Accelerating Breeding for Drought Tolerance in Sorghum (Sorghum bicolor): An Integrated Approach

Abstract

Sorghum (Sorghum bicolor) is the main food crop for people living in marginal areas. They are faced with a number of production challenges including; drought, insect pests, diseases, soil fertility and striga weeds. To adapt to current and future stresses, there is a dire need to develop tolerant cultivars using multistress lines and varieties from wide genetic backgrounds. Toward better integrated approaches; we conducted participatory field screening in hot spot locations for drought, striga weed and major leaf spot fungal diseases on the 20 lines making mini-core sorghum germplasm. Lines carrying key traits of resistance to stresses have been recycled into the breeding program. The study also identified biochemical traits that could potentially be used as surrogate traits for the selection of tolerant genetic resources with improved yields. Nuclear male fertile crosses have been derived for exploiting differences in the cytoplasm for enhancing resistance. It also integrated variability in phytochemicals and cytoplasmic resistance to develop multi-parent sorghum lines and populations possessing potentially favorable adaptive alleles. In conclusion; unique traits and breeding strategies for sorghum adapted to the dry lowlands have been identified to lay a foundation for a modernized and market-oriented sorghum breeding program to the advantage.

1. Introduction

Sorghum (Sorghum bicolor) is the main food crop for people living in marginal areas. They are faced with a number of production challenges including; drought, insect pests, diseases, soil fertility and weeds such as striga. Of recent, drought has been the major production challenge limiting sorghum production in Uganda by preventing improved cultivars from expressing their full genetic potential. Three mechanisms namely; drought escape, drought avoidance and drought tolerance are involved in drought resistance. Functional drought resistance categories are based on unique morphological, physiological and biochemical traits working together with genetic factors determined under growth chamber/controlled screening. Nevertheless, morphological and physiological characters show different types of inheritance pattern (i.e., monogenic and polygenic) and different gene actions (additive and non-additive). This implies that the heritability of drought resistance from different genotypes is not consistent so cannot be relied on. The breeding procedure commonly practiced for handling segregating generations affects the rate of genetic progress that can be made under stress that is, yield. Therefore drought resistance is best selected using secondary traits which depict the interaction between water stress, weather variables and the plant [1]. For example, the stay green color is a secondary trait resulting from biochemical adaptation to water stress leading to a change in chlorophyll content. There are genotypes expressing various degrees of stay green traits identified from screening experiments [2, 3, 4, 5]. For that reason, a large amount of genetic variability has been reported among sorghum germplasm for their reaction to drought stress necessary for setting a breeding scheme. Therefore innovative breeding as an aspect of forward-looking approach is critical while exploiting wide genetic variation of relevant plant characteristics with farmers who support the plant breeding industry. The approach is inspired by [6, 7], who have described steps and methodologies involved in setting priorities in the breeding program/breeding scheme. They argue that the views of farmers need to be considered to come with a deeper understanding about how yield and quality can be increased within the local production system and its specific risk management strategies. Therefore, incorporated resistance to drought must improve positively agronomic characteristics as well as the quantity and quality of harvested products. To address the availability of suitable varieties in the long term, the sorghum improvement program in Uganda has considered the following integrated approaches, in line with the early stages of product profile development under the dry lowland agroecology. Research objective 1: Participatory exploration of sorghum breeding targets. Research objective 2: Identification of genotypes with broad adaptation. Research objective 3: Profile elite germplasm on the basis of phytochemical defense compounds to exploit major factors of the evolution of the crop. Research objective 4: To test pollen fertility restoration of selected parents in cytoplasmic male sterile (CMS) background, to exploit heterosis for productivity and resistance to stresses in the derived lines and populations.

1.1 Participatory exploration of drought tolerance breeding targets in sorghum

The approach was through engaging farmers using a system approach in product profile development, to ensure practical actions by beneficiaries exposed to drought stresses are contextualized. This will contribute to harnessing opportunities and generate context-based technologies and innovations for dry lowland agroecologies of Uganda. This chapter compares farmers’ sorghum traits and breeding targets by breeders/scientists.

