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Faculteit Bio-ingenieurswetenschappen

Academiejaar 2011 2012

Are conservation agriculture practices an interesting option for the smallholder farmer communities of the

Okhahlamba Local Municipality, KwaZulu-Natal, South Africa?

Romain Elleboudt Promotor: Dr. ir. Stijn Speelman

Masterproef voorgedragen tot het behalen van de graad van Master in de bio-ingenieurswetenschappen: Milieutechnologie

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Copyright declaration This is an unpublished Masters of Science thesis and is not prepared for further distribution. Both the author and the promoter give the permission to use this thesis for consultation and to copy parts of it for personal use. Every other use is subject to the copyright laws; more specifically the source must be explicitly specified when using results from this thesis. The Promoter The Author Dr. ir. Stijn Speelman Romain Elleboudt

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Acknowledgments First of all, I would like to thank my promoter Dr. Stijn Speelman whose approachability and constructive feedback kept my motivation high at all stages of progress. I would also like to thank Jan Cools and Tom DHaeyer at ANTEA for their guidance and their coordination efforts that made the integration of this master thesis in the AFROMAISON project and my research trip to South Africa possible. Next, a lot of my gratitude goes to people in South Africa. I am thankful to the entire team of the Institute of Natural Resources, in particular Dr. Chris Dickens, Fonda Lewis, Kate Pringle, Leo Quayle, Dave Cox, Jon McCosh, Zibonele Nxele, Jenny Mitchell, Jackie Robinson, Angela Pillay, Nisha Rabiduth and Mandisa Ndaba. They all contributed to a wonderful and enriching experience in South Africa. A special thank to Dr. Chris Dickens for having reviewed the draft version of my thesis and made pertinent remarks and suggestions. Many thanks also to the team of the African Conservation Trust, in particular Meridy Pfotenhauer and Khumbulani Ndaba. Without them, my fieldwork in the Mnweni Valley would not have been possible. Thanks to all the farmers in the Mnweni Valley who participated to the interviews and helped me grasp the realities faced by smallholder farmers in the Okhahlamba Local Municipality. I also wish to thank Dr Terry Everson and Monique Salomon at the University of KwaZulu-Natal for their advice. Finally, I am very grateful to Dr. Hendrik Smith who provided the data of the LandCare project and was always available for further explanations.

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Table of contents Acknowledgments .............................................................................................................................. iii Table of contents ................................................................................................................................. iv List of abbreviations ........................................................................................................................... vi Abstract ..................................................................................................................................................vii Samenvatting ..................................................................................................................................... viii 1 Chapter 1: Introduction ............................................................................................................... 1 1.1 Socio-­‐economic and agricultural context of the municipality ............................................. 1 1.1.1 Background ...................................................................................................................................................... 1 1.1.2 The two faces of agriculture in the municipality.............................................................................. 1

1.2 Environmental issues in the Okhahlmaba Local Municipality ............................................ 5 1.2.1 Importance of the natural capital of the OLM for the provision of ecosystem services .. 5 1.2.2 Land uses threatening the ecosystem services ................................................................................. 6

1.3 Conservation agriculture for sustainable land management in the communal areas of the OLM .......................................................................................................................................................... 7 1.4 Poor adoption of conservation agriculture by South African smallholders ................... 8 1.5 Definition of the research questions and outline of research approach........................ 10

2 Chapter 2: Literature review .................................................................................................. 11 2.1 Conservation agriculture ................................................................................................................ 11 2.1.1 Principles and practices .......................................................................................................................... 11 2.1.2 Benefits of CA ............................................................................................................................................... 12 2.1.3 -­‐controlling and water-­‐regulating practice ............................ 13 2.1.4 Impacts of CA on yield and yield variability .................................................................................... 14 2.1.5 CA in the South African context ............................................................................................................ 16

2.2 Efficiency analysis .............................................................................................................................. 18 2.2.1 Introduction ................................................................................................................................................. 18 2.2.2 Different approaches to efficiency analysis..................................................................................... 18 2.2.3 Data envelopment analysis .................................................................................................................... 19 2.2.4 Efficiency analysis for the comparison of different technologies .......................................... 25 2.2.5 Efficiency analysis and sustainable land management............................................................... 25 2.2.6 Expanding the classical EA to fit specificities of CA ..................................................................... 26 2.2.7 Analysing efficiency scores with a Tobit regression.................................................................... 28

3 Chapter 3: Materials and methods ....................................................................................... 30 3.1 Identification of constraints to CA adoption in the OLM ...................................................... 30 3.2 Efficiency analysis .............................................................................................................................. 30 3.2.1 Data description .......................................................................................................................................... 30 3.2.2 Data processing and assumptions ....................................................................................................... 33 3.2.3 Methodology ................................................................................................................................................. 34

4 Chapter 4: Results ....................................................................................................................... 37 4.1 Constraints to the adoption of CA by smallholders in the OLM ......................................... 37 4.1.1 Biophysical constraints ............................................................................................................................ 37 4.1.2 Technical constraints ................................................................................................................................ 39 4.1.3 Infrastructural constraints ..................................................................................................................... 40 4.1.4 Financial constraints ................................................................................................................................. 43 4.1.5 Societal and cultural constraints ......................................................................................................... 43 4.1.6 Policy constraints ....................................................................................................................................... 45

4.2 Efficiency analysis .............................................................................................................................. 46 4.2.1 Preliminary analysis ................................................................................................................................. 46 4.2.2 Calculation of technical efficiencies .................................................................................................... 47 4.2.3 Calculation of allocative and economic efficiencies ..................................................................... 49

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4.2.4 Tobit regression .......................................................................................................................................... 50 5 Chapter 5: Discussion and recommendations .................................................................. 52 5.1 Efficiency analysis .............................................................................................................................. 52 5.1.1 Yield improvements .................................................................................................................................. 52 5.1.2 Efficiency measures ................................................................................................................................... 52 5.1.3 The way forward ........................................................................................................................................ 54

5.2 Constraints to adoption of CA in the OLM .................................................................................. 54 5.3 Providing the financial incentive for the adoption of CA: PES ........................................... 55

6 Conclusions ................................................................................................................................... 56 7 References ..................................................................................................................................... 57 8 Appendix 1: Agrochemical inputs used for the farmer-­‐managed trials of the Bergville/Emmaus LandCare project ......................................................................................... 66 9 Appendix 2: Description of some of the procedures of the farmer-­‐managed trials ... 67

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List of abbreviations

AE: Allocative efficiency

CA: Conservation agriculture

CRM: Crop-residue mulching

CRS: Constant Returns to Scale

CT: Conventional tillage

DEA: Data Envelopment Analysis

DMU: Decision making unit

DMC: Direct-seeding mulch-based cropping

EA: Efficiency analysis

FAO: Food and Agriculture Organisation of the United Nations

GM: Genetically modified

KZN: KwaZulu-Natal

MAP: Mean annual precipitation

NT: No-tillage

OLM: Okhahlamba Local Municipality

PES: Payment for ecosystem services

SE: Scale efficiency

SFA: Stochastic frontier analysis

SLM: Sustainable land management

SOM: Soil organic matter

SWCT: Soil and water conservation technologies

TE: Technical efficiency

TFP: Total Factor Productivity

TP: Traditional practices

VRS: Variable Returns to Scale

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Abstract The Maloti-Drakensberg area in South Africa is a very important region regarding the provision of land- and water-based ecosystem services. However, several unsustainable land management practices in the region are reducing both the quantity and quality of these services. One of the threats comes from the agricultural activities in the smallholder farmer communities of the Okhahlamba Local Municipality (OLM), which lead to severe land degradation and soil erosion. Conservation agriculture (CA) has the potential to address these problems while at the same time improving the yields of the farmers. By combining minimum soil disturbance (reduced or no tillage) with a permanent organic soil cover and crop rotations and associations, CA ensures the conservation of soil and water resources in the long term. Several projects have tried to promote CA among smallholder communities in South Africa, but adoption has been relatively poor and in most cases abandonment has been observed once the free input delivery assured by the projects came to an end. This thesis identifies the most important constraints that hamper the sustained adoption of CA by small-scale farmers in the OLM. The constraints can be classified as biophysical, technical, financial, infrastructural, cultural and political. While most constraints apply to traditional subsistence farming in Africa in general, the infrastructural and political constraints that characterise the OLM act as distinct bottlenecks to broad-scale adoption. Policy makers should address these problems first, if future projects aiming at promoting CA are to be really successful. Although CA generally requires larger quantities of agrochemical inputs (in particular herbicides), it is often claimed to improve input-use efficiency and increase farm income. Therefore, this thesis also compares CA and conventional agriculture through an efficiency analysis, based on the data from farmer-managed trials of a local CA project. Technical, allocative and economic efficiency are calculated for both practises using a data envelopment analysis (DEA) approach. Subsequently, the efficiencies scores are regressed in a Tobit model to assess the influence of the adopted practise and the evolution of efficiencies over time. The adoption of CA shows to have a significant negative impact on both technical and economic efficiency. In addition, there is no clear trend of improving efficiency for CA over the four years of the project. The lower efficiency of CA might be due to with agrochemical inputs, leading to an inappropriate and inefficient utilization. Moreover, four years might not be enough for the benefits of CA in terms of yields to fully materialise. This

-term experimental data. In any case, the lower technical and economic efficiency in the initial years of practice might act as a disincentive for adoption by the resource-constrained and risk averse smallholders of the OLM. On the other hand, several environmental benefits of CA are located at the societal level and manifest almost immediately after the adoption of CA (such as the reduction of the siltation of watercourses). For these reasons, a payment for ecosystem services (PES) scheme could be implemented to provide the necessary financial incentive for CA adoption and thus secure the provision of ecosystem services in the long term.

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Samenvatting De Maloti-Drakensberg regio in Zuid-Afrika vervult een cruciale rol bij het leveren van ecosysteemdiensten, zowel met betrekking tot water als bodem. Een aantal niet-duurzame landbeheer praktijken verminderen echter zowel de kwantiteit als de kwaliteit van deze diensten. Een van de bedreigingen wordt veroorzaakt door de landbouwactiviteiten van de kleinschalige boerengemeenschappen van de Okhahlamba Local Municipality (OLM), die leiden tot ernstige bodemerosie en -degradatie. Conservation agriculture (CA) biedt de mogelijkheid om deze problemen aan te pakken en tegelijkertijd betere opbrengsten te garanderen voor de boeren. Door het combineren van een minimale bodemverstoring (verminderde of geen grondbewerking) met een permanente organische bodembedekking en teeltwisselingen zorgt CA voor betere waterretentie en het behoud van bodemvruchtbaarheid. Meerdere pogingen werden reeds ondernomen om het gebruik van CA aan te moedigen bij kleinschalige boeren in Zuid-Afrika, maar het succes van CA blijft zeer beperkt en in de meeste gevallen keren de boeren terug naar hun traditionele praktijken wanneer de financiële ondersteuning ophoudt. In deze masterproef worden de belangrijkste barrières onderzocht die de aanwending van CA door kleinschalige boeren in de OLM belemmeren. De barrières zijn zowel van biofysische, technische, financiële, infrastructurele, culturele en politieke aard. De meeste daarvan zijn van toepassing op de traditionele zelfvoorzienende landbouwsector in Afrika in het algemeen, maar de infrastructurele en politieke beperkingen die de OLM kenmerken, vormen specifieke knelpunten. Beleidsmakers zouden eerst deze problemen moeten aanpakken indien ze de slaagkansen van toekomstige CA projecten wensen te verbeteren. Hoewel CA meestal grotere hoeveelheden agrochemische producten (voornamelijk herbiciden) vereist dan traditionele landbouw, wordt vaak beweerd dat de productie efficiëntie hoger ligt en dat de landbouwinkomens verbeteren. Daarom wordt in het tweede deel van deze masterproef door middel van een efficiëntie analyse CA vergeleken met traditionele landbouw, gebruik makend van experimentele data van een lokaal CA project. Technische, allocatieve en economische efficiëntie worden berekend voor beide praktijken met behulp van data envelopment analysis (DEA). Vervolgens wordt met een Tobit regressie de invloed van de praktijken nagegaan en wordt de evolutie van de efficiënties over de tijdsspanne van het project geanalyseerd. Het gebruik van CA blijkt een significant negatieve impact te hebben op zowel de technische en economische efficiëntie. Bovendien is er geen duidelijke trend van een verbetering van de efficiëntie van CA tijdens het vierjarig project. De lagere efficiëntie van CA valt mogelijks te verklaren door het feit dat de kleine boeren weinig ervaring hebben met het gebruik van agrochemische producten, waardoor ze deze verkeerd of inefficiënt gebruiken. Bovendien is het mogelijk dat vier jaar niet genoeg is opdat de voordelen van CA in termen van opbrengsten volledig tot uiting zouden komen. Dit benadrukt de noodzaak om de efficiëntie van CA ook op basis van lange termijn experimenten na te gaan. In ieder geval kan men verwachten dat de lagere technische en economische efficiëntie, die in de eerste jaren van CA toepassing waarschijnlijk zijn, de arme, risicomijdende boeren van de OLM ontmoedigd om over te schakelen op CA. Anderzijds, komen de positieve effecten van CA op de ecosysteemdiensten de hele maatschappij ten goede en zijn deze effecten meestal onmiddellijk waarneembaar (bv. een merkbare vermindering van de verzilting van rivieren). Daarom zou men een payment for ecosystem services (PES) systeem tot stand kunnen brengen, die de nodige financiële prikkel voor het gebruik van CA teweegbrengt en op die manier de bescherming van de ecosysteemdiensten garandeert op lange termijn.

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1 Chapter 1: Introduction

1.1 Socio-­‐economic and agricultural context of the municipality

1.1.1 Background The Okhahlamba Local Municipality (OLM) is situated in the province of KwaZulu-Natal, South Africa. It is one of the five Local Municipalities that fall under the UThukela District Municipality. It is bordered in the west by the international boundary with Lesotho, the Free State province in the north and three other Local Municipalities in the east and south. The municipality is predominately rural with only a few small urban areas in Bergville, Winterton, Cathkin Park and Geluksberg. The most notable natural feature of the region are the Drakensberg mountain range bordering the municipality in the west and the Thukela River taking source in these mountains and then flowing eastward through the rest of the KwaZulu-Natal (KZN) Province. A number of dams have been constructed along the river, as part of large water transfer schemes. These two major natural elements, coupled with the past segregation of KZN into commercial and communal tenure areas, have been the main influences for the settlement patterns and development in the region (OLM, 2012). The population of the municipality is estimated around 151 441 people (2007 census), with 28 509 households, the predominant group being the black Africans (85%). The level of education of the residents is very low. 38% of the people over the age of twenty have had no schooling at all and only 35% has had an education higher than primary school (OLM, 2012). In general there are very few employment opportunities. This has resulted in relatively high unemployment levels, both for skilled and unskilled labour. The unemployment rate is 18%,

1. The latter group includes a high number of people being unable to work because of HIV/AIDS. As a matter of fact, the municipality is struck by a high rate of HIV/AIDS infections. The lack of employment opportunities leads to dramatic figures regarding income levels. 82% of the population does not receive any form of income, while only 4% of the remaining part earns more than 800 ZAR2 per month (OLM, 2010). Infrastructure and basic service provision form a serious problem in the municipality. Only 63% of the households have a direct access to water within their dwelling. The majority of the households (75%) does not have access to basic sanitation. 52% uses pit latrines, which results in ground and surface water contamination and risks of waterborne diseases. Bulk electricity supply in the area is very limited. In the rural areas there is clear lack of all-weather roads, while the existing infrastructure is in poor conditions due to lack of maintenance. Most members of the rural communities live in traditional houses made from mud blocks and with a thatch or corrugated iron roof (OLM, 2012).

1.1.2 The two faces of agriculture in the municipality The OLM consists of a total of 344 000 hectares, of which 23% is available for arable

1 As defined by Statistics South Africa, this group includes full time students, housewives, the disabled who cannot work, retired people and others who cannot work. 2 ZAR stands for South African Rand, the currency of South Africa. On the 19th of August 2012 the exchange rate with the euro was 1 ZAR = 0,0974 EUR.

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production (inclusive of afforestation) (OLM, 2012). Traditionally, agriculture has always been an important economic activity. However, there is a clear difference between the commercial agriculture on large properties of privately owned farmland and the subsistence agriculture by smallholder farmers on communal land. As can be seen in Figure 1, there is clear geographical division between the two forms of agriculture. While the commercial farms, which are mainly owned by white farmers, are located on the lower and more fertile lands within the municipality, most of the small-scale farming occurs in the higher, less fertile parts, at the foothills of the Northern Drakensberg mountains. This is a direct result of the previous segregation policies that reserved the fertile land for white farmers, forcing the poor rural population to marginal areas (Durning, 1990; Arnalte, 2006).

Figure 1 - Map of the Okhahlamba Local Municipality with the major land cover classes (based on

2008 KZN Wildlife Land Cover). The red colour indicates subsistence, smallholder agriculture, while green stands for commercial, large-scale agriculture. Courtesy of the Institute of Natural Resources,

Pietermaritzburg.

1.1.2.1 Commercial agriculture The major commercial farming activities in the region consist of beef, mutton and dairy production combined with cropping of both dryland crops like maize, potatoes and soybeans and irrigated crops like wheat and vegetables (Wood, 2011). Because of the severe winters, the palatability on the natural rangelands is highly seasonal, requiring the farmers to

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supplementary feed their animals with fodder crops and cultivated pastures. With the support of the pre-democracy government, the commercial farmers were able to raise capital to develop the necessary infrastructure, such as impounding basins and irrigation systems (Zunckel, 2003). Comm (OLM, 2012). It still is an important economic sector although its relative importance in terms of turnover is diminishing.

