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Nucleic acid quantification, allowing us to accurately know the copy number of target nucleic acids, is significant for diagnosis, food safety, agricultural production, and environmental protection. However, current digital quantification methods require expensive instruments or complicated microfluidic chips, making it difficult to popularize in the point-of-care detection. Paper is an inexpensive and readily available material. In this study, we propose a simple and cost-effective paper membrane-based digital loop-mediated isothermal amplification (LAMP) method for nucleic acid quantification. In the presence of DNA fluorescence dyes, the high background signals will cover up the amplicons-formed bright spots. To reduce the background fluorescence signals, a quencher-fluorophore duplex was introduced in LAMP primers to replace non-specific fluorescence dyes. After that, the amplicons-formed spots on the paper membrane can be observed; thus, the target DNA can be quantified by counting the spots. Take Vibrio parahaemolyticus DNA detection as an instance, a good linear relationship is obtained between the light spots and the copy numbers of DNA. The paper membrane-based digital LAMP detection can detect 100 copies target DNA per reaction within 30 min. Overall, the proposed nucleic acid quantification method has the advantages of a simple workflow, short sample-in and answer-out time, low cost, and high signal-to-noise, which is promising for application in resourced limited areas. [ABSTRACT FROM AUTHOR]

Sustaining life on the Earth with its ever-growing population is forcing changes in people’s way of life, industrial and agricultural production, exploitation of energy resources, and approaches to ecology. We face continual growth in the world’s population, and the demand for materials is growing even more rapidly. Every manufacturing and consumer sector is looking for ways to save energy and materials, attempting to minimize their negative impacts on the environment. In the pulp and paper industry, one of the segments in which progress can be made and help protect the planet is to reduce the brightness of paper. Such a reduction would lead to a lowering in the energy and material costs associated with paper production. [ABSTRACT FROM AUTHOR]

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Producing sugar from lignocellulosic biomass is a promising resilient food solution to counter the near-total global failure of food production due to the agricultural collapse that would likely follow an abrupt sunlight reduction catastrophe such as a nuclear winter, a supervolcanic eruption, or a large asteroid or comet impact. This study examines how quickly edible sugar production could be ramped up globally by repurposing pulp and paper mills, sugarcane biorefineries, corn biorefineries, and breweries for lignocellulosic sugar production. A sub-unit component comparison to the NREL 2017 Biochemical Sugar Model indicates that 84%, 65%, 37%, and 39% of ISBL unit components are present, respectively. Fast construction methods were studied to analyze how this and other industrial foods could be rapidly leveraged in a catastrophe. Results suggest that the world’s current sugar demand could quickly be fulfilled by repurposing pulp and paper mills for lignocellulosic sugar production, given 5 months of production ramp-up and 24/7 construction. This method could reduce construction time to an estimated 32% of the original at an increased labor cost of 1.47 times, resulting in sugar production beginning 5 months after the catastrophe at a retail cost of $0.82 USD/kg. This could not only contribute a significant share of the food requirement after the catastrophe (∼28% within the first year), but also be key to preventing global starvation between the time at which global food storages run dry and other resilient food solutions can scale up significantly. This study aims to serve as the basis for more comprehensive scenario analyses. More research is needed to characterize material and labor constraints to fast response in more depth; repurposing and fast construction pilot studies and food safety studies are recommended. [ABSTRACT FROM AUTHOR]

Plastic mulch is used for agricultural production to improve crop performance and mediate local environmental concerns. Paper mulch is an alternative to plastic mulch, it is effective and more biodegradable. The performance of plastic and paper mulch materials in agricultural production varies according to climatic environments, production practices, and crops. The objective of this study was to investigate the performance of paper mulch compared with plastic mulch and bare soil for tomato (Solanum lycopersicum L.) field production in 2018 and 2021. Data on weed control, soil temperature, and tomato yield were recorded. Weed coverage and weeding time were reduced in paper and plastic mulch treatments compared to bare soil. Soil temperature in 2021 was lower in paper and plastic mulch treatments than in bare soil. Tomato yield was greater in plastic mulch than paper mulch and bare soil. Tomato yield for paper mulch treatments was not different from that of bare soil treatments. Paper mulch offered comparable weed control benefits as plastic mulch, and comparable tomato yield as bare soil. [ABSTRACT FROM AUTHOR]

Mulching with recycled paper has the potential to be used in agricultural production and can be adopted in strawberry cultivation. Therefore, the objective of this study was to evaluate the agronomic characteristics, water consumption and technical coefficients of strawberry cultivated with recycled paper as mulch. The treatments consisted of strawberry cultivation in lysimeters with recycled paper mulch and without soil cover. The recycled paper used was 187 μm-thick. The irrigation system was installed with drippers whose flow rate was 2 L h−1. Strawberry irrigation requirements were 317 and 394 mm, with and without mulch, respectively. Mulching with recycled paper did not have a significant effect on the average number of fruits, fresh fruit mass, fruit waste per plant, average fruit yield and water productivity. In relation to the technical coefficients, maximum values of the evaporation coefficient (Ke) of 0.40 (calculated with the evapotranspiration of the grass—L) and 0.28 (calculated with the Penman–Monteith ETo—PM) were obtained for OPM. The Kc values for the initial and full development stages were 0.31 and 0.84 (L) and 0.24 and 0.66 (PM), respectively. NDVI can be used to estimate strawberry Kc values. Recycled paper as mulch reduces the water consumption of strawberry crops and does not alter the agronomic characteristics. [ABSTRACT FROM AUTHOR]

