Understanding and modelling the ambiguous impact of off-farm income on tropical deforestation

Figures & data

Figure 1. Schematic to show the normalization of a distance between the best decision outcome and the actually achieved decision outcome (here 200) for objective i and future u, assuming 500 as the ideal and −50 as the anti-ideal reference point. For instance, in this illustrative example 500 could represent the net present value (NPV, measured in US$/ha) of the most profitable LULC type, whereas −50 would be the NPV of the least profitable LULC type. The achieved decision outcome (200 US$/ha) represents the weighted mean of the NPVs of all LULC types comprising the landscape composition.

Figure 2. a. Current and optimized long-term target landscape compositions for various scenarios. The baseline scenario without consideration of off-farm income has been adopted from Knoke et al. (2020). b. Influence of off-farm income on distances between best and achieved decision outcomes for farms with and without re-allocation of labor, given the current landscape composition. We show only the decision criteria influenced by off-farm income (NPVs and required labor). The distances for the three objectives are important drivers of change, as the optimization seeks to minimize the maximum distance. As discount rates are uncertain (Weitzman, 1998) we considered net present values (NPVs) for two discount rates, representing more moderate (5%) and higher time preferences (8%). Each individual symbol represents one uncertainty scenario.

Table 1. Influence of the proportion of off-farm income on the proportion of natural forest cover remaining after 30 years of simulation (initial forest cover is 50%).

	With re-allocation of labor		Without re-allocation of labor
Proportion of off-farm income	Proportion of natural forest after 30 years	Natural forest lost over 30 years	Proportion of natural forest after 30 years	Natural forest lost over 30 years
[%]	[%]	[percent points]	[%]	[percent points]
0	29.1	20.9	29.1	20.9
10	30.7	19.3	28.6	21.4
20	32.2	17.8	27.9	22.1
30	33.1	16.9	27.1	22.9
40	34.7	15.3	25.8	24.2
50	38.7	11.3	24.1	25.9
60	44.4	5.6	21.4	28.6
70	49.5	0.5	16.9	33.1

Figure 3. Changes of annual deforestation rates resulting from the consideration of off-farm income. Deforestation rates of the baseline scenario form the benchmark against which deforestation rates under the influence of off-farm income are assessed.

Figure 4. Trajectories of the proportion of natural forest under different scenarios.

Table 2. Results of our mechanistic land-use allocation model versus empirical evidence of the influence of off-farm income on tropical deforestation in Latin America.

Expectation	Model result	Empirical evidence
Smallholder farming leads to deforestation	For all studied scenarios optimized long-term target landscape compositions showed conversion of natural forest into pasture (Figure 2).	“The imbalanced coincidence of abundant forestland, scarce off-farm assets, free household labour and time yields near-inevitable deforestation as colonos convert the resources at their disposal (land, forest) to on-farm investments (pasture) … ” (Sloan, 2008, p. 432)
The proportion of off-farm income of total household income influences deforestation	The higher the proportion of off-farm income, the lower the loss of natural forest over 30 years on farms with re-allocation of labor (Table 1).	Off-farm work can draw away labor which would otherwise be used for deforestation (Barbier, 2010). As an example, Thapa et al. (1996) showed how off-farm income reduces women’s participation in agriculture with this pressure on natural forest (see, also Barbier & Burgess, 2001). In Vasco et al. (2020), p. 38% more off-farm work reduced deforestation by 28%. Off-farm employment also reduces interest in clearing forest (Pacheco et al., 2011). Araujo et al. (2019) obtained an average reduction of deforestation by 0.07% for an increase of 1% in relative off-farm income on Brazilian farms. In addition, Reyes et al. (2018) showed that income generated from forest extraction is negatively correlated with off-farm income (see, also Ojeda Luna et al., 2020).
	The higher the proportion of off-farm income, the higher the loss of natural forest over 30 years on farms without re-allocation of labor (Table 1). Increased income without decreasing the on-farm labor force thus accelerates deforestation (Figures 2 and 3).	Support: Wealthy farmers choose more lucrative non-agricultural work and invest into agricultural activities (Murphy, 2001). They do not use off-farm income to replace farm income, but hire additional labor associated with high deforestation rates (Pacheco, 2009; Mena et al., 2006), Bennett et al. (2018) showed how additional financial resources becoming available boost deforestation. Generally, an increase in income accelerates deforestation (Culas, 2007). No support: Mullan et al. (2018) did not find that income changes alter the rates of forest clearing and concluded that the total labor force is not a significant determinant of cleared area.
Off-farm income on farms with labor re-allocation can halt deforestation	Deforestation would no longer be necessary to maintain or enhance the livelihoods of extensive farm households, when their income is dominated by off-farm income (proportion of off-farm income $\ge 70\mathrm{\%}$, Table 1).	Some farmers who include off-farm income into their livelihood strategy apply deintensification and some ultimately step out of agriculture (Caulfield et al., 2021).
Increased income used to intensify agriculture with subsequent decreases of deforestation	The intense pasture LULC type is hardly affected by additional off-farm income. Even with labor and capital available, farmers tend to continue deforestation before intensifying agriculture. This highlights ambiguity about the social acceptability for farmers (Figure 3 and 4).	“The odds of deforestation decrease with herd size as stocking densities tend to increase most markedly only after colonos convert most of their forest cover to rough pasture.” (Sloan, 2008. p. 431). Similar results published by Kaimowitz and Angelsen (2008).

