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

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.

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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]

Acta Agrobotanica ◽

10.5586/aa.1975.001 ◽

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.

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Effects of fallow tillage on winter wheat yield and predictions under different precipitation types

PeerJ ◽

10.7717/peerj.12602 ◽

2021 ◽

Vol 9 ◽

pp. e12602

Author(s):

Yu Feng ◽

Wen Lin ◽

Shaobo Yu ◽

Aixia Ren ◽

Qiang Wang ◽

...

Keyword(s):

Winter Wheat ◽

Meteorological Factors ◽

Wheat Yield ◽

Control Variable ◽

Northern China ◽

Small Error ◽

Soil Water Storage ◽

Growth Stages ◽

Yield Prediction ◽

Fallow Period

In northern China, precipitation that is primarily concentrated during the fallow period is insufficient for the growth stage, creates a moisture shortage, and leads to low, unstable yields. Yield prediction in the early growth stages significantly informs field management decisions for winter wheat (Triticum aestivum L.). A 10-year field experiment carried out in the Loess Plateau area tested how three tillage practices (deep ploughing (DP), subsoiling (SS), and no tillage (NT)) influenced cultivation and yield across different fallow periods. The experiment used the random forest (RF) algorithm to construct a prediction model of yields and yield components. Our results revealed that tillage during the fallow period was more effective than NT in improving yield in dryland wheat. Under drought condition, DP during the fallow period achieved a higher yield than SS, especially in drought years; DP was 16% higher than SS. RF was deemed fit for yield prediction across different precipitation years. An RF model was developed using meteorological factors for fixed variables and soil water storage after tillage during a fallow period for a control variable. Small error values existed in the prediction yield, spike number, and grains number per spike. Additionally, the relative error of crop yield under fallow tillage (5.24%) was smaller than that of NT (6.49%). The prediction error of relative meteorological yield was minimum and optimal, indicating that the model is suitable to explain the influence of meteorological factors on yield.

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

Agronomy ◽

10.3390/agronomy11061245 ◽

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.

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

Sensors ◽

10.3390/s22020549 ◽

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.

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Spatiotemporal variability of soil nutrients and the responses of growth during growth stages of winter wheat in northern China

PLoS ONE ◽

10.1371/journal.pone.0203509 ◽

2018 ◽

Vol 13 (12) ◽

pp. e0203509 ◽

Cited By ~ 1

Author(s):

Baowei Su ◽

Gengxing Zhao ◽

Chao Dong

Keyword(s):

Winter Wheat ◽

Soil Nutrients ◽

Northern China ◽

Spatiotemporal Variability ◽

Growth Stages

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

PLoS ONE ◽

10.1371/journal.pone.0246874 ◽

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.

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