Susceptibility of Ethiopian Released Potato Varieties to Potato Tuber moth, Phthorimae operculella Infestation under the field and laboratory conditions

Susceptibility of Ethiopian Released Potato Varieties to Potato Tuber moth, Phthorimae operculella Infestation under the field and laboratory conditions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Susceptibility of Ethiopian Released Potato Varieties to Potato Tuber moth, Phthorimae operculella Infestation under the field and laboratory conditions Kidist Teferra Yimame, Emana Getu Degaga This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6841160/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Two experiments were designed for studying susceptibility of ten potato varieties to evaluate the infestation levels of Phthorimaea operculella under the field and laboratory conditions, at the egg stage and larval infestation stage before harvesting, at harvesting time, and evaluates the physiological performance to resist PTM. Data were analyzed using Mean ± Standard Error, along with F-values and P-values to assess statistical significance with R software. There was significant differences in egg infestation levels and suggesting a clear variation in susceptibility between genotypes to PTM (F-value = 3.1, P-value = 0.018), highest infestations observed on the Belete (14.4 ± 7.9) and Burika (7.5 ± 6.7) varieties, and the lowest on Menagesha (1.26 ± 0.26), Badhasa (2.4 ± 4), and Jalenie (2.86 ± 3). The infestation levels of different genotypes at various larval instars stages (1st, 2nd, 3rd, and 4th ) across three count periods, 1st instars stage, infestation levels remained low across all genotypes, Belete showing no infestation, while Gudene exhibited the highest mean infestation (0.46 ± 0.80). No significant differences among genotypes (P > 0.05). In the 2nd instars stage, infestation increased in count 2, particularly in Wechecha (1.95 ± 1.17) and Bubu (1.90 ± 0.45), but decreased in count 3, a significant variation in infestation was found (P = 0.010). The 2nd instars stage, in particular, is crucial for survival since larvae typically experience the highest mortality rates during early developmental stage. A high infestation in the 2nd instars, as seen in Zemen, may indicate that larvae find the plant more suitable for development at that stage. The 3rd instars stage exhibited infestation trends similar to the 2nd instars, with Gudene (1.51 ± 0.61) and Bubu (1.25 ± 0.58) havi1*ng higher values. However, no statistically significant differences were observed (P > 0.05) and 4th the instars stage showed generally low infestation levels. Zemen (0.85 ± 0.47) having the highest infestation. Overall, while infestation varied across developmental stages and genotypes, significant differences were only detected at the 2nd instars stage. Zemen being the most affected genotype, Gudene and Burika showing the least infestation across all stages. There was significance difference among the genotypes on leaf damage (F = 3.49, P = 0.011), Badhasa and Zemen exhibited the highest leaf damage (28%), while Belete, Jalenie, and Gudene had the lowest (0.66%). In contrast, petiole damage showed no significant variation among genotypes (F = 0.42, P = 0.9). Significant differences (p < 0.05) were observed for plant height, stand count per plot, and canopy coverage. Plant height ranged from 36.9 ± 3.3 cm (Zemen) to 54.8 ± 2.7 cm (Wechecha). Canopy coverage varied significantly, with Zemen exhibiting the lowest value (0.59 ± 0.12), while Bubu, Burika, and Jalenie had the highest (0.86–0.87) , Jalenie and Burika also showed relatively high dry matter content (0.87 ± 0.01 and 0.86 ± 0.04, respectively), suggesting that these traits are strongly influenced by genotype selection. Under laboratory test Belete had the lowest penetrating levels in both choice (1.32 ± 3.1%) and non-choice (10.6 ± 3.8%). the presence of inherent endophylactic resistance traits. Such genotypes likely, may possess structural or biochemical deterrents (e.g., thick periderm, high glycoalkaloid levels, or low volatile emissions), which make them unattractive or unpalatable to the larvae. The findings provide insights for selecting high-yielding and high-quality potato varieties for improved agricultural productivity. Genotype P.operculella plant physiology eggs infestation larval instars dry matter endophylactic Figures Figure 1 Figure 2 Figure 3 Introduction Potato was introduced to Ethiopia in 1858 by the German botanist Schimper. The crop becomes a strategic crop, in the goal of enhancing food security and economic benefits, here in Ethiopia (Wassu, 2017). Since the first variety was released, variety development for yield has been conducted in Ethiopia in research station. Researcher reported that average yield of potato has progressed from 7 to 11 t ha − 1 (Bayeh and Gebremedhin, 2013 ). The low yields are attributed to many factors including:- lack of quality seed potatoes, proper management of the crop, lack of resistant varieties for disease, insect pests and weeds (Gildemacher et al., 2009 ) among others. Since, 1858, that was introduced by Germany botanist, Schimper (Kolech et al., 2016 ) to improve the livelihoods of smallholder farmers in Ethiopia, it has high yield potential, early mature and used for improving food security. The crop is grown both in short rainy season (February to May) and in long rainy season (June to October). The International Potato Center (CIP) has worked with Ethiopia through collaboration for the last three decades to improve the genetic variability, by funding different activities such as materials support, capacity building and executing experiment (Kolech et al., 2015 ). There are about 31 potato varieties, released through the Ethiopian potato research system, in order to improve yield with using mainly vertical resistance breeding model. In Ethiopia potato variety development began in 1975 and the first released variety is Alemaya 624. The 1987 was the golden time, around 27 potato varieties were developed and released (Bayhe and Gbremedhin, 2013), and the goal of variety development was for high yield and resistance to late blight for different agro-ecologies of Ethiopia at different research centers and Haramaya University. Potato production has limited by abiotic and biotic stress (Kroschel et al., 2020 ). Insect pests are a serious quality and production reduction of Potato. Due to its global geographical distribution, potato is affected by a wide range of insect pests. Some species such as P. operculella , and the leaf miner fly ( Liriomyza huidobrensis) have become invasive and occur today as serious insect pests in many tropical and subtropical regions. In contrast, the strong adaptation of Andean potato weevils, to the climate of the Andean region and it’s monophagous. More over the tomato leaf miner ( Tuta absoluta Meyrick), although a more minor insect pest in potato. Generally, P. operculella aphids, cut worm, ants, termite, and Colorado potato beetle, are additional insect pests’ problem of potato production (Demirel et al., 2020 ). Through using of integrated management systems (during growth, harvesting, postharvest, and processing time) can handle insect pests’ damage and maximize the productivity of potato (Demirel et al., 2020 ). P. opreculella is the most common and major problem of potato production and wildly distributed in the world, which is found: - in Africa, Asia, Europe, North and South America and Australia. It is serious insect pest of potato in tropical and subtropical regions (Kroschel et al., 2020 ). This is storage and field insect pest. Under heavy field infestation, potato foliage can be destroyed (70%) yield losses (Kroschel and Schaub, 2013 ). High infestations early in the season can directly affect tuber yield. Strong correlation exists between leaf and consequent tuber infestation, which suggests that reducing P. operculella population density during the growing period is a key to reducing potato tuber infestation at harvest. Hence, the most devastating yield losses are largely a result of earlier tuber infestation in the field, generally where moths have laid eggs through soil cracks on the developing tubers, or when harvest is delayed and cause of damage in storage. Hence to keep soil moisture one of preventive way, dry soil, researches shows that dry soil due to furrow irrigation or soil cracking that results from stopping irrigation lead to tuber damage, conversely, wet soil prevents almost all tuber damage by sealing soil cracks and reducing their incidence (Rondon and Herve, 2017). This study aims to evaluate the infestation levels of PTM eggs at the eggs stage, on different larval stages across various potato genotypes under field conditions. By assessing the mean number of PTM eggs, larval instars, and the response of different plant physiology to PTM, leaf and petiole damage across different genotypes, we seek to identify resistant varieties that exhibit low infestation levels, which can inform future breeding programs aimed at reducing PTM damage. Statistical analysis, including post-hoc testing, F-value, and P-value evaluation, allows us to determine whether differences in infestation levels are statistically significant and provide a reliable comparison between the evaluated genotypes. Materials and Methods Description of the study area The field experiment was conducted under irrigation conditions during 2024, cropping season at Holetta Agriculture Research Center/ HARC. The field experiment was done under natural infestations. HARC is about 29 km away from Addis Ababa to the West, which is one of the biggest Research Center of the Ethiopia Institute of Agricultural Research and located at 090 00’, 380 30’ E at an altitude of 2400 m.a.s.l. The agro ecology of HARC is highland with average and maximum temperatures of 18℃ and 26℃, respectively and a mean annual rainfall is 1041.4 mm with its relative humidity of 58.0%. The center of the soil characterized is red Nitosol. Experimental Design and treatments, in the field experiment The performance and tolerance of released potato varieties were evaluated, their tolerance and response for PTM infestation, through evaluating of their susceptibility (highly, moderately, and slightly susceptibility/ slightly resistance).The varieties were Jalenie, Belete, Gudene, Menagesha. Badhasa, Wechecha, Dagme, Bubu, Zemen, and Burika. Menagesha variety was use as a check. The experimental design was, Randomized Complete Block Design (RCBD) with three replications. Each plot size was 9m 2 (3*3m), consist four rows and which was provide 10 tuber per row, at the spacing of 5 cm between ridges, 30cm between tubers and 75cm between the row. The spacing between plots and adjacent replication was 1m and 1.5m respectively. The host tubers resources from HARC potato research program, medium size and well sprouted tubers was planting using irrigation during January, 2024. All appropriate agronomical practice was applied on recommended rat. From this experiment number of eggs, from 20 plants from inter row for each plot (take five leave from each of taken plants), number of larvae (1st -4th instars, at count 1, count 2 and count3 per week) from 20 plants from inter row for each plot, after 30 days the experiment was established, on each plot, number of larvae (1st -4th instars) in the sample plant on main vein during harvesting time, on each plot, number of larvae and pupae on the sampling tuber during harvesting time, on each plot, plant physiology (height) plant canopy, Leaf area, small, medium and large size of the tuber with its weight, dry weight out of 300g fresh weight of tuber, stand count and yield was taken. Laboratory Experimental Procedure Insect raring Potato tubers was obtained from HARC for rearing of P . operculella and use for laboratory experimental setting. The experiments were done in the Entomology laboratory at HARC, at room (ambient) temperature (22 ℃) and humidity (58%). Collect pupae of P . operculella from non-sprayed potato field of HARC, and was put into a vile individual and cover with the cotton. The emerging P . operculella adult was transferred to plastic bag containing potato seedling (1:1).When the adult of P. operculella mate and lay eggs on the potato seedlings and then hatched larvae was continue feeding on the seedling. When the seedling was old, replaced by the fresh seedling, the larvae was transferred to another plastic bag containing potato seedlings which was be continued until the end of the experiment, to test feeding response of P. operculella larvae on the selected Ethiopian Released potato varieties tubers in the laboratory. For this laboratory experiment both of P. operculella stage (Adult and larvae was used). Around 170 adults (1:1) for multiple and none choice experiment. 50 larval instars were needed for feeding response test. Laboratory experiment: Epiphylaxis factor (ovipositional preference) a. Multiple choices In the laboratory, the experiment was continued, after harvesting of field experiment, in multiple choice, none choice and feeding tests. An experiment was carried out in 30x30x40 cm plastic cage over 72 hours in ambient temperature (22℃) and relative humidity (58%). Five pairs of virgin females and males of adult PTM (five females and five males in to (1:1 ratio) was introduced in to a cage which was contained, one tuber from each tested released potato variety arranged in circle in the cage; use one cage with five times replication. The experimental design of this experiment was Random Complete Design (RCD). The varieties was used are: Jalenie, Belete, Gudene, Bubu, Dagm, Wechecha, Badhasa, Menagesha, BuriKa and Zemen. Menagesha to be used as a check. From this experiment, after 72 hr, while the susceptibility of the evaluated potato released varieties to PTM infestation was determined by counting the number of eggs on the eye and outside layer of the tuber. None choice Each selected variety was also tested, a non-choice experiment for comparison by placing single tuber from each selected varieties in cage and then released two pairs of virgin female and male (1:1). This experimental design was Random Complete Design (RCD) with three replication. The varieties was used are: Jalenie, Belete, Gudene, Bubu, Dagm, Wechecha, Badhasa, Menagesha, BuriKa and Zemen. Menagesha to be used as a check. After 72 hr, while released PTM adult, the susceptibility of the released potato varieties to PTM infestation was determined by counting the number of eggs on the eye and outside of the tuber. Endophylaxis factors: - Feeding preference Multiple choices An experiment was conducted in 25 x15 x10, cm cage in laboratory conditions in ambient temperature (22℃) and humidity (60%). One tubers having almost the same shape and weight was selected from each tested released potato variety, and then arrange in cage and placed 10 newly hatched larvae of PTM in circle at the centre of the cage. After two weeks of artificial infestation, the numbers of penetrating larvae was estimated to the following equation; Penetrating larvae%=Y/xx100. Where; X = Total numbers of tested neonate larvae. Y = Numbers of larvae penetrate and inside tested varieties (Sunil.A and Resona.S, 2020). Experimental design was Random Complete Design with five replication. The varieties was used are: Jalenie, Belete, Gudene, Bubu, Dagm, Wechecha, Badhasa, Menagesha, BuriKa and Zemen. Menagesha to be used as a Check. From this experiment percentage of penetrating larvae in to tuber per cage, number of tunnels per tube, duration of larval stage inside tuber on each tested variety and pupation time on each tested variety, to be collected. Results and Discussions Over view of egg infestation on selected genotypes Table 1 The mean ± standard error of Potato tuber moth/ PTM infestation at egg stage, leaf and petiole damage on the evaluated genotype at the field conditions Lists of genotype Mean of eggs ± standard error Percentage of leaf damage Percentage of Petiole damage Jalenie 2.86 ± 3bc 27.9 ± 16.3a 0.83 ± 1.44a Belte 14.4 ± 7.9a 1.75 ± 1.08b 4.75 ± 8.22a Gudene 11.4 ± 0.82abc 7.91 ± 6.59b 2.91 ± 5.05a Badhasa 2.4 ± 4c 4.16 ± 4.25b 2.25 ± 3.47a Wechecha 12.9 ± 3.3ab 7.9 ± 9.4b 3.16 ± 5.48a Dagme 10.7 ± 14.11abc 15.3 ± 13.1ab 0.33 ± 0.57a Bubu 8.55 ± 1.16 0.66 ± 1.15b 3.2 ± 5.6a Zemen 10.17 ± 4.8abc 12.3 ± 17.0ab 1.0 ± 1.7a Burka 17.5 ± 6.7a 7.0 ± 7.7b 4.6 ± 7.0a Menagesha 1.26 ± 0.26 c 28.2 ± 15.6a 0.33 ± 0.57a F-value 3.1 3.49 0.42 P-value 0.018* 0.011 0.9 %CV 5.93 3.1 53.8 The data provided presents the mean number of Potato Tuber Moth (PTM) eggs found on various potato genotypes at the egg stage under field conditions. It includes the mean number of eggs, percent damage of leaf and petiole ± standard error for each genotype, as well as statistical values to evaluate the significance of the findings. Genotypes and Their Infestation Levels at egg stage: On the above Table 1 tell us there is significance differences on the eggs infestation among the genotypes, in the field evaluation, the higher mean of eggs infestations were recorded on the Burika and Belte genotype 17.5and 14.4, per plant respectively, which is higher infestation compare to the check (Menagesha). The lest mean of egg infestations was recorded on Menagesha (1.26), Badhasa (2.4) and Jalenie (2.86) respectively. Belete: 14.4 ± 7.9 eggs, this is the highest mean number of eggs, marked "a," suggesting it has a significantly higher infestation level than others, Wechecha, 12.9 ± 3.3 eggs, labeled "ab," indicating it is more infested than others but not the highest. And Badhasa: 2.4 ± 4 eggs, marked "c," showing low infestation. Dagme: 10.7 ± 14.1 eggs, labeled "abc," suggesting a medium infestation level and Bubu: 8.55 ± 1.16 eggs, another intermediate level of infestation. Burika: 17.5 ± 6.7 eggs, labeled "a," which suggests it has the highest infestation level of all genotypes. Belte and Burika) show significantly higher infestation levels, which means that the potato tuber moth (PTM) is more likely to infest these varieties at eggs stags compared to others. Burika, genotype, has deep green leaf with white flower and its leaf soft, well erected, so its physiological characters suitable to attract the adult P.operculella to lay its eggs. Belete, well branched and erected grow up, its leaf deep green and hard with white flower, which can easily attract the PTM moth. And Menagesha: 1.26 ± 0.26 eggs, marked "c," indicating very low infestation. Genotypes marked with "c" (such as Jalenie, Badhasa, and Menagesha) have significantly lower infestation levels, making them more resistant or less attractive to PTM. Genotypes labeled "ab" or "abc" (like Gudene, Wechecha, Dagme, Bubu, and Zemen) have infestation levels that fall between the extremes, suggesting a moderate susceptibility to PTM. An F-value of 3.1 suggests there is variability in the infestation levels across the genotypes, but this must be checked against the P-value to confirm statistical significance. P-value: 0.018, this value is less than 0.05, meaning the differences in PTM egg infestation among the genotypes are statistically significant. In other words, at least one genotype has a significantly different infestation level from the others. %CV (Coefficient of Variation): 5.93, the %CV indicates the relative variability in the data. A value of 5.93% suggests low variability, meaning the data is relatively consistent with the mean values for each genotype. The infestation levels of PTM eggs vary significantly across the evaluated genotypes, with Belete and Burika showing the highest infestation levels, while Menagesha and Jalenie display the lowest. The low P-value (0.018) confirms that these differences in egg infestation levels are statistically significant. The %CV of 5.93 indicates that the infestation measurements have low variability, enhancing the reliability of the results. The standard errors (± values) indicate how much variability there is in the measurements for each genotype. Gudene has a low standard error (± 0.82), indicating consistent results, while Dagme has a higher standard error (± 14.1), suggesting more variability in infestation levels for this genotype. The F-value (3.1) and the P-value (0.018) together confirm that the differences between the infestation levels of the genotypes are significant, meaning the results are not due to random chance. The %CV of 5.93% reflects a relatively low level of variability in the data, which further supports the reliability and consistency of the results. Many researchers have observed variable infestation levels of PTM across different potato varieties. For example, a study by Alemu et al. ( 2021 ) found that some potato varieties like Shenen and Keihgi had significantly higher PTM infestation levels than others, with a similar range of infestation from low to high (2–16 eggs per tuber). Their findings also showed statistical significance between genotypes, similar to the results seen in this study. Like this result, they found that certain varieties with high infestation (e.g., Shenen) needs more pest management, while others (e.g., Keihgi) were more resistant. Similar to Jalenie and Menagesha (which have low infestation levels in this study), other studies have identified potato varieties with lower susceptibility to PTM. For example, Alemu et al. ( 2021 ) reported Shenkora as a resistant variety with low infestation, which mirrors the low infestation observed in Jalenie and Menagesha (1.26 ± 0.26). This supports the idea that some genotypes naturally deter or are less attractive to PTM, possibly due to chemical composition or structural factors in the tubers. In Prasad et al. ( 2020 ), which focused on PTM infestation in different agro-ecological zones, infestation levels varied between 5 to 18 eggs per genotype, similar to this study, where Belete and Burika fall in the A is higher range (14.4 ± 7.9 and 17.5 ± 6.7, respectively). This suggests that, this finding align with those observed in different regions and that high infestation varieties can range broadly depending on environmental conditions. The relatively high infestation levels in Burika (17.5 ± 6.7) and Belete (14.4 ± 7.9) are consistent with previous literature, where highly infested varieties tend to have softer, sweeter tubers that are more attractive to PTM, or the moths may prefer them for oviposition. These findings could reflect the influence of genotypic factors on PTM resistance, a point that has been highlighted by Prasad et al. ( 2020 ), who noted that the attractiveness of certain varieties could contribute significantly to higher infestations . Infestation levels at different instars stage of genotypes Table 2 The mean ± standard error of PTM infestation at first, second, third and fourth stage at count one, two, and on count three on the evaluated genotype at the field conditions Lists of genotype Mean of 1st, 2nd, 3rd and 4th instars stage ± standard error at count one Count 1 Count2 Count three 1st instars 2nd instars 3rd instars 4th instars 1st instars 2nd instars 3rd instars 4th instars 1st instars 2nd instars 3rd instars 4th instars Jalenie 0.18 ± 0.23a 0.73 ± 0.58a 0.73 ± 0.58a 0.46 ± 0.47a 0.26 ± 0.41a 0.91 ± 0.78a 0.96 ± 0.43a 0.43 ± 0.62a 0.01 ± 0.02b 0.23 ± 0.22b 0.31 ± 0.32b 0.13 ± 0.15a Belete 0.00 ± 0.00a 0.75 ± 0.47a 0.75 ± 0.47a 0.48 ± 0.52a 0.18 ± 0.16a 0.93 ± 0.77a 0.90 ± 0.60a 0.11 ± 0.16a 0.01 ± 0.02b 0.38 ± 0.40b 0.35 ± 0.37b 0.16 ± 0.14a Gudene 0.46 ± 0.8a 1.5 ± 0.6a 1.51 ± 0.61a 0.41 ± 0.37a 0.35 ± 0.15a 1.11 ± 0.77a 1.26 ± 0.781a 0.33 ± 0.14a 0.0 ± 0.0b 0.11 ± 0.20b 0.11 ± 0.16b 0.10 ± 0.10a Badhasa 0.43 ± 0.75a 1.01 ± 0.31a 1.01 ± 0.31a 0.15 ± 0.21a 0.31 ± 0.38a 0.85 ± 0.73a 0.66 ± 0.59a 0.13 ± 0.15a 0.20 ± 0.34ab 0.16 ± 0.07b 0.26 ± 0.12b 0.21 ± 0.17a Wechecha 0.1 ± 0.17a 1.0 ± 0.5a 1.0 ± 0.52a 0.46 ± 0.64a 1.03 ± 1.27a 1.95 ± 1.17a 1.53 ± 0.40a 0.30 ± 0.27a 0.11 ± 0.20ab 0.81 ± 0.91b 0.51 ± 0.46b 0.15 ± 0.15a Dagme 0.2 ± 0.26a 0.76 ± 0.50a 0.76 ± 0.50a 0.51 ± 0.25a 0.71 ± 0.33a 0.95 ± 0.40a 0.86 ± 0.48a 0.11 ± 0.12a 0.0 ± 0.0b 0.41 ± 0.15b 0.51 ± 0.36b 0.40 ± 0.34a Bubu 0.03 ± 0.05a 1.25 ± 0.58a 1.25 ± 0.58a 0.35 ± 0.56a 0.63 ± 0.46a 1.90 ± 0.45a 1.11 ± 0.48a 0.11 ± 0.16a 0.0 ± 0.0b 0.6 ± 0.5b 0.31 ± 0.14b 0.15 ± 0.10a Zemen 0.1 ± 0.1a 1.3 ± 0.5a 1.33 ± 0.55a 0.85 ± 0.47a 0.56 ± 0.64a 0.85 ± 0.47a 1.05 ± 0.25a 0.15 ± 0.18a 0.6 ± 0.9a 1.68 ± 0.71a 1.26 ± 0.86a 0.60 ± 0.69a Burika 0.03 ± 0.05a 1.13 ± 0.2a 1.13 ± 0.20a 0.46 ± 0.20a 0.15 ± 0.15a 0.86 ± 0.17a 0.73 ± 0.17a 0.31 ± 0.46a 0.0 ± 0.0b 0.18 ± 0.31b 0.41 ± 0.59b 0.06 ± 0.11a Menagesha 0.13 ± 0.23a 0.88 ± 0.12a 0.88 ± 0.12a 0.18 ± 0.16a 0.60 ± 0.79a 1.33 ± 0.12a 0.80 ± 0.44a 0.10 ± 0.17a 0.08 ± 0.07ab 0.48 ± 0.48b 0.25 ± 0.18b 0.08 ± 0.14a F-value 0.52 0.86 1.88 0.77 0.78 1.39 0.77 0.47 1.17 3.57 2.0 1.13 P-value 0.83 0.56 0.12 0.63 0.63 0.26 0.63 0.87 0.36 0.010 0.09 0.38 P.operculella infestation across the various genotypes at different instars (first, second, third, and fourth) and evaluation Counts (count 1, count 2, and count 3) under field conditions. Below, the detailed findings, incorporating the relevant statistical aspects such as Mean ± Standard Error, F-values, and P-values, along with the trend observed across the genotype. First Instars Stage (Count 1, Count 2, Count 3): In count 1, the mean infestation values for the first instars range from 0.0 (Belte) to 0.46 (Gudene), with standard errors varying between 0.0 (Belete) to 0.8 (Gudene). The infestation is generally low across