A guide for the utilization of Health Insurance Review and Assessment Service National Patient Samples

The claims data of the Health Insurance Review and Assessment Service (HIRA) is an important source of information for healthcare service research. The claims data of HIRA is collected when healthcare service providers submit a claim to HIRA to be reimbursed for a service that they provided to patients. To improve the accessibility of healthcare service researchers to claims data of HIRA, HIRA has developed the Patient Samples which are extracted using a stratified randomized sampling method. The Patient Samples of HIRA consist of five tables: a table for general information (Table 20) containing socio-demographic information such as gender, age and medical aid, indicators for inpatient and outpatient services; a table for specific information on healthcare services provided (Table 30); a table for diagnostic information (Table 40); a table for outpatient prescriptions (Table 53) and a table for information on healthcare service providers (Table of providers). Researchers who are interested in using the Patient Sample data for research can apply via HIRA's website (https://www.hira.or.kr).

Thematic accuracy assessment of the 2011 National Land Cover Database (NLCD)

Accuracy assessment is a standard protocol of National Land Cover Database (NLCD) mapping. Here we report agreement statistics between map and reference labels for NLCD 2011, which includes land cover for ca. 2001, ca. 2006, and ca. 2011. The two main objectives were assessment of agreement between map and reference labels for the three, single-date NLCD land cover products at Level II and Level I of the classification hierarchy, and agreement for 17 land cover change reporting themes based on Level I classes (e.g., forest loss; forest gain; forest, no change) for three change periods (2001-2006, 2006-2011, and 2001-2011). The single-date overall accuracies were 82%, 83%, and 83% at Level II and 88%, 89%, and 89% at Level I for 2011, 2006, and 2001, respectively. Many class-specific user's accuracies met or exceeded a previously established nominal accuracy benchmark of 85%. Overall accuracies for 2006 and 2001 land cover components of NLCD 2011 were approximately 4% higher (at Level II and Level I) than the overall accuracies for the same components of NLCD 2006. The high Level I overall, user's, and producer's accuracies for the single-date eras in NLCD 2011 did not translate into high class-specific user's and producer's accuracies for many of the 17 change reporting themes. User's accuracies were high for the no change reporting themes, commonly exceeding 85%, but were typically much lower for the reporting themes that represented change. Only forest loss, forest gain, and urban gain had user's accuracies that exceeded 70%. Lower user's accuracies for the other change reporting themes may be attributable to the difficulty in determining the context of grass (e.g., open urban, grassland, agriculture) and between the components of the forest-shrubland-grassland gradient at either the mapping phase, reference label assignment phase, or both. NLCD 2011 user's accuracies for forest loss, forest gain, and urban gain compare favorably with results from other land cover change accuracy assessments.

Genotypic diversity and migration patterns of Phytophthora infestans in the Nordic countries

In this study we investigated the genotypic diversity and the migration patterns of Phytophthora infestans in the Nordic countries. Isolates of P. infestans from outbreaks in 43 fields sampled in 2008 were collected using stratified sampling with country, field, and disease foci as the different strata. Microsatellites were used as markers to determine the genotypic variation in the sampled material. The results show a high genotypic variation of P. infestans in the Nordic countries with most of the genotypes found only once among the collected isolates. The major part of the genotypic variation was observed within the fields, with low differentiation between the fields. The observed low association of alleles among loci is consistent with frequent sexual reproduction of P. infestans in the Nordic countries. Coalescence analyses did not support a single common population for the four countries, thus indicating some degree of geographic differentiation. The analyses of migration patterns showed differing levels of gene flow among the Nordic countries. No correlation between migration rates and geographical distance could be seen. This could be explained by different degrees of genetic similarity between the pathogen populations in the different countries.

