Epidemics of Northern Leaf Blight (Exserohilum turcicum) as influenced by Epidemiological Drivers and Agronomic Factors in the Major Maize-Growing Agro-Ecologies of Ethiopia

Epidemics of Northern Leaf Blight (Exserohilum turcicum) as influenced by Epidemiological Drivers and Agronomic Factors in the Major Maize-Growing Agro-Ecologies of Ethiopia | 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 Epidemics of Northern Leaf Blight (Exserohilum turcicum) as influenced by Epidemiological Drivers and Agronomic Factors in the Major Maize-Growing Agro-Ecologies of Ethiopia Mesele Haile, Zelalem Bekeko, Dagne Wegary, Habtamu Terefe, Suresh Lingadahalli Mahabaleshwara This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9198560/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Northern Leaf Blight (NLB), caused by Exserohilum turcicum , is a major constraint to maize production in Ethiopia. This study aimed to investigate the epidemiological drivers and spatial patterns of NLB across major maize-growing regions to inform integrated management strategies. Methods A field survey was conducted across 18 districts in Oromia, Amhara, and SNNPR regions over two cropping seasons (2021–2022), encompassing 1,080 farms. Disease severity was quantified using Percent Severity Index (PSI) and Lesion Area (LA). Spatial, agronomic, and environmental data were analyzed using chi-square tests, correlation analysis, and logistic regression. Results Pronounced spatial and temporal variations in NLB severity were observed. Oromia exhibited the highest overall disease pressure (PSI: 63.0%), followed by SNNPR (62.1%) and Amhara (60.6%). At the zonal level, Sidama recorded the most severe infections (PSI: 70.6%). Moist and semi-moist mid-altitude zones showed the highest disease pressure (PSI: 70.3%), attributed to cooler temperatures and higher rainfall (> 1,200 mm). Temporal trends showed higher severity in 2021 (PSI: 64.0%) than 2022 (60.2%). Cropping systems and cultivar selection significantly modulated disease outcomes. Sole cropping exacerbated severity (PSI: 68.4%), while intercropping reduced risk (PSI: 54.2%). Susceptible cultivars like BH540 (PSI: 77.1%) suffered severe infections, whereas resistant hybrids such as BH661 (PSI: 51.7%) mitigated impact. Logistic regression identified late planting (OR = 42.53), susceptible cultivars (OR = 24.51), and high-risk agro-ecology (OR = 7.22–16.07) as the top risk factors. Conclusion This study highlights the multifactorial nature of NLB epidemics. Effective management requires spatially-targeted integrated strategies combining resistant cultivars, optimized planting schedules, diversified cropping systems, and balanced nutrition to enhance maize productivity and safeguard smallholder livelihoods in Ethiopia. Agro-ecology Cropping systems Disease severity Ethiopia Integrated management Maize cultivars Spatio-temporal variation Figures Figure 1 Introduction Maize ( Zea mays L.) is a cornerstone of global food security, serving as a primary dietary energy source for over 1.2 billion people in sub-Saharan Africa (SSA) while also supporting livestock feed and agro-industrial value chains (Erenstein et al. 2022 ; Krishna et al. 2023 ). In Ethiopia, maize is dominant cereal, contributing approximately 32.5% of total cereal output and occupying nearly 2.1 million hectares of cultivated land (Asfaw et al., 2024 ). The crop is particularly crucial for smallholder farmers, who rely on it for both subsistence and income generation, with maize accounting for over 20% of smallholder caloric intake (Belachew et al. 2022 ; FAOSTAT 2023 ). Despite its significance, maize production in Ethiopia remains far below its potential, with average yields stagnating at 3.2–3.8 tons ha − 1 compared to the global average of 5.8 tons ha − 1 (Erenstein et al., 2022 ). A major factor behind this yield gap is disease pressure, with annual losses estimated at 20–50% due to fungal and viral infections such as Maize Lethal Necrosis Disease (MLND), Turcicum or Northern Leaf Blight (NLB), Gray Leaf Spot (GLS), and stalk/ear rots (Deressa et al. 2024 ; Keno et al. 2018 ; Mohammed et al. 2023b ; Regassa et al. 2020 ). These losses disproportionately affect resource-poor farmers, exacerbating food insecurity and rural poverty (Flora et al. 2023 ). Among these, Northern Leaf Blight (NLB), caused by the fungal pathogen Exserohilum turcicum (Pass.) Leonard & Suggs, is the most economically damaging, reducing yields by 30–70% under conducive environmental conditions (Gidi 2023 ; Ijaz and Fan 2024 ). The disease thrives in humid, mid- to high-altitude regions (1,500-2,400 m.a.s.l.), where moderate temperatures (18–27°C) and prolonged leaf wetness (≥ 12 hours of dew or rain) facilitate spore germination and infection (Sharma 2023 ). In Ethiopia, NLB is most severe in the highlands of Oromia, Amhara, and SNNPR regions, where maize cultivation is intensive (Keno et al. 2018 ). Studies have documented Percent Severity Index (PSI) levels exceeding 60% in untreated fields, leading to substantial grain yield reductions (Asfaw et al. 2024 ). The persistence of NLB is exacerbated by multiple agronomic and systemic constraints. A significant issue is the continued reliance on susceptible local maize varieties, which lack genetic resistance to E. turcicum (Chivasa et al. 2022 ). Additionally, monocropping, delayed planting, and poor weed management create microenvironments that favor disease spread (Pakdaman and Mohammadi 2018 ). Fertilizer imbalances, particularly excessive nitrogen application, have also been linked to increased NLB severity by promoting dense, moisture-retentive canopies (Gupta et al. 2017 ). Beyond agronomic factors, systemic barriers such as limited access to fungicides, weak extension services, and climate change-induced weather variability further impede disease control (Omotoso et al. 2023 ). Despite the significant economic impact of NLB, critical knowledge gaps persist in understanding disease dynamics and effective management strategies. A key limitation is the lack of localized data on disease severity metrics-including lesion area (LA) and percent severity index (PSI)-across Ethiopia's diverse agro-ecological zones (AEZs), hindering targeted interventions (Mohammed et al. 2023a ; Terefe et al. 2023 ). While previous studies have established broad relationships between environmental factors and NLB prevalence (Sintayehu et al. 2018 ), the complex interactions between agro-ecological conditions and farming practices remain poorly understood. For instance, although crop rotation with legumes has shown potential to disrupt disease cycles elsewhere (Zhang et al. 2019 ), its effectiveness in Ethiopia's unique farming systems requires further validation. Similarly, while improved hybrid varieties demonstrate varying levels of resistance, their adoption remains limited due to insufficient extension services and a lack of location-specific performance data (Chivasa et al. 2022 ; Yirga et al. 2020 ). This study aims to address these gaps through a comprehensive field survey to analyze NLB epidemic drivers, evaluate adopted maize cultivars, and assess disease management practices. The ultimate goal is to generate evidence-based, location-specific recommendations that enhance maize productivity and support national food security efforts. Materials and Methods Study Area and Sampling procedures The study was conducted across Ethiopia’s three principal maize-growing regions-Oromia, Amhara, and the Southern Nations, Nationalities, and Peoples' Region (SNNPR)-to capture country’s agro-ecological diversity. Over two cropping seasons (2021 to 2022), 1,080 maize farms were surveyed across 18 districts (Fig. 1 ). The sites spanned three agro-ecological zones as described by Abate et al.(2015): moist lower mid-altitudes (900-1,500 m.a.s.l.; 900-1,200 mm annual rainfall), moist and semi-moist mid-altitudes (1,700-2,000 m.a.s.l.; 1,000–1,200 mm), and moist highlands (2,001–2,400 m.a.s.l. ; >1,200 mm). These zones were selected to represent gradients in elevation, temperature, and rainfall, which are critical for maize production and E. turcicum epidemiology. Disease Assessment and Data Collection The assessment of NLB severity was conducted using a systematic 'X' pattern sampling strategy to achieve comprehensive spatial coverage of each farm. Within this framework, five standardized 9 m² quadrants were sampled per farm. From each quadrant, five plants were systematically selected for a detailed evaluation. The primary quantitative metric was the lesion area (LA), calculated in cm² by measuring the length and diameter of three representative lesions on both the ear leaf and its adjacent leaves. Complementing this direct measurement, a visual severity estimate was assigned to each plant following the established protocol of Price et al. ( 2016 ), which utilizes a 1–9 grading scale. This scale delineates three severity tiers: low (grades 1–3, ≤ 10% leaf area affected), moderate (grades 4–6, 11–30% affected), and high (grades 7–9, > 30% affected). To synthesize the individual plant ratings into a single, comparable metric for each farm, the Percent Severity Index (PSI) was calculated. The formula used was: $$\:\text{P}\text{S}\text{I}=\left[\frac{\sum\:\left(\text{N}\text{u}\text{m}\text{b}\text{e}\text{r}\:\text{o}\text{f}\:\text{p}\text{l}\text{a}\text{n}\text{t}\text{s}\:\text{i}\text{n}\:\text{e}\text{a}\text{c}\text{h}\:\text{g}\text{r}\text{a}\text{d}\text{e}\times\:\text{G}\text{r}\text{a}\text{d}\text{e}\:\text{v}\text{a}\text{l}\text{u}\text{e}\right)}{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{n}\text{u}\text{m}\text{b}\text{e}\text{r}\:\text{o}\text{f}\:\text{p}\text{l}\text{a}\text{n}\text{t}\text{s}\:\text{a}\text{s}\text{s}\text{e}\text{s}\text{s}\text{e}\text{d}\:\:\times\:\text{M}\text{a}\text{x}\text{i}\text{m}\text{u}\text{m}\:\text{g}\text{r}\text{a}\text{d}\text{e}\:\text{v}\text{a}\text{l}\text{u}\text{e}\:\:}\right]\times\:100,$$ Concurrently, a comprehensive set of agronomic and environmental variables was documented for each sampling site, including the specific maize cultivar grown (e.g., BH540, BH661, local landraces), the cropping system (sole, inter-, or mixed cropping), the type of fertilizer applied (NPS or NPS+UREA), planting time (categorized as early, on-time, or late), weed management status (weeded, semi-weeded, or unweeded), and the crop's growth stage at the time of assessment (blister, milk, or dough). Key agro-ecological variables, namely altitude and rainfall, were also recorded. Data Analysis The descriptive statistics for PSI and LA are summarized. Pairwise associations between disease metrics and agronomic factors were tested using Pearson’s correlation (for continuous data) (Ndo et al. 2022 ). Categorical predictors were evaluated via chi-square tests with Cramer’s V (thresholds: 0.5 = large effect). Multicollinearity among predictors was assessed using variance inflation factors (VIF < 5) (Dormann et al. 2013 ). Generalized Linear Models (GLMs) with binomial distributions analyzed binary severity outcomes (high vs. low), with model selection guided by Akaike (AIC) and Bayesian (BIC) Information Criteria (Burnham and Anderson 2004 ). Odds ratios (OR) quantified the influence of risk factors on NLB severity (Johnson and Omland 2004 ). All analyses were conducted in R v4.5.2, utilizing the readxl (Ulyanov et al., 2025 ), car (Weisberg and Fox, 2019 ), and base packages. Results Spatial and Temporal Patterns of NLB Severity Northern Leaf Blight (NLB) was observed across all surveyed farms during the 2021–2022 cropping seasons, though disease severity varied significantly. The study revealed significant regional differences in disease severity, with Oromia exhibiting the highest values for lesion area (LA: 25.4 cm²) and percent severity index (PSI: 63.0%), indicating more severe infection compared to Amhara and SNNPR (Table 1 ). At the zonal level, Sidama recorded the highest severity (LA: 28.9 cm², PSI: 70.6%), while Halaba had the lowest (PSI: 49.9%). Additionally, yearly trends showed higher disease pressure in 2021 (PSI: 64.0%) than in 2022 (PSI: 60.2%), suggesting that environmental factors such as rainfall and temperature fluctuations may influence disease progression. Agro-Ecological and Cropping System Influences Agro-ecological conditions played a crucial role in disease severity. Moist and semi-moist mid-altitude areas had the highest infection levels (LA: 29.2 cm², PSI: 70.3%), whereas moist lower mid-altitude regions experienced the lowest severity (PSI: 51.0%). Among cropping systems, sole cropping was associated with greater disease impact (PSI: 68.4%) compared to intercropping or mixed cropping (PSI: 52.5–54.2%). Monocropping maize led to higher severity (PSI: 65.7%), as did high crop density (PSI: 68.7%). In contrast, crop rotation (PSI: 53.6%) and low to intermediate planting density (PSI: 56.0-61.4%) helped reduce disease risk. Cultivar Susceptibility and Management Practices Maize cultivar selection significantly affected disease outcomes. Highly susceptible varieties included BH540 (PSI: 77.1%) and local landraces (PSI: 74.0%), while BH661 (PSI: 51.7%) and P3812W (PSI: 51.6%) demonstrated strong resistance. Poor weed management emerged as the most critical risk factor, with unweeded fields showing the highest severity (PSI: 79.3%, LA: 34.6 cm²). Proper weeding reduced disease severity by 25.4 percentage points (PSI: 53.9%). Late planting was equally detrimental, resulting in PSI values of 79.1% (LA: 34.6 cm²), whereas early or on-time planting maintained lower severity (PSI: 52.8–59.8%). Impact of Fertilizer Application Fertilizer use also influenced disease severity. Fields treated solely with NPS fertilizer had elevated infection levels (PSI: 73.7%, LA: 31.5 cm²). However, combining NPS with urea reduced severity significantly (PSI: 55.3%), highlighting the importance of balanced nutrient management in mitigating disease risk. Table 1 Northern leaf blight severity metrics (mean ± SE) [lesion area (LA), percent severity index (PSI)] in Ethiopian maize farms during 2021 to 2022 production seasons. Variable Variable class LA PSI Variable Variable class LA PSI Region Amhara 23.8 ± 0.48 60.6 ± 0.76 Agro-ecology Lower_Mid 18.8 ± 0.31 51.0 ± 0.53 Oromia 25.4 ± 0.38 63.0 ± 0.60 Moist 21.5 ± 0.43 55.9 ± 0.64 SNNPR 24.9 ± 0.53 62.1 ± 0.98 Semi_Moist 29.2 ± 0.35 70.3 ± 0.56 Zone Agew Awi 21.9 ± 0.97 58.8 ± 1.79 Cultivar AMH851 21.0 ± 0.65 56.2 ± 0.92 East Welega 24.1 ± 0.65 58.5 ± 1.04 BH540 32.5 ± 0.74 77.1 ± 1.04 Gurage 20.3 ± 0.90 54.2 ± 1.58 BH546 25.1 ± 0.77 64.0 ± 1.09 Halaba 19.4 ± 0.98 49.9 ± 1.81 BH661 20.5 ± 0.50 51.7 ± 0.56 Jimma 26.3 ± 0.79 64.3 ± 1.22 DK777 21.7 ± 0.72 58.1 ± 0.98 Sidama 28.9 ± 0.78 70.6 ± 1.52 Local 30.8 ± 0.66 74.0 ± 1.01 Silte 27.1 ± 1.16 65.4 ± 1.75 P30G19 25.8 ± 0.80 64.7 ± 1.57 West Shewa 24.3 ± 0.74 63.7 ± 1.08 P3506W 25.3 ± 0.81 64.1 ± 1.32 West Arsi 26.9 ± 0.84 65.6 ± 1.38 P3812W 20.8 ± 0.55 51.6 ± 0.91 West Gojam 24.2 ± 0.54 61.0 ± 0.84 Cropping System Inter cropped 21.8 ± 0.40 54.2 ± 0.60 District Abeshege 20.3 ± 0.90 54.2 ± 1.58 Mixed cropped 19.7 ± 0.46 52.5 ± 0.79 Ambo 25.1 ± 1.10 64.1 ± 1.66 Sole cropped 27.6 ± 0.35 68.4 ± 0.55 Bako Tibe 23.6 ± 0.98 63.3 ± 1.39 Cropping History Maize 26.7 ± 0.32 65.7 ± 0.52 Bure 22.0 ± 0.96 57.0 ± 1.29 Rotation 20.4 ± 0.36 53.6 ± 0.60 Dangila 21.9 ± 0.97 58.8 ± 1.79 Fertilizer NPS 31.5 ± 0.38 73.7 ± 0.62 Dembecha 24.5 ± 1.10 59.6 ± 1.84 NPS+UREA 20.9 ± 0.25 55.3 ± 0.40 Hawassa zuria 27.0 ± 1.10 65.2 ± 1.92 Population High 28.3 ± 0.50 68.7 ± 0.82 Jabi Tehnan 22.3 ± 0.97 60.0 ± 1.50 Intermediate 24.4 ± 0.42 61.4 ± 0.69 Jimma Arjo 22.6 ± 0.77 55.3 ± 0.95 Low 21.7 ± 0.36 56.0 ± 0.59 Mecha 28.1 ± 1.11 67.4 ± 1.74 Growth stage Blister 19.7 ± 0.45 53.1 ± 0.67 Negele Arsi 29.0 ± 1.18 70.6 ± 1.93 Dough 27.9 ± 0.35 67.9 ± 0.59 Omo Nada 24.2 ± 1.02 59.2 ± 1.55 Milk 21.3 ± 0.38 55.0 ± 0.56 Sankura 27.1 ± 1.16 65.4 ± 1.75 Weed condition Semi weeded 24.0 ± 0.38 60.7 ± 0.62 Sekoru 28.5 ± 1.15 69.4 ± 1.64 Not weeded 34.6 ± 0.35 79.3 ± 0.55 Shalla 24.7 ± 1.14 60.6 ± 1.76 Weeded 20.2 ± 0.29 53.9 ± 0.47 Sibu Sire 25.5 ± 1.03 61.8 ± 1.76 Planting time Early 19.5 ± 0.30 52.8 ± 0.51 Wera Dijo 19.4 ± 0.98 49.9 ± 1.81 Late 34.6 ± 0.34 79.1 ± 0.55 Wondo Gent 30.9 ± 1.05 76.0 ± 2.14 On time 23.5 ± 0.33 59.8 ± 0.54 Year 2021 2022 25.1 ± 0.39 64.0 ± 0.58 24.5 ± 0.35 60.2 ± 0.65 Associations and Predictive Models Correlation Analysis The chi-square analysis demonstrated highly significant associations (p < 0.001) between various agronomic factors and both NLB severity metrics (Table 2 ). LA and PSI were strongly positively correlated (coefficient = 0.82). Among the agronomic factors, planting time and weed management emerged as the strongest predictors of NLB severity, with Cramer’s V values exceeding 0.60. Fertilizer application ( V = 0.55–0.57), cultivar selection ( V = 0.43–0.56). Agro-ecological zone ( V = 0.50–0.54) also exhibited large effect sizes. District-level variations ( V = 0.32–0.35) had a greater effect than broader zonal ( V = 0.22–0.26) or regional ( V = 0.05–0.06) differences, highlighting the importance of localized factors. Table 2 Association between agro-ecological, agronomic variables and Northern Leaf Blight severity metrics [lesion area (LA), percent severity index (PSI)] in Ethiopian maize farms: Chi-Square Test Results with Cramer’s V Effect Sizes. Variable LA PSI Cramer’s V p-value Cramer’s V p-value Region 0.06 0.132 0.06 0.162 Zone 0.26 0.001 0.26 0.001 District 0.32 0.001 0.35 0.001 Year 0.02 0.461 0.07 0.016 Agro-ecology 0.52 0.001 0.54 0.001 Variety 0.43 0.001 0.56 0.001 Cropping System 0.34 0.001 0.47 0.001 Cropping history 0.33 0.001 0.33 0.001 Fertilizer 0.56 0.001 0.57 0.001 crop density 0.29 0.001 0.33 0.001 Growth stage 0.41 0.001 0.45 0.001 Weed management 0.60 0.001 0.61 0.001 Planting time 0.62 0.001 0.61 0.001 *All chi-square tests were significant at p 0.5 = large effect. Regression Analysis The logistic regression model for lesion area (AIC = 824.9) identified agro-ecological zone as a major determinant. Compared to the Lower moist zone, maize in moist highlands (OR = 5.32) and moist and semi-moist mid-altitudes (OR = 6.46) had significantly higher odds of developing larger lesion (Table 3 ). Landraces (OR = 6.22) and BH540 (OR = 3.67) were the most susceptible. NPS+UREA application (OR = 0.35) and late planting (OR = 32.78) were protective and risk factors, respectively. Table 3 Logistic regression analysis for lesion area (LA) of Northern Leaf Blight (NLB) in Ethiopian maize farms. Predictor Predictor class β SE OR p_value (Intercept) -3.17 0.92 0.04 0.001 Year 2022 -0.39 0.25 0.68 0.117 Agro ecology Moist highlands 1.67 0.41 5.32 0.001 Moist and semi-moist mid-altitudes 1.87 0.38 6.46 0.001 Cropping System Mixed cropping -0.53 0.30 0.59 0.076 Sole cropping -0.36 0.22 0.70 0.106 Fertilizer NPS+UREA -1.04 0.26 0.35 0.001 Growth stage Dough 0.95 0.27 2.58 0.001 Milk 0.44 0.30 1.54 0.141 Planting time Late 3.49 0.39 32.8 0.001 On time 1.24 0.21 3.45 0.001 Cultivar BH540 1.30 0.49 3.67 0.008 BH546 0.45 0.42 1.56 0.283 BH661 -0.15 0.38 0.86 0.695 DK777 0.38 0.43 1.46 0.380 Land races 1.83 0.47 6.22 0.001 P30G19 1.10 0.49 3.01 0.024 P3506W 0.97 0.48 2.62 0.045 P3812W 0.18 0.45 1.20 0.693 OR= Odds Ratio, SE= Standard Error. AIC = 824.9, BIC = 919.61, log-likelihood = -393.45. Bold p-values indicate significance at p < 0.05 The model for Percentage Severity Index (AIC = 641.04) demonstrated strong explanatory