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This paper assesses four relevant climate actions in Indian agricultural sector through the lens of United Nation's Sustainable Development Goals (SDGs) to understand how climate actions (SDG 13) are linked with other SDGs and their targets and what measures can be taken to scale up these actions. Four board categories of climate actions are considered for this exercise: shift to less-flooded paddy water management practices; minimizing overuse of nitrogen-based fertilizer, system of rice intensification and climate smart agricultural practices. A mixed method has been followed: first, scoping review of literature between 2010 and 2022 is undertaken to capture the growing body of literature and then a normalized weighted score is used to understand the net strength of the linkages. Scoping review helped in identifying 100 studies spanned across Indo-Gangetic Plain (IGP) and the eastern part of Peninsular India. Findings from the scoring method suggest that these practices have linkages with 15 SDGs and as many as 43 SDG targets. Almost all linkages are positive (synergy) but there are a few negative linkages (trade-offs) as well. Trade-offs include exacerbating inequalities including gender, high upfront cost, requirement of skilled labor, lack of access to information and finance. This paper provides a detailed description of these trade-offs and suggests measures to tackle and minimize them. [ABSTRACT FROM AUTHOR]

Background: One common renewable energy source for substituting fossil sources is photovoltaic (PV) systems. However, installing PV systems in agricultural areas can lead to competition with other land uses. These projects, therefore, often encounter problems with social acceptance in affected communities. Especially from the perspective of nature conservation targets, conflicts can arise. These potential differences are still under-researched but represent important knowledge for the societally broadly accepted design of such facilities and their contribution to energy transformation. In this paper, we investigate the perspectives of nature conservationists on PV in the region of Brandenburg, Germany. We comparatively analyse attitudes towards ground-mounted photovoltaics (GM-PV) and agrophotovoltaics (APV). APV combines energy supply and agricultural production on the same land and could thus be a possible solution for mitigating land-use conflicts. Results: We investigated the degree of local acceptability and positive and negative influencing factors through a qualitative text analysis of ten interviews with local representatives and position papers by Nature Conservation Associations. Our findings show a growing consensus around basic assumptions of the need for renewable energies, the prioritised support for PV systems on rooftops over the installation on agricultural land (GM-PV and APV), and the necessity for PV systems to be compatible with nature conservation objectives. Regarding specific site decisions on agricultural land, we find diverging attitudes when comparing the content of position papers and the responses of interviewees. The interviewees advocate taking into account local interests and specific regional conditions, the effects of PV systems on the local environment, and the need for distributional justice. Large-scale plants are locally less accepted than smaller ones and there is a more open attitude towards the expansion of APV than of GM-PV on agricultural land. However, a range of concerns regarding consequences for landscape and biodiversity persists, and further research and clarification is required to address these issues. Conclusions: We conclude that basic ecological standards and the demands of local stakeholders and interest groups should be taken into account when planning, implementing and reviewing PV projects in the future. [ABSTRACT FROM AUTHOR]

In 2007/2008, a triple crisis of food, fuel and finance sparked a global rush for agricultural land; tens of millions of hectares were acquired, primarily by foreign investors, within countries in the Global South. Amidst those transactions, intergovernmental organizations, national governments, investors, and community members envisioned what "success" of such investments entails. Although not explicitly defined, each stakeholder had different conceptualizations and measures of it, based upon the descriptions used and desired outcomes sought. Despite a large amount of literature analyzing the global rush for land, as far as we are aware no one has analyzed the diverse viewpoints about what success entails. This paper compares conceptualizations among four key stakeholder groups, based on ideal types from dominant narratives, and develops a typology of ideal stakeholder framing of success to allow comparisons of uses and thereby provide a foundation for researchers who are assessing the global land rush. This paper provides clarity about widely used, but inconsistently defined, framing providing an important foundation for clarity of meaning and comparative differences between stakeholders. The typology advances the discourse on the land rush by providing nuance to this widely used framing and makes explicit its diverse meanings. [ABSTRACT FROM AUTHOR]

This paper seeks to draw a research agenda for unveiling the underlying bottlenecks and possibilities for improving the agricultural total factor productivity in the European Union (EU). To this aim, the paper first surveys the key challenges for the modern agricultural economy. Then, the case of the EU is discussed by looking at the convergence in the partial productivity indicators (including the GHG emission) and growth efficiency that relate to the gap in the total factor productivity growth. The results suggest that the EU member states exhibited growth inefficiency (i.e., a gap in the total factor productivity growth) of 5–9% on average during 2004–2021. The paper also discusses the possibilities to utilize the production theory for assessing the pathways towards improvements in the productivity and sustainability of the EU agriculture. [ABSTRACT FROM AUTHOR]

The African continent is confronted with interconnected challenges of water, energy, and food insecurity, particularly affecting marginalized urban residents. Decentralized Water-Energy-Food (WEF) systems have emerged as potential solutions to alleviate these issues exacerbated by climate change. Despite scholarly acknowledgment of constraints related to WEF, there is a growing global trend towards trials of innovative decentralized WEF systems. This paper contributes to the body of research aimed at demonstrating the applicability of decentralised WEF systems at local level, by showcasing the potential of such systems to enhance water, energy and food security in informal settlements. Focusing on Diepsloot slums in Johannesburg, South Africa, the paper applied space analysis, least-cost modeling of sack farming, and renewable energy technologies in providing WEF solutions that demonstrate feasibility, sustainability and decentralised solutions for informal settlements, compared to the popular broad-based solutions that often neglect these marginalised communities. Utilizing Geographic Information Systems (GIS), AquaCrop crop modeling, financial analysis, and linear optimization modelling urbs, the study provides a comprehensive analysis of past climate trends, water sources, spatial plant arrangements, and a proposed community centre. The proposed Phezulu 'Up' growers' model and the positive return on investment further demonstrate the feasibility of decentralized WEF systems. Policymakers are encouraged to promote these WEF systems to bolster urban livelihoods and achieve Sustainable Development Goals (SDGs), notably SDG 2, 6, and 7, in marginalized African urban areas. Recommendations and areas of further research include looking into issues of community engagement, partnerships, risk mitigation, strategies for equity and inclusivity and local knowledge integration during WEF implementation, and robust critique of WEF practical interventions and comparative analysis with other WEF models. Overall, the study provides a blueprint for inclusive and sustainable urban development, emphasizing the importance of localized WEF solutions and community empowerment. [ABSTRACT FROM AUTHOR]

