Farming Systems Characterization in Three Communities from the Barotse Floodplains, Zambia: Relating Landscape with Production and Diversity

Home , Lealui

Farming systems characterization in three communities from the Barotse floodplains, Zambia: Relating landscape with production and diversity

Minor Thesis Report Nutrition Sensitive Landscape project CGIAR research programs AAS and A4NH

Trinidad del Río

December, 2014

Farming systems characterization in three communities from the Barotse floodplains, Zambia: Relating landscape with production and diversity

Minor Thesis Report Nutrition Sensitive Landscape project CGIAR research programs AAS and A4NH

Student name: Trinidad del Río Student registration number: 820306177080 Study program: MSc. Organic Agriculture Course name: MSc Thesis Farming Systems Ecology Code number: FSE 80424

Supervisor: Dr.ir. JCJ (Jeroen) Groot Examiner: Dr.ir. EA (Egbert) Lantinga

Table of Content

Preface 2

Abstract 3

Introduction 4 Objectives 6

Methodology 7

Results 8 Land types classification 8 Farmers interviews 10 1. Community of Lealui 10 2. Community of Mapungu 26 3. Community of Nalitoya, Sifuna and Nembwere 42 Summary Results 61

Discussion 66 Land types and farms interviews 66 Additional recommendations to complement the NSL project 69 1. Efficient use of energy 69 2. Closing cycles: using human excreta for plant production 70 3. Use of remote sensing techniques for monitoring different indicators of vegetation and soils 70

References 72

Appendix 75 Fields and houses location 75 Fields Maps 76 Soil sample and analyses 87 Production destination and prices 88 Labor Requirements 91 Pesticides and/or fertilizers used in the farm 98 Available tools 99 Challenges and constraints 103 Additional questions 106 Production Summary 108 Tillage activities per household 109

Preface

The present study was conducted as a contribution to the “Nutrition Sensitive Landscape” project, which is part on CGIAR research programs Aquatic Agricultural Systems (AAS) and Agriculture for Nutrition and Health (A4NH). This work would not have been possible without the help of many people for whom I am very grateful. First, I am particularly indebted to my supervisor at Wageningen University, Dr.ir. Jeroen Groot for offering me this opportunity. Particular thanks to Roseline Remans, Fabrice De Clerck, Gina Kennedy, Kate Longley, Steven Cole and again to Jeroen Groot, for their valuable support in different stages of my work.

I would like to especially acknowledge Natalia Estrada and Monica Pasqualino for their essential help on the daily life during my stay in Zambia. I significantly learned from their work and their presence also represented an important emotional support while been far from home. The work done by Monica on the seasonal calendars and by Natalia on the participatory mapping, allowed me to get a general idea of the context of each assessed community in order to channel the interviews in a better way. I am also very grateful to all the community facilitators and farmers for sharing their knowledge, dreams and personal thoughts; for the willingness to work together and for their kindness and hospitality. I hope this work could contribute to this research program, and especially to generate at least a small contribution on the life improvement of the community members in the Barotse floodplain.

The understanding of the different land types existing in the area constituted a learning process throughout the two months that I stayed in the Barotseland. My first approach to the landscape context was through a very informative chat I had with Carl Wahl, the coordinator from conservation agriculture from Concern Worldwide. I would like to give special thanks to Mr. Yaba Kabesanu, an extension officer from the Ministry of Agriculture and Livestock, for his patience as a teacher on local land classification during great part of this period. Many thanks also to Mulele Sibeso and Judith for their important help and pleasant company. To all the WorldFish team in Mongu and Lusaka for their readiness to share the facilities with us, which enormously eased our job there.

Back in Wageningen I would like to thank to Minke Stadler, Wampie van Schouwenburg and last but not least, to my friend Katja Kuivanen.

“There are two pieces of bread. You eat two. I eat none. Average consumption: one bread per person” (Nicanor Parra, Chilean anti-poet)

2 Abstract

Land use change is an often overlooked driver of change in diets, nutrition and food security, especially for rural communities. The synergies between a food systems approach to food security and nutrition and landscape approaches to integrated biodiversity in farming systems should be investigated and built on. This specific initiative involved participatory research in order to understand the dynamics of smallholder farming systems in three communities of the Barotse floodplain in the western region in Zambia. By taking into account what food is produced or available within the landscape in different seasons and how people's actions affect surrounding ecosystems, the present study aimed to provide information to identify how people with different interests within the landscape can work together in order to optimize agriculture, conservation and human well-being. This is a first descriptive study of the case study area from an agronomic perspective. The gathered information from this report was linked to two other studies that were carried out simultaneously: one related to human nutrition and the other connected to landscape assessment. The main work done consisted of generating a database of farming systems considering information about household members, field characteristics, management, and additional qualitative information. The results showed that farmers in the three communities recognized different land types to which they assigned specific soil characteristics and flooding risks that determine specific agricultural management requirements. Soil fertility and flooding risk of one land type were usually negatively correlated: land types located closer to water and which, therefore, were more prone to early flooding, tended to have higher organic matter content. Considering the whole assessed area, only four crops (maize, rice, cassava and sweet potato) occupied 73.6% of the cultivated area and almost half of the assessed area was planted with rice or maize. Lack of seeds and knowledge were the two main constraints referred by farmers for increasing the variety of production. Crop yields were generally low. Crop yields, farm productivity and crop diversity were affected by differences between farmers in terms of gender, age, family composition, wealth level, farming practices and land-types and spatial distribution of fields. Some opportunities extracted from the interviews for improving farm performance regarding productivity and diversity of food produced throughout the year are discussed in this report.

3 Introduction

Dietary diversity is universally recognized as a key component of healthy diets (Ruel, 2003). At each step in the nutrition value chain (from production to consumption) there are opportunities for safeguarding nutrition quality. By recognizing critical points in the chain, the nutritional value of food can be covered and promoted (Thompson and Amoroso, 2011). With up to 70 to 80% of the poor in developing countries dependent upon agriculture for their income and livelihoods, food and nutrition security is unlikely to be achieved without considerable attention being given to the food and agriculture sector (FAO, 2015). Resources must be made available for agricultural and rural improvement at a level that reflects the key role agriculture has in building sustainable livings for the world’s poorest people (Thompson and Amoroso, 2011). For achieving good nutrition in vulnerable populations, diversity of food production (or availability) must be secured throughout the whole year. In an assessment carried out by Ferguson et al., (1993) in Malawi (Zambian neighbor country), the diversity of items consumed by children was dependent on seasonality, therefore spatial attention should be put on understand the timing of production of food with different nutritional facts.

FAO is advocating that unless more attention is given to food-based interventions that encourage dietary diversity and the consumption of nutritionally rich foods, the goal of ending hunger may not be achieved. Food and agriculture-based strategies (including food production, dietary diversification and food fortification) focus on food as the primary tool for improving the quality of the diet and for overcoming and preventing malnutrition and nutritional deficiencies. The approach stresses the multiple benefits derived from enjoying a variety of foods, recognizing the nutritional value of food for good nutrition, and the importance and social significance of the food and agricultural sector for supporting rural livelihoods (Thompson and Amoroso, 2011).

In order to overcome the world’s nutrition problems, nutrition must become a cross cutting issue, with concrete commitment and attention from a wide range of disciplines. This notion is the basis for the promotion of nutrition-sensitive approaches to economic growth, development, agriculture and food systems (nutrition-specific interventions target malnutrition directly, whereas nutrition- sensitive interventions target the causes of malnutrition by integrating nutrition into policies and programs in diverse sectors). There have been frequent calls for the international community to prioritize the identification of ways to leverage agriculture (and agricultural landscapes) to improve nutrition (and health). Land use change is an often overlooked driver of change in diets, nutrition and food security, especially for rural communities. The synergies between food systems approach to food security and nutrition and landscape approaches to integrated biodiversity in farming systems should be investigated and built on (Powell et al., 2013).

Landscapes can determine nutrition at different scales: defining what, where and how food is produced; affecting production seasonality and availability; field management and farm systems constraints and opportunities for increasing biodiversity. Tscharntke et al. (2005) described the necessity of the use a landscape perspective evaluate the effects of agricultural land use for the conservation of biodiversity, and its relation to ecosystem services such as pollination and biological control via complementarity and sampling effects. Coppolillo (2000) also used the understanding of landscape-scale patterns to assess the livestock management in east Africa. Scherr and McNeely (2008) stated that agricultural landscapes could be designed and managed to host wild biodiversity of many types, with neutral or even positive effects on agricultural production and livelihoods.

4 Low-input agroecosystems depend on synergies of plant diversity and the continuing function of the soil microbial community, and its relationship with organic matter to maintain the integrity of the agroecosystem (Deugd et al., 1998). It is essential for scientists to understand that most pest management methods used by farmers can also be considered soil fertility management strategies and that there are positive interactions between soils and pests that once identified, can provide guidelines for optimizing total agroecosystem function (Altieri 2002). Ecological studies suggest that more diverse plant communities are more resistant to disturbance and more resilient to environmental perturbations like drought (Tilman et al., 1996). In agricultural situations this means that polycultures exhibit greater yield stability and less productivity declines during a drought than in the case of monocultures (Altieri 2002).

This study is part of the Nutrition-sensitive landscapes (NSL) project, which embraces an approach of systems-thinking for more sustainable diets, improved well-being and a healthier and more resilient ecosystem. It is focused on building diversity into landscapes and food systems to provide multiple sources of nutrients and vital ecosystem services (the benefits that people get from nature such as pollination, clean air and water, and natural pest and disease control). Taking into account what food is produced or available within the landscape, how people's actions affect surrounding ecosystems, and how people with different interests within the landscape can work together to optimize agriculture, conservation and human wellbeing. Bioversity International, working with partners through the CGIAR Research Program on Agriculture for Nutrition and Health, is piloting nutrition-sensitive landscape approaches in order to link agricultural biodiversity to dietary diversity; provide insights to sustainable intensification of agriculture; inform agricultural-based nutrition interventions; better understand the relationship between dietary diversity, environmental and social variables and improve the resilience of the populations that live in the landscape to adapt to changing conditions (Bioversity 2015).

A biodiverse agroecosystem is able to sponsor its own functioning (Altieri 2002). A field is seen as a complex system in which ecological processes originated under natural circumstances also occur, e.g. nutrient cycling, predator/prey interactions, competition, symbiosis, successional changes, etc. Implicit in agroecological research is the idea that, by considering these ecological relationships and processes, agroecosystems can be managed to improve production and to produce more sustainably, with less negative ecological or social impacts and fewer external inputs (Gliessman, 1998). Ecological concepts are utilized to favor natural processes and biological relations that optimize synergies that diversified farms are able to sponsor their own soil fertility, crop protection and productivity. By assembling crops, animals, trees, soils and other factors in spatial/temporal diversified schemes, several processes are enhanced (Altieri 2002). These processes are critical in defining the sustainability of agricultural systems (Vandermeer et al., 1998).

This specific initiative within the NSL project involved participatory research in order to understand the dynamics of smallholder farming systems. Although the present assessment focused at the farm scale, it also considered how the location and distribution of plots within the landscape affected people livelihoods determining their nutrition throughout the year. Before proposing any solutions for improving farming systems it is essential to understand the complex links between people, nature and agricultural landscapes and how these interactions are affecting farm production and diversity. This specific initiative within the NSL project involved Participatory Action Research in order to understand the dynamics of smallholder farming systems and to assess how the landscape affects their livelihoods determining their nutrition throughout the year.

Improved agricultural production provides opportunities to sustainably reduce poverty, food insecurity and malnutrition and thereby improve the quality of life. Narrowing the nutrition gap means increasing the availability, access and actual consumption of a diverse range of foods. This

5 means first assessing and identifying the nutrition gaps and then taking action to close them. (Thompson and Amoroso, 2011). This is a first descriptive study of the case study area from an agronomic perspective. The gathered information from this report is linked to two studies that were carried out simultaneously:

- Related to human nutrition: Participatory Action Research Contributing to the Identification of Sustainable Diet Options in the Barotse Floodplain, Zambia. Conducted by Monica Pasqualino. - Connected to landscape assessment: Understanding the Spatial-Temporal variability of the Barotse Landscape to improve nutrition and ecosystem services provisioning with a gender perspective, carried out by Natalia Estrada.

Objectives

1. To generate a detailed database of farming systems as starting input for further analysis with multi-objective optimization modeling at the farm and landscape scale.

3. To make an inventory of crop production in different land types in order to understand how the landscape determines production opportunities and constraints.

3. To identify seasonal production diversity and performance in space.

4. To evaluate the feasibility of soil sampling technology (soil doc), introducing soil-sampling procedure to farmers.

6 Methodology

This study was conducted in three AAS focal communities of the Barotse floodplain in the western region in Zambia: Lealui, Mapungu and Nalitoya (Figure 1). Together with the CGIAR teams and Ministry of Agriculture and Livestock, this study undertook a farming system assessment.

Figure 1. Assessed communities.

The main work done consisted of generating a database of farming systems. Specifically, detailed information of 13 households was gathered by means of participatory interviews. Each interview pertained to the livelihoods of the household members throughout the year; the location of their fields and their management associated with particular land types; crop seasonality, yield performance, associated labor requirement, general costs, food and destination; additional information regarding their dreams, concerns and needs.

In addition to a regular interview of questions and answers, farmers were requested to draw their fields with respective crops during different seasons (rainy, hot and cold). The date and duration of the seasons where defined by each farmer. This information was used to estimate crops area percentages within a field. All fields were mapped (Appendix) for further area calculation and estimation of field average altitude, distribution and accessibility. The information on the land type classification was derived from farmers description during the interviews, visits to their fields; the participatory mapping activities; talks with Mr. Yaba Kabesanu (extension officer from the MAL). This information also was constantly been complemented by local knowledge given by the community facilitators and farmers.

Furthermore, one complementary activity was carrying out: an introductory training was given for soil sampling and analysis with Soildoc. Consisting of a kind of portable lab, Soildoc is a new - technology which can be used to measure soil pH, NO3 , P and CE among other soil parameters. This activity intended to: 1. Test a participatory research method for soil sampling together with the extension officers, community facilitators and farmers. 2. Explore its acceptance and recognition of the importance of using the Soildoc technology in the future. The samples were collected mostly from the interviewed farmer fields.

7 Results

Land types classification

According to local knowledge, the Barotse Floodplain can be classified in different land types. A representation of the landscape in the dry and wet period is shown in Figures 2 and 3, respectively. The sketches show land types which could be find in an area from the Zambezi river until the ponds. The labels in green are asociated with ecosistems servises (forest and nesting sites) This distance varies in the Barotse according to the extension of the plains and saana. As an example, close to Nalitoya community, the distance between the river and the ponds (upland) is around 20 km. A general description of main land types is shown in Table 1. Both the sketch and the land type description table were corrected and complemented with the information gathered by Natalia Estrada.

Figure 2. Landscape and land types representation during the dry season.

Figure 3. Landscape and land types representation during the wet season.

8 Table 1. Land types description. Information from the Information from the Plots / Field work (average values) Communities Commonly grown crops in each Plain Saana Upland land type and in each community: Description Land Type Fertility Soil Close Soil Soil Soil Soil Soil Soil Mapungu (M), Nalitoya (N) and (Singular/ Moisture to Moisture Color Moisture Color Moisture Color Lealui (L). Plural) water Mulapo/ high medium close It is usually located in low areas, Not cultivated.

Milapo close to water sources in the dry season and flooded during the wet period. It’s a low land within the libala with a concave shape. Sitapa / high high close This land type is similar to a Maize (M), Pumpkin (M), Squash

Litapa Mulapo, the difference is that it is (yellow), Groundnuts (M,L), Rape used for agriculture activities (it is (M,L), Watermelons (M,L), potato cultivated). Although these land (L,M), Okra (L), Rice (N), Squash types have usually better soil (white) (M,N), Local sugarcane (M, fertility than most of the other land N), Squash (orange) (N), Sorghum types, they have high risk of early (N), Carrots (L), Chinese cabbage floodings. The production is not (L), Cassava (L), Tomato (M), always higher compared to lower Hibiscus (M), Cowpeas (M) fertility land types since crops have a shorter growing period or they are damaged by water. The water start receding at the mid-end of the cold season. The suitable period for cultivation would depend on the location and crop cycle length. Sishanjo / high high close Located at the edge of the Mukulo Rice (N), Vegetables (N), Sweet

Lishanjo (boundary between Libala and Up- potatoes (N), Tomato (N), Maize Land). It is similar to Sitapa but (N), Rape (N), Cabbage (N), located in the saana. Receives Cowpeas (N). water from ponds (mostly underground water) and from the river (or canals). This land is fertile, but when the water is drained by the canal (plus agricultural activities), the organic matter content decreases on time. The water start receding at the mid-end of the cold season. The suitable period for cultivation would depend on the location and crop cycle

6 Information from the Information from the Plots / Field work (average values) Communities Commonly grown crops in each Plain Saana Upland land type and in each community: Description Land Type Fertility Soil Close Soil Soil Soil Soil Soil Soil Mapungu (M), Nalitoya (N) and (Singular/ Moisture to Moisture Color Moisture Color Moisture Color Lealui (L). Plural) water length. Wet low medium close, It is located slightly higher than Maize (N), Cassava (N), Sweet

Litongo / far Sitapa and Sishanjo. Soils are sandy potatoes (N,L), Wheat (L), Banana Matongo and the organic matter is low. (N), Pumpkin (M,N), Orange (N), Therefore the production depends Mulonono (N), Mumosomoso wild on manure applications. The dry or fruit (N), Rape (M, N), African wet classification can change Cabbage (M,N), Amaranths (M), through time. If a Matongo has Pumpkin (N), Rice (L, N), Mango moisture still in October, is a Wet (N), Onion (N), Tomato (N), Litongo. Sometimes wet and dry Eggplant (N), Groundnuts (N), classification could be within the Cowpeas (N), Watermelon (N) same plot (mix litongo) Dry very low low far It is located slightly higher than wet Maize (N), Cassava (N), Pear Millet

Litongo / litongo. Soils are sandy and the (N), Wheat (L), Rice (L), Cowpeas Matongo organic matter is low even low than (N), Livingstone yam (N), (N), wet litongo. Production highly Pumpkin (N), Local squash (N), depends on manure applications and rains. Cassava is a good option Trees: because needs lower soil moisture. -Muzauli: edible red seed from It is not possible to grow other African rosewood. crops during the dry season without -Cashew nut tree. irrigation. Fruit trees and bushes -Mahuluhulu (Strychnos spinosa). can grow in these areas. The dry or -Mungongo: edible leaves for wet classification can change salad. through time. If the soil dries out -Mombole: edible fruits. completely in August, it is a Dry Litongo. Sometimes wet and dry classification could be within the same plot (mix litongo). Litema/ medium low far It is a forest field. It has sandy and Cassava (M, N), Pear millet (N),

Matema low organic matter soils. They Bambara groundnuts (N), usually cut bushes and some trees Livingstone yam (N), Hibiscus (M), do cultivate cassava. There are Groundnuts (N), Maize (N), trees separated ~ 40m. Some have Sorghum (M), Sweet potato (M), been cut down to allow light and Tomato (N) space for cassava. It cannot get

