1. Introduction
Tremendous effort and expense have been put into improving the accuracy, readability, and applicability of weather and climate predictions, and most experts would agree that these weather and climate forecasts can benefit agricultural production if used effectively. However, despite correspondingly tremendous efforts to inform farmers about the availability and potential usefulness of such products (HPRCC 1994), farmers’ attitudes toward and use of weather and climate forecasts have changed little over the decades (Rayner et al. 2005; Vogel and O’Brien 2006; Millner 2008). This lack of change points to a need for new and more effective methods for transferring improved weather and climate products into applications to benefit farming decisions.
Second, when information dissemination is not accompanied by experience and practice, not only is learning undermined, so is motivation. For example, according to the theory of planned behavior and theory of reasoned action (Ajzen 1991, 2001; see also Bandura 1994, 1997, and the discussion in Hu et al. 2006), personal experiences can affect farmers’ beliefs about the utility of the predictions for certain outcomes (i.e., their outcome expectancies) and change their attitudes and motivation to use the predictions in decisions. Third, constructivist (e.g., Piaget 1932; Vygotsky 1978) and other approaches suggest that motivation to use knowledge and information is most enhanced when people “own” and actively contribute to that knowledge and information, rather than having it merely presented to them. McCown (2005), for example, suggests that farmers must, through personal experience in relevant contexts, come to their own understanding of weather predictions because farmers are reluctant to use information in their decision making unless that information is generated from their own experience.Early cognitive theories typically treated … learning as the acquisition of knowledge and skills thought to be useful in a wide variety of settings … . Situative theorists challenge this assumption of a cognitive core independent of context and intention … . They posit, instead, that the physical and social contexts in which an activity takes place are an integral part of the activity, and that the activity is an integral part of the learning that takes place within it.
Consideration of learning and motivation theories suggests that new and more effective transition models would include contextualized information, personal experience using that information, and collaborative interaction that contributes to that information. In this paper we briefly describe the design and preliminary testing of a model including these features through the use of a test-of-concept prototype. The prototype implements these features and also incorporates scaffolding and feedback, two additional important features of educational design.
2. A prototype of a new model and method
The structure and flow of our prototype are illustrated in Fig. 1. For ease of access and to enhance its potential scalability, the demonstration prototype is Web based (online at http://driftwood.unl.edu/farmsmart).
a. Resources: Providing access to contextualized information
A major component of the prototype is the resources section, where users have access to a list of products, additional informational resources about the products, and links to any of the products that have a Web presence (“Resources” in Fig. 1). These informational resources also include how to navigate each product’s Web site to locate specific predictions and historical archives, how to read and interpret the product, and how to use it or limitations of its use in various types of farming decisions. Albeit we have already argued that “information provision” is not sufficient to ensure its use, a key feature of the resources in this model is that they are linked to case scenarios in which the information might be useful, thereby providing contextualized information. Provision of such resources is important because farmers are often faced with numerous, conflicting sources of climate and weather information and are left to wonder which sources they should trust in making specific decisions (Hu et al. 2006) or how to find and interpret information useful for specific situations. Provision of trustworthy and clear information can ease access and improve understanding of weather products and has been regarded as having an important influence on farmers’ personal attitudes about particular forecasts as well as being important for enhancing their general forecast use (Artikov et al. 2006).
b. Case scenarios: Opportunities for building personal experience
Within the Web-based prototype, farmers can visit an area called ThinkAboutIt (TAI), which presents case scenarios involving weather product information and decision-making opportunities. TAI is a Web-based technology (composed of the components inside the dashed-line box in Fig. 1) developed at the University of Nebraska at Lincoln to teach critical thinking. We chose to use case scenarios to provide farmers opportunities to build personal experience and practice applying weather products to specific situations based on a large body of literature on case methods. Case methods involve authentic, real decisions, accompanied by opportunity for reflection and feedback, and have been employed as an efficient supplement to apprenticeship training in numerous professions (e.g., Bruning et al. 2008; Greenwood 2000). In addition to involving realistic problem-solving situations (i.e., case scenarios), low-stakes learning opportunities, and careful individual or shared reflection among peers (e.g., Merseth 1991; McDade 1995; Kreber 2001), use of case methods has been found to relate to enhanced critical thinking, transfer of learning, and improved motivation (Bruning et al. 2008; Enos et al. 2003; McDade 1995; PytlikZillig et al. 2009, manuscript submitted to J. Educ. Psychol.; Stolovitch and Yapi 1997).
