Meta-analysis of yield-emission trade-off in direct seeded vs. puddled transplanted rice: Towards a cleaner and sustainable production K. Srikanth Reddy a,b, C.M. Parihar a,*, P. Panneerselvam b,**, Ayan Sarkar a, Kiranmoy Patra a, Sneha Bharadwaj c, D.R. Sena d, G. Sreeja Reddy a, Alok Sinha a, Rajkumar Dhakar a, Virender Kumar e, Hari Sankar Nayak a,f a ICAR-Indian Agricultural Research Institute (IARI), New Delhi, India b International Rice Research Institute (IRRI), South Asia Regional Centre, Varanasi, India c ICAR-Indian Agricultural Research Institute (IARI), Assam, India d International Water Management Institute, New Delhi, India e International Rice Research Institute (IRRI), Los Banos, Philippines f School of Integrative Plant Science, Cornell University, Ithaca, NY, USA A R T I C L E I N F O Keywords: Mitigation Carbon footprints Direct seeded rice Global warming potential Puddled transplanted rice Sustainability Yield-emission trade-off A B S T R A C T Conventional rice production through puddled transplanted rice-PTR is tillage, water, energy, and capital intensive. Furthermore, it is a major contributor to greenhouse gas (GHGs) emissions. In this regard, Direct seeded rice-DSR can be a potential alternative to PTR for reducing GHGs emissions, while sustaining yields. However, depending upon agroclimatic situation, whether the effect of DSR on GHGs emission and yield are consistent or not, as compared to PTR need a comprehensive analysis. To bridge this knowledge gap, we per- formed a meta-analysis synthesizing 876 paired measurements from 54-peer-reviewed studies to understand how DSR impacts N2O and CH4 emissions, global warming potential-GWP (heat-trapping potential of greenhouse gases compared to CO2), yield and C-footprint-CFP (environmental impact in CO2 eq. due to concerned activity). Compared to PTR, DSR decreased CH4 emissions by 70%, GWP by 37% and CFP by 34%, despite 85% increase in N2O emissions. However, this shift comes with 11% decrease in yield. To decipher the primary factors driving these outcomes, we conducted subgroup analyses by taking environmental conditions and management practices as predictors in a random effect model. Low to medium pH soils, zero tillage, puddled soil (wet DSR), conven- tional flooding, and high nitrogen rates (>200 kg/ha) are found to be favorable for DSR with comparable yields but posing a discrepancy with environmental sustainability benefits. Therefore, further research to evaluate DSR across agro-ecologies, management practices are needed to optimize yields with lower GWP and CFP. 1. Introduction Global warming is a primary challenge for entire world. The global surface temperature has been raised by 1.1 ◦C above the preindustrial levels by 2011–2020 (IPCC, 2023). Greenhouse gases (GHGs) such as carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) are the prime contributors to global warming leading to climate change. Agri- culture being one of the major sources of these gases is responsible for 16.2 billion tonnes of CO2 equivalent (Gt CO2 eq.) GHG emissions, that accounts to 22% of overall GHGs emissions (FAO, 2023). As of present day, CH4 and N2O gases makes up to 32% and 14% of the overall agri-food systems emissions, respectively. Among agricultural practices, rice cultivation is one of the most important anthropogenic sources of GHGs emission with a share of 10% in agri-food systems emissions (FAO, 2023). Noticeably, rice cultivation stands as the second largest contributor to CH4 emissions followed by enteric fermentation (Smartt et al., 2016), beside it contributes to 11% of worldwide agricultural N2O emissions (Zhang et al., 2021). CH4 and N2O gases are significant and primary contributors to GHGs emission from rice cultivation, possessing a global warming potential (GWP) of 28 and 298 times higher than that of CO2 over a 100-year time horizon, respectively (IPCC, 2001). In Asia, rice serves as a cornerstone of the diet, representing 86% of * Corresponding author. ** Corresponding author. E-mail addresses: pariharcm@gmail.com (C.M. Parihar), p.panneerselvam@irri.org (P. Panneerselvam). Contents lists available at ScienceDirect Cleaner Environmental Systems journal homepage: www.journals.elsevier.com/cleaner-environmental-systems https://doi.org/10.1016/j.cesys.2024.100238 Received 5 July 2024; Received in revised form 7 November 2024; Accepted 18 November 2024 Cleaner Environmental Systems 16 (2025) 100238 Available online 20 November 2024 2666-7894/© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ). global rice cultivation and 90% of production (FAO, 2022). On the flip side, Asia has emerged as the leading emitter of GHGs, contributing 6.8 Gt CO2 eq, which represents 42% of total emissions from the agriculture sector. Notably, India and China, as the leading rice producers, are accountable for 47% and 56% of CH4 and N2O emissions associated with rice cultivation (FAO, 2023). Across most Asian countries, farmers predominantly practice puddled transplanted rice (PTR), involving intensive tillage, high water usage, and energy consumption, exacer- bating GHGs emissions (Gupta et al., 2016). Despite these environ- mental concerns, PTR continues to be widely adopted due to its ability to provide higher andmore stable yields, whichmake it a preferredmethod for many farmers aiming for consistent production. However, the long-term sustainability of such practices is under threat due to their negative environmental impact. Thus, there’s a pressing need to adopt alternative and cleaner methods to meet global rice demand sustainably. Direct seeded rice (DSR) is one of such rice cultivation method that involves direct sowing of seeds into non-puddled, saturated/unsaturated soil (Liu et al., 2015a), with lower labor engagement, soil disturbance, water consumption, cost-efficient and conduciveness to mechanization. DSR benefits from the absence of puddling, resulting in significant en- ergy and fossil fuel savings, thereby contributes to increased carbon sequestration and reduces both carbon and water footprints (Mishra et al., 2021). Apart from this, the adoption of DSR can serve as a prac- tical approach for implementing resource conservation technologies such as zero tillage (ZT) in rice cultivation (Rao et al., 2007). ZT-DSR, offers additional benefits like soil health improvement, and farm eco- nomic viability (Ghosh et al., 2022). Due to these advantages, DSR is experiencing rapid adoption across Asia-Pacific rice-growing regions and is also gaining momentum in other parts of the world. In these regions, DSR has proven to be a successful cultivation method, offering nearly comparable yields to traditional practices while increasing economic returns and reducing environ- mental impacts. (Pittelkow et al., 2014; LaHue et al., 2016; Devkota et al., 2022). DSR encompasses wet DSR (sowing pre-germinated seeds on puddled/wet soil), dry DSR (sowing dry seeds in dry soil) and water seeding (Kumar and Ladha, 2011). Among these, dry DSR and wet DSR are the most commonly used establishment methods. The lack of intensive puddling in dry DSR makes it challenging to maintain standing water, leading to frequent wetting, and drying cycles in the soils. This prevents the formation of reduced soil conditions, and significantly curbs CH4 emissions. On the other hand, wet DSR, characterized by prolonged flooding, has yielded mixed results in terms of CH4 emissions, with some studies reporting significantly lower emissions (Setyanto et al., 2018; Li et al., 2023), while others showing higher emissions (Li et al., 2019) or no significant difference compared to PTR (Pandey et al., 2012; Kumar et al., 2023). In contrast, N2O emissions are lower under saturated anaerobic conditions in wet DSR. However, dry DSR shows a substantial increase in N2O emissions, primarily attributed to modified management practices. Despite numerous studies, there’s no definitive estimation of the extent to which N2O production increases, as reported increments vary across research (Gupta et al., 2016; Xu et al., 2022). The ambiguity surrounding CH4 and N2O emissions in DSR highlights the importance of accurately estimating the GWP. The variability in GHGs emissions is