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.

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.

A taxonomic study of the South African terricolous weevil genus Pentatrachyphloeus Voss (Coleoptera: Curculionidae: Entiminae: Trachyphloeini)

Zootaxa ◽  
2019 ◽  
Vol 4574 (1) ◽  
pp. 1
Author(s):  
ROMAN BOROVEC ◽  
JIŘÍ SKUHROVEC
Keyword(s):  
South Africa ◽  
South African ◽  
Host Plants ◽  
Free State ◽  
Taxonomic Study ◽  
Immature Stages ◽  
Eastern Cape ◽  
North West ◽  
Ecological Information ◽  
Kwazulu Natal

The genus Pentatrachyphloeus Voss, 1974, with two known species, is redefined and compared with related genera. An additional thirty seven new species are described here: P. andersoni sp. nov. (South Africa, Mpumalanga); P. baumi sp. nov. (South Africa, Gauteng); P. brevithorax sp. nov. (South Africa, KwaZulu-Natal); P. bufo sp. nov. (South Africa, Mpumalanga); P. endroedyi sp. nov. (South Africa, Mpumalanga); P. exiguus sp. nov. (South Africa, Mpumalanga); P. frici sp. nov. (South Africa, Limpopo); P. grobbelaarae sp. nov. (South Africa, KwaZulu-Natal); P. hanzelkai sp. nov. (South Africa, KwaZulu-Natal); P. holubi sp. nov. (South Africa, Mpumalanga); P. howdenae sp. nov. (South Africa, Mpumalanga); P. hystrix sp. nov. (South Africa, Mpumalanga); P. insignicornis sp. nov. (South Africa, KwaZulu-Natal); P. kalalovae sp. nov. (South Africa, Gauteng); P. kuscheli sp. nov. (South Africa, KwaZulu-Natal); P. laevis sp. nov. (South Africa, Mpumalanga); P. lajumensis sp. nov. (South Africa, Limpopo); P. leleupi sp. nov. (Zimbabwe, Manica); P. lesothoensis sp. nov. (Lesotho, Qacha’s Nek); P. machulkai sp. nov. (South Africa, Free State); P. marshalli sp. nov. (South Africa, KwaZulu-Natal); P. muellerae sp. nov. (South Africa, Mpumalanga); P. musili sp. nov. (South Africa, Limpopo); P. ntinini sp. nov. (South Africa, KwaZulu-Natal); P. oberprieleri sp. nov. (South Africa, Gauteng, North West); P. pavlicai sp. nov. (South Africa, Free State); P. rudyardi sp. nov. (South Africa, Limpopo); P. schoemani sp. nov. (South Africa, Limpopo); P. soutpansbergensis sp. nov. (South Africa, Limpopo); P. spinimanus sp. nov. (South Africa, Mpumalanga); P. stingli sp. nov. (South Africa, Limpopo); P. tenuicollis sp. nov. (South Africa, Mpumalanga); P. tuberculatus sp. nov. (South Africa, Mpumalanga); P. vavrai sp. nov. (South Africa, Eastern Cape); P. vossi sp. nov. (South Africa, Mpumalanga); P. vrazi sp. nov. (South Africa, Limpopo) and P. zikmundi sp. nov. (South Africa, Free State). All of the species are keyed and illustrated; ecological information is presented only where available. All species seem to be very localised, being known only from one or only a very limited number of localities. Immature stages or host plants are not known for any of the species. The species are distributed as follows: South Africa: Mpumalanga (13), Limpopo (8), KwaZulu-Natal (7), Free State (3), Gauteng (3), Eastern Cape (3), North West (1); Lesotho: Qacha’s Nek (1) and Zimbabwe: Manica (1). 

scholarly journals Impact of Climate Change on Maize Yield

2020 ◽  
Vol 58 (4) ◽  
Author(s):  
Clarietta Chagwiza ◽  
Phrasia Mapfumo ◽  
Michael Antwi
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Climate Change Impact ◽  
Pearson Correlation ◽  
Maize Yield ◽  
Free State ◽  
North West ◽  
Kwazulu Natal ◽  
North West Province ◽  
Change Impact

