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This research investigated the pre-service teachers' self-regulation, self-efficacy, and mathematics performance in blended learning during the post Coronavirus disease 2019 (COVID-19) pandemic in a state university using a descriptive correlational design. There were 201 pre-service teachers who were identified using simple random sampling. They answered the two-set survey questionnaire, which assessed their self-regulation and self-efficacy, while their performances were assessed using their grades in the subject. The data gathered were treated using descriptive and inferential statistics. The results revealed that they had high self-regulation while they had a very high level of self-efficacy. At the same time, they had a very satisfactory performance in mathematics. Moreover, their self-regulation in terms of planning, monitoring, and adjusting was significantly correlated with their math performance while reflecting is not. However, no significant relationship was found between their self-efficacy and mathematics performance. Thus, instructors are encouraged to conduct monitoring during blended learning to encourage pre-service teachers to maintain their high level of self-regulation and self-efficacy in learning mathematics. In addition, future researchers may explore the same variables to validate the findings of the study because these findings are limited only to pre-service teachers and were conducted during the post COVID-19 pandemic.

Smartphones are widely used among high school students nowadays. With their constant use of this technology, they have become more dependent on it. This study assessed the extent of smartphone utilisation and the academic performance of the 195 junior high school students at a public national high school. Respondents were identified using stratified random sampling and were asked to answer a survey questionnaire assessing their smartphone utilisation during their math classes while their performance in the subject was determined using their First Quarter grades. The data gathered were treated using descriptive and inferential statistics. The results revealed that the respondents had very satisfactory performance and they often use their smartphones during their math classes. Smartphone utilisation has a negative negligible correlation with the student's academic performance in Mathematics. Furthermore, Smartphone utilisation has no significant relationship between the respondents' math performance. Hence, teachers are encouraged to monitor students' use of gadgets during their math classes.

This research determined the predictors of the adaptive behaviour of children with autism who are enrolled in special education centres using a descriptive correlational design. There were 40 children with autism whose adaptive behaviour was assessed in terms of their communication, daily living, social and motor skills using the modified Vineland Adaptive Behaviour Scales (Vineland-3) together with their profile. Data gathered were treated using descriptive and inferential statistics. The results showed that most of the children were males whose average age was 9.6 years who belonged to low-income families and were enrolled in 2 to 3 years. The children had moderately low adaptive skills in all areas of the adaptive behaviour. Moreover, the profiles of the children were not significant predictors of the four adaptive skills being assessed. Thus, it is recommended that other factors may be considered to identify the predictors of the adaptive behaviour of children with autism.

It has always been a challenge to improve student learning outcomes. Stakeholders in higher education institutions need to go beyond traditional methods and develop new practices to elevate student's performance in mathematics. This research assessed the influencers of student Mathematics performance and also determined the issues and concerns encountered by the students in learning Mathematics. The descriptive-correlational method was employed using a survey questionnaire. There were 370 respondents in the sample and it was selected using simple random sampling from the population of the students at Cebu Technological University North Cell Campus. The data were statistically analyzed through percentage, frequency counts, weighted mean, ranking and Chi-square test for significant difference. The results revealed that home/family, school environment, classmates/peers, community, media and emerging technology were not significantly affecting the performance of the students in Mathematics. However, attending Mathematics classes as a perquisite of their courses was the main problem encountered by the respondents of this study. Based on these data analysis, the researchers proposed human and material resource development plan for enhancing students' performance in Mathematics. Moreover, the researchers recommend future research to dig deeper into the factors inside and outside school premises to identify influencers that influence students' academic performance especially in Mathematics.

Core Ideas DSSAT was calibrated for simulating soil N concentration during the maize growing season. Two-step validation included research experiments and 49 commercial maize fields. Model performance was fair in predicting SOM mineralization and N management impacts. The model captured effects of early season rainfall on soil N variability across sites. Farmers have limited knowledge of inorganic soil nitrogen (N) concentration during maize (Zea mays L.) growth in the US Midwest, particularly after periods of wet spring weather. The objectives of this study were to calibrate the Decision Support System for Agrotechnology Transfer (DSSAT) model for predicting inorganic soil N concentration using data from three field experiments in Illinois, to evaluate model performance against three independent sites and additional data from 49 commercial maize fields, and to assess the impacts of rainfall variability on the predicted decrease in soil N concentration early in the growing season. Model calibration included adjustments to soil organic matter (SOM) decomposition parameters based on predicted soil organic carbon concentration (obtained from gSSURGO) and soil drainage rates. Model performance was considered “fair” in predicting SOM mineralization dynamics and the effects of fall vs. spring N fertilizer application across the validation datasets (normalized RMSE, 21.2–25.7%). The model also captured the variability in soil N concentration across 49 commercial fields (R2 = 0.68–0.88; slope, 0.99–1.24), with higher cumulative rainfall from January to July (>800 mm) reducing predicted soil N availability compared with fields receiving less rainfall (500–600 mm). Results suggest that DSSAT has the potential to estimate soil N availability across variable weather patterns, soil properties, and fertilizer management scenarios in Illinois. However, future work is needed to further improve model accuracy, especially if it is to be used as a decision support tool for farmers.

