Minimum-Data Analysis of Technology Adoption and Impact Assessment for Agriculture-Aquaculture Systems John Antle Oregon State University Roberto Valdivia Montana State University AquaFish CRSP RD-IA Meeting, Seattle Oct

Reminder: Our Goals Learn about data, model for IA Design IA for AquaFish investigations Plan data collection –What data do we need? –Does your project have data? –Do you need to collect more data? –Set up a work plan for data collection and preparation. Plan analysis & presentations

Impact Assessment using the TOA-MD model Technology Adoption leads to Impacts –Define the relevant farm populations –Describe existing system and new system(s) based on improved technology (without vs with, baseline vs counterfactual) –Simulate adoption of new system(s) in the farm populations –Quantify impacts of adoption using Indicators

Logical structure of TOA-MD: Adoption analysis                        Farm population w/base tech & base indicators (poverty, sustainability)                        Sub-populations: non-adopters (base tech & indicators) adopters (improved tech, indicators)   Result: r% adopters, (1-r)% non-adopters Adoption

Logical structure of TOA-MD: Impact analysis Example: Poverty System 1 income distribution (Poverty = 65%) System 2 income distribution (Poverty = 25%) System 1 & 2 income distribution (Poverty = 45% ) 1-r% non-adopters r% adopters

TOA-MD Components System characterization Adoption rate Population (Strata) Impact indicator design Opportunity cost distributionOutcome distributions Indicators and Tradeoffs Design Data Simulation

Design Populations and strata –Spatial: agro-ecozones, political units, watersheds, location –Socio-economic: farm size, wealth, age, gender Systems –Crop, livestock, aquaculture subsystems –Sub-systems composed of activites –Farm household Impact indicators –Population mean

Design (cont.) Impact indicators –Economic Mean farm or per capita income Poverty –Environmental On-farm: soil quality Off-farm: water quality, GHG emissions –Social Nutrition Health Intra-household distribution

Design (cont.) Indicator design –Population means –Probability of exceeding a threshold Headcount poverty: % below poverty line Nutritional threshold Environmental threshold

TOA-MD Components System characterization Population (Strata) Impact indicator design Opportunity cost distributionOutcome distributions Design Data

Economic –Farmed area in each stratum (ha) –Population mean net returns for each system in each stratum Yield, price, costs of production, and land allocation to each activity in each system Or … direct observation of returns to the whole system –Standard deviation of net returns by system and stratum –Correlation between returns to each system Environmental and social –Mean and CV for each outcome for each system –Correlation with net returns

Data (cont.) Household: means and CVs for –Farm (ha), herd (head or TLU) and pond (ha) sizes –Family size (number of persons) –Non-agricultural income

TOA-MD Components System characterization Adoption rate Population (Strata) Impact indicator design Opportunity cost distributionOutcome distributions Indicators and Tradeoffs Design Data Simulation

Adoption –Baseline: all farms using system 1 –System 2 becomes available, r% adopt, (1-r)% continue to use system 1 Impact Indicators –Indicators calculated for each system and each stratum –Population indicators are a weighted average of indicators for farmers using each system in each stratum.

Implementing IA: Agricultural Systems Design Farm Household Income Health & nutrition Distribution & assets Crop system productivity costs (variable, fixed) input use (fertilizers, other) land allocation soil & water management Aquaculture system productivity costs feed water & waste management pond construction and maintenance Livestock system productivity costs herd health feed quantity & quality manure management Environmental processes on-farm (soils) off-farm (runoff, erosion, GHG, pond effluent)

The Malawi Case Study: Integrated Agriculture-Aquaculture population: southern Malawi strata: 5 southern districts systems: system 1: maize, beans other subsistence crops system 2L: system 1 with small ponds, low integration system 2H: system 1 with larger ponds, irrigated vegetables, high integration random survey of farms by strata and system Subsistence crops Aquaculture Irrigated vegetables

The Malawi Case Study We will use the case study to show you the kinds of data we need to do impact assessment of aquaculture systems At the same time, we will evaluate the data needed for your projects and design a plan to acquire them and implement IA Subsistence crops Aquaculture Irrigated vegetables

Exercise 1: Populations, Systems & Indicators 1.Define the populations and strata for your investigations (you may want to draw a map) 2.Develop a schematic diagram for the existing or baseline system (system 1). 3.Develop a schematic diagram for the system(s) with improved technologies (system 2). 4.Define the relevant indicators for your investigations.

Modeling Adoption Rates Farmers choose practices to maximize expected returns Farmers expect to receive v 1 ($/ha/season) for system 1 Farmers who adopt system 2 expect to earn v 2 ($/ha/season) The opportunity cost of changing from system 1 to system 2 is defined as ω = v 1 – v 2 Note: ω < 0 means gain from adoption of system 2 ω > 0 means loss from adoption of system 2 Returns vary spatially, so opportunity cost varies spatially, and is described with the distribution  (ω)

Construct spatial distribution of opportunity cost ω < 0 (adopters) ω > 0 (non-adopters) (ω)(ω)

Opportunity cost (  ) and adoption rate (r)

0 ()()  100 Opportunity cost and adoption rate: 1 farm ω =20, r = 0 r (%)

0 ()()  100 Opportunity cost and adoption rate: 1 farm ω =-10, r = 1 r (%)

0 ()()  Rate 100 Opportunity cost and adoption rate: 2 farms

0 ()()  Rate 100 Opportunity cost and adoption rate: 3 farms

0 ()()  Rate 100 Opportunity cost and adoption rate: Many farms

()()  < ω r Derivation of adoption rate from spatial distribution of opportunity cost ω < 0 r (0)

Varying the Adoption Rate We often want to use the model to see the effects of a range of different adoption rates E.g., low adoption rates may occur because farmers cannot get access to the technology or lack resources to invest E.g., high adoption rates may occur because farmers are required by government regulations to use a technology that reduces pollution We assume farmers choose system 2 if it is more profitable than system 1 As we “force” the model to lower or higher adoption rates, average farm income in the population will be lower than at the profit-maximizing adoption rate.

