Genetic potential and inheritance pattern of agronomic traits in faba bean under free and infested Orobanche soil conditions.

BACKGROUND: Orobanche is an obligate parasite on faba bean in the Mediterranean region, causes considerable yield losses. Breeding tolerant faba bean genotypes to Orobanche is pivotal to sustain production and ensuring global food security, particularly considering the challenges posed by population growth. In the present study, seven faba bean lines and four testers were used in a line×tester mating design during 2020-2021 and 2021-2022 growing seasons. The eleven parents and their 28 F1 crosses were evaluated under Orobanche free and naturally infested soils. RESULTS: The results demonstrated considerable variations among the evaluated genotypes, wide diversity among the parental materials, and heterotic effects for all studied agronomic traits under Orobanche-free and infested soils. Orbanche infestation displayed a significant adverse impact on all the studied agronomic traits. The genotypes Line1, Line2, Line3, and Line5 displayed superior performance under Orobanche-infested conditions and recorded the highest values of all studied agronomic traits. Additionally, Line1, Line2, Line3, Line5, and Line7 exhibited desirable significant GCA for most evaluated traits under the two infestation conditions. The obtained crosses displayed significant negative or positive heterosis for studied agronomic characters such as plant height, number of branches per plant, number of pods per plant, number of seeds per plant, and seed weight per plant were observed. Furthermore, specific cross combinations such as Line2×Sakha3, Line3×Nubaria5, Line7 × Nubaria5, Line6×Nubaria1, Line5×Sakha3, Line1×Sakha3, and Line1 × Nubaria5 exhibited superior performance in seed yield and contributing traits under Orobanche-infested conditions. Moreover, these specific crosses showed superior efficacy in reducing dry weight of Orobanche spikes. The results obtained from GGE biplot analysis closely aligned with those from the line×tester procedure, affirming the significance of GGE biplot as a valuable statistical tool for assessing genotype combining ability in line× tester data. Both additive and non-additive gene actions were reported to be predominantly involved in the inheritance of the studied agronomic traits in faba bean. CONCLUSIONS: The detected genetic diversity within the evaluated faba bean genotypes and their developed crosses exhibits substantial potential for improving faba bean productivity under Orobanche-infested conditions. The parental genotypes, Line1, Line2, Line3, Line5, and Line7, were identified as effective and promising combiners. Moreover, the developed crosses Line2×Sakha3, Line3×Nubaria5, Line7×Nubaria5, Line6×Nubaria1, Line5×Sakha3, Line1×Sakha3, and Line1×Nubaria5 could be considered valuable candidates for developing high-yielding and tolerant faba bean genotypes to Orobanche.

Integrating multiple statistical indices to measure the stability of photosynthetic pigment content and composition in Brassica juncea (L.) Czern germplasm under varying environmental conditions.

Understanding the stability of photosynthetic pigments is crucial for developing crop cultivars with high productivity and resilience to the environmental stresses. This study leveraged GGE biplot, WAASB, and MTSI indices to assess the stability of content and composition of photosynthetic pigments in leaves and siliques of 286 Brassica juncea (L.) Czern. genotypes across three environments. The GGE biplot analysis identified NRCQR-9901 as the best genotype in terms of chlorophyll 'a' under conditions of high irradiance and long days (E1). For chlorophyll 'b' and total chlorophyll, NC-533728 performed the best. AJ-2 and NPJ-208 had the maximum total carotenoids levels in leaves. RLC-2 was characterized by maximum values for chlorophyll a, chlorophyll b, and total chlorophyll in the siliques. The low irradiance, short days, and moderate to high temperatures (E2) seemed perfect for the synthesis of photosynthetic pigments. NPJ-182 shows the maximum concentrations of chlorophyll 'a', total chlorophyll, and total carotenoids in leaves. Conversely, IC-597869, RE-389, and IC-597894 exhibited the highest concentrations of chlorophyll 'b' under an environment characterized by low light intensity, shorter daylights, and low temperatures (E3) during flowering and siliqua formation stages. The combined analysis found NPJ-182, NC-533728, CN-105233, RLC-2, CN-101846, JA-96, PBR-357, JM-3, and DTM-34 as top performers with high stability. Comparative transcriptome analysis with two stable and high-performing genotypes (PBR-357 and DTM-34) and two average performers revealed upregulation of critical photosynthesis-related genes (ELIP1, CAB3.1, ELIP1.5, and LHCB5) in top performers. This study identified promising trait donors for use in breeding programs aimed at improving the mustard crop's photosynthetic efficiency, productivity, and stability.

