Agrekon - latest Issue
Volume 55, Issue 4, 2016
Agriculture in a developmental state : finding the nexus for development(al) agricultural economistsAuthor Moraka Nakedi MakhuraSource: Agrekon 55, pp 303 –330 (2016) http://dx.doi.org/http://dx.doi.org/10.1080/03031853.2016.1257687More Less
First of all, I feel very honoured and humbled to present this prestigious FR Tomlinson Memorial Lecture, particularly in my home province of Limpopo where I cut my teeth of service as an agricultural economist. It all started in the early days of my primary schooling where I observed extension officers assisting farmers to manage their micro-farm enterprises at our Eldorado village in Blouberg. It was a place well-endowed with underground water, good arable soil and sweet grazing land (though with less than normal rainfall). Years later, I had the opportunity to serve the Agricultural Economics profession through the Agricultural Economics Association of South Africa (AEASA) Management Committee at different (if not all) portfolios. Then, I persuaded the association to open up to agricultural economists from different spaces, particularly from the provinces.
Source: Agrekon 55, pp 331 –355 (2016) http://dx.doi.org/http://dx.doi.org/10.1080/03031853.2016.1243064More Less
This article examines maturity effects for futures contracts listed on the South African Futures Exchange (SAFEX). Three classes of derivative contracts are examined agricultural, metals and energy futures. Estimation of the Samuelson effect is by the ordinary least squares (OLS) approach using the volatility estimator in Garman and Klass (1980), Parkinson (1980) and Serletis (1992). The analysis simultaneously tests for the Samuelson effect, while establishing significance of traded volume, change in open interest and bid-ask spread on intraday volatility. Multicollinearity and seasonality are incorporated to examine if maturity effects remain in the contracts. Findings are that only wheat supports maturity effects. However, white maize and silver volatility decline as time-to-maturity diminishes. The implications of the results for traders and market participants are discussed.
Author Herman GeyerSource: Agrekon 55, pp 356 –376 (2016) http://dx.doi.org/http://dx.doi.org/10.1080/03031853.2016.1243753More Less
The article analyses the operation of poverty traps in South African agriculture through an analysis of the 2007 agricultural census. The poverty trap is a self-reinforcing mechanism in the market in which small variances in initial conditions can result in bimodality into differential economic steady-states resulting in multiple equilibria. Reasons for this include the savings trap, creating locally increasing rates return due to indivisible lumpy capital inputs; the technological trap in which the diminishing marginal rates of technological substitution diminishes productivity growth; the demographic trap in which population growth reduces the capital-labour ratio, increasing consumption and pure time costs; the stochastic returns trap in which producers without access to risk smoothing mechanisms assume greater risks; and the liquidity trap in which present and future capital values exceed current agricultural revenues. The study demonstrates that the failure of land reform and small-scale agriculture assistance programmes is thus a product of market failures, not policies. The analysis indicates that locally increased returns, symptomatic of the savings trap exists with initial average expenditures of smaller firms exceeding the average incomes. The technology trap is also evident with average incomes of small firms is also very low relative to the average capital asset values. The mean productivity of workers in terms of revenue per employee supports the argument for productivity declines in small firms due to the demographic trap. Initial declines in income per ton outputs of smaller firms validate the stochastic returns poverty trap.
Source: Agrekon 55, pp 377 –410 (2016) http://dx.doi.org/http://dx.doi.org/10.1080/03031853.2016.1243059More Less
The establishment of the World Trade Organization in 1995 and the subsequent proliferation of regional and bilateral trade agreements resulted in the decline of global tariffs. However, other trade and regulatory measures have increased and thus restricted potential trade to some extent. These measures, non-tariff measures (NTMs), have also affected intra-SADC trade as there was no evidence of growth in the trade that needed to accompany the decline in tariffs. The extent of the impact of NTMs on SADC trade is still not fully understood due to lack of such data, which has effectively affected the quality of research in this area. In this article, data on NTMs related to SADC agricultural products for ten countries was compiled to shed some light on these measures, as well as to make them transparent. The results confirm that these countries have increased their use of NTMs over the period 2000 to 2010. As a result, on average one product was subjected to 17 NTMs in 2010. The Southern African Customs Union is the leader in the use of NTMs, while Malawi had the least incidences of NTMs. Most of the NTMs are applied on fruits, meat, dairy, vegetables and cereal products. The use of sanitary and phytosanitary measures (SPS) and of export measures was increasing faster than other categories were. Finally, there is an indication that NTMs are used as substitutes for the declining tariffs. NTMs are trade restricting, and if they are not addressed, they will continue to reverse the gains of the SADC free trade area, as well as other initiatives of trade liberalisation.
