Agricultural labor, COVID-19, and potential implications for food security and air quality in the breadbasket of India.

To contain the COVID-19 pandemic, India imposed a national lockdown at the end of March 2020, a decision that resulted in a massive reverse migration as many workers across economic sectors returned to their home regions. Migrants provide the foundations of the agricultural workforce in the 'breadbasket' states of Punjab and Haryana in Northwest India.There are mounting concerns that near and potentially longer-term reductions in labor availability may jeopardize agricultural production and consequently national food security. The timing of rice transplanting at the beginning of the summer monsoon season has a cascading influence on productivity of the entire rice-wheat cropping system. To assess the potential for COVID-related reductions in the agriculture workforce to disrupt production of the dominant rice-wheat cropping pattern in these states, we use a spatial ex ante modelling framework to evaluate four scenarios representing a range of plausible labor constraints on the timing of rice transplanting. Averaged over both states, results suggest that rice productivity losses under all delay scenarios would be low as compare to those for wheat, with total system productivity loss estimates ranging from 9%, to 21%, equivalent to economic losses of USD $674 m to $1.48 billion. Late rice transplanting and harvesting can also aggravate winter air pollution with concomitant health risks. Technological options such as direct seeded rice, staggered nursery transplanting, and crop diversification away from rice can help address these challenges but require new approaches to policy and incentives for change.

Identifying high-yield low-emission pathways for the cereal production in South Asia.

Increasing agricultural production to meet the growing demand for food whilst reducing agricultural greenhouse gas (GHG) emissions is the major challenge under the changing climate. To develop long-term policies that address these challenges, strategies are needed to identify high-yield low-emission pathways for particular agricultural production systems. In this paper, we used bio-physical and socio-economic models to analyze the impact of different management practices on crop yield and emissions in two contrasting agricultural production systems of the Indo-Gangetic Plain (IGP) of India. The result revealed the importance of considering both management and socio-economic factors in the development of high-yield low-emission pathways for cereal production systems. Nitrogen use rate and frequency of application, tillage and residue management and manure application significantly affected GHG emissions from the cereal systems. In addition, various socio-economic factors such as gender, level of education, training on climate change adaptation and mitigation and access to information significantly influenced the adoption of technologies contributing to high-yield low-emission pathways. We discussed the policy implications of these findings in the context of food security and climate change.

Evaluation of multiple satellite precipitation products for rainfed maize production systems over Vietnam.

High-resolution reliable rainfall datasets are vital for agricultural, hydrological, and weather-related applications. The accuracy of satellite estimates has a significant effect on simulation models in particular crop simulation models, which are highly sensitive to rainfall amounts, distribution, and intensity. In this study, we evaluated five widely used operational satellite rainfall estimates: CHIRP, CHIRPS, CPC, CMORPH, and GSMaP. These products are evaluated by comparing with the latest improved Vietnam-gridded rainfall data to determine their suitability for use in impact assessment models. CHIRP/S products are significantly better than CMORPH, CPC, and GsMAP with higher skill, low bias, showing a high correlation coefficient with observed data, and low mean absolute error and root mean square error. The rainfall detection ability of these products shows that CHIRP outperforms the other products with a high probability of detection (POD) scores. The performance of the different rainfall datasets in simulating maize yields across Vietnam shows that VnGP and CHIRP/S were capable of producing good estimates of average maize yields with RMSE ranging from 536 kg/ha (VnGP), 715 kg/ha (CHIRPS), 737 kg/ha (CHIRP), 759 kg/ha (GsMAP), 878 kg/ha (CMORPH) to 949 kg/ha (CPC). We illustrated that there is a potential for use of satellite rainfall estimates to overcome the issues of data scarcity in regions with sparse rain gauges.

A new two-decade (2001-2019) high-resolution agricultural primary productivity dataset for India.

