This dissertation highlights the need for a formal methodology to be developed in order to unpack complicated supply chains and to publish information that explains how the farm value or farm to retail price spread of certain products can be calculated and how these results are to be analysed. It is for this reason that the study reviews and applies the methodology used for the calculation of price spreads and farm values. It applies the methodology to five food supply chains of maize, fresh milk, beef, poultry and sugar. The analysis of farm values and spread has already been developed in an international context but it has not of yet been applied in the South African context. It is therefore the aim of this dissertation to illustrate how this methodology can be applied here and how this can be done on a continuous basis. The main objectives of the study are: <ul> <li>To review and apply the methodology used for the calculation of price spreads and farm value, as well as to analyse trends of five agricultural commodities in the food sector.</li> <li>To understand not so much on what is behind the previous rise in food prices, but rather on why; when the farm or producer prices fall, do retail prices on certain goods not fall by the same margin? The question that needs to be asked is who or what is responsible for this? A detailed analysis of the supply chain of various products could prove invaluable in the process of understanding price movements.</li> <li>To investigate the degree of transparency of information in the South African food sector is investigated by looking at the market share that the various supermarket chains hold. Since competition and concentration of role players within this sector of the economy plays such a vital role in the determination of the market’s fairness, it is important that the size and the percentage of market share that the retailers hold in the market is researched and understood. A special section focuses on the market share that some retailers hold as a percentage share of the entire supermarket retail sector.</li> <li>To discuss the estimation of the specific cost incurred, at various levels,within the maize-to-maize meal and beef-to-beef products supply chains, in detail. This involves designing a framework for the continuous analysis of food prices and costs contained within these two supply chains and understanding the costs incurred by the different role players.</li> </ul> In applying the methodology to estimate farm value and farm to retail price spread it is determined some of the commodities such as beef, milk and sugar experienced a slight widening of the farm to retail price spread, while the opposite occurred with the price spread of maize meal and broiler meat. A widening farm to retail price spread shows that farmers’ share in terms of the retail price is declining and as a result their share of the final product has become less. Farmers in the beef, milk and sugar sectors experienced this while maize and chicken farmers experienced the opposite, in other words a narrowing spread and as a result they are earning more of the final product. In applying the various econometric tests in order to test for asymmetric price behaviour in the various supply chains it was found that in four of the five supply chains the transmission of increases in producer prices where not smoothly and timely transmitted to the retail price. The models that fared worst in the analyses were those of the sugar, beef, fresh milk and a part of the maize supply chain. The inabilities of the models to show any form of significance, even when tested economic theory is applied indicate that something is amiss within the supply chains. Asymmetric price transmissions, a lack of accurate data or unjust market behaviour by role players within the supply are some of the factors that could be responsible for this. The analysis in chapter 5 is based on these findings. A proposed framework for an in depth analysis of such a supply chain is documented there. The detailed analysis of costs and margins in the maize to maize meal and beef supply chains, have shown that there are many stages along the supply chain, where various costs and profits can have severe influences. In chapter 5 a detailed analysis has been done on this with the objective of developing a framework that can be applied to an industry. This chapter lends specific detail as to where the influences of such costs can be the greatest. The results point out that, of the five supply chains, only two of them, namely chicken and maize (from farm gate to miller), adhered to some form of economic theory, whereas the other three either suffered from insignificant/unrepresentative data or actual price transmission asymmetry. On the basis of this, the supply chains of both super maize meal and the five selected beef cuts were unpacked with the profit margin and the role player’s cost of formation at the different levels within the value chains. A conclusion can be made that parts of the maize supply chain (milldoor to retailer), the beef supply chain, the sugar supply chain and the dairy supply chain all suffer from asymmetric price transmissions or alternatively, a data discrepancy. This conclusion is drawn from the fact that the Error Correction Models ECMs for these specific industries failed most of the diagnostic tests and contained some insignificant variables. The diagnostic tests did not only test for misspecification but included a standard procedure, using the Jarque Bera test for normality, the ARCH LM test for heteroscedasticity, the White test for heteroscedasticity as well as the Breusch Godfrey test for serial correlation. The fact that the ECMs of these supply chains had these problems does give rise to a concern as to the transmission of prices within some of the supply chains within the South African food industry. The applied methodology used in unpacking of the supply chains, was applied with the aim of developing a framework that can be adapted and used for similar analyses in future. The aim of this methodology was solely on developing and applying a methodology to two of the five supply chains, partly based on the results in chapter 4 but also on the importance of the commodities in the South African food industry, and to illustrate, by using real data, how this framework can benefit future research. / Dissertation (MCom(Agric Economics))--University of Pretoria, 2006. / Agricultural Economics, Extension and Rural Development / unrestricted