1.2 Materials and methods

1.2.1 Sorghum varieties and breeding process

The current six popular sorghum varieties in Uganda were developed between 2011 and 2017 and are phenotypically distinct. There exists a number of other elite cultivars from the breeding program following six year breeding program to release. That is; from germplasm evaluation through initial screening, making crosses, advancing populations (F₁–F₅) as single plants selections under the pedigree system. This is followed by stages of preliminary yield trials (PYT) and advanced yield trials (AYT) in designated places. It is then followed by adaptation trials, national performance tests and release. Yield improvements currently have stagnated and there is a need to replace the current varieties released five years ago but suitable to dry lowland agroecology. Therefore participatory evaluation of potential candidate cultivars for replacement was done in 2017 B (short rain season), to set better targets for innovative breeding and selection approaches. This was to enhance the germplasm base for key traits that stabilize production and could increase genetic gains in farmers’ fields (Table 1). This summarizes the priority traits related to drought tolerance and drought escape given by the two groups of sorghum farmers, during the field day organized for the Teso and the Lango farmers of Uganda in the 2017 B season. These groups of farmers are from different sorghum production systems. The participants were encouraged to screen the on-station field experiment block and pick up the eye-catching ones for in-depth discussion. The scores and ranks for each group were summarized into four categories. The prioritization of drought tolerance and escape were mentioned along with the description of traits that will contribute to the stability of production (Table 1). Specific ranking based on value for use (Table 2) and ranking based on gender following focus group discussions in the field (Table 3).

Priority	Plant traits	Explanation/Description	Breeders related views
High priority	Robustness	Both groups rated high^1; Robustness is associated with (Vigor, Leafy, stout stem, plant health, seed yield, less lodging). The traits are indicative of high tolerance to drought or can escape drought	Biological-yield; Selection based on selection-index; Heterosis/genetic distance of parents; Leaf area/stay green; Node diameter; Tillers
Medium-High priority	Large seeds /Yield; (Table 2)	One group rated high^1and other medium^2; Large seeds are attractive and marketable	Harvest-index; Threshing percentage; Seed size(weight); Good agronomic practices
	Panicle-width; panicle length
Medium-Low priority	Uniform; Early maturity; Non-senescent	One group rated high^1and other medium^2	-Genotype by environment interaction; -Production areas; -Agroecology; -Certified seed
Low-priority	Plant color/Seed color	One group rated high^1and the other low^4	Anthocyanin/alkaloid; Content; Stem sugar; Agromorphological characters; Quality flour

Table 1.

Prioritization of identified selection traits by two participatory groups. Each group consisted of a random mix of six researchers and 30 farmers male and female. The farmers ranked groups of perceived drought tolerance traits from 1 to 4 and rated from 1 to 4 in order of priority.

Genotype	Yld	Good for food	Disease and pest resist	Local Mrkt	Attractive seed color	Early maturity	Bird damage	Average	Rank
SESO1	4	3	5	5	3	1	4	3.57	8th
IESV24029SH XICSB479-1	3	3	2	3	4	3	3	3.00	7th
NAROSORGH 2	1	1	1	1	1	2	1	1.14	1st
NAROSORGH3	1	3	3	4	3	2	3	2.71	6th
SESO3	3	1	4	1	1	1	2	1.86	4th
IESV24029SH	1	1	1	1	1	3	1	1.33	3rd
SEREDO × SRN 39H2-2-1	1	2	2	2	2	2	2	1.86	4th
IESV92207DL	1	5	1	5	3	4	5	3.43	10th
IESV92034DLSEL2	2	5	1	5	3	4	5	3.57	9th
KAK7780	1	1	1	1	1	2	2	1.29	2nd
Value	18	25	21	28	22	24	28
Rank	1st	5th	2nd	6th	3rd	4th	6th

Table 2.

Matrix ranking of traits by farmers (based on value for use), by pairwise wise ranking method.

Brief explanation of the results:

Low numbers are most preferred and therefore i.e., 1 is the best and 5 is the worst.

Highly valued traits are yield, disease and pest resistance and attractive seed color.

Best overall candidate varieties for release are; KAK77880 and IESV24029SH.

Yld = Yield, method described by [7], most farmer participants associated grain color with variety, hence able to describe agromorphological differences.

Genotype	Females; N = 15	Males; N = 15	Average	Rank
SESO1	8	10	9	9th
IESV24029SH XICSB479-1	6	7	6.5	6th
NAROSORGH 2	4.5	1	2.8	4th
NAROSORGH3	9	6	7.5	8th
SESO3	1	5	3	3rd
IESV24029SH	3	2	2.5	1st
SEREDO × SRN 39H2-2-1	4.5	4	4.3	5th
IESV92207DL	7	8.5	7.7	7th
IESV92034DLSEL2	10	8.5	9.7	10th
KAK7780	2	3	2.5	1st

Table 3.

Ranking based on gender following focus group discussions in the field.

Highly ranked candidate varieties; KAK-7780, IESV24029SH, SESO3.

