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Communication networks used by smallholder livestock farmers during disease outbreaks: Case study in the Free State, South Africa

  • Christopher Ugochukwu Nwafor EMAIL logo and Ifeoma Chinyelu Nwafor
Published/Copyright: October 22, 2022
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Open Agriculture
From the journal Open Agriculture Volume 7 Issue 1

Abstract

Smallholder livestock farmers routinely use existing communication networks as their information sources. This study explored these information sources, the frequency of contacts, and perceived usefulness of livestock health-related information received from these sources. Respondents were randomly selected from two farming districts in the Free State province. Using a mix of descriptive and correlation analyses, it categorized respondents according to their total information scores (TISs) and explored the relationship between their scores and socioeconomic characteristics. Findings show that 65% of farmers scored a high reliance on fellow farmers and extension officials. Mass media sources such as radio and television scored low on perceived usefulness. The correlation coefficients for age (−0.228), farming experience (0.183), extension visits (0.002), and information contacts (0.214) were significant (p < 0.05). Level of education (0.256), herd size (0.067), and perceived usefulness of information contacts (0.252) were also significant (p < 0.01). Gender, household size, income, cooperative participation, and access to financial services were not correlated to the TISs of respondents. It was recommended that mass media sources in the area be supported by extension communication specialists to disseminate livestock health-related information.

Keywords: correlation; disease outbreak; extension; information score; livestock; perceived usefulness; smallholder farmer

1 Introduction

The smallholder livestock sector significantly contributes to the economy and rural livelihoods in many African countries [1,2]. In South Africa, the Department of Agriculture, Forestry and Fisheries estimates show that the livestock sector contributes approximately 40% to agricultural incomes. The national response to livestock health issues has improved because of its linkage to the economy, public health, and food security. There is a recognition that inadequate control and prevention of livestock disease outbreaks may have huge negative economic and social impacts. The recognized connections among livestock health, public health, human nutrition, and welfare outcomes have also resulted in the conceptualization of the “one health” approach [3]. This association between humans, animals, and the surrounding environment is particularly close in developing countries, where resource-poor farming communities raise animals [4].

Economic loss from livestock diseases may be due to many reasons, including additional expenses incurred in treatment or prevention of the disease and production losses from unrealized potential or animal mortalities. Additional losses may be quantified under the reduction of income from earnings because of imposition of trade sanctions, a ban on livestock movement affecting traders, discontinuation of livestock auctions, large-scale cull of animals, livestock quarantine costs, and reduced value of products, among many others.

An effective response to outbreaks of livestock diseases requires an appropriately designed and delivered message [5], convincing farmers to take necessary action to protect their livestock and farm enterprise. Although the prevention and control of disease outbreaks ultimately depends on the adoption of best livestock health management practices [6], farmers’ social referents influence their adoption of these requisite standards. Using communication networks that appeal to farmers’ internal motivators hence elicits the desired behavior and increases their likelihood of adopting the identified health management intervention. Coombs [7] earlier summarized the importance of an effective communication channel during a disease outbreak or crisis. This is the conceptual basis for the situational crisis-communication approach found in the literature, where scholars have posited a change in communication channels following a crisis [8]. Other scholars have reported a lack of clarity about how the emergence of a disease outbreak changes information-seeking behavior among farmers [9].

In South Africa, the African Swine Flu (ASF) and Rift Valley Fever (RVF) disease outbreaks have been documented in the Fezile-Dabi and Lejweleputswa districts of the Free State Province, which is considered as one of the provinces severely affected by these diseases [10]. Also, a recent outbreak of the ASF disease in pigs was reported during May 2019 in the Free State [11]. Earlier, the World Health Organization documented widespread Rift Valley Fever Virus (RVFV) infection affecting sheep, goats, and cattle on farms within the Free State and a number of other provinces. According to official reports, approximately 78 farms reported laboratory-confirmed animal cases, with extensive livestock deaths [12]. Meanwhile, anecdotal evidence indicated an even greater number of infected farms and clusters of local outbreaks among animals kept by smallholder livestock farmers across South Africa [13].

2 Problem statement

Farmer’s communication networks play a vital role in their understanding of diseases and preventive measures taken in response to the outbreak of livestock diseases. These measures are important for reducing both production losses and mortalities in livestock, while improving herd health and overall productivity. The knowledge of communication networks among livestock farmers enables the dissemination of effective messaging for disease prevention and control, as well as undertaking significant interventions that meet local information needs. Moreover, existing local agricultural information systems need to be well understood so that they can be adequately used, managed, and improved where necessary.

