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. Key policy insights

Artificial intelligence (AI) represents technologies with human‐like cognitive abilities to learn, perform, and make decisions. AI in precision agriculture (PA) enables farmers and farm managers to deploy highly targeted and precise farming practices based on site‐specific agroclimatic field measurements. The foundational and applied development of AI has matured considerably over the last 30 years. The time is now right to engage seriously with the ethics and responsible practice of AI for the well‐being of farmers and farm managers. In this paper, we identify and discuss both challenges and opportunities for improving farmers’ trust in those providing AI solutions for PA. We highlight that farmers’ trust can be moderated by how the benefits and risks of AI are perceived, shared, and distributed. We propose four recommendations for improving farmers’ trust. First, AI developers should improve model transparency and explainability. Second, clear responsibility and accountability should be assigned to AI decisions. Third, concerns about the fairness of AI need to be overcome to improve human‐machine partnerships in agriculture. Finally, regulation and voluntary compliance of data ownership, privacy, and security are needed, if AI systems are to become accepted and used by farmers.

In industrialized societies, techno-optimism is a belief that human ingenuity, through improved science and technology, will ultimately provide remedies to most current and future threats to human well-being, such as diseases, climate change, and poverty. Here we examine (a) whether techno-optimism is found among Midwestern corn and soybean farmers and (b) how this confidence in human ingenuity influences their support for climate change adaptation. By examining data from a survey of nearly 5,000 grain farmers in the Midwestern United States, we found that greater techno-optimism can reduce farmers’ support for climate change adaptation and increase their propensity to express a preference to delay adaptation-related actions. This study advances our understanding of how social and cognitive factors influence farmers’ attitude toward climate change. Findings from this study can also help extension educators to develop outreach programs that are sensitive to farmers’ views about the ability of science and technology to solve climate change–related issues.

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

The use of intelligent decision support systems (DSS) in precision farming provides an opportunity to improve agricultural recommendations and reduce the impacts of agriculture on the environment. Despite the benefits offered by DDS, many farmers remain skeptical of using these hardware and software tools, and their adoption rates have remained low. A survey of 312 South Dakota farmers examined the barriers and opportunities for their engagement with DSS. Exploratory factor analysis was used to analyze 13 Likert scale survey items that probed farmers’ concerns about DSS. Factor loadings indicated that farmers’ concerns are related to high cost, insufficient knowledge, lack of confidence, and cyber security and privacy. A latent profile analysis (LPA) method was used to classify respondents into profiles or groups based on their dimensions of concerns (cost, knowledge, confidence, and security). Results of the LPA revealed that the sample of farmers could be grouped into four distinct profiles that ranged from low to high confidence in the use of DSS for agronomic decision‐making. Giving attention to farmer comfort/concern profiles allows for a more inclusive and better targeted engagement with farmers and potentially increase the adoption of PA. This knowledge can be vital for technology developers, policymakers, and extension services who are keen to promote PA usage among large‐, medium‐, and small‐scale farmers in the United States.