Ecology

Stratigraphic context of the Portfjeld Formation
The Portfjeld Formation is the lowermost formation of the Franklinian Basin in southern Peary Land (Figs. 1 and 2a, b), resting unconformably on Mesoproterozoic sandstones of the Independence Fjord Group and localized erosionally truncated outliers of Neoproterozoic tillites and associated carbonates of inferred Marinoan affinity (for regional stratigraphic reviews, see17,18). The carbonate-dominated Portfjeld Formation is overlain, at a karstified unconformity (Fig. 2c), by transgressive fluvial to marine shelf siliciclastics of the Buen Formation. The sandstone-dominated lower member of the Buen Formation yields trace fossils of early Cambrian age19, while the mudstone-dominated upper member contains rich faunas of Cambrian Series 2 (Stage 3–4) age20.
Fig. 1: Field photographs of locality of the Portfjeld biota.

a Portfjeld Formation—basal Buen sandstones at Midsommersøer, notice the conspicuous band of dark cherty dolomites. b Detail of Portfjeld Formation, west of Midsommersøer, with the same darky cherty dolomites overlain by thrombolitic mounds, showing the lithostratigraphic horizon yielding the Portfjeld biota. c View looking east along Wandel Dal with Midsommersøer, taken from the fossil locality (off shot left). Scale bar valid for a.

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Fig. 2: Geography, geology, and stratigraphy of the study area, North Greenland.

a Geological map showing the sample locality at Midsommersøer, North Greenland. b Stratigraphic schemes in northern Ellesmere Island and North Greenland. c Stratigraphic section through the Portfjeld Formation at the western end of Midsommersøer compared with δ13Ccarb (o/oo PDB) values indicating the Shuram–Wonoka anomaly (about 570–560 Ma), “F” indicates the sample locality.

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The Portfjeld Formation comprises two discrete stratigraphic packages separated by a regionally developed karstic unconformity. The lower succession, about 170-m thick, is dominated by dolostones with rare limestones and represents two transgressive–regressive cycles of a carbonate ramp. Typical facies include hummocky cross-stratified intraclast-rich grainstones and cherty dark dolostones of the mid- and outer ramp, and ooid–pisoid grainstones and varied microbial facies of the inner ramp, including columnar and meter-scale domal stromatolites and thrombolitic bioherms. The capping hiatal surface shows penetrative and multi-generational karstic features extending some 40 m beneath the surface (Fig. 2c), including extensive interstratal solution, brecciation, and successive cave/vug/fracture fills and cementation, testifying to a protracted period of subaerial exposure. The transgressive succession of the upper Portfjeld Formation (c. 70–90 m thick) comprises fluvial sandstones and mudstones succeeded by high-energy shallow marine carbonate and siliciclastic facies, truncated upwards by dolines and karstic collapse structures at the Portfjeld–Buen formation boundary.
Chemostratigraphy shows that the δ13C of the Portfjeld Formation carbonate samples range from +4‰ to −8‰. Positive δ13C values persist over the lower c. 40 m of the formation, before a marked negative shift of 12‰ down to values of −8‰. A more gradual increase characterizes the δ13C values up-section through the karstified strata to the karstic unconformity at 167 m, following which there is a clear stabilization in δ13C values to values around 0 to −1‰ for the remainder of the succession (Fig. 2c).
The δ13C database for Neoproterozoic carbonate sections has proliferated within the last 30 years to the point where a δ13C compilation curve can act as a chemostratigraphic correlation tool for newly studied sections. Utilizing chemostratigraphy as a chronology tool involves the correlation of globally coherent geochemical perturbations and trends in vertical carbonate successions, within a broadly understood timeframe. This is particularly useful when attempting to refine age estimates for successions that lack abundant biostratigraphical and/or radiometric data. Utilizing the most up-to-date δ13C chemostratigraphic framework21, the asymmetric negative δ13C excursion and more gradual recovery displayed by the mid-section of the Portfjeld Formation can be aligned with the most extreme C-isotope variation recorded in Earth’s history: the Shuram–Wonoka anomaly. The form and magnitude (∼12‰) of this δ13C excursion, as well as a nadir value of −8‰, are unique to the Shuram–Wonoka anomaly and deter its alignment with other Neoproterozoic excursions, as well as the Basal Cambrian Isotope Excursion, BACE21. The Shuram–Wonoka anomaly is recognized intercontinentally in Late Ediacaran strata22 and provides a broad chronostratigraphic marker to constrain the biostratigraphy presented in this study. Williams and Schmidt22 noted that the Wonoka excursion spanned an interval of up to 10 Myr. from about 570 to 560 Ma and was recognized in shallow marine shelf environments on three palaeocontinents with low palaeolatitudes ( More

