RESEARCH ARTICLE

 

Comparing the financial benefits of different grain production systems in South Africa's summer rainfall region

 

 

Mary Maluleke^I; Nie van Schalkwyk^II; Anika de Beer^II; Hendrik Smith^{I, III}; James Blignaut^{I, II, III, YV}; Jaap Knot^I; Gerrie Trytsman^I; Liane Erasmus^I

^IASSET Research, Sedgefield, South Africa
^IIRestore Africa Funds, Stellenbosch, South Africa
^IIISouth African Environmental Observation Network (SAEON), Pretoria, South Africa
^IVSchool for Public Leadership, Stellenbosch University, Stellenbosch, South Africa

Correspondence

 

 

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ABSTRACT

There is growing evidence that soil degradation, among other factors, has led to both the decline and constraint of agriculture in southern Africa. Conservation and regenerative agriculture (CA/RA) have been proposed as a grain crop production system that could slow down, halt or even reverse some of these disturbing trends. But the question remains whether it is financially viable. We sought to find an answer to this question by comparing the financial returns of a CA/RA system over a 20-year period to its alternatives, namely conventional tillage (CT) and no-tillage (NT) production systems. The cumulative free cash flow (CFCF) of the average between the realistic and conservative CA/RA scenarios in year 20 is considerably higher than that of the other systems under investigation. The CFCF for CA/RA in year 20 in Mpumalanga is estimated to be ZAR86 million, compared to -ZAR51 million for CT and about ZAR4 million for NT. That is a net difference between ZAR137 million (compared to CT) and ZAR82 million (compared to NT). In the Maluti area, the CFCF for the CA/RA production system is estimated at ZAR26 million, compared to -ZAR66 million for CT and -ZAR19 million for NT. In the North-West production area, the CFCF for the CA/RA production system is estimated at ZAR35 million, compared to -ZAR9 million for CT and about ZAR21 million for NT. The differences between the CFCF of the CA/RA system and the other systems represent the financial opportunity cost of not converting to the CA/RA system.
SIGNIFICANCE:
• Soil degradation leads to both the decline and constraint of agriculture in southern Africa.
• Conservation and regenerative agriculture (CA/RA) have been proposed as a grain crop production system that could slow down, halt or even reverse some of these disturbing trends.
• CA/RA are financially more viable in three grain production areas of South Africa.
• The cumulative free cash flow (CFCF) CA/RA in year 20 is considerably higher than that of the other systems.
• The differences between the CFCF of the CA/RA system and the other systems represent the financial opportunity cost of not converting to the CA/RA system.

Keywords: crop production systems, conservation and regenerative agriculture, financial analysis, investment J-curve

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Introduction

There is growing evidence that factors such as land and soil degradation, water insecurity and changes in climatic conditions have led to both the decline and constraint of agriculture at all levels in southern Africa.^1-3 These factors pose a major risk to the continuity and sustainability of the sector with an adverse impact on national and household food security. Given the widespread poverty and South Africa's precarious socio-economic context, the country is in great need of taking proactive steps to reduce all threats to food production.^4,5 The agriculture sector, be it small-scale, subsistence or commercial, must adapt to the current conditions and mitigate the risk factors in a way that protects the environment and its natural resources and reinforces production and sustainability.^6,7 To achieve this, the sector must embrace resilient, productive and profitable production systems that will restore, protect and sustain the health and productivity of the country's natural resources, notably its soil.

According to the Food and Agriculture Organization of the United Nations⁸, about 60% of South Africa's commercial farming sector, on an area basis, employs conventional deep-tillage and monocrop grain crop-livestock production systems. While this system has proven its high productive capacity in the past, it is very disruptive and leads to environmental degradation. This reduces a farm's resilience, sustainability and profitability.^9,10 Unfortunately, a vicious circle emerges. Conventional production systems' focus on maximising yields on increasingly degraded and disturbed soils makes them more dependent on the intensive and increasing use of external inputs to boost productivity, manage diseases and control pests, which, in turn, leads to further degradation, the sterilisation of the soil, and increased land requirements.^11-13 To combat this vicious circle of yield maximisation that requires high external inputs and leads to degradation that requires further external inputs to secure the yields, prudent or virtuous alternatives are required. These sought after virtuous alternatives must enable farmers to both adapt to climate variation and restore the land, while reducing the cost of production and upholding yield (Table 1).^14-19

Conservation and regenerative agriculture (CA/RA) has been proposed as one such alternative and virtuous production system.^20,21 CA/RA systems have gained much ground, mainly due to their long-term ecological and economic benefits.^22-27 CA/RA is, however, not a recipe, but an approach embedded in five principles and practices: minimum soil disturbance, maximum cropping diversity, permanent organic soil cover, maintenance of a living root for as long as possible, and integration of livestock.^28,29 Although CA/RA has gained momentum, the relative success, feasibility and applicability of the system are still debated.^30-32 Questions have been raised about (1) the lack of evidence with respect to a detailed financial analysis that quantifies the real costs and benefits of adopting or switching to CA/RA, and (2) the perceived risk and uncertainty associated with the adoption of CA/RA due to its relatively high initial costs and management ambiguities. In light of this, the objectives of this study were the following:

• To evaluate the financial implications (costs and benefits) of adopting the CA/RA systems relative to both conventional tillage (CT) and no-till (NT) systems.