Doubly-robust estimators of treatment-specific survival distributions in observational studies with stratified sampling

Observational studies are frequently conducted to compare the effects of two treatments on survival. For such studies we must be concerned about confounding; that is, there are covariates that affect both the treatment assignment and the survival distribution. With confounding the usual treatment-specific Kaplan-Meier estimator might be a biased estimator of the underlying treatment-specific survival distribution. This article has two aims. In the first aim we use semiparametric theory to derive a doubly robust estimator of the treatment-specific survival distribution in cases where it is believed that all the potential confounders are captured. In cases where not all potential confounders have been captured one may conduct a substudy using a stratified sampling scheme to capture additional covariates that may account for confounding. The second aim is to derive a doubly-robust estimator for the treatment-specific survival distributions and its variance estimator with such a stratified sampling scheme. Simulation studies are conducted to show consistency and double robustness. These estimators are then applied to the data from the ASCERT study that motivated this research.

Approximation rates for neural networks with general activation functions

We prove some new results concerning the approximation rate of neural networks with general activation functions. Our first result concerns the rate of approximation of a two layer neural network with a polynomially-decaying non-sigmoidal activation function. We extend the dimension independent approximation rates previously obtained to this new class of activation functions. Our second result gives a weaker, but still dimension independent, approximation rate for a larger class of activation functions, removing the polynomial decay assumption. This result applies to any bounded, integrable activation function. Finally, we show that a stratified sampling approach can be used to improve the approximation rate for polynomially decaying activation functions under mild additional assumptions.

Common Comorbidities of Stroke in the Canadian Population

OBJECTIVES

Although comorbidity increases the health care and community support needs for patients, and the burden for the health care system, there are few population-based studies on comorbidity in patients with stroke. This study aims to evaluate the occurrence of important comorbidities among stroke patients in the Canadian population.

METHODS

Data from the population-based 2011-2012 Canadian Community Health Survey containing responses from 124,929 participants covering about 98% of the Canadian population when weighted were examined and analyzed by means of logistic regression models.

RESULTS

There was a statistically significant association between stroke history and multiple comorbid risk factors. Stroke prevalence increased in individuals with heart disease (odds ratio (OR): 3.80, 95% confidence interval (CI): 3.77-3.84), hypertension (OR: 1.97, 95% CI: 1.95-1.99), diabetes (OR: 1.74, 95% CI: 1.72-1.75), mood disorder (OR: 2.14, 95% CI: 2.12-2.17), and chronic obstructive pulmonary disease (COPD) (OR: 1.46, 95% CI: 1.44-1.48) compared to others without the condition. Of 2067 participants with stroke, 1680 (81.3%) had one or more comorbid conditions (heart disease, hypertension, diabetes, mood disorder, or COPD) that coexist with stroke and 48% had two or more. Comorbidity increased with age, and two-thirds of stroke patients with comorbid medical conditions were 60 years of age or older.

CONCLUSION

This population-based study provides evidence of comorbidity between stroke and other conditions that include heart disease, hypertension, diabetes, mood disorder, and COPD. Canadian individuals with stroke have a high burden of comorbidity. Health care systems need to recognize and respond to the strong association of comorbidity and stroke occurrence. This key factor should be considered when allocating resources.

Prevalence of nutritional risk in the non-demented hospitalised elderly: a cross-sectional study from Norway using stratified sampling

There is a lack of accurate prevalence data on undernutrition and the risk of undernutrition among the hospitalised elderly in Europe and Norway. We aimed at estimating the prevalence of nutritional risk by using stratified sampling along with adequate power calculations. A cross-sectional study was carried out in the period 2011 to 2013 at a university hospital in Norway. Second-year nursing students in acute care clinical studies in twenty hospital wards screened non-demented elderly patients for nutritional risk, by employing the Nutritional Risk Screening 2002 (NRS2002) form. In total, 508 patients (48·8 % women and 51·2 % men) with a mean age of 79·6 (sd 6·4) years were screened by the students. Mean BMI was 24·9 (sd 4·9) kg/m², and the patients had been hospitalised for on average 5·3 (sd 6·3) d. WHO's BMI cut-off values identified 6·5 % as underweight, 48·0 % of normal weight and 45·5 % as overweight. Patients nutritionally at risk had been in hospital longer and had lower average weight and BMI compared with those not at risk (all P < 0·001); no differences in mean age or sex were observed. The prevalence of nutritional risk was estimated to be 45·4 (95 % CI 41·7 %, 49·0) %, ranging between 20·0 and 65·0 % on different hospital wards. The present results show that the prevalence of nutritional risk among elderly patients without dementia is high, suggesting that a large proportion of the hospitalised elderly are in need of nutritional treatment.

