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Article

Evaluation of Yield-Related Morphological, Physiological, Agronomic, and Nutrient Uptake Traits of Grain Sorghum Varieties in the Kerala Region (India)

by
Swathy Anija Hari Kumar
1,2,*,
Usha Chacko Thomas
2,
Yazen Al-Salman
3,
Francisco Javier Cano
4,
Roy Stephen
2,
P. Shalini Pillai
2 and
Oula Ghannoum
1
1
Hawkesbury Institute for the Environment, Western Sydney University, Richmond, NSW 2753, Australia
2
College of Agriculture, Kerala Agricultural University, Vellayani, Trivandrum 695522, India
3
Centre for Crop Systems Analysis, Wageningen University & Research, 6700 AK Wageningen, The Netherlands
4
Instituto de Ciencias Forestales (ICIFOR-INIA), CSIC, Carretera de la Coruña km 7.5, 28040 Madrid, Spain
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(10), 2320; https://doi.org/10.3390/agronomy15102320
Submission received: 9 August 2025 / Revised: 25 September 2025 / Accepted: 29 September 2025 / Published: 30 September 2025
(This article belongs to the Section Farming Sustainability)
Browse Figures
Versions Notes

Abstract

Climate change poses a significant threat to crop production, particularly in tropical and semi-arid regions. Sorghum (Sorghum bicolor (L.) Moench), a resilient C4 cereal, has high photosynthetic efficiency and abiotic stress tolerance, making it a key crop for food, fodder, and feed security. This study evaluated agronomic and physiological traits influencing the yield performance of 20 sorghum varieties under field conditions in Kerala, India. The data were analyzed using a randomized block design (RBD) in GRAPES software, and a principal component analysis was performed in R. Variety CSV 17 exhibited the highest grain yield (GY) (3760 kg ha−1) and harvest index (HI) (43), with early flowering, early maturity, a high chlorophyll content (CHL), and minimal nitrogen (N), phosphorus (P), and potassium uptake. Conversely, CSV 20 produced the highest stover yield (22.5 t ha−1), associated with greater leaf thickness (LT), lower canopy temperature, taller plant height (PH), increased leaf number (LN), and extended maturity. Leaf temperature (Tleaf) was negatively correlated with the quantum yield of photosystem II (ΦPSII) and panicle length (PL), which were strong predictors of grain weight. The principal component analysis revealed that PC1 and PC2 explained 21% and 19% of the variation in the grain and stover yield, respectively. Hierarchical partitioning identified the potassium content (K%), CHL, Tleaf, leaf area index (LAI), ΦPSII, and LT as key contributors to the GY, while the SY was primarily influenced by the LN, nitrogen content (N%), maturity duration, PH, and ΦPSII. These findings highlight the potential of exploiting physiological traits for enhancing sorghum productivity under summer conditions in Kerala and similar environments.

