Agricultural Areas Research Articles

Growing in Scarcity: Pre-Hispanic Rain-Fed Agriculture in the Semi-Arid and Frost-Prone Andean Altiplano (Bolivia)

Ancient Andean agricultural landscapes have been the subject of a large number of archaeological and agro-ecological studies, which generally refer to regions with favourable environmental conditions or, in the case of arid and semi-arid environments, those with irrigation facilities. The aim of this article is to present and analyse the pre-Hispanic rain-fed farming systems widely represented in two adjacent regions of Bolivia’s arid and cold southern Altiplano. The search for archaeological agricultural areas combined aerial analysis and field surveys. Agro-ecological characterisation was based on historical and ethnographic studies of the region’s present-day populations. Despite their geographical proximity, similar environmental conditions, and same agropastoral way of life, the typology of cultivated areas developed in the southern altiplano differs significantly. Within this same framework of adaptation and resilience, the sectorisation of agricultural systems observed in these two regions reveals a regional productive specialisation that favoured internal exchanges and exchanges with other regions. These differences are related to two models of non-centralised, low-inequality societies—one strongly based on cohesion and the other characterised by greater fragmentation and social conflict—underlining the limits of strict environmental determinism in shaping agricultural landscapes. These results provide new food for thought in the debate on the use and value of rain-fed agricultural practices and more broadly on the diversity of adaptations by human societies in extreme and unstable environmental contexts.

CA-Markov prediction modeling for the assessment of land use/land cover change in two sub-basins of the Tocantins-Araguaia River Basin.

Due to the anthropogenic pressures of expansion areas for livestock and agricultural production in the Brazilian Cerrado, it is of paramount importance to understand the dynamics of land use/land cover (LULC) changes in this region. Thus, we investigated LULC changes in two sub-basins of the Tocantins-Araguaia River basin from 1997 to 2015 and consequently projected future changes for the timespan between 2030 and 2050. The Formoso sub-basin experienced significant expansion of agricultural and pasture areas, whereas the Sono sub-basin limited farmland expansion (more stable native vegetation) due to substantial protected areas, trends that were also observed for future projections (2030 and 2050). Pastureland in the Formoso sub-basin increased by 5.8%, while the Sono sub-basin saw significant gains in cultivated land, according to change detection analyses during the 1997-2015 period. High stability probabilities of no change (> 70%) for grassland areas in the Sono River sub-basin and pasturelands in the Formoso River sub-basin were computed. The CA-Markov model demonstrated a high consistency level with actual LULC classes for both sub-basins, as indicated by an overall Kappa coefficient above 0.8. Future projections for 2030 and 2050 show a substantial expansion of agriculture and pasture in both sub-basins, driven by specific factors such as soil organic carbon stocks, distance from rural settlements, and proximity to rivers. Short- and mid-term simulations indicate substantial expansion of agriculture and pasture in both basins, with potential adverse impacts on water erosion. Consequently, developing policies for soil management and sustainable land use planning is essential for agroecosystem sustainability, promoting a balanced approach to economic development while addressing climate change and anthropogenic challenges.

Seminatural areas act as reservoirs of genetic diversity for crop pollinators and natural enemies across Europe

AbstractDespite increasing recognition of the importance of the multiple dimensions of biodiversity, including functional or genetic diversity as well as species diversity, most conservation studies on ecosystem service‐providing insects focus on simple diversity measures such as species richness and abundance. In contrast, relatively little is known about the genetic diversity and resilience of pollinators or natural enemies of crop pests to population fragmentation and local extinction. The genetic diversity and demographic dynamics of remnant populations of beneficial insects in agricultural areas can be a useful indicator proving additional insights into their conservation status, but this is rarely evaluated. Although gene flow between agricultural and seminatural areas is key to maintaining genetic diversity, its extent and directionality remain largely unexplored. Here, we apply a pan‐European sampling protocol to quantify genetic diversity and structure and assess gene flow between agricultural and nearby seminatural landscapes in populations of two key ecosystem service‐providing insect species, the lady beetle Coccinella septempunctata, an important predator of aphids and other crop pests, and the bee pollinator Andrena flavipes. We show that A. flavipes populations are genetically structured at the European level, whereas populations of C. septempunctata experience widespread gene flow across the continent and lack any defined genetic structure. In both species, we found that there is high genetic connectivity between populations established in croplands and nearby seminatural areas and, as a consequence, they harbor similar levels of genetic diversity. Interestingly, demographic models for some regions support asymmetric gene flow from seminatural areas to nearby agricultural landscapes. Collectively, our study demonstrates how seminatural areas can serve as genetic reservoirs of both pollinators and natural enemies for nearby agricultural landscapes, acting as sources for recurrent recolonization and, potentially, contributing to enhancing ecosystem service and crop production resilience in the longer term.