• To assess the short-, medium- and long-term risks associated with both the adoption and non-adoption of CA/RA, and what implications these have on food security and the food system.

Given these objectives, an answer to the following question was sought: Is CA/RA the more financially desirable management practice when compared to its alternatives, namely CT and NT? We sought to address this question by employing an Excel-based financial modelling approach using primary data for the three production systems (CA/RA, CT, and NT) from six fully statistical trials from three different summer rainfall grain production regions in South Africa. Herein, we do not argue for or against CA/RA's claims with respect to its environmental benefits or its superior ability to adapt to climate change and other challenges. We seek to provide insights into the short-, medium- and long-term financial performance of the three crop production systems over a 20-year period.

 

Materials and methods

Site description

Data from six on-farm trials in summer grain crop production areas in South Africa has been used. These areas are the Mpumalanga Highveld area (three trial sites), the Maluti area in the eastern Free State (two trial sites), and the North West Province (data from one study group and one trial) (see Figure 1). The latter data were collected by the Delareyville study group in cooperation with Noord-Wes Koöperasie (NWK), a local farmers' cooperative, as well as an on-farm trial done in collaboration with the Ottosdal No-till Club. All the on-farm trials were implemented through the CA Farmer Innovation Programme (FIP) with funding from The Maize Trust and coordination by ASSET Research.³³

The three regions lie in the summer grain crop production area of South Africa with average annual rainfall varying between 500 mm (in the west) and 900 mm (in the east). Daily maximum temperatures during the growing season are frequently above 20 °C, especially in the west. Rainfall in these areas typically occurs in the form of thunderstorms with frequent long dry spells in between. The arable soils in these crop production areas vary considerably. However, deep, well-drained sandy soils dominate in the dryer western parts, while shallower sandy-loam soils dominate in the wetter eastern areas. These soils naturally have low soil organic carbon (SOC) content of between around 1% in the west and 2% in the east. The average SOC level in soils under annual cropland in SouthAfrica has been reduced by 46% due to tillage³⁴, and combined with bare fallow tilled fields, the soils are extremely susceptible to erosion, leading to average long-term soil loss rates from water erosion of 13 tonnes/ha/year under annual grain crops³⁵. This is about equivalent to 3 tonnes of soil lost for every tonne of maize produced per year.

On-farm trials as the basis for innovation platforms and modelling

The on-farm trials were conducted for the 2020/2021 and 2021/2022 production seasons during which the three dominant farming systems discussed above, namely CT, NT and CA/RA, were compared. The basic description of the trial design and treatments at the sites, inclusive of the crop rotations followed, are shown in Table 2.

 

Data collection

Trial data were collected on production income and input costs (expenditure) as per the typical production budget. These data were used to calculate various financial indicators, such as the gross and net margins of cash crops, cover crops and livestock production (see Table 3). Grain yield was determined per farm by the farmer using combined harvesters with digital yield report equipment, dividing the total kg of grain by the distance or area covered. All modelled yields were verified against the farm's long-term averages. Grain income per ton was derived from SAFEX rates at the time of harvest minus a standardised fee for grain handling and storage. Additional income from livestock through grazing of crop residues and cover crops was derived from farm-collected production data, that is, dry matter (DM) yield of the cover crop, feed use efficiency and feed conversion ratios to determine kg of meat produced per ha multiplied by meat prices for the type of animal (i.e. beef, sheep) used. All farm expenditure was discussed and verified with the trial farmers based on their commercial enterprise experience. Actual costs were used for fertiliser, herbicide and pesticide rates applied, multiplied per unit cost as per that season's farm expenditure sheets. Diesel prices would fluctuate during the season; therefore, one annual rate was used, based on the farmer's price per litre of diesel. All commercial farmers are granted a lower diesel price than consumer pump prices due to bulk purchases. All farm equipment (mechanisation) passes are described per system. The diesel use per pass is based on the actual cost per farm, based on farmer experience. The diesel used per farm per pass can therefore differ per trial (and mechanisation tables) depending on the type of equipment and farm conditions. Farm expenditure included all variable input costs. Overhead costs were obtained per farm and verified per farming system (CA/RA, NT, and CT). Overhead costs differed per farm and comprised administration, land purchase and labour costs.

Model assumptions

Uniform assumptions

The Microsoft Excel-based financial cost-benefit model used the above trial design and data to calculate and compare a range of financial indicators for all the production systems at the six trial sites, most notably the cumulative free cash flows (CFCFs) (after taking into consideration loan settlements and the value of livestock at the end of the modeling period) and the average free cash flow/ha in real terms (AFCs). CFCF is defined as cash flow that is available to shareholders (the owners) of a company and is free of any claims from other stakeholders. The macro assumptions (see Table 4) have been kept uniform for all three production systems.

In addition to these macro assumptions (Table 4), we kept the production per production area and crop uniform (Figure 2). These are based on the averages obtainable in each production region. For cover crops, additional tonnes of biomass from both intercropping and double cover cropping were included. Unlike cash crops, covered crop biomass (yield) was included only under the CA/RA production system.

Production system-specific assumptions

Three sets of production system-specific assumptions were necessary. These relate to the cover crop utilisation, the efficiency of the major production inputs (fertiliser, herbicides, and pest control), and the capital replacement period of each. In addition, to allow for system-wide variation under the CA/RA system, optimistic and conservative scenarios were considered. These assumptions are as follows:

1. Intercrop utilisation

• Zero for CT and NT

• CA/RA

• Optimistic scenario: based on regional average biomass (Figure 2).