An investigation on the prevalence and patterns of multi-morbidity among a group of slum-dwelling older women of Kolkata, India.

BACKGROUND: Multi-morbidity is a pervasive and growing issue worldwide. The prevalence of multi-morbidity varies across different populations and settings, but it is particularly common among older adults. It poses substantial physical, psychological, and socio-economic burdens on individuals, caregivers and healthcare systems. In this context, the present study aims to provide an insight on the prevalence and degree of multi-morbidity; and also, on the relationship between level of multi-morbidity and morbid conditions among a group of slum-dwelling older women. METHODS: This community based cross-sectional study was conducted in the slum areas of urban Kolkata, West Bengal, India. It includes total 500 older women, aged 60 years or above. Pre-tested schedules on so-demographic and morbidity profile have canvassed to obtain the information by door-to-door survey. To determine the relationship between the level of multi-morbidity and morbid conditions, correspondence analysis has performed. RESULTS: The study revealed three most prevalent morbid conditions- back and/or joint pain, dental caries/cavity and hypertension. The overall prevalence of multi-morbidity was 95.8% in this group of older women. It was highly over-represented by the oldest-old age group (80 years and above). Majority were found to suffer from five simultaneous morbid conditions that accounted for 15.2% of the total respondents. All of the oldest-old women of this study reported to suffer from more than two medical conditions simultaneously. Three distinct groups were formed based on the inter-relationship between level of multi-morbidity and morbid conditions. The group 1 and 2 represents only 27.8% and 18% of the total sample. Whereas, group 3 comprises the highest level of morbidities (≥ 6) and 52.8% of total sample, and strongly related with general debilities, cardiac problems, asthma/COPD, gastrointestinal, musculoskeletal problems, neurological disorders, hypothyroidism and oral health issues. CONCLUSION: The findings confirmed the assertion that multi-morbidity in slum living older adults is a problem with high prevalence and complexity. This study proposes an easily replicable approach of understanding complex interaction of morbidities that can help further in identifying the healthcare needs of older adults to provide them with healthy and more productive life expectancy.

Multi-location evaluation of field pea in Indian climates: eco-phenological dynamics, crop-environment relationships, and identification of mega-environments.

Characterization of crop-growing environments in relation to crop's genotypic performance is crucial to harness positive genotype-by-environment interactions (GEI) in systematic breeding programs. Given that, the study aimed to delineate the impact of diverse environments on crop phenology and yield traits of dwarf-statured field pea, pinpointing location(s) favoring higher yield and distinctiveness within breeding lines. We tested twelve field pea breeding lines across twenty locations in India, covering Central Zone (CZ), North Western Plain Zone (NWPZ), North Eastern Plain Zone (NEPZ), and Northern Hill Zone (NHZ). Across these locations, maximum and minimum temperatures during flowering (TMAXF, TMINF) and reproductive period (TMAXRP, TMINRP) ranged 18.9-28.3, 3.3-18.0, 15.0-30.8, and 7.9-22.1oC, respectively. Meanwhile, notable variations in phenological and agronomic traits (coefficient of variation) were observed: flowering (31%), days to maturity (21%), reproductive period (18%), grain yield (48%), and 100-seed weight (18%). Combined ANOVA demonstrated an oversized impact of environment (81%) on yield, while genotype and GEI effects were 2% and 14%, respectively. The variables TMINF, TMINRP, and cumulative growing degree-day showed positive correlations with yield, while extended vegetative and maturity durations negatively influenced yield (p < 0.05). Additionally, linear mixed-models and PCA results explained that instability in crop phenology had significant influence on field pea yield. Seed weight was markedly varied within the locations (9.9-20.8 g) and both higher and lower seed weights were associated with lower yields (Optimal = 17.1 g). HA-GGE biplot-based on environment focus-scaling demonstrated three mega-environments and specific locations viz. Kota (CZ), SK Nagar (CZ), Raipur (CZ), Sehore (CZ), and Pantnagar (NWPZ) as the ideal testing-environments with high efficiency in selecting new genotypes with wider adaptability. The study findings highlight distinct impact of environments on crop phenology and agronomic traits of field pea (dwarf-type), hold substantial value in designing efficient field pea (dwarf-type) breeding program at mega-environment scale.

