Ratoon rice allows millions of smallholders to meet production and environmental goals

Ratoon rice allows millions of smallholders to meet production and environmental goals | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Biological Sciences - Article Ratoon rice allows millions of smallholders to meet production and environmental goals Patricio Grassini, Shen Yuan, Fuping Fang, Yucheng Wang, Fei Wang, and 34 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7339561/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract China aims to increase rice production and reduce associated negative environmental impact. However, increasingly limiting and expensive rural labor has driven a shift from double- to single-season rice, posing a threat to national food security. Here, we evaluated the impact of an alternative ratoon rice system on production and environmental outcomes using smallholders’ survey data together with spatial crop modeling. We showed that ratoon rice adoption would ensure rice self-sufficiency and drastically reduce nutrient losses, pesticide use, greenhouse gas emissions, and labor requirements. We conclude that ratoon rice is a viable option to meet the dual goal of increasing production and reducing negative environmental impacts. Biological sciences/Plant sciences/Plant ecology Earth and environmental sciences/Ecology/Agroecology Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Farming systems in China have experienced profound changes over the past 20 years. Rapid economic growth has led to massive migration from rural to urban areas, labor shortage, and higher farm wages 1-3 . One of the consequences of these trends has been a shift from a labor-intensive double-season rice system (DR), including two crops per year, towards a single-season rice system (SR) that includes one crop 4 . This transition has led to a decline in rice harvested area, further exacerbated by cropland conversion for urban, industrial, and recreational uses 5,6 . These trends are concerning because rice represents the basis of the diet for 1.4 billion people in China 7,8 . Consequently, the Chinese government has set explicit goals to increase rice self-sufficiency, and even generate a rice surplus, which seems vital for national security in the context of trade disruptions due to pandemics and geopolitical conflicts 9,10 . Additionally, China has set ambitious goals to reduce the environmental footprint from crop production 11 . Failure to achieve these goals could have far-reaching consequences in the rest of the world, including higher food prices, land conversion, and climate change 12,13 . At question is which innovations can help China make rice systems more productive, profitable, and sustainable. Within this challenging context, the ratoon rice system (RR) has emerged as a viable option. Ratoon rice refers to the crop that regrows from the stubble of the previously harvested main season crop ( Extended Data Fig. 1 ) 14,15 . Cultivation of RR has been practiced since the Western Jin dynasty (3 rd -4 th century) and described in an ancient Chinese poem as follows: “ At high noon, the grass in verdant grace, a cow leads its calf, a gentle trace. When autumn’s done its fruitful race, the fields are ripe, and from the rice stubble, new rice grains rise, a second cradle ( 晌午草青牛引犊，经秋田熟稻生孙 ) ” 16 . The RR area has fluctuated over time because of different factors including low and unstable yield, wide adoption of high-yielding modern varieties and associated technologies, difficulties in adopting mechanical harvesting, and, more recently, labor shortage 17-21 . The days of RR seemed over by the early 2000s, when its area did not exceed a few thousand hectares across the country. However, since 2010s, there has been a renewed interest in developing a high-yielding mechanized RR system with smaller labor requirement as an option to mitigate the negative impact of DR-SR transition on rice production 19,22-24 . Moreover, experimental evidence shows that RR can also contribute to reducing the negative environmental footprint 7,25,26 . At issue is the extent to which RR can help China achieve national production and environmental goals from local to national scales. Here we evaluate the agronomic, economic, and environmental performance of RR using a combination of crop modeling and analysis of smallholder survey data ( see Methods ) . We assessed the impact of RR adoption from farm to national scales and discussed implications for policy and agricultural research and development (AR&D) programs. We conclude that RR is a viable option to meet the dual challenge of achieving rice self-sufficiency while drastically reducing the environmental impact in a context of labor shortage and climate change. Results Adoption of ratoon rice in smallholder fields We investigated RR adoption in southern China, a region that has the biophysical conditions to allow RR cultivation ( Fig. 1A, Extended Data Fig. 2, Supplementary Figs. S1-3 ). Within this area, RR area has increased sharply from 0.4 to 2.1 million hectares between 2009 and 2022, driven by development of the mechanized rice ratooning technology 17 , which allows mechanical harvest of the main season without damaging the yield of the subsequent ratoon season. Combination of research and extension support has allowed farmers to obtain RR annual yields that are 40% higher than SR, and comparable to those in DR ( Fig. 1B ). The RR expansion occurred at expense of both DR and SR area, including traditional growing areas of SR and DR in Henan and Guangdong province, respectively ( Fig. 1A ). At present, RR accounts for 10% of and 7% of the rice harvested area in southern China and 7% nationwide, being cultivated by ca. 3.5 million smallholders ( Fig. 1A, Supplementary Fig. S1, Supplementary Table S1 ). Drivers for adoption of ratoon rice and associated benefits We analyzed survey data collected across 23,250 smallholders over multiple years to understand the drivers for RR adoption ( Supplementary Figs. S4 and S5, Supplementary Tables S2-12 ). The annual rice yield was 7.9, 12.0, and 11.4 Mg ha -1 y -1 for SR, DR, and RR, respectively, with higher inter-annual yield variation in the ratoon season of RR (10%) compared with yields in the DR and SR systems (<5%) ( Fig. 2A, Supplementary Figs. S4-6 ). Skipping land preparation and crop establishment in the ratoon season led to labor and input savings compared with DR while its shorter growth duration reduced pesticide, labor, and irrigation inputs ( Fig. 2A, Supplementary Figs. S3 and S5, Supplementary Tables S5 and S6 ). On the other hand, inputs and labor were higher in RR compared with SR due to the additional crop cycle. However, the combination of high yields with intermediate inputs, labor, and production costs in RR led to the highest yield per unit of input and labor among the three rice systems and two- and three-fold larger profit than in SR and DR, respectively ( Fig. 2B, Supplementary Fig. S5 ). Besides production and economic benefits, RR also achieved better environmental outcomes ( Fig. 2B, Supplementary Fig. S5 ). For example, the global warming potential (GWP) per unit yield was lowest and eco-efficiency highest in RR compared with the other systems ( Fig. 2B ). The RR system also showed a substantial improvement in nitrogen-use efficiency compared with DR and SR, reducing nitrogen surpluses by 56% and 3%, respectively ( Fig. 2B ). Scaled up to the current area (2.1 million hectares), RR adoption led to positive impacts on rice production (7 Mt), with reduced labor requirements and environmental impact per unit yield ( Fig. 2B, Supplementary Fig. S5, Supplementary Table S7 ). This calculation considers the direct impacts derived from SR-RR transition as well as the indirect RR benefits derived from mitigation of the yield loss associated with the DR-SR transition. Upscaling potential impacts of ratoon rice adoption from local to national scales We assessed the national impact of RR adoption on production, labor, and environmental outcomes. To do that, we used a well-validated crop model to estimate the attainable yield (defined as 80% of yield potential, see Methods ) for RR, DR, and SR across the main rice producing areas in China following a bottom-up approach based on local weather, soil, and crop system data ( Extended Data Figs. 2 and 3, Supplementary Fig. S7, Supplementary Table S13 ). Subsequently, we estimated attainable production and environmental outcomes for different scenarios of RR adoption over the next 20 years. Our analysis shows that continuation of historical trends in the harvested area of SR and DR without change in RR area (BAU) will lead to 16% smaller rice harvested area and 15% lower attainable production by 2040 (Figs. 3A and B, Supplementary Tables S14-17) . Shifting from DR to RR instead of SR (DR-RR scenario) would reduce by half the negative impact of future DR area loss on rice production. Both scenarios (BAU and DR-RR) would still allow rice self-sufficiency due to smaller demand by the year 2040. However, they will not allow China to meet the goals of increasing rice production and surplus and rice imports will likely be required in some years due to year-to-year variation in farmer yields ( Fig. 3B, Supplementary Fig. 6 ). On the other hand, implementation of current policy to maintain DR area while allowing some SR area (19% of 13 million hectares of SR area in the 14 provinces) to switch to RR (SR-RR) will allow China to reach a large rice surplus, equivalent to 27% of domestic demand by 2040. Likewise, full RR adoption (FRR) in areas that allow its cultivation and without substitution of other food crops ( e.g., wheat and rapeseed) will lead to a relatively large rice surplus (33% of domestic demand). However, there is an important distinction between SR-RR and FRR. While both scenarios allow China to generate a rice surplus, only FRR leads to a substantial reduction in N losses, GWP intensity, pesticide use, and labor requirement ( Figs. 3D-F ). While achieving attainable yield is feasible for farmers with access to inputs, markets, and extension services 7,27 , it would require national yield gain rates that are two-fold higher than those observed over the past 20 years 28 . Consequently, the actual rice production by 2040 could be lower than the attainable one due to limited yield growth. For example, extrapolation of historical rates of yield gain for DR and SR shows that China will not achieve rice self-sufficiency by 2040 in the BAU scenario (SSR=0.96), leading to an annual deficit of 8 million tons ( Extended Data Fig. 4, Supplementary Tables S18-20 ). This import requirement is equivalent to ca. two million hectares of rice in Southeast Asia 29 . On the other hand, SSR will change little in the DR-RR scenario (SSR=1.04) and increase substantially in the SR-RR and FRR scenarios (SSR=1.13 and 1.16, respectively). At the end, these findings further highlight the crucial importance of adopting RR to ensure rice self-sufficiency in China couples with relentless efforts to maintain or accelerate current rates of yield improvement through agronomic management and breeding efforts. To support policy-making and guide investments in AR&D programs, we analyzed the impacts of each RR scenario on attainable rice production at sub-national scale and compared it with BAU (Fig. 4) . Largest benefit of switching from DR to RR instead of SR (DR-RR scenario) was in Hunan, which is the main DR producing province at present. On the other hand, maintaining DR area and replacing some SR by RR (SR-RR scenario) led to a positive impact in the Yangtze River Valley because DR area was maintained, and in southwest China because SR was replaced by RR. Likewise, full RR adoption wherever feasible and without crop substitution (FRR scenario) led to positive impacts on crop production, with only a slight decrease (-3%) in Guangdong province because the dominant DR system is replaced by RR (Fig. 4) . Discussion Our analysis based on smallholder survey data and crop modeling shows that RR can play a central role in ensuring food security in China while improving economic and environmental outcomes, consistent with findings from experimental data 30 . Compared with the traditional DR and SR systems, RR exhibited higher efficiency in the use of inputs, leading to lower emissions, nitrogen losses, and pesticide per unit yield (Fig. 2) . Additionally, RR is more profitable and requires less labor, making it a viable cropping system option given the rural-urban demographic shift in China 3 . We also showed that the current national rice policy program (SR-RR) and the full RR adoption scenarios (FRR) reached similar production goals, ensuring rice self-sufficiency in both cases (Fig. 3, Extended Data Figs. 3 and 4, Supplementary Table S6) . However, the SR-RR is not compatible with the current desire to improve environmental outcomes since GWP and nutrient surplus remain similar to the current baseline 11 . Conversely, adopting RR substantially reduces the overall GWP, nitrogen surplus, pesticide load, and labor requirement, contributing to achieve the dual goal of achieving rice self-sufficiency while reducing negative environmental impacts. There are still constraints for RR adoption. A primary challenge in scaling up RR is the negative impact of mechanical harvesting of the main season, which damages stubbles and affects the axillary bud development in ratoon season 19,31 . To mitigate it, optimized water management strategies, including heavy soil drying before the harvest of main season and post-harvest irrigation, are