Open-cast coal mining is a globally prevalent method of coal extraction that significantly disrupts terrestrial ecosystems, particularly through its impact on soil health and biological resilience. The process often results in the exposure of pyrite-bearing strata, leading to Acid Mine Drainage (AMD), nutrient depletion, and disruption of microbial functionality. This study explores how different durations of land uses/types and recovery durations influence soil chemical and biological properties in paddy-growing landscapes of Assam, India. Assam is a region predominantly characterized by strongly acidic Inceptisols, with limited buffering capacity and high vulnerability to degradation. We hypothesized that prolonged recovery time following mining cessation would result in gradual ecological restoration of soil quality. Four categories of land use were studied: C1 (active mining), C2 (recovery for 5 years), C3 (recovery for 10 years), and C4 (uncontaminated paddy field as control). Soils in active mining areas (C1) exhibited severe acidification (pH 4.3–5.3), attributed to AMD, with the lowest pH recorded in C1. This was accompanied by marked depletion of macronutrients—Nitrogen (N), Phosphorus (P), and Potassium (K). Microbial analysis revealed the lowest bacterial populations and Soil Microbial Biomass Carbon (SMBC) in C1, indicating biological stress, whereas C3 showed a partial resurgence in microbial activity and nutrient status. These findings suggest that the cessation of mining activities initiates a slow but measurable trajectory of soil health recovery, defined here as the gradual improvement in soil chemical and biological functions, in post-mining landscapes, though full ecological functionality may require targeted interventions.

This research focuses on the development of an automated water discharge measurement instrument aimed at improving the sustainability of aquifer water management. Traditional methods for measuring water discharge are typically manual, labor-intensive, time-consuming, and susceptible to human error, making them inefficient and costly for large-scale or long-term applications. To address these limitations, an advanced system was designed using ultrasonic sensors integrated with a microcontroller-based data acquisition and processing unit. The system is capable of accurately measuring both water level and flow rate in real-time. Extensive testing and calibration were conducted under varying conditions to evaluate the performance of the device. Results demonstrated a high degree of accuracy, with an average error margin of less than 5%. The automated instrument significantly reduces the need for manual labor and associated operational costs, while enhancing the reliability and consistency of the measurements. Its compact design and adaptability make it suitable for a wide range of water management scenarios, including remote or resource-constrained environments. The system also supports data logging, which enables continuous monitoring and historical data analysis for informed decision-making. This innovation offers a scalable and sustainable solution for efficient water resource management. Its successful implementation can help optimize water usage, prevent resource depletion, and support long-term environmental conservation efforts. Future enhancements may include integration with Internet of Things (IoT) platforms to enable remote access, predictive analytics, and automated alerts. Such developments can further increase the instrument’s impact in advancing smart water management systems for sustainable development.

Rice (Oryza sativa L.) is a major food crop for half the world’s population but is constantly endangered by a variety of pathogens. Nucleotide-Binding Site Leucine-Rich Repeat (NBS-LRR) genes are important genes in plant innate immunity through which plants respond to pathogen effectors directly. The objective of this study was to conduct an extensive next-generation genomic analysis of the rice NBS-LRR gene family to understand its involvement in plant–microbe interactions and for its potential use in crop protection.

We performed an integrative computational analysis with bioinformatics methodologies. Genes were predicted by a database search and the hidden Markov model and confirmed by the conserved domain database. This analysis consisted of gene structure visualization, motif identification with the MEME suite, phylogenetic reconstruction with MEGA, GO term enrichment analysis, protein structure prediction with αFold2, and cis-regulatory element analysis from upstream of the gene.

An analysis showed that there were 10 conserved motifs in whole NBS-LRR: motifs 1, 4, and 5 were important in recognizing pathogens. Phylogenetic analysis revealed eight evolutionary lineages, and gene duplication events were found in Groups 4 and 7. In GO enrichment, similar functions, including the identification of roles in defense responses, ATP binding, and plasma membrane, were verified. Seven OSDRP protein structure predictions were of high confidence (98.2-100%) and were homologous to one LRR receptor-like kinase and six Toll-like receptors. The cis-regulatory study showed wide distribution of MYB and G-box motifs, and the first gene was identified as a transcriptional hotspot.

