An investigation into inter‐plot interactions, in experiments with mildew on barley, using balanced designs

Abstract

SUMMARY Serial designs, balanced for effects of neighbours, were used in 1975–1977 to investigate interactions between plots of spring barley given different treatments for the control of powdery mildew (Erysiphe graminis DC. f. sp. hordei Mérat). Differences in amounts of disease, between similarly treated plots, could be related largely to the treatments applied to the neighbouring plots and to wind directions. Amounts of disease in plots were usually increased if the upwind neighbour was untreated but untreated plots themselves were also affected by neighbours, having least disease where the upwind neighbour was sprayed early. Differences in mildew due to treatment were reflected in grain yield. Yields also provided evidence of interactions between plots. Interference, as well as altering the average response to treatments, can also contribute to variability in experiment results and lead to substantial losses in efficiency. The yields in 1975 and 1976 provided strong evidence of profiles of fertility. In the analyses, adjustment by covariates, employed to allow for these profiles, gave substantial net gains in accuracy.

Resistance to rice sheath blight (Rhizoctonia solani Kühn) [(teleomorph: Thanatephorus cucumeris (A.B. Frank) Donk.] disease: current status and perspectives

“…This aggregation of ShB is a central feature of rice ShB epidemics (Gou et al 1983;Savary et al 1997Savary et al , 2001Gong and Zhang 2002;Tan and Wang 2002) and a critical element to assess resistance (Zadoks and Schein 1979). Inter-plot interaction (Jenkyn et al 1979), on the other hand, is of little concern in the case of this disease. Experimental plots do not need to be large, since disease progress from an initial source rarely exceeds 1 m even under the most favorable conditions (Savary et al 1995).…”

Analysis of Agricultural Field Trials in the Presence of Outliers and Fertility Jumps

“…A numerical maximisation of p(OIY) is therefore required to obtain 9. in the mildew control trial (Draper and Guttman, 1985;Jenkyn et. al., 1979) (5) is not surprising when one notes its similarities with Restricted Maximum Likelihood (REML) estimation Thompson, 1971: Laird andWare, 1982).…”

Spread of Sclerotinia stem rot of soybean from area and point sources of apothecial inoculum

“…Determination of dispersal, deposition and disease gradients in crops that are hosts to S. sclerotiorum may be useful in identification of inoculum sources (Gregory and Lacey 1964); evaluation of the importance of primary inoculum (Rowe and Powelson 1973); determination of minimum crop field isolation distances from potential inoculum sources (McCartney and Fitt 1985); detection of interplot interference within and interpretation of results from small plot experiments (Jenkyn et al 1979); and proper scheduling of chemical sprays (Legg 1983). In pathosystems that do not involve S. sclerotiorum (Aylor 1987;Jenkyn and Bainbridge 1974;Legg and Powell 1979;McCartney and Bainbridge 1984;McCartney and Fitt 1987;Mundt 1989;Roche et al 1995), the amount of disease (incidence or severity) or the number of pathogen spores has been found to be highest near the inoculum source, to decrease with distance from the source and to generally follow the pattern predicted by the power law model (Gregory 1968), simple exponential model (Kiyosawa and Shiyomi 1972), or modifications of the general exponential model of Lambert et al (1980) (Jeger 1990;Mundt 1989;Mundt and Leonard 1985).…”