Agricultural Environment Research Articles

Development of Livestock-Associated Methicillin-Resistant Staphylococcus aureus (LA-MRSA) Loads in Pigs and Pig Stables During the Fattening Period

Livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA) is widespread in European pig production and poses an occupational hazard to farm workers and their household members. Farm workers are exposed to LA-MRSA through direct contact with pigs and airborne transmission, enabling bacteria to be carried home in the nose or on the skin. Consequently, it is important to consider LA-MRSA in a one-health context, studying human exposure by examining LA-MRSA levels in pigs, air, and dust in the farm environment. In this investigation, LA-MRSA levels were quantified in nasal swab samples from the pigs, air samples, and dust at three different time points in a farm rearing pigs from approx. 30 kg until slaughter. Sampling was repeated across seven batches of pigs, resulting in the analysis of 504 nasal swab samples, alongside air and dust samples. LA-MRSA was cultured and quantified on MRSA2 agar plates. Findings revealed significant batch-to-batch variation and a significant 94.1% decrease in LA-MRSA levels during the rearing period. Despite this decline, all nasal swab samples tested positive, with the highest level reaching 353,000 cfu in a sample. Among the 42 air samples, LA-MRSA levels were low to moderate, with a maximum of 568 and an average of 63 cfu/m3. In the 28 dust samples collected during the second and third sampling periods, LA-MRSA counts were high, reaching up to 37,272 cfu/g, with an average of 17,185 cfu/g. The results suggest that while LA-MRSA levels in pigs decrease with age, reaching low levels before slaughter, the bacterium remains highly abundant in dust, posing an occupational hazard to farm workers.

Exposure to organophosphate insecticides induces behavioral changes and acetylcholinesterase inhibition in Apis mellifera

European honey bee (Apis mellifera L.) is an essential pollinator that contributes significantly to the global ecosystem and agricultural productivity. However, their population has been facing unprecedented threats, primarily due to their exposure to various pesticides, including organophosphates. These pesticides are being widely used in agriculture to control insect pests due to their efficacy, but their non-selective nature raises concerns about their impact on honey bees. Insecticides viz., chlorpyriphos 20 Emulsifiable Concentrate, dimethoate 30 Emulsifiable Concentrate, and profenophos 50 Emulsifiable Concentrate at a range of 0.005–0.09 per cent concentration were evaluated through two modes of application viz., topical and oral. Acetylcholinesterase (AChE) activity was measured at various intervals (1 and 24 hours) to assess enzyme inhibition. Behavioral observations and statistical analyses, including factor analysis with Eigen values, were employed to evaluate the impact of exposure on bee behavior and physiological responses.The results revealed two factors with Eigen value > 0.95 in both topical as well as oral method which accounted for 88.28 and 88.80 per cent of the variation in behaviour, respectively. Insecticides applied to honey bee A. mellifera in both topical as well as oral methods resulted in significant inhibition of the Acetyl choline esterase enzyme (AChE) activity. Studies revealed higher AChE inhibition (%) in oral method as compared to topical method. AChE inhibition percentage increased from 25.15 (1 Hour after treatment) to 58.25 (24 Hours after treatment) with lower concentration (0.005) of chlorpyriphos in topical method while as it reached from 27.66 to 60.94 with same concentration and same time in oral method of application. AChE inhibition percentage increased from 35.81 (1 Hour after treatment) to 78.30 (24 Hours after treatment) with higher concentration (0.06) of chlorpyriphos in topical method while as it reached from 40.35 to 80.18 with same concentration and same time in oral method. Similar trend was observed in dimethoate, and profenophos where AChE inhibition increased from 17.30, 27.15 (1 Hour after treatment) to 57.18, 61.81 (24 Hours after treatment), respectively in topical method and 20.67, 28.80 (1 Hour after treatment) to 59.85 64.04 (24 Hours after treatment), respectively in oral method. Similarly, with higher concentrations of dimethoate (0.07), and profenophos (0.09), per cent inhibition increased from 34.54, 38.60 (1 Hour after treatment) to 75.68, 79.62 (24 Hours after treatment), in topical method and 37.25, 41.23 (1 Hours after treatment) to 77.86, 82.73 (24 Hours after treatment), in oral method, respectively. Thorough risk assessments are vital for evaluating the effects of agrochemicals on Apis mellifera. The findings highlight the necessity for updated pesticide regulations and broadened conservation strategies that take into account the diverse ways pollinators are exposed to agrochemicals in the environment. The study assessed the impact of different insecticides on Apis mellifera by comparing topical and oral exposure methods. The study also aimed to analyze the behavioral effects of insecticide exposure on Apis mellifera, assessing variations in response and enzyme inhibition.The research focused on evaluating Acetylcholinesterase (AChE) inhibition to better understand the risks posed to honeybees in agricultural environments.

