Response of potato genotypes to bacterial wilt caused by Ralstonia solanacearum (Smith) (Yabuuchi et al.) in the tropical highlands. 6 Genotype x environment interaction and stability of potato tuber yield and bacterial wilt resistance in Kenya.
Introduction
Origin and distribution of potato
Genetics of Solanum tuberosum
The inheritance of heterosis occurs through small genes or through the side effects of the most important genes. The greatest number of interlocus interactions will also occur as the frequency of tetraallelic loci increases.
Combining ability studies in potato
This is because in related material the number of different alleles is likely to be limited. In conditioning the after-cooking discoloration in potatoes, GCA was reported to be more important than SCA (Dalianis et al., 1966; Killick, 1977).
Farmers‘ preferences and participatory variety development
Tai (1976) reported that interprogeny variation in tuber yields and number of tubers per plant was dominated by SCA, while GCA was more important for average tuber weight and specific gravity. In yet another study, GCA was found to dominate in determining total tuber yield, number of tubers per plant and plant appearance, while average tuber weight depended on both GCA and SCA (Brown and Caligari, 1989).
Genotype X environment interaction
Another study found that GCA was more important in determining the inheritance of number of stems, stolon length, plant appearance, skin color, tuber shape, tuber yield, eye depth, number of tubers per plant, average tuber weight, harvest index, leaf weight and total biomass (Neele et al. , 1991). Breeders usually want genotypes that show little interaction with the environment because they are stable (Yan et al., 2007).
Potato production in Kenya
16 informal seed sources that include farm-saved (self supply), local markets or neighbors (Kaguongo et al., 2008; FAO, 2013). Besides late blight, viral diseases are a serious problem hampering potato production in Kenya (Kaguongo et al., 2008).
Bacterial wilt
However, the disease is effectively controlled using fungicides, although this significantly increases production costs, thereby discouraging most small-scale farmers (Kaguongo et al., 2008). Most potatoes in Kenya are grown from seed tubers retained by farmers from previous harvests, acquired from local markets or from neighbors (Khurana and Garg, 2003; Kaguongo et al., 2008).
Bacterial wilt symptoms on potatoes
18 Plants with foliar symptoms can produce apparently healthy and diseased tubers, while plants showing no symptoms of the disease can sometimes produce diseased tubers (Martin and French, 1985; Hayward, 1991; EPPO, 2004). In Kenya, certified and apparently healthy (but latently infected) potato seed tubers produced at altitudes of 1520-2120 meters above sea level showed infection when planted at lower altitudes (Nyangeri et al., 1984).
Causal organism of bacterial wilt
The species is also widespread at higher latitudes, reaching southern Sweden and southern Argentina (Champoiseau et al., 2009). The R3bv2A variety is the main cause of bacterial wilt of potatoes in the Kenyan highlands (Smith et al., 1995).
Dissemination and survival of R3bv2A
Pathogen survival in soil is reduced by extreme cold and the presence of antagonistic microorganisms, while volunteer host plants allow bacterial survival across seasons (Martin and French, 1985; Hayward, 1991; Milling et al. , 2009). Survival also depends on the breed involved; race1 usually persists in the soil for many years due to its many hosts, while R3bv2A tends to persist for several years due to its limited hosts (Martin and French, 1985; Champoiseau et al., 2009).
Management of bacterial wilt on potatoes
- Phytosanitation and cultural practices
- Use of disease-free tuber seeds
- Quarantine
- Crop rotation
- Intercropping
- Delayed planting
- Soil amendments
- Chemical control
- Biological control agents
- Host resistance
- Nature of resistance
- Inheritance of resistance to bacterial wilt
- Search for resistance
25 Some potato varieties are, at least in some regions, less susceptible to bacterial wilt (Champoiseau et al., 2010). To improve bacterial wilt of potatoes, continuous development of resistant varieties is necessary (Champoiseau et al., 2010).
The utility of potato resistance to Ralstonia solanacearum for integrated control of bacterial wilt. Incidence of potato blight caused by Ralstonia solanacearum in Kenya and opportunities for intervention.
Introduction
For example, most processors in Kenya prefer cultivars with long and white skins for French fries, while cultivars with round and red skins are preferred for chips (Walingo et al., 1998). In Kenya, red-skinned cultivars were found to be more popular than white-skinned varieties in Meru Central District, while the opposite was found in Nyandarua District (Kaguongo et al., 2008).
Materials and methods
Survey sites and descriptions
43 Meru Central district is located in the former Eastern Province and represents potato growing areas in the Mount Kenya region. In the district, potatoes are mainly produced in Kibirichia and Abothuguchi West divisions, located on the northern slopes of Mount Kenya.
Sampling method, data collection and analysis
Results
- Farmers and farm characteristics
- Potato farming system
- Major potato marketing constraints
- Potato production constraints
- Management of bacterial wilt
Over 75% of farmers in the surveyed divisions cited diseases as the main constraint to potato production (Table 2.12). After harvesting, most farmers in all surveyed divisions (except Molo) throw the rotten tubers into a pit and pit (Table 2.16).