A direct observation method for auditing large urban centers using stratified sampling, mobile GIS technology and virtual environments

BACKGROUND

With the expansion and growth of research on neighbourhood characteristics, there is an increased need for direct observational field audits. Herein, we introduce a novel direct observational audit method and systematic social observation instrument (SSOI) for efficiently assessing neighbourhood aesthetics over large urban areas.

METHODS

Our audit method uses spatial random sampling stratified by residential zoning and incorporates both mobile geographic information systems technology and virtual environments. The reliability of our method was tested in two ways: first, in 15 Ottawa neighbourhoods, we compared results at audited locations over two subsequent years, and second; we audited every residential block (167 blocks) in one neighbourhood and compared the distribution of SSOI aesthetics index scores with results from the randomly audited locations. Finally, we present interrater reliability and consistency results on all observed items.

RESULTS

The observed neighbourhood average aesthetics index score estimated from four or five stratified random audit locations is sufficient to characterize the average neighbourhood aesthetics. The SSOI was internally consistent and demonstrated good to excellent interrater reliability. At the neighbourhood level, aesthetics is positively related to SES and physical activity and negatively correlated with BMI.

CONCLUSION

The proposed approach to direct neighbourhood auditing performs sufficiently and has the advantage of financial and temporal efficiency when auditing a large city.

Analysis of multiple survival events in generalized case-cohort designs

Generalized case-cohort design has been proposed to assess the effects of exposures on survival outcomes when measuring exposures is expensive and events are not rare in the cohort. In such design, expensive exposure information is collected from both a (stratified) randomly selected subcohort and a subset of individuals with events. In this article, we consider extension of such design to study multiple types of survival events by selecting a proportion of cases for each type of event. We propose a general weighting scheme to analyze data. Furthermore, we examine the optimal choice of weights and show that this optimal weighting yields much improved efficiency gain both asymptotically and in simulation studies. Finally, we apply our proposed methods to data from the Atherosclerosis Risk in Communities study.

Nonparametric Bayes modeling with sample survey weights

In population studies, it is standard to sample data via designs in which the population is divided into strata, with the different strata assigned different probabilities of inclusion. Although there have been some proposals for including sample survey weights into Bayesian analyses, existing methods require complex models or ignore the stratified design underlying the survey weights. We propose a simple approach based on modeling the distribution of the selected sample as a mixture, with the mixture weights appropriately adjusted, while accounting for uncertainty in the adjustment. We focus for simplicity on Dirichlet process mixtures but the proposed approach can be applied more broadly. We sketch a simple Markov chain Monte Carlo algorithm for computation, and assess the approach via simulations and an application.

Pharmacovigilance from social media: An improved random subspace method for identifying adverse drug events

OBJECTIVE

Recent advances in Web 2.0 technologies have seen significant strides towards utilizing patient-generated content for pharmacovigilance. Social media-based pharmacovigilance has great potential to augment current efforts and provide regulatory authorities with valuable decision aids. Among various pharmacovigilance activities, identifying adverse drug events (ADEs) is very important for patient safety. However, in health-related discussion forums, ADEs may confound with drug indications and beneficial effects, etc. Therefore, the focus of this study is to develop a strategy to identify ADEs from other semantic types, and meanwhile to determine the drug that an ADE is associated with.

MATERIALS AND METHODS

In this study, two groups of features, i.e., shallow linguistic features and semantic features, are explored. Moreover, motivated and inspired by the characteristics of explored two feature categories for social media-based ADE identification, an improved random subspace method, called Stratified Sampling-based Random Subspace (SSRS), is proposed. Unlike conventional random subspace method that applies random sampling for subspace selection, SSRS adopts stratified sampling-based subspace selection strategy.

