Towards appropriate mainstreaming of "Theory of Change" approaches into agricultural research for development: Challenges and opportunities.

Food insecurity persists in many parts of Africa and Asia, despite ongoing agricultural research for development (AR4D) interventions. This is resulting in a growing demand for alternative approaches to designing and evaluating interventions in complex systems. Theory of Change (ToC) is an approach which may be useful because it enables stakeholders to present and test their theories and assumptions about why and how impact may occur, ideally within an environment conducive to iterative reflection and learning. However, ToC is yet to be appropriately mainstreamed into development by donors, researchers and practitioners. We carried out a literature review, triangulated by interviews with 26 experts in African and Asian food security, consisting of researchers, advisors to programs, and donors. Although 17 (65%) of the experts had adopted ToC, their responses and the literature revealed four challenges to mainstreaming: (i) different interpretations of ToC; (ii) incoherence in relationships among the constituent concepts of ToC; (iii) confused relationships between ToC and project "logframes"; and (iv) limitations in necessary skills and commitment for enacting ToC. A case study of the evolution of a ToC in a West African AR4D project over 4 years which exemplified these challenges is presented. Five recommendations arise to assist the mainstreaming of ToC: (i) select a type of ToC suited to the relative complexity of the problem and focal system of interest; (ii) state a theory or hypotheses to be tested as the intervention progresses; (iii) articulate the relationship between the ToC and parallel approaches (e.g. logframe); (iv) accept that a ToC is a process, and (v) allow time and resources for implementers and researchers to develop ToC thinking within projects. Finally, we suggest that communities of practice should be established among AR4D and donor organisations to test, evaluate and improve the contribution that ToCs can make to sustainable food security and agricultural development.

Contribution of the veterinary profession to the Australian animal health surveillance system and opportunities for future contributions.

Animal health surveillance is crucial for early detection of emergency animal diseases and effective responses. However, surveillance systems are complex and rely on the contributions of many animal health stakeholders. Veterinarians are key stakeholders in this system, given their role and skills in investigating, diagnosing, and reporting notifiable diseases. This study investigated the contribution of the veterinary workforce to the Australian animal health surveillance system and opportunities for future involvement. To achieve the aims of the study, an online cross-sectional survey among the veterinary profession was conducted. Descriptive statistics and regression analyses were used to provide an overview and investigate drivers of attitudes and practices of veterinarians in relation to animal health surveillance. A total of 311 usable responses were obtained, with 191 being from veterinarians who worked in private practice in the previous 12 months. Among private practitioners, 58.6% worked with companion animals, 34.0% were mixed practice veterinarians and 7.3% were equine veterinarians. Over half (56.6%) of all participants considered themselves active participants in the local animal disease management system. The level of confidence in understanding the reporting system and knowing and identifying signs of endemic and exotic diseases was moderate among those working in private practice, with companion animal veterinarians reporting the lowest levels of confidence (p < 0.05). Approximately 40% of veterinarians had taken samples for diagnosis for notifiable diseases in the last year, with just over 20% reporting a notifiable disease. Awareness of and participation in training and surveillance programs for animal diseases by veterinarians was low, with those working in private practice having lower levels of both awareness and participation for most programs. In relation to potential future contribution to the surveillance system, over half of participants reported being interested and available to undertake surveillance work on behalf of the government, with those in mixed practice reporting higher levels of interest (69.6%) compared to those in companion (49.5%) and equine practice (30.8%). However, key challenges identified were related to perceived conflict of interest, and tensions between client needs and government agenda, followed by profitability and suitability of the business. This study provides evidence of a significant existing contribution by the veterinary profession to the surveillance system, and the capacity and willingness to increase this contribution. However, there are gaps in awareness, confidence and participation, as well as financial and veterinary-client relationship challenges that should be considered in any future planning to strengthen the Australian surveillance system.

On-farm evaluation of a predictive model for Australian beef and sheep producers' vulnerability to an outbreak of foot and mouth disease.

