1. Introduction
Sorghum [Sorghum bicolor (L.) Moench], a versatile C4 grass with increasing prominence, is attracting attention due to its exceptional adaptability, drought tolerance, and diverse applications [1]. Across California’s varied ecological zones, from the Central Valley to the arid Imperial Valley, sorghum offers a unique opportunity to address the needs of both the dairy and biofuel industries, contributing to a more sustainable agricultural landscape under conditions of recurring droughts and uncertain water supplies associated with climate change. The California dairy industry, a significant economic driver generating over $57.7 billion annually in recent years [2], faces challenges in securing a sustainable and cost-effective feed supply due to rising costs and competition for water resources. Meanwhile, biofuel production is gaining momentum as a renewable energy source, with California leading the nation in implementing clean energy policies such as the Low Carbon Fuel Standard that promotes biofuel alternatives to reduce greenhouse gas emissions [3].
Sorghum’s significance extends beyond regional boundaries, playing a crucial role in global food security and sustainable agriculture. As the fifth most important cereal crop worldwide, sorghum serves as a staple food for over half a billion people, particularly in arid and semi-arid regions of developing countries [4]. Its exceptional drought tolerance and ability to thrive under low-input conditions make it invaluable in the face of climate change and water scarcity [5].
For dairy farmers, high-quality forage is essential for maintaining healthy herds and efficient milk production. Sorghum’s potential as a forage crop lies in its ability to deliver both yield and quality across a range of environments and production constraints [6,7]. Quality and productive indexes such as relative feed quality (RFQ) and milk/ton have been developed that are suitable for improved decision-making in feed rations [8,9]. RFQ is a single numerical value that combines the digestibility and intake potential of the feed, providing a good measure of overall forage quality for ruminant animals. milk/ton, on the other hand, reflects the efficiency of feed conversion into milk production. By evaluating these metrics alongside biomass yield, we can identify sorghum hybrids that offer a win-win scenario for dairy farmers—high-quality, cost-effective forage that supports milk production and contributes to a more sustainable dairy industry, especially for dry cows and heifers [10].
Sorghum’s potential extends beyond the dairy and beef industry. As a biofuel feedstock, it offers several advantages [11,12]. Unlike corn [Zea mays L.], a traditional biofuel feedstock, sorghum requires less water and can be economically productive both in high-productivity farmland as well as in more marginal lands where soil fertility, salinity, or soil water-holding capacity can be limitations [6,13]. Additionally, sorghum varieties can be specifically bred to optimize biofuel production traits [13,14]. Biofuel research on sorghum explores various conversion technologies, with ethanol being a leading biofuel generation method. The process involves fermenting sorghum sugars into ethanol production, a renewable alternative to gasoline [15]. However, research is ongoing to explore other next-generation biofuels and chemical by-products derived from sorghum biomass, such as biogasoline and biodiesel. By analyzing the compositional properties of sorghum hybrids, we can identify varieties with traits favorable for the broad category recognized as biofuel production. This approach, alongside evaluations of forage quality characteristics, can help in selecting sorghum hybrids that offer a versatile solution for California’s agricultural needs. Extensive research has explored sorghum’s potential for both food and forage. Studies have established its high biomass yield and remarkable adaptability to various environmental conditions, including drought and heat stress [16,17,18]. Additionally, other research has explored the relationship between sorghum’s composition traits, such as starch, fiber content (acid detergent fiber, neutral detergent fiber), and bioethanol production efficiency, paving the way for optimizing sorghum varieties for bioconversion processes [19,20]. Despite this promising research, a knowledge gap exists regarding the multi-location performance, genotype-by-environment interaction (GxE), and stability of sorghum hybrids across California’s diverse environments. GxE interaction in this context refers to how a sorghum hybrid’s performance can vary depending on the location’s specific growing conditions.
Understanding genotype-by-environment (GxE) interaction is crucial for optimizing sorghum production across California’s diverse climatic regions. California’s agricultural landscape spans a wide range of environments, from the hot and arid Imperial Valley to the more temperate Central Valley, each presenting unique challenges and opportunities for crop growth [21]. GxE interaction refers to the differential response of genetic varieties to varying environmental conditions, which can significantly impact crop performance, yield stability, and quality traits [22]. In the context of sorghum cultivation in California, GxE interactions may influence important characteristics such as drought tolerance, heat resistance, and biomass production [23]. By thoroughly investigating these interactions, we can identify sorghum hybrids that not only perform well in specific environments but also maintain stability across diverse conditions [11]. This knowledge is particularly valuable for California’s agricultural sector, as it faces increasing pressure from climate change and water scarcity [24]. Elucidating GxE interactions will enable more informed breeding strategies and variety recommendations, ultimately contributing to the development of resilient and high-performing sorghum cultivars tailored to California’s varied agroecosystems [25].
The primary objective of this study, therefore, was to evaluate the performance and stability of 59 sorghum hybrids across diverse environments in California over five years (2019–2023), focusing on biomass yield, forage quality traits, and biofuel-relevant chemical composition. By identifying genotypes that excel in both yield and quality across various environments, this research aimed to provide valuable insights for future cropping decisions in animal feed, biofuel, and chemical byproduct feedstock production in California.
2. Materials and Methods
2.1. Planting Materials and Field Evaluation
A total of 59 sorghum hybrids comprising (i) traditional forage sorghum (non-bmr), (ii) brown midrib (bmr) sorghum, (iii) photoperiod-sensitive (PS) sorghum, and (iv) photoperiod-sensitive bmr (PS bmr) sorghum. Of the 59 hybrids, 27 and 20 were non-bmr and bmr respectively, while 9 were photoperiod-sensitive sorghum (Table S1). Brown midrib (bmr) sorghum is distinguished by its characteristic brownish coloration in the leaf midrib. This coloration is a visible indicator of the genetic mutation that reduces lignin content, which enhances forage digestibility and nutritional value for ruminant livestock [26]. The performance of the 59 sorghum genotypes was evaluated in three locations: the University of California, Division of Agriculture and Natural Resources (UC-ANR) Kearney Agricultural Research and Extension Center, Parlier (KARE) and West Side Research and Extension Center, Five Points (WSREC), and the University of California Davis Research Farm (Davis), for five years (2019–2023). The three sites had different precipitation, environments (Table 1), and soil types, with KARE having Hanford sandy loam soil while WSREC and Davis had Panoche clay loam and Yolo loam soil, respectively. At the Kearney Agricultural Research and Extension Center (KARE), we implemented a dual planting date strategy for each year of the study. All 59 sorghum hybrids were planted at two different dates at this location, typically about 24–30 days apart. This approach was designed to capture the performance of hybrids under different planting windows within the same growing season, providing insight into their adaptability to varying early-season conditions.
Planting was done using either an Almaco 4-row plot planter (Nevada, Lowa USA) or Wintersteiger self-propelled drill planter (Salt Lake City, UT, USA) at a rate of 247,105 seeds ha−1. Evaluation trials were established as randomized complete block design (RCBD) comprised of four-row plots on 30-inch (0.76 m) wide raised beds. Fertilizer application followed recommendations for forage sorghum production, considering differences in soil N, P and K status, with fertilizer applications made as needed to maintain those soil nutrients at levels considered non-limiting to yields. Weeds were managed with Dual Magnum® pre-plant herbicide, while Gramaxone® (Paraquat or N,N′-dimethyl-4,4′-bipyridinium dichloride, Syngenta; Greensboro, NC, USA) and Maestro® 4EC (Bromoxynil (55%, Nufarm; Alsip, IL, USA)) were used as post-emergence herbicides. Sugar cane aphid (when present) was controlled using one or two within-growing season applications of Sivanto prime® Insecticide (Flupyradifurone (17.09%, Bayer, Leverkusen, Germany)) at a rate of 14 oz per acre (1.03 L/ha). Table 1 provides detailed information on the accumulated heat units of 60-degree F (15.56 °C) at 30, 60, and 90 days after planting, as well as for the full growing season across all sites and years. These data allowed for a comprehensive comparison of the thermal environments experienced by the sorghum hybrids throughout the study.