This detailed study contributes to our understanding of the molecular composition and evolution of rice NBS-LRR genes. The recognition of eight phylogenetic clades and the clustering of regulatory elements provide excellent candidates for the breeding of disease-resistant crops, laying the groundwork for the development of next-generation crop protection strategies.

Agricultural ecosystems in Northeastern Bangladesh are increasingly vulnerable to climate-induced stressors, particularly rising temperatures and seasonal droughts. This study aims to assess the spatiotemporal variations in vegetation health under climate stress in the Sylhet region over the last two decades using remote sensing techniques. The Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) were derived from Landsat satellite imagery to evaluate trends in vegetation and surface thermal conditions. Seasonal NDVI and LST values were analyzed across major cropping seasons to understand the ecological response of agricultural land to climatic variability. The relationship between vegetation health and surface temperature was quantified using statistical comparison techniques to identify patterns and intensity of climate stress. Preliminary trends indicate that increased LST correlates with reduced vegetation cover in lowland agricultural zones, while elevated regions with forest or tree cover show inverse patterns. Spatial hotspots of thermal stress and drought-prone areas were also identified. The findings highlight the increasing pressure on agricultural productivity due to rising surface temperatures and vegetation stress, particularly during the dry Rabi season. This research provides actionable insights for agronomists, planners, and policymakers in promoting climate-resilient agriculture and sustainable land management in subtropical regions such as Sylhet.

A study was conducted at the Modibbo Adama University Research Farm to evaluate the influence of different levels of urea fertilizer on the nutritive values of Brachiaria hybrid (Mulato II). The trial aimed to address the persistent issue of poor forage quality in tropical livestock systems by improving pasture productivity and nutritional composition through appropriate fertilization. Brachiaria Mulato II was established and treated with four levels of urea fertilizer: 0 kg/ha (control), 50 kg/ha, 100 kg/ha, and 150 kg/ha. Forage samples were harvested at 9- and 11-week post-establishment, and proximate composition was analyzed to determine crude protein (CP), ash, crude fiber (CF), dry matter (DM), ether extract (EE), and nitrogen-free extract (NFE). The results revealed that urea application significantly enhanced the crude protein content and reduced crude fiber across treatments, particularly at 100 kg/ha, indicating an optimal balance between nutrient uptake and growth. The CP values ranged from 8.15% in the control to 14.52% in the 100 kg/ha treatment at 9 weeks, while CF decreased from 31.44% in the control to 23.36% in the 100 kg/ha treatment. Ash and DM contents also improved with moderate fertilization, suggesting enhanced mineral content and biomass yield. However, application beyond 100 kg/ha showed diminishing returns in nutritional gains. The study concludes that applying 100 kg/ha of urea fertilizer to Brachiaria Mulato II enhances forage quality, making it suitable for improving ruminant nutrition in tropical regions. These findings support the integration of moderate fertilization into pasture management practices for sustainable livestock production.