<p>Aflatoxins, secondary metabolites produced by the molds <i>Aspergilllus flavus</i> and<i> A. parasiticus</i>, are estimated to affect upwards of 25% of the world’s global food supply. For Low and Middle-Income Countries like Kenya, a combination of trade, economic, and health challenges related to aflatoxin contamination present a serious threat to food and national security. One option for reducing aflatoxin risks in countries like Kenya is deploying small-scale, reprocessing technologies that degrade aflatoxin in contaminated food products. One potential technology for reprocessing is small-scale extrusion (60 pph) like the TechnoChem Mini-Extruder™.</p><p> First, to understand the extent of aflatoxin contamination in Kenyan maize, two field work trials were conducted in Uasin Gishu County, Kenya. Aflatoxin levels from each sample were analyzed and compared to a variety of agro-economic variables (e.g. farm size) using a stepwise multiple linear regression. Upon analysis, only 5% of maize samples collected during field work tested positive for unsafe levels of aflatoxin ( >10 ppb). Thus, the resulting regression model is highly biased towards predicting low aflatoxin levels. Such bias makes any inferences to predict high aflatoxin levels in maize largely inconclusive. The inherent heterogeneity of aflatoxin and the history of wide-spread contamination in Kenya further supports the conclusion that more studies are needed to understand the true extent of aflatoxin contamination in Uasin Gishu maize.</p><p> Second, to test the effectiveness of small-scale extrusion on aflatoxin degradation in maize, contaminated samples were processed at varying motor frequencies (15, 38, and 50 hz) and moisture contents (35, 40, 45 %wb). Moisture content is significant (p-value < 0.05) in aflatoxin degradation. Total aflatoxin degradation varied between 11 and 83% depending on processing conditions. Maximum degradation occurred at 40 %wb product moisture with a residence time of 265.1 s and an effective shear rate of 56.5 1/s. Thermal degradation is considered negligible due to low temperature increases. Consequently, all degradation is attributed to shear forces inside the extruder. Shear rates were approximated using the Harper model with moisture content and residence time being the most significant factors affecting shear effects on aflatoxin degradation. Although significant aflatoxin degradation occurred in the extruder, further studies are necessary to understand the role of processing parameters on aflatoxin degradation before small-scale extrusion can be confirmed as a viable reprocessing technology.</p>

With a growing world population, estimated to 9.6 billion in 2050, the world food demand is estimated to increase with 45-50 %. One way to meet the demand is to increase the areal yield from the agricultural sector, where rain-fed agriculture has the highest potential. 95 % of the agriculture in Sub-Saharan Africa is rain-fed and the same region is predicted to holds the largest share of poor people in 2015. Since 40-70 % of the rural households highly depend of on-farm sources, investments to increase the agriculture productivity target both the poverty alleviation in the region as well as the world’s food security. By a tripartite methodology, this study analyzed the use of small-scale rain water harvesting (RWH) ponds for supplemental irrigation (SI) of cereals to reduce the inter-annual variability and to increase the areal yield in semi-arid areas in Ethiopia. A physically based simulation model (CoupModel) considering the plant-soil-atmosphere system was used to study how a C4-plant responded to different irrigation scenarios with 30 years climate data (1980-2009) from six regions in Ethiopia. Moreover, two years field data with maize yield from Triple Green project’s experimental fields in Ethiopia was used to analyze the correlation between SI and yield. Finally, ten farmers that used RWH ponds for SI of cereals within Triple Green project were interviewed to find out their perception of the RWH and SI. The model results showed that irrigation almost eliminated the inter-annual variability and increased the areal yield for all the climates. SI was most efficiently used in areas with more than 900 mm precipitation/year were the two annual rain periods could be bridged to create a prolonged growth season (&gt;180 days). The mean annual irrigation water demand was estimated to 224 mm distributed over 7 irrigation events. The field results showed a moderate but significant 10 % increase of the areal yield with SI. None of the farmers wanted to use the RWH for SI of cereals, instead they wanted to use it to water their livestock, grow cash crop seedlings and fruit trees. If the future world food demand is to be targeted, the study suggests societal investments to build infrastructure to collect, store and distribute water for irrigation.

