Is Agricultural Sector Growth a Precondition for Economic Growth? The Case of South Africa

Poonyth, Daneswar; Hassan, Rashid M.; Kirsten, Johann F.; Calcaterra, M.

doi:10.1080/03031853.2001.9524950

Cited by 12 publications

(6 citation statements)

References 9 publications

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“…The time series analysis is utilized for the investigation of the economic data of Pakistan for the 1981-2005 period where a bidirectional causality relationship has been found between the agricultural sector and the economic growth (Azra et al, 2012). The economic data of South Africa is studied in another work for the period of 1973-1997 using the ordinary least squares method where it is concluded that 1% growth in the agricultural revenue impacts the economic growth more than 1% (Poonyth et al, 2001). In another study, the economic data of North African countries are investigated using the gravity model in the period of 1982-2006 and it is concluded that agricultural revenue and the gross domestic product have positive correlation (Bakari and Mabrouki, 2018).…”

Section: Literature Surveymentioning

confidence: 99%

Nowcasting the Agricultural Revenue and the Agricultural Land in Türkiye: An Autoregressive Multilayer Perceptron Network Approach

Tuncsiper¹

2023

CJMS

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The food prices are on the rise globally and this fact increases the importance of the agriculture sector. Considering the significance of the agriculture, the nowcasting of the agricultural revenue and the agricultural land of Türkiye are considered in this work. The agricultural revenue and the agricultural land data are taken from the official sources followed by their seasonal-trend analysis. The seasonality and nonlinearity of the agricultural revenue and the agricultural land are then carefully investigated. As the next step, a multilayer perceptron network is developed in Python programming language for the modelling and nowcasting the agricultural revenue and the land data. The developed multilayer perceptron network is built as an autoregressive network structure enabling the utilization of the previous values of the data as inputs. The 75% of the data is taken as the training data and the developed network is trained for the revenue and the land data separately. Then, the remaining 25% of the data is used as the test data. As the next step, the actual agricultural revenue and the agricultural land data are plotted together with the results of the developed autoregressive multilayer perceptron network. The plots indicate an accurate modelling and nowcasting. In addition, the performance metrics of the developed model such as the root mean square error, mean absolute error, mean absolute percentage error and the coefficient of determination are also calculated. The values of these performance metrics verify the accuracy of the developed autoregressive multilayer perceptron network for the modelling and nowcasting of the agricultural revenue and the agricultural land data. It is argued that the nowcasting of the agricultural revenue is an important concept for the estimation of the food prices therefore the developed model will be helpful for the food prices planners.

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Section: Literature Surveymentioning

confidence: 99%

Nowcasting the Agricultural Revenue and the Agricultural Land in Türkiye: An Autoregressive Multilayer Perceptron Network Approach

Tuncsiper¹

2023

CJMS

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“…Furthermore, the importance of the agricultural sector to further strengthen industrial growth can be realized in many ways. An increase in agriculture income generates capital for the industrial sector through trade and increases the autonomous demand for industrial goods (Lewis, 1954; Kuznet, 1966; Poonyth et al ., 2001). To summarize, as stated by Hwa (1988), agriculture aids industrialization through the supply of labor, capital and raw materials as inputs to other sectors, encouraging the demand for industrial goods.…”

Section: Literature Review and Hypothesis Developmentmentioning

confidence: 99%

Variations in economic growth across states in Malaysia: an exploratory analysis

Yusof

Kalirajan

2020

JES

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PurposeThe study contributes to the aim of regional development policy in reducing regional disparities, by examining the spatial balance in socioeconomic development across the states of Malaysia based on composite development index (CDI). Besides, the study has attempted to understand the issues in the development gaps across Malaysian states by evaluating the factors that explain the variation in economic growthDesign/methodology/approachThis study uses three-stage least squares (3SLS) and bootstrap sampling and estimation techniques to examine the factors that explain the variations in the growth of development across the states in Malaysia. The analysis involves 13 states in Malaysia (Johor, Melaka, Negeri Sembilan, Pulau Pinang, Perak, Perlis, Selangor, Kedah, Kelantan, Pahang, Terengganu, Sabah and Sarawak) from 2005 to 2015.FindingsThe pattern in the spatial socioeconomic imbalance demonstrates a decreasing trend. However, the development index reveals that the performance of less developed states remained behind that of the developed states. The significant factors in explaining the variation in growth across the Malaysian states are relating to agriculture, manufacturing, human capital, population growth, Chinese ethnicity, institutional factors and natural resources.Research limitations/implicationsThe authors focused on Malaysian states over the period between 2005 and 2015. The authors encountered some limitations in obtaining relevant data such as international factors and technological change that might also explain the variation in economic growth as the data on these variables are not reported at the state level. Moreover, the data on GSDP by sector was only available from the year 2005. Second, the study is based on secondary data. Future studies might examine the factors that contribute to the development gap across Malaysian states through interviews or questionnaires and compare the findings with the existing results. Despite its limitations, this study contributes to the existing literature that emphasizes on spatial balance of socioeconomic in a developing country, focusing on Malaysian states.Practical implicationsThese findings provide guidance for policymakers by understanding key potential areas to reduce the disparity in economic growth across Malaysian states by understanding their impact on the growth.Originality/valueThis study employs different method of 3SLS and bootstrap sampling and estimation techniques in examining the factors that explain the variations in the growth of development across the states in Malaysia.

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“…Although the programme promotes the intensive production of maize, the report indicates that only 1.88 kg of maize per kg of an additional subsidised fertiliser is achieved. Ramaila, Mahlangu and Du Toit (2011) reported that Poonyth et al (2001) contests that return on investment in agriculture is far less than in the nonagriculture sector, while Ricker-Gilbert et al (2013) argue further that the inputs support programme in both Malawi and Zambia had minimal effect on the retail price of maize.…”

Section: Impact Of Agricultural Production Input Support Programmementioning

confidence: 99%

Agricultural Production Inputs Support Programme As A Socioeconomic Development Strategy

Mkhonto

Musundire

2019

S Afr. Jnl. Agric. Ext.

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This paper presents the impact of the Agricultural Production Inputs Support Programme on crop yield, and reflects challenges and opportunities for improving agricultural production to ensure socioeconomic development and sustainability. Policy-makers assume that farmers who receive subsidised agricultural inputs produce high yields, generate income to sustain production, and reduce poverty. A structured interview schedule was used to collect data from horticultural crop farmers who received agricultural production inputs from the provincial agricultural department during the 2014/15 season in Mopani District. Findings indicate no significant linear relationship between agricultural production inputs received and crop yield. In addition, there was no significant linear relationship between agricultural production practices and horticultural crop yield. However, the study observed that production practices such as soil testing for nutrient content, fertilisation, irrigation scheduling, use of hybrid seeds, and nursery propagated seedlings are critical for farmers to experience high crop yield. This highlights the importance and need to strengthen extension services to improve production practices. It is therefore concluded that provision of production inputs is a necessary strategy to ensure improved agricultural production. This study thus recommends a careful selection of recipients with skills to farm, sound farming experience, improved level of education, and capacity building of the farmers.

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Is Agricultural Sector Growth a Precondition for Economic Growth? The Case of South Africa

Cited by 12 publications

References 9 publications

Nowcasting the Agricultural Revenue and the Agricultural Land in Türkiye: An Autoregressive Multilayer Perceptron Network Approach

Nowcasting the Agricultural Revenue and the Agricultural Land in Türkiye: An Autoregressive Multilayer Perceptron Network Approach

Variations in economic growth across states in Malaysia: an exploratory analysis

Agricultural Production Inputs Support Programme As A Socioeconomic Development Strategy

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