Although perception related to agricultural risks has received significant attention in the literature [14,15,16], few studies have explored the factors that influence the way farmers respond to livestock health risks, and how they obtain relevant information during a disease outbreak [17]. Previous studies have highlighted how farmers switch information sources [8] and the utility of radio for spreading information because of its accessibility and popularity [18,19,20]. In addition, Hagar [21] reported changes in information needs at varying stages of a disease outbreak, and how farmers switch from information providers to become information seekers. The importance of a sense-making approach to information seeking and the availability of both formal and informal channels in information seeking were all reported. Manyweathers et al. [22] and Kolich [23] espoused the concept of trust among participants within a social network. Although Baker et al. [24] and Kijazi et al. [25] found a reliance on veterinarians or livestock technicians during a disease outbreak, they bemoaned the lack of a clear source of information during outbreaks. Within all of these findings, there is a consensus that communication strategies need to link decision makers and all stakeholders [26].

This study accentuates the case of African Swine Flu Virus (ASFV) disease outbreaks, one of the most significant risks currently facing the smallholder pig farming sector, to explore these factors with a particular focus on the role of communication networks. Pig farming is considered as one of the few livelihood opportunities available to smallholder farmers within the affected areas in the Fezile-Dabi and Lejweleputswa districts.

Despite the multiplicity of information sources available to livestock farmers, the communication networks used by smallholder livestock farmers in the affected districts are inadequately understood. Without an in-depth knowledge of communication networks used by smallholder livestock farmers, messaging on measures for preventing the occurrence of notifiable diseases of livestock such as ASFV or RVF may be disseminated using poorly used information sources. This unfortunately results in farmers not being well-informed. Also, an understanding of farmer’s communication networks will justify the resources used and policies put in place for reaching livestock farmers with required information pertaining to their livestock enterprise. Muange et al. [27] averred that although communication networks were an important avenue for the dissemination of information to farmers, the use of these networks was rarely investigated.

This study therefore addressed the need to understand how livestock farmers within the area sourced livestock health management information. It explored crisis-communication through the social networks that livestock farmers partake in, and the novelty lies in the mediation of information flows through interpersonal networks for accessing information among smallholder livestock farmers. Hence, the study objectives were to:

  1. Assess the socioeconomic characteristics of livestock farmers within the study area.

  2. Identify sources of information relied on by farmers during the outbreak of diseases.

  3. Measure the perceived usefulness of these information sources to livestock farmers.

  4. Determine the relationship between socioeconomic characteristics of respondents and total information scores (TISs).

3 Conceptual framework

The social network theory focuses on the role of social relationships in transmitting information [28] and also highlights how influence may be channeled to enable change in behavior. An analysis of the network shows relationships between people and indicates how formal or informal interactions either promote or hinder the dissemination of knowledge and information [29]. Social networks are recognized as a form or source of social capital differing in composition, structure, and size [30] and found in relationships maintained by individuals which can influence decisions [31] or patterns of behavior. Communication networks describe regular patterns of interpersonal relationships through which information flows and is also linked to a social network.

Social networks mediate information flow and have been found useful for an understanding of both formal and informal relations. Lois [32] observed that humans were actors embedded in an intricate network of social relationships and aligns with the pronouncement that networks do play a key part in the shaping of social life [33]. The social network theory and analysis have been extensively used in the literature for a variety of behavior issues linked to information search and use. They include predicting fertility behavior [32], an understanding of educational reform practices among teachers [34], support for the implementation of change in healthcare setting [35], or the search for new connections to meet identified interests [36]. The concepts of centrality, brokerage, and multilevel networks are prominent in understanding how individuals and groups are linked, transmit information, influence behavior, and contribute to building relationships in the network.

In the agricultural sector, the analysis of farmer behavior has benefitted from a tradition among social science scholars, sociologists, and extension agents who have diligently used the social network theories for insights into decision-making, adoption behavior, and communication links involving farmers. Many studies that apply the social network theory have used any or a combination of three main approaches which include socio-metric analysis (reliant on graph-theory methods), interpersonal relations (with a focus on the formation of groups among individuals), or on the anthropology tradition (which explores the structure of community relations). These approaches fit with those identified as belonging to either graphic characterization, social influence, or social selection models [34].

Studies using social networks in agriculture have concentrated on the diffusion of technology, participation in value chains or commercialization, and the adoption of improved varieties or innovation. Mekonnen et al. [37] examined adoption of row planting among crop farmers, whereas Thuo et al. [38] explored information about seed varieties and productivity. Ramirez [39] analyzed the effect of social interactions in shaping decision-making during the adoption of new technologies. Nyantakyi-Frimpong et al. [40] examined how environmental outcomes were linked to network structures, whereas Muange et al. [27] analyzed social networks and their role in the spread of information on improved seed varieties. Abdul-Rahaman and Awudu [41] applied social network analysis in linking value-chain participation and market outcomes among crop farmers, whereas Mwema and Crewett [42] investigated the determinants of crop commercialization.

