Estimation of Alfalfa (Medicago sativa l.) yield under RCP4.5 and RCP8.5 climate change projections with ANN in Turkey
Keywords:Forage farming, Meadows, Pastures, Artificial neural network, HadGEM2-ES, Time series, Plant growth model.
Alfalfa is one of the most widely cultivated forage crops in the world. Alfalfa farming is carried out on approximately 35 million ha of land worldwide with an annual production amounting to 255 million tons. The average alfalfa cultivated area is about 637 000 ha with production of 13 million tons and yield of 2 200 kg da-1 in Turkey. It is expected that climate change will have significantly different effects on its production and yield in future. Therefore, the aim of the study was to predict the effect of climate change on the yield of alfalfa via selected Artificial Neural Network (ANN) according to RCP4.5 and RCP8.5 climate change scenarios. In line with this, first of all the best ANN structure among 176 different ANN alternatives consisting of various input parameters, learning rates, decay and neuron numbers to predicts alfalfa yield was selected. The ANN training/test dataset used in the study were composed of the alfalfa cultivation statistics, the soil parameters and the climatological data. Alfalfa yield for years 2020-2060 and 2060-2100 in 79 provinces of Turkey are predicted by using best ANN model, according to climate change projections (HadGEM2-ES RCP4.5 and RCP8.5). The ANN was able to calculate alfalfa yield with 0.827 coefficient of determination and 0.813 Nash-Sutcliff coefficient. It is understood that the alfalfa plant can resist climate change and its yield tend to increase or decrease in regions, where there will be an increase or decrease in precipitation in the same order as result of climatic change. It is predicted that the highest yield increase will be in Artvin (6%) (a province of the Eastern Anatolia region) and the maximum yield decrease will be noted in Siirt (9%) (a province of the South eastern Anatolia region). This research may be considered as a creative prediction approach for the alfalfa yield estimation.
How to Cite
Copyright (c) 2023 MAUSAM
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All articles published by MAUSAM are licensed under the Creative Commons Attribution 4.0 International License. This permits anyone.
Anyone is free:
- To Share - to copy, distribute and transmit the work
- To Remix - to adapt the work.
Under the following conditions:
- Share - copy and redistribute the material in any medium or format
- Adapt - remix, transform, and build upon the material for any purpose, even