BACKGROUND AND OBJECTIVE

Filipino farmers face unique occupational challenges that increase the risk of mental health issues, particularly anxiety. This study aims to determine the different personal, environmental, socioeconomic, occupational, and psychosocial factors associated with anxiety symptoms among Filipino farmers in Central Luzon.

Chain referral sampling method was used to recruit participants for the study, who underwent screening based on the eligibility criteria. Eligible participants were then asked about anxiety symptoms using the Generalized Anxiety Disorder-7 (GAD-7), while the validated, researcher-constructed Data Collection Tool for Factors Associated with Anxiety Symptoms (DCFAAS) was used to determine the farmers’ exposure to a variety of factors. Microsoft Excel was utilized in computing for frequency and percent distribution of participants, in each factor. Binary logistic regression was used to compute crude and adjusted odds ratio of each factor thru IBM SPSS Statistics®.

Among the 113 eligible farmers enrolled in the study, only 19 (16.8%) experienced anxiety symptoms, with excessive worrying, which was seen among 45 participants (39.8%). The mental health of Filipino farmers was significantly affected by the presence of physical illness (OR = 10.70 [95% CI 1.367, 83.773]) and having relatives affecting work completion (OR = 6.45 [95% CI 1.346, 30.896]). 

Despite the low prevalence of anxiety symptoms in this study, the findings suggest government policies to improve mental health service access to farmers, to integrate psychosocial support into agricultural programs, and to address family-related work pressures. By addressing these factors, it can improve farmer productivity and promote overall well-being, putting emphasis on the mental health of the Filipino farmers.

Agriculture, the strategic cornerstone of national long-term stability, is undergoing a fundamental shift from resource-dependent to technology-driven, driven by global food security and ecological conservation needs. Traditional agriculture can no longer sustain the growing food demand. Scientific and technological advancements are fundamental guarantees for ensuring food supply security and are the primary driver for future agricultural development. This special issue compiles the latest research advancements from diverse experts, covering fields such as microbe-driven green agriculture, pesticide technology innovation, intelligent agricultural machinery, smart manufacturing, and molecular design breeding fundamentals. It aims to inspire researchers to explore cutting-edge directions in future agriculture, promote interdisciplinary collaboration and technological integration, and thereby drive innovative breakthroughs and industrial transformation in agricultural modernization.
Agriculture/methods* ; Crops, Agricultural/genetics* ; Food Supply ; Biotechnology ; Pesticides

The escalating pressure from global population growth, climate change, and resource consumption is intensifying the burden on traditional agricultural production. Against this backdrop, soil degradation and pollution present increasingly severe challenges, creating a vicious cycle with rising food demands. Maintaining soil health and its ecosystem services has thus become a critical prerequisite for achieving sustainable agriculture in the future. This review explores the impacts of soil carbon (C) and nitrogen (N) dynamics on soil microbial communities and their interactions. Soil C and N are key determinants of microbial diversity and community structure, intrinsically linked to soil C/N cycling, crop productivity, and ecological balance. Environmental factors such as nitrogen fertilizer application, organic matter amendment application, litter decomposition, elevated CO₂ concentrations, and nitrogen deposition significantly influence soil C and N dynamics. Changes in soil C and N content regulate microbial community dynamics and the synergistic, competitive, and antagonistic interactions among microorganisms. Meanwhile, microbial communities actively respond to alterations in soil C and N availability. The resulting shifts in microbial communities and their interactions subsequently regulate soil C/N cycling and ecosystem stability, ultimately influencing ecosystem functions. By elucidating the mechanisms underlying soil carbon-nitrogen-microbial interactions, this review significantly advances our understanding of soil ecosystem responses and feedback mechanisms in the context of global change, while also providing crucial practical guidance for enhancing soil fertility and promoting sustainable agricultural development through microbial regulation.
Soil Microbiology ; Nitrogen/metabolism* ; Carbon/metabolism* ; Soil/chemistry* ; Bacteria/growth & development* ; Fungi/metabolism* ; Ecosystem ; Fertilizers ; Agriculture

