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

In recent years, there has been rapid growth of Photovoltaic (PV) system integration into diesel-based remote microgrids to reduce the diesel fuel consumption. However, due to low correlation of PV power availability with the load as well as uncertainty and variability of the PV power, the benefits of the integration have not been achieved properly. A large energy reserve is required to compensate the fluctuation and improve reliability, which leads to increased operational cost. Solar irradiance forecasting helps to reduce the reserve requirement and improve the PV energy utilization. In this paper, a novel solar irradiance forecasting using Markov Switching Model is proposed for remote microgrids. This forecasting method uses locally available historical irradiance data of the microgrid location to predict day-ahead irradiance. The case study for validating this method for Brookings, SD resulted in Root Mean Square Error (RMSE) of 99.6 W/m² for 2008 and 106.8 W/m² for 2011.