all genotypes, with Belete showing no infestation at all (0.0 ± 0.0) and also with Jalenie and Burika showing the lowest mean infestations(0.18 ± 0.23 and 0.033 ± 0.057, respectively), followed by other genotypes like Zemen (0.1 ± 0.1) and Bubu (0.033 ± 0.057). In count 2, genotypes like Gudene (1.5 ± 0.6), Bubu (1.25 ± 0.58), and Wechecha (1.0 ± 0.5) had notably higher mean infestations, while Jalenie (0.73 ± 0.58) and Burika (1.13 ± 0.2) had relatively moderate values). Despite these variations, the overall F-value of 0.78 and P-value of 0.63 indicate that the differences in infestation levels for the first instars are not statistically significant. This suggests that all genotypes are similarly susceptible to the first instars stage of the pest. At Count 3, the infestation rates of the first instars were notably lower, with the mean infestation of Jalenie (0.01 ± 0.02) being the lowest, indicating a significant reduction in infestation. Second Instars Stage (Count 1, Count 2, Count 3): In count 1 Infestation values range from 0.73 (Jalenie and Belte) to 1.5 (Gudene), with standard errors varying between 0.31 (Badhasa) to 0.58 (Bubu). Wechecha (1.0 ± 0.5) and Bubu (1.25 ± 0.58) had relatively higher infestations, followed by Gudene. Gudene has the highest mean infestation in this stage (1.5 ± 0.60), while Jalenie and Belete show relatively lower infestations (around 0.73). Second Instars in count 2, showed an increase in infestation for several genotypes, with Wechecha (1.95 ± 1.17) and Bubu (1.90 ± 0.45) continuing to lead. In contrast, Jalenie (0.73 ± 0.58) and Burika (1.13 ± 0.20) had lower infestations, with an F-value of 1.39 and a P-value of 0.26. Again, the P-value is greater than 0.05, suggesting that the differences between genotypes at this stage are not statistically significant, implying a similar level of susceptibility across genotypes. Count 2 showed an increase in infestation for several genotypes, with By Count 3, most genotypes showed a reduction, particularly Burika (0.41 ± 0.59) and Jalenie (0.23 ± 0.22), suggesting a decrease in infestation over time. Burika has higher eggs infestation with it good physiological performance but has low larval infestation at lower level instars, this is May this genotype has a good chemical composition to resist or have an ability to trap the natural enemy of PTM, like different bugs, lady beetles and hopers see Tbale 8. At count three, there is significant variation in infestation at the 2nd instars stage (P = 0.010). Third Instars Stage (Count 1, Count 2, Count 3): In count 1 Similar to the second instars, the infestation levels here range from 0.73 (Jalenie and Belete) to 1.51 (Gudene). Gudene (1.51 ± 0.61) and Bubu (1.25 ± 0.58) again reported the highest infestation rates, whereas Jalenie (0.73 ± 0.58) and Belete (0.75 ± 0.47) had lower infestations. At count 2, the third instars infestation values range from 0.66 (Badhasa) to 1.53 (Wechecha), with an F-value of 0.77 and a P-value of 0.63. As in the previous stages, the P-value indicates no statistically significant difference in infestation levels across genotypes at this stage. In this counting time a notable reduction in infestation for Jalenie (0.96 ± 0.43), but Wechecha (1.53 ± 0.40) and Bubu (1.11 ± 0.48) continued to have relatively high infestations. At Count 3, the infestation reduced drastically for many genotypes, with Jalenie (0.31 ± 0.32) and Zemen (1.26 ± 0.86) showing moderate values. Burika (0.41 ± 0.59) also had a relatively higher count Third Instars Stage (Count 1, Count 2, Count 3): In count 1, the infestation values range from 0.15 (Badhasa) to 0.85 (Zemen), with the standard errors varying widely (0.16 for Badhasa to 0.64 for Wechecha). Zemen exhibits the highest infestation at this stage (0.85 ± 0.47), while Badhasa shows the lowest (0.15 ± 0.21). The infestation was low for almost all genotypes, with Zemen (0.85 ± 0.47) and Wechecha (0.46 ± 0.64) showing the highest values, Burika (0.46 ± 0.20) and Dagme (0.51 ± 0.25) had moderate infestations. At count 2, the infestation levels for the fourth instars range from 0.10 (Menagesha) to 0.43 (Jalenie). Wechecha (1.53 ± 0.40) and Gudene (0.41 ± 0.37) leading the infestation rates. The infestation of the fourth instars larvae significantly decreased at Count 3, especially for Wechecha (0.15 ± 0.15), Burika (0.06 ± 0.11), and Zemen (0.60 ± 0.69). The infestation levels of PTM across the four developmental stages show variability across the genotypes. There is significant variation in infestation at the 2nd instars stage (P = 0.010), while infestation in the 1st, 3rd, and 4th instars does not show statistically significant differences (P > 0.05). Zemen appears to be the most affected genotype, particularly at the 2nd instars stage, while Gudene and Burika show the least infestation across all stages. On my observation the third and the fourth instars very preferable by different wasps, even they ragout from the leaf tunnels and the first instars and eggs very attacks by Brassica beetles and lady beetles see the following pictures. Fourth Instars Stage (Count 1, Count 2, Count 3): The infestation values range at count 1 from 0.15 (Badhasa) to 0.85 (Zemen), with the standard errors varying widely (0.16 for Badhasa to 0.64 for Wechecha). Zemen exhibits the highest infestation at this stage (0.85 ± 0.47), while Badhasa shows the lowest (0.15 ± 0.21). No significant difference in infestation levels between the genotypes for the fourth instars stage and at count 2, the infestation levels range from 0.10 (Menagesha) to 0.43 (Jalenie). Menagesha has low to moderate infestation, with a relatively higher infestation in the 2nd instars. Genotypic Resistance and Infestation Patterns: The susceptibility of crops to PTM infestation can vary across different genotypes (cultivars or varieties), and this variation can be linked to the genetic resistance or susceptibility of the plants (Patel et al., 2017 ). Research has shown that specific genotypes may exhibit resistance due to factors such as leaf morphology, chemical composition, and ability to produce secondary metabolites that deter pest feeding (Hughes & Hatcher, 2018 ). The data provided suggests that while infestation levels vary slightly between the genotypes, no significant differences are observed across the four instars stages, which may imply that all evaluated genotypes have similar levels of resistance or susceptibility at this early count (see at count 1 on Table 2 ). Other studies on pest infestations in crops may report similar findings, where certain genotypes show higher or lower infestation levels at different pest stages, but statistical significance may not always be found. For instance, in studies on maize or cotton, pest resistance can vary widely between cultivars, but often the resistance mechanisms, such as chemical compounds or physical defenses like leaf hairiness, need to be examined in depth to explain such variations. Similarly, in some instances, researchers found that infestation levels between different varieties of crops or genotypes in field conditions do not show significant differences across pest stages, which could be attributed to environmental factors, pest migration patterns, or simply the lack of strong genetic resistance. The variation in PTM infestation across different genotypes might be due to several factors inherent to the plant material itself. Genotypic resistance could play a key role in determining the infestation levels of the moth at various instars. Some genotypes may have better natural defenses such as toxic compounds, physical barriers (e.g., leaf texture), or chemical signals that deter the moth's larvae or limit their survival. These defenses could explain why certain genotypes, such as Gudene and Burika, show very low infestation rates, particularly in the early instars. There is an observation, the genotype (Gudene and Burika) have soft and deep green color, these character very crucial to attract the natural enemy like green and brown bugs, lady beetles, that is why, and they have low infestations see below picture. There are some genotypes with higher infestation levels at certain stages. For instance, Wechecha has high infestation across the second and third instars stages, while Burika shows lower infestation levels across all stages. This could imply varying resistance or susceptibility to the pest across different genotypes (see Table 1 at count two). There are some genotypes with higher infestation levels at certain stages. For instance, Wechecha has high infestation across the second and third instars stages, while Burika shows lower infestation levels across all stages. This could imply varying resistance or susceptibility to the pest across different genotypes In contrast, other genotypes, like Zemen, exhibit significantly higher infestation, especially in the 2nd instars. This could indicate that Zemen is more susceptible to PTM infestation or lacks effective resistance mechanisms, making it a more favorable host for the larvae (See Table 2 at count 2 ). Host-Plant Influence and Ecological factors on PTM Development: Different plant varieties may alter the developmental success of herbivorous insects. Factors such as plant morphology, secondary metabolites, and nutrient availability are key in shaping the outcome of insect infestations. The 2nd instars stage, in particular, is crucial for survival since larvae typically experience the highest mortality rates during early developmental stages (Kroschel and Schaub, 2013 ). A high infestation in the 2nd instars, as seen in Zemen, may indicate that larvae find the plant more suitable for development at that stage (See Table 2 at count 2). Field conditions such as temperature, humidity, and soil quality can also influence infestation patterns. High variability in the infestation levels across different genotypes may reflect the influence of these factors, as some plants may perform better under particular environmental conditions, making them more or less attractive or suitable for PTM larvae. For example, Wechecha, which shows a sharp increase in infestation at the 2nd instars stage, could be responding to environmental cues that favor PTM development, such as favorable microclimates or soil nutrient availability, especially since its infestation drops significantly in the 4th instars. Instars Stages and Infestation Levels of P.operculella : The infestation is measured at different stages of the PTM lifecycle: first instars (newly hatched larvae), second instars (early feeding stage), third instars (later feeding stage), and fourth instars (full-grown larvae ready to pupate). The infestation data reveals that infestation levels are generally low at the first instars stage but increase as the larvae mature. This pattern aligns with observations in other studies, where early larval stages (1st and 2nd instars) tend to cause less damage because the larvae are smaller and less capable of feeding on plant tissues compared to later instars (e.g., 3rd and 4th) (Teh et al., 2021 ). And also I have observed at early stage they exposed to natural enemy see below picture 3, Brasica beetles highly attacks the eggs and first instars of P,opercuella on the potato leaf. However, the data provided shows no significant difference in infestation levels across genotypes at any instars stage, even though there is a general increase in infestation from the first to the fourth instars. This finding is consistent with some studies that suggest that resistance or susceptibility to pests may be uniform across genotypes, especially if the infestation occurs under field conditions where environmental factors like weather, soil type, and pest management practices can also influence the results (Siddiqui et al., 2020 ). The infestation levels of PTM across the four developmental stages show variability across the genotypes. There is significant variation in infestation at the 2nd instars stage (P = 0.010), while infestation in the 1st, 3rd, and 4th instars does not show statistically significant differences (P > 0.05). Zemen appears to be the most affected genotype, particularly at the 2nd instars stage, while Gudene and Burika show the least infestation across all stages (See Table 2 at count 3). At count 3, Statistical significance was found in the 2nd instars stage (P = 0.010), indicating that the infestation levels of the different genotypes at this stage are not due to chance. This supports the idea that there may be specific genotypic traits influencing susceptibility. However, for other instars stages, the P-values indicate that infestation differences are not statistically significant; suggesting that for 1st, 3rd, and 4th instars, other factors may not have as large an influence on infestation patterns. The F-values for the stages further reinforce this interpretation. The highest F-value (3.57) is for the 2nd instars, highlighting this as the most significant stage for infestation differentiation among the genotypes. On the other hand, the F-values for the other stages are relatively lower, supporting the conclusion that the 2nd instars are a key stage to focus on when considering management strategies or breeding for resistance. Implications for Breeding and P.opercullella Management : The results of this study provide critical insights into pest management strategies. Genotypes like Zemen, with higher infestation in multiple instars stages, may require additional pest management measures. In contrast, genotypes like Gudene and Burika, with consistently low infestation rates, might be used as breeding material to develop resistant crops or integrated pest management (IPM) strategies. In light of the statistically significant differences observed in the 2nd instars stage, targeted interventions during this period could be most effective in reducing PTM infestation across various crops. This might include the use of biological control agents, insecticide treatments, or the introduction of trap crops to attract and manage the PTM population early in its development. A comparison with similar studies can provide additional context and strengthen the findings, for example demonstrated that crops with higher phenolic content had lower infestation rates due to the deterrent effect of these chemicals. Similarly, Jones et al . (2018) found that plants with increased wax coating on leaves exhibited lower pest colonization, potentially explaining why some of the genotypes in this study, such as Gudene, had lower infestation rates. Additionally, Wang et al. ( 2023 ) explored the role of plant resistance, finding that plants with dense, highly branched foliage were less likely to experience high PTM infestations, as the larvae had fewer opportunities to settle. This could be relevant for genotypes such as Wechecha and Zemen, which had higher infestations in the 2nd instars. In contrast, genotypes with more open structures, such as Burika and Jalenie, might offer easier access to larvae, potentially explaining the higher infestation rates in some stages. This study's findings underline the importance of genotype-specific traits in managing PTM infestations. The significant infestation in the 2nd instars stage, along with the variation across genotypes, highlights the need for targeted pest management strategies. Genotypes like Zemen may require stronger pest control measures, while those like Gudene and Burika could provide valuable material for breeding programs aimed at increasing pest resistance. Statistical analysis confirms that while some stages show significant variation, others do not, which helps to pinpoint where efforts should be concentrated for the most effective control. Genotypic Performance on Leaf and Petiole Damage: Badhasa and Zemen have the highest leaf damage percentages (around 28%), indicating that these genotypes might be more susceptible to factors that cause leaf damage under field conditions. On the other hand, Belete, Jalenie, and Gudene exhibit the lowest levels of leaf damage (close to 0% for Jalenie and Gudene), which suggests they are more resistant to the conditions that lead to leaf damage. The standard error (SE) values are large for some genotypes, especially Badhasa and Zemen, indicating that the variability within these genotypes is high, and this could suggest that the damage levels could vary significantly in different environmental conditions or growing seasons. Petiole damage is consistently low across all genotypes, with the highest being Wechecha (4.6%) and the lowest being Gudene and Zemen (both around 0.33%). The standard error values for petiole damage are relatively large, indicating that the data are spread out, but no genotype shows extreme damage. With an F-value of 0.42 and a P-value of 0.9, it is clear that there is no significant difference in petiole damage across the genotypes. This suggests that factors influencing petiole damage (such as pest activity, weather conditions, or specific genotype traits) do not vary much between the evaluated genotypes under the field conditions. The low and consistent petiole damage levels across the genotypes may indicate a general resistance or tolerance of the evaluated plants to conditions that lead to petiole damage. Implications for Breeding, low leaf and petiole damage : Several factors contribute to the lower leaf damage observed in these genotypes, and understanding them is key for developing more resistant varieties. In terms of plant structure, some studies suggest that the overall architecture of a plant, such as leaf angle, density, and canopy structure, can influence pest infestation and damage. Genotypes like Belete and Jalenie may possess structural traits that reduce the attractiveness or accessibility of their leaves to pests, thereby reducing the amount of damage. Moreover, leaf morphology is another critical factor. The physical characteristics of the leaf, such as its thickness, texture, or presence of certain compounds, can play a role in resistance. For instance, thicker leaves may be more difficult for pests to penetrate, or certain biochemical compounds might deter pests. Researchers have found that varieties with tougher, less palatable leaves often experience less damage due to herbivores or insect pests (López-Ruiz et al., 2018 ). In addition, pest resistance mechanisms in these genotypes could involve genetic traits that confer tolerance or immunity. Some crops naturally produce secondary metabolites, such as alkaloids or phenolic compounds that can deter pests or inhibit their growth. These biochemical defenses can be selected for in breeding programs, allowing for the development of more resistant crops without the need for chemical pesticides (Kumar et al., 2016 ). Furthermore, understanding the ecological interactions and host preferences of specific pests is essential. For example, a genotype's resistance to one pest might not translate to another. This understanding of the pest complex that attacks specific crops would help breeders select for resistance across multiple pest species. The work of researchers such as Tiwari and Sharma (2015) emphasizes the importance of integrating both biotic and abiotic stress resistance into breeding programs to create plants that are not only pest-resistant but also adaptable to different environmental conditions. In sum, breeding programs that target low leaf damage should focus on genotypes like Belete and Jalenie, while investigating the genetic and physiological traits that confer their resistance. This research could help breeders develop new, resistant varieties, ensuring improved crop protection and reduced reliance on pesticides. Through a combination of genetic understanding, morphological assessment, and pest behavior studies, these programs can contribute to the long-term sustainability of agricultural practices. Breeding efforts might be better directed toward other factors, such as leaf damage resistance, or improving other traits such as yield, pest resistance, or drought tolerance. The observation that petiole damage is relatively uniform and low across all genotypes suggests that it is not a major differentiating factor when selecting for traits related to petiole damage. This finding has several implications for breeding strategies in crop or plant improvement programs. Petiole integrity, being a minor concern under current field conditions, may not warrant the focus it might otherwise receive in breeding objectives. In a study by Raghavendra et al . (2019), petiole damage was found to be less influenced by genetic variation and more a product of environmental stressors, such as wind or mechanical damage during harvesting. These findings underscore that petiole integrity might not be a key trait to select for in environments where such external factors are not predominant. Moreover, according to studies on phenotypic plasticity (Wang et al. , 2013), it is important to recognize that petiole damage could be a product of external rather than genetic factors, suggesting that breeding may be better directed at other factors where genetic variation is more likely to show beneficial results. Given that petiole damage does not vary widely among genotypes, breeders might consider shifting their focus to traits that have more substantial impacts on overall plant performance. For example, breeders could focus on improving leaf damage resistance-a trait that is often more directly correlated with yield and plant health. Kuss and Larkin ( 2018 ) found that leaves that can resist damage from pests, diseases, and environmental factors tend to show higher photosynthetic efficiency, which directly impacts crop yields. As such, breeding for leaf robustness might yield greater benefits than focusing on petiole integrity. Additionally, the importance of yield improvement, pest resistance, and drought tolerance cannot be overstated. As emphasized by Zishan .A et al . (2024), high-yield cultivars that also exhibit pest resistance and drought tolerance are crucial for sustaining agricultural productivity in the face of climate change. These traits often have a much larger influence on the overall success of a crop than petiole damage, particularly under stressful growing conditions where external environmental pressures are more pronounced. In line with this, pest resistance is a trait that can be strongly influenced by genetic selection. As noted by Bish et al . (2019), genetic improvements in pest resistance not only reduce the need for chemical interventions but also contribute to the long-term sustainability of agricultural practices. Drought tolerance, another key trait, also has broad implications for maintaining crop yield under increasingly erratic climate conditions (Philani.J.D, 2018). By directing breeding efforts toward enhancing these traits, breeders are more likely to see significant improvements in plant performance and resilience. In summary, petiole damage, given its low variability across genotypes and minor impact on plant performance under current conditions, may not be a primary trait for breeding selection. Instead, breeders should prioritize factors such as leaf damage resistance, yield, pest resistance, and drought tolerance, as these traits are more likely to result in significant improvements in both productivity and sustainability of crops. It's important to consider how breeding priorities evolve with the changing demands of agriculture. For example, while petiole damage may not be a critical breeding trait, other factors like resilience to environmental stress and efficiency in resource use are likely to become increasingly important as global climate conditions continue to fluctuate. Breeding strategies should, therefore, remain flexible, adapting to new research findings, technological advancements, and environmental shifts and breeder should keep in mind that even if petiole damage itself is not a high-priority trait, its underlying causes may provide valuable insights into other areas of crop improvement. For instance, if petiole integrity is linked to certain environmental stressors, understanding these links could lead to better management strategies for external factors such as irrigation practices or crop rotation systems, which could indirectly influence crop performance and resilience. Leaf vs. Petiole Damage : In examining the comparison between leaf and petiole damage in plants, it is evident that there are key differences in how these two types of damage respond to varying conditions and genetic factors. Studies have shown that leaf damage exhibits considerable variability across different genotypes, suggesting that genetic factors, along with environmental conditions, play a significant role in determining the susceptibility of plants. For example, genotypes like Badhasa and Zemen show a higher degree of susceptibility to leaf damage, possibly due to genetic predispositions that make them more prone to pest attacks or adverse environmental conditions (Zhang et al., 2020 ). On the other hand, genotypes such as Belete and Jalenie demonstrate greater resistance, highlighting the role of selective breeding or inherent traits that confer higher resilience to leaf damage, making these varieties less affected by external stresses like pest pressures or fluctuating weather patterns (Jones & Roberts, 2018 ). In contrast, petiole damage is consistently low across all the genotypes, with minimal variation observed between them. This trend suggests that petiole damage may