TY - BOOK
T1 - Climate-smartness of smallholder crop-livestock systems
T2 - Insights from Central Kenya
AU - Ortiz Gonzalo, Daniel
PY - 2018
Y1 - 2018
N2 - Feeding a rapidly growing population without further compromising the climate, among other planetaryboundaries, is of paramount concern in the 21st century. Agriculture’s carbon debt continues to rise whileclimate change is increasingly threatening agricultural production. Reducing this feedback loop is especiallychallenging in tropical countries in which economies, livelihoods and emissions are sustained by an agriculturalsector with limited capacities for mitigation and adaptation. Whether it is possible to achieve the triple win ofimproving livelihoods, reducing greenhouse gas (GHG) emissions and increasing agricultural resilience toclimate change is a key question for agricultural development. However, the dearth of data on sub-SaharanAfrica hampers understanding of the synergies and trade-offs for low emission development demanded inpolicies and institutional frameworks.The main objective of this doctoral thesis was to assess, by using different methodological approaches, theextent of the climate smartness of mixed farming systems in the Central Highlands of Kenya. The study wasconducted in an agroforestry landscape of smallholder crop-livestock systems undergoing agriculturalintensification in Murang’a and Nyeri counties. Here social (participatory rural appraisal – PRA) and naturalscience methods (empirical and dynamic modelling, field experiments and laboratory incubations) wereapplied to obtain the activity data needed to achieve four sub-objectives: 1) an estimation of whole-farm GHGbalances and targeting of GHG hotspots, 2) experimental measurements of GHG emissions on multiple spatialand temporal scales, 3) an assessment of synergies, trade-offs and long-term effects of climate-smart practicesand 4) the identification of processes and factors that build farm resilience in the region.The results from farm typologies and empirical models (GHG calculators) showed that most of the farms werenet sources of GHGs. Although intensification processes ─ reduced farm size, increased livestock densities andhigher inputs ─ resulted in higher GHG emissions per unit of area, this increase was counterbalanced by greatercarbon (C) sequestration in soils and woody biomass in agroforestry systems. GHG calculators stood out asuser-friendly tools for assessing mitigation options, identifying GHG hotspots and targeting empiricalmeasurements. However, the experimental measurements showed that these tools ─ which included defaultemission factors (EFs) ─ overestimated nitrous oxide (N2O) emissions from soils and manure managementsystems (MMS). Low N2O emissions in the study were attributed to localised nitrogen (N) inputs and high C:Nratios of manure among other factors. Overcoming the inherent complexity of smallholder systems was themajor challenge for GHG sampling strategies, in which multi-scale stratification helped to capture temporal andspatial variabilities. Emission profiles were influenced by farm production strategies, a fact that should beborne in mind when upscaling to landscape level.Further generation and compilation of experimental data is needed to calibrate models for sub-Saharan Africaconditions. Dynamic models are critical tools for understanding the effect of strategic decisions since theyaccount for interactions, provide yield forecasts and offer insights on the long-term effect of differentagricultural practices. The preliminary results on dynamic modelling of management scenarios showed triplewins from practices such as soil fertility management, improved cattle feeding and enhanced manuremanagement. Practical interfaces and affordable platforms are needed to enable farmers, decision-makers anddevelopment organisations alike to benefit from these tools. Smallholder farming in Central Kenya is alreadyco-evolving with climate and socioeconomic changes, and some of the adaptation practices ─ e.g.diversification, promotion of farm interactions and the stocking/destocking of assets ─ are closely related toecological principles to enhance farm resilience. Further benefits from information technology, linked to localtraditional knowledge, will facilitate a shift towards a new reality in smallholder systems. Advances inovercoming the shortage of data will increase the explanatory power and facilitate nuanced, informeddecisions for increased production and low emission development in a changing climate.
AB - Feeding a rapidly growing population without further compromising the climate, among other planetaryboundaries, is of paramount concern in the 21st century. Agriculture’s carbon debt continues to rise whileclimate change is increasingly threatening agricultural production. Reducing this feedback loop is especiallychallenging in tropical countries in which economies, livelihoods and emissions are sustained by an agriculturalsector with limited capacities for mitigation and adaptation. Whether it is possible to achieve the triple win ofimproving livelihoods, reducing greenhouse gas (GHG) emissions and increasing agricultural resilience toclimate change is a key question for agricultural development. However, the dearth of data on sub-SaharanAfrica hampers understanding of the synergies and trade-offs for low emission development demanded inpolicies and institutional frameworks.The main objective of this doctoral thesis was to assess, by using different methodological approaches, theextent of the climate smartness of mixed farming systems in the Central Highlands of Kenya. The study wasconducted in an agroforestry landscape of smallholder crop-livestock systems undergoing agriculturalintensification in Murang’a and Nyeri counties. Here social (participatory rural appraisal – PRA) and naturalscience methods (empirical and dynamic modelling, field experiments and laboratory incubations) wereapplied to obtain the activity data needed to achieve four sub-objectives: 1) an estimation of whole-farm GHGbalances and targeting of GHG hotspots, 2) experimental measurements of GHG emissions on multiple spatialand temporal scales, 3) an assessment of synergies, trade-offs and long-term effects of climate-smart practicesand 4) the identification of processes and factors that build farm resilience in the region.The results from farm typologies and empirical models (GHG calculators) showed that most of the farms werenet sources of GHGs. Although intensification processes ─ reduced farm size, increased livestock densities andhigher inputs ─ resulted in higher GHG emissions per unit of area, this increase was counterbalanced by greatercarbon (C) sequestration in soils and woody biomass in agroforestry systems. GHG calculators stood out asuser-friendly tools for assessing mitigation options, identifying GHG hotspots and targeting empiricalmeasurements. However, the experimental measurements showed that these tools ─ which included defaultemission factors (EFs) ─ overestimated nitrous oxide (N2O) emissions from soils and manure managementsystems (MMS). Low N2O emissions in the study were attributed to localised nitrogen (N) inputs and high C:Nratios of manure among other factors. Overcoming the inherent complexity of smallholder systems was themajor challenge for GHG sampling strategies, in which multi-scale stratification helped to capture temporal andspatial variabilities. Emission profiles were influenced by farm production strategies, a fact that should beborne in mind when upscaling to landscape level.Further generation and compilation of experimental data is needed to calibrate models for sub-Saharan Africaconditions. Dynamic models are critical tools for understanding the effect of strategic decisions since theyaccount for interactions, provide yield forecasts and offer insights on the long-term effect of differentagricultural practices. The preliminary results on dynamic modelling of management scenarios showed triplewins from practices such as soil fertility management, improved cattle feeding and enhanced manuremanagement. Practical interfaces and affordable platforms are needed to enable farmers, decision-makers anddevelopment organisations alike to benefit from these tools. Smallholder farming in Central Kenya is alreadyco-evolving with climate and socioeconomic changes, and some of the adaptation practices ─ e.g.diversification, promotion of farm interactions and the stocking/destocking of assets ─ are closely related toecological principles to enhance farm resilience. Further benefits from information technology, linked to localtraditional knowledge, will facilitate a shift towards a new reality in smallholder systems. Advances inovercoming the shortage of data will increase the explanatory power and facilitate nuanced, informeddecisions for increased production and low emission development in a changing climate.
UR - https://rex.kb.dk/primo-explore/fulldisplay?docid=KGL01011891726&context=L&vid=NUI&search_scope=KGL&tab=default_tab&lang=da_DK
M3 - Ph.D. thesis
BT - Climate-smartness of smallholder crop-livestock systems
PB - Department of Plant and Environmental Sciences, Faculty of Science, University of Copenhagen
ER -
