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 Drought Characterization and Trend Detection Using the Reconnaissance Drought Index for Setsoto Municipality of the Free State Province of South Africa and the Impact on Maize Yield

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 2993 ◽  
Author(s):  
Hadisu B. Abubakar ◽  
Solomon W. Newete ◽  
Mary C. Scholes
Keyword(s):  
South Africa ◽  
Drought Index ◽  
Rate Of Change ◽  
Free State ◽  
Trend Detection ◽  
Maximum Temperature ◽  
Large Variability ◽  
Reconnaissance Drought Index ◽  
Free State Province ◽  
The Impact

The reconnaissance drought index (RDI) for the Setsoto municipality of the Free State province in South Africa was calculated for the period between 1985 and 2019 at 3 month (October–December), 6 month (October–March), and 12 month (October–September) intervals. Rainfall and minimum and maximum temperature data from four weather stations (Clocolan, Ficksburg, Marquard, and Senekal) were used for this study to characterize drought using “DrinC” software together with the Mann Kendall test with Sen’s slope to detect drought trends and the rate of change. Extreme, severe, and moderate droughts were recorded for all the stations, with RDIs ranging from −3.6 to −1.0 at different temporal scales. The years 1991, 1994, 2006, 2011, and 2015 were highlighted using the RDI 3, 6, and 12 month calculations. Results showed that the yield decreased either in the year of the drought or in the subsequent year, due to the exact timing of the low-rainfall events in the season and soil moisture storage. Yields were low, on average 2.5 tons ha−1 year−1, with high variability. Optimal growing conditions are essential in the early part of the season, October–December, for maximizing yield; if droughts are experienced at this time then the yield is more greatly impacted than if the droughts occur later in the season. Spatial analysis shows a large variability of drought patterns across the Municipality, over the years, with the 3 month RDI values giving a more detailed picture of this variability than the 6 and 12 month RDI values.

scholarly journals The Study of Frost Occurrence in Free State Province of South Africa

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Mokhele Edmond Moeletsi ◽  
Mphethe Tongwane ◽  
Mitsuru Tsubo
Keyword(s):  
South Africa ◽  
Early Onset ◽  
Late Onset ◽  
Kendall Test ◽  
Free State ◽  
Free State Province ◽  
Growing Period ◽  
Frost Free Period ◽  
Light Medium

The study investigated the cessation, onset, and duration of light, medium, and heavy frost in Free State province of South Africa using minimum temperatures from 1960 to 2015. Trends in the frost indices were assessed using the Man-Kendall test. Onset of frost varied spatially with earlier onset over the northern, eastern, and southeastern parts. Areas of early onset also experience late cessation of frost resulting in shorter growing period of less than 240 days. The western parts have longer growing period exceeding 240 days due to earlier cessation of frost and relatively late onset of frost. Trends for the frost-free period (growing period) show contrasting negative and positive trends with isolated significant trends.

scholarly journals Effects of Cropland Expansion on Temperature Extremes in Western India from 1982 to 2015

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 489
Author(s):  
Jinxiu Liu ◽  
Weihao Shen ◽  
Yaqian He
Keyword(s):  
Land Use ◽  
Heat Flux ◽  
Land Cover ◽  
Minimum Temperature ◽  
Negative Impact ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum ◽  
Temperature Extremes ◽  
Western India

India has experienced extensive land cover and land use change (LCLUC). However, there is still limited empirical research regarding the impact of LCLUC on climate extremes in India. Here, we applied statistical methods to assess how cropland expansion has influenced temperature extremes in India from 1982 to 2015 using a new land cover and land use dataset and ECMWF Reanalysis V5 (ERA5) climate data. Our results show that during the last 34 years, croplands in western India increased by ~33.7 percentage points. This cropland expansion shows a significantly negative impact on the maxima of daily maximum temperature (TXx), while its impacts on the maxima of daily minimum temperature and the minima of daily maximum and minimum temperature are limited. It is estimated that if cropland expansion had not taken place in western India over the 1982 to 2015 period, TXx would likely have increased by 0.74 (±0.64) °C. The negative impact of croplands on reducing the TXx extreme is likely due to evaporative cooling from intensified evapotranspiration associated with croplands, resulting in increased latent heat flux and decreased sensible heat flux. This study underscores the important influences of cropland expansion on temperature extremes and can be applicable to other geographic regions experiencing LCLUC.

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