Corn Leaf Research Articles

FMaize: A Seamless Image Filtering and Deep Transfer EfficientNet-b0 Model for Sub-Classifying Fungi Species Infecting Zea mays Leaves

Identification of fungi infecting Zea mays leaves and sub-classifying them to have correct course management in the earlier stages is lucrative. To develop a nondestructive and low-cost classification model of corn leaves infected by Setosphaeria turcica (ST), Cercospora zeae-maydis (CZM), and Puccinia sorghi (PS) fungi using image filtering and transfer learning model. Corn leaf images were categorized based on fungal-infection and stored in an image library. All images were then processed to show different intensities and then utilized to filter the images. An original RGB-based CNN model has been compared with selected pre-trained models of VGG16 and EfficientNet-b0 with inputs of both unfiltered and filtered RGB images. Results showed that the EfficientNet-b0 with filtered images model (fMaize) exhibited the highest accuracy of 97.63%, sensitivity of 97.99%, specificity of 97.38, quality index of 97.68%, and F-score of 96.48%. Consequently, the experimental results revealed that deep transfer learning models fed with filtered images produced higher accuracy than models that simply employed RGB images. Thus, transfer learning was proven to be a valuable tool in enhancing CNN image classification accuracy.

Maize rhizosphere modulates the microbiome diversity and community structure to enhance plant health

Metagenomic has been explored in investigating microbiome diversity. However, there is limited available information on its application towards securing plant health. Hence, this study adopts the metagenomic approach to unravel the microbiome diversity associated with healthy (LI and MA) and Northern corn leaf blight (NCLB) infected (LID and MAD) maize rhizosphere in the maize growing field at Lichtenburg and Mafikeng, North-West province of South Africa. The extraction of whole DNA from the respective healthy and diseased rhizosphere soils was conducted and sequenced using shotgun metagenomics. A total of 12 bacteria, 4 archaea and 2 fungal phyla were found as predominant across the fields with the use of the SEED subsystem database. The most predominant bacteria phyla included Proteobacteria, Dienococcus-Thermus, Gemmatimonadetes, Chlorobi, Cyanobacteria, Planctomycetes, Verrucomicrobia, Acidobacteria, Firmicutes, Chloroflexi and Bacteroidetes. Archaea consisted of Euryarchaeota, Thaumarchaeota, Crenarchaeota and Korachaeota, while Ascomycota and Basidiomycota were the dominant fungal phyla. Microbial abundance and diversity were higher in the rhizosphere of healthy maize (LI and MA) rhizosphere as compared to the NCLB diseased (LID and MAD), in the order LI > MA > LID > MAD. At phylum and genus level, alpha diversity index showed no significant (p > 0.05) difference in the abundance of the microbial community of healthy and NCLB infected maize rhizosphere, while beta analysis produced a significant (p = 0.01) difference in the microbial diversity in the soil. Taken together, the study revealed that the abundance of microbial diversity in the maize rhizosphere influences the efficacy of the rhizosphere microbiome to modulate microbial functions towards managing and sustaining plant health.

Genetic Diversity, Population Structure and Mating Type Distribution of Setosphaeria turcica on Corn in Midwestern China

Setosphaeria turcica is the causal agent of northern corn leaf blight (NCLB), which is a destructive foliar disease of corn around the world. To date, limited information is available on the genetic diversity, population structure, and mating type distribution of the pathogen in the mid-west of China. In this study, based on single nucleotide polymorphism (SNP) markers and mating type-specific primers, we characterized 117 S. turcica isolates collected from Henan, Hebei, Shanxi, and Shaanxi provinces in China. Based on the developed 33 SNP markers, all isolates can be categorized into two genetic groups. Each group consisted of isolates from all four provinces. The Nei's gene diversity of four populations ranged from 0.328 to 0.419 with a mean of 0.391. The analysis of fixation index (Fst) and gene flow (Nm) suggested that low genetic differentiation and high gene flow existed among four geographic populations. The analysis of molecular variance (AMOVA) demonstrated that the principal molecular variance existed within populations (98%) rather than among populations (2%). The analysis of mating type loci revealed that two mating types (MAT1-1 and MAT1-2) were basically in equilibrium in all four populations. These findings advance our understanding of the genetic diversity, population structure and mating type distribution of S. turcica on corn in the mid-west of China and will aid in developing efficient strategies to control NCLB.

