Spatial heterogeneity in smallholder oil palm production

https://doi.org/10.1016/j.forpol.2022.102731Get rights and content

Highlights

  • We analyze spatial variation of oil palm production in smallholder setting of Indonesia.

  • We employ structured additive regression models with nonlinear spatial effects, so-called geosplines.

  • We find large spatial differences, and much of the variation remains unexplained.

  • Land titles and proximity to urban centers are positively associated with oil palm productivity.

  • Knowledge about spatial differences can help geographically target support programs for agricultural productivity growth and broader sustainable development.

Abstract

Oil palm production is an important income source for millions of smallholder farmers in the tropics. Oftentimes, yields in smallholder systems are low, entailing larger area requirements and higher than necessary rates of deforestation. Smallholder performance differs spatially, even within countries and provinces. A better understanding of this spatial variation can help to design more effective support programs to preserve forestland and foster sustainable economic development. Here, we use survey data from smallholder oil palm producers in Sumatra, Indonesia, to identify spatial variation in yields, input use, and output prices by employing structured additive regression models with nonlinear spatial effects, so-called geosplines. Our results confirm large spatial differences. Some of these differences can be explained. For instance, possession of land titles and proximity to urban centers are positively associated with oil palm productivity. However, much of the spatial variation cannot be explained by variables typically included in socioeconomic studies. The geosplines help to control for possible bias even when the identified spatial variation cannot be fully explained. From a policy perspective, knowing where regions with high and low yields and input intensities are located can help to geographically target suitable support programs. From a research perspective, more work on trying to explain spatial heterogeneity in smallholder production is needed, possibly combining quantitative and qualitative approaches.

Introduction

Smallholder farm households in developing countries represent the largest share of the world's most impoverished people (De La, Campos, Villani, Davis, and Takagi, 2018). As these households rely on farming for their livelihoods, improving agricultural productivity and incomes remain crucial development objectives (UN, 2019). Progress towards achieving these objectives requires understanding of how agricultural production activities vary spatially and what factors contribute to this spatial variation (Marenya and Barrett, 2007). For instance, knowledge about spatial differences can help geographically target those farmers that are most likely to benefit from certain support programs. However, explicit analysis of spatial variation is not yet very common in the literature and is often hampered by limitations in appropriate data and methodologies.

Several existing studies try to consider spatial heterogeneity in smallholder systems using various approaches, such as examining the role of geographic proximity to urban centers and infrastructure conditions for innovation diffusion (Damania et al., 2017; Ebata, Velasco Pacheco, and von Cramon-Taubadel, 2017; Holloway and Lapar, 2007; Knowler and Bradshaw, 2007; Rudolf, Romero, Asnawi, Irawan, and Wollni, 2020; Tessema, Asafu-Adjaye, Kassie, and Mallawaarachchi, 2016; Vandercasteelen, Beyene, Minten, and Swinnen, 2018; Wollni and Andersson, 2014). Province, district, or village dummies are also often included as covariates in regression models to control for unobserved regional effects. However, using proximity measures and regional dummies assumes that spatial heterogeneity follows linear patterns or coincides with administrative boundaries, which is not always the case (Steinhübel, Wegmann, and Mußhoff, 2020; Steinhübel and von Cramon-Taubadel, 2021). Any remaining spatial heterogeneity that is not explained by the standard covariates included may lead to biased estimates, especially when jointly correlated with unobserved factors and the outcome variables of interest. This is considered a serious limitation in much of the existing literature.

A second limitation is that existing studies analyzing spatial heterogeneity focus on annual crops only, without considering perennial plantation crops. Plantation crops such as oil palm and rubber are important sources of income for many smallholder farmers. These crops, which remain planted in the field for many years, differ from their annual counterparts in many ways, including differences in capital requirements, growing and harvesting periods, water and input needs, carbon sequestration potentials, and effects on various other environmental dimensions (Corley and Tinker, 2016). Given that oil palm and rubber are typical export crops that are highly integrated into global supply chains, farm households' management systems and risk coping strategies are also quite different from those of households cultivating annual crops for own consumption or local markets (Kühling, Alamsyah, and Sibhatu, 2022).