The current study was conducted at the different villages of the Arajiline block of Varanasi district, Uttar Pradesh, India during November, 2022-June, 2023 to evaluate the variability of soil properties and nutrient indexof soils. The random sampling technique was used to collect 40 soil samples at different locations with the help of GPS at 0-15 cm depth. The collected soil samples were air-dried in shade at room temperature, passed through a 2 mm filter paper, and analyzed for different physico-chemical properties. The result revealed that the TS (range 8.2-81.3%), VFS (range 4.2-35.5%), Silt (range 10.1-54.2%), Clay (range 7.7-38.8%), BD (range 1.08-1.45 g cm-3), PD (range 2.04-2.33), Porosity (range 30.24-51.56%) pH (range 6.4-7.8), EC (range 0.1-0.3 dS m-1), OC (0.2-0.6%), and available N, P, K, S, Ca, and Mg varied from 130.12-260.30 kg ha-1, 14.2-23.2 kg ha-1, 145.0-240.0 kg ha-1, 7.5-17.6 kg ha-1, 3.8-8.2 cmol(P+)/kg, and 2.1-4.1 cmol(P+)/kg, respectively. The findings indicated that the studied soils were slightly acidic to moderately acidic and free from salinity hazards; 70% of the soil samples were low in organic carbon, 30% were medium in organic carbon, and 100% of the soil samples were low in available N and medium in available P, K, and S. 100% of soil samples were found to be sufficient in exchangeable Ca and Mg. The findings of this research could help in crop nutrient management, fertilizer recommendation, and decision-making for increasing agricultural production and farmer profitability. [ABSTRACT FROM AUTHOR]

Climate-change-induced unpredictable weather patterns are adversely affecting global agricultural productivity, posing a significant threat to sustainability and food security, particularly in developing regions. Wealthier nations can invest substantially in measures to mitigate climate change's impact on food production, but economically disadvantaged countries face challenges due to limited resources and heightened susceptibility to climate change. To enhance climate resilience in agriculture, technological solutions such as the Internet of Things (IoT) are being explored. This paper introduces a digital twin as a technological solution for monitoring and controlling temperatures in a greenhouse tunnel situated in Stellenbosch, South Africa. The study incorporates an aeroponics trial within the tunnel, analysing temperature variations caused by the fan and wet wall temperature regulatory systems. The research develops an analytical model and employs a support vector regression algorithm as an empirical model, successfully achieving accurate predictions. The analytical model demonstrated a root mean square error (RMSE) of 2.93 °C and an R 2 value of 0.8, while the empirical model outperformed it with an RMSE of 1.76 °C and an R 2 value of 0.9 for a one-hour-ahead simulation. Potential applications and future work using these modelling techniques are then discussed. [ABSTRACT FROM AUTHOR]

Bamboo has being used as alternative raw material for construction, reinforcing fibers, paper production, among other applications. Although its recognized potentials as raw material, there are doubts about its environmental performance compared to traditional wood-based products, including paper production, which hinders bamboo-based paper plants in large scales. This study aims to assess the environmental performance of producing office paper from bamboo. Emergy synthesis (with 'm') and global warming potential indicators are calculated and compared with the traditional eucalyptus paper-based production. Results highlights the importance in including the renewability fractions of each input resources into emergy calculations for production systems with high human-labor intensity such as the bamboo agricultural production. Office paper produced from bamboo has similar renewability (28%), moderate environmental load (3.23 vs. 2.49), and emergy unsustainability (0.34 vs. 0.60) compared to paper produced from eucalyptus, but bamboo showed lower performance for global efficiency (568 vs. 442 sej/ton paper), emergy yield (1.09 vs. 1.49), and emergy investment (10.79 vs. 2.05). Focusing on global warming potential, office paper produced from bamboo releases 98 kgCO 2 eq. /ton paper compared to 56–267 kgCO 2 eq. /ton paper for eucalyptus. Notwithstanding, bamboo-based office paper demands four times more land area of agricultural production than eucalyptus, but it has a positive social aspect by requiring higher amount of direct human labor. This work shows the advantages of using eucalyptus rather than bamboo in producing office paper from an emergy and land demand perspectives, while global warming can still be considered inconclusive. Future efforts should consider a quantitative and qualitative analysis of human labor availability in both systems, as well an economic analysis to support discussions towards more sustainable office paper production from different raw materials. • Environmental load of bamboo and eucalyptus for office paper production are compared. • Bamboo has lower emergy performance than eucalyptus. • Including the renewability fraction of labor and services better reflects emergy results. • Bamboo paper emits 98kgCO2eq/ton, eucalyptus paper emits 56–267kgCO2eq/ton. • Bamboo paper needs 4x land compared to eucalyptus and requires more human labor. [ABSTRACT FROM AUTHOR]

A completely lab-made paper/graphene-based 3D pop-up device (3D-PP) for the analysis of carbaryl (CBR), a pesticide widely used in crop production, is proposed. The device is composed of a nitrocellulose sensor equipped with reduced graphene oxide (rGO) and a foldable 3D paper pop-up. The rationale of the device is based on 3D and 2D configurations to allow the whole analytical procedure. In the 3D arrangement sample pre-concentration and hydrolysis occur; after folding (2D), contact with the complementary nitrocellulose-based sensor is ensured. The sensor, constituted by nanostructured graphene film, allows to obtain the necessary electroanalytical performance. 3D-PP has been shaped and nanostructured using a CO 2 -laser plotter and finished using low-cost benchtop instruments, ensuring in-series manufacturing of the device modules. 3D-PP includes the main steps required for CBR determination, i.e. (i) pre-concentration/purification, (ii) hydrolysis, (iii) medium neutralization, and (iv) electrochemical detection. 3D-PP was challenged for CBR quantification in model solutions and different grain samples. The response linearity (1.5–33 μM) and LOD (0.4 μM) obtained ensures CBR determination at the European-law admitted maximum residue levels (MRL). As proof of applicability, to simulate contaminates samples, different grains (i.e. soft and durum wheat, kamut, and barley) were spiked, before extraction, with CBR at three concentration levels corresponding to 0.5x, 1x, and 1.5x MRL; in all cases, quantitative and reproducible recoveries were obtained (93–109%; RSD ≤ 6%, n = 3). 3D-PP is produced using cellulosic components and solvent-free approaches, includes all CBR analysis steps, and returns reliable results in 15 min, resulting portable and usable with a smartphone [Display omitted] • A paper/graphene-based device for integrated carbaryl (CBR) analysis is proposed. • CO 2 -laser was used to shape and nanostructure the paper-based platform. • Complete CBR analysis is strategically enclosed in a 3D foldable paper pop-up (3D-PP). • 3D-PP design and sensing ability allow CBR residues analysis at EU-law limits. • 3D-PP analysis ability was proven in soft and durum wheat, kamut, and barley. [ABSTRACT FROM AUTHOR]

