study was to identify within which period of the dry season is the maximum temperatures related to onset dates, the correlations were run from the dry period of November up to February. Once periods of maximum temperature significantly related to rainy season onset were identified, regression analysis was performed to develop predictive models for each site.

22.3 Results

The findings in this study were outlined district per district, first reporting on the survey findings followed by statistical analysis.

Characteristics of Survey Areas

Local expertise on indigenous knowledge may be influenced by a person's livelihood, gender, age and education. As regards to gender, in each of the sites, more men than women participated in the survey (Table 22.4). The percentage of men ranged from 73% in Namulonge to 81% in Tororo. The women ranged from 19% in Tororo to 27% in Namulonge.

Elderly people are assumed to be the custodians of indigenous knowledge and hence a majority of elderly people were included in the survey. The percentage of respondents over 40 years of age ranged from 55% in Jinja to 89% in Namulonge (Table 22.4).

Crop and Livestock Production Systems

Table 22.5 reveals that cereal crops were the main crops grown in each of the survey, sites. Masindi, Wakiso and Jinja have perennial crops like coffee. Most of the crops like maize, cassava, beans, bananas, and millet double as both cash and food crops. Rains influence cereal crops, which implies that both household food supply and income are affected by the performance of the seasonal rains. The rankings were done with respect to major crop in the region.

Table 22.4. Percentage of gender representation and age range of respondents

Site

Female

Male

Age range 20-30 yr

40-50 yr

60-80 yr

Masindi (60)

23

77

31

39

30

Namulonge (60)

27

73

10

47

42

Jinja (80)

23

77

45

44

11

Tororo (80)

19

81

16

35

49

Table 22.5. Main cash crops and food crops for each site

Rank

Namulonge

Jinja

Tororo

Masindi

Cash crops

1

Coffee

Coffee

Cotton

Tobacco

2

Maize

Maize

Millet

Coffee

3

Cassava

Beans

Maize

Rice

4

Banana

Sweet potatoes

Cassava

Cassava

5

Beans

Tomatoes

Rica

Beans

Food crops

1

Sweet potatoes

Maize

Millet

Cassava

2

Banana

Beans

Cassava

Sweet potatoes

3

Maize

Sweet potatoes

Maize

Millet

4

Cassava

Bananas

Sorghum

Beans

5

Bean

Cassava

Sweet potatoes

Rainfall Seasons in the Survey Areas

In general the rainfall patterns experienced in all four of the sites is bimodal i.e. they experience two rainfall seasons and two dry seasons. Figure 22.2 indicates the average monthly rainfall for each site. The light gray color indicates months with rainfall amount below 100 mm. The dark gray color indicates months with rainfall amounts above 100 mm, which is considered as wet months. On average, the first rains stretch in March to May and the second rains from September to November. Basalirwa et al. (1993) findings reveals the sites Masindi and Tororo have different climate zones while Jinja and Wakiso are the same climate zones. As such at each site there are differences in terms of onset dates, rainfall amounts and duration. For example Fig. 22.2 reveals that Masindi has shorter duration of the first rains compared to the rest of the sites and Tororo has more seasonal rainfall amounts.

Production Problems

From the major production problems at the four study sites (Table 22.6), it can be seen that climate risks rank as one of the five major problems faced at all the four sites. At Tororo, climate issues rank as the number one production problem while at the rest of the three sites, climate issues follow pests and diseases.

Jan Feb Mar Apr May Juri Jul Aug Sep Oct Nov Dec

Fig. 22.2. Average monthly rainfall (mm) at; a Masindi; b Namulonge; c Jinja; d Tororo

Jan Feb Mar Apr May Juri Jul Aug Sep Oct Nov Dec

Fig. 22.2. Average monthly rainfall (mm) at; a Masindi; b Namulonge; c Jinja; d Tororo

Table 22.6. Major production problems at each site

Rank3

Masindi

Wakiso

Jinja

Tororo

1

Pests and diseases

Pests and diseases

Pests and diseases

Climate issues

2

Climate issues

Climate issues

Climate issues

Labor shortage

3

Labor shortage

Markets

Markets

Pests and diseases

4

Markets

Land pressure

Land pressure

Markets

5

Land pressure

Labor shortage

Labor shortage

Crop varieties

a Ranking is based percentages facing the problem 7

- highest %, 5 - lowest °/

The major climate issues affecting the farmers include droughts, floods and hailstorms, erratic rains and delayed onset of rains. These affect the farming activities like harvesting, planting, grazing, plowing, weeding, watering, etc.