2.2. Forage Quality Components Proximate Analysis
At harvest, samples were collected from the forage chopper in the field, weighed, put in coolers for transport, and then placed in a forced-air Gruenberg oven (Model T35HV216, Williamsport, PA, USA) set at 50 °C for two days. After drying, samples were weighed to calculate field moisture content, with samples then sealed and sent to Dairyland Laboratory Inc. (Arcadia, WI, USA) for further quality analyses. Ground samples were tested for fat (ether extract) using the Association of Official Agricultural Chemists (AOAC) [27] method 920.39 on a Foss Soxtec 2047 instrument (Gemini Lab, Apeldoorn, The Netherlands), crude protein (CP) using the combustion method (AOAC official method 990.03 (2012)), ash content (AOAC method 942.05, 1996), acid detergent fiber (ADF), and lignin (AOAC method 973.18 (1996)), with slight modification, which included the use of sea sand for filter aid. Neutral detergent fiber (NDF) was analyzed using AOAC method 2002.04 (2005), with a slight modification of sea sand as a filter aid and Whatman GF/C filter paper for residue collection, while starch was assessed using the acid hydrolysis method [28]. Fiber digestibility was assessed by measuring neutral detergent fiber digestibility over 30 h (NDFD30) in a flask using the Goering and Van Soest method [29], with a modification in the composite inoculum which contained a strained ruminal fluid and blended ruminal solids.
Sugars, such as water-soluble carbohydrates (WSC sugars), and ethanol-soluble carbohydrates (ESC sugar), were assessed to determine the readily available carbohydrates in feed as a quick source of energy. For ethanol-soluble carbohydrates, 80% ethanol was used to extract sugars, and ESC sugars were determined using sucrose as a standard carbohydrate [30]. Water-soluble carbohydrates were extracted using extraction methods adapted from Derias [31] and detected using neutral detergent-soluble carbohydrates’ nutritional relevance and analysis [30].
The quality and productive indexes relative forage quality (RFQ) and milk/ton were calculated and used to compare the feed quality of different sorghum genotypes. RFQ was calculated from total digestible nutrients and digestible dry matter. Total digestible nutrients comprised crude protein, ether extract fatty acids, neutral detergent fiber, nitrogen-free NDF, 48-h in vitro NDF digestibility, and non-fibrous carbohydrates. A relative forage quality of 100 was considered the average score across multiple types of forage to generate a relative index value and was represented as a fully mature alfalfa hay. Sorghum genotypes with RFQ greater than 100 were considered of higher quality than full-bloom alfalfa hay. Therefore, based on the RFQ score, the feed quality for a sorghum hybrid was graded either as utility (<90), standard (91–110), select (111–140), choice (141–160), prime (161–185), and supreme grade (>185). Milk/ton 2013 was used to rank samples for different sorghum hybrids, showing the potential milk yield per ton of forage dry matter. Each sorghum hybrid milk/ton score could then be compared to the average alfalfa-grass milk yield of 3000 lb/ton value found in prior studies [9]. Correlation between different compositional traits was determined using Pearson correlation. Pearson correlation between different compositional traits was done using the corr_coef function of the Metan R package, v.1.18.0 [32].
2.3. Genotype and Genotype-Environment Analysis by Mixed Effect Model
Analysis of genotype performance was accessed using Best Linear Unbiased Prediction (BLUP) based on a mixed effect model with random genotypes and interaction. In this approach, the genotypes, GxE interaction, and blocks were treated as random effects, while environment (locations) were considered fixed effects as given in the following equation:
where yijk is the phenotypic value of the kth genotype in the jth block of the ith environment, μ is the overall mean, Ei is the fixed effect of the ith environment, b(j|i) is the random effect of the jth block nested within the ith environment, Gk is the random effect of the kth genotype, (GE)ik is the random interaction effect of the ith environment and the kth genotype, and eijk is the random error term.Genetic parameters for assessing genotype-environment interaction were analyzed as described by [33] and implemented using the gamen_met function of the Metan R package v.1.18.0. Genetic parameters assessed included broad sense heritability, the coefficient of determination of the interaction effects (GEr2), the heritability on the mean basis (h2mg), the selective accuracy (Accuracy), the genotype-environment correlation (rge), the genotypic coefficient of variation (CVg), the residual coefficient of variation (CVr), and the ratio between genotypic and residual coefficient of variation (CV ratio).
2.4. Stability Analysis and Multi-Trait Selection
A weighted average of absolute score (WAASB) generated from the linear mixed effect model as a singular value decomposition (SVD) of the matrix of the BLUP for the genotype x environment interaction effects was used to assess the stability of each sorghum hybrid in all tested environments. A superior index of weighted average absolute scores from BLUP for yields (WAASBY) [32] that combines the mean performance of each hybrid and its WAASB score was generated using a waasb function (REML/BLUP method). The superiority index was implemented by assigning 65:35 weights to yield and stability. Based on this index, the sorghum hybrids were ranked based on their yield and stability. A genotype was considered ideal if it had both high mean yield performance and high stability across the environments. A multi-trait selection index based on factor analysis and ideotype design [34] was used to select the best sorghum genotypes with high biomass yield, relative feed quality, and high milk/ton for feed and those with high biomass yield and starch content or NDF for first- and second-generation biofuel production. The selected sorghum hybrids were visualized using radar plots [35].
3. Results
3.1. Variance Components and Genetic Parameters
The variance components and genetic parameters of both yield and compositional components are presented in Table 2 and Table 3. The results show that the sorghum hybrids (GEN), environment (ENV), and genotype by environment interaction (GEI) effects are highly significant (p < 0.001, p < 0.05) for all the traits except fat- and water-soluble sugars, which had non-significant GEN and GEI, respectively. These results revealed the presence of the three types of effects influencing the performance of the sorghum genotypes in California. Of the three, the largest proportion of variance was due to genotype.
The biomass yield of different sorghum hybrids showed a substantial genetic variation (18.10%) and significant genotype by environment interaction (6.82%), suggesting that some hybrids performed better under specific environmental conditions. Among the compositional components and quality indexes, relative feed quality, lignin, and starch had the highest variance attributed to genotype, with 32.99, 31.99, and 31.33%, respectively, while relative feed quality and NDFD30 had negligible environmental influence (0.74% and 0.14%, respectively). However, these sorghum hybrids showed a significant GxE interaction, suggesting that different genotypes might have varying quality and digestibility profiles depending on the environment.
Broad sense heritability for all the traits was moderate except for biomass yield (0.18) and crude protein (0.18) (Table 3). The value of heritability on the mean basis (h2mg) was higher ranging from 0.56 to 0.81 indicating a moderate to high genetic influence for all the traits. Traits with high heritability (0.78–0.81) were milk/ton, relative feed quality, fat, starch, lignin, and water-soluble sugar while others such as crude protein, biomass yield, and NDFD30 had moderate heritability (0.56–0.77). The coefficient of determination of interaction effect (GEIr2) for all the traits was very low, ranging from 0.02 to 0.21. This confirmed a weak interaction between genotype and environment, showing that the screened sorghum hybrid performance was relatively constant across different environments. Therefore, selecting the “best” sorghum genotypes for all environments is important.
