Keywords
Varieties, sorghum, adaptable, tidal swamplands, sandy soil, MGIDI, GGE biplot.
This article is included in the Agriculture, Food and Nutrition gateway.
Sorghum has potential as a source of material for food, bioenergy, and animal feed, making it a worthy candidate for promotion. This cereal thrives in regions characterized by low moisture and dry conditions. To address the diminishing availability of arable dry land, it may be necessary to explore the cultivation of sorghum insorghum in tidal swamplands and sandy soils.
Twelve sorghum varieties were evaluated in tidal swamplands during the rainy and dry seasons, as well as in sandy soil during the dry season, using two levels of organic fertilizers to create six test environments. The experiments were arranged in a completely randomized block design with three replications. To choose sorghum varieties with features that closely resemble an idealized sorghum variety, the Multi-trait Genotype-Ideotype Distance Index (MGIDI) was utilized. Simultaneously, genotype plus genotype-environment interaction (GGE) biplots were employed to determine the best circumstances for choosing broadly adaptable varieties that exhibit desirable features, as well as to find varieties that thrive environmental contexts.
Based on the MGIDI ranking on the average across environment, two varieties, i.e., Numbu and Kawali were selected. However selected varieties in each environment were differ due to significant variety-environment interaction. In terms of grain weight, the Soper 7 Agritan variety exhibits adaptability across diverse environments, while the Numbu variety likewise demonstrates versatility in various environmental conditions. When evaluating forage yield, several adaptable varieties have emerged. Tidal swamplands treated with a high application of organic fertilizer, as well as sandy soils, provide optimal environments for selecting broadly adaptable varieties that focus on both grain and forage yields.
Adaptable varieties differ for various groups of environments and different traits under consideration. Optimal environments for identifying broadly adaptable varieties varied by trait. The multitrait genotype-ideotype distance index proves to be a valuable tool for selecting varieties based on multiple traits. In parallel, the GGE biplot effectively identifies adaptable varieties based on individual traits.
Varieties, sorghum, adaptable, tidal swamplands, sandy soil, MGIDI, GGE biplot.
The sorghum crop (Sorghum bicolor L.) plays a significant role as a source of food, bioenergy, and animal feed materials. As a food source, it provides carbohydrate sources and other essential nutrients, including proteins, polyunsaturated fatty acids, and high fiber. The utilization of sorghum can then be promoted for food diversification.1,2 As a source of bioenergy, it produces biomass that can be processed through fermentation, gasification, and fast pyrolysis to generate various biofuels, including bioethanol, biodiesel, bio-oil, biogas, biohydrogen, and other bio-derived products.3–6 Sorghum also serves as a source of feed for animals.
Sorghum (Sorghum bicolor) is a highly adaptable crop that thrives in diverse agroecosystems due to its genetic diversity and resilience to various environmental stresses.7–9 Sorghum crops are primarily cultivated in drylands due to their drought resistance, which is attributed to their evolution in arid regions. As a drought-resistant crop, sorghum is widely cultivated in many areas, including semi-arid and arid zones in Africa, Asia, the Middle East, Central America, North America, and Australia.4,5,10
In Indonesia, sorghum is mainly cultivated in dry lands. However, the availability of dry lands for sorghum cultivation continually reduced due to land conversion for non-agricultural purposes and competition with other crops, prompting the need to expand sorghum cultivation to tidal swamplands and sandy soil areas, which are quite promising and widely available in Indonesia. It was estimated that 8.92 million hectares of tidal swamplands and 2.10 million hectares of sandy soils were available for agriculture in Indonesia.11,12
Swamplands are low-lying lands that are regularly flooded. It consists of two types of lands, i.e., tidal and inland swamplands. Tidal swamplands are swamplands that are influenced by sea tides. It can be further classified based on tidal influence into types A, B, C, and D.11 Tidal swamplands of type A are those lands influenced by spring and neap tides, whereas type B are those influenced by neap tides only. Suppose there is no flooding, i.e., only a rise in the water table during the tides, then those lands are classified as type C, while type D is not influenced by sea tides at all, and thus, basically a dry land in the swampy areas. Inland swamps are areas formed in the inland valley where water originates from an upstream river or rainfall. Sandy soil contains a high proportion of sand particles, i.e., more than 60% of sand by volume, derived from sedimentary rock. It has a gritty texture, excellent drainage, poor nutrient retention, and good airflow.13
The expansion of sorghum cultivation into tidal swamplands and sandy soils necessitates the development of varieties that can thrive in these environments. A crop’s adaptability is defined by its ability to grow and yield well under varying environmental conditions. Consequently, high phenotypic performance and consistency across different environments serve as critical indicators of adaptability. While sorghum’s adaptability has been investigated in dryland environments14–18—encompassing a range of climates from semi-arid and dry to humid,19,20 as well as21 various agroclimatic conditions,22–26 differing altitudes,27 and diverse fertilizer applications17,28—there is a notable lack of research focusing on sorghum’s response to high rainfall and inundation, as well as to nutrient-poor and pyrite-containing soils characteristic of tidal swamplands and sandy soils. This gap presents a valuable opportunity for further exploration. A recent 2023 study indicated that tidal swamplands cancould support sorghum cultivation, with soil acidity and limited nutrient availability29,30 identified as the primary challenges. Additional research is needed not only in swampy areas but also in other agroecosystems featuring sandy soils to assess the suitability of this crop for these environments.