REAL TIME CORN LEAF DISEASE DETECTION USING CONVOLUTION NEURAL NETWORK

Agriculture is the primary resource of livelihood, and the economy of our country highly depends on agricultural productivity. For this reason, plant disease detection places a vital role in the agriculture sector. According to one survey, in India, nearly 70% of the population depends on agriculture which is composed of many crops. Disease identification in plants is very challenging for farmers as well as for researchers. We proposed a 24-layer deep learning model in our paper using convolution neural networks (CNN) for the detection of corn leaf diseases by using real time image dataset as input. The CNN model is trained with different corn leaf image samples and model performance is tested and is reported with the evaluation metrics. The obtained results are compared with CNN pre-defined models which shows the superior performance of the proposed model compared to other state-of-the-art approaches.

Effect of Diet on the Midgut Microbial Composition and Host Immunity of the Fall Armyworm, Spodoptera frugiperda.

The fall armyworm (Spodoptera frugiperda, J.E. Smith) is one of the most important agricultural pests in the world and causes serious damage to many significant crops. Insect gut microbiota plays a vital role in host immunity, digestion, and development, helping the higher organism colonize in a new environment. However, the effects of different diets on midgut microbial composition and host immunity in S. frugiperda remain unclear. So far, no reports have compared the gut microbiota of fall armyworm reared using an artificial diet compared to corn leaf in Guangzhou, China. High-throughput 16S rRNA sequencing technology was applied to gain insight into the composition of the gut microbiota of S. frugiperda feeding on corn leaf (field diet) and on a starch-rich artificial diet (lab diet). The fall armyworm gut microbiota was dominated by the bacterial phyla Firmicutes and Proteobacteria. Despite the difference in diet, the core bacterial community was represented by the genus Enterococcus. However, the bacterial community is dominated by a few phylotypes, namely operational taxonomical units 1 (OTU1) (Enterococcus casseliflavus), OTU3 (Enterobacteriaceae), OTU2 (Weissella), and OTU4 (Clostridium), accounting for 97.43% of the total OTUs in the complete dataset. A significant difference was identified in the bacterial communities between the "lab diet" and the "field diet" groups. OTU1 and OTU2 were significantly higher in the "field diet" group, whereas OTU3 and OTU4 were higher in the "lab diet" group. A phylogenetic investigation of the communities by reconstruction of unobserved states (PICRUSt) predicted functional analysis indicates the presence of several genes associated with plant biomass degradation. Importantly, antibiotic-mediated perturbation of the midgut microbial community significantly impacts the expression profile of the important immune genes of the host. Furthermore, the oral reintroduction of gut bacterial isolates (E. mundtii and E. gallinarum) significantly enhances host resistance to AcMNPV infection. Taken together, our results indicate that diet composition is an important driver in shaping insect gut microbiome and immune gene expression, ultimately playing an important role in the pest defense system.

Phytochemical screening, polyphenols content and antioxidant activity of by-products of two corn variety

Phytochemical screening, polyphenols content, and antioxidant activity of the different parts of the white and purple corn (white corn cob, WCC; white corn leaf, WCL; white corn grain, WCG; purple corn cob, PCC; purple corn leaf, PCL, and purple corn grain, PCG) were evaluated. PCG had a higher content of fat (9.7%) and carbohydrates (77%), while WCG had a higher protein content (15.8%), the leaves had a higher fiber content (37 - 40%). The phytochemical profile was positive for alkaloids, carbohydrates, phytosterols, phenols, and terpenoids in all samples. The total phenolic content, WCC presented the highest (P < 0.05) value (225 mg GAE/100 g sample) and in lower concentration in PCG. However, PCG had a higher content of total flavonoids (104 mg CE/100 g sample). The results suggest that agroindustry wastes from corn (cob and leaf) could be considered a potential source of fiber, carbohydrates, and bioactive compounds with antioxidant capacity.

First report of maize yellow mosaic virus causing maize reddening in Henan,China.