Tucker3-PCovR: The Tucker3 principal covariates regression model.

In behavioral research, it is very common to have manage multiple datasets containing information about the same set of individuals, in such a way that one dataset attempts to explain the others. To address this need, in this paper the Tucker3-PCovR model is proposed. This model is a particular case of PCovR models which focuses on the analysis of a three-way data array and a two-way data matrix where the latter plays the explanatory role. The Tucker3-PCovR model reduces the predictors to a few components and predicts the criterion by using these components and, at the same time, the three-way data is fitted by the Tucker3 model. Both the reduction of the predictors and the prediction of the criterion are done simultaneously. An alternating least squares algorithm is proposed to estimate the Tucker3-PCovR model. A biplot representation is presented to facilitate the interpretation of the results. Some applications are made to empirical datasets from the field of psychology.

Graphical analysis of multi-environmental trials for wheat grain yield based on GGE-biplot analysis under diverse sowing dates.

BACKGROUND: Information on the nature and extent of genetic and genotype × environment (GE) interaction is extremely rare in wheat varieties under different sowing dates. In the present study, the GGE biplot method was conducted to investigate genotype × environment interaction effects and evaluate the adaptability and yield stability of 13 wheat varieties across eight sowing dates, in order to facilitate comparison among varieties and sowing dates and identify suitable varieties for the future breeding studies. RESULTS: Considerable genotypic variation was observed among genotypes for all of the evaluated traits, demonstrating that selection for these traits would be successful. Low broad sense heritability obtained for grain yield showed that, both genetic and non-genetic gene actions played a role in the control of this trait, and suggested that indirect selection based on its components which had high heritability and high correlation with yield, would be more effective to improve grain yield in this germplasm. Hence, selection based on an index may be more useful for improvement of this trait in recurrent selection programs. The results of the stability analysis showed that the environmental effect was a major source of variation, which captured 72.21% of total variation, whereas G and GE explained 6.94% and 18.33%, respectively. The partitioning of GGE through GGE biplot analysis showed that, the first two PCs accounted for 54.64% and 35.15% of the GGE sum of squares respectively, capturing a total of 89.79% variation. According to the GGE biplot, among the studied varieties, the performance of Gascogen was the least stable, whereas Sirvan, Roshan, and Pishtaz had superior performance under all sowing dates, suggesting that they have a broad adaptation to the diverse sowing dates. These varieties may be recommended for genetic improvement of wheat with a high degree of adaptation. CONCLUSION: The results obtained in this study demonstrated the efficiency of the GGE biplot technique for selecting high yielding and stable varieties across sowing dates.

Volatile Oil Yield and Composition, Total Phenolic Content, Antioxidant Activity and Secondary Metabolite Content of Collected Thymus praecox Species in Rize.

In this study, the volatile oil yield (Clevenger), volatile oil (VO) composition (Gas Chromatography), phenolic contents (UV-VIS Spectrophotometer), antioxidant activities (UV-VIS Spectrophotometer) and secondary metabolite content (High Pressure Liquid Chromatography) of 11 Thymus praecox subspecies were evaluated. The most detected chemical class were oxygenated monoterpenes (55.18-86.1 %) in investigated samples. In the present study rosmarinic acid, isoquercitrin, gallocatechin and thymol could be detected in high amounts. The min. and max. content values of Flora/Field Samples were 1543.241 and 890.3-1425.3 for rosmarinic acid, 139.44-287.894 and 129.9-312.2 for thymol, 38.619-121.424 and 26.3-112.9 for gallocatechin as mg/g DW. Principal Component Analysis was used to differentiate Thymus praecox species regarding volatile oil composition and secondary metabolite content. The results demonstrated that T. praecox collected from the Rize flora and cultivated afterwards showed variability based on investigated characteristics. Finally, the Thymus praecox samples displaying high bioactive compounds present useful information for further investigations and applications.

