Dataset on proximate composition, starch content, agronomic performance, and GGE biplot analysis of newly developed maize hybrids across three cropping systems in Indonesia

Abstract

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.

Specifications Table

Subject	Agricultural and Biological Sciences; Plant Breeding; Food Chemistry.
Specific subject area	maize hybrid proximate quality and multi-environment evaluation
Data format	Analyzed data
Type of data	raw, processed, analyzed
Data collection	Raw data were collected from field and laboratory observations of 18 newly developed maize hybrids and one commercial check hybrid evaluated under three cropping systems: sole maize cropping (JHT), maize–soybean intercropping (JHK), and maize–sweet potato intercropping (JHU). The experiment was arranged in a randomized complete block design with three replications. Field observations included number of leaves, leaf length, leaf width, chlorophyll content, and grain yield. Grain yield was recorded from each experimental plot and converted into tons per hectare. Grain samples were collected from each genotype × cropping system × replication combination after harvest. The samples were dried, shelled, ground into fine flour, homogenized, and stored in airtight containers before laboratory analysis. Laboratory observations included moisture content, ash content, crude fat, crude protein, carbohydrate content, crude fibre, and starch content. The processed dataset was analyzed using combined ANOVA, G×E heatmap visualization, and GGE biplot analysis using PBSTAT-GE 3.6.2 and RStudio [1,2]. Instrument: https://apps.pbstat.com/reports/pbstat-ge/ Version RStudio 2025.05.0 + 496 ``Mariposa Orchid'' Release (f0b76cc00df96fe7f0ee687d4bed0423bc3de1f8, 2025-05-04) for windows
Data source location	Samples were collected from the Sanggar Penelitian Latihan dan Pengembangan Pertanian (SPLPP) Universitas Padjadjaran in Arjasari, Bandung, West Java, Indonesia. The location is in latitude of 7.06112 S, longitude 107.64645 E, and altitude 950 m above sea level (asl.).
Data accessibility	Repository name: Mendeley Data Data identification number: DOI: 10.17,632/pd6yghvcf5.1 Direct URL to data: https://data.mendeley.com/datasets/pd6yghvcf5/1
Related research article	-

1. Value of the Data

• •This dataset provides integrated information on proximate composition, starch content, agronomic traits, and yield of newly developed maize hybrids evaluated under sole cropping and intercropping systems [3,4,5].
• •The data are valuable for maize breeders because they support the identification of hybrids with desirable grain quality traits, especially high starch, carbohydrate, protein, and yield performance.
• •The dataset can be reused by researchers in plant breeding, food science, feed science, and crop production to compare hybrid performance across diversified tropical cropping systems [3,4,6,7].
• •The combined ANOVA, G×E heatmaps, and GGE biplot outputs allow users to evaluate genotypic variation, environmental effects, genotype × environment interaction, stability, and specific adaptation of maize hybrids [2,8,9].
• •The dataset supports future studies on multi-trait selection, hybrid stability analysis, and the development of maize cultivars suitable for food, feed, and starch-based industries in tropical environments [[10], [11], [12], [13]].

2. Background

Maize (Zea mays L.) is an important cereal crop for food, feed, and industrial raw materials, particularly as a source of carbohydrate, starch, protein, and energy. In tropical agricultural systems, maize productivity and grain quality are strongly influenced by cropping system and crop-management practices. Intercropping systems, such as maize with soybean or sweet potato, are increasingly promoted to improve land-use efficiency and support sustainable crop production [3,5]. However, the performance of maize hybrids under different cropping systems may vary due to genotype × cropping-system interaction, which reflects management-induced differences in light, water, and nutrient competition rather than independent agroecological environments [3,6,7].

In maize breeding, selection is commonly focused on grain yield and agronomic performance. However, the development of superior hybrids also requires attention to grain quality traits, especially proximate composition and starch content. Traits such as protein, fat, carbohydrate, crude fibre, and starch are important for determining the suitability of maize hybrids for food, feed, and starch-based industries. Therefore, integrating nutritional quality traits with agronomic and yield traits provides a more comprehensive basis for hybrid selection [4,10,11].

Multi-environment evaluation across independent environments is commonly used to identify hybrids with stable and desirable performance, and the GGE biplot approach is widely used for this purpose to visualize genotype performance, environmental discriminating ability, representativeness, mean performance, stability, and specific adaptation [8,9]. In maize, GGE biplot and related stability approaches have been widely applied to evaluate genotype × environment interaction and yield stability across multi-location or multi-year trials [7,[12], [13], [14], [15], [16], [17]]. In the present dataset, the three cropping systems (sole maize, maize–soybean intercropping, and maize–sweet potato intercropping) were established at a single location and season and are therefore management treatments rather than independent environments in the classical multi-environment-trial sense; the GGE biplot framework is nonetheless applied, consistent with its growing use for genotype × management-system interaction, to summarize genotype responses, while the discriminativeness/representativeness interpretation is read here as reflecting cropping-system contrasts rather than sampling of a wider target population of environments. Heatmaps and GGE biplots were used to summarize the response of maize hybrids across the three cropping systems.

3. Data Description

The dataset contains proximate composition, starch content, agronomic traits, and yield data of 18 newly developed maize hybrids evaluated under three cropping systems: sole maize cropping (JHT), maize–soybean intercropping (JHK), and maize–sweet potato intercropping (JHU). The dataset was processed using combined analysis of variance (ANOVA), genotype × environment (G×E) heatmaps, and GGE biplot analysis to describe hybrid performance, environmental response, stability, and specific adaptation. Because the three cropping systems were implemented at a single site and growing season, the genotype × environment (G×E) effects reported below are interpreted as genotype × cropping-system interaction rather than genotype × independent-environment interaction (see Background). Carbohydrate content is calculated by difference rather than measured independently (see Experimental design, materials and methods) and is therefore not statistically independent of the other five proximate traits; this dependency should be kept in mind when interpreting carbohydrate alongside the directly measured traits in the heatmaps and GGE biplots.

Trait codes (T1–T12) used throughout the tables and figures below are defined in Table 4. Genotype × cropping-system mean values underlying all analyses in this article are deposited in full in the Mendeley Data repository (DOI: 10.17632/pd6yghvcf5.1).

Table 4.

Information.

Traits	Information
T1	Moisture Content
T2	Ash Content
T3	Fat Content
T4	Protein Content
T5	Carbohydrate Content
T6	Crude Fiber Content
T7	Starch Content
T8	Number of Leaves
T9	Leaf length
T10	Leaf width
T11	CC1
T12	Yield

Table 1 presents the combined ANOVA for four proximate traits: moisture content, ash content, fat content, and protein content. The environment effect was highly significant for moisture, fat, and protein content, and significant for ash content. Genotype and G×E interaction effects were highly significant for all four traits, indicating that proximate composition differed among hybrids and that hybrid responses varied across the three cropping systems. The low coefficient of variation for moisture and protein content indicates good experimental precision for these traits.

Table 1.

Mean squares from analysis of variance (ANOVA) for proximate traits in maize hybrids evaluated across three different cropping systems.

Source	Df	1	2	3	4
Environment(E)	2	19.2059**	0.1401*	8.1413**	33.2453**
Replication/E	6	0.0146	0.0160	0.0587	0.0025
Genotype (G)	17	1.4852**	0.2606**	2.6870**	1.9467**
GxE	34	2.5039**	0.1692**	2.2741**	1.0347**
Pooled Error	102	0.0106	0.0118	0.0577	0.0107
Total	161	23.2202	0.5977	13.2188	36.2399
Mean		11.3300	1.4900	5.2500	8.5500
CV (%)		0.9094	7.2900	4.5700	1.2100

Df = degrees of freedom, CV = Coefficient of variation, MS = mean square,*Significance at p < 0.05, **Significance at p < 0.01, 1 = moisture content, 2 = ash content, 3 = fat content, and 4 = protein content.

