Rice Management Decisions Using Process-Based Models With Climate-Smart Indicators

Abstract

Irrigation strategies are keys to fostering sustainable and climate-resilient rice production by increasing efficiency, building resilience and reducing Greenhouse Gas (GHG) emissions. These strategies are aligned with the Climate-Smart Agriculture (CSA) principles, which aim to maximize productivity whilst adapting to and mitigating climate change. Achieve such mitigation, adaptation, and productivity goals- to the extent possible- is described as climate smartness. Measuring climate smartness is challenging, with recent progress focusing on the use of agronomic indicators in a limited range of contexts. One way to broaden the ability to measure climate-smartness is to use modeling tools, expanding the scope of climate smartness assessments. Accordingly, and as a proof-of-concept, this study uses modeling tools with CSA indicators (i.e., Greenhouse Intensity and Water Productivity) to quantify the climate-smartness of irrigation management in rice and to assess sensitivity to climate. We focus on a field experiment that assessed four irrigation strategies in tropical conditions, Continuous Flooding (CF), Intermittent Irrigation (II), Intermittent Irrigation until Flowering (IIF), and Continuous soil saturation (CSS). The DNDC model was used to simulate rice yields, GHG emissions and water inputs. We used model outputs to calculate a previously developed Climate-Smartness Index (CSI) based on water productivity and greenhouse gas intensity, which score on a scale between−1 (lack of climate-smartness) to 1 (high climate smartness) the climate-smartness of irrigation strategies. The CSS exhibited the highest simulation-based CSI, and CF showed the lowest. A sensitivity analysis served to explore the impacts of climate on CSI. While higher temperatures reduced CSI, rainfall mostly showed no signal. The climate smartness decreasing in warmer temperatures was associated with increased GHG emissions and, to some extent, a reduction in Water Productivity (WP). Overall, CSI varied with the climate-management interaction, demonstrating that climate variability can influence the performance of CSA practices. We conclude that combining models with climate-smart indicators can broaden the CSA-based evidence and provide reproducible research findings. The methodological approach used in this study can be useful to fill gaps in observational evidence of climate-smartness and project the impact of future climates in regions where calibrated crop models perform well.

Phenology, gas exchange, biomass accumulation, and irrigated rice yield under alternative irrigation managements

“…This irrigation management approach is also documented in the study by Arenas‐Calle et al. (2022). Four cultivars (BRS A702 CL, BRS Catiana, BRS Pampeira, and IRGA 424) were allocated to the subplots.…”

“…The cumulative average water application for each irrigation management strategy in the two seasons was 32,436;1,413; 17,039; and 23,104 m 3 ha −1 for CFC, IFA, IFC, and IFF, respectively. This irrigation management approach is also documented in the study by Arenas-Calle et al (2022). Four cultivars (BRS A702 CL, BRS Catiana, BRS Pampeira, and IRGA 424) were allocated to the subplots.…”

“…According to Arenas‐Calle et al. (2022), this practice has the potential to reduce water consumption by up to 20% compared to conventional methods. Previous studies have consistently reported similar yields when comparing continuous and intermittent flooding techniques (Avila et al., 2015; Lampayan et al., 2015; Tarlera et al., 2016).…”

“…This approach entails cyclic fluctuations between irrigation and brief withholding of water without the complete drainage of fields, allowing the natural hydrological cycle to occur. According to Arenas-Calle et al (2022), this practice has the potential to reduce water consumption by up to 20% compared to conventional methods. Previous studies have consistently reported similar yields when comparing continuous and intermittent flooding techniques (Avila et al, 2015;Lampayan et al, 2015;Tarlera et al, 2016).…”

The fate of rice crop residues and context-dependent greenhouse gas emissions: Model-based insights from Eastern India

“…Long-term adoption (i.e., 100 years) is required for sequestration permanence, and net GHG balance can shift over time, even from a sink to a source ( Lugato et al, 2018 ). DNDC has been used in other studies to simulate biogeochemical processes and GHG fluxes in rice-based systems ( Arenas-Calle et al, 2022 ; Babu et al, 2006 ; Zhu et al, 2019 ) and also to characterize nutrient cycling in the rice-wheat systems of India ( Pathak et al, 2006 ). Wang et al (2020) reported a correlation (R) between the observed and simulated CH4 of 0.90 (p < 0.01).…”