Nano/Micro-Structural Supramolecular Biopolymers: Innovative Networks with the Boundless Potential in Sustainable Agriculture

Sustainable agriculture plays a crucial role in meeting the growing global demand for food while minimizing adverse environmental impacts from the overuse of synthetic pesticides and conventional fertilizers.In this context,renewable biopolymers being more sustainable offer a viable solution to improve agricultural sustainability and production.Nano/micro-structural supramolecular biopolymers are among these innovative biopolymers that are much sought after for their unique features.These biomaterials have complex hierarchical structures,great stability,adjustable mechanical strength,stimuli-responsiveness,and self-healing attributes.Functional molecules may be added to their flexible structure,for enabling novel agricultural uses.This overview scrutinizes how nano/micro-structural supramolecular biopolymers may radically alter farming practices and solve lingering problems in agricultural sector namely improve agricultural production,soil health,and resource efficiency.Controlled bioactive ingredient released from biopolymers allows the tailored administration of agrochemicals,bioactive agents,and biostimulators as they enhance nutrient absorption,moisture retention,and root growth.Nano/micro-structural supramolecular biopolymers may protect crops by appending antimicrobials and biosensing entities while their eco-friendliness supports sustainable agriculture.Despite their potential,further studies are warranted to understand and optimize their usage in agricultural domain.This effort seeks to bridge the knowledge gap by investigating their applications,challenges,and future prospects in the agricultural sector.Through experimental investigations and theoretical modeling,this overview aims to provide valuable insights into the practical implementation and optimization of supramolecular biopolymers in sustainable agriculture,ultimately contributing to the development of innovative and eco-friendly solutions to enhance agricultural productivity while minimizing environmental impact.

Journal of Integrative Agriculture(JIA)Instruction to Authors

Aims and Scope Journal of IntegrativeAgriculture(JIA),formerly Agricuiltural Sciences in China(ASC),founded in 2002,is sponsored by Chinese Academy of Agricultural Sciences(CAAS),co-sponsored by Chinsese Association of Agricultural Science Societies(CAAsS).The latest IF is 4.8.JIA seeks to publish those papers that are influential and will significantly advance scientific understanding in agriculture fields worldwide.JIA publishes manuscripts in the categories of Commentary,Review,Research Article,Letter and Short Communication,focusing on the core subjects:Crop Science Horticulture·Plant ProtectionAnimal Science·Veterinary Medicine·Agro-ecosystem&Environment·Food Science·Agricultural Economics and Management·Agricultural Information Science.

Journal of Integrative Agriculture(JIA)Instruction to Authors

Description Journal of Integrative Agriculture(JIA),formerly Agricultural Sciences in China(ASC),founded in 2002,is sponsored by Chinese Academy of Agricultural Sciences(CAAS),co-sponsored by Chinese Association of Agricultural Science Societies(CAASS).

Journal of Integrative Agriculture Instruction to Authors

Description Journal of Integrative Agriculture(JIA),formerly Agricultural Sciences in China(ASC),founded in 2002,is sponsored by Chinese Academy of Agricultural Sciences(CAAS),co-sponsored by Chinese Association of Agricultural Science Societies(CAASS).JIA is a peer-reviewed and multi-disciplinary international journal and published monthly in English.JIA Editorial Board consists of 289 well-respected scholars of agricultural scientific fields.

Spatio-Temporal Agricultural Drought Quantification in a Rainfed Agriculture, Athi-Galana-Sabaki River Basin

