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Resilience factors in African farming systems
- 1. Resilience in eastern and southern Africa’s farming systems Erin Wilkusa, Peter deVoilb, Paswel Marenyac, Sieg Snappd, John Dixone, Daniel Rodriguezf aQueensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland cInternational Maize and Wheat Improvement Center (CIMMYT) dMichigan State University
- 2. Research approach: Resilience Resilience: The capacity of a household to maintain enough food Adequate food supply: 2100 kcal and 60 g protein per consumption equivalent per day
- 3. Outline Research approach Results The future
- 4. Questions Question 1: What explains household resilience in eastern and southern Africa? Question 2: What change(s) has the greatest potential to enhance household resilience? Environmental stress (E)? Management practice (M)? Household characteristics (H)? E1 E2 M1 M2 H1 H2 …Combinations
- 5. Research approach Sample of Dutch dairy farms (1981) Van der Ploeg et al., 2009 Farm diversity, classification schemes and multifunctionality. J. Env. Mangement 90, 124-131 Divergent trajectories • Diversity of underlying attributes (disparities) • Uneven distribution of responses/ benefits (inequitable benefits)
- 6. Research approach: Workflow Predicted Resilience indicator Outputs • Household response to alternative rainfall scenarios and management practices (disaggregated by household type) Household stress Whole-farm model (APSFarm) Clean data Multivariate statistics and outcome model (GLM) • Functional farming system typologies Inputs • Household survey data (n=3550 households, 5 countries, 3 years) • Historical precipitation data • Alternative precipitation scenarios • Alternative management practices Outputs • Resilience/Risk levels • Resilience/Risk factors
- 7. Multivariate statistics and outcome model (GLM) Research approach: Workflow Predicted Resilience indicator Outputs • Household response to alternative rainfall scenarios and management practices (disaggregated by household type) Household stress Whole-farm model (APSFarm) Clean data • Functional farming system typologies Inputs • Household survey data (n=3550 households, 5 countries, 3 years) • Historical precipitation data • Alternative precipitation scenarios • Alternative management practices Outputs • Resilience/Risk levels • Resilience/Risk factors
- 8. Research approach: Workflow Predicted Resilience indicator Outputs • Household response to alternative rainfall scenarios and management practices (disaggregated by household type) Household stress Whole-farm model (APSFarm) Clean data Multivariate statistics and outcome model (GLM) • Functional farming system typologies Inputs • Household survey data (n=3550 households, 5 countries, 3 years) • Historical precipitation data • Alternative precipitation scenarios • Alternative management practices Outputs • Resilience/Risk levels • Resilience/Risk factors
- 9. Research approach: Household Stress 2010 Ranking 2010 Tercile
- 10. Research approach: Workflow Outputs • Household response to alternative rainfall scenarios and management practices (disaggregated by household type) Household stress Whole-farm model (APSFarm) Clean data Multivariate statistics and outcome model (GLM) • Functional farming system typologies Inputs • Household survey data (n=3550 households, 5 countries, 3 years) • Historical precipitation data • Alternative precipitation scenarios • Alternative management practices Predicted Resilience indicator Outputs • Resilience/Risk levels • Resilience/Risk factors
- 11. Research approach: Resilience outcome Food available Household-sourced food available Remaining food Total available stock after harvest Food sources Livestock products Off farm income Household crop production Stock before harvest Harvest Crop sold Seed Gifted away Post-harvest loss Remaining Stock Consumed Liquid assets Purchased/given Consumed Livestock sold Consumed
- 12. Research approach: Resilience outcome Food available Household-sourced food available Remaining food Total available stock after harvest Food sources Livestock products Off farm income Household crop production Liquid assets Purchased/given Consumed Livestock sold Consumed Kg, kcal, g protein
- 13. Research approach: Resilience outcome Household-sourced food available Unavailable food Total available stock after harvest Food sources Livestock products Off farm income Household crop production Liquid assets Purchased/given Consumed Livestock sold Consumed Household size and composition (Consumption equivalents) Food demand Food surplus or deficit Food available
- 14. Research approach: Resilience outcome 52%30% Density plot of food availability outcomes for household producers. The food requirement threshold for energetic needs (2100 kcal/CE/day) is indicated with a horizontal line. The food requirement threshold for protein needs (60 g/CE/day) is indicated with the vertical line Ethiopia Mozambique
- 15. Research approach: Workflow Outputs • Resilience/Risk levels • Resilience/Risk factors Outputs • Household response to alternative rainfall scenarios and management practices (disaggregated by household type) Household stress Whole-farm model (APSFarm) Clean data Multivariate statistics and outcome model (GLM) • Functional farming system typologies Inputs • Household survey data (n=3550 households, 5 countries, 3 years) • Historical precipitation data • Alternative precipitation scenarios • Alternative management practices Predicted Resilience indicator
- 16. Research approach: Workflow Principal component analysis A subset of variables for analysis • Identify collinearity and reduce dimensions of the survey dataset • Keep variables with the highest loading value for each PC (with eigenvalue above 1) Outcome model Resilience/risk factors • Fit Model of Food Availability (multinomial log-linear model) Regression tree Main resilience thresholds • Construct a regression tree based on the model Functional farming system typologies Classified households • Group households using main resilience thresholds as classification criteria