power, (Table 4 ). Agro-ecological zones were the strongest environmental predictors, with moist and semi-moist mid-altitudes having 16.07 times higher odds of sever NLB compared to lower moist zones. BH540 (OR = 24.51) and local landraces (OR = 13.45) were highly susceptible, while BH661 (OR = 0.15) and P3812W (OR = 0.28) were resistant. Late planting was the most critical risk factor (OR = 42.53). Sole cropping increased risk (OR = 1.82), while mixed cropping reduced it (OR = 0.38). Table 4 Logistic Regression Analysis for Percentage Severity Index (PSI) of Northern Leaf Blight (NLB) in Ethiopia maize farms. Variable Variable class β SE OR P_Value (Intercept) -4.30 87 0.01 0.001 Region Oromia 0.00 28 1.00 0.993 SNNPR 0.55 29 1.74 0.060 Agroecology Moist highlands 1.98 42 7.22 0.001 Moist and semi-moist mid-altitudes 2.78 38 16.07 0.001 Cropping System Mixed -0.97 40 0.38 0.014 Sole 0.60 25 1.82 0.017 Fertilizer NPS+UREA -0.58 26 0.56 0.029 Planting time Late 3.75 45 42.53 0.001 On time 1.06 25 2.90 0.001 Cultivar BH540 3.20 62 24.51 0.001 BH546 1.23 44 3.43 0.005 BH661 -1.89 52 0.15 0.001 DK777 0.47 46 1.60 0.300 Land races 2.60 48 13.46 0.001 P30G19 1.66 48 5.26 0.001 P3506W 1.12 45 3.07 0.014 P3812W -1.27 49 0.28 0.010 OR= Odds Ratio, SE =Standard Error. AIC = 641.04, BIC = 730.77, null deviance = 1473.41, residual deviance = 605.04. Bold p-values indicate significance at p < 0.05. Discussion Regional and Agro-Ecological Influences on Northern Leaf blight Severity The study revealed significant regional and agro-ecological variations in NLB severity, with Oromia, particularly in moist highlands and semi-moist mid-altitude zones, exhibiting the highest disease pressure. These findings align with previous research demonstrating that E. turcicum thrives in cooler (18–27°C), high-rainfall (> 1000 mm) environments, where prolonged leaf wetness (> 12 hours/day) facilitates spore germination and infection (Javed et al., 2022 ; Saeed et al., 2023 ). The elevated severity in mid-altitude zones (1,700-2,400 m.a.s.l.) can be attributed to the interaction of moderate temperatures (20–24°C) and high humidity (> 80%), which optimize fungal sporulation and lesion expansion (Navarro et al. 2021 ; Paul and Munkvold 2005 ). The temporal disparity in severity (PSI: 64.0% in 2021 vs. 60.2% in 2022) underscores the role of inter-annual climatic variability, particularly erratic rainfall distribution and temperature fluctuations, in modulating epidemic intensity (Fitt et al. 2024 ; Kumar and Mukhopadhyay 2025 ). Cropping Systems and Cultivar Susceptibility The identification of sole cropping and high-density planting as major drivers of NLB severity corroborates previous research showing that monoculture systems and dense plant spacing create favorable microclimates for the pathogen (Berger 2024 ; Terefe et al. 2023 ). In contrast, intercropping and crop rotation significantly reduced NLB pressure, likely by disrupting pathogen life cycles and improving canopy ventilation (Huang et al. 2024 ; Kinyua et al. 2024 ; Zou et al. 2024 ). Cultivar susceptibility was a critical determinant. The high susceptibility of BH540 and landraces (PSI > 70%) is consistent with their lack of known resistance genes (Badu-Apraku et al. 2021 ; Welz and Geiger 2000 ). The strong resistance in BH661 and P3812W likely derives from quantitative trait loci (QTL) for partial resistance (Ranganatha et al., 2021 ), underscoring the effectiveness of genetic resistance in reducing NLB-related losses (Berger et al., 2020 ; Onwunali and Mabagala, 2020 ). Agronomic Practices and Disease Management Late planting and poor weed management emerged as the most significant risk factors. Late-planted maize often coincides with peak humidity and rainfall, creating ideal infection conditions (Nsibo et al. 2024 ; Wise et al. 2023 ). Weeds can act as alternative hosts and compete with maize, exacerbating stress and susceptibility (Yamuna et al. 2021 ). Effective weed management enhances airflow and reduces leaf wetness, lowering infection rates (Kristó et al., 2022 ; Svec et al., 2020 ). The NPS+UREA combination significantly reduced NLB severity compared to NPS alone, validating findings that balanced nitrogen and phosphorus nutrition enhances maize resistance by promoting robust cell wall development and systemic acquired resistance (Zinsou et al. 2020 ). 2020). Nitrogen deficiency weakens plant defenses (Samarina et al. 2024 ), while balanced fertilization can mitigate disease (Richard et al. 2022 ). Implications for Predictive Modeling and Future Research The strong correlation between LA and PSI highlights their utility for predictive modeling of E. turcicum epidemics (Peddicord et al. 2025 ; Sharma 2023 ). The logistic regression models effectively quantified the influence of key factors, providing a robust framework for risk assessment (Delfani et al. 2024 ). Future research should leverage genomic tools to identify QTL for resistance (Prasanna et al. 2020 ; Wisser et al., 2011 ) and integrate remote sensing and machine learning to enhance early warning systems (Terentev et al. 2022 ). Participatory research involving farmers will be critical for scaling solutions sustainably (Jindo et al. 2021 ; Olita et al. 2024 ). Conclusions This study elucidates the multifactorial nature of NLB epidemics in Ethiopian maize systems. Agro-ecological zonation emerged as a dominant epidemiological driver, with moist highland and semi-moist mid-altitude zones exhibiting the highest disease pressure. Cultivar selection proved pivotal, with resistant hybrids like BH661 demonstrating strong mitigation potential. Agronomic practices profoundly modulated disease expression, with late planting identified as the most critical risk factor. Diversified cropping systems and balanced fertilization significantly reduced disease risk. The temporal variability and predominance of localized factors necessitate decentralized, participatory approaches to disease management. Collectively, these results demonstrate that effective NLB management requires integrating resistant cultivars, optimized planting schedules, balanced nutrition, and diversified cropping systems tailored to specific agro-ecological contexts. Declarations Conflict of interests The authors declare no conflicts of interest. Author Contributions All authors contributed to the preliminary analysis, manuscript drafting, and critical review. Mesele Haile led the study conception, design, data collection, and initial manuscript draft. Zelalem Bekeko and Dagne Wegary contributed to study design, final data analysis, and manuscript finalization. Habtamu Terefe and Suresh L.M. assisted in data organization, figure preparation, and initial drafting. All authors reviewed, provided feedback, and approved the final manuscript. Acknowledgements This study was part of a Ph.D. program at Haramaya University, supported financially by the Ethiopian Institute of Agricultural Research (EIAR) and technically by Haramaya University. The authors thank the participating farmers, research teams at Bako and Hawassa maize research centers for field data collection, and anonymous reviewers for their contributions. Data Availability With the exception of meteorological data, the majority of the study's data reported in the manuscript are provided in the article. Further datasets are available from the corresponding author upon justified request. References Abate, T., Shiferaw, B., Menkir, A., Wegary, D., Kebede, Y., Tesfaye, K., Kassie, M., Bogale, G., Tadesse, B., Keno, T. (2015). Factors that transformed maize productivity in Ethiopia. Food Security , 7 (5), 965–981. Asfaw, D. M., Asnakew, Y. W., Sendkie, F. B., Abdulkadr, A. A., Mekonnen, B. A., Tiruneh, H. D., Ebad, A. M. (2024). 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Climate change and variability in sub-Saharan Africa: A systematic review of trends and impacts on agriculture. Journal of Cleaner Production , 414 , 137487. Onwunali, M. R. O., Mabagala, R. B. (2020). Assessment of yield loss due to northern leaf blight in five maize varieties grown in Tanzania. Journal of Yeast and Fungal Research , 11 (1), 37–44. Pakdaman, S., Mohammadi, G. (2018). Weeds, Herbicides and Plant Disease Management BT - Sustainable Agriculture Reviews 31: Biocontrol (E. Lichtfouse (ed.); pp. 41–178). Springer International Publishing. Paul, P. A., Munkvold, G. P. (2005). Influence of Temperature and Relative Humidity on Sporulation of Cercospora zeae-maydis and Expansion of Gray Leaf Spot Lesions on Maize Leaves . 89 (6). Peddicord, L., Xavier, A., Cryer, S., Barr, J., Heijden, G. van der. (2025). Scalable Prediction of Northern Corn Leaf Blight and Gray Leaf Spot Diseases to Predict Fungicide Spray Timing in Corn. Agronomy , 15 (2). Prasanna, B. M., Nair, S. 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Zou, Y., Liu, Z., Chen, Y., Wang, Y., Feng, S. (2024). Crop Rotation and Diversification in China: Enhancing Sustainable Agriculture and Resilience. Agriculture , 14 (9). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9198560","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":623438655,"identity":"ce21451f-d3be-42ec-be9a-24b86b6b3857","order_by":0,"name":"Mesele Haile","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIie2QsUrDQBjH/8eBWQ66fsHBJxAigagQ9VVyFDpFly4dSjkRksUHyCD6CvoGlUC6BMTNzZQOXW8sCK1H6BCEq7qJ3G/8uB+/+z7A4firNEACzhU06IdK0ipMsaKj7JZbBUxx0RlalcM8nzfJCFe9nF0v4tHJwbHyXhcC8cSmRHUdBkmNIZXsJkxrOrqbimEoMLBWord0j2QGqUqW7V9mZPYRA79AaVfel8uVXEM+lCz/OF3TxVbZ7KggMgnIR1MxRyNZwKtIY2pX6jSkpCL5ZHbxbyvqF1zwQAd9X9mU2Wyu9TiW9y/ls16NJ2eF197wvGerbOn+gosACL4RvuA1v3vvcDgc/51PprpTnI6HjYUAAAAASUVORK5CYII=","orcid":"","institution":"Ethiopian Institute of Agricultural Research","correspondingAuthor":true,"prefix":"","firstName":"Mesele","middleName":"","lastName":"Haile","suffix":""},{"id":623438656,"identity":"4e9d6e05-0668-4a22-b547-1357f7d29ce9","order_by":1,"name":"Zelalem Bekeko","email":"","orcid":"","institution":"Haramaya University","correspondingAuthor":false,"prefix":"","firstName":"Zelalem","middleName":"","lastName":"Bekeko","suffix":""},{"id":623438657,"identity":"d2a01f2c-38fa-4001-941c-0fd68688f100","order_by":2,"name":"Dagne Wegary","email":"","orcid":"","institution":"International Maize and Wheat Improvement Center","correspondingAuthor":false,"prefix":"","firstName":"Dagne","middleName":"","lastName":"Wegary","suffix":""},{"id":623438658,"identity":"3b6c6200-9ef9-416b-84dc-4cc108a0d5e7","order_by":3,"name":"Habtamu Terefe","email":"","orcid":"","institution":"Haramaya University","correspondingAuthor":false,"prefix":"","firstName":"Habtamu","middleName":"","lastName":"Terefe","suffix":""},{"id":623438660,"identity":"c6a1abcf-d014-46e4-a4c6-e8a840bff39e","order_by":4,"name":"Suresh Lingadahalli Mahabaleshwara","email":"","orcid":"","institution":"International Maize and Wheat Improvement Center (CIMMYT)","correspondingAuthor":false,"prefix":"","firstName":"Suresh","middleName":"Lingadahalli","lastName":"Mahabaleshwara","suffix":""}],"badges":[],"createdAt":"2026-03-23 09:40:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9198560/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9198560/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107337104,"identity":"b270c76a-d392-4067-9f7c-e06d007331ed","added_by":"auto","created_at":"2026-04-20 13:44:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":641196,"visible":true,"origin":"","legend":"\u003cp\u003eMajor maize growing districts surveyed for northern leaf blight survey in Ethiopia, during the 2021 and 2022 cropping seasons.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9198560/v1/a03d6c9f35b182cf3760cb77.png"},{"id":107704938,"identity":"0179acc7-8471-46bd-bc30-83cb1cd9655a","added_by":"auto","created_at":"2026-04-24 09:04:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1224522,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9198560/v1/d9ce757a-1aaa-4f2a-a3a6-1b8e60014cc2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Epidemics of Northern Leaf Blight (Exserohilum turcicum) as influenced by Epidemiological Drivers and Agronomic Factors in the Major Maize-Growing Agro-Ecologies of Ethiopia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMaize (\u003cem\u003eZea mays\u003c/em\u003e L.) is a cornerstone of global food security, serving as a primary dietary energy source for over 1.2\u0026nbsp;billion people in sub-Saharan Africa (SSA) while also supporting livestock feed and agro-industrial value chains (Erenstein et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Krishna et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In Ethiopia, maize is dominant cereal, contributing approximately 32.5% of total cereal output and occupying nearly 2.1\u0026nbsp;million hectares of cultivated land (Asfaw et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The crop is particularly crucial for smallholder farmers, who rely on it for both subsistence and income generation, with maize accounting for over 20% of smallholder caloric intake (Belachew et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; FAOSTAT \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite its significance, maize production in Ethiopia remains far below its potential, with average yields stagnating at 3.2\u0026ndash;3.8 tons ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e compared to the global average of 5.8 tons ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Erenstein et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A major factor behind this yield gap is disease pressure, with annual losses estimated at 20\u0026ndash;50% due to fungal and viral infections such as Maize Lethal Necrosis Disease (MLND), Turcicum or Northern Leaf Blight (NLB), Gray Leaf Spot (GLS), and stalk/ear rots (Deressa et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Keno et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mohammed et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e; Regassa et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These losses disproportionately affect resource-poor farmers, exacerbating food insecurity and rural poverty (Flora et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong these, Northern Leaf Blight (NLB), caused by the fungal pathogen \u003cem\u003eExserohilum turcicum\u003c/em\u003e (Pass.) Leonard \u0026amp; Suggs, is the most economically damaging, reducing yields by 30\u0026ndash;70% under conducive environmental conditions (Gidi \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ijaz and Fan \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The disease thrives in humid, mid- to high-altitude regions (1,500-2,400 m.a.s.l.), where moderate temperatures (18\u0026ndash;27\u0026deg;C) and prolonged leaf wetness (\u0026ge;\u0026thinsp;12 hours of dew or rain) facilitate spore germination and infection (Sharma \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In Ethiopia, NLB is most severe in the highlands of Oromia, Amhara, and SNNPR regions, where maize cultivation is intensive (Keno et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Studies have documented Percent Severity Index (PSI) levels exceeding 60% in untreated fields, leading to substantial grain yield reductions (Asfaw et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe persistence of NLB is exacerbated by multiple agronomic and systemic constraints. A significant issue is the continued reliance on susceptible local maize varieties, which lack genetic resistance to \u003cem\u003eE. turcicum\u003c/em\u003e (Chivasa et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, monocropping, delayed planting, and poor weed management create microenvironments that favor disease spread (Pakdaman and Mohammadi \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Fertilizer imbalances, particularly excessive nitrogen application, have also been linked to increased NLB severity by promoting dense, moisture-retentive canopies (Gupta et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Beyond agronomic factors, systemic barriers such as limited access to fungicides, weak extension services, and climate change-induced weather variability further impede disease control (Omotoso et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the significant economic impact of NLB, critical knowledge gaps persist in understanding disease dynamics and effective management strategies. A key limitation is the lack of localized data on disease severity metrics-including lesion area (LA) and percent severity index (PSI)-across Ethiopia's diverse agro-ecological zones (AEZs), hindering targeted interventions (Mohammed et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; Terefe et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While previous studies have established broad relationships between environmental factors and NLB prevalence (Sintayehu et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the complex interactions between agro-ecological conditions and farming practices remain poorly understood. For instance, although crop rotation with legumes has shown potential to disrupt disease cycles elsewhere (Zhang et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), its effectiveness in Ethiopia's unique farming systems requires further validation. Similarly, while improved hybrid varieties demonstrate varying levels of resistance, their adoption remains limited due to insufficient extension services and a lack of location-specific performance data (Chivasa et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yirga et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study aims to address these gaps through a comprehensive field survey to analyze NLB epidemic drivers, evaluate adopted maize cultivars, and assess disease management practices. The ultimate goal is to generate evidence-based, location-specific recommendations that enhance maize productivity and support national food security efforts.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area and Sampling procedures\u003c/h2\u003e \u003cp\u003eThe study was conducted across Ethiopia\u0026rsquo;s three principal maize-growing regions-Oromia, Amhara, and the Southern Nations, Nationalities, and Peoples' Region (SNNPR)-to capture country\u0026rsquo;s agro-ecological diversity. Over two cropping seasons (2021 to 2022), 1,080 maize farms were surveyed across 18 districts (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The sites spanned three agro-ecological zones as described by Abate et al.(2015): moist lower mid-altitudes (900-1,500 m.a.s.l.; 900-1,200 mm annual rainfall), moist and semi-moist mid-altitudes (1,700-2,000 m.a.s.l.; 1,000\u0026ndash;1,200 mm), and moist highlands (2,001\u0026ndash;2,400 m.a.s.l. ; \u0026gt;1,200 mm). These zones were selected to represent gradients in elevation, temperature, and rainfall, which are critical for maize production and \u003cem\u003eE. turcicum\u003c/em\u003e epidemiology.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDisease Assessment and Data Collection\u003c/h3\u003e\n\u003cp\u003eThe assessment of NLB severity was conducted using a systematic 'X' pattern sampling strategy to achieve comprehensive spatial coverage of each farm. Within this framework, five standardized 9 m\u0026sup2; quadrants were sampled per farm. From each quadrant, five plants were systematically selected for a detailed evaluation. The primary quantitative metric was the lesion area (LA), calculated in cm\u0026sup2; by measuring the length and diameter of three representative lesions on both the ear leaf and its adjacent leaves. Complementing this direct measurement, a visual severity estimate was assigned to each plant following the established protocol of Price et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which utilizes a 1\u0026ndash;9 grading scale. This scale delineates three severity tiers: low (grades 1\u0026ndash;3, \u0026le;\u0026thinsp;10% leaf area affected), moderate (grades 4\u0026ndash;6, 11\u0026ndash;30% affected), and high (grades 7\u0026ndash;9, \u0026gt;\u0026thinsp;30% affected).