1. The effect of the multi-stakeholder extension services towards crop choice and farmer’s income Author: Mr. Joseph Sello Kau Co-authors: Mr. Elias Mashayamombe, Mr. Brian Mandikiana and Ms. Cynthia Ngwane ICAE conference 19-20 April 2016, Istanbul, Turkey

2. PRESENTATION LAY-OUT INTRODUCTION PROBLEM STATEMENT OBJECTIVES METHODOLOGY RESULTS CONCLUSION AND RECOMMENDATIONS

3. Figure 1: Intercropping and mulching practices at OR Tambo Source:Picture taken during the 2012 impact study The intercropping & agro-processing at HVC

4. Introduction, continues…. ARC Extension Model Is Baya in-house Ext. model (village monitors &, 6 visits/year), Focus: food security & commercial, agro-processing, Co-op, Certified fruit trees offered at 50% discount, 13 demos in 2009, by 2011, 3347 HH had 50 545 fruit trees Other Agencies Prov. Dept Agric. Potato SA (loans) & Ilima NGO (equipment) Ad hoc visits, offers fertilizer & vegetable seedlings for free,

5. Problem Statement In SA, poor public Agric. Ext. weak linkages in researchers & Ext, poor qualifications & limited HR, In 2005, ratio 1:1400 between Ext officers to Farmers, ARC work with NGO (Is Baya), with good extension models, OR Tambo, high poverty and unemployment (social grants), Problem: encroachment Ext. agencies, negative towards commercialization,

6. OBJECTIVES OF THE STUDY Using certain crops as proxy, for the Ext. agencies involved, determine effect towards crop choice & farm income, To analyze the effect of demographics on crop choice in order to assess how individuals respond to Ext. agency advice,

7. METHODOLOGY Sampling: 45 villages in 7 Muni. in OR Tambo, 2 HH (90 HH) 2 surveys: 2010 baseline and 2012 Impact study Focus group, field interviews, telephonic, draw maps Table 1:A sampling mix for HVC study Size of land Local municipality¼ ha½ ha1 ha2ha or more Number of HH (90) Inguza Hill4561429 KSD424010 Mbashe13004 Mhlontlo11002 Mnguma11002 Nyandeni266317 Port Saint Johns0971026 1327232790 Source:Derived from the 2012 impact study

8. Methodology continues….

9. RESULTS Descriptive analyses No. of HHPlot Size (ha)No of treesAv. No trees/HHFarm Inc./annumAv. Inc./HH 360.25 -0.514964234620962 3013804127659202197 242 & more545022724912110380 90Average 1.510750Average 119R349,661R3885 Table 2 Results for the descriptive analyses

10. Results continues Step-wise regression Potato: high income =1484.62 +9820.25*potatoes; Pr = 0.023 R-squared = 57% SourceValueStandard errorTPr > |t|Lower bound (95%)Upper bound (95%) Intercept 1484.6182096.5580.7080.481-2681.8505651.086 Cabbage 0.000 Spinach 0.000 Potatoes 9820.2464240.4972.3160.0231393.14918247.343 Maize 0.000 Table 3:Stepwise regression with outliers

11. Results continues Step-wise regression SourceValue Standard error TPr > |t| Lower bound (95%) Upper bound (95%) Intercept1109.700584.0991.9000.061-51.4512270.851 Cabbage0.000 Spinach0.000 Potatoes0.000 Maize782.886719.4721.0880.280-647.3772213.150 Table 4Results of stepwise regression without outliers Maize: high farm income =1109.7 +782.886*maize; Pr = 0.280 (not significant) R 2 =14% (not confident)

12. Results continues…(multi logistic regression).. CabbageMaizePotatoesSpinach Age 0.0170246-.0019258-.0001345-.015243 (0,016) * (0,749)(0.979)(0.001) * Males 0.20609090.30179710.04993-.0541791 (0.074)**(0,007)*(0.612)(0.518) Education -.00294540.03027660.026219-.0157512 (0.862)(0.055) ** (0.072) ** (0.189) Employ 0.1723141 -0,0060045-0.15314220.0583174 (0.169) (0,959)(0.114)(0.505) Plot size -.0612466-.0170880.09358410.0097982 (0.124)(0.522)(0.030) * (0.604) Grant 0.00492810.04625550.017057-.0090964 (0.882)(0.145)(0.631)(0.623) Fertilizer -.073956-.01175640.0487544-.0591997 (0.537)(0.916)(0.618)(0.461) Table 5:Demographics on choice of crops, testing at 5% & 10% stats. Sig.

13. Results continues….. Multivariate regression analyses Table 6:Effect of demographics on crop-choice, testing at 5%& 10%, DV:log(Farm Income)Index Age-.051977 (0,100) ** 0.0031775 Male0.1061745 (0,859)-.3032329 Education0.0111121 (0,892)-.0061982 Employing labour1.697272 (0,010) * 0.8815504 Plot size0.3216162 (0.037) * 0.3169508 Log (Grant)0.0655873 (0.691)0.0558377 Fertilizer1.049266 (0.077)* * Index (HVC)0.2840381 Pr ** (10% test; Pr * (5% test)

14. Results continues….. Effect of gender, cross tabs Table 7:Summary statistics for gender and High Value Crop Means and Frequencies of Farm Income No._HVC Gender1-3 fruit trees4-6 fruit trees≥ 6 fruit trees F680.76921155.38461507.125 No. of HH13 8 M160001732.24243618.8889 No. of HH14339 Total274617

15. Results continues….. Effect of education, cross tabs Table 8:Summary statistics for education and High Value Crop Means, and Frequencies of Farm Inc No._HVC Edu-years1-3 fruit4-6 fruit≥ 6 fruit trees <5 years4701230.2667760 No of HH5152 (5 years -10 years)5137.51634.58333185.1538 No of HH122413 Edu>10 years168852071.4286850 No of HH1072 Total274617

16. CONCLUSION AND RECOMMENDATIONS Planting material important, yet commercial and plot size key, Traditional crops, potato & maize, but maize less income Males more land & specialization, few HVC but high income, Women empowerment necessary: access to land & resources Various Ext. agencies to integrate services, ARC to intensify provision of certified fruit trees,

17. I thank you for your audience