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<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Sustain. Food Syst.</journal-id>
<journal-title>Frontiers in Sustainable Food Systems</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Sustain. Food Syst.</abbrev-journal-title>
<issn pub-type="epub">2571-581X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fsufs.2024.1395547</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Sustainable Food Systems</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Climate smart agriculture: assessing needs and perceptions of California&#x00027;s farmers</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name><surname>Ikendi</surname> <given-names>Samuel</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
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<name><surname>Pinz&#x000F3;n</surname> <given-names>Natalia</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
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<name><surname>Koundinya</surname> <given-names>Vikram</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
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<name><surname>Taku-Forchu</surname> <given-names>Namah</given-names></name>
<xref ref-type="aff" rid="aff5"><sup>5</sup></xref>
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<name><surname>Roche</surname> <given-names>Leslie M.</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff6"><sup>6</sup></xref>
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<name><surname>Ostoja</surname> <given-names>Steven M.</given-names></name>
<xref ref-type="aff" rid="aff7"><sup>7</sup></xref>
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<name><surname>Parker</surname> <given-names>Lauren E.</given-names></name>
<xref ref-type="aff" rid="aff7"><sup>7</sup></xref>
<xref ref-type="aff" rid="aff8"><sup>8</sup></xref>
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<name><surname>Zaccaria</surname> <given-names>Daniele</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff9"><sup>9</sup></xref>
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<name><surname>Cooper</surname> <given-names>Mark H.</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
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<name><surname>Diaz-Ramirez</surname> <given-names>Jairo N.</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff10"><sup>10</sup></xref>
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<name><surname>Brodt</surname> <given-names>Sonja</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff11"><sup>11</sup></xref>
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<name><surname>Battany</surname> <given-names>Mark</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff12"><sup>12</sup></xref>
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<name><surname>Rijal</surname> <given-names>Jhalendra P.</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff13"><sup>13</sup></xref>
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<name><surname>Pathak</surname> <given-names>Tapan B.</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff14"><sup>14</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x0002A;</sup></xref>
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<aff id="aff1"><sup>1</sup><institution>University of California Agriculture and Natural Resources</institution>, <addr-line>Davis, CA</addr-line>, <country>United States</country></aff>
<aff id="aff2"><sup>2</sup><institution>University of California</institution>, <addr-line>Merced, Merced, CA</addr-line>, <country>United States</country></aff>
<aff id="aff3"><sup>3</sup><institution>Department of Human Ecology, University of California, Davis</institution>, <addr-line>Davis, CA</addr-line>, <country>United States</country></aff>
<aff id="aff4"><sup>4</sup><institution>Department of Animal Science, University of California, Davis</institution>, <addr-line>Davis, CA</addr-line>, <country>United States</country></aff>
<aff id="aff5"><sup>5</sup><institution>Natural Resources Institute, Division of Extension, University of Wisconsin Natural Resources Institute</institution>, <addr-line>Madison, WI</addr-line>, <country>United States</country></aff>
<aff id="aff6"><sup>6</sup><institution>Department of Plant Sciences, University of California, Davis</institution>, <addr-line>Davis, CA</addr-line>, <country>United States</country></aff>
<aff id="aff7"><sup>7</sup><institution>United States Department of Agriculture California Climate Hub</institution>, <addr-line>Davis, CA</addr-line>, <country>United States</country></aff>
<aff id="aff8"><sup>8</sup><institution>Institute of the Environment, University of California, Davis</institution>, <addr-line>Davis, CA</addr-line>, <country>United States</country></aff>
<aff id="aff9"><sup>9</sup><institution>Department of Land, Air and Water Resources, University of California, Davis</institution>, <addr-line>Davis, CA</addr-line>, <country>United States</country></aff>
<aff id="aff10"><sup>10</sup><institution>University of California: Desert Research and Extension Center</institution>, <addr-line>Holtville, CA</addr-line>, <country>United States</country></aff>
<aff id="aff11"><sup>11</sup><institution>University of California Sustainable Agriculture Research and Education Program</institution>, <addr-line>Davis, CA</addr-line>, <country>United States</country></aff>
<aff id="aff12"><sup>12</sup><institution>University of California San Luis Obispo County Extension</institution>, <addr-line>San Luis Obispo, CA</addr-line>, <country>United States</country></aff>
<aff id="aff13"><sup>13</sup><institution>University of California Integrated Pest Management Statewide Program</institution>, <addr-line>Davis, CA</addr-line>, <country>United States</country></aff>
<aff id="aff14"><sup>14</sup><institution>Department of Civil and Environmental Engineering, University of California, Merced</institution>, <addr-line>Merced, CA</addr-line>, <country>United States</country></aff>
<author-notes>
<fn fn-type="edited-by"><p>Edited by: Sruthi Narayanan, Clemson University, United States</p></fn>
<fn fn-type="edited-by"><p>Reviewed by: Yefan Nian, Clemson University, United States</p>
<p>Meghnaa Tallapragada, Temple University, United States</p></fn>
<corresp id="c001">&#x0002A;Correspondence: Tapan B. Pathak <email>tpathak&#x00040;ucmerced.edu</email></corresp>
</author-notes>
<pub-date pub-type="epub">
<day>14</day>
<month>06</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="collection">
<year>2024</year>
</pub-date>
<volume>8</volume>
<elocation-id>1395547</elocation-id>
<history>
<date date-type="received">
<day>04</day>
<month>03</month>
<year>2024</year>
</date>
<date date-type="accepted">
<day>24</day>
<month>05</month>
<year>2024</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x000A9; 2024 Ikendi, Pinz&#x000F3;n, Koundinya, Taku-Forchu, Roche, Ostoja, Parker, Zaccaria, Cooper, Diaz-Ramirez, Brodt, Battany, Rijal and Pathak.</copyright-statement>
<copyright-year>2024</copyright-year>
<copyright-holder>Ikendi, Pinz&#x000F3;n, Koundinya, Taku-Forchu, Roche, Ostoja, Parker, Zaccaria, Cooper, Diaz-Ramirez, Brodt, Battany, Rijal and Pathak</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p></license>
</permissions>
<abstract>
<p>California is the largest agricultural economy in the United States; however, its current and projected climate risks pose significant challenges. Farmers will need to adapt to climate change in their farming practices. The goal of this needs assessment was to understand farmers&#x00027; perceptions and experiences with climate change exposures; the risk management practices they currently use; and what tools and resources would assist them in making strategic decisions. A statewide survey was conducted through Qualtrics with farmers (<italic>n</italic> = 341). Results showed that 67% of the farmers <italic>agree (agree</italic> &#x0002B; <italic>strongly agree)</italic> that climate change is happening, and 53.1% agreed that actions are required. Moreover, historically underrepresented farmers were <italic>very concerned</italic> about climate change-related impacts related to water, temperatures, and natural disasters. Farmers are currently <italic>implementing</italic> adaptation practices related to water management, soil health, and renewable energy and are also seeking insurance and government assistance programs to increase agricultural resilience. They also expressed <italic>interest</italic> and a <italic>high need</italic> for information on those adaptation practices to acquire skills and knowledge to manage various challenges of farming in variable climates. Also, the assessment established that farmers (47.5%) use decision-support tools, mostly weather stations (22.4%); and 51.9% indicated their interest in using online tools designed to translate climate information into forms that support production decision-making. Farmers (60.8%) responded that they would or may attend workshops to learn about adaptation practices. The findings of this needs assessment will inform the development of extension education programs on climate-smart agriculture for farmers in California and elsewhere.</p></abstract>
<kwd-group>
<kwd>needs assessment</kwd>
<kwd>extension program development</kwd>
<kwd>climate adaptation</kwd>
<kwd>climate change</kwd>
<kwd>climate-smart agriculture</kwd>
<kwd>decision support tools</kwd>
<kwd>California farmers</kwd>
</kwd-group>
<contract-num rid="cn001">2022-68017-36358</contract-num>
<contract-sponsor id="cn001">National Institute of Food and Agriculture<named-content content-type="fundref-id">10.13039/100005825</named-content></contract-sponsor>
<counts>
<fig-count count="6"/>
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<ref-count count="73"/>
<page-count count="15"/>
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<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Climate-Smart Food Systems</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="s1">
<title>1 Introduction</title>
