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Soil salinity and freshwater scarcity are among the major global threats to sustainable development owing to their adverse impacts on agricultural productivity especially in arid and semi-arid regions. There is a need to find sustainable alternatives such as salt-tolerant crops and fish to improve people's livelihoods in marginal areas. This study aimed to maximize the growth and yield of striped catfish (Pangasianodon hypophthalmus) and quinoa (Chenopodium quinoa) cultivated under a biosaline integrated aquaculture-agriculture system. The study was laid in a randomized completely block design of three saline effluent treatments under three replicates: 5000 ppm (T1), 10,000 ppm (T2), 15,000 ppm (T3), and control (T0). Agro-morphological and physiological attributes of quinoa were measured. The crop yield in biomass and mineral element composition was also studied. Additionally, fish growth performance parameters such as feed intake and efficiency, growth, and survival rate were also calculated. Our results indicated that irrigating quinoa with saline aquaculture effluents above 10,000 ppm enhanced the plant growth, yield, and nutrient content of seeds. Furthermore, rearing striped catfish in saline water reaching up to 15,000 ppm did not have adverse impacts on the growth and survival of fish. Overall, integrating catfish and quinoa production under a salinity regime of 10,000 ppm could be a potential solution to ensuring alternative food sources in marginal areas.

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The complex-enhanced hierarchical relationship among multiple stakeholders in the water-environment-agriculture interactive system has been overlooked. This study develops a leader-follower-enhanced framework (named as FCMLP) that integrates variable-weight combination prediction model, multi-level programming, and fuzzy credibility constrained programming, which can effectively address the above problems under uncertainties. Five water ecological carrying capacity (WECC) statuses are treated as a critical constraint into the modeling framework to improve the accuracy of decision-making. An interactive fuzzy satisfaction algorithm is advanced for solving this multi-level problem, in which COD discharge minimization, economic benefits maximization, and grain yield maximization are taken as the upper-, middle-, and lower-level goals, respectively. The framework is applied to plan the cross-regional water-environment-agriculture interactive system in the Beijing-Tianjin-Hebei and Yangtze River Economic Belt. Solutions reveal that increased WECC status and credibility level would decrease 1.40%-1.74%, 0.71%-9.61%, and 1.63%-2.26% of water resources allocation, COD emissions, and economic benefits, respectively. Crop area and grain yield would dramatically decline by 4.13%-4.46% and 4.03%-4.67% when a credibility level increases from 0.8 to 1, respectively. The overall satisfactory degree would range from 0.58 to 0.70, which illustrates interactive decision-making process of multiple stakeholders. Significant differences can be observed in the optimized schemes of water resources allocation and environmental-economic-agricultural performances among various models. The amounts of allocated water resources, pollutant discharge, and economic output from the FCMLP model would be respectively 11.30%-13.45%, 14.90%-15.21%, and 73.12%-73.48% higher than those from the environment- and agriculture-oriented schemes, yet 13.81%, 32.05%, and 15.29% lower than those from the economy-oriented scheme. Some water adaptability countermeasures are given for ensuring the scientific operation of the South-to-North Water Transfer Project and alleviating conflicts between water source and receiving areas. Further exploration of the optimization scheme of water-environment-energy-agriculture system driven by climate change is still required for guaranteeing the dynamic balance of regional resources.

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The agricultural sector is pivotal to food security and economic stability worldwide. Corn holds particular significance in the global food industry, especially in developing countries where agriculture is a cornerstone of the economy. However, corn crops are vulnerable to various diseases that can significantly reduce yields. Early detection and precise classification of these diseases are crucial to prevent damage and ensure high crop productivity. This study leverages the VGG16 deep learning (DL) model to classify corn leaves into four categories: healthy, blight, gray spot, and common rust. Despite the efficacy of DL models, they often face challenges related to the explainability of their decision-making processes. To address this, Layer-wise Relevance Propagation (LRP) is employed to enhance the model's transparency by generating intuitive and human-readable heat maps of input images. The proposed VGG16 model, augmented with LRP, outperformed previous state-of-the-art models in classifying corn leaf diseases. Simulation results demonstrated that the model not only achieved high accuracy but also provided interpretable results, highlighting critical regions in the images used for classification. By generating human-readable explanations, this approach ensures greater transparency and reliability in model performance, aiding farmers in improving their crop yields.

