Reducing tillage frequency and intensity and returning crop residues to the soil are two agricultural management practices to increase soil organic carbon (SOC). Timing and rates of nitrogen fertilizer, coupled with t...
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Reducing tillage frequency and intensity and returning crop residues to the soil are two agricultural management practices to increase soil organic carbon (SOC). Timing and rates of nitrogen fertilizer, coupled with tillage methods and residue incorporation, can also influence nitrous oxide emissions. In this study, we used measured data from a long term (23 years) tillage and residue management experiment, under intensive grain corn production, together with the DNDC (DeNitrification-DeComposition) process-based model, to ascertain the most effective fertilizer and tillage management practices to reduce nitrous oxide emissions and nitrate leaching, and increase soil carbon stocks. The field experiment consisted of no-till [NT], reduced [RT], and conventional tillage [CT], with and without corn residue return. After calibration and validation, using field observed data, the DNDC model was run with 108 management scenarios (six N application rates, spring single N fertilization, spring split N fertilization and fall fertilization, three tillage practices and two residue-return options). N application rate was predicted to be a significant driver for corn yield, SOC sequestration, N2O emissions and nitrate leaching, indicating the need to identify a critical N rate to both optimize production and achieve sustainability goals. Split (vs. single) N application did not significantly affect corn yield, nitrate leaching, N2O emissions or SOC, while fall fertilization should be avoided due to greater nitrate leaching and higher N2O emissions as well as lower crop yield. Although stimulating N2O emissions, both NT and residue return are recommended for their ability to enhance SOC stocks. Results of this study can be used for policy development regarding carbon sequestration and GHG emissions for intensive grain cropping systems in the humid regions of North America.
Advances in sensing and computation have accelerated at unprecedented rates and scales, in turn creating new opportunities for natural resources managers to improve adaptive and predictive management practices by coup...
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Advances in sensing and computation have accelerated at unprecedented rates and scales, in turn creating new opportunities for natural resources managers to improve adaptive and predictive management practices by coupling large environmental datasets with machine learning (ML). Yet, to date, ML models often remain inaccessible to managers working outside of academic research. To identify challenges preventing natural resources managers from putting ML into practice more broadly, we convened a group of 23 stakeholders (i.e., applied researchers and practitioners) who model and analyze data collected from environmental and agricultural systems. Workshop participants shared many barriers regarding their perceptions of, and experiences with, ML modeling. These barriers emphasized three main areas of concern: ML model transparency, availability of educational resources, and the role of process-based understanding in ML model development. Informed by workshop participant input, we offer recommendations on how the ecological modelling community can overcome key barriers preventing ML model use in natural resources management and advance the profession towards data-driven decision-making.
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