Spatial variability and key determinants of wilt complex disease: Insights from a survey in major lentil growing areas of Ethiopia

Spatial variability and key determinants of wilt complex disease: Insights from a survey in major lentil growing areas 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 Spatial variability and key determinants of wilt complex disease: Insights from a survey in major lentil growing areas of Ethiopia Tolesa Bedasa Abdisa, Chemeda Fininsa Gurmessa, Habtamu Terefe Yetayew, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6933637/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Lentil ( Lens culinaris ) is the most vital legume crop for dietary protein and income source to smallholder farmers in Ethiopia. However, its production and productivity are severely constrained by soilborne diseases, particularly the lentil wilt complex disease. The disease is caused by a combination of soilborne pathogens such as Fusarium oxysporum , Rhizoctonia solani , Sclerotium rolfsii , and Pythium species resulting in significant yield losses. Lentil wilt complex caused by Fusarium spp . is the predominant lentil wilt causal pathogen. The objectives of this study were to assess lentil wilt distribution, incidence, and determine its association with agroecological and agronomic cropping practices in Ethiopia. A survey of 170 lentil fields was conducted during the 2023 main cropping season in eight major lentil growing districts. Disease incidence data was analyzed using descriptive statistics and χ2-based correspondence analysis to map associations with independent variables. The association of disease incidence with biophysical factors were analyzed using logistic regression. The disease was present in all fields, with incidence ranging from 15.6% in Siyadebenawayu to 57.2% in Lume. The study showed that highly significant associations (P ≤ 0.001) between disease incidence and variables of district, altitude, planting date, soil type, drainage, seedbed preparation, and weeding practices. Plant population and growth stage significantly influenced outcomes (P ≤ 0.05), while previous crop, fungicide, and fertilizer application had no significant impact (P ≤ 0.05). Logistic regression showed higher wilt incidence in Lume (6.2 times) than Siyadebrenawayu, local varieties (6.2 times) than improved varieties, poorly drained (5.1 times) than well-drained soils, and black soil (2.6 times) than sandy loam. The findings indicated the need for targeted crop and cultural practices to reduce lentil wilt complex and improve sustainable production in Ethiopia's main lentil growing areas. Biophysical factors Cropping practices Disease incidence Lens culinaris Logistic regression Lentil wilt complex Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Lentil ( Lens culinaris Medik.,) is the most important crop in many pulse growing countries (Erskine et al., 2011 ; Montejano-Ramírez and Valencia-Cantero, 2024 ) with its origins in the Near East (Sarker, 2006 ; Pandey et al., 2024). This crop is highly valuable for the well-being of small-scale farmers, serving as sources of protein, cash, and animal feed (Absar et al., 2021 ; Suri et al., 2022 ). Lentil can fix nitrogen and contribute to improving soil fertility in the cereal-based crop production systems (Mesfin et al., 2020; Kumar et al., 2024). According to Durazzo et al. ( 2013 ), it has high antioxidants due to the presence of phytochemicals, such as phenolic acids, flavanols, saponins, and condensed tannins. Globally, lentil is annually cultivated in an area of 5 million hectares of land with a production of about 4.8 million tons (FAOSTAT, 2023 ). Ethiopia is considered as a center of diversity and the largest lentil producers in Africa (FAOSTAT, 2023 ). Mainly cultivated in Amhara, Oromia, and Tigray regions (Lijalem, 2009 ). Lentil is grown across a wide range of altitudes, from 1600 to 2700 meters above sea level (m.a.s.l,) and soil types during the main and small rainy seasons (Tolesa, 2018 ). It can grow in well-drained soil, which can range from sandy to clay-loam (Ozdemir, 2002). Lentil thrives in sandy-loam soils rich in phosphorus and potassium but are highly sensitive to waterlogging, which can quickly kill the crop (Brennan et al., 2002). Lentil productivity significantly increased from 0.6 t ha –1 in 2003 to 1.4 t ha –1 in 2018 in Ethiopia. However, between 2018 and 2021, the national average productivity on farmers' fields showed a slight overall decline (Tebkew and Bizuwork, 2023 ). The lentil productivity ranged from 0.208 t ha –1 in Morocco (lowest) to 2.6 t ha –1 in China (highest), with Egypt, New Zealand, Australia, and Tajikistan also exceeding 2 ha –1 , while countries like India, Canada, and the U.S. produced between 1–1.2 t ha –1 FAOSTAT ( 2023 ). Despite its multiple benefits, the production and productivity of lentil in Ethiopia is significantly lower (1.4 t ha –1 ) compared to its potential yields (3.6 t ha –1 ) under well-managed conditions (FAOSTAT, 2023 ). This yield gap is attributable to several biotic (plant diseases, insect pests and weeds) and abiotic (unfavorable temperature, soil fertility, moisture stress, waterlogging, and frost) factors (Nigussie et al., 2009) and limited genetic base of the local landraces (Takele et al., 2022 ). A recent survey result showed that diseases and lack of improved lentil variety’s seeds are the major lentil production constraints in Ethiopia (Lemma Zemedu, unpublished data of 2024, personal communication). In Ethiopia and elsewhere, about 20 lentil diseases have been reported (Negussie et al., 2008). The major biotic constraints of lentil are wilt/root rot complex and rust ( Uromyces viciae-fabae ) disease (Erskine et al., 2011 ; Williamson-Benavides and Dhingra, 2021). Lentil wilt complex is caused by co-occurrence of different fungal pathogens, including Fusarium oxysporum f.sp. lentis , collar rot ( Sclerotium rolfsii ), black root rot ( F. solani ), and wet root rot ( Rhizoctonia solani ), which together significantly adversely impact lentil cultivation (Chandra et al., 2019 ). However, the primary constraint to lentil production and productivity in Ethiopia is the wilt complex, primarily involving Fusarium spp. (Tebkew and Bizuwork, 2023 ). However, farmers still grow wilt-susceptible cultivars due to a lack of resistant varieties in Ethiopia (Chilot et al., 2016 ). Lentil wilt complex is primarily caused by Fusarium oxysporum f. sp. lentis and other soil-borne pathogens, is a multifactorial disease influenced by host susceptibility, pathogen variability, and environmental conditions including soil type, moisture, and temperature (Jiménez-Fernández et al., 2016 ). This complex disease produces overlapping symptoms, complicating accurate diagnosis. Infected plants initially exhibit wilting, often beginning with the lower leaves, followed by a progressive yellowing that spreads upward. Under field conditions, wilt complex symptoms in lentils are characterized by the wilting of older leaves, stunted plant growth, weakening and curling of lower leaves, which progressively affect the stem of the infected plant, ultimately leading to wilting and drying (Pouralibaba et al., 2017 ). A key symptom, including vascular browning and blockage near the stem base, is a feature of Fusarium infections, as the pathogen blocks the vascular system. Root rot and lesions on the stem and root collar are also common, weakening the plant and making it prone to lodging. In severe cases, individual plants or large field patches can die if favorable conditions, like warm and moist soil, persist. The disease is often exacerbated in fields with waterlogging conditions, as the pathogens remain in the soil (Pande et al., 2005 ; Jiménez-Fernández et al., 2016 ). According to Sara and Robert ( 2018 ), the combination of high temperatures and wet conditions probably favor disease severity. Monocropping leads to pathogen build (Kumar et al., 2021 ) whereas crop rotation with non-host reduces inoculum (Pande et al., 2005 ). Lentil wilt pathogen causes serious yield losses in different countries varying from 5‒72% in Syria (Bayaa et al., 1997 ), 10‒50% in Pakistan (Chaudhary et al., 2009 ), as high as 70% in Czechoslovakia (Bojdova and Sinsky, 1990 ), up to 50% in India (Nisa et al., 2021 ), and 10‒66% in Algeria (Belabid et al., 2002). Under favorable environmental conditions, the disease can cause 100% yield losses on susceptible lentil varieties. In 1991, an outbreak of lentil root rot in eastern Alberta resulted in up to 70% reduction in lentil yields (Hwang et al., 1994 ). This is an evident that lentil wilt complex epidemics are increasing and cause reduction to lentil production in many countries. Multiple efforts have been made to control lentil wilt complex, with resistant varieties being cost-effective. However, frequent emergence of new pathogen races regularly breaks down host resistance, and no released variety in Ethiopia is fully resistant to lentil wilt complex (Negussie et al., 2008). Chemical fungicides such as carbendazim and mancozeb have shown some efficacy against Fusarium spp. in vitro and in field trials (Singh et al., 2021 ), but widespread use is constrained by cost and environmental concerns. Breeding efforts in countries like India and Canada have identified moderately resistant genotypes (Kumar et al., 2015 ; Banniza et al., 2016 ), yet no single genotype offers durable resistance against the complex of pathogens, including Fusarium oxysporum f. sp. lentis , Rhizoctonia solani , and Sclerotium rolfsii . Integrated approaches using biocontrol agents like Trichoderma harzianum and Pseudomonas fluorescens show potential in controlled conditions (Mahanta et al., 2019 ), but field performance is inconsistent and context dependent. The situation calls for implementing urgent and comprehensive control options. Yet, to design effective management strategies for lentil wilt complex, it is crucial to keep record of information about the distribution of diseases in major cultivation areas and to understand the variability of the causing pathogens (Meftahi et al., 2019 ) as well as the disease. However, there is insufficient information on the distribution, relative importance, status, and estimates of yield loss attributed to the disease, and identity of the pathogens causing wilting of lentils in Ethiopia. In addition, the association of the disease with cropping systems and practices and environment factors are undetermined. Addressing these knowledge gaps and generating these data types are important for developing integrated lentil wilt management strategies in Ethiopia and other lentil growing areas worldwide. Therefore, the objectives of this study were to (1) assess the distribution, and incidence of wilt complex across major lentil-growing regions of Ethiopia; and (2) determine the association of disease epidemics with biophysical and agronomic factors that influence disease pressure across the study areas. 2. Materials and Methods 2.1. Description of the study areas The lentil wilt complex disease survey was conducted in three zones across eight major lentil growing districts (Ada’a, Bacho, Gimbichu, Ilu, Lume, Siyadebrena wayu, Minjar and Moretina Jiru) from early September to November during the 2023 main cropping season (Fig. 1 ). The geographic location (altitude and longitude), mean minimum and maximum temperatures, relative humidity, and total annual rainfall of the survey districts are presented in Table 1 . The latitudes and longitudes of the survey areas ranged from 35.71–10° N to 38.15–39.56° E. The districts are located at an altitude range of 1520–2700 m a.s.l. Predominantly the soil types were black and vertisols, with clay to sandy textures that influence water retention and drainage play key factors in disease expression. Major crops include tef, wheat, chickpea, grass pea, and lentil, often in rotation, which may maintain inoculum levels due to the presence of susceptible hosts. Poor drainage, particularly in black clay soils, combined with variable rainfall patterns during critical crop stages, likely intensifies disease pressure across surveyed zones. Table 1 Geographic location, altitudinal range, mean minimum and maximum annual temperatures, total annual rainfall, and relative humidity of survey areas in Ethiopia, during the 2023 main cropping season District Geographic location Temperature ( o C) Rainfall (mm) RH (%) Altitude (m.a.s.l.) Longitude Latitude Min. Max. Lume 1830─2253 39.12 8.32 12.38 28.27 140.92 68.42 Ada’a 1827─2274 38.95 8.73 13.38 26.27 118.27 68.42 Gimbichu 2256─2435 37.63 7.45 13.02 23.23 180.71 77.60 Bacho 2093─2135 39.20 8.2 13.19 21.96 281.91 84.87 Ilu 2042─2184 38.41 8.8 10.87 21.60 180.57 73.49 Minjar 1520─1780 39.42 8.92 13.62 25.64 112.40 63.57 Moretina Jiru 2638─2681 10.01 10.02 11.96 21.02 189.76 68.91 Siyadebrena wayu 2601─2700 39.07 9.78 11.96 21.02 182.46 68.91 Geographical ranges of the survey districts were recorded using a global positioning system. RH = Relative humidity; Max = Maximum annual temperature; and Min = Minimum annual temperature. (Source: Ethiopian Meteorological Institute, 2023) 2.2. Sampling procedures and sample units Survey areas such as East Shewa, and Southwest Shewa were selected from Oromia National Regional State, while North Shewa was considered from Amhara National Regional State. Districts were purposively selected from each zone based on lentil area coverage (CSA, 2023), and field accessibility by main and feeder roads. Three districts from each East Shewa (Ada'a, Gimbichu, and Lume), North Shewa (Siyadebrina Wayu, Minjar and Moretina-Jiru) zone and two districts from Southwest Shewa (Bacho and Ilu) zone were included in the survey. Districts were selected purposively based on historical data on the prevalence of lentil wilt complex disease, lentil production importance, and accessibility for repeated assessments. In each selected district, three to five farmers’ associations (FAs) were purposively selected in consultation with development (agricultural extension), proportional to lentil production intensity and cultivation area. A total of 170 sample fields were considered by randomly identifying five farmers' fields per FAs. The survey utilized questionnaires that was prepared in local languages (Afaan Oromo and Amharic) to collect lentil wilt disease, agroecological, and agronomic practices data from farmers with the support of development agents, and crop experts, telling us the lentil growers and potential areas. The questionaries were administered through face-to-face personal interviews. The survey was executed during the flowering and pod setting growth stages of the crop, as the highest infection rates were reported earlier during these stages (Das et al., 2022 ). To ensure a comprehensive evaluation of the disease, a systematic sampling approach was employed, considering the spatial clustering or aggregation pattern exhibited by the lentil wilt complex disease. The assessment of wilt in every FAs involved an initial random sampling, followed by subsequent sampling at intervals of 2–5 km from the initial field. Within each farmer's field, careful observations were made regarding the field's size to establish equidistant sampling points. The first quadrat for disease assessment was randomly chosen, and sampling was conducted within a 0.25 m 2 quadrat area in a ‘W’ fashion at five designated spots (Bebber et al., 2013 ). 2.3. Disease assessment Both healthy and infected plants within a sample quadrat were counted from each selected field displaying characteristic symptoms of lentil wilt complex. To assess the disease, all plants within each quadrat were considered as sampling units. To determine the prevalence of wilt complex, the number of infested fields were calculated as a percentage of the total number of fields assessed per district. The incidence of wilt complex disease was determined by counting the number of plants exhibiting typical symptoms in each quadrat, following the protocol developed by Das et al. ( 2022 ). These symptoms include progressive yellowing and drooping of lower leaves, sudden wilting of entire plants without foliar lesions, browning or blackening of vascular tissues, root rot, collar region discoloration, and poor root development. In advanced stages, partial or complete plant collapse and necrosis at the stem base are commonly observed. The formulae used to calculate the percentage of disease prevalence and incidence are as follows: $$\:\text{D}\text{i}\text{s}\text{e}\text{a}\text{s}\text{e}\:\text{p}\text{r}\text{e}\text{v}\text{a}\text{l}\text{e}\text{n}\text{c}\text{e}\:\left(\text{%}\right)=\:\frac{\text{N}\text{u}\text{m}\text{b}\text{e}\text{r}\:\text{o}\text{f}\:\text{f}\text{i}\text{e}\text{l}\text{d}\text{s}\:\text{s}\text{h}\text{o}\text{w}\text{i}\text{n}\text{g}\:\text{w}\text{i}\text{l}\text{t}\:\text{c}\text{o}\text{m}\text{p}\text{l}\text{e}\text{x}\:\text{s}\text{y}\text{m}\text{p}\text{t}\text{o}\text{m}\text{s}\:\text{p}\text{e}\text{r}\:\text{d}\text{i}\text{s}\text{t}\text{r}\text{i}\text{c}\text{t}}{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{n}\text{u}\text{m}\text{b}\text{e}\text{r}\:\text{o}\text{f}\:\text{f}\text{i}\text{e}\text{l}\text{d}\text{s}\:\text{v}\text{i}\text{s}\text{i}\text{t}\text{e}\text{d}\:\text{p}\text{e}\text{r}\:\text{d}\text{i}\text{s}\text{t}\text{r}\text{i}\text{c}\text{t}}\:\times\:100$$ $$\:\text{D}\text{i}\text{s}\text{e}\text{a}\text{s}\text{e}\:\text{i}\text{n}\text{c}\text{i}\text{d}\text{e}\text{n}\text{c}\text{e}\:\left(\text{%}\right)=\:\frac{\text{N}\text{u}\text{m}\text{b}\text{e}\text{r}\:\text{o}\text{f}\:\text{p}\text{l}\text{a}\text{n}\text{t}\text{s}\:\text{i}\text{n}\text{f}\text{e}\text{c}\text{t}\text{e}\text{d}\:\text{p}\text{e}\text{r}\:\text{q}\text{u}\text{a}\text{d}\text{r}\text{a}\text{t}}{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{n}\text{u}\text{m}\text{b}\text{e}\text{r}\:\text{o}\text{f}\:\text{p}\text{l}\text{a}\text{n}\text{t}\text{s}\:\text{i}\text{n}\text{s}\text{p}\text{e}\text{c}\text{t}\text{e}\text{d}\:\text{p}\text{e}\text{r}\:\text{q}\text{u}\text{a}\text{d}\text{r}\text{a}\text{t}}\times\:100$$ During the survey, farmers were personally consulted to gather insights into their knowledge of the disease, cultural practices and field histories. Lentil field geographical information (latitude, longitude, and altitude), crop growth stages, weeding practices, occurrence and status of lentil wilt complex disease, previous crop rotations, sowing dates, seedbed preparation methods, plant population, fertilizer and fungicide applications, soil type and drainage conditions were recorded. In addition, about twenty individual plant samples displaying typical symptoms of wilt complex disease were collected from each farming area for identification of the causal pathogen (s). These samples were carefully preserved in labeled paper bags, along with records of the collection location, sample number, and date. Lentil field biophysical data were collected using standardized scientific protocols. Geographic coordinates (latitude, longitude, altitude) were recorded using a GPS device. Crop growth stages, weeding practices, sowing dates, seedbed preparation, fertilizer and fungicide applications, and previous crop rotations were documented through field observation and farmer interviews. Lentil wilt complex occurrence was monitored by field scouting, with disease incidence and scored the wilted plants in the quadrat. Plant population was estimated via quadrat counts. Soil type and drainage conditions were assessed through field observation. 