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Freshwater scarcity, salinity, and poor soil fertility are the major challenges affecting both food and feed productions in arid and semi-arid regions of the world. Utilization of brackish water in the production of saline-tolerant fish and valuable field crops under an integrated system is promising in the maximization of yield per crop. The aim of this study, therefore, was to (1) assess the effect of saline aquaculture wastewater on the growth, yield, forage quality, and nutritive composition of sorghum seeds and (2) assess the effect of different water qualities on the survival, growth performance, and health status of Pangasianodon hypophthalmus. The experiment was conducted in a randomized completely block design of four salinity treatments with three replicates, i.e., control (freshwater mixed with inorganic fertilizers), 5000 ppm, 10,000 ppm, and 15,000 ppm. Our results indicated that although the control exhibited the highest growth (plant height, leaf number, internode number, leaf area, and soil-plant analysis development), grain, and forage yield, no significant differences were noted among the treatments. Likewise, no significant difference in the grain nutrient composition was noted among all the treatments. Assessment of the forage quality revealed improved crude protein content in the control compared to the saline treatments. However, no significant differences in the leaves and stalks fiber fractions were noted among all the treatments. Furthermore, rumen fermentation in terms of in vitro digestibility indicated no significant differences in the in vitro digestible dry matter, digestible organic matter, metabolic energy, net energy, microbial protein, short-chain fatty acids, and total dissolved nutrients among the treatments. However, rearing P. hypophthalmus in water salinities exceeding 10,000 ppm reduced the growth performance and health status of fish. Therefore, the integration of sorghum and P. hypophthalmus production in water salinities not exceeding 5000 ppm is a viable alternative to maximize brackish water productivity in freshwater-scarce regions.

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Smart agricultural systems have received a great deal of interest in recent years because of their potential for improving the efficiency and productivity of farming practices. These systems gather and analyze environmental data such as temperature, soil moisture, humidity, etc., using sensor networks and Internet of Things (IoT) devices. This information can then be utilized to improve crop growth, identify plant illnesses, and minimize water usage. However, dealing with data complexity and dynamism can be difficult when using traditional processing methods. As a solution to this, we offer a novel framework that combines Machine Learning (ML) with a Reinforcement Learning (RL) algorithm to optimize traffic routing inside Software-Defined Networks (SDN) through traffic classifications. ML models such as Logistic Regression (LR), Random Forest (RF), k-nearest Neighbours (KNN), Support Vector Machines (SVM), Naive Bayes (NB), and Decision Trees (DT) are used to categorize data traffic into emergency, normal, and on-demand. The basic version of RL, i.e., the Q-learning (QL) algorithm, is utilized alongside the SDN paradigm to optimize routing based on traffic classes. It is worth mentioning that RF and DT outperform the other ML models in terms of accuracy. Our results illustrate the importance of the suggested technique in optimizing traffic routing in SDN environments. Integrating ML-based data classification with the QL method improves resource allocation, reduces latency, and improves the delivery of emergency traffic. The versatility of SDN facilitates the adaption of routing algorithms depending on real-time changes in network circumstances and traffic characteristics.

