@article{fdi:010081484,
  title = {{E}nvironmental characterization and yield gap analysis to tackle genotype-by-environment-by-management interactions and map region-specific agronomic and breeding targets in groundnut},
  author = {{H}ajjarpoor, {A}mir and {K}holova, {J}. and {P}asupuleti, {J}. and {S}oltani, {A}. and {B}urridge, {J}. and {D}egala, {S}. {B}. and {G}attu, {S}. and {M}urali, {T}. {V}. and {G}arin, {V}. and {R}adhakrishnan, {T}. and {V}adez, {V}incent},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he high degree of {G}enotype by {E}nvironment by {M}anagement ({G}x{E}x{M}) interactions is a serious challenge for production and crop improvement efforts. {T}his challenge is especially true for a crop like groundnut that is often grown as a rainfed crop in diverse environments and management, leading to considerable production fluctuations among regions and seasons. {D}eveloping a means to characterize the drivers of variable yield and to identify region specific breeding objectives were the main motivations for this research, using groundnut production in {I}ndia, as a case study for rainfed crops. {H}istorically, five groundnut production areas have been considered by {I}ndian crop improvement programs. {O}ur objectives were to assess the relevance of this zonation system and possibly to re-define production areas with a higher degree of similarities into homogeneous production units ({HPU}s). {T}owards this, we used yield gap analysis and the geo-biophysical characters of the production region to understand and deal with {G}x{E}x{M} interactions. {W}eather and soil data, crop parameters, and management information data were collected and groundnut production was simulated at the district scale over 30 consecutive years. {C}onsequently, the geographic distribution of the potential yields and the yield gaps were first estimated to understand the main production limitations in a given region. {L}arge and variable yield gaps (with a mean of -70 %) were observed and results revealed a readily exploitable production gap (- 8 {M} tons), which might be bridged by following recommended agronomic practices. {W}ater deficit limited the yield potential by an average of 40 %, although with large variability among districts. {H}owever, large and variable yield gaps remained. {T}o resolve the unexplained variation, principal component and cluster analysis of agronomic model output together with geo-biophysical indicators for each district were carried out. {T}his resulted in seven {HPU}s, having well-defined production-limiting constraints. {G}rouping by {HPU} greatly reduced variance in actual and simulated yields, as compared to grouping across all groundnut production zones in {I}ndia. {T}he {HPU} based approach delimited precise geographic regions within which {HPU}-specific {G}x{M} products could be designed by crop improvement programs to boost productivity.},
  keywords = {{H}omogeneous production units ; {P}otential yield ; {SSM} model ; {C}rop design ; {T}arget population of environment ; {INDE}},
  booktitle = {},
  journal = {{F}ield {C}rops {R}esearch},
  volume = {267},
  numero = {},
  pages = {108160 [15 p.]},
  ISSN = {0378-4290},
  year = {2021},
  DOI = {10.1016/j.fcr.2021.108160},
  URL = {https://www.documentation.ird.fr/hor/fdi:010081484},
}