Environmental and variety-based adaptation research, including the use of biofertilizers and nutrients, as well as the influence of climate on swampy and sandy soils, are phenomena that require study. Given that superior sorghum varieties can adapt or tolerate climate change or stress. Intercropping and integrated nutrient management, as well as land and water management practices, are key adaptations that can enhance the health and productivity of marginal soils and are effective in increasing sorghum yields.29
The purposes of this research are: 1. to identify a high-performance variety based on multiple traits and beneficial characteristics in tidal swamplands and sandy soils. 2. To determine adaptable varieties in tidal swampland and sandy soil, and 3. To determine the best environment to test broadly adaptable varieties. The high-performance and adaptable varieties were selected using the Multi-Trait Genotype-Ideotype Distance Index (MGIDI) and Genotype plus Genotype vs Environment (GGE) biplot.
MGIDI is a tool for selecting plant genotypes and ranking agronomic treatments based on multiple traits. It integrates various traits into a single index. It could be used to select varieties and their interaction with an environment close to the ideal type of sorghum in tidal swamplands and sandy soils.31–33 MGIDI embedding weight to prioritize traits, reduce dimensionality, and enhance selection accuracy.32,33 Some studies have shown that MGIDI can lead to significant selection gains across various traits.34 The GGE biplot is a graphical tool for studying the performance of varieties in multiple tested environments. The biplot illustrates the two factors (G and GE) that are important in variety evaluation. The GGE biplot displays the first two principal components (PC1 and PC2) derived from environment-centered data, i.e., when the effect of environment is removed from the multi-environment data of the cultivar. This method has been employed in numerous studies to investigate adaptability and genotype-environment interaction in sorghum.35–50
The experiments were conducted from October 2022 to February 2023 (wet season) and from July to November 2024 (dry season) in tidal swamplands at Petak Batuah Village, Dadahup Sub-district, Kapuas Regency, and from August to December 2023 (dry season) in sandy soils at Sidodadi Village, Bukit Batu Sub-district, Palangka Raya City, Central Kalimantan Province, Indonesia.
This study used 12 varieties of sorghum ( Table 1). The Cereal Crop Instrument Standard Testing Centre (CCISTC) is the source of all seeds. Table 1 shows some of the main characteristics of these varieties. Other materials needed include soil conditioners such as dolomite and chicken manure. The inorganic fertilizers are Urea, NPK, SP-36, and KCl. Several insecticides were applied as required.
Varieties (code) | Origin | Pest and disease resistance* | Plant age at 50% flowering (dap)** | Carbohydrates (%)*** | Tanin (%) | Yield (t ha−1) |
---|---|---|---|---|---|---|
Super 1 (V1) | CCISTC germplasm collection | Aphis (R), Anthracnose, leaf rust, and leaf blight (R) | 56 | 71.30 | 0.110 | 5.70 |
Super 2 (V2) | Introduction from ICRISAT | Aphis (R), Anthracnose, leaf rust and leaf blight (R) | 60 | 75.60 | 0.300 | 6.30 |
Suri 3 Agritan (V3) | Introduction from ICRISAT | Aphis (R), Anthracnose and leaf spot (R) | 54 | 64.06 | 0.077 | 6.00 |
Suri 4 Agritan (V4) | Introduction from ICRISAT | Aphis, Anthracnose, and leaf spot (MR) | 55 | 64.93 | 0.013 | 5.70 |
Mandau (V5) | Introduction from IRRI | stem borers (R), Anthracnose and leaf rust (R) | 65 | 76.00 | na | 4.00–5.00 |
Soper 6 Agritan (V6) | Introduction from ICRISAT | Aphis and leaf rust (HR), leaf spot, and Anthracnose (MR) | 64 | 66.88 | ± 0.070 | 6.00 |
Soper 7 Agritan (V7) | Crossing of Numbu/15011-B | leaf rust and leaf spot (R), Anthracnose and stem rot (HR) | 59–65 | 63.90 | 0.210 | 12.93 |
Numbu (V8) | India | Aphis (R), leaf rust and leaf spot (R) | 69 | 84.58 | na | 4.00–5.00 |
Soper 9 Agritan (V9) | Crossing 4-183-A/Numbu | leaf rust (R), leaf spot, Anthracnose, and stem rot (HR) | 62–65 | 63.86 | 0.210 | 14.40 |
Kawali (V10) | India | Aphis (MR), leaf rust and leaf spot (R) | 70 | 87.87 | na | 4.00–5.00 |
Bioguma II Agritan (V11) | Improvement nt Numbu | leaf rust (R), Anthracnose (MR), and stem rot (HR) | 69–75 | 61.40 | 0.140 | 9.39 |
UPCA S1 (V12) | 56B | na | 60–70 | 66.50 | 0.215 | 7.38 |
The experiment was conducted in tidal swamplands of type C (Inceptisols) and sandy soils (Entisols). Intensive tillage was practiced. After one week, two seeds were planted per planting hole, with 0.6 m between rows and a 0.25 m planting distance. A replanting operation was performed 7–14 days after planting. Thinning was conducted 30 days after planting, leaving a single plant per pot. Weeds were controlled manually, with hoeing 26 and 46 days after planting.