also mirrored in the diverse grain yield outcomes observed in DSR. Some studies have reported lower yields in DSR compared to conventional methods (Kumar and Ladha, 2011; Bana et al., 2020), while others have found comparable yields (Peramaiyan et al., 2023), and a few have even documented higher yields (Huang et al., 2011; Kumar et al., 2023). Nevertheless, numerous studies conducted in Asia demonstrates that DSR is more financially rewarding than PTR, despite its lower or at par yields, attributed to reduced input costs (Gautam, 2008; Panneerselvam et al., 2020; Dey et al., 2024). The GHGs flux from soil, and grain yield in DSR are significantly influenced by complex interactions with multiple environmental conditions (such as climate, region, organic matter con- tent, bulk density, porosity, soil type, pH, Eh and temperature etc.) and managemental practices (including DSR type, tillage, water, weed and nutrient management). Given this complexity, a detailed and specific study with comprehensive assessment of the performance of DSR tech- nology through meta-analysis is required. To the authors’ best knowl- edge, a comprehensive and quantitative appraisal of the DSR on grain yield and environmental aspects, including GHGs (CH4 and N2O) emission, GWP and overall carbon footprint (CFP) has not been docu- mented. This meta-study, further, intended to test the hypothesis that implementation of DSR could reduce environmental impact while pre- serving rice productivity in comparison with PTR; specifically looking at i) do DSR significantly impacts the grain yield, GHGs (CH4 and N2O) emission, GWP and overall CFP?, ii) what are the optimum environ- mental and managemental aspects for sustainability of DSR practice?, and iii) the interplay and impact of nitrogen (N) application rates on studied response ratio (RR) variables. 2. Materials and methods 2.1. Literature search and review process A thorough appraisal of peer-reviewed studies published during last 23-years (between 2000 and 2023) was conducted through compre- hensive searches over multiple online platforms. Distinct search terms, either individually or in combination were employed in the search process with “PTR” or “Puddled Transplanted Rice”, “Direct Seeded Rice” or “DSR”, “GHG” or “Green House Gases”, “N2O” or “nitrous oxide”, “CH4” or “methane” and, “GWP” or “Global Warming Potential” and “Carbon footprint”. We followed PRISMA (preferred reporting items for systematic reviews and meta-analyses) guidelines for our literature search as depicted in Fig. 1 (Lori et al., 2017). We identified 9649 related publications in total, and after refinement, we included 54 studies comprising 876 pairs of observations (150 for Methane, 133 for Nitrous oxide, 111 for GWP, 443 for yield and 39 for CFP) in this meta-analysis. This is because individual research articles included multiple treatments Fig. 1. The chart shows the overview of identified, excluded, and included studies in this meta-analysis. PRISMA flow diagram modified from Lori et al. (2017). K.S. Reddy et al. Cleaner Environmental Systems 16 (2025) 100238 2 (see Supplementary Table 1). The geographic locations of the study sites are shown in Fig. 2. 2.2. Screening and criteria of selection To avoid selection bias, we exclusively incorporated papers that satisfied the following criteria: (1) data must originate solely from field experiments carried out in Asia; (2) field studies were required to feature side-by-side assessments among treatments with DSR (serving as experimental treatment) and PTR (serving as control treatment); (3) studies must have to report the number of replicates and any of the following measures of variability such as standard deviation, critical difference, standard error, or confidence intervals within the experi- mental design; (4) and studies must report at least one of the mentioned target variables: emissions of N2O or CH4, GWP, crop yield and CFP. The database consisted of mean values obtained from replicates for each treatment. Measurements taken from different sites or treatments with varied cultural and management practices within a given study were treated solely as an autonomous field measurements. The raw data for the analysis was either acquired directly from tables or mined from graphs employing Web Plot Digitizer tool (version 4.7) (https://auto meris.io/). 2.3. Data treatment and groupings To untangle the influence of other co-varying factors on GHGs emission, crop yield and CFP, two primary categorical variables: envi- ronment conditions and management practices were considered, excluding the studies with data availability constraint (Table 1). The collected data was additionally categorized based on the following secondary criteria; climate, soil organic carbon (SOC), soil pH, soil type, region, tillage, DSR type, water and N management (Table 1). GWP was either calculated (when N2O and CH4 emissions were stated) or extracted either from figures or tables from the similar experiment. We omitted soil CO2 emissions from the analysis due to the likelihood of higher CO2 absorption compared to emissions, resulting in a negative CO2 flux from the rice fields (IPCC, 2013). CH4 and N2O fluxes were standardized to CO2 equivalents for calculating overall GWP associated to emissions. The GWP (kg CO2 eq. ha− 1) was calcualted as follows (IPCC, 2001); GWP=(CH4 x 25) + (N2O x 298) CFP was either directly obtained or calculated from the data provided in the publications as follows (Parihar et al., 2018): CFP = Total GHG emissions Grain yield Total GHG emissions= EAIs+(ECH4 x 25) + (EN2O x 298) EAIs= ∑ (Ai x EFi) where, EAIs represents GHGs emissions from all agricultural inputs- EAIs; ECH4 denotes the collective soil CH4 emissions during the rice cultivation periods; 25 is the GWP factor of CH4 over a 100-year horizon; similarly, EN2O represents the collective N2O emissions; 298 represents the GWP factor for N2O emissions over 100-year horizon; Ai stands for the quantity of each agricultural input; and EFi represents the emission factor of each distinct agricultural input used. The emission factor in our study was considered from Eco invent Database v3.9 & v3.9.1 (Moreno Ruiz et al., 2020) and Chinese Life Cycle Database (CLCD) (Liu et al., 2020). 2.4. Effect size derivation and statistical analysis The effective size opted for our study was the response ratio (RR), DSR (E: Experiment) was treated as the dependent variable – relating to PTR (C), which was enumerated first by computing the RR (Hedges et al., 1999): RR= XE XC Fig. 2. Geographical distribution map of the studies. Table 1 Categories used in meta-analysis to describe the environmental and management conditions. Factors Categories A. Environmental conditions Climate Tropical Subtropical ​ ​ Soil type Fine (silt clay, clay, sandy clay) Medium (clay loam, loam, silt, silty clay loam, silt loam) Coarse (sandy clay loam, loamy sand, sandy loam) ​ Soil pH Acidic (≤6.5) Neutral (6.6–7.3) Alkaline (>7.3) ​ SOC Low (<0.5%) Medium (0.5%–0.75%) High (>0.75%) ​ Region India China OAC (other Asian countries) ​ B. Management practices Tillage CT (Conventional tillage) ZT (Zero tillage) ​ ​ DSR type Dry DSR Wet DSR ​ ​ Water management Aerobic AWD (Alternate wetting and drying) CF (Conventional flooding) ​ Nitrogen rate Control (0 kg N ha− 1) Low (<100 kg N ha− 1) Medium (100–200 kg N ha− 1) High (>200 kg N ha− 1) K.S. Reddy et al. Cleaner Environmental Systems 16 (2025) 100238 3 where, XE and XC are the conveyed means for the DSR and PTR, respectively, for individual observation. lnRR= ln XE XC = ln XE − ln XC The natural log for each respective RR (lnRR) value was quantified to standardize the data sets. If the resultant RR value is zero, RR < 1 and RR > 1, it specifies that the treatment had no, negative and positive outcome on variable, respectively. The sampling variance (vi) accompanying with each lnRR was assessed using the following equation (Hedges et al., 1999): vi = (SDE) 2 nE x (XE)2 + (SDC) 2 nC x (XC)2 where, X is means, SD is standard deviations, and n is sample sizes in both PTR and DSR for a provided case. Variability measurements excluding SDs i.e., least significant differ- ence (LSD) and coefficient