Search Results (2)

Effective residue management is crucial for maintaining soil organic carbon (SOC) in upland rice systems, particularly under diverse fertilization and planting management practices. This study investigates the impacts of residue management in upland rice fields using the CQESTR model through simulation of SOC dynamics over a 20-year period. The first 10 years served as a spin-up period for carbon pool stabilization in the model, followed by simulations under varying nitrogen (N) application rates and planting date management strategies. Experiments for various N application rates and planting times were conducted during 2018–2019 and 2019–2020. In 2019, 30% and in 2020, 100% of the residue was returned, and these data were used for evaluating model performance. Subsequently, we modeled predictions for residue retention levels of 100%, 70%, 50%, and 30% to assess their effects on SOC. The results indicated a good agreement between the simulated and observed data for model performance evaluation with an MSD value of 9.13. Lack of correlation (0.44) accounted for 5% of MSD, indicating a good agreement between the simulated and observed SOC values. The highest change in SOC was observed at 100% residue return under moderately delayed planting, potentially due to higher crop productivity and residue retention, and moderate climatic conditions. Reduced residue retention gradually declined the SOC stocks, especially under low N input. Delays in planting exacerbated negative impacts, possibly due to low crop productivity and reduced residue return. Despite the limited number of years of data and inconsistent management practices, the overall trends highlight the importance of residue retention under different N fertilization and planting management strategies. This research serves as a preliminary study for sustainable management practices to enhance long-term soil carbon sequestration in upland rice systems in southern Thailand. Long-term evaluations are necessary using the observed data and the CQESTR model application for applicable recommendations. Full article

Soil organic matter (SOM) stocks are crucial for soil fertility and food provision and also contribute to climate change adaptation and mitigation. However, assessing SOM changes in cropping systems is difficult due to the varying quantity and quality of input data. SOM processes have been described by several models, but these are complex and require high amounts of input data. In this work, we identified and selected frameworks that simulate SOM pools and stocks as well as the effects of different management practices. We also required that the frameworks be easily accessible for farm-related end users and require limited and accessible amounts of input data. In all, six frameworks met our inclusion criteria: SOCRATES (Soil Organic Carbon Reserves and Transformations in EcoSystems), CCB (CANDY and-Carbon Balance), AMG, CENTURY, CQESTR, and RothC (Rothamsted Carbon Model). We collected information on these frameworks and compared them in terms of their accessibility, the model time steps used, the nutrient cycles included in the simulation, the number of SOM pools, and the agricultural management options included. Our results showed that CCB was the most robust of the frameworks considered, while AMG, CQESTR, and RothC performed the least well. However, all frameworks have strengths which may match the specific requirements and abilities of individual users. Full article