Agricultural Fields Research Articles

ANALYTICAL EVALUATION OF THE EFFECTIVENESS OF EQUITY MANAGEMENT IN AGRICULTURAL, FORESTRY AND FISHERY ENTERPRISES IN UKRAINE

The purpose of the research is to generalize the theoretical and methodological aspects and implementation an analytical evaluation of the effectiveness of the management of equity of agricultural, forestry and fishery enterprises of Ukraine using the data of official statistics on large, medium and small enterprises for ten years (2013-2022) as a basis for directing analysis results for the further search for reserves to improve the efficiency of equity management. Methodology. The basis of the research methodology is a dialectical approach, an analysis of data on the composition and structure of equity capital of agricultural, forestry and fishing enterprises of Ukraine for the period from 2013 to 2022 with the division of enterprises into large, medium and small using official statistical information, published on the website of the State Statistics Service of Ukraine. The results of the research showed that in the period from 2013 until 2022, the largest increase in the equity capital of agricultural, forestry and fishing enterprises of Ukraine was observed at the end of 2015 compared to the end of 2014 due to the simultaneous increase in the value of the indicator at enterprises of different sizes. In the structure of equity capital, the share of retained earnings (uncovered loss) was the most significant. In the vast majority of the research years, at average and small enterprises, cash revenue exceeded the amount of equity capital, while at large enterprises, from 2016 to 2022, equity capital made less than one turnover per year. As the calculations showed, large enterprises in the field of agriculture, forestry and fisheries of Ukraine do not properly take advantage of the possibilities of the influence of the scale of production on the formation of indicators of the efficiency of the use of equity. Practical implications. The practical significance of the obtained results lies in their focus on confirming the expediency of the further search for reserves to increase the efficiency of the use of equity, first of all, by large enterprises in the field of agriculture, forestry and fisheries of Ukraine. Value / Originality. The results of the conducted research serve as the basis for further in-depth analysis and search for reserves to increase the efficiency of equity management at different sized of business entities.

Effect of Artificial Humic Acids Derived from Municipal Sludge on Plant Growth, Soil Fertility, and Dissolved Organic Matter

Due to its high nutrient utilization efficiency, liquid organic fertilizer has become a research hotspot in the field of agricultural planting. Artificial humic acids, which are near-nature products, can be deemed as a green liquid organic fertilizer, but few studies have been reported, which has limited their further application. In this study, artificial humic acids were derived from municipal sludge, and their effect on rice growth, soil fertility, and dissolved organic matter was investigated using multi-chamber root box experiments. The shoot and root biomass of rice can be significantly enhanced by artificial humic acids, and the heavy metal concentration in rice was within safe limits. Artificial humic acids can limit the decrease in soil pH, especially in the far-rhizosphere zone, and improve the distribution of nutrients in the rhizosphere, near-rhizosphere, and far-rhizosphere zones. The use of artificial humic acids led to a significant decrease in soil electrical conductivity. The dissolved organic carbon content in the root zone was significantly increased, and the fluorescence intensity of dissolved organic matter in the rhizosphere was significantly increased. The proportion of specific components of dissolved organic matter was just slightly changed in the rhizosphere and near-rhizosphere zones. Artificial humic acids promoted the humification of dissolved organic matter in the near-rhizosphere and far-rhizosphere zones. The findings indicate that the environmental impact of artificial humic acids is significantly different from conventional chemical fertilizers, and they show huge potential in the agriculture field.