3.2. Correlation Between Compositional and Yield Traits Among Different Sorghum Genotypes
Compositional traits and feed quality indexes were correlated to identify traits positively influencing feed quality and productivity (milk/ton). Pearson’s correlation revealed that fat, starch, and relative feed quality (RFQ) were highly correlated (p < 0.001) with milk/ton (Figure 1). Other traits with high positive correlation include (i) starch with fat and RFQ with 0.56 and 0.86, respectively, (ii) RFQ with fat (0.83, p < 0.001), (iii) biomass yield with height (0.62, p < 0.001), ethanol soluble sugars (0.44, p < 0.001) and lignin (0.46, p < 0.001), (iv) NDFD30 and ash (0.65, p < 0.001), (v) water- and ethanol-soluble sugar with lodging, and (vi) plant height with fiber (ADF, NDF and lignin).
Milk/ton and RFQ had a significantly high negative correlation with structural carbohydrates such as ADF (−0.87, p < 0.001) and NDF (−0.82, p < 0.001). Therefore, sorghum hybrids with high fiber had relatively low relative feed quality and milk/ton. Structural carbohydrates (ADF, NDF, and lignin) also had a significantly high negative correlation with fats and starch. Other traits with negative correlation included (i) biomass yield and crude protein (−0.78, p < 0.001), and (ii) biomass yield and ash content (−0.65, p < 0.001).
From these correlations, it can be deduced that sorghum hybrids with high biomass yield had high fiber content which is ideal for biofuel production but not for high-quality animal feed formulation. These results also revealed that sorghum genotypes with high fat and starch content produced feed with high milk/ton value.
3.3. Mean Performance of Different Sorghum Genotypes
The 59 sorghum hybrids showed variation in performance in the 15 environments (three locations and five-year evaluation) (Table 4 and Table S2). Biomass yield ranged between 18.71 and 33.50 tons per acre (at 65% moisture content) with the highest yield recorded with UCSG35 (75.10 Mg/ha), UCSG53 (68.37 Mg/ha), and UCSG34 (67.21 Mg/ha). Considering the best linear unbiased prediction (BLUP) values, the three best-yielding sorghum hybrids did not perform comparatively well in some compositional traits such as crude protein, starch, and quality indexes (RFQ and milk/ton).
Based on the production index (milk/ton), the evaluated sorghum hybrids varied greatly ranging between 2123.89 and 3119.35 lb/ton. Sorghum hybrids with the highest milk/ton were (i) UCSG51 (3119.35 lb/ton), (ii) UCSG28 (3027.72 lb/ton), and (iii) UCSG08 (3002.47 lb/ton). Though these hybrids had high milk/ton values, their biomass yield was average, especially for UCSG28 and UCSG08. However, the BLUP values for all the traits of these hybrids were high, showing that their performance across all the evaluated traits was good. These results show that there is a yield and quality penalty as breeders strive to develop high-yielding forage sorghum hybrids.
The relative feed quality of feed samples from different sorghum hybrids varied from 89.69 to 146.25. The two sorghum hybrids with the highest milk/ton (UCSG51 and UCSG08) also had the highest relative feed quality (146.25 and 140.90, respectively). Other sorghum hybrids with high RFQ values included UCSG20 (136.86), UCSG50 (134.35), and UCSG18 (132.07). In general, sorghum hybrids with high RFQ also had relatively high milk/ton.
To evaluate sorghum hybrids in terms of digestibility and energy provision, both NDFD30 and starch content were analyzed. NDFD30, which measures fiber digestibility of feed after 30 h, varied between 45.92 and 57.09% of neutral detergent fiber (NDF) digested. Sorghum hybrids UCSG33, UCSG36, and UCSG10 had the highest NDFD30 values (57.09, 54.57, and 54.30%, respectively). On the other hand, starch content varied greatly in different feed samples ranging from 2.25 to 27.25%. Furthermore, the sorghum hybrids with the highest RFQ (UCSG51 and UCSG20) also had the highest starch content of 27.25 and 21.38%, respectively.
Based on the RFQ scale, the evaluated sorghum hybrids were categorized into four groups; choice, select, standard, and utility. No sorghum hybrid had feed quality with an RFQ value above 160, therefore no sorghum hybrid was categorized as supreme or prime (Table 5). Only two hybrids (UCSG51 and UCSG08) had samples that were characterized as choice feed grade (Table 4). Most of the samples (35) had RFQ ranging from 110 to 139 and were, therefore, categorized as a select grade (Table 5).
3.4. Mean Performance and Stability of Sorghum Hybrids in Different Environments in California
The stability of the sorghum hybrids in different environments across multiple years was assessed using a superior index of weighted average absolute scores from BLUP for yield (WAASBY), which facilitates simultaneous selection for yield and stability. Based on the WAASBY values, a total of 33 sorghum hybrids performed above average in terms of biomass yield and stability in all the environments (Figure 2). Among these, the five most productive and stable sorghum hybrids in all locations and across the time of evaluation were UCSG53, UCSG36, UCSG09, UCSG35 and UCSG34. The highest-performing and most stable sorghum hybrid (UCSG53) outperformed all the other sorghum hybrids, with WAASBY values above 80.
Some plant responses across the three environments showed high variation, with sorghum hybrids at KARE having comparatively lower yields (Figure 3). The difference may be attributed to the differences in weather and soil conditions that affected the performance of the evaluated sorghum hybrids. Daily peak temperatures are typically higher at the KARE location than at the Davis site. Analysis of the heat unit accumulation data from Table 1 revealed significant variations across sites and years. For instance, the full season heat unit accumulation ranged from 1611 to 2487 growing degree days. These differences in thermal environments correlated with variations in biomass yield, with higher heat unit accumulation generally associated with increased biomass production. However, the sandy loam soil at the KARE site has much lower soil water-holding capacity than soils at the other two sites. Among the hybrids that performed well in terms of biomass yield, only eight hybrids consistently had above-average yield at KARE. These included UCSG35, UCSG53, UCSG34, UCSG21, UCSG51, UCSG39, UCSG27, and UCSG37. These results showed the importance of identifying sorghum hybrids that would consistently produce high biomass yield, even in adverse conditions.
When a random-effect model was used, where all effects (genotypes, environments, genotype and environment interaction, and blocks nested within environments) were assumed to be random factors, the sorghum hybrids were categorized into four groups (groups I–IV) based on biomass production and stability (Figure 4). Group I had sorghum hybrids that were both not productive and not stable, group II had hybrids that were highly productive but not stable, group III had hybrids that were highly stable but not productive, and Group IV comprised sorghum hybrids that were both highly productive and stable. Among the four groups, groups III and IV had the highest number of sorghum hybrids. Sorghum hybrids with the lowest WAASB values, close to zero (highly stable), and above average yield were UCSG09, UCSG53 and UCSG36.
