Analysing the socio-financial determinants shaping food production in the South African agriculture sector

Habanabakize, Thomas; Dickason-Koekemoer, Zandri Article Analysing the socio-financial determinants shaping food production in the South African agriculture sector Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Habanabakize, Thomas; Dickason-Koekemoer, Zandri (2024) : Analysing the socio-financial determinants shaping food production in the South African agriculture sector, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-13, https://doi.org/10.1080/23322039.2024.2426541 This Version is available at: https://hdl.handle.net/10419/321671 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: 2332-2039 (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Analysing the socio-financial determinants shaping food production inthe South African agriculture sector Thomas Habanabakize & Zandri Dickason-Koekemoer To cite this article: Thomas Habanabakize & Zandri Dickason-Koekemoer (2024) Analysing the socio-financial determinants shaping food production inthe South African agriculture sector, Cogent Economics & Finance, 12:1, 2426541, DOI: 10.1080/23322039.2024.2426541 To link to this article: https://doi.org/10.1080/23322039.2024.2426541 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 13 Nov 2024. Submit your article to this journal Article views: 435 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 FINANCIAL ECONOMICS | RESEARCH ARTICLE Analysing the socio-financial determinants shaping food production in the South African agriculture sector Thomas Habanabakize and Zandri Dickason-Koekemoer TRADE, North-West University, Vanderbijlpark, South Africa ABSTRACT The 2023 Sustainable Development Goals (SDGs) encompass a specific objective that is to ‘end hunger, achieve food security, enhance agriculture, and improve nutrition’. The realization of this goal relies heavily on efficient agriculture sector management in developing countries. Consequently, assessment of factors that influence agricultural production is one of the way to solve issues that hinders the attainement of the SDGs. This paper aims to investigate the determinants of food production within the South African agricultural sector. Employing econometric methods like Johansen and Canonical Cointegration, VAR and VECM, this study analyes annual time series data spanning from 1961 to 2022. The analysis indicates that factors such as accessible financing, heightened agricultural sector investment, fertilizer usage, and rural demographic growth positively influence food production; contributing to mitigating food insecurity in South Africa. Conversely, elevated lending rates and inflation pose challenges to South African food production. Thise results indicate the role played by monetary policy to improve food production in South Africa. To bolster food production in South Africa, policymakers shoul focus on enhancing agricultural skills, easing credit conditions to improve financial accessibility, and encouraging active investment from government and private sectors in agricultural activities. IMPACT STATEMENT The current study offers vital insights into the multifaceted determinants of food production within the South African agricultural sector, directly contributing to achieving the 2023 Sustainable Development Goals (SDGs), particularly the aim of “ending hunger, achieving food security, enhancing agriculture, and improving nutrition.”By employing rigorous econometric analyses on comprehensive time series data, this research enhances the existing literature by identifying and elucidating various bottlenecks that obstruct production growth in the South African agricultural sector. Additionally, the study conducts a thorough investigation into the impact of key factors of production in agriculture, providing evidence-based recommendations and robust strategies designed to alleviate hunger and malnutrition through enhanced food production techniques. It underscores the critical significance of targeted investment growth, agricultural skills enhancement, and improved financial accessibility as essential strategies for boosting agricultural productivity. The findings are not only relevant to South Africa but also offer valuable lessons for other developing countries facing similar agrarian challenges. By illuminating the pathways to improved food production and food security, this study serves as a foundational resource for policymakers, agricultural practitioners, and other stakeholders striving to create sustainable food systems that can effectively eradicate hunger and elevate nutrition standards on a global scale. This study highlights the usefulness of a collaborative approach incorporating government initiatives, private sector investments, and community engagement to foster a resilient agricultural sector capable of adapting to changing socio-economic conditions. ARTICLE HISTORY Received 28 March 2024 Revised 21 October 2024 Accepted 3 November 2024 KEYWORDS Agriculture; agro-finance; food production; food security; SDGs; South Africa SUBJECTS Economics; International Economics; International Finance; Finance JEL CLASSIFICATIONS O13; E23; E44 1. Introduction One of the major global problems that persist in many developing countries is the issue of food insecurity (Jacobsen et al., 2013). Despite advancements in technology aimed at assisting agricultural activities CONTACT Zandri Dickason-Koekemoer [email protected] North-West University, TRADE, Vanderbijlpark, South Africa ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2426541 