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Climate change is one of the main threats to modern society. This phenomenon is associated with an increase in greenhouse gas (GHGs, mainly carbon dioxide—CO2) emissions due to anthropogenic activities. The main causes are the burning of fossil fuels and land use change (deforestation). Climate change impacts are associated with risks to basic needs (health, food security, and clean water), as well as risks to development (jobs, economic growth, and the cost of living). The processes involving CO2 capture and storage are gaining attention in the scientific community as an alternative for decreasing CO2 emissions, reducing its concentration in ambient air. The carbon capture and storage (CCS) methodologies comprise three steps: CO2 capture, CO2 transportation, and CO2 storage. Despite the high research activity within this topic, several technological, economic, and environmental issues as well as safety problems remain to be solved, such as the following needs: increase of CO2 capture efficiency, reduction of process costs, and verification of the environmental sustainability of CO2 storage.

normalized difference vegetation index (NDVI) --- techno-economic analysis --- the Loess Plateau --- power-to-methane --- GHG mitigation --- CO2 capture and utilization --- cement production with CO2 capture --- oxyfuel --- knowledge mapping --- technological evolution --- CO2 capture --- CO2 capture in industry --- IGCC --- InVEST --- electricity production --- life cycle assessment --- carbon storage --- carbon capture and storage --- CO2 capture retrofitability --- TBAB --- supercritical CO2 --- hydrate --- membrane-assisted CO2 liquefaction --- micromorphology --- synthetic natural gas --- stability map --- carbon capture and storage (CCS) --- renewable power --- carbon density --- fossil fuels --- chilled ammonia --- CCS --- cement --- MEA-based absorption --- carbon capture --- energy dependence --- calcium looping --- CO2 separation --- CO2 pipeline --- flow instability --- CiteSpace --- anti-agglomerant

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The concept of nitrogen gap (NG), i.e., its recognition and amelioration, forms the core of this book entitled Site-Specific Nutrient Management (SSNM). Determination of the presence of an NG between fields on a farm and/or within a particular field, together with its size, requires a set of highly reliable diagnostic tools. The necessary set of diagnostic tools, based classically on pedological and agrochemical methods, should be currently supported by remote-sensing methods. A combination of these two groups of methods is the only way to recognize the factors responsible for yield gap (YG) appearance and to offer a choice of measures for its effective amelioration. The NG concept is discussed in the two first papers (Grzebisz and Łukowiak, Agronomy 2021, 11, 419; Łukowiak et al., Agronomy 2020, 10, 1959). Crop productivity depends on a synchronization of plant demand for nitrogen and its supply from soil resources during the growing season. The action of nitrate nitrogen (N–NO3), resulting in direct plant crop response, can be treated by farmers as a crucial growth factor. The expected outcome also depends on the status of soil fertility factors, including pools of available nutrients and the activity of microorganisms. Three papers are devoted to these basic aspects of soil fertility management (Sulewska et al., Agronomy 2020, 10, 1958; Grzebisz et al., Agronomy 2020, 10, 1701; Hlisnikovsky et al., Agronomy 2021, 11, 1333). The resistance of a currently cultivated crop to seasonal weather variability depends to a great extent on the soil fertility level. This aspect is thoroughly discussed for three distinct soil types and climates with respect to their impact on yield (Hlisnikovsky et al., Agronomy 2020, 10, 1160—Czech Republic; Wang et al., Agronomy 2020, 10, 1237—China; Łukowiak and Grzebisz et al., Agronomy 2020, 10, 1364—Poland). In the fourth section of this book, the division a particular field into homogenous production zones is discussed as a basis for effective nitrogen management within the field. This topic is presented for different regions and crops (China, Poland, and the USA) (Cammarano et al., Agronomy 2020, 10, 1767; Panek et al., Agronomy 2020, 10, 1842; Larson et al., Agronomy 2020, 10, 1858).

