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Linking weather generators and crop models for assessment of climate forecast outcomes
(Elsevier Science, 2010-02)
Agricultural production responses to climate variability require salient information to support decisions. We coupled a new hybrid stochastic weather generator (combining parametric and nonparametric components) with a ...
Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield
(MDPI, 2016-10)
A timely and accurate crop yield forecast is crucial to make better decisions on crop management, marketing, and storage by assessing ahead and implementing based on expected crop performance. The objective of this study ...
Forecasting of macro aggregates using yield curve information
(UniandesMaestría en EconomíaFacultad de Economía, 2015)
This paper obtains the forecasts of Colombian macroeconomic variables and the yield curve by jointly modeling their dynamics. For this purpose, I use unrestricted Bayesian Vector Auto Regressive (VAR) models and the ...
Relationship between MODIS-NDVI data and wheat yield : a case study in northern Buenos Aires province, Argentina
(2015-07-18)
In countries like Argentina, whose economy depends heavily on crop production, the estimation of harvests is an elementary requirement. Besides providing objectivity, the use of remote sensing allows estimating yield in ...
Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index
(Elsevier Science, 2014-05)
Soil moisture availability affects rainfed crop yield. Therefore, the development of methods for pre-harvest yield prediction is essential for the food security. A study was carried out to estimate regional crop yield using ...
Early Maize Yield Forecasting from Remotely Sensed Temperature/Vegetation Index Measurements
(Institute of Electrical and Electronics Engineers, 2016-01)
High and low soil moisture availability is one of the main limiting factors-Affecting crops productivity. Thus, determination of the relationship between them is crucial for food security and support importing-exporting ...
Forecasting large covariance matrices: comparing autometrics and LASSOVAR
(2019)
This study aims to compare the performance of two well known automatic model selection algorithms, Autometrics (Hendry and Krolzig, 1999; Doornik, 2009), LASSOVAR and adaptive LASSOVAR (Callot et al., 2017) for modelling ...
A random walk through the trees: Forecasting copper prices using decision learning methods
(Elsevier, 2020)
We investigate the accuracy of copper price forecasts produced by three decision learning methods. Prior evidence (Liu et al. Resources Policy, 2017) shows that a regression tree, a simple decision learning model, can be ...