Best Crop Rotation Selection with GIS-AHP Technique Using Soil Nutrient Variability

Crop selections and rotations are very important in optimising land and labour productivities, enhancing higher cropping intensities, producing better crop yield. A land suitability analysis system based on the analytical hierarchy process (AHP) technique coupled with the Geographic Information System (GIS) software environment can be a unique tool for better crop selection. The AHP-GIS technique was used in land suitability analysis in crop rotation decisions, for rice-jute (Kharif season) and potato-lentil (Rabi season) crops in the Hooghly District, West Bengal, India. The study area covering 291 ha was classified based on eight major soil nutrient levels with 70 randomly selected plots for soil sampling and analysis. The soil nutrient variability was examined with descriptive statistics followed by best semivariogram-based model selection for kriging interpolation in the ‘R’ software environment. The pairwise comparison matrix based ranking of parameters and giving weights was carried out considering the importance of each parameter for specific crops. The total area, being under the major rice-potato belt, could be classified from the suitability view point to the ‘highly suitable’(S1) class occupying 29.2%, and ‘not suitable’ (N) class; 4.5% for rice, about 6.5% of land is ‘highly suitable’ (S1), ‘and nearly 2.1% area is ‘not suitable’ (N) for jute; and 21.3% is ‘highly suitable’ (S1) for potato and 12.4% for lentil crops. The yield maps showed nearly 75% and 90% of the area for rice and potato crops, respectively, gave sound crop yield. Furthermore, the GIS platform was used for crop rotation analysis to spread multiple seasons ensuring better crop management in long run. Overall, 25% of the rice crop area for jute in Kharif and 8% of potato crop area for lentil in the Rabi season were recommended as replacements

Flood Susceptibility Mapping through the GIS-AHP Technique Using the Cloud

Flood susceptibility mapping is essential for characterizing flood risk zones and for planning mitigation approaches. Using a multi-criteria decision support system, this study investigated a flood susceptible region in Bihar, India. It used a combination of the analytical hierarchy process (AHP) and geographic information system (GIS)/remote sensing (RS) with a cloud computing API on the Google Earth Engine (GEE) platform. Five main flood-causing criteria were broadly selected, namely hydrologic, morphometric, permeability, land cover dynamics, and anthropogenic interference, which further had 21 sub-criteria. The relative importance of each criterion prioritized as per their contribution toward flood susceptibility and weightage was given by an AHP pair-wise comparison matrix (PCM). The most and least prominent flood-causing criteria were hydrologic (0.497) and anthropogenic interference (0.037), respectively. An area of ~3000 sq km (40.36%) was concentrated in high to very high flood susceptibility zones that were in the vicinity of rivers, whereas an area of ~1000 sq km (12%) had very low flood susceptibility. The GIS-AHP technique provided useful insights for flood zone mapping when a higher number of parameters were used in GEE. The majorities of detected flood susceptible areas were flooded during the 2019 floods and were mostly located within 500 m of the rivers’ paths

Land Suitability Assessment for Potato Crop using Analytic Hierarchy Process Technique and Geographic Information System

Agricultural land-suitability analysis is a prerequisite to achieve optimum utilization of the available land resources for sustainable agricultural production. The Analytical Hierarchy Process (AHP) technique coupled with Geographic Information System (GIS) can be a unique tool for land-suitability studies. AHP-GIS technique based on soil nutrient criteria of soil texture, pH, organic carbon, electric conductivity, available nitrogen, phosphorous, potassium, and zinc was used for land-suitability assessment in Tarkeswar Block, Hooghly district, West Bengal for growing potato. The study area was classified into suitable land categories based on soil nutrient levels analysed from 50 randomly-selected plot-based soil samples. Pair-wise comparison matrix-based ranking was computed considering the importance of each criterion for potato crop in the area. Suitability maps were developed and analysed in ArcGIS software environment. The total area was classified into ‘highly suitable’ class occupying 61 ha (21%), ‘moderately suitable’ class in 195 ha (67%), ‘marginally suitable’ class in 32 ha (11%) and ‘not suitable’ class in 3 ha (1%) land areas. Yield distribution map of potato crop showed nearly 106 ha (36.5%) area producing higher tuber yield of 20 t.ha-1. The proposed suitability map was validated against crop yield map where nearly 253 ha (87%) area classified under ‘highly suitable’ to ‘moderately suitable’ classes was found to give better potato yield of 15 t.ha-1or more. The AHP-GIS technique can be used for crop rotation analysis for multiple seasons for better crop selection in long run

Prediction of soil nutrients through PLSR and SVMR models by VIs-NIR reflectance spectroscopy

