Biology subjects

Cazon, L. I.

Publications and source records attributed to Cazon, L. I..

5 recordsLinked to original sources

bioRxiv2026

Yield losses associated with peanut smut incidence in Argentina: a quantitative synthesis across field studies

Peanut smut, caused by Thecaphora frezzii, is an important constraint to peanut production in Argentina, but quantitative estimates of yield losses across environments remain limited. We quantified the relationship between disease incidence and kernel yield using 922 observations from 26 field studies conducted in Cordoba, Argentina, between 2021 and 2025. Study-specific incidence-yield relationships were analyzed using linear regression, random-effects meta-analysis, and linear mixed-effects models. Peanut smut incidence was consistently associated with yield reduction across studies. The estimated damage coefficient ranged from 24.2 to 28.7 kg ha-{superscript 1} per 1% increase in disease incidence, corresponding to a relative yield reduction of 0.74-0.87% of attainable yield. In contrast, attainable yield varied markedly among studies, ranging from 1,370 to 5,409 kg ha-{superscript 1}. Although an exploratory segmented analysis suggested a breakpoint near 12% incidence, subsequent moderator analyses, study- specific regressions, and normalized response curves provided no evidence of a biologically meaningful change in the damage coefficient across incidence or yield classes. These results indicate that differences among environments were primarily associated with attainable yield rather than with changes in the magnitude of disease-associated yield loss. The resulting damage function provides a quantitative basis for yield-loss assessment and disease management in peanut.

plant biology↗

bioRxiv2026

A standard area diagram for potato common scab: comparable performance of image- and object-based validation

Potato common scab (Streptomyces sp.) is an economically important disease that reduces the quality and market value of tubers. A key aspect in developing management strategies involves accurately quantifying the disease. Due to the three-dimensional nature of the tuber and the heterogeneous distribution of lesions across its surface, visual estimates of severity can be challenging. Therefore, the objectives of this study were to develop and validate a standard area diagram (SAD) for estimating common scab severity on potato tubers and to compare validation outcomes obtained using real tubers and digital images. A SAD comprising six severity levels (from 1.3 to 66.8%) was developed based on image analysis of naturally infected tubers. Validation was conducted using two complementary approaches in which inexperienced raters evaluated either real potato tubers or digital images of the same tubers under unaided and aided conditions. Accuracy, bias components, and inter-rater reliability were quantified using absolute error metrics, Lins concordance correlation coefficient, intraclass correlation coefficients, and overall concordance correlation coefficients. Use of the SAD significantly improved accuracy, reduced systematic bias, and increased inter-rater reliability across both validation approaches. No significant differences were detected between assessments conducted on real tubers and images, although image-based evaluations showed a slight, non-significant tendency toward reduced scale and location bias under aided conditions. These results demonstrate that a dimension-aware SAD integrating information across the full tuber surface enhances the reliability and reproducibility of visual severity assessments and supports the use of image-based evaluations for training, large-scale surveys, and remote or collaborative applications involving three-dimensional plant organs.

plant biology↗

bioRxiv2025

LLS-SevEst - Late leaf spot severity estimator. A machine learning approach to assessing Nothopassalora personata in peanut.

Late leaf spot (LLS), caused by Nothopassalora personata, is the most damaging foliar disease in peanut production worldwide, leading to significant yield losses if not properly managed. Accurate disease severity assessment is crucial for evaluating fungicide efficacy and implementing effective management strategies. This study aimed to develop and validate an automated image analysis model, LLS-SevEst, for quantifying LLS severity in peanut leaves. A dataset of 190 scanned leaf images was analyzed using three approaches: a fixed threshold-based segmentation, morphological preprocessing, and K-means clustering. Exploratory analyses revealed distinct brightness patterns between healthy and diseased tissues, guiding the development of classification functions. The threshold-based model yielded high false positive rates due to its inability to account for natural leaf variation, while the morphological preprocessing method improved segmentation marginally but still required manual adjustments. The K-means clustering approach achieved superior segmentation by objectively differentiating healthy tissue, lesions, and background, and showed high potential for automated, reproducible disease severity estimation. Future work should focus on integrating deep learning and expanding the dataset to improve model robustness and adaptability to other foliar pathosystems.

plant biology↗

bioRxiv2023

Peanut Smut: A scientometric analysis for a pathosystem that concerns the Argentine peanut industry.

Since its first report in commercial batches in 1995, the prevalence and yield impact caused by smut disease have increased rapidly in peanut fields. At the same time, various working groups have studied this pathosystem using different approaches, contributing to the scientific knowledge of the disease. By recognizing the importance of a thorough bibliographic review and meticulous organization of information, the process of initiating new research projects becomes more effective. In light of this, the aim of this work was to provide a comprehensive scientometric analysis of the evolution of peanut smut research, spanning from its inception to the current day. For this purpose, we compiled bibliographic data about the disease and extracted information to calculate metrics. We observed that a smaller proportion of the scientific production was presented in peer-reviewed journals, the prevalent topics were epidemiology and breeding, and the collaborative endeavors were crucial for the scientific advancement in the study of this pathosystem. Additionally, the researchers with the most significant presence in the publications, the involved institutions, and the impact of the produced papers, among other trends were identified. Although there have been many scientific-technological advances in peanut smut over the years, this information is not reflected in scientific papers in peer-reviewed journals, which represents a great challenge for researchers involved in this topic. It is crucial to continue generating knowledge that contributes to the integrated management of this complex pathosystem. This will prevent further yield losses and the spread of the pathogen to new production areas.

plant biology↗

bioRxiv2023

Pyricularia Populations are Mostly Host-Specialized with Limited Reciprocal Cross-Infection Between Wheat and Endemic Grasses in Minas Gerais, Brazil

Wheat blast, caused by Pyricularia oryzae Triticum (PoT), is an emergent threat to wheat production. Current understanding of the evolution and population biology of the pathogen and epidemiology of the disease has been based on phylogenomic studies that compared the wheat blast pathogen with isolates collected from grasses that were invasive to Brazilian wheat fields. Genetic similarity between isolates from wheat and grasses lead to the conclusion that significant cross-infection occurs, especially on signalgrass (Urochloa spp.); and this in turn prompted speculation that its widespread use as forage is a key driver of the diseases epidemiology. We reanalyzed data from those studies and found that all but one of the isolates from non-wheat hosts were members of PoT and the related Lolium-adapted lineage (PoL1), which meant that the Pyricularia populations typically found on endemic grasses had not yet been sampled. To address this shortcoming, we performed a comprehensive sampling of blast lesions in wheat crops and endemic grasses found in and away from wheat fields in Minas Gerais. A total 1,368 diseased samples were collected (976 leaves of wheat and grasses and 392 wheat heads) which yielded a working collection of 564 Pyricularia isolates. We show that, contrary to earlier implications, PoT was rarely found on endemic grasses and, conversely, members of grass-adapted populations were rarely found on wheat. Instead, most populations were host-specialized with constituent isolates usually grouping according to their host-of-origin. With regard to the dominant role proposed for signalgrass in wheat blast epidemiology, we found only one PoT member in 67 isolates collected from signalgrass grown away from wheat fields, and only three members of Urochloa-adapted populations among hundreds of isolates from wheat. Cross-inoculation assays on wheat and a signalgrass used in pastures (U. brizantha) suggested that the limited cross-infection observed in the field may be due to innate compatibility differences. Whether or not the observed level of cross-infection would be sufficient to provide an inoculum reservoir, or serve as a bridge between wheat growing regions, is questionable and, therefore, deserves further investigation.

microbiology↗