Thesis (M. Sc. Agriculture (Agricultural Economics)) -- University of Limpopo, 2021 / Small-scale farmers are the drivers of many countries in Africa and play an important role in livelihood creation among the poor in rural areas (DAFF, 2012). The efficient use of scarce resources in promoting agricultural production has encouraged a considerable amount of research in determining efficiency differentials of small-scale farmers (Chiona, 2011); especially those engaged in maize as a staple commodity in many parts of the world. The study examined the effect of access to tractor service on technical efficiency of small-scale maize farmers following the implementation of the Masibuyele Emasimini programme in the Mpumalanga province. The objectives of the study were to: (i) Compare and identify the socio-economic characteristics of small-scale maize farmers in the three selected districts of the study, (ii) analyse the socio-economic factors influencing small-scale maize farmers’ access to tractor service, and to (iii) measure technical efficiency of farmers who have access to tractor service. The data collection was carried out in three districts of the Mpumalanga province, that is, Ehlanzeni, Nkangala and Gert Sibande. Farmers producing maize were purposively selected for the study because maize is the most staple food produced in the province, especially on a small-scale level. To effectively cover the study area, a simple random technique was used for sampling with a semi-structured questionnaire administered to 101 farmers. The three districts are heterogeneous in technical aspects, and were therefore treated separately in terms of data collection, analysis and report of findings. The data were further analysed using descriptive statistics, the logistic regression and Cobb-Douglas production function model to address objective one, two and three mentioned above, respectively. The results of the logistic regression model indicated that out of the 9 (Nine) socio-economic variables included in the analysis, 6 (Six) of them (Farmer’s association, irrigation, farmer’s level of education, gender, ownership of land and household size) were found to be significant and influencing access to v tractor service by small-scale maize farmers. Technical efficiency levels revealed that farmers with access to tractor service were more technically efficient than those without access in all districts of the Mpumalanga province. For example, the average technical efficiency for small-scale farmers with access to tractor service in the Ehlanzeni district was 0.68; about 41% higher than those without access with an average technical efficiency of about 0.27. The Cobb-Douglas results on the other hand, revealed that farmers in the Mpumalanga province are experiencing technical inefficiency in maize production due to decreasing returns to scale. Access to tractor service was also negatively insignificant towards maize production in both the Ehlanzeni and Nkangala district, and was found to have a positive but insignificant effect in Gert Sibande. Policy implications are that to improve the efficiency of tractor service (rendered by the Masibuyele Emasimini programme) towards maize production; government should focus on significant factors influencing the access of the following by small-scale maize farmers and the factors are machinery, irrigation, gender, and ownership of land, farmer’s level of education, farmer’s association, and household and land size per district. / National Research Foundation (NRF)

Thesis (M. A. Agriculture (Agricultural Economics)) -- University of Limpopo, 2021 / Maize seeds differ according to varieties.The traditional maize varieties(also referred to as (Landraces)are maize varieties that have been cultivated and subjected to selection by farmers for generations.They retain a distinct identity and lack formal crop improvement. Improved maize varieties,on the other hand,are bred with characteristics such as drought and disease tolerance. This research was conducted to determine the attributes preferred by farmers when making a maize varietal choice.To be specific, the study aimed to achieve the following objectives:(i) Identify and describe socio economic characteristics of smallholder maize farmers’ in Mogalakwena Municipality; (ii) Analyse socioeconomic characteristics of smallholder maize farmers in Mogalakwena Municipality; (iii) Identify different maize varieties grown by smallholder farmers in Mogalakwena