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Characteristic agro-ecological features of soil populations of bean root rot pathogens
Author’s Accepted Manuscript Characteristic agro-ecological features of soil populations of bean root rot pathogens Bita Naseri, Shina Ansari Hamadani
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S2452-2198(17)30059-9 http://dx.doi.org/10.1016/j.rhisph.2017.05.005 RHISPH66
To appear in: Rhizosphere Received date: 15 March 2017 Revised date: 6 May 2017 Accepted date: 6 May 2017 Cite this article as: Bita Naseri and Shina Ansari Hamadani, Characteristic agroecological features of soil populations of bean root rot pathogens, Rhizosphere, http://dx.doi.org/10.1016/j.rhisph.2017.05.005 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Epidemiology & bean root rot pathogens
Characteristic agro-ecological features of soil populations of bean root rot pathogens
Bita Naseri1*, Shina Ansari Hamadani2
1
Plant Protection Research Department, Kermanshah Agricultural & Natural Resource
Research & Education Center, AREEO, Kermanshah, Iran.
2
Department of Environmental Monitoring, Environment Protection Organization, Pardisan
Eco Park, Hakim Highway, Tehran, Iran.
*Correspondence. Phone: +98 83 3835 8128; Fax: +98 83 3835 8070; Mobile: +98 91 2622 4561; Email: [email protected]
Abstract Associations of soil populations of the predominant root rot pathogens with a number of agro-ecological features were assessed in bean production fields in Zanjan province, Iran. According to multivariate analyses, bean market class, herbicide, P-fertilizer, previous crop, region, field size, sand content, soil pH and texture, and urea use affected densities of Fusarium oxysporum and Macrophomina phaseolina populations in farm soils. For F. solani, bean class, herbicide and P-fertilizer significantly affected soil population density. Region, soil pH and sand were associated with Rhizoctonia solani populations in soil. Lower total soil populations of four root rot pathogens were associated with Red beans, larger fields, the
Kheirabad and Khodabandeh regions, P-fertilization, lack of trifluralin and urea application, and at higher levels of electrical conductivity, pH and sand of field soil. With this knowledge it should be possible to identify bean-root-rot-suppressive agro-ecosystems.
Keywords: agro-ecosystem; epidemiology; generalized linear model; Phaseolus vulgaris; soil-borne fungi.
1. Introduction Common or dry bean (Phaseolus vulgaris L.) constitutes an important legume food crop in Iran. Beans are widely cultivated over the whole country and more than 109,000 ha of dry beans are planted annually (Anonymous 2014). The lack of noticeable improvement in Iranian bean productivity with dry bean yields going from 223,303 tonnes in 2007 to 226,000 tonnes in 2014 (Anonymous 2014) is believed to be due in large part to diseases caused by soil-borne fungal pathogens. Depending on soil and environmental conditions, these pathogens may infect beans in any possible combination, resulting in a wide range of disease complexes (Abawi & Pastor-Corrales 1990). In Zanjan, Iran, surveys conducted in commercial bean farms during the last decade established that the most important of root rot pathogens are: Fusarium solani, Rhizoctonia solani, Macrophomina phaseolina and F. oxysporum. Yield losses in Zanjan's plantations due to root rots have been estimated as high as 65% (Naseri 2008). Root rots are present in almost all bean fields each year and yield loss caused by these pathogens is dependent on agro-ecological conditions (Naseri & Marefat 2011). Furthermore, earlier reports demonstrated that certain cultural factors can influence the intensity of root rot diseases in bean crops (Naseri & Marefat 2011; Naseri 2013b; 2014a,b,c; Naseri & Moradi 2015; Naseri et al. 2016). Because the fast spread of this disease complex in the rhizosphere of bean has been associated with high R. solani and F. solani
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populations in the soil (Naseri & Mousavi 2015), a systematic understanding of these soilborne pathogens in association with agro-ecosystem is also required. To meet this requirement, macro-scale epidemiological studies can not only benefit from a high diversity in pathogens population associated with various cropping systems, but also extend the applicability of findings to other farming areas different from the study region. Based on to multiple assessments mode during the growing season, the populations of these soil-borne pathogens in field soil varied among pathogen species and experimental sites (Naseri & Mousavi 2015). It is still unclear how much of this variation can be explained by differences in the application of fertilizers and herbicides, market classes of beans, previous crop, soil texture, soil electrical conductivity and pH. Such information assists in identifying important factors that affect inocula dynamics in farm soils for experimental designs and for disease management purposes. In recent years, interest in research on disease-suppressive soils has been wide spread. The capacity of a soil to allow a pathogen to survive, develop and cause infection of host plants is called soil receptivity (Amir & Alabouvette 1993). It is clear, however, that the level of soil suppressiveness is based on complex and poorly understood microbiological mechanisms (Cook & Baker 1983; Mazzola 2002), which in turn are affected by physico-chemical properties of soil environments. The dependence of soil suppressiveness on soil texture for R. solani (Otten & Gilligan 1998) and F. oxysporum f. sp. lini (Amir & Alabouvette 1993), on electrical conductivity for F. oxysporum f. sp. lactucae (Chitarra et al. 2013), on rotation for F. solani f. sp. phaseoli and R. solani (Burke & Kraft 1974), on soil pH for F. oxysporum f. sp. lini in flax (Linum usitatissimum; Höper et al. 1995), on soil phosphate for F. oxysporum in cotton (Gossypium hirsutum) and flax (Jones et al. 1989) and R. solani in cauliflower (Brassica oleracea; Chauhan et al. 2000) has been documented. Furthermore, no additional soil chemical variable such as concentrations of phosphorus, calcium and iron, which were irrelevant to epidemics occurred by F. solani in soybean
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commercial farms (Scherm et al. 1998), was included in this research. Kühn et al. (2009) also found no association of soil phosphorus, nitrogen and potassium with Rhizoctonia epidemics in commercial sugar beets. In Zanjan province, beans are produced over a wide range of soil types (Naseri 2013a). There is an increased interest in the design and use of disease-suppressive agroecosystems (Maia et al. 2010). In view of the above-mentioned reports and the scarcity of information on the effects of herbicides and fertilizers on soil populations of the fungi causing root rot of Phaseolus beans, a study was undertaken to examine the possible implications that such rhizospheric interactions may have for an effective control of root rots in this crop. The objectives of this investigation were to: (1) characterize the associations of bean class, farm size, herbicide, P-fertilizer, previous crop, region and urea variables with populations of F. solani, R. solani, M. phaseolina and F. oxysporum in farm soils, (2) determine the influence of soil EC, pH and texture on populations of pathogenic fungi, and (3) evaluation of the relationships between these agro-ecological properties and soil populations of the fungal pathogens of bean.
2. Materials and methods 2.1. Farm assessment The purpose of this study was to identify agro-ecological factors suppressive to bean root rot pathogens prevalent in the rhizosphere to improve the disease control. To achieve these goals, two closely related regional subprojects, one on agrosystems and the other on soil, were performed in 2007. The study covered the area which was cropped with bean in 2007 season estimated at 6,149 ha in Zanjan province, Iran. To conduct the soil sampling, 13 producer fields as experimental sites were randomly selected from the three main bean growing regions. Data on farm size, previous crop grown before bean, the application of herbicide, P-
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fertilizer and urea, and the market class of bean were provided by the farmers (Table 1). Only first application of herbicide, P-fertilizer and urea (before V3 stage) was considered for these factors, because the effects of these factors on soil populations were examined at both V3 and R9 stages. Zanjan has a cold semi-arid climate, with a hot and dry season from June to September. Mean annual rainfall and temperature is 315.4 mm and 11.7˚C, respectively. According to the report from the Zanjan meterological office (2007), the average monthly temperatures during the bean growing season were 20.1, 22.4 and 22.7˚C for June through August. In 2007, the crops received 28.9, 9.6 and 26.4 mm total monthly rainfall from June to August.
2.2. Soil assessment The complete details on soil sampling, fungal isolation, and identification were provided by Naseri and Mousavi (2015). For each farm, four samples (ca 1 kg in total) from the soil surrounding bean roots were collected (Table 2). Thus, 104 soil samples (field observations), taken from 13 experimental sites at V3 (vegetative stage with opening of the first trifoliate leaf) and R9 (pod maturity) growth stages of common bean (Van Schoonhoven & PastorCorrales, 1987), were assessed for pathogens populations. Four soil samples per field were pooled before fungal assessments. Then 1 ml aliquots of the 10-4 dilution of a soil subsample in Ringer’s solution (McLean & Ivimey-Cook 1941) were plated on four plates of soilextract-streptomycin-agar (SEA, James 1958) per subsample to estimate the number of colony forming units (CFU) per gram for F. solani, R. solani, M. phaseolina and F. oxysporum. This resulted in four population counts per field and thus 52 population records for each pathogen as lab observations of the 13 fields at a given sampling date. Fungal cultures on SEA were purified by subculturing single spores or hyphal tip on PDA and were identified according to microscopic examination of morphological characteristics after one
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week incubation using a taxonomic key (Domsch et al. 1980). Soil electrical conductivity (EC), pH and texture were determined at each sampling date according to standard methods as previously described (Naseri 2013a). According to USDA classification scheme for soil textural analysis (Gee & Bauder 1986), the soil types of experimental sites were as follows: clay loam in 8, clay in 3, loam in 1, and sandy clay loam in 1 farm. Clay-loamy soil was the predominant soil type in the three main bean growing regions studied.
