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Multi-trait selection using the MGIDI for simultaneously improving rapeseed performance and energy-eco efficiency in dryland farming


Abstract

Attaining sustainability in agroecosystems requires balancing trade-offs between maximizing yield, optimizing energy efficiency, and minimizing environmental footprints. Yet, determining the optimal configuration of tillage and fertilisation regimes to satisfy these multifaceted goals under dryland farming conditions remains a complex agronomic challenge. This two-year field experiment aimed to identify the most efficient integrated management strategy for winter rapeseed (Brassica napus L.) using a split-plot design comprising three tillage intensities and ten integrated fertilisation protocols. For the first time in rapeseed agronomic assessment, the Multi-trait Genotype-Ideotype Distance Index (MGIDI) was deployed to rank treatments based on 23 morphological, yield-related, quality, and environmental traits. Factor analysis reduced data dimensionality into two principal factors (agronomic and yield components: 63.19% and sustainability and energy indices: 28.25%) which collectively accounted for over 91% of the total variance. The MGIDI analysis identified conventional tillage combined with farmyard manure as the treatment closest to the ideotype. Analysis of Selection Differentials (SD) revealed that this strategy yielded substantial gains over the grand mean, including increases in oil performance (+ 26.09%), net energy (+ 31.13%), seed yield (+ 16.76%), and energy use efficiency (+ 71.62%). Concurrently, marked reductions were observed in specific energy (-51.73%) and, most notably, total CO2 equivalent emissions (-98.94%). These findings demonstrate the robustness of the MGIDI as a multi-criteria decision-making tool in cropping systems, highlighting conventional tillage supplemented with farmyard manure as the superior strategy for maximizing productivity and eco-efficiency in dryland farming rapeseed.

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The authors gratefully acknowledge the University of Guilan, Rasht, Iran.

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This research received no external funding.

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Majid Majidian or Atefeh Sabouri.

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Not applicable. This study did not involve human participants or animals.

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Khoshhal-Zolpirani, F., Majidian, M., Banaeian, N. et al. Multi-trait selection using the MGIDI for simultaneously improving rapeseed performance and energy-eco efficiency in dryland farming.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-74193-0

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  • DOI: https://doi.org/10.1038/s41598-026-74193-0

Keywords

  • Carbon emissions
  • Energy sustainability
  • Factor analysis
  • Multi-trait selection
  • Selection differential


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