Cogent Food & Agriculture ISSN: 2331-1932 (Online) Journal homepage: www.tandfonline.com/journals/oafa20 Transpiration efficiency in the major races of Ethiopian sorghum: unraveling genetic variation for drought adaptation Abel Debebe Mitiku , Tileye Feyissa , Alemu Tirfessa Woldetensaye , Habte Nida Chikssa , Temesgen Matiwos Menamo , Tewodros Mesfin Abebe & Kassahun Bante To cite this article: Abel Debebe Mitiku , Tileye Feyissa , Alemu Tirfessa Woldetensaye , Habte Nida Chikssa , Temesgen Matiwos Menamo , Tewodros Mesfin Abebe & Kassahun Bante (2025) Transpiration efficiency in the major races of Ethiopian sorghum: unraveling genetic variation for drought adaptation, Cogent Food & Agriculture, 11:1, 2516768, DOI: 10.1080/23311932.2025.2516768 To link to this article: https://doi.org/10.1080/23311932.2025.2516768 © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group View supplementary material Published online: 01 Jul 2025. 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However, drought stress significantly limits its productivity, emphasizing the need to enhance drought tolerance traits. One such trait is transpiration efficiency (TE), which reflects a plant’s ability to use water efficiently. This study tested a core collection of 182 Ethiopian sorghum accessions, representing major races for drought tolerance related traits. The accessions were grown in 16 l lysimeters under a rain-out shelter, employing a randomized complete block design with three replications. Our findings revealed significant genetic variation (p < 0.001) in TE and related traits among sorghum races. Durra races exhibited the highest TE (6–8.5 kg/L), while intermediate races (dura-caudatum, kafir-caudatum, dura-guinea) showed moderate. Conversely, kafir-caudatum, kafir-bicolor, guinea-bicolor, guinea-caudatum, dura-caudatum and bicolor exhibited the lowest TE. Strong correlations were found between TE and traits such as total shoot fresh weight, total shoot dry weight, and leaf number. Principal component analysis identified key traits influencing TE, while cluster analysis grouped accessions into four distinct clusters, with Cluster IV containing genotypes with the highest TE. These findings highlight the potential of TE in breeding water-efficient sorghum varieties and provide valuable genetic resources for enhancing drought tolerance. 1.  Introduction Sorghum ranks as the fifth most produced grain worldwide (FAO, 2024), with global production reaching approximately 57.96 million tons from around 40 million hectares in the 1919/20 season, primarily driven by contributions from the United States, Nigeria and Ethiopia. In sub-Saharan Africa, sorghum is a crucial food source, providing subsistence security for nearly 500 million people (Andiku et  al., 2021). Its versa- tility extends to human consumption, animal feed, fuel sources and gluten-free diets (Verma et  al., 2018). Sorghum is vital in arid and semi-arid regions due to its resilience and lower water requirements com- pared to other cereals (Dunjana et  al., 2022; Kumar et  al., 2011; Ndlovu et  al., 2021). In Ethiopia, sorghum is a key cereal crop, ranking third in production and making a substantial con- tribution to the nation’s grain output. In the 2017/18 crop year, the total sorghum production reached around 5.5 million metric tons, cultivated over approximately 1.5 million hectares (CSA, 2018). It is par- ticularly essential for subsistence farmers in drought-prone areas, offering multiple uses (food, animal feed, firewood, construction and local beer production) and adapting well to the country’s varied and challenging climates (Adugna, 2007). However, sorghum yields in Ethiopia are lower than the global average, primarily due to moisture stress, diseases and pests, and reliance on lower-yielding local variet- ies (Amelework et  al., 2016; Gebretsadik et  al., 2014). Despite numerous studies aimed at improving © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group CONTACT Abel Debebe Mitiku aabbeelldebebe@gmail.com Department of Agricultural Biotechnology, Melkassa Agricultural Research Center, Ethiopian Institute of Agricultural Research, Adama, Ethiopia Supplemental data for this article can be accessed online at https://doi.org/10.1080/23311932.2025.2516768. https://doi.org/10.1080/23311932.2025.2516768 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. ARTICLE HISTORY Received 27 November 2024 Revised 21 February 2025 Accepted 2 June 2025 KEYWORDS Transpiration efficiency; sorghum races; drought tolerance; core collection; durra; lysimeter SUBJECTS Agriculture & Environmental Sciences; Botany; Biodiversity & Conservation mailto:aabbeelldebebe@gmail.com https://doi.org/10.1080/23311932.2025.2516768 https://doi.org/10.1080/23311932.2025.2516768 http://creativecommons.org/licenses/by/4.0/ http://crossmark.crossref.org/dialog/?doi=10.1080/23311932.2025.2516768&domain=pdf&date_stamp=2025-7-1 2 A. D. MITIKU ET AL. sorghum production, the accelerating effects of climate change exacerbate biotic and abiotic stresses, posing significant challenges to sorghum production globally (Yarnell, 2008). Drought tolerance, in sorghum, is a complex trait with low heritability for direct yield selection. The low heritability of crop yield under drought conditions has led breeders to focus on secondary drought-adaptive traits with moderate to high heritability that reflect the physiological stress responses (Kapanigowda et  al., 2013). These include physiological, agronomical and morphological traits like shoot and root architecture; for instance, stay-green, chlorophyll content, transpiration efficiency (TE) and root system architecture (Borrell, Mullet, et  al., 2014; Lafitte et  al., 2003; Sinclair, 2012; Tuberosa, 2012; Zhi et  al., 2022). Ethiopia is the center origin and diversity for sorghum (de Wet et  al., 1972) thus it harbors a wealth of genetic variation in its diverse agro-ecologies (Mekbib, 2007; Mola & Ejeta, 2021). For example, Ethiopian landrace BTx642 was one of the source of stay-green trait, a key source of post-flowering drought response in sorghum (Mahalakshmi & Bidinger, 2002). TE is the total dry matter produced per unit of water transpired, improvement of this maximizes