Shobha Poudel

As the primary source of global food, fiber, and energy, agriculture plays an imperative role in human development. However, the sector is under pressure to expand production while simultaneously minimizing climate change impacts and enhancing adaptive capacity. These pressures demand a fundamental shift toward more sustainable and resilient production systems. Climate-smart agriculture (CSA) has emerged as a robust framework to address these challenges, synchronizing three critical goals: increasing productivity, enhancing resilience to climate shocks, and lowering emissions.However, sustainability must also be financially viable. For farmers, the decision to adopt a CSA practice is ultimately an investment decision. For society, the relevant calculus is broader, because many of the benefits of CSA, such as avoided emissions, improved soil health and biodiversity, accrue as positive externalities that markets do not reward. The gap between private and social returns, together with the costs of information, finance and learning, largely determines whether profitable practices are actually adopted. This Research Topic highlights how CSA innovations can generate measurable economic returns while advancing environmental sustainability and resilience under climate change. Based on this economic evidence, we aim to inform policymakers and investors with evidence-based research materials necessary to scale resilient solutions for a changing world.The distinctive contribution of this Research Topic lies in its explicit economic orientation. The ten articles collected here place economic evaluation at the center, using a notably diverse analytical toolkit: farm-level cost-benefit analysis, benefit-cost ratios from large-scale field demonstrations, net present value and sensitivity analysis, energy-and carbon-adjusted cost assessment, social cost-benefit analysis with externality valuation, real option analysis, treatment-effect and endogenous switching models of welfare impacts, panel econometrics, and a systematic review. The evidence spans South Asia, Sub-Saharan, West and Central Africa, China and Canada, and production systems ranging from cereals and dairy to aromatic plants and insect-based feed. Alltogether, the articles allow us to ask not only whether CSA pays, but for whom, under which institutional conditions, over what time horizon and under what degree of uncertainty.The overarching theme of these articles is the promotion of sustainable and climate-smart agricultural systems that align economic performance with environmental stewardship. Their key goals include: demonstrating the profitability and cost-effectiveness of CSA innovations using metrics such as net returns, benefit-cost ratios and net present values; quantifying the gap between private and social returns once environmental and social externalities are valued; assessing the welfare effects of CSA on rural livelihoods, employment and food security; identifying the adoption costs, information constraints and institutional barriers that prevent profitable practices from being taken up; examining how digital tools, finance, precision technologies and policy frameworks alter these costs and incentives; and evaluating how nutrient and resource management strategies can lower production costs while improving soil health and reducing greenhouse gas emissions. Together, these goals link the economic evaluation of agricultural practices to broader progress toward climate resilience and the Sustainable Development Goals.The articles emphasize that the economic viability of CSA innovations depends not only on productivity gains, but also on the institutional context, access to information, and the adoption of inclusive dissemination strategies. The contributions show that, across diverse agroecological and socio-economic settings, sustainable agricultural transitions are most effective when climate-smart practices are evaluated through integrated economic, environmental and social lenses that account for heterogeneity among farmers and production systems.Rather than presenting the articles individually, we organise them around five themes: profitability and productivity; environmental performance and its economic value; digitalisation and innovation; gender and inclusion; and institutional and policy dimensions. Several articles speak to more than one theme and are discussed accordingly. Table 1 compares the ten studies by setting, analytical approach, economic metrics and main economic result. Geyo (2025) shows the economic feasibility of crop diversification toward high-value, climateresilient crops. Applying farm-level cost-benefit analysis, the study demonstrates that despite cultivating in smaller land areas, rosemary production nearly doubles the net revenue compared to wheat. Geyo highlights the potential of medicinal and aromatic plants to increase smallholder incomes, reduce land-use pressure, and diversify livelihood alternatives in climatevulnerable regions.The study of Meena et al. (2025) assesses the productivity and profitability of the major staple crop system and provides strong empirical evidence that CSA practices can improve both. With large-scale field demonstrations the authors explain that timely sowing combined with resource-conserving technologies such as zero tillage and the Happy Seeder increases wheat yields, net returns, and benefit-cost ratios. The findings offer relevant economic justification for scaling climate-resilient technologies in subtropical agroecosystems, which are exposed to climate variability.Chelang'a et al. ( 2025) examine the adoption and welfare impacts of climate-smart dairy strategies among smallholder producers in Central Kenya using a multinomial endogenous switching regression model. They found that the adoption of integrated climate-smart dairy practice bundles encompassing improved feeding, breeding, and herd management can significantly increase milk productivity and farm...