crucial to minimize mechanical damage and alleviate heat stress on axillary buds 17,19 . Similarly, timing of nitrogen application is critical for promoting bud regeneration and tiller growth 32 . To date, there has been no explicit breeding program for RR, and the varieties used in RR cultivation were typically selected from those originally bred for SR or DR 18 . Thus, widespread adoption of proper crop management technologies of RR across millions of smallholders will require substantial research and extension support. In addition, successful adoption will also depend on tuning current agricultural policies. For example, current programs provide strong financial support for farmers cultivating DR and smaller and equal support for those practicing RR and SR, despite the additional harvest of the ratoon season 33 . Greater support can help farmers adopt RR instead of SR when moving away from DR and mitigate the associated negative production impact while improving environmental and economic outcomes. We note that adoption of RR system could benefit smallholders not only in China but also in other regions with similar biophysical and socio-economic conditions, especially at a time in which rice systems are being challenged by their negative environmental impacts and high labor requirements 7,31,34,35 . Our analysis is subject to several limitations and uncertainties. First, there is uncertainty on data inputs (weather and crop calendar) and capacity of the model to estimate yield potential. We calibrated and validated the crop models using independent datasets, and the comparison of simulated and observed yields from both datasets provides confidence in the robustness and accuracy of the models for simulating yield potential across diverse climates and rice systems ( Extended Data Fig. 5 ). We also made an explicit effort to use measured weather data and accurate crop calendar and validate our yield potential estimates with measured yields from well-managed rice experiments where crops received enough fertilizer and pesticides to avoid nutrient limitations and yield reductions due to biotic stresses ( Supplementary Table S21 ). Likewise, there may be biases in the survey data due to inappropriate representation of the farmer population. However, comparisons of yields derived from our survey data against those from official statistics and previous studies showed good agreement, giving confidence about the representativeness and accuracy of our farmer survey data ( Supplementary Fig. S8, Supplementary Table S22 ). Due to lack of local emission factors, our estimation of GHG emissions was largely based on IPCC tier-2 factors. Although the use of local factors may modify absolute emissions, we do not expect it will modify the conclusions derived from the comparison of rice systems or production scenarios. Our study did not account for the potential negative impact of climate change on rice yield 36 , which is negligible given the short time period of our study (15 years), the positive impact of CO 2 enrichment, and agronomic adaptation to warmer environments through changes in planting date and variety 37-39 . Indeed, we believe that rice production in our FRR scenario is probably underestimated given that climate-induced warming in northern China may allow further expansion of RR cultivation 40,41 , and the proportion of SR that is being rotated with other crops ( e.g. , rapeseed and wheat) is likely to be smaller ( see Methods ). Despite these uncertainties, the main conclusions about the positive impacts of RR adoption on production, environmental, and economic outcomes seem robust and not likely to change due to these uncertainties. Methods Data sources. Fourteen rice-producing provincial-level administrative regions (hereafter called provinces) where RR, SR, and/or DR systems are grown in China were selected for our analysis ( Extended Data Figs. S1 and S2, Supplementary Fig. S1 ). These included Anhui, Fujian, Guangxi, Hubei, Hunan, Jiangxi, Yunnan, and Zhejiang (RR, SR, and DR systems), Guizhou, Henan, Jiangsu, Sichuan, and Chongqing (RR and SR systems), and Guangdong (RR and DR systems) ( see Supplementary Information Text Section 1, Extended Data Fig. 2, Supplementary Fig. S1 ). Collectively, these provinces accounted for 70% (SR), 97% (DR), and 99% (RR) of current rice production and harvested area 28 ( Supplementary Table S1 ). Following previous studies 7 , we followed a two-step approach to identify major producing regions and dominant rice systems in each province. First, we identified the major rice-growing region(s) in each province. Then, we determined the dominant rice systems in each region. For example, we selected three regions in Hubei, with the three rice systems (i.e., RR, SR, and DR) practiced in two regions and only SR in the other region. Across the 14 provinces, our study included 183 system-region combinations, with 103, 40, and 40 cases corresponding to SR, DR, and RR systems, respectively ( Extended Data Fig. 2 ). Agronomic data were collected through local farm survey across 23,250 smallholders over 2020-2022 using structured questionnaires ( Extended Data Fig. 2 ). The data included yield, cropping system, farm size, tillage practices, methods of crop establishment, sowing and transplanting dates, mechanization for various field operations, seeding rates, nutrient fertilizer rates, pesticide use, irrigation amount and management, energy sources for irrigation pumping, grain drying, labor, and straw management practices ( Supplementary Tables S2 and S3 ). Labor inputs were estimated based on direct questions regarding the number of man-hours required for various field operations. Average values for each rice system were first calculated based on the on-farm survey data within each province ( Supplementary Tables S4-6 ). These provincial averages were then weighted by the annual rice harvested area of each cropping system in each province to generate the overall average values reported in this study, considering all 14 provinces collectively ( Fig. 2 ). Rice grain yields were calculated at a standard moisture content of 140 g H 2 O kg -1 grain, with separate data provided for each season, utilizing data from at least three recent rice-growing seasons within each respective rice system. The 14 provinces encompass a diverse range of biophysical and socio-economic environments, leading to average rice yields ranging from 5.5 to 10.8 Mg ha -1 (SR) and 4.3 to 8.0 Mg ha -1 (early-season of DR) and 4.3 to 7.7 Mg ha -1 (late-season of DR) from our survey ( Supplementary Fig. S2, Supplementary Tables S4-6 ). In the case of RR system, yields range between 6.1 to 9.2 Mg ha -1 (main season) and 1.6 to 5.1 Mg ha -1 (ratoon season). For each province, accuracy of survey data was evaluated using other independent datasets, such as those from the National Bureau of Statistics of China, published journal articles, and reports. For example, database yields of SR and DR were compared against those from the National Bureau of Statistics of China for each of the 14 provinces. Agreement between yield data sources was satisfactory, as indicated by the relatively small root mean square error (0.3 Mg ha -1 ), which represented 5% of the average statistical yield, without biases across the entire yield range ( Supplementary Fig. S8 ). Similarly, we compared the yield of RR at both provincial and national levels with data from statistics and previous publications, showing good agreement across different sources ( Fig. 1, Supplementary Table S22 ). Nitrogen surplus and global warming potential . We estimated a number of parameters related to environmental outcomes, including irrigation water and pesticide use, nitrogen (N) surplus, and global warming potential (GWP), separately on an area- and yield-basis to capture their impacts in relation to both land use and production efficiency ( Fig. 2, Supplementary Fig. S5 ). For each rice growing season within each system, N surplus was calculated by determining the difference between N inputs and N outputs 42 ( see Supplementary Information Text Section 2, Fig. 2, Supplementary Fig. S5 ). The N inputs included synthetic and organic fertilizers and biological N fixation 35,43 . The N outputs primarily consisted of the N removed through the harvested grain and straw. Estimates for synthetic N fertilizer inputs were derived from existing databases, while organic fertilizer inputs were calculated based on type and amount of manure and associated N concentration derived from the local literature 44 . We assumed a biological N fixation rate of 25 kg N per hectare per crop for lowland rice 45 . We note that this study did not include atmospheric deposition and irrigation water as N inputs, as these are typically offset by N losses through leaching, volatilization, and denitrification processes 46 . The N outputs were calculated by accounting for the N removed with the harvested grain and straw. Straw N removal was estimated considering field-specific straw management, including left as mulch, incorporated into the soil, burned in the field, or removed from the field. The N content in rice grain was determined based on average rice grain yield for each season and grain N concentration ( Supplementary Table S11 ). Straw biomass was estimated using a harvest index of 0.5, which is the ratio of grain weight to total above-ground biomass on a dry-matter basis 47 . The N losses from straw were determined based on the amount of straw N remaining in the field after harvest and the associated N loss as determined by reported straw management practices ( Supplementary Table S12 ). We note that achieving a zero N surplus is not the objective, as this would lead to depletion of soil organic matter 48 . Therefore, we set a threshold N surplus of 75 kg N per hectare as a critical level to identify excessive N, which could lead to significant reactive N losses, as N losses tend to increase when N surplus exceeds this threshold 49,50 . We also assessed yield-scaled N surplus by calculating the ratio between N surplus and grain yield. In the case of greenhouse gas (GHG) emissions associated with rice production, we examined emissions of carbon dioxide (CO 2 ), methane (CH 4 ), and nitrous oxide (N 2 O). Our assessment focused on three primary sources of GHG emissions: (i) the production, packaging, and transportation of agricultural inputs such as seeds, fertilizers, pesticides, and machinery ( Supplementary Tables S8 and S9 ); (ii) emissions resulting from the direct use of fossil fuels for farm operations like irrigation pumping; and (iii) CH 4 and N 2 O emissions generated during the cultivation of rice in paddy fields 7,51 ( see Supplementary Information Text Section 3 ). Given that intensive irrigated rice production in lowland areas typically maintains or increases soil organic matter 52,53 , we excluded CO 2 emissions and carbon sequestration from soil in our GHG emission calculations. We estimated annual GHG emissions from the production, storage, and transportation of various agricultural inputs by applying the rates of these inputs and their respective GHG emission coefficients 7 ( Supplementary Table S8 ). Total N 2 O emissions due to N inputs to paddy fields were assessed through both direct and indirect pathways 54 . Direct soil N 2 O emissions were calculated based on the N surplus, defined as the difference between applied N inputs and the accumulated N in aboveground biomass at physiological maturity. We used the N-balance approach proposed by van Groenigen et al. 55 to estimate direct soil N 2 O emissions for each rice season. Indirect N 2 O emissions were estimated using the IPCC methodology, assuming they constitute 20% of direct N 2 O emissions 56 . The CH 4 emissions from rice paddy fields were quantified according to IPCC guidelines, taking into account factors such as the duration of the rice cultivation period, water management practices during both the growing season and the pre-season period, and the type and quantity of organic amendments applied, including straw, manure, and compost 57 . We started with the baseline CH 4 emission factor and adjusted it to reflect the specific crop management practices observed in the selected regions. All GHG emissions were converted to CO 2 -equivalent (CO 2 -eq) values using the 100-year global warming potentials of CH 4 and N 2 O, which are 25 and 298 times the intensity of CO 2 on a mass basis, respectively 58 . For each rice growing season in each rice system and region, we calculated the GWP by summing the CO 2 , CH 4 , and N 2 O emissions, all expressed as CO 2 -eq. The average GHG emissions for each rice system were calculated for each province. Next, these provincial averages were weighted by the annual rice harvested area of each rice system in the respective provinces to obtain the overall average values per rice system ( Fig. 2 ). Economic analysis . For each rice season across different rice systems and provinces, we estimated production costs, gross revenue, net profits, and the benefit-to-cost ratio. The production costs included expenditures related to human labor, machinery for field operations such as tilling, plowing, seeding, transplanting, harvesting, and grain drying. It also includes the cost of rice seeds for both inbred and hybrid rice varieties, nitrogen, phosphorus, and potassium fertilizers, organic fertilizer, irrigation water, electricity for irrigation, sprayer, and grain drying, and herbicides, pesticides, and fungicides. Prices were derived from market rates, which included charges from local service