Determinantes de la estrategia de adaptación de los pequeños agricultores de palma aceitera al cambio climático en Bengkulu, Indonesia

Irawan, Andi; Syakir, Muhammad

doi:10.1590/1806-9479.2019.186452

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Original Article • Rev. Econ. Sociol. Rural 57 (3) • Jul-Sep 2019 • https://doi.org/10.1590/1806-9479.2019.186452 copiar

Determinantes de la estrategia de adaptación de los pequeños agricultores de palma aceitera al cambio climático en Bengkulu, Indonesia

Autoria SCIMAGO INSTITUTIONS RANKINGS

Resumen

Bengkulu es una de las 10 provincias de Indonesia, que se convirtió en un centro de producción de palma de aceite, donde las plantaciones de palma aceitera son la mayor superficie de tierra entre los cultivos de plantaciones en esta región. Este estudio tiene como objetivo específico determinar cómo influyen los factores determinantes en las estrategias de adaptación de los pequeños agricultores de palma aceitera sobre el cambio climático en la provincia de Bengkulu, Indonesia. Se empleó el método de regresión logística binaria para aclarar las variables independientes que influyen en la estrategia de los agricultores adaptada al cambio climático. La experiencia de los agricultores y el gasto de sus hogares tienen un efecto positivo y significativo en la diversificación de los cultivos, mientras que el precio de la palma aceitera influye positivamente en el uso del desmonte sin quemarse. Los factores que más influyen en la adaptación de los agricultores al cambio climático son la membresía de cooperación de los agricultores y la pertenencia al grupo de agricultores para la extensión agrícola. Debido a su educación y experiencia no son un determinante importante de las estrategias adaptadas al cambio climático, el grupo de agricultores para la extensión agrícola fue muy importante en la adopción de estrategias integrales de adaptación al cambio climático, así como la comprensión y habilidad de implementar estrategias adaptadas al clima el cambio entre los pequeños agricultores necesita mejorarse por medio de la agencia de extensión del gobierno. Por lo tanto, se cree que garantizar el acceso a la información sobre el cambio climático através de agentes de extensión crea conciencia y condiciones favorables para la adopción de prácticas agrícolas adecuadas al cambio climático. También significa que se recomienda encarecidamente mejorar el conocimiento y las habilidades del personal de servicios de extensión sobre el cambio climático y las estrategias de adaptación, y hacer que los servicios de extensión sean más accesibles para los agricultores. Para el trabajo futuro, se debe considerar la realización de una nueva investigación que involucre más áreas con propiedad diversificada, no solo la propiedad de los pequeños agricultores, sino también las empresas privadas y estatales. Además de eso, el estudio sobre la acción del gobierno y la política para acelerar la adaptación de los agricultores también es importante para ser ejecutado en el futuro.

Palabras clave:
adaptación; cambio climático; palma aceitera; Indonesia; modelo logit

