Exploring Feasible Yields for Cassava Production for Food and Fuel in the Context of Smallholder Farming Systems in Alto Molócuè, Northern Mozambique

Exploring feasible yields for cassava production for food and fuel in the context of smallholder farming systems in Alto Molócuè, Northern Mozambique

Sanne van den Dungen

Msc thesis Plant Production Systems

June 2010

Wageningen University, The Netherlands 2

Exploring feasible yields for cassava production for food and fuel in the context of smallholder farming systems in Alto Molócuè, Northern Mozambique

Sanne van den Dungen MSc Thesis Plant Sciences PPS 80436 36 credits

June 2010

Supervisors: Ir. Sander de Vries Dr. Ir. Gerrie van de Ven

Examiner: Prof. Dr. Ken E. Giller (Plant Production Systems)

Plant Production Systems Group Wageningen University P.O. Box 430, 6700 AK, The Netherlands

Preface and acknowledgements From the first contact I had with ir. Sander de Vries, Dr. Ir, Gerrie van de Ven and Prof. Dr. Ken Giller, I was able to express my personal motivations and wishes concerning this thesis. I would especially like to thank Sander and Gerrie for their patience and support during the length of this thesis. I was able to shape the research in, what for me was the reason to come to Wageningen: explorative research on farming systems in Sub‐Saharan Africa. I am very happy to have been able to pursue this dream.

There are many people I would like to thank for making the four months of fieldwork research possible, in Northern Mozambique 2009. First of all I would like to thank Sicco Kolijn for his contaminating enthusiasm and motivation. I admire his network and am thankful for the help I received in finding a suitable region for research, for hospitality and concerns. Because of his connections I was able to link up to World Vision Quelimane under the supervision of Brian Hilton. I thank Brian for his support in introducing me to the district of Alto Molócuè and World Vision staff and thank World vision for all the support and facilities I received. At World Vision Alto Molócuè, I met Sansao Honwana, who has to been much more than a supervisor to me and providing me with a place to stay and all the support from his staff I could ask for. Thanks Raol for countless Saturdays picking me up from a week’s work in the villages even though it was your free day. In Mugema, Nacuaca and Gafaria I thank all the farmers co‐ operating in the research and being patient with me. Most of all I am grateful for the insights I was able to get besides their farming systems: the everyday of life, the stories, the food, traditions and customs. Antonio, Pedro and Gustodio have been amazingly patient with me during the length of fieldwork, Trying to answer all my questions and providing me with solid friendship and support. The guest families: the family of Antonio, Donna Celesta and the family of Xavier I show gratitude to their hospitality for letting me camp at their premises and joining their meals. I had the honour to have Bruno Golden as my translator in the beginning of the fieldwork. There is absolutely no way of expressing my appreciation to you. Many times during fieldwork I have thought about how to be able to show to you what it was like having you to help me. I trust you have felt it and I could not have wished for a better friend and expert to guide me. Thanks to Armindo Cambule and his staff at the University of Eduardo Mondlane, Maputo for analyzing the numerous soil samples I collected.

When at the start of this project my dad told me “focus on success, focus on finishing” I confess I had trouble believing him. I could have never finished without the help of many people surrounding me. From a distance my parents have been supporting me in good and bad times, always there with good advice and motivation. I thank my sisters and brother for the support, my boyfriend Ofir Benjamin for overseas love and Eva Gies for on the spot help. My dear friend Loes Mertens wrote to me before leaving to Mozambique: “know that on your journeys the whole universe will come together to help you!” With countless people more to thank, she could not have been more right.

Summary Currently the world’s energy supply system for the 21st century is under intensive debate. Renewable energy sources, especially biomass, play an important role in the discussion where several scientists have tried to identify high potential areas for the production of biomass for energy. African countries such as Mozambique are currently under review, because of a favourable climate, ample land availability and low population density (Batidzirai et al., 2006). Cassava, a potential crop for the production of bio‐ethanol, is currently being discussed as a feasible option for biofuel production by smallholder farmers as well as maize and sorghum. However current (cassava) yields are low and farmers have very limited resources for yield improvement.

This study was developed with the overall goal to explore feasible yields for cassava production for food and fuel in the context of smallholder farming systems in Alto Molócuè, Northern Mozambique with the objectives of i) assessing the heterogeneity between farms by making a rapid farm characterisation, ii) estimating current yields of cassava, sorghum and maize of selected smallholder farmers, iii) explaining current yields of cassava, sorghum and maize, iv) making a yield gap analysis between actual yields from selected crops from field estimations and feasible potential yields simulated using the FIELD model, v) collecting parameter input used in the FIELD model.

Three villages (Mugema, Nacuaca and Gafaria) were selected in the district of Alto Molócuè to represent variability at district level. Experts, key‐informants, back ground information, and first approach farm characterisation interviews were used to describe and categorize bio‐physical, socio‐economic and farm management practices variability found at village, farm and field level. A farm typology based on expert knowledge and K‐means clustering was developed to analyse variability found at farm level. A dynamic simulation model FIELD was parameterized with field data, and simulation scenarios were developed from the rapid farm characterisation interviews and informal meetings with farmers. The model was run to study and quantify the cassava yield response of different farm management practices. Due to circumstances, model simulations were only based on soil fertility properties of the sampled fields.

Simulated yield variability between sites was mainly explained by inherent soil properties, such as soil texture (clay %) which determined exchangeable K level for a large extent. Simulated no input yields in Gafaria were found higher compared to Nacuaca because of: a higher clay content, higher N and K level. Simulated yields for Gafaria ranged between an average of 2.0 and 10.8 t/ha and were found lower for Nacuaca: 1.8 and 3.9 t/ha. Within village, yields were grouped into low yielding fields (1st quartile), medium yielding fields (2nd and 3rd quartile) and high yielding fields (4th quartile). Simulations of continuous cultivation showed yields in Gafaria to drop in average by 24% for 2nd & 3rd quartile fields (stabilizing at 3.1 t ha‐1), 27% for 4th quartile fields (stabilizing at 8.0 t ha‐1), while yields of 1st quartile fields did not change during time and remained at around 2 t ha‐1 over a time span of 25 years. Yields in Nacuaca did not change over time for 1st and 2nd & 3rd quartile fields and remained at 1.8 and 2.3 t ha‐1. Yield of 4th quartile fields dropped in average by 22% till it stabilized around 3.2 t ha‐1 .

Amongst farmers, a large variation of crop residue management was found. In Mugema, Nacuaca and Gafaria 48, 33 and 60% of the respondents of the rapid farm characterisation incorporated crop residues in the soil and 89% of all farmers asked included grass in preparing of the ridges/hills for cassava.

However a large part of the same respondents would use fire for clearing of the field: 59%, 80% and 50% in Mugema, Nacuaca and Gafaria. The effect of maize residue application on cassava fresh yield was analysed with the help of the simulation model FIELD. Addition of maize residues had a positive effect on fresh cassava yield with an increase in yield of between 156 – 237kg/ ha in Nacuaca and 189‐449 kg/ha in Gafaria over the three quartiles used per ton maize residue applied.

In general most farmers would only consider the use of manure if they would produce horticultures. Generally manure was piled up and removed not to be of further use. Simulated yields comparing no manure and manure application showed an increase of 402‐482 kg and 433‐1034 kg increase/ ton DM manure applied for Nacuaca and Gafaria subsequently.

Average yield response per kg applied nutrient was highly variable comparing villages and comparing quartiles. Increasing levels of potassium was most effective on soils in Nacuaca, while a higher response (increase kg fresh cassava / kg applied N, P or K ha‐1) for phosphorus was found in Gafaria. First and second quartile classified fields had a lower response and remained at a low yield level compared to the higher yielding fourth quartile fields. High increase in yield was obtained with NPK fertiliser application (100:22:83 N:P:K) ranging from 37 to 65 kg yield increase per kg fertiliser added in Gafaria and between 49 and 67 kg yield increase per kg fertiliser added in Nacuaca. Improved fertiliser management was developed to increase yield response per kg fertiliser applied with the help of available nutrients from crop residues and manure. An increase in yield of 6, 21, and 35% was attained for 4th quartile‐2nd & 3rd quartile and 1st quartile fields for Nacuaca. For Gafaria yield increase due to improved management was even higher: 22, 34 and 40% yield increase for 4th quartile‐2nd & 3rd quartile and 1st quartile fields.

Ranked as the most important crop by 60% and 70% of the respondents in Nacuaca and Gafaria respectively, cassava plays an important role for farmers in the research villages. Only one third of the farmers in Gafaria (26%) sold cassava compared to 42% in Nacuaca and 48% in Mugema. Overall cassava was mostly sold within the community (82% of respondents). When sold cassava was sold as less than half of the produced quantity: 47, 49 and 43% respectively for Mugema, Nacuaca and Gafaria. If farmers would be involved in the new emerging cassava for the bio fuel market, with current production levels, the surplus being sold to neighbours with insufficient cassava production at the moment, would be sold for biofuel purposes. This thesis has discussed ways to increase yields starting from low capital required residue and manure applications to higher capital investments needed if fertiliser would be applied Outgrower schemes for e.g. the production of tobacco are existing and providing farmers with fertiliser under contract production. These existing outgrower schemes could provide with examples and frameworks on how commercial firms can arrange input purchases or input credit for farmers under production contracts.

SUMMARY...... 5

1.1 Introduction to Mozambique...... 11

1.2 Agro climatic conditions and land use...... 11

1.3 Poverty and agriculture ...... 12

1.4 Potential crops for bio fuel: cassava, maize and sorghum ...... 12 1.4.1 Reasoning in favour of biofuel production...... 13 1.4.2 Reasoning against biofuel production ...... 14

1.5 Sustainability ...... 14

1.6 Introduction to research area...... 15 1.6.1 Introduction to Zambézia province...... 15 1.6.2 Alto Molócuè ...... 16

1.7 Objectives...... 18

1.8 Theoretical background...... 19

1.9 Outline of the thesis...... 20

2. MATERIALS AND METHODS...... 21 2.1.1 Introduction to work phases and working steps ...... 21 2.1.2 Study area: Bio-physical and socio-economic description...... 23

2.2 Farm sampling and characterisation...... 25 2.2.1 Rapid farm characterisation ...... 25 2.2.2 Farm typology...... 25 2.2.3 Second and third round interviews...... 26

2.3 Field biophysical characterisation...... 26 2.3.1 Soil sampling and analysis ...... 26 2.3.2 Field measurements...... 27

2.4 Yield estimations...... 27

2.5 Income calculations ...... 29 2.5.1 Household income calculations...... 29 2.5.2 Gross margin fertilizer analysis...... 29

2.6 Statistical analysis...... 30

2.7 Dynamic simulation of crop performance and nutrient balances at field scale ...... 31 2.7.1 Model for dynamic simulation at field scale: FIELD...... 31

2.7.2 Developing scenario’s...... 33

3 RESULTS ...... 35

3.1. Characteristics at village level ...... 35 3.1.1 Village biophysical variability ...... 35 3.1.2 Socio-economic variability at village level ...... 39 3.1.3 Land use and management practices...... 47

3.2 Between farm variability...... 52 3.2.1 Short characterisation per farm type ...... 52

4. DYNAMIC SIMULATION OF THE IMPACT OF MANAGEMENT ON CASSAVA YIELDS ...... 55 4.1 Introduction...... 55 4.1.1 Initial simulated yields: Base run...... 55 4.1.2 Nutrient yield response ...... 59

4.2 Management simulations ...... 60 4.2.1 Cassava residues ...... 60 4.2.2 Maize residue ...... 62 4.2.3 Manure application...... 63 4.2.4 Fertiliser use...... 65 4.2.5 Cost benefit analysis of fertiliser use ...... 65 4.2.5 Cost benefit analysis of fertiliser use ...... 66 4.2.6 Improved management practises...... 67 4.2.6 Improved management practises...... 68

4.3 Discrepancies simulations and observations ...... 70

5. DISCUSSION...... 73

5.1. Evaluating the effect of farm management on cassava yield ...... 73 5.1.1 Incorporation of crop residues...... 73 5.1.2 Manure application...... 74 5.1.3 Use of fertiliser...... 74 5.1.4 Feasibility of yield increase scenarios...... 76 5.1.5 Model FIELD considerations...... 77

5.2 Evaluation of feasibility...... 78

5.3.Methological considerations ...... 80 5.3.1 The use of a farm typology ...... 80 5.3.2 Yield estimations...... 81 5.3.3 Limitations of research...... 81

6. CONCLUSIONS...... 83

REFERENCES...... 85

APPENDIX I: ADDITIONAL BACKGROUND INFORMATION ...... 88

APPENDIX II: SOIL FERTILITY ANALYSIS ...... 89

APPENDIX III: SOCIO-ECONOMIC ANALYSIS ...... 91

APPENDIX IV: FARMERS ESTIMATIONS AND QUEFTS MAIZE YIELD ESTIMATIONS...... 94

APPENDIX IV (CONT): CASSAVA AND SORGHUM YIELD ESTIMATIONS ...... 95

APPENDIX V: FARM TYPOLOGY ...... 96

APPENDIX VI: MODEL ASSUMPTIONS ...... 98

APPENDIX VII: FIELD SIZE CASSAVA AND MAIZE...... 99

APPENDIX VIII: CASSAVA AND MAIZE YIELD ESTIMATIONS ...... 100

APPENDIX IX: FARM TYPOLOGY CONSIDERATIONS...... 103

APPENDIX X: QUARTILE CONSIDERATIONS...... 104

APPENDIX XI: PRINCIPAL COMPONENT ANALYSIS...... 106

APPENDIX XII: RAPID FARM QUESTIONNAIRES...... 108

1 Introduction

1.1 Introduction to Mozambique Mozambique is located in southern Africa and has around 80 Mha of land and a coastline of 2500 km, a population density of 22/km2 and approximately 19 million inhabitants. Projections estimate an increase to 28 million inhabitants by the year 2025 (Batidzirai et al., 2006). Mozambique has been reported as one of the poorest countries despite the high economic growth during the 1990s and has only recently been recovering from 16 years of devastating civil war and resulting famine (Bias and Donovan, 2003 ; Maria and Yost, 2006). An estimated 80% of Mozambican labour is currently used in agriculture; productivity however is low as agriculture only contributes up to ¼ of the GDP (Bias and Donovan, 2003). According to Wils (2002) three macro‐ socio‐ecological zones can be defined in Mozambique: The Northern region (Niassa, Cabo Delgado and Nampula), The Central region (Zambézia, Tete, Manica and Sofala) and the Southern region (Inhambane, Gaza and Maputo, see Figure 1). The Northern region covers around 50% of the total land area and is inhabited by 33% of the population. The Central region consists out of 29% of the total land area with 41% of the Mozambican population. Finally the FiguurFigure 1.1. OverviewOverview ofof Mozambique'sMozambique's Southern region forms 21% of the total land area and provides a provinces provinces home for 26% of the population. These regions were derived based on the existing administrative, demographic and hydrologic characteristics of the country.

1.2 Agro climatic conditions and land use The county exhibits great diversity in geological and climatic diversity: from low lying coastal plains (<200 m) to humid tropical mountainous regions (>1000 m) and rainfall varying from 400 mm in the southwest to 2600 mm per year in the mountainous North‐west regions. The central and northern region have the most fertile land which covers an area of around 26 million hectares (MAM and MEM, 2008). According to the same report 5 million hectares are currently being used as agricultural land with an estimated increase by 2025 to 10 to 19 million hectares according to different studies. According to the Biofuel assessment for Mozambique (MAM and MEM, 2008) this wide range of estimations corresponds to an as wide spectrum of assumptions used in scenario studies to calculate land available for plantations of bio‐fuel crops.

Mozambique suffers from the same problems faced by most African countries: high temperature and high intensity of rain have generally caused high rates of weathering and organic matter decomposition. This results in soils with a low nutrient level, low cation exchange capacity and they are susceptible to erosion. Besides an initial low fertility of the soil, crop management contributes to further depletion of soil fertility (Bias and Donovan 2003). Soil organic matter is an important parameter in crop yield analysis as is awareness of soil heterogeneity in the different production areas (Tittonell, 2007).

1.3 Poverty and agriculture In 2000, 70% of the Mozambican population lived below the poverty line (UNDP, 2000). Around three million families are farming in the rural area and obtain 70% of their food from their own on‐farm production (MAM and MEM, 2008). Interestingly enough the poor and the less poor (classification used by Bias and Donovan, 2003) have approximately the same amount of land per household (each plot of land is called a machamba). The differences however lie in the amount and the type of input the poor and less poor can use on their land. For example the amount of land irrigated is higher for the less poor. Furthermore questionnaires (MINAG, 2007) show that only 4% of the farmers use fertiliser; generally, the amount of inputs used in Mozambique is very low which is reflected in low crop yields. The low usage of inputs such as improved seed, fertiliser and technology is mainly due to limited capital, mechanization and poor access to financial support for the farmers. The Mozambique Biofuel Assessment (MAM and MEM, 2008) concludes that the amount of land is not a limiting factor for poor peasants, but rather the capacity to work the land. The average maize yield for Mozambique is approximately 0.9 tons/ha, which is the lowest yield in Southern Africa: Malawi (1.7 tons/ha), South Africa (2.8 tons/ha), Swaziland (1.5 tons/ha), Tanzania (1.2 tons/ha), Zambia (1.5 tons/ha) and Zimbabwe with 1.5 tons/ha (FAO, 2005). Compared to the global average of 4.5 tons/ha these averages are low, in European countries the production is overall higher and records exist of 15 tons/ha (MAM and MEM, 2008).

Mozambican agriculture can be characterized by a large number of small‐scale producers that have to rely on rain fed production systems for their subsistence farming. Most important crops grown by small holder farmers in Mozambique are (in order of importance): maize, cassava, cowpeas, groundnuts, sorghum, rice, bambara beans/nuts, pigeonpeas , sweet potato, common beans, and millet (Bias and Donovan, 2003). Maize, cassava and sorghum are grown by 79%, 63% and 27% of the of small holder farm households with a national production of 150, 755 and 34 metric tons subsequently (Bias and Donovan, 2003; MINAG, 2007). Maize and cassava are the most common crops grown by the poor and the less poor. The very poor depend on maize, cassava and sweet potatoes. The less poor are able to spend more on millet and sorghum (Bias and Donovan, 2003). This shows the diversity in diet throughout the country. Cash crops such as cashews and cotton have a relatively marginal importance and are grown more frequently by the less poor. In percentages the poor grow 6.1% of their crops as cash crops compared to 26% by the less poor. Usually a low amount of only 10% of the maize production can be accounted for as marketable output for both poor and less poor households (MAM and MEM, 2008). This implies mainly subsistence agriculture primarily based on the production of maize and cassava.

1.4 Potential crops for bio fuel: cassava, maize and sorghum Cassava is seen a potential feedstock crop for the production of bio‐ethanol for several reasons. Firstly, the crop cassava is able to adapt to a wide range of growing conditions using minimal inputs. Secondly, because cassava has an unbound window for planting and harvesting it can be produced all year round. Thirdly cassava can be stored as dried chips, waiting for further processing, making all year production of ethanol by ethanol plants possible (Nguyen et al., 2007).

Sorghum provides the staple food of many of the world’s poorest and most food insecure people in the semi‐arid tropics of the world. Because of its genetics sorghum is able to grow in hot and dry agro‐ ecological zones where it would be difficult to grow other food grain crops. Sorghum can produce grains and stover in areas with frequent droughts (Borikar et al., 2007). Sweet sorghum (Sorghum bicolor L. Moench) is similar to grain sorghum in appearance and agronomic performance. Because of its C4 metabolism sorghum is photosynthetically efficient. Like sorghum, sweet sorghum can be cultivated over a wide range of environments; the difference between the two is that sweet sorghum stores much of its photosynthates as sugar in the stalks. However sweet sorghum also produces reasonable grain yields. According to Reddy (2007) sweet sorghum could be grown on the 23.4 million hectares of dry land in Africa (55% of global sorghum area) where it could produce more biomass and grain if yield‐ enhancing technologies would be stimulated by the bio fuel market.

