Examining Field Crop Farmers’ Climate Change Perceptions, Adaptation Strategies, and Resilience in Florida: A spatial econometric approach

Objective 1. Evaluate the effects of Florida field-crop farmers' perceptions of climate variability and attitudes on adaptation and climate resilience strategies.

Background

        Climate variability directly threatens Florida's agricultural production and sustained growth. Climate-adaptive agriculture practices are often considered productive ways to cope with and attenuate the adverse effects of weather shocks and climate-exacerbated hazards (Amadu et al., 2020; Carter et al., 2018; Howden et al., 2007; IPCC, 2001, 2014; 2022; Khanal et al., 2018; Mendelsohn, 2000; Tambet & Stopnitzky, 2021; Wheeler & von Braun, 2013). However, in many countries, farmers and growers—who served as an essential part of adapting to climate change—may not be incentivized to adapt to climate change even if they have perceived the climate change risks and the benefits of adopting these climate-adaptive practices (Eitzinger et al., 2018; Mitter et al., 2019; Talanow et al., 2021; Zamasiya et al., 2017; Martinich & Crimmins, 2019). Numerous indications denoted that a set of limits and barriers impeded farmers' timely and informed endeavors of climate change adaptation (Barnes et al., 2020; Gifford, 2011; Gifford & Chen, 2017; van Valkengoed & Steg, 2019; Zhu et al., 2021).

        Apart from the physical and technological factors, the psychological and socio-economic barriers or drivers of farmers' climate change adaptation have been increasingly discussed and examined in recent years. Gifford (2011) and Barnes et al. (2020) systematically discussed these psychological and social determinants. Gifford summarized seven psychological barriers to climate change adaptation: limited cognition about climate change, ideological worldviews, sunk costs and behavior momentum, and perceived risk of change. The latter analyzed the social determinants of climate change adaptation, i.e., how different domains of adaptive capacity—assets, flexibility, organization, learning, socio-cognitive constructs, and agency—drive varied adaptive and transformative responses to climate change. Therefore, the foundation for designing reasonable and practical intervention policies entails a comprehensive understanding of farmers' psychological procedures for adapting to climate change.

Methods

        UF Institutional Review Board (IRB) (IRB#202102640) was obtained in December 2021. We collaborated with the UF/IFAS Center for Public Issues Education (PIE) to design our questionnaire and collect the responses from field crop farmers in the Southern United States. Two researchers at the PIE Center worked with us to conduct the survey, including selecting sample areas and determining sample size, sampling procedures, and survey methods. The data collection occurred over six months, namely three stages. In the first stage (July to September 2022), we contacted several Florida agricultural organizations and invited them to distribute the questionnaire to their organization members. These organizations were the Florida Farm Bureau, National Women in Agriculture Association-Florida Chapter, Florida Black Farmers and Agriculturalists Association, Florida Organic Farmers, and Farmworker Association of Florida. Then, in the second stage (October 2022), we extended our survey regions to other states in the Southeastern United States, such as Georgia, South Carolina, North Carolina, Alabama, and Mississippi. Likewise, we shared the questionnaire with local organizations to inquire them to distribute the survey. In the last stage (December 2022), we further extended our geographical reach for data collection and included other states in the Southern United States, such as Kentucky, Missouri, and Oklahoma.

        The initial instrument was drafted in March 2022 and edited in several rounds. First, we sent the raw version of the instrument to each member of the Advisory Council (AC) for cognitive testing. Based on their comments and suggestions, we then refined our instruments. Next, we discussed the instruments with the dissertation committee, composed of climate change and sustainable agriculture experts. Based on their recommendations and edits, we developed the final version of the instrument. Ultimately, the instrument included questions on risk perceptions of climate change, theory of planned behavior and protection motivation theory constructs (e.g., attitude toward adaptation strategies, self-efficacy, subjective norms), climate resilience, networks, a sequence of climate-adaptive agricultural strategy (such as using stress-tolerant crop varieties, conservation tillage, and changing planting dates), demographics, and farm characteristics. Regarding compensation, every participant who completed the questionnaire and provided the required contact information (i.e., name, email address, and phone number) received a $25 prepaid Walmart e-gift card.