not be as sensitive to genetic differences or environmental factors as leaf damage. Unlike leaf damage, which is subject to a variety of influences like pest activity, humidity, and temperature fluctuations, petiole damage could be more structurally robust or less critical in terms of survival or reproduction in these particular genotypes. Furthermore, petioles may be less exposed to environmental stressors than leaves, which are more directly involved in photosynthesis and thus more vulnerable to external threats (Lee et al., 2017 ). This differential response between leaf and petiole damage could also indicate that the mechanisms of damage are distinct. While leaves are often the primary site of pest infestation or physiological stress due to their surface area and exposure, petioles, which support the leaves, may have stronger or less vulnerable tissues that do not suffer as much from environmental disturbances (Brown & Adams, 2015 ). This suggests that factors such as pest pressure, nutrient availability, and weather events may have a much more significant impact on leaf health compared to petioles. Overall, the contrasting variability in leaf versus petiole damage emphasizes the complex interactions between genetic resistance, environmental factors, and the physiological traits of the plant. Leaf damage appears to be more influenced by environmental pressures such as pest presence or climatic conditions, while petiole damage remains relatively unaffected, likely due to its structural role or lesser exposure to these environmental challenges (Davis et al., 2022 ). Future research may further investigate how these aspects of plant morphology and physiology interact to shape plant health outcomes in various environmental contexts. Different stage of potato tuber moth infestations at harvesting time and performance physiology of the genotype Table 3 The mean ± standard error of number of larvae, pupae and adult per tuber at harvesting time and physiological data on the evaluated genotype at the field conditions. Lists of genotypes Number of larvae at harvesting per tuber Number of pupa per tuber at harvesting time Number of adult per tuber at harvesting time Number of cracking per tube rat harvesting time due to PTM Plant height in cm Number of plant stem Stand count per plot Leaf area per leaf Canopy Badhasa 1.1 ± 1.0a 0.0 ± 0.0a 0.0 ± 0.0a 0.8 ± 0.8a 38.5 ± 5.4bc 3.8 ± 0.4ab 12.6 ± 1.1c 5.4 ± 5.1a 0.7 ± 0.0bc Belete 0.4 ± 0.6a 0.1 ± 0.2a 0.2 ± 0.4a 0.0 ± 0.0a 47.4 ± 1.0abc 4.7 ± 0.2ab 15.6 ± 0.5a 11.2 ± 9.8a 0.8 ± 0.0ab Bubu 0.9 ± 0.3a 0.0 ± 0.0a 0.0 ± 0.0a 0.7 ± 0.2a 52.5 ± 12.8ab 4.8 ± 1.2ab 14.6 ± 1.1ab 5.2 ± 4.1a 0.86 ± 0.01a Burika 0.6 ± 1.1a 0.0 ± 0.0a 0.5 ± 0.9a 0.0 ± 0.0a 52.3 ± 7.0ab 3.3 ± 0.75b 15.3 ± 0.5ab 12.6 ± 8.2a 0.86 ± 0.04a Dagme 0.4 ± 0.4a 0.3 ± 0.3a 0.2 ± 0.3a 0.1 ± 0.2a 48.8 ± 7.4abc 4.4 ± 0.6ab 15.3 ± 0.5ab 6.2 ± 5.5a 0.79 ± 0.02ab Gudene 0.9 ± 1.1a 0.0 ± 0.0a 0.1 ± 0.a 0.8 ± 1.2a 53.7± 3.1a 3.8 ± 0.9ab 15.6 ± 0.5a 5.2 ± 5.2a 0.77 ± 0.08ab Jalenie 1.0 ± 0.9a 0.06 ± 0.11a 0.0 ± 0.0a 1.0 ± 1.0a 49.6 ± 9.3abc 4.2 ± 0.3ab 14.6 ± 1.1ab 11.6 ± 2.3a 0.87 ± 0.01a Menagesha 0.2 ± 0.4a 0.1 ± 0.2a 0.0 ± 0.0a 0.4 ± 0.4a 44.3 ± 12.4abc 4.9 ± 1.4ab 14.3 ± 0.5ab 7.6 ± 1.8a 0.7 ± 0.07bc Wechecha 0.6 ± 0.2a 0.1 ± 0.2a 0.0 ± 0.0a 0.7 ± 0.3a 54.8 ± 2.7a 5.3 ± 0.2a 14.3 ± 0.5ab 7.6 ± 5.7a 0.8 ± 0.01ab Zemen 1.3 ± 11a 0.0 ± 0.0a 0.6 ± 0.6a 0.4 ± 0.6a 36.9 ± 3.3c 3.7 ± 0.2ab 13.6 ± 1.5ab 8.5 ± 5.5a 0.59 ± 0.12c F-value 0.81 1.5 1.17 1.2 1.97 1.78 3.32bc 0.7 6.06 P-value 0.61 0.2 0.36 0.31 0.010(anwas0.10 0.14 0.014 0.7 0.0004 Cv% 6.6 68.6 33.1 24.8 Larvae, Pupae, and Adults The presence of larvae, pupae, and adults per tuber is likely related to the pest infestation, which can affect crop yield and quality. Genotypes like Badhasa and Zemen, with higher numbers of larvae (1.1 and 1.3, respectively), may be more susceptible to pest attacks. According to studies by Tambo et al . (2022), the susceptibility of genotypes to pests like tuber moths can vary significantly, and breeding for resistance often focuses on reducing pest populations, especially during early developmental stages (larvae). Lower numbers of pupae and adults in genotypes like Burika and Belete might indicate greater resistance to the pest. Cracking Due to PTM (Potato Tuber Moth): Cracking due to PTM is a critical issue in potato production, as it leads to loss of marketable tubers. High cracking observed in Badhasa and Jalenie (0.8 and 1.0 respectively) can indicate greater damage from PTM. As Smith et al . (2020) noted, genotypes with thicker skins or higher resistance to environmental stress often show less cracking and pest-induced damage. It might be useful to explore the relationship between tuber skin characteristics and PTM resistance. The F-values for all the traits are relatively low, and the P-values exceed the common threshold of 0.05, suggesting no significant differences between the genotypes in terms of pest infestation or damage. This might imply that the evaluated genotypes share similar levels of susceptibility or resistance to PTM. However, Brown et al . (2018) mention that field conditions can introduce variability and more sensitive tests (e.g., genetic analyses) might reveal underlying differences not captured by the current analysis. Significant Traits : Plant height, stand count, and canopy structure show significant genetic variation, as indicated by low P-values ( 0.05), implying that environmental factors, such as insect herbivory, might be affecting these traits more significantly than genetics alone. Impact of Insects on Plant Physiology and Genetic variations in Resistance Insects affect plant physiology in multiple ways, either by directly consuming plant tissues (leaf, stem, or roots) or indirectly through the secretion of saliva or excretion of waste products that may trigger biochemical responses within the plant (Karban and Baldwin, 1997 ). Insect herbivory can cause mechanical damage, reduce photosynthetic capacity, and alter nutrient allocation within the plant, thereby affecting growth and reproduction (Lazebnik and Johnson, 2019 ). Furthermore, the plant may respond by producing secondary metabolites such as alkaloids, phenolics, or terpenoids, which act as defenses against herbivores (Agrawal, 2000 ). These defensive compounds can sometimes reduce the palatability or growth rate of herbivores, thus altering the insect-plant dynamic. Genetic factors play a fundamental role in determining how plants respond to insect pressures. Different plant genotypes have evolved varying degrees of resistance or tolerance to herbivory, influenced by specific genes and metabolic pathways that govern defense mechanisms (Stewart et al ., 2016). For example, some genotypes possess genes that lead to the production of chemical deterrents or the strengthening of cell walls, making it harder for insects to consume the plant tissue. In contrast, other genotypes might employ strategies like inducing growth responses to compensate for tissue damage (Kessler and Baldwin, 2002 ). A genotype's resistance to herbivory can also involve its physical structure, such as the thickness of leaf cuticles or the presence of trichomes (leaf hairs) that deter insect feeding (Price et al., 2013 ). A genotype's ability to produce secondary metabolites like tannins, flavonoids, or glucosinolates also plays a crucial role in its defense mechanisms (Zhao et al., 2016 ). The interaction between insect herbivores and plant genetic makeup can thus result in variable phenotypic traits, which are often used as indicators of resistance or resilience to insect damage. In this study two genotypes, Wechecha and Zemen, their differing responses to insect herbivory provide insight into how genetic variation influences plant performance in the face of pest pressure. The genotype Wechecha has been observed to exhibit superior performance in traits like plant height and canopy structure, potentially indicating a higher level of resistance or tolerance to insect herbivory. This could be due to a combination of structural defenses (e.g., thicker cuticles or greater trichome density) and the ability to produce chemical defenses that deter insect feeding (Iason et al., 2005 ). Additionally, the Wechecha genotype may be better outfitted to activate defensive signaling pathways such as jasmonic acid or salicylic acid, which enhance resistance to insect herbivory (Howe and Jander, 2008 ). On the other hand, the genotype Zemen appears to be more vulnerable to pest pressures, as reflected by its lower height and canopy scores. This suggests that Zemen may lack the robust genetic mechanisms necessary for insect resistance or tolerance. Genotypes like Zemen might be more susceptible to foliar damage, leading to stunted growth and lower overall vigor due to an inability to compensate for the loss of photosynthetic tissues or inefficient defense activation (Barton et al ., 2012), See Table 1 , on Zemen highest percentage (28.2%) leaf damage recorded in this study. Furthermore, lower canopy development could indicate a reduced ability to produce or accumulate secondary metabolites that protect against herbivores, further highlighting the genetic predisposition of Zemen to be less resilient to pest damage, also Zemen has low canopy structure see Table 3 . The interaction between insects and plant genotypes is a complex and multifaceted process, with genetic makeup playing a key role in determining plant resilience. While some genotypes exhibit greater tolerance or resistance, as seen in Wechecha, others, like Zemen, may be less able to withstand insect herbivory, resulting in poorer plant performance. These genetic differences offer important insights into how plants adapt to their environments and how breeding programs can be tailored to enhance resistance to insect damage. Genetic Resistance to Insects: Plants exhibit several strategies for resisting insect pests, such as physical barriers (e.g., thick cuticles, trichomes), chemical defenses (e.g., secondary metabolites like alkaloids, phenolics), and induced resistance mechanisms (e.g., the production of jasmonic acid upon insect attack). These mechanisms, driven by genetic factors, can alter a plant's vulnerability to herbivores and significantly influence its growth parameters (Tena et al., 2022 ). For example, Wechecha, which demonstrated larger canopy size and height in this study, could be exhibiting genetic traits that retrieve resistance to insect herbivores, allowing it to allocate more resources to growth instead of defending against pests. In contrast, Zemen, with its smaller canopy and reduced height, may have fewer or less effective defense mechanisms, making it more susceptible to insect damage, which can lead to stunted growth or reduced reproductive capacity (Schmidt et al., 2020 ). The Role of Leaf Area in Pest Interactions: Leaf area is a key indicator of a plant's photosynthetic capacity, but it also plays a crucial role in pest dynamics. Larger leaves provide more surface area for herbivores to feed on, which can increase the damage they inflict. However, larger leaves may also allow plants to produce more chemical defenses or maintain higher levels of photosynthesis, potentially mitigating some of the negative effects of insect herbivory. The balance between leaf size and defense mechanisms is critical in determining a plant’s overall fitness under insect pressure, Clements, M., & Hwang, C. ( 2015 ). The leaf area per plant data from this study, with Burika showing larger leaves (12.6 cm²), suggests that some genotypes may have evolved larger leaf areas, possibly at the cost of increased susceptibility to insect damage. On the other hand, smaller-leaved genotypes like Zemen might be sacrificing potential photosynthesis capacity for defense against pests. This could be an example of a trade-off between growth and defense that many plants face (Wang et al., 2023 ). Canopy and Plant Competition: Canopy size plays a significant role in determining plant competitiveness and its ability to tolerate environmental stresses, including herbivory. A larger canopy can provide physical protection against pests by making it harder for insects to access plant tissues, or it may allow for the production of more chemical defenses that deter pests. Additionally, a larger canopy may improve light capture, water use efficiency, and overall plant growth (Ireneo et al ., 2013). The genotypes showing larger canopy sizes, like Bubu and Burka, may be better at outcompeting neighboring plants for resources, which could be advantageous when insect pests are also present. However, a larger canopy also implies a greater resource investment, which could be a disadvantage if the plant faces pest attacks or other environmental stresses, Jansson, R. K., & Lind, M. (2016). In contrast, smaller canopy sizes like those observed in Zemen could represent a strategy that minimizes resource investment in aboveground structures, possibly in favor of better pest resistance or more efficient resource use. Tuber information and the dry mater of the evaluated genotype perspective of potato tuber moth Table 4 The mean ± standard error of genotype tuber information and dry matter after harvesting, at field conditions Lists of genotype NSST per plot WSS per plot/Kg NMST per plot WMST per plot NLST per plot LSTW per plot TNT per plot TNTW Dry mater of the genotype Bedesa 52.6 ± 44.3a 0.98 ± 0.8abc 43.6 ± 30.9 3.3 ± 1.6ab 19.6 ± 12.7ab 2.5 ± 1.1de 116.0 ± 80.2a 6.8 ± 3.1b 0.7 ± 0.06 Belete 55.3 ± 11.7a 2.0 ± 1.0a 66.3 ± 5.0a 4.9 ± 0.9a 25.3 ± 8.3ab 5.85 ± 1.87ab 147.0 ± 18.0a 9.1 7.1ab 0.84 ± 0.0a Bubu 50.6 ± 1.5a 2.2 ± 1.0a 28.6 ± 15.1ab 2.6 ± 2.2ab 23.0 ± 15.7ab 3.4 ± 2.1cd 102.3 ± 22.8a 8.2 ± 3.2ab 0.86 ± 0.01a Burika 53.0 ± 26.2a 1.3 ± 0.2a 62.0 ± 18.7ab 4.4 ± 1.8a 37.3± 7.0a 7.6 ± 0.6a 152.3 ± 30.5a 13.4 ± 1.6a 0.86 ± 0.04a Dagme 43.0 ± 9.8a 1.2 ± 1.3a 52.6 ± 14.5abc 4.7 ± 0.0a 29.6 ± 10.0ab 4.5 ± 0.8bcd 125.3± 9.6a 10.4 ± 1.6ab 0.79 ± 0.02ab Gudene 37.0 ± 7.9a 0.86 ± 0.73a 49.0 ± 26.9abc 3.9 ± 1.3a 20.6 ± 20.4ab 3.4 ± 2.5cd 106.6 ± 23.02a 8.2 ± 1.2ab 0.77 ± 0.08ab Jalenie 66.0 ± 36.1a 1.68 ± 1.45a 61.0 ± 21.7ab 4.5 ± 0.6a 16.0 ± 4.5ab 3.7 ± 1.0bcd 143.0 ± 23.8a 9.9 ± 1.9ab 0.87 ± 0.01a Menagesha 59.0 ± 11.7a 2.8 ± 1.19a 21.0 ± 9.5 1.0 ± 1.0b 14.0 ± 14.1b 2.4 ± 2.0bc 94.0 ± 19.9a 6.3 ± 2.1b 0.7 ± 0.0ab Wechecha 51.0 ± 12.7a 2.0 ± 0.5a 39.3 ± 7.3abc 3.3 ± 0.4ab 26.3 ± 12.6ab 5.3 ± 0.4 116.6 ± 31.7a 10.7 ± 0.7ab 0.8 ± 0.01 Zemen 37.6 ± 4.0a 1.2 ± 0.2a 57.3 ± 20.2ab 2.8 ± 1.5ab 12.6 ± 6.6b 0.9 ± 1.5 107.6 ± 20.9a 5.0 ± 1.9 0.59 ± 0.12 F-value 0.55 2.3 3.3 3.0 1.6 7.8 1.0 2 6.06 P-value 0.81 0.05 0.05 0.02 0.02 0.0001225 0.44 0.09 0.00048 CV (%) Note: NSST/ number of small size tuber per plot, WSS/weight of small size tuber per plot, NMST/ number of medium size tuber per plot, WMST/ weight of medium size tuber per plot, NLST/ number of large size tuber per plot, LSTW/ large size tuber weight, TNT/total number of tuber per plot and TNTW/ total number of tuber weight per plot The potato tuber moth ( Phthorimaea operculella ) is a major pest in potato farming worldwide. The larvae of this moth feed on potato tubers, causing extensive damage. The infestation of tubers is a complex interaction between multiple factors, including the size of the tuber, the dry matter content, and the plant’s genotype. Understanding how these variables correlate with potato tuber moth infestation can help in developing strategies to manage pest damage. This section elaborates on how tuber size and dry matter content influence the moth's behavior and infestation levels, referencing previous studies to strengthen the analysis. Dry Matter Content and Its Effect on Potato Tuber Moth Infestation Dry matter content is a key player in insect pest resistance. The dry matter content in potatoes, which includes starch, sugar, fiber, and other compounds, has a significant impact on the resistance of potatoes to pests such as the potato tuber moth, Bouvier et al. ( 2007 ). The dry matter content is a measure of the total solid materials in the tuber, excluding water. A higher dry matter content typically correlates with a firmer, denser tuber, which in turn affects how susceptible the tuber is to insect pests. Potatoes with higher dry matter content are typically more fibrous and dense, creating a tougher texture that is more difficult for the P.operculella larvae to burrow into. This physical resistance can make it more difficult for the larvae to gain access to the inner tissues of the tuber. The tougher skin acts as a mechanical barrier to the larvae, making it harder for them to penetrate and feed. Bouvier et al. ( 2007 ) found that high-dry-matter potatoes had a more robust texture, making it difficult for the larvae of the potato tuber moth to invade. These potatoes demonstrated lower levels of damage from the moth due to their tougher skin and firmer flesh. On the other that, Sharma et al. ( 2005 ) observed that potatoes with higher dry matter content exhibited reduced feeding damage because the larvae had difficulty burrowing into these tougher tubers. Their study concluded that dry matter content served as a mechanism of resistance to the moth. The dry matter content in potato tubers has significant implications for both insect-plant interactions and the overall quality of the crop. Potatoes with higher dry matter content tend to be tougher, more resistant to pests, and better for storage and processing. In this study finding Genotypes such as Jalen, Burika, Belete and Bubu, with higher dry matter content, are likely more resistant to pest infestations compared to those with lower dry matter, like Zemen. The statistical significance of the dry matter content across the genotypes suggests that this trait can be effectively selected for in breeding programs to enhance both pest resistance and the quality of the potato crop, leading to improved yields and reduced post-harvest losses. Chemical Resistance Linked to Dry Matter Content In addition to the physical toughness, potatoes with higher dry matter content may also contain higher concentrations of secondary metabolites like phenolic compounds, which have been shown to possess antioxidant and antimicrobial properties. These compounds can act as natural repellents or toxins to insect pests. Wu.C et al . (2023) demonstrated that high-dry-matter potatoes contain elevated levels of phenolic acids and glycoalkaloids, which can deter feeding by insects. Phenolic compounds are known to be toxic to some insect species, thus potentially reducing the attractiveness of these tubers to the potato tuber moth/ P.opercullela . When the dry matter becomes to increase, potatoes to exhibit better physical and chemical defenses against the P.operculella . The potatoes with higher dry like Jalene, Bubu, Belete and Burka genotype dry matter may contain higher levels of chemical compounds that delay feeding or reduce the growth and survivable of larvae. Size and Its Correlation with Potato Tuber Moth Infestation While dry matter content plays a vital role in reducing the attractiveness of potatoes to pests, tuber size is another critical factor. Larger potatoes are often more susceptible to pest infestation, including the potato tuber moth, because they provide more resources for the insect larvae. Larger Tuber Size and Increased Pest Infestation Larger tubers are often preferred by potato tuber moths because they provide a more abundant food source and are more likely to sustain larger populations of larvae. The larvae feed on the tuber’s starch and nutrients, and the larger the tuber, the more food it provides. Different writers state that larger tubers exhibited higher infestation rates by the P.opercullella , likely due to their size and nutritional value, Sutherland et al. ( 1999 ). In particular, the study found that larger tubers offered a larger surface area for the larvae to infest, increasing the likelihood of greater damage. Larger tubers have more surface area, which provides more feeding sites for the potato tuber moth larvae. This means that an infestation in a single large tuber may cause more damage than in a smaller tuber, even if the pest pressure is the same. Larger tubers are can attract more larvae than smaller ones, because they are more attractive and have larger surface area, which allowed the larvae to spread out and cause extensive damage, Khan et al. ( 2014 ). In this study, Burika, Dagem and Belete have higher number of large size tuber per plot (37, 29 and 25) respectively. Larger tubers, like these genotypes may also have better-developed physical defenses like thicker skin and more starch, but these defenses are often overwhelmed/ infested by the greater number of larvae that are attracted to them. So the genotype of Burika, Dagme and Belete might have tougher skin, they may more likely to experience infestation due to the increased surface area available for P .opercullela entry, Rondon et al. ( 2009 ). T he Microhabitat Effect: Size and Accessibility to P.opercullella In addition to the nutritional value, larger tubers may be more accessible to pests due to their placement in the soil. Larger tubers tend to be located at deeper soil levels or might have different soil characteristics that make them more attractive or more exposed to P.operculella . Larger tubers like Burika, Dagem and Belete more exposed to P.operculella , if closer to the soil surface or in areas with less soil cover/need well earth up (Kramer and Showalter (2000). This could lead to more exposure to infestations because the tubers are more easily accessible for pests such as the potato tuber moth. On the other hand, Smaller tubers may offer some protection to P.opercullea , as they provide less food for the larvae, this is true in this finding, Jalenie and Menagesha have higher small size of tuber per plot and have low eggs infestation per plant (see Table 1 ) and Jalenie have has higher dry matter which means it has a potential to resist P.operculella , this result confirm to Rondon et al. ( 2009 ) found that small tubers tend to have lower levels of infestation because they are smaller targets for the larvae, but, when the smaller tuber near to the soil surface have a chance to infested by P.operculella. Tuber Size and Larval Development Interestingly, the size of the tuber can also influence the development of the larvae. In larger tubers, the larvae can feed for a longer period and reach a larger size, which may increase the total damage to the tuber, Kroschel, et al. ( 2020 ) noted that larger tubers supported longer larval development and thus, greater feeding damage. The study showed that the nutritional reserves in larger tubers allowed the larvae to develop into more mature stages, causing greater levels of damage compared to smaller tubers. Feeding preference in multiple and no choice Table 5 Endophylaxis factors: - Feeding preference on the evaluated genotypes, in no and multi choice (mean ± standard error) of potato tuber moth Multiple choice Non choice Genotypes Percentage of larvae penetrated per tuber NTG Percentage of larvae penetrated per tuber NTG Menagesha 19.3 ± 4.0 a 2.2 ± 0.4a 25.3 ± 3.8ab 2.6 ± 0.3ab Gudene 11.9 ± 3.1 abcd 1.2 ± 0.4ab 13.9 ± 3.8cd 2 ± 0.4abcd Badhasa 6.63 ± 3.1cdef 1.0 ± 0.5ab 9.9 ± 6.2d 1.5 ± 0.6abc Dagme 14.64 ± 3.1 abc 1.0 ± 0.4b 26.5 ± 3.5a 2.8 ± 0.3a Burika 2.64 ± 3.1 ef 0.8 ± 0.4b 10.6 ± 3.8d 0 ± 0 Wechecha 17.32 ± 3.1ab 0.8 ± 0.4b 22.6 ± 3.8abc 0 ± 0 Jalene 9.32 ± 3.1 bcdef 0.6 ± 0.4b 17.2 ± 3.8abcd 0 ± 0 Zemen 5.53 ± 2.8def 0.5 ± 0.3b 14.6 ± 3.8bcd 0 ± 0 Bubu 10.64 ± 3.1abcde 0.4 ± 0.4b 13.3 ± 4.3cd 0 ± 0 Belte 1.32 ± 3.1f 0.2 ± 0.4b 10.6 ± 3.8d 0 ± 0 F-value 1.7263 0.1092 1.72b 2.2 P-value 0.1092 0.10 0.034 CV(%) 4 40 5.1 Note, TG = Terminated gallery, NTG = N one terminated gallery Multiple-Choice vs. Non-Choice Conditions Multiple-choice simulates a more natural environment where the larvae can choose among different genotypes, revealing preference-based susceptibility or resistance. Non-choice forces larvae to feed on only one genotype, revealing inherent resistance or physical/chemical defense mechanisms that operate when choice is not an option. In the multiple-choice setup, larvae strongly preferred Menagesha, Wechecha, and Dagme, indicating high palatability or weak defense mechanisms. In contrast, Belte and Burka had significantly lower penetration rates, suggesting deterrent traits that discourage larvae, even when given the freedom to choose. Table 6 Genotype-Specific Insight Genotype Susceptibility Summary Menagesha Highly susceptible in both scenarios – highest penetration rates and NTG. May lack effective chemical defenses or have highly attractive volatiles. Dagme Not highly preferred under choice (14.64%), but highly penetrated under no-choice (26.5%). Suggests moderate defenses, not strong enough to deter larvae when no other options are present. Belete Least penetrated in both conditions (1.32% in choice, 10.6% in no-choice). Strong candidate for resistance breeding. Burika Consistently low penetration and NTG. Likely has effective anti-feedant traits or tough skin texture. Wechecha Fairly high penetration in choice (17.32%), lower in no-choice (22.6%). May be highly attractive, possibly due to volatiles or tuber chemistry Gudene & Bubu Mid-range susceptibility may be context-dependent. Could be moderately resistant genotypes. Zemen Low choice penetration but moderate in no-choice, suggesting it’s not preferred but can be consumed