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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]

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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]

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]

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]

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]

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]

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]

Given the 'subsistence' character of smallholder production, agricultural production diversification is often seen as an effective strategy for smallholders to improve their diets' diversity and nutritional status, yet the existing evidence remains inadequate. The study applies bibliometric data from the "Web of Science" database to synthesize 46 papers from developing countries to explore the relationship between production diversity, dietary diversity, and nutrition in smallholder households. The study identifies the most influential journals, authors, organizations, and countries and reveals research themes related to agricultural production and food security. This data analysis can help researchers target potential collaborators and access influential literature in agricultural production diversity and dietary diversity research. In addition, the results showed that agricultural production diversity potentially influences households' dietary diversity, with mixed results: Agricultural production diversification is the primary way to improve food and nutritional security among smallholder families with low socio-economic status, inaccessible transportation, and poverty; market access and trade have more potential to improve dietary diversity among smallholder households with well-developed markets and higher income levels; the significant measures of agricultural production diversity include Crop Counts, FGPD, SI, and SWDI; the significant measures of dietary diversity include HDDS and IDDS. This paper provides a roadmap for agricultural production and food security researchers by conducting a systematic review of the literature, summarizing some research methods and perspectives applicable to local socio-economic development. [ABSTRACT FROM AUTHOR]

The illegal occupation of farmland for building houses has impact not only on the land resources, but also the agricultural production, ecological environment, and food security. In order to protect farmland resources, maintain stable agricultural production activities and improve ecological environment, this paper designs and implements a time-point approval and dynamic monitoring solution and system for the problem of rural illegal occupation of farmland for constructing houses by comprehensively utilizing the satellite remote sensing technology (RS) and geographic information system technology (GIS). The result shows that the time-point approval and dynamic monitoring system can identify the illegal construction of houses accurately, thus this work can curb the generation of new illegal houses, and support the special governance of the construction of illegally occupied farmland effectively and continuously. [ABSTRACT FROM AUTHOR]

The potential adverse effects of maize leaf diseases on agricultural productivity highlight the significance of precise disease diagnosis using effective leaf segmentation techniques. In order to improve maize leaf segmentation, especially for maize leaf disease detection, a hybrid optimization method is proposed in this paper. The proposed method provides better segmentation accuracy and outperforms traditional approaches by combining enhanced Particle Swarm Optimisation (PSO) with Firefly algorithm (FFA). Extensive tests on images of maize leaves taken from the Plant Village dataset are used to show the algorithm's superiority. Experimental results show a considerable decrease in Hausdorff distances, indicating better segmentation accuracy than conventional methods. The proposed method also performs better than expected in terms of Jaccard and Dice coefficients, which measure the overlap and similarity between segmented sections. The proposed hybrid optimization method significantly contributes to agricultural research and indicates that the method may be helpful in real scenarios. The performance of proposed method is compared with existing techniques like K-Mean, OTSU, Canny, FuzzyOTSU, PSO and Firefly. The overall performance of the proposed method is satisfactory. [ABSTRACT FROM AUTHOR]

Accurate delineation of individual agricultural plots, the foundational units for agriculture-based activities, is crucial for effective government oversight of agricultural productivity and land utilization. To improve the accuracy of plot segmentation in high-resolution remote sensing images, the paper collects GF-2 satellite remote sensing images, uses ArcGIS10.3.1 software to establish datasets, and builds UNet, SegNet, DeeplabV3+, and TransUNet neural network frameworks, respectively, for experimental analysis. Then, the TransUNet network with the best segmentation effects is optimized in both the residual module and the skip connection to further improve its performance for plot segmentation in high-resolution remote sensing images. This article introduces Deformable ConvNets in the residual module to improve the original ResNet50 feature extraction network and combines the convolutional block attention module (CBAM) at the skip connection to calculate and improve the skip connection steps. Experimental results indicate that the optimized remote sensing plot segmentation algorithm based on the TransUNet network achieves an Accuracy of 86.02%, a Recall of 83.32%, an F1-score of 84.67%, and an Intersection over Union (IOU) of 86.90%. Compared to the original TransUNet network for remote sensing land parcel segmentation, whose F1-S is 81.94% and whose IoU is 69.41%, the optimized TransUNet network has significantly improved the performance of remote sensing land parcel segmentation, which verifies the effectiveness and reliability of the plot segmentation algorithm. [ABSTRACT FROM AUTHOR]

