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.

Winter Wheat (Triticum aestivum) Growth Stage Effect on Paraquat Bioactivity

1991 ◽  
Vol 5 (2) ◽  
pp. 439-441
Author(s):  
Randy L. Anderson ◽  
David C. Nielsen
Keyword(s):  
Triticum Aestivum ◽  
Winter Wheat ◽  
Growth Stage ◽  
Time Of Day ◽  
Growth Stages ◽  
Leaf Stage ◽  
Time Of Application ◽  
Biomass Reduction

Paraquat was applied at 0.28 and 0.56 kg ai ha-1to winter wheat at five growth stages at 0800, 1300, and 1600 hr to determine whether growth stage or time of application influenced winter wheat response to paraquat. Paraquat bioactivity was affected by growth stage. Biomass reduction by paraquat was 84% when winter wheat was in the 1 to 3 leaf stage, but only 68% when application was delayed until tillering. Paraquat bioactivity continued to decrease at later growth stages. The time of day when paraquat was applied did not affect its bioactivity on winter wheat.

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 Evaluation of Three Portable Optical Sensors for Non-Destructive Diagnosis of Nitrogen Status in Winter Wheat

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5579
Author(s):  
Jie Jiang ◽  
Cuicun Wang ◽  
Hui Wang ◽  
Zhaopeng Fu ◽  
Qiang Cao ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Optical Sensors ◽  
Accurate Estimation ◽  
Growth Stages ◽  
N Accumulation ◽  
N Nutrition ◽  
N Concentration ◽  
Non Destructive ◽  
N Status ◽  
Leaf N

The accurate estimation and timely diagnosis of crop nitrogen (N) status can facilitate in-season fertilizer management. In order to evaluate the performance of three leaf and canopy optical sensors in non-destructively diagnosing winter wheat N status, three experiments using seven wheat cultivars and multi-N-treatments (0–360 kg N ha−1) were conducted in the Jiangsu province of China from 2015 to 2018. Two leaf sensors (SPAD 502, Dualex 4 Scientific+) and one canopy sensor (RapidSCAN CS-45) were used to obtain leaf and canopy spectral data, respectively, during the main growth period. Five N indicators (leaf N concentration (LNC), leaf N accumulation (LNA), plant N concentration (PNC), plant N accumulation (PNA), and N nutrition index (NNI)) were measured synchronously. The relationships between the six sensor-based indices (leaf level: SPAD, Chl, Flav, NBI, canopy level: NDRE, NDVI) and five N parameters were established at each growth stages. The results showed that the Dualex-based NBI performed relatively well among four leaf-sensor indices, while NDRE of RS sensor achieved a best performance due to larger sampling area of canopy sensor for five N indicators estimation across different growth stages. The areal agreement of the NNI diagnosis models ranged from 0.54 to 0.71 for SPAD, 0.66 to 0.84 for NBI, and 0.72 to 0.86 for NDRE, and the kappa coefficient ranged from 0.30 to 0.52 for SPAD, 0.42 to 0.72 for NBI, and 0.53 to 0.75 for NDRE across all growth stages. Overall, these results reveal the potential of sensor-based diagnosis models for the rapid and non-destructive diagnosis of N status.

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 Variation Characteristics in Growth Stages of Winter Wheat (Triticum Aestivum L.) And Prediction Model

2021 ◽  
pp. 737-746
Author(s):  
Weili Wang ◽  
Xuhui Zhang ◽  
Zhaotang Shang
Keyword(s):  
Climate Change ◽  
Triticum Aestivum ◽  
Winter Wheat ◽  
Prediction Model ◽  
Late Stage ◽  
Growth Stage ◽  
Triticum Aestivum L ◽  
Meteorological Conditions ◽  
Growth Stages ◽  
Variation Characteristics

The variation characteristics of growth stages of winter wheat (Triticum aestivum L.) with the climate change were measured by designing its stability and prediction model. Results showed the trend of stability of growth stage of winter wheat in Jiangsu province of China was an S-shaped curve indicating the growth of winter wheat was more stable in late stage. The lengths of early and late stages of growth were in inverse proportion. Specifically, when the early stage was prolonged, the late stage was shortened, which ensured the relative stability of the length of growth stage. The length of growth stage was correlated with the meteorological conditions. Thus, favorable meteorological conditions contributed to the stability of growth stages of winter wheat. Along with the climate change, the basic statistical characteristics of growth stage remained stable. Each stage drifted moderately under the variation of meteorological conditions, typically during the stage of vegetative growth. The growth process can be regulated by means of variety improvement, adjustment of sowing time and density, reasonable fertilization, and the use of growth regulators. These measures are able to counteract the influences of climate change on winter wheat production and ensure the production security. Bangladesh J. Bot. 50(3): 737-746, 2021 (September) Special

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 Efficacy of Fungicides against Fusarium Head Blight Depends on the Timing Relative to Infection Rather than on Wheat Growth Stage

