Data and code for "Cover crop inclusion and residue retention improves soybean production and physiology in drought conditions" CONTEXT: Soybean (Glycine max (L.) Merr.) planting has increased in central and western North Dakota despite frequent drought occurrences that limit productivity. Soybean plants need high photosynthetic and transpiration rates to be productive, but they also need high water use efficiency when water is limited. Retaining crop residues and including cover crops in crop rotations are management strategies that could improve soybean drought resilience in the northern Great Plains. OBJECTIVE: We aimed to examine how a management practice that included cover crops and residue retention impacts agronomic, ecosystem water and carbon dioxide flux, and canopy-scale physiological attributes of soybeans in the northern Great Plains under drought conditions. METHODS: We compared two soybean fields over two years with business-as-usual and aspirational management that included residue retention and cover crops during a drought year. This comparison was based on yield, aboveground biomass, Phenocam images, and fluxes from eddy covariance and ancillary measurements. These measurements were used to derive meteorological, physical, and physiological attributes with the ‘big leaf’ framework. RESULTS: Soybean yields were 29% higher under drought conditions in the field managed in a system that included cover crops and residue retention. This yield increase was caused by extending the maturity phenophase by 5 days, increasing agronomic and intrinsic water use efficiency by 27% and 33%, respectively, increasing water uptake, and increasing the rubisco-limited photosynthetic capacity (Vcmax25) by 42%. CONCLUSIONS: The inclusion of cover crops and residue retention into a cropping system improved soybean productivity because of differences in water use, phenology timing, and photosynthetic capacity. IMPLICATIONS: These results suggest that farmers can improve soybean productivity and yield stability by incorporating cover crops and residue retention into their management practices because these practices allow soybean plants to shift to a more aggressive water uptake strategy. Data Half_Hourly.csv: Half hour data from eddy covariance towers Management.csv: data about field management Phenocamdata.csv: The output of 1_phenocam.Rmd code Predicted_Height_LAI.csv: The output of 3_Inferring_LAI_and_Height.Rmd Vegetation.csv: biomass and yield data Code 1_phenocam.rmd: Code to download Phenocam data and identify phenophase transition dates. 2_Daily_CO2_Water_Fluxes.Rmd: Code to analyze daily carbon and water fluxes (Figure 1, 2 3 and Table 2). 3_Inferring_LAI_and_Height.Rmd: Code to calculate the predicted LAI and height for each day. The output is used in the big-leaf framework. 4_Big_Leaf.Rmd: Code for the big-leaf ecophysiology estimates (Figure 4, 5 and 6; Table 3 and 4). 4_Data_Dictionary_Variables: Code to identify the data dictionary variables.
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