Research & information: general --- Biology, life sciences --- Technology, engineering, agriculture --- Triticum aestivum L. --- farmyard manure --- mineral fertilizers --- crude protein content --- soil properties, site-specific requirements --- yield --- site-specific nitrogen management --- regional optimal nitrogen management --- net return --- nitrogen use efficiency --- spatial variability --- temporal variability --- seed density --- N uptake --- indices of N productivity --- mineral N --- indigenous Nmin at spring --- post-harvest Nmin --- N balance --- N efficiency --- maximum photochemical efficiency of photosystem II --- chlorophyll content index --- soil enzymatic activity --- biological index fertility --- nitrogenase activity --- microelements fertilization (Ti --- Si --- B --- Mo --- Zn) --- soil --- nitrate nitrogen content --- contents of available phosphorus --- potassium --- magnesium --- calcium --- cardinal stages of WOSR growth --- PCA --- site-specific nutrient management --- soil brightness --- satellite remote sensing --- crop yield --- soil fertility --- winter wheat --- winter triticale --- vegetation indices --- NDVI --- grain yield --- number of spikes --- economics --- normalized difference vegetation index (NDVI) --- on-the-go sensors --- winter oilseed rape → winter triticale cropping sequence --- N input --- N total uptake --- N gap --- Beta vulgaris L. --- organic manure --- weather conditions --- soil chemistry --- sugar concentration --- climatic potential yield --- yield gap --- soil constraints --- subsoil --- remote sensing-techniques --- field --- a field --- crop production --- sustainability --- homogenous productivity units --- nitrogen indicators: in-season --- spatial --- vertical variability of N demand and supply --- spectral imagery

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The concept of nitrogen gap (NG), i.e., its recognition and amelioration, forms the core of this book entitled Site-Specific Nutrient Management (SSNM). Determination of the presence of an NG between fields on a farm and/or within a particular field, together with its size, requires a set of highly reliable diagnostic tools. The necessary set of diagnostic tools, based classically on pedological and agrochemical methods, should be currently supported by remote-sensing methods. A combination of these two groups of methods is the only way to recognize the factors responsible for yield gap (YG) appearance and to offer a choice of measures for its effective amelioration. The NG concept is discussed in the two first papers (Grzebisz and Łukowiak, Agronomy 2021, 11, 419; Łukowiak et al., Agronomy 2020, 10, 1959). Crop productivity depends on a synchronization of plant demand for nitrogen and its supply from soil resources during the growing season. The action of nitrate nitrogen (N–NO3), resulting in direct plant crop response, can be treated by farmers as a crucial growth factor. The expected outcome also depends on the status of soil fertility factors, including pools of available nutrients and the activity of microorganisms. Three papers are devoted to these basic aspects of soil fertility management (Sulewska et al., Agronomy 2020, 10, 1958; Grzebisz et al., Agronomy 2020, 10, 1701; Hlisnikovsky et al., Agronomy 2021, 11, 1333). The resistance of a currently cultivated crop to seasonal weather variability depends to a great extent on the soil fertility level. This aspect is thoroughly discussed for three distinct soil types and climates with respect to their impact on yield (Hlisnikovsky et al., Agronomy 2020, 10, 1160—Czech Republic; Wang et al., Agronomy 2020, 10, 1237—China; Łukowiak and Grzebisz et al., Agronomy 2020, 10, 1364—Poland). In the fourth section of this book, the division a particular field into homogenous production zones is discussed as a basis for effective nitrogen management within the field. This topic is presented for different regions and crops (China, Poland, and the USA) (Cammarano et al., Agronomy 2020, 10, 1767; Panek et al., Agronomy 2020, 10, 1842; Larson et al., Agronomy 2020, 10, 1858).