To explore Australian sheep and beef producer vulnerability to an emergency animal disease outbreak, Bayesian Network models have been developed, with the ultimate goal of creating risk management tool for outbreak preparedness. These models were developed using multiple stakeholder elicitation including modelling experts, epidemiologists and on-farm stakeholders, including on-farm/survey data. An evaluation of the model's predictive capacity was conducted, using independent, blinded on-farm vulnerability assessments. Nine properties were visited, four each with sheep and beef enterprises, and one mixed enterprise. There were some discrepancies between the model predictions and on-farm assessment in the beef enterprises, with greater disparity with the sheep properties. Discrepancies between the model predictions and on-farm assessments have created opportunities for examination of the data collection process for the model development, the model itself and the on-farm assessment process. Bayesian Network approaches that allow for the inclusion of both continuous and discrete variables may improve the usefulness of these models, avoiding the loss of nuanced data by the need for discretisation of continuous variables, as will the inclusion of input from on-farm stakeholders in model development. Future work includes more data collection to improve the sensitivity of the model predictions, and a deeper, systemic exploration of the factors that may impact Australian producers' vulnerability to an emergency animal disease outbreak.

Australian veterinarians' perspectives on the contribution of the veterinary workforce to the Australian animal health surveillance system.

This study investigated the involvement of private veterinarians in surveillance activities and the veterinary workforce's contribution to the Australian animal health surveillance system. The perception that there is overall a decreased engagement by veterinarians in surveillance outcomes at a time when there is increased need for bolstering of surveillance systems was investigated. Three key questions were considered: (1) What is the current contribution of private veterinarians to the Australian surveillance system? (2) What is the veterinary professions capacity to assume a more prominent role in surveillance? (3) What is the interest and ability of the veterinary profession in Australia to undertake this surveillance role now and into the future? Semi-structured telephone interviews were conducted with 17 private veterinarians with data analyzed qualitatively to identify key themes. Results demonstrate that private veterinarians are aware of their responsibilities and are engaged in surveillance activities at both formal and informal levels. The key challenges associated with current and future contributions were related to workload, remuneration, conflicts of interest and clarity over how responsibility for surveillance is shared amongst those involved in the system. The study has demonstrated that even amongst an engaged population, barriers do need to be addressed if private veterinarians are to be tasked with increasing their involvement in animal health surveillance activities.

One size does not fit all: Exploring the economic and non-economic outcomes of on-farm biosecurity for bovine viral diarrhoea virus in Australian beef production.

Bovine viral diarrhoea virus (BVDV) is a disease of global importance, affecting the production and welfare of cattle enterprises through poor reproductive performance and calf mortality. In Australia, the prevention of BVDV introduction and spread is primarily achieved with on-farm biosecurity; however, the use of these practices can vary amongst producers. Economic utility is commonly identified as a contributor to the uptake of on-farm biosecurity, but other factors such as animal welfare, producer priorities and introduction risk also influence farmer behaviour. This study uses an individual-based, stochastic simulation model to examine the economic and non-economic value of 23 on-farm biosecurity combinations for the control of BVDV in Australian beef farms without (N0) and with (N1) a neighbouring population of persistently infected (PI) cattle. Combinations of quarantine of purchased bulls (Q), hygiene during herd health events (H), double-fencing adjacent boundaries with neighbouring farms (F) and vaccination against BVDV (V) were tested. This study is the first to simulate the use of strategic PI exposure (PI) as an alternative to V, a contentious practice performed by some Australian beef farmers. Introduction of BVDV into a naïve 300-breeder self-replacing beef herd was achieved through the purchase of PI bulls (N0 and N1 herds) and over-the-fence contact with neighbouring PI animals (N1 herds only). The predicted median cumulative loss due to BVDV over a 15-year period was AUD$172/breeder and AUD$453/breeder for an N0 and N1 herd, respectively. Early establishment of BVDV in the simulation period was found to be the primary factor contributing to economic loss. Consequently, the Q and QF combinations resulted in the highest predicted average annual cost-benefit for BVDV-free N0 and N1 herds. In the five years following establishment of BVDV, use of QP (N0 herds) and V (N1 herds) combinations were most cost-effective. Combinations that involved V and P (in conjunction with F in N1 herds) also resulted in the lowest number of PI animals sold to other farms or feedlots over the simulation period. However, in both N0 and N1 herds, P resulted in the highest number of infected cattle, which has implications for poor animal welfare and increased antimicrobial use on Australian beef farms. The outcomes reported in this study can guide decisions to prevent BVDV introduction and spread on extensive beef farms using on-farm biosecurity, based on the risk of BVDV exposure and the priorities of the individual farmer.