Municipality,and (iv) determine and analyse factors influencing farmers’choice ofa maize variety. Descriptive statistics and the Multinomial Logistic Regression Model were used for data analysis.The results of the study revealed that 64% of the respondents had formal education.This meant that they have the capability to grasp more information, if provided with trainings. It was found that 75% of the farmers did not have access to extension service which is supposed to play a significant role in agricultural information dissemination.The most grown maize variety was land race varieties which constituted 59.5%. This percentage was said to be resultant from limited access to the seed market. Infact,80% of the farmers had to travel an average of 42 kilomteres to access the market which also had a limited number of varieties.The Multinomial Logistic Regression Model revealed that only 5 variables (Educational level, farm size, yield, extension contact and knowledge of maize varieties )were significantat1%,5%,1%,1% and1%, respectively.The majority of farmers were old people with little access to extension service and an inadequate farming knowledge which lead to a highper centage of farmers continuing to grow landrace varieties. Based on the findings, this study recommend further research on attributes that influence farmers varietal choice and Government intervention in provision for resources and development of existing and new infracstrcture to encourage extension service delivery. Keywords:Landrace,improvemaizevariety,smallholderfarmer

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Genetically modified food is a reality for many modern-day consumers around the world. With the introduction of GM food to the food market, consumers were faced with a number of new products and also familiar products containing new ingredients. The introduction of genetically modified food products to food markets around the world, led to a lot of controversy. In many cases consumer attitudes and perceptions of GM food products were revealed as fears, concern for, and avoidance of the new technology. Consumer attitudes, perceptions and acceptance towards the use of genetically modified foods or -food ingredients are currently highly relevant issues for role-player such as researchers, government, food companies, biotechnology companies, retailers and farmers all over the world.The importance of genetically modified food products in South Africa is increasing, even though the debate surrounding genetically modified food products lags behind many other (often more developed) parts of the world. Genetically modified white maize is among the agricultural crops approved for commercial production in South Africa. The production of genetically modified white maize in South Africa increased dramatically from its introduction in the 2001/2002-production season. White maize, especially in the form of super- and special maize meal, is an extremely important staple food source for consumers of all age groups in South Africa. The implication of the significant increase in the cultivation of genetically modified white maize is that the product is entering the South African food market at an increasing rate. In reality South African consumers are increasingly exposed to food products containing genetically modified white maize. This goes hand in hand with increasing consumer awareness regarding genetically modified food issues.The general objective of the dissertation is to develop an understanding of the perceptions, attitudes, acceptance and knowledge of South African urban consumers, regarding GM white maize as a staple food product within South Africa. The specific objectives are to identify trade-offs between selected attributes of maize meal and to determine the relative importance of selected GM characteristics within the trade-offs by means of a conjoint experiment, to construct market segments based on the outcomes of a conjoint experiment, to determine the effect of consumer perceptions on the sensory experience of white maize porridge and to determine the knowledge, perceptions and GM food acceptance of the different market segments.Quota sampling was applied to obtain a random sample of 80 urban white-maize consumers, based on the LSM (Living Standard Measures) market segmentation tool. The respondents participated in sensory evaluation of maize porridge. This was followed by a conjoint experiment designed around three selected product