The trials were arranged in a randomized complete block design (RCBD) with two-factor treatments and three replications. The first factor was tested environments consisted of E1 = tidal swamplands applied with 500 kg ha−1 chicken manure in the wet season, E2 = tidal swamplands applied with 1000 kg ha−1 chicken manure in the wet season, E3 = tidal swamplands applied with 500 kg ha−1 chicken manure in the dry season, and E4 = tidal swamplands applied with 1000 kg ha−1 chicken manure in the dry season, E5 = sandy soils applied with 500 kg ha−1 chicken manure in the dry season, and E6 = sandy soils applied with 1000 kg ha−1 chicken manure in the dry season. The second factor was 12 varieties of sorghum ( Table 1). Each plot consisted of eight 5.0-m long rows and sixteen 4.0-m long rows. The utilized area was defined as the area occupied by the central row. The entire plot was applied with 1000 kg ha−1 of dolomite. The observed traits are given in Table 2.
2.4.1 Analysis of variance
Multivariate Analysis of Variance for all traits according to the following model:
Where the observed t-th traits at k-th block under the i-th environment of the j-th variety, is the k-th block effect, i = 1, 2…e; j = 1, 2, 3 … v; k = 1, 2, 3; t = 1, 2 … p. is the i-th environmental effect, is the j-th variety effect, is the interaction of the variety and the environment, and is the experimental error at the t-th trait of the j-th variety planted at the experimental unit under the i-th environment and k-th block. Multivariate analysis of variance (MANOVA) was conducted for p traits based on model (1). Based on the MANOVA results, the means of the significant effects are extracted to construct two-way tables with variety means or a combination of variety-environment means in rows and traits in columns. The elements of the two-way tables are then rescaled so that all columns have values 0-100 as follows31,34
Where and are the maximum and minimum values of traits j in after rescaling, respectively; and are the original maximum and minimum value of the trait j, and is the original value for the jth trait of the i-th variety, for traits with higher values, = 100 and ; conversely, if the lower values are desired, then = 100.
2.4.2 Factor analysis
The V * = (rVij)vxp, i.e., the rescaled V, are then subject to factor analysis to group variables based on their correlation. All variables within a particular group are expected to be highly correlated with one another but have relatively small correlations with variables in different groups.
The estimation of the factorial scores for each row in is according to the following model:
Where X is a px1 vector of a row of (the rescaled values of ), is the px1 vector of the standardized mean, L is a pxf matrix of factorial loadings, f is a px1 vector of common factors, and is a px1 vector of residuals. p and f are the number of traits and common factors retained. The initial loadings are computed considering only factors with eigenvalues of the correlation matrix of or higher than 1. The varimax rotation criteria are used for the analytic rotation and estimation of final loadings. The scores are then obtained as follows.
S is a v x f matrix with factorial scores, V * is a v x p matrix with rescaling means, and A is a p x f matrix of canonical loading. R is a p x p correlation matrix between the traits, and f is the number of factors retained. Factors associated with the eigenvalue of the matrix greater than one are maintained.
2.4.3 Multitrait -Genotype-Ideotype-Distant Index (MGIDI)
The MGIDIi for the i-th treatment, defined as the Euclidean distance between the scores of the i-th treatment and the ideal type, is computed as follows.
