Single Regression Equation Research Articles

The moderating effect of environmental performance on the relationship between sustainability assurance quality and firm value: a simultaneous equations approach

PurposeThis study elucidates the interactive relationship of sustainability assurance (SA) quality with corporate environmental sustainability performance (CESP) and firm value and explores the moderating impact of CESP on the SA quality–firm value relationship.Design/methodology/approachThe sample comprises 320 firm-year observations of 40 companies listed on the Cotation Assistée en Continu (CAC 40) from 2010 to 2019. The authors use the simultaneous equations model to capture the CESP and SA quality–firm value relationship and apply the three-stage regression and generalised method of moments approaches to address possible endogeneity.FindingsThe results show that CESP, as assessed by International Organisation for Standardisation (ISO) 14001 certification, has a significant positive effect on firm value, the relevance of which implies that in the case of good environmental performance, society's perception of a firm is much more favourable; consequently, the firm is likely to be rewarded with a premium value in capital markets. In addition, environmental performance has a stronger interaction with SA quality, acting as a moderator variable; thus, greater SA quality signals credibility owing to increased eco-efficiency. The authors interpret their findings within a multi-theoretical framework that draws insights from legitimacy, stakeholders and signalling theoretical perspectives.Originality/valueThis study contributes to the literature by re-examining the relationship between SA quality and firm value. It also provides new evidence of the moderating effect of CESP on the SA quality–firm value nexus. Specifically, this study explores the joint effects of credibility and eco-efficiency on market confidence in sustainability information. The authors use a simultaneous equation model to capture the reciprocal association between SA quality and firm value, whereas prior studies on SA quality and market performance have frequently used single-equation regression. The authors also find that CESP positively moderates the relationship between SA quality and firm value. Including CESP and exploring the moderating impact of eco-efficiency on the SA quality–firm value relationship is a novel approach.

Even correctly specified and well-estimated regression models can mislead

It should be possible to draw causal conclusions from happenstance data. However, there are many well-known reasons for doubting the causal interpretation of single equation regression models based on such data. Still, hope springs eternal. The hope is founded on the belief that if the function linking the response variable to the predictor variables was known and its parameters estimated from plentiful data then one could predict what change in the response variable is caused by a change in a predictor variable. But what if this foundational belief was incorrect?I use a thought experiment to show even perfect models can lead to incorrect conclusions. The problem is that to say what change in the response variable is caused by a change in a predictor variable one must assume that all the other predictor variables remain unchanged. This may not be possible or may require changes to reality that are outside of the model, changes that almost certainly will not exist. To interpret the estimated model equation correctly one must trace all real-world consequences of holding the predictor variables constant. This is not easy to do. The history of regression-based research about the road safety effect of speed supports my case.

Comparative Evaluation of Empirical Approaches and Artificial Intelligence Techniques for Predicting Uniaxial Compressive Strength of Rock

The uniaxial compressive strength (UCS) of rocks is one of the key parameters for evaluating the safety and stability of civil and mining structures. In this study, 386 rock samples containing four properties named the load strength (PLS), the porosity (Pn), the P-wave velocity (Vp), and the Schmidt hardness rebound number (SHR) are utilized to predict the UCS using several typical empirical equations (EA) and artificial intelligence (AI) methods, i.e., 16 single regression (SR) equations, 2 multiple regression (MR) equations, and the random forest (RF) models optimized by grey wolf optimization (GWO), moth flame optimization (MFO), lion swarm optimization (LSO), and sparrow search algorithm (SSA). The root mean square error (RMSE), determination coefficient (R2), Willmott’s index (WI), and variance accounted for (VAF) are used to evaluate the predictive performance of all developed models. The evaluation results show that the overall performance of AI models is superior to empirical approaches, especially the LSO-RF model. In addition, the most important input variable is the Pn for predicting the UCS. Therefore, AI techniques are considered as more efficient and accurate approaches to replace the empirical equations for predicting the UCS of these collected rock samples, which provides a reliable and effective idea to predict the rock UCS in the filed site.