This study employs a quantitative approach to comprehensively investigate the full propagation process of agricultural drought, focusing on pigeon peas (the most grown crop in the AGS Basin) planting seasonal variations. The study modelled seasonal variabilities in the seasonal Standardized Precipitation Index (SPI) and Standardized Agricultural Drought Index (SADI). To necessitate comparison, SADI and SPI were Normalized (from −1 to 1) as they had different ranges and hence could not be compared. From the seasonal indices, the pigeon peas planting season (July to September) was singled out as the most important season to study agricultural droughts. The planting season analysis selected all years with severe conditions (2008, 2009, 2010, 2011, 2017 and 2022) for spatial analysis. Spatial analysis revealed that most areas in the upstream part of the Basin and Coastal region in the lowlands experienced severe to extreme agricultural droughts in highlighted drought years. The modelled agricultural drought results were validated using yield data from two stations in the Basin. The results show that the model performed well with a Pearson Coefficient of 0.87 and a Root Mean Square Error of 0.29. This proactive approach aims to ensure food security, especially in scenarios where the Basin anticipates significantly reduced precipitation affecting water available for agriculture, enabling policymakers, water resource managers and agricultural sector stakeholders to equitably allocate resources and mitigate the effects of droughts in the most affected areas to significantly reduce the socioeconomic drought that is amplified by agricultural drought in rainfed agriculture river basins.

Journal of Integrative Agriculture(JIA)Instruction to Authors

Description Journal of Integrative Agriculture(JIA),formerly Agricultural Sciences in China(AsC),founded in 2002,is sponsored by Chinese Academy of Agricultural Sciences(CAAS),co-sponsored by Chinese Association of Agricultural Science Societies(CAASS).JIAis a peer-reviewed and multi-disciplinary international journal and published monthly in English.JIA Editorial Board consists of 289 well-respected scholars of agricultural scientific fields.

Journal of Integrative Agriculture(JIA)

Instruction to Authors Aims and Scope Journal of Integrative Agriculture(JIA),formerlyAgricultural Sciences in China(ASC),founded in 2002,is sponsored by Chinese Academy of Agricultural Sciences(CAAS),co-sponsored by Chinsese Association of Agricultural Science Societies(CAAsS).

Security Analysis in Smart Agriculture: Insights from a Cyber-Physical System Application

Smart agriculture modifies traditional farming practices,and offers innovative approaches to boost production and sustainability by leveraging contemporary technologies.In today’s world where technology is everything,these technologies are utilized to streamline regular tasks and procedures in agriculture,one of the largest and most significant industries in every nation.This research paper stands out from existing literature on smart agriculture security by providing a comprehensive analysis and examination of security issues within smart agriculture systems.Divided into three main sections-security analysis,system architecture and design and risk assessment of Cyber-Physical Systems(CPS)applications-the study delves into various elements crucial for smart farming,such as data sources,infrastructure components,communication protocols,and the roles of different stakeholders such as farmers,agricultural scientists and researchers,technology providers,government agencies,consumers and many others.In contrast to earlier research,this work analyzes the resilience of smart agriculture systems using approaches such as threat modeling,penetration testing,and vulnerability assessments.Important discoveries highlight the concerns connected to unsecured communication protocols,possible threats from malevolent actors,and vulnerabilities in IoT devices.Furthermore,the study suggests enhancements for CPS applications,such as strong access controls,intrusion detection systems,and encryption protocols.In addition,risk assessment techniques are applied to prioritize mitigation tactics and detect potential hazards,addressing issues like data breaches,system outages,and automated farming process sabotage.The research sets itself apart even more by presenting a prototype CPS application that makes use of a digital temperature sensor.This application was first created using a Tinkercad simulator and then using actual hardware with Arduino boards.The CPS application’s defenses against potential threats and vulnerabilities are strengthened by this integrated approach,which distinguishes this research for its depth and usefulness in the field of smart agriculture security.

Irrigated Agriculture Facing the Challenge of Climate Change: Adaptation Strategies for Farmers in the Irrigated Perimeters of Môle Saint-Nicolas, Haiti

Môle Saint-Nicolas, like all other communes in the Republic of Haiti, faces increasing climate variability, impacting agricultural production and water resources. Consequently, there is a pressing need for adaptation to these climatic changes. This research aims to showcase the adaptation strategies deployed by farmers to cope with the increasing climate variability. Surveys were conducted through group and individual discussions with a randomly selected cohort of 150 farmers. Two types of analysis were performed: quantitative and qualitative. The quantitative data analysis was conducted using Statistical Package for the Social Sciences (SPSS) software. The findings reveal that farmers have perceived changes in rainfall patterns, temperature, wind, and their environment. These changes manifest as irregular rainfall, higher temperatures, prolonged drought periods, violent winds accompanied by rain, premature cessation of rains, and reduced flow from water sources. In response, the most common adaptation strategies adopted include selecting new cultivars, early-maturing varieties, crop rotation and diversification, canal dredging, new soil preparation methods, upstream water source protection, and micro-watershed management. The significance of this research lies in its contribution to enhancing farmers’ adaptive capacities by alerting stakeholders in the irrigated perimeters about the consequences of climate change, thereby incorporating the real needs of farmers in future projects.