Table 2 summarizes the combined ANOVA for carbohydrate content, crude fibre content, and starch content. Environment, genotype, and G×E interaction effects were highly significant for all three traits. This indicates that carbohydrate, crude fibre, and starch content were strongly affected by cropping system, genetic background, and their interaction. The very low CV values for carbohydrate and starch content suggest high reliability of the dataset for grain quality and starch-related evaluation.

Table 2.

Mean squares from analysis of variance (ANOVA) for starch content and related- traits in maize hybrids evaluated across three different cropping systems.

Source	Df	5	6	7
Environment(E)	2	10.4403**	5.1702**	2308.6857**
Replication/E	6	0.1373	0.0382	0.0182
Genotype (G)	17	7.1752**	0.3078**	47.8869**
GxE	34	8.9373**	0.3733**	91.2009**
Pooled Error	102	0.1035	0.0255	0.0041
Total	161	26.7936	5.9150	2447.7960
Mean		71.9200	2.1400	53.19
CV (%)		0.4473	7.4500	0.1211

Df = degrees of freedom, CV = Coefficient of variation, MS = mean square,*Significance at p < 0.05, **Significance at p < 0.01, 5 = carbohydrate, 6 = crude fibre, and 7 = starch.

Table 3 shows the combined ANOVA for grain yield and four related agronomic traits: number of leaves, leaf length, leaf width, and chlorophyll content. Significant cropping-system, genotype, and G×E (genotype × cropping-system) effects were detected for all traits. These results demonstrate that the agronomic performance and yield of the maize hybrids were influenced by cropping system and that each hybrid showed a different adaptive response under sole cropping and intercropping systems. Yield showed a relatively higher CV than several other traits, indicating greater sensitivity to cropping-system conditions.

Table 3.

Mean squares from analysis of variance (ANOVA) for yield and related traits in maize hybrids evaluated across three different cropping systems.

Source	Df	8	9	10	11	12
Environment(E)	2	1.5432**	996.2461**	7.9761*	1546.5884*	51.9556**
Replication/E	6	4.4143**	31.0533**	1.4040**	254.3330**	4.2118**
Genotype (G)	17	1.5437**	135.2304**	0.5274**	53.4339**	3.7419**
GxE	34	0.4789**	63.3072**	1.1247**	58.3345**	7.2158**
Pooled Error	102	0.0696	6.1894	0.1322	13.9691	0.8288
Total	161	8.0497	1232.0260	11.1644	1926.6590	67.9539
Mean		11.6900	101.7100	10.9400	43.7300	8.78
CV (%)		2.2600	2.4500	3.32	8.5500	10.37

Df = degrees of freedom, CV = Coefficient of variation, MS = mean square,*Significance at p < 0.05, **Significance at p < 0.01, 8 = number of leaves, 9 = Leaf length, 10 = leaf width, and 11 = chlorophyll content,12 = yield.

Fig. 1, Fig. 2, Fig. 3 present G×E heatmaps for the evaluated traits. Fig. 1 displays the heatmaps for moisture, ash, fat, and protein content. These heatmaps visualize the variation in proximate composition among hybrids across JHT, JHK, and JHU. Fig. 2 shows the heatmaps for carbohydrate, crude fibre, and starch content, allowing comparison of grain quality and starch accumulation patterns under different cropping systems. Fig. 3 presents the heatmaps for number of leaves, leaf length, leaf width, chlorophyll content, and yield, showing the agronomic and yield response of each hybrid to the three cropping systems.

Fig. 1.

G × E Heatmap for Moisture Content (a), Ash Content (b), Fat Content (c), Protein Content (d) in three cropping systems. The cropping systems include maize sole cropping (JHT), maize and soybean intercropping (JHK), and maize and sweet potato intercropping (JHU).

Fig. 2.

G × E Heatmap for Carbohydrate Content (e), Crude Fiber Content (f) and Starch Content (g) in three cropping systems. The cropping systems include maize sole cropping (JHT), maize and soybean intercropping (JHK), and maize and sweet potato intercropping (JHU).

Fig. 3.

G × E Heatmap for Number of Leaves (h), Leaf length (i), Leaf width (j), Chlorophyll content (CC1- k), and Yield (l) in three cropping systems. The cropping systems include maize sole cropping (JHT), maize and soybean intercropping (JHK), and maize and sweet potato intercropping (JHU).

Fig. 4, Fig. 5, Fig. 6 show the GGE biplot analysis for discriminativeness versus representativeness of environments. Fig. 4 presents this analysis for moisture, ash, fat, and protein content. Fig. 5 presents the same analysis for carbohydrate, crude fibre, and starch content. Fig. 6 presents the analysis for number of leaves, leaf length, leaf width, chlorophyll content, and yield. These figures help identify which cropping system was most effective in discriminating hybrid performance and which environment was most representative for evaluating each trait.

Fig. 4.

GGE biplot: discriminativeness vs. representativeness for Moisture Content (a), Ash Content (b), Fat Content (c), Protein Content (d) in three cropping systems. The cropping systems include maize sole cropping (JHT), maize and soybean intercropping (JHK), and maize and sweet potato intercropping (JHU).

Fig. 5.

GGE biplot: discriminativeness vs. representativeness for Carbohydrate Content (e), Crude Fiber Content (f) and Starch Content (g) in three cropping systems. The cropping systems include maize sole cropping (JHT), maize and soybean intercropping (JHK), and maize and sweet potato intercropping (JHU).

Fig. 6.

GGE biplot: discriminativeness vs. representativeness for Leaves (h), Leaf length (i), Leaf width (j), Chlorophyll content (CC1- k), and Yield (l) in three cropping systems. The cropping systems include maize sole cropping (JHT), maize and soybean intercropping (JHK), and maize and sweet potato intercropping (JHU).

Fig. 7, Fig. 8, Fig. 9 present the GGE biplot analysis for mean performance versus stability. Fig. 7 describes the mean and stability patterns for moisture, ash, fat, and protein content. Fig. 8 describes the stability of carbohydrate, crude fibre, and starch content. Fig. 9 describes the mean performance and stability of agronomic traits and yield. These figures are useful for identifying hybrids with both desirable trait values and stable performance across cropping systems.

Fig. 7.

GGE biplot: mean vs. stability of Moisture Content (a), Ash Content (b), Fat Content (c), Protein Content (d) in three cropping systems. The cropping systems include maize sole cropping (JHT), maize and soybean intercropping (JHK), and maize and sweet potato intercropping (JHU).

Fig. 8.

GGE biplot: mean vs. stability of Carbohydrate Content (e), Crude Fiber Content (f) and Starch Content (g) in three cropping systems. The cropping systems include maize sole cropping (JHT), maize and soybean intercropping (JHK), and maize and sweet potato intercropping (JHU).

Fig. 9.

GGE biplot: mean vs. stability Number of Leaves (h), Leaf length (i), Leaf width (j), Chlorophyll content (CC1- k), and Yield (l) in three cropping systems. The cropping systems include maize sole cropping (JHT), maize and soybean intercropping (JHK), and maize and sweet potato intercropping (JHU).

Fig. 10, Fig. 11, Fig. 12 present the which-won-where GGE biplots. Fig. 10 identifies the best-performing hybrids for moisture, ash, fat, and protein content in specific cropping systems. Fig. 11 identifies superior hybrids for carbohydrate, crude fibre, and starch content. Fig. 12 identifies the best hybrids for number of leaves, leaf length, leaf width, chlorophyll content, and yield. These biplots support the identification of hybrids with specific adaptation to particular cropping systems as well as hybrids with broader adaptation across environments.

Fig. 10.

GGE biplot: which-won-where of Moisture Content (a), Ash Content (b), Fat Content (c), Protein Content (d) in three cropping systems. The cropping systems include maize sole cropping (JHT), maize and soybean intercropping (JHK), and maize and sweet potato intercropping (JHU).

Fig. 11.

GGE biplot: which-won-where of Carbohydrate Content (e), Crude Fiber Content (f) and Starch Content (g) in three cropping systems. The cropping systems include maize sole cropping (JHT), maize and soybean intercropping (JHK), and maize and sweet potato intercropping (JHU).