https://doi.org/10.1080/23322039.2024.2426541 and increasing food production, challenges in achieving food security continue to plague these countries. One of the primary obstacles to food security is the rapid population growth coupled with limited arable land available for agricultural purposes. The high population growth rate and limited arable land create an imbalance between food demand and supply. This means that the amount of food needed surpasses the available supply, resulting in heightened food insecurity (Miladinov, 2023). Difficult access to finance, high production costs due to inflation rates, and the migration of youth from rural to urban areas in search of a better life are additional factors that impede the food production process and further exacerbate food insecurity in developing countries (Jacobsen et al., 2013; Pawlak & Kołodziejczak, 2020). As discord between population growth and food production in these countries remains a global concern; increasing food production has become one of the United Nations’Sustainable Development Goals, as outlined in the second goal: ‘to achieve food security at all levels, improve nutrition for all, and promote sustainable agriculture’. To address these challenges and strive towards the UN’s sustainable goal, governments and social stakeholders have implemented various strategies. These strategies encompass adapting to climate change, improving water and soil management, promoting agricultural diversity, and enhancing farmers’ capacity and financial access. It is expected that the implementation of these strategies will play a significant role in developing countries, as the majority of their labour force relies on the agricultural sector, which in turn contributes to their economic growth (Matthew et al., 2019). Correspondingly, agriculture plays a pivotal role in the South African economy, as it contributes to poverty reduction, food security, social development, job creation, and economic growth even during difficult economic conditions. For instance, despite the impact of the COVID-19 pandemic on economic activities, the agricultural sector still contributed approximately 2.3 percent to the country’s GDP in 2020 (Matsei, 2021). This contribution was the highest over a decade, as indicated in Table 1. The role of the agriculture sector in South Africa’s job creation is crucial and greatly significant in rural areas. Highlighting its significant impact on aggregate employment, Sihlobo (2023) argues that in the third quarter of 2023, over 956000 people were employed in the agriculture sector. Additionally, although the direct contribution of agriculture to the GDP may be relatively small, it remains a crucial source of raw materials for the manufacturing sector. Around 70 percent of agricultural output is utilized in the production of final goods in the manufacturing industry, while only about 30 percent is directly consumed (Matsei, 2021). This emphasizes the important role of agriculture in the manufacturing process. Beyond farming, the food production industry encompasses various activities such as transportation, processing, packaging, and distribution. While machinery and technology may play a role in these processes, human capital is also essential. Therefore, the agriculture sector creates jobs not only in farming but also in the broader food production industry. Furthermore, the agriculture sector has a significant impact on a country’s currency exchange rate through its trade balance. Figure 1 shows that between July 2016 and June 2021, the sector experienced a trade surplus, highlighting its contribution to the country’s economy. Irrespective of agriculture sector performance and its contribution to economic growth, Dlamini et al. (2023)’s findings suggest that food security remains a serious issue in South Africa. One of the reasons for food insecurity might be that the produced food is not sufficient to sustain all South African households. Nonetheless, given the role of the agriculture sector and its contribution to food production, Table 1. Contribution of agriculture to GDP (2011–2020). Year Total value added (R’million) Contribution of agriculture value added (R’million) Contribution of agriculture as % of total value added 2011 2 724 400 58 739 2,2 2012 2 932 879 60 003 2,0 2013 3 183 618 63 121 2,0 2014 3 414 943 70 342 2,1 2015 3 624 908 71 904 2,0 2016 3 891 559 82 406 2,1 2017 4 173 328 93 400 2,2 2018 4 341 292 90 148 2,1 2019 4 523 580 81 337 1,8 2020 4 428 711 101 650 2,3 Source: Matsei (2021). 2 T. HABANABAKIZE AND Z. DICKASON-KOEKEMOER economic growth and employment creation, the current paper aims to analyse the financial and social determinants of food production in South Africa. The existing literature highlights only the importance of access to finance as a solo means to improve agriculture production. This article breaks new ground by taking a holistic approach to addressing food insecurity, recognizing that social factors play a crucial role in improving the production process. While existing literature often focuses solely on the importance of access to finance, this article goes beyond that, emphasizing the need to consider the broader social context in which agriculture operates. By considering social factors, this article offers a more comprehensive understanding of the challenges facing agricultural production. It argues that addressing these social factors and improving financial accessibility is essential for sustainable and equitable improvements in food production and food security. 