The implementation of robotic dogs in automatic detection and surveillance of red imported fire ant nests.

BACKGROUND: The Red Imported Fire Ant (RIFA), scientifically known as Solenopsis invicta, is a destructive invasive species causing considerable harm to ecosystems and generating substantial economic costs globally. Traditional methods for RIFA nests detection are labor-intensive and may not be scalable to larger field areas. This study aimed to develop an innovative surveillance system that leverages artificial intelligence (AI) and robotic dogs to automate the detection and geolocation of RIFA nests, thereby improving monitoring and control strategies. RESULTS: The designed surveillance system, through integrating the CyberDog robotic platform with a YOLOX AI model, demonstrated RIFA nest detection precision rates of >90%. The YOLOX model was trained on a dataset containing 1118 images and achieved a final precision rate of 0.95, with an inference time of 20.16 ms per image, indicating real-time operational suitability. Field tests revealed that the CyberDog system identified three times more nests than trained human inspectors, with significantly lower rates of missed detections and false positives. CONCLUSION: The findings underscore the potential of AI-driven robotic systems in advancing pest management. The CyberDog/YOLOX system not only matched human inspectors in speed, but also exceeded them in accuracy and efficiency. This study's results are significant as they highlight how technology can be harnessed to address biological invasions, offering a more effective, ecologically friendly, and scalable solution for RIFA detection. The successful implementation of this system could pave the way for broader applications in environmental monitoring and pest control, ultimately contributing to the preservation of biodiversity and economic stability. © 2024 Society of Chemical Industry.

Molecular Modification Strategies of Nitrilase for Its Potential Application in Agriculture.

Some feed source plants will produce secondary metabolites such as cyanogenic glycosides during metabolism, which will produce some poisonous nitrile compounds after hydrolysis and remain in plant tissues. The consumption of feed-source plants without proper treatment affect the health of the animals' bodies. Nitrilases can convert nitriles and have been used in industry as green biocatalysts. However, due to their bottleneck problems, their application in agriculture is still facing challenges. Acid-resistant nitrilase preparations, high-temperature resistance, antiprotease activity, strong activity, and strict reaction specificity urgently need to be developed. In this paper, the application potential of nitrilase in agriculture, especially in feed processing industry was explored, the source properties and catalytic mechanism of nitrilase were reviewed, and modification strategies for nitrilase application in agriculture were proposed to provide references for future research and application of nitrilase in agricultural and especially in the biological feed scene.

Gelatin Nanoparticles can Improve Pesticide Delivery Performance to Plants.

Nanomaterials associated with plant growth and crop cultivation revolutionize traditional concepts of agriculture. However, the poor reiterability of these materials in agricultural applications necessitates the development of environmentally-friendly approaches. To address this, biocompatible gelatin nanoparticles (GNPs) as nanofertilizers with a small size (≈150 nm) and a positively charged surface (≈30 mV) that serve as a versatile tool in agricultural practices is designed. GNPs load agrochemical agents to improve maintenance and delivery. The biocompatible nature and small size of GNPs ensure unrestricted nutrient absorption on root surfaces. Furthermore, when combined with pesticides, GNPs demonstrate remarkable enhancements in insecticidal (≈15%) and weed-killing effects (≈20%) while preserving the efficacy of the pesticide. That GNPs have great potential for use in sustainable agriculture, particularly in inducing plant growth, specifically plant root growth, without fertilization and in enhancing the functions of agrochemical agents is proposed. It is suggested conceptual applications of GNPs in real-world agricultural practices.

Corrigendum: Paradise by the far-red light: Far-red and red:blue ratios independently affect yield, pigments, and carbohydrate production in lettuce, Lactuca sativa.

[This corrects the article DOI: 10.3389/fpls.2024.1383100.].

Spatial Variation in Agricultural BMPs and Relationships with Nutrient Yields Across New York State Watersheds.