The environmental consequences of residue removal practices to support cellulosic biofuel production remain poorly understood. In the U.S. Midwest, corn (Zea mays L.) stover removal combined with no-till practices may increase or decrease soil N2O emissions by influencing soil moisture, temperature, and nutrient dynamics, yet empirical evidence from long-term field experiments is inconsistent. We investigated the effects of residue management (residue retained or removed) and tillage (chisel-till or no-till) on cumulative soil nitrous oxide (N2O) emissions, grain yield, and yield-scaled N2O emissions in a 3-year study initiated 10 years after treatment implementation in a long-term, continuous corn experiment in Illinois, United States. Crop yields were affected by treatment in only one of three study years, with the combination of residue removal and no-till reducing yields compared to both chisel-till treatments. Cumulative N2O emissions, soil inorganic N concentrations, and yield-scaled N2O emissions differed over the 3-year period and were significantly affected by tillage, with no response to residue management. In 2 years, no-till decreased cumulative N2O emissions and yield-scaled N2O emissions by an average of 64% and 60%, respectively. Correlations between daily N2O fluxes and soil moisture, temperature, and inorganic N concentrations suggested that the relative importance of these variables changed depending on year and treatment. While more research across a range of sites and management practices is needed, our findings support previous studies which have challenged IPCC methodology assumptions regarding the effects of residue removal on N2O emissions. We conclude there is inherent difficulty in predicting the impacts of residue removal due to the complexity of soil processes underlying N2O emissions coupled with inter-annual weather variability in this rainfed continuous corn system. Future efforts to evaluate the net greenhouse gas emissions of cellulosic biofuel production may benefit from accounting for this uncertainty.

The flipped classroom enables teachers to accommodate the different needs of their students. With in-class activities, learning gaps and strengths can be addressed individually, allowing a more personalized approach to teaching. This research assessed the potential influence of the Flipped Classroom utilization towards the mathematics achievement in Rational Functions competencies of the Grade 11 Computer Systems Servicing and General Academic Strand students of Talisay City National High School in Talisay City Division for the school year 2023-2024. A descriptivecorrelational research design was utilized in this study using a survey questionnaire and achievement test. There were 134 respondents in the sample, and it was selected using simple random sampling from the population of the students at Talisay City National High School. The data were statistically analyzed through percentage, frequency counts, weighted mean, and Pearson-r correlational test for significant relationships. The findings revealed that the level of acceptability in terms of the pedagogical aspect of the utilization of the flipped classroom significantly influences the student's academic achievement in Mathematics. The researcher highly recommends that the Flipped Classroom Utilization Enhancement Plan be implemented and could serve as a supplementary strategy in addressing the students' varied learning needs, particularly in mathematics. [ABSTRACT FROM AUTHOR]

Two of the most important agricultural practices aimed at improving soil properties are crop rotations and no-tillage, yet relatively few studies have assessed their long-term impacts on crop yields and soil greenhouse gas (GHG) emissions. The objective of this study was to determine the influence of tillage and crop rotation on soil GHG emissions and yields following 15 years of treatment implementation in a long-term cropping systems experiment in Illinois, USA. The experimental design was a split-plot RCBD with crop rotation as the main plot: (continuous corn [Zea mays L.] (CCC), corn-soybean [Glycine max (L.) Merr.] (CS), continuous soybean (SSS), and corn-soybean-wheat [Triticum aestivum L.] (CSW); with each phase of each crop rotation present every year) and tillage as the subplot: chisel tillage (T) and no-tillage (NT). Tillage increased the yields of corn and soybean. Tillage and crop rotation had no effect on methane (CH₄) emissions (p = 0.4738 and p = 0.8494 respectively) and only rotation had an effect on cumulative carbon dioxide (CO₂) (p = 0.0137). However, their interaction affected cumulative nitrous oxide (N₂O) emissions significantly (p = 0.0960); N₂O emissions from tilled CCC were the greatest at 6.9 kg-N ha⁻¹-yr⁻¹; while emissions from NT CCC (4.0 kg-N ha⁻¹-yr⁻¹) were not different than both T CS or NT CS (3.6 and 3.3 kg-N ha⁻¹⁻yr⁻¹, respectively). Utilizing just a CS crop rotation increased corn yields by around 20% while reducing N₂O emissions by around 35%; soybean yields were 7% greater and N₂O emissions were not affected. Therefore results from this long-term study indicate that a CS rotation has the ability to increase yields and reduce GHG emissions compared to either CCC or SSS alone, yet moving to a CSW rotation did not further increase yields or reduce N₂O emissions.

