Daily soil surface CO2 flux during non-flooded periods in flood-irrigated rice rotations

Abstract

Carbon dioxide (CO 2 ) emitted from the soil, as a result of root and microorganism respiration, is a major process in the global carbon cycle. Since CO 2 production is dependent on oxygen availability, prolonged saturated soil conditions in rice (Oryza sativa) can decrease the quantity of soil carbon released in the form of CO 2 over time. At present, a deficiency exists in the scientific literature on soil surface CO 2 flux in well-established, flood-irrigated rice systems, which are flooded for approximately 3 months a year during the rice growth period. Plenty of studies have examined soil surface CO 2 flux in dryland cropping systems and methane emissions in paddy-grown rice, but flood-irrigated rice does not easily fall into either of these categories due to the cyclic nature of seasonal flooding. Therefore, this is the first study to examine daily soil surface CO 2 flux during non-flooded periods in well-established, flood-irrigated rice rotations. For a comprehensive analysis, soil surface CO 2 flux was measured for 2 years on 10 different rice-based rotations, which had been managed using conventional tillage or no-tillage for 10 and 11 years. Rotations included continuous rice and various combinations of rice rotated with soybean (Glycine max), corn (Zea mays), and/or winter wheat (Triticum aestivum) and were located on a silt-loam soil in the Mississippi River Delta region of Arkansas in the USA. Results showed that 7 of the 16 measurement dates differed in daily soil surface CO 2 flux among tillage and/or crop rotations. However, these differences were determined to be from crop maturity, in relation to early-or late-season planting, instead of patterns of long-term flood irrigation. Years that rice was grown reduced the cumulative CO 2 emissions, but substantial differences over time were minimized in rotations with soybean or corn. Findings from this experiment are valuable in the scientific understanding of carbon gas cycling in rice-based cropping systems because the aerobic periods between flooding were evaluated, which is the time period often ignored when examining carbon gas emissions in rice. Overall, this study provides evidence that the commonly used rice-based cropping systems reach a somewhat equilibrium state in daily CO 2 fluxes over time, regardless of the frequency in periodic soil saturation.

Forecasting Rice Status for a Food Crisis Early Warning System Based on Satellite Imagery and Cellular Automata in Malang, Indonesia

“…In addition to the increase in settlement LULC as one of the contributors to rice field shrinking, the fact that agricultural dry lands, agroforestry, and production forests remained stable or sometimes even increased showed that rice field is more vulnerable to changes than other LULC types [43]. Some factors that enhance a rice field's vulnerability to changing to other LULC types are: (i) less flexibility for farmers to rotate due to flooded fields; (ii) farmers with less flexibility regarding rotations are not able to grow other crops to allow for new market opportunities, and converting dry agricultural lands to rice fields is also not possible due to the extended time that is needed to build rice field ecosystems [44], making it difficult for smallholder farmers to make their living during the transition periods; (iii) the flooded condition limits modern tools and innovations, which could prevent younger generations from engaging in rice farming [45]. Based on these drivers, the increase in agroforestry and agricultural dry lands, along with the reduction in rice fields, could be associated with rice fields' conversion to agroforestry and agricultural dry land for easier management.…”

Dynamic Mapping of Paddy Rice Using Multi-Temporal Landsat Data Based on a Deep Semantic Segmentation Model

“…Dry seeding systems are standard practices in the mid-south of the U.S. (e.g., Arkansas) [1,42,55]. Dry seeding is the practice of planting rice either in rows or broadcasting and then lightly incorporating seed into soil.…”

“…Liner interpolation Time series data with regular time intervals can overcome the spatial heterogeneity of observation numbers and generate consistent time series. Previous efforts have used linear interpolation to generate equally spaced time series data and used it for crop classification and achieved good classification results [28,42]. The specific method was to first determine the starting time of the time series according to statistics from the USDA website (https://quickstats.nass.usda.gov/, accessed on 21 May 2021).…”