7 Information from the Information from the Plots / Field work (average values) Communities Commonly grown crops in each Plain Saana Upland land type and in each community: Description Land Type Fertility Soil Close Soil Soil Soil Soil Soil Soil Mapungu (M), Nalitoya (N) and (Singular/ Moisture to Moisture Color Moisture Color Moisture Color Lealui (L). Plural) water flooded as likaña land type does (Mapungu). Lilako / medium medium far Lilako is the name for cultivated Maize, Pumpkin, Cowpeas (M,N),

Malako plots located close to the house. Sweet potatoes (M,L), Amaranthus They could be also other land types (N), Rape (M,L), Squash (yellow, (e.g. lizulu, sishanjo or litongo). As white) (L), Groundnuts (L), Rice (L), these fields are closer to the house, African cabbage (N,L), Hibiscus (N), they are often cultivated with Sorghum (M), Eggplant (L), Guava vegetables and they are generally (L), Tomato (L), Banana (L), Okra better maintained (irrigation, (L), Onion (L), Cabagge (L), Pepper weeding, manure incorporation). (L), Rice (L), Local squash (L), Cowpeas (L), Water melon (L) Likaña low medium close, Land type located in the Saana and Cassava (M, L), Groundnuts (L), far close to water. It has sandy and low Sweet potatoes (L), Bambara fertility soil. It is located lower than groundnuts (M, L), Squash (yellow, litema therefore it can get flooded. white) (M), Hibiscus (M), Cowpeas This is probably because the water (M), Rice (M), Watermelons (M) table level changes drastically between the rainy and the dry season. Likaña usually has ridges to drain water. Cassava is commonly grown in these fields. Libala low medium close, Literally means plain in the upland. Rice (M), Sweet potatoes (M),

Saana far It has natural grass and it can get Bambara groundnuts (M) flooded sometimes in the rainy season. The soil fertility is relatively good (better than likaña); therefore these fields are commonly not been fertilized. Rice is a usual crop chosen for these land types. Litunda / low medium close, It’s in the banks of the rivers and Maize (M, N), Pumpkin,

Matunda far it’s a little bit elevated with an Groundnuts (M), Rape (M), Sweet elongated shape. People prefer to potato (L, M), Rice (L), Cassava (L, cultivate on this type of land M), Wheat (L), Squash (yellow, because during dry season they still white and orange) (N), African have enough soil moisture and has cabbage (M), Tomato (M), Mango a lower risk of flooding. Soil can be (M), Cowpeas (M)

8 Information from the Information from the Plots / Field work (average values) Communities Commonly grown crops in each Plain Saana Upland land type and in each community: Description Land Type Fertility Soil Close Soil Soil Soil Soil Soil Soil Mapungu (M), Nalitoya (N) and (Singular/ Moisture to Moisture Color Moisture Color Moisture Color Lealui (L). Plural) water sandy or loamy, potentially due to past river movement / deposition. Lizulu/ low medium far Mazulu are small hills that tend to Maize (L,M, N), Sweet potatoes

Mazulu be far from water. Usually they (lower part) (L, M, N), Pumpkin have a circular shape (some are (M,L,N), (lower part) (M,L, N), manmade). Due to the hilly shape Rape (also nursery) (M,L), there could be different growing Sorghum (L,M,N), Groundnuts areas within one lizulu according to (lower area) (M,L,N), Local the water proximity. sugarcane (L), Squash (yellow and white) (M,N), African cabbage (L), Squash orange (L,M), Water melon (M), Wheat (L), to (L, M), Cabbage (L), Okra (N). Saana Medium Far Is within Libala, usually at the end Rice (N,L), (N,L), Sweet potatoes

of the plains. Saana has a different (L) type of grass (Muangue). Libumbu: thick vegetation high trees. Libuta: smaller trees.

Soil Color

It is noteworthy to consider that within one land type there are usually different characteristics. The terrain micro-relief leads to lower and higher areas in one land type classification resulting in different flooding risks. Moreover, there could be soil type differences in small distances (e.g. Sitapa plot number 1.3.2). These small area variations are considered by farmers and greatly affects their crops choices and land management. Two examples of this: 1) Given that mazulu are small hills, the higher parts are usually plant with crops like maize, sorghum or tomato. Meanwhile, the lower part of a lizulu usually is planted with rice or sweet potato. 2) Another option is growing the same crop within the whole plot but differing in planting and harvesting dates (e.g. dry and wet litongo in the same plot number 3.5.3 or Lilako number 1.3.1). Another important issue is that there could be subtle differences between the same land type classification names between two different communities: for instance the lower part of a Lizulu will not be always exposed to floods in Nalitoya as it happened in Lealui.

9 Farmers interviews

1. Community of Lealui

It is a royal community. The main village is located in a kind of ‘island’ (a big lizulu); many inhabitants also live on the surroundings in other smallest mazulu. During the wet season almost all land gets flooded and it was only possible to access the community by means of water transportation. A road connecting Mongu and Kalabo (passing through Lealui) was been built in July-August and the inhabitants of Lealui expect to have benefits regarding a better access to markets.

Different fields of the interviewed farmers are shown in Figure 4, where different colors indicate different households. Close to the center of the image is located the main village. With the exception of farmer 1.1, most of fields are relatively far from the house (indicated by circles). In addition to the distance it is important to consider that due to flooded areas it can take a long time to get to the fields walking.

Figure 4. Distribution of fields of the interviewed farmers from Lealui.

Farmer 1.1: Mumeka Lubinda

Besides being a farmer, Mumeka is a community facilitator from Lealui. He lives relatively far from the community center and it is necessary to use a canoe to reach his house and plots. All plots are located reasonably close to the family house. He is practicing no tillage and mulching since 2004 and since 2012 he is growing wheat, which is an uncommon crop planted in the area (plot 1.1.3). The information of the household members is described in Table 2. The plots information and the crops seasonality are shown in Table 3 and 4 respectively. More detailed information is provided in the Appendix.

Table 2. Household members. Farm Labor Household work Highest school level Name Gender (M/F) At school? Age (Years) days/week h/day h/day achieved Mumeka M 54 6 7 to 8 1 No 10 Namwaka F 35 6 8 to 9 No 9 Nyambe M 14 3 2 Yes 6 Mumeka M 19 3 2 1 Yes 11

Table 3. Field information. Plot Average Production Production Production Crop Land type Field Area (m2) Crop Area (m2) Production Notes number Altitude (masl) perception unit (Mg ha-1) 1.1.1 Rice Lizulu 2825 1975 1016 High 15 Bags 3.8 1.1.2 Rice Lizulu High 3 Bags 2.15 697 698 1017 Birds ate it 1.1.2 Sorghum Lizulu No harvest 0 0 all Last year got 110 kg from 5 kg of First year on this 1.1.3 Wheat Lizulu 1395 1015 High - seeds area (smaller area) Rest of the Low quality, 1.1.3 Tomato Lizulu 337 1010 Low 6 Basket 3.71 field is not 2990 drought cultivated Destroyed by yet floodings. Reason 1.1.3 Maize Lizulu 556 1011 Low 2.25 why he changed to wheat 0.63 Borders, first 1.1.3 Pumpkin Lizulu 40 1011 - 200 Units units/ha year 1.1.3 Sweet potato Lizulu (lower part) 259 1012 First year previous 1.1.3a Carrot Sitapa No harvest 0 Floodings years 200 kg 721 721 1007 Chinese 1.1.3a Sitapa Good 750 Units 10 units/ha Floodings cabbage 1.1.4 Rape Lilako 70 High 3 or 4 Bags 25 120 1.1.4 Eggplant Lilako 10 120 Units units/ha 165 1019 1.1.4 Guava Lilako 1.1.4 Tomato Lilako 30 1.1.4 Banana Lilako 4 Cluster

11 Table 4. Crop seasonality.

Regarding animals, currently the family has ten village chickens. They use the eggs, meat and sell around 20 chickens per year. Before they had more chickens but they died of diseases.

12 Comparing before and after 2004, Mumeka perceives a decrease on labor requirements and costs since he does not need to pay to plough the soil. In his opinion weeding the wheat is not difficult and does not take much time. Moreover, he recognizes a general increase of yields due to increase of soil fertility. The farmer measured the soil nutritional status by observing the growth of a type of grass (in Lozi called liboa-tepe), which is an indicator of good soil fertility. This year the wheat was planted late because the water only started reseeding in June.

Regarding the rice management doing conservation farming, the farmer explained that only the first time before growing rice in a new area, it is necessary to plough because the grass is too dense and high. After that first time the soil will not be disturbed again. Rice is sown in rows (one foot space between rows) and covered with soil. When the rainy season starts and the seeds start geminating the farmer starts weeding. At this moment the weeds are small and it is easier to remove them. After the water level increases the grass will not grow and later, when the water recedes again, the land will be clean (no weeds). He also built soil made barriers (mixed with weeds) to prevent wild fish (which comes with the flooding) of eating the rice in early stages. This activity requires more labor. When the water level increases and passes over the barrier to the field, the rice has already grown and the leaves are hard enough avoiding the fish to eat them. After harvesting the rice the residues are left in the field. When the water comes it is decomposed and incorporated into the soil while weeding.

One big production problem in the plains is the birds that eat the crops, specially rice and sorghum. Scaring the birds represents more labor for this family than weeding. Last year he lost all the sorghum production (field 1.1.2). The improvised solution was to cut the sorghum residues and broadcast rice seeds. This is the explanation why he harvested only 3 bags of rice in this plot, equivalent to 2.15 Mg ha-1 approximately. If it was planted as it is described above, he could get 6 or 7 bags (around 4.5 Mg ha-1). In the case that sorghum could perform well (not eaten by the birds) he was thinking of intercropping it with cowpeas. He is also planning to grow cowpeas after maize. Mumeka also mentioned that he has problems with roaming cattle from neighbor farmers. Because they are grazing everywhere, sometimes he has to pass the night in the field to avoid animals to eat the crops.

For fertilizing he buys 2 canoes full of manure per year. He covers it with grass and irrigates the manure every other day during a week. After it develops fungus he applies it in the tomato basins.

14 Farmer 1.2: Amusa Mubukuanu

Amusa lives close to the clinic (around 15 minutes walking from the community center) with his wife and children (Table 5). He has two house plots (lilako) one situated right next to the house (lizulu) and the other is located 180 m downhill (Sitapa). Both lilako are fertilized with collected cattle manure. He could also fertilize with bat manure that he can get for free (many bats lives in houses roofs and their manure can be collected). However he was not sure on how to apply it and the proper doses for the crops. During the hot season the plots the whole family irrigates the plot using buckets during the hot season from 15 to 20 hours (“earlier it is too hot and this is not good for the plants”).

The other four plots (mazulu) are located from 1.6 to 2.1 km (straight line) apart of the farmstead. The access to these plots is difficult specially when the area gets flooded. To reach the fields even in August it is necessary to cross one canal and flooded areas. On the mazulu the manure is heaped and covered with soil and grass. After tillage he makes holes and puts four handfuls of manure, covers them with soil, irrigates and sows the maize seeds. Most of the mazulu from Amusa and Mulela (farmer 1.3) have local areas, which they call “sikela”. These are extremely salty soils; no vegetation grows in these areas. The information of the household members is described in Table 5. The plots information and the crops seasonality are shown in Table 6 and 7 respectively. More detailed information is provided in the Appendix.

Table 5. Household members.

Farm Labor Household work Highest school Name At school? level achieved days/week h/day h/day Amusa Mubukuanu 6 9 2 No 12 Inonge Lutopu 6 8 3 No 9 Lubasi Amusa 4 7 3 No 12 No Amusa Amusa 3 8 1 12 Ñambe Amusa 5 3 1 Yes 12 Sisii 5 3 1 Yes 12 Mubukuanu 5 3 1 Yes 9 Mubita 5 3 1 Yes 5 Inonge 5 3 1 Yes 4 Lilato 2 3 1 Yes 2 Muhalu 2 3 1 Yes 1

15 Table 6. Field information.

Average Production Production Production Plot number Crop Crop Area (m2) Altitude Production Notes perception unit (Mg ha-1) (masl)

1.2.1 Maize 1330 1019 Low 4 Bags 1.50 1.2.1 Rice 6716 1015 Low 3 Bags 0.22 1.2.1 Sweet potato 1330 1019 Low 2 Bags 0.60 Sometimes instead of maize

Sometimes (not every year) the farmers grows sweet potato. 1.2.1 Sweet potato 16333 1014.3 The crop area depends on labor and money

1.2.2 Maize 465 1017 Low 2 Bags 2.15

1.2.2 Rice 1427 1016 0.35 Low 1 Bags 1.2.3 Rice 1216 1016 Low 0.7 Bags 0.29 1.2.4 Rice 1747 1 0.29 Bags 3.16 (carrot) Cabbage, eggplant, sweet Carrot (6 to 10) 1.2.5 340 1014 Bags 2.06 pepper, onion, carrot Cabbage (2) (cabbage)

1.2.5 Tomato 136 1015 High 10 Basket 25.74 Nurcery: cabbage, eggplant, 1.2.5 rape, sweet pepper, onion, 58.4 1015 Onion: weeds problem carrot 1.2.5 Okra 159 1015 High 25 Bags 47.17 1.2.5 Sweet potato 212 1015 1 Bags 1.89 1.2.6 Rape 401 1017 Low 7 Bags 6.11 Disease (virus) 1.2.6 Rice 1100 1016 3 Bags 1.36 1.2.6 Sweet potato 87 1017 0.5 Bags 2.29 1.2.6 Maize 3.37 floods can damage the crops 2 to 6 Bags 593 1017 Pumpkin, local squash, 1.2.6 Intercropped with maize cowpeas, water melon

Table 7. Crop seasonality.

17 18

Farmer 1.2: Mulela Situbeko

Although Mulela is married, she is in charge of the farm because her husband is a member of the village parliament (Kuta). He does not get paid for this work but they can get access to special “royalties” like a big canoe to transport the harvested crops. The husband sometimes helps Mulela, especially for harvest time.

The family has one home field (plot 1.3.1), which is relatively close to water and where they grow diverse of kind of vegetables. In the sitapa (plot 1.3.2) she grows rice in most of the area and cassava in a small area. Collected manure from the surroundings is added to the soil. The result of the soil analysis done in this field is shown in the Appendix. In the past she also grew sweet potato but it was often ruined by water (otherwise it performed well, she could harvest around 3 bags of 50 kg* in approximately 360 m2). She is planning to grow pearl millet this year. The rice is planted early to avoid the fish, which comes when the area gets flooded. The mazulu are located from 4.4 to 4.7 km (straight line) from the homestead, which means 2 to 2.5 h walking to get there. She observed that the soil surrounding mukakani (Acacia sieberiana) does not loose moisture as fast as the rest of the plot (Picture 1.3-4). Regarding animals, currently the family has four village chickens and eight ducks. They consume the eggs and meat. The information of the household members is described in Table 8. The plots information and the crops seasonality are shown in Table 9 and 10 respectively. More detailed information is provided in the Appendix.

Table 8. Household members. Household Highest Farm Labor Name Gender (M/F) work At school? school level Age (Years) days/week h/day h/day achieved Mulela Situbeko F 62 6 10 (6) No 9 Kota M 74 1 10 (6) No 11 Mushambatua Kapelua F 22 1 10 (6) No 12 Mokongola Mulela Muanga F 20 1 10 (6) No 12 Mutumba Kabisa F 16 1 3 2.5 Yes 7 Masani F 15 1 3 2.5 Yes 8 Mukelelabo Namwaka F 5 1 3 2.5 Yes 0 Muanga Mulima F 1 No 0 Mulimanwngenga

* Correspond to a 50 kg mealie mill bag filled with sweet potato. The real weight needs validation.

19 Table 9. Plots information. Plot Field Area Crop Area Average Production Production Production (kg Crop Land type Production Notes number (m2) (m2) Altitude (masl) perception unit (Mg ha-1) 1.3.1 Sorghum Lilako 260 High 2 Bags 3.85 1.3.1 Sweet potato Lilako 247 High 6 to 7 Bags 1.54 1.3.1 Tomato Lilako 20 662 1012 Pumpkin & Performs well when 1.3.1 Lilako 125 High 120 Units 9600 units ha-1 local squash it is planted early 1.3.1 Onion Lilako 10 Plough by hand 1.3.2 Rice Sitapa 5206 1016 High 8 to 10 Bags 0.91 (hoe). cv. nerika and 5564 jacket 1.3.2 Cassava Sitapa 358 1015 Med-High 1 Bags 1.12 1.3.3 Rice Lizulu (lower part) 1959 1019 High 6 Bags 1.53 cv. super 2694 1.3.3 Maize Lizulu 735 1021 High 8 Bags 5.44 1.3.4 Rice Lizulu (lower part) 1207 1008 High 5 Bags 2.07 1909 1.3.4 Maize Lizulu 702 1010 Low 2 Bags 1.42 1.3.5 Rice Lizulu (lower part) 2946 1015 Low 2 Bags 0.34 cv. super 3616 1.3.5 Maize Lizulu 670 1018 High 1 Bags 0.75 1.3.6 Rice Lizulu (lower part) 2045 1013 High 3.5 Bags 0.86 cv. super 2642 1.3.6 Maize Lizulu 597 1015 Low 1.5 Bags 1.26 1.3.7 Rice Lizulu (lower part) 1547 1010 High 2 Bags 0.65 1894 1.3.7 Maize Lizulu 347 1016 High 4 Bags 5.76 1.3.8 Rice Lizulu (lower part) 536 536 1011 High 2 Bags 1.87 cv. super

20 Table 10. Crop seasonality.

22 Farmer 1.4: Lisuanizó Kamona

Lisuanizó is married but she is the responsible for the farm. His husband main activity is fishing. The sales of fish and scones (Table 11) represent the daily income for the family. The farm consists in three plots located 0.8, 1.4 and 2 km (straight line) from the homestead. Besides the walking distance to the fields, the farmer has to cross a canal (plot 1.4.1) and a lagoon (plot 1.4.3) to reach them. She applied manure as kutuliza in plot 1.4.2 and for mazulu 1.4.1 (practice of shifting manures: keep the cattle for 2 days in the same place. Then, the cattle are moved to another spot and the fertilized area is plough). For the field 1.4.3 she collects manure manually from the surroundings. She pays from 50 to 200 ZMW per plot for kutuliza (Table 12). Rice is broadcasted. The plots information and the crops seasonality are shown in Table 13 and 14 respectively. More detailed information is provided in the Appendix.

