Characterizing soil hydrology in the Indo-Gangetic plain of Bihar, India: Methods and preliminary results.

In the Eastern Gangetic Plain (EGP) soil hydrology is a major determinant of land use and also governs the ecosystem services derived from cropping systems, particularly greenhouse gas (GHG) emissions from rice fields. To characterize patterns of soil hydrology in these, daily field monitoring of water levels was conducted during the monsoon (kharif) season in a comparatively wet (2021) and dry (2022) year with flooding depth and drainage tracked with field water tubes across 47 (2021) and 183 (2022) locations. Fields were clustered into hydrologic response types (HRT) which can then be used for land surface modelling, land use recommendations, and to target agronomic interventions that contribute to sustainable development outcomes. Clusters based on two methods of summarizing a single information source were compared. The information source was a time-series of field water-level observations, and the two methods were (1) the original time-series and their first differences and (2) a set of derived hydrologic descriptors that are conceptually related to greenhouse gas (GHG) emissions. Clustering was (1) by k-means with an optimization of cluster numbers and (2) by hierarchical clustering with the same number of clusters as identified by k-means. Hydrologic behaviour shifted dramatically between growing seasons, and it was not possible to identify consistent HRT's across years. The clusters had only a weak relation with soil properties, almost no relation with farmer perception of relative landscape position, and no relation with rice establishment method. Clusters based on time-series were moderately well predicted in the dry year 2022 by optimized random forest models, with the most important predictors being the number of irrigations, seasonal precipitation, pre-monsoon groundwater levels, seasonal groundwater level change, and pH, this latter as a surrogate for landscape position and other soil properties. In the wet year 2021 clusters were (poorly) predicted by just seasonal precipitation and pre-monsoon groundwater levels. This shows the complex relation of soil hydrology with landscape position and land management, as well as synoptic climate. By contrast, clusters based on the descriptors were not well-matched with those from the time-series, and could not be well predicted by random forest models. This shows that different clustering criteria may result in different interpretations of the landscape hydrology and thus different heuristics for anticipating the hydrology of a given field under different management choices.

Disappearing cities on US coasts.

The sea level along the US coastlines is projected to rise by 0.25-0.3 m by 2050, increasing the probability of more destructive flooding and inundation in major cities1-3. However, these impacts may be exacerbated by coastal subsidence-the sinking of coastal land areas4-a factor that is often underrepresented in coastal-management policies and long-term urban planning2,5. In this study, we combine high-resolution vertical land motion (that is, raising or lowering of land) and elevation datasets with projections of sea-level rise to quantify the potential inundated areas in 32 major US coastal cities. Here we show that, even when considering the current coastal-defence structures, further land area of between 1,006 and 1,389 km2 is threatened by relative sea-level rise by 2050, posing a threat to a population of 55,000-273,000 people and 31,000-171,000 properties. Our analysis shows that not accounting for spatially variable land subsidence within the cities may lead to inaccurate projections of expected exposure. These potential consequences show the scale of the adaptation challenge, which is not appreciated in most US coastal cities.

The fate of rice crop residues and context-dependent greenhouse gas emissions: Model-based insights from Eastern India.

Crop residue burning is a common practice in many parts of the world that causes air pollution and greenhouse gas (GHG) emissions. Regenerative practices that return residues to the soil offer a 'no burn' pathway for addressing air pollution while building soil organic carbon (SOC). Nevertheless, GHG emissions in rice-based agricultural systems are complex and difficult to anticipate, particularly in production contexts with highly variable hydrologic conditions. Here we predict long-term net GHG fluxes for four rice residue management strategies in the context of rice-wheat cropping systems in Eastern India: burning, soil incorporation, livestock fodder, and biochar. Estimations were based on a combination of Tier 1, 2, and 3 modelling approaches, including 100-year DNDC simulations across three representative soil hydrologic categories (i.e., dry, median, and wet). Overall, residue burning resulted in total direct GHG fluxes of 2.5, 6.1, and 8.7 Mg CO2-e in the dry, median, and wet hydrologic categories, respectively. Relative to emissions from burning (positive values indicate an increase) for the same dry to wet hydrologic categories, soil incorporation resulted in a -0.2, 1.8, or 3.1 Mg CO2-e change in emissions whereas use of residues for livestock fodder increased emissions by 2.0, 2.1, or 2.3 Mg CO2-e. Biochar reduced emissions relative to burning by 2.9 Mg CO2-e in all hydrologic categories. This study showed that the production environment has a controlling effect on methane and, therefore, net GHG balance. For example, wetter sites had 2.8-4.0 times greater CH4 emissions, on average, than dry sites when rice residues were returned to the soil. To effectively mitigate burning without undermining climate change mitigation goals, our results suggest that geographically-target approaches should be used in the rice-based systems of Eastern India to incentivize the adoption of regenerative 'no burn' residue management practices.