For studies specifically in the livestock sector, Pali et al. [43] explored the adoption of breeding strategies, whereas Vishnu et al. [30] analyzed the configuration of social networks among farmers that is related to their acquisition of information on livestock technologies. Many of these studies have applied econometric models or graphs to visualize and interpret their data. This is common practice among communication behavior scholars, which often ignores the social nature of interactions that lead to information sharing. The nature of these interactions makes it difficult to accurately predict or measure behavior empirically [44], requiring a combination of approaches depending on the context and priorities of participants. We see a gap in available studies which have primarily relied on either graphic visualization and/or regression models to reach their conclusions. This study combines various approaches for exploring social network interactions that lead to information exchange and influence farmer behavior, with an emphasis of the link between TISs and socioeconomic features. Furthermore, there are very few studies that explored information sources from a social network perspective and their influence on the management of disease outbreaks among livestock farmers in South Africa.

This study used social networks for exploring information source and usage among livestock farmers during a disease outbreak. It contributes to the constantly evolving literature related to local agricultural information systems, information sources, and farmer communication networks. In the agricultural information system conceptual framework, an information system is considered as a structure that includes people, technology, and methods which are organized to collect, process, transmit, and disseminate data or translate it into useful information [45]. The communication network is made up of interconnected persons who are linked by a set of ties along the flow of information [46], which also indicates how the system is structured. It describes a regular pattern of contacts between people which can be considered as information exchange in a specific social system. Communication networks are useful because of interaction and exchange of information among members of the system, and these can be evaluated from the usefulness of information sources [47].

4 Methodology

4.1 Study area

The Fezile Dabi and Lejweleputswa districts of the Free State Province are considered as important agricultural areas, located uniformly with other districts at about 1,300 m above sea level and consisting of grasslands with summer rains, cold winters, and lots of sunshine. The districts are shown in Figure 1.

Figure 1 Map of the Free State Province showing its districts and the study area. Source: https://www.businessinsa.com/free-state/.
Figure 1

Map of the Free State Province showing its districts and the study area. Source: https://www.businessinsa.com/free-state/.

4.2 Sample frame, data collection, and measurement

The study respondents were identified by stratified random sampling using the list of livestock farmers provided by the district office of the Department of Agrarian Reform in the Free State. The list contained names of registered livestock farmers residing in Fezile-Dabi and Lejweleputswa districts. These districts were selected because of recently reported incidence of notifiable livestock disease outbreaks. From each of these administrative districts, livestock farmers were selected for the survey. A total of 129 respondents were sampled for this study. The sample size for this study was calculated using the Raosoft sample volume calculation method based on a 5% error rate and 95% reliability level.

A pilot survey of 12 farmers was conducted earlier to test the clarity, reliability, and validity of our questionnaire. The questionnaire was then revised and finalized in accordance with proffered suggestions. The questionnaire consisted of four sections and 28 questions, and the internal consistency of answers given to the questions prepared according to the 4-point Likert scale was measured with reliability analysis. The Cronbach’s α, which is a numerical coefficient of reliability, was determined as 0.69, 0.66, and 0.74, respectively.

The study applied the TIS technique to determine the information source(s) that respondents relied on for livestock health information and calculated the perceived usefulness of such information sources in mitigating livestock health concerns. Frequency of contact with different information sources and the perceived usefulness of information also contributed to determining the TIS. The TIS combines the frequency of information contact with information sources and their usefulness into a variable [45], reflecting not only the quantity but also the quality of a particular information source. Table 1 shows the various variables used in the analysis and a description of their characteristics.

Table 1

Variables used and description

Variable Category Type Measure (Description)
Gender Independent Nominal Binary (male/female)
Age Independent Ordinal Numeric/category (years)
Education level Independent Interval Years/category (years)
Size of household Independent Ordinal Numeric/category (persons)
Herd size Independent Ordinal Numeric/category (quantity)
Farming experience Independent Ordinal Numeric (years)
Income Independent Ordinal Numeric (amount)
Cooperative participation Independent Nominal Binary (yes/no)
Extension visits Independent Ordinal Numeric (number of visits)
Financial services Independent Nominal Binary (yes/no)
Frequency of information contact Independent Ordinal Numeric (number of contacts)
Usefulness of information source Independent Ratio Scale (low/medium/high)
TIS Dependent Ratio Percentage

Source: Author’s compilation.

