CHAPTER 4. RESEARCH FINDINGS ON OBJECTIVE 1
4.2 Characteristics of sampled household
From the surveyed households, the highest frequency (13.8 %) of respondents was from Goromonzi ward 11, followed by wards 4 (13.1 %) and 12 (13.0 %) in the same district, whilst the least frequency (6.3 %) was in Goromonzi ward 5. In Goromonzi there are more households compared to Murewa and this may have resulted in more responses from Goromonzi than Murewa. In FGDs, there were 55 participants from the four sites where the FGDs were conducted, with an average of about 14 participants per FGD. The number of participants for the FGDs falls within the recommended for such discussions (Stewart and Shamdasani, 1990). It is important to study these characteristics as they have an effect on how household members make decisions at household level (Chege, Lemba, Semenye and Muindi, 2016). Explanatory variables of adoption of fodder as a business are divided into three constructs, that is, farmers’
characteristics, household endowments and information sources (Table 4.1).
Table 4.1: Household characteristics of sample households in Goromonzi and Murewa
Males (281 respondents) Females (133 respondents) All respondents (414)
Household Characteristics Mean Std. dev. Mean Std. dev. Mean Std. dev. Sig. diff
Age of Head of Household 54.39 ±14.863 56.14 ±11.537 54.95 ±13.891 0.231
Household membership size 5.36 ±2.393 4.95 ±2.096 5.23 ±2.307 0.090*
Household labour size 3.62 ±1.848 3.26 ±1.683 3.50 ±1.802 0.060*
Years in farming 20.21 ±12.662 21.53 ±12.235 20.63 ±12.527 0.318
Homestead area (ha) 0.40 ±0.604 0.29 ±0.332 0.36 ±0.534 0.052*
Field crop area (ha) 1.45 ±1.116 1.32 ±1.082 1.41 ±1.106 0.269
Paddock area (ha) 0.12 ±0.375 0.11 ±0.534 0.12 ±0.432 0.764
Garden (ha) 0.28 ±0.350 0.24 ±0.379 0.27 ±0.359 0.250
Other area (ha) 0.07 ±0.408 0.02 ±0.195 0.05 ±0.354 0.258
Total area (ha) 2.33 ±1.752 1.99 ±1.604 2.22 ±1.711 0.057*
Response Count (281) Count (133)
Level of education
Never 9 4
Primary level 75 65
Secondary level 170 59
Tertiary level 28 5
Significant * at 1%.
This shows that females in female headed households were much older than the males. The smallholder population at study sites show that there is a more mature generation. More than two thirds of the household heads were older than 40 years. There is a small percentage (less than 10 %) of the younger generation. The average age of the head of household is within the economically productive age as outlined by Mandara, (1998) although carry-over of farming knowledge and activities might be jeopardised in the area (Anderson, Marita and Musiime, 2016).
Marital status - On marital status, the highest percentage (70.8 %) of household heads are married, followed by 24.4 % of household heads who are widowed and the least being heads of households that are single (2.2 %) as shown in Figure 4.1.
Figure 4.1: Marital status of head of household. Source: Developed from research results
Head of household’s educational level - Of the sampled households, 3.1 % have never been to school, whilst 33.8 %, 55.3 % and 8.0 % reached primary, secondary and tertiary levels respectively. There were less females compared to males at each level of education. Results also indicated that of all male head of households, 60.5 % reached secondary level, whilst 10 % reached as far as tertiary level (getting to university level and gaining professional qualifications). Only 3.2 % of males had never been to school. On the other hand, the highest percentage (48.9 %) of female head of households reached as far as primary level only, with the rest having not been to school (3.0 %), 44.4 % reached secondary level and 3.8 % reached tertiary level. Results showed that less females reached secondary compared to the males. Males reached secondary education level, which is higher than that reached by their female counterparts. Females
Single 2%
Married 71%
Divorced 3%
Widowed 24%
Single Married Divorced Widowed
continue to face challenges in accessing education facilities at higher levels despite the fact that strides have been made worldwide to increase female enrolment in educational institutions (UNESCO, 2012).
Jayachandran, (2014) attributes this to socio-cultural factors, besides the economic environment faced by the societies especially in the developing world.
Some researchers, Wafula, Oduol, Oluoch-Kosura, Muriuki, Okello, Mowo (2015) and Okello, Zhou, Kwikiriza, Ogutu, Barker, Schulte-Geldermann, Atieno and Ahmed (2016) found age of household head (agehh) having significant effect on the adoption of new technologies. Young people have more probability of becoming aware of production and marketing of fodder seed, and take up the technology as a business.
Fetien Abay et al., 2009 noted that young farmers have the chance to be educated and be exposed to new technology.
