# Synergistic Effects of Supplemental Irrigation and Foliar Selenium Application on Dynamics Characteristics of Soil Respiration and Its Components in Millet Field

## TL;DR

This study examines how supplemental irrigation and selenium application affect soil respiration and its components in millet fields in semi-arid regions.

## Contribution

The study reveals novel synergistic effects of irrigation and selenium on soil respiration dynamics and identifies key drivers using machine learning.

## Key findings

- Supplemental irrigation increased CO2 emissions, while selenium application suppressed them.
- Autotrophic respiration dominated soil respiration and was strongly influenced by soil temperature.
- The CS treatment reduced cumulative CO2 emissions and improved water use efficiency.

## Abstract

Soil respiration (Rs) plays a pivotal role in carbon cycling within semi-arid ecosystems. In our millet field experiment, we measured Rs, autotrophic respiration (Ra), heterotrophic respiration (Rh), water consumption (ET), yield (Y), water use efficiency (WUE), and key soil environmental properties to examine the effects of supplemental irrigation and selenium application on Rs dynamics and to clarify the controlling factors. The experiment was conducted from 2023 to 2024 with four treatments and three replicates per treatment each year. These treatments comprised conventional rainfed (CK), supplemental irrigation (SI, 50 mm), rainfed with Se addition (CS, 67.84 g·hm−2), and supplemental irrigation with Se addition (SIS). SI increased CO2 emissions in the millet field, whereas selenium application (CS) suppressed them. Ra was the dominant component of Rs and was 1.03–4.01 times greater than Rh. SI and CS significantly affected cumulative CO2 emissions through Ra (p < 0.05), whereas their effects on Rh were minor. The CS treatment resulted in the lowest cumulative CO2 emissions at 4233 and 4009 g·m−2 in 2023 and 2024, respectively. Diurnal variation patterns of Rs, Ra, and Rh differed across millet growth stages. Both supplemental irrigation and selenium application improved soil water retention, soil enzyme activity, and soil organic matter (SOM), and moderated soil temperature. Classification and Regression Tree (CART) algorithm analysis revealed that Ra was primarily driven by soil temperature, with a feature weight of 86.95% determined by CART based on machine learning, whereas Rh was mainly influenced by soil enzyme activity, with a feature weight of 76.11%. The CS treatment enhanced production while promoting emission mitigation. The combined SIS treatment achieved the highest WUE and maintained a lower Rs than SI. These findings suggest an environmentally sustainable management strategy for millet production in semi-arid regions. However, due to the limited number of parcels in this study, further field-scale validation and additional experimental research involving multiple levels of supplemental irrigation and Se addition are necessary.