Fig. 12.

GGE biplot: which-won-where Number of Leaves (h), Leaf length (i), Leaf width (j), Chlorophyll content (CC1- k), and Yield (l) in three different cropping systems. The cropping systems include maize sole cropping (JHT), maize and soybean intercropping (JHK), and maize and sweet potato intercropping (JHU).

Table 4 provides the trait codes and descriptions used in the dataset and figures. These codes help readers interpret the variables presented in the heatmaps and GGE biplots.

Overall, Table 1, Table 2, Table 3 provide statistical evidence of significant genotype, environment, and G×E effects, while Fig. 1, Fig. 2, Fig. 3, Fig. 4, Fig. 5, Fig. 6, Fig. 7, Fig. 8, Fig. 9, Fig. 10, Fig. 11, Fig. 12 provide visual interpretation of hybrid responses, environmental discrimination, stability, and specific adaptation. Together, these data support the selection of maize hybrids with superior proximate quality, starch content, agronomic performance, and yield stability under diversified tropical cropping systems.

Genotype × cropping-system mean values (± standard deviation, n = 3 replications) for all 12 traits are presented in Table 5 (proximate traits), Table 6 (carbohydrate, crude fibre and starch content), and Table 7 (agronomic traits and yield), complementing the ANOVA summaries in Table 1, Table 2, Table 3 and the full dataset deposited in the Mendeley Data repository.

Table 5.

Genotype × cropping-system mean values (± SD) for proximate traits.

Cropping system	Genotype	Moisture (%)	Ash (%)	Fat (%)	Protein (%)
Sole maize (JHT)	JH1	12.91 ± 0.02	1.49 ± 0.18	5.10 ± 0.02	8.25 ± 0.05
Sole maize (JHT)	JH2	12.71 ± 0.04	1.55 ± 0.16	6.78 ± 0.35	7.69 ± 0.23
Sole maize (JHT)	JH3	11.12 ± 0.09	1.45 ± 0.04	5.66 ± 0.28	8.10 ± 0.06
Sole maize (JHT)	JH4	10.68 ± 0.10	1.66 ± 0.10	6.25 ± 0.07	8.40 ± 0.01
Sole maize (JHT)	JH5	11.51 ± 0.14	1.29 ± 0.03	4.00 ± 0.07	6.71 ± 0.16
Sole maize (JHT)	JH6	13.18 ± 0.06	1.09 ± 0.14	4.88 ± 0.72	6.99 ± 0.02
Sole maize (JHT)	JH7	10.32 ± 0.00	1.28 ± 0.04	6.20 ± 0.00	7.47 ± 0.03
Sole maize (JHT)	JH9	11.40 ± 0.02	1.35 ± 0.08	5.30 ± 0.16	7.83 ± 0.02
Sole maize (JHT)	JH10	11.97 ± 0.07	2.52 ± 0.01	5.64 ± 0.27	9.12 ± 0.07
Sole maize (JHT)	JH11	12.07 ± 0.02	1.53 ± 0.04	5.23 ± 0.15	8.06 ± 0.08
Sole maize (JHT)	JH12	14.11 ± 0.02	1.24 ± 0.04	7.21 ± 0.07	8.23 ± 0.13
Sole maize (JHT)	JH13	13.11 ± 0.05	1.32 ± 0.17	5.71 ± 0.32	7.23 ± 0.04
Sole maize (JHT)	JH14	12.31 ± 0.01	1.21 ± 0.06	7.04 ± 0.19	6.60 ± 0.00
Sole maize (JHT)	JH15	12.60 ± 0.05	1.29 ± 0.01	4.91 ± 0.05	6.58 ± 0.00
Sole maize (JHT)	JH16	12.05 ± 0.17	2.15 ± 0.12	6.49 ± 0.34	7.13 ± 0.08
Sole maize (JHT)	JH17	13.16 ± 0.03	1.07 ± 0.02	4.91 ± 0.35	7.95 ± 0.11
Sole maize (JHT)	JH18	8.26 ± 0.08	1.36 ± 0.17	4.87 ± 0.09	6.63 ± 0.14
Sole maize (JHT)	JH19	12.87 ± 0.11	1.77 ± 0.06	5.81 ± 0.37	8.75 ± 0.01
Maize–soybean intercropping (JHK)	JH1	11.43 ± 0.05	1.80 ± 0.11	3.04 ± 0.03	9.30 ± 0.06
Maize–soybean intercropping (JHK)	JH2	11.11 ± 0.16	1.41 ± 0.05	5.39 ± 0.07	10.17 ± 0.03
Maize–soybean intercropping (JHK)	JH3	10.47 ± 0.38	1.83 ± 0.09	5.62 ± 0.06	10.22 ± 0.05
Maize–soybean intercropping (JHK)	JH4	11.23 ± 0.00	1.50 ± 0.14	5.89 ± 0.17	10.20 ± 0.16
Maize–soybean intercropping (JHK)	JH5	10.95 ± 0.08	1.39 ± 0.05	4.19 ± 0.02	8.71 ± 0.00
Maize–soybean intercropping (JHK)	JH6	10.55 ± 0.04	1.59 ± 0.09	5.41 ± 0.01	8.92 ± 0.02
Maize–soybean intercropping (JHK)	JH7	11.43 ± 0.04	1.59 ± 0.08	5.53 ± 0.16	8.09 ± 0.08
Maize–soybean intercropping (JHK)	JH9	10.79 ± 0.02	1.75 ± 0.38	7.68 ± 0.03	9.31 ± 0.04
Maize–soybean intercropping (JHK)	JH10	10.24 ± 0.17	2.10 ± 0.09	5.78 ± 0.02	9.15 ± 0.04
Maize–soybean intercropping (JHK)	JH11	10.89 ± 0.26	1.30 ± 0.04	4.92 ± 0.08	8.56 ± 0.03
Maize–soybean intercropping (JHK)	JH12	10.44 ± 0.10	1.40 ± 0.05	6.30 ± 0.02	8.73 ± 0.03
Maize–soybean intercropping (JHK)	JH13	11.15 ± 0.06	1.38 ± 0.03	4.50 ± 0.50	9.13 ± 0.04
Maize–soybean intercropping (JHK)	JH14	10.20 ± 0.09	1.71 ± 0.15	5.77 ± 0.02	9.02 ± 0.06
Maize–soybean intercropping (JHK)	JH15	11.61 ± 0.03	1.50 ± 0.10	4.37 ± 0.07	8.54 ± 0.02
Maize–soybean intercropping (JHK)	JH16	11.22 ± 0.00	1.51 ± 0.01	3.89 ± 0.02	8.58 ± 0.02
Maize–soybean intercropping (JHK)	JH17	10.34 ± 0.01	1.44 ± 0.13	5.31 ± 0.02	9.37 ± 0.02
Maize–soybean intercropping (JHK)	JH18	11.59 ± 0.09	1.22 ± 0.01	5.73 ± 0.30	8.75 ± 0.19
Maize–soybean intercropping (JHK)	JH19	11.39 ± 0.14	1.36 ± 0.01	4.37 ± 0.40	9.31 ± 0.07
Maize–sweet potato intercropping (JHU)	JH1	11.12 ± 0.01	1.39 ± 0.10	5.88 ± 0.20	7.83 ± 0.38
Maize–sweet potato intercropping (JHU)	JH2	10.66 ± 0.06	1.48 ± 0.09	4.78 ± 0.37	9.65 ± 0.26
Maize–sweet potato intercropping (JHU)	JH3	11.11 ± 0.07	1.37 ± 0.15	6.62 ± 0.11	8.91 ± 0.02
Maize–sweet potato intercropping (JHU)	JH4	11.02 ± 0.01	1.34 ± 0.02	4.44 ± 0.06	9.62 ± 0.01
Maize–sweet potato intercropping (JHU)	JH5	11.27 ± 0.08	1.61 ± 0.14	5.62 ± 0.12	8.07 ± 0.02
Maize–sweet potato intercropping (JHU)	JH6	11.57 ± 0.11	1.30 ± 0.01	4.08 ± 0.28	8.95 ± 0.01
Maize–sweet potato intercropping (JHU)	JH7	11.49 ± 0.16	1.36 ± 0.12	4.95 ± 0.09	8.86 ± 0.11
Maize–sweet potato intercropping (JHU)	JH9	10.71 ± 0.04	1.47 ± 0.02	4.04 ± 0.02	8.67 ± 0.07
Maize–sweet potato intercropping (JHU)	JH10	10.99 ± 0.11	1.50 ± 0.03	4.40 ± 0.42	8.94 ± 0.06
Maize–sweet potato intercropping (JHU)	JH11	11.43 ± 0.06	1.42 ± 0.00	4.98 ± 0.61	8.29 ± 0.01
Maize–sweet potato intercropping (JHU)	JH12	10.87 ± 0.06	1.44 ± 0.09	5.63 ± 0.19	8.12 ± 0.07
Maize–sweet potato intercropping (JHU)	JH13	10.78 ± 0.01	1.54 ± 0.07	5.17 ± 0.38	9.31 ± 0.05
Maize–sweet potato intercropping (JHU)	JH14	10.38 ± 0.06	1.77 ± 0.29	4.90 ± 0.00	10.11 ± 0.04
Maize–sweet potato intercropping (JHU)	JH15	10.54 ± 0.03	1.40 ± 0.06	3.55 ± 0.29	9.17 ± 0.05
Maize–sweet potato intercropping (JHU)	JH16	11.19 ± 0.04	1.24 ± 0.04	4.90 ± 0.08	8.12 ± 0.22
Maize–sweet potato intercropping (JHU)	JH17	10.77 ± 0.25	1.31 ± 0.10	4.22 ± 0.23	9.12 ± 0.07
Maize–sweet potato intercropping (JHU)	JH18	11.37 ± 0.10	1.42 ± 0.02	5.51 ± 0.02	8.81 ± 0.03
Maize–sweet potato intercropping (JHU)	JH19	11.38 ± 0.05	1.61 ± 0.13	4.42 ± 0.10	9.13 ± 0.03