\u003c/p\u003e \u003cp\u003eTo synthesize the individual plant ratings into a single, comparable metric for each farm, the Percent Severity Index (PSI) was calculated. The formula used was:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{P}\\text{S}\\text{I}=\\left[\\frac{\\sum\\:\\left(\\text{N}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r}\\:\\text{o}\\text{f}\\:\\text{p}\\text{l}\\text{a}\\text{n}\\text{t}\\text{s}\\:\\text{i}\\text{n}\\:\\text{e}\\text{a}\\text{c}\\text{h}\\:\\text{g}\\text{r}\\text{a}\\text{d}\\text{e}\\times\\:\\text{G}\\text{r}\\text{a}\\text{d}\\text{e}\\:\\text{v}\\text{a}\\text{l}\\text{u}\\text{e}\\right)}{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{n}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r}\\:\\text{o}\\text{f}\\:\\text{p}\\text{l}\\text{a}\\text{n}\\text{t}\\text{s}\\:\\text{a}\\text{s}\\text{s}\\text{e}\\text{s}\\text{s}\\text{e}\\text{d}\\:\\:\\times\\:\\text{M}\\text{a}\\text{x}\\text{i}\\text{m}\\text{u}\\text{m}\\:\\text{g}\\text{r}\\text{a}\\text{d}\\text{e}\\:\\text{v}\\text{a}\\text{l}\\text{u}\\text{e}\\:\\:}\\right]\\times\\:100,$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eConcurrently, a comprehensive set of agronomic and environmental variables was documented for each sampling site, including the specific maize cultivar grown (e.g., BH540, BH661, local landraces), the cropping system (sole, inter-, or mixed cropping), the type of fertilizer applied (NPS or NPS+UREA), planting time (categorized as early, on-time, or late), weed management status (weeded, semi-weeded, or unweeded), and the crop's growth stage at the time of assessment (blister, milk, or dough). Key agro-ecological variables, namely altitude and rainfall, were also recorded.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eThe descriptive statistics for PSI and LA are summarized. Pairwise associations between disease metrics and agronomic factors were tested using Pearson\u0026rsquo;s correlation (for continuous data) (Ndo et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Categorical predictors were evaluated via chi-square tests with Cramer\u0026rsquo;s V (thresholds: \u0026lt; 0.3\u0026thinsp;=\u0026thinsp;small effect; 0.3\u0026ndash;0.5\u0026thinsp;=\u0026thinsp;moderate; \u0026gt; 0.5\u0026thinsp;=\u0026thinsp;large effect).\u003c/p\u003e \u003cp\u003eMulticollinearity among predictors was assessed using variance inflation factors (VIF\u0026thinsp;\u0026lt;\u0026thinsp;5) (Dormann et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Generalized Linear Models (GLMs) with binomial distributions analyzed binary severity outcomes (high vs. low), with model selection guided by Akaike (AIC) and Bayesian (BIC) Information Criteria (Burnham and Anderson \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Odds ratios (OR) quantified the influence of risk factors on NLB severity (Johnson and Omland \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). All analyses were conducted in R v4.5.2, utilizing the \u003cem\u003ereadxl\u003c/em\u003e (Ulyanov et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), \u003cem\u003ecar\u003c/em\u003e (Weisberg and Fox, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and \u003cem\u003ebase\u003c/em\u003e packages.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSpatial and Temporal Patterns of NLB Severity\u003c/h2\u003e \u003cp\u003eNorthern Leaf Blight (NLB) was observed across all surveyed farms during the 2021\u0026ndash;2022 cropping seasons, though disease severity varied significantly. The study revealed significant regional differences in disease severity, with Oromia exhibiting the highest values for lesion area (LA: 25.4 cm\u0026sup2;) and percent severity index (PSI: 63.0%), indicating more severe infection compared to Amhara and SNNPR (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). At the zonal level, Sidama recorded the highest severity (LA: 28.9 cm\u0026sup2;, PSI: 70.6%), while Halaba had the lowest (PSI: 49.9%). Additionally, yearly trends showed higher disease pressure in 2021 (PSI: 64.0%) than in 2022 (PSI: 60.2%), suggesting that environmental factors such as rainfall and temperature fluctuations may influence disease progression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAgro-Ecological and Cropping System Influences\u003c/h2\u003e \u003cp\u003eAgro-ecological conditions played a crucial role in disease severity. Moist and semi-moist mid-altitude areas had the highest infection levels (LA: 29.2 cm\u0026sup2;, PSI: 70.3%), whereas moist lower mid-altitude regions experienced the lowest severity (PSI: 51.0%). Among cropping systems, sole cropping was associated with greater disease impact (PSI: 68.4%) compared to intercropping or mixed cropping (PSI: 52.5\u0026ndash;54.2%). Monocropping maize led to higher severity (PSI: 65.7%), as did high crop density (PSI: 68.7%). In contrast, crop rotation (PSI: 53.6%) and low to intermediate planting density (PSI: 56.0-61.4%) helped reduce disease risk.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCultivar Susceptibility and Management Practices\u003c/h3\u003e\n\u003cp\u003eMaize cultivar selection significantly affected disease outcomes. Highly susceptible varieties included BH540 (PSI: 77.1%) and local landraces (PSI: 74.0%), while BH661 (PSI: 51.7%) and P3812W (PSI: 51.6%) demonstrated strong resistance. Poor weed management emerged as the most critical risk factor, with unweeded fields showing the highest severity (PSI: 79.3%, LA: 34.6 cm\u0026sup2;). Proper weeding reduced disease severity by 25.4 percentage points (PSI: 53.9%). Late planting was equally detrimental, resulting in PSI values of 79.1% (LA: 34.6 cm\u0026sup2;), whereas early or on-time planting maintained lower severity (PSI: 52.8\u0026ndash;59.8%).\u003c/p\u003e\n\u003ch3\u003eImpact of Fertilizer Application\u003c/h3\u003e\n\u003cp\u003eFertilizer use also influenced disease severity. Fields treated solely with NPS fertilizer had elevated infection levels (PSI: 73.7%, LA: 31.5 cm\u0026sup2;). However, combining NPS with urea reduced severity significantly (PSI: 55.3%), highlighting the importance of balanced nutrient management in mitigating disease risk.\u003c/p\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\u003eNorthern leaf blight severity metrics (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE) [lesion area (LA), percent severity index (PSI)] in Ethiopian maize farms during 2021 to 2022 production seasons.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" 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=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePSI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVariable class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePSI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmhara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e23.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e60.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAgro-ecology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLower_Mid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e18.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e51.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOromia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e25.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e63.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMoist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e21.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e55.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNNPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e24.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e62.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSemi_Moist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e29.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e70.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eZone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgew Awi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e21.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e58.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eCultivar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAMH851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e21.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e56.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEast Welega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e24.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e58.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBH540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e32.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e77.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGurage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e20.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e54.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBH546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e25.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e64.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHalaba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e19.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e49.