• Conservative scenario: reduced to accommodate internal and external conditions that might affect the planting, growth cycle and yield of cover crops. In the Mpumalanga region, maize intercrop and soya intercrop yields were reduced from 2 tons each to 1.2 and 1.5, respectively; in the Maluti region, these were reduced to 0.7 and 1, respectively, while for the North West these were reduced to 1 and 1.5 tons, respectively. Utilisation rates were not changed but kept the same as in the optimistic scenario (DCC = 50%, SCC = 50%, WCC = 70% and IC = 70%).

2. Cover crop utilisation:

• Zero for CT and NT

• CA/RA:

• Optimistic scenario: based on regional average biomass (Figure 2).

• Conservative scenario: reduced to accommodate internal and external conditions that might affect the planting, growth cycle and yield of cover crops. In the Mpumalanga and Maluti regions, SCC and WCC yields were reduced to 6 and 1.5 tons, respectively, while in the North West, these were reduced to 10 and 1.5, respectively. Utilisation rates were not changed but kept the same as in the optimistic scenario (DCC = 50%, SCC = 50%, WCC = 70% and IC = 70%).

3. Input efficiency (fertiliser, herbicides and pest control):

• CT: 0.5% increase in input volume required per annum, adding up to an overall 110% input requirement by year 20.

• This assumption is because tillage will continue degrading the soil's fertility, which will require increasing amounts of synthetic fertilisers.^40,41

• NT: kept constant at 100% of the initial input volume requirement.

• This assumption is because, while no-till systems do not lead to physical soil degradation, they also do not lead to an adequate restoration of soil health and fertility, lacking crop diversity, biomass, and livestock integration.^42-44

• CA/RA:

• Optimistic scenario: Kept constant at 100% of the initial volume required during the first 4 years for Mpumalanga and Maluti (5 years for the North West), followed by a 10% annual decrease in volume for the next five years, and stabilised at 50% of the initial requirement thereafter.

• Conservative scenario: Kept constant at 100% of initial volume required during the first 4 years for Mpumalanga and Maluti (5 years for North West); followed by a 5% annual decrease in volume required for the next 10 years; stabilising at 50% of the initial requirement thereafter.

This assumption is based on sufficient evidence that fully integrated crop-livestock CA/RA systems lead to the restoration of soil health, increased natural fertility and, hence, the reduction of required synthetic fertiliser quantities.^45-47

4. Capital replacement period:

• Every 5 years for CT and every 8 years for NT

• CA/RA:

• Optimistic scenario: every 8 years

• Conservative scenario: every 7 years

This assumption was based on data from VKB, farmer co-workers, and justified by the reduced number of passes and implements (units) in the NT and CA/RA systems.⁴⁵ The conservative scenario is to accommodate the additional planting and harvesting of cover crops that is not applicable in the NT systems.

The modelling exercise, which ran over a period of 20 years, started with all the production systems on the same level, implying that they all had to incur the necessary costs (operating and capital) unique to each right at the start.

 

Results

We analysed the above-stated question of whether CA/RA is the more profitable practice compared to CT and NT in two ways. First, by observing the financial performance of each system by means of a year-on-year system-specific analysis of the average discounted free cash flow in real terms (AFC) (R/ha) and overall net benefit or cost (loss) achievable in each system. Figure 3 illustrates the region-specific AFCs/ha over 5-, 10-, 15- and 20-year periods to provide a regional overview of how each system performs on average. It should be noted that all cost values are inflated by 6% and all revenue values by 5%, as defined in Table 4. This implies a marginal reduction in nominal cash flows only offset by possible production changes, as highlighted above. To compare the results, we calculated real cash flows using a discount rate of 7%. Aside from any productivity gains, a declining trend in all the AFCs is thus to be expected. Second, we compared the relative financial performance of the three different systems over the long term (20 years) by means of cumulative free cash flows (CFCFs). Finally, the results were broken into a financial analysis of the cost of major production inputs, total costs of production, revenue from crop and cattle production, and debt uptake.

A comparative analysis of the average discounted free cash flow in real terms of various summer grain production systems

The AFC/ha under the CT system in Mpumalanga declined significantly from about ZAR1 960/ha in year 5 to -ZAR900 in year 20 in real terms (Figure 3). The AFCs/ha under the NT system is relatively constant for the first 10 years at approximately ZAR2 200/ha, but then declines to about ZAR560/ha in real terms by year 20 due to cost-push effects. Both the optimistic and conservative scenarios of the CA/RA systems behave differently, following an inverse trend. The AFC at year 5 ranges between ZAR900/ha and ZAR2 760/ha, peaking at almost ZAR3 600/ha in year 10, and settles between ZAR1 130/ha and ZAR3 100/ha in real terms in year 20. There are, therefore, no negative cash flows over time in real terms despite the cost-push effects. The results in both Maluti and the North West (Figure 3) mirror those in Mpumalanga, albeit at a marginally lower level in Maluti. In the North West region, CT indicates longer periods of positive returns for the CT system than Maluti and Mpumalanga.

