Hotspots of vulnerability and disruption in food value chains during COVID-19 in South Africa: industry- and firm-level “pivoting” in response

We use a primary data set from a survey of medium and large firms and farms in the beef, citrus, and maize value chains in South Africa during March-June 2020, the early and late phases of the initial COVID-19 lockdowns. We have five main findings. (1) The initial lockdown regulations declared as “essential” the product (vertical) value chains but left as “inessential” the important “lateral” value chains delivering labour, materials, and logistics to the segments of the vertical value chains. This hurt the three vertical value chains as constraints in the laterals choked key segments of the verticals. (2) Vulnerability of the whole value chain emanated from vulnerability to shocks of critical “hotspot” linchpin segments (such as livestock auctions) or infrastructure (such as at ports). (3) Collective, industry-level “pivoting” was crucial both to organize the private sector response and to interact with government to course-correct on COVID-19 policies. (4) Responses to pre-COVID-19 challenges (such as drought and international phytosanitary rule changes) had prepared the beef and citrus value chain actors to respond collectively to the pandemic challenges. (5) Individual firm- and segment-level “pivoting” was also crucial for resilience, such as cattle auctions going on-line with the help of e-commerce firms.http://www.tandfonline.com/loi/ragr202023-06-24hj2023Agricultural Economics, Extension and Rural Developmen

Phylodynamic Reconstruction Reveals Norovirus GII.4 Epidemic Expansions and their Molecular Determinants

Noroviruses are the most common cause of viral gastroenteritis. An increase in the number of globally reported norovirus outbreaks was seen the past decade, especially for outbreaks caused by successive genogroup II genotype 4 (GII.4) variants. Whether this observed increase was due to an upswing in the number of infections, or to a surveillance artifact caused by heightened awareness and concomitant improved reporting, remained unclear. Therefore, we set out to study the population structure and changes thereof of GII.4 strains detected through systematic outbreak surveillance since the early 1990s. We collected 1383 partial polymerase and 194 full capsid GII.4 sequences. A Bayesian MCMC coalescent analysis revealed an increase in the number of GII.4 infections during the last decade. The GII.4 strains included in our analyses evolved at a rate of 4.3–9.0×10−3 mutations per site per year, and share a most recent common ancestor in the early 1980s. Determinants of adaptation in the capsid protein were studied using different maximum likelihood approaches to identify sites subject to diversifying or directional selection and sites that co-evolved. While a number of the computationally determined adaptively evolving sites were on the surface of the capsid and possible subject to immune selection, we also detected sites that were subject to constrained or compensatory evolution due to secondary RNA structures, relevant in virus-replication. We highlight codons that may prove useful in identifying emerging novel variants, and, using these, indicate that the novel 2008 variant is more likely to cause a future epidemic than the 2007 variant. While norovirus infections are generally mild and self-limiting, more severe outcomes of infection frequently occur in elderly and immunocompromized people, and no treatment is available. The observed pattern of continually emerging novel variants of GII.4, causing elevated numbers of infections, is therefore a cause for concern

A spatial variant of the Gaussian mixture of regressions model

In this study the nite mixture of multivariate Gaussian distributions is discussed in detail including the derivation of maximum likelihood estimators, a discussion on identi ability of mixture components as well as a discussion on the singularities typically occurring during the estimation process. Examples demonstrate the application of the nite mixture of univariate and bivariate Gaussian distributions. The nite mixture of multivariate Gaussian regressions is discussed including the derivation of maximum likelihood estimators. An example is used to demonstrate the application of the mixture of regressions model. Two methods of calculating the coe cient of determination for measuring model performance are introduced. The application of nite mixtures of Gaussian distributions and regressions to image segmentation problems is examined. The traditional nite mixture models however, have a shortcoming in that commonality of location of observations (pixels) is not taken into account when clustering the data. In literature, this shortcoming is addressed by including a Markov random eld prior for the mixing probabilities and the present study discusses this theoretical development. The resulting nite spatial variant mixture of Gaussian regressions model is de ned and its application is demonstrated in a simulated example. It was found that the spatial variant mixture of Gaussian regressions delivered accurate spatial clustering results and simultaneously accurately estimated the component model parameters. This study contributes an application of the spatial variant mixture of Gaussian regressions model in the agricultural context: maize yields in the Free State are modelled as a function of precipitation, type of maize and season; GPS coordinates linked to the observations provide the location information. A simple linear regression and traditional mixture of Gaussian regressions model were tted for comparative purposes and the latter identi ed three distinct clusters without accounting for location information. It was found that the application of the spatial variant mixture of regressions model resulted in spatially distinct and informative clusters, especially with respect to the type of maize covariate. However, the estimated component regression models for this data set were quite similar. The investigated data set was not perfectly suited for the spatial variant mixture of regressions model application and possible solutions were proposed to improve the model results in future studies. A key learning from the present study is that the e ectiveness of the spatial variant mixture of regressions model is dependent on the clear and distinguishable spatial dependencies in the underlying data set when it is applied to map-type data.Dissertation (MSc)--University of Pretoria, 2017.StatisticsMScUnrestricte