Maize is produced almost throughout the country, however most of the production (70%) is concentrated in the central region and in the north (MINAG, 2007). As sorghum, maize is a C4 crop which under semi arid ecological conditions is favourable.

Government permission for using these three crops for biofuels is currently heavily debated in Mozambique. However, in the scope of explorative research they will still be considered in this research, but the main focus will be on cassava.

1.4.1 Reasoning in favour of biofuel production Currently the world’s energy supply system for the 21st century is under intensive debate. Renewable energy sources, especially biomass, play an important role in the discussion where several scientists have tried to identify high potential areas for the production of biomass for energy. African countries such as Mozambique are currently under review (Batidzirai et al., 2006). Large scale biomass conversion to for example ethanol could offer a sustainable alternative for part of the current African fuel consumption or provide export opportunities. Scenario studies estimate biomass production potential up to 134 EJ/ yr for Africa under specified conditions such as the use of abandoned agricultural land and non‐productive land (Hoogwijk, 2004). Another study more than doubles Hoogwijks projection to around 317 EJ/yr on 700 Mha in sub‐Saharan Africa but with an assumption of eight fold production increase (Smeets et al., 2004). Mozambique seems to have large potential for the production of biomass for bio‐fuels; by several reports it is considered a promising region within the south of Africa due to the relative abundance of land resources, favourable environmental conditions and low population density. For Mozambique a potential biomass production of 6,670 PJ/ yr using a segregation for land productivity classes is estimated (Batidzirai et al., 2006).

Smallholder biofuel production generates employment and income opportunities in the region by growing the feedstock and processing it; additional welfare benefits may be expected if feedstock is converted locally. However the poor technical knowhow related to feedstock and conversion, capital availability for start‐up costs, lack of private sector capacity and support, market development and secure land tenure are often cited as limitations to small‐scale agricultural development (Ewing and Msangi, 2008).

1.4.2 Reasoning against biofuel production In large parts of the country maize, cassava and sorghum are considered as staple food hence using these crops for ethanol production could jeopardize food security. From surveys held in 96/97 it was concluded that a large amount of farmers growing the primary crops mentioned above were not able to sell them: both in rural areas as for the urban poor with agricultural land. Cassava was grown throughout the country but rarely sold (Bias and Donovan, 2003). Prices of those crops could rise and availability could be limited creating problems for those without land or insufficient production. Local processing mills that transfer maize into meal for household consumption using raw material could be put into problems by the demand for maize for ethanol (MAM and MEM, 2008). However if production would increase, maize could be considered for ethanol production, especially in certain parts of the north and the south where sorghum accounts for a larger part of peoples diet. It will be important to include the impact of a diet crop being used for ethanol in that dietary region on household food expenditure in the modelling since this is lacking in previous studies. For all crops investigated (maize, cassava and sorghum) household consumption and surplus for the market therefore needs to be identified. One of the main biophysical problems in the production of the crops mentioned above is soil fertility.

1.5 Sustainability Since its introduction in 1987 by the Brundtland Commission the concept of sustainability has been of increasing importance in science, policy formulation and practice. In the Brundtland report sustainability has been defined as “meeting the needs of the present generation without compromising the ability of future generations to meet their needs” (Brundtland, 1987). However since the introduction of the concept more than 300 different definitions have been developed. A shared characteristic amongst these is the notion that sustainability is aimed at improving both the current and the future quality of live. In the scope of scenario studies for biofuel production for the future, sustainability of farming systems is a key factor. Sustainable agriculture is a form of agriculture concerned about the ability of an agro‐ecosystem to remain productive in the long term. Sustainable agriculture has three major aspects namely the ecological (environmental), economic and social sustainability (Werf and Petit, 2002).

The degree of sustainability of production systems for maize, cassava and sorghum can be investigated using sustainability indicators, which have ecological, economic and social dimensions. The major factors affecting sustainability should be reflected in these indicators and they should be quantifiable as well. This statement requires thorough analysis and knowledge of the conditions in the area of interest and of the information already available. Sustainability indicators used in research can for example be: balances of N,P and K in the soil and an index of biocide use (Jansen et al., 1995). But indicators used can be quite elaborate and broad in approach as presented in the work of Florin et al. (2010) where 18 explicit causal statements were derived from the biofuel debate, each capturing two to eight important indicators. As a first step in order to make an assessment on sustainable production of cassava, maize and sorghum by smallholder farmers in Northern Mozambique, current yield and production methods have to be analysed.

1.6 Introduction to research area Zambézia province was selected as an interesting province for conducting the research. Below the province and selected district will be further discussed, in section 2.1 the selection of the villages within Alto Molócuè district will be explained.

1.6.1 Introduction to Zambézia province Zambézia province produces more than one quarter of the countries cassava, about 18% of the total maize production and 15% of the country’s total sorghum production and this share remains stable over the years (Bias and Donovan, 2003; MAM and MEM, 2008). In total Mozambique’s area covers 79 mln hectares of which 12% is arable land suitable for cropping (MAM and MEM, 2008). Zambézia province occupies 105.000 km2 of land with 27.500 km2 suitable for arable production of which 23% is cropped with cassava, 37% with maize and 5% with sorghum (Statoids, 2007). Other crops grown are pigeonpea and rice (Table A 1 in the Appendix). Around 26% of the area of Zambézia province is suitable for arable production, which is the highest percentage compared to other provinces such as Nampula (24%) Maputo (17%) and Inhambane (14%) (MAM and MEM, 2008).

The highest yields per ha for cassava in Mozambique have been recorded in Zambézia and Nampula province (Bias and Donovan, 2003). Average production ranges from 6.4 t/ha in Southern Province Gaza to about 9.0 and 10 ton/ha in more Northern Provinces Zambézia and Nampula respectively (Table 1). High yield losses ranging from 21‐33% are mainly due to prevalence of diseases and plagues such as cassava brown streak, cassava mosaic virus and termites. Sorghum yield data are unavailable but estimated around 0.3‐0.6 t/ha with a potential between 0.8 and 2 t/ha. For maize a range of 0.4‐1.3 t/ha and an estimated potential of 5‐6 t/ha is estimated by the Ministry of Agriculture and Fisheries (MAP, 1997).

Table 1. Yields of fresh cassava (t/ha) per Province (IITA, 2003)

Provincince Marketable harvest (t/ha) Total harvest (t/ha) Yield loss (%) Cabo Delgado 8.4 10.9 22.9 Nampula 10.0 13.9 28.1

Zambézia 9.0 12.0 25.0 Manica 7.9 9.9 20.2 Inhambane 7.6 11.0 30.9

Gaza 6.4 9.5 32.6

Average 8.0 11.2 28.6

1.6.2 Alto Molócuè Alto Molócuè district is one of the largest districts of Zambézia province and covers an area of 6.386 km2 which equals 6% (DRAM, 2007; Molócuè, 2008). Located in the north of the province in between Quelimane in the south (circa 400km) and Nampula in the north (219km) at national road number one, latitude 16°15’ O and longitude 37°15’ W). The district borders in the north with the river Ligonha, which separates Zambézia from Nampula province, in the south by the district of Ile, in the west by the district of Gurué and in the east by the district of Gile (Figure 2).

Figure 2. Zambézia Province and the district of Alto Molócuè,

The district of Alto Molócuè is characterized by its mountains (900m) in the west in the direction of Ile and Gurué, its plains (200‐300m) in the south and centre and low riverbeds of the main river Molócuè that explain the name of the district (alto = high in Portuguese). Different crops can be grown according to the variation in elevation. Along the rivers: rice, coconut, sugarcane, tubers, fruits and vegetables and grassland. The plains make up 2/3 of the districts surface undulating gradually in the direction of the mountainous zones. Primary crops grown here: maize, sorghum, beans, sunflower, cotton, sesame, sweet potato, cassava, fruits, cashew, tobacco and horticultures as well as grass for cattle. The mountainous zone is cropped with: coffee, maize, sorghum, Irish potato, sweet potato, beans, sesame, soy, cassava and fruits. Because of its differences in elevation two climatic zones can be identified: tropical humid (90‐100% humidity) and a tropical mountainous climate. Average yearly temperature varies between the plains and the mountainous area around 26°C and 20°C respectively. Two seasons can be identified in the two climatic zones present in the district: the hot and rainy season from October till April and the dry and fresh season from Mai until September. Yearly rainfall varies between 1000 mm on the plains and 1300 mm in the mountains and is distributed in a uni modal pattern with peaks from December to March (Figure 3)(DRAM, 2007).

Figure 3. Average rainfall distribution for the district of Alto Molócuè and Zambézia Province in mm (October 2007‐June 2009)

1.7 Objectives The overall goal of this work was to explore feasible yields for cassava production for food and fuel in the context of smallholder farming systems in Alto Molócuè, Northern Mozambique. This was done by meeting with the following objectives in three phases.

Phase I objective was aimed at a concise literature study to provide with agronomic background information on Mozambique and to explore the debate about potentials and drawbacks of biofuel potential. The second phase was aimed at providing with information and insights from field work. The third phase needed the input of the first and second phase for scenario development.

Phase I

‐ To make an exploration in literature to give agronomic background information in Mozambique and to explore the biofuel debate in Mozambique

Phase II

- To asses heterogeneity between farms by making a rapid farm characterisation - To estimate current yields of cassava and sorghum of selected smallholder farmers - To explain current yields of cassava and sorghum of selected smallholder farmers in relation to soil, landscape and management variability - To collect parameter input to be used in model FIELD

Phase III

- To make a yield gap analysis between actual yields from field research and feasible yields simulated using model FIELD

1.8 Theoretical background The methodological approach followed in this thesis assumes that the sources of variability found at different levels and scales are nested within each other: the smaller scale (e.g. field) variability contributes to variability at larger scale (farm or village). Figure 4 illustrates how in a given region comparisons can be made at village level: capturing average farm and field characteristics and comparing between study villages, a step further downscaling farms can be compared using a developed typology of farms within and between villages. Subsequently fields can be compared between typologies and between villages.

Figure 4. Illustration of conceptual approach of variability at different levels in the research (for an n amount of fields, farms and villages in a given region), arrows indicate comparisons between components

Farm typology A typology can be used as a way of representing the diversity of farming systems and production units in a region (Poussin et al., 2008). As concluded in previous studies on smallholder farmers, heterogeneity can be large and in a specific area farming systems can be highly diverse (Tittonell, 2003; Tittonell, 2007). Therefore a farm categorization in typologies can be used as a tool in further research to try describe and explain these diverse farming systems. The criteria for this farm typology are based on the research objectives of the research. According to Maton et al. (2005) a distinction can be made in two types of methods to build a typology upon: 1) the “positivist method”, based on statistical analysis of farm surveys (Mignolet et al., 2001) and 2) the “constructivist method” where expert knowledge is the basis of the typology (Perrot and Landais, 1993). A third could be described by “participatory method” used by Tittonell (2003) and Fermont (Fermont et al., 2009) in which farmers group themselves into typologies designed for the purpose of a specific research. In this research the constructivist method was used as a first step: expert knowledge from extension officers working in the research area was used to make a selection of 25 farms per village (1 in 4 farmers).

The positivist method followed as the second step in which the output of the rapid farm characterisation formed the basis of designing a typology.

1.9 Outline of the thesis The Sections 1.1 to 1.6 have provided the background information on Mozambique and the biofuel debate. This is used as a starting point for the rest of the thesis. Chapter 2 introduces the methodology used during the different phases and working steps and explains in several subchapters at which level analysis are done. Chapter 3 is divided into three parts aimed at describing farm heterogeneity at different scales of analysis. Part 1 (Section 3.1) analyses results found at village level: comparing between villages in terms of soil fertility, socio‐economic characteristics, production activities and managerial aspects. Part 2 (Section 3.2) focuses on analyzing differences found comparing within villages based on the designed typology. Subsequently Chapter 4 presents the results of FIELD model simulations on cassava crop yields under different farm management scenarios. Chapter 5 is used for a general discussion and Chapter 6 for concluding remarks and recommendations. Each chapter gives a brief introduction about its contents and when indicated also a short summary at the end to make some important concluding remarks to facilitate interpretation of the chapters. Many tables can be found in the Appendices; within the main text explicit reference to the appendix is made.

2. Materials and methods

2.1.1 Introduction to work phases and working steps Three work phases and four main working steps can be identified in this thesis to cope with the variability found at different scales and to comply with the multiple objectives presented earlier (Chapter 1.7). Figure 5 below gives a schematic overview of the work phases, working steps and the interactions between the different components.

The first phase (Phase I) consisted of an elaborate literature review to provide a solid background for the start of fieldwork. It provided necessary knowledge on the methods needed in the following steps of the research such as the design of a questionnaire for the rapid farm characterisation and how to develop a farm typology.

The second phase was aimed at field work and analysis of the data collected. Each step used a previous step for input and provided information for the following one (Figure 5) For example previous to Step 1 experts were consulted and introduction visits to the area were made that provided the input needed to start the rapid farm characterisation in the 3 villages. With the help of key informants, 25 farmers in each village were chosen (74 in total, one missing). Socio‐economic and managerial data was collected as well as farmers´ yield estimations. The first approach questionnaire was used for this step (Appendix A 23). The output of Step 1 served as a basis for Step 2: the typology. After development of this farm typology selected farmers (a subset of 10 per village) was revisited for a second and third round of questionnaires to cross‐check and validate previous data and answers. During these second and third visits soil samples were taken and area and harvest measurements for the crops of focus were done. As a third step results were analysed at multiple scales for the villages, farms and fields (Figure 4).

Phase III continues at the end of step 3 from phase II: the analysis provided a framework for feasible scenarios of improved management by farmers on field and farm level. Main working step 4 in phase III was used for modelling to simulate the influence of farm management on cassava crop yield.

Figure 5. Illustration of methodological approach followed in four working steps. Output of the previous step served in several cases as input for the following step

2.1.2 Study area: Biophysical and socioeconomic description The district of Alto Molócuè was chosen before arrival in collaboration with the locally, nationally and internationally active aid organization World Vision and agricultural experts of the region. Upon arrival in the area in the end of April 2009, key informants (extension officers, researchers and NGO’s) were contacted in the district of Alto Molócuè. After several meetings in which the objectives were explained to those key informants three potential research villages were selected. Study sites with different soil types, different importance of cassava in the farming system and different distance to the markets were selected. The procedure was facilitated using a Global Position System (GPS) device to record GPS coordinates and a GIS soil map (Appendix A24) of the district to cross check for soil type variability between the sites. Main characteristics of the selected study sites are listed below in Table 2.

Table 2. Some biophysical and socio‐economic characteristics of the selected villages (IIAM, 1996; Report, 2007; unknown, 2007)

Mugema Nacuaca Gafaria

Biophysical Altitude (masl) 610 711 710

Dominant soil types (FAO) Ferric Acrisol (sandy) Cambio Arenosol Ferric Acrisol (loamy)

Description soil type low levels of plant nutrients, a fine‐textured subsurface layer, low levels of plant excess aluminium, and high low water and nutrient holding nutrients, excess erodibility, capacity aluminium high erodibility, high contents of kaolinitic clay and iron and aluminium oxides, Landscape Plains, slightly undulating, rivers Plains, slightly undulating Hilly surroundings

Socio‐economic Population density (inh km‐2) 43.5 24 20 Distance to major urban areas 5 km (dirt road) 15 km (highway) 15 km (highway) Production activities Main Food crops Maize, sorghum, beans, cassava Maize, cassava, sorghum, beans Cassava, sorghum, maize Main Cash crops Common bean, maize, Maize, pigeonpea, sesame, Pigeonpea pigeonpea, tobacco, sesame cassava, tobacco

Initially it was the intention to use the three selected villages Mugema, Nacuaca and Gafaria for all three survey rounds, soil samples and harvest estimations. Unfortunately due to unforeseen circumstances and limitations in time Mugema was only visited for the rapid farm characterisation. This made inclusion of Mugema for purpose of designing a farm typology possible, but further analysis on soil samples and harvest estimations not possible. Result from the rapid farm characterisation in Mugema will therefore only be used in the first part of Chapter 3: the analysis on village level (Section 3.1.2) and in the chapter on farm typology (Section 3.2).

2.2 Farm sampling and characterisation

2.2.1 Rapid farm characterisation After the introduction and site selection local leaders in the villages were consulted about the possibility and approval for carrying out the survey. In all three villages permission was obtained and a suitable key informant per village was selected. The key informant then helped to select 25 households per village, with contrasting household characteristics according to his knowledge. Special attention was paid to avoid sampling bias by taken extra notice of the spatial distribution of selected farmers over the area of study. Age distribution, ratio male: female respondents and NGO involvement of interviewees was taken into account. Consequently surveys for the rapid farm characterisation were designed to capture bio‐ physical, socio‐economic and managerial aspects of each selected farm and questions were checked through triangulation. Emphasis was placed on quantifying yields, sales and the use of inputs such as animal manure and hired labour and to explore the farming system in general. Overall the survey was aimed at characterisation of typical smallholder farm in term of size, composition, production systems, agricultural practices, labour and land with a focus on cassava, sorghum and maize. A mixture of structured and un‐structured questions was used and carried out by the author, a translator and the key informant, which allowed for further questioning if answers given were unclear or not corresponding with observations. The questionnaire is given in Appendix Table A 23.

2.2.2 Farm typology After the first round of questionnaires (May‐ June 2009) obtained data was processed using statistical software (SPSS 17.0) and Excel. Relationships between important variables on crop yield, crop management, socio‐economics, farming system and animal husbandry were explored using Pearson bivariate correlations. Data from the rapid farm characterisation was analysed and farm household typologies were constructed using the following multivariate statistical techniques as proxies for categorization; Principal Component Analysis (PCA) and Cluster Analysis (CA) and were combined with expert knowledge and preliminary household characterisation during fieldwork. PCA can be defined as a tool for data structuring. Socio‐economic data from the first round survey was previously log or square root transformed and standardized for comparable variances and checked for correlations. For correlations higher than r > 0.6 only one of the two variables was kept. Components retained in Principal Component Analysis followed Kaiser’s criterion that suggest retaining all components with eigenvalue higher than 1. The resulting component matrix with factor loadings of each variable for each of the components was subsequently used to find the subset of variables belonging best to the retained components (Field, 2005). Next, variables selected from the PCA were used in cluster analysis according to the theory presented in work of Zha et al., (2001) and Ding et al., (2004) arguing that PCA automatically projects to the subspace where global solution of K‐means clustering lie; PCA facilitates K‐ means clustering to find near‐optimal solutions. Cluster analysis seeks to classify entities (e.g. farm households) according to their (dis)similarities in terms of their characteristics represented by the variables chosen (Everitt, 1984 cited in (Bidogeza et al., 2007)). Ideally cluster analysis provides clusters composed of entities very similar to each other and dissimilar to entities of other clusters. However cluster analysis alone was not thought to be sufficient to design the farm household typology upon: a

25 preliminary characterisation with help of experts and exploration of field data was used to compare different cluster outputs and to choose the most appropriate one.

2.2.3 Second and third round interviews A subset of 2 farmers per typology (of the initially proposed typology) per village were chosen. The initial first made typology was based on dividing the farmers according to farm income, farm involvement and several wealth indicators suggested by experts. This approach will be discussed in Chapter 5.3.1 further in this thesis. Later the used typology was revised, but the first typology made during fieldwork was used for the selection of second and third round questionnaires. Besides asking for further details on crop and soil fertility management, marketing and pest & diseases, the second round of questionnaires was used to cross‐check important information from the first round. For this second part of surveys fields of cassava, sorghum, maize and fallow fields were visited together with the farmer. During the first round of questionnaires and visits to farmers it was observed that what is called a machamba (field) by farmers, is upon a closer look quite heterogeneous and could be divided in more homogenous sub‐ machambas. A cassava field was considered as such if it would have the same characteristics for: i) time of planting (in months), ii) ridges or heaps and iii) use of mixed cropping. For sorghum and maize only the use of intercrop was used to identify a sub‐field. Since farmers would fallow only a subplot of the whole field, only the part used in the next cropping season was measured and recorded in the survey. After defining the area was calculated using a differential GPS. Subsequently, the areas were plotted as polygon in ArcGIS software in GIS soil layers.