Indigenous Rainfall Indicators

Farmers have developed different approaches in responding problems related to rainfall.

Determining the Right Planting Time

The main criteria farmers in these regions use to determine the right time for planting their crops are rainfall onset followed by the calendar months (Table 22.7). At the onset of the rains, the farmers, wait for at least 2-3 showers then they consider planting their seeds. However the onset of rains should be within the expected months for planting. For example for this region, the showers should begin, in the months of late February or early March.

Other criteria include winds blowing westwards, rising temperatures and development of cloud cover.

Major Rainfall Indicators Farmers Use to Forecast Onset of First Rains

The main five rainfall indicators farmers use to forecast rains are winds, temperatures, clouds, birds and trees (see Table 22.8). The winds, temperatures and clouds are common atmospheric elements observed by both meteorologists and farmers. However unlike the farmers who keep the records in their minds, the meteorologists observe and keep the records on different mediums like paper and computer, which can felicitate follow up analysis.

Table 22.7. Farmers indicators of right planting time in different regions

Serial number

Indicator

Masindi

Wakiso

Jinja

Tororo

1

Rainfall onset

62

42

34

15

2

Calendar months

27

40

31

3

Trees shade leaves

5

48

4

Clouds darken

6

7

9

25

5

Soil gets wet

10

2

6

Winds blow eastwards

11

20

7

Winds blow westward

8

1

8

Winds blow southwards

2

4

4

9

Winds change direction

3

2

10

Temperature increase

7

10

1

11

Birds movement

5

1

8

12

Radio climate forecasts

3

13

Moon shape

2

7

14

Thunder and lightening

1

3

Table 22.8. Major rainfall indicators used by farmers to forecast onset of first rains

Rank

Indicator

Masindi

Wakiso

Jinja

Tororo

1

Winds

73

41

55

78

2

Clouds

49

31

46

55

3

Thunder

29

12

16

4

Temperatures

21

36

29

36

5

Moon

21

3

1

6

Insect

14

15

1

7

Trees

11

17

4

3

8

Birds

11

17

9

4

9

Mountain

6

10

Mist

2

3

11

Humidity

13

12

Months

7

1

Although, farmers have a range of local indicators, there are specific indicators that are regarded as more reliable than others. Table 22.9 reveals that the most reliable indicators are temperatures, winds, clouds and birds.

Forecasts of Rains by Farmers

Table 22.10 reveals that the majority of farmers find it easier to forecast the first rains than the 2nd seasonal rains. This is contrary to meteorologists who find it easier to forecast 2nd rains than 1st rains.

The ability of farmers being able to forecast 1st rains could provide scientific clues to meteorologists to forecast the rains better.

Table 22.9. Reliable indicators used by farmers to forecast onset of rains

Table 22.10 reveals that the majority of farmers find it easier to forecast the first rains than the 2nd seasonal rains. This is contrary to meteorologists who find it easier to forecast 2nd rains than 1st rains.

The ability of farmers being able to forecast 1st rains could provide scientific clues to meteorologists to forecast the rains better.

