Agronomy

Soil degradation is a global problem and refers to the reduction or loss of the biological and economic productive capacity of the soil resource. In Europe, the countries most affected by soil degradation are undoubtedly those of the Mediterranean basin. Among these, Italy shows clear signs of degradation, with different characteristics, especially in the southern regions, where climatic and meteorological conditions strongly contribute to it. Apulia, the Tavoliere plain in particular, is a fragile and very sensitive ecosystem due to its intrinsic characteristics and the level of anthropic exploitation. Agricultural production pays the highest price, as increasing desertification due to climate change and the loss of agricultural land severely limit the extent of land available to produce food for an ever-growing population. Plant growth-promoting bacteria (PGPB) could be a low-cost and long-term solution to restore soil fertility, as they provide a wide range of benefits in agriculture, including increasing crop productivity, improving soil nutrient levels and inhibiting the growth of pathogens. This review shows how PGPB can be used to improve the quality of soils, their impact on agriculture, their tolerance to abiotic stresses (drought, salinity, heavy metals and organic pollutants) and their feasibility. The use of PGPB could be promoted as a green technology to be applied in marginal areas of Apulia to increase soil fertility, reduce pollution and mitigate the impacts of abiotic stresses and climate change. This is supported by a series of studies showing that the growth of plants inoculated with PGPB is superior to that of non-inoculated plants. Full article

Accurate estimation and effective portioning of actual evapotranspiration () into soil evaporation (E) and plant transpiration (T) are important for increasing water use efficiency (WUE) and optimizing irrigation schedules in croplands. In this study, E/T partitioning was performed on rates measured using the eddy covariance (EC) technique in three winter wheat growing seasons from October 2020 to June 2023. The variation in the crop coefficients (, α, and ) were quantified by combining the and reference evapotranspiration rates using the Penman–Monteith, Priestley–Taylor, and Hargreaves equations. In addition, the application of models based on the modified crop coefficient (, α, and ) was proposed to estimate the rates. According to the obtained results, the average cumulative , T, and E rates in the three winter wheat growth seasons were 471.4, 265.2, and 206.3 mm, respectively. The average T/ ratio ranged from 0.16 to 0.72 at the different winter wheat growth stages. Vapor pressure deficit (VPD) affected the rates at a threshold of 1.27 KPa. The average , α, and values in the middle stage were 1.34, 1.54, and 1.21, respectively. The measured rates and rates estimated using the adjusted , α, and showed regression slope coefficients of 0.96, 0.99, and 0.96, and coefficients of determination (R²) of 0.92, 0.93, and 0.90, respectively. Therefore, the Priestley–Taylor-equation-based adjusted crop coefficient is recommended. The adjusted crop-coefficient-based models can be used as valuable tools for local policymakers to effectively improve water use. Full article

The spatiotemporal heterogeneity in the concentrations of atmospheric CO₂ and tropospheric O₃ is increasing under climate change, threatening food security. However, the impacts of short-term elevated CO₂ or O₃ on undamaged kernels in rice remain poorly understood, especially the impacts of their combination. We conducted an open-top chamber experiment to examine the impacts of short-term elevated CO₂ (+200 ppm, eCO₂) and O₃ (+40 ppb, eO₃) on undamaged kernels in rice cultivars (NJ5055 and WYJ3). We found eCO₂ significantly reduced undamaged kernels by 35.2% and 66.2% in NJ5055 and WYJ3, respectively. EO₃ significantly reduced undamaged kernels by 52.4% and 47.7% in NJ5055 and WYJ3, respectively. But the combination of eCO₂ and eO₃ did not affect the undamaged kernels in both cultivars. Moreover, we found that undamaged kernels were significantly correlated with chalky kernels (r = −0.9735). These results highlighted that changes in chalky kernels are most responsible for the changes in undamaged kernels in rice under eCO₂ and eO₃. This study demonstrated that undamaged kernels in rice are fragile to climate change factors like short-term eCO₂ and eO₃, and reducing chalky kernels is one of the most important adaptations to sustain food security in the future. Full article