A Provably Secure and Lightweight Two-Factor Authentication Protocol for Wireless Sensor Network

Wireless Sensor Networks (WSNs) are rapidly being integrated into various fields, significantly impacting and facilitating many aspects of human life. However, the increasingly prominent security issues associated with WSNs have become a significant challenge. This paper provides an in-depth analysis of the security challenges faced by WSNs in resource constrained and open communication environments. As a key component of the Internet of Things (IoT), a WSN can perceive, collect and transmit physical environmental data in real-time, and is widely used in military, medical, agricultural and other fields. However, the insecurity of communication channels and unauthorized user access pose severe threats to network security and data integrity. To address these challenges, this paper proposes a provably secure two-factor authentication protocol. This protocol utilizes a Chebyshev chaotic map and a two-factor authentication mechanism, which not only enhances security in WSNs but also improves authentication efficiency. The protocol is validated through security proof and performance experiments, demonstrating excellent security, functionality and efficiency. This provides strong support for secure and efficient communication in WSNs across various application scenarios.

Review Paper on Agricultural Terrain Traversal Rocker Bogie Mechanism

Abstract: This paper details the design and implementation of a terrain traversal system leveraging the rocker-bogie mechanism, specifically tailored for agricultural applications where uneven terrain presents significant challenges. The rocker-bogie suspension system, renowned for its application in planetary rovers, is innovatively adapted to navigate the complexities of agricultural fields. The primary objective of this research is to create a prototype that minimizes soil disturbance while effectively maneuvering over obstacles such as rocks, furrows, and varying soil conditions. Through this approach, the prototype aims to enhance operational efficiency in critical agricultural tasks, including planting, monitoring, and maintaining crops. The system's design incorporates advanced navigation capabilities, potentially integrating sensors for obstacle detection and GPS for precise positioning. This research addresses the pressing need for low-cost, scalable solutions in the realm of agricultural mechanization, ultimately contributing to improved productivity and reduced labor costs in farming operations. The findings from this study are anticipated to provide significant insights into the future of agricultural technology, paving the way for further innovations in automated systems that can adapt to the dynamic challenges of modern agriculture. The successful deployment of this prototype not only underscores the feasibility of the rocker-bogie mechanism in agricultural settings but also highlights its potential to revolutionize practices in the industry.

Fabrication of Remotely Controllable Robotic Weed-Ejector Vehicle

Weed is an unwanted plant in an agriculture field that keeps the crop plants deprived of sunlight, fertilisers and water. If not removed, weed reduces the crop output to a larger extent i.e., endangering the farmer's interests. There are three main reasons for leaving weed to grow in fields and substantial loss of crop output 1) non-availability of agricultural labour, 2) fear of snake bites and 3) higher amounts of time required for weed injection. As of there are no successful ROVs (Remotely operating vehicles) to get to the weed and capable of pulling off the weed without the farmer physically entering into the agricultural fields. The Proposed model reduces farmers getting exposed to snake bites during weed ejection and reduces the time and labour requirement for weed ejection. The project involves the study of different mechanisms required for weed pull-off from the agricultural fields and remote-control systems, modelling of components, fabrication of components, assembly and testing of the Remotely Controllable Robotic Weed-Ejector Vehicle.

Fine-grained recognition algorithm of crop pests based on cross-layer bilinear aggregation and multi-task learning

Fine-grained recognition of crop pests is a crucial concern in the field of agriculture, as the accuracy of recognition and generalization ability directly affect the yield and quality of crops. Aiming at the characteristics of crop pests with a wide variety of species, small inter-class and large intra-class differences in external morphology, as well as the problems of uneven sample distribution and noisy labels in fine-grained image datasets under complex environments, we propose a fine-grained recognition model of crop pests (MT-MACLBPHSNet) based on cross-layer bilinear aggregation and multi-task learning, which consists of three key modules: the backbone network module, the cross-layer bilinear aggregation module, and the multi-task learning module. A new union loss function is designed in the primary task of the multi-task learning module, which is used to alleviate the two problems existing in the model training fine-grained image datasets. The experimental results show that the model effectively balances the model complexity and recognition accuracy in a comparative analysis with several existing excellent network models on the IP102-CP13 dataset, with the recognition accuracy reaching 75.37%, which is 7.06% higher than the Baseline model, and the F1-score reaching 67.06%. Additionally, the generalization of the model is also verified on the IP102-VP16 dataset, and the model outperforms most of the models in terms of recognition accuracy and generalization ability, which can provide an effective reference for fine-grained recognition of crop pests.

