Carbon footprint and water footprint of rice and wheat production in Punjab, India

Highlights

• Nitrogen fertilizer use is the greatest hotspot for mitigation.

• Geoclimatic factors do not majorly affect CF variation, management practices do.

• Disparity between CROPWAT estimates and actual irrigation water use is established.

• Solely using theoretical WF values can lead to wrong conclusions and inappropriate policies.

• Government role is crucial in mitigating CF and WF by revamping policies.

Abstract

Carbon footprint and water footprint assessments can be powerful tools to guide sustainable food production systems. The present study simultaneously quantified the carbon footprint (CF) and water footprint (WF) of rice and wheat production in the five agro-climatic zones of Punjab, India using farm survey data. Further, the variability in CF among the five agro-climatic zones and farm sizes was analysed. The carbon footprint per unit area of rice and wheat was found to be 8.80 ± 5.71 and 4.18 ± 1.13 t CO₂eq/ha respectively. The CF per tonne of rice and wheat was 1.20 ± 0.70 and 0.83 ± 0.23 t CO₂eq/t respectively. Large farms had 39% lower CF per tonne of rice compared to small farms. Residue burning, direct methane emissions and fertilizer use were the most important factors that contributed to the CF of rice and wheat production in Punjab. Nitrogen fertilizer use was identified as the major hotspot for mitigation. The average WF of rice and wheat was found to be 1097 and 871 m³/t respectively. A disparity between CROPWAT estimates of blue WF and actual blue water use was established indicating the need for actual blue WF accounting, particularly for flood irrigated crop production. Additionally, policy measures based on ground situation are discussed and the major role of local government policies in mitigating carbon and water footprint is highlighted.

Introduction

Food production has large impacts on both global warming and freshwater consumption. The agriculture, forest and other land use (AFOLU) sector, which accounted for 24% of the global greenhouse gas (GHG) emissions in 2010, is the second largest sectoral GHG emitter (Smith et al., 2014). Agriculture is also the largest freshwater user accounting for 70% of global freshwater withdrawals, and 95% of water withdrawals in some developing countries (FAO, 2017). India, a leading food producer and predominantly agrarian nation, is the third largest GHG emitter (WRI, 2014), and a severely water-stressed country. It is among the 17 highly water-stressed countries in the world, with three times higher population than the combined population of all the other 16 countries (WRI, 2019). With its growing population, India is facing the twin challenge of increasing food production, given its limited water resources, while mitigating the associated GHG emissions.

Carbon footprint (CF) and water footprint (WF) are two widely used ‘pressure indicators’ that measure the human use of natural resources and the anthropogenic emissions; and can be used to direct mitigation policy (Ercin and Ertug Hoekstra, 2012). Few studies have utilised farm survey data for CF assessments (Yan et al., 2015; Arunrat et al., 2016; Zhang et al., 2017; Tahmasebi et al., 2018); and studies reporting WF of crops using farm level data are negligible (Cao et al., 2018; Ghosh and Chakma, 2019). Previous estimates of GHG emissions from crop production using life cycle assessment (LCA) approach in India were also based on secondary data sources (Pathak et al., 2010; Vetter et al., 2017; Benbi, 2018). Moreover, CF accounting at a high resolution, including crop management practices, like residue burning, has not been reported earlier in any of the LCA based study from India. Variability in CF among agro-climatic regions or farm sizes has not been studied previously in India. It is important to study regional variations to identify the hotspots across regions so that mitigation efforts can be specifically and effectively targeted. In this regard, primary data can capture the variability that is lost when statistical data is used (Schafer and Blanke, 2012). Based on first-hand information, survey-based studies reflect the existing regional diversity in farming practices that can be critical in environmental assessments (Zhang et al., 2017; Tahmasebi et al., 2018). This becomes significant in case of production of staple crops like rice and wheat that are produced in large quantities.

The study region, Punjab, also known as ‘the granary of India’ is the highest contributor to the central pool of grains in India (Government of India, 2018). It contributed 25.5% and 35.5% respectively in the central pool of rice and wheat during 2018–19 (ENVIS, 2020). The high productivity of the region was made possible due to introduction of irrigation, high yield variety (HYV) seeds, and consequent higher inputs of fertilizers and chemicals (Sarkar and Das, 2014). However, post green revolution, this has caused the over-exploitation of natural resources to the extent that sustainability of the present farming system of Punjab is uncertain unless major steps are taken towards improvement (Hira, 2009; Benbi, 2018).

Rice and wheat have the largest blue water footprints among crops (Mekonnen and Hoekstra, 2011). Water consumption is directly linked with the GHG emissions of groundwater irrigation, and therefore, there is an opportunity for concurrent mitigation of both. In Punjab, 99.3% of the gross cropped area is irrigated; of this, groundwater irrigation accounts for 71% (Statistical Abstracts of Punjab, 2019). Rice cultivation has expanded into non-suitable porous and coarse soils solely due to irrigation (Chauhan et al., 2012). Therefore, simultaneous assessment of CF and WF, based on farmer inputs, has the potential to reveal key insights such as the disparity between theoretical and actual water consumption. This disparity can be critical in policy framing. This study simultaneously estimated the CF and WF of crop production using farm survey data. CF and WF was previously estimated for tomato (Page et al., 2012) and pumpkin (Schafer and Blanke, 2012) using primary data of crop production. To the best of our knowledge, no such studies on grain production have been attempted globally. Therefore, the present study was planned to address this gap by quantifying both CF and WF of rice and wheat using farm survey data. It also aimed to analyse the variability in CF of different agroclimatic zones and farm sizes; assess the contribution of different inputs and suggest policy measures for mitigation of GHG emissions and water consumption from agriculture in a local as well as global context.