Kinetic study, byproducts characterization and photodegradation pathway of profoxydim in a biochar water soil system

The study focused on the photodegradation of profoxydim, a low-toxicity cyclohexanedione herbicide commonly used in rice crops, under simulated sunlight conditions. Profoxydim’s behavior in paddy field conditions is not well understood, and this research aimed to fill that gap, particularly examining the effect of commonly utilized organic amendments such as biochar (BC) on its degradation. Results indicated that profoxydim degrades rapidly, with a half-life of 2.4 ± 0.3 h in paddy water and 1.03 ± 0.1 h in paddy soil. However, when BC was introduced, the degradation slowed significantly, extending the half-lives to 3.1 ± 0.2 h in water and 3.07 ± 0.5 h in soil. The study identified five degradation products (DPs) using TOF mass accuracy measurements and MS/MS spectra fragmentation. Two of these DPs were found to be more stable than profoxydim itself. Additionally, the research proposed a novel photodegradation pathway, highlighting processes such as homolytic C-N bond cleavage, photoisomerization, and photoinduced oxidation. The study’s findings contribute new insights into the environmental fate of profoxydim, offering a deeper understanding of its transformation in rice paddy fields and aiding in the assessment of potential risks associated with its residues in agricultural environments.

Ignoring the food route underestimated human health risk from potentially toxic elements in agricultural environments of Ziyang, Shaanxi, China

Ignoring the food route underestimated human health risk from potentially toxic elements in agricultural environments of Ziyang, Shaanxi, China

A new strategy based on multi-source remote sensing data for improving the accuracy of land use/cover change classification.

Land Use/Cover Change (LUCC) plays a crucial role in sustainable land management and regional planning. However, contemporary feature extraction approaches often prove inefficient at capturing critical data features, thereby complicating land cover categorization. In this research, we introduce a new feature extraction algorithm alongside a Segmented and Stratified Principal Component Analysis (SS-PCA) dimensionality reduction method based on correlation grouping. These methods are applied to UAV LiDAR and UAV HSI data collected from land use types (e.g., residential areas, agricultural lands) and specific species (e.g., tree species) in urban, agricultural, and natural environments to reflect the diversity of the study area and to demonstrate the ability of our methods to be applied in different classification scenarios. We utilize LiDAR and HSI data to extract 157 features, including intensity, height, Normalized Digital Surface Model (nDSM), spectral, texture, and index features, to identify the optimal feature subset. Subsequently, the best feature subset is inputted into a random forest classifier to classify the features. Our findings demonstrate that the SS-PCA method successfully enhances downscaled feature bands, reduces hyperspectral data noise, and improves classification accuracy (Overall Accuracy = 91.17%). Additionally, the CFW method effectively screens appropriate features, thereby increasing classification accuracy for LiDAR (Overall Accuracy = 78.10%), HIS (Overall Accuracy = 89.87%), and LiDAR + HIS (Overall Accuracy = 97.17%) data across various areas. Moreover, the integration of LiDAR and HSI data holds promise for significantly improving ground fine classification accuracy while mitigating issues such as the 'salt and pepper noise'. Furthermore, among individual features, the LiDAR intensity feature emerges as critical for enhancing classification accuracy, while among single-class features, the HSI feature proves most influential in improving classification accuracy.

