scholarly journals SPATIO-TEMPORAL CHANGES IN WHEAT CROP CULTIVATION IN INDIA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 385-395
Author(s):  
P. Krishnan ◽  
P. Aggarwal ◽  
N. Mridha ◽  
V. Bajpai
Keyword(s):  
Hot Spots ◽  
Crop Production ◽  
Hot Spot ◽  
Space Time ◽  
Background Information ◽  
Wheat Crop ◽  
Cold Spot ◽  
Indian States ◽  
Crop Cultivation ◽  
Production Area

<p><strong>Abstract.</strong> This study was conducted to understand the changes in spatiotemporal characteristics of wheat crop production including the changes in area and yield. We employed the emerging hot and cold spot analysis along with space time cube and space-time cluster density analysis to study the spatial changes in wheat crop production, area and yield, and understand the changes in spatiotemporal features. We made a comprehensive analysis of the changes in wheat crop production, area and yield on pan India basis for the period from 1999 to 2015. The major findings were: (a) During the study period significant increase in wheat yield occurred within the North Indian states of Punjab and Haryana and intensifying hot spots appeared within the Indo-Gangetic plains. (b) The Analysis of the area under wheat cultivation showed a persistent hot spot in the Northern states of Uttarakhand and Uttar Pradesh, Punjab and Haryana, with new hot spots observed in the regions of Central India during the years 2014 and 2015. (d) The analysis of the wheat crop production showed significant new cold spots in Rajasthan and Gujarat, with intensifying hotspots emanating into the lower delta regions of Ganges. Present study also revealed the potential of GIS based data models when related with additional background information, to segregate the most significant clusters of changes (increase / decrease) happening over active wheat crop cultivation. We expect the results from this study to help in increasing the wheat crop yield and production in the future.</p>

scholarly journals Energy Assessment of Rice- Wheat Cropping Systems in Agro-Climatic Zone-1 of Haryana State

2021 ◽  
pp. 62-69
Author(s):  
Parmod Sharma ◽  
. Yadvika ◽  
Kanishk Verma ◽  
Y. K. Yadav ◽  
. Ravi
Keyword(s):  
Energy Expenditure ◽  
Total Energy ◽  
Crop Production ◽  
Energy Use ◽  
Cultural Practices ◽  
Cropping Systems ◽  
Rice Crop ◽  
Climatic Zone ◽  
Wheat Crop ◽  
Crop Cultivation

The aim of study to examined the operation-wise and source wise energy use in wheat and rice crop production.  Present study was conducted in four districts of Haryana namely Kurukshetra, Karnal, Kaithal and Sonipat, which are situated at the bank of Yamuna canal and comes under agro climatic zone-1. In this study total 1080 farmers from 120 villages in different categories (360 from each group) were interviewed and information on various input in wheat and rice crop production was collected during winter and rainy seasons consecutive two years i.e. 2018-19 and 2019-20. Based on the collected information, all the cultural practices in wheat and rice crop production were identified and converted into energy by using standard energy equivalents. Results showed that total operation-wise energy expenditure by large, medium and small farmer's was 43693.82, 42557.21 and 41915.70 MJ/ha respectively in rice crop production. In case of wheat crop cultivation total operation-wise energy consumed by large, medium and small farmer's was 26472.74, 26576.39 and 25644.18 MJ/ha respectively. In both the crop production irrigation and fertilizer share more than 75 % of the total energy.  Fertilizer alone accounted approximately 40 % 0f total energy followed by irrigation and it was also estimated that large group farmer's consumed more energy as compared to medium and small categories farmers in cultivation of rice and wheat crop. Total source-wise energy expenditure  by large, medium and small farmer's was 39402.40, 36579.49 and 36332.21.70 MJ/ha  respectively in rice crop production. In case of wheat crop cultivation total source-wise energy consumed by large, medium and small farmer's was 19969.47, 20486.03 and 20180.73 MJ/ha respectively. From the study it was concluded that energy consumption has a positive relationship with the yield.

