Modeling optimal irrigation scheduling under conjunctive use of canal water and poor quality groundwater in semi-arid region of northwestern India

In the state of Punjab, India available water resources are inadequate to meet the irrigation needs of the crops. Optimal irrigation scheduling includes allocation of limited water supply to several crops so, as to maximize the net benefits and reduce the stress of the crops during its growing season. Dynamic programming technique of optimization has been adopted for seasonal allocation of water for multiple crops (Wheat, Barley, Mustard and Gram). The stochastic nature of canal water releases of Golewala distributary for 20 years (1982-2001) was estimated by gamma distribution. Based on this expected values of canal water releases were computed as 3766.41, 4138.76, 4422.2, 4674.5 and 4918.95 hectare – meter (ha-m) corresponding to 10%, 20%, 30%, 40% and 50% risk levels of canal water releases in the distributary. The conjunctive use of canal water along with bad quality ground water offers sustainable water allocation option based on water production function. The seasonal allocation is done corresponding to different combinations of canal water and ground water at different risk levels of canal water. The seasonal water has been further redistributed on weekly basis by making use of dated water production functions and soil water balance equation. The potential evapotranspiration was estimated by Penman Montieth method and actual evapotranspiration was estimated on the basis of soil moisture balance in the study area. Economic co-efficient, crop areas, and crops growth stage stress effects are included in the mathematical formulation at both levels. The weekly allocation takes into account the initial moisture content along with limitations in terms of channel capacity, available water supply and soil storage capacity. The allocation of water was 97% and 3% for wheat and mustard crop respectively. Model did not allocate water to barley and gram crops in the catchment area. The seasonal water was redistributed on weekly basis with different risk levels of potential evapotranspiration. The weekly allocation of water varied from 0 – 22.5 mm for 10% risk level of evapotranspiration. The risk level of evapotranspiration did not much affect the allocation and varied from 278.08– 79.01 for full season. The net returns for 10% and 50% risk levels of canal water and 30% ground water were 8.51% and 32.42% higher than existing net returns observed in the command area. The increase in the ground water amount beyond 30% tends to have an adverse effect on the yield of the crops

Growth, yield and nutrient uptake of guava (Psidium Guavaja L.) affected by soil matric potential, fertigation and mulching under drip irrigation

Our objective was to examine the effect of plastic mulching, three soil matric potentials (SMP) treatments    {I1(-20 kPa), I2(-40 kPa), and I3(-60 kPa)} and three fertigation levels {F1(100%), F2(80%), and F3(60%) recommended dose of fertilizer} under drip irrigation conditions for nutrient uptake, growth parameters and yield in guava plants.  The experiments were set up in factorial randomized block design with eighteen treatment combinations.  The experiments were conducted during the year 2012-13.  The investigation indicated that the plant canopy spread in (N/S and E/W) directions was greatly affected by different treatments.  However, non-significant effects of interaction parameters were found on plant height, crop volume and plant girth.  The maximum yield was obtained in MI2F2 (68.66 kg per plant and 22.86 t ha-1) followed by NMI2F2 (66.50 kg per plant and 22.14 t ha-1) treatments.  The maximum percentage of high quality (fruit levels A and B) were 48.2% and 50.1% in -40 kPa  irrigation treatment for mulch and no mulch conditions under 100% application of recommended dose of fertilizers.  The varying range of leaf nutrients observed for different treatments of irrigation, fertigation and mulch is  1.26-1.74% N, 0.14-0.26% P, 0.44-0.88% K, 36.33-74.23 ppm Zn, 11.33-32.76 ppm Cu, 415.6- 557.3 ppm Fe, 26.80- 39.06 ppm Mn, 0.533-0.762 % Mg and 3.42-5.06% Ca.  Based on the results above, it is recommended that controlling SMP between -40 kPa to -45 kPa at 0.2 m depth immediately under the drip emitter and fertilizer dose of 80% recommended dose of fertilizer can be used as an indicator for drip irrigation scheduling in semi-arid region of northwest India.   Keywords: fertilizer application, irrigation strategies, pressure head, tensiometer, leaf uptak

Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM<inf>2·5</inf> air pollution, 1990–2019: an analysis of data from the Global Burden of Disease Study 2019