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBH661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e20.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e51.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJimma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e26.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e64.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDK777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e21.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e58.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSidama\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e28.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e70.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLocal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e30.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e74.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSilte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e27.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e65.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP30G19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e25.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e64.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWest Shewa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e24.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e63.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP3506W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e25.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e64.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWest Arsi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e26.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e65.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP3812W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e20.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e51.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWest Gojam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e24.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e61.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCropping\u003c/p\u003e \u003cp\u003eSystem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInter cropped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e21.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e54.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"17\" rowspan=\"18\"\u003e \u003cp\u003eDistrict\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbeshege\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e20.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e54.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMixed cropped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e19.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e52.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmbo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e25.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e64.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSole cropped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e27.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e68.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBako Tibe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e23.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e63.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCropping History\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMaize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e26.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e65.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e22.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e57.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRotation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e20.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e53.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDangila\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e21.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e58.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFertilizer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e31.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e73.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDembecha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e59.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNPS+UREA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e20.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e55.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHawassa zuria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e27.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e65.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e28.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e68.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJabi Tehnan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e22.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e60.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e24.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e61.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJimma Arjo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e22.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e55.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e21.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e56.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMecha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e28.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e67.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGrowth stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBlister\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e19.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e53.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegele Arsi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e29.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e70.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDough\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e27.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e67.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOmo Nada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e24.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e59.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMilk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e21.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e55.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSankura\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e27.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e65.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eWeed condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSemi weeded\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e24.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e60.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSekoru\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e28.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e69.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot weeded\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e34.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e79.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShalla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e24.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e60.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWeeded\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e20.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e53.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSibu Sire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e25.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e61.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePlanting time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEarly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e19.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e52.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWera Dijo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e19.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e49.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e34.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e79.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWondo Gent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e30.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e76.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOn time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e23.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e59.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e25.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e64.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e60.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e \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 \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociations and Predictive Models\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eCorrelation Analysis\u003c/h2\u003e \u003cp\u003eThe chi-square analysis demonstrated highly significant associations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between various agronomic factors and both NLB severity metrics (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). LA and PSI were strongly positively correlated (coefficient\u0026thinsp;=\u0026thinsp;0.82). Among the agronomic factors, planting time and weed management emerged as the strongest predictors of NLB severity, with Cramer\u0026rsquo;s \u003cem\u003eV\u003c/em\u003e values exceeding 0.60. Fertilizer application (\u003cem\u003eV\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.55\u0026ndash;0.57), cultivar selection (\u003cem\u003eV\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.43\u0026ndash;0.56). Agro-ecological zone (\u003cem\u003eV\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.50\u0026ndash;0.54) also exhibited large effect sizes. District-level variations (\u003cem\u003eV\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.32\u0026ndash;0.35) had a greater effect than broader zonal (\u003cem\u003eV\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.22\u0026ndash;0.26) or regional (\u003cem\u003eV\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05\u0026ndash;0.06) differences, highlighting the importance of localized factors.