Livestock Research for Rural Development 26 (6) 2014 Guide for preparation of papers LRRD Newsletter

Citation of this paper

Likelihood of agricultural technologies adoption by pastoralist communities: The case of camel improvement technologies and information in Marsabit and Isiolo Counties of northern Kenya

S G Kuria, A Murage*, H K Walaga and J Lesuper

Kenya Agricultural Research Institute, P.O. Box 147-60500 Marsabit Kenya, Kenya
kuriasg@gmail.com
* Agricultural Research Institute, P.O. Box 25 Naivasha, Kenya

Abstract

A study was conducted in Laisamis, Isiolo and Garbatula administrative districts of the arid northern Kenya to determine the rate of knowledge diffusion and the factors most likely to influence technology adoption among pastoralists following introduction of some camel improvement technologies and information. Different methodologies including capacity building of dissemination agents, field days and demonstrations were used to roll out the technologies. A semi-structured questionnaire was used to collect the data where a total of 183 randomly selected respondents were interviewed in the three districts with the help of trained local enumerators under close supervision of the research team. Chi square tests and the ordered probit regression model were employed in data analysis.

The overall response of the farmers following introduction of the camel improvement technologies was positive with the level of use of the technologies ranging between zero and 31%. Further, the results clearly showed that if pastoralists were well trained, they are more likely to adopt technologies compared to the non-trained ones. The rate of knowledge diffusion from the trained to untrained farmers was apparently low. Gender emerged as an important social factor influencing adoption of technologies with women being less likely to adopt technologies compared to men. In conclusion, the pastoralists in northern Kenya responded well to the introduced technologies. This however varied from site to the other and also appeared to be influenced by the economic and environmental circumstances of the farmers.

Key words: capacity building, improved camel productivity, pastoralists economy, technology dissemination


Introduction

Agriculture remains the backbone of the economy of Kenya with livestock being the most important in pastoral areas. The camel has a special place in the pastoral economy owing to its unique adaptations. Due to high population pressure, encroachment of pastoralist areas by farming communities from other parts of the country is on the rise reducing the area traditionally available for grazing by pastoralists. There is therefore need to intensify production for improved food security through adoption of superior production and post-harvest technologies. The government of Kenya and development partners have devoted substantial resources to improve environmental conditions and increase agricultural productivity. However, adoption of modern technology has been limited where small-scale agriculture remains characterized by minimal use of external inputs. This has been attributed to factors including high cost of acquiring agricultural technologies and other inputs, poor access to credit, poor or lack of infrastructure, inefficient extension services, low profitability/delayed returns to investment of technologies, inappropriateness of technologies, low education levels, the risk averse nature of the pastoralists, socio cultural factors, among others (Ndjeunga and Bantilan 2002, Lamboll et al 2003).

This particular study was conducted with two objectives; i) establish who was likely to do what and the level of spread of the knowledge following introduction of camel improvement technologies in several sites, ii) determine factors most important in influencing technology adoption amongst pastoralists of northern Kenya.


Materials and methods

Location

The study was conducted in three administrative districts of northern Kenya namely Laisamis, Isiolo and Garbatula. The communities involved in the study were Rendille (Laisamis), Somali and Turkana (Isiolo) and Somali in Garbatula. The study districts which lie approximately between longitude 3735’ to 4030’E and latitude 0100’ to 0300'N were purposely selected due to their high potential for commercializing camel rearing as informed by high population of consumers and easy access due to fairly good infrastructure. The communities inhabiting these areas are among the leading camel keepers in Kenya with about 20% of Kenya camel population estimated to be 2.97 million (GoK 2009) under their custody. The study districts are classified as arid, falling in ecological zones V and VI. Rainfall ranges from 120 mm in low lying areas to 700 mm per annum in the highlands and is in most cases received within a short duration. The rainfall pattern is bimodal with peaks in April and November. Temperatures vary from 23 to 34 oC with the period between January and April being very hot (MLFD 2003). Evaporation is high, exceeding 2600 mm annually over most of the area. Owing to the harsh climatic conditions, the area is best suited for nomadic pastoralism.

Technologies

The technologies which were being disseminated included; camel breeds in Kenya, characteristics of good breeding males and females, breeding guidelines, pregnancy diagnosis, management of pregnant females a month before calving, signs of labour, calving management, management of the calves up to four months of growth including (colostrum feeding, diarrhoea management, management of ecto-parasites), management of weaners, important forage species for camels, watering guidelines, supplementary feeding, age determination and its importance, estimation of live weight for marketing and drug administration purposes, record keeping for commercial rearing of camels and, economically important camel diseases.