2.4. Data Analysis Descriptive analysis and χ 2 statistics were used to determine differences of lentil wilt complex incidence and among independent variables and variable classes. Disease incidence data were categorized into binomial qualitative groups following Yuen ( 2006 ) and Ali and Terefe (2021). Disease incidence was categorized into binary classes using thresholds of ≤ 20% and > 20% based on Iqbal et al. ( 2005 ) (Table 2 ). Contingency tables displayed the bivariate relationship between disease incidence and independent variables (biophysical factors). Logistic regression through the GENMOD procedure was employed to analyze the association of wilt complex incidence with biophysical factors using SAS (2014). The effects of independent variables on the disease of incidence were evaluated at three different stages. First, single-variable models were used to examine the relationship between disease incidence and each independent variable. Next, the association of each variable was validated by incorporating it at both the start and end of the logistic regression model. Finally, only variables showing significant associations in single and multiple models were included in a reduced multi-variable model for an in-depth analysis. The odds ratio and deviance reduction were calculated for each variable as it was added to the reduced multiple variable model (Yuen, 2006 ; Yitayih et al., 2021). Parameter estimates and their standard errors were calculated for both single and multi-variable models. These estimates were exponentiated to generate odds ratios, allowing for the interpretation of relative risks compared to a reference point (Yuen, 2006 ). Deviations compared models, and likelihood ratio tests (LRTs) evaluated variable significance using chi-square values (McCullagh and Nelder, 1989), identifying key factors influencing lentil wilt complex to inform targeted management strategies. Correspondence analysis was employed to explore and visualize the associations between lentil wilt complex incidence and various biophysical and cropping practice factors. This multivariate technique enables graphical representation of contingency tables, effectively revealing relationships between two sets of categorical variables of explanatory (rows) and response (columns) (Greenacre, 1984 ). In this study, disease incidence classes served as the response variables, while cropping practices and biophysical factors, like planting methods, soil type, drainage, previous crops, were treated as explanatory variables. Correspondence analysis plots were constructed based on Chi-square (χ²) distances, with each axis representing a dimension of variation within the dataset. The analysis was performed in r software using the FactoMiner and Factoshiny packages, with interpretation based on axis inertia, variable contributions, and point proximity to assess the strength and direction of associations (Greenacre, 1984 ; Serge et al., 1993 ). This approach facilitated a clearer understanding of how different agronomic and environmental factors relate to levels of disease incidence. 3. Results 3.1. General features of surveyed fields Lentil wilt complex disease was found in all lentil growing areas and identified as a major lentil production constraint during the survey. The disease symptoms observed in infected plants included wilting, yellowing, stunting and necrosis (Fig. 2 a‒d). The disease incidence varied with geographical location and environmental conditions. Altitudes in the survey areas ranged from 1520 m a.s.l. in Minjar to 2700 m a.s.l. in Siyadebrena Wayu. Local lentil varieties covered 79.4% of the fields, while improved varieties accounted only for 20.59% of total inspected farms. Farmers used to plant lentil in June, July, August, September and October, depending on rainfall distribution and cropping system. Early, mid, and late planting accounted for about 25.88%, 33.53%, and 40.59% respectively. Soil types varied, with black soil being predominant (55.29%), followed by light clay (24.71%) and sandy loam (20.0%) soils. Regarding weed management practices, 46.47% of the fields were had weedy, 40.59% were weed-free, and 12.94% experienced weed re-emergence. Seedbed preparation was primarily flat (58.82%), with ridge furrow methods used in 41.18% of the observed fields. Tef ( Eragrostis tef ), wheat, chickpea, lentil, and grass pea were the most common crops grown in the survey areas. Yet, wheat (65.88%) and tef (33.53%) were predominant among previously grown crops in the survey fields. Farmers rarely applied seed treatment chemicals (2.35%), while most of the farmers applied fertilizers (53.53%) to grow lentil. During the survey lentil was at pod filling (50.0%), flowering (26.47%), and pod forming (23.53%) growth stages (Table 2 ). Although inspected fields were affected by lentil wilt complex, farmers in the studied areas lacked the use of proper management practices to control the disease. Many farmers referred to the disease as wagi (rust) rather than recognizing it as lentil wilt. In the surveyed areas, none of the growers used seed treatment fungicides for lentil wilt disease management due to a lack of awareness about their availability. While some farmers applied fungicides such as Ridomil Gold, mancozeb, and propiconazole, they often used them without fully understanding their purpose or effectiveness (Table 2 ). 3.2. Lentil wilt prevalence and incidence The survey revealed a 100% prevalence of lentil wilt complex across all districts, with varying incidence levels. Among districts, Lume exhibited the highest incidence (57.18%), while Siyadebrena Wayu had the lowest disease incidence (15.65%). Lentil fields planted early (from June 26 to July 30, 2023) showed a significantly higher incidence of 49.62% than those planted late (from September to October 5, 2023) in the season, which recorded only disease incidence of 23.80% (χ 2 = 34.03, P < 0.001). A higher lentil wilt incidence (39.61%) was obtained from weedy fields than weed free fields (24.18%). A ridged-furrow planting method reduced disease incidence by 18.12% as compared to flatbed planting, which experienced a wilt incidence of 60.33%. Local varieties exhibited a significantly higher disease incidence (35.48%) than improved varieties such as Alemaya and Derash (18.12%). Agronomic practices, such as effective weed management, substantially reduced disease incidence, with incidence compared to 39.61% in weedy fields (χ 2 = 11.359, P < 0.00342). Disease incidence varied significantly across districts, ranging from the lowest at Siyadebrenawayu (15.65%) to the highest at 57.18% in Lume district (χ 2 = 43.177, P = 3.084e-07). The Ada’a district recorded the second highest average incidence of 40.76%, followed by Minjar district (32.9%) (Table 2 ). Altitudinal variations also influenced disease occurrence, with fields at 1500–2000 meters experienced the highest incidence (38.96%), while fields above 2501 meters had the lowest (18.68%) incidence (χ 2 = 25.34, P = 3.142e-06) (Table 3 ). Disease incidence was also found to be influenced by crop growth stages, peaking during flowering (46.62%) and dropping to 25.24% during the pod-filling growth stage. Soil type and drainage were key factors affecting disease prevalence and incidence. Black soil had the highest incidence (39.75%), followed by light clay (33.75%) and sandy loam (21.69%) (Table 2 ). Poor drainage further exacerbated disease incidence, with poorly drained fields reporting 60.55% incidence compared to 22.79% in well-drained fields. Ridge furrow bed planting significantly reduced incidence (18.33%) compared to flatbed planting (41.35%) (χ 2 = 39.50, P = 2.641e-09). Similarly, fields with higher plant population (> 100 plants per 100 m²) exhibited lower disease incidence (30.33%) than sparsely populated lentil fields. Fertilized fields showed lower incidence (24.38%) than non-fertilized fields (38.44%). Despite slight reductions in disease incidence, fields with fungicide applied had the lowest (31.92%) disease incidence compared to fields with no fungicide (33.92%). Additionally, fields previously planted with wheat showing higher incidence (33.26%) compared to those following tef (29.39). Table 2 Disease contingency table for logistic regression analysis and mean disease incidence of lentil wilt complex for different independent variables in eight districts (n = 170) in major lentil growing areas of Ethiopia, during the 2023 main growing season Variable Variable class Number of fields Disease incidence (%) Incidence (mean ± SE) Variable Variable class Number of fields Disease incidence (%) Incidence (mean ± SE) ≤ 20 > 20 ≤ 20 > 20 District Ada'a 22 7 15 40.76 ± 4.29 Planting date Early 44 8 36 49.62 ± 3.64 Becho 10 3 7 29.81 ± 3.88 Medium 57 24 33 28.67 ± 2.48 Gimbichu 25 11 14 27.32 ± 4.33 Late 69 36 33 23.80 ± 1.84 Ilu 14 4 10 32.25 ± 4.53 Soil type Black 94 33 61 39.75 ± 2.34 Lume 24 3 21 57.18 ± 4.54 Light clay 42 28 14 33.75 ± 2.4 Minjar 25 8 17 32.9 ± 3.64 Sandy loam 34 10 24 21.69 ± 3.51 Moratinajiru 25 16 9 21.71 ± 3.29 Soil drainage Good 128 66 62 22.79 ± 1.18 Siyadebrenawayu 25 18 7 15.65 ± 1.56 Poor 42 7 35 60.55 ± 2.55 Altitude 1500–2000 37 8 29 38.96 ± 3.41 Weed practice Weed free 69 37 32 24.18 ± 1.95 2000–2500 83 30 53 36.91 ± 1.85 Weed present 79 22 57 39.61 ± 2.69 ≥ 2501 50 34 16 18.68 ± 23.0 Re-emerged 22 8 14 28.65 ± 3.96 Variety Improved 36 26 10 18.12 ± 2.61 Bed type Flat 100 20 80 41.35 ± 1.65 Local 134 41 94 35.48 ± 1.85 Ridge furrow 70 47 23 18.33 ± 1.45 Plant population > 101 122 47 75 30.33 ± 1.32 Previous crop Tef 58 19 39 29.39 ± 1.89 ≤ 100 48 22 26 32.56 ± 1.61 Wheat 112 49 63 33.26 ± 2.3 Growth stage Flowering 45 10 35 46.62 ± 3.66 Fungicide Applied 5 3 2 31.92 ± 1.6 Pod filling 85 42 43 25.24 ± 1.85 Not applied 166 67 99 33.92 ± 1.68 Pod forming 40 17 23 30.36 ± 3.05 Fertilizer Applied 91 21 70 24.38 ± 2.08 Not applied 79 46 33 38.44 ± 1.3 Planting date referred to as early = end of June to July; medium = August planting; and late = early September to October. Plant population was determined within 1 m 2 quadrat where low refers to ≤ 100 lentil plants m − 2 and high refers to > 101 plants m − 2 . Good and poor soil drainages are defined as well drained soil moisture and poorly drained soil moisture, respectively. The soil type classification was made based on visual observation where black is defined as heavy black or deep dark color, and not well-drained soil type; light clay soil is better drained soil type than the black soil; and sandy loam is defined as light dark soil in color and well-drained soil type. 3.4. Correspondence among independent variables and disease incidence The multiple correspondence analysis plot revealed variables associated with high disease incidence include early planting date (PD1), poor soil drainage (SD2), heavy or black soil type (ST1), light clay soil type (ST2), and flat seedbed preparation (SBP1). Agronomic practices such as low plant population (PP2), lack of fertilizer (FT2) and fungicide application (FG1), as well as weedy fields (WP2) and re-emerged weeds (WP3), were also strongly aligned with high disease occurrence. In addition, local varieties (VR2), flowering growth stage (GS1), growing in low-altitude areas (AL1), and in districts such AR5 (Lume), AR1 (Ada’a), Minjar, AR4 (Ilu), and AR2 (Becho), were clustered near the high disease dimension, indicating their potential contribution to disease vulnerability (Figs. 3 and 4 ). These altitudinal trends complement the spatial patterns observed in areas (AR) and zones (ZN), indicated as high-risk hotspots requiring prioritized interventions like improved drainage and improved (resistant) varieties (Fig. 4 ). Similarly, soil drainage (χ² = 46.932, P < 0.0001), district (χ² = 43.177, P < 0.0001), and seed bed preparation (χ² = 39.504, P < 0.0001) showed the strongest effects or highly significant associations with disease incidence (Table 3 ). Conversely, variables associated with low disease incidence include late planting date (PD3), good soil drainage (SD1), and sandy loam soil type (ST3), along with ridge-furrow seedbed preparation (SBP2). Weed-free fields (WP1), use of fertilizers (FT1) and fungicides (FG2), higher plant population density (PP1), and improved varieties (VR1) were closely associated with low disease incidence (Fig. 3 ). These factors were observed in high-altitude areas (AL3) and in districts like Minjar (AR6), Muratinajiru (AR7), and Siyadebrenawayu (AR8) (Fig. 4 ), which appeared clustered near the low disease incidence point on the multiple correspondence analysis plot, suggesting their protective role against lentil wilt complex. The alignment of correspondence analysis with regression results reinforces the validity of the identified disease drivers. Both methods consistently associate high disease incidence with early planting, poor drainage, flat seedbeds, low plant population, and absence of inputs (fertilizer and fungicide), while low incidence aligned with improved varieties, ridge-furrow beds, and good drainage. This convergence of multivariate and statistical approaches enhances confidence in the findings and supports the robustness of the identified factors influencing lentil wilt complex. Table 3 Correspondence analysis of lentil wilt complex disease incidence in relation to cropping practices and agroecological factors Independent variable Degrees of freedom Chi-square (χ 2 ) P-value Zone 2 12.811 0.0017 District 7 43.177 < 0.0001 Growth stage 2 10.678 0.0048 Planting date 2 14.482 0.0007 Variety 1 22.235 < 0.0001 Altitude 2 25.341 < 0.0001 Soil type 2 19.979 < 0.0001 Soil drainage 2 46.932 < 0.0001 Weeding practice 2 11.359 0.0034 Seed bed preparation 2 39.504 < 0.0001 Previous crop 1 1.8576 0.173 Fertilizer application 1 21.144 < 0.0001 Fungicide application 1 0.67396 0.412 Plant population 2 0.94689 0.623 P < 0.0001 = Highly significant, P < 0.01 = Significant, P ≥ 0.05 = Not significant 3.3. Association of lentil wilt complex disease incidence with biophysical factors The logistic regression analysis revealed that most of the biophysical variables had a strong association with incidence of lentil wilt complex (Table 4 ). Independent variables, including districts, altitude, lentil variety, soil type, soil drainage, planting date, seedbed preparation, and weed management practice, were found to have highly significant (P < 0.0001) associations with the incidence of lentil wilt complex when entered first and last into the logistic regression models. Previous crops, crop growth stage, and plant population also showed a significant (P < 0.05) association with disease incidence when entered first and last into the models. Fungicide application was significant (P = 0.0205) when it entered first into the model, but it lost its significant (P = 0.9304) association when it entered last into the model, while fertilizer application was non-significant in both models (Type 1, P = 0.2894 and Type 3, P = 0.0717) (Table 4 ). Table 4 Logistic regression model for lentil wilt complex disease incidence and likelihood ratio test on independent variables in major lentil growing areas in Ethiopia during the 2023 cropping season Independent variable df Lentil wilt complex incidence, LRT a Type 1 analysis (VEF) Type 3 analysis (VEL) DR Pr > χ 2 DR Pr > χ 2 District 7 179.1 < 0.0001 75.6 < 0.0001 Altitude 2 172.8 < 0.0001 72.0 < 0.0001 Previous crop 1 116.6 0.0078 72.0 0.0096 Crop growth stage 2 167.7 < 0.0001 71.8 0.0198 Lentil variety 1 122.6 < 0.0001 39.9 < 0.0001 Soil type 2 138.9 < 0.0001 43.9 < 0.0001 Soil drainage 1 102.2 < 0.0001 38.2 < 0.0001 Planting date 2 157.0 < 0.0001 47.5 < 0.0001 Seedbed preparation 1 98.9 < .0001 38.3 < 0.0001 Plant population 1 117.3 0.0061 39.7 0.0203 Weeding practice 2 160.7 < 00.0001 51.7 < 0.0001 Fungicide application 1 157.4 0.0205 51.4 0.9304 Fertilizer application 1 118.7 0.2894 50.7 0.0717 a LRT = Likelihood ratio test; VEF = Variables entered first into the model; VEL = Variable entered last into the model; DR = Deviance reduction; Pr = Probability of an χ 2 value exceeding the deviance reduction; χ 2 = Chi square; df = Degrees of freedom The probability of a high (> 20%) wilt complex disease incidence was 6.2 and 1.91 times greater in Lume and Ada’a districts than Siyadebrinawayu district. It was also 2.67 and 1.5 times higher in low and mid altitudes than in high altitudes (≥ 2001). Local varieties had a 6.18 times higher risk or disease pressure than improved ones. The probability of incidence exceeding 20% was 1.97 and 1.62 times higher during flowering and pod formation than during pod filling growth stage. There were about 2.01 and 1.75 times higher probability that lentil wilt complex incidence would exceed ( ≥ 20%) in early and medium than late planting date, respectively. The probability of the occurrence of high (> 20%) lentil wilt complex incidence was 2.64 and 1.37 times higher than sandy loam in black and light clay soil tpe, respectively. Moreover, poorly drained soils had a 5.05 times higher risk than well-drained soil (Table 5 ). Table 5 Analysis of deviance, natural logarithms of odds ratio, standard error of wilt complex disease incidence and likelihood ratio test on independent (added) variables in reduced regression model. Added variable a Residual Deviance b Lentil wilt complex, LRT c Variable class Estimate Log e (odds ratio) d SE e Odds ratio df DR Pr > χ 2 Intercept 229.9516 – – – Intercept –0.213 0.48 – District 179.1302 7 50.82 < 0.0001 Ada'a 0.647 0.35 1.91 Becho 0.061 0.09 1.06 Gimbichu 0.053 0.08 1.06 Ilu 0.078 0.24 1.08 Lume 1.823 0.52 6.20 Minjar 0.078 0.24 1.08 Moratina Jiru 0.042 0.06 1.04 Siyadebrena wayu 0 * 1 Altitude 172.8269 2 3.80 < 0.0001 < 1500 0.983 0.48 2.67 1501–2000 0.397 0.38 1.50 ≥ 2001 0 * 1 Lentil variety 122.9892 1 15.91 < 0.0001 Local 1.822 0.74 6.18 Improved 0 * 1.00 Crop growth stage 167.6591 2 8.53 < .0001 Flowering 0.676 0.37 1.97 Pod forming 0.482 0.33 1.62 Pod filling 0 * 1 Plant population 117.2506 1 7.53 0.0061 ≥ 100 –0.476 0.34 0.62 < 100 0.000 0.00 1 Planting date 156.9673 2 8.79 < 0.0001 Early 0.697 0.38 2.01 Medium 0.563 1.45 1.75 Late 0 * 1 Soil type 138.8950 2 17.15 < 0.0001 Black 0.972 0.45 2.64 Light clay 0.316 0.29 1.37 Sandy loam 0 * 1 Previous crop 176.6245 1 2.60 0.0081 Tef –0.782 0.39 0.46 Wheat 0 * 1 Soil drainage 102.2218 1 14.16 < 0.0001 Poor 1.620 0.43 5.05 Good 0 * 1 Weeding practice 160.6623 1 2.10 < 0.0001 Weed present 0.496 0.48 1.64 Weed free 0 * 1 Seedbed preparation 98.8915 1 2.88 < 0.0001 Flat 0.794 0.86 2.21 Ridge furrow bed 0 * 1 a Variables added into the model in order of presentation in the table. b Unexplained variations after fitting the model; c LRT = Likelihood ratio test; DR = Deviance reduction; Pr = Probability of an χ2 value exceeding the deviance reduction; χ2 = Chi square. d Estimates from the model with all independent variables added; e SE = standard error; * Reference group. 