crop production per unit of water used (A. L. Fletcher, 2020). Thus, the trait is crucial for evaluating genetic variation in water use efficiency (WUE; Unkovich et  al., 2018). It is key trait associated with drought tol- erance, could maintain productivity even under limited water availability (Raymundo et  al., 2023). Studies have documented genotypic variation in TE across diverse sorghum germplasm under various environ- mental conditions (Grégoire et  al., 2024; Leakey et  al., 2019; Pandey et  al., 2021; Salehi-Soghadi et  al., 2023; Vadez et  al., 2014). Considerable efforts have been made to enhance sorghum productivity, focus- ing on areas like exploring the genetic diversity of local landraces, identifying traits linked to drought tolerance, discovering genes associated with drought-adaptive traits such as stay-green and developing drought-tolerant varieties (Girma et  al., 2019; Kapanigowda et  al., 2012; Menamo et  al., 2021; Wondimu et  al., 2021). Keeping all these points in view, and given the effectiveness of evaluating Ethiopian diverse core sorghum germplasm, particularly focusing on various races of sorghum and their TE, are lacking. Thus, addressing this gap could facilitate the development of water-efficient sorghum cultivars. Therefore, this study aimed to characterize Ethiopian core-collection sorghum germplasm for TE and to explore the relationship between TE and other drought-related traits. 2.  Materials and methods 2.1.  Plant materials This study utilized a diverse panel of 182 sorghum accessions. The panel originated from the core col- lection of Ethiopian sorghum germplasm as described by Girma et  al. (2020). Four improved varieties (Chiro, Melkam, B35 and Macia) were used as controls, along with landraces representing various sor- ghum races. The check varieties have demonstrated proven benefits for high yield, seed quality, stay-green traits for post-drought tolerance, and are suitable for growth in both highland and lowland areas of Ethiopia (Adugna, 2007; V. B. R. Prasad et  al., 2021). The racial composition consisted of the main races of bicolor (9%), caudatum (18%), durra (18%), guinea (15%), kafir (3%) and intermediate races of: durra-caudatum (12%), guinea-bicolor (7%), durra-kafir (2%), guinea-caudatum (2%), kafir-bicolor (2%), kafir-caudatum (2%) and durra-guinea (1%) (Table S1). 2.2.  Growth conditions and experimental design The experiment was conducted at Melkassa Agricultural Research Center (MARC), Ethiopia, under a white-net house using large pots/buckets (30 cm diameter, 28 cm height) that served as lysimeters. Four reference pots were used to determine the water-holding capacity of the soil. Each pot was predrilled for drainage and filled with 15 kg of soil. The soil was then slowly saturated with water until it began to drip from the bottom. To prevent evaporation, the pots were sealed with black plastic and left to drain for 1 week. After draining, the four reference pots were weighed and their average weight (20.3 kg) was used as drained upper limit of the soil’s water-holding capacity in this experiment, then after deducting https://doi.org/10.1080/23311932.2025.2516768 Cogent Food & Agriculture 3 0.5 kg to avoid water lodging it became which was (19.8 kg) was used as a target weight for watering throughout the experiment. The experiment was performed in a randomized complete block design with three replicates. The experiments were conducted in three batches, each containing 180 experimental units. Five check acces- sions were included in each batch. Pots with a diameter of 30 cm and a height of 28 cm were filled with 15 kg of topsoil. Urea and DAP fertilizer were incorporated into the soil at a rate of 0.3 g/kg of soil. Then, five seeds were sown in each pot (Figure 1a), and after seedling emergence, thinning was gradually carried out until only one healthy plant remained per pot (Figure 1b, c). Then, the pots were sealed with black plastic bags (Xin et  al., 2008). After sealing the pots (Figure 1d), the initial weight of each pot was recorded. To ensure consistent moisture levels, water was added until the weight reached the predeter- mined upper limit (target weight). Throughout the experiment, pot weights were measured daily before the plants were irrigated (Figure 1e, f ). Irrigation occurred every other day until harvest at the 12 expanded leaf stages, with the amount of water applied equivalent to the amount transpired by the plant since the previous measurement (Figure 1f ). The temperature at the experimental site was recorded at regular intervals during the experimental period (Table S2). 2.3.  Data collection and analysis At harvest, the final weight of each lysimeter was recorded. Then, data on plant height (PH), leaf number (LN), tiller number (TN) and chlorophyll content (Soil Plant Analysis Development [SPAD]) were collected before the shoots were cut at the base of the stem. Then, the shoots were separated into leaves and stems, and the leaf area (LA) was measured with a LA meter (LI-3100, LI-COR Inc., Lincoln, Nebraska 68504-0425, USA). After that, the fresh weight of the shoot was measured with a balance, and the shoot dry weight (SDW) of each experimental plant was determined after the fresh shoot biomass (ShB) was oven-dried at 70 °C until it reached a constant weight. The TE of each experimental plant was calculated on a shoot basis by dividing the amount of ShB produced by each plant by total water transpired from each plant in kilogram from cover to harvest (Lu et  al., 2018). The analysis of variance (ANOVA) for the traits was carried out using the META-R linear model-R package. Figure 1.  