2.3 Field biophysical characterisation

2.3.1 Soil sampling and analysis For the cassava fields separate samples were collected from rows and from ridges/hills. Together with farmers a distance of 20 cm from the stem was chosen for sampling in ridges/hills to avoid damaging the tubers. To avoid oversampling the same number of soil samples in the ridges/hills as in between the ridges/hills was taken with an Edelmann auger from the topsoil (the first 15 cm). Sorghum and maize fields were sampled at a distance of 20 cm from the stem as well. Fallow fields were sampled in the zigzag line design paying attention to sampling not closer than 20 cm from a bush, shrub or plant.

The number of samples per field depended on field size and varied between 5 and 20 samples. After intensive mixing from the subsample a compound sample of 500 g was made. Samples were air‐dried, weighed, crushed and sieved through 2 mm sieves and kept at room temperature before further analysis. Chemical analysis was done at the Universidade de Eduardo Mondlane, Maputo (UEM) for soil organic C, total N (%), available P, exchangeable K, Ca and Mg, pH, CEC, sand (%), silt (%) and clay (%) from the composite samples (Appendix Table A 3)

134 topsoil samples were taken from 90 individual fields (defined in 2.2.3 above) growing cassava, sorghum, maize and fallow using systematic sampling in a zigzag line design over the field (Keizer et al., 1984).

2.3.2 Field measurements Bio‐physical information per field was collected and included e.g. visible signs of erosion, characterisation of surrounding fields (cultivated or fallow), and management: e.g. crop rotation, mixed cropping, history of cultivation, labour, weeding and handling of crop residues.

Distance in beds, distance between rows and height at 10 randomly chosen plants in each identified cassava field were measured and recorded. A weed management score from 1 (very poor) to 5 (very good) was used to quantify presence of weeds and subsequently weed management of the farmer.

Plant density (plants m‐1) and plant population (PP, plants ha‐1) were estimated by considering the average over at least 5 distances between rows (BR, in meters) and between plants spacing (BP, in meters) to calculate the expected plant population according to following equation:

Pl. density (plants m‐2) = 1 / (BR x BP)

Because non productive stalks were replaced with productive stalks a correction factor for crop survival was not included. This means that for estimation of plant population (PP, plants ha‐1) the assumption was made that all individual plants are productive and at least 5 measuring points between rows and between plants are representative for plant density in each individual field.

2.4 Yield estimations Yield estimations of several crops produced by the farmers in the villages were estimated using the following different approaches:

• farmers estimates of household production (per crop produced in ton per household) • farmers estimates of crop production per individual field (for cassava, sorghum and maize fields in t/ha) • calculations from harvest estimations in the field (only for cassava in t/ha) • calculation from harvest estimations in the storage facility (only for sorghum in t/ha)

Farmers estimating crop production for the household Overall farmer’s harvest estimations were derived from the first interview in which all crops were listed and average yearly production quantified. Most quantities indicated by farmers had to be converted to SI units, by asking the farmer for each product the quantity estimated. An example of conversions used is presented in Table 3 below. The following 2 return visits were used to cross‐check information given on the three crops of focus (cassava, sorghum and maize) for normal years of production, both husband and wife separately.

Table 3. Conversions used for farmer household production per crop

Crop Unit kg Form Cassava basket 50 dried Maize bag 70-80 grains Sorghum can 50 grains Rice bag 50 grains

Farmers estimating crop yield per individual field During the three rounds of farm visits farmers were asked to estimate cassava harvest (kg dried cassava), maize and sorghum per field visited. Only for cassava a conversion from dried to kg fresh weight tubers using 33% DM as a rule of thumb was used (Fermont et al., 2009).

Calculations estimating cassava yield from harvest measurements in the field In consensus with the farmer, a number of plants were selected for harvest by the researcher. The individual plants were specifically chosen to represent average plants present in each field. Farmers helped in finding representative plants by clearing the soil around the cassava plant and judging its representative characteristics by tuber size and amount of tubers. Selected plants were uprooted, soil removed and weighed as a whole. Next tubers were removed and weighed in total, subsequently marketable tubers were selected and weighed. Tubers were considered marketable if > 3cm in diameter, damage and disease free. Name of cultivar was recorded together with the notation bitter or sweet. Fresh tuber weight (IFW in kg plant‐1) could then be calculated out of the average of measured 5 individual plant tuber weights corrected for months after planting (MAP) and set on a standard of 12 months. Total fresh tuber weight (TFW in t ha‐1) was calculated using following equation:

TFW (t ha‐1)=IFW (kg plant‐1) x Plant population (PP, in plants ha‐1)

Calculations estimating sorghum yield from harvest estimations in the storage facility In the research area sorghum is harvested in between June and July. Initially the objective was to be part of the harvest of sorghum in order to make harvest measurements. Because not all farmers were able to estimate the amount of sorghum harvested (usually referred to as “heap and store”) an alternative had to be found. Sorghum throughout the research area is stored in an identical way: the whole panicle is piled up in a heap of panicles. The size of the storage of sorghum of farmers revisited was measured and adjusted to geometrical shapes. For these shapes size in m3 was calculated. From a subset of farmers a few panicles were collected. These were weighed as full panicle and volumetric (m3) size of the panicle estimated using a beaker filled with water. After drying the harvest index of the panicle (HIpanicle) was estimated. Using following equation grain dry weight (GDW in t/ha) of harvested sorghum was calculated:

Total panicle weight (kg in storage) = Geometrical shape representing storage (m‐3) /average panicle size (m‐3) * average panicle weight (kg)

GDW (t/ha) = Total panicle weight (kg in storage) * HIpanicle * (1/ area cultivated (ha))

Area cultivated was determined with a differential GPS (see section 2.2.3) and recalculated to mono culture sorghum by using a standard proportion of sorghum grown in mixed cropping in combination with a specific other crop or crops (see Table 19 for proportions used). Mixed cropped fields were recalculated to hypothetical mono culture fields by estimation of the proportion of land occupation per crop. As an example, for sorghum: pigeonpea mixed cropping systems a ratio of 3:1 could be found. This meant that to calculate sorghum yield/ha, area cultivated was multiplied by 75% to correct for mixed cropping.

2.5 Income calculations

2.5.1 Household income calculations A distinction in sources of income was made following the assumption that total estimated farm income is the sum of income from crops, income from selling animals and animal products and off farm sources of income as listed below:

Total estimated farm income = Income from crops + Income from animals + Off farm income (other sources of income)

Income from crop production Gross value per crop or crop product (GVC) per household was estimated by multiplying fresh weight (FW) of the product by the average selling price. Average selling prices and estimated fresh weights were collected during the three farm visits. The sum of all GVC subsequently formed total income from crop production in New Mozambican Meticals (MZN).

Income from animal production Gross value from animal production (GVA) per household was estimated by multiplying the number of animal type sold times average selling price. A summation of GVA’s per animal type plus the value of animal products sold formed the income from total animal production (GVAT).

Off farm income During the interviews farmers were asked for involvement in off farm activities such as making blocks, sieves or stoves, having a contract job (e.g. driver or teacher) and receiving pensions and remittances on a yearly basis. The sum of all activities belonging to non‐farm sources of income is off farm income.

2.5.2 Gross margin fertilizer analysis Partial gross margins of NPK fertiliser for simulated yields were calculated for a total of 90 individual fields in both Nacuaca and Gafaria. Average wholesale market prices for 2009 were used for the full ‐1 fertiliser package (NPK: 100:22:83, i.e. 100 kg N:22 kg P2O5:83 kg K2O ha ) at a price of 1200 MZN per 50 kg bag (New Mozambican Metical : 1US$ = 27 MZN) for the period April until September, 2009. Average selling prize of cassava was used at 1000MZN/ Mg fresh cassava tubers and average selling price of maize at 2.5 MZN/kg grains: recorded as common selling prices in both villages. Transport costs were not included in the calculation since it was a common practice in the villages for the buyers to come to the farmer to make the purchase. The Value/ Cost (VCR) ratio was calculated by dividing the marginal revenue by the marginal costs. As suggested by Kelly (2006) under small scale agriculture a VCR of two or higher gives a big enough economic incentive to farmers to adopt it.

2.6 Statistical analysis

Analysis of socioeconomic diversity Comparisons across sites and farm typologies in terms of socio‐economic indicators, land use and managerial variables were done through calculation of descriptive statistics and analysis of variance. This analysis was done with the explanatory factors Village and Typology and their interaction, and means were compared with the 5% LSD or 10% LSD if indicated. A principal component analysis (PCA) was conducted using the socio‐economic data (previous log or square root transformed and standardized for comparable ranges) to identify important proxy indicators further used in the development of the typology. Due to the experimental design of three villages and four farm types in a non‐equal distribution the ANOVAs were conducted under the unbalanced treatment structure of offered in SPSS (17.0) under Univariate Generalized Mixed Models.

Soil fertility variability Soil fertility status was assessed at village, farm and field level through calculation of descriptive statistics and analysis of variance with Village or Quartile as explanatory factors. This analysis was done based on soil properties pertaining to individual fields (N=90) expressed as results from chemical analysis of soil organic C, total N (%), available P, exchangeable K, Ca and Mg, pH, CEC, sand (%), silt (%) and clay (%) contents in a composite sample per field. The model FIELD was used to divide fields according to their yields in four significantly different quartiles, to cope with variability within villages.

Explaining simulated yield variability To be able to identify variables best explaining yield differences a multiple linear regression was carried out. As a first step variables were checked for correlation using Spearman’s and Pearson’s correlation analyses. For any pair of abiotic, biotic and management variables with inter‐correlations (r) greater than 0.7 only one variable was retained and subsequently these were taken as independent variables. The entire data set was used for the analysis using GenStat’s all‐subset regression routine and the best model was selected (version 13.1). Impact of the measurement error for each explanatory variable on the regression coefficient was allowed to be <5%

2.7 Dynamic simulation of crop performance and nutrient balances at field scale

2.7.1 Model for dynamic simulation at field scale: FIELD In this thesis two models for simulation of crop yields were used: FIELD for cassava yield simulations and QUEFTS for maize yield simulations. In this section both models will be discussed, but the model FIELD will be discussed more elaborately because of its importance in the modelling in this thesis.

To be able to explore short to medium term consequences of management options on cassava yield and soil fertility indicators a model was used. Several models on cassava crop production exist but as stated in research done by Fermont (2009) none of them has been calibrated for African conditions. A model FIELD was developed as a crop and soil sub model of FARMSIM, which is a bio‐economic model developed to analyse tradeoffs between farming systems and environments focusing on strategic decision‐making and embracing the spatial and temporal variability of smallholder systems (FArm‐scale Resource Management SIMulator; www.africanuances.nl)(Tittonell, 2008). In overall FARMSIM consists out of the components FIELD (crop‐soil), LVSIM (livestock) and HEAPSIM (manure) modules that are functionally integrated and allowing for feedbacks between these entities on farm scale (Figure 6). In this thesis only the crop‐soil component FIELD will be used.

FIELD focuses on long‐term changes in soil fertility (C, N, P and K interactions between nutrients) which determines crop production. It simulates crop responses to management interventions such as mineral fertiliser, application of manure or organic amendments. Different fields within the farm are treated as separate entities with their own set of soil properties.

Figure 6. Schematic representation of the relationships between different modules of the FARMSIM model (Tittonell, 2008).

Total fresh cassava yield (kg ha‐1) is calculated on the basis of seasonal resources (light, water and nutrients), their availability of those and use efficiencies according to the conceptual model:

Crop production = Resource availability x Resource capture x Resource conversion efficiency

Crop biomass is calculated based on the intercepted amount of photosynthetically active radiation (PAR) which is only a fraction of total PAR during the growing season. ‘Light determined yield can subsequently be estimated by using a light conversion efficiency coefficient and is affected by management factors such as cultivar choice, planting density and planting time. Water‐limited crop production is calculated based on seasonal rainfall data and site and crop specific water use efficiency coefficient and therefore depends on data availability of the study sites and has to be derived from literature otherwise. Nutrient‐limited crop production is calculated from nutrient availabilities and nutrient use efficiency of the crop. Other nutrient sinks for example leaching and gaseous losses of N or immobilization in the soil organic matter act as competition components to crop uptake an can be used to derive the nutrient capture efficiency of the crop. To be able to calculate resource‐limited crop production in FIELD the minimum of water‐limited and nutrient limited crop production (determined by the availability and use efficiency of N, P, K and their interactions ) has to be taken following Liebscher’s Law of Optimum (Van Keulen, 1995). Reduction factors such as weed competition are used to calculate actual yield and derived from multiplying actual biomass production with a harvest index coefficient (Tittonell, 2008).

A sensitivity analysis can be used to study the relative variation in model outputs in response to changes in model input or parameters. The model was run on a number of fixed parameter settings presented in Appendix Table A 12. The relative partial sensitivity of the model input can be calculated with the help of following equation (Tittonell, 2003):

(dO/O) / (dI/I)

In this equation (dO/O) is the relative change in model output and (dI/I) is the relative change in the value of parameter or input data. Sensitivity was calculated as the average sensitivity to changes in the value of parameters that were set according to information from the field (if available) or systematically (e.g. increase and decrease of 10%). Calibration, validation and the sensitivity analysis have been done in previous research by de Vries (2010)

To be able to simulate maize yields on the same fields as the cassava yields are simulated, QUEFTS was used. The ideas underlying QUEFTS are based on the results of many years of soil fertility research in Surinam and Kenya by B.H. Janssen and co‐workers. QUEFTS stands for Quantitative Evaluation of the Fertility of Tropical Soils. QUEFFTS is both a model and a computer program and can be used as a tool in nutrient management, land evaluation and fertiliser recommendations. It is however not a dynamic simulation model (as is FIELD) because it does not involve a series of time steps (Hoffland et al., 2008). In this research maize yields have been calculated based on soil fertility indicators, assuming no serious growth limitations other than soil fertility are present. The only means of validating the model were by the use of farm yield estimations by farmers.

2.7.2 Developing scenario’s The modelling part of this thesis focuses on cassava, the major staple food crop in the farms under study in Central to North Mozambique. In the simulation no distinction for different genotypes was made so the outcomes are the result of an ‘average’ performing cassava genotype.

Crop management The model was run for six different regimes affecting short‐ and long term crop and soil productivity listed in Table 4 : 1) no input farm practise, 2) increasing cassava residue returned to soil, 3) increasing amendment of maize based organic residues, 4) increasing amendment of on farm manure, 5) increasing amendment of NPK fertiliser, 6) increasing amendment of NPK fertiliser plus manure and organic residue amendment. For each management regime the model was run for all fields sampled in both villages (N=90) for a period of 10 years. A list of fixed model parameter input can be found in the Appendix A 12.

Table 4. Overview of management scenarios applied

Scenario Name Quantity

I No input - II Cassava residues 10% and 55% remaining in the field III Maize residues 1-5 t/ha (steps of 1 t DM) IV Small stock farm manure 0.125, 0.25, 0.5, 0.75, 1.0 t DM ha-1 V NPK fertiliser 25, 50, 100, 150, 200, 250, 350 kg ha-1 VI Improved NPK management NPK + 0.25t DM ha-1 manure + 1t DM ha-1 pigeonpea residues

Manure Nutrient input from manure application was calculated by the production of manure (kg year‐1 animal‐1), the dry matter content and the N, P and K fractions in the model. Animal manure production was assumed to be collected for 50% in the kraal. Remaining manure from animals ranging was assumed not to be collected and did not contribute to the nutrient input in the model. In the study region both pigs and goats were kept, none of the farmers owned cattle, so for the purpose of simulation we only focussed on pig and goat manure. Pig manure production has been estimated to be a fraction 0.20 of a live stock unit (LSU) manure production and a goat estimated to make up for 0.10 LSU (Dougill et al., 2002). We assumed manure production of 750 kg year‐1 per livestock unit following estimations from Geurts (1990). Estimated nutrient content of the collected small stock manure in the kraal (goat and pig manure combined) was set on 2.0%N, 0.4%P and 2.0%K taken from research done by Dougill et al., (2002) with a dry matter content of 50%. Manure application was simulated in steps of 0.1, 0.25, 0.5, 1.0, 1.5, 2.0 t fresh manure ha‐1 with a DM% of 50% (0.125, 0.25, 0.5, 0.75, 1.0 t DM manure ha‐1) covering a range representing manure of 1 pig for a cassava field with a size of 0.75 ha up to the maximum amount of manure of 11 pigs and 23 goats on 0.84 ha.

Crop residue To account for the amount of nutrients returned to the soil in the form of crop residues the recycled amounts of N, P and K, carbon content (C%) and lignin content of the residues was derived from

33 literature for the different crop residues used in the management regimes. For the purpose of the different management regimes two types of residues were used: maize and pigeonpea. The amount of nutrient in the residue was estimated using literature choosing a medium nutrient composition for maize, and an upper nutrient composition for pigeonpea. For maize 1%N, 0.2%P and 1%K was used derived from work done by Dass (1979). For pigeonpea a high nutrient content of 4%N, 0.4%P and 4%K was estimated to represent a hypothetical high nutrient rich legume. Lignin content was taken from literature at rates of 5.4% and 10.7% and of total DM content for maize and pigeonpea (Bernard and Jean‐Marc, 1986) for maize and (Mapfumo and Mtambanengwe, 2004) for pigeonpea.

For maize farmers’ estimations on maize yield and QUEFTS simulations were used to calculate maize biomass production and an HI of 40% was derived from work done by Hay and colleagues (2001). The effect of cassava residue incorporation was simulated in two scenarios: 10% and 55% cassava residue incorporation. 10% remaining residue complies with standard farm practice of removing all crop residues and having some remains (in e.g. litter fall or some stalk remaining). The proposed 55% cassava crop residues is in line with harvesting the tubers and stalks for planting but leaving the remaining stems and leaves (instead of e.g. burning). Lignin content was assumed to be 8.4% of total DM and nutrient content of crops residues set at 1.2% N, 0.2% P and 0.7%K , derived from Putthacharoen et al.(1998).

Table 5. Nutrient amendment in scenario simulations

Maize1 Cassava Pigeonpea1 Small stock residues residues1 manure HI 40 45 30 - DM (%) 64 26 53 50 Per ton DM applied N (kg/ha) 9.0 12 36.0 20.0 P (kg/ha) 1.8 2 3.6 4.0 K (kg/ha) 9.0 7 36.0 20.0 1Fraction incorporated was set at 0.9 (meaning 0.9 t of every ton added to the soil gets incorporated into the soil)

Fertiliser Fertiliser application was simulated using the most commonly available fertiliser formulation (100:22:83, N:P:K) at different application rates of 25, 50, 100, 150, 200, 250 and 350 kg ha‐1. Although not considered the most optimal package, still the choice was made to use this specific fertiliser package.

Improved management An extra scenario was added to the crop management scenarios to upgrade fertiliser application using nutrient sources from available crop residues and small stock manure. Frequently grown pigeonpea crop residue was chosen for its high nutrient content at a rate of 1 t/ha (available crop residue at average yields). Additional manure application was set on 0.25 t DM/ha corresponding to the manure of 2 pigs for an average size cassava field of 0.3 ha (see Appendix Table A 15).

3 Results

3.1. Characteristics at village level The following section (Section 3.1.1) analyses results found at village level: comparing between the villages Nacuaca and Gafaria for bio‐physical variability.