1. Introduction

Climate change is increasingly affecting crop production across many regions of the world due to rising temperatures, reduced precipitation frequency, and a higher incidence of extreme weather events [1,2,3]. Its negative impact is more pronounced in tropical regions than in temperate zones, where climate-related risks leave smallholder farmers particularly vulnerable, primarily due to their dependence on rainfed agriculture [4,5,6]. This challenge is compounded by the fact that most research on crop tolerance to abiotic stress has been focused on temperate agricultural systems, with uncertain applicability in tropical and subtropical contexts [7]. Water deficit influences a wide range of plant functions, from transpiration, photosynthesis, leaf and root growth, and reproduction to underlying physiological processes such as cell division, water transport, cell wall properties, metabolism, and the detoxification of reactive oxygen species.
As a result, cultivating hardy and resilient crops tested under tropical field conditions has been recommended as an alternative strategy for mitigating crop productivity losses due to climate hazards across large parts of Asia and Africa [8,9]. Growing a wider range of well-adapted cultivars is also suggested to enhance farm-level resilience to future climate change [10].
Sorghum, a major global C4 crop, possesses high photosynthetic efficiency and strong tolerance to abiotic stresses [11]. It is cultivated for its nutritious grain, high-quality fodder and feed, and is widely grown in semi-arid tropical regions, areas most vulnerable to climate change [12,13]. Additionally, sorghum holds significant potential as a bioenergy crop, with sweet sorghum yielding the highest ethanol per unit area among crops [14]. In India, sorghum ranks third among food grains after rice and wheat and is the second most important dryland crop after pearl millet [15]. It is cultivated in two main seasons: kharif (rainy) and rabi (post-rainy).
In recent years, summer cultivation of sorghum—either as a rice fallow or a direct crop—has expanded in Maharashtra, Andhra Pradesh, and Karnataka, taking advantage of its drought resilience. This trend highlights the need to investigate the drivers of sorghum productivity and to identify varietal traits suited to multiple growing seasons. Several morphological and phenological traits in sorghum are closely linked to economically valuable outcomes [16]. Traits such as plant height (PH), days to flowering (DTF), grain filling period (GFP), days to maturity (DTM), leaf area, and dry matter accumulation are significantly influenced by environmental conditions [17], making them useful indicators for selecting high-yielding sorghum varieties [18].
At the leaf level, morphological characteristics such as leaf width (LW) and leaf length (LL) determine the total leaf-blade area, which has been shown to correlate negatively with grain yield (GY) in sorghum [19]. Recently, LW has also been negatively associated with leaf water use efficiency (WUE) in sorghum [20,21]. WUE is a critical determinant of yield under stress conditions and is considered an important component of drought resistance in crops [22]. Enhancing WUE is therefore essential for developing resilient and productive farming systems [23].
As drought varies in intensity, traits conferring tolerance to moderate drought, which sustains yield, differ from those linked to survival under severe drought, underscoring the need for context-specific strategies [24]. A recent case study in maize introduced the water stress degree (Dws) index, showing that drought reduced photosynthesis, leaf water content, and biomass in proportion to Dws, with reproductive-stage drought causing greater and less reversible impacts than vegetative-stage stress [25]. Plant responses to drought occur across multiple timescales. Short-term responses, such as stomatal regulation and adjustments in photosynthesis, buffer plants against rapid fluctuations in water availability, while longer-term effects on growth and development ultimately determine yield outcomes [26].
The balance between plant transpiration and soil evaporation, and consequently ecosystem-level WUE, is largely controlled by LAI and total leaf area [27]. An increase in LAI improves light capture [28], which enhances photosynthetic efficiency relative to water loss. Understanding genotypic variation in these traits can support the identification of sorghum lines with improved WUE and greater tolerance to water stress.
Increasing WUE by reducing LW may negatively impact photosynthesis by lowering LAI, revealing a possible trade-off between WUE and productivity. Additionally, narrower leaves are expected to improve thermoregulation under high temperatures because plants require less water for cooling [20,29,30]. Higher leaf temperature (Tleaf) can adversely affect photosynthesis, which is essential for carbon assimilation and crop growth [31]. Changes in total transpiration rates and leaf biomass also influence root development, thereby affecting the uptake of nutrients, particularly nitrogen, which is crucial for photosynthesis and grain formation [32,33]. Exploring the balance between thermoregulation, productivity, and WUE-related traits in our germplasm was a central goal of this study.
Although sorghum varieties have been extensively characterized across different Indian regions, information on their suitability and yield potential under Kerala’s agroecological conditions remains limited [34,35,36,37,38]. Morphological traits such as panicle length, number and length of primary branches per panicle, and plant height vary widely within Indian sorghum germplasms [34,36,39]. Leaf traits including LW, leaf length (LL), and stomatal density are genetically determined [19]. These leaf traits are easier to phenotype in large germplasm collections compared to more complex physiological traits. Therefore, linking leaf morphology with physiological and agronomic traits such as yield, drought tolerance, and WUE is valuable for breeding and selecting genotypes for improvement.
Our overarching aim was to screen Indian sorghum genotypes for variation in traits that influence WUE and to analyze the factors contributing to variation in grain and stover yield. We also sought to assess the performance of these varieties under summer conditions in Kerala. The specific objectives were to (1) evaluate the agronomic and physiological traits affecting the yield performance of twenty sorghum varieties in AEU 8 (Southern Laterites); (2) identify the extent of variation in leaf morphology among these varieties; (3) examine how morphological traits influence underlying physiological processes related to grain yield, such as photosynthesis and nutrient uptake; and 4) analyze the contribution of these traits to grain and stover yield to identify potential trade-offs. As this study was primarily focused on physiological traits, the objectives were addressed within a single year of field experimentation.

2. Materials and Methods

The experiment was conducted under field conditions at the Instructional Farm, College of Agriculture, Vellayani, Kerala, India, located at latitude 8.5° North, longitude 76.9° East, and twenty-nine meters above mean sea level. The study period was from October 2021 to February 2022. Twenty Sorghum bicolor (L.) Moench varieties (Table 1) were collected from various research stations across India to evaluate their yield and suitability for cultivation in AEU 8 (Southern Laterites). These varieties are known for their high yield in their original growing conditions and variation in leaf width (LW). The experimental site’s soil was sandy clay loam with a pH of 6.5, electrical conductivity of 0.17 dS m−1, organic carbon content of 0.9%, and low available nitrogen, potassium, and phosphorus, at 87.81, 110, and 52.6 kg ha−1, respectively.
The field was leveled and divided into forty plots, each measuring 4.5 × 4.5 m. Dried cow dung was applied at 5 t ha−1 as an organic nutrient source. Additional nitrogen (N), phosphorus (P), and potassium (K) were supplied through urea (46% N), factamphos (20% N and 20% P2O5), and muriate of potash (60% K2O). The recommended nitrogen dose of 95 kg ha−1 was applied in two equal splits: half as basal application and the remainder 30 days after sowing. The full phosphorus and potassium doses, each at 45 kg ha−1, were applied basally.
A plant density of approximately 148,000 plants ha−1 was maintained, with a row spacing of 45 cm and an intra-row spacing of 15 cm. Each experimental plot measured 4.5 m × 4.5 m and consisted of multiple rows of a single genotype. For gap filling, seeds were germinated in pot trays, and 14-day-old seedlings were transplanted. Supplemental irrigation was applied based on an IW/CPE ratio of 0.6 from sowing until crop establishment, with the final irrigation before the onset of regular rainfall applied on 18 November 2021. During the cropping period, a total of 669.8 mm rainfall was received. From December onwards, due to reduced rainfall, irrigation was provided weekly until harvest to maintain adequate soil moisture.
The experiment followed a randomized block design (RBD) with two replications and twenty genotypes. Uniform initial plant populations were maintained across plots. Six plants were randomly selected from the center of each plot, tagged, and used for observations, with averages calculated for analysis.