of variation (CV) mentioned in the studies were used to compute their corresponding SDs. In case, if CV is mentioned for a response variable, then it was multiplied with the variable mean in order to get the SD. Moreover, experiments that re- ported LSD of the response variable were transformed to standard errors (SE) using the equation provided below (Quinn et al., 2020): SE= LSD t0.975,n ̅̅̅̅̅̅̅̅ 2bn √ where, t resembles to the t-test value, n represents the sample number, and b indicates the replication number. Furthermore, SE was multiplied by the square root of sample size (n) in order to compute the SD, as below given equation: SD= SE x ̅̅̅ n √ where, n represents the size of the sample. Additionally, if confidence interval (CI) and mean were provided in the data, then SDwas computed as: SD=(CIu − CIl) x ̅̅̅̅ n √ 2Zα 2 where, CIu and CIl indicates the upper and lower limits of CI respec- tively, and Zα/2 denotes the Z score for a given level of significance equal to 1.645, when α = 0.10 and 1.96, when α = 0.05. SD was treated as 1/ 10 of means, in cases where no SD or CI or SE were reported (Tian et al., 2015). The weighting factor (wi) for each study is computed using the in- verse variance ratio: wi = 1/vi, and mean effect sizes of RRs were computed using the weighted meta-analysis as follows: lnRR= ∑ (lnRRi x wi) wi where, lnRRi and wi were the effect size and weight from the ith com- parison, respectively. To ease interpretation, the results were expressed in percentage changes for all the moderators and were computed from the below equation: elnRR − 1 x 100 We have opted the random-effect model for computation of the grouped effect sizes from environmental and management co-variates in the R statistical software, version 4.2.3 (R Core Team, 2022), using the “metafor” package. We chose the random-effects model due to the diverse geographical locations of the studies included, each reflecting varying environmental, agricultural, and management con- ditions. Unlike the fixed-effects model, which assumes a single, uniform effect size across all studies, the random-effects model recognizes that the true effect size can differ between studies due to factors such as location, climate, soil types, and crop management practices. This model allows for variability in effect sizes by accounting for both within-study variation and between-study heterogeneity. In contrast, the fixed-effects model assumes that all studies estimate the same true effect, making it suitable when the studies are homogenous and share similar conditions. However, given the diversity of our data, the random-effects model provides a more flexible and realistic approach, offering a more generalized and accurate understanding of the overall trends. By incorporating study-specific variations, the random-effects model better captures the complexities of data pooled from different regions and conditions, ensuring a more robust synthesis of the findings. The provided categorical variable with effect size means at each level was considered significantly different from zero (i.e., DSR differ signif- icantly from PTR for a provided variable) if its 95% CI did not overlap with the zero. Furthermore, they were deemed significantly different, if their 95% CIs did not include, accompanied by p-values. All statistical analyses and plots, including regressions, forest plots, and funnel plots of RR among different variables, were prepared using R software (R Core Team, 2022). 3. Results 3.1. Overall effect of DSR and PTR comparison on GHGs emission, crop yield and CFP The meta-analysis unveiled a considerable difference in all the investigated variables, i.e., CH4 (n = 150), N2O (n = 133), GWP (n = 111), grain yield (n = 443) and CFP (n = 39). Overall, DSR significantly reduced CH4 emissions by 69.9% (95% CI= − 74.9%, − 64.0%), GWP by 36.7% (95% CI = − 43.6%, − 28.9%), grain yield by 10.8% (95% CI = − 12.7%, − 8.8%) and CFP by 34.4% (95% CI = − 41.8%, − 25.9%) compared to PTR (Fig. 3). In contrast, N2O emissions were increased significantly by 84.6% (95% CI = 47.9%, 130.4%) in DSR, compared to PTR (Fig. 3). To describe the statistical significance level of studies included under meta-analysis, funnel plots were drawn separately for each studied variable (Fig. 4). The plot shows the observed effect sizes (lnRR) on the x-axis against standard error (inverse of precision) of the corresponding effect size on y-axis. Similarly, the plots in Fig. 5 de- scribes the distribution of lnRR of each outcome variable under different study. Fig. 3. Overall effects of DSR on greenhouse gas (CH4 and N2O) fluxes, global warming potential (GWP), grain yield and carbon footprint (CFP). Parentheses numbers indicate the number of observations and error bars represent 95% confidence intervals; at *p < 0.05, **p < 0.01, and ***p < 0.001. K.S. Reddy et al. Cleaner Environmental Systems 16 (2025) 100238 4 3.2. Comparative effect of DSR and PTR on CH4 emission 3.2.1. Environmental conditions All the environmental conditions exhibited the significant reduction of CH4 emissions in DSR over PTR (Fig. 6). CH4 emissions were signif- icantly reduced by 57.6% (95% CI = − 65.6%, − 47.7%) in tropics and 74.2% (95% CI = − 79.6%, − 67.4%) in subtropics. Considering various soil textural classes, it was evident that both fine (lnRR = − 74.3%, 95% CI = − 82.5%, − 62.4%) and coarse (lnRR = − 74.8%, 95% CI= − 83.5%, − 61.5%) textured soils exhibited a comparable reduction in CH4 emis- sions. Whereas medium textured soils exhibited a relatively lower reduction (lnRR = − 65.1%, 95% CI = − 71.8%, − 56.9%) in CH4 emis- sions. Moreover, acidic, neutral and alkaline soils revealed a more or less similar reduction in CH4 emissions by ~70% (Fig. 6). Soil organic car- bon content significantly influenced the CH4 emission, resulting 63.2% (95% CI = − 71.7%, − 52.4%), 85.2% (95% CI = − 92.6%, − 70.4%) and 72.2% (95%CI= − 80.1%, − 61.1%) reduction in low, medium, and high SOC soils, respectively (Fig. 6). Comparing among the Asian countries, in India, a greater CH4 emission reduction were observed (lnRR = − 78.2%, 95% CI = − 83.1%, − 72%), whereas low profound difference was noticed in China (lnRR= − 53.8%, 95% CI= − 65.2%, − 38.8%) and other Asian countries (lnRR = − 59.1%, 95% CI = − 71.3%, − 41.8%). 3.2.2. Management practices Various crop establishment and management practices significantly reduced the CH4 emission from DSR over PTR, favouring its adaption (Fig. 7). Among the tillage practices, both in ZT and CT, the CH4 emis- sions were lowered by 69.0% (95% CI = − 77.7%, − 57.1%) and 70.4% (95% CI = − 76.1%, − 63.3%), respectively. However, the decrease of CH4 emissions in dry DSR (lnRR= − 80.9%, 95% CI= − 84.8%, − 75.9%) was nearly twice as much as that observed in wet DSR (lnRR = − 41.6%, 95% CI = − 51.4%, − 29.8%). Among different water management practices, the decrease in CH4 emissions unfolded as follows, 84.6% (95% CI = − 89.1%, − 78.2%), 74.8% (95% CI = − 81%, − 66.5%) and 37.2% (95% CI = − 46.6%, − 26.2%) in aerobic, AWD and CF respec- tively (Fig. 7). Similarly, across different N application rates, the CH4 emissions were reduced by 54% (95% CI = − 66.7%, − 36.3%), 83% (95% CI = − 87.6%, − 76.6%), 58.3% (95% CI = − 66.9%, − 47.4%) and 75% (95% CI = − 86.8%, − 52.5%) in control, low, medium, and high N application rates, respectively. Fig. 4. The funnel plot explains the precision of each study (a) CH4, (b) N2O, (c) GWP and (d) grain yield; blue and red coloured dots indicate the studies with low (<0.2 SE) and high (>0.2 SE) standard error (SE), respectively. Filled and unfilled circles indicate lnRR value of each study with positive and negative response with respect to overall mean, respectively.