Assessing Vulnerabilities and Adaptation Approaches

2.13 Assessing Vulnerabilities and Adaptation Approaches: Useful to Usable Tools LS Prokopy, AS Mase and R Perry-Hill, Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN, USA MC Lemos School of Natural Resources and Environment, University of Michigan, Ann Arbor, MI, USA Ó 2013 Elsevier Inc. All rights reserved.

2.13.1 2.13.2 2.13.3 2.13.3.1 2.13.3.2 2.13.4 2.13.4.1 2.13.4.2 2.13.4.3 2.13.4.4 2.13.5 2.13.5.1 2.13.5.2 2.13.5.3 2.13.5.4 2.13.6 2.13.7 References

2.13.1

Introduction Vulnerability and Adaptive Capacity Literature Review: Vulnerability, Adaptive Capacity, and Climate Information Seasonal Climate Forecasts as a Decision Support Tool Usability of SCFs Assessing Vulnerabilities and Adaptive Capacities Who Is the Subject of the Study? Acquiring a Sampling Frame Probability Sampling Nonprobability Sampling Methods Surveys Interviews Mental Models Interviews Participatory Approaches Case Study: Understanding Vulnerability and Adaptive Capacity to Enhance Usability of Climate Information in the North Central Region of the United States Conclusion

Introduction

Global and regional climatic variability has the potential to cause impacts to agricultural operations around the world. For example, arid parts of the world could expand and serious constraints to crop cultivation are underway (Fischer et al. 2005). In other places, climatic shifts could create more potential agricultural land and better growing conditions if adequate soil nutrients also coexist (Fischer et al. 2005). However, the overall impacts of climatic stress may be negative and thus threaten global food security (Nelson et al. 2009). It is, therefore, critically important to assess farmers’ vulnerabilities and capacities to respond to negative impacts of climatic stresses. To meet this challenge, there is a concerted effort around the world to both assess the vulnerability and adaptive capacity of farming households, as well as to better understand different ways to manage climate-related risk. This research suggests that one critical determinant of adaptive capacity of agricultural systems is the ability to use climate knowledge and decision support tools to inform preventive action and adaptation options to climate-related impacts. This chapter reviews the literature focusing on the role of climate information use (or lack thereof) by farming households in shaping their adaptive capacity to climate impacts. It first reviews the empirical literature exploring the opportunities and constraints to use of climate information (especially Seasonal Climate Forecasts, SCFs) and how it may contribute to reduce the threat of climatic vulnerability to

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farming. It then discusses how this literature has assessed vulnerabilities and adaptation approaches and the social science research methods used. Finally, it concludes with an example of how social science data can be combined with climate modeling to reduce vulnerabilities and enhance adaptive capacities.

2.13.2

Vulnerability and Adaptive Capacity

The IPCC (2007) defines vulnerability as a function of a system’s exposure, sensitivity, and adaptive capacity to climate impact; that is, vulnerability refers to “. the exposure and sensitivity of [a] system to hazardous conditions and the ability . of the system to cope, adapt or recover from the effects of those conditions” (Smit and Wandel 2006, p. 286). Adaptive capacity, in turn, is defined as “the potential or ability of a system, region, or community to adapt to the effects or impacts of climate change” (Reid et al. 2007, p. 612). (Table 1 shows common determinants of adaptive capacity.) Hence, the vulnerability of farmers to drought, for example, depends on how exposed and sensitive they are to the event and how much capacity they have to respond and recover after it happens (IPCC 2007). The three dimensions of vulnerability – exposure, sensitivity, and adaptive capacity – are shaped not just by the magnitude and frequency of the climate event, but also by the timing and the multitude of deficits and capacities farming households have to cope with or adapt to climate stress. From this perspective, vulnerability is not a static concept, but varies

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Table 1

Determinants of adaptive capacity

Determinant

Description

Awareness

The ability to accurately identify the signals of change and their implications The availability of and access to technologic options for adaptation The availability of resources for adapting (including financial capital and physical resources) The structure of critical institutions, including the allocation of decisionmaking authority The skills, education, experiences, and general abilities of individuals The informal social networks and collective life of a community, as it influences the ability and willingness of residents to work together for common community goals The ability of a system to manage risks, including sharing the risk among the stakeholders The ability of decision makers to manage information, including the processes by which information is acquired and assessed