2.1. Growth Attributes

All growth parameters were measured at 30, 60, and 90 days after sowing (DAS), and at harvest (crop maturity, which varied by genotype). PH in cm was recorded from the ground level to the tip of the uppermost leaf before flowering, and to the tip of the ear head after flowering, in the tagged plants. The total number of tillers per plant was counted from the tagged plants and expressed as tillers per plant. The total number of leaves per plant was determined by counting the fully expanded leaves. LL and LW were measured at the midpoint of the lamina of the fourth fully expanded leaf from the top, and leaf area was calculated using the following formula:
L e a f   A r e a   =   L   ×   L W   ×   6.18
where L is the length (cm) of 4th leaf from top, LW is the width (cm) of 4th leaf from top, 6.18 is the constant obtained by multiplying 0.75 by leaf number 4 constant, i.e., 8.242 [40].
LAI was worked out using the following formula:
L A I = L e a f   a r e a   p e r   p l a n t ÷   L a n d   a r e a   o c c u p i e d   b y   t h e   p l a n t
The total leaf area per plant was estimated using Equation (1).
The land area occupied per plant was calculated as
L a n d   a r e a   p e r   p l a n t = R o w   S p a c i n g × P l a n t   S p a c i n g
LAD is the growth analysis parameter used to correlate dry matter yield with LAI by integrating the LAI with time [41]. LAD considers both the duration and extent of photosynthetic tissue of the crop canopy. The LAD is expressed in days.
L A D = ( L 1 + L 2 / 2 ) × ( t 2 − t 1 )
where L1 is LAI at the first stage, L2 is LAI at the second stage, and (t2 − t1) = time interval in days. These stages are 30, 60, and 90 days and the harvest times highlighted above.
Once approximately 50% of the plants in the population initiated flowering, the number of days since planting was recorded as DTF [42]. DTM was recorded as the number of days from sowing to physiological maturity, or the age when the grains are fully ripe [43].

2.2. Agronomic Traits and Yield

PL was measured from the base to the tip of the panicle in the tagged plants, and the mean value was expressed in centimetres (cm). To determine GNP, the panicles from the tagged plants were cut, sun-dried, threshed separately, and the grains from each panicle were manually counted and weighed to obtain the GWP. TW was recorded by weighing 1000 grains collected from the net plot.
At maturity, all plants from the net plot were harvested and sun-dried for one week. The panicles were then separated, threshed, and the grains collected. The weight of grains from each plot was recorded and converted to kg ha−1 to estimate GY. Stover was sun-dried in the respective plots by turning periodically for one week, then bundled and weighed to obtain the S (kg ha−1).

2.3. Physiological Measurements

The MultispeQ device (PhotosynQ, East Lansing, MI, USA) was used to obtain rapid measurements of several physiological traits, including Tleaf, LT, CHL, and ΦPSII. Measurements were taken on young, fully expanded leaves at midday using the MultispeQ at 90 DAS [44].

2.4. Nutrient Content in Plants and Uptake from the Soil

At harvest, samples from the tagged plants were collected. The plant material (stems and leaves) was chopped, shade-dried, and then oven-dried at 70 °C until a constant dry weight was obtained. The dried samples were ground to pass through a 0.5 mm mesh. The required quantities were digested and used for nutrient analysis.
N % was estimated using the modified micro-Kjeldahl method [45]. Phosphorus content was determined calorimetrically by the vanado-molybdate yellow color method using a spectrophotometer [45]. Potassium content was analyzed using the flame photometric method [45]. Nutrient uptake was calculated by multiplying the N, P, or K content by the dry weight of plants and expressed in kg ha−1.
N u t r i e n t   u p t a k e   k g   h a − 1 = N u t r i e n t   c o n t e n t   b y   p l a n t   % × D r y   w e i g h t   o f   S t o v e r k g   h a − 1 100

2.5. Statistical Analysis

Statistical analysis was performed using GRAPES 1.0.0 (General R-based Analysis Platform Empowered by Statistics) developed by the Department of Agricultural Statistics, College of Agriculture, Vellayani [46]. Data normality was tested using the Shapiro–Wilk test. Analysis of variance (ANOVA) was carried out using the randomized block design (RBD) module in GRAPES, which accounts for replication across two plots and within-plot error. Post hoc tests were performed to assess differences between groups.
Pearson’s correlation analysis was conducted to evaluate the significance of relationships and to obtain correlation coefficients. Principal component analysis (PCA) was performed in R to identify the six major contributors (highest coefficients) to PC1 and PC2 (Table S3). To evaluate the relative contribution of these six contributors to variations in GY and SY, hierarchical partitioning analysis was carried out using the hier.part package in R.