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BACKGROUND: Soil salinity, soil infertility, and freshwater scarcity are among the major constraints affecting agricultural ecosystems in arid and semi-arid regions of the world. Hence, there is a need to look for salt-tolerant crops and fish that can be successfully cultivated and reared respectively in such harsh environments. The implementation of biosaline integrated aquaculture-agriculture systems (IAAS) utilizing both salt-tolerant crops and fish could improve food and feed production in arid and semi-arid regions. This study, therefore, investigated the influence of salinity on the biometric traits of striped catfish (Pangasianodon hypophthalmus) and barley (Hordeum vulgare) under an IAAS. METHOD: The experiment followed a randomized completely block design of three salinity treatments with three replicates namely, T0: Control (freshwater mixed with chemical fertilizers), T1: 5,000 ppm, T2: 10,000 ppm, and T3: 15,000 ppm. RESULTS: Irrigating barley with saline aquaculture wastewater at different salinities (5,000 ppm, 10,000 ppm, and 15,000 ppm) did not significantly affect the agro-morphological parameters (internode number per plant, stalk diameter, leaf number per plant, leaf area index, and leaf chlorophyll content (SPAD)) of the plants at 90 days after sowing. Moreover, the forage yield and forage quality in terms of fiber fraction, nutrient composition, and in vitro digestibility of the forage biomass were not severely affected by high salinity compared to the control (freshwater and inorganic fertilizers). Our results also showed that rearing striped catfish in saline water not exceeding 10,000 ppm did not negatively impact the growth performance (final weight, body weight gain, feed conversion ratio, specific growth rate, condition factor, and survival) and the health status of the fish. CONCLUSIONS: The integration of striped catfish and barley production in water salinities below 15,000 ppm could be a feasible alternative in safeguarding food and feed security in regions affected by soil salinity, soil infertility, and freshwater scarcity. Moreover, the salinity regime of 5,000 ppm could bring higher economic gains to farmers regarding higher crop yields (fish and forage yield).

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Intensive farming practices have increased the consumption of chemical-based pesticides and fertilizers thereby creating health issues for humans and animals and also causing a deterioration in the natural ecosystem. The promotion of biomaterials synthesis could potentially lead to the replacement of synthetic products and improve soil fertility, protect plants from pathogen attacks, and enhance the productivity of the agricultural sector resulting in less environmental pollution. Microbial bioengineering involving the use and improvement of encapsulation using polysaccharides has the required potential to address environmental issues and promote green chemistry. This article describes various encapsulation techniques and polysaccharides which have an immense applicable capability to encapsulate microbial cells. The review elucidates the factors that may result in a reduced viable cell count during encapsulation, particularly using the spray drying method, where a high temperature is required to dry the suspension, this may damage the microbial cells. The environmental advantage of the application of polysaccharides as carriers of beneficial microorganisms, which do not pose a risk for soil due to their full biodegradability, was also shown. The encapsulated microbial cells may assist in addressing certain environmental problems such as ameliorating the unfavourable effects of plant pests and pathogens, and promoting agricultural sustainability.

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In recent decades, networked smart devices and cutting-edge technology have been exploited in many applications for the improvement of agriculture. The deployment of smart sensors and intelligent farming techniques supports real-time information gathering for the agriculture sector and decreases the burden on farmers. Many solutions have been presented to automate the agriculture system using IoT networks; however, the identification of redundant data traffic is one of the most significant research problems. Additionally, farmers do not obtain the information they need in time, such as data on water pressure and soil conditions. Thus, these solutions consequently reduce the production rates and increase costs for farmers. Moreover, controlling all agricultural operations in a controlled manner should also be considered in developing intelligent solutions. Therefore, this study proposes a framework for a system that combines fog computing with smart farming and effectively controls network traffic. Firstly, the proposed framework efficiently monitors redundant information and avoids the inefficient use of communication bandwidth. It also controls the number of re-transmissions in the case of malicious actions and efficiently utilizes the network's resources. Second, a trustworthy chain is built between agricultural sensors by utilizing the fog nodes to address security issues and increase reliability by preventing malicious communication. Through extensive simulation-based experiments, the proposed framework revealed an improved performance for energy efficiency, security, and network connectivity in comparison to other related works.