Knoke, T., Paul, C., Rammig, A., Gosling, E., Hildebrandt, P., Härtl, F., Peters, T., Richter, M., Diertl, K.-H., Castro, L.M., Calvas, B., Ochoa, S., Valle‐Carrión, L.A., Hamer, U., Tischer, A., Potthast, K., Windhorst, D., Homeier, J., Wilcke, W., … Bendix, J. (2020). Accounting for multiple ecosystem services in a simulation of land-use decisions: Does it reduce tropical deforestation? Global Change Biology, 26(4), 2403–2420. https://doi.org/10.1111/gcb.15003

 PubMed Web of Science ®Google Scholar

Weitzman, M.L. (1998). Why the Far-Distant Future Should Be Discounted at Its Lowest Possible Rate. Journal of Environmental Economics and Management, 36(3), 201–208. https://doi.org/10.1177/0160017604266026

 Web of Science ®Google Scholar

Sloan, S. (2008). Reforestation amidst deforestation: Simultaneity and succession. Global Environmental Change, 18(3), 425–441. https://doi.org/10.1016/j.gloenvcha.2008.04.009

 Web of Science ®Google Scholar

Barbier, E.B. (2010). Poverty, development, and environment. Environment and Development Economics, 15, 635–660. https://doi.org/10.1017/S1355770X1000032X

 Web of Science ®Google Scholar

Thapa, K.K., Bilsborrow, R.E., & Murphy, L. (1996). Deforestation, land use, and women’s agricultural activities in the Ecuadorian Amazon. World Development, 24(8), 1317–1332. https://doi.org/10.1016/0305-750X(96)00041-1

 Web of Science ®Google Scholar

Barbier, E.B., & Burgess, J.C. (2001). The Economics of Tropical Deforestation. Journal of Economic Surveys, 15(3), 413–433. https://doi.org/10.1111/1467-6419.00144

 Web of Science ®Google Scholar

Vasco, C., Valdiviezo, R., Hernández, H., Tafur, V., Eche, D., & Jácome, E. (2020). Off-Farm Employment, Forest Clearing and Natural Resource Use: Evidence from the Ecuadorian Amazon. Sustainability, 12(11), 4515. en. https://doi.org/10.3390/su12114515

 Web of Science ®Google Scholar

Pacheco, P., Aguilar-Støen, M., Börner, J., Etter, A., Putzel, L., & Del Diaz, M.C.V. (2011). Landscape Transformation in Tropical Latin America: Assessing Trends and Policy Implications for REDD+. Forests, 2(1), 1–29. https://doi.org/10.3390/f2010001

 Web of Science ®Google Scholar

Araujo, C., Combes, J.-L., & Féres, J.G. (2019). Determinants of Amazon deforestation: The role of off-farm income. Environment and Development Economics, 24, 138–156. https://doi.org/10.1017/S1355770X18000359

 Web of Science ®Google Scholar

Reyes, R., Nelson, H., & Zerriffi, H. (2018). Firewood: Cause or consequence? Underlying drivers of firewood production in the South of Chile. Energy for Sustainable Development, 42, 97–108. https://doi.org/10.1016/j.esd.2017.10.006

 Web of Science ®Google Scholar

Ojeda Luna, T., Zhunusova, E., Günter, S., & Dieter, M. (2020). Measuring forest and agricultural income in the Ecuadorian lowland rainforest frontiers: Do deforestation and conservation strategies matter? Forest Policy and Economics, 111, 102034. https://doi.org/10.1016/j.forpol.2019.102034

 Web of Science ®Google Scholar

Murphy, L.L. (2001). Colonist Farm Income, Off-Farm Work, Cattle, and Differentiation in Ecuador’s Northern Amazon. Human Organization, 60(1), 67–79. https://doi.org/10.17730/humo.60.1.38rjx0aldu349vvh

 Web of Science ®Google Scholar

Pacheco, P. (2009). Smallholder Livelihoods, Wealth and Deforestation in the Eastern Amazon. Human Ecology, 37(1), 27–41. https://doi.org/10.1007/s10745-009-9220-y

 Web of Science ®Google Scholar

Mena, C.F., Bilsborrow, R.E., & McClain, M.E. (2006). Socioeconomic drivers of deforestation in the Northern Ecuadorian Amazon. Environmental Management, 37(6),802–815. eng. https://doi.org/10.1007/s00267-003-0230-z

 PubMed Web of Science ®Google Scholar

Bennett, A., Ravikumar, A., & Paltán, H. (2018). The Political Ecology of Oil Palm Company-Community partnerships in the Peruvian Amazon: Deforestation consequences of the privatization of rural development. World Development, 109, 29–41. https://doi.org/10.1016/j.worlddev.2018.04.001

 Web of Science ®Google Scholar

Culas, R.J. (2007). Deforestation and the environmental Kuznets curve: An institutional perspective. Ecological Economics, 61(2–3), 429–437. https://doi.org/10.1016/j.ecolecon.2006.03.014

 Web of Science ®Google Scholar

Mullan, K., Sills, E., Pattanayak, S.K., & Caviglia-Harris, J. (2018). Converting Forests to Farms: The Economic Benefits of Clearing Forests in Agricultural Settlements in the Amazon. Environmental and Resource Economics, 71(2), 427–455. https://doi.org/10.1007/s10640-017-0164-1

 Web of Science ®Google Scholar

Caulfield, M.E., Hammond, J., Fonte, S.J., Florido, M.A., Fuentes, W., Meza, K., Navarette, I., Vanek, S.J., & van Wijk, M. (2021). Unpicking the Inter-relationships Between Off-Farm Livelihood Diversification, Household Characteristics, and Farm Management in the Rural Andes. Frontiers in Sustainable Food Systems, 5, 295. https://doi.org/10.3389/fsufs.2021.724492

 Google Scholar

Kaimowitz, D., & Angelsen, A. (2008). Will Livestock Intensification Help Save Latin America’s Tropical Forests? Journal of Sustainable Forestry, 27(1–2), 6–24. https://doi.org/10.1080/10549810802225168

 Google Scholar