Apparent Gap Research Articles

Investigation on variations of apparent stress in the region in and around the rupture volume preceding the occurrence of the 2021 Alaska MW8.2 earthquake

On July 29, 2021, a large earthquake of MW8.2 occurred south of the Alaska Peninsula. To investigate the spatial-temporal changes of crustal stress in the earthquake-stricken area before this event, we selected 159 earthquakes of 4.7 ≤ MW ≤ 6.9 that occurred in the epicentral region and its surroundings between January 1980 and June 2021 to study the temporal variation and spatial distribution of their apparent stress. In addition, we analyzed the correlation between seismic activities and Earth’s rotation and explored the seismogenic process of this earthquake. The crustal stress rose from January 2008 to December 2016. This period was followed by a sub-instability stage from January 2017 until the occurrence of the MW8.2 earthquake. The average rate of apparent stress change in the first five years of the stress increase period was roughly 2.3 times that in the last four years. The lateral distribution of the apparent stress shows that the areas with apparent stress greater than 1.0 MPa exhibited an expanding trend during the seismogenic process. The maximum apparent stress was located at the earthquake epicenter during the last four years. The distribution of the apparent stress in the E-W vertical cross section revealed that an apparent stress gap formed around the hypocenter during the first five years of the stress increase period, surrounded by areas of relatively high apparent stress. After the Alaska earthquake, most parts of this gap were filled in by aftershocks. The seismic activities during the sub-instability stage exhibited a significant correlation with Earth’s rotation.

The Food Gap and Food Security of Poultry Meat in Saudi Arabia

This research aimed to explore the current situation of the food gap and the food security of poultry meat in the Kingdom, based on secondary published data by the food and Agriculture Organization of the United Nations (FAO), the Ministry of Environment, Water and Agriculture, the General Authority for statistics, the Arab Organization for Agricultural Development (AOAD), and the international information Network. The results indicated a decline in self-sufficiency at a statistically significant annual rate of about 1.5%, representing about 2.9% of the annual average self-sufficiency rate during the study period. This is consistent with the economic logic in terms of increasing the volume of imports at a rate that exceeds 6.3% annually and exceeds the rate of consumption growth (4.43%) over the rate of growth of domestic production (1.73%). The apparent gap increased at an annual statistically significant rate of about 38.5 thousand tons, representing about 7.1% of the annual average gap estimated at around 543.8 thousand tons. It was also found that the most influential factors on the self-sufficiency ratio of poultry meat were the number of imports and the domestic production. Therefore, the strategic stock of poultry meat was estimated at around 621.4 thousand tons, which is sufficient for local consumption for about 7 months, while the value of the food security factor was about 0.58, reflecting the existence of relative food security of poultry meat in the Kingdom. The results of the study also showed the size of the additional loans and investments needed to achieve self-sufficiency and food security for poultry meat in the Kingdom in line with the National Transformation Program and the Kingdom's Vision 2030.

A Cross Sectional Study on Prevalence and Factors Associated with Family Planning in the Context of COVID-19 among Women of Reproductive Age in a Rural Town Council in Eastern Uganda

Aim: This study aimed to assess the Family Planning Prevalence and Factors associated with Family Planning in face of COVID-19, among women of reproductive age in rural town council in Amuria district, Eastern Uganda. Little evidence exists of the factors associated with family planning use in face of COVID-19 in resource limited settings in Eastern Uganda. Our findings will close this apparent research gap and inform national and district Family planning guidelines and policies.
 Materials and Methods: We used a quantitative research approach and cross sectional design to determine prevalence and factors associated with family planning use, among women of reproductive age in the face of Covid-19 in Amuria Town council in Uganda. The respondents for the study were chosen using purposive sampling technique. We used questionnaires to collect data from study participants. Quantitative data was compiled and fed into the computer using Microsoft Excel and then exported to Stata 15.
 Results: The Prevalence was at 17.1% lower than the national target of 50%. 58.4% of our women were not using family planning because of the anticipated side effects. 50% those using family planning completed ordinary level education. The married women were 0.2 times less likely to use family planning. Single ladies were 4.6 times more likely to use family planning as compared to widowed, divorced and married.
 Conclusion: The prevalence of Family planning in Amuria Town Council was 17.1% much lower than the National Target. Being married and low level of education was associated with low uptake of Family planning in context of COVID-19. Family planning programs should put more focus on the married women and involve their husbands in implementation of their programs.

