| 1. |
- Thomas, HS, et al.
(författare)
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- 2019
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swepub:Mat__t
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| 2. |
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| 3. |
- Drake, TM, et al.
(författare)
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Surgical site infection after gastrointestinal surgery in children: an international, multicentre, prospective cohort study
- 2020
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Ingår i: BMJ global health. - : BMJ. - 2059-7908. ; 5:12
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Tidskriftsartikel (refereegranskat)abstract
- Surgical site infection (SSI) is one of the most common healthcare-associated infections (HAIs). However, there is a lack of data available about SSI in children worldwide, especially from low-income and middle-income countries. This study aimed to estimate the incidence of SSI in children and associations between SSI and morbidity across human development settings.MethodsA multicentre, international, prospective, validated cohort study of children aged under 16 years undergoing clean-contaminated, contaminated or dirty gastrointestinal surgery. Any hospital in the world providing paediatric surgery was eligible to contribute data between January and July 2016. The primary outcome was the incidence of SSI by 30 days. Relationships between explanatory variables and SSI were examined using multilevel logistic regression. Countries were stratified into high development, middle development and low development groups using the United Nations Human Development Index (HDI).ResultsOf 1159 children across 181 hospitals in 51 countries, 523 (45·1%) children were from high HDI, 397 (34·2%) from middle HDI and 239 (20·6%) from low HDI countries. The 30-day SSI rate was 6.3% (33/523) in high HDI, 12·8% (51/397) in middle HDI and 24·7% (59/239) in low HDI countries. SSI was associated with higher incidence of 30-day mortality, intervention, organ-space infection and other HAIs, with the highest rates seen in low HDI countries. Median length of stay in patients who had an SSI was longer (7.0 days), compared with 3.0 days in patients who did not have an SSI. Use of laparoscopy was associated with significantly lower SSI rates, even after accounting for HDI.ConclusionThe odds of SSI in children is nearly four times greater in low HDI compared with high HDI countries. Policies to reduce SSI should be prioritised as part of the wider global agenda.
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| 4. |
- Lozano, Rafael, et al.
(författare)
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Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017
- 2018
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Ingår i: The Lancet. - : Elsevier. - 1474-547X .- 0140-6736. ; 392:10159, s. 2091-2138
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Tidskriftsartikel (refereegranskat)abstract
- Background: Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of “leaving no one behind”, it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990–2017, projected indicators to 2030, and analysed global attainment. Methods: We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0–100, with 0 as the 2·5th percentile and 100 as the 97·5th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator. Findings: The global median health-related SDG index in 2017 was 59·4 (IQR 35·4–67·3), ranging from a low of 11·6 (95% uncertainty interval 9·6–14·0) to a high of 84·9 (83·1–86·7). SDG index values in countries assessed at the subnational level varied substantially, particularly in China and India, although scores in Japan and the UK were more homogeneous. Indicators also varied by SDI quintile and sex, with males having worse outcomes than females for non-communicable disease (NCD) mortality, alcohol use, and smoking, among others. Most countries were projected to have a higher health-related SDG index in 2030 than in 2017, while country-level probabilities of attainment by 2030 varied widely by indicator. Under-5 mortality, neonatal mortality, maternal mortality ratio, and malaria indicators had the most countries with at least 95% probability of target attainment. Other indicators, including NCD mortality and suicide mortality, had no countries projected to meet corresponding SDG targets on the basis of projected mean values for 2030 but showed some probability of attainment by 2030. For some indicators, including child malnutrition, several infectious diseases, and most violence measures, the annualised rates of change required to meet SDG targets far exceeded the pace of progress achieved by any country in the recent past. We found that applying the mean global annualised rate of change to indicators without defined targets would equate to about 19% and 22% reductions in global smoking and alcohol consumption, respectively; a 47% decline in adolescent birth rates; and a more than 85% increase in health worker density per 1000 population by 2030. Interpretation: The GBD study offers a unique, robust platform for monitoring the health-related SDGs across demographic and geographic dimensions. Our findings underscore the importance of increased collection and analysis of disaggregated data and highlight where more deliberate design or targeting of interventions could accelerate progress in attaining the SDGs. Current projections show that many health-related SDG indicators, NCDs, NCD-related risks, and violence-related indicators will require a concerted shift away from what might have driven past gains—curative interventions in the case of NCDs—towards multisectoral, prevention-oriented policy action and investments to achieve SDG aims. Notably, several targets, if they are to be met by 2030, demand a pace of progress that no country has achieved in the recent past. The future is fundamentally uncertain, and no model can fully predict what breakthroughs or events might alter the course of the SDGs. What is clear is that our actions—or inaction—today will ultimately dictate how close the world, collectively, can get to leaving no one behind by 2030.
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| 5. |
- Stanaway, Jeffrey D., et al.
(författare)
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Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: A systematic analysis for the Global Burden of Disease Study 2017
- 2018
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Ingår i: The Lancet. - 1474-547X .- 0140-6736. ; 392:10159, s. 1923-1994
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Tidskriftsartikel (refereegranskat)abstract
- Background The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk-outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk-outcome pairs, and new data on risk exposure levels and risk- outcome associations. Methods We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk-outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017.
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| 6. |
- Feigin, Valery L., et al.
