Nitrogen dioxide as proxy indicator of air pollution from fossil fuel burning in New Delhi during lockdown phases of COVID-19 pandemic period: impact on weather as revealed by Sentinel-5 precursor (5p) spectrometer sensor.

There has been a long-lasting impact of the lockdown imposed due to COVID-19 on several fronts. One such front is climate which has seen several implications. The consequences of climate change owing to this lockdown need to be explored taking into consideration various climatic indicators. Further impact on a local and global level would help the policymakers in drafting effective rules for handling challenges of climate change. For in-depth understanding, a temporal study is being conducted in a phased manner in the New Delhi region taking NO2 concentration and utilizing statistical methods to elaborate the quality of air during the lockdown and compared with a pre-lockdown period. In situ mean values of the NO2 concentration were taken for four different dates, viz. 4th February, 4th March, 4th April, and 25th April 2020. These concentrations were then compared with the Sentinel (5p) data across 36 locations in New Delhi which are found to be promising. The results indicated that the air quality has been improved maximum in Eastern Delhi and the NO2 concentrations were reduced by one-fourth than the pre-lockdown period, and thus, reduced activities due to lockdown have had a significant impact. The result also indicates the preciseness of Sentinel (5p) for NO2 concentrations.

Model-based ensembles: Lessons learned from retrospective analysis of COVID-19 infection forecasts across 10 countries.

Mathematical models of different types and data intensities are highly used by researchers, epidemiologists, and national authorities to explore the inherently unpredictable progression of COVID-19, including the effects of different non-pharmaceutical interventions. Regardless of model complexity, forecasts of future COVID-19 infections, deaths and hospitalization are associated with large uncertainties, and critically depend on the quality of the training data, and in particular how well the recorded national or regional numbers of infections, deaths and recoveries reflect the the actual situation. In turn, this depends on, e.g., local test and abatement strategies, treatment capacities and available technologies. Other influencing factors including temperature and humidity, which are suggested by several authors to affect the spread of COVID-19 in some countries, are generally only considered by the most complex models and further serve to inflate the uncertainty. Here we use comparative and retrospective analyses to illuminate the aggregated effect of these systematic biases on ensemble-based model forecasts. We compare the actual progression of active infections across ten of the most affected countries in the world until late November 2020 with "re-forecasts" produced by two of the most commonly used model types: (i) a compartment-type, susceptible-infected-removed (SIR) model; and (ii) a statistical (Holt-Winters) time series model. We specifically examine the sensitivity of the model parameters, estimated systematically from different subsets of the data and thereby different time windows, to illustrate the associated implications for short- to medium-term forecasting and for probabilistic projections based on (single) model ensembles as inspired by, e.g., weather forecasting and climate research. Our findings portray considerable variations in forecasting skill in between the ten countries and demonstrate that individual model predictions are highly sensitive to parameter assumptions. Significant skill is generally only confirmed for short-term forecasts (up to a few weeks) with some variation across locations and periods.

Potential of conservation agriculture modules for energy conservation and sustainability of rice-based production systems of Indo-Gangetic Plain region.

Rice-based cropping systems are the most energy-intensive production systems in South Asia. Sustainability of the rice-based cropping systems is nowadays questioned with declining natural resource base, soil degradation, environmental pollution, and declining factor productivity. As a consequence, the search for energy and resource conservation agro-techniques is increasing for sustainable and cleaner production. Conservation agriculture (CA) practices have been recommended for resource conservation, soil health restoration and sustaining crop productivity. The present study aimed to assess the different CA modules in rice-based cropping systems for energy conservation, energy productivity, and to define energy-economic relations. A field experiment consisted of four different tillage-based crop establishment practices (puddled-transplanted rice followed by (fb) conventional-till maize/wheat (CTTPR-CT), non-puddled transplanted rice fb zero-till maize/wheat (NPTPR-ZT), zero-till transplanted rice fb zero-till maize/wheat (ZTTPR-ZT), zero-till direct-seeded rice fb zero-till maize/wheat (ZTDSR-ZT)), with two residue management treatments (residue removal, residue retention) in rice-wheat and rice-maize rotations were evaluated for energy budgeting and energy-economic relations. Conservation-tillage treatments (NPTPR-ZT, ZTTPR-ZT, and ZTDSR-ZT) reduced the energy requirements over conventional tillage treatments, with the greater reduction in ZTTPR-ZT and ZTDSR-ZT treatments. Savings of energy in conservation-tillage treatments were attributed to reduced energy use in land preparation (69-100%) and irrigation (23-27%), which consumed a large amount of fuel energy. Conservation-tillage treatments increased grain and straw/stover yields of crops, eventually increased the output energy (6-16%), net energy (14-26%), energy ratio (25-33%), and energy productivity (23-34%) as compared with CTTPR-CT. For these energy parameters, the treatment order was ZTDSR-ZT ≥ ZTTPR-ZT > NPTPR-ZT > CTTPR-CT (p < 0.05). Crop residue retention reduced net energy, energy ratio, and energy productivity when compared with residue removal. Our results of energy-economic relations favored the "conservative hypothesis," which envisages that energy and monetary investments are not essentially the determinants of crop productivity. Thus, zero tillage-based crop establishments (ZTTPR-ZT, ZTDSR-ZT) in rice-based production systems could be the sustainable alternative to conventional tillage-based agriculture (CTTPR-CT) as they conserved non-renewable energy sources, reduced water requirement, and increased crop productivity.