Modeling the Effect of Bovine Viral Diarrhea Virus in Australian Beef Herds.

Bovine viral diarrhea virus (BVDV) is an economically important disease in Australian beef farming. The disease typically results in low-level production losses that can be difficult to detect for several years. Simulation modeling can be used to support the decision to control BVDV; however, current BVDV simulation models do not adequately reflect the extensive farming environment of Australian beef production. Therefore, the objective of this study was to develop a disease simulation model to explore the impact of BVDV on beef cattle production in south-east Australia. A dynamic, individual-based, stochastic, discrete-time simulation model was created to simulate within-herd transmission of BVDV in a seasonal, self-replacing beef herd. We used the model to simulate the effect of herd size and BVDV introduction time on disease transmission and assessed the short- and long-term impact of BVDV on production outputs that influence the economic performance of beef farms. We found that BVDV can become established in a herd after a single PI introduction in 60% of cases, most frequently associated with the breeding period. The initial impact of BVDV will be more severe in smaller herds, although self-elimination is more likely in small herds than in larger herds, in which there is a 23% chance that the virus can persist for >15 years following a single incursion in a herd with 800 breeders. The number and weight of steers sold was reduced in the presence of BVDV and the results demonstrated that repeat incursions exacerbate long-term production losses, even when annual losses appear marginal. This model reflects the short- and long-term production losses attributed to BVDV in beef herds in southeast Australia and provides a foundation from which the influence and economic utility of BVDV prevention in Australian beef herds can be assessed.

The goat industry in Australia: Using Bayesian network analysis to understand vulnerability to a foot and mouth disease outbreak.

Australia's goat industry is one of the largest goat product exporters in the world, managing both farmed and wild caught animals. To protect and maintain the competitive advantage afforded to the Australian goat industry by the absence of many diseases endemic elsewhere, it is important to identify the vulnerability of producers to livestock disease incursions. This study developed a framework of producer vulnerability built from the beliefs and practices of producers that may impact on their likelihood of exposure and response capacity to an emergency animal disease (EAD), using foot and mouth disease as a model. A cross-sectional questionnaire gathered information on producer/enterprise demographics, animal health management and biosecurity practices, with 107 participating in the study. The biosecurity measures that were most commonly implemented by producers were always using animal movement documentation for purchased stock (74.7 %) and isolating new stock (73.1 %). However, moderate to low uptake of biosecurity protocols related to visitors to the property were reported. Response capacity variables such as checking animals daily (72.0 %) and record keeping (91.7 %) were reported by the majority of respondents, with 40.7 % reporting yearly veterinary inspection of their animals. Using the vulnerability framework, a Bayesian Network model was developed and populated by the survey data, and the relationships between variables were investigated. Six vulnerability profiles were developed, with three levels of exposure (high, moderate, low) and two levels of response capacity (high, low), as described by producer demographics and practices. The most sensitive exposure variables on producer vulnerability included implementation of visitor biosecurity and control of feral animals. Results from this study can inform risk based perspectives and decisions around biosecurity and surveillance resource allocation within the goat industry. The results also highlight opportunities for improving Australia's preparedness for a future EAD incursion by considering producer behaviour and beliefs by applying a vulnerability framework.

Stakeholder mapping in animal health surveillance: A comparative assessment of networks in intensive dairy cattle and extensive sheep production in Australia.

The capacity to rapidly identify and respond to suspicion of animal disease is fundamental to protecting the integrity of the Australian livestock industry. An incursion of a nationally significant endemic, emerging or exotic animal disease could be disruptive and economically damaging for the industry, broader community and national economy. To counter this potential threat, a surveillance system that includes general and targeted activities exists at a jurisdictional and national level. Such a system requires a collaborative effort from all involved to work towards a common goal, reflecting the notion of shared responsibility. As in all systems, the animal health surveillance system can be enhanced or constrained by the relationships of the players involved. This study focusses on two livestock industries, dairy cattle and sheep, exploring the interrelationships between all stakeholders, and their role within the Australian animal health surveillance system. A stakeholder mapping exercise was undertaken, including a depiction of the perceived level of stakeholder interest and influence on producers' animal health surveillance practices and/or the surveillance system. Results from these activities were expanded upon through interviews. The findings reveal complex networks and a system that is, at times, constrained by institutional and individual barriers such as communication between and within stakeholders, and uncertainty about the consequences of reporting a suspected emergency disease. Whilst these challenges have the potential to negatively impact the robustness of the animal disease surveillance system, the study also provides clear evidence of strong and effective relationships amongst many of the key individuals and organisations.

