The National Academy of Agricultural Research Management is a national-level Natural Resource Service training institute for ARS cadre located in Hyderabad, Telangana, India..
Climate change is accelerated by increasing levels of greenhouse gases (GHGs) as a result of human activity, particularly the release of carbon dioxide (CO2). Soil carbon (C) sequestration, or the transfer of atmospheric CO2 to soil organic matter (SOM) with long-term stabilization within the soil, is an important process of C removal from the atmosphere. For the accounting of soil C and offset markets in most countries including Australia, the standard soil sampling depth is 0–30 cm, although deeper sampling is recommended for more accurate C stock assessments and to capture long-term sequestration potential. While 30 cm soil depth accounts for most short-term management impacts on C storage, a significant portion of soil C is stored below this depth (i.e., deep soil C), and sampling at greater depths can provide a more complete account of total C stocks and potential sequestration benefits. This paper aims to provide a comprehensive review, including a bibliometric analysis and a critical discussion of the link between deep soil C storage and sequestration potential in relation to climate change mitigation and soil health. Deep soil layers contain over 850 Pg C worldwide, which is approximately 50
ABSTRACT Infant foods and baby formulas are becoming increasingly popular across the globe owing to their ease of consumption and nutritional value specific to infants. Impurities may find their way into the food chain at any point from the acquisition of raw materials to final packaging, causing serious health hazards. Fruit and vegetable‐based infant foods have also been reported to be contaminated with plant toxins, mycotoxins, and microbial toxins, in addition to microbial residues that can prove extremely hazardous for infants. Current trends in artificial intelligence (AI) bring exciting opportunities for the instantaneous and nondestructive analysis of such contaminants. AI‐driven methods, such as machine learning (ML) and deep learning (DL) algorithms, are capable of analyzing vast datasets from spectroscopic, imaging, or sensor inputs to detect trace amounts of toxic substances with high accuracy. This review aims to identify the different poisonous substances and contaminants that are harmful to infant health and to emphasize how AI systems can enhance the identification, tracking, and prevention of such contaminants in baby food items.
Amid growing global concerns over environmental degradation, food security, and climate change, organic farming has emerged as a key pillar of sustainable agriculture. This scientometric analysis of global research from 2003 to 2023 examined trends across 11,561 publications, revealed an annual growth rate of 8.8 % and an average of 24.27 citations per document, emphasizing the growing significance of organic farming in both academic and practical spheres. Scientific collaborations across 33,576 authors, with an average of 4.4 co-authors per paper, underscore the interdisciplinary and international scope of organic farming research. The USA, Germany, and Italy played a major role in advancing the field of organic farming through publications in highimpact journals, facilitating the rapid and effective communication of research findings. The Scientometric analysis further revealed that the key research areas of organic farming include soil fertility, biological pest control, climate-resilient practices, and socioeconomic aspects. Emerging interest in precision agriculture and digital tools suggests a shift towards technologically enhanced organic farming. However, the concerns relating to the scalability and economic viability of organic farming practices, particularly for smallholders, remain critical challenges. This analysis offers strategic insights to inform future research directions, policy development, and sustainable agricultural transformation.
Melamine (MA) detection in milk and milk products has attracted much attention since the discovery that MA-adulterated food causes serious damage to the kidney, leading to urinary calculus, acute kidney failure, and bladder cancer. Most methods measure MA through various chromatographic techniques, which require skill, time consuming, tedious, expensive instrumentation and preprocessing of samples. Aptamer-based biosensors are a promising choice for MA analysis due to their advantages over instrumental analysis and immunoassays. Thymine (T) in single-stranded DNA can specifically bind to MA through a T-MA-T triple hydrogen bonding (NH⋯O and NH⋯N) motif, which serves as the fundamental recognition principle for many DNA-based biosensors. In this review, we aim to report the latest research progress in the field of aptasensors based on different sensing technologies (including colorimetric, fluorescence, surface enhanced Raman scattering (SERS), resonance Rayleigh scattering (RRS), and electrochemical) for rapid detection of MA in milk products. While these sensing techniques enable accurate and rapid detection, portable, high sensitivity, high efficiency, and simple operation, they are constrained by matrix interference, limited stability, poor reproducibility, and insufficient standardization. Overcoming these hurdles to achieve large-scale implementation requires future research to enhance device robustness, selectivity, and data-processing capabilities. This review will inspire future research on aptamer-based biosensing for real time monitoring of MA adulteration in order to safeguard human health and food safety.
Introduction:The identification and advancement of superior maize hybrids under the All India Coordinated Research Project (AICRP) on Maize rely on multi-environment evaluation integrating grain yield, maturity, and agronomic performance. Interpretation of large multi-environment datasets is often complex, time-consuming, and susceptible to subjectivity, highlighting the need for objective and reproducible decision-support tools. This study evaluated the effectiveness of REMATTOOL-R (Relative Maturity Adjustment Tool in R) in validating the existing hybrid advancement framework adopted under the AICRP on Maize. Methods:Multi-environment trial data from the National Initial Varietal Trial (NIVT)-Late conducted during Kharif 2020-21 across five locations representing the Central West Zone (CWZ) of India were analysed. The dataset comprised 45 entries, including 40 experimental hybrids, four commercial checks, and one filler entry. REMATTOOL-R integrated grain yield with days to 50% anthesis, grain moisture at harvest, and harvested plant stand to facilitate simultaneous evaluation of grain yield, maturity, and adaptation-related traits. Least-square means generated from mixed-model analysis were used to identify superior hybrids based on a predefined grain yield superiority threshold (≥5%) over the standard check while maintaining comparable maturity and agronomic performance. Results:REMATTOOL-R enabled rapid visualization and integrated assessment of multiple agronomic traits, allowing objective identification of superior hybrids. Five experimental hybrids-PM 21109L (Entry 30), R8050 (Entry 35), PM 21111L (Entry 32), BIO 978 (Entry 4), and DKC 9226 (Entry 9)-recorded ≥5% higher grain yield than the standard check Bio 9682 while maintaining statistically comparable days to 50% anthesis, grain moisture at harvest, and harvested plant stand. All five hybrids identified by REMATTOOL-R corresponded with the official AICRP decisions for advancement from NIVT to Advanced Varietal Trial-I (AVT-I), while three hybrids (R8050, PM 21111L, and DKC 9226) progressed further to AVT-II during subsequent testing cycles, confirming the reliability of the analytical framework. Discussion:The findings demonstrate that REMATTOOL-R provides an efficient, transparent, and reproducible framework for the simultaneous evaluation of grain yield, maturity, and adaptation-related traits in maize multi-environment trials. By complementing the existing AICRP hybrid evaluation procedure, the tool facilitates objective advancement decisions and reduces subjectivity associated with manual interpretation of complex datasets. REMATTOOL-R therefore represents a valuable decision-support approach for coordinated maize breeding programmes and has considerable potential for application in large-scale hybrid evaluation systems.
