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Stephen Welch

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Rini Akmeliawati

International Islamic University Malaysia

Pierfrancesco Lombardo

Università degli Studi "La Sapienza" di Roma

Hossein Sadegh Lafmejani

Università degli Studi di Parma (Italy)

Tomoyuki Ichiba

University of California, Santa Barbara

Mohamed I. Riffi

Islamic University of Gaza

Camila Sardeto Deolindo

University of Aarhus

Przemysław Kupidura

Warsaw University of Technology

Rini Akmeliawati

International Islamic University Malaysia

Lyudmila S Mihaylova

The University of Sheffield

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Papers by Stephen Welch

US hard winter wheat yield loss attributed to compound hot-dry-windy events

Figshare, 2022

CERES-Maize Phenology Estimation

The Dataset included in this paper is associated with PlosOne Paper entitled "Estimating par... more The Dataset included in this paper is associated with PlosOne Paper entitled "Estimating parametric phenotypes that determine anthesis date in zea mays: Challenges in combining ecophysiological models with genetics". This includes all simulation input, output, and observed data used in the stud

Proximal hyperspectral sensing and data analysis approaches for field-based plant phenomics

Computers and Electronics in Agriculture, 2015

Field-based plant phenomics requires robust crop sensing platforms and data analysis tools to suc... more Field-based plant phenomics requires robust crop sensing platforms and data analysis tools to successfully identify cultivars that exhibit phenotypes with high agronomic and economic importance. Such efforts will lead to genetic improvements that maintain high crop yield with concomitant tolerance to environmental stresses. The objectives of this study were to investigate proximal hyperspectral sensing with a field spectroradiometer and to compare data analysis approaches for estimating four cotton phenotypes: leaf water content (C w), specific leaf mass (C m), leaf chlorophyll a + b content (C ab), and leaf area index (LAI). Field studies tested 25 Pima cotton cultivars grown under well-watered and water-limited conditions in central Arizona from 2010 to 2012. Several vegetation indices, including the normalized difference vegeta

Proximal hyperspectral sensing and data analysis approaches for field-based plant phenomics

Computers and Electronics in Agriculture, Oct 1, 2015

Field-based plant phenomics requires robust crop sensing platforms and data analysis tools to suc... more Field-based plant phenomics requires robust crop sensing platforms and data analysis tools to successfully identify cultivars that exhibit phenotypes with high agronomic and economic importance. Such efforts will lead to genetic improvements that maintain high crop yield with concomitant tolerance to environmental stresses. The objectives of this study were to investigate proximal hyperspectral sensing with a field spectroradiometer and to compare data analysis approaches for estimating four cotton phenotypes: leaf water content (C w), specific leaf mass (C m), leaf chlorophyll a + b content (C ab), and leaf area index (LAI). Field studies tested 25 Pima cotton cultivars grown under well-watered and water-limited conditions in central Arizona from 2010 to 2012. Several vegetation indices, including the normalized difference vegeta

Quantifying Methane Production at a Cattle Grazing Site using Open-Path Dual-Comb Spectroscopy

To better quantify methane emissions resulting from grazing cattle, a controlled methane release ... more To better quantify methane emissions resulting from grazing cattle, a controlled methane release at an agricultural site is performed using dual comb spectroscopy. The achieved methane concentration precision is below 10 nmol/mol. The Author(s) Work of the US Government and not subject to copyright.

Developing Guidelines for Replanting Grain Sorghum: II. Improved Methods of Simulating Caryopsis Weight and Tiller Number