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Building an intelligent interface between plants and the environment is of paramount importance for real-time monitoring of the health status of plants, especially promising for high agricultural yield. Although the advancement of various sensors allows automated monitoring, developing a sustainable power supply for these electronic devices remains a formidable challenge. Herein, a waterproof and breathable triboelectric nanogenerator (WB-TENG) is designed based on poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) nanofibers embedded with fluorinated carbon nanotubes (F-CNT) microspheres, which was realized by simultaneous electrospinning and electrospraying, respectively. Using carbon nanotubes (CNT) as the electrode, the WB-TENG shows micro-to-nano hierarchical porous structures and high electrostatic adhesion, exhibiting a high output power density of 330.6 µW cm-2, breathability, and hydrophobicity. Besides, the WB-TENG can be conformally self-attached to plant leaves without sacrificing the intrinsic physiological activities of plants, capable of harvesting typical environmental energy from wind and raindrops. Results demonstrate that the WB-TENG can serve as a sustainable power supply for a wireless plant sensor, enabling real-time monitoring of the health status of plants. This work realizes the concept of constructing a plant compatible TENG with environment adaptivity and energy scavenging ability, showing great potential in building a self-powered agriculture system.

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Integrons are hot spots for acquiring gene cassettes from the environment and play a major role in the bacterial evolution and dissemination of antimicrobial resistance (AMR), thus posing a serious threat. There are currently studies on integrons and antibiotic resistance genes; however, the presence and association of integrons in different agricultural crops and their subsequent dissemination and role in AMR have not been reported previously. This study examines the abundance of integrons, their gene cassette diversity in various crop soils, and their role in the dissemination of AMR in the southern region of China. Samples from different agri-crop soil, such as rice (R.S), sugarcane (S.S), citrus (C.S), banana (B.S), agricultural runoff (the point where the runoff of all sites meet (R.O)), and wild (non-agricultural) soil (W.S), were collected. Quantitative PCR was used to determine the abundance of integrons, and clone libraries were constructed to examine the gene cassette arrays. All the tested samples were found positive for Class-I (CL1) integrons and revealed a higher concentration and higher relative abundance of R.S than the others, with the least found at the W.S site. The W.S CL1 cassette arrays were found empty, and no putative conserved domains were found. The R.O was found to contain a high number of gene cassettes with various functions, while the smallest number of gene cassettes was found in the S.S among the crop soils. Most of the gene cassettes presented by the R.O were primarily shared with other sites, and the antibiotic-resistant genes were consistently observed to be dominant. The constructed clone libraries represented a diverse gene cassette array with 16% novel gene cassettes that play a vital role in pathogenesis, transportation, biosynthesis, and AMR. Most resistance-related gene cassettes were associated with the genes encoding resistance to quaternary ammonium compound (QAC) and aminoglycosides. This study highlights the significant differences in the abundance of integrons among various agricultural soils and offers deep insight into the pools of gene cassettes that play a key role in the dissemination of integrons and AMR.

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Poor wastewater management that results from a lack of appropriate sanitation infrastructure contributes to increasing health risks in urban areas in Côte d'Ivoire. We assessed the health risks associated with the use of wastewater for watering salad destined for human consumption, to help local authorities in developing appropriate risk mitigation measures for Yamoussoukro, the political capital of Côte d'Ivoire. We applied a stochastic approach based on quantitative microbiological risk assessment (QMRA), focusing on wastewater for farming activities and salad consumption at the household level. Farming activities rely on a large degree on contaminated water and are conducted without any protection. The QMRA highlights that the poor quality of watering water increased the microbiological risk of the two assessed groups of urban farmers and individual households. The annual risk of infection due to watering wastewater in the city is estimated at 0.01 per person per year (pppy) for Giardia lamblia and 0.2 pppy for Escherichia coli O157:H7. The annual risk from salad consumption is 0.01 pppy for G. lamblia and 0.9 pppy for E. coli O157:H7. Both the annual risks from farming activities and salad consumption were higher than the tolerable standard of risk of 10-4 pppy as defined by the World Health Organization. There is a need to conduct a risk analysis and a cost-effectiveness study on intervention to improve public health and the livelihoods of the producers which are women in majority in Yamoussoukro.

