Jackknife Method Research Articles

Great Gerbils (Rhombomys opimus) in Central Asia Are Spreading to Higher Latitudes and Altitudes.

The great gerbil (Rhombomys opimus) is a gregarious rodent in Central Asia and is one of the major pests found in desert forest and grassland areas. The distribution changes and migration routes of R. opimus in Central Asia under climate change remain unexplored. This study employed multi-model ensemble, correlation analysis, jackknife method, and minimum cumulative resistance (MCR) model to simulate the potential habitat of R. opimus under current and future (2030 and 2050) climate scenarios and estimate its possible migration routes. The results indicate that the ensemble model integrating Random Forest (RF), Gradient Boosting Machine (GBM), and Maximum Entropy Model (MaxEnt) performed best within the present climate context. The model predicted the potential distribution of R. opimus in Central Asia with an area under the curve (AUC) of 0.986 and a True Skill Statistic (TSS) of 0.899, demonstrating excellent statistical accuracy and spatial performance. Under future climate scenarios, northern Xinjiang and southeastern Kazakhstan will remain the core areas of R. opimus distribution. However, the optimal habitat region will expand relative to the current one. This expansion will increase with the rising CO2 emission levels and over time, potentially enlarging the suitable area by up to 39.49 × 104 km2. In terms of spatial distribution, the suitable habitat for R. opimus is shifting toward higher latitudes and elevations. For specific migration routes, R. opimus tends to favor paths through farmland and grassland. This study can provide guidance for managing and controlling R. opimus under future climate change scenarios.

Carbon Stock Estimation of Poplar Plantations Based on Additive Biomass Models

Accurate estimation of biomass and carbon stocks in forest ecosystems is critical for understanding their roles in carbon sequestration and climate change mitigation. Currently, the development of stand biomass models and carbon stock estimation at the regional scale has emerged as a prominent research priority. In this study, 225 Populus spp. (poplar) trees in Shandong Province, China, were destructively sampled to obtain the biomass of their components. Two models (MS1 and MS2) were developed using allometric equations and the seemingly unrelated regression (SUR) method to ensure additive properties across tree components. The model evaluation employed the leave-one-out jackknife (LOO) method, considering statistics such as adjusted R-squared (Ra2), root mean square error (RMSE), mean absolute percent error (MAPE), and mean absolute error (MAE). The results from our models demonstrated high accuracy, with MS2 slightly outperforming MS1 after incorporating tree height as an independent variable. The models reliably estimated component-specific biomass and carbon stocks, with distinct variations observed in the carbon content among foliage (47.14 ± 2.07%), branches (47.26 ± 2.48%), stems (47.67 ± 2.21%), and roots (46.37 ± 2.78%). Carbon stocks in poplar plantations increased with the diameter class, ranging from 5 to 35 cm and correspondingly from 3.670 to 172.491 Mg C ha−1. As the diameter class increases, the carbon allocation strategy of poplars aligns with the CSR strategy, transitioning from prioritizing growth competition to emphasizing self-stabilization. Our research proposes a robust framework for assessing biomass and carbon stocks in poplar plantations, which is essential for evidence-based forest management strategies.

ANALYSIS OF PATH NONPARAMETRIC TRUNCATED SPLINE MAXIMUM CUBIC ORDER IN BANKING CREDIT OF RISK BEHAVIOR MODEL

Path analysis tests the relationship between variables through cause and effect. The assumption of linearity must be met before conducting further tests on path analysis. If the shape of the relationship is nonlinear and the shape of the curve is unknown, a nonparametric approach is used, one of which is a truncated spline. The purpose of this study is to estimate the function and obtain the best model on the nonparametric truncated spline path of linear, quadratic, and cubic orders with 1 and 2-knot points and determine the significance of the best function estimator in banking credit of risk behavior model through the jackknife resampling method. This study uses secondary data through questionnaires to KPR debtor consumers, as many as 100 respondents. Based on the results of the analysis, it is known that the best-truncated spline nonparametric path model is the quadratic order of 2 knots with a coefficient of determination of 85.50%; the significance of the best-truncated spline nonparametric path estimator shows that all exogenous variables have a significant effect on endogenous variables.