providers for machinery use and market prices for labor and agricultural inputs ( Supplementary Table S10 ). We note that we did not consider costs from land rental values, taxes, and machinery depreciation. Considering that rice farming in these provinces is predominantly managed by smallholders, we excluded the rental value of land from our estimation, as there is generally no cash cost associated with land use. Additionally, we omitted machinery depreciation since it is typically included in the service providers’ fees. Gross return was determined by multiplying grain yield by the official 2022 rice grain purchase price set by the central government, which varied by rice system and season: US$ 397 Mg -1 for SR, US$ 388 Mg -1 for early season of DR and main season of RR, and US$ 403 Mg -1 for late season of DR and ratoon season of RR 59 . For DR and RR systems, the total production cost and gross return were computed by aggregating the values from the two rice seasons within each system. Net profit was calculated by the difference between gross return and production cost and return on investment (ROI) as the ratio between net profit and production cost ( Fig. 2, Supplementary Fig. S5 ). Our economic analysis utilized an average exchange rate of US$ 1 to CNY 6.5 for the 2020-2022 period. Attainable yield estimation . We followed the protocols of the Global Yield Gap Atlas (GYGA, www.yieldgap.org) to identify a number of representative sites of the rice producing area in China ( Extended Data Fig. 2 ). Our methodology involved selecting reference RWS based on existing weather stations, distribution of harvested rice area, and a climate zone (CZ) scheme ( see Supplementary Information Text Section 4 ). This approach considers spatial variation in three critical variables influencing crop yield potential, including annual growing-degree days, aridity index, and temperature seasonality 60 . We also incorporated expert insights from local agronomists and extension agents in each of the provinces, based on the Spatial Production Allocation Model map (SPAM 2020, www.mapspam.info) as a reference. We selected CZs that accounted for more than 5% of national rice harvested area. A 100 km radius buffer was drawn around each RWS, with their borders clipped as needed to ensure that buffer extent does not go beyond the CZ where the stations were located. We prioritized the selection of buffers, starting with the largest harvested area and proceeding to the second largest, while excluding buffers that overlapped with selected ones by more than 20%. This iterative process continued until the cumulative coverage of selected buffers encompassed at least 50% of the national total harvested rice area. The final selection of sites was reviewed and validated by local agronomists and extension agents to ensure accurate representation of harvested rice areas, with particular attention given to RR, which exhibits uneven national distribution. Ultimately, we identified 32, 21, and 26 RWS and 12, 8, and 13 CZs for the SR, DR, and RR systems, respectively, across the 14 provinces and 12 RWS and 8 CZs in the remaining provinces ( see Supplementary Information Text Section 4, Extended Data Fig. 2 ). Selected RWS buffers and associated CZs in the 14 provinces collectively covered 49% and 74% of national total rice harvested area, respectively. Altogether, selected RWS buffers and CZs from all provinces accounted for 63% and 91% of the national rice harvested area, respectively. To estimate national rice production capacity, we used attainable yield, defined as 80% of the yield potential for irrigated rice ( Extended Data Fig. 3 ). This value is a reasonable yield goal for farmers with good access to markets, inputs, and extension services, as demonstrated in several regions 7,27 . Yield potential was simulated using the ORYZA v3 model for SR and DR, and the ORYZA_R model for RR. The ORYZA v3 model has been well validated in field experiments and is widely used to simulate rice yield potential in various cropping systems, including single-, double-, and triple-season rice systems across both irrigated and rainfed environments in temperate, subtropical, and tropical regions 7,61 . The ORYZA_R model, which was developed by adding a reserve pool submodule to ORYZA v3, simulates the growth and development of both the main and ratoon seasons in RR. ORYZA v3 has also been validated in field experiments with various treatments and rice varieties where crops were managed to avoid water and nutrient limitations and kept free of biotic stresses 62 . To calibrate and validate crop genetic coefficients for study, crop management data and yield were compiled from high-yielding experiments conducted across different regions and dominant rice varieties, under conditions free from nutrient limitations and biotic or abiotic stresses. This dataset included crop establishment methods, sowing and transplanting dates, seeding rates, planting densities, water management including dates and volumes of irrigation, nutrient management including dates and rates of application, and harvest date. Regarding crop growth, data were collected on the dry weight of green and senesced leaves, stems, and panicles at different growth stages, as well as yield at maturity. On-site daily weather data were available, including daily minimum and maximum temperatures, solar radiation, precipitation, relative humidity, and wind speed. In the case of rice varieties, we aimed to simulate those with broad adaptability that are widely grown in each rice system in each of the provinces. For example, in the case of Hubei Province, Huanghuazhan (inbred rice) and Yangliangyou6 (hybrid rice) were used for SR, while Zhongjiazao17 (inbred) and Liangyou287 (hybrid) were used in early season and Xiangzaoxian45 (inbred) and Tianyouhuazhan (hybrid) were used in late season, in DR. Likewise, Huahuazhan (inbred) and Fengliangyouxiang1 (hybrid) were used in RR ( Supplementary Table 15 ). The agreement between simulated and observed values was assessed using the correlation coefficient, root mean square error (RMSE), and normalized RMSE (RMSEn), expressed as a percentage of the mean observed value. Observed yields across experiments involving various rice systems and varieties ranged from 4.4 to 11.7 Mg ha -1 and were in close alignment with simulated values, as evidenced by a relatively low RMSE of 0.66 Mg ha -1 , equivalent to 8% of the mean observed yield in the calibration dataset ( Extended Data Fig. 4 ). In the validation dataset, RMSE (0.95 Mg ha -1 ) corresponded to 11% of the mean observed yield, also indicating a reasonable level of agreement. These results provide confidence that the calibrated models are robust and capable of accurately simulating yield potential across diverse climates and rice systems in China. We simulated yield potential for each rice cycle (SR, early and late seasons of DR, main and ratoon seasons of RR) within each rice system for each of the RWS buffers. Compiling weather data spanning multiple consecutive years is essential for accurately simulating rice yield potential and accounting for its inter-annual variability. In our study, we utilized 20 years of daily weather data from 2003 to 2022, including daily solar radiation, minimum and maximum temperatures, precipitation, vapor pressure deficit, and wind speed, which is deemed sufficient for yield potential simulations under both favorable and unfavorable environments. These data were sourced from the China Meteorological Data Service Centre for each RWS ( Extended Data Fig. 2 ). Weather data were subjected to quality control procedures to address possible missing values and rectify erroneous entries 63,64 . For the whole dataset, the proportion of missing values for each of the six weather variables did not exceed 0.1% across all RWS. For these missing values, a linear interpolation was applied to fill gaps in the data. Simulations were performed based on actual establishment date, daily measured weather data, and genetic coefficients of representative rice varieties ( see Supplementary Information Text Section 5 ). In locations where both inbred and hybrid rice varieties were planted by farmers, we conducted separate simulations for their yield potential ( Supplementary Fig. S7, Supplementary Table S13 ). The average yield potential for each rice cycle was calculated based on the proportion of the area of inbred and hybrid rice 65 . Annual yield potential was estimated by summing the yields from the early and late seasons for DR and the main and ratoon seasons for RR. Current (2020-2022) and future (2040) rice demand. We took the average (2020-2022) annual domestic rice demand in China as the baseline for our scenario assessment. Annual domestic rice demand was determined using data on national rice production, imports, exports, and stock changes over the same period from 66,67 ( Supplementary Table S17 ). To project future rice demand in China by 2040, we multiplied the projected population-based on the UN’s medium fertility variant of population projections-by the estimated per-capita rice consumption for 2040 68 . The 2040 per-capita consumption was calculated by analyzing the relative change from the 2020-2022 using several models, including the International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) database 69 , the Rice Economy Climate Change model 70 , the China-specific version of Model of Agricultural Production and its Impact on the Environment (MAgPIE-China) 71 , and the Asia-Pacific Integrated Model CGE (AIM/CGE) 72 ( Supplementary Table S17 ). Finally, the per-capita rice consumption estimate for 2040 was obtained by averaging the outputs across these models. We found that domestic rice demand in 2040 will decline by 8% compared to the 2020-2022 average due to a combination of decreasing population size and per capita rice demand 68 ( Supplementary Table S17 ). Scenario assessment. To assess future national rice self-sufficiency and potential surpluses or deficits, we compared estimated rice demand for 2040 with projected attainable rice production under various RR area expansion scenarios with, based on an attainable yield level ( Fig. 3 ). Using attainable production as a benchmark provides a more realistic and agronomically sound estimate of future rice supply, offering valuable insights to inform policy decisions, AR&D programs, and resource allocation 29 . To evaluate changes in rice harvested area, we considered four RR area change scenarios. The first scenario involves continuation of historical trends for SR and DR areas, following the observed patterns of harvested area during the past 30 years (1993-2022) without changes in RR area (BAU) ( see Supplementary Information Text Section 6, Supplementary Tables S14-16 ). The second scenario reallocates future DR area losses to RR instead of SR (DR-RR). The third scenario implements current policies aimed at maintaining DR area 73 , while allowing some SR areas to transition to RR (SR-RR), which would increase the harvested area of both the main and ratoon season of RR to 6.8 million hectares, in alignment with the government’s target for RR expansion by 2040. The fourth scenario proposes complete replacement of SR and DR with RR in areas suitable for RR cultivation, without substituting other food crops (FRR) 74 ( Fig. 3 ). In this scenario, to avoid impacting winter food crops, we excluded rice-wheat and rice-rapeseed areas from the RR-suitable zones. The rice-wheat areas for each province were derived from Zhang 75 , while the rice-rapeseed areas were estimated by multiplying the national total rice-rapeseed area by each province’s share of the national rapeseed area 28,76 , due to the unavailability of provincial-level data on rice-rapeseed areas. The national total harvested rice area, attainable production, nitrogen losses, GWP, labor input, and pesticide usage associated with rice farming were estimated under each of the four scenarios ( Fig. 3 ). We determined the harvested rice area for each province by adjusting the areas of SR, DR, and RR according to our scenario-specific assumptions for the 14 RR-planting provinces, while assuming a continuation of historical trends in the remaining provinces. Attainable rice production was calculated based on the harvested area of RR, SR, and /or DR in each province and their respective attainable yields (defined as 80% of yield potential). Nitrogen losses, GWP, labor input, and pesticide usage were estimated by multiplying the current per-unit-area values of each metric for each system in each province by the corresponding harvested area in each scenario. The total values for SR, DR, and RR systems were obtained by aggregating across all provinces, including 14 selected provinces and the remaining provinces ( Extended Data Fig. 2 ). The national totals were calculated by summing across all three systems. Our scenario assessment focused on estimating the aggregated self-sufficiency ratio and the rice surplus or deficit, defined as the ratio and difference between annual rice production and annual rice demand, respectively. All reported rice yield, production, per-capita rice demand, and total rice demand in our study were standardized to paddy rice at a moisture content of 140 g H 2 O kg -1 rice grain. We converted per-capita rice demand to paddy rice by dividing initially reported milled rice data from the USDA and FAO databases by a milling rate of 0.67 67 . Meanwhile, we note that achieving attainable yield by 2040 would require national yield growth rates to double those observed over the past two