SELF-LLP: Self-supervised learning from label proportions with self-ensemble

In this paper, we tackle the problem called learning from label proportions (LLP), where the training data is arranged into various bags, with only the proportions of different categories in each bag available. Existing efforts mainly focus on training a model with only the limited proportion information in a weakly supervised manner, thus result in apparent performance gap to supervised learning, as well as computational inefficiency. In this work, we propose a multi-task pipeline called SELF-LLP to make full use of the information contained in the data and model themselves. Specifically, to intensively learn representation from the data, we leverage the self-supervised learning as a plug-in auxiliary task to learn better transferable visual representation. The main insight is to benefit from the self-supervised representation learning with deep model, as well as improving classification performance by a large margin. Meanwhile, in order to better leverage the implicit benefits from the model itself, we incorporate the self-ensemble strategy to guide the training process with an auxiliary supervision information, which is constructed by aggregating multiple previous network predictions. Furthermore, a ramp-up mechanism is further employed to stabilize the training process. In the extensive experiments, our method demonstrates compelling advantages in both accuracy and efficiency over several state-of-the-art LLP approaches.

Generating and testing the efficacy of transgenic Cas9 in Tribolium castaneum.

CRISPR/Cas9 genome editing has now expanded to many insect species, including Tribolium castaneum. However, compared to Drosophila melanogaster, the CRISPR toolkit of T. castaneum is limited. A particularly apparent gap is the lack of Cas9 transgenic animals, which generally offer higher editing efficiency. We address this by creating and testing transgenic beetles expressing Cas9. We generated two different constructs bearing basal heat shock promoter‐driven Cas9, two distinct 3′ UTRs, and one containing Cas9 fused to EGFP by a T2A peptide. Analyses of Cas9 activity in each transgenic line demonstrated that both designs are capable of inducing CRISPR‐ mediated changes in the genome in the absence of heat induction. Overall, these resources enhance the accessibility of CRISPR/Cas9 genome editing for the Tribolium research community and provide a benchmark against which to compare future transgenic Cas9 lines.

Adaptive plasticity in activity modes and food web stability.

Natural ecosystems are comprised of diverse species and their interspecific interactions, in contrast to an ecological theory that predicts the instability of large ecological communities. This apparent gap has led ecologists to explore the mechanisms that allow complex communities to stabilize, even via environmental changes. A standard approach to tackling this complexity-stability problem is starting with a description of the ecological network of species and their interaction links, exemplified by a food web. This traditional description is based on the view that each species is in an active state; that is, each species constantly forages and reproduces. However, in nature, species’ activities can virtually stop when hiding, resting, and diapausing or hibernating, resulting in overlooking another situation where they are inactive. Here I theoretically demonstrate that adaptive phenotypic change in active and inactive modes may be the key to understanding food web dynamics. Accurately switching activity modes can greatly stabilize otherwise unstable communities in which coexistence is impossible, further maintaining strong stabilization, even in a large complex community. I hypothesize that adaptive plastic change in activity modes may play a key role in maintaining ecological communities.

Process-Driven Modelling of Media Forensic Investigations-Considerations on the Example of DeepFake Detection.

Academic research in media forensics mainly focuses on methods for the detection of the traces or artefacts left by media manipulations in media objects. While the resulting detectors often achieve quite impressive detection performances, when tested under lab conditions, hardly any of those have yet come close to the ultimate benchmark for any forensic method, which would be courtroom readiness. This paper tries first to facilitate the different stakeholder perspectives in this field and then to partly address the apparent gap between the academic research community and the requirements imposed onto forensic practitioners. The intention is to facilitate the mutual understanding of these two classes of stakeholders and assist with first steps intended at closing this gap. To do so, first a concept for modelling media forensic investigation pipelines is derived from established guidelines. Then, the applicability of such modelling is illustrated on the example of a fusion-based media forensic investigation pipeline aimed at the detection of DeepFake videos using five exemplary detectors (hand-crafted, in one case neural network supported) and testing two different fusion operators. At the end of the paper, the benefits of such a planned realisation of AI-based investigation methods are discussed and generalising effects are mapped out.

