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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Spatial modelling improves genetic evaluation in smallholder breeding programs.

Maria L Selle1, Ingelin Steinsland2, Owen Powell3

  • 1Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway. maria.selle@ntnu.no.

Genetics, Selection, Evolution : GSE
|November 17, 2020
PubMed
Summary
This summary is machine-generated.

Spatial modeling improves genetic evaluation accuracy in smallholder dairy systems by accounting for herd relationships. This method enhances the separation of genetic and environmental effects, benefiting both phenotyped and non-phenotyped animals.

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Area of Science:

  • Animal Genetics
  • Quantitative Genetics
  • Agricultural Science

Background:

  • Statistical models are crucial for separating genetic and environmental influences on animal traits.
  • Smallholder systems present challenges in genetic evaluation due to small herd sizes and limited genetic connections.
  • Spatial relationships between herds and environmental covariates offer potential solutions.

Purpose of the Study:

  • To evaluate the effectiveness of spatial modeling in improving genetic evaluation for dairy cattle in smallholder systems.
  • To assess if incorporating spatial relationships enhances the separation of genetic and environmental effects.
  • To determine the impact of geographically referenced environmental covariates on genetic evaluation accuracy.

Main Methods:

  • Simulations and real dairy cattle data were used to test the hypothesis.
  • Environmental variation was modeled using independent herd effects and distance-based spatial effects.
  • Models were compared using both pedigree and genomic data.

Main Results:

  • Standard models inaccurately separate genetic and environmental effects in smallholder settings.
  • Spatial modeling significantly increased genetic evaluation accuracy for all animals.
  • Environmental covariates provided minimal additional accuracy beyond simple distance-based spatial relationships.
  • The benefits of spatial modeling were most pronounced in challenging separation scenarios.
  • Spatial modeling proved effective with both pedigree and genomic data.

Conclusions:

  • Spatial modeling effectively improves genetic evaluation in smallholder systems by enhancing environmental connectedness between herds.
  • This approach leads to better separation of genetic and environmental effects, crucial for accurate breeding decisions.
  • Routine implementation of spatial modeling is recommended for smallholder genetic evaluations and may benefit other populations.