decades 28 , making it challenging to reach attainable production ( Fig. 3 ). Therefore, we estimated the projected national total rice production by extrapolating yield trends to 2040 based on the observed patterns for each rice system in each province over the past 30 years (1993-2022) ( see Supplementary Information Text Section 6, Extended Data Fig. 4, Supplementary Tables S18-20 ). To support policy making and guide investments in AR&D programs, we calculated the absolute and relative changes in attainable production at the provincial level ( Fig. 4 ). For each of the 14 RR-planting provinces, we compared the projected attainable production in 2040 under three scenarios of reallocating future DR area loss to RR (DR-RR), implementing current policies to maintain DR area while allowing some SR area to transition to RR (SR-RR), and complete replacement of SR and DR with RR in suitable areas without substituting other food crops (FRR) against the BAU scenario, which assumes a continuation of historical trends in SR and DR area without changes in RR area. Declarations Data availability Data on rice yield potential from the Global Yield Gap Atlas (GYGA) can be accessed at www.yieldgap.org. National-level data on harvested rice area, production, export, import, and stock change are available from FAOSTAT at www.fao.org/faostat. Provincial-level data on rice yield, harvested area, and production from the National Bureau of Statistics of China (NBSC) can be found at https://www.stats.gov.cn. Information on rice distribution from the SPAM map is available at www.mapspam.info. Current and future population data from the United Nations are accessible at https://population.un.org/wpp/. Data on current per-capita rice demand from the FAOSTAT and USDA database can be found at www.fao.org/faostat and https://apps.fas.usda.gov/psdonline/app/index.html#/app/advQuery, respectively. The source data are provided with this paper. Acknowledgements We would like to thank local agronomists and extension agents in each province for their help in data collection: Wanhua Xu, Peng Hu, Xiaohong Zhang, Zaigao Chen, Feng Hu, Daogui Liu, Konghao Li, Guomei Zhu, Yu Huang, Huayin Wang, Xiangdong Wang, and Yifei Huang (Anhui); Hongliang Wei (Guangdong); Qianchi Mo, Qibin Jiang, Qidong Huang, Jialiang Liang, Renmin Liang (Guangxi); Shilu Huang (Guizhou); Taiping Qian, Jifu Chen, Lei Xie (Hubei); Huayin Wang, Longsheng Liu, Yichun Xie, Wenping Wu, Kexiu Zhang, Wenping Wu, Jinking Peng, Wen Yang, Jiahong Liu, Guiping Zhang, Xiaojin Hu (Hunan); Yanli Li, Hongshu Zhang, Hongyang Wang, Jie Li, Xiaochun Lu, Jingdong Sun (Jiangsu); Zhonglai Liu, Mingzhu Sun, Haiping Liu (Jiangxi); Haining Yang, Yingping Chen, Yuqian Deng (Sichuan); Feng Yang, Sandan Yan, Xingkuan Chuan (Yunnan); Jianliang Cai, Dequan Jiang (Zhejiang); Xingzhong Yang, Xianglun Zhao, Houxian Liu (Chongqing). We acknowledge the support from the National Key Research and Development Program of China (2022YFD2301003), the National Natural Science Foundation of China (T2261129473), the Hubei International Science and Technology Cooperation Project (2024EHA059), the Fundamental Research Funds for the Central Universities (2662024JC012), the Young Elite Scientists Sponsorship Program by CAST (2022QNRC001), and the Young Top-notch Talent Cultivation Program of Hubei Province. Author contributions S.Y. and P.G. conceived and designed the study. F.F., F.W., G.Y., C.D., B.W., J.Z., L.X., C.Z., D.Y., X.L., C.Y., B.X., Q.T., X.H., C.S., W.W., Y.Z., Y.X., X.L., F.X., J.L., C.Y., T.L., J.Z., J.X., S.T., Q.Z., X.L., K.C., J.H., A.L., K.L., T.L., L.Z. and S.P. provided and compiled the data analyzed in this study. S.Y., Y.W., Y.F. and P.G. performed the spatial analysis, simulation, and data analysis. S.Y. and P.G. wrote the paper, with contributions from all authors. 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Field Crops Res. 318 , 109614 (2024). Additional Declarations There is NO Competing Interest. Supplementary Files YuanetalSupportinginformation.docx Supplementary Information ExtendedDataFigures.docx Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yongjin","middleName":"","lastName":"Zhou","suffix":""},{"id":499784575,"identity":"d6ed0bb3-87e1-44d8-ba85-1d77469f8a30","order_by":21,"name":"Youzun Xu","email":"","orcid":"","institution":"Anhui Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Youzun","middleName":"","lastName":"Xu","suffix":""},{"id":499784576,"identity":"f48e5f69-d310-4e83-b6d0-e6c656382a37","order_by":22,"name":"Xiangchen Liu","email":"","orcid":"","institution":"Xinyang Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xiangchen","middleName":"","lastName":"Liu","suffix":""},{"id":499784577,"identity":"d5c95791-1e09-45f9-99fb-b341a90c9a97","order_by":23,"name":"Fuxian Xu","email":"","orcid":"","institution":"Sichuan Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Fuxian","middleName":"","lastName":"Xu","suffix":""},{"id":499784578,"identity":"08b1ec40-284e-4d7f-9ef0-1eb368c26162","order_by":24,"name":"Jingyong Li","email":"","orcid":"","institution":"Chongqing Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jingyong","middleName":"","lastName":"Li","suffix":""},{"id":499784579,"identity":"75582947-fb9a-4eed-b370-9233192d9033","order_by":25,"name":"Congdang Yang","email":"","orcid":"","institution":"Yunnan Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Congdang","middleName":"","lastName":"Yang","suffix":""},{"id":499784580,"identity":"f88b7bf3-5f53-4872-ae39-d593b8b4ec33","order_by":26,"name":"Tianfeng Liang","email":"","orcid":"","institution":"Guangxi Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Tianfeng","middleName":"","lastName":"Liang","suffix":""},{"id":499784581,"identity":"2e53f207-ad85-4c53-8cc0-31914000fe76","order_by":27,"name":"Jianfu Zhang","email":"","orcid":"","institution":"Fujian Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jianfu","middleName":"","lastName":"Zhang","suffix":""},{"id":499784582,"identity":"535b7f1a-9958-40ea-a658-ccf2cd0aa2ab","order_by":28,"name":"Jing Xiang","email":"","orcid":"","institution":"China National Rice Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Xiang","suffix":""},{"id":499784583,"identity":"e4ccb26a-52b4-463c-9e70-df9f1b67869e","order_by":29,"name":"She Tang","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"She","middleName":"","lastName":"Tang","suffix":""},{"id":499784584,"identity":"477d0cb9-4173-459c-bed7-ba4681ac21a5","order_by":30,"name":"Quanzhi Zhao","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Quanzhi","middleName":"","lastName":"Zhao","suffix":""},{"id":499784585,"identity":"3c01a738-15b3-4a50-89f4-f7439c412ff1","order_by":31,"name":"Xiaoxiao Li","email":"","orcid":"","institution":"Anhui Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoxiao","middleName":"","lastName":"Li","suffix":""},{"id":499784586,"identity":"377f601f-5be1-4a68-8ff2-435016b0b08c","order_by":32,"name":"Kehui Cui","email":"","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Kehui","middleName":"","lastName":"Cui","suffix":""},{"id":499784587,"identity":"b9ddbfb0-ad21-43c1-acc1-2e7fd1bc34fe","order_by":33,"name":"Jianliang Huang","email":"","orcid":"https://orcid.org/0000-0002-4700-0329","institution":"College of Plant Science \u0026 Technology, Huazhong Agricultural University, Wuhan 430070,China","correspondingAuthor":false,"prefix":"","firstName":"Jianliang","middleName":"","lastName":"Huang","suffix":""},{"id":499784588,"identity":"9c5b87a4-2c79-4312-959f-daa23c3736c5","order_by":34,"name":"Akang Liu","email":"","orcid":"","institution":"National Agro-Tech Extension and Service Center Grain Crop Technology Division","correspondingAuthor":false,"prefix":"","firstName":"Akang","middleName":"","lastName":"Liu","suffix":""},{"id":499784589,"identity":"e555e464-297d-4ce8-acba-4519cf7e2d4d","order_by":35,"name":"Ke Liu","email":"","orcid":"https://orcid.org/0000-0002-8343-0449","institution":"University of Tasmania","correspondingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Liu","suffix":""},{"id":499784590,"identity":"1f609aa5-144f-4629-a365-8a406401959a","order_by":36,"name":"Tao Li","email":"","orcid":"","institution":"International Rice Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Li","suffix":""},{"id":499784591,"identity":"8b719df2-70ec-4dfa-8f7f-f96e1022d9f1","order_by":37,"name":"Lu Zhang","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Zhang","suffix":""},{"id":499784592,"identity":"772b1f43-5f9c-4746-8800-38c29041eb6f","order_by":38,"name":"Shaobing Peng","email":"","orcid":"https://orcid.org/0000-0003-1696-9409","institution":"Huazhong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Shaobing","middleName":"","lastName":"Peng","suffix":""}],"badges":[],"createdAt":"2025-08-10 14:55:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7339561/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7339561/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91300156,"identity":"e4a20fab-c5b0-400e-84c3-7e3c5f181eae","added_by":"auto","created_at":"2025-09-15 04:56:17","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":382509,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTrends in ratoon rice harvested area and average yield of single-season, double-season, and ratoon rice systems. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e(A) Proportion of ratoon rice harvested area (main plus ratoon season area) relative to total harvested rice area in southern China (highlighted in red in the inset) over the past 20 years (2002-2022). Inset shows the location of Henan (HN) and Guangdong (GD), which are traditional single- (SR) and double-season rice (DR) producing provinces, respectively. (B) Average yield of SR, DR, and RR for the 2021-2022 period. Error bars represent the standard deviation from the mean. Different letters above bars denote significant difference among rice systems according to Tukey’s test (α=0.05). Data are provided as Source Data.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7339561/v1/2306496dc365b550dd0d4ac8.jpg"},{"id":91300159,"identity":"8e95242d-42c5-42e2-8f4f-a40f5bd8193d","added_by":"auto","created_at":"2025-09-15 04:56:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":760540,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eComparison of (A) annual yield (and its variation), profit, and input use efficiency and (B) yield-scaled inputs and environmental outcomes across single-season, double-season, and ratoon rice systems\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e. Figure shows radar charts for (A) annual yield (and its variation), profit, return on investment (ROI), nitrogen use efficiency (NUE), and eco-efficiency, and (B) yield-scaled metrics including production cost, labor, irrigation water, pesticide, N surplus, and global warming potential (GWP). For each metric, data were normalized relative to the maximum value across the three rice systems. Absolute values of are provided in Supplementary Table 7. Data are provided as Source Data.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7339561/v1/43c75cddb501d9020ee9ef48.png"},{"id":91300157,"identity":"cdcad105-18a0-418b-b175-fe3d7a0d9186","added_by":"auto","created_at":"2025-09-15 04:56:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":509178,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eProjected national-level impacts under different scenarios of ratoon rice adoption by year 2040. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e(A) Harvested rice area, (B) attainable production, (C) nitrogen losses, (D) global warming potential (GWP), (E) labor input, and (F) pesticide. Scenarios include continuation of historical trends without changes in RR area (BAU), reallocation of future DR area loss to RR (DR-RR), maintenance of DR area and some transition of SR to RR (SR-RR), and complete replacement of SR and DR with RR where biophysically feasible and without substituting other food crops (FRR). Baseline values around the year 2020-2022 are shown (horizontal dashed lines). Also shown is the rice self-sufficiency (production-to-demand ratio) in 2040. Data are provided as Source Data.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7339561/v1/5f14d3d2142afc3391cb68e2.png"},{"id":91300158,"identity":"572be6bb-16af-4c13-9227-7f9f576c1a48","added_by":"auto","created_at":"2025-09-15 04:56:17","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":66534,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eImpact of different scenarios of RR adoption on attainable annual rice production. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eChanges were estimated as the difference between a given scenario and the business-as-usual scenario for each rice-producing province. Both absolute (A, C, E) and relative changes (B, D, F) are shown. Scenarios include (A, B) re-allocation of future DR area loss to RR (DR-RR), (C, D) implementing current policies to maintain DR area while allowing some SR area to transition to RR (SR-RR), and (E, F) complete replacement of SR and DR with RR in areas suitable for RR cultivation and without substitution of other crops (FRR). Projections are based on an attainable yield level (80% of yield potential). Only provinces with \u0026gt;1% of national rice harvested area are shown. Data are provided as Source Data.