July 29-350-Pranay Ranjan
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July 29-350-Pranay Ranjan
- 1. Explaining the use of online agricultural decision support tools with weather or climate information in the Midwestern United States 1 Pranay Ranjan, Ph.D. Postdoctoral Research Associate Purdue University Email: ranjanp@purdue.edu Junyu Lu, Ph.D. Postdoctoral Research Associate Purdue University Email: lu727@purdue.edu
- 2. Background Increasingly variable weather patterns due to climate change significantly impact agricultural productivity Agricultural decision support tools (DSTs) can reduce risks and uncertainty A variety of online agricultural DSTs have been developed from different sources Adoption rate of DSTs is low 2
- 3. Decision support tools from different sources Climate FieldView™ MyDTNTM Pioneer Field360 ISU Corn Nitrogen Calculator UNL CornSoyWater Subscription or purchased weather/climate tools Free and publicly available weather/climate information provided by a company Free weather/climate services provided by a university or government agency including Extension 3
- 4. The Theory of Planned Behavior Intention Attitude towards the behavior Subjective norms Perceived behavior control Descriptive social norms Injunctive social norms Behavior (Ajzen, 1988, 1991; Fishbein & Ajzen, 2010) Intervening factors 4
- 5. Adoption of DSTs Attitude towards DSTs Concerns about weather or climate affecting farm management Climate change beliefs Perceived risks of climate variability Adaptation attitude towards climate change Social-demographic characteristics (e.g., age, gender, year of farming/working, education, farm size) General propensity to adopt a new technology Descriptive social norm and reference group Injunctive social norm and reference group Perceived behavioral control to deal with weather-related risks and threats The Theory of Planned Behavior Perception and belief Personal Characteristics 5
- 6. Research Questions What percentage of farmers and advisors use online DSTs? Do farmers and advisors differ significantly with respect to their use of online DSTs? What factors significantly influence the use of online DSTs? 6
- 7. Methods Farmer’s Survey (Mailed Questionnaire) Distribution time: June 14, 2016 to October 7, 2016 Targets: corn producers in 12 states Survey sent out: 6,840; Response rate: 39.1% Advisor’s Survey (Online Questionnaire) Distribution time: November 7, 2016 to December 14, 2016 Targets: all relevant agricultural advisors in 12 states, including crop advisors, agricultural extension employees, agronomist, conservation employee, etc. Survey sent out: 10,760; Response rate: 29.0% Maps credit: Prokopy et al., 2017 7
- 8. RESULTS 8
- 9. Percentage of respondents using DSTs 47% 23% 31% 17% 80% 67% 49% 19% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% Any of above three providers Free weather/climate services provided by a university or government agency including Extension (e.g., ISU corn nitrogen rate calculator, UMissouri Nitrogen Watch, UNL CornSoyWater, etc.) Free and publicly available weather/climate information provided by a company (e.g., FieldViewTM Prime, Pioneer360 tools, etc.) Subscription or purchased weather/climate tools (e.g., MyDTNTM, Fieldview Plus or Pro, etc.) Advisors Farmers Hypothesis Testing p-value 0.114 <0.001 <0.001 <0.001 Dependent Variable 9
- 10. Theory of Planned Behavior & DST usage 3.07 3.04 3.4 3.56 3.14 3.9 3.59 3.87 2.5 3 3.5 4 I have the knowledge and technical skill to deal with any weather-related threats to the viability of my farm operation [to my clients’ farms]. I want to meet the expectations of others [my clients’expectations] when it comes to using decision support tools with weather or climate information. Other farmers [advisors] like me are using decision support tools with weather or climate information to help with farm decisions [in their advising work]. When farmers [advisors] use tools with weather or climate information to aid decisions, it can result in better farm outcomes (related to yield, profit, and/or the environment). Advisors Farmers Attitude towards the use of DSTs Descriptive social norm and reference group Injunctive social norm and reference group Perceived behavioral control MWW test p-value < 0.001 < 0.001 < 0.001 0.011 Note: the scale of those items is 1: Strongly disagree, 2: Disagree, 3: Neither agree nor disagree, 4: Agree, and 5: Strongly agree; the statements listed in bracket are for advisors. 10