Selection of oat (Avena sativa L.) drought-tolerant genotypes based on multiple yield-associated traits.

BACKGROUND: Most plant breeding and agricultural practices are based on selecting genotypes for yield. However, this is inadequate to screen crop varieties for specific attributes, such as drought tolerance. In this study, we quantified the response of oat (Avena sativa L.) plant physiological and morphological traits to drought stress and selected some key traits to establish a genotype by yield*trait (GYT)-based method for ranking 30 oat genotypes. The effectiveness of this method was also evaluated under drought conditions. RESULTS: Water-deficit treatment significantly reduced leaf chlorophyll, root morphological traits, groat yield and associated components, such as mean grain weight. We observed that the genotypes 'JUSTICE' and 'BOLINA' had the smallest and largest yield loss, respectively, after exposure to drought stress, but showed opposite trends in the biomass allocation of roots and grains. This indicated that drought tolerance was highly dependent on the distribution of photoassimilates. Our results also illustrated that the GYT method is a trade-off approach and more effective in selecting oat ideotypes under drought conditions than the yield-related index method because it combines yield, yield stability, and related agronomic traits in the calculation process. CONCLUSION: Drought-tolerant genotypes had more biomass allocated to roots and grains with higher chlorophyll content and better root structure, e.g. longer root lengths than drought-sensitive lines. By integrating yield and yield-related traits, the GYT approach is more practical than traditional single-trait selection methods when assessing drought tolerance. © 2023 His Majesty the King in Right of Canada. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. Reproduced with the permission of the Minister of Agriculture and Agri-Food Canada.

Screen for low-arsenic-risk rice varieties based on environment-genotype interactions by using GGE analysis.

Arsenic (As) accumulation in rice is a global health concern that has received increased attention in recent years. In this study, 12 rice genotypes were cultivated at four As-contaminated paddy sites in Taiwan. According to the different crop seasons and As levels in the soil, the sites were further divided into 18 environmental conditions. For As in soils, results showed that 67% of the studied environments were likely to represent As contamination. For As in rice, the mean total As concentration in brown rice grains ranged from 0.17 to 0.45 mg kg-1. The analysis of variance for the environment effect indicated that grain As concentration was mainly affected by the environmental conditions, suggesting that there was a remarkable degree of variation across the trial environments. According to the combination of the GGE biplot and cumulative distribution function of order statistics (CDFOS) analysis, five genotypes-TCS17, TCS10, TT30, KH139, and TC192-were regarded as stable, low-risk genotypes because the probability of grain As concentration exceeding the maximum permissible concentration (MPC) was lower for these genotypes across all environmental conditions. Particularly, TCS17 was recommended to be the safest rice genotype. Thus, grain As levels in the selected genotypes were applied to assess the health risk to Taiwanese residents associated with As exposure through rice consumption. Results showed that the upper 75th percentile values of the hazard quotient were all less than unity. This suggested that the health risk associated with consuming the selected rice genotypes was acceptable for most of the residents. The methodology developed here would be applicable to screen for stable, low-As-risk rice genotypes across multiple field environments in other regions or countries.

Strawberry crinkle (SCV) and Strawberry mottle (SMoV) viruses affect yield and physio-biochemical responses of three strawberry cultivars under different seasonal conditions.

BACKGROUND: The current study was performed to evaluate the impact of Strawberry crinkle virus (SCV) and Strawberry mottle virus (SMoV) viruses, in single and mixed infections, on the yield and physio-biochemical traits of strawberry plants with different genetic backgrounds. Accordingly, the virus-free plantlets were produced via meristem culture and were then transplanted in a research field in different seasons. METHODS AND RESULTS: The results indicated that genetic background clearly affects the responses of the strawberry plants to the viral infection. In addition, considering the effects of changing environmental factors, different seasons had significant influences on the strawberry responses to the virus infection. The results indicated that the components indicating destructive effects were significantly higher during the summer season. Reverse transcription-polymerase chain reaction using viral specific primers revealed 345 and 461 bp fragments for SCV and SMoV viruses, respectively, where all plants were positive for studied viruses. The comparison between the influence of SCV and SMoV viruses on the host plants indicated that SCV caused more damaging effects on the strawberry fruit yield and physio-biochemical traits. Simultaneous infection with SCV and SMoV severely impacted the strawberry plants compared with the effect of each virus individually. CONCLUSION: It is recommended to keep the temperature of the greenhouses producing strawberry at the cooler levels to prevent the spreading of the aphid-transmitted viruses.

