Dataset on proximate composition, starch content, agronomic performance, and GGE biplot analysis of newly developed...

This data article presents a dataset on proximate composition, starch content, and agronomic performance of newly developed maize hybrids evaluated across three cropping systems in Indonesia: sole maize cropping, maize-soybean intercropping, and maize-sweet potato intercropping. Because all three cropping systems were established at a single location and growing season, they are treated here as management treatments rather than independent environments, and genotype × cropping-system interaction is analyzed accordingly. Eighteen newly developed maize hybrids and one commercial check hybrid were tested using a randomized complete block design with three replications. The dataset includes seven grain quality traits, namely moisture, ash, fat, protein, carbohydrate (calculated by difference from the other proximate traits), crude fibre, and starch content, as well as four agronomic traits (number of leaves, leaf length, leaf width, and chlorophyll content) and grain yield. Combined analysis of variance showed significant effects of cropping system, genotype, and genotype × cropping-system interaction for most traits, indicating substantial variation among hybrids and cropping systems. Genotype × cropping-system mean values for all traits, heatmap visualizations, and GGE biplot analyses (discriminativeness/representativeness, mean performance/stability, and which-won-where patterns) are provided, and the complete dataset is available in the Mendeley Data repository. This dataset is useful for maize breeders, food and feed researchers, and crop scientists interested in selecting maize hybrids with superior nutritional quality, starch potential, yield performance, and adaptation to diversified tropical cropping systems.