Abstract
The need to adapt to decrease farm vulnerability to adverse contextual events has been extensively discussed on a theoretical basis. We developed an integrated and operational method to assess farm vulnerability to multiple and interacting contextual changes and explain how this vulnerability can best be reduced according to farm configurations and farmers’ technical adaptations over time. Our method considers farm vulnerability as a function of the raw measurements of vulnerability variables (e.g., economic efficiency of production), the slope of the linear regression of these measurements over time, and the residuals of this linear regression. The last two are extracted from linear mixed models considering a random regression coefficient (an intercept common to all farms), a global trend (a slope common to all farms), a random deviation from the general mean for each farm, and a random deviation from the general trend for each farm. Among all possible combinations, the lowest farm vulnerability is obtained through a combination of high values of measurements, a stable or increasing trend and low variability for all vulnerability variables considered. Our method enables relating the measurements, trends and residuals of vulnerability variables to explanatory variables that illustrate farm exposure to climatic and economic variability, initial farm configurations and farmers’ technical adaptations over time. We applied our method to 19 cattle (beef, dairy, and mixed) farms over the period 2008–2013. Selected vulnerability variables, i.e., farm productivity and economic efficiency, varied greatly among cattle farms and across years, with means ranging from 43.0 to 270.0 kg protein/ha and 29.4–66.0% efficiency, respectively. No farm had a high level, stable or increasing trend and low residuals for both farm productivity and economic efficiency of production. Thus, the least vulnerable farms represented a compromise among measurement value, trend, and variability of both performances. No specific combination of farmers’ practices emerged for reducing cattle farm vulnerability to climatic and economic variability. In the least vulnerable farms, the practices implemented (stocking rate, input use…) were more consistent with the objective of developing the properties targeted (efficiency, robustness…). Our method can be used to support farmers with sector-specific and local insights about most promising farm adaptations.
Highlights
The biophysical, economic and social context of agricultural production is increasingly unpredictable and volatile (Thompson and Scoones, 2009), driven by complex and interrelated contextual changes, including scarcity of natural resources, climate change, increasing food demand, and administrative regulation
Actual impact is the impact that remains after accounting for adaptive capacity, especially adaptations implemented by system managers confronted with climatic and economic variability
The integrated and operational method we developed enables assessing farm vulnerability to multiple contextual changes and explains how this vulnerability can be reduced according to farm configurations and farmers’ technical adaptations over time
Summary
The biophysical, economic and social context of agricultural production is increasingly unpredictable and volatile (Thompson and Scoones, 2009), driven by complex and interrelated contextual changes, including scarcity of natural resources, climate change, increasing food demand, and administrative regulation. The vulnerability of any system (at any scale) is considered a function of exposure and sensitivity of that system to a range of hazards and the adaptive capacity of the system to cope with, adapt to, or recover from the effects of these conditions (Smit and Wandel, 2006). Exposure usually refers to the duration, extent and frequency of climatic and economic perturbations influencing the system (Adger, 2006). Exposure and sensitivity determine the potential impact that occurs given the climatic and economic variability. Adaptive capacity is the degree to which a system can adjust to, moderate, or offset the potential impacts, or take advantage of opportunities created by a given climatic or economic event (Schneider et al, 2001). Actual impact is the impact that remains after accounting for adaptive capacity, especially adaptations implemented by system managers confronted with climatic and economic variability
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