2.3. Analytical methods At first step, the associations amongst 13 experimental sites according to 13 agro-ecological properties were evaluated using the principal coordinates (PCO) procedure (GENSTAT, Rothamsted Experimental Station, Harpenden, UK). PCO provided coordinates for each site or commercial field according to a similarity matrix. Sites closer together in PCO plot are more similar in agro-ecological composition. Because standard linear regression models consider each observation independent and then evaluate correlations among observations from the same farm (Maia et al. 2010), each lab observation or pathogen CFU record per field/stage was examined as independent individual. Thus, 52 CFU records per pathogen at each sampling time were used for analysis with df being 51. Before statistical analysis, data on the population of each fungal species at V3 and R9 stages were log-transformed. Total soil populations over growth stages and pathogens were also considered in the analysis that measures the sum of pathogens densities at both V3 and R9 stages. Accumulated soil population at either V3 or R9 refers to summed densities of the four pathogens at each stage. Multiple regression and multivariate analyses in the form of generalized linear models (GLMs) were performed to find significant associations within the data set using GenStat 6.1. The non-metric variables were regarded as factors in GLMs to determine the significant differences in the soil populations (dependent variables) between
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the levels of bean class, herbicide, P-fertilizer, previous crop, region, soil texture, and urea (independent variables). This meant that the first class of each factor was considered as the reference level to compare factor classes with each other. Associations of the soil population with metric agro-ecological data (EC, farm size, clay, sand, silt and pH) were simultaneously analyzed in the same model with the above-mentioned factors or non-metric variables. Due to linkages between soil-texture fractions (data not shown), only sand content was chosen for analyses. Two-way or higher order interactions between the factors and variables resulted in no estimation of parameters due to zero error variance. Therefore, only main effects of the agro-ecological traits (independent variable) on soil populations (dependent or response variable) of the pathogens were examined. The GLM method enabled joint analysis of the information on variables which were heterogeneous in nature (metric and non-metric). Then 15 soil population models were developed as represented in Table 2. To respect the condition of the test validity, the minimum number of individuals in factor categories was fixed at 5. Thus, most of independent non-metric variables were divided into two classes to represent the binary nature of the data (Table 1). For instance, the soil-texture variable was categorized into the two classes as follows: clay loam or others. In addition, previous findings were considered in the categorization of factors, e.g. more severe epidemics of Fusarium root rot were detected in Red beans compared to Pinto and White beans (Naseri and Marefat 2011). Likewise, earlier documents on significant differences between classes of herbicide and urea applications, previous crop (legumes & non-legumes), region (Abhar, Kheirabad, Khodabandeh), soil texture (Naseri 2013a,b, 2014a,b,c; Khodagholi et al. 2013; Naseri et al. 2016; Naseri and Moradi 2015; Naseri and Mousavi 2013) in the bean-Fusarium-Macrophomina-Rhizoctonia pathosystems were used to describe factor categories in the present study of pathogens populations in field soils. GLM offer a method of analysis in which important properties of
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binary data can be accounted for (McCullagh & Nelder, 1989). A stepwise procedure was then used to fit the model and remove irrelevant variables from the multivariate regression while finally still including as much of the soil-population-variability as possible. Van den Berg et al. (2012) also used GLM procedure to model CFU-based population densities of soilborne pathogens according to soil variables.
3. Results A significant parameter estimated by GLM indicated the noticeable contribution of variable or factor to response variable (Table 3). The positive and negative parameter estimated values indicated the direct and indirect associations with the response variable, the soil population of pathogens. The ranges for metric agro-ecological variables were as follows: 1.1-3.6 dS/m for EC, 0.3-2 ha for farm size, 24-44% clay, 14-54% sand, 22-42% silt, and 7.4-7.8 for pH. The PCO plots represented the experimental sites which were distributed normally according to the 11 agro-ecological properties (Fig. 1). Fields closer together across the PCO plot appeared to have more similar agro-ecosystems, and vice-versa.
3.1. Soil populations of F. oxysporum GLM analysis demonstrated that F. oxysporum populations at pod maturity (R9) stage were greater in Pinto and White beans than in Red beans. A greater soil EC was significantly linked to a smaller pathogen population at vegetative stage (V3) of bean growth. Populations of F. oxysporum at R9 and its accumulated population over the stages were different (P < 0.05) between the categories of herbicide, P-fertilizer and texture, with smaller populations of the pathogen being detected in trifluralin-applied and P-fertilized farms, and clay-loam soils. Planting beans after legumes lowered (P < 0.05) the accumulated fungal population in comparison with rotation with non-legumineous crops. At V3 stage, soil populations of F.
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oxysporum in farms located in region no. 2, Kheirabad, were significantly lower than those in region no. 1, Abhar. The accumulated F. oxysporum populations in region no. 1 were greater than the regions no. 2 and 3 (Khodabandeh). Farm size, soil pH and sand content were reversely linked to F. oxysporum populations in farm soil at either V3 or R9 stages, and to the accumulated population of the pathogen over the two stages. These indirect linkages demonstrated smaller populations in larger farms, and at higher levels of sand-content and soil-pH. Urea application increased (P < 0.05) soil population of F. oxysporum at V3 and R9, and the accumulated population over the stages.
3.2. Soil populations of F. solani Lower soil population levels of F. solani at V3 for Red beans, P-fertilization and trifluralin application differed (P < 0.05) from those for the Pinto and White beans, the lack of Pfertilization, and other herbicides, respectively. There did not appear to be any marked effect of farm size, previous crop, region, soil EC, pH, sand content, texture or urea on F. solani populations.
3.3. Soil populations of M. phaseolina Smaller populations of M. phaseolina at V3 in Red-bean, P-fertilized and region no. 2 farms differed (P < 0.05) from those without P-fertilization, Pinto-White beans, and region no. 1, respectively. At V3 stage, the application of trifluralin was associated with a lower pathogen population in the soil compared to the use of other herbicides. Larger M. phaseolina populations at R9 and the accumulated population over the stages in trifluralin-applied farms differed (P < 0.05) from those for the other herbicides. For the cropping history factor, larger population (at R9) of M. phaseolina and its accumulated population in bean farms rotated with legumes differed significantly from that for farms with non-legumes as previous crop.
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M. phaseolina populations at V3 and R9, and its accumulated population were linked (P < 0.05) to farm size, soil sand and pH. At V3 stage, lower population levels were detected in larger farms, and at higher levels of sand-content and soil-pH. However, populations of the pathogen at R9 and its accumulation over the two stages were smaller at lower levels of sandcontent and soil-pH, and in smaller farms. At vegetative stage of bean growth, smaller populations of M. phaseolina in clay-loam soils and unfertilized farms were different (P < 0.05) from those detected for the other types of soil texture and for urea fertilization. While larger M. phaseolina populations both at R9 and accumulated over the stages were observed in clay-loam soils and unfertilized farms.
3.4. Soil populations of R. solani For R. solani populations in field soils, there were not any significant effects of bean class, farm size, herbicide, P-fertilizer, previous crop, soil EC and texture, and urea application at either growth stage. However, smaller soil populations of R. solani at R9 in region no. 2 differed (P < 0.05) from region no. 1. Lower sand content of the farm soils was associated with larger R. solani populations in the soil determined at V3 and R9 stages. Smaller soil populations of R. solani at V3 stage was linked to lower levels of pH.
3.5. Total soil populations of root rot pathogens It should be noted that none of the agro-ecological factors and variables were associated with the total populations of the root rot pathogens studied at the pod maturity stage. The total population of the pathogens at V3 and accumulation over the stages were different (P < 0.05) between the levels of bean class, with Red beans being associated with smaller populations. At V3 stage, the larger total population of the root rot pathogens was associated with lower soil EC and pH levels. A significantly lower total fungal soil population at V3 stage was
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determined for either P-fertilization or lack of trifluralin application. Lower total pathogens populations at V3 in farms located in the regions no. 2 and 3 differed (P < 0.05) from that for the region no. 1. A lower accumulated total fungal population over the two stages was detected for the region no. 2 compared to the region no. 1. Farm size and sand content of soil were reversely linked to the total populations of the pathogens at V3 and accumulation over the stages. This indicated that smaller populations were observed in larger fields and greater sand contents. For urea fertilization, larger total population of pathogens at V3 and the accumulated population for urea-fertilized farms differed (P < 0.05) from the lack of ureafertilization.
