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Patterns of nutrient allocation and management in smallholder
farming system in Massingir District, Mozambique. A case study of
Banga village
Wilson José Leonardo
MSc Thesis Plant Production Systems
June 2007
Patterns of nutrient allocation and management in smallholder farming system in
Massingir District, Mozambique. A case study of Banga village
Wilson José Leonardo
MSc Thesis Plant Sciences
PPS 80436
June 2007
Supervisor: Ken E. Giller (Plant Production Systems)
Advisor: Jessica Milgroom (Plant Production Systems)
Examiner: Nico de Ridder (Plant Production Systems)
Plant Production Systems Group
Wageningen University
P.O. Box 430, 6700 AK, The Netherlands
Table of contents
ACKNOWLEDGMENTS ................................................................................................................................ 1 SUMMARY ......................................................................................................................................................... 3 ABBREVIATIONS AND ACRONYMS....................................................................................................... 7 LIST OF FIGURES ........................................................................................................................................... 9 LIST OF TABLES ............................................................................................................................................ 11 1. INTRODUCTION ................................................................................................................................ 13
1.1 LIMITATIONS OF NUTRIENT BALANCES ............................................................................................. 16 1.2 OBJECTIVE OF THE STUDY .................................................................................................................. 18 1.3 HYPOTHESIS......................................................................................................................................... 18 1.4 OUTLINE OF THE THESIS..................................................................................................................... 18
2. THE STUDY AREA .............................................................................................................................. 21 2.1 BIOPHYSICAL DESCRIPTION ........................................................................................................... 21 2.2 SOCIO-ECONOMIC DESCRIPTION................................................................................................... 24 2.3 JUSTIFICATION AND CRITERIA FOR THE SELECTION OF THE RESEARCH SITE ........................... 24
3 MATERIALS AND METHODS ......................................................................................................... 27 3.1 LITERATURE REVIEW........................................................................................................................... 27 3.2 INTERVIEWS WITH KEYS INFORMANTS............................................................................................... 27 3.3 RAPID FARMING SYSTEM CHARACTERIZATION (RFSC) ................................................................... 27 3.4 CLASSIFICATION OF FARMERS IN CLASSES ACCORDING TO THEIR WEALTH .................................. 28 3.5 LIVELIHOOD STRATEGY: ANALYTICAL FRAMEWORK ...................................................................... 28 3.6 INDIVIDUAL HOUSEHOLD SURVEY..................................................................................................... 29 3.7 NUTRIENT BALANCES ......................................................................................................................... 30
3.7.1 Soil sampling ............................................................................................................................... 32 3.7.2 Nutrient stocks ............................................................................................................................ 33 3.7.3 Calculations of nutrient balances................................................................................................ 33
3.8 STATISTICAL ANALYSIS........................................................................................................................ 40 4 RESULTS................................................................................................................................................. 41
4.1 THE HISTORY OF THE VILLAGE ..................................................................................................... 41 4.2 THE DIFFERENT TYPES OF FIELDS FOUND IN BANGA .................................................................. 43 4.3 NUTRIENT FLOW MAPS .................................................................................................................. 49 4.4 THE FIVE CAPITALS AND SOIL FERTILITY MANAGEMENT ............................................................ 49
4.4.1 The natural capital ...................................................................................................................... 49 4.4.2 Financial capital.......................................................................................................................... 51 4.4.3 Social capital ............................................................................................................................... 53 4.4.4 Human capital ............................................................................................................................ 54 4.4.5 Physical capital ............................................................................................................................ 55
4.5 AGRICULTURE ................................................................................................................................ 55 4.5.1 Farming system in Banga Village ............................................................................................... 55 4.5.2 Cropping season and labour allocation ....................................................................................... 56 4.5.3 Livestock and grazing system ...................................................................................................... 58
4.6 WEALTH GROUPS DIVISION ........................................................................................................... 60 4.7 VARIABILITY OF THE SOIL PROPERTIES IN DIFFERENT FIELD TYPES ........................................... 65 4.8 CALCULATION OF N, P AND K BALANCES................................................................................... 69
4.8.1 Partial balances ........................................................................................................................... 69 4.8.2 Full balances ................................................................................................................................ 70
4.9 MAIZE YIELD (KG HA-1) IN DIFFERENT WEALTH CLASSES ............................................................. 72 5 DISCUSSION......................................................................................................................................... 73 6 CONCLUSIONS ................................................................................................................................... 81 REFERENCES ................................................................................................................................................. 85
APPENDICES...................................................................................................................................................93 APPENDIX 1. HOUSEHOLD INTERVIEW (FIRST RAPID FARMING SYSTEM CHARACTERIZATION-RFSC).....................................................................................................................95 APPENDIX 2. INDIVIDUAL QUESTIONNAIRE USED IN INDIVIDUAL INTERVIEW ..............98 APPENDIX 3. NUTRIENT STOCKS IN DIFFERENT FIELD TYPES FOUND AT BANGA VILLAGE..........................................................................................................................................................101 APPENDIX 5. TOTAL AREA COVERED BY GOWENE AND BANHINE’S FIELDS AT BANGA VILLAGE..........................................................................................................................................................103 APPENDIX 6. MANURE INPUTS (IN2) AND WET DEPOSITION (IN3) (KG HA-1) ......................104 APPENDIX 7. BIOLOGICAL N FIXATION (IN4) AND HARVEST PRODUCT (OUT 1).............105 APPENDIX 8. CROP RESIDUES (OUT 2) AND LEACHING N AND K (OUT 3) ...........................106 APPENDIX 9. DENITRIFICATION (OUT 4)...........................................................................................107 APPENDIX 10. EROSION (OUT 5) ............................................................................................................108 APPENDIX 11. PARTIAL NUTRIENT BALANCES FOR MAIZE (KG HA-1) ...................................109 APPENDIX 12 ANALYSIS OF VARIANCE OF VARIABLES SOC, N, P, K, CEC AND C:N RATIO. .............................................................................................................................................................110
I dedicate this thesis to:
The farmers in Banga Village who regularly watch their crops fail due to the drought.
I wish I could provide you with small-scale irrigation equipment, and more drought
tolerant germplasm.
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Acknowledgments
I am indebted to the International Fellowship Programs (IFP) for providing me with funds to
pursue this MSc study. I would like to thank my IFP contact person - Dra Célia Diniz, for
always supporting me throughout the course of my studies. She also assisted me in all the
administrative issues related to this scholarship.
I would like to express my appreciation to the people of Banga for having allowed me to do
this study there. I am especially thankful to all who gave me the opportunity to interview
them. Thanks to the staff at Agriculture Directorate, especially to Mr. Filimone and Mr.
Marcos for helping me to select the village.
I am thankful to my supervisor Prof. Ken Giller who’s academic guidance and advice was
crucial. When I first arrived at Wageningen University I was fortunate enough to meet Ken.
By attending his course, I discovered that he was an interesting person to work with. His
knowledge of the complexities and heterogeneities of smallholder farming system in Sub-
Saharan Africa inspired in me an interest in working with him as my supervisor. Thanks for
giving me the opportunity to explore the area of soil nutrient management.
I would like to thank my advisor Jessica Milgroom for her continuous support in the thesis.
She was always there to listen and give advice. I remember the discussions held with her at
different stages of the research. These discussions inspired me to think about the best way to
conduct this study.
I would like to thank Dr. Roland Brouwer from Eduardo Mondlane University in
Mozambique for assisting me in different stages of writing the report. He provided
suggestions and critical comments to improve the thesis. He has been playing an important
role in my education career since he was my lecturer at university during my BSc.
They are many people who did not contribute directly to the thesis, but have been friends
and colleagues since I arrived at Wageningen University, so I would like to thank them for
their unconditional support – Adugna, Munisi, Deon, Yann, Byjesh, Jéróme, Jobert,
Guillaume, Solenn and Alex.
I would like to thank ICRISAT-Mozambique staff- Dr. Dominguez, Celso, Ivone, Gino and
Vilanculos for let me use your office, and your friendship during my fieldwork.
I will not forget the contribution received from Milly during the ups and downs of my
studies. Sometimes, when I felt depressed, she was there supporting me.
To my brothers and sisters Benvinda, Júlia, Mariza, Bernardo and Quincardete who
understood the importance of this course to my career. I will never forget your support.
2
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Summary
The alarming picture of nutrient depletion that emerged from the nutrient balances
calculated by Stoorvogel and Smaling at different scales in Sub-Saharan Africa,
motivated researchers to carry out multi-scaled studies in order to understand the
underlying causes behind this nutrient depletion. Some of these studies have confirmed
that nutrient depletion leading to poor soil fertility is largely determined by past and
current land use systems and management practices, and pointed at the importance of
differences in farming techniques between poor and wealthy households. In some
farming systems, one common scenario is reported: positive nutrient balances for
wealthiest farmers and negative for poorest farmers.
In Mozambique, the soil nutrient balance studies which were conducted at the national
level in different land use types showed annual depletion rates for cultivated fields of 33
kg nitrogen, 7 kg phosphorus and 25 kg of potassium per hectare per year on average.
For small-scale farming, the studies showed annual depletion rates of 47.9 kg ha-1 of N,
9.9 kg ha-1 of P and 36.5 kg ha-1 of K for maize, which is the most important crop in
Mozambican smallholder cropping systems. These studies did not take into account the
wealth status of the farmers despite of the widespread acceptance that land use systems
and management practices differ between poor and wealthy households.
It has also been argued that in some farming systems the gradients of nutrient depletion
increase with the distance from homestead. This occurs because farmers tend to
concentrate manure collected from the ‘kraals’ and other organic matter (ashes, kitchen
scraps, garbage) in the fields closer to the homestead, while fields further away often
receive no fertilizers amendments.
The main objective of this thesis was to test if the patterns of nutrient depletion
reported in many studies conducted in sub-Saharan Africa holds true in Mozambican
smallholder farming systems. In order to achieve this objective, soil nutrient stocks and
partial nutrients balance analysis were carried out at field level, in Banga Village, Gaza
province, Southern Mozambique. General information about the farming systems in
these villages was gathered through interviews with key informants, focus group
discussions, field visits and semi-structured interviews with individual farmers. About 25
households participated which were randomly selected out of 90 households. Eight
households comprising of different wealth groups within the village were selected for
detailed characterization of their farming systems. The information collected covered
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the main characteristics of the households, land availability, main activities, main crops,
production constraints, production orientation, nutrient management, coping strategies
and wealth indicators.
Agriculture and livestock husbandry are the main activities practiced in two different
land units: the alto (closer to homestead) and baixo (far-away). Agriculture is practiced
without using external inputs and is highly dependent on rainfall. Maize is the main
crop in the village and is cultivated in all land units, although those further away are
considered to have better yields. Drought is a recurrent phenomenon in the study area
and it severely affects the maize yield. A lack of other crops which are tolerant to
drought have limited farmers from diversification of crop production. The sale of
charcoal, animals, remittances and local brews constitute the main sources of income
when crops fail. There are different exchange relationships that underpin their
livelihood strategies including kukashela, xicoropa, tsimo and kuwekissa. All these are
related to the exchange of labour for land preparation.
Farmers indicated cattle ownership as the main factor in dividing wealth classes. Based
on this, four groups were identified: the Very Low Resource Endowment (VLRE), the
Low Resource Endowment (LRE), the Medium Resource Endowment (MRE) and High
Resource Endowment (HRE).
Soil nutrients stocks and chemical properties (N, P, K, SOC and CEC) were
significantly higher (p <0.005) in the far-away fields compared to those close to the
homestead. The reason appears to be the inherent properties of the soils. Within the
same field type, there were no significant differences in soil nutrient stocks between
farmers belonging to different resource endowment groups. The stocks range was:
Mananga (SOC 0.43 (%), 0.06 (%) N, 3.84 mg kg-1 P and 1.18 cmol kg-1 K), Gowene (SOC
1.53 (%), 0.13 (%) N, 53.3 mg kg-1 P and 3.09 cmol kg-1 K) and Banhine (SOC 1.1 (%),
0.12 (%) N, 27.5 mg kg-1 P and 2.4 cmol kg-1 K). Despite the lack of external inputs, and
with the exception of N and SOC, the levels of the other properties in far-away fields
were in adequate levels for crop production, especially maize crop.
Partial nutrients balances (N, P and K) for maize in the most important field type
(Banhine) were strongly negative for all resource endowment groups. The partial N
balances were: -27.3 kg ha-1, -21.5 kg ha-1, and -18.8 kg ha-1and -30.4 kg ha-1, for the
VLRE, LRE, MRE and HRE groups, respectively. In case of P the values were: -2.8 kg
ha-1, -2.1 kg ha-1, -1.6 kg ha-1 and -2.9 kg ha-1, for the VLRE, LRE, MRE and HRE
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groups, respectively, and for the K were: -32.3 kg ha-1, -24.8 kg ha-1, -21.4 kg ha-1 and -
35.0 kg ha-1 for the VLRE, LRE, MRE and HRE groups, respectively.
The results of this study contradict earlier research results that indicate a close
relationship between wealth status and level of nutrients. The study also refutes other
findings that revealed that fertility levels tend to decrease with distance. The land tenure
security does not influence the likelihood of soil fertility investment.
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Abbreviations and acronyms
CL Community Leaders DDA Direcção Distrital de Agricultura
District Agricultural Directorate DINA Direcção Nacional de Agricultura
National Agricultural Directorate EW Extension Workers FAO Food and Agriculture Organization of the United Nations FewsNet Early Warning Systems Network FRELIMO Frente de Libertação de Moçambique
Front for the Liberation of Mozambique GPS Global Position System HRE High Resource Endowment ICRISAT International Crop Research Institute for Semi-Arid Tropics IFPRI The International Food Policy Research Institute IIAM Instituto de Investigação Agrária de Moçambique Institute of Agrarian Research of Mozambique INAM Instituto Nacional de Meteorologia National Institute of Meteorology INE Instituto Nacional de Estatística National Institute of Statistic INGC Instituto Nacional de Gestão de Calamidades National Disaster Management Institute INIA Instituto Nacional de Investigação Agronómica National Institute for Agronomic Research LNP Limpopo National Park LRE Low Resource Endowment MRE Medium Resource Endowment NGOs Non-Governmental Organization RENAMO Resistência Nacional Moçambicana Mozambican National Resistance RFSC Rapid Farming Characterization SPSS Statistic Packages for Social Sciences UEM Universidade Eduardo Mondlane Eduardo Mondlane University USLE Universal Soil-Loss Equation VLRE Very Low Resource Endowment
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List of figures
Figure 1. Distribution of rainfall in mm through the year from a meteorological station
in Massingir District. The values are averaged over the last 20 years, and were calculated
on the basis of raw data provided by INAM (2006).......................................................... 21
Figure 2. Map of Massingir district showing the location of Banga. FewsNet, INGC and
UEM (2004)......................................................................................................................... 23
Figure 3. Mananga, Gangene, Gowene, Banhine and Cowene’s fields. ............................... 31
Figure 4. Cattle pulling sleigh – a traditional sledge used for transport. ............................. 37
Figure 5. The four fields types in Banga. ........................................................................... 45
Figure 6. Cattle grazing in Gowene’s fields. ....................................................................... 46
Figure 7. Different sources of remittances in Banga village expressed as a frequency of
the total number of households. N= 25 household.......................................................... 52
Figure 8. Number of farmers owning livestock (N=25) ................................................. 59
Figure 9. Resource endowed per different wealth groups at Banga Village ..................... 63
Figure 10. Resource endowed per different wealth groups – case study .......................... 63
Figure 11. Cattle distribution among locals and immigrants ........................................... 64
Figure 12. Soil organic carbon (SOC) in different field types at Banga. .......................... 66
Figure 13. Linear relationship between SOC (%) and CEC (cmol kg-1). ......................... 66
Figure 14. Cation Exchange Capacity (CEC) in different field types at Banga. .............. 67
Figure 15. Total nitrogen content in different field types at Banga. ................................ 67
Figure 16. The available P in different field types at Banga.............................................. 68
Figure 17. Exchangeable K in different field types at Banga............................................. 69
Figure 18. N, P and K inputs (a, b and c, respectively) for Banhine’s fields in different
resource endowment groups at Banga. .............................................................................. 70
Figure 19. Average maize yield in different wealth groups. ............................................. 72
Figure 20. Model of soil nitrogen and crop yield dynamics in a no-input system (after
FAO, 2004b). Source: Smaling and Dixon (2006). ............................................................ 77
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List of tables
Table 1. Mean monthly temperature from 1986 to 2006 measured at Massingir. ........... 22
Table 2. Number of fields sampled per wealth classes. ..................................................... 32
Table 3. Relation between bulk density and texture class ................................................ 33
Table 4. Soils properties of Gowene, Banhine and Mananga fields. .................................. 48
Table 5. Different forms of access to land in Banga Village ............................................. 51
Table 6. Crop calendar in Banga (October-May) .............................................................. 57
Table 7. Coping strategies used by farmers in Banga during emergency ......................... 58
Table 8. Main characteristics of the four different resource endowment groups at Banga.
............................................................................................................................................. 62
Table 9. Nutrient stocks in different field types (N, P and K, in kg ha-1) at Banga (See
Appendix 3 for the calculations). ....................................................................................... 76
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1. Introduction Soil scientists involved in research related to the problems of soil fertility in Africa argue
that soil fertility depletion is the main biophysical factor limiting the productivity of
smallholder farms (World Bank, 1989; Stoorvogel et al., 1993; Sanchez et al., 1997). The
growing human population has led to land scarcity forcing people to explore marginal
lands that are not suitable for agriculture. In many cases, land productivity declines due
to the shortness of the fallow periods, soil erosion, and overgrazing (World Bank, 1989).
The socio-economic environment and the biophysical conditions of the location
(climate and soils) cause variation in soil fertility at regional, village, farm and field
levels. At each level nutrients can be depleted at different rates, and these rates are
largely determined by the past and current land use systems and management practices
(Smaling et al., 1993; Smaling et al., 1997; Doran, 2002). These land use systems and
management practices differ between poor and wealthy households (Crowley and
Carter 2000; Scoones, 2001). Measurement of the variability of soil fertility of different
socio-economic groups in Zimbabwe showed that partial nutrient balances were positive
for the wealthiest farmers and negative for the poorest farmers (Zingore et al., 2006).
However, an impression that this pattern holds true everywhere would be misleading.
