MAPPING ADOPTION OF ISFM PRACTICES STUDY

Transcription

1 MAPPING ADOPTION OF ISFM PRACTICES STUDY The Case of Kenya, Rwanda & Zambia Ephraim Nkonya, Carlo Azzari, Edward Kato, Jawoo Koo, Generose Nziguheba, & Bernard Vanlauwe

2 Outline Summary of results Introduction Changing IT landscape in rural areas & potential & challenges of conducting mobile phone surveys Study objectives and corresponding analytical Methods Mobile phone survey results compared to traditional in-person interview Impact of ISFM on yield & carbon sequestration Drivers of ISFM adoption conclusions

3 Summary of results Mobile phone survey heavily biased to young farmers. Correction of errors of data collected using mobile phones is a challenge, setting rules in survey template may increase dropout rate Adoption rate of integrated soil fertility management (ISFM) is lowest even though it is the most profitable, sequesters more carbon and sustainable practice. Why? Ltd capacity of extension agents to provide advisory services on ISFM ISFM is knowledge intensive. Higher education major adoption driver Access to all-weather roads ISFM is labor intensive especially if organic inputs involve manure application

4 Introduction Data collected using phones are representative if 80% of the targeted population owns a phone (Croke et al 2012). Mobile phone survey allows more frequent data collection allowing collection of food consumption, and other data which are hard to recall However, mobile phone surveys pose statistical challenges This study examines the data collected using mobile phones and an equivalent data collected using traditional face to face survey Source: World Bank data, 2017

5 Study objectives Determine adoption rate of ISFM in Kenya, Rwanda and Zambia Design methodological approaches for determining adoption rate of innovations using mobile phones Analyze drivers of adoption of ISFM in the case study countries; Analyze impact of ISFM on yield and carbon sequestration Analytical methods Mobile phone survey (Rwanda); Traditional hhd survey (Kenya & Rwanda (secondary data) Address potential bias of mobile survey Econometric approaches Crop simulation (DSSAT model)

6 Methodology Mobile phone interviews conducted by GeoPoll Stratified random sampling using Age-sets to address bias towards younger and well-off farmers. Age-sets from nationally representative survey of 2012 Market access & population density at district level Treatment districts where TSBF/CIAT & IITA conducted research and promotion of ISFM. Matched them with control Population- Market access Low-Low- High Treatment (ISFM) district Matching control District Sample size for treatment & control (each) Kayonza Nyagatare 85 High-high Musanze Huye 180 Medium- Gagenke Rusizi 235 High Total 1000

7 Response rate of round 1 survey Count Percent Surveys Sent 71, % Opted in 4,606 6% Completed survey 970 1% Dropped off % Refused % Ineligible 3,307 5% Non-response 66,469 93% Age group (Years) 2012/13 household survey 2016 mobile phone survey Percent < >

8 Attrition of farmers who participated in round I District Round 1 Round 2 % Sample Attrition Gagenke Huye Kayonza Musanze Nyagatare Rusizi Out of sample districts Total 1, Attrition across treatment type Control Treatment Total 1, Attrition was high but paired test showed that it was random

9 Comparison of mobile phone vs traditional survey results

10 Maize yield SMS survey & traditional survey, Rwanda Soil management Mean Median SD N Yield, SAS survey 2016 practice Tons/ha Tons/ha ISFM Fert only No inputs Total Note: SAS = Seasonal Agricultural survey, 2016, Rwanda Agriculture Board

11 Area harvested reported by mobile phone respondents >100 times larger than national average Statistic Maize Bean Plantain Potato Mean (ha): SAS survey Mean (ha): mobile phone survey SAS survey area as % of mobile phone survey area

12 Adoption of soil fertility management practices & maize improved seeds

13 IMPACT OF ISFM ON YIELD & SOIL CARBON

14 Impact of adoption of ISFM on maize yield

15 trends of soil organic carbon changes over time

16 ISFM is very profitable & sustainable, yet its adoption rate is lowest Why?

17 Drivers of adoption of ISFM Access to rural services Advisory services especially on agroforestry (in Kenya) All-weather roads Male plot owner/decision maker Education as ISFM is education intensive

18 Conclusions Mobile phone interview is a useful tool for collection of food consumption & other hard to remember data at much higher frequency. But there is a lot that needs to be done to address its heavy bias to young and well-farmers ISFM is profitable, sustainable, and can reduce climate-related risks, yet its adoption is lowest Increasing capacity of extension agents require: Short-term training to in-service extension agents Agricultural training institutes syllabus needs to incorporate, ISFM, CSA and other new agricultural issues