Search Results (217)

Small-scale farms deserve attention and support because they play crucial and important roles. Apart from ensuring provision of food security, they also provide other economic, environmental, and social–cultural benefits. In the United States of America, these farms are agriculturally, culturally, and geographically different. They have varied needs that trigger an array of distinct biophysical, socioeconomic, and institutional challenges. The effects of these challenges are exacerbated by economic uncertainty, technological advancements, climate change, and other environmental concerns. To provide ideal services to the small-scale farm audience, it is necessary to understand these challenges and opportunities that can be leveraged to enhance their productivity and profitability. This article reviews the challenges faced by small-scale farming in the United States of America. It then reviews possible pathways to enhance their productivity and profitability. The review revealed that U.S. small-scale farms face several challenges. They include accessing farmland, credit and capital, lack of knowledge and skills, and technology adoption. Others are difficulties to insure, competition from corporations, and environmental uncertainties associated with climate change. The paper then reviews key pathways to enhance small-scale farmers’ capacities and resilience with a positive impact on their productivity and profitability. They are enhanced cooperative extension services, incentivization, strategic marketing, annexing technology, and government support, among others. Based on the diversity of farms and their needs, responses should be targeted towards individual needs. Since small-scale farm products have an effect on human health and dietary patterns, strategies to increase productivity should be linked to nutrition and health. Full article

To achieve regional sustainable development, the low-carbon transformation of agriculture is essential, as it serves both as a significant carbon source and as a potential carbon sink. This study calculated the agricultural carbon emissions in Liaocheng from 2010 to 2022 by analyzing processes including crop cultivation, animal husbandry, and agricultural input. Additionally, a simulation model of the water–energy–food–carbon nexus (WEFC-Nexus) for Liaocheng’s agricultural production process was developed. Using Vensim PLE 10.0.0 software, this study constructed a WEFC-Nexus model encompassing four major subsystems: economic development, agricultural production, agricultural inputs, and water use. The model explored four policy scenarios: business-as-usual scenario (S1), ideal agricultural development (S2), strengthening agricultural investment (S3), and reducing agricultural input costs (S4). It also forecast the trends in carbon emissions and primary sector GDP under these different scenarios from 2023 to 2030. The conclusions were as follows: (1) Total agricultural carbon emissions exhibited a three-phase trajectory, namely, “rapid growth (2010–2014)–sharp decline (2015–2020)–gradual rebound (2021–2022)”, with sectoral contributions ranked as livestock farming (50%) > agricultural inputs (27%) > crop cultivation (23%). (2) The carbon emissions per unit of primary sector GDP (CEAG) for S2, S3, and S4 decreased by 8.86%, 5.79%, and 7.72%, respectively, compared to S1. The relationship between the carbon emissions under the four scenarios is S3 > S1 > S2 > S4. The relationship between the four scenarios in the primary sector GDP is S3 > S2 > S4 > S1. S2 can both control carbon emissions and achieve growth in primary industry output. Policy recommendations emphasize reducing chemical fertilizer use, optimizing livestock management, enhancing agricultural technology efficiency, and adjusting agricultural structures to balance economic development with environmental sustainability. Full article

Nanomaterials have emerged as a transformative technology in agricultural science, offering innovative solutions to improve plant–microbe interactions and crop productivity. The unique properties, such as high surface area, tunability, and reactivity, of nanomaterials, including nanoparticles, carbon-based materials, and electrospun fibers, render them ideal for applications such as nutrient delivery systems, microbial inoculants, and environmental monitoring. This review explores various types of nanomaterials employed in agriculture, focusing on their role in enhancing microbial colonization and soil health and optimizing plant growth. Key nanofabrication techniques, including top-down and bottom-up manufacturing, electrospinning, and nanoparticle synthesis, are discussed in relation to controlled release systems and microbial inoculants. Additionally, the influence of surface properties such as charge, porosity, and hydrophobicity on microbial adhesion and colonization is examined. Moreover, the potential of nanocoatings and electrospun fibers to enhance seed protection and promote beneficial microbial interactions is investigated. Furthermore, the integration of nanosensors for detecting pH, reactive oxygen species, and metabolites offers real-time insights into the biochemical dynamics of plant–microbe systems, applicable to precision farming. Finally, the environmental and safety considerations regarding the use of nanomaterials, including biodegradability, nanotoxicity, and regulatory concerns, are addressed. This review emphasizes the potential of nanomaterials to revolutionize sustainable agricultural practices by improving crop health, nutrient efficiency, and environmental resilience. Full article