Technology Resources

Institutions

Human capital Social capital

Risk management

Information management

Table developed by Reid, S., B. Smit, W. Caldwell, and S. Belliveau, 2007: Vulnerability and adaptation to climate risks in Ontario agriculture. Mitig. Adapt. Strat. Global Change, 12, 609–637 from Yohe, G., R. S. J. Tol, 2001. Indicators for social and economic coping capacity–moving toward a working definition of adaptive capacity. Glob. Environ. Change, 12, 25–40 and Smit , B., and O. Pilifosova, 2001: Adaptation to climate change in the context of sustainable development and equity. In: IPCC Working Group II (Ed.), Climate Change 2001: Impacts, Adaptation and Vulnerability. Earth Scan, Geneva).

both spatially and temporally, by livelihood system and socioeconomic group (Liverman 1999; Vásquez-León 2002). Regarding adaptation, how farmers have responded to climate variability in the past has often informed our understanding of how they might respond in the future. For example, based on past response, Agrawal (2008) has suggested a typology of five categories of adaptation strategies to climate stress among poor farmers: diversification, mobility (e.g., migration), storage, communal pooling, and exchange. Among resource-richer farmers, other options to manage risk include buying farm insurance and proactive planning using decision support tools such as SCFs. Table 1 describes common determinants of adaptive capacity. This chapter specifically reviews the role of SCFs as a determinant of adaptive capacity and examines the different research methods the empirical social science literature has employed in understanding the vulnerabilities and capacities of farming households and the potential role for climate information to modulate them.

2.13.3 Literature Review: Vulnerability, Adaptive Capacity, and Climate Information The ability to forecast or project climate (short-term epochs, seasonal, and long term) and plan ahead has often been

assumed to be critical to decreasing farming systems’ vulnerability. The rationale is that if farmers know about the potential negative impact of the meteorologic events ahead of time, they can protect their crops (e.g., from frost and floods), apply climate-resistant technology (e.g., plant drought-resistant varieties or crops or use irrigation), pool risk (e.g., through diversification or storage), or buy insurance that would allow them to recover and start over. All these actions in principle increase their adaptive capacity; that is, their ability to prevent, cope, and recover from climate-related negative impact. Not surprisingly, through history, farmers have consistently devised different ways to predict climatic patterns to prevent and respond to their negative effects. However, the challenge of predicting climate is well documented and different approaches, from folk forecasts to scientific modeling, carry substantial levels of uncertainty that modulate their usefulness to farmers and other decision makers (e.g., see Lemos and Rood 2010). Although there has been significant investment in understanding the usability of climate forecasts around the world, relatively little is known about how climate information has affected the overall vulnerability of farmers. Rather than assessing outcomes of forecast use, most of the research so far has focused on understanding whether and why farmers use climate information or not. For the past 30 years, one particular kind of climate prediction, seasonal climate forecasting has been the subject of significant research, especially in the social sciences, to understand the opportunities and constraints of using emerging technology to forecast climate up to a year in advance. Part of the motivation for this research has been to assess the value of forecasts as a decision-support tool. For example, while researching farmers in Florida, Cabrera et al. (2007) sought to estimate the value of climate information in reducing farm risk by helping farmers decide on management strategies that could both reduce negative impacts of climatic shifts, but also know when to take advantage of changes in weather conditions that would be more favorable for growing than expected given past conditions. Another goal of empirical research has been to use evidence from SCF application as an analog to understand potential challenges to the use of other kinds of climate information, especially global climate projections.

2.13.3.1 Seasonal Climate Forecasts as a Decision Support Tool Since becoming available in the 1990s, various studies have consistently extolled the potential value of seasonal climate forecasts (Zebiak and Cane 1987; Magalhães et al. 1988; Glantz 1996; Ropelewski and Lyon 2003; Harrison 2005) to decrease farming vulnerability. In the mid-1980s, scientists interested in climate dynamics discovered the mechanisms of the El NiñoSouthern Oscillation (ENSO) phenomenon well enough to be able to predict the onset of its warm (El Niño) or cool (La Niña) phase several months to even a year in advance. The ENSO phenomenon and its teleconnections wreak havoc on many tropical and subtropical regions of the world, disrupting normal patterns of rainfall to cause severe droughts and catastrophic flooding. The possibilities raised by climate scientists and policy makers alike that such a devastating climate variability could be anticipated and planned for were very appealing, especially if

