DOI: 10.6086/d1c10t
title: Data from: Water-conscious management strategies reduce per-yield irrigation and soil emissions of CO2, N2O, and NO in high-temperature forage cropping systems.

description: Agriculture produces large emissions of carbon dioxide (CO2), nitrous
 oxide (N2O), and nitric oxide (NO), especially in high-temperature
 agroecosystems, where management approaches for reducing these emissions
 are needed. A promising management solution to increase water infiltration
 and reduce trace gas emissions is subsurface drip irrigation, a method
 which increases rhizosphere access to water and nitrogenous fertilizers.
 In a multi-year field study, we compared per-yield irrigation and soil
 emissions for flood- and drip-irrigated field plots in southern California
 during two seasons and between two forage crops differing in fertilizer
 requirements: alfalfa (Medicago sativa L.) and sudangrass (Sorghum bicolor
 ssp. Sudanese). We monitored soil climate and emission responses to
 irrigation using a custom array of automated chambers connected to trace
 gas analyzers that measured gas fluxes continuously every 30 minutes. We
 found that, compared to flood-irrigated fields, drip irrigation in alfalfa
 increased yield by 7%, decreased irrigation demand by 11%, and decreased
 CO2 emissions by 59%, N2O by 14%, and NO by 27%. Drip irrigation in
 sudangrass increased yield by 6%, decreased irrigation by 68%, increased
 CO2 emissions by 3%, and decreased both N2O and NO emissions by 62%. In
 both crops, differences between irrigation types were strongest in the
 summer when flooded soil produced the largest pulses of N2O and NO
 relative to small drip-irrigated pulses. As agriculture continues to
 intensify in warmer climates, implementation of subsurface drip irrigation
 can help reduce agroecosystem contributions to climate change and air
 pollution while increasing crop yields.
description: Harvested biomass and
 irrigation data were collected at field scale; for these variables, we
 calculated water use efficiency (WUE) of productivity by dividing
 harvested dry biomass by total irrigation applied per field per harvest.
 We batch-processed instantaneous soil flux, temperature,  and moisture
 data for each emissions collection campaign using methods adapted from
 Andrews and Jenerette (2020) <em>Plant and Soil</em>.
 Instantaneous fluxes of CO<sub>2</sub>,
 N<sub>2</sub>O, and NO were calculated as the regression
 coefficient of linear increase in gas concentration during the 2.5-minute
 active measurement period, corrected for soil collar dimensions and
 atmospheric parameters following the Ideal Gas Law (Davidson et al., 
 2000). Soil temperature and moisture were averaged over this same period
 as well. Instantaneous fluxes of each gas were compiled and integrated
 with instantaneous soil temperature and moisture measurements at 30-minute
 resolution using a publicly-accessible R script (https://github.com/handr003/TraceGasArray). Logistical constraints for trace gas measurements forced a decision to not replicate treatments but allowed us to measure high-resolution emission trajectories with replication across different field rows. With time series of these high-resolution measurements, we could interpret interactive responses to rapid changes in soil conditions, such as during re-wetting, that could be missed or misinterpreted at coarser temporal scales. We extracted the magnitude and timing of each peak flux and climate parameter, calculated as the maximum instantaneous measurement recorded over an irrigation event. We also extracted per-irrigation mean temperature, moisture, and daily emission values, the latter of which were calculated as the integrated area under each time series curve using linear trapezoidal method divided by the duration of time following the irrigation event and prior to the next one.  To link soil emissions to field-scale irrigation and yield measurements, we extracted per-yield emissions values for each trace gas and field. We multiplied each chamber’s mean per-irrigation daily emissions by the length of each harvest period to calculate each chamber’s per-harvest emissions. We then divided this per-harvest emission estimate by per-area yield to calculate the amount of each trace gas emitted per amount yield as estimated by each chamber. These calculations are similar to those used to estimate greenhouse gas intensity (GHGI), a measure which has been used to evaluate the global warming impact of irrigation in agriculture (McGill et al., 2018). Representing emissions in this way allowed us to compare the effectiveness of drip irrigation across time and between crops by taking into account differences in harvest cycles and plant physiological differences.