Functional traits in agroecology: Advancing description and prediction in agroecosystems

Martin, Adam R.; Isaac, Marney E.

doi:10.1111/1365-2664.13039

Cited by 57 publications

(30 citation statements)

References 67 publications

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“…Furthermore, GRooT facilitates the integration of root traits into studies of related scientific disciplines, e.g. soil science and agronomy (Wood et al, 2015;Martin & Isaac, 2018). The completion of this standardized, curated and publicly-available database provide immediate benefit to the research community from ready-to-use data (Gallagher et al, 2019) and provide additional direction helping experts to identify gaps that need to be filled to increase completeness of global root trait data.…”

Section: Discussionmentioning

confidence: 99%

Global Root Traits (GRooT) Database

Guerrero‐Ramírez

Mommer²,

Freschet

et al. 2020

Preprint

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Motivation: Trait data are fundamental to quantitatively describe plant form and function. Although root traits capture key dimensions related to plant responses to changing environmental conditions and effects on ecosystem processes, they have rarely been included in large-scale comparative studies and global models. For instance, root traits remain absent from nearly all studies that define the global spectrum of plant form and function. Thus, to overcome conceptual and methodological roadblocks preventing a widespread integration of root trait data into large-scale analyses we created the Global Root Trait (GRooT) Database. GRooT provides ready-to-use data by combining the expertise of root ecologists with data mobilization and curation. Specifically, we (i) determined a set of core root traits relevant to the description of plant form and function based on an assessment by experts, (ii) maximized species coverage through data standardization within and among traits, and (iii) implemented data quality checks. Main types of variables contained:GRooT contains 114,222 trait records on 38 continuous root traits. Spatial location and grain:Global coverage with data from arid, continental, polar, temperate, and tropical biomes. Data on root traits derived from experimental studies and field studies. Time period and grain: Data recorded between 1911 and 2019Major taxa and level of measurement: GRooT includes root trait data for which taxonomic information is available. Trait records vary in their taxonomic resolution, with sub-species or varieties being the highest and genera the lowest taxonomic resolution available. It contains information for 184 sub-species or varieties, 6,214 species, 1,967 genera and 254 families. Due to variation in data sources, trait records in the database include both individual observations and mean values. Software format:GRooT includes two csv file. A GitHub repository contains the csv files and a script in R to query the database.

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Section: Discussionmentioning

confidence: 99%

Global Root Traits (GRooT) Database

Guerrero‐Ramírez

Mommer²,

Freschet

et al. 2020

Preprint

Self Cite

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“…Recent studies highlighted the potential of using comparative functional ecology in agroecological studies, in particular to compare ecosystem services provided in agrosystems by various levels of cultivated and non‐cultivated biodiversity (Martin and Isaac 2015, 2018, Wood et al 2015). The trait‐based approach has a strong potential to address questions in order to understand how the functioning of organisms scales up to that of ecosystems (Reich et al 1992, Lavorel and Garnier 2002, Lavorel and Grigulis 2012, Reichstein et al 2014) and controls some of the services they deliver to humans (Díaz et al 2006, 2007, Faucon et al 2017), including those delivered by agriculture (Garnier and Navas 2012).…”

Section: Introductionmentioning

confidence: 99%

Seasonal and interannual variations in functional traits of sown and spontaneous species in vineyard inter‐rows

et al. 2020

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The trait‐based approach can address questions in order to understand how the functioning of organisms scales up to that of ecosystems and controls some of the services they deliver to humans, including in agriculture. However, the importance of interspecific vs the intraspecific trait variability (ITV) for classifying species according to their traits in agrosystems on a large diversity of pedoclimatic situations and cropping systems remains still open. Here, we addressed three questions: How do measured traits vary across years and seasons? Are species rankings conserved across years and season? And which traits and species are the more stable and repeatable for sown and spontaneous species? We conducted a two‐year experiment in a vineyard, and we measured four leaf and plant functional traits of 14 sown species and 43 spontaneous species that grew among sown species. Traits were measured at two key phenological stages for grapevine: budburst and flowering during two successive years with contrasted rainfall (2017 and 2018). We studied seasonal and interannual trait variations, rankings between species, and variance partitioning. The species factor explained the greatest part of trait variations across years and seasons. Sown and spontaneous species traits varied in the same way, and traits related to plant dry matter contents were the more stable across periods. Moreover, species rankings were conserved across years and seasons for all traits except plant height. Sown species showed better ranking conservation than spontaneous species overall. The trait‐based approach seems promising for the comparison of various cropping systems involving sown and spontaneous species, and may help identifying service crop species related to specific agroecosystem services. Further research is needed to bring more knowledge on trait variations under a diversity of agrosystems, and to improve theoretical frameworks that would help the design of sustainable agrosystems that provide multiple ecosystem services.

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“…While much contemporary research on plant functional traits has focused on ‘natural ecosystems’, trait-based research also has clear application in agricultural systems ( Martin and Isaac 2015 ; Milla et al 2015 ; Wood et al 2015 ; Martin and Isaac 2018 ). For example, the world’s 65 most common crops occupy ~1.2 billion ha or 8.1 % of the Earth’s land surface ( Martin and Isaac 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Inter- and intraspecific variation in leaf economic traits in wheat and maize

et al. 2018

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Wheat and maize are among the world’s most important crops, covering nearly 400 million ha of Earth’s land surface (an area roughly twice the size of Mexico) and playing a major role in the diets and livelihoods of millions of people. Leaf characteristics (or leaf traits) of these crops—including photosynthetic rates—are widely used in models that predict how wheat and maize will respond to environmental change. Using a large global database, we evaluate how leaf traits of wheat and maize differ across cultivars, and link this variability to climate conditions. In doing so, we assess how leaf traits can be expected to vary under climate change, and in turn, influence predictions of future food security.

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Functional traits in agroecology: Advancing description and prediction in agroecosystems

Cited by 57 publications

References 67 publications

Global Root Traits (GRooT) Database

Global Root Traits (GRooT) Database

Seasonal and interannual variations in functional traits of sown and spontaneous species in vineyard inter‐rows

Inter- and intraspecific variation in leaf economic traits in wheat and maize

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