Literature DB >> 18943002

Steps in predicting the relationship of yield on fungicide dose.

N D Paveley, R Sylvester-Bradley, R K Scott, J Craigon, W Day.   

Abstract

ABSTRACT A set of hypothetical steps has been defined, which links fungicide dose to marketable yield, whereby (i) increasing dose decreases symptom area, according to a dose-response curve, (ii) decreased symptom area increases crop green area index (GAI), (iii) increasing GAI increases fractional interception of photosynthetically active radiation, (iv) increased fractional interception increases crop dry matter accumulation, and (v) yield increases, depending on the partitioning of dry matter to the marketable fraction. One equation represented all five steps. By integrating this equation for light interception during the yield forming period and differentiating with respect to the ratio of fungicide cost over yield value, an analytical solution was obtained for the economic optimum dose. Taking published ranges of parameter values for the Septoria tritici wheat pathosystem as an example, yield-response curves and optimum doses were biologically plausible when compared with data from four field experiments. The analytical and empirical results imply that the dose required to optimize economic return will vary substantially between sites, seasons, and cultivars. Sensitivity analyses identified parameters describing specific facets of disease severity, fungicide efficacy, and assimilate partitioning as most influential in determining the dose optimum.

Entities:  

Year:  2001        PMID: 18943002     DOI: 10.1094/PHYTO.2001.91.7.708

Source DB:  PubMed          Journal:  Phytopathology        ISSN: 0031-949X            Impact factor:   4.025


  2 in total

1.  Exploitation of Diversity within Crops-the Key to Disease Tolerance?

Authors:  Adrian C Newton
Journal:  Front Plant Sci       Date:  2016-05-20       Impact factor: 5.753

2.  Durable resistance to crop pathogens: an epidemiological framework to predict risk under uncertainty.

Authors:  Giovanni Lo Iacono; Frank van den Bosch; Chris A Gilligan
Journal:  PLoS Comput Biol       Date:  2013-01-17       Impact factor: 4.475

  2 in total

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