Characterizing plant trait(s) for improved heat tolerance in field pea (Pisum sativum L.) under subtropical climate.

Field pea is highly sensitive to climatic vagaries, particularly high-temperature stress. The crop often experiences terminal heat stress in tropical climates indicating the need for the development of heat-tolerant cultivars. Characterization and identification of stress-adaptive plant traits are pre-requisites for breeding stress-tolerant/adaptive cultivar(s). In the study, a panel of 150 diverse field pea genotypes was tested under three different temperature environments (i.e., normal sowing time or non-heat stress environment (NSTE), 15 days after normal sowing time or heat stress environment-I (LSHTE-I), and 30 days after normal sowing time or heat stress environment-II (LSHTE-II)) to verify the effect of high-temperature environment, genotype, and genotype × environment interaction on different plant traits and to elucidate their significance in heat stress adaptation/tolerance. The delayed sowing had exposed field pea crops to high temperatures during flowering stage by + 3.5 °C and + 8.1 °C in the LSHTE-I and LSHTE-II, respectively. Likewise, the maximum ambient temperature during the grain-filling period was + 3.3 °C and + 6.1 °C higher in the LSHTE-I and LSHTE-II over the NSTE. The grain yield loss with heat stress was 25.8 ± 2.2% in LSHTE-I, and 59.3 ± 1.5% in LSHTE-II compared to the NSTE. Exposure of crops to a high-temperature environment during the flowering stage had a higher impact on grain yield than the heat stress at the grain filling period. Results suggested that the reduced sink capacity (pod set (pod plant-1), seed set (seed pod-1)) was the primary cause of yield loss under the heat stress environments, while, under the NSTE, yield potential was mostly attributed to the source capacity (plant biomass). The high-temperature stress resulted in forced maturity as revealed by shrinkage in crop period (5-11%) and reproductive period (15-36%), prominently in long-duration genotypes. The failure of pod set in the upper nodes and higher ovule abortion (7-16%) was noticed under the high-temperature environments, particularly in the LSHTE-II. Multivariate analysis results revealed seed set, pods plant-1, last pod bearing node, and plant biomass as a critical yield determinant under the heat stress. The GGE biplot suggested that the genotypes G-112, G-114, and G-33 had higher potential to sustain yield coupled with higher stability across the environments and, thus, could serve as a source for breeding heat-tolerant high yielding cultivars.

Biochemical composition of selected lines from sorghum (Sorghum bicolor L.) landraces.

MAIN CONCLUSION: Present findings revealed a great variation in protein, oil, starch, tannin and phytic acid-like biochemical attributes of sorghum genotypes. GT bi-plot analysis was used for assessment of biochemical analysis results. Sorghum is quite resistant to negative environmental conditions. Thus, it has become the basic source of nutrient in majority of developing countries. It is also used as a supporting product against several diseases. In this study, biochemical composition of new 154 sorghum lines, selected from Turkish sorghum landraces through morphological, biochemical and molecular studies, was determined. Present findings revealed based on average of two years that crude protein content of 80 lines (> 9.65%), digestible protein content of 94 lines (> 56.25%), crude oil content of 75 lines (> 3.87%), resistant starch ratio of 32 lines (> 3.93%), starch content of two lines (> 77.07%) and amylose content of 10 lines (> %25.26) were greater than standard cultivar with the greatest values and amylopectin contents of 74 lines (< 47.75%) were lower than the standard cultivar with the lowest values. As the average of years, phytic acid contents and condense tannins contents of the lines, respectively, varied between 0.02 (IS 12850/1)-6.49% (PI 177161 03/2) and 0.09 (IS 12819/1)-5.38% (PI 255738 02/3). With this study, several sorghum lines superior than the standard cultivars were identified. Those lines can be used for direct cultivar registration in short run and can be used as parent materials in further breeding studies to be conducted for various purposes in long run.

Integrating genomic selection with a genotype plus genotype x environment (GGE) model improves prediction accuracy and computational efficiency.

