scholarly journals Sensor based calibration study for in-season nitrogen management of winter wheat in Turkey

2020 ◽  
Vol 6 (2) ◽  
pp. 204-211
Author(s):  
Erdinc Savasli ◽  
Oguz Onder ◽  
Cemal Cekic ◽  
Hasan Mufit Kalayci ◽  
Ramis Dayioglu ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Vegetation Index ◽  
Agricultural Research ◽  
Nitrogen Nutrition ◽  
Stem Elongation ◽  
Block Design ◽  
Vegetation Indices ◽  
Nutrition Status ◽  
Growth Stages ◽  
Yield Estimation

The aims of this study were to compare the responses of four winter wheat cultivars to nitrogen fertilization with vegetation indices calculated using spectral reflection (GreenSeeker hand-held sensor) and to estimate in-season yield (INSEY) using the vegetation indices. The field experiment was conducted at Transitional Zone Agricultural Research Institute of Eskisehir province, Turkey in 2007-2008, 2008-2009 and 2009-2010 growing seasons. The experimental layout was a 2factor factorial in the randomized complete block design. Nitrogen rates were 0, 40, 80, 120, 160 and 200 kg N ha-1. Vegetation Index (NDVI) was obtained at growth stages of Zadoks 24 (tillering stage), Zadoks stage 30 (stem elongation), Zadoks stage 31 (the first node is visible) and Zadoks stage 32 (the second node is visible). The results revealed that Zadoks stage 30 was the most realistic reading time. NDVI had the advantage of providing information on biomass, in addition to nitrogen nutrition status of crops, enabling in-season yield estimation possible. Therefore, NDVI based calibration equations were preferred for testing in the fields of actual farmers for the last year of study. A comparison of the system with traditional farmer applications indicated that yield estimation obtained by the new system was quite similar yields with 13.2 kg ha-1 less N in the spring (ZD 3.0), showing its economically promising value. Asian J. Med. Biol. Res. June 2020, 6(2): 204-211

scholarly journals Sulu Şartlar İçin Geliştirilen Ekmeklik Buğday Çeşitlerine Optik Sensor ile Azotlu Gübre Tavsiyesi

2020 ◽  
Vol 8 (9) ◽  
pp. 1882-1891
Author(s):  
Erdinc Savasli
Keyword(s):  
Agricultural Research ◽  
Stem Elongation ◽  
Block Design ◽  
Vegetation Indices ◽  
Transitional Zone ◽  
Growth Stages ◽  
Growing Seasons ◽  
Randomized Complete Block Design ◽  
Winter Wheat Cultivars ◽  
Nitrogen Rates

This study was conducted at Transitional Zone Agricultural Research Institute in Eskisehir, in 2017-2019 growing seasons. In the study, responses of four winter wheat cultivars (Atay85, Hat 31, Yunus and Nacibey) to nitrogen fertilization under irrigation conditions were compared with vegetation indices based on spectral reflection and In- Season Estimates of Yield calculated from these indices. GreenSeekerTM (NTech Industries, Inc., Ukiah,CA) hand-held sensor was used for this purpose. The experimental layout were used 0, 4, 8, 12, 16 and 20 kg N/da nitrogen rates 2 factor factorial in randomized complete block design in the experiment. Vegetation indices (NDVI) were obtained at growth stages Zadoks2,4, Zadoks3,0, Zadoks3,1 and Zadoks3,2. Zadoks3,0 (stem elongation) was found to be the most realistic reading time. A comparison of the system with traditional farmer applications, based on the average of 3 experiment fields, the new system was shown to give similar yields with 2,8 kg/da less N in the spring (ZD3,0), showing its economically promising value. The sensor application is determined to be 2% more economically effective than farmer application. Economic nitrogen dose respectively Atay85, Hat 31, Yunus and Nacibey was determined as the nitrogen dose 12,6 kgN/da, 14,1 kgN/ da, 14,4 kg N/da and 17,9 kgN/da.

scholarly journals Use of an Active Canopy Sensor Mounted on an Unmanned Aerial Vehicle to Monitor the Growth and Nitrogen Status of Winter Wheat