Intelligent inspection and quality control of table olives

Quality is a key factor of product-marketing in the field of agriculture and food industry. A number of researches have suggested the use of vision computer systems and machine learning (ML) techniques essentially to inspect imperfection surfaces and defects in fruit products. Following the trend, this paper still relies on the use of vision computer system by assuring an intelligent detection of table olive defect, with a certainty of its quality control. The specifically exploited computer vision system consists of automatically extracting the texture, colour, and shape features mainly from the global thresholding segmented image. With reference to the extracted features, the newly introduced system has the capacities of distinguishing between the defected and the healthy olive fruits rapidly and effectively at the same time. Subsequently, it is capable of identifying and specifying the diseased table olive. This system is also helpful for estimating the table olive size automatically. The experimental findings are indicative of the high accuracy of the binary classification algorithm, reaching 99, 32%, the average processing time for just one olive is about 0.4 s, which could meet the requirements of the real-time applications, and the error in estimating the size of the table olives does not exceed 10%.

Small-sample cucumber disease identification based on multimodal self-supervised learning

It is difficult and costly to obtain large-scale, labeled crop disease data in the field of agriculture. How to use small samples of unlabeled data for feature learning has become an urgent problem that needs to be solved. The emergence of self-supervised contrastive learning methods and self-supervised mask learning methods can solve the problem of missing labels on the training data. However, each of these paradigms comes with its own advantages and drawbacks. At the same time, the features learned by dataset in a single modality are limited, ignoring the correlation with other modal information. Hence, this paper introduced an effective framework for multimodal self-supervised learning, denoted as MMSSL, to address the task of identifying cucumber diseases with small sample sizes. Integrating image self-supervised mask learning, image self-supervised contrastive learning, and multimodal image-text contrastive learning, the model can not only learn disease feature information from different modalities, but also capture global and local disease feature information. Simultaneously, the mask learning branch was enhanced by introducing a prompt learning module based on a cross-attention network. This module aided in approximately locating the masked regions in the image data in advance, facilitating the decoder in making accurate decoding predictions. Experimental results demonstrate that the proposed method achieves a 95% accuracy in cucumber disease identification in the absence of labels. The approach effectively uncovers high-level semantic features within multimodal small-sample cucumber disease data. GradCAM is also employed for visual analysis to further understand the decision-making process of the model in disease identification. In conclusion, the proposed method in this paper is advantageous for enhancing the classification accuracy of small-sample cucumber data in a multimodal, unlabeled context, demonstrating good generalization performance.

Studies on the Detection of Viruses Infecting Capsicum Species in the Northern Zone of Cross River State, Nigeria

The objective of this study was to determine the occurrence, detection, and geographical spread of viruses that infect pepper (Capsicum spp.) in six towns spanning two local government areas (Yala and Ogoja) in the northern zone of Cross River State. A total of 348 leaf samples from symptomatic and asymptomatic pepper plants were randomly collected from farmer's fields during a survey in 2023–2024. The samples were then preliminary tested using an antigen coated plate enzyme-linked immunosorbent assay (ACP-ELISA) with seven antibodies specific for ToLCV, Cowpea aphid borne mosaic virus (CABMV), Cucumber mosaic virus (CMV), Potato virus X (PVX), Tobacco mosaic virus (TMV), and Pepper veinal mottle virus (PVMV). The findings showed that the viruses that infect pepper in Yala had a high cumulative mean incidence, accounting for 56.9% (198 infected samples). Yache in Yala had the highest virus incidence at 90%, while Bansara in Ogoja had the lowest at 40%. At different rates, ToLCV, CMV, PVY, and PVMV were found in the samples; PVX, TMV, and CABMV were not found. The CMV virus was the most commonly discovered of them. Additionally, mixed infections between the viruses—between ToLCV and PVY in Yache and between CMV and PVMV in Bansara—were found. This is the first report on account of viruses found in pepper in the northern part of Cross River State. The findings show that the viruses that cause the infections are common and might potentially be a threat to the production of pepper in that region.