4. Discussion Lentil wilt complex is the most devastating lentil disease, and it was found to be widely distributed and prevalent in all fields per each district inspected during the survey. The disease incidence was varied among district, altitude, lentil variety, soil type, soil drainage, planting date, seedbed preparation, weeding practices previous crops, crop growth stage, and plant population (Table 4 ). Similarly, Das et al. ( 2022 ) reported that wilt pathogen incidence can vary based on factors such as soil type, weather conditions, cropping patterns, cultivated varieties, and agroclimatic conditions. These factors may support sporulation and conidia germination, infection, establishment and development of lentil wilt complex. A previous study in Uttar Pradesh, India, indicated that variable in wilt incidence across cultivated fields, which has been attributed to differences to location specific factors such as climate, crop variety, and soil type (Bekele, 2007). The current study indicated that district, black soil type, flatbed planting, low altitude zones, use of local varieties, early planting, weed-infested fields, and previous cultivation of wheat were strongly associated with the lentil wilt complex disease incidence, and had significant contribution to the development of the lentil wilt complex disease epidemics. The incidence of lentil wilt complex was closely linked to agroecological and environmental factors. Elevated disease levels occurred in low-altitude districts such as Lume, Ada’a, and Minjar, where higher temperatures, poor soil drainage, and black soils created conditions conducive to pathogen persistence and infection. Lume recorded the highest incidence (57.18%), linked to high temperature (28.27°C max), low rainfall (140.92 mm), and poor drainage. Conversely, cooler, higher-altitude areas like Moretina Jiru and Siyadebrina Wayu (≥ 2600 m.a.s.l.) with lower temperatures (~ 21°C max) and better drainage had the lowest incidence (< 22%). These findings align with previous reports indicating that wilt complex and other soilborne diseases are exacerbated by warm temperatures, excessive soil moisture, and poor aeration (Nasir et al., 2003). Ali and Terefe (2021) further emphasized that factors like district, soil type, moisture levels, crop growth stage, and plant density significantly influence Fusarium wilt incidence in chickpea. Furthermore, diverse environmental conditions across zones play a crucial role in the varying incidence of lentil wilt epidemics from one district to another (Singh et al., 2020 ). The high incidence of lentil wilt complex in Lume district can be attributed to several factors. The district likely has poor soil drainage, as black soil (associated with higher disease incidence) and flatbed planting (41.35% incidence) were common, creating waterlogged conditions favorable for fungal pathogens. The current study confirmed that black soil was found to have the highest association with higher wilt incidence (39.75%) and a 2.64 times greater risk of disease incidence than sandy loam soil. This can be attributed to its heavy and poorly drained nature, which favors pathogen survival and proliferation. Previous studies (Geletu et al., 1994 ; Rachana, 2002 ) have also linked black soil high moisture retention to the aggravation of wilt complex pathogens. Similarly, Yimer et al. ( 2018 ) indicated a higher incidence of chickpea wilt and root rot in dark black soils. In flat planting systems, excessive soil moisture creates anaerobic conditions that restrict oxygen availability while increasing carbon dioxide concentration in the root zone, thereby establishing an environment highly favorable for the proliferation of moisture-loving fungal pathogens. These hypoxic conditions compromise root respiration and functionality, ultimately reducing crop productivity and predisposing plants to root rot and wilt diseases (Midmore, 2015). In this study, higher lentil wilt complex incidence was observed in low- and mid-altitude areas, where the risk was 2.67 times and 1.50 times greater than high altitude areas, respectively. In a related disease, Asfaw and Negash (2020) found the highest incidence of chickpea wilt/root disease (41.55%) in altitudes ranging 1952‒2200 m a.s.l., while the lowest incidence (17.5%) was obtained at altitudinal ranges 2393‒2474 m a.s.l. This finding is likely due to the prevailing warmer conditions at low altitudes, which are reported to favor higher disease intensity compared to cool environmental conditions. Warmer and drier conditions are favor for pathogen initiation, multiplication, and survival (Singh et al., 2021 ). Rao (2014) and Mastewal et al. (2022) also noted that elevated temperatures at lower altitudes significantly contribute to wilt symptom development, with a more pronounced effect than cooler conditions. In the present assessment, varietal differences further influenced disease levels, with the continued use of local cultivars likely promoting pathogen inoculum buildup and sustaining high disease pressure. Fields planted with local varieties showed significantly higher disease incidence (35.48%) than improved ones, likely due to their genetic susceptibility and continued use, which facilitates inoculum buildup (Mengistu and Negussie, 1994 ; Assfaw and Negash, 2020 ; Nazneen et al., 2024 ). This suggests that improved varieties may possess partial resistance or enhanced vigor that helps suppress wilt progression, consistent with previous studies advocating the use of resistant genotypes as a primary management strategy (Bayaa and Erskine, 1998). Similar trends were observed by Mengistu and Negussie ( 1994 ) and Assfaw and Negash ( 2020 ), who reported that chickpea wilt incidence reaching up to 100% in local varieties while improved ones recorded only 21% wilt incidence in northwestern Ethiopia. This variation could also be attributed to the continuous cultivation of susceptible lentil varieties, which promotes inoculum buildup and increases disease pressure, implying that continuous cropping is largely practiced in the traditional intensive agriculture system, resulting in the high incidence of soil-borne diseases (Hu et al., 2023 ). Moreover, access to obtain improved lentil varieties was limited for most farmers in the study areas. This has caused low seed replacement rates and the continued use of disease-susceptible varieties, which increased fungal inoculum and disease pressure. Hence, farmers primarily relied on local lentil varieties from uncertain sources, raising the risk of disease outbreaks. In this regard, Setotaw ( 2006 ) indicated that only about 9% of lentil growers in Ethiopia’s central highlands adopted improved varieties, although improved lentil varieties reported to lower disease rates (Assfaw and Negash, 2020 ). During the survey, it was observed that planting dates also significantly affected wilt incidence. Fields sown early (end of June to July) showed the highest disease incidence (49.62%), while late planting (September to October) had the lowest (23.80%). Early planting may expose crops to higher temperatures and prolonged soil moisture, which favor pathogen multiplication and survival (Nasir et al., 2003). In contrast, late sowing might escape peak pathogen pressure due to cooler and drier conditions during critical growth stages (Singh and Kapoor, 1993). According to Tebkew and Bizuwork ( 2023 ), early planting, depending on soil types and the maturity period of the varieties, can encounter excessive moisture (waterlogging), which promotes wilt and root rot diseases that may destroy the crop. High mean lentil wilt incidence was highly associated with flowering and pod forming growth stages than the pod filling growth stage, which was likely 1.6‒times risky at the later growth stage. This trend aligns with previous findings indicating that the flowering stage is critical for vascular wilt pathogens due to peak physiological activity and nutrient flow, which favor rapid pathogen colonization (Bayaa and Erskine, 1998). During this stage, plant stress and open vascular tissues may increase susceptibility to pathogens such as Fusarium spp. and Rhizoctonia spp. Yan ( 2012 ) also reported that the faba bean gall disease epidemic reached its peak during the flowering and pod formation stages but declined by the late podding. The current findings are consistent with Das (2022) and Chaudhry et al. ( 2007 ) who reported that F. oxysporum f. sp. ciceri infects chickpea during seedling, flowering, and pod forming growth stages, with highest mortality typically occurring during the flowering and podding growth stages under conditions of sudden temperature rise and water stress. This might be attributed to the progression of wilt from early stages leading to higher individual plant death, coupled with the plants shifting in resources translocation during flowering toward reproductive processes to produce seeds. In this study, weed management practices showed a strong association with lentil wilt complex incidence. Fields with poor or no weeding had significantly higher disease levels compared to weed-free fields. In this study, wilt incidence was 39.61% in fields with weeds and only 24.18% in weed-free fields, indicating that weed presence contributes to disease pressure. Weeds compete with lentil plants for nutrients, water, and light, weakening crop vigor and making plants more susceptible to infection. Broadleaf weeds, particularly from the Solanaceae family, exacerbate disease incidence by acting as alternate hosts for wilt disease. Previously Zewde et al. (2007) and Sahile et al. ( 2008 ) reported that heavy weed infestation can weaken crop vigor by competing for essential resources, thereby creating favorable conditions for disease development and observed across various host-pathogen systems. Eshetu et al. ( 2013 ) similarly observed higher wilt complex incidence in weed infested faba bean fields. On the other hand, the primary factor contributing to the low lentil yield is inadequate weeding or pest control (Bejiga and Degago, 2000 ). In contrast, weed-free fields showed a reduced incidence by minimizing competition for water and nutrients, enhance soil drainage, and promote better air circulation under the canopy and suppressed wilt development by reducing alternative pathogen hosts and alleviating crop competition in align with Gudero (2018). Surprisingly, the incidence of lentil wilt complex was influenced by previous crop history, with fields previously planted with wheat showing higher disease levels than those with tef, likely due to enhanced inoculum buildup. Fungicide use had limited impact, as disease incidence remained similar between treated (31.92%) and untreated (33.92%) fields, possibly due to resistance or misapplication. Farmers commonly used non-specific fungicides such as Ridomil Gold and mancozeb without proper guidance, often targeting unrelated diseases. This aligns with Daraj et al. (2017), who emphasized that poor crop management and use of susceptible varieties contribute to wilt complex development and spread. Conversely, low lentil wilt complex incidence was consistently linked to improved varieties, high-altitude environments, sandy loam soils, late planting, pod-filling stage, ridge-furrow planting, weed-free fields, and tef as the preceding crop. Improved lentil varieties recorded significantly lower disease levels (18.12%), likely due to genetic resistance and better agronomic traits (Ghosh et al., 2013 ; Assfaw and Negash, 2020 ). High-altitude areas exhibited reduced disease incidence due to cooler temperatures that suppress pathogen activity (Mastewal et al ., 2022). Also, sandy loam soils, with better aeration and drainage, also limited pathogen proliferation and supported healthy root systems (Mesfin and Gebru, 2020 ). Late planting minimized exposure to early season moisture and reduced pathogen pressure (Navas-Cortés et al ., 1998). Ridge-furrow planting enhanced drainage and aeration, significantly lowering disease incidence, as confirmed by Sharma et al. ( 2018 ) and Tolesa and Asrat ( 2019 ). Moreover, tef as a preceding crop was associated with reduced wilt levels, indicating that crop rotation with non-hosts helps suppress soilborne inoculum. The logistic regression and correspondence analyses revealed that district, variety type, planting date, crop growth stage, seedbed type, weed infestation, soil type, drainage condition, and preceding crop were significantly associated with lentil wilt complex incidence and played critical roles in disease development. Certain variables, such as poor drainage, flat planting, local varieties, and early planting, had stronger contributions to increased disease incidence than others (Tables 2 – 4 ). The model highlighted the relative importance of each factor, confirming that lentil wilt epidemics are shaped by interactions among multiple agroecological and management-related variables. Recognizing the influence of these factors is essential for developing effective and sustainable disease management strategies tailored to local conditions. 5. Conclusion The study indicated that lentil wilt complex is the most prevalent, widely distributed and is a major problem across major lentil growing districts of Ethiopia. The results revealed that the disease incidence varied with districts, altitudinal ranges, time of planting, crop growth stage, varieties grown, preceding crops, soil type, soil drainage condition and weeding practice. This study identified key biophysical and agronomic factors significantly associated with the incidence of lentil wilt complex across major production areas in Ethiopia. Logistic regression and correspondence analyses consistently showed that high disease pressure was strongly linked to poor soil drainage, flatbed planting, early sowing, local cultivars, black soil types, and fields with weed infestation and wheat as the preceding crop. These factors contribute to favorable conditions for pathogen survival, buildup, and infection, and should be prioritized in wilt complex management strategies. Conversely, lower disease incidence was consistently observed under improved lentil varieties, ridge-furrow planting systems, late sowing dates, high-altitude zones, pod-filling stages, sandy loam soils, and in fields following tef as a rotation crop. These practices and environmental conditions can be recommended as components of an integrated disease management (IDM) package to suppress lentil wilt complex in affected regions. Future research should focus on refining these findings through multi-location field experiments, exploring the genetic resistance of improved varieties against complex pathogens, and assessing the role of microbial soil health and crop rotations in reducing pathogen inoculum. Further work is also needed to evaluate farmer practices, enhance fungicide targeting, and promote knowledge-based interventions for sustainable disease control. Declarations Conflict of interests We declare that there are no known financial conflicts of interest or personal relationships that could have affected the research presented in this paper. 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A., (2006). The Fusarium Laboratory Manual. Wiley-Blackwell Publishing Professional, Ames, IA, USA, 212. http://dx.doi.org/10.1002/9780470278376. Lijalem, K., (2009). Improving Production and Productivity of Chickpea and Lentil in Ethiopia Production Manual. Melkasa, Ethiopia. Mahanta, D., Bora, S., & Deka, P., (2019). Biocontrol potential of Trichoderma harzianum and Pseudomonas fluorescens against soil-borne pathogens of lentil under controlled conditions. Biocontrol Science and Technology , 29(10), 951–965. https://doi.org/10.1080/09583157.2019.1635081. Meftahi, M., Nourollahi, K., & Mirabbalou, M., (2019). Genetic diversity of Ascochyta lentis in Ilam and Kermanshah provinces using SSR Markers. DOI: 10.22043/mi.2020.122233. Mehmood, Y., Khan, M.A., Nazir, J., Muhammad, B., & Arif, J., (2013). Effect of soil and environmental factors on chickpea wilts disease caused by Fusarium oxysporum f.sp. ciceris . Pak. J. Phytopathol . 