1.1.2.2 Small-­‐scale agriculture in the communal areas In the communal areas in the west of the municipality the situation is totally different. The area consists of two tribal territories, the Amangwane and the Amazizi, where the formally recognized Traditional Authorities are in place. These areas are characterized by high settlement densities in the planned areas and more sparse and scattered settlements in the more remote areas (OLM, 2012). In the Amazizi area the settlements are concentrated along the Thukela river before it flows into the northern part of the artificial lake created by the Woodstock dam. In the Amangwane area they concentrate around this lake, in the Mnweni valley and around the little town of Emmaus (see Figure 1). In most of the wards of the communal areas the so- d to a division of the land into three main resource areas: the homestead area, the crop fields and the rangeland (van Niekerk, 2007). This division is still visible today, although customary laws and traditional land planning have been put in place again after the fall of the apartheid regime. Crops and vegetables Most households have received some land from the local chief (Nkosi) but this land remains community-owned. The traditional system provide

, but there is no real individual ownership through title deeds (Bolliger, 2007). On this land most people engage in small-scale cultivation, mainly for household food security. The main field crops are maize, and to a lesser extent dry bean, millet, sorghum and potato (Wood, 2011). These crops are mostly cultivated on a plot close to the homestead (usually <0,5 ha) and one or several outfields (usually between 1 and 5 ha) at some distance from the homestead (Bolliger, 2007). A survey among smallholder households in the Upper-uThukela catchment done by Crookes and Lyne (2003) indicated that the total arable land area per household was approximately 1,75 ha in the Amangwane communities and 0,85 ha in Amazizi communities. Only 43% of all households had arable allotments larger than 1 ha. Bolliger (2007) reports that usually only the wealthier members of the community are able to engage in this kind of cultivation. For the others, the agricultural activities are essentially limited to vegetable production at a small scale in home gardens, or when present, community gardens. Unlike the commercial farmers, the field crops of the smallholders are not irrigated and rely entirely on natural rainfall, which can be very erratic in the region. The vegetable gardens on the other hand, are watered to some extent, depending on the vicinity of streams or boreholes. As said before, for most households the production is solely for household consumption. When a surplus is produced it is mostly of limited quantity and sold locally, mostly to other members of the community (Zunckel, 2003; OLM, 2012). Most households have to supplement their own production with food products bought in the urban areas of the municipality. Livestock Traditional livestock keeping is another important component of the smallholder farming system of the region. But again, this activity is mostly reserved to the people having enough

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financial resources to afford buying animals to maintain a herd. The most important animals are cattle, goats and to a lesser extent sheep. Around 55% of the households in communal areas of the region are reported to own cattle (OLM, 2002; Bolliger, 2007). There is a wide range of reasons for keeping livestock, which can be categorized into agricultural, food and socio-cultural purposes (Chonco, 2009). Firstly, cattle are important as they provide draught power for ploughing and transport, and manure to fertilize the fields. Secondly, livestock is a source of meat and milk. Finally, besides supplying skin to make traditional items, cows and goats are used for several traditions, rituals and social transactions (e.g. dowry), while herd size also reflects the social status of the owners. Animals can be sold when cash is needed and in that way the herd acts as a buffer against adversity (Salomon, 2006), but the sale of livestock is far from being a business activity. Livestock keeping and crop cultivations are two closely interlinked components of the traditional small-scale agricultural system of the communal areas on the foothills of the Drakensberg. The grazing system follows the cropping cycle. When the crops are growing during summer, the cattle are transferred away from the fields to the higher surrounding natural grasslands where they can graze freely. In the winter, when the crops have been harvested, the animals are moved back closer to the fields, so that they can graze on the crop residues in addition to the rangeland (van Niekerk, 2007). The particularity of this traditional system is that all arable land not under cultivation becomes communal grazing land in the winter. Temporarily, exclusive rights to use the arable land do not apply anymore and in theory no one

Throughout the communal lands there is an apparent lack of control of grazing livestock. Herding systems have disappeared and fencing is not used because of its cost and fence theft (Salomon, 2006). This leads to damage to field crops and severe overgrazing of the natural rangelands of the mountain slopes (see § 1.2.2). Furthermore, because the cattle is left unattended far away from the homesteads there is a major problem of stock theft. This issue is making livestock a risky investment and provokes a steady decline in the absolute number of animals in the region (Bolliger, 2007). Livelihoods of smallholders and constraints for agricultural development As agriculture is mostly practised for subsistence purposes it does not constitute a significant source of income. For most households, wage income also represents only a small part of their earnings because job opportunities are rare and where employment is available, wages are low (Nsuntsha, 2000). Crookes et al. (2003) reported that approximately 60% of the adults of working age in the communal areas are unemployed. This is why families have developed a strong dependence on state pensions claimed by older members of the household, and on remittances from relatives working outside the municipality. However, most official figures and studies about household income composition in the Amangwane and Amazizi areas miss out an important source of cash, namely the illegal sale of cannabis (dagga). The plant is grown on the inaccessible mountain slopes by small-scale farmers and then sold to local drug barons. The drug is then transported to the rest of the country or smuggled over the border with Lesotho. The trans-boundary drug trade is often associated with cattle theft, generating conflicts and a general feeling of insecurity in the population (Sandwith, 2002). Cannabis commerce is possibly the most important element of the informal sector within the district (OLM, 2012). Although for most rural households farming provides only a small fraction of their total earnings, a significant number of them remains very dependent on agriculture, primarily for household food security (Crookes et al., 2003). On the other hand, some wealthier farmers with

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more extended land resources are trying to emerge from subsistence agriculture and make it a somewhat profitable occupation. Both categories of farmers encounter major problems to sustain, respectively extend their agricultural activities in the context of the OLM. A fundamental constraint is the lack of effective farmer support, in terms of services and information, especially in the more remote areas of the district. Small-scale farmers have little access to inputs such as improved seeds, fertilizers, lime, pesticides, herbicides, fences and traction power to work the land. And insufficient demand for these products makes it uneconomical to hold stocks in the area (OLM, 2002). Credit opportunities are almost totally absent and market access is problematic, because of the small volumes produced and the remoteness and scarcity of local markets. Access to transport is difficult and expensive and the price competition set by the commercial farmers is high, which further discourages small-scale commercial production. Although attempts were made to stimulate the land rental market, which had some positive impacts on allocative efficiency and equity (Crookes et al., 2003), it remains difficult for farmers to hire more land. Additionally, despite the fact that large tracts of formerly commercial land have already been transferred to the poor communities through land reform programs, the beneficiaries are not well-targeted and do not receive the necessary follow up support to start successful farming activities on the acquired land (Wood, 2011). Finally, small-scale farmers generally have insufficient knowledge and skills concerning both technical and managerial aspects of farming and are demanding for external advise (OLM, 2002). Due to a limited capacity, extension officers are often unavailable in the more remote areas.

1.2 Environmental issues in the Okhahlmaba Local Municipality

1.2.1 Importance of the natural capital of the OLM for the provision of ecosystem services

As mentioned in the previous section, the most outstanding landscape elements characterizing the OLM are the northern Drakensberg mountains. These mountains are part of the Maloti-Drakensberg mountain range spanning the eastern border between Lesotho and the South African provinces of Orange Free State, KwaZulu-Natal and the Eastern Cape. It is the highest mountain range of southern Africa, with several peaks above 3 400 meters, and can be classified as a high-altitude fire-prone grassland ecosystem (Blignaut et al., 2010). The ecosystem functions as a large and crucial water catchment, being one of the rare in southern Africa where long-term annual precipitation exceeds evaporation (Zunckel, 2003). Major rivers of the region, such as the Senqu (Lesotho), the Orange (South Africa) and the Thukela have their source in this mountain range. They supply water to various national and international inter-basin transfer schemes, providing water to a large part of the southern African sub-continent. One of the most important sub-catchments of the watershed is the Upper-uThukela catchment, which is almost entirely enclosed within the borders of the OLM. As a whole, this catchment

(Nsuntsha, 2000), and is of particular importance for the water provision of the Gauteng province (including Johannesburg), the economic hub of South Africa (Zunckel, 2003). In addition to its invaluable importance as a watershed, the mountain range is also well endowed with biodiversity resources. Especially the diversity of endemic plant species is remarkable, making the Maloti-Drakensberg a recognized centre for endemism (Zunckel, 2003). From an ecosystem point of view it is thus a refuge for a diversity of species and contains precious genetic resources. Furthermore, the scenic beauty and rich cultural heritage (with thousands of Khoisan/Bushman rock art paintings) attracts tourists and hikers from all over the world.

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This set of unique characteristics was the motivation for a number of conservation efforts, in the form of national parks and conservation programs. The Royal Natal National Park and the UKhahlamba / Drakensberg Park were established, and in 2000 the latter was registered by UNESCO as a World Heritage Site. As a result a significant portion of the mountainous areas of the OLM are now conservation areas. Besides these water-related, biodiversity-based, recreational and cultural services, the Maloti-Drakensberg Transfrontier Project identified some additional important ecosystem services, including carbon sequestration, climate regulation, erosion control and sediment retention, food and raw material production and grazing (MDTP, 2007).

1.2.2 Land uses threatening the ecosystem services Three major categories of land uses can be identified within the boundaries of the OLM: commercial agriculture, communal land use and conservation areas (van Niekerk, 2007). Obviously, the activities linked to these three categories differ considerably and so does the potential impact of the various activities on the ecosystem services delivered by the natural capital of the region. The commercial farming sector poses some threats both to the quantity and the quality of the water flowing from the mountains through the farmlands before it reaches the end-users. Water quantity is reduced as water is pumped out of the streams for irrigation and the indigenous vegetation is replaced by more productive, but also more water-consuming, food and feed crops and trees (for commercial forestry). Also, the construction of dams to create water reserves for irrigation, can interfere with natural surface flows and groundwater recharge (van Niekerk, 2007). Water quality can be negatively influenced by the uses of pesticides, herbicides and fertilizers and by some cultivation and tilling techniques that cause erosion and consequently sedimentation. In the conservation areas, threats could be summarized as inadequate fire management and the infestation of alien plants (van Niekerk, 2007). It is the role of the several conservation agencies operating in the area to adapt their management plans (e.g. the season and frequency chosen to burn firebreaks) and clearing techniques for alien plant removal to preserve the integrity of the sensitive high land ecosystems. In between the protected areas of the High Berg and the commercial farms on the lower lands in the east of the OLM, lie the communal areas. While, the three land use groups can have a negative impact on ecosystem service provisioning, and an integrated catchment-wide approach is needed to secure these services on the long run, this thesis will only consider the communal land uses. As was mentioned earlier, agriculture is still an important component of the life of the communities living in the communal areas. They primarily rely on the natural resources of the region to sustain their agricultural production: the natural grasslands for their livestock, and fertile land for their cultivations. However, the absence of effective community-wide land use management plans has had negative effects on the conservation of natural resources. A large portion of this area is subject to degradation, and the loss of grass cover has led to decreased water infiltration, increased runoff and severe soil erosion (van Niekerk, 2007; Blignaut et al., 2010; Wood, 2011). This degradation is particularly pronounced in the more mountainous parts of the communal areas, where the shallow and friable soil and the steep slopes make the land more vulnerable to eroding forces. The land degradation and erosion has seriously affected some of the land-based and water-related ecosystems services over the past decennia, prompting for remedial actions.

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There are different communal land uses and practices that act as driving forces for these environmental problems. These are mentioned as dark shaded boxes in Figure 2, and include (Nsuntsha, 2000; van Niekerk, 2007; Blignaut et al., 2010; Wood, 2011):

overstocking and poor livestock management, leading to overgrazing of some parts of the natural grasslands

uncontrolled livestock movements, outside cattle access routes lack of fire management, leading to annual out of season burning in late summer and/or

early winter (while the recommended fire regime is a biennial spring burn) and high frequency of wild fires (lack of fire breaks)

inappropriate tillage practices (extensive ploughing, no contour channels nor contour vegetation strips) and cropping on steep slopes (especially cannabis cultivation)

harvesting of indigenous trees for firewood and cutting of thatch-grass for roofing of houses

use of river-sand and soil for buildings

Figure 2 further illustrates how these various driving forces, through a number of physical processes, ultimately result in ecosystem service deterioration and other problems (these are indicated as light shaded boxes). The intermediary processes have to be understood together with the natural rainfall patterns of the area. There is high rainfall over the summer period with heavy thunderstorms (exacerbating erosion of denuded land) and low rainfall in the winter. The latter makes a good infiltration of the little rainfall crucial to sustain base flow of rivers and enable groundwater recharge. Although the mentioned land-uses and practices are not all contributing to the problem to the same extent, they all have to be taken into account when trying to find integrated solutions. However, this thesis will focus solely on the tillage and cultivation practices associated with the croplands of the communal areas

1.3 Conservation agriculture for sustainable land management in the communal areas of the OLM

To reduce the degradation and erosion of the land used for cropping, more sustainable land management practices are needed. The poor socio-economic conditions in the rural communities and the relatively low productivity of the small-scale agricultural sector, further urge for practices that have the potential to increase crop yields and food security. In this context, conservation agriculture (CA) is a promising technology that could address the environmental issues and realize sustainable agricultural production. Conservation agriculture

an approach to managing agro-ecosystems for improved and sustained productivity, increased profits and food security while preserving and enhancing the resource base and the environment (FAO, 2012). It relies on the simultaneous implementation of three interlinked principles:

1. Minimum mechanical soil disturbance, or if possible, no tillage at all. 2. Permanent organic soil cover. 3. Diversification of crop species, with crop rotations and associations.

In fact, rather than being a fixed combination of agricultural practices, CA is a toolkit that provides flexible solutions that can be adapted to local agro-ecological conditions to achieve sustainable land management, in particular the conservation of soil and water resources (Derpsch et al., 2010). While the most direct and visible benefits are situated at the field-scale,

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providing the basic motivation for individual adoption of the practices, CA has proven to have beneficial effects at the watershed level when broad adoption is realized (Laurent et al., 2011).

1.4 Poor adoption of conservation agriculture by South African smallholders

Although the worldwide success of CA is undoubted, the adoption is mainly concentrated in North and South America (Derpsch et al., 2010). Africa is still lagging behind with a share of merely 0,3%. Within the African continent, South Africa is taking the lead with 368.000 ha under NT (Derpsch et al., 2010), but it is almost exclusively practised on large scale

Figure 2 - Schematic representation of the practices in the communal areas that act as driving forces (dark shaded boxes) for the environmental problems in the OLM, and that lead to ecosystem services degradations (light shaded boxes), together with the intermediary processes (Adapted from(Fowler,

1999; Blignaut et al., 2010).

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commercial farms (FAO, 2010). Nonetheless, over the past decade or so, several extension projects have tried to propagate NT and CA in the smallholder farmer communities of South Africa. One of these projects was the Bergville/Emmaus LandCare project. Running from 2000 to 2005, the project was aiming to

e and diffuse sustainable land management technologies for local farmers in order to address soil degradation and conservation issues and increase farm productivity and income in the Bergville district (Emmaus ward) of KwaZulu- (Smith et al., 2005). Farmer-managed trials were implemented by a number of selected leader farmers of the Potshini community (see Figure 1 for geographical location). The objective of these trials was to see if the different components of conservation agriculture were practical, applicable and adapted to

situations (Smith et al., 2005). In addition, the new technology was compared with the traditional farming practices (inter alia conventional tillage) in space and over time by setting up two separated plots for each farmer. The project led to some promising results and a considerable number of farmers were applying CA principles at the end of the project (20 leader farmers and 365 trained farmers in 2005) (Smith et al., 2005). However, Bolliger (2007), who thoroughly investigated smallholders no-tillage adoption in KwaZulu-Natal in 2005, reported that adoption was generally limited to a

received, rather than the entire land. And, according to him, only a few (5-10) of the leader farmers in Potshini were becoming fairly competent CA practioners. Nowadays, there are 26 smallholders practising CA in Potshini and a few more in the rest of the Upper-uThukela (Hendrik Smith, personal communication, 8th March 2012) Even if it questionable if this can be called a local success, it is clear that, in general, adoption of CA by South African subsistence farmers is very slow and persistence with the application of CA, once projects come to an end and (financial) support is suspended, is poor (Bolliger, 2007; Smith et al., 2010; Sterve, 2010; Stronkhorst et al., 2010). Several general factors have been highlighted in the international literature as constraints for smallholder CA adoption, particularly in the Southern and South African context (Bolliger et al., 2005; Lal, 2007; Wall, 2007; FAO, 2009; Friedrich and Kassam, 2009; Giller et al., 2009; Nkala et al., 2011). However, as agro-ecological and socio-economic conditions vary considerably within the region, only a case-by-case analysis can determine which are the specific factors that stand in the way of the scaling-up of small-scale CA. The first part of this thesis will evaluate which are the specific constraining factors for adoption of CA by the small-scale farmers of the communal areas of the OLM. One of the general barriers that is systematically mentioned in the context of resource-constrained farmers, is external input reliance. Indeed, CA application is often linked with higher external input needs, especially in the initial phase of adoption (Steiner, 1998; Wall, 2007; FAO, 2009; Liniger et al., 2011; FAO, 2012). In particular, the use of more herbicides is necessary to suppress weed emergence, as weeds are no longer ploughed into the field when no-tillage is practised (Steiner, 1998; Wall, 2007). This raises two important questions:

To what extent is the higher input application being translated in a higher productivity in small scale farming systems? In other words: what is the technical efficiency of smallholder CA and is it really higher compared to traditional, low-input practices?

Are the costs of these inputs (when they are not provided for free through extension projects) compensated by the claimed higher yields of CA and consequent potential higher income? In other words: what is the economic efficiency of smallholder CA?

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These questions are especially relevant because CA is claimed to improve the efficiency of input-use and increase farm income (Erenstein et al., 2008; FAO, 2009, 2012). Nevertheless, some authors report a lower technical efficiency for African small-scale farmers using agrochemicals (Msuya et al., 2008; Oduol et al., 2011). This can possibly be explained by

especially when they do not receive appropriate help and advice (Bolliger, 2007). To look for an answer for these questions, data will be used from the above-mentioned farmer-managed trials, in which CA and traditional practices were compared. The purpose will be to deepen the data analysis that was done within the project, which merely consisted of comparing the average yields for the two technologies. This thesis will consider the different input amounts that were used for the two technologies to calculate their efficiencies.

1.5 Definition of the research questions and outline of research approach

In practice, this thesis will be articulated around the following research questions:

1. Which constraining factors are hampering the adoption of conservation agriculture by small-scale farmers in the communal areas of the Okhahlamba Local Municipality?

2. What was the technical efficiency (TE), allocative efficiency (AE) and economic

efficiency (EE) of the leader farmers of the Bergville/Emmaus LandCare project applying CA practices and are these efficiencies higher compared to traditional practices? Sub questions:

i. How do these efficiencies vary over time for the CA practices? Hypothesis: Efficiencies of CA will improve during the project, as the farmers become more familiar with the new technology.

ii. How does the TE vary between the farmers, comparing the two technologies?

Hypothesis: Due to the complexity of CA and higher managerial requirements, there will be a higher variability for CA farmers, with some skilled farmers picking up the technology quickly and others needing more time to improve their efficiencies.

To seek an answer to these questions, the following approach will be used. The first research question will be approached on the basis of a literature review of the constraining factors for smallholders CA adoption, combined with a detailed analysis of the specific agro-ecological and socio-economic conditions that prevail in the communal areas of the OLM. Efficiency measures can be measured using different mathematical methods. Data Envelopment Analysis (DEA) is one of these and has been used by many researchers to investigate efficiency in the agricultural sector (Coelli et al., 2002; Chirwa, 2003; Speelman et al., 2007; Msuya et al., 2008; Begum et al., 2011). Technical and economic efficiencies will be calculated using DEA and subsequently analysed with the statistical program SPSS to provide an answer to the sub questions.