3. Results

3.1. Trait Variation

A significantly higher grain yield was observed for CSV 17, whereas a significantly lower GY was observed in N15 (Table S4, Figure 1A). The days to 50% flowering (DTF) across the various varieties revealed a notable range, spanning from 58 days to 86 days, with CSV 17 being the earliest and M35-1 the most delayed (Table S4, Figure 1C). For DTM, HC 260 had the significantly highest mean value of 135 days and CSV 17 had the significantly lowest mean value of 100 days (Table S4, Figure 1D). The narrow-leaved varieties with better yield were identified as N14 and NTJ2. At 90 DAS and at harvest, the LAI of CSV36 was significantly the highest (7.01 and 6.52, respectively) and was on par with that of CSV 31 (6.97 and 6.46, respectively) (Table S5). CSV 27 showed the highest ΦPSII (0.47), while N15 had the lowest ΦPSII (0.31). CO32 had the highest relative chlorophyll content (52.16), whereas N15 (34.84) had the lowest.

3.2. Influence of Leaf Morphology and Physiology on Grain and Stover Yield

The chlorophyll content (CHL) had a positive effect on the GY and GWP (Table S3 and Figure 2c) but had no correlation with nutrient traits or LAI (except for a weak correlation for LAI at harvest; Figure 2d). We expected cooler canopies to have an effect on both the SY and GY, underpinned by reduced LW, but we did not find such a correlation. Instead, we found that increasing the leaf thickness (LT) made the leaves cooler (Figure 3c), leading to higher ΦPSII and lower ΦNPQ (Figure 3a,b,d).
Hence, we found a possible link between early flowering, which leads to a high LAI, and CHL, translating into a higher GY. We expected this mechanism to be underpinned by narrow leaves, cooler leaves, higher photosynthesis, and hence WUE, but we did not find such relationships. On the contrary, the influence of thicker leaves on the cooler canopy is an unexpected result, but one we discuss below. SY, on the other hand, was not influenced by physiology but rather on nutrient accumulation and increases in leaf number (LN) (Table S2, Figure 4).

3.3. Trait Correlation Analysis

The GY showed strong positive correlations with the GWP and GNP, while the SY was strongly associated with N uptake and P uptake. In contrast, significant negative correlations were observed between SY and HI, and between ΦPSII and ΦNPQ (Figure S1).

3.4. Contribution of Component Traits

To uncover some of the relationships we expected, we conducted a principal component analysis on the data (Table S3 and Figure 5a). Indeed, we found that PC1 explained the GY (21%, Table S3) and PC2 explained the SY (19%, Table S3). We then conducted a hierarchical analysis on the six variables with the highest explanatory power for each of the two components. The GY (Figure 5b) was explained by K%, CHL, Tleaf, LAI, ΦPSII, and LT, indicating a complex interaction between morphological, agronomical, and physiological traits. The SY (Figure 5c) was explained by the leaf number (LN), N%, DTM, PH, LAI, and ΦPSII, indicating that the SY was indeed more influenced by delayed flowering and maturity, allowing nutrient accumulation, and the green leaf area.

4. Discussion

We grew a set of 20 sorghum genotypes grown across India to evaluate their yield performance under tropical conditions in Kerala, India. We attempted to use the performance of these genotypes under these conditions to test whether variations in leaf width and other morphological traits determine yield variation. Our main findings were as follows: (1) there is significant variation in yield potential among the genotypes under Keralan conditions and (2) the yield and harvest index were mostly influenced by the crop duration and chlorophyll content, while (3) a higher nutrient uptake was a mechanism enabling vegetative growth and a high stover yield with no to minor negative effects on the GY; hence, (4) leaf morphological features had little direct influence on yield variation and the harvest index.