4. Discussion The science of epidemiology is concerned with the patterns of pathogen spread in host plants and the large number of parameters that influence those patterns, and provide the essential information to develop protective cropping methods. In other words, epidemiology deals with the determination of the set of risk describers, from which control recommendations are subsequently deduced. This especially requires primary consideration of specific regional crop-environment-pathogen interactions before developing integrated disease management programs aimed at sustainability and improvements of the bean growing system. This was a preliminary agro-ecological survey with the object of identifying the best agronomic and soil descriptors of fungal populations in the natural soil under commercial production conditions. Subsequently, the GLM test was used to simply assess the suppressiveness of the soils to the predominant root rot pathogens based on the 13 agro-ecological characteristics studied, in order to develop more efficient management strategies based on further plot-scale investigations. To the best of knowledge, this study is the first to illustrate the complexity of fungal population patterns in terms of the combination of four different bean-pathogen-soil
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pathosystems, with a manageable number of farming and soil factors. This preliminary research highlights that progress in suppressing these pathogens can be made by using a combination of management options. Although the study focused on multivariate analyses of relationships between the agro-ecological and soil-population variables, the findings revealed the importance of certain indicators in predicting the population densities of the pathogens in farm soils of bean crops that should be further continued in future research. Because extensive soil microbiological studies require molecular methods which were inaccessible in this study, CFU counting restricted the present macro-scale examination to one-year results collected from 13 experimental sites. Likewise, Scherm et al. (1998) performed a CFU-based study of F. solani f.sp. glycines populations in commercial soybeans (Glycine max) in 1995 (six fields) and 1996 (three fields). They ignored environmental variations across two years and combined nine-field data for 10 soil traits, presumably to improve degrees of freedom (df = 225) and model resolution by adding to data without increasing the number of variables. Earlier macro-scale findings reported more severe epidemics of Fusarium and Rhizoctonia root rots in either pinto or white beans when compared with red beans (Naseri & Marefat 2011; Naseri 2013b). Singh et al. (2007) reported an acceptable level of resistance to Rhizoctonia root rot in most Great northern, Pink, Pinto and Red cultivars. However, the association of bean class with population density of root rot pathogens in the soil of bean farms is not well understood. The present research provided new information on the lower densities of F. oxysporum, F. solani and M. phaseolina populations in soils collected from red-bean farms. Future investigation may elucidate the mechanisms responsible for restricting populations of the above-mentioned pathogens in Red-bean-field soils. Chitarra et al. (2013) reported no effect of nutrient solution EC (electrical conductivity) ranging from 1.5-1.6 to 4.0-4.2 mS/cm on Fusarium wilt caused by F. oxysporum f.sp. lactucae in hydroponically grown lettuce (Lactuca sativa). However, they
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did not examine the effect of soil EC on the pathogen populations in the soil. EC, which is the most common measure of soil salinity, indicates the ability of an aqueous solution to carry an electric current (Chitarra et al. 2013). GLM results provided new information on the importance of soil EC, ranged from 1 to 4 mS/cm, as a population indicator for F. oxysporum in the soil under commercial bean production conditions. This approach indicates the necessity of incorporating soil EC in future small-scale research to improve outcomes of rootrot-control programs. There are a number of publications available on how herbicides interact with plant pathogens in the soil. Bentazon, paraquat and trifluralin have been reported to be toxic to Colletotricum truncatum, Dreschlera teres, and F. solani, respectively, at the rates recommended for crops (Caulder et al. 1987; Yu et al. 1988; Toubia-Rahme et al. 1995). Preliminary assessments indicate that the application of herbicides in particular trifluralin intensified epidemics of Fusarium and Rhizoctonia root rot across the commercial farms studied in the main bean growing region in Zanjan (Naseri & Moradi 2015; Naseri et al. 2016). Thus, attempts were made to describe the linkages of herbicide variables with populations of root rot pathogens under farm conditions. This appears to be the first report on the association of trifluralin on the population densities of F. oxysporum, F. solani and M. phaseolina in bean-farm soils. Moreover, the findings evidenced larger densities of F. oxysporum and F. solani, but smaller populations of M. phaseolina following trifluralin applications. This might be attributed to differential responses of these pathogens to trifluralin which merit further experimentation to reveal corresponding physiological causes of the interactions. Phosphate (PO4), which plays a central role as a reactant in plant cell metabolism and promotes root growth, is the least accessible macronutrient in many ecosystems (Abel et al. 2002). Soil phosphate has been reported as an effective control agent for a number of
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pathosystems such as F. oxysporum in cotton and linseed (Jones et al. 1989) and R. solani in cauliflower (Chauhan et al. 2000). Elsewhere, there were no associations between the concentration of phosphorus and population densities of F. solani f.sp. glycines in soybean soils in the USA (Scherm et al. 1998). However, such associations for individual crops will vary from soil to soil and disease to disease, so should be determined precisely for a given geographical area based on the results of the current study. Based on this macro-scale study, the application of P-fertilizers by bean growers was associated with lower soil population densities for F. oxysporum, F. solani and M. phaseolina compared to non-P-fertilized bean farms. This appears to be the first record on the interaction of P-fertilizer usage with the population density of bean root rot pathogens under farm conditions. Subsequent careful monitoring of accessible phosphorus levels and its balance with other nutrients in agricultural soils is also required to be considered in an overall strategy for disease management purposes. In the traditional management of Fusarium root rot, rotations of bean crops with cereal grains and alfalfa were the most effective methods among those practices tested by Burke & Miller (1983). Preceding crop effected bean root rots caused by F. solani, R. solani, M. phaseolina and F. oxysporum (Naseri 2013b 2014b; Naseri & Marefat 2011; Naseri & Tabande 2017). Smaller populations of F. solani f.sp. phaseoli and R. solani were observed in the control bean farms (no bean crops had been grown) in comparison with the pathogens populations previously accumulated in the soil of research sites where beans were grown in monoculture for 15 yr. (Burke & Kraft 1974). M. phaseolina population in an arid soil increased under continuous monocropping with a susceptible host, but considerable reduction was recorded with a change in crop sequence (Lodha et al. 1990). The present research showed the significant role of crop rotation in the variability of F. oxysporum and M. phaseolina populations, in particular when the pathogens populations at both growth stages
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were summed. Furthermore, growing non-legumes (potato or wheat) before bean corresponded with smaller M. phaseolina and larger F. oxysporum populations. Thus, future investigations to determine proper crop sequences for minimizing soil populations of root rot pathogens in bean cropping systems should consider the prevalence of pathogen. The findings of the present study demonstrated a significant consistent effect of region on populations of F. oxysporum, M. phaseolina and R. solani in farm soil. Such noticeable regional differences might be related to variations in cropping history, fertility, soil texture and weather conditions that merit further investigation. Thus, regional influences on the presence of root rot pathogens in the soil should be considered in improving soil management programs designed for the sustainable production of bean crops. According to previous publications, farm size was associated directly with charcoal root rot, caused by M. phaseoli, and inversely with Fusarium wilt, caused by F. oxysporum (Naseri 2014b,c). The present regional study evidenced linkages of M. phaseolina and F. oxysporum populations in the soil with farm-size variable. Field size, which may reflect not only the measure of host plants available to root rot pathogens for infection and inoculum production, but also the kind of farm management practices, e.g. mechanical or manual sowing, fertilizers and herbicides inputs, can be considered as the descriptor of M. phaseolina and F. oxysporum populations in bean-farm soil. The differential responses of the pathogens to changes in farm size should be noticed in future research. Certain factors, like low organic matter and poor moisture retention capacity in sandy soils (Otten & Gilligan 1998), have been thought to favor the prevalence of R. solani. Amir and Alabouvette (1993) reported that the inoculum density of F. oxysporum f.sp. lini decreased faster and to a lower threshold in a conducive (sand 96% and clay 2.5%) than in a suppressive soil (clay 37%, silt 44%, sand 19%), suggesting the dependence of soil fungistasis on soil texture. The improved survival of the pathogen following the addition of
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clay to the sandy soil showed that such modifications of the texture may induce changes in microbial activities resulting in changes in soil properties (Amir & Alabouvette 1993). Höper et al. (1995) also determines that soil suppressiveness to flax Fusarium wilt was dependent on soil texture, so that the suppression increased with additions of clay minerals at soil pH 7. Whereas soil texture, ranging from loamy sand to heavy clay, had no apparent affect on the vertical distribution of microsclerotial populations of M. phaseolina (Bruton & Reuveni 1985). This regional study evidenced the noticeable role of soil-texture and sand content in fluctuations of F. oxysporum, M. phaseolina and R. solani populations across the producers' farms studied. A greater sand content lowered early season F. oxysporum, M. phaseolina, R. solani, total pathogens and late season F. oxysporum, R. solani densities, but increased late season M. phaseolina and its over-season accumulated populations. Clay-loamy soils also appeared to be conducive to M. phaseolina and its over-season accumulated populations, but suppressive to early season M. phaseolina, late season F. oxysporum and its over-season accumulated populations According to differential responses of the pathogens densities to soil texture, recommendations on proper soil type for bean cultivation should concern the pathogens densities in field soil. In USA, there was no association between pH (6.0-7.5) and population densities of F. solani f. sp. glycines in the soil of soybean commercial farms (Scherm et al. 1998). Elsewhere, soil suppressiveness to Fusarium wilt of flax was correlated with soil pH and populations of fluorescent pseudomonads, so that the suppressiveness level increased with rising soil pH from 4 to 7 (Höper et al. 1995). One explanation for the effect of pH was that lower population densities were due to the domination of beneficial bacteria (fluorescent pseudomonads) in the farm soil at a higher pH. Another explanation was that a higher pH reduces the availability of the micronutrients required for F. oxysporum virulence and sporulation (Jones et al. 1989). The present findings added to available knowledge that
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population densities of F. oxysporum, R. solani and M. phaseolina in the soil of bean farms corresponded with soil pH. In the present study, GLM analyses recognized urea application as a descriptor of soil populations of F. oxysporum and M. phaseolina at the vegetative (V3) and pod maturity (R9) stages of bean growth. Moreover, fertilization at bean farms with urea corresponded with increasing total population of root rot pathogens in the soil. This appears to be the first record of the linkage of urea with the prevalence of bean root rot pathogens in farm soils. Furthermore, the differential reactions of F. oxysporum and M. phaseolina densities to urea application should be considered when developing soil management programs for bean farms.