Trends in Global Agricultural Carbon Emission Research: A Bibliometric Analysis

As climate change intensifies and countries actively pursue carbon peaking and carbon neutrality targets, agriculture has emerged as a significant source of carbon emissions. A comprehensive analysis of global agricultural carbon emission research can enhance the agricultural environment and achieve a mutually beneficial outcome for environmental protection and economic development. Despite the evolution of research domains and methodologies, the global context remains closely connected to the current state of the discipline. Drawing on the Web of Science core collection, this paper develops a knowledge network framework, examines the current status and hotspots of agricultural carbon emissions, forecasts future development trends, and analyzes the findings using CiteSpace visualization software. The findings indicate that the number of papers on agricultural carbon emissions has been increasing annually, with minor fluctuations; time series analysis and sustainable development have emerged as the current focal points, and relevant institutions are collaborating increasingly closely. However, cooperation among scholars requires further enhancement. Countries such as China, the United States, and Germany are the primary nations for paper publication. The hotspot analysis reveals a high frequency of keywords such as greenhouse gas emissions and climate change, indicating that research on agricultural carbon emissions has matured and the emphasis has shifted from accounting to management. This paper develops a domain knowledge framework to assist readers in understanding agricultural carbon emission patterns and provide resources for further research. Follow-up studies should enhance both comprehensiveness and breadth, promote interdisciplinary cooperation, provide a scientific foundation for policymakers, and outline future research directions.

Environmental determinants of West Nile virus vector abundance at the wildlife-livestock interface.

The diversity and abundance of vectors are essential parameters in the transmission dynamics of West Nile virus (WNV) between its avian reservoirs and clinically susceptible mammalian species. Knowing the determinants of vector abundance could be thus useful in preventing West Nile fever (WNF) cases and associated socio-economic impact. We designed a survey at the wildlife-livestock interface to test the hypothesis that variations in environmental favourability between anthropized and wild scenarios modulate WNV vector abundance and transmission risk. In a continental Mediterranean region where WNF has recently emerged, we selected nine sampling sites and allocated three areas to every site with a decreasing gradient of wildlife-livestock interaction: A1-a horse farm where interaction is maximal; A2-a zone of intermediate interaction 500-1000 m from the farm; and A3-an entirely wild zone of low interaction 1-5 km from the farm. At a fortnightly frequency, we estimated mosquito abundance at each of the 27 study sites in May-December 2018 and April-July 2019. We estimated bird and mammal abundance, collected meteorological information and characterised mosquito habitat at the site scale. Thereafter, we studied the determinants of Culex spp., Culex pipiens sensu lato (s.l.) Linnaeus, 1758 (Diptera: Culicidae) and Culex theileri Theobald, 1903 abundance by constructing negative binomial generalised linear mixed models. We identified 20 mosquito species, with a notable predominance of Culex spp. and, particularly, of Cx. pipiens s.l. We found differences in the spatiotemporal distribution of Culex spp. abundance and confirmed our hypothesis by finding important effects of local environmental variations in abundance. The accumulated rainfall in fortnights 4-14 and the mean temperature of the two fortnights before sampling were positively and statistically significantly associated with the abundance of Cx. pipiens s.l. (Z = 13.09, p < 0.001, and Z = 9.91, p < 0. 001, respectively) and Culex spp. (Z = 13.35, p < 0.001, and Z = 6.99, p < 0.001, respectively), while the mean temperature of the two previous fortnights was a positive statistically significant predictor (Z = 14.69, p < 0.001) of the abundance of Cx. theileri. The farm environment was the most conducive predictor to hosting Culex spp. compared with wild settings. Our results indicate that continental Mediterranean environments are favourable for WNV circulation and maintenance, especially the environment of anthropized rural settings such as farms. These results will have an impact on the spatiotemporal risk prediction of WNF emergence in continental Mediterranean environments.