Assessing Vulnerabilities and Adaptation Approaches: Useful to Usable Tools

the worst climate-related impacts on poor populations worldwide could be avoided (Broad et al. 2002). Although scientists did have some spectacular breakthroughs in ENSO predictability in the past two decades, the reality on the ground soon tempered the initial expectations. On the one hand, some users have been able to benefit from this new information. For example, many Pacific Island nations respond to El Niño outlooks and avoid potential disasters from water shortages; similarly, agricultural producers in Australia and the United States have been better able to cope with swings in their commodity production associated with drought (Hammer et al. 2001; Hartmann et al. 2002; Pagano et al. 2002; Hogan et al. 2010). On the other hand, these uses have been mostly in more developed regions and within well-funded and resourced groups in society. However, even in more developed countries such as the United States, documented SCF use among farmers has been low. For example, coming from similar perspectives, Hu et al. (2006), and PytlikZillig et al. (2010) have argued that overall, farmers’ attitudes toward SFC use have remained poor and actual use has not increased despite improvements in climate predictions. Moreover, US farmers report a low level of understanding concerning climate variability and attach higher importance to short-term weather impacts at local spatial scales. In less developed regions, more often than not, the poorest of the poor have not benefited from the development of SCFs and, in some instances, have actually been negatively affected by it. For example, in Peru fishing companies have used SFC as one input to lay off workers when facing the possibility of a weak season and in NE Brazil, rain fed farmers’ access to selected seed has been conditioned to a favorable forecast by the government (for a more detailed description see Broad et al. 2002; Lemos 2003).

2.13.3.2

Usability of SCFs

In theory, SCFs have many applications including supporting agricultural planning, water management, and disaster preparedness and response. In assessing the value of SCFs for the past 15 years, a rich empirical literature focusing on different sectors in different parts of the developed and developing worlds has emerged both in the social and natural sciences (Zebiak and Cane 1987; Pagano et al. 2002; Johnston et al. 2004; Harrison 2005; Vogel and O’Brien 2006; Lemos and Dilling 2007; Meinke et al. 2008; Gilles and Valdivia 2009; Roncoli et al. 2009; Hu et al. 2006; PytlikZillig et al. 2010). Findings from this literature suggest that successful application of SCF tends to follow a systems approach in which the SCF is contextualized to the decision situation and embedded within an array of other information relevant for risk management (Lemos and Dilling 2007). However, in practice, climate information is often perceived as ‘outside’ of the experience and routine of decision makers. For example, in the United States, PytlikZillig et al. (2010) contend that a significant hurdle to forecast use by farmers is that information about climate forecasts and tools is typically available in situations other than their “day-to-day activities and actual experiences” (p. 1334). In other words, climate information and forecasts are typically presented out of context, and therefore less useful for agricultural adaptation than they could be.

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Research also finds that institutional and organizational factors play a critical role in fostering or constraining the use of SCFs in different sectors. In the Pacific Northwest US, environmental regulation (e.g., Endangered Species Act) has critically constrained the ability of decision makers to apply SCFs because it limited the range of decisions that could be made to guarantee compliance (Callahan et al. 1999). In Florida, Cabrera et al. (2007) found that government farm programs may be limiting (or failing to encourage) farmers’ use of climate information by restricting the types of crops farmers can grow, and by raising crop prices that encourage larger monoculture farms that are less adaptable to climate variability. In addition, programs that seek to stabilize farm income tend to reduce farmers’ perceptions of risk and urgency to adapt to climatic shifts (Cabrera et al. 2007). Another critical finding from this literature is that the value of SCFs is fundamentally dependent on different users’ ability, capacity, and willingness to respond to SCFs. Even if potential users have access to information, they may be powerless or unwilling to respond effectively. This applies both for highly regulated, wealthy institutions and for those individuals and groups living on the margin of survival. Much has been written, for example, on the lack of institutional capacity to respond to improved scientific predictions of stream flow, seasonal weather patterns, and climate (Pulwarty and Redmond 1997; Rayner et al. 2005). In the United States, water managers constrained by the high levels of accountability of their decision environment, prefer to rely on their professional experience rather than on SCFs to guide their management decisions (O’Connor et al. 2005; Rayner et al. 2005). Just having better information available does not necessarily lead to an improved response. For example, in the United States, Hu et al. (2006) found that changes to external factors (such as the accuracy of forecasts) have not resulted in increased use of information and adaptation by farmers, and argue that the focus of research should shift to understanding ‘internal’ reasons for using or not using forecasts – social and psychological factors – from the farmers’ perspective. Similarly, among poor farmers in Africa and Latin America, the general lack of alternatives both in terms of technology and access to financial and human resources act as a critical constraint to their ability to use SCFs. Lack of resources among the most climate-vulnerable rain-fed farmers also curbs their ability to respond to forecasts even if they have access to them (Hammer et al. 2001; Ingram et al. 2002; Lemos et al. 2002). Similarly, extreme adaptive strategies such as changing livestock species are often not considered because of the risk, cost, and time involved in making such significant changes (Coles and Scott 2009). Other factors, such as perception of climate change, membership in networks, belief that information use will lead to economic and environmental benefits, and cultural traits and contexts. Credibility of the information and those producing it also influence willingness to use climate information (Roncoli 2006, Hu et al. 2006; Crane et al. 2010, Marshall et al. 2011). Significant effort has been put into projects to help potential beneficiaries of SCFs realize those benefits, in both wealthy and poor regions. Importantly, successes are usually associated with an infusion of additional resources that potential users of SCFs were lacking in the first place, along