Consequences of Land Use Changes on Native Forest and Agricultural Areas in Central-Southern Chile during the Last Fifty Years

Chile’s central-south region has experienced significant land use changes in the past fifty years, affecting native forests, agriculture, and urbanization. This article examines these changes and assesses their impact on native forest cover and agricultural land. Agricultural data for Chile (1980–2020) were obtained from public Chilean institutions (INE and ODEPA). Data on land use changes in central and south Chile (1975–2018), analysed from satellite images, were obtained from indexed papers. Urban area expansion in Chile between 1993 and 2020 was examined using publicly available data from MINVIU, Chile. Additionally, photovoltaic park data was sourced from SEA, Chile. Field crop coverage, primarily in central and southern Chile, decreased from 1,080,000 ha in 1980 to 667,000 ha in 2020, with notable decreases observed in cereal and legume crops. Conversely, the coverage of export-oriented orchards and vineyards increased from 194,947 ha to 492,587 ha. Forest plantations expanded significantly, ranging from 18% per decade in northern central Chile to 246% in the Maule and Biobío regions. This was accompanied by a 12.7–27.0% reduction per 10 years in native forest. Urban areas have experienced significant growth of 91% in the last 27 years, concentrated in the Mediterranean climate region. Solar photovoltaic parks have begun to increasingly replace thorn scrub (Espinal) and agricultural land, mirroring transformations seen in other Mediterranean nations like Spain and Portugal.

Predictive statistical models for monitoring antimicrobial resistance spread in the environment using Apis mellifera (L. 1758) colonies

The rise of antimicrobial resistance (AMR) is one of the most relevant problems for human and animal health. According to One Health Approach, it is important to regulate the use of antimicrobials and monitor the spread of AMR in the environment as well. Apis mellifera (L. 1758) colonies were used as bioindicators thanks to their physical and behavioural characteristics. During their foraging flights, bees can intercept small particles, including atmospheric particulate matter, etc., and also microorganisms. To date, the antimicrobial surveillance network is limited to the sanitary level but lacks into environmental context. This study aimed to evaluate the use of A. mellifera colonies distributed throughout the Emilia-Romagna region (Italy) as indicators of environmental antimicrobial-resistant bacteria. This was performed by creating a statistical predictive model that establishes correlations between environmental characteristics and the likelihood of isolating specific bacterial genera and antimicrobial-resistant strains. A total of 608 strains were isolated and tested for susceptibility to 19 different antimicrobials. Aztreonam-resistant strains were significantly related to environments with sanitary structures, agricultural areas and wetlands, while urban areas present a higher probability of trimethoprim/sulfamethoxazole-resistant strains isolation. Concerning genera, environments with sanitary structures and wetlands are significantly related to the genera Proteus spp., while the Escherichia spp. strains can be probably isolated in industrial environments. The obtained models showed maximum values of Models Accuracy and robustness (R2) of 55 % and 24 %, respectively. The results indicate the efficacy of utilizing A. mellifera colonies as valuable bioindicators for estimating the prevalence of AMR in environmentally disseminated bacteria. This survey can be considered a good basis for the development of further studies focused on monitoring both sanitary and animal pathology, creating a specific network in the environments of interest.

Performance of Mehlich-1 and Mehlich-3 extractors in quantifying phosphorus in soils fertilized with liquid swine manure

Phosphorus is considered the most limiting essential macronutrient for plant production in soils of tropical climates. As the chemical and physical properties of the soil influence the availability of P to crops, the use of suitable extractants can increase the precision of the results obtained in soil analysis to enable the maximization of the efficiency of using phosphate fertilizers in agriculture. With this, the objective was to evaluate the capacity of the Mehlich-1 (M1) and Mehlich-3 (M3) extractors to determine the extractable P content in the soil in agricultural areas fertilized with liquid swine manure. The sampled sites were under the sandy loam (Sl) and sandy (S) textural class and were separated into four plots (repetitions) of 15 hectares each, where soil samples were collected in three depths 0.00-0.05; 0.05-0.10; and 0.10-0.20 m. For a depth of 0.20 m, the average levels of extractable P found in the evaluated textural classes were 34.83 (Sl) and 12.56 mg·dm-3 (S) for extractor M1; and 2.91 (Sl) and 0.81 mg·dm-3 (S) for extractor M3. It is clear that, although the Mehlich-1 solution has a greater capacity to extract the P content than the Mehlich-3 solution, both extractants can be used to extract the P content in sandy loam/sandy soils fertilized with liquid swine manure in the Cerrado region of Mato Grosso (Brazil).