Influence of conservation agriculture-based production systems on bacterial diversity and soil quality in rice-wheat-greengram cropping system in eastern Indo-Gangetic Plains of India.

Introduction: Conservation agriculture (CA) is gaining attention in the South Asia as an environmentally benign and sustainable food production system. The knowledge of the soil bacterial community composition along with other soil properties is essential for evaluating the CA-based management practices for achieving the soil environment sustainability and climate resilience in the rice-wheat-greengram system. The long-term effects of CA-based tillage-cum-crop establishment (TCE) methods on earthworm population, soil parameters as well as microbial diversity have not been well studied. Methods: Seven treatments (or scenarios) were laid down with the various tillage (wet, dry, or zero-tillage), establishment method (direct-or drill-seeding or transplantation) and residue management practices (mixed with the soil or kept on the soil surface). The soil samples were collected after 7 years of experimentation and analyzed for the soil quality and bacterial diversity to examine the effect of tillage-cum-crop establishment methods. Results and Discussion: Earthworm population (3.6 times), soil organic carbon (11.94%), macro (NPK) (14.50-23.57%) and micronutrients (Mn, and Cu) (13.25 and 29.57%) contents were appreciably higher under CA-based TCE methods than tillage-intensive farming practices. Significantly higher number of OTUs (1,192 ± 50) and Chao1 (1415.65 ± 14.34) values were observed in partial CA-based production system (p ≤ 0.05). Forty-two (42) bacterial phyla were identified across the scenarios, and Proteobacteria, Actinobacteria, and Firmicutes were the most dominant in all the scenarios. The CA-based scenarios harbor a high abundance of Proteobacteria (2-13%), whereas the conventional tillage-based scenarios were dominated by the bacterial phyla Acidobacteria and Chloroflexi and found statistically differed among the scenarios (p ≤ 0.05). Composition of the major phyla, i.e., Proteobacteria, Actinobacteria, and Firmicutes were associated differently with either CA or farmers-based tillage management practices. Overall, the present study indicates the importance of CA-based tillage-cum-crop establishment methods in shaping the bacterial diversity, earthworms population, soil organic carbon, and plant nutrient availability, which are crucial for sustainable agricultural production and resilience in agro-ecosystem.

Exposure Levels of Airborne Fungi, Bacteria, and Antibiotic Resistance Genes in Cotton Farms during Cotton Harvesting and Evaluations of N95 Respirators against These Bioaerosols.

The USA is the third-leading cotton-producing country worldwide and cotton farming is common in the state of Georgia. Cotton harvest can be a significant contributor to airborne microbial exposures to farmers and nearby rural communities. The use of respirators or masks is one of the viable options for reducing organic dust and bioaerosol exposures among farmers. Unfortunately, the OSHA Respiratory Protection Standard (29 CFR Part 1910.134) does not apply to agricultural workplaces and the filtration efficiency of N95 respirators was never field-tested against airborne microorganisms and antibiotic resistance genes (ARGs) during cotton harvesting. This study addressed these two information gaps. Airborne culturable microorganisms were sampled using an SAS Super 100 Air Sampler in three cotton farms during cotton harvesting, and colonies were counted and converted to airborne concentrations. Genomic DNA was extracted from air samples using a PowerSoil® DNA Isolation Kit. A series of comparative critical threshold (2-ΔΔCT) real-time PCR was used to quantify targeted bacterial (16S rRNA) genes and major ARGs. Two N95 facepiece respirator models (cup-shaped and pleated) were evaluated for their protection against culturable bacteria and fungi, total microbial load in terms of surface ATP levels, and ARGs using a field experimental setup. Overall, culturable microbial exposure levels ranged between 103 and 104 CFU/m3 during cotton harvesting, which was lower when compared with bioaerosol loads reported earlier during other types of grain harvesting. The findings suggested that cotton harvesting works can release antibiotic resistance genes in farm air and the highest abundance was observed for phenicol. Field experimental data suggested that tested N95 respirators did not provide desirable >95% protections against culturable microorganisms, the total microbial load, and ARGs during cotton harvesting.

Oxalate-enhanced solubility of lead (Pb) in the presence of phosphate: pH control on mineral precipitation.