A novel polerovirus maize yellow mosaic virus (MaYMV) has been discovered in Asia (Chen et al. 2016; Lim et al. 2018; Sun et al. 2019; Wang et al. 2016), East Africa (Guadie et al. 2018; Massawe et al. 2018) and South America (Gonçalves et al. 2017). MaMYV was first reported to infect maize (Zea mays L.) showing yellow mosaic symptoms on the leaves in Yunnan, Guizhou, and yellowing and dwarfing symptoms on the leaves in Anhui provinces of China in 2016 (Chen et al. 2016; Wang et al. 2016). An East African isolate of MaYMV has recently been shown to induce leaf reddening in several maize genotypes (Stewart et al. 2020). To our knowledge the leaf reddening symptoms in maize was not reported in China and MaYMV was not reported in Henan province, China. A survey of viral diseases on maize was carried out during the autumn of 2021 in Zhengzhou (Henan province), China. During the survey, the leaves showing reddening symptoms were observed on maize plants in all four fields investigated. Symptomatic leaves of 12 plants from four fields of Xingyang county, Zhengzhou (n=12) were collected and mixed for metatranscriptomics sequencing, and total RNA was extracted and subjected to an rRNA removal procedure using a Ribo-zero Magnetic kit according to the manufacturer's instructions (Epicentre, an Illumina® company). cDNA libraries were constructed using a TruSeq™ RNA sample prep kit (Illumina). Barcoded libraries were paired-end sequenced on an Illumina HiSeq X ten platform at Shanghai Biotechnology Co., Ltd. (Shanghai, China) according to the manufacturer's instructions (www.illumina.com). In total 67607392 clean reads were de novo assembled using CLC Genomics Workbench (version:6.0.4). 105796 contigs were obtained. The assembled contigs were queried by homology search tools (BLASTn and BLASTx) against public database(GenBank). One 5,457 nucleotide (nt) long contig with the most reads of 558826 was obtained and blast analysis showed it shared 99.3% nt sequence identity (99% coverage) with MaYMV Yunnan4 isolate (KU291100).. According to the sequencing data no other plant viruses except MaYMV were present in the sequencing data. To confirm the presence of this virus, twelve leaf samples showing reddening symptoms were detected by RT-PCR using specific primer pairs for CP full length open reading frame (F: ATGAATACGGGAGGTAGAAA, R: CTATTTCGGGTTTTGAACAT). Amplicons with expected size of 594 bp were gained in seven samples and three of them were cloned into pMD18T vector and sequenced. The three isolates (OM417795, OM417796, and OM417797) shared 99.16% to 99.83% nt sequence identity with MaYMV-Yunnan3 isolate (KU291100). Further P0 sequence analysis of the three samples (OM417798, OM417799, and OM417800) with primer pairs F: ATGGGGGGAGTGCCTAAAGC/R: TCATAACTGATGGAATTCCC showed they shared 99.5% to 99.62% nt sequence identity with MaYMV-Yunnan3 isolate.To our knowledge, this is the first report of the occurrence of MaYMV infecting maize in Henan, China. Besides, our finding firstly discovered reddening symptoms caused by MaYMV on maize in China which is different from the previous symptoms observed in the other three provinces of China possibly due to the different maize varieties grown in different areas. According to our investigation, maize showing reddening symptoms was common in the fields. Henan province is the main corn production area in China. Corn leaf aphid (Rhopalosiphum maidis), the insect vector of MaYMV, is an important pest of corn in Henan province, thereby the occurrence of MaYMV might cause potential threat to maize production in China.

Mining synergistic genes for nutrient utilization and disease resistance in maize based on co-expression network and consensus QTLs.