Central Asia is considered a region highly vulnerable to climate change impact and susceptible to climate-induced migration. Rural populations throughout the region are particularly at risk of experiencing adverse effects of climate variability and (im) mobility due to increased exposure to environmental hazards and distress that can severely affect agricultural productivity. This paper explores how climate change impacts migration trends in rural areas of Central Asia with an aim to contribute to the research on climate mobility in the region focusing on most vulnerable inhabitants. By examining the ongoing region's environmental degradation with increased aridity and retreating glaciers that affect local agriculture, water and food security, this paper suggests possible outcomes of such effects on rural mobility trends across five Central Asian countries. [ABSTRACT FROM AUTHOR]

The conflict between agricultural production and environmental protection, especially vegetable production, is one of the world's most prominent concerns. The acceptance degree and possible response of new agricultural operation subjects, represented by Farmers Professional Cooperatives (FPCs), to green production policies are a core issue that must be considered when designing environmental policies in the future. Based on field survey data of 192 FPCs of vegetables in Shanghai, China, this paper uses a choice experiment method to test the willingness of cooperatives to adopt green production behaviors under different policy settings and to identify the differences under different operation scales. The results show that both mandatory policies and incentive policies have an impact on the willingness of cooperatives to adopt green production behaviors, but significant differences exist among FPCs of different operation scales. Specifically, the willingness of large-scale FPCs to adopt green production behavior is affected by key technology training and income subsidies, whereas the willingness of small-scale FPCs is only affected by income subsidies; the willingness of medium-scale FPCs is simultaneously affected by key technology training, income subsidies, and penalties. Therefore, when formulating relevant policies in the future, policy-makers should consider local conditions, including the endowment heterogeneity of the production and operation subjects, so as to enhance the level of green production. [ABSTRACT FROM AUTHOR]

The formation of the scientific and intellectual potential of the agri-food complex contributes to the achievement of sustainable socio-economic growth. The article proposes models for the development of the scientific and intellectual potential of the Russian agri-food complex. An analysis of the activities of existing institutes for the development of agricultural scientific research was carried out. The assessment of the state of scientific and intellectual potential was carried out on the basis of cross-country comparisons of indicators of the number of researchers and expenditures on research and development. Empirically, imbalances were identified between the number of researchers and internal R&D costs. The positive experience of forming the scientific and intellectual potential of countries with developed market economies has been systematized, priority directions and stimulation of innovation and investment development have been identified. The practical significance of the research results lies in increasing the efficiency of agricultural production based on the development of scientific and intellectual potential. [ABSTRACT FROM AUTHOR]

Powdery mildew, caused by the fungal pathogen Erysiphe, is a widespread and economically important disease affecting broad beans (Viciafaba). This paper addresses the detection and treatment of powdery mildew in fava beans, covering various aspects such as symptomology, pathogen identification, disease surveillance techniques, and integrated management strategies. In this paper, we propose a mathematical model for broad bean powdery mildew detection and deep learning-based detection that improves accuracy and training efficiency. First, a conversion from the RGB format to his HSV format is performed and image segmentation is done. Then apply a random forest classifier to derive the results. The segmented leaves are then sent into a transfer learning model that has been trained on a dataset of sick leaves on a simple background. Additionally, the model is examined to identify the developmental stage of the Erysiphe family. By understanding the complexity of this disease, farmers and researchers can improve disease management and promote sustainable broad bean production. Therefore, for intelligent agriculture, environmental preservation, and agricultural productivity, the deep learning algorithms proposed in this article are crucial. [ABSTRACT FROM AUTHOR]

The non-judicious use of water at the farm level in traditional irrigation application methods is a present-day concern across the world that can be resolved by enhancing application efficiency through the adoption of advanced irrigation techniques. Sprinkler irrigation is a method that has high application efficiency, which can be further increased when coupled with automation toward precision irrigation. The objectives of this review are to summarize the main aspects of sprinkler and precision irrigation and their development, scope, and future prospects specifically in Asian countries. In this paper, a modified methodology, inspired by PRISMA guidelines, was used to explore the available literature to summarize the existing knowledge in the field. Regarding the technological aspects of the analyzed works, it became evident that sprinkler systems are an efficient method to not only irrigate crops (with 39% water saving) but also for the application of fertilizers with higher efficiency (>35%) and water productivity (>14.1%) compared with gravity irrigation systems. Moreover, this paper highlights the prominent features of precision irrigation for maximizing agricultural productivity. The use of sprinkler irrigation with precision applications using automation with a sensor-based mechanism for field data collection, data transformation, data analysis, and operation of IoT-based automatic solenoid valves can save 20–30% more irrigation water and increase crop yield by 20–27%. An analytical understanding and knowledge of the field were used to draw conclusions that are thought-provoking for scientists, researchers, and other stakeholders. [ABSTRACT FROM AUTHOR]