Pictorial representation of experimental steps in screen house. https://doi.org/10.1080/23311932.2025.2516768 4 A. D. MITIKU ET AL. The model is Rep Gen Covij ijY i j= + + + +∈µ where Yij is the trait of interest, μ is the overall mean effect, Repi is the effect of the ith replicate, Genj is the effect of the jth genotype, Cov is the effect of the covariate and εij is the effect of the error asso- ciated with the ith replication and jth genotype, which is assumed to be independently and identically distributed (iid) normal with mean zero and variance σε 2. META-R was used for basic statics and linear mixed model analysis, and the replicates match the entire block described by Alvarado et  al. (2020). It is also used to compute broad sense heritability (H2) of a given trait at an individual environment is cal- culated as H g g e n 2 2 2 2 = +( ) σ σ σ / Rep where σg 2 and σe 2 are the genotype and error variance components, respectively, and nRep is the num- ber of replicates. Phenotypic correlation, the observable correlation between variables, which includes genotypic and environmental effects, was calculated from the variance covariance components using the formula by Miller et  al. (1958) as follows: Phenotypic correlation phenotypic covariance of and pX pY = × X Y σ σ2 2(( ) where, σ2p of X = phenotypic variance for character X, and σ2p of Y = phenotypic variance for char- acter Y. Linear regression and cluster analyses were carried out using the R-program. Principal compo- nent analysis (PCA), linear regression and multiple regressions were performed using the built-in R functions prcomp and princomp. Moreover, cluster analysis was conducted using the cluster R package in the R program (version 4.1.2). 2.4.  Path coefficient analysis Path analysis was computed using Dewey and Lu (1959) approach to evaluate the direct and indirect effects of TE with related traits. As follows: r P r pij ij ik kj= + Σ where, rij = mutual association between the independent character (i) and dependent character (j) as measured by the genotypic correlation coefficients. Pij = direct effects of the independent character (i) on the dependent variable (j) as measured by the genotypic path coefficients and Σrikpkj = Sum of the elements of independent character (i) on a given dependent character (j) through all other independent characters (k). 3.  Results 3.1.  Phenotypic variation in TE and related traits To assess the effect of environmental variations across the three batches, we analyzed correlations between TE and related traits using five check genotypes, which were repeated in multiple batches (Figure S1). The results showed that TE and related traits were positively correlated across all experimen- tal runs. ANOVA identified significant treatment variations (p < .001) across all nine traits studied in the 182 genotypes, with the exception of LA (Table 1). The germplasm exhibited wide range of variation across all studied traits. For example, TE averaged 5 g/kg, with the value ranging from 1 g/kg to 9 g/kg. The PH https://doi.org/10.1080/23311932.2025.2516768 Cogent Food & Agriculture 5 ranged from 32 cm to 184 cm, TLA from 593 cm2 to 11365.58 cm2, total fresh weight (TFW) from 94 g to 963 g, total dry weight (TDW) from 11 to 202 g and the total transpired water (TTW) from 3.3 L to 44.5 L (Table 2). Heritability estimates of these traits were moderate. A racial analysis of TE revealed that genotypes from the durra race exhibited the highest TE, followed by bicolor, caudatum, guinea and kafir races (Table S4). This suggests that a larger proportion of durra genotypes demonstrate superior TE compared to other sorghum races. While, among the mixed races guinea-bicolor showed highest TE. Conversely, intermediate races like kafir-caudatum, kafir-bicolor, guinea-caudatum, dura-caudatum, bicolor and caudatum generally exhibited lower TE. Table S3 summa- rize characteristics of the top 10 genotypes with their variable treatments. The genotype ETSL_101101 was characterized by its small dry weight and total leaf area (TLA). On the other hand, ETSL_101492 had the smallest TLA, LN and the highest number of tiller (Nti) groups. Moreover, Table 3 provides the con- trasting (the highest and the lowest) 10 genotypes for TEs and related traits and the geographic origin of top 10%, checks and known genotypes for TEs. For instance, the genotype ETSL_101160, ETSL_101506, ETSL_101443, ETSL_100722 and ETSL_101292 originates from the dryland area of Tigray, Raya Azebo; Illubabor, Gechi; Tigray, Rya Azebo; Gambela; North Wollo, Guba Lafto Ethiopia respectively. 3.2.  Correlation and PCA of shoot TE and related traits Correlation analysis revealed that a strong positive association between TE and TFW followed by TDW and total leaf number (TLN). These correlations were statistically significant (p < .01), with R-squared val- ues of 0.38 for TFW, 0.32 for TDW and 0.15 for TLN (Figure 2). Furthermore, TDW exhibited the highest positive correlations with TFW, TLA, TLN, PH, TTW and Nti. Similarly, TLN displayed the highest positive correlations with TLA, Nti, TFW and TDW. In contrast, TTW had the highest negative relationship with TE. Additionally, SPAD values were negatively associated with the TDW, TTW, TLA and TLN. To identify key factors influencing TE, we employed PCA on all traits. Consistent with the correlation results, PCA revealed that TLN, TFW, LA, TDW and Nti were the most influential traits. The first two prin- cipal components (PC1 and PC2) explained 17.2% and 31.9% of the total variation, respectively (Figure 3). 3.3.  Path analysis of TE with other traits The path coefficient analysis revealed that TDW (0.79) had the maximum positive direct effect on TE while SPAD, TFW, TDW, TLN and PH showed small positive effect (Table 4). This trait also had highly sig- nificant phenotypic correlation with TE. Whereas, TLA, TTW and Nti showed negative direct effect. Though, Table 1.  