Africa’s land reform

Federal Department of Economic Affairs, Education and Research EAER Agroscope

www.agroscope.ch I good food, healthy environment

Siphe Zantsi, G. Mack, Anke Möhring, Kandas Cloete, Jan Greyling and Stefan Mann

29 ^th October 2019

Building an agent-

based model for South

Africa’s land reform

Building an agent-based model for South Africa’s land reform | Agroeconet 29^thOctober 2019 2 Siphe Zantsi, Gabi Mack, Anke Moehring, Kandas Cloete, Jan Greyling and Stefan Mann

Presentation Outline

 Motivation

 Historical background of land reposition

 South African land reform and its components

 Progress with land redistribution

 Model scenarios and research questions

 Model description

 First pilot results of a baseline scenario and discussion

 Preliminary conclusions and way forward

Building an agent-based model for South Africa’s land reform | Agroeconet 29^thOctober 2019 3 Siphe Zantsi, Gabi Mack, Anke Moehring, Kandas Cloete, Jan Greyling and Stefan Mann

 Like many other African, Asian and American countries, South Africa was colonised and land was forcefully taken from the natives .

 Similar to other former colonial states, when the first democratic government took power in 1994, a three-pronged land reform policy was adopted based on World Bank “willing seller – willing buyer” (WS-WB).

 Three prongs:

1. Land tenure

2. Land Restitution 3. Land Redistribution

 Our study focuses on Land Redistribution prong.

Historical background of land reposition

Building an agent-based model for South Africa’s land reform | Agroeconet 29^thOctober 2019 4 Siphe Zantsi, Gabi Mack, Anke Moehring, Kandas Cloete, Jan Greyling and Stefan Mann

Skewed racial land distribution in South Africa

 Unfair distribution of land:

 Dualistic agricultural farm structure

 ±2.3 million smallholders farming on 14% of land

 ± 28 000 commercial farmers farming on 80% of land

 0.05% of SA’s ±56 million population

Building an agent-based model for South Africa’s land reform | Agroeconet 29^thOctober 2019 5 Siphe Zantsi, Gabi Mack, Anke Moehring, Kandas Cloete, Jan Greyling and Stefan Mann

 ~ 10% of agricultural commercial farm land (78 413 227) have been redistributed since 1994.

 A plethora of challenges have been cited for the perceived slow progress in land redistribution.

 Among the cited reasons is failure of the WS-WB, such that there is not enough land on the open market.

 Further, reason is that there is no sufficient budget to pursue land redistribution at a faster pace as desired.

 However, there is no scientific empirical evidence of such claims.

Progress with land redistribution so far

Building an agent-based model for South Africa’s land reform | Agroeconet 29^thOctober 2019 6 Siphe Zantsi, Gabi Mack, Anke Moehring, Kandas Cloete, Jan Greyling and Stefan Mann

Pace of farm redistribution across the country

0 50 100 150 200 250 300 350 400

Redistributed farms in the past 10 years

EC FS GP KZN LP MP NC NW WC

Building an agent-based model for South Africa’s land reform | Agroeconet 29^thOctober 2019 7 Siphe Zantsi, Gabi Mack, Anke Moehring, Kandas Cloete, Jan Greyling and Stefan Mann

Public expenditure on land reform

Building an agent-based model for South Africa’s land reform | Agroeconet 29^thOctober 2019 8 Siphe Zantsi, Gabi Mack, Anke Moehring, Kandas Cloete, Jan Greyling and Stefan Mann

 Unlike the pure agricultural sciences, in agricultural economics it is not possible to do experiments with farm households.

 Kremmydas (2012) has argued that Agent Based Models are used for agricultural policy as ‘virtual laboratory experiments’.

 Thus, modeling and simulation have emerged to provide a solution for testing the impact of policy scenarios analysis in the economic and social sciences.

 ABM has been widely applied in modelling land use impacts (see Berger, 2001; Berger et al. 2006, Mohring et al., 2016; Berger et al., 2017), among others.

 However, in South African land reform, ABM has been hardly applied.

Why do we need ABM for modelling land reform

policy?

Building an agent-based model for South Africa’s land reform | Agroeconet 29^thOctober 2019 9 Siphe Zantsi, Gabi Mack, Anke Moehring, Kandas Cloete, Jan Greyling and Stefan Mann

 How much land could potentially be available on the market from farmers willing to exit? Is this land more or less than the current redistribution rate?

 What type of farm land will be available (grazing, field crops, forestry, grapes)?

 If this land is subdivided, how much farm income can we get? Is the income reasonable to attract smallholders willing to move to the commercial farms?

 How much budget will the state need to rent the available farms and provide operating capital for the new emerging farmers?

