Adoption of zero grazing and impact on livestock keepers’ knowledge of cattle reproductive parameters in Western Kenya

J Nalunkuuma, H Affognon¹, S W Kingori¹, D Salifu¹ and F K Njonge²

Makerere University, P. O .Box 7062, Kampala-Uganda.
justinen64@gmail.com
¹ International Centre for Insect Physiology and Ecology, P.O Box 30772 – 00100 Nairobi, Kenya
² Jomo Kenyatta University of Agriculture and Technology, P.O BOX 62000, Nairobi, Kenya

Abstract

A cross sectional survey questionnaire was administered in 520 livestock farms in Kisii and Bungoma counties of western Kenya. The objective was to determine factors influencing adoption of zero grazing farming and the impact of zero grazing on farmers’ knowledge of the cattle reproductive parameters. Data were analyzed using logistic regression, two-sample t test, and ordinary least squares model (OLS).

The results of the logistic regression model revealed that adoption of zero grazing was influenced by age of household head, years of schooling, number of school going children, number of exotic cattle , being above the poverty line of Ksh 2,500, dependency ratio and number of cross breed cattle. The two-sample t- test revealed that farmers who practiced zero grazing were more keen on the reproduction of their animals. The OLS regression predicted the variable of interest, practice of zero grazing and number of exotic cattle owned to have strongly influenced farmers’ knowledge of the cattle reproductive parameters. Other variables that had a weak significant and positive impact on farmers’ knowledge were age of household head, milk production as the most important reason for keeping cattle and herd size. To increase farmers’ investment in zero grazing production system, the study recommends, investments in education of farmers, increasing access of farmers to higher yielding cattle and improving their poverty status. Since participation in zero grazing enhances farmers’ knowledge of the cattle reproductive parameters, efforts should be made to encourage farmers’ adoption of zero grazing as it gives room for knowledge enhancement with positive implication on the success of the dairy industry.

Keywords: grazing systems, knowledge scores, logistic regression model, technology adoption

Introduction

Zero grazing farming contributes to increase milk production. It was introduced in Kenya by National Dairy Development Project (NDDP), in 1979. The practice has gained traction and is widespread in the country. Currently, a majority of the 3.5 million dairy cattle in Kenya are reared and stall- fed in zero grazing units (FAO 2011). The adoption of zero grazing addresses the problem of small land sizes used as grazing land, low productivity of indigenous cattle and disease challenges under the free range grazing system (Muma 1994; Baltenweck et al 1998). Farmers embraced the zero grazing system to intensify dairy production in Kenya (Bebe et al 2003). However, farmers’ knowledge of cattle reproductive parameters is an important factor which has implications on the success of the dairy zero-grazing industry (Meena et al 2012).

This study analyses factors influencing adoption of zero grazing, as well as the impact of adoption of zero grazing on farmers’ knowledge of cattle reproductive parameters. The implication of these results are discussed therein.

Material and methods

Study sites

A field survey was conducted in western Kenya where livestock keeping, a principal economic activity, is done through free grazing, tethering, and/or zero grazing (Amadalo et al 2003). The study was done in Kisii and Bungoma counties. The local zebu, Friesian and Ayrshire breeds are kept under extensive, semi-intensive, and zero grazing dairy farming systems predominates (Ouma et al 2003, Jaetzold et al 2005).

Sampling of farms

Data were collected from a random sample of 520 livestock farmers, stratified by category of livestock keepers: farmers who practice zero grazing and those who do not. Two sample frames were constructed for the two groups with the help of District Veterinary Officers (DVO) in each county (Kisii and Bungoma). Two hundred and eighteen (218) zero grazing livestock keepers and 302 livestock keepers who don’t practice zero grazing were randomly selected from each sample frame respectively. A structured questionnaire was used to collect data on socio-economic and demographic characteristics, dairy cattle breeds, herd size, and knowledge on cattle reproductive parameters.

Statistical analysis

Descriptive statistics were used to summarize the socio-economic and demographic characteristics of the farmers. The Logistic regression model was used to determine factors influencing farmers’ adoption of zero grazing dairy farming system. Percentage knowledge score was computed for each livestock farmer using a scale of 1 for each correct response for each cattle reproductive parameter and zero for incorrect response. Two-sample t-test was used to compare knowledge of the cattle reproductive parameters between the two groups (zero and non zero grazing farmers) and Ordinary Least Squares (OLS) regression was performed to determine the impact of practicing zero grazing on farmers’ knowledge of the cattle reproductive parameters using the composite knowledge score.