(For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.) K.S. Reddy et al. Cleaner Environmental Systems 16 (2025) 100238 5 3.3. Comparative effect of DSR and PTR on N2O emission 3.3.1. Environmental conditions Unlike CH4, N2O emissions increased in DSR compared to PTR (Fig. 8). While no significant distinction in N2O emissions was noted between DSR and PTR in tropical regions, a significant difference was reported under subtropical climatic conditions (lnRR = 120%, 95% CI = 66%, 148.6%). Highest N2O emissions were noticed in coarse-textured soil with increment of 121.3% (95% CI = 58.0%, 145.2%) followed by medium texture soil, 48.1% (95% CI = 36.2%, 61.0%). However, no significant difference was found under fine-textured soils. Among different pH levels, acidic and neutral pH soils exhibited comparatively less increase in N2O emissions with 48.6% (95% CI= 4.5%, 111.2%) and 22.6% (95%CI= 2.7%, 46.3%), respectively, whereas in alkaline soils, a higher increase in N2O emissions was reported with 61.1% (95% CI = 27.8%, 103.1%) increase over PTR. In comparison between the soils with different SOC levels, low and medium SOC soils exhibited a sig- nificant increase in N2O emissions, with increments of 67.9% (95% CI = 55.9%, 80.9%) and 30.3% (95% CI = 18.8%, 43.0%), respectively. In contrast to this, in high SOC soils N2O emissions did not differ Fig. 5. The caterpillar plot shows the lnRR value distribution of individual studies together with their 95% confidence intervals and estimates are ordered by their magnitude: (a) CH4, (b) N2O, (c) GWP and (d) grain yield. The vertical dashed line indicates the no response of the study. The polygon at bottom in- dicates the mean lnRR value. Fig. 6. Influence of varied environmental conditions on CH4 fluxes under DSR conditions. Parentheses numbers indicate the number of observations and error bars represent 95% confidence intervals; at *p < 0.05, **p < 0.01, and ***p < 0.001. (OAC: Other Asian countries). Fig. 7. Influence of varied management practices on CH4 fluxes under DSR conditions. Parentheses numbers indicate the number of observations and error bars represent 95% confidence intervals: at *p < 0.05, **p < 0.01, and ***p < 0.001. (CT: Conventional tillage; ZT: Zero tillage; AWD: Alternate wetting and drying; CF: Conventional flooding). Fig. 8. Influence of varied environmental conditions on N2O fluxes under DSR conditions. Parentheses numbers indicate the number of observations and error bars represent 95% confidence intervals; at *p < 0.05, **p < 0.01, and ***p < 0.001. (OAC: Other Asian countries). K.S. Reddy et al. Cleaner Environmental Systems 16 (2025) 100238 6 significantly (Fig. 8). At regional basis, India showed a pronounced in- crease in N2O emissions with 111.5% (95% CI = 67%, 148.3%) increase in DSR over PTR. While, China exhibited a comparatively lower and significantly higher N2O emissions with 48.7% (95% CI = 28.9%, 71.5%). Surprisingly, other Asian countries (OAC) concluded with non- significant result (Fig. 8). 3.3.2. Management practices The N2O emissions exhibited variations across all management practices, as illustrated in Fig. 9. In CT fields, N2O emissions were 62% higher (lnRR = 62.0%, 95% CI = 32.4%, 98.4%); and in ZT fields N2O emissions were twice as that of the CT (lnRR= 132.2%, 95%CI= 41.3%, 281.3%) under DSR. Among different crop establishment methods, dry DSR significantly increased N2O emissions by 147.3% (95% CI= 79.5%, 240.7%) over PTR. Whereas we found no significant difference in the wet DSR (Fig. 9). Comparing different water management practices, AWD irrigation practice was found to be the highest N2O emitter, fol- lowed by aerobic and CF with 197.9% (95% CI = 85.6%, 378.0%), 63.5% (95% CI = 4.6%, 155.5%) and 29.6% (95% CI = 9.9%, 52.8%) more N2O emission over PTR, respectively. Amongst different N appli- cation rates, control and high N application rates were found non- significant for N2O emissions (Fig. 9). In contrast, low and medium N application rates resulted in 43.9% (95% CI = 17.7%, 75.8%) and 138.2% (95% CI = 62.7%, 248.9%) higher N2O emissions in DSR over PTR. 3.4. Comparative effect of DSR and PTR on GWP 3.4.1. Environmental conditions All the examined environmental conditions had a substantial impact on GWP in DSR over PTR estbalishment methods (Fig. 10). When DSR was practiced in subtropical climatic conditions (lnRR = − 40.3%, 95% CI= − 49%, − 30.2%) a greater reduction in GWPwas observed than that of tropical climatic conditions (lnRR = − 26.6%, 95% CI = − 32.3%, − 20.4%). The DSR under fine, medium, and coarse-grained soils resul- ted in 72.8% (95% CI = − 86.6%, − 45%), 32.1% (95% CI = − 39.3%, − 24.0%) and 26.3% (95% CI = − 36.1%, − 15.1%) decrease in GWP, respectively. DSR showed a significant and substantial decrease in GWP for acidic, neutral, and alkaline soils compared to PTR. This decrease was higher in acidic soil, 67.6% (95% CI = − 79.7%, − 48.3%) followed by alkaline soil, 43.8% (95% CI = − 53.9%, − 31.6%) and neutral soil, 22.1% (95% CI= − 27.2%, − 16.7%), respectively. Among different SOC level, greater reduction was noticed in high SOC (lnRR = − 57.6%, 95% CI = − 70.5%, − 38.9%) soils followed by low SOC (lnRR = − 34.4, 95% CI = − 44.5%, − 22.3%) soils, whereas the reduction of GWP in medium SOC (lnRR = − 21.0%, 95% CI = − 27.9%, − 13.4%) was comparatively modest. According to the results of this meta-study, China (lnRR = − 45.6%, 95% CI = − 58.0%, − 29.6%) resulted highest reduction for GWP when DSR is practiced over PTR (Fig. 10), whereas India (lnRR = − 30.1%, 95% CI = − 35.9%, − 23.8%) and OAC (lnRR = − 30.6%, 95% CI = − 37.4%, − 23.0%) have a quite lower and almost similar degree of reduction of GWP, when DSR is practiced over PTR. Fig. 9. Influence of varied management practices on N2O fluxes under DSR conditions. Parentheses numbers indicate the number of observations and error bars represent 95% confidence intervals; at *p < 0.05, **p < 0.01, and ***p < 0.001. (CT: Conventional tillage; ZT: Zero tillage; AWD: Alternate wetting and drying; CF: Conventional flooding). Fig. 10. Influence of varied environmental conditions on GWP under DSR conditions. Parentheses numbers indicate the number of observations and error bars represent 95% confidence intervals; at *p < 0.05, **p < 0.01, and ***p < 0.001. (OAC: Other Asian countries). Fig. 11. Influence of varied management practices on GWP under DSR condi- tions. Parentheses numbers indicate the number of observations and error bars represent 95% confidence intervals; at *p < 0.05, **p < 0.01, and ***p < 0.001. (CT: Conventional tillage; ZT: Zero tillage; AWD: Alternate wetting and drying; CF: Conventional flooding). K.S. Reddy et al. Cleaner Environmental Systems 16 (2025) 100238 7 3.4.2. Management practices Similar to the environmental conditions, all the management prac- tices considerably reduced GWP under DSR over PTR (Fig. 11). The influence of tillage practices on GWP were noteworthy, resulting in 41.7% (95% CI = − 50.3%, − 31.6%) and 26.4% (95% CI = − 36.0%, − 15.2%) reduction of GWP in CT and ZT under DSR, respectively. Be- tween DSR types, dry DSR offers an impressive potential for GWP reduction, with a 42.1% (95% CI = − 51.5%, − 29.6%) drop. Whereas in wet DSR (lnRR = − 28.4%, 95% CI = − 39.1%, − 16.0%) the reduction was moderate and lower than dry DSR. Among different water man- agement practices, aerobic practice provides a great opportunity for GWP reduction (lnRR = − 51.3%, 95% CI = − 68.3%, − 25.2%), but in case of AWD and CF practices, they have lesser degree of reduction in GWP (Fig. 11). The high N application rate revealed a huge potential for GWP mitigation with a sound reduction of 72.8% (95% CI = − 86.6%, − 45%), whereas low and medium N application rates showed a reduc- tion of 22.2% (95% CI = − 26.6%, − 17.5%) and 32.5% (95% CI = − 41.5%, − 22.1%), respectively. However, in N control the GWP reduction was 45.7% (95% CI = − 60.2%, − 25.9%). The emission reduction for N rate must be interpreted in conjunction with the rela- tionship between N rate and the lnRR, which we reported in later sections. 