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CROP-LIVESTOCK INTENSIFICATION IN SOUTHERN AFRICA: DRIVERS, OPPORTUNITIES AND CROP RESIDUE UTILIZATION SABINE HOMANN-KEE TUI; JONATHAN TANGANYIKA; FELISBERTO MAUTE; DANIEL NKOMBONI; NKULULEKO MPOFU;TIMOTHY GONDWE; PAULA DIAS; SHADRECK NCUBE; ANDRE F. VAN ROOYEN SLP project :Optimizing livelihood and environmental benefits from crop residues in smallholder crop-livestock systems in sub-Saharan Africa and South Asia ( www.vslp.org ) 10th African  Crop Science Society Conference  10-13 October 2011  in  Maputo
Southern Africa: Drastic increases in the demand for agricultural products Source:  Adopted from Capacity Development Initiative in Modernizing Food Systems—Michigan State, Makerere, Stellenbosch and Pretoria Universities, 2010)  Strong urbanization and income growth Growing food markets and changes in composition (more meat, dairy, fresh and processed food) 5-6 times the marketed food between 2010 and 2050
… vs declining per capita food production Source: Chilonda  et al.,  2007
SA: Most extensive sites with strong potential for intensification India Bangladesh Kenya India Ethiopia Ethiopia Zimbabwe Nigeria Malawi Mozambique Niger Niger Source: Adopted from Valbuena  et al.,  2011
Crop livestock systems and interactions milk feed $ feed $ $ feed crop residues nutrients $ fertilisers On-farm feed crop residues nutrients $ fertilisers manure Investment capacity Labor availability? Access to cash/credit? Access to information? Where to invest? What type of crops? How many animals? What type of feed? Returns? Source: Adopted from Rufino (2009) draft power draft power
Crop livestock intensification and integration: non-linear Extensive mixed systems Intensive  specialized systems Crop livestock integration       National and local level drivers   1 3 2
Objectives of this scoping study Use farming systems analysis in the context of national and local drivers. Determine  site specific entry points for moving farmers in mixed crop-livestock systems up the development pathways.
Study sites and research methods Site selection :  Southern Africa - most extensive site Village selection :  8 villages per country  Distance from markets and roads Village level surveys :  Focus group discussions  ~  30 farmers of different wealth, gender and age per each village (n=24) Household surveys :  Quantitative interviews 20 households per village, stratified by wealth (n=480)
Major systems drivers 650 17 5 + + + 600 21 19 ++ ++ ++ 700 59 10 +++ +++ + Rainfall (mm annual average)  Densities  - Human (2008, pers/km 2 ) - Cattle (2008, head/km 2 ) Soil fertility and land management Extension support  Market development Changara Nkayi Mzimba Local level drivers 3.9 23.2 15.7 29.9 12.6 6.2  14.1 44.8 169.8 2.2 13.2 33.6 9.9 -6.3 10.6 Nat. budget for agriculture (2006)  Agric value added (% of GDP, 2000-06) Livestock (% agric. gross value, 2009) Net imports (Mio USD, 2008)  -Maize -Milk Mozambique Zimbabwe Malawi Country level drivers
Diversity of farming systems Mzimba in Malawi: intensified crop oriented farming systems ,[object Object],[object Object],[object Object]
Nkayi in Zimbabwe: integrated crop livestock systems ,[object Object],[object Object],[object Object]
Changara in Mozambique: Extensive livestock oriented systems ,[object Object],[object Object],[object Object]
Household’s sources of income
Household’s expenditures
Levels of maize production and uses 90.2 (10.8) 2.2 (7.8) 85.8 (17.5) 6.7 (13.7) 82.9 (20.1) 11.0 (14.9) Consumption (%) Sales (%) Grain uses 0 0 13.6 50.0 9.7 (28.3) 486.8 (1152.5) 41.8 96.2 273.2 (117.9) 148.9 (393.8) 42.5 3.8 Fertilizer  (kg/ha) Org. manure   (kg/ha) Hybrid seed (% HH) Draft power (% HH) Investment  41.3 364.8(235.3) 98.8 756.3(858.0) 100.0 1595.6 (1141.7) % HH Yields (kg/ha) Production Changara Nkayi Mzimba Units
Levels of cattle production and uses 75.4 (11.3) 22.3 (11.0) 2.4 (5.7) 61.1 (24.1) 15.5 (20.2) 23.5 (24.7) 60.3 (22.1) 24.6 (22.5) 12.1 (22.4) Rangelands CR grazed  in situ CR collected and fed Dry season feeding (%) 12.9 (19.5) 12.6 (16.3) 0 15.8 (23.9) 3.5 (9.5) 1.1 (3.8) 9.7 (20.1) 5.1 (9.9) 2.9 (7.3) Mortality Sales  Consumption  Herd dynamics (%) 34.4 7.9 (6.7) 57.5 5.4 (5.0) 26.3 4.9 (4.2) HH with cattle (%) Herd size (TLU) Ownership Changara Nkayi Mzimba Units
Crop residue utilization  High pressure on CR 
Site-specific opportunities and entry points for interventions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Thank you!

