The rapid movement of water through favorable channels in the soil is referred to as preferential flow, by passing the slower and more diffuse flow through the soil matrix, this is known as preferential flow. The intricate network of interconnected macrospores in the three-dimensional structure of preferential flow has a wide range in connection, shape, and size. The profile of the preferred flow is determined by previous soil moisture levels, vegetation, soil properties, land use, and rainfall intensity and duration. The texture, structure, and amount of organic matter significantly affect the preferential flow routes, which in turn affects the size and distribution of macrospores in the soil. By changing the pore structure and soil characteristics, land use activities including tillage, compaction, and the use of fertilizers and pesticides can also have an impact on the structure of the preferred flow. Depending on the type and intensity of the vegetation cover, vegetation can either encourage or obstruct preferred flow^[1]. Major elements that affect the beginning and spread of preferential flow channels include rainfall characteristics including intensity, duration, and spatial distribution. The development and existence of preferential flow channels are influenced by the previous soil moisture levels. Understanding the preferred flow's three dimensional structure and the variables that impact is necessary for predicting and managing water flow in soil systems. This knowledge is helpful for developing more effective and sustainable agriculture and water management techniques as well as for predicting the fate and transit of pollutants in soil and groundwater systems. Preferential flow is a fundamental phenomenon in soil hydrology that influences how water and solutes move through the soil. In this mechanism, water moves through preferred channels, such as macropores, soil fissures, and root channels, as opposed to the more uniform matrix flow. Predicting pollutant movement in the environment, comprehending the hydrological cycle, and managing soil moisture and nutrients in agricultural systems are just a few of the benefits of understanding preferred flow. For several reasons, research on preferred flow frequently takes place on agricultural land. First, due to changes in soil structure, texture, and organic matter content, agricultural soils are frequently characterized by a high degree of heterogeneity^[2]. This may lead to the development of preferred flow routes. For instance, frequent plowing of the soil can produce aggregates of dirt connected by pores, resulting in the construction of continuous channels through which water can flow more preferentially. Like this, the root systems of plants can build pathways for the movement of nutrients and water, further encouraging preferential flow.

Agricultural land offers a very consistent and accessible surface for gathering data and running preferred flow tests. It is simpler to set up experimental plots and monitor soil water content at various depths and locations in agricultural fields since they are frequently big and flat. Utilizing agricultural land for preference flow studies can also shed light on how agricultural management techniques like tillage, crop residue management, and irrigation can either promote or obstruct preferential flow^[3]. The addition of organic matter can improve the production of macropores and boost preferential flow, but tillage can disturb soil aggregates and macropores and hence limit preferential flow. Knowledge gained from research on preferred flow in agricultural land can be used to improve agricultural management procedures like nutrient and irrigation planning. Farmers can optimize irrigation schedules and prevent overwatering, which can result in waterlogging and nutrient leaching, by understanding how water and solutes travel through the soil. Similarly, to this, knowing how nutrients travel through soil can assist farmers in creating specialized nutrient management plans that lower the likelihood of nutrient loss and boost crop output. Pollutants may travel through favored flow pathways from the soil surface to the groundwater like pesticides, fertilizers, and other chemicals, which can contaminate sources of drinking water^[4]. Researchers can create models to forecast contaminant transport and create plans to reduce the danger of groundwater pollution by researching preferred flow in agricultural soils.

To analyze characteristic behavior and the three-dimensional path of preferential flow, it was decided to conduct our research trials on Adaptive Research Farm Sheikhupura Pakistan. (Located at Sarwar Shaheed Road Civil Lines, Sheikhupura, Punjab, Pakistan) (E 73° 29′ 7.12″, N 31° 34′ 15.24″). Two compaction trials were conducted to study the three-dimensional dynamics of soil solute transport and water flow. Then we analyzed all the pictures using Origin Lab, Sketchup 2021, and Image-Pro Plus 6.0 and established a three-dimensional model of the water and solute flow in the soil. The compaction trials were conducted on well-structured clay soil (45-54 percent clay) at the Adaptive Research Farm Sheikhupura, Punjab, Pakistan (Upper and Lower, roughly 500 m apart).

The approach that is most usually used to describe how preferential flow channels are distributed is three-dimensional image analysis with dye tracers. In this study, this methodology was used. The tracer experiments were conducted in September 2022 on Adaptive Research Farm Sheikhupura. Four Plots (120 cm by 120 cm) having a depth of 30 cm: Two at the upper side and two at the lower site, were designed to check the soil compaction and three-dimensional Preferential flow pattern of water and solute. The compaction treatment was performed by a Combine Harvester Machine, with a tire pressure of 44 psi and with a total load of 3 145 kg tire inflation pressure (tire size: 18.4/15-306 PR). Both locations were moldboard plowed to a depth of roughly 20 cm after compaction. The research area was covered by vegetation, and no crop was standing on it.

The following Tab. 1 lists some of the basic features of the soils at the various sites.