(författare)
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Global, regional, and national burden of neurological disorders, 1990–2016 : a systematic analysis for the Global Burden of Disease Study 2016
- 2019
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Ingår i: Lancet Neurology. - : Elsevier. - 1474-4422 .- 1474-4465. ; 18:5, s. 459-480
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Tidskriftsartikel (refereegranskat)abstract
- Background: Neurological disorders are increasingly recognised as major causes of death and disability worldwide. The aim of this analysis from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 is to provide the most comprehensive and up-to-date estimates of the global, regional, and national burden from neurological disorders.Methods: We estimated prevalence, incidence, deaths, and disability-adjusted life-years (DALYs; the sum of years of life lost [YLLs] and years lived with disability [YLDs]) by age and sex for 15 neurological disorder categories (tetanus, meningitis, encephalitis, stroke, brain and other CNS cancers, traumatic brain injury, spinal cord injury, Alzheimer's disease and other dementias, Parkinson's disease, multiple sclerosis, motor neuron diseases, idiopathic epilepsy, migraine, tension-type headache, and a residual category for other less common neurological disorders) in 195 countries from 1990 to 2016. DisMod-MR 2.1, a Bayesian meta-regression tool, was the main method of estimation of prevalence and incidence, and the Cause of Death Ensemble model (CODEm) was used for mortality estimation. We quantified the contribution of 84 risks and combinations of risk to the disease estimates for the 15 neurological disorder categories using the GBD comparative risk assessment approach.Findings: Globally, in 2016, neurological disorders were the leading cause of DALYs (276 million [95% UI 247–308]) and second leading cause of deaths (9·0 million [8·8–9·4]). The absolute number of deaths and DALYs from all neurological disorders combined increased (deaths by 39% [34–44] and DALYs by 15% [9–21]) whereas their age-standardised rates decreased (deaths by 28% [26–30] and DALYs by 27% [24–31]) between 1990 and 2016. The only neurological disorders that had a decrease in rates and absolute numbers of deaths and DALYs were tetanus, meningitis, and encephalitis. The four largest contributors of neurological DALYs were stroke (42·2% [38·6–46·1]), migraine (16·3% [11·7–20·8]), Alzheimer's and other dementias (10·4% [9·0–12·1]), and meningitis (7·9% [6·6–10·4]). For the combined neurological disorders, age-standardised DALY rates were significantly higher in males than in females (male-to-female ratio 1·12 [1·05–1·20]), but migraine, multiple sclerosis, and tension-type headache were more common and caused more burden in females, with male-to-female ratios of less than 0·7. The 84 risks quantified in GBD explain less than 10% of neurological disorder DALY burdens, except stroke, for which 88·8% (86·5–90·9) of DALYs are attributable to risk factors, and to a lesser extent Alzheimer's disease and other dementias (22·3% [11·8–35·1] of DALYs are risk attributable) and idiopathic epilepsy (14·1% [10·8–17·5] of DALYs are risk attributable).Interpretation: Globally, the burden of neurological disorders, as measured by the absolute number of DALYs, continues to increase. As populations are growing and ageing, and the prevalence of major disabling neurological disorders steeply increases with age, governments will face increasing demand for treatment, rehabilitation, and support services for neurological disorders. The scarcity of established modifiable risks for most of the neurological burden demonstrates that new knowledge is required to develop effective prevention and treatment strategies.Funding: Bill & Melinda Gates Foundation.
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| 7. |
- Kumar, Deepak, et al.
(författare)
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Multi-ahead electrical conductivity forecasting of surface water based on machine learning algorithms
- 2023
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Ingår i: Applied water science. - : Springer Nature. - 2190-5487 .- 2190-5495. ; 13:10
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Tidskriftsartikel (refereegranskat)abstract
- The present research work focused on predicting the electrical conductivity (EC) of surface water in the Upper Ganga basin using four machine learning algorithms: multilayer perceptron (MLP), co-adaptive neuro-fuzzy inference system (CANFIS), random forest (RF), and decision tree (DT). The study also utilized the gamma test for selecting appropriate input and output combinations. The results of the gamma test revealed that total hardness (TH), magnesium (Mg), and chloride (Cl) parameters were suitable input variables for EC prediction. The performance of the models was evaluated using statistical indices such as Percent Bias (PBIAS), correlation coefficient (R), Willmott’s index of agreement (WI), Index of Agreement (PI), root mean square error (RMSE) and Legate-McCabe Index (LMI). Comparing the results of the EC models using these statistical indices, it was observed that the RF model outperformed the other algorithms. During the training period, the RF algorithm has a small positive bias (PBIAS = 0.11) and achieves a high correlation with the observed values (R = 0.956). Additionally, it shows a low RMSE value (360.42), a relatively good coefficient of efficiency (CE = 0.932), PI (0.083), WI (0.908) and LMI (0.083). However, during the testing period, the algorithm’s performance shows a small negative bias (PBIAS = − 0.46) and a good correlation (R = 0.929). The RMSE value decreases significantly (26.57), indicating better accuracy, the coefficient of efficiency remains high (CE = 0.915), PI (0.033), WI (0.965) and LMI (− 0.028). Similarly, the performance of the RF algorithm during the training and testing periods in Prayagraj. During the training period, the RF algorithm shows a PBIAS of 0.50, indicating a small positive bias. It achieves an RMSE of 368.3, R of 0.909, CE of 0.872, PI of 0.015, WI of 0.921, and LMI of 0.083. During the testing period, the RF algorithm demonstrates a slight negative bias with a PBIAS of − 0.06. The RMSE reduces significantly to 24.1, indicating improved accuracy. The algorithm maintains a high correlation (R = 0.903) and a good coefficient of efficiency (CE = 0.878). The index of agreement (PI) increases to 0.035, suggesting a better fit. The WI is 0.960, indicating high accuracy compared to the mean value, while the LMI decreases slightly to − 0.038. Based on the comparative results of the machine learning algorithms, it was concluded that RF performed better than DT, CANFIS, and MLP. The study recommended using the current month’s total hardness (TH), magnesium (Mg), and chloride (Cl) parameters as input variables for multi-ahead forecasting of electrical conductivity (ECt+1, ECt+2, and ECt+3) in future studies in the Upper Ganga basin. The findings also indicated that RF and DT models had superior performance compared to MLP and CANFIS models. These models can be applied for multi-ahead forecasting of monthly electrical conductivity at both Varanasi and Prayagraj stations in the Upper Ganga basin.