Using a Bayesian Network Predictive Model to Understand Vulnerability of Australian Sheep Producers to a Foot and Mouth Disease Outbreak.

To maintain and strengthen Australia's competitive international advantage in sheep meat and wool markets, the biosecurity systems that support these industries need to be robust and effective. These systems, strengthened by jurisdictional and livestock industry investments, can also be enhanced by a deeper understanding of individual producer risk of exposure to animal diseases and capacity to respond to these risks. This observational study developed a Vulnerability framework, built from current data from Australian sheep producers around behaviors and beliefs that may impact on their likelihood of Exposure and Response Capacity (willingness and ability to respond) to an emergency animal disease (EAD). Using foot and mouth disease (FMD) as a model, a cross-sectional survey gathered information on sheep producers' demographics, and their practices and beliefs around animal health management and biosecurity. Using the Vulnerability framework, a Bayesian Network (BN) model was developed as a first attempt to develop a decision making tool to inform risk based surveillance resource allocation. Populated by the data from 448 completed questionnaires, the BN model was analyzed to investigate relationships between variables and develop producer Vulnerability profiles. Respondents reported high levels of implementation of biosecurity practices that impact the likelihood of exposure to an EAD, such as the use of appropriate animal movement documentation (75.4%) and isolation of incoming stock (64.9%). However, adoption of other practices relating to feral animal control and biosecurity protocols for visitors were limited. Respondents reported a high uptake of Response Capacity practices, including identifying themselves as responsible for observing (94.6%), reporting unusual signs of disease in their animals (91.0%) and daily/weekly inspection of animals (90.0%). The BN analysis identified six Vulnerability typologies, with three levels of Exposure (high, moderate, low) and two levels of Response Capacity (high, low), as described by producer demographics and practices. The most influential Exposure variables on producer Vulnerability included adoption levels of visitor biosecurity and visitor access protocols. Findings from this study can guide decisions around resource allocation to improve Australia's readiness for EAD incursion and strengthen the country's biosecurity system.

Understanding the vulnerability of beef producers in Australia to an FMD outbreak using a Bayesian Network predictive model.

Effective and adaptable biosecurity and surveillance systems are crucial for maintaining and increasing Australia's competitive advantages in international markets, and for the production of high quality, safe animal products. These systems are continuously strengthened by ongoing government and industry investment. However, a better understanding of evolving disease risks and the country's capacity to respond to these risks is needed. This study developed a vulnerability framework based on characteristics and behaviours of livestock producers that impact exposure and response capacity to an emergency animal disease (EAD) outbreak among beef producers in Australia, with a focus on foot and mouth disease (FMD). This framework articulated producer vulnerability typologies to better inform surveillance resource allocation and future research direction. A cross-sectional study of beef producers in Australia was conducted to gather information on producers' demographics, husbandry characteristics, biosecurity and animal health management practices and beliefs, including those specific to FMD risk and response capacity. A Bayesian Network (BN) model was developed from the vulnerability framework, to investigate the complex interrelationships between variables and identify producer typologies. A total of 375 usable responses were obtained from the cross-sectional study. Regarding EAD exposure, producers implemented appropriate biosecurity practices for incoming stock, such as isolation (72.0 %), inspection for disease (88.7 %) and the use of vendor declarations (78.5 %); however, other biosecurity practices were limited, such as restriction of visitor access, visitor biosecurity requirements or feral animal control. In relation to response capacity, a moderate uptake of practices was observed. Whilst daily or weekly visual inspection of animals was reported by most producers (90.1 %), physical inspection was less frequent. Most producers would call a private veterinarian in response to unusual signs of disease in their cattle; however, over 40 % of producers did not cite calling a government veterinarian as a priority action. Most producers believe an FMD outbreak would have extremely serious consequences; however, their level of concern was moderate and their confidence in identifying FMD symptoms was low. The BN analysis identified six vulnerability typologies, with three levels of exposure (high, moderate, low) and two levels of response capacity (high, low), as described by producer demographics and practices. The model identified property size, number of cattle and exposure variables as the most influential to the overall producer vulnerability. Results from this study can inform how to best use current biosecurity and surveillance resources and identify where opportunities exist for improving Australia's preparedness for future EAD incursions.