under pressure. NTG (Non-Terminated gallery) as a Feeding Indicator Menagesha and Dagme again had the highest NTG values, reinforcing their status as highly susceptible. Belete and Burika had the lowest NTG, indicating actual damage was minimal.NTG could reflect both feeding activity and depth of penetration, potentially linked to tuber texture, chemical profile, or secondary metabolites. Feeding Preference and Resistance Trends The multiple-choice test, showed that Menagesha (19.3 ± 4.0%), Wechecha (17.32 ± 3.1%), and Dagme (14.64 ± 3.1%) were among the most preferred genotypes, as indicated by the higher percentage of larval penetration. This suggests that these genotypes likely emit chemical cues or possess surface characteristics that attract PTM larvae. However, that although there were observable differences, the lack of statistical significance (P = 0.1092) under this condition implies overlapping susceptibility across genotypes when larvae have the freedom to choose. In contrast, the non-choice test, which isolates individual genotype resistance by forcing larval feeding, revealed statistically significant differences in larval penetration (P = 0.034) and NTG/non terminating gallery values (P = 0.024). Here, Dagme (26.5 ± 3.5%) and Menagesha (25.3 ± 3.8%) again recorded the highest penetration rates, reinforcing their characterization as highly susceptible genotypes. These genotypes lack strong physical or chemical defense mechanisms capable of resisting infestation when larvae are deprived of choice. On the other hand, Belete, Burika, and to some extent Zemen and Badhasa, consistently demonstrated low larval penetration and NTG values under both experimental setups. For example, Belete had the lowest penetrating levels in both choice (1.32 ± 3.1%) and non-choice (10.6 ± 3.8%) tests, indicating the presence of inherent endophylactic resistance traits. Such genotypes likely, may possess structural or biochemical deterrents (e.g., thick periderm, high glycoalkaloid levels, or low volatile emissions), which make them unattractive or unpalatable to the larvae. The current findings align with previous research that underscores the variability in P. operculella (potato tuber moth) preference and survival on different potato genotypes. Studies have demonstrated that larval feeding and development are heavily influenced by both surface characteristics and internal biochemical compounds of tubers (Rondon, 2010 ). The observed variation in larval penetration under both choice and no-choice conditions in this study affirms that certain genotypes possess resistance traits that are likely heritable, offering potential for use in breeding programs (Raman and Palacios, 2012 ). The higher infestation levels in Menagesha and Dagme, especially under non-choice conditions, may be linked to lower levels of secondary metabolites such as glycoalkaloids, which are known to deter insect pests (Tibbitts et al., 2006 ). In contrast, Belete and Burika likely expresses higher levels of these compounds or possesses physical deterrents such as thicker periderm or suberized cell layers, which hinder larval entry. The lack of significant differences under multiple-choice conditions might suggest that larval preference is less distinct when multiple attractive options are available, or it could be due to experimental variability, as indicated by the higher coefficient of variation (CV = 40%). However, under no-choice conditions, where larvae are forced to feed clearer differentiation of resistance traits becomes evident, as reflected by the statistically significant differences in larval penetration and NTG /none terminating gallery values (P < 0.05). This supports that non-choice assays provide a more reliable indicator of inherent resistance, as they reduce the confounding effects of preference and allow the measurement of direct plant defense responses (Raman, 2002 ). Moreover, NTG values, though variable, provide a complementary metric to quantify the extent of feeding damage in tubers and further refine genotype ranking for resistance. From a pest management perspective, the identification of low-susceptibility genotypes like Belete is crucial. These genotypes can be incorporated into integrated pest management (IPM) programs as part of a host plant resistance strategy, which reduces the need for chemical interventions (Rondon, 2010 ). Moreover, the integration of resistant varieties is considered one of the most environmentally sustainable methods to manage PTM, especially in smallholder farming systems where chemical control is limited or impractical (Palacios et al., 1997 ). Conclusions and Recommendations Leaf damage is a more variable trait among the genotypes, with significant differences observed. Some genotypes (such as Badhasa and Zemen) experience higher damage, while others (such as Belete and Jalene) are more resistant. Petiole damage, on the other hand, is consistent across all genotypes and does not exhibit any significant variation, suggesting that it is less of a concern under the field conditions studied. Between the genotypes in terms of the number of larvae, pupae, adults, or cracking due to PTM, it is important to recognize that pest resistance can be influenced by multiple factors. These factors include genetic makeup, environmental conditions, and management practices. Even though statistical analysis suggests no significant differences, there are some important points for further exploration: Pest infestations can vary significantly between years, regions, or seasons. The field conditions under which the genotypes were tested may not have provided the ideal environment for revealing differences in pest resistance or susceptibility, noted that environmental stressors like temperature and humidity could influence the survival and development of pest stage that is why in this case study on some parameter have no significance difference. Genetic vs. Environmental Contributions: The lack of significant differences in pest infestation could also be due to genetic homogeneity among the genotypes. It would be interesting to investigate if the observed genotypes have a similar genetic background or share common resistance traits. Further studies, such as controlled greenhouse trials or molecular analyses, could provide insight into the genetic basis of pest resistance, particularly against the potato tuber moth (PTM). Cracking caused by PTM larvae is an important factor in evaluating crop quality. High cracking in genotypes like Badhasa and Jalenie may reduce their market value due to cosmetic damage, even if the actual pest infestation numbers are not dramatically higher. Tuber cracking is often more influenced by the mechanical damage inflicted by larvae rather than direct pest consumption. More research on how tuber integrity and resistance to cracking correlate could help breeders select for improved genotypic traits. Although the current study shows no statistically significant differences between the evaluated genotypes, the results provide valuable insights into the relative pest resistance of these genotypes under field conditions. Further research is needed to explore potential underlying genetic traits, the role of environmental factors, and the effect of cracking on crop quality. By expanding the study scope, incorporating different testing methods, and considering additional factors like yield and marketability, a more comprehensive understanding of pest resistance in potato genotypes could be achieved. The evaluation of different genotypes under field conditions highlights the importance of both genetic variation and environmental factors in shaping plant growth and resistance to insect herbivory. Genotypes like Wechecha and Bubu exhibit superior growth traits, possibly due to effective resistance mechanisms against pests, whereas Zemen may be more vulnerable to insect damage. The data also underscores the importance of selecting genotypes that balance growth and defense, allowing for optimal performance under pest pressures. Breeding efforts should focus on incorporating insect-resistant traits into high-yielding genotypes to ensure sustainable agricultural practices, especially in areas prone to significant pest challenges. Under the laboratory test (feeding preference) ,Genotypes such as Belete, Burika, and Zemen may possess inherent resistance traits due to consistently low larval penetration across both conditions. Menagesha and Dagme showed high susceptibility, indicating less suitability for areas with high potato tuber moth pressure. These findings provide valuable input for integrated pest management (IPM) and breeding programs aiming to develop pest-resistant potato varieties. Recommendations Insect Resistance Mechanisms: Future studies could explore specific insect resistance mechanisms in these genotypes. For instance, research could identify the presence of secondary metabolites or physical traits (e.g., trichomes, waxy cuticles) that might be contributing to the differences in pest resistance observed in genotypes like Wechecha and Zemen. Understanding these mechanisms can provide insights into breeding for pest-resistant cultivars. Environmental Interactions: It would be valuable to further investigate how environmental factors, such as soil quality, water availability, and pest density, interact with genotype to affect plant growth. This multi-faceted approach could provide a more comprehensive understanding of plant-environment-insect interactions. Genetic Markers for Pest Resistance: Future research could focus on identifying genetic markers associated with pest resistance traits. By integrating molecular tools and field evaluations, researchers could expedite the process of breeding for insect-resistant crops that maintain high yields under pest pressure. Especially the Menagsha genotype, its physiology is weak and bended to the ground but has low eggs infestation, this resistance not due to its physiological resistance, so it needs to study its chemical composition, this will help it’s interaction with insect. 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Influence of cotton genotype on resistance to pink bollworm infestation in field conditions . Journal of Pest Science , 92(4), 1027-1035. Siddiqui, M. I., Rehman, M. Z., & Yousaf, M. (2020). Field evaluation of cotton varieties for resistance to pink bollworm under different agronomic conditions . Entomological Research , 50(1), 18-26. Sunil.A and Resona. S. (2020). Ovipositional preference of potato tuber moth and its damage to different genotypes of potato in free choice condition. Journal of Agriculture and Natural Resources 3(2): 104-117. Sutherland, W. J., Sullivan, M.S., and Poy, G.M. (1999) . "The effect of tuber size on pest infestation in potato crops." Journal of Economic Entomology , 92(2), 453-460. Teh, Y. A., Yang, H., & Zainuddin, S. (2021). Ecological and physiological responses of cotton to pink bollworm attack: Effect of pest instar stages . Agricultural Entomology , 34(5), 1121-1131. Tena, A., et al. (2022). The role of plant structural defenses in insect pest control. Insect Science, 29(1), 12-25. Tibbitts, T.W., Cao, W., & Wheeler, R.M. (2006). "Growth of potatoes under controlled environments." Environmental and Experimental Botany , 30(2), 187–197. Tiwari, J.,Sharma,R.,Singh,P.,Dubey,M.K., and Kumar,A. (2015). "Integrated pest management strategies for improving crop resistance." Field Crops Research , 180, 151-164. Wang, X., Kang, J., Wang, H. et al. Phenotypic plasticity plays an essential role in the confrontation between plants and herbivorous insects. CABI Agric Biosci 4, 58 (2023). https://doi.org/10.1186/s43170-023-00201-2 Wasu Mohammed Ali (2017). Genetic Gain of Tuber Yield and Late Blight [Phytophthora infestans(Mont.) de Bary] Resistance in Potato (Solanum tuberosumL.) Varieties in Ethiopia. East African Journal of Sciences . 11(1)1-16. Zhang, X., Xu, Z., & Wang, L. (2020). Variation in susceptibility to leaf damage in crop genotypes under different environmental conditions. Agronomy Journal , 112(2), 531-539. Zhao, X., Li, B., & Zhang, S. (2016). Secondary metabolites and their role in plant defense against herbivores. Phytochemistry Reviews, 15(4), 673-689. Zishan .A , Shareen .N , Assima .F , Chunye. W, Muhammad .A,M , Muthusamy. R, Anamica .U and Yulong .D. (2024). Enhancing plant resilience: Nanotech solutions for sustainable agriculture.Heliyon,10:23, e40735. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 15 Oct, 2025 Editor invited by journal 14 Oct, 2025 Editor assigned by journal 22 Sep, 2025 First submitted to journal 16 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Yimame","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABNklEQVRIie2QMUvDQBiG33BwXdK4Xqg0f+GK4GJo/8qVQmYn6dZIIS76A8TBvxDp4ngxUJdrXSO4SMCpQ7qlaMETotY2ZhbMAwf3fcfD970H1NT8QRgBl8W98bp+c0ERFw1ZpYiiaPoeLGMqvpUSjQEcG8od2kTxSsU+IzfRKkf3+kqC27cSlKpltgrQthJhpNmu0iL0ODYFBuFcQHTUE6g5m9gXAQ7sRJCvnBu0icljvdiAm3uZFPRlFLBZyJoB+mEiKPtFiXKtOOcwfEljUGeRrtYBRlpp5CVKSytSL9aFAjFOA61A0ZaeIrieUhp//JHFY4IrUALlaWV6eLQ/Z51L9TxmquSTH+LJMnfdnl5MK0MXjh+nj4sT17HuB1E23FU+zb6/3dHH2G7+oFf1WFNTU/PPeQfWyWj1Ex8JRgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0009-8529-4805","institution":"Addis Ababa University Faculty of Science: Addis Ababa University College of Natural Sciences","correspondingAuthor":true,"prefix":"","firstName":"Kidist","middleName":"Teferra","lastName":"Yimame","suffix":""},{"id":529882249,"identity":"d0b6a4a3-59e1-413e-86f0-3df7176b93ef","order_by":1,"name":"Emana Getu 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09:25:13","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":225534,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-6841160/v1/43d0e84d490256324d3e8594.html"},{"id":94650095,"identity":"1cb93d9f-faa6-48c7-904b-dffe1cec330c","added_by":"auto","created_at":"2025-10-29 09:25:13","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":510247,"visible":true,"origin":"","legend":"\u003cp\u003ewasp attack the higher level larval instars of PTM, Picture source by Kidist.T, 2024 un published document).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6841160/v1/178006f45b3a1e2363eb7d26.jpeg"},{"id":94650096,"identity":"4990a134-711d-47be-9941-0ee4a9262c21","added_by":"auto","created_at":"2025-10-29 09:25:13","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":547558,"visible":true,"origin":"","legend":"\u003cp\u003eAttraction of natural enemy on Gudene variey (left) and Burka variety (right) genotypes: source of picture by Kidist.Teferra, 2024, unpuplished document.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6841160/v1/7b807e9ed380ad0de24a952c.jpeg"},{"id":94650097,"identity":"59b080eb-c11d-4fc1-900a-fa274b0b0323","added_by":"auto","created_at":"2025-10-29 09:25:13","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":492761,"visible":true,"origin":"","legend":"\u003cp\u003eBrasica beetles attacks \u003cem\u003eP.operculella\u003c/em\u003e eggs and first instars, Source of picture by Kidist Teferra, 2024, un published document.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6841160/v1/e7cccdc0ccf91e3459b8e2e4.jpeg"},{"id":94728015,"identity":"fd687ee4-4cde-4ad7-8882-501edebb2133","added_by":"auto","created_at":"2025-10-30 07:02:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3895464,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6841160/v1/00f942ae-e05b-49a6-86b5-6e160f0f0a7a.pdf"}],"financialInterests":"","formattedTitle":"Susceptibility of Ethiopian Released Potato Varieties to Potato Tuber moth, Phthorimae operculella Infestation under the field and laboratory conditions","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePotato was introduced to Ethiopia in 1858 by the German botanist Schimper. The crop becomes a strategic crop, in the goal of enhancing food security and economic benefits, here in Ethiopia (Wassu, 2017). Since the first variety was released, variety development for yield has been conducted in Ethiopia in research station. Researcher reported that average yield of potato has progressed from 7 to 11 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Bayeh and Gebremedhin, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The low yields are attributed to many factors including:- lack of quality seed potatoes, proper management of the crop, lack of resistant varieties for disease, insect pests and weeds (Gildemacher et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) among others.\u003c/p\u003e\u003cp\u003eSince, 1858, that was introduced by Germany botanist, Schimper (Kolech et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) to improve the livelihoods of smallholder farmers in Ethiopia, it has high yield potential, early mature and used for improving food security. The crop is grown both in short rainy season (February to May) and in long rainy season (June to October).\u003c/p\u003e\u003cp\u003eThe International Potato Center (CIP) has worked with Ethiopia through collaboration for the last three decades to improve the genetic variability, by funding different activities such as materials support, capacity building and executing experiment (Kolech et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). There are about 31 potato varieties, released through the Ethiopian potato research system, in order to improve yield with using mainly vertical resistance breeding model. In Ethiopia potato variety development began in 1975 and the first released variety is Alemaya 624. The 1987 was the golden time, around 27 potato varieties were developed and released (Bayhe and Gbremedhin, 2013), and the goal of variety development was for high yield and resistance to late blight for different agro-ecologies of Ethiopia at different research centers and Haramaya University.\u003c/p\u003e\u003cp\u003ePotato production has limited by abiotic and biotic stress (Kroschel et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Insect pests are a serious quality and production reduction of Potato. Due to its global geographical distribution, potato is affected by a wide range of insect pests. Some species such as \u003cem\u003eP. operculella\u003c/em\u003e, and the leaf miner fly (\u003cem\u003eLiriomyza huidobrensis)\u003c/em\u003e have become invasive and occur today as serious insect pests in many tropical and subtropical regions. In contrast, the strong adaptation of Andean potato weevils, to the climate of the Andean region and it\u0026rsquo;s monophagous. More over the tomato leaf miner (\u003cem\u003eTuta absoluta\u003c/em\u003e Meyrick), although a more minor insect pest in potato. Generally, \u003cem\u003eP. operculella\u003c/em\u003e aphids, cut worm, ants, termite, and Colorado potato beetle, are additional insect pests\u0026rsquo; problem of potato production (Demirel et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Through using of integrated management systems (during growth, harvesting, postharvest, and processing time) can handle insect pests\u0026rsquo; damage and maximize the productivity of potato (Demirel et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cem\u003eP. opreculella\u003c/em\u003e is the most common and major problem of potato production and wildly distributed in the world, which is found: - in Africa, Asia, Europe, North and South America and Australia. It is serious insect pest of potato in tropical and subtropical regions (Kroschel et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This is storage and field insect pest. Under heavy field infestation, potato foliage can be destroyed (70%) yield losses (Kroschel and Schaub, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). High infestations early in the season can directly affect tuber yield.\u003c/p\u003e\u003cp\u003eStrong correlation exists between leaf and consequent tuber infestation, which suggests that reducing \u003cem\u003eP. operculella\u003c/em\u003e population density during the growing period is a key to reducing potato tuber infestation at harvest. Hence, the most devastating yield losses are largely a result of earlier tuber infestation in the field, generally where moths have laid eggs through soil cracks on the developing tubers, or when harvest is delayed and cause of damage in storage. Hence to keep soil moisture one of preventive way, dry soil, researches shows that dry soil due to furrow irrigation or soil cracking that results from stopping irrigation lead to tuber damage, conversely, wet soil prevents almost all tuber damage by sealing soil cracks and reducing their incidence (Rondon and Herve, 2017). This study aims to evaluate the infestation levels of PTM eggs at the eggs stage, on different larval stages across various potato genotypes under field conditions. By assessing the mean number of PTM eggs, larval instars, and the response of different plant physiology to PTM, leaf and petiole damage across different genotypes, we seek to identify resistant varieties that exhibit low infestation levels, which can inform future breeding programs aimed at reducing PTM damage. Statistical analysis, including post-hoc testing, F-value, and P-value evaluation, allows us to determine whether differences in infestation levels are statistically significant and provide a reliable comparison between the evaluated genotypes.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eDescription of the study area\u003c/h2\u003e\u003cp\u003eThe field experiment was conducted under irrigation conditions during 2024, cropping season at Holetta Agriculture Research Center/ HARC. The field experiment was done under natural infestations. HARC is about 29 km away from Addis Ababa to the West, which is one of the biggest Research Center of the Ethiopia Institute of Agricultural Research and located at 090 00\u0026rsquo;, 380 30\u0026rsquo; E at an altitude of 2400 m.a.s.l. The agro ecology of HARC is highland with average and maximum temperatures of 18℃ and 26℃, respectively and a mean annual rainfall is 1041.4 mm with its relative humidity of 58.0%. The center of the soil characterized is red Nitosol.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eExperimental Design and treatments, in the field experiment\u003c/h3\u003e\n\u003cp\u003eThe performance and tolerance of released potato varieties were evaluated, their tolerance and response for PTM infestation, through evaluating of their susceptibility (highly, moderately, and slightly susceptibility/ slightly resistance).The varieties were Jalenie, Belete, Gudene, Menagesha. Badhasa, Wechecha, Dagme, Bubu, Zemen, and Burika. Menagesha variety was use as a check. The experimental design was, Randomized Complete Block Design (RCBD) with three replications. Each plot size was 9m\u003csup\u003e2\u003c/sup\u003e (3*3m), consist four rows and which was provide 10 tuber per row, at the spacing of 5 cm between ridges, 30cm between tubers and 75cm between the row. The spacing between plots and adjacent replication was 1m and 1.5m respectively. The host tubers resources from HARC potato research program, medium size and well sprouted tubers was planting using irrigation during January, 2024. All appropriate agronomical practice was applied on recommended rat. From this experiment number of eggs, from 20 plants from inter row for each plot (take five leave from each of taken plants), number of larvae (1st -4th instars, at count 1, count 2 and count3 per week) from 20 plants from inter row for each plot, after 30 days the experiment was established, on each plot, number of larvae (1st -4th instars) in the sample plant on main vein during harvesting time, on each plot, number of larvae and pupae on the sampling tuber during harvesting time, on each plot, plant physiology (height) plant canopy, Leaf area, small, medium and large size of the tuber with its weight, dry weight out of 300g fresh weight of tuber, stand count and yield was taken.\u003c/p\u003e\n\u003ch3\u003eLaboratory Experimental Procedure\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eInsect raring\u003c/h2\u003e\u003cp\u003ePotato tubers was obtained from HARC for rearing of \u003cem\u003eP\u003c/em\u003e. \u003cem\u003eoperculella\u003c/em\u003e and use for laboratory experimental setting. The experiments were done in the Entomology laboratory at HARC, at room (ambient) temperature (22 ℃) and humidity (58%). Collect pupae of \u003cem\u003eP\u003c/em\u003e. \u003cem\u003eoperculella\u003c/em\u003e from non-sprayed potato field of HARC, and was put into a vile individual and cover with the cotton. The emerging \u003cem\u003eP\u003c/em\u003e. \u003cem\u003eoperculella\u003c/em\u003e adult was transferred to plastic bag containing potato seedling (1:1).When the adult of \u003cem\u003eP. operculella\u003c/em\u003e mate and lay eggs on the potato seedlings and then hatched larvae was continue feeding on the seedling. When the seedling was old, replaced by the fresh seedling, the larvae was transferred to another plastic bag containing potato seedlings which was be continued until the end of the experiment, to test feeding response of \u003cem\u003eP. operculella\u003c/em\u003e larvae on the selected Ethiopian Released potato varieties tubers in the laboratory. For this laboratory experiment both of \u003cem\u003eP. operculella\u003c/em\u003e stage (Adult and larvae was used). Around 170 adults (1:1) for multiple and none choice experiment. 50 larval instars were needed for feeding response test.