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with the new technology of SCFs (Lemos and Dilling 2007). For the systems/contextualized approach to work, participants usually have to have not only financial resources to come to meetings and access to information through the media, but must also be within the ‘radar’ of organizers of workshops; that is, the selection of potential users is often connected to specific implementation projects rather than widespread dissemination. When resources are available, interaction between model developers and users has been found to be the most critical factor driving usability. For example, indepth studies of climate information use among water managers in the United States show that interaction between producers and users fundamentally influences the willingness of decision makers to apply climate information to their decision-making process (Engle 2010; Kirchhoff 2010). In Arizona, decision makers reported higher interest in using climate information after being given the opportunity to interact with climate model developers in a controlled social experiment (White et al. 2010). In Burkina Faso, a long-term SCF application project reported significant increase in use among poor rain-fed farmers after repeated interaction between project members and farmers (Roncoli et al. 2009). In Nebraska, farmers were more motivated to use forecasts or decision support tools when they felt they contributed to it; that is, when they have a sense of ‘ownership’ of the information rather than passively receiving it (PytlikZillig et al. 2010). Based on this rich empirical research, different studies have advanced a range of recommendations to increase climate information use, especially if the goal is to decrease farmers’ vulnerability. First, they recommend that predictions should be integrated into broader decision contexts that should take into consideration not just the magnitude and dimension of exposure to climate impacts, but also the sensitivities related to livelihoods, institutions, politics, cultures, and economic situation of potential users (Johnston et al. 2004; Klopper et al. 2006; Meinke et al. 2008; Roncoli et al. 2009). Thus, rather than providing forecasts as a ‘fix,’ governments should be supporting farmers to adapt sustainably (Hogan et al. 2010). Second, they suggest that proactive strategies rather than reactive ones should be encouraged. In Florida, Cabrera et al. suggest that seasonal climate forecasts would be most useful when they provide farmers with ‘offensive’ strategies that allow them to capitalize on favorable conditions, and for farmers who are more risk averse. In contrast, crop insurance and/or commodity loans while alleviating risk, make climate information seem less valuable to farmers. Third, communication, availability, perception of accuracy, training and credibility needs to be improved; for example, by targeting groups who have the most influence of farmers’ choice (Hu et al. 2006; Crane et al. 2010; Artikov et al. 2006).

2.13.4 Assessing Vulnerabilities and Adaptive Capacities As described, many studies have argued that understanding what motivates and limits the use of SCFs is not simple and involves accessing underlying social and psychological factors