4. Conclusion
Soil populations of the four major root rot pathogens were assessed in 13 bean production fields in Zanjan province, Iran.
F. oxysporum and M. phaseolina populations corresponded with bean class, herbicide, P-fertilizer, previous crop, region, field size, sand content, soil pH and texture, and urea use. Bean class, herbicide and P-fertilizer affected F. solani density, while region, soil pH and sand influenced R. solani population.
Lower total pathogens populations occurred in Red beans, larger fields, the Kheirabad and Khodabandeh regions, P-fertilization, lack of trifluralin and urea application, and at higher EC, pH and sand levels.
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References Abawi GS, Pastor-Corrales MA. 1990. Root rots of beans in Latin America and Africa: Diagnosis, research methodologies, and management strategies. Cali, Colombia: Centro Internacional de Agricultura Tropical.
Abel S, Ticconi CA, Delatorre CA. 2002. Phosphate sensing in higher plants. Physiol. Plant. 115:1-8.
Amir H, Alabouvette C. 1993. Involvement of soil abiotic factors in the mechanisms of soil suppressiveness to fusarium wilts. Soil. Biol. Biochem. 25:157–164. Anonymous. 2014. Agricultural Production Report in 2007 and 2014. Tehran, Iran: The Iranian Ministry of Agriculture. Bruton BD, Reuveni R. 1985. Vertical distribution of microsclerotia of Macrophomina phaseolina under various soil types and host crops. Agric. Ecosys. Environ. 12:165169. Burke DW, Kraft JM. 1974. Responses of beans and peas to root pathogens accumulated during monoculture of each crop species. Phytopathology. 64:546-549. Burke DW, Miller DE. 1983. Control of Fusarium root rot with resistant beans and cultural management. Plant Dis. 67:1312–1317. Caulder JD, Gottleib AR, Stowell L, Watson AK. 1987. Herbicidal compositions comprising microbial herbicides and chemical herbicides or plant growth regulators. Euro. Patent Applic. specific. 80:39-46. Chauhan RS, Maheshwari SK, Gandhi SK. 2000. Effect of nitrogen, phosphorus and farm yard manure levels on stem rot of cauliflower caused by Rhizoctonia solani. Agric. Sci. Digest. 20:36–38.
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Chitarra W, Pugliese M, Gilardi G, Gullino ML, Garibaldi A. 2013. Effect of silicates and electrical conductivity on Fusarium wilt of hydroponically grown lettuce. Commun Agric Appl Biol Sci. 78:555-557. Cook RJ, Baker KF. 1983. The nature and practice of biological control of plant pathogens. St Paul, MN, USA: APS. Domsch KH, Gams W, Anderson TH. 1980. Compendium of soil fungi. London: Academic Press. Gee GW, Bauder JW. 1986. Particle size analysis. In Methods of Soil Analysis. Part 1. Physical and Mineralogical Methods, ed. A. Klute. Madison, WI, USA: Soil Science Society of America. Höper H, Steinberg C, Alabouvette C. 1995. Involvement of clay type and pH in the mechanisms of soil suppressiveness to fusarium wilt of flax. Soil Biol. Biochem. 27:955–967. James N. 1958. Soil extract in soil microbiology. Can. J. Microbiol. 4:363-370. Jones JP, Engelhard AW, Woltz SS. 1989. Management of Fusarium wilt of vegetables and ornamentals by macro- and microelement nutrition. In: Soilborne Plant Pathogens: Management of diseases with macro-and microelements, ed. AW. Engelhard. USA: American Phytopathological Society. Khodagholi M, Hemmati R, Naseri B, Marefat A, 2013. Genotypic, phenotypic and pathogenicity variation of Fusarium solani isolates, the causal agent of bean root rots in Zanjan province. Iran. J. Pulses Res. 4:111-125. Kühn J, Rippel R, Schmidhalter U. 2009. Abiotic soil properties and the occurrence of Rhizoctonia crown and root rot in sugar beet. J. Plant Nut. Soil Sci. 172:661–668.
Lodha S, Mathur BK, Solanki KR. 1990. Factors influencing population dynamics of Macrophomina phaseolina in arid soils. Plant Soil. 125:75-80. 18
Maia SMF, Ogle SM, Cerri CC, Cerri CEP. 2010. Changes in soil organic carbon storage under different agricultural management systems in the Southwest Amazon Region of Brazil. Soil Till. Res. 106:177–184.
Mazzola M. 2002. Mechanisms of natural soil suppressiveness to soilborne diseases. Antonie van Leeuwenhoek 81:557–564.
McCullagh P, Nelder JA. 1989. Monographs on statistics and applied probability 37: general linear models. 2nd ed. London: Chapman and Hall. McLean MA, Ivimey-Cook WR, 1941. Plant Science Formulae. London, UK: Macmillan. Naseri B. 2008. Root rot of common bean in Zanjan, Iran: major pathogens and yield loss estimates. Australas. Plant Pathol. 37:546-551. Naseri B. 2013a. Epidemics of Rhizoctonia root rot in association with biological and physicochemical properties of field soil in bean crops. J. Phytopathol. 161:397–404. Naseri B. 2013b. Linkages of farmers’ operations with Rhizoctonia root rot spread in bean crops on a regional basis. J. Phytopathol. 161:814-822. Naseri B, 2014a. Bean production and Fusarium root rot in diverse soil environments in Iran. J. Soil Sci. Plant Nut. 14:177-188. Naseri B. 2014b. Charcoal rot of bean in diverse cropping systems and soil environments. J. Plant Dis. Protect. 121:20–25. Naseri B. 2014c. Sowing, field size, and soil characteristics affect bean-Fusarium-wilt pathosystems. J. Plant Dis. Protect. 121:171-176. Naseri B, Marefat A. 2011. Large-scale assessment of agricultural practices affecting Fusarium root rot and common bean yield. Eur. J. Plant Pathol. 131:179-195. Naseri B, Moradi P. 2015. Farm management strategies and the prevalence of Rhizoctonia root rot in bean. J. Plant Dis. Protect. 5:238-243.
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Naseri B, Mousavi SS. 2013. The development of Fusarium root rot and productivity according to planting date and depth, and bean variety. Australas. Plant Pathol. 42:133-139. Naseri B, Mousavi SS. 2015. Root rot pathogens in field soil, root and seed in relation to common bean (Phaseolus vulgaris) disease and seed production. Inter. J. Pest Manage. 61:60-67. Naseri B, Shobeiri SS, Tabande L. 2016. The intensity of a bean Fusarium root rot epidemic is dependent on planting strategies. J. Phytopathol. 164:147-154. Naseri B, Tabande L. 2017. Patterns of Fusarium wilt epidemics and bean production determined according to a large-scale dataset from agro-ecosystems. Rhizosphere. (In press). Otten W, Gilligan CA. 1998. Effect of physical conditions on the spatial and temporal dynamics of the soil-borne fungal pathogen Rhizoctonia solani. New Phytol. 138:629637. Scherm H, Yang XB, Lundeen P. 1998. Soil variables associated with sudden death syndrome in soybean fields in Iowa. Plant Dis. 82:1152-1157. Singh SP, Ter_an H, Lema M, Webster DM, Strausbaugh CA, Miklas PN, Schwartz HF, Brick MA. 2007. Seventy-five years of breeding dry bean of the western USA. Crop Sci. 47:981–989. Toubia-Rahme H, Ali-Haimoud DE, Barrett G, Albertini L. 1995. Inhibition of Dreschlera teres schleroid formation in barley straw by application of glyphosate or paraquat. Plant Dis. 79:595-598. Van den Berg W, Vos J, Grasman J. 2012. Multimodel inference for the prediction of disease symptoms and yield loss of potato in a two-year crop rotation experiment. Int. J. Agron. 1:1-9.
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Van Schoonhoven A, Pastor-Corrales MA. 1987. Standard System for the Evaluation of Bean Germplasm. Cali, Colombia: Centro Internacional de Agricultura Tropical. Yu SM, Templeton GE, Wolf DC. 1988. Trifluralin concentration and the growth of Fusarium solani f. sp. cucurvitae in liquid medium and soil. Soil Biol. Biochem. 20:607-612. Zanjan Meteorological Office. 2007. Annual weather reports. [Internet]. Available October 30, 2007, from: http://www. zanjanmet.ir/.
Table 1. The number of commercial bean farms and farm observations for categories of agroecological attributes. Factors Bean class Herbicide P-Fertilizer Previous crop Region
Factor categories (farms & observations no.) Pinto/White (5 & 20)
Red (8 & 32)
Bentazon/Paraquat/Roundupa (4 & 16)
Trifluralin (8 & 32)
Not applied (7 & 28)
Applied (6 & 24)
Legumesb (5 & 20)
Non-legumesc (8 & 32)
Abhar (5 & 20)
Kheirabad (5 & 20)
Khodabandeh (3 & 12)
Texture
Clay loam (8 & 32)
Others (5 & 20)
Urea
Not applied (5 & 20)
Applied (8 & 32)
a
Farmers used either bentazon, paraquat or roundup. 21
b
Alfalfa or bean.
c
Potato or wheat.