Table 11. Household members. Household Farm Labor Off Farm Labor Highest Gender work At Name school level Notes (M/F) Age Amount/day school? Days/week h/day h/day Month/year days/week h/day achieved (Years) (ZMW) She manages the farm. Lisuanizó F 55 6 7 2 12 6 2 20 No 7 Off farm labor: sell scones Husband. Off farm Sikufele M 70 6 2 11 6 7 20 No 7 labor: fishing Off farm labor: fishing Mwangala F 35 6 7 2 1 (July) 2 3 20 No 7 using baskets. Money for personal use Mwene F 15 1 7 1 Yes 7 Off farm labor: fishing Lubasi M 24 6 7 2 1 (July) 2 3 20 No 9 using baskets. Money for personal use Off farm labor: fishing Pelekelo M 22 6 7 2 1 (July) 2 3 20 No 9 using baskets. Money for personal use Mate M 14 1 1 1 Yes 9 1 + 8 Skufele F No month

Table 12. Land preparation cost: Plough plus labor Field number Cost ZMW 1.4.1 200 1.4.2 50 1.4.3 50

23 Table 13. Farm information. Average Plot Field Area Crop Area Production Production Production Crop Land type Altitude Production Notes number (m2) (m2) perception unit (Mg ha-1) (masl) 1.4.1 Maize Lizulu Medium 2 to 3 Bags 0.64 Pumpkin and 1968 1.4.1 Lizulu Intercropped squash Problems: 1.4.1 Tomato Lizulu 1968 984 1014 High 5 Basket 1.78 water and nutrients 1.4.1 Rape Lizulu 394 High 4 to 5 Bags 4.00 1.4.1 Cabbage Lizulu 492 High 1.4.1 Sweet Potato Lizulu 98.4 Wet cv. Nerika. 1.4.2 Rice 2500 2500 1012 High 10 Bags 2.00 Litongo Kutuliza Kutuliza 1.4.3 Maize Lizulu Medium 1 to 2 Bags 0.36 Pumpkin and 2101 2101 1015 1.4.3 Lizulu squash

Table 14. Crop seasonality. Sept Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Hot Season Plot number Crop Land type Rainy Season Cold season Hungy Period Fish Ban 1.4.1 Maize Lizulu Plant Harvest 1.4.1 Pumpkin & squash Lizulu Plant Harvest 1.4.1 Tomato Lizulu Plant Harvest 1.4.1 Rape Lizulu Plant Harvest 1.4.1 Cabbage Lizulu Plant Harvest 1.4.1 Sweet Potato Lizulu Plant Harvest 1.4.2 Rice Wet Litongo Plant Harvest 1.4.3 Maize Lizulu Plant Harvest 1.4.3 Pumpkin & squash Lizulu Plant Harvest

Regarding animals, she had some chicken but they died.

24 25 2. Community of Mapungu

The village is located in the Kalabo district in the border of plains and saana (Figure 5). Compared to the other two communities Mapungu has the wider saana area. The plots of in this community are distributed along a big area, with distances reaching up to 10.6 km (straight line) between two plots from the same farmer (farmer 2.4). Some farmers have alternative houses (which could be temporal or permanent) in the plain that are used to stay during the dry period in order to take care of their fields and animals better. This is the only community which has plots in likaña, libala saana and litunda land types.

Figure 5. Distribution of fields of the interviewed farmers from Mapungu.

26 Farmer 2.1: Sifuniso Imbuwa

Mrs. Sifuniso adopted many children throughout her life. Currently she lives with four girls and one who comes only during holidays (Figure 15). She also shares her land with other orphan children that do not live with her.

Table 15. Household members. Household Farm Labor Highest Gender work Name At school? school level Notes (M/F) Age days/week h/day h/day achieved (Years) Sifuniso Imbuwa F 66 6 7 3 No 7 Sifuniso Akufuna F 17 1.5 3 1.5 Yes 9 Nemakau F 13 1.5 3 1.5 Yes 5 Akufuna Nawa Sitali F 10 1.5 3 1.5 Yes 4 Naomi Sitenge F 10 1.5 3 1.5 Yes 3 Boarding school, Imbuwa Akufuna F 10 0 0 0 Yes 11 comes only on holydays

She divides her land in four plots contained in six different land types (Figure 16). The first three plots are relatively close from the house, in the border between the plain and the saana. The plot 2.1.1 (lizulu) is left fallow during hot season because it is too dry. They built a shelter to protect the tomatoes from the sun, together with the girls collects cattle manure from the plain applied it to tomato. The tomato performs very well and she sells almost the whole production. There are 3 options for selling tomato: Sell from house; exchange for fish; sell in Kalabo (old daughter goes). She mentioned that soil fertility from plots 2.1.3 and 2.1.1 is low and have decreased compared to the past. Plot 2.1.4 is located in the saana, 2.3 km (straight line) from the farmhouse. This plot is approximately 285 m long, which have different land types (see map in Appendix). Although it is not located in the plain, it has two areas that are considered to be sitapa, one is near a lagoon and the area which can be grown depends on the water level. The plots information is shown in Table 17. More detailed information is provided in the Appendix.

Table 16. Crop seasonality. Sept Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Plot number Land type Crop (Lozi) Hot Season Rainy Season Cold season Hungy Period Fish Ban 2.1.1 Maize Lizulu Plant Harvest 2.1.1 Tomato Lizulu Plant Harvest 2.1.1 Veggie Nursery Lizulu Plant Harvest 2.1.1 Rape Lizulu Plant Harvest 2.1.1 Sweet Potato Lizulu Plant Harvest 2.1.2 Groundnuts Litapa Harvest Plant 2.1.2 Rape Litapa Harvest Plant 2.1.2 Tomato Litapa Harvest Plant 2.1.2 Cabbage Litapa Harvest Plant 2.1.2 Sweet Potato Litapa Harvest Plant 2.1.3 Maize Lilako Plant Harvest 2.1.3 Pumpkin Lilako Plant Harvest 2.1.3 Small squash Lilako Plant Harvest 2.1.3 Rape Lilako Plant Harvest 2.1.3 1 Papaya Lilako 2.1.3 1 Mango Lilako 2.1.3 Tomato Lilako Plant Harvest 2.1.3 Sweet Potato Lilako Plant Harvest 2.1.4B Groundnuts Litapa Harvest Plant 2.1.4C Maize Lizulu Plant Harvest 2.1.4C Sweet Potato Lizulu Plant Harvest 2.1.4 D1 Sweet Potato Litema Plant Harvest 2.1.4 D2 Cassava Litema Harvest Plant 2.1.4A Tomato Litema Plant Harvest 2.1.4E Cassava Likaña Harvest Plant 2.1.4F Rice Litapa Plant Harvest

Table 17. Farm information. Field Average Plot Crop Crop Area Production Production Production Land type Area Altitude Production Notes number (Lozi) (m2) perception unit (Mg ha-1) (m2) (masl) Low fertilization and floodings. Stop 2.1.1 Maize Lizulu 3924 Low Almost 0 Bag planting maize Similar date, different 2.1.1 Tomato Lizulu 3210 1015 Medium 10 Basket 1.09 areas Veggie 2.1.1 Lizulu 20 Nursery 7134 2.1.1 Rape Lizulu 2497 Low 2 Bag 0.28 Similar date, different Sweet First Year. The planting is in stages, as 2.1.1 Lizulu 4637 1013 areas Potato the water recedes 2.1.2 Groundnut Litapa 946 Medium 1 Bag 0.53 2.1.2 Rape Litapa 488 High 5 Bag 3.59 Did not harvest all: floodings 2868 1012 2.1.2 Tomato Litapa 946 Medium 1.5 Basket 0.55 Did not harvest all: floodings 2.1.2 Cabbage Litapa 488 Medium 7 to 8 Bag 5.38 Sweet 2.1.2 Litapa 1388 278 1011 High 7 Bag 10.09 Potato 2.1.3 Maize Lilako Medium 2 0.75 2.1.3 Pumpkin Lilako Intercrop with maize 1330 Small 2.1.3 Lilako Intercrop with maize squash 2.1.3 Rape Lilako 330 1675 1017 2.1.3 1 Papaya Lilako 2.1.3 1 Mango Lilako 2.1.3 Tomato Lilako 1005 High 80 Basket 27.86 Sweet 2.1.3 Lilako 340 High 1 Bag 1.47 Potato The rest of the plot is 2.1.4B Groundnut Litapa 2390 538 1016 5 Bag 4.65 The area depends on the water level used by other children 2.1.4C Maize Lizulu 4339 Low 3 0.35 Sweet 4339 1018 2.1.4C Lizulu 1675 2.39 Potato High 10 Bag Sweet 2.1.4 D1 Litema 3020 3020 1019 1.32 Potato 2.1.4 D2 Cassava Litema 1844 1675 1020 Low 1 Bag 0.3 Early floodings 2.1.4A Tomato Litema 578 578 1021 Medium 15 Basket 9.08 2.1.4E Cassava Likaña 3599 3674 1020 Very low 2 to 4 Bag 0.33 2.1.4F Rice Litapa 7177 7177 1019 Very low 2.5 Bag 0.17 Yield is decreasing over time

29 30

Farmer 2.2: Susiku Nosiku

Susiku lives with her wife and children (Table 18). Since they have many fields located far away in the plain, later in the cold season part of the family usually stay temporally there in order to take care better those fields (Figure 6). During the rainy season most of the people are cultivating, therefore there are more job opportunities (peace work).

Table 18. Household members. Household Farm Labor Off Farm Labor Gender work At Name Notes (M/F) Age Amount/day school? Days/week h/day h/day Month/year days/month (Years) (ZMW)* Susiku nosiku M 62 6 6 No Sepiso mwangala F 47 6 6 3 4 1.5 10 No Piecework, Lungowe susiku F 21 6 6 3 4 1.5 10 No rainy Wabeyi F 19 6 4 3 4 1.5 10 No season Mungandi M 17 1 4 Yes Mwangala F 15 1 4 Yes Nalishebo F 13 1 2 Yes Sibeso F 10 1 2 Yes Mundiya M 2 No

They divide their production land in ten plots (Table 19). The fields set in the plain are 4 up to 5.8 km (straight line) far from their permanent house. They usually applied manure in the mazulu using borrowed oxen from relatives (shifting the animals to different zones within the plot). The crops seasonality is shown in Table 20. More detailed information is provided in the Appendix. The plots located in the saana are 0.29 up to 3 km (straight line) from the house. Regarding the sales of sweet potato, he mentioned that if they harvested in august it is much essayer to sell them, they usually sell everything before reaching Mongu by canoe.

Figure 6. Temporal huts located in the in the plain (August)

31 Table 19. Farm information. Production Production Production Plot number Crop Land type Field Area (m2) Crop Area (m2) Average Altitude (masl) Production Notes perception unit (kg Mg -1) 2.2.1 Maize & Pumpkin Lizulu 4317 4317 1009.3 abandoned plot yield depends on manure 2.2.2 Maize Lizulu 2514 2514 1009.7 usually high 8 to 30 bag 3.18 application flooded: the water took last year only got 2 bags 2.2.3 Maize Sitapa 2307 2307 1012 10 to 15 bag 2.60 the upper part of the soil because could not weed (erosion) Rape & other Veggies; Nursery 2.2.4A Lilako 329 329 sweet potato harvest earlier cause of 2.2.4 Groundnut Sitapa 686 high 10 bag 7.28 2288 1015 floodings 2.2.4 Maize Sitapa 1144 high 8 to 10 bag 3.93 yield depend on the water 2.2.5 Rice Libala saana 24441 8147 1015 2 bag 0.12 broadcasted sown level 2.2.6 Cassava Likaña 2629 2629 1014 14 bag 2.13 in 2011 2.2.7A Cassava nursery Lilako 392 intercropped 2.2.7A Maize Lilako 856 1020 2 to 3 bag 3.19 2.2.7B Maize Lilako 464 2.2.8A Maize Lilako 529 5 4.73 593 1018 2.2.8B Sweet Potato Nurcery Lilako 64 2.2.9 Mango tree Litunda 1321 1321 1019 2.2.9 Sweet Potato Litunda not sure harvest in different times for home consumption 2.2.10 Cassava Litunda 2.2.10 Maize Litunda 2805 2805 1021 15 bags 2.67 2.2.10 Cowpea Litunda

Table 20. Crop seasonality. Sept Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Hot Season Plot number Crop Land type Rainy Season Cold season Hungy Period 2.2.1 Maize & Pumpkin Lizulu Plant Harvest 2.2.2 Maize Lizulu Plant Harvest 2.2.3 Maize Sitapa Plant Harvest Plant 2.2.4A Rape & other Veggies; Nursery sweet potato Lilako Plant Harvest 2.2.4 Groundnut Sitapa Harvest Plant 2.2.4 Maize Sitapa Plant Harvest Plant 2.2.5 Rice Libala saana Plant Harvest 2.2.6 Cassava Likaña Harvest Plant 2.2.7A Cassava nursery Lilako 2.2.7A Maize Lilako Plant Harvest 2.2.7B Maize Lilako Plant Harvest 2.2.8A Maize Lilako Plant Harvest 2.2.8B Sweet Potato Nurcery Lilako 2.2.9 Mango tree Litunda Harvest 2.2.9 Sweet Potato Litunda Harvest Harvest Plant Plant 2.2.10 Cassava Litunda Harvest Plant 2.2.10 Maize Litunda Plant Harvest 2.2.10 Cowpea Litunda Plant Harvest 32 33 Farmer 2.3: Wamunyima Nyambe

The farmer lives with his wife and children in the Kamonga village (Table 21).

Table 21. Household members. Household Farm Labor Highest Gender work At Name school level Notes (M/F) Age school? days/week h/day h/day achieved (Years) Wamunyima Household work: M 44 5 3-4 1 No 12 Nyambe collect fire wood Jane Mukelabai F 37 5 3-4 3 No 7 Liywali Litia M 14 3 1 1 Yes Mukelabai Litia M 9 3 1 1 Yes Namwaka Likezo F 11 3 1 1 Yes Mundia Nyambe M 7 3 1 1 Yes Mubita Nyambe M 5 1 1 1 No Sepiso Nyambe F 1 No F 8 3 1 1 Yes Musanga Kutundi

The farmer divided his land in four plots (Table 22). The field 2.3.1 is a new field that was grown with cassava 10 years ago. It is located 0.7 km from the farmhouse. When a litema land type is left fallow for several years, is called Lihula. The farmer is planning to cut the trees and plant cassava in August or September (100% of the area) for home consumption (leaves and tubers). The tubers will be harvested after 2-3 years. The farmer is willing to intercrop with squash, pumpkin or cowpeas depending on his budget to buy seeds.

Field 2.3.2 is located close to the house. During the hot season he only maintains a small area with crops (local eggplant “impwa” and cabbages) because the soil is very sandy and therefore requires high irrigation frequency. Even when this field is relatively close to water, there is not enough labor available to irrigate the whole area by hand.

The farmer used field 2.3.3 for two years but next growing season he decided to move to another area since the soil fertility decreased considerably and subsequently yields where very low. The weed control management can possibly explain the cause of the soil nutrient depletion in this area. Every year, when the flooding water dries, the farmer faces a high weed development. The regular management is to wait until they dry, burned and plough manually or by oxen, depending on the budget. For this year the farmer is planning to plant cabbage in one small area. Pumpkin is usually intercropped with maize.

In the filed Field 2.3.4 cattle manure is sometimes applied. The management consists of keeping the cows for some time in restricted area, which will be later shift to another zone. This practice is known in Lozi as kutuliza. Since the farmer does not have animals (besides few chicken) he asks someone else to bring the cows to his land. After kutuliza they plough so the manure will not be removed by flooding. However, usually they bring the cattle late, preventing him to plant maize that year (in part or the whole area). This time mismatch is caused because the livestock owner used the manure first to fertilize his own fields before lending the cattle to someone else. Pumpkin is usually intercropped with maize. The crops seasonality is shown in Table 23.

They currently have two-village chicken. He wants to increase the number of chickens to sell chickens in the future and use small part for home consumption; therefore they do not consume eggs or meat. The farmer is planning to build a fence and to buy maize bran for the chicken. They are planning to enlarge their house and to build a fence to keep chickens.

34 Table 22. Farm information.

Altitude Production Plot number Crop Land type Area (m2) Production Production Unit Production (Mg ha-1) Notes (masl) perception

Plans to 2.3.1 Cassava Litema (lihula) 20727 1021 plant 2.3.2 Tomato 938 Medium 40 Basket 13.8 2.3.2 Tomato 80 1019 2.3.2 Local eggplant 80 First time Lilako 2.3.2 Cabbage 60 1017 Nursery 2.3.2 Maize 194 1022 Medium 2.3.2 Cassava 280 1021 Medium 2.3.3 Maize Low 5 Bag 0.5 Sitapa 5000 2.3.3 Pumpkin Medium Estimated Area 2.3.4 Maize High 15 Bag 0.75 Lizulu 10000 2.3.4 Pumpkin Medium

Table 23. Crop seasonality. Sept Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug

Plot number Crop Land type Hot Season Rainy Season Cold season Hungy Period Fish Ban 2.3.1 Cassava Litema (lihula) Plant 2.3.2 Tomato Plant Harvest 2.3.2 Tomato Plant Harvest Plant Harvest 2.3.2 Local eggplant Plant Harvest Plant Harvest Lilako 2.3.2 Cabagge Plant Harvest Plant Harvest 2.3.2 Maize 2.3.2 Cassava 2.3.3 Maize Harvest Sitapa 2.3.3 Pumpkin Harvest 2.3.4 Maize Plant Harvest Lizulu 2.3.4 Pumpkin Plant Harvest

35 36 Farmer 2.4: Lifasi Nasilele

Lifasi lives with his wife and five children (Table 24). This family has maybe a higher wealth level than the other interviewees. Besides the house they own in the village, they also have other houses in the plain, which are used by relatives. In certain periods they stay in those houses or in temporal ones. Because they possess extra infrastructure like an animal cart (Figure 7) and an electric mill they obtain off-farm income (Table 25). In addition, the family has cattle and a village chicken business (Table 26). The plots information and the crops seasonality are shown in Table 27 and 28 respectively. More detailed information is provided in the Appendix.

Regarding the animal management, during February, March and April (rainy season) the cattle are moved to the upper land and are lend to others for kutuliza (shifting manure). In May the cattle is brought back to the plains. Lifasi takes care of the animals in this period and use them to fertilize his fields. Later he also borrows the animal to fertilize neighbor fields. For maize fertilization he buys 3 or 4 extra bags of bat manure per year (20 ZMW per bag). In the past, when they had a cooperative he uses to buy D-Compound and Urea, but not anymore.

Figure 7. Animal cart used to sell firewood.

The farmer divides his production land in 12 plots, which five of them are located in the plains. The most remote plot is 7.7 km (straight line) from the homestead and 2 km from the “plain house”. Although he has cattle, kutuliza is not practiced in litapa because the access is difficult and he considers since it has loamy soil it is not necessary. In this land type weeds are controlled manually. He considers that the best maize seeds for litapa are cultivar 441. For mazulu the best cultivars in his opinion are 603, 604.

The remotest field located in the saana is approximately 4.8 km (straight line) from the farmstead. However, the access to these areas is much easier than to ones located in the plain. He would like to enlarge the field 2.4.4 (litema), which means to cut all the bushes. He commonly does not practice kutuliza in these fields because soil has good fertility. The rice is broadcasted, the land is ploughed and the yield is determined by the water level (rain and underground water table): if it is too low rice will not perform fine.

Lifasi mentioned that he observes a changing pattern comparing with the past: Now more people prefer fishing rather than farming because climate change (flooding) makes farming too self- sacrificing and risky.