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As diversidades de clima e de manejo interferem no desenvolvimento e na produtividade vegetal, uma vez que essas características estão diretamente correlacionadas ao genótipo e ao ambiente de cultivo. Neste estudo, o objetivo foi estimar os efeitos do tipo de manejo sob o desempenho agronômico de 12 linhas endogâmicas recombinadas (F7) de pimenta (Capsicum annuum L.), originadas do cruzamento entre um acesso de pimentão suscetível à mancha-bacteriana (UENF 1421) e um acesso de pimenta resistente (UENF 1381). Dois experimentos foram conduzidos, sendo um deles em condições de campo, em Campos dos Goytacazes, RJ e o outro em sistema orgânico e cultivo protegido, em Seropédica, RJ. Foram avaliados o número total de frutos (NTF), peso total de frutos (PTF), peso médio dos frutos (PMF), comprimento dos frutos (CF), diâmetro dos frutos (DF), relação comprimento/diâmetro do fruto (CF/DF) e a presença de capsaicina (CAPS). Foram realizadas a análise conjunta e o estudo da decomposição da interação genótipo x ambiente (G x A). Estimaram-se os componentes quadráticos genotípico e da interação G x A, o coeficiente de determinação genotípico e de variação genético e do índice de variação. A interação G x A foi significativa para o número, o peso total e médio de frutos, além do diâmetro do fruto e da relação comprimento/diâmetro. Com exceção do comprimento do fruto, o cultivo protegido e orgânico propiciou melhores médias para todas as características estudadas. Os parâmetros estudados mostraram que as causas de variação foram devidas às diferenças genéticas existentes entre as linhas e que o peso total de frutos foi a característica mais influenciada pelo manejo empregado. Entre as linhas pungentes, no cultivo em campo, a linha 2 mostrou alta capacidade produtiva e, para o cultivo protegido, a linha 8 obteve o maior número de frutos. Entre as não-pungentes, a linha 1 foi superior em ambos os ambientes.

Climate and management diversity play an important role in crop production, since these characteristics are related to genotype and cultivation environment. The aim of this study was to evaluate the effects of genotype, environment and genotype-environmental interaction (GE) in 12 recombinant inbred lines of C. annuum. These lines were originated from crosses between the following accessions: a sweet pepper cultivar (susceptible to bacterial spot) and a chili pepper (resistant to bacterial spot). Two tests were conducted, the first in a conventional agriculture system, under field conditions and the second in ecological or organic system, under greenhouse conditions. The field experiment was carried out in Campos dos Goytacazes, RJ. The greenhouse experiment was conducted in Seropédica, RJ. Both experiments were performed using a randomized block design with three replications. The measured variables were total fruit number (TFN); total fruit weight (TFW); mean fruit weight (MFW); fruit length (FL); fruit diameter (FD); length/diameter fruit ratio (FL/FD) and capsaicin presence (CAPS). Data obtained for each experimental condition were submitted to variance analysis, joint variance analysis, and partitioning of genotype environment interaction. Genotype and GE quadratic components, genotypic determination coefficient, genetic variation coefficient and variation index were determined. The GE interaction was significant for TFN, TFW, MFN, FD, and FL/FD. Cultivation under greenhouse conditions was responsible for the highest mean performance for every trait studied but FL. Values observed for genetic variation were greater than values obtained for environmental variation. For field conditions, the high yield ability observed for line 2 (pungent) is suggestive of a potential to be used as a commercial genotype. For greenhouse conditions, line 8 (pungent), had higher fruit number yield. Line 1 (non-pungent) also seems promising for commercial...