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This study aimed to analyze rice straw alternative managements to maximize the rice-soybean succession yield and to quantify their impacts on soil properties. The experiment was set up in a completely randomized design with six replicates. Nine rice straw managements were evaluated: straw burning (M1), straw harvesting (M2), straw incorporation with disc harrow and two passes (M3) or three passes (M4) of leveling disc harrow, straw incorporation with knife-roller and soybean no-tillage (M5), straw incorporation with knife-roller and two passes (M6) or three passes (M7) of leveling disc harrow, straw incorporation with two passes of knife-roller and two passes (M8) or three passes (M9) of leveling disc harrow. Straw incorporation by knife-roller provided the lowest soil organic matter (SOM) contents due to slow mineralization of fresh straw incorporated shallow into the soil, although SOM increased in the surface layer related to the initial content. Rice-soybean succession yield was more affected by the physical than by the soil chemical properties. The straw incorporation with one pass of knife-roller and two passes of leveling disc harrow (M6) can replace the straw burning, without affecting the rice-soybean succession yield and soil physical quality, with a reduction in machinery operations, allowing early soil tillage. Made available in DSpace on 2022-08-23T11:20:02Z (GMT). No. of bitstreams: 1 rc-2022.pdf: 351346 bytes, checksum: 084e3820154a362375a0dc1d9c8e46f3 (MD5) Previous issue date: 2022

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.