scholarly journals SUBNORMAL BIRTHWEIGHT AND INDUSTRIAL AIR POLLUTANTS – A COMPARISON OF SPATIAL-TEMPORAL HOT SPOT PATTERNS

2018 ◽  
Vol 23 (suppl_1) ◽  
pp. e38-e38
Author(s):  
Charlene Nielsen ◽  
Carl Amrhein ◽  
Jesus Serrano Lomelin ◽  
Osmar Zaiane ◽  
Alvaro Osornio Vargas
Keyword(s):  
Birth Weight ◽  
Low Birth Weight ◽  
Hot Spots ◽  
Hot Spot ◽  
Temporal Patterns ◽  
Space Time ◽  
Postal Code ◽  
Birth Registry ◽  
Time Pattern ◽  
Increasing Trend

Abstract BACKGROUND Disorders related to short gestation and low birth weight are the 2nd cause of infant death in Canada and have been increasing, especially in Alberta. Individual maternal risks are important but environmental exposures during pregnancy may restrict fetal growth. This contributes to small for gestational age (SGA: < tenth percentile weight for pregnancy duration) and low birth weight at term (LBWT: <2500 grams at ≥37 weeks-gestation). OBJECTIVES We examined the spatial-temporal patterns of SGA and LBWT with patterns of pollutants around conception, middle trimester, and birth. DESIGN/METHODS We aggregated postal code locations of mothers’ residences from the 2006–2012 birth registry in to space-time bins to analyze emerging hot spots. We applied the space-time pattern analysis on 70 industrial chemical emissions from the National Pollutant Release Inventory (NPRI) in estimated three month intervals. Then we statistically associated the classified patterns of SGA/LBWT with the pollutant patterns using the kappa statistic to determine how much the hot spot categories agree. The difference between kappa values indicated which trimester would be more important for which chemical. RESULTS ​There was an increasing trend for SGA (consecutive hot spots) and for LBWT (sporadic hot spots) in major urban centers. There was an increasing trend for 15 chemicals (varying hot spots). 28 chemical patterns had a kappa index greater than 0.2 with SGA or LBWT patterns. Although there is poor agreement between the space-time patterns, the maximum kappa values occurred mostly with LBWT and around birth. CONCLUSION Spatial-temporal patterns of chemicals identified in published literature (e.g. particulate matter and gases) agreed more with timing around conception; however, there were additional pollutants identified during the birth trimester. Our research is moving us toward a better understanding of the spatial-temporal link between environment and early health.

scholarly journals The geno-spatio analysis of Mycobacterium tuberculosis complex in hot and cold spots of Guangxi, China

2020 ◽  
Author(s):  
Dingwen Lin ◽  
Zhezhe Cui ◽  
Virasakdi Virasakdi ◽  
Prasit Palittapongarnpim ◽  
Angkana Chaiprasert ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Hot Spots ◽  
Hot Spot ◽  
Cold Spot ◽  
Sequencing Analysis ◽  
Spot Area ◽  
H37rv Strain ◽  
Lineage 2 ◽  
Cold Spots ◽  
Genomic Clusters

Abstract Background At present, there are few studies on polymorphism of Mycobacterium tuberculosis (Mtb) gene and how it affects the TB epidemic. Objective This study aimed to document the differences of polymorphisms between tuberculosis hot and cold spot areas of Guangxi Zhuang Autonomous Region, China. Methods The cold and hot spot areas, each with 3 counties, had been pre-identified by TB incidence for 5 years from the surveillance database. Whole genome sequencing analysis was performed on all sputum Mtb isolates from the detected cases during January and June 2018. Single nucleotide polymorphism (SNP) of each isolate compared to the H37Rv strain were called and used for lineage and sub-lineage identification. Pairwise SNP differences between every pair of isolates were computed. Analyses of Molecular Variance (AMOVA) across counties of the same hot or cold spot area and between the two areas were performed. Results As a whole, 59.8% (57.7% sub-lineage 2.2 and 2.1% sub-lineage 2.1) and 39.8% (17.8% sub-lineage 4.4, 6.5% sub-lineage 4.2 and 15.5% sub-lineage 4.5) of the Mtb strains were Lineage 2 and Lineage 4 respectively. The percentages of sub-lineage 2.2 (Beijing family strains) are significantly higher in hot spots. Through the MDS dimension reduction, the genomic population structure in the three hot spot counties is significantly different from those three cold spot counties (T-test p = 0.05). The median of SNPs distances among Mtb isolates in cold spots was greater than that in hot spots (897 vs 746, Rank-sum test p < 0.001). Three genomic clusters, each with genomic distance ≤ 12 SNPs, were identified with 2, 3 and 4 consanguineous strains. Two clusters were from hot spots and one was from cold spots.Conclusion Narrower genotype diversity in the hot area may indicate higher transmissibility of the Mtb strains in the area compared to those in the cold spot area.