Plant temperature acclimation is closely related to maintaining a positive carbon gain under future climate change. However, no systematic summary of the field has been conducted. Based on this, we analyzed data on plant temperature acclimation from the Web of Science Core Collection database using bibliometric software R, RStudio and VOSviewer. Our study demonstrated that a stabilized upward trajectory was noted in publications (298 papers) from 1986 to 2011, followed by a swift growth (373 papers) from 2012 to 2022. The most impactful journals were Plant Cell and Environment, boasting the greatest count of worldwide citations and articles, the highest H-index and G-index, followed by Global Change Biology and New Phytologist, and Frontiers in Plant Science which had the highest M-index. The USA and China were identified as the most influential countries, while Atkin was the most influential author, and the Chinese Academy of Sciences was the most influential research institution. The most cited articles were published in the Annual Review of Plant Biology in 1999. “Cold acclimation” was the most prominent keyword. Future plant temperature acclimation research is expected to focus on thermal acclimation and photosynthesis, which have important significance for future agricultural production, forestry carbon sequestration, and global food security. In general, this study provides a systematic insight of the advancement, trend, and future of plant temperature acclimation research, enhancing the comprehension of how plants will deal with forthcoming climate change. [ABSTRACT FROM AUTHOR]

Therefore, crop mechanism models play an important role in assessing the effects of climate change on crop production and proposing effective coping strategies. Climate change and its impacts on agricultural production and food security are a significant source of public concern around the world. The paper by Shayanmehr et al. [[13]] constructed a new integrated climate-hydrological-economic model to assess the impact of future climate change on water resources and crop production. [Extracted from the article]

One of the effective ways to crack the "Three Rural Issues" and promote rural revitalization is to improve agricultural labor productivity (ALP). However, at this stage, improving China's ALP is still facing many obstacles and bottlenecks. Promoting migrant workers returning home for entrepreneurship is an important breakthrough point for solving this problem. This paper regards the pilot policy of migrant workers returning home for entrepreneurship as a quasi-natural experiment and empirically investigates the influence of migrant workers returning home for entrepreneurship on ALP and explores its potential mechanism and heterogeneity using county area panel data from 2011–2019. It found the following: Firstly, the policy of migrant workers returning home for entrepreneurship significantly increases ALP. Secondly, migrant workers returning home for entrepreneurship indirectly leads to a rise in ALP through the promotion of agricultural mechanization production. Thirdly, the heterogeneity test demonstrates that migrant workers returning home for entrepreneurship purposes have a significant influence in enhancing ALP in the eastern areas, plains areas and non-agricultural strong areas. The findings of this paper not only provide an important real-world basis for the government to further support migrant workers returning home for entrepreneurship but also provide useful policy insights for the modernization and development of agriculture and rural areas. [ABSTRACT FROM AUTHOR]

This paper aims to present an updated evolution of vegetable agricultural production in Vrancea county, during the period 2017-2021, with focus on the county's contributions in total at the country level, using the National Institute of Statistics (NIS) as principal data source. In order to meet the objectives, the methodology of research was based on indicators frequently used in papers about vegetable agricultural production, represented as values and/or percentage units. In 2021, Vrancea county detained 108 civil employment population (with 17% less than in 2017), through 22 employers in agriculture (52% less than in 2017). Among the main findings from the analyse are the following: the areas cultivated with corn grains, oat, sunflower, vegetables (all), dried onion, tomatoes, total fruits trees, plum, apples, pears, cherries and burgundy decreased among the county between 2017 and 2021 period and the areas cultivated with wheat, barley and vineyards in bearing increased. About the production, at the county level, the production of wheat, oat, barley, total fruit trees, plum, apple, pears, cherries, burgundy and grapes increased and the productions like: corn grains, sunflowers, vegetables (all), dried onion, tomatoes, decreased in the analysed period (2017-2021). From the conclusions of this paper, we found out that besides the good image in terms of wine fields, is observed that total fruit production increased by 50%, which can mean a new growth area of the county, regarding of vegetable agricultural production. [ABSTRACT FROM AUTHOR]

Avocado is one of the most commercialized and profitable fruits in the international market. Its cultivation and production are centered in countries characterized by tropical and subtropical climatic conditions, many of them with emerging economies. Moreover, the use of technology is key to agricultural production improvement strategies. Using avocado crop data to forecast the potential impacts of biotic and abiotic factors, combined with smart farming technologies, growers can apply measures during a single production phase to reduce the risks caused by pests and weather variations. Therefore, this paper aims to distinguish the most relevant variables related to agroclimatic and phytosanitary events in avocado crops, their incidence on production and risk management, as well as the emerging technologies used for the identification and analysis of pests and diseases in avocados. A scientific literature search was performed, and the first search found 608 studies, and once the screening process was applied, 37 papers were included in this review. In the results, three research questions were answered that described the pests and diseases with high impact on avocado production, along with the data sources and the principal enabling technologies used in the identification of agroclimatic and phytosanitary events in avocados. Some challenges and trends in the parameterization of the technology in field conditions for data collection are also highlighted. [ABSTRACT FROM AUTHOR]