Analysis of variance for transpiration efficiency and related traits. SOV DF TE PH LN TN SPAD LA FW DW TTW Rep 2 6.35ns 43.1ns 0.59ns 0.19ns 27.09ns 6976326* 8174ns 848.89ns 73.67ns Trt 181 4.47*** 862.02*** 0.819*** 0.522*** 48.64*** 3320292*** 33370*** 669.99*** 51.94*** Res 362 2.57 467.04 0.517 0.323 27.42 1798701 19845 398.85 30.15 SOV, source of variation; DF, degree of variation; TE, transpiration efficiency (kg/m3); PH, plant height (cm); LN, leaf number; TN, tiller number; LA, leaf area (cm2); FW, fresh weight (g); DW, dry weight (g); TTW, total transpirable water (L). Table 2. D escriptive statistics and broad-sense heritability of transpiration efficiency and related traits in sorghum. Trait Unit Mean Range SD H2 (%)Max Min Transpiration efficiency (TE) gm/kg 5 9 1 0.51 42 Plant height (PH) cm 104 184 32 7.76 46 Total leaf area (TLA) cm2 4685 11365.58 593.4 482.11 46 Total leaf number (TLN) count 24 51 8.8 1.80 36 Chlorophyll content of leaves (SPAD) SPAD 51 83 35 1.75 44 Number of tiller (Nti) count 1.3 5 0 0.23 38 Total fresh weight (TFW) gm 515 963 94 42.74 41 Total dry Weight (TDW) gm 65 202 11 6.04 40 Total transpired water (TTW) l 14 44.5 3.3 1.74 42 Max, maximum; Min, minimum; SD, standard deviations; H2 (%), heritability in percent. https://doi.org/10.1080/23311932.2025.2516768 https://doi.org/10.1080/23311932.2025.2516768 6 A. D. MITIKU ET AL. TLN and TLA had significant phenotypic correlation with TE, their direct effect was negligible. The amount of transpirable water displayed a negative linear direct effect with the TE, suggesting that higher tran- spiration is linked to lower TE. This analysis suggests that TE is primarily influenced by water manage- ment and plant growth, with no clear connection to LA. 3.4.  Cluster analysis This cluster analysis grouped the 182 genotypes into four distinct clusters based on various traits such as TE, PH, TN, Nti, SPAD value, TFW, TDW and total transpirable water. Cluster means of the TE and Table 3.  High and low transpiration efficiency landraces (top 10%), checks, other known germplasm and their origin. Landrace Accession Origin TDW (g) TTW (L) TE (g/kg−1) ETSL_101160 Landrace Tigray, Raya Azebo 55.93 6.5 8.6 ETSL_101292 Landrace Amhara, North Wollo, Guba Lafto 55.13 6.83 8.1 ETSL_101506 Landrace Oromia, Illubabor, Gechi 54.5 6.83 8 ETSL_101443 Landrace Oromia, East Hararghe, Meta 82.43 10.63 7.8 ETSL_100722 Landrace Gambela 49.33 6.5 7.6 ETSL_100919 Landrace Amhara, East Gojjam, Guzamin 78.6 10.53 7.5 ETSL_100315 Landrace NA 58.68 8.43 7 ETSL_101138 Landrace Tigraye, Southern Tigraye, Raya Azebo 82.9 12.43 6.7 ETSL_100204 Landrace Oromia, West Shewa, Ilu Gelan 53.68 8.2 6.5 ETSL_100436 Landrace Oromia, West Wollega, Begi 142.56 22.43 6.4 Chiro Improved variety Oromia, East Hararghe, Meta 65.1 11.03 5.9 Macia Improved variety Oromia, East Shewa, Adama 61.42 10.58 5.8 PML981476 Landrace NA 66 12.1 5.5 B35 Improved variety Western Ethiopia, specifically the Gambela region 52.2 9.83 5.3 IS_25531 Landrace Butare, Colline Kabirizi 78.33 15.67 5 Melkam Improved variety Oromia, East Shewa, Adama 77.88 16.03 4.9 Assosa 1 Landrace NA 58.03 11.9 4.9 TAM428 Landrace NA 80.03 17.07 4.7 IS_38279 Landrace Amhara, South Wollo, Ambassel 52.8 13 4.1 IS_38331 Landrace Oromia, West Hararghe, Chercher & Adal 62.37 15.8 3.9 IS_38259 Landrace Amhara, North Wollo, Raya & Kobo 66.35 17.48 3.8 Bonsa Landrace NA 62.9 17.63 3.6 ICTG2372 Landrace NA 52.78 17.15 3.1 P9830 Landrace NA 49.9 17.6 2.8 TDW, total dry weight; TTW, total transpired water; TE, transpiration efficiency, NA, not available. Figure 2.  Correlation of TE and related traits. Cogent Food & Agriculture 7 related traits were depicted in Table 5. The distribution of genotypes across clusters was varies from 1% (Cluster III) to 58% (Cluster IV) (Figure 4 and Table 5). Cluster II contained genotypes with the highest values for TLA, PH, dry weight, fresh weight, TN and total transpirable water. For instance, ETSL_100428(caudatum), ETSL_100200(guinea), ETSL_100436(caudatum), ETSL_101559 (bicolor), ETSL_101418 (durra) and ETSL_100983 (caudatum). Cluster III is unique, containing two genotypes, ETSL_100349 (bicolor) and ETSL_100841 (Guinea bicolor). ETSL_100349 exhibited the lowest values for all measured traits (TLA, TDW and TFW). Cluster IV contained genotypes with the highest TE, chlorophyll content and TN. Examples include ETSL_101160 (race durra kafir), macia race and IS_38331 (race durra) (Tables S1 and 6). 4.  Discussions Drought is becoming a major sorghum production constraint in the face of climate change. Drought stress can significantly impact global sorghum productivity, leading to yield losses of up to 50–90% during critical growth stages in pre and post flowering (Assefa et  al., 2010; Yahaya et  al., 2023). For instance, in sub Saharan Africa drought caused yield losses of 36% and 55% during vegetative and reproductive of sorghum production stages respectively (Khalifa & Eltahir, 2023). Environmental variables like temperature Figure 3.  PCA of TE and related traits. Table 4. D irect (diagonal) and indirect (off-diagonal) effects TE and related traits in 182 sorghum genotypes. Traits PH TLA TLN SPAD TFW TDW TTW Nti PH 0.0337 0.0015 −0.0031 0.0039 0.0029 0.2282 0.2179 0.0003 TLA −0.0007 −0.0670 0.0312 −0.0157 0.0058 0.3539 −0.1738 −0.0055 TLN −0.0019 −0.0390 0.0535 −0.0114 0.0041 0.2414 −0.0900 −0.0103 SPAD −0.0026 0.0208 −0.0121 0.0505 −0.0026 −0.3002 0.3277 0.0016 TFW 0.0106 −0.0422 0.0238 −0.0142 0.0092 0.5851 −0.1926 −0.0036 TDW 0.0098 −0.0302 0.0164 −0.0193 0.0069 0.7862 −0.4472 −0.0018 TTW 0.0079 −0.0125 0.0052 −0.0178 0.0019 0.3782 −0.9296 −0.0007 Nti −0.0005 −0.0187 0.0281 −0.0042 0.0017 0.0702 −0.0342 −0.0196 Dependent Variable: TE. Predictors: Nti, TTW, PH, TLA, SPAD, TDW, TLN, TFW. Table 5.  Cluster means for TE and related traits of 182 sorghum genotypes. Traits Proportion C-I C-II C-iII C-IV TE 4.9 5.2 4.9 4.2 5.1 PH 107.3 111.9 108.1 108.9 100.3 TLA 4386.5 4434.9 5039.6 3421.5 4649.9 TLN 24.1 23.3 25.6 22.8 24.5 SPAD 51.6 51.4 49.6 54.0 51.5 TFW 491.6 509.9 555.3 397.6 503.5 TDW 62.4 65.7 70.3 50.7 62.9 TTW 14.0 14.1 15.6 12.7 13.4 Nti 1.3 1.1 1.3 1.5 1.3 C-I, II, III, IV are clusters 1–4. https://doi.org/10.1080/23311932.2025.2516768 8 A. D. MITIKU ET AL. and humidity play crucial roles in flowering under drought conditions (Hossain et  al., 2022). Higher tem- perature can cause stomata opening widely, increase water loss. Thus, understanding the interaction between environmental factors and transpiration need to be consider to develop more resilient cultivars to maintain high productivity (Shahzad et  al., 2021). TE is an important drought adaptation trait that plays a substantial role in crop growth in water-limited environments (Sinclair, 2012; Vadez et  al., 2014). Figure 4. D enderograph of four clusters for TE and related traits. Table 6. D issemination of 182 genotypes in different clusters. Cluster Levels and name of genotypes I 180 (ETSL_101101),181 (ETSL_101492),14 (ETSL_101416),38 (ETSL_101555),59 (ETSL_100619),139 (ETSL_101270),27 (ETSL_101856),42 (ETSL_101312),137 (ETSL_101508),141 (ETSL_100689),125 (ETSL_101671),138 (ETSL_100432),73 (IS_25531),126 (ETSL_100492),168 (ETSL_101138),128 (ETSL_101443),140 (ETSL_100902),176 (ETSL_100956),178 (ETSL_101029),179 (ETSL_100977) II 61 (ETSL_101559),92 (TAM428),135 (ETSL_101114),5 (ETSL_100919),18 (IS_38319),12 (PML981476),47 (ETSL_100739),50 (ETSL_100768)52 (ETSL_100707),10 (ETSL_101445),22 (ETSL_101006), 88 (ETSL_100890),103 (ETSL_100365),114 (ETSL_101232),80 (ETSL_100661),95 (ETSL_100877),115 (ETSL_101698),109 (ETSL_101687),70 (ETSL_101451),107 (ETSL_100634),1 (ETSL_100105),44 (ETSL_101676),100 (ETSL_100016),62 (ETSL_101258),89 (ETSL_101014),90 (ETSL_101000),118 (ETSL_101323),98 (ETSL_101858),101 (ETSL_100265),111 (ETSL_101673),167 (ETSL_101255),7 (ETSL_100408), 9 (ETSL_100428),13 (ETSL_100782),146 (ETSL_100702), 173 (ETSL_100091), 124 (ETSL_101418),85 (ETSL_100101),96 (ETSL_100073), 174 (ETSL_100431), 182 (ETSL_100436), 86 (ETSL_101436),67 (ETSL_100430), 78 (ETSL_100090),91 (ETSL_100450),108 (ETSL_100648),93 (ETSL_101578),68 (ETSL_101225), 97(ETSL_101369),175(ETSL_100303),60(ETSL_100444),66(ETSL_100978), 71 (ETSL_101719),79 (ETSL_100200),177 (ETSL_100983) III 120 (ETSL_100349), 142 (ETSL_100841) IV 148 (ETSL_101023),149 (ETSL_101704),145 (IS_38331),151 (ETSL_100669), 83 (ETSL_101097),117 (ETSL_100419),136 (ETSL_100951),110 (ETSL_101249),121 (ETSL_100842),171 (ETSL_100851),172 (ETSL_101061,69 (ETSL_101515), 102 (ICTG2372), 105 (ETSL_100406), 74 (P9830), 166 (ETSL_101686),75 (Bonsa),144 (ETSL_100020),162 (IS_38279),152 (ETSL_101021),164 (ETSL_100539),119 (ETSL_101248),64 (ETSL_100121),112 (ETSL_101128),116 (ETSL_101127),65 (ETSL_100699),143 (ETSL_100644),157 (ETSL_100072),134 (ETSL_100353),165 (ETSL_101523),20 (ETSL_101486),34 (ETSL_100722),31 (ETSL_101506),4 (ETSL_101310),37 (ETSL_101527),16 (ETSL_101262), 25 (ETSL_101305),8 (ETSL_101431),32 (ETSL_101470),6 (ETSL_100141),48 (ETSL_101058),40 (ETSL_101297),56 (Chiro),57 (Macia),33 (ETSL_101160),36 (ETSL_100544),51 (ETSL_100797),35 (ETSL_101292),55 (ETSL_100315),81 (ETSL_101254), 154 (ETSL_100079),127 (ETSL_100874),170 (ETSL_101210),11 (ETSL_101491),29 (ETSL_100945),45 (ETSL_100204),41 (ETSL_100666),131 (ETSL_100292),160 (ETSL_100837),21 (ETSL_100529),26 (ETSL_100745),24 (ETSL_101730), 46 (ETSL_100584),53 (ETSL_101640),113 (ETSL_100417),30 (ETSL_101586), 63 (ETSL_101231),76 (ETSL_100039),23 (ETSL_100284),43 (ETSL_100638), 129 (Assosa1),133 (ETSL_100246),39 (ETSL_100798),130 (ETSL_101256),2 (ETSL_101681),49 (ETSL_101446),58 (B35),17 (ETSL_101346),19 (ETSL_100476),161 (ETSL_101362),163 (ETSL_101217),77 (ETSL_100763),87 (IS_38259),82 (ETSL_100894),94 (ETSL_100120),99 (ETSL_100409), 106 (ETSL_101848),3 (ETSL_101535),15 (ETSL_101512),28 (ETSL_101766),54 (ETSL_100267),132 (ETSL_101408),84 (ETSL_101715),150 (ETSL_101163), 147 (ETSL_101038),158 (ETSL_100985), 155(ETSL_101118),153(ETSL_101404), 123(ETSL_100350), 156(ETSL_101161),159(ETSL_101712), 169 (ETSL_101277),122 (ETSL_101550),72 (ETSL_100328),104 (ETSL_100876) Cogent Food & Agriculture 9 The exploration of diverse Ethiopian sorghum landraces and racial variation allows breeders to improve TE in specific environments. This study demonstrated that the Ethiopian core sorghum landraces exhib- ited significant genetic variation in shoot TE, ranging from 1 to 9 gm/kg and related traits. Thus, a set of sorghum accessions with high and low TEs were identified from this experiment; sorghum lines with contrasting TEs are important for improving sorghum (Xin et  al., 2008). The durra race had the highest TE, while the caudatum race had the lowest TE and it was positively correlated with TFW, total shoot dry weight (TDW), TLN and TLA. Moreover, cluster analysis results based on TEs and related traits indicated that the genotypes could be classified into four groups. The experimental results revealed genotypic variation for many drought adaptive traits in sorghum such as stay green, TE, canopy temperature and chlorophyll content (Kapanigowda et  al., 2014; Kassahun et  al., 2010; P. V. V. Prasad et  al., 2006). TE is considered an ideal parameter for measuring genetic varia- tion in crop WUE (Unkovich et  al., 2018; Lakshmi et  al., 2020). This study revealed highly significant genetic variation in shoot TE similar to the findings of other studies (Mortlock & Hammer, 2000; van Oosterom et  al., 2021; Vadez et  al., 2011; Xin et  al., 2008). Our findings on genotypic variation in TEs align with previous research by Mortlock and Hammer (2000); Xin et  al. (2009), who reported that genotypes with TEs as high as 9 g/kg and 13 g/kg were recorded. This finding aligns with the expectation that core germplasm collections exhibit wide genetic diversity for essential agronomic and physiological traits (Fatokun et  al., 2018; Swarup et  al., 2021). Furthermore, our observation indicated that the durra race had the highest TE is consistent with previous reports (Vadez et  al., 2011, 2014). In this study, significant differences were observed in TE and related traits among sorghum races, which can be attributed to four main factors, as discussed by several researchers (de Wet et  al., 1972; Gamachu et  al., 2021; Menamo et  al., 2021; Mutava, 2012; Xiong et  al., 2015). First, genetic variation plays a key role, as each sorghum race has evolved under different environmental conditions, leading to differences in traits associated with drought tolerance, such as root characteristics, leaf size and stomatal distribution and conductance. Second, variations in physiological adaptations among races contribute to these differences. For example, the durra race is more efficient in water use during stress while optimizing photosynthesis, reflecting variations in carbon assimilation. Additionally, races differ in their response to water stress, with some maintaining green leaves for extended periods, which helps improve TE (de Wet et  al., 1972). Third, sor- ghum races have evolved distinct adaptations to specific environments; some are suited to dry, lowland areas, while others thrive in more humid regions. For instance, the bicolor and caudatum races are com- monly found in areas with abundant water, and they have developed mechanisms to cope with these conditions. Lastly, variations