Abstract
Rust is an important fungal disease of coffee that causes defoliation of plants, thus affecting production and yield of coffee beans. Management of coffee rust with fungicides should be based on disease incidence at the onset of the epidemic. Previously, sampling has been done in a two-step systematic plan: trees are sampled in a systematic pattern (in the shape of a W covering the field), and then leaves are randomly sampled within each selected tree. Since coffee in Puerto Rico is typically grown in areas with pronounced slopes, these plans require walking diagonally along slopes, which is not feasible for regular monitoring by farmers. In this work we compare different sampling plans in order to find one which can be carried out by the farmers and permits the efficient estimation of the disease incidence. Alternative methods are shown by simulation to be as efficient as the traditionally used plans under patterns of spatial dispersion similar to the ones present in the field.
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Macchiavelli, Raw E. and Rodriguez, Roclo
(1998).
"SAMPLING PLANS FOR MONITORING RUST IN COFFEE TREES,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1287
SAMPLING PLANS FOR MONITORING RUST IN COFFEE TREES
Rust is an important fungal disease of coffee that causes defoliation of plants, thus affecting production and yield of coffee beans. Management of coffee rust with fungicides should be based on disease incidence at the onset of the epidemic. Previously, sampling has been done in a two-step systematic plan: trees are sampled in a systematic pattern (in the shape of a W covering the field), and then leaves are randomly sampled within each selected tree. Since coffee in Puerto Rico is typically grown in areas with pronounced slopes, these plans require walking diagonally along slopes, which is not feasible for regular monitoring by farmers. In this work we compare different sampling plans in order to find one which can be carried out by the farmers and permits the efficient estimation of the disease incidence. Alternative methods are shown by simulation to be as efficient as the traditionally used plans under patterns of spatial dispersion similar to the ones present in the field.