3.1.1 Village biophysical variability Average value of soil fertility indicators varied between villages in the district as predicted by the variation in inherent soil types present. For most important soil fertility variables, box‐plots are presented in the Appendix Figure A 1, determination and calculation of the soil fertility variables used can be found in the Appendix A 3. Comparing fields under cultivation between villages showed a significant higher percentage of clay content in Gafaria compared to Nacuaca (25% compared to 12%, P<0.001). Sampled fields in Nacuaca were found mostly on greyish FAO classified Cambio Arenosol and on Ferric Acrisols in Gafaria (IIAM, 1996). Only 3 samples had a clay content higher than 40% (found in Gafaria). Most soils in Nacuaca had a clay content lower than 10% (42% of soils samples), compared to only 2 samples in Gafaria. 86% of samples from Gafaria had a clay content between 10 and 40%, compared to 58% in Nacuaca (Table 6).

The soil organic content (SOC in g/kg) differed considerably between sites 10.7 and 8.5 g/kg (P<0.07) for Gafaria and Nacuaca and can be explained for less than 15% by clay content of the soil (see Figure 7 below). Overall soils from both villages can be considered low in SOC according to the Institute of Agricultural Research soil fertility capability classification system (Maria and Yost, 2006). 87% and 77% of all soils sampled in Nacuaca and Gafaria respectively had a SOC content below 11.2 g/kg (Table 6).

Figure 7. Soil organic content (SOC in g/kg) plotted against clay content (%) for all fields sampled combined (N=90) in Gafaria and Nacuaca

Table 6. Percentage of fields (N=90) belonging to soil fertility class Very Low (VL), Low (L), Medium (M) and High (H) according to soil property groupings of Instituto Nacional des Investigação Agronómica (Maria and Yost, 2006)

% of fields grouped per soil fertility class Clay SOC N P K Al saturation

Nacuaca Class VL 12 14 L 42 75 48 17 23 66 M 58 14 52 77 52 33 H 6 12 1

Gafaria Class VL 3 L 5 74 5 21 67 M 87 24 92 79 45 31 H 8 3 55 2

Villages P< 0.001 ns 0.001 ns 0.007 ns

‐1 Fields of Gafaria were found significantly higher in exchangeable K (cmol(+) kg ) compared to Nacuaca ‐1 with an average of 0.46 compared to 0.21 cmol(+) kg respectively (P<0.001). Soils were divided between sandy soils, clay soils and other soils according to standards and classified for potassium level to compare between villages and correcting for clay content in the soil (Maria and Yost, 2006). 37% of fields sampled in Nacuaca had a very low to low potassium content in the soil while none of the fields sampled in Gafaria were classified as such. Medium potassium levels were found for 52% compared to 45% of the fields. High K‐levels were found for 12% compared to 55% of the samples taken in Nacuaca ‐1 and Gafaria respectively. Clay content (%) was positively correlated with exchangeable K (cmol(+) kg ), (r=0.738, P<0.001) and the percentage of clay could explain more than 53% of the variability found in K ‐1 (cmol(+) kg ) across sites (Figure 8).

Average amount of extractable phosphorus (P mg/100g) found was significantly higher (P<0.05) in Nacuaca (0.35, range 0.01‐1.39) compared to Gafaria (0.22, range 0.02‐0.98). 17% compared to 21% of the soils were classified as Low phosphorus content soils, 77% compared to 79% as Medium and 3 soils in Nacuaca as High comparing Nacuaca and Gafaria. For clay content (%) and extractable P (mg/100g) a negative relation was found (r=0.345, P<0.001) possibly indicating some phosphor binding by clay particles (Figure 9)

‐1 Figure 8. Exchangeable K (cmol(+) kg ) plotted against clay content for all soil samples combined (N=90) for Gafaria and Nacuaca

Figure 9. Extractable P (mg/100g) plotted against clay content (%) for all soil samples combined (N=90) for Gafaria and Nacuaca

Comparing fields between the two villages for total nitrogen content (N%) showed a significant higher average value for Gafaria compared to Nacuaca (0.13 compared to 0.11%, p<0.001). 48% of the fields sampled in Nacuaca fell into the classification low, compared to only 2 samples in Gafaria, 52% were classified as medium compared to 92% in Gafaria (Table 6). The ratio between soil carbon content (SOC) and total nitrogen did however not differ between villages (average C: N=10).

Sampled fields in both Nacuaca and Gafaria had a comparable pH, but the pH range of fields sampled differed: a wider range was found in Gafaria (pH 5.3‐6.8 for Nacuaca and pH 4.9‐7.4 for Gafaria). None of the fields sampled was classified as extremely acid (>4.5). 10% compared to 37% of the soils were classified as strongly acid (pH 4.5‐5.5), 87% compared to 55% as slightly acid to acid (5.5‐6.5) and only 2 samples from both villages were classified as neutral (6.5‐7.3) in Nacuaca and Gafaria subsequently (Table 7).

No differences were found for the Cation Exchange Capacity (CEC) of soils comparing both villages with an average of 24.6 cmol kg‐1 for Nacuaca and 26.0 for Gafaria. The Exchangeable Cation Exchange Capacity (ECEC) was found to be an average of 10 cmol kg‐1 for both villages. Base saturation (the sum of bases Caexch, Mgexch, Naexch and Kexch/ECEC) did not differ significantly with a total average of 67% in both villages.

Aluminium saturation (Alexch/CEC*100) can be an important indicator of aluminium toxicity on acid soils and is considered low, medium and high at ratio’s of 0‐15, 15‐35 and >35% aluminium saturation. Of the soils sampled 67% compared to 79% were classified as low and 33% and 21% as medium in Nacuaca and Gafaria (Table 6).

Table 7. Percentage of fields grouped in the different pH ranges presented for Nacuaca and Gafaria (N=90), classification used is derived from the Institute of Agricultural Research (Appendix Table A 2)

% of fields pH range Nacuaca Gafaria

4.5-5.5 10 37 5.6-6.5 87 55 6.6-7.3 4 5 7.4-7.8 3

Villages P< 0.007

3.1.2 Socioeconomic variability at village level This section focuses on results found at village level: comparing between the villages Mugema, Nacuaca and Gafaria for socio‐economic characteristics, production activities and managerial aspects.

The farms sampled in this study showed characteristics in terms of family structure, income sources and farm indicators that could consider them as representatives for the study area as compared to the results of the baseline study conducted in the area (OVATA mid term, 2004). All results in this subchapter are based on the total data set of farmers (N=74, three villages) unless indicated elsewhere.

Comparing villages no difference was found in the number of household heads being illiterate: an average of 11% of farmers per village did not go to school and had no knowledge how to read or write. The remaining rest differed in level of education from 1st class until 8th class as highest education (Appendix Table A 5). Men were more likely to have a higher education compared to women across villages, females had a higher illiteracy rate compared to males across villages (not shown).

Sampled farms showed differences as well as shared characteristics across sites, some of these characteristics are shown in Table 8 below. The average amount of fields per household was significantly higher in Gafaria compared to farmers in Mugema (3.7 compared to 2.6 respectively). Household members, the amount of people present at the farm, did not differ across sites with an average of 6‐7 family members.

Closest fields for all study sites were 1.0‐1.5 min walking away from the homestead. These fields would be in the immediate vicinity of the family houses and storage sheds. Farmers of Gafaria all had their fields close by the house (±25 minutes) whereas on average 154 minutes had to be walked to reach the furthest field in Nacuaca, Mugema was found intermediate with fields on 50 min walking distance.

The total amount of animals (including all animals) per household compared between study sites did not differ and consisted in average out of 9‐12 animals per farm household (hh‐1). The number of families owning livestock (goats and/or pigs) as well as the number of goats, pigs and chicken hh‐1 did not differ between villages (Table 8). More farmers in Nacuaca owned goats (36%) compared to 4% of the farmers in Mugema and Gafaria (not shown).

Table 8. Socio‐economic indicators and production activity indicators per household for the three villages of study (N=74)

Site

Indicator unit Mugema Nacuaca Gafaria P

Socio economic indicators Household members present # 5.6 6.7 7.0 ns Amount of machambas # 2.6a 3.2ab 3.7b 0.011 Distance to closest field min 1.0 1.5 1.0 ns Distance to furthest field min 50a 154b 24a 0.003 Months without food # .7a 1.3a 3.7b 0.001 Total income out of agriculture MZN1 5853ab 12286b 878a 0.038 that hire labour % 28 29 10 ns that have off farm income % 44 21 41 ns

Production activity indicators crops grown # 6.2a 7.2b 7.9b 0.009 cash crops grown # 1.0 1.0 1.0 ns Fruit trees grown # 9.4a 11.6a 26.0b 0.001

Households that own animals % 80 92 92 ns Households that own livestock % 48 67 64 ns number of animals # 11.8 10.4 9.2 ns number of goats # 1.0 9.7 5.0 ns number of pigs # 5.2 4.9 6.4 ns number of chicken # 3.2 2.6 2.9 ns

1 New Mozambican Metical : 1US$ = 27 MZN (period April until September, 2009)

Food (in) security Farmers were asked for the number of months per year with food insecurity/shortage. Respondents of Mugema were able to feed themselves in average all year except for <1 month, farmers of Nacuaca >1 month, respondents in Gafaria had a significant higher amount of months with food insecurity (3.7 p<0.001) compared to the other two villages (Table 8). The cropping calendar in the Appendix in Table A 7 shows the end of harvest season for all crops except cassava around September. Following months of October, November and December are the months of first rains and the fields have to be prepared for the new season. 52% of the respondents of Gafaria indicate the start of food shortage commencing from November onwards, compared to 4 % in Mugema and 13% in Nacuaca. For most farmers in Nacuaca food shortage commences later in January (29%). End of deficiency starts in February for farmers of Nacuaca (55%), but later for most farmers in Gafaria in March (24%) (Table 10). The months in which food deficiency ends cannot be explained by looking at the cropping calendar. It could be that some crops are harvested green or can be harvested earlier.

Because for most crops only one cropping season is possible due to uni‐modal rainfall pattern (see Figure 3) good storage facilities were indicated as important as good harvest (personal communication in Mugema, Nacuaca and Gafaria). Overall almost half of the respondents did not have a good storage facility (48%). A significant higher number of farmers of Gafaria did not have proper storage (74%) compared to Nacuaca (38%) and Mugema (25%). Farmers of Mugema were able to store for 9 months without problems of insects and degradation, significant higher than Nacuaca (4 months) and Gafaria (3 months) (Table 9). At times of food deficiency interviewees would have several strategies such as harvesting the leaves of the cassava to eat in the form of a sauce. Also they would go and ask neighbours for food or work for them and get payment in food (ganho‐ganho) (personal communication, three villages).

Table 9. Storage facility characteristics of respondents in Mugema, Nacuaca and Gafaria (N= 47)

N Mugema Nacuaca Gafaria Total N 2 21 24

Storage facility yes 75 62 26 52 no 25 38 74 48

Time of storage(months) 9 4 3 4

Table 10. Start and end month of food insufficiency indicated by the respondents (% of households) in Mugema, Nacuaca and Gafaria (N=74)

Mugema Nacuaca Gafaria N 25 24 25

Start August 4 0 0 October 4 4 12 November 4 13 52 December 12 17 16 January 12 29 16 February 0 8 4 March 4 0 0 no hunger 60 29 0

End April 8 0 8 May 0 4 16 June 0 0 4 November 4 0 0 December 12 8 0 January 4 0 4 February 4 46 20 March 8 13 48 no hunger 60 29 0

Labour Labour was mainly derived from the household although some farmers from the survey also hired labour from outside the farming household. Between 10 and 29% of the farmers hired labour on a regular and non‐regular basis to relief pressure during the peak season of labour for preparing the fields and harvesting of maize. Farmers themselves would work for their neighbour (ganho‐ganho) at times of hunger and get payment in food. Most commonly the whole family or the wife and husband contributed to the largest part of the labour provided by the family (Table 11).

Table 11. Household members contributing the most to farm labour (percentage per household, N=74)

Site

Mugema Nacuaca Gafaria Total N 25 24 25 74 Who works the fields the wife 8 8 5 7 most? wife and husband 40 75 33 50 whole family 40 8 48 31 husband 0 4 0 1 wife and children 12 4 10 9 wife and grandmother 0 0 5 1

Household income Total household income was calculated summing the considered three main sources of income: income from crop production, income from animal production and off farm income. The percentage of households being involved with activities belonging to the different sources of income are listed below in Table 12. Although no significant difference could be found in the amount of households being involved in the different economic activities mentioned, a difference could be found in the percentage off farm income contributed to total calculated household income comparing between villages. For 44% of the farmers involved in the survey from Gafaria off farm income contributing for an average of 75% of their total household income. For Mugema and Nacuaca 44 and 21% of the households had sources of off farm income, contributing to 19% and 21% respectively of their total calculated household income. Off farm income activities mentioned were making crafts (45%: such as blocks, sieves or stoves), being a middleman (21%: selling maize or beans) or working for others (14%), remittances or pensions were received by the remaining 21% (N=29 out of 74,Table 12b )

Table 12. Percentage of farms within the farm samples from the three sites that are involved in activities listed below and their proportion of total income (N=74)

Village Mugema Nacuaca Gafaria

% of households selling animals or animal products 28 33 40 % of households selling crops 96 83 100 % of households having sources of off farm income 44 21 44

% of income from animals or animal products 9 12 17 % of income from crops 72 81 50 % of income from off farm 19 21 33

Table 12b. Relative frequency (%) of off/non farm activities mentioned by the farmers in both Nacuaca and Gafaria having off‐farm activities (N=29)

Activity Percentage of farmers answering (%)

craft 45 pension 21 salesmen 21 work for other 14

Income from crop production Importance of on farm income from crops differed between villages: 72 and 81% of total income in Mugema and Nacuaca was derived from crop sales compared to 50% in Gafaria (see Table 12 ). Households of Gafaria in average had the lowest income from agriculture compared to Mugema and Nacuaca (878 compared to 5150 and 12285 MT hh‐1; Table 8) and a higher percentage of income attributed by selling of the alcoholic drink Ortega (37%, Figure 10). Ortega is an alcoholic beverage made from cassava and sorghum flour as ingredients, fermented for about a week and a medium to high (± 12‐20%) alcohol percentage. A significantly higher percentage of farmers were selling Ortega in Gafaria compared to farmers in Nacuaca and Mugema. Farmers in Mugema had a considerable higher percentage of beans sales contributing to their from farm income (35% P<0.057) compared to the other two villages and a significantly higher percentage of farmers sold beans. No difference however was found for the percentage selling of cassava, maize and sorghum per household comparing the villages (Table 13).

sorghum cassava sorghum cassava cassava ortega 4% sorghum 11% ortega 4% 12% 8% maize 13% 16% 16% 12%

maize cash crop 23% 14% maize cash crop 29% 19% beans ortega 27% 37%

beans beans cash crop 35% 20% 0% Mugema Nacuaca Gafaria

Figure 10. Pie charts averaging the different components for farm income for Mugema, Nacuaca and Gafaria

Even though between 26 and 47% of the farmers sell cassava, cassava only contributes up to 12% in average of the total income from farm (Figure 10). Higher value crops such as beans and maize are being sold by 30‐57% and 50‐80% of the farmers respectively (Table 13). Higher cassava production did lead to a higher percentage of cassava sales; farmers of Nacuaca estimate a significant higher cassava yields per household compared to farmers of Mugema and Gafaria while the percentage sold remained at a comparable level (43‐49% in average). Maize with an overall higher production per household in Nacuaca resulted in a higher percentage sold of total production (70%; Table 13). A representable subset of farmers in Nacuaca and Gafaria (N=17) was asked who they sell their cassava and maize to, results are listed below in Table 14. Cassava was commonly sold to people of the community (82%): neighbours that came and purchased the cassava at the farm. Less commonly cassava was sold to people from far (outside the community and salesmen (12 and 6%). For maize is was more common to sell to salesmen (53%) who would come and collect on the farm and resell later on.

Table 13. Percentage of farms selling the most commonly grown crops and beverage Ortega listed below in the three villages (only farmers growing the crop have been included see Table 12)

Site

Mugema Nacuaca Gafaria P< that sell cassava % 48 42 26 ns that sell maize % 39 57 29 ns that sell beans % 80c 57b 50a 0.077

that sell sorghum % 12 9 25 ns

that sell Ortega b a c % 67 27 75 0.024

% sale of total production Cassava % 47 49 43 ns Maize % 57ab 70b 41a 0.09 Pigeonpea % 60 57 62 ns Cowpea % 20 35 35 ns

Common bean % 63 56 50 ns

Sorghum % unknown unknown unknown ns

Ortega % unknown unknown unknown ns

Table 14. Frequency of farmers (%) selling cassava and maize to listed buyers below (N=17)

Cassava Maize Buyer: People of the community 82 40 People from far (road sellings) 12 7 Middleman from community 6 53

3.1.3 Land use and management practices

Main crops grown Most important crops grown in the three different study sites are listed below in (Table 15). Mugema and Nacuaca have a higher percentage of farmers growing maize compared to farmers of Gafaria (92% and 96% compared to 68%). Sorghum is grown by most farmers in all study sites, cassava is grown by fewer farmers in Mugema (84%) while Nacuaca and Gafaria have high percentages (100% and 92%) but this difference is not significant. Common bean, a high value crop, is grown more in Mugema, and less so in Nacuaca and Gafaria, pigeonpea is grown by most farmers in Gafaria (Table 15).

Table 15. Overview of crops (grown by % of farmers) in the three study sites

Cereals Tubers Legume Cash crops

n Maize Sorghum Rice Cassava Sweet Velvet Pigeon Cowpea Common Ground Sesame Tobacco Sun Mugema 25 92 100 24 84 96 0 60 80 84 12 0 12 20 Nacuaca 24 96 92 29 100 100 54 79 96 17 54 25 17 13 Gafaria 25 68 96 28 92 92 20 92 80 28 56 32 4 32

Chi 8.9 16 7.3 40.4 12.7 10.3 P 0.012 ns ns ns ns 0.001 0.026 ns 0.001 0.002 0.006 ns ns

60 and 70% of the farmers in Nacuaca and Gafaria mentioned cassava as their most important crop (Table 16, and Appendix Table A 6 ). Farmers mentioned cassava as the most important crop because of the ability to store cassava in the soil (mentioned in Gafaria), in Nacuaca cassava was mentioned most important because the benefit of selling the product besides consuming it. Maize is a popular crop for cash generation, only few farmers mentioned sorghum as the most important crop. This was related to the good taste of sorghum when made into a xima (from sorghum flour).

Table 16. Percentage of farmers that ranked cassava, maize or sorghum as the most important crop, by village.

n Cassava 1st Maize 1st Sorghum 1st Nacuaca 24 60 40 0 Gafaria 25 70 20 10

Estimated crop production per household Farmers in the three villages of study were asked to estimate average yearly crop production for normal years per household. The results in Table 17 are the outcome of the first round of questionnaires: the rapid farm characterisation. No significant difference in production level of the different crops was found except for maize and cassava. Farmers of Nacuaca estimated harvesting a higher quantity of maize per household compared to farmers in Gafaria, but not significantly higher than farmers in Mugema. The farmers in Gafaria estimated harvesting the lowest amount of cassava per household, followed by farmers of Mugema: highest production estimations for cassava were done by farmers of Nacuaca.

Table 17. Overview of estimated crop production for the different villages (in ton production/ household), only cassava is listed in fresh weight based on the rapid farm characterisation

Mugema Nacuaca Gafaria P< Estimated production (t/hh) Maize 1.0b 1.3b 0.2a 0.05 cassava 1.8b 3.3c 1.2a 0.042 pigeonpea 0.1 0.5 0.1 ns

groundnut 0.1 0.4 0.2

rice 0.6 0.2 0.3 ns sorghum 0.3 0.2 0.2 ns common bean 0.4 0.2 na ns cowpea 0.1 0.2 0.1 ns sunflower 0.1 0.1 0.1 ns

na= not applicable ns= not significant (no significant difference found (p<0.05) comparing the three villages

Cassava varieties used More than 10 different varieties were identified during field visits with the help of expert farmers. Most popular were Nabrinco (26%) and Chief (15%). Some of the cassava could not be identified because they were local varieties with no known name (15%). Farmers were eager to exchange with other farmers for more productive or disease free plants (N=74). Introduced varieties by NGO World Vision, such as Fernando Boa, were only found marginally (3% of all individual plants in survey). Observations during field visits and non formal meetings with farmers showed problems with disease free handling of plant material, where farmers were using plant material of diseased plants (e.g. cassava mosaic virus) to cultivate for the next growing season.