Where is the score of the i- th treatment in the j- th factor (i = 1, 2, …, t; j = 1, 2, …, f ), being t and f the number of treatments and factors, respectively; and is the jth score of the ideotype or ideal treatment. The treatment with the lowest MGIDI is closer to the ideal treatment, presenting the desired values for all the p traits. The traits are prioritized by putting the following weights (number in the bracket in front of the traits): (0.4) PH, (0.6) LC, (0.4) INC, (0.4) INL, (0.7) SD, (0.7) LW, (0.7) LL, (0.6) PL, (0.5) (PDW), (1.0) LWW, (0.3) BRIX, (1.0) SWW, (1.0) GY. The analysis was performed using R software version 4.3.3.51
2.4.4 GGE Biplot
The mean yield of variety i in environment j according to model (1) is:
If we delete Ei from Yij, then the environmental-centered data matrix M with the ij-th element
Where ; , being λk the kth eigenvalue from the SVD (k = 1,2, …, p) with p ; a is the single value partition factor for the Principal Component (PC) k; and are the PC score for variety i and environment j, respectively.
A Genetic plus Genetic-Environment interaction (GGE) biplot was used to examine the stability and adaptability of the varieties. The biplot’s abscissa represents the first principal component (PC1), indicating the phenotypic performance of the varieties, while the ordinate represents the second principal component (PC2), indicating the stability of the varieties. The two components account for the variation in varieties and the interaction between varieties and environments. By joining the variety’s coordinates that were most distant from the origin, a polygon was created that can be used to determine which varieties were the best (won) and where ( Figure 5 and Figure 10). The biplot is divided into sectors by drawing a dotted line perpendicular to the polygon’s sides from the origin of the biplot. The sectors depict environments that are most comparable to one another. The varieties with the best phenotypic performance in environments within a sector were those found near the polygon’s vertices in the sector. A group of environments where the same variety performs the best is called a mega-environment. Varieties in a sector without allocated environments are considered unfavorable to any environment and exhibit low phenotypic performance responsiveness.
The average environmental point, with coordinates representing the average PC1 and PC2 scores of the environments, was initially defined to create the Average Environmental Coordination (AEC). The AEC’s X-axis is a line between the biplot’s origin and the average environmental point. Simultaneously, the Y-axis is the line that runs perpendicular to the AEC’s X-axis in the biplot’s origin. The ordinate shows the interactions between each variety and its environment, whilst the AEC abscissa shows the phenotypic performance of varieties in the average environment. The arrow in the AEC axis indicates the direction of ascending phenotypic performance. The projection of each variety on the X-axis of AEC measures the mean phenotypic performance across environments.
In contrast, the projection on the Y-axis measures the stability of the variety in tested environments ( Figure 6, Figure 11). The ascending direction is the arrow in the abscissa, and the varieties projected above the origin in the direction of the arrow in the abscissa are above the average of the mean phenotypic performance; the higher the ordinate of the variety in the AEC coordinate is, the less stable it is. The best (adaptable) variety is the highest phenotypic performance and stability variety. This imaginary “ideal variety,” i.e., the best variety, is marked as a small circle in Figures 7 and 12. Varieties are ranked by their mean phenotypic performance and stability, as indicated by their closeness to the “ideal variety” ( Figures 7 and 12).
The ideal variety is based on its performance in the AEC. However, one may need to determine a test environment representing the average environment. A line vector was constructed from the biplot’s origin to each environmental point to evaluate the environment’s representativeness and discriminating power. The length of the vector represents the discriminating ability of the environment, while the angle between the vector and the X-axis of AEC measures the representativeness of the environment. The longer the vector and the smaller the angle, the higher the discriminating ability and representativeness of the environment associated with the vector ( Figures 8 and 13). The environment is then ranked based on its discriminativeness and representativeness ( Figure 9 and Figure 14).
The multivariate analysis of variance ( Table 3) found that variety means across environment (V) and variety-environment interaction (VE) have significant effects on the vector of traits, based on the Pillai trace Test (p < 0.0.01), indicating differences in the means of varieties across environments and such differences are affected by environment. The significant effect of variety-environment interaction means that the ranking of varieties within each environment is varied.