characteristic variables describing a 2.5kg packet of super white maize meal: “Brand variable”, “Genetic modification variable” and “Price variable”. Market segmentation was done through Ward’s hierarchical cluster analysis based on the conjoint results. The final phase of the experimental analysis involved the profiling of the identified clusters based on demographic variables, respondents’ knowledge of genetic modification and respondents perceptions, attitudes and acceptance towards genetically modified food.The limited sample size (80 respondents) could influence the ability of the results to reflect on the population of urban white maize consumers given the presence of GM food in the market. However, the experimental results should be seen in view of general trends in South Africa and available anecdotal evidence supporting the results of the study. The results of this study could go a long way in representing the results of a more representative sample of urban white maize consumers given the presence of GM food in the market.The cluster analysis revealed that the sample of urban, white maize consumers could be grouped into three meaningful and distinct market segments, based on their preferences for branded- versus non-branded white-grained maize meal, as well as their preferences for non-GM white maize meal versus GM white maize meal with various types of genetic manipulations. The “Anti-GM” segment (35% of the sample) is particularly negative towards GM food irrelevant of the type of genetic modification applied to the food. The “Pro-GM farmer sympathetic” segment (20% of the sample) is positive towards genetically modified food in cases where the farmer receives the benefit of the genetic modification. The “Pro-GM” segment (45% of the sample) is generally positive towards GM food, but especially when the consumer receives the benefit of the genetic modification. The results indicated that the differences among the cluster groups were more prominent than the differences among the LSM groups. Thus, the clusters were most effective to distinguish between sub-groups in the experimental sample.The results of the respondents’ knowledge of genetic modification indicated that there is some degree of confusion among respondents regarding the meaning of genetic modification, as well as discrepancies between perceived and actual knowledge levels of genetic modification. In general, the respondents’ knowledge of GM food is relatively low.A strong positive correlation was observed between the sample respondents’ exposure to GM food related terms and their perceived understanding of these issues, implying that the exposure caused the respondents to learn more about GM food related terms. The balanced GM food information presented to the respondents during the experimental procedure probably influenced their knowledge levels and opinions about GM food as the experiment evolved. Despite these observations the research methodology was still deemed as appropriate. The GM food knowledge gained by the respondents during the experiment could be seen as a simulation of situations where they could receive GM food information from external sources such as television, radio, magazines or newspapers. The cluster profiling revealed that urban white-grain maize consumers’ perceptions and attitudes towards GM food were the strongest distinguishing factors between the various market segments, especially the preferences of the various cluster groups for non-GM maize or maize that was genetically modified for consumer benefit or maize that was genetically modified for producer benefit. Demographic factors and GM knowledge aspects did not really contribute towards distinguishing between the clusters.The dissertation determined that there is a need for a better understanding of consumer perceptions, attitudes towards and acceptance of GM food products, which could enable producers and scientists to engage in more consumer driven product development and marketing activities. Consumer acceptance is the most critical factor for the success of GM food products within the South African food market place and could shape the future of the agricultural modern biotechnology industry and the agricultural sector in South Africa. / Dissertation (MSc (Agric) Agricultural Economics)--University of Pretoria, 2004. / Agricultural Economics, Extension and Rural Development / unrestricted

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No description available.

