Agricultural Areas Research Articles

Leaf morphological variability of Pyrus spinosa and Crataegus monogyna and their potential hybridization

This paper is aimed at investigating leaf morphological variability and possible hybridization between two species within the Rosaceae family: the one-seed hawthorn (Crataegus monogyna Jacq.) and the almond-leaved pear (Pyrus spinosa Forssk.). These two genera are taxonomically related and meet many prerequisites for successful hybridization between them, which prompted us to investigate the possibility of the presence of a long-described yet uninvestigated hybrid called ×Pyrocrataegus. The research was conducted along the Eastern Adriatic coast, where both species are widespread and often grow together in open woodlands, forest edges and abandoned agricultural areas. The examination of morphological variability was based on a morphometric analysis of seven populations using ten phenotypic traits of leaves. In general, our results showed great variability of leaf morphological traits within and between the studied populations, as well as a clear differentiation between the two species. The results of principal component analysis (PCA) showed a few intermediate individuals between the two species, indicating possible hybridization. However, as heteroblasty is present in P. spinosa, with its seedlings reported to have lobed, hawthorn-like leaves, dimorphism could also result from the reappearance of juvenile leaves on adult trees by means of rejuvenation. In order to draw a definitive conclusion about the existence of hybrid individuals in the next study, DNA markers and a much larger sample, especially morphologically intermediate individuals per population should be included.

Buff-tailed bumblebee, an underrated indicator of air pollution: a comparison of particulate matter accumulation by Bombus terrestris L. and Apis mellifera L.

Abstract Anthropogenic and natural particulate matter (PM) affects urban and agricultural areas and contaminates the bodies of Apis mellifera (honeybee) and Bombus terrestris (buff-tailed bumblebee). Although both species accumulate PM, scientific interest has primarily focused on A. mellifera as a pollution indicator. This study directly compared the efficacy of honeybees and bumblebees as indicators of PM and its associated trace elements (TEs). Insects were collected from ecological and conventional apple orchards and underwent quantitative analysis of total PM, PM size fractions, and TEs. To establish an environmental context, plant samples and bee products were obtained from both plantations, and screened for PM and/or TEs. Bombus terrestris accumulated 191.3 μg total PM per individual, whereas A. mellifera accumulated 64 μg. Particular PM size fractions were also significantly more abundant on bumblebees. Accumulation patterns of total and large PM on bumblebees differed between the ecological and conventional orchards. Total PM accumulated by both species combined correlated strongly with the total PM covering apple tree foliage in the ecological orchard (r = 0.836) and with grass in the conventional orchard (r = 0.851). The amount of total PM accumulated by B. terrestris strongly correlated with the concentrations of Fe (r = 0.927) and Mn (r = 0.91) in this species. Accumulation of Fe by A. mellifera correlated with the content of this metal in pollen (r = 0.912) and bee bread (r = 0.91), whereas the reverse trend was found for Mo in bee bread (r = –0.912). The results indicate that B. terrestris is a more accurate pollution indicator, with potentially greater efficacy in more polluted areas. The observed differences are most likely attributed to the distinct morphology and behavior of the species.

DIGITALIZATION IN PLANT PRODUCTION AND PLANT PROTECTION IN BULGARIA – CURRENT STATUS AND FUTURE GOALS

According to the "Strategic Plan for the Development of Agriculture and Rural Areas of the Republic of Bulgaria for the period 2023-2027", Bulgaria is significantly behind the other EU members in terms of digital technologies, especially in agriculture. The present study aimed to follow the historical development of agriculture up to the so-called “fourth revolution” - Digital agriculture (DA). DA can be considered from a scientific and practical point of view. Science can demonstrate and prove the benefits of using a given tool or software by comparing the results with those obtained using standard analysis methods. From the users' point of view, the product must be easy to use, accessible from a technical and economic point of view and, most importantly, provide accurate information. Many investigations on DA are made in Bulgaria, but most of them study it economically. Farmers have access to a variety of DA products, but unfortunately research shows that a small percentage of them use them. Although only 14% of farmers apply modern digital technologies, there is a trend towards increasing their interest in DA, which will accelerate in the future.