Crop rotation and intercropping All farmers (N=74) applied crop rotation (changing the sequence of crops per season per field), although some would grow the same crop for two consecutive years within the rotation (commonly applied for cassava, see Table 18). Regeneration of the soil was mentioned as one of the main reasons for crop rotation and was applied in a more or less defined sequence. Alternatively: when interviewees saw the yield of their crop declining over a sequence of years, on a specific field. Most farmers interviewed would apply a crop rotation based on their own experience and experimenting. Not all farmers would follow a clear and closed crop rotation and therefore results listed below only show examples of crop rotations mentioned by most farmers (Table 18, N=47). Most popular and mentioned is the crop rotation with maize occurring once every 4 years and cassava the other 3 years (23 % of respondents), the same rotation but replacing maize with sorghum was mentioned by 19% of the respondents. Pigeonpea was used by 15% to “regenerate” the soil after cassava followed by sorghum and cassava afterwards (personal communication, Gafaria and Nacuaca). The preference of replacing cassava the next year by a cereal crop was explained by farmers because of the difference in land preparation: for cassava ridges or heaps were formed during which the soil would be hand ploughed and turned up side down, the next year for sorghum the heaps/ridges would be flattened again (personal communication Gafaria and Nacuaca). Several farmers of Mugema however were convinced sorghum should be grown year after year to obtain maximum yield.

Table 18. Examples of crop rotations applied in the three villages under study (% mentioned, N=47)

Crop rotation Year 1 Year 2 Year 3 Year 4 Frequency mentioned (%)

Cassava maize cassava cassava 23 Cassava sorghum cassava cassava 19 Cassava pigeonpea sorghum cassava 15 Cassava cassava sorghum cassava 6 Cassava maize sorghum cassava 6 Cassava fallow fallow cassava 6 Sorghum sorghum sorghum sorghum 6 Maize bean maize bean 6

Mixed cropping of several crops during the same growing season was commonly found in the three villages for several crop combinations (Table 19). For example during planting of the cassava stalks sweet potato stalks were planted in between the stalks and harvested between June‐ July. Older cassava was cropped together with velvet bean (Mucuna pruriens): the bean uses the tall grown stalks of cassava to grow and ripen. Sorghum was often grown with pigeonpea. At none of the survey farms strip cropping was identified. Distribution of the mixed crops presented should be interpreted as follows: in a mix crop system with e.g. sorghum and pigeonpea for every pigeonpea plant 3 sorghum plants are found (sorghum: pigeonpea = 3:1). To calculate yields ha‐1 these ratios were used to recalculate yield for a hypothetical mono crop.

Table 19. Examples of mixed cropping in the three study sites (% mentioned by farmers, N= 45) and example of distribution of mixed crops found in the field by direct observations in the field

Mixed cropping Frequency mentioned (%) Distribution in field (crop:crop:crop) Cassava Velvet bean 29 4 1 Sorghum Pigeonpea 27 3 1 Sorghum Maize Pigeonpea 20 2 1 1 Groundnut Pigeonpea 11 4 1 Sorghum Maize 11 3 1 Cassava Sweet potato Cowpea 9 1 1 1 Cassava Maize Pigeonpea 7 4 1 1 Pigeonpea Cowpea 7 2 1

Residue management For all three crops investigated in the second and third round of the research (cassava, maize and sorghum), 3 main possible postharvest residue management practices were identified: 1. Heap and burn residues, 2. Burn whole field and 3. Incorporate residues. As a common practice farmers would dig gutters around their field to prevent the fire from spreading to their neighbour fields. At all sites all three residue management options were found, not in equal frequency, but not differing significantly comparing sites. More farmers in Gafaria seemed to be incorporating crop residues (Table 20). Of 19 farmers interviewed in the second round of survey, 68% indicated to be burning the cassava remainings after harvest and before preparing the land for a new crop. Reason given for this was the ‘difficulty of letting cassava rot in the field “: the perception of 85% of the farmers interviewed that cassava did not decay and residues would interfere with land preparation in the next cropping season, 42% of them used the ashes after burning: replacing salt in cooking and 32% incorporated cassava as part of a practise to “feed” the soil. Parts of the cassava incorporated back in the soil or in other cases burnt were: unused cassava stalks, older leaves and twigs. Leaves are commonly used as a vegetable and therefore only older more inedible leaves were returned or burned. Sorghum was incorporated by 27% of the interviewees while 63% replied sorghum not “to be possible to rot in the field” for same reasons mentioned above: 53% of them heaped and burn and remaining 10% set the field on fire (Table 21).

Table 20 Percentage of farmers complying with crop residue management options listed below at the three sites (=74)

Mugema Nacuaca Gafaria

% incorporating residues 48 33 60 % burning field 17 17 25 % heap and burn 42 63 25 % using manure 8 8 0 % using fertiliser 000

The highest amount of farmers (47%) to be incorporating plant residues was found for the crop maize. 33% of them heaped and burned the maize remainings and the rest set the whole field on fire. Overall the idea existed that maize rotted easier in the field and that it could feed the soil. During the first round of interview an observation was made on preparing the fields for cassava: farmers would form heaps or ridges by incorporating grass. 89% of farmers replied positive on conducting above observation and told that it was their only way to keep the soil productive.

Table 21. (Crop) residue management of cassava, maize, sorghum and grass followed by farmers of the second round interview (N=19)

(Crop)residue of: Cassava Maize Sorghum Grass

Practice

Heap and burn 68 33 53 11

Burn field 20 10

Incorporate 32 47 27 89

Although in Mugema, Nacuaca and Gafaria pigs were owned by 10, 8 and 8 farmers involved in the survey only 1 farmer in Mugema was using the manure to fertilise their field. Other farmers upon asking why they weren’t using pig manure would respond not having horticultures so the manure would not be useful (3 out of 25) the remaining would not know how to use it, and therefore left it unattended (N=23). Goats were owned by farmers in Mugema (n=1) and Nacuaca (n=7) but not in Gafaria. One farmer of Mugema and two from Nacuaca were using the goat manure as fertiliser. Same reasons were mentioned by the farmers leaving the manure unattended: no horticultures (n=1) and no knowledge how to use (n=4). None of the farmers involved in the survey was using fertiliser.

Summary This section focused on socio‐economic, production activities and managerial aspects variability found at village level. The three villages visited during the rapid farm characterisation were used of this analysis. Although not located far from each other (± 30‐40 km between villages) some important differences were found comparing Mugema, Nacuaca and Gafaria. At village level, the total income out of agriculture was found lower in Gafaria compared to Nacuaca (878 compared to 12286 and 5853 MZN). Brewing of Ortega (an alcoholic beverage made of cassava and sorghum flour) was found much more prominent in Gafaria, and made up a for a large part (37%) of the average total income from farming. Estimated maize and cassava production per household was also found lower compared to Nacuaca and Mugema. In overall the highest amount of months without food security were found in Gafaria.

3.2 Between farm variability This section analyses results found at village level: comparing between and within the villages Mugema, Nacuaca and Gafaria for socio‐economic characteristics, production activities and managerial aspects.

A Principal Component Analysis (PCA) done on the entire sample of complete farm surveys from the three villages (N= 71) and 22 variables resulted in eight principal components with eigenvalues greater than 1. Combined explaining 71% of the total original variability in the dataset, derived important variables are listed in Appendix Table A 30. The selection for the number of factors to be retained followed Kaiser’s criterion. Considering proxies from PCA, cluster membership and a preliminary characterisation made during fieldwork with the help of experts, four farm types were identified (see Table 22). Figure 11 illustrates the contribution of farm products listed to the income from farm per village per farm type, Table A 11 in the Appendix provides some additional information on variables per farm type, per village.

3.2.1 Short characterisation per farm type Farm type I can be characterized as the “best‐off” farmers. Farmers that grow a higher number of different food crops, own livestock (goat(s) and/or pig(s)) and a higher number of animals (Table 22). These farmers have the highest income from animal selling compared to other farm types. All grow maize and a high percentage of beans. Mostly income is generated by farming, comparable to Type II farmers, but higher than Type III and IV farmers and consist out of complete families (husband and wife present), working together on the fields and having a relatively high literacy rate.

Farm type II looks similar to farm Type I in income generating strategies but differs in livestock ownership and amount of different crops grown (less). Income from animals is less compared to Type I farmers but comparable to farm Type III and IV. They can be defined as ‘in between’ farmers.

Farm Type III consists of farmers relying mostly on off‐farm income and having a lower total income compared to Type I. Literacy rate is relatively high and farm Type III is composed of relatively younger (male) household heads. Use of hired labour is lower compared to farm Type I, and the lowest number of different crops is grown compared to all typologies. Subsequently a lower percentage grows maize, beans, cassava, cowpea and sorghum. In addition a lower percentage owns livestock, the number of animals and the income earnings from animals is less compared to farm Type I.

Farm type IV consists out of single women households, widow or separated and have in average an older household head compared to farm Type III. Income generating strategies are comparable to farm Type I and II where most income is derived from farm activities. Income from crop and animal selling is lower compared to Type I. A smaller percentage owns livestock and the number of animals is lower compared to farm Type I. The amount of months with food deficiency is higher compared to farm Type I. Farm work is done by less persons as the family consists out of less members compared to Type I and II

Table 22. Household indicators of the selected variables for cluster membership per farm type for Mugema, Nacuaca and Gafaria (N=71)

Income Own Distribution Age Off /non Food Number Grow Grow Farm Married from Number Small of households N hh farm deficiency of maize (% beans (% types (% of hh) crops of animals stock (%) head income (months) crops of hh) of hh) (MZN) (% of hh)

Mugema 1 18 4 44.3 100 3948 13 0.5 13.0 80 9.0 100 100 2 50 11 49.2 100 9459 3 0.5 9.0 50 6.4 100 80 3 23 5 34.6 80 267 54 1.4 3.0 40 4.6 80 60 4 9 2 48.0 0 835 30 2.0 5.0 100 6.5 100 100 SED (Farm type) 3.2 8 2462 7 0.3 1.5 10 0.4 6 8

Nacuaca 1 46 11 44.0 100 18049 2 1.3 13.5 100 8.6 100 80 2 25 6 31.0 100 3610 5 1.2 8.3 50 6.2 100 30 3 21 5 39.8 100 188 20 1.0 4.0 40 5.2 100 20 4 8 2 53.0 0 100 0 2.0 5.0 0 7.0 100 0 SED (Farm type) 2.8 6 3735 4 0.7 1.5 10 0.5 4 10

Gafaria 1 24 6 48.0 100 1665 32 3.7 11.2 100 9.7 100 50 2 32 8 45.8 90 1679 23 4.4 6.3 40 7.6 60 60 3 20 5 36.8 100 1436 89 2.8 9.2 60 6.6 60 0 4 24 6 51.0 0 1022 0 3.7 8.0 70 7.7 70 70 SED (Farm type) 2.8 9 154 9 0.3 1.4 10 0.4 10 10 SED (Sites) 1.7 5 1516 4 0.2 .8 6 0.2 4 6

Significance (P values) Village (V) ns ns 0.038 0.070 <0.001 ns ns 0.044 0.015 0.033 Farm Type (FT) 0.093 <0.001 0.042 <0.001 0.060 0.026 0.005 <0.001 0.003 0.027 Interaction V * FT ns ns ns ns ns ns ns ns ns ns

Figure 11. Their contribution and sources of income to total income for Nacuaca farmers categorized in farm type 4 indicated not selling any of the lists.

4. Dynamic simulation of the impact of management on cassava yields

4.1 Introduction Chapters before have illustrated variability between villages as well as within villages. The aim of the dynamic simulation is to study the effect of management decisions (e.g. crop residues use, manure and fertiliser application) on soil fertility and subsequently on simulated yields. Simulations were run for the entire set of soil fertility data available from individual fields (N=90) for Nacuaca and Gafaria, under different management scenario’s. Scenarios were designed to fit a framework of a range of feasibility as derived from rapid farm characterisation and the farm typology and aimed at yield increasing options. This means that although a large variation exists in farm management practices, taking the range of these management options should capture all feasible possibilities for each individual field suitable for simulation. Initial model assumptions used for all scenario’s are listed in Table A 12 in the Appendix and an overview of scenario input is presented in Chapter 2.6.2. Before presenting the results of the simulations on management, initial yields will be presented to form the starting point of the simulations will be discussed.

4.1.1 Initial simulated yields: Base run Overall simulated cassava yields (without any amendments) had a satisfactory range for Gafaria (2.0 to 10.8 t/ha fresh cassava yield) but seemed to be a bit low for Nacuaca (1.8 to 3.9 t/ha fresh cassava). However, because further site‐specific calibration of the model was not really possible because of lack of sufficient on field harvest estimations, simulated ranges in the model were accepted for scenario exploration purposes. Estimated, calculated and simulated yields are presented in the Appendix in Table A 9, for 10 farmers (Figure 12). It shows a discrepancy in between the range of: Estimated by farmers

Figure 12. Predicted versus observed cassava yields under no‐input management. Predicted cassava yields are generated using the FIELD model. Dashed lines represent 1:1 and 2:1 lines. G= Gafaria.

To be able to account for inherent soil fertility variability per field and to have a better understanding of yield variability within villages, classification in quartiles was introduced. Per village simulated yields per field were grouped into three groups: lowest yielding fields (first quartile), average yielding fields (second and third quartile) and highest yielding fields (fourth quartile) (Figure 13). Averages of the analysed soil properties are listed below per quartile and per village in Table 23.In Nacuaca soil organic carbon content (SOC g/kg) decreased from the 4th quartile towards the 1st quartile and differed significantly between the 3 quartiles (P<0.001). In Gafaria the 4th and 2nd & 3rd quartile were significantly higher compared to the first quartile but did not differ from each other. pH was highest for 1st quartile fields in Nacuaca compared to 2nd & 3rd quartile fields (P<0.02) but not significantly higher than quartile 4. For Gafaria no significant differences were found comparing pH between quartiles. Exchangeable K values were significantly highest for 4th quartile fields in both Nacuaca and Gafaria, 1st and 2nd & 3rd quartile fields did not differ from each other within villages. Simulated yields were significantly higher in Gafaria compared to Nacuaca and can be partly explained by the higher inherent amount of ‐1 exchangeable potassium (K in cmol(+) kg ) present in the soil (an average of 0.46 compared to 0.21 ‐1 cmol(+) kg , P<0.001) and this accounted for 37.5% of explained variance in yields (Appendix Table A 4).

Note on the use of quartiles As seen in Figure 13 below, 1st and 2nd & 3rd quartile yields represent more stabile and depleted soils (lower initial yields and little change in yield over time) while 4th quartile fields in both villages appear less stabile and decline at faster rate over time. Individual fields summarized for soil property characteristics are listed in Table 23 are the basis for further simulation scenarios presented in following subchapters. Soil texture (% of clay) is at the basis of calculations on inert carbon stocks in the FIELD model. At this state of research the quartiles cannot be explained by shared characteristics other than the parameters that influence FIELD output; they are spread over farmers of both villages and all four farm types (Appendix Table A 20). Also they cannot be explained by distance to the homestead as done for example by Rowe et al. (2006), where outfields appeared more dynamic in response to manure compared to infields. Dividing the fields of this study in home fields, middle fields and outfields per village did not explain grouping of the fields into one of the 3 quartiles (Appendix Table 21). Age (relative age after clearing of field) was not lower for fields grouped in the 4th quartile (Appendix Table 22). It would be interesting to dedicate further research into exploring the complex interactions between inherent geology, geomorphology and managerial practices and soil heterogeneity.

C D

Figure 13. Simulated average fresh cassava yields over time (25 years) for the first model run (no inputs) for fields classified as low yielding (1st quartile), medium (2nd and 3rd quartile) and high (4th quartile) for fields sampled in Gafaria (A, N=37) and Nacuaca (B, N=52). Range of simulated cassava yields (t/ha) for Gafaria (C) and Nacuaca (D). The box‐and‐whisker diagram include the range of 50% of the samples (rectangle), the median (cross bar), maximum and minimum (extremes of lines).

As seen in Figure 13 above after 25 years of continuous cassava cultivation, simulated yields in both villages decline from their initial starting points. In Gafaria simulated yields dropped on average by 24% for 2nd & 3rd quartile fields (stabilizing at 3.1 t ha‐1), 27% for 4th quartile fields (stabilizing at 8.0 t ha‐1), while yields of 1st quartile fields did not change and remained at around 2 t ha‐1. Yields in Nacuaca did not change over time for 1st and 2nd & 3rd quartile fields and remained at 1.8 and 2.3 t ha‐1. Yield of 4th quartile fields dropped in average by 22% till it stabilized around 3.2 t ha‐1.

Table 23. Soil fertility properties for the different fields divided per quartile based on simulated yields in FIELD (N= 90) in the 2 villages

-1 Particle size Exchangeable cations (cmol(+) kg ) Village Quartile Clay Sand SOC pH N P ECEC K+ Ca2+ Mg2+ (%) (%) (g/kg) (1:2:5) (g/kg) (mg/100g)

1st quartile 10.2 83.0 6.7 6.1 1.02 3.8 10.0 .11 4.9 1.6 2nd & 3rd quartile 13.2 80.8 8.4 5.8 1.06 3.2 8.8 .20 3.6 1.6 4th quartile 14.8 76.7 11.9 5.9 1.10 3.6 10.4 .33 4.7 1.9

Nacuaca Total 12.9 80.3 8.9 5.9 1.06 3.5 9.5 .21 4.2 1.7

P< ns ns 0.001 0.020 ns ns ns 0.001 0.015 ns

1st quartile 23.2 71.4 8.2 6.0 1.27 2.2 10.4 .36 5.5 1.6 2nd & 3rd quartile 24.2 66.4 12.2 5.6 1.37 2.5 9.9 .45 4.0 1.4 4th quartile 31.4 60.9 10.6 5.8 1.39 1.4 10.1 .59 4.7 1.7

Gafaria Total 25.9 66.1 10.8 5.7 1.35 2.2 10.1 .46 4.5 1.5

P< 0.063 0.078 0.038 ns ns ns ns 0.005 ns ns

P< (Village) 0.001 0.001 0.001 ns 0.01 0.04 ns 0.001 ns ns P< (Village x Quartile) ns ns 0.001 ns ns ns ns ns ns ns

SED (general) 1.1 1.2 0.4 0.05 0.04 0.3 0.3 0.02 0.2 0.08

4.1.2 Nutrient yield response Single nutrient applications were simulated using FIELD (e.g. N:P:K 100:0:0) to access yield response per kg applied nutrient. Simulations were done for a period of 10 years per individual field. Yield responses per kg applied nutrient varied between villages and quartiles and ranged from 0.2, 0.8, and 20.7 kg fresh yield per kg N,P and K applied for the first quartile fields in Nacuaca till 28.6, 90.6 and 34.1 kg fresh yield per kg N, P and K applied in the 4th quartile fields in Gafaria. As can be seen in Table 24 below the average yield response of N, P and K (in kg fresh cassava yield/ kg fertiliser applied) differs comparing the villages for N and P but not for applied K. Fields of Gafaria in general had a higher yield response per kg N or P applied compared to Nacuaca. Within villages and comparing between quartiles the 4th quartile fields had the highest yield response to N and P application. Comparing between the type of nutrient (N, P or K applied) field of Nacuaca had a higher yield response per kg potassium applied (ranging from 21‐37) while fields of Gafaria had a higher response to phosphorus application.

