Latest

• BWGS
• RSenaps
• rtiddlywiki
• htcondor
• rapsim
• RApsimNG
• APSIM
• expDB
• ncdf4cf
• APSIMWheatPhenology
• Weather Analysis
• Climate change enhances stability of wheat-flowering-date
• Uncovering the Drivers and Regional Variability of Cotton Yield in China
• Quantification of the Cumulative Shading Capacity in a Maize–Soybean Intercropping System Using an Unmanned Aerial Vehicle
• Modelling the impact of climate change on agriculture in Australia and Oceania.
• A Generic Model to Estimate Wheat LAI over Growing Season Regardless of the Soil-Type Background
• Scales of development for wheat and barley specific to either single culms or a population of culms
• Responses of wheat kernel weight to diverse allelic combinations under projected climate change conditions
• A cultivar phenology classification scheme for wheat and barley
• Compile APSIMNG from source codes
• Increased wheat price spikes and larger economic inequality with 2°C global warming
• Combine project management and data analysis using Tiddlywiki and RMarkdown
• Unsupervised Plot-Scale LAI Phenotyping via UAV-Based Imaging, Modelling, and Machine Learning
• Integrating crop growth model and radiative transfer model to improve estimation of crop traits based on deep learning
• Using Refnotes and Zotero in Tiddlywiki
• Phenological optimization of late reproductive phase for raising wheat yield potential in irrigated mega-environments
• Isolation and sequencing of a single copy of an introgressed chromosome from a complex genome for gene and SNP identification
• Improving Grain Yield via Promotion of Kernel Weight in High Yielding Winter Wheat Genotypes
• Detecting Sorghum Plant and Head Features from Multispectral UAV Imagery
• Global Wheat Head Detection 2021: An Improved Dataset for Benchmarking Wheat Head Detection Methods
• Understanding the Effects of Growing Seasons, Genotypes, and Their Interactions on the Anthesis Date of Wheat Sown in North China
• Comparison of Modelling Strategies to Estimate Phenotypic Values from an Unmanned Aerial Vehicle with Spectral and Temporal Vegetation Indexes
• Using a gene-based phenology model to identify optimal flowering periods of spring wheat in irrigated mega-environments
• Genotype specific P-spline response surfaces assist interpretation of regional wheat adaptation to climate change
• Optimization of APSIM NG using factorial simulations
• Coupling of machine learning methods to improve estimation of ground coverage from unmanned aerial vehicle (UAV) imagery for high-throughput phenotyping of crops
• Modeling the joint impacts of ozone and aerosols on crop yields in China: An air pollution policy scenario analysis
• Integrating Crop Growth Models with Remote Sensing for Predicting Biomass Yield of Sorghum
• Capture message, warnings and errors from a R function
• JS updates position of TOC for Rmarkdown file in blogdown
• Interactive correlation plot
• ModelingGlobal Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods
• Archive the big folder
• PhenoCopter
• Common Errors in APSIM Next Generation
• Build and deploy website for R package
• Does precipitation keep pace with temperature in the marginal double-cropping area of northern China?
• Calculation of thermal time
• A reduced‐tillering trait shows small but important yield gains in dryland wheat production
• Designing crops for adaptation to the drought and high‐temperature risks anticipated in future climates
• Enabling breeding of spring wheat for optimisation of flowering time for current and future climates by linking genetic maps to simulation model parameters
• From QTLs to Adaptation Landscapes: Using Genotype-To-Phenotype Models to Characterize G×E Over Time
• Combining Crop Growth Modeling and Statistical Genetic Modeling to Evaluate Phenotyping Strategies
• Assessment of traits related with water productivity in the Australian wheatbelt using an improved version of the APSIM-Wheat model.
• Establishing a value proposition for future traits in a climate-changing world.
• Extraction of phenotypic traits using unmanned aerial vehicle for field experiments in the agronomy and crop breeding
• Assessment of traits related with water productivity in the Australian wheatbelt using an improved version of the APSIM-Wheat model.