Anthropogenic pressure induced discontinuities of microbial communities along the river.

Microorganisms play a fundamental role in driving biogeochemical functions within rivers. Theoretically, the directional flowing nature of river contributes to the continuous downstream change pattern of microbial communities. This continuity is anticipated to be influenced by human activities as anthropogenic materials lead to the mixing of environmental substances and their resident microorganisms with local communities. Here, we conducted a field investigation along the Beiyun River, which successively flows through pristine forest areas, artificial urban and agricultural areas with a length of 184km, to explore the influence of anthropogenic events on microbial similarity, diversity, composition, co-occurrence, and assembly mechanisms in sediments along the river. Piecewise linear regression tests showed that discontinuities of microbial similarity occurred following the transitions from low to high anthropogenic pressure. LEfSe analysis illustrated that microorganisms associated with wastewater treatment plants and gut were differentially abundant in urban and agricultural streams. By quantifying contributions of ecological assembly processes, we found that the dominant role shifted from variable selection (60.78% in forest group) to homogenous selection (79.52% in urban group and 57.14% in agriculture group) as the differences in NH4+-N, NO3--N and NO2--N content decreased. Moreover, the complexity and stability of microbial networks were reduced from upstream forest streams to downstream urban and agricultural streams, indicating more fragmented networks. Our study provides enhanced knowledge about the factors controlling the microbial community assembly in rivers under increasing human pressure through the integration of physical, environmental, and ecological mechanisms, which can serve as a basis for predicting and responding to changes in ecosystem function under the intensified human pressure.

EVALUASI KESESUAIAN LAHAN UNTUK BUDIDAYA TANAMAN SEMANGKA (STUDI KASUS DI KECAMATAN TUAH MADANI, KOTA PEKANBARU)

Evaluation of land suitability is carried out to perceive the potential and limitation factors of an area as an agricultural development area. Air Putih Village is one of the potential area for watermelon development area. This research was conducted to perceive the level of actual and potential land suitability for watermelon cultivation in Air Putih Village, Tuah Madani District, Pekanbaru City in the form of a semi-detailed map scale of 1:25.000. The method used in this study was a purposive sampling survey based on a map of land units. There were 7 units of land covering an area of 330.56 ha with a total of 21 sample points. Data were sourced from field observation, analysis of chemistry and physics of soil samples at the laboratory and secondary data from climate data. Land suitability assessment was carried out using the limiting factor matching method. The actual land suitability was in class S3 and N with effective soil depth (rc), base saturation (nr), slope (eh) and soil texture (rc) as limiting factors. The class of the potential land suitability for watermelon cultivation were S2, S3 and N.

Monitoring of selected heavy metals, hematological and biochemical indicators in the blood of lacting dairy cows from the area of the active copper industry

There is a well-developed copper industry in Poland. Copper mining and processing involves processes that emit numerous chemical pollutants, including heavy metals, into the environment. Blood was collected once in the summer of 2021 and 2022 from dairy cows (aged 3–5 years) kept in small herds in the Legnica-Głogów copper district (Lower Silesia, Poland). Selected toxic metals (Cd, Cu, Hg, Pb, and Zn) were determined in the whole blood of the cows, whereas hematological parameters (WBC, RBC, PLT, MCV, MCH, MCHC), biochemical parameters (AST, GGT, TAS) and minerals (Ca, Fe, Mg, P inorganic) were measured in their blood serum. Generally, the indicators assessed were within reference values (with a few exceptions, e.g. Zn) and generally similar to those in cows kept in typically agricultural areas. This was also true of the toxic metals. The results provide no indication that the copper industry (LGOM) has a negative impact on the physiological condition of cattle.