Confirmation of Glyphosate Resistance in Annual Bluegrass (Poa annua) via EPSPS Duplication in a Soybean and Rice Rotation

Abstract Annual bluegrass (Poa annua L.) populations in turfgrass have evolved resistance to several herbicides in the United States (US), but there has been no confirmed resistance from an agricultural field. Recently, glyphosate failed to control a P. annua population found in a field in a soybean [(Glycine max (L.) Merr.] and rice (Oryza sativa L.) rotation in Poinsett County, Arkansas. The present study focused on determining the sensitivity of a putatively-resistant accession (R1) to glyphosate compared with two susceptible accessions (S1 and S2). The experiments included a dose-response study, 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) gene copy number and expression analysis, and assessment of mutations in EPSPS. Based on the dose-response analysis, R1 required 1,038 g ae ha-1 of glyphosate to cause 50% biomass reduction, whereas S1 and S2 only required 148.2 g and 145.5 g ae ha-1, respectively. The resistant index (RI) was approximately 7-fold relative to the susceptible accessions. Real-time polymerase chain reaction data revealed at least a 15-fold increase in the EPSPS copy number in R1, along with a higher gene expression. No mutations in EPSPS were found. Gene duplication was identified as the main mechanism conferring resistance in R1. The research presented here reports the first incidence of glyphosate resistance in P. annua from an agronomic field crop situation in the US.

Two Novel Fluorescence Probes Based on Caffeic Acid Derivative for Phosphate Ions and Their Applications in Biological Samples

Phosphate is widely used in industry and agriculture fields. However, excess accumulation of PO43− causes several adverse effects on the human body and ecological environment. Consequently, it is important to develop a simple method for the detection of PO43− concentration in the ecological environment and in vivo. Herein, two caffeic acid derivative-based fluorescence probes (BAM-HM and BAM-HH) were developed for the detection of phosphate. The BAM-HM probe could detect phosphate via fluorescence enhancement at 500 nm, with the detection limit being 0.612 µM. Meanwhile, the BAM-HH probe showed a significant turn-on signal at 450 nm after the addition of phosphate, and the detection limit was calculated to be 0.318 µM. The sensing mechanism was determined by 1H NMR and MS. Furthermore, the two probes (BAM-HM and BAM-HH) were applied for PO43−detection in living cells and water samples.

TopoSinGAN: Learning a Topology-Aware Generative Model from a Single Image

Generative adversarial networks (GANs) have significantly advanced synthetic image generation, yet ensuring topological coherence remains a challenge. This paper introduces TopoSinGAN, a topology-aware extension of the SinGAN framework, designed to enhance the topological accuracy of generated images. TopoSinGAN incorporates a novel, differentiable topology loss function that minimizes terminal node counts along predicted segmentation boundaries, thereby addressing topological anomalies not captured by traditional losses. We evaluate TopoSinGAN using agricultural and dendrological case studies, demonstrating its capability to maintain boundary continuity and reduce undesired loop openness. A novel evaluation metric, Node Topology Clustering (NTC), is proposed to assess topological attributes independently of geometric variations. TopoSinGAN significantly improves topological accuracy, reducing NTC index values from 15.15 to 3.94 for agriculture and 14.55 to 2.44 for dendrology, compared to the baseline SinGAN. Modified FID evaluations also show improved realism, with lower FID scores: 0.1914 for agricultural fields compared to 0.2485 for SinGAN, and 0.0013 versus 0.0014 for dendrology. The topology loss enables end-to-end training with direct topological feedback. This new framework advances the generation of topologically accurate synthetic images, with applications in fields requiring precise structural representations, such as geographic information systems (GIS) and medical imaging.