Triticum aestivum L. --- farmyard manure --- mineral fertilizers --- crude protein content --- soil properties, site-specific requirements --- yield --- site-specific nitrogen management --- regional optimal nitrogen management --- net return --- nitrogen use efficiency --- spatial variability --- temporal variability --- seed density --- N uptake --- indices of N productivity --- mineral N --- indigenous Nmin at spring --- post-harvest Nmin --- N balance --- N efficiency --- maximum photochemical efficiency of photosystem II --- chlorophyll content index --- soil enzymatic activity --- biological index fertility --- nitrogenase activity --- microelements fertilization (Ti --- Si --- B --- Mo --- Zn) --- soil --- nitrate nitrogen content --- contents of available phosphorus --- potassium --- magnesium --- calcium --- cardinal stages of WOSR growth --- PCA --- site-specific nutrient management --- soil brightness --- satellite remote sensing --- crop yield --- soil fertility --- winter wheat --- winter triticale --- vegetation indices --- NDVI --- grain yield --- number of spikes --- economics --- normalized difference vegetation index (NDVI) --- on-the-go sensors --- winter oilseed rape → winter triticale cropping sequence --- N input --- N total uptake --- N gap --- Beta vulgaris L. --- organic manure --- weather conditions --- soil chemistry --- sugar concentration --- climatic potential yield --- yield gap --- soil constraints --- subsoil --- remote sensing-techniques --- field --- a field --- crop production --- sustainability --- homogenous productivity units --- nitrogen indicators: in-season --- spatial --- vertical variability of N demand and supply --- spectral imagery

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Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others.

Research & information: general --- Geography --- geographic information system (GIS) --- pocket beaches --- coastal management --- Interreg --- climate change --- remote sensing --- drone --- Sicily --- Malta --- Gozo --- Comino --- systematic literature review --- anomaly intrusion detection --- deep learning --- IoT --- resource constraint --- IDS --- evapotranspiration --- penman-monteith equation --- artificial neural network --- canopy conductance --- Ziz basin --- water quality --- satellite image analysis --- modeling approach --- nitrate --- dissolved oxygen --- chlorophyll a --- time series analysis --- environmental monitoring --- water extraction --- modified normalized difference water index (MNDWI) --- machine learning algorithm --- hyperspectral --- proximal sensing --- panicle initiation --- normalized difference vegetation index (NDVI) --- green ring --- internode-elongation --- Sentinel 1 and 2 --- Copernicus Sentinels --- crop classification --- food security --- agricultural monitoring --- data analysis --- SAR --- random forest --- 3D bale wrapping method --- equal bale dimensions --- mathematical model --- minimal film consumption --- optimal bale dimensions --- round bales --- Sentinel-2 --- SVM --- RF --- Boufakrane River watershed --- irrigation requirements --- water resources --- sustainable land use --- agriculture --- invasive plants --- precision agriculture --- rice farming --- site-specific weed management --- nitrogen prediction --- 1D convolution neural networks --- cucumber --- crop yield improvement --- mango leaf --- CCA --- vein pattern --- leaf disease --- cubic SVM --- chlorophyll-a concentration --- transfer learning --- overfitting --- data augmentation --- guava disease --- plant disease detection --- crops diseases --- entropy --- features fusion --- machine learning --- object-based classification --- density estimation --- histogram --- land use --- crop fields --- soil tillage --- data fusion --- multispectral --- sensor --- probe --- temperature profile --- forest roads --- simulation --- autonomous robots --- smart agriculture --- environmental protection --- photogrammetry --- path planning --- internet of things --- modeling --- convolutional neural networks --- machine vision --- computer vision --- modular robot --- selective spraying --- vision-based crop and weed detection --- Faster R-CNN --- YOLOv5 --- band selection --- CNN --- NDVI --- hyperspectral imaging --- crops --- urban flood --- Sentinel-1a --- Synthetic Aperture Radar (SAR) --- 3D Convolutional Neural Network --- multi-temporal data --- land use classification --- GIS --- Coatzacoalcos --- algorithms --- clustering --- pest control --- site-specific --- virtual pests --- rice plant --- weed --- hyperspectral imagery --- sustainable agriculture --- green technologies --- Internet of Things --- natural resources --- sustainable environment --- IoT ecosystem --- hyperspectral remoting sensing --- crop mapping --- image classification --- deep transfer learning --- hyperparameter optimization --- metaheuristic --- soil attribute --- ordinary Kriging --- rational sampling numbers --- spatial heterogeneity --- sampling --- soil pH --- spatial variation --- ordinary kriging --- Land Use/Land Cover --- LISS-III --- Landsat --- Vision Transformer --- Bidirectional long-short term memory --- Google Earth Engine --- Explainable Artificial Intelligence