Table 6.

Genotype × cropping-system mean values (± SD) for carbohydrate, crude fibre and starch content.

Cropping system	Genotype	Carbohydrate (%)	Crude Fibre (%)	Starch (%)
Sole maize (JHT)	JH1	70.33 ± 0.22	2.30 ± 0.05	44.87 ± 0.05
Sole maize (JHT)	JH2	69.42 ± 0.80	2.40 ± 0.06	40.72 ± 0.02
Sole maize (JHT)	JH3	72.27 ± 0.07	1.97 ± 0.11	31.85 ± 0.11
Sole maize (JHT)	JH4	71.73 ± 0.18	2.34 ± 0.15	44.37 ± 0.03
Sole maize (JHT)	JH5	74.99 ± 0.03	2.07 ± 0.20	51.67 ± 0.04
Sole maize (JHT)	JH6	71.86 ± 0.75	2.10 ± 0.02	52.96 ± 0.04
Sole maize (JHT)	JH7	73.56 ± 0.07	2.36 ± 0.16	45.24 ± 0.03
Sole maize (JHT)	JH9	72.65 ± 0.13	2.05 ± 0.15	51.13 ± 0.03
Sole maize (JHT)	JH10	69.12 ± 0.28	2.66 ± 0.02	44.95 ± 0.01
Sole maize (JHT)	JH11	71.45 ± 0.16	2.14 ± 0.08	41.85 ± 0.05
Sole maize (JHT)	JH12	66.89 ± 0.18	1.65 ± 0.02	50.77 ± 0.10
Sole maize (JHT)	JH13	70.65 ± 0.18	1.78 ± 0.01	42.17 ± 0.11
Sole maize (JHT)	JH14	71.12 ± 0.13	2.20 ± 0.11	48.56 ± 0.03
Sole maize (JHT)	JH15	72.80 ± 0.14	2.08 ± 0.11	47.71 ± 0.11
Sole maize (JHT)	JH16	70.52 ± 0.52	2.55 ± 0.35	49.49 ± 0.18
Sole maize (JHT)	JH17	70.92 ± 0.30	1.46 ± 0.13	44.54 ± 0.03
Sole maize (JHT)	JH18	78.14 ± 0.21	2.40 ± 0.25	45.69 ± 0.12
Sole maize (JHT)	JH19	68.90 ± 0.29	2.43 ± 0.01	46.29 ± 0.02
Maize–soybean intercropping (JHK)	JH1	72.95 ± 0.09	1.53 ± 0.12	47.40 ± 0.07
Maize–soybean intercropping (JHK)	JH2	70.54 ± 0.34	1.83 ± 0.09	53.25 ± 0.04
Maize–soybean intercropping (JHK)	JH3	70.63 ± 0.49	1.83 ± 0.01	55.52 ± 0.07
Maize–soybean intercropping (JHK)	JH4	69.77 ± 0.48	1.73 ± 0.16	51.89 ± 0.04
Maize–soybean intercropping (JHK)	JH5	73.41 ± 0.13	1.54 ± 0.05	57.66 ± 0.04
Maize–soybean intercropping (JHK)	JH6	72.28 ± 0.05	1.79 ± 0.01	57.78 ± 0.07
Maize–soybean intercropping (JHK)	JH7	71.89 ± 0.21	1.86 ± 0.11	58.80 ± 0.11
Maize–soybean intercropping (JHK)	JH9	69.16 ± 0.43	2.14 ± 0.11	58.48 ± 0.08
Maize–soybean intercropping (JHK)	JH10	71.56 ± 0.27	2.26 ± 0.21	57.43 ± 0.07
Maize–soybean intercropping (JHK)	JH11	73.00 ± 0.33	1.59 ± 0.08	56.25 ± 0.07
Maize–soybean intercropping (JHK)	JH12	71.91 ± 0.06	2.02 ± 0.14	59.28 ± 0.04
Maize–soybean intercropping (JHK)	JH13	72.44 ± 0.64	1.83 ± 0.09	53.53 ± 0.07
Maize–soybean intercropping (JHK)	JH14	72.13 ± 0.12	2.23 ± 0.11	59.39 ± 0.07
Maize–soybean intercropping (JHK)	JH15	72.46 ± 0.09	1.86 ± 0.12	57.92 ± 0.11
Maize–soybean intercropping (JHK)	JH16	73.38 ± 0.05	1.33 ± 0.19	54.15 ± 0.07
Maize–soybean intercropping (JHK)	JH17	72.35 ± 0.15	1.49 ± 0.02	45.53 ± 0.07
Maize–soybean intercropping (JHK)	JH18	71.20 ± 0.36	2.04 ± 0.08	54.22 ± 0.07
Maize–soybean intercropping (JHK)	JH19	72.11 ± 0.16	1.87 ± 0.00	59.23 ± 0.07
Maize–sweet potato intercropping (JHU)	JH1	72.39 ± 0.49	2.76 ± 0.15	64.18 ± 0.04
Maize–sweet potato intercropping (JHU)	JH2	72.16 ± 0.13	2.67 ± 0.09	56.61 ± 0.04
Maize–sweet potato intercropping (JHU)	JH3	70.60 ± 0.33	2.40 ± 0.10	64.92 ± 0.03
Maize–sweet potato intercropping (JHU)	JH4	72.22 ± 0.04	2.53 ± 0.16	64.33 ± 0.04
Maize–sweet potato intercropping (JHU)	JH5	72.00 ± 0.33	2.63 ± 0.06	46.30 ± 0.11
Maize–sweet potato intercropping (JHU)	JH6	72.58 ± 0.12	1.91 ± 0.03	58.64 ± 0.11
Maize–sweet potato intercropping (JHU)	JH7	71.84 ± 0.34	2.46 ± 0.47	56.62 ± 0.03
Maize–sweet potato intercropping (JHU)	JH9	73.83 ± 0.07	2.15 ± 0.08	54.75 ± 0.07
Maize–sweet potato intercropping (JHU)	JH10	72.81 ± 0.65	2.63 ± 0.11	53.78 ± 0.04
Maize–sweet potato intercropping (JHU)	JH11	72.40 ± 0.69	1.92 ± 0.04	66.94 ± 0.04
Maize–sweet potato intercropping (JHU)	JH12	72.62 ± 0.04	2.26 ± 0.11	56.04 ± 0.04
Maize–sweet potato intercropping (JHU)	JH13	71.90 ± 0.27	2.98 ± 0.02	61.44 ± 0.07
Maize–sweet potato intercropping (JHU)	JH14	71.64 ± 0.41	2.66 ± 0.25	54.35 ± 0.11
Maize–sweet potato intercropping (JHU)	JH15	74.11 ± 0.32	2.23 ± 0.15	51.93 ± 0.04
Maize–sweet potato intercropping (JHU)	JH16	73.14 ± 0.39	1.75 ± 0.20	65.14 ± 0.08
Maize–sweet potato intercropping (JHU)	JH17	73.29 ± 0.10	3.27 ± 0.07	52.30 ± 0.07
Maize–sweet potato intercropping (JHU)	JH18	71.44 ± 0.15	2.95 ± 0.61	66.80 ± 0.03
Maize–sweet potato intercropping (JHU)	JH19	72.01 ± 0.12	1.73 ± 0.08	54.54 ± 0.04