Two two-way tables were extracted from the MANOVA: V (Vjt)vxp and EV (EV(ij)t)(ev)xp, where Vjt and EV(ij)t are the variety and variety-environment combination, respectively, of the t traits. Factorial loading after varimax rotation and their cumulative variance obtained in factor analysis on the variety mean matrix (V) are presented in Table 4. In contrast, the variety-environment combinations matrix (EV) is presented in Table 5. In both tables, four factors associated with an eigenvalue greater than one are retained along with their cumulative variance. The bold-faced numbers (greater than 0.50 in absolute value) in each table are the dominant factor loading of the traits to the associated factor. Hence, for example, in Table 4, internode count (INC), panicle dry weight (PDW), stem wet weight (SWW), and grain yield (GY) are associated with factor 1(FA1). Similarly, plant height (PH), Internode count (INC), internode length (INL), leaf length (LL), and BRIX are associated with the factor (FA2). Factor 3 is associated with panicle length (PL),) and BRIX. Factor 4 is associated with leaf count (LC), stem diameter (SD), leaf width (LW), panicle dry weight (PDW), and Leaf wet weight (LWW). Similar interpretations can also be held for Table 5. The result of factor analysis will then be used to calculate MGIDI.
3.3.1 Selected varieties
Figure 1 shows the ranking of the MGIDI of varieties averaged across environments. The selected varieties based on the MGIDI are Kawali (V10) and Numbu (V8), as indicated by the red dots in Figure 1.
3.3.2 Selected variety-environment combinations
Figure 2 presents the ranking of variety-environment combinations based on MGIDI. The red dot at the outer circle is the selected environment-variety combination. They are E6-V10, E6-V5, E6-V8, E6-V7, E2-V7, E6-V9, E6-V6, E2-V8, E1-V4, and E1-V10, E6_V11, E2_V10, E2_V4, E6_V2, where EiVj denotes the j-th variety planted at the i-th environment. The majority of selected varieties are those applied in sandy soil with a high rate of organic fertilizer (E6). Only four varieties (V4, V7, V8, or V10) were selected for tidal swamplands in the rainy season, and they were either applied at a high or low rate of organic fertilizer (E1 and E2).
3.3.3 Selected varieties in each environment
The multivariate analysis of variance, as shown in Table 3, indicates that the interaction between variety and environment is significant. This interaction suggests that the ranking of varieties varies across different environments. Therefore, it is necessary to select varieties in each environment by the MGIDI. The selection procedure is similar to that of average varieties across all environments and variety-environment combinations. However, the result of the factor analysis and the graphs of the ranking are not presented here. Table 6 presents the result of the selection.
The strengths and weaknesses of all varieties and selected varieties-environment combinations, which are accounted for by the proportion of each factor to their calculated MGIDI, are presented in Figure 3 and Figure 4, respectively. Each factor has a specific color line, as indicated by the legend. The closer the variety or variety-environment combinations are to the external edge of the polygon, with a specific color representing a particular factor, the smaller the contribution of the factor to the MGIDI. The smaller the contribution of a factor to the MGIDI of a variety/variety-environment combination, the closer the traits associated with the factor to the “ideal type.” Since we defined “ideal type” as those varieties or variety-environment combinations with the highest values in all traits (as selection goals), it implies that the traits associated with the factor are high in the varieties or variety-environment combinations.
The strengths and weaknesses of all varieties are shown in Figure 3. We should focus attention on the selected varieties, i.e., V8 and V10. Variety V8 is closely related to FA1 and V10 is closely related to FA4. It implies that FA1 has a small contribution to the MGDI of V8, and hence traits like internode count (INC), panicle dry weight (PDW), stem wet weight (weight (SWW), and grain yield (GY), which are associated with FA1, have high values in V8. Similarly, V10 exhibits high values in traits related to FA4, including leaf count (LC), stem diameter (SD), panicle dry weight (PDW), and leaf wet weight (LWW).
Figure 4 illustrates the strengths and weaknesses of selected variety-environment combinations. Unlike Figure 3, Figure 4 presents only the selected variety-environment combinations for simplicity, given the high number of variety-environment combinations. Factor FA1 makes a small contribution to the MGIDI of E1-V10, E6-V10, and E6-V6, indicating that traits associated with this factor in that variety-environment combination are similar to those in the variety-environment idiotype. Therefore, traits such as plant height (PH), internode count (INC), Internode Length (INL), leaf Length (LL), and BRIX have high values in that variety-environment combination. With similar reasoning traits associated with FA2, such as leaf count (LC) and panicle dry weight (PDW), these values must be high in variety-environment combinations E6-V8, E6-V7, E2-V7, E6-V5, and E6-V11. Two economically valuable traits, grain yield (GY) and stem wet weight (SWW), contribute to biomass production associated with FA3. This factor has made a small contribution to the MGIDI of E6-V7, E2-V7, E6-V9, and E6-V8. Therefore, these varieties must possess high values for both traits. Finally, traits that are associated with FA4 must have high values in E1_V4, E1_V10, E2-V4, E6_V2 and E2_V8.