3.5. Comparative effect of DSR and PTR on grain yield 3.5.1. Environmental conditions The changes in grain yield with DSR across different environmental conditions are presented in Fig. 12. Under both tropical and subtropical conditions, DSR exhibited a yield reduction of 10.8% (95% CI = − 17.9%, − 3.0%) and 11.7% (95% CI = − 13.7%, − 9.6%) compared to PTR, respectively. Among different soil types, the yield reduction in DSR was 13.0% (95% CI = − 20.6%, − 4.7%) in fine-textured soils, 8.1% (95% CI = − 10.6%, − 5.5%) in medium-textured soils, and 12.1% (95% CI = − 14.8%, − 9.2%) in coarse-textured soils, respectively (Fig. 12). Nevertheless, there was no significant yield reduction observed in acid and neutral soils (Fig. 12). Conversely, in alkaline soils, the yield reduction amounted to 12.6% (95% CI = − 14.4%, − 10.8%). Among different SOC levels, the yield reduction was 9.5% (95% CI = − 11.7%, − 7.2%), 6.8% (95% CI = − 10.5%, − 2.9%) and 14.9% (95% CI = − 19.2%, − 10.4%) in low, medium and high SOC soils, respectively. In comparison of different countries in Asia, China resulted in least reduction in yield of DSR over PTR (lnRR = − 5.8%, 95% CI = − 11.3%, − 0.01%), whereas in India (lnRR= − 11.7%, 95% CI= − 14.1%, − 9.3%) and OAC (lnRR = − 12.2%, 95% CI = − 15.7%, − 8.6%), the reduction was higher and comparable. 3.5.2. Management practices Both in ZT and CT, there was a considerable yield reduction in DSR, with a comparatively higher reduction noticed in CT (lnRR = − 13.5%, 95% CI = − 15.7%, − 11.2%) compared to ZT (lnRR = − 6.3%, 95% CI = − 9.8%, − 2.7%). In terms of different crop establishment methods, dry DSR prone to greater yield reduction by 13.2% (95% CI = − 15.1%, − 11.2%), whereas the yield reduction was modest in wet DSR (lnRR = − 5.3%, 95% CI = − 9.8%, − 0.6%). Amongst water management prac- tices, aerobic practice (lnRR = − 18.2%, 95% CI = − 21.1%, − 15.1%) Fig. 12. Influence of varied environmental conditions on grain yield under DSR conditions. Parentheses numbers indicate the number of observations and error bars represent 95% confidence intervals; at *p < 0.05, **p < 0.01, and ***p < 0.001. (OAC: Other Asian countries). Fig. 13. Influence of varied management practices on grain yield under DSR conditions. Parentheses numbers indicate the number of observations and error bars represent 95% confidence intervals; at *p < 0.05, **p < 0.01, and ***p < 0.001. (CT: Conventional tillage; ZT: Zero tillage; AWD: Alternate wetting and drying; CF: Conventional flooding). Fig. 14. Influence of employed environmental and management moderators on CFP under DSR conditions. Parentheses numbers indicate the number of ob- servations and error bars represent 95% confidence intervals; at *p < 0.05, **p < 0.01, and ***p < 0.001. (CT: Conventional tillage; ZT: Zero tillage; AWD: Alternate wetting and drying; CF: Conventional flooding). K.S. Reddy et al. Cleaner Environmental Systems 16 (2025) 100238 8 resulted with greater yield penalty followed by AWD practice (lnRR = − 7.8%, 95% CI = − 9.5%, − 6.0%). Notably, CF practice resulted in non- significant value (Fig. 13). The yield reduction in control, low, and medium N application rates were as follows 24.0% (95% CI = − 33.5%, − 13.1%), 10.8% (95% CI = − 14.1%, − 7.3%) and 10.5% (95% CI = − 12.9%, − 8.0%), respectively. 3.6. Comparative effect of DSR and PTR on CFP Due to the data limitation, only sensible sub set of co-variates were selected for estimating CFP and categorized as illustrated in Fig. 14. In subtropical climates there was significant reduction in CFP (lnRR = − 36.3%, 95%CI= − 44.1%, 27.4%). But in tropical climates the CFP was similar in DSR and PTR (Fig. 14). Among tillage practices, CFP was reduced by 27.0% (95% CI = − 37.9%, − 14.2%) and 41.3% (95% CI = − 50.5%, − 30.5%) in CT and ZT-DSR as compared to PTR, respectively. Moreover, among dry and wet DSR, dry DSR (lnRR= − 45.6%, 95% CI= − 51.7%, − 38.7%) has significant and greater reduction in CFP, whereas in wet DSR the CFP was similar to PTR (Fig. 14). Comparing different water management practices, the CFP of aerobic and AWD practices were decreased by 63.0% (95% CI= − 68.5%, − 56.6%) and 36.3% (95% CI = − 44.1%, − 27.4%), respectively in DSR over PTR. In CF practice, subjected to greater usage of water and other resources, the CFP was similar in DSR and PTR (Fig. 14). 3.7. Relationships between GHGs, grain yield and N application rates under DSR and PTR The inter-relationships were established among the responses of GHGs emissions to N application rates and grain yield (Fig. 15). Results specified that the lnRR of GHGs (CH4 and N2O) showed non-significant relation with the N application rates. Similarly, lnRR of CH4 emissions also showed a non-significant result with grain yield. However, lnRR of Fig. 15. Figure (a) and (b) explains the relationship between the log response ratio (lnRR) of CH4 and N2O fluxes with nitrogen application rate, respectively. Figure (c) and (d) explains the relationship between log response ratio (lnRR) of CH4 and N2O fluxes with log response ratio (lnRR) of grain yield, respectively. Fig. 16. Relationship between the grain yield and nitrogen application rate under both PTR and DSR conditions. (DSR: Direct seeded rice; PTR: Puddled transplanted rice). K.S. Reddy et al. Cleaner Environmental Systems 16 (2025) 100238 9 N2O emissions was significantly positively correlated with the grain yield under DSR conditions (Fig. 15). Likewise, a quadratic relationship between grain yields and N application rates under PTR and DSR con- ditions were also observed (Fig. 16). The relationship was highly sig- nificant and positively correlated under both the conditions (p< 0.001). These findings indicate the possibility of enhancing grain yields in DSR, akin to PTR, by implementing higher N application rates. 4. Discussion 4.1. Comparative effect of DSR and PTR on CH4 and N2O emission and GWP The reduction in CH4 and increase in N2O emissions realized in DSR fields can be attributed to agro-environmental factors like climate, tillage, type of DSR, N fertilization levels, water management strategies, soil properties, etc. (Bhattacharyya et al., 2013). These variables have a cumulative impact on seasonal variations and flow of CH4 and N2O emissions via mechanisms like production, oxidation, and transit within the rice ecosystem (Kim et al., 2016). In the context of PTR, GWP was primarily influenced by CH4 emissions. However, in DSR, CH4 emissions had a minimal effect on GWP, whereas N2O plays a crucial role, with greater emissions under aerobic conditions with it’s higher GWP. In our meta-analysis, we found that DSR led to a 36.7% reduction in GWP, compared to the conventional PTR (Fig. 3). Despite observing notably higher N2O emissions in DSR, the overall GWP of DSR was still lower than that of PTR, primarily attributed to significantly lower CH4 emis- sions in the DSR system. An in-depth exploration of the potential factors, utilizing diverse moderators from both environmental and management perspectives, was aided in uncovering the possible reasons behind the observed results in our study. 4.1.1. Environmental conditions Tropical and subtropical climatic conditions with their intrinsic characteristics of higher temperatures, increased activity of aerobic microbes and specific water management practices reduces CH4 emis- sions under DSR conditions than PTR (Bhattacharya et al., 2014). Additionally, under subtropical conditions there was a surge in N2O emissions, but no significant change was found for N2O emissions in tropics (Fig. 8). The relatively higher soil organic matter (SOM) content in subtropical conditions likely provided a surplus carbon source for anaerobic decomposition which could have led to increased activity of methanogens, under PTR conditions (Cheng-Fang et al., 2012). Tran- sitioning to DSR, which offers more aerobic conditions and higher redox potential, could have significantly reduced CH4 emissions but poten- tially intensified N2O emissions in subtropical over tropical regions due to increased availability of substrate for denitrifiers as well (Zou et al., 2007). Converse to this, in tropical conditions, comparatively higher precipitation, prevalence of CF practice could have led to lower reduc- tion and non-significant result for CH4 and N2O emissions, respectively (Yang et al., 2012). In subtropical conditions owing to greater reduction of CH4 emissions, GWP was reduced by 40%, even though the N2O emissions were doubled. Whereas in tropical conditions the reduction was only 27% due to comparatively lower reduction in CH4 emissions (Fig. 10). Irrespective of soil textures, there was significant difference in CH4 reduction (~70%) under DSR, as anaerobic conditions prevail regardless of soil texture in PTR. However, N2O emissions were