scholarly journals Forage Yield Estimation with a Process-Based Simulation Model

Forage Groups ◽  
2019 ◽  
Author(s):  
James R. Kiniry ◽  
Sumin Kim ◽  
M. Norman Meki ◽  
Mari-Vaughn V. Johnson
Keyword(s):  
Simulation Model ◽  
Forage Yield ◽  
Yield Estimation

Spatial Variability Effects on Precision and Power of Forage Yield Estimation

Crop Science ◽  
2017 ◽  
Vol 57 (3) ◽  
pp. 1383-1393 ◽  
Author(s):  
Raghuveer Sripathi ◽  
Patrick Conaghan ◽  
Dermot Grogan ◽  
Michael D. Casler
Keyword(s):  
Spatial Variability ◽  
Forage Yield ◽  
Yield Estimation

A Simulation Model of Forage Yield, Quality and Intake and Growth of Growing Cattle Grazing Cornstalks

1989 ◽  
Vol 67 (2) ◽  
pp. 581 ◽  
Author(s):  
S. Fernandez-Rivera ◽  
M. Lewis ◽  
T. J. Klopfenstein ◽  
T. L. Thompson
Keyword(s):  
Simulation Model ◽  
Cattle Grazing ◽  
Forage Yield ◽  
Growing Cattle ◽  
Yield Quality

scholarly journals RICE YIELD ESTIMATION THROUGH ASSIMILATING SATELLITE DATA INTO A CROP SIMUMLATION MODEL

2016 ◽  
Vol XLI-B8 ◽  
pp. 993-996
Author(s):  
N. T. Son ◽  
C. F. Chen ◽  
C. R. Chen ◽  
L. Y. Chang ◽  
S. H. Chiang
Keyword(s):  
Data Assimilation ◽  
Simulation Model ◽  
Cost Function ◽  
Crop Yield ◽  
Satellite Data ◽  
Rice Yield ◽  
Yield Estimation ◽  
Crop Simulation Model ◽  
Crop Simulation ◽  
The Cost

Rice is globally the most important food crop, feeding approximately half of the world’s population, especially in Asia where around half of the world’s poorest people live. Thus, advanced spatiotemporal information of rice crop yield during crop growing season is critically important for crop management and national food policy making. The main objective of this study was to develop an approach to integrate remotely sensed data into a crop simulation model (DSSAT) for rice yield estimation in Taiwan. The data assimilation was processed to integrate biophysical parameters into DSSAT model for rice yield estimation using the particle swarm optimization (PSO) algorithm. The cost function was constructed based on the differences between the simulated leaf area index (LAI) and MODIS LAI, and the optimization process starts from an initial parameterization and accordingly adjusts parameters (e.g., planting date, planting population, and fertilizer amount) in the crop simulation model. The fitness value obtained from the cost function determined whether the optimization algorithm had reached the optimum input parameters using a user-defined tolerance. The results of yield estimation compared with the government’s yield statistics indicated the root mean square error (RMSE) of 11.7% and mean absolute error of 9.7%, respectively. This study demonstrated the applicability of satellite data assimilation into a crop simulation model for rice yield estimation, and the approach was thus proposed for crop yield monitoring purposes in the study region.

scholarly journals MEP-2®: Programa de computador para manejo de praderas con bovinos en el trópico colombiano I. Desarrollo informático

2005 ◽  
Vol 6 (2) ◽  
pp. 14
Author(s):  
Luis Carlos Arreaza ◽  
Alberto Franco ◽  
Jorge Mayorga ◽  
Henry Mateus ◽  
Oscar Pardo ◽  
...  
Keyword(s):  
Simulation Model ◽  
Visual Basic ◽  
Numerical Results ◽  
Forage Yield ◽  
Farm Animals ◽  
Grass Species ◽  
Model Description ◽  
Computer Simulation Model ◽  
Pasture Management ◽  
Grass Growth

<p>Se diseñó y desarrolló una herramienta informática (Manejo Experto de praderas: MEP-2) para simular el comportamiento de las gramíneas tropicales frente al pastoreo con bovinos y para establecer los períodos de uso y recuperación de las praderas, en función de la producción de biomasa comestible, su calidad y consumo por parte de los animales. Así mismo, MEP-2<sup>®</sup> incorpora variables de calidad del forrraje y de distensión del rumen, de acuerdo con un modelo de simulación de consumo de materia seca (MS) simple y de fácil implementación. EI sistema fue estructurado previamente en hoja de cálculo y posteriormente trasladado a Visual Basic 6™; la información de identificación de la finca, tipo de ganado, especies de pastos y sistema de pastoreo se almacenó en formato Access™. El programa consta de seis ventanas que se abren secuencialmente una vez se diligencia la información solicitada: dos ventanas corresponden a la evaluación de la disponibilidad de forraje según el tipo de gramínea: erecta o postrada. Otras dos ventanas registran información de la finca y las praderas con sus características individuales. De las dos ventanas finales, una corresponde a los resultados de la simulación y la otra a su interpretación, además de algunas recomendaciones generales. El propósito de la herramienta es proporcionar a ganaderos y asistentes técnicos un sistema objetivo para la toma de decisiones en el manejo de las praderas contribuyendo de esta manera a su sostenibilidad y a una mayor eficiencia de los animales.</p><p> </p><p><strong>MEP-2™: A computer simulation model for tropical pasture management. I - Model description</strong></p><p>A software called <em>Manejo Experto de Praderas </em>(MEP 2.O™) was designed for simulating pasture use by cattle for predicting optimum pasture grazing and rest periods based on biomass yield, nutritive value and feed intake, according to a dry matter intake (DMI) simulation model. The programme was previously written on a spreadsheet and then transferred to Visual Basic 6™. The basic information relating to farm, animals grazing system and grass species was stored in Access™ format. The programme structure was built into six sequentially opening windows. Two were for calculating forage yield according to grass growth type (lying or erect). Two windows were for farm and paddock data and the last two windows were for results; one was for numerical results and one for interpreting  numerical results with recommendations for improving grazing management on cattle farms. The project was aimed at helping farmers by providing them with a tool for better decision making in pasture management, contributing towards pasture sustainability and greater animal efficiency.</p><p> </p><p> </p>

