Gabriel Granco

Undergraduates today are being pulled in two opposing directions of mastering Artificial Intelligence (AI) and addressing sustainability

A common problem for interdisciplinary sustainability research is that scientists trained in different disciplines are often not rowing their boat effectively in the same direction. Sustainability tools can aid the implementation of this team-melding process. Here, our purpose is to illustrate our Multi-step Integrated graphical and structured discussion eXercise (MIX) tool that transforms diverse disciplinary experts into an interdisciplinary team. We use a visual puzzle-solving approach based on the blind men and the elephant metaphor (BMEM) because this story illustrates the shortcomings of siloed viewpoints and the need to integrate multiple perspectives. Our six-step MIX tool provides step-specific objectives, group activities, discussion questions, and learning outcomes. Activities promote experiential learning for team problem solving. The step-specific structured discussions are designed to get each individual to change their focus from their own discipline (i.e., an elephant trunk, tail, leg, or other isolated pieces of the whole animal) to the team’s interdisciplinary goal (i.e., the whole elephant or the entire multi-faceted problem). In our example proof of concept, we show that a narrow focus on only economic yield (trunk), ecological conservation (legs), or human values (tail) misrepresents the biologically involved sustainability problem (elephant) and blocks innovative solutions.

This project aims to broaden AI education by developing and studying the efficacy of innovative learning practices and resources for AI education for social good. We have developed three AI learning modules for students to: 1) identify social issues that align with the SDGs in their community (e.g., poverty, hunger, quality education); 2) learn AI through hands-on labs and business applications; and 3) create AI-powered solutions in teams to address social is-sues they have identified. Student teams are expected to situate AI learning in their communities and contribute to their communities. Students then use the modules to en-gage in an interdisciplinary approach, facilitating AI learn-ing for social good in informational sciences and technology, geography, and computer science at three CSU HSIs (San Jose State University, Cal Poly Pomona and CSU San Bernardino). Finally, we aim to evaluate the efficacy and impact of the proposed AI teaching methods and activities in terms of learning outcomes, student experience, student engagement, and equity.

O agronegócio é um vetor importante para o crescimento econômico do Brasil. Dentre os produtos agrícolas, o café foi responsável por R$ 34,5 bilhões da produção do setor em 2020 (Confederação da Agricultura e Pecuária do Brasil, 2021 Confederação da Agricultura e Pecuária do Brasil – CNA. (2021). Panorama do agro. Recuperado em 23 de abril de 2023, de https://www.cnabrasil.org.br/cna/panorama-do-agrohttps://www.cnabrasil.org.br/cna/panorama-do-agro https://www.cnabrasil.org.br/cna/panoram... ). Nesse setor, o Brasil é o maior produtor e exportador mundial. A produção total da safra no ano de 2022 foi de 50,92 milhões de sacas de 60kg de café beneficiado (Empresa Brasileira de Pesquisa Agropecuária, 2022 Empresa Brasileira de Pesquisa Agropecuária – EMBRAPA. (2022). Safra dos Cafés do Brasil totaliza 50,92 milhões de sacas de 60kg de café em 2022. Brasília. Recuperado em 4 de abril de 2023, de https://www.embrapa.br/busca-de-noticias/-/noticia/77216868/safra-dos-cafes-do-brasil-totaliza-5092-milhoes-de-sacas-de-60kg-de-cafe-em-2022#:~:text=36%25%20do%20total-,A%20produ%C3%A7%C3%A3o%20total%20da%20safra%20dos%20Caf%C3%A9s%20do%20Brasil%20deste,%C3%A0%20safra%20passada%20de%202021 https://www.embrapa.br/busca-de-noticias... ). Desse total, 39,35 milhões de sacas foram exportadas (Empresa Brasileira de Pesquisa Agropecuária, 2023 Empresa Brasileira de Pesquisa Agropecuária – EMBRAPA. (2023). Exportações dos cafés do Brasil atingem 39,35 milhões de sacas e receita cambial recorde de US$ 9,23 bi em 2022. Brasília. Recuperado em 4 de abril de 2023, de https://www.embrapa.br/busca-de-noticias/-/noticia/77856850/exportacoes-dos-cafes-do-brasil-atingem-3935-milhoes-de-sacas-e-receita-cambial-recorde-de-us-923-bi-em-2022#:~:text=socioecon%C3%B4micos%20e%20ambientais-,Exporta%C3%A7%C3%B5es%20dos%20caf%C3%A9s%20do%20Brasil%20atingem%2039%2C35%20milh%C3%B5es%20de,9%2C23%20bi%20em%202022 https://www.embrapa.br/busca-de-noticias... ), sendo cerca de 20% de cafés especiais (Verde.Ag, 2023 Verde.Ag. (2023). Produção de cafés especiais: saiba quais são os desafios e as oportunidades. Recuperado em 4 de abril de 2023, de https://blog.verde.ag/encontro-com-gigantes/producao-de-cafes-especiais/ https://blog.verde.ag/encontro-com-gigan... ).