3.5. Multi-Trait Selection for High-Quality Forage and Biofuel Production
Based on the results in Table 2, there was variation among sorghum hybrids in terms of biomass yield and compositional traits for forage and biofuel production. Therefore, a multi-trait stability index was used to identify the stable genotypes combining good quality traits (RFQ and milk/ton for feed and high carbohydrate content for biofuel production) combined with high or moderately high rankings for biomass yield. A radar plot revealed a total of nine sorghum hybrids that were selected at a 15% selection intensity for either feed or biofuel production. These sorghum hybrids had high values for biomass yield, relative feed quality, and milk/ton. These included UCSG51, UCSG08, UCSG28, UCSG52, UCSG24, UCSG11, UCSG44, UCSG40 and UCSG27 (Figure 5). Of these, five sorghum hybrids were bmr forage sorghum while four were non-bmr sorghum (Table S1).
For biofuel production, the sorghum hybrids with high biomass yield also had high structural carbohydrates such as fiber (lignin and NDF) (Figure 1). Therefore, stable hybrids combining biomass yield, starch, and high NDF content were selected for both first- and second-generation biofuel production. The sorghum hybrids that were selected for first-generation biofuel include UCSG51, UCSG50, UCSG31, UCSG35, UCSG53, UCSG26, UCSG08, UCSG27 and UCSG23 (Figure 5). Five of the nine sorghum hybrids selected were non-bmr, while three were bmr forage sorghum, and one was a photoperiod-sensitive sorghum hybrid. For second-generation biofuel, Multi Trait Selection Index (MTSI) was used to select sorghum hybrids with high biomass yield and high fiber content (NDF). NDF comprises cellulose, hemicellulose, and lignin, which are important in the production of cellulosic biofuel (second-generation biofuel). Sorghum hybrids selected included UCSG29, UCSG02, UCSG26, UCSG01, UCSG27, UCSG35, UCSG08, UCSG44 and UCSG34. Of these, three hybrids, UCSG27 (non-bmr), UCSG35 (photoperiod-sensitive), and UCSG08 (bmr) were selected for both first- and second-generation biofuel production. Of all the sorghum hybrids selected for feed (Figure 5) and biofuel production (Figure 6 and Figure 7), only three were selected in both categories. These included UCSG51 (non-bmr), UCSG08 (bmr) and UCSG27 (non-bmr). These results show that high-quality multipurpose sorghum can be produced for high-quality feed and biofuel production.
4. Discussion
Sorghum as a drought-tolerant energy crop offers a useful alternative crop for animal feed (particularly dairy operations) and biofuel/bioenergy sectors. In this study, 59 sorghum hybrids were evaluated in three different environments in the Central Valley of California for five years. The study revealed a highly significant effect (p < 0.001) of genotype, environment, and GxE interaction for all the evaluated traits except fat and water-soluble sugars. This indicated that the tested commercial and experimental sorghum hybrids responded differently to the environment, confirming variance and phenotypic diversity among the assessed genotypes.
To dissect the overall phenotypic variance, the study found a significant influence of genotype on all the traits with a smaller but detectable effect from GxE interaction and minimal overall environmental effect. These results agree with what Ndiaye et al. [36] and Enyew et al. [37] reported. This suggests the evaluated biomass yield and some specific compositional traits are highly heritable. This was confirmed by the heritability on the mean basis (h2mg), which indicated a moderate to high heritability. The observed GxE interaction indicated that specific environmental factors might moderate the expression of genes associated with biomass yield and the different compositional traits in this study. However, the coefficient of determination of interaction effect (GELr2) for all the traits was low, confirming a weak interaction between genotype and environment.
4.1. Optimizing Production of Sorghum in California
Sorghum’s remarkable genetic diversity and tolerance to different abiotic stresses likely contribute to the strong genotypic effect observed in this study. While minimal, the significant Gx E interaction suggests that California’s diverse environmental conditions, such as variation in temperature, rainfall, and soil salinity [38], might influence the performance of specific sorghum genotypes (Figure 3). GxE interaction can be attributed to predictable and unpredictable environmental factors [22]. The three trial sites in this study had different weather conditions (micro-climates) and soils, with differences in soil texture, structure, and water-holding capacity. The Hanford sandy loam soil in KARE is a well-drained moderately coarse-textured soil with a lower water holding and nutrient retention capacity compared to Panoche clay loam (clay loam, balanced yet slightly clay-dominant texture) and Yolo loam (well-drained and well-balanced mixture of sand, silt, and clay) in WREC and Davis, respectively. The soils in WREC and Davis have good water and nutrient holding capacity, offering comparatively better conditions for achieving higher yields. This could explain the relatively low performance of different sorghum hybrids at KARE. However, eight sorghum hybrids—UCSG35, UCSG53, UCSG34, UCSG21, UCSG51, UCSG39, UCSG27, and UCSG37—maintained high biomass yield across locations and the years of evaluation. These demonstrated that sorghum can be bred to withstand adverse conditions and perform well in both good and unfavorable conditions.
The varying environmental conditions in California require the selection of stable sorghum hybrids for sustainable production. Ndiaye et al. [36] showed that sorghum genotypes with good phenotypic stability performed poorly in terms of yields. Though this may be true, our study revealed a few sorghum hybrids (UCSG35, UCSG53, UCSG21, UCSG51, and UCSG39) with high biomass yield and stability. Among these, UCSG53 and UCSG39 were selected as superior hybrids based on WAASBY score (Figure 2). Our research confirms the well-documented difficulty in breeding crop varieties with both high yield and stability. Factors limiting simultaneous high and stable biomass yield may include (i) the complex architecture of the biomass yield trait in sorghum, with many genes having differential expression to different environmental conditions [39], (ii) environmental sensitivity among the sorghum genotypes, (iii) physiological trade-offs in which genotypes focus on rapid growth while compromising their tolerance to stress [40], and (iv) gene-environment interaction, in which specific genes might have positive or negative effect on biomass depending on the environmental context [41].
4.2. Optimizing Sorghum for Forage and Biofuel Uses in California
This study also investigated the compositional characteristics of sorghum and its influence on feed quality and milk production potential (based on production indices developed for dairy cattle feeding). Results showed that sorghum hybrids with high fat and starch content were positively correlated with milk/ton, indicating their suitability for formulating high-quality dairy rations. On the other hand, milk/ton and relative feed quality (RFQ) were negatively correlated with structural carbohydrates, confirming that minimizing fiber content in sorghum forage benefits cow performance and milk production. Efforts to reduce fiber content by using brown midrib genes have demonstrated increased milk production, higher NDF digestibility, and overall forage quality [41,42]. It is important to note that a proper dietary fiber and starch ratio is also important in the maintenance of both the quality and quantity of the produced milk [43,44,45,46,47]. Therefore, breeders focusing on the development of high-quality forage for dairy production should (i) prioritize genotypes with high fat and starch levels to enhance the overall feed quality and milk production potential of sorghum forage, and (ii) target genotypes with reduced structural carbohydrates (ADF, NDF, and lignin) to improve digestibility and nutrient utilization by dairy cows.