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7339561/v1/3f81e536c9b6bc2c617612f9.jpg"},{"id":91300853,"identity":"e2c73782-5cc7-450a-9fde-eb9d8cf31ab4","added_by":"auto","created_at":"2025-09-15 05:12:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3589558,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7339561/v1/ff6b6d55-b9c8-46df-bd28-c2d1d99b17c6.pdf"},{"id":91300228,"identity":"9c1c84b2-dc62-43e4-a733-893658ae0f84","added_by":"auto","created_at":"2025-09-15 05:04:17","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":914380,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"YuanetalSupportinginformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7339561/v1/f934f9a8cf5074d4b790a04f.docx"},{"id":91300160,"identity":"da251b57-1e01-49dd-bc75-59da8cefdbc6","added_by":"auto","created_at":"2025-09-15 04:56:17","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1540711,"visible":true,"origin":"","legend":"","description":"","filename":"ExtendedDataFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-7339561/v1/e3f8c8e39f1f564b272dbf70.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Ratoon rice allows millions of smallholders to meet production and environmental goals","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFarming systems in China have experienced profound changes over the past 20 years. Rapid economic growth has led to massive migration from rural to urban areas, labor shortage, and higher farm wages\u003csup\u003e1-3\u003c/sup\u003e. One of the consequences of these trends has been a shift from a labor-intensive double-season rice system (DR), including two crops per year, towards a single-season rice system (SR) that includes one crop\u003csup\u003e4\u003c/sup\u003e. This transition has led to a decline in rice harvested area, further exacerbated by cropland conversion for urban, industrial, and recreational uses\u003csup\u003e5,6\u003c/sup\u003e. These trends are concerning because rice represents the basis of the diet for 1.4 billion people in China\u003csup\u003e7,8\u003c/sup\u003e. Consequently, the Chinese government has set explicit goals to increase rice self-sufficiency, and even generate a rice surplus, which seems vital for national security in the context of trade disruptions due to pandemics and geopolitical conflicts\u003csup\u003e9,10\u003c/sup\u003e. Additionally, China has set ambitious goals to reduce the environmental footprint from crop production\u003csup\u003e11\u003c/sup\u003e. Failure to achieve these goals could have far-reaching consequences in the rest of the world, including higher food prices, land conversion, and climate change\u003csup\u003e12,13\u003c/sup\u003e. At question is which innovations can help China make rice systems more productive, profitable, and sustainable.\u003c/p\u003e\n\u003cp\u003eWithin this challenging context, the ratoon rice system (RR) has emerged as a viable option. Ratoon rice refers to the crop that regrows from the stubble of the previously harvested main season crop (\u003cstrong\u003eExtended Data Fig. 1\u003c/strong\u003e)\u003csup\u003e14,15\u003c/sup\u003e. Cultivation of RR has been practiced since the Western Jin dynasty (3\u003csup\u003erd\u003c/sup\u003e-4\u003csup\u003eth\u003c/sup\u003e century) and described in an ancient Chinese poem as follows: “\u003cem\u003eAt high noon, the grass in verdant grace, a cow leads its calf, a gentle trace. When autumn’s done its fruitful race, the fields are ripe, and from the rice stubble, new rice grains rise, a second cradle (\u003c/em\u003e\u003cem\u003e晌午草青牛引犊，经秋田熟稻生孙\u003c/em\u003e\u003cem\u003e)\u003c/em\u003e”\u003csup\u003e16\u003c/sup\u003e. The RR area has fluctuated over time because of different factors including low and unstable yield, wide adoption of high-yielding modern varieties and associated technologies, difficulties in adopting mechanical harvesting, and, more recently, labor shortage\u003csup\u003e17-21\u003c/sup\u003e. The days of RR seemed over by the early 2000s, when its area did not exceed a few thousand hectares across the country. However, since 2010s, there has been a renewed interest in developing a high-yielding mechanized RR system with smaller labor requirement as an option to mitigate the negative impact of DR-SR transition on rice production\u003csup\u003e19,22-24\u003c/sup\u003e. Moreover, experimental evidence shows that RR can also contribute to reducing the negative environmental footprint\u003csup\u003e7,25,26\u003c/sup\u003e. At issue is the extent to which RR can help China achieve national production and environmental goals from local to national scales.\u003c/p\u003e\n\u003cp\u003eHere we evaluate the agronomic, economic, and environmental performance of RR using a combination of crop modeling and analysis of smallholder survey data \u003cem\u003e(\u003cstrong\u003esee Methods\u003c/strong\u003e)\u003c/em\u003e. We assessed the impact of RR adoption from farm to national scales and discussed implications for policy and agricultural research and development (AR\u0026amp;D) programs. We conclude that RR is a viable option to meet the dual challenge of achieving rice self-sufficiency while drastically reducing the environmental impact in a context of labor shortage and climate change.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eAdoption of ratoon rice in smallholder fields\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe investigated RR adoption in southern China, a region that has the biophysical conditions to allow RR cultivation (\u003cstrong\u003eFig. 1A, Extended Data Fig. 2, Supplementary Figs. S1-3\u003c/strong\u003e). Within this area, RR area has increased sharply from 0.4 to 2.1 million hectares between 2009 and 2022, driven by development of the mechanized rice ratooning technology\u003csup\u003e17\u003c/sup\u003e, which allows mechanical harvest of the main season without damaging the yield of the subsequent ratoon season. Combination of research and extension support has allowed farmers to obtain RR annual yields that are 40% higher than SR, and comparable to those in DR (\u003cstrong\u003eFig. 1B\u003c/strong\u003e). The RR expansion occurred at expense of both DR and SR area, including traditional growing areas of SR and DR in Henan and Guangdong province, respectively (\u003cstrong\u003eFig. 1A\u003c/strong\u003e). At present, RR accounts for 10% of and 7% of the rice harvested area in southern China and 7% nationwide, being cultivated by \u003cem\u003eca.\u003c/em\u003e 3.5 million smallholders (\u003cstrong\u003eFig. 1A, Supplementary Fig. S1, Supplementary Table S1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDrivers for adoption of ratoon rice and associated benefits\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed survey data collected across 23,250 smallholders over multiple years to understand the drivers for RR adoption (\u003cstrong\u003eSupplementary Figs. S4 and S5, Supplementary Tables S2-12\u003c/strong\u003e). The annual rice yield was 7.9, 12.0, and 11.4 Mg ha\u003csup\u003e-1\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e for SR, DR, and RR, respectively, with higher inter-annual yield variation in the ratoon season of RR (10%) compared with yields in the DR and SR systems (\u0026lt;5%) (\u003cstrong\u003eFig. 2A, Supplementary Figs. S4-6\u003c/strong\u003e). Skipping land preparation and crop establishment in the ratoon season led to labor and input savings compared with DR while its shorter growth duration reduced pesticide, labor, and irrigation inputs (\u003cstrong\u003eFig. 2A, Supplementary Figs. S3 and S5, Supplementary Tables S5 and S6\u003c/strong\u003e). On the other hand, inputs and labor were higher in RR compared with SR due to the additional crop cycle. However, the combination of high yields with intermediate inputs, labor, and production costs in RR led to the highest yield per unit of input and labor among the three rice systems and two- and three-fold larger profit than in SR and DR, respectively (\u003cstrong\u003eFig. 2B, Supplementary Fig. S5\u003c/strong\u003e). Besides production and economic benefits, RR also achieved better environmental outcomes (\u003cstrong\u003eFig. 2B, Supplementary Fig. S5\u003c/strong\u003e). For example, the global warming potential (GWP) per unit yield was lowest and eco-efficiency highest in RR compared with the other systems (\u003cstrong\u003eFig. 2B\u003c/strong\u003e). The RR system also showed a substantial improvement in nitrogen-use efficiency compared with DR and SR, reducing nitrogen surpluses by 56% and 3%, respectively (\u003cstrong\u003eFig. 2B\u003c/strong\u003e). Scaled up to the current area (2.1 million hectares), RR adoption led to positive impacts on rice production (7 Mt), with reduced labor requirements and environmental impact per unit yield\u0026nbsp;(\u003cstrong\u003eFig. 2B, Supplementary Fig. S5, Supplementary Table S7\u003c/strong\u003e). This calculation considers the direct impacts derived from SR-RR transition as well as the indirect RR benefits derived from mitigation of the yield loss associated with the DR-SR transition.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eUpscaling potential impacts of ratoon rice adoption from local to national scales\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe assessed the national impact of RR adoption on production, labor, and environmental outcomes. To do that, we used a well-validated crop model to estimate the attainable yield (defined as 80% of yield potential, \u003cstrong\u003e\u003cem\u003esee Methods\u003c/em\u003e\u003c/strong\u003e) for RR, DR, and SR across the main rice producing areas in China following a bottom-up approach based on local weather, soil, and crop system data (\u003cstrong\u003eExtended Data Figs. 2 and 3, Supplementary Fig. S7, Supplementary Table S13\u003c/strong\u003e). Subsequently, we estimated attainable production and environmental outcomes for different scenarios of RR adoption over the next 20 years. Our analysis shows that continuation of historical trends in the harvested area of SR and DR without change in RR area (BAU) will lead to 16% smaller rice harvested area and 15% lower attainable production by 2040 \u003cstrong\u003e(Figs. 3A and B, Supplementary Tables S14-17)\u003c/strong\u003e. Shifting from DR to RR instead of SR (DR-RR scenario) would reduce by half the negative impact of future DR area loss on rice production. Both scenarios (BAU and DR-RR) would still allow rice self-sufficiency due to smaller demand by the year 2040. However, they will not allow China to meet the goals of increasing rice production and surplus and rice imports will likely be required in some years due to year-to-year variation in farmer yields (\u003cstrong\u003eFig. 3B, Supplementary Fig. 6\u003c/strong\u003e). On the other hand, implementation of current policy to maintain DR area while allowing some SR area (19% of 13 million hectares of SR area in the 14 provinces) to switch to RR (SR-RR) will allow China to reach a large rice surplus, equivalent to 27% of domestic demand by 2040. Likewise, full RR adoption (FRR) in areas that allow its cultivation and without substitution of other food crops (\u003cem\u003ee.g.,\u003c/em\u003e wheat and rapeseed) will lead to a relatively large rice surplus (33% of domestic demand). However, there is an important distinction between SR-RR and FRR. While both scenarios allow China to generate a rice surplus, only FRR leads to a substantial reduction in N losses, GWP intensity, pesticide use, and labor requirement (\u003cstrong\u003eFigs. 3D-F\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eWhile achieving attainable yield is feasible for farmers with access to inputs, markets, and extension services\u003csup\u003e7,27\u003c/sup\u003e, it would require national yield gain rates that are two-fold higher than those observed over the past 20 years\u003csup\u003e28\u003c/sup\u003e. Consequently, the actual rice production by 2040 could be lower than the attainable one due to limited yield growth. For example, extrapolation of historical rates of yield gain for DR and SR shows that China will not achieve rice self-sufficiency by 2040 in the BAU scenario (SSR=0.96), leading to an annual deficit of 8 million tons (\u003cstrong\u003eExtended Data Fig.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4, Supplementary Tables S18-20\u003c/strong\u003e). This import requirement is equivalent to \u003cem\u003eca.\u003c/em\u003e two million hectares of rice in Southeast Asia\u003csup\u003e29\u003c/sup\u003e. On the other hand, SSR will change little in the DR-RR scenario (SSR=1.04) and increase substantially in the SR-RR and FRR scenarios (SSR=1.13 and 1.16, respectively). At the end, these findings further highlight the crucial importance of adopting RR to ensure rice self-sufficiency in China couples with relentless efforts to maintain or accelerate current rates of yield improvement through agronomic management and breeding efforts.