The adaptability and stability of each variety were studied, with valuable traits including grain yield and fresh forage yield (stems and leaves, expressed as wet weight). The GGE biplot on each of the two traits was used to study the adaptability and stability of varieties.
3.5.1 Grain yield
The “Which-won-where view” of the biplot on the grain yield (GY) and its polygon is displayed in Figure 5. Of the total GGE variation, the PC1 and PC2 contributed 52.44% and 33.61%, respectively. PC1 reflects the average performance (mean grain yield) of the varieties, while PC2 reflects the stability (variety-environment interaction) of the varieties/genotypes. Jointly, the two components account for 86.05% of the total genotype plus genotype × environment interaction. The polygon separated the biplot’s five sectors. The highest phenotypic performance (grain yield) variety was the variety at the vertex of the polygon. There are five varieties at the polygon’s vertices, i.e., V3, V7, V8, V11 and V12. These varieties are among the best. There are two mega-environments in the biplot. Mega-environment 1 consists of environments E1, E2, and E6 in one sector, with a variety at the vertex, V7, while mega-environment 2 consists of environments E3, E4, and E5, with a variety at the vertex, V8. Varieties V3, V11, and V12 were found in sectors with no allocated environment. Hence, they were less responsive and exhibited low phenotypic performance (in terms of grain yield) in all tested environments.
The mean and stability analysis depicted in Figure 6 shows that V7 has the highest mean in Mega-Environment 1, as it is the furthest left along the green AEC line. Note that the green AEC arrow points to the left; therefore, varieties further in that direction can be interpreted as having a higher mean performance (in grain yield). V8 is the second-highest performance, with similar reasoning. In terms of stability, which is reflected by the ordinates in AEC, V7, and V8 are moderately stable, although they are less stable than other varieties, such as V1, V2, and V10. The ranking of varieties based on their mean performance (in terms of grain yield) and stability is presented in Figure 7. The best varieties, which are the most adaptable, are those closest to the ideal variety (represented by the small circle near the arrow), an imaginary genotype or variety with the highest mean and stability. V7 is the most adaptable variety, followed by V8. Consequently, in mega-environment 1, i.e., tidal swamplands in rainy season applied with high rate (E2) or low rate organic fertilizer (E1), and in sandy soils applied with high rate organic fertilizer (E6), the adaptable variety is Soper 7 agritan (V7), while in tidal swampland at dry season applied with high rate (E4) or low rate organic fertilizer (E3) and in sandy soils applied with low rate of organic fertilizer (E5) the adaptable variety is Numbu (V8).
Since the “ideal variety” in the environmental average is only hypothetical, we may need to determine the phenotypic performance of the varieties in a particular tested environment that represents the average environment. For this purpose, we first determined the tested environment that was more discriminative and representative of the average environment—the discriminativeness and representativeness of all tested environments were analyzed in Figure 8. The highest line vector from the origin of the biplot to the environment “point” was the most discriminative environment. At the same time, the most representative is the line vector with the lowest angle to the average environment. The selected environments are ranked based on their discriminativeness and representativeness ( Figure 9). The center of the concentric circles in Figure 9 represents the ideal environment for selecting genotypes, i.e., the most discriminative and representative ones. The closer an environment is to this center, the better it ranks. Hence, E6 is the most discriminative and representative environment of the average environment. In other words, sandy soil applied with a high rate of organic fertilizer during the dry season (E6) is ideal for selecting broadly adaptive genotypes or varieties based on grain yield (GY).
3.5.2 Forage yield
Figure 10 depicts a biplot of sorghum varieties’ Forage yield (FY) and its polygon. PC1 and PC2 contributed 61.30% and 33.57%, respectively, and jointly accounted for 94.87% of the overall GGE variance. There are two mega-environments in the biplot. The first mega-environment is in the sector that contains E1, E3, and E5 tested environments, and the second mega-environment is in the sector that contains E2, E4, and E6 tested environments. We can define the first mega-environment as the environment applied with a low rate of organic fertilizer since all environments are those applied with a low rate of organic fertilizer (500 kg of chicken manure per hectare) in both types of land at both seasons.
For the same reason, we can define the second environment as the one applied with a high rate of organic fertilizer (1,000 kg of chicken manure per hectare). Varieties V3 and V4 are at the vertices of polygons within mega-environment one and, therefore, become suitable candidates for the best varieties in the environment. Variety V11 is the suitable candidate for the best varieties in mega-environment 2. Varieties V5, V6, and V7 were found in sectors with no environmental conditions, indicating that they are not responsive and exhibit low mean phenotypic performance in any tested environment.