Though soil nutrients play important roles in maintaining soil fertility and crop growth, their estimation requires direct soil sampling followed by laboratory analysis incurring huge cost and time. In this research work, soil nutrients were predicted using VIs-NIR reflectance spectroscopy (range 350–2500 nm) with Partial Least Squares Regression (PLSR) and Support Vector Machine Regression Model (SVMR) model through principal component analysis. Two hundred soil samples were collected from Tarekswar, Hooghly, West Bengal, India to predict eight selected soil nutrients, such as soil organic carbon (OC), pH, available nitrogen (N), available phosphorus (P), available potassium(K), electric conductivity (EC), zinc (Zn) and soil texture (sand, silt, and clay) levels. The OC content was predicted with sound accuracy (R2: 0.82, RPD: 2.28, RMSE: 0.13, RPIQ: 4.15 FD-SG), followed by P (R2: 0.71, RPD: 1.83, RMSE: 4575, RPIQ: 3.44 1st derivative). The soil parameters sensitive to the particular band of visible spectrum were also identified viz. wavelengths of 409, 444, 591 and 592 nm for OC, 430 and 505 nm for P, 464 nm for K; 580 nm for Zn, 492,511,596 and 698 nm for N; 493, 569 and 665 nm for EC; 492,567 and 652 nm for pH; 457 nm for sand and 515 nm for clay.The soil nutrient levels were predicted by PLSR and SVMR models through PCA and Sentinel 2 imagery and soil suitability map were also generated for seven soil parameters such as OC, pH, EC, N, P, K and clay content. Through map query tool in ArcGIS software environment the PLSR and SVMR model successfully identify the suitability class with level of accuracy of 87.2% and 88.9%, respectively, against the direct soil analysis based suitability mapping.The machine learning technique based soil nutrient and soil suitability prediction can be easily adopted in different regions. This will reduce the cost of laboratory soil analysis and optimize the total time requirement

Mapping groundwater potential zone in the subarnarekha basin, India, using a novel hybrid multi-criteria approach in Google earth Engine

Assessing groundwater potential for sustainable resource management is critically important. In addressing this concern, this study aims to advance the field by developing an innovative approach for Groundwater potential zone (GWPZ) mapping using advanced techniques, such as FuzzyAHP, FuzzyDEMATEL, and Logistic regression (LR) models. GWPZ was carried out by integrating various primary factors, such as hydrologic, soil permeability, morphometric, terrain distribution, and anthropogenic influences, incorporating twenty-seven individual criteria using multi-criteria decision models along with a hybrid approach for the Subarnarekha River basin, India, in Google earth engine (GEE). The predictive capability of the model was evaluated using a Multi-Collinearity test (VIF <10.0), followed by applying a random forest model, considering the weighted impact of the five primary factors. The hybrid model for GWPZ classification showed that 21.97 % (4256.3 km2) of the area exhibited very high potential, while 11.37 % (2202.1 km2) indicated very low potential for GW in this area. Validation of the groundwater level data from 72 observation wells, performed by the Area under receiver operating characteristic (AUROC) curve technique, yielded values ranging between 75 % and 78 % for different models, underscoring the robust predictability of GWPZ. The hybrid and LR-FuzzyAHP models demonstrated remarkable effectiveness in GWPZ mapping, indicating that the downstream and southern regions boast substantial groundwater potential attributed to alluvial soil and favorable recharge conditions. Conversely, the central part grapples with a scarcity of groundwater. It holds the potential to assist planners and managers in formulating strategies for managing groundwater levels and alleviating the impacts of future droughts

Spatial Analysis of Flood Hazard Zoning Map Using Novel Hybrid Machine Learning Technique in Assam, India

Twenty-two flood-causative factors were nominated based on morphometric, hydrological, soil permeability, terrain distribution, and anthropogenic inferences and further analyzed through the novel hybrid machine learning approach of random forest, support vector machine, gradient boosting, na&iuml;ve Bayes, and decision tree machine learning (ML) models. A total of 400 flood and nonflood locations acted as target variables of the flood hazard zoning map. All operative factors in this study were tested using variance inflation factor (VIF) values (&lt;5.0) and Boruta feature ranking (&lt;10 ranks) for FHZ maps. The hybrid model along with RF and GBM had sound flood hazard zoning maps for the study area. The area under the receiver operating characteristics (AUROC) curve and statistical model matrices such as accuracy, precision, recall, F1 score, and gain and lift curve were applied to assess model performance. The 70%:30% sample ratio for training and validation of the standalone models concerning the AUROC value showed sound results for all the ML models, such as RF (97%), SVM (91%), GBM (97%), NB (96%), DT (88%), and hybrid (97%). The gain and lift curve also showed the suitability of the hybrid model along with the RF, GBM, and NB models for developing FHZ maps

Spatial Analysis of Flood Hazard Zoning Map Using Novel Hybrid Machine Learning Technique in Assam, India