Objectives and scenarios

ILUPSA- state of work / 17.07.2018 10 Anke Möhring

Model description: Definition of agent population

Emergent Farm (EF) Commercial

farms

Small holder

remaining SH

Remaining Commercial

Farms (CF)

move without intervention

From T8

 new CF

T ₁ T _n

ILUPSA- bidding process / 06.06.2019 11 Anke Möhring & Kandas Cloete

Farm Optimization within the bidding process

Farm Optimization within the bidding process Farm Optimization within

the bidding process Farm Optimization within the

bidding process

EmergFarm EF_T0 = Available parcel after subdividing

Commer- cial Farmer B

Parcel 1 PF

 Income change

Parcel 1 PF

 Income change Commer-

cial Farmer A

Parcel 1 Sales (YieldC

+

YieldL

) - Cost(InvC

+

OpC

)

 Income after optimisation

Parcel 1 PF

 Income change Emerg.

Farm T1toT7

+ + +

Bidding process in year one

Building an agent-based model for South Africa’s land reform | Agroeconet 29^thOctober 2019 12 Siphe Zantsi, Gabi Mack, Anke Moehring, Kandas Cloete, Jan Greyling and Stefan Mann

 A multi-stage sampling approach was employed to sample 833 farmers.

 Sample was done in three provinces that house >60% of smallholders in the country.

 Face to face interviews

 Data comprised

 Farmer demographics

 Production- cost and output

 Aspirations

 Willingness to relocate to commercial farms

Data-base for modelling smallholders

Building an agent-based model for South Africa’s land reform | Agroeconet 29^thOctober 2019 13 Siphe Zantsi, Gabi Mack, Anke Moehring, Kandas Cloete, Jan Greyling and Stefan Mann

Modelling the typical homeland setting of smallholder farms

Building an agent-based model for South Africa’s land reform | Agroeconet 29^thOctober 2019 14 Siphe Zantsi, Gabi Mack, Anke Moehring, Kandas Cloete, Jan Greyling and Stefan Mann

 Data collection based on an online survey via survey monkey with 90% response rate

 Survey in all provinces

 Data comprised

 Farmer demographics

 Production- cost and output

 Farm income

 Willingness to exit or to partially exit

Data-base for modelling commercial

farms

Building an agent-based model for South Africa’s land reform | Agroeconet 29^thOctober 2019 15 Siphe Zantsi, Gabi Mack, Anke Moehring, Kandas Cloete, Jan Greyling and Stefan Mann

Distribution of commercial farms in S.A

Building an agent-based model for South Africa’s land reform | Agroeconet 29^thOctober 2019 16 Siphe Zantsi, Gabi Mack, Anke Moehring, Kandas Cloete, Jan Greyling and Stefan Mann

Province Count Actual share ideal share Add New total New share

Limpopo 68 7 7 120 188 7

KwaZulu Natal 139 15 9 90 229 9

Mpumalanga 61 6 9 170 231 9

Western Cape 464 49 17 0 464 18

Eastern Cape 104 11 10 150 254 10

Gauteng 10 1 4 100 110 4

North West 24 3 12 290 314 12

Northern Cape 38 4 13 300 338 13

Free State 31 3 19 470 501 19

South Africa 939 100 100 1690 2629 100

Database for modelling the commercial farms

Building an agent-based model for South Africa’s land reform | Agroeconet 29^thOctober 2019 17 Siphe Zantsi, Gabi Mack, Anke Moehring, Kandas Cloete, Jan Greyling and Stefan Mann

Modelling the heterogenous production types of commercial

farms

Building an agent-based model for South Africa’s land reform | Agroeconet 29^thOctober 2019 18 Siphe Zantsi, Gabi Mack, Anke Moehring, Kandas Cloete, Jan Greyling and Stefan Mann

1. Maintaining the status quo — voluntary exits of commercial farmers

2. Preferential smallholder produce procurement a Low EF price increase 5%

b Medium EF price increase 10%

c High EF price increase 15%

3. Expropriation scenarios

a Expropriation with compensation - 50%

b Expropriation with compensation - 25%

c Expropriation without compensation - 0%

4. Land tax

a Low land tax increase 10%

b Medium land tax increase 20%

c High land tax increase 30%

5. Operational Subsidies

a High subsidy

6. Transferred land switch to production of EF's original crop

ILUPSA scenarios and focus of this presentation

Building an agent-based model for South Africa’s land reform | Agroeconet 29^thOctober 2019 19 Siphe Zantsi, Gabi Mack, Anke Moehring, Kandas Cloete, Jan Greyling and Stefan Mann

First results for a pilot model: Land redistribution

Total commercial farm land(50

farms);

424659; 88%

Redistributed land; 60238;

12%

Total Land

Dryland;

60179; 100%

Irrigated land;

60; 0%

Redistributed Land

Building an agent-based model for South Africa’s land reform | Agroeconet 29^thOctober 2019 20 Siphe Zantsi, Gabi Mack, Anke Moehring, Kandas Cloete, Jan Greyling and Stefan Mann

