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Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The IITM-ESM climate model, released in 2015, includes the following components: aerosol: prescribed MAC-v2, atmos: IITM-GFSv1 (T62L64, Linearly Reduced Gaussian Grid; 192 x 94 longitude/latitude; 64 levels; top level 0.2 mb), land: NOAH LSMv2.7.1, ocean: MOM4p1 (tripolar, primarily 1deg; 360 x 200 longitude/latitude; 50 levels; top grid cell 0-10 m), ocnBgchem: TOPAZv2.0, seaIce: SISv1.0. The model was run by the Centre for Climate Change Research, Indian Institute of Tropical Meteorology Pune, Maharashtra 411 008, India (CCCR-IITM) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

Microalgae offer a great potential to contribute significantly as renewable fuels and documented as a promising platform for algae-based bio refineries. They provide solutions to mitigate the environmental concerns posed by conventional fuel sources; however, the production of microalgal biofuels in large scale production system encounters few technical challenges. High quantity of nutrients requirements and water cost constrain the scaling up microalgal biomass to large scale commercial production. Crop protection against biomass losses due to grazers or pathogens is another stumbling block in microalgal field cultivation. With our existing technologies, unless coupled with high-value or mid-value products, algal biofuel cannot reach the economic target. Many microalgal industries that started targeting biofuel in the last decade had now adopted parallel business plans focusing on algae by-products application as cosmetic supplements, nutraceuticals, oils, natural color, and animal feed. This review provides the current status and proposes a framework for key supply demand, challenges for cost-effective and sustainable use of water and nutrient. Emphasis is placed on the future industrial market status of value added by products of microalgal biomass. The cost factor for biorefinery process development needs to be addressed before its potential to be exploited for various value-added products with algal biofuel.

SummaryThe adaptation of extra short duration (ESD) pigeonpea (Cajanus cajan) genotypes to rainfed environments was studied on Alfisols and Vertisols at the ICRISAT Center between 1987 and 1989. Despite a slightly shorter crop duration, the grain yield of ESD genotypes was twice as large on Alfisols as on Vertisols. On both soil types, the rate of growth and grain yield were better in crops sown on time than in those where sowing was delayed. The population levels necessary to maximize yield varied among genotypes on Alfisols, where the grain yield of several ESD genotypes compared favourably with that of ICPL 87, a standard short duration genotype. However, none of the ESD genotypes yielded more than ICPL 87 on the Vertisols.

This chapter focuses on the evaluation of adaptive capacities of community-level human systems related to agriculture and food security. It highlights findings regarding approaches and domains to monitor and evaluate behavioral changes from CGIAR’s research program on climate change, agriculture and food security (CCAFS). This program, implemented in five West African countries, is intended to enhance adaptive capacities in agriculture management of natural resources and food systems. In support of participatory action research on climate-smart agriculture, a monitoring and evaluation plan was designed with the participation of all stakeholders to track changes in behavior of the participating community members. Individuals’ and groups’ stories of changes were collected using most significant change tools. The collected stories of changes were substantiated through field visits and triangulation techniques. Frequencies of the occurrence of characteristics of behavioral changes in the stories were estimated. The results show that smallholder farmers in the intervention areas adopted various characteristics of behavior change grouped into five domains: knowledge, practices, access to assets, partnership and organization. These characteristics can help efforts to construct quantitative indicators of climate change adaptation at local level. Further, the results suggest that application of behavioral change theories can facilitate the development of climate change adaptation indicators that are complementary to indicators of development outcomes. We conclude that collecting stories on behavioral changes can contribute to biophysical adaptation monitoring and evaluation.

