Assessment and Prediction of Maize Production Considering Climate Change by Extreme Learning Machine in Czechia

Machine learning algorithms have been applied in the agriculture field to forecast crop productivity. Previous studies mainly focused on the whole crop growth period while different time windows on yield prediction were still unknown. The entire growth period was separated into each month to assess...

Full description

Saved in:
Bibliographic Details
Published in:Agronomy (Basel) 2021-11, Vol.11 (11), p.2344
Main Authors: Maitah, Mansoor, Malec, Karel, Ge, Ying, Gebeltová, Zdeňka, Smutka, Luboš, Blažek, Vojtěch, Pánková, Ludmila, Maitah, Kamil, Mach, Jiří
Format: Article
Language:English
Subjects:
Agricultural land
Agricultural production
Agriculture
Algorithms
Artificial neural networks
Climate change
Corn
Crop growth
Crop production
Crop yield
Czech Republic
Decision trees
Discriminant analysis
extreme machine learning
Grain
Heat
Humidity
Learning algorithms
Machine learning
maize yield
Meteorological data
Neural networks
Precipitation
Predictions
Rice
Root-mean-square errors
Silage
Variables
Water deficit
Windows (intervals)
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Machine learning algorithms have been applied in the agriculture field to forecast crop productivity. Previous studies mainly focused on the whole crop growth period while different time windows on yield prediction were still unknown. The entire growth period was separated into each month to assess their corresponding predictive ability by taking maize production (silage and grain) in Czechia. We present a thorough assessment of county-level maize yield prediction in Czechia using a machine learning algorithm (extreme learning machine (ELM)) and an extensive set of weather data and maize yields from 2002 to 2018. Results show that sunshine in June and water deficit in July were vastly influential factors for silage maize yield. The two primary climate parameters for grain maize yield are minimum temperature in September and water deficit in May. The average absolute relative deviation (AARD), root mean square error (RMSE), and coefficient (R2) of the proposed models are 6.565–32.148%, 1.006–1.071%, 0.641–0.716, respectively. Based on the results, silage yield will decrease by 1.367 t/ha (3.826% loss), and grain yield will increase by 0.337 t/ha (5.394% increase) when the max temperature in May increases by 2 °C. In conclusion, ELM models show a great potential application for predicting maize yield.
ISSN:2073-4395
2073-4395
DOI:10.3390/agronomy11112344