This study investigates the transformative shift towards renewable energy sources, with a particular emphasis on solar energy in Indiana-a state historically dependent on fossil fuels, but now rapidly advancing in renewable energy development. As solar energy gains prominence in the state's energy portfolio, identifying the factors that influence public acceptance of solar energy technologies and land use type preferences becomes essential. The study examines the influence of climate change perceptions and land use preferences on the acceptance of solar energy. Utilizing a 2020 online survey of 789 Indiana residents, we investigate the relationship between respondents' acknowledgment of anthropogenic climate change and the factors predicting their acceptance of solar panels in Indiana and their installation across various land use types (e.g., Business, Residential, Farm, etc.). Our result shows that climate change perception (anthropogenic and non-anthropogenic climate change), age, urbanicity, length of stay in Indiana, and political ideation were statistically significant determinants of the agreement that solar panels should be used in Indiana. Further, anthropogenic climate change perception and political ideation as determinants of acceptance of different land use types for solar panels installation, demonstrating a positive relationship across most (6 out of 7 and 4 out of 7 respectively) land use types. Finally, we discuss the importance of understanding key stakeholder dynamics and characteristics to facilitate a targeted approach to successfully implement solar energy initiatives.

Precision Agriculture (PA) uses sensors, drones, and machine learning algorithms to provide farmers with site-specific information for targeted farm management decisions. These technological systems can reconfigure farm labor, replacing or displacing agrarian workers, especially unskilled, seasonal, hired, and migrant labor. Therefore, PA raises critical social questions that have implications for farmers’ autonomy and control over agrarian production systems. We critically examine the social consequences of PA through the theoretical lenses of accumulation by dispossession and the agrarian question of labor. We use data from six focus group discussions conducted during the Fall of 2019 in heterogeneous production systems in South Dakota and Vermont. We assert that agritech firms design PA technologies as accumulation strategies predicated on the dispossession of farmers’ autonomy and control over agrarian production systems. As such, PA is fundamentally reconfiguring the future of agrarian labor in the US food system.

As one solution to feeding a growing population with finite resources, some farmers, researchers, and agricultural technology providers (ATPs) have turned to precision agriculture (PA). PA is the practice of mapping out precise input application to maximize the yield. To do this, ATPs collect input and output data from farmers and use Artificial Intelligence and machine learning to build prescription maps, which farmers can program farm equipment to follow. The use of PA has allowed farmers to use less resources, which saves money and reduces environmental impact. However, technology is a two-sided coin, benefiting both end-users, the farmers, and ATPs differently. In agriculture, power asymmetry has been cited as a critical issue existing between farmers and ATPs,and this impacts farmers negatively. For farmers to deploy and have more control over data decision-making on their farms, AI assurance methods need to be integrated into their technologies. There are currently a few studies on this subject in agriculture, but many do not involve agricultural end-users or fall short of meeting the needs of the end-users. If end-users and policymakers are not able to understand how their data is collected and used in the agricultural AI models, they will not be able to make educated decisions about their work. This chapter proposes solutions to benefit all agricultural end-users, including prompting the use of participatory design and adopting more user-centered principles when integrating AI assurance models into agricultural technologies.

Climate-smart agriculture (CSA) is an important discourse among national governments in Africa and international policy circles to increase food productivity, build smallholder farmers' resilience to climate change, and mitigate greenhouse gas emissions. Despite presenting several potential economic and environmental benefits to farmers, its adoption among African smallholder farmers is low. Two important aspects that influence the adoption of CSA are inclusion and exclusion of farmers' local knowledge and how CSA is upscaled among smallholder farmers in Africa. This article uses a systematic review methodology to demonstrate that the existing literature (between 2010-2020) on CSA has substantially addressed issues that hinder its upscaling in Africa, such as heterogeneous farming systems, limited finance, high cost of agricultural inputs, and technology. However, only eight of 30 articles included in the systematic review indicate challenges pertaining to inclusion or exclusion of local knowledge in CSA practices and technologies. Policymakers and academics need to rethink how smallholder farmers' local knowledge can enhance opportunities and fulfil the potential to upscale CSA in Africa.

Agricultural decision support systems (DSSs) are hardware and software tools that utilize big data collected from satellites and drones, ground-based sensors, and analyzed with machine learning algorithms to provide site-specific farming recommendations. Despite the promise of DSSs to address many challenges of the farm economy, there are social and ethical concerns that need to be addressed. Utilizing a mixed-methods approach that consisted of focus group discussions and a follow-up survey questionnaire, we highlight the experiences and affectations of heterogeneous food system actors from Vermont and South Dakota. We find that DSSs transform agricultural knowledge production, reconfigure labor arrangements and unevenly distribute benefits and burdens among farmers. We suggest that agritech developers implement inclusive and deliberative processes when redesigning DSSs to engender ethical, equitable and sustainable improvements to food production systems. Inclusive processes of open deliberation are modalities of responsible innovation, tasked with mitigating frictions within socio-technical systems.