\u003c/p\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\u003eAssociation between agro-ecological, agronomic variables and Northern Leaf Blight severity metrics [lesion area (LA), percent severity index (PSI)] in Ethiopian maize farms: Chi-Square Test Results with Cramer\u0026rsquo;s V Effect Sizes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePSI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCramer\u0026rsquo;s V\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCramer\u0026rsquo;s V\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistrict\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgro-ecology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCropping System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCropping history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFertilizer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecrop density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrowth stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeed management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlanting time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\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\u003e*All chi-square tests were significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 unless noted. Cramer\u0026rsquo;s V thresholds: 0.1\u0026ndash;0.3\u0026thinsp;=\u0026thinsp;small effect, 0.3\u0026ndash;0.5\u0026thinsp;=\u0026thinsp;moderate, \u0026gt;\u0026thinsp;0.5\u0026thinsp;=\u0026thinsp;large effect.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRegression Analysis\u003c/h2\u003e \u003cp\u003eThe logistic regression model for lesion area (AIC\u0026thinsp;=\u0026thinsp;824.9) identified agro-ecological zone as a major determinant. Compared to the Lower moist zone, maize in moist highlands (OR\u0026thinsp;=\u0026thinsp;5.32) and moist and semi-moist mid-altitudes (OR\u0026thinsp;=\u0026thinsp;6.46) had significantly higher odds of developing larger lesion (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Landraces (OR\u0026thinsp;=\u0026thinsp;6.22) and BH540 (OR\u0026thinsp;=\u0026thinsp;3.67) were the most susceptible. NPS+UREA application (OR\u0026thinsp;=\u0026thinsp;0.35) and late planting (OR\u0026thinsp;=\u0026thinsp;32.78) were protective and risk factors, respectively.\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\u003eLogistic regression analysis for lesion area (LA) of Northern Leaf Blight (NLB) in Ethiopian maize farms.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredictor class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep_value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.17\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYear\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAgro ecology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoist highlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoist and semi-moist mid-altitudes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eCropping System\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed cropping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSole cropping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFertilizer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNPS+UREA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eGrowth stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDough\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMilk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlanting time\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOn time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCultivar\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBH540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBH546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBH661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDK777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.380\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLand races\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP30G19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP3506W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP3812W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eOR= Odds Ratio, SE= Standard Error. AIC\u0026thinsp;=\u0026thinsp;824.9, BIC\u0026thinsp;=\u0026thinsp;919.61, log-likelihood = -393.45. Bold p-values indicate significance at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe model for Percentage Severity Index (AIC\u0026thinsp;=\u0026thinsp;641.04) demonstrated strong explanatory power, (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Agro-ecological zones were the strongest environmental predictors, with moist and semi-moist mid-altitudes having 16.07 times higher odds of sever NLB compared to lower moist zones. BH540 (OR\u0026thinsp;=\u0026thinsp;24.51) and local landraces (OR\u0026thinsp;=\u0026thinsp;13.45) were highly susceptible, while BH661 (OR\u0026thinsp;=\u0026thinsp;0.15) and P3812W (OR\u0026thinsp;=\u0026thinsp;0.28) were resistant. Late planting was the most critical risk factor (OR\u0026thinsp;=\u0026thinsp;42.53). Sole cropping increased risk (OR\u0026thinsp;=\u0026thinsp;1.82), while mixed cropping reduced it (OR\u0026thinsp;=\u0026thinsp;0.38).\u003c/p\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\u003eLogistic Regression Analysis for Percentage Severity Index (PSI) of Northern Leaf Blight (NLB) in Ethiopia maize farms.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP_Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.30\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOromia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNNPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgroecology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoist highlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoist and semi-moist mid-altitudes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCropping System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFertilizer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNPS+UREA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePlanting time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOn time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCultivar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBH540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBH546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBH661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDK777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLand races\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP30G19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP3506W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP3812W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\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\u003eOR= Odds Ratio, SE =Standard Error. AIC\u0026thinsp;=\u0026thinsp;641.04, BIC\u0026thinsp;=\u0026thinsp;730.77, null deviance\u0026thinsp;=\u0026thinsp;1473.41, residual deviance\u0026thinsp;=\u0026thinsp;605.04. Bold p-values indicate significance at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRegional and Agro-Ecological Influences on Northern Leaf blight Severity\u003c/h2\u003e \u003cp\u003eThe study revealed significant regional and agro-ecological variations in NLB severity, with Oromia, particularly in moist highlands and semi-moist mid-altitude zones, exhibiting the highest disease pressure. These findings align with previous research demonstrating that \u003cem\u003eE. turcicum\u003c/em\u003e thrives in cooler (18\u0026ndash;27\u0026deg;C), high-rainfall (\u0026gt;\u0026thinsp;1000 mm) environments, where prolonged leaf wetness (\u0026gt;\u0026thinsp;12 hours/day) facilitates spore germination and infection (Javed et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Saeed et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The elevated severity in mid-altitude zones (1,700-2,400 m.a.s.l.) can be attributed to the interaction of moderate temperatures (20\u0026ndash;24\u0026deg;C) and high humidity (\u0026gt;\u0026thinsp;80%), which optimize fungal sporulation and lesion expansion (Navarro et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Paul and Munkvold \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The temporal disparity in severity (PSI: 64.0% in 2021 vs. 60.2% in 2022) underscores the role of inter-annual climatic variability, particularly erratic rainfall distribution and temperature fluctuations, in modulating epidemic intensity (Fitt et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kumar and Mukhopadhyay \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCropping Systems and Cultivar Susceptibility\u003c/h2\u003e \u003cp\u003eThe identification of sole cropping and high-density planting as major drivers of NLB severity corroborates previous research showing that monoculture systems and dense plant spacing create favorable microclimates for the pathogen (Berger \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Terefe et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In contrast, intercropping and crop rotation significantly