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This Special Issue on ‘Advances in Cereal Crops Breeding’ comprises 10 papers covering a wide range of subjects, including the expression-level investigation of genes in terms of salinity stress adaptations and their relationships with proteomics in rice, the use of genetic analysis to assess the general combining ability (GCA) and specific combining ability (SCA) in promising hybrids of maize, the use of DNA markers based on PCR in rice, the identification of quantitative trait loci (QTLs) in wheat and simple sequence repeats (SSR) in rice, the use of single-nucleotide polymorphisms (SNP) in a genome-wide association study (GWAS) in cereals, and Nanopore direct RNA sequencing of related with LTR RNA retrotransposon in triticale prior to the genomic selection of heterotic maize hybrids.

maize --- density tolerance --- combining ability --- gene effects --- genetic diversity --- rice --- salinity --- submergence tolerance --- blast --- SSR markers --- PCR analysis --- long non-coding RNAs --- seed development --- Nanopore sequencing --- retrotransposons --- triticale --- prediction accuracy --- mixed linear and Bayesian models --- machine learning algorithms --- training set size and composition --- parametric and nonparametric models --- drought stress --- dendrogram --- barley --- breeding --- marker-assisted selection --- genes --- genetic resources --- genome editing --- health benefits --- metabolomics --- oat --- QTL --- wheat --- Triticum aestivum L. --- QMrl-7B --- root traits --- grain yield --- nitrogen use efficiency --- GWAS --- salinity tolerance --- Vietnamese landraces --- abiotic stress --- root --- auxin --- YUCCA --- PIN --- proteomics --- mass spectrometry --- n/a

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Global crop production must substantially increase to meet the needs of a rapidly growing population. This is constrained by the availability of nutrients, water, and land. There is also an urgent need to reduce the negative environmental impacts of crop production. Collectively, these issues represent one of the greatest challenges of the twenty-first century. Sustainable cropping systems based on ecological principles are the core of integrated approaches to solve this critical challenge. This special issue provides an international basis for revealing the underlying mechanisms of sustainable cropping systems to drive agronomic innovations. It includes review and original research articles that report novel scientific findings on improvement in cropping systems related to crop yields and their resistance to biotic and abiotic stressors, resource use efficiency, environmental impact, sustainability, and ecosystem services.

nutrient use efficiency --- organic fertilization --- system approach --- Helianthus annuus L. --- catch crop --- Texas High Plains --- forage yield and quality --- living mulch --- nutrient cycling --- quality --- leguminous cover crop --- conservation --- light --- sustainable crop production --- crop rotation --- WHCNS --- stemborer --- complexity --- perennial --- manure --- maize production --- SOC and STN stocks --- cover crops --- forage pea --- yield --- SDS-PAGE analysis --- vineyard system --- double cropping --- wheat --- partial returns --- soybean --- vetch --- nitrogen use efficiency --- enzyme activities --- agrobiodiversity --- gross margin --- residue C and N release --- systematic review --- maize --- protein crops --- no-tillage --- environmental quality --- fall grazing --- kura clover --- cover crop --- organization --- scenario analyses --- cropping system design --- irrigation --- sustainable yield index --- multiple correspondence analysis (MCA) --- Acidic soil --- Europe --- Zea mais L. --- shade --- up-scaling --- water --- conservation agriculture --- water use efficiency --- Triticum aestivum L. --- forage sorghum --- N use efficiency --- nutrient balance --- organic cropping system --- forage --- durum wheat --- cropping systems --- nitrate --- grain yield --- nitrogen nutrition --- conventionalization --- crop residue incorporation --- cereal rye --- green manure --- straw decomposition --- hierarchical patch dynamics --- N uptake --- farmer’s perception --- pearl millet --- nitrogen --- faba bean --- agroecology --- harvesting strategies --- rice --- gluten fractions --- weed suppression --- economics --- mineral N fertilization --- push-pull technology --- growth --- potato (Solanum tuberosum)

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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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This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment.

Technology: general issues --- History of engineering & technology --- Environmental science, engineering & technology --- UAS --- multiple sensors --- vegetation index --- leaf nitrogen accumulation --- plant nitrogen accumulation --- pasture quality --- airborne hyperspectral imaging --- random forest regression --- sun-induced chlorophyll fluorescence (SIF) --- SIF yield indices --- upward --- downward --- leaf nitrogen concentration (LNC) --- wheat (Triticum aestivum L.) --- laser-induced fluorescence --- leaf nitrogen concentration --- back-propagation neural network --- principal component analysis --- fluorescence characteristics --- canopy nitrogen density --- radiative transfer model --- hyperspectral --- winter wheat --- flooded rice --- pig slurry --- aerial remote sensing --- vegetation indices --- N recommendation approach --- Mediterranean conditions --- nitrogen --- vertical distribution --- plant geometry --- remote sensing --- maize --- UAV --- multispectral imagery --- LNC --- non-parametric regression --- red-edge --- NDRE --- dynamic change model --- sigmoid curve --- grain yield prediction --- leaf chlorophyll content --- red-edge reflectance --- spectral index --- precision N fertilization --- chlorophyll meter --- NDVI --- NNI --- canopy reflectance sensing --- N mineralization --- farmyard manures --- Triticum aestivum --- discrete wavelet transform --- partial least squares --- hyper-spectra --- rice --- nitrogen management --- reflectance index --- multiple variable linear regression --- Lasso model --- Multiplex®3 sensor --- nitrogen balance index --- nitrogen nutrition index --- nitrogen status diagnosis --- precision nitrogen management --- terrestrial laser scanning --- spectrometer --- plant height --- biomass --- nitrogen concentration --- precision agriculture --- unmanned aerial vehicle (UAV) --- digital camera --- leaf chlorophyll concentration --- portable chlorophyll meter --- crop --- PROSPECT-D --- sensitivity analysis --- UAV multispectral imagery --- spectral vegetation indices --- machine learning --- plant nutrition --- canopy spectrum --- non-destructive nitrogen status diagnosis --- drone --- multispectral camera --- SPAD --- smartphone photography --- fixed-wing UAV remote sensing --- random forest --- canopy reflectance --- crop N status --- Capsicum annuum --- proximal optical sensors --- Dualex sensor --- leaf position --- proximal sensing --- cross-validation --- feature selection --- hyperparameter tuning --- image processing --- image segmentation --- nitrogen fertilizer recommendation --- supervised regression --- RapidSCAN sensor --- nitrogen recommendation algorithm --- in-season nitrogen management --- nitrogen use efficiency --- yield potential --- yield responsiveness --- standard normal variate (SNV) --- continuous wavelet transform (CWT) --- wavelet features optimization --- competitive adaptive reweighted sampling (CARS) --- partial least square (PLS) --- grapevine --- hyperparameter optimization --- multispectral imaging --- precision viticulture --- RGB --- multispectral --- coverage adjusted spectral index --- vegetation coverage --- random frog algorithm --- active canopy sensing --- integrated sensing system --- discrete NIR spectral band data --- soil total nitrogen concentration --- moisture absorption correction index --- particle size correction index --- coupled elimination