Nutrient restrictions and large-scale emergence of diseases are threatening the maize production. Recent findings demonstrated that there is a certain synergistic interaction between nutrition and diseases pathways in model plants, however there are few studies on the synergistic genes of nutrients and diseases in maize. Thus, the transcriptome data of nitrogen (N) and phosphorus (P) nutrients and diseases treatments in maize, rice, wheat and Arabidopsis thaliana were collected in this study, and four and 22 weighted co-expression modules were obtained by using Weighted Gene Co-expression Network Analysis (WGCNA) in leaf and root tissues, respectively. With a total of 5252 genes, MFUZZ cluster analysis screened 26 clusters with the same expression trend under nutrition and disease treatments. In the meantime, 1427 genes and 22 specific consensus quantitative trait loci (scQTLs) loci were identified by meta-QTL analysis of nitrogen and phosphorus nutrition and disease stress in maize. Combined with the results of cluster analysis and scQTLs, a total of 195 consistent genes were screened, of which six genes were shown to synergistically respond to nutrition and disease both in roots and leaves. Moreover, the six candidate genes were found in scQTLs associated with gray leaf spot (GLS) and corn leaf blight (CLB). In addition, subcellular localization and bioinformatics analysis of the six candidate genes revealed that they were primarily expressed in endoplasmic reticulum, mitochondria, nucleus and plasma membrane, and were involved in defense and stress, MeJA and abscisic acid response pathways. The fluorescence quantitative PCR confirmed their responsiveness to nitrogen and phosphorus nutrition as well as GLS treatments. Taken together, findings of this study indicated that the nutrition and disease have a significant synergistic response in maize.

Pengaruh Posisi Pemangkasan Daun Jagung Dalam Sistem Tumpang Sari Terhadap Pertumbuhan dan Hasil Kedelai

This study aims to determine the effect of corn leaf pruning on soybean growth and yield in an intercropping system. This research was carried out on farmers' land in Simpang Village, Berbak District, Tanjung Jabung Timur Regency from July - October 2020. This research was carried out using a Randomized Block Design (RBD) with 5 replications. Pruning treatments based on the position of the leaves on the stem were : without pruning (control/p0), pruning 3 leaves at a position near the soil surface (p1), pruning 3 leaves at a position below the cob (p2), and pruning 3 leaves at a position above the cob (p3). The variables observed included soybean plant height (cm), number of pods per plant, number of filled pods per plant, weight of 100 dry seeds (g), and soybean dry seed yield. The results showed that the highest soybean plant height was obtained without pruning and pruning 3 corn leaves near the soil surface, but produced the lowest number of pods per plant, number of filled pods, and the lowest soybean dry seed yield. The number of pods per plant, the number of containing per plant, and the highest soybean dry seed yield were obtained at pruning of 3 leaves under the cob and 3 leaves of corn above the cob. In corn, the highest yield was obtained by pruning 3 leaves below the cob and 3 leaves above the cob. The diversity of soybean dry seed yields in this study was determined jointly by the diversity of the number of pods per plant, the number of filled pods per plant, and the weight of 100 dry seeds. Partially, only the number of pods contained per plant and the weight of 100 dry seeds significantly played a role in determining the amount of dry soybean yield, while the number of pods per plant had no significant role in determining the diversity of dry seed yields. Land use with corn and soybean intercropping system can increase land productivity so that it is more efficient with LER value > 1 in all positions of corn leaf pruning. Keywords: pruning, intercropping corn, soybean

Impacts of corn intercropping with soybean, peanut and millet through different planting patterns on population dynamics and community diversity of insects under fertilizer reduction.

Corn is one of the key grain crops in China and the excessive use of chemical fertilizers and pesticides seriously damages the ecological environment in fields. To explore a more scientific and reasonable way to plant corn and simultaneously reduce the overuse of chemical fertilizers and pesticides, the impact of corn intercropping with soybean, peanut, and millet, respectively, through five planting patterns, including three intercropping patterns (2 corn rows to 2, 3 and 4 rows of soybean/peanut or 2, 4 and 6 millet rows, respectively) and two monoculture patterns of corn and soybean, peanut or millet under normal (600 kg/ha) and reduced (375 kg/ha) levels of NPK (N:P2O5:K2O = 15:15:15) fertilization on the population abundance and community diversity of insects, leaf nutrients, and induced plant hormones jasmonic acid (JA) and salicylic acid (SA) was studied in 2018 and 2019. The results showed that the insect community indexes of the species number (S), the diversity index (H), and the uniformity index (E) generally increased under intercropping and were significantly higher than those under corn monoculture. The prevalence of Asian corn borer (Ostrinia furnacalis) on the intercropping corn plants decreased by based on the average of seven surveys per year for each treatment 2.9 to 17 heads per 30 plants compared with that on the monoculture corn plants. The number of natural enemy insect species on corn plants under intercropping was significantly higher than that under corn monoculture. That is, intercropping may decrease the population of Asian corn borers by increasing S, H, E, and natural enemy insect species (NEI). Moreover, intercropping type and fertilizer level significantly affected corn leaf nutrient contents. Compared with the normal fertilizer level, fertilizer reduction significantly reduced the foliar contents of amino acids, soluble protein, and soluble sugar in corn plants. In addition, corn-soybean and corn-peanut intercropping significantly increased the three nutrient contents in corn leaves compared with corn monoculture. In terms of corn nutrients, intercropping could compensate for the effects of fertilizer reduction. The foliar content of JA in corn-soybean intercropping was significantly higher than in corn monoculture. Under corn-soybean and corn-peanut intercropping, SA was significantly lower than under corn monoculture. Overall, intercropping, not fertilizer reduction, can significantly increase insect community diversity while reducing the population abundances of the key insect pest species on corn plants. Intercropping reduced the SA content, increased amino acids and thus reduced the susceptibility of corn to the pest insects.