Leopard occupancy correlates with tiger and prey occurrences in the Parsa‐Koshi Complex, Nepal

AbstractLarge carnivores such as leopards (Panthera pardus) experience continuing threats from habitat loss and fragmentation, depletion of prey populations, and retaliatory killing following conflicts with humans. We aimed to identify factors affecting leopard occupancy and temporal overlap between leopards and tigers (P. tigris), their major prey, and human activities in the Parsa‐Koshi Complex (PKC), Nepal. We deployed 154 cameras for 21 days each along wildlife trails throughout PKC during December 2022–March 2023. We found low leopard occupancy (0.17 ± 0.04) in PKC, with greater occupancy rates within protected areas, particularly in Parsa National Park and its buffer zone. Human (63.10 ± 23) and livestock (36.46 ± 102) detections were higher across the PKC. Leopard occupancy was positively associated with the presence of tigers and prey. Temporal overlap was moderately high between leopards and tigers, as well as between leopards and their prey. Our research suggests that coexistence between leopards and tigers is likely largely facilitated by higher prey availability, which may reduce competition. Further, we demonstrate that maintaining forest cohesion can improve habitat for leopards. Intensifying use of agricultural areas could reduce forest fragmentation and increase leopard habitat overall, in turn reducing leopard depredation of livestock and improving leopard‐human co‐existence and conservation.

Tracing the trail of eroded fertile soils during a high intensity rainfall event: A fingerprinting study in war-torn tropical mountains

The depletion of fertile topsoil presents a critical challenge in tropical mountain agroecosystems. Impacts are intensified during heavy storm events that strip unprotected topsoils and pose risks to downstream water ecosystems. To better understand such dynamics, we investigated an agricultural mountainous catchment located on the Democratic Republic of the Congo shore of Lake Kivu. This area is characterised by weak governance systems exacerbated by protracted violent conflicts, which have prevented the application of remediation practices to address extensive land use changes, including deforestation and monoculture proliferation, notably banana plantations. Additionally, the spread of Banana Xanthomonas wilt (BXW) has further augmented land use impacts. Herein the complete diseased mat uprooting (CDMU) method employed to combat the disease has resulted in significant erosion and sediment export rates. Following the recovery of banana plantations from CDMU, sediment export rates continued to increase without a clear responsible party but more as a legacy of past and present land degradation activities and embedded soil erosion and connectivity process pathways. To analyse the impact of high-intensity storm events coupled with the aftermath of conflict instability and the effects of BXW on erosion dynamics, the sediment fingerprinting technique was implemented. We collected 60 sediment source samples across a 42km2 catchment, predominantly cultivated since the mid-20th century. Samples spanned four main land uses/land covers: seasonal crops, banana plantations, bare or degraded soil, and channel banks. Additionally, mixture/target samples were collected at the summit of six fluvial terraces located at different altitudes along the vertical profile of the river after a high-intensity storm event. These terraces serve as natural indicators of sediment accumulation under different flow intensities, with higher terraces representing sediment deposition during higher discharge. Furthermore, sediment samples were collected via a gravity core in delta deposits at the river's inflow into the lake, allowing us to identify the main sources contributing to fine sediment export to the lake. This methodology enabled us to assess how sediment provenance has changed in response to varying discharge levels while elucidating the primary sources responsible for fine sediment inputs to the lake and the subsequent water quality decrease. Our analysis indicates increased contributions occurred from channel banks and degraded areas as discharge increased. Despite steep channel banks and degraded soils being primary sediment sources on the terraces, agricultural and banana sources predominantly contributed to the fine sediments exported to the lake. These findings underscore the hazards of high-intensity storm events in these fragile ecosystems and highlight the role of current banana plantation and cropland management in degrading land and water quality. Understanding the primary factors driving land degradation and sediment export is crucial for preventing further ecosystem degradation and mitigating heightened instability in conflict-affected areas. This research suggests that remediation practices should be incentivised to create buffer structures in upslope agricultural areas and protect the fertile sediments from banana plantations from being exported to the lake if socio-cultural and economic hurdles to implementation can be overcome.

