Integrated assessment of groundwater quality dynamics and Land use/land cover changes in rapidly urbanizing semi-arid region.

Rapid urbanization worldwide, poses numerous environmental challenges between escalating land use land cover (LULC) changes and groundwater quality dynamics. The main objective of this study was to investigate the dynamics of groundwater quality and LULC changes in Sargodha district, Punjab, Pakistan. Groundwater hydrochemistry reveals acceptable pH levels (<8) but total dissolved solids (TDS), electrical conductivity (EC) and HCO3- showed dynamic fluctuations by exceeding WHO limits. Piper diagrams, indicated dominance by magnesium and bicarbonate types, underscoring the influence of natural processes and anthropogenic activities. Major ion relationships in 2010, 2015, and 2021 showed a high correlation (R2 > 0.85) between Na+ and Cl-, suggesting salinization. whereas, the poor correlation (<0.17) between Ca2+ and HCO3- does not support calcite dissolution as the primary process affecting groundwater composition. The examination of nitrate contamination in groundwater across the years 2010, 2015, and 2021 was found to be high in the municipal sewage zone, suggesting a prevailing issue of nitrate contamination attributed to urban activities. The Nitrate Pollution Index (NPI) reveals a concerning trend, with a higher proportion of samples classified under moderate to high pollution categories in 2015 and 2021 compared to 2010. The qualitative assessment of nitrate concentration on spatiotemporal scale showed lower values in 2010 while a consistent rise from 2015 to 2021 in north-east and western parts of district. Likewise, NPI was high in the north-eastern and south-western regions in 2010, then reduced in subsequent years, which may be attributed to effective waste management practices and alterations in agricultural practices. The health risk assessment of 2010 indicated Total Health Hazard Quotient (THQ) within the standard limit, while in 2015 and 2021, elevated health risk was observed. This study emphasizes the need to use multiple approaches to groundwater management for sustainable land use planning and regulations that prioritize groundwater quality conservation.

Advanced biomechanical analytics: Wearable technologies for precision health monitoring in sports performance.

Objective: This study investigated the impact of wearable technologies, particularly advanced biomechanical analytics and machine learning, on sports performance monitoring and intervention strategies within the realm of physiotherapy. The primary aims were to evaluate key performance metrics, individual athlete variations and the efficacy of machine learning-driven adaptive interventions. Methods: The research employed an observational cross-sectional design, focusing on the collection and analysis of real-world biomechanical data from athletes engaged in sports physiotherapy. A representative sample of athletes from Bahawalpur participated, utilizing Dring Stadium as the primary data collection venue. Wearable devices, including inertial sensors (MPU6050, MPU9250), electromyography (EMG) sensors (MyoWare Muscle Sensor), pressure sensors (FlexiForce sensor) and haptic feedback sensors, were strategically chosen for their ability to capture diverse biomechanical parameters. Results: Key performance metrics, such as heart rate (mean: 76.5 bpm, SD: 3.2, min: 72, max: 80), joint angles (mean: 112.3 degrees, SD: 6.8, min: 105, max: 120), muscle activation (mean: 43.2%, SD: 4.5, min: 38, max: 48) and stress and strain features (mean: [112.3 ], SD: [6.5 ]), were analyzed and presented in summary tables. Individual athlete analyses highlighted variations in performance metrics, emphasizing the need for personalized monitoring and intervention strategies. The impact of wearable technologies on athletic performance was quantified through a comparison of metrics recorded with and without sensors. Results consistently demonstrated improvements in monitored parameters, affirming the significance of wearable technologies. Conclusions: The study suggests that wearable technologies, when combined with advanced biomechanical analytics and machine learning, can enhance athletic performance in sports physiotherapy. Real-time monitoring allows for precise intervention adjustments, demonstrating the potential of machine learning-driven adaptive interventions.

Formulation and In Vitro Assessment of Polymeric pH-Responsive Nanogels of Chitosan for Sustained Delivery of Madecassoside.

