This review examines contributions of interdisciplinary (ID) research to understanding interactions between environmental quality, food production and food security. Global patterns of food insecurity and crop production are reviewed in relation to climate, land use and economic changes, as well as potential productivity increases compatible with environmental conservation. Interactions between food production and global processes make food insecurity a complex problem that requires ID analysis at local to global scales. Census and satellite data contribute to understanding of global cropland distribution. Analysis of land-use change exemplifies research between natural and social sciences. Quantitative modelling of global climate change impacts indicates relatively greater potential food insecurity in developing countries. International food security is increasingly interconnected through economic globalization and incentives for increased food production are required. Societies may not be able to expand available cropland without significant environmental risks; enhanced land and water productivity are the major opportunities available to increase food production. This requires renewed efforts in ID work to design and implement sound and efficient agricultural management practices. Models need to be informed by data from field experiments, long-term measurements and watershed monitoring by ground and remote sensing methods. Agricultural intensification may spare natural land but lead to increased pollution and water demand; reconciling conservation and productivity is a critical need. ID work provides many opportunities for synergies including conservation agriculture at the local level, efficient use of inputs, smarter land use taking into account spatial patterns and landscape ecology principles, and improved water management at field, system, watershed and basin levels. Goal-directed ID research is crucial, since producers, practitioners and policy makers should be involved. Geospatial, biotechnological and precision agriculture technologies linked with models can help inform strategies to achieve sustainable food production increases that maintain environmental quality. Implementation also requires ID work to overcome impediments due to human factors and facilitate adoption by farmers.