Agronomy Journal, 1997

Sorghum [Sorghum bicolor (L.) Moench] producers often have difficulty determining when plant stan... more Sorghum [Sorghum bicolor (L.) Moench] producers often have difficulty determining when plant stands are low enough to merit replanting. Our objective was to use SORKAM, a sorghum growth model, to develop guidelines for replanting grain sorghum. A necessary first step is validation of the model over an extensive range of management and environmental factors. Validation was accomplished using 19 field data sets representing 11 yr and six Kansas locations. Several nonparametric tests were used to compare observed and simulated yields, yield components, and phenological dates. In addition, sensitivities of yield and yield components were determined in response to yearly climate, planting date, plant population, and maturity class changes. Model sensitivities were compared with sensitivities calculated from field data. While phenological predictions were adequate, SOR-KAM could capture only 27 to 79% of grain yield variability at the locations tested. Yield predictions from different plant populations within a planting date were particularly inaccurate. The validation and sensitivity analyses showed that the poor yield predictions were the result of improper computation of tiller number and faulty partitioning of biomass to caryopsis weight. Partitioning errors translocated enough assimilate from culm to grain to make yields essentially constant across populations within a planting date. To use SORKAM to generate replant guidelines, improvements must be made in modeling the relationships among yield components and the source-sink relationship that determines caryopsis weight. S ORGHUM PRODUCERS must decide whether to replant after reductions in plant populations due to pest damage, adverse weather, or other factors. Because poor stands are infrequent, producer experience with replanting decisions is likely to be limited. Nevertheless, in 1993, more than 69 388 ha of grain sorghum were replanted in Kansas alone. Estimating the damaged stand's yield potential is difficult (especially given the variability of weather), which complicates the choice of appropriate management practices. Plant growth models, by dynamically simulating interactions among climate, crops, and management, offer the best opportunity for developing guidelines when previous experience is limited (Rosenthal and Gerik, 1990). Therefore, our approach was to use a grain sorghum growth model and long-term weather data to provide producers with informative yield predictions. We selected the SORKAM vl .0 model (Rosenthal et al., 1989), because it simulates yield components individually and we are familiar with its internal formulation. To generate replanting guidelines, however, a model must demonstrate accurate yield predictions over a wide range of planting dates, plant populations, hybrid maturity classes, and weather factors. In

A modeling paradigm for interdisciplinary water resources modeling: Simple Script Wrappers (SSW)

EGUGA, May 1, 2014

Holistic understanding of a water resources system requires tools capable of model integration. T... more Holistic understanding of a water resources system requires tools capable of model integration. This team has developed an adaptation of the OpenMI (Open Modelling Interface) that allows easy interactions across the data passed between models. Capabilities have been developed to allow programs written in common languages such as matlab, python and scilab to share their data with other programs and accept other program's data. We call this interface the Simple Script Wrapper (SSW). An implementation of SSW is shown that integrates groundwater, economic, and agricultural models in the High Plains region of Kansas. Output from these models illustrates the interdisciplinary discovery facilitated through use of SSW implemented models.

Handbook of Research on Computational Methodologies in Gene Regulatory Networks

... Educational Technologies Educational Technologies Subjects. Adult LearningApplied E-LearningB... more

Developments in Computer-Based IPM Extension Delivery and Biological Monitoring System Design

The design of biological monitoring systems for pest management

Wiley eBooks, 1979

The authors analyse the design of pest management field monitoring programmes, adopting a systems... more

Integrating transcriptomic network reconstruction and eQTL analyses reveals mechanistic connections between genomic architecture and Brassica rapa development

PLOS Genetics, Sep 12, 2019

Plant developmental dynamics can be heritable, genetically correlated with fitness and yield, and... more Plant developmental dynamics can be heritable, genetically correlated with fitness and yield, and undergo selection. Therefore, characterizing the mechanistic connections between the genetic architecture governing plant development and the resulting ontogenetic dynamics of plants in field settings is critically important for agricultural production and evolutionary ecology. We use hierarchical Bayesian Function-Valued Trait (FVT) models to estimate Brassica rapa growth curves throughout ontogeny, across two treatments, and in two growing seasons. We find genetic variation for plasticity of growth rates and final sizes, but not the inflection point (transition from accelerating to decelerating growth) of growth curves. There are trade-offs between growth rate and duration, indicating that selection for maximum yields at early harvest dates may come at the expense of late harvest yields and vice versa. We generate eigengene modules and determine which are co-expressed with FVT traits using a Weighted Gene Co-expression Analysis. Independently, we seed a Mutual Rank co-expression network model with FVT traits to identify specific genes and gene networks related to FVT. GO-analyses of eigengene modules indicate roles for actin/cytoskeletal genes, herbivore resistance/wounding responses, and cell division, while MR networks demonstrate a close association between metabolic regulation and plant growth. We determine that combining FVT Quantitative Trait Loci (QTL) and MR genes/WGCNA eigengene expression profiles better characterizes phenotypic variation than any single data type (i.e. QTL, gene, or eigengene alone). Our network analysis allows us to employ a targeted eQTL analysis, which we use to identify regulatory hotspots for FVT. We examine cis vs. trans eQTL that mechanistically link FVT QTL with structural trait variation. Colocalization of FVT, gene, and eigengene eQTL provide strong evidence for candidate genes influencing plant height. The study is the first to explore eQTL for FVT, and specifically do so in agroecologically relevant field settings.

