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...
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2021
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oai:doaj.org-article:182047a2452947e6b21bbb7bb9bad7332021-11-25T16:12:14ZAssessment and Prediction of Maize Production Considering Climate Change by Extreme Learning Machine in Czechia10.3390/agronomy111123442073-4395https://doaj.org/article/182047a2452947e6b21bbb7bb9bad7332021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4395/11/11/2344https://doaj.org/toc/2073-4395Machine 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.Mansoor MaitahKarel MalecYing GeZdeňka GebeltováLuboš SmutkaVojtěch BlažekLudmila PánkováKamil MaitahJiří MachMDPI AGarticleclimate changeCzech Republicextreme machine learningmaize yieldAgricultureSENAgronomy, Vol 11, Iss 2344, p 2344 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
climate change Czech Republic extreme machine learning maize yield Agriculture S |
| spellingShingle |
climate change Czech Republic extreme machine learning maize yield Agriculture S Mansoor Maitah Karel Malec Ying Ge Zdeňka Gebeltová Luboš Smutka Vojtěch Blažek Ludmila Pánková Kamil Maitah Jiří Mach Assessment and Prediction of Maize Production Considering Climate Change by Extreme Learning Machine in Czechia |
| description |
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. |
| format |
article |
| author |
Mansoor Maitah Karel Malec Ying Ge Zdeňka Gebeltová Luboš Smutka Vojtěch Blažek Ludmila Pánková Kamil Maitah Jiří Mach |
| author_facet |
Mansoor Maitah Karel Malec Ying Ge Zdeňka Gebeltová Luboš Smutka Vojtěch Blažek Ludmila Pánková Kamil Maitah Jiří Mach |
| author_sort |
Mansoor Maitah |
| title |
Assessment and Prediction of Maize Production Considering Climate Change by Extreme Learning Machine in Czechia |
| title_short |
Assessment and Prediction of Maize Production Considering Climate Change by Extreme Learning Machine in Czechia |
| title_full |
Assessment and Prediction of Maize Production Considering Climate Change by Extreme Learning Machine in Czechia |
| title_fullStr |
Assessment and Prediction of Maize Production Considering Climate Change by Extreme Learning Machine in Czechia |
| title_full_unstemmed |
Assessment and Prediction of Maize Production Considering Climate Change by Extreme Learning Machine in Czechia |
| title_sort |
assessment and prediction of maize production considering climate change by extreme learning machine in czechia |
| publisher |
MDPI AG |
| publishDate |
2021 |
| url |
https://doaj.org/article/182047a2452947e6b21bbb7bb9bad733 |
| work_keys_str_mv |
AT mansoormaitah assessmentandpredictionofmaizeproductionconsideringclimatechangebyextremelearningmachineinczechia AT karelmalec assessmentandpredictionofmaizeproductionconsideringclimatechangebyextremelearningmachineinczechia AT yingge assessmentandpredictionofmaizeproductionconsideringclimatechangebyextremelearningmachineinczechia AT zdenkagebeltova assessmentandpredictionofmaizeproductionconsideringclimatechangebyextremelearningmachineinczechia AT lubossmutka assessmentandpredictionofmaizeproductionconsideringclimatechangebyextremelearningmachineinczechia AT vojtechblazek assessmentandpredictionofmaizeproductionconsideringclimatechangebyextremelearningmachineinczechia AT ludmilapankova assessmentandpredictionofmaizeproductionconsideringclimatechangebyextremelearningmachineinczechia AT kamilmaitah assessmentandpredictionofmaizeproductionconsideringclimatechangebyextremelearningmachineinczechia AT jirimach assessmentandpredictionofmaizeproductionconsideringclimatechangebyextremelearningmachineinczechia |
| _version_ |
1718413300638679040 |