An advanced high resolution land use/land cover dataset for Iran (ILULC-2022) by focusing on agricultural areas based on remote sensing data

This study presents the first high-resolution Land Use/Land Cover dataset for Iran in 2022 (ILULC-2022), with a particular emphasis on the agricultural areas. This research employed a two-level Decision Tree Object-Oriented Image Analysis (OBIA-DT) model which incorporated segmentation of the study area derived from Google Earth images, and classification using multi-temporal information derived from Sentinel-2 satellite imagery. After segmentation of fine resolution images, the first level of the OBIA-DT model established based on the collected field datasets (about 52,000 field data were collected) to build a light LULC map which broadly identified agricultural land components without differentiating between irrigated and non-irrigated cultivations. The second level used multi-temporal indices derived from Sentinel-2 imagery and supplementary data layers to produce a complete LULC map wherein cropland areas was distinguished further into irrigated and rainfed lands, with four distinctive sub-classifications for irrigated lands. By employing this approach, a LULC map of all basins of Iran were classified into sixteen distinct classes, with different agricultural lands divided into two rainfed croplands (rainfed farming and agroforestry) and five irrigated lands (orchards, fall crops, spring crops, multiple crops, and fallow crops). According to the collected field data, the overall accuracy of ILULC-2022 maps exhibited a range from 85 to 97 % for basins with varying climates ranging from cold and temperate to hot and dry, respectively. Results reveal that the major irrigated crop classes had a user’s accuracy and producer’s accuracy ranging from 91 % to 96 %. Based on the findings of this study, the total area of agricultures in Iran encompasses 20.9 ± 2.1 million ha, constituting approximately 13 % of the Iran’s total land area. Within this agricultural expanse, irrigated (comprising irrigated lands and orchards) and rainfed agricultural lands are delineated as 10.2 ± 1.08 and 10.7 × ± 1.02 million ha, respectively, with most agricultural areas located in basins with moderate to humid climates. The ILULC-2022 dataset serves as a benchmark for future LULC change detection and is a valuable reference for efforts aimed at achieving sustainable development goals in Iran.

Vertical and horizontal variabilities of ammonia in an agricultural area of Southeast China.

Ammonia (NH3) plays a crucial role in the global nitrogen cycle, the increased NH3 emissions from agricultural activities impacting air, soil, water quality, and human health. Accurately estimating both the vertical and horizontal transport distances of NH3 are important for effective pollution control. Therefore, we used a helium-filled balloon mounted sampler to analyze the vertical profiles of NH3 emissions and their seasonal variations in an agricultural area of southeast China. The annual mean concentration of NH3 in this area was 6.49±0.39μgm⁻3. Vertical measurements revealed a gradual and steady decrease with increasing height, though no significant variation detected between different heights. Seasonal variations showed that the highest concentration occurred in summer (9.98±0.11μgm⁻3) and the lowest in winter (4.65±0.02μgm⁻3). Concentrations in summer significantly higher than those in the other three seasons. NH3 concentrations during the fertilizer application period were much higher than those in the non-fertilizer application time. We also monitored the horizontal transport distance of NH3 after fertilization. It is indicated that NH3 concentrations emitted from agricultural activities decreased exponentially with horizontal transport distance and could only be transported over short distances, up to 250m. Therefore, local emissions are the dominant source of atmospheric NH3. Abetments such as enhanced-efficiency fertilizer could reduce agriculture NH3 emission to promote air quality.

