Speaker: James W Jones (Jim) Emeritus Distinguished Professor, Agricultural and Biological Engineering, University of Florida.
The talk will cover overall perspective of both genetic and modeling and advanced methods for working with the genetic and phenotypic data with crop models and a perspective on promising future approaches.
International Society for Tropical Root Crops (ISTRC). Tropical roots and tubers in a changing climate: A critical opportunity for the world, program and abstracts of papers. Lima (Peru). International Potato Center (CIP); ISTRC; Universidad Nacional Agraria La Molina (UNALM). 2009. p. 170. AP(SB 209 I59.4 2009) (AN=72635)
Geolife is a fast growing group of ambitious multi-activity business and excellent track record of its growth in India. This energetic group is led by qualified professionals with a vision to touch the horizon in every field of our business interest. Our group business started in the year 1939 with textiles, is now diversified into Agri Inputs, Pharmacauticals, Confectionary, Dyes & Chemicals, Real Estate, Hospitality and Led Lighting in India and Globally.
Kibinge Coffee Farmers’ Co-operative Society Ltd.CIAT
KCFCS is a strong cooperative of 2,000 smallholder farmers in southwestern Uganda with 20 years of experience in coffee trading. Its vision is to empower farmers to market value-added coffee competitively. Key activities include buying, processing, and exporting coffee beans as well as providing farmers' training, financial services, agro inputs, and community projects. The cooperative has grown its sales and aims to build new facilities to streamline operations as it commits to its community through social, economic and environmental projects. Challenges include governance issues, national policies favoring multinationals, and high start-up costs of establishing cooperatives.
Crop models can be used to estimate crop yield and its variability under different climate scenarios, account for nitrogen use efficiency, and help inform agricultural management decisions. The document discusses different types of crop models and provides examples of some models that have been successfully used in agrometeorology, including for rice, wheat, maize, sugarcane, and potato crops. It also outlines some limitations and advantages of using crop models.
CROP SIMULATION MODELS AND THEIR APPLICATIONS IN CROP PRODUCTION.pptxSarthakMoharana
CROP SIMULATION MODELS AND THEIR APPLICATIONS IN CROP PRODUCTION
Crop growth is a very complex phenomenon and a product of a series of complicated interactions of soil, plant and weather.
Crop growth simulation is a relatively recent technique that facilitates quantitative understanding of the effects of these factors and agronomic management factors on crop growth and productivity.
These models are quantitative description of the mechanisms and processes that result in growth of crop. The processes could be physiological, physical and chemical processes of crop.
MAJOR & POPULAR CROP SIMULATION MODELS:
DSSAT (Decision Support System for Agrotechnology Transfer)
Aqua Crop
Info Crop
APSIM (Agricultural Production System Simulator
This document summarizes a research paper that proposes a system to analyze crop phenology (growth stages) using IoT to support parallel agriculture management. The system would use sensors to collect data on soil moisture, temperature, humidity and other parameters. This data would be input to a database. Then, a multiple linear regression model trained on past data would predict the optimal crop and expected yield based on the tested sensor data and parameters. This system aims to help farmers select crops and fertilization practices tailored to their specific fields' conditions.
Crop modeling for stress situations, cropping system , assessing stress through remote sensing, understanding the adaptive features of crops for survival under stress .
International Society for Tropical Root Crops (ISTRC). Tropical roots and tubers in a changing climate: A critical opportunity for the world, program and abstracts of papers. Lima (Peru). International Potato Center (CIP); ISTRC; Universidad Nacional Agraria La Molina (UNALM). 2009. p. 170. AP(SB 209 I59.4 2009) (AN=72635)
Geolife is a fast growing group of ambitious multi-activity business and excellent track record of its growth in India. This energetic group is led by qualified professionals with a vision to touch the horizon in every field of our business interest. Our group business started in the year 1939 with textiles, is now diversified into Agri Inputs, Pharmacauticals, Confectionary, Dyes & Chemicals, Real Estate, Hospitality and Led Lighting in India and Globally.
Kibinge Coffee Farmers’ Co-operative Society Ltd.CIAT
KCFCS is a strong cooperative of 2,000 smallholder farmers in southwestern Uganda with 20 years of experience in coffee trading. Its vision is to empower farmers to market value-added coffee competitively. Key activities include buying, processing, and exporting coffee beans as well as providing farmers' training, financial services, agro inputs, and community projects. The cooperative has grown its sales and aims to build new facilities to streamline operations as it commits to its community through social, economic and environmental projects. Challenges include governance issues, national policies favoring multinationals, and high start-up costs of establishing cooperatives.
Crop models can be used to estimate crop yield and its variability under different climate scenarios, account for nitrogen use efficiency, and help inform agricultural management decisions. The document discusses different types of crop models and provides examples of some models that have been successfully used in agrometeorology, including for rice, wheat, maize, sugarcane, and potato crops. It also outlines some limitations and advantages of using crop models.
CROP SIMULATION MODELS AND THEIR APPLICATIONS IN CROP PRODUCTION.pptxSarthakMoharana
CROP SIMULATION MODELS AND THEIR APPLICATIONS IN CROP PRODUCTION
Crop growth is a very complex phenomenon and a product of a series of complicated interactions of soil, plant and weather.
Crop growth simulation is a relatively recent technique that facilitates quantitative understanding of the effects of these factors and agronomic management factors on crop growth and productivity.
These models are quantitative description of the mechanisms and processes that result in growth of crop. The processes could be physiological, physical and chemical processes of crop.
MAJOR & POPULAR CROP SIMULATION MODELS:
DSSAT (Decision Support System for Agrotechnology Transfer)
Aqua Crop
Info Crop
APSIM (Agricultural Production System Simulator
This document summarizes a research paper that proposes a system to analyze crop phenology (growth stages) using IoT to support parallel agriculture management. The system would use sensors to collect data on soil moisture, temperature, humidity and other parameters. This data would be input to a database. Then, a multiple linear regression model trained on past data would predict the optimal crop and expected yield based on the tested sensor data and parameters. This system aims to help farmers select crops and fertilization practices tailored to their specific fields' conditions.
Crop modeling for stress situations, cropping system , assessing stress through remote sensing, understanding the adaptive features of crops for survival under stress .