25:52-58. Mengistu, H., & Negussie, T., (1994). Chickpea and lentil diseases research in Ethiopia. In : Proceedings of Cool-season Food legumes of Ethiopia: The First National Cool-season food Legumes Review Conference (Asfaw T, Bejiga G, Saxena MC, Solh MB, (Ed.)., 16-20 December 1993. Addis Abeba, Ethiopia: ICARDA/Institute of Agricultural Research. ICARDA, Aleppo. 346-366. Mesfin, B., & Gebru, T., (2020). Role of soil types in crop health. African Journal of Soil Science. 20 (1): 24 - 26. Midmore, D, Jansen, H., & Dumsday, R., (2015). Soil erosion and environmental impact of vegetable production in the Cameron Highlands, Malaysia. Agric. Ecosyst. Environ . 60 (1): 29 - 46. Montejano-Ramírez V., & Valencia-Cantero, E., 2024. The importance of lentil: an overview. Agriculture 14, 103; https://doi.org/10.3390/agriculture14010103. Nasir, A., & Bretag, T., (2003). Influence of sowing time on Fusarium wilt in lentils. Australian Journal of Experimental Agriculture , 37(4), 517–524. Nazneen, H., Das, R., Das, A., Dutta, S., Bhattacharya, S., Patar, S., Roy, S., Gupta, S. & Kumar, S., (2024). Disease spectrum and its molecular characterization in the lentil production system of lower Indo Gangetic plains. Front. Plant Sci . 15:1199016. doi: 10.3389/fpls.2024.1199016. Negussie, T., Pretorius, Z., & Bender, C., (1998). Components of rust resistance in lentil. Euphytica , 142: 55-64. Nigussie, T., Seid, A., Derje, G., Tesfaye, B., Chemeda, F., Adane, A., Abiy, T., Fekede, A., & Kiros, M., (2008). Review of Research on Diseases Food Legumes. In : Abraham Tadesse (Eds). Increasing crop production through improved plant protection. 1:85-124. Nisa, S., Ahmad, S., & Ali, M., (2021). Impact of Fusarium wilt on lentil production and management strategies in India. International Journal of Agricultural Sciences , 17(2), 85–93. https://doi.org/10.15740/HAS/IJAS/17.2/85-93. Pande, S., Chen, W., & Ford, R., (2005). Influence of crop rotation on soil-borne diseases of lentil. Plant Disease , 89(3), 297–302. https://doi.org/10.1094/PD-89-0297. Pouralibaba, H.R., Perez-de-Luque, A., & Rubiales, D., (2017). Histopathology of the infection on resistant and susceptible lentil accessions by two contrasting pathotypes of Fusarium oxysporum f.p. lentis European Journal of Plant Pathology . 48(1):53-63. Rachana, S., (2002). Effect of soil moisture and type on the development of root diseases in pulses. Journal of Plant Disease and Protection , 109(1), 15–21. Sahile, S., Ahmed, S., Fininsa, C., Abang, M., & Sakhuja, P.K., (2008). Survey of chocolate spot ( Botrytis fabae ) disease of faba bean ( Vicia fabae L.) and assessment of factors influencing disease epidemics in northern Ethiopia. Crop Protection . 27: 1457-1463. Sara, D., & Robert, M., (2018). Effects of environmental factors on wilt disease severity in legumes. Journal of Plant Pathology , 100(3), 645–652. Sarker, A., & Erskine. W., (2006). Recent progress in the ancient lentil. J. Agric. Sci. 144:19-29. SAS (Statistical Analysis Systems /Institute)., (2014). SAS/STAT user's guide, version 9.4 edition. SAS Institute Inc. Cary, North Carolina, USA. Serge, S., Nestor, F., Emmanuel, R., & Paul, S., (1993). A characterization of rice tungro epidemics in the Philippines from historical survey data, Plant Dis . 77 (4) 376–382. https://DOI:10.1094/PD-77-0376. Setotaw, F., (2006). Impact of technological change on housed production and food security in small holder agriculture; the case of wheat-tef based farming system in the central high lands of Ethiopia. Sharma, R.L., Swarnkar, V.K., Khirod, B., & Sahu. M.K., (2018). Effect of different land configuration techniques on fusarium wilt of pigeonpea (Cajanus cajan L.). Int. J. Curr. Micr. App. Sci .7(10): 3237-3245. doi.org/10.20546/ijcmas. 2018.710.375. Shimbahri, M., Girmay, G., Mitiku, H., Amanuel, Z., & Girma, D., (2020). Mineral fertilizer demand for optimum biological nitrogen fixation and yield potentials of legumes in northern Ethiopia. Sustainability 12 (16), 6449; https://doi.org/10.3390/su12166449 Singh, R., Sharma, P., & Kumar, S., (2021). Efficacy of carbendazim and mancozeb against Fusarium species causing wilt in lentil. Journal of Plant Protection Research , 61(3), 241–249. https://doi.org/10.24425/jppr. 137895. Singh, R., Kumar, A., & Sharma, P., (2020). Influence of environmental factors on the incidence and severity of lentil wilt disease across agroecological zones. Plant Disease Research , 35(4), 367–374. https://doi.org/10.1007/s42360-020-00310-4. Suri, G.K. Braich, S, Noy, D.M. Rosewarne, G.M. Cogan, N. and Kaur, S. 2022. Advances in lentil production through heterosis: Evaluating generations and breeding systems . 17(2):262-857. https://doi.org/10.1371/journal. phone.0262857. Takele, E., Firew, M., & Fikru, M., (2022). Genetic variability and characters association for yield, yield attributing traits and protein content of lentil ( Lens Culinaris Medikus) genotype in Ethiopia. CABI Agriculture and Bioscience. Tebkew, D. and Bizuwork, T. (eds.). (2023). Lentil Research in Ethiopia : achievements, gaps and prospects, Ethiopian Institute of Agricultural Research, Addis Ababa, Ethiopia. Tolesa, B., (2018). Distribution and management of Fusarium wilt ( Fusarium oxysporum f.sp. lentis ) of lentil ( Lens culinaris Medikus) in Central Highlands of Ethiopia. MSc thesis in Agriculture (Plant Pathology). Haramaya, Ethiopia: Haramaya University. Tolesa, B., & Asrat, Z., (2019). Evaluation of lentil varieties and seedbed types for the management of lentil fusarium wilt disease ( Fusarium oxysporum f. sp. lentis ) in central highlands of Ethiopia. Afr. J. Agric. Res . 14(24): 1012-1019. Williamson‑Benavides, B. A., Sharpe, R. M., Nelson, G., Bodah, E. T., Porter, L. D., & Dhingra, A., (2021). Identification of Root Rot Resistance QTLs in Pea Using Fusarium solani f . sp . pisi‑ Responsive Differentially Expressed Genes . Frontiers in Genetics , 12, 62–92. doi:10.3389/fgene. Yan, J. M., (2012). Study on blister disease of broad bean caused by Olpidium viciae Kusano [Master’s/Ph.D. thesis, Sichuan Agricultural University, China]. Yimer, S.M., Ahmed, S., Fininsa, C., Tadesse, N., Hamwieh, A., & Cook, D.R., (2018). Distribution and factors influencing chickpea wilt and root rot epidemics in Ethiopia. Crop Protection , 106:150–155. Yuen, J., (2006). Deriving Decision Rules. The Plant Health Instructor. Department of Forest Mycology and Pathology. Swedish University of Agricultural Sciences. DOI: 10.1094/PHI-A-2006 0517-01. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revisions 26 Dec, 2025 Reviewers agreed at journal 26 Jul, 2025 Reviewers invited by journal 07 Jul, 2025 Editor invited by journal 02 Jul, 2025 Editor assigned by journal 30 Jun, 2025 First submitted to journal 26 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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-6933637","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":481586626,"identity":"d67a250f-a47b-4ea0-811b-ac2060c5aef7","order_by":0,"name":"Tolesa Bedasa Abdisa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYFAC5gYgcQCMGD5USMiBxA48wKOBh4ERoYVxxhkbY7CWBGK1MPO2pSWCuAz4tNizNzY+/FFzR47v+PGHH2ecOZw+P+zwQ6AtdnK6DThs4TnYbMxz7Jmx5JkcY4kPFYdzN95OMwBqSTY2O4BDi0RimzQD2+HEDQdyGCSBtuRunJ0A0nIgcRtuLe0/f/w7XL/h/PPHv3nbDqcbzk7/QEhLGwNQZYLBjQQzaaD3E+SlcwjYcuZgszRv3zPDmTfemFkCA9lwg3ROwYEEA9x+YW9vPvjxx7c78nzn0x/fAEalvPzs9M0fPlTYyeHSggkMwCoNiFUOAvINpKgeBaNgFIyCkQAA4n9w0sXTrr8AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-8517-2287","institution":"EIAR: Ethiopian Institute of Agricultural Research","correspondingAuthor":true,"prefix":"","firstName":"Tolesa","middleName":"Bedasa","lastName":"Abdisa","suffix":""},{"id":481586627,"identity":"44e79778-a148-4304-bd61-6c29a3b5b3a2","order_by":1,"name":"Chemeda Fininsa Gurmessa","email":"","orcid":"","institution":"Haramaya University College of Agricultural and Environmental Sciences","correspondingAuthor":false,"prefix":"","firstName":"Chemeda","middleName":"Fininsa","lastName":"Gurmessa","suffix":""},{"id":481586628,"identity":"82f569ed-ca68-45e7-a6a0-f6fafdce3f06","order_by":2,"name":"Habtamu Terefe Yetayew","email":"","orcid":"","institution":"Haramaya University College of Agricultural and Environmental Sciences","correspondingAuthor":false,"prefix":"","firstName":"Habtamu","middleName":"Terefe","lastName":"Yetayew","suffix":""},{"id":481586629,"identity":"92d7d452-8136-4ac6-ad36-2e5a5afd9c50","order_by":3,"name":"Seid Ahmed Kemal","email":"","orcid":"","institution":"International Center for Agricultural Research in the Dry Areas","correspondingAuthor":false,"prefix":"","firstName":"Seid","middleName":"Ahmed","lastName":"Kemal","suffix":""},{"id":481586630,"identity":"13fc1454-2866-441e-a8b4-dab1cc116e2b","order_by":4,"name":"Martin J. Barbetti","email":"","orcid":"","institution":"University of Western Australia","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"J.","lastName":"Barbetti","suffix":""}],"badges":[],"createdAt":"2025-06-19 20:47:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6933637/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6933637/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86395671,"identity":"efcfecea-d21f-4747-87ba-c2e28fd410e6","added_by":"auto","created_at":"2025-07-10 07:51:39","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":233870,"visible":true,"origin":"","legend":"\u003cp\u003eMajor lentil growing districts surveyed for lentil wilt complex disease in Ethiopia, during the 2023 main cropping season.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6933637/v1/9b7495a73fa2cb1a46a743b4.jpeg"},{"id":86395663,"identity":"32f22cc8-bc1a-439b-9349-e0e2a7968f8f","added_by":"auto","created_at":"2025-07-10 07:51:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1270108,"visible":true,"origin":"","legend":"\u003cp\u003eTypical symptoms of lentil wilt complex under field conditions of Lume district (a), Ada’a (b), vascular discoloration at Lume (c), and (d) typical lentil wilt from field experiment and \u003cem\u003eSclerotium rolfsii\u003c/em\u003e at Minjar (e).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6933637/v1/ae47f60532eb67b1e55578af.png"},{"id":86397034,"identity":"e398a870-d494-488d-acce-420d50504e99","added_by":"auto","created_at":"2025-07-10 08:07:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":63600,"visible":true,"origin":"","legend":"\u003cp\u003eOrdination of twelve variables by correspondence analysis. Eleven cropping practices, including planting date (PD), variety (VR), planting population (PP), weeding practice (WP), growth stage (GS), seedbed preparation (SBP), soil drainage (SD), soil type (ST), previous crop (PC), fungicide application (FG), fertilizer application (FT), and lentil disease incidence (DI) are displayed.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6933637/v1/ab3c26b65719fdc02210e009.png"},{"id":86395665,"identity":"31e020d6-461d-40b1-9747-3537d03edcb9","added_by":"auto","created_at":"2025-07-10 07:51:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":43891,"visible":true,"origin":"","legend":"\u003cp\u003eOrdination of three variables by correspondence analysis. The active variables are: areas (AR1-AR8), zones (ZN1-ZN3), and lentil wilt complex disease incidence (DI).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6933637/v1/7e94968a00721296abfb02aa.png"},{"id":86398125,"identity":"532d5af3-fac1-4d98-840c-a3f3c097bd38","added_by":"auto","created_at":"2025-07-10 08:15:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3189905,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6933637/v1/8d32d67e-c9f5-418e-a681-08629f10f835.pdf"}],"financialInterests":"","formattedTitle":"Spatial variability and key determinants of wilt complex disease: Insights from a survey in major lentil growing areas of Ethiopia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLentil (\u003cem\u003eLens culinaris\u003c/em\u003e Medik.,) is the most important crop in many pulse growing countries (Erskine et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Montejano-Ram\u0026iacute;rez and Valencia-Cantero, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e with its origins in the Near East (Sarker, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Pandey et al., 2024). This crop is highly valuable for the well-being of small-scale farmers, serving as sources of protein, cash, and animal feed (Absar et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Suri et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Lentil can fix nitrogen and contribute to improving soil fertility in the cereal-based crop production systems (Mesfin et al., 2020; Kumar et al., 2024). According to Durazzo et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), it has high antioxidants due to the presence of phytochemicals, such as phenolic acids, flavanols, saponins, and condensed tannins.\u003c/p\u003e\u003cp\u003eGlobally, lentil is annually cultivated in an area of 5\u0026nbsp;million hectares of land with a production of about 4.8\u0026nbsp;million tons (FAOSTAT, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Ethiopia is considered as a center of diversity and the largest lentil producers in Africa (FAOSTAT, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Mainly cultivated in Amhara, Oromia, and Tigray regions (Lijalem, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Lentil is grown across a wide range of altitudes, from 1600 to 2700 meters above sea level (m.a.s.l,) and soil types during the main and small rainy seasons (Tolesa, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It can grow in well-drained soil, which can range from sandy to clay-loam (Ozdemir, 2002). Lentil thrives in sandy-loam soils rich in phosphorus and potassium but are highly sensitive to waterlogging, which can quickly kill the crop (Brennan et al., 2002). Lentil productivity significantly increased from 0.6 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e in 2003 to 1.4 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e in 2018 in Ethiopia. However, between 2018 and 2021, the national average productivity on farmers' fields showed a slight overall decline (Tebkew and Bizuwork, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe lentil productivity ranged from 0.208 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e in Morocco (lowest) to 2.6 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e in China (highest), with Egypt, New Zealand, Australia, and Tajikistan also exceeding 2 ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e, while countries like India, Canada, and the U.S. produced between 1\u0026ndash;1.2 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e FAOSTAT (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite its multiple benefits, the production and productivity of lentil in Ethiopia is significantly lower (1.4 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) compared to its potential yields (3.6 t ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) under well-managed conditions (FAOSTAT, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This yield gap is attributable to several biotic (plant diseases, insect pests and weeds) and abiotic (unfavorable temperature, soil fertility, moisture stress, waterlogging, and frost) factors (Nigussie et al., 2009) and limited genetic base of the local landraces (Takele et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A recent survey result showed that diseases and lack of improved lentil variety\u0026rsquo;s seeds are the major lentil production constraints in Ethiopia (Lemma Zemedu, unpublished data of 2024, personal communication). In Ethiopia and elsewhere, about 20 lentil diseases have been reported (Negussie et al., 2008). The major biotic constraints of lentil are wilt/root rot complex and rust (\u003cem\u003eUromyces viciae-fabae\u003c/em\u003e) disease (Erskine et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Williamson-Benavides and Dhingra, 2021). Lentil wilt complex is caused by co-occurrence of different fungal pathogens, including \u003cem\u003eFusarium oxysporum\u003c/em\u003e f.sp. \u003cem\u003elentis\u003c/em\u003e, collar rot (\u003cem\u003eSclerotium rolfsii\u003c/em\u003e), black root rot (\u003cem\u003eF. solani\u003c/em\u003e), and wet root rot (\u003cem\u003eRhizoctonia solani\u003c/em\u003e), which together significantly adversely impact lentil cultivation (Chandra et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, the primary constraint to lentil production and productivity in Ethiopia is the wilt complex, primarily involving \u003cem\u003eFusarium\u003c/em\u003e spp. (Tebkew and Bizuwork, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, farmers still grow wilt-susceptible cultivars due to a lack of resistant varieties in Ethiopia (Chilot et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLentil wilt complex is primarily caused by \u003cem\u003eFusarium oxysporum\u003c/em\u003e f. sp. \u003cem\u003elentis\u003c/em\u003e and other soil-borne pathogens, is a multifactorial disease influenced by host susceptibility, pathogen variability, and environmental conditions including soil type, moisture, and temperature (Jim\u0026eacute;nez-Fern\u0026aacute;ndez et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This complex disease produces overlapping symptoms, complicating accurate diagnosis. Infected plants initially exhibit wilting, often beginning with the lower leaves, followed by a progressive yellowing that spreads upward. Under field conditions, wilt complex symptoms in lentils are characterized by the wilting of older leaves, stunted plant growth, weakening and curling of lower leaves, which progressively affect the stem of the infected plant, ultimately leading to wilting and drying (Pouralibaba et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A key symptom, including vascular browning and blockage near the stem base, is a feature of Fusarium infections, as the pathogen blocks the vascular system. Root rot and lesions on the stem and root collar are also common, weakening the plant and making it prone to lodging. In severe cases, individual plants or large field patches can die if favorable conditions, like warm and moist soil, persist. The disease is often exacerbated in fields with waterlogging conditions, as the pathogens remain in the soil (Pande