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| 8. |
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PAU Smart Seeder: a novel way forward for rice residue management in North-west India
- 2024
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Ingår i: Scientific Reports. - : Nature Research. - 2045-2322. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- In winter, the paddy residues become wet during morning and late evening due to dew, which restricts the operation of sowing machines (Happy Seeder and Super Seeder) into paddy residues, as wet residues do not slide on furrow openers/tines. A PAU Smart Seeder (PSS) was developed and evaluated for a four-wheel tractor that can sow wheat with optimum crop establishment in combined harvested rice fields. The PSS were evaluated for its performance under varying straw load, forward speed, and rotor speed in terms of fuel consumption, field capacity, seed emergence, and grain yield. The crop establishment and wheat yield of PSS was also compared with the existing straw management machines Happy Seeder (HS) and Super Seeder (SS) under heavy paddy residue conditions. The effect of the straw load was more pronounced on dependent variables than the effect of the speed index. PSS performance was best at a forward speed of 2.6 km h−1, rotor speed of 127.5 rpm, and a straw load of 6 t ha−1. Average fuel consumption using PSS was lower than SS but higher than HS. Wheat emergence was higher by 15.6 and 25.7% on the PSS plots compared to HS and SS, respectively. Average wheat grain yield in PSS plots was significantly higher by 12.7 and 18.9% than SS and HS, respectively in one experiment, while the grain yield was similar for both PSS and HS in other experiments. PSS has a novel mechanism to manage paddy straw and simultaneously sow wheat into a heavy straw load (> 8 t ha−1) mixture of anchored and loose straw. In conclusion, PSS showed promise for in-situ management of rice straw as it eliminates most of the operational problems encountered by the existing seeders (HS and SS).
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| 9. |
- Roy, Dilip Kumar, et al.
(författare)
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Improving Forecasting Accuracy of Multi-Scale Groundwater Level Fluctuations Using a Heterogeneous Ensemble of Machine Learning Algorithms
- 2023
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Ingår i: Water. - : MDPI. - 2073-4441. ; 15:20
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Tidskriftsartikel (refereegranskat)abstract
- Accurate groundwater level (GWL) forecasts are crucial for the efficient utilization, strategic long-term planning, and sustainable management of finite groundwater resources. These resources have a substantial impact on decisions related to irrigation planning, crop selection, and water supply. This study evaluates data-driven models using different machine learning algorithms to forecast GWL fluctuations for one, two, and three weeks ahead in Bangladesh’s Godagari upazila. To address the accuracy limitations inherent in individual forecasting models, a Bayesian model averaging (BMA)-based heterogeneous ensemble of forecasting models was proposed. The dataset encompasses 1807 weekly GWL readings (February 1984 to September 2018) from four wells, divided into training (70%), validation (15%), and testing (15%) subsets. Both standalone models and ensembles employed a Minimum Redundancy Maximum Relevance (MRMR) algorithm to select the most influential lag times among candidate GWL lags up to 15 weeks. Statistical metrics and visual aids were used to evaluate the standalone and ensemble GWL forecasts. The results consistently favor the heterogeneous BMA ensemble, excelling over standalone models for multi-step ahead forecasts across time horizons. For instance, at GT8134017, the BMA approach yielded values like R (0.93), NRMSE (0.09), MAE (0.50 m), IOA (0.96), NS (0.87), and a-20 index (0.94) for one-week-ahead forecasts. Despite a slight decline in performance with an increasing forecast horizon, evaluation indices confirmed the superior BMA ensemble performance. This ensemble also outperformed standalone models for other observation wells. Thus, the BMA-based heterogeneous ensemble emerges as a promising strategy to bolster multi-step ahead GWL forecasts within this area and beyond.
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| 10. |
- Singh, Vicky, et al.
(författare)
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Effect of Different Tillage and Residue Management Options on Soil Water Transmission and Mechanical Behavior
- 2023
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Ingår i: Land. - : Multidisciplinary Digital Publishing Institute (MDPI). - 2073-445X. ; 12:10
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Tidskriftsartikel (refereegranskat)abstract
- Understanding the variability in the mechanical and hydrological soil characteristics resulting from diverse tillage and residue management practices is essential for evaluating the adoption of conservation strategies to preserve soil’s physical well-being. Zero-tillage techniques combined with residue retention or incorporation have gained widespread recognition for their capacity to conserve soil and water resources, reduce energy consumption, and enhance soil quality and environmental sustainability. Nevertheless, the choice of tillage and residue management options may vary depending on the geographical locations and specific soil conditions. To assess the impacts of four distinct tillage and residue management approaches, a two-year experiment (2020–2021 and 2021–2022) was conducted: T1: conventional tillage followed by wheat sowing after the removal of rice straw (CT-RS); T2: zero tillage with wheat sowing using a Happy Seeder while retaining rice straw (ZT+RS); T3: conventional tillage followed by wheat sowing after rice straw incorporation using a reversible mouldboard plough (CT+RS); T4: minimum tillage with wheat sowing using a Super Seeder with rice straw incorporation (MT+RS); the effects were recorded on the physical soil properties. Our findings indicate that zero tillage combined with residue retention (T2) had a positive influence on various physical soil attributes. Notably, significant differences were observed among the tillage and residue management options, particularly in terms of the bulk density with T1 exhibiting the highest values and the lowest being in T2, whereas the soil penetration resistance was lowest in T3 compared to T1. In the case of T3, sandy loam and clay loam soils had the highest measured saturated hydraulic conductivity values, measuring 5.08 and 4.57 cm h−1 and 4.07 and 3.73 cm h−1, respectively. Furthermore, T2 (zero tillage with residue retention) demonstrated the highest mean weight diameter (MWD) and maximum water stable aggregate. These results collectively underscore the positive effects of adopting zero tillage and retaining residue (T2) on soil structure and quality, particularly concerning the mechanical and hydrological soil properties.