Rice is a staple food crop comprising two main subspecies, indica and japonica, which have diverged through adaptation and domestication. While recent genome assemblies provide high-quality references, a single reference genome cannot capture the full extent of genetic variation in rice. A pangenome constructed from multiple individuals can overcome the limitations of using a single reference genome. In this study, the first Vietnamese rice pangenome was constructed using 20 rice landrace accessions and the IRGSP-1.0 reference genome. The pangenome was constructed using an iterative mapping and assembly approach, spanning 386.89 Mb, and included 13.64 Mb of novel sequences specific to Vietnamese landraces. Genome annotation identified 37,292 genes, including 33,710 core, 3,560 dispensable, and 22 unique genes. Among these, 3,116 genes were variably present across both indica and japonica groups but were not conserved within either group. From this set of shared variable genes, 122 genes were linked to important agronomic traits. Genes related to auxin and chlorophyll content included OsMKK6, OsMEK1, OsDLT10, OsPYL10, OsPYL3, and OsRCAR1. Genes involved in photosynthesis and transpiration included REL2, OsDLT10, and RSD1. Floral development was associated with OsbZIP47, while OsBOP3 and OsBSK3 contributed to inflorescence traits. Root development involved OsNAC2, OsCSLC7, OsMKK6, and OsMEK1. Grain length, shape, number, and weight were associated with key genes such as OsBSK3, OsCDKF2, REL2, OsJAZ10, OsTIFY11b, and OsWRKY19. Disease resistance genes were also identified, including OsCEBiP, Pikp1, Pikm5NP, xa47, and OsREM20. These shared variable genes provide valuable genetic resources for future rice improvement and breeding efforts.

Abstract

Introduction
Lathyrus (Lathyrus sativus L.), an important pulse crop, is valued for its high protein content and resilience to abiotic stresses. However, its cultivation is limited due to the presence of the neurotoxin β-ODAP, which causes neurolathyrism. Mutation breeding offers a viable approach to develop low-toxin, high-yielding varieties. This study aimed to evaluate genetic variability, estimate heritability, and identify superior mutants in M~4~ progenies of Lathyrus cv. NLK-73 for yield and related traits with reduced β-ODAP content.

Methods
Twenty-nine mutant progenies derived from gamma-ray-irradiated NLK-73 seeds, along with checks (NLK-73 and Ratan), were evaluated in a Randomized Block Design with three replications. The data recorded included days to first flowering, days to maturity, plant height, number of branches/plant, pods/plant, 100-seed weight, seed yield/plant, and β-ODAP content. Genetic parameters such as genotypic and phenotypic variance, heritability, and genetic advance were estimated.

Results
Analysis of variance revealed significant genetic variability among mutants for all traits. High genotypic coefficients of variation (GCVs) and heritability were observed for branches/plant (42.06%, 87.46%), pods/plant (20.86%, 48.79%), and seed yield/plant (16.83%, 41.40%). Moderate to high genetic advance was recorded for these traits, indicating additive gene action. Ten superior mutants (e.g., NLM-12, NLM-20, and NLM-23) were identified, exhibiting high yield (>23 g/plant), increased pods/plant (>49), and low β-ODAP content (<0.20%).

Conclusions
The study demonstrated the potential of mutation breeding to enhance yield and reduce β-ODAP in Lathyrus. Selected mutants with high heritability and genetic advance can be advanced for further trials, contributing to safer and more productive Lathyrus cultivars.

Mulberry (Morus spp.), one of the key sericultural crops, exhibits remarkable cytogenetic diversity, with natural chromosome-level variation ranging from 2n=14 to 2n=308. This broad spectrum—from diploid, triploid, tetraploid and hexaploid to the extreme polyploid decosaploid—offers a unique system to investigate the impact of genome duplication on plant physiology and morphology. We studied the influence of different cytotypes on traits associated with cell division, cell size, and biomass metrics. Principal Component Analysis (PCA) revealed that tetraploids exhibit the most favorable combination of traits, suggesting a vigorous and balanced expression of polyploid advantage. Beyond tetraploidy, many key parameters showed signs of downsizing with increasing ploidy levels, particularly in hexaploid and decosaploid genotypes. Moreover, improved storage capacity coincided with significantly higher LMA and a greater number per stomata. Collectively, inconsistent growth superiority reflects higher ploidy levels, suggesting that phenotypic expression in polyploids is influenced by the lower limit of cell size (stomatal length, stomatal width, and guard cell volume) and rate-limited attributes (leaf length, leaf width, leaf area, and petiole length). These patterns imply a trade-off: while moderate polyploidy (tetraploid) enhances morphological robustness, excessive polyploidy may incur growth penalties due to biophysical and nutrient constraints. Therefore, the current study highlights that benchmarking genome size/ploidy level for parental material selection is a crucial factor that may enhance cellular function and developmental efficiency, thereby maximizing leaf yield. Considering the inverse relationship between ploidy and growth performance, as well as biophysical and nutrient constraints, necessitates a deeper understanding of molecular mechanisms.

