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.

Predicting Winter Wheat Grain Quality Using Hyperspectral Data Based on Plant Nitrogen Status

2012 ◽  
Vol 524-527 ◽  
pp. 2132-2138 ◽  
Author(s):  
Hui Fang Wang ◽  
Ji Hua Wang ◽  
Mei Chen Feng ◽  
Qian Wang ◽  
Wen Jiang Huang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Nitrogen Content ◽  
Vegetation Index ◽  
Grain Quality ◽  
Leaf Nitrogen ◽  
Hyperspectral Data ◽  
Leaf Nitrogen Content ◽  
Wheat Grain ◽  
Quality Model ◽  
Plant Nitrogen

Quality of winter wheat from hyperspectral data would provide opportunities to manage grain harvest differently, and to maximize output by adjusting input in fields. In this study, two varieties winter wheat as the object, hyperspectral data were utilized to predict grain quality. Firstly, the leaf and stem nitrogen content at winter wheat anthesis stage was proved to be signification correctly with crude content and wet gluten. And the leaf relatedcoefficient more than stem at the anthesis. Then, spectral indices significantly correlated to plant nitrogen content at anthesis stage were potential indicators for grain qualities. The vegetation index, VI derived from the canopy spectral reflectance was signification correlated to the leaf nitrogen content at anthesis stage, and highly significantly correlated to the leaf nitrogen content. Based on above analysis, the predict grain quality model were build and the related coefficient were 0.86, 0.68, 0.84, 0.58 which were reached a very significant.The result demonstrated the model based on SIPI and RVI to predict different cultivars wheat grain quality were practical and feasible.

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 Prediction of Plant Nutrition State of Rice under Water-Saving Cultivation and Panicle Fertilization Application Decision Making

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1626
Author(s):  
Guan-Sin Li ◽  
Dong-Hong Wu ◽  
Yuan-Chih Su ◽  
Bo-Jein Kuo ◽  
Ming-Der Yang ◽  
...  
Keyword(s):  
Nitrogen Content ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Nitrogen Nutrition ◽  
Explanatory Power ◽  
Vegetation Indices ◽  
Regression Function ◽  
Coefficient Of Determination ◽  
Plant Nitrogen ◽  
Smart Water

Rice is a staple food crop in Asia. The rice farming industry has been influenced by global urbanization, rapid industrialization, and climate change. A combination of precise agricultural and smart water management systems to investigate the nutrition state in rice is important. Results indicated that plant nitrogen and chlorophyll content at the maximum tillering stage were significantly influenced by the interaction between water and fertilizer. The normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE), obtained from the multispectral images captured by a UAV, exhibited the highest positive correlations (0.83 and 0.82) with plant nitrogen content at the maximum tillering stage. The leave-one-out cross-validation method was used for validation, and a final plant nitrogen content prediction model was obtained. A regression function constructed using a nitrogen nutrition index and the difference in field cumulative nitrogen had favorable variation explanatory power, and its adjusted coefficient of determination was 0.91. We provided a flow chart showing how the nutrition state of rice can be predicted with the vegetation indices obtained from UAV image analysis. Differences in field cumulative nitrogen can be further used to diagnose the demand of nitrogen topdressing during the panicle initiation stage. Thus, farmers can be provided with precise panicle fertilization strategies for rice fields.

Analysis of genotypic variation for normalized difference vegetation index and its relationship with grain yield in winter wheat under terminal heat stress

2012 ◽  
Vol 131 (6) ◽  
pp. 716-721 ◽  
Author(s):  
Shahnoza Hazratkulova ◽  
Ram C. Sharma ◽  
Safar Alikulov ◽  
Sarvar Islomov ◽  
Tulkin Yuldashev ◽  
...  
Keyword(s):  
Heat Stress ◽  
Winter Wheat ◽  
Grain Yield ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Genotypic Variation ◽  
Terminal Heat Stress

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.

Change on detection of vegetation cover and soil salinity using GIS technique in Diyala Governorate, Iraq

2021 ◽  
Vol 30 (1) ◽  
pp. 148-158
Author(s):  
Haneen Adeeb ◽  
Yaseen Al-Timimi
Keyword(s):  
Soil Salinity ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Inverse Correlation ◽  
Salinity Changes ◽  
Gis Technique ◽  
Salinity Index ◽  
Remote Sensing Techniques ◽  
Strong Inverse Correlation