表 1(Tab. 1)

Tab. 1 The physical and chemical properties of the upper and the lower site, which are used in determining PF Pathways Site Plants name Soil organic matter/% Soil texture Bulk density/(mg·m-3) Electrical conductivity/(dS·m-1) pH Upper site Shabbat grass Cynodon dactylon 1.26±0.05 Loam 1.65±0.08 >4 8.5±0.2 Lower site Gajar Booti Parthenium 1.17±0.03 Clay Loam 1.46±0.06 < 4 8.1±0.15

Tab. 1 The physical and chemical properties of the upper and the lower site, which are used in determining PF Pathways

Each plot's (120 cm× 120 cm) area received an initial rainfall of 50 L of water utilizing a Spray Machine with a flat nozzle for the dye tracing test (The average annual rainfall at this area is 600-700 mm). Following 24 h, 50 L of Brilliant Blue solution (5 g/L) were given to each plot^[5]. Each plot contained three vertical soil profiles that were each 150 cm deep, and the profile walls underwent extensive knife preparation. After that, all values were logged before the preferred flow was measured using a measuring table. The soil was compacted to prevent dye tracer and steel frame intrusion after spraying. Using a sprayer with nozzles that were operated above the center of the frame, to identify preferential flow routes, brilliant blue FCF (5 g/L) was progressively sprayed on the soil's surface as a dye tracer. The dye solution was transported to the sprayer by a continuous flow pump. During the spraying experiment, an identical depth of water was sprayed at the earth between two steel frames (as clearly shown in Fig. 1) to prevent the vivid blue solution from penetrating laterally. After 24 h, when the frames were moved, the soil margins surrounding them were disturbed, making it impossible to study anything but the soil's center.

图 1(Fig. 1)

Fig. 1 Application of Brilliant Blue with hand pump

The main portion of the dye area was divided into two horizontal cross-sections, each measuring 120 cm by 120 cm. A digital camera was used to take vertical photos of each horizontal cross-section that can be seen in Fig. 1. After removing the litter layer, we collected soil samples from the surrounding soil of each plot and evaluated the mass moisture content, bulk density, total porosity, mechanical composition, pH, as well as conductivity, organic matter content, and hydraulic conductivity.

In the compacted and control plots at the Upper and Lower sites, two samples each (20 cm high, 10 cm in diameter) were obtained from the 30-50 cm layer of soil. The soil indicators (porosity, large macropores, small macropores, length of infiltration, bulk density, saturated hydraulic conductivity and air permeability as mentioned in Tab. 2) were analyzed after sampling in Soil Testing Laboratory in Sheikhupura Pakistan.

表 2(Tab. 2)

Tab. 2 Soil parameters of four plots Parameter Plot 1 Plot 2 Plot 3 Plot 4 Porosity/% 8.00 8.00 7.90 7.93 Large macropores>1.2 mm/% 0.46 0.35 0.16 0.09 Small macropores>0.3 mm/% 4.00 2.00 0.10 0 Number of pores >1.2 mm 642.30 652.10 448.60 302.80 Length of infiltration/cm 25.00 29.00 35.00 39.00 Bulk density/(g·cm-3) 1.31 1.38 1.37 1.46 Saturated hydraulic conductivity/(cm·h-1) 37.70 32.00 10.09 0.09 Air permeability/μm2 33.50 21.00 12.10 4.00

Tab. 2 Soil parameters of four plots

These soil indicators were analyzed as below.

1) Porosity: Porosity was determined by using the volume displacement method it involves measuring the volume of water that can be held by the soil and calculating the porosity based on the soil volume and water volume.

2) Large macropores and small macropores: Macropores are larger openings in the soil that allow for better water movement and aeration. These were assessed by using image analysis. Image analysis involves taking pictures of soil samples and analyzing them to determine the size and distribution of macropores.

3) Length of infiltration: The length of infiltration was determined by conducting a simple infiltration test.

4) Bulk density: It was determined by collecting a known volume of soil, drying it to remove moisture, and then measuring its mass. The bulk density was calculated by dividing the dry mass by the volume.

5) Saturated hydraulic conductivity: It was determined by using falling head permeameter. In this method flow of water was measured through a soil sample under controlled conditions.

6) Air permeability: It was determined by using methods like those used for hydraulic conductivity, but with air flow instead of water flow.

These images were captured using a DSLR camera by adjusting the lens at different positions. The pictures were then processed using SketchUp 2021 and Image-Pro Plus software. Adobe Photoshop CS3 was utilized to adjust the perspective effect of the digital photographs. The dyed areas were colored blue and gray, while the non-dyed sections were painted white. Image analysis was performed using SketchUp 2021, Adobe Photoshop CS3, and Image-Pro Plus 6.0 software. This analysis allowed for the detection and quantification of preferred flow pathways based on the dye-stained portions and unstained areas. Excavation of dye tracing profiles was conducted during the dye tracing tests at the field sites.Then, dye-tracing images were taken of the plots in bright light. Because of the differences in groundwater levels between the two sites, photos obtained from the soil surface down to 100 cm depth were analyzed at the upper site, whereas images taken from the soil surface down to just 70 cm depth were analyzed at the lower site. To analyze the images, software such as SketchUp 2021 and Adobe Photoshop CS3 were used to distinguish dyed areas from non-dyed areas through changes in brightness, contrast, hue, and saturation. The dyed parts were replaced with black and the non-dyed parts with white. The Image-Pro Plus 6.0 software was used to transform the image into numerical matrices and determine the size and spatial location of the preferred flow pathways. The software's classification and count features aided in distinguishing the different size classes and spatial locations of the preferred flow. The data obtained included the coordinates and radius of the dyed regions. Additionally, image rectification and statistical methods were employed to detect the locations of plant roots in the horizontal images. The stained area served as the basis for computing the radius of influence, and the area of each colored zone was equal to the number of pixels each plaque occupied, which were approximately represented as rings. Then, a data file containing the coordinates at the center point and radius of the dyed regions was produced. After initial image rectification, an artificial statistical method was used to detect the locations of the plant roots in the horizontal images.