Extant systematic literature reviews on the topic of climate smart agriculture (CSA) have mainly focused on two issues: reviewing framing of the CSA discourse in the academic and policy literature; and policy initiatives in the Global South that enhance the adoption of climate-smart agricultural practices. Yet, there is little systematic investigation into how international organizations can help smallholder farmers manage agricultural systems to respond to climate change. Analyzing these organization's priorities and highlighting their knowledge gaps are crucial for designing future pathways of CSA. We intend to use this article to identify overarching CSA themes that can guide large international organizations to focus their CSA agenda in the hope of achieving goals associated with food security and sustainable intensification. We specifically ask the following question: How have the key CSA topics and themes emerged in the gray literature of international organizations between 2010 and 2020? We adopted a topic modeling approach to identify how six international organizations engaged with several topics related to CSA. Following the Latent Dirichlet Allocation (LDA) approach, we identified eight topics in the documents, representing four overarching themes: gender research, weather and climate, CSA management and food security. We found that there is insufficient discussion on the issues relating to governance measures and gender mainstreaming, with a larger focus on techno-managerial measures of CSA. We conclude that research and training related to CSA must offer opportunities for marginalized and disproportionately vulnerable populations to participate and raise their voices and share innovative ideas at different levels of governance. This article is categorized under: Climate and Development > Social Justice and the Politics of Development Vulnerability and Adaptation to Climate Change > Institutions for Adaptation

Puerto Rico has been subject to complex and compounding effects of multiple disasters, exacerbated by sociopolitical, climactic, and geographical challenges that complicate relief and resilience. Interdisciplinary teams are uniquely suited to traverse emerging challenges in post-disaster settings, but there are few studies that leverage transdisciplinary skill sets and virtual co-production of knowledge to build on local autonomous responses. Communities are key sources of information and innovation which can serve as a model for recovery amidst disaster. Thus, an interdisciplinary team of emerging scholars collaborated with Caras con Causa, a local organization in Catan similar to o, Puerto Rico, to develop processes for enhancing autonomous responses to disaster events through participatory pathways, specifically highlighting local knowledge and preferences. The results of this collaboration include: (1) an iterative process model for transdisciplinary co-production in virtual settings and (2) key highlights from post engagement reflections including community-scale definitions of disaster, and limitations to virtual collaboration amidst disaster. Together, these results yielded critical insights and lessons learned, including recommendations for improved project communication methods within transdisciplinary and virtual collaborations. Collectively, the process, it's resulting products, and the post-engagement reflections demonstrate a pathway for scholars and community members to engage disaster resilience challenges. These strategies are most effectively practiced through focused collaboration with community stakeholders and are paramount in solving real-world challenges related to the increasing complex of compounding disasters.

Advances in precision agriculture (PA), driven by big data technologies and machine learning algorithms can transform agriculture by enhancing crop and livestock productivity and supporting faster and more accurate on and off-farm decision making. However, little is known about how PA can influence farmers' sense of self, their skills and competencies, and the meanings that farmers ascribe to farming. This study is animated by scholarly commitment to social identity research, and draws from socio-cyber-physical systems research, domestication theory, and activity theory. This conceptualization of PA within these theoretical perspectives helps to render visible how big agricultural data and machine learning algorithms can affect meaning, doing, and being for US farmers. Through analysis of data from six focus group discussions and follow-up surveys with stakeholders across the PA value chain, this paper shows that PA tools can necessitate farmers to learn and develop new competencies such as flying drones and interpreting yield maps. At the same time, PA can shape new meaning of farm work and new expectation about a 'good farmer', changing what it means to be a 'successful' farmer from someone who is not only a data observer or data gatherer but also validators of PA models by using their local knowledge of agronomic and environmental phenomenon. We conclude that PA can alter social expectations about farming by reorienting the role of farmers. Policymakers and agriculture extension and outreach programmers can develop more socially relevant PA knowledge and innovation if they can attend to both new and traditional 'good farmer' identities.

To improve the economic and environmental sustainability of agriculture, information is needed on how to target research, teaching, and outreach programs. However, conducting survey research in general, and with agricultural producers specifically, is increasingly challenging given issues such as declining response rates and limited resources. While studies examining the best practices for promoting higher response rates exist, few focus explicitly on agricultural producers. In three separate surveys conducted with agricultural producers in South Dakota in 2018 and 2019, we included experiments testing how token pre-incentives, a research partnership, and response mode options impacted response rates. We also examined how sample source and email augmentations influence survey responses. The study findings indicate that providing pre-incentives and multiple simultaneous response options can increase response rates with agricultural producers. On the other hand, email augmentation to mail surveys, sample source, and identification of select institutional research partnerships appear to have minimal effects.