4.1. Determinants of Increased Yield in Sorghum

The high GY of CSV 17 echoed similar results found in an initial varietal trial in which a GY of 3807 kg ha−1 was obtained for CSV 17 [47]. Through our correlations, one factor that that appeared to be a key determinant of yield was the chlorophyll content (Figure S2C, Figure 5a). This is despite the negative correlation between nutrient uptake and HI, as the nutrient content is usually a strong indicator of chlorophyll content [48,49,50,51]. Maintaining the chlorophyll content is usually associated with the stay-green trait, which is now considered a basis for increased sorghum productivity [52]. CHL is also a key indicator of photosynthetic capacity, which directly influences biomass accumulation and grain filling [53], and, indeed, this is further confirmed by the indirect relationship between ΦPSII and GY (Figure 5a,b). This is slightly surprising, as correlations between photosynthetic parameters and the GY are elusive, especially in C4 crops like sorghum that already exhibit high photosynthetic rates [54].
However, the stay-green trait indicates that leaf production and leaf resource use can lead to higher yields [55], and we find a general trend in our data between increasing LAI and GY (Table S2, Figure 5). This could mean that the SY and GY are correlated, but we did not find evidence of this (Table S2). The SY increased based on a significantly higher LN (Figure 5c and Table S2), while the LN had no effect on the GY. The effect of chlorophyll could be that, combined with reduced LN, maintaining green leaf function at grain filling means increased plant water use and, hence, nutrient translocation, likely increasing yield. The aforementioned study also found that stay-green QTLs decreased the canopy leaf area as a mechanism to save water for later in the season for grain filling [52,55]. However, maintaining a high leaf area can contribute to better ground cover, which in turn reduces soil moisture evaporation and maximizes water use efficiency and possibly yield [56,57]. Also, maintaining the leaf area enables the plant to maximize sunlight absorption [58]. Hence, maintaining the LAI along with a higher CHL likely enabled a causal increase in photosynthesis and allowed its conversion into GY.
The mechanism of maximizing photosynthesis and light capture and converting it into yield would require a higher growth rate, and, indeed, the short flowering duration of high-yield genotypes like CSV 17 (58 DAS) and their shorter time to maturity (100 days) compared to other varieties confirms this. This is the opposite of higher-SY genotypes like CSV 20, which had longer durations to flowering and maturity (80 and 120 DAS, respectively). Early flowering and shorter maturity periods are advantageous traits in rainfed agriculture, as they enable the crop to complete its life cycle before the onset of adverse weather conditions or drought stress [59,60], while also allowing all available ground water to be used quickly and not be lost to run-off or evaporation throughout the season. Reducing soil evaporation could also be enabled by a quicker growth rate and canopy closure, as shown here with the higher LAI of the high-yield varieties. On the other hand, longer crop cycles coincided with increased vegetative growth and increased leaf production (Table S2). This means that increased light and carbon capture on a whole-plant scale did lead to the allocation of resources to vegetative growth, but without the increased translocation of nutrients to the grains, it resulted in lower overall yields (Table S2). Instead, increases in LN lead to increased nutrient uptake and nitrogen content in the vegetative parts, which remain there for long periods, driving a longer crop life span that results in a higher SY but reduces the HI. This would also likely come with narrower leaves, reducing the LAI of some late-maturing genotypes, and obscuring any relationship between the LW and GY. Longer-duration genotypes experience their maximum rate of vegetative growth later in the season, closer to flowering and grain filling, which are powered by increased nutrient uptake rates as the season progresses (Figure 5c). This only benefits vegetative growth as the yield and nutrient uptake rates did not correlate, and this means sorghum might be dependent on nutrient translocation for grain filling rather than on extra nutrient uptake [61].

4.2. Influence of Leaf Morphology and Physiology on Yield

We found little influence of LW on GY or SY to an extent. LW was more an indicator of vegetative growth dynamics. The genotypes that flowered later tended to increase the LW in new leaves as the season progressed (Figure S2A), increasing light and nutrient capture, especially K (Table S2). Increases in nutrient uptake correlated negatively with HI (Table S2). Hence, a combination of an increase in LN and LW concurrently increased the leaf area and SY by stimulating an increase in nutrient uptake and, most likely, water use. Hence, LW can be a good indicator of reduced HI within our germplasm, as LW signals increased investment in leaves (over grains) and longer seasons (rate of LW increase between 60 and 90 days correlated with longer DTF; see Table S2), increasing the rate of nutrient uptake and not nutrient translocation for grain filling. Consequently, SY exhibited a significant positive correlation with N uptake, P uptake, and K uptake [62,63] (Table S2). This also signifies that a shorter-duration crop would likely require less fertilizer over a season to produce the same or more yield. Hence, there is a clear trade-off between longer growth and higher vegetative growth and GY in our sorghum germplasm.
Physiologically, we expected LW to affect T and photosynthesis. While cooler leaves did lead to a higher photosynthetic efficiency (ΦPSII) (Figure 3a,d), this correlation was not influenced by LW. There was a curious effect of the leaf thickness (LT), where increased LT led to leaves being cooler than the air (Figure 3c), which indicates that increased LT comes with increased evaporative cooling and, hence, increased transpiration rates and water use. This could be a minor indicator that increased investment in leaf biomass and not just leaf area increases the transpirative demand without any gains in yield; however, higher leaf thickness has been linked with increased leaf nitrogen and photosynthesis rates, but we did not find that here [64]. Another explanation could be that thicker leaves might have bigger xylem vessels and stomatal size, likely increasing their transpirative capacity [21,65].
Under well-watered conditions, variations in leaf morphology had minimal impact on yield, as increases in LW and LAI likely stimulated both transpiration and growth, balancing increased water use. In contrast, under drought stress, a reduced LW could conserve water and improve yield stability by ensuring water availability during grain filling. As shown in Figure 5, the leaf thickness and LAI contributed positively to yield, but their effects were smaller compared with other traits. Well-bred characteristics such as the stay-green trait, reflected in the strong contribution of CHL (Figure 5c), may have dominated yield determination in genotypes with differing morphologies. Future work should therefore evaluate morphological responses under water-limited conditions to clarify their role in yield variation.

