scholarly journals Yield prediction and nitrogen recommendation in maize using normalized difference vegetation index

2013 ◽  
Vol 3 ◽  
pp. 82-88 ◽  
Author(s):  
TB Karki
Keyword(s):  
Grain Yield ◽  
Growth Stage ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Correlation Coefficients ◽  
Growth Stages ◽  
Strong Positive Correlation ◽  
Positive Correlation ◽  
Nutrient Levels ◽  
Seed Sowing

A study was carried out using three maize genotypes with three levels of nitrogen (30 kg, 60 kg and 120 kg per hectare) during the summer season of 2010 and 2011with the aim of predicting maize (Zea mays L.) yield through the Normalized difference vegetation index (NDVI). The NDVI was recorded at different times throughout the growing season using a Greenseeker™ handheld sensor. Significant effect of genotypes and nutrient levels on the NDVI was observed at different growth stages of maize. There was positive correlation between the NDVI and grain yield. In the first season, the correlation coefficients were 0.90, 0.92, 0.76 and 0.73, respectively at 15, 45, 75 and 110 days after seeding. In the second season, the correlation coefficients were 0.80, 0.92, 0.77 and 0.75 respectively at 15, 45, 75 and 110 days after seeding. The NDVI based N calculator showed that irrespective of genotypes, yield potentials under farmers' levels of nutrient management were almost half of the recommended doses of nitrogen. The amount of N to be top dressed decreased with increased crop duration. Grain yield varied significantly due to season, genotypes and nutrient levels. NDVI was affected due to season, stages of the crop (DAS), genotypes and nutrient levels. Interaction effects were significant for season x genotype, growth stage x genotype, growth stage x nutrient levels, genotype x nutrient levels and genotype x growth stage x nutrient levels. There was a strong positive correlation between NDVI and grain yields of hybrid maize at 15 and 45 DAS, but this correlation declined thereafter. This means that N top-dressed at or after 75 days of seed sowing will not increase grain yield as significantly as N applied earlier in the season. In contrast, topdressed N was producing significant effects on the open pollinated Rampur Composite even after 75 days of seed sowing. Further confirmation of the finding could be useful for top dressing N in the maize crop. Agronomy Journal of Nepal (Agron JN) Vol. 3. 2013, Page 82-88 DOI: http://dx.doi.org/10.3126/ajn.v3i0.9009

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 Scale-Dependent Effects of Growth Stage and Elevational Gradient on Rice Phyllosphere Bacterial and Fungal Microbial Patterns in the Terrace Field

2022 ◽  
Vol 12 ◽  
Author(s):  
Pei Wang ◽  
Jianping Dai ◽  
Luyun Luo ◽  
Yong Liu ◽  
Decai Jin ◽  
...  
Keyword(s):  
Plant Growth ◽  
Growth Stage ◽  
Elevational Gradient ◽  
Fungal Communities ◽  
Growth Stages ◽  
Strong Positive Correlation ◽  
Elevation Gradients ◽  
Positive Correlation ◽  
Α Diversity ◽  
Phyllosphere Bacteria

The variation of phyllosphere bacterial and fungal communities along elevation gradients may provide a potential link with temperature, which corresponds to an elevation over short geographic distances. At the same time, the plant growth stage is also an important factor affecting phyllosphere microorganisms. Understanding microbiological diversity over changes in elevation and among plant growth stages is important for developing crop growth ecological theories. Thus, we investigated variations in the composition of the rice phyllosphere bacterial and fungal communities at five sites along an elevation gradient from 580 to 980 m above sea level (asl) in the Ziquejie Mountain at the seedling, heading, and mature stages, using high-throughput Illumina sequencing methods. The results revealed that the dominant bacterial phyla were Proteobacteria, Actinobacteria, and Bacteroidetes, and the dominant fungal phyla were Ascomycota and Basidiomycota, which varied significantly at different elevation sites and growth stages. Elevation had a greater effect on the α diversity of phyllosphere bacteria than on that phyllosphere fungi. Meanwhile, the growth stage had a great effect on the α diversity of both phyllosphere bacteria and fungi. Our results also showed that the composition of bacterial and fungal communities varied significantly along elevation within the different growth stages, in terms of both changes in the relative abundance of species, and that the variations in bacterial and fungal composition were well correlated with variations in the average elevation. A total of 18 bacterial and 24 fungal genera were significantly correlated with elevational gradient, displaying large differences at the various growth stages. Soluble protein (SP) shared a strong positive correlation with bacterial and fungal communities (p < 0.05) and had a strong significant negative correlation with Serratia, Passalora, unclassified_Trichosphaeriales, and antioxidant enzymes (R > 0.5, p < 0.05), and significant positive correlation with the fungal genera Xylaria, Gibberella, and Penicillium (R > 0.5, p < 0.05). Therefore, it suggests that elevation and growth stage might alter both the diversity and abundance of phyllosphere bacterial and fungal populations.