Madecassoside, a triterpenoid saponin compound mainly isolated from the gotu kola herb (Centella asiatica), shows an extensive range of biological activities, including antiapoptotic, antioxidant, anti-inflammatory, moisturizing, neuroprotective, and wound healing effects. It has been highly used in the management of eczema, skin wounds, and other diseases. Due to poor oral bioavailability, membrane permeability, and intestinal absorption, the clinical application of the madecassoside is limited. Hence, a drug carrier system is needed that not only sustains the release of the madecassoside but also overcomes the drawbacks associated with its administration. Therefore, the authors prepared novel pH-responsive chitosan-based nanogels for the sustained release of madecassoside. Free radical polymerization technique was used for cross-linking of polymer chitosan and monomer methacrylic acid in the presence of cross-linker N',N'-methylene bis(acrylamide). The decrease in polymer crystallinity after polymerization and development of nanogels was demonstrated by XRD and FTIR analysis. The effects of nanogel contents on polymer volume, sol-gel analysis, swelling, drug loading, and release were investigated. Results indicated that high swelling and maximum release of the drug occurred at pH 7.4 compared to pH 1.2 and 4.6, indicating the excellent pH-sensitive nature of the engineered nanogels. High swelling and drug release were perceived with the integration of a high quantity of chitosan, while a decline was observed with the high integration of N',N'-methylene bis(acrylamide) and methacrylic acid contents. The same effects of nanogel contents were shown for drug loading too. Sol fraction was reduced, while gel fraction was enhanced by increasing the chitosan load, N',N'-methylene bis(acrylamide), and methacrylic acid. The Korsmeyer-Peppas model of kinetics was trailed by all nanogel formulations with non-Fickian diffusion. The results demonstrated that prepared nanogels can be employed for sustained release of the madecassoside.

A band gap and photoluminescence properties engineering in BaO semiconductor for ultraviolet (UV) photodetector applications: A comprehensive role of co-doping.

We report on ultra-violet (UV) photodetectors based on BaO nanoparticles by the detailed investigation of band gap and photoluminescence properties. The BaO nanomaterials were fabricated by the modified sol-gel technique. The innovation of co-doping can modulate the photoluminescence or sensing properties by narrowing the band gap related to enhancing the high carrier concentration, higher electronic lifetime, and low carriers recombination. It is investigated that the BaO nanoparticles with co-doping reveals a highly reduced band gap and exceptional photoluminescence properties as compared to the pristine BaO nanoparticles due to hindering carrier,s recombination for Ultra-violet (UV) photodetectors. The optical studies revealed that the addition of co-dopants in BaO host material creates new energy sites, so the band gap declines up to 1.31 eV as compared to that of pristine BaO (1.36 eV). The photoluminescence properties recorded with photoluminescence (PL) spectroscopy were recorded which revealed the decrease in PL intensity due to the hindering of carriers recombination with the addition of co-dopant metal ions. Furthermore, the inclusion of co-dopant metals results in an improvement in electrical conductivity because of a decline in carrier recombination, according to an I-V characteristic study. This factor contributes to enhance the photoluminescence properties of BaO which, in turn, contributes to enhance the sensing capability of the photodetector device. These obtained features modify optoelectronic properties are far superior as compared to that of previously reported literature on BaO nanomaterials, and the synthesized BaO semiconductor material becomes a potential candidate for efficient use in the ultraviolet (UV) photodetectors device applications.

A Novel Approach of Polyvinyl Alcohol/Acrylic Acid Based Hydrogels for Controlled Delivery of Diclofenac Sodium.

AIM AND OBJECTIVE: The aim of this study was to prepare polyvinyl alcohol/acrylic acid (PVA/AA) hydrogels for the controlled release of diclofenac sodium and to develop PVA/AA hydrogels as controlled release carriers to overcome not only the side effects of diclofenac sodium but also sustain its release for an extended period. BACKGROUND: Diclofenac sodium is employed for relieving pain and fever. The half-life of diclofenac sodium is very short (1-2 h). Hence, multiple intakes of diclofenac sodium are required to maintain a constant pharmacological action. Multiple GI adverse effects are produced as a result of diclofenac sodium intake. METHOD: A free radical polymerization technique was used for crosslinking PVA with AA in the presence of APS. EGDMA was used as a cross-linker. FTIR and XRD confirmed the preparation and loading of the drug by prepared hydrogels. An increase in the thermal stability of PVA was shown by TGA and DSC analysis. Surface morphology was investigated by SEM. Similarly, water penetration and drug loading were demonstrated by porosity and drug loading studies. The pH-sensitive nature of PVA/AA hydrogels was investigated at different pH values by swelling and drug release studies. RESULTS: The development and drug loading of PVA/AA hydrogels were confirmed by FTIR and XRD analysis. TGA and DSC indicated high thermal stability of prepared hydrogels as compared to unreacted PVA. SEM indicated a hard and compact network of developed hydrogels. The swelling and drug release studies indicated maximum swelling and drug release at high pH as compared to low pH values, indicating the pH-sensitive nature of prepared hydrogels. Moreover, we demonstrated that drug release was sustained for a prolonged time in a controlled pattern by prepared hydrogels by comparing the drug release of the developed hydrogels with the commercial product Cataflam. CONCLUSION: The results indicated that prepared PVA/AA hydrogels can be used as an alternative approach for the controlled delivery of diclofenac sodium.