The methods used in dissemination of these technologies and information were;

1. Baseline/benchmarking surveys – the surveys were conducted in the dissemination sites i.e. Laisamis, Isiolo and Garbatula and the data collected would be used to assess the impact.

2. Capacity building of dissemination agents – carefully selected dissemination agents mainly targeting relevant government extension staff, non-governmental and community based organizations implementing livestock development programs and, community based animal health groups were trained as trainers. After the training, the dissemination agents were equipped with training materials and were expected to build the capacity of camel producers on the technologies and information.

3. Field days – the camel production improvement technologies were displayed during two field days i.e. one organized by KARI and another jointly organized with the Ministries of Agriculture and Livestock Development. Application and the benefits of the various technologies and information were explained to the clients during the field days.

4. Field demonstrations – practical demonstrations on the application of the various technologies and information were held especially during field days and capacity building sessions for the dissemination agents.

Data collection and statistical analysis

A semi-structured questionnaire was used to collect the data where a total of 183 randomly selected respondents were interviewed in the three districts with the help of trained local enumerators under close supervision of the research team. The respondents were broken down as follows: Laisamis – 22 trained and 36 untrained totalling to 58; Isiolo – 21 trained and 34 untrained totalling to 55; Garbatula – 38 trained and 32 untrained totalling to 70. In Garbatula, the respondents had been trained by either the KARI research team or the trained trainers while in Isiolo and Laisamis, the training for respondents had been conducted by trained trainers only.

Crosstabs Chi-Square Tests were used to summarize categorical variables, in this case the level of use of the different technologies by camel pastoralists in the different dissemination sites at the time of the study. Assuming a normal cumulative distribution function of the dependent variable which in this case was the adoption, ordered probit model (Ayuk 1997) was run to determine the extent to which a number of independent variables were influencing the adoption.


Results and discussion

Table 1: Frequency cross tabs for the use of different technologies

Technology

Mean

Garbatula

Isiolo

Laisamis

Chi

N

%

N

%

N

%

N

%

If the farmer was aware of other camel breeds

16

14.4

12

11

21

19.3

14

12.8

 

If the farmer retired females after 6 calvings

8

7.6

5

4.6

20

18.3

0

0.0

***

If culling of old females was done

2

2.1

3

2.8

2

1.8

2

1.8

If the farmer selected breeding bull for mating the females

10

9.5

14

12.8

17

15.6

0

0.0

***

If the farmer kept a bull aged 12 years and below

29

26.9

18

16.5

35

32.1

35

32.1

If retired males were castrated

15

14.1

1

0.9

33

30.3

12

11.0

28.8***

If the farmer herded males and females separately

5

4.3

2

1.83

4

3.7

8

7.33

If the farmer understood the importance of body weight in breeding

12

11.0

17

15.6

16

14.7

3

2.8

***

If body weight determination was done using a tape measure

4

3.7

6

5.5

6

5.5

0

0.0

***

If age determination was done through dentition

5

4.6

10

9.2

5

4.6

0

0.0

***

If de-worming of calves was done

9

8.0

4

3.7

18

16.5

4

3.7

9.28***

If calves were given mineral supplements

7

6.1

16

14.7

3

2.8

1

0.9

54.6***

If ticks control was done in calves

10

9.0

16

14.7

12

11.0

2

1.8

32.5***

If the farmer watered the camels every 7 days

24

22.0

18

16.5

32

29.4

22

20.2

4.064

*** p<0.001

Across the sites, an average of 14% (N=16) of respondents were aware of the existence of camel breeds other than what they were rearing. The respondents largely (average of 8%, N=8) did not stop old females beyond 6 calvings from breeding with the exception of Isiolo (Table 1). Very few farmers (average of 2%, N=2) removed the old females from the herd even in situations where such camels were retired from breeding. Although respondents in all the sites were generally using young bulls for breeding as recommended (average of 27%, N=29), none of those in Laisamis cared about the quality attributes of the bull compared to those in Isiolo and Garbatula (average of 14%, N=16). While respondents in Isiolo were leading in castrating retired breeding bulls (30.3%, N=33), respondents across sites herded the bulls together with the females which may be attributed to labour constraints.

Respondents in Garbatula and Isiolo were aware of the importance of live weight in breeding (average of 15%, N=16) with an average of 5.5% (N=6) estimating the live weight of their camels using a tape measure as recommended. Respondents in Laisamis seemed not to have appreciated the importance of the technology. While less than 10% (N=15) were using dentition to estimate the age of camels, an average of 22% (N=72) had embraced the watering interval recommendation in all the sites. Sixteen point five (16.5%, N=18) of respondents in Isiolo were practicing de-worming in calves while 14.7% (N=16) of respondents in Garbatula were practicing mineral supplementation and tick control in calves. Rest of the respondents across the sites were not taking keen interest in de-worming, mineral supplementation and tick control in calves as recommended perhaps as a result of not having appreciated the importance of the three factors in calf survival and growth.