Desertification, deforestation, climate change, global warming, and economic instability are leading to serious problems in many countries. Two of these negative factors are the decrease in agriculture areas and food scarcity. This situation caused many countries to reconsider their agriculture polices and think of new solutions. The United Nations also addressed the problem via its "SDG—Sustainable Development Goals" agenda. One of its major goals is the change towards green and sustainable communities that can produce their own food. Rooftop farming in places with suitable climatic conditions was encouraged by the "SDG" and governments of many countries. The paper discusses the potential of utilizing Egyptian residential rooftops for rooftop farming, the Egyptian urban fabric is very dense with residential buildings. Taking the Egyptian "Asmarat" residential neighborhood as an exercise, the study aims to fulfilling the following three goals: 1- On a food security Level: if rooftop farming is extensively applied across all cities and villages in Egypt, a mass production threshold of exporting vegetables and fruits may be reached that will significantly participate in the overall national GDP, 2- On a socio-economic level: rooftop farming will create jobs for informal workers and the jobless, turning an individual from a jobless person to a busy one is the best practical solution for changing behavioral norms from the tendency towards crime, hanging around on the streets, and drug addiction towards a decent and productive society., and 3- On an environmental level.: Large cities suffer from crowds, high building densities, pollution, and scarcity of open green areas. Rooftop farming increases the "Urban Green Space—UGS" of the city, reduce pollution, decrease global warming, and purify the atmosphere. [ABSTRACT FROM AUTHOR]

Background: Thermal upgrading is an important element in the economic transformation of Poland towards a low-emission economy. This paper presents the results of research into the current status and plans of inhabitants of rural areas in Poland with regard to thermal upgrading of residential buildings and other buildings used in the agricultural activity of the study participants. The main purpose of the article is to identify thermal modernization needs and to analyse factors influencing individual thermal modernization plans in terms of replacement or modernization of heating systems. A key element of the analyses was the issue of upgrading priorities and the dependency between declared intentions in terms of thermal upgrading work and the production and economic features of the farms studied. Results: The main research material were surveys conducted with a randomly selected sample of 480 farming households in Poland. The analyses used, amongst others, multiple correspondence analysis (MCA) to determine and assess the relationships between the variables studied. Detailed correspondence analysis shows that there are strong dependencies between plans for thermal upgrading of farm buildings and the system of agricultural production (φ2 = 0.1503), the economic size of farms (φ2 = 0.1100), and the location of farms (φ2 = 0.0947). Conclusions: The research showed that there is a need for thermal upgrading in the examined area of study, especially with regard to the replacement or modernisation of heating systems. The issue of thermal upgrading of residential and farm buildings in rural areas in Poland requires support and engagement at all levels of administration, as well as the modernisation of assistance programmes. [ABSTRACT FROM AUTHOR]

Green nanotechnology has significant potential for use in agriculture particularly due to their antifungal properties, ability to control fungal diseases and reduce the reliance on chemical fungicides. Biotic stresses in agriculture have caused widespread damage worldwide, and green NPs provided eco-friendly alternatives to traditional chemical treatments, which are frequently toxic and harmful to the ecosystem. Green NPs could become an important tool in modern agricultural practices and environmental remediation if appropriate research is conducted to identify cost-effective production methods as well as safe and sustainable applications. In order to understand the potential of green NPs for sustainable agriculture and identify potential risks, research is ongoing into the effectiveness in agriculture sectors. Research update on green NPs is presented in this paper using data published on science direct over the last 15 to 20 years to clarify and understand the antifungal mechanisms of green metallic NPs, carbon and graphene nanotubes, nanocomposites as well as other type of nanomaterials. These green NPs are found to be more effective against pathogens on crops and humans than conventional fungicide approaches. They are very effective against fungi that affect cereal crops, including Fusarium oxysporum, Botrytis cinerea, and Candida species, etc. The green NPs developed using green synthesis methods are both cost-effective and environmentally friendly. Moreover, research is also required to identify the best methods for applying green NPs for crop production and sustainable agriculture. Furthermore, research should be undertaken to establish the most cost-effective methods of making and deploying green nanoparticles at a large field size study where there is fungal attack that diminishes agricultural output and affects global crop production. [ABSTRACT FROM AUTHOR]

Agriculture in developing countries faces numerous challenges such as price fluctuation and non-uniformity, presence of untrusted middlemen in the supply chain, crop wastage due to inadequate storage facilities or long commutes, and lack of proper monitoring. Blockchain technology has emerged as a promising approach to solve these challenges. Even though a number of solutions are recently proposed in the literature, they lack in several aspects: (1) lack of proper regulatory interface, (2) absence of proper agricultural supply chain, (3) absence of adequate storage facilities, (4) absence of a fair payment system, (5) poor monitoring and auditing system, (6) non-inclusion of government policies, and (7) missing experimental validation. This paper presents a novel blockchain-based smart agriculture system that overcomes the above-mentioned issues. In particular, our solution covers product traceability in the supply chain, elimination of counterfeit products, easily accessible cold storage facilities, fair payment procedure between buyers and sellers, a transparent process of distributing government financial benefits to farmers, and overall government monitoring. We present a prototype implementation of our proposed system using Ethereum. The backend smart contracts are developed using Solidity programming language, whereas the frontend is developed using ReactJS. The experimental evaluation using Hyperledger Caliper benchmark tool shows an encouraging result, demonstrating the system performance in terms of execution gas costs, CPU utilization, average latency, and transaction throughput. [ABSTRACT FROM AUTHOR]

The United Nations (UN) emphasizes the pivotal role of sustainable agriculture in addressing persistent starvation and working towards zero hunger by 2030 through global development. Intensive agricultural practices have adversely impacted soil quality, necessitating soil nutrient analysis for enhancing farm productivity and environmental sustainability. Researchers increasingly turn to Artificial Intelligence (AI) techniques to improve crop yield estimation and optimize soil nutrition management. This study reviews 155 papers published from 2014 to 2024, assessing the use of machine learning (ML) and deep learning (DL) in predicting soil nutrients. It highlights the potential of hyperspectral and multispectral sensors, which enable precise nutrient identification through spectral analysis across multiple bands. The study underscores the importance of feature selection techniques to improve model performance by eliminating redundant spectral bands with weak correlations to targeted nutrients. Additionally, the use of spectral indices, derived from mathematical ratios of spectral bands based on absorption spectra, is examined for its effectiveness in accurately predicting soil nutrient levels. By evaluating various performance measures and datasets related to soil nutrient prediction, this paper offers comprehensive insights into the applicability of AI techniques in optimizing soil nutrition management. The insights gained from this review can inform future research and policy decisions to achieve global development goals and promote environmental sustainability. [ABSTRACT FROM AUTHOR]

The integral development of agriculture and tourism heavily depends on natural resources, especially agricultural resources, and it also may contribute to the sustainability of agriculture. The sustainability of agriculture is usually measured by the agricultural eco-efficiency. The main objective of this paper is to demonstrate the impact of the agriculture and tourism integration on agricultural eco-efficiency, so as to reflect the role of the agriculture and tourism integration on promoting agricultural sustainability. Applying panel data of 19 provinces in Yangtze River Basin and Yellow River Basin in China from 2008 to 2019, this paper uses a PSTR model to demonstrate the impact of agriculture and tourism integration on agricultural eco-efficiency and its mechanisms. It is found that in the study area, the integration of agriculture and tourism can contribute to improvements in agricultural eco-efficiency, and when levels of integration are high, the promotional effect is enhanced. However, impacts vary among different regions to some extent. In terms of influencing mechanisms, inputs of agricultural labor, sown crop area, effective irrigation area and other factors decrease with continuous improvements of integration. The input of pesticides, fertilizers and other factors does not decrease in the initial stage of integration but undergoes a rapid decrease after crossing a threshold value. And it demonstrates that the integration of agriculture and tourism is beneficial for improving the agricultural eco-efficiency, so that it will contribute to agricultural sustainability. Meanwhile, this paper offers a new research perspective for discovering effects of agriculture and tourism integration, providing implications for formulating regional policies for the integration. [ABSTRACT FROM AUTHOR]