Livestock population surveys are crucial for grassland management tasks such as health and epidemic prevention, grazing prohibition, rest grazing, and forage–livestock balance assessment. These tasks are integral to the modernization and upgrading of the livestock industry and the sustainable development of grasslands. Unmanned aerial vehicles (UAVs) provide significant advantages in flexibility and maneuverability, making them ideal for livestock population surveys. However, grazing livestock in UAV images often appear small and densely packed, leading to identification errors. To address this challenge, we propose an efficient Livestock Network (LSNET) algorithm, a novel YOLOv7-based network. Our approach incorporates a low-level prediction head (P2) to detect small objects from shallow feature maps, while removing a deep-level prediction head (P5) to mitigate the effects of excessive down-sampling. To capture high-level semantic features, we introduce the Large Kernel Attentions Spatial Pyramid Pooling (LKASPP) module. In addition, we replaced the original CIoU with the WIoU v3 loss function. Furthermore, we developed a dataset of grazing livestock for deep learning using UAV images from the Prairie Chenbarhu Banner in Hulunbuir, Inner Mongolia. Our results demonstrate that the proposed module significantly improves the detection accuracy for small livestock objects, with the mean Average Precision (mAP) increasing by 1.47% compared to YOLOv7. Thus, this work offers a novel and practical solution for livestock detection in expansive farms. It overcomes the limitations of existing methods and contributes to more effective livestock management and advancements in agricultural technology. Full article

Feed conversion ratio (FCR) is a critical indicator of production efficiency in livestock husbandry. Improving FCR is essential for optimizing resource utilization and enhancing productivity. Traditional methods for FCR optimization rely on experience and long-term data collection, which are time-consuming and inefficient. This study explores the feasibility of predicting long-term FCR using short-term FCR data based on machine learning techniques. We employed nineteen machine learning algorithms, including Linear Regression, support vector machines (SVMs), and Gradient Boosting, using historical datasets to train and validate the models. The results show that the Gradient Boosting model demonstrated superior performance, achieving a coefficient of determination (R²) of 0.72 and a correlation of 0.85 between predicted and actual values when the testing interval exceeded 40 kg. Therefore, we recommend a minimum feeding measurement interval of 40 kg. Furthermore, when the testing interval was set to 40 kg and further refined to the range of 50–90 kg, the model achieved an R² of 0.81 and a correlation of 0.90 for FCR prediction in the 30–105 kg range. Among the 19 machine learning algorithms tested, Gradient Boosting, LightGBM, and CatBoost showed superior performance, with Gradient Boosting achieving the best results. Considering practical production requirements, the 50–90 kg feeding stage proved to be the most ideal for FCR testing. This study provides a more effective method for predicting feed efficiency and offers robust data support for precision livestock farming. Full article

The production of duckweed (Lemnaceae) as a novel protein source could make a valuable contribution to human nutrition. The greatly reduced habitus of duckweed enables simple cultivation with extremely low space requirements, making this free-floating freshwater plant ideal for substrate-free and vertical cultivation in controlled environment agriculture. Of particular importance in the design of a plant-producing Indoor Vertical Farming process is the determination of light intensity, as artificial lighting is generally the most energy-intensive feature of daylight-independent cultivation systems. In order to make the production process both cost-effective and low emission in the future, it is, therefore, crucial to understand and mathematically describe the primary metabolism, in particular the light utilization efficiency. To achieve this, a growth model was developed that mathematically describes the combined effects of plant density and light intensity on the growth rate of Lemna minor L. and physiologically explains the intraspecific competition of plants for light through mutual shading. Furthermore, the growth model can be utilized to derive environmental and process parameters, including optimum harvest quantities and efficiency-optimized light intensities to improve the production process. Full article