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2 Chapter 2: Literature review

2.1 Conservation agriculture

2.1.1 Principles and practices

The FAO defines conservation agriculture (CA) an approach to managing agro-ecosystems for improved and sustained productivity, increased profits and food security while preserving and enhancing the resource base and the environment (FAO, 2012). It is based on an integrated management of soil, water and biological resources through the simultaneous application of three complementary principles that aim at enhancing biological processes above and below the ground. These are (FAO, 2012):

1. Continuous minimum mechanical soil disturbance (reduced tillage or minimum tillage), and when possible complete absence of soil tillage (no-tillage or zero-tillage).

2. Permanent organic soil cover. 3. Diversification of crop species, through crop rotations and associations.

Although the FAO definition seems to have gained wide acceptance, some confusion remains because CA brings different practices under a general heading. Strictly seen, the application of reduced tillage or no-tillage (NT) on itself does not constitute CA (Hobbs, 2007; Erenstein et al., 2008). But because the aspect of minimum mechanical soil disturbance is considered as the

--

-residue mulching (CRM) and direct-seeding mulch-based cropping (DMC) (Giller et al., 2009).

The first principle of CA implies the direct seeding of crops without the mechanical preparation of seedbeds and with minimal soil disturbance after the harvest of the previous crop. Specialized equipment is used to penetrate the soil cover, create a seeding hole and place the seed into that hole. The purpose is to minimize the size of the seeding hole and the associated displacement of the soil. The land preparation is reduced to slashing or rolling the weeds, previous crop residues or cover crops and, in case it is necessary, applying herbicides for weed control and fertilizers for soil enrichment. By minimizing soil disturbance, organic matter is decomposed more slowly and hence nutrients are released more progressively. Moreover, this prevents the structural degradation and erosion of the soil (FAO, 2012). The main purpose of the permanent organic soil cover (mulch layer) - the second principle of CA - is to protect the soil from the physical impact of rain and wind, but it also has a stabilising effect on the moisture content and temperature of the upper soil layers (Erenstein, 2002; FAO, 2012). Consequently, a more favourable micro-climate for plant root development is created in these upper layers. Moreover, the surface layers become a habitat for micro-organisms and insects, which contribute to mulch decomposition and its incorporation and mixing with the soil. The mulch eventually becomes humus, increasing the soil organic matter (SOM) content and helping physical stabilization of the soil structure. The macropores formed by larger organisms of the soil fauna, such as earthworms, allow fast water infiltration in case of heavy rainfall events. This process is sometimes referred to as "biological tillage" (FAO, 2012).

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In practise, the soil cover can be guaranteed through the retention of crop residues after harvest or the planting of cover crops, especially multi-purpose crops that have several beneficial effects, such as fixing nitrogen, restoring soil porosity, repelling pests, etc. While the FAO does not specify the extent of soil cover required for successful CA, the US Conservation Technology Information Center (CTIC, 1999) defines a minimum of 30% of soil surface covered with crop residues at the moment of planting, for a tillage and planting system

The third principle of CA crop rotations and associations is of great importance for pest and weed control. Crop rotations interrupt infection cycles between subsequent crops and enable the regeneration of the soil fertility. Crop associations take advantage of the beneficial physical and chemical interactions between different plant species to generate a more diverse and resilient agro-ecosystem (FAO, 2012). Effective weed control is a particular challenge in CA because under NT weeds are no longer ploughed into the field (Steiner, 1998; Wall, 2007). Although soil cover reduces weed emergence to a certain extent, herbicides are mostly inevitable, especially in the first years of CA implementation (Steiner, 1998; Wall, 2007; FAO, 2009; Liniger et al., 2011; FAO, 2012).

2.1.2 Benefits of CA The benefits of CA can be categorized as agronomic, economic and environmental. Agronomic benefits arise from the improved soil productivity that is accomplished through (Steiner, 1998; Derpsch et al., 2010; Liniger et al., 2011; FAO, 2012):

Build-up and protection of the SOM. This organic matter aids soil aggregation and nutrient retention.

Reduced soil erosion by water and wind and reduced surface runoff, preventing the scouring of fertile topsoil, carrying with it seeds and fertilizers.

Improved rainfall infiltration and, as a result of better water storage capacity and reduced evaporation losses, an improved in-soil water conservation. This reduces crop water stress in periods of droughts.

Improved soil structure, leading to a better root growth and movement of water and root-respiration gases.

Improved nutrient distribution thanks to the diversity of plant species involved in the crop rotations and associations, and the associated diversity in root depths.

Increased nitrogen fixation through nitrogen-fixing crops and certain plant symbionts Enhanced biodiversity and biotic activity under and above the ground.

The higher soil productivity normally results in higher crop yields, although the positive effect might only become apparent after several years and other factors might be more influential. This will be discussed in detail in § 2.1.4. Economic benefits include (Steiner, 1998; Derpsch et al., 2010; Liniger et al., 2011; FAO, 2012):

Reduced amount of labour and energy required for soil preparation, since the land is no longer intensively ploughed. This can lead to significant cost savings and can be a great advantage in regions with labour shortage.

Reduced time requirement for soil preparation, enabling timely sowing and thus leading to reduced planting risk and improved yield potential.

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Higher yields arising from an improved soil productivity and thus potentially a higher income from production sale and a higher profitability.

Environmental and societal benefits become apparent when the effects described above are reproduced across enough farms in a contiguous micro-catchment within a landscape (Derpsch et al., 2010; Kassam and Friedrich, 2011). These benefits are associated with certain ecosystem services and include (Steiner, 1998; Derpsch et al., 2010; Liniger et al., 2011; FAO, 2012):

More regular water flow in streams and rivers throughout the year as a result of a slow release of the infiltrated water. Concretely this means a sustained flow in the dry season and a reduction of flooding after heavy rainfall events.

Improved recharge of aquifers, leading to more reliable yields from wells and boreholes. Less erosion and hence less sedimentation in rivers, dams, hydroelectric power plants

and irrigation systems. This leads to cleaner surface water and less damage to infrastructure and therefore reduced water treatment costs and infrastructure maintenance costs.

Increased carbon sequestration because soil degradation is reduced below the rate of soil regeneration.

Improvement of air quality due to reduced dust blow-up by means of wind erosion. All the above-mentioned benefits are only potential benefits and might not be as pronounced in all agro-ecological conditions. Most of them are based on the correct (and often simultaneous) application of the tree principles of CA. This is however not always possible, due to biophysical, socio-economic or cultural constraints. These constraints will be discussed in the specific context of smallholder agriculture in the Okhahlamba Local Municipality in Chapter 4.

2.1.3 -­‐controlling and water-­‐regulating practice

Since CA is brought forward in this thesis as a potential solution to the land degradation issues that affect the Okhahlamba Local Municipality, it is interesting to explore this aspect in further detail, although CA has several other positive effects, as mentioned above.

At the plot level, the minimum soil disturbance and the maintenance of a mulch layer effectively contribute to a reduced soil erosion and increased infiltration. The mulch layer acts as a protective layer that increases the resistance to overland water and wind flows, reduces the physical impact of raindrops and enhances soil surface aggregate stability through both physical and biological processes (Erenstein, 2002). Figure 3 illustrates this schematically. One can expect erosion to decline asymptotically to zero as soil cover increases (Erenstein, 2002). It is estimated that a ground cover of 30% reduces soil erosion by approximately 80% (Wall, 2007). The reduction in soil erosion on fields under CA has been extensively quantified, both in temperate and tropical conditions (McGregor et al., 1990; Alegre et al., 1991; Khybri et al., 1991; Langdale et al., 1994; Michels et al., 1995; Bissett and Oleary, 1996; Thierfelder et al., 2005; , 2008; Madarász et al., 2011), but seems to be strongly dependent on local environmental conditions. Therefore it is useful to have data from experiments that were specifically done in the OLM. Kosgei et al. (2007) assessed the influence of tillage on field scale water fluxes at an experimental site in the Potshini catchment, in vicinity of the trials where the Bergville/Emmaus LandCare project took place. They measured, inter alia, the soil moisture and runoff on maize plots under no-tillage (NT) and conventional tillage (CT). Soil moisture of NT plots was higher compared to that of CT plots throughout the season, and nearly twice as much runoff was generated from CT plots when compared to NT plots. Since a

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decreased runoff directly reduces erosion rates (see Figure 3), the study highlights the fact that CA can be an effective measure to reduce the degradation of crop fields in the OLM, especially because fields are often located on steep slopes.

Figure 3 - Effects of CA on soil conservation at the plot level (Source: Erenstein, 2002) As mentioned above, benefits of CA become apparent at the scale of a watershed when the practises are adopted by a sufficient number of farms within the catchment (Derpsch et al., 2010; Kassam and Friedrich, 2011). The most visible positive impact at that larger scale is the reduced siltation of watercourses. This is a particularly desirable effect in the case of the OLM, given the high number of dams and hydroelectric power plants in the Upper-uThukela catchment and the existing problems related to sedimentation and silt build-up. Siltation of dams reduces their capacity and lifespan, rising managerial costs as a result of increased maintenance and need for augmentation and replacement schemes (MDTP, 2007). In some countries catchment-level soil and water conservation programmes that promote conservation agriculture have successfully been implemented (Minella et al., 2009; Kassam et al., 2011b; Laurent et al., 2011). Minella et al. (2009) conducted a monitoring study in a rural catchment in Southern Brazil before and after the introduction of CA practices. They measured significant reductions in storm runoff, maximum flow rate and sediment yield after the implementation of the practices. Over a 2-year period, annual sediment yield decreased by 70,1%. Source fingerprinting techniques confirmed that it was mainly the reduced sediment yield from the cultivated fields that had contributed to the lower siltation levels of the streams in the catchment. Other studies showed that CA implementation at the landscape scale improved habitat and water quality and the benthic macro invertebrates community (Yates et al., 2006) and had a positive effect on the bird fauna (Madarász et al., 2011).

2.1.4 Impacts of CA on yield and yield variability Although erosion control and water flow regulation are desirable outcomes of CA from the perspective of the policy makers, they do not constitute a sufficient incentive for its adoption by farmers. Farmers will most probably emphasise the productivity aspects of CA, with conservation as complementary benefit (Erenstein, 2002). According to Erenstein et al. (2008),

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the main driver for acceptance of CA is the combination of a positive effect on crop yield and a -term profitability of adoption.

Conservation agriculture is claimed to allow yields comparable with, or often even higher than those under conventional agricultural practices (Wall, 2007; Liniger et al., 2011; Rusinamhodzi et al., 2011; FAO, 2012), but results are highly dependent on the location and the crops being cultivated. Additionally, while the increased yields have extensively being demonstrated in humid tropical and temperate regions, where CA is already largely being used, there is still very limited evidence of yield improvements in semi-arid and dry sub-humid smallholder agro-ecosystems (Rockström et al., 2009). For maize production in East and Southern Africa, on-farm research showed yield increases of 20 to 120% compared to conventional techniques (Rockström et al., 2009). In the study by Kosgei et al. (2007) in the Potshini catchment of the OLM, which was mentioned above, yield increases of 168%, 133% and 120% were obtained for no-tillage plots compared to traditional tillage plots for two farmer-managed and one research-managed trial. It is essential to remark that in this study the same fertilizer treatment was used for both tillage techniques, making the yield results relatively comparable. Unfortunately, this is not always the case. Indeed, it is not unusual that full CA packages, including fertilizers, herbicides and/or

(Giller et al., 2009). This means that it is impossible to segregate the specific effect of CA from the stimulation of crop growth due to the additional input usage or better seeds. As the LandCare project, which is considered in this thesis, also compares a full CA package with traditional practices, a real evaluation of the CA practice can only be made when also taking into account the inputs used. This therefore confirms the importance of the efficiency analysis approach taken here. It also seems to be uncertain when the positive yield effects start to manifest. Giller et al. (2009) reviewed a number of long-term experiments in Africa and concluded that the introduction of CA can result in yield benefits in the long-term, but that in the short-term (and this might be up to nearly 10 years) yield losses or no yield gains are just as probable (see Figure 4). This might be a serious reason for non-adoption or abandonment, as short-term benefits are essential for resource-constrained smallholders (Giller et al., 2009). According to Liniger et al. (2011), initial yield increases are mostly around 10-20% if all other conditions remain the same, while a 100% increase can be observed when the introduction comes with ripping or sub-soiling and fertiliser use. The authors state that that in any case significant yield increases can only be observed after 4-5 years of continuous CA application. The yield effect might also depend on other factors apart from the accomplishment of no-tillage and a sufficient mulch cover. Rusinamhodzi et al. (2011) performed a meta-analysis of long-term effects of CA on maize grain yields under rain-fed conditions. They showed that the success of CA in terms of productivity strongly depends on suitable crop rotations and an adequate fertiliser application, especially nitrogen fertiliser. The latter is due to the fact that crop residue mulching alters nitrogen availability and fertiliser use efficiency. Cereal crop residues typically have a high C:N-ratio and temporary immobilise nitrogen in the soil, implying a lower N-availability for the crop. Therefore, at low nitrogen application levels, conventional systems generally result in better yields than CA (Erenstein, 2002). The clearest benefit of CA responsible for yield improvement is probably its water conserving effect (Steiner, 1998). This applies especially in regions where drought stress is a frequent problem. Owing to moisture retention, CA will be particularly beneficial in dry years, reducing the productive risk and yield oscillations (Erenstein, 2002). This is distinctively relevant to the context of smallholder rain-fed maize production in the OLM, as the Bergville area often

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experiences a mid-summer dry spell in January (Smith et al., 2005). Such dry spells retard plant growth, and especially grain filling, reducing maize grain yields markedly (Walker and Schulze, 2006). But, once again the benefits from improved water availability might not be fully realised unless nutrient deficiencies, common in the soils of the semi-arid regions of Sub-Saharan Africa, are also resolved (Rockström and Barron, 2007). Figure 4 - Maize grain yields difference (t ha-1) between CA and conventional tillage of five studies in

Africa (Source: Giller et al., 2009)

2.1.5 CA in the South African context

Conservation agriculture under the form of no-tillage originated in the 1960s in North America, New Zealand, Australia and southern Brazil out of a necessity to fight soil degradation and erosion (Bolliger et al., 2006; FAO, 2012). From southern Brazil the practice spread to the rest of South America, where it quickly became an important alternative to conventional tillage (Bolliger et al., 2006). Despite the fact that the worldwide area under NT increased tremendously over the decades, from 2.8 million ha in 1973 to 45 million ha in 1999 and 111 million ha in 2009 (Derpsch et al., 2010), the adoption of NT and CA remains concentrated in the parts of the world were it first arose. In 2010 North and South America, Australia and New Zealand still accounted for 96% of the global area under NT. Africa remains the continent with the most limited adoption with a share of just 0,3% in 2010 (Derpsch et al., 2010). This share is expected to rise in the future as CA is now widely promoted in the continent. However, this process is still in the early stages of building capacities and setting up structures for upscaling (FAO, 2009; Derpsch et al., 2010). The FAO has the goal to attain at least 30% of African farm and rangelands under the simultaneous application of the three principles of CA by 2015 (FAO, 2009). CA practices have emerged in several African countries, most notably in South Africa, Zimbabwe, Zambia and Kenya. South Africa is taking the lead with 368 000 ha under NT in 2010 (Derpsch et al., 2010), which represents approximately 2% of the arable area (Liniger et al., 2011). However, it is almost exclusively practised on large scale commercial farms (FAO, 2010), most of which are located in the high potential areas of the province of KwaZulu-Natal. The Bergville/Winterton region in the OLM in particular, has the most dynamic and ardent commercial NT farmers of the province (Bolliger, 2007). This is where NT adoption started in the 1970s, primarily driven by the long-term multi-site tillage research programme started at

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the Cedara College (Birch and Havenga, 2011). Thereafter, leading farmers played a major role in promoting NT locally and the movement culminated in the establishment of the farmer

-Till Club of KwaZulu-demonstration events that attract attendees from all over Africa (Wood, 2011). Private companies responded to the increasing interest for NT by importing modern no-till planters and putting improved herbicides and glyphosate resistant maize and soybean cultivars on the market. This has lead to an exponential adoption of NT among commercial farmers of KwaZulu-Natal and it is estimated that in 2009 about 70% of the area under annual crops in the province was under NT (Birch and Havenga, 2011). However, over the decades the NT movement in KwaZulu-Natal (KZN) and other parts of (South) Africa has continuously ignored smallholder farmers. It is only recently that governments, research institutes and extension agencies have taken initiatives to adapt and promote CA for small-scale farmers (Erenstein et al., 2008). These initiatives mostly have the underlying idea of redressing historical imbalances, improving food security and socio-economic conditions in poor rural areas and unlock the agricultural potential of the former homelands by increasing productivity and sustainability of smallholder farms.

Affairs and the ARC-ISCW 3 LandCare projects in the provinces of Mpumalanga, KZN and the Eastern Cape funded by the South African National Department of Agriculture. Although the two initiatives had very different approaches, Bolliger (2007), who thoroughly investigated

NT and CA adoption in KwaZulu-Natal in 2005, reported that adoption in both projects was very low. Moreover, when the technologies were adopted, this was mostly only partially. Usually, adoption only consisted of not ploughing, but without maintaining a mulch layer as soil cover, and adoption was often limited to only available land. Only the Mlondozi LandCare project in the Mpumalanga Province seems to have achieved some reasonable adoption rates. In the Bergville/Emmaus LandCare project in KZN, from which yield data will be used in the analytical part of this thesis, adoption rarely

ble. Most of the farmers who where practising CA during the project, reverted back to conventional tillage once the project stopped (Sterve, 2010; Stronkhorst et al., 2010). Nevertheless, the project has managed to turn a few (5-10) of the leader farmers into enthusiastic and fairly competent CA practioners (Bolliger, 2007). The diagnosis of poor or unsustained adoption of CA practises by South African smallholders aligns itself with a more general observation throughout Africa. Giller et al. (2009) and Nkala et al. (2011) have gathered evidences from several African countries that show that most extension projects have failed in their objective of bringing about the massive uptake of CA technologies in the smallholder-farming sector and that adoption is rarely spontaneous, but rather linked to the free input packages that are provided during the project. Gowing and Palmer (2008) drew the conclusion that there has been practically no adoption of CA in most countries of Sub-Saharan Africa, with only small groups of adopters in Ghana, Zambia and Tanzania. These findings have lead to an ardent debate in the scientific literature about the appropriateness of CA in the context of African smallholders. Numerous publications (Bolliger et al., 2005; Lal, 2007; Wall, 2007; FAO, 2009; Giller et al., 2009; Nkala et al., 2011) have assessed under which ecological and socio-economic conditions CA presents real chances of success in Africa and which constraints stand in the way of effective adoption by smallholders. Rather than reviewing these general conditions and constraints here, the most relevant ones 3

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will be discussed directly in the context of the Okhahlamba District Municipality in Chapter 4.