(Artikov et al. 2006; Hu et al. 2006; Marshall et al. 2011). To that end, a variety of social science methods have been used to try to understand farming systems’ adaptive capacity and perceived vulnerabilities to climate change. In recent years, many vulnerability studies have covered a wide spectrum of risks and hazards while stimulating a rich debate about the many conceptual approaches to vulnerability and the methods to assess and measure it. Several factors contribute to the lack of consistency in the literature, including the multitude of disciplines and communities involved, the different contexts, scales, and areas of analysis, and diverging sources of methodology (Bohle 2001 cited by Kasperson and Kasperson 2001). One issue of particular debate is the dichotomy between top-down/ground-up strategies of vulnerability analysis. Although top-down approaches predominate in the literature, increasingly studies agree that there is a need to combine global/national level data with ethnographic and household survey approaches from the ground. For example, in their evaluation of vulnerability assessment methods for food security, Stephen and Downing (2001, p. 115) point out that socioeconomic indicators only contribute to one part of an entire information system. Hence, currently predominant assessments models look at climate hazard as one among many other stressors that shape farming households’ vulnerability. This chapter reviews the most commonly used research methods to assess farmers’ vulnerability, paying special attention to the strengths and weaknesses of these approaches. An important consideration in conducting research about agricultural vulnerability and adaptive capacity is research design. The selected research design drives decisions about appropriate methods to use to collect information (Prokopy 2011). The choice of research design should be driven by theory and the research questions one is trying to answer. Research designs can include cross-sectional (one point in time) and longitudinal data collection. They can include experimental designs and case studies. One of the considerations in research design is ‘unit of analysis’; that is, who is the subject of the study.

2.13.4.1

Who Is the Subject of the Study?

Despite the fact that many actors make decisions that influence farming systems’ vulnerability and adaptive capacity, most research on this topic focuses on the farmer or the farming household as the unit of analysis. Figure 1 illustrates the many different scales of decision making and factors that influence the choice of farming practices (crop variety, fertilization choices, planting time, access to technology, etc.). Although the landowner and/or operator usually make the majority of farming decisions, they are influenced by both family members and advisors (e.g., extension agents, crop consultants) (Coles and Scott 2009; Hu et al. 2006). In turn, people making and influencing decisions are informed by the larger policy environment and past experiences, including past climatic conditions. The decisions themselves are shaped by farm characteristics such as farm size, soil quality, access to water, and so on. In any study to assess vulnerabilities and adaptive capacity, the starting point is to determine who to study.

Assessing Vulnerabilities and Adaptation Approaches: Useful to Usable Tools

Figure 1

Different scales of decision making and factors that influence choice of farming practices.

2.13.4.2

Acquiring a Sampling Frame

A sampling frame is a complete list of all people meeting the criteria determined by the study. For studies in which generalizability to a complete population is the goal, a random sample or census of this population will usually be assessed. For studies in which in-depth understanding is desired, developing a random sampling frame is less important and results from nonrandom sampling approaches (e.g., snowballing, purposive sampling – in which subjects are chosen because of some desirable characteristic) are likely to be less generalizable to other contexts. Finding a sampling frame for conventional farmers in developed countries can be relatively straightforward. For example, Smit et al. (1996) were able to access a list of all farmers in three townships in southwestern Ontario through municipal office records for their quantitative farmer climatic variation adaptation survey. However, finding a sampling frame for nontraditional farmers may be more difficult. Smaller and more diverse farms are less likely to participate in government programs (Nickerson and Hand 2009). Also, because of barriers to certified organic adoption (Constance and Choi 2010), many farms that are essentially organic are not certified. One solution in the United States is to access publically available county landowner records. By surveying all landowners with more than five acres in a particular geographic location, one captures the majority of the nontraditional farming study population. However, this strategy introduces elements who are not part of the study population (nonagricultural landowners). Although these data can be discarded, this method may result in a substantial waste of money and time.

2.13.4.3

Probability Sampling

For quantitative research to be generalizable to a larger population, a probability sampling method must be used. The

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most basic probability sampling method is simple random sampling, in which the people to be studied are selected from the complete sampling frame only by chance. Generally, this method involves using a computer program to assign each case a number and then generate a random selection of the appropriate sample size. One problem with simple random sampling is that certain strata in a population might not be well represented in the sample. For example, to compare farmers in the United States based on ethnicity, a simple random sample of farmers is not likely to result in an adequate number of minority farmers for comparisons to be made. In this case, disproportionate stratified sampling is needed. In other words, the sampling frame is divided on the basis of a particular stratum, which in this case is ethnicity. Then, each section of the sampling frame is sampled randomly, but not in proportion to the representation of each stratum (i.e., ethnicity) in the population. That is, a higher proportion of the less common strata will be selected than by simple random selection, so that each stratum is well represented in the sample.

