Abstract
Peanut smut (Thecaphora frezzii) is one of the most important peanut diseases in Argentinian peanut production. This monocyclic soil-borne pathogen transforms kernels into spore masses. Spore liberation from broken infected pods during the harvest process is supposed to be the main mechanism of inoculum spread, with the subsequent spread among fields increasing the soil inoculum for future peanut cropping seasons. However, we are unaware of any published study on the role of wind (in terms of speed and direction) in how far smut spores spread. Therefore, we conducted an observational study where passive spore traps were distributed at harvest around six fields placed at 100, 200, 300, and 400 m away from each field’s centroid in four cardinal directions. Three time slices were sampled: from the beginning of harvest to 90-, 180-, and 270-minutes continuously during harvest. Wind speed and direction were recorded at each trap. A generalized additive model was fitted to describe the spore spread. Modeling the dispersal shows that the spread is influenced by wind speed and the smut severely damaged pods incidence present at the harvested field. Additionally, spore size and proportion of different smut spore types were assessed (from a single unit spore to a 5-multinuclear propagule). No statistical differences were observed in the proportion of the spore types trapped. However, fewer spores were trapped at distances farther from the harvested area. This work led us to understand a fundamental component of the peanut smut cycle and epidemiology, which is to design management strategies. For example, avoiding harvest on windy days (typically >10 km h-1) to prevent the distant spread of inoculum for subsequent seasons or predicting the risk surrounding an infected field.
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Data availability
A research compendium containing the raw data and R scripts used in the analysis of this paper is available at https://doi.org/https://doi.org/10.17605/OSF.IO/TK978.
References
Agüero D (2017) Mercado internacional y nacional del maní. In: Fernandez E, Giayetto O (Eds.) El cultivo de maní en Córdoba Departamen. pp. 411–433.
Akaike H (1974) A new look at the statistical model identification. IEEE Transactions on Automatic Control 19(6):716–723
Arias SL, Mary VS, Velez PA, Rodriguez MG, Otaiza-González SN, Theumer MG (2021) Where does the peanut smut pathogen, Thecaphora frezzii, fit in the spectrum of smut diseases? Plant Disease 105(9):2268–2280
Asinari F, Paredes JA, Monguillot JH, Rago AM (2019) Últimos años de registros del carbón del maní, ¿hacia donde vamos? In XXXIV Jornada Nacional del Maní. General Cabrera, Córdoba, Argentina
Astiz Gasso M, Leis R, Marinelli A (2008) Evaluación de incidencia y severidad del carbón de maní (Thecaphora frezzii) en infecciones artificiales, sobre cultivares comerciales de maní. In 1 Congreso Argentino de Fitopatología. p (Vol. 161).
Aylor D (2017) CHAPTER 2: Patterns of Disease Spread. In: Aerial Dispersal of Pollen and Spores pp. 15–27
Aylor DE (1990) The role of intermittent wind in the dispersal of fungal pathogens. Annu Rev Phytopathol 28(1):73–9
Bergamin Filho A (1996) Doenças de plantas tropicais: epidemiologia e controle econômico. Agronômica Ceres
CAM (2023) Cámara Argentina del maní. Clúster manisero. https://camaradelmani.org.ar/cluster-manisero. Accessed 16 Jan 2023
Cazón LI, Paredes JA, Rago AM (2018) The biology of Thecaphora frezzii smut and its effects on argentine Peanut production. Advances in Plant Pathology. London: IntechOpen Ltd 31-46
Dunn PK (2004) Occurrence and quantity of precipitation can be modelled simultaneously. Int J Climatol: A J Royal Meteorol Soc 24(10):1231–1239
El Jarroudi M, Karjoun H, Kouadio L, El Jarroudi M (2020) Mathematical modelling of non-local spore dispersion of wind-borne pathogens causing fungal diseases. Appl Math Computation 376:125107
Fiers M, Edel-Hermann V, Chatot C, Le Hingrat Y, Alabouvette C, Steinberg C (2012) Potato soil-borne diseases. A review. Agron Sustain Dev 32(1):93–132
Gideon S (1978) Estimating the dimension of a model. Ann Stat 6(2):461–464
Grewling Ł, Bogawski P, Szymańska A, Nowak M, Kostecki Ł, Smith M (2020) Particle size distribution of the major Alternaria alternata allergen, Alt a 1, derived from airborne spores and subspore fragments. Fungal Biology 124(3–4):219–227
Habel K, Grasman R, Gramacy RB, Mozharovskyi P, Sterratt DC (2023) Geometry: mesh generation and surface tessellation. R package version 0.4.7. https://CRAN.R-project.org/package=geometry. Accessed 6 Mar 2024
Hartig F (2022) DHARMa: residual diagnostics for hierarchical (Multi-Level / Mixed) regression models. R package version 0.4.6. https://CRAN.R-project.org/package=DHARMa. Accessed 6 Mar 2024
Hasan MM, Dunn PK (2010) A simple Poisson–gamma model for modelling rainfall occurrence and amount simultaneously. Agric For Meteorol 150(10):1319–1330
Hastie TJ, Tibshirani RJ (1990) Generalized additive models. Chapman & Hall/CRC, New York/Boca Raton
Hau B, de Vallavieille-Pope C (2006) Wind-dispersed diseases. In The Epidemiology of Plant Diseases pp.387–416
Jordan DL, Buol GS, Brandenburg RL, Reisig D, Nboyine J, Abudulai M, ... Rhoads J (2022) Examples of Risk Tools for Pests in Peanut (Arachis hypogaea) Developed for Five Countries Using Microsoft Excel. J Integr Pest Manag 13(1), 20.
