The characteristics of the farm and the farmer that affect the adoption of biosecurity on smallholder poultry farms in Indonesia

S H Susilowati, I Patrick*, M Iqbal and T Jubb**

Indonesian Center for Agriculture Socio Economic and Policy Studies, Bogor, Indonesia
srihery@yahoo.com
* Institute for Rural Futures, University of New England, Australia
** Livestock Health Systems Australia

Abstract

Highly Pathogenic Avian Influenza (HPAI) is endemic in most provinces in Indonesia. Its presence in the country since 2003 has provided the impetus for the Indonesian Government (GoI) to encourage the adoption of biosecurity in smallholder broiler and layer farms. In order to identify cost-effective biosecurity for these farms it is first necessary to identify the biosecurity activities already adopted and the reasons behind this level of adoption. This paper identifies the biosecurity activities already adopted by farmers, and the farm and farmer characteristics that influence this adoption. This paper develops the discussion of how to measure adoption and then use these measures as dependent variables in identifying the factors that influence adoption. The dependent variable is an aggregated Biosecurity Control Score which ranks and aggregates farmers’ adoption of 44 Biosecurity Control Indicators.

The analysis identified that older, more educated farmers with larger families are more likely to adopt better biosecurity in layer and broiler farms. On layer farms, farmers with fewer non-poultry sources of income will have better biosecurity. The farm characteristic that may influence biosecurity adoption in both layer and broiler farms is farm area. In broiler farms the number and average capacity of farms are also important.

Keywords: chickens, disease, eggs, food safety, HPAI, meat

Introduction

HPAI and the Indonesian poultry industry

The poultry industry is becoming increasingly important in Indonesia with a steady increase in demand for chicken meat and egg products sourced from both traditional markets and supermarkets. This is in response to an increasing population and standard of living. Since 2007, demand has been increasing for both broiler (7% per year) and layer (4% per year) DOCs (day old chicks) (DAB 2011). However, average per capita consumption of poultry meat is 7 kilograms per year in Indonesia which is still low in comparison to other developing countries such as China, Malaysia, and Thailand i.e., 50 kilograms, 36 kilograms, and 16 kilograms respectively (DGLS 2011). The Indonesian government sees a significant opportunity for the domestic poultry industry to play a role in increasing the level of protein available to consumers and assisting poverty alleviation.

The industry in Indonesia is dominated by production systems that supply live birds to consumers in traditional markets. The demand for a cheap source of protein and a desire to purchase fresh product has led to the development of a low cost, high risk production and transport system that encourages efficient feed conversion but does not encourage disease control or food safety. This however is changing, there is a growing modern supermarket sector in Indonesia and in other developing countries where consumers are concerned with these issues (Reardon et al 2009). There is a growing opportunity for smallholder farmers to receive better prices for products meeting more stringent production and processing requirements.

Interwoven with the modernization of the market was the introduction of highly pathogenic avian influenza (HPAI) to Indonesia in 2004 (FAO 2011). HPAI has become an economically and socially important disease in Indonesia and is now endemic in 31 of the 33 provinces. In 2011, HPAI outbreaks were recorded in 22 provinces. The largest number of human cases and mortalities worldwide has occurred in Indonesia, by June 2011, Indonesia had reported 178 human cases with 146 fatalities. Sporadic outbreaks of HPAI still occur in Indonesia and continue to threaten human and poultry health. Continuing concerns with HPAI in Indonesia may be changing consumer behaviour and encouraging them to purchase poultry products sourced from biosecure farms and certified as safe (Graham et al 2008, Nerlich et al 2009). A reduction in HPAI incidence will benefit farmers (higher income), consumers (food safety) and society (a reduction in pandemic risk).

The role of biosecurity

With other poultry diseases such as Newcastle disease (ND) also causing economic loss in the Indonesian poultry industry, improving farm biosecurity is one area that can potentially improve the economic and social livelihoods of Indonesian small-scale commercial poultry producers (Ahlers et al 2009). Poultry biosecurity is a set of preventive measures designed to reduce the risk of disease transmission onto and from the farm (Fasina et al 2011, Permin and Detmer 2007, Patrick and Jubb 2010). These measures are a combination of systems and practices that are a response to the specific risks faced by individual producers. In poultry farming, biosecurity is defined as management to keep diseases out of the flock and if diseases get in, good biosecurity will limit disease spread around and off the farm. It is regarded as key to controlling poultry diseases and improving both poultry and human health and livelihoods (Beach et al 2007, FAO 2008, Patrick and Jubb 2010,Fasina et al 2011). While improving biosecurity will reduce the risk of losses due to disease, there is still a lack of adoption especially in the non-industrial commercial poultry sector (NICPS) and the backyard or village chicken sector.

