Dataset for "Cover crop inclusion and residue retention improves soybean production and physiology in drought conditions"

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.

数据集描述翻译：

数据与代码集“混种覆盖作物与残留物保留提升干旱条件下大豆产量与生理机能”

背景：尽管在北达科他州中部和西部地区频繁发生干旱，大豆（Glycine max (L.) Merr.）的种植面积仍在增加。大豆植株需具备高光合作用和蒸腾速率以实现高产，但在水资源有限的情况下，它们同样需要较高的水分利用效率。保留作物残留物并在轮作中混种覆盖作物是提高北部大平原大豆干旱抗逆性的管理策略。

目标：本研究旨在探讨在干旱条件下，混种覆盖作物与残留物保留这一管理措施如何影响大豆在北部大平原的农学、生态系统水分与二氧化碳通量，以及冠层尺度的生理属性。

方法：我们对两年的两个大豆田进行了比较，分别基于常规管理与包含残留物保留和覆盖作物的理想化管理。这一比较基于产量、地上生物量、Phenocam图像以及涡度协方差和辅助测量得到的通量。这些测量数据被用于通过‘大叶’框架推导出气象、物理和生理属性。

结果：在包含覆盖作物和残留物保留的系统中管理的大豆田在干旱条件下产量提高了29%。这一产量的增加归因于成熟期物候相延长了5天，农学水分利用效率和内在水分利用效率分别提高了27%和33%，增加了水分吸收，以及将鲁宾斯二氧化碳限制的光合能力（Vcmax25）提高了42%。

结论：将覆盖作物和残留物保留纳入耕作体系，通过改变水分利用、物候时间表和光合能力，提高了大豆的生产力。

启示：这些结果表明，农民可以通过将覆盖作物和残留物保留纳入其管理实践来提高大豆的生产力和产量稳定性，因为这些做法使大豆植株能够转向更为激进的吸水策略。

数据：
Half_Hourly.csv：涡度协方差塔的半小时数据
Management.csv：关于田间管理的数据
Phenocamdata.csv：1_phenocam.Rmd代码的输出
Predicted_Height_LAI.csv：3_Inferring_LAI_and_Height.Rmd的输出
Vegetation.csv：生物量和产量数据
代码：
1_phenocam.rmd：下载Phenocam数据并识别物候相转换日期的代码
2_Daily_CO2_Water_Fluxes.Rmd：分析每日碳和水通量的代码（图1、2、3和表2）
3_Inferring_LAI_and_Height.Rmd：计算每日预测的LAI和高度的代码。该输出用于大叶框架
4_Big_Leaf.Rmd：大叶生态生理估算的代码（图4、5和6；表3和4）
4_Data_Dictionary_Variables：识别数据字典变量的代码。