Tamon Fumoto

Biological N₂ fixation (BNF) and ratoon rice growth are biological processes that mediate N and C cycling in rice paddy ecosystems, but their contributions to paddy soil fertility have rarely been evaluated in a quantitative and unified manner. In this study, we analyzed the contribution of BNF and ratoon rice growth to soil N fertility at six rice paddy sites in four prefectures of Japan, combining 2-year field monitoring and simulation using the DNDC-Rice biogeochemistry model. Across the sites and years, ratoon rice was found to accumulate up to 30 kg N ha⁻¹ without fertilization and irrigation after main rice harvest. BNF was not measured but estimated to be 33–63 kg N ha⁻¹ yr⁻¹ at the six sites, by applying a newly built BNF model after calibration against a literature dataset. Based on the simulations using DNDC-Rice under typical local management strategies, we estimated the following contributions of BNF and ratoon rice to soil N fertility, with variations based on the climate, soil properties, and management, as follows: (a) BNF and ratoon rice contributed 4–33% and 3–23% of the N supply from soil during the main rice season, respectively. (b) While BNF contributed 3–29% of the main rice N uptake, that from ratoon rice was much lower (6% or less), presumably because the decomposition of ratoon rice residue induced N immobilization during the main rice season. (c) Although the major part of N gain by BNF was being lost via denitrification and N leaching, BNF was contributing up to 6.6% of the organic N pool at the 0–30 cm soil layer. Ratoon rice was working to save N loss by reducing N leaching, consequently contributing up to 3.3% of the soil N pool. These findings provide quantitative insights into what roles BNF and ratoon rice play in paddy soil fertility.

At 6 rice paddy sites in 4 prefectures of Japan, we analyzed the contribution of biological N2 fixation (BNF) and ratoon rice growth to soil N fertility, combining 2-year field monitoring and simulation by the biogeochemistry model DNDC-Rice. Across the sites and years, ratoon rice was found to accumulate up to 30 kg N ha-1 without fertilization and irrigation after main rice harvest. Applying a newly built BNF model calibrated against literature data, BNF at the 6 sites were estimated to be 33-63 kg N ha-1 yr-1. Based on the simulations by DNDC-Rice under the locally typical managements, we estimated the contribution of BNF and ratoon rice to soil N fertility, being varied due to the climate, soil properties and management, as follows: (a) BNF and ratoon rice contributed 4-33% and 3-23% of N supply from soil during main rice season, respectively. (b) Contribution to main rice N uptake was 3-29% from BNF, whereas 6% or less from ratoon rice. (c) Although major part of N gain by BNF was being lost by denitrification and N leaching, BNF was contributing 88-349 kg N ha-1 (1.5-6.6%) of the organic N pool in 0-30cm soil layer. Ratoon rice was working to save N loss by reducing N leaching, consequently contributing 14-183 kg N ha-1 (0.2-3.3%) of the soil N pool. We suppose this is the first analysis that quantified the contribution of BNF and ratoon rice growth to paddy soil fertility.

The DNDC-Rice model effectively simulates yield and greenhouse gas emissions within a paddy system, while its performance under upland conditions remains unclear. Using data from a long-term cover crop experiment (fallow [FA] vs. rye [RY]) in a soybean field, this study validated the DNDC-Rice model’s performance in simulating soil dynamics, crop growth, and C-N cycling processes in upland systems through various indicators, including soil temperature, water-filled pore space (WFPS), soybean biomass and yield, CO₂ and N₂O fluxes, and soil organic carbon (SOC). Based on simulated results, the underestimation of cumulative N₂O flux (25.6% in FA and 5.1% in RY) was attributed to both underestimated WFPS and the algorithm’s limitations in simulating N₂O emission pulses. Overestimated soybean growth increased respiration, leading to the overestimation of CO₂ flux. Although the model captured trends in SOC stock, the simulated annual values differed from observations (−9.9% to +10.1%), potentially due to sampling errors. These findings indicate that the DNDC-Rice model requires improvements in its N cycling algorithm and crop growth sub-models to improve predictions for upland systems. This study provides validation evidence for applying DNDC-Rice to upland systems and offers direction for improving model simulation in paddy-upland rotation systems, thereby enhancing its applicability in such contexts.