\u003c/p\u003e\n\u003cp\u003eTo support policy-making and guide investments in AR\u0026amp;D programs, we analyzed the impacts of each RR scenario on attainable rice production at sub-national scale and compared it with BAU \u003cstrong\u003e(Fig. 4)\u003c/strong\u003e. Largest benefit of switching from DR to RR instead of SR (DR-RR scenario) was in Hunan, which is the main DR producing province at present. On the other hand, maintaining DR area and replacing some SR by RR (SR-RR scenario) led to a positive impact in the Yangtze River Valley because DR area was maintained, and in southwest China because SR was replaced by RR. Likewise, full RR adoption wherever feasible and without crop substitution (FRR scenario) led to positive impacts on crop production, with only a slight decrease (-3%) in Guangdong province because the dominant DR system is replaced by RR \u003cstrong\u003e(Fig. 4)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur analysis based on smallholder survey data and crop modeling shows that RR can play a central role in ensuring food security in China while improving economic and environmental outcomes, consistent with findings from experimental data\u003csup\u003e30\u003c/sup\u003e. Compared with the traditional DR and SR systems, RR exhibited higher efficiency in the use of inputs, leading to lower emissions, nitrogen losses, and pesticide per unit yield \u003cstrong\u003e(Fig. 2)\u003c/strong\u003e. Additionally, RR is more profitable and requires less labor, making it a viable cropping system option given the rural-urban demographic shift in China\u003csup\u003e3\u003c/sup\u003e. We also showed that the current national rice policy program (SR-RR) and the full RR adoption scenarios (FRR) reached similar production goals, ensuring rice self-sufficiency in both cases \u003cstrong\u003e(Fig. 3, Extended Data Figs. 3 and 4, Supplementary Table S6)\u003c/strong\u003e. However, the SR-RR is not compatible with the current desire to improve environmental outcomes since GWP and nutrient surplus remain similar to the current baseline\u003csup\u003e11\u003c/sup\u003e. Conversely, adopting RR substantially reduces the overall GWP, nitrogen surplus, pesticide load, and labor requirement, contributing to achieve the dual goal of achieving rice self-sufficiency while reducing negative environmental impacts.\u003c/p\u003e\n\u003cp\u003eThere are still constraints for RR adoption. A primary challenge in scaling up RR is the negative impact of mechanical harvesting of the main season, which damages stubbles and affects the axillary bud development in ratoon season\u003csup\u003e19,31\u003c/sup\u003e. To mitigate it, optimized water management strategies, including heavy soil drying before the harvest of main season and post-harvest irrigation, are crucial to minimize mechanical damage and alleviate heat stress on axillary buds\u003csup\u003e17,19\u003c/sup\u003e. Similarly, timing of nitrogen application is critical for promoting bud regeneration and tiller growth\u003csup\u003e32\u003c/sup\u003e. To date, there has been no explicit breeding program for RR, and the varieties used in RR cultivation were typically selected from those originally bred for SR or DR\u003csup\u003e18\u003c/sup\u003e. Thus, widespread adoption of proper crop management technologies of RR across millions of smallholders will require substantial research and extension support. In addition, successful adoption will also depend on tuning current agricultural policies. For example, current programs provide strong financial support for farmers cultivating DR and smaller and equal support for those practicing RR and SR, despite the additional harvest of the ratoon season\u003csup\u003e33\u003c/sup\u003e. Greater support can help farmers adopt RR instead of SR when moving away from DR and mitigate the associated negative production impact while improving environmental and economic outcomes. We note that adoption of RR system could benefit smallholders not only in China but also in other regions with similar biophysical and socio-economic conditions, especially at a time in which rice systems are being challenged by their negative environmental impacts and high labor requirements\u003csup\u003e7,31,34,35\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eOur analysis is subject to several limitations and uncertainties. First, there is uncertainty on data inputs (weather and crop calendar) and capacity of the model to estimate yield potential. We calibrated and validated the crop models using independent datasets, and the comparison of simulated and observed yields from both datasets provides confidence in the robustness and accuracy of the models for simulating yield potential across diverse climates and rice systems (\u003cstrong\u003eExtended Data Fig. 5\u003c/strong\u003e). We also made an explicit effort to use measured weather data and accurate crop calendar and validate our yield potential estimates with measured yields from well-managed rice experiments where crops received enough fertilizer and pesticides to avoid nutrient limitations and yield reductions due to biotic stresses (\u003cstrong\u003eSupplementary Table S21\u003c/strong\u003e). Likewise, there may be biases in the survey data due to inappropriate representation of the farmer population. However, comparisons of yields derived from our survey data against those from official statistics and previous studies showed good agreement, giving confidence about the representativeness and accuracy of our farmer survey data (\u003cstrong\u003eSupplementary Fig. S8, Supplementary Table S22\u003c/strong\u003e). Due to lack of local emission factors, our estimation of GHG emissions was largely based on IPCC tier-2 factors. Although the use of local factors may modify absolute emissions, we do not expect it will modify the conclusions derived from the comparison of rice systems or production scenarios. Our study did not account for the potential negative impact of climate change on rice yield\u003csup\u003e36\u003c/sup\u003e, which is negligible given the short time period of our study (15 years), the positive impact of CO\u003csub\u003e2\u003c/sub\u003e enrichment, and agronomic adaptation to warmer environments through changes in planting date and variety\u003csup\u003e37-39\u003c/sup\u003e. Indeed, we believe that rice production in our FRR scenario is probably underestimated given that climate-induced warming in northern China may allow further expansion of RR cultivation\u003csup\u003e40,41\u003c/sup\u003e, and the proportion of SR that is being rotated with other crops (\u003cem\u003ee.g.\u003c/em\u003e, rapeseed and wheat) is likely to be smaller (\u003cstrong\u003e\u003cem\u003esee Methods\u003c/em\u003e\u003c/strong\u003e). Despite these uncertainties, the main conclusions about the positive impacts of RR adoption on production, environmental, and economic outcomes seem robust and not likely to change due to these uncertainties.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eData sources.\u003c/strong\u003e Fourteen rice-producing provincial-level administrative regions (hereafter called provinces) where RR, SR, and/or DR systems are grown in China were selected for our analysis (\u003cstrong\u003eExtended Data Figs. S1 and S2, Supplementary Fig. S1\u003c/strong\u003e). These included Anhui, Fujian, Guangxi, Hubei, Hunan, Jiangxi, Yunnan, and Zhejiang (RR, SR, and DR systems), Guizhou, Henan, Jiangsu, Sichuan, and Chongqing (RR and SR systems), and Guangdong (RR and DR systems) (\u003cstrong\u003esee Supplementary Information Text Section 1,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eExtended Data Fig. 2, Supplementary Fig. S1\u003c/strong\u003e). Collectively, these provinces accounted for 70% (SR), 97% (DR), and 99% (RR) of current rice production and harvested area\u003csup\u003e28\u0026nbsp;\u003c/sup\u003e(\u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e). Following previous studies\u003csup\u003e7\u003c/sup\u003e, we followed a two-step approach to identify major producing regions and dominant rice systems in each province. First, we identified the major rice-growing region(s) in each province. Then, we determined the dominant rice systems in each region. For example, we selected three regions in Hubei, with the three rice systems (i.e., RR, SR, and DR) practiced in two regions and only SR in the other region. Across the 14 provinces, our study included 183 system-region combinations, with 103, 40, and 40 cases corresponding to SR, DR, and RR systems, respectively (\u003cstrong\u003eExtended Data Fig. 2\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAgronomic data were collected through local farm survey across 23,250 smallholders over 2020-2022 using structured questionnaires (\u003cstrong\u003eExtended Data Fig. 2\u003c/strong\u003e). The data included yield, cropping system, farm size, tillage practices, methods of crop establishment, sowing and transplanting dates, mechanization for various field operations, seeding rates, nutrient fertilizer rates, pesticide use, irrigation amount and management, energy sources for irrigation pumping, grain drying, labor, and straw management practices (\u003cstrong\u003eSupplementary Tables S2 and S3\u003c/strong\u003e). Labor inputs were estimated based on direct questions regarding the number of man-hours required for various field operations. Average values for each rice system were first calculated based on the on-farm survey data within each province (\u003cstrong\u003eSupplementary Tables S4-6\u003c/strong\u003e).\u0026nbsp; These provincial averages were then weighted by the annual rice harvested area of each cropping system in each province to generate the overall average values reported in this study, considering all 14 provinces collectively (\u003cstrong\u003eFig. 2\u003c/strong\u003e). Rice grain yields were calculated at a standard moisture content of 140 g H\u003csub\u003e2\u003c/sub\u003eO kg\u003csup\u003e-1\u003c/sup\u003e grain, with separate data provided for each season, utilizing data from at least three recent rice-growing seasons within each respective rice system.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe 14 provinces encompass a diverse range of biophysical and socio-economic environments, leading to average rice yields ranging from 5.5 to 10.8 Mg ha\u003csup\u003e-1\u003c/sup\u003e (SR) and 4.3 to 8.0 Mg ha\u003csup\u003e-1\u003c/sup\u003e (early-season of DR) and 4.3 to 7.7 Mg ha\u003csup\u003e-1\u003c/sup\u003e (late-season of DR) from our survey (\u003cstrong\u003eSupplementary Fig. S2, Supplementary Tables S4-6\u003c/strong\u003e). In the case of RR system, yields range between 6.1 to 9.2 Mg ha\u003csup\u003e-1\u003c/sup\u003e (main season) and 1.6 to 5.1 Mg ha\u003csup\u003e-1\u003c/sup\u003e (ratoon season). For each province, accuracy of survey data was evaluated using other independent datasets, such as those from the National Bureau of Statistics of China, published journal articles, and reports. For example, database yields of SR and DR were compared against those from the National Bureau of Statistics of China for each of the 14 provinces. Agreement between yield data sources was satisfactory, as indicated by the relatively small root mean square error (0.3 Mg ha\u003csup\u003e-1\u003c/sup\u003e), which represented 5% of the average statistical yield, without biases across the entire yield range (\u003cstrong\u003eSupplementary Fig. S8\u003c/strong\u003e). Similarly, we compared the yield of RR at both provincial and national levels with data from statistics and previous publications, showing good agreement across different sources (\u003cstrong\u003eFig. 1, Supplementary Table S22\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNitrogen surplus and global warming potential\u003c/strong\u003e. We estimated a number of parameters related to environmental outcomes, including irrigation water and pesticide use, nitrogen (N) surplus, and global warming potential (GWP), separately on an area- and yield-basis to capture their impacts in relation to both land use and production efficiency (\u003cstrong\u003eFig. 2, Supplementary Fig.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eS5\u003c/strong\u003e). For each rice growing season within each system, N surplus was calculated by determining the difference between N inputs and N outputs\u003csup\u003e42\u003c/sup\u003e (\u003cstrong\u003esee Supplementary Information Text Section 2, Fig. 2, Supplementary Fig. S5\u003c/strong\u003e). The N inputs included synthetic and organic fertilizers and biological N fixation\u003csup\u003e35,43\u003c/sup\u003e. The N outputs primarily consisted of the N removed through the harvested grain and straw. Estimates for synthetic N fertilizer inputs were derived from existing databases, while organic fertilizer inputs were calculated based on type and amount of manure and associated N concentration derived from the local literature\u003csup\u003e44\u003c/sup\u003e. We assumed a biological N fixation rate of 25 kg N per hectare per crop for lowland\u0026nbsp;rice\u003csup\u003e45\u003c/sup\u003e. We note that this study did not include atmospheric deposition and irrigation water as N inputs, as these are typically offset by N losses through leaching, volatilization, and denitrification processes\u003csup\u003e46\u003c/sup\u003e. The N outputs were calculated by accounting for the N removed with the harvested grain and straw. Straw N removal was estimated considering field-specific straw management, including left as mulch, incorporated into the soil, burned in the field, or removed from the field. The N content in rice grain was determined based on average rice grain yield for each season and grain N concentration (\u003cstrong\u003eSupplementary Table S11\u003c/strong\u003e). Straw biomass was estimated using a harvest index of 0.5, which is the ratio of grain weight to total above-ground biomass on a dry-matter basis\u003csup\u003e47\u003c/sup\u003e. The N losses from straw were determined based on the amount of straw N remaining in the field after harvest and the associated N loss as determined by reported straw management practices (\u003cstrong\u003eSupplementary Table S12\u003c/strong\u003e). We note that achieving a zero N surplus is not the objective, as this would lead to depletion of soil organic matter\u003csup\u003e48\u003c/sup\u003e. Therefore, we set a threshold N surplus of 75 kg N per hectare as a critical level to identify excessive N, which could lead to significant reactive N losses, as N losses tend to increase when N surplus exceeds this threshold\u003csup\u003e49,50\u003c/sup\u003e. We also assessed yield-scaled N surplus by calculating the ratio between N surplus and grain yield.