Genotype-by-environment interaction (GEI) is one of the major factors affecting the prediction accuracy of genomic selection (GS) models. Standard models have low power to model complex GEI, and they fail to predict phenotypes in unobserved environments. Here, we developed a novel prediction model that account for GEI, named 3GS, that combines genotype plus genotype × environment (GGE) analysis with GS. The model calculates the principal components (PCs) of the environmental phenotypes using GGE analysis and predict the performance of these PCs using standard GS models before converting the GEBVs of these PCs (pcGEBVs) back to the original phenotypes. We demonstrated three advantages of the new model. First, 3GS showed significantly higher prediction accuracy primarily for deviated environments that have low to negative correlations to other environments. Second, 3GS can predict new genotypes in unobserved environments with high accuracy. Third, the computational complexity of 3GS increases linearly with increasing the number of environments and the population size, unlike the standard models that exhibit exponential increase, making it hundreds of times faster than the standard models for large data sets. 3GS can improve prediction accuracy with minimal resources in modern breeding programmes in which massive populations get multi-environment phenotypes with high-throughput techniques.

Field-Relevant New Sources of Resistance to Anthracnose Caused by Colletotrichum truncatum in a Mungbean Mini-Core Collection.

Anthracnose is a prevalent disease of mungbean in Asian countries and Sub-Saharan Africa. It is caused by multiple Colletotrichum species. The high levels of anthracnose resistance in mungbean have not been studied in depth in India, but genetic resistance is desired. In this study, we identified the causal agent of mungbean anthracnose in two regions of India as Colletotrichum truncatum through morphological and molecular methods. A set of 296 mungbean mini-core accessions developed by WorldVeg was screened under a natural disease pressure from July to September (kharif season) in 2016, 2017, and 2018 in Hyderabad (a hot spot for anthracnose) to identify anthracnose resistance. Based on disease severity scores, 22 accessions were consistently anthracnose resistant under the categories of immune, highly resistant, and resistant with scores ranging from ≥1.0 to ≤3.0 during the period of study. Furthermore, based on the agronomic performance, anthracnose resistance in Hyderabad, and other desirable traits, a subset of 74 mungbean accessions was selected from 296 mini-core accessions. These accessions were evaluated under natural disease pressure from July to September in 2018 and 2019 in Palampur (another hot spot for anthracnose) to determine the variation in anthracnose resistance. Out of the 74 accessions, two accessions were resistant in 2018; in 2019, one was immune, nine were highly resistant, and 15 were resistant. Combined analysis of variance of 65 accessions common in Hyderabad and Palampur revealed highly significant effects of environment, genotype (accessions), and genotype × environment interaction on the disease severity. The combined GGE biplot analysis of data across years and locations confirmed that the seven accessions MC-24, MC-51, MC-75, MC-127, MC-207, MC-208, and MC-292 were resistant during 2016 to 2018 in Hyderabad, and only in 2019 in Palampur, and the same accessions were moderately resistant in 2018 in Palampur. The seven resistant accessions identified from both test locations could be used as potential donors in the anthracnose resistance breeding program.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Secretomics: a biochemical footprinting tool for developing microalgal cultivation strategies.

Microalgae offer a promising source of biofuel and a wide array of high-value biomolecules. Large-scale cultivation of microalgae at low density poses a significant challenge in terms of water management. High-density microalgae cultivation, however, can be challenging due to biochemical changes associated with growth dynamics. Therefore, there is a need for a biomarker that can predict the optimum density for high biomass cultivation. A locally isolated microalga Cyanobacterium aponinum CCC734 was grown with optimized nitrogen and phosphorus in the ratio of 12:1 for sustained high biomass productivity. To understand density-associated bottlenecks secretome dynamics were monitored at biomass densities from 0.6 ± 0.1 to 7 ± 0.1 g/L (2 to 22 OD) in batch mode. Liquid chromatography coupled with mass spectrometry identified 880 exometabolites in the supernatant of C. aponinum CCC734. The PCA analysis showed similarity between exometabolite profiles at low (4 and 8 OD) and mid (12 and 16 OD), whereas distinctly separate at high biomass concentrations (20 and 22 OD). Ten exometabolites were selected based on their role in influencing growth and are specifically present at low, mid, and high biomass concentrations. Taking cues from secretome dynamics, 5.0 ± 0.5 g/L biomass concentration (16 OD) was optimal for C. aponinum CCC734 cultivation. Further validation was performed with a semi-turbidostat mode of cultivation for 29 days with a volumetric productivity of 1.0 ± 0.2 g/L/day. The secretomes-based footprinting tool is the first comprehensive growth study of exometabolite at the molecular level at variable biomass densities. This tool may be utilized in analyzing and directing microalgal cultivation strategies and reduction in overall operating costs.