Table 2. The sampling design to study soil populations of major root rot pathogens in commercial bean farms. Soil sampling level
No of soil samples collected At vegetative stage
At pod maturity stage
Observation
1
1
Farm
4
4
Abhar
20
20
Kheirabad
20
20
Khodabandeh
12
12
Region
22
Table 3. Generalized linear models of soil populations of Fusarium oxysporum (Fo), F. solani (Fs), Macrophomina phaseoli (Mp), and Rhizoctonia solani (Rs) according to agro-ecological attributes in commercial bean farms. Variablesa Bean class
EC
Herbicide P-Fertilizer Previous
Models Fo-V3
-b
-2.3c
-
-
3.1
-
(1.5) Fo-Acc
Fs-V3
-
4.7
-
-
(2.1) Mp-V3
2.4
-
(0.9) Mp-R9
-
-
-
-
Rs-R9
-
-
-
-
3
(ha)
-
-3.7
-
-2.7
-0.1
-11.6
(1.2)
(0.1)
(3.4)
-3.6
-0.2
-8.9
6.5
6.5
(1.7)
(0.1)
(4.6)
(3.1)
(3.0)
Total -V3
Total-Acc
7.6
-6.7
6.8
-5.4
-8.6
-5.8
-0.3
-18.2
11.1
11.5
(3.0)
(3.6)
(2.7)
(2.4)
(3.7)
(2.3)
(0.1)
(6.4)
(4.3)
(4.2)
2.0
-4.2
-
-
-
-
-
-
-
-
(0.7)
(1.7)
2.6
-3.7
-
-2.5
-
-2.0
-0.1
-5.9
3.4
3.4
(1.3)
(1.6)
(1.0)
(0.1)
(2.8)
(1.9)
(1.8)
-4.8
-
4.1
0.1
9.5
-8.2
-6.8
(1.4)
(0.1)
(3.9)
(2.6)
(2.5)
3.5
0.1
6.7
-6.6
-5.5
(1.1)
(0.0)
(3.2)
(2.0)
(1.8)
-
-0.1
2.1
-
-
(0.0)
(1.2)
-0.1
-
-
-
-
16.2
-
-
(1.0) -2.9
-
-
(1.6) -
-1.7
-
-
(0.6) -
-
-
-
-
-1.6
9.6
-6.7
2.4
-6.7
(3.0)
(2.1)
(0.9)
(2.4)
14.9
-
-
-
-
-
(5.3) a
-
-
-
(0.04)
-8.7
-2.9
-6.1
-0.4
-20.1
(2.6)
(1.4)
(1.9)
(0.1)
(6.7)
-12.9
-
-7.2
-0.6
-
(4.2)
(0.2)
(4.6)
(4.8) -
17.3 (8.6)
Acc = accumulation of populations for two growth stages of bean; EC = electrical
conductivity; V3 = vegetative stage; R9 = pod maturity stage. b
(2.4)
(2.6)
-
-
7.3
(2.2)
-2.9
-
-
-4.4
(0.9) c
Urea
4.6
(1.0) Rs-V3
Sand Soil pH Texture
2
(1.3)
(1.8) Mp-Acc
Size
crop
(1.4) Fo-R9
Region
- = non-significant, P > 0.05.
23
c
Parameter estimates; bold numbers = P < 0.05; non-bold numbers = P < 0.10; numbers
inside brackets represent standard errors; df = 51.
Fig. 1. Principal coordinates plot of 13 commercial bean fields (points) according to agroecological characteristics.
24
Highligths
Due to the lack of information on the joint interactions of agro-ecological features on bean root rot pathogens, this study examined a manageable number of rhizospheric interactions in commercial farms.
Lower total populations of F. oxysporum, F. solani, M. phaseolina and R. solani occurred in Red beans, larger fields, the Kheirabad and Khodabandeh regions, P-fertilization, lack of trifluralin and urea application, and at higher EC, pH and sand levels.
This epidemiological information could be useful in the development of future experiments to mange bean root rots.
25
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PII: DOI: Reference:
S2452-2198(17)30059-9 http://dx.doi.org/10.1016/j.rhisph.2017.05.005 RHISPH66
To appear in: Rhizosphere Received date: 15 March 2017 Revised date: 6 May 2017 Accepted date: 6 May 2017 Cite this article as: Bita Naseri and Shina Ansari Hamadani, Characteristic agroecological features of soil populations of bean root rot pathogens, Rhizosphere, http://dx.doi.org/10.1016/j.rhisph.2017.05.005 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Epidemiology & bean root rot pathogens
Characteristic agro-ecological features of soil populations of bean root rot pathogens
Bita Naseri1*, Shina Ansari Hamadani2
1
Plant Protection Research Department, Kermanshah Agricultural & Natural Resource
Research & Education Center, AREEO, Kermanshah, Iran.
2
Department of Environmental Monitoring, Environment Protection Organization, Pardisan
Eco Park, Hakim Highway, Tehran, Iran.
*Correspondence. Phone: +98 83 3835 8128; Fax: +98 83 3835 8070; Mobile: +98 91 2622 4561; Email: [email protected]
Abstract Associations of soil populations of the predominant root rot pathogens with a number of agro-ecological features were assessed in bean production fields in Zanjan province, Iran. According to multivariate analyses, bean market class, herbicide, P-fertilizer, previous crop, region, field size, sand content, soil pH and texture, and urea use affected densities of Fusarium oxysporum and Macrophomina phaseolina populations in farm soils. For F. solani, bean class, herbicide and P-fertilizer significantly affected soil population density. Region, soil pH and sand were associated with Rhizoctonia solani populations in soil. Lower total soil populations of four root rot pathogens were associated with Red beans, larger fields, the
Kheirabad and Khodabandeh regions, P-fertilization, lack of trifluralin and urea application, and at higher levels of electrical conductivity, pH and sand of field soil. With this knowledge it should be possible to identify bean-root-rot-suppressive agro-ecosystems.
Keywords: agro-ecosystem; epidemiology; generalized linear model; Phaseolus vulgaris; soil-borne fungi.
1. Introduction Common or dry bean (Phaseolus vulgaris L.) constitutes an important legume food crop in Iran. Beans are widely cultivated over the whole country and more than 109,000 ha of dry beans are planted annually (Anonymous 2014). The lack of noticeable improvement in Iranian bean productivity with dry bean yields going from 223,303 tonnes in 2007 to 226,000 tonnes in 2014 (Anonymous 2014) is believed to be due in large part to diseases caused by soil-borne fungal pathogens. Depending on soil and environmental conditions, these pathogens may infect beans in any possible combination, resulting in a wide range of disease complexes (Abawi & Pastor-Corrales 1990). In Zanjan, Iran, surveys conducted in commercial bean farms during the last decade established that the most important of root rot pathogens are: Fusarium solani, Rhizoctonia solani, Macrophomina phaseolina and F. oxysporum. Yield losses in Zanjan's plantations due to root rots have been estimated as high as 65% (Naseri 2008). Root rots are present in almost all bean fields each year and yield loss caused by these pathogens is dependent on agro-ecological conditions (Naseri & Marefat 2011). Furthermore, earlier reports demonstrated that certain cultural factors can influence the intensity of root rot diseases in bean crops (Naseri & Marefat 2011; Naseri 2013b; 2014a,b,c; Naseri & Moradi 2015; Naseri et al. 2016). Because the fast spread of this disease complex in the rhizosphere of bean has been associated with high R. solani and F. solani
1
populations in the soil (Naseri & Mousavi 2015), a systematic understanding of these soilborne pathogens in association with agro-ecosystem is also required. To meet this requirement, macro-scale epidemiological studies can not only benefit from a high diversity in pathogens population associated with various cropping systems, but also extend the applicability of findings to other farming areas different from the study region. Based on to multiple assessments mode during the growing season, the populations of these soil-borne pathogens in field soil varied among pathogen species and experimental sites (Naseri & Mousavi 2015). It is still unclear how much of this variation can be explained by differences in the application of fertilizers and herbicides, market classes of beans, previous crop, soil texture, soil electrical conductivity and pH. Such information assists in identifying important factors that affect inocula dynamics in farm soils for experimental designs and for disease management purposes. In recent years, interest in research on disease-suppressive soils has been wide spread. The capacity of a soil to allow a pathogen to survive, develop and cause infection of host plants is called soil receptivity (Amir & Alabouvette 1993). It is clear, however, that the level of soil suppressiveness is based on complex and poorly understood microbiological mechanisms (Cook & Baker 1983; Mazzola 2002), which in turn are affected by physico-chemical properties of soil environments. The dependence of soil suppressiveness on soil texture for R. solani (Otten & Gilligan 1998) and F. oxysporum f. sp. lini (Amir & Alabouvette 1993), on electrical conductivity for F. oxysporum f. sp. lactucae (Chitarra et al. 2013), on rotation for F. solani f. sp. phaseoli and R. solani (Burke & Kraft 1974), on soil pH for F. oxysporum f. sp. lini in flax (Linum usitatissimum; Höper et al. 1995), on soil phosphate for F. oxysporum in cotton (Gossypium hirsutum) and flax (Jones et al. 1989) and R. solani in cauliflower (Brassica oleracea; Chauhan et al. 2000) has been documented. Furthermore, no additional soil chemical variable such as concentrations of phosphorus, calcium and iron, which were irrelevant to epidemics occurred by F. solani in soybean
2
commercial farms (Scherm et al. 1998), was included in this research. Kühn et al. (2009) also found no association of soil phosphorus, nitrogen and potassium with Rhizoctonia epidemics in commercial sugar beets. In Zanjan province, beans are produced over a wide range of soil types (Naseri 2013a). There is an increased interest in the design and use of disease-suppressive agroecosystems (Maia et al. 2010). In view of the above-mentioned reports and the scarcity of information on the effects of herbicides and fertilizers on soil populations of the fungi causing root rot of Phaseolus beans, a study was undertaken to examine the possible implications that such rhizospheric interactions may have for an effective control of root rots in this crop. The objectives of this investigation were to: (1) characterize the associations of bean class, farm size, herbicide, P-fertilizer, previous crop, region and urea variables with populations of F. solani, R. solani, M. phaseolina and F. oxysporum in farm soils, (2) determine the influence of soil EC, pH and texture on populations of pathogenic fungi, and (3) evaluation of the relationships between these agro-ecological properties and soil populations of the fungal pathogens of bean.