Studies conducted in Zimbabwe and Ethiopia by Scoones (2001) showed that
improvement and decline of soil fertility are not precisely correlated to wealth and asset
status, as some of the wealthiest farmers were found with the highest levels of nutrient
depletion.
Moreover, Smaling et al. (1996) and Scoones (2001) pointed out that the net flow of
resources is not the same for different fields belonging to a single farm household.
Consequently, studies at farm level may not reveal the complex spatial dynamics of soil
fertility management within the farm, and extrapolation of farm level data to regional or
national levels can be misleading. Bearing this in mind, field level research can improve
our understanding on the complexities of nutrient flows within a single farm.
Many authors reported gradients of nutrient depletion increasing with the distance from
the homestead (see for example Prudencio, 1993; Ruthenberg, 1980; Tittonell et al.,
2005; Zingore et al., 2006). This is because in some farming systems farmers tend to
concentrate manure collected from the ‘kraals’ and other organic matter (ashes, kitchen
scraps, garbage) in the fields closer to the homestead (Zingore et al., 2006; Mapfumo and
14
Giller, 2001), and further away fields often receive no organic amendments and
inorganic fertilizers (Giller et al., 2006). Crop management practices such as delayed
planting and low crop density also contribute to these gradients in crop productivity
(Tittonell et al., 2007). For instance, plant density affects the production, which on its
turn affects amount of organic matter that is returned to the soil through litter or other
crop residues and the nutrient extraction rates. Fields with low fertility levels are
normally planted late and receive less attention, because people preferably invest more
resources in fields that are expected to produce more yields or high value crops.
This present study has been carried out in Banga Village, Massingir District, which is
located in Gaza Province, Southern Mozambique to test if the pattern of nutrient
depletion reported in many studies conducted in sub-Saharan Africa holds true in these
smallholder farming systems. In Mozambique, a soil nutrient balance study conducted
at the national level in different land use types showed annual depletion rates for
cultivated fields of 33 kg nitrogen (N), 7 kg phosphorus (P) and 25 kg of potassium (K)
per hectare on average (Folmer et al., 1998). For small-scale farming, the study showed
depletion rates of 47.9 kg/ha of N, 9.9 kg/ha of P and 36.5 kg/ha of K for maize, the
most important crop in Mozambican smallholder cropping systems. These depletion
rates are thought to contribute to a rapid decline in soil fertility which affects the land
productivity (ibid).
The farming system in Massingir consists basically of two types of fields: the fields in
depressions and on riverbanks and the fields on the higher grounds. As people reside on
the higher grounds, the lower lying fields are located farther from the homestead than
those on the higher grounds. In Mozambique these fields in depressions and on river
bank or lowlands, namely baixo are inherently more fertile than those closer to
homestead, the higher grounds normally called alto.
In areas like Massingir District with low population pressure (4.9 inhabitants/km2)
(District Government, 2006), where farming systems are extensive, no external inputs
are used, weakly integrated in agricultural markets, and agricultural production is
mainly for household consumption, production systems are thought to have the
potential to be sustainable (Barrow, 1998). This happens because farmers can leave the
land to rest and the new vegetation can restore the “initial” fertility level. The time
needed for restoring initial fertility depends on many factors (Upadhyay, 1995) such as
the inherent characteristics of the soil, and interactions between vegetation and
15
management (Szott et al., 1999). As a result, a wide range of required fallow periods have
been reported. For example, a study conducted in Venezuela by Watters (1971) reports
the crop/fallow time ratio of at least 3 years cropping followed by 15 years of fallow;
according to Giller and Palm (2004) fallow periods of at least 15-20 years were reported
for West Africa; Nye and Greenland (1960) mention a fallow period of 10 years for the
humid tropics, while for the same climatic conditions a periods over 20 years were
considered as optimal by Tchoundjeu et al. (1999). However, as soon as the competition
for land intensifies, the sustainability of this land use system becomes threatened.
Therefore, under conditions of high pressure on land, sustainable production requires
investment in soil fertility management practices.
In Massingir, apart from the competition that may exist among farmers over the more
fertile areas and areas with better access to water, the establishment of the Great
Limpopo Transfrontier Park in 2001, which covers large areas of the district, is
increasing the pressure on land. The restrictions on land use imposed by the creation of
this park imply additional challenges to the farmers striving to produce enough food to
supply their households.
Under the establishment program of the park, the community of Macavene (inside the
park) will be resettled close to the Banga Village (outside the park). The resettlement
process will also increase the pressure on the land and other natural resources. The
restrictions on land use ensuing from the creation of the Park also affect existing grazing
practices. According to De Jager (2003) and Shepherd and Soule (1998), communal
grazing is thought to be a way of gathering nutrients from the outfields and transferring
these through the dung to the areas used for crop cultivation. In low population density
environments, this can lead to a slightly positive nutrient balance at farm level.
However, as De Jager (2003) pointed out this can only happen if sufficient amount of
communal land is available. On the top of that, in order to evaluate the grazing
contribution it is necessary to include factors that might affect the quality of manure
(Lekasi et al., 2001), crop nutrient uptake (Rufino et al., 2006) as well as the number and
the type of herd owned by farmers (Lekasi et al., 2002). For instance, moisture
availability limits the crop N capture, and in old manures the process of N realising can
be slow when it is presented as stable organic N (Rufino et al., 2006).
The partial nutrient balance analysis (NPK) is used as an indicator to understand the
patterns of nutrient allocation and soil management which can lead to nutrient
16
depletion and poor soil fertility, within different wealth classes and within the most
representative field types in the village of Banga. The nutrient balance analysis was
combined with the analytical framework to analyse sustainable livelihood.
The study aims to create the basis for intensive discussions between the policy makers
(DDA, government) and other stakeholders (local farmers, NGOs) in order to address
the issue of nutrient depletion. The new decentralization policy in Mozambique, which
consider the “district as the pool of development” opens a window of opportunities for
the researchers to carryout participative studies that address the main agricultural
problems.
Although the study does not claim to be representative of all the socio-physical niches in
smallholder farming system in Massingir District, it provides some important insight
about the sustainability of the farming system in some areas of this district. In addition,
the study has merit of being the first research in Mozambique which looks at soil
variability between different fields belonging to a single household, and relate this
variability to the wealth status of the household.
1.1 Limitations of nutrient balances
Although nutrient balances are a useful starting point to understanding the
sustainability of a land use system, it has, as other methodologies, some limitations that
should be taken into consideration when drawing conclusions. This study highlights the
following limitations:
- A nutrient balance itself can not explain the sustainability of the land use system;
therefore, nutrient stocks analysis and soil properties must be considered (Janssen,
1999; Vanlauwe and Giller, 2006). Indeed, the nutrient balance is just an indication
of present in and out flows of nutrients within the system; it does not provide
information on the current status of nutrient stocks. When this available pool of
nutrients is not taken into account, the results from nutrient balances can be
misleading and inappropriate polices can be recommended. Vanlauwe and Giller
(2006) reported an example from Malawi where an international scientist based on
the analysis of crop export off the farm and fertilizers inputs to conclude that the
major problem in Malawi was potassium. However, many studies conducted on
soil nutrient deficiencies in the same country showed no evidence of potassium
deficiency (ibid).
17
- Scoones and Toulmin (1998) highlighted the importance of considering the spatial
and temporal variations on nutrients flows, this because the level in which a certain
study is undertaken (plot, field, farmer, village, national or global) has implications
for the way the data should be analysed. While the nutrient balance at field level
could be negative due to the crop harvest removals without any input, at large-scale
such as a village nutrient gains and losses are may be in balance because of reasons
such as input imports (manure) from communal grazing areas (Roy et al., 2003).
Positive nutrient balances have been observed at field level due to the different
nutrient allocation pattern within different plots belonging to a single farm (De
Ridder et al., 2004; Zingore et al., 2006). The temporal dynamics of the number of
livestock owned as well as the grazing system (extensive or intensive) affect the
amount of inputs turned to the system and should be considered on soil nutrient
balances analysis. Scoones and Toulmin (1999) highlight some studies attempted to
determine the area of rangeland needed to graze livestock and provide enough
manure for one hectare of crop in many African farming. The results were highly
variable varying from zero up to 45 ha (ibid). These examples show the difficulties
on the quantification of manure contribution.
- Apart from the nutrient inputs of mineral fertilizers or organic manure and the
outputs from harvested products and removal of crop residues, it is hard to obtain
information for the other components of the methodology. As a result, its values
are frequently derived from transfer functions and literature (Smaling and Dixon,
2006). Even the organic manure composition highly variable. For instance, the
nutrient contents reported in the literature may not be as that used in the system at
same at application time. The trial conditions from which these values are collected
may not representative of the study area. Also different cultivars of each crop vary
in nutrient uptake. For instance, high productivity cultivars would be able to
remove more nutrient than the low productivity varieties. Other uncertainties of
using transfer functions for nutrient balance calculations are reported by Faerge
and Magid (2004) and De Ridder et al. (2004).
Taken into account the uncertainties of the nutrient balance approach, I decided to
calculate partial nutrient balances considering inputs that are relatively easy to measure
such as fertiliser, manure, crop harvest and residues and compare to full nutrient
18
balances. The partial nutrient balances can be useful indicators and be used without
making too many assumptions.
1.2 Objective of the study
The overall objective of this case study is to improve our understanding of farmers’
behaviour, their interactions and socio-economic conditions that directly impact their
farming system, since farmers are not identical in terms of their resource endowment
and agricultural practices.
In light of the main objective, the specific objectives are as follows:
(i) To collect basic socio-economic and biophysical information in Banga
Village;
(ii) To identify the most important land types (alto and baixo) and the criteria
farmers use for their choice and management practices for these land types;
(iii) To assess whether farmers allocate inorganic fertilizers, manure or other
organic fertilizers to their fields and to quantify these inputs;
(iv) To identify options for improvement of the current agricultural practices to
improve the soil fertility level.
1.3 Hypothesis
On basis of above objectives and background information, the following hypotheses are
formulated:
1. The wealth status of the household has no influence on nutrient balance at field
level – positive or negative;
2. The magnitude of nutrient depletion values does not depends on wealth, but on
the biophysical conditions of the location;
3. Fields in baixo (far away fields) are more fertile than those in alto (closer to
homestead), thus receive more attention (input and management).
1.4 Outline of the thesis
After describing the background in which this study was carried out, (Chapter 2) I
describe the study area in terms of biophysical and socio-economic conditions. In
Chapter 3, I describe the different methods used in the thesis. Chapter 4 presents the
19
results of the nutrient balances analysis in different resource endowment groups. This
chapter also looks at five capitals (social, physical, financial, natural, and human) in the
context of soil fertility management. Chapter 5 presents a general discussion, and in
chapter 6, I present the main conclusions of the thesis.
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2. The study area
2.1 Biophysical description
The study was conducted in Banga Village, Massingir District, located in the North of
Gaza Province in the Southern Mozambique. This district is located at about 360 km
from Maputo, the capital of Mozambique and it is bounded in the North by
Chicualacula District, on the South by Magude District, on the East by Mabalane and
Chókwè districts and by South Africa in the West. The altitude is about 148 m above
sea level and around latitude 23o 55’18.0 S and longitude 32o 09’42.0 E (measured with
Garmin (GPSII) Global Position System (GPS)). The total geographical area of the
district is about 5.858 km2 (INE, 1997).
The climate is semi-arid and the mean annual rainfall is about 320 mm, distributed in a
unimodal pattern (October to March), with peaks in December, January and February
(Figure 1). With exception of Cubo Village were farmers can benefit from residual
moisture from Massingir Dam, which allows them to have more than one cropping
season, the majority of the farmers in the district rely on unpredictable and low rains,
concentrated between October and March. However, in case of occasional rainfall out
of this period, farmers can still plant their crops.
0102030405060708090
100
JAN
FEBMAR
APRMAY
JUN
JUL
AUGSEP
OCTNOV
DEC
Months
Rai
nfal
l (m
m)
Figure 1. Distribution of rainfall in mm through the year from a meteorological station in Massingir District. The values are averaged over the last 20 years, and were calculated on the basis of raw data provided by INAM (2006).
22
The temperature data shows that the mean annual maximum temperature is relatively high: between 33 oC in the summer (September – March) and about 29 oC in the winter (April -August) (Table 1). Table 1. Mean monthly temperature from 1986 to 2006 measured at Massingir.
Temperature oC Months
Maximum Minimum
January 34.1 20.0
February 33.6 20.0
March 33.2 19.7
April 31.7 17.6
May 29.0 14.3
June 27.7 11.6
July 27.0 11.1
August 28.9 12.7
September 30.7 15.3
October 32.0 16.9
November 33.3 18.9
December 33.3 20.6
Source: Calculated on the basis of raw data provided by INAM, 2006.
The district of Massingir is part of the Limpopo River Basin, one of the biggest rivers in
the Southern Africa Region (Fews Net, 2003). Nutrient availability and unpredictable
and low rainfall are limiting factors controlling land productivity in this area (Geurts et
al., 1999).
According to FAO (2004) two main groups of soils can be found in this basin, namely:
“old soils formed on deeply weathered parent materials, influenced by earlier eroded
surfaces”; and “relatively young soils, formed on the more recent eroded surfaces, or on
alluvial deposits”. INIA (1995) classify these soils as Mollic Fluvisols. These two groups
of soils characterise the Banga situation, especially in baixo fields.
23
The village of Banga is about 18 km away from Massingir Village and is lies around latitude 24o 00’ 01.0 S and longitude 32o 18’01.8 E (GPS). Figure 2 shows the map of Massingir District.
Figure 2. Map of Massingir district showing the location of Banga. FewsNet, INGC and UEM (2004).
24
2.2 Socio-economic description
According to the District Administration, the total number of inhabitants in the district
is about 27757 which corresponds to 4.9 inhabitants per square kilometer. The
dominating ethnic group is Changana (INE, 1997). Migrants from different part of the
district and other regions of the country are also found in Massingir district. The
heritage system is patriarchal and polygamy is an important characteristic of these
communities.
According to FewsNet (2003) the district is located in Food Economies Zones 37 and 38,
where the main sources of food and cash income are mainly: agriculture, livestock
(cattle, goats and poultry), hunting, production and sale of charcoal, traditional
beverages and remittances from migrant labour in South Africa. Because vast areas of
Massingir are characterized by semi-arid climate, the livestock production (cattle and
goats) and exploitation of forestry resources are the main coping strategies in time of
crises (crop failure due to drought) and emergency such as medicines expensive, tuition
fees for the sons and other (ICRISAT/DINA, 2003). Livestock also provides animal
draught power to the farmers and is a means of transportation.
2.3 Justification and criteria for the selection of the research site
Massingir District was selected for the study due to its agro-ecological features (low and
unpredictable rainfall, rain fed agriculture), its farming system (mixed crop-livestock), its
natural resources and the establishment the Limpopo National Park (LNP).
The case study village was selected because Banga’s farming system became the most
productive in the district after the 2000 floods that devastated the Cubo Village,
previously the most productive area (Agriculture director and extension officers,
personal communication) and it is representative of the diversity of agroecological
conditions found in Massingir including soil types. Parallel to crop production, farmers
in Banga keep animals: cattle, goats, poultry and other. This was an important criteria
for my study because animal keeping is a potential source of organic inputs to improve
soil fertility.
Farmers in Banga also have access to the natural resources such as water from the Rio
dos Elefantes (Elephant River) (a potential source of water for irrigation) and forestry
(for charcoal production and wood fuel). While water from Elephant River can
contribute nutrients through irrigation, the increase production that results from
25
irrigation will lead to greater nutrient extraction. Thus the need for fertilizers and
organic nutrient inputs will be increased.
The resettlement of the Macavene Village to an area adjacent to Banga Village will
undoubtedly increase the pressure on land, forestry and other resources. The pressure
on land and forestry will lead to land degradation, therefore nutrient depletion. These
sources have an important role in the local livelihood strategies, so that the
unsustainable use of these natural resources will jeopardize the future of these
communities.
The willingness of the farmers to participate in the research also was taken into account.
27
3 Materials and methods
In order to understand soil fertility changes in the smallholder farming systems in Banga
as well as the “sustainability” of these systems, a combination of seven research methods
was adopted in this thesis. These methods are: (1) Literature review, (2) interviews with
key informants (3) rapid farming system characterization (RFSC), (4) a household
survey, (5) the classification of farmers in classes according to their wealth (adapted from
the work conducted by Zingore et al., 2006), (6) the livelihood strategy analytical
framework as proposed by Scoones (1998), and (7) the Stoorvogel and Smaling (1990)
methodology for assessing nutrient balances in Sub-Saharan Africa.
3.1 Literature review
The literature review encompassed farming system description, main livelihood
strategies, and others. The review provided the basis of the various methods applied in
this research for data collection and analysis. It also provided a series of basic data on the
district, farming systems and crops. This part covered the readily available grey
literature kept about the district.
3.2 Interviews with keys informants
The study area was selected after discussions with District Director of Agriculture
(DDA), Extension Workers (EW), District Administration, and Community Leaders
(CL). These interviews took place in the first 3 weeks of September (before the cropping
season). They gave insight in the climatic conditions, the main production systems,
alternatives livelihood strategies and other information related to physical and socio
economic conditions of farmers in Banga Village. The interviews with keys informants
also provided some information about the history of the village.
3.3 Rapid farming system characterization (RFSC)
The RFSC was conducted in order to gather general information on the farming system
as well as the livelihoods in Banga village in order to complete the objectives (i to iii) of
the thesis. The RFSC was based on concepts of the Nutrient Use in Animal and
Cropping Systems-Efficiency and Scales (Africa-Nuances), which aims at “generating an
integrated framework of data bases and computer models, that can be used to analyse current
livelihoods, explore options for their development and reveal trade-offs between farming
livelihoods and the environment in Sub-Saharan Africa” (http://www.africanuances.nl). It
28
took place in the period between September and October and addressed issues such as
available natural resource (land and water), land tenure system, nutrient management,
and others. The techniques used in the RFSC were household semi-structured interviews
(Appendix 1) and field visits. Before the survey, I tested the questions in Chibotane
Village, also Massingir District, on a sample of 30 farmers randomly selected in order to
adapt them to the Banga situation.
For the survey I used a sample of 25 households randomly selected out of 90 households
in the village of Banga. The purpose of field visits was to have an insight on the issues
gathered during the household data collection. All interviews were conducted by the
author in the local language (Shangana). During the interviews a local guide was present.