37 Table 24. Household members. Household Highest Farm Labor Gender work school Name At school? (M/F) level Age (Years) days/week h/day h/day achieved Lifasi Nasilele M 48 6 6 2 No 12 Nalucha Nwambe F 47 6 6 2 No 9 Mulema F 24 6 6 2 Yes 7 Nwambe M 17 1 2 1 Yes 9 Masueñeho M 14 1 2 1 Yes 5 Mulako M 12 1 2 1 Yes 1 Muyunda M 4 1 No

Table 25. Off farm income Off Farm Income Description Price (ZMW)* Unit Unit per year Sell mataka (kind of brooms) 5 Broom 20 to 30 Sell papyri mats 10 Mat 20 to 30 Electric mill 3 10 kg Sell fire ire wood 100 Full cart 2 *One Euro ≈ 7.8 ZMW (Zambian kwacha)

Table 26. Animals Animal Number/Farm Uses Notes Cost Type Reproduce Very rarely for Village 15 for selling in consumption. Son's chicken the future business Plough, Sell animals when they Oxen 8 80 ZMW per month manure are old (around 1 per for taking care of year). Brother has 2 Milk, plough, the animals + oxen and 1 cows (they Cow 5 manure, sell medicine (20 ZMW managed them all (milk) per animal per year) together)

38 Table 27. Farm information. Average Plot Crop Area Production Production Production Crop Land type Altitude Production Notes number (m2) perception unit (Mg ha-1) (masl) Libala 2.4.1 Rice 14280 1019 Medium 9 Bags 0.32 Did not grow last year: late cv. super Saana 2.4.2 Cassava Likaña 1253 1019 High 11 to 14 Bags 3.99 This year low harvest: early floodings 2.4.3 Maize Lilako 1 Bags 2.04 Sweet 2.4.3 Lilako 2 Bags 3.27 Potato Pumpkin 245 1021 2.4.3 and local Lilako eggplant Intercropped with maize 2.4.3 Hibiscus Lilako Pick leaves 2.4.3 Cassava Lilako 714 5 Bags 2.80 Sweet 2.4.4 Litema 445 1023 Medium 5 to 6 Bags 4.94 Not this year (no seeds) Potato 2.4.5 A G. Nuts Sitapa 1266 1015 High 11 Bags 4.34 2.4.5 B G. Nuts Sitapa 2.4.5 B Cowpeas Sitapa 474 1014 Low 0.5 0.53 Floodings 2.4.5 B Maize Sitapa Next year 2.4.6 Maize Sitapa Low 3 or 4 4 for home cons Pumpkin 2.4.6 and Sitapa 1503 1015 Squash 2.4.6 Hibiscus Sitapa Sweet 2.4.7 Lizulu 471 1018 Potato Pumpkin 2.4.8 and Local Lizulu 21500 1021 Squash 2.4.8 Maize Lizulu Low 4 Bags 0.09 Low fertility 2.4.9 A Maize Lizulu 10526 1023 0 Bags 0 No harvest: floodings Planted late 2.4.9 B Maize Lizulu 8946 1025 High Mid date plant 18 Bags 1.21 2.4.9 C Maize Lizulu 7433 1022 Medium Too dry Planted early Sweet 2.4.9 D Lizulu 868 0 No harvest: floodings Potato 1023 0 2.4.10 Cassava Likaña 1513 1019 Not this year 2.4.11 Cassava Likaña 1125 1019 2.4.12 Cassava Likaña 1631 1019

39 Table 28. Crop seasonality. Sept Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Plot Rainy Season Cold season Crop Land type number Hungy Period Fish Ban 2.4.1 Rice Libala Saana Plant Harvest 2.4.2 Cassava Likaña Harvest Harvest Plant Plant Plant Plant 2.4.3 Maize Lilako Plant Harvest 2.4.3 Sweet Potato Lilako Plant Harvest 2.4.3 Pumpkin & Malaka Lilako Plant Harvest Harvest 2.4.3 Hibiscus Lilako Plant (leaves) 2.4.3 Cassava Lilako Plant any time and harvest after one year 2.4.4 Sweet Potato Litema Plant Harvest 2.4.5 A G. Nuts Sitapa Plant Harvest Plant 2.4.5 B G. Nuts Sitapa Plant Harvest Plant 2.4.5 B Cowpeas Sitapa Plant Harvest 2.4.5 B Maize Sitapa Harvest Plant 2.4.6 Maize Sitapa Plant Harvest 2.4.6 Pumpkin & Squash Sitapa Plant Harvest Harvest 2.4.6 Hibiscus Sitapa Plant (leaves) 2.4.7 Sweet Potato Lizulu Harvest Plant 2.4.8 Pumpkin and Local Squash Lizulu Plant Harvest 2.4.8 Maize Lizulu Plant Harvest 2.4.9 A Maize Lizulu Plant No harvest 2.4.9 B Maize Lizulu Plant Harvest 2.4.9 C Maize Lizulu Plant Harvest 2.4.9 D Sweet Potato Lizulu Plant No harvest 2.4.10 Cassava Likaña Harvest Harvest Plant Plant Plant Plant 2.4.11 Cassava Likaña Harvest Harvest Plant Plant Plant Plant 2.4.12 Cassava Likaña Harvest Harvest Plant Plant Plant Plant

40 41 3. Community of Nalitoya, Sifuna and Nembwere

These three villages where considered as one community since they are located very close to each other. Nalitoya is located closer to the upland compared with Lealui and Mapungu. In general, the plots are distributed closer to the homesteads (Figure 8). It is the only community that has plots located in the sishanjo land type. Most of the houses are located in the border between dry litongo and wet litongo or sishanjo where the change in soil moisture and nutrient content is very drastic between these two areas. Although none of the farmers use to control weeds by burning, some of their fields had ashes and burned material. They explained that sometimes the fire from neighbor fields passes to their plots.

Most of the plots are relatively close to a canal; the farthest is approximately 0.7 km away (straight line). When the canal is maintained clear, the canal channeling and land drainage improves. This practice leads to an increase of surrounding areas that can be used for agricultural purposes (Lishanjo). However, the canal clearance must be maintained every year and this is not well organized. Last year it was done by the community members and they received plots in exchange of their work. However, it is not possible to deploy this solution every year. The farmers in general realize on the benefits of clearing the canal but they demand tools for doing the labor properly and time efficiently.

Figure 8. Distribution of fields of the interviewed farmers from Nalitoya.

42 Farmer 3.1: Mubita Sitali

Mubita lives with his wife and children (Table 29). Beside being a farmer, he is also a community facilitator. The plots information and the crops seasonality are shown in Table 30 and 31 respectively. More detailed information and soil analyses results from one dry litongo and sishanjo are provided in the Appendix.

Most of the plots are close the house with exception of mazulu, which are 3.3 km (straight line) from the farmhouse. Kutuliza practice is not usually done in this farm. Instead they collect cattle or bat manure (25 kg a week approximately) or buy it (15 ZMW per 25 kg bag) to fertilize dry matongo and mazulu. If there are any residues they are incorporated using hoes. In mazulu he uses mukakani leaves (local bush) as green manure (Picture 3.1-1).

Table 29. Household members. Household Highest * Farm Labor Off Farm Labor Gender Age work At school Name Notes (M/F) (Years) days/ month/ days/ amount/day school? level h/day h/day week year month (ZMW)* achieved Off farm Mubita M 51 6 8 to 9 1 3 1 or 2 50 No 9 labor: peace Sitali work Off farm Inonge F 48 6 8 to 9 3 3 1 or 2 50 No 7 labor: peace Lubinda work Mubita M 21 3 1 1 Yes 10 Mubita Pumulo M 19 3 1 1 Yes 11 Stali M 16 3 1 1 Yes 8 Monday F 11 3 1 1 Yes 5 Lutangu M 7 3 1 Yes 1 Pumulo F 3 0 0 1 No * Besides the farm work, the head of the household goes fishing once a week on June, July and August. * Children help cooking and collecting firewood *One Euro ≈ 7.5 ZMW (Zambian kwacha)

Table 30. Farm information.

Crop Area Field Area Altitude Production Production Plot number Crop Land type Production Production Unit (m2) (m2) (masl) perception (Mg ha-1)

3.1.1 Maize Lizulu 2410 1013 High 8 to 10 Bag 1.87 6076 3.1.1 Tomato Lizulu 3666 1012 Low 6 or 7 Basket 0.62 3.1.2 Maize Lizulu High 6 to 8 Bag 0.88 3993 1012 3.1.2 Sweet potato Lizulu 5093 High 3.1.2 Tomato Lizulu 1100 1011 Low 4 or 5 Basket 2.86 3.1.3 Rape Sishanjo 231 1012 High 3 to 4 Bag 5.30 3.1.3 Cabbage Sishanjo 231 1012 Medium 7 to 8 Bag 11.35 3.1.3 Tomato Sishanjo 1012 High 8 to 10 Basket 6.81 463 1388 3.1.3 Rice Sishanjo 1012 Very low 2.5 to 3.5 Bag 3.24 3.1.3 Cowpeas Sishanjo 231 1012 Very low 0.5 Bag 1.08 3.1.3 Sweet potato Sishanjo 231 1012 Medium 3 to 4 Bag 6.05 3.1.4 Rice Sishanjo 1298 1012 Very low 6 to 8 Bag 2.70 2163 3.1.4 Maize Sishanjo 865 1014 Very low 2 to 4 Bag 1.73 3.1.5 Cassava Sishanjo 1013 Very low 2.5 Bag 0.49 2037 3.1.5 Maize Sishanjo 3395 1013 Low 2 to 3 Bag 0.61 3.1.5 Maize Sishanjo 1358 1013 Low 2 to 3 Bag 0.92 3.1.6 Rice Sishanjo 1187 1013 Very low 5 to 6 Bag 2.32 1187 3.1.6 Maize Sishanjo 1187 1013 Low 2 to 3 Bag 1.05 3.1.7 Maize Dry litongo 1304 1017 Very low 1 Bag 0.38 1304 3.1.7 Cassava Dry litongo 1304 1017 Medium 3 to 4 Bag 1.07 3.1.8 Millet Dry litongo 2410 2410 1016 Medium 2 to 3 Bag 0.52

They had 20 chickens before but they died.

44 Table 31. Crop seasonality. Sept Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Hot Season Plot number Crop Land type Rainy Season Cold season Hungy Period Fish Ban 3.1.1 Maize Lizulu Plant Harvest 3.1.1 Tomato Lizulu Plant Harvest 3.1.2 Maize Lizulu Plant Harvest 3.1.2 Sweet potato Lizulu Plant Harvest 3.1.2 Tomato Lizulu Plant Harvest 3.1.3 Rape Sishanjo Plant Harvest 3.1.3 Cabagge Sishanjo Plant Harvest 3.1.3 Tomato Sishanjo Harvest Harvest Plant Plant Plant Harvest 3.1.3 Rice Sishanjo Plant Harvest 3.1.3 Cowpeas Sishanjo Harvest Plant 3.1.3 Sweet potato Sishanjo Plant Harvest 3.1.4 Rice Sishanjo Plant Harvest 3.1.4 Maize Sishanjo Harvest Plant 3.1.5 Cassava Sishanjo Harvest Plant 3.1.5 Maize Sishanjo Plant Harvest Plant 3.1.5 Maize Sishanjo Plant Harvest Plant 3.1.6 Rice Sishanjo Plant Harvest 3.1.6 Maize Sishanjo Plant Harvest Plant 3.1.7 Maize Dry litongo Plant Harvest 3.1.7 Cassava Dry litongo Harvest Plant 3.1.8 Millet Dry litongo Plant Harvest

46 Farmer 3.2: Isaac Inambao

He lives with his wife and children (Table 32). They divide his productive land in 4 plots; two are relatively close to the homestead while the other is 1.9 km approximately away (Table 33). Crops seasonality and animal information are shown in Table 34 and 35, respectively. He practices kutuliza when it is possible and yields strongly vary depending on this practice. After kutuliza he plough to incorporate the manure and/or the residues. Weeding is done by hand. Because of health problems, he did fertilize and could not spend much time in the rice fields; therefore he harvested three bags less compared to the previous year. There is a demonstration plot as part of a program from the ministry of agriculture. He is planning to get cabbage seeds from there. Last year they brought the seeds too late so the crop did not performed well (rain).

Table 32. Household members. Household Farm Labor Gender work Highest school Name At school? Notes (M/F) Age level achieved days h/day h/day (Years) Isaac Inambao M 55 6 7 No 10 Nalukui F 43 6 7 1 No 8 Namulokua F 17 6 4 2 No 6 Only comes on Namjeke F 18 1 5 Yes 11 holidays Ilitongo F 12 1 4 1 Yes 5 Likando M 9 1 4 Yes 1 Lubasi M 7 1 4 Yes 1 Mwaka F 1 No

47 Table 33. Farm information. Field Crop Area Altitude Production Production Production Plot number Crop Land type Production Notes Area (m2) (m2) (masl) perception Unit (Mg ha-1) 3.2.1 Maize Dry litongo 1014.5 High 15 Bag 2.68 Kutuliza Only in the borders, close to the 3.2.1 Pumpkin Dry litongo 2803 1014.5 Low * house 3.2.1 Local squash (malaka) Dry litongo * 3.2.1 Sweet potatos Wet litongo 190 1014 2 Bag 4.21 Nursery 3.2.1 Ground nuts Wet litongo 0.5 Bag 0.27 921 Intercropped 3.2.1 Cowpeas Wet litongo 0.5 Bag 0.27 4835 3.2.1 Onion Wet litongo 168 Medium 3.2.1 Rape Wet litongo 168 High 4 to 6 Bag 10.4 3.2.1 Cabbage Wet litongo 168 1014 3.2.1 Banana Wet litongo 2 trees Two trees 3.2.1 Tomato Wet litongo 168 3 Basket 6.24 3.2.1 Eggplant Wet litongo 168 First time he grows eggplant 3.2.1 Cassava Wet litongo 80 a bit Extra area in the south of the field 3.2.2 Maize Dry litongo Upper part. No kutuliza Medium 6 Bag 1.22 3.2.2 Maize Wet litongo 2456 1015 Lower part. No kutuliza 3.2.2 Pumpkin Wet litongo 4402 Intercropped 3.2.2 Ground nuts Wet litongo 896 1010.3 3.2.2 Cassava Wet litongo 1050 1012.5 3.2.3 Sorghum Lizulu Low 2.5 Bag 0.44 3.2.3 Okra Lizulu 2845 2845 1008 Intercropped 3.2.3 Pumpkin Lizulu * 3.2.3 Local squash (malaka) Lizulu * Only sometimes, close to the canal. Use 1 bag for home consumption and 3.2.4 A Maize Sishanjo 3208 Low 2 Bag 0.31 the other as payment for weeding (8 11457 people) less area if he grows maize in that 3.2.4 A Rice Sishanjo 9166 1006 low-medium 9 to 10.5 Bag 0.52 area. Yield depends on kutuliza 3.2.4 A Sweet potatos Sishanjo 2291 1006.5 3.2.4 B Rice Sishanjo 3047 3047 1007 5 to 6.5 Bag 0.98 Yield depends on kutuliza * Pumpkin: in total only 10 units because they eat the leaves. * In total 4 bags of local squash.

Table 34. Crop seasonality. Sept Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Hot Season Plot number Crop Land type Rainy Season Cold season Hungy Period Fish Ban 3.2.1 Maize Dry litongo Plant Harvest 3.2.1 Pumpkin Dry litongo Harvest Plant 3.2.1 Local squash (malaka) Dry litongo Harvest Plant 3.2.1 Sweet potato Wet litongo Plant Harvest 3.2.1 Ground nuts Wet litongo Plant Harvest 3.2.1 Cowpeas Wet litongo Plant Harvest 3.2.1 Onion Wet litongo Harvest Plant Transplant 3.2.1 Rape Wet litongo Plant Harvest 3.2.1 Cabbage Wet litongo Plans to Plant 3.2.1 Banana Wet litongo 2-3 clusters per year 3.2.1 Tomato Wet litongo Plant Harvest 3.2.1 Eggplant Wet litongo Plant 3.2.1 Cassava Wet litongo Harvest Plant 3.2.2 Maize Dry litongo Plant Harvest 3.2.2 Maize Wet litongo Plant Harvest 3.2.2 Pumpkin Wet litongo Harvest Plant 3.2.2 Ground nuts Wet litongo Plant Harvest 3.2.2 Cassava Wet litongo Harvest Plant 3.2.3 Sorghum Lizulu Plant Harvest 3.2.3 Okra Lizulu Plant Harvest 3.2.3 Pumpkin Lizulu Plant Harvest 3.2.3 Local squash (malaka) Lizulu Plant Harvest 3.2.4 A Maize Sishanjo Plant Harvest 3.2.4 A Rice Sishanjo Plant Harvest 3.2.4 A Sweet potato Sishanjo Plant Harvest 3.2.4 B Rice Sishanjo Plant Harvest

Table 35. Animals Animal Type Number/Farm Uses Village chicken 1 Reproduce for selling in the future Duck 6 Meat *One Euro ≈ 7.5 ZMW (Zambian kwacha)

49 50 Farmer 3.3: Phenos Musiwa

Phenos is the youngest of the interviewed farmers. He lives with his wife, daughter, brothers and sisters (Table 36). Most of the household members (including him) have off farm work (Table 37). He recognized that they do not spend enough time working in the farm and therefore they are getting low yields. For example he did not plant sweet potato this year because he delayed to finish the nursery.

He divides his productive land in five plots (Table 39). Two of his plots are located relatively close to the homestead while three approximately 2 km away. However, they have easy access in contrast to farm 3.3.5, which is close to the house but to get there requires crossing flooded areas with high grass (Picture 3.3-2). Plot 3.3.5 is a new field that he got from clearing the canal last year. Although he does not have enough time and labor availability to grow this field, he will try to grow rice because he is afraid that it would be taken away if he does not use it. Rice production in plot 3.3.2 is low due to low soil fertility. For three plots he ploughs with his own ox (Table 38) plus three others borrowed from farmer 3.5 (in exchange of his ox to plough other fields and taking care of the animals). To plough the other two plots he pays 600 ZMW (everything included). The animals are not enough to practice kutuliza in all the plots in a proper way. Crops seasonality is shown in Table 40 and more detailed information is provided in the Appendix.

Table 36. Household members. Household Gender Farm Labor At Highest school Name work Notes (M/F) school? level achieved Age (Years) days/week h/day h/day Phenos Off Farm Labor: at M 30 6 6 No Musiwa power lines Ethel F 24 6 6 2 No Inambao M 17 6 6 No 5 1 (1 day per Wakumelo M 15 2 5 Yes 6 week) 1 (1 day per Mwiya F 12 1 4 Yes 6 week) 1 (1 day per Musiwa M 8 1 4 Yes 2 week) 1 (1 day per Likando M 11 4 Next year 2 week) No 1 (1 day per Mwenda M 11 4 Next year 2 week) No Munalula F 9 2 4 Yes 1 Chuma F 1

Table 37. Off Farm labor Off Farm Labor Total income Gender Name Month Days per Amount Payment Units per per year Notes (M/F) per year month (ZMW)* unit year (ZMW)* Phenos Off Farm Labor: at M 3 6 800 Month 2400 Musiwa power lines Ethel F Sell brooms. 1 or 2 3 Broom 375 1125 times a year to Senanga Mwiya F (250 brooms each time). Inambao M Sell papyri mats. 1 or 2 12 Mat 33 400 times a year to Senanga Wakumelo M (200 ZMW each time) *One Euro ≈ 7.5 ZMW (Zambian kwacha)

Table 38. Animals. Animal Type Number per farm Cost per year (ZMW)* Uses

Oxen 1 35 (medicine) Plough, manure

One Euro ≈ 7.5 ZMW (Zambian kwacha)

Table 39. Farm information.