Analyzing Soil Pollution by Image Processing and Machine Learning at Contaminated Agricultural Field

Due to the fast advancement of big data, applying Machine Learning (ML) techniques to detect Soil Pollution (SP) at Potentially Contaminated Sites (PCS) across many sectors and regional sizes has emerged as a prominent research focus. The challenges in acquiring essential indices of SP sources and routes result in present methodologies exhibiting low predictive accuracy and an inadequate scientific foundation. This study gathered environmental data concerning heavy metal and organic contamination from 200 PCS across six representative sectors. Twenty-one indices derived from fundamental data, potential SP from products and materials, SP efficacy, and the migrating capability of SP were employed to build the SP detection index method. The research integrated the score into the new characteristic group, including 11 indicators using consolidation computation. The newly selected feature subset was utilized for training ML designs, including Random Forests (RF), Support Vector Machines (SVM), and Multilayer Perceptrons (MLP), and evaluated to ascertain its impact on SP recognition methods. The study findings indicated that the four newly developed indices by feature fusion exhibit an association with SP comparable to that of the original index. The component analysis suggests that several indices related to fundamental information, contamination potential from products and raw materials, and SP prevention levels significantly influence SP to varying extents. The index of the migratory capability of soil contaminants has minimal influence on the classification job of SP detection inside PCS. This research introduces a novel technological approach for identifying SP via big data and ML techniques while offering an overview and scientific foundation for PCS's environmental administration and SP mitigation.

MAR-YOLOv9: A multi-dataset object detection method for agricultural fields based on YOLOv9

With the development of deep learning technology, object detection has been widely applied in various fields. However, in cross-dataset object detection, conventional deep learning models often face performance degradation issues. This is particularly true in the agricultural field, where there is a multitude of crop types and a complex and variable environment. Existing technologies still face performance bottlenecks when dealing with diverse scenarios. To address these issues, this study proposes a lightweight, cross-dataset enhanced object detection method for the agricultural domain based on YOLOv9, named Multi-Adapt Recognition-YOLOv9 (MAR-YOLOv9). The traditional 32x downsampling Backbone network has been optimized, and a 16x downsampling Backbone network has been innovatively designed. A more streamlined and lightweight Main Neck structure has been introduced, along with innovative methods for feature extraction, up-sampling, and Concat connection. The hybrid connection strategy allows the model to flexibly utilize features from different levels. This solves the issues of increased training time and redundant weights caused by the detection neck and auxiliary branch structures in traditional YOLOv9, enabling MAR-YOLOv9 to maintain high performance while reducing the model’s computational complexity and improving detection speed, making it more suitable for real-time detection tasks. In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. At the same time, the model size was reduced by 9.3%, and the number of model layers was decreased, reducing computational costs and storage requirements. Additionally, MAR-YOLOv9 demonstrated significant advantages in detecting complex agricultural images, providing an efficient, lightweight, and adaptable solution for object detection tasks in the agricultural field. The curated data and code can be accessed at the following link: https://github.com/YangxuWangamI/MAR-YOLOv9.

Fabrication of Novel Porous Nano-pesticides by Modifying MPN onto Cu-TCPP MOFs to Enhance Bactericidal Efficacy and Modulate Its Bioavailability.

Nano-pesticides have attracted much attention in the field of agriculture, due to existing problems such as decreased bactericidal effect and poor adhesion. An environmentally friendly metal porphyrin (Cu-TCPP)-based nanocarrier pesticide release of diniconazole (DIN) was designed to enhance bactericidal efficacy and modulate its bioavailability in a multidimensional manner by constructing a metal phenolic network (MPN) encapsulation. The introduction of the MPN prevents the DIN from prematurely escaping from the Cu-TCPP@DIN@MPN in the environment and gives it strong interfacial adhesion to resist rain washing. The resulting Cu-TCPP@DIN@MPN nanoparticles (NPs) showed a lamellar stacked embedded structure, which improved the inhibition of Fusarium oxysporum (90.9%) and photostability (67.2%), while they do not affect healthy plant growth and meet the relevant food safety requirements for DIN residues. This work provides new ideas for the development of superior photostable, adhesive, rainwater erosion-resistant, and sustainable nanocarrier pesticides.