Indicator

Masindi

Wakiso

Jinja

Tororo

Temperatures

13

39

61

13

Winds

77

29

45

79

Birds

17

6

4

Clouds

51

16

15

63

Insects

5

12

Trees

2

8

1

Frogs

5

Months

3

1

Mist

3

1

1

Moon

5

2

3

Mountain

3

Lightening

21

15

River Nile

3

Radio

3

Humidity

3

Table 22.10. Percentage of farmers forecasting the first and second seasonal rains

Local Wind Systems

Farmers have a range of names they give to the winds they observe in their region. The names are based on direction of the winds, place of origin and speed. For example in Wakiso the wind locally known as Walusi, blows southwards from a hill called Walusi. Another type of wind in Wakiso is Kikunguta associated with a high speed of wind. This indicates that farmers are observant of subtle details of wind dynamic and may relate to their use as rainfall indicators

Time of Appearance of Wind Indicators

Most often the winds used for rainfall forecasts exhibit themselves in the month of February followed by March (Table 22.11). At times the winds appear in December and January. This means that these indicators can be used to forecast onset of first rains using mainly February winds and sometimes as early as December and January winds.

Operational Use of the Winds

The operational use of the wind indicators is based mainly on the change of wind direction. During dry season the wind blow in a particular direction and as the season is about to begin, the wind direction changes. Results show that directionality noted by respondents could be almost any combination. The direction and speed of winds are important features farmers use to forecast seasonal rains. During the January-March dry season, the winds usually blow strongly westwards. As the seasonal rains are approaching, the winds change direction, and blow eastwards. Winds blowing eastwards are heavily linked with the onset of seasonal rains. The above farmers' observations are consistent with findings by Camberlin and Wairoto (1997) and Okoola (1999). As such observing the time of the year when the winds change direction from blowing westwards to eastwards of the region could be used to forecast ahead of time when seasonal rains may start.

In addition to winds, farmers experience different temperature conditions in their place. These conditions have their local names as shown in the Appendix (Tables A22.1-A22.4). The names of the local temperature conditions are associated with the humidity, time

Table 22.11. Percentage occurrence of winds

Month

Masindi

Wakiso

Jinja

Tororo

December

23

2

9

15

January

49

8

33

34

February

64

49

45

85

March

64

12

18

60

of temperature increase and decrease. As shown in Table 22.12 below, the majority of farmers use the increase in temperatures during a dry season as signals for early onset of first rains. In Masindi, there is a clear indication that increase in temperature indicates early onset while a temperature decrease indicates late onset of first rains. The same indication is reflected in Wakiso, Jinja and Tororo sites. However apart from Masindi, which associates a clear decrease in temperatures with late onset, at the rest of the sites no such association was seen.

The occurrence of these temperature conditions is mainly in the month of February followed by March (Table 22.13). This suggests that the temperature conditions in February could be used to forecast onset of first rains. Though the farmers use February temperatures to forecast first rains a week ahead, there is potential for application of this predictor in the month of January.

Lead Time at which Farmers Make Forecasts of Onset of First Rains

The survey indicated a wide range of lead-times at which farmers can forecast onset of first rains. Table 22.14 shows that the majority of farmers in Masindi, Wakiso and Tororo can forecast rains 1-2 weeks ahead. However at Jinja, the majority of farmers can forecast rains 3-4 weeks ahead.

Table 22.12. Percentage of farmers who use different temperature conditions to forecast onset of first rains

Masindi

Wakiso

Jinja

Tororo

Category

Early Late

Early

Late

Early Late

Early Late

Temperature increase

20 8

22

7

31 16

18 9

Temperature decrease

2 15

7

7

8 11

6 6

Table 22.13. Monthly occurrence of temperature conditions described by farmers

Month

Masindi

Wakiso

Jinja

Tororo

December

26

0

5

13

January

51

17

23

20

February

66

51

40

78

March

46

20

16

55

Table 22.14. Percentage lead-time at which farmers forecast onset of first rains

Week

Masindi

Wakiso

Jinja

Tororo

1

43

15

8

18

2

30

43

11

18

3

3

3

10

3

4

8

28

Current Use and Accuracy of the Rainfall Indicators

Even though there is a range of rainfall indicators, the majority of farmers use mainly 1-2 indicators. A few of them can use up to three rainfall indicators (Table 22.15)

Although the farmers use these indicators, they also experience conflicting results. As indicated in Table 22.16, between 25-56% of the farmers experience conflicting results from the forecasts made using the rainfall indicators.

The farmers' experience of conflicts in their forecasts could be an opportunity to build confidence in scientific rainfall forecasts since they also face the same problem.