Indigenous and local communities (ILCs) are the most impacted by climate change, as they face a combination of environmental and socio-economic challenges. However, ILCs hold extensive, complex, and rich knowledge and deep-rooted understandings of climate and environmental change that can inform climate change adaptation (CCA) strategies. Through a literature review, the study investigates the effectiveness of Indigenous knowledge and CCA. This helped to critically and systematically interrogate the adaptive measures. Using case studies of India, Nepal, and Sri Lanka, this study specifically examines the ILCs can partner with authorities, lead and co-learn in resilience building at local and regional levels. Firstly, through literature review, we understand the dominant adaptation strategies employed across the region and how they vary in effectiveness. Secondly, through the knowledge co-production approach, we mapped (1) Indigenous and traditional practices and (2) the major actions and roles to be taken up by various levels of institutions for the case study areas with high vulnerability. The adaptation strategies range from tangible to intangible practices/knowledge systems, which are grouped as social, physical, environmental, institutional, and cultural. Research findings from the preliminary surveys of the case study areas from the Southeast Asia region indicate that indigenous applied community knowledge, such as microclimate adaptation strategies, seasonal livelihood resource mapping, and environmental stress management, are highly effective at the local level. This approach will not only support adaptive transformation in response to climate change events but also strengthen community cohesion by promoting the integration of indigenous, local, and long-practiced strategies by communities into current and future climate action frameworks. The study advocates an innovative three step process ensuring participatory, locally grounded knowledge co-production approach to climate resilience that supports livelihoods in vulnerable regions.

South Asia encounters distinct challenges and heightened vulnerabilities to climate change while striving for food security. Climate-smart agriculture (CSA) plays an imperative role to improve agricultural productivity, enhance climate resilience, and reduce greenhouse gas emissions. However, economic assessments of CSA rely on traditional cost-benefit analysis (CBA) that does not incorporate social and environmental externalities and fails to capture investment flexibility and externalities under climate crisis. This study provides an integrated economic evaluation framework combining CBA, externality valuation, and real option analysis (ROA) to assess the viability and strategic value of CSA interventions across Nepal, India, and Sri Lanka. Using household survey data along with focus group discussion and willingness to pay (WTP), data from high-value crops of 10 CSA practices were evaluated for their financial (NPV) and social net present value (SNPV), social internal rate of return (SIRR), and real option analysis. The analysis shows that precision nutrient management (NPV = USD 510.21/ha, IRR = 24%, BCR = 1.04), crop rotation (NPV=USD1464.39/ha, IRR = 44%, BCR = 1.08), mixed cropping (NPV = 513.49/ha, IRR = 41%, BCR = 1.07), and drip irrigation (NVP = 636.25/ha, IRR = 29%, BCR = 1.02) are economically feasible CSA practices. When environmental and social externalities such as women’s empowerment, biodiversity conservation, and greenhouse gas mitigation are included, the overall value of CSA practices increases significantly. Real option analysis, based on the Black–Scholes model, showed crop rotation (SNPV = 3492.94, SIRR = 46.47%) and precision nutrient management (SNPV = USD3024.70, SIRR = 56.24%) exhibited high option. The findings underscore the importance of integrating real options analysis and externality valuation into CSA planning and policy. This approach supports more informed, adaptive, and socially inclusive investment decisions that is critical for advancing sustainable agriculture and climate resilience in South Asia.