Feedlot-produced ammonia emissions quantified using dual-comb spectroscopy

Conference on Lasers and Electro-Optics, 2021

Ammonia flux from a cattle feedlot is characterized using two integrated-path dual-comb spectrosc... more

Comparison of Livestock Emissions Measurements Using Open-Path Dual-Comb Spectroscopy and Closed-Path Cavity Ring-Down Spectroscopy

Optical Sensors and Sensing Congress, 2020

Open-path dual-comb spectroscopy is used to quantify CO2, CH4, H2O and NH3 concentrations adjacen... more

Correction: Integrating transcriptomic network reconstruction and eQTL analyses reveals mechanistic connections between genomic architecture and Brassica rapa development

PLOS Genetics, Oct 8, 2020

Mapping and Predicting Non-Linear<i>Brassica rapa</i>Growth Phenotypes Based on Bayesian and Frequentist Complex Trait Estimation

G3: Genes, Genomes, Genetics, Apr 1, 2018

Predicting phenotypes based on genotypes and understanding the effects of complex multilocus trai... more Predicting phenotypes based on genotypes and understanding the effects of complex multilocus traits on plant performance requires a description of the underlying developmental processes, growth trajectories, and their genomic architecture. Using data from Brassica rapa genotypes grown in multiple density settings and seasons, we applied a hierarchical Bayesian Function-Valued Trait (FVT) approach to fit logistic growth curves to leaf phenotypic data (length and width) and characterize leaf development. We found evidence of genetic variation in phenotypic plasticity of rate and duration of leaf growth to growing season. In contrast, the magnitude of the plastic response for maximum leaf size was relatively small, suggesting that growth dynamics vs. final leaf sizes have distinct patterns of environmental sensitivity. Consistent with patterns of phenotypic plasticity, several QTL-by-year interactions were significant for parameters describing leaf growth rates and durations but not leaf size. In comparison to frequentist approaches for estimating leaf FVT, Bayesian trait estimation resulted in more mapped QTL that tended to have greater average LOD scores and to explain a greater proportion of trait variance. We then constructed QTL-based predictive models for leaf growth rate and final size using data from one treatment (uncrowded plants in one growing season). Models successfully predicted non-linear developmental phenotypes for genotypes not used in model construction and, due to a lack of QTL-by-treatment interactions, predicted phenotypes across sites differing in plant density. KEYWORDS Function-Valued Traits quantitative genetics Brassica rapa leaf development high-throughput phenotyping, phenotypic plasticity, Bayesian vs. frequentist, genotype to phenotype Expressed phenotypes reflect the independent and combined effects of genetic and environmental inputs over time. However, understanding the relationship between genotype, the environment, and phenotype can be complicated. For example, phenotypic plasticity generated via genotype-by-environment interactions can alter the course of development, allowing a single genotype to exhibit multiple distinct phenotypes (BRADSHAW 1965; SCHLICHTING AND PIGLIUCCI 1998; DIGGLE 2002). Alternatively, phenotypic traits may be buffered against environmental effects, a phenomenon referred to as canalization (WADDINGTON 1954; RICE 1998; DEBAT AND DAVID 2001; HALL et al. 2007). Predicting phenotypes based on genotypes across multiple environments is therefore complicated by differential environmental sensitivity, yet is critical for understanding and predicting crop yields and evolutionary outcomes (

RISK-BASED DESIGN OF METEOROLOGICAL NETWORKS FOR INTEGRATED PEST MANAGEMENT11Contribution No. 81-501-A of the Kansas Agricultural Experiment Station. The findings and opinions expressed herein are solely those of the author and do not necessarily reflect those of the Kansas Agricultural Experimen...