Table 24. N, P, K nutrient response: kg fresh cassava / kg N, P or K applied

Fertiliser response kg fresh cassava/ kg fertiliser applied Village N P K N Nacuaca First quartile 13 0.2a 0.8a 20.7a 2nd and 3rd 26 0.2a 6.7a 26.6ab 4th quartile 13 1.5b 25.3b 36.9b

P < 0.034 0.003 0.05 SED 0.21 2.78 2.41

Gafaria First quartile 9 0.2a 5.8a 19.8 2nd and 3rd 19 4.0a 40.0a 27.6 4th quartile 9 28.6b 90.0b 34.1

P< 0.001 0.002 ns SED 2.78 9.20 2.90

P < (Village) 0.001 0.001 ns P < (Quartile x Village) 0.001 0.02 Ns

4.2 Management simulations A large variation of crop residue practises was found not so much between villages but more within, where farmers adopted a wide range of practises to keep their fields productive. In Mugema, Nacuaca and Gafaria 48, 33 and 60% of the respondents of the rapid farm characterisation incorporated crop residues in the soil and 89% of all farmers asked included grass in preparing of the ridges/hills for cassava. There was an increasing awareness of the advantages of crop residue incorporation as a result of extension workers and farmers sharing new knowledge with their neighbours. Still a large part of the same respondents would use fire for clearing of the field: 59%, 80% and 50% in Mugema, Nacuaca and Gafaria showing multiple practises to be possible within a household (both burning as incorporation of residues, Table 21).

4.2.1 Cassava residues Simulations were run for incorporation of cassava remaining in the field. Currently, as discussed in Chapter 3.2, cassava residues are only incorporated in the soil by 32% of the farmers in the three villages. As a common practise, besides using the stalks for next growing season, cooking salt is being produced from the cassava stalks. Two scenarios of incorporation were compared: 10% cassava residues remaining on the field (base run) and 55% cassava residues remaining. In the last scenario the use of new plant material was taken into account and leaving the remaining in the field. Nutrient content of the cassava residues was estimated at 1.2%N, 0.2%P and 0.7%K (Putthacharoen et al., 1998) corresponding to 12kg N, 2kg P and 7kg K per ton DM residues applied.

Difference in fresh cassava yield (t/ha) comparing 55% with 10% cassava residue remaining in the field are shown below in Figure 14. As can be seen the difference between the two scenario’s are highest in Gafaria and in general at the 4th quartile fields (P<0.002, Table 25). The difference in yield ranged in average from 91 to 148 t/ha in Nacuaca and 110 to 782 t/ha in Gafaria for the tenth year of simulation.

Highest yields could be found at the 4th quartile, this automatically leads to a higher quantity of cassava residues remaining in the field.

Figure 14. Difference in fresh cassava yield (t/ha) comparing 10% with 55% cassava residue management for Gafaria (A) and Nacuaca (B) per quartile. Note the different y‐axis.

Table 25. Difference in fresh cassava yield (t/ha) comparing 55% with 10% cassava residue management for Nacuaca and Gafaria per quartile, simulated for year 3 and 10

Difference in yield (kg ha-1) comparing 55% with 10% Village Year 3 Year 10 N Nacuaca First quartile 13 93a 91a 2nd and 3rd 26 122a 116a 4th quartile 13 262b 148b

P < 0.001 0.002

Gafaria First quartile 9 106a 110a 2nd and 3rd 19 283a 313a 4th quartile 9 838b 782b

P< 0.001 0.001

P < (Village) 0.001 0.001 P < (Quartile x Village) 0.001 0.001

4.2.2 Maize residue The main purpose of the model was the option to explore the effect of different management scenarios on cassava yield in terms of feasibility and efficiency.

Maize residue incorporation was simulated in different applications ranging from 0 to 5 Mg ha‐1 year‐1 and was based on the rapid farm characterisation and follow up surveys and maize yield estimations (Chapter 2.6.2). To simulate these different strategies an organic amendment was included in the model with an assumed quality of 1%N, 0.2%P and 1.0%K a median value based on Dass et al. (1979). This corresponds to 9 kg N, 1.8 kg P and 9 kg K per ton DM maize residue applied and an incorporation fraction of 0.9 (Table 5, Chapter 2.6.2). Running the simulation for all fields in both Nacuaca and Gafaria, for 10 years showed a linear increase of between 155 ‐256 kg and 189‐449 kg fresh cassava yield per ton ha‐1 maize residue applied significantly higher (P<0.05) in Gafaria compared to Nacuaca (Table 26 and Figure 15).

Table 26. Mean yield increase (in kg ha‐1) per t DM residue applied for the 3rd and 10 year presented as averages per quartile per village

Mean yield increase (kg ha-1) per ton residue applied Village Year 10 N Nacuaca First quartile 13 169.7a (± 41) 155.5a (± 17) 2nd and 3rd 26 255.8a (± 76) 188.6a (± 53) 4th quartile 13 355.4b (± 164) 236.5b (± 106)

P < 0.001

Gafaria First quartile 9 192.3a (± 51) 188.7a (± 49) 2nd and 3rd 19 315.3ab (± 147) 298.3a (± 159) 4th quartile 9 443.9b (± 345) 449.0a (± 234)

P< 0.006

P < (Village) 0.001 P < (Quartile x Village) 0.046

Figure 15. Fresh cassava yield (t DM/ha) plotted per quartile against maize residue applied for Gafaria (A) and Nacuaca (B) for the tenth year of application

4.2.3 Manure application In general most farmers would only consider the use of manure if they would produce horticultural crops. It was a widespread thought across farmers that we encountered consistently during field work and discussions with farmers. Often pigs were kept in confined space during the day and goats stayed in their kraal or returned at the set of evening. Generally manure was piled up and removed when necessary and disposed of as a piled heap together with household waste. Only one farmer was using goat manure on rice production and was sometimes collecting goat manure from neighbours. However he was not sharing his knowledge on manure with others and remained the only one using the source of nutrients. Clearly a discrepancy of knowledge existed amongst all three villages visited about how to use the manure of animals in possession.

Small stock manure application was simulated in application rates of 0 to 1 t DM ha‐1 year‐1 (0, 0.125, 0.25, 0.5, 0.75, 1.0 t DM ha‐1). Nutrient concentrations in manure were assumed to be 2.0 %N, 0.4%P and 2.0%K derived from small stock manure nutrient content presented by Dougill et al. (2002). This corresponds to 20 kg N, 4 kg P and 20 kg K per ton DM manure applied. No significant difference was found for mean yield increase (kg ha‐1) per t DM manure applied comparing villages in the third year, but after stabilization in the 10th year yield increase was significantly higher in Gafaria (see Table 27 and Figure 16). Simulated yields comparing no manure and manure application showed an increase of 402‐ 482 kg and 433‐1034 kg yield increase/ ton DM small stock manure applied for Nacuaca and Gafaria subsequently.

Table 27. Mean yield increase (kg ha‐1) per ton DM manure applied for the 3rd and 10 year presented as averages per quartile per village

Mean yield increase (kg ha-1) per t DM manure applied Village Year 3 Year 10 N Nacuaca First quartile 13 409.4a (± 43) 402.0 (± 42) 2nd and 3rd 26 437.6a (± 40) 426.0 (± 40) 4th quartile 13 611.8b (± 140) 482.0 (± 182)

P < 0.001 ns

Gafaria First quartile 9 437.0 (± 51) 433.4 a (± 62) 2nd and 3rd 19 538.8 (± 205) 514.8 a (± 240) 4th quartile 9 628.8 (± 366) 1033.8 b (± 510)

P< ns 0.001

P < (Village) ns 0.001 P < (Quartile x Village) ns 0.001

(t fresh/ha) (t fresh/ha)

Figure 16. Fresh cassava yield (t/ha) plotted per quartile against small stock manure application (t fresh manure/ha) for Gafaria (A) and Nacuaca (B) for the tenth year of application

4.2.4 Fertiliser use To simulate the effect of different fertiliser application rates an ´available` NPK fertiliser was used: i.e. a fertiliser package was chosen to run in simulations that was known to farmers. NPK fertiliser application (100:22:83 N:P:K) in kg/ha was simulated for all fields in both villages. Fertiliser response differed comparing between villages and between quartiles. Ranging from 37 to 65 kg yield increase per kg fertiliser added in Gafaria and between 49 and 67 kg yield increase per kg fertiliser added in Nacuaca after stabilization (Table 28 and Figure 17). After stabilization (year 10) yield increase was not significantly higher in Gafaria compared to Nacuaca. Within Nacuaca, yield increase was highest on 4th quartile fields, while within Gafaria yield increase was lowest for 4th quartile fields. Figure 17 below shows yields of 4th quartile fields in Gafaria to reach maximum attainable yield at a lower NPK application compared to 2nd & 3rd and 1st quartile fields.

Table 28. Mean yield increase (kg ha‐1) per kg fertiliser applied (100: 22:83) for the 3rd and 10 year presented as averages per quartile per village

Mean yield increase (kg ha-1) per kg NPK applied Village Year 3 Year 10 N Nacuaca First quartile 13 49.3a 49.4 a 2nd and 3rd 26 57.5b 60.3 b 4th quartile 13 66.5c 66.1 b

P < 0.001 0.014

Gafaria First quartile 9 53.1b 59.2b 2nd and 3rd 19 65.1b 67.2 b 4th quartile 9 36.7a 44.0 a

P< 0.001 0.001

P < (Village) 0.001 ns P < (Quartile x Village) 0.001 0.001

Figure 17. Fresh cassava yield (t/ha) plotted against NPK65 application (kg/ha) (100:22:83) for fields divided into 3 quartiles (from top to down: 4th, 2nd & 3rd quartile and 1st quartile fields) for Gafaria (A) and Nacuaca (B) for the

tenth year of cultivation 4.2.5 Cost benefit analysis of fertiliser use A partial gross margin analysis of both villages for all fields divided per quartiles showed a difference in the benefit for NPK fertiliser. Fertiliser benefit is expressed in VCR (Value Cost Ratio) in which a VCR of 2 or above translates into a yield increase of 1.2 t/ha for cassava, and a yield increase of 0.5 t/ha for maize and is seen as acceptance threshold for farmers (Kelly, 2006) . 1st quartile fields in both villages had in general a lower response to fertiliser applications below 250 kg /ha. 2nd & 3rd quartiles appeared intermediate in response with most fields above VCR 2 around 250 kg and 150 kg/ha NPK application for Nacuaca and Gafaria respectively. Lowest applications were needed to reach a VCR of 2 or above for 4th quartile fields with application rates starting at 25kg/ha for both villages until 100 kg/ha for Gafaria and between 25 and 200 kg/ha for Nacuaca (Table 29).

Below Figure 18 serves as an illustration of a Value Cost Ratio for the same fields, same fertiliser package and same applications (100:22:83 N:P:K) for simulated maize yield (using QUEFTS) and simulated cassava (FIELD). It can be used to show the result of an allocation of the fertiliser, previously used in simulations for cassava, on a higher value crop such as maize. As can be seen while for cassava for all three quartiles VCR floats above and below threshold of VCR 2 (in dotted line), VCR for maize stays well above 2 for both villages for all three quartiles.

Table 29. Partial gross margin analysis for NPK fertiliser use (100:22:83 N:P:K) for simulated fresh cassava yields in both villages, average fields with VCR ≥ 2 (%) per quartile.

Fertiliser application (NPK, kg/ha)

N 25 50 75 100 150 200 250 350

Nacuaca VCR ≥ 2 (%) First quartile 9 31 39 31 31 39 39 62 100 2nd + 3rd 19 65 58 54 58 69 81 89 69 4th quartile 10 92 92 92 92 92 100 77 0

Gafaria VCR ≥ 2 (%) First quartile 12 33 22 22 22 44 56 56 78 2nd + 3rd 26 74 74 84 95 100 90 74 11 4th quartile 13 80 90 90 80 30 0 0 0

Figure 18. Value Cost Ratio (VCR) plotted against fertiliser application (kg N/ha) (100:22:83 N:P:K) of maize for Gafaria (A) and Nacuaca (B) and for cassava in Gafaria (C) and Nacuaca (D). Note the important differences in the scales of the y‐axis.

4.2.6 Improved management practises Finalizing nutrient management several combinations were made of crop residue incorporation, manure application and fertiliser application. Figure 19 below illustrates yield increase (%) comparing NPK application without any extra amendments with improved NPK application (added 0.5t/ha manure and 1t/ha pigeonpea residue, reasoning is explained in Chapter 2.6.2). Pigeonpea was chosen to represent a high quality nutrient rich residue legume with a high nutrient content of 4%N, 0.4%P and 4%K, corresponding to 36kg N, 3.6 kg P and 36kg K. For Nacuaca 4th quartile fields show little responsive to improved management practices while 1st and 2nd & 3rd quartile fields show high response at low NPK application rates. For Gafaria fresh cassava increase because of improved management dropped from almost 40% yield at zero NPK application rates to below 10% at higher NPK application rates showing a decline in the effect of improved management (combining both inorganic fertilisers with organic fertilisers) at higher NPK application rates in both villages.

A B

Figure 19. Relative yield increase (%) comparing NPK application (normal) with improved fertiliser management (NPK application with 0.5t manure and 1t pigeonpea residues) for Gafaria (A) and Nacuaca (B)

Figure 20 below shows fresh cassava yield over time (10 years) for different lower input scenarios. It allows for comparisons between a low dose of fertiliser (25kg/ha), crop residue application, manure application and a combination of both. Fertiliser package application (N:P:K 100:22:83) has, as expected, the highest impact on fresh yield compared to the other low input scenarios. The time interval of 10 years shows a decline in yield over the beginning of years towards stabilization in the tenth year. State of stabilization is reached earlier for lower quartile fields (2nd & 3rd and 1st quartile yields).

Figure 20. Fresh cassava yield (t/ha) plotted against time (years) for different low input management practices (no input, 1t/ha manure application, 1 t/ha maize residues, combined manure and maize residues and 25kg/ha fertiliser application plotted for each individual quartile per village. Gafaria: 4th quartile (A), 2nd & 3rd quartile (B), 1st quartile (C) and Nacuaca: 4th quartile (D), 2nd & 3rd quartile (E), 1st quartile (F) for a period of 10 years, year zero represents initial yield (base run). 69

4.3 Discrepancies simulations and observations The villages selected for the study showed important and interesting differences for further research in terms of socio‐economic and bio‐physical aspects within a relative small distance from each other (± 30km). Farm surveyed revealed socio‐economic characterisations of households that were in agreement with the results of the baseline study conducted in the area (OVATA mid term, 2004).

Gafaria seemed to be less off compared to Nacuaca in the socio‐economic analysis at village level (Chapter 3.2). Not only was the total income out of agriculture less compared to Nacuaca (878 compared to 12286 MZN) other wealth indicators such as production of maize, beans and cash crop production seemed to be lower in Gafaria compared to Nacuaca. Brewing of Ortega (an alcoholic beverage made of cassava and sorghum flour) was found much more prominent in Gafaria and considered by extension workers as a sign of higher poverty. Estimated average maize and cassava production per household was also found lower in Gafaria (1.2 t cassava and 0.2 t maize) compared to Nacuaca (3.3 t cassava and 1.3 t maize) (Table 17). Calculated yields in t/ha from field work were estimated to be higher for Nacuaca compared to Gafaria (Appendix Table A 9).The period of food insecurity was found to be longer Gafaria (Table 8).

In contrast to expectations from the socio‐economic analysis on village level done in this thesis (Chapter 3.1.2) fields sampled in Gafaria had higher soil fertility compared to fields of Nacuaca. Important soil ‐1 fertility variables such as exchangeable potassium (K in cmol(+) kg ), SOC (g/kg) and N (g/kg) were found to be significantly higher in fields sampled in Gafaria and predicted higher simulated yields by the FIELD model (2.0 to 10.8 t/ha fresh cassava yield) compared to Nacuaca (1.8 to 3.9 t/ha fresh cassava). As summarized in Appendix Table A 16, both simulated cassava and maize yields (t/ha) were significantly higher compared to yields in Nacuaca (P<0.05).

We could not explain why the village with higher soil fertility (Gafaria) had a lower farm income, comparing Gafaria and Nacuaca. During field work it was noticed that fields of farmers in Gafaria were not as big as fields of farmers in Nacuaca. GPS measurements were taken to assess field size and to compare between villages. Cassava fields in Gafaria were found significantly smaller in total size (ha) and corrected for field size per available labour (ha labor‐1) compared to fields in Nacuaca (Table A 15). In addition a negative relation was found between an increase in clay percentage and field size in ha (Figure 21). After calculation cassava household production (productivity of cassava x size of field) did not show any significant differences comparing villages (Table A 17). This could correct partly for the higher simulated yields found in Gafaria; the soil might be more fertile, the fields are smaller as a result of a higher clay percentage.

In informal meetings farmers mentioned having a shorter period of preparing the field compared to farmers of Nacuaca illustrated by the cropping calendar presented in Table 7. Both villages depend solely on human labour (often referred to as força = power, by interviewees) was the limiting factor for field size as well as available labour. Start of a downwards spiral could be observed in some farm households in Gafaria: bad health or a reduction in available labour made some farmers have smaller fields than preferred, yields were not sufficient and some farmers even mentioned consuming seed

70 material of maize and beans because of food insufficiency and harvesting cassava pre‐mature (> 6 months). After a lesser harvest, fields have to be prepared again starting from October. Being in less good health interviewees replied ending up with smaller fields again. The kick‐off of these downwards spirals can be found in dry years in which the farmers of Gafaria can not timely prepare the fields: the higher clay content makes it more difficult to prepare the ridges for cassava compared to a more sandy soil in Nacuaca. Farmers of Gafaria seem more depending on the first rains to start preparing fields.

4 Gafaria Nacuaca 3.5

2.5

1.5 Field size (ha) size Field

0.5

0 0 102030405060 Clay (%)

Figure 21. Field size (ha) plotted against clay content (%) for fields in Gafaria and Nacuaca (N=90)

5. Discussion

5.1. Evaluating the effect of farm management on cassava yield In the farming systems observed in the study sites cassava is grown without fertiliser or manure inputs, however some households do incorporate crop residues but on a small scale only. As reported by Sittibusaya (1993) if cassava is grown continuously on the same soil, without adequate fertiliser or manure, soil productivity may decline due to nutrient depletion and soil erosion. Cassava yields dropped from 26‐30 t ha‐1 to 10‐12 t ha‐1 after 20‐30 yrs of cassava cultivation. In previous research the main driver of yield decrease over time, soil C, was recorded to have a faster decay for sandy soils (Zingore et al., 2007). In that research the equilibrium of yield stability was reached at different times for soils of different textures. This is comparable to this research done where yields of more clayey textured Gafaria had a higher decline in yield over time compared to the more sandy soil type fields of Nacuaca

5.1.1 Incorporation of crop residues Yields under two scenarios of remaining cassava residues were compared: 55% remaining and 10% remaining residues. The difference in yield ranged in average from 91 to 148 kg/ha in Nacuaca and 110 to 782 kg/ha in Gafaria for the tenth year of simulation. Because highest yields could be found at the 4th quartile, this automatically led to a higher quantity of cassava residues remaining in the field. Research done by Howeler (2000) showed that incorporation of plant tops at harvest slowed down the yield decline over time.

The effect of maize residue application on cassava fresh yield was analysed with the help of the simulation model FIELD. Addition of maize residues had a positive effect on fresh cassava yield with an increase in yield of between 156 – 237 kg/ha in Nacuaca and 189‐449 kg/ha in Gafaria over the three quartiles used per ton maize residue applied.

Maize and cassava residues are examples crop residues that can be used by the farmers in the research village and could be an alternative to burning. Cassava litter fall is an important source of nutrients: it contains highly mineralisable N due to a high %N (2.5%) and a low lignin content. It also provides as an extra food source for the most food insecure households. Unless brought from outside the farming field, incorporation of crop residues are merely recycled within the system, unless derived from biological N‐ fixation by e.g. leguminous crops and can explain the limitations to yield response.