scholarly journals The geno-spatio analysis of Mycobacterium tuberculosis complex in hot and cold spots of Guangxi, China

2020 ◽  
Author(s):  
Dingwen Lin ◽  
Zhezhe Cui ◽  
Virasakdi Virasakdi ◽  
Prasit Palittapongarnpim ◽  
Angkana Chaiprasert ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Hot Spots ◽  
Hot Spot ◽  
Cold Spot ◽  
Sequencing Analysis ◽  
Spot Area ◽  
H37rv Strain ◽  
Lineage 2 ◽  
Cold Spots ◽  
Genomic Clusters

Abstract Background At present, there are few studies on polymorphism of Mycobacterium tuberculosis (Mtb) gene and how it affects the TB epidemic. This study aimed to document the differences of polymorphisms between tuberculosis hot and cold spot areas of Guangxi Zhuang Autonomous Region, China. Methods The cold and hot spot areas, each with 3 counties, had been pre-identified by TB incidence for 5 years from the surveillance database. Whole genome sequencing analysis was performed on all sputum Mtb isolates from the detected cases during January and June 2018. Single nucleotide polymorphism (SNP) of each isolate compared to the H37Rv strain were called and used for lineage and sub-lineage identification. Pairwise SNP differences between every pair of isolates were computed. Analyses of Molecular Variance (AMOVA) across counties of the same hot or cold spot area and between the two areas were performed. Results As a whole, 59.8% (57.7% sub-lineage 2.2 and 2.1% sub-lineage 2.1) and 39.8% (17.8% sub-lineage 4.4, 6.5% sub-lineage 4.2 and 15.5% sub-lineage 4.5) of the Mtb strains were Lineage 2 and Lineage 4 respectively. The percentages of sub-lineage 2.2 (Beijing family strains) are significantly higher in hot spots. Through the MDS dimension reduction, the genomic population structure in the three hot spot counties is significantly different from those three cold spot counties (T-test p = 0.05). The median of SNPs distances among Mtb isolates in cold spots was greater than that in hot spots (897 vs 746, Rank-sum test p < 0.001). Three genomic clusters, each with genomic distance ≤ 12 SNPs, were identified with 2, 3 and 4 consanguineous strains. Two clusters were from hot spots and one was from cold spots. Conclusion Narrower genotype diversity in the hot area may indicate higher transmissibility of the Mtb strains in the area compared to those in the cold spot area.

scholarly journals Identifying diabetes management opportunity areas in the USA

2020 ◽  
Vol 8 (1) ◽  
pp. e000293 ◽  
Author(s):  
Michael Topmiller ◽  
Kyle Shaak ◽  
Peter J Mallow ◽  
Autumn M Kieber-Emmons
Keyword(s):  
Hot Spots ◽  
Diabetes Management ◽  
Hot Spot ◽  
The United States ◽  
Cold Spot ◽  
United States Census ◽  
Preventable Hospitalisation ◽  
Cold Spots ◽  
Medicare Spending ◽  
Management Services