Agricultural nutrients nitrogen and phosphorus can subsequently be transported to waterways and are often managed through the adoption of best management practices (BMPs). However, we have a poor understanding of how the use of BMPs varies spatially and how BMP adoption might be related to nutrient yields in surface waters. To address this, we performed a survey of agricultural landowners across New York State and compared this with estimates of annual incremental nitrogen and phosphorus yields of agricultural origin from the Spatially Referenced Regressions On Watershed attributes (SPARROW) model. Using these socio-behavioral data and SPARROW predictions, we perform colocation analysis to identify areas where watersheds with high nutrient yield from agriculture are collocated with non-use of agricultural BMPs. This colocation analysis offers a novel methodology for identifying areas where monitoring of waterways and promotion of best management practices could be targeted to achieve the greatest benefits.

Nesting Biology of Megachile (Chrysosarus) jenseni (Hymenoptera: Megachilidae) in Two Contrasting Pampean Agroecosystems: A Potential Pollinator for Alfalfa?

Several crops depend on both managed and wild bees to produce fruits and/or seeds, and the efficiency of numerous wild bees is higher than that of some managed species. Therefore, knowing and understanding the required resources for wild bees could enabled the establishment of management practices to increase their populations. Here, we provide information about the nesting biology of Megachile (Chrysosarus) jenseni, a Faboideae-specialist bee species. Based on observations from two populations occurring in contrasting agroecosystems, this bivoltine species showed common behavioral features shared with other species of subgenus Chrysosarus, such as the use of petal pieces and mud as nesting materials and the utilization of pre-existing cavities. Both studied populations showed a bivoltine life cycle with a rapid early-summer generation and a second generation, with most individuals overwintering. Main causes of mortality were unknown diseases (or other factors), causing the death of preimaginal stages. Moreover, this species was attacked by a cleptoparasite megachilid (Coelioxys remissa), a parasitic eulophid wasp (Melittobia sp.), and a bee fly (Anthrax oedipus). Finally, we discussed the potential use of this leaf-cutter bee species for alfalfa pollination.

Spectral enhancement of PlanetScope using Sentinal-2 images to estimate soybean yield and seed composition.

Soybean is an essential crop to fight global food insecurity and is of great economic importance around the world. Along with genetic improvements aimed at boosting yield, soybean seed composition also changed. Since conditions during crop growth and development influences nutrient accumulation in soybean seeds, remote sensing offers a unique opportunity to estimate seed traits from the standing crops. Capturing phenological developments that influence seed composition requires frequent satellite observations at higher spatial and spectral resolutions. This study introduces a novel spectral fusion technique called multiheaded kernel-based spectral fusion (MKSF) that combines the higher spatial resolution of PlanetScope (PS) and spectral bands from Sentinel 2 (S2) satellites. The study also focuses on using the additional spectral bands and different statistical machine learning models to estimate seed traits, e.g., protein, oil, sucrose, starch, ash, fiber, and yield. The MKSF was trained using PS and S2 image pairs from different growth stages and predicted the potential VNIR1 (705 nm), VNIR2 (740 nm), VNIR3 (783 nm), SWIR1 (1610 nm), and SWIR2 (2190 nm) bands from the PS images. Our results indicate that VNIR3 prediction performance was the highest followed by VNIR2, VNIR1, SWIR1, and SWIR2. Among the seed traits, sucrose yielded the highest predictive performance with RFR model. Finally, the feature importance analysis revealed the importance of MKSF-generated vegetation indices from fused images.

Greenhouse gas emissions in the Indian agriculture sector and mitigation by best management practices and smart farming technologies-a review.

The growing demand for agricultural products, driven by the Green Revolution, has led to a significant increase in food production. However, the demand is surpassing production, making food security a major concern, especially under climatic variation. The Indian agriculture sector is highly vulnerable to extreme rainfall, drought, pests, and diseases in the present climate change scenario. Nonetheless, the key agriculture sub-sectors such as livestock, rice cultivation, and biomass burning also significantly contribute to greenhouse gas (GHG) emissions, a driver of global climate change. Agriculture activities alone account for 10-12% of global GHG emissions. India is an agrarian economy and a hub for global food production, which is met by intensive agricultural inputs leading to the deterioration of natural resources. It further contributes to 14% of the country's total GHG emissions. Identifying the drivers and best mitigation strategies in the sector is thus crucial for rigorous GHG mitigation. Therefore, this review aims to identify and expound the key drivers of GHG emissions in Indian agriculture and present the best strategies available in the existing literature. This will help the scientific community, policymakers, and stakeholders to evaluate the current agricultural practices and uphold the best approach available. We also discussed the socio-economic, and environmental implications to understand the impacts that may arise from intensive agriculture. Finally, we examined the current national climate policies, areas for further research, and policy amendments to help bridge the knowledge gap among researchers, policymakers, and the public in the national interest toward GHG reduction goals.