2020 ◽  
Vol 12 (22) ◽  
pp. 3684
Author(s):  
Jie Jiang ◽  
Zeyu Zhang ◽  
Qiang Cao ◽  
Yan Liang ◽  
Brian Krienke ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Dry Matter ◽  
Vegetation Index ◽  
Nutrition Status ◽  
Growth Stages ◽  
Growth Indices ◽  
N Accumulation ◽  
N Nutrition ◽  
Red Edge ◽  
Aerial Vehicle

Using remote sensing to rapidly acquire large-area crop growth information (e.g., shoot biomass, nitrogen status) is an urgent demand for modern crop production; unmanned aerial vehicle (UAV) acts as an effective monitoring platform. In order to improve the practicability and efficiency of UAV based monitoring technique, four field experiments involving different nitrogen (N) rates (0–360 kg N ha−1) and seven winter wheat (Triticum aestivum L.) varieties were conducted at different eco-sites (Sihong, Rugao, and Xinghua) during 2015–2019. A multispectral active canopy sensor (RapidSCAN CS-45; Holland Scientific Inc., Lincoln, NE, USA) mounted on a multirotor UAV platform was used to collect the canopy spectral reflectance data of winter wheat at key growth stages, three growth parameters (leaf area index (LAI), leaf dry matter (LDM), plant dry matter (PDM)) and three N indicators (leaf N accumulation (LNA), plant N accumulation (PNA) and N nutrition index (NNI)) were measured synchronously. The quantitative linear relationships between spectral data and six growth indices were systematically analyzed. For monitoring growth and N nutrition status at Feekes stages 6.0–10.0, 10.3–11.1 or entire growth stages, red edge ratio vegetation index (RERVI), red edge chlorophyll index (CIRE) and difference vegetation index (DVI) performed the best among the red edge band-based and red-based vegetation indices, respectively. Across all growth stages, DVI was highly correlated with LAI (R2 = 0.78), LDM (R2 = 0.61), PDM (R2 = 0.63), LNA (R2 = 0.65) and PNA (R2 = 0.73), whereas the relationships between RERVI (R2 = 0.62), CIRE (R2 = 0.62) and NNI had high coefficients of determination. The developed models performed better in monitoring growth indices and N status at Feekes stages 10.3–11.1 than Feekes stages 6.0–10.0. To sum it up, the UAV-mounted active sensor system is able to rapidly monitor the growth and N nutrition status of winter wheat and can be deployed for UAV-based remote-sensing of crops.

scholarly journals The use of NDVI to determine the nitrogen content in winter wheat plants in the Stavropol Territory

2019 ◽  
Vol 191 (12) ◽  
pp. 19-30
Author(s):  
I. STORCHAK ◽  
I. V. Chernova ◽  
F. Eroshenko ◽  
Tatiana Voloshenkova ◽  
Elena Shestakova
Keyword(s):  
Winter Wheat ◽  
Nitrogen Content ◽  
Vegetation Index ◽  
Grain Quality ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Research ◽  
Nitrogen Nutrition ◽  
Inverse Correlation ◽  
Research Centre ◽  
Federal State

Abstract. Lack of nitrogen leads to a decrease in yield and grain quality in winter wheat plants. Therefore, it is necessary to monitor nitrogen nutrition throughout the period of growth and development of plants, which will quickly assess the need for fertilizing to obtain high yields of quality grain. Therefore, the aim of the study was to establish the possibility of using the normalized difference vegetation index (NDVI) to control the nitrogen content in winter wheat plants in the Stavropol territory. Methods. The work was performed in federal state budgetary scientific institution “North-Caucasian Federal Agricultural Research Centre” at the production of winter crops. Selection of plant samples (sheaf material) was carried out according to the generally accepted method. Repeated – 4x. Determination of the chemical composition of plant organs was carried out by the method of V. T. Kurkaev with co-authors, and the content of chlorophyll – Milaeva and Primak. Results. Since the quality of winter wheat grain directly depends on the nitrogen supply of plants, the relationships between the nitrogen content in winter wheat plants and the values of the vegetation index NDVI were studied. High correlation coefficients between these indicators are obtained. Thus, the average of Rcorr fields.in 2012 it was equal to –0.89, and in 2013 and 2014 –0.82. In addition, due to the dependence of nitrogen content on the amount of chlorophyll, it was possible to analyze the correlation between these indicators and NDVI fields, which showed that a stable relationship (inverse) is observed in the case of the amount of chlorophyll per unit biomass (mg/g), which is estimated on average at –0.79. The interrelation between grain quality and earth remote sensing data is revealed. It is most clearly seen in the case of the maximum and average NDVI for the period from the resumption of spring vegetation to full ripeness of winter wheat. Scientific novelty. For the first time in the conditions of unstable humidification of the Stavropol territory, a high inverse correlation between the vegetation index NDVI and the nitrogen content in winter wheat plants was determined, which on average is estimated by the correlation coefficient equal to –0.84.

