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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]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

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]

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]

Purpose: This paper aims to analyse the pattern of structural transformation and productivity growth of 15 major Indian states at a ten-sector level of disaggregation from 1983 to 2017. Design/methodology/approach: The analysis has been carried over in a ten-sector disaggregated level through construction of the labour and output data from various micro data sets. Findings: The majority of Indian states have bypassed the stage of industrialization, wherein labour previously engaged in agriculture has transitioned directly into the modern services sector while skipping the manufacturing. There are no sign of convergence of sectoral productivities and the heterogeneity among Indian states persists throughout the time period. The growth performance of states are not positively associated with the movement of labour across sectors as measured by the structural transformation index (STI). This goes against the narrative that structural transformation help reduce the misallocation of factors. Despite an increase in educational attainment of workers across all sectors, more than one-third of agricultural workers still remain either illiterate or lack formal schooling. Among sectors, construction (C) and trade, hotels and restaurants (THR) have absorbed the majority of workers who have left agricultural jobs. Finance, insurance, real estate and business services (FIRB), electricity, gas and water supply (EGWS) and mining and quarrying (MQ) are the three sectors that have seen significant gains in labour productivity during the study period. Originality/value: To the best of the authors' knowledge, this is the first attempt to analyse structural change and productivity growth in the Indian economy using Indian states as critical geographical marker. The results are new and add value to the literature. [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 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]

Land consolidation has become an organized and widely implemented project in rural areas. However, research remains somewhat scarce on the contributing mechanism of land consolidation to farmer income in developing countries. Using provincial panel data from China, this study employs a fixed effects model to analyze the influence of high-standard farmland construction on farmer income and underlying mechanisms. Results suggest that the implementation of high-standard farmland construction can significantly augment farmer income. Reduction in agricultural production cost, improvement in agricultural management benefit and increase in non-farm income have led to the boost in income. The relationship between income and high-standard farmland construction exhibits regional disparities, with the most significant impacts concentrated in economically developed areas and low relief degree areas. Our findings suggest that it is imperative for the Chinese government to persistently promote the establishment of high-standard farmland, and further enhance its positive influence on advancing agricultural cost-effectiveness and expanding non-farm income channels for farmers. [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]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Agricultural development is crucial to feed the growing population. Most farmers tend to cultivate the crops which will give the more economical benefits besides checking the suitability of the crop according to the soil conditions. Use of technology in the agricultural sector leads the sustainable improvements in the agricultural production. Machine learning approach to suggest the suitable crop based on the soil parameters can help the farmers to cultivate the crops accordingly and can produce more yield. In this paper Random Forest Classifier is used to train the Machine Learning model on soil dataset using Python. Model performance is evaluated using confusion matrix and classification report having precision, recall and F1 score. Model accuracy achieved is 99% without parameter tuning. [ABSTRACT FROM AUTHOR]

Why do modern states allow parts of their territory to be governed by non‐state actors? We study this question using the Frontier Crimes Regulation (FCR) in Pakistan, a British Colonial law abrogated only in 2018, which left governance to pre‐colonial tribal councils in large parts of modern day Pakistan. In areas where the FCR did not apply, the British and then Pakistani state built modern political and bureaucratic institutions. Using primary legal documents, we build a dataset of when and where the FCR applied between 1901 and 2012. The territorial extent of the formal state is both cleanly demarcated by this law and varies substantially over time, permitting an empirical examination of the determinants of state control. The data reveal that the Green Revolution's potential to transform agriculture played a major role in extending the formal state. The law was repealed first from areas where agricultural productivity benefited the most from the Green Revolution. This is consistent with a model in which technological changes that shift the returns to control influence where states choose to govern. This paper is part of the Economica 100 Series. Economica, the LSE "house journal" is now 100 years old. To commemorate this achievement, we are publishing 100 papers by former students, as well as current and former faculty. Michael Callen is an Associate Professor at the LSE. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of land expropriation on the livelihoods of farmers expropriated from the peripheries of Addis Ababa, Ethiopia. A mixed methods approach, including household surveys, key informant interviews, field visits, and document reviews, was employed. A sample of 349 expropriated farmer household heads from four purposively selected sub-cities was drawn using systematic random sampling. The findings reveal significant adverse effects of land expropriation on the livelihoods of expropriated farmers, such as income losses, unemployment, and reduced agricultural production, primarily due to a lack of transparency and consultation during the expropriation process, inadequate compensation, and insufficient resettlement support provided by the city administration. Consequently, the living conditions of most farmers deteriorated in the post-expropriation. To address these effects, the study recommends enhancing procedural clarity, ensuring meaningful participation and consultation with affected farmers, and providing comprehensive resettlement support, including access to housing, employment opportunities, credit, and social services, to help affected farmers transition to new livelihoods. [ABSTRACT FROM AUTHOR]