scholarly journals Hyperspectral field reflectance measurements to estimate wheat grain yield and plant height

2006 ◽  
Vol 63 (2) ◽  
pp. 130-138 ◽  
Author(s):  
Alexandre Cândido Xavier ◽  
Bernardo Friedrich Theodor Rudorff ◽  
Mauricio Alves Moreira ◽  
Brummer Seda Alvarenga ◽  
José Guilherme de Freitas ◽  
...  
Keyword(s):  
Grain Yield ◽  
Plant Height ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Narrow Band ◽  
Broad Band ◽  
Growth Stages ◽  
Sensing Applications ◽  
Field Reflectance ◽  
Reflectance Measurements

Hyperspectral crop reflectance data are useful for several remote sensing applications in agriculture, but there is still a need for studies to define optimal wavebands to estimate crop biophysical parameters. The objective of this work is to analyze the use of narrow and broad band vegetation indices (VI) derived from hyperspectral field reflectance measurements to estimate wheat (Triticum aestivum L.) grain yield and plant height. A field study was conducted during the winter growing season of 2003 in Campinas, São Paulo State, Brazil. Field canopy reflectance measurements were acquired at six wheat growth stages over 80 plots with four wheat cultivars (IAC-362, IAC-364, IAC-370, and IAC-373), five levels of nitrogen fertilizer (0, 30, 60, 90, and 120 kg of N ha-1) and four replicates. The following VI were analyzed: a) hyperspectral or narrow-band VI (1. optimum multiple narrow-band reflectance, OMNBR; 2. narrow-band normalized difference vegetation index, NB_NDVI; 3. first- and second-order derivative of reflectance; and 4. four derivative green vegetation index); and b) broad band VI (simple ratio, SR; normalized difference vegetation index, NDVI; and soil-adjusted vegetation index, SAVI). Hyperspectral indices provided an overall better estimate of biophysical variables when compared to broad band VI. The OMNBR with four bands presented the highest R² values to estimate both grain yield (R² = 0.74; Booting and Heading stages) and plant height (R² = 0.68; Heading stage). Best results to estimate biophysical variables were observed for spectral measurements acquired between Tillering II and Heading stages.

scholarly journals Normalized Difference Vegetation Index and Chlorophyll Content for Precision Nitrogen Management in Durum Wheat Cultivars under Semi-Arid Conditions

2021 ◽  
Vol 13 (7) ◽  
pp. 3725
Author(s):  
Ferhat Kizilgeci ◽  
Mehmet Yildirim ◽  
Mohammad Sohidul Islam ◽  
Disna Ratnasekera ◽  
Muhammad Aamir Iqbal ◽  
...  
Keyword(s):  
Grain Yield ◽  
Durum Wheat ◽  
Chlorophyll Content ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Screening Tools ◽  
Growth Stages ◽  
Quality Traits ◽  
Wheat Genotypes ◽  
N Deficiency