2.2 Efficiency analysis

2.2.1 Introduction Studies that attempt to measure efficiency differences among farms are dominated by the use of productivity measures such as yield per hectare and cost per unit of output (Coelli et al., 2002). While these measures are easy to calculate and interpret, they can also be quite ambiguous. For instance, the comparison of yields-per-hectare between two farms or between two different practises is inappropriate when the amounts of non-land inputs used (such as labour, fertilisers and pesticides) differ between the two farms or practises. Cost per unit of output is a more useful measure in these cases, but it can still be misleading when the farm-gate input prices vary across the sample. In addition, a simple cost comparison fails to explain what portion of the cost difference is due to inefficient use of a certain input package (technical inefficiency) and what part is due to improper input ratios, given the prices of the different inputs in that package (allocative inefficiency). Therefore, the concept of efficiency analysis (EA) is increasingly being applied in the field of agricultural economics. Several publications have reviewed the application of EA in an agricultural context, both in developed and developing countries (Battese, 1992; Thiam et al., 2001; Bravo-Ureta et al., 2007).

2.2.2 Different approaches to efficiency analysis Several alternative approaches to measure productive efficiency have evolved, grouped into non-parametric and parametric approaches, with data envelopment analysis (DEA) and stochastic frontier analysis (SFA) respectively as most popular techniques. Parametric approaches will be briefly discussed first, to then go into details of the non-parametric DEA, as the latter will be used in this thesis. Parametric approaches A production function can be interpreted as a function that gives the maximum possible output that can be produced from given quantities of a set of inputs. In the context of EA the word

unction sets a limit to the range of possible observations (Førsund et al., 1980). The main characteristic of parametric frontier approaches is that a functional form is imposed on the production function, the most common functional form being the Cobb-Douglas production function (Førsund et al., 1980). Parametric frontiers can be further sub-divided into deterministic and stochastic frontiers. In the deterministic model it is assumed that any deviation from the f 4 inefficiency, while the stochastic approach assumes that part of the deviations from the frontier can also be due to random events (reflecting measurement errors and statistical noise). As agricultural production can be influenced by random exogenous shocks like droughts, the stochastic approach is often preferred when measuring efficiency in an agricultural context (Msuya et al., 2008).

4 In the agricultural context a firm, or a decision making unit (DMU) like it is often called, is a farm or an individual farmer.

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Non-parametric approaches Non-parametric approaches do not impose a functional form on the production frontiers. Instead, mathematical programming is used to estimate the frontier, only on the basis of the inputs-outputs-data of the different firms. The most popular non-parametric approach has been data envelopment analysis (DEA) (Coelli et al., 2005). The DEA methodology has some important advantages. First of all, because it is non-parametric, no assumptions have to be made concerning the functional form of the frontier that models the specific production technology under investigation, nor does the distribution of the inefficiency term need to be known (Førsund et al., 1980). Secondly, it is possible to construct a frontier using the joint data from different technologies, which allows the comparison of two or more production methods (as will be the case in this study) (Speelman et al., 2007). Moreover, small sample sizes do not significantly affect the efficiency measures computed with DEA, as long as the number of inputs is not too high compared to the number of observations (Chambers et al., 1998; Thiam et al., 2001). Indeed, when only a limited number of observations are available, expanding the number of inputs entails the risk that a disproportionate number of firms will lie on the frontier, producing an upward trend in efficiency scores (Nunamaker, 1985; Coelli et al., 2005). Finally, unlike parametric approaches such as the least-square or maximum likelihood technique, DEA works even for data that is characterised by limited input variability, i.e. when several firms use the same input quantities (Jaenicke, 2000). The major drawback of DEA is that the frontier is computed directly from the data set and is therefore particularly susceptible to extreme observations (outliers), measurement errors and other noise in the data due to random events (Førsund et al., 1980; Alene and Zeller, 2005). Just as in the case of the deterministic parametric techniques, deviations from the frontier will

agricultural data, considering that production is variable due to factors such as weather, pests and diseases (Mazvimavi et al., 2012). However, several studies comparing stochastic and deterministic techniques have shown a high correlation between the two methods (Thiam et al., 2001; Alene and Zeller, 2005).

2.2.3 Data envelopment analysis

2.2.3.1 Efficiency measures The theoretical foundations of DEA were laid out by Farrell (1957). He suggested that the efficiency of a firm, or more generally any decision making unit (DMU), consists of two elements: technical efficiency, which can be seen as the ability of the DMU to obtain maximal output from a given set of inputs, and allocative efficiency, which is related to the ability of the DMU to choose the optimal proportion of the different inputs, given their respective prices (Coelli, 1996). The two efficiency measures can be combined to calculate the economic efficiency, also called overall efficiency. These theoretical concepts were later translated into mathematical programming problems by Charnes et al. (1978). Technical efficiency

is to see it as the degree to which a DMU produces the maximum feasible output from a given

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set of inputs. In this definition the focus is on output maximisation and this is thus called an output-oriented efficiency measure. On the other hand, TE can also be seen as the degree to which a DMU uses the minimum feasible amount of inputs to produce a given level of output. Here, input minimisation is emphasized, so one speaks of an input-oriented efficiency measure. Both definitions lead to different but similar programming problems. The output-orientation will be presented in detail because this will be the most appropriate approach for the analysis of the data of the LandCare project. Afterwards, the main differences with the input-orientation will be briefly explained.

Output-oriented technical efficiency

Given production data of a number of DMUs, the DEA production frontier is constructed using a linear programming approach, yielding a piece-wise linear frontier enveloping the observed input and output data and simultaneously providing the TE measures (Coelli et al., 2002). The model is presented for , each producing M outputs

-th DMU, input and output data can be represented by the column vectors xi and yi, respectively. The KxN input matrix X and the MxN output matrix Y In this case, the DEA model used to calculate the output-oriented TE is (Coelli, 1996): Max Subject to - i + Y xi X (1)

n Nx1 vector of constants. The above equation is solved once for each i-

1 represents the proportional increase in outputs that could be achieved by the i-th DMU, with input quantities held constant. 1/ defines a TE score that varies between zero and one. A value of 1 indicates that the DMU lies on the frontier and thus, by definition, is a technically efficient DMU. It should be noted that eq. (1) is only valid for constant returns to scale (CRS). This will be discussed further in the text. There is an intuitive interpretation for the problem in eq. (1). For each i-th farm, solving eq. (1) means trying to radially expand the output vector yi as much as possible, while remaining within the feasible output set. The outer-boundary of this set is a piece-wise linear isoquant,

expansion of the output vector yi produces a projected point, (X , Ytechnology. This point is a linear combination of some observed data points, and the constraints in eq. (1) ensure that the point cannot lie outside the feasible set. This is illustrated in Figure 5 for a simplified case with two outputs (y1 and y2) and one input (x). For the DMU represented by point Q in Figure 5, the radial expansion yields the projected

at constitute the production frontier (or isoquant), A, B and C, are technically efficient because it is not possible for any of

possibility set.

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Input-oriented technical efficiency A similar approach is used to calculate TE in the input orientation. To model input minimisation instead of output maximisation, (eq. 1) is modified (Coelli, 1996):

Min Subject to -yi + Y i X (2) where is a scalar, is a Nx1 vector of constants. Again, the above equation is solved once for each i-th DMU to obtain each time a value for , which is the technical efficiency score for the DMU. The value of will always be between 0 and 1. A similar graphical interpretation can be conceived, the major difference being that TE is now calculated by reducing the input vector xi of the i-th DMU until a projected point is found on the isoquant. It should be stressed that the output- and input-orientated approach will estimate exactly the same frontier (Coelli, 1996)

constant returns to scale, but may differ between the two techniques when a variable returns to scale technology is assumed (see later). Allocative Efficiency and Economic Efficiency If input price information (in the case of a input-orientation) or output price information (in the case of a output-orientation) is available, allocative efficiencies can also be calculated (Coelli, 1996). In the first case, an allocative efficient DMU is using input quantities in such proportions that it minimises the costs of the input package (at a certain output level). In the output-orientation, an allocative efficient DMU is producing output quantities in such proportions that is maximises the total revue (at a certain input level). Here, only the input-oriented allocative efficiency (AE) and economic efficiency (EE) will be discussed because this approach will be more relevant for the analysis of the data of the LandCare project.

Figure 5 - Graphical representation of the calculation of technical efficiency in a simple output-oriented DEA

problem

S

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Input-oriented allocative and economical efficiency Given an input prices vector wi that corresponds to the input vector xi for the i-th DMU, the input-oriented EE is obtained by solving the following additional cost minimisation problem (Coelli, 1996):

Min wi i*, Subject to -yi + Y xi* X

(3) where xi* (which is calculated by the model) is the cost-minimising vector of input quantities for the i-th DMU. The EE of the i-th DMU is calculated as: EE = wi xi* / wi xi (4) Hence, CE is the ratio of minimum cost to observed cost for the i-th farm. The AE is then calculated by dividing the EE with the earlier obtained TE: AE = EE / TE (5) Scale efficiency The models described in eq. (1), (2) and (3) are valid for constant returns to scale (CRS). This means that for every DMU that produces a certain output y using an input x (x and y being vectors), it is feasible to produce an output a.y using an input a.x, for any scalar a. When increased amounts of inputs used do not proportionally increase the amount of output produced, variable returns to scale (VRS) have to be considered. When one assumes VRS, eq. (1), (2) and (3) can be adapted by simply adding the following convexity constraint to each model (Coelli, 1996): (6) Where N1 is an Nx1 vector of ones, and is an Nx1 vector of constants. The calculation of scale efficiency is illustrated in Figure 6 for the simple case of one input (x) and one output (y). The CRS and VRS frontiers are indicated in the figure. Under CRS, the output-oriented TE of the point P is the distance PPC. Under VRS, however, the TE would only be PPV. The difference between these two TE measures, PCPV, can be attributed to scale inefficiency. These efficiency measures can be expressed as follows: TECRS =AP/APC (7) TEVRS =AP/APV (8) And the scale efficiency (SE) can be calculated by: SE = APV/APC (9)

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Hence: SE = TECRS/TEVRS (10) One expects, a priori, that VRS prevail in agriculture, as production increase is rarely proportional to input increase (e.g. applying twice the amount of fertiliser will most probably not bring about a doubled crop yield). Therefore, substantial scale inefficiencies can be present, in particular for small-scale farmers. As a matter of fact, several authors that assessed SE in the smallholder-farming sector have reported significant scale inefficiencies (Chirwa, 2003; Solís et al., 2006; Speelman et al., 2007; Msuya et al., 2008; Thibbotuwawa et al., 2012). On the other hand, some others observed very little scale inefficiencies with smallholders cultivating very small plots (Coelli et al., 2002; Alene and Zeller, 2005; Alene et al., 2006; Haji, 2007). For this reason, scale inefficiencies always have to be verified by calculating TE under both a CRS and VRS assumption and determine if these differ significantly.

2.2.3.2 Panel data and the Malmquist TFP index

In some cases farm-level data are available across different time periods. The ability to observe each DMU more than once can lead to a more accurate estimation of efficiency compared to a single cross-section in time (Greene, 1993). Moreover, the access to panel data enables the assessment of productivity change over time. This change in productivity can be further

technological change (movement of the technology frontier itself) (Coelli, 1996). The calculation involves the use of DEA-like linear programming models to obtain the so-called Malmquist Total Factor Productivity (TFP) index. The Malmquist TFP index measures the change between two observations of a same DMU by computing the ratio of the distances in each time period relative to a common production frontier, constructed through DEA (Coelli et al., 2005). These distances are in fact nothing else than technical efficiencies of the observations in relation to a certain reference frontier. To illustrate this, consider a DMU whose production is characterised by the output vector yt and input vector xt in time period t and by the vectors yt+1 and xt+1 in time period t+1. One can

Figure 6 - Graphical representation of the calculation of scale efficiency in a simple output-

oriented DEA problem

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calculate four distance measures5: do

t(xt,yt): the technical efficiency of point (xt,yt) relative to the frontier in period t do

t+1(xt,yt): the technical efficiency of point (xt,yt) relative to the frontier in period t+1 do

t(xt+1,yt+1): the technical efficiency of point (xt+1,yt+1) relative to the frontier in period t do

t+1(xt+1,yt+1): the technical efficiency of point (xt+1,yt+1) relative to the frontier in period t+1 These distance measures can easily be calculated using a DEA model. For example, the second distance measure will be find be solving the following linear programming problem: [do

t+1(xt,yt)]-1 = max Subject to - it + Yt +1 xit X t +1 (11) Once the four distance functions have been calculated, the output-based Malmquist TFP index that describes the change in TFP between period t and t+1 is computed as follows (Färe et al., 1994):

mo(xt+1, yt+1, xt, yt ) =dot (xt+1, yt+1)dot (xt, yt )

dot+1(xt+1, yt+1)dot+1(xt, yt )

1/2

(12)

A value greater than one indicates a TFP growth, a value smaller than one indicates a TFP decrease. Actually, this index is the geometric mean of two output-based Malmquist productivity indices, the first one using the period t technology as a reference, the second one using the period t+1 technology as a reference. It can be shown that eq. (12) is equivalent to:

mo(xt+1, yt+1, xt, yt )=dot+1(xt+1, yt+1)dot (xt, yt )

dot (xt+1, yt+1)dot+1(xt+1, yt+1)

dot+1(xt, yt )dot+1(xt, yt )

1/2

(13)

In eq. (13) the first fraction outside the brackets is the TE change and the second term is the technological change. The Malmquist TFP index can be calculated for every DMU and for every pair of time periods for which data is available. Another interesting application of panel data is so-called window analysis. In a window

DMU in each different period is treated as if it were a "different" DMU (Cooper et al., 2011). A frontier can then be constructed based on this pooled data set. This is a particularly useful approach when only a small number of accuracy of the frontier estimation (Sipiläinen et al., 2008).

5 Output-oriented distance measures, based on ouput-oriented technical efficiencies, are presented here.

-oriented distance measures can be defined in a similar way.

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2.2.4 Efficiency analysis for the comparison of different technologies The most common application of EA in an agricultural context is the assessment of efficiency differences among several farms within a certain sector (e.g.(Coelli et al., 2002; Chirwa, 2003; Msuya et al., 2008; Begum et al., 2011; Thibbotuwawa et al., 2012). Mostly, the efficiency scores are subsequently regressed against a certain number of farm-specific variables in order to explain the differences in efficiency (examples provided in the same publications). The explanatory variables that have been used most frequently in analyses of developing country agriculture are farmer education and experience, contacts with extension, access to credit, and farm size (Bravo-Ureta and Pinheiro, 1993). Although there might be substantial differences in these socio-economic variables between the farms and different farms might use different kinds of inputs (and this might lead to efficiency differences), it is mostly assumed that the farms are characterised by the same production technology. Fewer studies have used EA to investigate the efficiency differences among farms that use clearly distinct production techniques (Sipiläinen et al., 2008; Heumesser and Schmid, 2010; Dung et al., 2011; Mazvimavi et al., 2012). The major problem when one considers different technologies is that this implies the existence of different production frontiers. However, as a general rule, efficiency scores calculated relative to one frontier cannot be compared with efficiency scores calculated relative to another frontier ( et al., 2008). One way to overcome this problem and make a comparison of efficiency measures possible, is to pool the data from the different technologies in one data set and construct a so-called metafrontier that hinges on this pooled data set (Battese et al., 2004; et al., 2008). This metafrontier will envelop the separate frontiers (called group frontiers). Hence, if these group frontiers are drawn as well, the efficiency of the DMU of a certain group measured relative to the metafrontier can be further decomposed into a component that measures the distance with reference to its group frontier and a component that measures the distance between its group frontier and the metafrontier. The findings of Sipiläinen et al. (2008) demonstrate the importance of constructing a common frontier when different technologies are compared. They compared the efficiencies of conventional and organic Finnish crop farms using a DEA approach. In a comparison with two separate frontiers, the average efficiencies of the two farming methods did not differ significantly. However, in a second analysis based on a pooled data set, the conventional crop farms were found to be clearly more technically efficient than the organic farms.

2.2.5 Efficiency analysis and sustainable land management In the context of this thesis it is interesting to consider the results of studies that have applied EA to assess the efficiency of sustainable land management (SLM) practices in general and CA in particular. Solís et al. (2006) found that, among small farms in Honduras and El Salvador, those with higher levels of investments in soil conservation showed higher average TE than those with lower levels of investments. Mahadevan (2008) concluded that Fijian sugarcane farmers who practised conservation practices such as contour planting, residue retention or planting of vetiver grass, had significantly higher TE. Jara-Rojas et al. (2012) observed a positive relationship between the adoption of soil and water conservation technologies (SWCT) and the farm-level TE of small-scale farmers in Chile.

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On the other hand, Oduol et al. (2011), who used data from 2130 smallholder farmers in Uganda, Rwanda and the DRC, concluded that adoption of SWCT disseminated through past research projects, had had a negative impact on the TE of the farmers in the Lake Kivu region (pooled sample). Dung et al. (2011) evaluated the TE of Indian farmers engaged in rice-wheat cropping systems, who are using CA principles that were promoted in a recent project. They found that the mean TE of farmers involved in the project, was significantly higher than that of farmers who had not been involved in the project. Heumesser and Schmid (2010) used a bio-physical process model to simulate the outputs of several alternative crop management systems in the Austrian Marchfeld region, consisting inter alia of minimum, reduced or conventional tillage. The results of the environmentally integrated EA based on this simulated outputs, pointed out that crop management systems with minimum tillage, low fertilizer application and irrigation were most often rated as technically efficient. Finally, the most interesting findings come from the work of Mazvimavi et al. (2012) as it

3-year panel sample of smallholder farmers in Zimbabwe, to compare productivity and TE of maize production under CA and conventional farming. The data emanated from households practising both practices side by side in a non-experimental setting (i.e. not bounded to a research or extension project). Productivity and efficiency scores were calculated with a SFA of the pooled data. Joint frontier estimates indicated that farmers produced 39% more maize output in CA than in conventional farming (higher productivity) but that TE levels were approximately the same (about 68%) for both practices.