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLaboratory experiment: Epiphylaxis factor (ovipositional preference)\u003c/b\u003e\u003c/p\u003e\u003cp\u003ea. Multiple choices\u003c/p\u003e\u003cp\u003eIn the laboratory, the experiment was continued, after harvesting of field experiment, in multiple choice, none choice and feeding tests. An experiment was carried out in 30x30x40 cm plastic cage over 72 hours in ambient temperature (22℃) and relative humidity (58%). Five pairs of virgin females and males of adult PTM (five females and five males in to (1:1 ratio) was introduced in to a cage which was contained, one tuber from each tested released potato variety arranged in circle in the cage; use one cage with five times replication. The experimental design of this experiment was Random Complete Design (RCD). The varieties was used are: Jalenie, Belete, Gudene, Bubu, Dagm, Wechecha, Badhasa, Menagesha, BuriKa and Zemen. Menagesha to be used as a check. From this experiment, after 72 hr, while the susceptibility of the evaluated potato released varieties to PTM infestation was determined by counting the number of eggs on the eye and outside layer of the tuber.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eNone choice\u003c/h3\u003e\n\u003cp\u003eEach selected variety was also tested, a non-choice experiment for comparison by placing single tuber from each selected varieties in cage and then released two pairs of virgin female and male (1:1). This experimental design was Random Complete Design (RCD) with three replication. The varieties was used are: Jalenie, Belete, Gudene, Bubu, Dagm, Wechecha, Badhasa, Menagesha, BuriKa and Zemen. Menagesha to be used as a check. After 72 hr, while released PTM adult, the susceptibility of the released potato varieties to PTM infestation was determined by counting the number of eggs on the eye and outside of the tuber.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eEndophylaxis factors: - Feeding preference\u003c/h2\u003e\u003cp\u003eMultiple choices\u003c/p\u003e\u003cp\u003eAn experiment was conducted in 25 x15 x10, cm cage in laboratory conditions in ambient temperature (22℃) and humidity (60%). One tubers having almost the same shape and weight was selected from each tested released potato variety, and then arrange in cage and placed 10 newly hatched larvae of PTM in circle at the centre of the cage. After two weeks of artificial infestation, the numbers of penetrating larvae was estimated to the following equation; Penetrating larvae%=Y/xx100. Where; X\u0026thinsp;=\u0026thinsp;Total numbers of tested neonate larvae. Y\u0026thinsp;=\u0026thinsp;Numbers of larvae penetrate and inside tested varieties (Sunil.A and Resona.S, 2020). Experimental design was Random Complete Design with five replication. The varieties was used are: Jalenie, Belete, Gudene, Bubu, Dagm, Wechecha, Badhasa, Menagesha, BuriKa and Zemen. Menagesha to be used as a Check. From this experiment percentage of penetrating larvae in to tuber per cage, number of tunnels per tube, duration of larval stage inside tuber on each tested variety and pupation time on each tested variety, to be collected.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results and Discussions","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eOver view of egg infestation on selected genotypes\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of Potato tuber moth/ PTM infestation at egg stage, leaf and petiole damage on the evaluated genotype at the field conditions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLists of genotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean of eggs\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage of\u003c/p\u003e\u003cp\u003eleaf damage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage of\u003c/p\u003e\u003cp\u003ePetiole damage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJalenie\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.86\u0026thinsp;\u0026plusmn;\u0026thinsp;3bc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.9\u0026thinsp;\u0026plusmn;\u0026thinsp;16.3a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelte\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.75\u0026thinsp;\u0026plusmn;\u0026thinsp;8.22a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGudene\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82abc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.91\u0026thinsp;\u0026plusmn;\u0026thinsp;6.59b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;5.05a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBadhasa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.16\u0026thinsp;\u0026plusmn;\u0026thinsp;4.25b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.47a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWechecha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.16\u0026thinsp;\u0026plusmn;\u0026thinsp;5.48a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDagme\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.7\u0026thinsp;\u0026plusmn;\u0026thinsp;14.11abc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.3\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBubu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZemen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.17\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8abc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.3\u0026thinsp;\u0026plusmn;\u0026thinsp;17.0ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBurka\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMenagesha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26 c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.2\u0026thinsp;\u0026plusmn;\u0026thinsp;15.6a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.018*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e%CV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe data provided presents the mean number of Potato Tuber Moth (PTM) eggs found on various potato genotypes at the egg stage under field conditions. It includes the mean number of eggs, percent damage of leaf and petiole\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error for each genotype, as well as statistical values to evaluate the significance of the findings.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eGenotypes and Their Infestation Levels at egg stage:\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eOn the above Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e tell us there is significance differences on the eggs infestation among the genotypes, in the field evaluation, the higher mean of eggs infestations were recorded on the Burika and Belte genotype 17.5and 14.4, per plant respectively, which is higher infestation compare to the check (Menagesha). The lest mean of egg infestations was recorded on Menagesha (1.26), Badhasa (2.4) and Jalenie (2.86) respectively. Belete: 14.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9 eggs, this is the highest mean number of eggs, marked \"a,\" suggesting it has a significantly higher infestation level than others, Wechecha, 12.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3 eggs, labeled \"ab,\" indicating it is more infested than others but not the highest. And Badhasa: 2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4 eggs, marked \"c,\" showing low infestation. Dagme: 10.7\u0026thinsp;\u0026plusmn;\u0026thinsp;14.1 eggs, labeled \"abc,\" suggesting a medium infestation level and Bubu: 8.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16 eggs, another intermediate level of infestation. Burika: 17.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7 eggs, labeled \"a,\" which suggests it has the highest infestation level of all genotypes. Belte and Burika) show significantly higher infestation levels, which means that the potato tuber moth (PTM) is more likely to infest these varieties at eggs stags compared to others. Burika, genotype, has deep green leaf with white flower and its leaf soft, well erected, so its physiological characters suitable to attract the adult \u003cem\u003eP.operculella\u003c/em\u003e to lay its eggs. Belete, well branched and erected grow up, its leaf deep green and hard with white flower, which can easily attract the PTM moth. And Menagesha: 1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26 eggs, marked \"c,\" indicating very low infestation. Genotypes marked with \"c\" (such as Jalenie, Badhasa, and Menagesha) have significantly lower infestation levels, making them more resistant or less attractive to PTM. Genotypes labeled \"ab\" or \"abc\" (like Gudene, Wechecha, Dagme, Bubu, and Zemen) have infestation levels that fall between the extremes, suggesting a moderate susceptibility to PTM. An F-value of 3.1 suggests there is variability in the infestation levels across the genotypes, but this must be checked against the P-value to confirm statistical significance. P-value: 0.018, this value is less than 0.05, meaning the differences in PTM egg infestation among the genotypes are statistically significant. In other words, at least one genotype has a significantly different infestation level from the others. %CV (Coefficient of Variation): 5.93, the %CV indicates the relative variability in the data. A value of 5.93% suggests low variability, meaning the data is relatively consistent with the mean values for each genotype.\u003c/p\u003e\u003cp\u003eThe infestation levels of PTM eggs vary significantly across the evaluated genotypes, with Belete and Burika showing the highest infestation levels, while Menagesha and Jalenie display the lowest. The low P-value (0.018) confirms that these differences in egg infestation levels are statistically significant. The %CV of 5.93 indicates that the infestation measurements have low variability, enhancing the reliability of the results.\u003c/p\u003e\u003cp\u003eThe standard errors (\u0026plusmn;\u0026thinsp;values) indicate how much variability there is in the measurements for each genotype. Gudene has a low standard error (\u0026plusmn;\u0026thinsp;0.82), indicating consistent results, while Dagme has a higher standard error (\u0026plusmn;\u0026thinsp;14.1), suggesting more variability in infestation levels for this genotype. The F-value (3.1) and the P-value (0.018) together confirm that the differences between the infestation levels of the genotypes are significant, meaning the results are not due to random chance. The %CV of 5.93% reflects a relatively low level of variability in the data, which further supports the reliability and consistency of the results. Many researchers have observed variable infestation levels of PTM across different potato varieties. For example, a study by Alemu et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that some potato varieties like Shenen and Keihgi had significantly higher PTM infestation levels than others, with a similar range of infestation from low to high (2\u0026ndash;16 eggs per tuber). Their findings also showed statistical significance between genotypes, similar to the results seen in this study. Like this result, they found that certain varieties with high infestation (e.g., Shenen) needs more pest management, while others (e.g., Keihgi) were more resistant. Similar to Jalenie and Menagesha (which have low infestation levels in this study), other studies have identified potato varieties with lower susceptibility to PTM. For example, Alemu et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported Shenkora as a resistant variety with low infestation, which mirrors the low infestation observed in Jalenie and Menagesha (1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26). This supports the idea that some genotypes naturally deter or are less attractive to PTM, possibly due to chemical composition or structural factors in the tubers. In Prasad et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which focused on PTM infestation in different agro-ecological zones, infestation levels varied between 5 to 18 eggs per genotype, similar to this study, where Belete and Burika fall in the A is higher range (14.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9 and 17.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7, respectively). This suggests that, this finding align with those observed in different regions and that high infestation varieties can range broadly depending on environmental conditions.\u003c/p\u003e\u003cp\u003eThe relatively high infestation levels in Burika (17.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7) and Belete (14.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9) are consistent with previous literature, where highly infested varieties tend to have softer, sweeter tubers that are more attractive to PTM, or the moths may prefer them for oviposition. These findings could reflect the influence of genotypic factors on PTM resistance, a point that has been highlighted by Prasad et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who noted that the attractiveness of certain varieties could contribute significantly to higher infestations\u003c/p\u003e\u003cp\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eInfestation levels at different instars stage of genotypes\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of PTM infestation at first, second, third and fourth stage at count one, two, and on count three on the evaluated genotype at the field conditions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLists of genotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"12\" nameend=\"c13\" namest=\"c2\"\u003e\u003cp\u003eMean of 1st, 2nd, 3rd and 4th instars stage\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error at count one\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eCount 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eCount2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e\u003cp\u003eCount three\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1st instars\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2nd instars\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3rd instars\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4th instars\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1st instars\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2nd instars\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3rd instars\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4th instars\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1st instars\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2nd instars\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e3rd instars\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e4th instars\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJalenie\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelete\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGudene\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.781a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBadhasa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWechecha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDagme\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBubu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZemen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBurika\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMenagesha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e3.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eP.operculella\u003c/em\u003e infestation across the various genotypes at different instars (first, second, third, and fourth) and evaluation Counts (count 1, count 2, and count 3) under field conditions. Below, the detailed findings, incorporating the relevant statistical aspects such as Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;Standard Error, F-values, and P-values, along with the trend observed across the genotype.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eFirst Instars Stage (Count 1, Count 2, Count 3):\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn count 1, the mean infestation values for the first instars range from 0.0 (Belte) to 0.46 (Gudene), with standard errors varying between 0.0 (Belete) to 0.8 (Gudene). The infestation is generally low across all genotypes, with Belete showing no infestation at all (0.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0) and also with Jalenie and Burika showing the lowest mean infestations(0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23 and 0.033\u0026thinsp;\u0026plusmn;\u0026thinsp;0.057, respectively), followed by other genotypes like Zemen (0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1) and Bubu (0.033\u0026thinsp;\u0026plusmn;\u0026thinsp;0.057). In count 2, genotypes like Gudene (1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6), Bubu (1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58), and Wechecha (1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5) had notably higher mean infestations, while Jalenie (0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58) and Burika (1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2) had relatively moderate values). Despite these variations, the overall F-value of 0.78 and P-value of 0.63 indicate that the differences in infestation levels for the first instars are not statistically significant. This suggests that all genotypes are similarly susceptible to the first instars stage of the pest. At Count 3, the infestation rates of the first instars were notably lower, with the mean infestation of Jalenie (0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02) being the lowest, indicating a significant reduction in infestation.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eSecond Instars Stage (Count 1, Count 2, Count 3):\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn count 1 Infestation values range from 0.73 (Jalenie and Belte) to 1.5 (Gudene), with standard errors varying between 0.31 (Badhasa) to 0.58 (Bubu). Wechecha (1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5) and Bubu (1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58) had relatively higher infestations, followed by Gudene. Gudene has the highest mean infestation in this stage (1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60), while Jalenie and Belete show relatively lower infestations (around 0.73).\u003c/p\u003e\u003cp\u003eSecond Instars in count 2, showed an increase in infestation for several genotypes, with Wechecha (1.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17) and Bubu (1.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45) continuing to lead. In contrast, Jalenie (0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58) and Burika (1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20) had lower infestations, with an F-value of 1.39 and a P-value of 0.26. Again, the P-value is greater than 0.05, suggesting that the differences between genotypes at this stage are not statistically significant, implying a similar level of susceptibility across genotypes.\u003c/p\u003e\u003cp\u003eCount 2 showed an increase in infestation for several genotypes, with By Count 3, most genotypes showed a reduction, particularly Burika (0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59) and Jalenie (0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22), suggesting a decrease in infestation over time. Burika has higher eggs infestation with it good physiological performance but has low larval infestation at lower level instars, this is May this genotype has a good chemical composition to resist or have an ability to trap the natural enemy of PTM, like different bugs, lady beetles and hopers see Tbale 8. At count three, there is significant variation in infestation at the 2nd instars stage (P\u0026thinsp;=\u0026thinsp;0.010).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eThird Instars Stage (Count 1, Count 2, Count 3):\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn count 1 Similar to the second instars, the infestation levels here range from 0.73 (Jalenie and Belete) to 1.51 (Gudene). Gudene (1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61) and Bubu (1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58) again reported the highest infestation rates, whereas Jalenie (0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58) and Belete (0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47) had lower infestations.\u003c/p\u003e\u003cp\u003eAt count 2, the third instars infestation values range from 0.66 (Badhasa) to 1.53 (Wechecha), with an F-value of 0.77 and a P-value of 0.63. As in the previous stages, the P-value indicates no statistically significant difference in infestation levels across genotypes at this stage. In this counting time a notable reduction in infestation for Jalenie (0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43), but Wechecha (1.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40) and Bubu (1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48) continued to have relatively high infestations.\u003c/p\u003e\u003cp\u003eAt Count 3, the infestation reduced drastically for many genotypes, with Jalenie (0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32) and Zemen (1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86) showing moderate values. Burika (0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59) also had a relatively higher count\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eThird Instars Stage (Count 1, Count 2, Count 3):\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn count 1, the infestation values range from 0.15 (Badhasa) to 0.85 (Zemen), with the standard errors varying widely (0.16 for Badhasa to 0.64 for Wechecha). Zemen exhibits the highest infestation at this stage (0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47), while Badhasa shows the lowest (0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21). The infestation was low for almost all genotypes, with Zemen (0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47) and Wechecha (0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64) showing the highest values, Burika (0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20) and Dagme (0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25) had moderate infestations. At count 2, the infestation levels for the fourth instars range from 0.10 (Menagesha) to 0.43 (Jalenie). Wechecha (1.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40) and Gudene (0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37) leading the infestation rates.\u003c/p\u003e\u003cp\u003eThe infestation of the fourth instars larvae significantly decreased at Count 3, especially for Wechecha (0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15), Burika (0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11), and Zemen (0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69). The infestation levels of PTM across the four developmental stages show variability across the genotypes. There is significant variation in infestation at the 2nd instars stage (P\u0026thinsp;=\u0026thinsp;0.010), while infestation in the 1st, 3rd, and 4th instars does not show statistically significant differences (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Zemen appears to be the most affected genotype, particularly at the 2nd instars stage, while Gudene and Burika show the least infestation across all stages. On my observation the third and the fourth instars very preferable by different wasps, even they ragout from the leaf tunnels and the first instars and eggs very attacks by Brassica beetles and lady beetles see the following pictures.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eFourth Instars Stage (Count 1, Count 2, Count 3):\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe infestation values range at count 1 from 0.15 (Badhasa) to 0.85 (Zemen), with the standard errors varying widely (0.16 for Badhasa to 0.64 for Wechecha). Zemen exhibits the highest infestation at this stage (0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47), while Badhasa shows the lowest (0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21). No significant difference in infestation levels between the genotypes for the fourth instars stage and at count 2, the infestation levels range from 0.10 (Menagesha) to 0.43 (Jalenie). Menagesha has low to moderate infestation, with a relatively higher infestation in the 2nd instars.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eGenotypic Resistance and Infestation Patterns:\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe susceptibility of crops to PTM infestation can vary across different genotypes (cultivars or varieties), and this variation can be linked to the genetic resistance or susceptibility of the plants (Patel et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Research has shown that specific genotypes may exhibit resistance due to factors such as leaf morphology, chemical composition, and ability to produce secondary metabolites that deter pest feeding (Hughes \u0026amp; Hatcher, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The data provided suggests that while infestation levels vary slightly between the genotypes, no significant differences are observed across the four instars stages, which may imply that all evaluated genotypes have similar levels of resistance or susceptibility at this early count (see at count 1 on Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOther studies on pest infestations in crops may report similar findings, where certain genotypes show higher or lower infestation levels at different pest stages, but statistical significance may not always be found. For instance, in studies on maize or cotton, pest resistance can vary widely between cultivars, but often the resistance mechanisms, such as chemical compounds or physical defenses like leaf hairiness, need to be examined in depth to explain such variations. Similarly, in some instances, researchers found that infestation levels between different varieties of crops or genotypes in field conditions do not show significant differences across pest stages, which could be attributed to environmental factors, pest migration patterns, or simply the lack of strong genetic resistance.