136 research outputs found

    Trend assessment of changing climate patterns over the major agro-climatic zones of Sindh and Punjab

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    The agriculture sector, due to its significant dependence on climate patterns and water availability, is highly vulnerable to changing climate patterns. Pakistan is an agrarian economy with 30% of its land area under cultivation and 93% of its water resources being utilized for agricultural production. Therefore, the changing climate patterns may adversely affect the agriculture and water resources of the country. This study was conducted to assess the climate variations over the major agro-climatic zones of Sindh and Punjab, which serve as an important hub for the production of major food and cash crops in Pakistan. For this purpose, the climate data of 21 stations were analyzed using the Mann–Kendall test and Sen's slope estimator method for the period 1990–2022. The results obtained from the analysis revealed that, in Sindh, the mean annual temperature rose by ~0.1 to 1.4°C, with ~0.1 to 1.2°C in cotton-wheat Sindh and 0.8 to 1.4°C in rice-other Sindh during the study period. Similarly, in Punjab, the mean annual temperature increased by ~0.1 to 1.0°C, with 0.6 to 0.9°C in cotton-wheat Punjab and 0.2 to 0.6°C in rainfed Punjab. Seasonally, warming was found to be highest during the spring season. The precipitation analysis showed a rising annual precipitation trend in Sindh (+30 to +60 mm) and Punjab (+100 to 300 mm), while the monsoon precipitation increased by ~50 to 200 mm. For winter precipitation, an upward trend was found in mixed Punjab, while the remaining stations showed a declining pattern. Conclusively, the warming temperatures as found in the analysis may result in increased irrigation requirements, soil moisture desiccation, and wilting of crops, ultimately leading to low crop yield and threatening the livelihoods of local farmers. On the other hand, the increasing precipitation may favor national agriculture in terms of less freshwater withdrawals. However, it may also result in increased rainfall-induced floods inundating the crop fields and causing water logging and soil salinization. The study outcomes comprehensively highlighted the prevailing climate trends over the important agro-climatic zones of Pakistan, which may aid in devising an effective climate change adaptation and mitigation strategy to ensure the state of water and food security in the country

    XVI Agricultural Science Congress 2023: Transformation of Agri-Food Systems for Achieving Sustainable Development Goals

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    The XVI Agricultural Science Congress being jointly organized by the National Academy of Agricultural Sciences (NAAS) and the Indian Council of Agricultural Research (ICAR) during 10-13 October 2023, at hotel Le Meridien, Kochi, is a mega event echoing the theme “Transformation of Agri-Food Systems for achieving Sustainable Development Goals”. ICAR-Central Marine Fisheries Research Institute takes great pride in hosting the XVI ASC, which will be the perfect point of convergence of academicians, researchers, students, farmers, fishers, traders, entrepreneurs, and other stakeholders involved in agri-production systems that ensure food and nutritional security for a burgeoning population. With impeding challenges like growing urbanization, increasing unemployment, growing population, increasing food demands, degradation of natural resources through human interference, climate change impacts and natural calamities, the challenges ahead for India to achieve the Sustainable Development Goals (SDGs) set out by the United Nations are many. The XVI ASC will provide an interface for dissemination of useful information across all sectors of stakeholders invested in developing India’s agri-food systems, not only to meet the SDGs, but also to ensure a stable structure on par with agri-food systems around the world. It is an honour to present this Book of Abstracts which is a compilation of a total of 668 abstracts that convey the results of R&D programs being done in India. The abstracts have been categorized under 10 major Themes – 1. Ensuring Food & Nutritional Security: Production, Consumption and Value addition; 2. Climate Action for Sustainable Agri-Food Systems; 3. Frontier Science and emerging Genetic Technologies: Genome, Breeding, Gene Editing; 4. Livestock-based Transformation of Food Systems; 5. Horticulture-based Transformation of Food Systems; 6. Aquaculture & Fisheries-based Transformation of Food Systems; 7. Nature-based Solutions for Sustainable AgriFood Systems; 8. Next Generation Technologies: Digital Agriculture, Precision Farming and AI-based Systems; 9. Policies and Institutions for Transforming Agri-Food Systems; 10. International Partnership for Research, Education and Development. This Book of Abstracts sets the stage for the mega event itself, which will see a flow of knowledge emanating from a zeal to transform and push India’s Agri-Food Systems to perform par excellence and achieve not only the SDGs of the UN but also to rise as a world leader in the sector. I thank and congratulate all the participants who have submitted abstracts for this mega event, and I also applaud the team that has strived hard to publish this Book of Abstracts ahead of the event. I wish all the delegates and participants a very vibrant and memorable time at the XVI ASC