Direct and mediation effect of supply chain complexity drivers on supply chain performance: an empirical evidence of organizational complexity theory

PurposeIn today's globalized business environment, growing supply chain complexity (SCC) is arguably a major threat to the firm's business continuity with an adverse impact on the firm's competitive advantage and business performance. Researchers, though, investigated the impact of SCC drivers on a firm's operational performance, but the key question “Which supply chain complexity drivers severely impact the supply chain performance (SCP)?” remains largely unanswered from empirical research. The present study aims to decompose the SCC into four major constituting sub-categories (upstream, operational, downstream and external) to explore the causal impact of SCC drivers on SCP in direct and mediated manner.Design/methodology/approachThe indicators applied for measuring constructs in the “Measurement model” are obtained from existing literature to increase the validity and reliability of the model. First, a pilot survey involving 25 SC managers from various manufacturing firms was conducted for indicator refinement and content validation. Second, the large-scale response data were collected through extensive surveys. This research explores the causality by testing the hypothesis applying Structural Equation Modeling (SEM) based on the responses received from 246 firms.FindingsThe study investigates the impact of SCC drivers on SCP through direct and mediation effect. The results indicate that upstream and operational SCC drivers play a mediating role in managing SCP. The findings reveal that upstream and operational SCC drivers adversely impact the SCP. Furthermore, the impact of downstream complexity on SCP is moderated through operational complexity drivers. The result explains the theoretical relation among SCC drivers supported by empirical validity.Practical implicationsThe outcome offers practical relevance to supply chain (SC) managers in SCC and SCP management. Knowing the effect of SCC drivers among themselves and on SCP will facilitate the SC managers in devising the right strategies. The study provides a framework for prioritizing the resource in addressing the SCC issues among many.Originality/valueThe study addresses the apparent gap in the literature by modeling the impact of SCC drivers on SCP, which remained largely unexplored. First, it contributes to developing complex relationships among SCC drivers. Second, the direct and mediated causal effect of the SCC drivers individually and combinedly on SCP are explicated.

With Clear Intention—An Ethical Responsibility Model for Robot Governance

There is much discussion about super artificial intelligence (AI) and autonomous machine learning (ML) systems, or learning machines (LM). Yet, the reality of thinking robotics still seems far on the horizon. It is one thing to define AI in light of human intelligence, citing the remoteness between ML and human intelligence, but another to understand issues of ethics, responsibility, and accountability in relation to the behavior of autonomous robotic systems within a human society. Due to the apparent gap between a society in which autonomous robots are a reality and present-day reality, many of the efforts placed on establishing robotic governance, and indeed, robot law fall outside the fields of valid scientific research. Work within this area has concentrated on manifestos, special interest groups and popular culture. This article takes a cognitive scientific perspective toward characterizing the nature of what true LMs would entail—i.e., intentionality and consciousness. It then proposes the Ethical Responsibility Model for Robot Governance (ER-RoboGov) as an initial platform or first iteration of a model for robot governance that takes the standpoint of LMs being conscious entities. The article utilizes past AI governance model research to map out the key factors of governance from the perspective of autonomous machine learning systems.

Learning Analytics implementations in universities: Towards a model of success, using multiple case studies


 
 
 In these pioneering days of Learning Analytics in higher education, universities are pursuing a diverse range of in-house implementation strategies, with varying degrees of success. In this exploratory study we compare and contrast the approaches taken at three demographically different Australian universities. The comparison is made in the context of Delone and McLean’s information system success model (1992). In time, a consensus-driven method for using Learning Analytics to improve student learning outcomes will eventuate, including individualized learning, but we are still some distance from this level of maturity. It seems likely that user-friendly proprietary platforms will prosper in the climate of uncertainty. Participants in the study see potential in Learning Analytics but are not sure about how best to realize that potential as the implementation of Learning Analytics systems at Australian universities are still very much in their infancy. Proprietary approaches offering sophisticated functionality seem likely to emerge and take precedence over the trial and error approach. This study addresses an apparent gap in the research as limited studies exist targeting both learning analytics and information system success. The methodology taken explores the research topic through a qualitative lense utlising thematic analysis. The study concludes that digital interventions such as Learning Analytics has great potential to optimize teaching and learning practices. Information systems success research can provide insights into what works and what does not in terms of Learning Analytics implementations. The discipline needs to be systematized for efficient implementation, and must deliver tangible benefits over time.
 