Insights into measles virus: Serological surveillance and molecular characterization.

BACKGROUND: Measles has been a significant public health concern in Pakistan, especially in the Khyber Pakhtunkhwa (KPK) province, where sporadic and silent epidemics continue to challenge existing control measures. This study aimed to estimate the prevalence and investigate the molecular epidemiology of the measles virus (MeV) in KPK and explore the vaccination status among the suspected individuals. METHODS: A cross-sectional study was conducted between February and October 2021. A total of 336 suspected measles cases from the study population were analyzed for IgM antibodies using Enzyme-Linked Immunosorbent Assay (ELISA). Throat swabs were randomly collected from a subset of positive cases for molecular analysis. Phylogenetic analysis of MeV isolates was performed using the neighbor-joining method. The vaccination status of individuals was also recorded. RESULTS: Among the suspected participants, 61.0% (205/336) were ELISA positive for IgM antibodies, with a higher prevalence in males (64.17%) compared to females (57.04%). The majority of cases (36.0%) were observed in infants and toddlers, consistent with previous reports. The majority of IgM-positive cases (71.7%) had not received any dose of measles vaccine, highlighting gaps in vaccine coverage and the need for improved immunization programs. Genetic analysis revealed that all MeV isolates belonged to the B3 genotype, with minor genetic variations from previously reported variants in the region. CONCLUSION: This study provides valuable insights into the genetic epidemiology of the MeV in KPK, Pakistan. The high incidence of measles infection among unvaccinated individuals highlights the urgency of raising awareness about vaccine importance and strengthening routine immunization programs.

Neuroprotective potential of Mentha piperita extract prevents motor dysfunctions in mouse model of Parkinson's disease through anti-oxidant capacities.

Parkinson's disease (PD) is the second most common neurodegenerative disease in the world. Neurodegeneration of the substantia nigra (SN) and diminished release of dopamine are prominent causes of this progressive disease. The current study aims to evaluate the protective potential of ethanolic extract of Mentha piperita (EthMP) against rotenone-mediated PD features, dopaminergic neuronal degeneration, oxidative stress and neuronal survival in a mouse model. Swiss albino male mice were assigned to five groups: control (2.5% DMSO vehicle), PD (rotenone 2.5 mg/kg), EthMP and rotenone (200mg/kg and 2.5mg/kg, respectively), EthMP (200 mg/kg), and Sinemet, reference treatment containing levodopa and carbidopa (20 mg/kg and rotenone 2.5mg/kg). Behavioral tests for motor functional deficit analysis were performed. Anti-oxidant capacity was estimated using standard antioxidant markers. Histopathology of the mid-brain for neurodegeneration estimation was performed. HPLC based dopamine level analysis and modulation of gene expression using quantitative real-time polymerase chain reaction was performed for the selected genes. EthMP administration significantly prevented the rotenone-mediated motor dysfunctions compared to PD group as assessed through open field, beam walk, pole climb down, stepping, tail suspension, and stride length tests. EthMP administration modulated the lipid peroxidation (LPO), reduced glutathione (GSH), and superoxide dismutase (SOD) levels, as well as glutathione-s-transferase (GST) and catalase (CAT) activities in mouse brain. EthMP extract prevented neurodegeneration in the SN of mice and partially maintained dopamine levels. The expression of genes related to dopamine, anti-oxidant potential and synapses were modulated in M. piperita (MP) extract treated mice brains. Current data suggest therapeutic capacities of MP extract and neuroprotective capacities, possibly through antioxidant capacities. Therefore, it may have potential clinical applications for PD management.

A Hybrid Approach for Energy Consumption and Improvement in Sensor Network Lifespan in Wireless Sensor Networks.

