由物种分布模型确定的农业生态改善了基因型与环境相互作用的模型拟合,从而为小农系统确定性能最佳的鸡种

IF 3.7 2区 农林科学 Q2 FOOD SCIENCE & TECHNOLOGY
F. Kebede, Hans Komen, T. Dessie, O. Hanotte, Steve Kemp, S. Alemu, John W M Bastiaansen
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引用次数: 0

摘要

动物生产性能是遗传效应、环境影响及其相互作用的结果。了解环境对性能的影响对于确定适合特定环境的品种非常重要。农业生态区划通常用于对环境进行分类,并在将品种广泛引入新环境之前对其性能进行比较。环境类,也被称为农业生态学,传统上是根据农学上重要的环境预测因子来定义的。我们假设,我们自己在物种水平上对牲畜的农业生态进行分类,并纳入最重要的环境预测因子,可以通过环境相互作用(GxE)估计比传统方法改善基因型。我们收集了分布在埃塞俄比亚多个环境中的改良鸡品种的生长性能数据。我们应用物种分布模型(SDMs)来确定最相关的环境预测因子,并将鸡生产性能测试点分组为农业生态学。我们拟合线性混合效应模型(LMM),对传统农业生态和sdm定义的农业生态进行模型比较。然后利用广义加性模型(GAMs)在种水平上可视化sdm识别的环境预测因子对鸡活重的影响。当基于sdm识别的环境预测因子定义农业生态时,模型在LMM中的拟合得到改善。GAMs生成的部分依赖图(pdp)显示了环境预测因子与体重之间的复杂关系。我们的研究结果表明,候选品种的多环境性能评估应基于sdm定义的环境类别或农业生态学。此外,我们的研究表明,GAMs非常适合于可视化生物气候因素对牲畜生产性能的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Agroecologies defined by species distribution models improve model fit of genotype by environment interactions to identify the best performing chicken breeds for smallholder systems
Animal performance is an outcome of genetic effects, environmental influences, and their interaction. Understanding the influences of the environment on performance is important to identify the right breeds for a given environment. Agroecological zonation is commonly used to classify environments and compare the performance of breeds before their wider introduction into a new environment. Environmental classes, also referred to as agroecologies, are traditionally defined based on agronomically important environmental predictors. We hypothesized that our own classification of agroecologies for livestock at a species level and incorporating the most important environmental predictors may improve genotype by environment interactions (GxE) estimations over conventional methodology. We collected growth performance data on improved chicken breeds distributed to multiple environments in Ethiopia. We applied species distribution models (SDMs) to identify the most relevant environmental predictors and to group chicken performance testing sites into agroecologies. We fitted linear mixed-effects models (LMM) to make model comparisons between conventional and SDM-defined agroecologies. Then we used Generalized Additive Models (GAMs) to visualize the influences of SDM-identified environmental predictors on the live body weight of chickens at species level. The model fit in LMM for GxE prediction improved when agroecologies were defined based on SDM-identified environmental predictors. Partial dependence plots (PDPs) produced by GAMs showed complex relationships between environmental predictors and body weight. Our findings suggest that multi-environment performance evaluations of candidate breeds should be based on SDM-defined environmental classes or agroecologies. Moreover, our study shows that GAMs are well-suited to visualizing the influences of bioclimatic factors on livestock performance.
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来源期刊
Frontiers in Sustainable Food Systems
Frontiers in Sustainable Food Systems Agricultural and Biological Sciences-Horticulture
CiteScore
5.60
自引率
6.40%
发文量
575
审稿时长
14 weeks
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