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

2019 ◽  
Vol 11 (4) ◽  
pp. 1145 ◽  
Author(s):  
Omolola Adisa ◽  
Joel Botai ◽  
Abiodun Adeola ◽  
Abubeker Hassen ◽  
Christina Botai ◽  
...  
Keyword(s):  
Neural Network ◽  
South Africa ◽  
Artificial Neural Network ◽  
Free State ◽  
Maximum Temperature ◽  
Maize Production ◽  
Free State Province ◽  
North West ◽  
Kwazulu Natal ◽  
Artificial Neural

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.

scholarly journals Impacts of Agroclimatic Variability on Maize Production in the Setsoto Municipality in the Free State Province, South Africa

Climate ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 147
Author(s):  
Abubakar Hadisu Bello ◽  
Mary Scholes ◽  
Solomon W. Newete
Keyword(s):  
South Africa ◽  
Minimum Temperature ◽  
Negative Impact ◽  
Free State ◽  
Maximum Temperature ◽  
Strong Positive Correlation ◽  
Maize Production ◽  
Free State Province ◽  
Growing Period ◽  
Maximum Temperatures

The majority of people in South Africa eat maize, which is grown as a rain-fed crop in the summer rainfall areas of the country, as their staple food. The country is usually food secure except in drought years, which are expected to increase in severity and frequency. This study investigated the impacts of rainfall and minimum and maximum temperatures on maize yield in the Setsoto municipality of the Free State province of South Africa from 1985 to 2016. The variation of the agroclimatic variables, including the Palmer stress diversity index (PSDI), was investigated over the growing period (Oct–Apr) which varied across the four target stations (Clocolan, Senekal, Marquard and Ficksburg). The highest coefficients of variance (CV) recorded for the minimum and maximum temperatures and rainfall were 16.2%, 6.2% and 29% during the growing period. Non-parametric Mann Kendal and Sen’s slope estimator were used for the trend analysis. The result showed significant positive trends in minimum temperature across the stations except for Clocolan where a negative trend of 0.2 to 0.12 °C year−1 was observed. The maximum temperature increased significantly across all the stations by 0.04–0.05 °C year−1 during the growing period. The temperature effects were most noticeable in the months of November and February when leaf initiation and kernel filling occur, respectively. The changes in rainfall were significant only in Ficksburg in the month of January with a value of 2.34 mm year−1. Nevertheless, the rainfall showed a strong positive correlation with yield (r 0.46, p = < 0.05). The overall variation in maize production is explained by the contribution of the agroclimatic parameters; the minimum temperature (R2 0.13–0.152), maximum temperature (R2 0.214–0.432) and rainfall (R2 0.17–0.473) for the growing period across the stations during the study period. The PSDI showed dry years and wet years but with most of the years recording close to normal rainfall. An increase in both the minimum and maximum temperatures over time will have a negative impact on crop yield.

Weather Forecasting Using Artificial Neural Network and Bayesian Network

2014 ◽  
Vol 18 (5) ◽  
pp. 812-817 ◽  
Author(s):  
Klent Gomez Abistado ◽  
◽  
Catherine N. Arellano ◽  
Elmer A. Maravillas ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bayesian Network ◽  
Weather Forecasting ◽  
Weather Conditions ◽  
Dew Point ◽  
Weather Data ◽  
Maximum Temperature ◽  
Prevailing Wind Direction ◽  
Artificial Neural

This paper presents a scheme of weather forecasting using artificial neural network (ANN) and Bayesian network. The study focuses on the data representing central Cebu weather conditions. The parameters used in this study are as follows: mean dew point, minimum temperature, maximum temperature, mean temperature, mean relative humidity, rainfall, average wind speed, prevailing wind direction, and mean cloudiness. The weather data were collected from the PAG-ASA Mactan-Cebu Station located at latitude: 10°19´, longitude: 123°59´ starting from January 2011 to December 2011 and the values available represent daily averages. These data were used for training the multi-layered backpropagation ANN in predicting the weather conditions of the succeeding days. Some outputs from the ANN, such as the humidity, temperature, and amount of rainfall, are fed to the Bayesian network for statistical analysis to forecast the probability of rain. Experiments show that the system achieved 93%–100% accuracy in forecasting weather conditions.

scholarly journals Influencing Factors of Maize Production in South Africa: The Case of Mpumalanga, Free State and North West Provinces

2020 ◽  
pp. 25-34
Author(s):  
Andisiwe Diko ◽  
Wang Jun
Keyword(s):  
South Africa ◽  
South African ◽  
Agricultural Practices ◽  
Maize Yield ◽  
Free State ◽  
The South ◽  
Maize Production ◽  
North West ◽  
Independent Variables ◽  
Multi Stage