RESULTS

A case study on heart disease discussion forums is performed to evaluate the effectiveness of the SSRS method. Experimental results reveal that the proposed SSRS method significantly outperforms other compared ensemble methods and existing approaches for ADE identification.

DISCUSSION AND CONCLUSION

Our proposed method is easy to implement since it is based on two feature sets that can be naturally derived, and therefore, can omit artificial stratum generation efforts. Moreover, SSRS has great potential of being applied to deal with other high-dimensional problems that can represent original data from two different aspects.

Analyzing the distribution of rabies clinics and achievements of standardized rabies clinics implementation in mainland China

BACKGROUND

For rabies prevention and treatment, the Chinese government has been establishing standardized rabies clinics since 2016. This study aimed to investigate the distribution of rabies clinics and the achievements of newly-implemented standardized rabies clinics in mainland China, for the purpose of providing further rabies control strategies.

METHODS

The number of rabies clinics, including per million inhabitants in each region, was determined. We sampled 1200 clinics from 8 provinces by multi-stage stratified sampling, and a questionnaire survey was carried out to record each clinic's achievements. Data collected from 1185 questionnaires were analyzed.

RESULTS

We found that rabies clinics were mostly located in the southwest, central, and eastern regions of China; these accounted for 67.1% of all clinics. The eastern and south regions showed the lowest number of rabies clinics per million inhabitants (0.15 and 0.12, respectively). The total standard-reaching rate of rabies clinics in mainland China was only 11.0%, with significant differences in the rate among regions (X² = 33.004, p <  0.001). Specifically, the qualified rates of supporting facilities and functional areas were 13.9% (X² = 34.003, p <  0.001) and 56.1% (X² = 9.943, p = 0.019), respectively. Vaccines with 2 different substrates and professional flushing equipment were provided by 40.5% (X² = 27.935, p = 0.001) and 37.7% (X² = 54.922, p = 0.001) of clinics, respectively.

CONCLUSION

Regional differences do exist in the distribution of rabies clinics in mainland China, with relative low number per million population in south and eastern China. There are few standardized rabies clinics in mainland China. Efforts are needed to establish supporting facilities, especially for wound treatment and vaccination. Future research should focus on the improvement of rabies clinics standardization.

Sample allocation balancing overall representativeness and stratum precision

PURPOSE

In large-scale surveys, it is often necessary to distribute a preset sample size among a number of strata. Researchers must make a decision between prioritizing overall representativeness or precision of stratum estimates. Hence, I evaluated different sample allocation strategies based on stratum size.

METHODS

The strategies evaluated herein included allocation proportional to stratum population; equal sample for all strata; and proportional to the natural logarithm, cubic root, and square root of the stratum population. This study considered the fact that, from a preset sample size, the dispersion index of stratum sampling fractions is correlated with the population estimator error and the dispersion index of stratum-specific sampling errors would measure the inequality in precision distribution. Identification of a balanced and efficient strategy was based on comparing those both dispersion indices.

RESULTS

Balance and efficiency of the strategies changed depending on overall sample size. As the sample to be distributed increased, the most efficient allocation strategies were equal sample for each stratum; proportional to the logarithm, to the cubic root, to square root; and that proportional to the stratum population, respectively.

CONCLUSIONS

Depending on sample size, each of the strategies evaluated could be considered in optimizing the sample to keep both overall representativeness and stratum-specific precision.

Implications of heterogeneous distributions of organisms on ballast water sampling

Ballast water sampling is one of the problems still needing investigation in order to enforce the D-2 Regulation of the International Convention for the Control and Management of Ship Ballast Water and Sediments. Although statistical "representativeness" of the sample is an issue usually discussed in the literature, neither a definition nor a clear description of its implications are presented. In this context, we relate it to the heterogeneity of the distribution of organisms in ballast water and show how to specify compliance tests under different models based on the Poisson and negative binomial distributions. We provide algorithms to obtain minimum sample volumes required to satisfy fixed limits on the probabilities of Type I and II errors. We show that when the sample consists of a large number of aliquots, the Poisson model may be employed even under moderate heterogeneity of the distribution of the organisms in the ballast water tank.