Sample collection
We sampled tissue from wild greater gliders from four locations that broadly represent the northern and southern distribution of the Petauroides latitudinal and longitudinal geographic range as part of a separate study to investigate relationships between animal physiology and climate (Fig. 4). The sites included Mount Zero-Taravale Australian Wildlife Sanctuary (19° 07′ 18″ S, 146° 04′ 42″ E, n = 18) and Blackbraes National Park (19° 34′ 39″ S, 144° 05′ 05″ E, n = 15) in North Queensland, and Bendoc State Forest (37° 10′ 35″ S, 148° 56′ 52″ E, n = 9) and Wombat State Forest (37° 29′ 50″ S, 144° 09′ 23″ E, n = 6) in Victoria. We then conducted additional field sampling in Redcliffe Vale, Queensland (21° 06′ 57″ S, 148° 56′ 58″ E, n = 18) (Fig. 4) as this was the suggested location of the proposed P. armillatus19,20. In addition, 12 museum specimen tissue samples were obtained from the Queensland Museum from greater gliders collected from northern, central and southern Queensland to investigate genetic structure in that area more broadly (Fig. 4). Additional information about the climate, geography and vegetation at each sample location can be found in Supplementary Material, (Table S1 online).
Figure 4

Location of the five study areas in eastern Australia in blue triangles (Blackbraes National Park (NP), Mount Zero-Taravale Sanctuary, Redcliffe Vale, Bendock State Forest (SF) and Wombat SF), and location of museum samples in orange squares (MS). The grey shading represents the current distribution of the greater glider from the Australian Species of National Environmental Significance Database49. This map was generated in R using the Australian coastline data from Geoscience Australia50 and multiple R packages (ggplot239, ggsn51, sp52,53, rgdal54, raster55).