Elsevier eBooks, 1982

Impact of water deficit and irrigation management on winter wheat yield in China

Agricultural Water Management, Sep 1, 2023

Simulation Data From Aistrup et al. Sustaining the Ogallala Aquifer Manuscript

Please see Readme.txt fileThese data are the simulation results described in the article by Aistr... more Please see Readme.txt fileThese data are the simulation results described in the article by Aistrup et al., "Sustaining the Ogallala Aquifer: From the Wells to People, A Holistic CNH Model," accepted for publication in Hydrology and Earth System Sciences (prepublication version at https://doi.org/10.5194/hess-2017-300). The data are being publicly shared in accordance with journal policy.National Science Foundation (NSF-CNH-0909515)

Simultaneous structure discovery and parameter estimation in gene networks using a multi-objective GP-PSO hybrid approach

International Journal of Bioinformatics Research and Applications, 2009

In this paper, we propose a hybrid genetic programming (GP) and multi-objective particle swarm op... more In this paper, we propose a hybrid genetic programming (GP) and multi-objective particle swarm optimization (PSO) algorithm for simultaneously identifying the structure and the parameters of a gene regulatory network. As a proof of concept the proposed algorithm is applied to evolve the flowering time control regulatory network in Arabidopsis thaliana. The goal is to find simplified genetic networks that can accurately predict the given data for various genetic lines planted in different environmental conditions. There are two objectives to be minimized: the error in prediction of the date when the plant flowers i.e. bolting date and the error in prediction of gene expression data for one unspecified gene present in the network. Simulation results are presented and discussed.

Large-effect flowering time mutations reveal conditionally adaptive paths through fitness landscapes in <i>Arabidopsis thaliana</i>

Proceedings of the National Academy of Sciences of the United States of America, Aug 16, 2019

US hard winter wheat yield loss attributed to compound hot-dry-windy events

Figshare, 2022

CERES-Maize Phenology Estimation

The Dataset included in this paper is associated with PlosOne Paper entitled "Estimating par... more The Dataset included in this paper is associated with PlosOne Paper entitled "Estimating parametric phenotypes that determine anthesis date in zea mays: Challenges in combining ecophysiological models with genetics". This includes all simulation input, output, and observed data used in the stud

Proximal hyperspectral sensing and data analysis approaches for field-based plant phenomics

Computers and Electronics in Agriculture, 2015

Field-based plant phenomics requires robust crop sensing platforms and data analysis tools to suc... more Field-based plant phenomics requires robust crop sensing platforms and data analysis tools to successfully identify cultivars that exhibit phenotypes with high agronomic and economic importance. Such efforts will lead to genetic improvements that maintain high crop yield with concomitant tolerance to environmental stresses. The objectives of this study were to investigate proximal hyperspectral sensing with a field spectroradiometer and to compare data analysis approaches for estimating four cotton phenotypes: leaf water content (C w), specific leaf mass (C m), leaf chlorophyll a + b content (C ab), and leaf area index (LAI). Field studies tested 25 Pima cotton cultivars grown under well-watered and water-limited conditions in central Arizona from 2010 to 2012. Several vegetation indices, including the normalized difference vegeta

Proximal hyperspectral sensing and data analysis approaches for field-based plant phenomics

