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The rhizosphere represents an intricate microenvironment, consisting of a complex network involving soil, root and soil microbes. Conditions in the rhizosphere exert a direct influence on the growth and yield of crops. The unregulated and widespread application of synthetic fertilizers has emerged as a grave concern for the sustainability of agriculture and the equilibrium of ecosystems. These chemical substances accumulate within the soil, leach into water sources and release into the atmosphere, persisting for decades and posing a substantial threat to the overall ecosystem. This issue is of significant concern, necessitating a potential solution that can only be realized through the involvement of microorganisms and organic amendments. Plant growth promoting rhizobacteria (PGPR) has assumed a pivotal role in addressing this concern. The established role of microorganisms in enhancing plant growth, managing nutrients and exerting biocontrol is well-documented. PGPR, present in the rhizosphere, has the capacity to transform numerous nutrients that are initially inaccessible to plants into forms that can be readily utilized. Additionally, PGPR synthesize plant hormones, secondary metabolites, antibiotics, stressrelieving compounds, chelating agents, and signaling molecules, enabling interactions with both beneficial and pathogenic organisms within the rhizosphere. Moreover, PGPR is involved in the improvement of soil physical properties, chemical properties and overall functioning that offers direct or indirect benefits to crop growth. [ABSTRACT FROM AUTHOR]

The first step in developing high yielding heterotic hybrids is to find restorers that can effectively restore the fertility of CMS lines. Two distinct and dominant nuclear genes for restoring fertility, Rf3 and Rf4, are primarily in charge of the fertility restoration of CMS-WA lines in rice. Identifying fertility restorer lines can be accelerated and made simpler by molecular mapping of Rf3 and Rf4. The present investigation was carried out to validate the presence of two fertility restorer genes Rf3 and Rf4 in the F4 generations of two populations viz., CBSN 25/WRM 21-24 (86 families) and CBSN 25/WRM 93-20 (79 families) using seven SSR molecular markers. The results revealed that 17 plants in the cross CBSN 25/WRM 21-24 and 47 plants in CBSN 25/WRM 93-20 were found to be double positives for both Rf3 and Rf4 genes. Hence, after stabilization these identified plants from both crosses could be used as male parents or restorers in CMS-based hybrid rice breeding programs. [ABSTRACT FROM AUTHOR]

Current dietary patterns are suboptimal for both human and planetary health(1,2). With growing consumer and business concerns around food sustainability, estimating the environmental footprint of foods and diets is pertinent. In many countries, supermarkets are the primary provider of foods and beverages; therefore, supermarket purchasing records represent a novel source of population dietary data that offers advantages over traditional methods(3). We developed a method for mapping greenhouse gas emissions (GHGE) to food and beverage products from a high-street retailer's portfolio, to enable the estimation of the environmental footprint of population diets when linked with sales information. We used data from the food and beverage portfolio of a high-street retailer in the United Kingdom (UK), including product name/description, categorisation, ingredients, and weight. We mapped these products to GHGE (kg CO2-eq/kg) using a global database on the average environmental footprint of food commodities(4). This mapping process involved three stages utilising different mapping approaches, guided by product sales data, which we extracted from the retailer's loyaltycard transactions for Yorkshire and the Humber (UK) region during 2022. Stage 1 involved categorising the products into Living Costs and Food Survey food categories and mapping each category to GHGE, where possible (food-category approach). Stage 2 involved splitting selected food categories (based on complexity, necessity of a better mapping, and sales) and creating a sub-category-specific mapping based on an indicator product, which was selected as most popular using sales data (food-sub-category approach). The indicator-product mapping represented a weighted average GHGE value calculated using information on product ingredients and their estimated proportions (ingredient approach). Stage 3 utilised word-searches in product descriptions to distinguish further between product types within selected prioritised subcategories. We used the estimated product-level GHGE (mapped GHGE × product weight) and sales data to estimate food-category contributions to total GHGE and assess how these estimations change by mapping stage. Of >28,000 products, 77.7%, 98.0% and 98.6% were mapped to GHGE at the end of stages 1, 2 and 3, respectively. Of the final product mappings, 40% were at a food-category level and 60% at least at a sub-category level. We calculated 153 product-specific GHGE using ingredients information for prioritised indicator products. When using mappings from stage 3 vs 1, the contributions of 'savoury snacks' and 'chocolate' to total GHGE were approximately four and two times higher respectively, due largely to improved mapping that accounted for product sub-category and ingredients. Mapping environmental sustainability metrics to a retail product dataset is feasible when using a staged approach, guided and prioritised by sales data. However, mapping approach and the estimations' variability should be considered. This method could be used for estimating the environmental footprint from food purchasing data, helping to inform responses towards promoting healthier and more sustainable diets. [ABSTRACT FROM AUTHOR]

