Estimating Generalized Soil‐water Characteristics from Texture

Saxton, K. E.; Rawls, W. J.; Romberger, J. S.; Papendick, R. I.

doi:10.2136/sssaj1986.03615995005000040039x

Cited by 1,565 publications

(542 citation statements)

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“…Community-level means for root biomass, RL, specific RL, and root nitrogen content, stratified by depth, were measured monthly during 2005 (40). Soil water-holding capacity was calculated from soil texture and structural data by using the SPAW model (43). Fertility was assessed by the NNI, which quantifies actual nitrogen availability for plant growth through the dilution of nitrogen in closed canopies (44).…”

Section: Methodsmentioning

confidence: 99%

Incorporating plant functional diversity effects in ecosystem service assessments

Dı́az

Lavorel

Bello

et al. 2007

Proc. Natl. Acad. Sci. U.S.A.

1,345

1,191

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Global environmental change affects the sustained provision of a wide set of ecosystem services. Although the delivery of ecosystem services is strongly affected by abiotic drivers and direct land use effects, it is also modulated by the functional diversity of biological communities (the value, range, and relative abundance of functional traits in a given ecosystem). The focus of this article is on integrating the different possible mechanisms by which functional diversity affects ecosystem properties that are directly relevant to ecosystem services. We propose a systematic way for progressing in understanding how land cover change affects these ecosystem properties through functional diversity modifications. Models on links between ecosystem properties and the local mean, range, and distribution of plant trait values are numerous, but they have been scattered in the literature, with varying degrees of empirical support and varying functional diversity components analyzed. Here we articulate these different components in a single conceptual and methodological framework that allows testing them in combination. We illustrate our approach with examples from the literature and apply the proposed framework to a grassland system in the central French Alps in which functional diversity, by responding to land use change, alters the provision of ecosystem services important to local stakeholders. We claim that our framework contributes to opening a new area of research at the interface of land change science and fundamental ecology.biodiversity ͉ land change ͉ mass ratio hypothesis ͉ plant functional traits G lobal environmental changes, including land use and land cover changes, have considerable impacts on the ecological properties of ecosystems and therefore on the ecosystem services (ES) that societies derive from them (1). Although links between ecosystem properties (EP) and ES are not always trivial, many ES and their changes can be reasonably quantified by EP that are routinely measured in ecological studies (2). Global change effects on EP can be direct, through their effects on physical and chemical processes and on the metabolism and behavior of organisms. Global change drivers can also influence EP indirectly through their impacts on biodiversity, either through their effects on local biota or by altering the ability of organisms to disperse through landscapes. Relevant changes in biodiversity are manifested through changes in plant functional diversity (FD), i.e., the value, range, and relative abundance of plant functional traits in a given ecosystem (2). Although often subtler than direct effects of global change drivers (3, 4), these indirect biotic effects remain a major source of uncertainty in predicting the impacts of global change on ES provision.Conceptual models accounting for links between the functional trait values of local plant communities and EP are scattered in the literature, and the conceptual connections between them are not always clear. We articulate the most important of those models i...

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Section: Methodsmentioning

confidence: 99%

Incorporating plant functional diversity effects in ecosystem service assessments

Dı́az

Lavorel

Bello

et al. 2007

Proc. Natl. Acad. Sci. U.S.A.

1,345

1,191

View full text Add to dashboard Cite

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“…Clay contents in plots 402, 409, and 311 were 16.25 %, 21.25 %, and 15.00 %, respectively, which was much higher than in other plots where soil water content was monitored. Higher clay contents in those three plots resulted in higher water-holding capacities (Saxton, Rawls, Romberger, & Papendick, 1986).…”

mentioning

confidence: 89%

Soil Moisture and Plant Canopy Temperature Sensing for Irrigation Application in Cotton