et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Jim\u0026eacute;nez-Fern\u0026aacute;ndez et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). According to Sara and Robert (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the combination of high temperatures and wet conditions probably favor disease severity. Monocropping leads to pathogen build (Kumar et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) whereas crop rotation with non-host reduces inoculum (Pande et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLentil wilt pathogen causes serious yield losses in different countries varying from 5‒72% in Syria (Bayaa et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), 10‒50% in Pakistan (Chaudhary et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), as high as 70% in Czechoslovakia (Bojdova and Sinsky, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), up to 50% in India (Nisa et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and 10‒66% in Algeria (Belabid et al., 2002). Under favorable environmental conditions, the disease can cause 100% yield losses on susceptible lentil varieties. In 1991, an outbreak of lentil root rot in eastern Alberta resulted in up to 70% reduction in lentil yields (Hwang et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). This is an evident that lentil wilt complex epidemics are increasing and cause reduction to lentil production in many countries. Multiple efforts have been made to control lentil wilt complex, with resistant varieties being cost-effective. However, frequent emergence of new pathogen races regularly breaks down host resistance, and no released variety in Ethiopia is fully resistant to lentil wilt complex (Negussie et al., 2008). Chemical fungicides such as carbendazim and mancozeb have shown some efficacy against \u003cem\u003eFusarium\u003c/em\u003e spp. in vitro and in field trials (Singh et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), but widespread use is constrained by cost and environmental concerns. Breeding efforts in countries like India and Canada have identified moderately resistant genotypes (Kumar et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Banniza et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), yet no single genotype offers durable resistance against the complex of pathogens, including \u003cem\u003eFusarium oxysporum f. sp. lentis\u003c/em\u003e, \u003cem\u003eRhizoctonia solani\u003c/em\u003e, and \u003cem\u003eSclerotium rolfsii\u003c/em\u003e. Integrated approaches using biocontrol agents like \u003cem\u003eTrichoderma harzianum\u003c/em\u003e and \u003cem\u003ePseudomonas fluorescens\u003c/em\u003e show potential in controlled conditions (Mahanta et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), but field performance is inconsistent and context dependent.\u003c/p\u003e\u003cp\u003eThe situation calls for implementing urgent and comprehensive control options. Yet, to design effective management strategies for lentil wilt complex, it is crucial to keep record of information about the distribution of diseases in major cultivation areas and to understand the variability of the causing pathogens (Meftahi et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) as well as the disease. However, there is insufficient information on the distribution, relative importance, status, and estimates of yield loss attributed to the disease, and identity of the pathogens causing wilting of lentils in Ethiopia. In addition, the association of the disease with cropping systems and practices and environment factors are undetermined. Addressing these knowledge gaps and generating these data types are important for developing integrated lentil wilt management strategies in Ethiopia and other lentil growing areas worldwide. Therefore, the objectives of this study were to (1) assess the distribution, and incidence of wilt complex across major lentil-growing regions of Ethiopia; and (2) determine the association of disease epidemics with biophysical and agronomic factors that influence disease pressure across the study areas.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Description of the study areas\u003c/h2\u003e\n \u003cp\u003eThe lentil wilt complex disease survey was conducted in three zones across eight major lentil growing districts (Ada\u0026rsquo;a, Bacho, Gimbichu, Ilu, Lume, Siyadebrena wayu, Minjar and Moretina Jiru) from early September to November during the 2023 main cropping season (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The geographic location (altitude and longitude), mean minimum and maximum temperatures, relative humidity, and total annual rainfall of the survey districts are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The latitudes and longitudes of the survey areas ranged from 35.71\u0026ndash;10\u0026deg; N to 38.15\u0026ndash;39.56\u0026deg; E. The districts are located at an altitude range of 1520\u0026ndash;2700 m a.s.l. Predominantly the soil types were black and vertisols, with clay to sandy textures that influence water retention and drainage play key factors in disease expression. Major crops include tef, wheat, chickpea, grass pea, and lentil, often in rotation, which may maintain inoculum levels due to the presence of susceptible hosts. Poor drainage, particularly in black clay soils, combined with variable rainfall patterns during critical crop stages, likely intensifies disease pressure across surveyed zones.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGeographic location, altitudinal range, mean minimum and maximum annual temperatures, total annual rainfall, and relative humidity of survey areas in Ethiopia, during the 2023 main cropping season\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDistrict\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eGeographic location\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTemperature (\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eRainfall (mm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eRH (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAltitude (m.a.s.l.)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLongitude\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLatitude\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1830─2253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e140.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAda\u0026rsquo;a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1827─2274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e118.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGimbichu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2256─2435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e180.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBacho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2093─2135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e281.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIlu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2042─2184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e180.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinjar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1520─1780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e112.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMoretina Jiru\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2638─2681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e189.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSiyadebrena wayu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2601─2700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e182.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eGeographical ranges of the survey districts were recorded using a global positioning system. RH\u0026thinsp;=\u0026thinsp;Relative humidity; Max\u0026thinsp;=\u0026thinsp;Maximum annual temperature; and Min\u0026thinsp;=\u0026thinsp;Minimum annual temperature. (Source: Ethiopian Meteorological Institute, 2023)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Sampling procedures and sample units\u003c/h2\u003e\n \u003cp\u003eSurvey areas such as East Shewa, and Southwest Shewa were selected from Oromia National Regional State, while North Shewa was considered from Amhara National Regional State. Districts were purposively selected from each zone based on lentil area coverage (CSA, 2023), and field accessibility by main and feeder roads. Three districts from each East Shewa (Ada\u0026apos;a, Gimbichu, and Lume), North Shewa (Siyadebrina Wayu, Minjar and Moretina-Jiru) zone and two districts from Southwest Shewa (Bacho and Ilu) zone were included in the survey. Districts were selected purposively based on historical data on the prevalence of lentil wilt complex disease, lentil production importance, and accessibility for repeated assessments. In each selected district, three to five farmers\u0026rsquo; associations (FAs) were purposively selected in consultation with development (agricultural extension), proportional to lentil production intensity and cultivation area. A total of 170 sample fields were considered by randomly identifying five farmers\u0026apos; fields per FAs. The survey utilized questionnaires that was prepared in local languages (Afaan Oromo and Amharic) to collect lentil wilt disease, agroecological, and agronomic practices data from farmers with the support of development agents, and crop experts, telling us the lentil growers and potential areas. The questionaries were administered through face-to-face personal interviews.\u003c/p\u003e\n \u003cp\u003eThe survey was executed during the flowering and pod setting growth stages of the crop, as the highest infection rates were reported earlier during these stages (Das et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). To ensure a comprehensive evaluation of the disease, a systematic sampling approach was employed, considering the spatial clustering or aggregation pattern exhibited by the lentil wilt complex disease. The assessment of wilt in every FAs involved an initial random sampling, followed by subsequent sampling at intervals of 2\u0026ndash;5 km from the initial field. Within each farmer\u0026apos;s field, careful observations were made regarding the field\u0026apos;s size to establish equidistant sampling points. The first quadrat for disease assessment was randomly chosen, and sampling was conducted within a 0.25 m\u003csup\u003e2\u003c/sup\u003e quadrat area in a \u0026lsquo;W\u0026rsquo; fashion at five designated spots (Bebber et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Disease assessment\u003c/h2\u003e\n \u003cp\u003eBoth healthy and infected plants within a sample quadrat were counted from each selected field displaying characteristic symptoms of lentil wilt complex. To assess the disease, all plants within each quadrat were considered as sampling units. To determine the prevalence of wilt complex, the number of infested fields were calculated as a percentage of the total number of fields assessed per district. The incidence of wilt complex disease was determined by counting the number of plants exhibiting typical symptoms in each quadrat, following the protocol developed by Das et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). These symptoms include progressive yellowing and drooping of lower leaves, sudden wilting of entire plants without foliar lesions, browning or blackening of vascular tissues, root rot, collar region discoloration, and poor root development. In advanced stages, partial or complete plant collapse and necrosis at the stem base are commonly observed. The formulae used to calculate the percentage of disease prevalence and incidence are as follows:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:\\text{D}\\text{i}\\text{s}\\text{e}\\text{a}\\text{s}\\text{e}\\:\\text{p}\\text{r}\\text{e}\\text{v}\\text{a}\\text{l}\\text{e}\\text{n}\\text{c}\\text{e}\\:\\left(\\text{%}\\right)=\\:\\frac{\\text{N}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r}\\:\\text{o}\\text{f}\\:\\text{f}\\text{i}\\text{e}\\text{l}\\text{d}\\text{s}\\:\\text{s}\\text{h}\\text{o}\\text{w}\\text{i}\\text{n}\\text{g}\\:\\text{w}\\text{i}\\text{l}\\text{t}\\:\\text{c}\\text{o}\\text{m}\\text{p}\\text{l}\\text{e}\\text{x}\\:\\text{s}\\text{y}\\text{m}\\text{p}\\text{t}\\text{o}\\text{m}\\text{s}\\:\\text{p}\\text{e}\\text{r}\\:\\text{d}\\text{i}\\text{s}\\text{t}\\text{r}\\text{i}\\text{c}\\text{t}}{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{n}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r}\\:\\text{o}\\text{f}\\:\\text{f}\\text{i}\\text{e}\\text{l}\\text{d}\\text{s}\\:\\text{v}\\text{i}\\text{s}\\text{i}\\text{t}\\text{e}\\text{d}\\:\\text{p}\\text{e}\\text{r}\\:\\text{d}\\text{i}\\text{s}\\text{t}\\text{r}\\text{i}\\text{c}\\text{t}}\\:\\times\\:100$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:\\text{D}\\text{i}\\text{s}\\text{e}\\text{a}\\text{s}\\text{e}\\:\\text{i}\\text{n}\\text{c}\\text{i}\\text{d}\\text{e}\\text{n}\\text{c}\\text{e}\\:\\left(\\text{%}\\right)=\\:\\frac{\\text{N}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r}\\:\\text{o}\\text{f}\\:\\text{p}\\text{l}\\text{a}\\text{n}\\text{t}\\text{s}\\:\\text{i}\\text{n}\\text{f}\\text{e}\\text{c}\\text{t}\\text{e}\\text{d}\\:\\text{p}\\text{e}\\text{r}\\:\\text{q}\\text{u}\\text{a}\\text{d}\\text{r}\\text{a}\\text{t}}{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{n}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r}\\:\\text{o}\\text{f}\\:\\text{p}\\text{l}\\text{a}\\text{n}\\text{t}\\text{s}\\:\\text{i}\\text{n}\\text{s}\\text{p}\\text{e}\\text{c}\\text{t}\\text{e}\\text{d}\\:\\text{p}\\text{e}\\text{r}\\:\\text{q}\\text{u}\\text{a}\\text{d}\\text{r}\\text{a}\\text{t}}\\times\\:100$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eDuring the survey, farmers were personally consulted to gather insights into their knowledge of the disease, cultural practices and field histories. Lentil field geographical information (latitude, longitude, and altitude), crop growth stages, weeding practices, occurrence and status of lentil wilt complex disease, previous crop rotations, sowing dates, seedbed preparation methods, plant population, fertilizer and fungicide applications, soil type and drainage conditions were recorded. In addition, about twenty individual plant samples displaying typical symptoms of wilt complex disease were collected from each farming area for identification of the causal pathogen (s). These samples were carefully preserved in labeled paper bags, along with records of the collection location, sample number, and date. Lentil field biophysical data were collected using standardized scientific protocols. Geographic coordinates (latitude, longitude, altitude) were recorded using a GPS device. Crop growth stages, weeding practices, sowing dates, seedbed preparation, fertilizer and fungicide applications, and previous crop rotations were documented through field observation and farmer interviews. Lentil wilt complex occurrence was monitored by field scouting, with disease incidence and scored the wilted plants in the quadrat. Plant population was estimated via quadrat counts. Soil type and drainage conditions were assessed through field observation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Data Analysis\u003c/h2\u003e\n \u003cp\u003eDescriptive analysis and \u0026chi;\u003csup\u003e2\u003c/sup\u003e statistics were used to determine differences of lentil wilt complex incidence and among independent variables and variable classes. Disease incidence data were categorized into binomial qualitative groups following Yuen (\u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e) and Ali and Terefe (2021). Disease incidence was categorized into binary classes using thresholds of \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;20% and \u0026gt;\u0026thinsp;20% based on Iqbal et al. (\u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Contingency tables displayed the bivariate relationship between disease incidence and independent variables (biophysical factors).\u003c/p\u003e\n \u003cp\u003eLogistic regression through the GENMOD procedure was employed to analyze the association of wilt complex incidence with biophysical factors using SAS (2014). The effects of independent variables on the disease of incidence were evaluated at three different stages. First, single-variable models were used to examine the relationship between disease incidence and each independent variable. Next, the association of each variable was validated by incorporating it at both the start and end of the logistic regression model. Finally, only variables showing significant associations in single and multiple models were included in a reduced multi-variable model for an in-depth analysis. The odds ratio and deviance reduction were calculated for each variable as it was added to the reduced multiple variable model (Yuen, \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e; Yitayih et al., 2021). Parameter estimates and their standard errors were calculated for both single and multi-variable models. These estimates were exponentiated to generate odds ratios, allowing for the interpretation of relative risks compared to a reference point (Yuen, \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). Deviations compared models, and likelihood ratio tests (LRTs) evaluated variable significance using chi-square values (McCullagh and Nelder, 1989), identifying key factors influencing lentil wilt complex to inform targeted management strategies.\u003c/p\u003e\n \u003cp\u003eCorrespondence analysis was employed to explore and visualize the associations between lentil wilt complex incidence and various biophysical and cropping practice factors. This multivariate technique enables graphical representation of contingency tables, effectively revealing relationships between two sets of categorical variables of explanatory (rows) and response (columns) (Greenacre, \u003cspan class=\"CitationRef\"\u003e1984\u003c/span\u003e). In this study, disease incidence classes served as the response variables, while cropping practices and biophysical factors, like planting methods, soil type, drainage, previous crops, were treated as explanatory variables. Correspondence analysis plots were constructed based on Chi-square (\u0026chi;\u0026sup2;) distances, with each axis representing a dimension of variation within the dataset. The analysis was performed in r software using the FactoMiner and Factoshiny packages, with interpretation based on axis inertia, variable contributions, and point proximity to assess the strength and direction of associations (Greenacre, \u003cspan class=\"CitationRef\"\u003e1984\u003c/span\u003e; Serge et al., \u003cspan class=\"CitationRef\"\u003e1993\u003c/span\u003e). This approach facilitated a clearer understanding of how different agronomic and environmental factors relate to levels of disease incidence.