Respondents were asked to identify which information sources they used, as well as the frequency of contact within a specified period, selected to coincide with the reported outbreak of livestock disease. Weights were then assigned to each information source depending on the frequency of contacts. These weights ranged from 0 to 2 (with 0 = no contact, 1 = few contacts, 2 = frequent contacts). The perceived degree of usefulness for each identified information source was also requested in relation to prevention and control of livestock diseases. Degree of usefulness of the information source was measured according to a four-point scale ranging from 0 to 3 (where 0 = not useful, 1 = slightly useful, 2 = useful, and 3 = very useful). The degree of usefulness of the information source was then weighted as follows: 0 = 0.0, 1 = 0.25, 2 = 0.50, and 3 = 0.75. We then correlated the degree of usefulness and frequency of contact to measure agreement among the two variables.

TIS for each information source was determined by multiplying the weighted number of contacts (FC) and degree of usefulness for the information source (IU) and may also be expressed as a percentage.

(1) TIS = [ FC i j × IU ij ] ,

where FC ij is the number of contacts with jth information channel to the ith farmer and IU ij is the usefulness of jth information channel to the ith farmer. Based on the TISs, their average and standard deviations, each information source was then classified accordingly to be weak, moderate, or strong. Furthermore, the relationship between socioeconomic characteristics of respondents and TIS was explored using a Pearson moment correlation coefficient.

5 Results and discussion

5.1 Demographic characteristics of respondents

Table 2 shows the personal characteristics of respondents indicating that 54% were males and 46% female. Most of the respondents were aged 56 years and above, making up 60% of the total respondents. Only 6% of the respondents were less than 36 years, with 33% of the survey respondents in the 36–55 year bracket. Almost half of the survey respondents (46%) had between five and seven persons in their household. Level of education among respondents varied, with 17% having no education, 36% had only a basic primary education, 20% attended a high school, and 27% indicated a post high school attendance. The number of livestock owned by 33% of the respondents was 50 or less, and 36 and 31% of the respondents had 51–100 animals and more than 100 animals, respectively.

Table 2

Demographic characteristics of survey respondents

Variable n = 129 %
Gender Female 59 46
Male 70 54
Age <36 years 9 7
36–55 years 43 33
56+ years 77 60
Household size <5 35 27
5–7 59 46
>7 35 27
Education None 22 17
Primary 47 36
High school 25 20
Post high school 35 27
Herd size 50 or less 43 33
51–100 46 36
More than 100 40 31

Source: Field survey 2019.

The data revealed that there were more male livestock farmers than females within the study area, with many of them at least 56 years old. The prevalence of older male livestock farmers has been reported in different studies on communal livestock production in South Africa [48,49]. Large household sizes comprising five or more persons are also common in many rural communities across South Africa. Levels of education vary and many rural dwellers, especially farmers, have only completed a primary education. This agrees with the reported limited education among many smallholder farmers in the study area [50]. The herd size among respondents was also reflective of the number of livestock kept by smallholder rural farmers.

5.2 Information sources used

Livestock farmers seek out different credible and reliable sources of information in the event of disease outbreak. The most commonly used sources of information include peers, veterinary technicians or extension staff from government departments, radio and television programs, and even knowledgeable livestock traders and family members. Table 3 lists the information sources used by respondents during the livestock disease outbreak, showing that farmer-to-farmer contacts as well as extension officials were the main sources of livestock health–related information. Radio was also used as an information source, followed by family members and television ranked in the order shown.

Table 3

Information source used by respondents for livestock disease information

Information source Rank Score Mean SD
Other farmers 1 645 14.33 1.53
Extension officials 2 196 5.02 1.32
Radio 3 105 4.90 1.22
Family members 4 74 4.56 1.26
Television 5 41 3.72 1.51

Source: Authors’ computation from survey 2019.

Peer networks have been reported as a highly effective information source which also influences the information-seeking behavior among farmers [51,52]. The importance of these personal relationships among farmers in information transfer has been noted with an indication that peer interaction was also an important learning tool [53]. The role of extension is important for information dissemination to livestock farmers, especially during an outbreak, and different sources have reported the reliance of farmers on extension officials. Although Chikaire et al. [54] admitted the responsibility of extension officials to provide information to farmers, Adisa [55] questioned the competence of some extension officials in animal health management issues. This probably contributed to the call for properly coordinated livestock health extension programs driven by trained and knowledgeable specialists [56].

