Leafspot is a fungal disease which occurs frequently in peanut and can cause almost complete defoliation if left untreated. The plant/disease interactions are complex, and it is difficult to predict the effect of altering one component in the system, such as timing of fungicide applications. The objective of this research was to develop a subroutine for the growth model PEANUT to enable the prediction of early leafspot development over a growing season and to predict leafspot's effect on peanut growth. A FORTRAN subroutine was developed for PEANUT which simulated early leafspot progression based on daily spore production, average daily temperature, and the area of healthy leaf tissue. Lesion initiation was calculated using a modified logistic equation, with the latent periods distributed normally in time as a function of ambient temperature. The predicted percent leafspot infection and percent defoliation agreed well with both the calibration and validation data. Total leaf mass, stem mass, and pod mass were also predicted well by the simulation model for 1988, the only year growth data for peanuts subjected to leafspot were available. More data are needed to validate the PEANUT/LFST model during years when yield losses occur due to leafspot. The leafspot subroutine developed in this study expands the ability of the growth and development model PEANUT to predict growth and yield under diseased conditions. In addition, the disease simulator can be used to improve the efficiency of breeding programs in selection for resistance to early leafspot. The next step in model development is to include the effects of fungicide on the spread of leafspot, and to use the model for investigating fungicide control strategies.
|Number of pages||10|
|Journal||Transactions of the American Society of Agricultural Engineers|
|State||Published - Mar 1991|
ASJC Scopus subject areas
- Agricultural and Biological Sciences (miscellaneous)