3.4 Classification of farmers in classes according to their wealth
Available information in Sub-Saharan Africa about nutrient management suggests a
direct connection between a household’s wealth status and investments made by the
household in soil fertility; as a consequence, rich farmers are thought to have relatively
better nutrient balances than poor farmers (Zingore et al., 2006; Tittonell et al., 2005).
Taking into account the findings of these authors and to address Hypothesis 1 of the
thesis, four (4) focus group discussions composed of both women and men were carried
out to understand the criteria they use to classify themselves according to wealth. This
involved 7-8 farmers per group belonging to different wealth groups within the village.
The discussions were recorded for further analysis and to crosscheck information. All
the discussions were conducted in local language to allow the participation of all farmers
within the group. The criteria used by farmers to determine wealth were used to
identify wealth groups or categories.
3.5 Livelihood strategy: Analytical framework
The livelihood strategy is a complex and dynamic matrix that enables people to meet
not only the need for food and generate income but also concerns cultural and social
choices (Ellis, 1998). Farmers’ households are highly heterogeneous in the way they use
available resources, therefore, an attempt to understand this heterogeneity is one step
forward in avoiding a single answer for all farmers. However, many studies on soil
fertility ignore the role that farmers play in influencing the processes of environmental
change (Roy et al., 2003). Acknowledging this complexity, an analytical framework has
29
been proposed for analysing livelihoods in relation to five capitals: human, social,
natural, financial and physical (Baumann, 2002).
1. Human capital that denotes skills, knowledge, ability to labour, good health
and physical capabilities of people.
2. Social capital which refers mainly to the social, institutional relationship and
ethno-cultural aspects.
3. Physical capital, this refers to the producer goods and physical infrastructures.
It includes tractors, irrigation schemes, motor pumps and roads.
4. Natural capital covers the available land either for livestock-crop production
or for building, as well as the forestry resources, fauna and water sources that can
be accessible to the communities, household or individuals.
5. Financial capital, this includes savings and other assets that can be easily
converted into cash or exchanged.
These capitals are interrelated and thus one can use one capital to strengthen another
(e.g., one can make money and buy cattle or land), one capital can compensate for the
loss of another (e.g., one loses cattle but can rely on social relations to borrow it from
neighbours or kin) – Brouwer and Nhassengo (2006). Use of the livelihood approach led
me to analyse how different wealth groups make a living and how this is related to the
way farmers manage soil fertility, e.g. by investing revenues in fertilizer or not. In this
way I expect to address the objective (iv) of the thesis.
3.6 Individual household survey
After following up on the farmers’ classification into wealth groups, two farmers’
households were selected in each of the four wealth groups to compose the case studies.
In total eight case study farms were selected. The case studies were used to obtain
detailed information about actual nutrient stocks; nutrient flows; qualitative data on soil
fertility management practices; different field types found in Banga; farmers’ choices
with regard to the most preferable fields; production constraints; production
orientations; and cropping patterns. This information was collected based on semi-
structured interviews developed from RFSC and Africa-Nuances questionnaires
(Appendix 2).
30
The farmers for the case studies were selected in such a way that, the variability of
different field types in Banga village was covered. To confirm this, the farmers were
asked to map the fields that belong to the 8 farmers that composed the case studies. The
information gathered at this stage addressed objectives (iii) and (iv) of the thesis.
3.7 Nutrient balances
Nutrient balances may help to understand the “sustainability” of the farming system
when it is combined with an analysis of soil nutrient stocks.
In this study, the methodology used by Stoorvogel and Smaling (1990); Smaling et al.
(1993) for assessing nutrient balances in Sub-Saharan Africa soils was adopted. Due to
the uncertainties and lack of validation of this methodology, Lesschen et al. (2007)
proposed some improvements in order to address these uncertainties at national level.
Thus, in this study I applied these improvements in my calculations. The main
components of the Stoorvogel and Smaling methodology are:
Inputs:
1- mineral fertilizers; 2- organic manure; 3- wet and dry deposition; 4- biological fixation
and 5- sedimentation;
Outputs:
1- harvested products; 2- removal crop residues; 3- leaching (N and K); 4- gaseous losses-
ammonia volatilization and denitrification and 5- soil loss due to erosion.
The methodology consists of the summation of all inputs minus all outputs. However,
in many farming systems a farm that belongs to a single household may comprise more
than one field. Thus, in order to define whether a flow can be considered as an input or
output the boundary of the system being analysed has to be defined.
On the basis of the literature, it was known that there are two different land types
(baixo and alto). On the basis of field visits with the farmers, these lands were identified
and the typology refined. For instance, the variability found in baixo lands allows its
division into 3 sub-fields. Lands in alto are called Mananga and those in baixo are:
Gowene, Banhine and Cowene1.
In this work, each field type and livestock were considered as a sub-system, because they
differ in terms of type of crops cultivated, landscape position, distance from homestead, 1 1 Cowene, Banhine, Gowene and Mananga are local classification of different field types found in Banga village. Detailed description of these fields is given in sections 4.2 and 4.3.
31
soils types and planting time. Thus, for farmers in Banga, the farm is composed by 6
sub-systems as follows: the fields in alto, the three fields in baixo, the transition fields
between alto and baixo and the livestock production. Figure 3 shows the different sub-
systems that compose farm and nutrient flows.
Figure 3. Mananga, Gangene, Gowene, Banhine and Cowene’s fields.
The original idea was to collect data before and after cultivation from all field types in
order to evaluate the impact of cropping on their fertility. As the 2006/7 cropping
season was seriously affected by a drought, this idea had to be changed. Thus, part of the
data collected was based on the information from the previous harvest 2005/06. During
the cropping season 2005/06 farmers planted in alto lands and baixo land types. In
Gowene and Mananga’s fields it was not possible to go into details (yields and areas), thus
only one set of data exists (nutrient stocks). Cowene’s fields, located close to the elephant
river are now less important within the farm, because some of its areas are covered by
coarse sand from the river during the 2000 floods. Thus, the partial nutrient balance was
calculated for Banhine’s field.
Although some flows between Banhine and Gowene, Banhine and Cowene, may be
expected, as a matter of simplification I treated each field type independently, so no
Manure
Harvest products
Gowene Cowene
Banhine
Livestock
Harvest products
Gaseous losses
Gaseous losses Gaseous
losses, erosion
Biological fixation
sedimentation Erosion
pastures
Gangene Banga village
deposition
Harvest product
Harvest product
Mananga
Gaseous losses, erosion
Erosion
Biological fixation
sedimentation
Crop residues and pastures
32
internal flows between field types were considered. The quantifications made at this
point addressed the research objective (iii) and the hypotheses (1), (2) and (3).
3.7.1 Soil sampling
In order to analyse the nutrient stocks in the fields, I collected topsoil (0-20 cm) samples
at fifteen (15) points in each field type: Banhine, Gowene and Mananga. For samples
collection, I used an auger of 10 cm length.
Taking into account that not all farmers from the case studies have both alto and baixo
fields, I sampled additionally 6 fields: five (5) of these fields belonging to medium
farmers’ wealth classes and one (1) to poor farmers’ wealth classes were sampled. Table 2
shows the number of fields sampled per wealth classes in alto and baixo.
Table 2. Number of fields sampled per wealth classes.
Alto Baixo Wealth
farmer’s group Mananga Gowene Banhine Cowene
Rich 1 - 2 -
Medium 1 4 2 -
Poor 1 2 2 -
Very poor 2 1 2 -
Total 5 7 8 0
The inclusion of additional fields increased the sample, allowing the comparison of soil
nutrient stocks between field types with less residual error. In total 15 fields were
sampled in baixo and 5 in alto. Soil samples were randomly collected and mixed in order
to have one composite sample per field.
All samples were air-dried and sieved through a sieve of 2 mm. After that, 200 g was
weighed for chemical analyses at the IIAM (Institute of Agrarian Research of
Mozambique) soil analysis laboratory in Maputo.
The total nitrogen was analysed using the Kjeldahl method (Bremmer and Mulvaney,
1982), available phosphorus by Olsen extraction (Olsen et al., 1954), soil carbon (SOC)
using the Walkley-Black procedure (Black, 1965) and soil pH in water using a 1:1.25
soil:solution ratio. Exchangeable Ca, Mg and K and cation exchange capacity (CEC) (in
ammonium acetate 1M at pH 7.0) were determined using methods described in Houba
33
et al. (1988), Holmgren et al. (1977) and USDA, SCS (1972). Particle size (texture) was
analysed using the modified Pipette Method (Gee and Bauder, 1986).
Some samples were submitted to a second laboratory analysis because of the lack of
consistency and confirmation in case of odd values or outliers, especially with relates to
the texture, nitrogen content and SOC.
3.7.2 Nutrient stocks
The nutrient stocks at each field were calculated as the product between of the content
of each nutrient in the topsoil and the weight of the topsoil. To get the weight of the
topsoil, I used the information that relates the bulk density and texture of the soil
(Agriculture compendium for rural development in the tropics and subtropics, 1981).
Table 3 show this relation.
Table 3. Relation between bulk density and texture class
Texture class
Sandy Sandy loam Loam Clay loam Silty clay Clay
Bulk density (g cm-3) Values Range
1.65
(1.55-1.80)
1.50
(1.40-1.60)
1.40
(1.35-1.50)
1.35
(1.30-1.40)
1.30
(1.25-1.35)
1.25
(1.20-1.30)
Source: Agriculture compendium for rural development in the tropics and subtropics
(1981). Ranges of values are shown in parenthesis.
3.7.3 Calculations of nutrient balances
Based on the information related to nutrient in and outflows of the fields, I calculated
the partial N, P, and K balances. Due to the failure of the crops during the season
2006/07, the calculations were performed using data from cropping system 2005/06.
The nutrient balance was only calculated for maize because is the main staple crop in
the research area.
Inputs
- IN 1: Mineral fertilizers
Fertilizers are needed to increase the crop production per unit of area. They also
compensate for the loss of nutrients through harvest. For the calculations of nutrient
balances this important source of nutrients has to be known and quantified.
- IN 2: Manure and other organic inputs
34
Manure and other organic material like plant residues are important sources of nutrients
for plant growth. In this study, I only considered the manure contribution (faeces and
urine) that the land receives through direct grazing during the off-season period (June -
September). I did not consider the crop residues because they are removed through
grazing. Throughout off-season period, animals graze mainly in the baixo fields. For the
calculations of the balance, I assumed that animals spend 12 hours in the fields during
daytime and another 12 hours in the kraal during the night. Assuming that the location
does not affect manure production, this implies that 50% of the faeces and urine is
dropped in the fields and 50% in the kraals at night.
Taking into account the grazing system in the research area (see chapter 4.6 for more
details) the faeces contribution to the soil fertility in each field is calculated on the basis
of the following formula:
Faeces per field = Total nr of cattle * [(amount of faeces/off-season period/ cattle)*0.5] * size of field
Total cultivated area
where: amount of faeces in kg; total area in ha.
Based on the results of the household survey and RFSC I estimated the numbers of
livestock and the total cultivated area. Information on the amount of manure produced
per head of cattle are not available for the research area, therefore, I used the value of
750 kg year-1, based on previous studies in Mozambique (Geurts et al., 1999). The values
for the chemical composition of manure that I used for these calculations are based on
the work conducted in Uganda by Bekunda and Manzi (2003) who have shown that
cattle manure is composed by 0.19 %N, 0.08 %P and 0.30 %K in dry matter. The
manure contribution to the nutrient balances is presented in the Appendix 6.
- IN 3: Wet and dry deposition
Wet and dry depositions can be an important pool of nutrient to soils. As local data on
atmospheric deposition (IN3) are not available in the area, I calculated the atmospheric
deposition of N, P and K based on the regression equation derived by Stoorvogel and
Smaling (1990). In case of the research area and all southern part of Mozambique, only
wet deposition is of importance. The nutrient input calculations were calculated follow
as:
35
IN 3 (N) = 0.14 * (rainfall) ½
IN 3 (P2O5) = 0.023 * (rainfall) ½
IN 3 (K2O) = 0.092 * (rainfall) ½
where: IN 3 is expressed in kg ha-1 yr-1 and the average rainfall in mm yr-1.
- IN 4: Biological fixation
Cowpea, groundnut and Bambara nuts are the only leguminous species cultivated by
Banga’s farmers that can contribute N through biological N2-fixation. These crops are
secondary to maize within the farming system in Banga village. As farmers said: “we
grow these crops just as a complementary food to our main meal (maize)”. Nevertheless,
I calculated the groundnut N contribution to the system. The groundnut was selected
because it is the second important crop after maize. The groundnut N content and
harvest index was based on a study conducted by Giller (2001), who showed that 60% of
groundnut N requirement is derived from biological fixation and that groundnut tend
to have a harvest index of 30%. The N content for groundnut was based on Bekunda
and Manzi (2003).
- IN 5: Sedimentation
Floods, runoff and irrigation contribute with considerable amount of nutrients. The
fields that can benefit can benefit from sedimentation are those located in baixo.
Irrigation is not used and floods have a frequency of almost 50 years, therefore, runoff is
more important in Banga. However, in this study the nutrient balances are only
calculated for Banhine, due to the limitations above presented (Section 3.1.7). Therefore,
the contribution of these sources to nutrient balance is not considered in the
calculations.
Outputs
- OUT 1: Harvested products
The yield and the nutrient content of the product allows for the quantification of the
amount of NPK removed through harvesting. Nutrient content data are taken from
Bekunda and Manzi (2003). Nutrient use efficiency is related to the cultivar, soil type
and technological level of the system (extension services, knowledge, etc.) (Stoorvogel,
Smaling and Windmeijer, 1993), however, in this study I did not consider it due to the
lack of data. Instead, the following simplified formula from (Roy et al., 2003) was used:
36
Nutrients in harvested products = ∑ (area * crop nutrient content) Total area
where:
Crop nutrient content = nutrient content *yield/100
Content: is the amount of nutrient in the residue (%) from Bekunda and Manzi (2003)
yield: crop yield (kg ha-1)
area: cultivated area from which the yield was recorded, in ha.
- Yield estimation (calculations)
The yield data were estimated from the last cropping season considered by farmers to
have been a good season, because during the fieldwork period all planted crops failed
due to the severe drought.
Farmers in the research area do not measure the yields after harvest; therefore, the yield
estimation was based on the number of farm carts and sleigh2 obtained from the harvest.
Men were not able to report these numbers; therefore, the estimations are based on the
information provided by women.
I attempted to calculate the kilograms of maize in each farm cart using the previous
harvest, but unfortunately farmers were not willing to remove their maize from their
grain stores and reload their carts. Thus, group discussions were conducted in order to
estimate the amount of maize in one farm cart. From these discussions, it was
considered that one farm cart contains 4 syilei and each sleigh contains 6 bags of 50 kg of
maize. Figure 4 shows cattle pulling sleighs containing gallons of water for consumption.
2 Sleigh is a traditional mean of transportation – a sort of ‘sleigh’ that is dragged across the ground.
37
Figure 4. Cattle pulling sleigh – a traditional sledge used for transport.
On the basis of a 15-household sample it was calculate that one 50 kg sugar-cane bag
contained approximately 20 kg of maize grain. The calculations made are presented in
Appendix 4.
Information on harvest index (HI) for the local varieties in Banga is not available, thus
this was estimated using the yield data from Cubo Village. For the calculation of HI a 9-
households sample in Cubo Village who cultivate the same variety as in Banga Village
was used (Appendix 4). For this, 20 plants at maturity stage in each household’s field
were harvested and the ear weight, grain weight and aboveground biomass (stover) were
measured (Appendix 4). The HI is calculated using following equation:
HI (%) = grain yield Total biomass
where: grain yield in kg per 20 plants; Total biomass = grain and stover yield, in kg
Knowing the HI allowed me to calculate the total maize biomass on the basis of the
weight of harvested grain estimated from last years harvest. To estimate the nutrient
content of maize grain and stover, I used the information provided by Bekunda and
Manzi (2003). These authors express the nutrient contents of maize grain and stover in
dry matter (kg). Thus, for the calculation of the nutrient contents of grain and stover in
Banga Village I assumed that the above-ground biomass (stover) and the grain at
38
harvested time have 12% of humidity. This assumption is based on minimum humidity
(%) necessary to store maize for one year (Weber, 1995). Full calculations are presented
in the Appendix 7.
- OUT 2: Removal of crop residues
The crop residues in all field types were grazed. I assumed that 80% of crop residues are
in fact removed by the animals, based on work done by Geurts et al. (1999). Only 8% of
the crop residue nutrients remain in the animal body and 72% of the nutrients are
excreted and return to the fields (ibid). The removal of nutrients is calculated with the
following formula (Roy et al., 2003):
Removal crop residues = ∑ (area * stover nutrient content) * removal factor
Total area
where:
Stover nutrient content = nutrient content*yield/100
Content: is the amount of nutrient in the residue (%) from Bekunda and Manzi (2003)
yield: crop yield (kg ha-1);
area: cultivated area from which the yield was recorded;
removal factor: 80%.
The contribution of this source to the soil nutrient removal is presented in the
Appendix 8.
- OUT 3: Leaching of N and K
In most tropical soils, P is often tightly bound by soil particles; therefore its loss
through leaching is considered to be negligible (Roy et al., 2003). Data on N and K
leaching are not available for the research area. Thus, I used the model provided by
Smaling et al. (1993) that calculate N as function of average rainfall, clay content (%) and
fixed mineralization rate of 2.5%. According to the same authors the K leached is
expressed as function of rainfall, clay content and exchangeable K. The N mineral is
calculated with the following equation:
Nmin = 20* Ntotal*M
where:
Nmin = total soil mineral (kg ha-1); M fixed mineralization rate (%)
39
- OUT 4: Gaseous losses
Nitrogen can be lost to atmosphere through denitrification and volatilization. For the
calculations of these losses I used the equation provided by Smaling et al (1993). The
equation has the following form:
DN = -9.4 + 0.13*clay content + 0.01 * R
where:
DN = denitrification in (%)
R = rainfall (mm yr-1); clay content (%)
- OUT 5: Water erosion
To estimate long-term average annual rate of erosion on a slope, I used the Universal
Soil Loss Equation (USLE) (Appendix 10). The equation has the following form (Roose,
1996).
