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Data envelopment analysis based optimization for improving net ecosystem carbon and energy budget in cotton (Gossypium hirsutum L.) cultivation: methods and a case study of north-western India

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Abstract

Cotton (Gossypium hirsutum L.) is an important fiber crop with high energy and C footprints. This study aimed at tracking C footprints through emission of greenhouse gases and energy flow nexus in cotton cultivation to frame policy to reduce C and energy footprints, while enhancing net ecosystem C budget and C sequestration in soils. We attempted to quantify and optimize C footprints and energy flow in cotton cultivation using integrated Life Cycle Assessment and Data Envelopment Analysis approach. The total input energy of 23,960 MJ ha−1 produced total output energy of 73,964 MJ ha−1. The energy use efficiency and energy productivity were 3.1 ± 0.1 and 0.083 ± 0.003 kg MJ ha−1, respectively. The Charnes–Cooper–Rhodes model based Data Envelopment Analysis approach elucidated 37 (out of total 65), while Banker–Charnes–Cooper model revealed 55 decision-making units as energy efficient. The energy footprints expressed as specific energy were significantly higher under current production situation (13.0 MJ kg−1), compared with optimum production situation (12.0 MJ kg−1). The Data Envelopment Analysis based optimized total energy input was significantly reduced by 1522 MJ ha−1 through chemical fertilizers (2.7–51.5%) and biocides (7.7–25.1%), which led to a significant reduction of ~9.2% of total C equivalent emissions having a technical mitigation potential of 114.3 kg CO2e ha−1. The negative values of net ecosystem C budget for efficient (− 4.04 Mg C ha−1) and inefficient (− 4.94 Mg C ha−1) decision making units revealed that these ecosystems act as net C source. The average technical efficiency of 0.87 ± 0.02 revealed that ~13% of total energy input could be saved without any impact on cotton productivity and environment. These results underpin the overwhelming significance of intensified extension efforts for efficient use of chemical fertilizers and discouraging farmers from unwarranted use of biocides in cotton in the north-western India.

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Abbreviations

RA :

Autotrophic respiration

BCC model:

Banker–Charnes–Cooper model

CE:

Carbon equivalent

CCR model:

Charnes–Cooper–Rhodes model

CPS:

Current production situation

DMU:

Decision making unit

RE :

Ecosystem respiration

EP :

Energy productivity

ER :

Energy use efficiency

GHGI:

Greenhouse gas intensity

GPP:

Gross primary production

NEE:

Net ecosystem exchange

NEG:

Net energy gain

NPP:

Net primary production

N2O:

Nitrous oxide

NPPAbove-ground biomass :

NPP as above-ground biomass yield

NPPBelow-ground biomass :

NPP as below-ground biomass yield

NPPEconomic yield :

NPP as economic yield

NPPLitter :

NPP as litter

NPPRhizodeposit :

NPP as rhizodeposition

OPS:

Optimum production situation

PTE:

Pure technical efficiency

SE:

Scale efficiency

RH :

Soil heterotrophic respiration

ΔSOC:

Change in soil organic C

ES :

Specific energy

S.E.M :

Standard error from mean

TE:

Technical efficiency

EI :

Total energy input

EO :

Total energy output

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Acknowledgements

The authors greatly acknowledge the farmers of the study region for their valuable time and making access to the data during personal interviews.

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  1. Punjab Agricultural University, Krishi Vigyan Kendra, Khokhar Khurd, Mansa, Punjab, 151505, India

    Pritpal Singh, Gurdeep Singh & G. P. S. Sodhi

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Singh, P., Singh, G. & Sodhi, G.P.S. Data envelopment analysis based optimization for improving net ecosystem carbon and energy budget in cotton (Gossypium hirsutum L.) cultivation: methods and a case study of north-western India. Environ Dev Sustain 24, 2079–2119 (2022). https://doi.org/10.1007/s10668-021-01521-x

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