Energy partitioning over an irrigated vineyard in arid northwest China: Variation characteristics, influence degree, and path of influencing factors

In this study, a two-year experiment in an irrigated vineyard was conducted to investigate the variations in energy fluxes (net radiation, Rn; latent heat flux, LE; sensible heat flux, H; and soil heat flux, G) and quantify the influence of arid advection and environmental factors (vapor pressure deficit (VPD), Rn, air temperature, wind speed, precipitation, and volumetric soil water content) on energy partitioning in arid Northwest China. For the diurnal variation, the peak values of the energy fluxes appeared at approximately 14:00, except for G (which appeared at approximately 16:00). LE was the main consumer of daytime (Rn>0) energy during the growing season (average LE/(Rn–G) was 87% and 89% in the two years), even during the shooting and leaf-fall stages. Arid advection, which affects energy partitioning mainly by increasing atmospheric evaporative demand and providing heat energy, contributed 6–60% and 1–56% of the average daytime LE during the two growing seasons, especially at the fruiting stage. Moreover, advection accounted for more than half of average daytime energy imbalances. We found that the arid advection induced by irrigation could be attenuated by selecting a reasonable irrigation time. According to the structural equation modeling (SEM) analysis, Rn had the strongest direct and positive regulation effect on LE at the half-hourly and daily scales, indicating that LE was limited by energy rather than water in the study area. The influence of Rn on LE gradually weakened with an increase in the time scale, whereas the effect of VPD increased, which may have been due to the smaller time-lag effect between VPD and LE. The diurnal variations in LE at approximately 08:00–19:30 and 19:30–08:00 were mainly controlled by the direct positive effects of Rn and VPD, respectively. The results obtained in this study will provide a better understanding of surface processes and help improve water resource management in arid agricultural areas.

AGRICULTURAL FIELD MANAGEMENT PROBLEMS: FROM CLASSIC AGRO-TECHNOLOGIES TO ARTIFICIAL INTELLIGENCE

The text is straightforward and precise. The agricultural sector has been significantly impacted by artificial intelligence (AI) technologies such as expert systems, natural language processing, speech recognition, and machine vision. This is due to factors such as the rising global population, increasing demand for food, changing weather conditions, and water availability. AI has not only increased the quantity of work in agriculture but also improved its quality. Researchers and scientists are now adopting new IoT technologies in smart farming to enable farmers to use AI technology for the advancement of seeds, crop protection, and fertilizers. This would enhance the financial viability of farmers and contribute to the general economic growth of the nation. AI is being used in three primary areas in agriculture: soil and crop monitoring, predictive analytics, and agricultural robots. Farmers are increasingly using sensors and soil sampling to collect data for farm management systems, which will be utilized for future studies and analysis. This paper enhances the area by doing a study of artificial intelligence applications in the agriculture industry. The text provides an introduction to AI, covering several AI techniques used in the agricultural sector, such as machine learning, the Internet of Things (IoT), expert systems, image processing, and computer vision.

Assessment of land use changes in Hung Yen province from 2010 to 2020, forecast to 2030 and proposed solutions for sustainable land use of the province

