Modeling oil palm crop for Brazilian climate conditions

https://doi.org/10.1016/j.agsy.2021.103130Get rights and content

Highlights

  • Oil palm production has been growing in Brazil, mainly in the Amazon region.

  • Agricultural modeling is an important tool to improve crop yield.

  • Air temperature and humidity affect oil palm crop yield.

  • Yield of oil palm is lower in younger plants compared to older plants.

  • Modeling the oil palm reproductive cycle is crucial to improve crop yield simulation.

Abstract

CONTEXT

The increasing world demand for palm oil led to the expansion of oil palm plantations, especially in the new lands in Southeast Asia, the main producing region in the world. The expansion of oil palm plantations has also occurred in Latin American countries, such as Brazil. Roughly 400 million hectares in Brazil are suitable for the planting of oil palm, but most of this area is currently covered by forest, mainly by the Amazon Rainforest. Climate change has reduced the extent of land suitable for oil palm cultivation in Brazil since under unfavorable climatic conditions, crop yields are reduced. To reconcile the increase in oil palm production in Brazil with the preservation of forests, modeling has been used as a tool to define the best suitable areas for planting expansion, as well as for the assessment of management techniques that aim to increase the yield.

OBJECTIVE

Thus, the object of this study was to implement the oil palm crop sub-model in the ECOSMOS integrated simulator and to evaluate its performance to simulate oil palm energy and carbon balance and the crop yield.

METHODS

The carbon allocation scheme for oil palm is quite different from the other crops of ECOSMOS. So, we use the sub-PFT (plant functional type) approach, where each phytomer in the plant evolves simultaneously, but individually.

RESULTS AND Conclusions

The results showed that the model was able to simulate net radiation (Rn), latent heat flux (LE), and net ecosystem CO2 exchange (NEE) with good accuracy. In contrast, the sensible heat flux (H) was not well simulated due to the lack of information on the soil's physical-hydric properties. The model simulated accurately the annual yield for plants aged between 12 and 25 years, whereas the yield was overestimated for plants aged outside this range. Also, the model better simulated the yield of genetic varieties with seasonal yield, compared to varieties with more uniform yield over the months.

SIGNIFICANCE

The robust and consistent results presented for most of the evaluated processes, especially for energy and carbon fluxes, make the oil palm sub-model described here suitable for improve the oil palm production in Brazil. For future studies, efforts should be directed to consider key factors for oil palm, such as the ratio between male and female inflorescences and the abortion rate of inflorescences, which affect crop yield, for a better understanding of the oil palm growth and production.

Introduction

The oil of the African palm (Elaeis guineensis Jacq.) is the vegetable oil with the highest production and commercialization volume in the world. According to the United States Department of Agriculture (USDA), the global production in 2019 was 74.60 million tons of processed oil, higher than the production of soy oil in the same period (56.85 million tons). The oil extracted from the palm fruit has several uses in the industry, such as in the production of food, detergents, cosmetics, and more recently in the production of biodiesel (Pirker et al., 2016). Most of the world's production comes from Southeast Asia. Indonesia is the main world producer, accounting for 57.19% of the world's production, followed by Malaysia (27.26%) and Thailand (3.99%), according to USDA. Together, these three countries account for almost 90% of the total palm oil production in the world.

Latin America has emerged as a feasible alternative to Southeast Asia to increase world palm oil production. Currently, Colombia is the main producer of palm oil in Latin America and the fourth-largest producer in the world. Brazil has a small contribution to the international market, ranking ninth and accounting for 0.72% of world production. However, the modest Brazilian oil palm production has been expanding. According to USDA, national production increased from 270 thousand tons in 2010 to 540 thousand million tons in 2019. This expansion is linked both to the creation of incentive governmental programs to increase palm oil production, such as Pronaf Eco Dendê (Monteiro de Carvalho et al., 2015), as well as the increase in domestic and international demand. The Pará state is the largest producer of palm oil in Brazil, accounting for 70% of the total planted area and 90% of national production.

In Brazil, more than 400 million ha are suitable for the cultivation of oil palm, but most of this area is currently covered by tropical forests, especially in the Amazon region (Pirker et al., 2016). The challenges imposed to the growth of oil palm production in Brazil due to competition by area with the Amazon Rainforest (Furumo and Aide, 2017; Vijay et al., 2016) and climate change (Almeida et al., 2017; Avila-Diaz et al., 2020; Da Silva et al., 2019; Paterson et al., 2017) require a deepening of knowledge about the plant's life cycle, including growth and yield in order to reduce the negative impacts of oil palm cultivation. Previous studies have shown how the environment and endogenous factors of the plant, such as age and reproductive cycle, affect the growth and yield of oil palm (Hoffmann et al., 2017; Woittiez et al., 2017). However, the contribution of climatic and meteorological conditions to carbon uptake and assimilation in oil palm is still unclear. Further, it is necessary to understand how these climatic conditions do affect crop yield.