On the Dynamic Relationship between Household Debt and Income Inequality in South Africa

This paper analyses the relationship between household debt and income inequality in South Africa for the period 1980–2021. We use two measures of inequality and estimate a vector error correction model (VECM) which includes household debt, inequality, and other macroeconomic variables. To test the robustness of our results, single equation models are used, which estimate household debt as a function of inequality and macroeconomic factors. We employ two measures of inequality, namely Gini coefficient and ratio of top and bottom income earners’ proportion of income. Furthermore, we use both household debt as a percentage of disposable income and household debt service costs as dependent variables in single equation regressions. The study finds a negative and significant relationship between household debt and income inequality in the long run, which contradicts the Rajan hypothesis in the South African case. Rather, we find that inequality in South Africa creates a bias in debt allocation towards high-income earners, whose incomes can easily absorb the extra debt (reduced ratio of debt to disposable income). There are therefore no socio-equity considerations in South African credit markets. We find growth in gross domestic product (GDP) per capita also has a moderating effect on the relationship between household debt and income inequality. High GDP per capita growth in the presence of high inequality reduces the impact of inequality on household debt and vice-versa. All other control variables take expected signs. These results are robust to changes in the inequality or household debt measures.

Isothermal oxidation kinetics of industrial South African chromite concentrates in air

The oxidation of chromite pellets prior to smelting in a submerged arc furnace is key to ensuring that maximum benefit can be derived in the subsequent smelting step. Oxidized pellet feed material reduces operating costs by increasing energy and reductant efficiency, as well as increasing chrome recoveries.There is significant compositional variance in chromites used in South African chromite smelting operations due to the different seams from which the materials are mined. The impact of these variances on the extent and rate of oxidation of chromite ores has not been studied before, and it was the impetus for this study.A detailed kinetic study was carried out on three different types of chromite ores, originating from different commercially mined seams in South Africa, specifically the UG2, LG6 and MG seams. Thermogravimetric analysis was performed on these samples in air at temperatures ranging from 873 to 1473 K, which was used as input to the kinetic analysis. The proposed mechanism of chromite oxidation was found to occur in two stages: initially by random nucleation and growth of a sesquioxide phase, followed by a three-dimensional diffusion-controlled mechanism. The activation energy for oxidation was found to be similar for UG2 and LG6 ore, but higher for MG at all tested temperatures.The effect of chromite composition, temperature, and time on the extent of oxidation (α) was further quantified and captured in a single regression equation, confirming that time and temperature increase the extent and rate of oxidation. The Ti content was found to be directly correlated with higher values of α for a given time and temperature. The regression equation partially resembles the functional form of the diffusivity equation for cations in chromite found in the literature. It was also found that the distribution between Cr and Al impedes oxidation, with higher Cr content resulting in lower values of α for a given time and temperature.

Statistical analysis of the solar diffuse fraction radiation using regression analysis of longitudinal data in India

In this study, the validity of the estimation of a single regression equation for the diffuse frac-tion across 22 stations in India using the two parameters: the clearness index and the sunshine ratio is tested. The homogeneity test based on Fisher’s statistics was applied to test the homo-geneity of the estimated parameters across all stations. The results showed that the p-value at the level of 5% for each model is smaller than 0.05, indicating that all stations were heteroge-neous. The Hierarchical Cluster Analysis (HCA) was used to classify the data into homoge-nous clusters. The results of HCA indicated that the longitudinal data were divided into four main clusters. For each cluster, the regression analysis was applied based on the longitudinal data then, the fixed effects model (FEM) and the random-effects model (REM) were used for the evaluation. Further, the Hausman test was applied to choose between the fixed effects model and the random-effects model. Finally, the results showed that the four best regression models were found for the selected stations in the study area.