In this paper, we propose an improved clustering algorithm for wireless sensor networks (WSNs) that aims to increase network lifetime and efficiency. We introduce an enhanced fuzzy spider monkey optimization technique and a hidden Markov model-based clustering algorithm for selecting cluster heads. Our approach considers factors such as network cluster head energy, cluster head density, and cluster head position. We also enhance the energy-efficient routing strategy for connecting cluster heads to the base station. Additionally, we introduce a polling control method to improve network performance while maintaining energy efficiency during steady transmission periods. Simulation results demonstrate a 1.2% improvement in network performance using our proposed model.

Artificial-Intelligence-Enhanced On-the-Fly Simulation of Nonlinear Time-Resolved Spectra.

Time-resolved spectroscopy is an important tool for unraveling the minute details of structural changes in molecules of biological and technological significance. The nonlinear femtosecond signals detected for such systems must be interpreted, but it is a challenging task for which theoretical simulations are often indispensable. Accurate simulations of transient absorption or two-dimensional electronic spectra are, however, computationally very expensive, prohibiting the wider adoption of existing first-principles methods. Here, we report an artificial-intelligence-enhanced protocol to drastically reduce the computational cost of simulating nonlinear time-resolved electronic spectra, which makes such simulations affordable for polyatomic molecules of increasing size. The protocol is based on the doorway-window approach for the on-the-fly surface-hopping simulations. We show its applicability for the prototypical molecule of pyrazine for which it produces spectra with high precision with respect to ab initio reference while cutting the computational cost by at least 95% compared to pure first-principles simulations.

MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows.

Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command-line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing service at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pretrained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries.

Molecular identification and genotyping of hepatitis E virus from Southern Punjab, Pakistan.

Hepatitis E is a global health concern. Hepatitis E virus (HEV) infection is endemic in Pakistan. HEV has four genotypes: HEV-1 through HEV-4. The genotypes HEV-1 and HEV-2 are associated with infection in humans, especially in countries with poor sanitation. The genotypes HEV-3 and HEV-4 are zoonotic and human infection takes place by consuming undercooked meat or being in contact with animals. The present study was designed to ascertain the presence of HEV in the Southern Punjab region of Pakistan. First, blood samples (n = 50) were collected from patients suspected of infection with the hepatitis E virus from the Multan District. The serum was separated and the samples were initially screened using an HEV IgM-ELISA. Second, the ELISA-positive samples were subjected to PCR and were genetically characterized. For PCR, the RNA extraction and complementary DNA synthesis were done using commercial kits. The HEV ORF2 (Open Reading Frame-2, capsid protein) was amplified using nested PCR targeting a 348 bp segment. The PCR amplicons were sequenced and an evolutionary tree was constructed using MEGA X software. A protein model was built employing the SWISS Model after protein translation using ExPASy online tool. The positivity rate of anti-HEV antibodies in serum samples was found as 56% (28/50). All Pakistani HEV showed homology with genotype 1 and shared common evolutionary origin and ancestry with HEV isolates of genotype 1 of London (MH504163), France (MN401238), and Japan (LC314158). Sequence analysis of motif regions assessment and protein structure revealed that the sequences had a similarity with the reference sequence. These data suggest that genotype 1 of HEV is circulating in Pakistan. This finding could be used for the diagnosis and control of HEV in the specific geographic region focusing on its prevalent genotype.

Isolation, characterization, and cloning of thermostable pullulanase from Geobacillus stearothermophilus ADM-11.

This study aimed to identify thermo-stable pullulanase-producing bacteria in soil samples of potato fields and food-producing companies. Pullulan agar medium was used to screen 17 bacterial strains, which were incubated at 65 °C. The isolate with the maximum activity (375U/ml) was selected and recognized as Geobacillus stearothermophilus ADM-11 by morphological, biochemical characterization, and 16S rRNA gene sequencing. The pullulanase production required optimum pH of 7 and temperature of 75 °C, respectively. The electrophoresis of purified pullulanase on SDS-polyacrylamide gel revealed 83 kDa of a molecular weight that is active at 70 °C and pH 7.0. It was also stable at 90 °C but its activity was decreased by 10 % at 100 °C. The action of pullulanase was increased and stabilized by Ca+2 among the metal ions. Beta and gamma-cyclodextrins inhibited enzyme activity while ethylenediaminetetraacetate (EDTA) and phenylmethylsulfonyl fluoride (PMSF) have no significant effect on pullulanase activity. A full-length pullulanase gene was amplified from G. stearothermophilus ADM-11 using genomic DNA 2.1 kb of PCR product which was then purified and ligated in the cloning vector pTZ57R using the TA cloning technique. Colony PCR confirmed cloning on the positive clones after the pullulanase gene had been ligated and subjected to restriction digestion. It revealed 74 % similarity with the reported pullulanase gene from Geobacillus sp. 44C. The thermostability of pullulanase and its ability to degrade raw pullulan may therefore have wide-scale applications in starch processing, the detergent business, and new biotechnological applications.