Indoor farming presents a sustainable response to urbanization and climate change, yet optimizing light use efficiency (LUE) remains vital for maximizing crop yield and minimizing energy use. This study introduces an IoT-based framework for adaptive light management in controlled environments, using lettuce (Lactuca sativa L.) as a model crop due to its rapid growth and sensitivity to light spectra. The system integrates advanced LED lighting, real-time sensors, and cloud-based analytics to enhance light distribution and automate adjustments based on growth stages. The key findings indicate a 20% increase in energy efficiency and a 15% improvement in lettuce growth compared to traditional static models. Novel metrics—Light Use Efficiency at Growth stage Canopy Level (LUEP) and Lamp Level (LUEL)—were developed to assess system performance comprehensively. Simulations identified optimal growth conditions, including a light intensity of 350–400 µmol/m²/s and photoperiods of 16–17 h/day. Spectral optimization showed that a balanced blue-red light mix benefits vegetative growth, while higher red content supports flowering. The framework’s feedback control ensures rapid (<2 s) and accurate (>97%) adjustments to environmental deviations, maintaining ideal conditions throughout growth stages. Comparative analysis confirms the adaptive system’s superiority over static models in responding to dynamic environmental conditions and improving performance metrics like LUEP and LUEL. Practical recommendations include stage-specific guidelines for light spectrum, intensity, and duration to enhance both energy efficiency and crop productivity. While tailored to lettuce, the modular system design allows for adaptation to a variety of leafy greens and other crops with species-specific calibration. This research demonstrates the potential of IoT-driven adaptive lighting systems to advance precision agriculture in indoor environments, offering scalable, energy-efficient solutions for sustainable food production. Full article

Nile tilapia, as an ideal model for studying sex differentiation, is a popular farmed fish worldwide with a stable XX/XY sex-determination system. In tilapia, ovarian differentiation is triggered by estradiol-17β (E2) production in undifferentiated gonads. In a previous study, we suggested that follicle-stimulating hormone (FSH) signaling might be involved in ovarian differentiation in Nile tilapia. In this study, we further investigated the role of FSH signaling in ovarian differentiation via aromatase expression, which converts testosterone to E2. Masculinization of XX fry by aromatase inhibitor or 17α-methyltestosterone leads to suppression of fshr expression. Feminization of XY fry by E2 treatment increased fshr expression from 15 days after hatching, when E2 treatment was terminated. XX tilapia developed ovaries harboring aromatase expression if fsh and fshr were double knockdowns by morpholino-oligo injections. Finally, the transcriptional activity in the upstream region of the aromatase gene (cyp19a1a) was further increased by FSH stimulation when HEK293T cells were co-transfected with foxl2 and ad4bp/sf1. Collectively, this study suggests that the role of FSH signaling is not critical in tilapia ovarian differentiation; however, FSH signaling may have a compensatory role in ovarian differentiation by increasing cyp19a1a transcription in cooperation with foxl2 and ad4bp/sf1 in Nile tilapia. Full article

Modern commercial poultry farming typically occurs indoors, where partial or complete environmental control is employed to enhance production efficiency. Maintaining optimal conditions, such as temperature, relative humidity, carbon dioxide, and ammonia levels, is essential for ensuring bird comfort and maximizing productivity. Monitoring the conditions of poultry houses requires reliable and intelligent management systems. This study introduces a Wireless Monitoring and Control System developed to regulate environmental conditions within poultry facilities. The system continuously monitors key parameters via a network of distributed sensor nodes, which transmit data wirelessly to a centralized control unit using Wi-Fi. The control unit processes the incoming data, stores it in a database, and adjusts actuators accordingly to maintain ideal conditions. A web-based dashboard allows users to monitor and control the environment in real time. Field testing confirmed the system’s effectiveness in keeping conditions optimal, supporting poultry welfare and operational efficiency. Full article