The three principles of biosecurity are segregation, cleaning and disinfection (Fasina et al 2011). Biosecurity activities range from simple, low cost measures such as putting locks on gates to the more costly measures such as using high-pressure water sprayers to clean cars and constructing shower blocks to secure visitors and workers as they enter the farm (Henson and Jaffee 2005, Permin and Detmer 2007). Some biosecurity activities are management changes, which may be low cost but require commitment from owners and farm workers to implement successfully. These management changes may include allocating a specific worker to a shed and not allowing staff to move from shed to shed. To minimize poultry disease movement, particularly in small scale farms also requires actions that minimize the movement of village chickens and wild birds (FAO 2008). It also requires that communities work together to minimize the movement of disease between farms.

Objectives

Poultry farmers, like all producers, respond to the information, cultural expectations and economic drivers that they face. These factors influence their understanding of their specific risks and hence their adoption, or lack of adoption, of measures to reduce this risk.

Apart from the cost of implementing some biosecurity measures (Siekkinen et al 2012, Van Steenwinkel 2011) and the lack of knowledge concerning movement of pathogens and the ability of biosecurity to impact this (Di Guiseppe et al 2008, Mossialos and Rudisill 2008), there are several farm and farmer characteristics that may influence the type and level of biosecurity measures adopted. These include; farmer’s attitude to risk, farm location farmer age and education, household structure, sources of income, management and marketing systems, and access to information and capital. In order to provide a recommendation for improving farm biosecurity, more information is required on current biosecurity implementation at farm level, and a better understanding is required of the factors that influence the adoption of biosecurity measures (Heffernan et al (2008).

This paper has two objectives:

• To construct a scoring system (biosecurity control score – BCS) that measures adoption of biosecurity activities, and

• To define the farm and farmer characteristics that influence the adoption of biosecurity on small-scale poultry farms in Indonesia

A better understanding of present levels of biosecurity adoption and the economic drivers and constraints faced by stakeholders (Leibler et al 2009) are necessary if appropriate government policy and market incentive structures can be established that encourage the reduction in poultry disease prevalence. The results from this study will be used to identify further the specific biosecurity activities that are cost-effective and able to be adopted by NICPS layer and broiler farmers.

Methodology

Primary data was collected from 120 smallholders in Bali (60 layer and 60 broiler farmers) and 108 smallholders in West Java (41 layer and 67 broiler farmers). These respondents were randomly selected from FAO and Provincial Department of Agriculture census data in West Java and Bali respectively (FAOID-USAID 2008, DGLS 2009).

Data was collected that allowed the construction of the BCS which represents a smallholder’s adoption of biosecurity. The survey collected data regarding the farmer’s management practices at 7 points or stages within the farm and then scored their activities with regard to effectiveness of reducing disease entry, movement around the farm and ability of disease to move from the farm. Each smallholder received a score for each of the following 7 stages:

1.       Vector/fomite status of farm inputs

2.       Traffic onto farm

3.       Biosecurity at farm boundary

4.       Biosecurity between farm boundary and shed

5.       Biosecurity at the shed door

6.       Traffic into the shed and

7.       Susceptibility of the layer and broiler flock.

The BCS is calculated by summing the biosecurity stage scores. This is a simple method and makes no comparison with regard to the importance of each stage in influencing on-farm biosecurity. It values each of the stages equally. This is similar to the approaches of Fraser et al (2009) who used a similar scoring system when evaluating adoption of biosecurity measures to minimize Campylobacter incidence in UK poultry farms and Dorea et al (2010) who developed a scoring system to measure farmer adoption of biosecurity in Georgia, USA. The differences in the BCS between broiler and layer smallholders were tested using t-ratio. It is this variable that is then used as the dependent variable in the simple regression analysis which identifies what farm and farmer characteristics influence biosecurity adoption.

