Spatial distribution of head smut (Sporisorium reilianum) of corn in Mexico
Knowledge about the spatial distribution of agricultural diseases is important for the elaboration of integrated pest management programs. Such knowledge allows the exact and adept development of sampling methods, control methods and risk valuation. Despite its importance, there have been few studie...
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| Autores principales: | , , , |
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| Lenguaje: | English |
| Publicado: |
Pontificia Universidad Católica de Chile. Facultad de Agronomía e Ingeniería Forestal
2011
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| Materias: | |
| Acceso en línea: | http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-16202011000200011 |
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| Sumario: | Knowledge about the spatial distribution of agricultural diseases is important for the elaboration of integrated pest management programs. Such knowledge allows the exact and adept development of sampling methods, control methods and risk valuation. Despite its importance, there have been few studies of the spatial distribution of head smut of corn in Mexico. This study aimed to determine the spatial distribution of this disease during the year 2007 in the State of México and to establish its spatial behavior with geostatistical techniques. Five points per plot were chosen for sampling. For each sampling point, 100 plants in a single row were counted, and the plants that presented symptoms of the disease were recorded. The geostatistical analysis used this data to estimate the experimental semivariogram, which was adjusted using theoretical models (spherical, exponential, Gaussian and logarithmic), in the Variowin 2.2 program. The semivariogram was verified by the geostatistical interpolation method or kriging through the cross validation, and cluster maps were subsequently made. The disease was present in 32 locations of 31 counties in Mexico State. All the locations presented a spatial behavior aggregated to the disease. Twenty-four locations were adjusted to the spherical model, seven locations were adjusted to the exponential model, and one location was fit to the Gaussian model. Lastly, it was possible to set aggregation maps in all models. |
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