Antifungal potential of zinc against leaf spot disease in chili pepper caused by Alternaria alternata.

The fungal pathogen, Alternaria alternata is responsible for causing leaf spot disease in many plants, including chili pepper. Zinc (Zn) an essential micronutrient for plant growth, also increases resistance in plants against diseases, and also acts as an antifungal agent. Here, in vitro effects of ZnSO4 on the propagation of A. alternata were investigated, and also in vivo, the effect of foliar application of ZnSO4 was investigated in chili pepper plants under disease stress. In vitro, ZnSO4 inhibited fungal growth in a dose-dependent manner, with complete inhibition being observed at the concentration of 8.50 mM. Hyphae and conidial damage were observed along with abnormal activity of antioxidant enzymes, Fourier-transform infrared spectroscopy confirmed the major changes in the protein structure of the fungal biomass after Zn accumulation. In vivo, pathogen infection caused the highest leaf spot disease incidence, and cumulative disease index, which resulted in a significant reduction in the plant's growth (length and biomass), and physiochemical traits (photosynthetic pigment, activity of catalase, peroxidase, polyphenol oxidase, and phenylalanine ammonia lyase). The heat map and principal component analysis based on disease, growth and, physico-chemical variables generated useful information regarding the best treatment useful for disease management. Foliar Zn (0.036 mM) acted as a resistance inducer in chili pepper plants that improved activities of antioxidants (CAT and POX), and defense compounds (PPO and PAL), while managing 77% of disease. The study indicated foliar ZnSO4 as an effective and sustainable agriculture practice to manage Alternaria leaf spot disease in chili pepper plants.

Comprehensive studies on optimization of ligno-hemicellulolytic enzymes by indigenous white rot hymenomycetes under solid-state cultivation using agro-industrial wastes.

The disposal of oil palm biomass is a huge challenge in Malaysian oil palm plantations. The aim of this study was to develop efficient solid-state cultivated (SSC) ligno-hemicellulolytic bio-degrader formulations of indigenous white-rot hymenomycetes (Trametes lactinea FBW and Pycnoporus sanguineus FBR) utilizing oil palm empty fruit bunches (EFB), rubber wood sawdust (SD) and vermiculite (V) either alone or in combination as substrates. Based on significant laccase (849.40 U mg-1 protein), xylanase (42.26 U g-1 protein) and amylase (157.49 U g-1 protein) production, SD+V (T5) and V (T3) were the optimum substrates for SSC of T. lactinea FBW. Whereas, utilizing EFB (T1) substrate for SSC of P. sanguineus FBR enhanced the production of MnP (42.51 U mg-1 protein), LiP (103.20 U mg-1 protein) and CMCase (34.39 U g-1 protein), enzymes. Apparently, this is the first study reporting on the protein profiles by T. lactinea FBW, producing two isoforms of un-purified laccase (~55 and 70 kDa) and MnP (~40 and 60 kDa) and a CMCase band (~60 kDa) during SSC on SD+V (T5) substrate. Interestingly, this is also the first report to document a single isoform of un-purified laccase (~50 kDa), MnP (~45 kDa), CMCase (~60 kDa) and xylanase (~55 kDa) by P. sanguineus FBR during SSC on empty fruit bunches substrate. The computed Principal Component Analysis (PCA) Biplot analysis elucidated the relationship between the solid substrate compositions, the hymenomycete strain, ligno-hemicellulolytic enzyme profiles, and cultivation time. Therefore, it is suggested to use PCA as a tool for multivariate analysis method for comprehensive selection and optimization of ligno-hemicellulolytic enzyme cocktails by the indigenous white rot hymenomycetes. These non-toxic (acute oral toxicity) formulations are safe to be used in field applications to efficiently degrade oil palm trunks and root mass that had been felled, chipped or pulverized under zero burning waste management program. This study could also serve as an alternative method for efficient utilization of agro-industrial waste as substrates for the development of cost-effective bio-degraders formulations for agro-waste management.