in stress-responsive genes among sorghum races also contribute to these differences (Bouchet et  al., 2017; Mekbib, 2007). Races vary in the expression of genes involved in stress responses, such as those related to antioxidants and osmo regulatory compounds, with examples includ- ing the ABA signaling pathway, which helps regulate stomatal behavior under stress conditions (Guo et  al., 2023). Durra race sorghum exhibits several key characteristics that enhance its performance in water-scarce environments (Borrell, Mullet, et  al., 2014). These include physiological adaptations, a deeper and more extensive root system, specialized leaf architecture and genetic traits that confer drought resilience. Among these characteristics, the stay-green trait stands out as particularly valuable (Teklay et  al., 2021). The relationship between stay-green traits and different sorghum races is significant, as distinct races may display varying levels of stay-green characteristics (Borrell, van Oosterom, et  al., 2014). This variation arises from the fact that different races have evolved under diverse environmental conditions, leading to unique adaptive traits, including stay-green (Hao et  al., 2021; Ochieng et  al., 2021). Stay-green is a quan- titative trait controlled by many genes, which are associated with greater biomass accumulation and a high intrinsic chlorophyll concentration (Rama Reddy et  al., 2014). Research has identified the potential use of stay-green quantitative trait loci to enhance plant water use and TE across various genetic backgrounds (Vadez et  al., 2011). Notably, lines such as B35, SC56 and E36-1 are recognized for their drought tolerance and stay-green characteristics (Mahalakshmi et  al., 2002). The inter relationship between TE and stay-green traits is crucial, especially in drought-prone environ- ments (Borrell et  al., 2014; Xin et  al., 2009). Higher TE enables plants to produce greater yields while utilizing less water, which is essential under water-limited conditions. Similarly, the stay-green trait allows plants to maintain leaf greenness and photosynthetic activity for longer periods after flowering, thereby 10 A. D. MITIKU ET AL. sustaining photosynthesis and biomass accumulation during times of water scarcity. Stay-green sorghum varieties tend to exhibit improved WUE, as they can continue to photosynthesize and grow even under stress. This sustained activity enhances TE, creating a strong interconnection between the two traits. Selection for stay-green characteristics can lead to improved TE, ultimately making sorghum plants more resilient and productive in water-limited conditions. Moreover, research indicated that both physiological factors, such as stomatal conductance, leaf carbon dioxide concentration, enhanced pho- tosynthetic capacity and leaf ash content, as well as environmental components like vapor pressure deficit, are linked to increased TE in sorghum under both well-watered and water-limited conditions (Xin et  al., 2009). This indicates that genotypes from the durra race possess promising traits that could be further harnessed for drought adaptation. Our study revealed that the caudatum race had the lowest TE, which contradicts previous work by Vadez et  al. (2011) who reported that the TE of the guinea race was the lowest. The differences between this study’s findings on sorghum race caudatum and those of other researchers might be due to factors, such as experimental conditions, genetic variation and environmental adaptations. Variations in growing conditions like soil type, irrigation and differences in cultivation practices (e.g. planting density) could influence results. Additionally, sorghum populations used in different studies had distinct genetic backgrounds. Environmental factors, such as geographic location and growing season, may also contribute to phenotypic variation (Kawecki et  al., 2012). Increased genetic variation in TE between genotypes can result in a number of useful consequences. Genotypes with greater TE are able to maintain adequate photosynthesis while minimizing water loss, enhancing their resilience to drought conditions (Jackson et  al., 2016; A. L. Fletcher, 2020). This charac- teristic is especially important in drought-prone areas; crops with higher TE will also be more adaptable to shifting environmental conditions (Natarajan et  al., 2021). Additionaly, they contribute to food security because they can continue to be productive even in water-stressed environments (Vadez et  al., 2014). In general, increased genetic diversity in TE offers the potential for the development of robust, fruitful and sustainable agricultural systems that are better equipped to handle the demands of a changing climate and an expanding world population. Moreover, in regions where water availability is known and suffi- cient, cultivating low TE genotypes can increase the productivity capacity of the genotypes so that these genotypes may exhibit more stable yields under those environmental conditions, enhancing farm resil- ience. Overall, the presence of a high TE trait in plants might help them increase biomass production using the same amount of soil water, or it could postpone the harmful effects of water deficit stress by preserving soil water (Xin et  al., 2008). We observed that the heritability of TE and related traits was moderate indicates that the effect of the environment on those traits is high. Since these traits are moderately heritable, breeders must evaluate candidates in different environments (drought-stressed and well-watered conditions) to account for envi- ronmental effects. Further, moderate heritability indicates the broad spectrum nature of the trait where, many traits like root architecture need to be consider to improve (Ismael et  al., 2021; Kapanigowda et  al., 2013; Lopez et  al., 2019). Moreover, significant positive correlations between shoot TE and total fresh shoot weight, TDW, TLN and TLA, similar to previous reports (Natarajan et  al., 2021; van Oosterom et  al., 2021). The positive correlation between TE and various plant traits is driven by several physiological processes, including stomatal regulation, photosynthesis, WUE, and growth dynamics. Stomatal