The graph of mean and stability ( Figure 11) showed that among the three varieties in mega-environment 1, V3 has a phenotypic performance (forage yield) below the average, while varieties V4 and V9 are above the average, with almost similar phenotypic performance. In genotype rank ( Figure 12), V9 is closer to the “ideal variety” than V3 or V4 in this mega-environment. However, it is further from the ‘ideal variety’ than V11, V2, and V8, which are in mega-environment 2. Therefore, we can conclude that in mega-environment 1, i.e., the environment in tidal swampland applied with low (E1) or high rate (E2) organic fertilizer and in sandy soils applied with low rate of organic fertilizer, the adaptable varieties are variety Soper nine 9 agritan (V9); while in mega-environment 2, i.e. tidal swamplands and sandy soil applied with high rate organic fertilizer, variety Bioguma agritan (V11) are the most adaptive variety.
Figure 13 analyses the discriminativeness and representativeness of all tested environments. Figure 14 gives the rank of the selected environment. Using the same reasoning as in the GGE biplot on grain yield, the tested environment E2 is the most discriminative and representative environment compared to the average environment. Therefore, tidal swampland applied with a high rate of organic fertilizer during the rainy season (E2) is ideal for selecting broadly adaptive genotypes/varieties based on forage yield (FY).
MGIDI incorporates trait information into a single value to rank varieties or variety-environment combinations based on their distance from an “ideal type.” The “ideal type” or “ideotype” is a hypothetical variety/variety-environment combination with the best possible value for each trait. It has been successfully applied to several studies to enhance the performance, productivity, quality, or adaptability of different crops.14 Each trait is assigned a weight based on its value or desirability, whereas superior varieties are those with the smallest distances from the ideal variety. The advantage of the MGIDI-based selection is that it incorporates several traits into the study and reduces the dimensions of these traits to just four factors that account for a significant portion of the variation. Finding varieties like ideotype types can be aided by the strengths and weaknesses of the selected varieties, as indicated by the contribution of each factor to the MGIDI. A helpful indicator for sorghum breeding or crop improvement would be the factors and their associated traits that contribute to the MGIDI of the selected varieties.
In contrast to the MGIDI, the GGE biplot tools only consider one trait at a time. In the GGE biplot technique applied in this study, we consider two valuable beneficial traits: grain yield and forage yield. The GGE biplot offers a more comprehensive evaluation of the best varieties across various environments (mega-environments). Furthermore, the ideal environment for identifying adaptable varieties, i.e., environments with representative and high-discriminating power, can be determined using the GGE biplot. Aside from the difference between MGIDI and GGE biplots, particularly in the traits they evaluate, comparing the results of the two methods in identifying the best varieties is worthwhile.
The GGE biplot has identified Soper 7 Agritan (V7) and Numbu (V8) as the top-performing varieties in terms of mean grain yield across various environments. These results differ somewhat from the varieties selected by the MGIDI, specifically V8 and V10. While V8 was chosen by the MGIDI and is acknowledged in the GGE biplot for its high mean grain yield, V10 was also selected by the MGIDI but does not perform as strongly in the GGE biplot, despite maintaining a mean grain yield above the average. Conversely, V7, not selected by the MGIDI, stands out as the top performer in grain yield according to the GGE biplot.
These discrepancies can be attributed to the fact that the contribution of FA1, which is associated with grain yield, is relatively small within the MGIDI for V8, thereby limiting its role in determining the highest mean grain yield. In contrast, the contribution of FA4, which does not relate to grain yield, is also minimal for V10 in the MGIDI. This difference implies that while V8 has significant value in terms of grain yield, V10 does not, although it may possess other traits related to FA4 that are unrelated to grain yield. The GGE biplot focuses solely on grain yield, which is why V7 was selected over V10.
The GGE biplot also shows the environment in which the varieties performed best in terms of their mean grain yield. During the rainy season, the Soper 7 agritan 7 (V7) variety is suitable for use in tidal swamplands during the wet season with either high-rate (E2) or low-rate organic fertilizers (E1), as well as in sandy soils with high-rate organic fertilizers (E6). Conversely, the Numbu (V8) variety is recommended for tidal swamplands during the dry season, particularly with high-rate (E4) or low-rate organic fertilizers (E3) and in sandy soils with low-rate organic fertilizers (E5). The MGIDI analysis indicated that variety V7 is also selected in the E2 environment and E3 environment, while V8 is selected in almost all environments except E1 and E3. These differences indicate that in specific environments (E1 and E2), V8 exhibits traits beyond grain yield that make it the closest to the ideal variety.