Independent variable	Measurement and unit	Expected effect	Research referred
Expenditure	Household expenditure in IDR (Indonesian currency)	+	Deressa et al. (2009)Deressa, T. T., Hassan, R. M., Ringler, C., Alemu, T., & Yesuf, M. (2009). Determinants of farmers’ choice of adaptation methods to climate change in the Nile Basin of Ethiopia. Global Environmental Change, 19(2), 248-255.; Sahu & Mishra (2013)Sahu, N. C., & Mishra, D. (2013). Analysis of perception and adaptability strategies of the farmers to climate change in Odisha, India. APCBEE Procedia, 5, 123-127.; Gebrehiwot & Van Der Veen (2013)Gebrehiwot, T., & Van Der Veen, A. (2013). Farm level adaptation to climate change: the case of farmer’s in the Ethiopian Highlands. Environmental Management, 52(1), 29-44.; Jianjun et al. (2015)Jianjun, J., Yiwei, G., Xiaomin, W., & Nam, P. K. (2015). Farmers’ risk preferences and their climate change adaptationstrategies in the Yongqiao District, China. Land Use Policy, 47, 365-372.; and Abid et al. (2016)Abid, M., Schilling, J., Scheffran, J., & Zulfiqar, F. (2016). Climate change vulnerability, adaptation and risk perceptions at farm level in Punjab, Pakistan. The Science of the Total Environment, 547, 447-460.
Expenditure	Household expenditure in IDR (Indonesian currency)	-	Menike & Arachchi (2016)Menike, L.M.C.S., & Arachchi, K.A.G.P.K. (2016). Adaptation to climate change by smallholder farmers in rural communities: Evidence from Sri Lanka. Procedia Food Science, 6, 288-292.
Farm size	Farm size in hectare	+	Gebrehiwot & Van Der Veen (2013)Gebrehiwot, T., & Van Der Veen, A. (2013). Farm level adaptation to climate change: the case of farmer’s in the Ethiopian Highlands. Environmental Management, 52(1), 29-44.; Alauddin & Sarker (2014)Alauddin, M., & Sarker, M. A. R. (2014). Climate change and farm-level adaptation decisions and strategies in drought-prone and groundwater-depleted areas of Bangladesh: an empirical investigation. Ecological Economics, 106, 204-213.; and Jianjun et al. (2015)Jianjun, J., Yiwei, G., Xiaomin, W., & Nam, P. K. (2015). Farmers’ risk preferences and their climate change adaptationstrategies in the Yongqiao District, China. Land Use Policy, 47, 365-372.
Farm size	Farm size in hectare	-	Candradijaya (2015)Candradijaya, A. (2015). Farmer adaptive capacity to climate change in strengthening rice sufficiency in Sumedang regency, West Java province (Ph.D thesis). Graduate School, Bogor Agricultural University, Bogor.
Oil palm farming total revenue	Total revenue of household’s farmer from oil palm farming in IDR (Indonesian currency)	+	Deressa et al. (2009)Deressa, T. T., Hassan, R. M., Ringler, C., Alemu, T., & Yesuf, M. (2009). Determinants of farmers’ choice of adaptation methods to climate change in the Nile Basin of Ethiopia. Global Environmental Change, 19(2), 248-255.; Sahu & Mishra (2013)Sahu, N. C., & Mishra, D. (2013). Analysis of perception and adaptability strategies of the farmers to climate change in Odisha, India. APCBEE Procedia, 5, 123-127.; Gebrehiwot & Van Der Veen (2013)Gebrehiwot, T., & Van Der Veen, A. (2013). Farm level adaptation to climate change: the case of farmer’s in the Ethiopian Highlands. Environmental Management, 52(1), 29-44.; Pires & Cunha (2014)Pires, M. V., & Cunha, D. A. (2014). Climate change and adaptive strategies in Brazil: the economic effects of genetic breeding. Revista de Economia e Sociologia Rural, 52(4), 627-642.; Jianjun et al. (2015)Jianjun, J., Yiwei, G., Xiaomin, W., & Nam, P. K. (2015). Farmers’ risk preferences and their climate change adaptationstrategies in the Yongqiao District, China. Land Use Policy, 47, 365-372. and Abid et al. (2016)Abid, M., Schilling, J., Scheffran, J., & Zulfiqar, F. (2016). Climate change vulnerability, adaptation and risk perceptions at farm level in Punjab, Pakistan. The Science of the Total Environment, 547, 447-460.
Oil palm farming total revenue		-	Menike & Arachchi (2016)Menike, L.M.C.S., & Arachchi, K.A.G.P.K. (2016). Adaptation to climate change by smallholder farmers in rural communities: Evidence from Sri Lanka. Procedia Food Science, 6, 288-292.
Oil palm price	Price per kilogram in IDR per kilogram	+	Asset and wealth of households are also needed to facilitate the adoption of adaptation strategies cause of being needed for adequate capital. (Carter & Barrett, 2006Carter, M. R., & Barrett, C. B. (2006). The economics of poverty traps and persistent poverty: an asset-based approach. The Journal of Development Studies, 42(2), 178-199.). Thus, the allegedly higher oil palm price means the greater farmer’s income, thus further enabling farmers to invest in climate change activities.
Age	Age of farmer in years	+	Deressa et al. (2009)Deressa, T. T., Hassan, R. M., Ringler, C., Alemu, T., & Yesuf, M. (2009). Determinants of farmers’ choice of adaptation methods to climate change in the Nile Basin of Ethiopia. Global Environmental Change, 19(2), 248-255.; Gebrehiwot & Van Der Veen (2013)Gebrehiwot, T., & Van Der Veen, A. (2013). Farm level adaptation to climate change: the case of farmer’s in the Ethiopian Highlands. Environmental Management, 52(1), 29-44.; Pires & Cunha (2014)Pires, M. V., & Cunha, D. A. (2014). Climate change and adaptive strategies in Brazil: the economic effects of genetic breeding. Revista de Economia e Sociologia Rural, 52(4), 627-642.; and Candradijaya (2015)Candradijaya, A. (2015). Farmer adaptive capacity to climate change in strengthening rice sufficiency in Sumedang regency, West Java province (Ph.D thesis). Graduate School, Bogor Agricultural University, Bogor.