Evaluation of the Relationship Between Growth Parameters for Spring-sown Onion Cultivation in Tohoku, Japan

Onion is one of the most productive vegetables in Japan, and onion growth models have been developed to predict its yield. However, onion growth is complex, and the selection of simple parameters is required to estimate bulb weight. This study utilized a 2-year dataset from a sequential growth study of spring-sown onions in the northeastern region of Japan to evaluate the correlation among growth parameters and to determine suitable parameters for estimating the dry weight of bulbs via multiple regression analysis. The results indicated that when bulb dry weight was the objective variable, the coefficients of determination of the single regression equation with plant height and leaf area as explanatory variables were 0.553 and 0.508, respectively, and the leaf parameter explained more than 50% of variance in bulb dry weight. Furthermore, the coefficient of determination was 0.912 when the leaf sheath basal diameter (ball width of the bulb) was used as an explanatory variable to explain the bulb dry weight. This indicates that there is a gap between the top and bulb growth. This study revealed the important parameters for predicting onion yield and demonstrated the switch in onion development from tops to bulbs.

Acetabulum Rekonstrüksiyonunda Erişkin İnsan Kuru Os coxae’sına ait Morfometrik Ölçümlerin Önemi

Objective: The aim of this study was to obtain morphometric measurements of adult human hip bones, examine the relationship among these measurement parameters, and develop regression equations to estimate the acetabular dimensions for acetabular reconstruction 
 Material and Method: Seventy-eight (39 right and 39 left) dry hip bones of unknown age and gender located in the laboratory of Çukurova University Faculty of Medicine, Department of Anatomy, were included in the study. Eleven hip bones with fractures, deterioration, deformities, and defects that would affect the measurements were excluded from the study. In our study, 14 morphometric measurements of hip bones were obtained. IBM SPSS program was used for statistical analysis.
 Results: Single and multiple regression equations were developed from the hip bone morphometric measurements for the estimation of the morphometric measurements of the acetabulum. The standard error of estimate (SEE) values ranged from ±1.818 mm to ±3.546 mm in single regression equations, and between ±1.633 mm and ±2.107 mm in multiple regression equations. A lower SEE value was obtained in multiple regression equations than in single regression equations. 
 Conclusion: The regression equations developed in this study will allow us to obtain personalized measurements, which will aid clinicians in the correct and safe placement of the implant in hip replacement surgeries as well as in the prevention of complications with the use of appropriate prostheses.

Stature estimation from the hand dimensions in the Eastern Saudi Arabian adult male population.

Background and aim:Anthropometry is widely used in preliminary forensic investigations involving the identification of unknown dismembered body parts. Stature estimation is an important indicator of forensic identification considered during such initial investigations. Different populations have different body sizes and proportions that affect the forensic anthropometric estimation of stature. Hence, the need for population-specific databases and analyses. The present study was conducted with the objective of estimating stature from the hand dimensions in the Eastern Saudi Arabian adult male population.Methods:The present study included 200 Eastern Saudi Arabian males aged from 20 to 56 years. Stature and hand dimensions of hand length, palm length, and hand breadth were measured as per standard anthropometric procedures. Linear and multiple regression equations were derived to estimate stature from the aforementioned hand dimensions.Results:Correlation coefficients between stature and the aforementioned hand dimensions were found to be statistically significant. The hand length and palm length showed higher correlation coefficients than the hand breadth. Single variable linear regression and multi-variable linear regression equations were derived to estimate stature from the hand dimensions. Higher correlation coefficients were obtained for multi-variable linear regression than single variable linear regressions.Conclusions:In conclusion, hand dimensions can be used as a reliable predictor to estimate stature in the Eastern Saudi Arabian adult male population. (www.actabiomedica.it)

The Millennial’s Investment Decisions: Implications of Financial Literacy, Motivation, and Digitalization

Investment as a means of developing assets is no longer a luxury activity reserved for a select few. Nowadays, as financial technology advances, everyone has equal access to investment information. This change generates new perspectives in which investing is not restricted to the wealthy, leading everyone, even on-budget millennials to be investors. The purpose of this research is to determine the variables affecting investing decisions at an early age, especially among millennials. This research adopted single-equation regression models to examine the dependent variable on several explanatory variables and estimate mean or average value. The data was generated using a questionnaire to 140 respondents. Three distinct variables utilized as independent variables are financial literacy, motivation, and the digitization of investing platforms. The findings indicate that financial literacy and motivation substantially affect millennials’ investment decisions, while digitization presents no correlation to the Millennials’ investment decisions.