Paddy runoff pollution is one of the major contributors to limiting the improvement of water quality in Taihu Lake Basin. Grass buffer strips (GBSs) are an effective measure to control paddy runoff pollution. However, most studies only consider a single inflow condition, and few studies have considered the effect of high-frequency rainfall. In this study, a field runoff simulation experiment was constructed to simulate the effect of GBSs on runoff nitrogen and phosphorus removal at different inflow volumes, inflow velocities, inflow concentrations, and rainfall frequencies. Results demonstrated that the larger the inflow volume, the faster the inflow velocity, and the lower the inflow concentration, the higher the runoff pollutant interception rate that occurred in GBSs, and the interception rate improved significantly with increasing GBS widths. The peak change point of removal rate occurred at a width of 15 m for NO₃⁻-N and TP and at a 25 m width for TN and NH₄⁺-N. The cumulative removal rate increased slowly after the change point. Although the peak cumulative removal rate appeared at a GBS width of 35~45 m. Considering the pollutants intercepted by GBSs and the emerging demand for land in this basin, 25 m was recommended as the optimum width to remove runoff pollutants. Full article

The Christiansen Uniformity Coefficient (CUC) describes the distribution of water in a sprinkler system. In this study, two types of models were developed to predict the Christiansen Uniformity Coefficient (CUC) of sprinkler irrigation systems: Artificial Neural Network (ANN), specifically the feed-forward neural networks, and multiple linear regression (MLR) models. The models were trained on a dataset of published research on the CUC of sprinkler irrigation systems, which included data on a variety of design, operating, and meteorological condition variables. In order to build the predictive model of CUC, 10 input parameters were used including sprinkler height (H), working pressure (P), nozzle diameter (D and d_a), sprinkler line spacing (S_L), sprinkler spacing (S_S), wind speed (WS), wind direction (WD), temperature (T), and relative humidity (RH). Fifty percent (50%) of the data was used to train ANN models and the remaining data for cross-validation (25%) and for testing (25%). Multiple linear regression models were built using the training data. Four statistical criteria were used to evaluate the model’s predictive quality: the correlation coefficient (R), the index of agreement (d), the root mean square error (RMSE), and the mean absolute error (MAE). Statistical analysis demonstrated that the best predictive ability was obtained when the models (ANN and MLR) utilized all the input variables. The results demonstrated that the accuracy of ANN models, predicting the CUC of sprinkler irrigation systems, is higher than that of the MLR ones. During the training stage, the ANN models were more accurate in predicting CUC than MLR, with higher R (0.999) and d (0.999) values and lower MAE (0.167) and RMSE (0.456) values. The R values of the MLR model fluctuated between 0.226 and 0.960, the d values oscillated from 0.174 to 0.979, the MAE values were in the range of 2.458% and 10.792%, and the RMSE values fluctuated from 2.923% to 13.393%. Furthermore, the study revealed that WS and WD are the most influential climatic parameters. The ANN model can be used to develop more accurate tools for predicting the CUC of sprinkler irrigation systems. This can help farmers to design and operate their irrigation systems more efficiently, which can save them time and money. Full article

This article uses a derived econometric model to estimate the impact of the physical properties of soil on its retention capacity and, subsequently, the impact of retention capacity on production potential. This is an important aspect considering climate change impacts, which are affecting food production across the world. An investigation of academic publications shows that very few studies address opportunities to price rainwater in relation to agricultural production. As such, the objective of the submitted article is to use soil physical property spatial data to create an econometric model. The econometric model itself determines the intensity and direction of action of the soil’s physical properties on the ability of the soil to hold rainwater. The results demonstrate the positive effect of physical properties such as porosity and humus content. Important information for farming practice is the relatively pronounced influence of soil acidity (pH) on its retention capacity, which is mainly the result of its effect on soil biogeochemical processes. The most significant variable in terms of the extent of action is the depth of the soil profile, which is in line with general assumptions. The actual evaluation of soil retention capacity was undertaken using an option with the use of a sensitivity analysis. In order to include the non-production function of the soil (retention capacity), we conclude for individual enhanced quality soil ecological units an increased price of 1–12%. These conclusions are particularly valuable because some soils may have a low production potential while also being highly valuable for their particular location in terms of their non-production potential (typically desirable floodwater retention, etc.). Considering climate change, this is a particularly topical issue. The use of enhanced-quality soil ecological units is reflected in a wide range of fields through legislative processes—determining rural land protection class and, especially in the tax obligations of agricultural entities, farming agricultural land. Full article

