Abstract
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.
Highlights
Climate change has established its reality on affecting temperatures and precipitation, with much evidence confirming the increase of global temperature and change in the rainfall rates
The maize yield from 2002–2018 was collected from the Czech Statistical Office (https://www.czso.cz) The water deficit data was calculated based on the Czech technical norm (C SN 750434), which uses standardized temperatures (ST) according to the long-term averages
As mentioned in 2.5, the input method of multiple linear regression (MLR) was utilized to analyze the influence of climate factors on silage and grain maize yields
Summary
Climate change has established its reality on affecting temperatures and precipitation, with much evidence confirming the increase of global temperature and change in the rainfall rates. Climate change is causing an impact on food production, and maize is no exception [3]. Temperature and precipitation are the two main climate factors influencing maize productivity. It will be reduced when extreme temperature events occur during pollination and are further exaggerated when there are water deficits. During the grain-filling period, warm temperatures above the upper threshold cause a reduction in yield. Model estimates suggest that for every 1 ◦C increase in temperature, there is nearly a 10% yield reduction [3]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