Using adherence to diabetes management guidelines as a case study, this paper applied a novel geospatial hot-spot and cold-spot methodology to identify priority counties to target interventions. Data for this study were obtained from the Dartmouth Atlas of Healthcare, the United States Census Bureau’s American Community Survey and the University of Wisconsin County Health Rankings. A geospatial approach was used to identify four tiers of priority counties for diabetes preventive and management services: diabetes management cold-spots, clusters of counties with low rates of adherence to diabetes preventive and management services (Tier D); Medicare spending hot-spots, clusters of counties with high rates of spending and were diabetes management cold-spots (Tier C); preventable hospitalisation hot-spots, clusters of counties with high rates of spending and are diabetes management cold-spots (Tier B); and counties that were located in a diabetes management cold-spot cluster, preventable hospitalisation hot-spot cluster and Medicare spending hot-spot cluster (Tier A). The four tiers of priority counties were geographically concentrated in Texas and Oklahoma, the Southeast and central Appalachia. Of these tiers, there were 62 Tier A counties. Rates of preventable hospitalisations and Medicare spending were higher in Tier A counties compared with national averages. These same counties had much lower rates of adherence to diabetes preventive and management services. The novel geospatial mapping approach used in this study may allow practitioners and policy makers to target interventions in areas that have the highest need. Further refinement of this approach is necessary before making policy recommendations.

scholarly journals Interaction analysis of Mycobacterium tuberculosis between host environment and highly mutated genes from population genetic structure comparison

2020 ◽  
Author(s):  
Zhezhe Cui ◽  
Dingwen Lin ◽  
Yue Chang ◽  
Jing Ou ◽  
Liwen Huang
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Low Income ◽  
Hot Spots ◽  
Interaction Analysis ◽  
Hot Spot ◽  
Minority Group ◽  
Independent Effect ◽  
Cold Spot ◽  
Structure Comparison ◽  
Significant Difference

Abstract ObjectiveWe aimed to investigate the genetic and demographic differences and interactions between areas where observed genomic variations in Mycobacterium tuberculosis (M. Tb) are distributed uniformly in cold and hot spots. MethodsThe cold and hot spot areas were identified using the reported incidence of TB over the previous 5 years. Whole genome sequencing was performed on 291 M. tb isolates between January and June 2018. Analysis of molecular variance (AMOVA) and a multifactor dimensionality reduction (MDR) model was applied to test gene-gene-environment interactions. Adjusted odds ratios (OR) and 95% confidence intervals (CI) were computed to test the extent to which genetic mutation affects the TB epidemic using a multivariate logistic regression model. ResultsThe percentage of the Beijing family strain in hot spots was significantly higher than that in cold spots (64.63% vs 50.69%, p = 0.022), among elderly, people from an ethnic minority group, those with a low income, low BMI and those having a history of contact with a TB patient (all p < 0.05).. Individuals from cold spot areas had a higher frequency of out-of-town travelling (p < 0.05). The mutation of Rv1186c, Rv3900c, Rv1508c, Rv0210 and a Intergenic Region (SNP site: 3847237) showed a significant difference between cold and hot spots. (p < 0.001). The best MDR model displayed a clear negative interaction effect of ethnicity with income (interaction entropy: -7.08%), mutation of Rv1186c (interaction entropy: -5.62%) and mutation of SNP position Rv0210 (interaction entropy: -4.26%). Through income, mutation of Rv1186c and mutation of Rv0210 had a low independent effect.ConclusionOur data suggests a statistically significant role of ethnicity, income and the polymorphisms of Rv1186c and Rv0210 genes in the transmission and development of M. tb. The results provide clues for the study of susceptibility genes of M. tb in different ethnic groups.

scholarly journals Hot and Cold Spot Areas of Household Tuberculosis Transmission in Southern China: Effects of Socio-Economic Status and Mycobacterium tuberculosis Genotypes