Farmers' Needs for Meteorological Information

Although meteorologists have made considerable advances in producing climate forecasts, these products mainly provide information on rainfall levels for the season. This product is important to the farmers, however according to Table 22.17, knowing when the rains will start is the most important climate information needed by the farmers (end users).

The climate information needs of farmers should guide the approaches meteorologist should take to serve farmers better. At the moment the service clearly follows a top-down approach yet the recently recommended approach in rural development is bottom-up. In a bottom-up approach the end users are involved and are asked to spell out their information needs. It is also the current notion in rural development that to serve the rural people better, improvements are needed on what they know and do. Table 22.17 clearly indicates that among the sample population, farmer's primary concern is to know when to plant. Interestingly, they also want assistance in forecasting rainfall, which the meteorological services are well positioned to do.

Table 22.15. Percentage number of rainfall indicators used by farmers at a time

Numberof indicators

Masindi

Wakiso

Jinja

Tororo

1

10

27

25

15

2

59

31

50

73

3

21

14

3

3

4

3

5

1

1

5

15

1

Table 22.16. Percentage of farmers experiencing conflicting results in forecasting rains

Site

Yes

No

Masindi

25

70

Wakiso

31

58

Jinja

30

50

Torero

56

36

Table 22.17. Services demanded by farmers from meteorologists

Interest

Know the right planting time Learn how to forecast rains Advise on rainfall forecasts Why changes in rainfall seasons Using rainfall forecasts Attend weather seminars Why poor crop yields

22.4

Summary Findings for Wakiso Survey

The findings confirm that the first important climate information needed by the farmers is when the seasonal rains will start. For this purpose, farmers look for local atmospheric conditions such as temperatures and winds. With reference to this, two hypotheses were derived as described below:

■ The direction and speed of winds provide signals as to when the wet season is likely to start.

■ The increase in local temperatures during the dry season signals when the wet season is likely to start.

While the hypotheses above are based on farmer's knowledge, their validity can be statistically validated and improved, using the very methods of scientific climate forecasting. However statistical validation requires records of weather observation made objectively. As such based on the technology used at most weather station, analysis of wind direction and force, observations had a high subjective element in reading using the Beaufort scale. However temperatures are read from thermometers, hence these readings are very objective. Therefore statistical validation was based on temperatures.

22.5

Statistical Validation of Farmers' Knowledge 22.5.1

Onset Dates of 1st Wet Season

Analysis of onset dates for the 1st seasonal rains for each site, indicate that the average onset dates are 70, 64, 63, and 57 for Masindi, Namulonge, Jinja and Tororo respectively. However the rains may set in as early as mid month of February (Julian day 45) and as late as end of month of March (Table 22.18).

One of the hypotheses derived from the farmers' knowledge is, that increase of temperatures during a dry season signals the onset of first rains approximately within a week's time. As such maximum temperatures during a dry season are related to the timing of onset of seasonal rains.

Figure 22.3 reveals the positive rise of the maximum temperatures for Masindi, Wakiso, Jinja and Tororo from the month of November to a higher value by end of February when the rains usually start. This confirms with the farmers experience of observing increase in temperatures as a signal to when the rains are about to start.

The relationship between the maximum temperatures and onset of rainfall can be used to develop models to forecast when the seasonal rains could start. This requires identifying the significant periods during the dry season when the relationship is strong.

Correlation of Rainfall Onset Dates with Maximum Temperatures

There is a statistical variation between the relationship of dry season maximum temperatures and onset dates. There are periods when the relationship is significantly strong and periods when the relationships are weak. To show the persistence of the relationship between the two variances for each times series (1991-2000, 1992-2001, 1993-2002 and 1994-2003) the different times series have been included in the graphs.