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Wild greater gliders were located through spotlight searches using high-powered, handheld torches (Ledlenser P7, Zweibrüder Optoelectronics GmbH and Co., Solingen, Germany) to detect greater glider eye shine. Individuals were then captured using a gas-powered, tranquilizer dart-gun (Montech Black Wolf; Tranquil Arms Company, VIC, Australia) and darts specifically designed for mammals between 400 and 2000 g (0.5 ml; Minidarts, Tranquil Arms Company) containing 30–60 mg Zoletil 100 (Zolazepan and Tiletamine 50:50; Virbac), depending on the estimated body mass of the animal. While still under sedation, captured individuals were weighed and sexed, and reproductive status was assessed. External measurements were taken with vernier callipers using external jaws to measure head length (tip of snout to occipital bone protuberance), head width (widest part of one zygomatic arch to the analogous location on the other side of the head), ear width (widest part of the ear when flattened), ear length (from tragus to the outermost edge of the ear, excluding fur), and knee to heal-hind limb (top of knee to base of the heel with limb flexed to ninety degrees). Body length (occipital bone protuberance to the base of the tail, following the spine, with head in-line with the plane of the body) and tail length (cloaca to the tip of the tail, excluding fur) were measured with a flexible tape measure. Each individual was marked with a PIT tag (AVID Microchip Company, CA, USA) implanted subcutaneously and a tissue sample was clipped from the margin of the ear for DNA analysis. All work involving live animals complied with animal ethics and relevant guidelines and regulations. The animal capture and tissue collection was approved by James Cook University (Animal Ethics Permits A2137, A2545).
Morphology data investigation
Principal components analysis of the eight measured morphological traits for greater gliders from the five sites (Taravale, Blackbraes, Redcliffe Vale, Bendoc, and Wombat) was performed in R, using the “prcomp” function. The plot was generated using the ggplot2 package37. We then used a canonical variate analysis (CVA) in R package MASS38 to analyse regional group structure (Northern, Central, and Southern) in the multivariate data. To explore whether there were significant differences in measured traits between sexes and account for unequal sample sizes, we used linear models with backward elimination variable selection method and Tukey’s post-hoc multiple comparison test. Linear models included each morphological trait as the response variable and region (Northern, Central, Southern), sex (male, female) and the interaction between region and sex. Insignificant variables were eliminated until only significant variables remained. Based on these results, sex was pooled and Tukey’s post-hoc multiple comparison test was used to compare measured morphological traits between regions. We also explored differences between sites with Tukey’s post-hoc multiple comparison test.
DNA extraction and sequencing
DNA was extracted by Diversity Arrays Technologies (DArT Pty Ltd, Canberra, Australia) using a NucleoMag 96 Tissue Kit (MachereyNagel, Duren, Germany) coupled with NucleoMag SEP (Ref. 744900) to allow automated separation of high-quality DNA on a Freedom Evo robotic liquid handler (TECAN, Miinnedorf, Switzerland). Tissue was first incubated overnight with proteinase K, adjusted in concentration depending on the tissue. Sequencing for SNP genotyping was done using DArTseq (DArT Pty Ltd, Canberra, Australia), which uses a combination of complexity reduction using restriction enzymes, implicit fragment size selection and next generation sequencing39, as described in detail by Kilian et al.40 and Georges et al.26. Essentially, DArTseq is an implementation of sequencing complexity-reduced representations41 and more recent applications on next generation sequencing platforms42,43. To achieve the most appropriate complexity reduction (the fraction of the genome represented, controlling average read depth and number of polymorphic loci), four combinations of restriction enzymes (Pstl enzyme combined with either Hpall, Sphl, Nspl or Msel) were evaluated and restriction enzyme combination of Pstl (recognition sequence 5′-CTGCAIG-3′) and Sphl (5′-GCATGIC-3′) was selected.
Amplification using polymerase chain reaction (PCR)26,44 and the conditions applied are as described in Georges et al.26. After PCR, equimolar amounts of amplification products from each sample were pooled and applied to cBot (Illumina) bridge PCR for sequencing on the Illumina Hiseq 2500. The sequencing (single end) was run for 77 cycles to yield sequence tags of 20–69 bp after removing adaptors.
SNP genotyping
Sequences generated from each lane were processed using proprietary DArT Pty Ltd analytical pipelines as described by Georges et al.26. In particular, one third of samples were processed twice from DNA, using independent adaptors, to allelic calls as technical replicates, and scoring consistency (repeatability) was used as the main selection criterion for high quality/low error rate markers. The DArT analysis pipelines have been tested against hundreds of controlled crosses to verify Mendelian behaviour of the resultant SNPs as part of their commercial operations. The resultant data set contained the SNP genotypes and various associated metadata of which CloneiD (unique identity of the sequence tag for a locus), repAvg (proportion of technical replicate assay pairs for which the marker score is identical), CallRate (proportion of individuals scored at a particular locus) and SnpPosition (position in the sequence tag at which the defined SNP variant base occurs) are of particular relevance to our analyses.
Additional SNP filtering
The SNP data and associated metadata were read into a genlight object ({adegenet}45) to facilitate processing with package dartR (version 1.8)46. We first removed all but one SNP from each sequence tag (12,782 SNPs removed) and retained only those loci supported by a read depth between 5 × and 100 × (8895 loci removed). Three individuals were removed from the dataset owing to an exceptionally poor call rate of less than 0.5 (MS9, MS12, MS8) and resultant monomorphic loci removed from the dataset. Loci with a repeatability less than 0.99 were removed (2380 loci) and finally, loci with a call rate of less than 0.95 were removed (9870 loci). We regard the data remaining after this additional filtering (11,317 SNP markers for 75 individuals) as highly reliable.
Visualization
Genetic similarity among individuals, populations and colour morphs was visualized using ordination (principal coordinates analysis or PCoA47) as implemented in the gl.pcoa and gl.pcoa.plot functions of dartR. A scree plot of eigenvalues guided the number of informative axes to examine48, taken in the context of the average percentage variation explained by the original variables (using the gl.pcoa.scree function in dartR).
Diagnosable units
Diagnosable units are populations or aggregations of populations that can be diagnosed by one or more fixed allelic differences. Individuals that were intermediate between two aggregations in the PCoA (T1 and T5) were removed as suspected hybrids or introgressed individuals and examined separately. A fixed difference analysis as implemented in dartR was undertaken, and pairs of populations with no fixed allelic differences were progressively amalgamated to yield a putative set of diagnosable operational taxonomic units (OTUs). Putative OTUs that were distinctive because of false positives (owing to sampling error) were identified using gl.fixed.diff() with test = TRUE in dartR (see Georges et al.26, R scripts are provided in the online data repository), and pairs of populations for which the count of fixed differences did not exceed the estimated false positive rate were also amalgamated. Counts of private alleles were obtained from gl.report.pa in dartR.
Genetic diversity
Expected heterozygosity was used as a measure of relative genetic diversity. Heterozygosity was obtained for each population from allele frequencies using the gl. report.heterozygosity function of dartR and pairwise comparisons of heterozygosity between populations were tested for significance using the gl.test.heterozygosity function in dartR (significance evaluated by re-randomizaton with 10,000 replicates).
Hybridisation
The genotypes of suspected hybrids/introgressed individuals (T1 and T5) were examined using New Hybrids28 without specifying parental source populations. Briefly, New Hybrids uses simulation to characterize likelihood bins for each of the parental populations, F1 hybrids generated by crossing the parentals, F2 hybrids, and backcrosses between the F1 hybrids and the parental populations. These bins are used to estimate the likelihood of an individual belonging to each bin, and these likelihoods are rendered and scaled to produce posterior probabilities of bin membership. Parameters were set as ThetaPrior = Jeffreys, PiPrior = Jeffreys, burnin = 10,000, sweeps = 10,000 and the default genotype frequency classes (P0, P1, F1, F2, F1 × P0, F1 × P1). New Hybrids gives a posterior probability of individual membership in each of the genotype frequency classes, allowing effective assignment of first generation hybrids (F1), second generation hybrids (F2) and backcrosses of F1 hybrids to the parental populations.
Relatedness
Two individuals with genotypes intermediate between Taravale/Blackbraes and Redcliffe Vale (T1 and T5) were found in the same cluster of trees in Taravale. Given their status and proximity, we wished to examine the possibility that they were in a parent–offspring relationship. We did this by assuming they were parent–offspring and examining the frequency of pedigree inconsistencies (e.g. one individual homozygous reference, the other homozygous alternate). Theoretically, the count of pedigree inconsistencies should be zero for true parent–offspring pairs, but errors in calling the SNPs generates false positives. To overcome this, we generated counts of pedigree inconsistencies for all pairs of individuals to generate a null expectation and identified putative pairs of parent–offspring as outliers using software to be included in the next release of dartR (gl.report.parent.offspring). More