\u003c/p\u003e\u003cp\u003eThe variation in PTM infestation across different genotypes might be due to several factors inherent to the plant material itself. Genotypic resistance could play a key role in determining the infestation levels of the moth at various instars. Some genotypes may have better natural defenses such as toxic compounds, physical barriers (e.g., leaf texture), or chemical signals that deter the moth's larvae or limit their survival. These defenses could explain why certain genotypes, such as Gudene and Burika, show very low infestation rates, particularly in the early instars. There is an observation, the genotype (Gudene and Burika) have soft and deep green color, these character very crucial to attract the natural enemy like green and brown bugs, lady beetles, that is why, and they have low infestations see below picture. There are some genotypes with higher infestation levels at certain stages. For instance, Wechecha has high infestation across the second and third instars stages, while Burika shows lower infestation levels across all stages. This could imply varying resistance or susceptibility to the pest across different genotypes (see Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e at count two). There are some genotypes with higher infestation levels at certain stages. For instance, Wechecha has high infestation across the second and third instars stages, while Burika shows lower infestation levels across all stages. This could imply varying resistance or susceptibility to the pest across different genotypes\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn contrast, other genotypes, like Zemen, exhibit significantly higher infestation, especially in the 2nd instars. This could indicate that Zemen is more susceptible to PTM infestation or lacks effective resistance mechanisms, making it a more favorable host for the larvae (See Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e at count 2\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eHost-Plant Influence and Ecological factors on PTM Development:\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eDifferent plant varieties may alter the developmental success of herbivorous insects. Factors such as plant morphology, secondary metabolites, and nutrient availability are key in shaping the outcome of insect infestations. The 2nd instars stage, in particular, is crucial for survival since larvae typically experience the highest mortality rates during early developmental stages (Kroschel and Schaub, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). A high infestation in the 2nd instars, as seen in Zemen, may indicate that larvae find the plant more suitable for development at that stage (See Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e at count 2). Field conditions such as temperature, humidity, and soil quality can also influence infestation patterns. High variability in the infestation levels across different genotypes may reflect the influence of these factors, as some plants may perform better under particular environmental conditions, making them more or less attractive or suitable for PTM larvae. For example, Wechecha, which shows a sharp increase in infestation at the 2nd instars stage, could be responding to environmental cues that favor PTM development, such as favorable microclimates or soil nutrient availability, especially since its infestation drops significantly in the 4th instars.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInstars Stages and Infestation Levels of\u003c/b\u003e \u003cb\u003eP.operculella\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eThe infestation is measured at different stages of the PTM lifecycle: first instars (newly hatched larvae), second instars (early feeding stage), third instars (later feeding stage), and fourth instars (full-grown larvae ready to pupate). The infestation data reveals that infestation levels are generally low at the first instars stage but increase as the larvae mature. This pattern aligns with observations in other studies, where early larval stages (1st and 2nd instars) tend to cause less damage because the larvae are smaller and less capable of feeding on plant tissues compared to later instars (e.g., 3rd and 4th) (Teh et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). And also I have observed at early stage they exposed to natural enemy see below picture 3, Brasica beetles highly attacks the eggs and first instars of \u003cem\u003eP,opercuella\u003c/em\u003e on the potato leaf.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eHowever, the data provided shows no significant difference in infestation levels across genotypes at any instars stage, even though there is a general increase in infestation from the first to the fourth instars. This finding is consistent with some studies that suggest that resistance or susceptibility to pests may be uniform across genotypes, especially if the infestation occurs under field conditions where environmental factors like weather, soil type, and pest management practices can also influence the results (Siddiqui et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe infestation levels of PTM across the four developmental stages show variability across the genotypes. There is significant variation in infestation at the 2nd instars stage (P\u0026thinsp;=\u0026thinsp;0.010), while infestation in the 1st, 3rd, and 4th instars does not show statistically significant differences (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Zemen appears to be the most affected genotype, particularly at the 2nd instars stage, while Gudene and Burika show the least infestation across all stages (See Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e at count 3).\u003c/p\u003e\u003cp\u003eAt count 3, Statistical significance was found in the 2nd instars stage (P\u0026thinsp;=\u0026thinsp;0.010), indicating that the infestation levels of the different genotypes at this stage are not due to chance. This supports the idea that there may be specific genotypic traits influencing susceptibility. However, for other instars stages, the P-values indicate that infestation differences are not statistically significant; suggesting that for 1st, 3rd, and 4th instars, other factors may not have as large an influence on infestation patterns. The F-values for the stages further reinforce this interpretation. The highest F-value (3.57) is for the 2nd instars, highlighting this as the most significant stage for infestation differentiation among the genotypes. On the other hand, the F-values for the other stages are relatively lower, supporting the conclusion that the 2nd instars are a key stage to focus on when considering management strategies or breeding for resistance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImplications for Breeding and\u003c/b\u003e \u003cb\u003eP.opercullella\u003c/b\u003e \u003cb\u003eManagement\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eThe results of this study provide critical insights into pest management strategies. Genotypes like Zemen, with higher infestation in multiple instars stages, may require additional pest management measures. In contrast, genotypes like Gudene and Burika, with consistently low infestation rates, might be used as breeding material to develop resistant crops or integrated pest management (IPM) strategies. In light of the statistically significant differences observed in the 2nd instars stage, targeted interventions during this period could be most effective in reducing PTM infestation across various crops. This might include the use of biological control agents, insecticide treatments, or the introduction of trap crops to attract and manage the PTM population early in its development.\u003c/p\u003e\u003cp\u003eA comparison with similar studies can provide additional context and strengthen the findings, for example demonstrated that crops with higher phenolic content had lower infestation rates due to the deterrent effect of these chemicals. Similarly, Jones \u003cem\u003eet al\u003c/em\u003e. (2018) found that plants with increased wax coating on leaves exhibited lower pest colonization, potentially explaining why some of the genotypes in this study, such as Gudene, had lower infestation rates. Additionally, Wang et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) explored the role of plant resistance, finding that plants with dense, highly branched foliage were less likely to experience high PTM infestations, as the larvae had fewer opportunities to settle. This could be relevant for genotypes such as Wechecha and Zemen, which had higher infestations in the 2nd instars. In contrast, genotypes with more open structures, such as Burika and Jalenie, might offer easier access to larvae, potentially explaining the higher infestation rates in some stages. This study's findings underline the importance of genotype-specific traits in managing PTM infestations. The significant infestation in the 2nd instars stage, along with the variation across genotypes, highlights the need for targeted pest management strategies. Genotypes like Zemen may require stronger pest control measures, while those like Gudene and Burika could provide valuable material for breeding programs aimed at increasing pest resistance. Statistical analysis confirms that while some stages show significant variation, others do not, which helps to pinpoint where efforts should be concentrated for the most effective control.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eGenotypic Performance on Leaf and Petiole Damage:\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eBadhasa and Zemen have the highest leaf damage percentages (around 28%), indicating that these genotypes might be more susceptible to factors that cause leaf damage under field conditions. On the other hand, Belete, Jalenie, and Gudene exhibit the lowest levels of leaf damage (close to 0% for Jalenie and Gudene), which suggests they are more resistant to the conditions that lead to leaf damage. The standard error (SE) values are large for some genotypes, especially Badhasa and Zemen, indicating that the variability within these genotypes is high, and this could suggest that the damage levels could vary significantly in different environmental conditions or growing seasons. Petiole damage is consistently low across all genotypes, with the highest being Wechecha (4.6%) and the lowest being Gudene and Zemen (both around 0.33%). The standard error values for petiole damage are relatively large, indicating that the data are spread out, but no genotype shows extreme damage. With an F-value of 0.42 and a P-value of 0.9, it is clear that there is no significant difference in petiole damage across the genotypes. This suggests that factors influencing petiole damage (such as pest activity, weather conditions, or specific genotype traits) do not vary much between the evaluated genotypes under the field conditions. The low and consistent petiole damage levels across the genotypes may indicate a general resistance or tolerance of the evaluated plants to conditions that lead to petiole damage.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003eImplications for Breeding, low leaf and petiole damage\u003c/b\u003e:\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eSeveral factors contribute to the lower leaf damage observed in these genotypes, and understanding them is key for developing more resistant varieties. In terms of plant structure, some studies suggest that the overall architecture of a plant, such as leaf angle, density, and canopy structure, can influence pest infestation and damage. Genotypes like Belete and Jalenie may possess structural traits that reduce the attractiveness or accessibility of their leaves to pests, thereby reducing the amount of damage.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMoreover, leaf morphology is another critical factor. The physical characteristics of the leaf, such as its thickness, texture, or presence of certain compounds, can play a role in resistance. For instance, thicker leaves may be more difficult for pests to penetrate, or certain biochemical compounds might deter pests. Researchers have found that varieties with tougher, less palatable leaves often experience less damage due to herbivores or insect pests (L\u0026oacute;pez-Ruiz et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In addition, pest resistance mechanisms in these genotypes could involve genetic traits that confer tolerance or immunity. Some crops naturally produce secondary metabolites, such as alkaloids or phenolic compounds that can deter pests or inhibit their growth. These biochemical defenses can be selected for in breeding programs, allowing for the development of more resistant crops without the need for chemical pesticides (Kumar et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurthermore, understanding the ecological interactions and host preferences of specific pests is essential. For example, a genotype's resistance to one pest might not translate to another. This understanding of the pest complex that attacks specific crops would help breeders select for resistance across multiple pest species. The work of researchers such as Tiwari and Sharma (2015) emphasizes the importance of integrating both biotic and abiotic stress resistance into breeding programs to create plants that are not only pest-resistant but also adaptable to different environmental conditions.\u003c/p\u003e\u003cp\u003eIn sum, breeding programs that target low leaf damage should focus on genotypes like Belete and Jalenie, while investigating the genetic and physiological traits that confer their resistance. This research could help breeders develop new, resistant varieties, ensuring improved crop protection and reduced reliance on pesticides. Through a combination of genetic understanding, morphological assessment, and pest behavior studies, these programs can contribute to the long-term sustainability of agricultural practices.\u003c/p\u003e\u003cp\u003eBreeding efforts might be better directed toward other factors, such as leaf damage resistance, or improving other traits such as yield, pest resistance, or drought tolerance. The observation that petiole damage is relatively uniform and low across all genotypes suggests that it is not a major differentiating factor when selecting for traits related to petiole damage. This finding has several implications for breeding strategies in crop or plant improvement programs. Petiole integrity, being a minor concern under current field conditions, may not warrant the focus it might otherwise receive in breeding objectives.\u003c/p\u003e\u003cp\u003eIn a study by Raghavendra \u003cem\u003eet al\u003c/em\u003e. (2019), petiole damage was found to be less influenced by genetic variation and more a product of environmental stressors, such as wind or mechanical damage during harvesting. These findings underscore that petiole integrity might not be a key trait to select for in environments where such external factors are not predominant. Moreover, according to studies on phenotypic plasticity (Wang \u003cem\u003eet al.\u003c/em\u003e, 2013), it is important to recognize that petiole damage could be a product of external rather than genetic factors, suggesting that breeding may be better directed at other factors where genetic variation is more likely to show beneficial results.\u003c/p\u003e\u003cp\u003eGiven that petiole damage does not vary widely among genotypes, breeders might consider shifting their focus to traits that have more substantial impacts on overall plant performance. For example, breeders could focus on improving leaf damage resistance-a trait that is often more directly correlated with yield and plant health. Kuss and Larkin (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) found that leaves that can resist damage from pests, diseases, and environmental factors tend to show higher photosynthetic efficiency, which directly impacts crop yields. As such, breeding for leaf robustness might yield greater benefits than focusing on petiole integrity.\u003c/p\u003e\u003cp\u003eAdditionally, the importance of yield improvement, pest resistance, and drought tolerance cannot be overstated. As emphasized by Zishan .A \u003cem\u003eet al\u003c/em\u003e. (2024), high-yield cultivars that also exhibit pest resistance and drought tolerance are crucial for sustaining agricultural productivity in the face of climate change. These traits often have a much larger influence on the overall success of a crop than petiole damage, particularly under stressful growing conditions where external environmental pressures are more pronounced.\u003c/p\u003e\u003cp\u003eIn line with this, pest resistance is a trait that can be strongly influenced by genetic selection. As noted by Bish \u003cem\u003eet al\u003c/em\u003e. (2019), genetic improvements in pest resistance not only reduce the need for chemical interventions but also contribute to the long-term sustainability of agricultural practices. Drought tolerance, another key trait, also has broad implications for maintaining crop yield under increasingly erratic climate conditions (Philani.J.D, 2018). By directing breeding efforts toward enhancing these traits, breeders are more likely to see significant improvements in plant performance and resilience. In summary, petiole damage, given its low variability across genotypes and minor impact on plant performance under current conditions, may not be a primary trait for breeding selection. Instead, breeders should prioritize factors such as leaf damage resistance, yield, pest resistance, and drought tolerance, as these traits are more likely to result in significant improvements in both productivity and sustainability of crops. It's important to consider how breeding priorities evolve with the changing demands of agriculture. For example, while petiole damage may not be a critical breeding trait, other factors like resilience to environmental stress and efficiency in resource use are likely to become increasingly important as global climate conditions continue to fluctuate. Breeding strategies should, therefore, remain flexible, adapting to new research findings, technological advancements, and environmental shifts and breeder should keep in mind that even if petiole damage itself is not a high-priority trait, its underlying causes may provide valuable insights into other areas of crop improvement. For instance, if petiole integrity is linked to certain environmental stressors, understanding these links could lead to better management strategies for external factors such as irrigation practices or crop rotation systems, which could indirectly influence crop performance and resilience.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003eLeaf vs. Petiole Damage\u003c/b\u003e:\u003c/h2\u003e\u003cp\u003eIn examining the comparison between leaf and petiole damage in plants, it is evident that there are key differences in how these two types of damage respond to varying conditions and genetic factors. Studies have shown that leaf damage exhibits considerable variability across different genotypes, suggesting that genetic factors, along with environmental conditions, play a significant role in determining the susceptibility of plants. For example, genotypes like Badhasa and Zemen show a higher degree of susceptibility to leaf damage, possibly due to genetic predispositions that make them more prone to pest attacks or adverse environmental conditions (Zhang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). On the other hand, genotypes such as Belete and Jalenie demonstrate greater resistance, highlighting the role of selective breeding or inherent traits that confer higher resilience to leaf damage, making these varieties less affected by external stresses like pest pressures or fluctuating weather patterns (Jones \u0026amp; Roberts, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn contrast, petiole damage is consistently low across all the genotypes, with minimal variation observed between them. This trend suggests that petiole damage may not be as sensitive to genetic differences or environmental factors as leaf damage. Unlike leaf damage, which is subject to a variety of influences like pest activity, humidity, and temperature fluctuations, petiole damage could be more structurally robust or less critical in terms of survival or reproduction in these particular genotypes. Furthermore, petioles may be less exposed to environmental stressors than leaves, which are more directly involved in photosynthesis and thus more vulnerable to external threats (Lee et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis differential response between leaf and petiole damage could also indicate that the mechanisms of damage are distinct. While leaves are often the primary site of pest infestation or physiological stress due to their surface area and exposure, petioles, which support the leaves, may have stronger or less vulnerable tissues that do not suffer as much from environmental disturbances (Brown \u0026amp; Adams, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This suggests that factors such as pest pressure, nutrient availability, and weather events may have a much more significant impact on leaf health compared to petioles.\u003c/p\u003e\u003cp\u003eOverall, the contrasting variability in leaf versus petiole damage emphasizes the complex interactions between genetic resistance, environmental factors, and the physiological traits of the plant. Leaf damage appears to be more influenced by environmental pressures such as pest presence or climatic conditions, while petiole damage remains relatively unaffected, likely due to its structural role or lesser exposure to these environmental challenges (Davis et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Future research may further investigate how these aspects of plant morphology and physiology interact to shape plant health outcomes in various environmental contexts.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDifferent stage of potato tuber moth infestations at harvesting time and performance physiology of the genotype\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of number of larvae, pupae and adult per tuber at harvesting time and physiological data on the evaluated genotype at the field conditions.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLists of genotypes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of larvae at harvesting per tuber\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of pupa per tuber at harvesting time\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNumber of adult per tuber at harvesting time\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNumber of cracking per tube rat harvesting time due to PTM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePlant height\u003c/p\u003e\u003cp\u003ein cm\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNumber of plant stem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eStand count per plot\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLeaf area per leaf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eCanopy\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBadhasa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4bc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0bc\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelete\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c5\"\u003e\u003cp\u003e0.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e52.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e15.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e12.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDagme\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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3.1a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08ab\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJalenie\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c8\"\u003e\u003cp\u003e14.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07bc\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWechecha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e54.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e14.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01ab\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZemen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e36.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e13.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12c\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.32bc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.010(anwas0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.0004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCv%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e68.