Katan J (2017) Diseases caused by soilborne pathogens: biology, management and challenges. Plant Pathol J 305–315.
Khaliq I, Fanning J, Melloy P, Galloway J, Moore K, Burrell D, Sparks AH (2020) The role of conidia in the dispersal of Ascochyta rabiei. Eur J Plant Pathol 158(4):911–924
Lichtemberg PSF, Moreira LM, Zeviani WM, Amorim L, De Mio LL (2022) Dispersal gradient of M. fructicola conidia from peach orchard to an open field. Eur J Plant Pathol 162(1):231–236.
Mahaffee WF, Stoll R (2016) The ebb and flow of airborne pathogens: Monitoring and use in disease management decisions. Phytopathology 106:420–431
Marinelli A, March GJ, Oddino C (2008) Aspectos biológicos y epidemiológicos del carbón del maní (Arachis hypogaea L.) causado por Thecaphora frezii Carranza & Lindquist. Agriscientia 25(1):1–6
McCartney A, West J (2007) Dispersal of fungal spores through the air. Mycology Series 25:65
Munir M, Wang H, Dufault NS, Anco DJ (2020) Early detection of airborne inoculum of Nothopassalora personata in spore trap samples from peanut fields using quantitative PCR. Plants 9:1–15
Norros V, Rannik Ü, Hussein T, Petäjä T, Vesala T, Ovaskainen O (2014) Do small spores disperse further than large spores? Ecology 95(6):1612–1621
Paredes JA (2017) Importancia regional del carbón del maní (Thecaphora frezzii) y efecto de ingredientes activos de fungicidas sobre la intensidad de la enfermedad. MSc thesis, Río Cuarto, Universidad Nacional de Rio Cuarto
Paredes JA, Cazón LI, Bima M, Kearney MI, Nicolino JM, Rago AM (2017) Protocolo de toma de muestras y evaluación para un correcto relevamiento del carbón del maní. In XXXII Jornada Nacional de Maní. General Cabrera, Argentina
Paredes JA, Asinari F, Monguillot JH, Edwards Molina JP, Oddino C, Rago AM (2019) Incidencia del carbón del maní en función del inóculo de Thecaphora frezii en el suelo. In XXXIV Jornada Nacional de Maní. General Cabrera, Argentina
Paredes JA, Cazon LI, Oddino C, Monguillot JH, Rago AM, Molina JE (2021) Efficacy of fungicides against peanut smut in Argentina. Crop Protection 140:105403
Paredes JA, Edwards Molina JP, Cazón LI, Asinari F, Monguillot JH, Morichetti SA, Rago AM, Torres AM (2022a) Relationship between incidence and severity of peanut smut and its regional distribution in the main growing region of Argentina. Trop Plant Pathol 47:233–244
Paredes JA, Pérez IA, Monguillot JH, Asinari F, Rago AM, Torres A (2022b) Incremento de esporas de carbón durante un ciclo de cultivo de maní. In XXXVII Jornada Nacional de Maní. General Cabrera, Argentina
R Core Team (2023) R: a Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Available in https://www.R-project.org. Accessed 6 Mar 2024
Rago AM, Cazón LI, Paredes JA, Molina JPE, Conforto EC, Bisonard EM, Oddino C (2017) Peanut smut: from an emerging disease to an actual threat to Argentine peanut production. Plant Disease 101(3):400–408
Rekah Y, Shtienberg D, Katan J (1999) Spatial distribution and temporal development of Fusarium crown and root rot of tomato and pathogen dissemination in field soil. Phytopathology 89(9):831–839
Rieux A, Soubeyrand S, Bonnot F, Klein EK, Ngando JE, Mehl A et al (2014) Long-distance wind-dispersal of spores in a fungal plant pathogen: estimation of anisotropic dispersal kernels from an extensive field experiment. PLoS One 9(8):e103225
Rogers SL, Atkins SD, West JS (2009) Detection and quantification of airborne inoculum of Sclerotinia sclerotiorum using quantitative PCR. Plant Pathology 58(2):324–331
Schneider CA, Rasband WS, Eliceiri KW (2012) NIH Image to ImageJ: 25 years of image analysis. Nature methods 9(7):671–675