Source: Agrekon 55, pp 411 –435 (2016) http://dx.doi.org/http://dx.doi.org/10.1080/03031853.2016.1243061More Less
The South African wheat industry has been under severe pressure in recent years. Prescribed high wheat quality, which is enforced via cultivar release criteria, is believed to have negatively influenced the productivity and competitiveness of producers. The main hypothesis is that producers deliver lower yields because of high quality requirements and are not compensated for this high quality since prices are still determined by the lowest import parity price. Whether or not this is actually the case must be determined, firstly, by identifying the factors that influence the price of wheat, and secondly, by identifying the factors that do not influence the price of wheat but nevertheless have an adverse effect on producersâ?? productivity. In this study, a hedonic price model, built on the premise that price is a function of all the characteristics that the product possesses, is used to precisely determine the factors that impact - or otherwise - on price levels in the South African wheat industry. The authors apply the hedonic price model using a three-step process to obtain the best-fitting model for the available data. The results reveal that variations in price are mainly a function of Colour, P/L, Defects and Fall, and that these factors should form the basis of the prescribed quality to producers. By knowing the wheat characteristics that must be included in, and excluded from, the release criteria (prescribed quality) system, producers will be able to produce goods that positively impact their productivity as well as their competitiveness.
The impact of social grants on the propensity and level of use of inorganic fertiliser among smallholders in KwaZulu-Natal, South AfricaSource: Agrekon 55, pp 436 –457 (2016) http://dx.doi.org/http://dx.doi.org/10.1080/03031853.2016.1243063More Less
This article assesses the extent to which social grants relieve liquidity constraints and improve inorganic fertiliser use among South African smallholders. A total of 984 farming households were randomly selected from four districts of KwaZulu-Natal, and data were analysed using the double-hurdle model. The econometric results indicated that use of social grants had a positive impact on the level of fertiliser use, while increasing dependency on social grants had no significant negative impact. The positive influence of social grants on the amount of inorganic fertiliser used suggests that these grants play a significant role in alleviating the liquidity constraints faced by poor farmers. This result is consistent with the presence of credit constraints that limit poor households' ability to invest in modern farming technologies. To increase technology adoption among the poor, the study recommends that policymakers should address imperfections in the rural credit markets, increase smallholders' assets in order to increase their risk-bearing capacity and improve the expected profitability of using inorganic fertiliser.
Commercialisation of food crops and farm productivity : evidence from smallholders in central AfricaSource: Agrekon 55, pp 458 –482 (2016) http://dx.doi.org/http://dx.doi.org/10.1080/03031853.2016.1243062More Less
Commercialisation of agriculture has long been considered an important driver of intensification, production, food security and farm incomes in Africa. This article investigates whether commercialisation is able to increase the intensification and yield of banana and legumes in central Africa. The study utilises survey data from 480 smallholder farmers in selected regions in rural Rwanda and the Democratic Republic of Congo (DRC). The findings show a positive effect of commercialisation on improved seed varieties use and food crop yields, even after controlling for an endogeneity problem. There is no strong evidence of commercialisation effect on fertilizer use among the sampled farm households. Apart from commercialisation, better education, larger farm sizes, access to markets and credit facilities, good roads and extension contacts are necessary for farmers to increase input use and crop yields. Overall, these findings suggest that programmes targeting to increase smallholder farm productivity through commercialisation will only work if they consider production and marketing conditions surrounding the target households.
Real-time grain commodities price predictions in South Africa : a big data and neural networks approachSource: Agrekon 55, pp 483 –508 (2016) http://dx.doi.org/http://dx.doi.org/10.1080/03031853.2016.1243060More Less
The prices of agricultural grain commodities are known to be volatile due to several factors that influence these prices. Moreover, different combinations of these factors, such as demand, supply and macroeconomic indicators are responsible for the price volatility at different times. Big Data presents opportunities to collect and integrate datasets from several sources for the purpose of discovering useful patterns and extracting actionable insights that can be used to gain competitive advantage or improve decision making. Neural Networks presents research opportunities for training computer algorithms to model linear and non-linear patterns that might exist in datasets for the purpose of extracting actionable insights such as making predictions. This article proposes a Big Data and Neural Networks approach for predicting prices of grain commodities in South Africa. It was identified that disparate data that influence the grain commodities market can be acquired, integrated and analysed in real-time to predict future prices of grain commodities. By utilising SAP HANA as the enabling Big Data technology, data acquired from several sources was used to create an integrated dataset, and a predictive model was developed using Backpropagation Neural Network algorithms. This model was used to predict the daily spot prices of white maize on the Johannesburg Stock Exchange (JSE) at the end of each trading day. The initial results indicate that the approach can be scientifically used to predict future prices of grain commodities in a real-time environment.