Hydrogeochemistry and prediction of arsenic contamination in groundwater of Vehari, Pakistan: comparison of artificial neural network, random forest and logistic regression models.

Arsenic contamination in the groundwater occurs in various parts of the world due to anthropogenic and natural sources, adversely affecting human health and ecosystems. The current study intends to examine the groundwater hydrogeochemistry containing elevated arsenic (As), predict As levels in groundwater, and determine the aptness of groundwater for drinking in the Vehari district, Pakistan. Four hundred groundwater samples from the study region were collected for physiochemical analysis. As levels in groundwater samples ranged from 0.1 to 52 µg/L, with an average of 11.64 µg/L, (43.5%), groundwater samples exceeded the WHO 2022 recommended limit of 10 µg/L for drinking purposes. Ion-exchange processes and the adsorption of ions significantly impacted the concentration of As. The HCO3- and Na+ are the dominant ions in the study area, and the water types of samples were CaHCO3, mixed CaMgCl, and CaCl, demonstrating that rock-water contact significantly impacts hydrochemical behavior. The geochemical modeling indicated negative saturation indices with calcium carbonate and other salt minerals, encompassing aragonite, calcite, dolomite, and halite. The dissolution mechanism suggested that these minerals might have implications for the mobilization of As in groundwater. A combination of human-induced and natural sources of contamination was unveiled through principal component analysis (PCA). Artificial neural networks (ANN), random forest (RF), and logistic regression (LR) were used to predict As in the groundwater. The data have been divided into two parts for statistical analysis: 20% for testing and 80% for training. The most significant input variables for As prediction was determined using Chi-squared analysis. The receiver operating characteristic area under the curve and confusion matrix were used to evaluate the models; the RF, ANN, and LR accuracies were 0.89, 0.85, and 0.76. The permutation feature and mean decrease in impurity determine ten parameters that influence groundwater arsenic in the study region, including F-, Fe2+, K+, Mg2+, Ca2+, Cl-, SO42-, NO3-, HCO3-, and Na+. The present study shows RF is the best model for predicting groundwater As contamination in the research area. The water quality index showed that 161 samples represent poor water, and 121 samples are unsuitable for drinking. Establishing effective strategies and regulatory measures is imperative in Vehari to ensure the sustainability of groundwater resources.

Factors influencing the adoption of improved cultivars: a case of peach farmers in Pakistan / Fatores que influenciam a adoção de cultivares melhoradas: um caso de produtores de pêssego no Paquistão

ABSTRACT: This study aimed to investigate factors influencing the adoption of improved cultivars (ICs) in peach production in Khyber Pakhtunkhwa province of Pakistan. A total of 270 respondents were randomly selected from the three different cultivated areas of Khyber Pakhtunkhwa, namely, Peshawar, Nowshera and Swat. Binary choice model was used in this study to categorise the ICs of peach farmers into adoption and non-adoption. The study identifies that socio-economic, institutional farm resources, and climatic factors are influencing the adoption of ICs of peach production. Results of the estimated model reveal that farmer's age, education, household size, membership, cell phone, farm size, extension services and the role of the non-government organization have a positive effect on adoption of ICs. In addition, farmer's experience, off-farm income, livestock and machinery ownership, credit access and inputs prices have a positive and significant impact on ICs adoption. Moreover, results of the logit model demonstrate that climatic related factors have a highly significant and positive impact on the adoption of ICs. These results suggested that institutional services should be strengthened to provide managerial and technical skills on ICs technology adoption and on time provision of financial services to enhance the productivity of peach farmers.