Field Area Crop Area Average Production Production Plot number Crop Land type Production Production unit Notes (m2) (m2) Altitude (masl) perception (Mg ha-1)

Started harvesting before 3.3.1 A Maize Wet Litongo 1832 Medium 1 Bag 0.27 3054 1013 for home consumption 3.3.1 A Sweet Potato Wet Litongo 611 1 0.65 Not harvested all at once Depends Depending on manure 3.3.1 B Millet Dry Litongo 4475 4475 1016 1.5 to 7 Bags 0.45 on kutuliza application 3.3.2 Maize Wet Litongo Low 5031 3.3.2 Pumpkin Wet Litongo Low 2 Units Mostly eat the leaves 5031 1011 3.3.2 Rice Nursery Wet Litongo 503 3.3.2 Rice Wet Litongo 4528 Low 2.5 Bags 0.28 3.3.3 Rice Sishanjo 12352 12352 1015 Low 8 Bags 0.32 3.3.4 Sweet Potato Sishanjo 306 Low 1.5 Bags 1.96 3201 1013 3.3.4 Rice Sishanjo 2895 Low 3 Bags 0.52 No labor, weeds, needs 3.3.5 Rice Sishanjo 3467 3467 1016 Low 1 Bags 0.14 support

Table 40. Crop seasonality. Sept Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Hot Season Plot Crop Land type Rainy Season Cold season number Hungy Period Fish Ban 3.3.1 A Maize Wet Litongo Harvest Plant 3.3.1 A Sweet Potato Wet Litongo Plant Harvest 3.3.1 B Millet Dry Litongo Plant Harvest 3.3.2 Maize Wet Litongo Harvest Plant 3.3.2 Pumpkin Wet Litongo Harvest Plant 3.3.2 Rice Nurcery Wet Litongo Plant Transplant 3.3.2 Rice Wet Litongo Plant Harvest 3.3.3 Rice Sishanjo Plant Harvest 3.3.4 Sweet Potato Sishanjo Plant Harvest 3.3.4 Rice Sishanjo Plant Harvest 3.3.5 Rice Sishanjo Plant Harvest

52 53 Farmer 3.4: Monde Mulonda

Monde lives with her grandchildren (Table 41). She gets irregular financial support from their parents (50 or 100 each month or a bag of mealie meal). She has eight plots although she does not cultivate all of them due to lack of manpower (Table 43). Regarding the residues management, the animals usually come and eat the remains but they do not stay enough to fertilize the land. She collects manure from the plain (a bag of 25 kg with grounded manure per day). Off farm income and crops seasonality and animal information is shown in Tables 42, 44 and 45 respectively. More detailed information is provided in the Appendix.

Table 41. Household members. Gender Farm Labor Highest school level Name Age (Years) At school? (M/F) Days h/day achieved

Monde Mulonda F 59 6 5 No 7 Mudenda Monga M 27 1 3 Yes 9 Brigitte F 13 1 3 Yes 5 Simbulwa M 8 1 3 Yes 2 Gift Shingumbe M 7 1 3 Yes 2 Mulonda Mulonda M 7 1 3 Yes 2 Kalalukanulonda M 5 1 3 No

Table 42. Off farm income. Off Farm Income Total income Unit per Notes Description Price (ZMW)* Unit per year year Sell mataka (brooms) 3 Broom 200 600 Can be also exchange for maize Sell papyri mats 10 Mat 12 120 Buy in the river and sell at the Sell fish 400 village One Euro ≈ 7.5 ZMW (Zambian kwacha)

54 Table 43. Farm information. Average Field Area Crop Area Production Production Production Plot number Crop Land type Altitude Production Notes (m2) (m2) perception unit (Mg ha-1) (masl) 3.4.1 Rice Sishanjo 7398 7398 1011 Low 16 Bags 1.08 3.4.2 Rice Sishanjo 8384 1009 Med 28 Bags 1.67 Before yield was higher 3.4.2 Rape Sishanjo 3.4.2 Tomato Sishanjo 12248 3864 1009 Upper part of the field Sweet 3.4.2 Sishanjo potato 3.4.3 Rice Sishanjo 4483 4483 1014 High 10 Bags 0.89 3.4.4 Maize Lizulu 3000 Low 6 Bags 1.00 Crop area needs validation 3.4.4 Rice Lizulu 3000 High 20 Bags 3.33 Crop area needs validation Sweet 3.4.4 Lizulu potato Cowpeas 10165 4165 1015 3.4.4 and Lizulu groundnuts 3.4.4 Okra Lizulu Intercropped with maize, ≠ 3000 3.4.4 Pumpkin Lizulu years 3.4.5 Maize Lizulu 1882 Low 2 Bags 0.53 Intercropped Home 3.4.5 Okra Lizulu 235 1 Bags 1.49 with maize 2352 1011 consumption Sweet 3.4.5 Lizulu 235 1 Bags 1.70 Home consumption potatoes 3.4.6 Maize Lizulu 1918 Low 2 Bags 0.52 Sweet 3.4.6 Lizulu 1630 Low 3 Bags 0.74 potato 1918 1010 Harvest 3.4.6 Cassava Lizulu 288 after 3 years Harvest on 3.4.7 Cassava Litema hungry 9112 9113 1020 Not this year period Doe not have 3.4.7 Millet Litema High 4 Bags 0.22 seeds Harvest 3.4.8 Cassava Dry Litongo Low 2698 2698 1017 after 3 years 3.4.8 Maize Dry Litongo Low 0.6 Bags 0.11

55 Table 44. Crop seasonality. Sept Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Hot Season Field number Crop Land type Rainy Season Cold season Hungy Period Fish Ban 3.4.1 Rice Sishanjo Plant Harvest 3.4.2 Rice Sishanjo Plant Harvest 3.4.2 Rape Sishanjo Plant Harvest 3.4.2 Tomato Sishanjo Harvest Plant 3.4.2 Sweet potato Sishanjo Plant Harvest 3.4.3 Rice Sishanjo Plant Harvest 3.4.4 Maize Lizulu Plant Harvest 3.4.4 Rice Lizulu Plant Harvest 3.4.4 Sweet potato Lizulu Plant Harvest 3.4.4 Cowpeas and grounnuts Lizulu Plant Harvest 3.4.4 Okra Lizulu Plant Harvest 3.4.4 Pumpkin Lizulu Plant Harvest 3.4.5 Maize Lizulu Plant Harvest 3.4.5 Okra Lizulu Plant Harvest 3.4.5 Sweet potatos Lizulu Plant Harvest 3.4.6 Maize Lizulu Plant Harvest 3.4.6 Sweet potato Lizulu Plant Harvest 3.4.6 Cassava Lizulu Plant harvest tubbers after 3 years (leaves after 3 month) 3.4.7 Cassava Litema Plant harvest tubbers after 3 years (leaves after 3 month) 3.4.7 Millet Litema Plant Harvest 3.4.8 Cassava Dry Litongo Plant harvest tubbers after 3 years (leaves after 3 month) 3.4.8 Maize Dry Litongo Plant Harvest

Table 45. Animals Animal Type Number/Farm Uses Notes

Village chicken 13 Eggs, meat, manure Feeds them with maize sub product (moluku). Good productivity Oxen 1 Plough She also borrows another oxen

57 Farmer 3.5: Mundia Siloka

Mundia lives with her mother and children (Table 46). Fields information crops seasonality are shown in Tables 47 and 48 respectively. She owns cattle (Table 49), which is used to plough and fertilize the soil (except for the lizulu that is too far). Farmer 3.3 takes care of her animals. More detailed information and the result from the soil analysis done in plot 3.5.1 are provided in the Appendix. She does not collect manure.

Table 46. Household members. Farm Labor Household work Highest school Name Gender (M/F) At school? Age (Years) days/week h/day h/day level achieved Mundia Siloka F 42 5 6 No 7 Kabati Itwi F 92 No Namukuolo F 18 2 2 1 No 8 Inambao M 8 1 2 1 Yes 2 Kufekisa M 7 1 2 1 Yes 1 Nalukui F 5 No Nalishebo M 3 No

Table 47. Farm information. Field Average Crop Area Production Production Production Field number Crop Land type Area Altitude Production Notes (m2) perception unit (Mg ha-1) (m2) (masl) 3.5.1 Maize Lizulu 1652 1010 Medium 3 Bag 0.91 depend on rains 1944 3.5.1 Groundnut Lizulu 292 1010 High 1 Bag 1.71 Sishanjo/wet 3.5.2 Rice 4461 4238 1007 Low 9 Bag 1.06 litongo 3.5.3 Maize Dry Litongo 220 0.8 Bag 1.82 Upper part High Alternates 3.5.3 Maize Wet Litongo 2 1.95 with sweet Water 512 Lower part 3.5.3 Wet Litongo potato Melon Only field Not sure, harvest when Sweet 732 1012 with 3.5.3 Wet Litongo 220 needed: home Lower part Potato kutuliza Alternates consumption with maize Sweet 3.5.3 Dry Litongo 512 Upper part Potato 3.5.3 Cowpeas Dry Litongo 220 High Upper part 3.2.4 Rice Sishanjo 1101 1101 1007 Low 2 Bags 0.91 got the field late

58 Table 48. Crop seasonality. Sept Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Hot Season Field number Crop Land type Rainy Season Cold season Hungy Period Fish Ban 3.5.1 Maize Lizulu Plant Harvest 3.5.1 Groundnut Lizulu Plant Harvest 3.5.2 Rice Sishanjo/wet litongo Plant Transplant Harvest 3.5.3 Maize Dry Litongo Plant Harvest 3.5.3 Maize Wet Litongo Plant Harvest 3.5.3 Water Melon Wet Litongo Plant Harvest 3.5.3 Sweet Potato Wet Litongo Plant Harvest 3.5.3 Sweet Potato Dry Litongo Plant Harvest 3.5.3 Cowpeas Dry Litongo Plant Harvest 3.2.4 Rice Sishanjo Plant Transplant Harvest

Table 49. Animals Animal Type Number/Farm Uses Notes Oxen 2 Plough, manure The animals eat their crop residues (except from lizulu it Cow 2 Milk, plough, manure is too far). Phenos (farmer 3.3) takes care if the cattle

60 Summary Results

The summary of the answers of the farmers regarding their challenges and constraints and tillage activities information are shown in Table 50 and 51 respectively.

Table 50. Summary of farmers recognized production challenges and constraints. Percentage of Challenges and Constraints Notes Interviewed Farmers (%) Low soil fertility moderating 85 productivity Crop pest and diseases (including Rice and Sorghum (birds and worms) Vegetables (specially 62 birds) tomato) Livestock pest and diseases 15 Fluctuating and/or low market 38 prices Distance and access to Market 54 places Lack of access to support in 23 farming (extension services) One farmer mentioned of lack of knowledge of how to get Lack of access to credits. 69 them High cost of good seeds 77 More varieties, early maturing High cost of fertilizers 62 High cost of pesticides 31 Good seeds are unavailable 23 High post harvest losses 0 Lack of processing equipment 8 Changing rainfall patterns 92 Early floods and droughts (unpredictable climate) Irrigation facilities, Plough, animals (for power and Lack of implements 62 manure), hoe, equipment for Irrigation, equipment to apply pesticides, tools to clear the canal About planting dates and management of different types Lack of knowledge 15 of soil Requires too much time to finish a task (e.g. weeding) and Labor 23 it is not possible to finish on time

Table 51. Summary of farm tillage activities and power sources. Percentage of Tillage activities / Interviewed Farmers Notes Power source (%) Ridges 54 Practiced at least in one area of the farm Mulching 54 Practiced at least in one field (or to one crop) of the farm 7.5% on the whole farm and 7.5% only close to the house Minimum tillage 15 (lilako) Crop rotation 54 Practiced at least in one area (or few crops) of the farm By hand 100 By oxen 77 Borrowed, rented or owned

None of the farmers made compost.

61 Table 52 shows the summary of answers provided by interviewed farmers to additional questions.

Table 52. Summary of farmers answers to additional questions. Additional questions Answers Summary Are you planning to expand 100% would like to expand the growing area in order to have more food to or reduce your farming sell and for consumption. However, they recognized that this would area? require more labor and time (which they actually do not have). 31% said that incorporated all the residues. The reasons said for the Why do you not incorporate remaining 69% for not incorporating all the residues are due to: 56% lack more crop residues as of labor; 56% lack of knowledge; 22% lack of tools and 11 to feed the mulch? livestock Why do you not practice all 8% practices all the principles 31% recognize the benefit of these practices the principles of and 15% do not. The reasons mentioned for the remaining 92% for not conservation agriculture? practicing one or more of these practices are: 83% lack of knowledge; 25% (Mulching, minimum tillage, lack of labor; 17% lack of tools and 8% mentioned that soil moisture is not crop rotation) suitable for these practices (too dry or too wet) Could you do more 15% said that could not do more intercrop. The reasons mentioned for the intercrop? If yes, why you remaining 85% for not intercropping more are: 91% lack of knowledge; don't do so? 55% needs seeds; 36% lack of labor 23% said that could not feed more animals with their residues. The Could you feed more reasons mentioned for the remaining 85% for not feeding more animals animals with the crop are: 100% there are not enough animals; 18% use it as green manure or residues? mulch What obstacles stand in the 92% mentioned lack of seeds; 61% lack of knowledge; 15% lack of labor; way of increasing the 15% lack of implements; 8% low soil fertility; 8% diseases number of crop grown? Comparing to your neighbor 38% answered to have a higher production level than the rest; 31 similar farmers, your yields are…. and 31% lower

In addition to the table above, 100% of the interviewed farmers showed high willingness on sharing farming experiences and knowledge regarding different practices with their neighbors and also with other communities. All supported the idea that is essential to improve diversity and yields as groups of households in order to help each other and to be able to negotiate better prices.

The Barotse Royal Establishment (BRE) is responsible for assigning the land to each household according to the household size. From the 13 interviewed farmers the average total area per farm is 3 ha (Table 53) ranging from 0.7 to 5 ha (variation of 17% of the total area). The average area per farm in this study almost twice as large as the 1.7 ha reported by Turpie et al., (1999). Taking the size of the household into account, the land to person ratio is on average 0.3 ± 0.07 ha/person. Mapungu community displayed higher total area per farm (average 4.8 ± 0.9 ha) compared to Nalitoya (average 2.8 ± 0.9 ha) and Lealui (average 1.6 ± 0.6 ha). Lealui and Nalitoya presented area variations of 37 of 30% respectively. Nalitoya showed the highest coefficient of variation (35% of the total assessed area in Nalitoya). It is important that these areas are not always productive. In some cases they owned even more land but they do not cultivate them or they borrowed to others. The measured area did not match with the area they answered to have in the interviews.

62 Table 53. Total area per farm and per family member. Household Area (m2) per Farm Total Area Community members household Notes Code (m2) (n°) member Lealui 1.1 4 7398 1850 Only 16162 are currently Lealui 1.2 11 32495 2954 (and usually) planted Lealui 1.3 8 19517 2440 Lealui 1.4 8 6569 821 Average ± SEM 16495 ± 6099 2016 ± 458 Borrows land to other Mapungu 2.1 6 36012 6002 children Mapungu 2.2 9 44400 4933 Only 16632 m2 are Mapungu 2.3 9 37359 4151 currently planted Mapungu 2.4 7 74193 10599 Average ± SEM 47991 ± 8926 6421 ± 1443 Nalitoya 3.1 8 25506 3188 Nalitoya 3.2 8 26586 3323 Borrows part of wet Nalitoya 3.3 10 31580 3158 litongo this year Has more land but Nalitoya 3.4 7 50374 7196 borrows it Nalitoya 3.5 7 8238 1177 Average ± SEM 28457 ± 8661 3609 ± 1259 Total Average ± SEM 30787 ± 5276 3984 ± 746

Considering the whole assessed area, almost half was planted with rice or maize (Figure 9). Even though usually maize fields are intercropped with species from the cucurbit family, it does not represent much diversity of food. Due to some intercropped areas, the total area for these percentages calculation have been slightly overestimated. Only four crops occupied 73.6% of the cultivated area. When the vegetables areas are added all together (tomato, rape, okra, hibiscus, cabbage, eggplant (local and common) and other vegetables), they represent 8.8% of the evaluated fields. A large fraction of the vegetables production is sold (Appendix). Compared to the statistics shown by Turpie et al., (1999), the area percentage planted with maize has been reduced, being replaced by an increase in crop the proportion grew with rice, cassava and sweet potato.

Figure 9. Crop fraction areas within the assessed fields.

63 Rice and Maize are not only occupying a great portion of the grown area but are also present in all (maize) or most (rice) of the interviewed farms (Figure 11). The presence fraction of a certain crop also shows differences with Turpie et al., (1999), where rice is present in 92.3% of the total number of farms while in the latter study was 20%, the choice to grow vegetables also seem to be higher.

Figure 10. Proportion of existing crops within interviewed farmers.

Yields where dimensioned by farmers in 50 kg bags of mealie mill. Since a bag of this type filled with other crop does not necessarily weight 50 kg, these yield where estimated using assumptions. It is therefore necessary to validate the absolute values. The assumptions used to estimate the real weights of a one 50 kg mealie mill bag are shown in Table 54.

Table 54. The assumptions used to estimate the real weight Approximate weight (kg) in one 50 Crop kg bag of mealie meal Sweet potato and cassava 40 Groundnut, rice and maize 50 Cabbage and rape 35

Figure 11 shows the variability in rice and maize yields within and between farms. Generally lower rice yields are produce in Mapungu relatively to the other two farms. The magnitudes of yield variation within farms are not specific for particular communities.

Figure 11. Rice and Maize average yields (Mg ha-1) ± SEM considering different plots within the farm.

Figure 12 shows the overall average crops yields (fresh product) and their variation considering all grown areas from the assessed fields. Tomato yields are measured in baskets. One full basket was assumed to weight 35 kg, although the material and small difference on the basket size could affect the real mass. This could explain the big variations. Tomatoes are usually grown in areas close to the house and therefore irrigation, fertilization and weeds are often controlled better.

Figure 12. Overall average crops yield (fresh product) ± SEM

65 Discussion

Land types and farms interviews

Farmers in the three communities recognized different land types to which they assigned specific soil characteristics and flooding risks. Soil fertility and flooding risk of one land type were usually negatively correlated: land types located closer to water and which, therefore, are more prone to early floodings, tended to have higher organic matter content. These land types in turn determine management requirements such as cultivating options, seasonality and thus, the actual yields. This classification should be considered for a natural resource management strategy that privileges the temporal and spatial diversification of cropping systems as this leads to higher productivity and likely to greater stability and ecological resiliency (Altieri, 2002).