- 11. Concerns about weather or climate affecting farm management Perceived risks of climate variability Note: the scale of those items is 1: Strongly disagree, 2: disagree, 3: Neither agree nor disagree, 4: Agree, and 5: Strongly agree 2.96 3.51 3.51 3.13 3.66 3.76 2.5 3 3.5 4 Changes in weather patterns are hurting my farm operation [the operations of farmers I advise]. In the past 5 years, I have noticed more variable/unusual weather across the Corn Belt. In the past 5 years, I have noticed more variable/unusual weather on my farm [in my area]. Advisors Farmers < 0.001 < 0.001 < 0.001 MWW test p-value 2.84 3.04 2.5 3 3.5 4 How concerned are you about weather or climate affecting farm management in your area? MWW test p-value < 0.001 Note: the scale of this item is 1: Not at all concerned, 2: Slightly concerned, 3: Moderately concerned, and 4: Very concerned 11
- 12. 3.24 3.28 3.22 3.41 3.47 3.49 3.49 3.65 3 3.2 3.4 3.6 3.8 4 There is enough evidence that climate is changing. Climate change will cause more extreme weather events in my area. Human activities are contributing to climate change. Climate change is happening. < 0.001 < 0.001 < 0.001 MWW test p-value < 0.001 Climate change beliefs 3.39 3.43 3.71 3.67 3 3.2 3.4 3.6 3.8 4 Changing practices to cope with increasing climate variability is important for the long- term success of my farm [the farmers I advise]. It is important for farmers to adapt to climate change to ensure the long-term success of U.S. agriculture. Advisors Farmers Note: the scale of those items is 1: Strongly disagree, 2: disagree, 3: Neither agree nor disagree, 4: Agree, and 5: Strongly agree Adaptation attitude towards climate change < 0.001 < 0.001 12
- 13. General propensity to adopt a new technology < 0.001 MWW test p-value 2.91 3.53 2 2.5 3 3.5 4 On a continuum of "early adopter" to "late adopter", where would you place yourself? Advisors Farmers 13
- 14. Independent Variables Farmers Advisors 𝛃 Significance 𝛃 Significance Attitude towards the use of DSTs - - Descriptive social norm and reference group + *** + *** Injunctive social norm and reference group - + *** Perceived behavioral control to deal with weather-related risks and threats + + ** Concerns about weather or climate affecting farm management + *** + ** Perceived risks of climate variability - + Climate change beliefs - - Adaptation attitude towards climate change + ** + Farmers: farm size in 1000 acres Advisor: average size of clients' farms in 1000 acres + *** + ** Farmers: year of farming Advisors: year of working as an agricultural advisor + ` - Age - ** + * Education + * + ` Gender (Female as baseline) + ` + *** General propensity to adopt a new technology + *** + ** Significance level: ` indicate significant at 0.1 (marginal evidence), * indicate significant at 0.05, ** indicate significant at 0.01, *** indicate significant at 0.001 Logistic regression model results 14
- 15. Conclusions Advisors report significantly higher adoption rate than farmers in using DSTs from free sources. Farmers and advisors differ in number of ways. Use of DSTs can be partly attributed to the Theory of Planned Behavior. Factors predicting the use of DSTs are different between farmers and advisors. 15
- 16. Implications Partnering or collaborating with local community to share the success story in using DSTs. Building up both farmers’ and advisors’ knowledge and technical skills in using DSTs. Leveraging farmer networks of “innovators” and “early adopters”. 16
- 17. www.AgClimate4U.org Authors: Junyu Lu, Purdue University Ajay S. Singh, California State University, Sacramento Vikram Koundinya, University of California, Davis Pranay Ranjan, Purdue University Tonya Haigh, University of Nebraska - Lincoln Jackie M. Getson, Purdue University Jenna Klink, University of Wisconsin - Madison Linda S. Prokopy, Purdue University Special thanks: Members in Natural Resources Social Science Lab, Purdue University Acknowledgement: 17
Notas del editor
- Agricultural decision support tools (DSTs) that incorporate weather or climate information can reduce risks and uncertainty in the agricultural planning and management decisions, for example, what crops and cultivars to plant, when to plant crops, when to apply fertilizers and pesticides, what is the forecast of weather, etc. A variety of online agricultural DSTs integrating soil, weather, and crop information have been developed from different sources