Financial inclusion, corporate social responsibility and firm performance – analysis of interactive relationship

PurposePrior studies on corporate social responsibility (CSR) and performance have frequently used unidirectional, single-equation regression although the literature recommends the reciprocal association of CSR with firm performance. This paper aims to elucidate the interactive relationship of CSR spending with financial inclusion (FI) and firm performance. The study also explores the moderating impact of the level of FI on the CSR-firm performance relationship.Design/methodology/approachThis study uses a simultaneous equations model to capture the FI, CSR and firm performance relationships and apply a three-stage regression approach and generalised method of moments approach to address possible endogeneity.FindingsThe results confirm a positive association of CSR spending with performance but a negative relationship of FI with performance. This paper also finds that FI negatively moderates the CSR spending-performance relationship.Practical implicationsThe positive impact of CSR spending and the negative impact of FI on performance in mandatory CSR regimes provides valuable input in policy formulation. The results of the study will also be useful to national and international organisations, such as the International Monetary Fund and the World Bank.Originality/valueThis study uses a simultaneous equations model to capture the reciprocal association of CSR spending with firm performance, whereas prior studies on CSR and performance have frequently used unidirectional, single-equation regression. This paper also finds that FI negatively moderates the CSR spending- performance relationship. Including FI and exploring the moderating impact of the level of FI on the CSR-firm performance relationship is novel.

Investigating the fatal pedestrian crash occurrence in urban setup in a developing country using multiple-risk source model

Pedestrian fatalities and injuries are a major public health burden in developing countries. In the safety literature, pedestrian crashes have been modelled predominately using single equation regression models, assuming a single underlying source of risk factors. In contrast, the fatal pedestrian crash counts at a site may be an outcome of multiple sources of risk factors, such as poor road infrastructure, land use type, traffic exposures, and operational parameters, site-specific socio-demographic characteristics, as well as pedestrians’ poor risk perception and dangerous crossing behavior, which may be influenced by poor road infrastructure and lack of information, etc. However, these multiple sources are generally overlooked in traditional single equation crash prediction models.In this background, this study postulates, and demonstrates empirically, that the total fatal pedestrian crash counts at the urban road network level may arise from multiple simultaneous and interdependent sources of risk factors, rather than one. Each of these sources may distinctively contribute to the total observed crash count.Intersection-level crash data obtained from the “Kolkata Police”, India, is utilized to demonstrate the present modelling methodology. The three-components mixture model and a joint econometric model are developed to predict fatal pedestrian crashes. The study outcomes indicate that the multiple-source risk models perform significantly better than the single equation regression model in terms of prediction ability and goodness-of-fit measures. Moreover, while the single equation model predicts total fatal crash counts for individual sites, the multiple risk source model predicts crash count proportions contributed by each source of risk factors and predicts crashes by a particular source.

Changes of Bone Strength Evaluated by CT-based Finite Element Methods in Radiotherapy for Bone Metastases of the Spine in Comparison With Bone Density

