DETERMINANTS OF HOUSEHOLDS PARTICIPATION IN TREE PLANTING ACTIVITIES ATTHE REDD+ PROJECT SITES IN SOUTHERN LEYTE, PHILIPPINES

Transcription

1 1 DETERMINANTS OF HOUSEHOLDS PARTICIPATION IN TREE PLANTING ACTIVITIES ATTHE REDD+ PROJECT SITES IN SOUTHERN LEYTE, PHILIPPINES Glorybeth C. Castillo Research System Analyst Office of the Vice-President for Research and Extension Visayas State University ViSCA, Baybay City, Leyte Tel. No.: Dr. Pedro T. Armenia Professor, Department of Economics College of Management & Economics Visayas State University ViSCA, Baybay City, Leyte or Tel. No.: , or

2 2 DETERMINANTS OF HOUSEHOLDS PARTICIPATION IN TREE PLANTING ACTIVITIES AT THE REDD+ PROJECT SITES IN SOUTHERN LEYTE, PHILIPPINES ABSTRACT This paper, which made use of survey data collected from the 797 in selected upland areas in Southern Leyte was conducted to: ( 1) identify the determinants of households participation in tree planting activities and ( 2) draw policy implications and recommendations to serve as inputs for planning and decision-making by the stakeholders of the REDD+ project and other individuals and institutions involved in upland farming and related initiatives. Results of the binary logit regression showed that the households in the REDD+ project site, members in organizations, with experiences in environmental risk, with access to natural resources, number of household members, and annual on-farm income were positively associated with participation in tree planting activities. Keywords: REDD+, environmental practices, tree planting, binary logit regression

3 3 INTRODUCTION Deforestation and forest degradation, through agricultural expansion, conversion of pasture land, infrastructure development, destructive logging, fires, among other factors, accounts for nearly 20% of global greenhouse gas emissions. REDD also known as Reducing Emissions from Deforestation and Forest Degradation is a set of steps designed to use market/financial incentives to reduce the emissions of greenhouse gases from deforestation and forest degradation. Moreover, REDD plus (REDD+) is similar to REDD, but instead of just reducing deforestation and degradation, it includes other activities such as sustainable management of forests and the enhancement of forests carbon stocks. Southern Leyte, which exhibits one of the remaining Philippine forests, is being considered as the site of the REDD+ project since upland farming is its most important economic activity. Upland household residents in the REDD+ project sites who have access to natural resources and its timber and non-timber forest products and by-products harvest and either consume or sell these products as alternative source of income. This implies heavy dependence on their farm to support family needs and more reasons to encroach the forest to do farming or harvest forest products. Livelihood activities done by households contribute to the destruction of the forest. The changes in the existing practices of upland household residents in Southern Leyte in terms of natural resource harvesting and extraction put pressure on land cultivation. The heavy dependence on the forest system for upland farming in Southern Leyte has created a case of deforestation, thus making it an appropriate site of REDD+ program. The success of environmental conservation practices depend largely on the participation of the households. Their participation in environmental activities such as tree planting helps preserve biodiversity and other natural resources. Participation in environmental activities is an important way to help save the environment. Households or individuals with diverse profiles participate in different environment-related activities. Governments and various organizations who are dedicated to conservation of the environment conduct environmental activities to help preserve it. However, not all individuals are involved in these environmental activities. There are different factors which contribute to their decision to participate or otherwise. Hence, this paper presents an analysis of one of the major activities of the households in the study sites. More specifically, it ventured into the analysis of factors affecting households decision to participate in one environment-related activity which is tree planting. The study attempted to describe and identify the major determinants of participation in tree planting activities and practices among upland household residents in the REDD+ sites in Southern Leyte, Philippines. REVIEW OF RELATED LITERATURE Illiteracy is one factor that could affect a person s decision (Brahml and Thankur, 2011; Alassaf et.al., 2011). The narrow vision about project activities, like the Hariyali Project in Himachal Pradesh in India was attributed to the lack of education among the people, which influenced their participation. Likewise, the study of Brahmi and Thakur (2011) conforms to the result of Oram (1988) as cited by Lwayo and Maritim (2003) which indicated formal education as a vital aspect in a farmer s decision to adopt farm forestry and thus, influences the effectiveness of the decision to participate in such ventures. For example, an educated farmer can readily have access to information on the value of farm forestry. Naidu (1992) as cited by Brahmi and Thakur (2011) also viewed people s participation and appropriate education were important factors. But, Thoai and Rañola in 2010 asserted that the level of education is not a significant factor affecting an upland farmer s decision to participate or not in forest management programs in the northwest mountainous regions of Vietnam. Furthermore, Alassaf et al. (2011) found out that age affects the decision of upland farmers to participate in forest management programs. Older farmers are more likely to participate in the forest management programs because their opportunities to be employed or engaged in other livelihood activities is more limited compared to younger people who tend to have more employment choices. Age, which reflects upland farmer s farm experience, is one of the important factors affecting the decision of