- To understand what factors will influence the use of DSTs, this study was built on the Theory of Planned Behavior The Theory of Planned Behavior suggests that an individual’s intentions to perform a given behavior depend upon three types of evaluations they make: attitude towards performing the behavior, subjective norms, and perceived behavioral control. An attitude towards a behavior is based on an individual’s positive or negative evaluation of the likely outcome of a particular behavior. Subjective norms refers to individuals’ perception and belief of how other important individuals think about them if they perform the behavior. There are two important types of social norms: descriptive social norms and injunctive social norms. A descriptive social norm is defined as an individual’s perception of how others typically behave within a given settings An injunctive social norm is defined as an individual’s perceptions about what should or ought to be done which is generally agreed or shared by the members of a social group. Perceived behavioral control refers to an individual’s perception and belief of whether they are capable of performing a given behavior and whether they have controls over its performance We hypothesized that the use of DSTs might be partly attributed to the constructs discussed in the Theory of Planned Behavior.
- In addition to the Theory of Planned Behavior, according to previous literature, we also hypothesized that the use of DSTs also could be partly attributed to stakeholders’ psychological factors, for example, (see slides). We hypothesized that higher level of concerns about weather or climate affecting farm management, stronger perceived risks of climate variability, stronger climate change beliefs, and more positive attitudes towards climate change would all have a positive effect on the adoption of DSTs.
- In order to understand those research questions, we distributed two surveys. 6,840 addresses were selected to receive a questionnaire. email addresses were identified to receive a questionnaire
- Only 17% of farmers and 19% of agricultural advisors have used the Subscription or purchased weather/climate tools. There is no statistically significant difference between farmers and advisors in using this type of DSTs. The agricultural advisors show much higher adoption rate than the farmers in using free and publicly available weather/climate information, especially the service provided by a university or government agencies. We combined those three sources and generate a new variable representing any of above three providers
- Positive attitude towards the use of DSTs.
- Positive adaptation attitude towards climate change.
- Coefficient – indicates direction of the finding. PBC – Advisors who are confident that they can deal with weather related risks are more likely to adopt one or more DSTs. Age – Younger farmers more likely to adopt; older advisors more likely to adopt, which indicates more experience. Gender – Marginal evidence that male farmers are more likely to use DSTs.
- Our findings highlight the needs to promote or advertise free DSTs by a university or government agency including Extension for more use among farmers to increase the adoption rate.
- To promote the use of DSTs, partnering or collaborating with farmers and advisors in local community to communicate or share the success story in using DSTs could enhance the trustworthiness of DSTs and hence increase the adoption rate. The result indicates that both farmers and advisors are not confident that they have the knowledge and technical skill to deal with weather-related threats. This suggests the needs for building up both farmers’ and advisors’ knowledge and technical skills in using DSTs to reduce weather-related uncertainties and risks. Since the general propensity to adopt a new technology has a positive effect on the probability of using DSTs, DST educators could consider leveraging farmer networks of “innovators” and “early adopters” in the watershed, to bolster the salience of descriptive social norms and subsequently promote usage and uptake of DSTs, for example, those farmers who adopted agricultural best management practices (BMPs).