The different land types described in Table 1 could be spatially proximate to one another, even within the same plot. Local soil classifications and farmers decision-making show that the Barotse farms are not an exception to Tittonell and Giller’s (2013) statement that these patterns may form the basis of a new form of precision agriculture adapted to the African smallholder context. Designing such forms of precision agriculture requires identifying three categories of fields in the landscape: “responsive”, “non-responsive but productive” and “non-responsive degraded”. The latter authors explained the term ‘responsive’ as the capacity of the soil to react to fertilization. Several farmers (especially in Mapungu) mentioned abandoning plots such as sitapa, lizulu located in the plain and sishanjo (which have relatively high soil nutrient content) due to a decrease in yields caused by low soil fertility. The first step for this land identification in the landscape was achieved in the present study. Although each land type has intrinsic soil characteristics, the responsiveness may depend on field management. The understanding of the landscape and the perfect use of each land type according to its individual qualities could represent big opportunities for improving farm performance regarding productivity and diversity of food produced throughout the year. Some examples and ideas extracted from the interviews made in this study are:

- Managing rice fields in the plains so as wild fish could contribute to nutrient cycling while probably also controlling weeds (in sitapa, lower part of lizulu and sometimes wet matongo in the community of Lealui). - Using legume bushes (e.g. mukankani) as green manure to improve soil fertility in Mazulu. - Trying innovative crops for the area (such as wheat) and exploring uses and markets for native plants which grow easily in certain land types (e.g. mabonobono, picture 1.3-5) -Improving the synergies with local legume bushes and fruit trees (e.g. mahuluhulu and muzauli). Especially in land types like dry mantongo, matema and mazulu, which are highly dependent on rains and have low soil fertility; agroforestry could have great potential by regulating and improving ecosystem functions such as nutrient recycling, water use, species diversity and reducing agrochemical pollution (Carsan et al., 2014) while providing greater agro-biodiversity, and ensuring longer term stability of C storage in fluctuating environments (Henry et al., 2009). Diversity should be promoted together with the encouragement of growing native crops and trees, which are already adapted to local conditions. - Since farmer 1.2 commented that their ancestors used “sikela” (extremely salty soils where nothing grows) as salt, it could be evaluated if it could be useful for preserving fish.

There are many limiting and reducing factors (Tittonell and Giller, 2013) that can explain the yield variation within farms (Figure 11). In the case of farmer 1.1, rice yields where much lower in plot 1.1.2 because they were planted late (and sown by broadcasting) as a solution to Sorghum failure (which was eaten by birds). For farmers 1.2 and 1.3 the variation in maize yields is probably a

66 combination of the remoteness, labor availability and the presence and size of the “sikela”, which due to the salinity would determined the area where crops can grow. Maize yield differences in 2.4 are explained by loss of production due to floodings and droughts associated with wrong planting dates. In Nalitoya, maize yield variations are due to manure being applied only in certain plots (farm 3.2). The comparatively lower rice production in Mapungu is due to the fact that in this community rice is only planted in libala saana, and the production in these land types depends mainly on the water level, which in some cases is not sufficient (sitapa 2.1.4B is an exception because is located in the saana). Also the soil nutrient depletion mentioned above could be an explanation for the low production levels in Mapungu.

None of the interviewed farmers indicated that land availability seemed to be an obstacle to food production. However all of them would like to increase their field’s sizes because they relate higher yields with bigger areas instead of increasing productivity of the current ones. At the same time they recognize that as they increase the productive area they would need more labor and recourses, which they do not have. On the other hand, in addition to the inherent land type, soil fertility, flooding risk, and management; the production, crop diversity and sustainable agricultural intensification in the Barotse farmlands largely depends on the plot distance from the homestead (permanent or temporal), field accessibility and available manpower. Labor deficiency, in addition to field remoteness and inaccessibility, often leads to late planting, exposure of bare soil to the first torrential rains of the season, or to late or inefficient weeding during the season (Milgroom and Giller, 2013). Where land is not limiting, as in this case, the area cultivated is a more important determinant of household food security than the yield per unit area. In these cases, farmers often invest in hiring labor or ox ploughing rather than buying fertilizers or improved seeds to intensify production (Tittonell et al., 2010). Fields distance and inaccessibility and lack of labor are important constraints for agricultural intensification and diversification for subsistence agriculture in the Barotseland.

Regarding crop diversity, even though maize is the staple food in Zambia (as mealie meal), the reduction of area grown with maize compared to Turpie et al., (1999) could be related to the fact that generally other crops like sweet potato, cassava or rice do perform much better that maize in most of the land types surrounding the assessed communities. For example in litapa land types rice perform better than rice because these areas are prone to floodings. On matongo land types where crops are dependent on rains and where the soil is sandy and has low fertility, the stored reserves of root-tuber crops allowed them to perform better. This tendency would mean higher diversity of crops, having most of them higher nutritional value than mealie meal. Lack of seeds and knowledge were the two main constraints referred by farmers for increasing the variety of production. They also mentioned that sometimes they get free seeds from different organizations but usually they come too late in the season and therefore it not possible to sow them.

In all the assessed farms the yields were generally extremely low (Figure 12), which could be explained by different reasons. Although the sample of interviewed farmers was rather small, it captured widely diverse scenarios regarding gender and age of the household head; family composition; wealth level; farming practices; fields land types and distribution in space. The combination of these parameters affected the farm productivity and crop diversity to varying degrees. These variables are interconnected and therefore should be analyzed together. Regarding gender, there were two cases where the wife was in charge of everything regarding the farm because the husband practiced another activity. In such instances there was not a clear difference in productivity with the cases where the husbands were the household heads. When women were in charge of the family alone, lack of labor became an important constraint. This problem, combined with the age of the household members, field remoteness and animal possession, harmed the farm performance to different extents.

67 Wealth level determines cattle tenure and therefore the option of fertilization and maintenance of yields in time. Producing more will give the option to sell the surpluses, diversify production and sources of income. More than a cycle, the wealth level leads to a positive or negative spiral throughout time, where the rich become richer and the other way around. There was also a tendency observed in young aged families to find off farm jobs that lead to a decrease in farm production.

The information gathered in the present study represent the first input for further use in multi- objective optimization modeling at the farm scale (Groot et al., 2012). For this analysis it is important to consider that the proportion of production destined for home consumption and other uses depends on the production. Therefore, if the yield of one crop changed, the percentage will vary likewise. For the landscape modeling, the field’s distribution over a large area might mean a challenge due to the requirement to define the land use of every location within a landscape. Secondly, it would be good to explore specific objectives and interests (such as nutrition and health; ecosystem services; agro-biodiversity and production efficiency and sufficiency; gross margin; and a sustainable use of one resource like water or nitrogen) from different stakeholders involved in this project (farmers, MAL, CGIAR teams, Universities, etc.). Knowing these objectives, it is required to define their respective quantifiable indicators and measure them in order to generate different alternatives of landscape configuration to (re) design according to the predefined objectives (Groot et al., 2007; Groot et al., 2009).

The introductory training for soil sampling with Soildoc showed high interest and understanding from community members, and as it gives a general diagnosis of the soil chemical condition, it can be used to identify local chemical characteristics. The soil analysis is also complementary information for surveys to be used in farming system modeling (Groot et al., 2012). It is important to remember that adding nutrient inputs may result in highly variable crop responses across spatially heterogeneous farms. Tittonell and Giller (2013) mentioned that in smallholder farms as small as 0.5 ha efficiencies will vary enormously between poorly responsive fertile fields (normally the home fields: malako), to responsive or poorly responsive infertile fields (normally the outfields). Applying nutrient inputs in the most responsive fields of the farm will ensure most efficient nutrient use. Fertile home gardens (malako), litapa and lishanjo may be managed with ‘maintenance fertilization’, whereas building soil organic matter of degraded fields should be done before they can respond to nutrient inputs (matongo, mazulu and matema). The impact of input use should be analyzed considering time horizons longer than a single season.

It is essential to promote knowledge sharing in situ within and between communities. Although the willingness to share farming practices and ideas was observed, the great distance between farmers in some communities (especially in Lealui and Mapungu) hinders mobility and communication especially in the rainy season. Conservation farming practices done by farmer 1.1 appear to have good effects on long-term soil fertility and thus on the yields. These practices should be encouraged in the community of Lealui and in others having similar geo conditions (i.e. located in the plains). One of the recognized constraints of conservation agriculture is that it usually requires more labor for weeding since they need to be controlled manually instead of using the plough (Giller et al., 2011). Therefore the positive perception of conservation farming as a way to decrease manpower requirements and costs associated with ploughing is highly remarkable.

Considering differences in yields between farmers (Figure 12) and communities (Figure 11), a promising option for promoting adoption of sustainable farming practices, innovative managements to solve challenges (e.g. wild fish in rice fields) or improving yields is by showing real results and that the farmers themselves could share their experiences with others. For example differences on rice sown by seeded in rows vs. broadcasting. When seeds are sown in roads the depth for proper

68 germination is controlled better than when they are broadcasted and ploughed afterwards. Using the latter option seeds could remain uncovered and prone to be eaten by animals or too deep and unable to reach the surface.

It is noteworthy to remember that certain farm management practices must be proven and adapted to local conditions. For instance, as the lishanjo land type (Nalitoya) does not get completely flooded like rice fields in Lealui (Sitapa and lower part of Mazulu), the same weed management practice described in farm 1.1 cannot be replicated in an identical fashion in other communities located in slightly different landscapes. There are different weed species and weed growing patterns throughout the year. Conservation practices done by farmer 1.1 on rice fields could be in theory also practiced in Mapungu. However, the remoteness of these fields renders them unsuitable for growing rice, and birds can cause loss of the whole production. Moreover, it is necessary to implement a sustainable and participatory system that promotes community seed production according to their needs (e.g. short growing period, drought and salty-soil resistant, innovative species, etc.).

Additional recommendations to complement the NSL project

1. Efficient use of energy

Research done for the NSL project could be complemented with simple and cheap practical activities in the communities that could contribute to improved nutrition directly or indirectly. It is important that the community members experience short-term results in order to maintain their enthusiasm to participate in research. Although none of the farmers mentioned postharvest losses of cultivated crops as a constraint during the interviews, they all recognized a period that is characterized by the shortage of food. In addition to food production, food storage largely determines nutrition opportunities to provide a higher diversity of food to keep a balance diet throughout the year. The use of solar energy for drying fruit and vegetables could improve seasonal the diversity of food availability in rural communities (Figure 13 Left). For example the surpluses of mangos could be dried and consumed later in the year where fruits are less available. It could even represent a source of extra income by selling of products with an added value. The construction of simple solar ovens or rocket stoves (Figures 13 Middle and Right respectively) could lead to decreased and more efficient use of firewood.

Figure 13. Examples of simple ways to improve the use of energy. Left: solar dryer. Middle: solar oven. Right: rocket stove.

2. Closing cycles: using human excreta for plant production

By constructing Urine Diverting Dry Toilets (UDDT) it is possible to separate urine flows (UF) from fecal matter (FM) (approximately 1.5 l/person/day and 0.8 kg/person/day respectively). After composting UF and FM for 6 and 18 months respectively, a source of fertilizer free from pathogens such as Ascaris lumbricoides and Escherichia coli is created. After the compost is ready, urine should be applied and mixed with irrigation water at a ratio of 1:10 (urine:water). This “free” fertilizer should be used as much as possible and needed. This is a technology that needs adaptation depending on the conditions and thorough training for people to use it. The UDDT can be used with the cheap pit latrines that are common in the rural of developing countries. They do not require electricity and/or tap water (Heinonen-Tanski and van Wijk-Sijbesma 2005).

3. Use of remote sensing techniques for monitoring different indicators of vegetation and soils

RS can provide authentic sources of information for identifying, classifying, mapping, monitoring, and planning of natural resources and disasters mitigation and management as a whole. It can provide data required for site-specific management (Usha and Singh, 2013). RS technology is a key component of precision agriculture and it has the potential of detecting and characterizing agricultural productivity based on biophysical attributes of crops and/or soils. In combination with ground data, the information gained from RS could have meaningful uses providing accurate and timely information to guide agronomic and economic decision-making (Liaghat and Balasundram, 2010).

Remote sensing (RS) is advancing quickly and showed potential for applications in estimating vegetation cover, absorbed photosynthetically active radiation (APAR), Leaf Area Index (LAI), chlorophyll content, canopy water content, biomass, carbon, structure of the canopy, crop biomass, soil properties, soil moisture and nutrient content, crop yield estimation, damage by biotic and a biotic stresses (Usha and Singh, 2013). Relatively new RS multispectral and hyperspectral sensors are swiftly generating vast amounts of data in a relatively cost effective manner and at higher spatial and spectral resolutions (Liaghat and Balasundram, 2010). Using RS it is not only possible to assess the special but also the time dimension, supporting modeling by monitoring changes through time (e.g. C accumulation, duration of the growing season, planting dynamics and detecting areas most likely to be affected by seasonal changes) (Herold, 2014). By analyzing spectral signatures it is also possible to estimate some soil parameters such as organic mater, moisture and mineralogical composition (Stoner and Baumgardner, 1981).

As an example, a lightweight hyperspectral mapping system (HYMSY) campaign lasting throughout the summer of 2013 was performed on agricultural experimental fields in The Netherlands (Mucher et al., 2014). The objective of the study was to evaluate how Unmanned Aerial Vehicle (UAV) based hyperspectral observations could be adopted for mapping and monitoring of a variety of arable crops (Figure 14). An important improvement has been developed in the processing chain for georectification of the hyperspectral push-broom data. The processing chain uses a photogrammetric algorithm to produce a Digital Surface Model (DSM) (Suomalainen et al., 2014).

Figure 14. Figure 4. Left: A collage image of the HYMSY dataset from a single flight at 120 m altitude. A RGB orthomosaic at 34 mm GSD generated from the aerial images (Left background). The Digital Surface Model at 77 mm resolution visualized with hill shading effect (Right background). A false color composite (RGB = 800, 650, 550 nm respectively) of the hyperspectral dataset of the first flight line at 320 mm GSD (Front). Right: Photo of HYMSY mounted on an Aerialtronics Altura AT8 v1 octocopter UAV. (Source: Suomalainen et al., 2014).

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74 Appendix

Fields and houses location

Locations Lealui Locations Mapungu Locations Nalitoya Code Code Code Latitude Longitude Latitude Longitude Latitude Longitude 1.1 15° 13.655'S 22° 59.553'E 2.1 15° 5.265'S 22° 47.460'E 3.1 15° 48.078'S 23° 18.871'E 1.1.1 15° 13.551'S 22° 59.516'E 2.1.1 15° 5.307'S 22° 47.554'E 3.1.1 15° 47.078'S 23° 17.336'E 1.1.2 15° 13.633'S 22° 59.537'E 2.1.2 15° 5.277'S 22° 47.577'E 3.1.2 15° 47.024'S 23° 17.372'E 1.1.3 15° 13.616'S 22° 59.611'E 2.1.3 15° 5.274'S 22° 47.484'E 3.1.3 15° 48.115'S 23° 18.837'E 1.1.4 15° 13.661'S 22° 59.552'E 2.1.4 B 15° 5.287'S 22° 46.237'E 3.1.4 15° 48.105'S 23° 18.878'E 1.2 15° 13.904'S 23° 1.463'E 2.1.4 C 15° 5.299'S 22° 46.194'E 3.1.5 15° 48.095'S 23° 18.901'E 1.2.1 15° 13.268'S 23° 2.327'E 2.1.4 D 15° 5.299'S 22° 46.167'E 3.1.6 15° 48.108'S 23° 18.961'E 1.2.2 15° 13.231'S 23° 2.114'E 2.1.4 E 15° 5.279'S 22° 46.128'E 3.1.7 15° 48.067'S 23° 19.024'E 1.2.3 15° 13.260'S 23° 2.182'E 2.1.4 F 15° 5.329'S 22° 46.091'E 3.1.8 15° 48.038'S 23° 18.946'E 1.2.4 15° 13.164'S 23° 2.339'E 2.2 15° 5.374'S 22° 47.480'E 3.2 15° 47.428'S 23° 17.614'E 1.2.5 15° 13.823'S 23° 1.409'E 2.2.1 15° 3.734'S 22° 50.185'E 3.2.1 15° 47.421'S 23° 17.556'E 1.2.6 15° 13.891'S 23° 1.451'E 2.2.2 15° 3.791'S 22° 50.235'E 3.2.2 15° 47.466'S 23° 17.572'E 1.3 15° 13.328'S 23° 1.141'E 2.2.3 15° 4.428'S 22° 49.624'E 3.2.3 15° 46.578'S 23° 17.045'E 1.3.1 15° 13.314'S 23° 1.129'E 2.2.4 15° 4.358'S 22° 49.444'E 3.2.4 A 15° 46.555'S 23° 17.072'E 1.3.2 15° 13.454'S 23° 1.441'E 2.2.5 15° 6.463'S 22° 46.068'E 3.2.4 B 15° 46.650'S 23° 17.041'E 1.3.3 15° 12.901'S 23° 3.554'E 2.2.6 15° 5.759'S 22° 46.826'E 3.3 15° 47.968'S 23° 18.466'E 1.3.4 15° 12.850'S 23° 3.575'E 2.2.7 15° 5.400'S 22° 47.483'E 3.3.1 A 15° 47.937'S 23° 18.313'E 1.3.5 15° 12.862'S 23° 3.667'E 2.2.8 15° 5.403'S 22° 47.457'E 3.3.1 B 15° 47.866'S 23° 18.336'E 1.3.6 15° 12.884'S 23° 3.727'E 2.2.9 15° 5.400'S 22° 47.310'E 3.3.2 15° 47.529'S 23° 17.509'E 1.3.7 15° 12.764'S 23° 3.646'E 2.2.10 15° 5.452'S 22° 47.354'E 3.3.3 15° 47.375'S 23° 17.346'E 1.3.8 15° 12.750'S 23° 3.668'E 2.3 15° 4.688'S 22° 46.754'E 3.3.4 15° 47.566'S 23° 17.458'E 1.4 15° 13.456'S 23° 1.088'E 2.3.1 15° 4.731'S 22° 46.226'E 3.3.5 15° 48.098'S 23° 18.461'E 1.4.1 15° 13.571'S 23° 1.885'E 2.3.2 15° 4.669'S 22° 46.754'E 3.4 15° 47.578'S 23° 17.801'E 1.4.2 15° 13.529'S 23° 1.523'E 2.4 15° 5.157'S 22° 47.179'E 3.4.1 15° 47.767'S 23° 17.808'E 1.4.3 15° 13.592'S 23° 1.174'E 2.4.1 15° 7.712'S 22° 46.455'E 3.4.2 15° 47.620'S 23° 17.333'E 2.4.2 15° 5.943'S 22° 47.618'E 3.4.3 15° 47.879'S 23° 17.781'E 2.4.3 15° 5.170'S 22° 47.176'E 3.4.4 15° 47.817'S 23° 17.796'E 2.4.4 15° 5.914'S 22° 47.672'E 3.4.5 15° 47.665'S 23° 17.310'E 2.4.5 A 15° 4.542'S 22° 49.942'E 3.4.6 15° 47.687'S 23° 17.342'E 2.4.5 B 15° 4.579'S 22° 49.946'E 3.4.7 15° 47.429'S 23° 18.058'E 2.4.6 15° 3.906'S 22° 50.890'E 3.4.8 15° 47.540'S 23° 17.788'E 2.4.7 15° 4.265'S 22° 50.077'E 3.5 15° 47.978'S 23° 18.473'E 2.4.8 15° 4.020'S 22° 51.070'E 3.5.1 15° 46.830'S 23° 17.262'E 2.4.9 A 15° 4.123'S 22° 51.146'E 3.5.2 15° 47.529'S 23° 17.404'E 2.4.9 B 15° 4.062'S 22° 51.195'E 3.5.3 15° 48.013'S 23° 18.489'E 2.4.9 C 15° 4.008'S 22° 51.217'E 2.4.9 D 15° 3.983'S 22° 51.211'E 2.4.10 15° 5.968'S 22° 47.572'E 2.4.11 15° 5.940'S 22° 47.593'E 2.4.12 15° 5.918'S 22° 47.578'E

