Latent Root Distributions

2014 ◽  
Author(s):  
Robb J. Muirhead
Keyword(s):  
Latent Root ◽  
Root Distributions

scholarly journals Root Response to Soil Water Status via Interaction of Crop Genotype and Environment

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 708
Author(s):  
Phanthasin Khanthavong ◽  
Shin Yabuta ◽  
Hidetoshi Asai ◽  
Md. Amzad Hossain ◽  
Isao Akagi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Root Distribution ◽  
Water Status ◽  
Soil Layer ◽  
Shoot Biomass ◽  
Soil Conditions ◽  
Crop Adaptation ◽  
Soil Moisture Status ◽  
Root Distributions

Flooding and drought are major causes of reductions in crop productivity. Root distribution indicates crop adaptation to water stress. Therefore, we aimed to identify crop roots response based on root distribution under various soil conditions. The root distribution of four crops—maize, millet, sorghum, and rice—was evaluated under continuous soil waterlogging (CSW), moderate soil moisture (MSM), and gradual soil drying (GSD) conditions. Roots extended largely to the shallow soil layer in CSW and grew longer to the deeper soil layer in GSD in maize and sorghum. GSD tended to promote the root and shoot biomass across soil moisture status regardless of the crop species. The change of specific root density in rice and millet was small compared with maize and sorghum between different soil moisture statuses. Crop response in shoot and root biomass to various soil moisture status was highest in maize and lowest in rice among the tested crops as per the regression coefficient. Thus, we describe different root distributions associated with crop plasticity, which signify root spread changes, depending on soil water conditions in different crop genotypes as well as root distributions that vary depending on crop adaptation from anaerobic to aerobic conditions.

scholarly journals Facial Reconstruction as a Regression Problem

2011 ◽  
pp. 68-87
Author(s):  
Maxime Berar ◽  
Françoise Tilotta ◽  
Joan A. Glaunès ◽  
Yves Rozenholc ◽  
Michel Desvignes ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Principal Component ◽  
Latent Root ◽  
Facial Reconstruction ◽  
Computer Assisted ◽  
Regression Methods ◽  
Tissue Surface ◽  
Tissue Surfaces ◽  
Statistical Shape ◽  
The Individual

This chapter presents a computer-assisted method for facial reconstruction. This method provides an estimation of the facial outlook associated with unidentified skeletal remains. Current computer-assisted methods using a statistical framework rely on a common set of points extracted form the bone and soft-tissue surfaces. Facial reconstruction then attempts to predict the position of the soft-tissue surface points knowing the positions of the bone surface points. This chapter proposes to use linear latent variable regression methods for the prediction (such as Principal Component Regression or Latent Root Root Regression) and to compare the results obtained to those given by the use of statistical shape models. In conjunction, the influence of the number of skull landmarks used was evaluated. Anatomical skull landmarks are completed iteratively by points located upon geodesics linking the anatomical landmarks. They enable artificial augmentation of the number of skull points. Facial landmarks are obtained using a mesh-matching algorithm between a common reference mesh and the individual soft-tissue surface meshes. The proposed method is validated in terms of accuracy, based on a leave-one-out cross-validation test applied on a homogeneous database. Accuracy measures are obtained by computing the distance between the reconstruction and the ground truth. Finally, these results are discussed in regard to current computer-assisted facial reconstruction techniques, including deformation based techniques.

Nitrogen fertilization and planting models regulate maize productivity, nitrate and root distributions in semi-arid regions

2020 ◽  
Vol 200 ◽  
pp. 104636 ◽  
Author(s):  
Qianmin Jia ◽  
Liye Yang ◽  
Haoyun An ◽  
Shan Dong ◽  
Shenghua Chang ◽  
...  
Keyword(s):  
Nitrogen Fertilization ◽  
Arid Regions ◽  
Maize Productivity ◽  
Semi Arid ◽  
Root Distributions

scholarly journals Towards understanding tree root profiles: simulating hydrologically optimal strategies for root distribution

2001 ◽  
Vol 5 (4) ◽  
pp. 629-644 ◽  
Author(s):  
M. T. van Wijk ◽  
W. Bouten
Keyword(s):  
Water Uptake ◽  
Root Distribution ◽  
Root Zone ◽  
Evolutionary Game ◽  
Optimal Strategies ◽  
Single Root ◽  
Root Interactions ◽  
Model Configuration ◽  
The One ◽  
Root Distributions

Abstract. In this modelling study differences in vertical root distributions measured in four contrasting forest locations in the Netherlands were investigated. Root distributions are seen as a reflection of the plant’s optimisation strategy, based on hydrological grounds. The "optimal" root distribution is defined as the one that maximises the water uptake from the root zone over a period of ten years. The optimal root distributions of four forest locations with completely different soil physical characteristics are calculated using the soil hydrological model SWIF. Two different model configurations for root interactions were tested: the standard model configuration in which one single root profile was used (SWIF-NC), and a model configuration in which two root profiles compete for the same available water (SWIF-C). The root profiles were parameterised with genetic algorithms. The fitness of a certain root profile was defined as the amount of water uptake over a simulation period of ten years. The root profiles of SWIF-C were optimised using an evolutionary game. The results showed clear differences in optimal root distributions between the various sites and also between the two model configurations. Optimisation with SWIF-C resulted in root profiles that were easier to interpret in terms of feasible biological strategies. Preferential water uptake in wetter soil regions was an important factor for interpretation of the simulated root distributions. As the optimised root profiles still showed differences with measured profiles, this analysis is presented, not as the final solution for explaining differences in root profiles of vegetation but as a first step using an optimisation theory to increase understanding of the root profiles of trees. Keywords: forest hydrology, optimisation, roots

scholarly journals Maize root distributions strongly associated with water tables in Iowa, USA

2019 ◽  
Vol 444 (1-2) ◽  
pp. 225-238 ◽  
Author(s):  
Virginia A. Nichols ◽  
Raziel A. Ordóñez ◽  
Emily E. Wright ◽  
Michael J. Castellano ◽  
Matt Liebman ◽  
...  
Keyword(s):  
Maize Root ◽  
Water Tables ◽  
Root Distributions
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