Figure 22.4a reveals that for Masindi the relationship between the maximum temperatures for months December up to February is positive with onset date of first rains. The pattern of the relationship is the same for all the three series. The strong positive

Table 22.18. Average onset dates for the first seasonal rains Masindi Namulonge Jinja

Table 22.18. Average onset dates for the first seasonal rains Masindi Namulonge Jinja

Average

70

64

63

57

Std

12

12

12

10

Min

45

45

45

45

Max

92

84

84

70

Fig. 22.3. Time series of 5-day average maximum temperatures for all sites based on data from 19892000 (December-January)
Fig. 22.4. Correlation values between maximum temperatures and rainfall onset dates for; a Masindi; b Namulonge; c Jinja; d Tororo

correlation is between the Julian days 354 and 360. The strongest correlation is r = +0.93 on day 356.

As for Namulonge the relationship during the month of November is negative, which gradually changes to a positive relationship by late February (Fig. 22.4b). A significant relationship is shown during the days 306 to 322 with particular highly strong negative relationship for Julian days 316-320.

Though the farmers can use the February temperatures to forecast the rains, another opportunity exists during the month of November. The strong relationship during this month suggests that the maximum temperatures for the month can be used to forecast the onset of 1st wet rains three months ahead.

In the case of Jinja, the relationship is generally negative during the months of December, which gradually changes to positive one by the end of February (Fig. 22.4c).

The onset dates based on a rain day threshold of 2.45 mm, show a relationship with maximum temperatures during the months of November to February. However during the month of November the relationship is negative, which gradually changes to a positive relationship by late February. A significant relationship is shown during the days 306 to 322 with particular highly strong negative relationship for Julian days 316-320.

At Tororo, the onset dates based on a rain day threshold of 2.45 mm, show a relationship with maximum temperatures during the months of November to February. The relationship is generally positive during the months of November and February (Fig. 22.4d). Significant relationship is shown during the 1st week of January with particular highly strong positive relationship for Julian day 5.

Though the farmers can use the February temperatures to forecast the rains, another opportunity exists at the beginning of the month of January. The strong relationship during this month suggests that the maximum temperatures for the month can be used to forecast the onset of 1st wet rains ahead of two months.

22.6

Regression Models Derived from the Relationships 22.6.1

Masindi District

Based on the rain day threshold of 2.45 mm, there is highly strong relationship (r = +0.93) between 5-day average maximum temperatures centered on Julian day 356 and onset dates for the first rains (Table 22.19).

A linear equation from this relationship was derived as 356y = 16.297* - 393.673 (Fig. 22.5a) Using the 5-day average maximum temperatures centered on Julian day 356, the above equation could be used to forecast the onset date of the first rains 2 months ahead.

Wakiso District

The relationship of average maximum temperatures and onset dates based on rain day threshold of 2.45 mm is highly strong centered on Julian day 319. The correlation value

Table 22.19. Details of regression models derived for different survey sites

Table 22.19. Details of regression models derived for different survey sites

Fig. 22.5. Linear forecasting models for; a Masindi; b Namulonge; c Jinja; d Tororo (temperature data in °C)

of the relationship is r = -0.91 (Table 22.19). From this relationship, a forecasting model was derived as shown in Fig. 22.5b.

The linear equation derived from the graph in Fig. 22.5b is 319y = -0.425X + 353.115. The predicted onset date based on 5-day average maximum temperatures was centered on Julian day 319. Using this forecasting model, the start date of the first rains can be forecasted 3 months ahead.

Jinja District

Based on the rain day threshold of 4.95 mm, there is highly strong relationship (r = +0.75) between 5-day average maximum temperatures centered on Julian day 307 and first rains onset dates (Table 22.19).

A linear equation from the relationship above was derived 307y = 12.149X - 274.021 (Fig. 22.5c). Using the 5-day average maximum temperatures centered on Julian day 307, the above equation could be used to forecast the onset date of the first rains ahead of 3 months.

Tororo District

The relationship of average maximum temperatures and onset dates based on rain day threshold of 2.45 mm is highly strong centered on Julian day 7. The correlation value of the relationship is r = +0.72 (Table 22.19). From this relationship, a forecasting model was derived as shown in Fig. 22.5d.

The linear equation derived from the graph in Fig. 22.5d is 7y = 3.889X - 62.967. While 7y represents a predicted on set date based on 5-day average maximum temperatures centered on Julian day 7. Using this forecasting model, the start date of the first rains can be forecasted months ahead.