Increasing soil salinization can severely restrict local agricultural production. Planting alfalfa is considered an effective measure to ameliorate saline–alkali soil. However, it remains unclear how alfalfa planting years affect the sustained impact on soil and rhizosphere microecology. This study analyzed the effects of alfalfa planted 3, 6, and 8 years ago on soil physicochemical properties and key soil enzyme activities and investigated the rhizosphere microbial community structure and diversity. The results indicate that cultivating alfalfa plants for six years can improve soil physicochemical properties and enhance soil fertility to a certain extent. This is attributed to a higher abundance of plant growth-promoting bacteria, such as Bradyrhizobium and Allorhizobium, as well as degradation bacteria, such as Flavobacterium, Stenotrophomonas, Brevundimonas, and Massilia, in the rhizosphere of alfalfa plants. These microorganisms promote alfalfa growth, improve soil quality, and inhibit the accumulation of autotoxins. This not only maintains high alfalfa yields but also optimizes soil physicochemical properties and enzyme activity, facilitating more effective nutrient cycling and metabolic processes in the soil. However, extending plant growth to 8 years is not beneficial. Full article

Progress in agricultural productivity and sustainability hinges on strategic investments in technological research. Evolving technologies such as the Internet of Things, sensors, robotics, Artificial Intelligence, Machine Learning, Big Data, and Cloud Computing are propelling the agricultural sector towards the transformative Agriculture 4.0 paradigm. The present systematic literature review employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to explore the usage of Machine Learning in agriculture. The study investigates the foremost applications of Machine Learning, including crop, water, soil, and animal management, revealing its important role in revolutionising traditional agricultural practices. Furthermore, it assesses the substantial impacts and outcomes of Machine Learning adoption and highlights some challenges associated with its integration in agricultural systems. This review not only provides valuable insights into the current landscape of Machine Learning applications in agriculture, but it also outlines promising directions for future research and innovation in this rapidly evolving field. Full article

Scald caused by the fungal pathogen Rhynchosporium commune is a significant foliar disease affecting barley production on a global scale, and it leads to substantial reductions in both yield and quality of barley. In the current study, the reactions of 40 Turkish barley (Hordeum vulgare L.) varieties to scald were evaluated under natural conditions in Çanakkale and Kırşehir in 2021–2022, and Antalya and Siirt locations in 2022–2023 growing seasons. Field trials were conducted according to randomized block design with three replications in each year; the spore concentration was 1 × 10⁶ spores per mL, and it was applied to the varieties three times at different growth stages. The reactions of barley varieties were assessed using a newly designed two-digit scale ranging from 11 to 99. Based on their scale values, the varieties were categorized as immune (0), resistant (11–35), moderately resistant (36–55), moderately susceptible (56–75), and susceptible (76–99). In addition, genotype plus genotype-by-environment (GGE) interactions of scale values were analyzed through GGE Biplot and explained 97.65% of the total variation. The ranking of genotypes based on scale groups generally showed consistency with GGE Biplot results, but GGE Biplot offered a more detailed classification, especially for moderately susceptible varieties. The relationship between the two methods indicated the relative stability of variety reactions, as GGE Biplot analysis also considered genotype stability. In conclusion, the use of the newly developed scale for evaluating scald reactions in barley gives reliable results. In addition, identified resistant varieties can serve as valuable genetic resources for further breeding studies. Full article

Soil productivity and crop yield were examined in response to legume-based short crop rotation under conventional (CT) and no-till (NT) tillage practices in saline meadow-alluvial soils of the arid region in Bukhara, Uzbekistan. Compared with the CT treatment, crop yield was consistently higher under NT, i.e., winter wheat 9.63%, millet 9.9%, chickpea 3.8%, and maize 10.7% at the first experiment cycle during 2019–2021. A further crop productivity increase was observed at the second experiment cycle during 2021–2023 under NT when compared to CT, i.e., winter wheat 17.7%, millet 31.2%, chickpea 19.6%, and maize 19.1%. An increase in total phyto residue by 20.9% and root residue by 25% under NT compared to CT contributed to the improvement in soil structure and played a vital role in the sustained improvement of crop yields. In turn, the increased residue retention under NT facilitated soil porosity, structural stability, and water retention, thereby improving soil quality and organic matter content. Soil salinity more significantly decreased under NT than in CT, reducing salinity buildup by 18.9% at the 0–25 cm and 32.9% at the 75–100 cm soil profiles compared to CT. The total forms N and P were significantly increased under NT when compared to CT, while the efficiency of the applied crop rotation was essential. This study showed the essential role of the NT method with legume-based intensive cropping in the maintenance of soil health and crop yield, thereby touching on recent advances in agro-biotechnology and the sustainable land management of drylands. Full article