The highest means across environments have also been identified via the GGE biplot on forage yield (FY), using a logic like that of the GGE biplot on grain yield (GY). Like the grain yield, varieties differ from those chosen using the MGIDI. The Bioguma (V11) variety has the highest mean in tidal swamplands during both the rainy (E2) and dry seasons (E4), as well as in sandy soil (E6), when using high-rate organic fertilizer. Meanwhile, the Soper 9 agritan (V9) variety has the highest mean in tidal swamplands in both the wet and dry seasons, with a low rate of organic fertilizer (E1 and E3), as well as in sandy soil during the dry season, with a low rate of organic fertilizer (E1, E3, and E5). These differences indicated a variation in selecting grain yield and forage yield. Some varieties, however, are dual-purpose varieties, i.e., higher in grain yield as well as forage yield mean
GGE biplot also determined the stability of the varieties in each group of environments. Soper 7 agritan (V7) and Numbu (V8) varieties that have the highest mean on grain yield, and Bioguma (V11) and Soper 9 Agritan (V9), which also have the highest mean in forage yield in their respective environments, are also relatively stable or have low variety-environment interactions. Therefore, they are adaptable varieties with the highest phenotypic mean (grain yield and forage yield) in the respective environments. Specifically, Soper 7 Agritan (V7) is adaptable in Mega-environment 1, and Numbu (V8) is adaptable in Mega-environment 2, as indicated by the GGE biplot on grain yield. At the same time, Soper 9 Agritan (V9) is adaptable in Mega-environment 1, and Bioguma (V11) is adaptable in Mega-environment 2, as shown in the GGE biplot for forage yield. The MGIDI, however, cannot identify adaptable varieties. The selection of a Variety-Environment combination can only determine which variety has the highest ranking in MGIDI. However, the selected variety-environment combination indicated that most varieties have a high MGIDI ranking, hence being close to ideal genotypes in environments E6, E4, and E2, which is like the result of the GGE biplot on forage yield.
The best environments for choosing broadly adaptive varieties could also be identified using the GGE biplot. These environments include tidal swamplands that are fertilized with a high rate of organic fertilizer during the rainy season (E2) to maximize forage yield and sandy soil that is fertilized with a high rate of organic fertilizer during the dry season (E6) to enhance grain yield. A high level of organic fertilizer enhances the environment’s ability to discriminate and represent the average environment.52,53 High rates of organic fertilizer have a significant impact on crops in tidal swamplands during the rainy season because they increase the populations of facultative and anaerobic microbes, which can help slow down the release of nutrients, add organic matter that can help bind particles in otherwise waterlogged areas, and help microbes release nutrients from organic material. In contrast, organic fertilizer increases fertility in sandy soils during the dry season by releasing nutrients slowly, a process that is particularly important in nutrient-poor sands. This process improves biological activity and soil life while also reducing compaction and erosion. These conditions will enhance environmental productivity, particularly for responsive varieties, thereby increasing the discriminating power of the environment.
A limitation of this study is that the tested environments are not sufficiently varied, so adaptability is not significantly broad. Variations of environments depend only on the type of agroecosystem (tidal swamplands and sandy soil), seasons (dry and rainy seasons), and rate of organic fertilizer applications. In other words, this study did not cover the wide variability in tidal swamplands and sandy soils. Other limitations, particularly in applying the MGIDI, are the limited number of traits observed, which does not involve some important traits. Nevertheless, with such limitations, we can still recommend adaptable varieties and a testing environment for testing the broadly adaptable ones, which require a higher rate of organic fertilizer application in tidal swamplands as well as in sandy soils.
Organic fertilizers significantly enhance the productivity and sustainability of agricultural practices in tidal swamplands and sandy soil. They are vital for improving soil fertility,20,21 enhancing crop productivity,54 reducing environmental impacts,55 and supporting sustainable agricultural practices in tidal swamps.56 Combined with traditional Knowledge and an integrated farming system, their use can transform these marginal lands into productive agricultural areas. The expansion of sorghum farming to tidal swamplands should consider using fertilizer and soil amelioration to improve soil fertility.
Adaptable varieties differ for various groups of environments and different traits under consideration. Optimal environments for identifying broadly adaptable varieties varied by traits. The multitrait genotype-ideotype distance index proves to be a valuable tool for selecting varieties based on multiple traits. In parallel, the genotype plus genotype interaction biplot effectively identifies adaptable varieties based on individual trait.
Ethical approval and consent were not required for this study, as it did not involve human participants, animal subjects, or sensitives data. The research focused on analyzing experimental data using publicly available software.
The data underlying this study are available in Figshare at https://doi.org/10.6084/m9.figshare.29364263 for data excell (multitraits observation on sorghum)57 and https://doi.org/10.6084/m9.figshare.29497829 for R code.58
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
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