The　crop　variety　yield　prediction　is　widely　used　to　select　new　varieties　and　select　suitable　planting　areas　for　them,　but　it　still　suffers　from　multiple　grand　challenges,　including　sparse　data　and　complex　interaction　mechanisms　between　the　variety　and　different　environments.　Moreover,　accurately　predicting　the　yield　of　corn　varieties　often　faces　the　problem　of　missing　trait　data,　making　it　even　more　difficult.　This　study　reports　a　corn　variety　yield　prediction　model　based　on　generative　adversarial　network　and　graph　attention　network　(YPM-GANGAT)　to　fill　in　the　missing　trait　attributes　and　achieve　corn　variety　yield　prediction　by　establishing　spatial　and　temporal　correlations　for　multi-environment　testing　samples.　Specifically,　we　first　construct　a　variety　spatiotemporal　graph　to　utilize　the　structural　features　of　the　graph　to　effectively　and　fully　express　information　such　as　relationships　between　different　corn　varieties　planting　data　from　different　locations.　Then,　we　make　a　sharedlatent　space　assumption　between　the　structural　features　of　the　graph　and　the　attribute　features　of　each　node　in　the　graph.　This　helps　in　developing　a　distribution　matching　based　on　graph　neural　network　(GNN)　and　adversarial　strategy　to　generate　new　data　to　fill　in　the　missing　attributes.　Finally,　we　predict　the　corn　variety　yield　based　on　the　filled　node　attributes　and　two　different　types　of　GNN　models.　The　experimental　results　show　that　the　proposed　missing　trait　imputation　method　outperforms　the　other　five　common　data　imputation　methods,　and　the　proposed　GNN-based　corn　yield　prediction　model　has　better　performance　as　compared　to　the　other　five　machine　learning-based　models　and　two　deep　learning-based　models.　This　study　demonstrates　a　novel　solution　for　improving　the　accuracy　of　large-scale　corn　variety　yield　prediction.