Background: Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM2·5 originating from ambient and household air pollution. Methods: We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM2·5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure–response curve from the extracted relative risk estimates using the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM2·5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM2·5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals. Findings: In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM2·5 exposure, with an estimated 3·78 (95% uncertainty interval 2·68–4·83) deaths per 100 000 population and 167 (117–223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13·4% (9·49–17·5) of deaths and 13·6% (9·73–17·9) of DALYs due to type 2 diabetes were contributed by ambient PM2·5, and 6·50% (4·22–9·53) of deaths and 5·92% (3·81–8·64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South America. Since 1990, the attributable burden has increased by 50%, driven largely by population growth and ageing. Globally, the impact of reductions in household air pollution was largely offset by increased ambient PM2·5. Interpretation: Air pollution is a major risk factor for diabetes. We estimated that about a fifth of the global burden of type 2 diabetes is attributable PM2·5 pollution. Air pollution mitigation therefore might have an essential role in reducing the global disease burden resulting from type 2 diabetes. Funding: Bill & Melinda Gates Foundation

Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM2.5 air pollution, 1990-2019 : an analysis of data from the Global Burden of Disease Study 2019

Background Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM2.5 originating from ambient and household air pollution.Methods We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM2.5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure-response curve from the extracted relative risk estimates using the MR-BRT (meta-regression-Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM2.5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM2.5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals.Findings In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM2.5 exposure, with an estimated 3.78 (95% uncertainty interval 2.68-4.83) deaths per 100 000 population and 167 (117-223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13.4% (9.49-17.5) of deaths and 13.6% (9.73-17.9) of DALYs due to type 2 diabetes were contributed by ambient PM2.5, and 6.50% (4.22-9.53) of deaths and 5.92% (3.81-8.64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South America. Since 1990, the attributable burden has increased by 50%, driven largely by population growth and ageing. Globally, the impact of reductions in household air pollution was largely offset by increased ambient PM2.5.Interpretation Air pollution is a major risk factor for diabetes. We estimated that about a fifth of the global burden of type 2 diabetes is attributable PM2.5 pollution. Air pollution mitigation therefore might have an essential role in reducing the global disease burden resulting from type 2 diabetes. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd.Peer reviewe

Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM2.5 air pollution, 1990-2019 : An analysis of data from the Global Burden of Disease Study 2019

Background Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM2·5 originating from ambient and household air pollution. Methods We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM2·5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure–response curve from the extracted relative risk estimates using the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM2·5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM2·5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals. Findings In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM2·5 exposure, with an estimated 3·78 (95% uncertainty interval 2·68–4·83) deaths per 100 000 population and 167 (117–223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13·4% (9·49–17·5) of deaths and 13·6% (9·73–17·9) of DALYs due to type 2 diabetes were contributed by ambient PM2·5, and 6·50% (4·22–9·53) of deaths and 5·92% (3·81–8·64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South America. Since 1990, the attributable burden has increased by 50%, driven largely by population growth and ageing. Globally, the impact of reductions in household air pollution was largely offset by increased ambient PM2·5. Interpretation Air pollution is a major risk factor for diabetes. We estimated that about a fifth of the global burden of type 2 diabetes is attributable PM2·5 pollution. Air pollution mitigation therefore might have an essential role in reducing the global disease burden resulting from type 2 diabetes

Remote Sensing-Based Prediction of Temporal Changes in Land Surface Temperature and Land Use-Land Cover (LULC) in Urban Environments

Pakistan has the highest rate of urbanization in South Asia. The climate change effects felt all over the world have become a priority for regulation agencies and governments at global and regional scales with respect assessing and mitigating the rising temperatures in urban areas. This study investigated the temporal variability in urban microclimate in terms of land surface temperature (LST) and its correlation with land use-land cover (LULC) change in Lahore city for prediction of future impact patterns of LST and LULC. The LST variability was determined using the Landsat Thermal Infrared Sensor (TIRS) and the land surface emissivity factor. The influence of LULC, using the normalized difference vegetation index (NDVI), the normalized difference building index (NDBI), and the normalized difference bareness index (NDBaI) on the variability LST was investigated applying Landsat Satellite data from 1992 to 2020. The pixel-level multivariate linear regression analysis was employed to compute urban LST and influence of LULC classes. Results revealed that an overall increase of 41.8% in built-up areas at the expense of 24%, 17.4%, and 0.4% decreases in vegetation, bare land, and water from 1992–2020, respectively. Comparison of LST obtained from the meteorological station and satellite images showed a significant coherence. An increase of 4.3 °C in temperature of built-up areas from 1992–2020 was observed. Based on LULC and LST trends, the same were predicted for 2025 and 2030, which revealed that LST may further increase up to 1.3 °C by 2030. These changes in LULC and LST in turn have detrimental effects on local as well as global climate, emphasizing the need to address the issue especially in developing countries like Pakistan

Diversity, phenology and biological spectrum of tree flora in upper Tanawal, district Mansehra, KP, Pakistan