The paper aims to present models of agricultural production optimization as tools for managers to increase yield, crop structure, crop rotation, increased efficiency and profitability. The two optimization models are: Case 1: Optimization of yield depending on wheat price and subsidy for increasing net return and profitability in wheat culture; Case 2: Optimal model for optimizing crop rotation in cereal culture with maximum income, having two solutions: (a) Basic optimal primal solution and (b) Basic optimal dual solution. The methodology included the calculations regarding the specific indicators reflecting the economic efficiency in wheat cropping for Case 1 and determination of the optimal primal and dual solutions assisted by SOLVER application from MS Excel for Case 2. The two examples come from vegetal farming but, other models of optimization could be developed in animal production for improving livestock structure by species and category and also in animal feeding setting up optimized feed ratios to sustain production. [ABSTRACT FROM AUTHOR]

This paper argues that rapid agricultural productivity growth is necessary for the expansion of manufacturing employment as a share of total employment. This hypothesis is tested utilizing data from 15 African nations. The estimation technique utilized is dynamic ordinary least squares and represents the first use of this technique to study the hypothesis that agricultural productivity growth is an important factor in the growth of manufacturing employment. The results indicate that agricultural productivity growth is a significant positive factor in the expansion of manufacturing employment. This is important in that productivity growth in agriculture in Sub-Saharan Africa has until recently been very sluggish. Thus, if manufacturing is to play an important role in this region, then significant investment in raising agricultural productivity needs to occur. [ABSTRACT FROM AUTHOR]

The increasing global warming trend has resulted in the mass loss of most glaciers. The Urumqi Vally, located in the dry and cold zone of China, and its widely dispersed glaciers are significant to the regional ecological environment, oasis economic development, and industrial and agricultural production. This is representative of glaciers in Middle Asia and represents one of the world's longest observed time series of glaciers, beginning in 1959. The Urumqi Headwater Glacier No. 1 (UHG-1) has a dominant presence in the World Glacier Monitoring Service (WGMS). This paper supplies a comprehensive analysis of past studies and future modeling of glacier changes in the Urumqi Valley. It has received insufficient attention in the past, and the mass balance of UHG-1 was used to verify that the geodetic results and the OGGM model simulation results are convincing. The main conclusions are: The area of 48.68 ± 4.59 km2 delineated by 150 glaciers in 1958 decreased to 21.61 ± 0.27 km2 delineated by 108 glaciers in 2022, with a reduction of 0.47 ± 0.04 km2·a−1 (0.96% a−1 in 1958–2022). The glacier mass balance by geodesy is −0.69 ± 0.11 m w.e.a−1 in 2000–2022, which is just deviating from the measured result (−0.66 m w.e.a−1), but the geodetic result in this paper can be enough to reflect the glacier changes (−0.65 ± 0.11 m w.e.a−1) of the URB in 2000–2022. The future loss rate of area and volume will undergo a rapid and then decelerating process, with the fastest and slowest inflection points occurring around 2035 and 2070, respectively. High temperatures and large precipitation in summer accelerate glacier loss, and the corresponding lag period of glacier change to climate is about 2–3 years. [ABSTRACT FROM AUTHOR]

Agriculture represents the "cause" and the "victim" of climate change. Almost 30 percent of greenhouse gas emissions come from the agricultural sector. They contribute to global warming and therefore significantly affect the sustainability of agricultural production systems. The aim of the paper is to determine which factors have the greatest influence on the greenhouse gas emission from agriculture. For this purpose, a dynamic panel analysis for 26 members of the European Union in the period from 2013 to 2021 in the paper is conducted. The results of the analysis suggest that the capacity for biofuel production, organic agricultural production and greenhouse gas emissions from the previous period have the greatest impact on the same emissions from agriculture. The results of this research can serve policy makers in formulating strategies for the development of food systems that will pollute the environment to a lesser extent and use available resources more rationally. [ABSTRACT FROM AUTHOR]

The problem of high grain production costs, which is not conducive to sustainable agricultural development and food security, is highlighted in the context of China's "large country and small household farmers". Reducing the grain production costs through factor allocation and organizational drive has become particularly important. Based on 768-grain peasant households in China, this paper uses OLS regression and robust regression to examine the effects of agricultural factor inputs and cooperatives on grain production costs. It analyzes the synergistic and substitution effects between farmers' factor inputs and cooperatives in grain production. It was found that: (1) in farmers' grain production, reductions in the grain production costs can be realized by expanding the area under cultivation, improving the use of agricultural machinery, and increasing technological inputs; (2) a reduction in the grain production costs can also be realized through cooperatives driving farmers into grain production; (3) cooperatives can provide farmers with various types of agricultural production services in grain production and cooperative-driven substitution effects between the agricultural factor inputs of farm households. The findings of this paper contribute to the enrichment of research in the field of agricultural production and are important for enhancing agricultural sustainability and reducing grain production costs. [ABSTRACT FROM AUTHOR]

