Prioritizing climate-smart livestock technologies in rural Tanzania: a minimum data approach

Crop-livestock production systems play an important role in the livelihoods of many rural communities in sub-Saharan Africa (SSA) but are vulnerable to the adverse impacts of climate change. Understanding which farming options will give the highest return on investment in light of climate change is...

Full description

Bibliographic Details
Main Authors: Shikuku, Kelvin Mashisia, Valdivia, Roberto O., Paul, Birthe K., Mwongera, Caroline, Winowiecki, Leigh Ann, Läderach, Peter R.D., Herrero, Mario, Silvestri, Silvia
Format: Journal Article
Language:Inglés
Published: Elsevier 2017
Subjects:
Online Access:https://hdl.handle.net/10568/75727
_version_ 1855516960920436736
author Shikuku, Kelvin Mashisia
Valdivia, Roberto O.
Paul, Birthe K.
Mwongera, Caroline
Winowiecki, Leigh Ann
Läderach, Peter R.D.
Herrero, Mario
Silvestri, Silvia
author_browse Herrero, Mario
Läderach, Peter R.D.
Mwongera, Caroline
Paul, Birthe K.
Shikuku, Kelvin Mashisia
Silvestri, Silvia
Valdivia, Roberto O.
Winowiecki, Leigh Ann
author_facet Shikuku, Kelvin Mashisia
Valdivia, Roberto O.
Paul, Birthe K.
Mwongera, Caroline
Winowiecki, Leigh Ann
Läderach, Peter R.D.
Herrero, Mario
Silvestri, Silvia
author_sort Shikuku, Kelvin Mashisia
collection Repository of Agricultural Research Outputs (CGSpace)
description Crop-livestock production systems play an important role in the livelihoods of many rural communities in sub-Saharan Africa (SSA) but are vulnerable to the adverse impacts of climate change. Understanding which farming options will give the highest return on investment in light of climate change is critical information for decisionmaking. While there is continued investment in testing adaptation options using on-farm experiments, simulation models remain important tools for ‘ex-ante’ assessments of the impacts of proposed climate-smart agricultural technologies (CSA). This study used the Ruminant model and the Trade-offs Analysis model for Multi-Dimensional Impact Assessment (TOA-MD) to assess how improved livestock management options affect the three pillars of CSA: increased productivity, improved food security, and reduced greenhouse gas (GHG) emissions. Our sample was stratified into: 1) households with local cow breeds (n = 28); 2) households with improved dairy cow breeds (n = 70); and 3) households without dairy cows (n = 66). Results showed that the predicted adoption rates for improved livestock feeding among households with improved dairy cows (stratum 2) were likely to be higher compared to householdswith only local cows (stratum1). Both householdswith local cows and thosewith improved cows had increased incomeand food security.However, overall poverty reduction was only modest for households with local cows. Expected methane emissions intensity declined with adoption of improved livestock feeding strategies both in stratum 1 and stratum 2, and greater impacts were observed when households in stratum 2 received an additional improved cow breed. Providing a cow to households that were not keeping cows showed substantial economic gains. Additional research is, however, needed to understand why those farms currently do not have cows, which may determine if the predicted adoption rates are feasible.
format Journal Article
id CGSpace75727
institution CGIAR Consortium
language Inglés
publishDate 2017
publishDateRange 2017
publishDateSort 2017
publisher Elsevier
publisherStr Elsevier
record_format dspace
spelling CGSpace757272025-03-13T09:44:00Z Prioritizing climate-smart livestock technologies in rural Tanzania: a minimum data approach Shikuku, Kelvin Mashisia Valdivia, Roberto O. Paul, Birthe K. Mwongera, Caroline Winowiecki, Leigh Ann Läderach, Peter R.D. Herrero, Mario Silvestri, Silvia climate-smart agriculture agriculture food security livestock ruminants agricultura climáticamente inteligente agricultura seguridad alimentaria ganado rumiante Crop-livestock production systems play an important role in the livelihoods of many rural communities in sub-Saharan Africa (SSA) but are vulnerable to the adverse impacts of climate change. Understanding which farming options