diff --git a/pandas/core/sparse/array.py b/pandas/core/sparse/array.py
index 5c1cf8c773501..9025f248e26b6 100644
--- a/pandas/core/sparse/array.py
+++ b/pandas/core/sparse/array.py
@@ -595,14 +595,12 @@ def fillna(self, value, downcast=None):
         if issubclass(self.dtype.type, np.floating):
             value = float(value)
 
-        if self._null_fill_value:
-            return self._simple_new(self.sp_values, self.sp_index,
-                                    fill_value=value)
-        else:
-            new_values = self.sp_values.copy()
-            new_values[isnull(new_values)] = value
-            return self._simple_new(new_values, self.sp_index,
-                                    fill_value=self.fill_value)
+        new_values = self.sp_values.copy()
+        new_values[isnull(new_values)] = value
+        fill_value = value if isnull(self.fill_value) else self.fill_value
+
+        return self._simple_new(new_values, self.sp_index,
+                                fill_value=fill_value)
 
     def sum(self, axis=0, *args, **kwargs):
         """
diff --git a/pandas/tests/sparse/test_frame.py b/pandas/tests/sparse/test_frame.py
index 654d12b782f37..5d75616464fa1 100644
--- a/pandas/tests/sparse/test_frame.py
+++ b/pandas/tests/sparse/test_frame.py
@@ -1252,6 +1252,7 @@ def test_from_scipy_correct_ordering(spmatrix):
     tm.skip_if_no_package('scipy')
 
     arr = np.arange(1, 5).reshape(2, 2)
+
     try:
         spm = spmatrix(arr)
         assert spm.dtype == arr.dtype
@@ -1267,6 +1268,33 @@ def test_from_scipy_correct_ordering(spmatrix):
     tm.assert_frame_equal(sdf.to_dense(), expected.to_dense())
 
 
+def test_from_scipy_object_fillna(spmatrix):
+    # GH 16112
+    tm.skip_if_no_package('scipy', max_version='0.19.0')
+
+    arr = np.eye(3)
+    arr[1:, 0] = np.nan
+
+    try:
+        spm = spmatrix(arr)
+        assert spm.dtype == arr.dtype
+    except (TypeError, AssertionError):
+        # If conversion to sparse fails for this spmatrix type and arr.dtype,
+        # then the combination is not currently supported in NumPy, so we
+        # can just skip testing it thoroughly
+        return
+
+    sdf = pd.SparseDataFrame(spm).fillna(-1.0)
+
+    # Returning frame should fill all nan values with -1.0
+    expected = pd.SparseDataFrame({0: {0: 1.0, 1: np.nan, 2: np.nan},
+                                   1: {0: np.nan, 1: 1.0, 2: np.nan},
+                                   2: {0: np.nan, 1: np.nan, 2: 1.0}}
+                                  ).fillna(-1.0)
+
+    tm.assert_frame_equal(sdf.to_dense(), expected.to_dense())
+
+
 class TestSparseDataFrameArithmetic(object):
 
     def test_numeric_op_scalar(self):