A comparative analysis of the CFCFs of various summer grain production systems

The CFCFs of the various production systems in the three areas are shown in Figure 4. There is a consistent trend across the three regions of high initial free cash flow accumulation under the CT production system that, over time, becomes an accumulated negative cash flow. The CFCF of the NT and conservative CA/RA scenarios track each other in most cases, with the CA/RA scenario slightly higher than the NT in Mpumalanga but lower in the North West and virtually the same for the Maluti region. The CFCFs of the optimistic scenario are at a much higher level in all cases. Despite this, the pattern of the CFCF follows what is known as the investment curve, also known as the J-curve. This curve indicates an initial decline in free cash flow for a period, whereafter it takes an upward turn, cumulating net positive cash flow. The results are summarised in Table 5. The average of the two CA/RA systems in year 20 is considerably higher than that of all the other systems for all three production areas. For example, the CFCF in year 20 for Mpumalanga is estimated to be ZAR86 million, compared to -ZAR51 million for CT and about ZAR4 million for NT. That is a net difference of between ZAR137 million when compared to CT and ZAR82 million compared to NT. The smallest difference is compared to the NT system in the North West of ZAR14 million. These differences represent the financial opportunity cost of not converting to CA/RA systems. No additional economic benefit with respect to any possible environmental benefit has been included.

A comparative breakdown of the cost, revenue and debt uptake of various summer grain production systems

Considering the total direct allocated variable cost (TDAVC) (ZAR/ha), Table 6 shows that, over the 20-year period, CT had the highest % growth rate followed by NT, while CA/RA have the lowest growth rate. The total cost of the four major production inputs also shows the same trend as the TDAVC/ha with fertiliser and herbicides as key drivers.

CT generated the highest total revenue from crop production throughout the 20-year period, followed by NT, while CA/RA generated the lowest crop revenue. However, the opposite is observed for total revenue from cattle production, where both CT and NT generate the lowest revenue relative to CA/RA, which has the highest revenue at an exponential rate. Figure 5 shows the total net accumulated cattle and crop revenue. Of the three systems, CA/ RA requires the most initial cattle investment (J-curve) throughout the period.

In terms of debt (loan repayments and finance costs), CT incurred the highest uptake over the period (shown by a steep upward trendline), followed by NT, while CA/RA incurred the lowest uptake. The same was observed for wear and tear (see Figure 6).

 

Discussion

The results indicate that, in the short term (5 years), CT had the highest average free cash flow in real terms on a per-hectare basis, followed by NT with CA/RA producing the lowest returns on investment. In the long term (10-20 years), the opposite is observed, wherein CT produced the highest losses, followed by NT, while CA/RA produced the best return on investment. These results are consistent across the three regions, with minor variations.

Short-term financial implications of CT, NT and CA/RA production systems

The high initial gains under the CT system might be due to: the continued high cash flow generated from crop production (Figure 5); the absence of additional investments required for extra cattle (Table 7); and relatively lower finance costs for the farm (Figure 6) and loan repayments toward additional cattle and infrastructure investments at the start of the period (Figure 6). Similar reasons apply for the high initial gains under NT farming. These positive returns, however, accrue for a relatively longer period than CT due to: relatively lower operational costs incurred through the years, resulting in prevailing cost savings; delayed capital replacement costs; and additional ecological benefits that translate into significant economic value to NT farmers and their operations (Tables 1 and 6).^10,43,45,48 Contrariwise, the initial negative return (financial dip) observed under the CA/RA system is consistent with the general understanding in literature that farmers often experience an additional financial expense when introducing the principles of fully integrated CA/RA systems.^49,50 The results of this study confirm that the main factor contributing to the initial dip relates to the additional livestock investment required as a tool for soil restoration at the start of the period (Table 7). During the initial transition period, the incorporation of double-cover crops in the crop rotation system resulted in forgone cash crop revenue (Figure 5), while the average free cash flow of crops was still high.⁵¹ This situation changes after the transition period because the integration process has an associated time-lag effect wherein (1) the financial benefits (i.e. the reduction in inputs) of the additional financial investment and spending (on cattle) are not immediate but are dependent on the restoration process⁴⁶; and (2) the average free cash flow of cropping under CT drops due to increasing volumes and costs of total direct allocated variable and capital costs.⁵²

Medium- to long-term financial implications of CT, NT and CA/RA production systems

Looking at the CT results, the main contributing factors to this medium- to long-term decline in both AFCs/ha and CFCFs include the (1) year-on-year increase in the volume and cost of major production inputs (fertiliser, herbicides, pest control and fuel/diesel) required due to declining soil health (Table 6); (2) an overall excessive rise in the farmer's cost/ha before marketing costs (Table 6a); (3) expensive capital replacement costs (every 5 years); (4) high wear and tear expenditure (Figure 6), (5) the recurrent debt uptake for mechanisation through high loan repayments and finance costs (Figure 6) - all lead to the decline in AFCs/ha. The magnitude of this decline, however, is also largely influenced by the negative impacts associated with CT practices (Table 1 ).^51,53-55 Apart from the positive start in NT results, the results also indicate a downward trendline in the longer term that varies in magnitude of losses across the three regions. This finding supports the existing argument that the success and performance of NT is to some extent in contexts, regions and climate-specific and can be limited (Table 1). The potential additional cattle revenue forgone under both CT and NT systems also adds to the gap in the performance of the two systems relative to CA/RA (Figure 5).