The production of healthy agricultural products has increased the demand for innovative and sustainable plant protection technologies, and the rapid advancement of nanotechnology has brought revolutionary breakthroughs to traditional agriculture. Nanocarrier-based drug delivery systems can not only significantly improve the utilization efficiency of pesticides, achieving enhanced efficacy and reduced application, but also decrease the pesticide residues and environmental pollution. Additionally, they have made breakthrough progress in the stability and persistence of RNA pesticides. This review summarized the research progress on nanopesticides and RNA pesticides, focusing on the mechanisms of nanocarriers in improving pesticide bioactivity and RNA interference (RNAi) efficiency. It also systematically summarized the types of nanomaterials and their applications in pest and disease management and provided an in-depth outlook for the future development of nanopesticides and RNA pesticides, which provided technical support for the high-quality development of agriculture in the future.
Pesticides/chemistry* ; Nanotechnology ; Nanostructures ; RNA ; Agriculture/methods* ; RNA Interference ; Drug Delivery Systems

Accurately obtaining the crop quantity and density is not only crucial for the demand-based input of water and fertilizer in the field but also vital for ensuring the yield and quality of crops. Aerial photography by unmanned aerial vehicles (UAVs) can quickly acquire the distribution image information of crops over a large area. However, the accurate recognition of a single type of dense targets is a huge challenge for most recognition algorithms. Taking banana seedlings as an example in this study, we captured the images of banana plantations by UAVs from high altitudes to explore an efficient recognition method for dense targets. We proposed a strategy of "cut-recognition-stitch" and constructed a counting method based on the improved Faster R-CNN algorithm. First, the images containing highly dense targets were cropped into a large number of image tiles according to different sizes (simulating different flight altitudes), and the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm was adopted to improve the image quality. A banana seedling dataset containing 36 000 image tiles was constructed. Then, the Faster R-CNN network with optimized parameters was used to train the banana seedling recognition model. Finally, the recognition results were reversely stitched together, and a boundary deduplication algorithm was designed to correct the final counting results to reduce the repeated recognition caused by image cropping. The results show that the recognition accuracy of the Faster R-CNN with optimized parameters for banana image datasets of different sizes can reach up to 0.99 at most. The deduplication algorithm can reduce the average counting error for the original aerial images from 1.60% to 0.60%, and the average counting accuracy of banana seedlings reaches 99.4%. The proposed method effectively addresses the challenge of recognizing dense small objects in high-resolution aerial images, providing an efficient and reliable technical solution for intelligent crop density monitoring in precision agriculture.
Musa/growth & development* ; Crops, Agricultural/growth & development* ; Algorithms ; Neural Networks, Computer ; Unmanned Aerial Devices ; Seedlings/growth & development* ; Image Processing, Computer-Assisted/methods* ; Photography ; Agriculture/methods*

Synthetic biology, recognized as one of the most revolutionary interdisciplinary fields in the 21st century, has established innovative strategies for the genetic improvement of rice through the integration of multidisciplinary technologies including genome editing, genetic circuit design, metabolic engineering, and artificial intelligence. This review systematically summarizes recent research advancements and breakthrough achievements in the application of synthetic biology in the genetic improvement of rice, focusing on three critical domains: yield improvement, nutritional quality fortification, and reinforcement of disease resistance and abiotic stress tolerance. It elucidates that synthetic biology enables precise genomic and metabolic pathway engineering through modular, standard, and systematic approaches, effectively overcoming the limitations of conventional breeding methods characterized by prolonged cycles and restricted trait modification capabilities. The implementation of synthetic biology has facilitated synergistic improvement of multi-traits, thereby providing critical technical references for developing elite rice cultivars with superior productivity and nutritional value. These technological breakthroughs hold significant implications for ensuring global food security and promoting green and sustainable development of agriculture.
Oryza/growth & development* ; Synthetic Biology/methods* ; Metabolic Engineering ; Plant Breeding/methods* ; Gene Editing ; Genetic Engineering/methods* ; Plants, Genetically Modified/genetics* ; Disease Resistance/genetics*

Nitrogen use efficiency (NUE) is a pivotal indicator for achieving high wheat yields and sustainable resource utilization. This paper reviews recent research advances in the NUE of wheat, emphasizing genotypic variations, physiological mechanisms, molecular regulation, and agronomic management practices. Furthermore, this paper analyzes the critical regulatory nodes in nitrogen uptake, transport, assimilation, and redistribution, summarizes the current research bottlenecks, and makes an outlook on the future research directions. We then propose a strategy integrating emerging biotechnologies with precision agronomic management to enhance both wheat yields and NUE. This review aims to offer a theoretical framework for breeding nitrogen-efficient wheat cultivars and promoting the eco-friendly production of wheat.
Triticum/growth & development* ; Nitrogen/metabolism* ; Plant Breeding

