Extensive Field Research Articles

Managing anticipation and reference-dependent choice

Extensive field and experimental evidence shows that reference points shape behavior. But, what shapes the reference point? Candidates put forward in the literature range from the status quo, to rational expectations and the narrow focus of dreaming or worrying about a single possible outcome. This paper develops a model that includes all of these candidate sources. It does so, by allowing the reference point to be any convex combination of the outcomes possible under a consumption lottery. We introduce new solution concepts for reference-dependent choices, characterize these solution concepts on the level of choice data and identify the model’s parameters.

A novel proposed classification system for rock slope stability assessment

The present study introduces “rock slope instability score (RSIS)” a novel classification system for assessing rock slope stability. It takes into account geological and geotechnical parameters, as well as the impact of human activities and triggering parameters, which have become more frequent due to climate change and few of them have been ignored in existing classifications. The study focuses on rock slopes of various lithologies from the Indian Himalayas. The development of this new classification system is based on the examination of 81 different rock slopes from various states of the Indian Himalayas. Extensive field surveys, rock sampling, geotechnical laboratory tests, and ground measurements have been conducted at the various slope sites to establish a comprehensive scoring system for the stability assessment. The distributions of weightage to each parameter have been considered, corresponding to its degree of impact in causing slope instability. Sensitivity analysis of all defined parameters of RSIS system has revealed that the majority of the parameters exhibit a strong positive correlation, with Pearson correlation coefficients ranging from 0.74 to 0.61. However, two parameters, namely discontinuity dip and the relationship between slope & discontinuity direction, gives moderate relationship with correlation coefficient values of 0.48 and 0.41, respectively. To avoid any designer biasness in the system, several individuals gathered data set at different times. The proposed classification system has demonstrated a strong correlation with the actual slope condition, and it is quite promising. The outcome of RSIS classification for studied 81 slopes classified 2 slopes under stable condition, 21 slopes as partially stable, 44 as unstable, and 14 as completely unstable.

UAV and Deep Learning: Detection of selected riparian species along the Ganga River

Abstract. Environmental protection and sustainable natural resource management are being recognised worldwide as essential goals to safe guard human health and well-being. Riparian zones, that face the highest decline in freshwater biodiversity, are of prime conservation priority because they are essential for regulating climate, preserving aquatic-terrestrial biodiversity, maintaining ground water recharge and restoring rivers. In today's fast-paced data-driven environment, artificial intelligence (AI) is the precise answer to a wide range of problems including biodiversity conservation and wildlife management. Leveraging advancements like Uncrewed/Unmanned Aerial Vehicles (UAVs) and AI has resulted innovative strides in wildlife conservation. This study utilised UAV imagery to record high-resolution data of aquatic habitat and species along the Ganga River and employed deep learning algorithms to analyse the data. Through extensive field surveys in the Hastinapur Wildlife Sanctuary, 7,025 photos representing a variety of environments, including 20,000 annotated samples of aquatic animals such as turtles and gharials were generated. Vision based computing capabilities such as pattern recognition model were developed to identify these species. To enrich and enhance the dataset and model, we used different image pre-processing techniques. Slight rotation (±5 degrees), minor cropping (up to 10%), and adjustments in brightness, saturation, and shear (±15%) were applied. Controlled blur (up to 0.5%) and exposure modifications (±5%) were also implemented on the image dataset to improve accuracy. Three Convolutional Neural Network (CNN) architectures, single-stage detectors named YOLO v7, YOLO v8, and Roboflow 3.0, were used for detecting the select species. Results show YOLO v8 excels, achieving mean average precision (mAP) of 98.8% for gharial and 92.2% for turtle detection, with a rapid average detection time of 0.308 seconds per frame at 3200 × 3200 resolution. Additionally, our model demonstrates real-time species detection capability through innovative frame sampling techniques with UAVs. This methodology provides promising technique to collect scientific data on IUCN red listed turtles and critically endangered gharials, allowing detection, monitoring, and real time counting with minimal intrusion. In conclusion, the fusion of UAVs and deep learning promises to revolutionize habitat monitoring, aiding conservation decision-making.