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1549
Author(s):  
Elisa González-Domínguez ◽  
Pierluigi Meriggi ◽  
Matteo Ruggeri ◽  
Vittorio Rossi
Keyword(s):  
Fusarium Head Blight ◽  
Mixed Models ◽  
Untreated Control ◽  
Growth Stage ◽  
Growth Stages ◽  
Head Blight ◽  
Wheat Growth ◽  
Fungicide Application ◽  
Time Of Application ◽  
Fungicide Efficacy

Fungicides used to control Fusarium head blight (FHB) are commonly applied at the wheat growth stage considered to be most susceptible, i.e., anthesis. We compared the efficacy of the most commonly used fungicide groups that were applied following two strategies: (i) at pre-defined growth stages, from the first half of heading to the end of flowering (experiment 1, in 2013 to 2015), or (ii) based on timing of infection by F. graminearum, specifically at 10, 7, 4, or 1 day before, or 3 or 5 days after artificial inoculation of the fungus (experiment 2, in 2017 and 2018). Fungicide efficacy was evaluated in terms of FHB incidence, FHB severity, and DON contamination by using generalised mixed models. In experiment 1, all fungicide groups reduced FHB severity and DON but only by <50% compared to an untreated control, with no differences among fungicides or growth stages at time of application. In experiment 2, the efficacy of fungicides was higher for applications at 1 or 4 days before inoculation than at 7 or 10 days before or 3 or 5 days after inoculation, with differences among fungicide groups. Based on our results, the timing of fungicide application for FHB control should be based on the time of F. graminearum infection rather than on wheat phenology.

scholarly journals Does the Organ-Based N Dilution Curve Improve the Predictions of N Status in Winter Wheat?

Agriculture ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 500
Author(s):  
Ke Zhang ◽  
Xue Wang ◽  
Xiaoling Wang ◽  
Syed Tahir Ata-Ul-Karim ◽  
Yongchao Tian ◽  
...  
Keyword(s):  
Winter Wheat ◽  
High Efficiency ◽  
Eastern China ◽  
Relative Yield ◽  
N Fertilizer ◽  
Growth Stages ◽  
Dilution Curve ◽  
Area Index ◽  
N Concentration ◽  
N Status

Accurately summarizing Nitrogen (N) content as a prelude to optimal N fertilizer application is complicated during the vegetative growth period of all the crop species studied. The critical nitrogen (N) concentration (Nc) dilution curve is a stable diagnostic indicator, which performs plant critical N concentration trends as crop grows. This study developed efficient technologies for different organ-based (plant dry matters (PDM), leaf DM (LDM), stem DM (SDM), and leaf area index (LAI)) estimation of Nc curves to enrich the practical applications of precision N management strategies. Four winter wheat cultivars were planted with 10 different N treatments in Jiangsu province of eastern China. Results showed the SDM-based curve had a better performance than the PDM-based curve in N nutrition index (NNI) estimation, accumulated N deficit (AND) calculation, and N requirement (NR) determination. The regression coefficients ‘a’ and ‘b’ varied among the four critical N dilution models: Nc = 3.61 × LDM–0.19, R2 = 0.77; Nc = 2.50 × SDM–0.44, R2 = 0.89; Nc = 4.16 × PDM–0.41, R2 = 0.87; and Nc = 3.82 × LAI–0.36, R2 = 0.81. In later growth periods, the SDM-based curve was found to be a feasible indicator for calculating NNI, AND, and NR, relative to curves based on the other indicators. Meanwhile, the lower LAI-based curve coefficient variation values stated that leaf-related indicators were also a good choice for developing the N curve with high efficiency as compared to other biomass-based approaches. The SDM-based curve was the more reliable predictor of relative yield because of its low relative root mean square error in most of the growth stages. The curves developed in this study will provide diverse choices of indicators for establishing an integrated procedure of diagnosing wheat N status, and improving the accuracy and efficiency of wheat N fertilizer management.

Factors Influencing Winter Wheat (Triticum aestivum) Injury from Sulfonylurea Herbicides

1990 ◽  
Vol 4 (4) ◽  
pp. 724-730 ◽  
Author(s):  
Kenneth L. Ferreira ◽  
Thomas K. Baker ◽  
Thomas F. Peeper
Keyword(s):  
Triticum Aestivum ◽  
Winter Wheat ◽  
Growth Stage ◽  
Field Experiments ◽  
Temperature Fluctuations ◽  
Post Treatment ◽  
Sulfonylurea Herbicides ◽  
Wheat Growth ◽  
Factors Influencing ◽  
Cultivar Selection

Field experiments were conducted to determine factors that predispose winter wheat to injury by sulfonylurea herbicides. Wheat was injured occasionally when herbicides were applied postemergence in November or when tank mixed with malathion. CGA 131036 at 28 or 56 g ha-1was less injurious than chlorsulfuron or DPX-G8311 at 26 or 53 g ha-1, and preemergence treatments of chlorsulfuron and DPX-G8311 were less injurious than preplant incorporated or postemergence treatments. Wheat growth stage, minimum post-treatment daily temperature, and summed diurnal temperature fluctuations after treatment and after first post-treatment rainfall were correlated with wheat injury. Grazing and cultivar selection did not affect injury.

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.

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