		-	Roco et al. (2014)Roco, L., Engler, A., Bravo-Ureta, B., & Jara-Rojas, R. (2014). Farm level adaptation decisions to face climatic change and variability: evidence from Central Chile. Environmental Science & Policy, 44, 86-96.
Experience	Number of years in oil palm farming	+	Festiani (2011)Festiani, R. A. (2011). Climate change impacts on revenue and determinants of farmers adapting to climate change; case study at village Kemukten, Sub-district Kersana, District Brebes (Undergraduate thesis). Bogor Agricultural University, Bogor.; Bryan et al. (2013)Bryan, E., Ringler, C., Okoba, B., Roncoli, C., Silvestri, S., & Herrero, M. (2013). Adapting agriculture to climate change in Kenya: Household strategies and determinants. Journal of Environmental Management, 114, 26-35.; Roco et al. (2014)Roco, L., Engler, A., Bravo-Ureta, B., & Jara-Rojas, R. (2014). Farm level adaptation decisions to face climatic change and variability: evidence from Central Chile. Environmental Science & Policy, 44, 86-96.; and Jianjun et al. (2015)Jianjun, J., Yiwei, G., Xiaomin, W., & Nam, P. K. (2015). Farmers’ risk preferences and their climate change adaptationstrategies in the Yongqiao District, China. Land Use Policy, 47, 365-372.
Education	1 if Senior high school and above and otherwise=0	+	Deressa et al. (2009)Deressa, T. T., Hassan, R. M., Ringler, C., Alemu, T., & Yesuf, M. (2009). Determinants of farmers’ choice of adaptation methods to climate change in the Nile Basin of Ethiopia. Global Environmental Change, 19(2), 248-255.; Kurniawati (2011)Kurniawati, F. (2011). The impact of climate change on revenues and determinants of farmers adapting to climate change: a case study at village Purwasari, Sub-district Dramaga, Bogor (Undergraduate thesis). Bogor Agricultural University, Bogor.; Yegbemey et al. (2013)Yegbemey, R. N., Yabi, J. A., Tovignan, S. D., Gantoli, G., & Haroll Kokoye, S. E. (2013). Farmers’ decisions to adapt to climate change under various property rights: a case study of maize farming in northern Benin (West Africa). Land Use Policy, 34, 168-175.; Alauddin & Sarker (2014)Alauddin, M., & Sarker, M. A. R. (2014). Climate change and farm-level adaptation decisions and strategies in drought-prone and groundwater-depleted areas of Bangladesh: an empirical investigation. Ecological Economics, 106, 204-213.; Jianjun et al. (2015)Jianjun, J., Yiwei, G., Xiaomin, W., & Nam, P. K. (2015). Farmers’ risk preferences and their climate change adaptationstrategies in the Yongqiao District, China. Land Use Policy, 47, 365-372.; Menike & Arachchi (2016)Menike, L.M.C.S., & Arachchi, K.A.G.P.K. (2016). Adaptation to climate change by smallholder farmers in rural communities: Evidence from Sri Lanka. Procedia Food Science, 6, 288-292.; and Abid et al. (2016)Abid, M., Schilling, J., Scheffran, J., & Zulfiqar, F. (2016). Climate change vulnerability, adaptation and risk perceptions at farm level in Punjab, Pakistan. The Science of the Total Environment, 547, 447-460.
Farmer cooperation membership	Membership in farmer cooperation if yes = 1 and no=0	+	Ehiakpor et al. (2016)Ehiakpor, D. S., Abbeam, G. D., & Baah, J. E. (2016). Cocoa farmer’s perception on climate variability and its effects on adaptation strategies in the Suaman district of western region, Ghana. Cogent Food & Agriculture, 2(1), 1-12.;
Farmer cooperation membership	Membership in farmer cooperation if yes = 1 and no=0	-	Bryan et al. (2013)Bryan, E., Ringler, C., Okoba, B., Roncoli, C., Silvestri, S., & Herrero, M. (2013). Adapting agriculture to climate change in Kenya: Household strategies and determinants. Journal of Environmental Management, 114, 26-35.
Membership in farmer’s group for agricultural extension	Membership in farmer’s group for agricultural extension if yes = 1 and no=0	+	Deressa et al. (2009)Deressa, T. T., Hassan, R. M., Ringler, C., Alemu, T., & Yesuf, M. (2009). Determinants of farmers’ choice of adaptation methods to climate change in the Nile Basin of Ethiopia. Global Environmental Change, 19(2), 248-255.; Below et al. (2012)Below, T. B., Mutabazi, K. D., Kirschke, K., Franke, C., Sieber, S., Siebert, R., & Tscherning, K. (2012). Can farmers’ adaptation to climate change be explained by socio-economic household-level variables? Global Environmental Change, 22(1), 223-235.; Bryan et al. (2013)Bryan, E., Ringler, C., Okoba, B., Roncoli, C., Silvestri, S., & Herrero, M. (2013). Adapting agriculture to climate change in Kenya: Household strategies and determinants. Journal of Environmental Management, 114, 26-35.; Yegbemey et al. (2013)Yegbemey, R. N., Yabi, J. A., Tovignan, S. D., Gantoli, G., & Haroll Kokoye, S. E. (2013). Farmers’ decisions to adapt to climate change under various property rights: a case study of maize farming in northern Benin (West Africa). Land Use Policy, 34, 168-175.; Gebrehiwot & Van Der Veen (2013)Gebrehiwot, T., & Van Der Veen, A. (2013). Farm level adaptation to climate change: the case of farmer’s in the Ethiopian Highlands. Environmental Management, 52(1), 29-44.; and Tambo (2016)Tambo, J. A. (2016). Adaptation and resilience to climate change and variability in north-east Ghana. International Journal of Disaster Risk Reduction, 17, 85-94. http://dx.doi.org/10.1016/j.ijdrr.2016.04.005 http://dx.doi.org/10.1016/j.ijdrr.2016.0...