The conducted three-dimensional studies have significantly enhanced our understanding of key characteristics related to soil water movement. It is now evident that soil water movement is a highly intricate and dynamic process, subject to the influences of various factors, including soil properties, the presence of preferential pathways, and the specific characteristics of water input. In the context of dye tracing tests illustrated in Fig. 2, Brilliant Blue, when introduced with water, infiltrates the soil through diverse pathways such as leaching or runoff. Upon entering the soil, it engages with soil water, giving rise to a complex three-dimensional flow pattern.

图 2(Fig. 2)

Fig. 2 Pattern of four plots for dye tracing tests

This movement of Brilliant Blue is intricately linked to factors such as soil type, texture, structure, and moisture content. Notably, its progression is notably swifter and deeper within sandy soils as opposed to clay soils. To gain a clearer insight into the intricate three-dimensional movement of Brilliant Blue, techniques like soil sampling and chemical analysis were skillfully employed, enabling the detection of its presence at varying depths and locations within the soil. Fig. 3 presents a visual representation of preferential flow within the soil in a three-dimensional perspective. Both a side view and a downward view are provided, enhancing our understanding of this phenomenon.

图 3(Fig. 3)

Fig. 3 Downward movement 3D structure of preferential flow inside the soil

Similarly, Fig. 4 captures the real-time movement of Brilliant Blue within the soil over a 24-hour period. The image effectively demonstrates that Brilliant Blue reaches a maximum depth of 15.5 cm in the soil. The movement of FCP (Fast-Flowing Colored Particles, presumably) is influenced by a range of factors, including root systems, pores, and cracks. It is noteworthy that Brilliant Blue attains its maximum depth in areas where roots are present^[6]. This study employed Sketch Pro software to create a three-dimensional representation of Brilliant Blue's movement, aiding in a more comprehensive understanding. The depth of movement was quantified using a steel frame.

图 4(Fig. 4)

Fig. 4 Downward movement 3D structure of preferential flow inside the soil

Fig. 5 represents the real downward movement in Brilliant Blue and water below the soil surface and four different angles pictures has been taken (after 24 hours of foliar application of mixture) from four different plots to show the clear downward movement of Brilliant Blue^[7].

图 5(Fig. 5)

Fig. 5 Pattern of preferential flow of water in 04 plots after 24 h

In summary, the outcomes of this research underscore the intricate nature of soil water movement, highlighting the interplay between soil characteristics, preferential pathways, and water input. The visual representations provided in the figures and the tabulated soil parameters offer valuable insights into these processes. The detailed examination and synthesis of these results provide a robust foundation for advancing our understanding of soil hydrology and related phenomena^[8].

In conclusion, this study has successfully unraveled the intricate nature of preferential flow within soil, shedding light on the emergence of distinct flow paths. Through meticulous three-dimensional analyses, a comprehensive comprehension of these preferential flow patterns has been achieved. By employing advanced techniques encompassing soil sampling, chemical analysis, and image analysis software, water and dye movement in the soil have been effectively visualized^[9].

The obtained imagery and data have been instrumental in identifying and quantifying preferential flow pathways, discerning between dye-stained segments and unstained regions. Exploring diverse infiltration conditions, we investigated the interplay between infiltration quantities and preferential flow path attributes. Notably, we established a direct correlation between infiltration rates and both dye penetration depth and the number of preferential flow paths.

This study unveiled a nuanced distribution of preferential flow channels across varying soil depths and infiltration intensities^[10]. Evident at smaller scales and shallower soil strata, preferential flow showcased localized and uneven characteristics. In contrast, as scale increased and soil depth augmented, preferential flow exhibited a more organized and structured demeanor, guided by larger soil structures.

Moreover, this investigation into compaction's influence on soil pore structure and transport characteristics highlighted a reduction in large mesopore percentage. This, in turn, adversely impacted soil hydraulic conductivity and air permeability. These insights into the impact of compaction broaden our comprehension of preferential flow dynamics^[11].

By synthesizing these findings, this study enriches the realm of soil hydrology. The unraveling of key characteristics, coupled with a nuanced grasp of three-dimensional flow patterns and spatial distribution, holds profound implications for water movement within agricultural systems. Through addressing intricate scientific queries, this research advances our knowledge of soil hydrology, paving the way for informed strategies in managing water movement in agricultural contexts.