Table 7.

Genotype × cropping-system mean values (± SD) for agronomic traits and yield.

Cropping system	Genotype	No. of Leaves	Leaf Length (cm)	Leaf Width (cm)	Chlorophyll (CC1)	Yield (t/ha)
Sole maize (JHT)	JH1	11.01 ± 0.23	102.58 ± 1.59	11.50 ± 0.00	45.56 ± 11.82	7.19 ± 0.63
Sole maize (JHT)	JH2	11.89 ± 0.02	119.88 ± 7.14	11.15 ± 0.11	48.80 ± 11.04	9.83 ± 2.02
Sole maize (JHT)	JH3	11.50 ± 0.45	104.96 ± 0.51	10.80 ± 0.02	47.91 ± 12.25	8.45 ± 0.63
Sole maize (JHT)	JH4	11.93 ± 0.01	95.96 ± 1.37	11.64 ± 0.18	50.15 ± 6.13	7.31 ± 0.50
Sole maize (JHT)	JH5	11.35 ± 0.03	105.33 ± 0.58	11.60 ± 0.64	42.72 ± 2.42	9.33 ± 0.50
Sole maize (JHT)	JH6	11.33 ± 0.41	109.25 ± 0.00	10.95 ± 0.73	46.22 ± 8.76	7.25 ± 1.07
Sole maize (JHT)	JH7	10.94 ± 0.22	99.58 ± 1.15	10.68 ± 0.64	45.41 ± 3.87	10.07 ± 0.62
Sole maize (JHT)	JH9	11.53 ± 0.41	106.92 ± 1.88	10.90 ± 0.36	46.88 ± 7.90	8.82 ± 0.50
Sole maize (JHT)	JH10	12.19 ± 0.20	105.67 ± 3.61	12.23 ± 0.27	44.19 ± 0.57	7.56 ± 0.00
Sole maize (JHT)	JH11	11.12 ± 0.36	102.17 ± 0.72	10.65 ± 0.43	50.60 ± 4.90	6.93 ± 0.88
Sole maize (JHT)	JH12	11.04 ± 0.38	114.00 ± 2.60	11.14 ± 0.00	57.10 ± 7.30	8.32 ± 0.50
Sole maize (JHT)	JH13	11.68 ± 0.15	93.50 ± 1.30	11.23 ± 0.63	49.78 ± 2.10	6.30 ± 0.00
Sole maize (JHT)	JH14	12.14 ± 0.39	112.08 ± 0.72	11.48 ± 0.20	46.77 ± 2.17	8.95 ± 0.38
Sole maize (JHT)	JH15	11.61 ± 0.03	107.04 ± 1.66	12.19 ± 0.19	57.69 ± 0.97	10.46 ± 0.38
Sole maize (JHT)	JH16	12.07 ± 0.28	102.25 ± 0.43	11.62 ± 0.26	33.34 ± 3.94	7.69 ± 1.13
Sole maize (JHT)	JH17	11.55 ± 0.32	107.50 ± 1.73	11.10 ± 0.38	38.19 ± 4.23	9.01 ± 1.70
Sole maize (JHT)	JH18	11.45 ± 0.30	104.54 ± 3.97	12.43 ± 0.17	43.47 ± 4.37	7.75 ± 1.70
Sole maize (JHT)	JH19	10.86 ± 0.08	95.62 ± 4.11	11.17 ± 0.13	53.71 ± 12.75	6.43 ± 0.88
Maize–soybean intercropping (JHK)	JH1	11.10 ± 0.50	99.22 ± 2.23	10.30 ± 0.50	45.21 ± 2.83	11.35 ± 0.00
Maize–soybean intercropping (JHK)	JH2	12.40 ± 0.80	101.77 ± 7.07	11.50 ± 0.10	48.65 ± 5.43	9.46 ± 0.63
Maize–soybean intercropping (JHK)	JH3	12.00 ± 1.00	103.44 ± 3.56	11.60 ± 0.40	44.15 ± 3.55	8.19 ± 0.63
Maize–soybean intercropping (JHK)	JH4	12.30 ± 0.50	91.90 ± 4.20	11.20 ± 0.00	50.83 ± 7.41	8.82 ± 0.00
Maize–soybean intercropping (JHK)	JH5	11.20 ± 0.20	103.18 ± 0.65	10.20 ± 0.00	49.31 ± 0.77	8.82 ± 1.26
Maize–soybean intercropping (JHK)	JH6	12.00 ± 1.00	112.48 ± 0.48	10.80 ± 0.60	45.66 ± 6.52	10.09 ± 1.26
Maize–soybean intercropping (JHK)	JH7	10.60 ± 0.80	99.20 ± 0.70	11.30 ± 0.10	49.67 ± 4.05	10.09 ± 0.00
Maize–soybean intercropping (JHK)	JH9	12.00 ± 1.00	102.72 ± 2.92	11.20 ± 0.40	43.89 ± 0.35	6.93 ± 1.89
Maize–soybean intercropping (JHK)	JH10	12.50 ± 1.10	101.82 ± 5.02	10.40 ± 0.60	39.05 ± 2.15	10.72 ± 1.89
Maize–soybean intercropping (JHK)	JH11	11.70 ± 1.10	102.37 ± 1.00	11.40 ± 0.60	51.06 ± 0.22	8.82 ± 1.26
Maize–soybean intercropping (JHK)	JH12	11.40 ± 0.60	109.95 ± 2.55	11.30 ± 0.10	41.98 ± 2.94	11.35 ± 0.00
Maize–soybean intercropping (JHK)	JH13	11.80 ± 0.80	97.53 ± 2.23	10.40 ± 0.20	44.02 ± 2.44	9.46 ± 1.89
Maize–soybean intercropping (JHK)	JH14	12.50 ± 0.70	110.28 ± 3.62	10.80 ± 0.20	46.52 ± 0.34	10.09 ± 1.26
Maize–soybean intercropping (JHK)	JH15	11.50 ± 0.50	106.63 ± 2.60	10.00 ± 1.00	48.13 ± 1.09	7.56 ± 0.00
Maize–soybean intercropping (JHK)	JH16	12.30 ± 1.30	101.05 ± 0.68	10.00 ± 0.00	43.79 ± 1.57	10.72 ± 0.63
Maize–soybean intercropping (JHK)	JH17	12.10 ± 1.10	105.45 ± 4.55	12.30 ± 0.30	46.84 ± 2.92	11.98 ± 0.63
Maize–soybean intercropping (JHK)	JH18	12.00 ± 1.20	107.70 ± 2.80	10.10 ± 0.70	50.57 ± 0.01	11.35 ± 1.26
Maize–soybean intercropping (JHK)	JH19	11.80 ± 0.60	103.65 ± 2.65	10.50 ± 0.50	47.44 ± 2.46	12.61 ± 1.26
Maize–sweet potato intercropping (JHU)	JH1	11.50 ± 0.30	91.80 ± 0.20	11.00 ± 0.40	39.83 ± 3.31	8.82 ± 0.00
Maize–sweet potato intercropping (JHU)	JH2	12.10 ± 0.10	91.40 ± 0.40	11.30 ± 0.30	31.79 ± 1.69	6.30 ± 0.00
Maize–sweet potato intercropping (JHU)	JH3	11.80 ± 0.20	96.50 ± 3.10	10.90 ± 0.50	39.43 ± 6.69	7.56 ± 0.00
Maize–sweet potato intercropping (JHU)	JH4	11.90 ± 0.70	90.40 ± 0.40	10.60 ± 0.20	40.28 ± 0.66	7.56 ± 0.00
Maize–sweet potato intercropping (JHU)	JH5	11.20 ± 0.20	96.30 ± 0.10	10.40 ± 1.00	36.13 ± 0.33	6.30 ± 0.00
Maize–sweet potato intercropping (JHU)	JH6	11.40 ± 0.20	107.30 ± 3.90	10.50 ± 0.70	37.51 ± 0.41	10.09 ± 0.00
Maize–sweet potato intercropping (JHU)	JH7	11.00 ± 0.00	95.20 ± 2.80	10.60 ± 0.00	39.24 ± 10.56	10.09 ± 0.00
Maize–sweet potato intercropping (JHU)	JH9	11.60 ± 0.40	93.70 ± 2.70	10.90 ± 0.10	39.23 ± 5.81	8.82 ± 0.00
Maize–sweet potato intercropping (JHU)	JH10	11.60 ± 0.00	96.40 ± 2.00	10.50 ± 0.30	35.99 ± 5.23	7.56 ± 0.00
Maize–sweet potato intercropping (JHU)	JH11	13.00 ± 0.20	97.40 ± 1.40	10.10 ± 0.50	33.36 ± 5.82	7.56 ± 1.26
Maize–sweet potato intercropping (JHU)	JH12	10.80 ± 0.60	93.50 ± 2.30	10.30 ± 0.10	38.79 ± 3.81	8.82 ± 0.00
Maize–sweet potato intercropping (JHU)	JH13	10.40 ± 0.20	101.30 ± 4.30	10.80 ± 0.20	34.46 ± 5.96	11.35 ± 0.00
Maize–sweet potato intercropping (JHU)	JH14	12.10 ± 0.30	93.67 ± 0.08	9.80 ± 0.60	35.36 ± 1.44	6.30 ± 0.00
Maize–sweet potato intercropping (JHU)	JH15	12.50 ± 0.30	98.30 ± 1.50	11.00 ± 0.60	42.96 ± 3.90	8.07 ± 1.76
Maize–sweet potato intercropping (JHU)	JH16	12.20 ± 0.40	99.90 ± 1.30	10.60 ± 1.20	42.90 ± 1.56	7.56 ± 0.00
Maize–sweet potato intercropping (JHU)	JH17	12.00 ± 0.20	96.90 ± 0.30	10.40 ± 0.40	35.70 ± 2.30	6.93 ± 0.63
Maize–sweet potato intercropping (JHU)	JH18	12.10 ± 0.10	103.80 ± 1.80	11.50 ± 0.10	39.52 ± 4.10	10.09 ± 1.26
Maize–sweet potato intercropping (JHU)	JH19	11.90 ± 0.10	99.20 ± 4.60	9.70 ± 0.30	33.61 ± 1.89	8.19 ± 3.15