1.3 Results and discussion

High-valued traits considered for drought tolerance and escape by farmers are high plant vigor and robustness to nurture the seeds of high quality and hence high yield. These traits were identified from three genotypes; KAK-7780, IESV 24029SH and SESO3. Further description of the robustness trait takes care of plant health and associated responses to biotic and abiotic stresses (pests, diseases, drought and soil fertility). Additionally, it dictates the maturity and uniformity of the crop and hence was rated highly by participating farmers. Drought is a complicated trait to screen; robustness and vigor show good nutrition and good germination. This is how farmers quickly demonstrate yield potential. Furthermore, farmers emphasized morphological traits that breeders do not systematically select for. Breeders, on the other hand, rely on quantitative measurements but are aware of the heritability of specific traits and the influence of genotypes by the environment on plants growing under optimal conditions. Therefore, observed selection goals of farmers and breeders fit into the description of [8], who stated that science takes a reductionist approach toward breeding that is, to reduce the number of variables considered but study them in detail. Farmer’s positive selection in favor of healthy, good germinating, vigoros, large panicles and large seeds demonstrate their ability to select stable genetic responses. These selection traits have actual advantages on yield under rain-fed conditions. Number of factors have been found to be associated with resistance in sorghum and include; seedling vigor, glossiness, morphological and biochemical characteristics. Any condition such as drought, low fertility and temperature makes the plant susceptible to attack by insect pests and diseases [9]. Besides involving farmers in target setting and selection, it is important to consider the selection environment. Selection is more efficient when the correlation between selection and the target environment is high and increases selection efficiency for direct and indirect selection for broader or specific adaptation [10]. This study recommends selection indices that will help in selecting seedlings at a very early stage without losing important information. The farmers and scientists preferred selections made from advanced lines to be planted in regional adaptation trials to analyze genotype by environment interaction to prove the need for more varieties to be released with certainty. This will help define clusters of target environments based on differences in environmental parameters, production systems, and farmer preferences [11]. From the participatory study; drought resistance is best measured in terms of robustness (vigor) and plant health traits as priority traits when designing product profiles (Table 2).

1.4 Sectional conclusion

Several relevant traits are often considered simultaneously in plant breeding, particularly when selection is done by farmers. The relationship among them determines breeding strategies and response to selection (study 2). There is a need to develop an index which when applied to sorghum seedlings during the first eight weeks of development in the field (at anthesis), will indicate the best progenies and best plants within the progeny. The possible vigor parameters associated with drought tolerance include; Plant height, node diameter, basal stem sugar, internode length, Leaf area, leaf dry weight and stay green from where correlations can be calculated from non-senescent genotypes. Some of these traits identified can reasonably be bred depending on their level of expression. Therefore, it is a need to conduct multi-location trials to help to monitor levels and expression of adaptive associated traits in plants that will be incorporated into the model while taking account of environmental variance in the expression of traits.

2. Determination of yield stability of rain-fed sorghum and GGE biplot analysis of multi-environment trials

2.1 Materials and methods

Drought, yield and biotic stress traits are quantitatively inherited and highly environmentally interactive and require multi-environment testing to accurately characterize them. Therefore integrating the yield and stability of genotypes tested in very unpredictable environments is an important breeding strategy to identify superior sorghum genotypes for the rain-fed areas of Uganda. It helps to determine the existence of different mega-environments for maximizing genetic gains. This was achieved by testing 20 elite genotypes identified in study 1, in four locations via GGE (genotype + genotype-by-environment) biplot analysis. Data from two seasons (2019b–2020b) of rain-fed sorghum were used in this study. The materials consist of elite germplasm; landraces, advanced breeding lines and popular cultivars within crop improvement programs and introductions which contribute to variation and differentiation. Table 4 is a representative sample of all the diversity in the large collection and would facilitate the enhanced use of sorghum germplasm in the breeding programs for major economic and production traits. They were selected together with farmers as having the best attributes for food and niche markets. The information on genotype-environment interaction was a useful supplement for classifying genotypes. The nature of genotype-environment interaction helps in the development suitable procedures for selection and the nature of stability for vital characters. Meanwhile, multivariate analysis was used to offer a more complete examination of data by looking at all possible independent variables and their relationships to one another. Finally, levels of similarity were computed as percentages among genotypes for key variables useful for planning crosses.