Потенциальный биоклиматический ареал Ephedra distachya L.

The paper presents modern data on the distribution of Ephedra distachya L. an attempt was made to model the bioclimatic range of the species using the maximum entropy method and compare the obtained data with the range defined in the report «Flora of the USSR», taking into account additions from the monographs «Flora of the European Part of the USSR» and «Geography of Trees plants of the USSR». MaxEnt modeling of the potential range of E. distachya was carried out on the basis of the herbarium collections of Saratov State University (SARAT), the GBIF database and the Plantarium electronic atlas. A total of 6973 species location points were used. In accordance with the obtained bioclimatic model, in the modern climate the most favorable conditions for the existence of the species are on the coasts of the Black, Azov, Marmara, Mediterranean and Caspian Seas, in the territories of the former AzSSR, Ukrainian SSR and Georgian SSR. In the RSFSR (modern Russian Federation), the species is optimally located in the Volga, North Caucasus and Southern federal districts. A sharp narrowing of the potential range is observed in the Ural and Central Federal Districts of the Russian Federation, as well as in the territories of the former TurSSR and KazSSR. The accuracy of the model is confirmed by the high AUC (Area Under Curve) score, which is 0.933 for training data and 0.930 for test data. Climatic parameters have been established that influence the distribution of the species, in which its existence is optimal. The greatest contribution to the construction of the model after permutation was made by isothermality (32%), average annual temperature (23.1%), average monthly daily temperature amplitude (12.4%), as well as the maximum temperature of the warmest month (11.6%). A correlation analysis of the parameters that made the greatest contribution after permutation was carried out. When estimating the error using the jackknife method, a variable (average annual temperature) was obtained that contains the most information that is not in other variables; the exclusion of this variable leads to deterioration of the model.

Potential Controlling Factors and Landslide Susceptibility Features of the 2022 Ms 6.8 Luding Earthquake

On 5 September 2022, a Ms 6.8 earthquake struck Luding County, Ganzi Tibetan Autonomous Prefecture, Sichuan Province, China. This seismic event triggered over 16,000 landslides and caused serious casualties and infrastructure damages. The aim of this study is to perform the detailed landslides susceptibility mapping associated with this event based on an updated landslide inventory and logistic regression (LR) modeling. Firstly, we quantitatively assessed the importance of different controlling factors using the Jackknife and single-variable methods for modeling landslide occurrence. Subsequently, four landslide susceptibility assessment models were developed based on the LR model, and we evaluated the accuracy of the landslide susceptibility mappings using Receiver Operating Characteristic (ROC) curves and statistical measures. The results show that ground motion has the greatest influence on landslides in the entire study area, followed by elevation, while distance to rivers and topographic relief have little influence on the distribution of landslides. Compared to the NEE plate, PGA has a greater impact on landslides in the SWW plate. Moreover, the AUC value of the SWW plate significantly decreases for lithological types and aspect, indicating a more pronounced lithological control over landslides in the SWW plate. We attribute this phenomenon primarily to the occurrence of numerous landslides in Permian basalt and tuff in the SWW plate. Otherwise, the susceptibility results based on four models indicate that high-susceptibility areas predicted by different models are distributed along both sides of seismogenic faults and the Dadu Rivers. Landslide data have a significant impact on the model prediction results, and the model prediction accuracy based on the landslide data of the SWW plate is higher.

