Taiwo Bintu Ayinde, Charles F. Nicholson, Benjamin Ahmed
Abstract Achieving Net Zero Emissions in vegetable production systems is a critical challenge in dryland climates of low- and middle-income countries, yet limited data exists to assess the feasibility of such systems. This study employs life cycle inventory methods to evaluate key performance metrics, including yield per land area, production costs, cumulative energy demand (CED), global warming potential (GWP), and water use (WU) for Controlled Environment Agriculture (CEA) in screen houses and field-based tomato production systems in Northern Nigeria. The findings reveal that CEA, despite its high production cost of ₦24,070.80 per m², achieves the highest yield of 28.57 kg per m². Additionally, CEA demonstrates superior efficiency, exhibiting the lowest C ED (0.025 MJ/kg) and GWP (0.76 kg CO₂-eq/kg). In contrast, rainfed field production, while having the lowest cost (₦58.45 per m²), results in the lowest yield (0.08 kg/m²) and the highest GWP (34,545.8%). Irrigated field production performs moderately, with a production cost of ₦150.38 per m², a yield of 0.22 kg per m², and a GWP of 12,572.4%. A key factor influencing yield variation across production systems is the difference in tomato varieties cultivated in open-field and CEA environments. CEA relies on hybrid varieties optimized for controlled conditions, whereas open-field farming utilizes varieties adapted to outdoor environmental fluctuations, contributing to disparities in yield potential. This study highlights the trade-offs between cost, yield, energy efficiency, and environmental impact across different production models. The results underscore the advantages of adopting more efficient and controlled cultivation methods like CEA, offering potential pathways for sustainable and environmentally responsible agricultural practices in regions facing climate and resource constraints.
The development of a kilometer-scale E3SM Land Model (km-scale ELM) is an integral part of the E3SM project, which seeks to advance energy-related Earth system science research with state-of-the-art modeling and simulation capabilities on exascale computing systems. Through the utilization of high-fidelity data products, such as atmospheric forcing and soil properties, the km-scale ELM plays a critical role in accurately modeling geographical characteristics and extreme weather occurrences. The model is vital for enhancing our comprehension and prediction of climate patterns, as well as their effects on ecosystems and human activities. This study showcases the first set of full-capability, km-scale ELM simulations over various computational domains, including simulations encompassing 21.6 million land gridcells, reflecting approximately 21.5 million square kilometers of North America at a 1 km x 1 km resolution. We present the largest km-scale ELM simulation using up to 100,800 CPU cores across 2,400 nodes. This continental-scale simulation is 300 times larger than any previous studies, and the computational resources used are about 400 times larger than those used in prior efforts. Both strong and weak scaling tests have been conducted, revealing exceptional performance efficiency and resource utilization. The km-scale ELM uses the common E3SM modeling infrastructure and a general data toolkit known as KiloCraft. Consequently, it can be readily adapted for both fully-coupled E3SM simulations and data-driven simulations over specific areas, ranging from a single gridcell to the entire North America.
Mahmoud Mbarak, Manmeet Singh, Naveen Sudharsan
et al.
Accurate soil moisture prediction during extreme events remains a critical challenge for earth system modeling, with profound implications for drought monitoring, flood forecasting, and climate adaptation strategies. While land surface models (LSMs) provide physically-based predictions, they exhibit systematic biases during extreme conditions when their parameterizations operate outside calibrated ranges. Here we present NoahMP-AI, a physics-guided deep learning framework that addresses this challenge by leveraging the complete Noah-MP land surface model as a comprehensive physics-based feature generator while using machine learning to correct structural limitations against satellite observations. We employ a 3D U-Net architecture that processes Noah-MP outputs (soil moisture, latent heat flux, and sensible heat flux) to predict SMAP soil moisture across two contrasting extreme events: a prolonged drought (March-September 2022) and Hurricane Beryl (July 2024) over Texas. When comparing NoahMP-AI with NoahMP, our results demonstrate an increase in R-squared values from -0.7 to 0.5 during drought conditions, while maintaining physical consistency and spatial coherence. The framework's ability to preserve Noah-MP's physical relationships while learning observation-based corrections represents a significant advance in hybrid earth system modeling. This work establishes both a practical tool for operational forecasting and a benchmark for investigating the optimal integration of physics-based understanding with data-driven learning in environmental prediction systems.