This paper focuses on the urgent issue of minimising the impact of pollutants on aquatic life in river ecosystems. Our innovative approach involves the integration of mathematical modelling and strategic control methods to counteract the negative consequences of industrial and agricultural activities. The model, developed in a one-dimensional context, captures the complex dynamics of species population and pollutant concentration. Using an optimisation framework, we strive to achieve a harmonious balance that limits pollution, enhances species diversity and optimises control expenditure. Ultimately, we seek to harmonise industrial progress with ecological vitality, promoting the sustainability of river ecosystems for generations to come. [ABSTRACT FROM AUTHOR]

The agriculture Internet of Things (IoT) has been widely applied in assisting pear farmers with pest and disease prediction, as well as precise crop management, by providing real-time monitoring and alerting capabilities. To enhance the effectiveness of agriculture IoT monitoring applications, clustering protocols are utilized in the data transmission of agricultural wireless sensor networks (AWSNs). However, the selection of cluster heads is a NP-hard problem, which cannot be solved effectively by conventional algorithms. Based on this, This paper proposes a novel AWSNs clustering model that comprehensively considers multiple factors, including node energy, node degree, average distance and delay. Furthermore, a novel high-performance cluster protocol based on Gaussian mutation and sine cosine firefly algorithm (GSHFA-HCP) is proposed to meet the practical requirements of different scenarios. The innovative Gaussian mutation strategy and sine–cosine hybrid strategy are introduced to optimize the clustering scheme effectively. Additionally, an efficient inter-cluster data transmission mechanism is designed based on distance between nodes, residual energy, and load. The experimental results show that compared with other four popular schemes, the proposed GSHFA-HCP protocol has significant performance improvement in reducing network energy consumption, extending network life and reducing transmission delay. In comparison with other protocols, GSHFA-HCP achieves optimization rates of 63.69%, 17.2%, 19.56%, and 35.78% for network lifespan, throughput, transmission delay, and packet loss rate, respectively. [ABSTRACT FROM AUTHOR]

In farming, clashes between humans and animals create significant challenges, risking crop yields, human well-being, and resource depletion. Farmers use traditional methods like electric fences to protect their fields but these can harm essential animals that maintain a balanced ecosystem. To address these fundamental challenges, our research presents a fresh solution harnessing the power of the Internet of Things (IoT) and deep learning. In this paper, we developed a monitoring system that takes advantage of ESP32-CAM and Raspberry Pi in collaboration with optimised YOLOv8 model. Our objective is to detect and classify objects such as animals or humans that roam around the field, providing real-time notification to the farmers by incorporating firebase cloud messaging (FCM). Initially, we have employed ultrasonic sensors that will detect any intruder movement, triggering the camera to capture an image. Further, the captured image is transmitted to a server equipped with an object detection model. Afterwards, the processed image is forwarded to FCM, responsible for managing the image and sending notifications to the farmer through an Android application. Our optimised YOLOv8 model attains an exceptional precision of 97%, recall of 96%, and accuracy of 96%. Once we achieved this optimal outcome, we integrated the model with our IoT infrastructure. This study emphasizes the effectiveness of low-power IoT devices, LoRa devices, and object detection techniques in delivering strong security solutions to the agriculture industry. These technologies hold the potential to significantly decrease crop damage while enhancing safety within the agricultural field and contribute towards wildlife conservation. [ABSTRACT FROM AUTHOR]

Diagnostic checking is an important predefined step before using autoregressive models. Although many portmanteau tests were proposed for diagnostic checking, they still struggle with the issue of significant size distortion. In this paper, we develop new diagnostic checking methods based on jackknife empirical likelihood. It is demonstrated that the suggested testing statistics asymptotically have a typical chi-squared distribution. To verify the performance of the finite sample, some simulations are constructed. Additionally, a real example of five agricultural futures is provided to illustrate the merits of our diagnostic checking procedure. [ABSTRACT FROM AUTHOR]

This paper introduces the concept of complex quadratic Diophantine fuzzy sets (CQDFS) which serves as a robust framework for effectively dealing with uncertainty within decision-making challenges. The study comprehensively explores the properties and characteristics of CQDFS by conducting a systematic comparative analysis to demonstrate its superiority over existing techniques in managing complex fuzzy information. This foundation contributes to the theoretical understanding of CQDFS and also provides valuable practical insights into a wide range of transportation strategies and economic efficiency. These insights offer practical solutions to enhance decision-making within these important and interconnected domains. The proposed complex information system serves as a versatile and adaptive tool, significantly strengthening the flexibility available for addressing complex decision-making challenges, particularly in contexts such as the management of GPS-enabled cargo vehicles and cargo loading operations, where precision and efficiency are of great importance. [ABSTRACT FROM AUTHOR]

Wireless sensors are used for smart building monitoring, biomedical applications, intelligent home appliances, urgent disaster management and precision agriculture production. These devices today operate with batteries that are very difficult to change. The major problem hindering the widespread deployment of wireless sensor networks is the need to constantly replace them because their batteries expires. We can see that it is possible to charge them using energy harvested from solar, vibration, thermal or ambient radio frequency sources. we can implement sustainable health surveillance systems, agriculture, mining, environmental protection and national infrastructure, which will improve the quality of life, save money and achieve ecological goals. We realized that it would be wise to specify a field of application for internet of things in order to be able to clarify the identification of the problem of energy consumption and its relation with renewable energies. In addition, one of the most interesting applications for the socio-economic fields in the world in recent years is Smart Farming. In this paper, we reviewed a state of the art on the use of renewable energy harvesting in precision agriculture. [ABSTRACT FROM AUTHOR]

In Ethiopia, on-farm agrobiodiversity and the Productive Safety Net Program (PSNP) play a key role in building smallholders' resilience. However, the impact of PSNP on on-farm agrobiodiversity is not yet well investigated. In this paper, we develop an analytical framework that links PSNP participation to on-farm agrobiodiversity. Both diverse farming systems and PSNP require labour inputs while providing income stabilization, which might result in a negative relationship between the two. Conversely, higher income from PSNP might allow farmers to increase their long-term on-farm investments, as opposed to the strategies oriented toward the highest immediate profit or calorie intake outcome. We base our empirical analysis on the World Bank's Ethiopian Socioeconomic Survey, a panel dataset encompassing nearly 3000 respondents and a Tobit model, based on Difference-in-Difference and the Propensity-Score Matching methods. We find that Ethiopia's PSNP has a negative impact on farm labour input, both in terms of labour intensity and duration. Furthermore, our results show that participation in the program is associated, on average, with lower on-farm crop diversity. We conclude that the PSNP participation may be crowding-out production stabilizing farming activities, such as intercropping or cover cropping, that are more labour intensive. Our findings call for embedding tools in the new phase of the PSNP (2021–2025) that could incentivise on-farm resilience-oriented investments, in particular leading to higher crop diversification. [ABSTRACT FROM AUTHOR]

