O intercepto (β0) representa o rendimento potencial (0% sveridade). O coeficiente angular (β1) representa a magnitude do incremento/decrescimento no rendimento (kg/ha) por cada ponto porcentual (pp) de incremento da severidade.

Random Models

Slope - global

## 
## Random-Effects Model (k = 26; tau^2 estimator: ML)
## 
##    logLik   deviance        AIC        BIC       AICc  
## -113.0589    83.5525   230.1177   232.6339   230.6395  
## 
## tau^2 (estimated amount of total heterogeneity): 252.9034 (SE = 83.6136)
## tau (square root of estimated tau^2 value):      15.9029
## I^2 (total heterogeneity / total variability):   91.58%
## H^2 (total variability / sampling variability):  11.88
## 
## Test for Heterogeneity: 
## Q(df = 25) = 192.6181, p-val < .0001
## 
## Model Results:
## 
## estimate       se     zval     pval    ci.lb    ci.ub 
## -24.0635   3.4347  -7.0059   <.0001 -30.7955 -17.3316

Intercept - global

## 
## Random-Effects Model (k = 26; tau^2 estimator: ML)
## 
## tau^2 (estimated amount of total heterogeneity): 521071.0538 (SE = 149678.0291)
## tau (square root of estimated tau^2 value):      721.8525
## I^2 (total heterogeneity / total variability):   98.35%
## H^2 (total variability / sampling variability):  60.76
## 
## Test for Heterogeneity: 
## Q(df = 25) = 1820.3373, p-val < .0001
## 
## Model Results:
## 
##  estimate        se      zval      pval     ci.lb     ci.ub 
## 3541.2686  144.1147   24.5726    <.0001 3258.8090 3823.7282

Incluindo uma variavel moderadora qualitativa: pressão de doença (Resposta em rendimento, sendo Baixa pressão quando a resposta máxima da aplicação de fungicida foi de <30%, ou “Alta pressão” se Ry máxima >30%)

Slope - moderators

## 
## Mixed-Effects Model (k = 26; tau^2 estimator: ML)
## 
##    logLik   deviance        AIC        BIC       AICc  
## -109.8112    77.0572   225.6224   229.3967   226.7134  
## 
## tau^2 (estimated amount of residual heterogeneity):     178.3367 (SE = 62.0017)
## tau (square root of estimated tau^2 value):             13.3543
## I^2 (residual heterogeneity / unaccounted variability): 88.00%
## H^2 (unaccounted variability / sampling variability):   8.34
## 
## Test for Residual Heterogeneity: 
## QE(df = 24) = 122.9217, p-val < .0001
## 
## Test of Moderators (coefficient(s) 1,2): 
## QM(df = 2) = 69.9524, p-val < .0001
## 
## Model Results:
## 
##                estimate      se     zval    pval     ci.lb     ci.ub
## Ry_pres21Low   -15.7775  4.0866  -3.8608  0.0001  -23.7872   -7.7679
## Ry_pres22High  -32.0371  4.3180  -7.4194  <.0001  -40.5003  -23.5739

Intercept - moderators

## 
## Mixed-Effects Model (k = 26; tau^2 estimator: ML)
## 
##    logLik   deviance        AIC        BIC       AICc  
## -206.9530   120.9769   419.9060   423.6803   420.9969  
## 
## tau^2 (estimated amount of residual heterogeneity):     464040.8071 (SE = 133830.1195)
## tau (square root of estimated tau^2 value):             681.2054
## I^2 (residual heterogeneity / unaccounted variability): 98.10%
## H^2 (unaccounted variability / sampling variability):   52.67
## 
## Test for Residual Heterogeneity: 
## QE(df = 24) = 1292.1657, p-val < .0001
## 
## Test of Moderators (coefficient(s) 1,2): 
## QM(df = 2) = 677.9612, p-val < .0001
## 
## Model Results:
## 
##                 estimate        se     zval    pval      ci.lb      ci.ub
## Ry_pres21Low   3772.5637  192.4454  19.6033  <.0001  3395.3776  4149.7498
## Ry_pres22High  3307.7264  193.0182  17.1369  <.0001  2929.4178  3686.0351
b0.est b0.inf b0.sup
Low 3772.56 3395.38 4149.75
High 3307.73 2929.42 3686.04
b1.est b1.inf b1.sup
Low -15.78 -23.79 -7.77
High -32.04 -40.50 -23.57