2.2.6 Expanding the classical EA to fit specificities of CA

2.2.6.1 Modelling undesirable outputs

Classical EA approaches generally model an agricultural production technology merely as a process that uses a certain number of inputs such as labour, land, capital, machinery, fertilizers, pesticides and irrigation water, to produce one or more outputs, usually crops or livestock (Gomes, 2010). However, in recent years, an increasing number of studies have tried to extend this basic model to include the environmental externalities associated with agricultural production (Reinhard et al., 2000; Seiford and Zhu, 2002; Briec and Chambers, 2008; Heumesser and Schmid, 2010; Picazo-Tadeo et al., 2011; Hoang and Alauddin, 2012). As a matter of fact, agriculture can have both negative environmental externalities, such as pesticide or nutrient leaching and soil erosion, and positive environmental externalities, such as biodiversity generation and landscape preservation. To model these externalities, the positive externalities can be grouped with production output (e.g. crop yield) to form a set of desirable outputs that have to be maximised, while the negative externalities form a set of undesirable outputs that have to be minimised. However, in the standard output-oriented DEA model, as described by eq. (1), decreases in outputs are not allowed. Therefore, Seiford and Zhu (2002) proposed an approach to expand this model to enable undesirable output minimisation. Considering that the data of the i-th DMU are expressed as the column vectors xi, yg

i and ybi, representing inputs, desirable (good) outputs and undesirable

(bad) outputs respectively (and the data of the whole sample as the matrices X , Yg and Yb), the linear programming problem becomes:

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Max Subject to (14) where is the so- 6 undesirable output vector of the i-th DMU (and the corresponding matrix for the whole of the sample), calculated as follows:

(15) in which w is an appropriate translation vector to let all negative output vectors be positive. Eq. (14) will expand desirable outputs and contract undesirable outputs as much as possible until a projected point is found on the frontier. 1/ is a measure of TE, interpretable in the same manner as in eq. (1). This approach was used in the above-mentioned environmentally integrated DEA study of Heumesser and Schmid (2010) to compare different crop management techniques, including minimum tillage. They accounted for desirable outputs such as soil organic carbon stocks and undesirable outputs such nitrate emissions and soil sediment losses. A similar model could be used to compare efficiencies of CA and conventional farming. It is noteworthy that other approaches to include undesirable outputs exist. For example, an undesirable output could also be included as an input, since a higher input usage also has negative influence on efficiency. However, Seiford and Zhu (2002) argue that the resulting DEA model does not reflect the true production process and thus prefer the above mentioned approach.

2.2.6.2 Modelling dynamic effects and soil capital When panel data is available and one wants to follow the evolution of the efficiency of an agricultural practise in time, another modelling problem emerges. Indeed, agricultural production is a dynamic process where the production of one year is often influenced by the production and the management decisions in the previous years. The connection between

year, termed soil capitalproducts in the following year (Briec and Chambers, 2008). This is especially true for CA, as the main purpose of CA is to sustain and protect this soil capital through practices such as mulching and crop rotations.

6 Seiford and Zhu (2002) us -linear DEA model of Färe et al. (1989) into a linear DEA model. Theoretical background is provided in their article.

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One interesting modelling approach is the one described by Jaenicke (2000). Based on the work of Chambers and Lichtenberg (1995), he proposed a dynamic DEA method to model rotational crop production that treats soil capital as an intermediate output linking consecutive annual productions in a multi-year rotation. Soil capital can be represented by a number of biological, physical and chemical soil quality indicators. Figure 7 is a schematic representation of the model (see Jaenicke (2000) for the detailed analytical approach).

2.2.7 Analysing efficiency scores with a Tobit regression As mentioned before, it is common in EA to regress the efficiency scores against a certain number of farm-specific variables, such as farmer education, farm size and access to credit, in order to explain the differences in efficiency. However, because the efficiency scores are censored variables, bounded between zero and one, a regression model has to be used that accounts for this censoring rule to avoid biased and inconsistent parameter estimates (Maddala, 1983). The Tobit model satisfies this condition and has been used by numerous studies to regress efficiency scores (Coelli et al., 2002; Speelman et al., 2007; Begum et al., 2011; Thibbotuwawa et al., 2012). In the case of the dependent variable (the efficiency score Ei) being censored from above at the value 1, the Tobit model is specified as:

where j are the parameters of interest corresponding to the explanatory variables xj 2)). The joint significance of all the variables within the model can be assessed using different test statistics, such as the Wald statistic (Speelman et al., 2007), which follows a chi-squared distribution. Having discussed the principles of CA and its potential as an erosion-controlling agricultural

Figure 7 - Schematic representation of Jaenicke's dynamic DEA model, here illustrated for the case of a 3-year crop rotation (adapted from Jaenicke, 2000)

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adoption in the OLM (first research question). Based on the theoretical principles of EA that were presented in this chapter and using the data from the LandCare project mentioned in the introduction, the second part of this research will be dedicated to the construction of a DEA model to esquestion).

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3 Chapter 3: Materials and methods

3.1 Identification of constraints to CA adoption in the OLM There is a large body of literature dedicated to the various biophysical, socio-economic and political factors that can hamper or are hampering a widespread adoption of conservation agriculture. Knowler and Bradshaw (2007) have reviewed 23 studies of CA adoption and identified nearly 170 different variables that had significantly influenced adoption in these studies. However, they concluded that there are few, if any, factors that apply universally and argued that esearch should probably aim to produce results that are meaningful for

For the first part of this thesis I tried to identify the most critical constraints that stand in the way of CA adoption in the smallholder farmer communities of the OLM, or that might explain why adoption has been so slow and unsustained until now. To do so, I reviewed several articles that (inter alia) discussed such barriers and I extracted the constraints that were relevant to the context of the OLM. Next, I sought specific information concerning the local agro-ecologic and economic conditions of the OLM to assess how and to what extent the selected constraints formed a hindrance to local CA adoption.

3.2 Efficiency analysis

3.2.1 Data description

3.2.1.1 The Bergville/Emmaus LandCare Project In order to address the land degradation issues and reinvigorate smallholder agriculture in the rural areas of South Africa, the South African National Department of Agriculture (NDA) funded the Agricultural Research Council's Institute for Soil, Climate and Water (ARC-ISCW) to launch LandCare projects in a few selected key areas of the country. One of these projects was launched in the Bergville district of KwaZulu-Natal. The Bergville/Emmaus LandCare Project started in August 2000 and ran until the end of the 2004/2005 cropping season. The overall objective of the project was to technologies for local farmers in order to address soil degradation and conservation issues and increase farm productivity and income in the Bergville district (Emmaus ward) of KwaZulu- (Smith et al., 2005). To achieve this goal the ARC-ISCW decided to follow a highly participatory approach, involving the whole Potshini community. It was articulated around six pillars:

On-farm experimentation (using CA principles) Awareness and communication (through farmer field days) Local (social) institution-building Training of trainers Farmer-to-farmer extension Partnerships with other organisations in the region

Two types of on-farm experimentation were designed: researcher-managed trials and farmer-

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managed trials. The researcher-managed trials consisted of a number of rigorous scientific experiments aiming at assessing the influence of different practices, including conservation tillage, fertilizing, liming, intercropping and crop rotations, on crop yields, soil parameters and plant nutrients. These trials were started in the first cropping season of the project, involving several farmers of the community, under supervision of the researchers. In the meanwhile,

-managed trials. The objective of these trials was to see if the different components of CA were practical, applicable and adapted to the specific (non-ideal) conditions of small-scale farming (Smith et al., 2005). Additionally, the new practices were to be compared with the traditional farming practices in space and over time by setting up two separated plots for each farmer. These trials were started in the second season of the project (2001/2002), and continued for four seasons until the project terminated at the end of 2005. As the project progressed, the

-to-farmer extension and share their technical knowledge with other farmers. The efficiency analysis of this thesis will be based on data of the farmer-managed trials.

3.2.1.2 Characteristics of the farmer-­‐managed trials The experimental plots of the farmer-managed trials were established on the farms of the eighteen leader farmers in October 2001. These farms are located within the Potshini community, approximately half-way between Bergville and Emmaus (see Figure 1). The area is characterised by highly acidic Avalon soils with a sandy loam texture. The mean annual precipitation in Potshini, measured from the nearby Bergville weather station located 10 km away, averages at 710 mm per annum. However, this rainfall is strongly seasonal and occurs mainly in summer (October March). The mean annual potential evaporation is approximately 1750 mm per annum and the mean annual temperature is 17,4°C (Smith et al., 2005). Each farmer established two plots of 1000 m2 each, to compare CA with the traditional practices (TP). CA plots

certain guidelines. Farmers were provided with the necessary inputs and were trained by local extension officers on how to apply these inputs. The input quantities are mentioned in Appendix 1. Box 1 of Appendix 2 describes the guidelines followed to cultivate the CA plots. Although the importance of a mulch layer was emphasized, the extent of ground cover remained at an unsatisfactory level of merely 15% (recall the 30% threshold of the CTIC). The main cause for this was the fact that animals grazed one the residues when the plots were left unattended (since most farmers do not have fences around their fields). Some farmers also intentionally used part of the residues to feed their animals, since this is often the only way to guarantee a sufficient nutrition for the animals in the winter, when the grasslands are of low quality (Smith et al., 2005). TP plots As it was not the purpose of the project to compare the results of TP plots with each other, but merely to have a benchmark for comparison with the CA plots, no guidelines were conceived to warrant uniformity among the farmers. Basically, every farmer cultivated his/her fields like

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he/she had always done. However, input quantities were set at the same level for all the farmers (see Appendix 1). No liming was performed and no herbicides were applied (weeding with hand hoes). Ploughing was mostly performed with a Saffim mouldboard plough, pulled by a span of oxen. Changes over time Because a participatory and adaptive approach was chosen for the farmer-managed trials, inputs and practices were changed or improved as the project progressed. As can be seen in Appendix 1, some of the fertilisers, pesticides and herbicides used on the CA plots were replaced by other ones. The only, but important change regarding CA practises was the introduction of crop rotations at the beginning of the 2002/2003 season (second season of the farmer-managed trials). The 1000m2 CA plots were split into three equal parts and managed as follows: one third was planted with maize, another third with soya beans and in the last third an intercropping system was started. The maize and soya beans were used in rotation, while the intercropping system had maize as main crop and either soya beans, dry beans, lablab or cowpeas as intercrop. Only the maize plot in rotation will be considered here. The input quantities of this plot were reduced to one third of their previous levels, so that the quantity per unit of land remained unchanged (see Appendix 1). On the other hand, the input quantities of the TP plots did not change over the years and it is reasonable to assume that the practises used on these plots remained unchanged as well.

3.2.1.3 Data collection in the farmer-­‐managed trials Yield estimates To obtain comparable results, a standard method was used to calculate the maize yields, both for CA and TP plots. Box 2 of Appendix 2 describes this method. Soil samples In the beginning, the project team intended to determine a number of soil parameters after each cropping season, so that the effect of CA on soil fertility and soil quality could be assessed. However, the soil sampling and analysis turned out to be a time-consuming and complex procedure and was therefore not continued until the end of the project. Moreover, part of the collected data set was lost or could not be matched with the different farmers anymore. For these reasons, the available data set of soil parameters was insufficient to develop a model that takes environmental externalities or dynamic effects into account, although I initially intended to use such a model. Hence, maize yield was the only output considered in the EA. Rainfall data Since precipitations in the Upper-uThukela catchment are known to be very erratic (Smith et al., 2005) and rainfall can have a strong influence on rainfed maize production in SSA (Rusinamhodzi et al., 2011), it was decided to include rainfall data as a explanatory variable in the model. This data was obtained from the Bergville weather station located 10 km away from Potshini.

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3.2.2 Data processing and assumptions

3.2.2.1 Yield data Theoretically, the data set should have consisted of 18 x 2 x 4 = 144 yield observations (18 farmers, two plots each, during 4 years). However, for several reasons the actual workable data set consisted of only 90 observations. First of all, some data were missing because not all the yield samples could be analysed. Farmers were trained on the sampling procedure at the onset of the project, but some of them failed to do it correctly, leading to unreliable samples (Smith et al., 2004). Secondly, three obvious outliers with a yield of zero (no maize production) were excluded. It was checked for other outliers (extremely low or high yields), but a box plot analysis did not indicate the presence of any of these. Finally, a lot of observations were missing for the two last seasons (2003/04 and 2004/05), especially for TP plots. For the last season even no observations at all were available for the TP plots. This was because the farmers completely switched to CA on their farms in the last season, abandoning the TP plots. The project team decided not to stop this, since it was a result of the adaptive nature of the experiment and no real formal statistical approach was intended for the farmer-managed trials.

3.2.2.2 Inputs data A first important remark is that data was only available for easily quantifiable inputs (lime, fertilisers, herbicides and pesticides). No precise information was available on the quantity of seeds used by each farmer, since the landrace maize seeds were not provided by the project but

did not keep any records of the number of man-hours they spend for the cultivation of their two plots. This is unfortunate, considering that a reduced labour requirement, which is repeatedly mentioned in the literature as one of the major advantages of CA (Steiner, 1998; Hobbs, 2007; FAO, 2012), is contested by several other authors (Wall, 2007; Giller et al., 2009). Finally, precise equipment usage and costs were not determined. For these reasons it was decided to consider only the aforementioned agrochemical inputs for the EA. In some way, this is justifiable since these are the only inputs incurring repeated and direct costs for the farmers, while the other inputs are long-term investments (equipment) or available nearly free of costs (familial land received from the chief, own seeds, family labour7). As can be seen in Appendix 1 the kinds of inputs that were used varied between CA and TP and between the years for CA. In total, thirteen different products were used. Clearly, to avoid the number of inputs of the model to be too high compared to the number of observations, the different products had to be grouped in a limited number of categories. For the fertilisers it was chosen to calculate the NPK-content of each product and consequently add up the respective quantities of each of the three elements for the different fertilisers used by a farmer, to obtain total quantities of N, P and K applied by each farmer (in kg). A similar approach was not possible for the different kinds of pesticides and herbicides since these products do not share

o aggregate these inputs was to sum their respective

value of the herbicide package or pesticide package was taken as a proxy for the quantity applied. Several researchers have used this approach to deal with these input categories in EA (Wossink and Denaux, 2006; Speelman et al., 2007; Msuya et al., 2008). Finally, although lime was broadcasted only once at the onset of the experiments, it was decided to spread the 7 Family labour, although not always available, has a low opportunity cost considering the very limited employment opportunities in the OLM.

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applied quantity over the four years of the project. The reason for this is that lime has a long-term effect on crop growth, keeping the pH of the soil at a more optimal level for several years. Thus, attributing the entire amount (500kg) to the first year would have unreasonably increased the efficiency scores of the following years, while the crops were still benefiting of the lime. Nevertheless, the buffering effect in the topsoil decreases with time as the lime is gradually dispersed in the soil profile. So it was chosen to allocate decreasing amounts to consecutive seasons, roughly following an exponentially decreasing trend: 250kg, 150kg, 75kg, 25kg. It should be noted that, considering the entire set of input combinations, there is only a limited input variability, since different farmers in one year use exactly the same input combinations, for one practise. As mentioned earlier, unlike parametric approaches, DEA models can deal with such a limited variability.

3.2.2.3 Rainfall data

period that will have an influence on its yields, only the precipitations for this period were considered. In practise, the monthly precipitations (in mm) were summed for the months November to April, to use the total amount as an explanatory variable (see later).

3.2.3 Methodology

3.2.3.1 Preliminary analysis Before carrying out the EA, the average yields were compared for the two practices over the years. It was then checked if the difference between the two practices for each season was significant, using a independent- -test with the computer program SPSS. An independent-samples test was chosen since the observations come from different plots, although there is a link between CA and TP plots since they are cultivated by the same farmers.

3.2.3.2 Malmquist test to assess technological change To be able to construct a DEA frontier with reasonable accuracy and to avoid too many

the number of inputs plus outputs. As a rule of thumb there should always be at least three times as many observations as there are inputs plus outputs (Cooper, 1995 in(Chambers et al., 1998). So in this case, there should be at least 3 x (6inputs + 1output) = 21 observations each season. For season 2003/04 and 2004/05, however, only sixteen and thirteen observations respectively were available. To increase the number of observations in order to avoid the above-mentioned problems, the only solution was to pool the observations of the different years in one data set and construct the frontier based on this larger set. However, one condition for this to be done, is that there should be no significant technological change over the years. Indeed, if this would be the case, comparing the efficiency scores of observations from different years would be problematic, since higher (lower) scores in one year might not only be due to TE improvement (decrease), in other words movements relative to the frontier, but also due to technological progress (regress). Hence, it was planned to perform a Malmquist test to assess whether any technological change was present. This test, however, can only be done with a so- data set, i.e.

the data set, this test would only have been possi

35

into account. For example, to perform an analysis over the four years of the project, data was

were present in the last year.

have corresponded to a total of 3 x 14 = 42 observations, not even half of the 90 observations.

to unreliable results concerning technological change. For this reason, no satisfactory Malmquist test could be performed. Nevertheless, it is reasonable to assume that no significant technological change will manifest in just four years of observations.

3.2.3.3 Calculation of technical efficiencies For the calculation of the TE an output-oriented approach was chosen. This was the most appropriate orientation considering the experimental setting of the famer-managed trials, where input quantities were fixed by the project team and freely available for every farmer. In some way, the focus was thus to maximise the output (maize yield) at a fixed input level. To check for possible scale inefficiencies the TE was calculated both under CRS and VRS. The EA was run with the computer program LIMDEP. A first method to assess the influence of CA on TE is to compare average efficiency scores for both techniques and verify whether the difference is significant. To do so, a non-parametric Mann-Whitney U-test was used. This test can be seen as the non-parametric equivalent of the Student t-test and has been used in many EA studies to compare the efficiency of two different technologies (Lansink et al., 2002; Wossink and Denaux, 2006; Denaux, 2007). The non-parametric approach is required since the efficiency scores are censored at zero and one.

3.2.3.4 Calculation of allocative and economic efficiencies On the other hand, to calculate the AE and EE, an input-orientation was chosen. This was more relevant than an output-orientation since the prices of the inputs vary between the two techniques (and between the years), leading to different costs for different input combinations and hence, possible allocative inefficiency. Moreover, there is only one output, whose price (the market price of maize) is considered to be the same for all the farmers. It might seem contradictory to use different orientations for the calculation of TE and EE, but, as will be seen later, the choice of orientation has no influence on the efficiency scores because CRS prevail in this case.

used in that case. The cost of each package was the sum of the costs of its constituents. The input prices that were used were the retail prices that were valid in Bergville at the time of the project, i.e. prices farmers would have had to pay if it they had purchased the inputs themselves.