Colchicine is a substance used to induce mutations in order to regulate important agronomic traits. The genotypes Pavlodarskoe 4, Quartet, and PI 289324, originating from Kazakhstan, the Russian Federation, and Hungary, respectively, were used as materials. The objective of this study was to investigate the effects of different colchicine concentrations (0.0, 0.04, 0.06, 0.08, and 0.1%) and treatment times (6, 12, and 24 h) on the agronomic traits of proso millet (Panicum miliaceum L.) and to assess the genetic diversity of the M₂ generation using inter simple sequence repeat (ISSR) markers. The experiment was conducted in 2021 for the M₁ generation and in 2022 for the M₂ generation, from May to September. The percentage of field germination decreased with increasing colchicine concentrations and exposure durations. The mean field germination percentages were 48.57% in Pavlodarskoe 4, 43.28% in Quartet, and 53.14% in PI 289324 under colchicine treatment. Chlorophyll-defective M₁-M₂ plants were obtained using various colchicine concentrations and exposure periods. The highest number of mutational modifications was attained with the 0.08–0.1% concentrations of colchicine. Based on the research results, a total of 248 plants with chlorophyll-defective mutations were selected from 2214 plants. The growing seasons of M₁ and M₂ plants were shortened by higher colchicine concentrations (0.08–0.1%) combined with soaking times of 12 and 24 h. Thus, the longest growing season (84 days) was observed with a 6 h treatment time for PI 289324, while the shortest (78 days) was recorded for 12 and 24 h treatments. The possibility of obtaining morphological mutations using colchicine has been confirmed. The ISSR primers amplified a total of 1333 fragments; 1281 bands were found to be polymorphic, and 52 bands were monomorphic. The percentage of polymorphism varied from 80 to 100%, with an average of 96.11%. Most of the different allelic bands were detected when applying the 0.08% colchicine concentration. These positive variations are a great opportunity to use colchicine as a tool for improving agronomic traits in plant breeding. Full article

Excess Na⁺ and high pH result in poor structures in Saline-Sodic soils, which reduces extracellular enzyme activity (EEA) and causes nutrient limitations. The application of manure improved the Physical-Chemical properties of soil and balanced the soil nutrient supply, which was reflected in the soil EEAs and stoichiometry. Five experimental treatments were designed according to the manure application duration as follows: manure application for 11 years (11a), 16 years (16a), 22 years (22a), and 27 years (27a) and a control treatment with no manure application (CK). The results of the redundancy analysis (RDA) showed that physical properties (mean weight diameter (MWD)) and EEA (β–glucosidase (BG)) significantly increased and bulk density (ρ_b) significantly decreased when the nutrient content increased. Additionally, soil pH, electrical conductivity (EC), exchangeable sodium percentage (ESP) and sodium adsorption ratio (SAR) significantly decreased after manure application. Based on stepwise multiple linear regression models (SMLR), total nitrogen (TN) was the dominant variable that significantly increased EEA, and the Mantel test showed that soil C:N significantly influenced enzyme stoichiometry. Furthermore, RDA showed that pH, soil C:N and TN were the main factors influencing EEAs and enzyme stoichiometry. Soil EEAs significantly increased with TN and decreased with pH and soil C:N, which affected enzyme stoichiometry. The enzyme stoichiometry increased from 1:2.1:1.2 and 1:2.7:1.5 to 1:1.7:1.2, and the vector angle (vector A) increased, which showed that the N limitation was relieved after the application of manure. The vector length (vector L) showed no significant difference in the C limitation at depths of 0–20 cm and significantly increased at depths of 20–40 cm. In conclusion, soil EEAs and stoichiometry improved with changes in TN and soil C:N, and pH decreased with changes in the soil structure after the application of manure, which accelerated the soil nutrient cycle and balanced the soil nutrient supply. Full article