Epigenetics refers to a heritable phenomenon that dynamically modulates gene expression without altering the DNA sequence, through molecular mechanisms such as DNA methylation, histone modification, non-coding RNA, chromatin remodeling, and RNA modifications. In plants, these modifications are extensively involved in key biological processes, including flowering time, gametogenesis, stress responses, and immune defenses. Over the past few decades, the research on epigenetics has gradually shifted from fundamental studies primarily conducted in Arabidopsis thaliana to investigations in various crop species such as rice and tomato. This transition has revealed the multifaceted roles of epigenetic regulation in shaping agronomic traits. This review integrates current knowledge of epigenetic regulatory mechanisms and their functions in plant responses to both biotic and abiotic stresses. Epigenetic editing tools such as CRISPR-dCas9 enable targeted DNA methylation or histone acetylation. Emerging transformation technologies, including magnetic nanoparticles and virus-based delivery systems, have the potential to overcome the bottlenecks of plant regeneration, offering new possibilities for precise epigenetic editing. In future agriculture, it is essential to further elucidate multi-layered epigenetic regulatory mechanisms at the single-cell level, develop efficient delivery systems, and leverage artificial intelligence to advance the application of epigenetic breeding for sustainable agricultural development.
Epigenesis, Genetic/genetics* ; Crops, Agricultural/genetics* ; Plant Breeding/methods* ; DNA Methylation/genetics* ; Gene Editing ; Disease Resistance/genetics* ; CRISPR-Cas Systems

BACKGROUND AND OBJECTIVE

Filipino farmers face unique occupational challenges that increase the risk of mental health issues, particularly anxiety. This study aims to determine the different personal, environmental, socioeconomic, occupational, and psychosocial factors associated with anxiety symptoms among Filipino farmers in Central Luzon.

Chain referral sampling method was used to recruit participants for the study, who underwent screening based on the eligibility criteria. Eligible participants were then asked about anxiety symptoms using the Generalized Anxiety Disorder-7 (GAD-7), while the validated, researcher-constructed Data Collection Tool for Factors Associated with Anxiety Symptoms (DCFAAS) was used to determine the farmers’ exposure to a variety of factors. Microsoft Excel was utilized in computing for frequency and percent distribution of participants, in each factor. Binary logistic regression was used to compute crude and adjusted odds ratio of each factor thru IBM SPSS Statistics®.

Among the 113 eligible farmers enrolled in the study, only 19 (16.8%) experienced anxiety symptoms, with excessive worrying, which was seen among 45 participants (39.8%). The mental health of Filipino farmers was significantly affected by the presence of physical illness (OR = 10.70 [95% CI 1.367, 83.773]) and having relatives affecting work completion (OR = 6.45 [95% CI 1.346, 30.896]). 

Despite the low prevalence of anxiety symptoms in this study, the findings suggest government policies to improve mental health service access to farmers, to integrate psychosocial support into agricultural programs, and to address family-related work pressures. By addressing these factors, it can improve farmer productivity and promote overall well-being, putting emphasis on the mental health of the Filipino farmers.

Germplasm degeneration occurs during the long-term cultivation of root-and rhizome-derived traditional Chinese medicine(RR-TCM), which seriously restricts the high-quality development of their industry. Therefore, it is urgent to solve the problem of germplasm degeneration through variety breeding. In this paper, based on previously published research articles, monographs, and news reports, the research progresses on the number and origins, breeding methods, and selection of new varieties of RR-TCM listed in the Chinese Pharmacopoeia(Edition 2020) were summarized and analyzed. The results show that there are 169 kinds of RR-TCM listed in the Chinese Pharmacopoeia(Edition 2020), originated from 223 origins with three breeding methods(i.e., seed propagation, vegetative reproduction, and tissue culture), and there are 215 species derived from seed propagation, 177 species derived from vegetative reproduction, and 164 species derived from tissue culture. To date, there are 62 origins breeding new varieties through conventional breeding, cross breeding, mutation breeding, ploidy breeding, or modern biotechnology breeding methods, including 57 origins breeding 145 new varieties through conventional breeding, 10 origins breeding 43 new varieties through mutation breeding, and seven origins breeding 12 new varieties through cross breeding method. They are used mainly to improve yield, disease resistance, and active ingredient content, but only a few new varieties have been widely used. This review will provide useful references in variety breeding, quality breeding, and standardized planting of RR-TCM.
Plant Breeding/methods* ; Plant Roots/growth & development* ; Rhizome/growth & development* ; Drugs, Chinese Herbal ; Plants, Medicinal/classification* ; Medicine, Chinese Traditional