Unravelling wetland connectivity: a comparative analysis of landscape structure and connectivity between natural and unnatural (artificial) wetlands in Van Sub-basin

This study compares natural and artificial wetlands in Türkiye’s Van sub-basin, exploring human impacts on landscape connectivity and species diversity. It examines how habitat fragmentation affects species isolation and long-term viability, involving 14 natural and 9 artificial wetlands to improve conservation strategies. A four-stage methodology was used to analyze wetland connectivity. First, focal nodes and resistance surfaces were defined using Corine 2018 data and topographic wetness index (TWI). Second, resistance maps were created. Third, habitat fragmentation was assessed using Fragstats software, analysing metrics like PLAND, and CONNECT. Finally, connectivity was modeled using Circuitscape software. The study revealed that natural wetlands, despite their dominance (PLAND = 89.89% for lakes), did not ensure effective connectivity. Artificial wetlands often served as crucial connectivity points, with a cumulative current value of 0.99. Land cover characteristics significantly impacted connectivity, with agricultural and forest areas promoting better connectivity. The combined analysis showed a slight increase in connectivity value (4.01687) compared to natural wetlands alone (4.01654) but a substantial increase (305%) compared to artificial wetlands alone. Artificial wetlands significantly contribute to landscape connectivity, particularly in areas with heterogeneous agricultural landscapes. Effective conservation should consider both wetland types and focus on enhancing connectivity across landscapes to mitigate habitat fragmentation and support resilient ecosystems. The study highlights the importance of integrated approaches, as the combined natural and artificial wetland network improved overall landscape connectivity by 0.0082% compared to natural wetlands alone.

Does the digital economy exhibit multiplier effects? A case study on the optimization of agricultural production structure in rural digital economy

ABSTRACT The multiplier effect of the digital economy exists in production activities, so is the structure of agricultural production, which is closely related to it, affected by the digital economy? Based on the panel data of 30 provinces from 2011 to 2020. The results show that: (1) The development level of China's rural digital economy shows a general upward trend, showing regional differences with high levels in the southeastern coastal regions and low levels in the central and western regions. (2) The rural digital economy significantly contributes to agricultural production structure upgrading (APSU), this positive effect is more obvious in the main grain-producing areas, areas with a high degree of land transfer, and agricultural production agglomeration areas. (3) The rural digital economy achieves APSU by promoting technological innovation, facilitating industrial integration, and stimulating consumption upgrading, while there is a multiplier effect for this impact, that is, the existence of Metcalfe's law in APSU was verified for the first time. The multiplier effect stems not only from the rural digital economy, but also from the synergistic effect formed by technological innovation, industrial integration, and consumption upgrading, jointly playing a role in increasing marginal effects.

Predicting Illegal Forest Encroachment in Bamori Range, Guna, Madhya Pradesh Using Machine Learning

Illegal forest encroachment represents a critical challenge in the Bamori Range, Guna District, Madhya Pradesh. The conversion of forested land into agricultural areas disrupts biodiversity and threatens ecological stability. This study presents a predictive model, utilizing machine learning, to identify areas prone to illegal land-use changes. By employing spatial and temporal datasets within a Random Forest framework, the model achieved a high accuracy of 94% and an ROC AUC score of 0.9796. Key predictors, such as the "Rate of Spread" and "Neighbor Crop Count," emerged as significant drivers of encroachment, providing actionable insights for forest management. This approach offers a data-driven solution to enhance conservation strategies and mitigate environmental degradation.

Dryland cropping in different Land uses of Senegal using Sentinel-2 and hybrid ML method

ABSTRACT In rainfed and dryland agricultural areas with smallholder farms (less than 2 ha), crop diversity is high due to farmers' decisions and local climatic conditions, leading to a complex spatial–temporal distribution of crops. Monitoring and mapping crops is crucial for food security and implementing agricultural support programs. This study aims to map crop types across Senegal using Sentinel-2 satellite imagery and the limited ground reference data available, which has been increasing recently. The study compares conventional supervised classification algorithms to unsupervised classification algorithms using high-resolution satellite imagery. Crop type classification for 2020 in Senegal employed supervised machine learning algorithms, including Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) on the Google Earth Engine (GEE) cloud platform, and the unsupervised Iso-clustering classification algorithm with Spectral Matching Techniques (SMTs). Due to limited ground data, supervised classifiers achieved 45-55% accuracy, whereas the unsupervised semi-automatic approach achieved over 75% accuracy. The study indicates that supervised classifiers' performance depends on ground data quantity, while SMT shows good performance even with limited ground data. This SMT approach is valuable for classifying crop types in dryland areas with smallholder farms and diverse cropping patterns.