Likewise, as the ecological functions and services are restored and the benefits associated with the CA/RA system accrue (Table 1), farmers start to accumulate positive financial returns on their investment (indicated by the turning point of the J-curve). These positive returns are mainly driven by the gradual reduction in the volume cost of major inputs required and the overall cost of production (Table 6). Prevailing benefits such as sustained crop productivity and long-term yields also contribute to positive returns through a year-on-year increase in crop revenue, while the benefit of additional biomass contributes to a year-on-year increase in livestock revenue (Figure 5). Significantly low loan repayments, finance costs and wear and tear (Figure 6); altogether, farm profitability increases, which strengthens the financial position and sustainability of CA/RA farmers and their farming operations.^10,41 These positive returns are sustained through the medium- to long-term period and can be observed by the upward/positive trendline after the transition period. Although the long-term upward trendline in the CA/RA system is not smooth but fluctuating (yet positive), this can be attributed to the machinery replacement cycle and the recurring double cover crop with livestock rotation included in the model configuration. When replacement takes place, free cash flow is severely compromised; and when cover crops are in rotation, the revenue from cover crop and livestock on certain fields is lower than on fields with cash crops.^2,56 It is from this premise that the debate around the financial viability of cover crops and associated trade-offs occurs.^57,58 In this case, farmers need to consider the long-term ecological benefits of cover crops and the economic value they add to the whole farming operation.

 

Conclusion

The agricultural sector is in dire need of transformation and regeneration. The challenge of feeding a growing population will continue to increase with negative environmental impacts, variable climate conditions, rising external production costs and limited land.^59,60 This, in addition to rising costs of production and farm debt uptake, a growing area under production but reduced productivity and declining number of farmers in South Africa. Farmers must start to incorporate sustainable principles geared toward climate change mitigation and adaptation, and if the adoption process is not accelerated, those lacking proper adaptation will quickly reach even higher levels of risk and unprofitable farming operations. Those who have adopted a NT farming system have already started to improve both the physical and biological characteristics of soils. However, this study emphasised the need to transcend the limitations of pure NT systems to a better alternative that will make more significant and lasting changes to soil properties, enhance yield sustainability and safeguard farm profitability. It further showed that CA/RA can relieve farmers of enormous financial risk that has the potential to grow exponentially over the medium to long term by prioritising the restoration of soil and ecosystem goods and services. Various studies have proven that CA/RA can reduce a farm's heavy reliance on expensive inputs, offer significant cost savings and loss-avoidance, and provide supplementary profitgenerating opportunities through additional livestock and feed revenue. The results of this study also corroborate existing studies by providing evidence-based support that indicates that, relative to CT and NT, CA/RA offers the best/maximum return on investment in absolute terms, and even more so on a risk-adjusted basis.

As such, this study supported the message that (1) there are sound strategies to successfully navigate through the transition period when adopting and adapting to CA/RA; (2) active farmer networks and support by sharing past knowledge and experiences of success stories can contribute significantly to the inspiration and guidance of farmers starting this new journey; and (3) often NT and CA/RA practices have less total risks than CT if applied correctly (sometimes even in the short term but especially in the long term when taking into consideration the impact of future possible drought conditions among other expected climate variations and extremes). Granting that different conclusions can be drawn from the relevant information available, the reality remains that short-term needs must be balanced with long-term environmental, food and financial sustainability. The existing body of work implores policymakers to consider this reality because the ultimate decision to encourage the adoption of or conversion to CA/RA is centred on their perception of how such a move will alter their overall business risk.

 

Acknowledgements

We thank Ms Leandri vd Elst for her editorial and administrative support in the preparation of the manuscript. We also gratefully acknowledge the comments received from the anonymous reviewers.

Funding

We thank the Maize Trust for their financial support of the Conservation Agriculture project coordinated by ASSET Research which has, in part, contributed to this study.

Data availability

Data pertaining to this study are available on request and can be found at https://assetresearch.org.za/conservation-agriculture/.

Declaration of AI use

We declare that we have not used AI tools in the preparation of this manuscript.

Authors' contributions

M.M.: Conceptualisation, methodology, data collection, data analysis, validation, data curation, writing - the initial draft, writing - revisions, project leadership, project management. N.v.S. Conceptualisation, methodology, data analysis, validation, data curation, writing - revisions. A.d.B.: Conceptualisation, writing - revisions. H.S.: Conceptualisation, methodology, data analysis, validation, data curation, writing - the initial draft, writing - revisions, project leadership, project management. J.B.: Conceptualisation, methodology, data analysis, validation, data curation, writing - the initial draft, writing - revisions, project leadership, project management. J.K.: Conceptualisation, data collection, data analysis, writing - revisions. G.T.: Conceptualisation, data collection, writing -revisions. L.E.: Conceptualisation, data collection, writing - revisions.

Competing interests

We have no competing interests to declare.