Pig traders' networks on the Kenya-Uganda border highlight potential for mitigation of African swine fever virus transmission and improved ASF disease risk management.

We applied social network analysis to pig trader networks on the Kenya-Uganda border. Social network analysis is a recently developed tool, which is useful for understanding value chains and improving disease control policies. We interviewed a sample of 33 traders about their experiences with trade and African swine fever (ASF), analyzed the networks they generated in purchasing pigs and selling pork and their potential contribution to modulating dissemination of the ASF virus (ASFV). The majority of the traders were aware of clinical signs of ASF and the risk of trade transmitting ASFV. Most said they avoided buying pigs from ASF outbreak villages or sick pigs but their experiences also indicated that inadvertent purchase was relatively common. Traders had early knowledge of outbreaks since they were contacted by farmers who had heard rumours and wanted to sell their pigs to avoid the risk of them dying. Individual traders bought pigs in up to nine villages, and up to six traders operated in a village. Although each trade typically spanned less than 5km, networks of the various traders, comprising movements of pigs from source villages to slaughter slabs/sites and retail outlets, and movement of pork to villages where it was consumed, linked up indirectly across the 100km×50km study area and revealed several trade pathways across the Kenya-Uganda border. ASF could potentially spread across this area and beyond through sequential pig and pork transactions. Regulation of the pig and pork trade was minimal in practice. The risk of ASFV being spread by traders was compounded by their use of poorly constructed slaughter slabs/sites with open drainage, ineffective or non-existent meat inspection services, lack of provision for biosecurity in the value chain, and sales of pork to customers who were unaware of the risks to their own pigs from contact with ASF infected pork. More effective regulation is warranted. However, limitations on government capacity, together with the strong self-interest that established traders have in reducing the disruption and financial losses that outbreaks cause, highlight the importance of governments and traders co-developing an approach to ASF control. Formation of trader organizations or common interest groups warrants government support as an important step in engaging traders in developing and implementing effective approaches to reduce the risk of ASF outbreaks.

Social network analysis provides insights into African swine fever epidemiology.

Pig movements play a significant role in the spread of economically important infectious diseases such as the African swine fever. Characterization of movement networks between pig farms and through other types of farm and household enterprises that are involved in pig value chains can provide useful information on the role that different participants in the networks play in pathogen transmission. Analysis of social networks that underpin these pig movements can reveal pathways that are important in the transmission of disease, trade in commodities, the dissemination of information and the influence of behavioural norms. We assessed pig movements among pig keeping households within West Kenya and East Uganda and across the shared Kenya-Uganda border in the study region, to gain insight into within-country and trans-boundary pig movements. Villages were sampled using a randomized cluster design. Data were collected through interviews in 2012 and 2013 from 683 smallholder pig-keeping households in 34 villages. NodeXL software was used to describe pig movement networks at village level. The pig movement and trade networks were localized and based on close social networks involving family ties, friendships and relationships with neighbours. Pig movement network modularity ranged from 0.2 to 0.5 and exhibited good community structure within the network implying an easy flow of knowledge and adoption of new attitudes and beliefs, but also promoting an enhanced rate of disease transmission. The average path length of 5 defined using NodeXL, indicated that disease could easily reach every node in a cluster. Cross-border boar service between Uganda and Kenya was also recorded. Unmonitored trade in both directions was prevalent. While most pig transactions in the absence of disease, were at a small scale (<5km) and characterized by regular agistment, most pig sales during ASF outbreaks were to traders or other farmers from outside the sellers' village at a range of >10km. The close social relationships between actors in pig movement networks indicate the potential for possible interventions to develop shared norms and mutually accepted protocols amongst smallholder pig keepers to better manage the risk of ASF introduction and transmission.