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1436 - Participation and Impact of SRI Training in Madagascar

Poster at the 4th International Rice Congress
Authors: Shigeki Yokoyama and Takeshi Sakurai
Title: Participation and Impact of Rice Cultivation Training: The Case of SRI in Madagascar
Venue: Bangkok International Trade and Exhibition Centre (BITEC), Bangkok, Thailand
Date: October 28-31, 2014

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1436 - Participation and Impact of SRI Training in Madagascar

  1. 1. 2. APPROACH AND METHOD A household survey was conducted in the south west of Lake Alaotra irrigated rice area in central highland of Madagascar in Nov-Dec 2012 (Fig. 1). The decision on training participation was made at household level, while application of technology may differ by plot. Therefore, analyses were conducted at both household and plot levels. Firstly, household characteristics of training participants were identified by probit analysis. Then, a propensity score matching was applied to evaluate the impacts of training at the plot level controlling for selection bias. Numbers of the samples are 24 households and 46 plots. 3. RESULTS The training participants are characterized as follows (Table 1). 1) Older and more educated household heads, suggesting well experienced farmers of high learning motivation, are more interested in SRI. 2) Small farm size, short distance to the fields from home, having irrigated fields, and ownership of tractors are facilitating factors of training participation. These conditions are crucial to maintain bund and canal, and to practice timely irrigation. 3) A high ratio of paddy area over total farmland owned, a high ratio of rented plots over the total operated paddy fields, and small number of livestock holdings, characteristics of rice oriented farmers, are also promoting training participation. The impacts of SRI training estimated by a propensity score matching are on the use of micro credit, chemical fertilizer application, number of weeding, and resulting in high yield. The use of manure, chemical pesticides, rotary weeder, and rice straw as fertilizer are not affected by SRI training (Table 2). 1. PURPOSE Given potential of high yield with low external inputs, training on the system of rice intensification (SRI) has been provided by various institutions in Madagascar. The impacts of the trainings were rarely evaluated yet, however. This study aimed to investigate the characteristics of SRI training participants and to evaluate its impacts on technology adoption and yield. Table 1. Determinant factors of SRI training participation1 4. SYNTHESIS AND APPLICATION Well motivated and rice oriented farmers tend to participate in SRI training. Controlling for these factors of seemingly inherent participants’ high productivity, the SRI training facilitated to adopt intensive technologies, resulted in an incremental yield of 2.89 t/ha. While some components of SRI were not significantly promoted by the training, best mix of SRI components should be systematized at farm level. Table 2. SRI training impacts: Propensity scoring matching1 Participation and Impact of Rice Cultivation Training: The Case of SRI in Madagascar (IRC14-0504) Shigeki YOKOYAMA (JIRCAS), Takeshi SAKURAI (Univ of Tokyo) 4th International Rice Congress, 27 October -1 November 2014, Bangkok, Thailand Explanatory variable Mean (STD) Marginal effect (z value) Operational farmland (ha) 5.06 (5.19) - 0.07 (3.25)*** Distance from home to field (km) 3.90 (2.64) - 0.04 (4.14)*** Paddy ratio over owned farmland 0.53 (0.41) 0.93 (4.12)*** Irrig. ratio over operational paddies 0.87 (0.30) 0.25 (1.84)* Own ratio over operational paddies 0.54 (0.42) - 0.40 (2.52)*** Family labor (12-65 yr) ratio 0.74 (0.20) - 0.84 (1.26) (Hand) Tractor ownership (number) 0.75 (0.61) 0.53 (3.46)*** Age of household head 43.8 (11.9) 0.11 (1.78)* Age2 of household head 2057 (1185) - 0.00 (1.62) Schooling yr of household head 8.54 (3.01) 0.03 (2.62)*** Tropical Livestock Unit per HH member 0.94 (1.39) - 0.28 (5.95)*** Pseud R2 0.57 N (SRI training participants) 24 (10) 1 Probit estimation (SRI training participant = 1, Non participant = 0) *** Significant at 1%, * 10% Dependent variable Mean (STD) Intervention effect (t value) Rice yield (paddy, t/ha) 2 4.24 (1.69) 2.89 (5.96)*** Micro credit use (dummy)3 0.50 (-) 0.87 (5.29)*** Chemical fertilizer use (dummy) 2 0.83 (-) 0.60 (4.72)*** Compost use (dummy) 2 0.96 (-) 0.01 (0.18) Pesticide/herbicide use (dummy) 2 0.78 (-) - 0.06 (0.17) Weeding times2 1.78 (0.92) 1.41 (3.79)*** Rotary weeder use (dummy) 2 0.67 (-) 0.08 (0.36) Fertilizing rice straw (2=all, 1=partly, 0=none) 2 1.02 (0.65) - 0.32 (0.53) 1Kernel matching by bootstrapping 50 times 2n=46 (plot wise), 3n=24 (household wise) *** Significant at 1% Fig. 1 Study site in Madagascar Lake Alaotra Acknowledgement This work was supported by JSPS KAKENHI Grant Numbers 25245038, 25252041. P557