Strip cropping increases yield and revenue: multi-year analysis of an organic system in the Netherlands

Intercropping is proposed as a promising strategy to meet future food demand while reducing agriculture’s environmental impact by re-diversifying agricultural fields. Strip cropping, a form of intercropping, has a potential to simultaneously deliver multiple ecosystem services including productivity, while facilitating management as strip width can be adjusted to the working width of available machines. While the yield performance of strip cropping systems is influenced by the interaction between neighboring crops, to date, empirical studies on the performance of various crop combinations in strip cropping systems are limited. Here we used three-year data (2020–2022) from a 64-ha organic strip cropping system in the Netherlands to (1) evaluate the effects of crop neighbors and strip cropping on yield and (2) explore if optimizing the allocation of crop neighbors in alternative strip cropping configurations can improve yield and revenue performances. We analyzed the edge effect and strip cropping effect on yield of six crops grown in strips, each neighboring a total of five crops. The yield data was then used to evaluate the performance of the current and alternative strip configurations in terms of LER and relative revenue. Results showed that except for the positive effect observed on potato when neighboring celeriac or broccoli, edge effects lacked statistical significance. Strip cropping effect varied per crop: positive for faba bean and parsnip, neutral for celeriac and potato, and negative for oat and onion. Analysis across crops showed an overall significant positive strip cropping effect on yield. These findings highlighted the value of analysis at the cropping system level in developing designs aimed at unlocking the potential of strip cropping. The positive but variable strip cropping effects observed in the current experimental design and the two alternative configurations suggests prioritizing an overall increased crop diversity over optimizing their spatial arrangement. While we demonstrated increased productivity with strip cropping, further research is needed to expand the database on optimal crop combinations, extending the evaluation beyond yield and revenue performances to facilitate broader adoption of strip cropping in the Netherlands and Western Europe.

Persistent chemiluminescence-based nanosensor for portable point-of-care testing of H2O2.

Anovel glow-type chemiluminescence (CL)-based nanosensing system was developed for sensitive and rapid point-of-care testing (POCT) of H2O2 in food. CuSe nanoparticles (CuSeNPs) have excellent peroxidase-like activity. After being modified with thiols of 4-mercaptophenylboronic acid (MBA) (CuSeNPs@MBA), luminol can be catalyzed to produce long-lasting CL in the presence of H2O2. The possible reason for the long-lasting glow-type CL behavior was explored. Under the optimized condition, H2O2 can be sensitively detected with improved repeatability. The limit of detection isas low as 0.30 μM. To meet the requirement of in situ and outside of laboratory detection, a 3D-printed portable device was designed which can eliminate the environmental interference to improve detection accuracy. The developed multifunctional platform also has the advantages of simple operation and low cost, suggesting its great potential for applications in food and agricultural fields.

Bounding Seed Loss from Isolated Habitat Patches

Dispersal of propagules (seeds, spores) from a geographically isolated habitat into an uninhabitable matrix can play a decisive role in driving population dynamics. ODE and integrodifference models of these dynamics commonly feature a “dispersal success” parameter representing the average proportion of dispersing propagules that remain in viable habitat. While dispersal success can be estimated by empirical measurements or by integration of dispersal kernels, one may lack resources for fieldwork or details on dispersal kernels for numerical computation. Here we derive simple upper bounds on the proportion of propagule loss—the complement of dispersal success—that require only habitat area, habitat perimeter, and the mean dispersal distance of a propagule. Using vector calculus in a probabilistic framework, we rigorously prove bounds for the cases of both symmetric and asymmetric dispersal. We compare the bounds to simulations of integral models for the population of Asclepias syriaca (common milkweed) at McKnight Prairie—a 14 hectare reserve surrounded by agricultural fields in Goodhue County, Minnesota—and identify conditions under which the bounds closely estimate propagule loss.