Optimizing population mean estimation in stratified sampling using linear cost: A simulation study

Improving efficiency has long been a focal challenge in sampling literature. However, simultaneously enhancing estimator efficacy and optimizing survey costs is a practical necessity across various fields such as medicine, agriculture, and transportation. In this study, we present a comprehensive family of generalized exponential estimators specifically designed for estimating population means within stratified sampling frameworks. Optimizing the survey cost is one the major challenges in the stratified sampling because the cost of the survey is fixed and decided before the survey. To optimize survey costs, we employ integer programming and Lagrange multipliers. We have carefully derived the Mean Square Error (MSE) of the proposed estimators and addressed this as an optimization problem to further refine estimator performance in light of cost constraints and optimal sample sizes. The results have been rigorously validated using real-world datasets, and both theoretical and empirical evaluations show that the proposed estimators significantly outperform existing alternatives. These findings underscore the estimators' practical relevance and theoretical robustness.

Compromise optimum allocation in neutrosophic multi-character survey under stratified random sampling using neutrosophic fuzzy programming

Survey sampling has wide range of applications in social and scientific investigation to draw inference about the unknown parameter of interest. In complex surveys, the sample information about the study variable cannot be expressed by a precise number under uncertain environment due fuzziness and indeterminacy. Therefore, this information is expressed by neutrosophic numbers rather than the classical numbers. The neutrosophic statistics, which is generalization of classical statistics, deals with the neutrosophic data that has some degree of indeterminacy and fuzziness. In this study, we investigate the compromise optimum allocation problem for estimating the population means of the neutrosophic study variables in a multi-character stratified random sampling under uncertain per unit measurement cost. We proposed the intuitionistic fuzzy cost function, modeling the fuzzy uncertainty in stratum per unit measurement cost. The compromise optimum allocation problem is formulated as a multi-objective intuitionistic fuzzy optimization problem. The solution methodology is suggested using neutrosophic fuzzy programming and intuitionistic fuzzy programming approaches. A numerical study includes the means estimation of atmospheric variables is presented to explore the real-life application, explain the mathematical formulation, and efficiency comparison with some existing methods. The results show that the suggested methods produce more precise estimates with less utilization of survey resources as compared to some existing methods. The Python is used for statistical analysis, graphical designing and numerical optimization problems are solved using GAMS.

Methodological design for the macro-location of a micropollutants monitoring network in tropical rivers: a case study in Cauca River

Establishing scientifically the macro-location of a micropollutants monitoring network in tropical Andean rivers is a complex process, because information gathering is restricted by high-cost of analysis and limited availability of analytical techniques, which lead to inadequate sampling strategies that hinder the representativeness of samples. Thus, this work proposes a methodology for determining the number of representative sampling sections in a micropollutant monitoring network to characterise the ecological risk in tropical Andean torrential rivers. The proposed methodology consists of four stages: identification of the potential sampling units by Spline interpolation; calculation of the number of representative sections for a stratified sampling with an acceptable level of confidence and error; spatial allocation of the potential sampling units into sections by hierarchical cluster analysis; and representation of the spatial distribution of the sampling sections through a geographic information system (GIS). The proposed methodology is dynamic, and therefore, it can be revisited as more data are obtained in the subsequent years; it has the possibility of being applied to other inter-Andean valley rivers that interact with the tropical Andean sloppy mountains and serves as a tool for decision making by environmental authorities regarding the optimisation of the existing monitoring networks in terms of micropollutants to promote sustainable management of water resources. The proposed methodology is applied in the Upper Cauca River Basin (UCRB), which is located in southwest Colombia, South America.