In addition, this study confirmed the positive correlation between biomass yield and fiber content (ADF, NDF, and lignin). This aligns well with the desired traits for biofuel feedstock as high fiber content is essential for efficient conversion to biofuel. This offers both a challenge and opportunity to sorghum breeders in the quest for a dual-purpose forage/biofuel sorghum. Developing a single sorghum variety optimized for high-quality dairy forage and biofuel feedstock presents challenges due to the opposing selection pressure on fiber content. Therefore, future research could explore potential “compromise traits” that offer a balance between moderate fiber content for adequate digestibility in dairy rations and sufficient fiber for biofuel conversion. The concept of “compromise traits” in sorghum breeding represents a balanced approach to developing varieties that serve multiple purposes. This approach aims to find an optimal middle ground between traits desirable for forage quality (high digestibility, low fiber content) and those beneficial for biofuel production (high biomass yield, high fiber content). For instance, moderating lignin content can improve digestibility for forage while maintaining adequate biomass yield and plant standability for biofuel production. Optimizing the cellulose to hemicellulose ratio can enhance both livestock digestibility and biofuel conversion efficiency. Brown midrib (bmr) mutations, which reduce lignin content, can be selectively incorporated to improve digestibility without significantly compromising yield. Starch and stem sugar content can be balanced to benefit both livestock feed energy and ethanol production for biofuels. Future breeding efforts could focus on developing ‘dual-purpose’ sorghum varieties that perform well for both forage and biofuel applications. This might involve employing genomic selection techniques to simultaneously improve multiple traits, exploring the genetic basis of these compromise traits, and developing selection indices that balance forage quality and biofuel potential [11,48,49].
However, to meet the vision of sustainability, sorghum breeders should develop separate breeding schemes for forage and biofuel sorghums. The observation that high biomass yield often comes at the expense of desirable feed or biofuel composition highlights a significant challenge in sorghum breeding. The multi-trait stability index successfully addressed this issue by allowing selection for genotypes combining high biomass yield with favorable quality traits. The identification of nine sorghum hybrids (Figure 5) exhibiting high biomass yield, relative feed quality, and milk/ton potential offers significant value to animal nutritionists and dairy producers. These genotypes represent promising candidates for breeding high-quality forage sorghum that can enhance animal performance and milk production. The selection of distinct sorghum hybrids for first-generation (starch-based) and second-generation (cellulose-based) biofuel production strategies demonstrates the versatility of the multi-trait approach. Genotypes like UCSG51, UCSG50, etc., with high biomass yield and starch content, are ideal candidates for first-generation biofuels (Figure 6), while those with high NDF content (e.g., UCSG26, UCSG23) (Figure 7) are well-suited for second-generation processes. The identification of three sorghum hybrids (UCSG51, UCSG08, UCSG27) exhibiting desirable traits for both feed and biofuel production represents a significant breakthrough. These multipurpose genotypes offer immense potential for sustainable agricultural practices, allowing growers to cater to diverse market demands while optimizing land use.
The identification of multipurpose sorghum hybrids in this study has significant implications for sustainable agricultural practices in California, particularly in optimizing land use and reducing environmental impact. These hybrids, which exhibit desirable traits for both feed and biofuel production, offer the potential for more efficient land utilization. By growing crops that serve multiple purposes, farmers can potentially reduce the total land area required for separate feed and biofuel production [50]. This approach aligns with the concept of sustainable intensification, where agricultural output is increased without expanding the land under cultivation [51]. Furthermore, multipurpose hybrids can contribute to reduced environmental impact by minimizing the need for separate cultivation, thereby potentially lowering overall water usage, fertilizer application, and greenhouse gas emissions associated with agricultural activities [52]. Our findings build upon previous studies that have encountered similar challenges in breeding multipurpose crops, such as the work on dual-purpose maize varieties by Carpita and McCann [53] and switchgrass breeding for both forage and bioenergy uses by Sarath et al. [53]. The identification of sorghum hybrids that balance competing demands for feed and biofuel traits demonstrates the potential for developing versatile crop varieties suited to California’s diverse agricultural needs. However, further research is needed to quantify the exact environmental benefits and land-use efficiencies that could be achieved through widespread adoption of these multipurpose sorghum hybrids.
The photoperiod sensitivity of certain sorghum hybrids plays a significant role in the observed yield–quality trade-off. Photoperiod-sensitive (PS) hybrids typically have an extended vegetative growth period, which allows for greater biomass accumulation but can result in lower forage quality at harvest [54]. This extended growth period leads to increased lignification and a lower leaf-to-stem ratio, factors that contribute to reduced digestibility and nutritional value [48]. Conversely, non-PS hybrids generally reach maturity earlier, resulting in potentially lower biomass yields but higher forage quality due to their higher leaf-to-stem ratios and lower lignin content [11]. The timing of harvest relative to the physiological maturity of PS and non-PS hybrids can significantly influence the final yield and quality parameters, with PS hybrids often showing higher yield potential but lower quality when harvested at the same calendar date as non-PS hybrids [14]. This dynamic underscores the importance of considering both hybrid type and harvest timing when selecting sorghum varieties for specific end-uses in California’s diverse agricultural landscape.
Overall, this study demonstrates the effectiveness of a multi-trait stability index in overcoming yield–quality trade-offs and identifying superior sorghum genotypes for specific applications. The identified feed and biofuel candidates, particularly the multipurpose varieties, pave the way for the development of more sustainable and versatile sorghum production systems. While this study provides valuable insights into the performance of sorghum hybrids across diverse Californian environments, it also highlights several avenues for future research. First, evaluating the identified multipurpose hybrids under different management conditions, such as varying irrigation regimes, fertilizer applications, and planting densities, would further elucidate their adaptability and potential for optimized production. This could include assessing their performance under deficit irrigation scenarios, which are increasingly relevant in water-scarce regions. Second, investigating the response of these hybrids to different harvest timings could reveal opportunities to balance yield and quality for specific end-uses. Third, conducting detailed economic analyses that consider both production costs and potential returns from multiple end-uses (feed, biofuel, and other byproducts) would provide valuable information for growers and industry stakeholders. Additionally, exploring the genetic basis of the traits that contribute to multipurpose functionality could inform targeted breeding efforts. Finally, long-term studies assessing the impact of these multipurpose hybrids on soil health, water use efficiency, and overall farm sustainability would be crucial for developing more resilient agricultural systems in California. These research directions would not only enhance the applicability of our findings but also contribute to the ongoing development of versatile, high-performing sorghum varieties suited to the evolving needs of California’s agricultural sector.
5. Conclusions
In this five-year study, 59 sorghum hybrids were evaluated across California to assess their potential for feed, biofuel, and dual-purpose applications. The research identified significant genetic diversity and heritability in sorghum, with superior genotypes exhibiting high biomass yield and desired compositional traits. The study highlighted the importance of considering environmental factors when breeding sorghum, with some hybrids performing well across diverse locations. For dairy feed, high fat and starch content with reduced fiber were desirable, while high fiber content aligned well with biofuel needs. The study identified exceptional multipurpose hybrids well-suited for both feed and biofuel production. These findings offer valuable insights for scientists, breeders, policymakers, and farmers, paving the way for more sustainable and versatile sorghum production systems in California.
Conceptualization, J.A.D., R.B.H. and D.H.P.; methodology, J.A.D., R.B.H., D.H.P. and J.A. (Jackie Atim); formal analysis, J.A.D., R.B.H., D.H.P., J.A. (Jackie Atim), T.K., J.P., C.M.d.B., T.S. and J.A. (Jorge Angeles); investigation, J.A.D., R.B.H., D.H.P., J.A. (Jackie Atim), J.P., C.M.d.B., T.S., J.A. (Jorge Angeles) and N.E.C.; resources, J.A.D., R.B.H., D.H.P. and J.A. (Jackie Atim); writing—original draft preparation, J.A. (Jackie Atim) and T.K.; writing—review and editing, J.A.D., R.B.H., D.H.P., J.A. (Jackie Atim), T.K. and N.E.C.; visualization, J.A. (Jackie Atim) and T.K.; supervision, J.A.D., R.B.H., J.A. (Jackie Aim) and D.H.P.; project administration, J.A.D., R.B.H., J.A. (Jackie Atim) and D.H.P.; funding acquisition, J.A.D., R.B.H., J.A. (Jackie Atim) and D.H.P. All authors have read and agreed to the published version of the manuscript.