The coordinated development of agriculture and tourism is very important. On the basis of the index system of the predecessors, the development effect index after the integration of the subsystems is added, so as to construct the evaluation index system of the integrated development of agriculture and tourism in Inner Mongolia. The entropy weight method is used to set the weight value for each index, and the panel data from 2001 to 2019 collected from the Inner Mongolia Statistical Yearbook and the China Yearbook are analyzed with the coupled coordination degree model. The research results show that the development of agriculture and tourism in Inner Mongolia has been highly coupled since 2004; it has been reluctantly coordinated from 2007 until 2019 to achieve high-quality coordination; from 2001 to 2011, the development of tourism lagged behind the development of agriculture, while after 2012, the development of agriculture has been lagging behind the tourism industry. Wang (Asia Pac J Tour Res 24(6):550–562, 2019), Li and Du (Sustainability 13(10), 2021) had got similar conclusions. The article puts forward suggestions from two aspects. On the one hand, using the tourism as the trigger for the development of agriculture tourism by developing more agricultural-related tourism products so as to fully tap the tourism attributes of agriculture. The marketing of tourism products should be consciously inclined to agriculture; on the other hand, the promoting the development of tourism can be realized by adopting modern agriculture, which includes cultivating new types of large agricultural households, supporting the development of large and medium-sized farms, promoting the development of green agriculture and industrialized and three-dimensional agriculture. The modern agriculture can be deeply integrated with the tourism industry, thereby promoting the development of tourism. The research results and suggestions put forward in this paper have certain reference significance for the coordinated development of agriculture and tourism in Inner Mongolia and Southeast Asia. [ABSTRACT FROM AUTHOR]

Since the 1990s, agribusiness expansion in Argentina involved the exponential growth of pesticide use throughout the country. Pesticide exposure has become a widespread problem in rural areas and farming towns, but protests and conflicts about this issue are the exception rather than the norm. Why? Based on archival work, interviews, and ethnographic observations, in this paper, we scrutinize pesticide governance at the subnational scale to elucidate this puzzle, focusing on a) informal arrangements (face-to-face negotiations between pesticide users and people affected by them), b) juridical conflicts (lawsuits against farmers over exposure of people and crops to herbicides), and c) regulatory challenges (when rural populations and environmentalists push for local ordinances to curb pesticide exposure). We find that local cultural codes encourage informal agreements and that social inequalities prevent conflicts from arising in the public sphere. The interventions of social movements and civil society organizations (or lack thereof) and the mobilization of expertise also shape pesticide governance. Our analysis highlights that pesticide governance is context-dependent or situated, and informed by subtle but significant power asymmetries. [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]

Agriculture is an essential sector that plays a necessary role in the economic improvement of a country. Prediction of plant diseases at the earliest stage may result in better yield and sustainable for growing population. The conventional method necessitates highly skilled inspectors to identify the phenotypic expression of different diseases. Alternatively, biochemical technologies offer more precise means of obtaining crop disease information by analyzing susceptible rice. However, these methods are time-consuming, expensive, reliant on laboratories, and require skilled professionals, rendering them unaffordable for most farmers. The paper aims to propose a solution to prevent infection at the earliest stage for the benefit of farmers. A novel crop disease detection model deploying a deep convolutional generative adversarial network (DC-GAN) and with multidimensional feature compensation Residual Neural Network (MDFC-ResNet) and named as DC-GAN-MDFC–ResNet, which aims at fine grained disease identification system detects from three aspects, bacterial leaf blight, leaf streak and panicle blight. Initially the input data undergone preprocessing using the several processes like data improvement, data normalization, and Singular value decomposition (SVD) to reduce the negative influence that the data set has on the training of the model. When compared to traditional convolution models, the suggested DC-GAN-MDFC–ResNet architecture exhibits in terms of highest classification accuracy, Segmentation free methodology and training stability. The experiments done in this work using Plant Village dataset which show the proposed technique offering improved recognition with the rate of 95.99% accuracy and generating higher quality samples compared to other well-known deep learning models. [ABSTRACT FROM AUTHOR]

The BELSAR dataset consists of high-resolution multitemporal airborne mono- and bistatic fully-polarimetric synthetic aperture radar (SAR) data in L-band, alongside concurrent measurements of vegetation and soil biogeophysical variables measured in maize and winter wheat fields during the summer of 2018 in Belgium. Its collection was funded by the European Space Agency (ESA) to address the lack of publicly-accessible experimental datasets combining multistatic SAR and in situ measurements. As such, it offers an opportunity to advance the development of SAR remote sensing science and applications for agricultural monitoring and hydrology. This paper aims to facilitate its adoption and exploration by offering comprehensive documentation and integrating its multiple data sources into a unified, analysis-ready dataset. [ABSTRACT FROM AUTHOR]

Agricultural producers adopt management practices that positively and negatively affect the lives of non-producers in their communities. CRB has important environmental and human health implications, and local non-producers might have different perceptions and attitudes from agricultural producers about crop residue burning. In this paper, we use a multi-stakeholder approach to study the issue of crop residue burning (CRB). Survey data were collected from a sample of producers in Arkansas who burn crop residue and a sample of non-producers who resided in the same counties as the producers. Non-producers may not be willing to compensate producers at an amount that would reduce the use of CBR. Non-producers do not fully understand some of the benefits of CRB, like reduced tillage or equipment savings cost, and producers are less likely to perceive increased greenhouse gas (GHG) emissions as a negative externality associated with CRB. A multi-stakeholder approach can provide more depth and breadth to understanding complex decisions about farm management practices, and these results have implications for policies that incentivize adopting best farm management practices. [ABSTRACT FROM AUTHOR]

Agricultural carbon emission reduction is inseparable from discussions about government systems, especially in countries with distinctive institutional arrangements such as China. Environmental regulation may influence the impact of environmental decentralisation on agricultural carbon intensity. Using panel data from 30 provinces in mainland China from 2000 to 2015, this paper depicts the spatiotemporal pattern and dynamic evolution of environmental decentralisation, environmental regulation, and agricultural carbon intensity. It uses the spatial Durbin model to empirically study the internal connections between the three. The empirical results show that the enhancement of environmental decentralisation, environmental administrative decentralisation, environmental supervision decentralisation, and environmental monitoring decentralisation is not conducive to reducing agricultural carbon intensity. The results also show that the enhancement of the intensity of environmental regulation of "terminal governance type" and "front-end governance type" is conducive to reducing agricultural carbon intensity, but this effect has a negative "space spillover" phenomenon. Other findings are as follows: (1) the enhancement of the two types of environmental regulation intensity is conducive to changing the impact of environmental decentralisation on agricultural carbon intensity from a "grabbing-hand" to a "helping-hand"; (2) compared with balanced grain-producing and marketing areas and major grain-marketing areas, environmental decentralisation and two types of environmental regulations have a greater negative impact on the agricultural carbon intensity of major grain-producing areas. Therefore, the central government should change how it evaluates political performance; accelerate the construction of a diversified performance evaluation system; increase the weight of low-carbon, green, and other indicators that reflect the friendly development of agriculture in the system; and encourage local governments to adjust the direction of agricultural production. [ABSTRACT FROM AUTHOR]