Ensuring food safety: Microfluidic-based approaches for the detection of food contaminants.

Detecting foodborne contamination is a critical challenge in ensuring food safety and preventing human suffering and economic losses. Contaminated food, comprising biological agents (e.g. bacteria, viruses and fungi) and chemicals (e.g. toxins, allergens, antibiotics and heavy metals), poses significant risks to public health. Microfluidic technology has emerged as a transformative solution, revolutionizing the detection of contaminants with precise and efficient methodologies. By manipulating minute volumes of fluid on miniaturized systems, microfluidics enables the creation of portable chips for biosensing applications. Advancements from early glass and silicon devices to modern polymers and cellulose-based chips have significantly enhanced microfluidic technology, offering adaptability, flexibility, cost-effectiveness and biocompatibility. Microfluidic systems integrate seamlessly with various biosensing reactions, facilitating nucleic acid amplification, target analyte recognition and accurate signal readouts. As research progresses, microfluidic technology is poised to play a pivotal role in addressing evolving challenges in the detection of foodborne contaminants. In this short review, we delve into various manufacturing materials for state-of-the-art microfluidic devices, including inorganics, elastomers, thermoplastics and paper. Additionally, we examine several applications where microfluidic technology offers unique advantages in the detection of food contaminants, including bacteria, viruses, fungi, allergens and more. This review underscores the significant advancement of microfluidic technology and its pivotal role in advancing the detection and mitigation of foodborne contaminants.

Assessing soil pollution concerns in proximity to Fence-type solar photovoltaic system installations.

This study conducted in the Kyungpook National University Eco-friendly Agriculture Research Centre between 2022 and 2023 investigates the environmental implications of fence-type solar photovoltaic (PV) systems in diverse agricultural settings. Despite the increasing adoption of solar energy for climate change mitigation, there is a noticeable gap in research regarding the potential environmental impact of these specific PV systems. Focusing on heavy metal concentrations, including Cadmium (Cd), Copper (Cu), Arsenic (As), Mercury (Hg), Lead (Pb), Hexavalent Chromium (Cr+6), Zinc (Zn), and Nickel (Ni), across distinct fields, the study reveals significant fluctuations. Notably, the Rice Field experienced a substantial increase in Cd levels from 0.47 mg/kg in 2022 to 1.55 mg/kg in 2023, while Cu and Pb concentrations decreased to acceptable levels in 2023. The findings underscore the dynamic nature of heavy metal concentrations, emphasizing the importance of continuous soil quality monitoring to prevent contamination. This research provides valuable insights into the impact of fence-type solar PV system installations on agricultural soil quality, emphasizing the urgent need to secure these ecosystems through vigilant monitoring and environmental management practices.

Alpine grazing management, breed and diet effects on coagulation properties, composition, and microbiota of dairy cow milk by commercial mountain based herds.