 

References

1. Thierfelder C, Baudron F, Setimela P Nyagumbo I, Mupangwa W, Mhlanga B, et al. Complementary practices supporting conservation agriculture in southern Africa. A review. Agron Sustain Dev. 2018;38(16):1-22. https://doi.org/10.1007/s13593-018-0492-8        [ Links ]

2. Mitchell J. Conservation agriculture systems. CABI Rev. 2019;14(1):1-25. https://doi.org/10.1079/PAVSNNR201914001        [ Links ]

3. El Chami D, El Moujabber M. Drought, climate change and sustainability of water in agriculture: A roadmap towards the nWrS2. S Afr J Sci. 2016;112(9/10), Art. #2015-0457. https://doi.org/10.17159/sajs.2016/20150457        [ Links ]

4. Statistics South Africa. Agricultural survey preliminary. Pretoria: StatsSA; 2019. Available from: http://www.statssa.gov.za/publications/P1101/P11012019.pdf        [ Links ]

5. Bardsley DK, Knierim A. Hegel, Beck, and the reconceptualization of ecological risk: The example of Australian agriculture. J Rural Stud. 2020;80:503-512. https://doi.org/10.1016/j.jrurstud.2020.10.034        [ Links ]

6. South African Department of Agriculture, Land Reform and Rural Development (DALRRD). Annual report. Pretoria: DALRRD; 2021. Available from: http://www.old.dalrrd.gov.za/Portals/0/Annual%20Report/DALRRD%20Annual%20Report%20%202020%20-%202021.pdf        [ Links ]

7. Van der Walt AJ, Fitchett JM. Statistical classification of South African seasonal divisions on the basis of daily temperature data. S Afr J Sci. 2020;116(9/10), Art. #7614. https://doi.org/10.17159/sajs.2020/7614        [ Links ]

8. UN Food and Agriculture Organization (FAO). An assessment of the adoption of conservation agriculture in annual crop-livestock systems in South Africa. Pretoria: FAO;2021. p. 1-28.         [ Links ]

9. Huggins DR, Reganold JP. No-till: The quiet revolution. Sci Am. 2008;1:70-77. Available from: http://geraghtyconsulting.ie/wp-content/uploads/2011/11/No-Till-The-Quiet-Revolution.pdf        [ Links ]

10. Derpsch R, Friedrich T, Kassam A, Hongwen L. Current status of adoption of no-till farming in the world and some of its main benefits. Int J Agric Biol Eng. 2010;3(1):1-25.         [ Links ]

11. Deloitte Africa Agribusiness Unit (DAAU). Four key success factors to growing agriculture opportunities in Africa. Johannesburg: DAAU; 2011. Available from: https://www.afrilogic.co.za/downloads/pdf/Agribusiness%204%20key%20success%20factors.pdf        [ Links ]

12. Luan Y Cui X, Ferrat M, Nath R. Dynamics of arable land requirements for food in South Africa: From 1961 to 2007. S Afr J Sci. 2014;110(1/2), Art. #2012-0088. https://doi.org/10.1590/sajs.2014/20120088        [ Links ]

13. Fresco LO. Some thoughts about the future of food and agriculture. S Afr J Sci. 2014;110(5/6), Art. #a0066. https://doi.org/10.1590/sajs.2014/a0066        [ Links ]

14. Bender MH. An Economic comparison of traditional and conventional agricultural systems at a county level: Appendices and expanded references. Am J Agric Res. 2001;16(1):2-15. https://doi.org/10.1017/S0889189300008808        [ Links ]

15. Mazvimavi K, Ndlovu PV Henry A, Murendo C. Productivity, and efficiency analysis of maize under conservation agriculture in Zimbabwe. Paper presented at: International Association of Agricultural Economists Conference, 20212 August 18-24; Foz do Iguaçu, Brazil. https://doi.org/10.22004/ag.econ.126767        [ Links ]

16. Swanepoel CM. Assessment of the potential of conservation agriculture management practices to sequester organic carbon in South African soils [PhD thesis]. Pretoria: University of Pretoria; 2018.         [ Links ]

17. Moswetsi G, Fanadzo M, Ncube B. Cropping systems and agronomic management practices in smallholder farms in South Africa: Constraints, challenges, and opportunities. J Agron. 2017;16:51-64. https://doi.org10.3923/ja.2017.51.64        [ Links ]

18. Sanchez AC, Kamau HN, Grazioli F, Jones SK. Financial profitability of diversified farming systems: A global meta-analysis. Ecol Econ. 2022;201(1), Art. #107595. https://doi.org/10.1016/j.ecolecon.2022.107595        [ Links ]

19. Omulo G, Birner R, Koller K, Simunji S, Daum T. Comparison of mechanized conservation agriculture and conventional tillage in Zambia: A short-term agronomic and economic analysis. Soil Tillage Res. 2022;221(1), Art. #105414. https://doi.org/10.1016/j.still.2022.105414        [ Links ]

20. Hobbs PR. Conservation agriculture: What is it and why is it important for future sustainable food production? J Agric Sci. 2007;145:127-137. https://doi.org/10.1017/S0021859607006892        [ Links ]

21. Friedrich T Derpsch R, Kassam A. Overview of the global spread of conservation agriculture. Field Actions Sci Rep. 2012;Special Issue 6:1-7. Available from: http://factsreports.revues.org/1941        [ Links ]

22. Landers JN, de Freitas PL, de Oliveira MC, da Silva Neto SP Ralisch R, Kueneman EA. Next steps for conservation agriculture. J Agron. 2021;11(12), Art. #2496. https://doi.org/10.3390/agronomy11122496        [ Links ]

23. Strauss JA, Swanepoel PA, Laker MC, Smith HJ. Conservation agriculture in rainfed annual crop production in South Africa. S Afr J Plant Soil. 2021;38(3):217-230. https://doi.org/10.1080/02571862.2021.1891472        [ Links ]

