Spatial variability of winter wheat growth based on the individual index and the population index

2014 ◽  
Author(s):  
Bei Cui ◽  
Xiaoyu Song ◽  
Wude Yang ◽  
Meichen Feng ◽  
Jihua Wang
Keyword(s):  
Winter Wheat ◽  
Spatial Variability ◽  
Wheat Growth ◽  
Population Index ◽  
Individual Index ◽  
The Individual

scholarly journals Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping

2021 ◽  
Vol 13 (6) ◽  
pp. 1144
Author(s):  
Mahendra Bhandari ◽  
Shannon Baker ◽  
Jackie C. Rudd ◽  
Amir M. H. Ibrahim ◽  
Anjin Chang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Grain Filling ◽  
Unmanned Aerial Systems ◽  
High Temporal Resolution ◽  
Wheat Growth ◽  
Spatial Domains ◽  
Temporal And Spatial

Drought significantly limits wheat productivity across the temporal and spatial domains. Unmanned Aerial Systems (UAS) has become an indispensable tool to collect refined spatial and high temporal resolution imagery data. A 2-year field study was conducted in 2018 and 2019 to determine the temporal effects of drought on canopy growth of winter wheat. Weekly UAS data were collected using red, green, and blue (RGB) and multispectral (MS) sensors over a yield trial consisting of 22 winter wheat cultivars in both irrigated and dryland environments. Raw-images were processed to compute canopy features such as canopy cover (CC) and canopy height (CH), and vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Excess Green Index (ExG), and Normalized Difference Red-edge Index (NDRE). The drought was more severe in 2018 than in 2019 and the effects of growth differences across years and irrigation levels were visible in the UAS measurements. CC, CH, and VIs, measured during grain filling, were positively correlated with grain yield (r = 0.4–0.7, p < 0.05) in the dryland in both years. Yield was positively correlated with VIs in 2018 (r = 0.45–0.55, p < 0.05) in the irrigated environment, but the correlations were non-significant in 2019 (r = 0.1 to −0.4), except for CH. The study shows that high-throughput UAS data can be used to monitor the drought effects on wheat growth and productivity across the temporal and spatial domains.

Differences in warming impacts on wheat productivity among varieties released in different eras in North China

2015 ◽  
Vol 153 (8) ◽  
pp. 1353-1364 ◽  
Author(s):  
C. Y. ZHENG ◽  
J. CHEN ◽  
Z. W. SONG ◽  
A. X. DENG ◽  
L. N. JIANG ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Biomass Production ◽  
Air Temperature ◽  
North China ◽  
Growth Period ◽  
Wheat Breeding ◽  
Wheat Growth ◽  
Positive Effects ◽  
Significant Difference

SUMMARYTen leading varieties of winter wheat released during 1950–2009 in North China were tested in a free-air temperature increase (FATI) facility. The FATI facility mimicked the local air temperature pattern well, with an increase of 1·1 °C in the daily mean temperature. For all the tested varieties, warming caused a significant reduction in the total length of wheat growth period by 5 days and especially in the pre-anthesis period, where it was reduced by 9 days. However, warming increased wheat biomass production and grain yield by 8·4 and 11·4%, respectively, on an average of all the tested varieties. There was no significant difference in the warming-led reduction in the entire growth period among the tested varieties. Interestingly, the warming-led increments in biomass production and grain yield increased along with the variety release year. Significantly higher warming-led increases in post-anthesis biomass production and 1000-grain weight were found in the new varieties compared to the old ones. Meanwhile, a significant improvement in plant productivity was noted due to wheat breeding during the past six decades, while no significant difference in the length of entire growth period was found among the varieties released in different eras. The results demonstrate that historical wheat breeding might have enhanced winter wheat productivity and adaptability through exploiting the positive effects rather than mitigating the negative impacts of warming on wheat growth in North China.

scholarly journals Variation of 13C and 15N enrichments in different plant components of labeled winter wheat (Triticum aestivum L.)

