Designing profitable, resource use efficient and environmentally sound cereal based systems for the Western Indo-Gangetic plains

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).

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 < 0.05) higher wheat grain yield in all CA-based scenarios (Sc2-Sc3, and Sc5-Sc7) compared to CT (Sc1 and Sc4). CA-based scenarios produced a ~ 9% higher wheat grain yield compared to farmers’ practice (FP; Sc1). Almost similar yield of wheat was recorded with CA-based management whether it was grown after rice or maize.

System yield (rice equivalent yield; REY) varied from 9.89 to 14.84 Mg ha⁻¹ over the study years (Table 1). Four-year mean system yield (rice equivalent) of CA-based Sc7 was 0.74 to 2.25 Mg ha⁻¹ (6–20%) higher than rest of the scenarios. The lowest system yield was recorded in Sc4 with maize-wheat on a FB with a 17% lower yield than Sc7, and 7–12% lower than the rest of the scenarios. System-level yield of Sc7 was consistently highest in all the study years, whereas Sc4 had the lowest yield. In terms of system productivity, among different practices, Sc2 (+ 5%) and Sc7 (+ 11%) were the most efficient management practices in the RW system and MW system, respectively.

Sustainable yield index (SYI)

The sustainable yield index (SYI) for rice, maize, wheat, and system are presented in Fig. S2. Highest SYI for rice/maize was observed under Sc2 (0.81) and Sc7 (0.81), while the lowest with CT-based maize system (Sc4). SYI for wheat was higher for CA-based management scenarios (Sc2, Sc5, and Sc7) (0.83–0.84) compared to CT-based scenarios (Sc1 and Sc4). Results indicated that wheat yields are more sustainable as compared to rice and maize. Compared to farmers’ practice, SYI was increased by 11 and 5% in Sc7 and Sc2, respectively. Results from our study clearly showed that CA-based Sc7 (maize-wheat-mungbean) is more sustainable than that of the other rice/maize-based scenarios.

Economic profitability

Crop production costs were mainly attributed to tillage/field preparation, crop establishment, field preparations, irrigation, fertilizer, pest management, harvesting/threshing, and man-days involved in agricultural production. The total production costs of rice and maize varied from 541 to 715 USD ha⁻¹ acros 4-years under different management scenarios (Table S1). Average (4-years’ mean) production costs of rice/maize was highest in CT-based rice (680 USD ha⁻¹) and followed by CT-based maize(630 USD ha⁻¹)and were lower (583-613USD ha⁻¹) in CA-based management scenarios (Sc2-Sc3 and Sc5-Sc7) (Table S1). Compared to Sc1, the total production cost was ~ 13% lower when rice was seeded under ZT and maize on PB (permanent beds) (Table S1). In contrast, net income was highest in CA-based Sc5 (991 USD ha⁻¹) followed by Sc7 (985 USD ha⁻¹), and was lowest in Sc4 (741 USD ha⁻¹) (Table 2). The net income of other CA-based scenarios (Sc2, Sc3, and Sc6) did not differ from Sc5 and Sc7. The net income of CA-based Sc5, Sc7, and Sc3 were 19, 18, and 12% higher, respectively compared to the CT-based RW system (835 USD ha⁻¹) (Table 2).

In the case of wheat, based on a 4-year average, the cultivation cost and net returns varied from 456 to 534 USD ha⁻¹ and 974 to 1192USD ha⁻¹, respectively (Table S1 and Table 2). Similarly to rice and maize, CT-based management practices (Sc1-USD 534 ha⁻¹ and Sc4-USD 495 ha⁻¹) recorded the highest cost of wheat cultivation (Table S1) and CA-based scenarios recorded the lowest cultivation cost of USD 461 ha⁻¹. Net income from wheat under CA-based management (Sc2, Sc3 and Sc5) was higher by 151–218 USD ha⁻¹ (+ 16–22%) compared to Sc1 (974 USD ha⁻¹) (Table 2).

The total cultivation cost and net returns ranged from 988 to 1290 USD ha⁻¹ and 1286 to 2592 USD ha⁻¹, respectively under different system based management scenarios over the years (Table S1 and Fig. 1). On 4-year average basis, the highest cost of cultivation was associated with Sc1 (1213 USD ha⁻¹) followed by Sc7 (1184 USD ha⁻¹) and Sc4 (1124 USD ha⁻¹) and, it was lowest with Sc3 (USD 1044 ha⁻¹) (Table S1). The net incomes of all CA-based scenarios were higher than CT-based scenarios (Sc1 and Sc4) by 260–514 USD ha⁻¹. CA-based Sc2, Sc3, Sc5, Sc6 and Sc7 recorded 18, 14, 19, 15 and 25% (4-years’ mean) higher net incomes, respectively compared to farmers’ practice (1810 USD ha⁻¹) (Table 2). CA-based Sc2 (+ 18%) under RW system and CA-based Sc7 (+ 25%) under MW system, were the most profitable management scenarios compared to Sc1 among all the management scenarios included in the study (Table 2).

Irrigation water use and water productivity

The amount of irrigation water applied varied from 1382 to 2495 mm ha⁻¹ in rice and 173 to 545 mm ha⁻¹ in maize over the 4-years (Fig. 2). Based on 4-year average, the irrigation water input decreased in the following order: Sc1 (2173 mm ha⁻¹) > 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 < 0.05) higher by ~ 19 and 85% (4-years’ mean) compared to CA-based rice (Sc2-Sc3) and maize (Sc5-Sc7) scenarios, respectively (Table 2). However, compared to CA-based rice (Sc2-Sc3), CA-based maize (Sc5-Sc7) saved ~ 79% of irrigation water. In the case of wheat, applied irrigation water varied from 285 to 555 mm ha⁻¹ across the 4-years (Fig. 2).

In wheat, the amount of irrigation water applied was almost similar across the different scenarios except in Sc5 (Fig. 2), where about 12% (4-years’ mean) less irrigation water was applied compared to CT-based Sc1(Table 2). Based on 4-year average, scenarios did not differ in irrigation inputs during wheat except Sc5 which had 45–55 mm ha⁻¹ (10–12%) lower irrigation input than rest of the scenarios (Table 2). At system level, the amount of applied water was significantly lowered by 16% (4-years’ mean) in CA-based rice systems (Sc2-Sc3) and by 70% (4-years’ mean) in maize-based systems (Sc4-Sc7), irrespective of management systems compared to CT-based RW system (2627 mm ha⁻¹). The general trend in irrigation water input in different scenarios across years and average of four-years followed the following trend: Sc1 > Sc2 = Sc3 > Sc7 > Sc4-Sc6.

Higher grain yield and low water use led to significantly (P < 0.05) higher irrigation water productivity (WP_I) under CA-based management systems in all the crops and cropping systems compared to CT-based scenario (Sc1) (Fig. 3). On 4-year average basis, CA-based rice (Sc2-Sc3) and maize (Sc5-Sc7) recorded ~ 27 and 664% higher WP_I compared to CT-based Sc1 (0.42 kg grain m⁻³) (Table 2). On 4-year average basis, mean WP_I in maize was 583, 612, 644 and 755% higher in order of Sc5 (2.59 kg grain m⁻³) > 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 WP_I by 9% (4-years’ mean) compared to Sc1 (1.21 kg grain m⁻³). CA-based management practices improved mean WP_I by 23 and 438% in RW and MW system, respectively compared to Sc1 (0.42 kg grain m⁻³).

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.

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).

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.

Source: Ecology - nature.com