Computers and Electronics in Agriculture, Oct 1, 2015

Field-based plant phenomics requires robust crop sensing platforms and data analysis tools to suc... more Field-based plant phenomics requires robust crop sensing platforms and data analysis tools to successfully identify cultivars that exhibit phenotypes with high agronomic and economic importance. Such efforts will lead to genetic improvements that maintain high crop yield with concomitant tolerance to environmental stresses. The objectives of this study were to investigate proximal hyperspectral sensing with a field spectroradiometer and to compare data analysis approaches for estimating four cotton phenotypes: leaf water content (C w), specific leaf mass (C m), leaf chlorophyll a + b content (C ab), and leaf area index (LAI). Field studies tested 25 Pima cotton cultivars grown under well-watered and water-limited conditions in central Arizona from 2010 to 2012. Several vegetation indices, including the normalized difference vegeta

Quantifying Methane Production at a Cattle Grazing Site using Open-Path Dual-Comb Spectroscopy

To better quantify methane emissions resulting from grazing cattle, a controlled methane release ... more To better quantify methane emissions resulting from grazing cattle, a controlled methane release at an agricultural site is performed using dual comb spectroscopy. The achieved methane concentration precision is below 10 nmol/mol. The Author(s) Work of the US Government and not subject to copyright.

Developing Guidelines for Replanting Grain Sorghum: II. Improved Methods of Simulating Caryopsis Weight and Tiller Number

Agronomy Journal, 1997

Sorghum [Sorghum bicolor (L.) Moench] producers often have difficulty determining when plant stan... more Sorghum [Sorghum bicolor (L.) Moench] producers often have difficulty determining when plant stands are low enough to merit replanting. Our objective was to use SORKAM, a sorghum growth model, to develop guidelines for replanting grain sorghum. A necessary first step is validation of the model over an extensive range of management and environmental factors. Validation was accomplished using 19 field data sets representing 11 yr and six Kansas locations. Several nonparametric tests were used to compare observed and simulated yields, yield components, and phenological dates. In addition, sensitivities of yield and yield components were determined in response to yearly climate, planting date, plant population, and maturity class changes. Model sensitivities were compared with sensitivities calculated from field data. While phenological predictions were adequate, SOR-KAM could capture only 27 to 79% of grain yield variability at the locations tested. Yield predictions from different plant populations within a planting date were particularly inaccurate. The validation and sensitivity analyses showed that the poor yield predictions were the result of improper computation of tiller number and faulty partitioning of biomass to caryopsis weight. Partitioning errors translocated enough assimilate from culm to grain to make yields essentially constant across populations within a planting date. To use SORKAM to generate replant guidelines, improvements must be made in modeling the relationships among yield components and the source-sink relationship that determines caryopsis weight. S ORGHUM PRODUCERS must decide whether to replant after reductions in plant populations due to pest damage, adverse weather, or other factors. Because poor stands are infrequent, producer experience with replanting decisions is likely to be limited. Nevertheless, in 1993, more than 69 388 ha of grain sorghum were replanted in Kansas alone. Estimating the damaged stand's yield potential is difficult (especially given the variability of weather), which complicates the choice of appropriate management practices. Plant growth models, by dynamically simulating interactions among climate, crops, and management, offer the best opportunity for developing guidelines when previous experience is limited (Rosenthal and Gerik, 1990). Therefore, our approach was to use a grain sorghum growth model and long-term weather data to provide producers with informative yield predictions. We selected the SORKAM vl .0 model (Rosenthal et al., 1989), because it simulates yield components individually and we are familiar with its internal formulation. To generate replanting guidelines, however, a model must demonstrate accurate yield predictions over a wide range of planting dates, plant populations, hybrid maturity classes, and weather factors. In

A modeling paradigm for interdisciplinary water resources modeling: Simple Script Wrappers (SSW)

EGUGA, May 1, 2014

Holistic understanding of a water resources system requires tools capable of model integration. T... more Holistic understanding of a water resources system requires tools capable of model integration. This team has developed an adaptation of the OpenMI (Open Modelling Interface) that allows easy interactions across the data passed between models. Capabilities have been developed to allow programs written in common languages such as matlab, python and scilab to share their data with other programs and accept other program's data. We call this interface the Simple Script Wrapper (SSW). An implementation of SSW is shown that integrates groundwater, economic, and agricultural models in the High Plains region of Kansas. Output from these models illustrates the interdisciplinary discovery facilitated through use of SSW implemented models.