Manyeki, Kubasu, Kirwa and Mnene, (2013), in their study revealed that higher educational level will lead to incease in adoption of technologies. However, this is not consistent with some studies by Jera and Ajayi, (2008) and Beshir, (2014) where farmer’s level of education did not have a significant effect on adoption of forages.Innovations and new technologies are better adopted by farmers who have the ability to see the embedded benefits to be attained (Murage et al., 2012; Manyeki, Kubasu, Kirwa and Mnene, 2013).
Household size - Results indicated that there is an average of 5 members in each household, whilst an average of 3 members are able to provide labour within the household. Household membership and labour availability within the household for both male and female headed households is significant (p<0.1).
ZimStat (2013) revealed that the average national household size for the same area is 4.2. Results from the study are higher as the sites are highly populated and may be a result of an increase over time since the last census. When labour is available, especially within the household, then farmers can venture into more agricultural activities, explore new technologies and on bigger land sizes (Beshir, 2014). This might be a result of the need for more labour. However, gender and type of work to be done also influence labour availability within the household of many rural farmers. There are certain roles that are assigned to specific gender within the households and this will affect labour availability (Mandara, 1998; Chingarande and Kandiwa, 2015).
Farming experience - Results for both male and female headed households indicated that they have an average of 20 years of farming experience, which shows they have gained some experience in crop and livestock farming activities. More years of farming experience have been found to influence adoption of technologies (Manyeki et al., 2013). An experienced farmer who is resource endowed and with a higher
income from agriculture and a large household labour size with access to information through membership to other farmer organisations, found it easier to invest in the fodder seed business.
Land holding - The average farm size per household is 2.2 ha. Males own more land than females, thus some of land ownership increases adoption of new farming practices and technologies (Manyeki et al., 2013). Also the World Bank (2000), in its report revealed that in Sub-Saharan Africa, women do not have rights to land, only through their husbands if they are still in marriage. However, some of these challenges being faced by women have now been addressed through different fora and policy amendments in the different countries (Jost, Kyazze, Naab, Neelormi, Kinyangi, Zougmore, et al., 2016).Economic factors such as size of land owned by household and the total number of livestock units owned by the households have the strongest influence on adoption of fodder seed business. Total land area was also significantly (p<0.1) different between male and female headed households. In the smallholder sector, land size indicates a sign of wealth and status within the community. Smallholder agriculture dominates in the study sites with dryland crop production and horticulture as main agricultural activities. Livestock activities are present although not as pronounced as crops.
Sixty-three percent of the total land area was allocated to field crops, 16.0 % to homestead, 12.0 % to garden, 5.0 % to paddocks and 2.0 % to other (for example orchards and gumtree plantations). It is normal practice among smallholder farmers to allocate more land to crop production as they want to produce food to meet household food security. Farm size of household (landsz) measured in hectares will also affect land committed to a new crop in relation to food crops and cash crops already grown by the household. Farm land size positively influence adoption especially in the early stages. This is because those farmers with limited land size are not likely willing to experiment with new technologies on their small pieces of land, especially when they are uncertain of the likely outcomes or benefits associated with them.
Asset ownership - Responses indicated that more than 50.0 % of the households own at least a wheelbarrow, plough, knapsack sprayer, bicycle and 2 mobile phones. Less than 20.0 % own a car, tractor or motorbike (Figure 4.3). Owning assets that are related to agricultural activities has been found to positively influence adoption of technologies, including forage seed production (Ramirez, 2013; Tolno, Kobayashi, Beshir, 2014; Ichizen, Esham, and Balde, 2015).
Assets ownership measured by asset index is expected to positively affect decision to engage in forage seed marketing because farmers with farming implements such as ploughs can timeously till land and have higher productivity levels. Over 50.0 % of the sampled households own a wheel barrow, mobile phone,
plough, knapsack sprayer and a bicycle. These are important basic assets for farmers to be able to carry out farming activities and only when carrying out specific activities will they require specialised equipment.
On the other hand, less than 15.0 % of respondents own a car, motorbike or tractor (Figure 4.2).
Figure 4.2: Percentage (%) of households indicating different assets owned in Goromonzi and Murewa
Membership to an organisation – Sixty-three percent of the households are not members of any farmer organisation. FGDs also revealed that a greater number of households are not members. Reasons cited during FGDs include farmer organisations are not visible within the communities, there are subscription fees paid and farmers cannot afford these, also there is not much benefit being a member besides information. However, some studies have revealed that when farmers become members of organisations, they tend to benefit much (Manyeki et al., 2013; Fon, 2015).
Access to technological information through membership to farmer organisations is also very important as farmers are persuaded to adopt based on the benefits and costs of each technology and can thus make informed decisions. Training in fodder seed production enables households to increase knowledge and skill on seed production and marketing.