Deep Learning (DL), a type of Machine Learning, has gained significant interest in many fields, including agriculture. This paper aims to shed light on deep learning techniques used in agriculture for abiotic and biotic stress detection in fruits and vegetables, their benefits, and the challenges faced by users. Scientific papers were collected from Web of Science, Scopus, Google Scholar, Springer, and Directory of Open Access Journals (DOAJ) using combinations of specific keywords such as: 'Deep Learning' OR' Artificial Intelligence' in combination with fruit disease', vegetable disease', 'fruit stress', OR 'vegetable stress' following PRISMA guidelines. From the initial 818 papers identified using the keywords, 132 were reviewed after excluding books, reviews, and the irrelevant. The recovered scientific papers were from2003 to 2022; 93 % addressed biotic stress on fruits and vegetables. The most common biotic stresses on species are fungal diseases (grey spots, brown spots, black spots, downy mildew, powdery mildew, and anthracnose). Few studies were interested in abiotic stresses (nutrient deficiency, water stress, light intensity, and heavy metal contamination). Deep Learning and Convolutional Neural Networks were the most used keywords, with GoogleNet (18.28%), ResNet50 (16.67%), and VGG16 (16.67%) as the most used architectures. Fifty-two percent of the data used to compile these models come from the fields, followed by data obtained online. Precision problems due to unbalanced classes and the small size of some databases were also analyzed. We provided the research gaps and some perspectives from the reviewed papers. Further research works are required for a deep understanding of the use of machine learning techniques in fruit and vegetable studies: collection of large datasets according to different scenarios on fruit and vegetable diseases, evaluation of the effect of climatic variability on the fruit and vegetable yield using AI methods and more abiotic stress studies. [ABSTRACT FROM AUTHOR]

The paper analyzed the evolution animal sector regarding livestock and production in Romania in the period 2007-2020. Two sub-periods: P1- 2007-2013 and P2 2014-2020 were compared for assessing if the achievements in P2 were superior. The data from the National Institute of statistics regarding livestock and production by species were processed using fixed basis index, structural index, regression equations, coefficient of determination, multi annual mean per decade, absolute and relative differences between P2 and P1, in order to emphasize the trends and changes during the studied period. The results emphasized the following: the number of bovines, pigs and poultry decreased, while the number of sheep and goats and bee families increased both in P2 and P1. The total animal live weight at slaughter diminished due to the lower and lower live weight of bovines and pigs. However, the increased live weight at slaughter of sheep, goats and poultry had a positive impact. Milk and egg production declined and continued in P2, while wool and honey production increased. Despite that agricultural production value in animal sector increased by +20,420 trillion Lei in 2020 versus 2007, it was by 68.61% smaller than the agricultural production value achieved in the vegetal sector. In consequence, the contribution of animal sector to agricultural output was just 31.39% in the year 2020. The gaps in domestic production have to be covered by imports to meet consumers' requirements and this will deepen the deficit in agro-food trade balance. The reduction of CO2 emissions in agriculture, where 50% come from animal sector, obliges farmers to set up strategies destined to promote animal products obtained from more environment friendly technologies. [ABSTRACT FROM AUTHOR]

The present paper addresses a topical issue for the agricultural production sector and it is based on an economic-financial indicators analysis for a series of 17 main agricultural crops. The practical research was carried out with information and data for 10 agricultural holdings in Iaşi County. They operate in the vegetable production sector and have a solid commercial representation on the national market. The main indicators used in the study are: crops and shared areas for each of the 10 analyzed units, considering the relevance of crops for the national economy and economic-financial indicators relevant for the design of the production activity: turnover, profit, employes number. The reference year for which the research was carried out is 2021. The work methodology mainly uses quantitative methods and descriptive analysis, with the processing and interpretation of data that have been organized in a design configured according to the specifics of the paper. In addition, qualitative information collected through interviews is used. The results of the paper indicate the relevance of basic agricultural crops for the performance of large enterprises. [ABSTRACT FROM AUTHOR]

Understanding biological mechanisms is fundamental for improving animal production and health to meet the growing demand for high-quality protein. As an emerging biotechnology, single-cell transcriptomics has been gradually applied in diverse aspects of animal research, offering an effective method to study the gene expression of high-throughput single cells of different tissues/organs in animals. In an unprecedented manner, researchers have identified cell types/subtypes and their marker genes, inferred cellular fate trajectories, and revealed cell‒cell interactions in animals using single-cell transcriptomics. In this paper, we introduce the development of single-cell technology and review the processes, advancements, and applications of single-cell transcriptomics in animal research. We summarize recent efforts using single-cell transcriptomics to obtain a more profound understanding of animal nutrition and health, reproductive performance, genetics, and disease models in different livestock species. Moreover, the practical experience accumulated based on a large number of cases is highlighted to provide a reference for determining key factors (e.g., sample size, cell clustering, and cell type annotation) in single-cell transcriptomics analysis. We also discuss the limitations and outlook of single-cell transcriptomics in the current stage. This paper describes the comprehensive progress of single-cell transcriptomics in animal research, offering novel insights and sustainable advancements in agricultural productivity and animal health. [ABSTRACT FROM AUTHOR]

The paper seeks to look into the role played by the rulers and the merchant class in the emergence of exchange centers (or market centers) in Western India, particularly Rajasthan and Gujarat, during the early medieval period. The paper tries to examine the combined efforts made by both the ruling class and private individuals in the agrarian expansion, which in turn encouraged exchange (trade and commerce) and ultimately paved way for the rise of different exchange centers in different parts of Western India. The focus is on the rising agricultural productivity, commercial transactions, and initiatives by rulers and merchants, which together provided suitable climate for the emergence of exchange centers. Rulers being aware of the revenue-generating potential of commerce themselves encouraged trade by establishing markets. Not only rulers but the merchants too helped in the growth of trade and commerce during the period of study. [ABSTRACT FROM AUTHOR]