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1. General features of surveyed fields\u003c/h2\u003e\u003cp\u003eLentil wilt complex disease was found in all lentil growing areas and identified as a major lentil production constraint during the survey. The disease symptoms observed in infected plants included wilting, yellowing, stunting and necrosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea‒d). The disease incidence varied with geographical location and environmental conditions. Altitudes in the survey areas ranged from 1520 m a.s.l. in Minjar to 2700 m a.s.l. in Siyadebrena Wayu. Local lentil varieties covered 79.4% of the fields, while improved varieties accounted only for 20.59% of total inspected farms. Farmers used to plant lentil in June, July, August, September and October, depending on rainfall distribution and cropping system. Early, mid, and late planting accounted for about 25.88%, 33.53%, and 40.59% respectively. Soil types varied, with black soil being predominant (55.29%), followed by light clay (24.71%) and sandy loam (20.0%) soils.\u003c/p\u003e\u003cp\u003eRegarding weed management practices, 46.47% of the fields were had weedy, 40.59% were weed-free, and 12.94% experienced weed re-emergence. Seedbed preparation was primarily flat (58.82%), with ridge furrow methods used in 41.18% of the observed fields. Tef (\u003cem\u003eEragrostis tef\u003c/em\u003e), wheat, chickpea, lentil, and grass pea were the most common crops grown in the survey areas. Yet, wheat (65.88%) and tef (33.53%) were predominant among previously grown crops in the survey fields. Farmers rarely applied seed treatment chemicals (2.35%), while most of the farmers applied fertilizers (53.53%) to grow lentil. During the survey lentil was at pod filling (50.0%), flowering (26.47%), and pod forming (23.53%) growth stages (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Although inspected fields were affected by lentil wilt complex, farmers in the studied areas lacked the use of proper management practices to control the disease. Many farmers referred to the disease as \u003cem\u003ewagi\u003c/em\u003e (rust) rather than recognizing it as lentil wilt. In the surveyed areas, none of the growers used seed treatment fungicides for lentil wilt disease management due to a lack of awareness about their availability. While some farmers applied fungicides such as Ridomil Gold, mancozeb, and propiconazole, they often used them without fully understanding their purpose or effectiveness (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Lentil wilt prevalence and incidence\u003c/h2\u003e\u003cp\u003eThe survey revealed a 100% prevalence of lentil wilt complex across all districts, with varying incidence levels. Among districts, Lume exhibited the highest incidence (57.18%), while Siyadebrena Wayu had the lowest disease incidence (15.65%). Lentil fields planted early (from June 26 to July 30, 2023) showed a significantly higher incidence of 49.62% than those planted late (from September to October 5, 2023) in the season, which recorded only disease incidence of 23.80% (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;34.03, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A higher lentil wilt incidence (39.61%) was obtained from weedy fields than weed free fields (24.18%). A ridged-furrow planting method reduced disease incidence by 18.12% as compared to flatbed planting, which experienced a wilt incidence of 60.33%. Local varieties exhibited a significantly higher disease incidence (35.48%) than improved varieties such as Alemaya and Derash (18.12%). Agronomic practices, such as effective weed management, substantially reduced disease incidence, with incidence compared to 39.61% in weedy fields (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;11.359, P\u0026thinsp;\u0026lt;\u0026thinsp;0.00342). Disease incidence varied significantly across districts, ranging from the lowest at Siyadebrenawayu (15.65%) to the highest at 57.18% in Lume district (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;43.177, P\u0026thinsp;=\u0026thinsp;3.084e-07). The Ada\u0026rsquo;a district recorded the second highest average incidence of 40.76%, followed by Minjar district (32.9%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Altitudinal variations also influenced disease occurrence, with fields at 1500\u0026ndash;2000 meters experienced the highest incidence (38.96%), while fields above 2501 meters had the lowest (18.68%) incidence (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;25.34, P\u0026thinsp;=\u0026thinsp;3.142e-06) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDisease incidence was also found to be influenced by crop growth stages, peaking during flowering (46.62%) and dropping to 25.24% during the pod-filling growth stage. Soil type and drainage were key factors affecting disease prevalence and incidence. Black soil had the highest incidence (39.75%), followed by light clay (33.75%) and sandy loam (21.69%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Poor drainage further exacerbated disease incidence, with poorly drained fields reporting 60.55% incidence compared to 22.79% in well-drained fields. Ridge furrow bed planting significantly reduced incidence (18.33%) compared to flatbed planting (41.35%) (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;39.50, P\u0026thinsp;=\u0026thinsp;2.641e-09). Similarly, fields with higher plant population (\u0026gt;\u0026thinsp;100 plants per 100 m\u0026sup2;) exhibited lower disease incidence (30.33%) than sparsely populated lentil fields. Fertilized fields showed lower incidence (24.38%) than non-fertilized fields (38.44%). Despite slight reductions in disease incidence, fields with fungicide applied had the lowest (31.92%) disease incidence compared to fields with no fungicide (33.92%). Additionally, fields previously planted with wheat showing higher incidence (33.26%) compared to those following tef (29.39).\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\u003eDisease contingency table for logistic regression analysis and mean disease incidence of lentil wilt complex for different independent variables in eight districts (n\u0026thinsp;=\u0026thinsp;170) in major lentil growing areas of Ethiopia, during the 2023 main growing season\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable class\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNumber of fields\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eDisease incidence (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eIncidence (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable class\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNumber of fields\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eDisease incidence (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eIncidence (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003eDistrict\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAda'a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40.76\u0026thinsp;\u0026plusmn;\u0026thinsp;4.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePlanting date\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eEarly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e49.62\u0026thinsp;\u0026plusmn;\u0026thinsp;3.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBecho\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29.81\u0026thinsp;\u0026plusmn;\u0026thinsp;3.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e28.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGimbichu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27.32\u0026thinsp;\u0026plusmn;\u0026thinsp;4.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e23.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIlu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32.25\u0026thinsp;\u0026plusmn;\u0026thinsp;4.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSoil type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e39.75\u0026thinsp;\u0026plusmn;\u0026thinsp;2.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLume\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e57.18\u0026thinsp;\u0026plusmn;\u0026thinsp;4.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLight clay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e33.75\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMinjar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSandy loam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e21.69\u0026thinsp;\u0026plusmn;\u0026thinsp;3.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMoratinajiru\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21.71\u0026thinsp;\u0026plusmn;\u0026thinsp;3.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSoil drainage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e22.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSiyadebrenawayu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e60.55\u0026thinsp;\u0026plusmn;\u0026thinsp;2.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAltitude\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1500\u0026ndash;2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38.96\u0026thinsp;\u0026plusmn;\u0026thinsp;3.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eWeed practice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eWeed free\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e24.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2000\u0026ndash;2500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e36.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eWeed present\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e39.61\u0026thinsp;\u0026plusmn;\u0026thinsp;2.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;2501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18.68\u0026thinsp;\u0026plusmn;\u0026thinsp;23.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRe-emerged\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e28.65\u0026thinsp;\u0026plusmn;\u0026thinsp;3.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eImproved\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18.12\u0026thinsp;\u0026plusmn;\u0026thinsp;2.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBed type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFlat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e41.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e35.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRidge furrow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e18.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePlant population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePrevious crop\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e29.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32.56\u0026thinsp;\u0026plusmn;\u0026thinsp;1.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eWheat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e33.26\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eGrowth stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFlowering\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e46.62\u0026thinsp;\u0026plusmn;\u0026thinsp;3.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFungicide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eApplied\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e31.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePod filling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNot applied\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e33.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePod forming\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30.36\u0026thinsp;\u0026plusmn;\u0026thinsp;3.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFertilizer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eApplied\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e24.38\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNot applied\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e38.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\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\u003ePlanting date referred to as early\u0026thinsp;=\u0026thinsp;end of June to July; medium\u0026thinsp;=\u0026thinsp;August planting; and late\u0026thinsp;=\u0026thinsp;early September to October. Plant population was determined within 1 m \u003csup\u003e2\u003c/sup\u003e quadrat where low refers to \u0026le;\u0026thinsp;100 lentil plants m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e and high refers to \u0026gt;\u0026thinsp;101 plants m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e. Good and poor soil drainages are defined as well drained soil moisture and poorly drained soil moisture, respectively. The soil type classification was made based on visual observation where black is defined as heavy black or deep dark color, and not well-drained soil type; light clay soil is better drained soil type than the black soil; and sandy loam is defined as light dark soil in color and well-drained soil type.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Correspondence among independent variables and disease incidence\u003c/h2\u003e\u003cp\u003eThe multiple correspondence analysis plot revealed variables associated with high disease incidence include early planting date (PD1), poor soil drainage (SD2), heavy or black soil type (ST1), light clay soil type (ST2), and flat seedbed preparation (SBP1). Agronomic practices such as low plant population (PP2), lack of fertilizer (FT2) and fungicide application (FG1), as well as weedy fields (WP2) and re-emerged weeds (WP3), were also strongly aligned with high disease occurrence. In addition, local varieties (VR2), flowering growth stage (GS1), growing in low-altitude areas (AL1), and in districts such AR5 (Lume), AR1 (Ada\u0026rsquo;a), Minjar, AR4 (Ilu), and AR2 (Becho), were clustered near the high disease dimension, indicating their potential contribution to disease vulnerability (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These altitudinal trends complement the spatial patterns observed in areas (AR) and zones (ZN), indicated as high-risk hotspots requiring prioritized interventions like improved drainage and improved (resistant) varieties (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Similarly, soil drainage (χ\u0026sup2; = 46.932, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), district (χ\u0026sup2; = 43.177, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and seed bed preparation (χ\u0026sup2; = 39.504, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) showed the strongest effects or highly significant associations with disease incidence (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eConversely, variables associated with low disease incidence include late planting date (PD3), good soil drainage (SD1), and sandy loam soil type (ST3), along with ridge-furrow seedbed preparation (SBP2). Weed-free fields (WP1), use of fertilizers (FT1) and fungicides (FG2), higher plant population density (PP1), and improved varieties (VR1) were closely associated with low disease incidence (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These factors were observed in high-altitude areas (AL3) and in districts like Minjar (AR6), Muratinajiru (AR7), and Siyadebrenawayu (AR8) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), which appeared clustered near the low disease incidence point on the multiple correspondence analysis plot, suggesting their protective role against lentil wilt complex.\u003c/p\u003e\u003cp\u003eThe alignment of correspondence analysis with regression results reinforces the validity of the identified disease drivers. Both methods consistently associate high disease incidence with early planting, poor drainage, flat seedbeds, low plant population, and absence of inputs (fertilizer and fungicide), while low incidence aligned with improved varieties, ridge-furrow beds, and good drainage. This convergence of multivariate and statistical approaches enhances confidence in the findings and supports the robustness of the identified factors influencing lentil wilt complex.