Based on the panel data of provincial units in China from 2000 to 2020, this paper uses the entropy method to calculate agricultural environmental regulation level in China from the perspectives of afforestation projects, water conservancy projects and environmental protection projects. Then, this paper calculates agricultural non-point source pollutant emissions in China by using the unit survey and evaluation method, and calculates agricultural green productivity growth in China by using the SBM directional distance function and Luenberger productivity index. On this basis, the influence of environmental regulation on agricultural green productivity growth in China and its mechanism are empirically analysed. The results show that:(1) During the sample period, agricultural environmental regulation level and agricultural green productivity growth increased significantly, while the emission density of agricultural non-point source pollution decreased significantly; (2) On the whole, environmental regulation significantly promoted agricultural green productivity growth in China, which was mainly reflected in the promotion of agricultural green technology progress change; (3) Environmental regulation can significantly promote agricultural green productivity growth in China through the channel of agricultural innovation investment. The innovation of this paper lies in the measurement of agricultural environmental regulation, and the analysis of the moderating effect of agricultural innovation investment on the impact of environmental regulation on agricultural green productivity growth in China. The research conclusions of this paper verify the 'Porter Hypothesis' theory in the field of agriculture, and have guiding significance for China to promote agricultural green productivity growth by relying on environmental regulation. [ABSTRACT FROM AUTHOR]

Copyright of Agricultural Outlook (1673-3908) is the property of Institute of Agricultural Information, Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Haors in Bangladesh are saucer-shaped, low-lying land depressions that form deep basins; they remain submerged for approximately half of the year, typically from June onwards. This fragile ecosystem spans over 2.0 million hectares in the northeastern region of the country, accounting for roughly 14% of the total areas, where approximately 19.4 million people reside. Factors including floods, flash floods, and low winter temperatures constrain agricultural productivity in the haor areas. It is a great challenge to change the haor areas from less productive to more productive land. This is a comprehensive analysis of the biophysical and socioeconomic characteristics of haors which also highlights the constraints and opportunities in agricultural production. It explores strategies for significantly increasing crop, livestock, and fish production within the haor ecosystem, in alignment with government policies. Some of the proposed agricultural development strategies for the haor areas include the development of short-duration, cold-tolerant crop varieties, such as Boro rice, utilizing relatively flood-free elevated lands and homesteads for vegetable production and promoting agricultural mechanization, livestock rearing, fisheries, and agribusiness development. The recommendations presented in this paper focus on enhancing crop yields, increasing cropping intensity, and boosting livestock and fish production; ultimately, they contribute to food security, poverty reduction, and improved livelihoods for the inhabitants of the haor areas. [ABSTRACT FROM AUTHOR]

Over the years, agricultural management practices are being improved as they integrate Information and Communication Technologies (ICT) and Precision Agriculture tools. Regarding sugarcane crop production, this integration aims to reduce production cost, enhance input applications, and allow communication among different hardware and datasets, improving system sustainability. Sugarcane mechanization has some particularities that mandate the development of custom solutions based on digital tools, which are being applied globally in different crops. Digital mechanization can be conceived as the application of digital tools on mechanical operation. This review paper addresses different digital solutions that have contributed towards the mechanization of sugarcane crop production. The process of digitalization and transformation in agriculture and its related operations to sugarcane are presented, highlighting important ICT applications such as real-time mechanical operations monitoring and integration among operations, demonstrating their contributions and limitations regarding management efficiency. In addition, this article presents the major challenges to overcome and possible guidance on research to address these issues, i.e., poor communication technologies available, need for more focus on field and crop data, and lack of data interoperability among mechanized systems. [ABSTRACT FROM AUTHOR]