To impart sustainability to modern intensive farming systems, environmental pollution caused by nitrogenous fertilizers in needs to be reduced by optimizing their doses. To estimate the grain yield and nutrtional quallity of wheat, the normalized difference vegetation index (NDVI) and chlorophyll content (SPAD) are potential screening tools to identify the N deficiency and screen out the promising cultivars. The two-year field study was comprised with five levels of nitrogen (N) (control, 50, 100, 150 and 200 kg N ha−1) and two durum wheat genotypes (Sena and Svevo). The experimental design was split-plot, in which N levels were placed in the main plots, while wheat genotypes were arranged in sub-plots. To predict the yield and quality traits, the NDVI and SPAD values recorded at heading, anthesis and milky growth stages were taken as response variables. The results revealed that N fertilization significantly influenced the SPAD and NDVI attributed traits of durum wheat, except NDVI at milky stage (NDVI-M) during the first year. The maximum value of NDVI was recorded by 150 kg N ha−1, while control treatment gave the minimum value. The grain yield was increased with the increasing dose of the N up to 100 kg N ha−1 (4121 kg ha−1), and thereafter, it was declined with further increased of N levels. However, the variation between the genotypes was not significant, except NDVI and SPAD values at the milky stage. The genotype Svevo had the highest NDVI values at all growth stages, while the genotype Sena recorded the maximum SPAD values during both years. Similarly, the N levels significantly influenced the quality traits (protein, wet gluten, starch test weight and Zeleny sedimentation) of both genotypes. The highly significant relationship of SPAD and NDVI with the grain yield and yield attributes showed their reliability as indicators for determining the N deficiency and selection of superior wheat genotypes for ensuring food security under climate change scenario.

scholarly journals Normalized Difference Vegetation Index as a Tool for Wheat Yield Estimation: A Case Study from Faisalabad, Pakistan

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Syeda Refat Sultana ◽  
Amjed Ali ◽  
Ashfaq Ahmad ◽  
Muhammad Mubeen ◽  
M. Zia-Ul-Haq ◽  
...  
Keyword(s):  
Grain Yield ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Field Experiments ◽  
Wheat Yield ◽  
Grain Filling ◽  
Research Area ◽  
Wheat Crop ◽  
Growth Stages ◽  
Wheat Cultivars

For estimation of grain yield in wheat, Normalized Difference Vegetation Index (NDVI) is considered as a potential screening tool. Field experiments were conducted to scrutinize the response of NDVI to yield behavior of different wheat cultivars and nitrogen fertilization at agronomic research area, University of Agriculture Faisalabad (UAF) during the two years 2008-09 and 2009-10. For recording the value of NDVI, Green seeker (Handheld-505) was used. Split plot design was used as experimental model in, keeping four nitrogen rates (N1= 0 kg ha−1,N2= 55 kg ha−1,N3=110 kg ha−1, andN4= 220 kg ha−1) in main plots and ten wheat cultivars (Bakkhar-2001, Chakwal-50, Chakwal-97, Faisalabad-2008, GA-2002, Inqlab-91, Lasani-2008, Miraj-2008, Sahar-2006, and Shafaq-2006) in subplots with four replications. Impact of nitrogen and difference between cultivars were forecasted through NDVI. The results suggested that nitrogen treatment N4(220 kg ha−1) and cultivar Faisalabad-2008 gave maximum NDVI value (0.85) at grain filling stage among all treatments. The correlation among NDVI at booting, grain filling, and maturity stages with grain yield was positive (R2 = 0.90;R2 = 0.90;R2 = 0.95), respectively. So, booting, grain filling, and maturity can be good depictive stages during mid and later growth stages of wheat crop under agroclimatic conditions of Faisalabad and under similar other wheat growing environments in the country.

scholarly journals Coastal Mangrove Response to Marine Erosion: Evaluating the Impacts of Spatial Distribution and Vegetation Growth in Bangkok Bay from 1987 to 2017

2020 ◽  
Vol 12 (2) ◽  
pp. 220 ◽  
Author(s):  
Han Xiao ◽  
Fenzhen Su ◽  
Dongjie Fu ◽  
Qi Wang ◽  
Chong Huang
Keyword(s):  
Spatial Distribution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Mangrove Ecosystem ◽  
Early Growth ◽  
Growth Stages ◽  
Human Disturbances ◽  
Vegetation Growth ◽  
Two Parameters ◽  
The Impact