Soil salinity is one of the most important problems of land degradation, that threatening the environmental, economic and social system. The aim of this study to detect the changes in soil salinity and vegetation cover for Diyala Governorate over the period from 2005 to 2020, through the use of remote sensing techniques and geographic information system. The normalized difference vegetation index (NDVI) and salinity index (SI) were used, which were applied to four of the Landsat ETM+ and Landsat OLI satellite imagery. The results showed an increase in soil salinity from 7.27% in the period 2005–2010 to 27.03% in 2015–2020, as well as an increase in vegetation from 10% to 24% in the same period. Also the strong inverse correlation between the NDVI and the SI showed that vegetation is significantly affected and directly influenced by soil salinity changes

scholarly journals Detecting Cover Crop End-Of-Season Using VENµS and Sentinel-2 Satellite Imagery

2020 ◽  
Vol 12 (21) ◽  
pp. 3524
Author(s):  
Feng Gao ◽  
Martha C. Anderson ◽  
W. Dean Hively
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Cover Crops ◽  
Cover Crop ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Research ◽  
Agricultural Landscapes ◽  
Absolute Difference ◽  
Sentinel 2

Cover crops are planted during the off-season to protect the soil and improve watershed management. The ability to map cover crop termination dates over agricultural landscapes is essential for quantifying conservation practice implementation, and enabling estimation of biomass accumulation during the active cover period. Remote sensing detection of end-of-season (termination) for cover crops has been limited by the lack of high spatial and temporal resolution observations and methods. In this paper, a new within-season termination (WIST) algorithm was developed to map cover crop termination dates using the Vegetation and Environment monitoring New Micro Satellite (VENµS) imagery (5 m, 2 days revisit). The WIST algorithm first detects the downward trend (senescent period) in the Normalized Difference Vegetation Index (NDVI) time-series and then refines the estimate to the two dates with the most rapid rate of decrease in NDVI during the senescent period. The WIST algorithm was assessed using farm operation records for experimental fields at the Beltsville Agricultural Research Center (BARC). The crop termination dates extracted from VENµS and Sentinel-2 time-series in 2019 and 2020 were compared to the recorded termination operation dates. The results show that the termination dates detected from the VENµS time-series (aggregated to 10 m) agree with the recorded harvest dates with a mean absolute difference of 2 days and uncertainty of 4 days. The operational Sentinel-2 time-series (10 m, 4–5 days revisit) also detected termination dates at BARC but had 7% missing and 10% false detections due to less frequent temporal observations. Near-real-time simulation using the VENµS time-series shows that the average lag times of termination detection are about 4 days for VENµS and 8 days for Sentinel-2, not including satellite data latency. The study demonstrates the potential for operational mapping of cover crop termination using high temporal and spatial resolution remote sensing data.

scholarly journals Sentinel-1 Data for Winter Wheat Phenology Monitoring and Mapping

2019 ◽  
Vol 11 (19) ◽  
pp. 2228 ◽  
Author(s):  
Ali Nasrallah ◽  
Nicolas Baghdadi ◽  
Mohammad El Hajj ◽  
Talal Darwish ◽  
Hatem Belhouchette ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Incidence Angle ◽  
Weather Conditions ◽  
Crop Monitoring ◽  
Temporal Series ◽  
Automated Method ◽  
Low Incidence ◽  
Phenological Phases

The ability of Synthetic Aperture Radar (SAR) Sentinel-1 data to detect the main wheat phenological phases was investigated in the Bekaa plain of Lebanon. Accordingly, the temporal variation of Sentinel-1 (S1) signal was analyzed as a function of the phenological phases’ dates observed in situ (germination; heading and soft dough), and harvesting. Results showed that S1 data, unlike the Normalized Difference Vegetation Index (NDVI) data, were able to estimate the dates of theses phenological phases due to significant variations in S1 temporal series at the dates of germination, heading, soft dough, and harvesting. Particularly, the ratio VV/VH at low incidence angle (32–34°) was able to detect the germination and harvesting dates. VV polarization at low incidence angle (32–34°) was able to detect the heading phase, while VH polarization at high incidence angle (43–45°) was better than that at low incidence angle (32–34°), in detecting the soft dough phase. An automated approach for main wheat phenological phases’ determination was then developed on the western part of the Bekaa plain. This approach modelled the S1 SAR temporal series by smoothing and fitting the temporal series with Gaussian functions (up to three Gaussians) allowing thus to automatically detect the main wheat phenological phases from the sum of these Gaussians. To test its robustness, the automated method was applied on the northern part of the Bekaa plain, in which winter wheat is harvested usually earlier because of the different weather conditions. The Root Mean Square Error (RMSE) of the estimation of the phenological phases’ dates was 2.9 days for germination, 5.5 days for heading, 5.1 days soft dough, 3.0 days for West Bekaa’s harvesting, and 4.5 days for North Bekaa’s harvesting. In addition, a slight underestimation was observed for germination and heading of West Bekaa (−0.2 and −1.1 days, respectively) while an overestimation was observed for soft dough of West Bekaa and harvesting for both West and North Bekaa (3.1, 0.6, and 3.6 days, respectively). These results are encouraging, and thus prove that S1 data are powerful as a tool for crop monitoring, to serve enhanced crop management and production handling.

