scholarly journals Rainy season characteristics of the Free State Province of South Africa with reference to rain-fed maize production

Water SA ◽  
2012 ◽  
Vol 38 (5) ◽  
Author(s):  
ME Moeletsi ◽  
S Walker
Keyword(s):  
South Africa ◽  
Rainy Season ◽  
Free State ◽  
Maize Production ◽  
Free State Province

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.

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.

Development of an agroclimatological risk tool for dryland maize production in the Free State Province of South Africa

2013 ◽  
Vol 95 ◽  
pp. 108-121 ◽  
Author(s):  
Mokhele Edmond Moeletsi ◽  
Seboko Gerard Moopisa ◽  
Sue Walker ◽  
Mitsuru Tsubo
Keyword(s):  
South Africa ◽  
Free State ◽  
Maize Production ◽  
Free State Province

scholarly journals Evaluation of an inverse distance weighting method for patching daily and dekadal rainfall over the Free State Province, South Africa

Water SA ◽  
2016 ◽  
Vol 42 (3) ◽  
pp. 466 ◽  
Author(s):  
Mokhele Edmond Moeletsi ◽  
Zakhele Phumlani Shabalala ◽  
Gert De Nysschen ◽  
Sue Walker
Keyword(s):  
South Africa ◽  
Inverse Distance Weighting ◽  
Free State ◽  
Weighting Method ◽  
Free State Province ◽  
Distance Weighting ◽  
Inverse Distance
Sign in / Sign up

Export Citation Format

Share Document