 

Characterization and optical properties of m-Li2ZrO3 prepared by optimized solid-state reaction

m-Li2ZrO3 powders were successfully prepared by solid-state reaction method using Li2CO3 and ZrO2 as raw materials. The synthesis was optimized by varying the ball-milling time (0–96 h); Li2CO3 excess (0 or 5 wt%), reaction temperature (700, 800, 900 or 1000 °C), and reaction time (3, 6, 9 or 12 h). The structural, morphological and optical properties of m-Li2ZrO3 powders were examined by X-Ray Diffraction, Thermogravimetric and Differential-Thermal analysis, Scanning Electron Microscopy, High-Resolution Transmission Electron Microscopy, Laser Diffraction, Dynamic Light Scattering and UV–Vis Diffuse Reflectance Spectroscopy. The results show that precursors suitable for the synthesis of fine powders require ball-milling times longer than or equal to 6 h. Highly crystalline m-Li2ZrO3 was synthesized under two distinctive calcination conditions as follows: 900 °C/6h without Li2CO3 excess or 1000 °C/12 h using 5 wt% of Li2CO3 excess. Particle size of as-synthesized powders was found to be in the range from 200 nm to 1 µm. m-Li2ZrO3 was found to be a wide band gap material with apparent optical band gap of 5.5 eV (direct) and 5.1 eV (indirect), which can be used in UV-C applications.

Pressure-driven Eu2+-doped K3Sc(PO4)2: A broad cyan-green emitting phosphor for closing the cyan cavity in solid-state lighting and applying in optical pressure sensor

In general, the apparent spectrum gap appearing in the cyan region (480 – 520 nm) will limit the application of the phosphor in the high-quality full-spectrum lighting. Therefore, the cyan-green emitting K3Sc(PO4)2:Eu2+ phosphor with peak at 495 nm we reported here is very essential to close the cyan gap. The cyan-green emission is caused by the highly condensed network and three different K sites for Eu2+ ions. Increasing the Eu2+ ions content causes the spectral tuning from 468 nm to 497 nm. K3Sc(PO4)2:0.05Eu2+ exhibits excellent thermal stability: the integrated intensity at 423 K are still 90.0% of initial intensity. The red shift with shift rate of 7.9 cm−1/kbar of the K3Sc(PO4)2:Eu2+ induced by pressure indicates that the phosphor has a good application prospect in optical pressure sensor. At ambient pressure, the identical profile and position of the normalized PL spectra in the compression and decompression process confirm the good structural rigidity and stability of the K3Sc(PO4)2. The color rendering index (Ra) and correlated color temperature (CCT) of the WLEDs devices under 50–500 mA working current are still maintain stable (Ra of 87.5 and CCT of 4505 K). These suggest the broad cyan-green emitting phosphor plays an important role in closing the cyan cavity of solid-state lighting and applying in optical pressure sensor.

ERP systems selection in multinational enterprises: a practical guide

The Enterprise Resource Planning (ERP) system selection is an early phase in the ERP adoption process. When organizations evaluate an ERP, they commonly develop their own selection criteria that usually involve various system and vendor related factors. While the selection process is critical, however, there is an apparent research gap in literature. The ERP selection effort also focuses on the system’s fit with the organizational requirements and needs. Thus, the selection phase is critical, because if an organization chooses an unfit ERP, the whole project could be predestined to fail. This research provides an overview of an ERP selection process at an overseas branch office of a multinational company. The process employed a simple multi-attribute rating technique (SMART) for evaluation. In addition, this research presents how cross-border data protection laws between the parent company and its branch have influenced the selection process. As the ERP system has been implemented successfully, the method and the selection factors have been proven adequate for the selection process.

Generalized self-concordant analysis of Frank–Wolfe algorithms

Projection-free optimization via different variants of the Frank–Wolfe method has become one of the cornerstones of large scale optimization for machine learning and computational statistics. Numerous applications within these fields involve the minimization of functions with self-concordance like properties. Such generalized self-concordant functions do not necessarily feature a Lipschitz continuous gradient, nor are they strongly convex, making them a challenging class of functions for first-order methods. Indeed, in a number of applications, such as inverse covariance estimation or distance-weighted discrimination problems in binary classification, the loss is given by a generalized self-concordant function having potentially unbounded curvature. For such problems projection-free minimization methods have no theoretical convergence guarantee. This paper closes this apparent gap in the literature by developing provably convergent Frank–Wolfe algorithms with standard mathcal {O}(1/k) convergence rate guarantees. Based on these new insights, we show how these sublinearly convergent methods can be accelerated to yield linearly convergent projection-free methods, by either relying on the availability of a local liner minimization oracle, or a suitable modification of the away-step Frank–Wolfe method.