TESTING OF WATER QUALITY AT LAKE ŠODERICA IN VARAŽDIN COUNTY

<p>The rapid growth of industrial, agricultural, and urban areas, along with rising living standards, invariably results in an increase in the amount of solid, liquid, and gaseous waste elements in water. Because of the variety of hazards that recreational waters pose to human health and well-being, a thorough evaluation of the sources and relative importance of the resulting health effects is necessary.  The testing of bathing water quality was conducted by determination of physical-chemical indicators at three locations in the lake Šoderica in Varaždin county. Lake Šoderica is an artificial lake formed by quarries and also used as a swimming area. The results demonstrated that the water was suitable for swimming and that all examined criteria agreed with the legislation. The results of the analyzed physical-chemical parameters show that the bathing water is satisfactory, but it is also necessary to determine the microbiological parameters. <strong><br /></strong></p>

Assessing hydrogeochemical facies and Groundwater Quality Index in rapidly urbanizing coastal region: a GIS-based approach with machine learning for enhanced management.

Groundwater is an essential freshwater source worldwide, but increasing pollution poses risks to its sustainability. This study applied a comprehensive approach to assess hydrogeochemical facies and groundwater quality in Odisha's large low-lying coastal regions. Analysis of 136 samples revealed that sodium (9.4%), potassium (40.8%), bicarbonate (2.1%), and chloride (2.1%) exceeded WHO limits. The Groundwater Quality Index (GQI) map classified 5.1% of samples as "excellent," 39.4% as "good," 31.3% as "poor," 13.8% as "very poor," and 10.2% as "unsuitable" for use. Additionally, the GQI values demonstrate a random spatial autocorrelation (- 0.06) likely due to diverse influences. The study identified the expansion of agricultural (43%) and built-up areas (13%) from the Land Use/Land Cover (LULC) map. Piper diagram and Gibbs plots suggest continued freshening, rock-water interaction, and seawater intrusion. Groundwater levels fall between 0 to 2m below ground level (mbgl), primarily due to excessive groundwater extraction. The Sodium (Na+) vs. Chloride (Cl-) cross plot shows most samples align with the mixing line, with some deviations indicating multiple contamination sources. The strong correlation (> 0.90) between total dissolved salts (TDS), electrical conductivity (EC), Na+, and Cl- signals seawater intrusion, highlighting the complex interaction between human activities and natural processes. The proposed machine learning (ML) models like random forest (RF), artificial neural network (ANN), decision tree, and linear regression (LR) offer a reliable alternative to traditional GQI methods, addressing the challenges of extensive sampling and data management. Among these, RF exhibited the highest predictive accuracy (coefficient of correlation (R2) = 95%), surpassing ANN (R2 = 82%), decision tree (R2 = 81%), and LR (R2 = 67%) as the most effective model for GQI prediction. Potassium (K+) stands out as a key indicator of contamination. GQI, LULC map, and ML methods improve understanding of contamination sources and support systematic groundwater management.

Land use-based assessment of surface-water quality using indices approaches

ABSTRACT This study assessed land-use impacts on surface-water quality and explored relationships between water indexes with water parameters. Twenty-seven water samples, collected from canals located in agricultural, industrial, and residential areas, were analyzed for 22 parameters. Water quality index (WQI), heavy metal pollution index (HPI), and metal quality index (MQI) results showed poor to very poor water quality across all land uses. Agriculture had the highest WQI (39), followed by residential (12) and industrial areas (7). Industrial areas exhibited the highest HPI and MQI, indicating higher heavy metal pollution in the areas. Stepwise multiple regression analysis revealed a significant correlation between WQI and electrical conductivity and chemical oxygen demand (COD), explaining 71% of WQI variance. Discriminant analysis differentiated the three land uses with 100% accuracy using turbidity, COD, biochemical oxygen demand, Mg, and, Na. Tailored management strategies should be developed for each land-used type to improve water quality in urban areas.