Radio broadcasts are a veritable means for disseminating information, and Girma et al. [57] highlighted its importance because of possible wide coverage, especially among rural communities where a majority have access to radio in their household [58,59]. This finding regarding the importance of radio for information dissemination to rural farmers is however at variance with Lamontagne-Godwin et al. [60] who reported the rare use of radio by respondents from their study. Family members and television were, however, ranked low as sources of information during a livestock disease outbreak.

With several available sources of information, farmers will seek out those sources that are knowledgeable with specific reference to livestock diseases. This might explain why family members were ranked lowly in this instance. Both Ubaldi et al. [61] and Lambiotte et al. [36] averred that while individuals may show preference for social interaction revolving around family and friends, they would routinely search for new external links for emerging issues. Also, in relation to the low ranking of television as an information source by respondents, Freeman and Mubichi [62] posited that certain characteristics of television explains why access is often limited, and despite its presence in many homes contributed little as a source of livestock health information during the disease outbreak.

5.3 Usefulness of information sources

The realization that not all information sources are credible nor provide reliable information has resulted in concepts such as “perceived usefulness” acquiring particular significance. Farmers’ perception of the usefulness of information sources and frequency of contact was used to determine their TISs which is presented in Table 4, and it shows that farmers’ colleagues and extension officials scored highest. These sources are followed by family members and mass media sources, i.e., radio and television.

Table 4

TISs of respondents

Sources used N = 129 Freq. (Month) Weight Total Perceived usefulness Weight TIS
Other farmers 45 15 2 30 3 0.75 67.5
Extension officials 39 8 3 24 4 1.00 96.0
Radio 21 5 3 15 2 0.50 15.0
Family members 15 25 2 50 1 0.75 37.5
Television 9 20 2 40 1 0.50 20.0

Source: Author’s computation from survey 2019.

Other farmers and extension officials have also been reported as a major information source among farmers [63,64], which provide opportunities for extension practitioners to use peer-to-peer learning. From their study, Kumar et al. [47] also identified other farmers and extension officials as belonging to a high-scoring group for information source usefulness among farmers. Among these groups are peers who provide issue-specific leadership to farmers, considered as opinion leaders who are influential in certain domains. They often possess important centrality measures within the social network.

Radio and television sources were scored low which underlines their poor perceived usefulness. The role of mass media in disseminating agricultural information is noted [6,65]; however, some studies have also reported their perceived ineffectiveness [66]. Low TIS obtained by mass media sources of information, including radio and television, is an indication of the need to provide targeted information related to livestock health, especially during periods of disease outbreaks. Farmers are however known to use different sources of information, and the inadequacy of any single source has been established [67].

As shown in Table 5, the TISs obtained for identified sources were categorized into low, medium, and high, based on the score range. The high category ranges from 50 up to 100 and included other farmers and extension officials, medium category ranged between 25 and less than 50 to include family members, and the low category scored less than 25 with radio and television included.

Table 5

Category based on TISs among respondents

TIS Category Score Range % Mean SD
Low Less than 25 23 52 27.97
Medium 25 and <50 12
High 50 and more 65

Source: Author’s compilation from survey 2019.

Sixty-five percentage of respondents had TISs in the high category, whereas 12 and 23% of respondents had score categories of medium and low, respectively. Among the respondents, there was a mean score of 52 with standard deviation of 27.9. This shows that the information sources categorized under “high” (other farmers and extension officials) were perceived by a majority of respondents to be most useful in providing livestock health-related information.

5.4 Relationship between socioeconomic characteristics and information score

Access to livestock health information and the perceived importance of information received by farmers may depend on their socioeconomic features. Information access and quality captured as TIS is an important dependent variable in the prevention and control of livestock diseases and may be influenced positively or negatively by various personal characteristics of the farmer such as gender, age, level of education, herd size, farming experience, participation in cooperatives, or access to extension and other services. These independent variables presented in Table 6 were correlated with the TISs of respondents.

Table 6

Correlation of TIS with respondents’ socioeconomic characteristics

Independent variables Coefficient (r) Significance
Gender −0.033 0.069
Age −0.228* 0.021
Education 0.256** 0.010
Household size 0.314 0.557
Livestock number 0.067** 0.005
Farming experience 0.183* 0.036
Income −0.124 0.237
Cooperative participation −0.092 0.065
Extension visits 0.002* 0.024
Financial services −0.145 0.198
Information contacts 0.214* 0.041
Usefulness of information 0.252** 0.002

**Significant correlation at 0.01 level; *significant correlation at 0.05 level.

Source: Field survey data 2019.