The research of plant seeds has always been a focus of agricultural and forestry research, and seed identification is an indispensable part of it. With the continuous application of artificial intelligence technology in the field of agriculture, seed identification through computer vision can effectively promote the development of agricultural and forestry wisdom. Data is the foundation of computer vision, but there is a lack of suitable datasets in the agricultural field. In this paper, a seed dataset named LZUPSD is established. A device based on mobile phones and macro lenses was established to acquire images. The dataset contains 4496 images of 88 different seeds. This dataset can not only be used as data for training deep learning models in the computer field, but also provide important data support for agricultural and forestry research. As an important resource in this field, this dataset plays a positive role in modernizing agriculture and forestry. [ABSTRACT FROM AUTHOR]

Agricultural waste is an economically and environmentally beneficial precursor material for the development of activated carbon (AC) due to its low price, wide sources, and large production scale. This makes it a crucial field for agricultural waste treatment and AC development. This paper reviews the research progress of agricultural waste-based activated carbon (AWAC), including the sources and characteristics of agricultural waste, and the effects of raw material characteristics and activation conditions on the performance of AWAC. It analyzes the advantages and disadvantages of various preparation methods of AWAC. Additionally, it explains the research status of AWAC in wastewater treatment, gas adsorption, catalyst carrier, and energy carrier. Finally, this paper summarizes the advantages and limitations of using agricultural wastes as raw materials for AC, and provides a prospect for raw material development, process optimization, and application expansion, taking into account the shortcomings of current research. [ABSTRACT FROM AUTHOR]

Globally, food production for an ever-growing population is a well-known threat to the environment due to losses of excess reactive nitrogen (N) from agriculture. Since the 1980s, many countries of the Global North, such as Denmark, have successfully combatted N pollution in the aquatic environment by regulation and introduction of national agricultural one-size-fits-all mitigation measures. Despite this success, further reduction of the N load is required to meet the EU water directives demands, and implementation of additional targeted N regulation of agriculture has scientifically and politically been found to be a way forward. In this paper, we present a comprehensive concept to make future targeted N regulation successful environmentally and economically. The concept focus is on how and where to establish detailed maps of the groundwater denitrification potential (N retention) in areas, such as Denmark, covered by Quaternary deposits. Quaternary deposits are abundant in many parts of the world, and often feature very complex geological and geochemical architectures. We show that this subsurface complexity results in large local differences in groundwater N retention. Prioritization of the most complex areas for implementation of the new concept can be a cost-efficient way to achieve lower N impact on the aquatic environment. [ABSTRACT FROM AUTHOR]

In Ethiopia, large-scale agricultural investment (LAI) is promoted to foster adaptation to climate change among smallholder farmers by improving farming conditions and productivity. However, little has been known about this condition so far. Therefore, the aim of this paper is to study the implications of LAI for the capacity for climate change adaptation among smallholder farmers, based on a study conducted in Merti district in the Central Rift Valley of Ethiopia. An asset-based approach was used to measure the adaptive capacity of smallholders, via data collected from a household survey, undertaken among smallholder households who had, or had not, benefited, from LAI, and supported by key informant interviews and focus group discussions. Descriptive and inferential statistics and qualitative thematic analysis methods were used to analyze the data. The study found that the adaptive capacity of smallholder households was improved under the influence of LAI. LAI motivated and prompted farmers to use technologies that fostered climate change adaption, including the use of irrigation, improved crop varieties, chemical fertilizer, and engagement in alternative sources of income, which improved their food security status. But this technology and knowledge transfer mainly occurred through observation (learning by doing), not by capacity-building. However, both group of smallholders (within and without contact with the LAI) received similar benefits through government rural extension services. Therefore, the paper concludes that scaling up these technologies and alternative income sources could enhance the adaptive capacity of smallholders, but that LAIs should contribute actual technical and financial support as part of benefit-sharing or corporate responsibility. [ABSTRACT FROM AUTHOR]

In Finland, agriculture is practiced in variable growing conditions that are further challenged by climate change and under external pressure caused by international competition, changing consumer preferences and demands, and the renewal of the European Union Common Agriculture Policy and Paris Agreement climate targets. Futures studies in agriculture have focused on scenario building with expert panels, though usually without farmers' involvement. This study focuses on farmers' views of shaping the future of Finnish agriculture. It builds on the disaggregative Delphi method and combines interviews and a representative survey of Finnish farmers. The study is based on a bottom-up process in which a farmer panel of 20 farms defined their views of the future in a semi-structured thematic interview. The views were turned into statements in a structured survey sent to the second panel, which comprised all Finnish farmers who had received agricultural subsidies in 2016, with 4401 respondents. The results were analyzed using quantitative factor analysis, which produced five future images for Finnish agriculture. The images were shown to the original farmer panel for reflection. They considered the probability of each future image and their own role in it. Technology solves as a future image was most favored by farmers, followed by Ecological and specializing small-scale production image. Business-as-usual was the least likely future images according to the interviewed farmers. This paper describes the process and discusses both methodological benefits and pitfalls, as well as farmers' future views of the forthcoming decades. [ABSTRACT FROM AUTHOR]

Agricultural extension, as an important part of modern agriculture, can promote the scientific transformation of the traditional agricultural production model. This paper analysed the impact of university agricultural technology extension on efficient and sustainable agriculture using difference-in-differences model (DID). The results showed that university agricultural technology extension plays a facilitating role by influencing the coordinated development and green development dimensions in efficient and sustainable agriculture; there is a moderating effect of modern agricultural industrial parked in university agricultural technology extension and efficient and sustainable agriculture; there are significant differences in the impact of university agricultural technology extension on efficient and sustainable agriculture across regions and different levels of development. The findings have important implications for evaluating the effectiveness of current university agricultural extension policies and how to further promote university agricultural extension. The study also established an evaluation index system for efficient and sustainable agriculture, explored the mechanism of university agricultural extension in promoting efficient and sustainable agriculture, and enriched relevant theoretical research. [ABSTRACT FROM AUTHOR]

With the advancement of technology, the demand for increased production efficiency has gradually risen, leading to the emergence of new trends in agricultural automation and intelligence. Precision classification models play a crucial role in helping farmers accurately identify, classify, and process various agricultural products, thereby enhancing production efficiency and maximizing the economic value of agricultural products. The current MobileNetV2 network model is capable of performing the aforementioned tasks. However, it tends to exhibit recognition biases when identifying different subcategories within agricultural product varieties. To address this challenge, this paper introduces an improved MobileNetV2 convolutional neural network model. Firstly, inspired by the Inception module in GoogLeNet, we combine the improved Inception module with the original residual module, innovatively proposing a new Res-Inception module. Additionally, to further enhance the model's accuracy in detection tasks, we introduce an efficient multi-scale cross-space learning module (EMA) and embed it into the backbone structure of the network. Experimental results on the Fruit-360 dataset demonstrate that the improved MobileNetV2 outperforms the original MobileNetV2 in agricultural product classification tasks, with an accuracy increase of 1.86%. [ABSTRACT FROM AUTHOR]