<p>California is the largest agricultural economy in the United States with a farmgate revenue exceeding $51 billion from farms, ranches, and plant nurseries (California Department of Food Agriculture, <xref ref-type="bibr" rid="B5">2022</xref>). California produces more than 400 commodities including more than 40 specialty crops due to its favorable Mediterranean and unique regional microclimatic zones (Pathak et al., <xref ref-type="bibr" rid="B51">2018</xref>; Rance, <xref ref-type="bibr" rid="B55">2023</xref>). Additionally, more than one-third of vegetables and two&#x02013;thirds of fruits and nuts in the U.S. are produced in California; 19 of which are not produced anywhere else in the country, including nuts&#x02014;almonds, walnuts, pistachios; vegetables&#x02014;artichokes, celery, garlic; dried fruits&#x02014;prunes, raisins; canned fruits&#x02014;olives, cling peaches; fresh fruits&#x02014;nectarines, kiwi, pomegranates, honeydew, figs, plums, table grapes; sweet rice; and lima beans.</p>
<p>However, the current and projected climate change in California (Luedeling et al., <xref ref-type="bibr" rid="B38">2009</xref>; Pathak et al., <xref ref-type="bibr" rid="B51">2018</xref>) poses significant challenges to its agricultural sector due to multiple phenomena that include increased heat waves, temperatures, droughts, wildfires, floods, and variable precipitation. These changes are directly and indirectly impacting agriculture such as declining yield, increases in weed, insect pests, and disease pressures (Matzrafi, <xref ref-type="bibr" rid="B42">2018</xref>; Pathak et al., <xref ref-type="bibr" rid="B51">2018</xref>; Swain et al., <xref ref-type="bibr" rid="B64">2018</xref>; Rijal et al., <xref ref-type="bibr" rid="B59">2021</xref>; Skend&#x0017E;i&#x00107; et al., <xref ref-type="bibr" rid="B62">2021</xref>; Jha et al., <xref ref-type="bibr" rid="B30">2024</xref>), phenology shifts (Pathak and Stoddard, <xref ref-type="bibr" rid="B53">2018</xref>), and declining chill (Luedeling et al., <xref ref-type="bibr" rid="B38">2009</xref>; Zhang et al., <xref ref-type="bibr" rid="B73">2021</xref>). Given the scale of production, such conditions provide a glimpse of a volatile future, which requires multifaceted approaches to address these challenges. Climate-smart agriculture (CSA) is one of the approaches that provides an opportunity to alleviate some of the risks associated with climate change in California (Lewis and Rudnick, <xref ref-type="bibr" rid="B36">2019</xref>). The Food and Agriculture Organization (FAO) describes CSA as an integrated approach that guides actions to transform agrifood systems toward climate-resilient practices, reducing farmer stress from climate impacts [Food and Agriculture Organization of the United Nations (FAO), <xref ref-type="bibr" rid="B14">2021</xref>].</p>
<p>The CSA approach holds particular importance in California, especially for beginning farmers, farmers with limited resources, and socially disadvantaged farmers who may lack access to technical assistance and often have fewer resources for adapting to climate change (Munden-Dixon et al., <xref ref-type="bibr" rid="B46">2018</xref>; Taku-Forchu et al., <xref ref-type="bibr" rid="B66">2023</xref>; Ikendi et al., <xref ref-type="bibr" rid="B25">2024</xref>). These farmers are adversely affected by climate impacts, for instance, the 2012&#x02013;2016 drought&#x02014;the worst in 1,200 years (Griffin and Anchukaitis, <xref ref-type="bibr" rid="B19">2014</xref>) greatly impacted the rural communities in the San Joaquin Valley (Howitt et al., <xref ref-type="bibr" rid="B22">2014</xref>; Jasechko and Perrone, <xref ref-type="bibr" rid="B28">2020</xref>). Large-scale farmers also faced challenges, exemplified by abnormally warm winter and spring temperatures in 2015 that resulted in more than $240 million in combined crop indemnity payments to major nut tree and fruit growers (Reyes and Elias, <xref ref-type="bibr" rid="B58">2019</xref>; Parker et al., <xref ref-type="bibr" rid="B49">2020</xref>).</p>
<p>Adaptation to climate change will be crucial in California and farmers will have an increased need for locally relevant and crop-specific information (Jagannathan et al., <xref ref-type="bibr" rid="B27">2023</xref>). Similarly, technical service providers (TSPs) from agencies, extension, private crop consulting, and other boundary-spanning organizations also will be faced with climate change questions from farmers, yet they often lack locally relevant adaptation and mitigation resources to help farmers implement decisions (Grantham et al., <xref ref-type="bibr" rid="B18">2017</xref>; Johnson et al., <xref ref-type="bibr" rid="B31">2023</xref>; Parker et al., <xref ref-type="bibr" rid="B48">2023</xref>). To address the climate and agriculture science knowledge gap in California, a transdisciplinary team of researchers and educators from the University of California designed a multifaceted climate-smart agriculture education project (<xref ref-type="fig" rid="F1">Figure 1</xref>).</p>


<fig id="F1" position="float">
<label>Figure 1</label>
<caption><p>Multifaceted pathways to climate-smart agriculture educational project concept in California.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fsufs-08-1395547-g0001.tif"/>
</fig>


<p>The objective of this study was to assess farmers&#x00027; perspectives, experience, and knowledge on climate change exposures, potential impacts, vulnerabilities and needs for tools, resources, and extension programs, including needs for field demonstration studies for implementing climate-smart agriculture practices. There have been several studies on farmers&#x00027; needs assessment in California published in the literature (for instance, Jackson et al., <xref ref-type="bibr" rid="B26">2011</xref>; California Department of Food Agriculture, <xref ref-type="bibr" rid="B4">2013</xref>; Surls et al., <xref ref-type="bibr" rid="B63">2015</xref>; Jasperse and Pairis, <xref ref-type="bibr" rid="B29">2020</xref>; Kanter et al., <xref ref-type="bibr" rid="B32">2021</xref>) but to the best of our knowledge, there has been no study published in the literature that is focused on farmers with such diverse cropping systems and their scales of operations. Information from this study will benefit the agricultural community in California and also provide valuable insights to agricultural communities worldwide with similar scales of operations.</p></sec>
<sec sec-type="methods" id="s2">
<title>2 Methodology</title>
<sec>
<title>2.1 Research design</title>
<p>This study was part of a statewide survey that adopted an electronic mail communication system through Qualtrics. A participatory and culturally responsive framework was adopted in the design of the survey instrument, taking advantage of the diverse expertise of the research team in climate and agricultural sciences, social sciences, and extension program development and evaluation (Koundinya et al., <xref ref-type="bibr" rid="B33">2023</xref>). The survey was designed according to the guidelines of Dillman et al. (<xref ref-type="bibr" rid="B10">2014</xref>) Tailored Design Method (TDM) which emphasizes focusing the questions on the study objectives, systematic design of survey instrument blocks, and effective communication during the survey implementation processes to induce desired social exchange with respondents The survey consisted of seven blocks/sections with 27 questions (<xref ref-type="table" rid="T1">Table 1</xref>) blended in multiple choices, Likert-type scales, and open-ended capturing an array of information from farmers.</p>
<table-wrap position="float" id="T1">
<label>Table 1</label>
<caption><p>Composition of the farmers&#x00027; climate adaptation needs assessment instrument.</p></caption>
<table frame="box" rules="all">
<thead>
<tr style="background-color:#919498;color:#ffffff">
<th valign="top" align="left"><bold>Blocks</bold></th>
<th valign="top" align="left"><bold>Questions</bold></th>
<th valign="top" align="left"><bold>Information collected</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">1</td>
<td valign="top" align="left">4</td>
<td valign="top" align="left">Characteristics include land access and use, major crops produced and their relative revenues, and zip codes to determine spatial locations</td>
</tr> <tr>
<td valign="top" align="left">2</td>
<td valign="top" align="left">5</td>
<td valign="top" align="left">Concerns and perspectives of farmers about impacts related to climate on farm operations</td>
</tr> <tr>
<td valign="top" align="left">3</td>
<td valign="top" align="left">2</td>
<td valign="top" align="left">Thoughts on climate adaptation practices farmers are currently implementing or interested in, and nature of information need for practice adoption. And the preferred method(s) to receive information on adaptation practices</td>
</tr> <tr>
<td valign="top" align="left">4</td>
<td valign="top" align="left">2</td>
<td valign="top" align="left">Perceptions of barriers to climate adaptation practices and whether farmers might participate in workshops</td>
</tr> <tr>
<td valign="top" align="left">5</td>
<td valign="top" align="left">6</td>
<td valign="top" align="left">Use of Decision Support Tools that aid decision-making by translating climate and weather information into usable information for farmers</td>
</tr> <tr>
<td valign="top" align="left">6</td>
<td valign="top" align="left">2</td>
<td valign="top" align="left">Farmer climate change perspectives and beliefs</td>
</tr> <tr>
<td valign="top" align="left">7</td>
<td valign="top" align="left">6</td>
<td valign="top" align="left">Farmer demographics including years farming since age 18, gender, age, race/ethnicity, category of farmers&#x02013;first generation, multigeneration, socially disadvantaged, and/or limited resource farmers</td>
</tr></tbody>
</table>
</table-wrap>


<p>The researchers of this study ensured content validity by exhausting all possible dimensions of the study objectives blended in 27 questions. Face validity was ensured by looking at the questions multiple times and discussing opinions through weekly meetings. Modifications were made including improving the question formatting and reducing the number of questions. The survey instrument was also pilot tested with 18 stakeholders and feedback was incorporated into the final survey.</p></sec>
<sec>
<title>2.2 Data collection</title>