This document provides an introduction to crop simulation models. It defines a model as a set of mathematical equations that mimic the behavior of a real crop system. Modeling involves analyzing complex problems to make predictions about outcomes. Simulation is the process of building models and analyzing systems. Crop models provide simple representations of crops. The document outlines different types of models and their purposes. It describes the key components and steps involved in building crop simulation models, including defining goals and variables, quantifying relationships, calibration, and validation. Finally, it discusses several popular crop models and their uses in farm management, research, and experimental applications.
This document summarizes a term paper on crop growth simulation models. It provides background on models, describing them as mathematical representations of crop systems. It outlines different types of models like mechanistic, deterministic, and dynamic models. Major steps in modeling like defining goals and variables, quantifying relationships, calibration, and validation are described. Popular crop models are highlighted, including DSSAT, AquaCrop, and APSIM. The uses of simulation models in research applications like yield prediction and evaluation of climate change are noted. Several examples of simulations and model outputs validating crop yields and variables are presented.
This document discusses the challenges and opportunities in biometry and trends in agricultural research at the Ethiopian Institute of Agricultural Research (EIAR). It outlines the major themes of EIAR's biometrics program, including addressing issues like spatial modeling and analysis of multi-level experiments. It also describes the diversity of methodologies used, developments in biometry, integration with information technology, training workshops, and the contributions of biometric support over the years.
CROP MODELING IN VEGETABLES ( AABID AYOUB SKUAST-K).pptxAabidAyoub
crop modeling is future in agriculture to tackle changing environment conditions and increase food security in the world. These models incorporate various factors such as climate, soil characteristics, agronomic practices, and crop physiology to predict crop yields, water usage, nutrient uptake, and other important parameters. Crop modeling helps in understanding the complex interactions between different variables affecting crop growth and assists farmers, researchers, and policymakers in making informed decisions related to crop management, resource allocation, and risk assessment.
Role of AI in crop modeling: Artificial Intelligence (AI) plays a significant role in enhancing crop modeling by leveraging advanced computational techniques to improve model accuracy, efficiency, and scalability. One of the most important aspects of precision farming is sustainability. Using artificial neural networks (ANNs), a highly effective multilayer perceptron (MLP) model. The most common type in crop modeling is DSSAT , DSSAT (Decision Support System for Agro-technology Transfer).The Decision Support System for Agro-technology Transfer (DSSAT) is a software application program that comprises crop simulation models for over 42 crops (as of Version 4.8.2) as well as tools to facilitate effective use of the models. The tools include database management programs for soil, weather, crop management and experimental data, utilities, and application programs. The crop simulation models simulate growth, development and yield as a function of the soil-plant-atmosphere dynamics.DSSAT and its crop simulation models have been used for a wide range of applications at different spatial and temporal scales. This includes on-farm and precision management, regional assessments of the impact of climate variability and climate change, gene-based modeling and breeding selection, water use, greenhouse gas emissions, and long-term sustainability through the soil organic carbon and nitrogen balances.In conclusion, crop modeling stands as a crucial tool in modern agriculture, offering a systematic approach to understanding and predicting crop growth dynamics in diverse environmental conditions. By simulating the complex interactions between various factors influencing crop development, including climate, soil properties, agronomic practices, and genetic traits, crop models provide valuable insights for farmers, researchers, and policymakers.
Presentation by Glenn Hyman and Ernesto Giron on agricultural trial database for climate change adaptation planning, at the 2011 ESRI International User Conference in San Diego, CA, July 14th, 2011
The Global Futures and Strategic Foresight (GFSF) team met in Rome from May 25-28, 2015 to review progress towards current work plans, discuss model improvements and technical parameters, and consider possible contributions by the GFSF program to the CRP Phase II planning process. All 15 CGIAR Centers were represented at the meeting.
‘Crop Modeling for Stress Situation , Assessing Stress through Remote Sensing’AmanDohre
‘Crop Modeling for Stress Situation , Assessing Stress through Remote Sensing’
Crop modeling for stress situations involves utilizing mathematical models to simulate plant growth, development, and responses under various stress conditions. These models integrate data on environmental factors, soil properties, and crop physiology to predict crop performance and yield potential. By simulating stress scenarios such as drought, heat, or salinity, crop models help assess the impact of stress on crop growth and yield, enabling proactive management decisions and adaptation strategies.
Assessing stress through remote sensing involves using satellite or aerial imagery to monitor crop health, stress levels, and productivity. Remote sensing techniques, such as multispectral or thermal imaging, detect subtle changes in plant reflectance and temperature associated with stress-induced physiological responses. These data are processed using advanced algorithms to generate stress indices and maps, providing valuable insights into spatial and temporal patterns of stress distribution across agricultural landscapes. Integrating crop modeling with remote sensing enables more accurate and timely assessments of stress impacts, facilitating targeted interventions and resource allocation for stress mitigation and crop management.
1. The document describes the key elements of designing quantitative experiments, including completely randomized designs, randomized complete block designs, and factorial experiments. It discusses the importance of replication, randomization, and estimating experimental error.
2. For quantitative experiments, treatments need to be clearly defined based on the research objectives. Random assignment of treatments to experimental units and proper randomization techniques are necessary to reduce bias and estimate experimental error.
3. Analysis of variance (ANOVA) is used to determine if observed treatment differences are statistically significant by comparing treatment and error variations. The F-test compares the mean squares to determine if treatments have real effects beyond experimental error.
This document discusses data transformation techniques for statistical analysis. It explains that if measurement data is not normally distributed or has unequal variances, transformation is necessary. It then outlines steps to test for normality in SPSS. The document focuses on three common transformations: logarithmic for count data with a wide range, square root for rare count events, and arcsine for proportional or percentage data to make distributions normal. Examples and formulas are provided for each transformation.
This document describes the Nutrient Tracking Tool (NTT), an interface and tool developed by researchers at Tarleton State University to evaluate the economic and environmental impacts of farm management practices using the APEX and FEM models. The NTT integrates national and regional weather, soil, management, and model output data. It allows users to simulate different management scenarios and conservation practices to analyze indicators like crop yields, nutrient losses, costs, and greenhouse gas emissions. The researchers have validated the NTT through multi-stage calibration and evaluation processes in different regions using measured field and statistical data. The NTT provides tabular and graphical outputs to compare scenarios and help inform management decisions.