Classification of Corn Diseases from Leaf Images Using Deep Transfer Learning

Corn is a mass-produced agricultural product that plays a major role in the food chain and many agricultural products in addition to biofuels. Furthermore, households in poor countries may depend on small-scale corn cultivation for their basic needs. However, corn crops are vulnerable to diseases, which greatly affects farming yields. Moreover, extreme weather conditions and unseasonable temperatures can accelerate the spread of diseases. The pervasiveness and ubiquity of technology have allowed for the deployment of technological innovations in many areas. Particularly, applications powered by artificial intelligence algorithms have established themselves in many disciplines relating to image, signal, and sound recognition. In this work, we target the application of deep transfer learning in the classification of three corn diseases (i.e., Cercospora leaf spot, common rust, and northern leaf blight) in addition to the healthy plants. Using corn leaf image as input and convolutional neural networks models, no preprocessing or explicit feature extraction was required. Transfer learning using well-established and well-designed deep learning models was performed and extensively evaluated using multiple scenarios for splitting the data. In addition, the experiments were repeated 10 times to account for variability in picking random choices. The four classes were discerned with a mean accuracy of 98.6%. This and the other performance metrics exhibit values that make it feasible to build and deploy applications that can aid farmers and plant pathologists to promptly and accurately perform disease identification and apply the correct remedies.

Preferential sorption of polysaccharides on mackinawite: A chemometrics approach

Marine sediments represent the most important sink for natural organic matter (NOM) across geological time spans. Strong associations between minerals, including iron oxides, and organic matter reaching the seafloor play a fundamental role in this preservation and have been known for some decades. Despite the importance of this protective mechanism in the balances of the global carbon budget, very little is known about the affinity of NOM for reduced iron species such as mackinawite (FeS) in the anoxic layers of sediment. In this study, equilibrium partition coefficients (Kd) for three types of NOM (soil extract, corn leaf extract and plankton extract) on FeS were determined through batch sorption experiments. Models were then built via multiple linear regression (MLR) and partial least squares regression (PLSR) in order to predict sorption Kd’s based on the chemical characteristics of the NOM. Investigation of the PLSR regression coefficients indicates that functional groups characteristic of polysaccharides are the greatest positive predictors of NOM sorption onto FeS at marine sediment porewater pH. This conclusion was independently verified by analyses of NOM FTIR spectra pre- and post-sorption. PLSR outperformed MLR with a lower root mean square error of prediction (RMSEP) of 53.4 L/kg compared to 64.0 L/kg. To our knowledge, this research presents a novel machine-learning approach to the quantitative modelling of NOM sorption to minerals found in anoxic marine environments.

Prediction of Growth Characteristics and Migration Period of Spodoptera frugiperda (Lepidoptera: Noctuidae) According to Temperature.