Consumer acceptance of sugar derived from genetically modified sugarcane in South Africa

The study investigated the perceptions of and acceptance by South African consumers regarding sugar derived from genetically modified sugarcane ('GM sugar'). The results are based on a demographically representative consumer survey in the Gauteng Province of South Africa, conducted in 2017. Logistic regression models indicated that socio-economic group and age were the most significant factors explaining consumers' willingness to purchase 'GM sugar.' Product acceptance decreased with socio-economic status and age. Additional information on the potential benefits of sugar derived from GM cane increased middle-income and more affluent consumers' willingness to purchase, with the opposite effect on low income consumers. Pro-GM consumers were willing to pay an 8.7% premium for 'GM sugar,' motivated by the environmental benefits argument. Positive marketing messages focusing on the potential role of 'GM sugar' in food security improvement, food affordability, and ensuring the future of the local sugar industry should be disseminated through appropriate marketing channels as presented in this paper.Hester Vermeulen, Marnus Gouse, Marion Delport, Marlene Louw, and Taaibah Miller (Bureau for Food and Agricultural Policy (BFAP), South Africa

Evaluating the demand for meat in South Africa : an econometric estimation of short term demand elasticities

The study aims to improve understanding of meat demand in South Africa through the estimation of a Linear Approximation of an Almost Ideal Demand System (LA/AIDS) for the South African meat complex which includes beef, mutton, pork and poultry. As the most widely consumed animal protein, a special focus is placed on poultry, which is disaggregated into two separate product groups, namely IQF portions and other poultry products, providing an improved understanding of demand preferences among different poultry cuts. In light of the changes that have occurred in both global agricultural markets and the South African consumer environment over the past decade, the model is estimated based on monthly data from January 2008 to September 2014, yielding short run elasticities. Expenditure elasticity estimates for IQF portions, other poultry products, pork, mutton and beef were 1.17, 1.24, 0.44, 1.07 and 0.8 respectively and the compensated own-price elasticities were estimated as −0.61, −0.43, −0.72, −0.96 and −0.11 for IQF portions, other poultry products, pork, mutton and beef, respectively. Most of the estimated elasticities conformed to a priori expectations, with the exception of poultry expenditure elasticities, which were higher than expected and in line with luxury goods, rather than normal goods, as the most affordable source of protein. Within the lower income consumer groups, where poultry dominates meat consumption, it was argued that meat in itself is a luxury good, reflected in the elasticities of poultry as the most affordable entry point into the meat market.http://www.tandfonline.com/loi/ragr202018-09-16hj2017Agricultural Economics, Extension and Rural Developmen

Combining theory and wisdom in pragmatic, scenario-based decision support for sustainable development

Researchers have increasingly acknowledged the relative strength of ‘hybrid’ approaches to scenario analysis for exploring the futures of coupled human-nature systems. In this paper, we explain, demonstrate, and provisionally evaluate the usefulness of a simple analytical framework, based on five categories of capital assets, as part of a protocol for overcoming the conversion problem in hybrid scenario analysis. Based on a preliminary application of the framework to a case study in South Africa, we suggest that the five capitals framework has the potential to improve expedience and counter the bias against ‘soft’ drivers in hybrid approaches to scenario analysis. However, in light of the methodological trade-off between rigour and expedience, we suggest that future research needs to compare the available protocols for hybrid scenario analysis by weighing up the relative gain in scenario quality versus the relative cost of scenario construction.The South African Maize Trusthttp://www.tandfonline.com/loi/cjep202019-04-04hj2018Agricultural Economics, Extension and Rural Developmen

Combining theory and wisdom in pragmatic, scenario-based decision support for sustainable development

<p>Researchers have increasingly acknowledged the relative strength of ‘hybrid’ approaches to scenario analysis for exploring the futures of coupled human-nature systems. In this paper, we explain, demonstrate, and provisionally evaluate the usefulness of a simple analytical framework, based on five categories of capital assets, as part of a protocol for overcoming the conversion problem in hybrid scenario analysis. Based on a preliminary application of the framework to a case study in South Africa, we suggest that the five capitals framework has the potential to improve expedience and counter the bias against ‘soft’ drivers in hybrid approaches to scenario analysis. However, in light of the methodological trade-off between rigour and expedience, we suggest that future research needs to compare the available protocols for hybrid scenario analysis by weighing up the relative gain in scenario quality versus the relative cost of scenario construction.</p