Gender difference was not found to correlate with TIS of respondents and was not significant in this study. However, Lamontagne-Godwin et al. [60] reported from their study that preferences in accessing information sources were different between male and female farmers. There was a significant negative correlation (−0.228) between age and TIS (p < 0.05), an indication that age affects information contacts and perceived usefulness. Motiang and Webb [68] also reported the reliance on few information contacts among older cattle farmers from their study. Contrarily, Rehman et al. [69] found that access to agricultural information was not influenced by age.

A positive correlation (0.256) existed between level of education and TIS (p < 0.05). Rehman et al. [69] and Elias et al. [70] reported a highly significant association between education and information, whereby higher levels of education were beneficial for increased access to useful information sources. The number of livestock owned (0.067) as well as the farming experience of respondents (0.183) was positively correlated to TISs at p < 0.01 and p < 0.05 levels of significance, respectively. Herd size was reported to be positively correlated to information needs [71], which aligns with the finding by Mittal and Mehar [72] showing that farm size influences farmer’s behavior in selecting different information sources. Kavithaa et al. [73] also noted that farming experience influenced information-seeking behavior. These imply that farmers with large numbers of livestock and farming experience sought more information and from diverse sources.

Furthermore, the number of extension visits (0.002) was positively correlated to TISs (p < 0.05) in this study. The finding agrees with many others showing the importance of extension officials in the provision of relevant information to farmers. However, Popoola et al. [74] and Molieleng et al. [75] reported the poor contribution by extension officials toward providing climate-related information to farmers. Crawford et al. [51] also submitted that the use of extension was the least effective tool for information sourcing among organic farmers in their study. These divergent views regarding the contribution of extension services to farmer’s information needs may arise from differences in delivery methods and objectives [76], which affects the effectiveness of public extension delivery in various countries.

Household size was not significant in the TIS of respondents, and this could be a result of the nature of information sought by respondents. Family members may not be technically proficient in matters related to livestock diseases and control measures. However, Onuekwusi and Atasie [77] and Koskei [78] reported the significant influence of household size for accessing information and use of information sources. Income, access to financial services, and cooperative membership did not correlate with respondents TIS. Nonetheless, Sakib et al. [79] reported that income had a positive and significant relationship with the use of information sources. Sheng Tey et al. [80] also submitted that access to credit improved farmers’ access to different information sources, which Yaseen et al. [81] admitted could sometimes be very costly. Brhane et al. [67] reported the failure of cooperatives in providing needed information to farmers from their study, which contradicts the assumption that cooperatives were an effective channel of communication to farmers [82]. Although membership of a cooperative increases awareness of information sources and availability [83], our finding suggests that cooperatives within the study area were probably not as effective as required, or that respondents from our study were not active members.

Predictably, the frequency of information contacts (0.214) and the perceived usefulness of information (0.252) were positively correlated with respondents TISs at the p < 0.05 and p < 0.01 levels, respectively. Pandit et al. [84] also used the frequency of contacts and usefulness of information to obtain TISs for comparison among farmers in their study. These variables jointly determine the direction and magnitude of the TISs. It also agrees with the finding by Okwoche et al. [85] of a significant relationship between information sources and information utilization. The two variables have shown that the use of any information source during the occurrence of a livestock disease outbreak depends on its perceived usefulness to the livestock farmer.

6 Conclusion

The study set out to examine communication networks used by farmers during a livestock disease outbreak, by exploring their primary information sources and the perceived usefulness of these sources. The TISs were obtained and used to categorize farmers into groups based on their scores. Socioeconomic characteristics of the livestock farmers were then correlated with their TISs to determine the existence of any association between personal features and frequent information contacts perceived useful for obtaining livestock health-related information.

Our findings show how social networks mediate information flows and amplify the importance of interpersonal contacts for successful information dissemination, especially considering livestock farmers’ reliance on their peers. It highlights the need for adequate extension coverage and regular visits to livestock farmers by extension officials, livestock health technicians, and veterinarians. Effective farmer cooperatives providing requisite information and services to its membership appeared to be lacking, whereas mass media sources such as radio and television were not perceived as useful sources of information by livestock farmers during the disease outbreak.

It is recommended that extension contacts be intensified during a disease outbreak, and mass media be used purposefully to disseminate livestock health information targeted at rural farmers. The impersonal sources of information should be supported by extension communication practitioners to increase coverage and effectiveness. Therefore, extension officials as well as policy makers have a key role to play in this regard. Policies that target the livestock farming sectors supported by community awareness and education activities will be beneficial for improving knowledge of livestock diseases among farmers and their associates.