Comparative Study in Controlling Outliers and Multicollinearity Using Robust Performance Jackknife Ridge Regression Estimator Based on Generalized-M and Least Trimmed Square Estimator

Regression analysis is one of the statistical methods used to determine the causal relationship between one or more explanatory variables to the affected variable. The problem that often occurs in regression analysis is that there are multicollonity and outliers. To deal with such problems can be solved using ridge regression analysis and robust regression. Ridge regression can solve the problem of multicollinearas by assigning a constant k to the matrix Z′Z. But in this method the resulting bias value is still high, so to overcome this problem, the jackknife ridge regression method is used. Meanwhile, to overcome outliers in the data using robust regression methods which have several estimation methods, two of which are the Generalized-M (GM) estimator and the Least Trimmed Square (LTS) estimator. The aim of the study is to solve the problem of multicollinearity and outliers simultaneously using robust jackknife ridge regression method with GM estimators and LTS estimators. The results showed that the robust ridge jackknife regression method with LTS estimator can control multicollinearity and outliers simultaneously better based on MSE, AIC and BIC values compared to the robust ridge jackknife regression method with GM estimators. This is indicated by the value MSE = -6.60371, AIC = 75.823 and BIC = 81.642 on LTS estimators that are of lower value than GM estimators.

Irish Amyotrophic Lateral Sclerosis Incidence: Age, Period, and Cohort Effects Using a Partial Least Squares Regression Model.

To investigate the underlying reasons for variability in the incidence rate of amyotrophic lateral sclerosis (ALS) within the Irish population between the years 1996 and 2021. The Irish ALS register was used to calculate the incidence and to subsequently extract age at diagnosis (age), year of diagnosis (period), and date of birth (cohort) for all incident patients within the study period (n = 2,771). An age-period-cohort (APC) model using partial least squares regression was constructed to examine each component separately and their respective contribution to the incidence while minimizing the well-known identifiability problem of APC effects. A dummy regression model consisting of 5 periods, 19 cohorts, and 16 age groups was used to examine nonlinear relationships within the data over time. The CIs for each of these were estimated using the jackknife method. The nonlinear model achieved R2 of 99.43% with 2-component extraction. Age variation was evident with those in the ages 65-79 years contributing significantly to the incidence (βmax = 0.0746, SE = 0.000410, CI 0.00665-0.00826). However, those aged 25-60 years contributed significantly less (βmin = -0.00393, SE = 0.000291, CI -0.00454 to -0.00340). Each successive period showed an increase in the regression model coefficient suggesting an increasing incidence over time, independent of the other factors examined-an increase of β from -0.00489 (SE = 0.000264, CI -0.00541 to -0.00437) to 0.00973 (SE = 0.000418, CI 0.0105-0.00891). A cohort effect was demonstrated showing that the contribution of those born between 1927 and 1951 contributed to a significantly greater degree than the other birth cohorts (βmax = 0.00577, SE = 0.000432, CI 0.00493-0.00662). Using the Irish population-based ALS Register, robust age, period, and cohort effects can be identified. The age effect may be accounted for by demographic shifts within the population. Changes in disease categorization, competing risks of death, and improved surveillance may account for period effects. The cohort effect may reflect lifestyle and environmental factors associated with the challenging economic circumstances in Ireland between 1927 and 1951. Age-period-cohort studies can help to account for changes in disease incidence and prevalence, providing additional insights into likely demographic and environmental factors that influence population-based disease risk.

A Stepwise Cosimulation Framework for Modeling Critical Elements in Copper Porphyry Deposits

The increased attention given to batteries has given rise to apprehensions regarding their availability; they have thus been categorized as essential commodities. Cobalt (Co), copper (Cu), lithium (Li), nickel (Ni), and molybdenum (Mo) are frequently selected as the primary metallic elements in lithium-ion batteries. The principal aim of this study was to develop a computational algorithm that integrates geostatistical methods and machine learning techniques to assess the resources of critical battery elements within a copper porphyry deposit. By employing a hierarchical/stepwise cosimulation methodology, the algorithm detailed in this research paper successfully represents both soft and hard boundaries in the simulation results. The methodology is evaluated using several global and local statistical studies. The findings indicate that the proposed algorithm outperforms the conventional approach in estimating these five elements, specifically when utilizing a stepwise estimation strategy known as cascade modeling. The proposed algorithm is also validated against true values by using a jackknife method, and it is shown that the method is precise and unbiased in the prediction of critical battery elements.