<div>Increasing the capability of maize hybrids to use nitrogen (N) more efficiently is a common goal that contributes to reducing farmer costs and limiting negative environmental impacts. However, development of such hybrids is costly and arduous due to the repeated need for laborious field and laboratory measurements of whole-plant biomass and N uptake in large early-stage breeding programs. This research evaluated alternative in-season methodologies, including field-based physiological measurements and hyperspectral remote imagery, as surrogate or predictive measures of important end-of-season N efficiency parameters. </div><div><br></div><div>Differences in the genetic potential of 285 hybrids (derived from test crosses to a single tester) with respect to N Internal Efficiency (NIE, grain yield per unit of accumulated plant N) were investigated at two Indiana locations in 2015. The hybrids (representing both early and late maturity groups) were grown at one low N rate and a single plant density. Germplasm sources included USDA, Dow AgroSciences, and “control” checks. Various secondary traits (plant height, stalk diameter, LAI, green leaf counts, and SPAD measurements) were evaluated for their potential role as surrogate measurements for N metrics at maturity (R6) such as plant N content or NIE. Four band (RGB, NIR) multispectral airborne remote sensing imagery at R1 and R3/R4 was also collected. The key findings were: 1) identification of the 10 highest and 10 lowest ranked hybrids for each maturity group in both grain yield and NIE categories, 2) the discovery of 5 top performing hybrids which had both high NIE and high yield, 3) strong correlations of leaf SPAD (at R1 and R2/R3) to grain yield or plant N at R6, 4) none of the surrogate measurements were significantly correlated to NIE, and 5) vegetation indices (NDVI and SR) from the remote imaging were not predictive of hybrid differences in yields or whole plant N content at R6. From these results we concluded genetic potential exists among current maize germplasm for NIE breeding improvements, but that more in-depth investigations were needed into other surrogate measures of relevant N efficiency traits in hybrid comparisons. </div><div><br></div><div>Next, hyperspectral imaging was investigated as a potential predictor of agronomic parameters related to N Use Efficiency (NUE, understood here as grain yield relative to applied N fertilizer input). Hyperspectral vegetation indices (HSI) were used to extract the image features for predicting N concentration (whole plant N at R6, %N), Nitrogen Conversion Efficiency (biomass per unit of plant N at R6, NCE), and NIE. Images were collected at V16/V18 and R1/R2 by manned aircraft and unmanned aerial vehicles (UAVs) at 50 cm spatial resolution. Nine maize hybrids, or a subset, were grown under N stress conditions with two plant densities over three site years in either 2014 or 2017. Forty HSI-based mixed models were analyzed for their predictability relative to the ground reference values of %N, NCE, and NIE. Two biomass and structural indices (HBSI1_682,855 and HBSI2_682,910 at R1) were predictive of NCE values and capably differentiated the highest and lowest ranked NCE hybrids. The highest prediction accuracy for NIE was achieved by two biochemical indices (HBCI_8515,550 at both V16 and R1, and HBCI9_490,550 at R1). These also allowed for hybrid differentiation of the highest and lowest ranked NIE hybrids. From these results, we concluded that accurate end-season prediction of hybrid differences in NCE and NIE was possible from mid-season hyperspectral imaging (yet not for %N). Furthermore, the quality of the predictions was dependent on image timing, actual HSI, and the targeted N parameter at maturity. </div><div><br></div><div>One benefit to hyperspectral imaging is that it can provide greater discrimination of imaged materials through its narrow, contiguous bands. However, the data are highly correlated in some ranges. This problem was mitigated through the use of partial least squares regressions (PLSR) to predict the three N parameters from the field data described previously. Data were divided into train and test; then ten-fold cross validation was performed. The twelve models evaluated included those with 89 image bands of 5 nm widths and a selected, reduced set of hyperspectral bands as predictors. The key findings were that PLSR models based on V16 and R1 images provided accurate predictions for final whole-plant %N (R² = 0.73, CV = 11%; R² = 0.68, CV = 10%) and NCE at R6 (R² = 0.71, CV = 10%; R² = 0.73, CV = 12%) but not NIE. Additionally, accurate hybrid differentiation was possible with the combination of hyperspectral imaging and PLSR at R1 to predict %N and NCE values at R6 stage. </div><div><br></div><div>The PLSR and HSI results from this research showed that hyperspectral imaging has the potential for prediction of NUE parameters through non-destructive remote sensing at a broad scale. Additional validation is needed through the study of other genotypes and locations. Nevertheless, practical application of these methods through the integration into early stage breeding programs may allow the early identification of the highest and lowest ranked hybrids providing data-driven decisions for selecting genotypes. Implementation of improved imaging approaches may drive the increased development of maize hybrids with superior NUE. </div><div><br></div>