highly affected by soil texture with greater emissions in coarse followed by medium textured soils under DSR (Fig. 8). In coarse soils, higher availability of macro pores and less water holding capacity favours conversion of ammonium (NH4 +) to nitrate (NO3 − ), with subsequent irrigation or pre- cipitation resulting in rapid denitrification (Feng and Yin, 1995). While in fine soils, poor drainage leads anaerobic condition for longer time, restricting nitrification and thus did not affect N2O emissions signifi- cantly (Liu et al., 2010). Due to this pattern in CH4 and N2O emissions, GWP was reduced in accordance with soil fineness with greater reduc- tion in fine soils followed by medium and coarse textured soils (Fig. 10). Soil pH plays a crucial role in CH4 production, with the highest pro- duction rates typically occurring in neutral pH conditions. In acidic and alkaline environments, CH4 emissions are significantly lower under DSR conditions (Fig. 6). Methanogens are most active in neutral pH (6.5–7.5) or slightly alkaline soils, and they are particularly sensitive to changes in soil pH. CH4 production is nearly eliminated when the soil pH is below 5.8 or above 8.8 (Wang et al., 1993a). In flooded rice fields, the pH tends to stabilize around 7.0, regardless of the initial pH of the soil, providing ideal conditions for CH4 production (Wang et al., 1993b). Although some reports indicate that CH4 oxidation can occur at low pH (2) and high pH (9.5), CH4 oxidation is negligible in soils with a pH below 5.0 (Malyan et al., 2016). As a result, switching to DSR helps maintain soil pH at levels that are unfavorable for methanogens, leading to a reduc- tion in CH4 emissions in both acidic and alkaline soils (Pathak et al., 2008). However, N2O emissions were observed to be higher at alkaline pH levels under DSR conditions. This is because autotrophic nitrifiers, which are most active in slightly alkaline soils, facilitate the conversion of ammonia (NH4 +) to nitrate (NO3 − ), thereby increasing N2O emissions (Parashar et al., 1991) (Fig. 8). In terms of GWP, the combined emissions of CH4 and N2O were lowest in acidic soils, followed by alkaline, and neutral pH soils (Fig. 10). In PTR, medium and high SOC soils exhibits increased availability of carbon sources for methanogens, contributing to higher CH4 emissions. In contrast, after converting to DSR in these soils, a larger decline in CH4 emission was observed. Surprisingly, the percentage increase in N2O emissions was also lower under medium and high SOC soils in DSR. This might be attributed to greater water-filled pores with higher water holding capacity associated with higher SOC, resulting restricted nitri- fication (Pathak et al., 2002). Parallelly due to these causes, the extent of reduction in GWP was higher in high SOC soils. However, there was not much pronounced and converse result noticed with low and medium SOC soils. In India, the higher percentage reduction in CH4 emissions, compared to China and other Asian Countries (OAC) can be attributed to the prevalence of dry DSR adoption (Kumar and Ladha, 2011). In contrast, China and OAC favour wet DSR potentially causing variability in CH4 emissions across these regions (Xiao et al., 2013). Probably aiding with these causes, there was a substantial increase in N2O emis- sions in India (111%), followed by China (49%). However, non-significant results were observed in the OAC (Fig. 8). China ach- ieved a more substantial decrease in GWP by 45%, while India and OAC experienced a comparable reduction of around 30%. This outcome may be attributed to the effective and optimal implementation of water, soil and N management, leading lower GWP in China. Environmental conditions serve as the intrinsic and primary drivers of GHG fluxes. Intermittent irrigation in DSR fosters aerobic soil con- ditions that curb CH4 emissions, contrasting with PTR’s persistent flooding, which sustains anaerobic environments favorable to meth- anogenesis. Warmer tropical and subtropical climates further amplify these effects: aerobic conditions in DSR accelerate organic matter decomposition, whereas PTR’s flooding increases CH4 emissions, particularly in soils with high organic content. Soil texture and pH add complexity—coarse soils in DSR promote N2O emissions through enhanced drainage, while PTR’s stable, neutral pH favors CH4 produc- tion. Regional practices, such as India’s dry DSR versus China’s wet DSR, modulate these emissions by altering water availability and microbial activity. Ultimately, while DSR generally reduces CH4 emissions, it may elevate N2O emissions depending on specific soil and climate conditions. 4.1.2. Management practices Irrespective of tillage practices, DSR reduced methane emission (~70% reduction) as compared to PTR. Mishra et al. (2021); and Kak- raliya et al. (2021) found similar results with comparable reduction of CH4 emissions under CT and ZT. The N2O emissions in ZT (132%) increased by two-fold over CT (62%) (Fig. 9). Under ZT, the combination K.S. Reddy et al. Cleaner Environmental Systems 16 (2025) 100238 10 of a high SOC content, along with optimal soil temperature and moisture regime, likely facilitated better nitrifiers activity, for increased NO3 − formation under aerobic conditions (Wu et al., 2018). Subsequently, after precipitation or irrigation, the high water-holding capacity of soils under ZT practices could sustain prolonged anaerobic conditions, favouring higher N2O emissions (Kakraliya et al., 2021). The reduction in CH4 emissions was similar under ZT and CT, but with accelerated N2O emissions under ZT, the reduction in GWP was lower in ZT compared to CT (Fig. 11). Water management varies in DSR according to its forms (dry DSR, wet DSR and water seeding) under practice. Therefore, the reduction in CH4 emission intensity was mainly driven by soil moisture levels and aeration (Wu et al., 2017). Dry DSR, aerobic conditions, wet DSR and CF had CH4 emission reduction of 86%, 80%, 42%, and 37%, respectively. In wet DSR and CF, field was frequently flooded and held near/above saturation; in certain cases, puddling was done before direct sowing in wet DSR, resulting in extended anaerobic conditions causing higher CH4 emissions (Liu et al., 2014a). Additionally, during the flowering stage, increased rice biomass in wet DSR and CF can facilitate higher CH4 emission through aerenchyma and offer carbon substrates for meth- anogenesis through root exudation and plant matter decomposition during senescence (Das and Baruah, 2008; Yao et al., 2024). Relatedly, in AWD, CH4 emissions were intermediate between CF and aerobic practices (Fig. 7), resulting variation in CH4 emissions between different DSR cultivation methods and water management approaches (Khosa et al., 2011). Furthermore, in AWD, the frequent wetting and drying of soil in DSR can potentially result in higher emissions of N2O, as the soil switches between microbial nitrification and denitrification processes more frequently than in aerobic system (Gupta et al., 2016). Addition- ally, high soil moisture in AWD over aerobic practice also results in micro-anaerobic condition, favouring more denitrification. The higher N2O emissions under aerobic, AWD and dry DSR may encourage poor N-use efficiency. Ultimately, dry DSR and aerobic water management practices entail substantial reduction in net GWP compared to wet DSR. However, there was a similar reduction of GWP between AWD and CF practices for GWP, attributed to higher N2O emissions in AWD, despite a greater reduction in CH4 emissions compared to CF (Fig. 11). Further, DSR offers long-term benefits for soil health, crop rotation, and pest management. Unlike PTR, DSR eliminates intensive tillage and puddling, preserving soil structure and preventing compaction (Gupta et al., 2016). With improved soil porosity and aeration, DSR supports beneficial microbial activity and organic matter retention, enhancing nutrient cycling and soil health. Also, DSR allows for quicker crop establishment and shorter turnaround times, making DSR followed crops well-suited for timely sowing with diverse crop rotation options (Kumar and Ladha, 2011). This adaptability benefits farmers by enabling the integration of legumes or high-value crops, which improve soil fertility and income potential. Additionally, DSR’s intermittent wetting and drying disrupts pest and disease cycles common in flooded systems, reducing reliance on chemical pesticides (Liu et al., 2014a; Li et al., 2023). Collectively, these advantages make DSR a sustainable approach that fosters resilient soils, crop diversity, and reduced pest pressures over time. Application of high N rate could potentially reduce N2O emissions through triggering the plant growth and increasing yield, resulting higher NUE and restricting its loss (Liao et al., 2021; Islam et al., 2024). The lower CH4 and N2O emissions can be attributed to the predominant use of high (>200 kg/ha) N application in China. These applications were primarily implemented in wet DSR and maintained under CF conditions. Moreover, it’s worth noting that there was a relatively few data points for high N application rates under both CH4 (14) and N2O (12) emissions. Consequently, the GWP also exhibited variation across different N application rates (Fig. 11). 