Human-Centered Robotic System for Agricultural Applications: Design, Development, and Field Evaluation

This paper introduce advancements in agricultural robotics in response to the increasing demand for automation in agriculture. Our research aims to develop humancentered agricultural robotic systems designed to enhance efficiency, sustainability, and user experience across diverse farming environments. We focus on essential applications where human labor and experience significantly impact performance, addressing four primary robotic systems, i.e., harvesting robots, intelligent spraying robots, autonomous driving robots for greenhouse operations, and multirobot systems, as a method to expand functionality and improve performance. Each system is designed to operate in unstructured agricultural environments, adapting to specific needs. The harvesting robots address the laborintensive demands of crop collection, while intelligent spraying robots improve precision in pesticide application. Autonomous driving robots ensure reliable navigation within controlled environments, and multirobot systems enhance operational efficiency through optimized collaboration. Through these contributions, this study offers insights into the future of agricultural robotics, emphasizing the transformative potential of integrated, experience-driven intelligent solutions that complement and support human labor in digital agriculture.

Covalent Organic Framework Packed Nanoporous Membrane for Continuous Removal of Bisphenol A from Agricultural Irrigation Wastewater.

BPA, a typical endocrine disruptor, poses a significant threat to the growth of crops and thereby jeopardizes sustainable agriculture products and human health. In this work, a water-stabilized imine covalent organic framework (TpBD-COF) packed nanochannel membrane was constructed. The TpBD-COF membrane achieves high selective removal of BPA attributed to the subdivision of the pores by the filled COF, which further reduces the porous size and effectively eliminates the distance barrier between the selective sites of TpBD-COF membranes and BPA. The selective removal ratio of BPA was 5.79 times higher than that of the bare membrane, while the removal capacity reached 6.78 nM cm-2 min-1. It can eliminate BPA from irrigation wastewater and ensure crop growth. The application of COF-filled nanoporous membrane provides not only a size-matching strategy for the development of specific continuous removal of BPA but also a theoretical reference for membrane removal of other organic pollutants in agricultural irrigation water environment.

Cattle identification based on multiple feature decision layer fusion

In farming scenarios, cattle identification has become a key issue for the development of precision farming. In precision livestock farming, single-feature recognition methods are prone to misjudgment in complex scenarios involving multiple cattle obscuring each other during drinking and feeding. This paper proposes a decision-level identification method based on the multi-feature fusion of cattle faces, muzzle patterns, and ear tags. The method utilizes the SOLO algorithm to segment images and employs the FaceNet and PP-OCRv4 networks to extract features for the cattle’s faces, muzzle patterns, and ear tags. These features are compared with the Ground truth, from which the Top 3 features are extracted. The corresponding cattle IDs of these features are then processed using One-Hot encoding to serve as the final input for the decision layer, and various ensemble strategies are used to optimize the model. The results show that using the multimodal decision fusion method makes the recognition accuracy reach 95.74%, 1.4% higher than the traditional optimal unimodal recognition accuracy. The verification rate reaches 94.72%, 10.65% higher than the traditional optimal unimodal recognition verification rate. The research results demonstrate that the multi-feature fusion recognition method has significant advantages in drinking and feeding farm environments, providing an efficient and reliable solution for precise identification and management of cattle in farms and significantly improving recognition accuracy and stability.

Revolutionizing automated pear picking using Mamba architecture

With the emergence of the new generation vision architecture Vmamba and the further demand for agricultural yield and efficiency, we propose an efficient and high-accuracy target detection network for automated pear picking tasks based on Vmamba, aiming to address the issue of low efficiency in current Transformer architectures. The proposed network, named SRSMamba, employs a Reward and Punishment Mechanism (RPM) to focus on important information while minimizing redundancy interference. It utilizes 3D Selective Scan (SS3D) to extend scanning dimensions and integrates global information across channel dimensions, thereby enhancing the model's robustness in complex agricultural environments and effectively adapting to the extraction of complex features in pear orchards and farmlands. Additionally, a Stacked Feature Pyramid Network (SFPN) is introduced to enhance semantic information during the feature fusion stage, particularly improving the detection capability for small targets. Experimental results show that SRSMamba has a low parameter count of 21.1 M, GFLOPs of 50.4, mAP of 72.0%, mAP50 reaching 94.8%, mAP75 at 68.1%, and FPS at 26.9. Compared with other state-of-the-art (SOTA) object detection methods, it achieves a good trade-off between model efficiency and detection accuracy.