        We utilized Partial Least Square Structural Modelling (PLS-SEM) to assess farmers' climate change adaptation decision-making in agricultural production. PLS-SEM is a causal-predictive approach that estimates partial model structures by combining principal components analysis with ordinary least square regressions (Hair et al., 2019). PLS-SEM is variance-based, accounting for the total variance and estimating the parameters using the total variance. Compared with the covariance-based SEM (CB-SEM) methods, PLS-SEM has strengths in weaker normality assumptions and large sample sizes (Hair et al., 2019, 2021). Therefore, given the small sample size and the complexity of our constructs in this research, the PLS-SEM would be a better choice for having a reliable and robust estimate of coefficients than the CB-SEM (Hair et al., 2019; Rigdon et al., 2017). However, the PLS-SEM requires a representative sample (Becker & Ismail, 2016). Our sample was not representative since we did not use a random sampling method. We utilized a weighted PLS-SEM (WPLS-SEM) method developed by Becker and Ismail Field (2016) to address these issues. In particular, we weighted our sample by gender and age based on the U.S. Agricultural Census. Gender and age are the population characteristics that are commonly used for weighting samples (Becker & Ismail, 2016; Cheah et al., 2021). In addition, our outcome variables (i.e., intentions and behaviors) are highly related to the weighting variables, and they are not endogenous variables, which satisfies the critical requirements of selecting weighting variables (Cheah et al., 2021). Accordingly, gender and age were used as weighting variables in this study. Lastly, we employed SmartPLS 4.0 to estimate and check the explanatory and predictive powers of our WPLS-SEM models.

Objective 2. Examine the impact of community-based networks and their interactions with climate change perceptions and attitudes on adaptation and climate resilience strategies.

Background  

        Farmers' active engagement in climate change adaptation is the premise of achieving climate-resilient development (Ado et al., 2019; Soubry et al., 2020). Nonetheless, evidence shows that many remain skeptical about human-induced climate change (Jones et al., 2017; Schuldt et al., 2018; Spence et al., 2012). Consequently, they have low adaptation intentions to climate change (Mitter et al., 2019). Other than that, distinguished from other agricultural innovations and technologies, farmers' decision-making on climate-adaptive farming practices may be impacted by psychological distance because the consequences of climate change would be too abstract, and people may perceive the risk of climate change as somewhat distant and diffuse over space and time (Haden et al., 2012; Van Lange & Huckelba, 2021). Networks are often considered essential for exchanging information, providing support, and sharing risks among network members (Chavas & Nauges, 2020; Chuang & Schechter, 2015), and for diffusing information and promoting agricultural technology adoption rates (Beaman & Dillon, 2018; Conley & Udry, 2010).

Methods

       The dependent variable in this study is farmers' adoption of adaptation and climate-resilient technologies. In particular, we differentiated two kinds of technologies, namely, incremental and transformative adaptations. The incremental adaptation concentrated more on short-term coping measures with the goal of reducing sensitivity and vulnerability (Kates et al., 2012; Wilson et al., 2020). The incremental adaptations included in this study are the following: use stress-tolerant crop varieties, change planting dates, change harvesting dates, conservation tillage (e.g., no-tillage, ridge tillage, strip tillage), and use cover crops. On the contrary, transformative adaptation targets fundamental system changes rather than single and small-scale improvements (Kates et al., 2012; Wilson et al., 2020). The transformative adaptations are scaling back operations (e.g., taking land out of production, de-stocking), using mixed crop-livestock systems, diversifying into other forms of production or different crops, and getting an off-farm job to supplement farm income (you and/or your spouse). We measured these adaptation strategies by binary variables. That is, one means farmers adopted this adaptation strategy on their farm, and zero otherwise. We aggregated these binary variables to represent farmers' adaptation intensity. 

        The key independent variables in this study are farmers' perceptions of and attitudes towards climate change and community-based networks. To capture farmers' climate change perceptions, we asked respondents' concerns on the following potential problems for farm operation: increased flooding, longer dry periods and drought, increased weed pressure, increased pest pressure, higher incidence of crop disease, more frequent extreme rains, increased in saturated soils and ponded water, increase heat stress on crops, increased loss of nutrients into waterways, and increased soil erosions. Besides, three 5-point Likert scales from "strongly disagree" to "strongly agree" are used to capture farmers' attitudes toward climate change. The three questions are: Using adaptation practices in my farm will benefit me (AT1); I will use adaptation practices, even if my production costs increase (AT2); and by using adaptation practices, I can reduce the losses from weather shocks (AT3). We then utilized factor analysis to reduce the factors for measuring climate change perception. We used two variables to capture farmers' community-based networks. We asked farmers how many peer farmers they spoke with when making business operation and production practice decisions on their farm in the last 12 months and how many extension agents and crop consultants they spoke with when making business operation and production practice decisions. We also contained a set of control variables in our analysis, such as gender, age, race, and household income. We further utilized a multi-linear regression model to analyze the impacts of community-based networks and their interactions with climate change perceptions and attitudes on adaptation and climate resilience strategies.