24. Burns EA. Regenerative agriculture: Farmer motivation, environment, and climate improvement. Policy Q. 2021;17(3):55-60. https://doi.org/10.26686/pq.v17i3.7133        [ Links ]

25. LaCanne CE, Lundgren JG. Regenerative agriculture: Merging farming and natural resource conservation profitably. PeerJ. 2018;6, e4428. https://doi.org/10.7717/peerj.4428        [ Links ]

26. Rhodes CJ. The imperative for regenerative agriculture. Sci Prog. 2017;100(1):80-129. https://doi.org/10.3184/003685017X14876775256165        [ Links ]

27. Blignaut J, Knot J, Smith H, Nkambule N, Crookes D, Saki A, et al. Promoting and advancing the uptake of sustainable, regenerative, conservation agricultural practices in South Africa with a specific focus on dryland maize and extensive beef production. Pretoria: ASSET Research; 2015. Available from: https://agbiz.co.za/uploads/documents/news/Newsletter/2015/150730_Promoting_ASSET.pdf        [ Links ]

28. Newton P, Civita N, Frankel-Goldwater L, Bartel K, Johns C. What is regenerative agriculture? A review of scholar and practitioner definitions based on processes and outcomes. Front Sustain Food Syst. 2020;4:1-11. https://doi.org/10.3389/fsufs.2020.577723        [ Links ]

29. Waswa SC, Mulyungi LM. Adoption of conservation agriculture in eastern Kenya: Identified and measured indicators of the sustainability of the CA practices. J Agric Ecol Res Int. 2021;22(5):52-62. https://doi.org/10.9734/JAERI/2021/v22i530202        [ Links ]

30. Knowler D, Bradshaw B. Farmers' adoption of conservation agriculture: A review and synthesis of recent research. Food Policy. 2006;32:25-48. https://doi.org/10.1016/j.foodpol.2006.01.003        [ Links ]

31. Greiner R, Patterson L, Miller O. Motivations, risk perceptions and adoption of conservation practices by farmers. Agric Syst. 2009;1(1):86-104. https://doi.org/10.1016/j.agsy.2008.10.003        [ Links ]

32. Ahnstro J, Höckert J, Bergea HL, Francis CA, Skelton P, Hallgren L. Farmers, and nature conservation: What is known about attitudes, context factors and actions affecting conservation? Renew Agric Food Syst. 2008;24(1):38-47. https://doi.org/10.1017/S1742170508002391        [ Links ]

33. ASSET Research. Project: Conservation agriculture [webpage on the Internet]. No date [cited 2024 May 13]. Available from: https://assetresearch.org.za/conservation-agriculture/        [ Links ]

34. Swanepoel CM, Swanepoel L, Smith HJ. A review of conservation agriculture research in South Africa. S Afr J Plant Soil. 2018;35(4):297-306. https://doi.org/10.1080/02571862.2017.1390615        [ Links ]

35. Le Roux JJ, Morgenthal TL, Malherbe J, Pretorius DJ, Sumner PD. Water erosion prediction at a national scale for South Africa. Water SA. 2008;34(3):305-314. https://doi.org/10.4314/WSA.V34I3.180623        [ Links ]

36. Grain South Africa (GSA). Production reports. Pretoria: GSA; 2021; 2022. Available from: https://www.grainsa.co.za/pages/industry-reports/production-reports        [ Links ]

37. South African Grain Information Services (SAgis). South African Futures Exchange.Pretoria: SAgis; 2021; 2022. Available from: https://www.sagis.org.za/safex_historic.html        [ Links ]

38. Red Meat Producers' Organization (RMIS). Prices [webpage on the Internet]. c2021 [updated 2022; cited 2024 May 13]. Available from: https://rpo.co.za/weekly-prices/        [ Links ]

39. Statistics South Africa(Stats SA). CPI history.Pretoria: Stats SA; 2021; 2022. Available from: https://www.statssa.gov.za/publications/Report-01-41-01/Report-01-41-012022.pdf        [ Links ]

40. Sosibo NZ, Muchaonyerwa P, Visser L, Barnard A, Dube E, Tsilo TJ. Soil fertility constraints and yield gaps of irrigation wheat in South Africa. S Afr J Sci. 2017;113(1/2), Art. #2016-0141. http://dx.doi.org/10.17159/sajs.2017/20160141        [ Links ]

41. Somasundaram J, Sinha NK, Dalal RC, Lal RL, Mohanty M, Naorem AK, et al. No-till farming and conservation agriculture in South Asia - issues, challenges, prospects and benefits. Crit Rev Plant Sci. 2020;39(3):236-279. https://doi.org/10.1080/07352689.2020.1782069        [ Links ]

42. Baker CJ, Saxton KE, Ritchie WR, Chamen WCT, Reicosky DC, Ribeiro F, et al. No-tillage seeding in conservation agriculture. 2nd ed. Rome: FAO; 2007. https://doi.org/10.1079/9781845931162.0000        [ Links ]

43. Bolliger A, Magid J, Amado TJC, Scora Neto F, Dos Santos Ribeiro MDF, Calegari A, et al. Taking stock of the Brazilian "zero-till revolution": A review of landmark research an farmers' practice. Adv Agron. 2006;91:47-110. https://doi.org/10.1016/S0065-2113(06)91002-5        [ Links ]