Land surface models are a tool widely used in studies involving the relationship between agricultural production and the climate. These models are able to simulate crop yields under different climatic conditions, explicitly simulating processes such as photosynthesis, respiration, and phenology, as well as the exchange of carbon, water, and energy between the biosphere and the atmosphere (Sellers, 1997). In addition, modeling allows to assess the impact of management techniques on carbon assimilation and yield, as well as assessing the suitability of areas for the cultivation of different agricultural species, avoiding the deforestation of low-yield areas. Several models simulate oil palm yield currently, such as APSIM-Palm (Huth et al., 2014), ECOPALM (Combres et al., 2013), PALMSIM (Hoffmann et al., 2014), and CLM-Palm (Fan et al., 2015). However, most of these models represent biophysical processes in a simplified way, such as the coupling between photosynthesis and plant transpiration. Few models, such as CLM-Palm, are able of simulating growth, yield, and exchanges of carbon, water, energy, and momentum between the ecosystem and the atmosphere. Those models can be applied, for example, to study the land cover and land use changes climate impacts considering not only the climate impacts through biogeochemical (CO2) emissions but also through the biophysical mechanisms (regulation of water and energy).

In this study, the ECOSMOS-Palm sub-model is presented, which simulates the growth and yield of oil palm through the structure of the ECOSMOS integrated simulator, which is based on the Agro-IBIS model (Foley et al., 1996; Kucharik and Brye, 2003). In addition, the sub-PFT scheme proposed by Fan et al. (2015) was used to implement phenology and carbon allocation to the plant pools (roots, trunk, leaves, and fruits). Thus, the objectives of this paper are (i) evaluate the oil palm crop sub-model implemented in the ECOSMOS model, (ii) to evaluate modeled crop carbon assimilation and energy balance through two micrometeorological flux tower measurements, and (iii) to investigate modeled crop yield in the major Brazilian producing region.

Section snippets

The ECOSMOS-Palm model

ECOSMOS is a biophysical growth model based on Agro-IBIS (Foley et al., 1996; Kucharik and Brye, 2003). Researchers from EMBRAPA (Brazilian Agricultural Research Company) have been improving the original model and implementing the most important crops in Brazil. One of the major changes in the ECOSMOS framework is that developers can add new a crop-PFT (plant functional type) as a module of the main program, and this crop module is linked with core model sub-routines that solves biophysical

Sensitivity analysis and calibration

The proposed sensitivity analysis framework was able to identify the most sensitive parameters to simulate energy and carbon fluxes. Fig. 3 presents the 33 parameters that significantly affect the simulation of at least one variable (H, LE, and NEE) on JAM site. NEE simulation was sensitive to the largest number of parameters, followed by H and LE (24, 18, and 10 parameters, respectively). For four parameters, the model was sensitive to simulate all variables simultaneously (H, LE, and NEE),

Climate dependence on oil palm carbon uptake efficiency

Previous studies have shown that oil palm achieves a high rate of photosynthesis compared to other C3 photosynthesis pathway plants, saturating at a PPFD (Photosynthetic Photon Fluence Density) of 1100 μmol m−2 s−1 (~240 W m−2 of PAR) in ~20 μmol-CO2 m−2 s−1 (Apichatmeta et al., 2017; Dufrene and Saugier, 1993). Also, the oil palm requires PAR of at least 230 MJ m−2 month−1 (Woittiez et al., 2017), which is commonly fulfilled in the equatorial region, where MOJ and site are located. On the

Conclusions

The new oil palm model presented proved to be able of simulating carbon and energy exchanges between the land surface and the atmosphere. The exception was the simulation of the sensible heat flux, which was affected by the lack of data on the soil's physical properties. In addition, the model was also able to simulate well the oil palm yield in the main producing region of Brazil. In general, the model better simulated the yield of plants from 12 to 25 years old and for varieties that showed

Declaration of Competing Interest

None.

Acknowledgement

This study was made possible thanks to the support of the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES, finance code 88882.437129/2019-01) and Agropalma SA. The Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, finance code 480210/2011-0), the Empresa Brasileira de Pesquisa Agropecuária (Embrapa), and Marborges Agroindústria SA also contributed to this study.

References (44)

  • N. Zhang et al.

    Review of soil thermal conductivity and predictive models

    Int. J. Therm. Sci.

    (2017)
  • C.T. Almeida et al.

    Spatiotemporal rainfall and temperature trends throughout the Brazilian Legal Amazon, 1973–2013

    Int. J. Climatol.

    (2017)
  • C.A. Alvares et al.

    Köppen’s climate classification map for Brazil

    Meteorol. Zeitschrift

    (2013)
  • A. Avila-Diaz et al.

    Assessing current and future trends of climate extremes across Brazil based on reanalyses and earth system model projections

    Clim. Dyn.

    (2020)
  • E. Barcelos et al.

    Oil palm natural diversity and the potential for yield improvement

    Front. Plant Sci.

    (2015)
  • J.P. Caliman et al.

    Effect of drought and haze on the performance of oil palm

    International Oil Palm Conference

    (1998)
  • N. Chen et al.

    Nonlinear response of ecosystem respiration to multiple levels of temperature increases

    Ecol. Evol.

    (2019)
  • R.B. Clapp et al.

    Empirical equations for some soil hydraulic properties

    Water Resour. Res.

    (1978)
  • J.-C. Combres et al.

    Simulation of inflorescence dynamics in oil palm and estimation of environment-sensitive phenological phases: a model based analysis

    Funct. Plant Biol.

    (2013)
  • R.H.V. Corley et al.

    The Oil Palm

    (2015)
  • B.J. Cosby et al.

    A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils

    Water Resour. Res.

    (1984)
  • L.C.N. Fonseca da et al.

    Fluxos de CO2 em Plantio de Palma de Óleo no Leste da Amazônia

    Rev. Bras. Meteorol.

    (2018)
  • Cited by (0)

    View full text