4.3. Screening Varieties for Water Use Efficiency in Southern Indian Conditions

The water use efficiency (WUE) and drought tolerance are closely related, and a higher WUE is a sign of drought tolerance [66]. Two ecological characteristics that are crucial for the plant drought response are early flowering time and WUE [67]. An improved WUE in crops depends on different physiological processes, like a higher leaf-level intrinsic WUE (iWUE), early crop vigor, and deeper roots [68]. Leaf anatomical traits like stomatal density (SD) and size of stomata (SS) are potential drivers for iWUE, which is related to the variations in LW in sorghum [20,65,69,70,71]. Morphological traits such as the LAI and LW can be positively correlated with WUE [72] but can also increase the rate of water use without significant carbon gain, decreasing the yield-based WUE [73]. While a higher LAI may improve light capture and transpiration efficiency, excessive canopy density can lead to mutual shading and reduced photosynthetic efficiency, especially at the crop level [74,75], highlighting the need for an optimal LAI range. In general, narrow leaves have a lower stomatal conductance and higher WUE relative to wider leaves in sorghum [65]. We did not find a correlation between narrow leaves and GY here, with a moderate-to-good yield observed in narrow-leaved varieties such as N14, NTJ2, and NTJ3, suggesting that narrow leaves do not necessarily limit productivity. Thus, despite the weak overall correlation, N14 and NTJ2, as narrow-leaved varieties with better yield, could be considered for WUE screening. Concurrent with this is selecting for a higher CHL, as producing a smaller canopy but with a higher chlorophyll content can give the advantage of higher yield due to light capture by chlorophyll and increasing light use efficiency, without the excess transpirative demand of the thick, dense canopy of wide-leaved genotypes and a high LAI [76]. And, finally, we show that a shorter crop life cycle under Keralan conditions increases the GY (Figure 3a). Shorter cycles maximize the use of available water [77]. Hence, the “genotype” that seems more effective under these conditions is a fast-growth-rate, “smaller” plant that grows quickly and translocates all nutrients to the panicle rather than accumulating nutrients from the soil for the sake of vegetative growth.

5. Conclusions

This study evaluated the yield performance of twenty sorghum varieties in Kerala, India, and identified significant differences in growth, yield, and morphological traits. A trade-off was observed between genotypes with vigorous vegetative growth, longer growth cycles, denser canopies, and higher nutrient uptake, versus those with shorter growth cycles that relied more on nutrient translocation and achieved a higher HI. The LW was not strongly correlated with the GY but showed weak associations with the DTF and stronger correlations with the LAI and LAD, suggesting that wider leaves may indicate longer growth cycles and reduced yield potential under extreme conditions. The CHL consistently emerged as a reliable predictor of the GY. Varieties such as CSV 17, CSV 15, CSV 20, NTJ2, and N14 combined narrower leaves, lower LAI, higher CHL, and greater GY, making them strong candidates for further improvement. Future research should aim to validate these findings through experiments with more replications and across multiple environments and should also focus on testing these genotypes under abiotic stresses, particularly heat and drought, to assess the interactions between genotype and environment and better understand their adaptive potential.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agronomy15102320/s1: Figure S1: Trait correlations. Figure S2. Growth parameters of sorghum measured at different growth stages. (A) LW (Leaf width); (B) LAI (Leaf area index); (C) LAD (Leaf area duration). Figure S3. Experimental field. Table S1. Physiological observations taken from twenty sorghum varieties at 90 DAS. Table S2. Pearson’s correlation matrix of the measured variables. Table S3. Component loadings and total variance of principal components from a principal component analysis (PCA) of the measured variables. Table S4. Yield attributes and yield of twenty sorghum varieties. Table S5. Plant and leaf morphology of sorghum varieties.

Author Contributions

S.A.H.K. and U.C.T. designed the experiment based on ideas by Y.A.-S., F.J.C., R.S., P.S.P. and O.G. S.A.H.K. analyzed all the data with help from Y.A.-S. and F.J.C. and wrote the manuscript with help from all the authors. U.C.T. and O.G. oversaw project execution. All authors have read and agreed to the published version of the manuscript.

Funding

Australian Research Council: CE140100015; Australian Research Council: DP210102730; MCIN/AEI: RYC2021-035064-I.