This abstract delves into the pressing social challenge of limited access to affordable agricultural technologies among farmers in Navalur, Karnataka, India. Specifically, the study concentrates on the labour-intensive and time-consuming process of manual seed sowing, which not only hampers efficiency but also takes a toll on the physical well-being of farmers, especially those engaged in small-scale agricultural activities. To tackle these issues, this paper proposes the development and implementation of a novel solution -- a seed sowing bot. The primary objective of this technological innovation is to alleviate the laborious aspects of seed sowing, offering a potential respite to farmers. The significance of addressing the gap in accessible agricultural technologies cannot be overstated, as it holds the key to empowering farmers, augmenting agricultural productivity, and contributing to sustainable rural development in Karnataka. By automating the seed sowing process, the proposed bot not only reduces the physical burden on farmers but also promises to enhance overall productivity. The abstract highlights the trajectory of conceptualization, creation, and evaluation of the seed sowing bot, showcasing its capability to effectively minimize manual labour while optimizing agricultural outcomes. The results underscore the feasibility and advantages of introducing this cost-effective technological solution to the farming community in Karnataka. In conclusion, this paper advocates for the adoption of the seed sowing bot as a strategic step towards addressing the challenges faced by farmers and promoting agricultural sustainability in the region. [ABSTRACT FROM AUTHOR]

In agricultural production, rapid and accurate detection of peach blossom bloom plays a crucial role in yield prediction, and is the foundation for automatic thinning. The currently available manual operation-based detection and counting methods are extremely time-consuming and labor-intensive, and are prone to human error. In response to the above issues, this paper proposes a natural environment peach blossom detection model based on the YOLOv5 model. First, a cascaded network is used to add an output layer specifically for small target detection on the basis of the original three output layers. Second, a combined context extraction module (CAM) and feature refinement module (FSM) are added. Finally, the network clusters and statistically analyzes the range of multi-scale channel elements using the K-means++ algorithm, obtaining candidate box sizes that are suitable for the dataset. A novel bounding box regression loss function (SIoU) is used to fuse the directional information between the real box and the predicted box to improve detection accuracy. The experimental results show that, compared with the original YOLOv5s model, our model has correspondingly improved AP values for identifying three different peach blossom shapes, namely, bud, flower, and falling flower, by 7.8%, 10.1%, and 3.4%, respectively, while the final mAP value for peach blossom recognition increases by 7.1%. Good results are achieved in the detection of peach blossom flowering volume. The proposed model provides an effective method for obtaining more intuitive and accurate data sources during the process of peach yield prediction, and lays a theoretical foundation for the development of thinning robots. [ABSTRACT FROM AUTHOR]

Agricultural insurance is a risk management tool developed to guarantee regional production and national food security. Based on survey data from 629 households in the Inner Mongolia autonomous region, China, this paper examines the impact of agricultural insurance on farmers' planting structure. The results show that: (1) Farmers' participation in agricultural insurance significantly affects their planting structure, especially lowering the level of specialization in production; insured households are about 3–6% lower than the non-insured, and there is a complementary effect between insurance participation and diversified planting. (2) For different types of farmers, participating in insurance has different effects on their agricultural production structure. Farmers purchasing insurance with low or medium levels in proportion to agricultural income significantly reduce the degree of production specialization. The effect of participation in insurance on large-scale farmers' adjustment of planting structure is more obvious. For pure grain farmers, participation in insurance will reduce planting specialization by about 3–4%. (3) Participation in agricultural insurance will encourage farmers to plant grain crops, but the effect is small. Therefore, we need to expand the coverage areas, increase insurance types, and improve coverage levels; the products should be more targeted and differentiated, strengthening farmers' participation in complete cost insurance and income insurance. [ABSTRACT FROM AUTHOR]