scholarly journals Within-Field Relationships between Satellite-Derived Vegetation Indices, Grain Yield and Spike Number of Winter Wheat and Triticale

Agronomy ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1842
Author(s):  
Ewa Panek ◽  
Dariusz Gozdowski ◽  
Michał Stępień ◽  
Stanisław Samborski ◽  
Dominik Ruciński ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Correlation Coefficients ◽  
Soil Nutrient ◽  
Growth Stages ◽  
Plant Growth Stages

The aims of this study were to: (i) evaluate the relationships between vegetation indices (VIs) derived from Sentinel-2 imagery and grain yield (GY) and the number of spikes per square meter (SN) of winter wheat and triticale; (ii) determine the dates and plant growth stages when the above relationships were the strongest at individual field scale, thus allowing for accurate yield prediction. Observations of GY and SN were performed at harvest on six fields (three locations in two seasons: 2017 and 2018) in three regions of Poland, i.e., northeastern (A—Brożówka), central (B—Zdziechów) and southeastern Poland (C—Kryłów). Vegetation indices (Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), modified SAVI (mSAVI), modified SAVI 2 (mSAVI2), Infrared Percentage Vegetation Index (IPVI), Global Environmental Monitoring Index (GEMI), and Ratio Vegetation Index (RVI)) calculated for sampling points from mid-March until mid-July, covering within-field soil and topographical variability, were included in the analysis. Depending on the location, the highest correlation coefficients (of about 0.6–0.9) for most of VIs with GY and SN were obtained about 4–6 weeks before harvest (from the beginning of shooting to milk maturity). Therefore, satellite-derived VIs are useful for the prediction of within-field cereal GY as well as SN variability. Information on GY, predicted together with the results for soil nutrient availability, is the basis for the formulation of variable fertilize rates in precision agriculture. All examined VIs were similarly correlated with GY and SN via the commonly used NDVI. The increase in NDVI by 0.1 unit was related to an average increase in GY by about 2 t ha−1.

scholarly journals Crop Yield Estimation Using Time-Series MODIS Data and the Effects of Cropland Masks in Ontario, Canada

2019 ◽  
Vol 11 (20) ◽  
pp. 2419 ◽  
Author(s):  
Jiangui Liu ◽  
Jiali Shang ◽  
Budong Qian ◽  
Ted Huffman ◽  
Yinsuo Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Winter Wheat ◽  
Negative Correlation ◽  
Linear Correlation ◽  
Vegetation Index ◽  
Growth Stages ◽  
Yield Estimation ◽  
Grain Yields ◽  
Positive Linear Correlation ◽  
Reflectance Data

This study investigated the estimation of grain yields of three major annual crops in Ontario (corn, soybean, and winter wheat) using MODIS reflectance data extracted with a general cropland mask and crop-specific masks. Time-series two-band enhanced vegetation index (EVI2) was derived from the 8 day composite 250 m MODIS reflectance data from 2003 to 2016. Using a general cropland mask, the strongest positive linear correlation between crop yields and EVI2 was observed at the end of July to early August, whereas a negative correlation was observed in spring. Using crop-specific masks, the time of the strongest positive linear correlation for winter wheat was found between mid-May and early June, corresponding to peak growth stages of the crop. EVI2 derived at peak growth stages of a crop provided good predictive capability for grain yield estimation, with considerable inter-annual variation. A multiple linear regression model was established for county-level yield estimation using EVI2 at peak growth stages and the year as independent variables. The model accounted for the spatiotemporal variability of grain yields of about 30% and 47% for winter wheat, 63% and 65% for corn, and 59% and 64% for soybean using the general cropland mask and crop-specific masks, respectively. A negative correlation during the spring indicated that vegetation index extracted using a general cropland mask should be used with caution in regions with mixed crops, as factors other than the growth conditions of the targeted crops may also be captured by remote sensing data.