2.13.4.4

Nonprobability Sampling

Although quantitative methods require large randomly selected samples, qualitative methods are more appropriate for small samples that are selected purposefully (Patton 2002). Magistro and Roncoli (2001) suggest that “a more localized, detailed understanding of a given geographical setting reflects the complexity of real life decisions and situations” regarding human adaptation to climate (p. 92). There are many strategies for selecting a sample in qualitative research. Key informants, or people who are knowledgeable or important in the community, and who are willing to cooperate with researchers, are often excellent subjects to

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interview. In situations in which the researcher does not know how to reach the appropriate sample or does not have enough cases in the sample, snowball sampling may be advantageous. In snowball sampling, the researcher asks each respondent to recommend other potential respondents (Patton 2002). Snowball sampling can, however, result in sampling bias. For example, Coles and Scott (2009) found that ‘snowballing’ to augment their sample of southeastern Arizona farmers and ranchers may have “resulted in a set of respondents that are more connected and informed than in general” (p. 300). In this case, some variability that would have been present with a more random sample is desired.

(2009) used a household survey to provide “the basis for a quantitative characterization of household perceptions of CCV [Climate Change and Variability], adaptation to CCV and changes in household resource management and livelihood strategies over the past 20 years” in the savanna zone of central Senegal (p. 808). In the context of these rural Senegalese villages, it was necessary for researchers to administer surveys in person. Using a mixed-methods approach, Mertz et al. (2009) also carried out informal interviews with key informants and focus groups to complement the quantitative analysis.

2.13.5.2

2.13.5

Methods

In general, the purpose of a study drives the choice of the appropriate social science method(s). If the purpose of the study is actually to change behavior, then participatory research may be most appropriate (Roncoli 2006), although other methods often have behavior change as a goal. Many studies will use a mixed-methods approach (use of more than one method, such as interviews followed by a survey) to develop a more comprehensive understanding of the issue.

2.13.5.1

Surveys

Surveys generally consist of a questionnaire administered through the mail, over the phone, on the Internet, or in person. The mode(s) used depend on characteristics of the target population, the information (addresses, phone numbers, emails, etc.) contained in the sampling frame, and the resources (money, staff, time, etc.) available to the researcher. At times, multiple modes are used for administering questionnaires to increase the response rate and therefore decrease response bias. As response rates increase, a wider diversity of people and opinions are surveyed, which makes the results more generalizable to the entire population (assuming a probability sampling approach was used). Questionnaires can consist of close-ended questions, in which people can check boxes that apply to them, or open-ended questions, in which they write out responses, or a combination of both. Considerations for developing and choosing questions include the research questions and relevant variables, the survey mode, past research and theory, and the respondents’ ability and willingness to answer. Careful question development and survey formatting is important not only to response rate, but also to validity. (Are the data an accurate reflection of reality?) For a comprehensive guide to developing and administering surveys, see Dillman et al. (2009). Many of the studies reviewed in this chapter used surveys to collect their data in the context of different theoretical and analytical approaches (Lemos et al. 2002; Hu et al. 2006; Mertz et al. 2009). Because Lemos et al. (2002) wanted to understand how different farming households used climate information to decrease their vulnerability to drought, they stratified their random sample by different geoclimatic regions in northeast Brazil. That allowed the research team to better assess how other factors beyond climatic and geographical factors (exposure) affected SCF use. Mertz et al.

Interviews

One commonly adopted method to acquire more detailed and rich data in mixed methods approaches (i.e., quantitative and qualitative) are in-depth interviews. These interviews are often used in exploratory research when the relevant variables are not well defined. They are also indicated in research in which variables are not easily quantifiable and in-depth description of factors explaining outcomes are necessary. For example, if a research goal is to understand how different past experiences influence farmer’s willingness to use climate information, indepth interviewing may be a good method. The same applies for information that may require a certain level of trust between informant and interviewer. In social science research, an interview generally involves a researcher asking an interviewee open-ended questions, a format that allows the respondent to provide content and meaning to the conversation. The conversation is recorded and then transcribed or the interviewer takes notes during or after the interaction. In our examples, different approaches to qualitative interviewing have been used. For example, because note- taking can be unreliable and not always consistent, the research teams collecting data in Crane et al. (2010) chose to record and transcribe their interviews with farmers in Georgia, the United States. However, other researchers have chosen to take detailed field notes during the interviews (Coles and Scott 2009). Still other teams, such as Cabrera et al. (2006) have a group of people participate in each of their interviews and then afterward have team members compare and discuss their notes to check for consistency. Interviews can also be conducted over the phone. Format and length of interviews also vary. In their study of farmers’ skill in using climate forecasts, Crane et al. (2010) used ‘semistructured’ interviews, which was essentially a hybridized conversational/interview guide approach. Rather than follow a structured interview guide, interviewers loosely followed a predetermined interview protocol, which “was designed to elicit information on farmers’ production systems, climatesensitive management decisions, use of weather and climate information systems, and potential application of seasonal climate forecasts” (p. 47). However, the researchers wanted interviewees to at least partly guide the conversation, allowing for a conversational flow and new perspectives and insights. Overall, with each study population, the object is to conduct as many interviews as needed to reach data saturation, or the point at which no new patterns or themes emerge from subsequent interviews. Interview guides are also useful when moderating focus groups, a type of small group interview. Focus groups are