Sui¹,

Fisher²,

Barnes³

2012

JAS

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There is a need to provide technical tools to cotton producers for appropriate management of irrigation in the Mid-South region of the U.S.A wireless sensor network was deployed in a cotton field to monitor soil water status for irrigation. The network included two systems, a Decagon system and a microcontroller-based system. The Decagon system consisted of soil volumetric water-content sensors, wireless data loggers, and a central data station. Sensor data collected by each data logger were wirelessly transferred to and stored in the data station. The microcontroller-based system was designed to be a low-cost data logger for monitoring Watermark water-potential sensors. An infrared thermometer was used in the field to measure plant canopy temperature for evaluating its usefulness in detecting water stress in cotton under humid conditions. Soil water and plant canopy temperature data were collected during the 2011 cotton growing season. Results indicated that the soil water sensors were able to measure the soil water status, and the measurements recorded by the systems reflected general trends of soil water change during the growing season. Soil water content decreased at a higher rate in 15-cm and 30-cm depths than in 60-cm depth before 60 days after planting. A sharp decrease of soil water content in 60-cm depth was observed from 60 to 80 days after planting. Sensor measurements responded to effects of soil texture on available water capacity. Canopy temperature of non-irrigated plants is 2-4 C higher than that of the irrigated plants during peak time of day. Supplemental irrigation increased yield by 14.2 % in this study. System installation and maintenance procedures developed worked well in general. Some installation and operational issues were found and need to be resolved for wider field operation and user acceptance.

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“…(16) is calculated as a function of soil clay content (f clay ; %) as proposed by Saxton et al (1986):…”

Section: Soil Ch 4 Diffusivity D Chmentioning

confidence: 99%

Soil Methanotrophy Model (MeMo v1.0): a process-based model to quantify global uptake of atmospheric methane by soil

et al. 2018

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Abstract. Soil bacteria known as methanotrophs are the sole biological sink for atmospheric methane (CH 4 ), a potent greenhouse gas that is responsible for ∼ 20 % of the humandriven increase in radiative forcing since pre-industrial times. Soil methanotrophy is controlled by a plethora of factors, including temperature, soil texture, moisture and nitrogen content, resulting in spatially and temporally heterogeneous rates of soil methanotrophy. As a consequence, the exact magnitude of the global soil sink, as well as its temporal and spatial variability, remains poorly constrained. We developed a process-based model (Methanotrophy Model; MeMo v1.0) to simulate and quantify the uptake of atmospheric CH 4 by soils at the global scale. MeMo builds on previous models by Ridgwell et al. (1999) and Curry (2007) by introducing several advances, including (1) a general analytical solution of the one-dimensional diffusion-reaction equation in porous media, (2) a refined representation of nitrogen inhibition on soil methanotrophy, (3) updated factors governing the influence of soil moisture and temperature on CH 4 oxidation rates and (4) the ability to evaluate the impact of autochthonous soil CH 4 sources on uptake of atmospheric CH 4 . We show that the improved structural and parametric representation of key drivers of soil methanotrophy in MeMo results in a better fit to observational data. A global simulation of soil methanotrophy for the period 1990-2009 using MeMo yielded an average annual sink of 33.5 ± 0.6 Tg CH 4 yr −1 . Warm and semi-arid regions (tropical deciduous forest and open shrubland) had the highest CH 4 uptake rates of 602 and 518 mg CH 4 m −2 yr −1 , respectively. In these regions, favourable annual soil moisture content (∼ 20 % saturation) and low seasonal temperature variations (variations < ∼ 6 • C) provided optimal conditions for soil methanotrophy and soil-atmosphere gas exchange. In contrast to previous model analyses, but in agreement with recent observational data, MeMo predicted low fluxes in wet tropical regions because of refinements in formulation of the influence of excess soil moisture on methanotrophy. Tundra and mixed forest had the lowest simulated CH 4 uptake rates of 176 and 182 mg CH 4 m −2 yr −1 , respectively, due to their marked seasonality driven by temperature. Global soil uptake of atmospheric CH 4 was decreased by 4 % by the effect of nitrogen inputs to the system; however, the direct addition of fertilizers attenuated the flux by 72 % in regions with high agricultural intensity (i.e. China, India and Europe) and by 4-10 % in agriculture areas receiving low rates of N input (e.g. South America). Globally, nitrogen inputs reduced soil uptake of atmospheric CH 4 by 1.38 Tg yr −1 , which is 2-5 times smaller than reported previously. In addition to improved characterization of the contemporary soil sink for atmospheric CH 4 , MeMo provides an opportunity to quantify more accurately the relative importance of soil methanotrophy in the global CH 4 cycle in the past and its capaci...

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Estimating Generalized Soil‐water Characteristics from Texture

Cited by 1,565 publications

References 0 publications

Incorporating plant functional diversity effects in ecosystem service assessments

Incorporating plant functional diversity effects in ecosystem service assessments

Soil Moisture and Plant Canopy Temperature Sensing for Irrigation Application in Cotton

Soil Methanotrophy Model (MeMo v1.0): a process-based model to quantify global uptake of atmospheric methane by soil

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