@article{fdi:010093479,
  title = {{I}n-silico optimization of peanut production in {I}ndia through envirotyping and ideotyping},
  author = {{H}ajjarpoor, {A}. and {P}avlík, {J}. and {H}ora, {J}. and {K}onopásek, {J}. and {P}usupuleti, {J}. and {V}adez, {V}incent and {S}oltani, {A}. and {F}eike, {T}. and {S}toces, {M}. and {J}arolimek, {J}. and {K}holová, {J}.},
  editor = {},
  language = {{ENG}},
  abstract = {{P}eanut ({A}rachis hypogaea {L}.) is an important cash crop with significant yield gaps, especially in developing countries. {O}ptimizing peanut production could foster economic growth for a significant number of smallholder farmers across the globe. {I}n this study, we used an in-silico cropping system model to simulate and optimize genotype x crop management ({G} x {M}) across {I}ndia that would narrow the existing peanut yield gaps. {F}or that, we simulated diverse {G} x {M} combinations across range of environments ({E}) in {I}ndia, considering three irrigation regimes typical for managing peanut production systems. {C}overing whole {I}ndia in a 0.5 degrees x0.5 degrees resolution, we simulated 60,480 {G} x {M} combinations for each grid, summing up to a total of 2.3 billion simulations and 1.02 {TB} output data. {T}his required well-structured high-performance computing ({HPC}) approaches, data management, and analytical capacities. {F}or this, we present the concept of a re-usable {HPC} system with interoperable modules, which can be readily adapted for different simulation setups. {W}e introduced the novel way of analyzing simulation outputs - "{I}ndex of {G}oodness" ({I}o{G}) - that aggregates key peanut production characteristics (grain and haulm production) and production risk failure. {I}o{G} is a simple way to evaluate the suitability of simulated {G}x{M} options from the perspective of end-users, including primary producers and crop improvement programs. {T}he generated output was used to identify the geographic regions (environmental clusters, {EC}) with high degree of similarities within each of the tested irrigation regimes. {F}or each cluster, we identified a specific suite of {G}x{M} to benefit peanut production and prioritize {G} targets for breeding. {I}n principle, irrigated cropping systems would benefit from high planting densities, long duration and vigorous crop types. {W}ith diminishing water availability (particularly in the {T}har {D}esert and {SE} {I}ndia), the optimal production included shorter duration crop types which could quickly respond to drought stimuli (i.e. close stomata and conserve soil water upon soil and atmospheric drought exposure). {T}hese traits should also be considered in phenotyping strategies to support context-specific breeding.},
  keywords = {{C}rop {M}odeling ; {H}igh {P}erformance {C}omputing ({HPC}) ; {G}enotype x {E}nvironment ; x {M}anagement ; ({G}x{E}x{M}) interaction ; {I}ndex of {G}oodness ({I}o{G}) ; {E}nvironmental {C}lustering ({EC}) ; {INDE}},
  booktitle = {},
  journal = {{C}omputers and {E}lectronics in {A}griculture},
  volume = {235},
  numero = {},
  pages = {110383 [16 p.]},
  ISSN = {0168-1699},
  year = {2025},
  DOI = {10.1016/j.compag.2025.110383},
  URL = {https://www.documentation.ird.fr/hor/fdi:010093479},
}