diff --git a/pyomo/contrib/piecewise/tests/test_nonlinear_to_pwl.py b/pyomo/contrib/piecewise/tests/test_nonlinear_to_pwl.py
index 29bfd8723a7..48c36c017fb 100644
--- a/pyomo/contrib/piecewise/tests/test_nonlinear_to_pwl.py
+++ b/pyomo/contrib/piecewise/tests/test_nonlinear_to_pwl.py
@@ -19,6 +19,8 @@
 from pyomo.contrib.piecewise.transform.nonlinear_to_pwl import (
     NonlinearToPWL,
     DomainPartitioningMethod,
+    lineartree_available,
+    sklearn_available,
 )
 from pyomo.core.base.expression import _ExpressionData
 from pyomo.core.expr.compare import (
@@ -45,8 +47,6 @@
     SolverFactory('gurobi').available(exception_flag=False)
     and SolverFactory('gurobi').license_is_valid()
 )
-lineartree_available = attempt_import('lineartree')[1]
-sklearn_available = attempt_import('sklearn.linear_model')[1]
 
 
 class TestNonlinearToPWL_1D(unittest.TestCase):
diff --git a/pyomo/contrib/piecewise/transform/nonlinear_to_pwl.py b/pyomo/contrib/piecewise/transform/nonlinear_to_pwl.py
index 6794a2428b6..26b4d533cbb 100644
--- a/pyomo/contrib/piecewise/transform/nonlinear_to_pwl.py
+++ b/pyomo/contrib/piecewise/transform/nonlinear_to_pwl.py
@@ -39,7 +39,7 @@
 from pyomo.common.collections import ComponentMap, ComponentSet
 from pyomo.common.config import ConfigDict, ConfigValue, PositiveInt, InEnum
 from pyomo.common.dependencies import attempt_import
-from pyomo.common.dependencies import numpy as np
+from pyomo.common.dependencies import numpy as np, packaging
 from pyomo.common.enums import IntEnum
 from pyomo.common.modeling import unique_component_name
 from pyomo.core.expr.numeric_expr import SumExpression
@@ -53,7 +53,28 @@
 from pyomo.repn.util import ExprType, OrderedVarRecorder
 
 
-lineartree, lineartree_available = attempt_import('lineartree')
+def _lt_importer():
+    import lineartree
+    import importlib.metadata
+
+    # linear-tree through version 0.3.5 relies on a private method from
+    # scikit-learn.  That method was removed in scikit-learn version
+    # 1.7.0.  We will report linear-tree as "unavailable" if
+    # scikit-learn is "too new."
+    lt_ver = packaging.version.parse(importlib.metadata.version('linear-tree'))
+    if lt_ver <= packaging.version.Version('0.3.5'):
+        import sklearn
+
+        skl_ver = packaging.version.parse(sklearn.__version__)
+        if skl_ver >= packaging.version.Version('1.7.0'):
+            raise ImportError(
+                f"linear-tree<=0.3.5 (found {lt_ver}) is incompatible with "
+                f"scikit-learn>=1.7.0 (found {skl_ver})"
+            )
+    return lineartree
+
+
+lineartree, lineartree_available = attempt_import('lineartree', importer=_lt_importer)
 sklearn_lm, sklearn_available = attempt_import('sklearn.linear_model')
 
 logger = logging.getLogger(__name__)
diff --git a/pyomo/solvers/tests/checks/test_xpress_persistent.py b/pyomo/solvers/tests/checks/test_xpress_persistent.py
index d5ec3a78d4a..65f7f18f45f 100644
--- a/pyomo/solvers/tests/checks/test_xpress_persistent.py
+++ b/pyomo/solvers/tests/checks/test_xpress_persistent.py
@@ -320,6 +320,10 @@ def test_add_column_exceptions(self):
         self.assertRaises(RuntimeError, opt.add_column, m, m.y, -2, [m.c], [1])
 
     @unittest.skipIf(not xpress_available, "xpress is not available")
+    @unittest.skipIf(
+        xpd.xpress_available and xpd.xpress.__version__ == '9.8.0',
+        "Xpress 9.8 always runs global optimizer",
+    )
     def test_nonconvexqp_locally_optimal(self):
         """Test non-convex QP for which xpress_direct should find a locally
         optimal solution."""