Calcium (Ca) plays a vital role as a macronutrient in the growth and development of plants. In order of decreasing solubility, Ca can be found in vegetal tissues as soluble Ca (Fraction I), bound Ca (mainly pectates, Fraction II), inorganic insoluble Ca (mainly phosphates and carbonates, Fraction III) and organic insoluble Ca or oxalate (Fraction IV). To explore the impact of Ca fertilizer application on plant growth and its allocation among different fractions, young citrus trees were fed over a complete vegetative cycle with a ⁴⁴Ca labeled fertilizer (T1-Ca), while control plants (T2) received no Ca fertilizer. The results showed that plants receiving Ca exhibited significantly greater biomass. ⁴⁴Ca derived from the fertilizer was localized mainly in sink organs (new flush leaves–twigs and fibrous roots). The primary fraction responsible for total Ca partitioning was Fraction II, followed by Fraction III or IV. Citrus plants, commonly found in calcareous soils, demonstrated improved growth with calcium treatments, indicating a positive link between calcium supplementation and enhanced development. The calcium supplied through the fertilizer (⁴⁴Ca) was predominantly concentrated in sink organs (mainly in Ca-pectate fraction), including new flush leaves and twigs above ground, as well as fibrous roots below ground. Full article

The sustainable use of water resources is of utmost importance given climatological changes and water scarcity, alongside the many socioeconomic factors that rely on clean water availability, such as food security. In this context, developing tools to minimize water waste in irrigation is paramount for sustainable food production. The evapotranspiration estimate is a tool to evaluate the water volume required to achieve optimal crop yield with the least amount of water waste. The Penman-Monteith equation is the gold standard for this task, despite it becoming inapplicable if any of its required climatological variables are missing. In this paper, we present a stochastic Bayesian framework to model the non-linear and non-stationary time series for the evapotranspiration estimate via Bayesian regression. We also leverage Bayesian networks and Bayesian inference to provide estimates for missing climatological data. Our obtained Bayesian regression equation achieves 0.087 mm · day${}^{-1}$ for the RMSE metric, compared to the expected time series, with wind speed and net incident solar radiation as the main components. Lastly, we show that the evapotranspiration time series, with missing climatological data inferred by the Bayesian network, achieves an RMSE metric ranging from $0.074$ to 0.286 mm · day${}^{-1}$. Full article

Fall dormancy plays important roles in the evaluation of alfalfa’s winter hardiness and in the selection of alfalfa breeding. A rapid and effective method to estimate the fall dormancy rating of alfalfa will shorten the breeding cycle. The purpose of this study is to test the correlations between the germination thermal time model parameters and the fall dormancy ratings and to evaluate the potential of the thermal-based fall dormancy methodology. Alfalfa cultivars with a series of fall dormancy ratings were used to study the responses of seed germination at six constant temperatures (5, 10, 15, 20, 25, 30 °C). The results showed that all cultivars had a relatively high germination percentage at all temperatures and the optimal temperature is 25 or 30 °C. Germination rate and base temperature significantly increased with the fall dormancy rating of alfalfa cultivars while thermal time (θ_T) decreased with the fall dormancy rating. The extremely significant linear regression relationships between the germination rate, base temperature (T_b), θ_T, and fall dormancy rating indicated that it is convenient and straightforward to predict the fall dormancy rating of unknown cultivars or lines using thermal time model parameters. This method can significantly shorten the selection and breeding cycles in alfalfa cultivation. Full article

Drought stress is one of the major abiotic stresses to wheat worldwide, with negative effects on wheat growth and yield. Assessing genetic variation and drought stress tolerance of key agronomic and physiological traits of spring wheat and screening germplasm resources for higher drought tolerance and yield stability are a prerequisite for developing new, better-adapted spring wheat varieties. This study evaluated nine important agronomic and physiological traits in 152 spring wheat cultivars under non-stress (NS) and drought-stress (DS) conditions. Under DS conditions, grain yield per plot (GYP) and grain weight per spike (GWE) were significantly reduced by 33.8% and 31.7%, and their drought-tolerance indexes (DIs) were only 0.66 and 0.69, respectively, indicating that GYP and GWE are the most susceptible traits to drought stress. The SPAD value of flag leave at flowering stage decreased by 13.9% under DS conditions, and the DI of SPAD was 0.86. In addition, DI-SPAD was significantly positively correlated with DIs of plant height (PH), grain number per spikelet (GPS), grain number per spike (GNS), GWE and GYP, indicating that the drought tolerance and yield of wheat are closely related to chlorophyll retention. Six wheat germplasm accessions were identified for their ability to sustain grain yield and improve drought tolerance simultaneously. These results provide insights into the genetic co-variation between grain yield and drought stress tolerance and provide a theoretical basis for the development of new wheat cultivars with excellent drought tolerance and high yields in the presence and absence of drought. Full article

