RESUMEN
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.
RESUMEN
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.