<p>Following the TDM, the main survey was distributed on February 28, 2023, through Qualtrics using 12,933 emails (including both farmers and ranchers) bought from Farm MarketID in California. Recipients (798) started their survey, and five reminders were sent. Overall, 610 recipients completed the survey and 310 were confirmed as farmers after sorting and cleaning the data that of possible AI-generated responses and human fraudulent responses using 31 fraud detection strategies generated by investigators (Pinz&#x000F3;n et al., <xref ref-type="bibr" rid="B54">2023</xref>). Additionally, we leveraged the networks of Extension associations in California to distribute the survey using QR codes and anonymous links. Additional cleaned responses from QR (22) and anonymous links (09) were added to the 310 email responses, giving a final total of 341 respondents. <xref ref-type="fig" rid="F2">Figure 2</xref> provides a distribution of farmers who provided zip codes.</p>
<fig id="F2" position="float">
<label>Figure 2</label>
<caption><p>Responses by counties and region for farmers who provided California zip codes. Some farmers provided multiple zip codes depending on the location of their farms.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fsufs-08-1395547-g0002.tif"/>
</fig>


<p>After establishing reliability through a pilot test, it was further determined using Cronbach&#x00027;s alpha (Drost, <xref ref-type="bibr" rid="B12">2011</xref>) on the Likert-scale responses. The extent of concern about climate-related impacts had 15 Likert items; alpha was 0.923. Interest in adaptation practices had 34 Likert items; alpha was 0.899. Need for information on adaptation practices also had 34 Likert items; alpha was 0.949. Perception of barriers to implementing adaptation practices had 23 Likert items; alpha was 0.915. All alpha coefficients were above 0.70, indicating high consistency (McNeish, <xref ref-type="bibr" rid="B43">2018</xref>).</p></sec>
<sec>
<title>2.3 Data analysis</title>
<p>Data were analyzed using SPSS and presented as measures of dispersions and variability. Also, a chi-square test was employed to determine the associations between perception variables (concerns, interest, implementation, needs, and barriers to adaptation) and farmer structure and background variables (regions, crops, ethnicity/race, gender, and ethnoracial background). Chi-square tests were also done for crops/regions. The study considered a significance level of &#x02264; 0.05.</p></sec></sec>
<sec sec-type="results" id="s3">
<title>3 Results</title>
<sec>
<title>3.1 Farmers sociodemographic characteristics</title>
<p>Farmers differed in their demographic characteristics (<xref ref-type="fig" rid="F3">Figure 3</xref>). Of the 294 farmers who responded to the gender question, 82.0% were male, and 16.3% were female. In comparison, California producers as a whole are 62% male and 38% female [United States Department of Agriculture (USDA) &#x02013; National Agricultural Statistics Service, <xref ref-type="bibr" rid="B69">2024</xref>], in this, our sample underrepresents female farmers. Most of the farmers identified as white (75.5%; <italic>n</italic> = 277), similarly, 80% of California producers identify as white (USDA 2024). Our study identified 52.3%; (<italic>n</italic> = 279) were multigenerational farmers, but these were not available in the agriculture census for comparison. The average age was 60 &#x000B1; 13.340 years (<italic>n</italic> = 277), with a minimum of 22 years and a maximum of 87 years. Most farmers had farmed for more than 20 years (65.2%; <italic>n</italic> = 293) since age 18, this reflects California producers where the majority (70%) have been farming for more than 11 years (USDA 2024).</p>
<fig id="F3" position="float">
<label>Figure 3</label>
<caption><p>Demographic profile of farmers. A socially disadvantaged farmer was defined as a farmer who has been subjected to racial or ethnic prejudices because of their identity as a member of a group without regard to their individual qualities (United States Department of Agriculture &#x02013; Natural Resources Conservation Service, <xref ref-type="bibr" rid="B68">2018</xref>).</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fsufs-08-1395547-g0003.tif"/>
</fig>
</sec>

<sec>
<title>3.2 Land tenure types</title>
<p>A total of 330 farmers responded and made use of multiple land tenure types including private land, privately leased, public leased, and/or were hired to manage farms. The majority (71%) used one tenure system; 23.6% used two systems; 5.2% used three and 0.3% used all four systems. Most farmers, 311 (94.2%), farmed on private land, with an average of 341.1 &#x000B1; 769.745, and a median of 72.0 with a maximum of 6,000 acres. Ninety-two farmers (27.9%) leased land, with an average of 613.2 &#x000B1; 1,030.633, a median of 200.0 with a maximum of 5,000 acres. Hired farmers 39 (11.8%) managed an average of 6,265.2 &#x000B1; 31,897.513, and a median of 400.0 with a maximum of 200,000 acres. Only two farmers (0.6%) on publicly leased land for an average of 1,850.0 &#x000B1; 2,474.874, and a median of 1,850.0 with a maximum of 3,600 acres.</p></sec>
<sec>
<title>3.3 Crop production profile</title>
<p>The majority (35.5%, <italic>n</italic> = 293) indicated that 76%&#x02212;100% of their income comes from farming, followed by 29.7% reporting up to 25%, 18.8% reporting 26%&#x02212;50%, and the smallest percentage (16.0%) indicating 51%&#x02212;75%. Of the 340 farmers who responded to specific crops produced, the majority participated in the production of fruits (51.2%) and nut trees (44.1%; <xref ref-type="fig" rid="F4">Figure 4</xref>). Relatedly, among farmers (<italic>n</italic> = 329), the majority reported that fruit (37.6%) and nut tree (31.8%) production contributed more than 50% of their annual income.</p>
<fig id="F4" position="float">
<label>Figure 4</label>
<caption><p>Main crops produced and corresponding annual revenue.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fsufs-08-1395547-g0004.tif"/>
</fig>


<p>Chi-square tests revealed statistically significant associations among geographic regions and crops grown. Nut production was associated (<italic>p</italic> &#x0003D; &#x0003C; 0.001) with farmers in the Northern (77.3%) and Southern (55.2%) San Joaquin Valley regions. Fruit production was associated in a statistically significant manner (<italic>p</italic> &#x0003D; &#x0003C; 0.001) with farmers in the North Coast (86.2%), Central Coast (84.2%), San Francisco Bay (83.3%), Southern (73.7%), and the South Urban (66.7%) regions. Production of grains and pulses was associated (<italic>p</italic> &#x0003D; &#x0003C; 0.001) with farmers in the Superior (32.0%) while vegetable production in the South Urban (33.3%) regions. The study also found that first-generation farmers (64.3%) were associated with fruit production (<italic>p</italic> = 0.003) whereas multigenerational farmers (50.3%) with nut production (<italic>p</italic> = 0.005).</p></sec>
<sec>
<title>3.4 Climate change-related impacts and perceptions</title>
<p>Farmers held varying perceptions regarding climate change and related impacts (<xref ref-type="fig" rid="F5">Figure 5</xref>). Up to 67% <italic>agree (agree</italic>&#x0002B;<italic>strongly)</italic> that climate change is happening and requires action (53.1%). Also, farmers, 66.8% <italic>agree (agree</italic>&#x0002B;<italic>strongly)</italic> that climate change is due to natural causes, while 55.7% <italic>agree (agree</italic>&#x0002B;<italic>strongly)</italic> that climate change is due to human causes. The majority <italic>agree (agree</italic>&#x0002B;<italic>strongly</italic>; 69.8%) that they are interested in learning more about the impacts of climate change on the agricultural industry. Relatedly, most farmers (59.0%, <italic>n</italic> = 339) expressed that they experienced greater climate change on their farms today than 10 years ago. Similarly, about one-third indicated that the impacts have been <italic>moderate</italic> (31.3%) whereas 27.7% (<italic>slight impact</italic>) 17.1% (<italic>great impact</italic>), 17.1 (<italic>no impact</italic>), and 6.8% experienced a <italic>severe impact</italic> in the last 10 years.</p>
<fig id="F5" position="float">
<label>Figure 5</label>
<caption><p>Perceptions of farmers about climate change-related impacts on their farms.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fsufs-08-1395547-g0005.tif"/>
</fig>
</sec>

<sec>
<title>3.5 Concerns about climate change-related impacts</title>
<p>The concerns were categorized into three categories: water, temperature, and disaster-related concerns (<xref ref-type="table" rid="T2">Table 2</xref>). Farmers were <italic>very concerned</italic> about water-related issues, especially increased uncertainty in water availability for irrigation (58.2%), reduced water availability for irrigation (54.7%), and reduced groundwater availability (51.3%). For temperature, farmers were <italic>very concerned</italic> about increased drought severity (37.4%) and crop damage due to extreme heat (26.5%) while among disasters, farmers were <italic>very concerned</italic> about increased crop loss (23.0%) and farm loss (19.8%).</p>


<table-wrap position="float" id="T2">
<label>Table 2</label>
<caption><p>Extent of concern about climate change-related impacts on the future of farmers&#x00027; operations.</p></caption>
<table frame="box" rules="all">
<thead>
<tr style="background-color:#919498;color:#ffffff">
<th valign="top" align="left"><bold>Climate change-related impacts</bold></th>
<th valign="top" align="left"><bold><italic>n</italic></bold></th>
<th valign="top" align="left" colspan="4"><bold>Percentage extent of concern</bold></th>
</tr>
</thead>
<tbody>
<tr style="background-color:#919498;color:#ffffff">
<td/>
<td/>
<td valign="top" align="left"><bold>Not at all</bold></td>
<td valign="top" align="left"><bold>Somewhat</bold></td>
<td valign="top" align="left"><bold>Concerned</bold></td>
<td valign="top" align="left"><bold>Very</bold></td>
</tr> <tr style="background-color:#dee1e1;color:#ffffff">
<td valign="top" align="left" colspan="6"><bold>Water-related concerns</bold></td>
</tr> <tr>
<td valign="top" align="left">Increased uncertainty in water availability for irrigation</td>
<td valign="top" align="left">340</td>
<td valign="top" align="left">8.2</td>
<td valign="top" align="left">12.6</td>
<td valign="top" align="left">20.9</td>
<td valign="top" align="left">58.2</td>
</tr> <tr>
<td valign="top" align="left">Reduced water availability for irrigation</td>
