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

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.

Variation of Some Biometric Parameters in Corn Ears in Relation to Plant Status

“…Results regarding the prediction of the corn crop production were communicated both in relation to the climatic conditions, as well as to the methods and techniques used, under statistical safety conditions (Maitah et al, 2021;Li et al, 2022).…”

Predictions and Estimations in Agricultural Production under a Changing Climate

“…In the first article [7], the authors attempted to predict corn yield in the Czech Republic using extreme machine learning. The advantage of the yield prediction approach presented in the article, compared to the classical approach, was the division of the entire growth period of corn into individual months.…”

Suitability Evaluation of Crop Variety via Graph Neural Network

“…Then, sunshine time directly determines the time of crop photosynthesis, affecting the various stages of crop growth. Maize is a short-day crop, and the whole growth period requires strong light, so sunshine time has a greater impact on crops [ 24 , 25 ]. Finally, because maize is a light-loving crop, it needs higher temperature during the whole growth period, so the effect of minimum temperature on maize growth is more obvious.…”

“…en, sunshine time directly determines the time of crop photosynthesis, affecting the various stages of crop growth. Maize is a shortday crop, and the whole growth period requires strong light, so sunshine time has a greater impact on crops [24,25].…”