4 4 upland farmers to participate in forest management programs in Vietnam as cited by Thoai and Rañola (2010). Lwayo and Maritim (2003) support these findings by asserting that age and the decision to adopt farm forestry have a positive relationship. The age of the farmer affects knowledge and awareness of activities in the surrounding environment. Age, as concluded by Lwayo and Maritim (2003), affects one s ability to adopt farm forestry. In the study conducted by Alassaf et al. (2011) and Lwayo and Maritim (2003), they found out that gender is not a significant factor affecting the decision of the upland farmers to participate in forest management. Thoai and Rañola (2010) also figured out that gender is negatively associated with the decision to continue farm activity in Jordan. For example, the male household head would not prefer to engage in farming activity, or the young ones, as they prefer other financially secured work. According to Thoai and Rañola (2010), households that depend greatly on fores t products as their livelihood are more willing to participate in forest management programs. High income derived from forestry helps farmers recognize the benefit of protecting the forest and therefore made them more willing to participate in the forest management programs. In Jordan, the share of farm income to the total household income has a positive relationship to farming activities in marginal areas. As long as farming could provide a higher income to the household over other ventures, the household would continue upland farming (Alassaf et al. 2011). In the study of Armenia et al. (2011), one of the reasons for the non-participation of households in environmental activities is the belief that environmental activities are for barangay officials only. In Brahmi and Thakur s study (2011), the village people in the Himachal Pradesh in India have lost t heir faith in government works and the village politics which results in poor participation in the Hariyali Project. Additionally, lack of awareness among participants to environmental activities was the primary cause of poor participation as cited by Brahmi and Thakur (2011). However, Thoai and Rañola (2010) put forward that to encourage the participation of upland farmer s in forest management programs, it is important to increase the level of awareness of upland farmers about the benefits of improved management of the forest. Furthermore, Alassaf et al. (2011) mentioned that family labor in the farm has a positive relationship with the farmer s decision to continue the farming activities owing to high cost of hired labor and its availability. Thoai and Rañola (2010) concluded that households of upland farmers with more family labor are more likely to participate in forest management programs. This factor is important because the management of the forest is labor-intensive and will require an adequate supply of household labor for different activities such as reforestation, protection, and others, thus, Thoai and Rañola s (2010) findings sustained the conclusion of Alassaf et al. (2011). Sources of Data METHODOLOGY Data for this paper was sourced out from the recently concluded research project entitled Socioeconomic Baseline for the REDD+ Project Sites in Southern Leyte, Philippines conducted by Armenia et al. in The survey data included 597 households from the REDD+ sites and 200 households from Non-REDD+ project sites located in Southern Leyte: Maasin City, and the municipalities of Bontoc, Silago, Sogod, and Tomas Oppus. The paper fully utilized the available and comprehensive database for the benefit of the REDD+ project stakeholders in Southern Leyte, Philippines. For the purposes of this paper, households were classified as: (a) participants and (b) non-participants in tree planting activities. Analytical Tool A binary logit regression model was employed to identify the factors that affect households' decision to participate in tree-planting activities. Given certain characteristics of households, the model was likewise used to predict the probability of participation or non-participation to tree-planting activities among households in the project sites. Table 1 shows the list and definition of the dependent and explanatory variables included in the analytical model adopted in this study.