Handbook of Research on Computational Methodologies in Gene Regulatory Networks

... Educational Technologies Educational Technologies Subjects. Adult LearningApplied E-LearningB... more

Developments in Computer-Based IPM Extension Delivery and Biological Monitoring System Design

The design of biological monitoring systems for pest management

Wiley eBooks, 1979

The authors analyse the design of pest management field monitoring programmes, adopting a systems... more

Integrating transcriptomic network reconstruction and eQTL analyses reveals mechanistic connections between genomic architecture and Brassica rapa development

PLOS Genetics, Sep 12, 2019

Plant developmental dynamics can be heritable, genetically correlated with fitness and yield, and... more Plant developmental dynamics can be heritable, genetically correlated with fitness and yield, and undergo selection. Therefore, characterizing the mechanistic connections between the genetic architecture governing plant development and the resulting ontogenetic dynamics of plants in field settings is critically important for agricultural production and evolutionary ecology. We use hierarchical Bayesian Function-Valued Trait (FVT) models to estimate Brassica rapa growth curves throughout ontogeny, across two treatments, and in two growing seasons. We find genetic variation for plasticity of growth rates and final sizes, but not the inflection point (transition from accelerating to decelerating growth) of growth curves. There are trade-offs between growth rate and duration, indicating that selection for maximum yields at early harvest dates may come at the expense of late harvest yields and vice versa. We generate eigengene modules and determine which are co-expressed with FVT traits using a Weighted Gene Co-expression Analysis. Independently, we seed a Mutual Rank co-expression network model with FVT traits to identify specific genes and gene networks related to FVT. GO-analyses of eigengene modules indicate roles for actin/cytoskeletal genes, herbivore resistance/wounding responses, and cell division, while MR networks demonstrate a close association between metabolic regulation and plant growth. We determine that combining FVT Quantitative Trait Loci (QTL) and MR genes/WGCNA eigengene expression profiles better characterizes phenotypic variation than any single data type (i.e. QTL, gene, or eigengene alone). Our network analysis allows us to employ a targeted eQTL analysis, which we use to identify regulatory hotspots for FVT. We examine cis vs. trans eQTL that mechanistically link FVT QTL with structural trait variation. Colocalization of FVT, gene, and eigengene eQTL provide strong evidence for candidate genes influencing plant height. The study is the first to explore eQTL for FVT, and specifically do so in agroecologically relevant field settings.

Feedlot-produced ammonia emissions quantified using dual-comb spectroscopy

Conference on Lasers and Electro-Optics, 2021

Ammonia flux from a cattle feedlot is characterized using two integrated-path dual-comb spectrosc... more

Comparison of Livestock Emissions Measurements Using Open-Path Dual-Comb Spectroscopy and Closed-Path Cavity Ring-Down Spectroscopy

Optical Sensors and Sensing Congress, 2020

Open-path dual-comb spectroscopy is used to quantify CO2, CH4, H2O and NH3 concentrations adjacen... more

Correction: Integrating transcriptomic network reconstruction and eQTL analyses reveals mechanistic connections between genomic architecture and Brassica rapa development

PLOS Genetics, Oct 8, 2020

Mapping and Predicting Non-Linear<i>Brassica rapa</i>Growth Phenotypes Based on Bayesian and Frequentist Complex Trait Estimation