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Weather
All the weather parameters measured during the study period were similar to the long-term averages (Fig. S1). During the study period (2014–2018), crops received an average annual rainfall of 763 mm, although its distribution was quite different amongst the rainy season (June–September) (Fig. S1). Rice/maize season in 2014, 2015, and 2016, 2017 received 485 (256 mm in September), 420 (255 mm in July), 533 (284 mm in August), and 695 mm (247 mm in June and 226 mm in September) of rainfall, respectively. In 1st year, the wheat crop receivedrainfall of 247 mm whereas in the 2nd, 3rd, and 4th years it was only 56, 96 and 78 mm, respectively.
Crops and system productivity
The management practices under different rice/maize-based scenarios influenced the crop grain yields over the 4-years (2014–2017) (Table 1). Scenarios with rice crops (Sc1-Sc3) did not differ in rice yields during the year 2014 and 2017, but CT direct seeded rice (Sc2) in the 2nd year (2015) and ZT direct seeded rice (Sc3) in the 3rd year (2016) produced 0.9 Mg ha−1 higher and 1.1 Mg ha−1 lower yield than farmers’ practice (Sc1), respectively (Table 1). Rice equivalent maize yields in CA-based scenarios (Sc6-Sc7) did not differ from scenarios with rice crops (Sc1-Sc3) in any of the study years. Rice equivalent maize yield of CA-based Sc5 with maize on PB, although was similar to Sc1 in all the years but was 1.41 Mg ha−1 lower than ZT-DSR (Sc3) in 1st year and 0.98 Mg ha−1 lower than CT-DSR (Sc2) in 2nd year. In contrast, rice equivalent yield (REY) of Sc4 with maize on fresh beds (FB) produced lower yields than one of the rice-based scenarios in three out of four years. These results suggest that maize performs better under CA-based management system than under conventional tillage system. Almost 5% higher yield of maize was recorded in the 1st year and 12–16% higher in the last three years under CA-based scenario (Sc7) compared to CT-based scenario (Sc4) and at par with Sc5. Based on the 4-years average, rice equivalent yield (REY) of Sc4 (maize on FB ) was 0.8 Mg ha−1 (12%) lower than Sc1 (business-as-usual) whereas other scenarios did not differ from each other in REY (Table 1).
Table 1 Effect of different scenarios on grain yields (Mg ha−1) of rice, maize, wheat and systems during 4-years (2014–2018).
Full size table