S-scheme heterojunction of CuBi2O4 supported Na doped P25 for enhanced photocatalytic H2 evolution

Rational design and construction of Step-scheme (S-scheme) photocatalyst have attracted considerable attention in the photocatalytic field. In this study, a series of Step-scheme (S-scheme) CuBi2O4/Na doped P25 photocatalysts (CBO/Na–P25) were synthesized through a facile hydrothermal method. CBO/Na–P25 photocatalyst without co-catalyst exhibited good photocatalytic performance for hydrogen evolution, and the H2 yield rate of the 7.5%CBO/Na–P25 sample achieved 2695.73 μmol/gcat/h after 4 h of illumination, which was more than 37.8, 13.7 and above 100 times those of pure P25, Na–P25 and CuBi2O4. The calculated apparent band gap of P25 decreased by Na doping and the construction of CuBi2O4/Na–P25 heterojunction contributed to efficient interfacial charge separation. Meanwhile, the S-scheme carrier transfer mechanism for the CuBi2O4/Na–P25 heterojunction photocatalyst was verified by the experimental results under different light wavelengths. This work demonstrated that the P25 based S-scheme heterojunction is a promising photocatalyst for hydrogen evolution.

Education for optimized Life Cycle Management: The Project e-CIRP and its insights into embedding circular economy aspects to product design via teaching

The integration of circular economy-based life cycle management (LCM) into product design and optimisation is essential for the transformation towards a circular economy (CE). However, companies often lack the expertise to adapt life-cycle design (LCD) thinking in their business operations and are in need of respective capacity building. To close this apparent gap is the aim of the project e-CirP (Embedding Circular Economy into Product Design and Optimization) where LUT University, Fraunhofer, Technical University of Denmark, University of Padova, Delft University of Technology, University of Helsinki and Metso Outotec have worked together to develop a program that allows Master students across Europe to learn how to integrate CE and Life Cycle Thinking principles into product design by analysing real industrial cases. In the project, modern pedagogical approaches have been applied. A modular training package covering general circular economy aspects, as well as detailed value chain perspectives, has been created. Next to the content-related aspects, a great focus was also on the support of so-called soft-skills development, e.g. through international student cooperation on case studies. The paper presents the perspective of participating students as well as the cooperating companies that supplied the industry cases to allow an overview of opportunities and challenges.

Harnessing Synergistic Biostimulatory Processes: A Plausible Approach for Enhanced Crop Growth and Resilience in Organic Farming.

Simple SummaryDemand for organically grown crops has risen globally due to its healthier and safer food products. From a sustainability perspective, organic farming offers an eco-friendly cultivation system that minimizes agrochemicals and producing food with little or no environmental footprint. However, organic agriculture’s biggest drawback is the generally lower and variable yield in contrast to conventional farming. Compatible with organic farming, the selective use of biostimulants can close the apparent yield gap between organic and conventional cultivation systems. A biostimulant is defined as natural microorganisms (bacteria, fungi) or biologically active substances that are able to improve plant growth and yield through several processes. Biostimulants are derived from a range of natural resources including organic materials (composts, seaweeds), manures (earthworms, fish, insects) and extracts derived from microbes, plant, insect or animal origin. The current trend is indicative that a mixture of biostimulants is generally delivering better growth, yield and quality rather than applying biostimulant individually. When used correctly, biostimulants are known to help plants cope with stressful situations like drought, salinity, extreme temperatures and even certain diseases. More research is needed to understand the different biostimulants, key components, and also to adjust the formulations to improve their reliability in the field.Demand for organically grown food crops is rising substantially annually owing to their contributions to human health. However, organic farm production is still generally lower compared to conventional farming. Nutrient availability, content consistency, uptake, assimilation, and crop responses to various stresses were reported as critical yield-limiting factors in many organic farming systems. In recent years, plant biostimulants (BSs) have gained much interest from researchers and growers, and with the objective of integrating these products to enhance nutrient use efficiency (NUE), crop performance, and delivering better stress resilience in organic-related farming. This review gave an overview of direct and indirect mechanisms of microbial and non-microbial BSs in enhancing plant nutrient uptake, physiological status, productivity, resilience to various stressors, and soil-microbe-plant interactions. BSs offer a promising, innovative and sustainable strategy to supplement and replace agrochemicals in the near future. With greater mechanistic clarity, designing purposeful combinations of microbial and non-microbial BSs that would interact synergistically and deliver desired outcomes in terms of acceptable yield and high-quality products sustainably will be pivotal. Understanding these mechanisms will improve the next generation of novel and well-characterized BSs, combining microbial and non-microbial BSs strategically with specific desired synergistic bio-stimulatory action, to deliver enhanced plant growth, yield, quality, and resilience consistently in organic-related cultivation.