Genetic characteristics and antimicrobial resistance of Staphylococcus aureus isolates from pig farms in Korea: emergence of cfr-positive CC398 lineage

BackgroundLivestock-associated Staphylococcus aureus (LA-SA) has gained global attention because of its ability to colonize farm animals and transmit to the environment and humans, leading to symptomatic infections and the spread of antimicrobial resistance (AMR). In the last decade, numerous studies have reported a high prevalence of S. aureus clonal complex (CC) 398 in pig farms.ResultsIn this study, 163 S. aureus isolates were collected from healthy pigs (n = 110), farm environments (n = 42), and farm workers (n = 11), and their AMR profiles and epidemiological characteristics were analyzed. We identified 51 (31.3%) methicillin-resistant S. aureus (MRSA) and 112 (68.7%) methicillin-susceptible S. aureus (MSSA), with 161 (98.8%) isolates belonging to the CC398 lineage. The highest prevalence of spa type t571 was observed among the CC398 isolates. All 47 sequence type (ST) 398 MRSA isolates carried staphylococcal cassette chromosome mec (SCCmec) V, while four ST541 isolates carried SCCmec IV. High levels of resistance to commonly used antibiotics, including phenicols, quinolones, lincosamides, macrolides, aminoglycosides, and tetracyclines, have been observed on Korean pig farms. Notably, 21 cfr-positive CC398 isolates (four ST541-SCCmec IV MRSA and 17 ST398 MSSA) displaying increased resistance to linezolid were identified in healthy pigs.ConclusionsIn summary, these findings suggest that the multidrug-resistant CC398 S. aureus lineage predominantly colonizes healthy pigs and farm environments in Korea. The emergence of cfr-positive S. aureus at human-animal interfaces presents a significant threat to food safety and public health.

Risk factors for antimicrobial usage and diseases in Dutch veal calf farms: a cross-sectional study

Antimicrobial use (AMU) is the main driver of antimicrobial resistance (AMR). In the Netherlands, the veal calf sector was among the largest consumers of antimicrobials in Defined Daily Doses Animal (DDDA) for the year of 2022. As preventive use in Dutch livestock farms is forbidden since 2011, most AMU is due to the herd health status which is affected by the farm environment in which the conditions for diseases to spread are created. The aim of this study was to determine which disease etiologies for group treatments are associated with AMU in rosé starter veal calves, and which modifiable technical risk factors on farm are associated with those diseases and with total AMU. Cross-sectional data were collected from 36 Dutch rosé starter veal calf farms in the Netherlands in 2021 using a digital survey. Linear regression analysis showed that the main indications for AMU were respiratory infections, for which mainly tetracyclines and macrolides were used. Partial least squares regression analysis (PLS) revealed 13 on-farm practices associated with the number of group treatments for respiratory diseases and 19 with total AMU. Overlapping variables in both PLS models were related to regrouping of calves, micro-climate conditions, water access and weaning strategies. Overall, these features focused on improving animal welfare and nutrition during production and enhancing a farm’s internal and external biosecurity. This study identified opportunities for reducing AMU in rosé starter veal calf farms, which thereby could contribute to limiting AMR emergence and spread.

Detection in Orchards of Predominant Azole-Resistant Candida tropicalis Genotype Causing Human Candidemia, Taiwan.