List of variable	Position	Mean	Std. Dev.	Measurement and unit
Diversification	Dependent variable	0.34	0.475	Dummy, value 1 for diversified farming and 0 for otherwise
Land clearing without slash and burning	Dependent variable	0.41	0.493	Dummy, value 1 if farmer is doing land clearing without slash and burning and 0 if otherwise
Mixcropping	Dependent variable	0.55	0.499	Dummy, value 1 if farmer is doing mix cropping and 0 if otherwise
Using crop residues as compost	Dependent variable	0.32	0.47	Dummy, value 1 if using crop residues as compost and 0 if otherwise
Crop residues for feeding livestock	Dependent variable	0.17	0.377	Dummy, value 1 if crop residues for feeding livestock and 0 if otherwise
Digging well for water harvesting	Dependent variable	0.44	0.498	Dummy, value =1 if digging well for water harvesting and 0 if otherwise
Expenditure	Independent	2516166	2318069	Continous
Farm size	Independent variable	2.86	3.36	Continous
Oil palm farming total revenue	Independent variable	5357726	16659770	Continous
Oil palm price	Independent variable	797.37	93.93	Continous
Age	Independent variable	42.93	11.52	Continous
Farmer’s experience	Independent variable	13.29	8.29	Continous
Education	Independent variable	0.67	0.47	Dummy, value 1 if high senior school and above and 0 if otherwise
Farmer cooperation membership	Independent variable	0.31	0.46	Dummy, value 1 if member of farmer cooperation and 0 if otherwise
Membership in farmer’s group for agricultural extension	Independent variable	0.81	0.39	Dummy, membership in farmer’s group for agricultural extension if yes = 1 and no=0