reduced NLB pressure, likely by disrupting pathogen life cycles and improving canopy ventilation (Huang et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kinyua et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zou et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCultivar susceptibility was a critical determinant. The high susceptibility of BH540 and landraces (PSI\u0026thinsp;\u0026gt;\u0026thinsp;70%) is consistent with their lack of known resistance genes (Badu-Apraku et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Welz and Geiger \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The strong resistance in BH661 and P3812W likely derives from quantitative trait loci (QTL) for partial resistance (Ranganatha et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), underscoring the effectiveness of genetic resistance in reducing NLB-related losses (Berger et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Onwunali and Mabagala, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAgronomic Practices and Disease Management\u003c/h2\u003e \u003cp\u003eLate planting and poor weed management emerged as the most significant risk factors. Late-planted maize often coincides with peak humidity and rainfall, creating ideal infection conditions (Nsibo et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wise et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Weeds can act as alternative hosts and compete with maize, exacerbating stress and susceptibility (Yamuna et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Effective weed management enhances airflow and reduces leaf wetness, lowering infection rates (Krist\u0026oacute; et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Svec et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe NPS+UREA combination significantly reduced NLB severity compared to NPS alone, validating findings that balanced nitrogen and phosphorus nutrition enhances maize resistance by promoting robust cell wall development and systemic acquired resistance (Zinsou et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). 2020). Nitrogen deficiency weakens plant defenses (Samarina et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), while balanced fertilization can mitigate disease (Richard et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eImplications for Predictive Modeling and Future Research\u003c/h2\u003e \u003cp\u003eThe strong correlation between LA and PSI highlights their utility for predictive modeling of \u003cem\u003eE. turcicum\u003c/em\u003e epidemics (Peddicord et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sharma \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The logistic regression models effectively quantified the influence of key factors, providing a robust framework for risk assessment (Delfani et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Future research should leverage genomic tools to identify QTL for resistance (Prasanna et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wisser et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and integrate remote sensing and machine learning to enhance early warning systems (Terentev et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Participatory research involving farmers will be critical for scaling solutions sustainably (Jindo et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Olita et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study elucidates the multifactorial nature of NLB epidemics in Ethiopian maize systems. Agro-ecological zonation emerged as a dominant epidemiological driver, with moist highland and semi-moist mid-altitude zones exhibiting the highest disease pressure. Cultivar selection proved pivotal, with resistant hybrids like BH661 demonstrating strong mitigation potential. Agronomic practices profoundly modulated disease expression, with late planting identified as the most critical risk factor. Diversified cropping systems and balanced fertilization significantly reduced disease risk. The temporal variability and predominance of localized factors necessitate decentralized, participatory approaches to disease management. Collectively, these results demonstrate that effective NLB management requires integrating resistant cultivars, optimized planting schedules, balanced nutrition, and diversified cropping systems tailored to specific agro-ecological contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of interests\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eAll authors contributed to the preliminary analysis, manuscript drafting, and critical review. Mesele Haile led the study conception, design, data collection, and initial manuscript draft. Zelalem Bekeko and Dagne Wegary contributed to study design, final data analysis, and manuscript finalization. Habtamu Terefe and Suresh L.M. assisted in data organization, figure preparation, and initial drafting. All authors reviewed, provided feedback, and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis study was part of a Ph.D. program at Haramaya University, supported financially by the Ethiopian Institute of Agricultural Research (EIAR) and technically by Haramaya University. The authors thank the participating farmers, research teams at Bako and Hawassa maize research centers for field data collection, and anonymous reviewers for their contributions.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eWith the exception of meteorological data, the majority of the study's data reported in the manuscript are provided in the article. 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Biosci.\u003c/em\u003e, 15621\u0026ndash;15629.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZou, Y., Liu, Z., Chen, Y., Wang, Y., Feng, S. (2024). Crop Rotation and Diversification in China: Enhancing Sustainable Agriculture and Resilience. \u003cem\u003eAgriculture\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(9).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Agro-ecology, Cropping systems, Disease severity, Ethiopia, Integrated management, Maize cultivars, Spatio-temporal variation","lastPublishedDoi":"10.21203/rs.3.rs-9198560/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9198560/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eNorthern Leaf Blight (NLB), caused by \u003cem\u003eExserohilum turcicum\u003c/em\u003e, is a major constraint to maize production in Ethiopia. This study aimed to investigate the epidemiological drivers and spatial patterns of NLB across major maize-growing regions to inform integrated management strategies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA field survey was conducted across 18 districts in Oromia, Amhara, and SNNPR regions over two cropping seasons (2021\u0026ndash;2022), encompassing 1,080 farms. Disease severity was quantified using Percent Severity Index (PSI) and Lesion Area (LA). Spatial, agronomic, and environmental data were analyzed using chi-square tests, correlation analysis, and logistic regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePronounced spatial and temporal variations in NLB severity were observed. Oromia exhibited the highest overall disease pressure (PSI: 63.0%), followed by SNNPR (62.1%) and Amhara (60.6%). At the zonal level, Sidama recorded the most severe infections (PSI: 70.6%). Moist and semi-moist mid-altitude zones showed the highest disease pressure (PSI: 70.3%), attributed to cooler temperatures and higher rainfall (\u0026gt;\u0026thinsp;1,200 mm). Temporal trends showed higher severity in 2021 (PSI: 64.0%) than 2022 (60.2%). Cropping systems and cultivar selection significantly modulated disease outcomes. Sole cropping exacerbated severity (PSI: 68.4%), while intercropping reduced risk (PSI: 54.2%). Susceptible cultivars like BH540 (PSI: 77.1%) suffered severe infections, whereas resistant hybrids such as BH661 (PSI: 51.7%) mitigated impact. Logistic regression identified late planting (OR\u0026thinsp;=\u0026thinsp;42.53), susceptible cultivars (OR\u0026thinsp;=\u0026thinsp;24.51), and high-risk agro-ecology (OR\u0026thinsp;=\u0026thinsp;7.22\u0026ndash;16.07) as the top risk factors.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study highlights the multifactorial nature of NLB epidemics. Effective management requires spatially-targeted integrated strategies combining resistant cultivars, optimized planting schedules, diversified cropping systems, and balanced nutrition to enhance maize productivity and safeguard smallholder livelihoods in Ethiopia.\u003c/p\u003e","manuscriptTitle":"Epidemics of Northern Leaf Blight (Exserohilum turcicum) as influenced by Epidemiological Drivers and Agronomic Factors in the Major Maize-Growing Agro-Ecologies of Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-20 13:44:18","doi":"10.21203/rs.3.rs-9198560/v1","editorialEvents":[{"type":"communityComments","content":1}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a0e7ed1a-8de2-475b-997d-be5809f7ca5a","owner":[],"postedDate":"April 20th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"254645578681721603046558992674533333725","date":"2026-05-06T06:56:42+00:00","index":15,"fulltext":""},{"type":"reviewerAgreed","content":"95350797247295967332932369123244885405","date":"2026-05-05T11:47:15+00:00","index":14,"fulltext":""},{"type":"reviewerAgreed","content":"246781327520468764533274773635616403035","date":"2026-05-05T11:31:43+00:00","index":13,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T13:44:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-20 13:44:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9198560","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9198560","identity":"rs-9198560","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}