Fluconazole-resistant clade 4 Candida tropicalis causing candidemia in humans has been detected in tropical/subtropical areas, including those in China, Singapore, and Australia. We analyzed 704 individual yeasts isolated from fruits, soil, water, and farmers at 80 orchards in Taiwan. The most common pathogenic yeast species among 251 isolates recovered from farmers were Candida albicans (14.7%) and C. parapsilosis (11.6%). In contrast, C. tropicalis (13.0%), C. palmioleophila (6.6%), and Pichia kudriavzevii (6.0%) were prevalent among 453 environmental isolates. Approximately 18.6% (11/59) of C. tropicalis from the environment were resistant to fluconazole, and 81.8% (9/11) of those belonged to the clade 4 genotype. C. tropicalis susceptibility to fluconazole correlated with susceptibilities to the agricultural azole fungicides, difenoconazole, tebuconazole, and triadimenol. Tandem gene duplications of mutated ERG11 contributed to azole resistance. Agriculture environments are a reservoir for azole-resistant C. tropicalis; discontinuing agricultural use of azoles might reduce emergence of azole-resistant Candida spp. strains in humans.

Exposure of Enchytraeus crypticus to ciprofloxacin - A multi- and transgenerational study

The increased consumption of antibiotics and their partial metabolism by the human and animal body have led to the frequent identification of such compounds in various environmental matrices. Ciprofloxacin (CIP) is one of the most widely used antibiotics worldwide and it has low mobility and high sorption to soil, leading to the potential to accumulate and persist on organic fertilizers (manure and sewage sludge) and agricultural soils. Therefore, the presence of CIP in the agricultural environment has become an emerging concern as it may negatively affect soil organisms and soil quality, and contribute to the spread of antibiotic-resistant bacteria, thus threatening food security and public health. In this study, we aimed to evaluate the multigenerational and transgenerational effects of CIP on the reproduction of Enchytraeus crypticus to properly assess the long-term impacts of such exposure. Our results for the multigenerational test showed that the reproduction of the organisms was affected from the third to the last (sixth) generation, highlighting the importance of long-term evaluation. There was also evidence of tolerance to reproduction of E. crypticus, as there was no statistical difference between concentrations for all generations, including for among the last three generations themselves. For the transgenerational tests, almost all results showed no statistical difference from the controls, indicating that the effects of CIP may be reversible when the organisms are transferred to clean media. However, since the last generations in the transgenerational tests showed significant differences from the first of the multigenerational tests, the adverse effects may still persist to some extent in the subsequent generations. Thus, when evaluating the effects of CIP on soil media, its persistence and behavior in this matrix must be considered. We strongly recommend that further long-term exposure studies be conducted and considered in regulatory decision-making, especially for persistent compounds.

Using Marker-Assisted Selection to Develop a Drought-Tolerant Rice Line with Enhanced Resistance to Blast and Brown Planthopper

Rice is a major global staple crop, but rising temperatures and freshwater shortages have made drought one of the most severe abiotic stresses affecting agriculture. Additionally, rice blast disease and brown planthopper infestations significantly impact yields. Therefore, developing water-saving, drought-resistant, high-yielding, and disease-resistant rice varieties is critical for sustainable rice production. The new water-saving and drought-resistant (WDR) rice ‘Huhan 1516’, bred using marker-assisted selection (MAS) and marker-assisted backcrossing (MABC) techniques, addresses these challenges. This variety is highly adaptable to drought-prone and water-scarce regions such as the Yangtze and Huai River basins. With its high yield, drought tolerance, and broad-spectrum resistance to rice blast (conferred by the Pi2 gene) and brown planthopper (BPH), ‘Huhan 1516’ is suitable for various farming systems and environments. Field trials show that this variety outperforms control varieties by 2.2% in yield and exhibits moderate resistance to both rice blast and brown planthopper. ‘Huhan 1516’ has been recognized as a new water-saving and drought-resistant rice variety by the state, and as a released cultivar, it has great potential for market promotion and application.

