Contrasting plant height can improve the control of rain-borne diseases in wheat cultivar mixture: modelling splash dispersal in 3-D canopies

Vidal, Tiphaine; Gigot, Christophe; Vallavieille‐Pope, C. de; Huber, Laurent; Saint‐Jean, Sébastien

doi:10.1093/aob/mcy024

Cited by 26 publications

(29 citation statements)

References 58 publications

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“…(2013) found that a susceptible cultivar was consistently protected in a mixture under low to moderate STB levels. Subsequent greenhouse experimentation (Vidal et al ., 2017) and a modelling study (Vidal et al ., 2018) have demonstrated the importance of canopy structure for the efficacy of a mixture.…”

Section: Introductionmentioning

confidence: 99%

Improved control of septoria tritici blotch in durum wheat using cultivar mixtures

et al. 2020

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Mixtures of cultivars with contrasting levels of resistance can suppress infectious diseases in wheat, as demonstrated in numerous field experiments. Most studies focus on airborne pathogens in bread wheat, while splash‐dispersed pathogens have received less attention, and no studies have been conducted in durum wheat. We conducted a 2‐year field experiment in Tunisia to evaluate the performance of cultivar mixtures with varying proportions of resistance (0%–100%) in controlling the polycyclic, splash‐dispersed disease septoria tritici blotch (STB) in durum wheat. To measure STB severity, we used a high‐throughput method based on digital image analysis of 3,074 infected leaves collected from 42 and 40 experimental plots during the first and second years, respectively. This allowed us to quantify pathogen reproduction on wheat leaves and to acquire a large data set that exceeds previous studies with respect to accuracy and precision. Our analyses show that introducing only 25% of a disease‐resistant cultivar into a pure stand of a susceptible cultivar provides a substantial reduction of almost 50% in disease severity compared to the susceptible pure stand. However, incorporating two resistant cultivars instead of one did not further improve disease control, contrary to predictions of epidemiological theory. Susceptible cultivars can be agronomically superior to resistant cultivars or be better accepted by growers for other reasons. Hence, if mixtures with only a moderate proportion of the resistant cultivar provide a similar degree of disease control as resistant pure stands, as our analysis indicates, such mixtures are more likely to be accepted by growers.

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Section: Introductionmentioning

confidence: 99%

Improved control of septoria tritici blotch in durum wheat using cultivar mixtures

et al. 2020

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“…Furthermore, pathogen dispersal mode may influence relationships with plant traits. For example, taller plants may be more susceptible to wind dispersed pathogens, whereas plants with foliage low to the ground may be more affected by pathogens from the soil surface (Marquis et al 2001;Robert et al 2018;Vidal et al 2018), suggesting that traits conferring tolerance, resistance, and escape may be pathogen dependent.…”

Section: Context Dependencementioning

confidence: 99%

Susceptibility of non-native invasive plants to novel pathogen attack -- do plant traits matter?

Fahey¹,

Koyama²,

Antunes³

2020

Preprint

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1. Invasive plants are considered major threats to biodiversity globally; however, our understanding of the long-term dynamics of invasion remains limited. 2. Over time, invasive plants can accumulate pathogens capable of causing population declines because invaders have a greater chance of encountering such pathogens as they spread and native pathogens adapt to use invasive plants as a resource over time. However, reports typically focus on individual species and ecologists lack a synthesis approach capable of predicting pathogen susceptibility in plant invaders. 3. Pathogen resistance and tolerance are tightly coupled to plant traits, which we suggest can provide a framework for understanding and predicting novel pathogen accumulation. 4. We reviewed the literature to synthesize plant traits associated with pathogen susceptibility and to determine the prevalence of novel pathogen accumulation on invasive plants. We then used these data and applied a multivariate model to associate plant traits with pathogen effects to predict pathogen susceptibility of invasive plants. Finally, we provide directions for future research. 5. Considering the emergence of trait-based approaches, comprehensive databases, and new data on individual invasions, advances in our understanding of invasive plant-pathogen interactions can lead to breakthroughs both at fundamental and management decision-making levels.

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“…These traits could be used to assist plant breeders in the selection of soybean genotypes. For example, they are used as indicators to identify early maturing soybean cultivars that can reduce yield losses due to diseases [59,60] and insect-pest complexes [61]. From a technical perspective, geometric traits can also be accurately measured using other imaging sensors, such as light detection and ranging (LIDAR) sensors, and color indices can be accurately acquired using a near-infrared spectroscopy camera.…”

Section: Future Workmentioning

confidence: 99%

High-Throughput Phenotyping Analysis of Potted Soybean Plants Using Colorized Depth Images Based on A Proximal Platform

Zhu

Guan

et al. 2019

Remote Sensing

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Canopy color and structure can strongly reflect plant functions. Color characteristics and plant height as well as canopy breadth are important aspects of the canopy phenotype of soybean plants. High-throughput phenotyping systems with imaging capabilities providing color and depth information can rapidly acquire data of soybean plants, making it possible to quantify and monitor soybean canopy development. The goal of this study was to develop a 3D imaging approach to quantitatively analyze soybean canopy development under natural light conditions. Thus, a Kinect sensor-based high-throughput phenotyping (HTP) platform was developed for soybean plant phenotyping. To calculate color traits accurately, the distortion phenomenon of color images was first registered in accordance with the principle of three primary colors and color constancy. Then, the registered color images were applied to depth images for the reconstruction of the colorized three-dimensional canopy structure. Furthermore, the 3D point cloud of soybean canopies was extracted from the background according to adjusted threshold, and each area of individual potted soybean plants in the depth images was segmented for the calculation of phenotypic traits. Finally, color indices, plant height and canopy breadth were assessed based on 3D point cloud of soybean canopies. The results showed that the maximum error of registration for the R, G, and B bands in the dataset was 1.26%, 1.09%, and 0.75%, respectively. Correlation analysis between the sensors and manual measurements yielded R2 values of 0.99, 0.89, and 0.89 for plant height, canopy breadth in the west-east (W–E) direction, and canopy breadth in the north-south (N–S) direction, and R2 values of 0.82, 0.79, and 0.80 for color indices h, s, and i, respectively. Given these results, the proposed approaches provide new opportunities for the identification of the quantitative traits that control canopy structure in genetic/genomic studies or for soybean yield prediction in breeding programs.

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Contrasting plant height can improve the control of rain-borne diseases in wheat cultivar mixture: modelling splash dispersal in 3-D canopies

Abstract: These results suggest that mixture effects on spore dispersal could be modulated by an adequate choice of architectural characteristics of cultivars. In particular, even small differences in plant height could reduce spore dispersal.

Cited by 26 publications

References 58 publications

Improved control of septoria tritici blotch in durum wheat using cultivar mixtures

Improved control of septoria tritici blotch in durum wheat using cultivar mixtures

Susceptibility of non-native invasive plants to novel pathogen attack -- do plant traits matter?

High-Throughput Phenotyping Analysis of Potted Soybean Plants Using Colorized Depth Images Based on A Proximal Platform

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