In our prototype scenario, we describe a practical, real-world farming irrigation decision. The scenario was written and revised based on critiques by farmers, extension agents, and crop consultants both from within and outside of the location featured in the case study (in our example, Franklin County, Nebraska). The final version of the prototype scenario describes the location, planting date, irrigation costs, and climate conditions since planting and asks farmers to make an irrigation decision using information from relevant weather products.
c. Discussion: A forum for collaborative interaction
Within the TAI tool, after farmers read and respond to the case scenario, they are given the opportunity to explore weather products in more detail (Fig. 1; TAI component 2) and are invited to answer at least four questions for each product. One multiple-choice question concerns the interpretation of the prediction or product, for example, “According to the 5-day Precipitation Forecast map, what is the predicted rainfall in your area over the next five days?” Another asks farmers to rate the usefulness of the information in the prediction to the decision-making case scenario, for example: “How useful is this information for making the case scenario decision?” Most important is that these two closed-ended questions are each paired with open-ended questions that allow farmers to contribute their opinions and expertise and to launch discussions with other users (Fig. 1; TAI component 3). To be specific, after the forecast interpretation question, users are asked to think critically and offer suggestions regarding how the presentation of the forecast can be improved for their use. In a similar way, after rating the usefulness of a weather product for making the case scenario decision, farmers are asked to justify and explain their rating relative to the specific case scenario context.
As discussed in section 2d, farmers do receive feedback on their answers to the questions. However, at this point it is important to note that a key feature of the design of the questions was that they solicited both farmer input and ownership of the knowledge they were creating and they engaged farmers in discussion with their peers. Farmer input and explanations are essential for enhancing their ownership of the new knowledge they are developing. In addition, explanation also can facilitate the learning of new skills, deeper learning, and better integration of new knowledge with prior knowledge (Ainsworth and Loizou 2003; Chi et al. 1989, 1994; Renkl 2002; Roscoe and Chi 2008; Roy and Chi 2005). Meanwhile, peer discussion can enhance many important outcomes, including understanding, critical thinking, and construction of complex knowledge (Gunawardena et al. 1997; Newman et al. 1997; Thomas 2002). Peer discussion also can create a social environment that can affect attitudes toward the use of the weather products. When respected peers accept and value the use of certain weather products for certain farming decisions, they also impact others’ attitudes toward the products (Ajzen 1985).
d. Coaching and others’ opinions: Scaffolding and feedback
Scaffolding and feedback are other important features of educational design incorporated into the model prototype. Scaffolding refers to supporting learners as they form new knowledge by providing guidance, and then gradually removing that support as learners gain experience with the tasks (Vygotsky 1978; Brown et al. 1989; Pea 2004). In our model, scaffolding is primarily provided in “coaching” within TAI. While users explore the weather products within the context of the case scenario administered by the TAI tool, a digital coach is available to provide context-specific hints and guidance whenever the user wants or needs it. The coach does not give the answers to the questions posed to the farmers but does bring up information about a specified forecast as well as a link back to the specific part of the resources that provides additional information. Some of the coaching information includes how to read and interpret the products, how to find the right information for specific locations and times of interest, and how the products might be applied in various farming operations.
In our prototype, feedback within TAI comes from both peers and experts. Upon answering a pair of questions (one closed and one open ended), users not only see their peers’ open-ended comments for discussion, but also are presented with a statistical and graphical display (a bar chart) of peer responses to the closed-ended question. This feedback is useful for affirming matching responses in the case in which one finds that most of his/her peers agree or disagree with his/her answer, for creating cognitive dissonance or disequilibrium that the producers are then motivated to resolve (de Lisi and Golbeck 1999). Users also are provided with expert feedback after each pair of questions. The “expert” provides a “best choice” answer to each question that is actually a composite answer constructed based on the judgments of experts, crop consultants, extension agents, and researchers. Users can bring up the expert opinion, which includes both what experts had judged as the best answer to the multiple-choice question and also the experts’ rationale (open-ended explanation) for the answer, so that users can compare this feedback with their own answers and explanations. From a learning perspective, such feedback has been shown to be important for helping learners to build competence and increase their self-efficacy (e.g., McCarthy et al. 1995; Schunk and Rice 1993). In addition, by explaining the rationale for the answer, the expert provides learners with a “cognitive apprenticeship” that can help users to understand better how to think critically about issues relevant to effectively using weather information in decision making (Pedersen and Liu 2002).