Although this study contributes to the understanding of livestock health information sources used by smallholder livestock farmers, it enunciates an urgent need to improve the content of media programs directed at livestock farmers, which will strengthen information dissemination through mass media sources in the area. We identify methodological limitations to this study as a number of other available analytical tools were not used, especially measures of centrality and multilevel networks to enrich the output and hence propose that future research endeavors should incorporate these social network concepts.

Acknowledgments

The earlier version of this article was deposited as a preprint on the Preprints platform at link: https://www.preprints.org/manuscript/202008.0539/v1.

  1. Funding information: The authors state no funding involved.

  2. Conflict of interest: The authors state no conflict of interest.

  3. Data availability statement: The dataset generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Received: 2020-08-29
Revised: 2022-06-11
Accepted: 2022-06-13
Published Online: 2022-10-22

© 2022 Christopher Ugochukwu Nwafor and Ifeoma Chinyelu Nwafor, published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/opag-2022-0119
Keywords for this article
correlation; disease outbreak; extension; information score; livestock; perceived usefulness; smallholder farmer
Creative Commons
BY 4.0

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  1. Regular Articles
  2. Foliar application of boron positively affects the growth, yield, and oil content of sesame (Sesamum indicum L.)
  3. Impacts of adopting specialized agricultural programs relying on “good practice” – Empirical evidence from fruit growers in Vietnam
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  15. Growth of Rucola on Mars soil simulant under the influence of pig slurry and earthworms
  16. Effect of potassium fertilizer application in teff yield and nutrient uptake on Vertisols in the central highlands of Ethiopia
  17. Dissection of social interaction and community engagement of smallholder oil palm in reducing conflict using soft system methodology
  18. Farmers’ perception, awareness, and constraints of organic rice farming in Indonesia
  19. Improving the capacity of local food network through local food hubs’ development
  20. Quality evaluation of gluten-free biscuits prepared with algarrobo flour as a partial sugar replacer
  21. Effect of pre-slaughter weight on morphological composition of pig carcasses
  22. Study of the impact of increasing the highest retail price of subsidized fertilizer on rice production in Indonesia
  23. Agrobiodiversity and perceived climatic change effect on family farming systems in semiarid tropics of Kenya
  24. Influences of inter- and intra-row spacing on the growth and head yield of cabbage (Brassica oleracea var. capitata) in western Amhara, Ethiopia
  25. The supply chain and its development concept of fresh mulberry fruit in Thailand: Observations in Nan Province, the largest production area
  26. Toward achieving sustainable development agenda: Nexus between agriculture, trade openness, and oil rents in Nigeria
  27. Phenotyping cowpea accessions at the seedling stage for drought tolerance in controlled environments
  28. Apparent nutrient utilization and metabolic growth rate of Nile tilapia, Oreochromis niloticus, cultured in recirculating aquaculture and biofloc systems
  29. Influence of season and rangeland-type on serum biochemistry of indigenous Zulu sheep
  30. Meta-analysis of responses of broiler chickens to Bacillus supplementation: Intestinal histomorphometry and blood immunoglobulin
  31. Weed composition and maize yield in a former tin-mining area: A case study in Malim Nawar, Malaysia
  32. Strategies for overcoming farmers’ lives in volcano-prone areas: A case study in Mount Semeru, Indonesia
  33. Principal component and cluster analyses based characterization of maize fields in southern central Rift Valley of Ethiopia
  34. Profitability and financial performance of European Union farms: An analysis at both regional and national levels
  35. Analysis of trends and variability of climatic parameters in Teff growing belts of Ethiopia
  36. Farmers’ food security in the volcanic area: A case in Mount Merapi, Indonesia
  37. Strategy to improve the sustainability of “porang” (Amorphophallus muelleri Blume) farming in support of the triple export movement policy in Indonesia
  38. Agrarian contracts, relations between agents, and perception on energy crops in the sugarcane supply chain: The Peruvian case
  39. Factors influencing the adoption of conservation agriculture by smallholder farmers in KwaZulu-Natal, South Africa
  40. Meta-analysis of zinc feed additive on enhancement of semen quality, fertility and hatchability performance in breeder chickens
  41. Meta-analysis of the potential of dietary Bacillus spp. in improving growth performance traits in broiler chickens
  42. Biocomposites from agricultural wastes and mycelia of a local mushroom, Lentinus squarrosulus (Mont.) Singer
  43. Cross transferability of barley nuclear SSRs to pearl millet genome provides new molecular tools for genetic analyses and marker assisted selection