Results and Discussion

Constructing the Biosecurity Control Score (BCS)

The BCS was derived from 44 biosecurity control indicators (BCIs) elicited from the smallholders through a survey. The first step in generating a BCS is to score each individual BCI (Appendix 1). A biosecurity indicator is, for example, the source of poultry feed, the actions taken to minimize pest and rodent entry, or the number and type of signs installed around the farm.

Most of the indicators have been allocated scores ranging from 1 to 3 (1 being low biosecurity, and 3 being high biosecurity). For example it is more biosecure to purchase farm inputs (indicators 1B to 1E) direct from the contractor or feed company (a score of 3) rather than from a poultry shop (score 2) or from another farmer (score 1). There are several indicators that have a broader range of responses, and therefore a broader range of scores. One of these is parking and vehicle washing. Low biosecurity with regard to this indicator (a score of 1) means there is no designated parking area, no car wash area and no high pressure pump available to clean vehicles as they enter. High biosecurity (a score of 7), indicates that a car park, car wash area and pressure pump are present. Scores of 2 to 6 indicate the presence of some but not all of these facilities.

The BCS can then be constructed in 2 ways. The first and simplest is to add these 44 BCIs together. This method makes no judgment on the importance of each individual BCI or each stage, it weighs them all equally. The second method was to aggregate the scores of the individual BCIs into stage scores and then divide this aggregate by the number of BCIs within this stage. This method treats every stage as equally important rather than each BCI. While both methods were used in the following analysis, no real differences were identified so it was decided to use the first method for simplicity.

Comparing the BCS

Bali and West Java

The BCS suggests that in Bali broiler smallholders have a significantly higher adoption of biosecurity activities than do layer smallholders (Table 1). This result may be surprising as it is sometimes expected that layer smallholders, given the longer life span of their chickens and greater difficulty in finding replacement chickens, may have more to lose from a disease outbreak than do broiler smallholders. However, it may be that broiler smallholders, who are mostly contracted to produce birds by larger companies, receive better technical and biosecurity advice than the layer smallholders who are more likely to be independent producers. 

In West Java the result is quite different. Layer farms have significantly higher BCS scores than broilers. In Bali, broiler smallholders have a higher biosecurity score for all control stages except traffic onto farm (Table 2). In West Java broiler smallholders tend to purchase their inputs from more biosecure sources, however, layer smallholders had significantly higher biosecurity scores for the risk stages; biosecurity at farm gate and susceptibility of flock. Layer farms tend to be laid out or structured in a more biosecure manner with a higher likelihood of having poultry sheds further from potential sources of disease. They also have better biosecurity at the farm gate (Stage 3).  

Broiler and layer farms

Rearranging the data by farm type allows comparison of layer farms in Bali and West Java and broiler farms in Bali and West Java. When comparing farms within the provinces, there are two noteworthy results (Table 3). Firstly, layer smallholders implement significantly more biosecurity measures in West Java than they do in Bali. Secondly, there is no real difference in biosecurity adoption on broiler farms between the two provinces. The reason for this may be that most broiler smallholders in the NICPS are contracted to large companies who have uniform contracts and expectations across Indonesia. Each contract has agreed activities to be performed and agreed biosecurity measures that should be implemented. The layer industry, however, is not beholden to large multinational companies to the same extent and, therefore, layer smallholders are able to make more independent decisions when it comes to biosecurity adoption. There may well be many other significant variables influencing adoption, an area that can be explored in greater depth in future work.

Table 4 compares, at the Stage level, layer farms between the provinces and broiler farms between the provinces. It considers, for example, the particular risk stages where Bali smallholders may be different to smallholders in West Java. There are significant differences in the adoption of biosecurity between both layer and broiler smallholders in these provinces. Broiler smallholders in Bali have better biosecurity between the farm gate and the shed, while broiler farmers in West Java have a significantly higher level of biosecurity in the sheds and also a better vaccination and poultry health program.

Amongst layer smallholders, there are also significant differences. Generally, layer smallholders in West Java have higher biosecurity scores than those in Bali. There are significant differences in five of the seven control stages. Layer smallholders in West Java source their farm inputs from more biosecure sources, and have better-structured farms than the smallholders in Bali. Their sheds are positioned further away from potential sources of pest and disease, have more biosecure infrastructure and management practices at the farm gate, and have more biosecurity measures at the entrances to the sheds.