opening and closing are influenced by factors such as water availability, light, humidity and temperature. Effective stomatal regulation helps balance water loss and CO2 uptake, supporting optimal plant growth. As a result, TE is closely linked to overall growth traits. Plants that can maintain a high TE while minimizing water loss tend to be more productive. A higher WUE allows plants to accumulate more biomass with less water (Vadez et  al., 2014). In this study the genotype ETSL_100428 race caudatum had the widest LA, whereas the genotype ETSL_101292 race durra had the narrowest LA, this variation is an adaptation mechanism to different environments. This result is supported by Zhi et  al. (2022) who reported that plant level TEs were nega- tively associated with leaf width. High TE can be negatively correlated with leaf width due to several physiological and morphological factors; to mention few wider leaves have a larger surface area, which can lead to increased water loss through transpiration. While a larger LA can enhance photosynthetic capacity, it also means more stomata and greater potential for water loss, which may reduce TE. Narrower Cogent Food & Agriculture 11 leaves, the ratio of water lost to carbon gained can be more favorable, enhancing TE. Narrower leaves can promote better water conservation while still allowing for efficient gas exchange. Further, in drought-prone environments, plants with narrower leaves may be better adapted, as they can reduce water loss while maintaining essential physiological processes (Vadez et  al., 2011; Zhi et  al., 2022). Moreover, TE and PH did not show high correlation, which implies that it is possible to improve these traits at the same time and similar result was reported by van Oosterom et  al. (2021). Overall, the cor- relations between TFW, TDW and shoot TE emphasize how crucial effective water use is for supporting plant development and output. Moreover, increased photosynthetic ability in plants can result in increased biomass accumulation and better crop yields (Wu et  al., 2019). This can be achieved through increasing LA and LN. Optimizing photosynthetic efficiency and improving crop performance can be achieved by breeding for TEs in conjunction with TLA and TLN. Fresh ShB and dry ShB exhibited strong positive linear correlations with TE. However, there was no apparent relationship between LA and TE, or between TE and SPAD measurements and similar results were reported (Marenco et  al., 2009). TDW has the strongest positive association with total fresh shoot weight, TLA, PH and total number of leaves, suggesting that when one of these growth parameters rises, the others tend to rise in proportion. The positive connections imply that plants with higher total fresh shoot weight, larger leaf area, higher PH, and more leaves are also accumulating more biomass (dry weight), as SDW is directly correlated with biomass accumulation. This indicates these factors are often tied to a plant’s ability to photosynthesize effectively and convert nutrients into growth. Negative cor- relations of SPAD with SDW, TLA, and TLN suggest an inverse relationship between chlorophyll content and these plant growth parameters. The negative correlation could reflect genetic factors where plants with more chlorophyll (SPAD readings) may have a slower growth rate or reduced leaf expansion for reasons that are related to the plant’s growth strategy (Xiong et  al., 2015). Moreover, gaining insight into the interrelationships among these variables can improve crop resil- ience, maximize resource utilization and support sustainable food production systems. The results of the cluster analysis revealed that 182 genotypes were classified into four clusters. Cluster IV comprised the genotypes with the highest TE, chlorophyll content and TNs. Identifying genotypes with the high- est TEs, chlorophyll contents and TNs in one cluster has the potential for significant advancements in crop breeding, agricultural productivity and sustainability. Moreover, these clusters allow breeders to do more targeted breeding that can improve sorghum variety well suited to specific ecology for instance high TE genotypes being targeted for future hybridization for low moisture environments. TE can be measured, at the shoot or whole plant level (A. Fletcher et  al., 2018). It is an ideal param- eter for measuring genetic variation in crop WUE (Unkovich et  al., 2018; Lakshmi et  al., 2020). Research has indicated that increasing TE can lead to increased grain yield in water-limited environments (Vadez & Ratnakumar, 2016). Lysimetric and gravimetric methods are the most commonly used methods to measure WUE at a large scale; for instance, a study by Vadez et  al. (2014) revealed that high WUE was related to high yield. However, many researchers have reported that TE can be measured relatively easily using sealed pots, by estimating the amount of water transpired by changes in pot weight over time excluding soil evaporation and deep drainage (Xin et  al., 2009; A. Fletcher et  al., 2018). This experiment favored shoot-based TE measurements over whole-plant measurements because obtaining accurate root biomass from whole plants is challenging. Additionally, the shoots were harvested at the 12th expanded leaf stage to avoid wilting of the leaves. This experiment used large pots as lysimeters. However, weigh- ing each pot before watering is labor-intensive and inconvenient. A portable automatic lysimeter would have been a more suitable option. Moreover, Xin et  al. (2008) suggested that a high-throughput and economical technique is required to screen the genetic diversity of TEs germplasm collections to effi- ciently improve TEs in sorghum. 5.  