    Applications of Machine Learning in Chemical and Biological Oceanography

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    Machine learning (ML) refers to computer algorithms that predict a meaningful output or categorize complex systems based on a large amount of data. ML is applied in various areas including natural science, engineering, space exploration, and even gaming development. This review focuses on the use of machine learning in the field of chemical and biological oceanography. In the prediction of global fixed nitrogen levels, partial carbon dioxide pressure, and other chemical properties, the application of ML is a promising tool. Machine learning is also utilized in the field of biological oceanography to detect planktonic forms from various images (i.e., microscopy, FlowCAM, and video recorders), spectrometers, and other signal processing techniques. Moreover, ML successfully classified the mammals using their acoustics, detecting endangered mammalian and fish species in a specific environment. Most importantly, using environmental data, the ML proved to be an effective method for predicting hypoxic conditions and harmful algal bloom events, an essential measurement in terms of environmental monitoring. Furthermore, machine learning was used to construct a number of databases for various species that will be useful to other researchers, and the creation of new algorithms will help the marine research community better comprehend the chemistry and biology of the ocean.Comment: 58 Pages, 5 Figure

    Remote Sensing Monitoring of Land Surface Temperature (LST)

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    This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research

    Remote Sensing of the Aquatic Environments

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    The book highlights recent research efforts in the monitoring of aquatic districts with remote sensing observations and proximal sensing technology integrated with laboratory measurements. Optical satellite imagery gathered at spatial resolutions down to few meters has been used for quantitative estimations of harmful algal bloom extent and Chl-a mapping, as well as winds and currents from SAR acquisitions. The knowledge and understanding gained from this book can be used for the sustainable management of bodies of water across our planet

    Proceedings of the XXVIIIth TELEMAC User Conference 18-19 October 2022

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    Hydrodynamic

    IoT Applications Computing

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    The evolution of emerging and innovative technologies based on Industry 4.0 concepts are transforming society and industry into a fully digitized and networked globe. Sensing, communications, and computing embedded with ambient intelligence are at the heart of the Internet of Things (IoT), the Industrial Internet of Things (IIoT), and Industry 4.0 technologies with expanding applications in manufacturing, transportation, health, building automation, agriculture, and the environment. It is expected that the emerging technology clusters of ambient intelligence computing will not only transform modern industry but also advance societal health and wellness, as well as and make the environment more sustainable. This book uses an interdisciplinary approach to explain the complex issue of scientific and technological innovations largely based on intelligent computing

    Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

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    The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future

    Remote sensing techniques in the analysis of change detection in the Algarve coast, Portugal