Discussion and conclusions
A negative correlation (r=-0.354) between bacterial wilt incidence and altitude was previously observed (Wakahiu et al., 2007). In another study, farmers in the main potato growing provinces in Kenya ranked high yields as the most important criterion for growing a particular cultivar (Ng'ang'a et al., 2003).
Mapping the potato bacterial wilt caused by Ralstonia solanacearum and its spread in major potato growing areas of Kenya. Chapter Three: Response of Potato Genotypes to Bacterial Wilt Disease in the Tropical Highlands of Kenya.
Introduction
The use of potato varieties resistant to bacterial wilt caused by Ralstonia solanacearum (Smith, 1896) (Yabuuchi et al., 1995) is probably the best treatment option for the disease. Locally acceptable cultivars with good resistance to bacterial wilt have yet to be identified in Kenya (Ateka et al., 2001).
Materials and methods
- Description of the study site
- Field layout, bacterial wilt inoculation and crop management
- Data collection
- Data analysis
- Selection of bacterial wilt resistant genotypes
The same genotypes were used in the second and third seasons; in the first season, four genotypes were different. In addition, the number of symptomatic tubers (i.e. the appearance of rot or bacterial ooze in the tuber eyes or soil adhering to the tuber eyes) and healthy-appearing (asymptomatic) tubers was determined.
Results
Weather data
Soil bacterial counts
Bacterial wilt incidence and tuber traits
81 Table 3.7. Average response and rank among 36 potato genotypes for some agronomic and bacterial wilt parameters ¨ during the first season. Average response and ranks among 36 potato genotypes for some agronomic and bacterial wilt parameters ¨ during the second season.
Ranks of genotypes based on selected traits
Mu gihembwe cya kabiri, genotypes icumi za mbere ni Roslin Bvumbwe, Kenya Sifa, Kenya Karibu, Ingabire, Musenyeri Gitonga, Sherekea, Nyayo, Kihoro, Robyjn w’Ubuholandi na clone 394034.7 kuri urwo rutonde (Imbonerahamwe 3.10).
Bacterial wilt resistance
Average rankings of 36 potato genotypes for bacterial wilt resistance based on %LI, DTOW, PSTTW, PSTTN and AUDPC in the first season. Mean rankings of 36 potato genotypes for bacterial wilt resistance based on %LI, DTOW, PSTTW, PSTTN and AUDPC in the second season.
Correlations among traits
The correlation between %LI and all other traits was not significant in the first two seasons. In the third season, the correlation between % LI and DTOW was negative and significant (P≤0.01) while the correlations between % LI and AUDPC, PSTTN and PSTTW were positive and significant (P≤0.01) (Table 3.17).
Discussion and conclusions
A later study found that Kenya Sifa and Kenya Karibu were the most resistant to bacterial wilt, while Dutch Robyjin and Tigoni were the most susceptible (Felix et al., 2010). Studies have shown that latent infection of tuber and plant susceptibility to bacterial wilt above ground are not correlated; the latent infection potential of the clone depends not only on BWI, but also on other factors such as the environment (Ciampi and Sequeira, 1980; Priou et al., 2001).
Assessment of latent infection rate in progeny tubers of advanced potato clones resistant to bacterial wilt: A new selection criterion. The objective of this study was to determine the genetic relationships between potato clones to complement other bacterial wilt resistance data to identify parents for a breeding program.
Introduction
Simple Sequence Repeat (SSR) markers detect highly repetitive regions in the genome that can be derived from untranslated regions and introns ( Ghislain et al., 2006 ). In solanaceous species, the microsatellite frequency is greater in the untranslated intron regions 5' (upstream of the gene) and 3' (downstream of the gene) (Smulders et al., 1997).
Materials and methods
Plant materials
The kit provides a high content of locus-specific polymorphic information and a high quality of multiples, as determined by clarity and reproducibility (Ghislain et al., 2009). They can also use genetic distance based on molecular markers to complement co-ancestry/pedigree analysis (Tarn et al., 1992; Gopal and Oyama, 2005) to avoid closely related parents and thus inbreeding depression and to provide genetic variation for further progress.
DNA sampling
SSR analysis
Results
Genetic polymorphisms
Cluster analysis among potato clones
Furaha=Sumrek iti Furaha; Karibu=Kenya Karibu; Gitonga=Obispo Gitonga; Faulu=Faulu ti Kenya; Mavuno=Kenya nga Mavuno; Meru- Meru Mugaruro. Ti ababa a henetiko a distansia iti nagbaetan ti C4 ken Tigoni Long (0.36) ket mangpasingked ti panagsuspetsa a ni Tigoni Long ket mabalin a nakalibas manipud iti germplasm ti CIP.