scholarly journals RICE YIELD ESTIMATION THROUGH ASSIMILATING SATELLITE DATA INTO A CROP SIMUMLATION MODEL

2016 ◽  
Vol XLI-B8 ◽  
pp. 993-996 ◽  
Author(s):  
N. T. Son ◽  
C. F. Chen ◽  
C. R. Chen ◽  
L. Y. Chang ◽  
S. H. Chiang
Keyword(s):  
Data Assimilation ◽  
Simulation Model ◽  
Cost Function ◽  
Crop Yield ◽  
Satellite Data ◽  
Rice Yield ◽  
Yield Estimation ◽  
Crop Simulation Model ◽  
Crop Simulation ◽  
The Cost

Rice is globally the most important food crop, feeding approximately half of the world’s population, especially in Asia where around half of the world’s poorest people live. Thus, advanced spatiotemporal information of rice crop yield during crop growing season is critically important for crop management and national food policy making. The main objective of this study was to develop an approach to integrate remotely sensed data into a crop simulation model (DSSAT) for rice yield estimation in Taiwan. The data assimilation was processed to integrate biophysical parameters into DSSAT model for rice yield estimation using the particle swarm optimization (PSO) algorithm. The cost function was constructed based on the differences between the simulated leaf area index (LAI) and MODIS LAI, and the optimization process starts from an initial parameterization and accordingly adjusts parameters (e.g., planting date, planting population, and fertilizer amount) in the crop simulation model. The fitness value obtained from the cost function determined whether the optimization algorithm had reached the optimum input parameters using a user-defined tolerance. The results of yield estimation compared with the government’s yield statistics indicated the root mean square error (RMSE) of 11.7% and mean absolute error of 9.7%, respectively. This study demonstrated the applicability of satellite data assimilation into a crop simulation model for rice yield estimation, and the approach was thus proposed for crop yield monitoring purposes in the study region.

scholarly journals Effect of Male Planting Date and Female Plant Population on Hybrid Maize Yield and Evaluation of Use of Hybrid-Maize Simulation Model for Grain Yield Estimation in Hybrid Maize Seed Production

2018 ◽  
Vol 10 (3) ◽  
pp. 333
Author(s):  
Lennin Musundire ◽  
Shorai Dari ◽  
John MacRoberts ◽  
H. S. Yang ◽  
John Derera ◽  
...  
Keyword(s):  
Grain Yield ◽  
Simulation Model ◽  
Seed Production ◽  
Planting Date ◽  
Plant Population ◽  
Female Plant ◽  
Weather Data ◽  
Yield Estimation ◽  
Maize Seed ◽  
Hybrid Maize

The study was carried out to determine the effect of male planting date (MPD) and female plant population (FPP) on the grain yield (GY) performance of a three-way hybrid and to evaluate Hybrid-Maize simulation model for grain yield estimation in hybrid seed maize production. Fifteen treatment combinations of five MPD as a deviation from the female planting date and three FPP replicated three times were used. The Hybrid-Maize simulation model programme was used to forecast the possible GY outcomes for the fifteen treatments of the experiment using estimated parameters and weather data for the 2006/7 season. The field experiment produced significant (P < 0.005) main effects but non-significant interaction effects for GY, yield components and antheis-silking interval (ASI). Female seed yield was affected by time of male pollen shed relative to female silking: ASI, with highest yields associated with close synchrony (ASI= +/-3 days). ASI had a significant effect on the number of kernels per ear (KPE), with the greatest KPE (318) associated with an ASI of +/-3 days. FPP effects on yield are typical for maize, showing a curvilinear response from low to high density. The optimum population density for GY was 5.4 plants m-2. Simulation output from the Hybrid-Maize simulation model showed an overestimation of GY compare to the observed yield. Furthermore, the model was unable to predict yields for the low FPP of 2.7 plants m-2. We found that Hybrid-Maize simulation model has limited potential for simulating hybrid maize seed production, as it does not accommodate limitations that may occur during the growing season: difference in male and female planting dates, pollen density and dispersion. Hence, the fixed parameters for the Hybrid-Maize simulation model can only be used in maize commercial production.

Field Design Factors Affecting the Precision of Ryegrass Forage Yield Estimation

2017 ◽  
Vol 109 (3) ◽  
pp. 858-869 ◽  
Author(s):  
Raghuveer Sripathi ◽  
Patrick Conaghan ◽  
Dermot Grogan ◽  
Michael D. Casler
Keyword(s):  
Forage Yield ◽  
Yield Estimation ◽  
Factors Affecting ◽  
Design Factors
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