Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Online measurement of soil organic carbon as correlated with wheat normalised difference vegetation index in a vertisol field.

ABSTRACT: This study explores the potential of visible and near infrared (vis-NIR) spectroscopy for online measurement of soil organic carbon (SOC). It also attempts to explore correlations and similarities between the spatial distribution of SOC and normalized differential vegetation index (NDVI) of a wheat crop. The online measurement was carried out in a clay vertisol field covering 10 ha of area in Karacabey, Bursa, Turkey. Kappa statistics were carried out between different SOC and NDVI data to investigate potential similarities. Calibration model of SOC in full cross-validation resulted in a good accuracy (R (2) = 0.75, root mean squares error of prediction (RMSEP) = 0.17%, and ratio of prediction deviation (RPD) = 1.81). The validation of the calibration model using laboratory spectra provided comparatively better prediction accuracy (R (2) = 0.70, RMSEP = 0.15%, and RPD = 1.78), as compared to the online measured spectra (R (2) = 0.60, RMSEP = 0.20%, and RPD = 1.41). Although visual similarity was clear, low similarity indicated by a low Kappa value of 0.259 was observed between the online vis-NIR predicted full-point (based on all points measured in the field, e.g., 6486 points) map of SOC and NDVI map.

SUBMITTER: Tekin Y

PROVIDER: S-EPMC4109388 | biostudies-other | 2014

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Similar Datasets

Deeper roots associated with cooler canopies, higher normalized difference vegetation index, and greater yield in three wheat populations grown on stored soil water.

Project description:Simple and repeatable methods are needed to select for deep roots under field conditions. A large-scale field experiment was conducted to assess the association between canopy temperature (CT) measured by airborne thermography and rooting depth determined by the core-break method. Three wheat populations, C306×Westonia (CW), Hartog×Drysdale (HD), and Sundor×Songlen (SS), were grown on stored soil water in NSW Australia in 2017 (n=196-252). Cool and warm CT extremes ('tails') were cored after harvest (13-32% of each population). Rooting depth was significantly correlated with CT at late flowering (r= -0.25, -0.52, and -0.23 for CW, HD, and SS, respectively, P<0.05 hereafter), with normalized difference vegetation index (NDVI) at early grain filling (r=0.30-0.39), and with canopy height (r=0.23-0.48). The cool tails showed significantly deeper roots than the respective warm tails by 8.1 cm and 6.2 cm in CW and HD, and correspondingly, greater yields by an average 19% and 7%, respectively. This study highlighted that CT measured rapidly by airborne thermography or NDVI at early grain filling could be used to guide selection of lines with deeper roots to increase wheat yields. The remote measurement methods in this study were repeatable and high throughput, making them well suited to use in breeding programmes.

| S-EPMC6760272 | biostudies-literature

Normalized Difference Vegetation Index as a tool for wheat yield estimation: a case study from Faisalabad, Pakistan.

Project description:For estimation of grain yield in wheat, Normalized Difference Vegetation Index (NDVI) is considered as a potential screening tool. Field experiments were conducted to scrutinize the response of NDVI to yield behavior of different wheat cultivars and nitrogen fertilization at agronomic research area, University of Agriculture Faisalabad (UAF) during the two years 2008-09 and 2009-10. For recording the value of NDVI, Green seeker (Handheld-505) was used. Split plot design was used as experimental model in, keeping four nitrogen rates (N1 = 0 kg ha(-1), N2 = 55 kg ha(-1), N3 = 110 kg ha(-1), and N4 = 220 kg ha(-1)) in main plots and ten wheat cultivars (Bakkhar-2001, Chakwal-50, Chakwal-97, Faisalabad-2008, GA-2002, Inqlab-91, Lasani-2008, Miraj-2008, Sahar-2006, and Shafaq-2006) in subplots with four replications. Impact of nitrogen and difference between cultivars were forecasted through NDVI. The results suggested that nitrogen treatment N4 (220 kg ha(-1)) and cultivar Faisalabad-2008 gave maximum NDVI value (0.85) at grain filling stage among all treatments. The correlation among NDVI at booting, grain filling, and maturity stages with grain yield was positive (R(2) = 0.90; R(2) = 0.90; R(2) = 0.95), respectively. So, booting, grain filling, and maturity can be good depictive stages during mid and later growth stages of wheat crop under agroclimatic conditions of Faisalabad and under similar other wheat growing environments in the country.

| S-EPMC4090434 | biostudies-other

Stay-green in spring wheat can be determined by spectral reflectance measurements (normalized difference vegetation index) independently from phenology.