This document discusses the implications of climate change on agriculture and small farmers' livelihoods. Crop prediction models are used to estimate the impact of climate change on the suitability of various crops. Results are then translated to analyze the effects on livelihoods using socioeconomic indicators and econometric models. Participatory workshops are recommended to identify best practices and adaptation strategies. While some crops may lose suitability, climate change also brings new opportunities. Adaptation requires site-specific management and preparing for change.
Crop modelling is useful for optimizing rice production. The document discusses rice crop modelling methodology and applications. It provides an overview of different types of crop models and their purposes. Statistical, mechanistic, deterministic, and stochastic models are described. The document also discusses important rice crop simulation models like DSSAT, APSIM, ORYZA1, and InfoCrop. It explains how these models work and the types of inputs they require. The validation of model outputs against observed field data is also demonstrated through sample tables and figures. Crop models help address issues in rice crops, optimize management practices, and evaluate impacts of climate change.
This document discusses an adaptive clinical trial design that was used in a phase III oncology study. The particular adaptation was an unblinded sample size re-estimation based on interim analysis results. This required changes to the SDTM and ADaM data models to account for the interim analysis cut-off dates. The reviewer guides were also updated to explain how to identify patients in the interim analysis and which analysis datasets to use for re-calculating results based on the interim and final cut-offs.
This document discusses using simulation tools to model plant breeding strategies and genetic systems. It provides examples of simulating cross prediction, gene pyramiding, and quantitative trait loci (QTL) experiments. Steps described include defining the gene-environment system, starting populations, breeding programs, running simulations, and comparing results. Two specific examples are given: simulating cross prediction and gene pyramiding, and combining QTL data from statistical analysis into a simulation to model marker-assisted selection and marker-assisted recurrent selection.
The document discusses a case study measuring a critical quality trait (CTQ1) at a manufacturing company. A measurement study of CTQ1 was conducted across four worldwide sites to evaluate the measurement system. The results showed high measurement error, with an overall %GRR of 94.3% and P/T ratio of 116%. When analyzed by site, two sites showed significant differences in CTQ1 averages. The high measurement variability masked potential process improvements. Improving the measurement system capability could help the company better understand real process variation and identify opportunities to optimize production.
Ensemble of Heterogeneous Flexible Neural Tree for the approximation and feat...Varun Ojha
This document describes research using an ensemble of heterogeneous flexible neural trees (FNTs) to predict the dissolution profiles of poly(lactic-co-glycolic acid) (PLGA) micro- and nanoparticles. The researchers trained multiple FNT regression models on different feature subsets and parameter settings. They then combined the models using an ensemble approach to improve predictive accuracy. Their best model achieved a root mean square error of 11.541 on test data, an improvement over other methods. Feature selection identified the most influential factors on PLGA dissolution. The ensemble of diverse FNT models and feature selection led to more accurate PLGA dissolution profile predictions.
Use of logistic, gompertz and richards functions for fitting normal and malfo...srajanlko
The document evaluates three mathematical functions - Logistic, Gompertz, and Richards - for fitting growth data of normal and malformed mango panicles. It finds that the Richards function provides the best fit with the lowest standard error and highest correlation coefficient. The Richards function is more flexible than the other two functions and can successfully model panicle growth under different treatments and conditions. It is superior for summarizing the growth data of both normal and malformed mango panicles.
Crop weather modeling involves using computer programs to simulate crop growth and development based on soil characteristics, weather conditions, and crop management practices. There are different types of crop models including statistical, mechanistic, deterministic, and stochastic models. Models can be used for applications like optimizing fertilizer use, crop yield forecasting, evaluating climate change impacts, and identifying management practices to minimize weather risks and yield gaps. Crop weather modeling provides useful insights for agricultural management and planning.
Durante la Semana de la Agricultura y la Alimentación, el Programa de Investigación del CGIAR en Cambio Climático, Agricultura y Seguridad Alimentaria – CCAFS, la Organización de las Naciones Unidas para la Alimentación y la Agricultura, FAO, y el Centro Internacional de Agricultura Tropical – CIAT, apoyaron la II Reunión Internacional de Ministros y altas autoridades de agricultura sobre agricultura sostenible y cambio climático con un documento base y su presentación sobre los retos que representa el cambio climático para la agricultura en Latino América y el Caribe.
Taller sobre intervenciones en nutrición, género y agricultura: situación actual y oportunidades futuras’, organizado por el CIAT y HarvestPlus en Ciudad de Guatemala. Leer más: http://ow.ly/XNIv30mGYBv
This document provides an introduction to crop simulation models. It defines a model as a set of mathematical equations that mimic the behavior of a real crop system. Modeling involves analyzing complex problems to make predictions about outcomes. Simulation is the process of building models and analyzing systems. Crop models provide simple representations of crops. The document outlines different types of models and their purposes. It describes the key components and steps involved in building crop simulation models, including defining goals and variables, quantifying relationships, calibration, and validation. Finally, it discusses several popular crop models and their uses in farm management, research, and experimental applications.
This document summarizes a term paper on crop growth simulation models. It provides background on models, describing them as mathematical representations of crop systems. It outlines different types of models like mechanistic, deterministic, and dynamic models. Major steps in modeling like defining goals and variables, quantifying relationships, calibration, and validation are described. Popular crop models are highlighted, including DSSAT, AquaCrop, and APSIM. The uses of simulation models in research applications like yield prediction and evaluation of climate change are noted. Several examples of simulations and model outputs validating crop yields and variables are presented.
This document discusses the challenges and opportunities in biometry and trends in agricultural research at the Ethiopian Institute of Agricultural Research (EIAR). It outlines the major themes of EIAR's biometrics program, including addressing issues like spatial modeling and analysis of multi-level experiments. It also describes the diversity of methodologies used, developments in biometry, integration with information technology, training workshops, and the contributions of biometric support over the years.
CROP MODELING IN VEGETABLES ( AABID AYOUB SKUAST-K).pptxAabidAyoub
crop modeling is future in agriculture to tackle changing environment conditions and increase food security in the world. These models incorporate various factors such as climate, soil characteristics, agronomic practices, and crop physiology to predict crop yields, water usage, nutrient uptake, and other important parameters. Crop modeling helps in understanding the complex interactions between different variables affecting crop growth and assists farmers, researchers, and policymakers in making informed decisions related to crop management, resource allocation, and risk assessment.