Radiotherapy (RT) is performed not only to reduce pain but also to prevent pathological fractures due to bone metastases of the spine. However, it is unclear to what extent radiotherapy increases bone strength. Bone density is sometimes used as a marker for assessment of local response to RT in spinal bone metastases. The purpose of this study was to evaluate the changes of bone strength using CT-based finite element (FE) methods in radiotherapy for metastases at the lumber vertebra in comparison with bone density.Five patients with metastases to the lumber vertebra who could be followed for more than 6 months were included. Primary lesions were lung cancer in all patients. All patients had osteolytic changes of the bone cortex in the vertebral body. Median radiation dose was 37.8 Gy in 14 fractions. CT images were taken before and at 1, 3, 6, 9 and 12 months after irradiation. Bone density was measured in Hounsfield units (HU) in each CT scan. The regions of interest were set to include the whole vertebral body of the metastatic spine and that of a non-irradiated unaffected neighboring spine used for reference. The 3-dimensional FE models of the same vertebrae were constructed from each CT image. The models consisted of 1- or 2-mm linear tetrahedral elements and the cortical shell with a thickness of 0.1 mm. A uniaxial compressive load with uniform distribution was applied on the upper surface of the vertebral body with the lower surface completely restrained. The predicted fracture load was defined to be present when at least one element or cortical shell failed. The ratios of the values on the follow-up CT to the values on the pre-irradiation CT were calculated, and single regression equations were determined for these ratios.At one month after irradiation, the bone density decreased in all except one case of the affected vertebral bodies, and the predicted fracture load decreased in all except one case of the unaffected vertebral bodies. In the affected vertebral bodies, the slopes of the single regression equations of bone density were positive in 4 out of 5 cases. The slopes of those of predicted fracture loads were negative in 3 out of that 4 cases. In the unaffected vertebral bodies, the slopes of the single regression equations of bone density were positive in 3 out of 5 cases. The slopes of predicted fracture loads were positive in those 3 cases.The bone strength might not improve in one month after irradiation. The predicted fracture load did not necessarily increase even if the bone density increased in the irradiated affected vertebral bodies. Bone density alone might not be sufficient as an indicator of bone strength. The FE method could evaluate the changes in bone strength in radiotherapy for bone metastases based on predicted fracture loads.

Improving Restoration Efficiency by Modeling Optimal Sowing Periods: A Case Study of Two Native Plants and Restoration of a Site

Efficient ecological restoration techniques are urgently required to minimize seed consumption and labor requirements. Here, we determined the optimal sowing period for two native species, Agastache rugosa (Korean mint) and Astilbe rubra (False goat’s beard), toward their use for ecological restoration of Mt. Gariwang, a site damaged by the 2018 PyeongChang Winter Olympics’ activities. We investigated the effects of daily mean temperature (DMT) and daily temperature range (DTR) on seed germination percentage, which decreased for both species with decreasing DTR and was optimal at a DMT of 25 °C and 17.5 °C for A. rugosa and A. rubra, respectively. We developed a single multiple regression equation that evaluated the effects of DMT and DTR simultaneously and determined the temperature scores when the germination percentage reached 85%. We applied the developed multiple regression equation by analyzing the temperature data of the restoration site. In addition, precipitation data analysis was added to set the optimal sowing period. As a result, the optimal sowing period for the two species was determined from May 21 to the end of May. This makes it possible to minimize seed consumption and labor requirements when sowing seeds. The model developed herein will be useful not only to guide the ecological restoration of Mt. Gariwang, but also for other regions using site-specific temperature data.

The contribution of human capital to foreign direct investment inflows in developing countries