• Estimation of plot variability using unmanned aerial vehicle (UAV) for the wheat breeding trials
• A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting
• Pixel size of aerial imagery constrains the applications of unmanned aerial vehicle in crop breeding
• A generic approach to modelling, allocation and redistribution of biomass to and from plant organs
• Effects of climate change on the extension of the potential double cropping region and crop water requirements in Northern China
• Accuracy assessment of plant height using an unmanned aerial vehicle for quantitative genomic analysis in bread wheat
• Improving process-based crop models to better capture genotype×environment×management interactions
• Modelling impact of early vigour on wheat yield in dryland regions
• A new probabilistic forecasting model for canopy temperature with consideration of periodicity and parameter variation
• Characterization of peach tree crown by using high-resolution images from an unmanned aerial vehicle
• Climate Data
• The Value of Tactical Adaptation to El Niño–Southern Oscillation for East Australian Wheat
• Direct and Indirect Costs of Frost in the Australian Wheatbelt
• Efficient characterization of orchard tree using high resolution images from unmanned aerial vehicle An effective method for crown characteristics analysis in orchard using UAV images
• Estimation of aboveground biomass and leaf area for a wheat trial index using unmanned aerial vehicle
• PhenoCopter: An cloud based solution for image processing captured by unmanned aerial vehicle in high throughput phenotyping
• APSIM Next Generation: Overcoming challenges in modernising a farming systems model
• Estimation of plant height using a high throughput phenotyping platform based on unmanned aerial vehicle and self-calibration: Example for sorghum breeding
• Modelling maize phenology, biomass growth and yield under contrasting temperature conditions
• McGET: A rapid image-based method to determine the morphological characteristics of gravels on the Gobi desert surface
• Projected impact of future climate on water-stress patterns across the Australian wheatbelt
• PhenoCopter: An cloud based platform to manage, process and visualize images captured by unmanned aerial vehicle for high throughput phenotyping
• The big impacts of image resolution on the estimation of ground coverage for wheat in UAV survey
• Optimizing soil-coring strategies to quantify root-length-density distribution in field-grown maize: virtual coring trials using 3-D root architecture models
• Estimation of canopy height using an unmanned aerial vehicle in the field during wheat growth season
• Assessment of canopy growth and development for three wheat cultivars under different water and nitrogen regimes
• Improving the relationships used to define frost damage to wheat in crop models
• A faster way to trim a raster file
• Economic assessment of wheat breeding options for potential improved levels of post head-emergence frost tolerance
• Dynamic monitoring of NDVI in wheat agronomy and breeding trials using an unmanned aerial vehicle
• EasyPCC: Benchmark Datasets and Tools for High-Throughput Measurement of the Plant Canopy Coverage Ratio under Field Conditions
• Quantifying high temperature risks and their potential effects on sorghum production in Australia
• Comparison of ground cover estimates from experiment plots in cotton, sorghum and sugarcane based on images and ortho-mosaics captured by UAV
• Dynamic monitoring of canopy structure by time serial photography of early stage growth of wheat
• The gravel coverage and size of Gobi desert analyzed by a rapid image-based method
• Identification of Earliness Per Se Flowering Time Locus in Spring Wheat through a Genome-Wide Association Study
• Improvement of the model capacity and assessment traits related with water use efficiency for wheat in Australia
• Recent changes in southern Australian frost occurrence: implications for wheat production risk
• Dynamic quantification of canopy structure to characterize early plant vigour in wheat genotypes
• A standardized workflow to utilise a grid-computing system through advanced message queuing protocols
• Do maize models capture the impacts of heat and drought stresses on yield? Using algorithm ensembles to identify successful approaches
• Do wheat breeders have suitable genetic variation to overcome short coleoptiles and poor establishment in the warmer soils of future climates?
• Quantitative evaluation of influence of PROSTRATE GROWTH 1 gene on rice canopy structure based on three-dimensional structure model
• Simplification of leaf surfaces from scanned data: Effects of two algorithms on leaf morphology
• Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis
• Velocity of temperature and flowering time in wheat – assisting breeders to keep pace with climate change
• Breeding for the future: How to adapt to frost, drought and heat impacts in Australian wheat
• Heat stress effects on grain sorghum productivity – biology and modelling
• Projected impacts of climate change on drought stresses in Australian wheat
• Quantification of direct and indirect cost of frost for the Australian wheatbelt
• The shifting influence of drought and heat stress for crops in Northeast Australia
• Crop modelling to aid crop improvement
• Gene-based prediction of heading time to target real-time and future climate adaptation in wheat
• Predicting heading date and frost impact in wheat across Australia
• Climate change adaptation in Australian wheat farm systems upscaled from farm to national scale
• The value of adapting to climate change in Australian wheat farm systems: farm to cross-regional scale
• Frost trends and their estimated impact on yield in the Australian wheatbelt
• National scale adaptation value in wheat farm systems by 2030, filling the yield gap or interactive benefits?
• Crop design for specific adaptation in variable dryland production environments
• Pheno-Copter: a low-altitude, autonomous remote-sensing robotic helicopter for high-throughput field-based phenotyping
• Pheno-Copter: A Low-Altitude, Autonomous Remote-Sensing Robotic Helicopter for High-Throughput Field-Based Phenotyping
• Breeding for the future: How to adapt to potential impacts of future frost, drought and heat events on Australian wheat?
• Breeding for the future – avoiding climate extremes and maximising yield of spring wheat in water-limited environments
• Crop design for specific adaptation in variable dryland production environment
• Predicting heading time of Australian wheat using effects of VRN1 and Ppd-D1
• Role of genotypic variation in response to increasing temperatures in wheat
• Using gene-based information to adapt wheat flowering time to avoid heat, frost and drought stresses in current and future climates
• Quantification of the effects of VRN1 and Ppd-D1 to predict spring wheat (Triticum aestivum) heading time across diverse environments
• Association genetics for earliness components and QTL-based ecophysiological predictions of heading date provide tools to optimize heading date through breeding
• Wheat response to drought and heat - Using crop modelling to prepare for the future
• Breeding for the future: what are the potential impacts of future frost and heat events on sowing and flowering time requirements for Australian bread wheat (Triticum aestivium) varieties?
• Crop improvement, ideotyping and modelling under climate change
• Frost and heat limits to the wheat flowering ‘window’ in present and future climates
• Assessment of the influence of global dimming on the photosynthetic production of rice based on three-dimensional modeling
• Assessment of the effects of leaf angle combinations on potential photosynthesis capacity of rice with 3-D models using high performance computing
• Three-dimensional digitization in situ of rice canopies and virtual stratified-clipping method.
• Comparison of architecture among different cultivars of hybrid rice using spatial light model based on 3D digitizing
• Canopy Architecture Quantification and Spatial Direct Light Interception Modeling of Hybrid Rice
• A field-based high-throughput method for acquiring canopy architecture using unmanned aerial vehicle images
• Vernalisation and photoperiod responses of diverse wheat genotypes