Agriculture Productivity and Environmental Degradation in Indonesia: A Time Series Analysis

Agriculture Productivity and Environmental Degradation in Indonesia: A Time Series Analysis

Chronic exposure to low concentration of diflubenzuron and pyriproxyfen induces brain oxidative stress and inflammation and alters locomotion in zebrafish (Danio rerio)

Diflubenzuron (DFB1) and pyriproxyfen (PPF) are pesticides widely used in agriculture and urban environments to control insect actions. The aim of this study was to evaluate the effects of chronic 30-day exposure to DFB (0.025 and 0.125 mg/L) and PPF (0.379 and 0.758 mg/L) on the behavior of juvenile zebrafish (Danio rerio). Fish were exposed to insecticides from early stage (4 h post fertilization) to 30 days post fertilization (dpf). At 45 dpf, fish were evaluated in the novel tank test, social behavior test, and mirror aggressive test. Brain gene expression related to oxidative stress and inflammation was also evaluated. DFB reduced locomotor parameters in the novel tank and aggression tests, while it induced to hyperactivity in the social behavior test. PPF reduced anxiety-like behavior, measured by the time spent in risky areas of the novel tank, and reduced aggression against the mirror image. There was a significant reduction in mRNA levels of the nfe2l2 gene (∼0.54 fold downregulated) and increase in levels of cat (PPF ∼1.8 fold change), gsr (PPF ∼1.5 fold change), gpx1a (PPF ∼1.6 and DFB 1.1 fold change), tnf-α (PPF 1.9 and DFB 2.2 fold change), and il-6 (PPF ∼1.2 and DFB 2.3 fold change). These endpoints are indicative of the threatening effects of insecticides to aquatic organisms and the need for alternative methods to control pests by using less harmful and safer substances for animal and human well-being, as well as for the environment.

A novel root hair mutant, srh1, affects root hair elongation and reactive oxygen species levels in wheat

BackgroundRoot hairs are single-celled projections on root surfaces, critical for water and nutrient uptake. Here, we describe the first short root hair mutant in wheat (Triticum aestivum L.), identified in a mutagenized population and termed here short root hair 1 (srh1).ResultsWhile the srh1 mutant can initiate root hair bulges, lack of subsequent extension results in very short root hairs. Due to its semi-dominant nature, heterozygous lines displayed intermediate root hair lengths compared to wild-type. Bulked segregant analysis in a BC1F3 segregating population genotyped via exome capture sequencing localized the genetic control of this mutant to a region on the long arm of chromosome 3A. Via RNA sequencing and bioinformatic analysis, we identified two promising candidate genes. The first was a respiratory burst oxidase homolog (RBOH) encoding gene TaNOX3-A, orthologous to RBOH genes controlling root hair elongation in rice (OsNOX3) and maize (ZmRTH5), that carries a missense mutation in a conserved region of the predicted protein. RBOHs are membrane bound proteins that produce reactive oxygen species (ROS) which trigger cell wall extensibility, allowing subsequent root hair elongation. Notably, reduced ROS levels were observed in srh1 root hair bulges compared to wild-type. The second candidate was the calreticulin-3 encoding gene TaCRT3-A, located within the wider srh1 interval and whose expression was significantly downregulated in srh1 root tissues.ConclusionsThe identification of a major effect gene controlling wheat root hair morphology provides an entry point for future optimization of root hair architecture best suited to future agricultural environments.

Optimizing Sorghum Yield and Quality: A Study on the Role of Plant Growth Regulators and Micronutrient Management Strategies

This study assessed the impact of plant growth regulators (PGRs) and micronutrients on sorghum (Sorghum bicolor L.) productivity in four locations viz., Palampur, Srinagar, Mandya, and Ayodhya during Kharif 2020-2021. Twelve treatments, combining PGRs and micronutrients, were evaluated. The best treatment (T10), applying 5 kg Zn and 2 kg B per hectare with a triacontanol (10 ppm) foliar spray at 30 days after sowing, resulted in the highest green fodder yield (493.2 q/ha), dry matter yield (125.6 q/ha), and crude protein yield (10.80 q/ha). It also improved the leaf-stem ratio (0.63), ADF (42.34%), and NDF (63.03%). Economic analysis showed a gross return of Rs. 130,208/ha, net return of Rs. 81,717/ha, and B:C ratio of 2.56. These findings provide actionable insights for enhancing sorghum productivity in semi-arid regions through integrated nutrient management, improving both food security and farmer incomes in challenging agricultural environments.