Crop Modeling

2013 ◽  
pp. 123-132
Author(s):  
Raffaello Cervigni ◽  
Riccardo Valentini ◽  
Monia Santini
Keyword(s):  
Crop Modeling

scholarly journals Global Analysis of Atmospheric Transmissivity Using Cloud Cover, Aridity and Flux Network Datasets

2021 ◽  
Vol 13 (9) ◽  
pp. 1716
Author(s):  
Ankur Srivastava ◽  
Jose F. Rodriguez ◽  
Patricia M. Saco ◽  
Nikul Kumari ◽  
Omer Yetemen
Keyword(s):  
Cloud Cover ◽  
Climatic Factors ◽  
Global Analysis ◽  
Empirical Relationship ◽  
Aridity Index ◽  
Global Scale ◽  
Empirical Models ◽  
Coefficient Of Determination ◽  
Crop Modeling ◽  
Atmospheric Transmissivity

Atmospheric transmissivity (τ) is a critical factor in climatology, which affects surface energy balance, measured at a limited number of meteorological stations worldwide. With the limited availability of meteorological datasets in remote areas across different climatic regions, estimation of τ is becoming a challenging task for adequate hydrological, climatic, and crop modeling studies. The availability of solar radiation data is comparatively less accessible on a global scale than the temperature and precipitation datasets, which makes it necessary to develop methods to estimate τ. Most of the previous studies provided region specific datasets of τ, which usually provide local assessments. Hence, there is a necessity to give the empirical models for τ estimation on a global scale that can be easily assessed. This study presents the analysis of the τ relationship with varying geographic features and climatic factors like latitude, aridity index, cloud cover, precipitation, temperature, diurnal temperature range, and elevation. In addition to these factors, the applicability of these relationships was evaluated for different climate types. Thus, empirical models have been proposed for each climate type to estimate τ by using the most effective factors such as cloud cover and aridity index. The cloud cover is an important yet often overlooked factor that can be used to determine the global atmospheric transmissivity. The empirical relationship and statistical indicator provided the best performance in equatorial climates as the coefficient of determination (r2) was 0.88 relatively higher than the warm temperate (r2 = 0.74) and arid regions (r2 = 0.46). According to the results, it is believed that the analysis presented in this work is applicable for estimating the τ in different ecosystems across the globe.

Leaf Area Index Estimates for Wheat from LANDSAT and Their Implications for Evapotranspiration and Crop Modeling 1

1979 ◽  
Vol 71 (2) ◽  
pp. 336-342 ◽  
Author(s):  
C. L. Wiegand ◽  
A. J. Richardson ◽  
E. T. Kanemasu
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Modeling ◽  
Area Index

scholarly journals Crop modeling defines opportunities and challenges for drought escape, water capture, and yield increase using chilling‐tolerant sorghum

Plant Direct ◽  
2021 ◽  
Vol 5 (9) ◽  
Author(s):  
Rubí Raymundo ◽  
Sarah Sexton‐Bowser ◽  
Ignacio A. Ciampitti ◽  
Geoffrey P. Morris
Keyword(s):  
Crop Modeling ◽  
Yield Increase

scholarly journals Vadose zone measurement and modeling

1997 ◽  
Vol 54 (spe) ◽  
pp. 22-26 ◽  
Author(s):  
J. W. Hopmans ◽  
V. Clausnitzer ◽  
K.I. Kosugi ◽  
D.R. Nielsen ◽  
F. Somma
Keyword(s):  
Vadose Zone ◽  
Sao Paulo ◽  
University Of California ◽  
Crop Modeling ◽  
Soil Physics ◽  
Ongoing Research ◽  
General Overview ◽  
Physics Program ◽  
Research Activities ◽  
The University

The following treatise is a summary of some of the ongoing research activities in the soil physics program at the University of California in Davis. Each of the four listed areas win be presented at the Workshop on special topics on soil physics and crop modeling in Piracicaba at the University of Sao Paulo. We limited ourselves to a general overview of each area, but will present a more thorough discussion with examples at the Workshop.

scholarly journals Heliaphen, an Outdoor High-Throughput Phenotyping Platform for Genetic Studies and Crop Modeling

2019 ◽  
Vol 9 ◽  
Author(s):  
Florie Gosseau ◽  
Nicolas Blanchet ◽  
Didier Varès ◽  
Philippe Burger ◽  
Didier Campergue ◽  
...  
Keyword(s):  
High Throughput ◽  
Crop Modeling ◽  
Genetic Studies ◽  
High Throughput Phenotyping

scholarly journals Soil water crop modeling for decision support in millet-based systems in the Sahel: a challenge

2014 ◽  
Vol 9 (22) ◽  
pp. 1700-1713 ◽  
Author(s):  
P. B. I. Akponikp ◽  
B. Grard ◽  
C. L Bielders
Keyword(s):  
Decision Support ◽  
Soil Water ◽  
Crop Modeling

Model-driven engineering applied to crop modeling

2015 ◽  
Vol 26 ◽  
pp. 173-181 ◽  
Author(s):  
Guillaume Barbier ◽  
Véronique Cucchi ◽  
David R.C. Hill
Keyword(s):  
Crop Modeling ◽  
Model Driven Engineering ◽  
Model Driven

Estimating Solar Irradiance for Crop Modeling Using Daily Air Temperature Data

1999 ◽  
Vol 91 (5) ◽  
pp. 845-851 ◽  
Author(s):  
Douglas G. Goodin ◽  
J. M. S. Hutchinson ◽  
Richard L. Vanderlip ◽  
M. C. Knapp
Keyword(s):  
Air Temperature ◽  
Solar Irradiance ◽  
Temperature Data ◽  
Crop Modeling

Crop Modeling and Applications: A Cotton Example

1997 ◽  
pp. 225-290 ◽  
Author(s):  
K. Raja Reddy ◽  
Harry F. Hodges ◽  
James M. McKinion
Keyword(s):  
Crop Modeling
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