Climate change has increased temperature, caused drought in places like North West Province, and reduced crop yield. This study investigated the climate change impact (rainfall) on maize yield (1987 -2017). The objectives were to determine the climate change impact on maize yield for Kwazulu-Natal, North West, and Free State Provinces of South Africa, assess the difference in climate change impact on maize yield between the three provinces. Rainfall and maize data were collected from WeatherSA and DAFF, respectively. A Pearson Correlation Analysis revealed a weak negative correlation between rainfall and maize for KwaZulu-Natal and Free State Provinces. However, for North West Province there was a weak positive correlation between maize yield and rainfall. Rainfall determines yield, if excessive, it becomes detrimental to crop yield. Climate change affected negatively on maize yield, rainfall above maize requirement was not beneficial to crop yield and drought reduced yield too. ANOVA results revealed that the group mean yield between the Provinces was different, with KwaZulu-Natal having the highest mean yield. The climate change impact on maize varied between provinces, KwaZulu-Natal Province was least affected, however, North West Province was the most negatively impacted with drought events leading to reduced maize yield.

scholarly journals A RISK PERCEPTION MODEL OF CLIMATE CHANGE FOR UNIVERSITY STUDENTS

2015 ◽  
Vol 14 (3) ◽  
pp. 339-350
Author(s):  
Shyang-Chyuan Fang ◽  
Tai-Yi Yu
Keyword(s):  
Climate Change ◽  
Risk Perception ◽  
University Students ◽  
Personality Traits ◽  
Latent Variables ◽  
Goodness Of Fit ◽  
Environmental Attitudes ◽  
Environmental Behavior ◽  
Partial Least Square ◽  
Environmental Value

This study establishes a behavioral model for university students by utilizing the theories of planned behavior and value-belief-norm, and proposes key latent variables for risk perception toward climate change to establish a structural equation model. Partial least squares analyses and three indicators are utilized to test the reliability, validity, and goodness-of-fit of the model. This study establishes a mixed model with formative and reflective indicators, and assesses both environmental concern and personality traits as formative indicators. Using standardized path coefficients, eight out of 10 paths demonstrate statistical significance, indicating that environmental value and environmental attitudes influence environmental behavior. Three of the five included personality traits (e.g., agreeableness, extraversion, and openness) demonstrate a positive correlation with environmental behavior and environmental attributes. Individuals’ risk perception positively influences their environmental value, environmental attitudes, and environmental behavior with respect to climate change. Keywords: climate change, environmental behavior, partial least square, personality trait.

scholarly journals I.—On the Diamond Fields of South Africa

1871 ◽  
Vol 8 (80) ◽  
pp. 50-60 ◽  
Author(s):  
T. Rupert Jones
Keyword(s):  
South Africa ◽  
Free State ◽  
The South ◽  
South Side ◽  
North West ◽  
South West ◽  
Vaal River

Diamond Region.—The diamond-bearing region in South Africa, as at present known, is chiefly within the valley ofthe Vaal River and some of its tributaries (as the Modder and the Vet); but it is known also to extend down the Orange (Gariep) Valley for a few miles after the junction of its two great branches, the Ky Gariep (Vaal) and the Nu Gariep (CradockRiver). Bloemhof on the Vaal, two hours (12 miles) south-west of Potscherfstroom (Trans-vaal), is the reported locality ofthe most northern diamond-find. Below, for a distance of 370 miles, the plain has yielded diamonds, at several places, on both sides of the river, at Hebron, Klipdrift (near Pniel), Zitzikammsi, Vogelstmis Pan, Sitlacomie's Village, Sikoneli's Village, Nicholson's Farm, Kalk Farm (near Litkatlong), etc.; and on the south side of tie Orange River, they have been found some miles north-west of Hopetown, at Probeerfontein, Roodekop, David's Pan, etc. Diamondsare also said to have been found a few miles east of Fauresmith, on a branch of the Modder, about 100 miles south by east of Litkatlong; also a few miles south of Winburg (also in the Orange River Free State), in the upper drainage of the Vet River, about 80 miles from the Vaal.

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.

Sign in / Sign up

Export Citation Format

Share Document