Spatio-Temporal Analysis of Ain Abid City (Algeria) Using Multi-Index Remote Sensing (2003-2023)

This study investigates the spatio-temporal changes in Ain Abid (Constantine, Algeria) between 2003 and 2023 using multi-index remote sensing analysis. Through Google Earth Engine platform and Landsat imagery (Landsat 5 TM and Landsat 8 OLI/TIRS), we examined land use/land cover changes and vegetation dynamics using three complementary vegetation indices: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Soil-Adjusted Vegetation Index (SAVI). Results reveal significant landscape transformations, including a marked increase in urban areas from 6.90 km² to 19.00 km², a dramatic 98% reduction in forest cover, and a doubling of agricultural vegetation areas. The correlation analysis between indices showed perfect correlation between NDVI and SAVI (r=1.00), while EVI provided complementary information with distinct correlations (r=0.76 and 0.61). Classification accuracy assessment yielded Kappa coefficients of 89.1% and 92.3% for 2003 and 2023, respectively. These findings highlight concerning trends in environmental degradation and landscape homogenization, emphasizing the urgent need for sustainable land management practices in this semi-arid region. The study provides valuable insights for policymakers and land managers in developing strategies for balanced territorial development and environmental conservation in Ain Abid.

EFFECTS OF DIFFERENT CONCENTRATIONS OF Cu+, Mn+, AND Ni+ IONS ON GLYCINE MAX GERMINATION

This study investigates the impact of varying concentrations of Cu+, Mn+, and Ni+ ions in sulfate salts on the germination and seed growth of soybeans (Glycine max). In regions with a high density of industrial complexes, agricultural lands often coexist with industrial activities, posing a risk to local food supplies and health. Wastewater from industrial centers, rich in heavy metals such as Cu, Ni, Pb, and Cr, can contaminate agricultural areas, affecting crops and seeds. This study explores the germination and growth of Glycine max seeds exposed to different concentrations (0.2 M, 0.1 M, and 0.05 M) of CuSO4, MnSO4, and NiSO4 salts. The study employs two-way ANOVA and regression analyses to assess the significance of solution types and concentrations on seed growth. The results indicate a strong negative correlation between increasing heavy metal concentrations and seed growth. However, no significant difference is observed among the effects of different metal ions. The study concludes that increased concentrations of Cu+, Mn+, and Ni+ ions in sulfate salts lead to a significant decrease in seed growth. The findings emphasize the potential risks of heavy metal contamination in agricultural practices and call for further research on the transfer of absorbed heavy metals in crops to human health. Presented at Bilim Armonisi International Youth Congress on December 15th, 2023.

Study on the Practical Path of Rural Inclusive Finance to Support Rural Revitalisation

The comprehensive promotion of rural revitalization cannot be separated from the strong support of finance, and inclusive finance, as an important channel for rural funding, has become an important driving force for achieving the modernization of agriculture and rural areas and continuously promoting rural revitalization. This article will take Tongchuan City as an example to deeply analyze the current development status of inclusive finance policies in Tongchuan City. It will explore the paths through which rural industry modernization and upgrading, as well as the formation of a new agricultural management pattern, will be promoted in practice. It will also discuss the challenges that will be faced in the future. Based on the impact mechanism of inclusive finance on rural revitalization, relevant policy recommendations will be proposed to establish and improve a new inclusive finance service system that is suitable for the characteristics of agriculture and rural areas under the background of rural revitalization, broaden the channels for fund utilization, guide more social funds to flow to rural areas, and further empower the transformation and upgrading of rural industries to consolidate the achievements of rural revitalization.