The aim of this study was to evaluate the technical and economic impact of using commercial phytogenic feed supplements and dried Greek Oregano leaves as feed additives on dairy sheep farms. Fifteen farms in the Greek region of Thessaly were divided into intervention and control farms, and techno-economic data were collected before and after supplementation through structured interviews and cost analysis. The results showed that the administration of certain phytogenic supplements and oregano to ewes resulted in improved animal health, higher milk yield, and lower production costs, which created a positive trend in the financial results of the farm. Further research is needed to accurately determine the ideal production stage of the animals for the interventions, the amount of supplements administered, and the selection of appropriate plant species, which would lead to better financial management of the farms. Full article

Vector-borne diseases play a significant role in veterinary health, impacting both wild and domestic animals and posing a major constraint on the development of animal husbandry worldwide. The current study aimed to highlight some of the factors involved in the appearance and dissemination of these emerging and re-emerging diseases, as well as the prevalence rate of certain species of pathogens, in cattle throughout Europe. Considering the complexity of vector–host systems, ticks can be mentioned as the first and most common vector involved in the transmission of pathogens in cattle. The highest prevalence was reported for two vector species: Ixodes ricinus and Haemaphysalis punctata. Another factor that contributes to the rapid identification of these diseases is the employed diagnostic method; thus, the most frequently employed techniques in Europe are: PCR, ELISA, and phylogenetic analysis of sequences. The prevalence of tick-borne infections in cattle is continuously increasing. The most frequent associations are Anaplasma spp., Babesia spp., Theileria spp., and Borrelia burgdorferi. Overall, this study highlights a rising occurrence and prevalence of vector-borne diseases in European cattle, underscoring the need for thorough monitoring of farms and vector hotspots—ideally within a “One Health” framework. Full article

Clavelina oblonga is an invasive tropical tunicate recently introduced into the Adriatic Sea as a consequence of globalization and climate change. Mussel aquaculture sites provide an ideal environment for this colonial ascidian, where it has recently become the dominant fouling species. This study represents the first investigation of its biological and physical characteristics, as well as its proximal, fatty acid, macroelement, trace element, and toxic metal composition. The entire-tissue chemical composition of C. oblonga resulted in 95.44% moisture. Its composite structure revealed several strong peaks, attributed to O-H, C-H, C-N, and C=O stretching, along with cellulose components overlapping with proteins and carbohydrates. The major fatty acids were palmitic, stearic, and docosahexaenoic acid, followed by docosanoic, elaidic, linoleic, and myristic acid. The saturated fatty acids, polyunsaturated fatty acids, and monounsaturated fatty acids comprised 51.37, 26.96, and 15.41% of the total fatty acids, respectively. Among the analysed trace and macroelements, aluminium and sodium were predominant. C. oblonga exhibited different concentrations of toxic metals, such as arsenic and lead, compared to fouled mussels in the Istria region. It appears that the tunicate has adapted to the environmental conditions of the Adriatic, reaching its maximum spread and biomass in mid-autumn. There is a strong possibility that C. oblonga could colonize and establish itself permanently in the Adriatic. This would have a strong negative impact on shellfish farming, the structure of the ecosystem, plankton biomass, and the distribution of other marine species. However, it also represents a biomass resource with high potential of utilization in different industries. Full article

