Maize and Wheat Production Trends in South Africa in a Deregulated Environment / Mielie en Koring Produksietendense in 'N Ge-Dereguleerde Markomgewing in Suid-Afrika

Breitenbach, Marthinus C.; Fényes, T I

doi:10.1080/03031853.2000.9523578

Cited by 6 publications

(5 citation statements)

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“…Wheat (Triticum aestivum L.) is an important crop in many parts of the world including South Africa, where it is the second largest component of the staple diet after maize. 1,2 Consequently, it is crucial to predict wheat yields as global wheat production is expected to decrease under conventional management as a result of climate variability. 3,4 Additionally, a challenge exists to feed a growing human population while avoiding environmental problems such as deforestation and land degradation.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting winter wheat yields using MODIS NDVI data for the Central Free State region

Mashaba¹,

Chirima²,

Botai³

et al. 2017

S. Afr. J. Sci.

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Consumption of wheat is widespread and increasing in South Africa. However, global wheat production is projected to decline. Wheat yield forecasting is therefore crucial for ensuring food security for the country. The objective of this study was to investigate whether the anthesis wheat growth stage is suitable for forecasting dryland wheat yields in the Central Free State region using satellite imagery and linear predictive modelling. A period of 10 years of Normalized Difference Vegetation Index data smoothed with a Savitzky–Golay filter and 10 years of wheat yield data were used for model calibration. Diagnostic plots and statistical procedures were used for model validation and assessment of model adequacy. The period 30 days before harvest during the anthesis stage was established to be the best period during which to use the linear regression model. The calibrated model had a coefficient of determination of 0.73, a p-value of 0.00161 and a root mean squared error of 0.41 tons/ha. Residual plots confirmed that a linear model had a good fit for the data. The quantile-quantile plot provided evidence that the residuals were normally distributed, which means that assumptions of linear regression were fulfilled and the model can be used as a forecasting tool. Model validation showed high levels of accuracy. The evidence indicates that use of Moderate Resolution Imaging Spectroradiometer data during the anthesis growth stage is a reliable, cost-effective and potentially time-saving alternative to ground-based surveys when forecasting dryland wheat yields in the Central Free State.

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Section: Introductionmentioning

confidence: 99%

“…soybean and canola). 1 Furthermore, timeous generation of yield projections will support timeous decisions concerning either importation or exportation of wheat. Therefore, the overall objective of this study was to develop a yield model for dryland winter wheat for the Central Free State region using MODIS data.…”

Section: Introductionmentioning

confidence: 99%

Forecasting winter wheat yields using MODIS NDVI data for the Central Free State region

Mashaba¹,

Chirima²,

Botai³

et al. 2017

S. Afr. J. Sci.

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“…Most of the wheat production is channelled towards human consumption; some uses include beverage, starch and straw production, with occasional uses in animal feed and bio-fuel production [ 12 ]. Wheat production has seen a steady decline by hectarage in the last three decades and has continued to fall since [ 11 , 13 ]. Among many factors, poor soil fertility and high pH, climate change, poor cultivar choices and environmental stressors such as disease breakouts, including yellow wheat stripe rust, have contributed to the observed losses in production rates, quality and yields [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Metabolite profiling of susceptible and resistant wheat (Triticum aestivum) cultivars responding to Puccinia striiformis f. sp. tritici infection

et al. 2023

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Background Puccinia striiformis f. sp. tritici (Pst) is an economically devasting disease that is prominent in cereal crops such as wheat (Triticum aestivum). The fungal pathogen can cause approximately 30–70% losses in crop productivity and yields. Pst has become difficult to manage due to its ease of transmission through wind dispersal over long distances, and intercontinental dispersal has been previously reported. The ease of transmission has resulted in further destruction because of new and more virulent strains infecting crops previously resistant to a different strain. Results In this study, a liquid chromatography-mass spectrometry-based untargeted metabolomics approach, in combination with multivariate data analytical tools, was used to elucidate the mechanistic nature of the defence systems of a Pst-resistant and a susceptible wheat cultivar infected with P. striiformis. We also investigated the time-dependant metabolic reconfiguration of infected plants over a four-week period. The untargeted metabolomic analysis revealed a time-course metabolic reprogramming involving phenylpropanoids (majority flavonoids), amino acids, lipids, benzoic acids, TCA cycle intermediates and benzoxazinoids responding to Pst infection. Interestingly, the results do not show a linear course for the decrease and increase (up-/down-regulation) of said classes of metabolites, but rather the up- or down-regulation of specific metabolites in response to the pathogen infection. The resistant Koonap cultivar had an abundance of phenolic compounds such as rutin, isoorintin-7-O-glucoside and luteolin-6-C-hexoside-O-hexoside. These compounds showed a decrease over time in control Koonap plants compared to an increase in Pst-infected plants. These metabolites were down-regulated in the susceptible Gariep cultivar, which could serve as biomarkers for plant responses to biotic stress and resistance against Pst. Conclusions Overall, an LC-MS-based metabolomics approach allowed for the metabolic profiling and analysis of the impact of plant-pathogen interactions on the overall plant metabolome and provided a real-time snapshot of the differential significant metabolic perturbations occurring in wheat plants responding to the Pst pathogen. The Pst-resistant Koonap cultivar showed a rapid accumulation of defence metabolites in response to pathogen infection compared to the susceptible Gariep cultivar. These findings provide insight into the mechanistic biochemical nature of plant-microbe interactions and the prospects of metabolic engineering for improved plant tolerance and resistance to biotic stresses.

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“…Maize is the most important crop in South Africa [1]. Grain production in South Africa consists of maize, followed by wheat and sorghum in order of importance [2,3]. Maize accounts approximately for 83% of the national grain production, wheat accounts for 14,7%, while sorghum accounts for 1,3% [4].…”

Section: Introductionmentioning

confidence: 99%

“…Van Zyl & Nel [15] evaluated the macro-economic role that the maize industry played in the South African economy during the 1970s and 1980s, but did not delve into details of the structural trends within the industry. Breitenbach & Fenyes [2] quantified production trends within the commercial maize and wheat industries during the 1985 to 1999 period, including a breakdown of maize production into its output and planted area components for South Africa. A study by Greyling & Pardey [20] highlighted key insights about the South African maize production in terms of commercial and smallholder share contribution.…”

Section: Introductionmentioning

confidence: 99%

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

Diko

Wang

2020

AJAAR

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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.

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Maize and Wheat Production Trends in South Africa in a Deregulated Environment / Mielie en Koring Produksietendense in 'N Ge-Dereguleerde Markomgewing in Suid-Afrika

Cited by 6 publications

References 1 publication

Forecasting winter wheat yields using MODIS NDVI data for the Central Free State region

Forecasting winter wheat yields using MODIS NDVI data for the Central Free State region

Metabolite profiling of susceptible and resistant wheat (Triticum aestivum) cultivars responding to Puccinia striiformis f. sp. tritici infection

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

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