The integration of the Internet of Things (IoT) has brought about transformative changes across diverse industries, and its application in agriculture, particularly in soil management, has become a focal point of research and innovation. This paper explores the paradigm shift enabled by IoT in detecting organic carbon in soil, with the overarching goal of advancing agricultural productivity and sustainability. Emphasizing the pivotal role of organic carbon in soil health, the research addresses the existing challenges associated with its detection and underscores the potential advantages offered by IoT-based solutions for precise and real-time monitoring. The study meticulously examines various IoT technologies, including sensors, data analytics, and wireless communication protocols, within the specific context of organic carbon detection. A thorough review of contemporary techniques, methodologies, and devices utilized in IoT-based organic carbon detection in soil is presented. The paper not only sheds light on the current state-of-the-art but also explores the advantages, limitations, and prospects inherent in the implementation of IoT-enabled systems in agriculture. The envisioned outcomes encompass enhanced soil management practices, heightened crop yields, and the simultaneous preservation of the environment. In essence, this research contributes to the expanding body of knowledge surrounding IoT applications in agriculture, specifically emphasizing the transformative potential of IoT-enabled organic carbon detection for revolutionizing farming practices and promoting sustainable agricultural development. [ABSTRACT FROM AUTHOR]

This paper focuses on the development and deployment of a dApp (decentralized Application) for Smart Crop Production Data exchange (SCPDx) that runs on Antelope blockchain/IPFS infrastructure. The paper emphasizes practical approaches to dApp design and deployment, analyses architectural patterns of dApps, and underlines the role of smart contracts in implementing complex functionality. The paper's contribution is the detailed description of the main smart contracts and the practical knowledge provided on the architecture and implementation of dApps, emphasizing the challenges and solutions in the development process, especially in the context of smart contract implementation. Future developments of the application towards additional data types processing, and design of an interface for leveraging, testing, and evaluating the performance of open source Large Language Models (LLMs) on specific datasets are commented on. [ABSTRACT FROM AUTHOR]

The accurate identification of crop aphids is an important aspect of improving agricultural productivity. Aphids are characterised by small targets and a body colour similar to their surroundings. Even the most advanced detectors can experience problems such as low detection accuracy and a high number of missed detections. In this paper, a multi-stream target detection model is proposed for fast and accurate detection of crop aphids in complex backgrounds. First, inspired by the human visual system, we propose a bionic attention (BA) approach. Unlike previous strategies, we do not improve the model but input additional category labels as bionic information streams into the network at the network input stage to support mainstream recognition, which improves the detection effect. In addition, through web crawling and manual screening, we construct an aphid dataset containing 2059 images, named IP_Aphids, based on IP102. In this paper, we combine the proposed BA with a number of classical target detection models, including YOLOv5s, YOLOv7-tiny, YOLOv8n, SSD, and faster R-CNN, and we also compare the BA with the classical attention mechanisms CBAM and SE. This approach allows the effectiveness of the method to be evaluated from multiple perspectives. The results show that combining the bionic information flow of BA with the original mainstream information flow as inputs to the five target detection models improves the mAP by 2.2%, 7%, 2.7%, 1.78%, and 1.12%, respectively, compared to the models using only the original inputs. In addition, the mAP of the YOLOv8n_BA model is higher than that of the YOLOv8n, YOLOv8n_CBAM, and YOLOv8n_SE models by 4.6%, 3.3%, and 2.7%, respectively. This indicates that the BA proposed in this study is significantly better than the classical attention to improve crop aphid detection, which provides a reference for crop aphid-related research. [ABSTRACT FROM AUTHOR]

The purpose of the paper was to assess the impact of climate changes on agricultural development and food security in Ukraine and Slovakia using statistical data from Statistical Office of the Slovak Republic, State Statistics Service of Ukraine, Eurostat, Food and Agriculture Organization and other sources. The comparison method was used to evaluate changes in agricultural development indicators, as well as climate changes in 1998-2021 in Ukraine and Slovakia. The main indicators of agricultural development used in the research were agricultural raw materials exports(%), agricultural raw materials imports (%), agriculture value added (% of GDP), agriculture value added (annual % growth), agriculture value added (current US$), cereal production (metric tons), cereal yield (kg per hectare), crop production index, food production index. Correlation and regression equations were used to establish the relationship between climatic factors and agricultural development indicators. The multiple correlations between air temperature and indicators of agricultural development was r=0.75 for Ukraine, r=0.69 for Slovakia. Using neuromodeling tools, the indicators of cereal production volumes and cereals yields in Ukraine and Slovakia in 2024-2033 were predicted. Tables were used to summarize the results. In conclusion, it should be noted that climatic conditions should be analyzed in each sector of crop production, and measures should be taken to adapt technologies for the gradual expansion of obligations for the production and export of agricultural products to world markets. [ABSTRACT FROM AUTHOR]

Food security, a vital determinant of overall health and well-being, hinges on the simultaneous achievement of four dimensions: physical availability of food, economic and physical access to food, utilization of nutrients, and stability over time. This paper explores the complex and multi-faceted food security situations in Kenya and Zambia, emphasizing key factors such as agricultural production, government policies, and socio-economic conditions. [ABSTRACT FROM AUTHOR]

The topic of the paper is tax and the tax system of the Byzantine Empire. The tax system analysed in this paper was created and applied from the 3rd to the 9th century. In this research, the author tried to point out the changes that took place in the property relations in the agriculture of the Empire, which in many ways affected the organization and method of tax collection. The paper provides an analysis from which it can be noted that the organization of tax collection followed the changes that took place in Byzantine society. Agricultural production in the first centuries was based on the relationship between the colonists and the landowners. That relationship began to change from the beginning of the 7th century, when an increasing number of free peasants began to appear in agricultural production. The Roman and then the Byzantine system of tax collection was based on the registration of the land in the cadastre. Every land owner had to be registered in the cadastre, and thus was obliged to pay taxes. The financial administration of the Empire gave estimates every year of how much tax should be collected. The tax system in these times is going through changes in the way it is collected. The old relationship in which head tax and land tax are collected is abandoned and a system of special assessments, household tax and land tax is introduced. The changes that occurred in the method of tax collection influenced the further development of relations in Byzantine agriculture. Throughout this period, taxes were collected in money, mostly gold. [ABSTRACT FROM AUTHOR]