44. Pittelkow CM, Liang X, Linquist BA, van Groenigen KJ, Lee J, Lundy ME, et al. Productivity limits and potentials of the principles of conservation agriculture. Nature Adv Onl Pub. 2015;517:365-368. https://doi.org/10.1038/nature13809        [ Links ]

45. Baker CJ, Saxton KE. The 'what' and 'why' of no-tillage farming. In: Baker CJ, Saxton KE, editors. No-tillage seeding in conservation agriculture. 2nd ed. Rome: FAO and CAB International; 2007. p. 1-10. Available from: https://openknowledge.fao.org/server/api/core/bitstreams/85bbfdfe-51e2-4351-9c45-38d6d531b2d3/content        [ Links ]

46. Derpsch R. No-tillage and conservation agriculture: A progress report. In: Goddard T, Zoebisch MA, Gan YT, Ellis W, Watson A, Sombatpanit S, editors. No-till farming systems. Special Publication No. 3. Bangkok: World Association of Soil and Water Conservation; 2008. p. 7-39.         [ Links ]

47. Dube E, Chiduza C, Muchaonyerwa P High biomass yielding winter cover crops can improve phosphorus availability in soil. S Afr J Sci. 2014;110(3/4), Art. #2013-0135. https://doi.org/10.1590/sajs.2014/20130135        [ Links ]

48. Jat ML, Gathala MK, Saharawat YS, Tetarwal JP Gupta R. Double no-till and permanent raised beds in maize-wheat rotation of north-western IndoGangetic plains of India: Effects on crop yields, water productivity, profitability, and soil physical properties. Field Crops Res. 2013;149:291-299. https://doi.org/10.1016/j.fcr.2013.04.024        [ Links ]

49. UN Food and Agriculture Organization. Adoption of conservation agriculture [webpage on the Internet]. No date [cited 2024 May 13]. Available from: http://fao.org/ag/ca/6c.html        [ Links ]

50. Mafongoyaa P, Rusinamhodzib L, Sizibac S, Thierfelderd C, Mvumie BM, Nhauf B, et al. Maize productivity and profitability in Conservation Agriculture systems across agro-ecological regions in Zimbabwe: A review of knowledge and practice. Agric Ecosyst Environ. 2016;220:211-225. https://doi.org/10.1016/j.agee.2016.01.017        [ Links ]

51. Knott S, Hoffmann W, Strauss J. The whole farm financial implications of different tillage systems on different crop rotations in the Swartland area of the Western Cape, South Africa. Int J Agic Manag. 2016;6(1):20-31.         [ Links ]

52. Thierfelder C, Rusinamhodzi L, Ngwira AR, Mupangwa W, Nyagumbo I, Kassie GT, et al. Conservation agriculture in Southern Africa: Advances in knowledge. Renewable Agric Food Syst. 2014;30(4):246-258. https://doi.org/10.1017/S1742170513000550        [ Links ]

53. Smith P Milne R, Powlson DS, Smith JU, Falloon P Coleman K. Revised estimates of the carbon mitigation potential of UK agricultural land. Soil Use Manag. 2006;16(4):293-295. https://doi.org/10.1111/j.1475-2743.2000.tb00214.x        [ Links ]

54. Brussaard L, de Ruiter PC, Brown GG. Soil biodiversity for agricultural sustainability. Agric Ecosyst Environ. 2007;121:233-244. https://doi.org/10.1016/j.agee.2006.12.013        [ Links ]

55. Chivenge PP, Murwira HK, Giller KE, Mapfumo P, Six J. Long-term impact of reduced tillage and residue management on soil carbon stabilization: Implications for conservation agriculture on contrasting soils. Soil Tillage Res. 2007;94(2):328-337. https://doi.org/10.1016/j.still.2006.08.006        [ Links ]

56. Smith HJ, Blignaut JN, Van Zyl A, Yssel D, Vienings E. Determining the carbon footprint of different maize farming systems within the summer rainfall crop production area in South Africa, Phase 3. Interim progress report to The Maize Trust. Pretoria: ASSET Research; 2021        [ Links ]

57. Scopel E, Triomphe B, Affholder F, Da Silva FAM, Corbeels M, Xavier JHV et al. Conservation agriculture cropping systems in temperate and tropical conditions, performances, and impacts. A review. Agron Sustain Dev. 2013;33:113-130. https://doi.org/10.1007/s13593-012-0106-9        [ Links ]

58. Pannell DJ, Llewellyn RS, Corbeels M. The farm-level economics of conservation agriculture for resource-poor farmers. Agric Ecosyst Environ. 2014;187:52-64. https://doi.org/10.1016/j.agee.2013.10.014        [ Links ]

59. Lobell DB, Burke MB, Tebaldi C, Mastrandrea MD, Falcon WP, Naylor RL. Prioritizing climate change adaptation needs for food security in 2030. Science. 2008;319:607-610. https://doi.org/10.1126/science.1152339        [ Links ]

60. Godfray HCJ, Beddington JR, Crute IR, Haddad L, Lawrence D, Muir JF, et al. Food security: The challenge of feeding 9 billion people. Science. 2010;327:812-818. https://doi.org10.1126/science.1185383        [ Links ]

 

 

Correspondence:
Mary Maluleke
Email: maryameliamaluleke@gmail.com

Received: 24 Oct. 2023
Revised: 13 May 2024
Accepted: 15 May 2024
Published: 31 July 2024

 

 

Editors: Teresa Coutinho; Lindah Muzangwa
Funding: None