Long time-series monitoring of mangroves to marine erosion in the Bay of Bangkok, using Landsat data from 1987 to 2017, shows responses including landward retreat and seaward extension. Quantitative assessment of these responses with respect to spatial distribution and vegetation growth shows differing relationships depending on mangrove growth stage. Using transects perpendicular to the shoreline, we calculated the cross-shore mangrove extent (width) to represent spatial distribution, and the normalized difference vegetation index (NDVI) was used to represent vegetation growth. Correlations were then compared between mangrove seaside changes and the two parameters—mangrove width and NDVI—at yearly and 10-year scales. Both spatial distribution and vegetation growth display positive impacts on mangrove ecosystem stability: At early growth stages, mangrove stability is positively related to spatial distribution, whereas at mature growth the impact of vegetation growth is greater. Thus, we conclude that at early growth stages, planting width and area are more critical for stability, whereas for mature mangroves, management activities should focus on sustaining vegetation health and density. This study provides new rapid insights into monitoring and managing mangroves, based on analyses of parameters from historical satellite-derived information, which succinctly capture the net effect of complex environmental and human disturbances.

scholarly journals Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries

Agronomy ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 340
Author(s):  
Ewa Panek ◽  
Dariusz Gozdowski
Keyword(s):  
Linear Regression ◽  
Grain Yield ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Arable Land ◽  
Early Spring ◽  
European Countries ◽  
Yield Prediction ◽  
Country Level ◽  
Using Data

In this study, the relationships between normalized difference vegetation index (NDVI) obtained based on MODIS satellite data and grain yield of all cereals, wheat and barley at a country level were analyzed. The analysis was performed by using data from 2010–2018 for 20 European countries, where percentage of cereals is high (at least 35% of the arable land). The analysis was performed for each country separately and for all of the collected data together. The relationships between NDVI and cumulative NDVI (cNDVI) were analyzed by using linear regression. Relationships between NDVI in early spring and grain yield of cereals were very strong for Croatia, Czechia, Germany, Hungary, Latvia, Lithuania, Poland and Slovakia. This means that the yield prediction for these countries can be as far back as 4 months before the harvest. The increase of NDVI in early spring was related to the increase of grain yield by about 0.5–1.6 t/ha. The cumulative of averaged NDVI gives more stable prediction of grain yield per season. For France and Belgium, the relationships between NDVI and grain yield were very weak.

scholarly journals A Spatial and Temporal Assessment of Vegetation Greening and Precipitation Changes for Monitoring Vegetation Dynamics in Climate Zones over Africa

2021 ◽  
Vol 10 (3) ◽  
pp. 129
Author(s):  
Vincent Nzabarinda ◽  
Anming Bao ◽  
Wenqiang Xu ◽  
Solange Uwamahoro ◽  
Madeleine Udahogora ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Forest Type ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Plant Health ◽  
Tree Cover ◽  
Climatic Zones ◽  
Dramatic Decline ◽  
Precipitation Changes

Vegetation is vital, and its greening depends on access to water. Thus, precipitation has a considerable influence on the health and condition of vegetation and its amount and timing depend on the climatic zone. Therefore, it is extremely important to monitor the state of vegetation according to the movements of precipitation in climatic zones. Although a lot of research has been conducted, most of it has not paid much attention to climatic zones in the study of plant health and precipitation. Thus, this paper aims to study the plant health in five African climatic zones. The linear regression model, the persistence index (PI), and the Pearson correlation coefficients were applied for the third generation Normalized Difference Vegetation Index (NDVI3g), with Climate Hazard Group infrared precipitation and Climate Change Initiative Land Cover for 34 years (1982 to 2015). This involves identifying plants in danger of extinction or in dramatic decline and the relationship between vegetation and rainfall by climate zone. The forest type classified as tree cover, broadleaved, deciduous, closed to open (>15%) has been degraded to 74% of its initial total area. The results also revealed that, during the study period, the vegetation of the tropical, polar, and warm temperate zones showed a higher rate of strong improvement. Although arid and boreal zones show a low rate of strong improvement, they are those that experience a low percentage of strong degradation. The continental vegetation is drastically decreasing, especially forests, and in areas with low vegetation, compared to more vegetated areas, there is more emphasis on the conservation of existing plants. The variability in precipitation is excessively hard to tolerate for more types of vegetation.