22.7

Discussion

The above results indicate common linkages between indigenous and scientific knowledge systems on climate observation. In either knowledge systems, there is practice of observing the atmospheric environment for the purpose of forecasting weather and climatic events. The practice of farmers suggests a strong need for climate forecasts to solve their agricultural production problems. Such findings are in line with studies by Roncoli et al. (2001) and Onyewotu (2000).

Weather and Climate Knowledge Systems

Although there are common linkages in both the climate knowledge systems, there are also noted differences among them. These differences are centered on the range and interval of observations, documentation, and forecasting methods. While the farmers have a holistic observation of the local environment indicators they observe, the meteorologists have selective but larger geographic observations. For example among the range of environmental indicators farmers observe, the scientists observe only temperatures, winds, clouds and precipitation. Meteorologists also have a set time interval to make the observations. The documentation system is another point of concern. Though the farmers observe a wide range of environment indicators, their observations are mainly recorded in their memory. However scientists keep historical records of the observations for deeper study. The results revealed that farmers are able to forecast the first rains easier than the second wet season. This is an interesting issue because scientists forecast the second rains easier than the first rains. The differences highlighted above indicate the opportunities meteorologists can use to develop better forecasts.

Outputs from Knowledge-Sharing

The common and different practices of observing environmental indicators by both farmers and meteorologists, for the purpose of forecasting seasonal rains form a good platform to produce needed climate information for end users. Through this study the farmers' indigenous climate practices, knowledge gaps, and farmers priority climate information needs are revealed. The scientific reasons for the ability of farmers to forecast the first rains better than the second rains need investigation. However suggestions may include the following. Farmers regard the first rains as the major rainfall season, when they produce most crops. As such there is always a lot of agriculture planning and production expectations. Secondly the dry season following the first rains is pronounced and longer than the dry season following the second rains. Therefore during the pronounced and longer dry season, rainfall indicators become well established for the farmers to easily relate them with seasonal rains. Steady winds in Uganda (Jameson 1970) are experienced at the height of the dry season in February. Thirdly, the variability of the first rains may be less than the second rains enabling farmers to master its developments. The influence of climate change to differences in degree of variability of both wet seasons could also be investigated.

Meteorologists forecast the second rains in Uganda easier than the first rains probably due to the following. The statistical models used by meteorologists are produced from global climate circulatory system, which are more pronounced during the second part of the year. Additionally the models are developed to detect extremes from the normal conditions. Therefore the forecasting models are more efficient to forecast the second rains, which are more variable than the first rains.

Farmers' Use of Local Forecasts

Although a basic seasonal rainfall forecast should indicate the onset time, rainfall amount and duration of the expected season, results from this study indicate that the majority of farmers use the environment indicators to forecast onset while the meteorologist basically forecast rainfall amount. Harmonizing the two climate forecasts could provide a better climate information package for the farmers. Interestingly, whereas the farmers can forecast the onset of first rains wet season with a lag period, additional results indicate that the majority of them wait for the rains to actually start to determine the right planting time. They wait for the rains to continue for at least 2-3 days. Others wait to experience the rains in the traditional planting calendar months of March. There could be various reasons for this scenario of which may include the following. First the lag period between when the farmers can use the indicators to forecast the onset of the rains and when the rains actually do happen is very short. For example use of increasing ambient night temperatures as signal that seasonal rains are about to start gives a short period. The correlation graphs of maximum temperatures and onset dates confirm this with strong relationships at the end of February when the season usually begins. Secondly although the farmers can forecast the onset of the rains, during the dry season the land is dry and hard to cultivate. So farmers wait for the rains to wet the soils to plough and plant. The third reason could be that although they can forecast the rains, but due to the increasing irregularity of seasonal rains, and climate change they may not be confident with their forecasts. Hence they wait for the rains to start then they plant.