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| 11. |
- Chettiyam Thodi, Muhamed Fasil, et al.
(författare)
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Using RS and GIS Techniques to Assess and Monitor Coastal Changes of Coastal Islands in the Marine Environment of a Humid Tropical Region
- 2023
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Ingår i: Water. - : MDPI. - 2073-4441. ; 15:21
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Tidskriftsartikel (refereegranskat)abstract
- Vypin, Vallarpadam, and Bolgatty are significant tropical coastal islands situated in the humid tropical Kerala region of India, notable for their environmental sensitivity. This study conducted a comprehensive assessment of shoreline alterations on these islands by integrating Remote Sensing (RS) and Geographic Information Systems (GIS) techniques. Utilizing satellite imagery from the LANDSAT series with a spatial resolution of 30 m, the analysis spanned the years from 1973 to 2019. The Digital Shoreline Analysis System (DSAS) tool, integrated into the ArcGIS software, was employed to monitor and analyze shoreline shifts, encompassing erosion and accretion. Various statistical parameters, including Net Shoreline Movement (NSM), End Point Rate (EPR), and Linear Regression Rate (LRR), were utilized to evaluate these changes. Additionally, the study aimed to discern the root causes of shoreline modifications in the study area, encompassing disturbances and the construction of new structures on these islands. The results conclusively demonstrated the substantial impact endured by these coastal islands, with accretion on both sides leading to the creation of new landmasses. This manuscript effectively illustrates that these islands have experienced marine transgression, notably evidenced by accretion. Anthropogenic activities were identified as the primary drivers behind the observed shoreline changes, underscoring the need for careful management and sustainable practices in these fragile coastal ecosystems.
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| 12. |
- Gawdiya, Sandeep, et al.
(författare)
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Field Screening of Wheat Cultivars for Enhanced Growth, Yield, Yield Attributes, and Nitrogen Use Efficiencies
- 2023
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Ingår i: Agronomy. - : MDPI. - 2073-4395. ; 13:8
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Tidskriftsartikel (refereegranskat)abstract
- Optimizing nitrogen (N) inputs is crucial for maximizing wheat yield and ensuring environmental sustainability. Wheat’s economic significance in India calls for a comprehensive evaluation of its ecological implications to develop a resilient production system. This study aimed to identify and evaluate ten wheat cultivars for their yield and N-use efficiency under varying nitrogen inputs (control (N0), half of the recommended nitrogen (N75), and the recommended nitrogen (N150)) using the surface application of neem-oil-coated urea. All N inputs were applied in three splits, basal, crown root initiation, and tillering stages, and an experiment was conducted in a split-plot design. The application of N150 gave the highest dry matter accumulation (DMA) at harvesting stage (AHS) (871 g m−2), seed/spike (60), grain yield (GY = 7.4 t ha−1), straw yield (SY = 8.9 t ha−1), harvest index (HI = 45.2%), protein (12.5%), and total uptake of N (TUN) (223 kg ha−1) by the cultivar ‘HD 3249’, being closely followed by the cultivar ‘HD3117’. Six cultivars (‘HD 3298’, ‘HD 3117’, ‘HD 3249’, ‘PBW 550’, ‘HD 3086’, ‘HD 2967’) out of the ten cultivars evaluated responded well to different input treatments with respect to the grain yield efficiency index (GYEI ≥ 1). Regarding N input, N75 and N150 recorded the highest increases in plant height, AHS (16.5%; 21.2%), dry matter accumulation (DMA) at 30 days after sowing (DAS) (37.5%; 64%), DMA-60 DAS (42%; 53%), DMA-90 DAS (39.5%; 52.5%), TILL-30 DAS (19.8%; 26.4%), TILL-60 DAS (33.3%; 44%), TILL-90 DAS (37.2%; 47.2%), seed/spike (8%; 10%), 1000-grain weight (7.8%; 12.2%), and protein content (23.3%; and 33%) when compared with N0. Furthermore, the application of N75 and N150 improved GY (72.1%; 142.6%), SY (61.1%; 110.6%), BY (65.5%; 123%), and HI by 4.4% and 9%, respectively, over N0. Nitrogen addition (N75 and N150) also significantly increased total nitrogen uptake (104.7%; 205.6%), respectively, compared to N0. The correlation analysis revealed a positive association among most of the crop parameters. Overall, our research results suggest that the cultivars ‘HD 3249’ and ‘HD 3117’ have the potential to be effective options for improving N utilization efficiency, grain yield, and GYEI in North-West India.
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| 13. |
- Gupta, Rajeev Kumar, et al.
