diff --git a/map2map/data/fields.py b/map2map/data/fields.py
index e36dc99..873f11e 100644
--- a/map2map/data/fields.py
+++ b/map2map/data/fields.py
@@ -172,10 +172,10 @@ class FieldDataset(Dataset):
             if shift is not None:
                 anchor[d] += torch.randint(shift, (1,))
 
-        in_fields = crop(in_fields, anchor, self.crop, self.pad)
+        in_fields = crop(in_fields, anchor, self.crop, self.pad, self.size)
         tgt_fields = crop(tgt_fields, anchor * self.scale_factor,
                           self.crop * self.scale_factor,
-                          np.zeros_like(self.pad))
+                          np.zeros_like(self.pad), self.size)
 
         in_fields = [torch.from_numpy(f).to(torch.float32) for f in in_fields]
         tgt_fields = [torch.from_numpy(f).to(torch.float32) for f in tgt_fields]
@@ -208,12 +208,20 @@ class FieldDataset(Dataset):
         return in_fields, tgt_fields
 
 
-def crop(fields, anchor, crop, pad):
+def crop(fields, anchor, crop, pad, size):
+    ndim = len(size)
+    assert all(len(x) == ndim for x in [anchor, crop, pad, size]), 'inconsistent ndim'
+
     new_fields = []
     for x in fields:
-        for d, (a, c, (p0, p1)) in enumerate(zip(anchor, crop, pad)):
-            begin, end = a - p0, a + c + p1
-            x = x.take(range(begin, end), axis=1 + d, mode='wrap')
+        ind = [slice(None)]
+        for d, (a, c, (p0, p1), s) in enumerate(zip(anchor, crop, pad, size)):
+            i = np.arange(a - p0, a + c + p1)
+            i %= s
+            i = i.reshape((-1,) + (1,) * (ndim - d - 1))
+            ind.append(i)
+
+        x = x[ind]
 
         new_fields.append(x)