Entry/NPT	Genotype	Origin	Pedigree	Traits
1	NAROSORGH 3	Uganda	IS8193 × SEREDO	Midge resistance
2	NAROSORGH 4	U.S.A-Perdue	GE17/1/2003A	Striga + Early
3	SILA	Kenya	SILA	Malting
4	NTJ2	Ethiopia	NTJ2	Malting + Grain size
5	IESV24029SH	Ethiopia	GADAM × IS8193	Food grain
6	IESV92172DL	Ethiopia	IESV92172DL	Drought + Dwarf
7	ASERECA 13-3-1	Sudan	GADAM	Striga, Drought
8	ASERECA 15-2-1	Sudan	GADAM	Striga
9	IESV24029SH × ICSB479-1	Uganda	IESV24029SH × ICSB479-1	Stemborer resistance + Male sterility
10	KAK-7780	Kenya	Landrace	Grain quality (food), drought
11	IESV142001	Ethiopia	IESV142001	Grain yield
12	ICSV142012	India	ICSV142012	Grain Yield
13	SEREDO X; SESO1	Uganda	SEREDO X; SESO1	Striga + grain Yield
14	IESV92207DL	Ethiopia	IESV92207DL	Drought
15	IESV92034DLSEL2	Ethiopia	IESV92034DLSEL2	Drought
16	ICSL71052	India	ICSL71052	Yield
17	EPURIPURI	Uganda	Tegemeo	Grain Yield
18	SESO2	Uganda	SRN39	Striga + grain yield
19	IESV23007DL	Ethiopia	IESV23007DL	Drought + grain yield
20	SEREDO	Uganda	SEREDO	Drought + striga

Table 4.

Genetic materials.

2.2 GGE analysis

In GGE biplot analysis, the first two principal components (PC1 and PC2), derived by subjecting the environment-centered yield bi-plot (Figure 1), to singular value decomposition (SVD). Principal component (PC1) was significant and location accounted for 17% of the total sums of squares. Genotype by environment variation was greater in the two seasons confirming that food productivity is threatened by environmental variables. The small yield variation due to location is relevant to cultivar evaluation. The yield obtained from across environments selected the following genotypes as they combine yield and stability and this should be considered during genotype selection. Genotypes with the above mean performance were; IESV 92207DL, IESV92024SH × ICSB 497-1, on the basis of the ATC (Average Tester coordinate X-axis) or (Average Tester coordinate Y –axis, the stability axis). The research identified two mega environments for sorghum under rain-fed areas. This has several implications for future breeding and genotype evaluations of sorghum that is, warm-dry ecology (Kumi) and sub-humid environments; (Namutumba, Pallisa and Iganga). The closer an environment is to this virtual environment (ATC axis); the better it is as a test environment [11]. Thus Pallisa and Namutumba are relatively favorable test environments and most representative and as well discriminative, suitable for multiple stress evaluation with a yield above 2000 kg/ha. Kumi was a most discriminative environment, probably due to the high level of striga from low fertile sandy soils which enhance water stress, hence strong genotype by environment interaction. The large part of the genotype × environment interaction was also indicated by a positive correlation between different yield components (Figure 2).

Figure 1.

Environment-focused comparison bi-plot shows the performance and stability of genotypes.

Figure 2.

Dendogram showing how sorghum founder lines can be clustered together in groups on the basis of important biochemical composition.

2.3 Cluster dendrograms based on grain yield

A dendogram with clusters was created from 12 elite lines based on their level of similarity in grain yield/ha (Figure 3). This is important for planning a crossing program; for example, using IESV24029SH × ICSB 497 and KAK-7780 could have a good combination of favorable alleles. They were also among the best four highly ranked genotypes from Additive Main effects and Multiplicative Interaction (AMMI) analysis (Table 5). Genotype KAK-7780 was selected from a landrace population that could possess both major and minor gene systems for stress protection which contributes to yield.

Figure 3.

Dendrogram with clusters based on level of similarity (%) in grain yield (Kgm/ha).

Number	Environment	Mean	Score	1	2	3	4
1	IGANGA	2517	24.63	NPT 21	NPT 9	NPT 14	NPT 5
4	PALLISA	2125	5.71	NPT 21	NPT 23	NPT 14	NPT 22
3	NAMUTUMBA	2664	1.26	NPT 15	NPT 5	NPT 14	NPT 9
2	KUMI	2612	−31.61	NPT 23	NPT15	NPT 1	NPT 5

Table 5.

AMMI ranking of the best four selections.

NPT 1 = NAROSORGH3, NPT 5 = IESV24029SH, NPT9 = IESV24029SH × ICSB479-1, NPT22 = SESO1.

NPT14 = IESV92207DL. NPT15 = IESV92034DLSEL2, NPT21 = NAROSORGH 2, NPT23 = SESO3.

2.4 Cluster dendogram based on plant height

Using IESV92034DLSEL2, KAK-7780, NAROSORGH 2 and IESV92207DL will be very useful for improved yield components. These differences could have resulted from differences in biomass production and seed weights and hence they are the best ranking candidates for rain-fed areas (Figure 4). Since the genotype cannot restrict their heights significantly across environments, it implies resistance operates against major sorghum production challenges (drought, disease and pests) contributing to stability. More research needs to be conducted to unravel the underlying principles for plant-stress interaction with respect to plant height so that they can be incorporated into breeding.