2. Materials and methods 2.1. Farm assessment The purpose of this study was to identify agro-ecological factors suppressive to bean root rot pathogens prevalent in the rhizosphere to improve the disease control. To achieve these goals, two closely related regional subprojects, one on agrosystems and the other on soil, were performed in 2007. The study covered the area which was cropped with bean in 2007 season estimated at 6,149 ha in Zanjan province, Iran. To conduct the soil sampling, 13 producer fields as experimental sites were randomly selected from the three main bean growing regions. Data on farm size, previous crop grown before bean, the application of herbicide, P-
3
fertilizer and urea, and the market class of bean were provided by the farmers (Table 1). Only first application of herbicide, P-fertilizer and urea (before V3 stage) was considered for these factors, because the effects of these factors on soil populations were examined at both V3 and R9 stages. Zanjan has a cold semi-arid climate, with a hot and dry season from June to September. Mean annual rainfall and temperature is 315.4 mm and 11.7˚C, respectively. According to the report from the Zanjan meterological office (2007), the average monthly temperatures during the bean growing season were 20.1, 22.4 and 22.7˚C for June through August. In 2007, the crops received 28.9, 9.6 and 26.4 mm total monthly rainfall from June to August.
2.2. Soil assessment The complete details on soil sampling, fungal isolation, and identification were provided by Naseri and Mousavi (2015). For each farm, four samples (ca 1 kg in total) from the soil surrounding bean roots were collected (Table 2). Thus, 104 soil samples (field observations), taken from 13 experimental sites at V3 (vegetative stage with opening of the first trifoliate leaf) and R9 (pod maturity) growth stages of common bean (Van Schoonhoven & PastorCorrales, 1987), were assessed for pathogens populations. Four soil samples per field were pooled before fungal assessments. Then 1 ml aliquots of the 10-4 dilution of a soil subsample in Ringer’s solution (McLean & Ivimey-Cook 1941) were plated on four plates of soilextract-streptomycin-agar (SEA, James 1958) per subsample to estimate the number of colony forming units (CFU) per gram for F. solani, R. solani, M. phaseolina and F. oxysporum. This resulted in four population counts per field and thus 52 population records for each pathogen as lab observations of the 13 fields at a given sampling date. Fungal cultures on SEA were purified by subculturing single spores or hyphal tip on PDA and were identified according to microscopic examination of morphological characteristics after one
4
week incubation using a taxonomic key (Domsch et al. 1980). Soil electrical conductivity (EC), pH and texture were determined at each sampling date according to standard methods as previously described (Naseri 2013a). According to USDA classification scheme for soil textural analysis (Gee & Bauder 1986), the soil types of experimental sites were as follows: clay loam in 8, clay in 3, loam in 1, and sandy clay loam in 1 farm. Clay-loamy soil was the predominant soil type in the three main bean growing regions studied.
2.3. Analytical methods At first step, the associations amongst 13 experimental sites according to 13 agro-ecological properties were evaluated using the principal coordinates (PCO) procedure (GENSTAT, Rothamsted Experimental Station, Harpenden, UK). PCO provided coordinates for each site or commercial field according to a similarity matrix. Sites closer together in PCO plot are more similar in agro-ecological composition. Because standard linear regression models consider each observation independent and then evaluate correlations among observations from the same farm (Maia et al. 2010), each lab observation or pathogen CFU record per field/stage was examined as independent individual. Thus, 52 CFU records per pathogen at each sampling time were used for analysis with df being 51. Before statistical analysis, data on the population of each fungal species at V3 and R9 stages were log-transformed. Total soil populations over growth stages and pathogens were also considered in the analysis that measures the sum of pathogens densities at both V3 and R9 stages. Accumulated soil population at either V3 or R9 refers to summed densities of the four pathogens at each stage. Multiple regression and multivariate analyses in the form of generalized linear models (GLMs) were performed to find significant associations within the data set using GenStat 6.1. The non-metric variables were regarded as factors in GLMs to determine the significant differences in the soil populations (dependent variables) between
5
the levels of bean class, herbicide, P-fertilizer, previous crop, region, soil texture, and urea (independent variables). This meant that the first class of each factor was considered as the reference level to compare factor classes with each other. Associations of the soil population with metric agro-ecological data (EC, farm size, clay, sand, silt and pH) were simultaneously analyzed in the same model with the above-mentioned factors or non-metric variables. Due to linkages between soil-texture fractions (data not shown), only sand content was chosen for analyses. Two-way or higher order interactions between the factors and variables resulted in no estimation of parameters due to zero error variance. Therefore, only main effects of the agro-ecological traits (independent variable) on soil populations (dependent or response variable) of the pathogens were examined. The GLM method enabled joint analysis of the information on variables which were heterogeneous in nature (metric and non-metric). Then 15 soil population models were developed as represented in Table 2. To respect the condition of the test validity, the minimum number of individuals in factor categories was fixed at 5. Thus, most of independent non-metric variables were divided into two classes to represent the binary nature of the data (Table 1). For instance, the soil-texture variable was categorized into the two classes as follows: clay loam or others. In addition, previous findings were considered in the categorization of factors, e.g. more severe epidemics of Fusarium root rot were detected in Red beans compared to Pinto and White beans (Naseri and Marefat 2011). Likewise, earlier documents on significant differences between classes of herbicide and urea applications, previous crop (legumes & non-legumes), region (Abhar, Kheirabad, Khodabandeh), soil texture (Naseri 2013a,b, 2014a,b,c; Khodagholi et al. 2013; Naseri et al. 2016; Naseri and Moradi 2015; Naseri and Mousavi 2013) in the bean-Fusarium-Macrophomina-Rhizoctonia pathosystems were used to describe factor categories in the present study of pathogens populations in field soils. GLM offer a method of analysis in which important properties of
6
binary data can be accounted for (McCullagh & Nelder, 1989). A stepwise procedure was then used to fit the model and remove irrelevant variables from the multivariate regression while finally still including as much of the soil-population-variability as possible. Van den Berg et al. (2012) also used GLM procedure to model CFU-based population densities of soilborne pathogens according to soil variables.
3. Results A significant parameter estimated by GLM indicated the noticeable contribution of variable or factor to response variable (Table 3). The positive and negative parameter estimated values indicated the direct and indirect associations with the response variable, the soil population of pathogens. The ranges for metric agro-ecological variables were as follows: 1.1-3.6 dS/m for EC, 0.3-2 ha for farm size, 24-44% clay, 14-54% sand, 22-42% silt, and 7.4-7.8 for pH. The PCO plots represented the experimental sites which were distributed normally according to the 11 agro-ecological properties (Fig. 1). Fields closer together across the PCO plot appeared to have more similar agro-ecosystems, and vice-versa.
3.1. Soil populations of F. oxysporum GLM analysis demonstrated that F. oxysporum populations at pod maturity (R9) stage were greater in Pinto and White beans than in Red beans. A greater soil EC was significantly linked to a smaller pathogen population at vegetative stage (V3) of bean growth. Populations of F. oxysporum at R9 and its accumulated population over the stages were different (P < 0.05) between the categories of herbicide, P-fertilizer and texture, with smaller populations of the pathogen being detected in trifluralin-applied and P-fertilized farms, and clay-loam soils. Planting beans after legumes lowered (P < 0.05) the accumulated fungal population in comparison with rotation with non-legumineous crops. At V3 stage, soil populations of F.
7
oxysporum in farms located in region no. 2, Kheirabad, were significantly lower than those in region no. 1, Abhar. The accumulated F. oxysporum populations in region no. 1 were greater than the regions no. 2 and 3 (Khodabandeh). Farm size, soil pH and sand content were reversely linked to F. oxysporum populations in farm soil at either V3 or R9 stages, and to the accumulated population of the pathogen over the two stages. These indirect linkages demonstrated smaller populations in larger farms, and at higher levels of sand-content and soil-pH. Urea application increased (P < 0.05) soil population of F. oxysporum at V3 and R9, and the accumulated population over the stages.
3.2. Soil populations of F. solani Lower soil population levels of F. solani at V3 for Red beans, P-fertilization and trifluralin application differed (P < 0.05) from those for the Pinto and White beans, the lack of Pfertilization, and other herbicides, respectively. There did not appear to be any marked effect of farm size, previous crop, region, soil EC, pH, sand content, texture or urea on F. solani populations.
3.3. Soil populations of M. phaseolina Smaller populations of M. phaseolina at V3 in Red-bean, P-fertilized and region no. 2 farms differed (P < 0.05) from those without P-fertilization, Pinto-White beans, and region no. 1, respectively. At V3 stage, the application of trifluralin was associated with a lower pathogen population in the soil compared to the use of other herbicides. Larger M. phaseolina populations at R9 and the accumulated population over the stages in trifluralin-applied farms differed (P < 0.05) from those for the other herbicides. For the cropping history factor, larger population (at R9) of M. phaseolina and its accumulated population in bean farms rotated with legumes differed significantly from that for farms with non-legumes as previous crop.
