Association of digital photo parameters and NDVI with winter wheat grain yield in variable environments

Morgounov, Alexey; Gummadov, Nurberdi; Belen, Savaş; Kaya, Yüksel; Keser, Mesut; Mursalova, Jamala

doi:10.3906/tar-1312-90

Cited by 26 publications

(27 citation statements)

References 16 publications

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“…Therefore, NDVI may be useful for GY prediction in disease-free conditions and as part of combined methods for disease-detection, but is not an appropriate index of GY assessment in disease-affected conditions. In agreement with previous reports that used digital indices from BreedPix software [23,49] and other image processors [50,51], GY was accurately predicted by RGB indices. As a further contribution, our study demonstrated that RGB indices are also able to predict GY and yield losses in yellow rust infected cultivars at the canopy level.…”

Section: Performance Of Vegetation Indicessupporting

confidence: 89%

Grain yield losses in yellow-rusted durum wheat estimated using digital and conventional parameters under field conditions

et al. 2015

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The biotrophic fungus Puccinia striiformis f. sp. tritici is the causal agent of the yellow rust in wheat. Between the years 2010-2013 a new strain of this pathogen (Warrior/Ambition), against which the present cultivated wheat varieties have no resistance, appeared and spread rapidly. It threatens cereal production in most of Europe. The search for sources of resistance to this strain is proposed as the most efficient and safe solution to ensure high grain production. This will be helped by the development of high performance and low cost techniques for field phenotyping. In this study we analyzed vegetation indices in the Red, Green, Blue (RGB) images of crop canopies under field conditions. We evaluated their accuracy in predicting grain yield and assessing disease severity in comparison to other field measurements including the Normalized Difference Vegetation Index (NDVI), leaf chlorophyll content, stomatal conductance, and canopy temperature. We also discuss yield components and agronomic parameters in relation to grain yield and disease severity.RGB-based indices proved to be accurate predictors of grain yield and grain yield losses associated with yellow rust (R 2 = 0.581 and R 2 = 0.536, respectively), far surpassing the predictive ability of NDVI (R 2 = 0.118 and R 2 = 0.128, respectively). In comparison to potential yield, we found the presence of disease to be correlated with reductions in the number of grains per spike, grains per square meter, kernel weight and harvest index. Grain yield losses in the presence of yellow rust were also greater in later heading varieties. The combination of RGB-based indices and days to heading together explained 70.9% of the variability in grain yield and 62.7% of the yield losses.(http://creativecommons.org/licenses/by-nc-nd/4.0/). Keywords:Wheat yellow rust Field phenotyping NDVI Phenology, Puccinia striiformis RGB-based indices Triticum durumT H E C R O P J O U R N A L 3 ( 2 0 1 5 ) 2 0 0 -2 1 0

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Section: Performance Of Vegetation Indicessupporting

confidence: 89%

Grain yield losses in yellow-rusted durum wheat estimated using digital and conventional parameters under field conditions

et al. 2015

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“…As found in previous studies in wheat, the RGB canopy indices measured at flowering were strongly correlated with GY [31,32,64]. For wheat under Mediterranean conditions, the reproductive stage is usually the best period for crop monitoring, since the crop is exposed to increasing stress (drought) conditions during the last part of the crop cycle.…”

Section: Relationship Of Vegetation Indices and Water Status Traits Wsupporting

confidence: 63%

Combined Use of Low-Cost Remote Sensing Techniques and δ13C to Assess Bread Wheat Grain Yield under Different Water and Nitrogen Conditions

et al. 2019

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Vegetation indices and canopy temperature are the most usual remote sensing approaches to assess cereal performance. Understanding the relationships of these parameters and yield may help design more efficient strategies to monitor crop performance. We present an evaluation of vegetation indices (derived from RGB images and multispectral data) and water status traits (through the canopy temperature, stomatal conductance and carbon isotopic composition) measured during the reproductive stage for genotype phenotyping in a study of four wheat genotypes growing under different water and nitrogen regimes in north Algeria. Differences among the cultivars were reported through the vegetation indices, but not with the water status traits. Both approximations correlated significantly with grain yield (GY), reporting stronger correlations under support irrigation and N-fertilization than the rainfed or the no N-fertilization conditions. For N-fertilized trials (irrigated or rainfed) water status parameters were the main factors predicting relative GY performance, while in the absence of N-fertilization, the green canopy area (assessed through GGA) was the main factor negatively correlated with GY. Regression models for GY estimation were generated using data from three consecutive growing seasons. The results highlighted the usefulness of vegetation indices derived from RGB images predicting GY.

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“…NDVI readings in this experiment were performed during anthesis period, and showed a positive association with yield. However, the association between the NDVI and the biomass (Marti et al, 2007) and grain yield (Morgounov et al, 2014) varied among the phenological phases in which the readings are made. Previous studies reported that the relationship is higher if the measurement is conducted at later stages, such as anthesis or grain filling period (Alvaro et al, 2007;Marti et al, 2007).…”

Section: Relationship Among Studied Traitsmentioning

confidence: 99%

Variation in NDVI values and relationship with grain yield in two-rowed winter barley

Mirosavljević¹,

Momčilović²,

Čanak³

et al. 2018

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The simple methods used for indirect biomass determination are recognized as potentially important tools for crop growth prediction. The normalized difference vegetation index (NDVI) is widely used for assessing crop biomass, yield, photosynthesis, and nitrogen management. Aims of this study were to determine the variation in NDVI values and relationship of the NDVI with the most significant agronomic properties in the elite genotypes of two-rowed winter barley. The experiment included elite two-rowed winter barley genotypes and was carried out in two growing seasons. NDVI were measured at mid-anthesis, while grain yield and grain yield components were determined from combine harvested samples at maturity. Results from this study showed that the influence of genotype was mainly responsible for variation in most studied traits. Grain yield of two-rowed barley cultivars was more related to high grain number per unit area than to grain weight. NDVI values at anthesis in both growing seasons were positively associated with grain yield (R 2 = 0.444 and R 2 = 0.386), grain number (R 2 = 0.387 and R 2 = 0.507) and plant height (R 2 = 0.352 and R 2 = 0.364), while relationship with grain weight was not significant. Significant genotypic variation for NDVI values and the presence of significant positive relationship with grain yield and grain number indicated that NDVI measurement at anthesis could be efficiently used for indirect identification of productive two-rowed winter barley genotypes.

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Association of digital photo parameters and NDVI with winter wheat grain yield in variable environments

Cited by 26 publications

References 16 publications

Grain yield losses in yellow-rusted durum wheat estimated using digital and conventional parameters under field conditions

Grain yield losses in yellow-rusted durum wheat estimated using digital and conventional parameters under field conditions

Combined Use of Low-Cost Remote Sensing Techniques and δ13C to Assess Bread Wheat Grain Yield under Different Water and Nitrogen Conditions

Variation in NDVI values and relationship with grain yield in two-rowed winter barley

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