\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\u003eCorrespondence analysis of lentil wilt complex disease incidence in relation to cropping practices and agroecological factors\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndependent variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDegrees of freedom\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChi-square (χ\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\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\u003eZone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0017\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\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43.177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0048\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlanting date\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0007\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\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAltitude\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoil type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.979\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoil drainage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46.932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeeding practice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeed bed preparation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39.504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious crop\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.8576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.173\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFertilizer application\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFungicide application\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.67396\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.412\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlant population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.94689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.623\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u0026thinsp;=\u0026thinsp;Highly significant, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u0026thinsp;=\u0026thinsp;Significant, P\u0026thinsp;\u0026ge;\u0026thinsp;0.05\u0026thinsp;=\u0026thinsp;Not significant\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Association of lentil wilt complex disease incidence with biophysical factors\u003c/h2\u003e\u003cp\u003eThe logistic regression analysis revealed that most of the biophysical variables had a strong association with incidence of lentil wilt complex (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Independent variables, including districts, altitude, lentil variety, soil type, soil drainage, planting date, seedbed preparation, and weed management practice, were found to have highly significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) associations with the incidence of lentil wilt complex when entered first and last into the logistic regression models. Previous crops, crop growth stage, and plant population also showed a significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) association with disease incidence when entered first and last into the models. Fungicide application was significant (P\u0026thinsp;=\u0026thinsp;0.0205) when it entered first into the model, but it lost its significant (P\u0026thinsp;=\u0026thinsp;0.9304) association when it entered last into the model, while fertilizer application was non-significant in both models (Type 1, P\u0026thinsp;=\u0026thinsp;0.2894 and Type 3, P\u0026thinsp;=\u0026thinsp;0.0717) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\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 model for lentil wilt complex disease incidence and likelihood ratio test on independent variables in major lentil growing areas in Ethiopia during the 2023 cropping season\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=\"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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eIndependent variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003eLentil wilt complex incidence, LRT\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eType 1\u003c/p\u003e\u003cp\u003eanalysis (VEF)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eType 3\u003c/p\u003e\u003cp\u003eanalysis (VEL)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePr\u0026thinsp;\u0026gt;\u0026thinsp;χ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePr\u0026thinsp;\u0026gt;\u0026thinsp;χ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\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\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e179.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e75.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAltitude\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e172.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e72.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious crop\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e116.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e72.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0096\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCrop growth stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e167.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e71.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0198\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLentil variety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e122.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e39.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoil type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e138.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e43.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoil drainage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e102.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e38.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlanting date\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e157.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e47.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeedbed preparation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e38.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlant population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e117.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e39.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0203\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeeding practice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e160.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;00.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e51.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFungicide application\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e157.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e51.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9304\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFertilizer application\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e118.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.2894\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e50.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0717\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e LRT\u0026thinsp;=\u0026thinsp;Likelihood ratio test; VEF\u0026thinsp;=\u0026thinsp;Variables entered first into the model; VEL\u0026thinsp;=\u0026thinsp;Variable entered last into the model; DR\u0026thinsp;=\u0026thinsp;Deviance reduction; Pr\u0026thinsp;=\u0026thinsp;Probability of an χ\u003csup\u003e2\u003c/sup\u003e value exceeding the deviance reduction; χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;Chi square; df\u0026thinsp;=\u0026thinsp;Degrees of freedom\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe probability of a high (\u0026gt;\u0026thinsp;20%) wilt complex disease incidence was 6.2 and 1.91 times greater in Lume and Ada\u0026rsquo;a districts than Siyadebrinawayu district. It was also 2.67 and 1.5 times higher in low and mid altitudes than in high altitudes (\u0026ge;\u0026thinsp;2001). Local varieties had a 6.18 times higher risk or disease pressure than improved ones. The probability of incidence exceeding 20% was 1.97 and 1.62 times higher during flowering and pod formation than during pod filling growth stage. There were about 2.01 and 1.75 times higher probability that lentil wilt complex incidence would exceed (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;20%) in early and medium than late planting date, respectively. The probability of the occurrence of high (\u0026gt;\u0026thinsp;20%) lentil wilt complex incidence was 2.64 and 1.37 times higher than sandy loam in black and light clay soil tpe, respectively. Moreover, poorly drained soils had a 5.05 times higher risk than well-drained soil (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnalysis of deviance, natural logarithms of odds ratio, standard error of wilt complex disease incidence and likelihood ratio test on independent (added) variables in reduced regression model.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAdded variable \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eResidual\u003c/p\u003e\u003cp\u003eDeviance \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eLentil wilt complex, LRT \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable class\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003cp\u003eLog\u003csub\u003ee\u003c/sub\u003e (odds ratio) \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSE \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOdds ratio\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePr\u0026thinsp;\u0026gt;\u0026thinsp;χ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e229.9516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;0.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003eDistrict\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003e179.1302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003e50.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAda'a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.647\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBecho\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGimbichu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIlu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLume\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.823\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMinjar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMoratina Jiru\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSiyadebrena wayu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAltitude\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e172.8269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e3.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;1500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.983\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1501\u0026ndash;2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.397\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;2001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLentil variety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e122.9892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e15.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLocal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eImproved\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eCrop growth stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e167.6591\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e8.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlowering\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePod forming\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePod filling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePlant population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e117.2506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e7.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.0061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;0.476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePlanting date\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e156.9673\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e8.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEarly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.697\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSoil type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e138.8950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e17.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLight clay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSandy loam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePrevious crop\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e176.6245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.0081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;0.782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWheat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSoil drainage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e102.2218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e14.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eWeeding practice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e160.6623\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWeed present\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWeed free\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSeedbed preparation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e98.8915\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFlat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.794\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRidge furrow bed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\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\u003csup\u003ea\u003c/sup\u003e Variables added into the model in order of presentation in the table. \u003csup\u003eb\u003c/sup\u003e Unexplained variations after fitting the model; \u003csup\u003ec\u003c/sup\u003e LRT\u0026thinsp;=\u0026thinsp;Likelihood ratio test; DR\u0026thinsp;=\u0026thinsp;Deviance reduction; Pr\u0026thinsp;=\u0026thinsp;Probability of an χ2 value exceeding the deviance reduction; χ2\u0026thinsp;=\u0026thinsp;Chi square. \u003csup\u003ed\u003c/sup\u003e Estimates from the model with all independent variables added; \u003csup\u003ee\u003c/sup\u003e SE\u0026thinsp;=\u0026thinsp;standard error; \u003csup\u003e*\u003c/sup\u003eReference group.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eLentil wilt complex is the most devastating lentil disease, and it was found to be widely distributed and prevalent in all fields per each district inspected during the survey. The disease incidence was varied among district, altitude, lentil variety, soil type, soil drainage, planting date, seedbed preparation, weeding practices previous crops, crop growth stage, and plant population (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Similarly, Das et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported that wilt pathogen incidence can vary based on factors such as soil type, weather conditions, cropping patterns, cultivated varieties, and agroclimatic conditions. These factors may support sporulation and conidia germination, infection, establishment and development of lentil wilt complex. A previous study in Uttar Pradesh, India, indicated that variable in wilt incidence across cultivated fields, which has been attributed to differences to location specific factors such as climate, crop variety, and soil type (Bekele, 2007).\u003c/p\u003e\u003cp\u003eThe current study indicated that district, black soil type, flatbed planting, low altitude zones, use of local varieties, early planting, weed-infested fields, and previous cultivation of wheat were strongly associated with the lentil wilt complex disease incidence, and had significant contribution to the development of the lentil wilt complex disease epidemics. The incidence of lentil wilt complex was closely linked to agroecological and environmental factors. Elevated disease levels occurred in low-altitude districts such as Lume, Ada\u0026rsquo;a, and Minjar, where higher temperatures, poor soil drainage, and black soils created conditions conducive to pathogen persistence and infection. Lume recorded the highest incidence (57.18%), linked to high temperature (28.27\u0026deg;C max), low rainfall (140.92 mm), and poor drainage. Conversely, cooler, higher-altitude areas like Moretina Jiru and Siyadebrina Wayu (\u0026ge;\u0026thinsp;2600 m.a.s.l.) with lower temperatures (~\u0026thinsp;21\u0026deg;C max) and better drainage had the lowest incidence (\u0026lt;\u0026thinsp;22%). These findings align with previous reports indicating that wilt complex and other soilborne diseases are exacerbated by warm temperatures, excessive soil moisture, and poor aeration (Nasir et al., 2003). Ali and Terefe (2021) further emphasized that factors like district, soil type, moisture levels, crop growth stage, and plant density significantly influence Fusarium wilt incidence in chickpea. Furthermore, diverse environmental conditions across zones play a crucial role in the varying incidence of lentil wilt epidemics from one district to another (Singh et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The high incidence of lentil wilt complex in Lume district can be attributed to several factors. The district likely has poor soil drainage, as black soil (associated with higher disease incidence) and flatbed planting (41.35% incidence) were common, creating waterlogged conditions favorable for fungal pathogens.\u003c/p\u003e\u003cp\u003eThe current study confirmed that black soil was found to have the highest association with higher wilt incidence (39.75%) and a 2.64 times greater risk of disease incidence than sandy loam soil. This can be attributed to its heavy and poorly drained nature, which favors pathogen survival and proliferation. Previous studies (Geletu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Rachana, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) have also linked black soil high moisture retention to the aggravation of wilt complex pathogens. Similarly, Yimer et al. (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) indicated a higher incidence of chickpea wilt and root rot in dark black soils. In flat planting systems, excessive soil moisture creates anaerobic conditions that restrict oxygen availability while increasing carbon dioxide concentration in the root zone, thereby establishing an environment highly favorable for the proliferation of moisture-loving fungal pathogens. These hypoxic conditions compromise root respiration and functionality, ultimately reducing crop productivity and predisposing plants to root rot and wilt diseases (Midmore, 2015).\u003c/p\u003e\u003cp\u003eIn this study, higher lentil wilt complex incidence was observed in low- and mid-altitude areas, where the risk was 2.67 times and 1.50 times greater than high altitude areas, respectively. In a related disease, Asfaw and Negash (2020) found the highest incidence of chickpea wilt/root disease (41.55%) in altitudes ranging 1952‒2200 m a.s.l., while the lowest incidence (17.5%) was obtained at altitudinal ranges 2393‒2474 m a.s.l. This finding is likely due to the prevailing warmer conditions at low altitudes, which are reported to favor higher disease intensity compared to cool environmental conditions. Warmer and drier conditions are favor for pathogen initiation, multiplication, and survival (Singh et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Rao (2014) and Mastewal et al. (2022) also noted that elevated temperatures at lower altitudes significantly contribute to wilt symptom development, with a more pronounced effect than cooler conditions.