First results for a pilot model: Land redistribution

Building an agent-based model for South Africa’s land reform | Agroeconet 29^thOctober 2019 21 Siphe Zantsi, Gabi Mack, Anke Moehring, Kandas Cloete, Jan Greyling and Stefan Mann

First results for a pilot model: Land redistribution

14’592 14’592

15’167 15’049

118 0

5000 10000 15000 20000 25000 30000 35000 40000

Commercial farm before land redistribtion

Commercial farm after land redistribtion

Emergent Farms

Arable and special crops (ha)

Cereals and grains Fruits Vegetables Graps Berries, nuts, citus,tea

Building an agent-based model for South Africa’s land reform | Agroeconet 29^thOctober 2019 22 Siphe Zantsi, Gabi Mack, Anke Moehring, Kandas Cloete, Jan Greyling and Stefan Mann

First results for a pilot model: Income distribution

49’706’578

55’874’008

1’486’888

0 10’000’000 20’000’000 30’000’000 40’000’000 50’000’000 60’000’000

Commercial farms before land redistribtion

Commercial farms after land redistribution

Emergent Farms

Average income per year in Rands

Building an agent-based model for South Africa’s land reform | Agroeconet 29^thOctober 2019 23 Siphe Zantsi, Gabi Mack, Anke Moehring, Kandas Cloete, Jan Greyling and Stefan Mann

 Can the farm income on subdivided farms attract potential emerging farmers currently farming on former homelands?

 YES.

 Average aspirational income for smallholders: R39 339 – R66 877/ production season or cycle (Zantsi & Mack, 2019).

 Both smallholder and emergent farm incomes > poverty line (R1 200/person/month based on StatsSA, 2018).

First results for a pilot model: Income distribution

Building an agent-based model for South Africa’s land reform | Agroeconet 29^thOctober 2019 24 Siphe Zantsi, Gabi Mack, Anke Moehring, Kandas Cloete, Jan Greyling and Stefan Mann

Investment costs (land acqui.) for EF

Operational

costs (prod.) for EF

Total costs for EF

Estimated State costs for EF

Mean (ZAR)

582 600 1 993 003 2 575 603 43 270 130 400

First results for a pilot model: required budget for land redistribution in ZAR

According to the NDP (2030 strategic plan), the state wants to

redistribute at least 30% (~ 8400 farms) of commercial farm land

Building an agent-based model for South Africa’s land reform | Agroeconet 29^thOctober 2019 25 Siphe Zantsi, Gabi Mack, Anke Moehring, Kandas Cloete, Jan Greyling and Stefan Mann

 Land availability: South Africa cannot solely rely on WS-WB approach to achieve land redistribution.

 Mostly land of poor quality becomes available for redistribution.

 Farm size and farm income on the subdivided redistribution farms can attract potential emerging farmers, despite the poor quality farms.

 In order to achieve land redistribution faster, state needs to allocate much more funds than the 2016 expenditure.

 A well organised and coordinated support for emergent farmers will be required to achieve land redistribution.

Preliminary conclusions

Building an agent-based model for South Africa’s land reform | Agroeconet 29^thOctober 2019 26 Siphe Zantsi, Gabi Mack, Anke Moehring, Kandas Cloete, Jan Greyling and Stefan Mann

 Alternative methods of making land available for redistribution are needed.

 Most of the alternatives are among our list of next scenarios

 Competition for markets will disadvantage emerging farmers because of small farm size and therefore, procurement

strategies will be necessary.

 E.g. Smallholder produce procurement

 A definitive period of support for emerging farmers is needed to have a sufficient budget.

Lessons drawn and implications for next scenarios

Building an agent-based model for South Africa’s land reform | Agroeconet 29^thOctober 2019 27 Siphe Zantsi, Gabi Mack, Anke Moehring, Kandas Cloete, Jan Greyling and Stefan Mann

Danke!

Agroscope good food, healthy environment

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Building an agent-based model for South Africa’s land reform | Agroeconet 29^thOctober 2019 28 Siphe Zantsi, Gabi Mack, Anke Moehring, Kandas Cloete, Jan Greyling and Stefan Mann

Land item Hectares

South Africa total 122 518 143

State-owned land ^{10 566 215}

Nature conservation, national parks, etc. ^{7 448 764}

State forests ^{1 812 478}

Department of Water Affairs ^{575 723}

Department of Defence ^{688 127}

Correctional Services ^{41 123}

Urban areas, towns and villages ^{11 357 935}

Farm land under traditional tenure ^{18 036 773}

Land use change due to urban sprawl, mining, expansion of parks and forests since 1994

4 143 993

Total area of farm land under freehold ^{78 413 227}

Historical background of land reposition