Figure 4.

Level of similarity (%) based on plant height.

2.5 Correlation between traits

Genetic variability for yield and seedling vigor components exits and they include; seed size, days to flowering (maturity), plant height, panicle length, panicle width, pest and disease resistance. They are a result of active physiological processes driven by active translocation and they influence dry matter accumulation. Plant height is strongly positively correlated to panicle length (PL) and moderately to days to flowering (DF) and hundred seed weight (HSWT). On the other hand, stem borer (SB) resistance is moderately positively correlated to stay green (SC), plant height (PHT) and days to flowering (Figure 2), where the correlation is low (R = 0.2) or negative, or both, little progress can be made. For example, the relationship between hundred seed weight and stay green could be due to competition for sink source relationship. So grain yield may not be true determinant for drought tolerance [12].

This could be because hundred seed weight is calculated after seed cleaning. For breeding purposes, it is therefore important to compare hundred seed weight (seed size) among genotypes with respect to the standard check/commercial varieties. The data should be interpreted based on physiological time of maturity, growth patterns, dry matter accumulation, partitioning of sink and genetic differences. Although many traits have been studied for their use in breeding for drought resistance, there is a general consensus among breeders that only a few of them can be recommended for use in practical breeding programs at this time. The study identified maturity (50% flowering), plant height, stay green [13].

2.6 Sectional conclusion

It is possible to select stable genotypes combining the number of yield components since genetic merit for these traits exist. For example, tall plants of a height of above 170 cm (centimeters) can be used to select improved grain yield components. Considering the strong weak negative correlation between turscicum leaf blight (TLB) infection with plant height and days to flowering, implies delayed flowering is associated with decreased diseased levels and is common among tall plants. The key traits are plant height, stay grain and days to 50% flowering which are most likely to improve the rate of genetic gains for drought tolerance. There is a need to exploit information gained from correlated traits and pedigree for the selection of parents with multiple traits [14, 15].

3. Determination of grain quality profile and phytochemical content of elite lines

3.1 Introduction

Adaptation to drought is a result of biochemical adaptation to water stress leading to a change in chlorophyll content, production of antioxidant scavenging enzymes, increase in proline content, production of secondary metabolites such as; alkaloids, terpenes, flavonoids, mevalonic acid, shikimic acid among others [4, 5]. Drought like other environmental changes can bring about marked differences in the defense chemistry of the plant. These qualitative traits (secondary metabolites) also control the aroma, taste and acceptability of products and can be integrated into the breeding pipeline at the priority setting and trait discovery stage. Variations in phytochemicals could be used to broaden the genetic base in the three current gene pools; food, feed, fodder through introgression to generate desirable genetic complexes (linkage groups). From breeder point of view, these phytochemicals can be grouped as valuable or useful and sometimes negative hampering the application of germplasm in a breeding program.

3.2 Materials and methods

The study evaluated the hypothesis that alkaloid content reduce in advanced generations as result of selection. They are however associated with serious side effects on products at high levels. This was investigated among the 20 breeding materials in study 2. The genotypes included; (7 cultivars, 4 progeny lines, 8 Varieties and 1 Landrace populations) making minicore-germplasm. There are from different gene pools and breeding history. Biochemical analysis was carried out at the National Crops Resources Research Institute (NACRRI) Bio-Nutrition Laboratory in 2019. Principles and methods of biometrical designs were applied according to the protocol developed by [16]. Absorbance was read at wavelength 470 nm. The variation with respect to chemical composition in genotypes was attributed to genetic differences. Canonical correlation analysis was used between sets of independent variables for data interpretation and cluster analysis for grouping genotypes.

3.3 Variability for biochemical contents in grain sorghum

The multivariate analysis utilized all the variations of traits in generic way to group genotypes with similar sets of traits and quantify the importance of various traits in grouping/clustering genotypes. Results revealed statistically significant (P < 0.001) differences among the sorghum accessions profiled for biochemical contents, demonstrating the influence of genotype with respect to checks (Table 6). The subset of sorghum minicore germplasm has been categorized into four clusters. This could be the influence of physiological and biochemical processes in modifying the plant in response to abiotic and biotic stresses [4, 17]. Cluster 1 in red (Figure 5), with the largest number of genotypes displayed more scope for selection against water stress as supported by positive and significant correlations (Figure 6). The coefficient of genotypic variance was above 80% indicative of substantial genetic diversity and prospects for improvement through chemical selection (Table 6). Low levels of phytochemicals of less than 4 mg/100 g were consistent as all released varieties were clustered together with checks supporting the hypothesis (Figure 5). Polyphenols were positively correlated among themselves but negatively associated to the levels of carbohydrates. The level of tannins was important in establishing groups and contributed a lot to the total variability among the accessions. Landraces and their derived lines and hybrids clustered together, hence exploitation of this material require a lot of chemical selection due to probably strong linkages with the wild (i.e., NPT 10, NPT 5 and NPT 9). Improvement of genetic gain for these plant chemical defense compounds might be possible through hybridization.