The primacy of ontology: a philosophical basis for research on religion in prison

ABSTRACT This paper suggests philosophical foundations for mixed methods research based on the philosophy of critical realism. In particular, it suggests that the critical realist idea of the primacy of ontology helps bridge the apparent paradigmatic gap between qualitative and quantitative research. It illustrates this foundational idea by showing why and how a multi-disciplinary team used a mixed methods approach to understand the significance of religion in prison through a multi-site study of religious conversion to Islam in prison and how this gives a better account than a single method approach. The mixed method research design used in the project sets out a new way of mapping and understanding religious conversion and differences within a faith group that draws on the emancipatory potential of critical realist thought.

An inside/outside Ramsey theorem and recursion theory

Inspired by Ramsey’s theorem for pairs, Rival and Sands proved what we refer to as an inside/outside Ramsey theorem: every infinite graph G G contains an infinite subset H H such that every vertex of G G is adjacent to precisely none, one, or infinitely many vertices of H H . We analyze the Rival–Sands theorem from the perspective of reverse mathematics and the Weihrauch degrees. In reverse mathematics, we find that the Rival–Sands theorem is equivalent to arithmetical comprehension and hence is stronger than Ramsey’s theorem for pairs. We also identify a weak form of the Rival–Sands theorem that is equivalent to Ramsey’s theorem for pairs. We turn to the Weihrauch degrees to give a finer analysis of the Rival–Sands theorem’s computational strength. We find that the Rival–Sands theorem is Weihrauch equivalent to the double jump of weak König’s lemma. We believe that the Rival–Sands theorem is the first natural theorem shown to exhibit exactly this strength. Furthermore, by combining our result with a result of Brattka and Rakotoniaina, we obtain that solving one instance of the Rival–Sands theorem exactly corresponds to simultaneously solving countably many instances of Ramsey’s theorem for pairs. Finally, we show that the uniform computational strength of the weak Rival–Sands theorem is weaker than that of Ramsey’s theorem for pairs by showing that a number of well-known consequences of Ramsey’s theorem for pairs do not Weihrauch reduce to the weak Rival–Sands theorem. We also address an apparent gap in the literature concerning the relationship between Weihrauch degrees corresponding to the ascending/descending sequence principle and the infinite pigeonhole principle.

Managing Density Stress to Close the Maize Yield Gap.

Continued yield increases of maize (Zea mays L.) will require higher planting populations, and enhancement of other agronomic inputs could alleviate density-induced stress. Row spacing, plant population, P-S-Zn fertility, K-B fertility, N fertility, and foliar protection were evaluated for their individual and cumulative impacts on the productivity of maize in a maize-soybean [Glycine max (L.) Merr.] rotation. An incomplete factorial design with these agronomic factors in both 0.76 and 0.51 m row widths was implemented for 13 trials in Illinois, United States, from 2014 to 2018. The agronomic treatments were compared to two controls: enhanced and standard, comprising all the factors applied at the enhanced or standard level, respectively. The 0.51 m enhanced management control yielded 3.3 Mg ha–1 (1.8–4.6 Mg ha–1 across the environments) more grain (25%) than the 0.76 m standard management control, demonstrating the apparent yield gap between traditional farm practices and attainable yield through enhanced agronomic management. Narrow rows and the combination of P-S-Zn and K-B fertility were the factors that provided the most significant yield increases over the standard control. Increasing plant population from 79,000 to 109,000 plants ha–1 reduced the yield gap when all other inputs were applied at the enhanced level. However, increasing plant population alone did not increase yield when no other factors were enhanced. Some agronomic factors, such as narrow rows and availability of plant nutrition, become more critical with increasing plant population when density-induced stress is more significant. Changes in yield were dependent upon changes in kernel number. Kernel weight was the heaviest when all the management factors were applied at the enhanced level while only planting 79,000 plants ha–1. Conversely, kernel weight was the lightest when increasing population to 109,000 plants ha–1 while all other factors were applied at the standard level. The yield contribution of each factor was generally greater when applied in combination with all other enhanced factors than when added individually to the standard input system. Additionally, the full value of high-input agronomic management was only realized when matched with greater plant density.