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7738
Author(s):  
Zhaoan Sun ◽  
Shuxia Wu ◽  
Biao Zhu ◽  
Yiwen Zhang ◽  
Roland Bol ◽  
...  
Keyword(s):  
Triticum Aestivum ◽  
Winter Wheat ◽  
Grain Filling ◽  
Triticum Aestivum L ◽  
N Fertilization ◽  
Wheat Growth ◽  
Filling Stage ◽  
N Enrichment ◽  
Two Stages ◽  
Grain Filling Stage

Information on the homogeneity and distribution of 13carbon (13C) and nitrogen (15N) labeling in winter wheat (Triticum aestivum L.) is limited. We conducted a dual labeling experiment to evaluate the variability of 13C and 15N enrichment in aboveground parts of labeled winter wheat plants. Labeling with 13C and 15N was performed on non-nitrogen fertilized (−N) and nitrogen fertilized (+N, 250 kg N ha−1) plants at the elongation and grain filling stages. Aboveground parts of wheat were destructively sampled at 28 days after labeling. As winter wheat growth progressed, δ13C values of wheat ears increased significantly, whereas those of leaves and stems decreased significantly. At the elongation stage, N addition tended to reduce the aboveground δ13C values through dilution of C uptake. At the two stages, upper (newly developed) leaves were more highly enriched with 13C compared with that of lower (aged) leaves. Variability between individual wheat plants and among pots at the grain filling stage was smaller than that at the elongation stage, especially for the −N treatment. Compared with those of 13C labeling, differences in 15N excess between aboveground components (leaves and stems) under 15N labeling conditions were much smaller. We conclude that non-N fertilization and labeling at the grain filling stage may produce more uniformly 13C-labeled wheat materials, whereas the materials were more highly 13C-enriched at the elongation stage, although the δ13C values were more variable. The 15N-enriched straw tissues via urea fertilization were more uniformly labeled at the grain filling stage compared with that at the elongation stage.

Understanding the trends and controlling factors of Indian Ocean acidification

2021 ◽  
Author(s):  
Kunal Madkaiker ◽  
Vinu Valsala
Keyword(s):  
Indian Ocean ◽  
Spatial Variability ◽  
Dissolved Inorganic Carbon ◽  
Total Alkalinity ◽  
Global Ocean ◽  
Strong Negative Correlation ◽  
Chemical Balance ◽  
Factors Affecting ◽  
Water Ph ◽  
The Individual

&lt;p&gt;The Indian Ocean (IO) is witnessing acidification of its surface waters as a consequence of the continuous rising of atmospheric CO&lt;sub&gt;2&lt;/sub&gt; concentration thus disrupting the biological and chemical balance of the ecosystem in the region. The basin wide spatial variability of biogeochemical properties induces spatial variability of surface water pH. This study investigates the seasonality and trends of surface pH over the IO bioprovinces and regionally assesses the individual contribution of the factors affecting its variability. Simulations from global ocean models (OTTM and ROMS) coupled with suitable biogeochemical modules were validated with pH observations over the basin, and used to discern the regional response of pH seasonality (1990-2010) and trend (1961-2010) to changes in ocean temperature (SST), Dissolved Inorganic Carbon (DIC), Total Alkalinity (ALK) and Salinity (S). DIC and SST are the major contributors to the seasonal variability of pH in almost all bioprovinces consistent in both model simulations. The acidification in IO basin of 0.0675 units during 1961-2010 is attributed to 69.28% contribution of DIC followed by 13.82% contribution of SST. For most of the regions DIC remains a dominant contributor to changing trend in pH except for the Northern Bay of Bengal and Around India (NBoB-AI) region, wherein pH trend is dominated by ALK (55.6%) and SST (16.8%). The interdependence of SST and S over ALK is significant in modifying the carbonate chemistry and biogeochemical dynamics of NBoB-AI and a part of tropical, subtropical IO. The strong negative correlation between SST and pH infers the increasing risk of acidification in the bioprovinces with the rising SST.&lt;/p&gt;&lt;p&gt;This study is an attempt to identify the regional influencers of pH variability so that adequate mitigation action can be planned and the acidification can be decelerated in near future.&lt;/p&gt;

scholarly journals Large spatial variations in the flux balance along the front of a Greenland tidewater glacier

