Search results for "Hunger"
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Additional file 1 of Blood and skeletal muscle ageing determined by epigenetic clocks and their associations with physical activity and functioning
2021
Additional file 1: Within-pair correlations in age acceleration in blood and in muscle. Additional file 2: Associations between DNAmAge age acceleration estimates and body composition and physical activity in blood. Additional file 3: Sensitivity analyses related to twin pair discordance in body mass index.
Additional file 1 of Multiple paths to cold tolerance: the role of environmental cues, morphological traits and the circadian clock gene vrille
2021
Additional file 1: Table S1. Information on fly collecting sites and years, and the exact coordinates (latitude, longitude) and altitudes for each collecting site. Table S2. A List of 19 bioclimatic variables used in the PCA (WorldClim database v2.1, 2.5 min spatial resolutions; current data 1970–2000; Fick and Hijmans 2017; www.worldclim.org ). Table S3. 19 bioclimatic variables for each site were extracted from WorldClim database v2.1. Table S4. Principal components with their variance, cumulative variance and Eigenvalues. Table S5. Contributions (loadings) of the altitude and 19 bioclimatic variables on the Principal Component (PC). Table S6. The best-fit model for CCRT, CTmin, body colo…
Additional file 1 of The genome sequence of the grape phylloxera provides insights into the evolution, adaptation, and invasion routes of an iconic p…
2020
Additional file 1: Figures. S1-S22, Table S1-S20, Methods and Results. Figure S1. Mitochondrial genome view of grape phylloxera. Figure S2. Proportion of transposable elements (TE) in the genome. Figure S3. GO terms of phylloxera-specific genes. Figure S4. Enriched GO terms in the phylloxera genome with and without TEs. Figure S5. Gene gain/loss at different nodes or branches. Figure S6. Species phylogenetic tree based on insect genomes and the transcriptomes of Planoccoccus citri and Adelges tsugae. Figure S7. Diagram of the gap-filling and annotation process. Figure S8. Urea cycle in D. vitifoliae and A. pisum. Figure S9. IMD immune pathway in D. vitifoliae.Figure S10. Phylogenetic tree o…
Additional file 1 of Multiple paths to cold tolerance: the role of environmental cues, morphological traits and the circadian clock gene vrille
2021
Additional file 1: Table S1. Information on fly collecting sites and years, and the exact coordinates (latitude, longitude) and altitudes for each collecting site. Table S2. A List of 19 bioclimatic variables used in the PCA (WorldClim database v2.1, 2.5 min spatial resolutions; current data 1970–2000; Fick and Hijmans 2017; www.worldclim.org ). Table S3. 19 bioclimatic variables for each site were extracted from WorldClim database v2.1. Table S4. Principal components with their variance, cumulative variance and Eigenvalues. Table S5. Contributions (loadings) of the altitude and 19 bioclimatic variables on the Principal Component (PC). Table S6. The best-fit model for CCRT, CTmin, body colo…
Comparison between SMOS Vegetation Optical Depth products and MODIS vegetation indices over crop zones of the USA
2014
The Soil Moisture and Ocean Salinity (SMOS) mission provides multi-angular, dual-polarised brightness temperatures at 1.4 GHz, from which global soil moisture and vegetation optical depth (tau) products are retrieved. This paper presents a study of SMOS' tau product in 2010 and 2011 for crop zones of the USA. Retrieved tau values for 504 crop nodes were compared to optical/IR vegetation indices from the MODES (Moderate Resolution Imaging Spectroradiometer) satellite sensor, including the Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVE), Leaf Area Index (LAI), and a Normalised Difference Water Index (NOW!) product. tau values were observed to increase during the…
Towards Quantifying Non-Photosynthetic Vegetation for Agriculture Using Spaceborne Imaging Spectroscopy
2021
Non-photosynthetic vegetation (NPV) has been identified as priority variable in the context of new spaceborne imaging spectroscopy missions. In this study we provide a first attempt to quantify NPV biomass from these unprecedented data streams to be provided by multiple recently launched or planned instruments. A hybrid workflow is proposed including Gaussian process regression (GPR) trained over radiative transfer model (RTM) simulations and applying active learning strategies. A soybean field data set including two dates with NPV measurements on yellow and senescent (brown) plant organs was used for model validation, resulting in relative errors of 13.4%. This prototype retrieval model wa…
Multitemporal and multiresolution leaf area index retrieval for operational local rice crop monitoring
2016
Abstract This paper presents an operational chain for high-resolution leaf area index (LAI) retrieval from multiresolution satellite data specifically developed for Mediterranean rice areas. The proposed methodology is based on the inversion of the PROSAIL radiative transfer model through the state-of-the-art nonlinear Gaussian process regression (GPR) method. Landsat and SPOT5 data were used for multitemporal LAI retrievals at high-resolution. LAI estimates were validated using time series of in situ LAI measurements collected during the rice season in Spain and Italy. Ground LAI data were collected with smartphones using PocketLAI, a specific phone application for LAI estimation. Temporal…
Crop Phenology Retrieval Through Gaussian Process Regression
2021
Monitoring crop phenology significantly assists agricultural managing practices and plays an important role in crop yield predictions. Multi-temporal satellite-based observations allow analyzing vegetation seasonal dynamics over large areas by using vegetation indices or deriving biophysical variables. This study presents a framework for automatic corn phenology characterization based on high spatial and temporal resolution time series. By using the Difference Vegetation Index (DVI) estimated from Sentinel-2 data over Iowa (US), independent phenological models were optimized using Gaussian Processes regression. Their respective performances were assessed based on simulated phenological indi…
Seasonal variations of leaf area index of agricultural fields retrieved from Landsat data
2008
Abstract The derivation of leaf area index (LAI) from satellite optical data has been the subject of a large amount of work. In contrast, few papers have addressed the effective model inversion of high resolution satellite images for a complete series of data for the various crop species in a given region. The present study is focused on the assessment of a LAI model inversion approach applied to multitemporal optical data, over an agricultural region having various crop types with different crop calendars. Both the inversion approach and data sources are chosen because of their wide use. Crops in the study region (Barrax, Castilla–La Mancha, Spain) include: cereal, corn, alfalfa, sugar bee…
2021
Nitrogen (N) is one of the key nutrients supplied in agricultural production worldwide. Over-fertilization can have negative influences on the field and the regional level (e.g., agro-ecosystems). Remote sensing of the plant N of field crops presents a valuable tool for the monitoring of N flows in agro-ecosystems. Available data for validation of satellite-based remote sensing of N is scarce. Therefore, in this study, field spectrometer measurements were used to simulate data of the Sentinel-2 (S2) satellites developed for vegetation monitoring by the ESA. The prediction performance of normalized ratio indices (NRIs), random forest regression (RFR) and Gaussian processes regression (GPR) f…