Color constancy mechanisms in virtual reality environments.

Prior research has demonstrated high levels of color constancy in real-world scenarios featuring single light sources, extensive fields of view, and prolonged adaptation periods. However, exploring the specific cues humans rely on becomes challenging, if not unfeasible, with actual objects and lighting conditions. To circumvent these obstacles, we employed virtual reality technology to craft immersive, realistic settings that can be manipulated in real time. We designed forest and office scenes illuminated by five colors. Participants selected a test object most resembling a previously shown achromatic reference. To study color constancy mechanisms, we modified scenes to neutralize three contributors: local surround (placing a uniform-colored leaf under test objects), maximum flux (keeping the brightest object constant), and spatial mean (maintaining a neutral average light reflectance), employing two methods for the latter: changing object reflectances or introducing new elements. We found that color constancy was high in conditions with all cues present, aligning with past research. However, removing individual cues led to varied impacts on constancy. Local surrounds significantly reduced performance, especially under green illumination, showing strong interaction between greenish light and rose-colored contexts. In contrast, the maximum flux mechanism barely affected performance, challenging assumptions used in white balancing algorithms. The spatial mean experiment showed disparate effects: Adding objects slightly impacted performance, while changing reflectances nearly eliminated constancy, suggesting human color constancy relies more on scene interpretation than pixel-based calculations.

Crude credit: The political economy of natural resource booms and sovereign debt management

Oil, gas, and minerals have notoriously adverse effects on institutional quality. But when global liquidity is high, risk-tolerant investors are more willing to lend to all borrowers, even resource-rich countries with low-quality institutions. Despite the availability of cheaper credit during commodity booms, we argue that countries do not increase current borrowing to mitigate future revenue shortfalls during commodity busts. Instead, they rely on resource windfalls to meet their current financing needs, fearing they would otherwise forfeit national policy discretion to global financial markets. We leverage primary evidence from extensive field research across five Latin American countries to show that national economic officials (i.e. finance ministers and central bank governors) are wary of high indebtedness, after past commodity booms ended in cycles of lofty spending, borrowing, and default. For sovereign borrowers, high bond market indebtedness often reduces government discretion over economic policy, whereas windfalls increase it; all else equal, governments will favor the latter. Using data on 22 Latin American and Caribbean countries from 1996 to 2020, we find that governments issue bonds less frequently, in smaller amounts, as their oil and gas production or GDP share from resource rents increases. These findings make an important contribution to our understanding of how commodity cycles affect global capital markets: sovereign borrowers do not fully leverage commodity booms to expand their fiscal space to finance more spending over time.

Suburban bubbles: emerging suburban gated communities in the Prague Urban Region

Gated communities, which physically embody prestige and exclusivity through their security features and gating practices, started proliferating in the United States and were subsequently copied by other countries worldwide. However, even when replicated, the suburban gated communities in the Prague Urban Region differ in terms of size and the function of their security features and gating practices. Applying the typology of Blakely and Snyder, the research results propose a new type of development: the bubble community. This term refers to social separation with rather weak and temporary enclosure practices. The research findings indicate that bubble communities are usually small developments with weak gating practices and security features, where residents prefer to live in their own small social bubbles. Analyzing data acquired from extensive field mapping, field survey, and photo documentation, the research focuses on the physical distribution and spatial and physical characteristics of the two surrounding administrative districts: Prague-West and Prague-East.

Design and fabrication of two-dimensional holographic grating waveguide with large field of view and high uniformity

Augmented Reality (AR) technology is a technique that integrates virtual im- ages with the real environment. With the continuous improvement of comput- ing power in mobile electronic products, mobile devices with AR capabilities are gradually becoming the next generation computing platform. When eval- uating the performance of holographic waveguides, key indicators include the field of view (FOV), eye relief, and eye box uniformity. This study proposes a method for designing holographic waveguides with a large field of view and high uniformity based on a two-dimensional holographic grating (2DHG). By lever- aging the multiplexing characteristics of photopolymer, we superimposed two incoherent one-dimensional gratings on a holographic material layer, achieving dual-channel propagation of the field of view, with each channel responsible for half of the FOV. In the end, these two channels are stitched together to form a complete FOV. This method not only enlarges the eye box but also helps im- prove the system's FOV. By controlling the transmittance of the exposure area, the uniformity of the brightness of the wide-field eye box is improved. Finally, we conducted a performance analysis of the prepared holographic waveguide, verifying the feasibility and correctness of the proposed 2DHG waveguide in two-dimensional eye box expansion and field of view extension. Through eye box expansion, the eye box reached 14 mm × 17 mm, the diagonal field of view reached 58◦, the eye relief is 15 mm, and the eye box brightness uniformity reached 79.7 %.

Rapid morphosedimentary development of a shallow subtropical embayment in response to extreme episodic flooding

Variability in supply of terrestrial sediments to the coastal zone impacts sediment transport processes and ecological dependencies. This study investigated surface sediment distribution and transportation within a 2020 km2 shallow embayment following a period of flood induced sediment supply. Extensive field sampling of 221 common sites between 2015 and the 2019 follow-up survey presented, identified significant reduction in fine sediment fractions, evident through a 367 km2 decrease in surface mud coverage. Wind-wave sediment transport processes controlled the spatial distribution of fine sediments, exporting 23 million tonnes of mud from the shallow sedimentation zones. In a broader context, sensitive benthic habitats such as seagrass meadows exhibited signs habitat recolonisation coinciding with areas of reduced mud content. Despite these promising short-term outlooks, the developed fine sediment budget suggests ongoing terrestrial sedimentation will exhaust key sediment sinks within Moreton Bay, effectively reducing the system’s ability to buffer future sedimentation events.

Real-Time Monitoring on the Chinese Giant Salamander Using RPA-LFD.

The Chinese giant salamander (Andrias davidianus), listed as an endangered species under "secondary protection" in China, faces significant threats due to ecological deterioration and the expansion of human activity. Extensive field investigations are crucial to ascertain the current status in the wild and to implement effective habitat protection measures to safeguard this species and support its population development. Traditional survey methods often fall short due to the elusive nature of the A. davidianus, presenting challenges that are time-consuming and generally ineffective. To overcome these obstacles, this study developed a real-time monitoring method that uses environmental DNA (eDNA) coupled with recombinase polymerase amplification and lateral flow strip (RPA-LFD). We designed five sets of species-specific primers and probes based on mitochondrial genome sequence alignments of A. davidianus and its close relatives. Our results indicated that four of these primer/probe sets accurately identified A. davidianus, distinguishing it from other tested caudata species using both extracted DNA samples and water samples from a tank housing an individual. This method enables the specific detection of A. davidianus genomic DNA at concentrations as low as 0.1 ng/mL within 50 min, without requiring extensive laboratory equipment. Applied in a field survey across four sites in Huangshan City, Anhui Province, where A. davidianus is known to be distributed, the method successfully detected the species at three of the four sites. The development of these primer/probe sets offers a practical tool for field surveying and monitoring, facilitating efforts in population recovery and resource conservation for A. davidianus.

An Ethno-Pharmacological Study of Wound Healing Medicinal Plants Used by Traditional Healers in Dhamtari, Chhattisgarh, India

Ethno-pharmacology is "the multidisciplinary scientific investigation of the biologically active substances that are customarily used." As a result, the ethno-pharmacological approach is founded on a corpus of research encompassing pharmacology, chemistry, and botany, among other fields. The research was conducted in the Indian state of Chhattisgarh's Dhamtari areas. Fifteen families and a total of eighteen therapeutic plants with ethno-pharmacological potency, particularly in wound healing, were identified at the study site. In the research areas, extensive field surveys were carried out between March 2021 and February 2023. On field excursions, native knowledge of wild medicinal plants was gathered through conversations, questionnaires, and in-person interviews. Plants were arranged according to their scientific names, families, common names, and parts that were utilised to cure or promote wound healing. In the survey, the dominant families of plants like Amaranthaceae, Liliaceae, and Fabaceae had two (02) species of medicinal importance, particularly for wound healing treatment. The Dhamtari region's rural inhabitants have traditionally utilised native flora for primary healthcare and the treatment of a variety of ailments. On the other hand, hardly much was recorded regarding traditional knowledge of therapeutic herbs. The Dhamtari people of the countryside claimed that as society developed, newer generations became less interested in the traditional knowledge of therapeutic herbs. Therefore, before they become extinct and are no longer available, it is imperative to document ethno-medicinal plants that can heal wounds. In order to properly conserve plants and traditional knowledge for the future, this ethno-botanical database will be valuable to scientists, naturalists, planners, policymakers and chemists.

Assessing the role of plant clinic advisory services in plant health improvement in Chakwal district Punjab

Plant clinic (plant-wise) is a global catalog that Punjab Agriculture Extension is currently implementing to reduce crop losses to improve food security and rural incomes. Plant clinics provide farmers with information that obligates them to lose less of their produce due to pest and disease outbreaks. Plant clinics are held once a week in a village where farmers come to get advice on crops under the protection of a shade. The goal of the desk monitoring was to assess the overall execution and the assistance provided by this initiative. The study covered the district Chakwal, and 225 total participants were randomly selected for data collection in the research area. The data was collected by a well-structured interview schedule consisting of closed-ended and open-ended questions to maintain the quality regarding farmers' perceptions of plant clinics. After data collection, it was analyzed by using SPSS. Descriptive statistical analysis, including frequencies, averages and percentages was undertaken. The results show that 55.6% of the respondents belonged to middle aged. About 60.0% work as private jobs, 44.0% attained matric level education and 60/0% of them were living in a nuclear family. According to 100.0% of the respondents, the plant clinic was providing services about disease management, pest control resources, and integrated pest management. All (100.0%) the respondents reported that extension field staff teaches them methods of weed control, pest identification, fungal and bacteria identification through plant clinic. About 66.7% of the respondents learned the manure application from plant clinic activities. According to 100.0% of the respondents, disease identification and control of the disease were very important. All (100.0%) of the respondents never visited the plant clinic daily. All (100.0%) of them communicated with office calls. The vast majority (90.2%) of the respondents contacted the plant clinic through the farm visit. The majority (82.2%) of respondents disclosed that crop production increased up to 25% with the adoption of the plant clinic services advises. The majority (82.2%) of plant clinics are working with value addition. All (100.0%) of the respondents had awareness about the disease’s services, pest identification & control, seed verities recommendation, and spraying technique services. About all (98.2%) of them also stated that they had increased awareness about the fertilizer’s application services from the plant clinic. The majority (82.7%) of the respondents enhanced awareness about the disease’s services, pest identification & control, seed varieties recommendation, and spraying technique services.

OpenWeedGUI: An Open-Source Graphical Tool for Weed Imaging and YOLO-Based Weed Detection

Weed management impacts crop yield and quality. Machine vision technology is crucial to the realization of site-specific precision weeding for sustainable crop production. Progress has been made in developing computer vision algorithms, machine learning models, and datasets for weed recognition, but there has been a lack of open-source, publicly available software tools that link imaging hardware and offline trained models for system prototyping and evaluation, hindering community-wise development efforts. Graphical user interfaces (GUIs) are among such tools that can integrate hardware, data, and models to accelerate the deployment and adoption of machine vision-based weeding technology. This study introduces a novel GUI called OpenWeedGUI, designed for the ease of acquiring images and deploying YOLO (You Only Look Once) models for real-time weed detection, bridging the gap between machine vision and artificial intelligence (AI) technologies and users. The GUI was created in the framework of PyQt with the aid of open-source libraries for image collection, transformation, weed detection, and visualization. It consists of various functional modules for flexible user controls and a live display window for visualizing weed imagery and detection. Notably, it supports the deployment of a large suite of 31 different YOLO weed detection models, providing flexibility in model selection. Extensive indoor and field tests demonstrated the competencies of the developed software program. The OpenWeedGUI is expected to be a useful tool for promoting community efforts to advance precision weeding technology.