3.2.3.5 Regression of efficiency scores with a Tobit model The following variables were used to carry out the Tobit regression:

-season rainfall (in mm)

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representing the product of the two previous variables. This variable was included in the analysis to see if one of the practises performed notably better under certain rainfall conditions. One would for example expect that, due to its positive impact on moisture retention, CA performs better in drier years.

that assume a value of 1 when the observation is for that season and 0 otherwise. The fourth season is symbolised by three zeros. This will enable the assessment of trends in efficiency during the project.

Hence, in this case the Tobit model for the efficiency score Ei (TE, EE or AE) is specified as

2)). The significance of

the model will be assessed using the Wald test.

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4 Chapter 4: Results

4.1 Constraints to the adoption of CA by smallholders in the OLM

4.1.1 Biophysical constraints

4.1.1.1 Low level of biomass production To ensure an adequate and permanent soil cover in a CA system, a sufficient amount of biomass has to be produced by the system throughout the year (Erenstein, 2002). The biomass that can contribute to a mulch layer can take the form of crop residues, cover crops, relay crops and weeds. In semiarid and arid regions, the limited precipitation and unfavourable temperatures lead to a relatively short growing season under rainfed agriculture and hence to a lower annual biomass production (Steiner, 1998). In his assessment of the potential of CA in the various agro-ecological zones of Africa, Steiner (1998) classifies all the regions with a mean annual precipitation (MAP) of less than 800 mm as low-potential regions. With the exception of the high altitude areas in the Drakensberg, the MAP in the OLM varies between 600 and 800 mm. Moreover, this rainfall is strongly seasonal, occurring mainly in summer with more than 80% of the rain coming from heavy thunderstorms, having no time to infiltrate in the soil (DWAF, 2001). This limited out-of-season rainfall leaves little opportunities to relay maize with cover crops. In addition, the cool winters with several frost events that characterise the region, restrict the choice of possible cover crops and their productivity. Furthermore, residues derived from maize, sorghum and millet, the main crops cultivated by the smallholders of the OLM, are considered to be of poor quality due to high C/N ratios and large amounts of readily decomposable carbon (Cadisch and Giller, 1997). These two characteristics result in prolonged nitrogen immobilisation in the soil, reducing nitrogen availability for crop growth in the short term and thus potentially augmenting the need for N-fertilisation (Giller et al., 1997). Also, at the low cropping densities typical for non-irrigated smallholder fields, the cereal residues mostly do not provide a sufficient soil cover. Finally, Nkala et al. (2011) rendered their use in smallholder CA systems in Mozambique impossible.

4.1.1.2 Alternative uses of crop residues In addition to the inherent difficulty to grow a sufficient amount of biomass for soil coverage, the biggest challenge in smallholder CA systems is to permanently retain enough of that biomass on the fields (Bolliger, 2007). Indeed, there are several alternative uses for crop residues, including feeding of livestock, building huts or fences and providing fuel for cooking and heating (FAO, 2009). In the case of the smallholder communities of the OLM, residues are not used for building purposes (wild thatch grass is used instead), nor are they a significant source of fuel (forest firewood is used instead). However, their use as feed for livestock is of utmost importance. Therefore, Smith et al. (2005) consider animals grazing on the fields during the winter as the biggest constraint for creating a good mulch layer in the Emmaus area. Indeed, the grazing value of residues in the winter is considered crucial because close, secure and palatable pasture for cattle is rare during this period. Since there are no traditions of producing hay or silage, purchasing animal feed or cutting and carrying fodder from elsewhere, all cropping fields are opened to communal cattle herds after a date fixed by the local chief (Nkosi), corresponding to the latest maize harvest (Bolliger, 2007). In theory some traditional rules exist that enable a farmer to protect some of his plots from the communal herds, but in

38

practise it is very difficult for a farmer to restrict grazing on his fields since cattle is mostly left unattended and fencing is expensive and not a realistic option on the outfields. The need for crop residues as supplementary livestock feed is becoming even more pressing as the number of livestock is exceeding the grazing capacity of the communal pastures. Moreover, the fact that the earliest planting date and latest harvest date are determined based upon the growing season of maize (usually October to June), restricts the choice of alternative main crops or cover crops since crops with a longer or different growing season cannot be grown, unless the farmer can afford to fence his fields (Sterve, 2010). Since this problem predominates in most African mixed farming systems and given the importance of livestock in arid and semi-arid areas, some authors argue that curtailing the use of residues as feed in favour of the creation of a mulch layer would be socially unacceptable (Twomlow et al., 2008; Giller et al., 2009)

be on raising productivity through the optimization of certain management practices, such as efficient input usage, effective weeding and better water management, rather than on mulch creation (Twomlow et al., 2008; Liniger et al., 2011). Whether the latter would be a good alternative remains an issue for debate, since several studies have reported yield decreases for NT without soil cover compared to conventional techniques (CIMMYT research mentioned in(Derpsch, 2003; FAO, 2009) and soil cover is crucial to achieve erosion reduction (Erenstein, 2002). Clearly, more adequate solutions have to be found to integrate CA in the mixed farming system of the OLM. Wall (2007) points out that any solution has to be designed by involving the whole community and raising awareness on the benefits of residue retention for soil cover. Erenstein et al. (2008) report some cases in Tanzania and Zimbabwe where this approach turned out to be successful, even leading to the re-enactment of forgotten traditional regulations that enabled farmers to deny access to their fields to grazing animals. However, in the context of the OLM this approach seems to be particularly challenging, since Bolliger (2007) reported that most of the South African smallholders he interviewed, disliked the idea of having to restrict winter residue grazing, saying that it was incompatible with their traditions. Most of the respondents also believed that CA was more about saving plough contractor costs and intensifying cropping with inputs rather than about keeping a permanent soil cover. It is also doubtful whether the use of fences to protect the fields from stray animals would be a workable solution in the OLM. First of all, most farmers can not afford fencing and secondly, during previous attempts to implement rotational grazing systems in the region, fence theft was reported as a recurring problem (Salomon, 2006). Stall-feeding and the production of unpalatable cover crops are two other solutions mentioned in the literature (Derpsch, 2003; Liniger et al., 2011), but just as is the case for fencing, they do not address the basic problem of competition between livestock and soil cover. Unless alternative feed sources are found or stocking numbers are reduced, farmers will probably still consider the opportunity cost of leaving residues on the fields too great in relation to the potential benefits of a permanent mulch layer. Especially because these benefits are difficult to quantify and might not be apparent in the first years of practise.

4.1.1.3 Weeds As mentioned earlier, weeds are a particular concern in CA, since these are no longer ploughed into the field. The potentially improved crop yields may never be achieved if weeds are not deterred from outcompeting the main crop (FAO, 2009). In the smallholder communities of the OLM weeds are traditionally controlled with hand hoes. This is already very labour intensive

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in conventional systems, but becomes even more labour demanding when the weed pressure increases under CA, especially because plant residues make this task more difficult and weeding has to be continued during the dry season to prevent seed formation and water loss (Steiner, 1998). For these reasons, herbicide application is often seen as inevitable, at least in the initial years of adoption (Steiner, 1998; Wall, 2007; FAO, 2009; Liniger et al., 2011; FAO, 2012) and it is actively promoted by extension projects as a necessary component of smallholder CA packages. However, Bolliger (2007) reported that, during several smallholder CA projects in South Africa, even with herbicide application, weed control remained a serious problem. An increase in noxious and hard to control weeds such as couch grass (Cynodon dactylon) was observed in some cases, despite that glyphosate was used at the recommended rate of 3 litres per hectare. According to Derpsch (2003) 4 litres per hectare are necessary and particular attention has to be paid to use clean (not muddy) water lowered to a pH of 3.5. Obviously, such high quantities of herbicide are not affordable for most smallholders and the use of water with these specifications are inconceivable in the rural setting of the OLM. Cover crops are often seen as a low cost alternative to (partially) supress weed emergence in smallholder CA, since the farmer can produce his own seeds. Leguminous cover crops can have the additional benefit of enhancing soil fertility and providing an edible product. In the case of the small-scale maize production in the OLM, dry bean (Phaseolus vulgaris), pigeon pea (Cajanus cajan), cowpea (Vigna unguiculata) and groundnut (Arachis hypogaea) can be suitable choices since these are drought tolerant plants that can grow on degraded soils (Derpsch, 2003; Smith et al., 2005).

4.1.1.4 Pests and diseases Some pests and diseases can be of particular concern under CA. In South Africa, Bolliger (2007) reported some mulch-related diseases such as grey leaf spot, while Steiner (1998) indicates that the fact that maize, sorghum or millet are mostly grown every year (occasionally intercropped but not really rotated with legumes) can be conducive to more frequent occurrence of plant diseases, since reinfection can occur through the residues. The obvious solution here is to introduce real crop rotations over two or three years, but this might be challenging considering that maize is a staple food for the rural populations of the OLM, so it is needed every year. Additionally, the short growing season limits the choice of rotational crops.

4.1.2 Technical constraints

4.1.2.1 High managerial requirements and information needs Compared to conventional agriculture, CA is a completely different production system with complex dynamics, requiring high management skills and a learning process by the farmer (FAO, 2012). Especially mulch maintenance, weed control and the choice of efficient crop rotations can present unusual challenges in comparison with traditional farming (Steiner, 1998). The knowledge- and management-intensive nature of CA can be a serious disincentive for inexperienced smallholders, particularly because the information is not available as a

-the-conditions of an individual farm (Friedrich and Kassam, 2009). Given that most smallholders in the OLM are illiterate and have little experience with new or alternative technologies, it is crucial that they receive the necessary support and that long-term on-farm demonstration trials are implemented by extension services (see further).

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4.1.2.2 Labour requirements

It remains questionable whether CA is an interesting option for smallholders who cannot purchase or have difficult access to herbicides. Indeed, most authors agree that, although there is a clear reduction in labour requirements for tillage, this will most probably be outweighed by increased labour for weeding when only manual weeding is practiced (Wall, 2007;; Erenstein et al., 2008;; Giller et al., 2009). This might in particular be valid in the context of the OLM, since weeding accounts for approximately 60% of the labour required for smallholder maize production under conservation tillage (Riches et al., 1997) and labour shortage is widespread in the smallholder farming sector of the OLM. The latter is due to the HIV / AIDS pandemic, the emigration of young men to urban areas for wage labour and the lack of interest in agriculture by the youth.

4.1.3 Infrastructural constraints

4.1.3.1 Lack of markets for equipment and inputs Under CA, some of the inputs such as fertilizers and insecticides will differ only marginally from the requirements of conventional tillage-based farming. Nevertheless, other inputs, such as herbicides, seeds for cover and rotational crops and in particular equipment for direct seeding, planting and residue management are mostly completely different (Friedrich and Kassam, 2009). Equipment According to Lal (2009) the lack of markets for no-till and direct seeding equipment can be seen as the principal constraint to adoption of CA in SSA. Mostly, implements are imported from Brazil and made available to farmers on an experimental basis, but no real markets exist, at least not for equipment adapted to African smallholders (FAO, 2009). The same problems were witnessed in South Africa: Smith et al. (2005) affirmed that the absence of appropriate implements, especially planters, was experienced as a major constraint to the implementation of the Bergville/Emmaus LandCare project. And, although a mechanisation strategy was carried out during the project to modify conventional implements for no-till purposes, Sterve (2010) reports that the lack of equipment is still the single most important factor causing abandonment of CA by farmers in Potshini. However, this situation might change in the near future, since, according to Johansen et al. (2012), the introduction of animal-drawn rippers and direct seeders for smallholders in SSA is now breaking through. The Magoye ripper is now relatively easy available and affordable for most smallholders, since it costs about US$ 25 (Johansen et al., 2012). Nevertheless, a ripper only serves to open shallow rip-lines, so seeding, fertilisation and covering still have to be done manually. Animal-drawn direct planters, on the other hand, permit to perform all these operations mechanically and in one pass. Because the first planters were introduced in Africa only in 2004, and local manufacturers were initially sceptical about these new machines, local production is only taking off now and implements are still expensive, ranging between US$ 500 and 600 (Johansen et al., 2012). For the purposes of the smallholders of the OLM it is promising that planters adapted for the seeding of maize, sorghum and beans are already available. However, according to the National Agricultural Directory 2011, only three

41

distributors8 commercialize animal-drawn direct planters on the South African market (DAFF, 2010). It is expected that, as farmer demand rises and local production increases, more distributors will offer smallholder-adapted solutions, which will reduce the costs of these implements over time. The main advantage of these rippers and direct planters is that they require considerably less draft power, compared to conventional deep-tillage implements. For example, according to Harrington and Erenstein (2005), shallow tine tillage requires only 14% of the traction power of tillage with a mouldboard plough. Similarly, the above-mentioned modifications of convential implements during the LandCare project in Potshini, resulted in a reduction of draft power from eight or ten oxen to only two oxen (Mhlongo, 2007). This could have a positive impact on yields in the OLM, since a lot of smallholders fail to plant their maize on time due to a lack of animals or because these animals are in poor condition after the winter (Smith et al., 2003). Moreover, Johansen et al. (2012) argue that this development could potentially act as an incentive to reduce the number of cattle being kept and thus reduce the competition for crop residues. This is an interesting effect, in the light of the problems of overstocking and overgrazing in the OLM. It is not sure, however, if this will also work as an incentive for the smallholders of the region. There, cattle are not only kept to provide traction power, but also have an important role related to traditions and social status (see § 1.1.2.2). Herbicides and seeds The access to herbicides is probably less problematic in the OLM, since the long-established commercial CA sector (especially in the Bergville and Winterton area) triggered the introduction of these products on local markets. However, the kind of seeds for cover and rotational crops suitable for small-scale CA differ from those for commercial purposes. Hence, they are still absent on local markets. Their introduction could be promoted or financed by the government to initiate smallholder CA in the OLM, since once the crops become established, the farmers could produce their own seeds (at least if these are non-patented seeds, see next paragraph).

4.1.3.2 Joint promotion of CA and GM seeds It should be stressed that, as is the case in other parts of the world, the promotion and implementation of CA in South Africa goes hand in hand with the introduction of genetically modified (GM) seeds. Indeed, the higher levels of herbicide application make the use of herbicide-tolerant crops more appropriate, or even unavoidable. In fact, the advent of herbicide-tolerant maize varieties 9 (in particular glyphosate- -maize) is considered as one of the major reasons for the success of no-till in KwaZulu-Natal (Smith et al., 2010). A reason for concern is that most projects that promote smallholder CA in (South) Africa are established in partnership with biotech companies (Ekboir et al., 2002; Bolliger, 2007; FAO, 2009; MEDPT, 2011), which often provide both the herbicides and corresponding GM seeds. Once the projects come to an end and free input supply stops, smallholders that want to continue practising CA are faced with the high costs of the GM seeds 8 Two South African distributors (Afritrac and Inttrac Trading) and one Zimbabwean (Hastt Zimbabwe). 9 In South Africa, five different herbicide-tolerant maize seed varieties have been approved for general release (involving two companies, Monsanto and Syngenta) and in the 2010/11 season 41,8% of the total maize production area had been planted with herbicide-tolerant maize (Esterhuizen, 2012).

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and, more importantly, the fact that they cannot produce their own seeds, but have to buy the patented GM seeds every year. This further aggravates the problem of external input reliance, which is incompatible with resource-constrained and vulnerable smallholders. (Esterhuizen, 2011)

4.1.3.3 Lack of output markets As denoted by Bolliger (2007), an additional problem in the rural areas of KZN, is the absence of lucrative or formal markets for excess crop yields. Local markets quickly become saturated and smallholders can hardly compete with large-scale commercial farmers in terms of the quantity, quality and timeliness required by commercial buyers. To my knowledge, the OLM counts only two maize milling facilities: Umbuso Maize Mills CC in Bergville and Drakensberg Milling Ltd in Winterton. Due to storage saturation or price fluctuations, however, farmers are often forced to travel as far as Ladysmith to be able to sell their surplus at a reasonable price (Stronkhorst et al., 2010), although most of them are discouraged by the difficult access to and the relatively high cost of local transportation (OLM, 2002). Clearly, this is not a favourable environment for smallholders to develop (small-scale) commercial activities. Consequently, they are less incentivised to engage in CA, since it is hard to compensate the higher input costs of CA with a cash income from their surplus production. In other words, even if CA has the potential of increasing the yields of the smallholders of the OLM to a level above household consumption, farmers might not see the advantage of switching to CA as long as access to profitable markets is impossible.

4.1.3.4 Lack of extension support and information

As noted earlier, CA is a knowledge-intensive technology. Since most of the smallholders of the OLM are not well connected to outside information systems (although this is improving with the advent of cell phones; access to the Internet remains almost non-existent), information sharing within the community is generally the only source of new knowledge (Wall, 2007). Hence, it is crucial that extension services or other external agents come into direct contact with farmers and diffuse the information within the different communities. This seems to be problematic in the OLM, since Bolliger (2007) reported that, among the interviewed smallholders that were willing to learn more about CA, many complained that they often had nobody to turn to. A quarter of the respondents mentioned that it was more difficult getting advice concerning CA compared to conventional farming, and, according to Bolliger, this caused herbicides and other inputs to be used less efficiently. As a matter of fact, it appears that there is a shortage of skilled extension officers with expertise in SLM practices in general and CA in particular (Wood, 2011), most of them being employed by NGOs or the private sector (OLM, 2002). Moreover, there is an apparent controversy around CA within government agricultural services, since they have traditionally tried to propagate ploughing among smallholders. Therefore, some officers still have a sceptical attitude towards the radically opposite approach of CA (Steiner, 1998). Stonkhorst et al. (2010) affirm that confusion is created in the communities of the Emmaus area, as contrasting projects implemented by different organisations coexist, some promoting CA and others relying on conventional tillage. They conclude that there is a lack of a coherent long-term vision expressed by the implementing organizations.

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4.1.4 Financial constraints The switch from conventional farming to CA implies a long-term investment, primarily in new equipment for direct seeding. Smallholders usually do not have the necessary capital for this kind of investment. In addition most of the existing equipment is becoming obsolete, while the farmer will probably not find a second hand market for it (Friedrich and Kassam, 2009). In addition to the initial investment, inputs like herbicides and fertilizers represent operational expenses that might not be affordable to the smallholders of the OLM, since most of the households have very low incomes consisting almost entirely of state pensions. Indeed, according to Sterve (2010), most CA farmers in Potshini state that they buy less or cheaper fertilizers than what is recommended based on soil sampling and some fail to apply crop rotations in some years because they cannot afford dry bean seeds. At these lower levels of fertilisation, the expected higher yields of CA might not manifest at all (Erenstein, 2002); see § 2.1.4) especially considering that the leached nature of the soils of the OLM in itself requires high levels of fertilisation (OLM, 2002). Finally, it has to be stressed that farmers might be reluctant to invest in a technology that lacks immediate returns on investment. In point of fact, the benefits of CA in terms of higher yields usually arise only after several years (Hobbs, 2007; Giller et al., 2009); see § 2.1.4). To gather the necessary capital, farmers could take individual loans (micro-financing) or cooperate to engage in joint investments such as a shared planter. The first option, however, might not be possible, since credit opportunities for smallholders are almost completely absent in the OLM (OLM, 2012).