\u003c/p\u003e\n\u003cp\u003eIn the case of greenhouse gas (GHG) emissions associated with rice production, we examined emissions of carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e), methane (CH\u003csub\u003e4\u003c/sub\u003e), and nitrous oxide (N\u003csub\u003e2\u003c/sub\u003eO). Our assessment focused on three primary sources of GHG emissions: (i) the production, packaging, and transportation of agricultural inputs such as seeds, fertilizers, pesticides, and machinery (\u003cstrong\u003eSupplementary Tables S8 and S9\u003c/strong\u003e); (ii) emissions resulting from the direct use of fossil fuels for farm operations like irrigation pumping; and (iii) CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO emissions generated during the cultivation of rice in paddy fields\u003csup\u003e7,51\u003c/sup\u003e (\u003cstrong\u003esee Supplementary Information Text Section 3\u003c/strong\u003e). Given that intensive irrigated rice production in lowland areas typically maintains or increases soil organic matter\u003csup\u003e52,53\u003c/sup\u003e, we excluded CO\u003csub\u003e2\u003c/sub\u003e emissions and carbon sequestration from soil in our GHG emission calculations. We estimated annual GHG emissions from the production, storage, and transportation of various agricultural inputs by applying the rates of these inputs and their respective GHG emission coefficients\u003csup\u003e7\u003c/sup\u003e (\u003cstrong\u003eSupplementary Table S8\u003c/strong\u003e). Total N\u003csub\u003e2\u003c/sub\u003eO emissions due to N inputs to paddy fields were assessed through both direct and indirect pathways\u003csup\u003e54\u003c/sup\u003e. Direct soil N\u003csub\u003e2\u003c/sub\u003eO emissions were calculated based on the N surplus, defined as the difference between applied N inputs and the accumulated N in aboveground biomass at physiological maturity. We used the N-balance approach proposed by van Groenigen et al.\u003csup\u003e55\u003c/sup\u003e to estimate direct soil N\u003csub\u003e2\u003c/sub\u003eO emissions for each rice season. Indirect N\u003csub\u003e2\u003c/sub\u003eO emissions were estimated using the IPCC methodology, assuming they constitute 20% of direct N\u003csub\u003e2\u003c/sub\u003eO emissions\u003csup\u003e56\u003c/sup\u003e. The CH\u003csub\u003e4\u003c/sub\u003e emissions from rice paddy fields were quantified according to IPCC guidelines, taking into account factors such as the duration of the rice cultivation period, water management practices during both the growing season and the pre-season period, and the type and quantity of organic amendments applied, including straw, manure, and compost\u003csup\u003e57\u003c/sup\u003e. We started with the baseline CH\u003csub\u003e4\u003c/sub\u003e emission factor and adjusted it to reflect the specific crop management practices observed in the selected regions. All GHG emissions were converted to CO\u003csub\u003e2\u003c/sub\u003e-equivalent (CO\u003csub\u003e2\u003c/sub\u003e-eq) values using the 100-year global warming potentials of CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO, which are 25 and 298 times the intensity of CO\u003csub\u003e2\u003c/sub\u003e on a mass basis, respectively\u003csup\u003e58\u003c/sup\u003e. For each rice growing season in each rice system and region, we calculated the GWP by summing the CO\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4\u003c/sub\u003e, and N\u003csub\u003e2\u003c/sub\u003eO emissions, all expressed as CO\u003csub\u003e2\u003c/sub\u003e-eq. The average GHG emissions for each rice system were calculated for each province. Next, these provincial averages were weighted by the annual rice harvested area of each rice system in the respective provinces to obtain the overall average values per rice system (\u003cstrong\u003eFig. 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEconomic analysis\u003c/strong\u003e. For each rice season across different rice systems and provinces, we estimated production costs, gross revenue, net profits, and the benefit-to-cost ratio. The production costs included expenditures related to human labor, machinery for field operations such as tilling, plowing, seeding, transplanting, harvesting, and grain drying. It also includes the cost of rice seeds for both inbred and hybrid rice varieties, nitrogen, phosphorus, and potassium fertilizers, organic fertilizer, irrigation water, electricity for irrigation, sprayer, and grain drying, and herbicides, pesticides, and fungicides. Prices were derived from market rates, which included charges from local service providers for machinery use and market prices for labor and agricultural inputs (\u003cstrong\u003eSupplementary Table S10\u003c/strong\u003e). We note that we did not consider costs from land rental values, taxes, and machinery depreciation. Considering that rice farming in these provinces is predominantly managed by smallholders, we excluded the rental value of land from our estimation, as there is generally no cash cost associated with land use. Additionally, we omitted machinery depreciation since it is typically included in the service providers’ fees. Gross return was determined by multiplying grain yield by the official 2022 rice grain purchase price set by the central government, which varied by rice system and season: US$ 397 Mg\u003csup\u003e-1\u003c/sup\u003e for SR, US$ 388 Mg\u003csup\u003e-1\u003c/sup\u003e for early season of DR and main season of RR, and US$ 403 Mg\u003csup\u003e-1\u003c/sup\u003e for late season of DR and ratoon season of RR\u003csup\u003e59\u003c/sup\u003e. For DR and RR systems, the total production cost and gross return were computed by aggregating the values from the two rice seasons within each system. Net profit was calculated by the difference between gross return and production cost and return on investment (ROI) as the ratio between net profit and production cost (\u003cstrong\u003eFig. 2, Supplementary Fig. S5\u003c/strong\u003e). Our economic analysis utilized an average exchange rate of US$ 1 to CNY 6.5 for the 2020-2022 period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAttainable yield estimation\u003c/strong\u003e.\u0026nbsp;We followed the protocols of the Global Yield Gap Atlas (GYGA, www.yieldgap.org) to identify a number of representative sites of the rice producing area in China (\u003cstrong\u003eExtended Data Fig. 2\u003c/strong\u003e). Our methodology involved selecting reference RWS based on existing weather stations, distribution of harvested rice area, and a climate zone (CZ) scheme (\u003cstrong\u003esee Supplementary Information Text Section 4\u003c/strong\u003e). This approach considers spatial variation in three critical variables influencing crop yield potential, including annual growing-degree days, aridity index, and temperature seasonality\u003csup\u003e60\u003c/sup\u003e. We also incorporated expert insights from local agronomists and extension agents in each of the provinces, based on the Spatial Production Allocation Model map (SPAM 2020, www.mapspam.info) as a reference. We selected CZs that accounted for more than 5% of national rice harvested area. A 100 km radius buffer was drawn around each RWS, with their borders clipped as needed to ensure that buffer extent does not go beyond the CZ where the stations were located. We prioritized the selection of buffers, starting with the largest harvested area and proceeding to the second largest, while excluding buffers that overlapped with selected ones by more than 20%. This iterative process continued until the cumulative coverage of selected buffers encompassed at least 50% of the national total harvested rice area. The final selection of sites was reviewed and validated by local agronomists and extension agents to ensure accurate representation of harvested rice areas, with particular attention given to RR, which exhibits uneven national distribution. Ultimately, we identified 32, 21, and 26 RWS and 12, 8, and 13 CZs for the SR, DR, and RR systems, respectively, across the 14 provinces and 12 RWS and 8 CZs in the remaining provinces (\u003cstrong\u003esee Supplementary Information Text Section 4, Extended Data Fig. 2\u003c/strong\u003e). Selected RWS buffers and associated CZs in the 14 provinces collectively covered 49% and 74% of national total rice harvested area, respectively. Altogether, selected RWS buffers and CZs from all provinces accounted for 63% and 91% of the national rice harvested area, respectively.\u003c/p\u003e\n\u003cp\u003eTo estimate national rice production capacity, we used attainable yield, defined as 80% of the yield potential for irrigated rice (\u003cstrong\u003eExtended Data Fig. 3\u003c/strong\u003e). This value is a reasonable yield goal for farmers with good access to markets, inputs, and extension services, as demonstrated in several regions\u003csup\u003e7,27\u003c/sup\u003e. Yield potential was simulated using the ORYZA v3 model for SR and DR, and the ORYZA_R model for RR. The ORYZA v3 model has been well validated in field experiments and is widely used to simulate rice yield potential in various cropping systems, including single-, double-, and triple-season rice systems across both irrigated and rainfed environments in temperate, subtropical, and tropical regions\u003csup\u003e7,61\u003c/sup\u003e. The ORYZA_R model, which was developed by adding a reserve pool submodule to ORYZA v3, simulates the growth and development of both the main and ratoon seasons in RR. ORYZA v3 has also been validated in field experiments with various treatments and rice varieties where crops were managed to avoid water and nutrient limitations and kept free of biotic stresses\u003csup\u003e62\u003c/sup\u003e. To calibrate and validate crop genetic coefficients for study, crop management data and yield were compiled from high-yielding experiments conducted across different regions and dominant rice varieties, under conditions free from nutrient limitations and biotic or abiotic stresses. This dataset included crop establishment methods, sowing and transplanting dates, seeding rates, planting densities, water management including dates and volumes of irrigation, nutrient management including dates and rates of application, and harvest date. Regarding crop growth, data were collected on the dry weight of green and senesced leaves, stems, and panicles at different growth stages, as well as yield at maturity. On-site daily weather data were available, including daily minimum and maximum temperatures, solar radiation, precipitation, relative humidity, and wind speed. In the case of rice varieties, we aimed to simulate those with broad adaptability that are widely grown in each rice system in each of the provinces. For example, in the case of Hubei Province, Huanghuazhan (inbred rice) and Yangliangyou6 (hybrid rice) were used for SR, while Zhongjiazao17 (inbred) and Liangyou287 (hybrid) were used in early season and Xiangzaoxian45 (inbred) and Tianyouhuazhan (hybrid) were used in late season, in DR. Likewise, Huahuazhan (inbred) and Fengliangyouxiang1 (hybrid) were used in RR (\u003cstrong\u003eSupplementary Table 15\u003c/strong\u003e). The agreement between simulated and observed values was assessed using the correlation coefficient, root mean square error (RMSE), and normalized RMSE (RMSEn), expressed as a percentage of the mean observed value. Observed yields across experiments involving various rice systems and varieties ranged from 4.4 to 11.7 Mg ha\u003csup\u003e-1\u003c/sup\u003e and were in close alignment with simulated values, as evidenced by a relatively low RMSE of 0.66 Mg ha\u003csup\u003e-1\u003c/sup\u003e, equivalent to 8% of the mean observed yield in the calibration dataset (\u003cstrong\u003eExtended Data Fig. 4\u003c/strong\u003e). In the validation dataset, RMSE (0.95 Mg ha\u003csup\u003e-1\u003c/sup\u003e) corresponded to 11% of the mean observed yield, also indicating a reasonable level of agreement. These results provide confidence that the calibrated models are robust and capable of accurately simulating yield potential across diverse climates and rice systems in China.\u003c/p\u003e\n\u003cp\u003eWe simulated yield potential for each rice cycle (SR, early and late seasons of DR, main and ratoon seasons of RR) within each rice system for each of the RWS buffers. Compiling weather data spanning multiple consecutive years is essential for accurately simulating rice yield potential and accounting for its inter-annual variability. In our study, we utilized 20 years of daily weather data from 2003 to 2022, including daily solar radiation, minimum and maximum temperatures, precipitation, vapor pressure deficit, and wind speed, which is deemed sufficient for yield potential simulations under both favorable and unfavorable environments. These data were sourced from the China Meteorological Data Service Centre for each RWS (\u003cstrong\u003eExtended Data Fig. 2\u003c/strong\u003e). Weather data were subjected to quality control procedures to address possible missing values and rectify erroneous entries\u003csup\u003e63,64\u003c/sup\u003e. For the whole dataset, the proportion of missing values for each of the six weather variables did not exceed 0.1% across all RWS. For these missing values, a linear interpolation was applied to fill gaps in the data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimulations were performed based on actual establishment date, daily measured weather data, and genetic coefficients of representative rice varieties (\u003cstrong\u003esee Supplementary Information Text Section 5\u003c/strong\u003e). In locations where both inbred and hybrid rice varieties were planted by farmers, we conducted separate simulations for their yield potential (\u003cstrong\u003eSupplementary Fig. S7, Supplementary Table S13\u003c/strong\u003e). The average yield potential for each rice cycle was calculated based on the proportion of the area of inbred and hybrid rice\u003csup\u003e65\u003c/sup\u003e. Annual yield potential was estimated by summing the yields from the early and late seasons for DR and the main and ratoon seasons for RR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCurrent (2020-2022) and future (2040) rice demand.