Use of Normalized Difference Vegetation Index to Assess N Status and Predict Grain Yield in Rice

SUMMARYThis study was aimed at comparing in-field parameters and remote sensing NDVI (normalized difference vegetation index) by both satellite (SAT) and unmanned aerial vehicle (UAV) for the assessment of early nitrogen (N) status and prediction of yield in winter wheat (Triticum aestivum L.). Six increasing N rates, i.e., 0, 40, 80, 120, 160, 200 kg N ha−1 were applied, half at tillering and half at shooting. Thus, when the crop N status was monitored between the two N applications, consecutive N treatments differentiated from each other by just 20 kg N ha−1. The following in-field and remote sensed parameters were compared as indicators of crop vegetative and N status: plant N% (w:w) concentration; crop N uptake (Nupt); ratio between transmitted and incident photosynthetically active radiation (PARt/PARi); leaf SPAD values, an indirect index for chlorophyll content; SAT and UAV derived NDVI. As reliable indicators of wheat N availability, in-field parameters were ranked as follows: PARt/PARi ≅ Nupt > SPAD ≅ N%. The PARt/PARi, Nupt and SPAD resulted quite strongly correlated to each other. At all crop stages, the NDVI was strongly correlated with PARt/PARi and Nupt. It is of relevance that NDVI correlated quite strongly to in-field parameters and grain yield at shooting, i.e., before the second N application, when the N rate can still be adjusted. The SAT and UAV NDVIs were strongly correlated to each other, which means they can be used alternatively depending on the context.

Download Full-text

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

Agronomy ◽

10.3390/agronomy11020340 ◽

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.

Download Full-text

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

Plant Breeding ◽

10.1111/pbr.12003 ◽

2012 ◽

Vol 131 (6) ◽

pp. 716-721 ◽

Cited By ~ 9

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

Download Full-text

In-Season Prediction of Corn Grain Yield Potential Using Normalized Difference Vegetation Index

Agronomy Journal ◽

10.2134/agronj2006.0103 ◽

2006 ◽

Vol 98 (6) ◽

pp. 1488-1494 ◽

Cited By ~ 171

Author(s):

R. K. Teal ◽

B. Tubana ◽

K. Girma ◽

K. W. Freeman ◽

D. B. Arnall ◽

...

Keyword(s):

Grain Yield ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Yield Potential ◽

Corn Grain ◽

Corn Grain Yield

Download Full-text

Temporal covariance structure of multi-spectral phenotypes and their predictive ability for end-of-season traits in maize

Theoretical and Applied Genetics ◽

10.1007/s00122-020-03637-6 ◽

2020 ◽

Vol 133 (10) ◽

pp. 2853-2868

Author(s):

Mahlet T. Anche ◽

Nicholas S. Kaczmar ◽

Nicolas Morales ◽

James W. Clohessy ◽

Daniel C. Ilut ◽

...

Keyword(s):

Grain Yield ◽

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Crop Improvement ◽

Genetic Correlations ◽

Random Regression ◽

Multiple Time ◽

Heritable Variation ◽

Using Data

Abstract Key message Heritable variation in phenotypes extracted from multi-spectral images (MSIs) and strong genetic correlations with end-of-season traits indicates the value of MSIs for crop improvement and modeling of plant growth curve. Abstract Vegetation indices (VIs) derived from multi-spectral imaging (MSI) platforms can be used to study properties of crop canopy, providing non-destructive phenotypes that could be used to better understand growth curves throughout the growing season. To investigate the amount of variation present in several VIs and their relationship with important end-of-season traits, genetic and residual (co)variances for VIs, grain yield and moisture were estimated using data collected from maize hybrid trials. The VIs considered were Normalized Difference Vegetation Index (NDVI), Green NDVI, Red Edge NDVI, Soil-Adjusted Vegetation Index, Enhanced Vegetation Index and simple Ratio of Near Infrared to Red (Red) reflectance. Genetic correlations of VIs with grain yield and moisture were used to fit multi-trait models for prediction of end-of-season traits and evaluated using within site/year cross-validation. To explore alternatives to fitting multiple phenotypes from MSI, random regression models with linear splines were fit using data collected in 2016 and 2017. Heritability estimates ranging from (0.10 to 0.82) were observed, indicating that there exists considerable amount of genetic variation in these VIs. Furthermore, strong genetic and residual correlations of the VIs, NDVI and NDRE, with grain yield and moisture were found. Considerable increases in prediction accuracy were observed from the multi-trait model when using NDVI and NDRE as a secondary trait. Finally, random regression with a linear spline function shows potential to be used as an alternative to mixed models to fit VIs from multiple time points.