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Crop-livestock intensification in Southern Africa: Drivers, opportunities and crop residue utilization

  • 1. CROP-LIVESTOCK INTENSIFICATION IN SOUTHERN AFRICA: DRIVERS, OPPORTUNITIES AND CROP RESIDUE UTILIZATION SABINE HOMANN-KEE TUI; JONATHAN TANGANYIKA; FELISBERTO MAUTE; DANIEL NKOMBONI; NKULULEKO MPOFU;TIMOTHY GONDWE; PAULA DIAS; SHADRECK NCUBE; ANDRE F. VAN ROOYEN SLP project :Optimizing livelihood and environmental benefits from crop residues in smallholder crop-livestock systems in sub-Saharan Africa and South Asia ( www.vslp.org ) 10th African Crop Science Society Conference 10-13 October 2011 in Maputo
  • 2. Southern Africa: Drastic increases in the demand for agricultural products Source: Adopted from Capacity Development Initiative in Modernizing Food Systems—Michigan State, Makerere, Stellenbosch and Pretoria Universities, 2010) Strong urbanization and income growth Growing food markets and changes in composition (more meat, dairy, fresh and processed food) 5-6 times the marketed food between 2010 and 2050
  • 3. … vs declining per capita food production Source: Chilonda et al., 2007
  • 4. SA: Most extensive sites with strong potential for intensification India Bangladesh Kenya India Ethiopia Ethiopia Zimbabwe Nigeria Malawi Mozambique Niger Niger Source: Adopted from Valbuena et al., 2011
  • 5. Crop livestock systems and interactions milk feed $ feed $ $ feed crop residues nutrients $ fertilisers On-farm feed crop residues nutrients $ fertilisers manure Investment capacity Labor availability? Access to cash/credit? Access to information? Where to invest? What type of crops? How many animals? What type of feed? Returns? Source: Adopted from Rufino (2009) draft power draft power
  • 6. Crop livestock intensification and integration: non-linear Extensive mixed systems Intensive specialized systems Crop livestock integration       National and local level drivers   1 3 2
  • 7. Objectives of this scoping study Use farming systems analysis in the context of national and local drivers. Determine site specific entry points for moving farmers in mixed crop-livestock systems up the development pathways.
  • 8. Study sites and research methods Site selection : Southern Africa - most extensive site Village selection : 8 villages per country Distance from markets and roads Village level surveys : Focus group discussions ~ 30 farmers of different wealth, gender and age per each village (n=24) Household surveys : Quantitative interviews 20 households per village, stratified by wealth (n=480)
  • 9. Major systems drivers 650 17 5 + + + 600 21 19 ++ ++ ++ 700 59 10 +++ +++ + Rainfall (mm annual average) Densities - Human (2008, pers/km 2 ) - Cattle (2008, head/km 2 ) Soil fertility and land management Extension support Market development Changara Nkayi Mzimba Local level drivers 3.9 23.2 15.7 29.9 12.6 6.2 14.1 44.8 169.8 2.2 13.2 33.6 9.9 -6.3 10.6 Nat. budget for agriculture (2006) Agric value added (% of GDP, 2000-06) Livestock (% agric. gross value, 2009) Net imports (Mio USD, 2008) -Maize -Milk Mozambique Zimbabwe Malawi Country level drivers
  • 10.
  • 11.
  • 12.
  • 15. Levels of maize production and uses 90.2 (10.8) 2.2 (7.8) 85.8 (17.5) 6.7 (13.7) 82.9 (20.1) 11.0 (14.9) Consumption (%) Sales (%) Grain uses 0 0 13.6 50.0 9.7 (28.3) 486.8 (1152.5) 41.8 96.2 273.2 (117.9) 148.9 (393.8) 42.5 3.8 Fertilizer (kg/ha) Org. manure (kg/ha) Hybrid seed (% HH) Draft power (% HH) Investment 41.3 364.8(235.3) 98.8 756.3(858.0) 100.0 1595.6 (1141.7) % HH Yields (kg/ha) Production Changara Nkayi Mzimba Units
  • 16. Levels of cattle production and uses 75.4 (11.3) 22.3 (11.0) 2.4 (5.7) 61.1 (24.1) 15.5 (20.2) 23.5 (24.7) 60.3 (22.1) 24.6 (22.5) 12.1 (22.4) Rangelands CR grazed in situ CR collected and fed Dry season feeding (%) 12.9 (19.5) 12.6 (16.3) 0 15.8 (23.9) 3.5 (9.5) 1.1 (3.8) 9.7 (20.1) 5.1 (9.9) 2.9 (7.3) Mortality Sales Consumption Herd dynamics (%) 34.4 7.9 (6.7) 57.5 5.4 (5.0) 26.3 4.9 (4.2) HH with cattle (%) Herd size (TLU) Ownership Changara Nkayi Mzimba Units
  • 17. Crop residue utilization  High pressure on CR 
  • 18.
  • 19.

Editor's Notes

  1. The advisory is an interpretation of probabilistic forecast for its agricultural significance Prepared in consultation with met, agricultural research and extension officers It provides location specific and crop specific suggestions that perform well under expected seasonal conditions The Advisory is currently distributed to participating farmers Currently working on an agricultural information system for wider reach and dissemination through FM (FM station Mbaitu is a partner) and other extension agents