Functional diversity and metabolic profile of microbial community of mine soils with different levels of chromium contamination.

Microbial communities provide useful information about any chemical and physical changes in the environment and play an essential role in maintaining soil fertility. Biolog® eco-plates method was used to study the functional diversity of microbial communities, and their correlation with soil organic carbon (OC), microbial biomass and activities, under three different soil conditions of Sukinda chromite mining area of Odisha, India during August 2016. The OC, available nitrogen, available phosphorus and available potash were significantly (p < 0.05) lower in in situ and overburden soils as compared to forest soil. The average development rate of average well color development values decreased with incubation time in all soil conditions. The utilization of six categories of carbon sources by soil microbes decreased with the increase in chromium load and biplot analysis suggested that carbohydrate, polymer and amino acid utilizing microbes were dominant in mining soils. The ecotoxicological status of chromite mine soil would be useful for formulating strategies of possible bioremediation program.

Pre evaluation of cassava (Manihot esculenta Crantz) germplasm for genotypic variation in the identification of K efficient genotypes through different statistical tools.

Among the tropical tuber crops, cassava (Manihot esculenta Crantz) deserves special attention as regards to its higher biological efficiency in terms of dry matter production which incidentally implies to the higher amount of nutrient extraction from the soil resulting in better response to the application of manures and fertilizers. Among the major nutrients, Potassium (K) is considered as the key nutrient for cassava owing to its influence both in tuber yield and tuber quality. The above facts as well as the availability of sufficient cassava genotypes in the germplasm collection of ICAR-CTCRI made us to initiate research work to screen cassava germplasm including the pre breeding lines. The objective being to identify K efficient genotypes which can yield well under limited availability of K so that the external application of K can be reduced. This paper describes the wide variation noticed during the pre evaluation of 83 elite genotypes which was done as a prelude in the screening and identification of K efficient genotypes. The characters studied were tuber yield, tuber characters, plant dry matter percentage, plant K content, tuber quality (starch, cyanogenic glucosides) attributes, physiological efficiency and plant biometric characters. The variation among the genotypes for the above traits was assessed by making some yardstick for classification which in turn helped in determining the percent distribution of genotypes in each category. The variation among the genotypes were further affirmed through principal component analysis, wherein the first five components explained more than 77% of variability and the cluster analysis performed grouped these genotypes into five clusters. The biplot showed the traits which are closely linked to the genotypes. The dendrogram constructed indicated similar genotypes to that of the clusters to the extent of more than 50% revealing the association of members with similar traits in clusters and dendrograms. The study helped in establishing the drastic variation among the genotypes along with identification of six genotypes viz., Aniyoor, 7 Sahya (2), 7 III E3-5, W-19, CR 43-8, 6-6 for further detailed experimentation to identify K efficient genotypes.

Temporal and geographical patterns of solid waste collected at recycling centres.

Citizens increasingly dispose their waste at household waste recycling centres (HWRC). To enhance the collection of recyclables materials, local authorities and waste management companies invest considerable resources in planning. While the planning of these centres requires a comprehensive understanding of collected solid waste, only limited studies have consistently investigated waste data from HWRC. To fill this knowledge gap, historical data for HWRC from the Greater Copenhagen, Central Zealand, Silkeborg and Djursland in Denmark were analysed with regards to temporal and geographical variation. The results showed the mass of collected waste varies seasonally; this trend was consistently seen during the period 2010-2016. Moreover, the data revealed that the total waste collected was principally driven by the number of visitors. The geometric bar plot and ternary plot depicted an increase in the percentage of recyclable materials, whereas the percentage of incinerated waste decreased during the period 2010-2016. The waste characterisation study indicated that about 7% of small miscellaneous combustible waste was brought in black plastic bag, although these bags were forbidden; the results suggest that the percentage of misplaced recyclable materials could considerably decrease if citizens bring their waste in clear plastic bags.