Experimental investigation and machine learning modeling of diethylenetriaminepentaacetic acid agents in sandstone rock wettability alteration: Implications for enhanced oil recovery processes

Chelating agents have garnered increasing attention due to their unique capabilities in addressing challenges associated with chemical enhanced oil recovery (CEOR) materials. This research investigates the transformative potential of diethylenetriaminepentaacetic acid (DTPA) chelating agent in altering sandstone rock wettability. Wettability and zeta potential measurements were used to assess the impact of crucial factors including DTPA concentration, salinity, and potential determining ions (PDIs) on contact angle and rock surface charges. The findings revealed that there is an optimal level for DTPA concentration and salinity that can shift rock wettability from an oil-wet to strongly water-wet state. Moreover, while PDIs in their natural concentrations had no significant impact on DTPA performance, a threefold increase in their concentration was found to adversely affect DTPA efficacy. After conducting rock wettability tests, two advanced machine learning (ML) techniques, namely Random Forest (RF) and Boosted Regression Tree (BRT), were employed to assess rock/oil contact angle based on the aforementioned factors. Data were collected from 240 experimental datasets of contact angles. The findings indicated that both models performed well, but BRT exhibited exceptional performance. Sensitivity analysis, conducted using the Jackknife method, revealed the order of importance for parameters affecting wettability as follows: PDIs > salinity > time > DTPA concentration.

Rejoinder to the letter: "Standard and reference-based conditional mean imputation: Regulators and trial statisticians be aware!"

We appreciate Cro et al.'s efforts to bring wider attention to the debate surrounding variance estimation for reference-based imputation methods. However, we believe that the way this debate is presented as "multiple imputation" versus "conditional mean imputation" can be misleading. Both of these imputation methods rely on identical assumptions and provide essentially identical treatment effect estimates. While conditional mean imputation naturally focuses on the frequentist repeated sampling variance, we show here that it can be easily adapted to target a variance with similar properties to Rubin's variance. Therefore, conditional mean imputation combined with jackknife resampling remains a valid and effective deterministic method for handling missing data under missing-at-random or reference-based assumptions regardless of the user's preference for variance estimation. We also reappraise the frequentist variance by arguing that it correctly reflects the strong assumptions of reference-based imputation. In contrast, we are not aware of any frequentist or Bayesian framework under which Rubin's variance provides correct inference.

Potential Habitat Prediction of Invasive Species Flaveria Bidentis in China Based on ArcGIS and MAXENT

The flaveria bidentis, an alien invasive plant that poses a threat to ecological stability and agricultural output, has invaded China since the early 1990s and may have the potential to spread further. An accurate assessment of its potential spread can provide a basis for early warning and prevention. The flaveria bidentis's probable habitat in China under present ecological circumstances was predicted via a maxent model that was constructed based on dispersal data and environmental factors. The result showed that the suitable habitat of the flaveria bidentis is currently widely distributed in China, making up around 3.98% of China's total land area, having a total size of about 381,795km2. The Jackknife method showed that the main factors that affected the distribution of the flaveria bidentis were the precipitation seasonality and the minimum temperature of the most frigid month. The model's AUC was 0.987, which means the precision of the forecast was quite high. The flaveria bidentis will continue to spread, therefore the pertinent departments ought to pay particular scrutiny and react promptly to set up protective measures in place.

