Evaluating vegetation indices for precision phenotyping of quantitative stripe rust reaction in wheat
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Wheat production and productivity is widely affected by stripe rust infection. Resistance to this disease governed by additive-gene effect is one of the recent strategies being adopted in wheat breeding programme. While the present phenotyping approaches for scoring, the plants reaction often vary from person to person. A novel, repeatable and reliable approach can enhance the efficiency of determining genetic variability in large number of genotypes. Taking into consideration the emerging non-invasive tools to assess the physiological status of plants. The present investigation was undertaken to explore utility of optical measurements to variation of the stripe rust reaction in wheat genotypes. One hundred and twenty Indian wheat genotypes representing released varieties, elite genotypes, genetic stocks, and local landraces were used for the study. The stripe rust epidemics in the field were initiated with Yr27-virulent P. Striiformis race 78S84. The Area under the Disease Progress Curve (AUDPC) values were calculated from four weekly visual estimates of disease severity which ranged from 0 to 2077. Normalized difference vegetation index (NDVI), Chlorophyll content index (CCI) and Plant Canopy temperature (CT) were recorded twice, 7 days apart, when disease severity approached maximum values on the susceptible controls. The results indicate that the temporal ground-based NDVI is most effective in studying quantitative rust reaction with a significant regression coefficient (r2=0.63) between AUDPC and NDVI data followed by chlorophyll content index (r2=0.37) and canopy temperature (r2=0.21).Keywords:
Rust (programming language)
Stripe rust
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A primary selection target for wheat (Triticum aestivum) improvement is grain yield. However, the selection for yield is limited by the extent of field trials, fluctuating environments, and the time needed to obtain multiyear assessments. Secondary traits such as spectral reflectance and canopy temperature (CT), which can be rapidly measured many times throughout the growing season, are frequently correlated with grain yield and could be used for indirect selection in large populations particularly in earlier generations in the breeding cycle prior to replicated yield testing. While proximal sensing data collection is increasingly implemented with high-throughput platforms that provide powerful and affordable information, efficient and effective use of these data is challenging. The objective of this study was to monitor wheat growth and predict grain yield in wheat breeding trials using high-density proximal sensing measurements under extreme terminal heat stress that is common in Bangladesh. Over five growing seasons, we analyzed normalized difference vegetation index (NDVI) and CT measurements collected in elite breeding lines from the International Maize and Wheat Improvement Center at the Regional Agricultural Research Station, Jamalpur, Bangladesh. We explored several variable reduction and regularization techniques followed by using the combined secondary traits to predict grain yield. Across years, grain yield heritability ranged from 0.30 to 0.72, with variable secondary trait heritability (0.0-0.6), while the correlation between grain yield and secondary traits ranged from -0.5 to 0.5. The prediction accuracy was calculated by a cross-fold validation approach as the correlation between observed and predicted grain yield using univariate and multivariate models. We found that the multivariate models resulted in higher prediction accuracies for grain yield than the univariate models. Stepwise regression performed equal to, or better than, other models in predicting grain yield. When incorporating all secondary traits into the models, we obtained high prediction accuracies (0.58-0.68) across the five growing seasons. Our results show that the optimized phenotypic prediction models can leverage secondary traits to deliver accurate predictions of wheat grain yield, allowing breeding programs to make more robust and rapid selections.
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The objectives of this study were to evaluate the ability of five selection indices to assess drought tolerance of durum wheat genotypes under a variety of environmental conditions and the relationships of canopy temperature depression (CTD) with drought indices. Eight durum wheat genotypes were planted in the rainfed and supplementary irrigation conditions for two years (2007-2009). Five drought tolerance indices including stress susceptibility index (SSI), stress tolerance index (STI), tolerance index (TOL), mean productivity (MP) and geometric mean productivity (GMP) were calculated. Canopy temperature depression (CTD) was used to estimate crop yield and to rank genotypes. CTD was measured at three stages, from the emergence of fifty percent of inflorescence (Zadoks Growth Scale54) to watery ripe stage (ZGS71). The results showed that the average values of CTD in durum wheat genotypes changed from 3.3 to 5.7°C at the ZGS69 stage. Genotypes in this stage (ZGS69) had highly significant differences and average of CTD showed that durum wheat canopy was the largest value in all ZGSs under both conditions. The significant and positive correlation of YP, MP, GMP, SSI, STI and CTD showed that these indices were more effective in identifying high yield genotypes under both conditions. Results also showed that CTD has played an important role to search physiological basis of grain yield of wheat, and can be successfully used as a selection criterion in breeding programs.
CTD
Drought Tolerance
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Rust (programming language)
Rank correlation
Growing season
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Eleven genotypes of wheat varieties were used to research the relation between normalized difference vegetation index(NDVI) and yield components and drought resistance under dryland conservation tillage condition.The results showed that NDVI values of different wheat genotypes differed significantly at different development stages.Value of wheat NDVI expressed a positive correlation with drought yield index at heading stage.Those who had higher NDVI value of heading had better drought yield index.Under the experimental conditions,Shimai 15,Shijiazhuang 8 and Yan Blu6439 have higher drought yield index than others.