Data Availability Statement

The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.

Acknowledgments

Swathy Anija Hari Kumar acknowledges the Dual degree PhD Scholarship granted by Western Sydney University and PhD Scholarship granted by Kerala Agricultural university. OG acknowledges funding from the ARC Centre of Excellence for Translational Photosynthesis (CE140100015) and Australian Research Council Discovery Project (DP210102730). Francisco Javier Cano acknowledges grant RYC2021-035064-I funded by MCIN/AEI/ 10.13039/501100011033 and “European Union Next Generation EU/PRTR”.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Abbreviations

The following abbreviations are used in this manuscript:
DASDays after sowing
DTFDays to 50% flowering
DTMDays to maturity
GWPGrain weight per panicle
GYGrain yield
HIHarvest index
iWUEIntrinsic water use efficiency
KPotassium
LADLeaf area duration
LAILeaf area index
LNNumber of leaves
LWLeaf width
NNitrogen
PPhosphorous
PHPlant height
PLPanicle length
RCRelative chlorophyll
SYStover yield
TleafLeaf temperature
TWTest weight
ΔTLeaf temperature differential
ΦNPQNon-photochemical quenching
ΦPSIIQuantum yield of photosystem II

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Figure 1. Yield, yield attributes, and nitrogen uptake of twenty sorghum varieties. (A) Grain yield (kg ha−1); (B) stover yield (kg ha−1); (C) days to 50% flowering (DTF); (D) days to maturity (DTM); (E) nitrogen uptake (kg ha−1). Bars represent mean values ± standard error (SE).
Figure 1. Yield, yield attributes, and nitrogen uptake of twenty sorghum varieties. (A) Grain yield (kg ha−1); (B) stover yield (kg ha−1); (C) days to 50% flowering (DTF); (D) days to maturity (DTM); (E) nitrogen uptake (kg ha−1). Bars represent mean values ± standard error (SE).
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Figure 2. The relationship between the grain yield and growth attributes. (a) GNP: number of grains per panicle vs. grain yield (GY) (kg/ha); (b) GNP: number of grains per panicle vs. GY: grain weight per panicle; (c) GWP: grain weight per panicle vs. leaf relative chlorophyll content; (d) leaf relative chlorophyll content vs. LAI at harvest. (e) Panicle length vs. test weight (TW); (f) GWP: grain weight per panicle vs. test weight (TW). Solid lines represent the best fit through the data. R2 values are from a Pearson product–moment correlation analysis. Degrees of statistical significance are represented as p < 0.001 (***), p < 0.05 (**), p < 0.1 (*).
Figure 2. The relationship between the grain yield and growth attributes. (a) GNP: number of grains per panicle vs. grain yield (GY) (kg/ha); (b) GNP: number of grains per panicle vs. GY: grain weight per panicle; (c) GWP: grain weight per panicle vs. leaf relative chlorophyll content; (d) leaf relative chlorophyll content vs. LAI at harvest. (e) Panicle length vs. test weight (TW); (f) GWP: grain weight per panicle vs. test weight (TW). Solid lines represent the best fit through the data. R2 values are from a Pearson product–moment correlation analysis. Degrees of statistical significance are represented as p < 0.001 (***), p < 0.05 (**), p < 0.1 (*).
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Figure 3. (a) ΦPSII: Actual quantum yield of photosystem II vs. leaf temperature; (b) ΦNPQ: non-photochemical quenching vs. leaf temperature. (c) Leaf thickness (mm) vs. leaf-to-air temperature differential (ΔTleaf) (°C). (d) ΔT (°C) vs. ΦPSII. Solid lines represent the best fit through the data. R2 values are from a Pearson product–moment correlation analysis Degrees of statistical significance are represented as p < 0.0001 (****), p < 0.1 (*).
Figure 3. (a) ΦPSII: Actual quantum yield of photosystem II vs. leaf temperature; (b) ΦNPQ: non-photochemical quenching vs. leaf temperature. (c) Leaf thickness (mm) vs. leaf-to-air temperature differential (ΔTleaf) (°C). (d) ΔT (°C) vs. ΦPSII. Solid lines represent the best fit through the data. R2 values are from a Pearson product–moment correlation analysis Degrees of statistical significance are represented as p < 0.0001 (****), p < 0.1 (*).
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Figure 4. The importance of time to flowering for yield attributes: (a) days to 50% flowering (DTF) vs. grain yield (GY)(kg/ha); (b) DTF vs. leaf area index (LAI) harvest; (c) days to 50% flowering (DTF) vs. HI (harvest index). R2 values are from a Pearson product–moment correlation analysis. Degrees of statistical significance are represented as p < 0.1 (*).
Figure 4. The importance of time to flowering for yield attributes: (a) days to 50% flowering (DTF) vs. grain yield (GY)(kg/ha); (b) DTF vs. leaf area index (LAI) harvest; (c) days to 50% flowering (DTF) vs. HI (harvest index). R2 values are from a Pearson product–moment correlation analysis. Degrees of statistical significance are represented as p < 0.1 (*).
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Figure 5. Multivariant trait analysis of grain and stover yield. (a) shows PCA1 vs. PCA2 components that explain variance among genotypes using the following variables: GY, SY, HI, PH, LAI harvest, leaf number at 90 DAS, leaf thickness, DTM, ΦPSII, Tleaf, relative chlorophyll content, and plant %N, %P, and %K. (b,c) show the hieratical analysis performed to explain grain yield (GY) (b) and stover yield (c) variation using the sixth most important variables selected from the list above, but excluding harvest index.
Figure 5. Multivariant trait analysis of grain and stover yield. (a) shows PCA1 vs. PCA2 components that explain variance among genotypes using the following variables: GY, SY, HI, PH, LAI harvest, leaf number at 90 DAS, leaf thickness, DTM, ΦPSII, Tleaf, relative chlorophyll content, and plant %N, %P, and %K. (b,c) show the hieratical analysis performed to explain grain yield (GY) (b) and stover yield (c) variation using the sixth most important variables selected from the list above, but excluding harvest index.
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Table 1. Description of sorghum varieties.
Table 1. Description of sorghum varieties.
Treatments/VarietiesCenter of CollectionPedigree/Parentage/Other Features
CSV 27ICAR-IIMRGJ 35 × E 35-1
Dual-purpose kharif variety, resistant to grain moulds, non-lodging and non-shattering
CSV 31ICAR-IIMRSPV 462 × SPV 1329
Kharif variety, resistant to grain mould, resistant to anthracnose and leaf blight
NTJ2RARS Nandyal, Andhra PradeshNandyal Tella Jona–2, white sorghum variety
CO32TNAU(APK 1 × M35-1)
CSV23ICAR-IIMRSPV 861 × SU 248
CSV15ICAR-IIMRSPV 475 × SPV462
CSV 20ICAR-IIMRSPV 946 × Kh89-246
CSV 36ICAR-IIMRSPV1231 × NSV13, kharif variety
CSV 39ICAR-IIMRSPV 772 × SPV 1754, kharif variety
NTJ 5RARS NandyalNandyal Tella Jona–5, white sorghum variety
M35-1UAS Dharwad Selection from maldandi landraces
CSV 17ICAR-IIMRSPV 946 × SPV 772
CSV 13ICAR-IIMR(IS12622 × 555) × IS 3612 × E35-1-52
N13RARS, NandyalYellow sorghum variety
NTJ 4RARS, NandyalNTJ 1 × CMS3
NTJ1RARS, NandyalNandyal Tella Jona–1
N 15RARS NandyalRabi sorghum
N 14RARS NandyalYellow sorghum variety
NTJ3RARS NandyalMJ 2092 × POD 24
HC 260CCS HAU, HisarSPV 103 × PC 9
ICAR: Indian Council of Agricultural Research; IIMR: Indian Institute of Millets Research; RARS: Regional Agricultural Research Station; TNAU: Tamil Nadu Agricultural University; CCS HAU: Chaudhary Charan Singh Haryana Agricultural University.
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MDPI and ACS Style

