Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Li X

PROVIDER: S-EPMC6760272 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Li Xiaoxi X Ingvordsen Cathrine H CH Weiss Michael M Rebetzke Greg J GJ Condon Anthony G AG James Richard A RA Richards Richard A RA

Journal of experimental botany 20190901 18

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 har ...[more]

PMID: 31089708

Similar Datasets

Project description:The estimation of long-term groundwater recharge rate ([Formula: see text]) is a pre-requisite for efficient management of groundwater resources, especially for arid and semi-arid regions. Precise estimation of [Formula: see text] is probably the most difficult factor of all measurements in the evaluation of GW resources, particularly in semi-arid regions in which the recharge rate is typically small and/or regions with scarce hydrogeological data. The main objective of this study is to find and assess the predicting factors of [Formula: see text] at an aquifer scale. For this purpose, 325 Iran's phreatic aquifers (61% of Iran's aquifers) were selected based on the data availability and the effect of eight predicting factors were assessed on [Formula: see text] estimation. The predicting factors considered include Normalized Difference Vegetation Index (NDVI), mean annual temperature ([Formula: see text]), the ratio of precipitation to potential evapotranspiration ([Formula: see text]), drainage density ([Formula: see text]), mean annual specific discharge ([Formula: see text]), Mean Slope ([Formula: see text]), Soil Moisture ([Formula: see text]), and population density ([Formula: see text]). The local and global Moran's I index, geographically weighted regression (GWR), and two-step cluster analysis served to support the spatial analysis of the results. The eight predicting factors considered are positively correlated to [Formula: see text] and the NDVI has the greatest influence followed by the [Formula: see text] and [Formula: see text]. In the regression model, NDVI solely explained 71% of the variation in [Formula: see text], while other drivers have only a minor modification (3.6%). The results of this study provide new insight into the complex interrelationship between [Formula: see text] and vegetation density indicated by the NDVI. The findings of this study can help in better estimation of [Formula: see text] especially for the phreatic aquifers that the hydrogeological ground-data requisite for establishing models are scarce.

Project description:Phenology of plants is important for ecological interactions. The timing and development of green leaves, plant maturity, and senescence affects biophysical interactions of plants with the environment. In this study we explored the agreement between land-based camera and satellite-based phenology metrics to quantify plant phenology and phenophases dates in five plant community types characteristic of the semi-arid cold desert region of the Great Basin. Three years of data were analyzed. We calculated the Normalized Difference Vegetation Index (NDVI) for both land-based cameras (i.e., phenocams) and Landsat imagery. NDVI from camera images was calculated by taking a standard RGB (red, green, and blue) image and then a near infrared (NIR) plus RGB image. Phenocam NDVI was calculated by extracting the red digital number (DN) and the NIR DN from images taken a few seconds apart. Landsat has a spatial resolution of 30 m², while phenocam spatial resolution can be analyzed at the single pixel level at the scale of cm² or area averaged regions can be analyzed with scales up to 1 km². For this study, phenocam regions of interest were used that approximated the scale of at least one Landsat pixel. In the tall-statured pinyon and juniper woodland sites, there was a lack of agreement in NDVI between phenocam and Landsat NDVI, even after using National Agricultural Imagery Program (NAIP) imagery to account for fractional coverage of pinyon and juniper versus interspace in the phenocam data. Landsat NDVI appeared to be dominated by the signal from the interspace and was insensitive to subtle changes in the pinyon and juniper tree canopy. However, for short-statured sagebrush shrub and meadow communities, there was good agreement between the phenocam and Landsat NDVI as reflected in high Pearson's correlation coefficients (r > 0.75). Due to greater temporal resolution of the phenocams with images taken daily, versus the 16-day return interval of Landsat, phenocam data provided more utility in determining important phenophase dates: start of season, peak of season, and end of season. More specific species-level information can be obtained with the high temporal resolution of phenocams, but only for a limited number of sites, while Landsat can provide the multi-decadal history and spatial coverage that is unmatched by other platforms. The agreement between Landsat and phenocam NDVI for short-statured plant communities of the Great Basin, shows promise for monitoring landscape and regional-level plant phenology across large areas and time periods, with phenocams providing a more comprehensive understanding of plant phenology at finer spatial scales, and Landsat extending the historical record of observations.

Project description:The loss of unimproved grassland has led to species decline in a wide range of taxonomic groups. Agricultural intensification has resulted in fragmented patches of remnant grassland habitat both across Europe and internationally. The monitoring of remnant patches of this habitat is critically important, however, traditional surveying of large, remote landscapes is a notoriously costly and difficult task. The emergence of small-Unmanned Aircraft Systems (sUAS) equipped with low-cost multi-spectral cameras offer an alternative to traditional grassland survey methods, and have the potential to progress and innovate the monitoring and future conservation of this habitat globally. The aim of this article is to investigate the potential of sUAS for rapid detection of threatened unimproved grassland and to test the use of an Enhanced Normalized Difference Vegetation Index (ENDVI). A sUAS aerial survey is undertaken at a site nationally recognised as an important location for fragmented unimproved mesotrophic grassland, within the south east of England, UK. A multispectral camera is used to capture imagery in the visible and near-infrared spectrums, and the ENDVI calculated and its discrimination performance compared to a range of more traditional vegetation indices. In order to validate the results of analysis, ground quadrat surveys were carried out to determine the grassland communities present. Quadrat surveys identified three community types within the site; unimproved grassland, improved grassland and rush pasture. All six vegetation indices tested were able to distinguish between the broad habitat types of grassland and rush pasture; whilst only three could differentiate vegetation at a community level. The Enhanced Normalized Difference Vegetation Index (ENDVI) was the most effective index when differentiating grasslands at the community level. The mechanisms behind the improved performance of the ENDVI are discussed and recommendations are made for areas of future research and study.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets