Excerpts from interviews.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"51962",title:"Application of Lean Methodologies in a Neurosurgery High Dependency Unit",doi:"10.5772/64715",slug:"application-of-lean-methodologies-in-a-neurosurgery-high-dependency-unit",body:'\nImagine that in a ward or in an acute care unit, a patient develops a sudden and severe laryngeal edema and stops breathing for obstruction of the respiratory tract. Nurse and medical staff starts‐up advanced life support (ALS) maneuvers. The primary and emerging interventions are to permeabilize the airway accessing the trachea through an endotracheal tube. Not being possible to access it by the usual routes, the only solution is to perform a tracheotomy or cricothyrotomy, using a tracheotomy surgical tray (TST) or an emergency cricothyrotomy kit (ECK). However, the health team most of the times do not know its existence, location, or has difficulties in accessing it in due time, what could have as consequence the loss of a life.
\nPortuguese Directorate‐General of Health (DGS) points out on the Circular Normative no. 15/ DQS/DQCO of 22/06/2010 [1] that “patients who are admitted in hospitals believe that they are being admitted to a safe environment. They feel confident that if their clinical condition gets worse, they are in the best place for a prompt and effective intervention. However, there is some evidence that this does not always happen” (p. 1). This Circular also states, “ALL inpatient areas should have easy and immediate access to equipment, supplies and emergency drugs. They should be organized and stored in a standardized way… throughout the health unit” (p.6). However, compliance with these recommendations depends, above all, on political and management decisions of legislators, regulators, managers, and industry providers, and it is at the health institutions jurisdiction to “adequate resources and create the structures that leads to quality professional practice” [2]. For the Portuguese Republic Government (Governo da República Portuguesa—GRP) is “fundamental, that the available resources are better used, avoiding waste, that is, improving management, transparency, and accountability for the use of money from the citizens” [3]. Corvi [4] draws attention to the “waste epidemic in health care,” as acknowledge by the GRP, and so a great opportunity to improve, being fundamental a spirit of continuous learning as part of “implementing a lean management system” [4].
\nThe Intensive Care Society [5] recommends “all critical care areas should have their own, appropriately stocked and checked difficult airway trolley to deal with airway and tracheostomy emergencies” (p.11). The absence or inaccessibility to this kind of equipment can lead to adverse events with huge impact on the safety and lives of patients, mainly the critical ones. To the matter of the impact of layout configurations in hospital environment, Soriano‐Meier et al. [6] points out that “inadequate facility layout negatively affects the performance of the service staff, the quality of care provision and the service temporally over time” (p. 255).
\nAt a particular neurosurgery high dependency unit (NHDU) of a central hospital in Lisbon, problems related to design, layout, architectural barriers, accessibility to life support equipment (LSE) and wastes of time, handling, and transport were identified. Those that may infer greater impact on patients and on the provision of care taken by nurses are as follows: (a) accessibility to LSE, (b) difficulty/inexperience in the use of the resuscitation trolley (RT), and (c) lack of knowledge of ECK and TST existence and location. This chapter summarizes the action research study embarked with the purpose of testing the application of Lean methodologies at NHDU for a quality and safety provision of care to acute neuropatients and to reduce at least by half, time, steps, and distance travelled by nurses’ accessing LSE. Gemba walk, value stream mapping, spaghetti diagrams, 5S, and JIT (just‐in‐time) were the main Lean methodologies used.
\nGemba is the Japanese term used for Shop floor, where products are produced, or where the services are provided [7, 8]. To start an improvement project, it is critical to analyze the current and real situation of an organization or its workplace. Therefore, the gemba should analyze processes, time of setups, physical layout and surroundings with an open mind, to detect where, how, and why clients and staff experience problems [7–9].
\nThe value stream mapping (VSM) is one of the main lean methodologies that engages waste elimination in any organization [10]. VSM will help to identify and analyze, for example, problems experienced by stakeholders, errors in medicine, flow of processes and work, financial analysis, among others. VSM allows checking (visual and graphically) the current state of a particular procedure, its productive time (value‐added), and the non‐productive time (non‐value‐added) [11].
\nJackson [12] argues that 5S is the foundation of the Toyota production system (TPS). What this methodology tries to ensure is an orderly organized workspace for an efficient and safe work environment [10], increased productivity, fewer errors, and less waste [8]. 5S represents the five levels of this methodology starting with the letter “S”. In Japanese vocabulary: SEIRI (sort), SEITON (set in order), SEISO (shine), SEIKETSU (standardize), and SHITSUKE (sustain). Smart [13] summarizes this methodology with the expression “a place for everything and everything in its place” (p. 62).
\nJIT is a production process that targets the optimization of the process as a whole in a continuous flow improvement and tries to answer to the organization or service needs. Briefly, it means producing no sooner, no later, neither more or less, only and just the necessary [8, 10].
\nAnother Lean methodology is the spaghetti diagram. This diagram consists on a graphic reproduction of the architectural floor plan of a structure, where you draw lines from one space to another, representing the path taken by employees, customers, and objects along a particular process (round trip) [12, 14]. It allows documenting and visualizing the physical flow, in order to identify waste motion or transportation, architectural barriers, and improvement opportunities to expedite process flow [15].
\nThis chapter is organized as follows: Following the introduction, a literature review on Lean philosophy is performed, then the methodology used in the research, and the results of the action research are described. Finally, some discussion and conclusions are drawn.
\nIt was through Krafcik [16] that the Lean term was released thus referring to the TPS as a lean production system: A system that uses less resources compared to the mass production systems. Less effort, less capital investment, less space, and less time [17]. The Lean philosophy is essentially focused on waste reduction as a means to increase actual value‐added, in order to fulfill customer needs and maintain profitability [17]. The fundamental focuses of Lean are respect for people, teamwork, waste elimination, continuous improvement, value, quality, and safety [8, 16–18]. Several authors have highlighted this and other key principles of Lean philosophy, such as follows: (i) customer relationship [19]; (ii) total quality management (TQM) [20]; (iii) JIT [21, 22]; (iv) pull production/flow [19, 20]; (v) supplier relationships/long‐term business relationship [21]; (vi) mistake‐proofing [23]; (vii) total productive maintenance (TPM) [22]; and (viii) physical layout [6]. At the operational level, the Lean paradigm is implemented using a number of techniques such as kanban, 5S, visual control, takt‐time, poke‐yoke, and single minute exchange of die (SMED) [24].
\nTo Imai [8], the importance of applying a Lean philosophy in an organization has at least three components: (1) any activity or process that does not add value is waste, independently of being practiced by people or machines; (2) the reduction or elimination of waste may be the most cost‐effective way for improving productivity and reduce operating costs instead of increasing investment in the hope of adding value. Moreover, investing in new equipment is expensive while eliminating waste, in most cases, has no costs. (3) Standardization of processes ensures quality and error prevention. Womack et al. [17] documented the benefits of Lean philosophy compared to the mass production model, arguing that this philosophy would succeed, not only in the automotive industry or aviation, but also in all activities from distribution, retail, and healthcare. Not being the solution to all the problems that health services faces today, the Lean philosophy can bring significant benefits to this sector and in a range of hospital areas [25], contributing to develop the continuous improvement into the organizational culture and improving quality of care, efficiency and effectiveness, while reducing costs, errors, and waste.
\nIn the Portuguese healthcare sector, the implementation of Lean philosophy has been focused on some specific areas such as quality [26, 27] logistics, supply and storage [28–30], agility and continuous process improvement [31–33], workplace reorganization [34], and reducing waiting times [35, 36]. Particularly in services such as community health centers [37], operating room, imaging, ophthalmology, outpatient, ward, pharmacy, and warehouse. Other studies focused on conducting systematic reviews [38, 39]. There is thus a research gap in applying the Lean philosophy to inhospital medical emergency, especially in inpatient critical care services.
\nThe methodology used in this study was an action research, supported by a longitudinal mixed method approach with a one‐group within‐subjects pretest‐posttest experimental type design.
\nLewin [40] suggests the existence of a cycle in action research. It begins with the diagnosis and identification of the problem(s) with all participants in a democratic way and then follows the proposal, planning interventions, and actions of change. Subsequently, the impact of the changes is monitored, the data collected, analyzed, interpreted, and finally results are reported. This is a flexible research methodology that integrates an exploratory action in order to investigate and support the implementation of changes according to the diagnosis raised [41]. Action research claims that the researcher participates in the change process since the changes suggested are implemented by himself, that is, he “take action to improve the practice and study … the effects of the action taken” [42]. Yin [43] considers this methodology as a variant of qualitative research that emphasizes the researcher action role and his active collaboration with the research participants.
\nThe research was performed at a level 2 patient care four‐bedded NHDU. This unit shares human resources, equipment, and materials with the 44‐bedded standard care neurosurgery and neurotraumatology wards. NHDU is a healthcare facility specialized in the care of neuropatients undergoing neurological, hemodynamic, and respiratory instability with the eventual need of non‐invasive or invasive ventilatory support by tracheotomy. These patients require critical care nursing and permanent vigilance that, although not requiring intensive care, may potential and quickly evolve to a severe status and thus the need of an immediate intervention. Nurse:patient ratio is 1:4. Located in one of an 802‐bedded triple hospital centre at the metropolitan area of Lisbon (Portugal), this centre serves about a million people population. Data available from 2013 institutional performance reports show a surgical movement of 1423 neurosurgeries and a bed occupancy rate of 87.7% and 91.4% at the neurotrauma and neurosurgery wards, respectively.
\nThe research was authorized by the NHDU Medical Director, the NHDU Chief Nurse, and the Ethics for Health Committee of the hospital centre. The unit of analysis is the NHDU with the corresponding nurse team. A convenience sampling was used attending to nurses’ availability during the period that took place the visit of the researcher. The two nurses of the management team (chief and coordinator) were excluded from this sample since the purpose was to simulate the performance of the direct care nurses. Thus, from a population of 20 nurses, a sample of 12 nurses (60%) was selected. This is a longitudinal research in which data were collected from two points in time, which allowed studying the changes that have occurred during the period in which it was conducted (November 2014 to January 2015).
\nThe research design follows several phases. The main three phases were (1) pre‐intervention, (2) intervention, and (3) post‐intervention, in which a simultaneous mixed method approach (qualitative and quantitative) was applied. The pre‐intervention phase was further divided into three sub‐phases: (i) diagnostic assessment (qualitative approach), (ii) simulation (quantitative approach), and (iii) proposal of changes (qualitative approach). The intervention phase consisted on the application of 5S and JIT lean methodologies. The post‐intervention phase was divided into two qualitative approaches: (i) simulation and (ii) unstructured interview.
\nThe pre‐intervention diagnostic assessment sub‐phase involved the following activities: (a) direct observation of the physical space performed by the participant researcher (PR) which focused mainly on the layout of the NHDU and the location of existing materials and equipment. To support the gemba walk, pictures and paper record with graphical representation of the service plan were used to complement the visual management and spaghetti diagram. The transition to digital record was made using Microsoft® Office® 2013 software. (b) Personal unstructured interviews performed by the PR to the nurse team, and questionnaires to identify the difficulties and constraints of nurses in their professional daily routines, especially in emergency situations. The questionnaires were anonymous and blind in order to guarantee their confidentiality. The participants returned them in a sealed envelope deposited at a container left in the nursing room. The analysis of questionnaires and interviews was performed using qualitative content analysis, and it was organized according to the research variables, the types of wastes considered by the Lean philosophy and the suggestions of change by the participants.
\nThe pre‐intervention simulation sub‐phase was accomplished by measuring time, distance, and number of steps (dependent variables) undertaken by nurses in the access to LSE (RT, ECK, TST, and automated infusion systems (AIS)). The simulation context was used because during the research it was not possible to monitor the tasks developed by nurses in a real context. As measuring instruments, the Nokia® 6230 mobile phone chronometer was used to monitor timing performance in seconds, rounded to the unit. Sixty meters’ tape Stanley PowerWinder® was used to measure the distance travelled by nurses, with data rounded to the first decimal place. The PR counted the number of steps, and the data were triangulated with the participant itself. The monitoring was performed from the point of departure (nurses’ station), arrival to LSE and return to the starting point with the respective LSE.
\nThe third pre‐intervention sub‐phase was completed by the suggestion of changes presented, as a proposal like determined by Lewin [40], to the Medical Director and Chief Nurse of NHDU.
\nThe intervention phase consisted on the application of lean 5S and JIT methodologies for the reorganization of physical space, equipment location, and NHDU inventory. The tasks performed by the researcher in this phase consisted on the organization of the contents in the NHDU large cabinet, relocation, and availability of TST and AIS. The reorganization of RT, ECK, and NHDU small cabinets was performed with the help of the nurses’ management and direct care team. Other human resources such as nurses’ aides and the hospital carpentry services were involved to perform small changes and to construct small furniture. Stock boxes abandoned in the hospital storage were recycled and used for better storage and visual management of cabinet contents.
\nThe post‐intervention phase was divided into two sub‐phases: (i) simulations, using the same methodology and equipments applied in the pre‐intervention. (ii) Unstructured interviews, using the same methodology as in the pre‐intervention to collect the opinion of nurses regarding the interventions made to the unit, and how this influenced their daily routines and professional practice.
\nThe quantitative results are presented comparing the pre‐intervention with the post‐intervention phases, allowing a more direct comparison of the data. The IBM SPSS Statistics version 21 and Microsoft® Office® 2013 Excel version 15 software were used for the statistical analysis of data. For the statistical hypothesis tests, the parametric Student’s t‐test with a significance level of 0.025 (one‐sided) was used, such as the nonparametric Wilcoxon W‐test with the exact significance of 0.025 (one‐sided) for the poorly distributed data situations [44]. Standardized response mean, calculated through MedCalc Statistical Software version 15.2.2, was used to analyze the effect size (Cohen’s d) of the intervention made by the application of Lean methodologies, representing the independent variable. The qualitative results are summarized in tables with transcription of the nurses opinions collected from the interviews and the summary of the answers given by them in the questionnaires. Spaghetti diagrams and photographs are also used for better contextualization.
\nAttending to the literature review and the pre‐intervention phase the following hypotheses are formulated:\n
H01: The difference of TST time of access between pre‐ and post‐intervention equals zero.
H02: The difference of TST distance of access travelled between pre‐ and post‐intervention equals zero.
H03: The difference of TST number of steps of access between pre‐ and post‐intervention equals zero.
H04: The difference of AIS time of access between pre‐ and post‐intervention equals zero.
H05: The difference of AIS distance of access travelled between pre‐ and post‐intervention equals zero.
H06: The difference of AIS number of steps of access between pre‐ and post‐intervention equals zero.
Throughout Gemba Walk, twelve unstructured interviews were carried out to nurses in order to identify their difficulties in their professional daily routines and what kind of improvements they would like to implement in NHDU (Table 1). The collected data focused mainly on the inadequate layout and location of equipment, poor organization of clinical material in NHDU cabinets and units of patients, obstacles, restricted circulation and workspaces, frequent journeys out of NHDU to supply missing materials and equipment, and difficulty in implementing improvements because of a great resistance to change. According to these interviews only 50% of nurses knew the existence and location of TST, and only 33.3% of nurses knew the ECK existence or location. For AIS and RT, all participants were aware of them. After Lean methodologies’ intervention and education, 100% of the participants were aware of all life support equipment.
\nIn addition to the interviews, questionnaires were delivered to 12 nurses and eight were returned, representing a 67% response rate. The purpose of the questionnaire was to identify the set of difficulties felt by nurses in their daily professional life in NHDU, mainly in emergencies, monitoring and surveillance of the acute neurosurgical patient. The questionnaire made also possible to study the kind of wastes (according to Lean philosophy) the nurses identify. The collected data from questionnaires are summarized in Table 2 that includes suggestions provided by the respondents.
\nA | \n“My greatest difficulty in NHDU is to always have to go out of the unit to look for supplies …either because we do not have a specific location for them either it was not replaced… Medication and serums, forget it…” | \n
B | \n“We should have an adequate level of stocks according to our needs and not have to always go ‘out there’ seek for supplies.” | \n
C | \n“The NHDU should be independent from all resources of Neurosurgery… Nurses and nurses’ aides should be dedicated to NHDU… Stock, equipment and supplies should be replenished regularly and directly by the supply and pharmacy services.” | \n
D | \n“The vital signs monitors should be fixed to the wall for not taking up space in patients’ desk . . . and because sometimes they drop of the desk, usually when pulled by confused patients.” | \n
E | \n“It’s hard to work when there is not enough space to move around the patient bed without going against curtains, literally upon us, against wheelchairs and other patient’s beds.” | \n
F | \n“There is neither space nor conditions to lift patients to an armchair or wheelchair.” | \n
G | \n“Patients from one bed can touch and reach things of next patients because everything is so tight and so close to each other… Patients are potentially contaminating each other … and we ourselves have a hard time for this cross‐contamination doesn’t happen, I am sure it does eventually happen. “ | \n
H | \n“We have no space to put a RT next to the patient’s units … it is impossible to make secure ALS with the available space that we have.” | \n
I | \n“We usually are trained in basic life support every year, but we should also be trained in the use of RT and ALS… I have some difficulties in perceiving the location of clinical materials in the RT because there is a bad visual perception of it.” | \n
J | \n“I have little practice in the use of the RT, mainly the defibrillator… We should have training…” | \n
K | \n“Practices adopted in NHDU goes against scientific evidences … but it is difficult here to make whatever change we need … some people do not understand what good practices are.” | \n
L | \n“The NHDU has a lack of identity and autonomy.” | \n
M | \n“There is a lack of standards for admittance and clearance of patients… Even the doctors and some nurses do not understand that we only have capacity for 4 patients” | \n
N | \n“We cannot take any initiative to improve anything, because they fear us to take their place.” | \n
O | \n“They never listen to us. They do not realize, or understand, the staff who are working with them. We could make a great contribution to the better functioning of the unit.” | \n
P | \n“There is a huge resistance to change… There is a fear of loss or prestige transfer.” | \n
Excerpts from interviews.
Data Collected from Questionnaires.
According to the previous results and analysis of the interviews, questionnaires, spaghetti diagrams, value stream mapping (data not shown), and simulations, a set of suggestions were proposed by the PR to the Medical Director and Chief Nurse of NHDU (third pre‐intervention sub‐phase). This proposal was drawn up from the data collected attending to the Lean philosophy, the recommendations of best practices, and the standards of Portuguese regulatory institutions. The proposal considers several suggestions for amendments procedures, layout updates of the physical space, RT and NHDU cabinets content, and different locations of the clinical material and equipment. Briefly, these suggestions were the following:
\n\nPlace suction probes supports on the wall at each bed side (accepted);
Place water bottles supports to ensure suction tubes washing after manipulation (accepted);
Place mobile IV pole with AIS mounted at each bed side (accepted);
Remove vital signs monitors from patients’ desks and fixate them on the wall (accepted);
ALS and RT handling workshops for nurse training and education (accepted);
RT standardization (accepted);
Place TST at NHDU next to nurse station (accepted);
Reorganization of NHDU cabinets to improve contents access, variety, and identification (accepted);
Place drug vault at NHDU (rejected);
Place double air and oxygen pressure regulators at each patient unit (rejected);
Place manual ventilator at each unit in the presence of tracheotomized patient (rejected);
Organize trolley with clinical material for isolation room (rejected);
Eliminate one of the beds to increase circulation space (rejected).
After approval, or disapproval, of each suggestion, Lean methodologies (5S, JIT) were undertaken to ensure a better and safer work environment for patients and staff. The cabinets were reorganized into categories to cover the various patients’ needs like breathing, elimination, circulation, and administration, dressings and skin integrity, feeding, individual protection equipment. Sliding frosted glasses were removed from the cabinets, and it was possible to reduce and optimize the occupied space without decreasing the amount of material, but rather increasing its variety and availability, as seen in Figure 1 that also illustrates the post‐intervention TST location. The patients’ units were likewise reorganized with the inclusion of supports for suction probes, water bottles, and AIS (in mobile IV poles). Vital signs monitors were placed at a new shelve on each patient’s unit and ALS, and RT workshops has been schedule for nurse training and education.
\nSupplies in NHDU large cabinet before and after Lean intervention.
Spaghetti diagram for TST access before and after Lean intervention.
The presence of tracheotomized patients or at risk of being tracheotomized in NHDU is constant; therefore, the availability and accessibility to LSE, particularly ECK and TST, are of extreme importance. In the pre‐intervention phase, TST was in the treatment room of neurosurgery, about 63 m (round trip) far from NHDU nurse station. Changing its location into the large cabinet inside NHDU the distance decreased to 6 m (round trip) from the nurse station. Figure 2 represents the spaghetti diagram made before and after Lean intervention for the TST.
\nTable 3 shows the quantitative results obtained from the simulations of TST accessibility before and after Lean intervention. Data show the reduction of waste in time (−87.35%), distance (−90.47%), and steps (−87.12%) achieved with the application of Lean methodologies. According to Cohen’s d, the effect size is large. Shapiro‐Wilk normality test (data not shown) rejected the normality of the distance distribution (p < 0.001). So, for a one‐sided significance level of 0.025, were accepted the alternative hypothesis that time (p = 0.0017), number of steps (p = 0.000015) and distance (p = 0.016) were statistical and significantly lower after the application of Lean methodologies.
\n\n | \n | Time | \nDistance | \nSteps | \n|||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
\n | \n | A | \nB | \n∆ | \n∆% | \nA | \nB | \n∆ | \n∆% | \nA | \nB | \n∆ | \n∆% | \n
\n | \n | Collective | \n\n | Paired | \n\n | Collective | \n\n | Paired | \n\n | Collective | \n\n | Paired | \n\n |
Collective and paired data | \nM | \n45.5 | \n4.58 | \n−40.5 | \n−87.35 | \n64.03 | \n6 | \n−58.03 | \n−90.62 | \n60.17 | \n7.5 | \n−52.5 | \n−87.12 | \n
\n | Mdn | \n39.5 | \n5 | \n−34 | \n−86.1 | \n63 | \n6 | \n−57 | \n−90.47 | \n60.05 | \n8 | \n−52.5 | \n−87.32 | \n
\n | SD | \n18.87 | \n0.79 | \n19.07 | \n5.08 | \n2.43 | \n0 | \n2.43 | \n0.34 | \n9.75 | \n0.91 | \n9.05 | \n1.71 | \n
\n | Max | \n76 | \n6 | \n−71 | \n−93.42 | \n69 | \n6 | \n−63 | \n−91.3 | \n70 | \n9 | \n−62 | \n−89.09 | \n
\n | Min | \n26 | \n3 | \n−21 | \n−80.77 | \n63 | \n6 | \n−57 | \n−90.48 | \n45 | \n6 | \n−38 | \n−84.44 | \n
\n | Range | \n50 | \n3 | \n−50 | \n−12.65 | \n3 | \n0 | \n6 | \n−0.82 | \n25 | \n3 | \n−24 | \n−4.65 | \n
t-test | \n\n | 95% CI | \n\n | \n | [−60.52; −20.48] | \n\n | \n | \n | \n | \n | \n | \n | [−61.99; −43] | \n
\n | \n | \n | t (df) | \n\n | −5.2 (5) | \n\n | \n | \n | \n | \n | \n | \n | −14.21 (5) | \n
\n | \n | \n | pa | \n\n | 0.0017 | \n\n | \n | \n | \n | \n | \n | \n | 0.000015 | \n
Wb‐test | \nZ | \n\n | \n | \n | \n | \n | \n | −2.264 | \n\n | \n | \n | \n | \n |
\n | pa | \n\n | \n | \n | \n | \n | \n | 0.016 | \n\n | \n | \n | \n | \n |
Effect size | \nCohen’s d | \n\n | \n | \n | −2.12 | \n\n | \n | −23.84 | \n\n | \n | \n | \n | −5.8 | \n
Results from TST accessibility.
A: Pre‐intervention (n = 6). B: Post‐intervention (n = 12).
aOne‐sided 0.025 significance.
bW‐test with exact significance.
Although the ECK is correctly located in the RT, 66.7% (n = 8) of nurses were unaware of its existence or location. For ethical reasons, there was an imperative and urgent need to educate them, which was done by the PR to all nurses’ team. In order to identify the difficulties of nurses in using the RT, simulations were performed. These simulations consisted in locating and accessing all RT contents, especially ECK. Through direct observation, it was found that all 12 nurses had some difficulties such as follows: safety seal breakage; retraction of safety latch; removal of back board; opening drawers by poor perception of the handle; finding and identifying critical medications and supplies; swing arm handling; and use of equipment including heart defibrillator. After the simulations, nurses justified their difficulties as a result of little practice and/or experience. An ALS and RT handling workshop intervention were scheduled to nurse’s continuous education plan.
\nIn the pre‐intervention phase, the AIS were in a storeroom forcing nurses to a constant movement and transportation of about 84 m (round trip). Lean 5S and JIT methodologies determined changing AIS location into a mobile IV pole next to the patients’ unit, permanently connected to electricity in order to ensure its permanent availability (Figures 3 and 4).
\nAIS location before and after Lean intervention.
Spaghetti diagram for AIS access before and after Lean intervention.
\n | Time | \nDistance | \nSteps | \n||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
\n | A | \nB | \n∆ | \n∆% | \nA | \nB | \n∆ | \n∆% | \nA | \nB | \n∆ | \n∆% | \n|
\n | Collective | \nPaired | \nCollective | \nPaired | \nCollective | \nPaired | \n|||||||
Collective and paired data | \nM | \n79.5 | \n3 | \n−76.5 | \n−96.27 | \n83.6 | \n3 | \n−80.6 | \n−96.41 | \n107 | \n4.5 | \n−102.5 | \n−95.83 | \n
Mdn | \n78.5 | \n3 | \n−76 | \n−96.29 | \n83.6 | \n3 | \n−80.6 | \n−96.41 | \n104 | \n4.5 | \n−99 | \n−95.84 | \n|
SD | \n8.13 | \n0.95 | \n7.44 | \n0.91 | \n0 | \n0 | \n0 | \n0 | \n9.72 | \n1.17 | \n8.85 | \n0.85 | \n|
Max | \n92 | \n5 | \n−87 | \n−97.40 | \n83.6 | \n3 | \n−80.6 | \n−96.41 | \n130 | \n6 | \n−124 | \n−97.00 | \n|
Min | \n65 | \n2 | \n−63 | \n−94.57 | \n83.6 | \n3 | \n−80.6 | \n−96.41 | \n97 | \n3 | \n−94 | \n−94.55 | \n|
Range | \n27 | \n3 | \n−24 | \n−2.83 | \n0 | \n0 | \n0 | \n−0 | \n33 | \n3 | \n−30 | \n−2.45 | \n|
t‐test | \n95% CI | \n\n | \n | [−81.23, −71.77] | \n\n | \n | \n | \n | \n | \n | \n | \n | \n |
\n | t (df) | \n\n | \n | −35.62 (11) | \n\n | \n | \n | \n | \n | \n | \n | \n | \n |
\n | pa | \n\n | \n | 5.64×10−13 | \n\n | \n | \n | \n | \n | \n | \n | \n | \n |
W b‐test | \nZ | \n\n | \n | \n | \n | \n | \n | −3.46 | \n\n | \n | \n | −3.06 | \n\n |
\n | pa | \n\n | \n | \n | \n | \n | \n | 0.00024 | \n\n | \n | \n | 0.00024 | \n\n |
Effect size | \nCohen’s d | \n\n | \n | −10.28 | \n\n | \n | \n | −5×1015 | \n\n | \n | \n | −11.58 | \n\n |
Results from AIS accessibility.
A: Pre‐intervention (n=6). B: Post‐intervention (n=12).
aOne‐sided 0.025 significance.
bW‐test with exact significance.
After the intervention and application of Lean methodologies, the AIS mean access was 96.27% in time, 95.83% in steps, and 96.41% in distance lower than in the pre‐intervention. The effect size is large (or very large) with d = −10.28 for time, d = −11.58 to number of steps, and d = −5×1015 to distance. For the hypothesis test, the Shapiro‐Wilk normality test rejected the normality of steps (p = 0.039) and distance (this one constant) distribution. So, for a one‐sided significance level of 0.025, were accepted the alternative hypothesis that time (p = 5.64×10−13), number of steps (p = 0.00024) and distance (p = 0.00024) were statistical and significantly lower after Lean methodologies application, as shown in Table 4.
\n\nThe results of quantitative data associated to the hypothesis test, the size effect, and the improvements in accessibility to TST and AIS are summarized in Table 5.
\n\nHypotheses | \nStatistical test | \np‐value | \nSize effect | \nPercentage variation (decrease) | \nPercentage variation (improvement) | \n
---|---|---|---|---|---|
H01: The difference of TST time of access between pre and post‐ intervention equals zero. | \nPaired samples t‐test | \n0.0017 | \n−2.12 | \n−87.35% | \n837.22% | \n
H02: The difference of TST distance of access travelled between pre and post‐intervention equals zero. | \nPaired samples W‐test | \n0.016 | \n−23.84 | \n−90.47% | \n950% | \n
H03: The difference of TST number of steps of access between pre and post‐intervention equals zero. | \nPaired samples t‐test | \n0.0000151 | \n−5.8 | \n−87.12% | \n687.46% | \n
H04: The difference of AIS time of access between pre and post‐ intervention equals zero. | \nPaired samples t‐test | \n5.64×10−13 | \n−10.28 | \n−96.27% | \n2733.8% | \n
H05: The difference of AIS distance of access travelled between pre and post‐intervention equals zero. | \nPaired samples W‐test | \n0.00024 | \n−5×1015 | \n−96.41% | \n2686.7% | \n
H06: The difference of AIS number of steps of access between pre and post‐intervention equals zero. | \nPaired samples W‐test | \n0.00024 | \n−11.58 | \n−95.84% | \n2310% | \n
Summary results from quantitative data.
aα = 0.025 one‐sided.
After all the action research phases performed, it was demonstrated that the application of Lean methodologies contributes for improving the accessibility to equipment and material that are essential to nurses’ safe practice. With the application of the Lean methodologies, it is possible to provide optimized care to acute neurosurgical patients, in emergency and life support situations. Lean methodologies such as Gemba walk and spaghetti diagram made possible to identify wastes and difficulties in LSE accessibility, organization, and provision of other clinical equipment and supplies, and security issues such as potential cross‐contamination provoked by exiguous work areas and architectural barriers. 5S and JIT philosophies together with interviews and questionnaires led to the development of a grounded interventional proposal for a functional and organizational harmonization of NHDU. Each suggestion on the proposal was then analyzed by medical and nurse unit managers giving deferral or refusal to certain interventions. The implementation of 5S and JIT methodologies led to the reorganization of NHDU and the allocation of the equipment closer to patients and nurses as well as to the decrease of waste, non‐value‐added activities and to significant improvements. These same results are argued in Carvalho et al. [45] since they defend that the layout must “reflect the need to reduce the time spent traveling” (p. 291) since “time ‘lost’ in travel between the various services… represents a cost to the organization in question, and that, in most cases, is not noticed or accounted for” (p. 291). For example, a nurse who searches for drugs, supplies, and equipment are doing it to serve the needs of patients, but may not notice that it can result in a waste of time, transport, handling, and human potential. But according to the Institute for Healthcare Improvement [46], if these materials were readily available when, how, and where they are needed (JIT), the time that nurses wasted looking for them would be instantly devoted to other more appropriate and critical tasks.
\nThrough action research and the application of Lean methodologies, nurses of NHDU actually take only 10% of time, 9.37% of the distance travelled and 12.46% of the steps spent accessing TST compared to pre‐intervention. The results of the intervention in AIS showed an improvement even more significant since the post‐intervention access time is just 3.77% of pre‐intervention time, the distance just 3.59%, and the number of steps only 4.21% compared to pre‐intervention. To achieve this, nurses were educated about the location of LSE, and the need for training these nurses in ALS and RT handling was identified. Wastes and barriers that conditioned rapid access and action to acute patients were identified, reduced, or removed. Time, steps, and distance travelled accessing LSE were shortened and reduced more than half (−87.12 to −96.41%).
\nThe same results were reached in other researches. Virginia Mason Medical Center (VMMC), in Seattle (USA), is credited to be one of the pioneers in healthcare industry to implement Lean by applying their own Virginia Mason Production System (based on TPS) [47]. Since 2001 VMMC makes efforts with the reorganization of spaces and workflows, minimizing transportation, and handling wastes, where all clinical equipment and supplies essential to care are placed in the point‐of‐use in UK Hereford Hospital, Lean methodologies led also to reductions of delay in nurses’ response time between 40 and 93% [48]. In Scotland, from a sample of 19 critical care units, nurses available time increased from 35 to 64%, in which 32% of these units reached changes greater than 100%, supported by the program Releasing Time to Care: The Productive Ward, based on Lean and six sigma methodology [49].
\nIn this study, there is a significant and serious lack of nurses’ knowledge on the existence and location of LSE. Intervention trough education, awareness, and change of its location resulted in an improvement of 100% to TST and 200% to ECK leading to health benefits for patient’s safety and quality of care. Still on the ECK and the RT, the simulation demonstrated the difficulties experienced by nurses in the use of the RT, particularly in opening it, use of drawers, location, and rapid visualization of contents. It was retrieved from this analysis that the imperative and urgent need for nurse’s professional training and the need for a clearly defined intervention criteria in emergency situations. This is in line with Silich et al. [50] that also highlights that informed and trained professionals provide better care with potential reduction of adverse events, bad practices, and less waste of resources.
\nCatchpole [51] argues that the undesirable effects of an inadequate working environment can result in fatigue, frustration, reduced performance, and human capacity, increased risk, and adverse events. Hence, the importance that health facility managers have and the impact of their decisions on patients and staff, and “usually, it is the intermediate and elementary level manager, involved in everyday decisions, that affect the care that is actually provided to patients” [52].
\nThis research was intended to interfere in the reality studied by solving identified problems in an effective and participatory manner (through action), not only explain it or proposing a problem solution. The impact for practice and health services (quality indicators, safety, and satisfaction) of the Lean interventions carried out by the PR is well grounded by the results. In this research, it was verified that 66.7% of nurses were unaware of the existence or location of ECK and 50% of the TST. The education intervention resulted in an improvement of knowledge of 100% in the TST and 200% in the ECK, leading to potentially high health gains for the patient, because trained professionals provide better care with fewer mistakes. Furthermore, this research identified needs for periodic training and education on ALS and RT practice. Through Lean methodologies such as 5S, JIT, and spaghetti diagrams, it was possible to decrease time, steps, and distance travelled by nurses accessing TST and AIS between 87.12% and 96.41% and to improve this accessibility between 687.46% and 2733.8%.
\nThese results confirm the contribution of this research to address the need of this healthcare unit to improve the care of neurosurgical acute/critically ill patients. The implementation of Lean 5S and just‐in‐time methodologies led to the reorganization of NHDU environment by allocating LSE closer to patients and nurses station, contributing by this way for improving the security and responsiveness of nurses’ team for having more knowledge and quick access to LSE. In addition, it contributes to overcoming emergency, life support situations, and day‐to‐day professional life action to the needs of patients, freeing up time and availability of nurses for direct care by a work environment with less waste of time, distance, steps, handling, and setup procedures.
\nAlthough not focused in this research, for the unit and hospital management, there are potential economic and financial benefits attained from the application of Lean methodologies through the following factors: hand labor and human capital gains by reducing the time required to perform certain tasks (setup time); reduction of the “snowball” effect that leads to the accumulation of everyday work; reprocessing gains from potential reduction of costs in time of hospital internment and patient morbidity.
\nBesides the advantages reached with the application of the lean methodologies the research findings, however, are tempered by several shortcomings such as the unavailability of participants to collaborate with the research and resistance to change. Financial impact of the intervention was not recorded. Moreover, the results cannot be generalized; other realities can compare them and encounter similar situations that may benefit with the application of Lean methodologies in an attempt to overcome their problems.
\nIt is expected that health professionals, especially their leaders and managers, can take some lessons from the different approaches adopted in this research and may act as a catalyst for future positive changes in all health services.
\nAs a suggestion for future research it would be interesting to study the financial impact (time saved vs. value/hour) of the application of these lean methodologies, the impact on the quality of nurses daily professional life (satisfaction, fatigue, stress, burnout) and on emergency scenarios (LSE accessibility/availability vs. morbidity and mortality).
\nLandscape topography is a key parameter influencing biogeochemical processes that occur in the near-surface layer of the earth [1]. In particular, the topography plays an important role in soil formation through regulating soil hydrological regimes and controlling the gravity-driven soil movements [2, 3, 4, 5, 6].
Quantitative and qualitative topographic information is essential in understanding the heterogeneity of soil chemistry and physics. Before the 1990s, geographic maps were the main source to quantify landscape topography in soil science [7]. Topographic variables, such as slope and plan and profile curvatures, were calculated manually from these maps to investigate their relationships with soil properties and to generate soil maps [8, 9, 10].
Along with the development in computer, aerial, space, and geographic technologies, the availability of high-resolution digital elevation models (DEMs) introduces a new technique in deriving digital terrain models (DTMs) and has been the main source for topographic information extraction in soil biogeochemical studies since the 1990s [7]. A DEM is a digital representation of the terrain surface elevation referenced to a vertical datum. A DTM is an enhanced DEM that has been augmented with breaklines and other observations to describe the land surface geometry [1, 11, 12]. The application of DTMs enables effectively reconstruct topographic landscape over a large scale. Recently, there are two main applications of DTMs in soil science. One is analyses of topographic influences on soil formation and movement, which would be introduced in Section 2. The other is modeling of relations between soil properties and topography and using the results to predict soil properties, which would be discussed in Section 3.
The objective of this chapter is to provide an overview of how topographic heterogeneity causes the spatial variability in soil properties. This chapter starts with an introduction of DTMs applications, which is then followed by reviews of investigations on topographic impacts on soil formation and movements and modeling of soil morphological, chemical, physical properties based on DTMs. The last section presents a case study of DTM-based analysis on how land topography affects soil carbon (C) dynamics.
DTMs are functions of morphometric variables that digitally represent the geometry of the land surface. Various techniques have been developed to generate different DTMs such as topographic metrics of slope, aspect, and curvature. Fifteen topographic metrics that have been reported to be highly correlated with soil properties, including slope gradient, slope aspect, profile curvature, plan curvature, general curvature, flow accumulation, topographic relief, topographic openness, upslope slope, flow path length, downslope index, catchment area, topographic wetness index, stream power index, and slope length factor, are introduced in this section (Table 1). Based on the spatial scope, the topographic metrics can be grouped into three main categories [7]:
Local topographic metrics: variables describe the surface geometry at a given point on the land surface. Slope gradient, slope aspect, and curvature related (plan, general and profile curvatures) metrics belong to this category.
Nonlocal topographic metrics: variables consider relative positions of a selected point, including catchment area, upslope slope, downslope index, flow path length, flow accumulation, topographic relief, and topographic openness.
Combined topographic metrics: variables integrate local and nonlocal topographic metrics considering both local surface geometry and relative positions of a point on the land surface. This group of metrics includes topographic wetness index, stream power index, and slope length factor.
Variables | Definition and formula |
---|---|
Slope gradient, G (radian) | An angular measure of the relation between a tangent plane and a horizontal plane |
Slope aspect, A (radian) | Direction of slope measured clockwise with north as 0 |
Profile curvature, P_Cur (1/m) | Slope change rates in the vertical plane |
Plan curvature, Pl_Cur (1/m) | Curvature in a horizontal plane |
General curvature, G_Cur (1/m) | Curvature of the surface itself |
Catchment area, CA (m2) | Upslope area contributing runoff to a given point on the land surface |
Upslope slope, Upsl (radian) | Mean slope of upslope area |
Downslope index, DI (radian) | Head differences along a flow path |
Flow path length, FPL (m) | Maximum distance of water flow to a location in the catchment |
Flow accumulation, FA (m2) | Land area that contributes surface water to an area in which water accumulates |
Topographic relief, TR (m) | Elevation difference between the highest (hmax) point in an area and a given point (hi) |
Positive topographic openness, PTO (radian) | Angular measure describing the relationship between surface relief and horizontal distance |
Topographic wetness index, TWI | Frequencies and duration of saturated conditions |
Stream power index, SPI | Erosive power of overland flow |
Slope length factor, LS | Distance from flow origin to a point where deposition begins |
Definitions and formulas of selected topographic metrics.
Ld is the horizontal distance to a point with an h meter elevation below the starting cell. CAs is specific catchment area. p, q, r, t, and s are partial derivatives of elevation (h),
These nonlocal and combined topographic metrics often reflect important physics involved in water and soil mass transfer processes considered to have important impacts on soil property patterns.
Slope gradient indicates the steepness of a line which directly influences the velocity of a gravity-driven flow [2]. For example, a steep area drains quickly and retains less soil than a flat area [4, 13]. Therefore, negative soil redistribution rates with high erosion possibilities are often observed in steep areas. The erosion processes tend to remove fine particles which are usually enriched in soil organic carbon (SOC), leading to low SOC content in a steep area [4, 14]. Meanwhile, the slope gradient can impact soil water content [2, 7]. For relatively flat areas, soil water content commonly decreases with slope gradient due to increased lateral flow and depositional crusts that decrease infiltration; while as slope steepens, rills may occur that can disrupt the crusts and favor greater infiltration, and therefore lead to a positive relationship between soil water content and slope gradient [15, 16, 17, 18].
Slope aspect shows the direction that a slope faces. This metric influences soil-water balance by affecting insolation and evapotranspiration [19]. Soil temperature and evapotranspiration tend to be lower, and soil water content tends to be higher in shady aspect areas. These environmental conditions can be favorable for slow decompositions of organic matter and high accumulations of soil C and nitrogen (N) content [20, 21, 22]. Soil water content impacted by slope aspect can further influence vegetation density, which may have impacts on runoff velocities and soil erosion rates [23].
Profile, plan, and general curvatures are important topographic factors controlling patterns of overland flow and soil water content. Profile curvature shows upwardly concave with positive values and upwardly convex with negative values (Figure 1a). This variable affects flow acceleration and deceleration and therefore influences soil redistribution and distribution patterns of SOC content [24, 25, 26]. A positive plan curvature value indicates a laterally convex surface and a negative value indicates a laterally concave surface [24] (Figure 1b). Water accumulates and soil water content decrease when flow diverges (positive plan curvature) and increase when flow converges (negative plan curvature) [7]. General curvature is the curvature of the land surface and describes peaks with positive values and valleys with negative values. This metric enables more accurate estimation of overland flow paths than plan and profile curvatures, and can significantly correlate with patterns of soil erosion and deposition [4, 27].
Diagrammatic illustration of (a) profile and (b) plan curvatures.
Catchment area and slope related nonlocal topographic metrics (upslope slope and downslope index) affect soil properties mainly through regulating soil water content. At a location, increases in water amount from upslope areas can increase water supply to the location and affect the water accumulation [28]. Therefore, positive correlations have been observed between the catchment area and soil water content [7]. Furthermore, as the catchment area increases, the chance for sediment deposition increases, and thus affects the soil C stocks [29]. The upslope slope relates to slopes in upslope contributing areas. Overland flow velocities are usually less at positions with lower values of upslope slope [1, 30]. The downslope index is a metric including dispersal (downslope) controls on drainage [31]. Since the drainage of a location is the balance between the water from a specific upslope contributing area and to a downslope area, this index usually shows a better representation of groundwater gradients and soil water content than slope gradient [31, 32].
The two flow-related nonlocal topographic metrics (flow path length and flow accumulation) reflect the impacts of soil hydrology on soil properties. The longer flow path length decreases overland flow velocity and increases infiltration [33, 34]. Increased erosion of fine particles can also be observed when flow path length increases [35, 36]. This metric has been widely used in soil erosion models, describing soil loss under flow divergence and convergence conditions [37, 38]. Flow accumulation mainly influences water conditions in soils. Flow volume and soil water content response positively to this metric, which in turn can influence the soil C stocks [39, 40].
For topographic relief, higher values suggest larger differences from the highest points, which would stimulate flow velocity, leading to more rapid downslope soil transport from low relief areas [4, 41]. Moreover, topographic relief influences landscape drainage characteristics. Tucker and Bras [42] found that drainage density was positively correlated with relief in semiarid and low-relief landscapes but negative related to relief in humid or high-relief landscapes. Areas with a broad range of relief may cover several altitudinal climatic zones with differences in vegetation types, further influencing weathering and denudation processes [43].
Topographic openness describes the distinction between relief and surrounding topographic features [44]. Convex landforms often exhibit high positive topographic openness values, whereas concave landforms typically have high negative topographic openness values (Figure 2) [44, 45]. Therefore, soil water content may change with this variable [46]. The low positive openness areas are more likely to be depressional areas with high soil water contents that provide suitable anaerobic environments for denitrification but impede aerobic SOC decomposition [4, 46].
Positive (α) and negative (β) topographic openness along two profiles.
Topographic wetness index combines a local topographic metric (slope) and a nonlocal topographic metric (upslope contributing area) [47]. It is considered as an indicator effectively reflecting the spatial distribution of wetness conditions as the upslope contributing area would impact groundwater level and soil water content, and the slope would influence drainage processes [3]. Areas with higher wetness index tend to be wetter. The topographic wetness index has been used to estimate the spatial distribution of hydrological and geochemical properties of soil, and significant correlations have been observed between this metric and soil C and N content [3, 4, 32, 46, 48, 49].
Stream power index takes into account both specific contributing area and slope. This metric is useful for characterizing potential erosive power of water flow [1]. When the slope gradient and catchment area increase, the amount of water from contributing area and the velocity of water flow increase, and consequently enhancing the erosive power of water [50]. Therefore, areas with larger stream power index values have greater potential to be erosive regions [4, 51]. Due to its impacts on erosive power, this metric can also be useful in understanding erosion-induced soil C and N dynamics [14, 52, 53].
Slope length factor includes the length and steepness of a slope and thus reflects the topographic impacts on erosion [54, 55]. As the slope length increases, the soil loss per unit area usually increases due to a greater runoff accumulation on a longer slope length that increases transport capacity of runoff; as slope steepness increases, soil loss also generally increases [54, 56]. This factor is essential in estimating soil transport and erosion by runoff [37, 38, 50, 56, 57].
In a DTM-based soil property model, the predictive variable could be the morphological, chemical, or physical property of soil. Development of DTM-based models follows two assumptions including that (1) the controls of topography on soil properties can be found through a relatively small set of soil samplings and topographic metrics, and (2) the statistical correlations between topography and soil properties are often strong. In this case, soil properties can be predicted based on the topographic metrics [7]. Due to the recent availability of large-scale, high-resolution DEMs, DTMs over large-scales can be derived. The DTM-based models benefit investigations in regions with limited observations and can generate spatially continuous soil property maps based on extrapolation.
Methods of DTM-based soil property prediction could be grouped into two categories [7]:
DTM-based models to predict quantitative soil properties based on statistical analyses. Multiple regression analysis, regression kriging, cokriging, and kriging with external drift are the widely used methods to predict quantitative soil properties.
DTM-based models to predict categorical soil properties. Statistical methods such as classification tree model, fuzzy logic, and discriminant analysis are usually employed in this category.
Multiple linear regression (MLR) simulates relationships between two or more independent variables and a dependent soil property variable by fitting to a linear equation. The DTM-based MLR models have been applied to study spatial patterns of soil structures, horizonation, and soil water content [12, 39, 58, 59, 60, 61, 62, 63, 64, 65], to explore spatial variability of cation exchange capacity and pH [62, 65, 66, 67], and to derive continuous quantitative maps of SOC, C isotopes, and nutrients over large spatial scales [4, 6, 12, 14, 46, 52, 68, 69]. In some modeling investigations, Hybrid regression methods were used to improve the efficiency of soil property prediction. Li et al. [4] combined stepwise MLRs with principal component analysis (PCA) for SOC mapping. Results suggested that the combination of DTM-based MLRs with PCA outperformed regular stepwise MLRs in the prediction of SOC and soil redistribution rates at a watershed scale.
Regression kriging (RK) is a spatial prediction combining an MLR with kriging of the regression residuals. The RK acts as a MLR model if the data used in the model have low spatial structure, and the method reduces to Ordinary kriging (OK) if there are no linear statistic correlations between the dependent variable and the ancillary variables [64]. Based on topographic and other environmental variables, numerous studies have applied RK for predicting spatial patterns of soil properties, such as soil horizon thickness [70, 71], soil structures [63, 70, 71], soil water content [72, 73], soil C content [63, 69, 74, 75], cation exchange capacity [66, 76], and soil hydraulic properties [77, 78]. Generally, this method is more accurate in soil property estimations than the OK, Cokriging, or MLR because residual values from kriging analysis were added to the regression [63, 70, 71, 73, 76, 77]. However, Zhu and Lin [72] reported that the RK performed worse than the OK for soil property prediction in relatively low relief areas.
Cokriging (CK) and kriging with external drift (KED) are also popular and practical spatial predicting techniques in digital soil mapping. The CK calculates soil properties by investigating topographic metrics in the kriging procedure and KED uses external ancillary topographic variables as kriging weights. Various studies have employed CK and KED to derive continuous maps of soil physical and chemical properties [65, 66, 70, 71, 76, 77, 78, 79, 80]. Some of these studies were also suggested that these techniques would be superior to OK in soil property estimation when the selected topographic metrics are highly correlated with the dependent variables [79, 81].