Cow milk microbiota has received increased attention in recent years, not only because of its importance for human health but also because of its effect on the quality and technological properties of milk. Several studies, therefore, have investigated the effect of various production factors on the microbial composition of milk. However, most of the previous studies considered a limited number of animals from experimental or single farm, which could have biased the results. Therefore, this study aimed to understand the effect of different alpine production systems on the compositional and microbiological quality of milk, considering commercial herds with different feeding intensities and cattle breeds. The results obtained in this work indicated that the month/season of sampling (July for summer or February for winter) more than farm, breed and cow diet exerted significant effects on cow milk parameters and microbiota. In particular, significant differences were observed for urea content in milk between sampling seasons. Differences in milk fat were mainly related to breed specific effects. From a microbiological point of view, statistically significant differences were found in presumptive lactic acid bacteria counts. Based on a culture-independent method, milk obtained in February harbored the highest number of Firmicutes (e.g., Lactobacillus) and the lowest number of Actinobacteria (e.g., Corynebacterium). Moreover, bacterial richness and diversity were higher in July/summer during alpine pasture season indicating a significant effect of pasture feed on the growth of bacterial communities. The results of this study highlighted the effect of month/season mainly related to differences in feeding management (e.g., access to pasture during vegetation period, concentrates supplementation) on composition and microbiota in milk.

Flexible wearable sensors for crop monitoring: a review.

Crops were the main source of human food, which have met the increasingly diversified demand of consumers. Sensors were used to monitor crop phenotypes and environmental information in real time, which will provide a theoretical reference for optimizing crop growth environment, resisting biotic and abiotic stresses, and improve crop yield. Compared with non-contact monitoring methods such as optical imaging and remote sensing, wearable sensing technology had higher time and spatial resolution. However, the existing crop sensors were mainly rigid mechanical structures, which were easy to cause damage to crop organs, and there were still challenges in terms of accuracy and biosafety. Emerging flexible sensors had attracted wide attention in the field of crop phenotype monitoring due to their excellent mechanical properties and biocompatibility. The article introduced the key technologies involved in the preparation of flexible wearable sensors from the aspects of flexible preparation materials and advanced preparation processes. The monitoring function of flexible sensors in crop growth was highlighted, including the monitoring of crop nutrient, physiological, ecological and growth environment information. The monitoring principle, performance together with pros and cons of each sensor were analyzed. Furthermore, the future opportunities and challenges of flexible wearable devices in crop monitoring were discussed in detail from the aspects of new sensing theory, sensing materials, sensing structures, wireless power supply technology and agricultural sensor network, which will provide reference for smart agricultural management system based on crop flexible sensors, and realize efficient management of agricultural production and resources.

Precision agriculture for wine production: A machine learning approach to link weather conditions and wine quality.

The agricultural sector, in particular viticulture, is highly susceptible to variations in the environment, crop conditions, and operational factors. Effectively managing these variables in the field necessitates observation, measurement, and responsive actions. Leveraging new technologies within the realm of precision agriculture, vineyards can enhance their long-term efficiency, productivity, and profitability. In our work we propose a novel analysis of the impact of pedoclimatic factors on wine, with a case study focusing on the Denomination of Controlled and Guaranteed Origin Chianti Classico (DOCG), a prime wine-producing region located in Tuscany, between the provinces of Siena and Florence. We first collected a novel dataset, where geographic information as well as wine quality information were collected, using publicly available sources. Using such geographic information retrieved and an unsupervised machine learning approach, we conducted an in-depth examination of pedoclimatic and production data. To collect the whole set of possibly relevant features, we first assessed the region's morphological attributes, including altitude, exposure, and slopes, while pinpointing individual wineries. Subsequently we then calculated crucial viticultural indices such as the Winkler, Huglin, Fregoni, and Freshness Index by utilizing daily temperature records from Chianti Classico, and we further related them to an assessment of wine quality. In addition to this, we designed and distributed a survey conducted among a sample of wineries situated in the Chianti Classico area, obtaining valuable insights into local data. The primary goal of this study is to elucidate the interrelationships between various parameters associated with the region, considering influential factors such as the environment, viticulture, and field operations that significantly impact wine production. By doing so, wineries could potentially unlock the full potential of their resources. In fact, through the unsupervised and correlation analysis we could elucidate the relationships existing between the pedoclimatic parameters of the region, considering the most important factors such as viticulture and field operations, and relate them to wine quality as for instance using the survey data collected. This study represents an unprecedent in the literature, and it could pave the path for future studies focusing on the importance of climatic factors into production and quality of wines.

The theory of the investment development path and agriculture in Eastern Europe.