Entry	Flavs		Carbh		Color		Pheno		Amylose		Tannins
NPT4	0.172	A	1.337	Cd	1.182	h	0.039	E	0.5817	b	0.2747	F
NPT 8	0.183	A	1.944	I	0.152	a	0.00500	A	0.737	h	0.225	E
NPT 6	0.207	B	1.773	H	1.432	j	0.00633	A	0.6037	c	0.0273	A
NPT 20	0.221	C	1.973	I	0.289	b	0.01367	Bc	0.7297	h	0.0677	Bc
NPT 18	0.237	D	1.735	H	1.008	f	0.024	D	0.6693	e	0.086	C
NPT 2	0.242	D	1.893	I	0.745	e	0.01533	C	0.733	h	0.025	A
NPT 12	0.265	E	1.928	I	1.915	m	0.077	G	0.8775	j	1.1453	I
NPT 7	0.29	F	1.776	H	1.105	g	0.00333	A	0.692	f	0.4253	G
NPT 14	0.326	G	1.763	H	1.334	i	0.16183	I	0.631	d	1.2475	J
NPT 13	0.332	G	1.693	Gh	1.5	jk	0.01067	B	0.9717	k	0.9553	H
NPT 16	0.35	H	1.261	Bc	1.355	i	0.15217	H	1.0715	m	1.1215	I
NPT 15	0.356	Hi	1.079	A	1.646	l	0.0695	F	0.9782	k	1.8665	Lm
NPT 3	0.358	Hi	1.488	Ef	1.191	h	0.014	Bc	0.5567	a	0.122	D
NPT 17	0.361	Hi	1.514	F	0.594	d	0.00667	A	0.7363	h	0.02	A
NPT 19	0.365	I	1.269	Bc	1.551	k	0.07133	F	0.8882	j	1.3975	K
NPT 11	0.395	J	1.611	G	1.648	l	0.00533	A	0.7103	g	0.0387	Ab
NPT 1	0.436	K	1.226	B	1.983	m	0.2915	J	0.8608	i	1.8372	L
NPT 10	0.562	L	1.496	Ef	0.506	c	0.0145	Bc	1.0525	l	0.9725	H
NPT 5	0.724	M	1.974	I	1.306	i	0.02267	D	1.5667	n	1.9015	M
NPT 9	0.884	N	1.402	De	0.749	e	0.01217	Bc	1.0787	m	1.9598	N

Table 6.

Variability for biochemical contents in grain sorghum in Serere in 2017 based on observed values of absorption spectrophotometer.

Checks NPT 3 = SILA, NPT 17 = EPURIPURI a, b, c, d, e, f, g, h, I, j, k, l, m, n = Mean separation.

Figure 5.

Correlation among key trait.

Figure 6.

Correlation among variables in grain sorghum in Serere in 2017 with their corresponding coefficient values and probabilities potential.

3.4 Correlated response to selection and indirect selection

A high level of carbohydrates was negatively correlated to the levels of polyphenols. Therefore, selection for large seed size of ≥3 gms/100 seed could reduce the concentration of polyphenols relative to the increase in water and carbohydrate (starch) content in the seed. In nature, the association among desirable traits can be negative as for the case of maize, (e.g., increasing grain yield is associated with lower protein content [18]. The variation for biochemical traits was represented by the two-dimensional scatter diagram that accounted for 70% of the variance. Genotypes; NPT15 (IESV92034DLSEL2) and NPT 19 (IESV23007DL), were plotted in the upper right quadrat. Meanwhile genotypes; NPT 5 (IESV24029SH), NPT 9 (IESV24029SH × ICSB 479) and NPT10 (KAK-7780) are intermediate occupying the lower right quadrant (Figure 7). The differences could reflect breeding, selection history and complex interrelationships between ecological factors important for parental selections for multiple traits [14, 15]. Diversity among genotypes has been categorized into groups of similar characteristics that can be used for designing optimized crossing strategies. Released varieties clustered together support the argument that selection and hybridization among themselves have taken place (Figure 5). The key traits that are most likely to improve the rate of genetic gains for grain quality traits are levels of tannins and carbohydrates since they are negatively correlated.