Hung Yen is a province in the Red River Delta, located in the center of the Northern Delta and the Northern Economic Zone (Hanoi - Hung Yen - Hai Duong - Hai Phong - Quang Ninh). Due to that favorable geographical location, in recent years, infrastructure and industrial parks in the province have developed strongly. The results of the assessment of land use changes in the period 2010-2020 in Hung Yen province show that the period 2015-2020 had more significant changes than the period 2010-2015. For industrial land, the period 2015-2020 saw an increase of 137.70%, while the period 2010-2015 saw an increase of 84.25%. Residential land, office buildings, transportation, and other non-agricultural land increased by 8.3% in the period 2015-2020, while it increased by 6.81% in the period 2010-2015. On the other hand, the agricultural land fund also saw a faster decline in the period 2015-2020 than in the period 2010-2015. For example, the area of land for perennial crops decreased by 18.71% in the period 2015-2020, while it decreased by 7.75% in the period 2010-2015. The results of the forecast of land use status to 2030 show that the area of non-agricultural land has surpassed that of agricultural land. In particular, the area of land for annual crops has decreased significantly, with a decrease of 8.88%, much higher than the 4.60% in the period 2010-2020. This type of land was used for growing rice and other vegetables to ensure food security for the province and the country. In addition, due to the shrinking agricultural land area, it is necessary to switch the crop structure to be suitable for climatic conditions as well as for each type of soil and terrain conditions. In addition, it is necessary to apply scientific and technical methods to agriculture to increase productivity and achieve high economic efficiency.

Optimal design and experiment of bionic vacuum suckers with high attachment properties

A variety of bionic vacuum suckers with papillae structures of different parameters were developed to improve the attachment performance of vacuum suckers. The oral sucker of the suckermouth catfish served as the bionic prototype and bionic suckers were made by taking the diameter of papillae, the number of single rows of papillae, the center distance between adjacent rows of papillae, and the distance between the outermost row of papillae and the edge as influencing factors. The single-factor tests and response surface tests were carried out respectively with the growth rates of vertical and horizontal attachment forces as the response values. Twenty-five groups of bionic suckers and one group of standard suckers were fabricated and tested for attachment forces. The experimental results revealed that the bionic suckers had higher vertical and horizontal attachment forces than standard suckers and that their maximum growth rates for these forces were 5.16% and 59.87%, respectively. This suggested that the bionic suckers had better attachment performance. The significant orders of the factors affecting response values were established and their interaction was also analyzed. Besides, the optimal parameter combination of papillae structures of the bionic suckers with optimal vertical and horizontal attachment properties was obtained, respectively. Finally, the influence mechanism of papillae structures on the attachment properties of bionic suckers was analyzed. The studies can serve as a guide for the application of bionic suckers in agricultural production areas such as fruit picking and handling, and the improvement of attachment performance of associated machinery.

Habitat characteristics and the rate of decline in a threatened farmland bird, the ortolan bunting Emberiza hortulana

Farmland habitats witness steep declines in biodiversity. One rapidly declining farmland species is the ortolan bunting Emberiza hortulana. In Finland, a staggering 99% of the population has been lost during the past 30 years. Changes in the breeding habitats have been proposed as a reason for the decline, although hazards during migration and wintering may also play a role.We gathered a 19‐year data set of Finnish ortolan buntings and studied which spatial characteristics, habitat features, and climate factors might explain the population growth rate at the singing‐group level. As explanatory variables we used region, density of small‐scale structures, proportion of agricultural area in the landscape, diversity of crop types, proportion of bare ground, and temperature and precipitation of previous breeding season.Higher population growth rates were associated with higher crop type diversity and higher proportion of bare ground. The mosaic of various crop plants and bare ground may provide a wider array of feeding, hiding and nesting places, and an easier access to food. Higher growth rates were also associated with landscapes dominated by interconnected agricultural land, which may reflect the species' sociability and avoidance of forested areas. The North Ostrobothnia region had higher growth rates compared to other regions.We suggest that northern populations of ortolan bunting should be targeted for further studies on feeding and breeding ecology as well as for urgent conservation actions, such as increasing crop type diversity and bare ground.

ASSESSING ANTHROPOGENIC PRESSURE ON LAND USE AND LAND COVER IN THE MAFERE SUB-PREFECTURE IN SOUTH-EAST COTE DIVOIRE

Several hectares of forest disappear every year in Cote dIvoire, particularly in the Aboisso Department. Despite the fight to preserve biodiversity and forest cover, numerous pressures (anthropogenic pressures) continue to be exerted on the forests, leading to their destruction and even increased deforestation. This deforestation has many consequences, including reduced biodiversity and climate change. This study was initiated to gain a better understanding of this reduction in forest area. The aim of the study is to assess the level of forest degradation in the Mafere sub-prefecture. The activities involve mapping and analyzing the dynamics of land use and land cover (LULC). To achieve these objectives, the study is based on satellite images (Landsat 5, 7 and 8) from 1986, 2000 and 2020. The results of LULC dynamics show, on the one hand, a decline in closed forest/swamp forest (96.29%), degraded forest (76.75%), crop/fallow (67.03%) and annual crops (38.35%) and, on the other hand, an increase in cocoa (675.25%), oil palm (193.13%), rubber (14.39%) and housing/bare land (101.26%). The extension of agricultural areas in search of resources (natural and financial), demographic pressures, still traditional cultivation techniques and climate change are the main factors causing the degradation of forests in the Mafere sub-prefecture.