4. Experimental Design, Materials and methods

4.1. Experimental design

The experiment was arranged in a randomized complete block design with three replications in each cropping system. The treatment structure consisted of 18 maize hybrid genotypes evaluated under three cropping systems. Each treatment combination was assigned to a plot, and all genotypes were evaluated under each cropping system.

4.2. Plant materials

Eighteen maize hybrid genotypes and one commercial check hybrid were used in this study. These genotypes were developed through hybridization of elite inbred lines derived from commercial sources and the maize breeding program at Universitas Padjadjaran. Several genotypes were derived from gamma irradiation-induced mutant lines to increase genetic variability and adaptability across environments. The observed traits included proximate composition, starch content, agronomic traits, chlorophyll content, and yield. The pedigree and origin of the evaluated maize hybrids are presented in Table 8.

Table 8.

Pedigree and origin of maize hybrids used in the study.

Code	Parental			Pedigree
	Male		Female
JH1	10,004	X	10,005	Male is derived from DR 4 and female is from DR 5
JH2	10,004	X	10,028	Male is derived from DR 4 and female is from BR 154
JH3	10,004	X	10,065	Male is derived from DR 4 and female is from MDR. 12.3.2
JH4	10,004	X	10,082	Male is derived from DR 4 and female is from MDR. 18.8.1
JH5	10,005	X	10,008	Male is derived from DR 5 and female is from DR 8.
JH6	10,005	X	10,056	Male is derived from DR 5, and female is from MDR 8.5.3
JH7	10,008	X	10,028	Male is derived from DR 8 and female is from BR 154
JH9	10,008	X	10,095	Male is derived from DR 8 and female is from MBR.153.15.1
JH10	10,037	X	10,082	Male is derived from MDR. 3.1.2 and female is from MDR 18.8.1
JH11	10,065	X	10,067	Male is derived from MDR 12.3.2 and female is from MDR 14.2.1
JH12	10,065	X	10,076	Male is derived from MDR 12.3.2 and female is from MDR. 16.6.14
JH13	10,065	X	10,082	Male is derived from MDR 12.3.2 and female is from MDR 18.8.1
JH14	10,065	X	10,084	Male is derived from MDR 12.3.2 and female is from MBR 153.3.2
JH15	10,065	X	10,095	Male is derived from MDR 12.3.2 and female is from MBR 153.15.1
JH16	10,076	X	10,082	Male is derived from MDR 16.6.14 and female is from MDR 18.8.1
JH17	10,082	X	10,056	Male is derived from MDR 18.8.1 and female is from MDR 8.5.3
JH18	10,091	X	10,094	Male is derived from MBR 153.10.2 and female is from MBR 153.14.1
JH19	Pertiwi 3			Commercial check Hybrid

4.3. Proximate analysis

Proximate analysis was conducted to determine the chemical composition of maize grain samples, including moisture, ash, crude fat, crude protein, crude fibre, carbohydrate, and starch content. Prior to analysis, harvested grain samples from each genotype × cropping system × replication combination were dried, ground into fine flour, homogenized, and stored in airtight containers.

4.4. Moisture content

Moisture content was determined using the oven-drying method based on AOAC 925.04. Approximately 10 g of ground maize sample was dried at 105 °C for 24 h. Moisture content was calculated gravimetrically based on weight loss after drying and expressed as a percentage.

4.5. Crude protein content

Crude protein content was determined using the Kjeldahl method based on AOAC 981.10. Approximately 1 g of sample was digested with one Kjeldahl catalyst tablet and 10 mL H₂SO₄ at 420 °C for 2 h. The digest was alkalized using 30% NaOH, distilled into 0.1 M HCl, and titrated with 0.25 M NaOH. Nitrogen content was converted into crude protein using a conversion factor of 6.25.

4.6. Crude fat content

Crude fat content was determined using the Soxhlet extraction method. A moisture-free maize flour sample was extracted with petroleum ether for 2 h. The solvent was evaporated, and the residue was dried at 105 °C overnight. Crude fat content was calculated gravimetrically and expressed as a percentage.