<td valign="top" align="left">340</td>
<td valign="top" align="left">8.8</td>
<td valign="top" align="left">14.4</td>
<td valign="top" align="left">22.1</td>
<td valign="top" align="left">54.7</td>
</tr> <tr>
<td valign="top" align="left">Reduced groundwater availability</td>
<td valign="top" align="left">339</td>
<td valign="top" align="left">11.2</td>
<td valign="top" align="left">13.9</td>
<td valign="top" align="left">23.6</td>
<td valign="top" align="left">51.3</td>
</tr> <tr>
<td valign="top" align="left">Increased crop/water stress</td>
<td valign="top" align="left">340</td>
<td valign="top" align="left">13.5</td>
<td valign="top" align="left">20.3</td>
<td valign="top" align="left">30.0</td>
<td valign="top" align="left">36.2</td>
</tr> <tr>
<td valign="top" align="left">Increased salinization</td>
<td valign="top" align="left">338</td>
<td valign="top" align="left">40.2</td>
<td valign="top" align="left">26.6</td>
<td valign="top" align="left">23.7</td>
<td valign="top" align="left">9.5</td>
</tr> <tr style="background-color:#dee1e1;color:#ffffff">
<td valign="top" align="left" colspan="6"><bold>Temperature-related concerns</bold></td>
</tr> <tr>
<td valign="top" align="left">Increased drought severity</td>
<td valign="top" align="left">340</td>
<td valign="top" align="left">12.6</td>
<td valign="top" align="left">24.1</td>
<td valign="top" align="left">25.9</td>
<td valign="top" align="left">37.4</td>
</tr> <tr>
<td valign="top" align="left">Increased crop damage due to extreme heat</td>
<td valign="top" align="left">340</td>
<td valign="top" align="left">18.2</td>
<td valign="top" align="left">31.2</td>
<td valign="top" align="left">24.1</td>
<td valign="top" align="left">26.5</td>
</tr> <tr>
<td valign="top" align="left">Increased frost damage</td>
<td valign="top" align="left">339</td>
<td valign="top" align="left">24.8</td>
<td valign="top" align="left">37.5</td>
<td valign="top" align="left">23.6</td>
<td valign="top" align="left">14.2</td>
</tr> <tr>
<td valign="top" align="left">Increased pest and disease pressure</td>
<td valign="top" align="left">336</td>
<td valign="top" align="left">19.6</td>
<td valign="top" align="left">36.3</td>
<td valign="top" align="left">31.8</td>
<td valign="top" align="left">12.2</td>
</tr> <tr>
<td valign="top" align="left">Reduced chill accumulations</td>
<td valign="top" align="left">334</td>
<td valign="top" align="left">29.9</td>
<td valign="top" align="left">37.4</td>
<td valign="top" align="left">23.1</td>
<td valign="top" align="left">9.6</td>
</tr> <tr style="background-color:#dee1e1;color:#ffffff">
<td valign="top" align="left" colspan="6"><bold>Disaster-related concerns</bold></td>
</tr> <tr>
<td valign="top" align="left">Increased crop loss due to climate-related disaster</td>
<td valign="top" align="left">339</td>
<td valign="top" align="left">16.8</td>
<td valign="top" align="left">30.4</td>
<td valign="top" align="left">29.8</td>
<td valign="top" align="left">23.0</td>
</tr> <tr>
<td valign="top" align="left">Increased farm loss due to climate-related disaster</td>
<td valign="top" align="left">337</td>
<td valign="top" align="left">19.3</td>
<td valign="top" align="left">32.0</td>
<td valign="top" align="left">28.8</td>
<td valign="top" align="left">19.8</td>
</tr> <tr>
<td valign="top" align="left">Increased crop damage due to wildfire/smoke</td>
<td valign="top" align="left">337</td>
<td valign="top" align="left">38.3</td>
<td valign="top" align="left">30.9</td>
<td valign="top" align="left">16.3</td>
<td valign="top" align="left">14.5</td>
</tr> <tr>
<td valign="top" align="left">Increased wildfires severity</td>
<td valign="top" align="left">337</td>
<td valign="top" align="left">30.3</td>
<td valign="top" align="left">35.3</td>
<td valign="top" align="left">21.1</td>
<td valign="top" align="left">13.4</td>
</tr> <tr>
<td valign="top" align="left">Increased flooding</td>
<td valign="top" align="left">337</td>
<td valign="top" align="left">38.3</td>
<td valign="top" align="left">36.2</td>
<td valign="top" align="left">16.0</td>
<td valign="top" align="left">9.5</td>
</tr></tbody>
</table>
</table-wrap>


<p>In a chi-square tabulation of farmers by ethnoracial background, specifically with water-related concerns, the proportions that were very concerned (36% underrepresented vs. 27.9% others) were significantly different (<italic>p</italic> = 0.067). So, there was a significant association between ethnoracial background and concern for water, with more underrepresented farmers being more concerned. Similarly, underrepresented farmers were 17.3% more likely to be <italic>very concerned</italic> with temperature-related issues than 7.4% of other farmers (<italic>p</italic> = 0.076); likewise, 14.7% of underrepresented farmers expressed significant concerns about disasters than 4.9% of other farmers (<italic>p</italic> = 0.021).</p>
<p>We also identified statistically significant differences in primary concerns between farming regions. Farmers in South San Joaquin Valley (71.6%), Central Coast (64.1%), and the Inland Empire (60.0%) were very concerned about reduced groundwater availability (<italic>p</italic> = 0.005). Increased drought severity was a very significant concern (<italic>p</italic> = 0.035) with farmers in the Inland Empire (80.0%), Central Coast (53.8%), and Southern (52.6%) regions. Farmers in the North Coast (39.3%) and Southern (36.8%) were very concerned with increased crop damage due to wildfires (<italic>p</italic> = &#x0003C; 0.001).</p>
<p>We also found statistically significant differences in primary concerns among different types of farming operations. Vegetable farmers significantly (<italic>p</italic> = 0.044) expressed more concerns about uncertainty in water availability for irrigation (74.4% <italic>very concerned</italic>). Fruit farmers significantly expressed more concerns regarding increased crop/water stress (38.5% very concerned; <italic>p</italic> &#x0003D; &#x0003C; 0.001), and increased crop damage due to extreme heat (32.8%; <italic>p</italic> &#x0003D; &#x0003C; 0.001). Grains and pulse farmers stood out significantly (<italic>p</italic> = 0.022) in terms of concerns with reduced groundwater availability (59.2%).</p></sec>
<sec>
<title>3.6 Implementation, interest, and need for information on climate adaptation practices</title>
<p>The top adaptation practices (<xref ref-type="table" rid="T3">Table 3</xref>) farmers were implementing included changes in irrigation practices (54.0%), reducing soil disturbance/no-till (47.9%), building soil organic matter/applying manure/compost (46.9%), using cover crops (39.3%), and mulching (39.3%). Mostly, farmers expressed interest in water harvesting (54.5%), transition to renewable energy (48.7%), drought-tolerant varieties (48.5%), reducing dependence on fossil fuels (48.1%), and applying for government assistance (45.8%). The highest information needs were securing access to insurance (21.6%), applying for government assistance (20.6%), transitioning to renewable energy (20.4%), building soil organic matter (19.7%), and water harvesting (19.4%).</p>


<table-wrap position="float" id="T3">
<label>Table 3</label>
<caption><p>The top 10 climate adaptation practices farmers are currently implementing, interested in, and in need for information.</p></caption>
<table frame="box" rules="all">
<thead>
<tr style="background-color:#919498;color:#ffffff">
<th valign="top" align="left"><bold>Climate-smart adaptation practices</bold></th>
<th valign="top" align="left" colspan="3"><bold>Interest and implementation (%)</bold></th>
<th valign="top" align="left" colspan="3"><bold>Need for information (%)</bold></th>
</tr>
</thead>
<tbody>
<tr style="background-color:#919498;color:#ffffff">
<td/>
<td valign="top" align="left"><bold>Not interested</bold></td>
<td valign="top" align="left"><bold>Interested</bold></td>
<td valign="top" align="left"><bold>Currently implementing</bold></td>
<td valign="top" align="left"><bold>No need</bold></td>
<td valign="top" align="left"><bold>Some need</bold></td>
<td valign="top" align="left"><bold>High need</bold></td>
</tr> <tr>
<td valign="top" align="left">Secure access to insurance</td>
<td valign="top" align="left">29.2</td>
<td valign="top" align="left">45.7</td>
<td valign="top" align="left">25.1</td>
<td valign="top" align="left">44.3</td>
<td valign="top" align="left">34</td>
<td valign="top" align="left">21.6</td>
</tr> <tr>
<td valign="top" align="left">Apply for government assistance</td>
<td valign="top" align="left">33</td>
<td valign="top" align="left">45.8</td>
<td valign="top" align="left">21.2</td>
<td valign="top" align="left">39</td>
<td valign="top" align="left">40.4</td>
<td valign="top" align="left">20.6</td>
</tr> <tr>
<td valign="top" align="left">Transition to renewable energy for farm use</td>
<td valign="top" align="left">21.7</td>
<td valign="top" align="left">48.7</td>
<td valign="top" align="left">29.7</td>
<td valign="top" align="left">37.2</td>
<td valign="top" align="left">42.5</td>
<td valign="top" align="left">20.4</td>
</tr> <tr>
<td valign="top" align="left">Build soil organic matter</td>
<td valign="top" align="left">17.8</td>
<td valign="top" align="left">35.3</td>
<td valign="top" align="left">46.9</td>
<td valign="top" align="left">38</td>
<td valign="top" align="left">42.3</td>
<td valign="top" align="left">19.7</td>
</tr> <tr>
<td valign="top" align="left">Water harvesting</td>
<td valign="top" align="left">34.7</td>
<td valign="top" align="left">54.5</td>
<td valign="top" align="left">10.9</td>
<td valign="top" align="left">39.1</td>
<td valign="top" align="left">41.5</td>
<td valign="top" align="left">19.4</td>
</tr> <tr>
<td valign="top" align="left">Change irrigation practices</td>
<td valign="top" align="left">18.4</td>
<td valign="top" align="left">27.6</td>
<td valign="top" align="left">54</td>
<td valign="top" align="left">42.3</td>
<td valign="top" align="left">41</td>
<td valign="top" align="left">16.7</td>
</tr> <tr>
<td valign="top" align="left">Reduce dependency on fossil fuels</td>
<td valign="top" align="left">41.3</td>
<td valign="top" align="left">48.1</td>
<td valign="top" align="left">10.6</td>
<td valign="top" align="left">45.8</td>
<td valign="top" align="left">38.5</td>
<td valign="top" align="left">15.7</td>
</tr> <tr>
<td valign="top" align="left">Use of cover crops</td>