\u003c/p\u003e\u003cp\u003eIn the present assessment, varietal differences further influenced disease levels, with the continued use of local cultivars likely promoting pathogen inoculum buildup and sustaining high disease pressure. Fields planted with local varieties showed significantly higher disease incidence (35.48%) than improved ones, likely due to their genetic susceptibility and continued use, which facilitates inoculum buildup (Mengistu and Negussie, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Assfaw and Negash, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nazneen et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This suggests that improved varieties may possess partial resistance or enhanced vigor that helps suppress wilt progression, consistent with previous studies advocating the use of resistant genotypes as a primary management strategy (Bayaa and Erskine, 1998). Similar trends were observed by Mengistu and Negussie (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) and Assfaw and Negash (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who reported that chickpea wilt incidence reaching up to 100% in local varieties while improved ones recorded only 21% wilt incidence in northwestern Ethiopia. This variation could also be attributed to the continuous cultivation of susceptible lentil varieties, which promotes inoculum buildup and increases disease pressure, implying that continuous cropping is largely practiced in the traditional intensive agriculture system, resulting in the high incidence of soil-borne diseases (Hu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, access to obtain improved lentil varieties was limited for most farmers in the study areas. This has caused low seed replacement rates and the continued use of disease-susceptible varieties, which increased fungal inoculum and disease pressure. Hence, farmers primarily relied on local lentil varieties from uncertain sources, raising the risk of disease outbreaks. In this regard, Setotaw (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) indicated that only about 9% of lentil growers in Ethiopia\u0026rsquo;s central highlands adopted improved varieties, although improved lentil varieties reported to lower disease rates (Assfaw and Negash, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDuring the survey, it was observed that planting dates also significantly affected wilt incidence. Fields sown early (end of June to July) showed the highest disease incidence (49.62%), while late planting (September to October) had the lowest (23.80%). Early planting may expose crops to higher temperatures and prolonged soil moisture, which favor pathogen multiplication and survival (Nasir et al., 2003). In contrast, late sowing might escape peak pathogen pressure due to cooler and drier conditions during critical growth stages (Singh and Kapoor, 1993). According to Tebkew and Bizuwork (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), early planting, depending on soil types and the maturity period of the varieties, can encounter excessive moisture (waterlogging), which promotes wilt and root rot diseases that may destroy the crop. High mean lentil wilt incidence was highly associated with flowering and pod forming growth stages than the pod filling growth stage, which was likely 1.6‒times risky at the later growth stage. This trend aligns with previous findings indicating that the flowering stage is critical for vascular wilt pathogens due to peak physiological activity and nutrient flow, which favor rapid pathogen colonization (Bayaa and Erskine, 1998). During this stage, plant stress and open vascular tissues may increase susceptibility to pathogens such as \u003cem\u003eFusarium\u003c/em\u003e spp. and \u003cem\u003eRhizoctonia\u003c/em\u003e spp. Yan (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) also reported that the faba bean gall disease epidemic reached its peak during the flowering and pod formation stages but declined by the late podding. The current findings are consistent with Das (2022) and Chaudhry et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) who reported that \u003cem\u003eF. oxysporum\u003c/em\u003e f. sp. \u003cem\u003eciceri\u003c/em\u003e infects chickpea during seedling, flowering, and pod forming growth stages, with highest mortality typically occurring during the flowering and podding growth stages under conditions of sudden temperature rise and water stress. This might be attributed to the progression of wilt from early stages leading to higher individual plant death, coupled with the plants shifting in resources translocation during flowering toward reproductive processes to produce seeds.\u003c/p\u003e\u003cp\u003eIn this study, weed management practices showed a strong association with lentil wilt complex incidence. Fields with poor or no weeding had significantly higher disease levels compared to weed-free fields. In this study, wilt incidence was 39.61% in fields with weeds and only 24.18% in weed-free fields, indicating that weed presence contributes to disease pressure. Weeds compete with lentil plants for nutrients, water, and light, weakening crop vigor and making plants more susceptible to infection. Broadleaf weeds, particularly from the Solanaceae family, exacerbate disease incidence by acting as alternate hosts for wilt disease. Previously Zewde et al. (2007) and Sahile et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) reported that heavy weed infestation can weaken crop vigor by competing for essential resources, thereby creating favorable conditions for disease development and observed across various host-pathogen systems. Eshetu et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) similarly observed higher wilt complex incidence in weed infested faba bean fields. On the other hand, the primary factor contributing to the low lentil yield is inadequate weeding or pest control (Bejiga and Degago, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). In contrast, weed-free fields showed a reduced incidence by minimizing competition for water and nutrients, enhance soil drainage, and promote better air circulation under the canopy and suppressed wilt development by reducing alternative pathogen hosts and alleviating crop competition in align with Gudero (2018).\u003c/p\u003e\u003cp\u003eSurprisingly, the incidence of lentil wilt complex was influenced by previous crop history, with fields previously planted with wheat showing higher disease levels than those with tef, likely due to enhanced inoculum buildup. Fungicide use had limited impact, as disease incidence remained similar between treated (31.92%) and untreated (33.92%) fields, possibly due to resistance or misapplication. Farmers commonly used non-specific fungicides such as Ridomil Gold and mancozeb without proper guidance, often targeting unrelated diseases. This aligns with Daraj et al. (2017), who emphasized that poor crop management and use of susceptible varieties contribute to wilt complex development and spread.\u003c/p\u003e\u003cp\u003eConversely, low lentil wilt complex incidence was consistently linked to improved varieties, high-altitude environments, sandy loam soils, late planting, pod-filling stage, ridge-furrow planting, weed-free fields, and tef as the preceding crop. Improved lentil varieties recorded significantly lower disease levels (18.12%), likely due to genetic resistance and better agronomic traits (Ghosh et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Assfaw and Negash, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). High-altitude areas exhibited reduced disease incidence due to cooler temperatures that suppress pathogen activity (Mastewal \u003cem\u003eet al\u003c/em\u003e., 2022). Also, sandy loam soils, with better aeration and drainage, also limited pathogen proliferation and supported healthy root systems (Mesfin and Gebru, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Late planting minimized exposure to early season moisture and reduced pathogen pressure (Navas-Cort\u0026eacute;s \u003cem\u003eet al\u003c/em\u003e., 1998). Ridge-furrow planting enhanced drainage and aeration, significantly lowering disease incidence, as confirmed by Sharma et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Tolesa and Asrat (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Moreover, tef as a preceding crop was associated with reduced wilt levels, indicating that crop rotation with non-hosts helps suppress soilborne inoculum.\u003c/p\u003e\u003cp\u003eThe logistic regression and correspondence analyses revealed that district, variety type, planting date, crop growth stage, seedbed type, weed infestation, soil type, drainage condition, and preceding crop were significantly associated with lentil wilt complex incidence and played critical roles in disease development. Certain variables, such as poor drainage, flat planting, local varieties, and early planting, had stronger contributions to increased disease incidence than others (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The model highlighted the relative importance of each factor, confirming that lentil wilt epidemics are shaped by interactions among multiple agroecological and management-related variables. Recognizing the influence of these factors is essential for developing effective and sustainable disease management strategies tailored to local conditions.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe study indicated that lentil wilt complex is the most prevalent, widely distributed and is a major problem across major lentil growing districts of Ethiopia. The results revealed that the disease incidence varied with districts, altitudinal ranges, time of planting, crop growth stage, varieties grown, preceding crops, soil type, soil drainage condition and weeding practice. This study identified key biophysical and agronomic factors significantly associated with the incidence of lentil wilt complex across major production areas in Ethiopia. Logistic regression and correspondence analyses consistently showed that high disease pressure was strongly linked to poor soil drainage, flatbed planting, early sowing, local cultivars, black soil types, and fields with weed infestation and wheat as the preceding crop. These factors contribute to favorable conditions for pathogen survival, buildup, and infection, and should be prioritized in wilt complex management strategies.\u003c/p\u003e\u003cp\u003eConversely, lower disease incidence was consistently observed under improved lentil varieties, ridge-furrow planting systems, late sowing dates, high-altitude zones, pod-filling stages, sandy loam soils, and in fields following tef as a rotation crop. These practices and environmental conditions can be recommended as components of an integrated disease management (IDM) package to suppress lentil wilt complex in affected regions. Future research should focus on refining these findings through multi-location field experiments, exploring the genetic resistance of improved varieties against complex pathogens, and assessing the role of microbial soil health and crop rotations in reducing pathogen inoculum. Further work is also needed to evaluate farmer practices, enhance fungicide targeting, and promote knowledge-based interventions for sustainable disease control.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of interests\u003c/h2\u003e\u003cp\u003eWe declare that there are no known financial conflicts of interest or personal relationships that could have affected the research presented in this paper.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eWe gratefully acknowledge the Ethiopian Institute of Agricultural Research, Bishoftu Agricultural Research Center, and the Protecting Ethiopian Lentil ACAR for financial support. Special thanks to Dr. Gezehagn, Dr. Tebkew Damte, and Mr. Tayu Shewaseged for their field survey assistance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbsar, M., Yasmeen, S., Mushahid, A., Zahid, H., Zeeshan, G., Saddam, H., \u0026amp; Abdul, Q. (2021). A destructive disease of lentil: Fusarium wilt of lentil. \u003cem\u003ePlant Archives\u003c/em\u003e 21:2117-2127. \u003c/li\u003e\n\u003cli\u003eAssfaw, D., \u0026amp; Negash, T., (2020). Spatial distribution and association of chickpea wilt/root rot epidemics with biophysical factors at West Shewa, Oromia Regional State, Ethiopia. \u003cem\u003eJournal of Plant Pathology \u0026amp; Microbiology\u003c/em\u003e, 11(9), 513. https://doi.org/10.35248/2157-7471.20.11.513.\u003c/li\u003e\n\u003cli\u003eAssfaw, D., \u0026amp; Negash, T., (2020). 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EIAR, Addia Ababa.\u003c/li\u003e\n\u003cli\u003eCSA, Cenrtral Statistical Agency 2023 \u0026ldquo;Agricultural Sample Survey (202/23). Report on Area and Production of Major Crops,\u0026rdquo; Statistical Bulletin 587, CSA, Addis Ababa, Ethiopia.\u003c/li\u003e\n\u003cli\u003eDarai, R., Gharti, D., Shrestha, R., Adhikari, B., \u0026amp; Bett, K. E., (2017). Comparative agronomic performance and reaction to Fusarium wilt of\u003cem\u003e Lens culinaris \u0026times; L. orientalis and L. culinaris \u0026times; L. ervoides \u003c/em\u003ederivatives. \u003cem\u003ePest Management Strategies in Pulses and Cereal Crops\u003c/em\u003e. 1:65-74.\u003c/li\u003e\n\u003cli\u003eDas, T., Sandham, T., Umbrey, Y., Mahapatra, S., \u0026amp; Das, S., (2022). 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Swedish University of Agricultural Sciences. DOI: 10.1094/PHI-A-2006 0517-01.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-plant-pathology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejpp","sideBox":"Learn more about [European Journal of Plant Pathology](http://link.springer.com/journal/10658)","snPcode":"10658","submissionUrl":"https://www.editorialmanager.com/ejpp/default2.aspx","title":"European Journal of Plant Pathology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Biophysical factors, Cropping practices, Disease incidence, Lens culinaris, Logistic regression, Lentil wilt complex","lastPublishedDoi":"10.21203/rs.3.rs-6933637/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6933637/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLentil (\u003cem\u003eLens culinaris\u003c/em\u003e) is the most vital legume crop for dietary protein and income source to smallholder farmers in Ethiopia. However, its production and productivity are severely constrained by soilborne diseases, particularly the lentil wilt complex disease. The disease is caused by a combination of soilborne pathogens such as \u003cem\u003eFusarium oxysporum\u003c/em\u003e, \u003cem\u003eRhizoctonia solani\u003c/em\u003e, \u003cem\u003eSclerotium rolfsii\u003c/em\u003e, and \u003cem\u003ePythium\u003c/em\u003e species resulting in significant yield losses. Lentil wilt complex caused by \u003cem\u003eFusarium spp\u003c/em\u003e. is the predominant lentil wilt causal pathogen. The objectives of this study were to assess lentil wilt distribution, incidence, and determine its association with agroecological and agronomic cropping practices in Ethiopia. A survey of 170 lentil fields was conducted during the 2023 main cropping season in eight major lentil growing districts. Disease incidence data was analyzed using descriptive statistics and χ2-based correspondence analysis to map associations with independent variables. The association of disease incidence with biophysical factors were analyzed using logistic regression. The disease was present in all fields, with incidence ranging from 15.6% in Siyadebenawayu to 57.2% in Lume. The study showed that highly significant associations (P\u0026thinsp;\u0026le;\u0026thinsp;0.001) between disease incidence and variables of district, altitude, planting date, soil type, drainage, seedbed preparation, and weeding practices. Plant population and growth stage significantly influenced outcomes (P\u0026thinsp;\u0026le;\u0026thinsp;0.05), while previous crop, fungicide, and fertilizer application had no significant impact (P\u0026thinsp;\u0026le;\u0026thinsp;0.05). Logistic regression showed higher wilt incidence in Lume (6.2 times) than Siyadebrenawayu, local varieties (6.2 times) than improved varieties, poorly drained (5.1 times) than well-drained soils, and black soil (2.6 times) than sandy loam. The findings indicated the need for targeted crop and cultural practices to reduce lentil wilt complex and improve sustainable production in Ethiopia's main lentil growing areas.\u003c/p\u003e","manuscriptTitle":"Spatial variability and key determinants of wilt complex disease: Insights from a survey in major lentil growing areas of Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-10 07:51:34","doi":"10.21203/rs.3.rs-6933637/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2025-12-26T17:08:32+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-07-26T17:36:06+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-07T08:03:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"European Journal of Plant Pathology","date":"2025-07-02T04:12:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-30T06:29:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Plant Pathology","date":"2025-06-26T14:49:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-plant-pathology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejpp","sideBox":"Learn more about [European Journal of Plant Pathology](http://link.springer.com/journal/10658)","snPcode":"10658","submissionUrl":"https://www.editorialmanager.com/ejpp/default2.aspx","title":"European Journal of Plant Pathology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"41e86d8e-8bc8-47d2-a2f4-67175ad6ae68","owner":[],"postedDate":"July 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-22T14:36:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-10 07:51:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6933637","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6933637","identity":"rs-6933637","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}