Application of Artificial Neural Network for Predicting Maize Production in South Africa

Omolola M. Adisa, Joel O. Botai, Abiodun M. Adeola, Abubeker Hassen, Christina M. Botai, Daniel Darkey and Eyob Tesfamariam
Additional contact information
Omolola M. Adisa: Department of Geography, Geoinformatics & Meteorology, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa
Joel O. Botai: Department of Geography, Geoinformatics & Meteorology, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa
Abiodun M. Adeola: South African Weather Service, Private Bag X097, Pretoria 0001, South Africa
Abubeker Hassen: Department of Animal and Wildlife Sciences, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa
Christina M. Botai: South African Weather Service, Private Bag X097, Pretoria 0001, South Africa
Daniel Darkey: Department of Geography, Geoinformatics & Meteorology, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa
Eyob Tesfamariam: Department of Plant and Soil Sciences, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa

Sustainability, 2019, vol. 11, issue 4, 1-17

Abstract: The use of crop modeling as a decision tool by farmers and other decision-makers in the agricultural sector to improve production efficiency has been on the increase. In this study, artificial neural network (ANN) models were used for predicting maize in the major maize producing provinces of South Africa. The maize production prediction and projection analysis were carried out using the following climate variables: precipitation (PRE), maximum temperature (TMX), minimum temperature (TMN), potential evapotranspiration (PET), soil moisture (SM) and land cultivated (Land) for maize. The analyzed datasets spanned from 1990 to 2017 and were divided into two segments with 80% used for model training and the remaining 20% for testing. The results indicated that PET, PRE, TMN, TMX, Land, and SM with two hidden neurons of vector (5,8) were the best combination to predict maize production in the Free State province, whereas the TMN, TMX, PET, PRE, SM and Land with vector (7,8) were the best combination for predicting maize in KwaZulu-Natal province. In addition, the TMN, SM and Land and TMN, TMX, SM and Land with vector (3,4) were the best combination for maize predicting in the North West and Mpumalanga provinces, respectively. The comparison between the actual and predicted maize production using the testing data indicated performance accuracy adjusted R 2 of 0.75 for Free State, 0.67 for North West, 0.86 for Mpumalanga and 0.82 for KwaZulu-Natal. Furthermore, a decline in the projected maize production was observed across all the selected provinces (except the Free State province) from 2018 to 2019. Thus, the developed model can help to enhance the decision making process of the farmers and policymakers.

Keywords: maize; climate; prediction; artificial intelligence (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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