Simple SummarySpodoptera frugiperda is a pest with a wide host range that causes severe damage to the agricultural sector around the world. The species, which was first recorded in South Korea in 2019, has been affecting corn farmers by harming the leaves and fruits. Unfortunately, measures to initially respond to the species have not been implemented appropriately due to the lack of information on the growth characteristics of S. frugiperda in the Korean climate. Against this backdrop, we analyzed the growth characteristics and degree of damage to corn leaves at different temperatures and found that the optimal temperature for the growth of S. frugiperda was in the range 28–32 °C, which is the temperature range during summer in Korea. It is most active during this season, affecting more number of farmers progressively. In this regard, more efforts are needed to control the pest in summer in Korea.In 2019, an outbreak of Spodoptera frugiperda (Smith) was first reported in Korea. This study aimed to determine the growth rate and feeding amount of S. frugiperda by temperature to establish the right time window for its control and management. Linear regression analysis was used to determine the growth period and thermal requirements of S. frugiperda. The longest growth period of 97.2 ± 1.2 days was observed at 16 °C, and the shortest growth period of 15.5 ± 0.7 days was observed at 36 °C. In terms of each growth stage, the pupal period was the longest at all temperatures, followed by the egg period. The maximum corn leaf feeding amount (6.61 g) was observed for the larvae grown at 16 °C, and the minimum (2.9 g) was observed at 36 °C. However, the daily feeding amount of S. frugiperda larvae was the highest at 28 °C and 32 °C. The hatching rate according to temperature exceeded 70% at 24 °C, 28 °C, and 32 °C, and the survival rate of larvae and pupae was 100% at 24 °C to 32 °C. Based on these results, a temperature range of 28 °C to 32 °C is proposed as the optimum temperature for the growth of S. frugiperda.

The northern corn leaf blight resistance gene Ht1 encodes an nucleotide-binding, leucine-rich repeat immune receptor.

Northern corn leaf blight, caused by the fungal pathogen Exserohilum turcicum, is a major disease of maize. The first major locus conferring resistance to E.turcicum race 0, Ht1, was identified over 50 years ago, but the underlying gene has remained unknown. We employed a map-based cloning strategy to identify the Ht1 causal gene, which was found to be a coiled-coil nucleotide-binding, leucine-rich repeat (NLR) gene, which we named PH4GP-Ht1. Transgenic testing confirmed that introducing the native PH4GP-Ht1 sequence to a susceptible maize variety resulted in resistance to E.turcicum race 0. A survey of the maize nested association mapping genomes revealed that susceptible Ht1 alleles had very low to no expression of the gene. Overexpression of the susceptible B73 allele, however, did not result in resistant plants, indicating that sequence variations may underlie the difference between resistant and susceptible phenotypes. Modelling of the PH4GP-Ht1 protein indicated that it has structural homology to the Arabidopsis NLR resistance gene ZAR1, and probably forms a similar homopentamer structure following activation. RNA sequencing data from an infection time course revealed that 1 week after inoculation there was a threefold reduction in fungal biomass in the PH4GP-Ht1 transgenic plants compared to wild-type plants. Furthermore, PH4GP-Ht1 transgenics had significantly more inoculation-responsive differentially expressed genes than wild-type plants, with enrichment seen in genes associated with both defence and photosynthesis. These results demonstrate that the NLR PH4GP-Ht1 is the causal gene underlying Ht1, which represents a different mode of action compared to the previously reported wall-associated kinase northern corn leaf blight resistance gene Htn1/Ht2/Ht3.

Genetic characterization and comparative assessment of response of biparental and multi-parental populations to northern corn leaf blight in maize (Zea mays L.)

To compare the efficiency of multiparental and biparental progenies to northern corn leaf blight (NCLB) resistance, F3 progenies were evaluated during two seasons. A total of 418 F3 progenies derived from multiple founder parents and 248 F3 progenies derived from two parents, namely, SKV50 (Resistant) and CM202 (Susceptible) were phenotyped for their response to the NCLB during summer and kharif 2020. A wide range of disease severity was observed among F3 progenies derived from multiple parents ranging from 28.12 to 62.93%. The difference in the variances between multiparent and biparental progenies was indicated by the significance of Levene’s test. High genotypic and phenotypic coefficient of variation was observed among multi-parental progenies than those derived from the biparental population. Reaction to NCLB showed moderate heritability of 57% and high genetic advance as per cent mean (20.70%) in the case of biparental population, whereas, multi-parental population exhibited a high heritability (68%) and high genetic advance as per cent mean (28.57%). The distribution of F3 progenies was found to be positively skewed and platykurtic indicating that the trait was under the control of large number of genes with complementary gene action.