With the enhancement of remote sensing image resolution and the rapid advancement of deep learning, land cover mapping is transitioning from pixel-level segmentation to object-based vector modeling. This shift demands more from deep learning models, requiring precise object boundaries and topological consistency. However, existing datasets face three main challenges: limited class annotations, small data scale, and lack of spatial structural information. To overcome these issues, we introduce IRSAMap, the first global remote sensing dataset for large-scale, high-resolution, multi-feature land cover vector mapping. IRSAMap offers four key advantages: 1) a comprehensive vector annotation system with over 1.8 million instances of 10 typical objects (e.g., buildings, roads, rivers), ensuring semantic and spatial accuracy; 2) an intelligent annotation workflow combining manual and AI-based methods to improve efficiency and consistency; 3) global coverage across 79 regions in six continents, totaling over 1,000 km; and 4) multi-task adaptability for tasks like pixel-level classification, building outline extraction, road centerline extraction, and panoramic segmentation. IRSAMap provides a standardized benchmark for the shift from pixel-based to object-based approaches, advancing geographic feature automation and collaborative modeling. It is valuable for global geographic information updates and digital twin construction. The dataset is publicly available at https://github.com/ucas-dlg/IRSAMap
Abstract Human activities have profoundly changed the land on which we live. In particular, land use and land management change affect the hydrology that determines flood hazard, water resources (for human and environmental needs) and the transport and dilution of pollutants. It is increasingly recognised that the management of land and water are inextricably linked (e.g. Defra, 2004). “Historical context, state of the science and current management issues” section of this paper addresses the science underlying those linkages, for both rural and urban areas. In “Historical context, state of the science and current management issues” section we discuss future drivers for change and their management implications. Detailed analyses are available for flood risk, from the Foresight Future Flooding project (Evans et al., 2004a,b) and other recent studies, and so we use flooding as an exemplar, with a more limited treatment of water resource and water quality aspects. Finally in “Science needs and developments” section we discuss science needs and likely progress. This paper does not address the important topic of water demand except for some reference to the Environment Agency's Water Resources Strategy for England and Wales (Environment Agency, 2009).
Introduction: Limited information is available regarding effect of vaccination on protection against Covid-19 infections and their severity as well. Objectives: In the present study, we assessed the effect of Covid-19 vaccination on incidences and severity of break through Covid-19 infections. Method: This retrospective study was conducted at a tertiary care center in Northern India during one calendar year, 1st August 2021 to 31st July 2022. The study population included Health-care workers (HCWs) who were treated for Covid 19 infection and had already received at least 1 dose of Covishield TM (AZD1222) Covid-19 vaccine. Results: Out of 1868 health care workers enrolled for the study, 513 contracted Covid-19 infections. Amongst infected HCWs, number of single and double doses of CovishieldTM (AZD1222) recipients were 112 and 401 respectively. Out of the 513 covid positive HCWs, 459 (89.4%) had mild disease, whereas 54 (10.6%) had moderate disease. None of the HCWs developed severe disease and no mortality was noted in either group. Conclusion: In this study, we found that immunization with two doses of CovishieldTM (AZD1222) vaccine was associated with decline in number of cases with moderate or severe Covid-19. Moreover, immunization with even single dose of CovishieldTM (AZD1222) vaccine prevented development of severe disease. Henceforth, it is concluded that although, immunization with CovishieldTM (AZD1222) could not protect all recipients from SARS-Cov-2 infection, it did prevent the progress of disease to severe grades.