Characterisation of farming systems in southern Rwanda

LISTE OF FIGURES

Figure 1: Framework 19

Figure 2. Farmers in wealth categories 24

Figure 3: Most important food crops 33

Figure 4: Most important income-earning crop/Act 34

Figure 5: Variability of soil carbon (1), available P (2), exchangeable K (3) and total N (4) within plots of farm. Plots n^o increases as plot position moves from homestead to further away from home 39

Figure 6: Variability of total N (1), exchangeable K (2), soil carbon (3), and Available P (4) in plots on farms in different wealth categories in Shanga 40

LIST OF APPENDICES

Appendix 1: Rapid Survey Questionnaire: 50

Appendix 2: Soil analysis results 52

Appendix 3: Chemical analysis interpretation 53

Appendix 4: List of farmers interviewed in Shanga cell 55

ACRONYMS AND ABREVIATIONS

C: Carbon

Ca: Calcium

Cm: Centimeter

FAO: Food and Agricultural Organization

GNP: Gross National Product

ha: Hectare

ISAR: Institut des Sciences Agronomiques du Rwanda

K: Potassium

Kg: Kilogram

m: meter

mm: millimeter

MINAGRI: Ministère de l'Agriculture et des Ressources Animales

MINECOFIN: Ministère des Finances et de la planification Economique

MINIPLAN: Ministère de la Planification

MININFRA: Ministère de l'Infrastructure

nm: nanometer

N: Nitrogen

P: Phosphorous

pH: Potentiel à l'Hydrogène

PPM: Partie Pour Million

SPSS: Statistical Package for Social Sciences

SSA: Sub-Saharan Africa

UK : United Kingdom

á: Alpha

%: Percentage

^oC: Celsius Degree

PART I. GENERAL INTRODUCTION

I.1. PROBLEM STATEMENT

Agricultural sector is the backbone of national economy in most of Sub-Saharian African (SSA) countries. In countries such as Rwanda, agriculture sector contributes up to 46% of GNP (MINICOFIN, 2004). African farmers operate in different environments, some having enough resources, others operating in resource constrained environment. In many farming systems in the tropics, strong gradients of decreasing soil fertility are found with increasing distance from the homestead (Ruthernberg, 1980: Prudentcio, 1993). Farmer manage crop and livestock production using organic and mineral nutrient resources and the net flow of resources is not equal for the various fields belonging to a single farm household (Smaling, 1996). Causes of variability in soil fertility management at different scales of analysis (i.e. region, village, farm and field) are both biophysical and socio-economic.

Variability at regional scale is determined by climate and dominant soil types, presence of and access to factor and product markets and historical, socio-cultural and ethnic aspect defining land use. The variability of soil fertility between different farm types within a village is associated within the «soilscape», such as the location along catenary (Duckers, 2002) and with differences in soil fertility management between poor and wealthy households (Crowley and Carter, 2000). Resource availability and the pattern of resource allocation to different activities are determined by household «wealth», and depend on household priorities and production strategies.

Rwanda like other SSA countries presents quite similar features of farming system; it is one of the most populated developing countries with a density of 500 inhabitants per km² of the arable areas (MINICOFIN, 2004). The majority of the Rwandan households are small agricultural producers dealing with subsistence oriented agriculture. Poor productivity of Rwandan agriculture is due to exhaustion of the ground, the insufficiencies of agricultural use of inputs and of the weak development of the markets and of infrastructures (www.rwandagateway.com, 2007). And then, the level of organization farming systems is complex within each agro-ecological and the analysis requires accurate information that is not always available in the literature. A careful analysis of the functioning of farming systems and the way different components interact between them is a key step in the design of possible interventions for improving productivity at farm level. Beside biophysical factors, farmer management strategies determine the kind of farming system the farmer is interested in and the productivity he gets out of it. With the proposed study, we intend to conduct a diagnostic study of different farming systems existing in Shanga cell of Maraba sector as the first step towards a detailed study of different farm types identified in the area. The study will also try to characterize soil fertility level on representative farms which we believe is linked with the socio-economic status of farmers.