8
M. phaseolina populations at V3 and R9, and its accumulated population were linked (P < 0.05) to farm size, soil sand and pH. At V3 stage, lower population levels were detected in larger farms, and at higher levels of sand-content and soil-pH. However, populations of the pathogen at R9 and its accumulation over the two stages were smaller at lower levels of sandcontent and soil-pH, and in smaller farms. At vegetative stage of bean growth, smaller populations of M. phaseolina in clay-loam soils and unfertilized farms were different (P < 0.05) from those detected for the other types of soil texture and for urea fertilization. While larger M. phaseolina populations both at R9 and accumulated over the stages were observed in clay-loam soils and unfertilized farms.
3.4. Soil populations of R. solani For R. solani populations in field soils, there were not any significant effects of bean class, farm size, herbicide, P-fertilizer, previous crop, soil EC and texture, and urea application at either growth stage. However, smaller soil populations of R. solani at R9 in region no. 2 differed (P < 0.05) from region no. 1. Lower sand content of the farm soils was associated with larger R. solani populations in the soil determined at V3 and R9 stages. Smaller soil populations of R. solani at V3 stage was linked to lower levels of pH.
3.5. Total soil populations of root rot pathogens It should be noted that none of the agro-ecological factors and variables were associated with the total populations of the root rot pathogens studied at the pod maturity stage. The total population of the pathogens at V3 and accumulation over the stages were different (P < 0.05) between the levels of bean class, with Red beans being associated with smaller populations. At V3 stage, the larger total population of the root rot pathogens was associated with lower soil EC and pH levels. A significantly lower total fungal soil population at V3 stage was
9
determined for either P-fertilization or lack of trifluralin application. Lower total pathogens populations at V3 in farms located in the regions no. 2 and 3 differed (P < 0.05) from that for the region no. 1. A lower accumulated total fungal population over the two stages was detected for the region no. 2 compared to the region no. 1. Farm size and sand content of soil were reversely linked to the total populations of the pathogens at V3 and accumulation over the stages. This indicated that smaller populations were observed in larger fields and greater sand contents. For urea fertilization, larger total population of pathogens at V3 and the accumulated population for urea-fertilized farms differed (P < 0.05) from the lack of ureafertilization.
4. Discussion The science of epidemiology is concerned with the patterns of pathogen spread in host plants and the large number of parameters that influence those patterns, and provide the essential information to develop protective cropping methods. In other words, epidemiology deals with the determination of the set of risk describers, from which control recommendations are subsequently deduced. This especially requires primary consideration of specific regional crop-environment-pathogen interactions before developing integrated disease management programs aimed at sustainability and improvements of the bean growing system. This was a preliminary agro-ecological survey with the object of identifying the best agronomic and soil descriptors of fungal populations in the natural soil under commercial production conditions. Subsequently, the GLM test was used to simply assess the suppressiveness of the soils to the predominant root rot pathogens based on the 13 agro-ecological characteristics studied, in order to develop more efficient management strategies based on further plot-scale investigations. To the best of knowledge, this study is the first to illustrate the complexity of fungal population patterns in terms of the combination of four different bean-pathogen-soil
10
pathosystems, with a manageable number of farming and soil factors. This preliminary research highlights that progress in suppressing these pathogens can be made by using a combination of management options. Although the study focused on multivariate analyses of relationships between the agro-ecological and soil-population variables, the findings revealed the importance of certain indicators in predicting the population densities of the pathogens in farm soils of bean crops that should be further continued in future research. Because extensive soil microbiological studies require molecular methods which were inaccessible in this study, CFU counting restricted the present macro-scale examination to one-year results collected from 13 experimental sites. Likewise, Scherm et al. (1998) performed a CFU-based study of F. solani f.sp. glycines populations in commercial soybeans (Glycine max) in 1995 (six fields) and 1996 (three fields). They ignored environmental variations across two years and combined nine-field data for 10 soil traits, presumably to improve degrees of freedom (df = 225) and model resolution by adding to data without increasing the number of variables. Earlier macro-scale findings reported more severe epidemics of Fusarium and Rhizoctonia root rots in either pinto or white beans when compared with red beans (Naseri & Marefat 2011; Naseri 2013b). Singh et al. (2007) reported an acceptable level of resistance to Rhizoctonia root rot in most Great northern, Pink, Pinto and Red cultivars. However, the association of bean class with population density of root rot pathogens in the soil of bean farms is not well understood. The present research provided new information on the lower densities of F. oxysporum, F. solani and M. phaseolina populations in soils collected from red-bean farms. Future investigation may elucidate the mechanisms responsible for restricting populations of the above-mentioned pathogens in Red-bean-field soils. Chitarra et al. (2013) reported no effect of nutrient solution EC (electrical conductivity) ranging from 1.5-1.6 to 4.0-4.2 mS/cm on Fusarium wilt caused by F. oxysporum f.sp. lactucae in hydroponically grown lettuce (Lactuca sativa). However, they
11
did not examine the effect of soil EC on the pathogen populations in the soil. EC, which is the most common measure of soil salinity, indicates the ability of an aqueous solution to carry an electric current (Chitarra et al. 2013). GLM results provided new information on the importance of soil EC, ranged from 1 to 4 mS/cm, as a population indicator for F. oxysporum in the soil under commercial bean production conditions. This approach indicates the necessity of incorporating soil EC in future small-scale research to improve outcomes of rootrot-control programs. There are a number of publications available on how herbicides interact with plant pathogens in the soil. Bentazon, paraquat and trifluralin have been reported to be toxic to Colletotricum truncatum, Dreschlera teres, and F. solani, respectively, at the rates recommended for crops (Caulder et al. 1987; Yu et al. 1988; Toubia-Rahme et al. 1995). Preliminary assessments indicate that the application of herbicides in particular trifluralin intensified epidemics of Fusarium and Rhizoctonia root rot across the commercial farms studied in the main bean growing region in Zanjan (Naseri & Moradi 2015; Naseri et al. 2016). Thus, attempts were made to describe the linkages of herbicide variables with populations of root rot pathogens under farm conditions. This appears to be the first report on the association of trifluralin on the population densities of F. oxysporum, F. solani and M. phaseolina in bean-farm soils. Moreover, the findings evidenced larger densities of F. oxysporum and F. solani, but smaller populations of M. phaseolina following trifluralin applications. This might be attributed to differential responses of these pathogens to trifluralin which merit further experimentation to reveal corresponding physiological causes of the interactions. Phosphate (PO4), which plays a central role as a reactant in plant cell metabolism and promotes root growth, is the least accessible macronutrient in many ecosystems (Abel et al. 2002). Soil phosphate has been reported as an effective control agent for a number of
12
pathosystems such as F. oxysporum in cotton and linseed (Jones et al. 1989) and R. solani in cauliflower (Chauhan et al. 2000). Elsewhere, there were no associations between the concentration of phosphorus and population densities of F. solani f.sp. glycines in soybean soils in the USA (Scherm et al. 1998). However, such associations for individual crops will vary from soil to soil and disease to disease, so should be determined precisely for a given geographical area based on the results of the current study. Based on this macro-scale study, the application of P-fertilizers by bean growers was associated with lower soil population densities for F. oxysporum, F. solani and M. phaseolina compared to non-P-fertilized bean farms. This appears to be the first record on the interaction of P-fertilizer usage with the population density of bean root rot pathogens under farm conditions. Subsequent careful monitoring of accessible phosphorus levels and its balance with other nutrients in agricultural soils is also required to be considered in an overall strategy for disease management purposes. In the traditional management of Fusarium root rot, rotations of bean crops with cereal grains and alfalfa were the most effective methods among those practices tested by Burke & Miller (1983). Preceding crop effected bean root rots caused by F. solani, R. solani, M. phaseolina and F. oxysporum (Naseri 2013b 2014b; Naseri & Marefat 2011; Naseri & Tabande 2017). Smaller populations of F. solani f.sp. phaseoli and R. solani were observed in the control bean farms (no bean crops had been grown) in comparison with the pathogens populations previously accumulated in the soil of research sites where beans were grown in monoculture for 15 yr. (Burke & Kraft 1974). M. phaseolina population in an arid soil increased under continuous monocropping with a susceptible host, but considerable reduction was recorded with a change in crop sequence (Lodha et al. 1990). The present research showed the significant role of crop rotation in the variability of F. oxysporum and M. phaseolina populations, in particular when the pathogens populations at both growth stages