scholarly journals Analysis of Drought Characteristics in Xilingol Grassland of Northern China Based on SPEI and Its Impact on Vegetation

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Siqin Tong ◽  
Yuhai Bao ◽  
Rigele Te ◽  
Qiyun Ma ◽  
Si Ha ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Pearson Correlation ◽  
Growing Season ◽  
Vegetation Coverage ◽  
Semiarid Region ◽  
Linear Regression Method ◽  
Upward Trend ◽  
Drought Effect ◽  
Positive Correlation

This research is based on the standardized precipitation evapotranspiration index (SPEI) and normalized difference vegetation index (NDVI) which represent the drought and vegetation condition on land. Take the linear regression method and Pearson correlation analysis to study the spatial and temporal evolution of SPEI and NDVI and the drought effect on vegetation. The results show that (1) during 1961–2015, SPEI values at different time scales showed a downward trend; SPEI-12 has a mutation in 1997 and the SPEI value significantly decreased after this year. (2) During 2000–2015, the annual growing season SPEI has an obvious upward trend in time and the apparent wetting spatially. (3) In the recent 16 years, the growing season NDVI showed an upward trend and more than 80% of the total area’s vegetation increased in Xilingol. (4) Vegetation coverage in Xilingol grew better in humid years and opposite in arid years. SPEI and NDVI had a significant positive correlation; 98% of the region showed positive correlation, indicating that meteorological drought affects vegetation growth more in arid and semiarid region. (5) The effect of drought on vegetation has lag effect, and the responses of different grassland types to different scales of drought were different.

Relationship amongst ResearchGate altmetric indicators and Scopus bibliometric indicators

2015 ◽  
Vol 116 (9/10) ◽  
pp. 564-577 ◽  
Author(s):  
RISHABH SHRIVASTAVA ◽  
Preeti Mahajan
Keyword(s):  
Sample Size ◽  
Correlation Coefficients ◽  
Strong Positive Correlation ◽  
Bibliometric Indicators ◽  
Pearson’S Correlation ◽  
Content Type ◽  
Positive Correlation ◽  
Pearson's Correlation ◽  
Different Characteristics ◽  
The Relationship

Purpose – The purpose of this paper is twofold. First, the study aims to investigate the relationship between the altmetric indicators from ResearchGate (RG) and the bibliometric indicators from the Scopus database. Second, the study seeks to examine the relationship amongst the RG altmetric indicators themselves. RG is a rich source of altmetric indicators such as Citations, RGScore, Impact Points, Profile Views, Publication Views, etc. Design/methodology/approach – For establishing whether RG metrics showed the same results as the established sources of metrics, Pearson’s correlation coefficients were calculated between the metrics provided by RG and the metrics obtained from Scopus. Pearson’s correlation coefficients were also calculated for the metrics provided by RG. The data were collected by visiting the profile pages of all the members who had an account in RG under the Department of Physics, Panjab University, Chandigarh (India). Findings – The study showed that most of the RG metrics showed strong positive correlation with the Scopus metrics, except for RGScore (RG) and Citations (Scopus), which showed moderate positive correlation. It was also found that the RG metrics showed moderate to strong positive correlation amongst each other. Research limitations/implications – The limitation of this study is that more and more scientists and researchers may join RG in the future, therefore the data may change. The study focuses on the members who had an account in RG under the Department of Physics, Panjab University, Chandigarh (India). Perhaps further studies can be conducted by increasing the sample size and by taking a different sample size having different characteristics. Originality/value – Being an emerging field, not much has been conducted in the area of altmetrics. Very few studies have been conducted on the reach of academic social networks like RG and their validity as sources of altmetric indicators like RGScore, Impact Points, etc. The findings offer insights to the question whether RG can be used as an alternative to traditional sources of bibliometric indicators, especially with reference to a rapidly developing country such as India.

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