The outdated investment development path results in Eastern Europe and the lack of focus on the agricultural sector necessitated this study. The generalised least squares estimator employed countries from 1993 to 2021 for agricultural sector data on 17 Eastern Europe. Eastern European agriculture is in the early phase of stage IV of the investment development path, consistent with the theory of the investment development path. Human capital enhanced net foreign direct investment. Agricultural trade openness, exchange rate, and inflation did not influence net foreign direct investment. Developed and transition countries in Eastern Europe were not distinguished regarding net foreign direct investment. Eastern European countries must increase agricultural growth relative to population growth. This would increase agricultural development. The increased income can be saved and channelled into domestic investments to spur additional growth. This would make capital available for export. The growth in human capital must be sustained to enhance technical know-how in agriculture that would accompany agricultural capital export. Agricultural sector managers of Eastern European countries must focus on enhancing the sector's supervisory and regulatory functions. The goal should be to reduce the costs of doing agricultural business through effective facilitation towards efficient agricultural markets.

Electric field-based air nanobubbles (EF-ANBs) irrigation on efficient crop cultivation with reduced fertilizer dependency.

The advent of air nanobubbles (ANBs) has opened up a wide range of commercial applications spanning industries including wastewater treatment, food processing, biomedical engineering, and agriculture. The implementation of electric field-based air nanobubbles (EF-ANBs) irrigation presents a promising approach to enhance agricultural crop efficiency, concurrently promoting environmentally sustainable practices through reducing fertilizer usage. This study investigated the impact of EF-ANBs on the germination and overall growth of agricultural crops in soil. Results indicate a substantial enhancement in both germination rates and plant growth upon the application of EF-ANBs. Notably, the introduction of ANBs led to a significant enhancement in the germination rate of lettuce and basil, increasing from approximately 20% to 96% and from 16% to 53%, respectively over two days. Moreover, the presence of EF-ANBs facilitates superior hypocotyl elongation, exhibiting a 2.8- and a 1.6-fold increase in the elongation of lettuce and basil, respectively, over a six-day observation period. The enriched oxygen levels within the air nanobubbles expedite aerobic respiration, amplifying electron leakage from the electron transport chain (ETC) and resulting in heightened reactive oxygen species (ROS) production, playing a pivotal role in stimulating growth signaling. Furthermore, the application of EF-ANBs in irrigation surpasses the impact of traditional fertilizers, demonstrating a robust catalytic effect on the shoot, stem, and root length, as well as the leaf count of lettuce plants. Considering these parameters, a single fertilizer treatment (at various concentrations) during EF-ANBs administration, demonstrates superior plant growth compared to regular water combined with fertilizer. The findings underscore the synergistic interaction between aerobic respiration and the generation of ROS in promoting plant growth, particularly in the context of reduced fertilizer levels facilitated by the presence of EF-ANBs. This promising correlation holds significant potential in establishing more sustainability for ever-increasing environmentally conscious agriculture.

Optimizing wheat productivity through integrated management of irrigation, nutrition, and organic amendments.

Enhancing wheat productivity by implementing a comprehensive approach that combines irrigation, nutrition, and organic amendments shows potential for collectively enhancing crop performance. This study examined the individual and combined effects of using irrigation systems (IS), foliar potassium bicarbonate (PBR) application, and compost application methods (CM) on nine traits related to the growth, physiology, and yield of the Giza-171 wheat cultivar. Analysis of variance revealed significant (P ≤ 0.05) main effects of IS, PBR, and CM on wheat growth, physiology, and yield traits over the two growing seasons of the study. Drip irrigation resulted in a 16% increase in plant height, leaf area index, crop growth rate, yield components, and grain yield compared to spray irrigation. Additionally, the application of foliar PBR at a concentration of 0.08 g/L boosted these parameters by up to 22% compared to the control. Furthermore, the application of compost using the role method resulted in enhanced wheat performance compared to the treatment including mix application. Importantly, the combined analysis revealed that the three-way interaction between the three factors had a significant effect (P ≤ 0.05) on all the studied traits, with drip irrigation at 0.08 g PBR rate and role compost application method (referred as Drip_0.08g_Role) resulting in the best performance across all traits, while sprinkle irrigation without PBR and conventional mixed compost method (referred as sprinkle_CK_Mix) produced the poorest results. This highlights the potential to synergistically improve wheat performance through optimized agronomic inputs.