4.1.5 Societal and cultural constraints

4.1.5.1 -­‐sets and risk aversion Although the traditional mind-set of smallholders might be a constraint to any new technology, this holds particularly for CA. In fact, Wall (2007) considers that a change in mind-set is one of the most important changes necessary to adopt CA. The plough is often considered as the ultimate symbol of agriculture and farmers traditionally believe that soil tillage is an essential component of farming. Moreover, it is often assumed that a clean farm is synonymous with hard work and good management. CA goes in direct opposition of these paradigms, since the soil tillage is avoided as much as possible and a typicdecomposing residues on the fields and the presence of several crops in intercropping. In this context, peer pressure and community norms can constitute substantial barriers to the adoption of CA in smallholder communities (Wall, 2007). This shows once more how important it is to involve the whole community instead of merely targeting a few progressive farmers.

aversion can also present a challenge to CA adoption since considerable amounts of inputs are necessary for a result that is not guaranteed in the first years of practise. This uncertainty is exacerbated by the erratic nature of the climatic conditions of the OLM. Bolliger (2007) recounts that most of the interviewed smallholders acknowledged that the use of fertilisers and herbicides associated with CA could improve crop yields, but that at least 25% set this against yield-diminishing climatic factors beyond their control such as late or irregular rains, early frosts or a humid harvest period. One way to overcome these mental obstacles would be the occurrence of inspiring examples (passionate and successful farmers, not simply short demonstration trials) in the several

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communities of the OLM. Ideally, these examples should be smallholders as well, so that potential adopters can identify themselves with the demonstrating farmers. Nevertheless, advantage could also be taken of the existence of experienced commercial CA farmers in the Bergville / Winterton region.

4.1.5.2 General apathy towards agriculture As was mentioned in the introduction, for the majority of the smallholders in the OLM, agriculture is mainly done for subsistence purposes and contributes only in a small extent to

d security, farming is seen as a supplementary livelihood rather than a career to aspire to (Bolliger, 2007), especially considering the lack of opportunities to intensify agricultural activities to profitable levels. This has led to a general apathy towards agriculture and the conviction that the ways out of poverty have to be found elsewhere. This feeling is markedly pronounced in the younger generations of the OLM, who totally lack interest in farming and regard it as an activity with low social status (Wood, 2011). As a consequence the smallholder farming sector is essentially run by elder people and destitute farmers, and only in rare cases by young dynamic smallholders. All this might lead to many smallholders of the OLM being unwilling to devote themselves to an agricultural system such as CA, since it requires commitment, constant planning and management and potentially additional labour, while at the same time it breaks with traditions and customs (Bolliger, 2007).

4.1.5.3 Lack of concern about environmental issues In Brazil, farmers started adopting CA because of the unprecedented soil erosion and land degradation that escalated during the 1960s. It was a profound crisis situation, in which erosion sometimes reduced productivity to such an extent, that farmers were unable to repay bank loans (Harrington and Erenstein, 2005). This prompted for a radical and immediate shift towards sustainable practices. The situation in the OLM is far from being as dramatic for the farmers, although soil degradation is also reaching alarming degrees. Consequently, the smallholders of OLM do not seem to consider degradation an urgent issue (Sterve, 2010) and in general soil erosion is perceived as a landscape rather than plot phenomenon (Hoffman et al., 1999). Moreover, CA is rather regarded as a mean of increasing yields (mainly through inputs) than as an erosion-controlling practise (Bolliger, 2007; Sterve, 2010). Hence, before the start of any CA implementation project in the OLM, general awareness concerning land degradation should be raised among the rural populations, and particular attention has to be paid to the

4.1.5.4 Gender issues and traditions As is the case for most smallholder communities in Africa, the agricultural labour force in the OLM predominately consists of women (Walker and Schulze, 2006). Ploughing, however, remains an activity performed almost exclusively by men. When a contractor has to be paid to execute this task, or when oxen have to be hired to obtain the necessary draft power, it will mostly also be the male members of the households paying for this, using their off-farm salaries or money external to the ordinary household budget (e.g. by selling livestock) (Bolliger, 2007). Weeding, on the other hand, is traditionally performed by women. Also, when agrochemical inputs are used, it is usually considered normal that they are purchased with the household budget, managed by the female members of the family. As a result, the adoption of CA potentially implies gender issues. First of all, as CA leads to a shift from ploughing to

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weeding, this might engender an unacceptable increase in the burden of labour on women, unless there is a reallocation of the gender roles in agricultural production, (Giller et al., 2009). Secondly, this might lead to financial instability for the poorest households, since the extra inputs would have to be paid with the household budget. Most of the communities in the OLM are still largely influenced by the androcentric Zulu culture, in which cattle raising - a typical masculine activity - is considered much more

(Bolliger, 2007). Accordingly, cultural norms and tribal regulations will rather encourage livestock keeping than efforts to improve crop production, especially if the latter happens at the expense of a source of feed for livestock.

4.1.5.5 Land tenure security Secure land tenure is often cited as a primordial prerequisite for CA adoption, since CA implies long-term investments as well as progressive benefits (Derpsch, 2003; Wall, 2007; FAO, 2009; Friedrich and Kassam, 2009). However, in their review of thirteen different studies assessing the influence of tenure security on CA adoption, Knowler and Bradshaw (2007) found only two studies where this factor was significantly and positively correlated to adoption. In the context of the communal lands of the OLM, tenure security is hard to define, since farmland remains community-­owned, although farme to

(Bolliger, 2007). The fact that they do not possess title deeds to their land and the existence of certain customary laws prevents them from selling their land or buying additional land. Although land can be rented, the land rental market is so inefficient and agricultural intensification is so hard to realise, that land is often left idle since this does not incur any opportunity cost (Crookes and Lyne, 2003). CA would probably have more chances of success, if clear ownership rights were assigned to individual farmers so that they would feel more committed to improve the fertility of their land and tackle erosion problems on their own plots. The stimulation of a land rental market would probably also be beneficial, since this would enable motivated farmers that witness the benefits of CA on their fields to intensify their farming activities to make them more profitable.

4.1.5.6 Historical background The problems of land degradation in the OLM are not new. In fact, they gradually manifested during the 20th century as population increased and black farmers were forced to migrate to more fragile, marginal land. Because of the visible impacts downstream, such as siltation of rivers and dams, the government implemented policies to protect the highest part of the mountains, again causing the displacement of small-scale farmers since they were held responsible for the erosion problems (Arnalte, 2006). Nowadays, these past oppressions and accusations continue to shape their attitudes towards government interventions, especially when it comes to environmental management and land degradation (Nsuntsha, 2000). Again, this calls for a careful approach, avoiding that farmers have the feeling that decisions are taken above their heads and that CA is imposed to them solely to address land degradation.

4.1.6 Policy constraints Clearly, considering all the above-mentioned infrastructural constraints, the introduction of CA in the smallholder sector of the OLM will have to happen with the support of governmental institutions if it is to be successful at a large scale. It is encouraging to see that several CA

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projects are launched all over South Africa, the majority being implemented by the Agricultural Research Council (Smith et al., 2010) and supported by national or provincial governments. However, these projects are mostly still in the stage of research and demonstration (Mazvimavi, 2011), while practical dissemination and long-term support seems to stay out for the moment. For example, the KwaZulu-Natal Department of Agriculture is conducting on-farm CA trials based on tractor, animal draft and manual means (Mazvimavi, 2011). Although the local government of the OLM is aware of the alarming situation regarding land degradation in its municipality, it does not seem to have concrete plans to tackle the problems. As deduced from the most recent Integrated Development Plan of the OLM, basic service provision will still be the top priority of the local government in the coming years, in view of the huge backlog concerning water provision and sanitation (OLM, 2012). Neither (smallholder) agricultural development, nor environmental rehabilitation figures in the list of key challenges of this document. Yet, the commitment and support of the local government is crucial, since it is responsible for, inter alia, the training of local extension officers. Together with traditional authorities, it has the closest link to the rural populations, which is crucial for the implementation of awareness raising campaigns. Governmental institutions can also have a coordinating role, creating appropriate innovation networks and linking different actors, including researchers, NGOs, inputs suppliers, equipment manufacturers and distributors, extension services and farmers. In South Africa, the communication concerning CA between all these institutions and stakeholders is reported to be poor (Mazvimavi, 2011). A final constraint at the policy level is the fact that the traditional authorities of the OLM are getting weaker and that traditional regulations related to land management are disappearing. Since the introduction of the municipalities, the local chiefs (Nkosi) have lost a lot of their (enforcement) powers. As a result, the traditional arrangements that used to regulate the use of arable lands and communal pastures are no longer effective. This has lead to an almost total lack of direction and implementation of sustainable land management practises (Stronkhorst et al., 2010).

4.2 Efficiency analysis

4.2.1 Preliminary analysis The averages of the maize yields of all the CA plots and all the TP plots together were calculated for each season. Then the difference between the average of the CA plots and the average of the TP plots was calculate and it was checked if the difference was significant using an independent samples Student t-test. The results are indicated in Table 1. We see that the yields were higher for CA plots compared to TP plots for season 2001/2002 (+ 21,50%) and season 2003/2004 (+ 61,50%). In the second season, however, CA plots had lower yields than TP plots (- 2,83%). For the last season no comparison could be made since the farmers abandoned the TP plots. Finally, considering all the observations for the four cropping seasons, the average yield of CA plots was 30,96% higher than that of TP plots. This difference was significant at the 1% level. Hence, we can conclude that, on average, significantly higher yields were obtained with CA.

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Table 1 - Average maize grain yields for the TP and CA plots of the farmer-managed trials during four

consecutive seasons

Season Practice Average yield (ton ha-1)

# observations Std. Deviation yield

Yield difference CA above TP

t-statistic

2001-02

TP 2,6168 15 1,378583

CA 3,1795 16 1,159392 + 21,50% 1,233°

TP & CA 2,9072 31 1,280904

2002-03 TP 3,2796 15 1,425881 CA 3,1867 15 1,222610 - 2,83% -0,192° TP & CA 3,2332 30 1,305895

2003-04 TP 3,0358 5 1,238059 CA 4,9029 11 1,501916 + 61,50% 2,418** TP & CA 4,3195 16 1,646658

2004-05 CA 4,6650 13 1,568951 - -

All seasons

TP 2,9607 35 1,377657

CA 3,8773 55 1,538200 + 30,96% 2,868***

TP & CA 3,5209 90 1,537055 Note 1: ** and *** denote significance at the 5% and 1% level respectively, ° denotes insignificance at the 10% level Note 2: There are no observations for TP plots for season 2004-05

4.2.2 Calculation of technical efficiencies Using the computer program LIMDEP, output-oriented TE estimates were calculated, both under CRS and VRS assumptions. Both approaches yielded exactly the same scores for all the

should be noted that input-oriented scores were calculated as well, but that these were also exactly the same. This is coherent with the fact that orientation has no effect on efficiency estimates when CRS prevail. The results are indicated in Table 2. Looking at the seasonal differences, the same trend can be observed as for the comparison of yields: the TE is higher for CA for season 2001/2002 and season 2003/2004 (both around +5%) while is it lower for the second season (-21%). However, only the latter difference is significant according to the Mann-Whitney U-test. Considering the four cropping seasons, the average TE of CA plots was 2% lower than that of TP plots, but the difference is not significant at the 10% level. Hence, on the basis of the U-test, one could conclude that, on average, TE does not differ significantly between both technologies. To answer the first subquestion how do the efficiencies of CA vary over time? we observe the evolution of the average TE for CA over the four years: 0,544 0,403 0,620 0,590. First of all, there definitely is no clear trend. The average TE first decreases in the second season, then increases in the third season and decreases again in the last season. Secondly, although the score is slightly higher in the last season compared to the first season (0,590 vs. 0,544), this difference proved to be insignificant at the 10% level according to a Mann-Whitney U-test. Finally, we can look at the evolution of the distribution of the efficiency scores

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of CA over the four seasons (see Table 3). In Table 3 for every season the two most represented classes are indicated. Similarly, no clear trend of improvement can be observed. In the last season, four out of thirteen farmers (approximately 30%), are still practising CA with a relatively low TE (between 0,2 and 0,4).

fficiency improved during the project, can probably be rejected. To be really conclusive, however, the effect of rainfall has to be taken into account, since this could explain the variation of yields (and thus efficiencies) between the seasons. This will be done in the Tobit regression.

Table 2 Average technical efficiency (TE) for the TP and CA plots of the farmer-managed trials during four consecutive seasons

Season Practice Average TE

# observations Std. Deviation TE

TE difference CA above TP

Mann-Whitney U-statistic

2001-02

TP 0,490 15 0,258

CA 0,544 16 0,198 + 0,054 144,5°

TP & CA 0,518 31 0,227

2002-03 TP 0,614 15 0,267 CA 0,403 15 0,155 - 0,211 60** TP & CA 0,509 30 0,240

2003-04 TP 0,569 5 0,232 CA 0,620 11 0,190 + 0,051 32° TP & CA 0,604 16 0,198

2004-05 CA 0,590 13 0,199 - -

All seasons

TP 0,554 35 0,258

CA 0,532 55 0,199 - 0,022 926°

TP & CA 0,540 90 0,223 Note 1: ** denotes significance at the 5% level , ° denotes insignificance at the 10% level Note 2: There are no observations for TP plots for season 2004-05

Table 3 - Frequency distribution of TE scores for CA plots 0<TE<0,2 0,2<TE<0,4 0,4<TE<0,5 0,5<TE<0,6 0,6<TE<0,8 0,8<TE<1 TE=1 TOTAL # % # % # % # % # % # % # % # %

CA

2001-02 0 0,00 4 25,00 3 18,75 3 18,75 5 31,25 0 0,00 1 6,25 16 100 2002-03 2 13,33 5 33,33 6 40,00 0 0,00 2 13,33 0 0,00 0 0,00 15 100 2003-04 0 0,00 2 18,18 1 9,09 1 9,09 4 36,36 3 27,27 0 0,00 11 100 2004-05 0 0,00 4 30,77 0 0,00 2 15,38 5 38,46 1 7,69 1 7,69 13 100 all seasons 2 3,64 15 27,27 10 18,18 6 10,91 16 29,09 4 7,27 2 3,64 55 100

To answer the second subquestion - how does the TE vary between the farmers, comparing the two technologies? we look at the standard deviations of TE scores, presented in Table 2. It is remarkable that the standard deviation of the CA plots is systematically lower than that of TP

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significant at the 5% level (when considering all the seasons together). This is contrary to the initial hypothesis, which stated that, due to the complexity of CA and the higher managerial requirements, there would be a higher variability for CA farmers.

4.2.3 Calculation of allocative and economic efficiencies Using the computer program LIMDEP, input-oriented EE and AE estimates were calculated, both under CRS and VRS assumptions. Again, nor the orientation, nor the returns to scale assumption had an impact on the efficiency estimates. Table 4 and Table 5 indicate the average scores for both practises.

Table 4 - Average allocative efficiency (AE) for the TP and CA plots of the farmer-managed trials during four consecutive seasons

Season Practice # observations Average AE

Std. Deviation AE

AE difference CA above TP

Mann-Whitney U-statistic

2001-02

TP 15 1,000 0,000

CA 16 0,832 0,000 - 0,168 0***

TP & CA 31 0,913 0,085

2002-03 TP 15 1,000 0,000 CA 15 0,896 0,000 - 0,104 0*** TP & CA 30 0,948 0,053

2003-04 TP 5 1,000 0,000 CA 11 0,958 0,000 - 0,042 0*** TP & CA 16 0,971 0,020

2004-05 CA 13 0,994 0,000 - -

All seasons

TP 35 1,000 0,000

CA 55 0,913 0,063 - 0,087 0***

TP & CA 90 0,947 0,065 Note 1: *** denotes significance at the 1% level Note 2: There are no observations for TP plots for season 2004-05 A first remark is that, in one season, the AE is the same for all the CA plots and the same for all the TP plots. This is due to the fact that, in one season and for each technology, exactly the same inputs were used (see Appendix 1). In this case, output quantity does not determine the AE, since the distance between the isocost line and the production frontier is the same for all

10. Moreover, the AE is equal to one for all the best considering the lower prices of TP inputs.

the 1% level according to a Mann-Whitney U-test. In other words, - remains the more allocative efficient technology.

10 In a simple case of two inputs and one output, this corresponds to combination lying on a straight line going through the origin. For all these determined by the same distance between the isocost line and the intersection of this straight line and the frontier.

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The increasing trend for CA is probably mainly due to the decreasing amounts of lime that are

Table 5 - Average economic efficiency (EE) for the TP and CA plots of the farmer-managed trials

during four consecutive seasons

Season Practice # observations Average EE

Std. Deviation EE

EE difference CA above TP

Mann-Whitney U-statistic

2001-02

TP 15 0,490 0,258

CA 16 0,453 0,165 - 0,037 121°

TP & CA 31 0,471 0,212

2002-03 TP 15 0,614 0,267 CA 15 0,361 0,139 - 0,253 49*** TP & CA 30 0,488 0,245

2003-04 TP 5 0,569 0,232 CA 11 0,594 0,182 + 0,025 29° TP & CA 16 0,586 0,191

2004-05 CA 13 0,587 0,197 - -

All seasons

TP 35 0,554 0,258

CA 55 0,488 0,192 - 0,066 827°

TP & CA 90 0,514 0,221 Note 1: *** denotes significance at the 1% level Note 2: There are no observations for TP plots for season 2004-05 Concerning the EE (which is in fact the product of the TE and AE), CA plots are less efficient than TP plots in the first and second season and for all the seasons pooled together, but are more efficient than TP plots in the third season. However, only the (negative) difference in the second season is significant at the 10% level according to the Mann-Whitney U-test. On the basis of this test, it can thus be stated that, on average and considering the four seasons of the project, the CA technology did not have a better EE. On the contrary, in one season it even had a significantly lower EE. Similarly to what was observed for TE, there is no clear trend of improving EE for CA. The average EE first decreases, then increase in the third season and decrease again in the last season. In this case, however, the difference between the first and last season (0,453 vs. 0,587) turned out to be significant at the 5% level according to a Mann-Whitney U-test. Hence, there is some improvement of the EE for CA during the project, although this is probably merely due to the depreciation of lime quantity, as was the case for AE.

