Dataset on proximate composition, starch content, agronomic performance, and GGE biplot analysis of newly developed maize hybrids across three cropping systems in Indonesia
Description
This dataset contains raw and processed data used for evaluating the grain quality, agronomic performance, and genotype × environment interaction of newly developed maize hybrids under diversified cropping systems. The experiment was conducted as part of a maize breeding program aimed at developing hybrids with superior nutritional quality, high starch content, stable yield, and broad adaptation for tropical agricultural systems. The dataset includes observations from 19 maize hybrids, consisting of 18 experimental hybrids and one commercial check hybrid, evaluated under three contrasting cropping systems: JHT: Sole maize cropping JHK: Maize–soybean intercropping JHU: Maize–sweet potato intercropping. The field experiment was arranged using a randomized complete block design (RCBD) with three replications. Grain samples collected from each genotype × cropping system × replication combination were subjected to proximate composition analysis following AOAC standard procedures. The evaluated grain quality traits included moisture content, ash content, crude fat, crude protein, carbohydrate content, crude fibre, and starch content. Agronomic observations included number of leaves, leaf length, leaf width, chlorophyll content, and grain yield. The dataset also contains processed outputs generated from combined analysis of variance (ANOVA), genotype × environment heatmap analysis, and GGE biplot analysis. These analyses were conducted to characterize environmental discrimination, genotype stability, and specific adaptation across cropping systems. This dataset may be useful for researchers working in maize breeding, grain quality evaluation, food and feed sciences, starch industry development, multi-environment trials, and genotype × environment interaction studies.
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Steps to reproduce
Field data were collected manually using standard agronomic measurement procedures. Chlorophyll content was measured using a SPAD meter. Grain quality analyses were conducted following AOAC standard methods. Combined ANOVA, heatmap visualization, and GGE biplot analyses were performed using PBSTAT-GE 3.6.2 and RStudio with the metan package. Several quality assurance procedures were implemented before data analysis: verification of plot labels, duplicate laboratory measurements, calibration of analytical instruments, screening for missing values and outliers, consistency checking between field and laboratory datasets, validation of ANOVA assumptions and GGE biplot outputs.