(författare)
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Biochar influences nitrogen and phosphorus dynamics in two texturally different soils
- 2024
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- Nitrogen (N) and phosphorus (P) are vital for crop growth. However, most agricultural systems have limited inherent ability to supply N and P to crops. Biochars (BCs) are strongly advocated in agrosystems and are known to improve the availability of N and P in crops through different chemical transformations. Herein, a soil-biochar incubation experiment was carried out to investigate the transformations of N and P in two different textured soils, namely clay loam and loamy sand, on mixing with rice straw biochar (RSB) and acacia wood biochar (ACB) at each level (0, 0.5, and 1.0% w/w). Ammonium N (NH4-N) decreased continuously with the increasing incubation period. The ammonium N content disappeared rapidly in both the soils incubated with biochars compared to the unamended soil. RSB increased the nitrate N (NO3–N) content significantly compared to ACB for the entire study period in both texturally divergent soils. The nitrate N content increased with the enhanced biochar addition rate in clay loam soil until 15 days after incubation; however, it was reduced for the biochar addition rate of 1% compared to 0.5% at 30 and 60 days after incubation in loamy sand soil. With ACB, the net increase in nitrate N content with the biochar addition rate of 1% remained higher than the 0.5% rate for 60 days in clay loam and 30 days in loamy sand soil. The phosphorus content remained consistently higher in both the soils amended with two types of biochars till the completion of the experiment.
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| 14. |
- Gupta, Rajeev Kumar, et al.
(författare)
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Influence of 11 years of crop residue management on rice productivity under varied nitrogen levels in the rice-wheat cropping system
- 2023
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Ingår i: Plant, Soil and Environment (Praha). - : Czech Academy of Agricultural Sciences. - 1214-1178 .- 1805-9368. ; 69:7, s. 333-343
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Tidskriftsartikel (refereegranskat)abstract
- The present study compares the six crop residue management techniques in main plots (since 2008) and three split nitrogen (N) levels, i.e., 75, 100 and 125 kg N/ha in subplots for rice crops for two years, i.e., 2019 and 2020, in sandy loam soil under field conditions. This experiment evaluated the long-term effect on rice productivity, soil organic carbon content and nutrient requirement in rice-wheat cropping system. The results revealed that different crop residue management practices and N levels significantly influenced rice growth, yield and yield attributes and improved nutrient uptake by grain and straw. Maximum grain yields of 20.8% and 17.8% higher over the conventional (no straw) treatment during 2019 and 2020, respectively, were recorded where the rice and wheat residue was re-tained or incorporated. The rice grain yield without residue responded significantly up to 125 kg N/ha. Whereas, with rice and wheat residue, rice grain yield did not respond to the application of N beyond 75 kg N/ha during both years.
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| 15. |
- Gupta, Rajeev Kumar, et al.
(författare)
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Interactive effects of long-term management of crop residue and phosphorus fertilization on wheat productivity and soil health in the rice–wheat
- 2024
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- In the context of degradation of soil health, environmental pollution, and yield stagnation in the rice–wheat system in the Indo-Gangetic Plains of South Asia, an experiment was established in split plot design to assess the long-term effect of crop residue management on productivity and phosphorus requirement of wheat in rice–wheat system. The experiment comprised of six crop residue management practices as the main treatment factor with three levels (0, 30 and 60 kg P2O5 ha–1) of phosphorus fertilizer as sub-treatments. Significant improvement in soil aggregation, bulk density, and infiltration rate was observed under residue management (retention/incorporation) treatments compared to residue removal or residue burning. Soil organic carbon (SOC), available nutrient content (N, P, and K), microbial count, and enzyme activities were also significantly higher in conservation tillage and residue-treated plots than without residue/burning treatments. The residue derived from both crops when was either retained/incorporated improved the soil organic carbon (0.80%) and resulted in a significant increase in SOC (73.9%) in the topsoil layer as compared to the conventional practice. The mean effect studies revealed that crop residue management practices and phosphorus levels significantly influenced wheat yield attributes and productivity. The higher grain yield of wheat was recorded in two treatments, i.e. the basal application of 60 kg P2O5 ha–1 without residue incorporation and the other with half the P-fertilizer (30 kg P2O5 ha–1) with rice residue only. The grain yield of wheat where the rice and wheat residue were either retained/incorporated without phosphorus application was at par with 30 and 60 kg P2O5ha–1. Phosphorus levels also significantly affected wheat productivity and available P content in the soil. Therefore, results suggested that crop residue retention following the conservation tillage approach improved the yield of wheat cultivated in the rice–wheat cropping system.
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| 16. |
- Gupta, Sanjeev, et al.
(författare)
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Sensitivity of daily reference evapotranspiration to weather variables in tropical savanna: a modelling framework based on neural network
- 2024
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Ingår i: Applied water science. - : Springer Nature. - 2190-5487 .- 2190-5495. ; 14:6
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Tidskriftsartikel (refereegranskat)abstract
- Accurate prediction of reference evapotranspiration (ETo) is crucial for many water-related fields, including crop modelling, hydrologic simulations, irrigation scheduling and sustainable water management. This study compares the performance of different soft computing models such as artificial neural network (ANN), wavelet-coupled ANN (WANN), adaptive neuro-fuzzy inference systems (ANFIS) and multiple nonlinear regression (MNLR) for predicting ETo. The Gamma test technique was adopted to select the suitable input combination of meteorological variables. The performance of the models was quantitatively and qualitatively evaluated using several statistical criteria. The study showed that the ANN-10 model performed superior to the ANFIS-06, WANN-11 and MNLR models. The proposed ANN-10 model was more appropriate and efficient than the ANFIS-06, WANN-11 and MNLR models for predicting daily ETo. Solar radiation was found to be the most sensitive input variable. In contrast, actual vapour pressure was the least sensitive parameter based on sensitivity analysis.
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| 17. |
- Gururani, Dheeraj Mohan, et al.