5 5 Table 1. List and definitions of variables used in the model VARIABLE DESCRIPTION Dependent Variable Y1 Participation on tree planting activities taking the corresponding values: 1=participant and 0=otherwise, Independent Variables X1 X2 X3 X4 X5 X6 X7 X8 X9 D1 D2 Age of the household headin years, Age of the spouse in years, Educational attainment of the household head (years of formal schooling), Educational attainment of the spouse (years of formal schooling), Household size, Total annual on-farm income of the household (Pesos), Total annual non-farm income of the household (Pesos), Number of parcel, Total farm area cultivated (hectares), Dummy variable for REDD+ site taking the value 1=if the household respondents was from the REDD+ project site and 0=if the household was from the non-redd+ site, Dummy variable for membership in organization with 1=member and 0=otherwise, D3 D4 D5 Dummy variable for household experience in environmental degradation taking the values:1=for those with experience and 0=otherwise, Dummy variable for awareness of the risk brought about by environmental degradation with a value 1=aware and 0=otherwise, Dummy variable for access to support and extension services taking 1=with access and 0=otherwise, D6 Dummy variable for access to natural resources taking the values 1=with access and 0=otherwise, D7 Dummy variable for household head making decision on acquiring farm inputs and farm activities taking 1=household head and 0=otherwise, D8 Dummy variable household head making decision in terms of production and livelihood activities with 1=household head and 0=otherwise.

6 6 Empirical model and Estimation Procedure The binary logit model was employed with the corresponding model specification: Y 1 = α0+α 1 X1+α 2 X2+α 3 X3+α 4 X4+ α 5 X5+α 6 X6+α 7 X7+α 8 X8+ α 9 X9+α 10 D1+ α 11 D2+α 12 D3+ α 13 D4+α 14 D5+α 15 D6+α 16 D7+ α 17 D8.. (Eqn. 1) The above model was estimated using the STATA V.11 Statistical Package. To ensure the validity of the results, the estimated model was subjected to a standard post-estimation diagnostic procedures such as model specification errors, goodness-of-fit, and multicollinearity tests. Focus Group Discussions Focus group discussions were conducted at the project sites to validate, verify, and/or highlight important findings from the analysis and to counter check whether the results of the model captured the actual field conditions of households in the project sites. The respondents feedback supplemented the discussion of the results of the logit model employed in the study. SUMMARY OF FINDINGS This section mainly presents the results of the logit model as well as the predicted probabilities of participation in tree-planting activities given certain characteristics of households in the REDD+ project sites. Results from the Logit Model The logit model was adopted to identify and describe the relationship between categorical outcome variable and one or more categorical and continuous predictor variables. In this paper, a binary logit regression model was employed to identify the factors that affect household-respondents decisions to participate in tree-planting activities. Although the coefficient of the logit model is not directly interpretable as the traditional regression analysis, in the case of household participation or nonparticipation in tree planting activities, variables with positive and significant coefficients in the model may be used to characterize those who opt to participate in environmental activities. On the other hand, factors with negative and significant coefficients may also be used to describe the characteristics of those who chose not to participate. Results of the logit regression on factors affecting participation to tree-planting activities are shown in Table 2. The positive and statistically significant factors that would characterize participation to the said activity were the following: REDD+ project site, membership in organization, experience in environmental risk, access to natural resources, household size, and total on-farm income. On the other hand, among the variables, non-farm income appeared to be negatively, but statistically significant. It should be noted that participation in tree planting activities by households in the REDD+ project sites has added income incentives or opportunities for households. As such, among households in the project sites, participation in such activity needs to be decided alongside other available opportunities and constraints. The likelihood of tree planting participation for households located in the REDD+ project sites partly confirmed the positive effect of the REDD+ project activities on tree planting. Also, the positive association between membership in organization and participation may be attributed to the fact that treeplanting activities are usually closely associated with membership and/or involvement in organizations. On the other hand, the positive association between participation and experience on environmental risks such as increase in temperature, drought, flooding due to typhoons, and soil erosion may be attributed to the perception or understanding on positive effects of tree-planting on the environment. Likewise, the positive association between access to natural resources and participation to tree-planting activities could perhaps be attributed to the proximity of participating households to where these natural resources can be found. Moreover, the positive association of household size participation could be simply due to more availability of household members who may alternatively get involved in the said activity. Conversely, the positive association of on-farm income and participation can be attributed to the fact that tree-planting can

