scholarly journals Morfogeneza pędów krzewiącej się pszenicy oraz dynamika ich elongacyjnego wzrostu [Morphogenesis of shoots of tillering plants of winter wheat and the dynamics of their elongation growth]

2015 ◽  
Vol 28 (1) ◽  
pp. 5-28
Author(s):  
Witold Drezner
Keyword(s):  
Winter Wheat ◽  
Elongation Growth ◽  
Growth Stages ◽  
Main Shoot ◽  
Wheat Growth

The morphogenesis of vegetative shoots of tillering plants of the winter wheat, the mode of identification and the description of the sequence of formation of individual shoots are presented. The average elongation growth of plants (e) in the successive growth stages are described as the sum of the increase of the main shoot (a) and of the side (secondary) shoots (Σ b) divided by the number of measured tillers (1) and by the time unit (t) according to the equation. By this method the correlation between the dynamics of winter wheat growth and the grade of tillering are described for three varieties.

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.

Discrepancies between observed and predicted growth stages in wheat

1997 ◽  
Vol 129 (4) ◽  
pp. 379-384 ◽  
Author(s):  
E. J. M. KIRBY ◽  
R. M. WEIGHTMAN
Keyword(s):  
Stem Elongation ◽  
Daily Maximum ◽  
Growth Stages ◽  
Main Shoot ◽  
Wheat Growth ◽  
Growing Seasons ◽  
Leaf Emergence ◽  
Prediction Scheme ◽  
Number Of Leaves ◽  
Input Variables

A model to predict wheat growth stage is briefly described. It is based on prediction of the number of emerged leaves and the final number of leaves on the main shoot, and the co-ordination between leaf emergence and apex development, including stem elongation. The input variables are daily maximum and minimum temperatures, date of sowing and site latitude, from which thermal time, vernalization and daylength are calculated.Selected growth stages were predicted for six sites in each of three growing seasons. The differences between observations made by independent observers and predictions were mostly 7 days or less but in three site–season combinations the average difference was >10 days. Observer errors were implicated and examined, but it is concluded that the prediction scheme must also have been partly responsible for the discrepancies.

Variations of winter wheat growth stages under climate changes in northern China

2011 ◽  
Vol 35 (6) ◽  
pp. 623-631 ◽  
Author(s):  
Jian-Ying YANG ◽  
Xu-Rong MEI ◽  
Qin LIU ◽  
Chang-Rong YAN ◽  
Wen-Qing HE ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Climate Changes ◽  
Northern China ◽  
Growth Stages ◽  
Wheat Growth

scholarly journals Modeling Soil Water Content and Crop-Growth Metrics in a Wheat Field in the North China Plain Using RZWQM2

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1245
Author(s):  
Kun Du ◽  
Yunfeng Qiao ◽  
Qiuying Zhang ◽  
Fadong Li ◽  
Qi Li ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Aboveground Biomass ◽  
North China Plain ◽  
North China ◽  
Crop Growth ◽  
Growth Stages ◽  
Mean Square ◽  
Wheat Growth ◽  
The North ◽  
The North China Plain

Soil water content (SWC) is an important factor restricting crop growth and yield in cropland ecosystems. The observation and simulation of soil moisture contribute greatly to improving water-use efficiency and crop yield. This study was conducted at the Shandong Yucheng Agro-ecosystem National Observation and Research Station in the North China Plain. The study period was across the winter wheat (Triticum aestivum L.) growth stages from 2017 to 2019. A cosmic-ray neutron probe was used to monitor the continuous daily SWC. Furthermore, the crop leaf area index (LAI), yield, and aboveground biomass of winter wheat were determined. The root zone quality model 2 (RZWQM2) was used to simulate and validate the SWC, crop LAI, yield, and aboveground biomass. The results showed that the simulation errors of SWC were minute across the wheat growth stages and mature stages in 2017–2019. The root mean square error (RMSE) and relative root mean square error (RRMSE) of the SWC simulation at the jointing stage of winter wheat were 0.0296 and 0.1605 in 2017–2018, and 0.0265 and 0.1480 in 2018–2019, respectively. During the rain-affected days, the RMSE (0.0253) and RRMSE (0.0980) for 2017–2018 were significantly lower than those of 2018–2019 (0.0301 and 0.1458, respectively), indicating that rain events decreased the model accuracy in the dry years compared to the wet years. The simulated LAIs were significantly higher than the measured values. The simulated yield value of winter wheat was 5.61% lower and 3.92% higher than the measured yield in 2017–2018 and in 2018–2019, respectively. The simulated value of aboveground biomass was significantly (45.48%) lower than the measured value in 2017–2018. This study showed that, compared with the dry and cold wheat growth period of 2018–2019, the higher precipitation and temperature in 2017–2018 led to a poorer simulation of SWC and crop-growth components. This study indicated that annual abnormal rainfall and temperature had a significant influence on the simulation of SWC and wheat growth, especially under intensive climate-change stress conditions.

scholarly journals Winter Wheat Nitrogen Estimation Based on Ground-Level and UAV-Mounted Sensors

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 549
Author(s):  
Xiaoyu Song ◽  
Guijun Yang ◽  
Xingang Xu ◽  
Dongyan Zhang ◽  
Chenghai Yang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Gaussian Process ◽  
Growth Stage ◽  
Gaussian Process Regression ◽  
Ground Level ◽  
Growth Stages ◽  
Wheat Growth ◽  
Level Indicator ◽  
N Concentration ◽  
Study Results