<td valign="top" align="left">26.9</td>
<td valign="top" align="left">33.8</td>
<td valign="top" align="left">39.3</td>
<td valign="top" align="left">47.7</td>
<td valign="top" align="left">37.1</td>
<td valign="top" align="left">15.2</td>
</tr> <tr>
<td valign="top" align="left">Reduce reliance on groundwater</td>
<td valign="top" align="left">29.1</td>
<td valign="top" align="left">38.3</td>
<td valign="top" align="left">32.6</td>
<td valign="top" align="left">41.4</td>
<td valign="top" align="left">44.1</td>
<td valign="top" align="left">14.5</td>
</tr> <tr>
<td valign="top" align="left">Use of drought-tolerant varieties</td>
<td valign="top" align="left">30.3</td>
<td valign="top" align="left">48.5</td>
<td valign="top" align="left">21.2</td>
<td valign="top" align="left">49.5</td>
<td valign="top" align="left">37.1</td>
<td valign="top" align="left">13.4</td>
</tr> <tr>
<td valign="top" align="left">Change market strategy</td>
<td valign="top" align="left">41.8</td>
<td valign="top" align="left">42.9</td>
<td valign="top" align="left">15.3</td>
<td valign="top" align="left">50.9</td>
<td valign="top" align="left">36.2</td>
<td valign="top" align="left">12.9</td>
</tr> <tr>
<td valign="top" align="left">Mulching</td>
<td valign="top" align="left">24.4</td>
<td valign="top" align="left">36.4</td>
<td valign="top" align="left">39.3</td>
<td valign="top" align="left">47.5</td>
<td valign="top" align="left">39.9</td>
<td valign="top" align="left">12.6</td>
</tr> <tr>
<td valign="top" align="left">Reduce soil disturbance</td>
<td valign="top" align="left">24.4</td>
<td valign="top" align="left">27.7</td>
<td valign="top" align="left">47.9</td>
<td valign="top" align="left">51.5</td>
<td valign="top" align="left">36.2</td>
<td valign="top" align="left">12.3</td>
</tr> <tr>
<td valign="top" align="left">Reduce input use</td>
<td valign="top" align="left">34.6</td>
<td valign="top" align="left">45.5</td>
<td valign="top" align="left">19.9</td>
<td valign="top" align="left">46.5</td>
<td valign="top" align="left">41.8</td>
<td valign="top" align="left">11.7</td>
</tr> <tr>
<td valign="top" align="left">Alter labor schedules to cope with heat</td>
<td valign="top" align="left">34.1</td>
<td valign="top" align="left">34.1</td>
<td valign="top" align="left">31.8</td>
<td valign="top" align="left">58.6</td>
<td valign="top" align="left">31.9</td>
<td valign="top" align="left">9.5</td>
</tr> <tr>
<td valign="top" align="left">Transition to perennial plants</td>
<td valign="top" align="left">57.6</td>
<td valign="top" align="left">13.8</td>
<td valign="top" align="left">28.6</td>
<td valign="top" align="left">75.3</td>
<td valign="top" align="left">20.1</td>
<td valign="top" align="left">4.7</td>
</tr></tbody>
</table>
<table-wrap-foot>
<p>Most adaptation practices and programs were among the top 10 that are currently being implemented, interested, and in need of information. <xref ref-type="supplementary-material" rid="SM1">Supplementary Table S1</xref> provides complete details of all 34 adaptation practices and programs traced in this study.</p>
</table-wrap-foot>
</table-wrap>


<sec>
<title>3.6.1 Adaptation practices by regions</title>
<p>Seven of the 34 adaptation practices presented in the survey showed statistically significant differences by regions with <italic>implementation:</italic> agroforestry, diversification, cover crops, new crops, fuel load management, windbreaks, and increasing acreage. The use of cover crops was mostly implemented by farmers in the North Coast (66.7%) and Central Coast (51.4%; <italic>p</italic> = 0.022). Farmers significantly implemented agroforestry in South Urban (50.0%; <italic>p</italic> = 0.035) and diversifying production (50.0%; <italic>p</italic> = 0.009). Switching to new crops was significantly (<italic>p</italic> = 0.059) implemented by farmers in Southern (26.3%); and fuel load management in San Francisco Bay (33.3%; <italic>p</italic> = 0.033). Farmers implemented wind breaks significantly in San Francisco Bay (33.3%; <italic>p</italic> = 0.001) and increase acreage in the Inland Empire (40.0%; <italic>p</italic> = 0.082).</p>
<p>As it relates to expressing <italic>interest</italic>, seven adaptation practices showed significant differences by region. Farmers in Superior (40.0%), North San Joaquin Valley (38.2%), Southern (36.8%), and South San Joaquin Valley (36.7%) significantly (<italic>p</italic> = 0.009) expressed interest in diversifying production. The interest in the use of cover crops was significant (<italic>p</italic> = 0.022) in Southern (47.4%), South San Joaquin Valley (44.4%), and Inland Empire (40.0%). Farmers in the Inland Empire expressed significant interest (<italic>p</italic> = 0.001) in adding windbreaks (80.0%) and similarly, agroforestry in the Inland Empire (60.0%), South Urban (50.0%), and North Coast (37.5%; <italic>p</italic> = 0.035); while switching to new crops was significantly (<italic>p</italic> = 0.059) expressed by farmers in South Urban (50.0%) and Superior (40.0%) regions.</p></sec>
<sec>
<title>3.6.2 Adaptation practices by farmer demographics</title>
<p>The study found statistically significant differences in <italic>implementation, interest, and need for information</italic> on adaptation practices among different ethnoracial backgrounds. First-generation farmers significantly <italic>implemented</italic> reducing dependence on fossil fuels (14.6%; <italic>p</italic> = 0.083) and switching to low-chill varieties (11.6%; <italic>p</italic> = 0.008) compared to other farmers. Similarly, they significantly expressed a <italic>high need</italic> for information about securing access to insurance (32.3%; <italic>p</italic> = 0.004), changing irrigation practices (24.0%; <italic>p</italic> = 0.081), and earning off-farm income (18.3%; <italic>p</italic> = 0.075). In contrast, multigenerational farmers significantly <italic>implemented</italic> altering labor schedules (39.6%; <italic>p</italic> = 0.031), reduced reliance on groundwater (37.9%; <italic>p</italic> = 0.036), reduced input use (25.7%; <italic>p</italic> = 0.017), and rotating crops (23.0%; <italic>p</italic> = 0.009). Similarly, they significantly expressed <italic>interest</italic> in switching to low-chill varieties (38.8%; <italic>p</italic> = 0.035) and rotating crops (25.9%; <italic>p</italic> = 0.009) compared to other farmers.</p>
<p>There was a statistically significant difference in the way limited resource farmers <italic>implemented</italic> earning off-farm income (22.8%; <italic>p</italic> = 0.009) and changed their market strategy (19.6%; <italic>p</italic> = 0.005). Similarly, they significantly expressed a <italic>high need</italic> for information with applying for government assistance (42.6%; <italic>p</italic> &#x0003D; &#x0003C; 0.001), transitioning to renewable energy (36.4%; <italic>p</italic> = 0.013), securing access to insurance (27.8%; <italic>p</italic> = 0.005), earning off-farm income (27.3%; <italic>p</italic> = 0.002), and changing market strategy (21.2%; <italic>p</italic> = 0.046). Socially disadvantaged farmers statistically significantly <italic>implemented</italic> applying for government assistance (34.5%; <italic>p</italic> = 0.024). These farmers significantly expressed a <italic>high need</italic> for information with applying for government assistance (55.2%; <italic>p</italic> &#x0003D; &#x0003C; 0.001), securing access to insurance (48.3%; <italic>p</italic> = 0.001), transitioning to renewable energy (44.8%; <italic>p</italic> = 0.006), and reducing dependency on fossil fuels (37.9%; <italic>p</italic> = 0.005) compared to other farmers.</p></sec></sec>
<sec>
<title>3.7 Extension education and outreach</title>
<p>Most farmers preferred emails (67.7% <italic>n</italic> = 294; <xref ref-type="table" rid="T4">Table 4</xref>); 41.3% (<italic>n</italic> = 298) indicated that they would attend workshops on adaptation practices and programs if organized, while 39.3% said no, and 19.5 responded &#x00027;maybe&#x00027;.</p>
<table-wrap position="float" id="T4">
<label>Table 4</label>
<caption><p>Methods to receive information on adaptation practices by farmers.</p></caption>
<table frame="box" rules="all">
<thead>
<tr style="background-color:#919498;color:#ffffff">
<th valign="top" align="left"><bold>Extension education and outreach indicators</bold></th>
<th valign="top" align="left"><bold>Frequencies</bold></th>
<th valign="top" align="left"><bold>Percentages</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Emails</td>
<td valign="top" align="left">199</td>
<td valign="top" align="left">67.7</td>
</tr> <tr>
<td valign="top" align="left">Print publications</td>
<td valign="top" align="left">163</td>
<td valign="top" align="left">55.4</td>
</tr> <tr>
<td valign="top" align="left">Extension education events</td>
<td valign="top" align="left">138</td>
<td valign="top" align="left">46.9</td>
</tr> <tr>
<td valign="top" align="left">Face-to-face</td>
<td valign="top" align="left">73</td>
<td valign="top" align="left">24.8</td>
</tr> <tr>
<td valign="top" align="left">Social media</td>
<td valign="top" align="left">43</td>
<td valign="top" align="left">14.6</td>
</tr> <tr>
<td valign="top" align="left">Group texting</td>
<td valign="top" align="left">17</td>
<td valign="top" align="left">5.8</td>
</tr> <tr>
<td valign="top" align="left">Webinars</td>
<td valign="top" align="left">03</td>
<td valign="top" align="left">1.0</td>
</tr> <tr>
<td valign="top" align="left">Farm visits</td>
<td valign="top" align="left">02</td>
<td valign="top" align="left">0.6</td>
</tr></tbody>
</table>
</table-wrap>

</sec>
<sec>
<title>3.8 Barriers to climate adaptation</title>
<p>In this study, 23 obstacles were presented (<xref ref-type="fig" rid="F6">Figure 6</xref>), of which government regulations (53.1%), high input cost (46.9%), labor access/cost (35.2%), access to water (33.5%), and access to investment capital/funds (32.4%) were the most &#x0201C;<italic>significant barriers</italic>&#x0201D; to adaptation.</p>
<fig id="F6" position="float">
<label>Figure 6</label>
<caption><p>Top 10 barriers to implementation of adaptation practices and programs by farmers. Also see <xref ref-type="supplementary-material" rid="SM1">Supplementary Table S2</xref> for complete details of all 23 barriers to adaptation.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fsufs-08-1395547-g0006.tif"/>