(författare)
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Mapping Prospects for Artificial Groundwater Recharge Utilizing Remote Sensing and GIS Methods
- 2023
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Ingår i: Water. - : MDPI. - 2073-4441. ; 15:22
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Tidskriftsartikel (refereegranskat)abstract
- The indiscriminate use of groundwater and its overexploitation has led to a significant decline in groundwater resources in India, making it essential to identify potential recharge zones for aquifer recharge. A study was conducted to determine such potential recharge zones in the Nandhour-Kailash River watershed. The study area included 1481 streams divided into 12 sub-basins (SWS). The results show that the downstream Saraunj sub-basins (SWS-11) and Odra sub-basins (SWS-12) were high priority and required immediate soil and water conservation attention. Sub catchments Lobchla West (SWS-4), Deotar (SWS-5), Balot South (SWS-8), Nandhour (SWS-9), and Nakoliy (SWS-10) had medium priority and were designated for moderate soil erosion and degradation. In contrast, sub-catchments Aligad (SWS-1), Kundal (SWS-2), Lowarnala North (SWS-3), Bhalseni (SWS-6), and Uparla Gauniyarao (SWS-7) had low priority, indicating a low risk of soil erosion and degradation. Using the existing groundwater level data, the potential map of groundwater was validated to confirm its validity. According to the guidelines provided by the Integrated Mission for Sustainable Development (IMSD), the results of the groundwater potential zones for good to very good zones have been integrated at the slope and stream order. In a 120.94 km2 area with a slope of 0–5% in first-order streams, 36 ponds were proposed, and in a 218.03 km2 area with a slope of 15% in first- to fourth-order streams, 105 retention dams were proposed and recognized as possible sites for artificial groundwater recharge. The proposed water harvesting structure may aid in continuously recharging these zones and benefit water resource managers and planners. Thus, various governmental organizations can use the results to identify possible future recharge areas.
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| 18. |
- Heddam, Salim, et al.
(författare)
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Hybrid river stage forecasting based on machine learning with empirical mode decomposition
- 2024
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Ingår i: Applied water science. - : Springer Nature. - 2190-5487 .- 2190-5495. ; 14:3
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Tidskriftsartikel (refereegranskat)abstract
- The river stage is certainly an important indicator of how the water level fluctuates overtime. Continuous control of the water stage can help build an early warning indicator of floods along rivers and streams. Hence, forecasting river stages up to several days in advance is very important and constitutes a challenging task. Over the past few decades, the use of machine learning paradigm to investigate complex hydrological systems has gained significant importance, and forecasting river stage is one of the promising areas of investigations. Traditional in situ measurements, which are sometime restricted by the existing of several handicaps especially in terms of regular access to any points alongside the streams and rivers, can be overpassed by the use of modeling approaches. For more accurate forecasting of river stages, we suggest a new modeling framework based on machine learning. A hybrid forecasting approach was developed by combining machine learning techniques, namely random forest regression (RFR), bootstrap aggregating (Bagging), adaptive boosting (AdaBoost), and artificial neural network (ANN), with empirical mode decomposition (EMD) to provide a robust forecasting model. The singles models were first applied using only the river stage data without preprocessing, and in the following step, the data were decomposed into several intrinsic mode functions (IMF), which were then used as new input variables. According to the obtained results, the proposed models showed improved results compared to the standard RFR without EMD for which, the error performances metrics were drastically reduced, and the correlation index was increased remarkably and great changes in models’ performances have taken place. The RFR_EMD, Bagging_EMD, and AdaBoost_EMD were less accurate than the ANN_EMD model, which had higher R≈0.974, NSE≈0.949, RMSE≈0.330 and MAE≈0.175 values. While the RFR_EMD and the Bagging_EMD were relatively equal and exhibited the same accuracies higher than the AdaBoost_EMD, the superiority of the ANN_EMD was obvious. The proposed model shows the potential for combining signal decomposition with machine learning, which can serve as a basis for new insights into river stage forecasting.
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| 19. |
- Jagadesh, M., et al.
(författare)
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Altering Natural Ecosystems Causes Negative Consequences on the Soil Physical Qualities: An Evidence-Based Study from Nilgiri Hill Region of Western Ghats, India
- 2023
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Ingår i: Land. - : Multidisciplinary Digital Publishing Institute (MDPI). - 2073-445X. ; 12:10
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Tidskriftsartikel (refereegranskat)abstract
- Land use change (LUC) has direct and indirect consequences on soil quality. To gain insight into how LUC influences the physical properties of soil, it can be advantageous to compare undisturbed ecosystems with those that have naturally evolved over time, as well as to use soil quality indices to pinpoint the sensitivity of each ecosystem and land use change (LUC). A soil survey was carried out in the six major ecosystems of the Nilgiri Hill Region: cropland (CL), deciduous forest (DF), evergreen forest (EF), forest plantation (FP), scrubland (SL), and tea plantation (TP), with those having an establishment for over 50 years being selected and analyzed for soil physical parameters. In addition, soil quality indices were also derived to pinpoint the vulnerability of each ecosystem to LUC. The results reveal that the changes in land use significantly altered the soil physical properties. The content of clay was higher in EF and DF and increased with the soil profile’s depth, whereas the sand content was higher in CL and TP and decreased with the depth increment. BD and PD were significantly lower in EF, DF, SL, and FP, whereas they were higher in CL and TP. PS and ASM followed a similar trend to BD and PD. Owing to undisturbed natural settings, an abundance of litter input, and higher carbon concentrations, the HC was higher in EF, DF, SL, and FP, whereas, in the case of anthropogenic-influenced ecosystems such as CL and TP, it was lower. We discovered that LUC has altered Ag S, WSA, and MWD. Due to tillage and other cultural practices, Ag S, WSA, and MWD were significantly lower in CL and TP. However, the results confirm that native ecosystems (EF and DF) with a higher carbon content prevent such degradation, thereby resulting in good Ag S, WSA, and MWD.