4.2. Comparative effect of DSR and PTR on grain yield Overall, our meta-analysis found that there was a ~10.7% yield penalty in DSR over PTR (Fig. 5). This result was in accord with many previous studies. Some inherent properties in DSR causing lower yields could be; i) Poor crop establishment due to scarce or excess soil moisture after sowing, inappropriate sowing depth and NH4 + toxicity to seeds (Ma et al., 2011). ii) Heavy weed infestation in DSR compared to PTR (Panneerselvam et al., 2020), iii) Moisture stress particularly in aerobic or AWD practice of DSR (Liu et al., 2014b) iv) Nematode infestation, due to prevailing dry and moist conditions under DSR (Gathala et al., 2011), v) Pest, disease and rodent damage susceptibility, due to higher seed rate and lack of thinning with dense foliage (Balasubramanian and Hill, 2002), vi) Crop lodging related problems (Tao et al., 2016). vii) Lack of DSR specific cultivars (Liu et al., 2014a), viii) Inappropriate N man- agement in DSR. All these factors are the potential causes for yield loss in DSR. 4.2.1. Environmental conditions on grain yield Significant and comparable yield reductions were observed in both tropical and subtropical climatic conditions, with slightly lower yield reductions in tropical climates. This better performance in tropical cli- mates might be attributed to higher precipitation and optimal temper- atures (~35 ◦C) during the growth period of DSR (Peng et al., 2006). In relation to soil texture and SOC, a marked reduction in yield was observed across all classifications, though the pattern of decline was not consistent throughout (Fig. 12). Fine-textured and high SOC soils exhibited the greatest yield reductions, followed by coarse-textured and low SOC soils (Fig. 12). This variability could be attributed to poor seedling establishment and nitrogen immobilization in fine-textured soil with high SOC. Additionally, high water holding capacity in these soils coupled with high seed rate in DSR can hamper the aeration and in- crease the susceptibility of plants to pests and diseases due to dense canopy, ultimately may cause yield reduction. However, our meta-analysis results deviated from numerous studies where, SOC and fine soil texture were consistently found to have a direct positive impact on the grain yield of rice (Shakoor et al., 2021; Bhuiyan et al., 2023). So, by maintaining optimum plant population there is a space for yield optimisation under these conditions. In case of soil pH, the percentage of yield reduction in DSR was directly proportional to soil pH, with 12.6% yield reduction under alkaline pH; whereas no significant yield loss under acidic and neutral pH (Fig. 12). As rice prefers optimum soil pH of 5.5–6.5, the growth and yields will be higher under such acidic soil conditions (Haefele et al., 2014). In PTR, puddling and CF practices will bring the soil to neutral pH (Wassmann et al., 1998). Whereas, in DSR these practices are avoi- ded, and soil pH tends to remain unchanged and yield penalty could be felt particularly under alkaline pH condition (Gathala et al., 2011). Further, micronutrient deficiencies particularly iron deficiency under high pH could also cause lower yields (Liu et al., 2014a). In regional basis, yield reduction in India and OAC was higher and comparable (~12%), whereas in China, yield penalty was low with marginal sig- nificance (p = 0.05) under DSR. This could be due to the prevalence of wet DSR, CF, and high N application. Aligning to this, in India and OAC, where a considerable area is under dry DSR along with prevailing practices like aerobic or AWD practices with low N application causes higher yield penalty (Gupta et al., 2016). Furthermore, difference in soil properties, climatic conditions and management practices could contribute to the variation in yield reduction. 4.2.2. Management practices on grain yield Significant yield penalty was found in both CT and ZT practices under DSR conditions. These results are aligned with the findings of Peng et al. (2006) and Kreye et al. (2009). In ZT there was comparatively low yield reduction than CT under DSR (Fig. 13). In ZT due to minimal soil disturbance, there was an improvement in soil physical structure, K.S. Reddy et al. Cleaner Environmental Systems 16 (2025) 100238 11 microbial activity, nutrient availability, organic matter, and C seques- tration. These all properties could aid in the ideal plant growth with optimum nutrient uptake, aeration, and soil moisture availability (Sharma et al., 2023). Among water management practices, in wet DSR and CF, less significant (p = 0.03) and non-significant result were noted, respectively. Whereas dry DSR, AWD and aerobic practices showed a significantly higher levels of yield reduction under DSR (Fig. 13). In aerobic and AWD practices, rice plants encounter water stress at some growth stages, hampering yield due to stunting, reduced chlorophyll content and increased electrolyte leakage (Jahan et al., 2014). Addi- tionally, anatomical changes such as hampered cell division and decreased intercellular space etc. leads to reduction in yield (Singh et al., 2016). Similar results of yield penalty with dry DSR (Malik and Yadav, 2008), aerobic (Kumar et al., 2019) and AWD practices (Singh et al., 2018) were noticed by many researchers. The causes for high responsiveness in DSR for N application could be variable. i) in DSR, due to lack of puddling there was a chance of N loss in form of NO3 − through leaching along with irrigation water (Zhang and Wang, 2002); ii) N was mostly supplied through urea in the oxidised zones, where urease activity is high and it hydrolyses urea (NH4 + form) into NH3 form, leading to volatilisation loss (Liu et al., 2015b) iii) Practices like alternate wetting and drying in DSR aggravates the N2O emissions. Cumulatively, all these forms of N loss will result in poor N use efficiency, leading to high responsiveness of DSR to increased N application. 4.3. Economic viability of DSR DSR offers a more economically viable alternative to PTR due to its lower labor and input requirements, which contribute to reduced pro- duction costs, even though the yields were lower (Kumar et al., 2023; Peramaiyan et al., 2023). In DSR systems, rice seeds are directly sown into the field without the labor-intensive processes of raising and transplanting seedlings. This saves substantial labor and time, particu- larly crucial in regions where labor shortages or rising wage costs are significant concerns. Additionally, DSR requires less water than PTR, as it avoids continuous flooding, thus conserving water resources and reducing irrigation expenses (Panneerselvam et al., 2020). The absence of intensive tillage and puddling in DSR also minimizes fuel consump- tion, contributing to further cost savings and lowering greenhouse gas emissions from fossil fuel use. Economic gains in DSR are enhanced by the compatibility of this method with conservation practices like zero tillage, which reduces soil disturbance and promotes better soil health over time (Gathala et al., 2011; Kakraliya et al., 2021). This compati- bility can help to improve crop yields and farm profitability. Moreover, DSR enables farmers to mechanize more easily, which can further optimize labor and time efficiency. Together, these factors make DSR an economically attractive option for farmers looking to maintain or improve yield while lowering operational costs and enhancing sustainability. 