</fig>

<sec>
<title>3.8.1 Significant barriers by regions</title>
<p>Access to locally adapted decision support tools (DSTs) was a <italic>significant barrier</italic> (<italic>p</italic> &#x0003D; &#x0003C; 0.001) to farmers in the Inland Empire (80.0%), and South Urban (50%). Similarly, farmers in the Inland Empire (40.0%), San Francisco Bay (33.3%), North San Joaquin Valley (26.0%), and Southern (23.5%) perceived access to detailed economic information (cost/benefit analysis) as a significant barrier (<italic>p</italic> = 0.058). The risk of implementing new practices was a significant barrier (<italic>p</italic> = 0.040) perceived by farmers in San Francisco Bay (50.0%), Superior (31.1%), Southern (29.4%), and Southern San Joaquin Valley (28.6%).</p></sec>
<sec>
<title>3.8.2 Significant barriers by farmers&#x00027; profile</title>
<p>First-generation farmers perceived labor access/cost (44.2% <italic>significant barrier</italic>; <italic>p</italic> = 0.028), access to water (38.5%; <italic>p</italic> = 0.092), and access to appropriate insurance (30.1%; <italic>p</italic> = 0.098). Multigenerational farmers perceived access to locally adapted DSTs (10.8%) as a <italic>significant barrier</italic> (<italic>p</italic> = 0.041) whereas socially disadvantaged farmers perceived access to appropriate equipment to implement climate adaptation practices (33.3%; <italic>p</italic> = 0.067), land ownership (33.3%; <italic>p</italic> = 0.005), and access to locally adapted monitoring tools (26.7%; <italic>p</italic> = 0.004). Limited resource farmers perceived access to investment capital (47.4% <italic>significant barrier</italic>; <italic>p</italic> = 0.039), labor access/cost (41.1%; <italic>p</italic> = 0.008), access to appropriate insurance (34.5%; <italic>p</italic> = 0.030), access to appropriate markets (32.1%; <italic>p</italic> = 0.013), and access to detailed economic information (22.8%; <italic>p</italic> = 0.026).</p></sec>
<sec>
<title>3.8.3 Significant barriers to crop production</title>
<p>Fruit farmers perceived labor access/cost (38.8% <italic>significant barrier</italic>; <italic>p</italic> = 0.036) and access to appropriate equipment (20.4%; <italic>p</italic> = 0.074) whereas nut tree farmers perceived government regulations (62.1%; <italic>p</italic> = 0.048) and high input cost (55.6%; <italic>p</italic> = 0.064) as significant barriers. Similarly, vegetable farmers perceived time (48.5%; <italic>p</italic> = 0.076), land ownership (42.4%; <italic>p</italic> = 0.003), and access to appropriate insurance (40.6%; <italic>p</italic> = 0.021) as significant barriers whereas grain and pulse farmers perceived access to appropriate markets (37.5%; <italic>p</italic> = 0.091) and land ownership (30.8%; <italic>p</italic> &#x0003D; &#x0003C; 0.001) as their significant barrier to adaptation efforts. Fiber farmers significantly perceived time (71.4%; <italic>p</italic> = 0.003), access to water (64.3%; <italic>p</italic> = 0.097), risk of implementing new practices (64.3%; <italic>p</italic> = 0.005), access to investment capital/funds (60.0%; <italic>p</italic> = 0.085), access to appropriate markets (57.1%; <italic>p</italic> = 0.013), and access to detailed economic information (46.7%; <italic>p</italic> = 0.006) as <italic>significant barriers</italic>.</p></sec></sec>
<sec>
<title>3.9 Decision support tools</title>
<p>On farmers&#x00027; experiences with DSTs, 47.5% &#x00027;use&#x00027; DSTs to aid farming decision-making. Similarly, 56.5% (<italic>n</italic> = 294) indicated that DSTs are helpful in their farm decision-making; 49.7% (<italic>n</italic> = 296) indicated using them <italic>within-in-season planning</italic>, 15.5% use them in <italic>long-term planning</italic>; 51.9% (<italic>n</italic> = 293) expressed their interest in using online DSTs. The study also asked farmers what DSTs they use and weather stations (<italic>n</italic> = 67), soil moisture sensors (<italic>n</italic> = 19), California Irrigation Management Information System [CIMIS] (<italic>n</italic> = 11), ETo (<italic>n</italic> = 10), and water sensors (<italic>n</italic> = 8) were most used. Other info and tools utilized included the National Oceanic and Atmospheric Administration (NOAA), eVeg, Jain Logic, IRROMETERS, and Fruition Sciences 360Viti.</p></sec></sec>
<sec sec-type="discussion" id="s4">
<title>4 Discussion</title>
<p>A needs assessment is the initial step toward developing adaptation strategies for farmers by understanding and documenting their perceptions, experiences, and knowledge of exposures, potential impacts, and social vulnerabilities. Also, to know what effective risk management practices they currently use, what tools and resources would assist them in making strategic decisions, what types of extension education activities would help them, and where to find essential resources. These aspects begin with a detailed understanding of stakeholder needs that allows for a focused use and mobilization of resources that are both measurable and trustworthy to decision-makers on the ground (Garst and McCawley, <xref ref-type="bibr" rid="B15">2015</xref>; Donaldson and Franck, <xref ref-type="bibr" rid="B11">2016</xref>). The discussions focus on farmers&#x00027; climate change perceptions, impacts, concerns, CSA practices, extension education programs, and climate DSTs they currently use to adapt to farming in variable climatic conditions in California.</p>
<sec>
<title>4.1 Climate change perceptions, impacts, and concerns</title>
<p>Climate change and the associated extreme events, including high temperatures, heatwaves, droughts, wildfires, erratic precipitation, floods, and storms are evident (Swain et al., <xref ref-type="bibr" rid="B65">2020</xref>), increasing the vulnerability of agriculture in California (Pathak et al., <xref ref-type="bibr" rid="B51">2018</xref>; Swain et al., <xref ref-type="bibr" rid="B64">2018</xref>; Parker et al., <xref ref-type="bibr" rid="B49">2020</xref>). Despite the ongoing debate on climate change perceptions across the United States (Sanders et al., <xref ref-type="bibr" rid="B61">2022</xref>), this study established that 67% of respondents <italic>agree (agree</italic>&#x0002B;<italic>strongly agree)</italic> that climate change is happening and also <italic>agree (agree</italic>&#x0002B;<italic>strongly agree)</italic> that it is happening due to natural (66.8%) and human (55.7%) causes. Moreover, 38.7% of all impacts were rated <italic>severe</italic> over the past 10 years among historically underrepresented farmers&#x02014;mostly first-generation, limited resource, socially disadvantaged&#x02014;who are mainly (42.9%) farming in the San Joaquin Valley.</p>
<p>Underrepresented farmers were overall <italic>very concerned</italic> about climate-change-related impacts in all three categories&#x02014;water&#x02013;related (i.e., the increasing uncertainty of available water, reduced underground water, crop/water stress, and salinization); temperature&#x02013;related (i.e., crop damage due to heat, drought, frost, pest and disease, and reduced chill); and disaster&#x02013;related (i.e., crop and farm losses, wildfire, smoke, and floods). This study found a connection between farmers&#x00027; responses and the 2012&#x02013;2016 drought in California that led to fallowing of thousands of acres, drying of wells in San Joaquin Valley (Howitt et al., <xref ref-type="bibr" rid="B22">2014</xref>; Jasechko and Perrone, <xref ref-type="bibr" rid="B28">2020</xref>); and indemnity payments to nut tree and fruit growers (Reyes and Elias, <xref ref-type="bibr" rid="B58">2019</xref>; Parker et al., <xref ref-type="bibr" rid="B49">2020</xref>).</p>
<p>The future climatic change in California is projected to impact water supply, storage, and reservoir operations; for instance, snowpacks in the Sierras are projected to decrease by 48% and 65% under low and high-emission scenarios respectively (Reich et al., <xref ref-type="bibr" rid="B57">2018</xref>). In the Sierra region, a notable shift has occurred, with a higher proportion of precipitation falling as rain rather than snow, leading to reductions in stored water and time regulation of snow; affecting the substantially irrigated San Joaquin Valley, Sacramento&#x02013;San Joaquin Delta, and Imperial regions (Mount et al., <xref ref-type="bibr" rid="B45">2018</xref>). We found connections between those assertions with significant <italic>concerns</italic> of farmers which were mainly water-related issues (see <xref ref-type="table" rid="T2">Table 2</xref>). Specifically, farmers in the South San Joaquin Valley (71.6%) and Central Coast (64.1%) were <italic>very concerned</italic> about reduced groundwater availability. Similarly, vegetable farmers significantly expressed more concerns about uncertainty in water availability for irrigation (74.4% <italic>very concerned</italic>) during spring months. The reduced availability of stored water introduces complexities for farmers, especially for irrigation insurance (Wright, <xref ref-type="bibr" rid="B72">2014</xref>; Maestro et al., <xref ref-type="bibr" rid="B39">2016</xref>; Ghosh et al., <xref ref-type="bibr" rid="B16">2021</xref>).</p>
<p>In temperature, warmer winters are projected to reduce winter chill accumulation, an important decision-making factor in orchards, especially in rootstock and cultivar selection (Parker and Abatzoglou, <xref ref-type="bibr" rid="B47">2019</xref>; Zhang et al., <xref ref-type="bibr" rid="B73">2021</xref>). In some nut species (almond), these aspects are further complicated due to the need to have a main variety and one or two pollinator varieties in the same orchard for cross-pollination. Relatedly, problems of insect pest population and incidence are directly linked to increases in greenhouse gas (GHG) emissions, rising temperatures, and relative humidity. Increased carbon dioxide, for instance, supports plant physiology but also influences insect fecundity, consumption rates, and density (Rijal et al., <xref ref-type="bibr" rid="B59">2021</xref>; Skend&#x0017E;i&#x00107; et al., <xref ref-type="bibr" rid="B62">2021</xref>; Jha et al., <xref ref-type="bibr" rid="B30">2024</xref>), and alters pesticide translocation and metabolism, reducing pesticide availability for target pests (Matzrafi, <xref ref-type="bibr" rid="B42">2018</xref>). Also increases in temperature increase the pest generations and their geographical expansion, like, Naval orangeworm spreading in the Coastal Ranges, Southern Sierra, and San Joaquin Valley (Pathak et al., <xref ref-type="bibr" rid="B52">2021</xref>; Mart&#x000ED;nez-L&#x000FC;scher et al., <xref ref-type="bibr" rid="B40">2022</xref>). The impacts that are seen by the scientific community and those perceived by farmers are aligned, regardless of whether they believe the climate changes are natural cycles or human driven.</p></sec>