Bioaerosol Exposures and Respiratory Diseases in Cannabis Workers.

PURPOSE OF REVIEW: This review investigates occupational inhalation hazards associated with biologically derived airborne particles (bioaerosols) generated in indoor cannabis cultivation and manufacturing facilities. RECENT FINDINGS: Indoor cannabis production is growing across the US as are recent reports of respiratory diseases among cannabis workers, including occupational asthma morbidity and mortality. More information is needed to understand how bioaerosol exposure in cannabis facilities impacts worker health and occupational disease risk. Preliminary studies demonstrate a significant fraction of airborne particles in cannabis facilities are comprised of fungal spores, bacteria, and plant material, which may also contain hazardous microbial metabolites and allergens. These bioaerosols may pose pathogenic, allergenic, toxigenic, and pro-inflammatory risks to workers. The absence of multi-level, holistic bioaerosol research in cannabis work environments necessitates further characterization of the potential respiratory hazards and effective risk prevention methods to safeguard occupational health as the cannabis industry continues to expand across the US and beyond.

Organic food consumers and producers: Understanding their profiles, perceptions, and practices.

The organic food market has been expanding across the globe as well as in Brazil. Therefore, since we considered the collection of information about the organic food market to be relevant, we decided to characterize the organic food market in the metropolitan region of Vitória, Espírito Santo. Accordingly, the aim of this study was to analyze the profile and perceptions of organic producers/traders regarding the production of organic foods, investigate the profile and perceptions of organic food consumers, and identify the factors that influence their consumption. A cross-sectional, descriptive, and exploratory survey was conducted in 2019 with 50 producers/traders and 281 organic food consumers utilizing semistructured questionnaires. A descriptive statistical analysis was performed using percentage values, media, and evaluation of proportion differences. Fisher's exact test was used to analyze differences in proportions. The producers/traders were 76.0 % male, 82.0 % white, 70.0 % married, and 60.0 % attended elementary school. The commercial production of organic foods is mainly motivated by health and financial issues. A lack of labor (52.0 %) and an absence of government/credit support (40.0 %) were the greatest obstacles related to organic food production. The consumers were mostly female (75.1 %), white people (53.4 %), married people (44.5 %), and people with at least one degree (76.5 %). While the consumption of mainly organic fruits and vegetables was motivated by aspects related to health, factors such as poor access to sales points (27.8 %) and price (26.7 %) also limited the increase in consumption. Both consumers and producers/traders related to the consumption and production of organic food with health concerns revealed the important role of these foods in promoting the population's quality of life. However, for this growing market, there is a need for an integration between political authorities, producers, and consumers.

Adoption of sustainable farming practices in Vietnam: A discourse of the determining factors.

To delve into the intricacies of sustainable agricultural practices, our study investigates both the behavioral and non-behavioral factors influencing farmers' decision-making processes. Employing the New Ecological Model (NEP) to capture social factors, our research framework integrates insights from the sustainable livelihood framework, which delineates five crucial types of livelihood capital: (1) human capital, (2) natural capital, (3) financial capital, (4) physical capital, and (5) social capital. This comprehensive approach enables us to incorporate additional non-behavioral factors and their impacts on farmers' decisions. We underscore the pivotal role of farmers' decisions in fostering sustainable agriculture, aligning with seven of the Sustainable Development Goals (SDGs). Leveraging survey data collected from 303 Vietnamese farmers, we validate our research framework using two analytical models: Structural Equation Model (SEM) and binary logit analysis. Our findings underscore the significant influence of farmers' risk propensity and concerns regarding food safety and environmental conservation on the adoption of organic farming practices. Notably, farmers' level of knowledge emerges as a critical determinant shaping their inclination towards sustainable agriculture. The study underscores the importance of targeted educational initiatives and awareness campaigns, identifying key determinants such as proximity to green spaces and farm size in shaping farmers' choices towards sustainable practices.