Hybrid renewable energy sources are sustainable and eco-friendly and challenge the alternative sources of conventional energy production facilities. Pakistan's present energy dilemma is a serious impediment to its economic progress. This paper proposes a techno-economic analysis of commercial-scale photovoltaic (PV) systems for commercial agricultural farms in Punjab, Pakistan. A survey was conducted to gather the load data of the farms from major cities of north, center, and south Punjab. For the PV system design, the K-means approach was used to cluster data from 93 farmers into nine clusters with similar electrical consumption. A complete technical, economic, and environmental study is undertaken of the PV systems deployed in five selected cities. The most practical locations are Attock and Multan, which have the lowest Levelized Cost of Energy at 5.52 and 5.37 cents/kWh, respectively. PV installations are nevertheless technically, economically, and environmentally feasible for all cities. Throughout its lifespan, the planned PV system has the potential for Faisalabad and Chiniot to minimize 154 metric tons of emissions, resulting in a greener environment. [ABSTRACT FROM AUTHOR]

Agricultural manufacturers (abbreviation: Manufacturers) are conventionally committed to carbon emission reductions under government subsidies to achieve carbon neutrality goals, such as manufacturer subsidies and consumer subsidies. In addition, with the increase in consumers' low-carbon preferences, a growing number of retailers exert sales efforts to promote carbon emission reduction. In this paper, we apply a game theory approach and focus on investigating how sales effort and different government subsidies promote manufacturers' carbon emission reductions. We draw several important conclusions: First, we find that government subsidies are not always conducive to promoting carbon emissions reductions. Second, we show that consumer subsidies impose a higher (lower) financial burden on the government than manufacturer subsidies when carbon abatement costs are above (below) a threshold. Furthermore, retailers are preferable to impose sales effort under consumer subsidies when the cost of sales effort is higher. Third, the results suggest that manufacturer subsidies are more conducive to maximizing social welfare when consumer low-carbon preferences are small in the presence of retailer-imposed sales effort. Something counterintuitive is that we find that social welfare is more likely to be optimal without government subsidies as consumer low-carbon preferences increase. [ABSTRACT FROM AUTHOR]

The Filipino agricultural sector is exposed to multiple climatic, economic, and social risks that will likely intensify in the near future. Building agroecological resilience has been proposed to protect small-scale farmers' livelihoods and improve food security in the context of (unexpected) shocks and disruptions, and slow system changes such as climate change. This paper argues that commons-based seed production, based on collective management and ownership of seeds and varieties, can play a central role in building resilience capacities in smallholder communities. I explore this by applying an indicator-based framework to assess the contribution of the Filipino farmer network Magsasaka at Siyentipiko para sa Pag-unlad ng Agrikultura (MASIPAG) to agroecological resilience. I find that the networks' commons-based seed governance builds agroecological resilience in various ways. By equipping small-scale farmers with the tools to regain control over seed production and breeding, they become stewards of an actively evolving collection of varieties. The in situ maintenance and development of traditional, open-pollinated varieties and a network of diversified trial and backup farms build up buffering capacities and foster agrobiodiversity and local adaptation. A focus on regionally available natural resources reduces vulnerabilities to external factors. Adaptive capacities are strengthened through a high degree of flexibility and responsiveness achieved by self-organization and polycentric organizational structures. Broad participation, shared learning and spaces for experimentation support the development of farmers' capacities to respond to diverse challenges. Commons-based approaches to seed governance can thus strengthen agroecological resilience and long-term food security in smallholder agricultural systems. [ABSTRACT FROM AUTHOR]

One of the essential components of human civilization is agriculture. It helps the economy in addition to supplying food. Plant leaves or crops are vulnerable to different diseases during agricultural cultivation. The diseases halt the growth of their respective species. Early and precise detection and classification of the diseases may reduce the chance of additional damage to the plants. The detection and classification of these diseases have become serious problems. Farmers' typical way of predicting and classifying plant leaf diseases can be boring and erroneous. Problems may arise when attempting to predict the types of diseases manually. The inability to detect and classify plant diseases quickly may result in the destruction of crop plants, resulting in a significant decrease in products. Farmers that use computerized image processing methods in their fields can reduce losses and increase productivity. Numerous techniques have been adopted and applied in the detection and classification of plant diseases based on images of infected leaves or crops. Researchers have made significant progress in the detection and classification of diseases in the past by exploring various techniques. However, improvements are required as a result of reviews, new advancements, and discussions. The use of technology can significantly increase crop production all around the world. Previous research has determined the robustness of deep learning (DL) and machine learning (ML) techniques such as k-means clustering (KMC), naive Bayes (NB), feed-forward neural network (FFNN), support vector machine (SVM), k-nearest neighbor (KNN) classifier, fuzzy logic (FL), genetic algorithm (GA), artificial neural network (ANN), convolutional neural network (CNN), and so on. Here, from the DL and ML techniques that have been included in this particular study, CNNs are often the favored choice for image detection and classification due to their inherent capacity to autonomously acquire pertinent image features and grasp spatial hierarchies. Nevertheless, the selection between conventional ML and DL hinges upon the particular problem, the accessibility of data, and the computational capabilities accessible. Accordingly, in numerous advanced image detection and classification tasks, DL, mainly through CNNs, is preferred when ample data and computational resources are available and show good detection and classification effects on their datasets, but not on other datasets. Finally, in this paper, the author aims to keep future researchers up-to-date with the performances, evaluation metrics, and results of previously used techniques to detect and classify different forms of plant leaf or crop diseases using various image-processing techniques in the artificial intelligence (AI) field. [ABSTRACT FROM AUTHOR]

Water resources are the source of life for people to survive, especially in agriculture. Water resources provide necessary support for agricultural economic development and irrigation management. Using the panel data from 2013 to 2019, this paper uses the coupled coordination model to explore the degree of synergy between agricultural water resources efficiency (AWRE) and agricultural economic development level (AEDL), and analyzes the spatial correlation of coupling coordination degree (CCD) between the AWRE and AEDL, and further predicts the relationship between the two. The results are as follows: (1) There are superb variations in the CCD among provinces, and the diploma of coordination is greater in the south than in the north; (2) In the spatial correlation pattern, high–high agglomeration areas exhibit an growing trend, and the variety of low–low agglomeration areas is small; and (3) Through the prediction, it is found that in the further, the CCD will show a trend of slow increase in future. For purpose of the coordinated perfection of the two systems, we need to completely discover the connotation of coordinated improvement and promote regional cooperation. [ABSTRACT FROM AUTHOR]