Responses of Alkali Grass (Puccinellia ciliata Bor) Genotypes to Geothermal Water

The increasing use of geothermal resources for generating electrical energy has brought the negative impact of geothermal fluids on the surrounding agricultural areas in Turkey. Alkali grass is one of the halophyte plant species that is spreading in marginal areas such as waterlogged, excessively alkaline and salty. We conducted the study to test the response of alkali grass genotypes to geothermal and mineral water used as irrigation water. Geothermal water negatively affected the observed traits by 14.01% (panicle length) and 64.21% (seed yield). The number of tillers per plant for biomass and the number of panicles per plant and plant height for seed yield were the determinant traits. Genotypes XII and XI were determined as seed-type, whereas genotypes IX and VIII were determined as biomass-type according to the Biplot graph.

Creating Climate-Resilient Crops by Increasing Drought, Heat, and Salt Tolerance

Over the years, the changes in the agriculture industry have been inevitable, considering the need to feed the growing population. As the world population continues to grow, food security has become challenged. Resources such as arable land and freshwater have become scarce due to quick urbanization in developing countries and anthropologic activities; expanding agricultural production areas is not an option. Environmental and climatic factors such as drought, heat, and salt stresses pose serious threats to food production worldwide. Therefore, the need to utilize the remaining arable land and water effectively and efficiently and to maximize the yield to support the increasing food demand has become crucial. It is essential to develop climate-resilient crops that will outperform traditional crops under any abiotic stress conditions such as heat, drought, and salt, as well as these stresses in any combinations. This review provides a glimpse of how plant breeding in agriculture has evolved to overcome the harsh environmental conditions and what the future would be like.

Prediction of Inland Excess Water Inundations Using Machine Learning Algorithms

Regularly, large parts of the agricultural areas of the Great Hungarian Plain are inundated due to excessive rainfall and insufficient evaporation and infiltration. Climate change is expected to lead to increasingly extreme weather conditions, which may even increase the frequency and extent of these inundations. Shallow “floods”, also defined as inland excess water, are phenomena that occur due to a complex set of interrelated factors. Our research presents a workflow based on active and passive satellite data from Sentinel-1 and -2, combined with a large auxiliary data set to detect and predict these floods. The workflow uses convolutional neural networks to classify water bodies based on Sentinel-1 and Sentinel-2 satellite data. The inundation data were complimented with meteorological, soil, land use, and GIS data to form 24 features that were used to train an XGBoost model and a deep neural network to predict future inundations, with a daily interval. The best prediction was reached with the XGBoost model, with an overall accuracy of 86%, a Kappa value of 0.71, and an F1 score of 0.86. The SHAP explainable AI method showed that the most important input features were the amount of water detected in the satellite imagery during the week before the forecast and during the period two weeks earlier, the number of water pixels in the surroundings on the day before the forecast, and the potential evapotranspiration on the day of the forecast. The resulting inland excess water inundation time series can be used for operational action, planning, and prevention.

Paisaje Agrario: Producción Agrícola Familiar en la Campaña Dange-Quitexe 2019-2022

Family farming in Angola has pre-colonial historical roots. It faced challenges during the colonial period and the war of independence, but after 1975, the government promoted it as essential for economic development. Despite the obstacles, the province of Uíge plays a crucial role in food security and rural development, receiving investment for strengthening and sustainable growth. The aim of the research was to analyse the influence of agricultural practices on food availability in the region and to represent the temporal evolution of land use and land cover in the agricultural production of families in the municipality of Dange-Quitexe. The methodology used was a temporal land use study, involving the collection of data on traditional agricultural practices, analysing soil conditions, defining sustainability goals and implementing management practices such as crop rotation, intercropping, water management and soil conservation techniques. The results of this study offered a deeper understanding of family farming over the two agricultural seasons, and provided valuable information to guide future actions and initiatives  in  the  area  of  agriculture  and  regional  development,  land  use  revealed significant  improvements  in  productivity  and  resilience,  the  implementation  of sustainable practices, such as crop rotation, intercropping and water management, resulted in healthier harvests and more stable yields over time, in addition, it presented the level of production for food security in the municipality of Dange-Quitexe-Uíge in the 2019-2022 agricultural seasons.