Analysis of the Surrounding Rock Full-Displacement Variation in Large-Span Mudstone Highway Tunnels

Analysis of the Surrounding Rock Full-Displacement Variation in Large-Span Mudstone Highway Tunnels

Recent Development in Intelligent Compaction for Asphalt Pavement Construction: Leveraging Smart Sensors and Machine Learning.

Intelligent compaction (IC) has emerged as a breakthrough technology that utilizes advanced sensing, data transmission, and control systems to optimize asphalt pavement compaction quality and efficiency. However, accurate assessment of compaction status remains challenging under real construction conditions. This paper reviewed recent progress and applications of smart sensors and machine learning (ML) to address existing limitations in IC. The principles and components of various advanced sensors deployed in IC systems were introduced, including SmartRock, fiber Bragg grating, and integrated circuit piezoelectric acceleration sensors. Case studies on utilizing these sensors for particle behavior monitoring, strain measurement, and impact data collection were reviewed. Meanwhile, common ML algorithms including regression, classification, clustering, and artificial neural networks were discussed. Practical examples of applying ML to estimate mechanical properties, evaluate overall compaction quality, and predict soil firmness through supervised and unsupervised models were examined. Results indicated smart sensors have enhanced compaction monitoring capabilities but require robustness improvements. ML provides a data-driven approach to complement traditional empirical methods but necessitates extensive field validation. Potential integration with digital construction technologies such as building information modeling and augmented reality was also explored. In conclusion, leveraging emerging sensing and artificial intelligence presents opportunities to optimize the IC process and address key challenges. However, cooperation across disciplines will be vital to test and refine technologies under real-world conditions. This study serves to advance understanding and highlight priority areas for future research toward the realization of IC's full potential.

Pedogenic Characterization of Alluvium-Derived Soils in the Arid Region of Rajasthan, India

South eastern part of the arid region of Rajasthan (India) is an alluvial plain developed from the sediments deposited by the river Luni and its tributaries, arising from the Aravali hill range. Soils of the region are medium to fine textured. Mean annual rainfall ranges between 350-400 mm, the soil temperature regime is hyperthermic, and the moisture regime is aridic, which restricts pedogenic processes. The objective of the study is to characterize the uniformity of the parent material and illuviation process. Extensive field work for the study of morphological characteristics of soil profiles revealed two major types of soils, viz., medium texture non-calcareous and fine texture calcareous. Sand fractionation and related parameters were worked out to ascertain the uniformity of the parent material. In medium texture non-calcareous profiles, medium sand (1-2 Φ) and fine sand (2-3 Φ) are dominant fractions, while in fine texture calcareous alluvium, the major particle sizes are fine sand (Φ 2-3), very fine sand (3-4 Φ), and coarse silt (Φ 4-5). The sorting coefficient in medium texture (1.49 to 1.61) and fine textured (1.41 to 1.81) soils indicated the well-sorted nature of the sediments (values less than 2.5). In both medium and fine texture soils, the depth-wise uniform distribution of all size sand fractions and the sorting coefficient indicated the uniformity of the parent material. Parameters such as clay/sand ratio, HA-carbon/FA-carbon ratio, CEC, and CaCO3, considered together, indicated that in medium texture non-calcareous soils, the clay and humus fractions have moved up to 80 cm depth, whereas in fine texture calcareous soils, the clay, humus fractions, and CaCO3 have moved up to 60 cm depth. These observations suggest that both medium and fine texture soils have undergone the illuviation process, but the process is more pronounced in fine texture calcareous soils.