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e33.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e24.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eLarvae, Pupae, and Adults\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe presence of larvae, pupae, and adults per tuber is likely related to the pest infestation, which can affect crop yield and quality. Genotypes like Badhasa and Zemen, with higher numbers of larvae (1.1 and 1.3, respectively), may be more susceptible to pest attacks. According to studies by Tambo \u003cem\u003eet al\u003c/em\u003e. (2022), the susceptibility of genotypes to pests like tuber moths can vary significantly, and breeding for resistance often focuses on reducing pest populations, especially during early developmental stages (larvae). Lower numbers of pupae and adults in genotypes like Burika and Belete might indicate greater resistance to the pest.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eCracking Due to PTM (Potato Tuber Moth):\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eCracking due to PTM is a critical issue in potato production, as it leads to loss of marketable tubers. High cracking observed in Badhasa and Jalenie (0.8 and 1.0 respectively) can indicate greater damage from PTM. As Smith \u003cem\u003eet al\u003c/em\u003e. (2020) noted, genotypes with thicker skins or higher resistance to environmental stress often show less cracking and pest-induced damage. It might be useful to explore the relationship between tuber skin characteristics and PTM resistance. The F-values for all the traits are relatively low, and the P-values exceed the common threshold of 0.05, suggesting no significant differences between the genotypes in terms of pest infestation or damage. This might imply that the evaluated genotypes share similar levels of susceptibility or resistance to PTM. However, Brown \u003cem\u003eet al\u003c/em\u003e. (2018) mention that field conditions can introduce variability and more sensitive tests (e.g., genetic analyses) might reveal underlying differences not captured by the current analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSignificant Traits\u003c/b\u003e: Plant height, stand count, and canopy structure show significant genetic variation, as indicated by low P-values (\u0026lt;\u0026thinsp;0.05), suggesting that genetics plays a role in shaping these traits. Non-Significant Traits: Traits like leaf area per plant and number of plant stems show less genetic variation (P-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05), implying that environmental factors, such as insect herbivory, might be affecting these traits more significantly than genetics alone.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eImpact of Insects on Plant Physiology and Genetic variations in Resistance\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eInsects affect plant physiology in multiple ways, either by directly consuming plant tissues (leaf, stem, or roots) or indirectly through the secretion of saliva or excretion of waste products that may trigger biochemical responses within the plant (Karban and Baldwin, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Insect herbivory can cause mechanical damage, reduce photosynthetic capacity, and alter nutrient allocation within the plant, thereby affecting growth and reproduction (Lazebnik and Johnson, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, the plant may respond by producing secondary metabolites such as alkaloids, phenolics, or terpenoids, which act as defenses against herbivores (Agrawal, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). These defensive compounds can sometimes reduce the palatability or growth rate of herbivores, thus altering the insect-plant dynamic. Genetic factors play a fundamental role in determining how plants respond to insect pressures. Different plant genotypes have evolved varying degrees of resistance or tolerance to herbivory, influenced by specific genes and metabolic pathways that govern defense mechanisms (Stewart \u003cem\u003eet al\u003c/em\u003e., 2016). For example, some genotypes possess genes that lead to the production of chemical deterrents or the strengthening of cell walls, making it harder for insects to consume the plant tissue. In contrast, other genotypes might employ strategies like inducing growth responses to compensate for tissue damage (Kessler and Baldwin, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). A genotype's resistance to herbivory can also involve its physical structure, such as the thickness of leaf cuticles or the presence of trichomes (leaf hairs) that deter insect feeding (Price et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). A genotype's ability to produce secondary metabolites like tannins, flavonoids, or glucosinolates also plays a crucial role in its defense mechanisms (Zhao et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The interaction between insect herbivores and plant genetic makeup can thus result in variable phenotypic traits, which are often used as indicators of resistance or resilience to insect damage.\u003c/p\u003e\u003cp\u003eIn this study two genotypes, Wechecha and Zemen, their differing responses to insect herbivory provide insight into how genetic variation influences plant performance in the face of pest pressure. The genotype Wechecha has been observed to exhibit superior performance in traits like plant height and canopy structure, potentially indicating a higher level of resistance or tolerance to insect herbivory. This could be due to a combination of structural defenses (e.g., thicker cuticles or greater trichome density) and the ability to produce chemical defenses that deter insect feeding (Iason et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Additionally, the Wechecha genotype may be better outfitted to activate defensive signaling pathways such as jasmonic acid or salicylic acid, which enhance resistance to insect herbivory (Howe and Jander, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOn the other hand, the genotype Zemen appears to be more vulnerable to pest pressures, as reflected by its lower height and canopy scores. This suggests that Zemen may lack the robust genetic mechanisms necessary for insect resistance or tolerance. Genotypes like Zemen might be more susceptible to foliar damage, leading to stunted growth and lower overall vigor due to an inability to compensate for the loss of photosynthetic tissues or inefficient defense activation (Barton \u003cem\u003eet al\u003c/em\u003e., 2012), See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, on Zemen highest percentage (28.2%) leaf damage recorded in this study. Furthermore, lower canopy development could indicate a reduced ability to produce or accumulate secondary metabolites that protect against herbivores, further highlighting the genetic predisposition of Zemen to be less resilient to pest damage, also Zemen has low canopy structure see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The interaction between insects and plant genotypes is a complex and multifaceted process, with genetic makeup playing a key role in determining plant resilience. While some genotypes exhibit greater tolerance or resistance, as seen in Wechecha, others, like Zemen, may be less able to withstand insect herbivory, resulting in poorer plant performance. These genetic differences offer important insights into how plants adapt to their environments and how breeding programs can be tailored to enhance resistance to insect damage.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003eGenetic Resistance to Insects:\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003ePlants exhibit several strategies for resisting insect pests, such as physical barriers (e.g., thick cuticles, trichomes), chemical defenses (e.g., secondary metabolites like alkaloids, phenolics), and induced resistance mechanisms (e.g., the production of jasmonic acid upon insect attack). These mechanisms, driven by genetic factors, can alter a plant's vulnerability to herbivores and significantly influence its growth parameters (Tena et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor example, Wechecha, which demonstrated larger canopy size and height in this study, could be exhibiting genetic traits that retrieve resistance to insect herbivores, allowing it to allocate more resources to growth instead of defending against pests. In contrast, Zemen, with its smaller canopy and reduced height, may have fewer or less effective defense mechanisms, making it more susceptible to insect damage, which can lead to stunted growth or reduced reproductive capacity (Schmidt et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003ch2\u003eThe Role of Leaf Area in Pest Interactions:\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eLeaf area is a key indicator of a plant's photosynthetic capacity, but it also plays a crucial role in pest dynamics. Larger leaves provide more surface area for herbivores to feed on, which can increase the damage they inflict. However, larger leaves may also allow plants to produce more chemical defenses or maintain higher levels of photosynthesis, potentially mitigating some of the negative effects of insect herbivory. The balance between leaf size and defense mechanisms is critical in determining a plant\u0026rsquo;s overall fitness under insect pressure, Clements, M., \u0026amp; Hwang, C. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The leaf area per plant data from this study, with Burika showing larger leaves (12.6 cm\u0026sup2;), suggests that some genotypes may have evolved larger leaf areas, possibly at the cost of increased susceptibility to insect damage. On the other hand, smaller-leaved genotypes like Zemen might be sacrificing potential photosynthesis capacity for defense against pests. This could be an example of a trade-off between growth and defense that many plants face (Wang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003eCanopy and Plant Competition:\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eCanopy size plays a significant role in determining plant competitiveness and its ability to tolerate environmental stresses, including herbivory. A larger canopy can provide physical protection against pests by making it harder for insects to access plant tissues, or it may allow for the production of more chemical defenses that deter pests. Additionally, a larger canopy may improve light capture, water use efficiency, and overall plant growth (Ireneo \u003cem\u003eet al\u003c/em\u003e., 2013). The genotypes showing larger canopy sizes, like Bubu and Burka, may be better at outcompeting neighboring plants for resources, which could be advantageous when insect pests are also present. However, a larger canopy also implies a greater resource investment, which could be a disadvantage if the plant faces pest attacks or other environmental stresses, Jansson, R. K., \u0026amp; Lind, M. (2016). In contrast, smaller canopy sizes like those observed in Zemen could represent a strategy that minimizes resource investment in aboveground structures, possibly in favor of better pest resistance or more efficient resource use.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003eTuber information and the dry mater of the evaluated genotype perspective of potato tuber moth\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of genotype tuber information and dry matter after harvesting, at field conditions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLists of genotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNSST per plot\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWSS per plot/Kg\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNMST per plot\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWMST per plot\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNLST per plot\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLSTW per plot\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTNT per plot\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eTNTW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eDry mater of the genotype\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBedesa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52.6\u0026thinsp;\u0026plusmn;\u0026thinsp;44.3a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8abc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.6\u0026thinsp;\u0026plusmn;\u0026thinsp;30.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.6\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1de\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e116.0\u0026thinsp;\u0026plusmn;\u0026thinsp;80.2a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelete\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e147.0\u0026thinsp;\u0026plusmn;\u0026thinsp;18.0a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9.1 7.1ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBubu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.6\u0026thinsp;\u0026plusmn;\u0026thinsp;15.1ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.7ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1cd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e102.3\u0026thinsp;\u0026plusmn;\u0026thinsp;22.8a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBurika\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53.0\u0026thinsp;\u0026plusmn;\u0026thinsp;26.2a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62.0\u0026thinsp;\u0026plusmn;\u0026thinsp;18.7ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e37.3\u0026plusmn; 7.0a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e152.3\u0026thinsp;\u0026plusmn;\u0026thinsp;30.5a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e13.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDagme\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52.6\u0026thinsp;\u0026plusmn;\u0026thinsp;14.5abc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8bcd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e125.3\u0026plusmn; 9.6a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e10.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02ab\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGudene\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.0\u0026thinsp;\u0026plusmn;\u0026thinsp;26.9abc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c1\"\u003e\u003cp\u003eZemen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57.3\u0026thinsp;\u0026plusmn;\u0026thinsp;20.2ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e107.6\u0026thinsp;\u0026plusmn;\u0026thinsp;20.9a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0001225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.00048\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCV (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNote: NSST/ number of small size tuber per plot, WSS/weight of small size tuber per plot, NMST/ number of medium size tuber per plot, WMST/ weight of medium size tuber per plot, NLST/ number of large size tuber per plot, LSTW/ large size tuber weight, TNT/total number of tuber per plot and TNTW/ total number of tuber weight per plot\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe potato tuber moth (\u003cem\u003ePhthorimaea operculella\u003c/em\u003e) is a major pest in potato farming worldwide. The larvae of this moth feed on potato tubers, causing extensive damage. The infestation of tubers is a complex interaction between multiple factors, including the size of the tuber, the dry matter content, and the plant\u0026rsquo;s genotype. Understanding how these variables correlate with potato tuber moth infestation can help in developing strategies to manage pest damage. This section elaborates on how tuber size and dry matter content influence the moth's behavior and infestation levels, referencing previous studies to strengthen the analysis.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDry Matter Content and Its Effect on Potato Tuber Moth Infestation\u003c/h3\u003e\n\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eDry matter content is a key player in insect pest resistance. The dry matter content in potatoes, which includes starch, sugar, fiber, and other compounds, has a significant impact on the resistance of potatoes to pests such as the potato tuber moth, Bouvier et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The dry matter content is a measure of the total solid materials in the tuber, excluding water. A higher dry matter content typically correlates with a firmer, denser tuber, which in turn affects how susceptible the tuber is to insect pests. Potatoes with higher dry matter content are typically more fibrous and dense, creating a tougher texture that is more difficult for the \u003cem\u003eP.operculella\u003c/em\u003e larvae to burrow into. This physical resistance can make it more difficult for the larvae to gain access to the inner tissues of the tuber. The tougher skin acts as a mechanical barrier to the larvae, making it harder for them to penetrate and feed. Bouvier et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) found that high-dry-matter potatoes had a more robust texture, making it difficult for the larvae of the potato tuber moth to invade. These potatoes demonstrated lower levels of damage from the moth due to their tougher skin and firmer flesh.\u003c/p\u003e\u003cp\u003eOn the other that, Sharma et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) observed that potatoes with higher dry matter content exhibited reduced feeding damage because the larvae had difficulty burrowing into these tougher tubers. Their study concluded that dry matter content served as a mechanism of resistance to the moth. The dry matter content in potato tubers has significant implications for both insect-plant interactions and the overall quality of the crop. Potatoes with higher dry matter content tend to be tougher, more resistant to pests, and better for storage and processing. In this study finding Genotypes such as Jalen, Burika, Belete and Bubu, with higher dry matter content, are likely more resistant to pest infestations compared to those with lower dry matter, like Zemen. The statistical significance of the dry matter content across the genotypes suggests that this trait can be effectively selected for in breeding programs to enhance both pest resistance and the quality of the potato crop, leading to improved yields and reduced post-harvest losses.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003eChemical Resistance Linked to Dry Matter Content\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn addition to the physical toughness, potatoes with higher dry matter content may also contain higher concentrations of secondary metabolites like phenolic compounds, which have been shown to possess antioxidant and antimicrobial properties. These compounds can act as natural repellents or toxins to insect pests. Wu.C \u003cem\u003eet al\u003c/em\u003e. (2023) demonstrated that high-dry-matter potatoes contain elevated levels of phenolic acids and glycoalkaloids, which can deter feeding by insects. Phenolic compounds are known to be toxic to some insect species, thus potentially reducing the attractiveness of these tubers to the potato tuber moth/\u003cem\u003eP.opercullela\u003c/em\u003e. When the dry matter becomes to increase, potatoes to exhibit better physical and chemical defenses against the \u003cem\u003eP.operculella\u003c/em\u003e. The potatoes with higher dry like Jalene, Bubu, Belete and Burka genotype dry matter may contain higher levels of chemical compounds that delay feeding or reduce the growth and survivable of larvae.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e\u003ch2\u003eSize and Its Correlation with Potato Tuber Moth Infestation\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eWhile dry matter content plays a vital role in reducing the attractiveness of potatoes to pests, tuber size is another critical factor. Larger potatoes are often more susceptible to pest infestation, including the potato tuber moth, because they provide more resources for the insect larvae.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec33\" class=\"Section3\"\u003e\u003ch2\u003eLarger Tuber Size and Increased Pest Infestation\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eLarger tubers are often preferred by potato tuber moths because they provide a more abundant food source and are more likely to sustain larger populations of larvae. The larvae feed on the tuber\u0026rsquo;s starch and nutrients, and the larger the tuber, the more food it provides. Different writers state that larger tubers exhibited higher infestation rates by the \u003cem\u003eP.opercullella\u003c/em\u003e, likely due to their size and nutritional value, Sutherland et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). In particular, the study found that larger tubers offered a larger surface area for the larvae to infest, increasing the likelihood of greater damage. Larger tubers have more surface area, which provides more feeding sites for the potato tuber moth larvae. This means that an infestation in a single large tuber may cause more damage than in a smaller tuber, even if the pest pressure is the same. Larger tubers are can attract more larvae than smaller ones, because they are more attractive and have larger surface area, which allowed the larvae to spread out and cause extensive damage, Khan et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In this study, Burika, Dagem and Belete have higher number of large size tuber per plot (37, 29 and 25) respectively. Larger tubers, like these genotypes may also have better-developed physical defenses like thicker skin and more starch, but these defenses are often overwhelmed/ infested by the greater number of larvae that are attracted to them. So the genotype of Burika, Dagme and Belete might have tougher skin, they may more likely to experience infestation due to the increased surface area available for \u003cem\u003eP .opercullela\u003c/em\u003e entry, Rondon et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eT\u003cb\u003ehe Microhabitat Effect: Size and Accessibility to\u003c/b\u003e \u003cb\u003eP.opercullella\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn addition to the nutritional value, larger tubers may be more accessible to pests due to their placement in the soil. Larger tubers tend to be located at deeper soil levels or might have different soil characteristics that make them more attractive or more exposed to \u003cem\u003eP.operculella\u003c/em\u003e. Larger tubers like Burika, Dagem and Belete more exposed to \u003cem\u003eP.operculella\u003c/em\u003e, if closer to the soil surface or in areas with less soil cover/need well earth up (Kramer and Showalter (2000). This could lead to more exposure to infestations because the tubers are more easily accessible for pests such as the potato tuber moth. On the other hand, Smaller tubers may offer some protection to \u003cem\u003eP.opercullea\u003c/em\u003e, as they provide less food for the larvae, this is true in this finding, Jalenie and Menagesha have higher small size of tuber per plot and have low eggs infestation per plant (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and Jalenie have has higher dry matter which means it has a potential to resist \u003cem\u003eP.operculella\u003c/em\u003e, this result confirm to Rondon et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) found that small tubers tend to have lower levels of infestation because they are smaller targets for the larvae, but, when the smaller tuber near to the soil surface have a chance to infested by \u003cem\u003eP.operculella.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec34\" class=\"Section3\"\u003e\u003ch2\u003eTuber Size and Larval Development\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eInterestingly, the size of the tuber can also influence the development of the larvae. In larger tubers, the larvae can feed for a longer period and reach a larger size, which may increase the total damage to the tuber, Kroschel, et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) noted that larger tubers supported longer larval development and thus, greater feeding damage. The study showed that the nutritional reserves in larger tubers allowed the larvae to develop into more mature stages, causing greater levels of damage compared to smaller tubers.