Scientific experts from different disciplines often struggle to mesh their specialized perspectives into the shared mindset that is needed to address difficult and persistent environmental, ecological, and societal problems. Many traditional graduate programs provide excellent research and technical skill training. However, these programs often do not teach a systematic way to learn team skills, nor do they offer a protocol for identifying and tackling increasingly integrated interdisciplinary (among disciplines) and transdisciplinary (among researchers and stakeholders) questions. As a result, professionals trained in traditional graduate programs (e.g., current graduate students and employed practitioners) may not have all of the collaborative skills needed to advance solutions to difficult scientific problems. In the present article, we illustrate a tractable, widely implementable structured process called RISE that accelerates the development of these missing skills. The RISE process (Route to Identifying, learning, and practicing interdisciplinary and transdisciplinary team Skills to address difficult Environmental problems) can be used by diverse teams as a tool for research, professional interactions, or training. RISE helps professionals with different expertise learn from each other by repeatedly asking team-developed questions that are tested using an interactive quantitative tool (e.g., agent-based models, machine learning, case studies) applied to a shared problem framework and data set. Outputs from the quantitative tool are then discussed and interpreted as a team, considering all team members’ perspectives, disciplines, and expertise. After this synthesis, RISE is repeated with new questions that the team jointly identified in earlier data interpretation discussions. As a result, individual perspectives, originally informed by disciplinary training, are complemented by a shared understanding of team function and elevated interdisciplinary knowledge.

Specialty crops with long economic life cycles have lower adaptability and flexibility to climate change, making long-term planning crucial. This study examines the impact of climate change on almond, citrus, pistachio, and walnut production in California, using a machine learning approach to estimate crop suitability under current and future environmental conditions. We used recent satellite-observed cropland data to generate an occurrence dataset for these crops. Ecological data including bioclimatic variables derived from global circulation models developed under the Coupled Model Intercomparison Project Phase 6 (CMIP6) and surface variables were used to model suitability. The bioclimatic variables relating to temperature and precipitation had the largest effect on each crop’s suitability estimation. The results indicate that suitable areas for almonds, citrus, and walnuts will change significantly within 20 years due to climatic change, and the change will be even greater by the end of the century, indicating a potential loss of 94% of the current suitable area. The results for pistachios indicate change in the spatial distribution of suitable area but the total area is predicted to remain near the current suitable area. Policymakers, researchers, and farmers must work together to develop proactive adaptation strategies to mitigate the negative effects of climate change on specialty crop production. The application of a species distribution model for agriculture suitability provides critical information for future work on adaptation to climate change, identifying areas to target for further analysis.

Climate change is a critical challenge for the global grape and wine industry, as it can disrupt grapevine growth, production, and wine quality. Climate change could influence the cost-effectiveness and growth of the wine industry in different wine regions since grapevine development is deeply dependent on weather (short-term) and climate (long-term) conditions. Innovation and new technologies are needed to meet the challenge. This review article addresses the impact of climate change on grapevines, such as vine phenology, pest and disease pressure, crop load, and grape and wine composition. It also reviews recent advances in the areas of viticultural manipulation and relevant technologies to potentially reduce the impact of climate change and help growers improve grape quality. Remote sensing is used for vineyard microclimate monitoring; thermal sensors combined with UAVs, aircraft, or satellites are used for water management; soil electrical conductivity sensors have been developed for soil mapping. Viticultural manipulations, such as regulated deficit irrigation for water use efficiency and berry-ripening delay for growing quality fruit, are also discussed. The review assesses future directions for further technological development, such as soil and vine water monitoring devises, precision viticulture, and artificial intelligence in vineyards.