E= A*K*LS*C*F
where:
E = mean annual loss (t ha-1 year-1)
A = is the rainfall and runoff factor by geographic location (mm yr-1);
K = is the soil erodibility factor;
LS = is the slope length-gradient factor;
C = is the crop/vegetation and management factor;
F = is the support practice factor.
A was calculated using the equation developed by Foster et al (1981);
A = 0.276*R*I30
where:
R= mean annual precipitation (mm yr-1)
I30= rainfall intensity (mm h-1). The vales 30, means that 30 mm fail per hour.
C was based on the study by Roose (1975) who have shown that C values for maize
range from 0.4 to 0.9, as cited by Morgan and Davidson (1986).
In Banga maize is planted in November and harvested in January. The mean annual
percentage of rainfall is 16%, 24%, 20% and 28% for November, December, January and
February, respectively. I assumed C values of 0.9, 0.6, 0.4 and 0.7 for the first, second,
third and fourth months, respectively. Prior to planting there is no crop, thus I assumed
C=0.9.
40
The K value was estimated for maize crop using the soil erodibility monograph (Morgan
and Davidson, 1986) based on the percentage of silt, % of organic matter, % of sand, soil
structure and permeability of the soil. There are no conservation measures in Banga;
therefore, I used P value of 1.0. (Morgan and Davidson, 1986).
The LS was based on the following equation (ibid):
LS=√L/22.13 * (0.065+0.045S+0.0065S);
where;
L=slope length (m) and S = slope steepness (per cent)
The calculations of these equation components are presented in the Appendix 10.
3.8 Statistical analysis
To analyse the information collected during the Rapid Farming System
Characterization, I used using the Statistical Programme for Social Science (SPSS 10.0
for windows). This information was expressed as frequencies (%) and average values for
different socio-economic data (e.g. Household size, livestock size, number of fields,
different forms of access to land, and other).
For the statistical significance of the differences between farms of different wealth status
for nutrient stocks (NPK) and partial nutrient balances (NPK), I used the Genstat
Discovery Edition 2.
41
4 Results
4.1 The history of the village
The history of Banga village can help us to understand the current soil fertility status.
This population suffered many re-locations phases which are important to highlight.
These re-locations can be organized according to five different historical epochs: the
colonial period (before 1975); the socialist or central planning period between
Independence in 1975 and 1989; abandonment due to warfare between 1989 and 1992;
reoccupation and resettlement after the Peace Agreement in 1992 to the floods in 2000;
and today after the 2000 floods. In every phase the land use intensity, defined as
cultivated land per total area available in each period was affected, which is most likely
to affect the soil fertility.
Colonial period
During discussions held with the key informants in the village, it transpired that the
name of the village “Banga” means “machete”. According to them, this name appears
because during the ancient times, when their forefathers occupied the land and started to
cut trees to build their houses and they found a machete abandoned in the forest.
Throughout this time the villagers were living dispersed in Banhine’s3 fields near to
Elephant River (low lying areas) practicing agro-pastoralism in Cowene, Banhine,
Gowene (also all depressions and on river bank) and in Mananga (higher grounds and
more drier, the out-fields or the far-away-fields).
From independence in 1975 until 1989
In 1975 Mozambique became independent from Portugal after a ten-years war.
Independence heralded a period dominated by central planning and a socialist vision on
development. As part of this, resettlement of rural populations in so-called communal
villages (very similar to the Ujamaa policy in neighbouring Tanzania) was implemented.
The communal villages were the strategy of socialist development pursued by the Front
for the Liberation of Mozambique (FRELIMO) government which consisted in the
concentration of the dispersed population in villages, with the aim of transforming their
production activities into collective action through cooperatives and state farms
(Isaacman, 1978; Coelho, 1998). Under this strategy, Banga’s populations were told to
3 Cowene, Banhine, Gowene and Mananga are local classification of different field types found in Banga village. Detailed description of these fields is given in Sections 4.2 and 4.3.
42
move homesteads from Banhine to be concentrated in Mananga. During this period
Banga’s farmers still practiced the crop and livestock production in Cowene, Banhine,
Gowene and Mananga’s fields. Although this strategy have been seen by some authors as
ambitious strategy of socialist development with negative impacts for communities
(Dinerman, 1994; Unruh, 1998), it also true that having people concentrated in one
place makes it possible to provide services and goods to more people at lower costs.
Examples of such services are medical assistance, consumer goods, clean water provision,
roads, education, and others.
In 1977 the Government of Mozambique became involved in a war with the
Mozambican National Resistance (RENAMO). This war reflected external conflicts (the
cold war) and internal tensions which sometimes had been exacerbated by government
policies. According to some authors, even the forced resettlement to communal villages
may have been a factor that contributed to this war (Hall e Young, 1997: 136-7;
O’Laughlin 1992; Abrahamsson e Nilsson 1995: 79-87; Pitcher 2002 quoted by Brouwer
2006a)4. In Banga, the civil war became extremely acute in the mid-1980s. As a result of
the deteriorated safety conditions in the Mananga area somewhere in 1986 the
population had to go back to Banhine. Unfortunately, in the middle of 1987, warfare
became even more intense and the Banhine area was not longer safe, so that the
population was forced to leave Banhine and to return to Mananga. But the war was so
dangerous that even the Mananga was no longer safe, and in the middle of 1989, the
villagers had to flee and seek refuge in the town of Massingir, South Africa and other
safe places.
From 1989 to 1992
The year 1989 marks the third period. When the Banga people fled to Massingir and
South Africa, their fields remained uncultivated and a fallow period started which only
ended with the cease-fire in 1992, when the General Peace Accord was signed. To what
extent the vegetation was able to recover it initial fertility level, is hard to determine.
Nevertheless, taking into account the subsistence agriculture that was and still is the
common practice among farmers in Banga, one will expect that the almost three (3)
years of “rest” will have improved the soil fertility status in the area.
From 1992 until 2000 4 Dinâmicas agrícolas e agrárias: desenvolvimento rural e meio ambiente, Roland Brouwer Maputo, International Workshop at FAEF, 20 de Setembro 2006.
43
With peace re-established, the population returned to their original residence, the low-
lying plains (Banhine) and started to rebuild their lives. However, at the start of new
millennium (by late January) Southern Africa was affected by torrential rainfall, and the
villages and valleys were suddenly full of water. These floods marked another episode of
resettlement: the post 2000 floods.
After the 2000 floods
Banga was one of the villages that were devastated by floods that destroyed crops and
livestock. In response to this calamity, the Government and NGOs started to mobilize
people to avoid building houses in flood-prone areas. The 2000 floods drastically affected
the Banhine, Cowene and Gowene fields with the worst scenario being observed in the
Cowene’s fields located along the banks of the Elephant River. Some of these fields were
totally covered by coarse sand from this river and as a result, could not longer be
farmed. However, the floods are also be seen as one way to bring nutrients to regenerate
soil fertility and improve the system.
4.2 The different types of fields found in Banga
Underneath the dynamic panorama of repeated resettlements between the low-lying and
higher areas in response to macro-political and natural events described in the previous
section, lies the local land-use system. This system forms, on the one hand, a constant
backdrop on top which all dynamic changes take place. On the other hand, it is also
subject to the impacts of the changes in the settlement patterns and other macro factors,
or both. Most literature describing agricultural land use in Southern Mozambique makes
a distinction between two types of lands: the up-lands (alto) and the low-lands (baixo). In
most areas, and Banga is no exception, it has been noted that farmers try to have fields
in both types of lands (ICRISAT/DINA, 2003). A more careful look at the different
land types in Banga, however, indicates that this simply dichotomy is inadequate. As it
is mention on the Section 3.1.7, the diversity in baixo, is so large that this type actually
consists of a three kinds of fields, which differ greatly in terms of landscape position,
colour and texture. On the basis of these differences, the local population divides the
baixo fields into three groups: Banhine, Gowene and Cowene. Arable land in alto has less
variability therefore, it has only one name: Mananga. There is a third type of land in the
higher and dryer stretches, the Gangane. The Gangene land is mainly areas of browse
44
with stones that limit crop production, and therefore they are not described in detail in
this report. It also makes the spatial transition between the alto and baixo fields.
People in Banga have maintained these names across generations. Figure 5 shows the
diagram representation of the 4 field types.
45
Figure 5. The four fields types in Banga.
46
A general description of the field types is given below: Banhine and Gowene
These two adjacent field types are rich alluvial deposits which make them more fertile in
comparison with Mananga and Cowene fields. The landscape position, texture, colour
and water holding capacity appear important features to differentiate them. Thus, fields
at the bottom valleys are called Gowene and are characterized by heavy texture, darker
colouring and a high water retention capacity. As a consequence Gowene fields are
difficult to plough immediately after rains because the soil is too heavy. Due to the
landscape position of Gowene it can benefit from the nutrients eroded in Banhine and
Gangene fields. The Banhine’s fields, adjacent to the Gowene’s fields have a slope of about
2%.
Contrary to the bottom fields, the fields at the top of the slope called Banhine are
slightly sandier, lighter, with relatively low water holding capacity and easier to plough
immediately after rains. Figure 6 show cattle grazing on Gowene’s fields.
Figure 6. Cattle grazing in Gowene’s fields.
Cowene
This field type is located along the bank of Elephant River and due to its proximity to
the water source has potential for vegetable production compared with the other fields.
As a result of the consecutive droughts that characterize the Banga Village, Cowene fields
used to be important for food security and coping strategies, because farmers used to
grow their crops on the river bank where water was available when there was no rain.
47
However, as mentioned above (Section 4.1) some of the Cowene fields are now covered
by coarse sand from the river deposited during the 2000 floods, and are no longer
suitable for crop production. Even the remaining Cowene’s fields are under-utilized by
the farmers in Banga Village because of lack of resources to establish irrigation, lack of
human resource or other means to control the monkeys that destroy their crops, and
the distance from the village to the Cowene fields.
Mananga
This field type is located in the upland part of Banga village, as opposed to Banhine,
Cowene and Gowene. It is characterized by soils with low water holding capacity and
sandier texture and is less preferable to farmers compared to Banhine and Gowene, as
shown by the fact that some farmers who have fields in Mananga often do not choose to
plant there. This field type is thought to be more suitable for groundnut, Bambara nut
and cowpea production. It is also used as a coping strategy during flooding periods,
however, it seems that farmers rapidly forget the danger of flooding and rely mostly on
crop production in Banhine and Gowene. In 2000, the last year of major flooding, the
total precipitation was about 900 mm, almost 3 times the annual mean (320 mm).
Gangene
The Gangene land is not used for agriculture because they are covered by stones that
limit the crop production, and therefore are mainly kept for livestock production.
These fields are an important source of grazing for cattle and goats throughout the year,
particularly during the cropping-season period. The total area covered by these fields is
about a quarter of total Mananga fields (see Appendix 5 for the total areas).
Table 4 provides a summary of their most important soil properties. The soils properties
of Cowene’s fields are not represented in the table because are less important in the
Banga farming system.
48
Table 4. Soils properties of Gowene, Banhine and Mananga fields. Field type pH in
water Conductivity 1:2.5 mS/cm
Texture (dominant)
Sand (%)
Silt (%)
Clay (%)
SOC (%)
N Total (%)
P Olsen (mg kg-1)
Exchangeable cations (cmol kg-1)
C/N ratio
K+ Mg++ Ca++ Banhine 6.73 0.34 Loam 45 40 15 1.1 0.12 27.51 2.43 5.92 14.82 15.3 Gowene 7.02 0.18 Silt clay loam 19 50 31 1.53 0.13 53.30 3.09 7.47 16.72 20.2 Mananga 6.56 0.07 Loam sandy 84 6 10 0.43 0.06 3.84 1.18 0.71 3.07 12.5 SD 0.28 0.25 26.9 18.7 9.5 0.55 0.04 24.46 0.85 2.92 6.24 3.8 The values were averaged. SD= total standard deviation.
49
4.3 Nutrient flow maps
In many farming systems the farms consist of one large, contiguous production unit
demarcated into smaller production units (Zingore et al., 2006). These smaller
production units differ significantly in terms of their capacity to sustain the crop
production, as a consequence and with the objective of maximize the available resources,
the attention to these units also differ. In such type of farming system, the elaboration
of flows resources maps helps to understand how decisions are made related to soil
fertility management. Knowing this is possible to identify steps of efficient and
inefficient stages for further improvement. Originally, taking into account these
findings, this research foresaw the drawing of nutrient flow maps at different fields
belonging to a single farmer (see chapter 3.1.7). However, after an initial survey of the
production system in research area, this activity was cancelled. It appeared to be
irrelevant because farmers do not apply any inputs to their fields.
4.4 The five capitals and soil fertility management
The livelihood strategies analysis was one of the methodologies used to better
understand how different wealth groups manage their soil and whether it is sustainable.
According to Butterworth et al. (2002) the livelihood comprises:
“the capabilities, assets (including material and social resources) and activities required
for a means of living. A livelihood is sustainable when it can cope with and recover from
stresses and shocks and maintain or enhance its assets and capabilities whilst not
undermining the natural resource base."
The integration of the livelihood approach in this study is intended to provide an
insight of the complexities of survival strategies by farmers in Banga Village, in relation
to the five capitals.
4.4.1 The natural capital
In this study the Natural capital covers the available land for livestock-crop production
and for construction, as well as the forestry resources, fauna and sources of water that
can be accessible to the communities. I start with the analysis of this capital by looking
at land tenure system.
Land tenure
Land tenure arrangements are thought to be a key factor for understanding soil nutrient
management (Ondiege, 1996; Scoones, 2001). Having this in mind, I analysed the
50
different land tenure arrangements in Banga to see whether this leads to a different soil
nutrient management system by farmers.
According to the Mozambican land system, the land is the property of the State. This
implies that people can acquire the right to use the land, but not to sell or buy it. This
definition of rights to land does not interfere with the way in which people locally get
access to land, as traditionally people obtain land through different mechanisms rather
than through the market.
The results of the RFSC show that farmers in Banga have five main forms of access to
land:
a. Husband’s family land: native people can inherit land from their family. This
land is mainly administrated by the eldest son.
b. Spouse’s family land: male immigrants who marry local women can gain access
to land from the spouse’s family.
c. Traditional leaders. The traditional leader has the right to allocate land to
someone who asks for it, especially when the person does not belong to the
village. However, in order to allocate the land, the traditional leader has to
consult the villagers whether the person fulfils all the requirements (e.g. not
being involved in criminal activities).
d. Borrowing: local people also lend lands to immigrants or family who seek for
the places to live and cultivated their crops.
e. Gift: some locals with huge land holdings offer their land to immigrants with no
or little land to grow their crops.
The information on the number of households in relation to the different forms of
access to the land found in Banga Village is presented in Table 5.
51
Table 5. Different forms of access to land in Banga Village
1 Confidence interval (95%) Forms of access to the land Frequency Percent (%)
Low upper
Husband’s family land 18 72 57 86.9
Spouse's family land 1 4 -2.5 10.5
Borrowed land 3 12 11.2 22.8
Granted by the traditional
leader
2 8
-1.0 17.0
Gift 1 4 -2.5 10.5
Sample size 25 100 1 Confidence interval is calculated with finite population corrected (Roger et al., 2003;
Porkess, 2004).
The sample data shows that the most prevailing form of land access is through the
husband’s family land (72%) (Table 5). This is probably because of the heritage system
that is patrilineal. Borrowed land appears in second place followed by the land obtained
through the traditional leaders. People who obtain land through spouse’s family land or
gift land are less common in the village.
The different form of obtaining access to land found in Banga Village are not related to
the way farmers manage the soil fertility, because none of the farmers in the village
apply any inputs or techniques to improve the current soil fertility status. The
incorporation of crop residues after direct grazing by cattle is the only practice
implemented by the farmers in Banga that can help to maintain soil fertility.
4.4.2 Financial capital
The local population has access to money through different channels. Products often
sold to gain cash include, (on the basis of human capital) charcoal (which builds on
natural capital), maize (on basis of natural and human capital), and chicken and
sometimes goat and cattle (on basis of financial capital).
Labour migration to South Africa, either legal or illegal, constitutes a common feature
of life for the people in Banga Village. The migration is mainly by men who seek
temporary jobs on farms. Although I was not able to quantify its contribution to
income, it is quite clear that it contributes substantially to the household economy of
people in Banga. The inter-regional analysis of South, Centre and North of
52
Mozambique done by Vletter (2007) in levels of development between households,
demonstrates that southern rural households were more developed and better-off than
other regions of the country due to the transfer of remittances from South Africa. The
study conducted by Brouwer and Nhassengo (2006) in Mabalane District also
highlighted the role of remittances in livelihood strategy.
Households in Banga are not an exception; they also receive remittances either from
their kin in South Africa or kin in Massingir Village. The sources of the remittances as
the relative weight are presented in Figure 7.
0
2
4
6
8
10
12
14
Husband inSouthAfrica
Kin inMassingir
Kin is SouthAfrica
None
Sources of remittances
Freq
uenc
y of
hou
seho
lds
Figure 7. Different sources of remittances in Banga village expressed as a frequency of the total
number of households. N= 25 household
The survey data showed that 48% of household declared that they received remittances,
and 40% of these remittances came from their kin in South Africa. Remittances from
their kin in Massingir Village account for 8% of the total population. Although people
can benefit from migration activities, it increases the women’s workload, as they
become temporarily the household leader. From Figure 7 we see that 2 of 25 household
have their husband working in South Africa.
Trees (mainly Colophospermum mopane) in the Mananga fields are used for commercial
charcoal production, wood fuel, selling posts and timber for the building of houses.
Commercial charcoal production is mainly done by migrant charcoal burners, who
make temporary camps in Mananga. However, with the consecutive droughts charcoal
production became a new coping strategy for the farmers in Banga, mainly by men.
These findings are also reported by Brouwer and Nhassengo (2006) in neighbouring
53
Mabalane District. Because of the labour required to cut the trees and burn them, it
excludes the households headed by women and elders. In order to benefit from this
natural capital, these excluded groups exchange their labour (kukashela) for charcoal.
They helping the charcoal producers to collect and concentrate the trees for burning and
receive some charcoal in kind.
As a result of the exploitation of trees for commercial charcoal production, the
deforestation rate is high. Most of the produce ends up on the Maputo market. This
finding is in agreement with other studies conducted in the field of natural resource use,
where agribusiness is one of the main causes of environmental degradation (Pereira et
al., 2001). The results from the survey showed that all (100%) of the households in the
village have some participation in the production and sale of charcoal locally to outside
buyers.