Strategy Adapted	Yes		No
Strategy Adapted	Frequency	Percentage	Frequency	Percentage
Diversification	43	33.3	86	66.7
Land clearing without slash and burning	52	40.31	77	59.69
Mixcropping	71	55.03	58	44.97
Using crop residues as compost	42	32.56	87	67.44
Crop residues for feeding livestock	22	17.05	107	82.94
Digging well for water harvesting	57	44.18	72	55.81

Diversification Pattern	Frequency	Percentage
Oil palm + rice	2	1.55
Oil Palm + rubber	23	17.82
Oil palm +cattle	3	2.32
Oil palm+ freshwater fish	1	0.77
Oil palm + rubber+ cattle	13	10.07
Oil palm +cattle + thorny palm + freshwater aquaculture	1	0.77
Total	43	33.3

No	Reasons for burning	Frequency	Percentage
1	Cheapest cost	9	6.97
2	Make soil easier to plant	9	6.97
3	The fastest way for land preparation	7	5.42
4	The clearest land for planting	20	15.51
5	Make soil more fertile	4	3.11
6	Without reason	28	21.71
	Total	77	59.69

Correlation	Expenditure	Farm size	Total revenue	Price	Age	Experience	Education (dummy)	Farmer cooperation membership (dummy)	Membership in agricultural extension group (dummy)	District (dummy)
Expenditure	1.000000
Farm size	0.350751	1.000000
Total revenue	0.216339	0.740825	1.000000
Price	0.058354	0.119542	0.039127	1.000000
Age	0.077893	0.0129	-0.091882	-0.177058	1.000000
Experience	0.030784	-0.02183	-0.059991	-0.195816	0.631945	1.000000
Education (dummy)	0.115128	0.165337	0.112305	0.153307	-0.428116	-0.313449	1.000000
Farmer cooperation membership (dummy)	-0.039142	-0.11298	-0.025278	-0.000930	0.116117	0.252224	0.011851	1.00000
Membership in agricultural extension group (dummy)	0.090488	-0.29005	-0.225426	0.000663	0.063400	0.130492	-0.084515	0.2343	1.000000
District (dummy)	-0.002301	-0.05832	0.012988	-0.230483	0.320018	0.374995	-0.011390	0.4327	-0.134770	1.00000

Independent variables	Dependent variables
	Diversification		Land clearing without slash-burn		Mixcropping		Crop residues as compost		Crop residues for feeding livestock		Digging wells for water harvesting
	coefficient	Prob.	coefficient	Prob.	coefficient	Prob.	coefficient	Prob.	coefficient	Prob.	coefficient	Prob.
Expenditure	8.45E-07^* *** , , * are significant at 1, 5 and 10% probability level, respectively a Dummy, 1 = senior high school and above and 0 = otherwise b Dummy, 1 = member of farmer cooperation and 0= otherwise c Dummy, 1= member of agricultural extension group and 0= otherwise d Dummy, 1= for Bengkulu Utara district and 0 for Muko-muko district	0.0038	-1.85E-07	0.164	-6.29E-08	0.425	5.9E-08	0.557	-4.84E-08	0.764	6.55E-08	0.589
Farm size	-	-			-	-	-0.1161	0.142	-	-	0.02285	0.765
Total revenue from palm oil farming	2.15E-08	0.2632	2.72E-08	0.581	-	-	-	-	-
Palm oil price	-0.0057	0.2728	0.0059**	0.019			0.0043*	0.072	0.0017	0.593	-0.0040	0.108
Age	-0.0539	0.2878	-0.0161	0.503	-0.0102	0.532	0.0155	0.542	-0.0414	0.233	0.0032	0.909
Experience	0.1371**	0.0377	-0.0459	0.226	-	-	-0.0138	0.705	0.0246	0.623	-0.0220	0.597
Education (dummy)^a	-0.0124	0.9903	-0537	0.912	-	-	0.3879	0.466	0.2368	0.751	0.7539	0.222
Membership in farmer cooperation ^b	0.9276	0.4045	0.9495*	0.074	0.5540	0.185	1.6839***	0.041	2.6284***	0.001	0.7004	0.328
Membership in agricultural extension group (dummy) ^c	-0.3484	0.7550	-1.2281**	0.033	1.2522***	0.013	-1.5933**	0.0101	0.9826	0.378	2.1728***	0.009
District (dummy)^d	6.6188***	0.0000	-0.4187	0.449	-	-	-1.9237***	0.0027	-3.3081***	0.002	-4.0586***	0.000
Constant	-1.3693	0.7527	-2.6410	0.239	-0.3878	0.632	-3.3950	0.137	-2.6360	0.418	2.4325	0.324

LR Statistic	120.3992***		27.5896***		10.9141**	0.027	26.04***		36.2195***		66.1462***
Prob (LR Statistic)	0.000000		0.0011		0.0275		0.002		0.000016		0.000000

Independent variables	Dependent variables
	Diversification	Land clearing without slash-burn	Mix cropping	Crop residues as compost	Crop residues for feeding livestock	Digging well for water harvesting
	Marginal effect	Marginal effect	Marginal effect	Marginal effect	Marginal effect	Marginal effect
Expenditure	8.45E-05^* *** , , * are significant at 1, 5 and 10% probability level, respectively Marginal effect = (eβi-1)*100%	-1.85E-05	-6.29E-06	5.9E-06	-4.84E-06	6.55E-06
Farm size	-		-	-10.961382	-	2.3113061
Total revenue from oil palm farming	2.15E-06	2.72E-06	-	-
Oil palm price	-0.5683	0.5917**		0.4309258*	0.1701446	-0.3992011
Age	-5.2473	-1.5971	-1.0148156	1.5620748	-4.0554725	0.3205125
Experience	14.6942**	-4.4862		-1.3705217	2.4905076	-2.1759765

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[1] Source: The authors.