Effective spraying is essential for modern agricultural production, to ensure a high-quality and abundant harvest. Simultaneously, it is crucial to minimise the negative impact of crop protection products on the environment. To achieve this, it is necessary to implement the appropriate technical and technological parameters for the treatment and to consider the conditions under which the treatment is carried out. The aim of this study was to determine the relationship between the speed of the sprayer, the pressure of the liquid, and the type of nozzles, as well as air temperature and wind speed, in terms of the degree of coverage of the sprayed surfaces. The degree of coverage was analysed by spraying water-sensitive papers placed on the artificial plant, positioned to obtain horizontal and vertical surfaces. The study found that standard single flat fan nozzles provided greater coverage on upper horizontal surfaces, while standard nozzles were more effective for vertical transverse approach surfaces at lower fluid pressures and travel speeds. Neural networks were used to develop models of the relationships studied. Models with high accuracy for the validation data set were obtained in the case of the coverage of the vertical transverse leaving surface and the upper level surface (R = 0.93 and R = 0.86). These models were used to determine the optimum values of the technical parameters of the spraying process under the selected weather conditions. The maximum spray coverage (41.49%) was predicted for the XR nozzle under the following conditions: pressure = 200 kPa, driving speed = 1.4 m·s−1, temperature = 21.73 °C and wind speed = 0.32 m·s−1. Based on the sensitivity analysis of the neural models it was found that the greatest effect on the coverage of the vertical transverse leaving surface was observed for temperature and the coverage of the upper level surface was mostly influenced by driving speed. [ABSTRACT FROM AUTHOR]

In an era of connectivity, the Internet of Things introduces smart solutions for smart and sustainable agriculture, bringing alternatives to overcome the food crisis. Among these solutions, smart greenhouses support crop and vegetable agriculture regardless of season and cultivated area by carefully controlling and managing parameters like temperature, air and soil humidity, and light. Smart technologies have proven to be successful tools for increasing agricultural production at both the macro and micro levels, which is an important step in streamlining small-scale agriculture. This paper presents an experimental Internet of Things-based small-scale greenhouse prototype as a proof of concept for the benefits of merging smart sensing, connectivity, IoT, and mobile-based applications, for growing cultures. Our proposed solution is cost-friendly and includes a photovoltaic panel and a buffer battery for reducing energy consumption costs, while also assuring functionality during night and cloudy weather and a mobile application for easy data visualization and monitoring of the greenhouse. [ABSTRACT FROM AUTHOR]

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]

Copyright of Zywnosc is the property of Polish Society of Food Technologists - Scientific Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

Farmland transfer is a critical component in facilitating agricultural scale management and improving agricultural production efficiency. This study examines the spatial distribution of farmland transfer in China and identifies the factors influencing it, offering valuable guidance for advancing China's farmland transfer practices. Through the application of mathematical statistics and GIS spatial analysis, the study investigates changes in spatial patterns related to the scale, rate, mode, and recipients of farmland transfer across China's 30 provinces from 2015 to 2020. Geographical detectors are also employed to identify the key factors influencing the extent and pace of farmland transfer. The study reveals that between 2015 and 2020, China's farmland transfer area increased from 29,789 to 37,638 million hectares. Provinces with abundant farmland resources generally experienced larger farmland transfers, while economically developed regions and major grain-producing areas saw higher rates of farmland transfers. The predominant mode of farmland transfer in China was leasing (subcontracting), accounting for over 80% of the total transferred area. Large-scale grain growers and family farms were significant participants in farmland transfers, acquiring approximately 60.1% of the transferred lands, followed by professional cooperatives (21.5%), enterprises (10.4%), and other entities (7.9%). Key factors influencing the farmland transfer area include the "regional farmland area", the "proportion of family farms supported by loans", and the "proportion of non-agricultural population", with explanatory powers of 0.663, 0.319, and 0.225, respectively. Notably, there is a substantial interaction between the "regional farmland area" and factors such as the "proportion of family farms supported by loans" and the "grain yield per unit area", with explanatory powers reaching 0.957 and 0.901, respectively. These findings offer valuable insights for promoting farmland transfer in agriculturally rich regions. Factors affecting the farmland transfer rate include "grain yield per unit area", "GDP per capita", and the "proportion of non-agricultural population", each with an explanatory power above 0.500. Moreover, their interactive explanatory powers with other indicators exceed 0.600, indicating that provinces with high agricultural productivity or economic development levels are more likely to undergo farmland transfer. The paper concludes by proposing strategies and recommendations to promote farmland transfer in both "large agricultural areas" and "metropolitan suburbs." [ABSTRACT FROM AUTHOR]