Table 2: Ordered probit results (site not included in the model)

Variables

Coefficient of probit model

Marginal effects for adoption level 30-49%

Marginal effects for adoption level >50%

Coef.

Std. Err.

z

p>|z|

dy/dx

Std. Err.

z

P>|z|

dy/dx

Std. Err.

z

P>|z|

Age

-0.013

0.010

-1.340

0.181

0.000

0.001

0.380

0.706

-0.004

0.003

-1.330

0.183

Gender

0.509

0.280

1.820

0.069

0.003

0.029

0.090

0.927

0.144

0.076

1.900

0.057

Educlev

0.160

0.237

0.680

0.499

-0.004

0.010

-0.340

0.735

0.048

0.070

0.670

0.500

Karitrain

1.082

0.537

2.020

0.044

-0.207

0.165

-1.260

0.209

0.394

0.204

1.930

0.054

Tottrain

0.346

0.284

1.220

0.223

-0.018

0.030

-0.600

0.546

0.108

0.092

1.170

0.243

Farmyear

0.004

0.011

0.350

0.727

0.000

0.000

-0.260

0.793

0.001

0.003

0.350

0.727

Logtlu

0.365

0.274

1.330

0.182

-0.008

0.021

-0.380

0.705

0.108

0.081

1.330

0.183

/cut1

-0.259

0.617

/cut2

1.373

0.628

Statistics

Number of obs

91.000

LR chi2(7)

15.660

Prob > chi2

0.028

Pseudo R2

0.085

Log likelihood

-84.040

                   

All the independent variables included in the ordered probit model had been hypothesized to influence adoption. However, the results in Table 2 shows that gender and karitrain (KARI trained) variables were significant and positive. The significant and positive gender variable implied that compared to female respondents, male respondents were likely to adopt more of the trained technologies (coefficient 0.509, p <0.1). The marginal effects for the adoption level > 50% was (0.144, p <0.1), meaning that the probability of male respondents adopting >50% of the trained technologies was 14.4% higher compared to female farmers. Result for the karitrain variable suggest that compared to respondents who were not trained at all, the respondents who were trained by KARI/KASAL team were more likely to adopt the trained technologies (coefficient 1.082, p<0.05). The marginal effect of 0.394 p<0.1 for the adoption level >50% implied that compared to the non-trained respondents, KARI trained respondents were likely to adopt over 50% of the trained technologies, with a probability of 39.4%. Note that the ordered probit coefficient measures the direction of relationship between the dependent variable (adoption) and the independent/explanatory variables and the coefficient can either be positive or negative. Marginal effects show the magnitude of change. The probit model was significant at p<0.05 (Prob > chi2 = 0.028).

Table 3: Ordered probit results (including site in the model)

Variables

Coefficient of probit model

Marginal effects for adoption level 30-49%

Marginal effects for adoption level >50%

Coef.

Std. Err.

z

P>|z|

dy/dx

Std. Err.

z

P>|z|

dy/dx

Std. Err.

z

P>|z|

Age

-0.011

0.011

-1.080

0.279

0.000

0.001

0.430

0.666

-0.003

0.002

-1.060

0.290

Gender

0.161

0.309

0.520

0.602

-0.003

0.011

-0.310

0.755

0.035

0.067

0.530

0.596

Educlev

-0.086

0.253

-0.340

0.735

0.003

0.009

0.270

0.785

-0.019

0.056

-0.340

0.734

Karitrain

0.400

0.632

0.630

0.527

-0.043

0.117

-0.360

0.715

0.105

0.193

0.550

0.584

Tottrain

0.182

0.310

0.590

0.557

-0.008

0.022

-0.380

0.706

0.042

0.074

0.570

0.571

Farmyear

0.009

0.012

0.760

0.448

0.000

0.001

-0.410

0.685

0.002

0.003

0.760

0.450

Logtlu

-0.253

0.311

-0.810

0.417

0.007

0.018

0.410

0.683

-0.056

0.071

-0.800

0.423

Garbatu

3.002

0.528

5.680

0.000

-0.547

0.092

-5.940

0.000

0.853

0.081

10.540

0.000

Isiolo

1.423

0.355

4.010

0.000

-0.068

0.078

-0.870

0.382

0.337

0.087

3.880

0.000

/cut1

-0.369

0.682

/cut2

1.909

0.727

Statistics

Number of observations

91.000

LR chi2(9)