The management practices influenced wheat grain yield over the years of experimentation (Table 1). Across study years, the grain yield of ZT wheat in CA-based scenario was either similar or higher than CT wheat. Results showed significantly (P  Sc2 = Sc3 (1753–1759 mm ha−1)  > S7 = Sc6 = Sc4 = S5 (289–365 mm ha−1) (Table 2). The same trend followed in all the study years except in the 4th year, where irrigation water input in Sc5 (maize on PB) was 109–154 mm ha−1 (22–28%) lower than Sc6 and Sc7 (ZT maize on flat beds). The amount of water applied in CT-based rice crop (Sc1; farmers’ practice) was significantly (P  Sc2 = Sc3  > Sc7  > Sc4-Sc6.
Higher grain yield and low water use led to significantly (P  Sc4 (2.25 kg grain m−3)  > Sc7 (2.15 kg grain m−3)  > Sc6 (2.06 kg grain m−3), respectively compared to Sc1 (0.30 kg grain m−3) (Table 2). In wheat, CA-based management practices increased WPI by 9% (4-years’ mean) compared to Sc1 (1.21 kg grain m−3). CA-based management practices improved mean WPI by 23 and 438% in RW and MW system, respectively compared to Sc1 (0.42 kg grain m−3).
Figure 3

Effect of different scenarios on irrigation water productivity (kg grain m−3) of rice, maize, wheat and systems during 4-years (2014–2018).