Aims: Maize is of great significance in the national food security of South Africa. Maize production levels in South Africa continue to decline, further deteriorating the situation of increased food insecurity, unemployment and increased poverty levels in the face of increasing population. This paper investigated fundamental variables influencing maize yield in the South African major maize producing regions. Study Design: A multi-stage stratified sampling method was employed to select maize producing farmers in the major maize producing provinces, namely Mpumalanga, Free State and North West provinces of South Africa. Furthermore, three districts were selected from which maize farmers were then selected. Methodology: Using linear multiple regression for a sample of 202 maize farmers, maize yield as a dependent variable was regressed against land size, fertilizer usage, labour, herbicides and seeds as independent variables. The paper employed the Cobb-Douglas production function to estimate parameters. The data obtained from the field were subjected to analysis using inferential statistics using SPSS v20. Results: The study showed that fertilizer, labour, and herbicides used in the production of maize in the study area were positively and statistically significant at a 5% confidence interval (P<0.05) with elasticity coefficients of 0.55, 0.47 and 0.198 respectively. The independent variables computed in the model had positive elasticity coefficients indicating a direct positive relationship between the input variables and maize output. The study also revealed that farmers in the study area were applying fewer amounts of fertilizer than the recommended rates per hectare. Conclusion: The study recommends that the South African government should supply inputs to maize farmers at subsidized rates to promote correct application rates and attain higher yields.  The promotion of good quality extension services to foster good agricultural practices in the production of maize is also recommended.

scholarly journals The Effect of Different Cover and Flooring Materials on Climatic Comfort in Landscape Design

2022 ◽  
Vol 9 (sp) ◽  
pp. 2571-2580
Author(s):  
Orhun Soydan ◽  
Ahmet Benliay
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Temperature Difference ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Ambient Air ◽  
Maximum Temperature ◽  
Wide Range ◽  
Artificial Neural

In this study, it is aimed to understand the effects of structural and vegetative elements that can be used in landscape designs on the temperature factor, which will greatly affect the climatic comfort, by using artificial neural networks. In this context, measurements were carried out in the morning (08:00-09:00), noon (13:00-14:00) and evening (17:00-18:00) of a total of 100 days, 50 days in each of the winter and summer seasons, at 7 randomly selected points in the Akdeniz University Campus. In these measurements, the temperature difference values of 11 cover elements on 7 different floor covering types were measured, and the ambient air temperature, humidity and wind values were also determined. The temperature differences between the areas where the flooring elements are exposed to direct sun and the shadow effect of different plant and cover elements were determined using an infrared laser thermometer. These values were processed with Neural Designer software and possible temperature difference prediction values were created for 57.750 different alternatives with the help of artificial neural network model from 837 sets of data. Evaluation shows that the maximum temperature difference is 15.6°C at noon in the summer months in the red tartan flooring material and Callistemon viminalis cover material. While the artificial neural network model predicts that there will be a high 2-3° C temperature difference for the alternatives, it has made predictions for temperature differences between 0-10°C in winter and 0-16°C in summer months. Although the temperature differences that will occur in the noon hours are distributed over a wide range of values, it seems that the morning and evening forecasts are concentrated between 0-7°C values. Also, it has been determined that the wind and humidity in the environment are more important factors than the ambient temperature in terms of temperature differences.

scholarly journals Variability of Satellite Derived Phenological Parameters across Maize Producing Areas of South Africa

2018 ◽  
Vol 10 (9) ◽  
pp. 3033 ◽  
Author(s):  
Omolola Adisa ◽  
Joel Botai ◽  
Abubeker Hassen ◽  
Daniel Darkey ◽  
Abiodun Adeola ◽  
...  
Keyword(s):  
Climate Change ◽  
South Africa ◽  
Goodness Of Fit ◽  
Partial Least Square ◽  
Climatic Variables ◽  
Free State ◽  
Entire Study ◽  
North West ◽  
Kwazulu Natal ◽  
Entire Study Area

Changes in phenology can be used as a proxy to elucidate the short and long term trends in climate change and variability. Such phenological changes are driven by weather and climate as well as environmental and ecological factors. Climate change affects plant phenology largely during the vegetative and reproductive stages. The focus of this study was to investigate the changes in phenological parameters of maize as well as to assess their causal factors across the selected maize-producing Provinces (viz: North West, Free State, Mpumalanga and KwaZulu-Natal) of South Africa. For this purpose, five phenological parameters i.e., the length of season (LOS), start of season (SOS), end of season (EOS), position of peak value (POP), and position of trough value (POT) derived from the MODIS NDVI data (MOD13Q1) were analysed. In addition, climatic variables (Potential Evapotranspiration (PET), Precipitation (PRE), Maximum (TMX) and Minimum (TMN) Temperatures spanning from 2000 to 2015 were also analysed. Based on the results, the maize-producing Provinces considered exhibit a decreasing trend in NDVI values. The results further show that Mpumalanga and Free State Provinces have SOS and EOS in December and April respectively. In terms of the LOS, KwaZulu-Natal Province had the highest days (194), followed by Mpumalanga with 177 days, while North West and Free State Provinces had 149 and 148 days, respectively. Our results further demonstrate that the influences of climate variables on phenological parameters exhibit a strong space-time and common covariate dependence. For instance, TMN dominated in North West and Free State, PET and TMX are the main dominant factors in KwaZulu-Natal Province whereas PRE highly dominated in Mpumalanga. Furthermore, the result of the Partial Least Square Path Modeling (PLS-PM) analysis indicates that climatic variables predict about 46% of the variability of phenology indicators and about 63% of the variability of yield indicators for the entire study area. The goodness of fit index indicates that the model has a prediction power of 75% over the entire study area. This study contributes towards enhancing the knowledge of the dynamics in the phenological parameters and the results can assist farmers to make the necessary adjustment in order to have an optimal production and thereby enhance food security for both human and livestock.

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