22.8

Conclusions

The above study reveals, the common practices that farmers and meteorologist use in observing atmospheric conditions in pursuit of forecasting seasonal rains for crop production. There are differences in the way both farmers and meteorologists observe and develop climate scenarios that each group can forecast. The farmer's practice of forecasting rains using their rainfall indicators highlights the importance of climate forecasts to them to ensure food security. The challenges farmers experience in producing and using climate forecasts is a development activity which is very critical to be addressed by the meteorologists. Through studying the indigenous climate knowledge systems, meteorologists can identify the priority climate information needed by farmers. Considering the farmer's priority climate information needs, and interest in improving their own local forecasts, a new paradigm of work from meteorologists is needed (Engel 1997) learning to incorporate the multiple rationalities of stakeholders, rather than promoting linear, exclusive and one-dimensional ways of thinking.

Donnelly (1998) points out that recent developments focus on capacity and institutional building. Since farmers use there indigenous knowledge at the local level as the basis for decisions pertaining to food security, understanding the farmers practice in forecasting seasonal rains may help meteorologists improve their services to the end users. As such building on indigenous knowledge (Gorjestani 2000) can be particularly effective in helping to reach the poor since indigenous knowledge is often the only asset they control, and certainly one with which they are very familiar. The development of forecasting models from this study confirms with the extension approach of helping farmers based on what they have and know.

References

Basalirwa CPK, Ogallo LJ, Mutua FM (1993) The design of a regional minimum raingauge network. Int

J Water Resour Res 9(4)1411-424 Camberlin P, Wairoto JG (1997) Intraseasonal wind anomalies related to wet and dry spells during the

"long" and "short" rainy seasons in Kenya. Theor Appl Climatol 58:57-69 Donnelly R (1998) Indigenous knowledge systems in sub Saharan Africa: an overview. Knowledge and Learning Group, African Region, The World Bank (IK Notes on Indigenous Knowledge and Practices 1, October 1998, available at http://www.worldbank.org/afr/ik/ikcomplete.pdf) Engel CH (1997) The social organization of innovation. Royal Tropical Institute, Amsterdam, The Netherlands

Gorjestani N (2000) Indigenous for development: opportunities and challenges. UNCTAD conference on traditional knowledge in Geneva, 1 November 2000 Jameson JD (1970) Agriculture in Uganda, 2nd edn. Oxford University Press

Nicholls N (1984) The stability of emprical long-range forecast techniques: a case study. J Appl Meteorol

23:143-147

Okoola RE (1999) Mid troposphere circulation patterns associated with extreme dry and wet episodes over equatorial eastern Africa during the northern hemisphere spring. J Appl Meteorol 38(8):1161-1169

Onyewotu L (2000) Determining of sowing date with increasing varying onsets of the rains. (available at http://www.agrometeorology.org/mdex.php?id=n&backPW=n&begm_at=w&tt_news=84) Roncoli C, Ingram K, Kirshen P, Jost C (2001) Burkina Faso: integrating indigenous and scientific rainfall forecasting. Knowledge and Learning Group, African Region, The World Bank (IK Notes on Indigenous Knowledge and Practices 39, December 2001, available at http://www.worldbank.org/afr/ ik/ikcomp lete.pdf)

Appendix

Table A22.1 Local names of temperatures conditions in Masindi

Rank

Name

%

1

Obutagasi

48

2

Omurombwe

28

3

Embeho

8

4

Akanyango

2

Table A22.2 Local names of temperatures conditions in Namulonge

Rank

Name

%

1

Ebugumu

44

2

Obutiti

7

3

Okubugujja

3

4

Kikome

3

5

Omututulu

2

6

Kyeya

2

7

Okubindabinda

2

8

Luwewowewo

2

9

Embuyaga

2

10

Empewo

2

11

Olufu

2

12

Kasambula

2

Table A22.3 Local names of temperatures conditions in

1

Lubugumu

4

2

Lwota

3

3

Nsaikya

3

4

Luwewowewo

3

5

Kasanaka

1

Table A22.4 Local names of temperatures conditions in Tororo

Rank

Name

%

1

Ngicho

29

2

Leitho

28

3

Mulengela

11

4

Kadis (Kidisa)

9

5

Nongino

8

6

Kothodisa

3

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