Shukla A, Panchal H, Mishra M, Patel PR, Srivastava HS, Patel P, Shukla AK (2014) Soil moisture estimation using gravimetric technique and FDR probe technique: a comparative analysis. Am Int J Res Formal Appl Nat Sci 8:89–92
Sparks AH, Forbes GA, Hijmans R, Garrett KA (2011) A metamodeling framework for extending the application domain of process-based ecological models. Ecosphere 2(8):1–14
Villari C, Mahaffee WF, Mitchell TK, Pedley KF, Pieck ML, Hand FP (2017) Early detection of airborne inoculum of Magnaporthe oryzae in turfgrass fields using a quantitative LAMP assay. Plant Disease 101(1):170–177
Wickham H, Averick M, Bryan J, Chang W, McGowan LDA, François R, … , Yutani H (2019) Welcome to the Tidyverse. J Open Source Softw 4(43):1686.
Willocquet L, Berud F, Raoux L, Clerjeau M (1998) Effects of wind, relative humidity, leaf movement and colony age on dispersal of conidia of Uncinula necator, causal agent of grape powdery mildew. Plant Pathology 47(3):234–242
Wood SN (2011) Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 73(1):3–36
WeatherSpark. Average Weather in Córdoba, Argentina. https://weatherspark.com/m/28147/5/Average-Weather-in-May-in-C%C3%B3rdoba-Argentina#Figures-WindDirection. Accessed on 16 Jan 2023
WPM (2021) World Peanut Magazine. ISSUE 01. https://camaradelmani.org.ar/wpmagazine-2/. Accessed 16 Jan 2023
Yee TW, Mitchell ND (1991) Generalized additive models in plant ecology. J Veg Sci 2(5):587–602
Acknowledgements
The authors thank Fundación Maní Argentino and the National Institution of Agricultural Technology (INTA) (project [I090]) for the financial support in the experiments. J.A. Paredes holds a Ph.D. fellowship launched by the Argentinean National Scientific and Technical Research Council (CONICET) and INTA, and the USQ Centre for Crop Health Advisory Group’s comments that helped improve the clarity of the manuscript.
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Juan Andres Paredes: Conceptualization, data collection, methodology, formal analysis, investigation, and writing
Adam Sparks: Validation, formal analysis, visualization, and writing
Joaquin Humberto Monguillot: Data collection
Alejandro Mario Rago: Project administration and supervision of the project
Juan Pablo Edwards Molina: Validation, formal analysis, visualization, and writing
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40858_2024_645_MOESM1_ESM.png
Supplementary file1 Wind roses illustration for all the experimental locations (fields), showing wind speed and direction recorded at times slices 90 (0–90 min.), 180 (0–180 min.) and 270 (0–270 min.) from the time of harvest commencing at 0 minutes. The notation [] represents a ‘closed interval’ indicating an interval and is inclusive of both lower and upper values, whereas the notation (] represents ‘half open interval’ indicating an interval is exclusive of the lower value but inclusive of the upper value(PNG 497 KB)
40858_2024_645_MOESM2_ESM.png
Supplementary file2 Diagnostic plots of the generalized additive model’s fit to the data. The plots show no major issues with the fit of the model to the data in the residuals or fitted values and response(PNG 288 KB)
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Paredes, J.A., Sparks, A.H., Monguillot, J.H. et al. Aerial spread of smut spores during peanut harvest. Trop. plant pathol. (2024). https://doi.org/10.1007/s40858-024-00645-5
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DOI: https://doi.org/10.1007/s40858-024-00645-5