Project description:The green area displayed by a crop is a good indicator of its photosynthetic capacity, while chlorophyll retention or 'stay-green' is regarded as a key indicator of stress adaptation. Remote-sensing methods were tested to estimate these parameters in diverse wheat genotypes under different growing conditions. Two wheat populations (a diverse set of 294 advanced lines and a recombinant inbred line population of 169 sister lines derived from the cross between Seri and Babax) were grown in Mexico under three environments: drought, heat, and heat combined with drought. In the two populations studied here, a moderate heritable expression of stay-green was found-when the normalized difference vegetation index (NDVI) at physiological maturity was estimated using the regression of NDVI over time from the mid-stages of grain-filling to physiological maturity-and for the rate of senescence during the same period. Under heat and heat combined with drought environments, stay-green calculated as NDVI at physiological maturity and the rate of senescence, showed positive and negative correlations with yield, respectively. Moreover, stay-green calculated as an estimation of NDVI at physiological maturity and the rate of senescence regressed on degree days give an independent measurement of stay-green without the confounding effect of phenology. On average, in both populations under heat and heat combined with drought environments CTgf and stay-green variables accounted for around 30% of yield variability in multiple regression analysis. It is concluded that stay-green traits may provide cumulative effects, together with other traits, to improve adaptation under stress further.

| S-EPMC3388823 | biostudies-literature

wheat field soil

Project description:Predictive microbial-based modelling of wheat yields and grain quality across a 500km transect in Quebec

| PRJNA749034 | ENA

Proximal Active Optical Sensing Operational Improvement for Research Using the CropCircle ACS-470, Implications for Measurement of Normalized Difference Vegetation Index (NDVI).

Project description:Active radiometric reflectance is useful to determine plant characteristics in field conditions. However, the physics of silicone diode-based sensing are temperature sensitive, where a change in temperature affects photoconductive resistance. High-throughput plant phenotyping (HTPP) is a modern approach using sensors often mounted to proximal based platforms for spatiotemporal measurements of field grown plants. Yet HTPP systems and their sensors are subject to the temperature extremes where plants are grown, and this may affect overall performance and accuracy. The purpose of this study was to characterize the only customizable proximal active reflectance sensor available for HTPP research, including a 10 °C increase in temperature during sensor warmup and in field conditions, and to suggest an operational use approach for researchers. Sensor performance was measured at 1.2 m using large titanium-dioxide white painted field normalization reference panels and the expected detector unity values as well as sensor body temperatures were recorded. The white panel reference measurements illustrated that individual filtered sensor detectors subjected to the same thermal change can behave differently. Across 361 observations of all filtered detectors before and after field collections where temperature changed by more than one degree, values changed an average of 0.24% per 1 °C. Recommendations based on years of sensor control data and plant field phenotyping agricultural research are provided to support ACS-470 researchers by using white panel normalization and sensor temperature stabilization.

| S-EPMC10255205 | biostudies-literature

Soil bacterial community shift correlated with change from forest to pasture vegetation in a tropical soil.

Project description:The change in vegetative cover of a Hawaiian soil from forest to pasture led to significant changes in the composition of the soil bacterial community. DNAs were extracted from both soil habitats and compared for the abundance of guanine-plus-cytosine (G+C) content, by analysis of abundance of phylotypes of small-subunit ribosomal DNA (SSU rDNA) amplified from fractions with 63 and 35% G+C contents, and by phylogenetic analysis of the dominant rDNA clones in the 63% G+C content fraction. All three methods showed differences between the forest and pasture habitats, providing evidence that vegetation had a strong influence on microbial community composition at three levels of taxon resolution. The forest soil DNA had a peak in G+C content of 61%, while the DNA of the pasture soil had a peak in G+C content of 67%. None of the dominant phylotypes found in the forest soil were detected in the pasture soil. For the 63% G+C fraction SSU rDNA sequence analysis of the three most dominant members revealed that their phyla changed from Fibrobacter and Syntrophomonas assemblages in the forest soil to Burkholderia and Rhizobium-Agrobacterium assemblages in the pasture soil.

| S-EPMC91543 | biostudies-literature

Using the Negative Soil Adjustment Factor of Soil Adjusted Vegetation Index (SAVI) to Resist Saturation Effects and Estimate Leaf Area Index (LAI) in Dense Vegetation Areas.

