Molecular characterization of vermicompost-derived IAA-releasing bacterial isolates and assessment of their impact on the root improvement of banana during primary hardening.

The hardening step of micropropagation is crucial to make the in vitro raised plants mature and further enhancing their survivability in the external environment. Auxin regulates various root physiological parameters in plant systems. Therefore, the present study aimed to assess the impact of three vermicompost-derived IAA-releasing microbial strains, designated S1, S2, and S3, as biofertilizers on in vitro raised banana plantlets during primary hardening. The High-Performance Thin-Layer Chromatography (HPTLC) analysis of these strains revealed a higher IAA content for S1 and S2 than that of S3 after 144 h of incubation. In total, seven different treatments were applied to banana plantlets, and significant variations were observed in all plant growth parameters for all treatments except autoclaved cocopeat (100%) mixed with autoclaved vermicompost (100%) at a 1:1 ratio. Among these treatments, the application of S3 biofertilizer: autoclaved cocopeat (1:1), followed by S2 biofertlizer: autoclaved cocopeat (1:1), was found to be better than other treatments for root numbers per plant, root length per plant, root volume, and chlorophyll content. These findings have confirmed the beneficial effects of microbial strains on plant systems and propose a link between root improvement and bacterial auxin. Further, these strains were identified at the molecular level as Bacillus sp. As per our knowledge, this is the first report of Bacillus strains isolated from vermicompost and applied as biofertilizer along with cocopeat for the primary hardening of banana. This unique approach may be adopted to improve the quality of plants during hardening, which increases their survival under abiotic stresses.

Microbiological properties of Beejamrit, an ancient Indian traditional knowledge, uncover a dynamic plant beneficial microbial network.

Beejamrit is an ancient organic formulation commonly used as a seed treatment in organic and natural farming in India. This low-cost formulation is primarily a product of dairy excreta (e.g., cow dung and cow urine) and forest soil, often supplemented with limestone. Growing data suggest that dairy excreta are the potential sources of enriched microbial niche, including several plant growth-promoting bacteria capable of synthesizing plant growth regulators. However, the microbiological properties of Beejamrit and their temporal changes after different incubation periods, delineating its application in seed treatment, remain largely unexplored. Here, we aimed to analyze the decomposition rate of Beejamrit over 7-consecutive days of incubation. This study further elucidates the microbial niche and their dynamics in Beejamrit, including the plant beneficial bacteria. We have shown that the population of plant beneficial bacteria, such as the free-living nitrogen fixers (FNFs) and the phosphate solubilizers (PSBs), proliferates progressively up to 4- and 5-days of incubation, respectively (p < 0.0001). This study also reports the total indolic content of Beejamrit, including indole 3-acetic acid (IAA), which further tends to oscillate in concentration based on the incubation periods incurred during the Beejamrit preparation. Our analyses, together, establish that Beejamrit provides a dynamic, microbe-based metabolic network and may, therefore, act as a plant biostimulant to crop plants. A plant-based bioassay finally demonstrates the role of Beejamrit in the seed treatment to improve seed germination, seedling survival rate, and shoot length trait in French beans (p < 0.01). In conclusion, this study highlights, for the first time, the scientific insights of Beejamrit as a potential seed priming agent in agriculture.

Multi-faceted impact and outcome of COVID-19 on smallholder agricultural systems: Integrating qualitative research and fuzzy cognitive mapping to explore resilient strategies.

The shock of Coronavirus Disease 2019 (COVID-19) has disrupted food systems worldwide. Such disruption, affecting multiple systems interfaces in smallholder agriculture, is unprecedented and needs to be understood from multi-stakeholder perspectives. The multiple loops of causality in the pathways of impact renders the system outcomes unpredictable. Understanding the nature of such unpredictable pathways is critical to identify present and future systems intervention strategies. Our study aims to explore the multiple pathways of present and future impact created by the pandemic and "Amphan" cyclonic storm on smallholder agricultural systems. Also, we anticipate the behaviour of the systems elements under different realistic scenarios of intervention. We explored the severity and multi-faceted impacts of the pandemic on vulnerable smallholder agricultural production systems through in-depth interactions with key players at the micro-level. It provided contextual information, and revealed critical insights to understand the cascading effect of the pandemic and the cyclone on farm households. We employed thematic analysis of in-depth interviews with multiple stakeholders in Sundarbans areas in eastern India, to identify the present and future systems outcomes caused by the pandemic, and later compounded by "Amphan". The immediate adaptation strategies of the farmers were engaging family labors, exchanging labors with neighbouring farmers, borrowing money from relatives, accessing free food rations, replacing dead livestock, early harvesting, and reclamation of waterbodies. The thematic analysis identified several systems elements, such as harvesting, marketing, labor accessibility, among others, through which the impacts of the pandemic were expressed. Drawing on these outputs, we employed Mental Modeler, a Fuzzy-Logic Cognitive Mapping tool, to develop multi-stakeholder mental models for the smallholder agricultural systems of the region. Analysis of the mental models indicated the centrality of "Kharif" (monsoon) rice production, current farm income, and investment for the next crop cycle to determine the pathways and degree of the dual impact on farm households. Current household expenditure, livestock, and soil fertility were other central elements in the shared mental model. Scenario analysis with multiple stakeholders suggested enhanced market access and current household income, sustained investment in farming, rapid improvement in affected soil, irrigation water and livestock as the most effective strategies to enhance the resilience of farm families during and after the pandemic. This study may help in formulating short and long-term intervention strategies in the post-pandemic communities, and the methodological approach can be used elsewhere to understand perturbed socioecological systems to formulate anticipatory intervention strategies based on collective wisdom of stakeholders.