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eFeeding preference in multiple and no choice\u003c/h3\u003e\n\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEndophylaxis factors: - Feeding preference on the evaluated genotypes, in no and multi choice (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error) of potato tuber moth\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eMultiple choice\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eNon choice\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotypes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePercentage of larvae penetrated per tuber\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eNTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePercentage of larvae penetrated per tuber\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNTG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMenagesha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0 a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3ab\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGudene\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1 abcd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8cd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4abcd\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBadhasa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.63\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1cdef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6abc\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDagme\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.64\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1 abc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBurika\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.64\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1 ef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWechecha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8abc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJalene\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1 bcdef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8abcd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZemen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.53\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8def\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8bcd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBubu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.64\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1abcde\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3cd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelte\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1f\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.7263 0.1092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e1.72b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCV(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote, TG\u0026thinsp;=\u0026thinsp;Terminated gallery, NTG\u0026thinsp;=\u0026thinsp;N\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eone terminated gallery\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eMultiple-Choice vs. Non-Choice Conditions\u003c/h3\u003e\n\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cb\u003eMultiple-choice\u003c/b\u003e simulates a more natural environment where the larvae can \u003cem\u003echoose\u003c/em\u003e among different genotypes, revealing preference-based susceptibility or resistance. Non-choice forces larvae to feed on only one genotype, revealing inherent resistance or physical/chemical defense mechanisms that operate when choice is not an option. In the multiple-choice setup, larvae strongly preferred Menagesha, Wechecha, and Dagme, indicating high palatability or weak defense mechanisms. In contrast, Belte and Burka had significantly lower penetration rates, suggesting deterrent traits that discourage larvae, even when given the freedom to choose.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGenotype-Specific Insight\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSusceptibility Summary\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMenagesha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHighly susceptible in both scenarios \u0026ndash; highest penetration rates and NTG. May lack effective chemical defenses or have highly attractive volatiles.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDagme\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot highly preferred under choice (14.64%), but highly penetrated under no-choice (26.5%). Suggests moderate defenses, not strong enough to deter larvae when no other options are present.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelete\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLeast penetrated in both conditions (1.32% in choice, 10.6% in no-choice). Strong candidate for resistance breeding.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBurika\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConsistently low penetration and NTG. Likely has effective anti-feedant traits or tough skin texture.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWechecha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFairly high penetration in choice (17.32%), lower in no-choice (22.6%). May be highly attractive, possibly due to volatiles or tuber chemistry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGudene \u0026amp; Bubu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMid-range susceptibility may be context-dependent. Could be moderately resistant genotypes.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZemen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow choice penetration but moderate in no-choice, suggesting it\u0026rsquo;s not preferred but can be consumed under pressure.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eNTG (Non-Terminated gallery) as a Feeding Indicator\u003c/p\u003e\u003cp\u003eMenagesha and Dagme again had the highest NTG values, reinforcing their status as highly susceptible. Belete and Burika had the lowest NTG, indicating actual damage was minimal.NTG could reflect both feeding activity and depth of penetration, potentially linked to tuber texture, chemical profile, \u003cb\u003eor\u003c/b\u003e secondary metabolites.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec37\" class=\"Section2\"\u003e\u003ch2\u003eFeeding Preference and Resistance Trends\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe multiple-choice test, showed that Menagesha (19.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0%), Wechecha (17.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1%), and Dagme (14.64\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1%) were among the most preferred genotypes, as indicated by the higher percentage of larval penetration. This suggests that these genotypes likely emit chemical cues or possess surface characteristics that attract PTM larvae. However, that although there were observable differences, the lack of statistical significance (P\u0026thinsp;=\u0026thinsp;0.1092) under this condition implies overlapping susceptibility across genotypes when larvae have the freedom to choose.\u003c/p\u003e\u003cp\u003eIn contrast, the non-choice test, which isolates individual genotype resistance by forcing larval feeding, revealed statistically significant differences in larval penetration (P\u0026thinsp;=\u0026thinsp;0.034) and NTG/non terminating gallery values (P\u0026thinsp;=\u0026thinsp;0.024). Here, Dagme (26.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5%) and Menagesha (25.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8%) again recorded the highest penetration rates, reinforcing their characterization as highly susceptible genotypes. These genotypes lack strong physical or chemical defense mechanisms capable of resisting infestation when larvae are deprived of choice.\u003c/p\u003e\u003cp\u003eOn the other hand, Belete, Burika, and to some extent Zemen and Badhasa, consistently demonstrated low larval penetration and NTG values under both experimental setups. For example, Belete had the lowest penetrating levels in both choice (1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1%) and non-choice (10.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8%) tests, indicating the presence of inherent endophylactic resistance traits. Such genotypes likely, may possess structural or biochemical deterrents (e.g., thick periderm, high glycoalkaloid levels, or low volatile emissions), which make them unattractive or unpalatable to the larvae.\u003c/p\u003e\u003cp\u003eThe current findings align with previous research that underscores the variability in \u003cem\u003eP. operculella\u003c/em\u003e (potato tuber moth) preference and survival on different potato genotypes. Studies have demonstrated that larval feeding and development are heavily influenced by both surface characteristics and internal biochemical compounds of tubers (Rondon, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The observed variation in larval penetration under both choice and no-choice conditions in this study affirms that certain genotypes possess resistance traits that are likely heritable, offering potential for use in breeding programs (Raman and Palacios, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe higher infestation levels in Menagesha and Dagme, especially under non-choice conditions, may be linked to lower levels of secondary metabolites such as glycoalkaloids, which are known to deter insect pests (Tibbitts et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In contrast, Belete and Burika likely expresses higher levels of these compounds or possesses physical deterrents such as thicker periderm or suberized cell layers, which hinder larval entry.\u003c/p\u003e\u003cp\u003eThe lack of significant differences under multiple-choice conditions might suggest that larval preference is less distinct when multiple attractive options are available, or it could be due to experimental variability, as indicated by the higher coefficient of variation (CV\u0026thinsp;=\u0026thinsp;40%). However, under no-choice conditions, where larvae are forced to feed clearer differentiation of resistance traits becomes evident, as reflected by the statistically significant differences in larval penetration and NTG /none terminating gallery values (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eThis supports that non-choice assays provide a more reliable indicator of inherent resistance, as they reduce the confounding effects of preference and allow the measurement of direct plant defense responses (Raman, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Moreover, NTG values, though variable, provide a complementary metric to quantify the extent of feeding damage in tubers and further refine genotype ranking for resistance.\u003c/p\u003e\u003cp\u003eFrom a pest management perspective, the identification of low-susceptibility genotypes like Belete is crucial. These genotypes can be incorporated into integrated pest management (IPM) programs as part of a host plant resistance strategy, which reduces the need for chemical interventions (Rondon, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Moreover, the integration of resistant varieties is considered one of the most environmentally sustainable methods to manage PTM, especially in smallholder farming systems where chemical control is limited or impractical (Palacios et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec38\" class=\"Section3\"\u003e\u003ch2\u003eConclusions and Recommendations\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eLeaf damage is a more variable trait among the genotypes, with significant differences observed. Some genotypes (such as Badhasa and Zemen) experience higher damage, while others (such as Belete and Jalene) are more resistant. Petiole damage, on the other hand, is consistent across all genotypes and does not exhibit any significant variation, suggesting that it is less of a concern under the field conditions studied.\u003c/p\u003e\u003cp\u003eBetween the genotypes in terms of the number of larvae, pupae, adults, or cracking due to PTM, it is important to recognize that pest resistance can be influenced by multiple factors. These factors include genetic makeup, environmental conditions, and management practices. Even though statistical analysis suggests no significant differences, there are some important points for further exploration:\u003c/p\u003e\u003cp\u003ePest infestations can vary significantly between years, regions, or seasons. The field conditions under which the genotypes were tested may not have provided the ideal environment for revealing differences in pest resistance or susceptibility, noted that environmental stressors like temperature and humidity could influence the survival and development of pest stage that is why in this case study on some parameter have no significance difference.\u003c/p\u003e\u003cp\u003eGenetic vs. Environmental Contributions: The lack of significant differences in pest infestation could also be due to genetic homogeneity among the genotypes. It would be interesting to investigate if the observed genotypes have a similar genetic background or share common resistance traits. Further studies, such as controlled greenhouse trials or molecular analyses, could provide insight into the genetic basis of pest resistance, particularly against the potato tuber moth (PTM).\u003c/p\u003e\u003cp\u003eCracking caused by PTM larvae is an important factor in evaluating crop quality. High cracking in genotypes like Badhasa and Jalenie may reduce their market value due to cosmetic damage, even if the actual pest infestation numbers are not dramatically higher. Tuber cracking is often more influenced by the mechanical damage inflicted by larvae rather than direct pest consumption. More research on how tuber integrity and resistance to cracking correlate could help breeders select for improved genotypic traits.\u003c/p\u003e\u003cp\u003eAlthough the current study shows no statistically significant differences between the evaluated genotypes, the results provide valuable insights into the relative pest resistance of these genotypes under field conditions. Further research is needed to explore potential underlying genetic traits, the role of environmental factors, and the effect of cracking on crop quality. By expanding the study scope, incorporating different testing methods, and considering additional factors like yield and marketability, a more comprehensive understanding of pest resistance in potato genotypes could be achieved.\u003c/p\u003e\u003cp\u003eThe evaluation of different genotypes under field conditions highlights the importance of both genetic variation and environmental factors in shaping plant growth and resistance to insect herbivory. Genotypes like Wechecha and Bubu exhibit superior growth traits, possibly due to effective resistance mechanisms against pests, whereas Zemen may be more vulnerable to insect damage. The data also underscores the importance of selecting genotypes that balance growth and defense, allowing for optimal performance under pest pressures. Breeding efforts should focus on incorporating insect-resistant traits into high-yielding genotypes to ensure sustainable agricultural practices, especially in areas prone to significant pest challenges. Under the laboratory test (feeding preference) ,Genotypes such as Belete, Burika, and Zemen may possess inherent resistance traits due to consistently low larval penetration across both conditions. Menagesha and Dagme showed high susceptibility, indicating less suitability for areas with high potato tuber moth pressure. These findings provide valuable input for integrated pest management (IPM) and breeding programs aiming to develop pest-resistant potato varieties.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec39\" class=\"Section2\"\u003e\u003ch2\u003eRecommendations\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eInsect Resistance Mechanisms: Future studies could explore specific insect resistance mechanisms in these genotypes. For instance, research could identify the presence of secondary metabolites or physical traits (e.g., trichomes, waxy cuticles) that might be contributing to the differences in pest resistance observed in genotypes like Wechecha and Zemen. Understanding these mechanisms can provide insights into breeding for pest-resistant cultivars.\u003c/p\u003e\u003cp\u003eEnvironmental Interactions: It would be valuable to further investigate how environmental factors, such as soil quality, water availability, and pest density, interact with genotype to affect plant growth. This multi-faceted approach could provide a more comprehensive understanding of plant-environment-insect interactions.\u003c/p\u003e\u003cp\u003eGenetic Markers for Pest Resistance: Future research could focus on identifying genetic markers associated with pest resistance traits. By integrating molecular tools and field evaluations, researchers could expedite the process of breeding for insect-resistant crops that maintain high yields under pest pressure. Especially the Menagsha genotype, its physiology is weak and bended to the ground but has low eggs infestation, this resistance not due to its physiological resistance, so it needs to study its chemical composition, this will help it\u0026rsquo;s interaction with insect. Again Belete genotype shows high eggs infestation, but has low larval infestation, why is it? ,it needs clear clarification through study its genetically resistance\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cb\u003e- we have no conflict interest on this research\u003c/b\u003e\u003c/p\u003e\u003ch2\u003eFunding:-\u003c/h2\u003e\u003cp\u003ewe have no received financial support for this research\u003c/p\u003e\u003ch2\u003eAuthor(s) contributions;-\u003c/h2\u003e\u003cp\u003eKidist Teferra Yimame writer of this research proposal, maker of the research and full writer of this paper and Emana Getu degage, is supervisor.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eMany thank those two organizations, Ethiopian Institute of Agricultural Research (EIAR) for study leaving and Addis Ababa University (AAU), we also thanks to Holetta potato research program.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgrawal, A. 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Varieties in Ethiopia. \u003cem\u003eEast African Journal of Sciences\u003c/em\u003e. 11(1)1-16.\u003c/li\u003e\n\u003cli\u003eZhang, X., Xu, Z., \u0026amp; Wang, L. (2020). \u003cstrong\u003eVariation in susceptibility to leaf damage in crop genotypes under different environmental conditions.\u003c/strong\u003e \u003cem\u003eAgronomy Journal\u003c/em\u003e, 112(2), 531-539.\u003c/li\u003e\n\u003cli\u003eZhao, X., Li, B., \u0026amp; Zhang, S. (2016). \u003cem\u003eSecondary metabolites and their role in plant defense against herbivores.\u003c/em\u003e Phytochemistry Reviews, 15(4), 673-689.\u003c/li\u003e\n\u003cli\u003eZishan .A , Shareen .N , Assima .F , Chunye. W, Muhammad .A,M , Muthusamy. R, Anamica .U and Yulong .D. (2024). Enhancing plant resilience: Nanotech solutions for sustainable agriculture.Heliyon,10:23, e40735.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-tropical-insect-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtis","sideBox":"Learn more about [International Journal of Tropical Insect Science](http://link.springer.com/journal/42690)","snPcode":"42690","submissionUrl":"https://www.editorialmanager.com/jtis/default2.aspx","title":"International Journal of Tropical Insect Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Genotype, P.operculella, plant physiology, eggs infestation larval instars, dry matter, endophylactic","lastPublishedDoi":"10.21203/rs.3.rs-6841160/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6841160/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eTwo experiments were designed for studying susceptibility of ten potato varieties to evaluate the infestation levels of\u003c/em\u003e \u003cb\u003ePhthorimaea operculella\u003c/b\u003e \u003cem\u003eunder the field and laboratory conditions, at the egg stage and larval infestation stage before harvesting, at harvesting time, and evaluates the physiological performance to resist PTM. Data were analyzed using Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;Standard Error, along with F-values and P-values to assess statistical significance with R software. There was significant differences in egg infestation levels and suggesting a clear variation in susceptibility between genotypes to PTM (F-value\u0026thinsp;=\u0026thinsp;3.1, P-value\u0026thinsp;=\u0026thinsp;0.018), highest infestations observed on the Belete (14.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9) and Burika (7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7) varieties, and the lowest on Menagesha (1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26), Badhasa (2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4), and Jalenie (2.86\u0026thinsp;\u0026plusmn;\u0026thinsp;3). The infestation levels of different genotypes at various larval instars stages (1st, 2nd, 3rd, and 4th ) across three count periods, 1st instars stage, infestation levels remained low across all genotypes, Belete showing no infestation, while Gudene exhibited the highest mean infestation (0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80). No significant differences among genotypes (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In the 2nd instars stage, infestation increased in count 2, particularly in Wechecha (1.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17) and Bubu (1.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45), but decreased in count 3, a significant variation in infestation was found (P\u0026thinsp;=\u0026thinsp;0.010).\u003c/em\u003e The 2nd instars stage, in particular, is crucial for survival since larvae typically experience the highest mortality rates during early developmental stage. A high infestation in the 2nd instars, as seen in Zemen, may indicate that larvae find the plant more suitable for development at that stage. \u003cem\u003eThe 3rd instars stage exhibited infestation trends similar to the 2nd instars, with Gudene (1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61) and Bubu (1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58) havi1*ng higher values. However, no statistically significant differences were observed (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) and 4th the instars stage showed generally low infestation levels. Zemen (0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47) having the highest infestation. Overall, while infestation varied across developmental stages and genotypes, significant differences were only detected at the 2nd instars stage. Zemen being the most affected genotype, Gudene and Burika showing the least infestation across all stages. There was significance difference among the genotypes on leaf damage (F\u0026thinsp;=\u0026thinsp;3.49, P\u0026thinsp;=\u0026thinsp;0.011), Badhasa and Zemen exhibited the highest leaf damage (28%), while Belete, Jalenie, and Gudene had the lowest (0.66%). In contrast, petiole damage showed no significant variation among genotypes (F\u0026thinsp;=\u0026thinsp;0.42, P\u0026thinsp;=\u0026thinsp;0.9). Significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were observed for plant height, stand count per plot, and canopy coverage. Plant height ranged from 36.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3 cm (Zemen) to 54.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7 cm (Wechecha). Canopy coverage varied significantly, with Zemen exhibiting the lowest value (0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12), while Bubu, Burika, and Jalenie had the highest (0.86\u0026ndash;0.87)\u003c/em\u003e, Jalenie \u003cem\u003eand\u003c/em\u003e Burika \u003cem\u003ealso showed relatively high dry matter content (0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 and 0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04, respectively), suggesting that these traits are strongly influenced by genotype selection.\u003c/em\u003e Under laboratory test Belete had the lowest penetrating levels in both choice (1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1%) and non-choice (10.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8%). the presence of inherent endophylactic resistance traits. Such genotypes likely, may possess structural or biochemical deterrents (e.g., thick periderm, high glycoalkaloid levels, or low volatile emissions), which make them unattractive or unpalatable to the larvae. \u003cem\u003eThe findings provide insights for selecting high-yielding and high-quality potato varieties for improved agricultural productivity.\u003c/em\u003e\u003c/p\u003e","manuscriptTitle":"Susceptibility of Ethiopian Released Potato Varieties to Potato Tuber moth, Phthorimae operculella Infestation under the field and laboratory conditions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-29 09:24:57","doi":"10.21203/rs.3.rs-6841160/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-10-15T07:34:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"International Journal of Tropical Insect Science","date":"2025-10-14T13:20:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-22T08:01:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Tropical Insect Science","date":"2025-09-17T02:34:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-tropical-insect-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtis","sideBox":"Learn more about [International Journal of Tropical Insect Science](http://link.springer.com/journal/42690)","snPcode":"42690","submissionUrl":"https://www.editorialmanager.com/jtis/default2.aspx","title":"International Journal of Tropical Insect Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"dae7b7f0-a2d1-4673-aa05-16999c157a24","owner":[],"postedDate":"October 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-11T05:50:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-29 09:24:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6841160","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6841160","identity":"rs-6841160","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}