Effects of oregano essential oil, cobalt and synergistic of both of them on rumen degradation rate and fermentation characteristics for corn silage

This study used oregano essential oil (EO), cobalt (Co) and synergistic of both of them additives instead of antibiotics to explore the effects of different additives on the corn silage fibre structure degradation, nutrient degradation rate and rumen fermentation characteristics of sheep. The additives used in the study as a product were provided by Ralco, Inc., USA. The Latin square experimental design was adopted with 5 treatments. The following treatments were fed through the rumen fistula: carrier (Car), carrier + EO, carrier + Co, carrier + EO + cobalt (EOC) and no additives as the control (CON). These treatments were fed to five sheep for five rounds at a dose of 80 mg/kg. At 48 h of degradation, the area of degraded corn leaf and husk reached 100%, SEM results showed that except for Car and CON treatment, there were obvious destroyed fibre structure differences under EO, Co and EOC treatment, showing total disappearance of leaf epidermal cells, palisade tissue and spongy tissue, especially under EOC. Under EOC treatment, DMD48 h, CPD48 h, NDFD48 h and ADFD48 h increased by 3.33%, 3.89%, 7.64% and 5.67%, respectively, compared with CON, while NDFD48 h and ADFD48 h increased by 4.70% and 6.05% under Co (p > 0.05). The NH3-N concentration in rumen fluid decreased by 19.36%, 27.20% and 19.48%, while the microbial protein (MCP) content increased by 6.32%, 4.29% and 5.97% (p > 0.05) under EO, Co and EOC treatments compared with CON at 48 h (p < 0.05). The total VFA concentration under EO, Co and EOC treatment was lower than CON at 6 and 48 h (p = 0.016, p = 0.022) and the propionate and acetate concentrations were higher under EOC treatment than CON at 48 h (p > 0.05). Feeding EOC increased the fibre structure degradation and nutrient degradation rate of corn silage and improved rumen fermentation in sheep, resulting in better effects than feeding EO or Co alone. HIGHLIGHTS This article revealed the significance of oregano essential oil synergistic with cobalt (EOC) on rumen fermentation characteristics and nutrient degradation rate of sheep. In this study, the degradation of leaves in the rumen was observed by scanning electron microscopy, and the degradation area was calculated by electron microscopy for the first time. The results of this study will provide the theoretical basis for EOC as a substitute for antibiotics.

Linkage mapping and genomic prediction to infer QTL regions associated with resistance to gray leaf spot and northern corn leaf blight in tropical maize

Linkage mapping and genomic prediction to infer QTL regions associated with resistance to gray leaf spot and northern corn leaf blight in tropical maize

Endophytic Metarhizium robertsii suppresses the phytopathogen, Cochliobolus heterostrophusand modulates maize defenses.

Fungi in the genus Metarhizium (Hypocreales: Clavicipitaceae) are insect-pathogens and endophytes that can benefit their host plant through growth promotion and protection against stresses. Cochliobolus heterostrophus (Drechsler) Drechsler (Pleosporales: Pleosporaceae) is an economically-significant phytopathogenic fungus that causes Southern Corn Leaf Blight (SCLB) in maize. We conducted greenhouse and lab-based experiments to determine the effects of endophytic M. robertsii J.F. Bisch., Rehner & Humber on growth and defense in maize (Zea mays L.) infected with C. heterostrophus. We inoculated maize seeds with spores of M. robertsii and, at the 3 to 4-leaf stage, the youngest true leaf of M. robertsii-treated and untreated control plants with spores of C. heterostrophus. After 96 h, we measured maize height, above-ground biomass, endophytic colonization by M. robertsii, severity of SCLB, and expression of plant defense genes and phytohormone content. We recovered M. robertsii from 74% of plants grown from treated seed. The severity of SCLB in M. robertsii-treated maize plants was lower than in plants inoculated only with C. heterostrophus. M. robertsii-treated maize inoculated or not inoculated with C. heterostrophus showed greater height and above-ground biomass compared with untreated control plants. Height and above-ground biomass of maize co-inoculated with M. robertsii and C. heterostrophus were not different from M. robertsii-treated maize. M. robertsii modulated the expression of defense genes and the phytohormone content in maize inoculated with C. heterostrophus compared with plants not inoculated with C. heterostrophus and control plants. These results suggest that endophytic M. robertsii can promote maize growth and reduce development of SCLB, possibly by induced systemic resistance mediated by modulation of phytohormones and expression of defense and growth-related genes in maize.