Factors affecting biosecurity adoption on NICPS farms

The previous section has summarized the levels of biosecurity adopted by broiler and layer smallholders in Bali and West Java. This section identifies the factors that influence a small-holder’s capacity or desire to implement biosecurity activities. These can be classified as:

                        1. Smallholder characteristics

                        2. Farm characteristics, and

                        3. Social/community characteristics

With regard to the smallholders, factors including the age of the household head, level of education, the number of household members, farm ownership structure and sources of income may influence adoption of biosecurity. Some of the basic data collected from the survey regarding these factors is presented in Table 5.

Ownership of assets may influence the level of biosecurity adoption. Information was collected concerning the area of land and livestock (including cattle, village chickens, pigs and ducks) owned as well as other household assets. Information was also collected on their level of borrowings and savings.

Biosecurity adoption may also be influenced by poultry farm size and capacity. This may include the number of farms and sheds, total land area of farms, total and average shed capacity. The location of the farm may also be important. The survey collected data on factors such as: the distance to other commercial poultry producers, elevation of sheds, and the distance of sheds from main roads, houses, live bird markets, wetlands, neighboring sheds, feed shed, offices, parking areas, and boundary fences. Once again summary data for a selection of these variables is presented in Table 6.

Social capital may play important role in determining biosecurity adoption. Social capital is embedded in social interaction and attitudes among communities, and may affect a community’s response to disease-related risks (Patrick et al 2010). For example, people who are unwilling to trust other people may respond differently to a village outbreak of HPAI compared to those who are prepared to work with the community. Similarly, the perception of the community and their attitude towards HPAI outbreaks can be categorized as a positive or negative response to biosecurity risks and adoption of biosecurity measures.

This study uses a similar methodology for measuring social capital as described in Patrick et al (2010). Respondents were asked questions concerning their attitudes to community and leadership and their expected responses to issues and problems that may arise in their community. Two variables were constructed from these responses. The first was social capital, the second agency. Social capital was a measure of the strength of community ties and community interaction while agency is the ability to actually utilize this social capital to benefit the community. It is expected that both social capital and agency may have a positive influence on the level of biosecurity adoption as smallholders may realize that strong community level biosecurity adoption will have greater individual farm benefits than individual and isolated farmer adoption.

Factors influencing the adoption of biosecurity

The dependent variable in this analysis was calculated using the total poultry farm BCS score which ranks and aggregates farmers’ adoption of the 44 biosecurity control indicators (BCIs). This section discusses the results of a multiple regression model (Table 7).

The previous discussion indicated that the BCS for layer farmers differed significantly from the broiler farmers in both Bali and West Java. It also showed that the BCS for layer farms differed between the two locations while for broiler farmers there was no difference. Therefore, in this analysis respondents were divided into layer and broiler farmers in order to identify factors influencing the adoption of biosecurity. Separate analysis is undertaken for each group.

Characteristics of farmers

For layer and broiler farm respondents, the regression model indicated that the farmer characteristics that significantly influence the adoption of farm biosecurity measures were age and education level of the household head. The older and more educated the farmer the higher the adoption of biosecurity activities. Older farmers may have more control over their decision making and more confidence to make improved managerial decisions. More educated farmers may be more able to understand the biosecurity concept and see the potential importance of implementing these management changes. 

Non-poultry income had a negative effect on the adoption of biosecurity activities on layer farms but not on broiler farms. That is, the higher the level of non-poultry income on layer farms the lower the adoption of biosecurity.  Layer farmers who were more dependent on the income from egg production were more likely to attempt to protect their assets and maybe improve efficiency than were farmers who had other priorities.   

The number of household members had a positive significant influence on biosecurity control in broiler farms. Conversely, this had no significant influence in layer farms. While it was expected that the higher the number of family members may lead to a lower level of biosecurity adoption, this analysis showed that, in fact, the higher the number of household members, the higher the adoption of biosecurity activities. It may be that farmers understand the risks posed by many people having access to their farm and find it easier to insist on good biosecurity adoption with their family members as opposed to hired labour. Broiler farms tend to be smaller than layer farms and, hence, can rely on family labour rather than hired labour. Broiler farms are also probably less labor intensive than layer farms.