75 Fields Maps

Farmer 1.1. Mumeka Lubinda

Farmer 1.2. Amusa Mubukuanu

Farmer 1.3. Mulela Situbeko

1.4 Lisuanizó Kamona

78 2.1. Sifuniso Imbuwa

2.2.Susiku Nosiku

80 2.3. Nyambe Wamunyima

2.4. Lifasi Nusilele

82 3.1. Mubita Sitali

83 3.2. Isaac Inambao

3.3. Phenos Musiwa

3.4. Monde Mulonda

85 3.5. Mundia Siloka

86 Soil sample and analyses

Active C Sampling N (mg) per NO N (mg) K (mg) per P (mg) per S (mg) per kg Site SubLocation Village Latitude Longitude Farmer name Depth (cm) Crop Analysis Date EC (dS cm-1) pH H 0 pH CaCl 3 K (cmol) (mg) per kg Notes Date 2 2 kg Soil per kg Soil kg Soil kg Soil Soil Soil Litongo Senanga Nalitoya S 15.48054 E 23.18955 Mubita Sitali 15 Maize 21/08/14 22/08/14 0.22 7.20 6.24 0.00 0.00 10.00 0.26 4.08 10.55 -20.16 Low yields Gets flooded in the lower Sishanjo Senanga Nalitoya S 15.48106 E 23.18844 Mubita Sitali 15 Rice 21/08/14 22/08/14 0.27 7.33 6.73 -22.00 -5.00 28.00 0.72 0.00 7.94 328.32 part of the field Plough with oxen. Soil Lizulu Senanga Nalitoya S 15.46845 E 23.17254 Mundia Siloka 15 Maize 21/08/14 22/08/14 0.20 7.22 6.68 -52.00 -11.82 6.00 0.15 1.80 11.86 280.80 compaction. Mulela Sitapa Mongu Lealui S 15.134665 E 23.1472 15 Cassava 20/08/14 22/08/14 0.27 7.49 6.74 -22.00 -5.00 36.00 0.92 0.00 8.81 748.08 Mushambeta Mulela Sitapa Mongu Lealui S 15.13484 E 23.1484 15 Rice 20/08/14 22/08/14 0.02 7.29 6.59 -26.00 -5.91 2.00 0.05 0.00 7.07 225.36 Lower production Mushambeta Mulela Sitapa Mongu Lealui S 15.13484 E 23.1484 15 Rice 20/08/14 22/08/14 0.05 7.30 6.49 -24.00 -5.45 34.00 0.87 1.44 6.22 336.24 Higher production Mushambeta Conservation farming. Lizulu Mongu Lealui S 15.14155 E 23.00144 Doreen Kaumba 15 Wheat 20/08/14 22/08/14 1.20 6.70 6.14 20.00 4.55 10.00 0.26 1.08 6.63 1278.72 Sandy part, gets dry faster Lizulu Mongu Lealui S 15.14145 E 23.00109 Doreen Kaumba 15 Wheat 20/08/14 22/08/14 0.45 7.24 6.75 -78.00 -17.73 100.00 2.56 0.00 8.16 756.00 Conservation farming Lizulu Mongu Lealui S 15.14166 E 23.00144 Doreen Kaumba 15 Tomato 20/08/14 22/08/14 2.00 7.11 6.83 -4.00 -0.91 156.00 4.00 276.00 13.81 732.24 Had maize before

87 Production destination and prices

One Euro ≈ 7.5 ZMW (Zambian kwacha) Selling percentages depends Farm 1.1 Crop or Animal Sell price Sell* (%) Sell (n˚) Sell unit Notes (product) (ZMW) Rice 28 120 Bag 11% use for seeds Wheat 60 300 Bag 10% use for seeds Carrots 100 2 8 units Cabbage 90 1.5 Unit Pumpkin 75 2 Unit Eggplant 95 5 2 Units Tomato 90 55 Basket Chicken 20 15 Chicken *Depends on the yield

Farm 1.2 Crop Sell* (%) Sell price (ZMW) Sell unit Notes

Carrots 90 80 Bag Maize 40 80 Bag Rice 80 150 Bag Cabbage 100 3 Unit Leaves are small because of Rape 90 1 15 leaves disease (virus) Pumpkin 80 2 Unit Eggplant 95 1 Unit Sweet Potato 60 70 Bag Tomato 60 100 Basket Okra 80 200 Bag Onion 40 350 Bag *Depends on the yield

Farm 1.3

Crop % Seeds % Exchange to labor % Home consumption

Rice 30 30 40 Maize 10 90 Sweet potato 50 50

Farm 1.4 Crop Sell* (%) Sell price (ZMW) Sell unit Tomato 90 50 Basket Cabbage 90 2 Unit Rape 90 1 10 leaves

88 Farm 2.1 Sell price Crop Sell* (%) Sell unit Notes (ZMW) Price depends on the market (supply Tomato 95 50 to 95 Basket and demand) % Depends on the yield. If they have Sweet potato 35 25 Kg enough, they sell to buy mealie meal. Keep 5 bags (3 for consumption, 2 Rice 120 50 kg for seeds) rest for sell

Farm 2.2 Sell price Crop Sell (%) Sell unit (ZMW) Rice 60 120 Bag Sweet Potato 80 60 Bag Groundnut 10

Farm 2.3 Crop or Animal Sell price Sell (%) Sell unit Notes (product) (ZMW) 50 ZMW from April Tomato 75 50 to 90 Basket to may; 50 ZMW in June and July Depending on the Cabbage 75 2 to 7 Unit size Farm 2.4 Crop or Animal Sell price Sell (%) Sell* (n˚) Sell unit Notes (product) (ZMW) The remain is 20% for Groundnut 40 2 Cup seeds and 40% for consumption Sweet Potato 40 2 Hip Hip= 10 to 12 units % Depends on the yields (25 bags for home Maize 65 consumption) rest for sell or to pay labor Chicken 4 or 5 20 Unit Milk 5 l per day 10 2.5 l During hot season

Farm 3.1 Sell price Crop Sell* (%) Sell unit (ZMW) Tomato 30-50 40 Basket Rape 50 2 8 to 10 leaves Cabbage 50 2 Unit Rice 70 90 Bag *Depends on the yield

89 Farm 3.2 Sell price Crop Sell (%) Sell unit Notes (ZMW) Sorghum 20 35 10 kg To make beer Rape 20 2 8 leaves Keep 25 kg for seeds and the rest is for home Rice 30 100 Bag consumption Onion 20 1 1 or 2 units Pumpkin 10 5 Unit Local squash 10 1 3 units Tomato 20 1 4 units

Farm 3.3 Crop Sell (%) Sell price (ZMW) Sell unit Rice 50 100 10 kg Sorghum 50 120 8 leaves 2 200 gr Sweet Potato 50 65 25 kg

3.4 Crop Sell (%) Sell price (ZMW) Sell unit Notes Save one bag for Rice 50 90 to 100 Bag seeds. Sell at Senanga.

3.5 Crop Sell (%) Sell price (ZMW) Sell unit Notes Sell 2 bags and exchange 4 bags for Rice 55 100 Bag maize (also 4 bags). Kept 5 bags for home consumption.

90 Labor Requirements

Higher workload in the rainy season.

Farm 1.1 Person Total time Crop Labor task Hired? Notes Cost n˚ (h) Land Wheat 2 No 27 Only in the morning preparation Wheat Planting 2 No 45 Only in the morning Wheat Weeding 2 No 51 Wheat Harvest 4 No 27 Only in the morning Include build of protection barrier for Land Rice 2 (+3) No 204 early stages (fish). preparation Other family members help Rice Planting 2 No 102 600 m2/person per 20 ZMW/person per day Rice Weeding 6 Yes: 4 204 day approximately 480 ZKW in total The children also Rice Harvest 2 No 102 help Only part for home Rice Pounding 2 No 112.5 consumption Only in the morning. Land Tomato 2 No 4.5 He irrigates in the preparation afternoon. Tomato Planting 2 No 9 Tomato Weeding 2 No 9 Approximately 20 min/day; 2 Tomato Harvest 2 No 3 times/week for 2 month

Farm 1.2 Crop Hired? Total time (h) Notes Cost Rice Yes 59.5 Plot 1.2.1 (+ 4 oxen) 1000 ZMW (whole plot) Rice Yes 42.5 Plot 1.2.1(soften the soil) 500 ZMW (whole plot) Rice Yes 51 Plot 1.2.1 900 ZMW Rice Yes 102 Plot 1.2.1 pay with rice Rice Locally Maize Yes 51 Plot 1.2.1 Included in rice cost Maize Yes 42.5 Plot 1.2.1 Included in rice cost Maize Yes 42.5 Plot 1.2.1 625 ZMW Maize No/Yes 42.5 Plot 1.2.1 Pay with maize Maize Yes 51 Plot 1.2.1 500 ZMW Maize No 32 Part is sale in Mongu, the rest locally 10 ZMW/bag Vegetables Yes: 2 153 Include children 360 ZKM Vegetables No 48 Vegetables Yes: 4 48 300 ZMW Vegetables No 20 approximately Vegetables Locally

91 Farm 1.3 Total time Crop Labor task Person n˚ Hired? Notes Cost (h) Land Oxen. 24 hours per lizulu Rice 3 No 144 preparation (2 h per day for 2 weeks) Rice Soften the soil 3 No 480 Mulela and the children Broadcast / Mulela and the children. Rice 3 No 20 cover Oxen for covering Family + 2 to 3 Rice Weeding No 480 people Rice Harvest 4 No 720 4 days. Transport by Rice Shelling 4 No canoe Land Maize Oxen. Land preparation preparation 4 No 50 and planting together Maize Planting Maize Weeding Family (4) No 480 Maize Harvest Family (4) No 60 60 Maize Processing 2 No 1 day to Mongu by boat ZMW Land Per pieces as the water Sweet Potato preparation recedes 3 weeks (as the water Sweet Potato Planting 1 No recedes) Sweet Potato Weeding 1 + children No 24 1 h/day for 1 month Land Has to be fast because of Sweet Potato 2 to 3 Yes 15 preparation floodings Sweet Potato Land 3 to 4 No 120 Plat in ridges (lizulu) preparation Sweet Potato Planting 2 to 3 No 120 (lizulu) Sweet Potato Weeding 1 + children No 900 (lizulu) Sweet Potato Take when is needed, no Harvest (lizulu) flooding danger

Farm 1.4 Crop Labor task Person n˚ Hired? Total time (h) Notes Cost Land Maize 3 Yes 14 * preparation Maize Planting 2 to 3 No 7 Maize Weeding 4 to 5 No 84 Maize Harvest 4 to 5 No 42 Maize Shelling Family No 24 2 h/day for 2 weeks Maize Processing 2 No 7 5 ZMW/bag + 5 Maize Sales 9 At Mongu - 10ZMW/canoe Land Rice 2 Yes 7 preparation Softening the soil Rice Planting 5 No 84 and broadcast of seeds Rice Weeding 5 No 84 Rice Harvest 5 No 36 2 weeks (morning) Rice Shelling 5 No 42 6 ZMW/bag + Rice Polishing 2 No At Mongu 3 25 ZMW (car) Tomato, cabbage Land 2 to 3 No 42 and rape preparation Tomato, cabbage Planting 2 to 3 No 42 and rape

92 Tomato, cabbage Weeding 3 No 14 and rape Cabbage Harvest 2 No 7 30 min/day; 2 Tomato and rape Harvest 1 to 2 No 12 days/week for 3 month Sales Harvest 2 No 8 At Mongu 10 ZMW (boat)

Farm 2.1 Crop Labor task Person n˚ Hired? Total time (h) Notes Cost Plus oxen and Maize Land preparation 2 Yes 14 400 ZMW/ha plough Maize Planting 1 + 4 (children) No 14 10 ZMW per Maize Weeding 6 + 4 (children) Yes: 5 179 100 m2 Maize Harvest 2 to 3 Yes: 1 or 2 42 Maize Shelling 17.5 Plus oxen and Rice Land preparation 2 Yes 35 400 ZMW/ha plough Rice Planting 1 No 35 Takes 252 h 10 ZMW per Rice Weeding 6 + 4 (children) Yes: 5 168 when no workers 100 m2 are hired Takes 168 h 10 ZMW per Rice Harvest 6 + 4 (children) Yes: 5 84 when no workers bag are hired When the yield is Rice Shelling 1 + 4 (children) No 24.5 good By canoe to Rice Sales 120 Mongu. 4 days of journey Cassava Land preparation 4 + 4 (children) Yes: 3 462 Cassava and planting During 1 month Cassava Weeding 4 + 4 (children) Yes: 3 84 only some days Cassava Harvest 3 Yes: 2 84 Land preparation Sweet potato 4 + 4 (children) Yes: 3 280 and planting Sweet potato Weeding 1 + 4 (children) no Need to harvest Sweet potato Harvest 5 + 4 (children) Yes: 4 42 fast (floodings) Tomato Land preparation 2 Yes 7 Oxen Tomato Planting 1 + 4 (children) No 35 Tomato Weeding 1 + 4 (children) No 63 Every other day Tomato Harvest 4 (children) No 84 for 2 month 2 h a day for a Tomato Selection 2 No 40 month (5 day per week)

93 Farm 2.2 Crop Labor task Person n˚ Hired? Total time (h) Notes Maize Land preparation 4 No 18 Maize Planting 1 No 6 Maize Weeding 6 No 90 Maize Harvest 3 No 36 Maize Shelling 7 to 8 No 12 Rice Land preparation 4 No 18 Rice Planting 1 No 18 Rice Weeding Rice Harvest 7 to 8 No 72 Rice Shelling 5 No 30 By canoe to Mongu. 4 days of Rice Sales 2 No 144 journey Land preparation and Cassava 3 No 120 planting Cassava Harvest 5 No 30 Land preparation and Groundnut 1 No 108 planting Groundnut Weeding 7 to 8 No 72 Groundnut Harvest 7 to 8 No 36 Groundnut Sales 2 No 10 To Kalabo Land preparation and Sweet Potato 3 No 72 planting Sweet Potato Harvest 3 No 36 Sweet Potato Sales 2 10

Farm 2.3 Crop Labor task Person n˚ Hired? Total time (h) Notes Maize Land preparation 2 No 25 Maize Planting 3 Yes 12 Maize Weeding 2 No 60 Maize Harvesting (high yields) 2 No 20 to 25 Maize Harvesting (low yields) 2 No 5 to 8 Maize Shelling & threshing 2 No 1 per 70 kg Maize Processing 2 to 4 No 0.5 Tomato Land preparation 3 to 5 No 10 ( weeds) Tomato Land preparation 3 to 5 No ( weeds) Tomato Planting 1 No 25 Tomato Weeding 2 No 25 Harvest last 2 month. Every 2 Tomato Harvesting 2 No 60 days for 30 min to 2 h 40 to 60 (depending on Tomato Selection 3 to 5 2 hours per day every 2 days labor) Tomato Sales 2 No 7 By canoe to Kalabo Cabbage Land preparation 2 No 10 ( weeds) Cabbage Land preparation 2 No 25 ( weeds) Cabbage Transplanting 2 No 25 Cabbage Weeding 2 No 25 Harvest last 1 month. Two Cabbage Harvesting 2 No 8 times a week for 1 h To Kalabo or Mongu (Mongu Cabbage Sales 2 No 7 is more expensive)

94 Farm 2.4 Crop Labor task Person n˚ Hired? Time (h) Notes Maize Land preparation 5 Yes: 3 30 Plus 4 oxen

Maize Planting 5 Yes: 3 42

Maize Weeding 8 Yes: 2 54 Maize Harvest & shelling 5 No 36 Maize Sale Sale from home Rice Land preparation 2 No 12 Plus 4 oxen Rice Planting 4 No 48 24 h per field -> 48 in total Rice Harvest 3 No 18 To Kalabo. 10 ZMW per bag plus 35 ZMW Rice Shelling 2 No 10 (canoe) Groundnut Land preparation 4 Yes: 2 21 Plus 4 oxen Groundnut Planting 1 No 30 Wife is in charge Groundnut Harvest 3 No 42 Groundnut Shelling 3 No 30 Groundnut Sale Sale from home

Farm 3.1 Crop Labor task Person n˚ Hired? Time (h) Notes Cost Cabbage and Rape Land preparation 2 No 45 Cabbage and Rape Planting 2 No 8 Cabbage and Rape Weeding 2 No 90 Cabbage and Rape Harvest 2 No 45 Cabbage and Rape Sale 2 No At the village and in Senanga Millet Land preparation 2 No 90 Millet Planting 2 No 28 Millet Weeding 2 No 135 Millet Harvest 2 No 40 storage Maize Land preparation 2 No 113 Maize Planting 2 No 45 Maize Weeding 2 No 135 Maize Harvest 2 No 28 Maize Shelling 2 No 24 No 30 Maize Processing 2 1/bag ZMW/bag Tomato Land preparation 2 No 90 Tomato Planting 2 No 8 Tomato Weeding 2 No 90 No 1-2 h/day for 2 days/week for 2 Tomato Harvest 2 12 month Tomato Selection No 24 1 h/day for 2 months Tomato Sale 1 No 18 1 to 2 days (Senanga) Rice Land preparation 2 No 63 Rice Planting 2 No 68 Rice Weeding 2 No 135 Rice Harvest 2 No 67.5 Depends on the yield Rice Drying 2 No 12 1 day No Come to the village or at the road or Rice Sale 1 12 at Senanga (1 day) Cassava Land preparation 2 No 198 It is far Cassava Planting 2 No 198 Cassava Weeding 2 No 135 Cassava Harvest 2 No 48 1-2 days/week for 2 month Cassava No Sweet Potato Land preparation 2 No 8 Sweet Potato Planting 2 No 8 Sweet Potato Harvest 2 No 108 2 months (1-2 days/week)

Farm 3.2 Crop Labor task Person n˚ Hired? Time (h) Notes Maize Land preparation 3 No 28 Borrows 4 oxen No 2 oxen. One hold the plough and Maize Planting 1 + 3 children 28 children put the seeds Maize Weeding Whole family No 120 During 1 month Harvest and No Maize 2 84 shelling Maize Processing 2 No 1 At home Rice Land preparation 2 No 84 4 oxen Rice Planting 2 + 3 children No 147 Rice Transplanting 2 + 3 children No 360 Harvest and No Rice 2 168 shelling No Mostly sell from home but takes 1 Rice Sales 1 10 day for selling in Senanga Land preparation No Sorghum 4 7 4 oxen and planting Sorghum Weeding 2 + 3 children No 120 Sorghum Harvest (cut) 2 No 7 Sorghum Harvest (collection) 2 No 7 Sorghum Shelling 2 No 14 Sorghum Sales No Sell from home Land preparation & No Sweet potato 2 7 planting Only when is needed for home Sweet potato Harvest (cut) consumption

