7,085 research outputs found
Using numerical plant models and phenotypic correlation space to design achievable ideotypes
Numerical plant models can predict the outcome of plant traits modifications
resulting from genetic variations, on plant performance, by simulating
physiological processes and their interaction with the environment.
Optimization methods complement those models to design ideotypes, i.e. ideal
values of a set of plant traits resulting in optimal adaptation for given
combinations of environment and management, mainly through the maximization of
a performance criteria (e.g. yield, light interception). As use of simulation
models gains momentum in plant breeding, numerical experiments must be
carefully engineered to provide accurate and attainable results, rooting them
in biological reality. Here, we propose a multi-objective optimization
formulation that includes a metric of performance, returned by the numerical
model, and a metric of feasibility, accounting for correlations between traits
based on field observations. We applied this approach to two contrasting
models: a process-based crop model of sunflower and a functional-structural
plant model of apple trees. In both cases, the method successfully
characterized key plant traits and identified a continuum of optimal solutions,
ranging from the most feasible to the most efficient. The present study thus
provides successful proof of concept for this enhanced modeling approach, which
identified paths for desirable trait modification, including direction and
intensity.Comment: 25 pages, 5 figures, 2017, Plant, Cell and Environmen
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Evaluating soil nitrate dynamics in an intercropping dripped ecosystem using HYDRUS-2D.
The competition mechanisms between crop species for water and nutrients, especially nitrate (NO3-N), in intercropping ecosystems are still poorly understood. Therefore, an experiment involving high (300 kg ha-1 for corn and 250 kg ha-1 for tomato), medium (210 kg ha-1 for corn and 175 kg ha-1 for tomato), and low (150 kg ha-1 for corn and 125 kg ha-1 for tomato) N-fertilizer applications (HF, MF, LF, respectively) was conducted in the corn and tomato intercropping ecosystem during 2014 (a calibration period for modeling) and 2015 (a validation period for modeling). The modified HYDRUS-2D code was used to analyze soil NO3-N concentrations (SNC) in the middle between corn rows (Pc), between corn and tomato rows (Pb), and between tomato rows (Pt), NO3-N exchange in the horizontal direction between different regions, NO3-N leaching from the corn, the bare, and the tomato region, and N uptake by crops. Simulated SNCs were in good agreement with measurements, with RMSE, NSE, and MRE of 0.01-0.06 mg cm-3, 0.75-0.98, and 8.7-19.1%, respectively, during the validation period (2015). Average SNCs in the 0-40 cm soil layer were different between Pc, Pt, and Pb. Intensive NO3-N exchange in the horizontal direction occurred during the second stage (Day After Sowing [DAS] 37-113 in 2014; DAS 29-120 in 2015). NO3-N exchange between the corn and bare regions was lower than between the tomato and bare regions due to smaller concentration gradients. However, in the vertical direction, NO3-N leaching from the corn region in both years was 4.1 and 8.8 times larger, respectively, than from the tomato region under HF since NO3-N mainly moved from the tomato region to the corn region. Our results reveal the competition between corn and tomato for N and provide a rationale for formulating and optimizing different fertilizer regimes for different crops in the intercropping ecosystem
Masticatory biomechanics in the rabbit : a multi-body dynamics analysis
Acknowledgement We thank Sue Taft (University of Hull) for the µCT-scanning of the rabbit specimen used in this study. We also thank Raphaël Cornette, Jacques Bonnin, Laurent Dufresne, and l'Amicale des Chasseurs Trappistes (ACT) for providing permission and helping us capture the rabbits used for the in vivo bite force measurements at la Réserve Naturelle Nationale de St Quentin en Yvelines, France.Peer reviewedPublisher PD
A general constitutive model for dense, fine particle suspensions validated in many geometries
Fine particle suspensions (such as cornstarch mixed with water) exhibit
dramatic changes in viscosity when sheared, producing fascinating behaviors
that captivate children and rheologists alike. Recent examination of these
mixtures in simple flow geometries suggests inter-granular repulsion is central
to this effect --- for mixtures at rest or shearing slowly, repulsion prevents
frictional contacts from forming between particles, whereas, when sheared more
forcefully, granular stresses overcome the repulsion allowing particles to
interact frictionally and form microscopic structures that resist flow.
Previous constitutive studies of these mixtures have focused on particular
cases, typically limited to two-dimensional, steady, simple shearing flows. In
this work, we introduce a predictive and general, three-dimensional continuum
model for this material, using mixture theory to couple the fluid and particle
phases. Playing a central role in the model, we introduce a micro-structural
state variable, whose evolution is deduced from small-scale physical arguments
and checked with existing data. Our space- and time-dependent model is
implemented numerically in a variety of unsteady, non-uniform flow
configurations where it is shown to accurately capture a variety of key
behaviors: (i) the continuous shear thickening (CST) and discontinuous shear
thickening (DST) behavior observed in steady flows, (ii) the time-dependent
propagation of `shear jamming fronts', (iii) the time-dependent propagation of
`impact activated jamming fronts', and (iv) the non-Newtonian, `running on
oobleck' effect wherein fast locomotors stay afloat while slow ones sink
Principal Fitted Components for Dimension Reduction in Regression
We provide a remedy for two concerns that have dogged the use of principal
components in regression: (i) principal components are computed from the
predictors alone and do not make apparent use of the response, and (ii)
principal components are not invariant or equivariant under full rank linear
transformation of the predictors. The development begins with principal fitted
components [Cook, R. D. (2007). Fisher lecture: Dimension reduction in
regression (with discussion). Statist. Sci. 22 1--26] and uses normal models
for the inverse regression of the predictors on the response to gain reductive
information for the forward regression of interest. This approach includes
methodology for testing hypotheses about the number of components and about
conditional independencies among the predictors.Comment: Published in at http://dx.doi.org/10.1214/08-STS275 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Geo-Spatial Analysis in Hydrology
Geo-spatial analysis has become an essential component of hydrological studies to process and examine geo-spatial data such as hydrological variables (e.g., precipitation and discharge) and basin characteristics (e.g., DEM and land use land cover). The advancement of the data acquisition technique helps accumulate geo-spatial data with more extensive spatial coverage than traditional in-situ observations. The development of geo-spatial analytic methods is beneficial for the processing and analysis of multi-source data in a more efficient and reliable way for a variety of research and practical issues in hydrology. This book is a collection of the articles of a published Special Issue Geo-Spatial Analysis in Hydrology in the journal ISPRS International Journal of Geo-Information. The topics of the articles range from the improvement of geo-spatial analytic methods to the applications of geo-spatial analysis in emerging hydrological issues. The results of these articles show that traditional hydrological/hydraulic models coupled with geo-spatial techniques are a way to make streamflow simulations more efficient and reliable for flood-related decision making. Geo-spatial analysis based on more advanced methods and data is a reliable resolution to obtain high-resolution information for hydrological studies at fine spatial scale
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