  44. Detection of encapsulant addition in butterfly-pea (Clitoria ternatea L.) extract powder using visible–near-infrared spectroscopy and chemometrics analysis
  45. The willingness of farmers to preserve sustainable food agricultural land in Yogyakarta, Indonesia
  46. Transparent conductive far-infrared radiative film based on polyvinyl alcohol with carbon fiber apply in agriculture greenhouse
  47. Grain yield stability of black soybean lines across three agroecosystems in West Java, Indonesia
  48. Forms of land access in the sugarcane agroindustry: A comparison of Brazilian and Peruvian cases
  49. Assessment of the factors contributing to the lack of agricultural mechanization in Jiroft, Iran
  50. Do poor farmers have entrepreneurship skill, intention, and competence? Lessons from transmigration program in rural Gorontalo Province, Indonesia
  51. Communication networks used by smallholder livestock farmers during disease outbreaks: Case study in the Free State, South Africa
  52. Sustainability of Arabica coffee business in West Java, Indonesia: A multidimensional scaling approach
  53. Farmers’ perspectives on the adoption of smart farming technology to support food farming in Aceh Province, Indonesia
  54. Rice yield grown in different fertilizer combination and planting methods: Case study in Buru Island, Indonesia
  55. Paclobutrazol and benzylaminopurine improve potato yield grown under high temperatures in lowland and medium land
  56. Agricultural sciences publication activity in Russia and the impact of the national project “Science.” A bibliometric analysis
  57. Storage conditions and postharvest practices lead to aflatoxin contamination in maize in two counties (Makueni and Baringo) in Kenya
  58. Relationship of potato yield and factors of influence on the background of herbological protection
  59. Biology and life cycle Of Diatraea busckella (Lepidoptera: Crambidae) under simulated altitudinal profile in controlled conditions
  60. Evaluation of combustion characteristics performances and emissions of a diesel engine using diesel and biodiesel fuel blends containing graphene oxide nanoparticles
  61. Effect of various varieties and dosage of potassium fertilizer on growth, yield, and quality of red chili (Capsicum annuum L.)
  62. Review Articles
  63. Germination ecology of three Asteraceae annuals Arctotis hirsuta, Oncosiphon suffruticosum, and Cotula duckittiae in the winter-rainfall region of South Africa: A review
  64. Animal waste antibiotic residues and resistance genes: A review
  65. A brief and comprehensive history of the development and use of feed analysis: A review
  66. The evolving state of food security in Nigeria amidst the COVID-19 pandemic – A review
  67. Short Communication
  68. Response of cannabidiol hemp (Cannabis sativa L.) varieties grown in the southeastern United States to nitrogen fertilization
  69. Special Issue on the International Conference on Multidisciplinary Research – Agrarian Sciences
  70. Special issue on the International Conference on Multidisciplinary Research – Agrarian Sciences: Message from the editor
  71. Maritime pine land use environmental impact evolution in the context of life cycle assessment
  72. Influence of different parameters on the characteristics of hazelnut (var. Grada de Viseu) grown in Portugal
  73. Organic food consumption and eating habit in Morocco, Algeria, and Tunisia during the COVID-19 pandemic lockdown
  74. Customer knowledge and behavior on the use of food refrigerated display cabinets: A Portuguese case
  75. Perceptions and knowledge regarding quality and safety of plastic materials used for food packaging
  76. Understanding the role of media and food labels to disseminate food related information in Lebanon
  77. Liquefaction and chemical composition of walnut shells
  78. Validation of an analytical methodology to determine humic substances using low-volume toxic reagents
  79. Special Issue on the International Conference on Agribusiness and Rural Development – IConARD 2020
  80. Behavioral response of breeder toward development program of Ongole crossbred cattle in Yogyakarta Special Region, Indonesia
  81. Special Issue on the 2nd ICSARD 2020
  82. Perceived attributes driving the adoption of system of rice intensification: The Indonesian farmers’ view
  83. Value-added analysis of Lactobacillus acidophilus cell encapsulation using Eucheuma cottonii by freeze-drying and spray-drying
  84. Investigating the elicited emotion of single-origin chocolate towards sustainable chocolate production in Indonesia
  85. Temperature and duration of vernalization effect on the vegetative growth of garlic (Allium sativum L.) clones in Indonesia
  86. Special Issue on Agriculture, Climate Change, Information Technology, Food and Animal (ACIFAS 2020)
  87. Prediction model for agro-tourism development using adaptive neuro-fuzzy inference system method
  88. Special Issue of International Web Conference on Food Choice and Eating Motivation
  89. Can ingredients and information interventions affect the hedonic level and (emo-sensory) perceptions of the milk chocolate and cocoa drink’s consumers?
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