Early-season estimation and mapping of the nitrogen and phosphorus contents are essential for enhancing sugarcane production and preventing yield loss. Timely data acquisition is needed to estimate and map the nitrogen and phosphorus contents to facilitate decision-making and precision agriculture in sugarcane production. However, the traditional methods used to estimate and map the nitrogen and phosphorus contents are time-consuming, laborious, and expensive.

This study’s aims were to map the spatial and temporal variation in the nitrogen and phosphorus contents early in the sugarcane season using UAVs, machine learning algorithms, and soil and vegetation indices. The sugarcane plantations were in Emangweni in the Nkomazi Local Municipality, Mpumalanga, South Africa.

Soil and leaf samples, computed vegetation indices, and ground survey data were used as inputs for the machine learning algorithms. The performance of Random Forest, Support Vector Machines, and Partial Least Squares Regression was compared based on the accuracy of the models in estimating and mapping the nitrogen and phosphorus contents in sugarcane plantations. The Pearson Correlation Coefficient (R), p-Value (p), Coefficient of Determination (R²), and Root Mean Square Error (RMSE) were used to validate the accuracy of the machine learning algorithms.

Based on our results, Random Forest is expected to outperform Support Vector Machines and Partial Least Squares Regression in estimating and mapping the nitrogen and phosphorus contents. The Normalized Difference Red Edge is expected to perform better in estimating and mapping the nitrogen contents; however, a combination of vegetation indices will be required to estimate and map the phosphorus contents in sugarcane plantations.

Future studies employing unmanned aerial vehicles should focus on the estimation and mapping of the nitrogen, phosphorus, and potassium contents over the entire sugarcane season.

Potato Dextrose Agar (PDA) has been widely recognized in mycological research for its effectiveness in cultivating a broad spectrum of fungi, including edible mushrooms such as Pleurotus ostreatus. However, the high cost of commercial agar limits its accessibility for local mushroom growers. Therefore, this study aims to investigate the potential of low-cost agar formulations and other alternatives to PDA in a laboratory setting. Five treatments were applied: PDA (control), Malt Extract Agar (MEA), Murashige and Skoog (MS), Rice Water Agar (RWA), and Potato Sucrose Agar (PSA). The experiment was arranged in a complete block design with three replications. Data were collected at two stages of oyster mushroom cultivation: pure and spawn culture. The mycelial growth quality was determined. Results revealed that, at the pure culture stage, RWA had the highest mycelial growth rate at 9 days after inoculation (DAI) with 7.73 cm, but there was no statistical difference compared to PSA and PDA. MEA obtained the highest mycelial weight at 1.08 g. PSA, MEA, and RWA favored mycelial visual parameters, while PDA, PSA, and MEA favored thickness. RWA showed the highest growth vigor, and MEA and PDA the highest mycelial density. RWA completed the stage in 9.30 days, including the time mycelium appears at 1.03 days. No contamination occurred at RWA. At the spawn culture stage, RWA had the highest mycelial growth rate at 11.50 cm in 12 DAI. RWA was also high at mycelial visual parameters. MEA completed the stage in 14.87 days with comparable results in mycelial germination in all treatments except MS. Contamination occurred at the spawn stage, with high contamination observed in MS at 26.67%. Contamination during spawn and culture stages is influenced by inoculation technique and environmental conditions, not just the media. Temperature and RH significantly affected weight and color score. The MS medium showed the least favorable performance, likely due to its high salt content being unsuitable for fungal growth. This study provides a reference for mycelial growth quality on how mycelium behaves across two stages of oyster mushroom production.