<sec>
<title>4.2 Need for information on climate adaptation practices</title>
<p>Farmers are currently <italic>implementing</italic> several climate adaptation practices related to water resource management, soil health, and renewable energy and are also seeking out insurance and government assistance programs to increase agriculture resilience. Farmers expressed <italic>interest</italic> and a <italic>high need</italic> for information on those adaptation practices and programs (see <xref ref-type="table" rid="T3">Table 3</xref>) to acquire skills and knowledge to manage various challenges of farming in variable climates. Specifically, water resources are among the main issues farmers are <italic>very concerned</italic> about.</p>
<p>It is noted that increasing weather variability will worsen problems for water supply in California (Reich et al., <xref ref-type="bibr" rid="B57">2018</xref>); consequently, water will be limited and more regulated for all users (Mount et al., <xref ref-type="bibr" rid="B45">2018</xref>). According to California Department of Food Agriculture (<xref ref-type="bibr" rid="B5">2022</xref>), irrigated cropland in California is 9.4 million acres, of which 6.17 million acres are of annual crops and 3.21 million acres are of perennial crops, mostly fruits, and nut trees led by almonds (Mount et al., <xref ref-type="bibr" rid="B45">2018</xref>) in the San Joaquin Valley which accounts for over 60% of the farmland (Escriva-Bou et al., <xref ref-type="bibr" rid="B13">2023</xref>). Farmers will need cropping systems that use less water and benefit from rainfall and flows captured during the fall and winter months. Additionally, farmers will need high-precision automated irrigation systems to apply water in small amounts at frequent intervals, shifting from surface to Microirrigation, and effectively managing crop stress (Ayars et al., <xref ref-type="bibr" rid="B1">2024</xref>).</p>
<p>Farmers are currently <italic>implementing</italic> several soil health-related adaptation practices, more especially reducing soil disturbance/no-till, building soil organic matter by applying manure/compost, using cover crops, and mulching. This study noted that some of these practices, such as mulching, were of <italic>interest</italic>, and farmers similarly expressed a <italic>high need</italic> for information about these practices for building soil organic matter and using cover crops. Cover cropping is a conservation practice, improving biodiversity, ecosystem services, and yield index, making productionist agrosystems more sustainable and reconnecting human activities to the natural ecological community (Thompson, <xref ref-type="bibr" rid="B67">2017</xref>; Montgomery, <xref ref-type="bibr" rid="B44">2021</xref>; Ikendi, <xref ref-type="bibr" rid="B23">2023</xref>). However, cover crops earlier were not widely adopted in semi-arid western states and are grown on 4.8% of farmland in California (LaRose and Myers, <xref ref-type="bibr" rid="B35">2019</xref>). Nevertheless, an <italic>implementation</italic> rate of 39.3% was established among farmers who participated in this study. The low adoption of cover crops is mainly due to the lack of accurate water-related information i.e., the amount of water necessary to establish and maintain the cover crops as well as the paucity of information on their costs and benefits (DeVincentis et al., <xref ref-type="bibr" rid="B9">2022</xref>). Moreover, access to water was among the top five barriers that farmers perceived as significantly impeding their adaptation endeavors. One of the core aspects of this project (see <xref ref-type="fig" rid="F1">Figure 1</xref>) is to close the information gap through regional and crop-specific workshops (Ikendi et al., <xref ref-type="bibr" rid="B24">2023</xref>).</p>
<p>This study established that farmers (29.7%) are transitioning to renewable energy and many are expressing interest in transitioning to renewable energy (48.7%) and reducing dependence on fossil fuels (48.1%), which were both among the top adaptation practices identified. Despite their <italic>interest</italic>, there is a knowledge gap expressed by a <italic>high need</italic> for information on transitions and the reduction of dependency on fossil fuels. The Senate Bill (SB100 2018) laid out a decarbonization program with a vision for a zero-carbon economy by 2045 (California Air Resources Board, <xref ref-type="bibr" rid="B2">2020</xref>; Heidi et al., <xref ref-type="bibr" rid="B21">2022</xref>). The 2022 GHG inventory report shows that between 2000&#x02013;2020, the proportions of emissions by sector were transportation (38%), industrial (23%), in-state electricity (11%), agriculture and forestry (9%), residential (8%), commercial (6%) and imported electricity (5%) which all together emitted an estimated amount of 369.2 MMT CO<sub>2</sub>e (California Air Resources Board, <xref ref-type="bibr" rid="B3">2022</xref>). Also, California is hydrologically engineered with water conveyance systems spanning from the north with large distributions to central to southern California that entail increasing electricity demands.</p>
<p>Most irrigation takes place in the San Joaquin Valley, creating implications for the environmental footprint of nut crops (Marvinney and Kendall, <xref ref-type="bibr" rid="B41">2021</xref>), as well as the energy demand for water conveyance and groundwater pumping (Heidi et al., <xref ref-type="bibr" rid="B21">2022</xref>). Moreover, energy rates for agriculture have been increasing, with an estimated 65% rise over the past decade. In addition, the rate for 2023 was estimated to rise by 16.4%, increasing the operational costs. Investing in automated irrigation systems (Ayars et al., <xref ref-type="bibr" rid="B1">2024</xref>), and renewable energy and creating opportunities for optimal power generation and usage in agriculture can help the electricity system be decarbonized (Governor Gavin Newsom, <xref ref-type="bibr" rid="B17">2023</xref>). California is on a trajectory to zero carbon with statistics showing 59% of electricity already coming from renewable and zero-carbon resources.</p>
<p>Given the current variability of climate and its projected volatility to the end of the century, farmers are consistently looking toward securing access to insurance and/or seeking information about insurance programs. Multigenerational farmers are statistically significantly <italic>accessing</italic> insurance, whereas underrepresented farmers statistically expressed significant <italic>interest</italic> and a <italic>high need</italic> for information on access to insurance and this is expressed as the <italic>highest need</italic> overall. California had its worst drought ever recorded in 1,200 years between 2012&#x02013;2016 based on paleoclimate reconstructions (Griffin and Anchukaitis, <xref ref-type="bibr" rid="B19">2014</xref>) which caused severe crop losses and drying of wells especially in the San Joaquin Valley. However, there are Multiple Peril Crop Insurance products available to farmers in the San Joaquin Valley, especially covering losses caused by failure of the irrigation systems (Wright, <xref ref-type="bibr" rid="B72">2014</xref>; Maestro et al., <xref ref-type="bibr" rid="B39">2016</xref>).</p>
<p>Nevertheless, there are complexities involved in insurance that require thorough education of farmers to understand what alternatives benefit best (Wright, <xref ref-type="bibr" rid="B72">2014</xref>; Ghosh et al., <xref ref-type="bibr" rid="B16">2021</xref>). For instance, insurance bases its expected water availability on how much water is available in reservoirs, levels of soil moisture and snow packs, and crop season precipitation; making it difficult given the fact that snow packs are projected to decrease, and more precipitation falls as rain in the Sierras instead of snow reducing the stored water (Reich et al., <xref ref-type="bibr" rid="B57">2018</xref>). Moreover, insurance does not cover crop losses due to non-natural causes like repurposed water allocations, for instance, environmental regulations (Escriva-Bou et al., <xref ref-type="bibr" rid="B13">2023</xref>) limiting water supplies and usage in agriculture.</p>
<p>This study found that most farmers expressed <italic>interest</italic> in and <italic>a high need</italic> for information on government assistance programs, especially indicated by underrepresented farmers. Adapting to adverse climate requires government intervention and California has several assistance programs related to water resources, renewable energy, and soil health at the federal, state, and county levels (Lewis and Rudnick, <xref ref-type="bibr" rid="B36">2019</xref>). Since 2006, California has been enacting Acts in response to global warming and subsequently providing funds to move forward the CSA policy framework (p. 4). Federal assistance programs helping farmers include the Environmental Quality Incentives Program (EQIP), which promotes conservation practices to improve soil health, water quality, and irrigation efficiency (United States Department of Agriculture Natural Resource Conservation, <xref ref-type="bibr" rid="B70">2023</xref>). At the state level, the California Department of Food and Agriculture (CDFA) offers several CSA grant programs including, but not limited to, the Healthy Soils Program (HSP), which increases soil organic matter and decreases GHG emissions (California Department of Food and Agriculture, <xref ref-type="bibr" rid="B6">2023a</xref>); State Water Efficiency and Enhancement Program (SWEEP), which increases water use efficiency and decreases GHG emission (California Department of Food and Agriculture, <xref ref-type="bibr" rid="B7">2023b</xref>). The California Energy Commission has the Renewable Energy for Agriculture Program that supports the installation of renewable energy technologies on-site in agricultural operations (California Energy Commission, <xref ref-type="bibr" rid="B8">2023</xref>). At the county level, California has, for instance, San Joaquin Valley Air Board with the Agriculture Burn Alternatives Grant Program (San Joaquin Valley Air Pollution Control District, <xref ref-type="bibr" rid="B60">2023</xref>) and Zero Footprint Grants including Compost Connector Grants and Restore Grants, which together support farmer adaptation and resilience to climate change. Despite the availability of several government assistance programs, the study found an information gap as expressed by farmers in the high need for information related to those programs, indicating a need to educate farmers through regional and crop-specific extension education workshops (Ikendi et al., <xref ref-type="bibr" rid="B24">2023</xref>).</p></sec>