Role of AI in crop modeling: Artificial Intelligence (AI) plays a significant role in enhancing crop modeling by leveraging advanced computational techniques to improve model accuracy, efficiency, and scalability. One of the most important aspects of precision farming is sustainability. Using artificial neural networks (ANNs), a highly effective multilayer perceptron (MLP) model. The most common type in crop modeling is DSSAT , DSSAT (Decision Support System for Agro-technology Transfer).The Decision Support System for Agro-technology Transfer (DSSAT) is a software application program that comprises crop simulation models for over 42 crops (as of Version 4.8.2) as well as tools to facilitate effective use of the models. The tools include database management programs for soil, weather, crop management and experimental data, utilities, and application programs. The crop simulation models simulate growth, development and yield as a function of the soil-plant-atmosphere dynamics.DSSAT and its crop simulation models have been used for a wide range of applications at different spatial and temporal scales. This includes on-farm and precision management, regional assessments of the impact of climate variability and climate change, gene-based modeling and breeding selection, water use, greenhouse gas emissions, and long-term sustainability through the soil organic carbon and nitrogen balances.In conclusion, crop modeling stands as a crucial tool in modern agriculture, offering a systematic approach to understanding and predicting crop growth dynamics in diverse environmental conditions. By simulating the complex interactions between various factors influencing crop development, including climate, soil properties, agronomic practices, and genetic traits, crop models provide valuable insights for farmers, researchers, and policymakers.
Presentation by Glenn Hyman and Ernesto Giron on agricultural trial database for climate change adaptation planning, at the 2011 ESRI International User Conference in San Diego, CA, July 14th, 2011
The Global Futures and Strategic Foresight (GFSF) team met in Rome from May 25-28, 2015 to review progress towards current work plans, discuss model improvements and technical parameters, and consider possible contributions by the GFSF program to the CRP Phase II planning process. All 15 CGIAR Centers were represented at the meeting.
‘Crop Modeling for Stress Situation , Assessing Stress through Remote Sensing’AmanDohre
‘Crop Modeling for Stress Situation , Assessing Stress through Remote Sensing’
Crop modeling for stress situations involves utilizing mathematical models to simulate plant growth, development, and responses under various stress conditions. These models integrate data on environmental factors, soil properties, and crop physiology to predict crop performance and yield potential. By simulating stress scenarios such as drought, heat, or salinity, crop models help assess the impact of stress on crop growth and yield, enabling proactive management decisions and adaptation strategies.
Assessing stress through remote sensing involves using satellite or aerial imagery to monitor crop health, stress levels, and productivity. Remote sensing techniques, such as multispectral or thermal imaging, detect subtle changes in plant reflectance and temperature associated with stress-induced physiological responses. These data are processed using advanced algorithms to generate stress indices and maps, providing valuable insights into spatial and temporal patterns of stress distribution across agricultural landscapes. Integrating crop modeling with remote sensing enables more accurate and timely assessments of stress impacts, facilitating targeted interventions and resource allocation for stress mitigation and crop management.
1. The document describes the key elements of designing quantitative experiments, including completely randomized designs, randomized complete block designs, and factorial experiments. It discusses the importance of replication, randomization, and estimating experimental error.
2. For quantitative experiments, treatments need to be clearly defined based on the research objectives. Random assignment of treatments to experimental units and proper randomization techniques are necessary to reduce bias and estimate experimental error.
3. Analysis of variance (ANOVA) is used to determine if observed treatment differences are statistically significant by comparing treatment and error variations. The F-test compares the mean squares to determine if treatments have real effects beyond experimental error.
This document discusses data transformation techniques for statistical analysis. It explains that if measurement data is not normally distributed or has unequal variances, transformation is necessary. It then outlines steps to test for normality in SPSS. The document focuses on three common transformations: logarithmic for count data with a wide range, square root for rare count events, and arcsine for proportional or percentage data to make distributions normal. Examples and formulas are provided for each transformation.
This document describes the Nutrient Tracking Tool (NTT), an interface and tool developed by researchers at Tarleton State University to evaluate the economic and environmental impacts of farm management practices using the APEX and FEM models. The NTT integrates national and regional weather, soil, management, and model output data. It allows users to simulate different management scenarios and conservation practices to analyze indicators like crop yields, nutrient losses, costs, and greenhouse gas emissions. The researchers have validated the NTT through multi-stage calibration and evaluation processes in different regions using measured field and statistical data. The NTT provides tabular and graphical outputs to compare scenarios and help inform management decisions.
This document discusses the implications of climate change on agriculture and small farmers' livelihoods. Crop prediction models are used to estimate the impact of climate change on the suitability of various crops. Results are then translated to analyze the effects on livelihoods using socioeconomic indicators and econometric models. Participatory workshops are recommended to identify best practices and adaptation strategies. While some crops may lose suitability, climate change also brings new opportunities. Adaptation requires site-specific management and preparing for change.
Crop modelling is useful for optimizing rice production. The document discusses rice crop modelling methodology and applications. It provides an overview of different types of crop models and their purposes. Statistical, mechanistic, deterministic, and stochastic models are described. The document also discusses important rice crop simulation models like DSSAT, APSIM, ORYZA1, and InfoCrop. It explains how these models work and the types of inputs they require. The validation of model outputs against observed field data is also demonstrated through sample tables and figures. Crop models help address issues in rice crops, optimize management practices, and evaluate impacts of climate change.
This document discusses an adaptive clinical trial design that was used in a phase III oncology study. The particular adaptation was an unblinded sample size re-estimation based on interim analysis results. This required changes to the SDTM and ADaM data models to account for the interim analysis cut-off dates. The reviewer guides were also updated to explain how to identify patients in the interim analysis and which analysis datasets to use for re-calculating results based on the interim and final cut-offs.
This document discusses using simulation tools to model plant breeding strategies and genetic systems. It provides examples of simulating cross prediction, gene pyramiding, and quantitative trait loci (QTL) experiments. Steps described include defining the gene-environment system, starting populations, breeding programs, running simulations, and comparing results. Two specific examples are given: simulating cross prediction and gene pyramiding, and combining QTL data from statistical analysis into a simulation to model marker-assisted selection and marker-assisted recurrent selection.