The state of India was situated on fertile land and river deltas with appropriate agricultural land. In 2019, agricultural fields, primarily cropland, occupy more than 40% of the country's total surface area. Moreover, agricultural industry revenues less than 3% of the country's Gross Provincial Product (GPP). While manufacturing has become the country's major financial activity, accounting countries represent half of GPP's revenues. The objective of the study is to find a way to improve the financial profitability and efficiency of farming supply chain networks as follows: (1) Fixation of national-level targets for zonal-level groups information after assessment and forecasts that affect production and distribution of agriculture. (2) Producers risk can be reduced by directing people to multiple factory and industrialization options based on market assessment. (3) Insurance costs and reduction of bank borrowing by standardizing the connection between bankers and producers using the structure to centralise land information. (4), Stabilize the agricultural sector by looking at nearby potential destinations for production and regulation of the Public Distribution System (PDS) flow for the security stock. In this paper, the novel ML target prediction algorithm to inform the farmers about the market target product and improve the relationships between the farmer and bankers for centralizing the information about recent government plans. The crop prediction ML algorithm proposed to improve the revenue of agriculture field. [ABSTRACT FROM AUTHOR]

Carp is a key aquaculture species worldwide. The intensification of carp farming, aimed at meeting the high demand for protein sources for human consumption, has resulted in adverse effects such as poor water quality, increased stress, and disease outbreaks. While antibiotics have been utilized to mitigate these issues, their use poses risks to both public health and the environment. As a result, alternative and more sustainable practices have been adopted to manage the health of farmed carp, including the use of probiotics, prebiotics, phytobiotics, and vaccines to prevent disease outbreaks. Phytobiotics, being both cost-effective and abundant, have gained widespread acceptance. They offer various benefits in carp farming, such as improved growth performance, enhanced immune system, increased antioxidant capacity, stress alleviation from abiotic factors, and enhanced disease resistance. Currently, a focal point of research involves employing molecular approaches to assess the impacts of phytobiotics in aquatic animals. Gene expression, the process by which genetic information encoded is translated into function, along with transcription profiling, serves as a crucial tool for detecting changes in gene expression within cells. These changes provide valuable insights into the growth rate, immune system, and flesh quality of aquatic animals. This review delves into the positive impacts of phytobiotics on immune responses, growth, antioxidant capabilities, and flesh quality, all discerned through gene expression changes in carp species. Furthermore, this paper explores existing research gaps and outlines future prospects for the utilization of phytobiotics in aquaculture. [ABSTRACT FROM AUTHOR]

The China-Pakistan Economic Corridor (CPEC) is a cornerstone of China's Belt and Road Initiative (BRI). It aims to enhance regional trade and economic expansion in Pakistan. We adopted systematic review approach to investigate the agricultural trade narrative of the CPEC and identify future research avenues. Our study uses the Web of Sciences and Scopus database to extract the relevant literature. We executed the search query for 2013–2023. We followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement to identify the literature. The final analysis includes only 14 articles. We found that the literature mainly focuses on three topics, Gwadar port, road infrastructure, and agricultural complementarity and competitiveness. The review reveals the significant potential of CPEC on the agricultural trade of Pakistan. Based on the review, we point to avenues that could be considered in future research work to fully exploit the potential of CPEC in the agriculture sector of Pakistan. [ABSTRACT FROM AUTHOR]

Current climate issues pose a challenge to green development, so it is important to study the relationship between climate change and agricultural green total factor productivity (AGTFP). This paper constructs a panel smooth transition regression (PSTR) model using panel data from 2005 to 2019 in China to evaluate the relationship between climate change and agricultural green total factor productivity. Selecting agricultural operation scale, agricultural premium and green finance index as transfer variables, the PSTR model is constructed, respectively. The empirical results show that the impacts of different types of climate change on AGTFP have significant nonlinear characteristics. Smaller and larger agricultural operation scales can mitigate the negative impact of sunshine impact on AGTFP. Higher agricultural premiums and lower or higher green finance index can mitigate the negative impact of temperature shock and sunshine shock on AGTFP. Based on the research results, we put forward policy recommendations to encourage the expansion of agricultural operation scale, promote agricultural insurance and promote the construction of a green financial system. [ABSTRACT FROM AUTHOR]

Payment for watershed ecosystem services (PES) has been applied to water conservation and poverty reduction in an increasing number of developing countries. This study evaluates the effect of payment for watershed ecosystem services on farmers' income in conservation intervention areas using a difference-in-differences model and a panel dataset that covers 18 countries in the Xin'an River Basin in China for fourteen consecutive years (2006–2019). The results show that PES programs increase farmers' income and that the poverty reduction effect is sustainable. The PES programs mainly increase the farmers' income in conservation intervention areas through two paths: triggering the transfer of agricultural labor and promoting agricultural restructuring. PES programs are pro-poor and more conducive to increasing the income of farmers in upstream regions and counties with lower levels of economic development. This paper reveals the specific role played by PES in promoting rural poverty reduction in developing countries, providing insights into alleviating the contradiction between poverty and watershed ecosystem protection. [ABSTRACT FROM AUTHOR]

This paper explores agro-ecological implications of climate change and gendered adaptations in Northern Ghana. Drawing on feminist political ecology, sequential mix-method design was used to collect socio-economic and livelihood data through a survey of 300 smallholder farmers. Paired sample t test, simple linear regression and descriptive statistics were used to determined yield variation, gendered perception, and adaptive capacities to climate change. Three focus group discussions and 15 key informant interviews were conducted to validate the survey results. Findings show that climate change has threatened the sustainability of local livelihoods with a significant drop in food production over the last 15 years. Adaptation to climate change in the study area is polarized by gender, affecting productivity. This is because men perceive climate change and its impacts differently from women. Men were likely to adapt changing methods of farming and improvement in storage capacity, while women were likely to adapt migration and livelihood diversification. Policy toward building resilience to the impacts of climate change should recognize and adopt these gender adaptive capacities. [ABSTRACT FROM AUTHOR]

Meteorological variables are essential inputs for agricultural simulation models and the lack of measured data is a big challenge for the application of these models in many agricultural zones. Studies indicated that gridded meteorological datasets can be proper replacements for measured data. This paper aimed to examine a new gridded meteorological dataset namely CRU-JRA for crop modeling intents. The CRU-JRA is a 6-hourly dataset with a spatial resolution of 0.5° × 0.5° that was primarily constructed for modeling purposes. The CERES-Wheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) was used for the simulation of irrigated and rainfed wheat production systems in Iran. Results showed that the CRU-JRA maximum and minimum temperature values had a relatively fine accuracy with a normalized root mean square error (NRMSE) of 14% for the simulated grain yield. The performance of the CRU-JRA solar radiation values for the simulation of grain yield was similar with a NRMSE of 14.4%. The weakest performance was found for the CRU-JRA precipitation values with a NRMSE of 18.9%. Overall, the CRU-JRA dataset performed comparatively acceptable and similar to existing gridded meteorological datasets for crop modeling purposes in the study area, however further calibrations can improve the accuracy of the next versions of this dataset. More research is necessary for the investigation of the CRU-JRA dataset for agricultural modeling purposes across diverse climates. [ABSTRACT FROM AUTHOR]