Project: High Resolution dynamically downscaled CMIP5 climate data over India - The datasets in this project are developed as a part of a project associated with the Indian Institute of Technology Delhi, India, and the National Mission for Clean Ganga, Government of India. We have dynamically downscaled a coarser resolution CMIP5 GCM (CESMv1) climate data using the Weather Research and Forecasting (WRF) model for the current (2006-2015) and future (2091-2100) RCP8.5 emission scenario to produce a 10km resolution dataset over India. This dataset is expected to be of massive value to fine-scale regional modelling based climate change adaptive and mitigative studies in fields of water resources, energy, agriculture, and forestry over India. This project is financially supported by the National Mission for Clean Ganga (NMCG), Ministry of Jal Shakti, Department of Water Resources, River Development and Ganga Rejuvenation, Government of India, through grant number TE-16015/02/2019/NMCG. Summary: The Bias Corrected CESMv1 data for mid-century (2041-2050) for RCP8.5 emission scenario at coarser resolution has been downscaled to 10km resolution over India using the Weather Research and Forecasting (WRF) model. The climate variables included are 2m Temperature (t2m), relative humidity (rh), wind speed (wspd), total precipitation (prec), mean surface shortwave flux (sw), top-of-atmosphere outgoing longwave radiation (lw), mean surface latent (lhf) and sensible (shf) heat fluxes along with the latitude, longitude, and time information. The dataset covers the Indian National Territory region at a 369 x 369 grid. The data is available at three temporal resolutions: Daily TS, Monthly TS, and Monthly Climatology. The dataset has been structured into a total of 30 files (10 variables x 3 temporal resolutions) packed in self-explanatory NetCDF format. The daily, monthly, and monthly climatology files contain 369x369x3650, 369x369x30, and 369x369x12 data points, respectively. The entire dataset is about 30 GB in size. The precipitation files in the older version contained hourly accumulated values for every day. This version contains the correct daily accumulated, monthly accumulated and monthly climatology precipitation data.

The impacts of increasing natural climate disasters are threatening food security in the Asia-Pacific region. Rice is Asia’s most important staple food. Climate variability and change directly impact rice production, through changes in rainfall, temperature and CO2 concentrations. The key for sustainable rice crop is water management. Adaptation can occur through shifts of cropping to higher latitudes and can profit from river systems (via irrigation) so far not considered. New opportunities arise to produce more than one crop per year in cooler areas. Asian wheat production in 2005 represents about 43 % of the global total. Changes in agronomic practices, such as earlier plant dates and cultivar substitution will be required. Fisheries play a crucial role in providing food security with the contribution of fish to dietary animal protein being very high in the region – up to 90 % in small island developing states (SIDS). With the warming of the Pacific and Indian Oceans and increased acidification, marine ecosystems are presently under stress. Despite these trends, maintaining or enhancing food production from the sea is critical. However, future sustainability must be maintained whilst also securing biodiversity conservation. Improved fisheries management to address the existing non-climate threats remains paramount in the Indian and Pacific Oceans with sustainable management regimes being established. Climate-related impacts are expected to increase in magnitude over the coming decades, thus preliminary adaptation to climate change is valuable.

The basins of the Indus and Ganges rivers cover 2.20 million km2 and are inhabited by more than a billion people. The region is under extreme pressures of population and poverty, unregulated utilization of the resources and low levels of productivity. The needs are: (1) development policies that are regionally differentiated to ensure resource sustainability and high productivity; (2) immediate development and implementation of policies for sound groundwater management and energy use; (3) improvement of the fragile food security and to broaden its base; and (4) policy changes to address land fragmentation and improved infrastructure. Meeting these needs will help to improve productivity, reduce rural poverty and improve overall human development.

Biomass pretreatment incurs 40% of the overall cost of biorefinery operations. The usage of mushroom cultivation as a pretreatment/delignification technique, and bio-ethanol production from spent mushroom substrates, after subsequent pretreatment, saccharification and fermentation processes, have been reported earlier. However, the present pilot-scale, entirely-organic demonstration is one of the very first biorefinery models, which efficiently consolidates: biomass pretreatment; in-situ cellulase production and saccharification; mushroom cultivation, thereby improving the overall operational economy. During pretreatment, the oyster mushroom, Pluerotus florida VS-6, matures into distinct substrate mycelia and fruiting bodies. Consequential variations in the kinetics of growth, biomass degradation/substrate utilization, oxygen uptake and transfer rates, and enzyme production, have been analyzed. Signifying the first-time usage of a biomass mixture, comprising vegetative waste and e-commerce packaging waste, the 30 day-long, bio-economical, non-inhibitor-generating, catabolite repression-limited, solid-state in-situ pretreatment-cum-saccharification, resulted in: 78% lignin degradation; 13.25% soluble-sugar release; 18.25% mushroom yield; 0.88 FPU/g.ds cellulase secretion. The in-situ saccharified biomass, when sequentially subjected to ex-situ enzymatic hydrolysis and fermentation, showed 37.35% saccharification, and a bio-ethanol yield of 0.425 g per g of glucose, respectively. Apart from yielding engine-ready bio-ethanol, the model doubles as an agripreneurial proposition, and encourages mushroom cultivation and consumption.