Hello!

I just learned about Automatic Differentiation and of course need to give it a go using Julia

I have a function defined as:

```
Wendland(q) = aD*(1-q/2)^4 * (2*q+1)
```

Where aD in my case is 696.

And its derivative w.r.t. q:

```
WendlandDerivative(q) = aD*((5/8)*q*(q-2)^3)
```

By using ForwardDiff and Benchmark package I confirm that I get the same answers but vastly different timings:

```
using BenchmarkTools
using ForwardDiff
aD = 696
Wendland(q) = aD*(1-q/2)^4 * (2*q+1)
WendlandDerivative(q) = aD*((5/8)*q*(q-2)^3)
#Define derivative function using ForwardDiff
df = x -> ForwardDiff.derivative(Wendland, x)
# Benchmarks
@btime WendlandDerivative(u) setup=(u=1.1)
24.397 ns (2 allocations: 32 bytes)
-348.82649999999984
@btime df(u) setup=(u=1.1)
121.287 ns (6 allocations: 160 bytes)
-348.8264999999999
```

Am I doing something wrong; perhaps this is not the intented use case of AD? It perhaps works better on scale (i.e. arrays) than for just producing the derivative function and evaluating it a few times?

Kind regards