The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) is a conservation and trade convention regulating international trade in wild species listed under its various appendices. Party nations are required to have designated scientific and management authorities to administer CITES and, ideally, domestic enabling legislation for proper implementation. However, the complexity of the convention makes it difficult to implement in resource-constrained nations that lack expertise and commitment. Apart from the lack of resources and expertise in much of the global south, hurdles to CITES implementation arise from delays in the formation of necessary legislation, apathy in enforcing the legislation, and the gatekeeping of resources by leading government agencies. Nepal has a long and well-documented history of wildlife conservation and is party to most major global environmental conventions, although it has frequently lacked the ability to implement them fully. Recently, Nepal has formulated domestic policies and developed institutions supporting biodiversity conservation but still refrains from formulating and implementing some provisions. After a long history of only partial (at best) compliance, Nepal enacted domestic CITES-implementing legislation, the CITES Act of 2017, with progressive provisions for enforcement, wildlife farming, and resource utilization and conservation. Here, we used a mixed methods research approach involving published and gray literature reviews and key informant interviews with concerned stakeholders to understand the workings and modality of authorities under the 2017 Act. We explore its nuances and discuss potential challenges for its implementation over time. Though the new policy has many positive aspects in that it is progressive in shifting away from more classical, strict protection, we found that there are still shortcomings within national administrative structures and a lack of policy that coordinates and informs different government offices of their responsibilities and how they interface under the current federal system. This has resulted in ongoing obstacles to achieving fuller CITES implementation to date and, in some cases, also inhibits sustainable uses of biodiversity. Full article

Near-surface floating kelp farms constitute a Nature-Based Solution (NBS) capable of damping incident wind-generated waves, which might be beneficial to reduce wave overtopping on maritime structures. As the global mean sea level rises, the mean wave overtopping discharge is expected to increase. The incorporation of this NBS, as a green–grey solution, might be beneficial to mitigate this effect. Physical modelling experiments with random waves have been conducted to assess the ability of this NBS to reduce the mean wave overtopping discharge on a rubble mound breakwater. Results show that while the mean wave overtopping discharge was reduced by 47% with a kelp farm length of 50 m (prototype scale), a kelp farm of 200 m achieved a reduction of 93% for the tested conditions. This reduction is mainly a function of the ratio between floating kelp farm length and incident wavelength. An idealized case study at the Port of Leixões breakwater suggests that, under storm wave conditions with return period of 2 and 5 years, floating kelp farms could maintain mean wave overtopping discharges below present levels until 2070. Thus, this study highlights the relevance of incorporating NBS with existing coastal and port defence structures as an adaptation measure to mitigate climate change effects. Full article

Climate change will create significant challenges to agriculture. The effects on livestock productivity and crop production are highly dependent on weather conditions with consequences for food security. If agriculture is to remain a viable industry and to maintain future food security, the adaptations and the ideal timeframes for their implementation to mitigate against climate change impacts will be essential knowledge. This study aims to show how farms will be affected and will need to adapt to climate change, based on a holistic examination of the entire farming process. A modified Bayesian belief network (BBN) was used to investigate climate change impacts on livestock, crops, soil, water use, disease, and pesticide use through the use of 48 indicators (comprising climate, agricultural, and environmental). The seasonal impact of climate change on all aspects of farming was investigated for three different climate forcing scenarios (RCPs 2.6, 4.5, and 8.5) for four timeframes (2030, 2050, 2080, and 2099). The results suggest that heat stress and disease in both livestock and crops will require adaptations (e.g., shelter infrastructure being built, new crops, or cultivators grown). Pest intensity is expected to rise, leading to increased pesticide use and greater damage to crops and livestock. Higher temperatures will likely cause increased drought and irrigation needs, while increasing rain intensity might lead to winter flooding. Soil quality maintenance will rely increasingly on fertilisers, with significant decreases in quality if unsustainable. Crop yield will be dependent on new crops or cultivators that can cope with a changing climate being successful and market access; failure to do so could lead to substantial decrease, in food security. Impacts are more significant from 2080 onwards, with the severity of impacts dependent on season. Full article