R polynomal regression or group values and test between groups + outcome interpreatation

Question

I am trying to model the relation between a scar acquisition rate of a wild population of animals, and I have calculated yearly rates before.

If you see below the plot, it seems to me that rates rise through the middle of the period and than fall again. I have tried to fit a polynomial LM with the code

model1 <- lm(Rate~poly(year, 2, raw = TRUE),data=yearlyratesub) 
summary(model1)                         
model1 

I have plotted using:

g <-ggplot(yearlyratesub, aes(year, Rate)) + geom_point(shape=1) +  geom_smooth(method = lm, formula = y ~ poly(x, 2, raw = TRUE))
g

The model output was:

Call:
lm(formula = Rate ~ poly(year, 2, raw = TRUE), data = yearlyratesub)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.126332 -0.037683 -0.002602  0.053222  0.083503 

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)  
(Intercept)                -8.796e+03  3.566e+03  -2.467   0.0297 *
poly(year, 2, raw = TRUE)1  8.747e+00  3.545e+00   2.467   0.0297 *
poly(year, 2, raw = TRUE)2 -2.174e-03  8.813e-04  -2.467   0.0297 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.0666 on 12 degrees of freedom
Multiple R-squared:  0.3369,    Adjusted R-squared:  0.2264 
F-statistic: 3.048 on 2 and 12 DF,  p-value: 0.08503

How can I enterpret that now? The overall model p value is not significant but the intercept and single slopes are?

Should I rather try another fit than x² or even group the values and test between groups e.g. with an ANOVA? I know the LM has low fit but I guess it's because I have little values and maybe x² might be not it...?

Would be happy about input regarding model and outcome interpretation..