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

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.

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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.

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Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping

Remote Sensing ◽

10.3390/rs13061144 ◽

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.

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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

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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.

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Sentinel-1 Data for Winter Wheat Phenology Monitoring and Mapping

Remote Sensing ◽

10.3390/rs11192228 ◽

2019 ◽

Vol 11 (19) ◽

pp. 2228 ◽

Cited By ~ 17

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.

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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.

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