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.
We would like to express our sincere appreciation to the sorghum seed companies: Scott Seed Company, Dyna-Gro Seed Company, S&W Seed Company, Zinma Seed, Sorghum Partners, Mojo Seed, Grayland Ward Seed, Croplan, Richardson Seeds, Advanta-Alta, and Supra International for submitting their seeds for performance trials at various sites in California. We are also grateful for the coordination efforts of the United Sorghum Checkoff Program and the National Sorghum Producers. A special thanks goes to the dedicated farm crews from Kearney, Westside Research and Extension Centers, and the UC Davis farm, whose hard work made these evaluations possible. We also deeply appreciate the contributions of the intern students, Maikon F. Lemos, Ernesto Duran, and Kross Savas, who assisted throughout the process.
The authors declare no conflicts of interest.
Footnotes
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Enlarge this image.Figure 1. Correlation between different traits, quality (RFQ), and production indexes (milk/ton) among the 59 sorghum genotypes.
![View Image - Figure 2. Characterization of sorghum hybrids based on a superior index (WAASBY) [26] that combines the mean performance of each hybrid and its weighted average of absolute score (WAASB) stability value for the biomass yield. Sorghum hybrids were ranked based on the mean WAASBY value (blue for those that had values above the mean WAASBY value, while the red had values below the mean WAASBY).](https://media.proquest.com/media/hms/THUM/4/zhT2b?_a=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%3D%3D&_s=ZmMISWG9VhkH4aB%2FKJSaOHXgc3A%3D "View Image - Figure 2. Characterization of sorghum hybrids based on a superior index (WAASBY) [26] that combines the mean performance of each hybrid and its weighted average of absolute score (WAASB) stability value for the biomass yield. Sorghum hybrids were ranked based on the mean WAASBY value (blue for those that had values above the mean WAASBY value, while the red had values below the mean WAASBY).")
Enlarge this image.Figure 2. Characterization of sorghum hybrids based on a superior index (WAASBY) [26] that combines the mean performance of each hybrid and its weighted average of absolute score (WAASB) stability value for the biomass yield. Sorghum hybrids were ranked based on the mean WAASBY value (blue for those that had values above the mean WAASBY value, while the red had values below the mean WAASBY).
Enlarge this image.Figure 3. Performance of different sorghum hybrids in terms of biomass yield in the three locations (KARE, WREC and DAVIS) for the five years of evaluation. The general biomass yield at KARE was comparatively lower than that of WREC and DAVIS. The dotted line represents the mean biomass yield for the three locations and the five years of evaluation.
Enlarge this image.Figure 4. Categorizing sorghum hybrids based on the weighted average of absolute score (WAASB) index from the singular-value decomposition of the matrix of BLUPs for the GEI effects generated by a linear mixed model. Environment E1 (KARE), E2 (WREC) and E3 (DAVIS). Group I had sorghum hybrids that were both not productive and not stable, group II had hybrids that were highly productive but not stable, group III had hybrids that were highly stable but not productive, and Group IV comprised sorghum hybrids that were both highly productive and stable.
Enlarge this image.Figure 5. Radar plot showing the best sorghum hybrids selected for forage based on Yield, Milk, and RFQ using multi-trait stability index.
Enlarge this image.Figure 6. Radar plot showing the best sorghum hybrids selected for first-generation biofuel based on Yield, and starch content using multi-trait stability index.
Enlarge this image.Figure 7. Radar plot showing the best sorghum hybrids selected for second-generation biofuel based on Yield, and NDF using multi-trait stability index.
Accumulated Heat Units (base 60 F) at 30 days after planting (DAP), 60 DAP, 90 DAP, and the full growing season for the years 2019 through 2023 for the sites of the forage sorghum data sets at UC Davis, UC ANR West Side, and Kearney REC.
| Site | Year | Planting Date | Harvest Date | Season Duration (Days Planting to Harvest) | Accumulated Heat Units (Base 60 F) at Specific | Rainfall | Amount of Water Irrigated (mm) § | |||
|---|---|---|---|---|---|---|---|---|---|---|
| From Day 1 to 30 DAP | From Day 1 to 60 DAP | From Day 1 to 90 DAP | From Day 1 to Harvest | |||||||
| Davis | 2019 | 5 June | 26 September | 113 | 445 | 880 | 1359 | 1627 | 4.57 | - |
| 2020 | 29 May | 16 September | 110 | 405 | 856 | 1357 | 1611 | 10.67 | - | |
| 2022 | 7 June | 21 September | 106 | 450 | 928 | 1431 | 1659 | 2.03 | - | |
| 2023 | 25 May | 17 October | 145 | 266 | 718 | 1189 | 1780 | 29.46 | 556.3 | |
| WSREC | 2019 | 12 June | 27 September | 107 | 481 | 1065 | 1648 | 1868 | 16.26 | 266.7 |
| 2020 | 3 June | 5 September | 94 | 480 | 1052 | 1661 | 1735 | 17.53 | 401.3 | |
| 2021 | 8 June | 13 October | 127 | 586 | 1262 | 1786 | 2257 | 14.22 | 477.5 | |
| 2022 | 6 June | 28 September | 114 | 496 | 1178 | 1827 | 2254 | 18.03 | - | |
| 2023 | 6 June | 5 October | 121 | 422 | 988 | 1531 | 1909 | 0.00 | 586.5 | |
| KARE | 2019 | 2 May | 16 August | 106 | 230 | 773 | 1383 | 1704 | 3.05 | 478.5 |
| 2019 | 2 June | 13 September | 103 | 558 | 1173 | 1791 | 2029 | 2.29 | 449.8 | |
| 2020 | 4 May | 17 August | 105 | 388 | 870 | 1469 | 1789 | 9.91 | 424.7 | |
| 2020 | 1 June | 8 September | 99 | 504 | 1116 | 1736 | 1909 | 0.51 | 439.9 | |
| 2021 | 17 May | 16 September | 122 | 403 | 1117 | 1765 | 2383 | 1.02 | - | |
| 2021 | 11 June | 6 October | 117 | 692 | 1351 | 1941 | 2326 | 0.00 | - | |
| 2022 | 1 June | 5 October | 126 | 529 | 1180 | 1851 | 2487 | 6.60 | - | |
| 2023 | 24 May | 22 October | 151 | 361 | 972 | 1595 | 2399 | 12.70 | 654.1 | |
* Heat Units calculated using a 60-degree F (15.56 °C) base temperature, no maximum temperature horizontal cut-off, and single sine calculation method using daily high and low temperatures. CIMIS weather station data at each site were used for heat unit calculations. Planting and harvest dates for each site/year are shown, harvest season duration in days, and rainfall totals between planting and harvest are shown. §—Missing values (-), irrigation water amounts were planned and applied to essentially keep up with evapotranspiration demand of the crop. Data collected included emerged, final plant population, plant height (cm) at silage harvest, lodging (%) at silage harvest, moisture content at harvest, and forage yield for the two middle rows at silage harvest timing. Biomass yield (Mg/ha) was reported at 65% moisture content. The trials were harvested approximately 94 to 120 days after planting using a Wintersteiger Cibus forage chopper (Wintersteiger Inc, Salt Lake City, UT, USA).