G3: Genes, Genomes, Genetics, Apr 1, 2018

Predicting phenotypes based on genotypes and understanding the effects of complex multilocus trai... more Predicting phenotypes based on genotypes and understanding the effects of complex multilocus traits on plant performance requires a description of the underlying developmental processes, growth trajectories, and their genomic architecture. Using data from Brassica rapa genotypes grown in multiple density settings and seasons, we applied a hierarchical Bayesian Function-Valued Trait (FVT) approach to fit logistic growth curves to leaf phenotypic data (length and width) and characterize leaf development. We found evidence of genetic variation in phenotypic plasticity of rate and duration of leaf growth to growing season. In contrast, the magnitude of the plastic response for maximum leaf size was relatively small, suggesting that growth dynamics vs. final leaf sizes have distinct patterns of environmental sensitivity. Consistent with patterns of phenotypic plasticity, several QTL-by-year interactions were significant for parameters describing leaf growth rates and durations but not leaf size. In comparison to frequentist approaches for estimating leaf FVT, Bayesian trait estimation resulted in more mapped QTL that tended to have greater average LOD scores and to explain a greater proportion of trait variance. We then constructed QTL-based predictive models for leaf growth rate and final size using data from one treatment (uncrowded plants in one growing season). Models successfully predicted non-linear developmental phenotypes for genotypes not used in model construction and, due to a lack of QTL-by-treatment interactions, predicted phenotypes across sites differing in plant density. KEYWORDS Function-Valued Traits quantitative genetics Brassica rapa leaf development high-throughput phenotyping, phenotypic plasticity, Bayesian vs. frequentist, genotype to phenotype Expressed phenotypes reflect the independent and combined effects of genetic and environmental inputs over time. However, understanding the relationship between genotype, the environment, and phenotype can be complicated. For example, phenotypic plasticity generated via genotype-by-environment interactions can alter the course of development, allowing a single genotype to exhibit multiple distinct phenotypes (BRADSHAW 1965; SCHLICHTING AND PIGLIUCCI 1998; DIGGLE 2002). Alternatively, phenotypic traits may be buffered against environmental effects, a phenomenon referred to as canalization (WADDINGTON 1954; RICE 1998; DEBAT AND DAVID 2001; HALL et al. 2007). Predicting phenotypes based on genotypes across multiple environments is therefore complicated by differential environmental sensitivity, yet is critical for understanding and predicting crop yields and evolutionary outcomes (

RISK-BASED DESIGN OF METEOROLOGICAL NETWORKS FOR INTEGRATED PEST MANAGEMENT11Contribution No. 81-501-A of the Kansas Agricultural Experiment Station. The findings and opinions expressed herein are solely those of the author and do not necessarily reflect those of the Kansas Agricultural Experimen...

Elsevier eBooks, 1982

Impact of water deficit and irrigation management on winter wheat yield in China

Agricultural Water Management, Sep 1, 2023

Simulation Data From Aistrup et al. Sustaining the Ogallala Aquifer Manuscript

Please see Readme.txt fileThese data are the simulation results described in the article by Aistr... more Please see Readme.txt fileThese data are the simulation results described in the article by Aistrup et al., "Sustaining the Ogallala Aquifer: From the Wells to People, A Holistic CNH Model," accepted for publication in Hydrology and Earth System Sciences (prepublication version at https://doi.org/10.5194/hess-2017-300). The data are being publicly shared in accordance with journal policy.National Science Foundation (NSF-CNH-0909515)

Simultaneous structure discovery and parameter estimation in gene networks using a multi-objective GP-PSO hybrid approach

International Journal of Bioinformatics Research and Applications, 2009

In this paper, we propose a hybrid genetic programming (GP) and multi-objective particle swarm op... more In this paper, we propose a hybrid genetic programming (GP) and multi-objective particle swarm optimization (PSO) algorithm for simultaneously identifying the structure and the parameters of a gene regulatory network. As a proof of concept the proposed algorithm is applied to evolve the flowering time control regulatory network in Arabidopsis thaliana. The goal is to find simplified genetic networks that can accurately predict the given data for various genetic lines planted in different environmental conditions. There are two objectives to be minimized: the error in prediction of the date when the plant flowers i.e. bolting date and the error in prediction of gene expression data for one unspecified gene present in the network. Simulation results are presented and discussed.

Large-effect flowering time mutations reveal conditionally adaptive paths through fitness landscapes in <i>Arabidopsis thaliana</i>

Proceedings of the National Academy of Sciences of the United States of America, Aug 16, 2019