55.620

Prob > chi2

0.000

Pseudo R2

0.303

Log likelihood

-64.063

                     

The results in Table 3 show that inclusion of the site variable in the model rendered gender and karitrain variables insignificant. However, the site variable (garbatu and isiolo) was significant and positive. Both variables had positive coefficients implying that compared to Laisamis which was dropped for reference, respondents at Garbatula and Isiolo were more likely to adopt the trained technologies (coefficient = 3.002, and 1.423 respectively) both at p<0.001. The marginal effects for garbatu were significant at both adoption levels (ME = -0.547, p<0.001) for level 30 to 49% adoption, and ME = 0.853, p<0.001, for level of adoption > 50%. The negative sign for ME in level 30-49% adoption category, implies that compared to Laisamis which was the reference variable, respondents in Garbatula were 54.7% less likely to fall in this adoption category, BUT 85.3% more likely to be at > 50 % adoption category. This explanation applies for the Isiolo variables too. Note that the model was significant at p<0.001 (Prob > chi2 = 0.000). It is worth noting that the respondents in Isiolo and Garbatula were of Somali origin while those in Laisamis were of Rendille origin. The Somali community camel production system is more commercial oriented compared to that of the Rendille community and the former therefore are more motivated to take up new technologies than the latter. The respondents particularly in Isiolo interacted more with outsiders due to their location compared to those in Garbatula and Laisamis who were located in the interior. Most importantly, it is in Isiolo where camel rearing especially for milk production is most commercialized and this could explain why they quickly embraced the technology of using younger, more productive females for breeding. Rendille community is generally known to be more conservative compared to the Somali (Personal observation).

The results presented in Tables 2 and 3 clearly show that if farmers are well trained, they are likely to adopt technologies and in this particular case, camel keepers in Laisamis would trail Garbatula while Isiolo would lead the pack. It is certainly through the use of technologies that productivity of camels would improve. Previous agricultural technology adoption studies in arid and semi-arid areas (Juma et al 2009) reported disappointingly low results mainly due to the associated risks. The study by Juma et al however did indicate that frequent visits by agricultural extension officers enhanced adoption. Low adoption (37% and below) has also been reported in the Ethiopian arid and semi-arid areas (Kassie et al 2009), mainly attributed to the cost of acquiring the technologies and the risks in production environment. Costly technologies were least adopted. This observation compares favourably with the results of the current study where in the overall, the level of use of the technologies ranged between zero and 31% which was also in agreement with studies by Wabbi (2002) in Uganda.

Other studies (Feder et al 1985; Foster and Rosenzweig 1995; Nyangena 2008) which have explored the role of social factors on technology adoption revealed that production risk is an important element in agricultural production decisions, particularly in the uptake of farm technology and this tend to be worse in the arid and semi-arid environments. Korsching and Nowak (1983) had earlier identified attitudes to risk, institutional contacts, and farm size as having a significant bearing on decisions by farmers to adopt conservation farming practices in Zibambwe. If poor people are risk averse, they will be reluctant to invest in modern technology because that involves taking risks. Only economically secure farmers who are in possession of sufficient defense against downside risk will undertake profitable capital investments and innovations, while the majority of the poor remains caught up in a risk-induced poverty trap (Mosley and Verschoor 2005; Dercon and Christiansen, 2007; Yesuf and Bluffstone, 2009). Among other factors, whether to adopt a technology or not depends on the profitability of the technology, farmer education/learning, and other observed and unobserved differences among farmers and across farming systems (Suri 2005). For the current study, dissemination sites were typical arid/semi-arid environments with high uncertainties and thus risky. Profitability of the technologies introduced to the farmers could only be realized in the long run while the level of education of the farmers was low and this may also explain the low adoption.

Nsabimana and Masabo (2005) recorded a 27% level of adoption in Rwanda noting that the adopters received more farm visits from the technical staff than non-adopters in agreement with results of the current study. Further, the adopters were below 50 years and 80% had at least primary level education in contrast to the findings of the current study where age and level of education did not have significant influence on adoption. Derpsch (2005) and Hobbs (2007) working in Zibambwe also observed that those farmers who received continued support from both NGOs and the government extension services tended to intensify adoption of different conservation farming components in agreement with the findings of the current study.


Conclusions


Acknowledgements

The team wishes to thank the European Union and the Director KARI for funding this study through the Kenya Arid and Semi Arid Lands (KASAL) project. The contribution of the KASAL project coordinator by way of facilitating the study is recognized and regarded. Finally, the team acknowledges the support and cooperation by the camel producers in the respective working sites without which no data would have been collected.


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Received 6 October 2013; Accepted 21 April 2014; Published 1 June 2014

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