Abstract: Abstract: Agricultural information systems rely heavily on ontologies to realize intelligent and precision agricultural information services such as disease diagnosis and crop planting management. In the development of agricultural applications, due to the massive and cross domain knowledge required in the agricultural domain, it is impossible to develop applications after the completion of domain ontologies. Due to various reasons, ontologies are constantly modified, augmented, or evolved during the application development. Since ontologies are often tightly interwoven with applications, when changes occur in ontologies, the applications such as query services and decision support services that rely on them will be affected in different ways and may not function correctly. Therefore, it is important to provide mechanisms that fill the gap between ontology evolution management and the change management of knowledge based systems. In this paper, we proposed an approach to analyze and predict change impacts on user interface components when the underlying ontology is changed of its concepts. Our approach avoided the hard and error-prone task to analyze change impacts at the lower level, i.e., source code level. Instead, in our method, the change impact prediction was accomplished at the higher conceptual level. Specifically, we focused on the problem that when ontology concepts were changed, how to determine the affected user interface components of applications without diving into the source codes of the system. Our approach was based on constructing three matrices: the interface component dependency matrix, the ontology concept dependency matrix, and the ontology concept-user interface component correlation matrix, at the conceptual level. The interface component dependency matrix specified the direct reliance between interface components based on the shared interface variables of interface components. The ontology concept dependency matrix described the direct relationships between ontology concepts derived from domain ontology. The ontology concept-user interface component correlation matrix specified the direct dependencies between concepts and interface components. With the three matrices, we provided an algorithm to create the change impact propagation tree for each involved ontology concept. By treating the change impact propagation tree as a logical tree, we were able to calculate the change impact prediction probabilities for each concept and interface component. By setting appropriate prediction thresholds, we can obtain the predicted change impact results. To evaluate our approach of change prediction for interface components relating to ontology concepts, we applied the proposed method to the citrus fertilization and irrigation intelligent decision support system. The citrus decision support system was supported by a citrus fertilization and irrigation ontology, which contained 22 domain concepts. The decision support system had six user interface components. For each of the 22 concepts, we calculated the change impact probabilities for each of the six interface components by the change impact propagation trees. In addition, we obtained the actual data by analyzing the Java source codes of the citrus decision support systems. In order to compare the experiment data with the actual data, we set two empirical prediction thresholds, 5% and 10%, based on the existing related studies for filtering the experiment data. We applied two traditional statistic indicators, precision and recall, to evaluate the results. The final evaluation results showed that given the prediction threshold of 5%, the average precision of change impact prediction for the 22 concepts was 77% and the average recall was 98%. Given the threshold of 10%, the average precision of change impact prediction for the 22 concepts reached 85% and the average recall was 74%. There was a tradeoff between precision and recall, i.e., a higher precision indicated a lower recall. In our cases, the precision and recall rates for the both thresholds indicated satisfied results for our proposed change impact prediction approach. The proposed approach provides a feasible and effective solution to the challenging task of change management problem in agricultural information systems based on ontologies.