Objective 3. Analyze the role of climate change perceptions and community-based networks on climate change adaptation and resilience by farmers' intersectionality.

Background

        As a critical platform for interdisciplinary collaboration and dialogue, resilience is a systematic and complex concept (Folke, 2016). This concept has been widely used to analyze the human-nature interactions in social-ecological systems and bridge the role of social, economic, cultural, political, and environmental factors (Ifejika Speranza et al., 2014). The complexity of resilience is also embodied in the various definitions of resilience among different disciplines, and the meaning often depends on the research objectives and purposes. For example, from the disaster risk reduction perspective, resilience refers to the ability of a system and its parts to anticipate, absorb, accommodate, or recover from the effects of hazardous events in a timely and efficient manner (IPCC, 2012; Tambo, 2016). In the economic field, as Di Falco and Chavas (2008) described, "a 'resilient' agroecosystem is more capable of providing a vital service, such as food production, when challenged by a severe drought or by a large reduction in rainfall." Correspondingly, we defined climate resilience in the agricultural system as the capacity of a farming unit or agricultural system to cope with and adapt to the social, political, economic, and ecological challenges precipitated by changing climate and climatic events (Hirons et al., 2018). Intersectionality can be understood as gender and other social categories, such as age, race, and class, interacting to affect farmers' climate change adaptation experiences ( McCall, 2005; Van Aelst & Holvoet, 2016). For example, several studies have discussed the impact of intersectionality on climate change. Van Aelst and Holvoet (2016) found rural farmers' access to adaptive strategies depends on their gender and material status in Tanzania. Likewise, Lawson et al. (2020) studied female farmers' intersectional perceptions and adaptation strategies. They found that adopting adaptation strategies was affected by age, marital and residential status. Intersectionality also relates to climate justice (Mikulewicz et al., 2023). Females and other underserved groups are more vulnerable to climate change and have less adaptive capacity (Perkins, 2018), and therefore, more climate change adaptation policies and response initiatives should be provided for women and traditionally underserved stakeholders.

Methods

        In this study, we asked respondents two questions to measure resilience: "If an adverse weather shock occurred and affected your farm, how likely is it that your farm would be well prepared to absorb the shock (e.g., proper infrastructure, financial capacity, necessary risk, and mitigation safeguards)?" and "If an adverse weather shock occurred and affected your farm, how likely is it that your farm could recover and resume to normal operations within six months after that event?". The answers are measured by extremely likely, very likely, not very likely, and not at all likely.

        Like Objective 2, we measured farmers' climate change adaptation by several climate-adaptive strategies, including the use of stress-tolerant crop varieties, change planting dates, change harvesting dates, conservation tillage (e.g., no-tillage, ridge tillage, strip tillage), use cover crops, scale back operations (e.g., take land out of production, de-stocking), use mixed crop-livestock systems, diversify into other forms of production or different crops, and get an off0farm job to supplement farm income (you and/or your spouse). Similarly, we measured these adaptation strategies by binary variables. That is, 1 indicates farmers adopted this adaptation strategy on their farm, and 0 otherwise. Moreover, we aggregated these binary variables to represent farmers' adaptation intensity. Besides, we contained a set of demographics, such as gender, age, race, and household income, to capture the intersectionality. We utilized the t-test, ANOVA, and multi-linear regression model to analyze the effects of intersectionality on farmers' climate change adaptation and resilience.

Objective 4. Incorporate stakeholders' recommendations in the execution of project activities and disseminate findings on climate change adaptation and climate resilience among Florida field-crop farmers.

• We obtained IRB approval to facilitate focus groups and individual interviews with AC members (IRB#202102640).
• We recruited members for our Advisory Council (AC). Three Extension Faculty members at UF/IFAS graciously agreed to serve as members. Two farmers also agreed to serve in the AC.
• We have established an ad-hoc committee composed of three faculty members at UF with research expertise in regional climate change (Dr. Kevin Ash-Geography), agricultural economics (Dr. Trent Blare-Food and Resource Economics), and sustainable agriculture (Dr. Mickie Swisher- Center for Sustainable and Organic Food System
• The role of the AC is to provide targeted consensus-based inputs for the refinement and improvement of research activities and detailed reviews of progress for each of the research goals, objectives, and results.
• The Delphi method was used to reach consensus-based recommendations for the quality, utility, and appropriateness of the instrument used to collect data in this study. This process ensured scientific and methodological rigor, reduced research bias, and enhanced research outcomes and the potential quality of findings. It also aided in establishing face validity.

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