Unmatched spatially stratified controls: A simulation study examining efficiency and precision using spatially-diverse controls and generalized additive models

Unmatched spatially stratified random sampling (SSRS) of non-cases selects geographically balanced controls by dividing the study area into spatial strata and randomly selecting controls from all non-cases within each stratum. The performance of SSRS control selection was evaluated in a case study spatial analysis of preterm birth in Massachusetts. In a simulation study, we fit generalized additive models using controls selected by SSRS or simple random sample (SRS) designs. We compared mean squared error (MSE), bias, relative efficiency (RE), and statistically significant map results to the model results with all non-cases. SSRS designs had lower average MSE (0.0042-0.0044) and higher RE (77-80%) compared to SRS designs (MSE: 0.0072-0.0073; RE across designs: 71%). SSRS map results were more consistent across simulations, reliably identifying statistically significant areas. SSRS designs improved efficiency by selecting controls that are geographically distributed, particularly from low population density areas, and may be more appropriate for spatial analyses.

Evaluating the potential of wastewater-based epidemiology to estimate the retail illicit drug market size in Europe: A case study

BACKGROUND AND AIMS

Estimating the retail drug market size is complex due to its clandestine nature, yet useful for intervention and policy planning. Wastewater-based epidemiology (WBE) offers an alternative strategy to estimate population-wide consumption trends by analysing influent wastewater, avoiding self-reporting biases associated with other estimation techniques, e.g., consumption or expenditure-based models. This study evaluates the applicability of WBE for drug market size estimation.

METHODS

Using WBE data the drug market situation of amphetamine, cocaine, MDMA, and methamphetamine is estimated in European cities (n=75) through combination with price/purity information (Statistical Bulletin) published by the European Union Drugs Agency. Market sizes were estimated at three levels: (L1) pure drug quantities, (L2) adulterated retail drugs quantities, and (L3) market value. Furthermore, yearly consumption estimates from the commonly applied consecutive seven-day sampling is compared to a randomised stratified sampling approach in Brussels, Belgium.

RESULTS

Challenges include the availability and accuracy of price and purity data, which complicates cross-country comparisons and hinders the higher-levels calculations. Due to missing data, the market value (L3) could only be calculated for a limited number of cities, i.e., 39% (amphetamine), 39% (cocaine), 1% (MDMA), and 23% (methamphetamine). Furthermore, statistically significant differences were shown for amphetamine (up to 139% measurement error) and methamphetamine (up to 58% measurement error) between both sampling approaches.

CONCLUSIONS

Particularly at city level, WBE can complement traditional market size estimations, but significant uncertainties remain. Future research should expand one-week consecutive sampling to a randomised stratified sampling approach tailored to drug and location.

Mapping forest aboveground carbon stock of combined stratified sampling and RFRK model with mean annual temperature and precipitation

Accurately estimating forest aboveground carbon stock (ACS) is essential for achieving carbon neutrality. At present, most non-parametric models still have errors in estimating carbon stock in regions. Given the autocorrelation inherent in spatial interpolation, combining non-parametric models with spatial interpolation offers significant potential. In this study, we combined the random forest (RF) with the ordinary kriging and co-kriging of the mean annual temperature, precipitation, slope, and elevation to establish the random forest residual kriging (RFRK) model. Meanwhile, we also developed the multiple linear regression residual kriging (MLRRK) model and the random forest residual kriging (RFRK) model. Finally, we selected the optimal model for the estimation and mapping of the ACS. The results indicate that: (1) the model achieves an R2 of 0.871, P of 90.4%, and RMSE of 3.948 t/hm2; (2) the RFCK model with mean annual precipitation (RFCKpre) outperforms the one with mean annual temperature (RFCKtem), while the RFOK model exhibits the lowest accuracy; (3) the RFCKpre exponential model has the highest accuracy, with the highest R2 of 0.63 and RI (0.23), the lowest RMSE of 9.3 and SSR (41,612). These findings suggest that the RFRKpre model has improved the accuracy of estimating the ACS of regional forests.