Food security has become a concerning issue because of global climate change and increasing populations. Agricultural production is considered one of the key factors that affects food security. The changing climate has negatively affected agricultural production, which accelerates food shortages. The supply of agricultural commodities can be heavily influenced by climate change, which leads to climate-induced agricultural productivity shocks impacting crop prices. This paper systematically reviews publications over the past ten years on the factors affecting the prices of a wide range of crops across the globe. This review presents a critical view of these factors in the context of climate change. This paper applies a systematic approach by determining the appropriate works to review with defined inclusion criteria. From this, groups of key factors affecting crop prices are found. This study finds evidence that crop prices have been both positively and negatively affected by a range of factors such as elements of climate change, biofuel, and economic factors. However, the general trend is towards increasing crop prices due to deceasing yields over time. This is the first systematic literature review which provides a comprehensive view of the factors affecting the prices of crops across the world under climate change. [ABSTRACT FROM AUTHOR]

Large scale agricultural business entities (hereinafter referred to as "the large scale entities") are newly sprouted in the process of China's agricultural transformation from traditional production to modern mode, while the improvement of agricultural total factor productivity is an important driving force to realize agricultural transformation in China. Therefore, it is of great significance to explore whether the former has a promoting effect on the latter. First of all, the effective improvement of agricultural total factor productivity by the large scale entity development is analyzed from the two aspects of scale effect and technology utilization effect, and the effect mechanism is discussed in depth in this paper. Secondly, empirical test was carried out by using the panel data of prefecturelevel cities from 2015 to 2020. The benchmark regression results show that the development of large scale entities can significantly improve agricultural total factor productivity. The mechanism test results indicate that the development of large scale entities improves the agricultural total factor productivity by raising the agricultural mechanization level. The results of heterogeneity analysis show that the difference between large scale entities would affect the benchmarking results. Therefore, it is believed through the study as discussed in this paper that devoting greater effort to support the development of large scale entities is an important measure to improve agricultural total factor productivity and promote China's agricultural transformation, but it is necessary to act according to local conditions based on the difference of entity types in the process. [ABSTRACT FROM AUTHOR]

The article discusses the importance of crop tolerance to both abiotic and biotic stresses in agricultural research. Abiotic stresses include environmental factors like drought and extreme temperatures, while biotic stresses involve pests, diseases, and pathogens. Understanding and enhancing crop tolerance to these stresses is crucial for global food security and sustainable crop production. The article highlights various research papers from different countries that focus on topics such as abiotic stress (salinity, drought) and biotic stress (pests, diseases). The research papers explore different strategies, including breeding and genetic engineering, to enhance crop tolerance. [Extracted from the article]

The present paper deals with the kinetic-theoretic description of the evolution of systems consisting of many particles interacting not only with each other but also with the external world, so that the equation governing their evolution contains an additional term representing such interaction, called the 'forcing term'. Firstly, the interactions between pairs of particles are both conservative and nonconservative; the latter represents, among others, birth/death rates. The 'forcing term' does not express a 'classical' force exerted by the external world on the particles, but a more general influence on the effects of mutual interactions of particles, for instance, climate changes, that increase or decrease the different agricultural productions at different times, thus altering the economic relationships between different subsystems, that in turn can be also perturbed by stock market fluctuations, sudden wars, periodic epidemics, and so on. Thus, the interest towards these problems moves the mathematical analysis of the effects of different kinds of forcing terms on solutions to equations governing the collective (that is statistical) behaviour of such nonconservative many-particle systems. In the present paper, we offer a study of the basic mathematical properties of such solutions, along with some numerical simulations to show the effects of forcing terms for a classical prey–predator model in ecology. [ABSTRACT FROM AUTHOR]

Climate change-related impacts have hampered the productivity of agricultural lands in recent times, affecting food security globally. Novel technology-based agricultural production systems such as controlled-environment agriculture (CEA) are a way to reduce the impact of climatic variation and pests that harm current global crop production and ensure consistent crop development. These systems often use artificial lighting and soilless mediums to produce crops. This meta-analysis has investigated the key influencing factors on crop production within these systems, using previous studies on lettuce (the most cultivated crop in these systems) to understand what affects yield within CEA. This analysis has found that on average, CEA systems yield twice that of field-based agriculture (3.68 kg m−2 vs. 1.88 kg m−2), with the most influencing factors being the variety of cultivars grown, the season, the nutrient delivery method, and the lighting type. The cultivation time for this study was 40 days, with 94% of papers having trial periods of 70 days or less, much lower than field-based agriculture (60–120 days). Vertical farming (stacked vertical CEA cultivation) studies were found to especially drive up yield per area (6.88 kg m−2). The results of this meta-analysis are useful for starting to understand the key influencing factors on CEA growth and highlight the breadth of research ongoing in the CEA industry. [ABSTRACT FROM AUTHOR]

This document is an editorial from the Vadose Zone Journal, announcing a special section on soil physics in agricultural production, water resources, and waste management. The special section includes nine papers, some of which focus on African scenarios. The papers cover topics such as catchment hydrology, modeling of hydrological processes, uncertainty in modeling and measuring vadose zone hydrological processes, and nutrient dynamics. The special section aims to advance soil physics knowledge and its practical applications in agricultural and environmental management. The editorial also acknowledges the contributions of the authors and the legacy of Don and Betty Kirkham, who supported the 2022 Kirkham conference where the research was presented. [Extracted from the article]

The paper is devoted to crop production adaptation to climate change. It considers the problem of finding the future optimal structure of the sowing area, considering the possible climate change scenario. It implies finding a distribution of the sowing area for different groups of crops that would maximize the average annual yield while limiting the risk of shortfall in crop production due to adverse weather conditions. The authors have developed a mathematical model to solve this problem and used it for computations. [ABSTRACT FROM AUTHOR]