Here we study the precipitation of lead (Pb)-phosphate minerals over the pH range of 4.0 to 8.0 with and without oxalate, a ubiquitous and abundant low-molecular-weight organic acid derived from plants and microorganisms in environmental matrices. In the aqueous Pb-phosphate systems, phosphate precipitated Pb efficiently, reducing the dissolved Pb concentration below 1 µM at all the tested pH values, with the minimum solubility of about 0.1 µM measured at the intermediate pH of 6.0. The measured dissolved Pb and free Pb2+ ion activity were not in agreement with predictions from generally-accepted solubility products of the Pb phosphate minerals, particularly hydroxypyromorphite [Pb5(PO4)3OH]. Discrepancies between our measured Pb phosphate solubility products and older reported values are attributed to non-ideal behavior of these minerals (incongruent dissolution) as well as uncertainties in stability constants for soluble Pb-phosphate ion pairs. The presence of equimolar levels of oxalate and phosphate resulted in up to 250-fold increase in Pb solubility at acidic pH and about a 4-fold increase at pH 7.0, due to the strong suppression of Pb phosphate precipitation by oxalate and formation of soluble Pb-oxalate complexes. At pH 4.0 and 5.0, Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) identified a Pb-oxalate mineral phase as the only precipitate despite the presence of phosphate; in the absence of oxalate, Pb hydrogen phosphate, PbHPO4, stably formed under these acidic conditions. At pH 6.0 and greater, FTIR and XRD data revealed that Pb-phosphate [Pb3(PO4)2], and hydroxypyromorphite [Pb5(PO4)3OH] to a lesser extent, were the predominant precipitates both in the absence and presence of oxalate. Therefore, oxalate did not strongly interfere with Pb-phosphate mineral formation at aqueous pH greater than 6.0 but oxalate controlled Pb solubility at acidic pH values.

Carbonate determination in soils by mid-IR spectroscopy with regional and continental scale models.

A Partial Least Squares (PLS) carbonate (CO3) prediction model was developed for soils throughout the contiguous United States using mid-infrared (MIR) spectroscopy. Excellent performance was achieved over an extensive geographic and chemical diversity of soils. A single model for all soil types performed very well with a root mean square error of prediction (RMSEP) of 12.6 g kg-1 and was further improved if Histosols were excluded (RMSEP 11.1 g kg-1). Exclusion of Histosols was particularly beneficial for accurate prediction of CO3 values when the national model was applied to an independent regional dataset. Little advantage was found in further narrowing the taxonomic breadth of the calibration dataset, but higher precision was obtained by running models for a restricted range of CO3. A model calibrated using only on the independent regional dataset, was unable to accurately predict CO3 content for the more chemically diverse national dataset. Ten absorbance peaks enabling CO3 prediction by mid-infrared (MIR) spectroscopy were identified and evaluated for individual and combined predictive power. A single-band model derived from an absorbance peak centered at 1796 cm-yielded the lowest RMSEP of 13.5 g kg-1 for carbonate prediction compared to other single-band models. This predictive power is attributed to the strength and sharpness of the peak, and an apparent minimal overlap with confounding co-occurring spectral features of other soil components. Drawing from the 10 identified bands, multiple combinations of 3 or 4 peaks were able to predict CO3 content as well as the full-spectrum national models. Soil CO3 is an excellent example of a soil parameter that can be predicted with great effectiveness and generality, and MIR models could replace direct laboratory measurement as a lower cost, high quality alternative.

Knowledge, attitude, practice and preferences of contraceptive methods in udupi district, karnataka.

OBJECTIVE: To assess the knowledge, attitude, practice and preferences on contraceptive methods among the female population, to determine the association between knowledge and attitude on contraceptive methods with the variables. MATERIALS AND METHODS: A Descriptive survey of 136 females between 18-45 year of age were done using a structured knowledge questionnaire, structured attitude scale and opinionnaire on practice and preference during the month of January 2012 to February 2012 at Moodu Alevoor village, Udupi district, Karnataka. Simple random sampling was used to select the village and purposive sampling technique was used to select the sample. RESULTS: It was shown that 48.5% were of 26-35 years of age, 92% were Hindus, 45.6% had higher secondary education, 41.2% were house wives, 55.9% had family monthly income below 5000 rupees, 49.3% were from nuclear family, 64% were married between 19-25 years, 43.3% had 2-3 years of married life and 52.2% had one pregnancy. Majority (55.9%) had one living child and 98.5% got information through health personnel. Majority (67.60%) had moderate knowledge on contraceptive methods and 17.60% had high knowledge. Majority (87.50%) had favourable attitude and 12.50% had unfavourable attitude towards contraceptive methods. From the group of studied women 38.23% did not use any contraceptive methods, 19.85% used OCPs and minimum 1.47% used injection as contraceptive method. In this study 37.5% preferred OCPs as Rank 1, male condom (22.1%) as Rank 2 and injection (16.3%) as Rank 3. There was association between knowledge with educational status (χ(2) = 47.14, p = 0.001), occupation (χ(2) =15.81, p = 0.044), family monthly income (χ(2) =6.473, p = 0.039) and duration of marriage (χ(2)=6.721, p = 0.035). There was no association between attitude and the studied variables. CONCLUSION: The study showed that majority of the females had moderate knowledge and favourable attitude.