PurposeThis paper is about the effect of human capital on foreign direct investment (FDI). The purpose of this paper is to find out if developing countries with high levels of human capital (educated people and well-trained labour force) are more successful in attracting FDI. The underlying hypothesis has been tested repeatedly without reaching a consensus view or providing an answer to the basic question. This is to be expected because FDI is determined by a large number of factors, making the results sensitive to the selected set of explanatory variables, which forms the basis of the Leamer (1983) critique of the use of multiple regression to derive inference. Furthermore, confirmation bias and publication bias entice researchers to be selective in choosing the set of results they report.Design/methodology/approachThe technique of extreme bounds analysis, as originally suggested by Leamer (1983) and modified by Sala-i-Martin (1997), is used to determine the importance of human capital for the ability of developing countries to attract FDI. The authors use a cross-sectional sample covering 103 developing and transition countries.FindingsThe results show no contradiction between firms seeking human capital and cheap labour. No matter what proxy is used to represent human capital, it turns out that the most important factor for attracting FDI is the variable “employee compensation”, which is the wage bill, implying that multinational firms look for cheap and also skilled labour in the host country.Originality/valueIn this paper, the authors follow the procedure prescribed by Leamer (1983), and modified by Sala-i-Martin (1997), using extreme bounds analysis to distinguish between robust and fragile determinants of FDI, with particular emphasis on human capital. Instead of deriving inference from one regression equation by determining the statistical significance of the coefficient on the variable of interest, the extreme bounds or the distribution of estimated coefficients are used to distinguish between robust and fragile variables. This means that emphasis is shifted from significance, as implied by a single regression equation, to robustness, which is based on a large number of equations. The authors conduct tests on three proxies for human capital to find out if they are robust determinants of FDI and also judge the degree of robustness relative to other determinants.

Indium concentration in serum is an excellent predictor for assessing accumulated indium concentration in the lungs.

ObjectiveTo clarify whether indium in serum (In‐S) is an appropriate parameter for assessing accumulated indium concentration in the lungs (In‐L).MethodsDuring our approximately 15‐year Japanese cohort follow‐up, five male indium‐tin oxide (ITO) or/and indium trioxide‐exposed workers underwent lung surgical procedures to treat lung diseases or to confirm a diagnosis of lung impairments. We measured In‐L of these Cases 1‐5 and were able to assess the relationship between In‐L and the most recent In‐S. Another 1 Japanese case (Case 6) exposed to indium trioxide and indium hydroxide was referred from an article.ResultsCases 1 and 3 had lung cancer, Case 2 suffered from recurrent pneumothorax, and Case 4 had interstitial pneumonia with mild emphysema. Case 5 had severe emphysema with pulmonary hypertension and underwent bilateral lung transplantation. In Cases 1‐5, In‐L and In‐S ranged from 3.4 to 161.2 µg/g wet weight and 0.7 to 60.4 ng/mL, respectively, and In‐L/In‐S ratios ranged from 2484 to 4857. The slope of the single regression equation with zero intercept was 2767 and the correlation coefficient was 0.995. In contrast, Case 6 was extraordinarily outlying, but the reason is unclear.ConclusionsIn‐S is an excellent predictor for assessing indium load in the lungs in ITO or/and indium trioxide‐exposed workers. However, number of cases was only five and not enough to authorize definite conclusion. It is desirable to add more cases to confirm our conclusion.

Relationships between Dynamic Elastic Moduli in Shale Reservoirs

Sonic log compressional and shear-wave velocities combined with logged bulk density can be used to calculate dynamic elastic moduli in organic shale reservoirs. We use linear multivariate regression to investigate modulus prediction when shear-wave velocities are not available in seven unconventional shale reservoirs. Using only P-wave modulus derived from logged compressional-wave velocity and density as a predictor of dynamic shear modulus in a single bivariate regression equation for all seven shale reservoirs results in prediction standard error of less than 1 GPa. By incorporating compositional variables in addition to P-wave modulus in the regression, the prediction standard error is reduced to less than 0.8 GPa with a single equation for all formations. Relationships between formation bulk and shear moduli are less well defined. Regressing against formation composition only, we find the two most important variables in predicting average formation moduli to be fractional volume of organic matter and volume of clay in that order. While average formation bulk modulus is found to be linearly related to volume fraction of total organic carbon, shear modulus is better predicted using the square of the volume fraction of total organic carbon. Both Young’s modulus and Poisson’s ratio decrease with increasing TOC while increasing clay volume decreases Young’s modulus and increases Poisson’s ratio.