Due to the high pressure on use of this natural resource it is expected that in the near
future charcoal production will be no longer possible, taking away this source of income
and therefore potentially increasing the degree of poverty. The 2020 Vision (IFPRI, 1996
cited by Sanchez et al., 1997) highlights rural poverty in the Third World as the major
concern at present, stressing the need to think about the sustainability of natural
resource use.
Currently, to address the overexploitation of natural woodlands for charcoal
production, the community has the right to define which areas can be exploited by the
charcoal burners in order to replant or allow natural regeneration of the cleared area.
However, this system is not working to avoid the deforestation. The lack of human
resources to control some illegal charcoal burners is pointed out by the local leaders as
the key limitation to ensure a better exploitation of woodlands.
In dry season the available pasture is low in Banga, therefore the dry leaves of mopane
can form an important source of fodder for cattle, as is the case in the lowveld of
Zimbabwe, even though the leaves are very poor in quality (Giller, personal
communication).
4.4.3 Social capital
To understand the social capital, I attempted to look at the relationship between people,
between people and institutions, and other social networks. Households in Banga are
strongly allied with each other, where mutual support is a common feature. The strong
54
solidarity among villagers in Banga can be highlighted by the different exchange
relationships that underpin their livelihood strategies. These relations are: kukashela,
tsimo, kuwekissa and xicoropa, explained below.
Kukashela and tsimo are basically exchange of the use of cattle and ploughs for labour or
money. In Kukashela, farmers with cattle and ploughs cultivate someone else’s fields and
in exchange the beneficiary has to help him or her with weed control; while in, tsimo,
the compensation involves cash payment.
Kuwekissa occurs when people with a larger number of cattle from the same or other
village ask someone without or with a lesser number to look after his or her cattle. In
this strategy someone without cattle can benefit from some services provided by cattle
(animal traction and transport). This strategy is mainly used by women (either single or
widowers). Xicoropa is essentially exchange labour in large farms in Massingir Village or
in local development projects such as road construction, bridges over the rivers, and
others.
The relationships between farmers appear to affect the way land is prepared and differs
among Banga’s farmers. For instance, farmers who benefit from kukashela and tsimo will
see their fields affected by the way people doing kukashela and tsimo.
4.4.4 Human capital
Human capital refers essentially to the skills and knowledge and labour power of the
people. People in Banga have developed many skills that allow them to exploit the
natural resources in their area, deal with the uncertainty of the weather conditions and
maximize the benefits of the available labour by allocating that labour to one type of
field or another according to the prevailing conditions. For example, farmers do not
invest their scarce labour in ploughing fields in Gowene and Banhine during the first
week after the rains, because at this stage the soils are sticky, and therefore too difficult
to work. Instead, they invest their efforts in Mananga fields where the soils are sandier
and well drained.
Another example is the way in which they allocate crops to different fields. In case of
late arrival of rains, farmers do not plant their crops in Mananga fields. According to the
farmers this is a means to avoid the risk of crop failure, because these soils have a low
water retaining capacity (see Table 4). Instead they invest more in Banhine and Gowene
fields with more water retention capacity.
55
4.4.5 Physical capital
Analysing the physical capital, I found that two of 25 farmers have irrigation equipments
(water pumps, tubs) that are used to extract water from the Elephant River. As in many
parts of the country there is no local market where farmer can buy fertilizer or other
inputs. The lack of means of transportation that could allow them to transport the
manure to fields is also a limiting factor. To highlight this, only 7 households in the
village have an animal-drawn cart. The other farmers with cattle basically use the sleigh.
The available woods (natural capital) allow farmers to construct the sleigh.
The crop storage facilities (tsala) made by local material do not protect the grain from
pest attacks, thus, grain losses in the storage are reported by farmers. However, the
physical capital is not weak at all, while all farmers in Banga have at least one plough for
land preparation.
The lack of animal care facilities is jeopardizing the farmers’ effort to increase the
livestock production, as many animals are dying due to diseases.
4.5 Agriculture
4.5.1 Farming system in Banga Village
The cropping system includes maize (Zea mays L.), cowpeas (Vigna unguiculata (L.)
Walp.), beans (Phaseolus vulgaris L.), Bambara nut (Vigna subterranea (L) Verdc.),
groundnuts (Arachis hypogaea L.), sweet potatoes (Ipomea batatas L.), cassava (Manihot
esculenta L.), pumpkins and vegetables. Sweet potato, beans, pumpkins and vegetables
are mainly grown in Cowene; pumpkins are also grown in Banhine and Gowene, and
groundnuts and Bambara nut are mostly cultivated in Mananga. Maize and cowpeas are
grown in all types of fields. The maize crop is the most important of the village and one
of the few crops that is sold; therefore besides the physical properties used to
differentiate the field types in terms of soil fertility, maize yield is also used as an
indicator of soil fertility quality. As a farmer said during the group discussions:
“As long as the maize can grow well and we get a good yield, we do not need to apply any
input to the field; we just pray for the rain”.
The last part of this sentence leads us to the importance of the soil water infiltration and
holding capacity, the second important criteria to access soil quality by Banga’s farmers.
Although I understand the farmers’ argument, it is also true that in case of very little
rain the water holding capacity itself can not help.
56
All the crops used by farmers in Banga are sown in different geometric patterns from
intercropping to single crop (in case of maize). For instance, when maize is grown in the
same field with groundnut a typical pattern is one row of maize alternating with one
row of groundnut; when maize is grown with pumpkins the seeds of both crops are
sowed in the same “hole”. In addition to these combinations, maize is also found as sole
crop. As a result, it occupies most of the land within a farm.
Although the farming system encompasses a wide range of crops, the extent to which
these crops are planted in each cropping season depends largely on:
- Amount of rainfall received;
- The start of the rains in relation the cropping season;
- The distribution of the rainfall with time through the season (e.g. mid-season or
terminal drought periods);
- Spatial distribution of the rainfall among the different field types.
For instance, in the cropping season during which the study was carried out, none of the
farmers planted cowpea, groundnut, cassava or sweet potato because of the late arrival of
the rains and the failure of the rains which prevented cultivation of these crops. Instead,
all of the farmers cultivated maize intercropped with pumpkins, only.
4.5.2 Cropping season and labour allocation
Due to the unimodal pattern of rainfall distribution that prevails in the Massingir
region, there is only one main cropping season and this is strongly dependent on the
rains. The labour for agriculture involves all family members over 12 years old;
households, especially the richer farmers, also use additional labour that is locally hired.
Table 6 gives the cropping calendar used in Banga village as well as the social division of
the tasks by gender.
57
Table 6. Crop calendar in Banga (October-May) Months Activities Who is responsible Observation October Ploughing, mainly
in Mananga husband and sons Sons older than 18 years
November Ploughing and sowing
Wife, daughter, daughter in law and sons
Sons with age between 12 and 18 years
December Sowing and weed control
Wife, daughters, daughter in law and sons
Sowing in case of delay rains
January Weed control and harvesting
Husband, daughters, daughter in law
Middle January for harvesting
February Harvesting Husband, daughters, daughter in law
March Harvesting Husband, daughters, daughter in law
March: in case of late sowing
April Harvesting Husband, daughters, daughter in law
April: in case of late sowing
May Off-season Charcoal production and tradition beverages
Mainly household members older than 18 years
Off-season period (June- September)
June Charcoal production and tradition beverages
Mainly household members older than 18 years
These activities also take place during the cropping season, but more so in the off-season
July Charcoal production and tradition beverages
Mainly household members older than 18 years
August Charcoal production and tradition beverages
Mainly household members older than 18 years
September Charcoal production and tradition beverages
Mainly household members older than 18 years
The most important phases in terms of labour demand within the crop calendar in
Banga are: ploughing, sowing, weeding and harvesting, where weeding requires more
labour and is mainly done by women, manually or by using cattle.
58
4.5.3 Livestock and grazing system
Livestock, including cattle, goats and poultry (mainly chicken) are kept by most of the
farmers in Banga primarily as means of storing capitals and attenuating emergency
situations. Table 7 shows the different coping strategies used by farmers during
emergency.
Table 7. Coping strategies used by farmers in Banga during emergency
1 Confidence interval (95%)
Coping strategy Frequency Percent (%) Low Upper
Sell of animals only 7 28 12.9 43.1
Small business (biscuits) 1 4 -2.6 10.6
Sell traditional beverages 2 8 -1.1 17.1
Sell of charcoal only 8 32 1.6 47.7
None 2 8 -1.1 17.1
Sell of animals and traditional
beverages
1 4
-2.6 10.6
Sell charcoal and traditional
beverages
1 4
-2.6 10.6
Husband in South Africa 1 4 -2.6 10.6
Salary 1 4 -2.6 10.6
Rent oxen 1 4 -2.6 10.6
1 Confidence interval is calculated with finite population corrected (Roger et al., 2003;
Porkess, 2004).
The survey data shows that the most common coping strategy in the case of an
emergency is the sale of charcoal with 36% (Table 7). The sale of animals is the second
form of coping strategy most common in emergency situations with 32% of all
population. The sale of traditional beverages, 16% of the population, is another
important source of income during emergency. The number of farmers involved in
charcoal production should be considered carefully, while during my fieldwork I
observed that all farmers in the village were engaged on charcoal production due to the
severe drought in 2006/07 cropping season.
59
Household livestock numbers range from a 0 to 38 head per household with varying
ratios of cattle and goats. Descriptive statistics of number of animals owned per
household in Banga Village is presented in the Figure 8.
0
5
10
15
20
25
30
0 1 to 5 6 to 10 more than 10
Number of animals
Num
ber
of fa
rmer
scattlegoatspigschickensduckssheep
Figure 8. Number of farmers owning livestock (N=25)
Farmers in Banga generally own much cattle and chickens, followed by goats. The
number of cattle owned by each household varies between 0 and 23, while goats
numbers vary between 0 and 18. These animals are followed in number by duck, sheep
and pigs in decreasing order, respectively.
Currently livestock production is seen as a separate enterprise, while the interaction
between animal-crop is only made through direct grazing of crop residues. The animal
manure from cattle which can be used in agricultural is simply accumulated each year in
the kraals. However, as McIntire et al. (1992) pointed out, in areas where the population
pressure is increasing, and where there is in general low purchasing power to afford
mineral fertilizers, the interaction between animal and crop may increase. The farmers
in Banga may soon be forced to move toward this direction due to the resettlement of
Macavene Village close to them.
Livestock management consists of direct grazing of crop residues and natural pastures
during two distinct periods:
- Off season (June-October)
During this period fields in Mananga are very dry and are therefore not an option for
cattle grazing. As a consequence cattle graze mainly in Cowene, Banhine and Gowene
fields (on crop residues and natural pastures) and there is not a restriction on animal
movement, which means that cattle can graze different fields, and the time spent in each
60
field will depend on the amount of pastures and crop residues available. Animals spent
almost 12 hours per day in these fields.
- Cropping season (October to May)
Throughout this period cattle are only allowed to graze freely in Mananga and Gangene
communal land, therefore, farmers have to make fences in order to protect their crops
in Mananga (maize, groundnut, Bambara nut, pumpkin and cowpea). This has led to the
abandonment or the significant decline of cultivation on Mananga fields by the farmers
due to the labour needed to construct the fence to protect the crops against the cattle.
During this time animals rely on natural pasture, therefore, the cultivated Mananga
fields do not benefit from cattle excreta, unless the field was not cultivated in previous
years. In both periods sons of ages between 10-14 years are mainly responsible for cattle
herding, although some adults (men) may also help them, although their main task is to
look where the ‘good’ pastures is available.
4.6 Wealth groups division
There are different classifications of wealth groups and different ways to achieve such a
classification. In this study I first tried to have farmers classify themselves. This
approach, however, failed, because farmers were not willing to talk about this issue.
Apparently, it was too sensitive. Moreover, there was tendency among the farmers to
consider themselves as the poorest. A possible explanation for this attitude might be that
the history of the village is strongly connected to the relief programs that appeared in
the area after the 2000 floods. Presenting oneself as poor may be envisaged as a way to
get assistance from the government or from NGOs.
When it became clear that the original approach was failing, another approach was used.
This approach consisted in informal meetings with two farmers individually and with
four key informants as a group. The individual farmers were selected after a preliminary
analysis of data collected during the rapid farming system characterization. They
represent the “high resource” and the “low resource” farmer groups. The interviewed
“high resource” farmer owns 16 cattle, 18 goats, 28 chickens, 18 sheep (the only ones in
the village) and 4 ducks; the interviewed “low resource” farmer is widow without any
livestock or poultry. Contrary to the “high resource” farmer that was willing to provide
his perceptions about his own household wealth indicators, the “low resource” farmer
preferred to classify all the farmers in the village as poor.
61
As a result of this, wealth indicators were based on information provided by the key
informants and the “high resource” farmer (5 persons in total).
Instead of talking about rich or poor, the key informants and the “high resource” farmer
preferred to talk about the resource endowment (mainly livestock ownership), farmers’
limitations (age, physical deficiency), household size and marital status (single, widower,
unmarried, and divorced or separated). When the five informants were asked to rank
these indicators, cattle were considered to be the most important indicator. They
emphasized that cattle have various roles, such as draught power, water transport, and a
source of income in emergency situations. The selection of cattle as the main indicator
of wealth is in agreement with findings from others studies conducted on this topic (e.g.
Brouwer and Nhassengo, 2006; Zingore et al., 2007).
The application of these criteria resulted into the emergence of four different groups:
1. Very Low Resource Endowment (VLRE): comprising adults, deficient (blind or
handicapped), separated, unmarried and widows and widowers; all without
cattle, goats and chickens. These farmers have difficulties to prepare their fields
and are not able to do kukashela; they always ask for help from their parents and
neighbours to prepare their fields; some of them can only do xicoropa;
2. Low Resource Endowment (LRE): consisting of farmers with 0-5 cattle, or
single-headed households or female and male widowers younger than 35 years
old. According to the key informants, even though a person has no cattle, he or
she (younger than 35 years old) can still use alternative strategies such as xicoropa,
kukashela, tsimo, and charcoal production in order to compensate for the lack of
cattle to prepare their fields;
3. Medium Resource Endowment (MRE): includes farmers with 6-10 cattle;
according to the key informants, having this number of cattle allows him or her
to use at least three plough teams of two cattle each (6 cattle), enough to prepare
at least 10 ha per cropping season. In addition, farmers from this group can even
sell 2 cattle in case of an emergency and still have draught power to be used in
field preparation. They can also benefit from kukashela.
4. High Resource Endowment (HRE): farmers with more than 10 cattle. These
farmers are considered to be better-off because they can use more than 4 ploughs
teams to prepare their fields. They can also sell 4 cattle and invest the income in
62
an irrigation machine. In addition these farmers can benefit from the existing
trading strategies such as kukashela, xicoropa, tsimo, etc. This group also has an
ox-drawn cart, which can be rented out in exchange for maize. For instance, by
transporting one ox-drawn cart load from the field to homestead the owner
receives almost 20 kg of maize.
Table 8 shows the main characteristics of the four different resource endowment groups,
and Figure 9 the average resource of the farmers in the village, and Figure 10 in the
selected case study farmers at Banga.
Table 8. Main characteristics of the four different resource endowment groups at Banga.
Resource endowment Characteristics VLRE LRE MRE group HRE
Number of cattle Do not own cattle, goats and chickens
Own less than 5 cattle, can use at least one plough
Own between 6-10 cattle; can use at least three ploughs at same time
Own more than 10 cattle; can use at least 5 ploughs at same; have an ox-drawn cart
Marital status Widowed, unmarried, divorced separated
Widowed, unmarried, divorced separated
Not relevant Not relevant
Deficiency Blind person or handicap
None Not relevant Not relevant
Age Over 50 years below 35 years old
Not relevant Not relevant
Alternative strategies
Only xicoropa, kukashela
Xicoropa, kukashela, tsimo and charcoal production
Charcoal production
Charcoal production,
Who prepare the fields
Parents and neighbours
Themselves Themselves and small benefits from kukashela
Themselves and large benefits from kukashela
63
02468
101214161820
No of fields Household size Cattle Goats Chickens
Resource endowed
Freq
uenc
y of
hou
seho
ld
VLRELREMREHRE
Figure 9. Resource endowed per different wealth groups at Banga Village
0
5
10
15
20
25
No of fields Householdsize
Cattle Goats Chickens
Resource endowed
Freq
uenc
y of
hou
seho
ld
VRLE
LRE
MRE
HRE
Figure 10. Resource endowed per different wealth groups – case study
Instead of presenting the resources endowed as absolute values, a more realistic approach
is express present them per household members.
It is interesting to note that immigrants in Banga own a larger number of cattle than the
local people (Figure 11). In Banga, immigrants are people belonging to different parts of
the district or other regions of the country who came to Banga to seek better living
conditions or other reasons. Some of these immigrants have been living in Banga for
more than 50 years.
64
619N =
Naturality
ImmigrantsLocals
Num
ber o
f cat
tle p
ursu
ed
30
20
10
0
-10
Figure 11. Cattle distribution among locals and immigrants
The immigrant households owned on average 10.7 cattle compared with the 3.5 of the
local households. A huge variation in the number of cattle was registered between
immigrant households ranging from zero to 23 cattle per household when compared
with zero to 10 cattle per household in the local group. The variances of the sample
were 11.37 and 75.06 for local and immigrant groups, respectively. The t-test performed
to compare the two groups indicated that the difference was significant only at the 10%
probability level.
As cattle are the main factor in dividing wealth classes and the households with the
largest herds are immigrants, the HRE group is composed only of immigrant farmers.
However, as was mentioned above, there are some immigrant farmers with no cattle.
Additional information from group discussions
Apart from the wealth group division and case studies definition, the focus group
discussions added some insights and clarified information that was wrongly interpreted
through the RFSC. For instance, in relation to benefits of manure in improving the soil
fertility level, I was informed by the farmers in Banga, that the District Directorate of
Agriculture (DDA) in collaboration with the NGO ‘CARITAS’ had previously
organized meetings with farmers to discuss the benefits of applying manure to their
fields. During the group discussions meeting a farmer said:
65
“We know that fresh manure should be avoided because it can kill our crops, unless it is
dissolved in water. However, we do not apply it because on one hand our fields are highly
fertile and big, and on the other hand we do not have means to transport it to the fields”.