RESUMO: Este estudo tem como objetivo investigar os fatores que influenciam a adoção de cultivares melhoradas (ICs) na produção de pessegueiros na província de Khyber Pakhtunkhwa, no Paquistão. Um total de 270 entrevistados foram selecionados aleatoriamente a partir das três diferentes áreas cultivadas de Khyber Pakhtunkhwa, Peshawar, Nowshera e Swat. O modelo de escolha binária foi usado neste estudo para categorizar os ICs dos produtores de pêssego em adoção e não-adoção. O estudo identifica que fatores socioeconômicos, institucionais, recursos agrícolas e climáticos influenciam a adoção de ICs de produção de pêssego. Os resultados do modelo estimado revelam que a idade do agricultor, educação, tamanho do agregado familiar, adesão, telefone celular, tamanho da fazenda, serviços de extensão e o papel da organização não-governamental têm efeito positivo sobre a adoção de ICs. Além disso, a experiência do agricultor, a renda fora da propriedade, a posse de gado e maquinaria, acesso ao crédito e preços dos insumos têm um impacto positivo e significativo na adoção dos ICs. Os resultados do modelo logit demonstram que os fatores climáticos relacionados têm um impacto altamente significativo e positivo na adoção de ICs. Estes resultados sugerem que os serviços institucionais devem ser fortalecidos para prover habilidades gerenciais e técnicas na adoção de tecnologia de ICs e no fornecimento de serviços financeiros para aumentar a produtividade dos produtores de pêssego.

Testing long-run relationship between agricultural gross domestic product and fruits production: evidence from Pakistan / Teste de relação de longo prazo entre o produto interno bruto agrícola e produçãode frutos: evidência do Paquistão

ABSTRACT: This paper analyses the long-run relationship between agricultural gross domestic product (GDP) and fruits production of Pakistan's economy over a period of 1961-2015 by employing Johansen and autoregressive distributed lag (ARDL) modern econometric technique. Three fruits were selected namely mango, apple and peach in this study. Augmented Dickey-Fuller and Phillips-Perron tests were used to check the data stationarity and conclude that the series are integrated of order one. The Johansen approach was applied to check the joint evolution of all the variables for co-integration. The Johansen test suggests that a long-run co-integration exists between agricultural GDP and fruits production. Results of the ARDL model (Bound test) detect the existence of long-run relationship between agricultural GDP and fruits production. The coefficient of the short-run form of ARDL model reveals that all the fruits have a positive impact on agricultural GDP. Moreover, the coefficients of the long-run form of ARDL model have apositive and significant influence on agricultural GDP of Pakistan. These results suggest that a 1% increase in mango, apple and peach production will increase agricultural GDP by 0.06%, 0.03% and 0.03% respectively. Finally, forecast error variance decomposition and impulse response function results depict that mango, apple and peach production significantly contributes to agricultural GDP in the case of Pakistan.

RESUMO: Este artigo analisa a relação de longa duração entre o Produto Interno Bruto agrícola (PIB) e a produção de frutos na economia do Paquistão durante um período de 1961-2015, empregando Johansen e o modelo ARDL (Autoregressive Distributed Lag) técnica de cointegração ou técnica de cointegração vinculada. Três frutas foram selecionadas neste estudo: manga, maçã e pêssego. Testes aumentados de Dickey-Fuller e Phillips-Perron foram utilizados para verificar a estacionária de dados e concluir que a série está integrada na ordem um. A abordagem de Johansen foi aplicada para verificar a evolução conjunta de todas as variáveis para a cointegração. O teste de Johansen sugere que existe uma cooperação de longa duração entre o PIB agrícola e a produção de frutos. Os resultados do modelo ARDL (Bound test) detectam a existência de uma relação de longa duração entre o PIB agrícola e a produção de frutos. O coeficiente da forma de curto prazo do modelo ARDL revela que todos os frutos têm um impacto positivo no PIB agrícola. Além disso, os coeficientes da forma de longo prazo do modelo ARDL têm uma influência positiva e significativa sobre o PIB agrícola do Paquistão. Estes resultados sugerem que um aumento de 1% na produção de manga, maçã e pêssego irá aumentar o PIB agrícola em 0,6%, 0,3% e 0,3%, respectivamente. Finalmente, a previsão de desvio de erros e resultados da função de resposta de impulso retratam que a produção de manga, maçã e pêssego contribui significativamente para o PIB agrícola do Paquistão.