High-resolution mapping reveals a Ht3-like locus against northern corn leaf blight.

Northern corn leaf blight (NCLB), caused by the fungal pathogen Exserohilum turcicum, poses a grave threat to maize production worldwide. The resistance gene in A619Ht3, discovered decades ago, is an important genetic resource for NCLB control. By using a pair of near-isogenic lines (NILs) A619Ht3 and A619, together with the resistant and susceptible bulks derived from the cross of A619Ht3 and L3162 lines, we initially detected a Ht3-like (Ht3L) locus in bin 8.06 that was closely associated with NCLB resistance. We then performed five rounds of fine-mapping, which ultimately delimited the Ht3L locus to a 577-kb interval flanked by SNP markers KA002081 and KA002084. Plants homozygous for the Ht3L/Ht3L genotype exhibited an average reduction in diseased leaf area (DLA) by 16.5% compared to plants lacking Ht3L locus. The Ht3L locus showed extensive variation in genomic architecture among different maize lines and did not appear to contain any genes encoding canonical cell wall-associated kinases against NCLB. Moreover, the Ht3L locus was located ∼2.7 Mb away from the known Htn1 locus. We speculate that the Ht3L locus may contain a bona fide Ht3 gene or a novel NCLB resistance gene closely linked to Ht3. In practice, the Ht3L locus is a valuable resource for improving maize resistance to NCLB.

Physiological and Molecular Characteristics of Southern Leaf Blight Resistance in Sweet Corn Inbred Lines

Southern corn leaf blight is one of the most widespread foliar diseases in maize-producing areas worldwide and can seriously reduce the yield and quality of sweet corn. However, the molecular mechanisms underlying the disease in sweet corn have not been widely reported. In this study, two sweet corn inbred lines, resistant K13 (RK13) and susceptible K39 (SK39), were used to explore the disease resistance mechanism of southern leaf blight. We observed morphological characteristics and assessed the changes in protective enzymatic activity in sweet corn leaves after inoculation of C. heterostrophus. RNA-seq was performed to elucidate the transcriptional dynamics and reveal the key pathways involved in southern leaf blight resistance without pathogens (Mock) and at 1 and 3 days post inoculation (1 and 3 dpi). Differentially expressed genes (DEGs) were identified in the SK39 group (including three pairwise combinations: SK39−0d_vs_SK39−1d, SK39−1d_vs_SK39−3d and SK39−1d_vs_SK39−3d), the RK13 group (including three pairwise combinations: RK13−0d_vs_RK13−1d, RK13−1d_vs_RK13−3d and RK13−1d_vs_RK13−3d), and the SK39_vs_RK13 group (including three pairwise combinations: SK39−0d_vs_RK13−0d, SK39−1d_vs_RK13−1d, and SK39−3d_vs_RK13−3d). In our study, 9455 DEGs from the RK13 group, 9626 from the SK39 group, and 9051 DEGs from the SK39_vs_RK13 group were obtained. Furthermore, 2775, 163, and 185 DEGs were co-expressed at SK39_vs_RK13, RK13, and SK39, respectively. A functional analysis of the DEGs revealed that five pathways—i.e., photosynthesis, plant hormone signal transduction, MAPK signaling pathway, phenylpropanoid biosynthesis, and biosynthesis of secondary metabolites—and transcription factor families play crucial roles in disease resistance. The results from the present study enabled the identification of the JA and SA signaling pathways, which are potentially involved in the response to southern leaf blight in maize. Our findings also highlight the significance of ZIM transcription factors and pathogenesis-related (PR) genes during pathogen infection. This study preliminarily explored the molecular mechanisms of the interaction between sweet corn and C. heterostrophus and provides a reference for identifying southern leaf blight resistance genes in the future.