4.7. Ash content

Ash content was determined using a modified AOAC 938.08 method. The moisture- and fat-free sample was incinerated in a muffle furnace at 500 °C for 12 h. The remaining inorganic residue was weighed, and ash content was expressed as a percentage of dry sample weight.

4.8. Crude fibre content

Crude fibre content was determined following the AOAC procedure. The sample was sequentially digested under acidic and alkaline conditions to remove soluble components. The remaining residue was dried, weighed, ashed, and calculated as crude fibre content. The result was expressed as a percentage of dry matter.

4.9. Carbohydrate content

Carbohydrate content was calculated by difference using the following formula:

$$"\text{Carbohydrate}\text{content}"=100-\left("\text{moisture}"+"\text{ash}"+"\text{crude}\text{fat}"+"\text{crude}\text{protein}"+"\text{crude}\text{fibre}"\right)$$

The value was expressed as a percentage of grain dry matter or sample weight basis according to the proximate data format.

4.10. Starch content

Starch content was determined using the anthrone colorimetric method following acid hydrolysis, based on the procedure of Clegg [20]. Approximately 100 mg of ground maize flour (particle size < 1 mm) was hydrolysed with hydrochloric acid in a water bath, and the hydrolysate was neutralized with sodium hydroxide and filtered. An aliquot of the filtrate was reacted with anthrone reagent, and the absorbance was measured spectrophotometrically against a glucose standard calibration curve. Glucose concentration was converted to starch content using a conversion factor of 0.9, accounting for the molecular weight difference between glucose and starch, and expressed as a percentage of dry matter.

4.11. Data analysis

The statistical model used was:

$$Y_{ijk}=\mu +E_i+R{\left(E\right)}_{j\left(i\right)}+G_k+G{E}_{ik}+{\epsilon }_{ijk}$$

where:

• •$Y_{ijk}$= observed value of genotype $k$in environment/cropping system $i$and replication $j$;
• •$\mu$= overall mean;
• •$E_i$= effect of the $i$-th environment or cropping system;
• •$R{\left(E\right)}_{j\left(i\right)}$= effect of the $j$-th replication nested within the $i$-th environment;
• •$G_k$= effect of the $k$-th genotype;
• •$G{E}_{ik}$= genotype × environment interaction effect;
• •${\epsilon }_{ijk}$= experimental error.

Mean squares, significance levels, trait means, and coefficients of variation were calculated for each trait. Heatmaps were used to visualize genotype × environment responses across the three cropping systems. Combined ANOVA and multi-environment analysis are commonly used to partition genotype, environment (here, cropping system), and genotype × cropping-system interaction effects in crop performance datasets [6,7,12,13]. Multi-environment analysis and graphical visualization can also be supported using plant breeding statistical tools and R-based packages [1,2].

4.12. GGE biplot analysis

GGE biplot analysis was used to visualize genotype performance and genotype × cropping-system interaction for each trait. As noted in the Background and Data Description, the three cropping systems evaluated here are management treatments at a single site rather than independent multi-environment-trial environments; the biplot is applied by treating each cropping system as one ``environment'' axis, so its interpretation should be understood in that management-treatment sense. The analysis focused on three interpretations: discriminativeness versus representativeness of cropping systems, mean performance versus stability of genotypes, and which-won-where patterns. This approach has been widely used for interpreting multi-environment trial data and identifying stable or specifically adapted genotypes [8,9]. In maize, GGE biplot analysis has been applied to evaluate yield stability, adaptability, and genotype × environment interaction across diverse testing environments [7,[12], [13], [14], [15], [16], [17]].

The GGE biplot model was:

$$Y_{ij}-{\bar{Y}}_j={\lambda }_1{\xi }_{i1}{\eta }_{j1}+{\lambda }_2{\xi }_{i2}{\eta }_{j2}+{\epsilon }_{ij}$$

where:

• •$Y_{ij}$= mean performance of genotype $i$in environment $j$;
• •${\bar{Y}}_j$= mean performance of all genotypes in environment $j$;
• •${\lambda }_1$and ${\lambda }_2$= singular values for the first and second principal components;
• •${\xi }_{i1}$and ${\xi }_{i2}$= PC1 and PC2 scores for genotype $i$;
• •${\eta }_{j1}$and ${\eta }_{j2}$= PC1 and PC2 scores for environment $j$;
• •${\epsilon }_{ij}$= residual of the model.

The first two principal components were used to construct the biplots. Environments with longer vectors were considered more discriminating, while environments with smaller angles to the average environment axis were considered more representative. Genotypes located near the ideal genotype position were considered to have desirable mean performance and stability. With only three cropping systems, PC1 and PC2 together necessarily capture close to 100% of the genotype × cropping-system variation, so the biplots shown here should be read as a near-complete but low-resolution decomposition of a small dataset; complementary quantitative stability statistics (e.g., AMMI stability value, Shukla's stability variance, or Wricke's ecovalence) would strengthen this interpretation and are recommended for future multi-location extensions of this dataset (see Limitations)..

4.13. Cropping system management and agronomic data collection

The field experiment was conducted during one cropping season at Arjasari, Bandung, West Java, Indonesia, from mid-February to mid-June 2023 (Table 9, Table 10). Three maize cropping systems were evaluated: sole maize (JHT), maize–soybean intercropping (JHK), and maize–sweet potato intercropping (JHU). Because the three cropping systems were established at the same location and during the same growing season, they were treated as cropping-system management treatments rather than independent environments in the classical multi-environment trial sense. Accordingly, the interaction evaluated in this dataset represents genotype × cropping-system interaction and describes differential hybrid responses to the three management systems.

Table 9.

Cropping season, rainfall, temperature, relative humidity, type of agroclimate.

Cropping Season	Month	Temperature (°C)	Relative Humidity (%)	Rainfall (mm/ month)	Type of Agroclimate
Season 1 (Rainy to dry season)	February	22.50	81.49	443.40	AII2. Wet; ; dry month per year 3–7; wet month 5–9; crop index 2; pH 6; ultisol type of soil.
	March	23.00	86.06	373.6
	April	23.50	84.26	204.5
	May	23.5	84.01	247.7
	June	23.5	85.54	101.0
	Mean	23.20	84.27	274.04
Season 2 (Dry to rainy season)	July	23.50	78.56	76.50
	August	24.00	67.84	122.20
	September	24.00	79.34	172.60
	October	23.00	78.05	587.60
	November	24.00	85.40	365.10
	Mean	23.70	77.84	264.80

Table 10.

Results of soil chemical and texture analysis.

No.	Parameter	Unit	Result	Criteria	Method
1	pH : H2O *)	-	5.9	Slightly Acidic	Balittanah 2023 Chapter I.3 (Electrometry)
2	pH : KCl 1 N	-	5.3	-	Balittanah Tahun 2023 Chapter I.3 (Electrometry)
3	Organic – C *)	(%)	2.52	Medium	ISRIC, Procedure for Soil Analysis, Butir 5, 2002
4	Total – N *)	(%)	0.27	Medium	Balittanah Tahun 2023 Chapter I.8 (Titrimetry, Kjeldahl)
5	C/N	-	9	Low	Calculation
6	P₂O₅ HCl 25%	(mg/100 g)	98.58	Very High	25% HCl Extract, Spectrophotometry
7	P₂O₅ (Olsen)	(ppm P)	42.04	Very High	B/O Extract, Spectro-photometry
8	K₂O HCl 25%	(mg/100 g)	5.65	Poor	5% HCl Extract, AAS
9	Basic Cations:
	K-dd	(cmol.kg⁻¹)	0.17	Low	NH₄OAc (pH 7) Extract, AAS
	Na-dd	(cmol.kg⁻¹)	0.17	Low	NH₄OAc (pH 7) Extract, AAS
	Ca-dd	(cmol.kg⁻¹)	8.81	Medium	NH₄OAc (pH 7) Extract, AAS
	Mg-dd	(cmol.kg⁻¹)	3.20	High	NH₄OAc (pH 7) Extract, AAS
10	Cation Exchange Capacity	(cmol.kg⁻¹)	29.40	High	NH₄OAc (pH 7) Extract, Titrimetry
11	Base Saturation	(%)	42.03	Medium	Calculation
12	Al-dd	(cmol.kg⁻¹)	1.14	-	KCl 1 M Extract, Titrimetry
13	H-dd	(cmol.kg⁻¹)	0.00	-	KCl 1 M Extract, Titrimetry
14	Al-saturation	(%)	8.96	Low	Calculation
15	Texture
	Sand	(%)	16	Silty Clay Loam	Hydrometry
	Silt	(%)	55
	Clay	(%)	29

The experiment consisted of 18 newly developed maize hybrids and one commercial check hybrid. The genotypes were evaluated using a randomized complete block design with three replications within each cropping system. Each genotype × cropping-system combination was assigned to an experimental plot, allowing all genotypes to be evaluated under the three cropping systems.