Analysis of variance for yield and compositional traits of 59 sorghum genotypes evaluated across three environments in California.
| Df | Yield | CP § | WSC Sugar | NDFD30 | Lignin | Starch | Fat | Milk/Ton | RFQ §§ | |
|---|---|---|---|---|---|---|---|---|---|---|
| Mean Square | ||||||||||
| ENV | 2 | 6637.80 ** | 120.04 ** | 127.06 ** | 25.28 ** | 20.26 ** | 2256.09 ** | 3.19 ** | 1,029,431.38 ** | 3073.35 ** |
| GEN | 58 | 394.57 ** | 18.13 ** | 74.61 ** | 143.85 ** | 4.84 ** | 769.18 ** | 0.99 | 1,034,202.76 ** | 4710.53 ** |
| GEN:ENV | 116 | 120.36 ** | 7.66 ** | 12.39 | 33.64 * | 0.68 ** | 113.14 ** | 0.09 ** | 150,148.17 * | 743.70 ** |
| PC1 | 59 | 97.19 ** | 6.30 | 8.52 | 26.88 | 0.49 | 82.61 | 0.05 | 120,750.08 | 556.19 ** |
| PC2 | 57 | 65.99 | 3.61 ** | 5.69 | 22.38 | 0.26 | 61.67 | 0.04 | 57,853.17 | 286.63 |
| Residual | 1051 | 68.88 | 2.58 | 13.38 | 28.58 | 0.40 | 105.54 | 0.07 | 108,215.60 | 391.56 |
Yield: Biomass yield (Mg/ha at 65% moisture content), § CP: Crude Protein (%), WSC sugar: Water-Soluble Carbohydrates sugar (%), NDFD30: Neutral Detergent Fiber Digestibility at 30 h (%), §§ RFQ: Relative Feed Quality, Milk/ton: Potential milk production per ton of forage (lbs/ton). *—Significant at p < 0.05, and **—Significant at p < 0.001.
Estimation of variance components and genetic parameters from the mixed effect model.
| Yield | CP § | WSC Sugar | NDFD30 | Lignin | Starch | Fat | Milk/Ton | RFQ §§ | |
|---|---|---|---|---|---|---|---|---|---|
| Variance Components | |||||||||
| % ENV Variance | 10.05 | 4.35 | 1.67 | 0.14 | 4.62 | 3.17 | 4.2 | 10.08 | 0.74 |
| % GEN Variance | 18.10 | 19.07 | 25.08 | 22.63 | 31.99 | 31.33 | 37.80 | 29.41 | 32.99 |
| % GEN:ENV Variance | 6.82 | 16.12 | 8.24 | 10.48 | 9.03 | 9.22 | 7.26 | 8.54 | 10.41 |
| % explained PC1 | 60.40 | 64.90 | 60.8 | 66.00 | 66.00 | 58.1 | 54.9 | 68.4 | 67.4 |
| % explained PC2 | 39.60 | 35.60 | 39.20 | 34.00 | 34.00 | 41.9 | 45.1 | 31.6 | 32.6 |
| Mean | 51.18 | 8.28 | 8.17 | 50.88 | 4.07 | 12.06 | 2.41 | 2695.02 | 115.04 |
| SE | 0.63 | 0.06 | 0.12 | 0.17 | 0.02 | 0.3 | 0.01 | 11.28 | 0.72 |
| SD | 22.4 | 2 | 3.75 | 5.43 | 0.82 | 10.42 | 0.34 | 396.01 | 25.13 |
| CV | 97.9 | 24.16 | 45.96 | 10.67 | 20.19 | 86.41 | 14.29 | 14.7 | 21.85 |
| Genetic Parameters | |||||||||
| Heritability | 0.181 | 0.176 | 0.34 | 0.21 | 0.32 | 0.34 | 0.35 | 0.29 | 0.32 |
| GEIr2 | 0.068 | 0.211 | 0.02 | 0.04 | 0.06 | 0.05 | 0.04 | 0.03 | 0.08 |
| h2mg | 0.63 | 0.56 | 0.79 | 0.68 | 0.78 | 0.79 | 0.81 | 0.77 | 0.77 |
| Accuracy | 0.794 | 0.748 | 0.89 | 0.83 | 0.88 | 0.89 | 0.90 | 0.88 | 0.88 |
| rge | 0.083 | 0.256 | 0.02 | 0.05 | 0.09 | 0.08 | 0.07 | 0.05 | 0.12 |
| CVg | 18 | 10.458 | 27.27 | 4.92 | 11.24 | 51.09 | 8.18 | 8.06 | 12.47 |
| CVr | 36.661 | 19.511 | 39.72 | 9.38 | 15.65 | 68.81 | 10.76 | 12.25 | 17.23 |
| CV ratio | 0.491 | 0.536 | 0.69 | 0.53 | 0.72 | 0.74 | 0.76 | 0.66 | 0.72 |
Yield: Biomass yield (Mg/ha at 65% moisture content), § CP: Crude Protein (%), WSC sugar: Water-Soluble Carbohydrates sugar (%), NDFD30: Neutral Detergent Fiber Digestibility at 30 h (%), §§ RFQ: Relative Feed Quality, Milk/ton: Potential milk production per ton of forage (lbs/ton), Genetic parameters—Heritability the broad-sense heritability; GEr2 the coefficient of determination of the interaction effects; h2mg the heritability on the mean basis; Accuracy the selective accuracy; rge the genotype–environment correlation; CVg the genotypic coefficient of variation; CVr the residual coefficient of variation; CV ratio the ratio between genotypic and residual coefficient of variation.
Predicted genotypic mean of yield and compositional qualities of 59 sorghum genotypes evaluated in three locations in California.