The document discusses a case study measuring a critical quality trait (CTQ1) at a manufacturing company. A measurement study of CTQ1 was conducted across four worldwide sites to evaluate the measurement system. The results showed high measurement error, with an overall %GRR of 94.3% and P/T ratio of 116%. When analyzed by site, two sites showed significant differences in CTQ1 averages. The high measurement variability masked potential process improvements. Improving the measurement system capability could help the company better understand real process variation and identify opportunities to optimize production.
Ensemble of Heterogeneous Flexible Neural Tree for the approximation and feat...Varun Ojha
This document describes research using an ensemble of heterogeneous flexible neural trees (FNTs) to predict the dissolution profiles of poly(lactic-co-glycolic acid) (PLGA) micro- and nanoparticles. The researchers trained multiple FNT regression models on different feature subsets and parameter settings. They then combined the models using an ensemble approach to improve predictive accuracy. Their best model achieved a root mean square error of 11.541 on test data, an improvement over other methods. Feature selection identified the most influential factors on PLGA dissolution. The ensemble of diverse FNT models and feature selection led to more accurate PLGA dissolution profile predictions.
Use of logistic, gompertz and richards functions for fitting normal and malfo...srajanlko
The document evaluates three mathematical functions - Logistic, Gompertz, and Richards - for fitting growth data of normal and malformed mango panicles. It finds that the Richards function provides the best fit with the lowest standard error and highest correlation coefficient. The Richards function is more flexible than the other two functions and can successfully model panicle growth under different treatments and conditions. It is superior for summarizing the growth data of both normal and malformed mango panicles.
Crop weather modeling involves using computer programs to simulate crop growth and development based on soil characteristics, weather conditions, and crop management practices. There are different types of crop models including statistical, mechanistic, deterministic, and stochastic models. Models can be used for applications like optimizing fertilizer use, crop yield forecasting, evaluating climate change impacts, and identifying management practices to minimize weather risks and yield gaps. Crop weather modeling provides useful insights for agricultural management and planning.
Similar to Advances in gene-based crop modeling (20)
Durante la Semana de la Agricultura y la Alimentación, el Programa de Investigación del CGIAR en Cambio Climático, Agricultura y Seguridad Alimentaria – CCAFS, la Organización de las Naciones Unidas para la Alimentación y la Agricultura, FAO, y el Centro Internacional de Agricultura Tropical – CIAT, apoyaron la II Reunión Internacional de Ministros y altas autoridades de agricultura sobre agricultura sostenible y cambio climático con un documento base y su presentación sobre los retos que representa el cambio climático para la agricultura en Latino América y el Caribe.
Taller sobre intervenciones en nutrición, género y agricultura: situación actual y oportunidades futuras’, organizado por el CIAT y HarvestPlus en Ciudad de Guatemala. Leer más: http://ow.ly/XNIv30mGYBv
Impacto de las intervenciones agricolas y de salud para reducir la deficienci...CIAT
Este documento resume un estudio realizado en Guatemala para evaluar el impacto de entregar semilla biofortificada de frijol en aspectos socioeconómicos y de salud nutricional. El estudio utilizó un diseño de ensayo clúster aleatorio en comunidades rurales asignadas a recibir semilla biofortificada o no. Los resultados preliminares mostraron pocos cambios socioeconómicos entre grupos. Los resultados de línea base encontraron altas tasas de anemia y deficiencia de hierro, con el frijol contribuyendo signific
Agricultura sensible a la nutrición en el Altiplano. Explorando las perspecti...CIAT
Taller sobre intervenciones en nutrición, género y agricultura: situación actual y oportunidades futuras’, organizado por el CIAT y HarvestPlus en Ciudad de Guatemala. Leer más: http://ow.ly/XNIv30mGYBv
El rol de los padres en la nutrición del hogarCIAT
Este documento presenta los resultados preliminares de un estudio sobre las dinámicas intra-hogar y su impacto en la nutrición de familias agrícolas en Guatemala. Los hallazgos incluyen que las mujeres tienden a estar más desempoderadas que los hombres, y los niños en hogares con mujeres desempoderadas tienen más probabilidades de sufrir retraso en el crecimiento. Además, las preferencias de alimentos y labores varían entre hombres y mujeres dependiendo del ingreso disponible. Considerar tanto a padres como madres es importante para proyectos de nut
Scaling up soil carbon enhancement contributing to mitigate climate changeCIAT
This document summarizes Session 3 of a symposium on scaling up soil carbon enhancement to contribute to climate change mitigation. It discusses: 1) The potential for climate change
Impacto del Cambio Climático en la Agricultura de República DominicanaCIAT
El Banco Interamericano de Desarrollo (BID) y el Centro Internacional de Agricultura Tropical (CIAT), con el apoyo de los Programas de Investigación de CGIAR sobre Políticas, Instituciones y Mercados (PIM) y sobre Cambio Climático, Agricultura y Seguridad Alimentaria (CCAFS), se han asociado para comprender, a través de la ciencia, el impacto del cambio climático en cultivos claves y el impacto económico en la productividad de la agricultura en países de ALC.
BioTerra: Nuevo sistema de monitoreo de la biodiversidad en desarrollo por el...CIAT
BioTerra es un sistema innovador de monitoreo de la biodiversidad y sus amenazas desarrollado por el Programa Riqueza Natural de la Agencia de los Estados Unidos para el Desarrollo Internacional (USAID), y sus socios locales – el Centro Internacional de Agricultura Tropical (CIAT) y el Instituto Alexander von Humboldt (IAvH) – para apoyar al gobierno colombiano en el cumplimiento de las metas y compromisos de conservación de la biodiversidad. Este sistema busca complementar y aunar esfuerzos existentes de monitoreo de la biodiversidad y sus amenazas, a nivel nacional y regional.