2. Concise summation of literature and existing studies Agricultural food production is influenced by various factors, such as limited and inadequate land, changes in climate, availability of irrigation water, crop characteristics, and financial access for farmers. Among these factors, the availability of financial support is crucial for successful food production. However, due to the risks associated with farming, some banks and financial institutions are reluctant to provide funding for agricultural projects. As a result, the agricultural sector faces a significant issue of insufficient funding, despite employing more than 70 percent of the labour force in many developing economies (Food and Agriculture Organization (FAO)), 2011). The role of the agriculture sector in socioeconomic development, especially in achieving the Sustainable Development Goals by 2030, cannot be overstated. Hence, a comprehensive review of the literature on agro-financing and agro-food production is essential. The significance of the agricultural sector as the primary source of food has prompted both developed and developing countries to take proactive measures over the centuries, and this trend continues in developing nations today (Martin & Clapp, 2015). In developed countries, the agriculture sector laid the foundation for industrialization through financial regulation and support, while in many developing countries, agriculture remains a crucial sector for socioeconomic development (Chang, 2009; Garidzirai, 2020; Friedmann, 1982). It is essential to note that financial support is necessary during the agrarian process, but it is challenging to obtain. This difficulty in financing agricultural activities primarily stems from the fact that farming investments carry higher risks compared to other forms of economic investment, such as services and manufacturing. Consequently, without government assurance, private investors are more hesitant to invest in farming activities (Martin & Clapp, 2015). Agricultural development and food production are influenced not only by investment and allocation of resources to the sector but also by market conditions and exchange rates. The exchange rate plays a crucial role, particularly for food-exporting countries, as it interacts with exports. When the domestic Figure 1. Imports vs exports of agricultural products. Source: Matsei (2021). COGENT ECONOMICS & FINANCE 3 currency is strong and there is an increased global demand for food, farmers are incentivized to boost their production and supply in order to meet the demand and increase their profits. On the other hand, a weak domestic exchange rate leads to high production costs, reducing farmers’profits (Orman & Dellal, 2021). Additionally, farm size is a factor that not only affects aggregate production but also access to financial credit. Generally, larger plots used for food production result in higher output. Moreover, investors are more inclined to fund large-scale farmers as they primarily produce for commercial purposes, compared to small-scale farmers who typically produce for household consumption (Anupama & Falk, 2018). The next paragraph provides a concise review of empirical studies on food production and their respective outcome. Several studies investigated the importance of farmers’access to finance and its implications towards food production. Using ta double-hurdle approach, the study of Njogu et al. (2018) in Kenya analysed the role of financial access on 21576 farmers and the production capacity. The study’s inferential analysis discovered a significant and positive link between financial access and aggregate production. The authors of the study recommended improvement and easing of financial credits to farmers to increase food production and agricultural activity in Kenya. Using the Autoregressive Distribution Lag (ARDL) model, another study was conducted by Osabohien et al. in Nigeria to assess the effect of access to credit facilities and agricultural food production. Similar to the Njogu et al. (2018) results in the Kenyan agriculture sector, the Osabohien et al. findings indicated a significant positive impact of access to credit facilities to food production in Nigeria. Irrespective of easy credit facilities towards agricultural production both studies, the study by Rahji and Fakayode (2009) suggested small-scare farmers have limited access to financial credits as, in the perception of financial institutions, they present a high risk of defaulting their credits. Similar results were presented in the study by Odoemenem and Obinne specifying that small-scale farmers found difficulties in obtaining financial loans owing to their incapacity to afford loan collaterals. The issue of limited ability to access financial loans impedes small-scale farmers’ agricultural activities and thereafter reduces the contribution of the agricultural sector to socio-economic improvement. Nonetheless, the study by Adeleke et al. indicated that the case of incompatibility between farming size and loan access should be generalised as in the same place there exist investors willing to fund small-scale farmers. Besides the role of farmers’financial access in food production, Sebu (2013) considered also the lore of farmland as the combination of the latter with financial access can enhance the agricultural output. In his study, Sebu (2013) found that in Malawi farm sizes influence access to loans and the combination of the two results in high food production. 