Burning of crop residues is still a common practice for farmers in the research villages. Advantages of burning can be listed as 1). an increase in pH from ash alkalinity 2). improved access to land preparation, sowing and planting and 3). reduction of pests and diseases. The improvement of soil fertility by crop residue burning, depends on the quality and quantity of ash released by burning the vegetation. Key disadvantages to burning are losses of important nutrients due to volatization of nitrogen, sulfur and phosphorus and potassium in smaller quantities (Kato et al., 1999). With a raising awareness about the side effects of burning for the environment and wasting of plant nutrients, fire‐free land preparation alternatives are being sought. Instead of burning of crop residues mulching or incorporation of slashed

73 vegetation can modify the soil environment for plants and microbial organisms. The organic material of residues serves as a carbon rich substrate that subsequently is decomposed to soil organic matter by soil micro‐organisms. This can result however in initially immobilizing a large fraction of the available soil nutrients (Braakhekke et al., 1993). As a benefit on the long term crop residues will add to the soil organic matter, water holding capacity and build up of nutrients present. The expected results of incorporated residues have to be seen on a longer time scale: ash can provide nutrients to the first crop after clearance, slowly decomposing organic material however will take longer.

Farmers can be considered somehow in between adoption of both practises: burning has been a common practise of ancestors and according to some farmers was even taught in schools. Incorporation of crop residues is currently being promoted by NGO’s active in the region which can help in the adoption of crop incorporating practises. Labour limitations however could have farmers decide to still burn instead of incorporation because the remaining stalks in the field can hinder land preparations (personal communication, three villages). Fermont (2009) states that potential additional problems may include the adventitious sprouting of woodier stem parts.

5.1.2 Manure application Simulated yields comparing no manure and manure application showed an increase of 402‐482 kg and 433‐1034 kg yield increase/ ton DM manure applied for Nacuaca and Gafaria subsequently (Table 27). Research done by Rowe et al., (2006) showed a less steady decline of maize yields over 40 years of simulation for fields after forest clearance with manure application, compared to those without. Continuing depletion of nutrients resulted in a more efficient response to applied manure and a lesser yield decrease over time compared to the start year: manure seemed to level of yield decline especially for infields (fields close to the house). Manure application could therefore help to increase cassava yields and over time reduce yield decline from continuous cultivation.

Previous research across sub‐Saharan Africa has showed that differences in farmer‐induced soil heterogeneity can be assigned due to differences in availability of nutrient resources, and manure in particular (Zingore et al., 2007). When farmers will start adopting manure application for crops, farmer induced zones of soil fertility are to be expected: farmers would have to make decisions on which field and which crop the manure will be used. Comparing between farmers it could create a different potential for food production per capita (Tittonell et al., 2009).

5.1.3 Use of fertiliser Cassava is known to be well adapted to a low pH, high levels of exchangeable aluminium (Al) and low concentrations of phosphorus (P) in the soil (Howeler, 2002). Because of these characteristics cassava is able to grow well on more poorer and degraded soils. Average yield response per kg applied nutrient was highly variable comparing villages and comparing quartiles. Increasing levels of potassium was most effective on soils in Nacuaca, while a higher response (increase kg fresh cassava / kg applied N, P or K ha‐ 1) for phosphorus was found in Gafaria (Table 24). Overall first and second quartile classified fields had a lower response and remained at a low yield level compared to the higher yielding fourth quartile fields. In the setting of this experiment fertiliser response was only affected by inherent soil fertility properties.

As studied before cassava is highly responsive to fertiliser applications, high yields can be obtained and maintained only when adequate amounts of fertiliser and/ or manures are applied, yields increased by (Howeler, 2002). Research done by Fermont (2009) showed a highly variable fertiliser response (‐0.2 to 15.3 t ha‐1). In this research high increase of yield was obtained with fertiliser applications, NPK fertiliser application (100:22:83 N:P:K) resulted in strong increases of simulated fresh cassava yields in both villages. Ranging from 37 to 65 kg yield increase per kg fertiliser added in Gafaria and between 49 and 67 kg yield increase per kg fertiliser added in Nacuaca after stabilization (Table 28). Yield response to fertiliser application was much stronger compared to yield response from organic amendment (155 ‐ 256kg and 189‐449kg increase in fresh yield/ t residue added in Nacuaca and Gafaria respectively) and yield responses from manure application (402‐482 kg and 433‐1034kg increase/ ton DM manure applied).

The selected fertiliser package (N:P:K 100:22:83) was, looking at the yield response per kg applied nutrient, not the most optimal choice of fertiliser. Yield response to kg N applied was very low for all quartiles in Nacuaca and 1st and 2nd & 3rd of Gafaria. Only 4th quartile fields of Gafaria had a quite high yield response to kg N applied. A different fertiliser package aimed at increasing phosphorus and potassium could be recommendable for 4th quartile fields of Nacuaca and 1st, 2nd & 3rd quartile fields in Gafaria. 4th quartile fields in Gafaria seem to require a full fertiliser package with an emphasis on phosphorus. The quartiles can serve as a tool for the development of fertility niches within the farm: eventually these could avoid ‘blanket’ fertiliser recommendations and aim at a more efficient fertiliser management.

Low/Medium technologies could include adaptations of best‐bet options such as i) combined use of inorganic and organic fertiliser, ii) targeted micro‐dosages of fertiliser and iii) intercropping and or crop rotations with dual purpose legumes . Improved fertiliser management was developed to increase yield response per kg fertiliser applied with the help of available nutrients from crop residues and manure. An increase in yield of 6, 21, and 35% was attained for 4th quartile‐2nd & 3rd quartile and 1st quartile fields in Nacuaca. For Gafaria yield increase due to improved management was even higher: 22, 34 and 40% yield increase for 4th quartile‐2nd & 3rd quartile and 1st quartile fields (see Figure 19).

Within farms fields classified as 4th quartile (highest yielding fields) had the highest yield response to fertiliser package application. Even at low application rates (e.g. 25 kg NPK/ha) investment showed profitable (VCR> 2, Table 29).

5.1.4 Feasibility of yield increase scenarios The feasibility of the proposed scenario studies for model simulation could be assessed with the help of farm types. As stated in the work of Tittonell (2008) household diversity and livelihood strategies may have implications for the design of technology interventions to target smallholders and the relative impact of changes in policy.

In general available crop residues and farm manure are currently underutilized by farmers, mostly from reasons derived by lack of knowledge. NGO’s such as World Vision are currently involved in promoting both practices (use of residues and use of manure) as a way to increase soil fertility and subsequently crop yield. Fertiliser application, although resulting in a much higher yield response, carries some drawbacks such as the requirement of capital to invest in fertiliser. Its application could therefore be more optimized by the addition of crop residues and manure. With current market prices however, it might be even more economically interesting to apply available fertiliser to maize fields instead of using it for cassava.

Simulations made for fertiliser application show high potential for cassava yield increase. For both villages even at low NPK application (e.g. 25 kg ha‐1) investment showed profitable (VCR> 2). However to be able to purchase NPK fertiliser farm households need capital for investment. Farm type 1 and 2 farmers seem to have more capital available from crop and animal sellings (Figure 11). Farm type 3 has a much higher percentage of income out of non‐farming practises and doesn’t seem to be as highly dependent on agriculture, like the other farm types. As they do have capital to invest, they might lack the involvement and expertise required make investments in agricultural inputs such as fertiliser. Farm type 4 farmers are mostly single headed households that are not as involved in farming for income generation, but mostly to support themselves and the remaining of the family present. Typologies found in this research seem to be in line with characteristics of typologies found in previous research by Tittonell (2009).

Figure 22. Example of conceptual framework to make a feasibility assessment: Capital availability is plotted against manure availability per farmer belonging to one of four farm types

Improved management simulations included manure, crop residue and fertiliser in a complete application package. The feasibility of this package requires availability of manure. Comparing farm types, Type 1 and 2 farmers in general had the highest number of animals available on farm and were more likely to own small stock (goat or pig). Figure 22 tries to illustrate capital availability on farm and animals present classified per farm type. Farm types 1 and 2 seem to have as well a higher number of animals, as more capital for investment. From literature the hypothesis is proposed that farms having on‐farm income strategies (Farm type 1 and 2), are more focused on productivity and often more innovative. They can be characterized by an earlier adoption and adaptation of technologies and could serve as an example for other farmers in the community. Reij and Water‐Bayer (Reij and Bayer, 2001) even state that this may even facilitate the further outspread of technologies in the community.

5.1.5 Model FIELD considerations Simulated yield response by the FIELD model used in this research was only influenced by the soil fertility of the different fields used (N=90). Other variables such as weed management, pest and disease pressure and harvest age have not been included in the simulation, while previous research showed fertiliser response affected by soil fertility, rainfall and weed management (Fermont et al., 2009). It is therefore important to look at the simulated result from the viewpoint of soil fertility alone, keeping in mind several other variables that can influence actual yield.

Extra demand in labour, for the scenario’s involving crop residue incorporation and manure applications have not been taken into account in this study. Several attempts were made to quantify labour needs, but did not succeed in sufficient and reliable data. Especially for cassava residues, which can be difficult to handle in the field, extra labour could be required to cultivate the same field size. Even so, the size of field could suffer under the implication of cassava residue incorporation. At this stage of research, these remain uncertainties.

Another option of increasing yield quantity would be by increasing the size of fields. In both villages farmers indicated having more plots of land available and belonging to them but not having enough strength and labour to clear and prepare fields. Recently NGO World Vision International has started with bullocks’ traction projects and a training school for both animals and farmers has been set into place near Quelimane. As a pilot some farmers per village are selected to start having a pair of bullocks. They could be use to increase the area cultivated by farmers by using a plough for example. Still some major drawbacks of course have to be considered. The animals have to be taken care of, fed and trained to be useful in farm’s work.

5.2 Evaluation of feasibility

5.2. 1 Potential crops for biofuel; cassava, maize and sorghum Firstly the discussion of cassava as a biofuel crop will be focussed upon: being the most important crop of this study. Sorghum and maize will be used to illustrate and elaborate on the biofuel discussion. This thesis can merely provide with insights from research done in three villages as case studies in Alto Molócuè district.

Ranked as the most important crop by 60% and 70% of the respondents in Nacuaca and Gafaria respectively, cassava plays an important role for farmers in the research villages. As discussed before one of the main advantages of cassava is the storing of roots in the soil. While other crops such as maize and sorghum have a more defined time frame of harvest, cassava can be kept undisturbed for a later period of harvesting. Only one third of the farmers in Gafaria (26%) sold cassava compared to 42% in Nacuaca and 48% in Mugema. When sold cassava was sold as less than half of the produced quantity: 47, 49 and 43% respectively for Mugema, Nacuaca and Gafaria. From the interviews it was understood that mostly cassava is sold within the community (82%) but some was also sold to people coming from far (18%). If farmers would be involved in a emerging cassava for biofuel market, with current production levels, the surplus being sold to neighbours with insufficient cassava production at the moment, could be sold for biofuel purposes. This could be an important consideration to be investigated in further detail. The resilience of a community for food insecurity can be outbalanced if cassava is sold outside the community. Currently low priced (contributing to an average of 12% of total income) cassava could increase in value because of extra demand of the market from the biofuel industry. Therefore making it more expensive as a food crop purchase for cassava consumers.

As an illustration the crop maize can be used. As well as an important food crop, maize also serves as a commonly used crop to generate cash. Recent programmes such as People for People (PforP) try to increase food security in Mozambique and surrounding countries by buying farmers surplus of maize, common bean and pigeonpea. Farmers are offered a fixed price higher than the market price and are being organised in selling as a group by farmer associations. This project started in Mugema and Nacuaca, but not yet in Gafaria. It could offer a very interesting case study to follow price development of these crops mentioned, and in addition to follow up on the proportion sold of total production per household. Better options for selling: both an increase in price offered as a joint organization of sales of maize could increase maize selling and brings food consumption focus more on crops such as cassava.

For sorghum, another crop considered in this thesis as a potential biofuel, the discussion mostly focuses on sweet sorghum. At the moment grain sorghum is grown by almost all respondents in the research villages. Sorghum is sold by a small amount of the respondents: 12, 9 and 25% in Mugema, Nacuaca and Gafaria respectively and makes up for 4% in Mugema and Nacuaca and 16% in Gafaria. As perceived from the field work it is a highly preferred crop to keep for consumption in the form of xima or Ortega. If sweet sorghum would be grown for biofuel it has to be harvested green for the stalks. These stalks would have to be harvested to extract juice for bio‐ethanol. Currently sorghum stalks are only used for incorporation in the soil by 27% of the respondents, and because of the lack of cattle are not used for animal feed. Keeping diet preferences in mind, the importance of sorghum for flour could be a

78 bottleneck in the adoption of sweet sorghum cultivation. However, since farmers are familiar with sorghum growing practises, sweet sorghum could be introduced not as a crop to replace sorghum but as an extra cash crop. The same way, sesame has been introduced a couple of years ago to generate cash for the farmers while hardly being consumed.

The biggest question remains if current cassava yields can be increased. This thesis has discussed ways to increase yields starting from low capital required residue and manure applications to higher capital investments needed if fertiliser would be applied. Previous research has shown that only 4% of the farmers have access to credit, although rural banks are emerging and more micro‐credit incentives are being developed (MINAG, 2007). Outgrower schemes for e.g. the production of tobacco are excisting and providing farmers with fertiliser under contract production. These existing outgrower schemes could provide with examples and frameworks on how commercial firms can arrange input purchases or input credit for farmers under production contracts.

5.3.Methological considerations

5.3.1 The use of a farm typology Initially after the first rapid farm characterisation (May‐June 2009) a farm typology was designed to characterize farm types and have a better understanding on within and between village farm heterogeneity (and homogeneity). This ‘first’ typology was based on dividing farmers on farm income, their involvement in farming and several wealth indicators as suggested by experts (amount of animals, hired labour, maize and bean production). A total of 5 farm types were identified for all three villages (Mugema, Nacuaca and Gafaria) and 2 farmers per village were selected for re‐visit (excluding Mugema).

After returning from fieldwork and the start of analysis questions arose about the correctness of the chosen typology. Ideally, a chosen farm typology should capture the most important characteristics that would typify farm households according to their similarities within a typology and their dissimilarities between typologies. A re‐evaluation was made using the proposed methodology presented in Chapter 2.2.2. After designing the new typology, more profoundly based on the results of the rapid farm characterisation, and making divisions in farmers based on several variables and less strongly on farm income it was found that on revisiting the selected farmers a farm type group appeared under sampled. Farm type 3 farmers characterized by high off farm involvement seemed to have been not re‐ visited in sufficient amount in Gafaria and even more in Nacuaca where farm type 3 was left out totally (Appendix Table A 18). The first farm typology design was mainly based on variables presented in Appendix A 19.where a separate typology was made for farmers hiring labour. As shown farm management practices such as burning of crop residues were taken into dividing farmers over typologies. Pro’s and con’s for both types of typologies can be thought of but keeping in mind the function of a typology in this research: a tool for understanding variability between farmers within a village, the revised typology seems to have a more profound basis, even though sampling has been done with a less favourable division.

Another option for the design of a farm typology used by Titonell (2008) and Wilson (2007) would have been the participatory (wealth) ranking in which farmers group themselves in terms of their resource endowment. At the start of this research this method was considered, however at first introduction visits conversations about endowments of farmers seemed to be extremely sensitive. All three villages were involved in NGO aid and farmers seemed eager in receiving more attention from the NGO. Therefore the positivist method was used (Mignolet et., 2001) combined with the constructivist method (Perrot and Landais, 1993) trusting on expert knowledge for important variables to be included in the design of a typology. In overall an attempt has been made to categorize rural household families into typologies based on several indicators. However because of the complexity of a household these characteristics only capture a fragment of the household. The typology does not detain the dynamics, strategies and changes over time that are a feature observed in earlier research done on the welfare dynamics in rural Kenya and Madagascar (Barret, 2006). As stated by Tittonell (2009) it makes the kind of typology a mere snapshot of inventories of household resources and assets and therefore should also be used realizing its limitations.

5.3.2 Yield estimations Yield estimations in this study were done following several methods as presented in Chapter 2.4 and listed in the Appendix Table A 8, Table A 9 and Table A 10. For cassava three methods were used to estimate production/ha. Because of limitation in field work, only a limited amount of plants could be harvested per plot, calculations from harvest measurements remain uncertain. Previous research done by Hilton (2000) showed farmers underestimating their yields by 25‐50%. This would imply that actual yields should be considered somehow in between the range of (lowest to highest): estimated by farmers

For the purpose of comparison in this thesis it is assumed that all farmers underestimate and therefore the use of proportions (e.g. farm income as a percentage of total estimated income) is still valuable information for analysis. Quantitative data on difficult or sensitive estimations such as yield and income are mostly analysed as proportions per household. Furthermore, two return visits were used to cross‐ check given answers. The key informant also helped during interview to clarify answers with his observations and expectations.

5.3.3 Limitations of research In order to capture the complexity of different farming systems models can be used. These models should have the characteristic of simplifying the system, but without ‘oversimplifying’ a system that it becomes un useful for further analysis, this implies a certain complexity still being needed. To represent the total agricultural production system datasets on different components are required, these components can be listed into: 1) crop, 2) weather, 3) soil, 4) management and 5) socio‐economics. The order of these components is not hierarchical (Lansigan, 1998).

Earlier research of Fermont (2009) showed cassava productions in Kenya and Uganda to be affected by multiple abiotic and biotic constraints differing strongly between fields, sites and years. Effects were aggravated by sub‐optimal management practices leading farmers to yield only one fifth of the maximum yields recorded in the same region. In this study the focus has been mostly soil fertility constraints while keeping several parameters such as rainfall at an average level and similar in both villages. Therefore an important limitation of this research is the main focus on soil fertility and not so much on the interaction of cassava yield and for example weed management. Uncontrolled weeds in the first three months of crop age were found to reduce yields by 50‐65% (Melifonwu 1994) and a yield gap of around 5.0 ton ha‐1 (25% of maximum yield recorded) was found by Fermont (2009). An important pillar for integrated crop management for higher yields: resistant genotypes could not be evaluated because varieties uses were mostly local. Introduction of more disease resistant varieties could increase yields as observed in the work of Fermont (2009)

6. Conclusions Initially the aim of this study was to explore smallholder farming systems producing cassava, maize and sorghum in the scope of the current raging discussions on crops for biofuel production. Although Mozambique has been identified as a promising country for biofuel production little is known about current farming systems.

An important finding of this research was the high heterogeneity found comparing villages and within villages. Analysis at different levels: village, farm and field provided helpful tools and the design of a typology helped to analyse within village variability. An interdisciplinary approach in which bio‐physical, socio‐economic and farm management practices was used to explain variability can be considered key to this sort of research. As an example the case study of Gafaria can be mentioned: based on bio‐physical analysis alone Gafaria would be thought to be more fertile and higher in production compared to Nacuaca. From socio‐economic analysis the opposite was found: farmers in Gafaria seemed to be less well off compared to farmers in Nacuaca.

Cassava yield estimations, currently lacking in many research projects, have proven to be very difficult in this research. Initially the aim was to quantify yields of cassava, maize and sorghum but due to several unforeseen circumstances this could not be accomplished. Therefore explaining current yields in relation to soil, landscape and management variability was not possible. This is to be regretted because it could have provided with important insights on most important factors, besides bio‐physical properties, limiting yields.

As a result of this, simulated cassava simulated yields by the model FIELD, are based on soil fertility alone and not on interactions with crop management such as weeds and pests. It is a major limitation that has to be taken into account. Simulated yields of FIELD were higher than estimated yields by farmers but lower than calculated yields from field work. Harvest measurements are lacking to assess the goodness of fit for simulated and actual yield.

FIELD simulations suggest that all management scenarios (all improved scenario’s compared to current farmer practise) may have a positive effect on yield and yield development over time. Simulations show there is scope for improving smallholder yields, even at limited resources. Crop residue and manure application, considered as low input sources of nutrients, have a positive effect on yield and could reduce yield decline over time. The ability of farmers to adopt the several management scenarios proposed will be dependant on several factors such as labour availability for incorporation of crop residue and small stock ownership for the application of manure.