7 7 help increase the current and future income opportunities of participating households. While a number of factors are positively associated with participation to tree-planting activities, on the contrary, non-farm income appeared to be negatively associated with it. This result can be ascribed to the fact that households normally evaluate how they may be able to allocate efficiently their labor resources to competing activities. Hence, for some households earning more income from non-farm sources, getting involved in tree planting may not be an attractive venture. This implies that households earning higher income from non-farm sources such as remittances from abroad, operating businesses, and others would not most likely participate in tree-planting activities. Table 2.Logit model estimates on the factors affecting tree planting activities VARIABLES COEFFICIENT STD. ERROR P>[Z] Dummy Variables: REDD+ Project Site *** Member in an Organization *** Experienced environmental risk *** Awareness of environmental risk and degradation Access to support and extension services Access to natural resources ** Farm inputs/ farm activities Production and livelihood activities * Continuous Variables: Age (Household Head) Age (Spouse) * Educational attainment (head) Educational attainment (spouse) Household size *** On-farm income 2.83e-06** 1.27e Non-farm income -6.36e-06** 2.83e Number of parcels Total farm area in hectares *** Significant at 1%, ** significant at 5%, *significant at 10%. Predicted Probability of Participation in Tree Planting

8 8 The result of the logit regression model indicating positive and significant relationships between treeplanting and selected predictor variables can be used to predict the average probability of participation to tree-planting given several characteristics of farm households. For instance, the blue line in Figure 1 shows the average increasing probability of participation for a given household at given levels of onfarm income and with the following household characteristics: location relative to the REDD+ project site, membership in organizations, experience in environmental risk, and access to natural resources. On the contrary, the red line shows the very low probability of participation if the farmhousehold comes from the Non-REDD+ site, not a member in an organization, did not experience environmental risk, and without access to natural resources. This result has important implications to the REDD+ project implementation in terms of effectively Fig. 1.Predicted probability of participation to tree planting with given levels of on-farm income. targeting the desired attributes of farmer-beneficiaries to ensure higher chances of success and impact. The negative and statistically significant relationship between participation to tree-planting activities and non-farm income may also be used to predict the probability of participation of farm-households given their level of non-farm income and characteristics. Using similar household characteristics used in Figure 1, Figure 2 shows that the predicted probability of participation, as shown in the green line, decreases considerably as non-farm income increases. Once again, this result clearly shows that households earning considerable income from non-farm sources alongside absence of attributes identified with participation are important factors for consideration by the project in targeting upland household project beneficiaries. Figure 2.Predicted probability of participation to tree planting with given levels of non-farm income. CONCLUSIONS, POLICY IMPLICATIONS,AND RECOMMENDATIONS The results of the logit regression model which showed the positive association between participation in tree-planting and proximity of the household to the REDD+ project site, basically confirmed the positive effect of the REDD+ tree-planting component of the project. Likewise, the positive association between participation in tree-planting and membership in organization validated the fact that active participation in organizations is a critical component of project intervention. The positive association of on-farm income and participation in tree-planting and the negative association between participation and non-farm income indicate that households who are mainly dependent on on-farm income are more likely to participate in tree-planting activities than those who earned non-farm income. It is therefore recommended that proper targeting of project beneficiaries through appropriate