In Silico, In Vitro, and In Vivo Studies Indicate the Endocrine-Disrupting Effects of Cyproconazole Stereoisomers.

The widespread use of chiral triazole fungicide cyproconazole (CPZ) in agricultural fields has led to frequent detection of CPZ in the environment. The restriction of CPZ in the EU raised wide concerns regarding its potential endocrine-disrupting effects (EDEs). The present study was conducted to evaluate EDEs of CPZ stereoisomers in vitro, in silico, and in vivo. The reporter gene assay indicated that all CPZ stereoisomers were agonists to the human estrogenic receptor α. (2S,3S)-(+)- and (2R,3S)-(-)-CPZ exhibited stronger binding capacities to ERα compared with (2R,3R)-(-)- and (2S,3R)-(+)-CPZ. Our computational studies showed consistent results with reporter gene assay, elucidating the stereoselective binding mode of CPZ to estrogen receptor. In zebrafish embryos, the 96h-lethality of CPZ stereoisomers ordered (2R,3R)-(-)- > (2R,3S)-(-)- > (2S,3S)-(+)- > Rac- > (2S,3R)-(+)-CPZ. Stereoselective developmental toxicity of CPZ was observed while (2R,3S)-(-)-CPZ is the most toxic isomer. The estrogenic hormones were significantly decreased in (2S,3R)-(+)- and (2R,3S)-(-)-CPZ groups and enhanced in (2S,3S)-(+)- and (2R,3R)-(-)-CPZ, along with the gene expression in hypothalamic-pituitary-gonad axis altered. CPZ shows no thyroid hormone activity. These data clarified that CPZ is a new-found endocrine disruptor threatening human health and each stereoisomer of CPZ showed stereoselective EDEs by regulating the nuclear receptor-mediated gene expression.

Monitoring and Modelling Fluxes of Water and Nutrients to Surface Drainage Network From Irrigated Agricultural Fields in a Hydraulically Reclaimed Coastal Area

ABSTRACTThis paper describes the application of a physically based agrohydrological model (named FLOWS), coupled with a kinematic wave approach model (named KWV) for water and solute runoff routing, for interpreting the fate of water and nutrients coming from cultivated fields to surface drainage network located in ‘Piana del Sassu’ in the Arborea plain, a hydraulically reclaimed area with shallow groundwater. Modelling was supported by a large complex database on soil, groundwater and surface drainage water, which was used for establishing the boundary conditions for simulations, as well as for calibrating and validating the model. The model FLOWS provided the water and nutrient fluxes to the surface water, which were passed to the KWV model for their routing along the elementary fields in the experimental area and from these to the ditches and finally to the drainage channel. The modelling approach effectively predicted the water and solute distribution along the soil profile, as well as the losses of water and nutrients to the surface water. The results showed a significant amount of water and dissolved nutrients to flow quickly from the soil uppermost layer to the surface drainage network during both the irrigation season and during rainfall events. During irrigation applications, losses were mostly due to rainfall intensity exceeding the maximum infiltration velocity of the shallow soil layer in the case of sprinkler irrigation and to subsurface lateral drainage in the case of exceeding irrigation water provided by drip irrigation. This makes the Sassu plain a significant contributor of nutrients (nitrate and phosphorus) to the surface water. Consequently, even though the agricultural activities might not be an important issue for the groundwater vulnerability, the management of water and nutrients should be significantly improved to avoid ecohydrological threats to the important coastal water bodies present in the area.