Conclusion This study revealed significant genetic variation in Ethiopian core sorghum landraces across all nine traits among 182 genotypes. A racial analysis showed that genotypes from the durra race had the highest TE. The top five genotypes with the highest TE were ETSL_101160, ETSL_101506, ETSL_101443, ETSL_100722 and 12 A. D. MITIKU ET AL. ETSL_101292. A strong positive relationship between TE with TFW and TDW was observed. The genotype ETSL_101292, belonging to the durra race, exhibited the narrowest LA. This suggests that high TE is nega- tively correlated with leaf width due to various physiological and morphological factors. Cluster IV capture genotypes with the highest TE, highest chlorophyll content and TN. These findings highlight the potential of diverse genetic resources in the Ethiopian sorghum landrace to develop water-efficient cultivars. Previous research has shown that enhancing TE can improve grain yield in water-limited conditions. By identifying key genetic markers associated with TE and related traits, breeders could develop more drought-resistant varieties of sorghum, improving resilience and productivity in water-limited environments. Acknowledgments The author wishes to thank the National Sorghum and Millet Improvement Program at Melkassa Agricultural Research Center, Ethiopia for providing experimental planting materials. Author contributions CRediT: Abel Debebe Mitiku: Conceptualization, Data curation, Formal analysis, Writing – original draft; Tileye Feyissa: Supervision, Writing – review & editing; Alemu Tirfessa Woldetensaye: Supervision, Writing – review & editing; Habte Nida Chikssa: Supervision, Writing – review & editing; Temesgen Matiwos Menamo: Supervision, Writing – review & editing; Tewodros Mesfin Abebe: Supervision, Writing – review & editing; Kassahun Bante: Supervision, Writing – review & editing. All authors have approved the final draft of the manuscript. Disclosure statement The authors report there are no competing interests to declare. Funding This experiment was funded by the Ethiopian Institute of Agricultural Research (EIAR). About the authors Abel Debebe Mitiku is currently a PhD candidate in Biotechnology at Addis Ababa University, Ethiopia. I had M.SC. Degree in Plant Breeding and B.SC. Degree in plant science from Alemaya University, Ethiopia. I have been working in Ethiopian Institutes of Agricultural Research (EIAR), as researcher in Plant Biotechnology directorate. My research focuses on leveraging plant tissue culture, genomics, and high-throughput phenotyping to assess genetic variability and identify QTLs linked to abiotic stress tolerance and yield-related traits in staple crops. Prof. Tileye Feyissa holds MSc, in Genetics from Addis Ababa University, Ethiopia and a PhD in Plant Biotechnology from Swedish University of Agricultural Sciences (SLU). He is a senior lecturer and researcher at Addis Ababa University. He is recognized as well-known experienced project manager and leader, with a history of mentoring MSc and PhD students. Currently, Prof. Tileye is Associate Dean for Graduate Programs, College of Natural and Computational Science, Addis Ababa University. His research interests encompass plant tissue culture, genetic engineering and plant molecular breeding areas on plants of African importance, particularly Sub-Saharan Africa. More specifically, develop- ing abiotic and biotic stress resistant plants using molecular breeding and/or plant genetic engineering techniques. Dr. Alemu Tirfessa Woldetensaye holds MSc in plant breeding from Hawassa University, Ethiopia and a Ph.D. in plant science from The University of Queensland, Australia. He was a national coordinator for Ethiopian Institute of Agricultural Research sorghum and millet research. Moreover, he was a country coordinator for the Sorghum and Millet Innovation Lab (SMIL) program. Currently, he is a director of technical cooperation at global collaboration on sorghum and millet, college of Agriculture, Kansas State University. His research interests encompass modernizing conventional breeding, use of molecular markers and improving sorghum production in water limited production system vis-à-vis understanding of major physiological traits and crop simulation modeling. Dr. Habte Nida Chikssa holds MSc in plant science from the Hebrew University of Jerusalem, Rehovot, Israel and a Ph.D. in plant genetics from Purdue University, USA. He was a national coordinator for Ethiopian Institute of Agricultural Research sorghum and millet research. Currently, he is a post-doctoral research associate at Purdue University, USA. His research interests encompass understanding and improving of key adaptive traits such as Cogent Food & Agriculture 13 tolerance to drought and resistance to biotic constraints via using new generations’ technologies like QTL mapping and gene identification. Dr. Temesgen Matiwos Menamo holds MSc in plant biotechnology from Wageningen University, Netherlands and a Ph.D. in plant breeding from Jimma University College of Agricultural and Veterinary Medicine, Ethiopia. He is a senior lecturer and researcher at the university. He is recognized as well-known experienced project manager and leader, with a history of mentoring MSc and PhD students. Currently, he is a post-graduate program coordinator at the University. His research interests encompass on improving crop performance through genetic manipulation of root system architecture (RSA). Moreover, he is interested in application of advanced molecular techniques, bioinfor- matics analysis, and field-based experiments to identify and characterize key genes and genetic loci governing RSA in cereal crops, particularly sorghum, wheat, and barley. Dr. Tewodros Mesfin Abebe holds MSc in agronomy from Alemaya University, Ethiopia and a Ph.D. in Agronomy and crop physiology from University of Tasmania, Australia. His research focuses on systems agronomy, leveraging site-specific management, climate-smart approaches, and innovative tools to improve crop productivity and resil- ience while addressing soil health and climate challenges. He is also dedicated to advancing sustainable water man- agement and bridging research with scalable solutions for impactful agricultural transformation. Prof. Kassahun Bante holds MSc, in Plant Breeding and Genetics from Indian Agricultural Research Institute, New Delhi India and a PhD in Plant Biotechnology/molecular Breeding from University of Agricultural Sciences, Dharwad India. He is a senior lecturer and researcher at Jimma University. He is recognized as well-known experienced project manager and leader, with a history of mentoring MSc and PhD students. 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