There is a growing trend toward the use of renewable sources to produce clean energy and mitigate the effects of climate change. Second-generation lignocellulose biomasses, such as agro-residues, comprise a potential energy source as a byproduct of agriculture. Ecuador has optimal climate conditions that allow for the cultivation of different types of crops. This makes agriculture a relevant economic activity in the country; however, the residues obtained from agriculture have not been investigated for the establishment of a bioenergy industry. This study evaluated the potential of three varieties of sugarcane bagasse, named PR 980, CC 85-92, and CB 40-59, for bioenergy production in the Ecuadorian Andes. The bagasse was quantified by means of weighing, and then evaluated via calorimetry and thermogravimetric analysis as well as proximal and elemental analysis. The results showed that these materials met the criteria for direct combustion, exhibiting both a low nitrogen content of 0.30 ± 0.12% and ash values of 6.20 ± 1.20%. Among the analyzed varieties, CB 40-69 stood out as the most suitable for power generation within the cogeneration system; this was attributed to its superior dry calorific value of 17.37 ± 1.45 MJ kg⁻¹, greater presence of volatile materials, and negligible ash content. Variety CB 40-69 (157.91 t ha⁻¹) reported the highest biomass and bagasse production (56.32 t ha⁻¹). Analysis of the SCB structure concluded that the three varieties did not differ significantly in their contents of lignin, hemicellulose, and cellulose, which is essential to implementing an industrial process for bioethanol production. Full article

Micro-irrigation pipe network systems are commonly utilized for water transmission and distribution in agricultural irrigation. They effectively transport and distribute water to crops, aiming to achieve water and energy conservation, increased yield, and improved quality. This paper presents a model for the scaled micro-irrigation pipeline network system and provides a comprehensive review of the fundamental concepts and practical applications of optimization techniques in the field of pipeline network design. This paper is divided into four main sections: Firstly, it covers the background and theoretical foundations of optimal design for scaled micro-irrigation pipeline network systems. Secondly, the paper presents an optimal design model specifically tailored for scaled micro-irrigation pipeline networks. And then, it discusses various optimization solution techniques employed for addressing the design challenges of scaled micro-irrigation pipeline networks, along with real-world case studies. Finally, this paper concludes with an outlook on the ongoing research and development efforts in the field of scaled micro-irrigation pipeline network systems. In addition, this paper establishes a fundamental model for optimizing pipeline networks, to achieve minimum safe operation and total cost reduction. It considers constraints such as pipeline pressure-bearing capacity, maximum flow rate, and diameter. The decision-making variables include pipeline diameter, length, internal roughness, node pressure, future demand, and valve placement. Additionally, this paper provides an extensive overview of deterministic methods and heuristic algorithms utilized in the optimal design of micro-irrigation pipeline networks. Finally, this paper presents future research directions for pipeline network optimization and explores the potential for algorithmic improvements, integration of machine learning techniques, and wider adoption of EPANET 2.0 software. These endeavors aim to lay a strong foundation for effectively solving complex and challenging optimization problems in micro-irrigation pipeline network systems in the future. Full article

Endophytes are symbiotic microbes that are mutually beneficial to the plant host and whose number and diversity affect the strength of plant resistance to stresses. The infection of sweetpotato with the scab pathogen can lead to yield losses. However, little is known about how the endophytic flora in sweetpotato respond to scab pathogen infection. This study used high-throughput amplicon sequencing with Illumina’s MiSeq PE300 platform ITS and the 16SrRNA gene to analyze the composition and distribution of endophytic flora in the roots, stems, and leaves of sweetpotato plants infected with scab disease and those of healthy plants. The dominant endophytic fungi in sweetpotato were Ascomycota, while the dominant endophytic bacteria were Proteobacteria. The diversity of endophytic fungi in the healthy plants followed a root > stem > leaf trend, while an opposite trend was observed in the infected plants. The diversity pattern of endophytic bacterial flora showed a root > stem > leaf trend in both healthy and infected plants. The scab pathogen Elsinoë was classified under OTU87 and was enriched in the leaves and stems of the infected plants. OTU87 was negatively correlated with Acaulospora and positively correlated with eight other fungal taxa, including Cladosporium.Future research should focus on exploring potential biocontrol fungal resources for sweetpotato scab. Full article