- 17. Results: Functional typologies, Mozambique Normal rainfall, no fertilizer (37%) Rainfall Yes Normal Stress: Wet or dry Normal rainfall, fertilizer (15%) Fertilizer use Stressed (48% of surveyed households) No Functional typology Energy and protein deficit Enough energy and protein Enough energy, protein deficit Energy deficit, enough protein
- 18. Results: Functional typologies, Mozambique Normal rainfall, no fertilizer (37%) Rainfall Yes Normal Stress: Wet or dry Normal rainfall, fertilizer (15%) Fertilizer use Stressed (48% of surveyed households) No Functional typology Energy and protein deficit Enough energy and protein Enough energy, protein deficit Energy deficit, enough protein Fertilizer use
- 19. Results: Functional typologies, Mozambique 31 23% 46 0 Normal rainfall, fertilizer (15%) Stressed (48% of surveyed households) 20 21 58 1 67 17 16 0 Normal rainfall, no fertilizer (37%)
- 20. Results: Functional typologies, Mozambique 31 23% 46 0 Normal rainfall, fertilizer (15%) 20 21 58 1 67 17 16 0 Normal rainfall, no fertilizer (37%) Stressed (48% of surveyed households)
- 21. Results: Typology-specific Resilience/risk factors, Mozambique >1.5 <5.3 Household size (CE) >5.3 Cultivated land area (ha) <5 >1.8<1.8 Energy and protein deficit Enough energy and protein Enough energy, protein deficit Energy deficit, enough protein 34%28% 3% 35% Normal rainfall, no fertilizer (37%) Household size (CE)
- 22. Results: Major findings, Mozambique Resilience factor Implication Mozambique Environment Management Rainfall stress is pervasive Fertilizer use can help Typologies: Household-level Options exist for some types of households Rainfall stressed None identified Few resilience options under stress Normal rainfall Household size It is hard to feed a big family No fertilizer Cultivated land area Extensification is an option Fertilizer Livestock Intensification is another option
- 23. Results: Major findings, Ethiopia Resilience factor Implication Ethiopia Household-level Options exist for households • Land area • Household size • Livestock • Extensification • Feed fewer people • Mixed livestock-cropping Typologies: Large land Few additional options Sufficient food supply Small land Big house Few additional options Insufficient food supply with few options Small house Many livestock Environment Management Rainfall stress limits resilience Fertilizer use can help Few livestock Few additional options Uncertain food supply
- 24. Research approach: Workflow Outputs • Household response to alternative rainfall scenarios and management practices (disaggregated by household type) Household stress Whole-farm model (APSFarm) Clean data Multivariate statistics and outcome model (GLM) • Functional farming system typologies Inputs • Household survey data (n=3550 households, 5 countries, 3 years) • Historical precipitation data • Alternative precipitation scenarios • Alternative management practices Predicted Resilience indicator Outputs • Resilience/Risk levels • Resilience/Risk factors
- 25. Multivariate statistics and outcome model (GLM) Research approach: Workflow Predicted Resilience indicator Outputs • Household response to alternative rainfall scenarios and management practices (disaggregated by household type) Household stress Whole-farm model (APSFarm) Clean data • Functional farming system typologies Inputs • Household survey data (n=3550 households, 5 countries, 3 years) • Historical precipitation data • Alternative precipitation scenarios • Alternative management practices Outputs • Resilience/Risk levels • Resilience/Risk factors
- 27. APSFarm Synthetic climate records MarkSim APSFarm-LivSim Baseline household survey Household parameterisation 90 years of climate Soil water balance Soil nitrogen balance Crop 1, 2, … n Cattle Cultivated areas Crop stubble, pastures & common grazing Crop production Livestock production Goats & sheep Herd dynamics Intensification interventions Environmental outputs Reproduction Potential growth Nutrient requirements Feed intake Production (calves, milk) Actual growth Manure Feed quantity Feed quality Animal characteristics Growth curves LivSim (Rufino, et al., 2008)
- 28. The future Likelihoodofself-sufficiency Normal rainfall, fertilizer (15%) Stressed (48% of surveyed households) Normal rainfall, no fertilizer (37%)
- 29. Greatest potential benefits from intensification The future This approach has helped identify options for improving household resilience Next: Assess risk and benefits across E, M, H, and combinations
- 30. Thank you SIMLESA Farming systems research team
Editor's Notes
- how our research team is thinking about the resilience of household farming systems in ESA
- First terminology: - We can look at how resilient a household is across a range of conditions - Consumption equivalent - the number of household members, adjusted for age and gender specific consumption needs
- Three sections to the talk
- Historically we’ve found that households can have very different trajectories- this has been explained by underlying diversity And uneven distribution of benefits across diversifying variables An initial step in our modeling approach is to understand this underlying diversity and disaggregate our simulations accordingly
- Our research process has two phases. The first phase in green provides us with the functional farming system typologies that we include as an input in the second phase
- Under what conditions are households resilient and likely to have enough food. And when do they risk a food deficit? To answer this, we simulate households using the Whole farm model (APSFarm) APSFarm simulations show how certain groups and households within groups are likely to respond to a change in rainfall or management practice
- Survey data - includes household, management information and food information - household demographics (age, education) - assets, access to markets, ag. extension and off-farm work - On farm management and production – Consumption
- The inputs of phase 1 are household survey data and historical precipitation data We’re looking today at household survey data from Mozambique and Ethiopia – the data includes household demographics, assets, production activities, access to markets, involvement with extension efforts and off-farm work The countries had very different- in fact, inverse rainfall patterns