Approximately 90% of the world total paddies area and annual output of the rice production are concentrated in monsoon Asia, which has no more land/water resources for further expansion of cultivation. Most rice grows under lowland conditions where currently facing to the fresh water scarcity due to sea-water intrusion accelerated by sea-level rise and land-subsidence, and decelerating freshwater supply by upstream-dam construction. Since the rice production also requires large amount of water (3,000-5,000 L kg-1 rice), water-saving irrigation practice (e.g., Alternate Wetting and Drying, a.k.a., AWD) is desirable to be implemented in this region to save the water-demand sustainably, and irrigation status need to be evaluated for the decision making on sustainable food security. In addition to the significance of AWD’s role as an adaptation to drought risks, AWD also has a potential to act as an important mitigation-measure by reducing methane emission from paddy soils. This function is very important since rice cropping is responsible for approximately 11% of global anthropogenic CH4 emissions, and rice has the highest greenhouse gas intensity among the main food crops.In order to implement AWD in Asian rice paddies as a mitigation-measure based on a carbon pricing scheme, it is important to evaluate the spatial distribution of AWD paddy fields in the target region. For the detection of AWD-fields versus continuously-flooding fields, it is essential to develop method using EO data to detect soil inundation under rice plants at various growth stages.  In this study, ALOS-2/PALSAR-2 and Sentinel-1 data were used to combine the penetration capacity of L-band SAR data with C-band data capacity to monitor rice growth status with their high temporal resolution.The study was conducted in triple rice cropping systems in the Vietnamese Mekong delta (5 sites: Thot Not in Can Tho city; Chau Thanh, Cho Moi, Thoai Son and Tri Ton in An Giang Province, where AWD field campaign was conducted from 2012 to 2017. EO data consisted of ALOS-2/PALSAR-2 every 14 days in 2017/2018 in An Giang province at high resolution observation mode (3-6m resolution) and ScanSAR observation mode (25-100m resolution) every 42 days over the Mekong delta.As the result of the classification using the dual-polarization ALOS-2/PALSAR-2 data, soil inundation status could be detected during various rice growth stages. To evaluate rice productivity and GHG emissions from rice fields, we developed a simulation system based on the DeNitrification-Decomposition (DNDC) model which can assimilate PALSAR-2 inundation map and ground observed GHG -flux and rice growth status data on a pixel basis. For spatial extension, rice map, together with rice calendar (sowing date, rice growth status), required as inputs by DNDC are provided by the GeoRice project, based on the use of Sentinel-1 6-day time series. This paper presents the performance of multi-sensor data fusion to realize sustainable agricultural management by mitigating the GHGs emission while maintaining or improving regional fresh water use efficiency for stable food production under climate change pressure.

Two-year field experiments were conducted at Tamil Nadu Rice Research Institute, Aduthurai, Tamil Nadu, India, to evaluate the effect of continuous flooding (CF) and alternate wetting and drying (AWD) irrigation strategies on rice grain yield and greenhouse gas emissions from double-cropping paddy rice. Field observation results showed that AWD irrigation was found to reduce the total seasonal methane (CH₄) emission by 22.3% to 56.2% compared with CF while maintaining rice yield. By using the observed two-year field data, validation of the DNDC-Rice model was conducted for CF and AWD practices. The model overestimated rice grain yield by 24% and 29% in CF and AWD, respectively, averaged over the rice-growing seasons compared to observed values. The simulated seasonal CH₄ emissions for CF were 6.4% lower and 4.2% higher than observed values and for AWD were 9.3% and 12.7% lower in the summer and monsoon season, respectively. The relative deviation of simulated seasonal nitrous oxide (N₂O) emissions from observed emissions in CF were 27% and −35% and in AWD were 267% and 234% in the summer and monsoon season, respectively. Although the DNDC-Rice model reasonably estimated the total CH₄ emission in CF and reproduced the mitigation effect of AWD treatment on CH₄ emissions well, the model did not adequately predict the total N₂O emission under water-saving irrigation. In terms of global warming potential (GWP), nevertheless there was a good agreement between the simulated and observed values for both CF and AWD irrigations due to smaller contributions of N₂O to the GWP compared with that of CH₄. This study showed that the DNDC-Rice model could be used for the estimation of CH₄ emissions, the primary source of GWP from double-cropping paddy rice under different water management conditions in the tropical regions.

We have estimated methane (CH₄) emission from total rice (Oryza sativa L.) paddies in Japan by means of a process-based biogeochemistry model, DeNitrification-DeComposition(DNDC)-Rice, combined with a geographic information system (GIS) database of climate, soil and farming practices. In the GIS database, 2 million ha of rice paddies were divided into 17,408 units according to 136 climate areas, 16 soil types, four classes of drainage rate and two classes of groundwater level, to simulate CH₄ flux from each of the units applying the DNDC-Rice model. As a result, the national-scale CH₄ emission in 1990 was estimated to be 216 Gg carbon (C), 13% lower than a previous inventory estimated by the Tier 2 method. By our Tier 3 approach, a relatively higher CH₄ flux was estimated from eastern regions than from western regions of Japan, presumably due to the differences in climate and water management. Sensitivity analysis and uncertainty assessment indicated that it is important to account for the heterogeneity in soil properties such as field water capacity, iron (Fe) concentration and drainage rate, in order to reduce the uncertainty in regional estimates.