Download Full-text

Effect of Seed Distribution and Population on Maize (Zea maysL.) Grain Yield

International Journal of Agronomy ◽

10.1155/2014/125258 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8 ◽

Cited By ~ 5

Author(s):

Bee Khim Chim ◽

Peter Omara ◽

Natasha Macnack ◽

Jeremiah Mullock ◽

Sulochana Dhital ◽

...

Keyword(s):

Grain Yield ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Block Design ◽

N Uptake ◽

Plant Spacing ◽

Randomized Complete Block Design ◽

Seed Distribution ◽

Treatment Structure ◽

Number Of Seeds

Maize planting is normally accomplished by hand in the developing world where two or more seeds are placed per hill with a heterogeneous plant spacing and density. To understand the interaction between seed distribution and distance between hills, experiments were established in 2012 and 2013 at Lake Carl Blackwell (LCB) and Efaw Agronomy Research Stations, near Stillwater, OK. A randomized complete block design was used with three replications and 9 treatments and a factorial treatment structure of 1, 2, and 3 seeds per hill using interrow spacing of 0.16, 0.32, and 0.48 m. Data for normalized difference vegetation index (NDVI), intercepted photosynthetically active radiation (IPAR), grain yield, and grain N uptake were collected. Results showed that, on average, NDVI and IPAR increased with number of seeds per hill and decreased with increasing plant spacing. In three of four site-years, planting 1 or 2 seeds per hill, 0.16 m apart, increased grain yield and N uptake. Over sites, planting 1 seed, every 0.16 m, increased yields by an average of 1.15 Mg ha−1(range: 0.33 to 2.46 Mg ha−1) when compared to the farmer practice of placing 2 to 3 seeds per hill, every 0.48 m.

Download Full-text

Prediction of Maize Yields from In-Season GreenSeeker Normalized Difference Vegetation Index and Dry Biomass as Influenced by Different Nutrient Combinations

Journal of Agricultural Science ◽

10.5539/jas.v13n1p165 ◽

2020 ◽

Vol 13 (1) ◽

pp. 165

Author(s):

Hillary M. O. Otieno ◽

George N. Chemining’wa ◽

Shamie Zingore

Keyword(s):

Grain Yield ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Nutrient Management ◽

Fertilizer Application ◽

Growth And Yield ◽

Yield Prediction ◽

Maize Productivity ◽

Dry Biomass ◽

The Right

To mitigate low maize productivity, improve on-farm planning and policy implementation, the right fertilizer combinations and yield forecasting should be prioritized. Therefore, this research aimed at assessing the effect of applying different nutrient combinations on maize growth and yield and in-season grain yield prediction from biomass and normalized difference vegetation index (NDVI) readings. The research was done in Embu and Kirinyaga counties, in Central Kenya. Nutrient combinations tested were P+K, N+K, N+P, N+P+K, and N+P+K+Ca+Mg+Zn+B+S. The results showed consistently lowest and highest NDVI reading, dry biomass, and grain yields due to P+K and N+P+K+Ca+Mg+Zn+B+S treatments, respectively. Positive NDVI responses of 56%, 14%, 15%, and 15% were recorded with N, P, K, and combined Ca+Mg+Zn+B+S, respectively. These nutrients, in the same order, recorded 54%, 20%, 8%, and 18% positive responses with biomass. The GreenSeeker NDVI reading with grain yield and aboveground dry biomass with grain yield recorded R2 ranging from 0.23-0.53 and 0.30-0.61 (in Embu), and 0.31-0.64 and 0.30-0.50 (in Kirinyaga), respectively. When data were pooled, the prediction strength increased, reaching a maximum of 67% and 58% with NDVI and biomass, respectively. Yield prediction was even more robust when the independent variables were combined through multiple linear model at both 85 and 105 days after emergence. From this research, it is evident that the effects of balanced fertilizer application are detectable from NDVI readings—providing a tool for tracking and monitoring nutrient management effects—not just from the nitrogen perspective as commonly studied but from the combined effects of multiple nutrients. Also, grain yield could be accurately predicted early before harvesting by combining NDVI and biomass yields.

Download Full-text

Determining nitrogen deficiencies for maize using various remote sensing indices

Precision Agriculture ◽

10.1007/s11119-021-09861-4 ◽

2022 ◽

Author(s):

Brayden W. Burns ◽

V. Steven Green ◽

Ahmed A. Hashem ◽

Joseph H. Massey ◽

Aaron M. Shew ◽

...