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| 20. |
- Joshi, Bhupendra, et al.
(författare)
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A comparative survey between cascade correlation neural network (CCNN) and feedforward neural network (FFNN) machine learning models for forecasting suspended sediment concentration
- 2024
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- Suspended sediment concentration prediction is critical for the design of reservoirs, dams, rivers ecosystems, various operations of aquatic resource structure, environmental safety, and water management. In this study, two different machine models, namely the cascade correlation neural network (CCNN) and feedforward neural network (FFNN) were applied to predict daily-suspended sediment concentration (SSC) at Simga and Jondhara stations in Sheonath basin, India. Daily-suspended sediment concentration and discharge data from 2010 to 2015 were collected and used to develop the model to predict suspended sediment concentration. The developed models were evaluated using statistical indices like Nash and Sutcliffe efficiency coefficient (NES), root mean square error (RMSE), Willmott’s index of agreement (WI), and Legates–McCabe’s index (LM), supplemented by a scatter plot, density plots, histograms and Taylor diagram for graphical representation. The developed model was evaluated and compared with CCNN and FFNN. Nine input combinations were explored using different lag-times for discharge (Qt-n) and suspended sediment concentration (St-n) as input variables, with the current suspended sediment concentration as the desired output, to develop CCNN and FFNN models. The CCNN4 model with 4 lagged inputs (St-1, St-2, St-3, St-4) outperformed the other developed models with the lowest RMSE = 95.02 mg/l and the highest NES = 0.0.662, WI = 0.890 and LM = 0.668 for the Jondhara Station while the same CCNN4 model secure as the best with the lowest RMSE = 53.71 mg/l and the highest NES = 0.785, WI = 0.936 and LM = 0.788 for the Simga Station. The result shows the CCNN model was better than the FFNN model for predicting daily-suspended sediment at both stations in the Sheonath basin, India. Overall, CCNN showed better forecasting potential for suspended sediment concentration compared to FFNN at both stations, demonstrating their applicability for hydrological forecasting with complex relationships.
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| 21. |
- Karthik, Rayapati, et al.
(författare)
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Designing a productive, profitable integrated farming system model with low water footprints for small and marginal farmers of Telangana
- 2024
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- In the years 2021–2022 and 2022–2023, an experiment was carried out at the IFS Unit, College of Agriculture, PJTSAU, Rajendranagar in order to determine the best one-acre integrated farming system model for Telangana's small and marginal farmers. Seven farm models among which six models were developed by combining the various components i.e., cropping systems, fruit cropfodder crops and livestock components, in different proportions, and compared with rice-groundnut system which is a major farming approach in Telangana using randomized block design. The seven models were as follows: M1: Rice–Groundnut; M2: Rice–Groundnut, Pigeonpea + Sweetcorn (1:3)—Bajra, Bt cotton + Greengram (1:2)—Maize; M3: Rice–Groundnut, Pigeonpea + Sweetcorn (1:3)—Bajra, Pigeonpea + Maize (1:3)—Sunhemp; Napier grass, Sheep (5 + 1); M4: Rice–Groundnut, Pigeonpea + Sweetcorn (1:3)—Bajra, Bt cotton + Greengram (1:2)—Maize, Pigeonpea + Maize (1:3)—Sunhemp, Poultry unit; M5: Guava, Hedge Lucerne, Napier grass, Bt cotton + Greengram (1:2)—Maize, Sheep (5 + 1); M6: Guava, Bt cotton + Greengram (1:2)—Maize, Rice–Groundnut, Poultry; M7: Rice–Groundnut, Pigeonpea + Sweetcorn (1:3)—Bajra, Pigeonpea + Maize (1:3)—Sunhemp; Napier grass, Hedge lucerne, Poultry (100), Sheep (5 + 1). Based on a 2-year average, the Model M7 system produced 9980 Rice Grain Equivalent Yield(RGEY)kg of output per acre, with gross and net returns of ₹210,439 and ₹124,953 respectively, and recovered a B:C ratio of 2.46. It has recorded highest sustainable yield index (SYI) of 0.673 and value index of 0.772 with the lowest water footprint of 259.0 L/kg. This study reveals that adopting an integrated farming system is the optimal approach for effectively combining productive, financially rewarding, and diversified enterprises within a single acre of land.d. This system should be recommended for maximum benefits to smallto small and marginal farmers in Telangana's southern hills and plateau.
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| 22. |
- Murad, Sadia, et al.
(författare)
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Efficacy of DAP coated with bacterial strains and their metabolites for soil phosphorus availability and maize growth
- 2024
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- Phosphorus (P) use efficiency in alkaline/calcareous soils is only 20% due to precipitation of P2O5 with calcium and magnesium. However, coating Diammonium Phosphate (DAP) with phosphorus solubilizing bacteria (PSB) is more appropriate to increase fertilizer use efficiency. Therefore, with the aim to use inorganic fertilizers more effectively present study was conducted to investigate comparative effect of coated DAP with PSB strains Bacillus subtilis ZE15 (MN003400), Bacillus subtilis ZR3 (MN007185), Bacillus megaterium ZE32 (MN003401) and Bacillus megaterium ZR19 (MN007186) and their extracted metabolites with uncoated DAP under axenic conditions. Gene sequencing was done against various sources of phosphorus to analyze genes responsible for phosphatase activity. Alkaline phosphatase (ALP) gene amplicon of 380bp from all tested strains was showed in 1% w/v gel. Release pattern of P was also improved with coated fertilizer. The results showed that coated phosphatic fertilizer enhanced shoot dry weight by 43 and 46% under bacterial and metabolites coating respectively. Shoot and root length up to 44 and 42% with metabolites coated DAP and 41% with bacterial coated DAP. Physiological attributes also showed significant improvement with coated DAP over conventional. The results supported the application of coated DAP as a useful medium to raise crop yield even at lower application rates i.e., 50 and 75% DAP than non-coated 100% DAP application which advocated this coating technique a promising approach for advancing circular economy and sustainable development in modern agriculture.