will give the highest return on investment in light of climate change is critical information for decisionmaking. While there is continued investment in testing adaptation options using on-farm experiments, simulation models remain important tools for ‘ex-ante’ assessments of the impacts of proposed climate-smart agricultural technologies (CSA). This study used the Ruminant model and the Trade-offs Analysis model for Multi-Dimensional Impact Assessment (TOA-MD) to assess how improved livestock management options affect the three pillars of CSA: increased productivity, improved food security, and reduced greenhouse gas (GHG) emissions. Our sample was stratified into: 1) households with local cow breeds (n = 28); 2) households with improved dairy cow breeds (n = 70); and 3) households without dairy cows (n = 66). Results showed that the predicted adoption rates for improved livestock feeding among households with improved dairy cows (stratum 2) were likely to be higher compared to householdswith only local cows (stratum1). Both householdswith local cows and thosewith improved cows had increased incomeand food security.However, overall poverty reduction was only modest for households with local cows. Expected methane emissions intensity declined with adoption of improved livestock feeding strategies both in stratum 1 and stratum 2, and greater impacts were observed when households in stratum 2 received an additional improved cow breed. Providing a cow to households that were not keeping cows showed substantial economic gains. Additional research is, however, needed to understand why those farms currently do not have cows, which may determine if the predicted adoption rates are feasible. 2017-02 2016-06-14T16:37:47Z 2016-06-14T16:37:47Z Journal Article https://hdl.handle.net/10568/75727 en Open Access Elsevier Shikuku, Kelvin M.; Valdivia, Roberto O.; Paul, Birthe K.; Mwongera, Caroline; Winowiecki, Leigh A.; Läderach, Peter; Herrero, Mario; Silvestri, Silvia. 2016. Prioritizing climate-smart livestock technologies in rural Tanzania: A minimum data approach . Agricultural Systems. 151: 204-216.
spellingShingle climate-smart agriculture
agriculture
food security
livestock
ruminants
agricultura climáticamente inteligente
agricultura
seguridad alimentaria
ganado
rumiante
Shikuku, Kelvin Mashisia
Valdivia, Roberto O.
Paul, Birthe K.
Mwongera, Caroline
Winowiecki, Leigh Ann
Läderach, Peter R.D.
Herrero, Mario
Silvestri, Silvia
Prioritizing climate-smart livestock technologies in rural Tanzania: a minimum data approach
title Prioritizing climate-smart livestock technologies in rural Tanzania: a minimum data approach
title_full Prioritizing climate-smart livestock technologies in rural Tanzania: a minimum data approach
title_fullStr Prioritizing climate-smart livestock technologies in rural Tanzania: a minimum data approach
title_full_unstemmed Prioritizing climate-smart livestock technologies in rural Tanzania: a minimum data approach
title_short Prioritizing climate-smart livestock technologies in rural Tanzania: a minimum data approach
title_sort prioritizing climate smart livestock technologies in rural tanzania a minimum data approach
topic climate-smart agriculture
agriculture
food security
livestock
ruminants
agricultura climáticamente inteligente
agricultura
seguridad alimentaria
ganado
rumiante
url https://hdl.handle.net/10568/75727
work_keys_str_mv AT shikukukelvinmashisia prioritizingclimatesmartlivestocktechnologiesinruraltanzaniaaminimumdataapproach
AT valdiviarobertoo prioritizingclimatesmartlivestocktechnologiesinruraltanzaniaaminimumdataapproach
AT paulbirthek prioritizingclimatesmartlivestocktechnologiesinruraltanzaniaaminimumdataapproach
AT mwongeracaroline prioritizingclimatesmartlivestocktechnologiesinruraltanzaniaaminimumdataapproach
AT winowieckileighann prioritizingclimatesmartlivestocktechnologiesinruraltanzaniaaminimumdataapproach
AT laderachpeterrd prioritizingclimatesmartlivestocktechnologiesinruraltanzaniaaminimumdataapproach
AT herreromario prioritizingclimatesmartlivestocktechnologiesinruraltanzaniaaminimumdataapproach
AT silvestrisilvia prioritizingclimatesmartlivestocktechnologiesinruraltanzaniaaminimumdataapproach