Classification tree models (CTMs) are a major type of Decision Tree method used in soil science, in which the target variable is a categorical soil variable. This model applies a set of rules that use explanatory variables to split data into homogeneous subsets. The explanatory variables can be either categorical, such as geological unit number, soil unit, etc., or continuous, such as slope, elevation, topographic wetness index, etc., [82, 83]. Compared to mathematic functions, the tree structure can provide a more visualized explanation of relationships between explanatory variables and the target variable. The CTMs can be used to derive efficient predictions of soil taxonomic classes from local to large spatial scales [82, 83, 84, 85, 86, 87, 88, 89, 90, 91]. Soil drainage can also be effectively classified using the CTMs with soil profiles and topographic metrics as predictors [92, 93, 94, 95, 96].
The basic idea of fuzzy logic (FL) is to show “degrees of truth” for a variable. Soils are continuums in both geographic and attribute spaces. As a result, using 0 and 1 or discrete categories cannot provide sufficient information about soil properties. The FL overcomes the limitation. If a variable belongs to a set, the model would take a value between 0 to 1 instead of 0 or 1. Several studies have used the DTM-based FL to improve soil taxonomic classes in soil mapping [97, 98, 99, 100, 101], soil texture and soil horizonation prediction [98, 102, 103, 104, 105], and soil vulnerability classification [106]. Qi et al. [102] found that using the FL the accuracy of soil series name prediction increased 17% compared to the conventional soil survey. The FL was also combined with maximum likelihood regression to derive the prediction of some continuous soil properties [97].
Discriminant analysis (DA) is a type of supervised classification to assign objects to the most likely group among a lot of groups. It uses some observations (training dataset) to classify others. This method is applicable when correlations between soil property variables and independent variables are high [107]. It has been applied to differentiate soil taxonomic classes and to generate soil texture maps using multiple ancillary variables including topographic metrics [107, 108, 109, 110]. Several studies also demonstrated the feasibility of using DA in deriving soil drainage classes based on its relationships to topography and soil electrical conductivity [111, 112, 113, 114].
Artificial intelligence (AI) or machine learning gives computers a degree of sophistication to act intelligently [115]. Therefore, to be intelligence, computers should be able to learn from training datasets, correctly interpret external data, and apply learned knowledge to achieve specific goals. With increased computing power, massive sets of labeled data, and developed pre-trained models, increasingly researchers have applied AI to fields such as speech recognition, objective detection, visualization, machine translation, image processing and others [116]. However, it is not until the recent decade that the potential applications of AI on soil property prediction have come into more common awareness by scientists.
Artificial neural networks (ANNs) are a representative AI technique that has been applied to solve complex machine learning problems (Figure 3a). The method has similar data processes as a biological neural with nonlinear mapping structures, which consists of a set of interconnected units (neurons) [117]. The input neurons are predictors, linking to one layer of hidden neurons and finally linking to the output variables [118]. To obtain accurate prediction results, the network model is trained first by a set of observations. The weights that connect neurons are adjusted iteratively using the training dataset. After training, the model is applied to predict areas with the same input variables. ANNs outperform traditional statistics in handling large datasets even when the input data are noisy with low levels of precision due to the ability to reduce bias by evenly distributing training data across classes [119]. Various researchers have employed ANNs for efficient prediction of quantitative soil chemical and hydrological properties [118, 120, 121] and adequate mapping categorical soil taxonomic classes [122, 123, 124, 125, 126, 127, 128, 129] based on DTMs and environmental variables. Zhao et al. [93] also tested the feasibility of using ANNs for soil drainage classification and found an accuracy of 52% between field observations and digital classification.
Architectures of the (a) artificial neural networks and (b) deep learning.
Deep learning (DL) is considered as an advanced ANN (Figure 3b) that has been facilitated by recent advances in technology for highly parallel computing. In contrast to single hidden layer ANNs, DL algorithms allow the computer to learn on its own by multi-layer nonlinear transformations of the input training data [130]. For instance, such algorithms can define edges within images by training on multiple examples and perform automatic feature extraction without human intervention. Therefore, massive quantities of representative learning data are the prerequisite for effective estimation from DL. The architectures of DL include Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), and Recurrent Neural Networks (RNNs). CNNs are classic feedforward networks in which the hidden layers consist of convolutional layers, pooling layers, and fully-connected layers. The convolutional layers apply different convolution operations (filters) to pass results from local patches in the feature maps of the input or the previous layers to the next layers, enhancing certain features in the output. Neurons in the same convolutional layer share the same weight. The pool layers merge similar feature together, improving the robustness of features against noise and distortion. The convolutional and pooling layers are finally stacked to a fully-connected layer. The local connectivity in a convolution layer allows CNNs to achieve a better generation in output analysis and the shared weights increase the possibility to extract information of high complexity [131]. Padarian et al. [132] applied CNNs to predict SOC at multiple depths using elevation, slope, topographic wetness index, temperature and rainfall as input data. The results suggested that the CNNs reduced errors by 25% for SOC predictions than the conventional Cubist model. The DBNs are considered as a composition of unsupervised sub-networks, which are trained to maximize the likelihood of training data. Each sub-network serves as a visible layer used for unsupervised training of the next layer [133]. Song et al. [134] demonstrated the usefulness of DBNs in predicting soil water content in highly nonlinear forms over an irrigated field. RNNs have a “memory” called hidden state to remember all information that has been calculated, so the output of RNN loops connect to their past decision nodes based on the hidden state. The networks process an input sequence at one time, preserving the sequential information in the hidden state and producing the output sequence. Therefore, this model is especially useful for tasks containing sequential input [131]. Researchers have demonstrated the feasibility of using RNNs for hydrological study, although no reports were found using this approach to map soil properties based on topography [135, 136].
Random forest (RF) is another emerging method of AI and consists of an ensemble of classification and regression trees for prediction (Figure 4). Each tree is a random subset of features and uses a random set of the training data (about 2/3 of the available observations), which increases the diversity of the forest and decreases the correlation of individual trees. RF commonly has high efficiency and low bias and variance since the output is the average or majority voting of a large number of trees [137]. The method has been proved to be resistance to over-fitting because each tree is trained on a unique bootstrap subset and provide a reliable error estimate using Out-Of-Bag data (the remaining one-third of the observations) [138]. Because of the above advantages, increasing scientists have used RF in soil mapping. Combining topographic metrics, environmental variables, climate variables, or/and land cover as input, RF can predict quantitative and category soil properties. For quantitative soil property prediction, the output is the average of individual tree outputs. RF has been successfully applied to investigate spatial patterns of soil organic matters [139, 140, 141, 142] and to estimate soil texture [143]. Guo et al. [144] further developed soil organic matter prediction by combining RF with Residuals Kriging, for which the prediction accuracy increased dramatically (R2 = 0.86) compared to the method using RF only (R2 = 0.65). For categorical soil property classification, the output is obtained from voting by the majority on the correct classification. Several studies have demonstrated the feasibility of using RF for updating soil survey maps [145] and predicting soil classes in unmapped regions [146, 147, 148].
Architectures of the random forest model.
In this section, a case study about DTM-based modeling of SOC and soil redistribution (SR) was discussed to understand the impacts of topography on SR and SOC dynamics. We also compared efficiencies of three types of DTM-based models in predicting the soil properties. Cesium-137 (137Cs) was used to trace the SR process, and high-resolution light detection and ranging (LiDAR) data were applied to derive DEMs for DTM extraction. Based on the DEM-derived topographic information and field measured SOC density and SR rates, the multiple linear regression (MLR), MLR combined with principal component analysis (MLR-PCA), and MLR combined with factor analysis (MLR-FAn) were developed and discussed.
The study was carried out in Walnut Creek watershed (WCW), which is located in Boone and Story counties, Iowa, USA (Figure 5a, 41°55′–42°00′N; 93°32′–93°45′W). It has a humid continental climate. The landscape of this watershed is relatively flat with a low topography relief (2.03 ± 1.62 m). The typical soils are poor-drained Nicollet and Webster soils in the lowlands and well-drained Clarion in the uplands. More than 86% area of the watershed is cropland. Chisel plowing in autumn and spring disking are the current primary tillage operations. Directions of tillage practices in the WCW are mostly north-south or east-west, depending on the management and field configurations. Detailed information on climate, soils, and farming practices can be found in Hatfield et al. [149].
Location of a) Walnut Creek watershed (WCW), b) Site 1 and c) Site 2 (z-axis 15× elevation).
Two field sites were selected for intensive sampling investigation. Each site is approximately 15 ha. Site 1 is in the WCW (Figure 5b) and Site 2 is located between Boone and Ames (Figure 5c), which is within 10 km of the closest watershed boundary. Similar to the WCW, low reliefs (<4.6 m) were observed for both sites. Tillage practices at these two sites were along the north-south direction.
The SOC and 137Cs data used in this section have been reported in Ritchie et al. [25] and Li et al. [4, 52]. A total of 460 locations were randomly selected for WCW and 230 locations were selected for each site of Sites 1 and 2 (Figure 5). Topography information was extracted for all locations using the LiDAR-derived DEMs. For the watershed, 100 out of the 460 locations, including two 300-m transects, were chosen for field estimations of SOC content and 137Cs inventory in 2006. The field samplings for Sites 1 and 2 were collected in 2003. A 25 × 25 m grid was created for each site of Sites 1 and 2. The 230 samplings were obtained at grid nodes. At each location, we collected three samples that were located within a 1 m × 1 m quadrat from top 30 cm of soil using a push probe (3.2 cm diameter). At locations where sediment depositions were observed, deeper soils from the 30 to 50 cm layer were collected. Four reference soil samples for estimation of the baseline 137Cs inventory were collected from a local cemetery in WCW where no apparent soil redistribution had occurred from the 1950s. Trimble RTK 4700 global positioning system (GPS) was used to record the locations of sampling.
During laboratory analyses, bulk density was calculated after drying soil at 90°C for 48 hours based on the soil volume and dry mass weight. Then, the three samplings were mixed and sieved through a 2 mm screen. We ground subsamples that were taken from the composite soils to fine power with a roller mill and measured soil total C content by dry combustion at a temperature of 1350°C using an elemental analyzer (LECO CNS 2000, LECO Crop., St. Joseph, MI). Then, C content in CaCO3 was analyzed by dry combustion after the soil sample was baked in a furnace at 420°C for 16 hours. Estimates of SOC content (SOCcontent, %) were obtained from the differences between total C content and C content in CaCO3. SOC density (ρSOC, kg m−2) of the top 30 cm layer was calculated from the bulk density (ρbulk) and SOC content using the equation of ρSOC = SOCcontent × ρbulk × 0.3.
Measurement of 137Cs inventory used another subsample of the sieved soil sample and placed and sealed in a Marinelli beaker. The 137Cs concentration was estimated by Canberra Genie-2000 Spectroscopy System that receives input from Canberra high purity coaxial germanium crystals (HpC > 30% efficiency) into three 8192-channel analyzers through gamma-ray analysis. Analytic mixed radionuclide standard (10 nuclides) that follows the U.S. National Institute of Standards and Technology was applied for calibration the Spectroscopy System. The measurement precision is between ±4 and ± 6%. Unit of 137Cs concentration is in Becquerels per gram (Bq g−1) and was converted to 137Cs inventory in Becquerels per square meter (Bq m−2) using soil bulk density.
Calculation of SR rates based on 137Cs inventories was carried out by applying a Mass Balance Model II in a spreadsheet Add-in program [150]. Before running the model, parameters of tillage depth, proportion factor, and relaxation depth were set to 0.25 m, 0.5, and 4 kg m−2, respectively. The baseline 137Cs inventory estimated from the mean of 137Cs inventory in Ref. sites was 2657 Bq m−2 for Sites 1 and 2 in 2003 and 2526 Bq m−2 for the WCW in 2006. Positive SR rates were obtained when 137Cs inventories were higher than the baseline and the sites were referred as depositional sites; while eroded sites were considered when negative soil SR rates were estimated under conditions of lower 137Cs inventories than the baseline. Details of soil sampling and laboratory analyses can be found in Ritchie et al. [25] and Li et al. [4].
Fifteen topographic metrics that were discussed previously were used as ancillary variables for the development of the DTM-based SOC and SR models. All metrics were derived from DEMs generated from high resolution (1 m horizontal and 0.1 m vertical resolutions) LiDAR data [48]. Before generation of topographic metrics, inverse distance weighted interpolation was applied to produce 3 m spatial resolution DEMs after converting the raw LiDAR data to LAS files.
Topographic metrics were derived based on the 3 m DEMs after filtering twice by a 3-kernel low pass filter. Modules in an open-access software of the System for Automated Geoscientific Analysis (SAGA) v. 2.2.5 were applied to generated 14 of the selected topographic metrics including slope gradient (G), aspect (A), profile curvature (P_Cur), plan curvature (Pl_Cur), general curvature (G_Cur), flow accumulation (FA), positive topographic openness (PTO), upslope slope (Upsl), flow path length (FPL), downslope index (DI), catchment area (CA), topographic wetness index (TWI), stream power index (SPI), and slope length factor (LS). Topographic relief (TR) was calculated by the difference between a maximum elevation map within a specific area and the filtered 3 m DEMs. In order to reduce errors due to an arbitrary selection of the radius of the specific area, a series of maximum elevation maps with multiple radiuses including 7.5, 15, 30, 45, 60, 75, and 90 m, were used to generate TR maps with different spatial scales. Principal component analysis (PCA) and varimax rotated Factor Analysis (FAn) were used and converted the TR maps into two main topographic relief components (TRPC1 and TRPC2) and two topographic factors (TRFA1 and TRFA2). The detailed topographic metric processing can be found in Li et al. [52].
Spearman’s rank analysis was applied to understand the impacts of topographic metrics on SR and SOC distribution patterns. Due to high correlations between some of the topographic metrics, PCA and varimax rotated FAn were used to limit errors caused by collinearity between topographic variables. The PCA analyzed topographic metrics from the 460 locations of the WCW. Loadings for the first eight components that explained 90% of the variance of all metrics were selected and used to calculate topographic principal components (TPCs) at the field Sites 1 and 2. Similarly, eight topographic factors (TFAs) at field sites were also estimated based on loadings from the watershed using FAn with varimax rotation.
The stepwise linear regression with “leave-one-out” cross-validation was applied for MLR, MLR-PCA, and MLR-FAn model development using the topographic metrics from two field sites. Akaike Information Criterion was used to select variables contained in each model. The SOC density and SR rates were log-transformed to meet the assumption of residual normality. Model efficiencies were assessed with the following three criteria. The first one is the adjusted coefficient of determination (
The high-resolution topographic metrics derived from LiDAR data presented detailed topographic information in the WCW. Take field Site 1 as an example, Figure 6 exhibited characteristics of each topographic metric in response to the elevation. Seven topographic metric maps, including catchment area (CA, Figure 6f), downslope index (DI, Figure 6h), flow path length (FPL, Figure 6i), flow accumulation (FA, Figure 6j), topographic relief component 1 (TRPC1, Figure 6k), topographic relief factor 1 (TRFA1, Figure 6m), and topographic wetness index (TWI, Figure 6p), showed high values in depressional areas and low values in sloping and ridge areas. Positive topographic openness (PTO, Figure 6o) had a reverse pattern compared to the above seven metrics. It showed high values in ridge areas where a wider view of a landscape can be seen. For slope gradient (G, Figure 6a), upslope slope (Upsl, Figure 6g), topographic relief component 2 (TRPC2, Figure 6l), topographic relief factor 2 (TRFA2, Figure 6n), stream power index (SPI, Figure 6q), and slope length factor (LS, Figure 6r), high values were observed in sloping areas, but low values were found in ridges and depressional areas.
Topograhic metrics of Site 1 (z-axis 15× elevaton). The metrics include a) slope gradient (G), b) aspect (A), c) profile curvature (P_Cur), d) plan curvature (Pl_Cur), e) general curvature (G_Cur), f) catchment area (CA), g) upslope slope (Upsl), h) downslope index (DI), i) flow path length (FPL), j) flow accumulation (FA), k) topographic relief principal component 1 (TRPC1), l) topographic relief principal component 2 (TRPC2), m) topographic relief factor 1 (TRFA1), n) topographic relief factor 2 (TRFA2), o) positive topographic openness (PTO), p) topographic wetness index (TWI), q) stream power index (SPI), and r) slope length factor (LS).
Most topographic metrics showed significant correlations with SOC density and SR rates except A. The A was slightly correlated with SOC density (r = −0.097; P = 0.02) and insignificantly correlated with 137Cs inventory (P > 0.05) and SR rates (P > 0.05). Generally, stronger topographic controls on SOC density than 137Cs inventory and SR rates were observed (Table 2). TWI, TRFA1, TRPC1, CA, FPL, DI, FA, SPI, and TRFA2 were significantly positively correlated with SOC density and G, LS, PTO, Upsl, Pl_Cur, G_Cur, TRPC2, and P_Cur were significantly negatively correlated with SOC density. For both 137Cs inventory and SR rates, similar high related topographic metrics (|r| > 0.5) were observed, including TRPC1, TWI, TRFA1, G, and CA.
G | A | P_Cur | Pl_Cur | G_Cur | CA | Upsl | DI | FPL | |
---|---|---|---|---|---|---|---|---|---|
SOC | −0.687*** | −0.097* | −0.159** | −0.333*** | −0.288*** | 0.587*** | −0.315*** | 0.413*** | 0.500*** |
137Cs | −0.646*** | — | −0.210*** | −0.277*** | −0.286*** | 0.568*** | −0.209*** | 0.366*** | 0.490*** |
SR | −0.650*** | — | −0.205*** | −0.274*** | −0.282*** | 0.565*** | −0.217*** | 0.361*** | 0.487*** |
FA | TRPC1 | TRPC2 | TRFA1 | TRFA2 | PTO | TWI | SPI | LS | |
SOC | 0.204*** | 0.698*** | −0.171*** | 0.723*** | 0.143** | −0.451*** | 0.735*** | 0.165*** | −0.453*** |
137Cs | 0.192*** | 0.686*** | −0.095* | 0.623*** | 0.221*** | −0.437*** | 0.640*** | 0.160** | −0.433*** |
SR | 0.193*** | 0.687*** | −0.099* | 0.624*** | 0.218*** | −0.427*** | 0.647*** | 0.156*** | −0.438*** |
Spearman’s ranking correlation between topographic metrics and SOC density, 137Cs inventory, and soil redistribution (SR) rates.
P < 0.05.
P < 0.005.
P < 0.0001.
G and A are slope gradient and aspect, respectively; P_Cur, Pl_Cur, and G_Cur are profile curvature, plan curvature and general curvature, respectively; TRPC1 and TRPC2 are topographic relief components 1 and 2, respectively; TRFA1 and TRFA2 are topographic relief factors 1 and 2, respectively; PTO is positive topographic openness; Upsl is upslope slope; FPL is flow path length; DI is downslope index; CA is catchment area; TWI is topographic wetness index; SPI is stream power index; LS is slope length factor. The values in bold have correlation coefficient > 0.5 and values in italic and bold indicate the highest correlation coefficient for soil properties.
Topographic wetness index (TWI) was the most influential topographic factor with a correlation coefficient up to 0.735. The finding is consistent with the high TWI impacts on SOC in previous studies. The high impact of TWI suggests that soil water content distribution was an important driver of SOC dynamics in the WCW. In areas with high TWI and possibly elevated water content, litter decomposition rates decrease and plant productions increase, which increases SOC input and accumulations and results in high SOC density in the soil; while low soil water content areas provide an adequate environment for rapid aerobic decomposition of soil C, leading to a negative correlation between SOC and TWI [3, 4, 32, 46, 152].
Topographic relief (TR) was found to be the most important factor for 137Cs inventory and SR rates with correlation coefficients of 0.686 and 0.687, respectively. This metric was also highly correlated with SOC density. The strong effects of TR on soil properties may be due to its influence on flow velocity. The flow velocity reflects runoff shear stress, which would impact the sediment transport capacity of the runoff [153, 154]. Thus, as the TR increases, the flow velocity and runoff shear stress increases, leading to enhance in sediment transport capacity, which increases the transports of 137Cs and SOC-enriched fine fraction of sediments from low TR areas to the high TR areas.