Project description:Saturation effects limit the application of vegetation indices (VIs) in dense vegetation areas. The possibility to mitigate them by adopting a negative soil adjustment factor X is addressed. Two leaf area index (LAI) data sets are analyzed using the Google Earth Engine (GEE) for validation. The first one is derived from observations of MODerate resolution Imaging Spectroradiometer (MODIS) from 16 April 2013, to 21 October 2020, in the Apiacás area. Its corresponding VIs are calculated from a combination of Sentinel-2 and Landsat-8 surface reflectance products. The second one is a global LAI dataset with VIs calculated from Landsat-5 surface reflectance products. A linear regression model is applied to both datasets to evaluate four VIs that are commonly used to estimate LAI: normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed SAVI (TSAVI), and enhanced vegetation index (EVI). The optimal soil adjustment factor of SAVI for LAI estimation is determined using an exhaustive search. The Dickey-Fuller test indicates that the time series of LAI data are stable with a confidence level of 99%. The linear regression results stress significant saturation effects in all VIs. Finally, the exhaustive searching results show that a negative soil adjustment factor of SAVI can mitigate the SAVIs' saturation in the Apiacás area (i.e., X = -0.148 for mean LAI = 5.35), and more generally in areas with large LAI values (e.g., X = -0.183 for mean LAI = 6.72). Our study further confirms that the lower boundary of the soil adjustment factor can be negative and that using a negative soil adjustment factor improves the computation of time series of LAI.

| S-EPMC8002733 | biostudies-literature

Multifractal Downscaling of Rainfall Using Normalized Difference Vegetation Index (NDVI) in the Andes Plateau.

Project description:In this paper, a multifractal downscaling technique is applied to adequately transformed and lag corrected normalized difference vegetation index (NDVI) in order to obtain daily estimates of rainfall in an area of the Peruvian Andean high plateau. This downscaling procedure is temporal in nature since the original NDVI information is provided at an irregular temporal sampling period between 8 and 11 days, and the desired final scale is 1 day. The spatial resolution of approximately 1 km remains the same throughout the downscaling process. The results were validated against on-site measurements of meteorological stations distributed in the area under study.

| S-EPMC5268785 | biostudies-literature

The interaction between wheat roots and soil pores in structured field soil.

Project description:Wheat (Triticum aestivum L.) root growth in the subsoil is usually constrained by soil strength, although roots can use macropores to elongate to deeper layers. The quantitative relationship between the elongation of wheat roots and the soil pore system, however, is still to be determined. We studied the depth distribution of roots of six wheat varieties and explored their relationship with soil macroporosity from samples with the field structure preserved. Undisturbed soil cores (to a depth of 100 cm) were collected from the field and then non-destructively imaged using X-ray computed tomography (at a spatial resolution of 90 µm) to quantify soil macropore structure and root number density (the number of roots cm-2 within a horizontal cross-section of a soil core). Soil macroporosity changed significantly with depth but not between the different wheat lines. There was no significant difference in root number density between wheat varieties. In the subsoil, wheat roots used macropores, especially biopores (i.e. former root or earthworm channels) to grow into deeper layers. Soil macroporosity explained 59% of the variance in root number density. Our data suggested that the development of the wheat root system in the field was more affected by the soil macropore system than by genotype. On this basis, management practices which enhance the porosity of the subsoil may therefore be an effective strategy to improve deep rooting of wheat.

| S-EPMC7853603 | biostudies-literature

Vegetation change impacts on soil organic carbon chemical composition in subtropical forests.

Project description:Changes in the chemical composition of soil organic carbon (SOC) might strongly affect the global carbon cycle as it controls the SOC decomposition rate. Vegetation change associated with long-term land use changes is known to strongly impact the chemical composition of SOC; however, data on the impacts of vegetation change following disturbance events of short durations and succession that occur frequently in forest ecosystems via diverse management objectives on SOC chemical composition are negligible. Here we examined the impacts of vegetation changes on the chemical composition of SOC by sampling soils of native broad-leaved forests, planted mixed broad-leaved and coniferous forests, and tea gardens in eastern China. We used nuclear magnetic resonance spectroscopy to quantify SOC chemical composition. We found that among all components of SOC chemical composition, alkyl carbon (C) and aryl C were more liable to change with vegetation than other SOC components. Soil pH was negatively correlated to the relative abundances of alkyl C and N-alkyl C, and Shannon's index of overstory plant species was positively correlated to the relative abundances of phenolic C and aromaticity. Our results suggest that vegetation changes following short disturbance events and succession may strongly alter SOC chemical composition in forest ecosystems.

| S-EPMC4939599 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data