Cacao for Peace Activities for Tackling the Cadmium in Cacao Issue in Colo...CIAT
El taller ‘Cacao libre de cadmio’, organizado por el CIAT, CIRAD, y la AFD, se lleva a cabo del 12 al 14 de marzo en la sede del CIAT en Palmira,y tiene como objetivo integrar un consorcio de actores y disciplinas claves de la región, así como elaborar un proyecto de investigación aplicada que dé respuesta a este problema que afecta a los cacaoteros de Colombia, Perú y Ecuador. http://ow.ly/J43p30iU0UZ
Tackling cadmium in cacao and derived products – from farm to forkCIAT
El taller ‘Cacao libre de cadmio’, organizado por el CIAT, CIRAD, y la AFD, se lleva a cabo del 12 al 14 de marzo en la sede del CIAT en Palmira,y tiene como objetivo integrar un consorcio de actores y disciplinas claves de la región, así como elaborar un proyecto de investigación aplicada que dé respuesta a este problema que afecta a los cacaoteros de Colombia, Perú y Ecuador. http://ow.ly/J43p30iU0UZ
Cadmium bioaccumulation and gastric bioaccessibility in cacao: A field study ...CIAT
El taller ‘Cacao libre de cadmio’, organizado por el CIAT, CIRAD, y la AFD, se lleva a cabo del 12 al 14 de marzo en la sede del CIAT en Palmira,y tiene como objetivo integrar un consorcio de actores y disciplinas claves de la región, así como elaborar un proyecto de investigación aplicada que dé respuesta a este problema que afecta a los cacaoteros de Colombia, Perú y Ecuador. http://ow.ly/J43p30iU0UZ
Geographical Information System Mapping for Optimized Cacao Production in Col...CIAT
El taller ‘Cacao libre de cadmio’, organizado por el CIAT, CIRAD, y la AFD, se lleva a cabo del 12 al 14 de marzo en la sede del CIAT en Palmira,y tiene como objetivo integrar un consorcio de actores y disciplinas claves de la región, así como elaborar un proyecto de investigación aplicada que dé respuesta a este problema que afecta a los cacaoteros de Colombia, Perú y Ecuador. http://ow.ly/J43p30iU0UZ
El documento resume los resultados de una investigación sobre el contenido de cadmio en granos de cacao en Perú. La investigación analizó muestras de suelo, hojas y granos de cacao de varias regiones para determinar las relaciones entre los contenidos de cadmio. Los resultados mostraron que eliminar la testa de los granos tiende a disminuir el contenido de cadmio. Además, se proponen nuevos protocolos de poscosecha y prácticas agrícolas para reducir los contenidos de cadmio en el suelo, las plantas y los
Técnicas para disminuir la disponibilidad de cadmio en suelos de cacaoterasCIAT
El taller ‘Cacao libre de cadmio’, organizado por el CIAT, CIRAD, y la AFD, se lleva a cabo del 12 al 14 de marzo en la sede del CIAT en Palmira,y tiene como objetivo integrar un consorcio de actores y disciplinas claves de la región, así como elaborar un proyecto de investigación aplicada que dé respuesta a este problema que afecta a los cacaoteros de Colombia, Perú y Ecuador. http://ow.ly/J43p30iU0UZ
El taller ‘Cacao libre de cadmio’, organizado por el CIAT, CIRAD, y la AFD, se lleva a cabo del 12 al 14 de marzo en la sede del CIAT en Palmira,y tiene como objetivo integrar un consorcio de actores y disciplinas claves de la región, así como elaborar un proyecto de investigación aplicada que dé respuesta a este problema que afecta a los cacaoteros de Colombia, Perú y Ecuador. http://ow.ly/J43p30iU0UZ
El taller ‘Cacao libre de cadmio’, organizado por el CIAT, CIRAD, y la AFD, se lleva a cabo del 12 al 14 de marzo en la sede del CIAT en Palmira,y tiene como objetivo integrar un consorcio de actores y disciplinas claves de la región, así como elaborar un proyecto de investigación aplicada que dé respuesta a este problema que afecta a los cacaoteros de Colombia, Perú y Ecuador. http://ow.ly/J43p30iU0UZ
de la región, así como elaborar un proyecto de investigación aplicada que dé respuesta a este problema que afecta a los cacaoteros de Colombia, Perú y Ecuador. http://ow.ly/J43p30iU0UZ
El taller ‘Cacao libre de cadmio’, organizado por el CIAT, CIRAD, y la AFD, se lleva a cabo del 12 al 14 de marzo en la sede del CIAT en Palmira,y tiene como objetivo integrar un consorcio de actores y disciplinas claves de la región, así como elaborar un proyecto de investigación aplicada que dé respuesta a este problema que afecta a los cacaoteros de Colombia, Perú y Ecuador. http://ow.ly/J43p30iU0UZ
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills MN
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
BREEDING METHODS FOR DISEASE RESISTANCE.pptxRASHMI M G
Plant breeding for disease resistance is a strategy to reduce crop losses caused by disease. Plants have an innate immune system that allows them to recognize pathogens and provide resistance. However, breeding for long-lasting resistance often involves combining multiple resistance genes
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
Nucleophilic Addition of carbonyl compounds.pptxSSR02
Nucleophilic addition is the most important reaction of carbonyls. Not just aldehydes and ketones, but also carboxylic acid derivatives in general.
Carbonyls undergo addition reactions with a large range of nucleophiles.
Comparing the relative basicity of the nucleophile and the product is extremely helpful in determining how reversible the addition reaction is. Reactions with Grignards and hydrides are irreversible. Reactions with weak bases like halides and carboxylates generally don’t happen.
Electronic effects (inductive effects, electron donation) have a large impact on reactivity.
Large groups adjacent to the carbonyl will slow the rate of reaction.
Neutral nucleophiles can also add to carbonyls, although their additions are generally slower and more reversible. Acid catalysis is sometimes employed to increase the rate of addition.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
What is greenhouse gasses and how many gasses are there to affect the Earth.
Advances in gene-based crop modeling
1.
2. Gene-Based Crop Modeling
J. W. Jones, M. J. Correll, K. J. Boote, S. Gezan, and C. E. Vallejos
CIAT
Aug 4, 2015
Source: Monica Ozores-Hampton
3. Crop models can be considered as non-linear functions
Estimate GSPs (Genetic Coefficients), fit linear statistical model
to estimate GSPs vs. QTLs
Develop new statistical linear mixed effects models of G, E, and
GxE for different processes
• E.g., flowering date, node addition rate, leaf size, max number of
MS nodes, …
Integrate new relationships into existing DSSAT CROPGRO-
Bean model
Develop component process modules using linear or nonlinear
mixed effects models of traits vs. QTLs and environmental
factors, combine them to demo modular approach
Future – compare Genomic Prediction for beans similar to
Technow et al. Plos One 2015)
Discussion
Outline: Our Work in Modeling
CIAT
Aug 4, 2015
4. Dynamic Crop Models
Dynamic, variables of interest change over time (state variables)
Environment also changes over time
System of equations & not just a single variable to predict
Variables interact, typically in highly non-linear ways, varying
over time
There is not a single equation to calculate the response that one
is interested in (e.g., final yield of a crop)
Final yield (and other variables) may reach their final values in
many different ways, depending on genetics and environment
CIAT
Aug 4, 2015
5. General Form of a Dynamic System Model
Discrete Time/Difference Equation
Difference equation form, when time step
equals 1 (e.g., 1 day):
U1,t+1 = U1,t + g1[Ut, Xt, θ]
U2,t+1 = U2,t + g2[Ut, Xt, θ]
.