| Genotype | Yield | CP § | NDF30 §§ | Starch | WSC Sugar | RFQ §§§ | Milk/Ton |
|---|---|---|---|---|---|---|---|
| UCSG35 | 75.10 | 6.99 | 49.82 | 6.26 | 9.74 | 95.63 | 2608.80 |
| UCSG53 | 68.37 | 6.90 | 50.10 | 4.23 | 8.84 | 92.39 | 2537.02 |
| UCSG34 | 67.21 | 7.14 | 49.55 | 6.77 | 9.24 | 98.28 | 2587.77 |
| UCSG21 | 64.00 | 7.42 | 52.33 | 5.77 | 8.10 | 90.66 | 2460.20 |
| UCSG51 | 62.25 | 8.36 | 45.92 | 27.24 | 2.24 | 146.26 | 3119.35 |
| UCSG39 | 60.66 | 7.57 | 50.69 | 9.15 | 9.02 | 102.14 | 2628.38 |
| UCSG09 | 60.32 | 7.89 | 50.80 | 16.42 | 7.29 | 118.90 | 2806.50 |
| UCSG36 | 58.78 | 8.03 | 54.57 | 4.76 | 8.24 | 105.16 | 2467.82 |
| UCSG49 | 58.78 | 7.68 | 48.81 | 10.46 | 9.62 | 101.24 | 2629.32 |
| UCSG40 | 58.13 | 7.72 | 53.33 | 12.39 | 9.14 | 115.36 | 2868.67 |
| UCSG27 | 57.59 | 7.42 | 48.38 | 11.89 | 11.09 | 111.48 | 2761.56 |
| UCSG37 | 57.12 | 7.47 | 51.63 | 3.40 | 8.09 | 91.75 | 2396.33 |
| UCSG16 | 56.16 | 8.09 | 47.53 | 18.35 | 7.79 | 123.15 | 2882.14 |
| UCSG15 | 55.91 | 8.01 | 47.19 | 18.40 | 7.60 | 119.26 | 2892.71 |
| UCSG22 | 55.59 | 7.84 | 48.88 | 12.23 | 8.82 | 105.82 | 2613.97 |
| UCSG28 | 55.42 | 7.79 | 47.90 | 19.46 | 11.18 | 126.96 | 3027.72 |
| UCSG23 | 55.08 | 8.17 | 52.92 | 18.87 | 9.18 | 128.80 | 2840.29 |
| UCSG43 | 54.52 | 8.22 | 49.68 | 20.62 | 7.86 | 126.00 | 2888.93 |
| UCSG50 | 54.32 | 8.07 | 50.21 | 19.68 | 3.85 | 134.35 | 2860.30 |
| UCSG24 | 54.14 | 7.43 | 51.76 | 12.35 | 10.44 | 119.10 | 2856.26 |
| UCSG29 | 53.85 | 7.60 | 48.60 | 12.09 | 12.05 | 122.07 | 2920.74 |
| UCSG31 | 53.85 | 8.16 | 50.41 | 18.92 | 9.75 | 127.01 | 2895.93 |
| UCSG19 | 53.76 | 8.35 | 49.37 | 17.41 | 7.14 | 123.80 | 2843.71 |
| UCSG08 | 52.24 | 8.21 | 51.28 | 18.21 | 9.33 | 140.90 | 3002.47 |
| UCSG47 | 52.20 | 8.93 | 47.99 | 17.58 | 4.32 | 127.63 | 2883.23 |
| UCSG32 | 52.04 | 7.48 | 47.75 | 8.82 | 11.89 | 109.46 | 2704.11 |
| UCSG44 | 51.86 | 7.94 | 50.81 | 13.94 | 9.63 | 117.04 | 2751.60 |
| UCSG30 | 51.79 | 7.97 | 52.64 | 14.82 | 10.23 | 128.51 | 2866.31 |
| UCSG57 | 51.59 | 8.14 | 53.88 | 14.20 | 5.18 | 127.63 | 2762.59 |
| UCSG10 | 51.03 | 8.30 | 54.30 | 13.54 | 9.01 | 127.35 | 2746.70 |
| UCSG17 | 50.11 | 7.97 | 52.82 | 7.41 | 9.23 | 107.29 | 2585.44 |
| UCSG01 | 49.98 | 8.04 | 48.76 | 11.92 | 7.86 | 103.67 | 2610.03 |
| UCSG03 | 49.98 | 9.08 | 50.47 | 12.45 | 4.64 | 115.09 | 2708.76 |
| UCSG25 | 49.46 | 7.89 | 50.38 | 9.25 | 12.35 | 120.23 | 2791.44 |
| UCSG26 | 49.37 | 8.38 | 50.13 | 18.41 | 9.06 | 123.34 | 2851.89 |
| UCSG12 | 48.79 | 8.62 | 54.18 | 14.57 | 8.28 | 129.25 | 2794.23 |
| UCSG14 | 48.54 | 8.39 | 53.49 | 3.89 | 7.77 | 102.85 | 2360.30 |
| UCSG52 | 48.52 | 8.22 | 48.67 | 19.00 | 9.53 | 123.10 | 2882.97 |
| UCSG54 | 47.35 | 8.09 | 51.86 | 15.58 | 7.10 | 118.06 | 2761.89 |
| UCSG58 | 47.24 | 8.52 | 52.57 | 8.39 | 9.12 | 112.81 | 2510.22 |
| UCSG11 | 46.77 | 7.86 | 52.45 | 13.44 | 8.44 | 125.40 | 2733.28 |
| UCSG20 | 46.16 | 8.94 | 50.54 | 21.38 | 4.82 | 136.86 | 2938.00 |
| UCSG18 | 44.26 | 8.43 | 51.22 | 13.37 | 7.93 | 132.07 | 2842.28 |
| UCSG13 | 43.77 | 8.99 | 52.08 | 14.86 | 6.35 | 114.30 | 2640.68 |
| UCSG05 | 43.52 | 8.53 | 51.31 | 10.28 | 6.93 | 111.48 | 2577.24 |
| UCSG07 | 42.55 | 8.62 | 51.45 | 17.65 | 6.67 | 124.50 | 2818.90 |
| UCSG41 | 42.44 | 8.36 | 52.57 | 2.73 | 7.63 | 89.69 | 2217.43 |
| UCSG02 | 42.06 | 9.06 | 50.89 | 13.14 | 6.01 | 118.36 | 2719.22 |
| UCSG04 | 41.95 | 9.11 | 49.70 | 8.15 | 6.24 | 113.74 | 2601.91 |
| UCSG48 | 41.88 | 8.67 | 52.37 | 4.22 | 7.55 | 105.26 | 2457.07 |
| UCSG33 | 38.02 | 8.52 | 57.10 | 5.85 | 8.04 | 99.94 | 2401.14 |
| UCSG59 | 37.77 | 9.51 | 51.69 | 2.25 | 7.32 | 99.49 | 2123.90 |
§ CP—Crude protein, §§—Neutral Detergent Fiber digestibility after 30 h, §§§—RFQ, Relative Feed Quality. Mean: The average performance of each genotype across all environments. Yield (Mg/ha) is reported at 65% moisture content, RFQ is a unitless index where 100 represents the quality of full-bloom alfalfa hay, Milk/ton is calculated using the Milk2013 equation and represents the potential milk yield per ton of forage dry matter, NDFD30 is expressed as a percentage of NDF digested after 30 h of incubation, and all compositional traits (CP, WSC sugar, NDFD30, Lignin, Starch, Fat) are expressed as a percentage of dry matter. Bold-text genotypes had high relative yield and quality traits (RFQ and Milk/ton).
Categorization of sorghum genotypes based on relative feed quality score.
| RFQ Score | Grade ** | Number of Sorghum Genotypes |
|---|---|---|
| >185 | Supreme | 0 |
| 160–180 | Prime | 0 |
| 140–159 | Choice | 2 |
| 110–139 | Select | 35 |
| 90–109 | Standard | 21 |
| <90 | Utility | 1 |
** Grade—Supreme denotes the highest quality feed (exceptionally nutritious, highly palatable, and free from contaminants); Prime grade feed would be of very high quality, with excellent nutritional value and palatability formulated to meet or exceed nutritional requirements for the intended animals; Choice grade feed would be of good quality, providing solid nutritional value that meets all necessary nutritional standards for the animals it is intended for; Select grade feed would be of acceptable quality, meeting basic nutritional requirements and providing adequate nutrition for animals; Standard grade feed would meet minimum nutritional requirements, suitable for basic maintenance but may not be optimal for high-performance animals; and Utility grade feed would be of lower quality, that meets very basic nutritional needs but may not be ideal for promoting optimal growth or production.
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; Kaweesi, Tadeo 1
; Hutmacher, Robert B 2 ; Putnam, Daniel H 3 ; Pedraza, Julie 1 ; de Ben, Christopher M 3 ; Schramm, Tarilee 1 ; Angeles, Jorge 1 ; Clark, Nicholas E 1
; Dahlberg, Jeffery A 1
1 Kearney Agricultural Research & Extension Center, Agriculture and Natural Resources, University of California, Parlier, CA 93648, USA;
2 West Side Research & Extension Center, University of California, Five Points, CA 93624, USA;
3 Department of Plant Science, University of California Davis, One Shields Ave, Davis, CA 95616, USA;