3. Encouraging evidence from focus group surveys and discussion
To assess our preliminary test of concept, we presented the prototype to focus groups composed of farmers from south-central Nebraska. The two focus groups had 6 and 9 participants each, and 14 of them were male. Their ages ranged from 27 to 59 (average 44.5). They each reported 10–40 years of farming experience from 1000 to over 2000 acres of crop lands, most of which were irrigated.1 Thus, irrigation decisions such as whether, when, and how much to irrigate during the growing season were important to these participants. Most of them also indicated that they had computers at home; however, there was great variability in familiarity with and extent of computer use.
Members of each focus group completed a presurvey and then spent approximately 2 h exploring the system. They first were introduced to the prototype system by the researchers and then explored the system on their own. After exploring the system, users completed an anonymous postsurvey. Some questions in the postsurvey were identical to those in the presurvey to assess changes in farmers’ attitudes toward weather products (both generally and for specific products). Other questions asked for farmers to report the extent to which they had used weather products in the past (in general, as well as specific products used in the scenario) and the extent to which they intended to use them in the future. Following the postsurvey, researchers led the participants in a large group discussion, asking them to share their reactions to and impressions of the system and each of its components. Participants were specifically asked what they liked, disliked, felt was effective, or felt needed improvement. Results of these surveys and interactions are summarized below.
a. System evaluation
Evaluations of specific model features, including the decision-making scenarios, coaching, expert feedback, peer-discussion area, and resources, were generally positive (see Table 1) but also showed room for improvement. On a 0–6 scale, almost all mean ratings of system features are above the scale midpoint (3.0) except use of the resources (2.77). Thus, participants reported using and exploring the novel features of coaching, expert feedback, and peer discussion more than the informational resources. Farmers valued the expert/consultant materials highest and rated them as the most helpful and informative components of the model. Because the expert/consultant materials give specific details about how individual climate products can or should be used in making a specific decision, these high ratings may indicate farmers’ strong interest in and need for expert information concerning the application of weather products in specific situations. This interpretation was supported by the discussion among the focus groups after they had used the system, during which a number of farmers indicated they would like to have the discussion area include interaction with experts concerning questions related to their specific farms and fields.
b. Change in expectancies
To assess weather product–relevant expectancies before and after use of the model, participants were asked, “In your opinion, how likely is it that each of the following [general] weather forecasts and information are any good at producing the following outcomes?” One question asked participants to rate the likelihood that “precipitation forecasts are good for helping you to maximize profit.” Table 2 shows the average pre- and postanswers of the participants for the surveyed forecast–outcome combinations. The means in Table 2 show an interesting pattern of all positive changes (except for one item) in farmer outcome expectancies. The consistency of the increases over such a short time span and only one exposure to a prototype of the model argues for a potentially positive impact of this method of education/training on farmers’ beliefs, and therefore their attitudes about forecasts. Nonetheless, the effect sizes of the increases were small and not statistically significant when tested in our small sample. The very modest effect sizes might indicate the difficulty in changing farmer attitudes toward general categories of products, especially with only one exposure to a system. For example, a number of cognitive biases might be at work against such attitude changes, including confirmation biases—tendencies to give greater weight to information that is consistent with one’s beliefs and to discount evidence that is inconsistent (see Slovic 1987; Nickerson 1998). As an alternative, system users may not have felt the scenario decision was as significant as those they face and “own” in real life. Farmer comments after using the system did again suggest that farmers were more interested in specific information—including information from specific products and specific to their own farms and fields.
c. Willingness to use weather products and predictions
Prior to interacting with the training system, participants saw the name and a picture of each specific weather and climate product/prediction that would be available for use in the system and were asked to rate the extent to which they have allowed it to influence their decisions in the past. After going through the training, participants were asked the extent to which they intended to use those weather products in the future. Results in Table 3 show that the average ratings for postsurvey questions were significantly and substantially higher than that for the presurvey questions. These relatively large effects contrast with the small effects reported for general forecasts, and are consistent with the farmers’ desire for and focus on specific products and information.
To assess the extent to which participants felt they had changed their willingness to use various forecasts in a more global sense, we asked them, “Compared to the last time you did this survey, please rate the extent to which each of the following general types of weather forecasts and information will influence your irrigation decisions in the future.” The ratings shown in Table 4 indicate that, on average, the participants would use the products/predictions slightly more. All answers fell into the 0–2 range on the scale from −3 to +3 and are statistically significant in single-tailed Student’s t test comparing the mean with 0. However, the averages for each item were less than +1 on the scale from −3 to +3, indicating the perceived positive changes were small. Results in Table 4 indicate that the largest reported changes are in intention to use temperature and wind predictions. During focus-group discussions, farmer comments suggested that it was obvious to use precipitation products but use of temperature and wind products within the scenario increased the salience of the relevance of these products for irrigation decisions, resulting in their increased willingness to use them in going from pre- to postexposure to the system.