- Our environment data is based on long-term gridded rainfall data This gives us a historical rainfall dataset based for each household-based on the grid the household falls within Household Rainfall within the survey period is compared to thee households’ long term average Rainfall during the survey period is ranked based on the deviation from the norm- 50 is normal, 100 is very wet and 0 is very dry. 3. We get three categories of environmental stress Dry, normal or wet based on the tercile of the deviation ranking
- We just looked at the households and our characterization of household stress Now we’ll look at how we derive the resilience outcome- food availability
- Food availability was calculated based on household survey data. Household reported each food product they obtained from livestock, off-farm income, household crop production In Ethiopia the main livestock products were poultry, beef and pork. Cereal crops were white corn meal, sorghum and teff and legumes were various beans and enset (like a plantain or banana)
- Household reported quantities of each food product in kg which we then converted into kcal and protein using product-specific conversion factors
- To determine if they had enough food we had to compare the quantity they had available to the quantity they required. Their food demand was estimated based on their household size and composition- adjusting for age and gender, we assumed a food requirement of 2100 kcal and 60 g protein per consumption equivalent per day
- Households fell into four quadrants. Households in the bottom left quadrant fall under the threshold for meeting the energetic and protein needs of the household
- We just finished looking at the resilience outcome. Now we want to know: what influenced household risk of having insufficient food available to meet household demand? We use multivariate statistics and an outcome model to identify resilience/risk factors We look at the household- management- and environment levels
- The stages of analysis and outcomes
- The main outcomes of this process are include in the regression tree depicted here: The Resilience risk factors- rainfall and fertilizer use Thresholds are- normal versus wet or dry, fertilize use yes or not- these are simple here because we are dealing what categorical and binary data Function Typologies – Normal rainfall with fertilizer, normal rainfall no fertilizer, stressed Each branching point represents a point that significantly changed the likelihood that a household had enough food The pie charts represent the percentage of households within each group that had enough food or some kind of deficit. The yellow portion of the pie is the likelihood that a household with those characteristics will have enough food
- Fertilizer use didn’t change likelihood that households under stress would have enough food
- Do household characteristics explain different food availability outcomes within a typological group? We can run these analyses for each typological group separately
- Household characteristics didn’t significantly increase the likelihood that households under stress would have enough food
- Within the group that had normal rainfall and did not use fertilizer Household size and land area influenced the likelihood that households had enough food You were more likely to have enough energy if you had few mouths to feed ad a lot of land We can also do this for the group that used fertilizer
- Ethiopia and Mozambique had very different sources of resilience. In Ethiopia- Households were more likely to have enough food if they had more land, more livestock and fewer mouths to feed.
- This regression tree shows the major determinants and of food availability- identified in the food availability model – Land under cultivation, consumption equivalents and total livestock units Households were more likely to have enough food if they had more land, more livestock and fewer mouths to feed. The tree also shows the threshold values for each of these resilience factors The households that fall within the same terminal branch fall into a functional type. Food availability still varies within a typological group. The pie charts show the proportion of households within the group that had enough energy and protein and those that didn’t. The yellow portion of the pie is the likelihood that a household with those characteristics will have enough food
- Food availability still varies within What resilience within a typological group? Did rainfall stress or management contribute/lessen household resilience within a type of household? We go back and fit a multinomial log-linear model of Food Availability
- Only in household with small land, few household members and many livestock
- The likelihood that a household had enough food was primarily depended on fertilizer use The household that used fertilizer was significantly more likely to have enough food
- Households that didn’t use fertilizer were sensitive to rainfall conditions They were less likely to have enough protein if it was wet- These results are household based on a small sample of households. The three clusters observed in the wet density plot suggest other underlying factors might explain this variability
- The story was different in Ethiopia The major sources of resilience were at the household-level- that’s can be good news for households as are probably more able to adjust household strategies than the rain
- Second round
- How are households likely to respond to a change in management or rainfall stress> To answer this, we simulate households using the Whole farm model (APSFarm)
- Households can have very different responses- Even those that are grouped together based on important functional characteristics. this has been explained by underlying diversity Within the typological groups we just constructed - We see underlying diversity in food availability as well as variability in characteristics that impact resilience.
- They simulated each household before and after adding fertilizer and found that the household with the highest level of uncertainty was expected to gain the most from intensifying This approach helps us target groups of households and individuals to optimize resilience with the intensification tools we have in our toolbox.
- I would now go on to evaluate risk across environments