Anija Hari Kumar, S.; Chacko Thomas, U.; Al-Salman, Y.; Cano, F.J.; Stephen, R.; Pillai, P.S.; Ghannoum, O. Evaluation of Yield-Related Morphological, Physiological, Agronomic, and Nutrient Uptake Traits of Grain Sorghum Varieties in the Kerala Region (India). Agronomy 2025, 15, 2320. https://doi.org/10.3390/agronomy15102320

AMA Style

Anija Hari Kumar S, Chacko Thomas U, Al-Salman Y, Cano FJ, Stephen R, Pillai PS, Ghannoum O. Evaluation of Yield-Related Morphological, Physiological, Agronomic, and Nutrient Uptake Traits of Grain Sorghum Varieties in the Kerala Region (India). Agronomy. 2025; 15(10):2320. https://doi.org/10.3390/agronomy15102320

Chicago/Turabian Style

Anija Hari Kumar, Swathy, Usha Chacko Thomas, Yazen Al-Salman, Francisco Javier Cano, Roy Stephen, P. Shalini Pillai, and Oula Ghannoum. 2025. "Evaluation of Yield-Related Morphological, Physiological, Agronomic, and Nutrient Uptake Traits of Grain Sorghum Varieties in the Kerala Region (India)" Agronomy 15, no. 10: 2320. https://doi.org/10.3390/agronomy15102320

APA Style

Anija Hari Kumar, S., Chacko Thomas, U., Al-Salman, Y., Cano, F. J., Stephen, R., Pillai, P. S., & Ghannoum, O. (2025). Evaluation of Yield-Related Morphological, Physiological, Agronomic, and Nutrient Uptake Traits of Grain Sorghum Varieties in the Kerala Region (India). Agronomy, 15(10), 2320. https://doi.org/10.3390/agronomy15102320

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