Slope gradient (G) was another important factor for SOC density, 137Cs inventory, and SR rates with absolute correlation coefficients larger than 0.6. Our findings are consistent with those of other researches, reporting high erosion rates in areas with relatively steep slopes [151, 155]. In agricultural fields, the main erosion processes include both water and tillage erosion [156, 157, 158]. Soil and associated SOC are transported to downslope due to gravity-driven lateral transport by overland and concentrated flows. Tillage operations would also cause redistribution of soil by small downslope movements of soil associated with each operation. Furthermore, as discussed in Section 2, G increase could enhance runoff and decrease infiltration, reducing water content in soil in the flat watershed area [15, 17, 18]. The controls of G on water and tillage erosion and water content could be related to the high slope impacts on soil properties in agricultural areas.
Since slope aspect (A) showed a weak correlation with SOC and no significant correlations with 137Cs inventory and SR rate, we removed the A for the following DTM-based model development. Therefore, 17 topographic metrics, including slope gradient (G), curvature related metrics (P_Cur, Pl_Cur, and G_Cur), catchment area (CA), upslope slope (Upsl), downslope index (DI), flow path length (FPL), flow accumulation (FA), topographic relief principal components 1 and 2 (TRPC1 and TRPC2), topographic relief factors 1 and 2 (TRFA1 and TRFA2), positive topographic openness (PTO), topographic wetness index (TWI), stream power index (SPI), and slope length factor (LS) were used for building the MLR models. We only used TRPC1 and TRPC2 to represent topographic relief for MLR-PCA development and TRFA1 and TRFA2 for MLR-FAn.
The MLR, MLR-PCA, and MLR-FAn models were developed based on topographic and soil property data at the two field sites (Table 3). The MLR models showed the best simulations of SOC and SR rates with the highest
Model | NSE | RSR | ||
---|---|---|---|---|
MLR | ||||
SOC | 2.98 + 0.071TRFA1 − 4.23G-9.29G_Cur + 0.0004FPL + 0.030TRPC2 + 0.103Pl_Cur + 0.063DI† | 0.723 | 0.727 | 0.522 |
SR | 2.12 − 3.12G + 0.107DI + 0.019TRFA2 + 0.0002FPL + 0.915Upsl + 0.010TRFA1 − 0.002SPI-1.53G_Cur | 0.655 | 0.659 | 0.584 |
MLR-PCA | ||||
SOC | 2.94 − 0.060TPC2 − 0.024TPC3 + 0.051TPC7 + 0.037TPC1 | 0.684 | 0.686 | 0.560 |
SR | 2.11 + 0.013TPC1 + 0.032TPC7 − 0.028TPC2−0.016TPC3 − 0.010TPC6 | 0.625 | 0.629 | 0.609 |
MLR-FAn | ||||
SOC | 2.92 − 0.101TFA1 + 0.074TFA4 + 0.045TFA7 + 0.026TFA8 + 0.037TFA2 − 0.027TFA3 | 0.706 | 0.710 | 0.538 |
SR | 2.10−0.047TFA1 − 0.011TFA3 + 0.026TFA7 + 0.034TFA4 + 0.025TFA8 | 0.620 | 0.624 | 0.613 |
Models of soil organic carbon (SOC) density and soil redistribution (SR) rate based on cropland field sites 1 and 2.
The order of predictors is based on the stepwise selection procedure.
Although the MLR showed the best simulation performance for the two field sites, the MLR-PCA had the highest prediction efficiency when applying models to predict the spatial patterns of SOC and SR rate over the watershed (Figure 7a). The SOC predictions by MLR-PCA explained 60% of the variability in observed SOC in the WCW. The NSE value was larger than 0.5 (0.591) and RSR value was <0.7 (0.639), which suggested a satisfactory performance of SOC prediction by the MLR-PCA model. The prediction efficiencies of MLR and MLR-FAn models were lower than the MLR-PCA model with correlation coefficients of 0.39 and 0.49, respectively. Based on these results, the SOC map over the watershed was generated based on the MLR-PCA model (Figure 8). The derived SOC map captured the majority spatial variability in SOC density as reflected by consistent spatial patterns between observed and simulated SOC density. High values of SOC density were observed in depressions and low values were found in ridges and sloping areas.
Comparison of (a) soil organic carbon (SOC) density (kg m−2) and (b) soil redistribution (SR) rate (t ha−1 year−1) simulations to observations over the Walnut Creek watershed.
Soil organic carbon (SOC) map obtained from the MLR-PCA model (a) within the Walnut Creek watershed and (b) along two transects.
Lower efficiencies were observed for SR rate than SOC density when compared the model predictions in the WCW (Figure 7b). The MLR-PCA SR model showed the highest correlation coefficient. However, the low NSE and high RSR values indicated that the model could not well predict SR rates when applying a model developed at field-scale for predictions at watershed-scale.
The better performance of MLR-PCA models relative to MRL models may be due to the exclusion of multicollinearity by PCA. High correlations (|r| > 0.8) were observed for some of topographic metrics, such as G and Upsl, and G_Cur and Pl_Cur. Uncertainty increases due to the high collinearity because models can be significantly influenced by small changes in the high collinearity predictors [159]. Thus, the MLR models were less stable with lower efficiencies in predicting SOC density and SR rates when applying to different spatial scales. The use of PCA could eliminate the multicollinearity and increase the stability of model since the PCA converted the 15-dimension topographic dataset to eight mutually independent combinations (TPCs) [159, 160].
Furthermore, by analysis of TPC loadings, hidden relationships between topographic metrics were uncovered, which could be another advantage of using the PCA [160]. For example, in this study, we selected TPCs 1, 2, 3, 6, and 7 for model development (Table 4). The high loading (|loading| > 0.35) topographic metric in TPC1 was G_Cur (−0.353), and thus, this component was associated with runoff divergence. G (0.475), TWI (−0.465), Upsl (0.419), and LS (0.396) were the high loading metrics for TPC2, which indicated that TPC2 was associated with soil water content. TPC3 were associated with runoff volume since the high loading metrics were FA (0.482), SPI (0.460), and CA (0.400). TPCs 6 and 7 were associated with runoff velocity and flow acceleration, respectively. Based on the TPCs, we can obtain a better understanding of the controlling components for SOC distribution and SR. For the low-relief agriculture watershed under study, the spatial patterns of SOC and SR rate were mainly impacted by soil water content (TPC2) and runoff divergence (TPC1), respectively, according to the priority of TPCs used in model development. This conclusion is also consistent with findings by Fox and Papanicolaou [161] that indicated flow divergence significantly influenced soil erosion from uplands in a low-relief watershed.
TPC1(25%) | TPC2(24%) | TPC3(14%) | TPC6(5%) | TPC7(4%) | |
---|---|---|---|---|---|
G | 0.062 | 0.475 | −0.035 | −0.013 | −0.183 |
P_Cur | −0.290 | 0.000 | 0.346 | −0.070 | −0.002 |
Pl_Cur | −0.283 | 0.107 | −0.001 | 0.485 | 0.383 |
G_Cur | −0.353 | 0.054 | 0.275 | 0.025 | 0.100 |
FA | 0.297 | −0.042 | 0.482 | 0.179 | 0.131 |
TRPC1 | 0.309 | −0.193 | −0.237 | 0.113 | −0.116 |
TRPC2 | 0.234 | 0.266 | −0.118 | 0.084 | 0.597 |
PTO | −0.330 | 0.092 | 0.258 | −0.292 | 0.217 |
Upsl | 0.187 | 0.419 | −0.143 | −0.066 | 0.012 |
FPL | 0.147 | −0.168 | −0.088 | −0.703 | 0.407 |
DI | 0.103 | −0.220 | −0.164 | 0.184 | 0.435 |
CA | 0.326 | −0.128 | 0.400 | −0.160 | −0.092 |
TWI | 0.053 | −0.465 | −0.067 | 0.185 | −0.047 |
SPI | 0.345 | −0.014 | 0.460 | 0.169 | 0.080 |
LS | 0.256 | 0.396 | 0.050 | 0.011 | −0.072 |
Loadings in the selected topographic principal components (TPCs) calculating based on topographic metrics at the two field sites.
G is slope gradient; P_Cur, Pl_Cur, and G_Cur are profile curvature, plan curvature and general curvature, respectively; TRPC1 and TRPC2 are topographic relief components 1 and 2, respectively; PTO is positive topographic openness; Upsl is upslope slope; FPL is flow path length; DI is downslope index; CA is catchment area; TWI is topographic wetness index; SPI is stream power index; and LS is slope length factor.
The values in bold indicate loadings > 0.35.
The lower efficiencies of MLR-FAn than MLR-PCA may be because the latter approach diminishes the risk of over-fitting the models. The difference between PCA and FAn is that PCA considers all of the variance in the matrix, including unique, error and shared variance; while FAn extracts and exhibits shared variance only. Although some studies were preferable to FAn because of its ability to understand the underlying structure by extracting latent shared variance [162, 163], others also proved that there were almost no practical differences between the two methods [164, 165]. In this study, we found that both methods had similar performance during model calibration in small-scales. However, including more predictors in MLR-FAn models may enhance the instability of models and increase uncertainties during extrapolating prediction points over a large-scale [159, 166].
This case study demonstrated the importance of topography on soil properties in the low-relief watershed. DTM-based models are feasible for SOC predictions at different spatial scales. By combining MLR with PCA, the model efficiencies increased during soil property prediction. The DTM-based mapping techniques can be improved by further refinement remotely sensed data, improvement of the topographic dataset, and development of modeling techniques such as including Hybrid Regression and Artificial Intelligence techniques. The large-scale soil property maps can provide a more sound scientific basis for understanding of the mechanisms underlying the topographic impacts on soil movement in agricultural landscapes and the fate of SOC at the watershed and regional scales.
This research was supported by the USDA Natural Resources Conservation Service in association with the Wetland Component of the National Conservation Effects Assessment Project (NRCS 67-3A75-13-177). All data used in this study has been published on the USDA Ag Data Commons and can be accessed using DOI:
No conflict of interest exists in the submission manuscript.
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