Spatial variations in groundwater hydrochemistry, sources, and controls across catchments on Chinese Loess Plateau

Study regionThis study was carried out at the Chinese Loess Plateau (CLP), which is located in the middle reach of the Yellow River in the northern China. Study focusUnderstanding groundwater hydrochemistry and its controlling factors in regions like CLP with thick unsaturated zones is challenging due to limited accessibility. The primary objective of this study was to investigate groundwater hydrochemistry, sources, and controls, and assess groundwater suitability for drinking and irrigation purposes across 14 major catchments in CLP. New hydrological insights for the regionOur results showed that the composition of groundwater dissolved solutes is primarily influenced by weathering rock, particularly silicate rock. However, human activities, especially in agriculture and urban areas, also have a notable impact on groundwater solutes. The presence of elevated nitrate in groundwater in various regions underscores the urgency of addressing long-term nitrogen pollution. Environmental changes such as climate change, erosion rates, and land-use patterns are key factors shaping groundwater quality. As air temperature decreases and erosion intensifies, along with more intensive land use, ion concentrations in groundwater tend to rise. Note that, approximately 91% of groundwater in CLP is suitable for direct drinking, but ensuring proper drainage is crucial to prevent sodium damage during irrigation. These findings enhance our understanding of the factors influencing groundwater solutes and are essential for effectively managing groundwater quality in CLP.

Deep learning based crop-type mapping using SAR and optical data fusion

Accurate crop maps are essential in various applications related to food security management activities. Remote Sensing is the primary data source for land-use and land-cover monitoring applications. However, high spectral, spatial, and temporal variations of crop types during their phonological stages make crop mapping challenging. According to the complementary behaviors of optical and radar data, a deep learning-based feature-level fusion strategy is proposed for accurate crop-type mapping. The proposed method uses Sparse Auto-Encoders (SAE) as its base classifier and can involve spatial information in two data-preparation and post-processing steps. Involving spatial information in the classification process can significantly improve crop mapping. The post-processing step uses the guided filter as an edge-aware filtering process that uses field boundary information that implicitly exists in data. Accuracy analysis was performed using two pairs of RapidEye and UAVSAR data from two agricultural areas in Canada. The suitability and higher performance of the proposed method were demonstrated compared to traditional machine learning and common decision-level fusion methods. Accuracy metrics were improved by at least 3% and 1% in two datasets. Class F-scores were also significantly enhanced in oat and wheat classes (up to 20%).

REMOTE SENSING AND GOOGLE EARTH ENGINE FOR RAPID FLOOD MAPPING AND DAMAGE ASSESSMENT: A CASE OF TYPHOON GONI (ROLLY) AND VAMCO (ULYSSES)

Abstract. Flooding is a devastating natural disaster with global repercussions, affecting human life, crop production, and infrastructure. The increasing frequency and unpredictability of flood events, driven by climate change, highlight the urgent need for rapid and accurate flood mapping and damage assessment. This study explores the use of Google Earth Engine, a web-based platform that utilizes satellite imagery and geospatial data, for flood mapping and damage assessment. By incorporating localized datasets, such as region-specific land cover maps and population density information, the developed workflow provides a robust tool for assessing flood extent, identifying high-risk areas, and estimating the impact on cropland and urban centers. Key findings showed that Cagayan province presented the highest potential flood area, covering approximately 55,063 hectares. This province also had the largest population at risk, with around 39,440 individuals potentially exposed to the effects of flooding. Moreover, the cropland in Cagayan province experienced the most substantial impact, with around 91.26% being affected due to the swelling of the Cagayan River. Camarines Sur experienced severe devastation in urban areas, with about 269 hectares affected by flooding from the Bicol River. This study's findings contribute to a comprehensive understanding of flood impacts beyond the extent of flooding, facilitating informed decision-making, emergency response planning, and targeted interventions to mitigate the effects of floods on communities, agriculture, and urban areas. The approach presented here can be applied in other regions, supporting improved flood management and mitigation strategies for national.

Trophic ecology of Japanese eels in small rivers of urban and agricultural areas

Trophic ecology of Japanese eels in small rivers of urban and agricultural areas