The Kullback–Leibler Divergence and the Convergence Rate of Fast Covariance Matrix Estimators in Galaxy Clustering Analysis

We present a method to quantify the convergence rate of the fast estimators of the covariance matrices in the large-scale structure analysis. Our method is based on the Kullback–Leibler (KL) divergence, which describes the relative entropy of two probability distributions. As a case study, we analyze the delete-d jackknife estimator for the covariance matrix of the galaxy correlation function. We introduce the information factor or the normalized KL divergence with the help of a set of baseline covariance matrices to diagnose the information contained in the jackknife covariance matrix. Using a set of quick particle mesh mock catalogs designed for the Baryon Oscillation Spectroscopic Survey DR11 CMASS galaxy survey, we find that the jackknife resampling method succeeds in recovering the covariance matrix with 10 times fewer simulation mocks than that of the baseline method at small scales (s ≤ 40 h −1 Mpc). However, the ability to reduce the number of mock catalogs is degraded at larger scales due to the increasing bias on the jackknife covariance matrix. Note that the analysis in this paper can be applied to any fast estimator of the covariance matrix for galaxy clustering measurements.

Impacts of climate change and human activity on the potential distribution of Aconitum leucostomum in China

Aconitum leucostomum is a poisonous grass that disturbs grassland populations and livestock development, and its spread is influenced by climate change and human activities. Therefore, exploring its potential distribution area under such conditions is crucial to maintain grassland ecological security and livestock development. The present study initially selected 39 variables that may influence the spatial distribution of A. leucostomum, including bioclimate, soil, topography, solar radiation, and human footprint data; the variables were screened by Spearman's correlation coefficient and the jackknife method. Twenty variables were finally identified, and three types of models based on the maximum entropy (MaxEnt) model were constructed to predict the distribution of A. leucostomum within China under three shared economy pathways (SSP126, SSP245, and SSP585): A: prediction of environmental variables under the current climate model; B: prediction of environmental variables + human footprint under the current climate model; and C: prediction of environmental variables under the future climate model (including the 2030s, 2050s, and 2070s). The effects of human activities and climate change on the potential geographic distribution of A. leucostomum were explored separately. The results show that precipitation seasonality, human footprint, solar radiation and mean diurnal range are the main factors affecting the distribution of A. leucostomum. Human activities inhibit the spread of A. leucostomum, and climate change promotes its growth, with areas of high suitability and area variation mainly in northern Xinjiang and northern Yunnan. With climate change, in the future, the distribution center of A. leucostomum shows a tendency to migrate to the southeast on the horizontal gradient and to move to higher altitudes on the vertical gradient. This study provides a positive reference value for the control of A. leucostomum and the maintenance of grassland ecological security.

A novel directional-oriented method for predicting shear wave velocity through empirical rock physics relationship using geostatistics analysis

This study attempts to design a novel direction–oriented approach for estimating shear wave velocity (VS) through geostatistical methods (GM) using density employing geophysical log data. The research area involves three hydrocarbon wells drilled in carbonate reservoirs that are comprised of oil and water. Firstly, VS was estimated using the four selected empirical rock physics relationships (ERR) in well A (target well), and then all results were evaluated by ten statistical benchmarks. All results show that the best ERR is Greenberg and Castagna, with R2 = 0.8104 and Correlation = 0.90, while Gardner's equation obtained the poorest results with R2 = 0.6766 and correlation = 0.82. Next, Gardner's method was improved through GM by employing Ordinary Kriging (OKr) in two directions in well A, and then Cross-Validation and Jack-knife methods (JKm and CVm, respectively) were used to assess OKr's performance and efficiency. Initially, CVm and JKm were employed to estimate Vs using the available density and its relationship with shear wave velocity, where the performance of CVm was better with R2 = 0.8865 and correlation = 0.94. In this step, some points from the original VS were used to train the data. Finally, Vs was estimated through JKm and using the relationship between the shear wave velocity of two wells near the target well, including wells B and C; however, in this step, the original shear wave velocity of the target well was completely ignored. Reading the results, JKm could show excellent performance with R2 = 0.8503 and Corr = 0.922. In contrast to previous studies that used only Correlation and R-squared (R2), this study further provides accurate results by employing a wide range of statistical benchmarks to investigate all results. In contrast to traditional empirical rock physics relationships, the developed direction-oriented technique demonstrated improved predicted accuracy and robustness in the investigated carbonate field. This work demonstrates that GM can effectively estimate Vs and has a significant potential to enhance VS estimation using density.