2019 ◽  
Vol 13 (3) ◽  
pp. 911-925 ◽  
Author(s):  
Till J. W. Wagner ◽  
Fiamma Straneo ◽  
Clark G. Richards ◽  
Donald A. Slater ◽  
Laura A. Stevens ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Variability ◽  
Ocean Model ◽  
Complex Interplay ◽  
Flux Balance ◽  
Tidewater Glacier ◽  
Glacier Front ◽  
The Individual ◽  
High Spatial Variability ◽  
Frontal Ablation

Abstract. The frontal flux balance of a medium-sized tidewater glacier in western Greenland in the summer is assessed by quantifying the individual components (ice flux, retreat, calving, and submarine melting) through a combination of data and models. Ice flux and retreat are obtained from satellite data. Submarine melting is derived using a high-resolution ocean model informed by near-ice observations, and calving is estimated using a record of calving events along the ice front. All terms exhibit large spatial variability along the ∼5 km wide ice front. It is found that submarine melting accounts for much of the frontal ablation in small regions where two subglacial discharge plumes emerge at the ice front. Away from the subglacial plumes, the estimated melting accounts for a small fraction of frontal ablation. Glacier-wide, these estimates suggest that mass loss is largely controlled by calving. This result, however, is at odds with the limited presence of icebergs at this calving front – suggesting that melt rates in regions outside of the subglacial plumes may be underestimated. Finally, we argue that localized melt incisions into the glacier front can be significant drivers of calving. Our results suggest a complex interplay of melting and calving marked by high spatial variability along the glacier front.

scholarly journals The Modification of Difference Vegetation Index (DVI) in middle and late growing period of winter wheat and its application in soil moisture inversion

2019 ◽  
Vol 131 ◽  
pp. 01098
Author(s):  
Zhang Hong-wei ◽  
Huai-liang Chen ◽  
Fei-na Zha
Keyword(s):  
Soil Moisture ◽  
Winter Wheat ◽  
Vegetation Index ◽  
Vegetation Coverage ◽  
Growth Stages ◽  
Wheat Growth ◽  
Inversion Result ◽  
Growing Period ◽  
The Difference ◽  
Different Growth Stages

In the middle and late growing period of winter wheat, soil moisture is easily affected by saturation when using MODIS data to retrieve soil moisture. In this paper, in order to reduce the effect of the saturation caused by increasing vegetation coverage in middle and late stage of winter wheat, the Difference Vegetation Index (DVI) model was modified with different coefficients in different growth stages of winter wheat based on MODIS spectral data and LAI characteristics of variation. LAI was divided into three stages, LAI ≤ 1 < LAI ≤, 3 < LAI, and the adjusting coefficient of α=1, α=3, α=5, were taken to modifying the Difference Vegetation Index(DVI). The results show that the Modified Difference Vegetation Index (MDVIα) can effectively reduce the interference of saturation, and the inversion result of soil moisture in the middle and late period of winter wheat growth is obviously superior to the uncorrected inversion model of DVI.

scholarly journals Winter Wheat Growth Monitoring Using Multi-temporal TerraSAR-X Dual-polarimetric Data

2014 ◽  
Vol 48 (4) ◽  
pp. 471-476 ◽  
Author(s):  
Rei SONOBE ◽  
Hiroshi TANI ◽  
Xiufeng WANG ◽  
Nobuyuki KOBAYASHI ◽  
Hideki SHIMAMURA
Keyword(s):  
Winter Wheat ◽  
Growth Monitoring ◽  
Wheat Growth ◽  
Multi Temporal
Sign in / Sign up

Export Citation Format

Share Document