Results and discussion

Farmer characteristics

The descriptive statistics indicate that the mean age of farmers was 48.12 (± 0.56) ranging from 20 to 87 years, with an average number of 9 years spent in school (Table 1). The average number of school going children was 2.53 (± 0.10) per household ranging from 0 to 16. The average herd size was 3.28 (± 0.10) with crossbreed cattle being the most popular; mean number 1.23 (± 1.68) followed by exotic cattle and small ruminants with an average of 1.09 (± 0.08) and 0.86 (± 0.07) respectively. The mean dependency ratio was 1.16 per household (Table 1). Fifty eight percent (58%) of the livestock keepers selected within the two counties are not zero grazing farmers while 41.92 % practice zero grazing (Table 2). Overall, majority of the household heads in the survey were male household heads (81.35%) and 18.65% were female household heads. The monthly income for majority (82.69%) of the farmers was above poverty line of 2,500 Ksh. In addition, 53.08% of the farmers owned at least one or more means of transport with 64.22% of those being zero grazing farmers (Table 2).

Factors influencing adoption of zero grazing dairy farming system

Logistic regression results are presented in Table 3. The results indicate that years spent in school, number of school going children and number of exotic cattle had a very strong significant (P < 0.01) influence adoption of zero grazing farming. Age of the household head, being above the poverty line of Ksh 2,500 and dependency ratio of household head had a strong significant (P < 0.05) influence on adoption of zero grazing farming. Number of cross breed cattle had a weak (P < 0.10) influence on zero grazing adoption.

The observed positive relationship between age of household head and adoption of zero grazing was as hypothesized. This implies that older farmers are more likely to adopt zero grazing. This could stem from the fact that older people could have saved capital which enable them to invest in advanced technologies such as zero grazing, which young people could not afford due to a likelihood of having no savings. The odds ratio of age of household head is 1.03, which implies that older household heads are 1.03 times more likely to adopt zero grazing. The study is consistent with the findings of (Murage and IIatsia 2011, Kafle and Shah 2012) who observed a positive and significant relationship between age and technology adoption.

Years of schooling had a positive and significant influence on adoption of zero grazing farming. These findings are consistent with the findings of Fita et al (2012), Karamjit et al (2009), Murage and IIatsia (2011), Musaba (2010). We supposed and it was hypothesized that educated household heads acquire, understand, and disseminate new technologies within a shorter time compared to less educated household heads (Ebojei et al 2012, Kafle and Shah 2012). Furthermore, education whether it be self or in children enables people realize the importance and benefits of adopting new technologies (Musaba 2010). The odds ratio implies that farmers who have more years of schooling are 1.10 times more likely to adopt zero grazing than those with less years of schooling.

As hypothesized prior, ownership of exotic cattle influenced adoption of zero grazing as exotic cattle yield higher under zero grazing. This finding agrees with other studies Kaaya et al (2005) and Murage and IIatsia (2011) showing that livestock keepers that keep exotic breed are more likely to adopt new technology in dairy farming. The odds ratio for number of exotic breed cattle owned implies that number of exotic cattle breeds was 5.0 times more likely to influence positively the adoption of zero grazing (Table 3).

As hypothesized, the study showed that higher household incomes would lead to adoption. Zero grazing farming requires relatively higher initial investment cost to meet the initial zero grazing investment requirements that involve buying  higher yielding cows and building a stall which a poor farmer with very limited/no household incomes cannot afford. Therefore, farmers with higher household incomes are more likely to take up zero grazing. The odds ratio for being above poverty line of Ksh 2,500 in Kenya was 2.9, which implies that households that are above the poverty line are 2.9 times more likely to adopt zero grazing. The findings of this study is in consistence with the findings of Irungu et al (1998), Waithaka et al (2007) and Shields et al (2012) who observed a positive relationship between household income and adoption of a new technology.

The dependency ratio influenced adoption of zero grazing. The positive relationship between dependency ratio and adoption of zero grazing implies that the higher the dependency ratio, the higher the need to expand the household income base by setting up income generating activities so as to generate more incomes to feed and maintain the non working population within the household. The odds ratio for dependency ratio implies that dependency ratio is 1.4 times more likely to influence the adoption of zero grazing. However, findings from Ojaiko et al (2007) and Ojaiko (2011) studies found opposite relationship.  

Results also show that the variable of number of crossbreed cattle positively influenced adoption of zero grazing as hypothesized. The odds ratio for number of crossbreed implies that number of crossbreed cattle is 1.22 times more likely to influence adoption of zero grazing and therefore a household with more crossbreed cattle was more likely to adopt zero grazing production system. The management of crossbreed cattle is not very different from exotic breeds of cattle, which is a breed of choice for zero grazing practice. The findings of this study are however, consistent with Kaaya et al (2005) who argued that ownership of crossbred cattle influenced adoption of improved technology (Table 3).