4. Concluding remarks
To successfully transfer costly weather and climate products into meaningful information that farmers can use in their decisions, farmers must understand the products and have the skills and motivation to extract the relevant pieces of information and apply them to specific decision contexts. Based on consideration of educational and motivational theories, we contend that provision of contextualized information and opportunities for farmers to gain experience using the products and to contribute their own expertise to a situated knowledge base can enhance farmer knowledge and motivation to use the products in their decisions. In its emphasis on farmer input and contributions, our approach may be compared to other participatory approaches involving stakeholders in the creation of decision-making tools (e.g., Cabrera et al. 2008; Jagtap et al. 2002). However, our model places greater emphasis on principles of learning through practice and motivation. In addition, our model proposes the use of the Internet for all aspects of the model.
Preliminary test results of our model are encouraging. We cannot infer that these results, which are based on a small sample and the use of a single case scenario, represent generalizable conclusions applicable to other populations. However, the results are suggestive of the model’s promise. The most promising aspects of the model included feedback from experts and aspects most relevant to farmers’ own specific situations. Farmers’ desire for expert over peer feedback suggests that they may be looking for feedback that they find trustworthy, and that the role of farmer trust in the transition of weather information into actual use should be further explored in future research. In addition, although climate and weather predictions are far from being detailed enough for specific locations where climate information may be desired, results from this study suggest that the effectiveness of the model might be most powerful if the case scenarios are designed to be maximally relevant to farmers’ specific and unique situations.
Results suggested that use of this model also may be most likely to immediately affect perceptions of the specific forecasts featured in the prototype, but not of general categories of forecasts. Given that the effects on general outcome expectancies were small, future work should examine the power of multiple exposures to these methods, especially in larger and more diverse samples and additional contexts. Further development and tests of this model and the implementation of refined and tested modules in various public domains such as regional climate centers have the potential to improve effective use of climate predictions in agricultural and other production decisions. Given the social importance of these decisions, the outcomes of which impact food availability, economics, the environment, and other areas, further development of such transition models is of immediate and substantial importance.
Acknowledgments
We thank Derrel Martin, Steve Melvin, and several others for their critical evaluations of the case scenario, resources, coaching, and expert materials built in this transition model. Comments and suggestions from three anonymous reviewers and the editor are appreciated and substantially improved an earlier draft of this paper. This research was supported by NOAA NCTP/TRACS Grant NA05OAR4311139 to the University of Nebraska at Lincoln.
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A schematic of the prototype system.
Citation: Journal of Applied Meteorology and Climatology 49, 6; 10.1175/2010JAMC2270.1
Evaluations of transition-module components (ratings were made using a scale from 0 = “not at all” to 6 = “very much”). Here N is the number of participants; Min and Max are minimum and maximum rating to the question, respectively; M and SD are mean and standard deviation of the rating data, respectively; and Cov is the standard deviation divided by the mean.
Pre- and postanswers to the question, How likely it is that weather products will produce various outcomes? (0 = extremely unlikely, 3 = moderately likely, 6 = extremely likely). Here, η2 is a measure of effect size similar to R2 and Pwr is the observed power of the analyses, i.e., the probability of finding a significant effect given the size of the effect and the number of participants; p is the two-tailed significance level.
Rated past (pre) and intended future (post) influence of specific weather products and predictions on irrigation decisions. (Note: N = 15 in the “pre” and “post” surveys). For each data product and prediction, participants were asked to report how much they had used (or intended to use in the future) the product/prediction in irrigation decisions. Answer options ranged from 0 = never to 1 = seldom to 6 = “a great deal.”
Ratings for the question, “Compared to before using FarmSmart and TAI, how much will different types of weather data products and predictions influence your production decisions in the future?” (rated on a scale ranging from −3 = “a great deal less,” to 0 = the same as before, to +3 = “a great deal more than before”).
For this study, we sought farmers who irrigated and would thus be faced with irrigation decisions. Relative to the farmers in the larger-scale study of Hu et al. (2006), which also examined farmers in southeastern Nebraska, members of the sample presented in this paper were younger and farmed and irrigated more acres. The participants in the 2006 study were aged 19–92 (average age 52), averaged 30 years of farming experience, and farmed an average of 781 acres, with less than one-half of those irrigated.