The impact of climate change, arguably global warming and resulting drought, is one of the most escalating agricultural challenges affecting crop productivity. Therefore, effective water management is critical in agricultural practices. The impact of climate change, arguably global warming and resulting drought, is one of the most escalating agricultural challenges affecting crop productivity. Therefore, effective water management is critical in agricultural practices.T The analysis of plant leaves presents an opportunity to gauge irrigation status through automated solutions to encourage broader adoption among farmers. Currently, there is a notable absence of AI methods in the literature for detecting tomato plant irrigation status through leaf analysis. Addressing this gap, we propose a novel end-to-end deep learning (DL)-based method, inspired by the ResNet-50 model. Our model trims unnecessary blocks and reduces larger kernels, significantly streamlining the model to better fit with the leaf image dataset related to the tomato irrigation status. We evaluate our method using a newly developed dataset and find outstanding performance (Precision: 99.05%, Recall: 99.01%, F1-score: 99.01%, mean-average F1: 98.98%, weighted-average F1: 98.95%, Kappa: 98.61%, accuracy: 98.90%) while comparing with the pre-trained DL models. Additionally, our model has fewer parameters and lower floating-point operations (FLOPs), enhancing its efficiency and suggesting its potential for more cost-effective and productive irrigation management practices.

Climate-smart agriculture (CSA) is a climate-resilient practice that stands out globally as an important practice through which we can deal with emerging challenges through adaptation and mitigation to increase crop productivity and resilience. Despite its significance, a comprehensive cost–benefit analysis of the adoption of these practices has not yet been carried out. This study aims to bridge the knowledge gap between the cost and effectiveness of CSA practices adopted by small-scale farmers in growing rice, wheat and maize, the most staple crops in the Gandaki River Basin of Nepal. In this study, net present value (NPV), internal rate of return (IRR), benefit–cost (BC) ratio, net benefit investment (NK) ratio and payback period, along with the value of externalities (social and environmental), were employed to assess the profitability of CSA practices. The findings indicate that almost all the CSA practices analyzed were profitable, with the exception of solar water management in maize with very low IRR (6%) and a longer payback period. The outcome of this study offers valuable insights for farmers in choosing profitable CSA technology and for policy makers in promoting better CSA technology, upscaling CSA practices, and formulating new agricultural policies and programs in the context of the changing climate.

Nepal relies heavily on agricultural production for food security and economic growth, with rice being a major staple crop that accounts for more than 25% of the country's agricultural contribution. The main rice growing season (June to October) is particularly vulnerable to monsoon rainfall-induced flooding damage. Accurate and transparent methods are crucial to assess rice crop damage and support mitigation and disaster response strategies in the country. This study developed a framework for rapid pre- and post-event crop status assessment using optical and microwave remote sensing images and estimate the actual loss based on its timing and intensity. The framework combines Sentinel 2 A/B images with Sentinel 1 SAR images to map rice areas, identify areas at risk, and quantify crop loss due to flooding. The crop status at the moment of flooding is estimated through time series analysis of rice phenology. The inundation area map and its temporal dynamics were developed using Sentinel 1 SAR images during the flooding and post-transplantation months. Statistical analysis was performed on the time-series pre- and post-flooding status of the rice crop to estimate rice yield loss and, finally, automate the method used in the region for yield loss estimation due to flooding in rice-growing areas.

South Asian countries face a disproportionate impact from disasters due to their unique topography, poverty, low literacy rates, and socio-economic status. Human activities, such as unplanned urbanization and poorly designed rural road networks, have further contributed to disasters in the region. The article explores the potential of nature-based solutions (NbS) as a means of addressing these challenges through the integration of green, blue, and grey infrastructure. The analysis evaluates the significance of NbS and examines policies and regional cooperation in Bangladesh, Bhutan, India, Nepal, and Sri Lanka, highlighting the importance of incorporating NbS into national policies and promoting collaboration among these countries. The study identifies the current low implementation of NbS in South Asia, with limited research in this area. While there are existing policy tools related to coastal zone management, water, forest, and urban development, policies related to NbS should be coherent, connected, and integrated with natural resources, climate change, disaster risk reduction, and socio-economic growth to achieve sustainable development in the region. Overall, the article emphasizes the need for effective policy implementation and research to enhance resilience to climate change and promote sustainable development in South Asia.