3. Research methodology As indicated in the introduction section, the study investigates socio-finance determinants of food production in the South African agriculture sector. The study applied the Johansen and canonical cointegration on annual time series data to achieve this objective. The data sample complies with 60 observations starting from 1961 to 2021. The choice of sample size was based on the data availability and the data was sourced from the World Development Indicator (WDI) and South African Reserve Bank (SARB). As presented in Table 2, the analysis of food production in South Africa comprises 6 (cash credit access, arable land, inflation rate, fertilizer consumption, rural population growth, and interest rate) independent variables. These variables were selected based on their role not only in food production but also in food affordability or food security. Table 2. Variable description and summary statistics. Variable Ellipsis Measurement Source Mean Max Min SD Food production FP Indexed 2004–2006 ¼100 WDI 4.115 4.739 3.472 0.345 Cash credits Access CCA Millions of rand SARB 8.059 10.200 4.905 1.587 Arable land ARL Hectares WDI 15.489 15.830 14.892 0.301 Inflation INF Annual percentages WDI 1.897 2.926 0.220 0.656 Fertilizer consumption FTC Kgs per hectare of arable land WDI 4.047 4.651 2.885 0.345 Rural population growth RPG annual percentages WDI 1.294 2.876 −1.092 1.361 Interest rate IR Annual percentages WDI 12.527 22.333 5.500 4.697 Source: Authors’compilation. 4 T. HABANABAKIZE AND Z. DICKASON-KOEKEMOER The descriptive statistics of selected variables are displayed in Table 2 below. The averages of used variables are 4.115; 8.059; 15.489; 1.897; 4.047; 1.294 and 12.527 for food production cash credits, arable land, food inflation, fertilizer consumption, interest rate and rural population growth respectively. As designated by the difference between maximum and minimum values, over the sample period, interest rate experienced the highest fluctuation with a standard deviation of 4.697 while the lowest variation was experienced in the arable land with a standard deviation of 0.301. This implies data since 1961 the number of people interested in farming did not increase significantly or other individuals interested did not have access (were not permissible) to arable land. The history of land distribution in South Africa backs these results as for decades, even centuries arable land remains a monopoly of certain groups of individuals (Hall & Kepe, 2017). The graphical representation or plot of used variables shows trends in all variables. While dominant trends for LFP, LCCA, LINF and LFTC are positive, INR, LARL and RGP experienced downward trends. These observed trends suggest that used data is not stationary at levels as shown in Figure 2. Thus, stationality or unit root test is required to determine integration order for each variable. 3.1. Model specification In the context of the current study, the relationship between food production and its determinants in South Africa is implicitly presented as follows: FPt¼fðCCAt,ARLt,FTCt,INFt,IRt,RGPt[1] Where the latter tis associated with each variable indicates the period. From Equation 1, a non-linear model is delivered and presented as follows: FPt¼aþCCAb1 tþARLb2 tþFTCb3 tþINFb4 tþIRb5 tþRGPb6 tþet[2] The process of addressing challenges that may arise in estimating equation 2 in its non-linear form involves employing the strategy of linearizing the equation. This was accomplished by applying a Figure 2. Graphical representation on used data. COGENT ECONOMICS & FINANCE 5 double-log transformation to equation 2, resulting in the formulation of equation 3. By utilizing the natural logarithm to transform the variables, a common unit of measurement is established. This establishment enables a more meaningful interpretation of the estimated coefficients, facilitating the comparison of the effects of different variables on the outcome. Furthermore, the transformation aids in reducing the occurrence of heteroscedasticity, which pertains to the uneven spread and variance of the error term. The linearization technique diminishes the likelihood of heteroscedasticity within the model, ensuring that the assumption of constant variance is met and improving the accuracy of the estimated coefficients. Additionally, the double-log transformation facilitates the estimation of a Best Linear and Unbiased Estimator (BLUE). The BLUE estimation yields efficient and unbiased estimates of the model coefficients, making it a desirable characteristic within the realm of econometric analysis. By linearizing equation 2 through the utilization of the transformation method, the resulting formulation of equation 3 enables the application of standard linear regression techniques, thereby enabling the estimation of the coefficients through the utilization of rigorous and well-established statistical methods. lnFPt¼u0þu1lnCCAtþu2lnARLtþu3lnFTCtþu4lnINFtþu5lnIRtþu6lnRGPtþet[3] Where ln represents natural logarithm, u0denotes the intercept term, u1is the coefficient of cash credit access or agro-financing, u2is the coefficient of arable hectares, u3is the coefficient of Fertilizer consumption, u4is the coefficient food inflation, u5the coefficient of landing interest rate, u6is the coefficient of rural population, tdenotes time and esignifies the error term. The expected results are that agro-financing, arable land, fertilizer consumption and population growth have a positive effect on food production while inflation and interest rates are expected to have a negative on food production. The expected results can, symbolically be presented as follows, u0>0, u2>0, u3>0, u6>0, u4< 0, u5<0:The reason behind the expected results is that an increase in agriculture funds will enable farmers to increase and speed up their farming productivity while large arable land, more fertilizer and more labour (population growth) would lead to high output. On the other hand inflation and interest rates are expected to reduce food production through low demand and high cost of production respectively. 