Figure 7.

Scatter plot showing the best selections on the upper right hand.

3.5 Sectional conclusion and recommendations

Chemical factors such as high carbohydrate content, less intensive color and tannin content are good attributes for better quality products. This study showed the value of exploiting the information in correlated traits that will contribute toward improving the accuracy of breeding values of the products such as bread and malt quality. Accurate selection of sorghum breeding lines can accelerate annual genetic gain for these correlated traits when used to generate an optimized crossing design (study 4); where there is a high and positive correlation between secondary traits such as color and target traits such as tannins and phenolic, then greater selection intensities can be applied to the secondary trait during screening in big populations. Positive results will be expected when the information is integrated with the pedigrees during breeding. Furthermore innovative research is important into processes that mitigate ant nutritional factors while enhancing bio-availability of proteins, amylose starch among the high tannin genotypes in the utilization of such materials in feed, food and beer value chains.

4. Pollen fertility restoration in diverse cytoplasmic sterility lines for improved populations

Superimposed on the major forces of evolution is variation due to the interaction of the mitochondria (cytoplasm) and nuclear genes in mediating resistance to some major stresses and physiological processes in the plant that needs to be exploited to enhance genetic variation. This material is useful in developing hybrids, inbred lines and gene pools based on combining ability for specific traits (components of grain yield). The study identified testers and restorers for extraction of lines from improved populations for yield-related traits such as; seed size, plant height panicle length, panicle width and resistance to stem borers.

4.1 Population development

The experiment was designed to make improvements in quantitative traits to maximize as much as possible the additive effects, maternal, as well as to gather genes with complementary dominant and epistatic effects in genotype. The hypothesis tested was that Hybrids derived from inbred lines (A-lines) with complementary heterotic groups have superior performance. (Table 7). A two-way cross hybrids were generated from seven pollinator testers of (Sorghum bicolor) with drought tolerant backgrounds mated to A₂ cytoplasmic sterile lines using an appropriate model [17]. The lines used were selected from previous studies (1–3) perceived to be containing favorable alleles for the prioritized traits of; robustness (vigor), large seeds, disease resistance and high threshing percentage. Data was analyzed using NCII model appropriate to line × tester crossing design with two reps in the 2022 A season. The variance between the testers was subdivided into variance within cytosteriles and that due to interaction.

SOURCE	DF	PHT	PANL	PWD	100 GNW	GRN YLD
Hybrids	21	251.5**	102.5**	18.8**	18.6**	1029.9**
Lines	5	7408***	296***	38.**	39.8**	2708**
Testers	8	4820***	166***	104.2**	62.9**	1589.9*
Between genotypes with A-lines	6	8641***	382***	147.6**	142.1***	654.1 ns
A-Line × Testers	30	636.3***	30.9***	2.9***	6.6 ns	408.3 ns
Hybrids × Replication	42	129**	30.9***	0.7***	5.5 ns	423.5.5**
Error	360	178.6	7.4	0.45	1.88	152.1
Var GCA		78.31	0.95	0.33	0.43	8.21
var SCA		46.35	0.4	0.07	0.01	0.51
VarGCA:Var SCA		1:0.59	1:0.42	1:0.21	1: 0.65	1:0.06

Table 7.

Analysis of variance for key yield components.

SCA = specific combining ability, GCA = General combining ability, Var = variance, ***=significant at 0.01 probability, **p = 0.1, *p = 0.05, ns = not significant, DF = degrees of freedom, PHT = Plant height, PANL = Panicle Length, GNW = Grain weight, GRNYLD = Grain yield per hectare.

4.2 Combining ability

Testers varied significantly for all characters (Tables 7 and 8). The differential behavior of the genotypes was reflected in general combining ability (GCA) effects. Variance for GCA is greater than the variance for specific combining ability (SCA) implying large additive gene effects and over-dominance effects. There is a possibility of deriving superior lines from such populations containing balanced cumulative effects of genes. Lines with highly significant positive GCA effect such as DINKIMASH-17 for grain yield could be useful for contributing favorable alleles for breeding for improved grain yield under drought conditions. Female inbred lines; ICSA 12, CK60A, P 9518A and ICSA 90001 and the male lines GE30, KAK-7780, NAROSORGH4, IESV98038/2SH and DINKIMASH-17, were selected for their desirable GCA effects for agronomic traits. Test cross F₁-derived F₂ families have been developed by exploiting differences in the cytoplasm (Table 8). The identified lines have a positive effect on fertility restoration. The effect of genes carried was likely large enough to influence the full seed set of the panicles among many families. The Partial sterility observed among some F1_S does not present a serious problem because the F₁ may be either selfed/backcrossed to recurrent parent Populations. This permits the best exploration of the intricate assortment of both major genes and genes with small effects for the traits under consideration [19]. The breeding products from this study are important in enhancing sorghum germplasm base by contributing favorable alleles for expressing vigor of yield and cytoplasmic pest and disease-mediated resistance [20].