Climate change is expected to change precipitation and temperature patterns, which will impact the hydrological regime in Asia. Most river systems in the region originate from the Hindu Kush-Himalayas, and the altered precipitation patterns pose a threat to their sustainability, making it a major concern for planners and stakeholders. Obtaining accurate data on precipitation distribution is crucial for water accounting, which poses challenge. To address this, gridded precipitation products developed from satellite imagery and modeling techniques have become a viable alternative or addition to observed rainfall. However, the accuracy of these products in the region is uncertain. In this study, we aim to evaluate and compare the seven most commonly used precipitation products for the regions to address this gap. The study evaluated seven rainfall products, namely APHRODITE, TRMM, CHIRPS, PERSIANN-CDR, CMORPH, WFDEI, and GPCC by comparing daily, dekadal, and monthly rainfall data to 168 stations data in six countries and 11 river basins in the HKH region. The analysis used four continuous statistical indicators (Pearson correlation coefficient, Bias, Root Mean Square Error, and Nash–Sutcliffe Efficiency coefficient) and two categorical indicators (Probability of Detection and False Alarm Ratio). APHRODITE consistently performed well in several basins with high r values and low RMSE values, but had positive or negative bias values in different basins. CMORPH had the lowest positive bias value in the Ganga_Brahmaputra basin, while GPCC showed the largest r value and lowest RMSE value in the Sindha basin. CHIRPS performed well in Afghanistan, but had positive bias values. GPCC performed well in Myanmar and Pakistan, but had negative or positive bias values. APHRODITE performed consistently well in Nepal, but had negative bias values. Overall, the performance of different gridded precipitation products varies depending on the country and type of evaluation.

Sustainable agricultural management requires knowledge of where and when crops are grown, what they are, and for how long. However, such information is not yet available in Nepal. Remote sensing coupled with farmers’ knowledge offers a solution to fill this gap. In this study, we created a high-resolution (10 m) seasonal crop map and cropping pattern in a mountainous area of Nepal through a semi-automatic workflow using Sentinel-2 A/B time-series images coupled with farmer knowledge. We identified agricultural areas through iterative self-organizing data clustering of Sentinel imagery and topographic information using a digital elevation model automatically. This agricultural area was analyzed to develop crop calendars and to track seasonal crop dynamics using rule-based methods. Finally, we computed a pixel-level crop-intensity map. In the end our results were compared to ground-truth data collected in the field and published crop calendars, with an overall accuracy of 88% and kappa coefficient of 0.83. We found variations in crop intensity and seasonal crop extension across the study area, with higher intensity in plain areas with irrigation facilities and longer fallow cycles in dry and hilly regions. The semi-automatic workflow was successfully implemented in the heterogeneous topography and is applicable to the diverse topography of the entire country, providing crucial information for mapping and monitoring crops that is very useful for the formulation of strategic agricultural plans and food security in Nepal.

Background: In the last two decades, Nepal has experienced an increase in both forest fire frequency and area, but very little is known about its spatiotemporal dimension. A limited number of studies have researched the extent, timing, causative parameters, and vulnerability factors regarding forest fire in Nepal. Our study analyzed forest fire trends and patterns in Nepal for the last two decades and analyzed forest fire-vulnerability risk based on historical incidents across the country. Results: We analyzed the spatial and temporal patterns of forest fires and the extent of burned area using the Mann-Kendall trend test and two machine-learning approaches maximum entropy (MaxEnt), and deep neural network (DNN). More than 78% of the forest fire burned area was recorded between March and May. The total burned area has increased over the years since 2001 by 0.6% annually. The forest fire-vulnerability risk obtained from both approaches was categorized into four classes—very high, high, low, and very low. Conclusions: Although burned area obtained from both models was comparable, the DNN slightly outperformed the MaxEnt model. DNN uses a complex structure of algorithms modeled on the human brain that enables the processing of the complex relationship between input and output dataset, making DNN-based models recommended over MaxEnt. These findings can be very useful for initiating and implementing the most suitable forest management intervention.