\u003c/strong\u003e We took the average (2020-2022) annual domestic rice demand in China as the baseline for our scenario assessment. Annual domestic rice demand was determined using data on national rice production, imports, exports, and stock changes over the same period from\u003csup\u003e66,67\u003c/sup\u003e (\u003cstrong\u003eSupplementary Table S17\u003c/strong\u003e). To project future rice demand in China by 2040, we multiplied the projected population-based on the UN’s medium fertility variant of population projections-by the estimated per-capita rice consumption for 2040\u003csup\u003e68\u003c/sup\u003e. The 2040 per-capita consumption was calculated by analyzing the relative change from the 2020-2022 using several models, including the International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) database\u003csup\u003e69\u003c/sup\u003e, the Rice Economy Climate Change model\u003csup\u003e70\u003c/sup\u003e, the China-specific version of Model of Agricultural Production and its Impact on the Environment (MAgPIE-China)\u003csup\u003e71\u003c/sup\u003e, and the Asia-Pacific Integrated Model CGE (AIM/CGE)\u003csup\u003e72\u003c/sup\u003e (\u003cstrong\u003eSupplementary Table S17\u003c/strong\u003e). Finally, the per-capita rice consumption estimate for 2040 was obtained by averaging the outputs across these models. We found that domestic rice demand in 2040 will decline by 8% compared to the 2020-2022 average due to a combination of decreasing population size and per capita rice demand\u003csup\u003e68\u003c/sup\u003e (\u003cstrong\u003eSupplementary Table S17\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScenario assessment.\u003c/strong\u003e To assess future national rice self-sufficiency and potential surpluses or deficits, we compared estimated rice demand for 2040 with projected attainable rice production under various RR area expansion scenarios with, based on an attainable yield level (\u003cstrong\u003eFig. 3\u003c/strong\u003e). Using attainable production as a benchmark provides a more realistic and agronomically sound estimate of future rice supply, offering valuable insights to inform policy decisions, AR\u0026amp;D programs, and resource allocation\u003csup\u003e29\u003c/sup\u003e. To evaluate changes in rice harvested area, we considered four RR area change scenarios. The first scenario involves continuation of historical trends for SR and DR areas, following the observed patterns of harvested area during the past 30 years (1993-2022) without changes in RR area (BAU) (\u003cstrong\u003esee Supplementary Information Text Section 6, Supplementary Tables S14-16\u003c/strong\u003e). The second scenario reallocates future DR area losses to RR instead of SR (DR-RR). The third scenario implements current policies aimed at maintaining DR area\u003csup\u003e73\u003c/sup\u003e, while allowing some SR areas to transition to RR (SR-RR), which would increase the harvested area of both the main and ratoon season of RR to 6.8 million hectares, in alignment with the government’s target for RR expansion by 2040. The fourth scenario proposes complete replacement of SR and DR with RR in areas suitable for RR cultivation, without substituting other food crops (FRR)\u003csup\u003e74\u003c/sup\u003e (\u003cstrong\u003eFig. 3\u003c/strong\u003e). In this scenario, to avoid impacting winter food crops, we excluded rice-wheat and rice-rapeseed areas from the RR-suitable zones. The rice-wheat areas for each province were derived from Zhang\u003csup\u003e75\u003c/sup\u003e, while the rice-rapeseed areas were estimated by multiplying the national total rice-rapeseed area by each province’s share of the national rapeseed area\u003csup\u003e28,76\u003c/sup\u003e, due to the unavailability of provincial-level data on rice-rapeseed areas.\u003c/p\u003e\n\u003cp\u003eThe national total harvested rice area, attainable production, nitrogen losses, GWP, labor input, and pesticide usage associated with rice farming were estimated under each of the four scenarios (\u003cstrong\u003eFig. 3\u003c/strong\u003e). We determined the harvested rice area for each province by adjusting the areas of SR, DR, and RR according to our scenario-specific assumptions for the 14 RR-planting provinces, while assuming a continuation of historical trends in the remaining provinces. Attainable rice production was calculated based on the harvested area of RR, SR, and /or DR in each province and their respective attainable yields (defined as 80% of yield potential). Nitrogen losses, GWP, labor input, and pesticide usage were estimated by multiplying the current per-unit-area values of each metric for each system in each province by the corresponding harvested area in each scenario. The total values for SR, DR, and RR systems were obtained by aggregating across all provinces, including 14 selected provinces and the remaining provinces (\u003cstrong\u003eExtended Data Fig. 2\u003c/strong\u003e). The national totals were calculated by summing across all three systems. Our scenario assessment focused on estimating the aggregated self-sufficiency ratio and the rice surplus or deficit, defined as the ratio and difference between annual rice production and annual rice demand, respectively. All reported rice yield, production, per-capita rice demand, and total rice demand in our study were standardized to paddy rice at a moisture content of 140 g H\u003csub\u003e2\u003c/sub\u003eO kg\u003csup\u003e-1\u003c/sup\u003e rice grain. We converted per-capita rice demand to paddy rice by dividing initially reported milled rice data from the USDA and FAO databases by a milling rate of 0.67\u003csup\u003e67\u003c/sup\u003e. Meanwhile, we note that achieving attainable yield by 2040 would require national yield growth rates to double those observed over the past two decades\u003csup\u003e28\u003c/sup\u003e, making it challenging to reach attainable production (\u003cstrong\u003eFig. 3\u003c/strong\u003e). Therefore, we estimated the projected national total rice production by extrapolating yield trends to 2040 based on the observed patterns for each rice system in each province over the past 30 years (1993-2022) (\u003cstrong\u003esee Supplementary Information Text Section 6, Extended Data Fig. 4, Supplementary Tables S18-20\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo support policy making and guide investments in AR\u0026amp;D programs, we calculated the absolute and relative changes in attainable production at the provincial level (\u003cstrong\u003eFig. 4\u003c/strong\u003e). For each of the 14 RR-planting provinces, we compared the projected attainable production in 2040 under three scenarios of reallocating future DR area loss to RR (DR-RR), implementing current policies to maintain DR area while allowing some SR area to transition to RR (SR-RR), and complete replacement of SR and DR with RR in suitable areas without substituting other food crops (FRR) against the BAU scenario, which assumes a continuation of historical trends in SR and DR area without changes in RR area.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData on rice yield potential from the Global Yield Gap Atlas (GYGA) can be accessed at www.yieldgap.org. National-level data on harvested rice area, production, export, import, and stock change are available from FAOSTAT at www.fao.org/faostat. Provincial-level data on rice yield, harvested area, and production from the National Bureau of Statistics of China (NBSC) can be found at https://www.stats.gov.cn. Information on rice distribution from the SPAM map is available at www.mapspam.info. Current and future population data from the United Nations are accessible at https://population.un.org/wpp/. Data on current per-capita rice demand from the FAOSTAT and USDA database can be found at www.fao.org/faostat and https://apps.fas.usda.gov/psdonline/app/index.html#/app/advQuery, respectively. The source data are provided with this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank local agronomists and extension agents in each province for their help in data collection: Wanhua Xu, Peng Hu, Xiaohong Zhang, Zaigao Chen, Feng Hu, Daogui Liu, Konghao Li, Guomei Zhu, Yu Huang, Huayin Wang, Xiangdong Wang, and Yifei Huang (Anhui); Hongliang Wei (Guangdong); Qianchi Mo, Qibin Jiang, Qidong Huang, Jialiang Liang, Renmin Liang (Guangxi); Shilu Huang (Guizhou); Taiping Qian, Jifu Chen, Lei Xie (Hubei); Huayin Wang, Longsheng Liu, Yichun Xie, Wenping Wu, Kexiu Zhang, Wenping Wu, Jinking Peng, Wen Yang, Jiahong Liu, Guiping Zhang, Xiaojin Hu (Hunan); Yanli Li, Hongshu Zhang, Hongyang Wang, Jie Li, Xiaochun Lu, Jingdong Sun (Jiangsu); Zhonglai Liu, Mingzhu Sun, Haiping Liu (Jiangxi); Haining Yang, Yingping Chen, Yuqian Deng (Sichuan); Feng Yang, Sandan Yan, Xingkuan Chuan (Yunnan); Jianliang Cai, Dequan Jiang (Zhejiang); Xingzhong Yang, Xianglun Zhao, Houxian Liu (Chongqing). We acknowledge the support from the National Key Research and Development Program of China (2022YFD2301003), the National Natural Science Foundation of China (T2261129473), the Hubei International Science and Technology Cooperation Project (2024EHA059), the Fundamental Research Funds for the Central Universities (2662024JC012), the Young Elite Scientists Sponsorship Program by CAST (2022QNRC001), and the Young Top-notch Talent Cultivation Program of Hubei Province.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.Y. and P.G. conceived and designed the study. F.F., F.W., G.Y., C.D., B.W., J.Z., L.X., C.Z., D.Y., X.L., C.Y., B.X., Q.T., X.H., C.S., W.W., Y.Z., Y.X., X.L., F.X., J.L., C.Y., T.L., J.Z., J.X., S.T., Q.Z., X.L., K.C., J.H., A.L., K.L., T.L., L.Z. and S.P. provided and compiled the data analyzed in this study. S.Y., Y.W., Y.F. and P.G. performed the spatial analysis, simulation, and data analysis. S.Y. and P.G. wrote the paper, with contributions from all authors. All authors reviewed and edited the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePeng, S. B. Reflection on China\u0026rsquo;s rice production strategies during the transition period. \u003cem\u003e Sin. Vitae\u003c/em\u003e \u003cstrong\u003e44\u003c/strong\u003e, 845\u0026ndash;850 (2014).\u003c/li\u003e\n\u003cli\u003eKnight, J., Deng, Q. \u0026amp; Li, S. The puzzle of migrant labour shortage and rural labour surplus in China. \u003cem\u003eChina Econ. Rev.\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 585\u0026ndash;600 (2011).\u003c/li\u003e\n\u003cli\u003eGao, J., Song, G. \u0026amp; Sun, X. Does labor migration affect rural land transfer? 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Optimizing tillage regimes in rice-rapeseed rotation system to enhance crop yield and environmental sustainability. \u003cem\u003eField Crops Res.\u003c/em\u003e \u003cstrong\u003e318\u003c/strong\u003e, 109614 (2024).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7339561/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7339561/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"China aims to increase rice production and reduce associated negative environmental impact. However, increasingly limiting and expensive rural labor has driven a shift from double- to single-season rice, posing a threat to national food security. Here, we evaluated the impact of an alternative ratoon rice system on production and environmental outcomes using smallholders’ survey data together with spatial crop modeling. We showed that ratoon rice adoption would ensure rice self-sufficiency and drastically reduce nutrient losses, pesticide use, greenhouse gas emissions, and labor requirements. We conclude that ratoon rice is a viable option to meet the dual goal of increasing production and reducing negative environmental impacts.","manuscriptTitle":"Ratoon rice allows millions of smallholders to meet production and environmental goals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-15 04:56:13","doi":"10.21203/rs.3.rs-7339561/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-sustainability","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natsustain","sideBox":"Learn more about [Nature Sustainability](http://www.nature.com/natsustain/)","snPcode":"","submissionUrl":"","title":"Nature Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"78b38f62-95aa-4577-9e2a-95f10897f456","owner":[],"postedDate":"September 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":53086258,"name":"Biological sciences/Plant sciences/Plant ecology"},{"id":53086259,"name":"Earth and environmental sciences/Ecology/Agroecology"}],"tags":[],"updatedAt":"2025-09-15T04:56:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-15 04:56:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7339561","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7339561","identity":"rs-7339561","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}