A better understanding of wheat nitrogen status is important for improving N fertilizer management in precision farming. In this study, four different sensors were evaluated for their ability to estimate winter wheat nitrogen. A Gaussian process regression (GPR) method with the sequential backward feature removal (SBBR) routine was used to identify the best combinations of vegetation indices (VIs) sensitive to wheat N indicators for different sensors. Wheat leaf N concentration (LNC), plant N concentration (PNC), and the nutrition index (NNI) were estimated by the VIs through parametric regression (PR), multivariable linear regression (MLR), and Gaussian process regression (GPR). The study results reveal that the optical fluorescence sensor provides more accurate estimates of winter wheat N status at a low-canopy coverage condition. The Dualex Nitrogen Balance Index (NBI) is the best leaf-level indicator for wheat LNC, PNC and NNI at the early wheat growth stage. At the early growth stage, Multiplex indices are the best canopy-level indicators for LNC, PNC, and NNI. At the late growth stage, ASD VIs provide accurate estimates for wheat N indicators. This study also reveals that the GPR with SBBR analysis method provides more accurate estimates of winter wheat LNC, PNC, and NNI, with the best VI combinations for these sensors across the different winter wheat growth stages, compared with the MLR and PR methods.

scholarly journals Plant phenomics & precision agriculture simulation of winter wheat growth by the assimilation of unmanned aerial vehicle imagery into the WOFOST model

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0246874
Author(s):  
Tianle Yang ◽  
Weijun Zhang ◽  
Tong Zhou ◽  
Wei Wu ◽  
Tao Liu ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Leaf Area ◽  
Unmanned Aerial Vehicle ◽  
Precision Agriculture ◽  
Model Simulation ◽  
Growth Stages ◽  
Wheat Growth ◽  
Area Index ◽  
Crop Models ◽  
Aerial Vehicle

The aim of this study is to optimize the simulation result of the WOFOST model and explore the possibility of assimilating unmanned aerial vehicle (UAV) imagery into this model. Field images of wheat during its key growth stages are acquired with a UAV, and the corresponding leaf area index (LAI), biomass, and final yield are experimentally measured. LAI data is retrieved from the UAV imagery and assimilated into a localized WOFOST model using least squares optimization. Sensitive parameters, i.e., specific leaf area (SLATB0, SLATB0.5, SLATB2) and maximum CO2 assimilation rate (AMAXTB1, AMAXTB1.3) are adjusted to minimize the discrepancy between the LAI obtained from the model simulation and inversion of the UAV data. The results show that the assimilated model provides a better estimation of the growth and development of winter wheat in the study area. The R2, RMSE, and NRMSE of winter wheat LAI simulated with the assimilated WOFOST model are 0.8812, 0.49, and 23.5% respectively. The R2, RMSE, and NRMSE of the simulated yield are 0.9489, 327.06 kg·hm−2, and 6.5%. The accuracy in model simulation of winter wheat growth is improved, which demonstrates the feasibility of integrating UAV data into crop models.

scholarly journals Growth and Phenology of Vulpia Myuros in Comparison with Apera Spica-Venti, Alopecurus Myosuroides and Lolium Multiflorum in Monoculture and in Winter Wheat

Plants ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1495
Author(s):  
Muhammad Javaid Akhter ◽  
Bo Melander ◽  
Solvejg Kopp Mathiassen ◽  
Rodrigo Labouriau ◽  
Svend Vendelbo Nielsen ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Weed Management ◽  
Lolium Multiflorum ◽  
Management Strategies ◽  
Integrated Weed Management ◽  
Growth Stages ◽  
Neighborhood Design ◽  
Alopecurus Myosuroides ◽  
Winter Cereals ◽  
Arable Weed

Vulpia myuros has become an increasing weed problem in winter cereals in Northern Europe. However, the information about V. myuros and its behavior as an arable weed is limited. Field and greenhouse experiments were conducted in 2017/18 and 2018/19, at the Department of Agroecology in Flakkebjerg, Denmark to investigate the emergence, phenological development and growth characteristics of V. myuros in monoculture and in mixture with winter wheat, in comparison to Apera spica-venti, Alopecurus myosuroides and Lolium multiflorum. V. myuros emerged earlier than A. myosuroides and A. spica-venti but later than L. multiflorum. Significant differences in phenological development were recorded among the species. Overall phenology of V. myuros was more similar to that of L. multiflorum than to A. myosuroides and A. spica-venti. V. myuros started seed shedding earlier than A. spica-venti and L. multiflorum but later than A. myosuroides. V. myuros was more sensitive to winter wheat competition in terms of biomass production and fecundity than the other species. Using a target-neighborhood design, responses of V. myuros and A. spica-venti to the increasing density of winter wheat were quantified. At early growth stages “BBCH 26–29”, V. myuros was suppressed less than A. spica-venti by winter wheat, while opposite responses were seen at later growth stages “BBCH 39–47” and “BBCH 81–90”. No significant differences in fecundity characteristics were observed between the two species in response to increasing winter wheat density. The information on the behavior of V. myuros gathered by the current study can support the development of effective integrated weed management strategies for V. myuros.

Targeting Ascophyllum nodosum (L.) Le Jol. extract application at five growth stages of winter wheat

2021 ◽  
Author(s):  
Stamatis Stamatiadis ◽  
Eleftherios Evangelou ◽  
Frank Jamois ◽  
Jean-Claude Yvin
Keyword(s):  
Winter Wheat ◽  
Ascophyllum Nodosum ◽  
Growth Stages

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.

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