Association between an anesthesia department development program for junior faculty and long-term production of publications: A longitudinal cohort study

BackgroundAn important mission of academic medical departments is to further the scholarship and education of its junior faculty. In 2013, Hindman et al. described the design and initial outcomes of a faculty development program for junior faculty at the University of Iowa Department of Anesthesia. In the current study, we reassessed whether the program increased the production of publications long-term. We included all department faculty, years before joining the department, and years after leaving the department, to control for the effects of simply being current faculty in the department, benefiting from its resources, and having had progressively more experience working. MethodsThe population studied was the faculty for any period between January 2006 and December 2022. The dependent variable was the count of publications in Scopus each year with the faculty member's Scopus identifier, 1996 through 2022. The two-year faculty development program included non-clinical time, two mentors, defined mentorship plan, didactic program, and financial support for clinical and/or laboratory studies. Statistical analyses were with logistic and Poisson random effect models for panel data, with standard errors estimated using jackknife resampling. ResultsAmong the 128 distinct faculty in the department from 2006 through 2022, the 10% with the most publications per year accounted for 54% of the total annual publications. The two-year program was completed by 41% (53/128). Completion of the faculty development program was associated with a 17% absolute increase in the predicted marginal probability of one or more publications per year, from 25% to 41%. The 95% confidence interval for the 17% absolute increase was 9% to 24% (P < .0001). The predictive marginal effect of completing the program was 1.7 more publications per year per faculty (95% confidence interval 1.1 to 2.4, P < .0001). The estimate was also 1.7 more publications per year while limiting consideration to the 108 faculty who joined the department after 1996 and including as an independent variable the count of publications the year before joining the department. ConclusionsA faculty development program for junior faculty can reliably increase the production of publications in an anesthesiology department by at least one per year. However, there is considerable heterogeneity in publication production among faculty.

A back propagation neural network-based adaptive sampling strategy for uncertainty surfaces

Owing to the lack of prior knowledge, the accurate reconstruction of surfaces with high uncertainty is dependent on the reasonable real-time selection of the next best point (NBP) during the sampling process. In this study, a new informative criterion called the MaxCWVar weighting shape effect is proposed for NBP selection. The responses to the geometric features of the candidate locations are predicted by a back propagation neural network (BPNN), which is then used in combination with the jackknife method to estimate the candidate uncertainty. The blade cross-section sampling case is considered to validate the flexibility and effectiveness of the proposed method. A comparison with other adaptive sampling strategies shows that BPNN-based response prediction is well-suited for allocating sample points. In contrast to other NBP selection criteria, the sample point distribution recommended by the MaxCWVar criterion is preferable as it improves the reconstruction accuracy and modeling efficiency. This study promotes the exploration of metrological methods for the fast and intelligent reconstruction of complex surfaces with high uncertainty.

The Level of Efficiency Comparison of Indirect Hypothesis Testing and The Total Effect of Bootstrap and Jackknife Resampling Based on Path Analysis (Studies on SRD and Firm Value Determination Models)

The Level of Efficiency Comparison of Indirect Hypothesis Testing and The Total Effect of Bootstrap and Jackknife Resampling Based on Path Analysis (Studies on SRD and Firm Value Determination Models)

Occurrence Regionalization of Kiwifruit Brown Spot in Sichuan.