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| 23. |
- Naresh, R. K., et al.
(författare)
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Long-term application of agronomic management strategies effects on soil organic carbon, energy budgeting, and carbon footprint under rice–wheat cropping system
- 2024
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- In the plains of western North India, traditional rice and wheat cropping systems (RWCS) consume a significant amount of energy and carbon. In order to assess the long-term energy budgets, ecological footprint, and greenhouse gas (GHG) pollutants from RWCS with residual management techniques, field research was conducted which consisted of fourteen treatments that combined various tillage techniques, fertilization methods, and whether or not straw return was present in randomized block design. By altering the formation of aggregates and the distribution of carbon within them, tillage techniques can affect the dynamics of organic carbon in soil and soil microbial activity. The stability of large macro-aggregates (> 2 mm), small macro-aggregates (2.0–2.25 mm), and micro-aggregates in the topsoil were improved by 35.18%, 33.52%, and 25.10%, respectively, over conventional tillage (0–20 cm) using tillage strategies for conservation methods (no-till in conjunction with straw return and organic fertilizers). The subsoil (20–40 cm) displayed the same pattern. In contrast to conventional tilling with no straw returns, macro-aggregates of all sizes and micro-aggregates increased by 24.52%, 28.48%, and 18.12%, respectively, when conservation tillage with organic and chemical fertilizers was used. The straw return (aggregate-associated C) also resulted in a significant increase in aggregate-associated carbon. When zero tillage was paired with straw return, chemical, and organic fertilizers, the topsoil's overall aggregate-associated C across all aggregate proportions increased. Conversely, conventional tillage, in contrast to conservation tillage, included straw return as well as chemical and organic fertilizers and had high aggregate-associated C in the subsurface. This study finds that tillage techniques could change the dynamics of microbial biomass in soils and organic soil carbon by altering the aggregate and distribution of C therein.
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| 24. |
- Ramulu, Chelpuri, et al.
(författare)
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A residue management machine for chopping paddy residues in combine harvested paddy field
- 2023
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- Nowadays, Combine Harvesters are the most commonly used device for harvesting crops; as a result, a large amount of plant material and crop residue is concentrated into a narrow band of plant material that exits the combine, challenging the residue management task. This paper aims to develop a crop residue management machine that can chop paddy residues and mix them with the soil of the combined harvested paddy field. For this purpose, two important units are attached to the developed machine: the chopping and incorporation units. The tractor operates this machine as the main source, with a power range of about 55.95 kW. The four independent parameters selected for the study were rotary speed (R1 = 900 & R2 = 1100 rpm), forward speed (F1 = 2.1 & F2 = 3.0 Kmph), horizontal adjustment (H1 = 550 & H2 = 650 mm), and vertical adjustment (V1 = 100 & V2 = 200 mm) between the straw chopper shaft and rotavator shaft and its effect was found on incorporation efficiency, shredding efficiency, and trash size reduction of chopped paddy residues. The incorporation of residue and shredding efficiency was highest at V1H2F1R2 (95.31%) and V1H2F1R2 (61.92%) arrangements. The trash reduction of chopped paddy residue was recorded maximum at V1H2F2R2 (40.58%). Therefore, this study concludes that the developed residue management machine with some modifications in power transmission can be suggested to the farmers to overcome the paddy residue issue in combined harvested paddy fields.
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| 25. |
- Singh, Vicky, et al.
(författare)
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Soil type and integrated nitrogen nutrient-rice straw residue management techniques affect soil microbes, enzyme activities and yield of wheat crop
- 2023
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Ingår i: Heliyon. - : Elsevier. - 2405-8440. ; 9:6
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Tidskriftsartikel (refereegranskat)abstract
- Sporadic burning of rice straw and the particulate air pollution caused consequently have created a pressing need for identification of practical environmentally sound in situ rice residue management methods. However, the agronomic interventions associated with the agri-inputs particularly the type of nitrogen fertilizer source must be worked out for these interventions. In a two-year field study performed at two different locations representing sandy loam and clay loam soil types, zero tillage with application of nitrophosphate (applied as basal dose through drilling) in combination with urea (applied at 1st irrigation + 3 foliar sprays of urea at weekly interval) significantly enhanced the grain and straw yield of wheat. The soil microbial viable cell counts and dehydrogenase and urease enzyme activities were also recorded to be highest in this treatment indicating the occurrence of higher living microbial population. The treatment × response variable Principle component analysis (PCA) biplot depicted relative variation among the residue management treatments/Nitrogen fertilizer sub-treatments and the enzyme activities as response variables. A variation in the soil organic content components was recognized through Fourier transform infra-red spectroscopy (FT-IRS) studies. Irrespective of the soil types under study, the FT-IR spectra exhibited presence of the aromatic carbon functional groups in residue incorporated treatments as compared to the no residue incorporation treatment.
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