Other information gathered during the group discussions was related to the distinction
between having a plough and having both a plough and cattle. I found out during the
group discussion that all households within the village have at least one plough,
although it does not mean they have direct access to animals for traction. It was also
possible to figure out that the social capital within the village allowed them to borrow
cattle from kin or to loan from friends. Some farmers earlier reported these cattle as
their own, which may have led to misinterpretation of the situation during data
analysis.
4.7 Variability of the soil properties in different field types
The % of SOC content were significantly different (p<0.001) between the three field
types (Appendix 12). As expected SOC contents across the fields decreased according to
the trend: Gowene > Banhine > Mananga. The average SOC concentration in Gowene,
Banhine and Mananga’s fields were 1.52%, 1.1% and 0.43%, respectively (Figure 12). The
Figure 12 shows a box-and-whisker plots of the SOC content in the soil. The box-and-
whisker plots shows five statistics – the minimum, the lower quartile, the median, the
upper quartile, the maximum. The box contains the middle 50% of data values. The line
drawn across the box is the sample median. The whiskers or line on either sides of the
box show the range of the lower or upper 25% of data values. Values which are between
one and a half (denoted with circle) and three (denoted with asterisks) box lengths from
either end of the box are outliers. For the farmers on the same field type and belonging
to different wealth groups, no differences were found in the SOC contents (Appendix
12).
66
578N =
Field type
ManangaGoweneBanhine
SOC
(%)
2.5
2.0
1.5
1.0
.5
0.0
10
Figure 12. Soil organic carbon (SOC) in different field types at Banga.
The SOC content in Banhine, Gowene and Mananga’s fields were positively correlated
with CEC (r=0.87) (Figure 13).
y = 17.937x + 0.5892
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
40.00
0.00 0.50 1.00 1.50 2.00 2.50
Figure 13. Linear relationship between SOC (%) and CEC (cmol kg-1).
The CEC of the soils was also significantly different between the field types (p<0.001)
(Appendix 12). The values were higher in Gowene’s (27.28 cmol kg-1) and lower in
Mananga’s fields 4.97 cmol kg-1 (Figure 14). In the same field type there were no
significant differences in the CEC of the soils between the wealth groups (Appendix 12).
67
478N =
Field types
ManangaGoweneBanhine
CEC
(cm
ol k
g-1)
40
30
20
10
0
medium
Poor
Figure 14. Cation Exchange Capacity (CEC) in different field types at Banga.
The total N of the soils differed significantly between the field types (p<0.001)
(Appendix 12). The higher values were observed in Gowene’s fields (0.13 g kg-1) and the
lower in Mananga’s field (0.06 g kg-1). The N values in different fields are shown in the
Figure 15. For the farmers on the same field type and belonging to different wealth
groups, no differences were found in the N contents (Appendix 12).
The were significant differences in C:N ratio between the field types (p<0.001)
(Appendix 12). The C:N ratio decreased according to the trend: Gowene > Banhine >
Mananga. The C:N ratio values were as follow: 11.3, 9.6 and 8.3, respectively.
578N =
Field type
ManangaGoweneBanhine
N T
otal
(%)
.16
.14
.12
.10
.08
.06
.04
.02
20
Figure 15. Total nitrogen content in different field types at Banga.
68
The analysis of variance revealed significant differences on the available P in the soils
between the field types (p<0.001) (Appendix 12). The availability increased in following
order: Mananga > Banhine > Gowene. The P concentration in Mananga, Banhine and
Gowene were 3.84 mg kg-1, 27.51 mg kg-1 and 53.3 mg kg-1, respectively (Figure 16). No
significant differences were observed in available P for the farmers on the same field
type and belonging to different wealth groups (Appendix 12).
478N =
Field types
ManangaGoweneBanhine
P O
lsen
(mg
kg-1
)
100
80
60
40
20
0
-20
Figure 16. The available P in different field types at Banga.
The exchangeable K in the soils were significantly different (p<0.001) between the field
types (Appendix 12). The values were higher in Gowene (3.09 cmol kg-1) and lower in
Mananga (1.18 cmol kg-1) (Figure 17). For the farmers on the same field type and
belonging to different wealth groups, no differences in exchangeable K were observed
(Appendix 12).
69
578N =
Field types
ManangaGoweneBanhine
K (c
mol
kg-
1)
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
.5
11
10
Figure 17. Exchangeable K in different field types at Banga.
4.8 Calculation of N, P and K balances
4.8.1 Partial balances
Partial balances take into account nutrients flows that are relatively easier to measure
without make many assumptions. These flows are: nutrient added to system through
inorganic and organic fertilizers, and nutrient removed through crop removal and crop
residues graze.
The partial N, P and K balances for maize at field scale were negative for the all farmer
wealth groups (Figure 18 (a), (b) and (c)). For all the nutrients, the depletion values were
higher for the HRE and VLRE groups. The MRE groups observed the lowest values for
all the nutrients. The magnitude of nutrient depletion between the wealth groups were
almost the same for all the nutrients.
70
(a) Maize: Banhine's fields
-35-30-25-20-15-10-505
Very poor Poor Medium Rich
Resource endowment groups
N in
puts
(kg/
ha)
INPOUT
(b) Maize: Banhine's fields
-4-3-2-1012
Verypoor Poor
Medium Rich
Resource endowment group
P In
puts
(kg/
ha)
INP
OUT
(c) Maize: Banhine's fields
-40-35-30-25-20-15-10-505
Very poor Poor Medium Rich
Resource endowment groups
K In
puts
(kg/
ha)
INPOUT
Figure 18. N, P and K inputs (a, b and c, respectively) for Banhine’s fields in different resource
endowment groups at Banga.
4.8.2 Full balances
As it mentioned above (see 1.1), the full nutrient balances calculated on the basis of
transfer functions has been widely criticised because of uncertainties around the way it
is calculated.
The equation coefficients presented by Smaling et al. (1993) to estimate the leaching of
N and K is appropriate for high rainfall conditions. The minimum value is 1350 mm
year-1, while in Banga the average precipitation is about 320 mm yerar-1. As pointed out
71
by (Faerge and Magid, 2004), the use of the transfer functions in areas of low rainfall and
in a low input agricultural system as Banga can lead to an overestimation of leaching of
N and K. Yet, I looked at possibilities to use the transfer functions from Lesschen et al.
(2007) that estimate leaching of N and K for national level, based on the following
equations:
OUT3 N (kg N ha-1year-1) = (0.0463 + 0:0037*(R/(C*L) * (FN +D*NOM – U)
OUT3 K (kg N ha-1year-1) = -6.87+0.0117*R+0.173 *FK – 0.265 * CEC
where:
R = precipitation (mm year-1)
C = clay (%)
L = layer thickness (m) = rooting depth
FN = mineral and organic fertilizer nitrogen (kg N ha-1)
D = decomposition rate of organic matter (1.6% year-1)
NOM = amount of nitrogen in soil organic matter (kg N ha-1)
U = uptake by crop (kg N ha-1 year-1)
FK = mineral and organic fertilizer potassium (kg K ha-1 year-1)
CEC = cation exchange capacity (cmol kg-1)
The application of the regressions equations yielded negative values for K leached and
overestimation of N leached (Appendix 8). The equations were developed to correct the
uncertainties of nutrient balances at national scale; therefore it has limited application
for the case study.
Denitrification estimated on the basis of Stoorvogel and Smaling (1990) transfer
functions lead to negative values. Thus, I attempt to estimate this loss using the
regression equation from Lesschen et al (2007). The equation estimates denitrification
for tropical conditions. The equation shows relation between precipitation, mineral and
organic fertilizer nitrogen and soil organic carbon content. The equation has the
following form:
OUT4 (kg N ha-1year-1) = 0.025 + 0.000855 * R + 0.130 * F + 0.117 * O
where:
R = precipitation (mm1year-1);
F = mineral and organic fertilizer nitrogen (kg N ha-1year-1);
O = soil organic carbon content (%).
72
The estimation of these losses is shown in the Appendix 9.
Due to the fact that some legumes can nodulate and fix N2, I estimated the contribution
of groundnut crop to the farming system in Banga, assuming that 60% of N demand is
derived from biological fixation. Based on the yield of 500 kg ha-1, the results showed an
annual contribution of 18 kg ha-1 (Appendix 8). However, the N contribution to the soil
fertility will depend on the net inputs from N2-fixation (Toomsan et al., 1995). The N
leached based on regression equations of Lesschen et al. (2007) resulted in overestimation
of N leached (Appendix 8).
Taking into account all uncertainties around the IN and OUT flows that are calculated
for full nutrient balances, the use of these transfer functions for Banga conditions seems
not to be realistic. Thus, I decided to calculate only the partial nutrient balances
4.9 Maize yield (kg ha-1) in different wealth classes
No significant differences on maize yield were observed between the farmers belonging
to different wealth groups (p=0.68) (Appendix 12). And, within the same wealth group,
higher variability was found in maize yield. The average yield (kg ha-1) in each wealth
group is presented in the Figure 19.
0200400600800
10001200140016001800
very poor Poor Medium Rich
Wealth classes
Mai
ze y
ield
(kg/
ha)
Figure 19. Average maize yield in different wealth groups.
73
5 Discussion
This section is divided in three parts: first, I discuss the variability of the soil chemical
properties (N, P, K and SOC) and CEC observed in different field types. Second, I
analyse the partial N, P and K balances in relation to the nutrient stocks. Finally I
discuss the nutrient balances in context of the five capitals in Banga.
(a) Nutrient stocks analysis
The fact that soil organic carbon (SOC) plays an important role in crop development is
due to its influence on soil structure, its position as a key source of mineralization of N
for production, its water holding capacity, and the fact that it enhances soil’s ability to
form complexes with metal ions and store nutrients (e.g. Lal, 1997; van Keulen, 2001).
Through continued cultivation without addition external inputs, soils carbon stocks are
likely to be lost, affecting the soil’s production capacity significantly. Therefore, SOC
stocks are often used as a key indicator of sustainability of farming system (Giller and
Vanlauwe, 2006; Manlay et al., 2007).
The data presented in section 4.2 indicate that soil organic carbon is low in all field
types. According to Metson (1961) as cited by Landon (1984) for general assessment of
SOC content, values between 2-4% are considered low. Despite the fact that SOC in
general is low in all field types, there is considerable variation between the field types.
The relative higher value of SOC found on Gowene’s fields compared with those of
Banhine and Mananga’s fields can be explained by the higher concentrations of clay and
silt in the Gowene’s fields. These finer particle-size fractions physically protect the SOC
from decomposition (Six et al., 2002; Plante et al., 2006). Soil Organic Carbon was
positively correlated with silt and clay contents, r=0.87 and r=0.77, respectively
(Appendix 12).
The differences in SOC observed between the different field types, seems not to be
rooted in differences in agricultural managements practices; rather they seem to be
attributed to the inherent properties of the soils: the Gowene’s soils are richer in clay and
silt (see Table 4). Vanlauwe and Giller (2006) reported close relationship between
increases of soil organic matter and increases of crops yields, due to the organic matter
return to the soil through roots and litters. Crop yield is directly related to total
biomass. In Banga, maize occupies the larger area within the field. Yet, the fact that no
significant differences in maize yields (in Banhine’s fields) were observed between the
74
wealth groups (p=0.68), suggests that there is no evidence of differences in the organic
matter returned to the soil through roots and litters between wealth groups. However,
this result should be interpreted with care because the standard error of observations
was too high (Appendix 5). The lack of application of external inputs and management
practices to improve the current soil fertility level, in all field types, also suggests that
the maize yields in other field types will depend on the inherent properties of the soil,
instead of differences on wealth status.
Soil organic carbon also affects the cation exchange capacity (CEC) of the soil (De
Ridder and Keulen, 1990). The CEC gives the soil potential to hold nutrients for plant
growth; therefore, it is important to estimate this soil capacity. The CEC is also
determined by the clay fraction (De Ridder and Keulen, 1990), thus, higher values of
CEC found in Gowene’s fields can be explained by the strong influence of the clay
content in this field type.
Nitrogen, phosphorus and potassium are macro-nutrients that have direct influence on
crop growth. After nitrogen, phosphorus is the most limiting nutrient for crop
production (Smithson and Giller, 2002). These nutrients are relatively easier to measure
and to quantify in most laboratories, therefore can be analysed at high degrees of
precision.
For general assessment of total N for good crop production, values between 0.1 and
0.2% are considered low (Landon, 1984). The values of total N in Gowene and Banhine
were high than those observed in the Mananga. One explanation could be the inherent
properties of the soils and the grazing system. During the off-season cattle graze crop
residues and available natural pastures in Gowene and Mananga, contributing through
direct depositions with some amount of nitrogen. In Mananga, farmers used to construct
fences to protect their crops against the cattle during the crop-season. These fences
remain in the field until the next crop-season. This could somehow limit the possibility
of the cattle to graze the crops residues in comparison with the Gowene and Banhine’s
fields. In addition, these two field types constitute a smaller area (almost half of
Mananga) which caused the manure distribution to be more concentrated than in
Mananga. In all field types the C:N ratio was less then 20 (average), showing that excess
N is presented and nitrification proceeds.
75
An indicative available P value (Olsen’s method) is considered low when it is below 4
mg kg-1 (Landon, 1984). The average values of the available P (53.3 and 27.51 mg kg-1) in
Gowene and Banhine fields, respectively, were much higher compared to Mananga’s
fields, and no appreciable response is expected with adequate phosphate fertiliser.
Contrary, the available P in Mananga (3.84 g kg-1) was below the critical values, thus
fertiliser response is most likely to occur. The lower available P can be explained by the
grazing and the crop production systems in the village (fences in Mananga’s fields). The
analysis done in case of the total N could also be applied to this nutrient if we consider
that the inherent soil properties and cattle manure through direct crop grazing are the
source of P in Banga farming system.
The exchangeable K will not be treated here, as this is present in relatively high amounts
in all field types analysed in this study (see Table 4).
(b) Interpretation of the partial nutrient balances (N, P and K)
The partial nutrient balances for maize at the field level were not related to the wealth
status of the household, and the HRE and VLRE farmers’ groups experienced higher
rates of nutrient removal. The higher rates observed could not be attributed to the
variability in soil fertility and management, while the analysis of nutrients stocks in
different fields belonging to different wealth groups showed no significant differences. A
possible explanation could be the slightly higher maize yield observed in these two
groups. Because the inputs are the same for all wealth groups, the output will define the
level of nutrient extraction.
The results of the nutrient balances found in Banga differ from those reported by
Zingore et al., 2006 and Tittonell et al., 2005 who found a close relationship with wealth
classes. The lack of inputs application in all wealth groups, the intensity of cropping
plus nutrient extraction, the length of land use, and land use cycle are some of the
possible reasons for the different results.
Because nutrient balances itself can not explain the sustainability of the system, I
analysed this results taking into account the nutrient stocks presented in the Table 9.
76
Table 9. Nutrient stocks in different field types (N, P and K, in kg ha-1) at Banga (See Appendix
3 for the calculations).
Field types SOC (kg ha-1) Ntotal (kg ha-1) Pavailable (kg ha-1) Kexchangeable (kg ha-1)
Gowene 40000 3500 140 3100
Banhine 31000 3200 77 2700
Mananga 13000 1700 12 1400
The sum of IN1+IN2 minus the sum of OUT1+OUT2 (Figure 18) for N in Banhine’s
fields showed the values -27.28 kg ha-1, -21.54 kg ha-1, -18.79 kg ha-1and -30.42 kg ha-1, for
the VLRE, LRE, MRE and HRE groups, respectively. These values suggest net
depletion of nutrient stocks. Moreover, if one assumes a linear relationship between the
current input and output rates and if one assumes that this relation remains the same
over the following years, after 10 years of continuous cultivation theses soils will be
exhausted. Although for the other field types measurements were not done due to the
limitations presented before (section 3.1.7), I would also expect here the same scenario as
in Banhine’s fields due to the fact that farmers do not apply any inputs to these fields.
In order to draw a final conclusion about N depletion, it is necessary to consider the soil
N and yield dynamics. The hypothetical example (Figure 20) presented by Smaling and
Dixon (2006) based on two soils with different N stocks in continuous cultivations with
only wet+dry deposition and biological fixation inputs, showed a constant maize crop
yields as long as the mineralized N was less than that removed through crop
products+crop residues outputs. Even when the above inputs are less than the two
outputs, there is tendency of N stabilization at lower level (ibid). The stabilization has
been also reported in studies of soil organic carbon dynamics (Six et al., 2002).
77
Figure 20. Model of soil nitrogen and crop yield dynamics in a no-input system (after FAO, 2004b). Source: Smaling and Dixon (2006).
In case of P the sum of IN1+IN2 minus OUT1+OUT2 (Figure 18) yield the values -
2.84 kg ha-1, -2.05 kg ha-1, -1.62 kg ha-1 and -2.87 kg ha-1, for the VLRE, LRE, MRE and
HRE groups, respectively. For this nutrient, the situation is not alarming probably due
to the manure contribution from directly grazing crop residues.
In case of K content the fields present adequate values suggesting that K is not a limiting
factor in Banga farming system.
(c) The nutrient balances and the five capitals in Banga
The purpose of this section is to link the different capitals that compose the livelihood
strategies in Banga (section 4.4) to the results of nutrient balances, and decisions on soil
fertility management. As we have seen above, the nutrient balances for all the resource
endowment groups are negative, basically due to the lack of inputs to the system.
During the individual interviews and group discussions with farmers the following
reasons for not investing in soil fertility improvement were highlighted:
- Their fields are highly fertile, therefore there is no need to improve;
- Their ancestor traditionally never applied manure;
- The lack of means to transport the manure to their fields;
- The availability of only a small amount of manure to cover all the fields.
The analysis of nutrient stocks in all field types showed that for Ntotal, the nutrient that
most limits crop growth in this case, values vary from high in Mananga to very high in
Gowene and Banhine’s fields. In case of available P the values were high in Gowene and
Banhine, and low in Mananga’s fields. The results from soil nutrient analysis are in
agreement with the farmers’ reasons for not investing on soil fertility improvement
(human capital).
78
Although not used by farmers in Banga, some farmers showed to have knowledge of the
benefits of using manure as well as the skills to apply them in the fields. But the labour,
and means needed to transport the available manure are limiting factors in Banga.