Knowledge of farmers on cattle reproductive parameters

On average, farmers’ composite knowledge score of cattle reproductive parameters ranged from 4.7% to 18.3%. The findings indicate that the average combined knowledge of farmers on all the parameters was, 10% ranging from 0% to 22.7%. The results are lower than those reported by other similar studies (Meena et al 2012). The low knowledge level is attributed to lack of contact of farmers with extension workers and extension agencies, lack of awareness from the veterinary and livestock production offices and unwillingness of farmers to pay for the cost of extension service. The findings revealed that farmers had limited knowledge on cattle reproductive parameters, especially knowledge of heat signs (6.5%) ranging from 0% to 14.7%  and duration from calving interval to first heat (4.7%) ranging from 0% to 40%, which are among the most important reproductive parameters. These farmers were relatively knowledgeable about the gestation period of cattle (17%) ranging from 0% to 42.9%, age at first heat (18%) ranging from 0%  to 60%, calving to conception interval (14%) ranging from 0% to 71.4% and pregnancy signs (13%)  ranging from 0% to 37.5% (Table 4).

Since zero grazing farmers’ do not breed using own bulls they are keener at observing and identifying animals coming on heat so that they can present them for breeding at the appropriate time thus had higher knowledge ( 11.59%) than their counter parts (8.82%) overall and differences were noted between the two groups ( P < 0.01) ( Table 5).

Impact of zero grazing on farmers’ knowledge of the cattle reproductive parameters

Table 6 presents the ordinary least squares model estimates on the impact of zero grazing on farmers’ knowledge of the cattle reproductive parameters. The results indicate that most of the explanatory variables had the hypothesized signs. However, only practicing zero grazing and the number of exotic cattle had a strong significant (P < 0.05) impact on farmers’ knowledge of the cattle reproductive parameter at 5%. The other variables that had a significant effect are age of household head, milk production being the most important reason for keeping cattle and herd size but these are significant only at 10%.

The practice of zero grazing (P < 0.01) and number of exotic cattle (P < 0.05) had a strong significant impact on farmers’ knowledge of cattle reproductive parameters. While other variables; age of the household head, milk production as the most important reason for keeping cattle and herd size had a significant impact on farmers’ knowledge ( P < 0.10). Variables such as years spent in school by household head, number of school going children, male household heads, ownership of one or more means of transport, number of crossbreed cattle, number of small ruminants, dependency ratio and being above poverty line of 2,500 Ksh had no significant influence on farmers knowledge of cattle reproductive parameters.

The variable of interest, practicing zero grazing farming affected farmers’ knowledge of the cattle reproductive parameters. Holding all other factors constant, farmers’ knowledge of cattle reproductive parameters was more likely to increase by 1.6 % if adoption of zero grazing increases by 1%. Zero grazing positively influences farmers’ knowledge of cattle reproductive parameters. This implies that adoption of zero grazing farming creates greater opportunities for farmers to search and have access to more knowledge through fellow farmers, extension agents, and research institutions among others (Table 6).

The number of exotic cattle positively (P < 0.05) influences farmers’ knowledge. Thus, increasing the number of exotic cattle by one exotic cow is likely to increase farmers’ knowledge of the reproductive parameters by 0.02%. It might be argued that farmers practicing intensive production systems attach higher importance to genetic improvement of their cattle (Bebe 2003).

The variable age of the household head had a positive and significant effect on farmers’ knowledge. Increasing the age of the farmer by one year increases farmers’ knowledge by 0.19%. This could stem from the fact that older people could have generated knowledge as per the parameters over the past years through experience and hands on hence are better positioned to be more knowledgeable than the young people with no/ limited /experience.

Milk production as the most important reason for keeping cattle had a positive and significant impact on knowledge. Attaining more milk yields requires a farmer to have knowledge on reproduction parameters. Knowledge on reproduction parameters is key in realization of increased milk production. Having milk production as the most important reason for keeping cattle increases farmers’ knowledge by 0.62% holding other factors constant.

There is also evidence that herd size is an important factor in influencing knowledge. Herd size appears significant and positive. This illustrates the importance of the size of the herd in influencing farmer’s knowledge. Presence of one additional cow acts as an additional channel of relevant livestock information for increased productivity. When the size of the herd is increased by one cow farmers’ knowledge is increases by 0.14%.

Conclusions

• Adoption of zero grazing dairy farming was influenced by age, years spent in school, number of school going children, number of exotic and crossbreed cattle, being above poverty line of 2,500 Ksh and dependency ratio of household head. The study demonstrated that, farmers who practice zero grazing had more knowledge on all the six cattle reproductive parameters than farmers who do not. OLS regression showed that, the practice of zero grazing and number of exotic cattle influenced farmers’ knowledge of the cattle reproductive parameters.
  
  
• Investments in education and training, income-raising activities at farm level would increase zero grazing adoption.
  
  
• Participation in zero grazing enhances farmers knowledge of the cattle reproductive parameters, efforts should be made to encourage farmers adopt zero grazing as it gives room for knowledge enhancement with positive implication on the success of the dairy industry.

Acknowledgment

This work was carried out while with the International Centre on Insect Physiology and Ecology. The funding by Regional Forum for Capacity Building in Agriculture (Ruforum) is highly acknowledged. Support from my supervisors is also highly acknowledged

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Received 5 February 2015; Accepted 10 August 2015; Published 1 September 2015

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