The development of cassava for ethanol could strongly increase the demand for cassava and could be an incentive for farmers to adopt technology packages to improve productivity and profitability of cassava production. However which farmers would be more suitable to involve in production increasing activities (e.g. the adoption levels of farmers to improved techniques) could be assessed with the help of farm typologies. Considered solely as a tool they can provide with insight on important farm characteristics and could be used in allocating promising pilot projects. Production orientation, resource possibilities and farm involvement could be important indicators derived from a Farm typology. Four different farm types over the three villages were identified in this study. From this research Farm Type I and II would be indentified as feasible candidates for pilot projects.

Within farms it was found that fields could be classified into quartiles corresponding to FIELD simulated yields. 4th quartile fields seemed to be more responsive to any nutrient amendment (in‐organic and organic) and had overall higher yields compared to 1st and 2nd & 3rd quartile fields. Allocation of available nutrients to these higher producing could induce soil fertility gradients over farms and should be taken into consideration.

Recommendations This study has provided insights in farming systems from three villages in Alto Molócuè district, Northern Mozambique and can underline the importance of studies focused on exploring the farming system. Biofuel projections are based on assumptions and estimations on national level, with 80% of the population of Mozambique being involved in agriculture one can imagine the heterogeneity in possibilities for production.

• On farm improved farm management trials could provide with more insights on factors affecting yields such as weeds, pests and diseases. The model FIELD could be improved and further calibrated to have a better fit; it could provide as a helpful tool in simulation of different scenario’s aimed at yield increase of smallholder grown cassava.

• Further research to quantify yields of cassava, maize and sorghum in general is needed.

• Additional research is needed to study the effect of the increase in demand for staple crop cassava in the research areas. With an increase in interest for maize, common bean and pigeonpea by food programmes such as People for People (PforP) these crops will get better prices and subsequently farmers could focus more on selling. Cassava is currently low priced compared to other crops and serves as a security crop for many farmers in the villages. In the scope of sustainable production and poverty elevation the changing role of cassava should be followed closely.

• A more in depth study of current outgrower schemes with tobacco can provide with useful information on examples how technological packages e.g. fertiliser could be supplied by companies in contract growing schemes.

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Appendix I: Additional background information

Table A 1. Crops grown in Zambézia province as a percentage of total arable area (Bias and Donovan 2003)

Crop (%) In ha

Maize 36.8 3864294 Cassava 23.4 2457187 Rice 14.1 1480613 Pigeonpea 7.6 798061 Sorghum 4.9 514539 Groundnuts 4.0 420032 Cowpeas 3.5 367528 Sweet potato 2.0 210016 Common beans 1.9 199515 Bambara beans/nuts 1.6 168012 Millet 0.3 31502

Table A 2. Soil property classification from the Institute of Agricultural Research (Maria and Yost, 2006)

Appendix II: Soil fertility analysis

Figure A 1. Range of relevant soil properties found for soil samples from Nacuaca (N=52) and Gafaria (N=38), Alto Molócuè District, Northern Mozambique. a) SOC (%), b) pH, c) Clay (%), d) N(%), e) K (meq/100g), f) P (mg/100g). The box –and‐whisker diagram includes the range of 50% of the samples

(rectangle), the median (cross bar), maximum and minimum (extremes of the line) and outliers (dots).

Table A 3. Soil fertility variables and their determination/calculation included in the soil fertility analysis

Variable Units Determination/calculation

Inherent properties Texture (clay, silt, sand) Soil laboratory, Eduardo Mondlane, Maputo2

Soil fertility Soil organic carbon g kg-1 Soil laboratory, Eduardo Mondlane, Maputo Total soil nitrogen (Nt) g kg-1 Soil laboratory, Eduardo Mondlane, Maputo C: N ratio - SOC/Nt Extractable P (Pextr) mg 100g Soil laboratory, Eduardo Mondlane, Maputo -1 Exchangeable Ca, Mg, Na, K cmol(+) kg Soil laboratory, Eduardo Mondlane, Maputo -1 Effective cation exchange cmol(+) kg Sum of bases (Caexch, Mgexch, Naexch, Kexch) + (H+Al) capacity (ECEC) -1 Exchangeable acidity (H+Al) cmol(+) kg Soil laboratory, Eduardo Mondlane, Maputo -1 pH (1:2:5) cmol(+) kg Soil laboratory, Eduardo Mondlane, Maputo

Table A 4. Subsets of (combined) soil fertility variables included in the multiple term regression model to explain cassava yield variability found per village and for the total set (N=90) and the percentage of the variance explained by them

Village Best subset of explanatory variables % of variance explained

Nacuaca C (g/100g) 36.5 -1 K (cmol(+) kg ) 40.1 -1 -1 K (cmol(+) kg ) + C:N ratio + Mg (cmol(+) kg ) 41.7 -1 -1 K (cmol(+) kg ) + Mg (cmol(+) kg ) 42.6

Gafaria Clay (%) 20.6 -1 K (cmol(+) kg ) 33.5 -1 K (cmol(+) kg ) + pH + C/N 36.4 -1 K (cmol(+) kg ) + pH 37.1

-1 Total set K (cmol(+) kg ) 37.5

2 Soil laboratory: agricultural faculty of Eduardo Mondlane University, Maputo, Mozambique

Appendix III: Socioeconomic analysis

Table A 5. Highest level of education followed by household head (%) in the three villages (N=74)

Village Mugema Nacuaca Gafaria Education 1st class 4 4 2nd class 4 4 3rd class 13 8 4th class12 13 12 5th class12 25 16 6th class16 17 20 7th class28 17 12 8th class 16 4 12

didn't go to school but knows how to read and write 4 didn't go to school, doesn't know how to read and write 12 4 0

Table A 6. Percentage of farmers ranking most important three crops in Nacuaca (N=24) and Gafaria (N=25)

Rank Crop Nacuaca Gafaria 1st cassava 60 70 1st maize 40 20 1st sorghum 0 10

2nd cassava 20 10 2nd maize 60 40 2nd sorghum 10 20 2nd pigeonpea 0 30

3rd sorghum 60 40 3rd cowpea 10 20 3rd pigeonpea 10 0 3rd maize 0 20 3rd groundnut 0 10 3rd cassava 10 0

Figure A 2. Relative importance of on‐farm income at Mugema, Nacuaca and Gafaria

Table A 7. Cropping calendar for Nacuaca and Gafaria for tubers, cereals and beans

Months of year Crop Village Oct Nov Dec Jan Feb March April May June July Aug Sep Oct Nov Cassava G Cassava Tubers N

Sweet potato

Sorghum

Cereals Maize

Rice

Cow pea

Common bean

Bambara nuts

Beans Groundnut

Velvet bean

Pigeon pea

prepare field and sow/plant weed harvest 93 G= Gafaria N= Nacuaca Appendix IV: Farmers estimations and QUEFTS maize yield estimations

Table A 8. Farmers Estimations maize yield (t/ha)

Estimated Area t/ha Residue

maize (kg) (ha) (t/ha)

Farmer 1 3.5 2.18 1.6 2.4 Farmer 2 0.4 0.16 2.3 3.4

Farmer 3 0.9 Farmer 4 0.4 1.14 0.4 0.6

Farmer 5 0.8 0.24 3.4 5.1 Farmer 6 0.3 0.10 2.5 3.7

Farmer 7 0.6 0.73 0.8 1.2 Farmer 8 0.8 0.81 1.0 1.6

Farmer 9 0.3 0.34 0.8 1.2 Farmer 10 0.02 0.23

Farmer 11 0.5 0.25 2.0 3.0 Farmer 12 0.5

Farmer 13 0.1

Farmer 14 0.1

Farmer 15 1.0 1.0 1.0 1.6 Farmer 16 0.2

A B

Figure A 3 (A) Frequency bar chart for simulated maize yield (t/ha) and the frequency over the number of fields (N=90). (B) Range of QUEFTS simulated maize yield for Nacuaca and Gafaria. The box and whisker diagram includes the range of 50% of the samples (rectangle), the median (cross bar) and minimum values (extremes of the line) and outliers (dots). Sample size Nacuaca N=53 and Gafaria N=37. 94

Appendix IV (cont): Cassava and sorghum yield estimations

Table A 9. Calculated yields from cassava harvest measurements, estimated yields by farmers and simulated yields in FIELD (t/ha). Blank spots are unknown (e.g, some farmers were not able to estimate cassava yield)

Cassava Village Calculated Farmer estimated Simulated (t/ha) (t/ha) (t/ha) Gafaria 11.2 15.4 Gafaria 8.0 Gafaria 7.7 12.1 Nacuaca 8.0 2.4 Nacuaca 17.8 4.0 Nacuaca 4.8 3.9 1.7 Nacuaca 8.2 3.2 3.1 Nacuaca 17.4 1.5 5.9 Nacuaca 7.9 16.3 7.5 Nacuaca 19.0 4.1 4.7 Nacuaca 14.3 5.1 1.8

Table A 10. Sorghum yield calculations from storage measurements

Storage (m) Total Total yield Estimated Cultivated length width height m3 t in storage by farmer (t) area (ha) Grain t/ha Straw t/ha Farmer 1 0.2 0.5 0.6 0.06 0.04 0.04 0.080 0.5 1.0 Farmer 2 0.3 0.6 1.3 0.22 0.15 0.4 0.337 0.4 0.9 Farmer 3 0.5 0.9 0.9 0.41 0.27 0.3 0.243 1.1 2.2 Farmer 4 0.3 1.0 0.7 0.21 0.14 0.4 0.100 1.4 2.8 Farmer 5 0.4 1.0 0.3 0.10 0.07 0.101 0.7 1.3 Farmer 6 1.8 1.8 0.4 1.30 0.86 0.3 0.695 1.2 2.5 Farmer 7 1.3 1.2 1.0 1.63 1.08 1.143 0.9 1.9 Farmer 8 0.9 1.0 0.6 0.51 0.34 0.500 0.7 1.3 Farmer 9 1.0 0.7 0.9 0.57 0.37 0.774 0.5 1.0 Farmer 10 0.5 1.4 0.5 0.27 0.18 0.2 0.125 1.4 2.9 Farmer 11 0.4 1.1 0.9 0.42 0.28 0.3 1.089 0.3 0.5 Farmer 12 1.0 1.6 0.3 0.54 0.36 0.565 0.6 1.3 Farmer 13 0.5 0.6 1.5 0.47 0.31 0.4 0.368 0.8 1.7

HI used: 33%

Appendix V: Farm typology

Table A 11. Additional socio‐economic and managerial indicators derived for the three villages Mugema, Nacuaca and Gafaria per farm type

Farm N Literate Income from animals Hired labour Total income type Distribution of Family labour (%) (MZN) (%) Incorporate residues Households (%) (%) (%) Mugema 1 18 4 100 100 3690 50 75 8075 2 50 11 100 70 720 40 55 10288 3 23 5 100 100 0 0 20 516 4 9 2 0 90 200 50 50 1535 SED (Farm type) 6 8 640 10 11 2588

Nacuaca 1 46 11 91 100 1455 50 55 19795 2 25 6 83 100 178 20 17 3955 3 21 5 100 100 32 20 20 320 4 8 2 50 50 0 0 SED (Farm type) 7 4 345 10 10 4090

Gafaria 1 24 6 100 100 519 20 33 1665 2 32 8 100 80 150 0 88 1679 3 20 5 100 100 216 0 60 1436 4 24 6 83 90 60 0 50 1022 SED (Farm type) 4 6 80 6 10 214 SED (Sites) 3 4 231 5 6 1635

Significance (P values) Village (V) ns ns ns 0.097 ns 0.083 Farm Type (FT) <0.001 0.055 0.04 0.051 ns ns

Interaction V * FT <0.001 ns ns ns ns ns

Appendix VI: Model assumptions Table A 12. Fixed model parameters used

Parameter unit

Land quality Slope m 100m-1 3,0 Slope length m 40 Bulk density kg m-3 1300 SOM C:P g C g-1 P 180 Top soil depth m 0.20 Water capture efficiency transpired/rained 0.25 Water conversion efficiency kg DM/ mm transpired 45

Climate Rainfall mm year-1 1079 Rain days Day year-1 144

Table A 13. Parameters used for different crop residues management practices simulated using the model

unit Maize Pigeonpea Sorghum

Fraction incorporated 0.9 0.9 0.9 Lignin content % of DM 5.4 10.7 13.5 MRDROC 0.8 0.8 0.8 N % of DM 1 4 0.4 P % of DM 0.2 0.4 0.05 K % of DM 1 4 0.8

Table A 14. Parameters used for manure application simulated using the model

unit Small stock manure

Fraction HUMCOM 0.53 DM content % of FM 50 N (% DM) % DM 2 P (% DM) % DM 0.4 K (% DM) % DM 2 Production goat* kg year-1 37.5 Production pig† kg year-1 75

* Based on 75 kg manure production year‐1 animal‐1 and 12 hours manure collection per day (time spend in kraal)

† Based on 150 kg manure production year‐1 animal‐1 and 12 hours manure collection per day (time spend in kraal)

Appendix VII: Field size cassava and maize

Table A 15. Land distribution for individual cassava fields, total cassava fields and maize fields for the different farm types at the two villages

Cassava Cassava Maize Field size Per family Per Total Per Per Total Per Per Farm labour member field size labour member field size labour member type N (ha) (ha) (ha) N (ha) (ha) (ha) N (ha) (ha) (ha) Nacuaca 1 23 0.38 0.19 0.05 8 1.218 0.574 0.165 8 0.453 0.254 0.093 2 4 0.20 0.10 0.03 2 0.404 0.210 0.065 2 0.171 0.096 0.036 3 3 0.49 0.39 0.12 4 3 0.36 0.20 0.05 2 0.729 0.585 0.146

SED 0.08 0.04 0.01 0.195 0.108 0.032 0.094 0.052 0.029

Gafaria 1 3 0.33 0.14 0.07 2 0.985 0.428 0.197 1 0.331 0.144 0.067 2 13 0.14 0.04 0.02 3 0.603 0.186 0.090 3 3 0.19 0.09 0.03 1 0.565 0.282 0.094 4 8 0.18 0.18 0.03 2 0.705 0.705 0.126 1 0.018 0.018 0.003

SED 0.03 0.03 0.01 0.132 0.141 0.022 0.157 0.063 0.032

Village 0.04 0.06 ns

P< Village x Farm type ns ns ns

Appendix VIII: Cassava and maize yield estimations

Table A 16. Simulated fresh cassava yields (t/ha) and maize yields (t/ha) using FIELD and QUEFTS subsequently per village and per farm type.

Simulated yields Cassava Maize Farm type N t/ha N t/ha Nacuaca 1 42 2.5 41 1.7 2 7 2.7 7 2.0 3 4 3 3.2 3 1.5

SED 0.14 0.08

Gafaria 1 6 7.2 6 2.9 2 15 4.1 15 2.1 3 5 4.7 5 1.9 4 12 6.0 11 1.9

SED 0.61 0.11

Village (P<) 0.001 0.02 Village x Farm type (P<) ns 0.011

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Table A 17. Simulated cassava and maize yield (t/ household) adjusted for field size per farm type for both villages

Cassava Maize Per Per Per Per Per Per N field labour member N field labour member Nacuaca 1 23 0.8 0.4 0.1 8 0.8 0.5 0.1 2 4 0.7 0.3 0.1 2 0.3 0.2 0.0 3 4 2 2.5 2.1 0.6

SED 0.19 0.13 0.04 0.18 0.11 0.03

Gafaria 1 3 3.5 1.5 0.7 1 1.3 0.5 0.3 2 13 0.4 0.1 0.1 3 3 0.7 0.3 0.1 4 8 0.7 0.7 0.1 1 0.04 0.04 0.01

SED 0.25 0.14 0.05 0.16 0.10 0.03

Village ns ns ns Village X FT 0.001 0.001 0.001

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Appendix IX: Farm typology considerations

Table A 18. Comparison of the division of selected farmers over the first farm typology (designed after the rapid farm characterisation) and the revised

‘First’ farm types ‘Revised’ farm types N N Nacuaca I 3 I 8 II 3 II 2 III 2 III IV 2 IV 2 V 2

Gafaria I 2 I 2 II 2 II 3 III 2 III 1

IV 2 IV 2 V

Table A 19. Important variables for design of first typology after rapid farm characterisation

Variabele Description Calculation

Income from farm (MT) All products sold have a direct Sum of product quantity * price of relation with agricultural practices product at selling conducted at the farm Hired labour Farmers hiring other people to help Outcome of questionnaire: yes or no them in preparing the land or harvest Burning fields Burning of crop residues present on Outcome of questionnaire: yes or no the field to prepare for a new growing season Incorporating residues The practice of returning crop Outcome of questionnaire: yes or no residues back to the field for rotting Selling of cassava Farmers that are selling cassava to % of farmers selling compared to neighbours or middlemen total farmers in survey % of income from Ortega A homebrew drink that is being sold % of farm income attributed by by farmers to earn some money selling homebrew drink frequently Amount of animal owned Number of animals owned by a Sum of all animals present and family owned by a family

103

Appendix X: Quartile considerations

Table A 20. Division of number of fields (total = 90) divided per village per farm type over quartiles

Quartile 1st 2nd & 3rd 4th Nacuaca Farm type 1 11 21 10

2 1 4 2 4 1 1 1 Gafaria Farm type 1 0 3 3 2 5 9 1 3 1 3 1 4 3 4 5 P< Villages ns P < Village x Quartile ns

Table A 21. Division of number of fields (total = 90) divided per village per field type over quartiles

Quartile 2nd & 1st 3rd 4th Nacuaca Field type Middle 9 21 10 Outfield 4 5 3

Gafaria Field type Home 6 5 5 Middle 1 8 3 Outfield 2 6 2

P< Villages ns P < Village x Quartile ns

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Table A 22. Years of cultivation of field and classification into quartiles

Village Years of N cultivation Nacuaca First quartile 13 4.2 2nd and 3rd 26 4.7 4th quartile 13 5.2

P < ns SED 0.4

Gafaria First quartile 9 5.8 2nd and 3rd 19 5.1 4th quartile 9 4.3

P< ns SED 0.27

P < (Village) ns P < (Quartile x Village) Ns

105

Appendix XI: Principal Component Analysis

Table 30. Uncorrelated important variables derived from the rapid farm characterization and their factor loadings from principal component analysis

Component 1 2 3 4 5 6 7 8

Civil status .205 -.630 .187 .049 .245 .216 .149 -.223

Incorporate residues .385 .409 -.075 -.041 .440 -.287 .350 -.154

Family members .277 -.059 .487 -.296 .450 .038 .071 -.117 present Amount of crops .482 .343 .333 .207 .050 .458 .120 -.102 grown Months with food -.180 .654 .359 .040 .005 -.206 -.005 -.080 deficiency

Gross income from .784 -.096 -.226 .107 -.124 -.239 -.092 -.057 farm (MZM)

Cowpea grown (n/y) .357 .378 -.259 .392 -.090 .530 -.043 -.040

Age of household .377 .368 -.205 -.551 -.083 .147 -.070 -.007 head Common bean grown .395 -.388 -.524 -.220 .128 .069 .091 .013 (n/y)

Number of animals .523 -.118 .489 -.198 -.116 -.281 .139 .019

Maize grown grown * .479 -.368 -.072 .214 .281 .184 -.192 .028 (n/y)

Amount of fields -.093 -.173 .493 -.009 -.442 .443 .335 .129

Cassava grown (n/y) .549 .256 -.033 .118 -.296 -.085 -.401 .142

Ownership of .201 .023 .325 -.630 .007 .192 -.419 .239 livestock (n/y) Hired labour (n/y) .370 -.283 -.019 -.011 -.495 -.258 .360 .318

Literacy (n/y) -.132 -.303 .341 .336 .255 -.115 -.420 .319

Off/non farm income .245 .253 .096 .305 .342 -.072 .237 .639

Colour (yellow) indicates taken into the cluster analysis because of high factor loading and/or indicated important variables by experts.

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Appendix XII: Rapid farm questionnaires

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Table A 23. Rapid farm characterisation questionnaires used in the survey

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111

112

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Table A 24. Soil map Alto Molócuè district. dots are visited farms

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