Maize was planted at a spacing of 0.75 m between rows and 0.20 m between plants. Each experimental plot measured 5 m × 3 m and consisted of four maize rows. Maize plant spacing (0.75 m × 0.20 m) was identical across all three cropping systems; only the number of maize rows per plot differed between sole cropping (four maize rows per plot) and the intercropping treatments. In the maize–soybean intercropping system (JHK), two rows of maize were alternated with two rows of soybean (cv. Grobogan); in the maize–sweet potato intercropping system (JHU), two rows of maize were alternated with two rows of sweet potato (cv. Rancing). In both intercropping systems, the companion crop (soybean or sweet potato) was sown two weeks before maize, and was planted at 0.75 m between rows and 0.25 m between plants within the companion-crop rows. The total number of maize plants per plot was not used as an analytical variable and is therefore not reported here, avoiding ambiguity arising from plot boundaries and plant establishment. For sole-cropping plots (four maize rows), grain yield was determined from the two central maize rows to minimize border effects; for the intercropping plots (two maize rows per plot), both maize rows were harvested for yield, since no additional maize border row was present within the intercropped plot.

Land preparation consisted of two ploughings followed by one harrowing. Weeds were controlled manually at 15 and 35 days after planting (DAP). Basal fertilization consisted of 150 kg urea, 100 kg SP-36, and 75 kg KCl ha⁻¹. A supplemental fertilizer application of 100 kg urea and 50 kg KCl ha⁻¹ was applied at 30 DAP. Irrigation was supplied by furrow irrigation approximately weekly or when required according to rainfall conditions. The same general maize management practices were applied across the three cropping systems.

The agronomic and physiological traits evaluated were number of leaves, leaf length, leaf width, chlorophyll content (CC1), and grain yield. Measurements of number of leaves, leaf length, and leaf width followed the maize characterization descriptors developed by the International Board for Plant Genetic Resources (IBPGR) and the International Maize and Wheat Improvement Center (CIMMYT) [18]. Three representative plants were selected from each plot for measurement of vegetative traits. Number of leaves was determined by counting the leaves on each selected plant after flowering and expressed as leaves plant⁻¹.

Leaf length was measured on the leaf subtending the uppermost ear, from the ligule to the leaf apex, using a measuring tape and expressed in centimetres (cm). Leaf width was measured at the midpoint of the same leaf blade used for leaf-length measurement and expressed in centimetres. Both measurements followed the IBPGR/CIMMYT descriptors for maize characterization [18].

Chlorophyll content (CC1) was measured at 100 DAP using a SPAD-502Plus Minolta chlorophyll meter. Measurements were taken from the first ear leaf, with one reading obtained per plant from three plants per plot. The recorded values were used to obtain the CC1 value for each plant, and the measurements were expressed in SPAD units.

Grain yield was determined from the harvestable area of each experimental plot. The four-row plots measured 5 m × 3 m, and only the two middle rows were harvested to minimize border effects. After harvest, the ears were shelled and grain weight was determined using a digital balance. Grain moisture at the time of weighing ranged from 25% to 40%. Grain weight was adjusted to a standard moisture content of 15% using the equation adapted from Tandzi and Mutengwa [19]. The adjusted grain weight was then converted to grain yield (t ha⁻¹) based on the harvested area.

For laboratory analysis, grain samples were collected separately for each genotype × cropping-system × replication combination after harvest. The samples were dried, shelled, ground into fine flour, homogenized, and stored in airtight containers until proximate composition and starch analyses were conducted.

Limitations

This dataset has several limitations. First, the experiment was conducted at one research location, namely SPLPP Universitas Padjadjaran, Arjasari, Bandung, West Java, Indonesia. Although three cropping systems were evaluated, the dataset does not yet represent broader agroecological zones. Therefore, further multi-location testing is needed to confirm the stability and adaptability of the selected hybrids across wider tropical maize-growing environments.

Second, the "environments" evaluated here are in fact cropping-system (management) treatments at a single site and season, namely sole maize, maize–soybean intercropping, and maize–sweet potato intercropping, rather than independent environments in the classical multi-environment-trial sense; the genotype × environment interaction reported in Table 1, Table 2, Table 3 is therefore genotype × cropping-system interaction, and GGE biplot interpretations of environmental discriminativeness and representativeness should be read as describing these three management contrasts rather than sampling of a wider target population of environments. These systems provide useful information on hybrid performance under diversified cultivation, but they do not represent fully controlled stress treatments. Light competition, soil moisture, nutrient availability, and crop competition may vary naturally within intercropping systems.

Third, the dataset focuses on proximate composition, starch content, selected agronomic traits, and yield. Other important quality traits, such as amino acid profile, amylose–amylopectin ratio, mineral composition, and feed digestibility, were not included. Additional biochemical and nutritional analyses would strengthen the use of this dataset for food, feed, and industrial starch applications.

Fourth, the number of tested hybrids was limited to 18 genotypes. Although this number is sufficient for preliminary hybrid evaluation, a larger and more genetically diverse set of hybrids would improve the robustness of genotype × environment analysis and broaden the relevance of the findings for maize breeding programs.

Fifth, the GGE biplot analysis provides useful visualization of stability, environmental discrimination, and which-won-where patterns, but the results should be interpreted together with ANOVA, trait means, and breeding objectives. GGE biplot interpretation is exploratory and should be validated through further field trials before candidate hybrids are recommended for release or commercialization [8,9].

Sixth, with only three cropping systems, the GGE biplots necessarily explain nearly all of the genotype × cropping-system variation with just two principal components, so the discriminativeness-versus-representativeness view is based on an average-environment axis defined by only two non-ideal points and should be interpreted cautiously. Complementary quantitative stability statistics not shown here — such as the AMMI stability value (ASV), Shukla's stability variance, Wricke's ecovalence, or a cultivar superiority measure — would provide readers with numerical stability estimates in addition to the visual biplot interpretation and are recommended for future analyses of this dataset.

Despite these limitations, the dataset provides a useful foundation for selecting maize hybrids with improved proximate composition, starch content, agronomic performance, and yield adaptation under sole cropping and intercropping systems in tropical environments.

Declaration of Generative AI and AI-Assisted Technologies in the Manuscript Preparation Process

During the preparation of this work the author(s) used [QUILBOLT and TURNITIN] in order to [EDIT and CHECK LANGUAGE]. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

Ethics Statement

This research did not involve human participants, animals, or data derived from social media platforms.

CRediT authorship contribution statement

Frengky Arya Saputra: Conceptualization, Validation, Methodology, Writing – review & editing. Adimas Pamuji: Writing – review & editing. Ade Ismail: Validation, Methodology, Writing – review & editing. M. Rafi Fauzan: Software, Validation, Writing – review & editing. Dedi Ruswandi: Conceptualization, Supervision, Resources, Funding acquisition, Methodology, Project administration, Writing – review & editing.

Data availability

• Mendeley DataDataset on proximate composition, starch content, agronomic performance, and GGE biplot analysis of newly developed maize hybrids across three cropping systems in Indonesia (Original data)