The digitalization of agriculture is rapidly changing the way farmers do business. With the integration of advanced technology, farmers are now able to increase efficiency, productivity, and precision in their operations. Digitalization allows for real-time monitoring and management of crops, leading to improved yields and reduced waste. This paper presents a review of some of the use cases that digitalization has made an impact in the automation of open-field and closedfield cultivations by means of collecting data about soils, crop growth, and microclimate, or by contributing to more accurate decisions about water usage and fertilizer application. The objective was to address some of the most recent technological advances that are leading to increased efficiency and sustainability of crop production, reduction in the use of inputs and environmental impacts, and releasing manual workforces from repetitive field tasks. The short discussions included at the end of each case study attempt to highlight the limitations and technological challenges toward successful implementations, as well as to introduce alternative solutions and methods that are rapidly evolving to offer a vast array of benefits for farmers by influencing cost-saving measures. This review concludes that despite the many benefits of digitalization, there are still a number of challenges that need to be overcome, including high costs, reliability, and scalability. Most of the available setups that are currently used for this purpose have been custom designed for specific tasks and are still too expensive to be implemented on commercial scales, while others are still in their early stages of development, making them not reliable or scalable for widespread acceptance and adoption by farmers. By providing a comprehensive understanding of the current state of digitalization in agriculture and its impact on sustainable crop production and food security, this review provides insights for policy-makers, industry stakeholders, and researchers working in this field. [ABSTRACT FROM AUTHOR]

Cotton is a crucial cash crop in the tribal belt of Odisha. It has a significant effect on the livelihood of the tribal people. The volatile socio-economic background of the tribal farmers is a major hindrance to the proper development of the agriculture sector. In this regard, it is important to study the possible factors affecting cotton productivity in the tribal region to boost the policy effectiveness for the development of this cash crop in the area. More precisely, how is their socio-economic condition reflected in their crop productivity? So, the development of the tribal farmers can develop this crop in the region. This study analyses the detrimental effect of socio-economic factors on cotton productivity in the tribal area of Odisha. The data are collected using a structured questionnaire and a multiple-sampling technique. To understand the effect of socioeconomic factors on productivity, the ordinary least square (OLS) method has been employed in this paper. The result shows that most of the farmers are illiterate for which productivity is degrading. The age and gender of the head of the household are significant contributors to the cotton productivity of the study area. Male-headed households are more productive than female-headed households. Per capita monthly income and proportion of irrigated land are also important factors to affect cotton productivity positively. However, the social group of the family has no significant effect on the cotton productivity in the study area. Policymakers and agriculturists should emphasize farmer literacy, farmer income and land irrigation facilities to boost the productivity of this crop in the studied region. [ABSTRACT FROM AUTHOR]

The olive tree (Olea europaea L.) is an evergreen tree that occupies 19% of the woody crop area and is cultivated in 67 countries on five continents. The largest olive production region is concentrated in the Mediterranean basin, where the olive tree has had an enormous economic, cultural, and environmental impact since the 7th century BC. In the Mediterranean region, salinity stands out as one of the main abiotic stress factors significantly affecting agricultural production. Moreover, climate change is expected to lead to increased salinization in this region, threatening olive productivity. Salt stress causes combined damage by osmotic stress and ionic toxicity, restricting olive growth and interfering with multiple metabolic processes. A large variability in salinity tolerance among olive cultivars has been described. This paper aims to synthesize information from the published literature on olive adaptations to salt stress and its importance in salinity tolerance. The morphological, physiological, biochemical, and molecular mechanisms of olive tolerance to salt stress are reviewed. [ABSTRACT FROM AUTHOR]

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The detection of tomatoes is of vital importance for enhancing production efficiency, with image recognition-based tomato detection methods being the primary approach. However, these methods face challenges such as the difficulty in extracting small targets, low detection accuracy, and slow processing speeds. Therefore, this paper proposes an improved RT-DETR-Tomato model for efficient tomato detection under complex environmental conditions. The model mainly consists of a Swin Transformer block, a BiFormer module, path merging, multi-scale convolutional layers, and fully connected layers. In this proposed model, Swin Transformer is chosen as the new backbone network to replace ResNet50 because of its superior ability to capture broader global dependency relationships and contextual information. Meanwhile, a lightweight BiFormer block is adopted in Swin Transformer to reduce computational complexity through content-aware flexible computation allocation. Experimental results show that the average accuracy of the final RT-DETR-Tomato model is greatly improved compared to the original model, and the model training time is greatly reduced, demonstrating better environmental adaptability. In the future, the RT-DETR-Tomato model can be integrated with intelligent patrol and picking robots, enabling precise identification of crops and ensuring the safety of crops and the smooth progress of agricultural production. [ABSTRACT FROM AUTHOR]