Globally, a third of total anthropogenic greenhouse gas emissions (GHGE) are produced by the food system(1). Estimating the carbon footprint of current diets is therefore important to consumers, businesses, and policymakers. With most home-consumed food in the United Kingdom (UK) purchased from supermarkets(2), supermarket purchasing records represent a novel data source that can provide insights into dietary patterns(3). These data are particularly useful in an environmental sustainability context as they provide information on the amounts of foods and beverages purchased, not just the amounts consumed (as in traditional dietary assessments). We estimated GHGE of foods and beverages purchased in Yorkshire and the Humber region of the UK using supermarket transaction data from primary-shopper loyalty cards over 12 months in 2022. We mapped a UK retailer's food and beverage products to GHGE (kg CO2-eq/kg) using data on the environmental footprint of food commodities(4), and grouped the products according to the Living Costs and Food Survey (LCFS) categories. The sustainability mapping process was guided by product sales (i.e., prioritising the most sold products and categories) and involved three stages utilising mapping approaches with different complexity, resulting in 98.6% of >28,000 store products being mapped. We estimated total GHGE of each product by multiplying the final mapped GHGE by the product weight (as sold). We then used these product-level GHGE estimations (kg CO2-eq/item) in conjunction with the sales data (number of items sold) to estimate the contribution of each product, and subsequently each LCFS category, to total GHGE from all purchases. When incorporating sales, the LCFS categories with the highest contributions to total GHGE included 'beef' (19.6%), 'milk' (9.8%), 'cheese and curd' (8.6%), 'ready meals' (6.9%), and 'poultry' (5.5%). The LCFS categories among the lowest contributors to total GHGE included 'confectionery products' (0.2%), 'pasta products' (0.4%) and 'soft drinks' (0.5%). Although some LCFS categories had higher GHGE per kg for their products, they were sold in smaller quantities, and therefore, their contributions to total GHGE were lower in total. For example, 'lamb' was in the top five LCFS categories with the highest GHGE per kg (39.7 kg CO2-eq/kg) but contributed to 1.4.% of total estimated GHGE when incorporating sales information, which was less than 'bread' (2.2%) and 'yoghurt' (1.7%). Our results highlight that although some foods might be very GHGE-intensive on a per weight basis, they have a lower overall GHGE impact if they are not frequently purchased in the population. These supermarket sales data are an important resource to understanding and subsequently tackling the environmental impact of the food system. Further research, including other environmental sustainability metrics (e.g., water and land use), is needed to provide a more comprehensive picture of the environmental footprint of foods and beverages purchased by UK consumers. [ABSTRACT FROM AUTHOR]

The present investigation was carried out to understand the genetic variability, association, cause and effect relationships between yield and its attributing agronomic and physiological traits in 23 F3 families of pre-breeding restorer germplasm in a hybrid rice program for six agronomic and three physiological characters. The variability and heritability parameters were grouped under the high category for the number of productive tillers per plant, leaf area per plant, dry matter production and single plant yield. The traits viz., the number of tillers per plant and panicle length, leaf area per plant and dry matter production showed a direct correlation with single plant yield and in cause and effect relationships. All traits had a direct influence on single plant yield except days to 50% flowering and chlorophyll index. At the intra-family level, six families namely 30, 35, 36, 46, 55 and 82 showed high variability and heritability values of all parameters for six traits. All nine traits exhibited normal distribution. [ABSTRACT FROM AUTHOR]

Background: Groundnut, commonly known as peanut, is a significant oil, food and feed legume crop grown in all seasons in Tamil Nadu, including kharif, rabi and summer and it is cultivated both under irrigated and rainfed conditions in all the seasons at Thiruvannamlai district. One of the most important applications of remote sensing in agriculture is a crop acreage and production estimation (CAPE). The CAPE's main goal is to estimate crop acreage and production of important crops, so that advanced food production, distribution and supply data were achieved. Methods: Multi-temporal Sentinel 1A SAR IW-GRD data with 20 m spatial resolution and 12 days temporal resolution of vertical - horizontal (V-H) polarization were downloaded for the period of 4th October 2020 to 8th January 2021 to have the full coverage during the crop growth period in the study area used for this work. Crop backscattering and multi-temporal features were extracted from MAPscape 5.2 automated pre-processing tool and its classified using supervised maximum likelihood classification for groundnut acreage extraction for Thiruvannamalai district. Result: The rabi groundnut area of Thiruvannamalai district of Tamil Nadu was estimated using SAR Sentinel-1A data as 32298 ha with a higher accuracy percentage of 87.4 and kappa coefficient of 0.75. [ABSTRACT FROM AUTHOR]