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Addressing the excessive input and inefficient use of agrochemicals are crucial for global food security, environmental protection, and human health. This paper offers a new idea from the perspective of outsourcing agricultural production. The impact of outsourcing on the frequency and intensity of agrochemical inputs were theoretically analyzed and empirically tested using a field survey of 1211 farmers in Heilongjiang, Henan, and Hunan, the major food-producing areas in China. A Logit regression framework was used to analyze the effect, a conditional mixture process (CMP) method was used to address potential endogeneity concerns, and a mediation effect model was used to dissect the mechanism. The results show that the effect of outsourcing on both input frequency and input intensity of agrochemicals was positive at the 1% significance level. The positive effect conclusion still holds even after addressing the potential endogeneity concerns, and in the sub-sample estimates for maize, wheat, and rice. We conclude that outsourcing can improve the utilization efficiency of agrochemicals by increasing the frequency of agrochemical inputs, but fail to solve the excessive agrochemical inputs and even leads to a further increase in the intensity of agrochemical inputs. Moreover, the mechanism for an increase in agrochemical input intensity due to outsourcing was explored, and it is more likely to be caused by inhibiting farmers' investment in soil improvement measures. [ABSTRACT FROM AUTHOR]

Background and Aims: Two main problems the globe currently facing are migration and weather variation. Weather change has a significant impact on the agricultural industry, which affects the majority of poor people. There is a dearth of adequate methodological documentation when examining the relationship between weather variation, agricultural output, and migration. We aimed to identify methodological reporting difficulties by reviewing the quantitative literature on weather‐related migration through agricultural channels. Methods: A systematic evaluation was conducted using papers published between January 2010 and June 2022, indexed in the SCOPUS, PUBMED, and Google Scholar databases. Using inclusion/exclusion criteria, we selected 22 original research articles out of 18,929 distinct articles for review, in accordance with the PRISMA guidelines. We extracted data from each study to understand how various concepts, research designs, and investigative techniques influence our understanding of migration patterns related to weather in the agricultural sector. Results: The majority (64%) of the study's data consisted of time series data. In 50% of the studies, secondary data were used. Additionally, 55% of these studies did not state the sample size. In 40% of the studies, model assumptions were fully adhered to, whereas in 36% of the studies, they were not followed at all. The majority of the articles used the Ordinary Least Squares technique, while about 41% applied the Two‐Stage Least Squares technique. Various tests were conducted across these studies, such as robustness checks (59.1%), endogeneity tests (31.8%), omitted variable bias tests (22.7%), sensitivity analyses (22.7%), and weak instrument tests (13.6%), to name a few. In the research we selected, the methodology section had various shortcomings and lacked organization. Furthermore, the justifications for deviations from model assumptions were unclear, potentially affecting the study outcomes. Conclusion: This study has important indications for researchers in studying climatic (weather) migration through agricultural channels besides for policymakers by giving a thorough review of the methods and techniques. Key points: Providing narrative insights on how various issues, research designs, and analytical techniques shape our understanding of the association between weather variation and migration via agricultural production.Providing a broad overview of the pertinent literature, covering 22 articles, as a complement to earlier studies.Our study has important ramifications for researchers studying climatic (weather) migration through agricultural channels as well as for policymakers by giving a thorough review of the methods and techniques currently used in this sector.Future studies on weather variation, agricultural production, and migration should use prudent and comparable models that capture whole climatic (weather) impacts on migration through mediating factors like agricultural production. [ABSTRACT FROM AUTHOR]

Using the CRISPR-Cas9 system to perform base substitutions at the target site is a typical technique for genome editing with the potential for applications in gene therapy and agricultural productivity. When the CRISPR-Cas9 system uses guide RNA to direct the Cas9 endonuclease to the target site, it may misdirect it to a potential off-target site, resulting in an unintended genome editing. Although several computational methods have been proposed to predict off-target effects, there is still room for improvement in the off-target effect prediction capability. In this paper, we present an effective approach called CRISPR-M with a new encoding scheme and a novel multi-view deep learning model to predict the sgRNA off-target effects for target sites containing indels and mismatches. CRISPR-M takes advantage of convolutional neural networks and bidirectional long short-term memory recurrent neural networks to construct a three-branch network towards multi-views. Compared with existing methods, CRISPR-M demonstrates significant performance advantages running on real-world datasets. Furthermore, experimental analysis of CRISPR-M under multiple metrics reveals its capability to extract features and validates its superiority on sgRNA off-target effect predictions. Author summary: Genome editing using the CRISPR-Cas9 system, particularly base substitutions directed by guide RNA, holds immense potential for applications in gene therapy and agricultural productivity. However, the risk of unintended off-target effects poses a challenge, as misdirection of the Cas9 endonuclease can lead to unintended genome alterations. While computational methods exist for predicting off-target effects, there remains a need for encoding methods with more representation space and deep learning models with generalization capability and the adaptability. This paper introduces CRISPR-M, an innovative approach addressing the limitations of existing methods in predicting off-target effects, especially for target sites with indels and mismatches. CRISPR-M employs a novel encoding scheme and a multi-view deep learning model, combining convolutional neural networks and bidirectional long short-term memory recurrent neural networks. The three-branch network structure enhances the prediction accuracy by considering multiple perspectives. Compared with previous representative methods, CRISPR-M exhibits remarkable performance advantages when applied to real-world datasets. The experimental evaluation of CRISPR-M, assessed by various metrics such as ROC, PRC, GC content and melting temperature, demonstrates its ability to extract meaningful features and establishes its superiority in predicting off-target effects of sgRNA. [ABSTRACT FROM AUTHOR]