Farm 3.3 Crop Labor task Person n˚ Hired? Time (h) Notes Cost (ZMW) Land Maize 4 No 36 preparation 250 Maize Planting 7 No 72 Maize Weeding 7 Yes: 3 72 Maize Harvest 3 No 36 Plus 1 or 2 Maize Transport 2 No 6 oxen Maize Shelling 2 No 5 ZMW per 20 kg Land 300 ZMW the big field and 200 ZMW Rice 3 Yes: 1 108 4 oxen preparation the small one Rice Planting 4 No 24 4 oxen Free Rice Transplanting 7 Yes: 3 360 3 month 20 ZKM per person per day Rice Weeding 6 Yes: 3 108 3 month 20 ZMW per person per day Rice Harvest 7 Yes: 4 30 10 ZMW per bag Rice Transport 2 No 6 Rice Sales Sales from home Land Millet preparation 4 No 12 4 oxen Millet Planting 3 No 36 Millet Transplanting 4 No 42 Millet Weeding 2 No 12 Millet Pounding 2 No 2 h per 20 kg Sales from Millet Sales home

96 Farm 3.4 Crop Labor task Person n˚ Hired? Time (h) Notes Cost (ZMW) 10 ZMW per Land person per day: Maize 4 Yes: 3 30 preparation 180 ZMW en total Maize Planting 1 No 10 Pays with mealie meal: 1 Maize Weeding 12 Yes: 10 or 11 120 One month flask per 10x15m Maize Harvest 2 Yes: 1 10 20 kg of maize Maize Shelling 1 No 5 6 ZMW per 20 Maize Processing kg Land Pays with Rice 6 to 8 Yes: 5 to 7 60 preparation mealie meal Borrowed Rice Planting 3 No 10 plough Pays with Rice Weeding 8 Yes: 7 180 mealie meal 10 ZMW per Rice Harvest 2 Yes: 2 64 bag Rice Transport 2 No 6 With the oxen 10 ZMW per Rice Sales 6 To Senanga bag Land Cassava 4 No 10 Plus oxen preparation Cassava Planting 3 No 10 Borrowed Cassava Weeding 1 No 35 3 h per week for Cassava Harvest 1 No 12 1 month

3.5 Crop Labor task Person n˚ Hired? Time (h) Notes Cost (ZMW) Maize Land preparation 3 No 6 Plus 4 oxen Maize Planting 1 No 30 Maize Weeding 1 No 60 Maize Harvest 1 No 18 Maize Shelling Family No 5 Maize Processing No 4 ZMW per 12 kg Rice Land preparation 3 No 18 Plus 4 oxen No Needs more labor Rice Planting 1 120 Broadcast Depends on money: 10 10 ZMW per Rice Weeding 1 to 10 Yes:9 120 to 480 people take 1 month. 10x15 m2 Alone is 4 month 20 ZMW + 25 kg Rice Harvest & Shelling 2 Yes:1 6 h per bag of rice (from a total of 7 bags) Rice Sales Sell from home Land preparation & Sweet potato 1 No 240 planting Harvest only when is required for home Sweet potato Harvest 1 consumption. Deadline to harvest is August in order to plant maize

97 Pesticides and/or fertilizers used in the farm

Farm 1.1 Product Name Crop Quantity per year Units Price (ZMW) Units Pesticide Red spider mite killer Vegetables 500 ml 40 500 ml Pesticide Kinalax Cabbage and rape 200 g 15 100 g

Farm 1.2 Product Quantity/acre Units Price (ZMW) Units Pesticide 100 ml 25 100 ml Pesticide 100 ml 25 100 ml Pesticide 100 ml 30 100 ml Pesticide 1 Flask 45 Flask

Farm 1.4 Product Name Crop Quantity per year Units Price (ZMW) Units Pesticide Logo Vegetables 1 Bottle 15 Bottle

Farm 2.1 Price Product Name Crop Quantity per year Units Units (ZMW) Fertilizer D-compound Maize and Tomato 2 Bag 200 Bag Tomato, Maize and Fertilizer Bat manure 5 Bag 25 Bag (50 kg) other Veg.

2.3 Quantity Product Name Crop Units Price (ZMW) Units per year Pesticide Saaf Tomato 500 g 45 500 g Pesticide Iboforce Tomato 1 l 15 100 ml Pesticide Rogo Tomato 1 l 15 100 ml Pesticide Dicofol Tomato 1 l 15 100 ml Fertilizer Soloba Vegetables 1 l 50 kg Tomato and Fertilizer D compound 2 Bags 100 Bag (50 kg) local eggplant Maize & Fertilizer Urea 2 Bags 7 2 kg cabbage Maize & Fertilizer Urea 2 Bags 100 Bag (50 kg) cabbage Cattle manure Fertilizer Maize (field 4) Free (kutuliza) Colected cattle Fertilizer Free manure Mainly Fertilizer Bat manure 100 kg 15 Bag (25 kg) cabbage

Farm 3.1 Product Name Crop Quantity/year Units Price (ZMW) Units Pesticide Emthane M-45 Vegetables 250 g 35 250 g Pesticide Metamidophos Maize 250 ml Fertilizer D compound Rice, Maize, Tom, Veg. 1 50 kg bag 250 Bag Fertilizer Urea Rice, Maize, Tom, Veg. 1 50 kg bag 250 Bag

Farm 3.2

Product Name Crop Quantity per year Units Price (ZMW) Units

Pesticide Logo Vegetables 100 ml 15 100 ml

Available tools

One Euro ≈ 7.5 ZMW (Zambian kwacha

Farm 1.1 Approximated total value Name Owned Note (ZMW) Axe 2 30 Panga machete 2 20 Sickle 1 10 Knapsack sprayer 1 300 Shovel 2 100 Hoe 4 60 Treadle pump 1 150 Bicycle 1 200 Cell phone 2 130 Inverter (charging cell phone) 1 150 Battery 1 50 Solar panel 2 300 Radio 1 50 TV 1 Not working 0

Farm 1.2 Approximated total value Name Total rental cost (ZMW) Note (ZMW) Panga machete 40 Axe 10 Pock axe Free 30 Sickle 50 Shovel 20 Hoe 30 Animal Cart 10 Per trip Cell phone 120 Radio 500 TV 400 Solar panel 200

Farm 1.3 Approximated total Name Owned Rented Borrowed value (ZMW) Axe 1 15 Sickle 2 to 3 Shovel 2 Hoe 1 3 Animal Cart 1 Yoke 1 Disc plough 1 Cell phone 2 300 Radio 1 70 TV 1 250 Battery 1 150

Farm 1.4 Approximated total value Name Owned Note (ZMW) Axe 1 30 Panga machete 1 35 Sickle 2 14 Shovel 1 70 Sometimes borrows Hoe 2 50 more Bicycle 1 400 Cell phone 1 72 Radio 1 30

Farm 2.1 Name Owned Rented Total rental cost (ZMW) Note Approximated total value (ZMW) Sickle 1 10 Hoe 1 30 Animal Cart 1 80 Harrow 1 60 Shovel 1 lima = 2500 m2. The Ox plough 100/lima price includes all land preparation. Cell phone 1 120 Radio 1 400 TV 500 Solar panel 150

Farm 2.2 Name Owned Borrowed Axe 1 Sickle 1 Shovel 1 Hoe 1 Animal cart 1 Yoke 1 Cell phone 1

100

Farm 2.3 Rental price Approximated Name Owned Rented Unit Borrowed Age (years) (ZMW) total value (ZMW) Panga Machete 1 15 20 Axe 1 15 10 Pick axe 1 10 20 Sickle 1 1 12 Knapsack sprayer 1 Animal Cart 1 60 Whole task Lima (2500 Ox plough 1 60 m2) Hoe 1 10 25 Cell phone 1 65

Farm 2.4 Approximated total Name Owned Note value (ZMW) Panga Machete 1 25 Axe 3 120 Sickle 1 10 Shovel 2 100 Hoe 5 150 Animal Cart 1 1800 Yoke 4 40 Ox Plough 1 800 Bicycle 1 Not working 200 Electric mill 1 18 million Cell phone 1 100 Radio 2 370 TV 2 One is not working 1150 Solar panel 1 100 Canoe 2 350

Farm 3.1 Approximated total Name Owned Borrowed value (ZMW) Axe 2 40 Hoe 1 1 20 Cell phone 1 150 Solar panel 1 150

Farm 3.2 Approximated total value Name Owned (ZMW) Axe 2 70 Sickle 1 20 Shovel 1 65 Hoe 4 60 Yoke 1 120 Ox plough 1 1000 Mortar pestle Cell phone 1 100 Radio 1 85 Battery (solar panel) 1 90 Solar panel 1 100

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Farm 3.3 Approximated total value Name Owned Borrowed (ZMW) Panga machete 1 45 Axe 3 70 Shovel 1 45 Hoe 2 30 Branding iron (mark cattle) 1 80

Animal cart 1 Cell phone 1 80

Farm 3.4 Approximated total Name Owned Borrowed value (ZMW) Panga machete 1 45 Axe 2 40 Sickle 4 48 Shovel 1 40 Hoe 4 40 Rake 1 30 Ox plough 1 Cell phone 1 90

Farm 3.5 Approximated Name Owned Borrowed total value (ZMW) Panga machete 1 45 Axe 2 40 Sickle 1 12 Shovel 3 120 Hoe 2 20 Mortar pestle 2 Ox plough 1 (not working) Radio 1 (not working)

102 Challenges and constraints Challenges and Household code Constraints 1.1 1.2 1.3 1.4 2.1 2.2 2.3 2.4 3.1 3.2 3.3 3.4 3.5 Yes. Low soil Bigger fertility problem Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes moderating in plots productivity without Kutuliza Bird eat Crop pest and rice and Rape, diseases Tomato (rainy Worms in sorghum tomato and Tomato Yes Yes Yes (including season) rice in the cabbage birds) lowland Livestock pest Yes Yes and diseases Only one Fluctuating market Low and/or low (Mongu): Yes Low prices Yes prices market prices high competition Far and Yes: Distance and difficult Too far Mongu No access to access Far Yes Transport and and market Market places during expensive Senanga flooding Lack of access to support in farming Yes Yes Yes (extension services) Yes. But there is a special Difficult Lack of Lack of access Difficult to program to get Yes knowledge Yes Yes Yes Yes to credits. get loans (silk loans to get loans program) that works like

103 Challenges and Household code Constraints 1.1 1.2 1.3 1.4 2.1 2.2 2.3 2.4 3.1 3.2 3.3 3.4 3.5 small banks

Varieties High cost of of seeds Cabbage Yes Yes Yes Cabbage Yes Yes Yes Yes good seeds (early maturity) High cost of Yes Yes Yes Yes Yes Yes Yes Yes fertilizers High cost of Yes Yes Yes Yes pesticides Senanga is too far. There is also a problem with the timing of seed Good seeds are Yes. Only in availability Yes unavailable Senanga Senanga (maize) and the 'improved seeds' do not always work (e.g. drought resistant seeds) High post

harvest losses Lack of processing Yes equipment Changing Yes. Need Yes, Droughts. Yes, Yes: early rainfall Floods management droughts Need droughts Yes floods and Early floods Yes Yes Yes Yes patterns are the advise: and prediction and early droughts (unpredictable biggest flooding floods of flooding floods

104 Challenges and Household code Constraints 1.1 1.2 1.3 1.4 2.1 2.2 2.3 2.4 3.1 3.2 3.3 3.4 3.5 climate) problem prediction behavior because damage our crops Hoes, Animals for Irrigation Equipment: Tools to plough, Plough, Lack of Irrigation manure; equipment. for Irrigation, clear the Yes animals, cattle, implements facilities irrigation Needs more to apply canal and money, hoe system manure pesticides farming. labor About planting dates Lack of and Yes knowledge management of different types of soil Labor: requires too much time to finish a task (e.g. Lack of labor Yes Yes weeding) and it is not possible to finish on time

105 Additional questions

Household code Additional questions 1.1 1.2 1.3 1.4 2.1 2.2 2.3 2.4 3.1 3.2 3.3 3.4 3.5 Stay. He would like Expand: Expand: Expand. to expand Expand: harvest Why are you the family is Expand: to Expand: More feed. Expand to Expand: (to improve the family Expand: more and Expand: Expand: planning to expand Expand: more growing, increase more food However feed the More food their life) is growing, bigger grow more food more (or reduce) your food needs more harvest, for family would need family and for family but they needs more harvest different and money harvest farming area? food and more food. and to sell more sell more. and to sale would need food kind of money manpower more crops implements and labor. Residues Labor and are tools Lack of Sometimes important Why do you not (plough, Labor implements: Incorporate Incorporate Labor burn the to feed the Incorporate Incorporate Needs incorporate more Lack of oxen) requirements plough (we Lack of all the all the intensive residues livestock all the all the more crop residues as knowledge requirement. and share it so it knowledge residues residues (watering) (lack of and it is residues residues manpower mulch? Lack of knowledge is not always knowledge) not knowledge available) beneficial (e.g. tomato) for yields. Mulching and Lack of Mulching: Why do you not minimum Mulching Knows that knowledge not practice all the tillage: not and Mulching could Only some on how to Lack of recognized principles of Willing to Crop Lack of recognized minimum and improve fields: lack do it right Land is too knowledge as beneficial conservation but lack of rotation: knowledge as tillage: lack minimum fertility but of labor (which dry or too on how to for yields. agriculture? tools and Lack of on how to do beneficial of tillage: lack lacks of and crops?). wet do it right; Lack of (Mulching, minimum labor know how' it right for yields. knowledge of knowledge knowledge See benefit lack of tools knowledge tillage, crop Lack of on how to knowledge on how to of mulching on how to rotation) knowledge do it right do it right in cassava do it right on how to do it right Now he has Lack of Lack of more Yes. Money knowledge Yes. Money Lack of knowledge knowledge on for seeds; Yes. Money of crops. Yes. Money for seeds; Could do you more knowledge of crops. how to lack of Lack of Lack of for seeds. One for seeds; lack of intercrop? If yes, Labor of crops. Time and diversify knowledge knowledge knowledge No Lack of cannot mix lack of knowledge No why you don't do intensive Some plots labor crops. of crops. of crops of crops knowledge of some knowledge of crops so? are too wet requirement Although he Lack of crops crops of crops and lack of (sishanjo) (all is spend needs more manpower because labor in rice) seeds they don’t

106 Household code Additional questions 1.1 1.2 1.3 1.4 2.1 2.2 2.3 2.4 3.1 3.2 3.3 3.4 3.5 perform well. Different seeds for different soils.

Yes. There are no Yes. She animals to He uses the use them as eat the Yes. There Yes. There Yes. There Yes. There Yes. There Yes. There Yes. There Yes. There residues as green residues. Could you feed more are no are no are no are no are no are no are no are no It is just mulch, but he manure; Sometimes animals with the animals to animals to animals to animals to No animals to animals to animals to animals to enough for could also There are other animal crop residues? eat the eat the eat the eat the eat the eat the eat the eat the her cattle feed more no animals eat the residues residues residues residues residues residues residues residues animals to eat the residues but residues they don’t stay enough (manure) Not Not enough enough Not enough money for money for Not enough Not enough Not enough Not enough Not enough Not enough money for Not enough seeds; Lack seeds; What obstacles money for money for money for money for money for money for seeds; Lack money for of Lack of stand in the way of seeds. Lack seeds. Some Not enough seeds; Lack of seeds; Lack seeds; Lack seeds; Lack of seeds; Lack knowledge knowledge increasing the Seeds Labor of areas are money for knowledge of of of of knowledge of of many of many number of crop implements very sandy seeds. many crops. knowledge knowledge knowledge of many knowledge crops. crops: how grown? (plough) and have low Poverty in of many of many of many crops. Lack of many Diseases to plant and labor soil fertility. general crops crops crops of crops and the seeds implements manpower in a good way. Lower: Comparing to your Higher and Lower: lack Lower: not Medium needs Better than neighbor farmers, higher crop Higher Similar Similar Similar of Higher enough time Lower Higher (similar) animals and most your yields are…. diversification implements in the field plough

107 Production Summary

Crop Farm code Production (Mg ha-1) SEM within farm Rice 1.1 2.97 0.82 Rice 1.2 0.50 0.22 Rice 1.3 1.17 0.25 Rice 1.4 2.00 Rice 2.1 0.17 Rice 2.2 0.12 Rice 2.4 0.32 Rice 3.1 2.75 0.27 Rice 3.2 0.75 0.23 Rice 3.3 0.32 0.08 Rice 3.4 1.74 0.55 Rice 3.5 0.99 0.08 Average ± SEM 1.15 ± 0.28 Maize 1.1 2.25 Maize 1.2 2.34 0.55 Maize 1.3 2.93 1.10 Maize 1.4 0.64 0.00 Maize 2.1 0.55 0.20 Maize 2.2 3.38 0.33 Maize 2.3 0.63 0.13 Maize 2.4 1.41 0.68 Maize 3.1 1.06 0.21 Maize 3.2 1.49 1.18 Maize 3.3 0.27 Maize 3.4 0.54 0.18 Maize 3.5 1.56 0.33 Average ± SEM 1.47 ± 0.28 Sweet Potato 1.2 1.59 0.51 Sweet Potato 1.3 1.54 Sweet Potato 2.1 3.82 2.10 Sweet Potato 2.4 4.94 Sweet Potato 3.1 6.05 0.00 Sweet Potato 3.2 4.21 Sweet Potato 3.3 1.31 0.65 Sweet Potato 3.4 1.22 0.48 Average ± SEM 3.09 ± 0.53 Cowpea 2.4 0.53 Cowpea 3.1 1.08 Cowpea 3.2 0.27 Average ± SEM 0.63 ± 0.24 Millet 3.1 0.52 Millet 3.3 0.45 Millet 3.4 0.22 Average ± SEM 0.40 ± 0.09 Tomato 1.1 3.71 Tomato 1.2 25.74 Tomato 1.4 1.78 Tomato 2.1 9.65 6.38 Tomato 2.3 13.75 Tomato 3.1 3.43 1.81 Tomato 3.2 6.24 Average ± SEM 9.19 ± 3.17

108 Crop Farm code Production (Mg ha-1) SEM within farm Sorghum 1.1 0.00 Sorghum 1.3 3.85 Sorghum 3.2 0.44 Average ± SEM 1.43 ± 1.32 Groundnut 2.1 2.59 2.06 Groundnut 2.2 7.28 Groundnut 2.4 4.34 Groundnut 3.2 0.27 Groundnut 3.5 1.71 Average ± SEM 3.24 ± 1.21 Cassava 1.3 1.40 Cassava 2.1 0.31 0.01 Cassava 2.2 2.13 Cassava 2.4 3.99 Cassava 3.1 0.78 0.29 Average ± SEM 1.72 ± 0.64

Tillage activities per household

Tillage Household code activities / used power 1.1 1.2 1.3 1.4 2.1 2.2 2.3 2.4 3.1 3.2 3.3 3.4 3.5 source and tool Sometimes Only in sishanjo Ridges Yes In Lilako Yes Yes sweet Maize and potato Mantongo Vegetables Tomato Maize during hot Only for Mulching Yes and and season Mantongo Yes seedlings eggplant Tomato (Litapa and Lilako) Minimum Only Yes tillage vegetables Yes: Only Crop Some Yes Only Lilako Yes groundnut some Yes rotation times and maize fields Litapa, Sitapa Likaña and By hand Yes Yes and Yes Yes Yes Yes Yes Yes Yes Yes Yes sweet Lilako potato Yes. Borrow: Yes. By oxen Yes Yes Yes Yes Yes Yes Yes Rented Lizulu Rented Hoe Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Axe Yes Plough Rented Borrow Yes Yes Yes Yes Borrows Yes

109