Upper Tanawallies is in the middle of the Western part of Hazara division, and includes the remote areas of four districts i.e. Mansehra, Abbottabad, Haripur and Tor-Ghar, having latitude 34°.34.40´ N to 34°.48.88´ N and 72°.84.27´ E to 73°.10.50´ E longitude. This area is loaded with plant diversity, and tree species in this area vary in their life form, leaf spectra and phenological behavior. There is no prior record of plants diversity in the area, hence, this study was conducted to explore the tree diversity, life form, leaf spectrum and phenology of the tree flora. A total 127 sampling stands (10 x 10 m2) were put into place in different locations in Upper Tanawallies to collect field data using the quadrat method. As an outcome, we learned that the region hosts 53 different tree species of 39 genera belonging to 25 tree families. The biological spectra of the trees were constructed according to Raunkiaer (1934). Results showed that Mesophanerophytes was the dominant life form class, contributing 34 (64%) of all tree species encountered, while Mesophyll and Microphyll was the dominant leaf size spectrum classes, each contributing to a count of 18 (34%) of all tree species. Frequent field visits were also carried out during the flowering and fruiting seasons in 2016/17. The result indicates that most tree species of the area show flowering during April-May (32%) while, maximum fruiting were recorded in June-July (36%). Our study concludes that anthropogenic activities on these forests should be reduced to overcome deforestation. This work will be the baseline for new research in the study area

Optimization of Water Distribution Systems Using Genetic Algorithms: A Review

Water distribution networks are crucial for supplying consumers with quality and adequate water. A water distribution system comprises connected hydraulic components which ensure water supply and distribution to meet demand. Optimization of water distribution networks is carried out to minimize resource utilization and expenditure or maximize the system’s efficiency and higher benefits. Genetic algorithms signify an effective search technique for non-linear optimization problems and have gained acceptance among water resources planners and managers. This paper reviews various developments in the optimization of water distribution systems using the technique of genetic algorithms. These developments are pertinent to creating novel systems for distributing water and the expansion, reinforcement, and rehabilitation process for prevailing water supply mechanisms.Licens fulltext: CC BY License</p

Identification of SNPs Related to <i>Salmonella</i> Resistance in Chickens Using RNA-Seq and Integrated Bioinformatics Approach

Potential single nucleotide polymorphisms (SNPs) were detected between two chicken breeds (Kashmir favorella and broiler) using deep RNA sequencing. This was carried out to comprehend the coding area alterations, which cause variances in the immunological response to Salmonella infection. In the present study, we identified high impact SNPs from both chicken breeds in order to delineate different pathways that mediate disease resistant/susceptibility traits. Samples (liver and spleen) were collected from Salmonella resistant (K. favorella) and susceptible (broiler) chicken breeds. Salmonella resistance and susceptibility were checked by different pathological parameters post infection. To explore possible polymorphisms in genes linked with disease resistance, SNP identification analysis was performed utilizing RNA seq data from nine K. favorella and ten broiler chickens. A total of 1778 (1070 SNPs and 708 INDELs) and 1459 (859 SNPs and 600 INDELs) were found to be specific to K. favorella and broiler, respectively. Based on our results, we conclude that in broiler chickens the enriched pathways mostly included metabolic pathways like fatty acid metabolism, carbon metabolism and amino acid metabolism (Arginine and proline metabolism), while as in K. favorella genes with high impact SNPs were enriched in most of the immune-related pathways like MAPK signaling pathway, Wnt signaling pathway, NOD-like receptor signaling pathway, etc., which could be a possible resistance mechanism against salmonella infection. In K. favorella, protein–protein interaction analysis also shows some important hub nodes, which are important in providing defense against different infectious diseases. Phylogenomic analysis revealed that indigenous poultry breeds (resistant) are clearly separated from commercial breeds (susceptible). These findings will offer fresh perspectives on the genetic diversity in chicken breeds and will aid in the genomic selection of poultry birds

Remote Sensing as a Management and Monitoring Tool for Agriculture: Potential Applications

Remote sensing technology has revolutionized agriculture management and monitoring by providing valuable information on crop health, soil conditions, weather patterns, and overall land management. The reflectance data are progressively being exploited in agriculture with the momenta gained in ground-based, airborne, and satellite remote sensing. The agriculture systems when managed conventionally don’t facilitate the proper utilization of resources and productivity potential of the soil. However, taking the aid of remote sensing techniques helps in boosting the productivity potential of the soil and optimizing the inputs.&nbsp; This paper aims to review the potential applications of remote sensing in agriculture and its role in improving productivity, resource efficiency, and sustainability. The paper discusses various remote sensing techniques, including satellite imagery, aerial photography, and sensor-based data collection, and their integration with advanced data analysis methods. The applications explored include biomass estimation, yield estimation, global food demand, salinity stress detection, drought monitoring, soil moisture content assessment, and change detection. The paper highlights the benefits and challenges associated with each application and provides insights into future research directions and technology advancements in the field of remote sensing for agriculture