scholarly journals Influence of uniconazole and plant density on nitrogen content and grain quality in winter wheat in South China

2009 ◽  
Vol 55 (No. 4) ◽  
pp. 159-166 ◽  
Author(s):  
H. Han ◽  
W. Yang
Keyword(s):  
Winter Wheat ◽  
Nitrogen Content ◽  
South China ◽  
Grain Quality ◽  
Protein Quality ◽  
Plant Density ◽  
Grain Protein ◽  
Processing Quality ◽  
Four Levels ◽  
Dough Development Time

Superior protein quality and consistent processing quality is needed for winter wheat marketing in South China. It has been shown that uniconazole concentration and plant density are certainly related to crop growth. An experiment was conducted to investigate the effects of uniconazole concentration and plant density on nitrogen content and grain quality in winter wheat (<I>Triticum aestivum</I> L.). Trials were managed to provide three levels of density (90 × 10<sup>4</sup>, 180 × 10<sup>4</sup>, and 270 × 10<sup>4</sup> per ha) over plots receiving four levels of uniconazole concentrations (0, 10, 20, and 40 mg/kg) which were applied to seeds before sowing. The results revealed that the contents of N accumulated in ear, stem, and leaf were higher in uniconazole concentrations than that in control, and the effect of uniconazole on main stem was bigger than that on tillers. The grain protein was significantly (LSD, <I>P</I> < 0.05) higher in uniconazole concentrations than that in control. Uniconazole at 20 mg/kg was the most favorable for improving grain protein and protein fractions. Application of uniconazole concentrations also significantly (LSD, <I>P</I> < 0.05) increased WGC (wet gluten content) and SDS (sedimentation volumes), prolonged DDT (dough development time) and DST (dough stable time), and improved WA (water absorption), increased VV (valorimeter value), and subsequently improved the processing quality of wheat grains. These results suggest that a combination of uniconazole concentration and plant density should be applied in South China.

scholarly journals Cattle Horn Shavings: A Possible Nitrogen Source for Apple Trees

Agronomy ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 540
Author(s):  
Juozas Lanauskas ◽  
Nobertas Uselis ◽  
Loreta Buskienė ◽  
Romas Mažeika ◽  
Gediminas Staugaitis ◽  
...  
Keyword(s):  
Ammonium Nitrate ◽  
Nitrogen Content ◽  
Nitrogen Nutrition ◽  
Agricultural Waste ◽  
Leaf Nitrogen ◽  
Mineral Nitrogen ◽  
Research Centre ◽  
Leaf Nitrogen Content ◽  
Apple Trees ◽  
Orchard Soil

The circular economy concept promotes the recycling of agricultural waste. This study was aimed at investigating the effects of cattle horn shavings on apple tree nitrogen nutrition. Ligol apple trees on P 60 rootstock were the object of the study. The experiment was conducted in the experimental orchard of the Institute of Horticulture, Lithuanian Research Centre for Agriculture and Forestry, from 2015 to 2018. Two fertiliser rates were tested: 50 and 100 kg/ha N. Horn shavings (14.1% N) were applied at the end of autumn or at the beginning of vegetation in the spring and in one treatment 100 kg/ha N rate was divided into two equal parts and applied both in autumn and spring. The effects of the horn shavings were compared with the effects of ammonium nitrate (34.4% N) and the unfertilised treatment. The lowest mineral nitrogen content was found in the unfertilised orchard soil and the soil fertilised with horn shavings in the spring at 50 kg/ha N equivalent. In all other cases, the fertilisers increased the soil’s mineral nitrogen content. The lowest leaf nitrogen content was found in apple trees that grew in the unfertilised orchard soil or soil fertilised in the spring with 50 kg/ha N of horn shavings (1.58–2.13%). In other cases, leaf nitrogen content was higher (1.77–2.17%). The apple trees with the lowest leaf nitrogen content produced the smallest average yield (34.5–36.6 t/ha). The highest yield was recorded from fruit trees fertilised with 50 kg/ha N of ammonium nitrate applied in spring or horn shavings applied in autumn (42.4 and 41.4 t/ha, respectively). The influence of horn shavings on the other studied parameters was similar to that of ammonium nitrate. Horn shavings, like nitrogen fertiliser, could facilitate nitrogen nutrition management in apple trees, especially in organic orchards, where the use of synthetic fertilisers is prohibited.

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