Winter wheat
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Stripe rust
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Increased biomass production could be an important criterion for future grain yield improvement in wheat (Triticum aestivum L.). Quick assessment of genetic variations for biomass production may become a useful tool for wheat breeders. The potential of using canopy spectral reflectance indices (SRI) to assess genetic variation for biomass production in winter wheat was evaluated. Three experiments were conducted for 2 yr (2003-2004 and 2004-2005) at Oklahoma State University, Stillwater, OK. The first experiment consisted of 25 winter wheat cultivars, and the other two experiments contained two sets of 25 F 4:6 and F 4:7 recombinant inbred lines from two crosses developed by breeding programs in the great plains of the United States of America. Three groups of SRI (vegetation-based, pigment-based, and water-based) were tested for their ability to assess biomass production at three growth stages (booting, heading, and grainfilling). The water index and the normalized water indices gave stronger genetic correlations (P < 0.01) and linear relationship for biomass production compared with the vegetation-based and pigment-based indices. The strong association of water-based indices with biomass was related to the canopy water content of the genotypes. Canopy water content was significantly (P < 0.05) correlated with biomass production. A strong positive association (P < 0.05) of grain yield and dry biomass was observed at the heading and grainfilling stages. Our study demonstrated the potential of using water-based SRI as a breeding tool to estimate genetic variability and identify genotypes with higher biomass production, and could eventually help to achieve higher grain yield in winter wheat. Key words: Wheat; biomass; grain yield; spectral reflectance index
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Breeding for Cercospora resistant sugar beet cultivars requires field experiments for testing resistance levels of candidate genotypes in conditions that are close to agricultural cultivation. Non-invasive spectral phenotyping methods can support and accelerate resistance rating and thereby speed up breeding process. In a case study, experimental field plots with strongly infected beet genotypes of different resistance levels were measured with two different spectrometers. Vegetation indices were calculated from measured wavelength signature to determine leaf physiological status, e.g., greenness with the Normalized Differenced Vegetation Index (NDVI), leaf water content with the Leaf Water Index (LWI) and Cercospora disease severity with the Cercospora Leaf Spot Index (CLSI). Indices values correlated significantly with visually scored disease severity, thus connecting the classical breeders’ scoring approach with advanced non-invasive technology.
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Plant type and irrigation scheme are key influencing factors of real-time yield estimation and monitoring of winter wheat in precision farming. In this paper, MODIS remote sensing data were used in combination with GPS and ground-truth non-remote sensing data to determine the dynamics of normalized difference vegetation index (NDVI) of winter wheat cultivars with different plant types under irrigation and non-irrigation conditions. The relationship between NDVI and yield of different winter wheat culti-vars in different growth stages was then analyzed. Results showed the trends in NDVI with developmental stages of different wheat cultivars were same, following a low-high-low curve. There were obvious differences in NDVI from jointing to booting stages for different cultivars, and NDVI for cultivars with horizontal plant types was higher than that for cultivars with erect plant types. It im-plied that the jointing-to-booting stage was the best period for identifying plant types of winter wheat cultivars. Even for the same cultivar, mean NDVI was obviously different at each growth stage for irrigated and non-irrigated lands. NDVI for irrigated winter wheat was higher than that for non-irrigated winter wheat, with a notable difference especially at the early heading stage. At early heading stage, NDVI was strongly correlated with yield in irrigated and non-irrigated lands. However, regression equation based on NDVI both in the early heading and filling stages gave better prediction for wheat yield than that based on NDVI only in the early heading stage. This was especially the case for non-irrigated wheat fields.
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Abstract An early prediction of crop biomass at maturity and yield is important in different circumstances. The use of spectral reflectance indices, such as the Normalized Difference Vegetation Index (NDVI), has been proposed as a fast, nondestructive way of estimating crop growth capacity. In this study, we examined whether NDVI assessment relatively early in the crop cycle may be useful for predicting final biomass and yield in wheat. To that end, NDVI was measured and biomass quantified regularly from tillering to maturity for six different wheat genotypes grown under a contrasting range of N and water availabilities. In addition, final biomass and yield were measured at maturity. In line with expectations from the literature, we found that NDVI at milk‐grain stage was well correlated to final yield and biomass. However, it was also observed that NDVI at the onset of stem elongation was also reasonably correlated to both attributes. Because crop growth in wheat from the end of tillering to anthesis is related to the determination of grain number and yield, we propose the use of NDVI at the onset of stem elongation as a complementary criterion for establishing the required late crop management (N fertilisation, irrigation) practices.
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Leaf rust epidemics of wheat, caused by Puccinia recondita f. sp. tritici, were analyzed for the 1972 to 1990 growing seasons. The disease severity values recorded for leaf rust in early and late bread-wheat planting dates at Pergamino were used to identify the best genetic and environmental predictors of disease severity. Leaf rust severity (early planting date) could be predicted (R 2 = 0.88) as a function of heat accumulation (base daily mean temperature >12°C), days with relative humidity >70% without precipitation, and a cultivar resistance index. For late planting date, the predictive value of meteorological variables decreased, while the importance of the resistance index increased over that found for the early seeded trials. In general, predicted and observed leaf rust severity levels agreed during 1994 to 1996 at Pergamino, and for trials (1991) that were grown at some distance from the area where the original data for model development were recorded.
Rust (programming language)
Wheat leaf rust
Soybean rust
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