13
were summed. Furthermore, growing non-legumes (potato or wheat) before bean corresponded with smaller M. phaseolina and larger F. oxysporum populations. Thus, future investigations to determine proper crop sequences for minimizing soil populations of root rot pathogens in bean cropping systems should consider the prevalence of pathogen. The findings of the present study demonstrated a significant consistent effect of region on populations of F. oxysporum, M. phaseolina and R. solani in farm soil. Such noticeable regional differences might be related to variations in cropping history, fertility, soil texture and weather conditions that merit further investigation. Thus, regional influences on the presence of root rot pathogens in the soil should be considered in improving soil management programs designed for the sustainable production of bean crops. According to previous publications, farm size was associated directly with charcoal root rot, caused by M. phaseoli, and inversely with Fusarium wilt, caused by F. oxysporum (Naseri 2014b,c). The present regional study evidenced linkages of M. phaseolina and F. oxysporum populations in the soil with farm-size variable. Field size, which may reflect not only the measure of host plants available to root rot pathogens for infection and inoculum production, but also the kind of farm management practices, e.g. mechanical or manual sowing, fertilizers and herbicides inputs, can be considered as the descriptor of M. phaseolina and F. oxysporum populations in bean-farm soil. The differential responses of the pathogens to changes in farm size should be noticed in future research. Certain factors, like low organic matter and poor moisture retention capacity in sandy soils (Otten & Gilligan 1998), have been thought to favor the prevalence of R. solani. Amir and Alabouvette (1993) reported that the inoculum density of F. oxysporum f.sp. lini decreased faster and to a lower threshold in a conducive (sand 96% and clay 2.5%) than in a suppressive soil (clay 37%, silt 44%, sand 19%), suggesting the dependence of soil fungistasis on soil texture. The improved survival of the pathogen following the addition of
14
clay to the sandy soil showed that such modifications of the texture may induce changes in microbial activities resulting in changes in soil properties (Amir & Alabouvette 1993). Höper et al. (1995) also determines that soil suppressiveness to flax Fusarium wilt was dependent on soil texture, so that the suppression increased with additions of clay minerals at soil pH 7. Whereas soil texture, ranging from loamy sand to heavy clay, had no apparent affect on the vertical distribution of microsclerotial populations of M. phaseolina (Bruton & Reuveni 1985). This regional study evidenced the noticeable role of soil-texture and sand content in fluctuations of F. oxysporum, M. phaseolina and R. solani populations across the producers' farms studied. A greater sand content lowered early season F. oxysporum, M. phaseolina, R. solani, total pathogens and late season F. oxysporum, R. solani densities, but increased late season M. phaseolina and its over-season accumulated populations. Clay-loamy soils also appeared to be conducive to M. phaseolina and its over-season accumulated populations, but suppressive to early season M. phaseolina, late season F. oxysporum and its over-season accumulated populations According to differential responses of the pathogens densities to soil texture, recommendations on proper soil type for bean cultivation should concern the pathogens densities in field soil. In USA, there was no association between pH (6.0-7.5) and population densities of F. solani f. sp. glycines in the soil of soybean commercial farms (Scherm et al. 1998). Elsewhere, soil suppressiveness to Fusarium wilt of flax was correlated with soil pH and populations of fluorescent pseudomonads, so that the suppressiveness level increased with rising soil pH from 4 to 7 (Höper et al. 1995). One explanation for the effect of pH was that lower population densities were due to the domination of beneficial bacteria (fluorescent pseudomonads) in the farm soil at a higher pH. Another explanation was that a higher pH reduces the availability of the micronutrients required for F. oxysporum virulence and sporulation (Jones et al. 1989). The present findings added to available knowledge that
15
population densities of F. oxysporum, R. solani and M. phaseolina in the soil of bean farms corresponded with soil pH. In the present study, GLM analyses recognized urea application as a descriptor of soil populations of F. oxysporum and M. phaseolina at the vegetative (V3) and pod maturity (R9) stages of bean growth. Moreover, fertilization at bean farms with urea corresponded with increasing total population of root rot pathogens in the soil. This appears to be the first record of the linkage of urea with the prevalence of bean root rot pathogens in farm soils. Furthermore, the differential reactions of F. oxysporum and M. phaseolina densities to urea application should be considered when developing soil management programs for bean farms.
4. Conclusion
Soil populations of the four major root rot pathogens were assessed in 13 bean production fields in Zanjan province, Iran.
F. oxysporum and M. phaseolina populations corresponded with bean class, herbicide, P-fertilizer, previous crop, region, field size, sand content, soil pH and texture, and urea use. Bean class, herbicide and P-fertilizer affected F. solani density, while region, soil pH and sand influenced R. solani population.
Lower total pathogens populations occurred in Red beans, larger fields, the Kheirabad and Khodabandeh regions, P-fertilization, lack of trifluralin and urea application, and at higher EC, pH and sand levels.
16
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Van Schoonhoven A, Pastor-Corrales MA. 1987. Standard System for the Evaluation of Bean Germplasm. Cali, Colombia: Centro Internacional de Agricultura Tropical. Yu SM, Templeton GE, Wolf DC. 1988. Trifluralin concentration and the growth of Fusarium solani f. sp. cucurvitae in liquid medium and soil. Soil Biol. Biochem. 20:607-612. Zanjan Meteorological Office. 2007. Annual weather reports. [Internet]. Available October 30, 2007, from: http://www. zanjanmet.ir/.
Table 1. The number of commercial bean farms and farm observations for categories of agroecological attributes. Factors Bean class Herbicide P-Fertilizer Previous crop Region
Factor categories (farms & observations no.) Pinto/White (5 & 20)
Red (8 & 32)
Bentazon/Paraquat/Roundupa (4 & 16)
Trifluralin (8 & 32)
Not applied (7 & 28)
Applied (6 & 24)
Legumesb (5 & 20)
Non-legumesc (8 & 32)
Abhar (5 & 20)
Kheirabad (5 & 20)
Khodabandeh (3 & 12)
Texture
Clay loam (8 & 32)
Others (5 & 20)
Urea
Not applied (5 & 20)
Applied (8 & 32)
a
Farmers used either bentazon, paraquat or roundup. 21
b
Alfalfa or bean.
c
Potato or wheat.
Table 2. The sampling design to study soil populations of major root rot pathogens in commercial bean farms. Soil sampling level
No of soil samples collected At vegetative stage
At pod maturity stage
Observation
1
1
Farm
4
4
Abhar
20
20
Kheirabad
20
20
Khodabandeh
12
12
Region
22
Table 3. Generalized linear models of soil populations of Fusarium oxysporum (Fo), F. solani (Fs), Macrophomina phaseoli (Mp), and Rhizoctonia solani (Rs) according to agro-ecological attributes in commercial bean farms. Variablesa Bean class
EC
Herbicide P-Fertilizer Previous
Models Fo-V3
-b
-2.3c
-
-
3.1
-
(1.5) Fo-Acc
Fs-V3
-
4.7
-
-
(2.1) Mp-V3
2.4
-
(0.9) Mp-R9
-
-
-
-
Rs-R9
-
-
-
-
3
(ha)
-
-3.7
-
-2.7
-0.1
-11.6
(1.2)
(0.1)
(3.4)
-3.6
-0.2
-8.9
6.5
6.5
(1.7)
(0.1)
(4.6)
(3.1)
(3.0)
Total -V3
Total-Acc
7.6
-6.7
6.8
-5.4
-8.6
-5.8
-0.3
-18.2
11.1
11.5
(3.0)
(3.6)
(2.7)
(2.4)
(3.7)
(2.3)
(0.1)
(6.4)
(4.3)
(4.2)
2.0
-4.2
-
-
-
-
-
-
-
-
(0.7)
(1.7)
2.6
-3.7
-
-2.5
-
-2.0
-0.1
-5.9
3.4
3.4
(1.3)
(1.6)
(1.0)
(0.1)
(2.8)
(1.9)
(1.8)
-4.8
-
4.1
0.1
9.5
-8.2
-6.8
(1.4)
(0.1)
(3.9)
(2.6)
(2.5)
3.5
0.1
6.7
-6.6
-5.5
(1.1)
(0.0)
(3.2)
(2.0)
(1.8)
-
-0.1
2.1
-
-
(0.0)
(1.2)
-0.1
-
-
-
-
16.2
-
-
(1.0) -2.9
-
-
(1.6) -
-1.7
-
-
(0.6) -
-
-
-
-
-1.6
9.6
-6.7
2.4
-6.7
(3.0)
(2.1)
(0.9)
(2.4)
14.9
-
-
-
-
-
(5.3) a
-
-
-
(0.04)
-8.7
-2.9
-6.1
-0.4
-20.1
(2.6)
(1.4)
(1.9)
(0.1)
(6.7)
-12.9
-
-7.2
-0.6
-
(4.2)
(0.2)
(4.6)
(4.8) -
17.3 (8.6)
Acc = accumulation of populations for two growth stages of bean; EC = electrical
conductivity; V3 = vegetative stage; R9 = pod maturity stage. b
(2.4)
(2.6)
-
-
7.3
(2.2)
-2.9
-
-
-4.4
(0.9) c
Urea
4.6
(1.0) Rs-V3
Sand Soil pH Texture
2
(1.3)
(1.8) Mp-Acc
Size
crop
(1.4) Fo-R9
Region
- = non-significant, P > 0.05.
23
c
Parameter estimates; bold numbers = P < 0.05; non-bold numbers = P < 0.10; numbers
inside brackets represent standard errors; df = 51.
Fig. 1. Principal coordinates plot of 13 commercial bean fields (points) according to agroecological characteristics.
24
Highligths
Due to the lack of information on the joint interactions of agro-ecological features on bean root rot pathogens, this study examined a manageable number of rhizospheric interactions in commercial farms.
Lower total populations of F. oxysporum, F. solani, M. phaseolina and R. solani occurred in Red beans, larger fields, the Kheirabad and Khodabandeh regions, P-fertilization, lack of trifluralin and urea application, and at higher EC, pH and sand levels.
This epidemiological information could be useful in the development of future experiments to mange bean root rots.
25