{"title":"土壤初级宏养分和微养分的时空变异性--对印度哥印拜陀地区 Anaimalai 区块的经验分析","authors":"Dhayalan Vaithiyanathan , Karuppasamy Sudalaimuthu","doi":"10.1016/j.jssas.2023.12.002","DOIUrl":null,"url":null,"abstract":"<div><p>This study reveals Spatio-temporal prognostication of macro and micronutrients present in entisols, inceptisols and alfisols soil groups at Anaimalai block of Coimbatore district, India through empirical analysis. The soil primary macronutrients and micronutrients for the period 2021–2032 were procured from Autoregressive Integrated Moving Average (ARIMA), Linear Regression (LR) and Logistic Regression (Log R) models by setting out 2005–2020 soil nutrient datasets as input. 1627 soil samples were taken out through composite sampling method in the month of February 2021. The laboratory soil analysis was carried out for macro and micronutrients computation. Computed values were validated with the 2021 soil nutrient empirical models. R<sup>2</sup> values 0.88, 0.83, 0.85, 0.89, 0.88, 0.92, 0.90, 0.92 and 0.85 for soil pH, EC, Available nitrogen, Available phosphorus, Available potassium, Iron, Manganese, Copper and Zinc respectively divulges the reliability of ARIMA model in forecasting the soil nutrients and are spatially exposed. Spatio-temporal visualization of soil nutrients for a decade with large nutrient database as input along with field validation brings out promising forecasting model that reverberates on futuristic policy making for attaining sustainable agro-productivity.</p></div>","PeriodicalId":17560,"journal":{"name":"Journal of the Saudi Society of Agricultural Sciences","volume":"23 3","pages":"Pages 245-259"},"PeriodicalIF":0.0000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1658077X23001236/pdfft?md5=1c4638020480bcd40ddc57fc6fd6356b&pid=1-s2.0-S1658077X23001236-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Spatio-Temporal variability of soil primary macro and micro nutrients – An empirical analysis on Anaimalai block, Coimbatore District, India\",\"authors\":\"Dhayalan Vaithiyanathan , Karuppasamy Sudalaimuthu\",\"doi\":\"10.1016/j.jssas.2023.12.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This study reveals Spatio-temporal prognostication of macro and micronutrients present in entisols, inceptisols and alfisols soil groups at Anaimalai block of Coimbatore district, India through empirical analysis. The soil primary macronutrients and micronutrients for the period 2021–2032 were procured from Autoregressive Integrated Moving Average (ARIMA), Linear Regression (LR) and Logistic Regression (Log R) models by setting out 2005–2020 soil nutrient datasets as input. 1627 soil samples were taken out through composite sampling method in the month of February 2021. The laboratory soil analysis was carried out for macro and micronutrients computation. Computed values were validated with the 2021 soil nutrient empirical models. R<sup>2</sup> values 0.88, 0.83, 0.85, 0.89, 0.88, 0.92, 0.90, 0.92 and 0.85 for soil pH, EC, Available nitrogen, Available phosphorus, Available potassium, Iron, Manganese, Copper and Zinc respectively divulges the reliability of ARIMA model in forecasting the soil nutrients and are spatially exposed. Spatio-temporal visualization of soil nutrients for a decade with large nutrient database as input along with field validation brings out promising forecasting model that reverberates on futuristic policy making for attaining sustainable agro-productivity.</p></div>\",\"PeriodicalId\":17560,\"journal\":{\"name\":\"Journal of the Saudi Society of Agricultural Sciences\",\"volume\":\"23 3\",\"pages\":\"Pages 245-259\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1658077X23001236/pdfft?md5=1c4638020480bcd40ddc57fc6fd6356b&pid=1-s2.0-S1658077X23001236-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the Saudi Society of Agricultural Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1658077X23001236\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Agricultural and Biological Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Saudi Society of Agricultural Sciences","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1658077X23001236","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Agricultural and Biological Sciences","Score":null,"Total":0}
Spatio-Temporal variability of soil primary macro and micro nutrients – An empirical analysis on Anaimalai block, Coimbatore District, India
This study reveals Spatio-temporal prognostication of macro and micronutrients present in entisols, inceptisols and alfisols soil groups at Anaimalai block of Coimbatore district, India through empirical analysis. The soil primary macronutrients and micronutrients for the period 2021–2032 were procured from Autoregressive Integrated Moving Average (ARIMA), Linear Regression (LR) and Logistic Regression (Log R) models by setting out 2005–2020 soil nutrient datasets as input. 1627 soil samples were taken out through composite sampling method in the month of February 2021. The laboratory soil analysis was carried out for macro and micronutrients computation. Computed values were validated with the 2021 soil nutrient empirical models. R2 values 0.88, 0.83, 0.85, 0.89, 0.88, 0.92, 0.90, 0.92 and 0.85 for soil pH, EC, Available nitrogen, Available phosphorus, Available potassium, Iron, Manganese, Copper and Zinc respectively divulges the reliability of ARIMA model in forecasting the soil nutrients and are spatially exposed. Spatio-temporal visualization of soil nutrients for a decade with large nutrient database as input along with field validation brings out promising forecasting model that reverberates on futuristic policy making for attaining sustainable agro-productivity.
期刊介绍:
Journal of the Saudi Society of Agricultural Sciences is an English language, peer-review scholarly publication which publishes research articles and critical reviews from every area of Agricultural sciences and plant science. Scope of the journal includes, Agricultural Engineering, Plant production, Plant protection, Animal science, Agricultural extension, Agricultural economics, Food science and technology, Soil and water sciences, Irrigation science and technology and environmental science (soil formation, biological classification, mapping and management of soil). Journal of the Saudi Society of Agricultural Sciences publishes 4 issues per year and is the official publication of the King Saud University and Saudi Society of Agricultural Sciences and is published by King Saud University in collaboration with Elsevier and is edited by an international group of eminent researchers.