A log rank type test in observational survival studies with stratified sampling

In randomized clinical trials, the log rank test is often used to test the null hypothesis of the equality of treatment-specific survival distributions. In observational studies, however, the ordinary log rank test is no longer guaranteed to be valid. In such studies we must be cautious about potential confounders; that is, the covariates that affect both the treatment assignment and the survival distribution. In this paper, two cases were considered: the first is when it is believed that all the potential confounders are captured in the primary database, and the second case where a substudy is conducted to capture additional confounding covariates. We generalize the augmented inverse probability weighted complete case estimators for treatment-specific survival distribution proposed in Bai et al. (Biometrics 69:830-839, 2013) and develop the log rank type test in both cases. The consistency and double robustness of the proposed test statistics are shown in simulation studies. These statistics are then applied to the data from the observational study that motivated this research.

Quantitative detection of serum biochemical indexes via UV-Vis-NIRS combined with deep neural networks

To achieve rapid, cost-efficient, convenient and accurate detection of five clinical serum biochemical indexes, namely glucose (GLU), triglycerides (TG), total cholesterol (TC), total protein (TP) and albumin (ALB), ultraviolet-visible-near infrared spectroscopy (UV-Vis-NIRS) technology combined with deep neural network (DNN) is firstly proposed in this study. The absorption spectra of 992 human serum are collected in 200-2500 nm. Different spectra preprocessing methods are studied and compared to eliminate interference, baseline offset, and highlight specificity information of biochemical indexes in the raw spectra. Moreover, the competitive adaptive reweighted sampling (CARS) algorithm is utilized to optimally select characteristic wavelengths related to biochemical indexes. A DNN, i.e., 1DCNN-LSTM model is established to quantitatively predict five biochemical indexes using stratified sampling with the training set and testing set divided in 7:3. Compared with the traditional machine learning (ML) and artificial neural network (ANN) algorithms, the results show that the quantitative prediction performances of 1DCNN-LSTM model are significant superior. Root mean square error of prediction (RMSEP) and determination coefficient (R2) of GLU, TG, TC, TP and ALB are 0.39 mmol/L, 0.36 mmol/L, 0.31 mmol/L, 1.26 g/L and 1.28 g/L, 0.97, 0.90, 0.93, 0.96 and 0.93, respectively. Finally, the advantage of UV-Vis-NIRS are verified by comparing with NIRS and UV-Vis alone. Results show that UV-Vis-NIRS combined with DNN can provide new idea and strong technical support in the clinical application of serum biochemical indexes detection.

Filling the agronomic data gap through a minimum data collection approach

Context

Agronomic data such as applied inputs, management practices, and crop yields are needed for assessing productivity, nutrient balances, resource use efficiency, as well as other aspects of environmental and economic performance of cropping systems. In many instances, however, these data are only available at a coarse level of aggregation or simply do not exist.

Objectives

Here we developed an approach that identifies sites for agronomic data collection for a given crop and country, seeking a balance between minimizing data collection efforts and proper representation of the main crop producing areas.

Methods

The developed approach followed a stratified sampling method based on a spatial framework that delineates major climate zones and crop area distribution maps, which guides selection of sampling areas (SA) until half of the national harvested area is covered. We provided proof of concept about the robustness of the approach using three rich databases including data on fertilizer application rates for maize, wheat, and soybean in Argentina, soybean in the USA, and maize in Kenya, which were collected via local experts (Argentina) and field surveys (USA and Kenya). For validation purposes, fertilizer rates per crop and nutrient derived at (sub-) national level following our approach were compared against those derived using all data collected from the whole country.

Results

Application of the approach in Argentina, USA, and Kenya resulted in selection of 12, 28, and 10 SAs, respectively. For each SA, three experts or 20 fields were sufficient to give a robust estimate of average fertilizer rates applied by farmers. Average rates at national level derived from our approach compared well with those derived using the whole database ( ± 10 kg N, ± 2 kg P, ± 1 kg S, and ± 5 kg K per ha) requiring less than one third of the observations.

Conclusions

The developed minimum crop data collection approach can fill the agronomic data gaps in a cost-effective way for major crop systems both in large- and small-scale systems.

Significance

The proposed approach is generic enough to be applied to any crop-country combination to guide collection of key agricultural data at national and subnational levels with modest investment especially for countries that do not currently collect data.