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 R2 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.