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Energy use efficiency
Energy equivalents for different agricultural operations used in the study are given in Table S2. The energy input and output (Tables S3 and S4), and energy use efficiency (EUE) of rice, maize, wheat and mungbean were influenced by the management practices and varied from year to year (Fig. 4). During rice/maize, higher EUE was observed in maize based scenarios (Sc4-Sc7) than in rice-based scenarios (Sc1-Sc3) (10.81–13.83 MJ MJ−1 versus 3.95–4.85 MJ MJ−1) (Table 2). Rice-based scenarios (Sc1-Sc3) did not differ in EUE. However, in maize-based scenarios (Sc4-Sc7), EUE of CA-based maize scenarios (Sc5-Sc7) was 17–28% higher than CT-based maize Sc4. Across years also, the same trend was observed with no difference in EUE of rice-based scenarios (Sc1-Sc3), whereas CA-based maize scenarios (Sc5-Sc7) had higher EUE than CT-based Sc4 (Table 2). In wheat crop, highest EUE was observed under CA-based scenarios (Sc2-Sc3 and Sc5-Sc7) compared to CT-based scenarios (Sc1 and Sc4) across all study years and based on four years’ average (9.26–10.05 MJ MJ−1 versus 7.44–7.84 MJ MJ−1), it is indicated that CA-based scenarios are more energy-efficient than those of CT-based scenarios (Fig. 4). In all the years, EUE of maize-based scenarios (Sc4-Sc7) were higher than rice-based scenarios (Sc1-Sc3) but within rice-based scenarios (Sc1-Sc3), results were more variable with higher EUE of CA-based Sc2 and Sc3 in 1st and 2nd year than CT-based scenarios (Sc1) but did not differ in 3rd and 4th year (Fig. 4). On system basis, the EUE of different scenarios decreased in the following order: Sc5 (11.92 MJ MJ−1)  > Sc6 = Sc7 (10.26–10.95 MJ MJ−1)  > Sc4 (9.25 MJ MJ−1)  > Sc3 = Sc2 (6.23–6.25 MJ MJ−1)  > Sc1 (5.05 MJ MJ−1) (Table 2). Maize-based scenarios (Sc5-Sc7) had 48 to 136% higher EUE than rice-based scenarios (Sc1-Sc3) suggesting maize-wheat based cropping systems were more efficient in energy use than rice–wheat based systems (Table 2). Scenario 3 (+ 24%) in RW and Sc5 (+ 136%) in MW system were the most energy-efficient among the different combinations of management practices in 4-years of study.
Figure 4

Effect of different scenarios on energy use efficiency of rice, maize, wheat and systems during 4-years (2014–2018).

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Methane (CH4) and nitrous oxide (N2O) emission from soil
Methane (CH4) was emitted only from the rice plots (Table 3). The estimated mean value of CH4 emission (kg CO2 eq. ha−1) was 39% lower in CA-based rice scenarios without continuous flooding (Sc2 and Sc3) compared to CT-based Sc1 with continuous flooding for  > 1 month (Table 3).
Table 3 Effect of different scenarios on GHGs emissions, C-sequestration and GWP of rice, maize, wheat and systems (based on 4-year average, 2014–18).
Full size table