Furthermore, the expansion of plantation crops is sometimes associated with negative externalities such as deforestation and biodiversity loss. Oil palm in particular has massively gained in importance over the last 30 years in Southeast Asia and other parts of the world. At least one-third of the worldwide oil palm land is estimated to be managed by smallholders (Descals et al., 2021; Qaim, Sibhatu, Siregar, and Grass, 2020). As oil palm can bear fruits all year round, the crop allows for a steady income stream largely independent of seasons (Edwards, 2019). Especially in Indonesia, the widespread cultivation of oil palm in the small farm sector was shown to be associated with rural poverty reduction and improvements in food security (Qaim, Sibhatu, Siregar, and Grass, 2020; Sibhatu, 2019). However, the massive recent expansion of the oil palm land in Southeast Asia is also associated with tropical deforestation, leading to biodiversity loss, greenhouse gas emissions, and other environmental problems (Cisneros, Kis-Katos, and Nuryartono, 2021; Qaim, Sibhatu, Siregar, and Grass, 2020). A rising number of studies looks at economic, social, and environmental effects of the oil palm boom (Drescher et al., 2016; Euler, Krishna, Schwarze, Siregar, and Qaim, 2017; Euler, Schwarze, Siregar, and Qaim, 2016b; Grass et al., 2020; Mehraban, Kubitza, Alamsyah, and Qaim, 2021; Qaim, Sibhatu, Siregar, and Grass, 2020; Santika et al., 2019). However, to the best of our knowledge, none of these studies explicitly examines spatial variation of oil palm productivity and management. Better understanding of such variation could help to design and geographically target smallholder support programs to enhance productivity on the already-cultivated land and thus reduce the need to further expand into forests.

In this study, we address both mentioned limitations in the existing literature. In particular, we investigate the spatial heterogeneity of smallholder oil palm production in Sumatra, Indonesia, where the crop substantially gained in importance during the last 30 years (Clough et al., 2016; Drescher et al., 2016; Romero, Wollni, Rudolf, Asnawi, and Irawan, 2019). The study area covers a large diversity of environmental and socioeconomic conditions, making the analysis of spatial heterogeneity especially interesting. We use data from a farm household survey and employ structured additive regression models with nonlinear spatial effects, so-called geosplines, which allow us to also identify and control for spatial heterogeneity that is not captured by standard covariates.

We find significant spatial variation in smallholder oil palm productivity and management. Possession of formal land titles, farmers' specialization in oil palm, and several other variables are positively associated with productivity, but we also show that about half of the spatial heterogeneity remains unexplained. Hence, the use of geosplines improves the reliability of the estimates.

The rest of this article is structured as follows. Section 2 provides a brief overview of oil palm production in Indonesia, and in Sumatra in particular. The data and statistical methods are described in section 3. Section 4 presents the results, while section 5 concludes.

Section snippets

Oil palm cultivation in Indonesia

Oil palm (Elaeis guineensis) is native to Central and West Africa and was hardly grown in other world regions until about 100 years ago. In Sumatra, oil palm was first introduced as an ornamental plant by the Dutch colonialists in the eighteenth century (Corley and Tinker, 2016). As an oil crop, oil palm has been cultivated in Sumatra and other parts of Indonesia at a smaller scale since the early twentieth century; since the late-1980s, production has expanded substantially (Dharmawan et al.,

Farm household survey

This study uses survey data from oil palm farmers in Jambi Province, one of the main oil palm producing regions on the Indonesian Island of Sumatra. The farm households to be included in the survey were selected through a multistage-cluster sampling approach (Romero, Wollni, Rudolf, Asnawi, and Irawan, 2019). At the first stage, five oil palm growing regencies in Jambi, namely, Muaro Jambi, Batanghari, Sarolangun, Tebo, and Bungo, were purposively selected (Fig. 1). These regencies cover most

Descriptive statistics

Descriptive statistics for the whole sample of oil palm farm households and by regency are shown in Table 2. On average, farmers own about 5.6 ha of land, most of which is used for oil palm production. Around 70% of the farmers' plots are formally titled. On average, about 45% of oil palm farms are flat, and about 40% are characterized by containing shallow topsoil organic matter. About 85% and 17% of the farmers experienced drought and floods in the previous 12 months, respectively. The large

Conclusion

The analysis of spatial heterogeneity in agricultural production has so far received little explicit attention in the literature. While a few previous studies had analyzed spatial variation in annual crops, no related earlier work existed for plantation crops. In this article, we have investigated spatial heterogeneity in oil palm production with data from smallholder farmers in Sumatra, Indonesia. Oil palm is a particularly relevant example. This crop was massively expanded in Indonesia and

Author statement

Kibrom T. Sibhatu: Conceptualization, Methodology, Data analysis, Writing - original draft preparation, Writing - Reviewing and Editing.

Linda Steinhübel: Conceptualization, Methodology, Data Analysis, Visualization, Writing - Reviewing and Editing.

Hermanto Siregar: Writing - Reviewing and Editing.

Matin Qaim: Conceptualization, Writing - Reviewing and Editing.

Meike Wollni: Conceptualization, Data Curation, Writing - Reviewing and Editing.

All authors approved the manuscript and its submission to

Funding

This study was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project ID 192626868 – SFB 990.

Declaration of Competing Interest

None is declared.

Acknowledgements

We are grateful for the comments and recommendations from the two anonymous reviewers and the editor, Runsheng Yin.

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