Discussion and conclusions
A comparative assessment of DNA fingerprinting techniques (RAPD, ISSR, AFLP and SSR) in tetraploid potato (Solanum tuberosum L.) germplasm. Chapter Five: Combining ability analysis of tuber yield and related traits and bacterial wilt resistance in potatoes.
Introduction
Furthermore, the genetic background of adaptation is crucial for the expression of resistance (Tung, 1992; Tung et al., 1993). Therefore, potato clones with a broad genetic background for both bacterial wilt resistance and adaptation tend to exhibit a high level of resistance that is stable across environments (Tung et al., 1993).
Materials and methods
- Study sites
- Plant materials and crosses
- Generation of true potato seed and F 1 seedlings
- Field management of F 1 seedlings
- Determination of combining abilities for bacterial wilt resistance and
- Data collection
- Data analysis
- Analysis of variance
- Estimation of general and specific combining ability effects
The weight of symptom and edible tubers was expressed as a percentage of the total yield. Resistance to bacterial wilt crossing was determined using ranking based on % LI, AUDPC, DTOW, PSTTW and PSTTN and the percentage of total infected tubers (PTIT).
Results
- Analysis of variance for crosses across sites
- Ranking of crosses for bacterial wilt resistance across sites
- General and specific combining ability estimates for selected tuber
- General and specific combining ability estimates for selected tuber
TTN=Total tuber number per ha; PSTTN= Percentage of symptomatic tubers (% of total tuber number per ha); PWTTW= Percentage true size tubers (% of total tuber weight in t ha-1); PSTTW= Percentage of symptomatic tubers (% of total tuber weight in t ha-1); AUDPC= Area under the disease progression curve; DTOW= Days to onset of wilting; DTM = Days to maturity. PSTTW= Percentage of symptomatic tubers (% of total tuber weight in t ha-1); AUDPC= Area under the disease progression curve; DTOW= Days to onset of wilting; DTM = Days to maturity.
Discussion and conclusions
This is in agreement with previous studies reporting that both major and minor genes are involved in the expression of resistance to bacterial wilt; and the inheritance of this resistance involves both additive and non-additive gene actions (Tung et al., 1993; Tung and Schmiediche, 1995). Moreover, epistasis appeared to be important in the inheritance of this resistance (Tung et al., 1992a; Tung et al., 1993).
Chapter Six: Genotype x Environment Interaction and Potato Tuber Yield Stability and Bacterial Wilt Resistance in Kenya. Potato families were ranked differently for resistance to bacterial wilt in four environments.
Introduction
Breeders mostly want high and stable genotypes that show minimal interaction with the environment (Yan et al., 2007). Additive main effects and multiplicative interaction (AMMI) analysis (Gauch and Zobel, 1997) and biplot analysis of genotype main effect and genotype x environment interaction (GGE) (Yan et al., 2000) are widely used and powerful tools.
Materials and methods
- Study sites
- Plant materials, families and agronomic management
- Inoculation of bacterial wilt
- Data collection
- Data analysis
- Analysis of variance
- AMMI model
- GGE biplot
Time to maturity was counted as the number of days from planting until 75% of the plants had. GGE biplots based on average environmental coordination (AEC) and the sign of the genotype-focused biplot (Yan and Kang, 2003) were used to determine yield performance and stability for the 48 potato families.
Results
- Weather conditions in the test environments
- Analysis of variance across environments
- Ranking for bacterial wilt resistance across environments
- AMMI analysis of variance
- Ranking of the best four AMMI selections per environment
- AMMI biplots: classification of families and environments
- GGE biplot analysis
The AMMI analysis of variance showed significant (P≤0.001) effects of the families, environments and the G x E interaction (Table 6.7). The ENVI 1 (short rains during 2013 at Kinale, Table 6.1) was the closest to ideal environment and therefore the most desirable of the four environments (Figure 6.5).
Discussion and conclusions
The potato families were ranked differently for resistance to bacterial wilt across the four environments; this was an indication of crossover GEI. The environment ENVI 1 (short rain during 2013 at Kinale) was the closest to ideal environment and therefore the most desirable test site of the four environments.
This study provided an insight into the magnitude of GEI for potato tuber yield and bacterial wilt resistance in Kenya. Kelman (ed.) Proceedings of the first international planning conference and workshop on the ecology and control of bacterial wilt caused by Pseudomonas solanacearum, Raleigh, North Carolina. January 18-24, 1976.
Introduction and research objectives
Research summary
The potato genotypes varied in their susceptibility to bacterial wilt and the most resistant genotypes were Kenya Karibu followed by Kenya Sifa. The study identified eight potato genotypes (Meru Mugaruro, Ingabire, Kenya Karibu, Sherekea, Kihoro, Tigoni, Bishop Gitonga and Cangi) to be used in a breeding program to improve bacterial wilt resistance in Kenyan germplasm.
Implications of the research findings to breeding potato for higher yield
The ENVI 1 environment (short rains in 2013 in Kinal) was the closest to the ideal environment and therefore the most desirable of the four test environments.