3.2. Unit root test Each study that employs time series to assess a relationship amongst two or more variables should first be subjected to stationarity tests. The latter assists in destemming an adequate cointegration approach following the established integration order. Additionally, the unit root tests are vital in ascertaining that the analysed time series are stationary and there the study outcomes are not spurious. The Augmented Dickey-Fuller test (ADF) and Phillips-Perron (PP) tests were used to establish the integration order of the study variables. It is important to note that the ADF is built on the first-order autoregressive process model (Box & Jenkins, 1970). Following the ADF approach, the following equation was applied to assess whether collected variables have unit roots or not. Yt¼; 1Yt−1þutt¼1, ..., T [4] Where ;1represents a constant term, utdenotes the non-systematic factor of the model that meets the features of the white noise process. The subsequent hypotheses are used to diagnose the absence or presence of a unit root in a specified variable: The null hypothesis H0:;1¼1 suggests that the variable contains a unit root and it is, consequently, not stationary at level, and can become stationary after the first difference I(1). The alternative hypothesis H0:;1 jj <1, suggests that the variable does not contain a unit root and is stationary at level I(0). The Phillips-Perron test follows the same procedure described in Equation 4. However, contrary to ADF which considers T-statistics, to assess unit root, the PP test considers Z-statistics for a similar outcome. The next section presents and discusses the study results. 6 T. HABANABAKIZE AND Z. DICKASON-KOEKEMOER 4. Results report and interpretations 4.1. Unit root test The unit root results from both tests (Augmented Dickey-Fuller test and Phillips-Perron) are presented in Table 3. All variables are stationary after the first difference with more than a 95 percent level of confidence as all series are statistically significant at less than one percent level (P-value <0.01). 4.2. Lag length determination Given that the unit root test results indicated that all variables under consideration are integrated of the first order, it is noteworthy to determine the number of lags to be included in the model. From the results displayed in Table 4, the majority of lag length criteria suggest the use of one lag. In this regard, Schwarz’s information Criterion (SC) is selected as the best owing to the study sample which is not too large, and the SC is the choice for a small sample. 4.3. Cointegration In order to analyse the relationship between the respondent variable (food production) and its explanatory variables (Cash credits, arable land, food inflation, fertilizer consumption, rural population growth, and interest rate), it is necessary to first confirm whether these variables are stationary. Once it is established that the variables are stationary, the next step is to assess the presence of a long-run relationship between them. The Johansen test for cointegration is a suitable approach to determine the presence of a long-run relationship when the variables are stationary at first difference. Through conducting this test, evidence of cointegration between the variables under consideration can be ascertained. The results of the Johansen test for cointegration, displayed in Table 5, indicate a rejection of the null hypothesis. This rejection suggests that there is indeed evidence of cointegration, indicating the presence of a long-run relationship between the variables. In other words, the Johansen test results provide valuable insights into the dynamics and interdependencies among the analysed variables, contributing to the general understanding of the factors influencing food production in South Africa. From the Johansen test results, the authors of the study proceeded with the establishment of a longrun relationship using the canonical cointegration regression. The cointegration test outcome in Table 5 indicates that a long-run relationship exists between food production, cash credits or investment, arable Table 4. Lag length selection. Lag LogL LR FPE AIC SC HQ 0 638.0445 NA 5.57e-20 −30.14498 −29.93811 −30.06915 1 840.9746 347.88021.18e-23−38.61784 −37.37665−38.16289 2 866.0126 36.96074 1.24e-23 −38.61965 −36.34413 −37.78558 3 891.3028 31.31168 1.40e-23 −38.63347 −35.32362 −37.42028 4 918.6165 27.31375 1.68e-23 −38.74364 −34.39947 −37.15133 5 955.7670 28.30516 1.64e-23 −39.32224 −33.94374 −37.35081 6 987.8650 16.81322 3.29e-23 −39.66024−33.24741 −37.30968 Source: Authors’compilation. Table 3. Unit root results. Variable ADF PP ConclusionLevel 1 st difference Level 1st difference Food production 0.9997 0.0000 0.9979 0.0000 I(1) Cash credits 0.5430 0.0000 0.5164 0.0000 I(1) Arable land 0.8865 0.0000 0.8631 0.0000 I(1) Inflation 0.3114 0.0000 0.2875 0.0000 I(1) Fertilizer consumption 0.5672 0.0000 0.5698 0.0000 I(1) Rural population growth 0.9161 0.0000 0.9232 0.0000 I(1) Interest rate 0.3507 0.0000 0.3833 0.0000 I(1) Source: Authors’compilation. COGENT ECONOMICS & FINANCE 7