4.4. Comparative effect of DSR and PTR on carbon footprint (CFp) CFp refers to the total GHGs emission throughout the life cycle of an event or product, including direct and indirect GHGs emission and changes in SOC storage (Sun et al., 2021). DSR adoption over PTR has decreased the overall CFp by 34.4%. This implies that DSR is a potential choice of rice cultivation with lower CFp and higher carbon sequestra- tion (Kakraliya et al., 2021). Lower CFp can be attributed to manifold reasons like, lower diesel consumption, labor engagement, water usage, tillage, methane emission and lower GWP while fertilizer and most of the chemicals were constant for both the systems (Singh et al., 2022). Even though at some instances higher emissions were observed in DSR like for N2O, machinery usage and higher herbicide usage at some cases, the overall CFp was lower in DSR. Compared to tropics, lower GWP from subtropical climates resulted with lower CFp under DSR. In case of tillage practices, ZT with minimal tillage, higher water uses efficiency and carbon sequestration, resulted lower CFp than CT (Ahmad et al., 2024). Moreover, in wet DSR and CF, employment of puddling and higher water usage leads CFp change to be non-significant than PTR. Additionally, because of lower water usage and prevalence of dry DSR, aerobic practice showed a significant higher reduction in CFp, while AWD practice showed intermediate result (Fig. 14). Additional studies across various climatic conditions, soil types and management practices are imperative to more accurately es- timate CFp under DSR. 4.5. Cleaner and sustainable crop management options for balanced crop yields and lower GHG emission under DSR We assessed the relationships between responses of GHGs fluxes with varying N application rates and crop yields to see if changes in GHGs fluxes were correlated with N rate and crop production (Fig. 15). Using paired data, we found that N2O emissions were negatively related to crop yields under DSR conditions, indicating a trade-off between crop yield and N2O emissions. As N loss in the form of N2O emissions con- tributes significantly in lowering NUE, the N2O emission showed a negative trend with grain yield. Significant positive correlations were found for grain yields with N application rate (Fig. 16). Additionally, the graph illustrates at any given point, NUE of DSR may consistently be lower than that of PTR, leading to reduced grain yields. Yun et al. (1993) recommended applying 40–50% more N to DSR compared to PTR to counteract N losses. In addition to nitrogen management, sustainable practices can enhance the environmental and agronomic outcomes of DSR. Adoption of conservation agriculture practices like minimum tillage, residue retention, and crop rotation improve soil structure, increase organic matter, and reduce CH4 emissions by promoting aerobic soil conditions. Precision water and nitrogen application using drip fertigation and soil moisture sensors helps maintain optimal moisture levels, conserving water, nitrogen and limiting N2O emissions by avoiding excessive wet- ting. Remote sensing and soil nutrient mapping enable targeted, site- specific nutrient application, which improves NUE and minimizes nutrient losses. Integrated weed management (IWM) through methods like crop rotation, mechanical weeding, and competitive rice varieties supports weed control while reducing herbicide reliance and main- taining soil health. Adopting DSR, supported by these sustainable practices, is a viable strategy for mitigating GHG emissions and lowering the CFP in rice production. By optimizing nutrient and water use, DSR can address trade-offs associated with N2O emissions and potential yield reductions, making it a holistic, environmentally resilient approach to rice cultivation. Further studies should focus on the on-farm perfor- mance of DSR compared to PTR. While production practices at research stations are typically maintained under ideal conditions, in farmers’ fields, the interaction of management practices with landscape vari- ability can significantly influence DSR’s contributions to both mitigation and food security. 5. Conclusions This study shows that DSR can cut CH4 emissions by 69.9%, GWP by 36.7% and CFP by 34.4%. However, there was an 84.6% concomitant surge in N2O emissions and yield forfeit of 10.7%. These effects appear to differ across environmental and management gradient with a considerable trade off observed between CH4 and N2O emissions. From the study it was evident that tropical regions, and soil properties like medium texture, acidic pH and medium SOC content, provide the most favorable environmental conditions for DSR with nominal or no yield penalty. Similarly, under management practices, CF and high N appli- cation rate has comparable yields, and wet DSR and ZT resulted in minimal yield reduction. Maintaining the optimum soil moisture with appropriate redox potentials could cumulatively maintain grain yields K.S. Reddy et al. Cleaner Environmental Systems 16 (2025) 100238 12 and cut down CH4 and N2O emissions. Additionally, adopting ZT, along with other CA practices in DSR cultivation could provide extra benefits like yield and soil health improvement, while maintaining ecological sustainability with lower GWP and CFP. Thus, there is a need for further verification on DSR technology with combinational studies of assorted management practices under varied agro-ecosystems for achieving a cleaner and sustainable rice production system, more importantly at landscape scale at farmers field. CRediT authorship contribution statement K. Srikanth Reddy: Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Data curation, Conceptualization. C.M. Parihar:Writing – review & editing, Supervision, Formal analysis, Conceptualization. P. Panneerselvam: Writing – review & editing, Supervision, Resources, Project administration. Ayan Sarkar:Writing – review & editing, Methodology, Formal analysis. Kiranmoy Patra: Writing – review & editing, Methodology, Formal analysis. Sneha Bharadwaj: Writing – original draft, Formal analysis. D.R. Sena: Writing – review & editing, Methodology, Formal analysis. G. Sreeja Reddy: Writing – original draft, Formal analysis, Data curation. Alok Sinha:Writing – review& editing. Rajkumar Dhakar:Writing – review & editing, Formal analysis. Virender Kumar: Writing – review & edit- ing, Resources, Project administration, Funding acquisition, Conceptu- alization. Hari Sankar Nayak:Writing – review & editing, Supervision, Methodology, Formal analysis, Conceptualization. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments The first author sincerely acknowledges Indian Council of Agricul- tural Research (ICAR), ICAR-Indian Agricultural Research Institute (IARI), for providing the scholarship and other facilities as well as assistance. We acknowledge the support received from International Rice Research Institute (IRRI), South Asia Regional Centre, Varanasi, India for facilitating the PhD research of the first author. The support received from Director and other staff of ICAR-IARI, New Delhi, Division of Agronomy, ICAR-IARI, New Delhi is also acknowledged. The support provided by Cereal Systems Initiative for South Asia (CSISA) and Direct seeded rice consortium (DSRC) projects is also acknowledged. We thank the authors whose data contributed to this analysis and who provided us with vital additional information about their research upon contact. Special thanks to Dr. Raj Singh, Dr. Renu Pandey, Dr. D.K. Sharma, Pr. Scientists, ICAR-IARI, New Delhi, Dr. S. L. Jat, Sr. Scientist, ICAR-IIMR, Dr. Sunil Kumar, ISARC, Dr. Debabrata Nath, ISARC for assistance in data management and analysis work. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.cesys.2024.100238. Data availability Data will be made available on request. 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