Assessing Vulnerabilities and Adaptation Approaches: Useful to Usable Tools

generally conducted with groups of 6–10 people from similar backgrounds, and several focus groups are usually conducted to ensure breadth in the data. The purpose of conducting focus groups is to collect responses from people in a social context in which respondents hear the reactions of others (Patton 2002). Focus groups are often used in concert with other methods either in the exploratory phase of research or as a way to supplement other data. As previously mentioned, Hu et al. (2006) conducted focus groups with Nebraska farmers in the exploratory stage of their research to identify and define key variables that should be used later in the survey. In their study of farmer climate change adaptation in Sahel, Mertz et al. (2009) used focus group data not only to develop the list of important concepts, but also to triangulate with data from key informant interviews and household surveys. Although focus groups offer efficiency in that numerous individuals are interviewed at one time and can be effective at getting high-quality data, there are a number of limitations. First, shy individuals or those with minority viewpoints may not have the opportunity to express their perspectives. Second, focus groups require a skilled moderator who can manage groups of people and effectively guide the conversation. Third, because confidentiality and anonymity are not assured, respondents might feel uncomfortable expressing embarrassing or controversial opinions. Therefore, focus groups are at times not an appropriate method for controversial topics (Patton 2002).

2.13.5.3

Mental Models Interviews

Mental models interviews are a type of interview that can be used to assess farmers’ perceptions of their vulnerability and adaptive capacity to climate shifts. The rationale for using mental models as an approach is the belief that the experiences people use to inform their decisions regarding complex issues are organized in a mental framework (or mental model) of associated or related concepts (Morgan et al. 2002). Mental models interviews are designed to capture current beliefs (correct and incorrect) without creating beliefs through the wording of questions, with the goal of visually displaying respondents’ mental models. These visualizations, also called concept maps, allow comparisons to be made between groups, including the public and experts (Morgan et al. 2002; Bostrom et al. 1992; Lazo et al. 1999). Mental models interviews, often used to understand risk perceptions, are typically semistructured interviews that begin with broad questions about the general topic of interest, to allow the interviewee to direct the flow of the conversation and express their views without the researcher imposing ideas or terms on the respondent (Morgan et al. 2002). A standardized interview guide should be developed and pretested with representative farmers and experts to ensure consistency across the interviews (Morgan et al. 2002). If the focus of the interviews is to identify gaps in the knowledge of farmers as compared to risk ‘experts,’ a mental model of agricultural vulnerability to climate variability could be constructed first, from interviews with these experts. Next, interviews would be conducted with farmers, and mental frameworks developed based on the expert model of vulnerability and adaptation to climate variability. This particular methodology does presume

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that experts have a more complete mental framework of the risks to agriculture from climate than do farmers. Mental models interviews with farmers and experts about this topic might initiate with a question like, “Tell me what you think about the effects of climate variability on your farm” or even more broadly, “Tell me what you think about the effects of climate variability on U.S. agriculture.” When there are particular areas of interest to the researchers, such as perceived vulnerability or adaptive capacity, specific prompts are included later in the interview guide so they can be addressed after the interviewee has had a chance to describe all of his or her initial thoughts about the broader topic. The mental models diagrams constructed from these interviews highlights the similarities and differences between the vulnerability and adaptation frameworks of farmers and climate experts. Areas of consensus and divergence uncovered by the interviews could be used to inform policies and improve communication between climate vulnerability and adaptation experts and the farmers they are trying to persuade.

