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

Author

Listed:
  • 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
    South African Weather Service, Private Bag X097, Pretoria 0001, South Africa
    School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Westville Campus, Private Bag X54001, Durban 4000, South Africa)

  • Abiodun M. Adeola

    (South African Weather Service, Private Bag X097, Pretoria 0001, South Africa
    School of Health Systems and Public Health, Faculty of Health Sciences, University of Pretoria, Private Bag X20, Hatfield 0028, 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)

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.

Suggested Citation

  • Omolola M. Adisa & Joel O. Botai & Abiodun M. Adeola & Abubeker Hassen & Christina M. Botai & Daniel Darkey & Eyob Tesfamariam, 2019. "Application of Artificial Neural Network for Predicting Maize Production in South Africa," Sustainability, MDPI, vol. 11(4), pages 1-17, February.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:4:p:1145-:d:208011
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    References listed on IDEAS

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    1. Klemme, Richard M. & Martin, Marshall A. & Whittaker, James K., 1978. "An Econometric Yield Response And Forecasting Model For Corn In Indiana," 1978 Annual Meeting, August 6-9, Blacksburg, Virginia 284174, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    2. Kaul, Monisha & Hill, Robert L. & Walthall, Charles, 2005. "Artificial neural networks for corn and soybean yield prediction," Agricultural Systems, Elsevier, vol. 85(1), pages 1-18, July.
    3. Intrator, Orna & Intrator, Nathan, 2001. "Interpreting neural-network results: a simulation study," Computational Statistics & Data Analysis, Elsevier, vol. 37(3), pages 373-393, September.
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    Cited by:

    1. Alexander Kocian & Luca Incrocci, 2020. "Learning from Data to Optimize Control in Precision Farming," Stats, MDPI, vol. 3(3), pages 1-7, July.
    2. Elbeltagi, Ahmed & Deng, Jinsong & Wang, Ke & Hong, Yang, 2020. "Crop Water footprint estimation and modeling using an artificial neural network approach in the Nile Delta, Egypt," Agricultural Water Management, Elsevier, vol. 235(C).
    3. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2021. "Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data," Land, MDPI, vol. 10(6), pages 1-21, June.
    4. Bhoomin Tanut & Rattapoom Waranusast & Panomkhawn Riyamongkol, 2021. "High Accuracy Pre-Harvest Sugarcane Yield Forecasting Model Utilizing Drone Image Analysis, Data Mining, and Reverse Design Method," Agriculture, MDPI, vol. 11(7), pages 1-21, July.

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