Kiwifruit brown spot caused by Corynespora cassiicola is the most significant fungal disease in Sichuan, resulting in premature defoliation, which had a significant impact on yield and fruit quality. The objective of the study was to determine the occurrence regularity and suitability of kiwifruit brown spot in Sichuan. The occurrence of the disease in the main producing region was continuously monitored, the maximum entropy (MaxEnt) model was used to predict its potential distribution, and the key environmental variables were identified using the jackknife method. The results indicated that kiwifruit brown spot was widely distributed across the entire producing region in Sichuan, predominantly affecting the variety "Hongyang". The incidence (p < 0.01) and disease index (p < 0.05) showed a significant positive correlation with the cultivar, and decreased with the altitude increasing. The average area under the ROC curve (AUC) of 10 replicates was 0.933 ± 0.012, with an accuracy of 84.44% in a field test, confirming the reliability of the predicted results. The highly suitable distribution areas of kiwifruit brown spot were mainly located in the Chengdu and Ya'an regions. The entire Panzhihua region was an unsuitable distribution area, and the entire Pujiang County and Mingshan District were highly suitable distribution areas. The key environmental variables affecting the potential distribution of kiwifruit brown spot included isothermality (24.3-33.7%), minimum temperature in August (16.3-23.6 °C), maximum temperature in July (25.5-31.2 °C), minimum temperature in June (15.6-20.9 °C), precipitation in August (158-430 mm), and average temperature in October (15.6-18.8 °C). This study provides a theoretical basis for the reasonable layout of the cultivar and the precise prevention and control of the disease.

Which method is optimal for estimating variance components and their variability in generalizability theory? evidence form a set of unified rules for bootstrap method.

The purpose of this study is to compare the performance of the four estimation methods (traditional method, jackknife method, bootstrap method, and MCMC method), find the optimal one, and make a set of unified rules for Bootstrap. Based on four types of simulated data (normal, dichotomous, polytomous, and skewed data), this study estimates and compares the estimated variance components and their variability of the four estimation methods when using a p×i design in generalizability theory. The estimated variance components are vc.p, vc.i and vc.pi and the variability of estimated variance components are their estimated standard errors (SE(vc.p), SE(vc.i) and SE(vc.pi)) and confidence intervals (CI(vc.p), CI(vc.i) and CI(vc.pi)). For the normal data, all the four methods can accurately estimate the variance components and their variability. For the dichotomous data, the |RPB| of SE (vc.i) of traditional method is 128.5714, the |RPB| of SE (vc.i), SE (vc.pi) and CI (vc.i) of jackknife method are 42.8571, 43.6893 and 40.5000, which are larger than 25 and not accurate. For the polytomous data, the |RPB| of SE (vc.i) and CI (vc.i) of MCMC method are 59.6612 and 45.2500, which are larger than 25 and not accurate. For the skewed data, the |RPB| of SE (vc.p), SE (vc.i) and SE (vc. pi) of traditional method and MCMC method are over 25, which are not accurate. Only the bootstrap method can estimate variance components and their variability accurately across different data distribution. Nonetheless, the divide-and-conquer strategy must be used when adopting the bootstrap method. The bootstrap method is optimal among the four methods and shows the cross-distribution superiority over the other three methods. However, a set of unified rules for the divide-and-conquer strategy need to be recommended for the bootstrap method, which is optimal when boot-p for p (person), boot-pi for i (item), and boot-i for pi (person × item).

Metodologia para estimativa de zonas de potencial produtivo a partir de dados de produtividade

The methodology proposed herein for identifying potentially productive zones from yield data captured by harvester onboard sensors aims to establish a viable and easy-to-implement method for defining management zones by running statistical procedures on data from the harvest monitor. To do this, yield data from maize (2018 winter/second growing season) and soybean (2019 growing season) were converted into ɀ-score values and compared at a 99.8% confidence interval of standard normal distribution ɀ. Simultaneously, the degree of linearity was evaluated and Jackknife resampling, for removing data outside the range (outliers) established by the ɀ table (&lt;-3.09 and &gt;3.09). Next, yield score-ɀ algebraic mapping was performed to obtain a mean crop map, then applying three classes from the probability intervals of a plus and minus deviation, resulting in a map of potentially productive zones (below average, average and above average yield). Using this method, 5.72% of the area exhibited low yield potential, 90.71% average potential and 3.57% high yield potential. This analysis method was easy and quick to perform and provided summarized information, facilitating additional field surveys and providing a basis for decision-making.