.
.
US,t+1 = US,t + gS[Ut, Xt, θ]
CIAT
Aug 4, 2015
6. Example
Final yield response to all variables during a season
Y =f (X;θ)
where
X represents all explanatory variables during a season,
θ represents all parameters of the dynamic model
f represents a function (typically implicit function)
• We could write this as
Y = simulated final grain biomass at harvest time, T, as affected by
explanatory variables (e.g., irrigation applied during a season) and by
all parameters
Dynamic System Model as a Response Model
CIAT
Aug 4, 2015
8. How Simulation Computes Responses
Figure 1.3. Computer program flow diagram showing how a simulation model is used
as a function such that any time a response is needed, the simulation is run to
calculate state variables for every time step, but return only the value of selected state
variable for the time of interest. In this case, we are interested in Y at a time t = 140.
CIAT
Aug 4, 2015
9. Quantities in the model that represent variations in crop
performance across cultivars or lines
GSPs are the same as “cultivar coefficients” that have been used
routinely in the models contained in DSSAT
Examples
• Phenology – e.g., duration to first flower under optimal conditions
• Size of leaves on the main stem
• Maximum rate of node appearance on the main stem under optimal conditions
• Number of seeds per pod (or per ear in maize)
Must be known for each cultivar to simulate its performance
Genotype-Specific Parameters (GSPs)
CIAT
Aug 4, 2015
10. Example, DSSAT CROPGRO-Bean Model using GSPs
0
500
1000
1500
2000
20 40 60 80
Leaf,Stem,orSeedMass
Days after Sowing
Leaf-Jatu-Rong
Leaf-Porrillo S.
Stem-Jatu-Rong
Stem-Porrillo S.
Seed-Jatu-Rong
Seed-Porrillo S.
Obs Leaf
Obs Leaf
Obs Stem
Obs Stem
Obs Seed
Obs Seed
Flw Sd
Flw Sd
R7
R7
Figure 6. Time course of leaf, stem, and seed mass accumulation of Jatu-Rong (Andean)
and Porrillo Sintetico (Meso-American) cultivars relative to time of first flower (Flw),
first seed (Sd), & beginning maturity (R7) (grown at Palmira, Colombia (data from Sexton et al., 1994, 1997).
CIAT
Aug 4, 2015
11. Application of Crop Models
Genotypes
G, M Selection for
Optimal Responses
Bean Crop
Model
Environment,
Management Data
Sim Phenotypic
Responses
Iterative
Exploration
GSPs
CIAT
Aug 4, 2015
12. TRIFL is a GSP in the existing bean model
TRIFL is the maximum rate of node appearance on the main stem,
number per day
Temperature has a major effect on how rapid new nodes appear on
the main stem
The model* in the DSSAT common bean model is:
GSP Example - TRIFL
𝑁𝐴𝑅(𝑡) = 𝑇𝑅𝐼𝐹𝐿 ∙ (
1
24
)
𝑇ℎ∗ − 𝑇𝑏𝑎𝑠𝑒
(𝑇𝑜𝑝𝑡1 − 𝑇𝑏𝑎𝑠𝑒)
where
NAR(t) = rate of new node or leaf appearance on the main stem on day t, #/day,
TRIFL = maximum node/main stem leaf addition rate, number per day,
Tbase = base temperature, below which the rate is 0.0, 0C,
Topt1 = temperature above which node addition rate remains its maximum value, 0C,
Thour = hourly temperature in the field where the crop is growing, 0C, and
𝑇ℎ∗
=
𝑇𝑏𝑎𝑠𝑒 𝑖𝑓
𝑇ℎ𝑜𝑢𝑟 𝑖𝑓
𝑇𝑜𝑝𝑡1 𝑖𝑓
𝑇ℎ𝑜𝑢𝑟 < 𝑇𝑏𝑎𝑠𝑒
𝑇𝑏𝑎𝑠𝑒 < 𝑇ℎ𝑜𝑢𝑟 < 𝑇𝑜𝑝𝑡1
𝑇𝑜𝑝𝑡1 < 𝑇ℎ𝑜𝑢𝑟
CIAT
Aug 4, 2015
13. TRIFL is a GSP
Tbase and Topt1 are not GSPs, but are species-dependent
parameters in the current bean model
Also, TRIFL has been used as fixed across cultivars in the past due
to lack of information
We now know that TRIFL varies significantly across lines/cultivars,
based on our NSF study
What about Tbase and Topt1?
Example will be given later in the week on how this new information
is affecting how we model beans
TRIFL Example (continued)
CIAT
Aug 4, 2015
14. Data are needed for each cultivar or genotype
In our NSF study, we had over 180 genotypes, and for each of them,
we had observations in the field at 5 locations
These data were used to estimate GSPs, as will be shown later in
the workshop
The basic idea is that we use the multi-location experiment
phenotypic data:
• Set initial GSPs as input to the simulation,
• compare simulated and observed phenotypic data,
• compute a measure of how close the simulated phenotypic data are to observed
• Vary the GSPs and search the range of feasible values until a criterion is met,
such as minimizing the sum of the differences (errors) squared (e.g., MSE basis)
or maximizes a likelihood function
Estimating GSPs
CIAT
Aug 4, 2015
15. GSP Estimation: Various Approaches, including
Bayesian MCMC for Model Development, Genomic
Prediction, etc.
RILs
Error/Likelihood
Bean Crop
Model
Multi-Location
Experiments
Phenotypic
Data
QTLs
(~traits)
Environment,
Management Data
Sim Phenotypic
Responses
Iterative
Estimation
GSPs
GSP* & QTL
effects
16. Adding Genetic Information for Application of Crop
Models (Ideotype Design, Selection of G, M for E,
Genomic Prediction)
Genotypes
G, M Selection for
Optimal Responses
Bean Crop
Model
QTLs
Environment,
Management Data
Sim Phenotypic
Responses
Iterative
Exploration
GSPs
CIAT
Aug 4, 2015
17. Current approaches – develop relationships between GSPs
and QTLs (e.g., White and Hoogenboom, 1996, 2003;
Messina et al., 2006; etc.)