2019 ◽  
Vol 16 (10) ◽  
pp. 1863 ◽  
Author(s):  
Zhezhe Cui ◽  
Dingwen Lin ◽  
Virasakdi Chongsuvivatwong ◽  
Edward A. Graviss ◽  
Angkana Chaiprasert ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Hot Spots ◽  
Hot Spot ◽  
Socioeconomic Factor ◽  
Skin Tests ◽  
P Value ◽  
Cold Spot ◽  
Medical Facility ◽  
Beijing Family ◽  
Household Contacts

The aims of the study were: (1) compare sociodemographic characteristics among active tuberculosis (TB) cases and their household contacts in cold and hot spot transmission areas, and (2) quantify the influence of locality, genotype and potential determinants on the rates of latent tuberculosis infection (LTBI) among household contacts of index TB cases. Parallel case-contact studies were conducted in two geographic areas classified as “cold” and “hot” spots based on TB notification and spatial clustering between January and June 2018 in Guangxi, China, using data from field contact investigations, whole genome sequencing, tuberculin skin tests (TSTs), and chest radiographs. Beijing family strains accounted for 64.6% of Mycobacterium tuberculosis (Mtb) strains transmitted in hot spots, and 50.7% in cold spots (p-value = 0.02). The positive TST rate in hot spot areas was significantly higher than that observed in cold spot areas (p-value < 0.01). Living in hot spots (adjusted odds ratio (aOR) = 1.75, 95%, confidence interval (CI): 1.22, 2.50), Beijing family genotype (aOR = 1.83, 95% CI: 1.19, 2.81), living in the same room with an index case (aOR = 2.29, 95% CI: 1.5, 3.49), travelling time from home to a medical facility (aOR = 4.78, 95% CI: 2.96, 7.72), history of Bacillus Calmette-Guérin vaccination (aOR = 2.02, 95% CI: 1.13 3.62), and delay in diagnosis (aOR = 2.56, 95% CI: 1.13, 5.80) were significantly associated with positive TST results among household contacts of TB cases. The findings of this study confirmed the strong transmissibility of the Beijing genotype family strains and this genotype’s important role in household transmission. We found that an extended traveling time from home to the medical facility was an important socioeconomic factor for Mtb transmission in the family. It is still necessary to improve the medical facility infrastructure and management, especially in areas with a high TB prevalence.

scholarly journals The geno-spatio analysis of Mycobacterium tuberculosis complex in hot and cold spots of Guangxi, China

2020 ◽  
Author(s):  
Dingwen Lin ◽  
Zhezhe Cui ◽  
Virasakdi Virasakdi ◽  
Prasit Palittapongarnpim ◽  
Angkana Chaiprasert ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Hot Spots ◽  
Hot Spot ◽  
Cold Spot ◽  
Sequencing Analysis ◽  
Spot Area ◽  
H37rv Strain ◽  
Lineage 2 ◽  
Cold Spots ◽  
Genomic Clusters

Abstract Background At present, there are few studies on polymorphism of Mycobacterium tuberculosis (Mtb) gene and how it affects the TB epidemic. This study aimed to document the differences of polymorphisms between tuberculosis hot and cold spot areas of Guangxi Zhuang Autonomous Region, China. Methods The cold and hot spot areas, each with 3 counties, had been pre-identified by TB incidence for 5 years from the surveillance database. Whole genome sequencing analysis was performed on all sputum Mtb isolates from the detected cases during January and June 2018. Single nucleotide polymorphism (SNP) of each isolate compared to the H37Rv strain were called and used for lineage and sub-lineage identification. Pairwise SNP differences between every pair of isolates were computed. Analyses of Molecular Variance (AMOVA) across counties of the same hot or cold spot area and between the two areas were performed. Results As a whole, 59.8% (57.7% sub-lineage 2.2 and 2.1% sub-lineage 2.1) and 39.8% (17.8% sub-lineage 4.4, 6.5% sub-lineage 4.2 and 15.5% sub-lineage 4.5) of the Mtb strains were Lineage 2 and Lineage 4 respectively. The percentages of sub-lineage 2.2 (Beijing family strains) are significantly higher in hot spots. Through the MDS dimension reduction, the genomic population structure in the three hot spot counties is significantly different from those three cold spot counties (T-test p = 0.05). The median of SNPs distances among Mtb isolates in cold spots was greater than that in hot spots (897 vs 746, Rank-sum test p < 0.001). Three genomic clusters, each with genomic distance ≤ 12 SNPs, were identified with 2, 3 and 4 consanguineous strains. Two clusters were from hot spots and one was from cold spots. Conclusion Narrower genotype diversity in the hot area may indicate higher transmissibility of the Mtb strains in the area compared to those in the cold spot area.