4.2.4 Tobit regression Table 6 contains the coefficients of the explanatory variables of the Tobit regressions of the TE scores and EE scores. The regression could not be performed for the AE scores due to the fact

-season rainfall does not have a significant effect on the efficiency scores. On the other hand,

affecting efficiency scores in Rainfall_practice

However, it is more likely that this is caused by chance, since the combined effect of rainfall

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and practice would also emerge in the TE regression if the effect would really be significant. Finally, there seems to be no seasonal effect for TE and EE (all the dummies have insignificant coefficients). It should be noted that the results of the Tobit regression are somewhat different than those obtained by assessing the significance of the observed differences with the Mann-Whitney U-test. On the basis of the U-test, the difference between the two technologies regarding TE and EE was found to be insignificant when considering the four seasons. The Tobit regressions, however, point towards a significant negative impact of CA on TE and EE. Hence, a general conclusion would be that CA is at best as technically and economically efficient as TP, but that the tendency is rather towards lower efficiencies. With respect to the evolution of efficiencies for the CA technology during the project, both approaches have the same outcome for TE: there is no significant improvement. Yet, the EE seems to have improved somewhat according to the U-test, while no seasonal effect emerges in the Tobit regression. Hence, we conclude that there are no clear evidences of a significant improvement of technical or economic efficiency for the CA technology over the four seasons of the project.

Table 6 - Tobit regression of efficiency scores using six explanatory variables

TE EE Variable Coefficient Std. Dev Coefficient Std. Dev

0) 4,462* 2,536 4,204* 2,404 season1_dummy -0,611° 0,459 -0,564° 0,436 season2_dummy -0,225° 0,254 -0,173° 0,241 season3_dummy 0,572° 0,526 0,664° 0,499 rainfall -0,006° 0,004 -,0006° 0,004 practice (CA) -1,362** 0,548 -1,154** 0,519 rainfall_practice 0,002° 0,001 ,002** 0,001

Wald statistic (D.f.: 6) 11,065* 16,541** Note 1: * and ** denote significance at the 10% and 5% level respectively, ° denotes insignificance at the 10% level Note 2: D .f. indicates the degrees of freedom

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5 Chapter 5: Discussion and recommendations

5.1 Efficiency analysis

5.1.1 Yield improvements As we saw, significantly higher yields were obtained with CA compared to traditional practices. On average, over the four seasons of the farmer-managed trials, the yields of CA plots were 31% higher than the yields of the TP plots. This corresponds with the results of Rockström et al. (2009), who found yield increases of 20 to 120% for maize production in East and Southern Africa. Nevertheless, it is a significantly poorer result than the increases of 120 to 168% that were obtained in the study of Kosgei et al. (2007) in Potshini, and the increase of 187% reported in the study of Mazvimavi et al. (2011) in Zimbabwe. A possible explanation for the relatively small yield difference is that some of the farmers have used the acquired knowledge or even part of the inputs meant to be used on the CA plots, to improve the production of the TP plots. As a matter of fact, the average yields obtained with the traditional practices (ca. 3 ton ha-1) was surprisingly high, considering that Taylor and Cairns (2001) and Smith et al. (2005) reported typical yields for smallholders in KZN of 1,5-2 ton ha-1. In any case, average yields under CA increased remarkably, reaching 4,9 and 4,6 ton ha-1 in the third and fourth season respectively. According to a business plan mentioned by Taylor and Cairns (2001), in conventional smallholder maize production in KZN, yields of at least 4 ton ha-1 are necessary for farmers to be able to pay off the costs of inputs. Since the business plan takes ploughing costs into account, the figure will probably be somewhat lower in the case of CA, since soil is not ploughed. This would mean that at the yield levels reached in the LandCare project, CA farming could be profitable.

5.1.2 Efficiency measures

5.1.2.1 Technical efficiency The average TE computed over the four seasons of the project was very comparable for both technologies, although TP performed slightly better (55,4% vs. 53,2%). As such, these values are relatively low, but they are in line with the results of Chirwa (2003), who calculated an average TE of 53,1% for smallholder maize farmers in Malawi. The fact that the average scores do not differ significantly between CA and TP, coincides with the findings of Mazvimavi et al. (2012), where Zimbabwean smallholder farmers reached approximately the same level of TE (about 68%) with conventional farming and CA. As we saw, although the difference in TE was small and not significant according to the Mann-Whitney U-test, the Tobit regression found a significant negative relationship between the TE scores and the practice of CA. The explanation of this negative correlation might be more

use of the CA inputs than to the lower efficiency of the CA technology itself. Indeed, even if the farmers received some training at the onset of the project, their limited experience with some inputs such as herbicides, combined with problems of literacy and numeracy might have led to an inefficient use of the inputs. This hypothesis is supported by the results of the efficiency analysis done by Msuya et al. (2008), in which

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Tanzanian smallholder maize producers who used agrochemicals were found to be less technically efficient compared to farmers not using these inputs. Inefficient input usage by smallholders in KZN has also been reported by Bolliger (2007). However, the lower TE of CA and the absence of a clear improvement of TE during the project might be only a temporary phenomenon. First of all, given the high managerial requirements of

of practise might not have been enough for the farmers to completely master the complex techniques of CA and improve their TE. A similar conclusion was drawn by Sipiläinen and Lansink (2005) concerning the conversion to organic farming. They found that the TE diminished when the switch took place but started to increase again after six years due to learning effects. Secondly, even if the farmers were using the inputs in a perfectly efficient way right from the start, the beneficial effects of CA in terms of yields would probably only materialise completely after several years. As was mentioned in the literature review, some authors assert that this might take from 4-5 years (Liniger et al., 2011) up to 10 years (Giller et al., 2009).

g-term experimental data. Unfortunately, as Derpsch et al. (2010) explain, the complex and knowledge-intensive nature of a CA system does not lend itself easily to long-term scientific examination. In addition, the typical short-term funding for agronomic research hampers such prolonged experiments.

5.1.2.2 Scale efficiency

unlikely that variable returns to scale can be observed when considering plots of such a small size. Moreover, land area was not included as an input because this would not have reflected true farm size but was simply determined by the experimental setting. Thus, in this analysis

-use-intensity, which was of a similar order of magnitude sence of scale inefficiency is probably only due to the fact

5.1.2.3 Allocative and economic efficiency

same input combination, the AE scores of all TP plots equal one, and the AE scores of the CA plots only vary between the seasons but not between the farmers. Hence, considered alone, these scores merely tell us that the TP technology has the cheapest input combination11. Nevertheless, they are useful when multiplied with the TE scores to obtain the estimates of EE. Looking at the EE, we can draw similar conclusions as for the TE. There seems to be little differences between the average EE scores of CA and TP, and the difference computed for the pooled data of the four seasons (TP: 55,4% ; CA: 48,8%) is not significant according to the U-test. Note, however, that the values are consistent with the findings of Coelli et al. (2002), who reported an average EE around 53% for small-scale rice farmers in Bangaldesh. Similarly to the case of TE, the Tobit regression found a significant and negative impact of the practise of CA on the EE scores. Here, the obvious explanation is that, in this experiment, CA inputs were considerably more expensive than TP inputs. This further penalised the CA technology, since 11 We also noticed an improvement in the EA of CA over the years, but, as was explained, this is probably only due to the decreasing quantities on lime.

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this higher price (and the higher quantities used) was not compensated by an important increase in yields. Hence, we conclude that, although the average yields obtained with CA were significantly higher than for TP, the results of the EA point towards lower technical and economic efficiency for the CA technology. However, this conclusion has to be interpreted with care in the view of some of the assumptions and simplifications that characterised this EA. Firstly, one has to keep in mind that this analysis was based on the data of research trials and not on actual farm-level data. Hence, numerous exogenous factors that could determine yields and efficiency

setting, such as input and output price fluctuations, are excluded. Also, the fact that the experiment was managed by smallholder farmers and that it was not the purpose of the project to carry out a rigorous or statistically exact comparison, might have opened the door to some errors and inconsistencies. Secondly, the total number of observations was relatively small and the proportions of CA and TP observations were unequal. Finally, and maybe most importantly, only agrochemical inputs were considered. No data could be included concerning equipment and draft animals, although these differ both in kind and quantity when comparing CA and TP. Labour was also not taken into account, while this might be the most decisive factor in the comparison of the two technologies, considering the potential reduction in labour requirements with CA.

5.1.3 The way forward Although the results clearly showed the added value of an EA compared to a simple comparison of the average yields of both technologies, a number of research aspects could have been added to this analysis to render it more useful at the policy level. Firstly, it could have been determined which inputs of the CA technology are most negatively affecting efficiency scores. This would indicate which inputs are used most inefficiently by the farmers and consequently need to be paid particular attention to by extension officers when they explain and demonstrate the new technology. Secondly, it would have been valuable to regress the efficiency scores against a number of socio-economic characteristics of the farmers, such as age, education, household income, access to extension and participation in other research projects. Unfortunately this kind of data was not collected during the project. Eventually, any future research that aims at assessing the efficiency of CA, should seek to expand the classical efficiency model to obtain environmentally integrated efficiency measures. This implies the acquisition of data related to soil quality (defined by biological, chemical and physical parameters) and soil erosion, so that the positive externalities of CA are included in the model. This would provide a more holistic view of the system and potentially yield very different results than in the case where agricultural production is considered as the only output.

5.2 Constraints to adoption of CA in the OLM We saw that there are several constraints that could hamper a widespread adoption of CA among the smallholder farmers of the OLM. Undoubtfully, the most important biophysical constraint is the competition for crop residues between livestock and soil cover. Considering the central role of livestock in the life of the rural communities of the OLM, this will probably be a delicate issue that has to be addressed with the involvement of the whole community. An inevitable first step towards a solution will be to tackle the problems of overstocking, especially since this is also causing degradation of the grasslands.

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adoption would most likely remain limited if the shortcoming of services and infrastructure that should support agricultural activities remains unresolved. Especially the lack of input and output markets that are accessible to smallholders acts as a bottleneck to agricultural development in general and to adoption of CA in particular. It is the role of policy makers to stimulate the emergence of such markets by creating the necessary innovation networks that link farmers, researchers, extension services, input providers, machinery manufacturers and commercial buyers. It is promising to see that several CA projects are launched all over South Africa and that coordination efforts are undertaken by organisations such as the African Conservation Tillage Network and the Conservation Agriculture Thrust. However, most initiatives are driven by short-term and erratic funding from governmental departments. Important actors, such as the No-till Club of KwaZulu-Natal are lobbying for long-term funding and political support, but without much success so far.

5.3 Providing the financial incentive for the adoption of CA: PES Although it is crucial to address the above-mentioned constraints to make adoption of CA possible, one has to keep in mind that the removal of all these barriers will, in se, not necessarily result in a broad-scale adoption of CA by the smallholders. As we saw, the economic efficiency of CA is possibly lower than that of TP, and the high investment and input costs might deter farmers from switching to CA, especially if the full benefits in terms of yields only become visible after several years. On the other hand, the societal benefits related to the reduction of soil erosion are almost immediate. This raises the question whether a payment for ecosystem services (PES) scheme could possibly provide the farmers with the financial incentive necessary to make the decision of converting to CA. Such financial incentive could bridge the initial period of lower economic returns and stimulate to continue to practise CA in the long-term. Considering the millions of water users12 that could potentially benefit from the enhancement of water quantity and quality achieved with CA, a PES scheme could generate an enormous and sustained cash flow. In addition, the societal benefits in terms of carbon sequestration and (soil) biodiversity could also be rewarded, although these services might be harder to quantify and might only manifest in the longer-term. CA-based PES schemes are already operational in different parts of the world. They include the agricultural carbon offset system in Alberta, Canada; the hydrological services from the Paraná III Basin in Brazil; the control of soil erosion in Spain and western Australia and the control of water erosion, dust storms and desertification in the Loess plateau in China (Kassam et al., 2011a). Actually, the possibility of implementing a PES scheme to restore and secure the ecosystem services of the OLM is already being investigated in the Maloti Drakensberg Transfrontier Project (MDTP, 2007; Blignaut et al., 2010). However, the project is focussing on grassland restoration and the problems of overgrazing and fire management, but does not consider the cropping fields and agricultural management, despite the fact that the unsustainable agricultural practises of the smallholders is also a significant source of erosion and land degradation. This issue should also be considered when developing a PES scheme and it is definitely worth exploring the role that conservation agriculture could play in the provision and protection of the ecosystem services of the region.

12 Recall the crucial role of the Upper-uThukela catchment in providing water to the southern African subcontinent through the numerous rivers, dams and national and international inter-basin transfers.

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6 Conclusions The natural capital of the Okhahlamba Local Municipality (OLM) fulfils a crucial role in the provision of ecosystem services both at a local and global scale. However, the erosion and land degradation caused by several unsustainable land management practises is reducing both the quantity and quality of these services. Three land use categories are involved: conservation areas, commercial agriculture and communal land use. The communal land consists primarily of vast grasslands used for grazing of the cattle, and cropping fields where small-scale food production for household consumption takes place. The lack of sound management of the grasslands and the use of unsustainable cultivation practises on the fields is dramatically decreasing the agricultural potential of the region. Focussing on the cropping fields, this thesis highlighted the potential of conservation agriculture (CA) to control erosion and safeguard the land- and water-based ecosystem services of the region, provided that a broad-scale adoption by the smallholders is realised. However, so far the adoption of CA among South African smallholders has been poor and often unsustained. The first part of this thesis identified the most important constraints that hamper the adoption of CA in the OLM. Apart from some intrinsic biophysical and technical constraints, the lack of appropriate infrastructure and services (mainly input and output markets and extension support) and the unfavourable policy environment function as bottlenecks that prevent smallholder CA adoption. Policy makers should address these problems first, if future projects aiming at promoting CA are to be really successful. In the second part of this thesis CA and conventional smallholder practices were compared through an efficiency analysis, using 4-year data from farmer-managed trials of a local LandCare project. It was demonstrated that significantly higher yields had been obtained with CA, but that the yield increase was insufficient to balance the higher input quantities and higher input costs of CA, leading to lower, or at best, similar technical and economic efficiencies. This result, however, is based on a short-term comparison that only considers agrochemical inputs. A more comprehensive and longer-term analysis might yield different results, especially considering that the benefits of CA manifest only gradually. In any case, this points out that there might be a lack of immediate agronomic and financial incentives for smallholders to switch from their traditional practises to CA. On the other hand, CA is highly desirable at the societal level and, if the current agricultural practises are continued in the long-term, this will undoubtedly lead to irreversible losses of the agricultural and environmental resources of the region. Therefore, it might be necessary to stimulate the transition to CA using external financial incentives. Since CA is not (yet) receiving the necessary support from the South African government and considering the erratic nature of governmental funding in general, a payment for ecosystem services (PES) scheme might be a more appropriate solution. Such a scheme could generate a sustained cash flow from the end-users that benefit from the ecosystems services preserved by CA to the small-scale farmers adopting CA. Looking at existing CA-based PES systems in the world, future research should examine how CA could be included in a PES scheme in the context of the OLM.

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8 Appendix 1: Agrochemical inputs used for the farmer-­‐managed trials of the Bergville/Emmaus LandCare project

Practice Season Category INPU T Specification Quantity used per plot*

CA

2001/02

LIME Dolomitic Lime 500 kg

FERTILIZERS MAP 25 kg LAN 25 kg

PESTICIDES Cutworm bait Decitab 1 kg Stalk Borer bait Kombat 1 kg

HERBICIDES Glyphosate Roundup 720 g

Lasso MT 0,50 L

2002/03 &

2003/04

FERTILIZERS

LAN at planting 6,8 kg LAN for top-dress 6,8 kg Superphosphate 5,1 kg KCl 3,4 kg

PESTICIDES Cutworm + Stalkborer bait Decitab tabelets 3 tab.

HERBICIDES Glyphosate SenatorExtra 0,33 L

Pre-emergence Dual Gold 0,10 L

2004/05

FERTILIZERS

LAN at planting 6,8 kg LAN for top-dress 6,8 kg Superphosphate 5,1 kg KCl 3,4 kg

PESTICIDES Cutworm + Stalkborer bait

Kemprin (1/3 for cutworms, 2/3 for stalkborer) 0,1 L

HERBICIDES Glyphosate SenatorExtra 0,33 L

Pre-emergence Dual Gold 0,08 L

TP all seasons

FERTILIZERS 2:3:2 (22) 20,0 kg PESTICIDES Stalkborer bait Bulldog 0,2 kg

* The size of the CA plots was 1000m2 during the first season and approximately 340m2 during the following seasons. Consequently input quantities were recalculated to maintain the same quantity per unit of land.

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9 Appendix 2: Description of some of the procedures of the farmer-­‐managed trials

B O X 1: Guidelines followed by the leader farmers to cultivate the C A plots of the farmer-managed trials

The first intervention on the plots was the application of 500kg of dolomitic lime (corresponds to 5ton/ha). The lime was broadcasted and then incorporated into the soil with a plough. This was only done once at the onset of the experiment; no maintenance liming was performed during the following seasons. Then, a first weeding operation was performed with herbicides, 2-3 weeks after the first rain. In the second or third week of November, 10 cm deep furrows were opened with hand hoes or a Magoye ripper and a mixture of fertilisers (see Appendix 1) was placed at the bottom. Maize seeds of a local landrace variety (with a high tolerance to acidic soils) were then planted in these furrows at a density of approximately 40 000 plants per ha (inter-row spacing of 0.9 m). The following operations in chronological order were: cutworm control with cutworm bait, second weeding operation (herbicides), top dress N-fertilisation (LAN) when maize was about knee-high, third weeding operation (with a hand hoe, left on surface to add to mulch), pest control when needed (cutworm and stalkborer bait), fourth weeding operation (hand hoe, add to mulch). At maturity, usually somewhere in the first two weeks of June, the maize cobs were harvested and weighted.

B O X 2: Standardised method to determine the yields of the C A and TP plots of the farmer-managed trials

Once the farmers had harvested the maize cobs, they threshed them by hand and the fresh weight of the grain was measured using a 50 kg scale. Three randomly selected samples of grain were taken for each plot, weighted and oven dried at 60 °C for 24 h. The samples were then cooled down and dried again in the oven until a constant weight was obtained. The moisture content of the grain at harvest was determined from the weight difference. The fresh weight was then standardized to 12.5% moisture content.