Profitability, energetics and GHGs emission estimation from rice-based cropping systems in the coastal saline zone of West Bengal, India.

This study compares thirteen rice-based cropping systems in the coastal part of West Bengal, India in terms of productivity, profitability, energetics, and emissions. Information on the crop management practices of these systems was collected on 60 farms through a questionnaire survey. Rice-bitter gourd system was observed to have the highest system yield (49.88 ± 4.34 tha-1yr-1) followed by rice-potato-ridge gourd (37.78 ± 2.77 tha-1yr-1) and rice-potato-pumpkin (36.84 ± 2.04 tha-1yr-1) systems. The rice-bitter gourd system also recorded the highest benefit:cost ratio (3.92 ± 0.061). The lowest system yield and economics were recorded in the rice-fallow-fallow system. Rice-sunflower system recorded highest specific energy (2.54 ± 0.102 MJkg-1), followed by rice-rice (2.14 ± 0.174 MJkg-1) and rice-fallow-fallow (1.91 ± 0.327 MJkg-1) systems, lowest being observed in the rice-bitter gourd (0.52 ± 0.290 MJkg-1) and rice-pointed gourd (0.52 ± 0.373 MJkg-1) systems. Yield-scaled GHGs (YSGHG) emission was highest (1.265 ± 0.29 t CO2eqt-1 system yield) for rice-fallow-fallow system and was lowest for rice-vegetable systems. To estimate the uncertainty of the YSGHG across different systems under study, Monte-Carlo Simulation was performed. It was observed that there was a 5% probability of recording YSGHG emission > 1.15 t CO2eqt-1 system yield from different cropping systems in the present experiment. Multiple system properties such as productivity, economics, energy, and emission from all rice-based systems taken together, the rice-vegetable system performed consistently well across parameters and may be practised for higher economic returns with judicious and sustainable utilization of resources in the coastal saline tracts of the region.

Maize yield in smallholder agriculture system-An approach integrating socio-economic and crop management factors.

Yield gaps of maize (Zea mays L.) in the smallholder farms of eastern India are outcomes of a complex interplay of climatic variations, soil fertility gradients, socio-economic factors, and differential management intensities. Several machine learning approaches were used in this study to investigate the relative influences of multiple biophysical, socio-economic, and crop management features in determining maize yield variability using several machine learning approaches. Soil fertility status was assessed in 180 farms and paired with the surveyed data on maize yield, socio-economic conditions, and agronomic management. The C&RT relative variable importance plot identified farm size, total labor, soil factors, seed rate, fertilizer, and organic manure as influential factors. Among the three approaches compared for classifying maize yield, the artificial neural network (ANN) yielded the least (25%) misclassification on validation samples. The random forest partial dependence plots revealed a positive association between farm size and maize productivity. Nonlinear support vector machine boundary analysis for the eight top important variables revealed complex interactions underpinning maize yield response. Notably, farm size and total labor synergistically increased maize yield. Future research integrating these algorithms with empirical crop growth models and crop simulation models for ex-ante yield estimations could result in further improvement.

Screening of rice landraces for salinity tolerance at seedling stage through morphological and molecular markers.

The present investigation was carried out to evaluate 33 rice landrace genotypes for assessment of their salt tolerance at seedling stage. Growth parameters like root length, shoot length and plant biomass were measured after 12 days of exposure to six different levels of saline solution (with electrical conductivity of 4, 6, 8, 10, 12 or 14 dS m (-1)). Genotypes showing significant interaction and differential response towards salinity were assessed at molecular level using 11 simple sequence repeats (SSR) markers, linked with salt tolerance quantitative trait loci. Shoot length, root length and plant biomass at seedling stage decreased with increasing salinity. However, relative salt tolerance in terms of these three parameters varied among genotypes. Out of the 11 SSR markers RM8094, RM336 and RM8046, the most competent descriptors to screen the salt tolerant genotypes with higher polymorphic information content coupled with higher marker index value, significantly distinguished the salt tolerant genotypes. Combining morphological and molecular assessment, four lanraces viz. Gheus, Ghunsi, Kuthiahara and Sholerpona were considered as true salt tolerant genotypes which may contribute in greater way in the development of salt tolerant genotypes in rice.