An efficient plant disease identification is prime important for Agricultural productivity. This paper proposes a method of automatic leaf extraction and classification with improved accuracy compared with existing techniques. Images acquired from natural fields are complex, with background information that needs to be accurately segmented to identify areas of interest. Grab cut is a semi-automatic approach to extract foreground objects, which might occasionally degrade an object's properties. This paper suggests a better Grab cut for automatically extracting leaves from real-field images. The enhanced dataset of 23,617 images with eleven tomato leaf classes are created using the plant village dataset and actual field images. This paper addresses a wide range of issues related to convolutional neural network optimization, including how the number of layers affects the results of leaf disease detection and the creation of tiny models for portable devices. Five models, VGG 16, MobileNet, and custom architecture with input sizes of 98 × 98, 160 × 160, and 256 × 256, have been optimized and evaluated through several trials. Training and testing involve varying the input size, the number of layers, the optimizer, the dropout, the batch normalization, and the transfer learning. This paper presents and analyzes the findings of thirty experiments on various architecture. The proposed modified sequential eleven-layer architecture achieves accuracy compared with MobileNet and a reduced model size of 3.9 MB for input size 160 and 4.5 MB for input size 98. [ABSTRACT FROM AUTHOR]

Measuring agricultural productivity is a multiscale spatiotemporal problem that requires multiscale solutions. In Vietnam, rice comprises a substantial portion of the cultivated area and is a major export crop that supplies much of the global food system. Understanding the when and where of rice productivity is vital to addressing changes to yields and food security, yet descriptive summarizations will vary depending on the spatial or temporal scale of analysis. This paper explores rice trends across Vietnam over a 19-year period, giving specific attention to modifiable spatiotemporal unit problems by evaluating productivity across multiple time periods and administrative levels. A generalizable procedure and tools are offered for visualizing multiscale time-series remote sensing data in matrix and map form, not only to elucidate the effects of modifiable spatiotemporal unit problems, but also to demonstrate how these problems serve as a useful research framework. Remote sensing indices (e.g., LAI and EVI) were evaluated against national and provincial estimates across Vietnam during multiple crop production periods using the Pearson Correlation Coefficient (PCC) to establish a relationship. To overcome challenges posed by long-term observations masking emerging phenomena, time-series matrices and multi-spatial and multi-temporal maps were produced to show when, where, and how rice productivity across Vietnam is changing. Results showed that LAI and EVI are favorable indices for measuring rice agriculture in Vietnam. At the province scale, LAI compared to nationally reported production estimates reached a Pearson's r of 0.960; 0.974 for EVI during the spring crop production period. For questions such as, "What portion of Vietnam exhibits a negative linear trend in rice production?", the answer depends on how space and time are organized. At the province scale, 25.4% of Vietnam can be observed as exhibiting a negative linear trend; however, when viewed at the district scale, this metric rises to 45.7%. This research contributes to the discussion surrounding ontological problems of how agricultural productivity is measured and conveyed. To better confront how agriculture is assessed, adopting a multiscale framework can provide a more holistic view than the conventional single spatial or temporal selection. [ABSTRACT FROM AUTHOR]

This paper shows the performance of HR collaboration on the environment, in other words, GHRM can increase agricultural productivity while protecting the environment. The method is qualitative with a case study on a farm in Cirebon Regency, West Java, which applies the concept of GHRM. Data were collected through interviews, observations, and document analysis through SWOT Analysis. The findings of GHRM can be achieved through fostering a workforce that cares about the environment, encouraging innovation and collaboration, influencing organisational culture and decision-making, and generating new businesses such as garden/nature tourism, educational tourism, and spiritual tourism. The novelty of the research from the SWOT analysis is that collaborative GHRM leads to new sustainable businesses. Limitations The study was only conducted in Cirebon Regency, West Java implications of the research directly on stakeholders and government through policies that favour collaboration for sustainable business. [ABSTRACT FROM AUTHOR]

In light of the environmental challenges currently facing humanity, the issue of the environmental sustainability of crop production is becoming increasingly pressing. This is due to the fact that global population growth and the related demand for food are placing significant pressure on the environment. Wheat is a strategic crop globally due to its extensive cultivation area, high production and consumption levels, and vital nutritional properties. It is cultivated across diverse climatic conditions and within various agricultural production systems. It is of the utmost importance to pursue sustainable wheat production on a global scale, given the necessity to protect the environment and climate. The application of life cycle assessment (LCA) enables the identification of potential avenues for enhancing wheat production processes, thereby reducing the negative environmental impacts associated with these processes. This paper presents a synthesis of the existing literature on the environmental LCA of wheat grain production. It compares the impacts of different production systems, highlights critical stages in wheat cultivation, and provides recommendations for sustainable practices and directions for future research. [ABSTRACT FROM AUTHOR]