Parents	PHT	PANL	PWD	100 GNW	GRN YLD
ICSA 12 × DINKIMASH-17	7.92**	0.8**	0.5 ns	0.5**	2.94**
ICSA 1630139 × DINKIMASH-17	5.44**	0.31**	0.35*	0.14**	0.3.07**
ICSA 1630139 × IESV92038/2SH	7.25	1.24	0.19*	0.61*	1.05
P9518A × NAROSORGH4	−6.75	1.29.	0.63*	0.89*	1.87
ICSA9 × GE30/1/1-2-2	11.24*	1.29*	0.1*	0.55*	1.87*
ICSA9001 × IESV92021DL-1-9-2-1	26.55*	1.04	0.17	7.42	0.44*
ISCA 6 × IESV 92038/2SH -2-1	0.87*	2.24	1.06**	0.47**	0.5*
ICSA8 × GE30/1/2013A-1-1-1-3	3.36	0.53	0.47	0.52	0.77
P9518A × NAROSORGH4	11.38*	3.24*	0.91	34.64*	2.68
ICSA × KAK-7780-1-5-1-3	2.34*	2,‘46**	0.37	6.72**	3.57*

Table 8.

Estimates of general combining ability for top 10 ranking genotypes.

Probability, **p = 0.1, *p = 0.05, ns = not significant, PHT = Plant height, PANL = Panicle Length, GNW = Grain weight, GRNYLD = Grain yield per hectare.

5. Perspectives: Toward breeding to for drought resistance

Drought resistance is a complex trait controlled by many linked genes. Therefore, the probability of selecting drought-tolerant lines increases measurably as the percentage of adapted genotypes in gene combination increases. This progressively will increase the rate of genetic gains. The account below gives a summary of components (insights) of a breeding strategy for drought resistance for a practical breeding program.

5.1 Identification of important secondary traits from multi-locational trials

Analysis of genotype-by environmental interaction (GEI) was carried out in selected elite breeding material using nine characters including grain yield. The study has highlighted important secondary traits well expressed under field screening conditions because of good interaction of genotype × environment with other weather variables. Such traits; stay green, plant height, vigor (size), day to flowering, disease and pest response. The secondary traits can be used to increase progress made with primary traits (functional resistance) that is determined under growth chamber conditions. Therefore toward better integrated approaches, field screening of elite sorghum lines was carried out in hot spot locations for key sorghum production constraints; that is, drought, striga, pests and leaf spot diseases. This is because incorporated resistance to drought must have a positive effect on agronomic attributes as well as the quantity and quality of harvested products (study 1). Therefore the research captured a wide genetic variation of relevant plant characters to include in designing a breeding scheme. The GEI was approached through variance components, regression and multivariate methods (study 2 and study 3). Through these analyses, genotypes and environments were grouped, and stable genotypes were identified and ranked. The GEI for yield was attributed to various traits and genotypic correlations determined between plant height, panicle length, panicle width, days to 50% flowering, hundred grain weight, grain yield/ha, pest and disease response. The correlations ranged from negative, low, moderate, and positive and this guided selection. These analyses helped to design optimal crossing design (study 4).

5.2 Development of multi-parent population using cytoplasmic male sterile system (CMS)

The selected good parents from study 2 and study 3 were evaluated to determine their effectiveness in a breeding scheme through line × tester (North Carolina II) mating design. General combining ability for agronomic characteristics and full fertility restoration ability was tested under A₂ form of CMS which is easily transmissible to the progeny. The use of such derived progenies helped to exploit diverse nuclear backgrounds to enhance stability. The use of CMS-derived crosses/progenies has improved; grain sets, hundred seed weight, germination capacity and vigor which are components of drought resistance. This will permit selection for drought resistance but guarantee essential gene recombination necessary for the stability and adaptability of breeding lines.

Acknowledgments

The study was supported by the National Agricultural Research Organization (NARO), International Crop Research Institute for Semi-Arid Tropics I (CRISAT) under the Accelerated Variety Improvement and Seed Delivery of legumes and cereals in Africa (AVISA) project, National Semiarid Resources Research Institute (NaSARRI), And the National Crops Resources Research Institute (NACRRI)-BIO NUTRITION LAB.

Conflict of interest

None.