Twenty-two flood-causative factors were nominated based on morphometric, hydrological, soil permeability, terrain distribution, and anthropogenic inferences and further analyzed through the novel hybrid machine learning approach of random forest, support vector machine, gradient boosting, naïve Bayes, and decision tree machine learning (ML) models. A total of 400 flood and nonflood locations acted as target variables of the flood hazard zoning map. All operative factors in this study were tested using variance inflation factor (VIF) values (<5.0) and Boruta feature ranking (<10 ranks) for FHZ maps. The hybrid model along with RF and GBM had sound flood hazard zoning maps for the study area. The area under the receiver operating characteristics (AUROC) curve and statistical model matrices such as accuracy, precision, recall, F1 score, and gain and lift curve were applied to assess model performance. The 70%:30% sample ratio for training and validation of the standalone models concerning the AUROC value showed sound results for all the ML models, such as RF (97%), SVM (91%), GBM (97%), NB (96%), DT (88%), and hybrid (97%). The gain and lift curve also showed the suitability of the hybrid model along with the RF, GBM, and NB models for developing FHZ maps

Total land suitability analysis for rice and potato crops through FuzzyAHP technique in West Bengal, India

AbstractA total land suitability analysis was carried out through FuzzyAHP technique for rice and potato crops in West Bengal, India. Around 21 most relevant crop suitability parameters were selected and classified under five primary criteria, such as terrain distribution parameter, static soil parameter, available soil nutrient, agriculture practice parameter, and local variation parameter for the study. The factors such as NDVI and SAVI values were estimated from Sentinel 2B images in “SNAP” toolbox software environment, whereas soil nutrients were estimated through standard laboratory methods. Individual parameter weights were assigned through the FuzzyAHP technique for sub-criteria as well as for primary criteria. The final crop suitability map was developed showing nearly 20% of the total area as highly suitable for rice crop, whereas nearly 39% of the area was found suitable for the potato crop. Comparing the prediction map with yield distribution, it was found that the southwest region of the study area is very suitable for both rice and potato crop with higher crop yields in the range of 5 t/ha and 20 t/ha, respectively. Six different machine learning models, namely random forest, support vector machine, AdaBoost, extreme gradient boosting, logistic regression, and naïve Bayes, were utilized for validation of the suitability maps. The support vector machine (SVM) learning model with the highest AUC (~80%) was found efficient for testing both rice and potato crop suitability. The economic status of farmers can be rejuvenated by selecting the best crop rotation through land suitability analysis

Psychological distress and burnout among healthcare worker during COVID-19 pandemic in India-A cross-sectional study.

BackgroundCOVID-19 has inundated the entire world disrupting the lives of millions of people. The pandemic has stressed the healthcare system of India impacting the psychological status and functioning of health care workers. The aim of this study is to determine the burnout levels and factors associated with the risk of psychological distress among healthcare workers (HCW) engaged in the management of COVID 19 in India.MethodsA cross-sectional study was conducted from 1 September 2020 to 30 November 2020 by telephonic interviews using a web-based Google form. Health facilities and community centres from 12 cities located in 10 states were selected for data collection. Data on socio-demographic and occupation-related variables like age, sex, type of family, income, type of occupation, hours of work and income were obtained was obtained from 967 participants, including doctors, nurses, ambulance drivers, emergency response teams, lab personnel, and others directly involved in COVID 19 patient care. Levels of psychological distress was assessed by the General health Questionnaire -GHQ-5 and levels of burnout was assessed using the ICMR-NIOH Burnout questionnaire. Multivariable logistic regression analysis was performed to identify factors associated with the risk of psychological distress. The third quartile values of the three subscales of burnout viz EE, DP and PA were used to identify burnout profiles of the healthcare workers.ResultsOverall, 52.9% of the participants had the risk of psychological distress that needed further evaluation. Risk of psychological distress was significantly associated with longer hours of work (≥ 8 hours a day) (AOR = 2.38, 95% CI(1.66-3.41), income≥20000(AOR = 1.74, 95% CI, (1.16-2.6); screening of COVID-19 patients (AOR = 1.63 95% CI (1.09-2.46), contact tracing (AOR = 2.05, 95% CI (1.1-3.81), High Emotional exhaustion score (EE ≥16) (AOR = 4.41 95% CI (3.14-6.28) and High Depersonalisation score (DP≥7) (AOR = 1.79, 95% CI (1.28-2.51)). About 4.7% of the HCWs were overextended (EE>18); 6.5% were disengaged (DP>8) and 9.7% HCWs were showing signs of burnout (high on all three dimensions).ConclusionThe study has identified key factors that could have been likely triggers for psychological distress among healthcare workers who were engaged in management of COVID cases in India. The study also demonstrates the use of GHQ-5 and ICMR-NIOH Burnout questionnaire as important tools to identify persons at risk of psychological distress and occurrence of burnout symptoms respectively. The findings provide useful guide to planning interventions to mitigate mental health problems among HCW in future epidemic/pandemic scenarios in the country