Determining the prevalence of Escherichia coli, Salmonella, and shiga toxin-producing Escherichia coli in manure of dairy lagoons.

The aim of this study was to determine the prevalence of microbial pathogens in manure of dairy lagoons in California. To determine pathogens in dairy manure stored in anaerobic lagoons of dairy farm, an extensive field study was conducted across California to sample manure from 20 dairy farms. Samples were analyzed to determine the prevalence of indicator E. coli, Shiga toxin producing E. coli (STEC), Salmonella, and E. coli O157:H7. To test the E. coli, STEC, and Salmonella, we used agar culture-based method followed by polymerase chain reaction (PCR) method. In addition, a real- time PCR based method was used to determine the presence of E coli O157:H7. Study demonstrated that the prevalence of Salmonella in manure sample is lower than E. coli. The presence of Salmonella was found in 2.26% of the samples, and both the culture-based and PCR methods yielded comparable outcomes in detecting Salmonella. Moreover, approximately 11.30% of the total samples out of the 177 were identified as positive for STEC by qPCR. These findings demonstrate that indicator E. coli are abundantly present in anaerobic lagoons. However, the presence of STEC, and Salmonella is substantially low.

Shape Effect of Rockfall Impacting Sandy Soil Cushion Layer–Reinforced Concrete Slab Composite Structure

The impact effects of falling rocks on sand–reinforced concrete slab composite protective structures involve several factors. Among them, the existing codes are unable to consider the effect of rockfall shape and the angle of contact between the rockfall and the object on the impact force as well as the depth of penetration. Based on extensive field investigation, this paper proposes a shape factor to simplify the rockfall into an ellipsoid and determines the shape and dimensions of the rockfall by three-dimensional axis length. Besides, a coupled SPH-FEM numerical calculation model is established and validated through comparison with a large-scale outdoor test of a rockfall impact protection structure. Finally, the effects of rockfall shape and impact angle on the symbolic parameters including impact force, impulse and energy in the impact process are revealed. The findings indicate that the maximum force and displacement of the midpoint of the bottom of the reinforced concrete slab have relative errors within 5.0 % when compared to the model test, confirming the precision of the models discussed in this paper. For the same rockfall, the peak force decreases with the impact angle increasing; taking the same volume of spherical rockfall as the reference, under the same rockfall pattern, the peak impact force and impulse amplification factor decreases with the increase in contact attitude angle. Additionally, the scaling effect becomes more pronounced when the shape factor of the rockfall is smaller; under the same shape factor, the impact depth of the cushion layer is the smallest when the attitude angle is 45°, and the maximum when the impact angle is 90°; the SPH-FEM coupling algorithm could reasonably reproduce the pit-forming process of sand and soil, and it is very effective in simulating the flow effect of soil particles under impact.

Motion planning and contact force distribution for heavy‐duty hexapod robots walking on unknown rugged terrains

AbstractHeavy‐duty hexapod robots have impressive stability, high load‐bearing capacity, and exceptional adaptability to rugged terrains. They are capable of working in challenging outdoor environments such as planetary exploration, disaster relief and mountain transportation. Their ability to traverse terrain requires effective motion planning and accurate force distribution, neither of which is currently at the level required for widespread practical applications. In this paper, the mechanical legs are divided into support and swing legs, and the adaptability of the hexapod robot to unknown rugged terrain is enhanced by introducing the Decomposition Quadratic Programming‐based Contact Force Distribution (DQP‐based CFD) method. Moreover, an efficient replanning strategy can handle accidental collisions between swinging legs and unmodelled obstacles. Extensive field experiments demonstrate the effectiveness of our proposed motion planning and contact force distribution methods.

Alfalfa Cultivation Patterns in the Yellow River Irrigation Area on Soil Water and Nitrogen Use Efficiency

Establishing lucerne field is an efficient way to protect natural steppes, alleviate conflicts between meadows and livestock, and promote the development of animal husbandry. However, problems such as extensive field management, valuing yield over quality, and low resource utilization are endemic in production. Exploring reasonable cultivation patterns can contribute to improving the current situation of artificial grassland production and promoting the high-quality development of husbandry and prataculture. Lucerne the field experiment was carried out in Jingtai, Gansu Province, China in 2021–2022; this study compared and analyzed the effects of three cultivation patterns—ridge tillage with plastic film mulching (PM), ridge tillage with biodegradable film mulching (BM), and traditional flat planting (FP)—on soil water, heat, and fertilizer, as well as lucerne growth, yield, quality, and water and nitrogen use efficiency. The results show that: (1) during the growth period of lucerne, PM and BM treatments augment the average moisture content of the soil layer of 0–120 cm by 31.19% and 24.03% compared to the FP treatment, respectively. In the soil layer of 0–40 cm, PM and BM treatments abate the soil moisture content of the ridges by an average of 19.29% and 7.89% compared to that in the ditches, respectively. In the soil layer of 40–120 cm, PM and BM treatments elevate the soil moisture content of the ridges by 4.40% and 4.65% on average compared to that in the ditches, respectively. The average soil temperature in a soil layer of 5–25 cm shows PM > BM > FP. In contrast with the FP treatment, PM and BM treatments increase the soil temperature of the ridges by an average of 1.87 °C and 0.96 °C and decrease that of the ditches by an average of 0.47 °C and 0.46 °C, respectively. After two years of planting, the three cultivation patterns all promote the soil nutrient content. Compared to the FP treatment, PM and BM treatments increase the organic matter content by 9.94% and 19.94%, respectively. (2) Ridge tillage with film mulching can evidently stimulate the growth of lucerne and enhance yield and quality. Compared to the FP treatment, PM and BM treatments enhance plant height by an average of 15.37% and 4.04%, stem diameter by an average of 34.14% and 14.58%, yield by an average of 21.20% and 14.77%, crude protein content by an average of 13.47% and 7.68%, and relative feed value by an average of 8.71% and 4.41%, respectively. (3) During the two-year growing period, the irrigation amount of lucerne was 508.60–615.30 mm, and the evapotranspiration was 563.70–761.80 mm. Compared to the FP treatment, PM and BM treatments hoist water use efficiency by an average of 43.50% and 17.56%, nitrogen partial factor productivity by an average of 21.20% and 15.22%, and net income by an average of 14.78% and 11.05%, respectively. In summary, in ridge tillage, both ordinary film mulching and biodegradable film mulching can create a favorable soil environment for lucerne growth and heighten production effect. The former has a better effect on advancing the lucerne production effect, and the latter exhibits superior performance in improving soil fertility.

Developmental characteristics of loess mudflow and correlation analysis with influencing factors

Loess mudflow is a unique geological hazard in the Loess Plateau, presenting a significant risk to the lives and properties of the region’s inhabitants. Defining and categorizing loess mudflow scientifically, as well as comprehending their disaster-prone environment and patterns of development, are crucial for implementing effective disaster prevention and mitigation strategies. Based on extensive field investigations and statistical analyses, this study defines and categorizes the loess mudflow into five types: liquefaction flow, freeze-thaw creep, subsurface erosion failure, collapse-mudflow, and erosion-enlargement. Statistical analysis reveals that the drainage area of loess mudflow is mostly concentrated between 0.15 and 30 km2, the bed slope typically ranges from 10‰–200‰, material storage in channels is mostly above 5 × 104 m3/km2, and the rainfall intensity triggering mudflow formation is generally greater than 10 mm/h; furthermore, a correlation analysis between the parameters of loess mudflow and the influencing factors reveals a significant linear growth relationship between mudflow volume and drainage area, and also reveals a significant power function relationship with the increase in channel bed slope. However, there is no significant correlation between mudflow volume and solid material storage, and mudflow density shows no significant correlation with drainage area, channel bed slope, or solid material storage. Finally, an empirical formula for estimating mudflow volume is proposed through regression analysis. The research findings aim to provide a theoretical basis for the study and risk assessment of loess mudflow disasters.