Leptothrix ochracea is known for producing large volumes of iron oxyhydroxide sheaths that alter wetland biogeochemistry. For over a century, these delicate structures have fascinated microbiologists and geoscientists. Because L. ochracea still resists long-term in vitro culture, the debate regarding its metabolic classification dates back to 1885. We developed a novel culturing technique for L. ochracea using in situ natural waters and coupled this with single-cell genomics and nanoscale secondary-ion mass spectrophotometry (nanoSIMS) to probe L. ochracea's physiology. In microslide cultures L. ochracea doubled every 5.7 h and had an absolute growth requirement for ferrous iron, the genomic capacity for iron oxidation, and a branched electron transport chain with cytochromes putatively involved in lithotrophic iron oxidation. Additionally, its genome encoded several electron transport chain proteins, including a molybdopterin alternative complex III (ACIII), a cytochrome bd oxidase reductase, and several terminal oxidase genes. L. ochracea contained two key autotrophic proteins in the Calvin-Benson-Bassham cycle, a form II ribulose bisphosphate carboxylase, and a phosphoribulose kinase. L. ochracea also assimilated bicarbonate, although calculations suggest that bicarbonate assimilation is a small fraction of its total carbon assimilation. Finally, L. ochracea's fundamental physiology is a hybrid of those of the chemolithotrophic Gallionella-type iron-oxidizing bacteria and the sheathed, heterotrophic filamentous metal-oxidizing bacteria of the Leptothrix-Sphaerotilus genera. This allows L. ochracea to inhabit a unique niche within the neutrophilic iron seeps.IMPORTANCELeptothrix ochracea was one of three groups of organisms that Sergei Winogradsky used in the 1880s to develop his hypothesis on chemolithotrophy. L. ochracea continues to resist cultivation and appears to have an absolute requirement for organic-rich waters, suggesting that its true physiology remains unknown. Further, L. ochracea is an ecological engineer; a few L. ochracea cells can generate prodigious volumes of iron oxyhydroxides, changing the ecosystem's geochemistry and ecology. Therefore, to determine L. ochracea's basic physiology, we employed new single-cell techniques to demonstrate that L. ochracea oxidizes iron to generate energy and, despite having predicted genes for autotrophic growth, assimilates a fraction of the total CO2 that autotrophs do. Although not a true chemolithoautotroph, L. ochracea's physiological strategy allows it to be flexible and to extensively colonize iron-rich wetlands.

Relationships between greenhouse gas emissions, yields, and soil properties are not well known. Utilizing two datasets from long-term cropping systems in Illinois, USA, our we aim to address these knowledge gaps. The objective of this study was to explore the relationships between the physical and chemical properties and greenhouse gas (GHG) emissions of soil, and cash crop yields over a four-year time-period and following 15 years of treatment implementation in Illinois, USA. The experimental layout was a split-plot arrangement involving rotation and tillage treatments in a randomized complete block design with four replications. The studied crop rotations were continuous corn [Zea mays L.] (CCC), corn-soybean [Glycine max (L.) Merr.] (CS), continuous soybean (SSS), and corn-soybean-wheat [Triticum aestivum L.] (CSW), with each phase being present for every year. The tillage options were chisel tillage (T) and no-tillage (NT). We used an array of multivariate approaches to analyze both of our datasets that included 31 soil properties, GHG emissions (N2 O, CO2, and CH4 ) and cash crop yields. The results from our analyses indicate that N2 O emissions are associated with a low soil pH, an increased Al concentration, the presence of soil nitrate throughout the growing season, an increase in plant available water (PAW) and an increased soil C concentration. Likewise, soil CO2 respiration was correlated with low pH, elevated Al concentrations, low Ca, increased PAW, higher levels of microbial biomass carbon (MBC), and lower water aggregate stability (WAS). Emissions of CH4 were associated with increased levels of MBC. Lastly, the yield index (YdI) was correlated with lower levels of soil Ca and available P and lower values of WAS. The association between high YdI and lower WAS can be attributed to tillage, as tillage lowers WAS, but increases yields in highly productive cropping systems in the Midwest.

