Adaptive Governance and Resilience Capacity of Farms: The Fit Between Farmers’ Decisions and Agricultural Policies

Greater resilience is needed for farms to deal with shocks and disturbances originating from economic, environmental, social and institutional challenges, with resilience achieved by adequate adaptive governance. This study focuses on the resilience capacity of farms in the context of multi-level adaptive governance. We define adaptive governance as adjustments in decision-making processes at farm level and policy level, through changes in management practices and policies in response to identified challenges and the delivery of desired functions (e.g. private and public goods) to be attained. The aim of the study is twofold. First, we investigate how adaptive governance processes at farm level and policy level influence the resilience capacity of farms in terms of robustness, adaptability and transformability. Second, we investigate the “fit” between the adaptive governance processes at farm level and policy level to enable resilience. We study primary egg and broiler production in Sweden taking into consideration economic, social and environmental challenges. We use semi-structured interviews with 17 farmers to explain the adaptive processes at farm level and an analysis of policy documents from the Common Agricultural Policy program 2014–2020, to explain the intervention actions taken by the Common Agricultural Policy. Results show that neither the farm level nor policy level adaptive processes on their own have the capacity to fully enable farms to be robust, adaptable and transformable. While farm level adaptive processes are mainly directed toward securing the robustness and adaptability of farms, policy level interventions are targeted at enabling adaptability. The farm- and the policy level adaptive processes do not “fit” for attaining robustness and transformability

A framework to assess the resilience of farming systems

Agricultural systems in Europe face accumulating economic, ecological and societal challenges, raising concerns about their resilience to shocks and stresses. These resilience issues need to be addressed with a focus on the regional context in which farming systems operate because farms, farmers’ organizations, service suppliers and supply chain actors are embedded in local environments and functions of agriculture. We define resilience of a farming system as its ability to ensure the provision of the system functions in the face of increasingly complex and accumulating economic, social, environmental and institutional shocks and stresses, through capacities of robustness, adaptability and transformability. We (i) develop a framework to assess the resilience of farming systems, and (ii) present a methodology to operationalize the framework with a view to Europe’s diverse farming systems. The framework is designed to assess resilience to specific challenges (specified resilience) as well as a farming system’s capacity to deal with the unknown, uncertainty and surprise (general resilience). The framework provides a heuristic to analyze system properties, challenges (shocks, long-term stresses), indicators to measure the performance of system functions, resilience capacities and resilience-enhancing attributes. Capacities and attributes refer to adaptive cycle processes of agricultural practices, farm demographics, governance and risk management. The novelty of the framework pertains to the focal scale of analysis, i.e. the farming system level, the consideration of accumulating challenges and various agricultural processes, and the consideration that farming systems provide multiple functions that can change over time. Furthermore, the distinction between three resilience capacities (robustness, adaptability, transformability) ensures that the framework goes beyond narrow definitions that limit resilience to robustness. The methodology deploys a mixed-methods approach: quantitative methods, such as statistics, econometrics and modelling, are used to identify underlying patterns, causal explanations and likely contributing factors; while qualitative methods, such as interviews, participatory approaches and stakeholder workshops, access experiential and contextual knowledge and provide more nuanced insights. More specifically, analysis along the framework explores multiple nested levels of farming systems (e.g. farm, farm household, supply chain, farming system) over a time horizon of 1-2 generations, thereby enabling reflection on potential temporal and scalar trade-offs across resilience attributes. The richness of the framework is illustrated for the arable farming system in Veenkoloniën, the Netherlands. The analysis reveals a relatively low capacity of this farming system to transform and farmers feeling distressed about transformation, while other members of their households have experienced many examples of transformation

Genetic Control of Canine Leishmaniasis: Genome-Wide Association Study and Genomic Selection Analysis

Background: the current disease model for leishmaniasis suggests that only a proportion of infected individuals develop clinical disease, while others are asymptomatically infected due to immune control of infection. The factors that determine whether individuals progress to clinical disease following Leishmania infection are unclear, although previous studies suggest a role for host genetics. Our hypothesis was that canine leishmaniasis is a complex disease with multiple loci responsible for the progression of the disease from Leishmania infection. Methodology/Principal Findings: genome-wide association and genomic selection approaches were applied to a population-based case-control dataset of 219 dogs from a single breed (Boxer) genotyped for ~170,000 SNPs. Firstly, we aimed to identify individual disease loci; secondly, we quantified the genetic component of the observed phenotypic variance; and thirdly, we tested whether genome-wide SNP data could accurately predict the disease. Conclusions/Significance: we estimated that a substantial proportion of the genome is affecting the trait and that its heritability could be as high as 60%. Using the genome-wide association approach, the strongest associations were on chromosomes 1, 4 and 20, although none of these were statistically significant at a genome-wide level and after correcting for genetic stratification and lifestyle. Amongst these associations, chromosome 4: 61.2-76.9 Mb maps to a locus that has previously been associated with host susceptibility to human and murine leishmaniasis, and genomic selection estimated markers in this region to have the greatest effect on the phenotype. We therefore propose these regions as candidates for replication studies. An important finding of this study was the significant predictive value from using the genomic information. We found that the phenotype could be predicted with an accuracy of ~0.29 in new samples and that the affection status was correctly predicted in 60% of dogs, significantly higher than expected by chance, and with satisfactory sensitivity-specificity values (AUC = 0.63)

Evaluating Strategies for Honey Value Chains in Brazil using a Value Chain Structure-Conduct-Performance (SCP) Framework

Development organizations have used value chain analysis in defining interventions for the honey business in major exporting countries like Brazil. Yet, the impact of interventions has been unclear. This paper aims at evaluating strategies of three honey value chain streams in Brazil, selected for a multiple case study between the years 2007–2011.Using the value chain Structure-Conduct-Performance (SCP) framework, likely successful strategies are identified by comparing stream performances. Next, the outcomes of this comparison are validated through questionnaires with experts. Understanding current stream strategies and local structural conditions, and fostering well-aligned strategies are found to be key for successful donor interventions

Mean farmer knowledge scores and coefficients from the censored regression of knowledge scores on the influencing factors (n = 434), standard errors in parentheses.

<p>Mean farmer knowledge scores and coefficients from the censored regression of knowledge scores on the influencing factors (n = 434), standard errors in parentheses.</p

Demographic and socioeconomic characteristics of farms in the sample.

<p>Demographic and socioeconomic characteristics of farms in the sample.</p

Farmers’ perceptions of the important requirements to obtain finance per finance provider type ^a (n = 434).

<p>Farmers’ perceptions of the important requirements to obtain finance per finance provider type <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0179285#t003fn001" target="_blank">^a</a> (n = 434).</p