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    The process of occupation in the Algarve area is millenary. The last decades have shown that economic activities in the region have caused significant changes in the use and occupation of the land. The main objective of this study was to correlate carbon sequestration and the evolution of land use and occupation, in the coastal zone of Algarve, Portugal, through the application of vegetation indices such as NDVI, PRI, CO2flux, compared with MODIS GPP and COPERNICUS Corine Land Cover, between the years 1990 and 2020. The results were expressed through digital cartography for better data visualization. The NDVI results demonstrate that the vigor and biomass produced by the coastal vegetation tended to increase. There was a decrease in areas without vegetation and areas with sparse vegetation, which were replaced mainly by the vegetation of moderate density and, secondly, by the vegetation of high density. The joint analysis of the indexes corroborates such results, PRI and CO2flux, which, related in a linear regression with the MODIS GPP, indicated that the study region has a great capacity to store carbon, mainly on the West Coast, where the highest density was observed of biomass and a consequent higher level of GPP. The results also indicated an abandonment of rural areas, which were taken over by vegetation and urban sprawl, as well as a growth of around 69% in areas destined for leisure areas, such as golf courses. The results also showed that the environmental preservation areas in the region, the RAMSAR and REDE NATURA 2000 Sites, did not suffer from changes in the use and occupation of their areas.É milenar o processo de ocupação na zona do Algarve. As últimas décadas mostraram que as atividades económicas da região têm provocado significativas alterações no uso e ocupação do Solo. O principal objetivo deste estudo foi correlacionar o sequestro de carbono e a evolução do uso e ocupação do solo, na zona costeira do Algarve, Portugal, através da aplicação de índices de vegetação como NDVI, PRI, CO2flux, comparados com os produtos MODIS GPP e COPERNICUS Corine Land Cover, entre os anos de 1990 e 2020. Os resultados foram expressos através cartografia digital para melhor visualização dos dados. Os resultados do NDVI demonstram que o vigor e biomassa produzidos pela vegetação litorânea tendeu a crescer. Houve uma diminuição das áreas sem vegetação e das áreas de vegetação esparsa, que foram substituídas, principalmente, por uma vegetação de densidade moderada e, em segundo lugar, por uma vegetação de alta densidade. Tais resultados são corroborados pela análise conjunta dos índices , PRI e CO2flux, que, relacionado numa regressão linear com os MODIS GPP, indicou que região de estudo tem uma grande capacidade de estocar de carbono, principalmente na Costa Oeste, onde foi observado a maior densidade de biomassa e consequente maior nível de GPP. Os resultados também indicaram que houve um abandono das áreas rurais, que foram tomadas pela vegetação e pela expansão urbana. Assim como um crescimento de cerca de 69% das áreas destinadas às zonas de lazer, como os campos de Golfe. Os resultados ainda mostraram que as áreas de preservação ambiental da região, os Sítios RAMSAR E REDE NATURA 2000, não sofreram com as alterações do uso e ocupação de suas áreas

    Novel spectral imaging instrumentation for environmental sensing in extreme environments

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    Spectral imaging techniques provide a valuable means of improving our understanding of the world around us. Environmental monitoring approaches that utilise these techniques are, therefore, essential to our understanding of the effects of climate change. Hyperspectral imaging applications are of particular benefit to a broad range of environmental monitoring scenarios, providing rich datasets that combine both spectral and spatial information, enabling intricate features and variations to be visualised. However, to date, most commercially available hyperspectral instrumentation remains bulky and expensive, significantly limiting their user-base and accessibility. These factors substantially limit the use of these instruments resulting in much of our information coming from a few well-resourced research teams across a limited number of more easily accessed field locations. These limitations, have a compounded effect on the quality and robustness of hyperspectral data outputs, particularly within more extreme settings, as the comparatively small sample of more accessible locations is not necessarily representative of the much larger whole. This thesis presents on the development and testing of three novel low-cost hyperspectral imaging instruments designed specifically for environmental monitoring applications, providing valuable, low-cost alternatives to currently available commercial systems. Specifically, the three instruments presented within this thesis are: a low-cost laboratory-based hyperspectral imager, a semi-portable instrument capable of accurate data capture within a laboratory setting; the Hyperspectral Smartphone, an ultra-low-cost smartphone-based fully portable hyperspectral imager; and a low-cost high-resolution hyperspectral imager capable of resolving mm-scale spatial targets. All instruments were rigorously tested to analyse and evaluate their performances. Each instrument was shown to perform well across a range of environmental monitoring applications demonstrating that expensive commercial instrumentation is not required to achieve accurate and robust hyperspectral imaging. These low-cost instruments could promote the widespread dissemination of accessible hyperspectral imaging equipment, facilitating the democratisation of hyperspectral measurement modalities across environmental monitoring applications and beyond
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