Why not continue this?
• Current models do not include GSPs for all processes and traits that
we now know are under genetic control (examples from this study)
• May need to modify environmental effects, interactions, in the model
• Current crop models are not ideally structured to make all of the
changes that are needed.
• Major changes are likely needed in many places, although some
code may be reusable
• Although some existing crop models are modular, new modules are
needed that are designed based on what we are now learning about
genetic control of processes and so that new modules can be easily
modified as more is learned, fine granularity
Need for a new gene-based model
CIAT
Aug 4, 2015
18. Example Results After Incorporating* Gene-
Based Component in CROPGRO-Bean
0
2
4
6
8
10
12
14
16
18
20 40 60 80 100
Days after Planting
Leaf number (Jamapa QTLs (-1) 0.3 m ro)
Leaf number (Calima QTLs (+1) 0.3 m ro)
0
1000
2000
3000
4000
5000
6000
20 40 60 80 100
Days after Planting
Grain wt kg/ha (Jamapa QTLs (-1) 0.3 m ro)
Tops wt kg/ha (Jamapa QTLs (-1) 0.3 m ro)
Grain wt kg/ha (Calima QTLs (+1) 0.3 m ro)
Tops wt kg/ha (Calima QTLs (+1) 0.3 m ro)
Main Stem Node
Number
Biomass and Pod Mass,
kg/ha
* Incorporated NAR to compute TRIFL only
CIAT
Aug 4, 2015
19. Need to account for G x E x M interactions on processes
Need to design for evolution as more knowledge about
genetic effects on crop components is obtained
Example Gene-based Model of bean leaf area
Design modules with QTL effects on CM processes
Still a work in progress
New Modular Approach
CIAT
Aug 4, 2015
20. 𝑵𝑨𝑹(𝒕) = 0.252 + 0.021 ∙ 𝑇𝐸𝑀𝑃 − 21.51 − 0.005 ∙
𝑆𝑅𝐴𝐷 − 17.38 − 0.004 ∙ 𝐷𝐿 − 12.74 − 0.010 ∙ 𝐵𝑛𝑔072 −
0.032 ∙ 𝐹𝐼𝑁 + 0.009 ∙ 𝐵𝑛𝑔083 − 0.008 ∙ 𝐷𝑖𝑀7−𝟕 − 0.004 ∙
𝐵𝑛𝑔072 ∙ 𝐷𝐿 − 12.74 − 0.003 ∙ 𝐹𝐼𝑁 ∙ (𝑇𝐸𝑀𝑃 − 21.51)
Linear Mixed Effects Model for NAR(t)
Bng072 Marker for QTL found to influence NAR, + 1 for Calima and -1 for Jamapa parental lines
Bng083 Marker for QTL found to influence NAR, equal to + 1 for Calima and -1 for Jamapa parental lines
DL Average daylength during time when nodes were being added in genotype g at site s (h)
DLmean Average daylength across sites in the experiment during node addition, h
Dim7-7 Gene or QTL found to influence NAR, equal to + 1 for Calima and -1 for Jamapa parental lines
FIN Gene or QTL found to influence NAR, equal to + 1 for Calima and -1 for Jamapa parental lines
NAR(t) Node addition rate, nodes per day added to the main stem for genotype g grown at site s
SRAD Average SRAD across sites in the experiments, MJ m-2 d-1
TEMP Average of daily mean temperature during the time when nodes were added, 0C
CIAT
Aug 4, 2015
21. NAR vs. Temperature
Parent Lines
0
0.1
0.2
0.3
0.4
0.5
0.6
0 10 20 30 40
NodeAdditionRate,#/d
Temperature, C
Jamapa (-1) Calima (+1)
0
0.1
0.2
0.3
0.4
0.5
0.6
0 10 20 30 40
NodeAdditionRate,#/d
Temperature, C
Jamapa (-1) Jamapa with Calima FIN
Calima (+1) Calima with Jamapa FIN
(a) (b)
CIAT
Aug 4, 2015
22. Modular Approach
Example of a module: model that computes node addition rate on day t (NAR(t))
CIAT
Aug 4, 2015
23. We know that temperature effects on most crop
growth processes is nonlinear
Also, this linear model uses mean temperature
during observation period, when we know that plants
respond non-linearly to temperature and should be
considered hourly
So, modules need to be dynamic and include
nonlinear effects
But, is Linear Model Adequate?
CIAT
Aug 4, 2015
25. What are the GSPs in the above equation?
Are they constant across environments?
Does this nonlinear formulation make sense relative to physiological
process and what we know?
Is it sufficiently robust? How can we determine this?
Will the GSPs in this equation remain fixed across genotypes?
Environments? Management?
Will “calibration” be needed after fitting these equations to field
data? If so, how will this differ from what we now do?
We should formulate nonlinear models based on mechanistic
knowledge, then estimate parameters using data from genetic family
across diverse environments.
What About GSPs?
CIAT
Aug 4, 2015
29. CommonBean Model: Integrating modules
Integrated
Modules
CommonBean Model
Initialize T storage matrix
Initialize d = VEDAP
Set initial state variables
Set hour h = 1
Read T for hour h
Calculate & update mean T
Hour = 24 ?
Run MSNOD.max Module
Run NAR Module
Run LAMS Module
Day (d) = End point (DAY) ?
End CommonBean Model
h=h+1
d=d+1
No
No
CIAT
Aug 4, 2015
34. Crop Model-Based Genomic Prediction
outperforms GBLUP
QTLs estimate Yield via
Crop Model function using GSPs
QTLs estimate Yield via
GBLUP
Yield=f(4 GSPs,Env)
35. Discussion
Demonstrated benefits of merging crop modeling
and genetics
Various methods are reasonable
Need new G,E nonlinear functions estimated using
mixed effects models, physiologically based with G
and E components (management also)
Modularity is important, short and long term
Paper in Special Issue
Genomic Prediction with crop models likely to
perform better than other methods (GBLUP)
CIAT
Aug 4, 2015