Oil Prices, Gasoline Prices and Inflation Expectations: A New Model and New Facts

The conventional wisdom that inflation expectations respond to the level of the price of oil (or the price of gasoline) is based on testing the null hypothesis of a zero slope coefficient in a static single-equation regression model fit to aggregate data. Given that the regressor in this model is not stationary, the null distribution of the t-test statistic is nonstandard, invalidating the use of the normal approximation. Once the critical values are adjusted, these regressions provide no support for the conventional wisdom. Using a new structural vector regression model, however, we demonstrate that gasoline price shocks may indeed drive one-year household inflation expectations. The model shows that there have been several such episodes since 1990. In particular, the rise in household inflation expectations between 2009 and 2013 is almost entirely explained by a large increase in gasoline prices. However, on average, gasoline price shocks account for only 39% of the variation in household inflation expectations since 1981.

Identification of Pesticide Residues and Prediction of Their Fate in Agricultural Soil

Fourteen pesticides were screened and determined through quick, easy, cheap, effective, rugged, and safe (QuEChERS) extraction process combined with GC-MS/MS in arid agriculture soil. The aims of the current investigation were to account the occurrence of organochlorine (OCP) and organophosphates (OPP) pesticide residues as well as other groups of pyrethroids (PYRs), carbamates, and biopesticides using a combined of QuEChERS and GC-MS/MS techniques in agriculture soils at Al-Kharj region, Saudi Arabia, and to investigate correlation between pesticide losses in soils and some physicochemical characteristics of pesticides including an octanol-water coefficient partition (Kow) and the vapor pressure (Vp). Prediction of pesticide fate by considering both pesticide and soil physio-chemical properties will facilitate the management of pesticide application and minimize the hazards of environmental pollution. The fate of pesticide residue in soils is generally controlled by soil/air exchange, water interaction, and biodegradation. The results indicated that 14 pesticide residues were measured in collected samples of various soils, spinosad, chlorpyrifos methyl, dimethoate, chlorpyrifos, lindane (γ-HCH), permethrin, and methomyl which were the most abundant sources of contamination in the studied region. p,p-DDT, o,p-DDT, bifenthion, β-cyfluthrin, and methidathion were less commonly detected. Single parameter least squares regression equations (sp-LSRE) for Vp and Kow against the loss of each pesticide residue showed a significant change in concentration levels (p < 0.05) between the two seasons. The results showed that vapor pressure and octanol-water partition coefficient data are not enough to model pesticide residue losses in arid low organic carbon soil. More soil-related data is needed to describe the dissipation mechanisms of these pesticide residues in the region.

Stemflow estimation models for Japanese cedar and cypress plantations using common forest inventory data

Although stemflow (SF) had been regarded as a small portion of the gross rainfall (GR), recent studies have revealed that, depending on the forest stand structure, the SF/GR ratio should not be neglected. This study derived SF/GR estimation models using common forest inventory data. A set of SF/GR ratio and forest inventory data (stand density (SD), total basal area (BA), mean diameter at breast height (DBH¯), mean tree height (H¯), canopy cover (CC), and leaf area index (LAI)) was collected from previous studies of Japanese cedar and cypress plantations. To further investigate the relation between SF/GR ratio and forest stand structures, we examined additional stand-structure variables (mean basal area (BA¯), mean stem surface area (SA¯), and total stem surface area (SA)) derived from the inventory data, and the stand-scale funneling ratio (FRstand) evaluating the efficiency of funneling rainwater. Among all the stand-structure variables, SD exclusively determined the SF/GR ratio, providing the best-fitting positive single linear regression equation as a density-based SF/GR model with a root mean square error (RMSE) of 2.4%. Although this model is useful for practical forest water management because it requires only SD which is the most basic forest inventory data, it has a weak point in meticulous forest water management because it cannot reflect the effect of tree growth on SF/GR ratio. Thus, we developed a size-based SF/GR model (RMSE = 2.0%) based on the strong relationship between the FRstand and DBH¯. This model is applicable to meticulous forest water management because it reflects the effects of not only SD but also tree growth by DBH on SF/GR ratio. These models derived from the common forest inventory data are potentially applicable to the evaluation and control of SF in forest water management.