Sorghum Productivity and Its Farming Feasibility in Dryland Agriculture:Genotypic and Planting Distance Insights

Sorghum(Sorghum bicolor L.Moench)is an essential food crop for more than 750 million people in tropical and sub-tropical dry climates of Africa,India,and Latin America.The domestic sorghum market in Indonesia is still limited to the eastern region(East Nusa Tenggara,West Nusa Tenggara,Java,and South Sulawesi).Therefore,it is crucial to carry out sorghum research on drylands.This research aimed to investigate the effect of sorghum genotype and planting distance and their interaction toward growth and sorghum’s productivity in the Gunungkidul dryland,Yogyakarta,Indonesia.In addition,the farm business analysis,including the feasibility of sorghum farming,was also examined.The research used a randomized complete block design(RCBD),arranged in a 5×4 factorial with 3 replicates.The first treatment consisted of 5 varieties(2 high-yielding varieties(Bioguma 1 and Kawali)and 3 local sorghum varieties(Plonco,Ketan Merah,and Hitam Wareng)).The second treatment consisted of 4 levels of planting distance,namely 50×20 cm,60×20 cm,70×15 cm,and 70×20×20 cm.Analysis of variance was used to analyze the data,where Duncan’s multiple range test(DMRT)was used post hoc.Plant height,panicle height,panicle width,panicle weight,stover weight,grains weight/plot,and productivity were significantly affected by sorghum varieties(p<0.05).However,there was no significant effect from the planting distance treatment and no interaction between planting distance and varietal treatments.Ketan Merah had the highest height,panicle length,and panicle width,while Bioguma 1 had the highest stover weight,panicle weight,grain weight/plot,and productivity.There was a significant linear regression equation,i.e.,productivity=0.0054–0.0003 panicle height+0.4163 grains weight/plot.Our findings on farm business analysis suggested that four out of five tested sorghum varieties were feasible to grow,except for the Ketan Merah variety.The most economically profitable sorghum variety to grow in Gunungkidul dryland was Bioguma 1.

Application of the Electrical Resistivity Method in Precision Agriculture of Coffee Cultivation, in the Kabiri Area, Ícolo e Bengo Township, Luanda, Angola

The electrical resistivity method is a geophysical tool used to characterize the subsoil and can provide an important information for precision agriculture. The lack of knowledge about agronomic properties of the soil tends to affect the agricultural coffee production system. Therefore, research related to geoelectrical properties of soil such as resistivity for characterization the region of the study for coffee cultivation purposes can improve and optimize the production. This resistivity method allows to investigate the subsurface through different techniques: 1D vertical electrical sounding and electrical imaging. The acquisition of data using these techniques permitted the creation of 2D resistivity cross section from the study area. The geoelectrical data was acquired by using a resistivity meter equipment and was processed in different softwares. The results of the geoelectrical characterization from 1D resistivity model and 2D resistivity electrical sections show that in the study area of Kabiri, there are 8 varieties of geoelectrical layers with different resistivity or conductivity. Near survey in the study area, the lowest resistivity is around 0.322 Ω·m, while the highest is about 92.1 Ω·m. These values illustrated where is possible to plant coffee for suggestion of specific fertilization plan for some area to improve the cultivation.