scholarly journals Interaction analysis of Mycobacterium tuberculosis between host environment and highly mutated genes from population genetic structure comparison

2020 ◽  
Author(s):  
Zhezhe Cui ◽  
Dingwen Lin ◽  
Yue Chang ◽  
Jing Ou ◽  
Liwen Huang
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Hot Spots ◽  
Interaction Analysis ◽  
Hot Spot ◽  
Genetic Mutation ◽  
Independent Effect ◽  
Multivariate Logistic Regression Model ◽  
Cold Spot ◽  
Structure Comparison ◽  
Significant Difference

Abstract Objective We aimed to investigate the genetic and demographic differences and interactions between areas where observed genomic variations in Mycobacterium tuberculosis (M. Tb) are distributed uniformly in cold and hot spots. Methods The cold and hot spot areas were identified using the reported incidence of TB over the previous 5 years. Whole genome sequencing was performed on 291 M. tb isolates between January and June 2018. Analysis of molecular variance (AMOVA) and a multifactor dimensionality reduction (MDR) model was applied to test gene-gene-environment interactions. Adjusted odds ratios (OR) and 95% confidence intervals (CI) were computed to test the extent to which genetic mutation affects the TB epidemic using a multivariate logistic regression model. Results The percentage of the Beijing family strain in hot spots was significantly higher than that in cold spots (64.63% vs 50.69%, p = 0.022), among elderly, people with a low BMI and those having a history of contact with a TB patient (all p < 0.05). Individuals from cold spot areas had a higher frequency of out-of-town travelling (p < 0.05). The mutation of Rv1186c, Rv3900c, Rv1508c, Rv0210 and a Intergenic Region (SNP site: 3847237) showed a significant difference between cold and hot spots. (p < 0.001). The MDR model displayed a clear negative interaction effect of age groups with BMI (interaction entropy: -3.55%) and mutation of Rv0210 (interaction entropy: -2.39%). Through the mutations of Rv0210 and BMI had a low independent effect (interaction entropy: -1.46%). Conclusion Our data suggests a statistical significant role of age, BMI and the polymorphisms of Rv0210 genes in the transmission and development of M. tb. The results provide clues for the study of susceptibility genes of M. tb in different population.

Amtrak Rail Trespasser Analysis using a GIS Space-Time Approach

2021 ◽  
pp. 1087724X2110032
Author(s):  
Michelle R. Oswald Beiler ◽  
Evan Filion
Keyword(s):  
High Risk ◽  
Hot Spots ◽  
Hot Spot ◽  
Statistical Significance ◽  
Time Of Day ◽  
Space Time ◽  
Incident Report ◽  
Activity Time ◽  
Point Density ◽  
Spatiotemporal Process

This research explores Amtrak trespass incident data from 2011 to 2019 using a GIS spatiotemporal process. The objective is to evaluate incident characteristics based on space, time, incident factors, and statistical significance. Incidents were first analyzed at the megaregional level, revealing Northern and Southern California as the highest trespassing risk in the country, followed by the Northeast and Great Lakes megaregions. A new standardized point density approach was applied to reveal incident clusters representing high-risk localities. Then, the optimized and emerging hot spot methods were applied to the top four megaregions. The results showed four Amtrak corridors as hot spots, including three along coastal California railways and the Philadelphia region. Trends for incident report factors were analyzed (e.g., pre-crash activity, time of day, location of impact). “Walking” prior to impact, occurrence in the “afternoon,” and crash location “on the tracks” were found to be the most prominent incident characteristics for those factors.

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