Global warming is one of the greatest threats to the social development of human beings. It is a typical example of global climate change, and has profoundly affected human production and life in various aspects. As the foundation of human existence, agricultural production is particularly vulnerable to climate change, which has altered environmental factors such as temperature, precipitation, and wind speed, and affected crop growth cycles, the frequency of extreme weather events, and the occurrence patterns of pests and diseases directly or indirectly, ultimately influencing crop yield and quality. This article reviews the latest research progress in this field, summarizes the impact of global climate change on agricultural production as well as the feedback mechanisms of agricultural activities on climate change, and proposes strategies for agricultural production to cope with global climate change. This paper aims to provide a scientific basis and suggestions for ensuring the sustainable development of agricultural production. [ABSTRACT FROM AUTHOR]

Advancing the construction and application of new quality productive forces is an essential prerequisite for achieving high-quality agricultural development and expediting the establishment of agricultural powerhouses. This study aims to elucidate the internal mechanisms through which new quality productivity contributes to high-quality agricultural development and to explore practical pathways for enhancing agricultural quality through its promotion. Utilizing panel data spanning 2012 to 2021 from 30 provinces and municipalities in mainland China, the entropy method is employed to gauge levels of new quality productivity and high-quality agricultural development. Additionally, employing research methodologies including SYS-GMM and threshold effect models, this study empirically investigates how the advancement of new quality productivity influences high-quality agricultural development. Our research reveals the following key findings: (1) The development of new quality productive forces significantly enhances high-quality agricultural development, exhibiting a heterogeneous distribution pattern favoring the "eastern region > western region > central region" and "northern region > southern region". (2) New quality productive forces can bolster the level of high-quality agricultural development by fostering innovation, coordination, openness, and shared development within its subsystems. However, they may impede progress by inhibiting improvements in green development within the subsystems. (3) The results of the threshold effect test demonstrate that the promotion effect of the development of new quality productive forces on high-quality agricultural development escalates with the level of high-quality agricultural development. Specifically, as the level of high-quality agricultural development exceeds the first threshold value of 0.1502, the promotion effect becomes significant; crossing the second threshold value of 0.2010 further amplifies this effect. This paper's primary marginal contribution involves empirically analyzing the potential nonlinear effects of advancing new quality productivity in enhancing the level of high-quality agricultural development. This enriches empirical research on how new quality productivity fosters the development of high-quality agriculture. [ABSTRACT FROM AUTHOR]

Agricultural scale management is an important means of improving agricultural production efficiency. To answer the controversy over whether different agricultural scale management models can improve production efficiency, this paper obtained 117,627 samples from 68 domestic and foreign literature and used meta-analysis to study the effects of land-scale operation (LSO) and service-scale operation (SSO) on agricultural production efficiency (APE). The moderators that affect the relationship between LSO, SSO, and APE are also examined. The research results show that (1) both LSO and SSO positively impact production efficiency, but LSO has a stronger impact. (2) The relationship between LSO and APE is positively regulated by the agricultural production and operation environment, characteristics of the agricultural location, degree of farmers' participation, and types of APE, and negatively regulated by the research situation. (3) The relationship between SSO and APE is negatively regulated by APE types and data types. Therefore, the government should promote agricultural dual-scale operation according to the local conditions through the complementary advantages of LSO and SSO, so as to fully release the promotion potential of APE. [ABSTRACT FROM AUTHOR]

MPROVING agricultural output worldwide to satisfy the demands of a rapidly expanding I population may be possible, in part, through the creation and use of novel fertilizers made possible by cutting-edge nanotechnology. As a matter of fact, according to the literature assessment, there are designed nanomaterials that, at specific concentrations, can improve plant development. These materials could be utilized as nanofertilizers in agriculture to boost crop yields and reduce pollution. Nanofertilizers that enhance plant development, nanofertilizers that include nutrients, and nanofertilizers that contain micronutrients are the four main types of nanomaterials discussed in this article. Concerning the chemical makeup of the nanomaterials, the size of the particles, the concentrations used, the plant species that benefited, the techniques of plant incubation, and the features and rates of plant growth promotion, each category is addressed separately. Furthermore, we take a close look at each type of nanofertilizer to determine its relevance, potential future paths, and special research needs in the pursuit of sustainable agriculture. Lastly, this paper argues that protecting the environment and increasing food production should be top research priorities, and they propose developing macronutrient nanofertilizers containing N and P. [ABSTRACT FROM AUTHOR]