scholarly journals Joint Retrieval of Winter Wheat Leaf Area Index and Canopy Chlorophyll Density Using Hyperspectral Vegetation Indices

2021 ◽  
Vol 13 (16) ◽  
pp. 3175
Author(s):  
Naichen Xing ◽  
Wenjiang Huang ◽  
Huichun Ye ◽  
Yu Ren ◽  
Qiaoyun Xie
Keyword(s):  
Winter Wheat ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Growth Stages ◽  
Growth Monitoring ◽  
Area Index

Leaf area index (LAI) and canopy chlorophyll density (CCD) are key biophysical and biochemical parameters utilized in winter wheat growth monitoring. In this study, we would like to exploit the advantages of three canonical types of spectral vegetation indices: indices sensitive to LAI, indices sensitive to chlorophyll content, and indices suitable for both parameters. In addition, two methods for joint retrieval were proposed. The first method is to develop integration-based indices incorporating LAI-sensitive and CCD-sensitive indices. The second method is to create a transformed triangular vegetation index (TTVI2) based on the spectral and physiological characteristics of the parameters. PROSAIL, as a typical radiative transfer model embedded with physical laws, was used to build estimation models between the indices and the relevant parameters. Validation was conducted against a field-measured hyperspectral dataset for four distinct growth stages and pooled data. The results indicate that: (1) the performance of the integrated indices from the first method are various because of the component indices; (2) TTVI2 is an excellent predictor for joint retrieval, with the highest R2 values of 0.76 and 0.59, the RMSE of 0.93 m2/m2 and 104.66 μg/cm2, and the RRMSE (Relative RMSE) of 12.76% and 16.96% for LAI and CCD, respectively.

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.

Effect of foliar application of iron and zinc on performance of transplanted rice in middle Gangetic plains of Bihar

2021 ◽  
Vol 8 (2) ◽  
Author(s):  
MOHAMMAD\ HASHIM ◽  
◽  
V K SINGH ◽  
K K SINGH ◽  
SHIVA DHAR ◽  
...  
Keyword(s):  
Agricultural Research ◽  
Foliar Application ◽  
Block Design ◽  
Indian Agricultural Research Institute ◽  
Ferrous Sulphate ◽  
Growth Yield ◽  
Growth Stages ◽  
Higher Plant ◽  
Gangetic Plains ◽  
Iron And Zinc

A field experiment was conducted during kharif season of 2015 and 2016 at research farmof the ICAR- Indian Agricultural Research Institute Regional Station Pusa, Samastipur, Biharto determine the foliar feeding of micronutrients (iron and zinc at different growth stages)on growth, yield and economics of rice in middle Gangetic plains of Bihar. The experimentwas laid out in randomized block design consisting of 9 treatments with 3 replications. Thetreatments consist of 0.5% spray of Zinc Sulphate and 1% spray of Ferrous Sulphate at fourdifferent growth stages i.e. 40, 50, 60 and 70 days aĞer transplanting (DAT) and one con-trol. The results shown significant increasing trends of growth, yield aĴributes and yield ofrice with four sprays of 1.0% solution of FeSO4at 40, 50, 60 and 70 days and three sprays ofZnSO4at 50, 60 and 70 days recorded significantly higher plant height, effective tillers/m2,panicle length, grains/panicle, 1,000-grain weight, biological yield, grain yield and straw yieldat maturity. These treatments also gave significantly higher net returns and benefit: cost ratioover the control.

scholarly journals Use of Landsat images for yield evaluation within a small plot