Understanding factors affecting non-participants’interest in community-supported agriculture

Community-supported agriculture(CSA)has emerged as a viable solution for addressing the agricultural challenges faced by countries like Indonesia.This study uses the wellestablished unified theory of acceptance and use of technology(UTAUT2)model to examine the interest in CSA of potential customers in Indonesia.A standardized questionnaire was distributed to 1200 respondents,and the data were analyzed using structural equation model-partial least square(SEM-PLS)in SmartPLS 4.0 software.The results capture potential CSA consumer interest and will help to improve CSA development strategies in Indonesia.The model explains 44.4%of customers’intentions,and identifies performance expectancy as the decisive factor in customers’willingness to participate in CSA.Performance expectancy(0.292),hedonic motivation(0.262),social influence(0.259),and facilitating conditions(0.086)positively influence customers’interest in participating in a CSA program.The adoption of CSA programs by both farmers and customers could be increased by implementing regulations that provide tax incentives and subsidies,offering training on sustainable farming practices,facilitating the establishment of distribution channels,and establishing guidelines for fair price and quality standards.This study shows the high potential for the implementation of CSA in Indonesia.It could also be used as a foundation for the development of new policies regarding sustainable agriculture markets in Indonesia.

Planetary health risks in urban agriculture

Urban agriculture is gaining recognition for its potential contributions to environmental resilience and climate change adaptation,providing advantages such as urban greening,reduced heat island effects,and decreased air pollution.Moreover,it indirectly supports communities during weather events and natural disasters,ensuring food security and fostering community cohesion.However,concerns about planetary health risks persist in highly urbanized and climate-affected areas.Employing electronic databases such as Web of Science and PubMed and adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines,we identified 55 relevant papers to comprehend the planetary health risks associated with urban agriculture,The literature review identified five distinct health risks related to urban agriculture:(1)trace metal risks in urban farms;(2)health risks associated with wastewater irrigation;(3)zoonotic risks;(4)other health risks;and(5)social and economic risks.The study highlights that urban agriculture,while emphasizing environmental benefits,particularly raises concerns about trace metal bioaccumulation in soil and vegetables,posing health risks for populations.Other well studied risks included wastewater irrigation and backyard livestock farming.The main limitations in the available literature were in studying infectious diseases and antibiotic resistance associated with urban agriculture.

Fertilization and Soil Ploughing Practices under Changing Physical Environment Lead to Soil Organic Carbon Dynamics under Conservation Agriculture in Rice-Wheat Cropping System: A Scoping Review

Ploughing and fertilization practices in rice-wheat system have deteriorated the soil carbon (C) pools. Conservation agriculture (CA) based management approaches have proven to enhance C sequestration and reverse the loss of soil-organic-carbon (SOC), which further enhances soil fertility. Different fractions of SOC pools react to the alterations in management practices and indicate changes in SOC dynamics as compared to total C in the soil. Higher SOC levels in soil have been observed in case of reduced/no-till (NT) practices than conventional tillage (CT). However, between CT and zero tillage/NT, total SOC stocks diminished with an increase in soil depth, which demonstrated that the benefits of SOC are more pronounced in the topsoil under NT. Soil aggregation provides physical protection to C associated with different-sized particles, thus, the improvement in soil aggregation through CA is an effective way to mitigate soil C loss. Along with less soil disturbance, residual management, suitable crop rotation, rational application of manures and fertilizers, and integrated nutrient management have been found to be effective in not only improving soil C stock but also enhancing the soil health and productivity. Thus, CA can be considered as a potential method in the build-up of SOC of soil in rice-wheat system.

Coping with the Impact of Climate Change: A Dive into Precision Agriculture in the United States

With the continued increase in the number of people that are food insecure globally, which could be increasing because of the ongoing Ukraine-Russia war, leading to reduction in international agribusinesses, coupled with drastic climate change exacerbating the problem of food insecurity, there is a constant need to come up with innovative approaches to solve this global issue. In this article, we articulated how precision agriculture can be a tool for ensuring food security in the United States. This study aims to reiterate the significance of precision agriculture in solving global food insecurity.