Making decisions in the agriculture sector is a complex and challenging process, especially when aiming for sustainability. Decision makers (DMs) face multiple contradictions, particularly when trying to reduce environmental and social impacts without decreasing economic profits. Therefore, the objective of this paper is to support a group of DMs in choosing the most sustainable agricultural practices, extraction method, and waste recovery management method so that the entire product life cycle becomes circular and sustainable. To achieve this goal, we propose a new multi-criteria framework to help DMs to rank the different life cycle scenarios. The main contributions of this article are as following: first, to build sustainable scenarios under the circular economy thinking, the Structured Analysis and Design Technique has been integrated to close and valorize the waste loops for each scenario. Then, the 2-Tuple model was used to deal with uncertainty in expert knowledge application. Afterward, a synthetic dynamic weight algorithm was used to aggregate the DMs opinions. Next, the VIKOR method was extended with the 2-Tuple model to rank the sustainability of each scenario. To illustrate the applicability of the proposed approach, a case study of olive oil life cycle in Sfax-Tunisia was conducted. Also, we perform a sensitivity analysis to reveal the effect of the subjective parameter variations on the initially obtained ranking. Finally, the obtained results prove that the proposed method is more accurate and effective for the agricultural sustainability problem. [ABSTRACT FROM AUTHOR]

The advent of Internet of Things (IoT) in agriculture has revolutionized the way farmers monitor and manage their crops. IoT-enabled sensors can provide real-time data on various environmental parameters such as temperature, humidity, soil moisture, and crop growth, which can be used to make informed decisions and optimize crop yield. However, the vast amount of data generated by these sensors poses a significant challenge in terms of data processing and communication. To address this challenge, clustering is often used to group the sensors into clusters and elect a cluster head (CH) to communicate with the gateway node. The selection of an appropriate CH and the optimal path for data transmission are critical factors that affect the performance of the IoT system. In this paper, we propose a novel approach to optimize the CH selection and path selection using modified fuzzy logic, Whale optimization algorithm (WOA) and enhanced crow swarm optimization (ECSO). Fuzzy logic is used to evaluate the relevant parameters such as energy, distance, overhead, trust, and node degree to select the most suitable CH. ECSO is then employed to find the optimal path for data transmission based on the selected CH. We evaluate the proposed approach using simulation experiments in a smart agriculture scenario. The results show that the proposed approach outperforms existing approaches in terms of throughput, packet delivery ratio, delay, and energy efficiency. The proposed system demonstrates substantial advantages in terms of throughput, with a remarkable throughput value of 68 Mbps, outperforming the other methods, including EQSR (12.5 Mbps), E2S-DRL (60 Mbps), and OSEAP. The packet delivery ratio (PDR) is also significantly higher for the proposed system, reaching 90.9%. The proposed approach can significantly improve the performance of IoT-enabled smart agriculture systems, leading to better crop yield and higher profitability for farmers. The results of the simulation experiments demonstrate the superiority of the proposed approach over the existing one's throughput, packet delivery ratio (PDR), delay, and energy consumption efficiency is found in the result section. [ABSTRACT FROM AUTHOR]

The frequency of extreme events is increasing on a global scale due to various factors, accounting for natural disasters such as landslides, floods, and droughts. The Heilongjiang province, which is agriculturally important for China, has suffered from several extreme rainfall events in the past as a high-latitude plain region. Analysis of historical rainfall characteristics of the Heilongjiang region helps to understand and predict the behavior of extreme rainfall events and provides a reference for reducing agricultural economic losses. Precipitation data from 1974 to 2017 is selected to estimate whether rainfall series in Heilongjiang obey the Gumbel-Logistic Model. We compared the Gumbel-Logistic Model based on the data length of 5 years, 10 years, and 20 years with the original 44-year data to determine the appropriate data partition length and explore the reasons for errors. Combining the empirical frequency and KS test, this paper concludes that the proposed model is appropriate for the representation of the joint distribution of monthly extreme rainfall and annual rainfall. The proposed model improves the rainfall assessment of these two positively correlated variables in Heilongjiang. Univariate and bivariate return periods and correlation coefficients are derived as the basis for assessing the risk of extreme rainfall. The proposed model obtained from the 5-year data length has the largest error, while the distribution curve is progressively smoother as the data length increases. [ABSTRACT FROM AUTHOR]

Nature-based Solutions (NbS) are integral to efforts to keep global warming below 2°C in accordance with the United Nations' 2015 Paris Agreement on Climate Change. Yet the transboundary governance dimensions of NbS remain unclear and largely undocumented. In Southeast Asia, NbS have emphasised the conservation and/ or sustainable commodification of carbon sinks found in terrestrial and mangrove forests, seagrass meadows, peatlands and agricultural soils. Mostly project-driven and fixed-term, these "solutions" have often failed to meet their social and ecological objectives. Increasingly, they have added to cross-border problems of: (1) displaced carbon emissions; and (2) economic migration and societal dispossession. This perspective paper delineates a transboundary governance research agenda to mitigate these trade-offs and enhance the co-benefits of NbS in carbon sinks. Building on NbS literature, it identifies cross-sector, multi-scalar and interdisciplinary pathways to improve transboundary cooperation, inclusion and equity in carbon sink governance in varying Southeast Asian contexts. [ABSTRACT FROM AUTHOR]

In recent years, the increasing demand for knowledge services and the challenges of information overload have posed significant problems in delivering personalized and efficient agricultural knowledge services. This paper presents a comprehensive framework that addresses the issues of vague user positioning, serious privacy leakage, and low efficiency in personalized knowledge services within the national agricultural knowledge intelligent service cloud platform. The proposed framework utilizes privacy-protected user portraits based on generative adversarial nets (GAN) and leverages the TextCNN-LSTM algorithm for agricultural knowledge service prediction. By embedding labels into the algorithm and employing data obfuscation techniques, the framework achieves accurate inference of user behavior while preserving user privacy. Experimental results demonstrate the effectiveness and accuracy of the proposed framework, highlighting its potential for regional precise positioning and recommendation of personalized agricultural knowledge services. Experimental data shows that the average absolute error and root-mean-square error of this method are 1.1997 and 1.4143, respectively, and compared with MLP, TextCNN, and LSTM models, and it has higher prediction accuracy. In recent years, the increasing demand for knowledge services and the challenges of information overload have posed significant problems in delivering personalized and efficient agricultural knowledge services. [ABSTRACT FROM AUTHOR]

There are several standardized meteorological, hydrological, agricultural, and environmental indices for drought classification into a set of dry categories all of which are derived from the basic concept of standardized precipitation index (SPI). Almost all these indices are based on crisp (bivalent) logic where boundary limits between neighboring categories are numbers without transboundary inclusiveness. The SPI procedure depends on the standard normal (Gaussian) probability distribution function (PDF) with zero mean and standard deviation equal to one. This paper proposed fuzzification of the SPI limits among categories for the inclusion of more than one category with different membership degrees. This method is referred to as the fuzzy SPI (FSPI) procedure, which provides drought tracing possibility and categorization. At the end for a numerical value, one can defuzzify the fuzzy result through defuzzification methods. FSPI provides preliminary warning system in terms of two or more fuzzy rule propositions and categories. Thus, one can know the logical alternatives of the drought behavior of a given hydro-meteorology variable. The application of the FSPI is presented for New Jersey State wise and Istanbul annual precipitation records. [ABSTRACT FROM AUTHOR]