9 9 characterization of farm-household beneficiaries should be done to ensure a high level of participation among households and project success. LITERATURE CITED ALASSAF, A., MAJDALWAI, M. AND NAWASH, O Factors Affecting Farmer s Decision to Continue Farm Activity in Marginal Areas of Jordan.African Journal of Agricultural Research.Vol.6 (12). Pp ANGELSEN, A., BOUCHER, D., BROWN, S., MERCKX, V., STRECK C., ZARIN, D., 2011.Guidelines for REDD+ Reference Levels: Principles and Recommendations. ARMENIA, P.T., BULAYOG, M.S.B., PATINDOL, T.A., GLOVA, N.M., SERIÑO, M.N.V Socioeconomic Baseline for the REDD+ Project Sites in Southern Leyte, Philippines. Visayas State University.Deutsche GesellschaftfürInternationaleZusammenarbeit (GIZ) ISBN BRAHMI M.K. AND THAKUR K.S Factors Affecting People Participation in Hariyaki Project UnderNalagarh Block of Himachal Pradesh BRIONES, N.D Environmental sustainability Issues in Philippine Agriculture. Vol. 2, Nos. 1&2 CHAUDHARY Q, A., 1986.Rural Poor s Participation And Small Watershed Projects in Bangladeshing- A case study CLOTHIER, L. AND FINCH, E Farming in the English Uplands.Defra Agricultural Change and Environmental Observatoy FAO AND IIRR (1995).Resource management or upland areas in Southeast Asia. House of Commons, Environment, Food and Rural Affairs Committee.Farming in the Uplands. Third Report of Session KUMMER, D. M., 1992.Agroforestry Systems.Kluver Academic Publishers, Printed in Netherlands. 18: LOGANANDHAN, N. AND BISWAJIT, M., 2005.Impact of Watershed development programme on awareness, knowledge and attitude of farmers in semi-arid region of Andhra Pradesh. 33(1): LWAYO, M. K. AND MARITIM, H. K Socio-economic Factors Affecting Farmers Decision to Adopt Farm Forestry; An Application of Multivariate Logistic Analysis in Busia District, Kenya B1 MELLINCK, W., RAO, Y.S., MACDICKEN, K.G Agroforestry in Asia and the Pacific. NAIDU, V. J.,.1992.Planning and people participation in India NAIR, P.K. RAMACHANDRAN.1993.An Introduction to Agroforestry. Kluwer Academic Publishers, Dordrecht, the Netherlands ORAM, P. A., Moving Towards Sustainability; Building the Agroecological Framework In Environment (30) (9) PARTRAP, T Sustainable Farming Systems in Upland Areas.Report of the APO Study Meeting on Sustainable Farming System on Upland Areas. APO 2004, ISBN: THOAI, T. Q. AND RAÑOLA, R. F. JR., 2010.Decision Making by Upland Farmers on Forest Management in the Northwest Mountainous Region of Vietnam.J.ISSAAS Vol. 16, No. 1:68-82 WEIDNER, S., BÜNNER, N., CASILLANO, Z.L., COME, R.S., ERHARDT, J., FROMMBERG, P., PEUSER, F., RINGHOF, E., Towards Sustainable land-use: A Socio-economic and Environmental Appraisal of Agroforestry Systems in the Philippine Uplands.Humboldt- UniversitӒtZu Berlin ; YUE, P The Environment Needs Public Participation. Accessed on June 5, 2012

10 10 APPENDIX Table 3. Distribution of household respondents by project site, membership in organization, experienced in environmental risk, access to natural resources, decision making in production and livelihood activities and by participation. Participation in Tree Planting Activities Total ITEMS Non-Participant Participant Count Col % Count Col % Count Col % REDD Non REDD REDD Total Membership in an Member Organization Non-Member Total Experienced Experiences Did not environmental risk Experienced Access to natural resources Decision Making on Production and Livelihood Activities Total With Access Without Access Total Household Head Other Household Member Total Table 4. Average age, household size, on farm income and non-farm income of the participating and nonparticipating households Participation in Tree Planting Activities Total ITEM Non-Participants Participants Mean Median Mean Median Mean Median Age (Spouse) Household size On-Farm Income Non-Farm Income

11 11 ACKNOWLEDGMENT The authors wish to acknowledge and express their deepest and sincerest gratitude to the following persons and institutions whose invaluable support and contributions made possible the completion of this study: The Deutsche GessellschaftfürInternationaleZusammenarbeit (GIZ) for the financial support provided in the study; Ms. Nelfa M. Glova, for imparting her ability and skills to improve this manuscript; Dr. Teofanes A. Patindol, for assisting the major author in conducting the fieldwork and focus group discussions in Southern Leyte; and To LEYECO IV Electric Cooperative, most especially to Dr. Jose M. Alkuino, Jr., for helping the major author acquire her scholarship grant and a thesis support.