Nutrient loss reduction strategies have recently been developed in the U.S. Midwest to decrease the environmental footprint associated with nitrogen (N) fertilizer use. Although these strategies generally suggest decreasing N rates and shifting the timing of N application from fall to spring, the spatiotemporal impacts of these practices on maize yield and fertilizer N use efficiency (NUE, kg grain yield increase per kg N applied) have not been assessed at the watershed scale using crop simulation models. We simulated the effects of N fertilizer rate (0, 168, 190, 224 kg N ha-1) and application timing [fall-applied N (FN): 100% N applied on 1 December; spring-applied N (SN): 100% N applied 10 days before planting; split N: 66% N applied on 1 December + 34% N applied 10 days before planting] on maize grain yield (GY) across 3042 points in Illinois during 2011-2015 using the DSSAT-CERES-Maize model. When simulations were scaled up to the watershed level, results suggest that increases in average maize GY for SN compared to FN occurred in years with higher than average winter rainfall (2011, 2013), whereas yields were similar (+/- 4%) in 2012, 2014, and 2015. Accordingly, differences in NUE for SN compared to FN were small (0.0-1.4 kg GY/kg N) when cumulative winter rainfall was < 300 mm, but increased to 0.1-9.2 kg GY/kg N when winter rainfall was > 500 mm at both 168 kg N ha-1 and 224 kg N ha-1. The combined practice of reducing N fertilizer amounts from 224 kg N ha-1 to 190 kg N ha-1 and shifting from FN to SN resulted in a wide range of yield responses during 2011-2015, with the probability of increasing yields varying from 70% of simulation points within a watershed. Positive impacts on both GY and NUE occurred in only 60% of simulations for this scenario, highlighting the challenge of simultaneously improving yield and NUE with a 15% N rate reduction in this region.

Mitigating nitrogen (N) losses from agriculture without negatively impacting crop productivity is a pressing environmental and economic challenge. Reductions in N fertilizer rate are often highlighted as a solution, yet the degree to which crop yields and economic returns may be impacted at the field-level remains unclear, in part due to limited data availability. Farmers are risk averse and potential yield losses may limit the success of voluntary N loss mitigation protocols, thus understanding field-level yield tradeoffs is critical to inform policy development. Using a case study of soil N2O mitigation in the US Midwest, we conducted an ex-post assessment of two economic and two environmental N rate reduction strategies to identify promising practices for maintaining maize yields and economic returns while reducing N2O emissions per unit yield (i.e. yield-scaled emissions) compared to an assumed baseline N input level. Maize yield response data from 201 on-farm N rate experiments were combined with an empirical equation predicting N2O emissions as a function of N rate. Results indicate that the economic strategy aimed at maximizing returns to N (MRTN) led to moderate but consistent reductions in yield-scaled N2O emissions with small negative impacts on yield and slight increases in median returns. The economic optimum N rate strategy reduced yield-scaled N2O emissions in 75% of cases but increased them otherwise, challenging the assumption that this strategy will automatically reduce environmental impacts per unit production. Both environmental strategies, one designed to increase N recovery efficiency and one to balance N inputs with grain N removal, further reduced yield-scaled N2O emissions but were also associated with negative yield penalties and decreased returns. These results highlight the inherent tension between achieving agronomic and economic goals while reducing environmental impacts which is often overlooked in policy discussions. To enable the development of more scalable environmental N loss mitigation strategies, yield tradeoffs occurring at the critical point of adoption (i.e. the farm-level) should be considered.

Plant N concentration (PNC) has been commonly used to guide farmers in assessing maize (Zea mays L.) N status and making in-season N fertilization decisions. However, PNC varies based on the development stage. Therefore, a relationship between biomass and N concentration is needed (i.e., critical N dilution curve; CNDC) to better understand when plants are N deficient. A few CNDCs have been developed and used for plant N status diagnoses but have not been tested in the US Midwest. The objective of this study was to evaluate under highly diverse soil and weather conditions in the US Midwest the performance of CNDCs developed in France and China for assessing maize N status. Maize N rate response trials were conducted across eight US Midwest states over three years. This analysis utilized plant and soil measurements at V9 and VT development stages and final grain yield. Results showed that the French CNDC (y = 34.0x−0.37, where y is critical PNC, and x is aboveground biomass) was better with a 91% N status classification accuracy compared to only 62% with the Chinese CNDC (y = 36.5x−0.48). The N nutrition index (NNI), which is the quotient of the measured PNC and the calculated critical N concentration (Nc) based on the French CNDC was significantly related to soil nitrate-N content (R2 = 0.38–0.56). Relative grain yield on average reached a plateau at NNI values of 1.36 at V9 and 1.21 at VT but for individual sites ranging from 0.80 to 1.41 at V9 and from 0.62 to 1.75 at VT. The NNI threshold values or ranges optimal for crop biomass production may not be optimal for grain yield production. It is concluded that the CNDC developed in France is suitable as a general diagnostic tool for assessing maize N status in US Midwest. However, the threshold values of NNI for diagnosing maize N status and guiding N applications vary significantly across the region, making it challenging to guide specific on-farm N management. More studies are needed to determine how to effectively use CNDC to make in-season N recommendations in the US Midwest.