The coronavirus pandemic (COVID-19) is transforming individuals, governments, and sectors globally. To mitigate the potential effects of COVID-19, various governments around the globe are implementing measures such as social distancing and lockdown strategies. However, these policies have unintended consequences. Although the main objective is to ensure that individuals are safe, a tradeoff exists between safety and entirely creating new challenges for society. This study explores the implications of policies such as social distance, lockdown, and curfews implemented by various national governments on food systems and greenhouse emissions. Using data from International Food and Policy Research, we identified limited transportation, lockdown, ban of public gathering, and closure of schools and religious institutions as prominent measures in mitigating the effects of COVID-19 by many countries. We observed these policies have unintended consequences for food systems and greenhouse gas emissions, mainly CO2. Implementing these policies show that CO2 emission reduced, and existing challenges of food systems are further aggravated with these policies. Governments need to have coordinated strategies to minimize unintended consequences of these policies.

Vaccine hesitancy remains a major barrier to the successful campaign of vaccine programs, including COVID-19, globally. Understanding themes in perceptions among populations regarding vaccine science can aid in improving program implementation as well as potentially reduce socially induced vaccine hesitancy. Social media has been shown to be an increasingly useful tool for rapidly understanding public perceptions regarding public health concerns including vaccine adoption. However, specific themes regarding vaccine perceptions immediately after the COVID-19 vaccine release to the public has yet to be investigated. This study aimed to investigate the perceptions surrounding the COVID-19 vaccine among United States, Brazil, and India Twitter users within weeks post vaccine release. We collected Twitter data through Meltwater software using keywords including coronavirus, vaccines, Pfizer-BioTech, Moderna, Johnson & Johnson/Jassen, AstraZeneca, Novavax, Sinovac-Biotech, Covaxin, Covishield, Sputnik, United States, Brazil, and India in our search query. These keywords were also combined in a Boolean search style (i.e. COVID-19 and Pfizer) to retrieve relevant social media posts on COVID-19 vaccination and vaccines. We used R software to remove usernames, weblinks and other personal information and then the Nvivo 12 statistical software to analyze tweets and draw meanings through a qualitative interpretative approach. Three key themes related to vaccine perception among 2,858 Twitter posts in the United States, Brazil, and India emerged in our analysis. These themes were mistrust in vaccine science (91.5%), religious push backs (5.4%), and politics of vaccination (3.5%). Several subthemes also emerged from these Twitter data. Identifying social implications of COVID-19 vaccine hesitancy is vital in combating vaccine related misinformation as well as provision of accurate vaccine related public communications regarding vaccine acceptance among populations globally.

In this paper, we estimate the dependence structure between international stock markets using copulas. Different relationships that exist in normal and extreme periods were estimated using Clayton copula. The Inference Functions for Margins method was used in estimating the clayton copula parameter thereby obtaining dependence estimates used in estimating Value-at-Risk. Extreme events are likely to alter the dependence structure of financial markets.This could have implications for investment decisions and ability to estimate the risk of financial markets crash. Results reveal that during the crisis period (2007-2009), maximum possible loss of market value is 75.9% and 77.6% with a confidence interval of 90% for the Kenya-Nigeria and Kenya-South Africa portfolios respectively. This implies that the Kenya-South Africa portfolio has the highest risk.

The paper analyzed shale oil development in the United States and its implications for the Nigerian economy. The analyses show that recent shifts in global energy market are characterized firstly by advancement in technology (horizontal drilling and hydraulic fracking) as a result of rising global crude oil price making shale production cheaper to explore and second, increase demand from Asia. The analysis reveals an increase in global oil production, decline in U.S. oil import from the world with increased reference to Nigeria. Further, the Nigerian macroeconomy has largely been affected by the recent global shift in energy market. Oil revenue has declined alongside exchange rate and external reserves with external debt rising. The study recommends therefore that greater weight should be assigned to engineering diversification of the economy from over dependence on oil revenue to reduce macroeconomic instability.

We analyzed the structural and employment implications of the recent Nigerian GDP rebasing exercise and the data released in its aftermath. Our analyses show that structural shifts revealed by the data are overstated by the extent of inclusion effects and by the extent to which the relative price effects are accurate. The estimates of employment elasticities of sectoral growth imply that the growth of manufacturing and service sectors generate approximately triple and quadruple the job creation elasticities of agricultural sector growth. The results suggest therefore, that the future of job creation in Nigeria lies more in policies that promote the competitiveness and growth of manufacturing and services. We recommend therefore that greater weight should be assigned to engineering sustainable structural changes, higher employment elasticity of growth in Nigeria hence, global competitiveness of the Nigerian economy.