Short-Term Statistical Forecasts of COVID-19 Infections in India.

COVID-19 cases in India have been steadily increasing since January 30, 2020 and have led to a government-imposed lockdown across the country to curtail community transmission with significant impacts on societal systems. Forecasts using mathematical-epidemiological models have played and continue to play an important role in assessing the probability of COVID-19 infection under specific conditions and are urgently needed to prepare health systems for coping with this pandemic. In many instances, however, access to dedicated and updated information, in particular at regional administrative levels, is surprisingly scarce considering its evident importance and provides a hindrance for the implementation of sustainable coping strategies. Here we demonstrate the performance of an easily transferable statistical model based on the classic Holt-Winters method as means of providing COVID-19 forecasts for India at different administrative levels. Based on daily time series of accumulated infections, active infections and deaths, we use our statistical model to provide 48-days forecasts (28 September to 15 November 2020) of these quantities in India, assuming little or no change in national coping strategies. Using these results alongside a complementary SIR model, we find that one-third of the Indian population could eventually be infected by COVID-19, and that a complete recovery from COVID-19 will happen only after an estimated 450 days from January 2020. Further, our SIR model suggests that the pandemic is likely to peak in India during the first week of November 2020.

Impact of conservation tillage in rice-based cropping systems on soil aggregation, carbon pools and nutrients.

Tillage intensive cropping practices have deteriorated soil physical quality and decreased soil organic carbon (SOC) levels in rice-growing areas of South Asia. Consequently, crop productivity has declined over the years demonstrating the need for sustainable alternatives. Given that, a field experiment was conducted for six years to assess the impact of four tillage based crop establishment treatments [puddled transplant rice followed by conventional tillage in wheat/maize (CTTPR-CT), non-puddled transplant rice followed by zero-tillage in wheat/maize (NPTPR-ZT), zero-till transplant rice followed by zero-tillage in wheat/maize (ZTTPR-ZT), zero-tillage direct seeded rice followed by zero-tillage in wheat/maize (ZTDSR-ZT)], two residue management treatments [residue removal, residue retention (~33%)], and two cropping systems [rice-wheat, rice-maize] on soil aggregation, carbon pools, nutrient availability, and crop productivity. After six years of rotation, in top 0.2 m soil depth, zero-till crop establishment treatments (ZTTPR-ZT and ZTDSR-ZT) had higher (p < 0.05) total organic carbon (TOC) over conventional tillage treatment (CTTPR-CT). Zero-till crop establishment treatments increased very-labile C faction (Cfrac 1) by 21% followed by labile fraction (Cfrac 2) (16%), non-labile fraction (Cfrac 4) (13%) and less-labile fraction (Cfrac 3) (7%). Notably, higher passive C-pool in conservation tillage practices over CTTPR-CT suggests that conservation tillage could stabilize the recalcitrant form of carbon that persists longer in the soil. Meantime, zero-till crop establishment treatments had higher (p < 0.05) water stable macro-aggregates, macro-aggregates: micro-aggregates ratio and aggregate carbon content over CTTPR-CT. The treatment NPTPR-ZT significantly increased soil quality parameters over CTTPR-CT. However, the effect was not as prominent as that of ZTTPR-ZT and ZTDSR-ZT. Retention of crop residue increased (p < 0.05) TOC (12%) and soil available nutrients mainly available-P (16%), followed by available-K (12%), DTPA-extractable Zn (11%), and available-S (6%) over residue removal treatment. The constructive changes in soil properties following conservation tillage and crop residue retention led to increased crop productivity over conventional CTTPR-CT. Therefore, conservation tillage (particularly ZTTPR-ZT and ZTDSR-ZT) and crop residue retention could be recommended in tropical rice-based cropping systems for improving soil quality and production sustainability.

Agronomic improvements can make future cereal systems in South Asia far more productive and result in a lower environmental footprint.

South Asian countries will have to double their food production by 2050 while using resources more efficiently and minimizing environmental problems. Transformative management approaches and technology solutions will be required in the major grain-producing areas that provide the basis for future food and nutrition security. This study was conducted in four locations representing major food production systems of densely populated regions of South Asia. Novel production-scale research platforms were established to assess and optimize three futuristic cropping systems and management scenarios (S2, S3, S4) in comparison with current management (S1). With best agronomic management practices (BMPs), including conservation agriculture (CA) and cropping system diversification, the productivity of rice- and wheat-based cropping systems of South Asia increased substantially, whereas the global warming potential intensity (GWPi) decreased. Positive economic returns and less use of water, labor, nitrogen, and fossil fuel energy per unit food produced were achieved. In comparison with S1, S4, in which BMPs, CA and crop diversification were implemented in the most integrated manner, achieved 54% higher grain energy yield with a 104% increase in economic returns, 35% lower total water input, and a 43% lower GWPi. Conservation agriculture practices were most suitable for intensifying as well as diversifying wheat-rice rotations, but less so for rice-rice systems. This finding also highlights the need for characterizing areas suitable for CA and subsequent technology targeting. A comprehensive baseline dataset generated in this study will allow the prediction of extending benefits to a larger scale.