| dc.creator |
Josephat, Peter |
|
| dc.creator |
Likangaga, Rose |
|
| dc.date |
2020-03-19T07:31:03Z |
|
| dc.date |
2020-03-19T07:31:03Z |
|
| dc.date |
2015 |
|
| dc.date.accessioned |
2022-10-20T13:25:30Z |
|
| dc.date.available |
2022-10-20T13:25:30Z |
|
| dc.identifier |
Josephat, P., & Likangaga, R. (2015). Analysis of effects of agriculture intervention using propensity score matching. Journal of Agricultural Studies, 3(2), 49-60. |
|
| dc.identifier |
http://dx.doi.org/ 10.5296 j as v 3 i 2 7339 |
|
| dc.identifier |
http://hdl.handle.net/20.500.12661/2190 |
|
| dc.identifier.uri |
http://hdl.handle.net/20.500.12661/2190 |
|
| dc.description |
Full Text Article
Also available at: http://dx.doi.org/ 10.5296 j as v 3 i 2 7339 |
|
| dc.description |
Nowadays the agriculture extension programme s are practiced in many parts of the world. There is a mixture of results about the effects of agriculture intervention programmes. The literature shows that the interventions are ineffective and have limited diffusion. On the other side, it shows that interventions are effective. Following different arguments about the effects of agriculture extension, this paper adopted Propensity Score Matching (PSM) to analyze the effects of District Agricultural Sector Investment Project (DASIP) using agriculture data. The study was conducted in rural Tanzania areas. It covered five regions namely Kagera, Mwanza, Mara, Simiyu and Kigoma. The study focused on agro ecological zone where corn is cultivated. Two methods which are questionnaire administration and direct oral interview were used to collect primary data. The collection of data using the questionnaire was done from both participants (359) and non participants (519). Before running the independent t test, the estimation of propensity score was done using Logistic regression. Thirteen confounding variables were used to estimate propensity scores.The effects of the intervention were analysed by considering four items namely the earnings from corn production, value of livestock owned value of household assets owned, and value of farm assets owned. The results show that none of the four factors had significant result as the p values are greater than 0.05. This implies that the earning between farmers participating in DASIP are not significant different from those who do not participate in the programme. The study recommends that the group activities should last longer rather than changing them
from time to time. |
|
| dc.language |
en |
|
| dc.publisher |
Macrothink Institute |
|
| dc.subject |
Agriculture extension programme |
|
| dc.subject |
PSM |
|
| dc.subject |
DASIP |
|
| dc.subject |
Farmer Field School |
|
| dc.subject |
Intervention |
|
| dc.subject |
Propensity Score Matching |
|
| dc.subject |
Agriculture extension |
|
| dc.title |
Analysis of effects of agriculture intervention using propensity score matching |
|
| dc.type |
Article |
|