Mixing process-based and data-driven approaches in yield prediction-Reference-Cited by-同舟云学术

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Mixing process-based and data-driven approaches in yield prediction

Published:2022-09 Issue: Volume:139 Page:126569
ISSN:1161-0301
Container-title:European Journal of Agronomy
language:en
Short-container-title:European Journal of Agronomy

Author:

Maestrini Bernardo,Mimić Gordan,van Oort Pepijn A.J.,Jindo Keiji,Brdar Sanja,Athanasiadis Ioannis N.,van Evert Frits K.

Publisher

Elsevier BV

Subject

Plant Science,Soil Science,Agronomy and Crop Science

Reference140 articles.

1. Performance evaluation of AquaCrop model for maize crop in a semi-arid environment;Abedinpour;Agric. Water Manag.,2012

2. Prediction of potato crop yield using precision agriculture techniques;Al-Gaadi;PLoS One,2016

3. Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach;Alvarez;Eur. J. Agron.,2009

4. Simulation of crop and water productivity for rice (Oryza sativa L.) using APSIM under diverse agro-climatic conditions and water management techniques in Sri Lanka;Amarasingha;Agric. Water Manag.,2015

5. On the accuracy of crop production and water requirement calculations: process-based crop modeling at daily, semi-weekly, and weekly time steps for integrated assessments;Ammar;J. Environ. Manag.,2019

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