Performance of SAP low germination stimulant 1 sorghum
under natural Striga infestation
The ultimate test for Striga resistance performance under natural
pest infestations. We, therefore, evaluated the resistance response of
the SAP lgs1 accessions in a Striga- infested field in
western Kenya by measuring Striga emergence using the metric of
Area Under Striga Number Progressive Curve (AUSNPC); a
modification of the area under the disease progress curve (AUDPC)
developed for plant pathology studies (Simko & Piepho 2012). In this
approach, a quantitative measurement of Striga infestation is
determined over time. For our case, we determined Strigainfestation using data collected at 42, 56, 70, 84 and 98 days after
planting.
Resistance measured by AUSNPC showed that most SAP lgs1 had highStriga resistance (Figure4a). We would like to point out SAPlgs1 PI533976 which had remarkably lower emergence (p value =
0.00017) compared to the base mean of all accessions. Furthermore, the
resistance of PI533976 was higher than that of known Strigaresistant varieties including SRN39, Framida and IS9830. Also striking
was the field resistance displayed by the SAP LGS1 PI655054
accession, described earlier in germination bioassays and pot
experiments. Field resistance of this genotype affirmed its importance
as a Striga resistant accession. Expectedly, some SAP lgs1accessions for example PI656040, PI585295, and PI656010 showed low
resistance, even comparable to the susceptible Shanqui Red andLGS1 PI533839. These same genotypes had displayed low resistance
in pot experiments. The low resistance could be attributed to low
post-attachment resistance. Two of these accessions (PI585295, PI656010)
that were assayed biochemically were also shown to produce relatively
large quantities of orobanchol (Figure 2c). Even though orobanchol is a
less potent stimulator of germination compared to 5-deoxystrigol,Striga populations from western Kenya are particularly sensitive
to high concentrations of orobanchol relative to populations from other
locations (Haussmann et al. 2004; Bellis et al. 2020).
In addition to Striga resistance screening, field evaluation
allowed us to determine the suitability of SAP lgs1 accessions
for growth in western Kenya as well as evaluate other desirable
agronomic traits (Table 1). Considering that the SAP accessions were
selected for photoperiodism sensitivity, we sought to determine days to
flowering by measuring maximum days to 50 % flowering (DFL50 %). We
found that DFL50 % of the latest and earliest genotypes averaged
between 76.67 in PI5610710 and 55 in PI533976 indicating that the SAP
accessions are in general early maturing and do not have variation in
flowering time. We also measured the associated trait of plant height
under field conditions. Results showed that the average plant height
ranged from 65 cm to 235 cm. The plant height of the SAP lgs1accessions are comparable to those of sorghum genotypes grown in Kenya
such as SRN39 (125.33.73cm), Framida (155.33 cm) and IS9830 (173.67 cm).
Lastly, we compared the average yield of SAP lgs1 lines to that
of popular Striga -resistant varieties in Kenya. The top 3 highest
yielding varieties were SAP lgs1 PI656094 (2.9667g), PI656096
(3.0333g), and SRN39 while the poorest yielding varieties were PI655979,
PI561071and PI533839. The yields are comparable to varieties Framida
(2.8667g), IS9830 (2.6333g) grown in Kenya indicating that the lines are
suitable for adoption in Kenyan agro ecological regions.
The other aspect of our field experiments was to determine the effect ofStriga infestation on growth of the sorghum plants – given thatStriga infection is associated with severe growth retardation
(Fujioka et al. 2019a). An effective way to determine the extent
to which Striga infection affects growth, and how best an
infected host copes with the infection is to correlate host growth andStriga emergence. Hosts that cope well with infection –
described as tolerant genotypes – show less severe effects of parasite
infection (Mwangangi et al. 2021). We found that Strigainfection reduced yield by up to 27 %. The most severe reduction in
growth was in PI576385 (26 %) while SRN39 and Shanqui Red incurred
losses of 6 % and 13 %. Remarkably, PI561071 and PI585295 did not
suffer significant yield losses. When we correlated sorghum’s yield withStriga emergence (AUSNPC) we found a weak positive correlation
between yield loss and AUSNP (R = 0.33, p = 0.25) indicating that most
genotypes were tolerant (Figure 4b). PI561071, PI656096, PI6506054
maintained high yields even with some Striga infestation.
To summarize our field experiments, we ranked Striga tolerance,
field resistance and other desirable agronomic traits in SAP lgs1lines to find the best performers. To achieve this, we used the Rank
summation index (RSI) method proposed by (Mulamba & Mock) where a
genotype is assigned a rank based on performance for each trait (Figure
4c). A final score (RSI) is then assigned based on all traits. Using
this analysis, the best 3 performing genotypes were SRN39, SAPlgs1 PI533976 (caudatum, USA), and SAP LGS1 PI656054 (Kafir,
South Africa).
Although further field evaluation is required, our results show that
some SAP lgs1 lines are well suited to environments in Kenya and
can potentially be used in Striga management programs. A
representative photograph depicting the performance of the Strigaresistant SAP lgs1 PI561071 against the susceptible LGS1PI533839
and Shanqui Red is shown in Figure 4d.