Nevertheless, the analysis of physical capitals showed that some farmers from the HRE
groups have animal-drawn carts that could be used to transport manure. Even for
farmers without this capital, one would expect that their could use the different forms of
relationship (kukashela, kuwekissa and tsimo- all social capitals) to transport the manure
to the Mananga’s fields, as they do with land preparation.
Another constraint in Banga is the amount of available manure that is too low to
increase the SOC and available P. During my fieldwork I also observed the low amount
of manure in kraals.
According to Landon (1984) values between 4-10% of SOC are considered medium.
Based on this information I estimated the amount of organic material needed to increase
the SOC from 1.9% in Banhine’s fields (see Table 4) to 4%, in the top 20 cm. The
Banhine’s fields have a bulk density of 1.40g cm-3. I assumed the relative rate of
decomposition of organic material as 0.06 yr-1 based on experiment in Senegal (Harpaz,
1975 cited by De Ridder and Keulen, 1990). My calculations showed that 3192 kg C per
ha per year is required to maintain the carbon at 1.9% level. To increase the level up to
4%, 8800 kg ha-1 of C is needed in addition. Thus, almost 62 ton of C is needed in total.
This is not feasible for Banga’s farmers due to the lack of labour and natural resource.
One way to solve this problem is to apply manure in combination with inorganic
fertilizers. However, fertilizer packed in large bags increases the price for small farmers
to buy fertilizers. Bearing in mind the low purchasing power among farmers in Banga,
and the low amount of available manure, sustainable alternatives have to be considered.
In this light, animal manure and legume rotations in combination with small packages
of inorganic fertilizers could be more realistic.
Financial capital (remittances, income from the sale of charcoal, chicken and sometimes
cattle) is part of the livelihood strategies among farmers in Banga. This capital could be
used to invest in soil fertility, however it does not happen. The lack of local input
markets (physical capital) limits the investment on external inputs such as inorganic
fertilizers. The risks of crop fail due to the consecutive droughts are high in Banga, and
farmers have been exposed to this situation for a long time. Farmer’s knowledge of the
79
production (risks of no return) environment, plus the perception that their fields are
highly fertile seems to be as main reasons for not investing in soil fertility.
81
6 Conclusions
The N, P and K have proven useful indicators of the farming system in Banga; in
combination with nutrient stocks analysis provided important insight about how the
system functions. The results of nutrient balances confirm Hypothesis (i) which states
that the wealth status of the household has no influence on nutrient balances at field
level. This differs from earlier research by Zingore et al., 2006 and Tittonell et al., 2005
which has shown a close relationship.
The fields belonging to all wealth groups show negative balances for maize, the main
crop in the research area. No significant differences between the different wealth groups
were observed with regard to the yield of maize. The variation within each group is
much higher than between the groups.
The analyses of N, P, and K balances show negative values. However, with the
exception of P in Mananga’s fields, the nutrients stocks remain above the critical values
established by Landon (1984) for good maize growth. This occurs despite the fact that
farmers in Banga do not apply external inputs to improve the soil fertility status. The
values of nutrient stocks can be attributed to the inherent properties of the soils,
intensity of cropping, and length of land use. The 2000 floods could also have
contributed to the actual fertility status.
The far-away fields (Gowene and Banhine) are inherently more fertile than those closer
to the homestead (Mananga), thus are more preferable for crop production. This also
confirmed the second and third hypotheses of the thesis and refutes the results of other
research that found that fertility would decrease with distance.
The soil organic carbon in all field types is below the critical values for general accepted
as needed for good crop production, while the available P was low in Mananga’s fields.
The use of inorganic fertilizer can help to improve the available P level in Mananga’s
fields, organic manure and legume crops can improve the SOC and N. Animal manure
and legume rotations in combination with small packages of inorganic fertilizers could
be a more realistic option. The exchange relationship among farmers in Banga
(kukashela, tsimo and kuwekissa) could be used to overcome the lack of labour and means
required to transport the available manure. The income from chicken and remittances
from kin could be used to purchase small packages of inorganic fertilizers.
82
Agriculture in Banga is mainly dependent on the rains, and maize is the main crop. The
lack of other germplasm that are more tolerant to the water stress, limits the possibility
of farmers to diversify crop production. Yet, with the consecutive droughts that
characterize this village, it is also questionable whether it is sustainable to have maize as
the main crop. It might be wiser to invest in cattle production rather than in soil, for
instance. Therefore, the incorporation of multipurpose crops that are more drought
tolerant seems to be a valuable alternative. Pigeon pea appears as good alternative for
drought conditions, it has advantage of being a multipurpose crop which can be
cultivated mixed with maize. The leaves can be used to improve the soil fertility; the
beans are rich in protein. Because farmers in Banga rarely eat their animals, this could be
seen at the same time as one way to improve their diet.
The present land availability is not a constraint in Banga. Therefore, farmers can still
open new fields especially in Mananga. With the expected increase in demand for land
due to the relocation of Macavene Village to the Banga area, this option may become no
longer feasible. During the fieldwork we learned that a new project was being prepared
by the PROCANA enterprise. This project entails the occupation of 25,000 ha of
Mananga fields for sugar cane production. This project will unquestionably increase the
restriction to access the land for grazing among Banga’s farmers even further. On the
other hand, sugar cane production will require external inputs such as mineral fertilizer.
The implementation of this project can eventually constitute an opportunity for the
farmers in Banga to get in contact with these inputs and to see or to compare the
benefits of their use, as the project intends to contract local farmers for sugar cane
production. If this is the case, it is expected that the farmers’ purchasing power will
increase. However, one should keep in mind that agriculture is only one among many
other activities that compose the livelihood strategies and the soil fertility issue is
probably a minor one. In addition, decisions within a household are subject to trade-
offs.
It should be stressed that the results of this research should be interpreted with care.
First, the nutrient balances presented here are based on the data of one year. Full
understanding can only be achieved if a long-term data series would be constituted.
Second, the nutrient balance analysis method itself is based on many assumptions
derived from research in other regions, which require local validation. Instead of using
83
these results as ‘recipe’ to guide policy towards soil fertility management, it should be
seen, as it was acknowledged by Scoones and Toulmin (1999) as a way to promote
debates between policy-makers, researchers and farmers about soil fertility.
84
85
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Appendices
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Appendix 1. Household interview (first Rapid Farming System Characterization-RFSC) Farmer name (household head) _______________________________________ Age: _______ Sex: ________________ Education (level) __________________
A. Household size/or labour:
Number of family members in the farm
Gender Position1 Male Female Total: 1: wife, son, etc
B. Land size, tenure and preparation Number of fields:
1. Mananga: _____
2. Banhine :______
3. Gowene: _______
4. Cowene: ______
Farm tenure 1. Own property
a. Mananga -> yes: no:
b. Banhine -> yes: no:
c. Gowene -> yes: no:
2. Heritage
a. Mananga -> yes: no:
b. Banhine -> yes: no:
c. Gowene -> yes: no:
3. Borrow
a. Mananga -> yes: no:
b. Banhine -> yes: no:
c. Gowene -> yes: no:
Land preparation 1. How the land is prepared:
a. oxen
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b. axe c. animal power (cattle) d. Tractor e. others
2. Sources of tools used to prepare the land:
3. In which fields it applied: a. Banhine b. Mananga c. Gowene
C. Livestock production
Type of animal
number of animals
Cattle Goats Pigs Chicken Duck Sheep Place for grazing during the crop season and off-season Communal Own land Location (baixo or alto):
D. Cropping system Main crop grow in each field type:
1. Banhine Cash crop food crop
2. Mananga Cash crop food crop
3. Gowene Cash crop food crop
4. Number and size of granaries: ________ E. Production orientation:
Crops orientation
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F. Coping strategies when crops fail G. Source off income and services
List all (cash crops, enterprises such as traditional beverages, oxen services, remittances, etc.) Crops enterprises services
Thanks very much
98
Appendix 2. Individual questionnaire used in individual interview
Detailed characterization of farming system Date: ________________________ site: _______________________________
Farmer name (household head) _______________________________________ Age: _______ Sex:_________________ Education (level)__________________ 1 Resources
Household size/or labour:
Number of family members in the farm Ages range1 Gender Male Female Below 1 threshold 12. Decision maker within HH on labour allocation issues: __________________ Number of family members outside the farm that have assisted you with farms activity? ___ Ages range Gender Male Female Activities Number of non-family members that assist in farm activity or hired (including the livelihood strategies)?
Gender Activity Male Female Total
Land preparation Sowing Weeding Harvest Farmers’ management skills
Who is responsible Skills Male Female Not applicable1
Land preparation Crop selection Seed selection Planting in rows Harvest
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Land Farm size and tenure: ____________ If unknown, the farm has to be measured. Tenure Type of land Cowene (ha) Banhine (ha) Gowene Mananga (ha) Own property Heritage Borrow
2 Assets Assets Function within household if applicable # wives Plough
Farm cart 3 Wealth Livestock: How many animals do have and for how long (months, years, etc) Animals Number Function within household Cattle Goats Pigs Chickens duck Sheep Common rangeland: Indicate access to communal land used for grazing (no units) (v) Farm description Production orientation
Orientation Main crops Subsistence Market Both Priorities (rank)
Farmers’ priorities: Farmers’ should indicate their most important objectives and rank them – relate this to self-categorisation (wealth) and self-definition (orientation) Crop allocation according to the field type Crops Type of field Cowene Banhine Gowene Mananga Preferable field Maize
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4) Income and access to other services List all (enterprises such as traditional beverages, oxen services, xicorapa, etc.) Crops enterprises services 5) Sources of remittances and off-farm employment: 6) Coping strategies when the crops fail
Thank very much
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Appendix 3. Nutrient stocks in different field types found at Banga Village Wealth classes
Field typeOM (%)
SOC (%)
N Total (%)
P Olsen (mg kg-1)
K (cmol kg-1)
Mg (cmol kg-1)
Ca (cmol kg-1)
Bulk density (g /cm3)
depth (cm)
1 ha vol (cm3)
weight of the soil (g)
SOC (kg/ha) N (kg/ha) P (kg/ha) K (g/kg) K (kg/ha)
Very poor Banhine 1.64 0.95 0.10 12.22 2.12 6.54 13.58 1.40 20 2.0E+09 2.8E+09 2.7E+04 2.7E+03 3.4E+01 8.3E-01 2.3E+03Very poor Banhine 2.03 1.18 0.11 39.59 2.92 6.81 12.30 1.40 20 2.0E+09 2.8E+09 3.3E+04 3.1E+03 1.1E+02 1.1E+00 3.2E+03Poor Banhine 1.81 1.05 0.13 8.66 2.18 6.59 11.75 1.40 20 2.0E+09 2.8E+09 2.9E+04 3.6E+03 2.4E+01 8.5E-01 2.4E+03Poor Banhine 2.03 1.18 0.11 46.39 2.68 5.78 13.46 1.40 20 2.0E+09 2.8E+09 3.3E+04 3.1E+03 1.3E+02 1.0E+00 2.9E+03Medium Banhine 1.43 0.83 0.09 23.66 2.52 5.45 17.36 1.40 20 2.0E+09 2.8E+09 2.3E+04 2.5E+03 6.6E+01 9.8E-01 2.8E+03Medium Banhine 2.55 1.48 0.15 36.03 2.08 3.01 18.28 1.40 20 2.0E+09 2.8E+09 4.2E+04 4.3E+03 1.0E+02 8.1E-01 2.3E+03Rich Banhine 2.07 1.20 0.11 32.32 2.68 6.73 13.07 1.40 20 2.0E+09 2.8E+09 3.4E+04 3.1E+03 9.0E+01 1.0E+00 2.9E+03Rich Banhine 1.64 0.95 0.13 21.19 2.28 6.48 18.77 1.40 20 2.0E+09 2.8E+09 2.7E+04 3.6E+03 5.9E+01 8.9E-01 2.5E+03
Average 1.10 3.1E+04 3.2E+03 7.7E+01 9.5E-01 2.7E+03SD 5.7E+03 5.8E+02 3.7E+01 1.2E-01 3.4E+02
Very poor Gowene 2.80 1.63 0.13 77.30 3.22 8.50 18.43 1.30 20 2.0E+09 2.6E+09 4.2E+04 3.4E+03 2.0E+02 1.3E+00 3.3E+03Poor Gowene 3.32 1.93 0.15 37.00 3.82 8.37 20.70 1.30 20 2.0E+09 2.6E+09 5.0E+04 3.9E+03 9.6E+01 1.5E+00 3.9E+03Poor Gowene 2.22 1.29 0.14 45.50 2.38 7.67 16.81 1.30 20 2.0E+09 2.6E+09 3.4E+04 3.6E+03 1.2E+02 9.3E-01 2.4E+03medium Gowene 2.60 1.51 0.13 83.50 3.28 4.68 9.99 1.30 20 2.0E+09 2.6E+09 3.9E+04 3.4E+03 2.2E+02 1.3E+00 3.3E+03medium Gowene 2.65 1.54 0.13 64.70 2.86 7.99 16.98 1.30 20 2.0E+09 2.6E+09 4.0E+04 3.4E+03 1.7E+02 1.1E+00 2.9E+03Medium Gowene 2.08 1.21 0.12 28.90 2.96 7.36 17.12 1.30 20 2.0E+09 2.6E+09 3.1E+04 3.1E+03 7.5E+01 1.2E+00 3.0E+03Medium Gowene 2.73 1.59 0.14 36.20 3.10 7.70 17.01 1.30 20 2.0E+09 2.6E+09 4.1E+04 3.6E+03 9.4E+01 1.2E+00 3.1E+03
Average 4.0E+04 3.5E+03 1.4E+02 1.2E+00 3.1E+03SD 6.1E+03 2.5E+02 5.6E+01 1.7E-01 4.5E+02
Poor Mananga 0.58 0.34 0.04 0.60 0.72 0.57 1.78 1.50 20 2.0E+09 3.0E+09 1.0E+04 1.2E+03 1.8E+00 2.8E-01 8.4E+02association Mananga 0.68 0.40 0.04 4.30 0.74 0.35 1.98 1.50 20 2.0E+09 3.0E+09 1.2E+04 1.2E+03 1.3E+01 2.9E-01 8.7E+02Medium Mananga 0.88 0.51 0.05 7.40 1.66 0.90 4.96 1.50 20 2.0E+09 3.0E+09 1.5E+04 1.5E+03 2.2E+01 6.5E-01 1.9E+03Very poor Mananga 0.68 0.40 0.05 4.60 0.92 0.65 2.58 1.50 20 2.0E+09 3.0E+09 1.2E+04 1.5E+03 1.4E+01 3.6E-01 1.1E+03Rich Mananga 0.92 0.53 0.10 2.30 1.86 1.09 4.07 1.50 20 2.0E+09 3.0E+09 1.6E+04 3.0E+03 6.9E+00 7.3E-01 2.2E+03
Average 1.3E+04 1.7E+03 1.2E+01 4.6E-01 1.4E+03SD 2.5E+03 7.5E+02 7.7E+00 2.1E-01 6.3E+02
SD= standard deviation
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Appendix 4. Harvest index estimates based on the Cubo Village data
Grain stoverTotal biomass HI
Household number
Grain (kg) in one 50kg bag
HI from Cubo Village
Total biomass in 50kg bag (kg)
Total biomass in one Sleigh (kg)
Total biomass in one farm cart (kg)
3.4 4.6 8 0.43 1 22.3 0.42 53.10 318.57 1274.293.7 5 8.7 0.43 2 19.8 0.42 47.14 282.86 1131.433.6 5.3 8.9 0.40 3 22.4 0.42 53.33 320.00 1280.003.3 4.8 8.1 0.41 4 21.3 0.42 50.71 304.29 1217.143.3 4.5 7.8 0.42 5 18.9 0.42 45.00 270.00 1080.003.7 5.4 9.1 0.41 6 19.6 0.42 46.67 280.00 1120.003.5 4.7 8.2 0.43 7 19.5 0.42 46.43 278.57 1114.293.4 4.5 7.9 0.43 8 20.6 0.42 49.05 294.29 1177.143.6 5.2 8.8 0.41 9 20.4 0.42 48.57 291.43 1165.71