<sec>
<title>4.3 Use of climate decision support tools</title>
<p>Information on climate change is one of the most valuable resources for adaptation planning. Climate scientists have a strong understanding of climate trends and have developed many trend and impact models (Laloyaux et al., <xref ref-type="bibr" rid="B34">2018</xref>; Swain et al., <xref ref-type="bibr" rid="B65">2020</xref>). Despite progress at both ends of the spectrum, DSTs that combine climate trends and impact analyses for specific contexts are still in their infancy (Haigh et al., <xref ref-type="bibr" rid="B20">2018</xref>; Ranjan et al., <xref ref-type="bibr" rid="B56">2020</xref>; Lu et al., <xref ref-type="bibr" rid="B37">2021</xref>). Our study corroborates this statement, with 50% of respondents reporting using DSTs and mostly used weather stations. However, the greatest hope is the high level of acceptance that DSTs are useful and the high expression of interest in using online DSTs designed to translate climate information into forms that support production decision-making.</p>
<p>The USDA had earlier echoed the need to transform technical climatic information into forms that farmers can use [United States Government Accountability Office (USGAO), <xref ref-type="bibr" rid="B71">2014</xref>]. Farmers need specific information on their unique crops for decision-making (Jagannathan et al., <xref ref-type="bibr" rid="B27">2023</xref>; Taku-Forchu et al., <xref ref-type="bibr" rid="B66">2023</xref>; Ikendi et al., <xref ref-type="bibr" rid="B25">2024</xref>), especially in California with more than 400 commodities (Pathak et al., <xref ref-type="bibr" rid="B51">2018</xref>) including high-value specialty crops that are highly sensitive to climate-related impacts (DeVincentis et al., <xref ref-type="bibr" rid="B9">2022</xref>; Parker et al., <xref ref-type="bibr" rid="B50">2022</xref>; Ikendi et al., <xref ref-type="bibr" rid="B25">2024</xref>; Jha et al., <xref ref-type="bibr" rid="B30">2024</xref>). In response, a team of researchers (Pathak et al., <xref ref-type="bibr" rid="B74">2023</xref>) from CalAgroClimate embarked on designing online DSTs. The team currently has five tools that project: frost and heat using high-resolution PRISM data within 800 m and 4 km grids, pests, crop phenology, and historical data. Tools like this have value only if they are utilized in farming decisions. For that purpose, tools like CalAgroClimate and other web-based portals need to be illustrated and disseminated to farmers in ways to address their specific needs.</p></sec></sec>
<sec sec-type="conclusions" id="s5">
<title>5 Conclusions</title>
<p>Farmers in California who participated in this study had varying perceptions and experiences regarding climate change and related impacts. However, we found that 67% <italic>agree</italic> that climate change is happening due to natural (66.8%) and/or human (55.7%) causes; moreover, 59.0% expressed that they experienced greater climate change on their farms today than 10 years ago. Importantly, 69.8% <italic>agree</italic> that they are interested in learning more about the impacts of climate change on the agricultural industry, providing a platform for climate and agriculture science researchers and educators to develop extension education programs to meet the needs of the farmers.</p>
<p>Generally, farmers were <italic>very concerned</italic> about water-related issues, specifically increased uncertainty in water availability for irrigation, reduced water availability for irrigation, and reduced groundwater availability. Closely related were temperature-related issues especially increased drought severity and crop damage due to extreme heat. The study found statistically significant associations between ethnoracial background and concern for water, temperature, and disaster-related issues with more underrepresented farmers expressing greater concerns and these farmers especially first-generation farmers were statistically significantly participating in fruit production. The study also identified statistically significant differences in primary concerns between farming regions with farmers in South San Joaquin Valley and Central Coast expressing greater concerns about reduced groundwater availability. Relatedly, increased drought severity was a significant concern with farmers in the Inland Empire, Central Coast, Southern, and North Coast regions whereas farmers in the North Coast and Southern were very concerned with increased crop damage due to wildfires and/or wildland fire smoke. Additionally, vegetable farmers significantly expressed greater concerns about uncertainty in water availability for irrigation while fruit farmers significantly expressed more concerns regarding increased crop/water stress and increased crop damage due to extreme heat.</p>
<p>This needs assessment also found that farmers were implementing several climate adaptation practices related to water resource management, soil health, and renewable energy and are also seeking out insurance and government assistance programs to increase their agricultural resilience. We found a connection between the farmers&#x00027; current adaptation practices and expression of interest in those practices, for instance, water harvesting, transition to renewable energy, drought-tolerant varieties, reducing dependence on fossil fuels, and applying for government assistance. We also found that among the top 10 climate change adaptation practices that farmers were <italic>implementing</italic> and also expressed <italic>interest</italic> in are the same practices they expressed a <italic>high need</italic> for information. These practices included securing access to insurance, applying for government assistance, transitioning to renewable energy, building soil organic matter, and water harvesting. However, the greatest obstacles to farmers&#x00027; adaptation efforts that they perceived as <italic>significant barriers</italic> included government regulations, high input cost, labor access/cost, access to water, and access to investment capital/funds. These findings will potentially inform the development of extension education programs on climate-smart agriculture for farmers in California.</p></sec>
<sec sec-type="data-availability" id="s6">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="sec" rid="s11">Supplementary material</xref>, further inquiries can be directed to the corresponding author.</p></sec>
<sec sec-type="ethics-statement" id="s7">
<title>Ethics statement</title>
<p>The studies involving humans were approved by University of California Davis under Institutional Review Board Number 1841798-2. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.</p></sec>
<sec sec-type="author-contributions" id="s8">
<title>Author contributions</title>
<p>SI: Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing &#x02013; original draft, Writing &#x02013; review &#x00026; editing. NP: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing &#x02013; review &#x00026; editing. VK: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Writing &#x02013; review &#x00026; editing, Visualization. NT-F: Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing &#x02013; review &#x00026; editing, Validation. LR: Conceptualization, Data curation, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing &#x02013; review &#x00026; editing, Formal analysis, Software. SO: Conceptualization, Data curation, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing &#x02013; review &#x00026; editing, Formal analysis. LP: Conceptualization, Data curation, Investigation, Methodology, Validation, Writing &#x02013; review &#x00026; editing, Formal analysis. DZ: Conceptualization, Data curation, Investigation, Methodology, Resources, Supervision, Writing &#x02013; review &#x00026; editing, Formal analysis, Project administration. MC: Conceptualization, Investigation, Methodology, Project administration, Resources, Writing &#x02013; review &#x00026; editing, Data curation, Formal analysis, Supervision. JD-R: Investigation, Methodology, Validation, Writing &#x02013; review &#x00026; editing, Conceptualization. SB: Investigation, Methodology, Validation, Writing &#x02013; review &#x00026; editing. MB: Investigation, Methodology, Validation, Writing &#x02013; review &#x00026; editing. JR: Investigation, Methodology, Validation, Writing &#x02013; review &#x00026; editing. TP: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing &#x02013; original draft, Writing &#x02013; review &#x00026; editing.</p></sec>
</body>
<back>
<sec sec-type="funding-information" id="s9">
<title>Funding</title>
<p>The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The study was funded by the U.S. Department of Agriculture&#x02014;National Institute of Food and Agriculture, Grant Number: 2022-68017-36358.</p>
</sec>
<sec sec-type="COI-statement" id="conf1">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="disclaimer" id="s10">
<title>Publisher&#x00027;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec sec-type="supplementary-material" id="s11">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fsufs.2024.1395547/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fsufs.2024.1395547/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Data_Sheet_1.PDF" id="SM1" mimetype="application/pdf" xmlns:xlink="http://www.w3.org/1999/xlink"/></sec>
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