The present study describes a new dataset that estimates seasonally integrated agricultural gross primary productivity (GPP). Several models are being used to estimate GPP using remote sensing (RS) for regional and global studies. Using biophysical and climatic variables (MODIS, SBSS, ECWMF reanalysis etc.) and validated by crop statistics, the present study provides a new dataset of agricultural GPP for monsoon and winter seasons in India for two decades (2001-2019). This dataset (GPPCY-IN) is based on the light use efficiency (LUE) principle and applied a dynamic LUE for each year and season to capture the seasonal variations more efficiently. An additional dataset (NGPPCY-IN) is also derived from crop production statistics and RS GPP to translate district-level statistics at the pixel level. Along with validation with crop statistics, the derived dataset was also compared with in situ GPP estimations. This dataset will be useful for many applications and has been created for estimating integrated yield loss by taking GPP as a proxy compared to resource and time-consuming field-based methods for crop insurance.

A high-resolution assessment of climate change impact on water footprints of cereal production in India.

Water footprint (WF), a comprehensive indicator of water resources appropriation, has evolved as an efficient tool to improve the management and sustainability of water resources. This study quantifies the blue and green WF of major cereals crops in India using high resolution soil and climatic datasets. A comprehensive modelling framework, consisting of Evapotranspiration based Irrigation Requirement (ETIR) tool, was developed for WF assessment. For assessing climate change impact on WF, multi-model ensemble climate change scenarios were generated using the hybrid-delta ensemble method for RCP4.5 and RCP6.0 and future period of 2030s and 2050s. The total WF of the cereal crops are projected to change in the range of - 3.2 to 6.3% under different RCPs in future periods. Although, the national level green and blue WF is projected to change marginally, distinct trends were observed for Kharif (rainy season-June to September) and rabi (winter season-October to February) crops. The blue WF of paddy is likely to decrease by 9.6%, while for wheat it may increase by 4.4% under RCP4.5 during 2050s. The green WF of rabi crops viz. wheat and maize is likely to increase in the range of 20.0 to 24.1% and 9.9 to 16.2%, respectively. This study provides insights into the influences of climate change on future water footprints of crop production and puts forth regional strategies for future water resource management. In view of future variability in the WFs, a water footprint-based optimization for relocation of crop cultivation areas with the aim of minimising the blue water use would be possible management alternative.

Cost-effective opportunities for climate change mitigation in Indian agriculture.

Long-term changes in average temperatures, precipitation, and climate variability threaten agricultural production, food security, and the livelihoods of farming communities globally. Whilst adaptation to climate change is necessary to ensure food security and protect livelihoods of poor farmers, mitigation of greenhouse gas (GHG) emissions can lessen the extent of climate change and future needs for adaptation. Many agricultural practices can potentially mitigate GHG emissions without compromising food production. India is the third largest GHG emitter in the world where agriculture is responsible for 18% of total national emissions. India has identified agriculture as one of the priority sectors for GHG emission reduction in its Nationally Determined Contributions (NDCs). Identification of emission hotspots and cost-effective mitigation options in agriculture can inform the prioritisation of efforts to reduce emissions without compromising food and nutrition security. We adopted a bottom-up approach to analyse GHG emissions using large datasets of India's 'cost of cultivation survey' and the '19th livestock census' together with soil, climate and management data for each location. Mitigation measures and associated costs and benefits of adoption, derived from a variety of sources including the literature, stakeholder meetings and expert opinion, were presented in the form of Marginal Abatement Cost Curves (MACC). We estimated that by 2030, business-as-usual GHG emissions from the agricultural sector in India would be 515 Megatonne CO2 equivalent (MtCO2e) per year with a technical mitigation potential of 85.5 MtCO2e per year through adoption of various mitigation practices. About 80% of the technical mitigation potential could be achieved by adopting only cost-saving measures. Three mitigation options, i.e. efficient use of fertilizer, zero-tillage and rice-water management, could deliver more than 50% of the total technical abatement potential.