2014 ◽  
Vol 60 (No. 11) ◽  
pp. 501-506 ◽  
Author(s):  
J. Kumhálová ◽  
F. Zemek ◽  
P. Novák ◽  
O. Brovkina ◽  
M. Mayerová
Keyword(s):  
Winter Wheat ◽  
Water Distribution ◽  
Vegetation Indices ◽  
Wheat Yield ◽  
Moisture Stress ◽  
Stress Index ◽  
Growth Stages ◽  
Landsat Images ◽  
Topographic Attributes ◽  
Small Plot

Many factors can influence crop yield. One of the most important factors is topography, which can play a crucial role especially in dry years. Plant variability can be monitored by many methods. This paper evaluates the suitability of vegetation indices derived from satellite Landsat 5 TM data in comparison with yield, curvature and topography wetness index over a relatively small field (11.5 ha). Imageries were chosen from the years 2006 and 2010, when oat was grown and from 2005 and 2011, when winter wheat was grown. These images were taken in June in the same growth stage for every crop. It was confirmed that derived indices from Landsat images can be used for comparison with yield and selected topographic attributes and it can explain yield variability, which can be influenced by water distribution during growth stages. Correlation coefficient between moisture stress index and winter wheat yield was &ndash;0.816 in the image acquisition date of 4. 6. 2011.

scholarly journals Influence of Soil Background on Spectral Reflectance of Winter Wheat Crop Canopy

2019 ◽  
Vol 11 (16) ◽  
pp. 1932 ◽  
Author(s):  
Elena Prudnikova ◽  
Igor Savin ◽  
Gretelerika Vindeker ◽  
Praskovia Grubina ◽  
Ekaterina Shishkonakova ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Soil Type ◽  
Spectral Reflectance ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Soil Surface ◽  
Wheat Crop ◽  
Crop Canopy ◽  
Winter Wheat Crop ◽  
Type Factor

The spectral reflectance of crop canopy is a spectral mixture, which includes soil background as one of the components. However, as soil is characterized by substantial spatial variability and temporal dynamics, its contribution to the spectral reflectance of crops will also vary. The aim of the research was to determine the impact of soil background on spectral reflectance of crop canopy in visible and near-infrared parts of the spectrum at different stages of crop development and how the soil type factor and the dynamics of soil surface affect vegetation indices calculated for crop assessment. The study was conducted on three test plots with winter wheat located in the Tula region of Russia and occupied by three contrasting types of soil. During field trips, information was collected on the spectral reflectance of winter wheat crop canopy, winter wheat leaves, weeds and open soil surface for three phenological phases (tillering, shooting stage, milky ripeness). The assessment of the soil contribution to the spectral reflectance of winter wheat crop canopy was based on a linear spectral mixture model constructed from field data. This showed that the soil background effect is most pronounced in the regions of 350–500 nm and 620–690 nm. In the shooting stage, the contribution of the soil prevails in the 620–690 nm range of the spectrum and the phase of milky ripeness in the region of 350–500 nm. The minimum contribution at all stages of winter wheat development was observed at wavelengths longer than 750 nm. The degree of soil influence varies with soil type. Analysis of variance showed that normalized difference vegetation index (NDVI) was least affected by soil type factor, the influence of which was about 30%–50%, depending on the stage of winter wheat development. The influence of soil type on soil-adjusted vegetation index (SAVI) and enhanced vegetation index (EVI2) was approximately equal and varied from 60% (shooting phase) to 80% (tillering phase). According to the discriminant analysis, the ability of vegetation indices calculated for winter wheat crop canopy to distinguish between winter wheat crops growing on different soil types changed from the classification accuracy of 94.1% (EVI2) in the tillering stage to 75% (EVI2 and SAVI) in the shooting stage to 82.6% in the milky ripeness stage (EVI2, SAVI, NDVI). The range of the sensitivity of the vegetation indices to the soil background depended on soil type. The indices showed the greatest sensitivity on gray forest soil when the wheat was in the phase of milky ripeness, and on leached chernozem when the wheat was in the tillering phase. The observed patterns can be used to develop vegetation indices, invariant to second-type soil variations caused by soil type factor, which can be applied for the remote assessment of the state of winter wheat crops.

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