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首页> 外文期刊>Agroforestry Systems >Empirical models to predict LAI and aboveground biomass of Coffea arabica under full sun and shaded plantation: a case study of South of Minas Gerais, Brazil
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Empirical models to predict LAI and aboveground biomass of Coffea arabica under full sun and shaded plantation: a case study of South of Minas Gerais, Brazil

机译:完全阳光和遮荫人工林下预测阿拉伯咖啡的LAI和地上生物量的经验模型:以巴西米纳斯吉拉斯州南部为例

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Leaf area index (LAI) and above ground biomass (AGB) are two parameters that are difficult to measure but very useful. In this paper we investigated the relationship between coffee biophysical properties and LAI and AGB in two coffee production systems: full sun (FS) and shaded with macadamia nuts (SH). The paper proposes an empirical relationship for calculating coffee AGB and coffee LAI which avoids destructive methods, using simple field measurements and agrometeorological data. Here, we reported that LAI is related to canopy structure but subject to strong seasonal variations, which can be identified using water requirements satisfaction index (WRSI). Coffee LAI answers to the decreased WRSI with 1 month lag (WRSI-1) and LAI values decreases more for FS systems than for SH systems during dry periods. The best empirical model to predict LAI for FS coffee production system was based on canopy height (ch) and WRSI-1 value. For SH systems, the best model used ch, WRSI-1 and the height of the first pair of branches. Coffee AGB values were measured using destructive analyses and an empirical equation was developed. Both coffee production systems stocked carbon, whereas the SH system stocked an increased carbon amount provided by the macadamia trees that contributed with 15 % of the total carbon above ground. Both systems can be considered mitigation techniques since they are able to remove atmospheric carbon and stock it in the biomass, which has been largely proposed as a compensation mechanism for greenhouse gas emissions.
机译:叶面积指数(LAI)和地上生物量(AGB)是两个很难测量但非常有用的参数。在本文中,我们调查了两种咖啡生产系统中的咖啡生物物理特性与LAI和AGB之间的关系:全日照(FS)和夏威夷果坚果(SH)。本文提出了一种使用简单的田间测量和农业气象数据来避免破坏性方法的计算咖啡AGB和咖啡LAI的经验关系。在这里,我们报道了LAI与冠层结构有关,但受强烈的季节变化影响,可以使用需水满意度指数(WRSI)进行识别。咖啡LAI可以解决WRSI降低且滞后1个月(WRSI-1)的问题,而在干旱期间,FS系统的LAI值比SH系统的降低更多。预测FS咖啡生产系统LAI的最佳经验模型是基于树冠高度(ch)和WRSI-1值。对于SH系统,最佳模型使用ch,WRSI-1和第一对分支的高度。使用破坏性分析测量了咖啡的AGB值,并建立了经验方程。两种咖啡生产系统都存储有碳,而SH系统存储的澳洲坚果树提供的碳量增加,占地上总碳的15%。两种系统都可以视为缓解技术,因为它们能够去除大气中的碳并将其存储在生物质中,而碳在很大程度上已被提议作为温室气体排放的补偿机制。

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