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Wheat (Triticum aestivum L.) is highly energy intensive winter crop grown in rice-wheat and cotton-wheat cropping systems in north-western India. In this study, energy input-output relationships for wheat cultivation were estimated from data collected from 80 wheat producers (40 each in rice-wheat and cotton-wheat cropping system) by face-to-face survey. Energy input through irrigation water was significantly (p < 0.05) higher by 517 MJ ha-1 (14.5%) in wheat under cotton-wheat (4093 ± 133 MJ ha-1), compared with rice-wheat (3576 ± 124 MJ ha-1); while energy input difference from other sources was non-significant among cropping systems. Data pooled for cropping systems revealed that chemical fertilizers comprised ∼45%, while irrigation, electricity and diesel fuel consumption each shared ∼15% towards total input energy (E I). Wheat cultivation was energy efficient with E I of 25.6 ± 0.5 GJ ha-1 and total output energy (E O) of 191.7 ± 3.6 GJ ha-1 produced net energy gain (NEG) of 166.1 ± 3.5 GJ ha-1. Data Envelopment Analysis (DEA) elucidated 28 decision making units (DMUs) as energy efficient with technical score (TE) of 1.00, while 52 DMUs (∼65% of total DMUs) with TE < 1.00 were inefficient. Therefore, by the adoption of recommendations of the study, 3819 ± 641 MJ ha-1 (∼12% of E I) energy saving is possible from different inputs in wheat cultivation. • Energy input was 26 GJ ha-1 and energy output was 192 GJ ha-1 in wheat cultivation. • Net energy gain of 166 GJ ha-1 shows that wheat cultivation is energy efficient. • Data envelopment analysis was used to optimize input energy in wheat cultivation. • Technical efficiency of 52 wheat farms (65% of total) was <1.00 • 12% (3819 MJ ha-1) of total input energy could be saved under optimized conditions. [ABSTRACT FROM AUTHOR]

Rice (Oryza sativa L.) is an important energy intensive cereal crop in India. In the present study, energy input and output were audited to optimize energy use in rice cultivation using data collected from 40 farmers during face-to-face interview. Average total energy input (E I) of 52.4 ± 1.3 GJ ha−1 produced total energy output (E O) of 259 ± 4.7 GJ ha−1, with energy productivity (E P) of 0.16 kg MJ−1. Results revealed that rice cultivation was energy efficient with energy ratio (E R) of 5.0 ± 0.12 and net energy gain (NEG) of 206 ± 4.5 GJ ha−1. In rice, irrigation water (∼40%) and chemical fertilizers (24.7%) had highest contribution towards E I. A non-parametric production function of DEA (Data Envelopment Analysis) applied to optimize energy use in rice, elucidated 20 decision making units (DMUs) as efficient, whilst 50% as inefficient. The average technical efficiency (TE), pure technical efficiency (PTE) and scale efficiency (SE) of rice farms were 0.94, 0.97 and 0.97, respectively. Our results revealed that between 9.4 and 2170 MJ ha−1 energy (0.2 and 46.9% of total saving) could be saved, if inefficient DMUs adopt the management practices of efficient DMUs. An average energy saving of 579 ± 166 MJ ha−1 by efficient DMUs accounts for ∼8.8% of the E I. • Energy input was 52.4 GJ ha−1 and energy output was 259 GJ ha−1 in rice cultivation. • Irrigation and fertilizers had highest contribution (64.7%) in total energy input. • Data envelopment analysis was used to optimize input energy use in rice cultivation. • Saving of 8.8% of total energy input is possible with current yield level. [ABSTRACT FROM AUTHOR]

Rice residue management (RRM) impacts ecosystems' sustainability due to change in energy use, C footprints (CFs) and net ecosystem C budget (NECB). The energy input-output and CFs were audited to optimize energy use and C equivalent emissions (CE) in wheat following RRM with Happy Seeder using data collected from 53 farmers during face-to-face interview. Average total energy input (E I) of 23.5 GJ ha−1 produced output energy of 204.8 ± 2.9 GJ ha−1, with energy productivity (E P) of 0.24 kg MJ−1. The net energy gain (NEG) of 181.3 ± 3.0 GJ ha−1 was achieved with energy use efficiency (E R) of 8.81 ± 0.19. Chemical fertilizers were the major hotspots of energy (∼48.4% of E I) and CFs (∼59.2% of CE) in wheat cultivation. Data Envelopment Analysis (DEA) elucidated 23 decision making units (∼43% of total) as energy efficient, whilst 30 as inefficient with average technical efficiency (TE), pure technical efficiency (PTE) and scale efficiency (SE) were 0.94, 0.97 and 0.94, respectively. DEA helped energy saving of 1462.8 MJ ha−1 (∼6.2%), mitigation of 107.8 kg CO 2 e ha−1 (∼7.6%) with significantly higher NECB of 716.4 kg C ha−1 (∼19.1%) due to change in soil organic C (ΔSOC) pool by 34.8 kg C ha−1 at efficient Happy Seeder sown wheat farms. [Display omitted] • Energy, C footprints (CFs) and NECB of Happy Seeder sown wheat was studied. • Wheat cultivation was energy efficient with net energy gain (NEG). • Chemical fertilizers were major hotspots of energy (∼48.4%) and CFs (∼59.2%). • DEA optimized energy and CFs were reduced by ∼6.2 and 7.6%, respectively. • Efficient wheat farms had ∼19.1% higher NECB, than the inefficient farms. [ABSTRACT FROM AUTHOR]