Assessment of water quality suitability for agriculture in a potentially leachate-contaminated region

Dump sites pose a significant threat to groundwater resources due to the possibility of leachate leakage into the aquifer.This study investigated the impact of leachate on groundwater quality in the southwest region of Zanjan City,Iran,where groundwater is utilized for drinking,agricultural,and industrial purposes.We analyzed 18 parameters of dump site leachate,including physicochemical,heavy metals,and bacterial properties,alongside 13 groundwater samples.Sampling was conducted twice,in November 2020 and June 2021,within a five-kilometer radius of the Zanjan dump site.We utilized the Leachate Pollution Index(LPI)to evaluate potential groundwater contamination by leachate leakage from nearby dumpsite.Additionally,due to the predominant agricultural activities in the study area,various indices were employed to assess groundwater quality for agricultural purposes,such as Sodium Adsorption Ratio(SAR),Soluble Sodium Index(SSI),Kelly Ratio(KR),and Permeability Index(PI).Our analysis revealed no observed contamination related to leachate in the study area according to the LPI results.However,with the persistent pollution threat,implementing sanitary measures at the dump site is crucial to prevent potential impacts on groundwater quality.Moreover,the assessment of groundwater quality adequacy for irrigation yielded satisfactory results for SAR,KR,and PI indices.However,during both the dry(November 2020)and wet seasons(June 2021),the SSP index indicated that 80%of the samples were not classified as excellent,suggesting groundwater may not be suitable for agriculture.Overal,our qualitative study highlights the significant impact of the dry season on groundwater quality in the study area,attributed to elevated concentration levels of the investigated parameters within groundwater sources during the dry season.

Construction and Application Exploration of Smart Agriculture Based on Big Data Technology

Big data finds extensive application and many fields.It brings new opportunities for the development of agriculture.Using big data technology to promote the development of smart agriculture can greatly improve the effect of agricultural planting,reduce the input of manpower and material resources,and lay a solid foundation for the realization of agricultural modernization.In this regard,this paper briefly analyzes the construction and application of smart agriculture based on big data technology,hoping to provide some valuable insights for readers.

Review on the Impact of Climate Change on Great Lakes Region’s Agriculture and Water Resources

This study investigates the multifaceted impacts of climate change on the Midwest region of the United States, particularly the rising temperatures and precipitation brought about by hot weather activities and technological advances since the 19th century. From 1900 to 2010, temperatures in the Midwest rose by an average of 1.5 degrees Fahrenheit, which would also lead to an increase in greenhouse gas emissions. Precipitation is also expected to increase due to increased storm activity and changes in regional weather patterns. This paper explores the impact of these changes on urban and agricultural areas. In urban areas such as the city of Chicago, runoff from the increasing impervious surface areas poses challenges to the drainage system, and agriculture areas are challenged by soil erosion, nutrient loss, and fewer planting days due to excessive rainfall. Sustainable solutions such as no-till agriculture and the creation of grassland zones are discussed. Using historical data, recent climate studies and projections, the paper Outlines ways to enhance the Midwest’s ecology and resilience to climate change.

Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies

This paper presents a comprehensive review of emerging technologies for the internet of things(IoT)-based smart agriculture.We begin by summarizing the existing surveys and describing emergent technologies for the agricultural IoT,such as unmanned aerial vehicles,wireless technologies,open-source IoT platforms,software defined networking(SDN),network function virtualization(NFV)technologies,cloud/fog computing,and middleware platforms.We also provide a classification of IoT applications for smart agriculture into seven categories:including smart monitoring,smart water management,agrochemicals applications,disease management,smart harvesting,supply chain management,and smart agricultural practices.Moreover,we provide a taxonomy and a side-by-side comparison of the state-ofthe-art methods toward supply chain management based on the blockchain technology for agricultural IoTs.Furthermore,we present real projects that use most of the aforementioned technologies,which demonstrate their great performance in the field of smart agriculture.Finally,we highlight open research challenges and discuss possible future research directions for agricultural IoTs.

Computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture

Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)approaches.Weed plays a vital role in influencing crop productivity.The wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are increased.Since the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity,this study presents a novel computer vision and deep learning based weed detection and classification(CVDL-WDC)model for precision agriculture.The proposed CVDL-WDC technique intends to prop-erly discriminate the plants as well as weeds.The proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine(ELM)based weed classification.The parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization(FFO)algorithm.A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced out-comes over its recent approaches interms of several measures.