N2O emission varied from 7 to 583 kg CO2 eq. ha−1 during the rice season (Table 3). The maximum amount of N2O emission (580–583 kg CO2 eq. ha−1) was observed in CA-based rice scenarios (Sc2-Sc3) followed by the maize-based scenarios (50–61 kg CO2 eq. ha−1) and was the lowest in CT-based rice Sc1 (7 kg CO2 eq. ha−1). The CA-based rice and maize scenarios produced 88 and 9 times higher N2O emission compared to Sc1, respectively. The N2O emission in the wheat season ranged between 50 to 102 kg CO2 eq. ha−1 (Table 3). The highest N2O emission was estimated with CA-based scenarios (Sc2-Sc3) (101–102 kg CO2 eq. ha−1) and followed by scenarios Sc5-Sc7 (72–73 kg CO2 eq. ha−1) and was lowest in CT-based scenarios Sc1 and Sc4 (50 kg CO2 eq. ha−1). The N2O emission in the wheat crop was increased by 57% under CA-based management scenarios compared to CT-based management scenario (Table 3). On system basis, CA-based rice and maize systems emitted 12 and 2.4 times more N2O compared to Sc1, respectively but methane emission was reduced to zero (Table 3). Overall CA-based cereal management systems emitted almost six-time higher N2O emission compared to farmers’ practice, irrespective of cropping systems (Table 3).
GHG emission associated with residue burning (kg CO2 eq. ha−1)
Crop residue burning is a common farmers’ practice in the western IGP. Therefore, GHG emission due to residue burning (kg CO2 eq. ha−1) was estimated with CT-based system of rice (Sc1; 278 kg CO2 eq. ha−1) and maize (Sc4; 69 kg CO2 eq. ha−1) cultivation (Table 3). In the case of wheat, the GHG emission due to residue burning (kg CO2 eq. ha−1) was estimated with CT-based cultivation of wheat in Sc1 (59 kg CO2 eq. ha−1) and Sc4 (40 kg CO2 eq. ha−1). No GHG emission (kg CO2 eq. ha−1) was considered due to burning where crop residues were retained/incorporated in CA-based management practices under different scenarios.
GHG emission due to energy consumption (kg CO2 eq. ha−1)
GHG emission due to energy consumption varied from 2414 to 2941, 1005 to 1126 and 1122 to 1299 kg CO2 eq. ha−1 in rice, maize, and wheat, respectively (Table 3). Compared to CA-based management scenarios, CT-based scenarios emitted more GHGs due to the higher consumption of electricity and diesel energy in all the crops and cropping systems. Compared to Sc1, GHG emission due to energy consumption from rice/maize season was 16–18% lower in CA-based rice scenarios (Sc2-Sc3) and 63–66% lower in maize-based scenarios (Sc4-Sc7) (Table 3). Overall, compared to Sc1, CA-based scenarios reduced ~ 17 and 63% of GHG emissions due to energy consumption in rice and maize across the years, respectively. Similarly, in wheat, CA-based scenarios (Sc2-Sc3 and Sc5-Sc7) reduced 10% GHG emission due to energy consumptions as compared to CT-based scenarios (Sc1 and Sc4). On the system basis, Sc2, Sc3, Sc4, Sc5, Sc6, and Sc7 recorded lower energy-related emission of GHG by 14, 15, 43, 50, 46, and 43% (4-years’ mean), respectively, relative to Sc1 (4240 kg CO2 eq. ha−1) (Table 3). Rice and maize-based systems recorded ~ 15 and 46% lower GHG related emissions, respectively compared to farmers’ practice (Sc1-4240 kg CO2 eq. ha−1).
Carbon (C) sequestration
The estimated C-sequestration was carried out in those scenarios where crop residues were retained/ incorporated during the study period. The C-sequestration varied with the amount of crop residue was recycled under different crops and cropping systems. Estimated C-sequestration in soil varied from 0 to − 625 kg CO2 eq. ha−1 in rice, 0 to − 908 CO2 eq. ha−1 in maize and 0 to − 1821 kg CO2 eq. ha−1 in wheat (Table 3). On system basis, the highest C-sequestration was estimated under CA-based management scenarios which varied in the following order of Sc7 (3039 kg CO2 eq. ha−1)  > Sc3 (2446 kg CO2 eq. ha−1)  > Sc2 (2086 kg CO2 ha−1)  > Sc6 (2070 kg CO2 eq. ha−1).
Total global warming potential (GWP)
Global warming potential (GWP) varied with crop management practices under different scenarios over the years. In 4-year, the total estimated GWP from rice was lower under the CA-based systems than CT-based system. On 4-year mean basis, the GWP under the CA-based rice (Sc2-Sc3) and maize (Sc5-Sc7) systems were lowered by ~ 28 and 90% compared to farmers’ practice (Sc1), respectively (Table 3). Within maize-based scenarios, the CA-based scenarios (Sc5-Sc7) reduced the GWP of maize by 77–83% compared to CT-based Sc4. The GWP in wheat varied from − 384 to 1409 kg CO2 eq. ha−1 based on 4 year average (Table 3). The 4 years mean GWP was significantly lower by 127–138% in CA-based RW system (Sc2-Sc3) and 96–99% in CA-based MW system (Sc5-Sc7) compared to Sc1, respectively (Table 3). The mean GWP of wheat under CT-based RW system (Sc1) was similar to CT-based MW (Sc1and Sc4) systems.
The crop management practices under different scenarios influenced the total GWP (CO2 eq. ha−1) in both the cropping systems (RW and MW system) during the study years (Table 3). On 4-years system mean basis, GWP under Sc2, Sc3, Sc4, Sc5, Sc6, and Sc7 were 48, 54, 59, 96, 95, and 107% lower compared to Sc1 (farmers’ practice), respectively. In CA-based RW and MW systems, GWP was estimated lower by 50 and 89% compared to CT-based Sc1(6451 kg CO2 eq. ha−1), respectively. More