Average 3.50 4.89 8.39 0.42 10 18.8 0.42 44.76 268.57 1074.29Standard deviation 0.16 0.35 0.49 0.01 11 21.2 0.42 50.48 302.86 1211.43
12 20.2 0.42 48.10 288.57 1154.2913 19.1 0.42 45.48 272.86 1091.4314 18.3 0.42 43.57 261.43 1045.7115 19.7 0.42 46.90 281.43 1125.71
Average 20.14 0.42 47.95 287.71 1150.86Standard deviation 1.23 2.94 17.64 70.54
Farm cart= 4 SleighOne Sleigh= 6 50 bags
Weight of 20 plants (kg) in Cubo Village Banga Village
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Appendix 5. Total area covered by Gowene and Banhine’s fields at Banga Village Area m2Banhine 1 Banhine 2 Banhine totaBanhine totaGowene (ha) total gowene+banhine (ha)
Very poor Raulina 8249 7676 15925 1.6 1.6Very poor Feliciana 11859 11859 1.2 0.2 1.4Poor Simione 25188 14118 39306 3.9 0.9 4.8Poor Jamal 10278 10278 1.0 1.0Medium Jaime 46100 46100 4.6 4.6Medium Janeiro 5916 19792 25708 2.6 2.6Rich Macuele 32970 8442 41412 4.1 4.1Rich Casamente 17777 13848 31625 3.2 3.2
Average size 2.8
Total area (banhine, Gowene) 90 HH 256.5
Nr HH sample 25Nr HH in Village 90 Number of farmers with Gowene's fie 13
Average size in Gowene (ha) 0.5Total gowene (ha) 6.5Total banhine (ha) 250.0 Average cattle 5.24Total cattle 471.6
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Appendix 6. Manure inputs (IN2) and wet deposition (IN3) (kg ha-1)
Field type area (ha)Manure per Manure perManure da Manure per Manure per o total manure in total manure N per kg drP per kg dry wK per kg dryN kg/off-seP kg/off-s K kg/off-seN Kg/ha P kg/ha K kg/ha
Banhine 1.6 725 60 2 242 113970.0 56985.0 47487.5 0.0019 0.0008 0.003 90.23 37.99 142.46 0.575 0.242 0.908Banhine 1.2 725 60 2 242 113970.0 56985.0 47487.5 0.0019 0.0008 0.003 90.23 37.99 142.46 0.419 0.176 0.661
0.497 0.209 0.784
Banhine 1.0 725 60 2 242 113970.0 56985.0 47487.5 0.0019 0.0008 0.003 90.23 37.99 142.46 0.361 0.152 0.570Banhine 3.9 725 60 2 242 113970.0 56985.0 47487.5 0.0019 0.0008 0.003 90.23 37.99 142.46 1.408 0.593 2.223
0.884 0.372 1.396
Banhine 4.6 725 60 2 242 113970.0 56985.0 47487.5 0.0019 0.0008 0.003 90.23 37.99 142.46 1.664 0.701 2.627Banhine 2.6 725 60 2 242 113970.0 56985.0 47487.5 0.0019 0.0008 0.003 90.23 37.99 142.46 0.928 0.391 1.465
1.296 0.546 2.046
Banhine 4.1 725 60 2 242 113970.0 56985.0 47487.5 0.0019 0.0008 0.003 90.23 37.99 142.46 1.495 0.629 2.360Banhine 3.2 725 60 2 242 113970.0 56985.0 47487.5 0.0019 0.0008 0.003 90.23 37.99 142.46 1.141 0.481 1.802
1.318 0.555 2.081
Wealth classes
Field type area (ha)rainfall (mm) N (kg/ha/yearP (kg/ha/year)K (kg/ha/year)
Very poor Banhine 1.6 320 2.50 0.41 1.65Very poor Banhine 1.2 320 2.50 0.41 1.65
1.4 2.5 0.4 1.6Poor Banhine 1.0 320 2.50 0.41 1.65Poor Banhine 3.9 320 2.50 0.41 1.65
2.5 2.5 0.4 1.6Medium Banhine 4.6 320 2.50 0.41 1.65Medium Banhine 2.6 320 2.50 0.41 1.65
3.6 2.5 0.4 1.6Rich Banhine 4.1 320 2.50 0.41 1.65Rich Banhine 3.2 320 2.50 0.41 1.65
3.7 2.5 0.4 1.6
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Appendix 7. Biological N fixation (IN4) and harvest product (OUT 1) % of area yield grain (k stover yield (k%N grain per %N stover pe N fom grain ( Nfrom stover total N per haN from fixation/ha tota area of legume (ha)
Banhine 30.0 238.9 557.4 0.04 0.008 9.6 4.5 14.0 8.4 0.41Banhine 30.0 174.0 406.0 0.04 0.008 7.0 3.2 10.2 6.1
17.7 Banhine 30.0 150.0 350.0 0.04 0.008 6.0 2.8 8.8 5.3 0.74Banhine 30.0 585.0 1365.0 0.04 0.008 23.4 10.9 34.3 20.6
17.5 Banhine 30.0 691.5 1613.5 0.04 0.008 27.7 12.9 40.6 24.3 1.01Banhine 30.0 385.6 899.8 0.04 0.008 15.4 7.2 22.6 13.6
18.8 Banhine 30.0 621.2 1449.4 0.04 0.008 24.8 11.6 36.4 21.9 1.10Banhine 30.0 474.4 1106.9 0.04 0.008 19.0 8.9 27.8 16.7
17.5
Wealth classes
Field type area (ha) nr of farm c nr of xyilei nr of 50 bags Grain yield (Grain yield (Grain yield N content P content K content OUT 1 N (kg/OUT 1 P (kOUT 1 K (kg/ha)
Very poor Banhine 1.6 4 16 96 1933.4 1701.4 1068.4 0.0112 0.001 0.0062 12.0 1.1 6.6Very poor Banhine 1.2 5 20 120 2416.8 2126.8 1833.4 0.0112 0.001 0.0062 20.5 1.8 11.4
1450.9 16.3 1.5 9.0Poor Banhine 1.0 4 16 96 1933.4 1701.4 1701.4 0.0112 0.001 0.0062 19.1 1.7 10.5Poor Banhine 3.9 6 22 132 2658.5 2339.5 599.9 0.0112 0.001 0.0062 6.7 0.6 3.7
1150.6 12.9 1.2 7.1Medium Banhine 4.6 8 32 192 3866.9 3402.9 738.1 0.0112 0.001 0.0062 8.3 0.7 4.6Medium Banhine 2.6 8 32 192 3866.9 3402.9 1323.7 0.0112 0.001 0.0062 14.8 1.3 8.2
1030.9 11.5 1.0 6.4Rich Banhine 4.1 16 64 384 7733.8 6805.7 1643.4 0.0112 0.001 0.0062 18.4 1.6 10.2Rich Banhine 3.2 12 48 288 5800.3 5104.3 1614.0 0.0112 0.001 0.0062 18.1 1.6 10.0
1628.7 18.2 1.6 10.1
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Appendix 8. Crop residues (OUT 2) and leaching N and K (OUT 3) MaizeWealth classes
Field type area (ha)nr of farm cart nr of xyile nr of 50 bagGrain yield (kgHI Total biomas stover yield N conten P content K conten OUT 2 N (kg)OUT 2 P (kg) OUT 2 K (kg)
Very poor Banhine 1.6 4 16 96 1068.4 0.42 2543.81 1475.41 0.006 0.001 0.015 8.9 1.18 17.7Very poor Banhine 1.2 5 20 120 1833.4 0.42 4365.32 2531.89 0.006 0.001 0.015 15.2 2.03 30.4
12.0 1.6 24.0Poor Banhine 1.0 4 16 96 1701.4 0.42 4051.02 2349.59 0.006 0.001 0.015 14.1 1.88 28.2Poor Banhine 3.9 6 22 132 599.9 0.42 1428.24 828.38 0.006 0.001 0.015 5.0 0.66 9.9
9.5 1.3 19.1Medium Banhine 4.6 8 32 192 738.1 0.42 1757.49 1019.34 0.006 0.001 0.015 6.1 0.82 12.2Medium Banhine 2.6 8 32 192 1323.7 0.42 3151.56 1827.91 0.006 0.001 0.015 11.0 1.46 21.9
8.5 1.1 17.1Rich Banhine 4.1 16 64 384 1643.4 0.42 3912.89 2269.48 0.006 0.001 0.015 13.6 1.82 27.2Rich Banhine 3.2 12 48 288 1614.0 0.42 3842.86 2228.86 0.006 0.001 0.015 13.4 1.78 26.7
13.5 1.8 27.0
Leaching N and K (Lesschen et al., 2007)Wealth classes
Field type area (ha) Precipita (mm year-1)
Clay (%) L=depth (m)zer kg/ha/year rate (%/year)Kg N ha/year)NOM = amouFN = mineral Fk=(mineral andCEC (cmol/kgOUT3 N (kg NOUT3
Very poor Banhine 1.6 320.0 15.0 0.2 0.57 1.6 20.8 266.0 0.6 0.91 22.24 160Very poor Banhine 1.2 320.0 15.0 0.2 0.42 1.6 35.7 308.0 0.4 0.66 22.03 181Poor Banhine 1.0 320.0 15.0 0.2 0.36 1.6 33.2 364.0 0.4 0.57 20.52 217Poor Banhine 3.9 320.0 15.0 0.2 1.41 1.6 11.7 308.0 1.4 2.22 21.92 190Medium Banhine 4.6 320.0 15.0 0.2 1.66 1.6 14.4 252.0 1.7 2.63 25.33 154Medium Banhine 2.6 320.0 15.0 0.2 0.93 1.6 25.8 428.4 0.9 1.47 23.37 261Rich Banhine 4.1 320.0 15.0 0.2 1.49 1.6 32.0 308.0 1.5 2.36 22.48 182Rich Banhine 3.2 320.0 15.0 0.2 1.14 1.6 31.4 364.0 1.1 1.80 27.53 218
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Appendix 9. Denitrification (OUT 4) Wealth classes
Field type area (ha)R=rainfall(mmO=SOC (%) Lechssen et al; (OUTOUT4 (stovoorgel)
Very poor Banhine 1.6 320 1.1 0.50 -4.25Very poor Banhine 1.2 320 1.1 0.48 -4.25
0.49 -4.25Poor Banhine 1.0 320 1.1 0.47 -6.20Poor Banhine 3.9 320 1.1 0.61 -4.25
0.54 -5.23Medium Banhine 4.6 320 1.1 0.64 -6.20Medium Banhine 2.6 320 1.1 0.55 -6.20
0.60 -6.20Rich Banhine 4.1 320 1.1 0.62 -4.25Rich Banhine 3.2 320 1.1 0.58 -6.20
0.60 -5.23
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Appendix 10. Erosion (OUT 5) Wealth classes
Field type area (ha) rainfall (mm)R K LS C F factor OUT5 (t/ac/yeOUT5 (kg /m OUT kg/ha
Very poor Banhine 1.6 320.0 10.18 0.03 0.39 0.05 1 0.01 0.0013 21.03Very poor Banhine 1.2 320.0 10.18 0.03 0.39 0.05 1 0.01 0.0013 15.32
18.17Poor Banhine 1.0 320.0 10.18 0.03 0.39 0.05 1 0.01 0.0013 13.21Poor Banhine 3.9 320.0 10.18 0.03 0.39 0.05 1 0.01 0.0013 51.50
32.35Medium Banhine 4.6 320.0 10.18 0.03 0.39 0.05 1 0.01 0.0013 60.88Medium Banhine 2.6 320.0 10.18 0.03 0.39 0.05 1 0.01 0.0013 33.95
47.41Rich Banhine 4.1 320.0 10.18 0.03 0.39 0.05 1 0.01 0.0013 54.69Rich Banhine 3.2 320.0 10.18 0.03 0.39 0.05 1 0.01 0.0013 41.76
48.23A rainfall erosion indexR 320 C Value Adjustmemt fWeighted C valuesMethod 3 (Foster, Lane, etc..) A=0.276*R*I(30) November 0.9 0.16 0.144I30 (mm year 20 December 0.6 0.24 0.144Tranf. Functio 0.276 January 0.4 0.20 0.08A 1766.4 Feb 0.7 0.28 0.196Convertion fa 173.6 Mar-Oct 1 0.07 0.07R 10.1751152 0.95 0.634K= clay (%) silt (%) sand(%) SOC(%)
15 40 45 1.9 C for year 0.05283333K LS slope factorLS (sqrt(L/22)*(0.065+0.045*S+0.0065*S^2)L 100S 2LS 0.3858933C(crop practic 0.45
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Appendix 11. Partial nutrient balances for maize (kg ha-1) Balances in Banhine
N Wealth IN 1 IN2 Total IN OUT1 OUT2 total OT Balance Very poor 0 0.50 0.50 16.25 12.02 28.27 -27.78 Poor 0 0.88 0.88 12.89 9.53 22.42 -21.54 Medium 0 1.30 1.30 11.55 8.54 20.09 -18.79 Rich 0 1.32 1.32 18.24 13.50 31.74 -30.42 P IN 1 IN2 Total IN OUT1 OUT2 total OT Balance Very poor 0 0.21 0.21 1.45 1.60 3.05 -2.84 Poor 0 0.37 0.37 1.15 1.27 2.42 -2.05 Medium 0 0.55 0.55 1.03 1.14 2.17 -1.62 Rich 0 0.55 0.55 1.63 1.80 3.43 -2.87 K IN 1 IN2 Total IN OUT1 OUT2 total OT Balance Very poor 0 0.78 0.78 9.00 24.04 33.04 -32.26 Poor 0 1.40 1.40 7.13 19.07 26.20 -24.81 Medium 0 2.05 2.05 6.39 17.08 23.48 -21.43 Rich 0 2.08 2.08 10.10 26.99 37.09 Balance
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Appendix 12 Analysis of variance of variables SOC, N, P, K, CEC and C:N ratio. Response variate: SOC_% Fitted terms: Constant + Wealth_classes + Field_type *** Summary of analysis *** d.f. s.s. m.s. v.r. F pr. Regression 5 3.4961 0.69923 15.18 <.001 Residual 14 0.6447 0.04605 Total 19 4.1408 0.21794 Percentage variance accounted for 78.9 Standard error of observations is estimated to be 0.215 * MESSAGE: The following units have large standardized residuals: Unit Response Residual 6 1.483 2.16 10 1.930 2.10 *** Estimates of parameters *** estimate s.e. t(14) t pr. Constant 1.084 0.109 9.95 <.001 Wealth_classes Poor 0.034 0.123 0.28 0.784 Wealth_classes Rich 0.035 0.156 0.23 0.824 Wealth_classes Very poor 0.014 0.135 0.10 0.921 Field_type Gowene 0.433 0.120 3.62 0.003 Field_type Mananga -0.666 0.123 -5.40 <.001 Parameters for factors are differences compared with the reference level: Factor Reference level Wealth_classes Medium Field_type Banhine ******************************************************************************************** Response variate: N_Total_% Fitted terms: Constant + Wealth_classes + Field_type *** Summary of analysis *** d.f. s.s. m.s. v.r. F pr. Regression 5 0.020332 0.0040665 12.36 <.001 Residual 14 0.004606 0.0003290 Total 19 0.024939 0.0013126 Percentage variance accounted for 74.9 Standard error of observations is estimated to be 0.0181 * MESSAGE: The following units have large standardized residuals: Unit Response Residual 6 0.1530 2.78 *** Estimates of parameters *** estimate s.e. t(14) t pr. Constant 0.10961 0.00921 11.90 <.001 Wealth_classes Poor 0.0068 0.0104 0.65 0.526 Wealth_classes Rich 0.0233 0.0132 1.77 0.099 Wealth_classes Very poor -0.0045 0.0114 -0.40 0.697 Field_type Gowene 0.0234 0.0101 2.31 0.036 Field_type Mananga -0.0587 0.0104 -5.63 <.001 Parameters for factors are differences compared with the reference level: Factor Reference level Wealth_classes Medium
111
Field_type Banhine Response variate: P_Olsen_mg_kg_1 Fitted terms: Constant + Wealth_classes + Field_type *** Summary of analysis *** d.f. s.s. m.s. v.r. F pr. Regression 5 7543. 1508.5 5.53 0.005 Residual 14 3822. 273.0 Total 19 11365. 598.1 Percentage variance accounted for 54.4 Standard error of observations is estimated to be 16.5 *** Estimates of parameters *** estimate s.e. t(14) t pr. Constant 28.56 8.39 3.40 0.004 Wealth_classes Poor -6.55 9.50 -0.69 0.502 Wealth_classes Rich -2.0 12.0 -0.17 0.870 Wealth_classes Very poor 4.3 10.4 0.42 0.682 Field_type Gowene 25.99 9.21 2.82 0.014 Field_type Mananga -23.88 9.50 -2.51 0.025 Parameters for factors are differences compared with the reference level: Factor Reference level Wealth_classes Medium Field_type Banhine ******************************************************************************************** Response variate: CN_ratio Fitted terms: Constant, Field_type *** Summary of analysis *** d.f. s.s. m.s. v.r. F pr. Regression 2 27.32 13.659 6.25 0.009 Residual 17 37.17 2.186 Total 19 64.49 3.394 Percentage variance accounted for 35.6 Standard error of observations is estimated to be 1.48 * MESSAGE: The following units have large standardized residuals: Unit Response Residual 20 5.34 -2.27 *** Estimates of parameters *** estimate s.e. t(17) t pr. Constant 9.576 0.523 18.32 <.001 Field_type Gowene 1.754 0.765 2.29 0.035 Field_type Mananga -1.236 0.843 -1.47 0.161 Parameters for factors are differences compared with the reference level: Factor Reference level Field_type Banhine ************************************************************************************************************ Regression Analysis ***** Response variate: K_cmol_kg_1 Fitted terms: Constant + Wealth_classes + Field_type *** Summary of analysis ***
112
d.f. s.s. m.s. v.r. F pr. Regression 5 11.008 2.2017 11.37 <.001 Residual 14 2.711 0.1937 Total 19 13.720 0.7221 Percentage variance accounted for 73.2 Standard error of observations is estimated to be 0.440 * MESSAGE: The following units have large standardized residuals: Unit Response Residual 10 3.820 2.07 *** Estimates of parameters *** estimate s.e. t(14) t pr. Constant 2.331 0.223 10.43 <.001 Wealth_classes Poor -0.030 0.253 -0.12 0.907 Wealth_classes Rich 0.353 0.319 1.11 0.287 Wealth_classes Very poor 0.084 0.276 0.30 0.767 Field_type Gowene 0.754 0.245 3.07 0.008 Field_type Mananga -1.232 0.253 -4.87 <.001 Parameters for factors are differences compared with the reference level: Factor Reference level Wealth_classes Medium Field_type Banhine ************************************************************************************************************ Response variate: Grain_yield_kg_ha_ _ Fitted terms: Constant, Wealth classes *** Summary of analysis *** d.f. s.s. m.s. v.r. F pr. Regression 3 449222. 149741. 0.56 0.670 Residual 4 1071205. 267801. Total 7 1520427. 217204. Residual variance exceeds variance of response variate Standard error of observations is estimated to be 517. *** Estimates of parameters *** estimate s.e. t(4) t pr. Constant 1031. 366. 2.82 0.048 Wealth_classes Poor 120. 517. 0.23 0.828 Wealth_classes Rich 598. 517. 1.16 0.312 Wealth_classes Very poor 420. 517. 0.81 0.463 Parameters for factors are differences compared with the reference level: Factor Reference level Wealth_classes Medium ********************************************************************* Response variate: CEC Fitted terms: Constant + Wealth_classes + Field_type *** Summary of analysis *** d.f. s.s. m.s. v.r. F pr. Regression 5 1597.5 319.51 27.14 <.001 Residual 14 164.8 11.77 Total 19 1762.3 92.76 Percentage variance accounted for 87.3 Standard error of observations is estimated to be 3.43 * MESSAGE: The following units have large standardized residuals: Unit Response Residual 12 17.95 -2.92 *** Estimates of parameters ***
113
estimate s.e. t(14) t pr. Constant 22.09 1.74 12.68 <.001 Wealth_classes Poor 0.60 1.97 0.31 0.765 Wealth_classes Rich 2.92 2.49 1.17 0.261 Wealth_classes Very poor 0.83 2.15 0.38 0.707 Field_type Gowene 4.89 1.91 2.56 0.023 Field_type Mananga -17.99 1.97 -9.12 <.001 Parameters for factors are differences compared with the reference level: Factor Reference level Wealth_classes Medium Field_type Banhine