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Comparing the Predictive Capacity of Allometric Models in Estimating Grass Biomass in a Desert Grassland
Rangeland Ecology & Management ( IF 2.3 ) Pub Date : 2024-01-25 , DOI: 10.1016/j.rama.2024.01.004
Adam T. Naito , Steven R. Archer , Philip Heilman

Allometric models provide a rapid, nondestructive means for estimating aboveground biomass (AGB) of perennial grass species. In the absence of site-specific models, allometric relationships developed at other sites at other times are often used. This implicitly assumes that size-biomass relationships are highly robust. In this study, we assess the comparability of allometric relationships developed at two points in time (2005 and 2015) on different soils on a Sonoran Desert savanna in southern Arizona. We used peak growing season field measurements to develop single-species and multispecies regression models using basal diameter and height to predict the current year's AGB for seven perennial grass species. Basal diameter exhibited the strongest relationship with AGB among single-species (adjusted = 0.54 to 0.87) and multispecies models (adjusted = 0.73). Inclusion of height did little to improve biomass predictions. Our models generally underestimated observed 2015 AGB on the loamy site, whereas models developed in 2005 on a sandier site overestimated the 2015 AGB. Results suggest site-specific allometric models should be used when possible. However, in lieu of such models, relationships developed at other sites or at other times may be appropriate depending on the level of precision needed to address a specific research question.

中文翻译:

荒漠草原草生物量异速生长模型预测能力比较

异速生长模型提供了一种快速、无损的方法来估计多年生草种的地上生物量(AGB)。在缺乏特定地点模型的情况下,通常使用在其他时间在其他地点开发的异速生长关系。这隐含地假设尺寸与生物量的关系是高度稳健的。在这项研究中,我们评估了亚利桑那州南部索诺兰沙漠稀树草原上两个时间点(2005 年和 2015 年)不同土壤上形成的异速生长关系的可比性。我们使用生长旺季现场测量来开发单物种和多物种回归模型,使用基径和高度来预测七种多年生草本植物当年的 AGB。在单物种(调整 = 0.54 至 0.87)和多物种模型(调整 = 0.73)中,基底直径与 AGB 的关系最强。纳入高度对于改善生物量预测几乎没有作用。我们的模型普遍低估了在壤质场地观测到的 2015 年 AGB,而 2005 年在沙地场地开发的模型则高估了 2015 年 AGB。结果表明,应尽可能使用特定地点的异速生长模型。然而,代替此类模型,在其他地点或其他时间建立的关系可能是合适的,具体取决于解决特定研究问题所需的精度水平。
更新日期:2024-01-25
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