Keyword(s):

Remote Sensing ◽

Grain Yield ◽

Nitrogen Fertilizer ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Nitrogen Deficiency ◽

Maize Grain Yield ◽

Nitrogen Inputs ◽

Chlorophyll Index ◽

Maize Grain

AbstractDetermining a precise nitrogen fertilizer requirement for maize in a particular field and year has proven to be a challenge due to the complexity of the nitrogen inputs, transformations and outputs in the nitrogen cycle. Remote sensing of maize nitrogen deficiency may be one way to move nitrogen fertilizer applications closer to the specific nitrogen requirement. Six vegetation indices [normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), red-edge normalized difference vegetation index (RENDVI), triangle greenness index (TGI), normalized area vegetation index (NAVI) and chlorophyll index-green (CIgreen)] were evaluated for their ability to detect nitrogen deficiency and predict grain maize grain yield. Strip trials were established at two locations in Arkansas, USA, with nitrogen rate as the primary treatment. Remote sensing data was collected weekly with an unmanned aerial system (UAS) equipped with a multispectral and thermal sensor. Relationships among index value, nitrogen fertilizer rate and maize growth stage were evaluated. Green NDVI, RENDVI and CIgreen had the strongest relationship with nitrogen fertilizer treatment. Chlorophyll Index-green and GNDVI were the best predictors of maize grain yield early in the growing season when the application of additional nitrogen was still agronomically feasible. However, the logistics of late season nitrogen application must be considered.

Download Full-text

Assessing Nitrogen Status in Potted Geranium through Discriminant Analysis of Ground-based Spectral Reflectance Data

HortScience ◽

10.21273/hortsci.47.3.343 ◽

2012 ◽

Vol 47 (3) ◽

pp. 343-348 ◽

Cited By ~ 5

Author(s):

Yun-wen Wang ◽

Bruce L. Dunn ◽

Daryl B. Arnall

Keyword(s):

Discriminant Analysis ◽

Chlorophyll Content ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Rocky Mountain ◽

Flower Color ◽

Production Costs ◽

N Application ◽

Chlorophyll Meter ◽

N Status

Nitrogen (N) deficiencies can significantly reduce plant growth as well as flower quantity and quality. However, excessive N application leads to increased production costs and may cause water contamination as a result of runoff. Ground-based remote sensing of plant chlorophyll content offers the possibility to rapidly and inexpensively estimate crop N status. The objective of this study was to test the reliability of three different Normalized Difference Vegetation Index (NDVI) measuring methods and Soil-Plant Analyses Development (SPAD) chlorophyll meter values as indicators of geranium (Pelargonium ×hortorum L.H. Bailey) N status. Two potted geranium cultivars, Rocky Mountain White and Rocky Mountain Dark Red, were supplied with N at 0, 50, 100, and 200 mg·L−1 levels, respectively. NDVI readings were measured at 45 cm above the canopy or media of individual plants or 45 cm above the canopy of a group of plants (four plants treated with the same N rate were placed together). Significant correlations existed between indirect chlorophyll content measurements of SPAD values and NDVI readings regardless of four-pot group or single-pot measurements with N application rates and leaf N concentration. Using a cross-validation technique in discriminant analysis, 70.8% to 79.2% of sample cases were correctly categorized to the corresponding N statuses including very deficient, deficient, and sufficient. Therefore, ground-based, non-destructive measurements of a chlorophyll meter and pocket NDVI unit were able to indicate N status. Considering that flower color can interfere with NDVI measurements, the chlorophyll meter may better determine N content when flowers are present.

Download Full-text

Vegetation indices in the prediction of biomass and grain yield of white oat under irrigation levels

Pesquisa Agropecuária Tropical ◽

10.1590/1983-40632018v4851523 ◽

2018 ◽

Vol 48 (2) ◽

pp. 109-117 ◽

Cited By ~ 3

Author(s):

Anderson Prates Coelho ◽

David Luciano Rosalen ◽

Rogério Teixeira de Faria

Keyword(s):

Grain Yield ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Flag Leaf ◽

Vegetation Indices ◽

Leaf Sheath ◽

Phenological Stage ◽

Mean Values ◽

Ripe Stage ◽

Irrigation Levels

ABSTRACT Vegetation indices are widely used to indicate the nutritional status of crops, as well as to estimate their harvest yield. However, their accuracy is influenced by the phenological stage of evaluation and the index used. The present study aimed to evaluate the accuracy of the Normalized Difference Vegetation Index (NDVI) and Inverse Ratio Vegetation Index (IRVI) in the prediction of grain yield and biomass of white oat cultivated under irrigation levels, besides indicating the best phenological stage for evaluation. The irrigation levels consisted of 11 %, 31 %, 60 %, 87 % and 100 % of the maximum evapotranspiration, with four replicates. The mean values for NDVI and IRVI were determined using an active terrestrial sensor, at four phenological stages (4, 8, 10 and 10.5.4). The white oat grain yield and biomass may be estimated with a high precision using the NDVI and IRVI. The NDVI was more accurate than the IRVI. The grain yield estimate was more accurate from the flag leaf sheath appearance stage (10), whereas, for the biomass, the best estimate was for the kernel watery ripe stage (10.5.4).

Download Full-text