Characteristics of farms

There are two variables, land area of farm and distance of farm to neighbour’s poultry that had a positive influence on biosecurity adoption in layer and broiler farms. As expected the larger the farm (in land area, not capacity) the higher the adoption of biosecurity activities. It may be that the respondents with larger farms understand the increased risk of disease entry and have the resources to do something about it.

The distance to a neighbor’s poultry influences a farmer’s decision to implement biosecurity but probably not in the way that was expected. It was expected that the closer the farm is to a source of disease, in this case a neighbor’s farm, the greater the attempt to minimize the ability of the disease to spread. However, this analysis has shown that the further the poultry farm from the source of risk, the higher the adoption of biosecurity measures. Another factor influencing the biosecurity control adoption was the distance from the farm to the nearest road. This factor had the same positive significance as did distance from neighbors, but this was only on broiler farms.  It appears that smallholders who are close to important sources of risk such as neighboring farms and roads may believe that it is a waste of time trying to reduce the risk while smallholders who have a natural advantage believe that the risk is manageable and worth investing in.

Whether the farm was independent or contract had no significant influence on the adoption of biosecurity both on broiler and layer farms. It may be that there weren’t enough broiler farms that were independently managed and layer farms that were under contract to provide a clear enough difference between the farms.

Broiler farmers who had more, and larger, farms adopted higher levels of biosecurity. This was because larger-scale broiler farms need to avoid the potential large losses caused by disease outbreaks. They are prepared to invest more time and money to minimize the risk of loss. The layer farmers studied were mostly larger-scale anyway with more uniform investment and implementation of biosecurity measures.

Conclusions

• This study has developed a useful method of measuring the adoption of biosecurity on poultry farms in Indonesia. The Biosecurity Control Score consists of 44 biosecurity control indicators that are grouped into seven stages from the sourcing and type of farm inputs, through to the activities undertaken to reduce the susceptibility of the flock to disease incursion. Every farm is, therefore, allocated a measure of biosecurity adoption which is an individual response to the social, economic, environmental and institutional factors influencing them.

• Using the BCS to compare smallholder adoption indicated that in Bali, broiler farmers had higher adoption levels than layer farmers. In West Java, on the other hand, the biosecurity adoption in layer farms was higher than in broiler farms. In West Java, the implementation of biosecurity measures in layer farms was better than in broiler farms particularly at the farm gate. In Bali, almost at every stage the implementation of biosecurity measures in broiler farms was better than in layer farms.

• Using the BCS as the dependent variable, this study identified the potential factors that influence the adoption of biosecurity activities. The regression analysis identified that older, more educated farmers with larger families are more likely to adopt better biosecurity in layer and broiler farms. On layer farms, farmers with fewer non-poultry sources of income will have better biosecurity.

• The farm characteristic that may influence biosecurity adoption in both layer and broiler farms is land area of the farm. In broiler farms the number and average capacity of farms are also important. The analysis suggested that variables related to farm size had a positive impact on biosecurity control; the larger the farm the better the biosecurity. The distance of layer and broiler farms from neighbor’s poultry and nearest road was also important; the greater the distance the better the biosecurity. 

• It is suggested that the unique characteristics of farmers and farms should be considered during the process of encouraging the improvement of poultry farm biosecurity. This is because each farmer has a different and unique set of characteristics and, therefore, a different set of appropriate responses. If biosecurity on small-scale poultry farms is to be encouraged in countries such as Indonesia, governments and contract companies need to understand the drivers of adoption in the NICPS and the types of smallholders that are more likely to improve biosecurity. These smallholders will be more able to respond to the increasing demand from supermarket consumers for chicken meat and egg products that originate from farms with higher levels of biosecurity and are marketed through processing and transport systems that meet higher food safety standards. Understanding a smallholder’s decision making process with regard to biosecurity adoption is the first step in the industry being able to better respond to the increasing demand for better quality poultry products

Acknowledgements

The authors wish to thank the Australian Center for International Agricultural Research (ACIAR) Project AH/2006/169 for funding this study.

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Appendix 1: Individual farm biosecurity control indicators

Received 26 March 2013; Accepted 11 April 2013; Published 1 May 2013

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