2.13.5.4

Participatory Approaches

Participatory approaches include numerous methods that involve research participants collaboratively in the research. Participatory approaches can be especially useful when the project goal is to achieve behavioral change or increase the usefulness of climate knowledge. One participatory approach employed by Cabrera et al. (2008) is to involve participants in the development of models. In an iterative process involving scientists, farmers, consultants, and other stakeholders, the team of researchers created the Dynamic North Florida Dairy Farm model (DyNoFlo), a model used to help farmers make management decisions with the goal of reducing nitrogen runoff while promoting profitability. Their methods included exploratory interviews, site visits, focus groups, a sondeo or rapid rural survey (Hildebrand 1981), and farmer trials of a prototype model. Because stakeholders were involved throughout the process of model development, the resulting DyNoFlo model was usable and useful to farmers. In another good example of how interaction between researchers and producers can increase understanding and accessibility, Ziervogel (2004) used a role-playing game to investigate how smallholder farmers in the African country of Lesotho would likely use seasonal forecasts in farm and household decision making. The role-playing game was conducted with small groups. First, participants collaboratively drew maps of their village. Then, three rounds, each representing 1 year, were commenced with a mock radio address announcing that season’s forecast. During each round, participants were asked to look at the map and describe the farming and household decisions they would make based on the given forecast. Although the resulting data were hypothetical, there are a few advantages to using a role-playing game in lieu of a survey. Namely, the speculative decision making is given more contextual realism in that participants are interacting and communicating with others, responding to the mock forecast, and using a map to think about resources (Ziervogel 2004). It would be futile to study farmer vulnerability and adaptation to climate variability without considering the benefits of the research to the farmers. According to Roncoli (2006), the

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aim of participatory research is to create change in the study community. Although qualitative and participatory studies do not lend themselves to generalizability and replication, they often result in participant learning and empowerment.

2.13.6 Case Study: Understanding Vulnerability and Adaptive Capacity to Enhance Usability of Climate Information in the North Central Region of the United States The goal of the Useful to Usable (U2U) project is to improve the resilience and profitability of grain farms in the north central region of the United States amid variable climate through the development and dissemination of decision support tools, resource materials, and training. The U2U project consists of a diverse group of state climatologists, crop modelers, agronomists, economists, and social scientists who are working collaboratively to develop decision support tools that are usable by farmers and their advisors and that meet their needs. Building on the findings of the studies reviewed in this chapter, the U2U project is collecting information from potential users that will help to develop tools that will actually be used to improve adaptive capacity and reduce vulnerabilities. Because the study area is too large to conduct in-depth participatory research, team members are initially surveying farmers throughout most of the region. However, the working hypothesis is that farmers may not ultimately be the ones using climate information in decision making and so there is a strong emphasis on finding out the needs of advisors – including extension agents, crop consultants, bankers, employees of the US Department of Agriculture, agricultural retailers, and others. Surveys of advisors are being conducted in four key states in the region. Both the producer and advisor surveys seek to find out when in the calendar year key farming decisions are made, whether people already use climate information in their planning, and what are current levels of adaptive capacity. To further understand how decisions get made and who influences producers, a stakeholder network analysis will also be conducted in these four states. After gathering and analyzing the survey data, climate model developers and social scientists will work together to develop decision support tools that meet the expressed needs and interests of end users. High resolution, gridded crop models will be developed for the region and the impact of climate and management decisions on crop yield and farm profitability will be determined for four representative farm types. Based on the calendar information that emerges from the surveys, the goal of the modeling will be to provide information to people at critical times of the year to help with both short- and long-term farm planning decisions. Outreach and training materials will be targeted at appropriate audiences (producers, advisors, or both) based on the outcomes of the network analysis. Recognizing that a participatory approach is ideal for developing tools that can actually change behaviors and increase adaptive capacity, the team will work interactively with producers and advisors to evaluate and adapt these tools and outreach materials in an iterative process of co-production in four states. In-depth interviews and focus groups will be used throughout this iterative process. Only after the tools are clearly

meeting needs in the four-state region will dissemination be extended across the entire 12-state region. This ambitious project is funded primarily by the USDA’s National Institute of Food and Agriculture started in early 2011. More information can be found at www.agclimate4u.org.

2.13.7

Conclusion

Gaining a better understanding of farmers’ vulnerabilities and adaptive capacities is essential for developing climate information that they can and will use to adapt. The literature review presented in this chapter illustrated that it is not always straightforward for farmers to use climate information. Only by better understanding gaps in adaptive capacity can these gaps be addressed. To that end, key social science methods such as the ones presented in this chapter can be used to identify and understand current levels of adaptive capacity. With this knowledge, the promise of collaboration with climate model developers to prepare climate forecasts and information in ways that farmers can use to improve their adaptive capacity can be fulfilled.

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