Varying the Adoption Rate (cont.) To simulate the effects of farmers using different adoption rates, the model can simulate the effect of giving all farmers an incentive payment or penalty (PAY) for using system 2. This payment is used to calculate the adoption rate, but it is not included in the calculation of farm income. With an incentive payment, famers use system 2 if: v 1 < v 2 + PAY or if v 1 – v 2 < PAY or if ω < PAY So when PAY = 0 we get the adoption rate that occurs without any incentive payment or penalty. This is the adoption rate that gives the highest average farm income in the population.

()() , Pay < ω < Pay 0 Adoption Rate PAY 0 Adoption rate with incentive payment (Pay) ω < 0 ω > Pay 0 Adoption rate with Pay 0 > 0

()() , Pay 100 Adoption Rate Using PAY to simulate effects of different adoption rates Adoption rates with Pay <0 Adoption rates with Pay >0 PAY < 0 PAY > 0

Parameters of the Distribution of Opportunity Cost Farms are heterogeneous so  = v 1 – v 2 varies across farms. We use the mean and variance of  in the MD model. Mean: E (  ) = E (v 1 ) – E (v 2 ) ($/ha) Suppose system 1 has one activity, then: E (v 1 ) = P 11 Y 11 – C 11 – f 11 P 11 = price of output for activity ($/Y/time) Y 11 = yield of activity 1 (Y/ha/time) C 11 = variable cost of activity 1 ($/ha/time) f 11 = fixed cost of activity 1 ($/ha/time) Note: subscripts are system, activity

System 2 with 1 activity: E (v 2 ) = P 21 Y 21 – C 21 – FC 21 P 21 = price of product of activity 1, system 2 Y 21 = output of activity 1, system 2 C 21 = variable cost of activity 1, system 2 f 21 = fixed cost of activity 1, system 2 Note: subscripts are system, activity

Variance of  :  i  standard deviation of returns, system i  ik  covariance of returns, system i and k ρ 12   12 /  1  2   2  E(  - E(  )) 2 = E({v 1 - E(v 1 )} – {v 2 - E(v 2 )}) 2 =   2 2 – 2  12 =   2 2 – 2  1  2 ρ 12

Multiple Activities in a System Suppose system 1 has 2 crop activities Each activity k uses a share W 1k of the land E.g., activity 1 = maize, activity 2 = beans farm size = 2 ha, maize = 1.5 ha, beans = 0.5 ha Then W 11 = 1.5/2 = 0.75, W 12 = 0.5/2 = 0.25 Now v ik = P ik Y ik – C ik – f ik, for i,k = 1,2 E (v 1 ) = W 11 v 11 + W 12 v 12 E (v 2 ) = W 21 v 21 + W 22 v 22 Remember: first subscript = system, second subscript = activity

Data: Malawi case study Surveys –Random sample of farms without IAA and with IAA in each stratum (district) Areas, farm size, household size, income, health, nutrition… Other economic data from secondary sources –Crop yields, prices, costs of production –Yield variances –Land use Environmental and social data, as needed: –Pond effluent & water quantity, quality data –Soil, climate data for crop system Estimate crop yields using crop simulation models? Estimate change in soil nutrients or soil carbon? –Nutrition: e.g., protein consumption

Exercise 2: Data Preparation 1.Open Database\malawi_tables.doc. Review the data tables for Malawi, note the way the data are organized by strata. 2.Open Database\MW_DATA_L.xls. This is the data template for the model. This file is set up with data to analyze adoption of System 2L, the aquaculture system with low integration. 3.Look at the Variable Description sheet in the data template, and then the other sheets with highlighted variables. Identify how data from the tables have been put into the sheets in the template. 4.As you review the data template, start preparing a list of data needed for the systems you identified in Exercise 1 for your investigations. Identify which variables are available, which ones may need to be obtained from a survey or from secondary sources.

Exercise 3: Run the Model for Adoption of the Low- Integration System (System 2L) 1.Open the model Econmod\TOA_MD6_AF.xls. 2.Following the directions given, load the data Database\MW_DATA_L.xls and run the model. 3.Examine the model output in the sheets and graphs. 4.Interpret the results, using the variable descriptions in the data template. 5.What is the predicted adoption rate for System 2L? 6.What is the poverty rate with zero adoption? How much is poverty reduced by adoption of System 2L? 7.Can you explain what happens to net returns per farm as the adoption rate varies?

Exercise 4: Set up a model for adoption of System 2H by farms using System 1 1.Find the data in the data tables for system 2H. 2.Open the data template for system 2L. Put the data for system 2H into the sheets where system 2 data belong. 3.Run the model. 4.Compare the results for System 2L and 2H. Can you explain the differences you see in adoption rates, poverty and nutrition outcomes?

Exercise 5: Sensitivity analysis to the parameter RHO12 Explain what RHO12 represents in the model. What happens when RHO12 = 1? (Hint: review the formula for the variance of ω) Run the model for adoption of System 2H with different values of RHO12 Can you explain why the adoption rate changes as you change RHO12? Would you say the model is “sensitive” to this parameter?

Exercise 6: Set up a model for adoption of System 2H by farms using System 2L Hint: you need data from the two data templates you have already used. Compare the results from the three models you have now run: can you explain the differences?

0 ()()  r (%)  = 0 r = Variance of opportunity cost and adoption rates Note effect of variance on adoption rate