Yield and nitrogen losses in oil palm plantations: Main drivers and management trade-offs determined using simulation

Pardon, Lénaïc; Huth, Neil; Nelson, Paul N.; Banabas, Murom; Gabrielle, Benoît; Bessou, Cécile

doi:10.1016/j.fcr.2017.05.016

Cited by 26 publications

(17 citation statements)

References 32 publications

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“…Modeling efforts to simulate OP behaviour and development also exist but they are mostly focused on agronomic variables (e.g. carbon allocation, yield, fertilization) and neglect carbon/water relations and surface energy fluxes (van Kraalingen et al 1989, Combres et al 2013, Huth et al 2014, Hoffmann et al 2014, Fan et al 2015, Okoro et al 2017, Pardon et al 2017. Only recently, Meijide et al (2017) employed a land surface model adapted to OP (CLM-Palm (Fan et al 2015)) to simulate water/energy fluxes at two OP plantations but changes with comparison to native forests were disregarded.…”

Section: Introductionmentioning

confidence: 99%

Ecohydrological changes after tropical forest conversion to oil palm

Manoli

Meijide

Huth

et al. 2018

Environ. Res. Lett.

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Given their ability to provide food, raw material and alleviate poverty, oil palm (OP) plantations are driving significant losses of biodiversity-rich tropical forests, fuelling a heated debate on ecosystem degradation and conservation. However, while OP-induced carbon emissions and biodiversity losses have received significant attention, OP water requirements have been marginalized and little is known on the ecohydrological changes (water and surface energy fluxes) occurring from forest clearing to plantation maturity. Numerical simulations supported by field observations from seven sites in Southeast Asia (five OP plantations and two tropical forests) are used here to illustrate the temporal evolution of OP actual evapotranspiration (ET), infiltration/runoff, gross primary productivity (GPP) and surface temperature as well as their changes relative to tropical forests. Model results from large-scale commercial plantations show that young OP plantations decrease ecosystem ET, causing hotter and drier climatic conditions, but mature plantations (age > 8−9 yr) have higher GPP and transpire more water (up to +7.7%) than the forests they have replaced. This is the result of physiological constraints on water use efficiency and the extremely high yield of OP (six to ten times higher than other oil crops). Hence, the land use efficiency of mature OP, i.e. the high productivity per unit of land area, comes at the expense of water consumption in a trade of water for carbon that may jeopardize local water resources. Sequential replanting and herbaceous ground cover can reduce the severity of such ecohydrological changes and support local water/climate regulation.

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Section: Introductionmentioning

confidence: 99%

Ecohydrological changes after tropical forest conversion to oil palm

Manoli

Meijide

Huth

et al. 2018

Environ. Res. Lett.

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“…Second, a legume understory, for example, Pueraria phaseoloides or Mucuna bracteata , is generally sown at the beginning of the growth cycle, and the N fixed by the legume was identified as one of the largest N fluxes (Pardon et al., 2016). The amount of legume understory was also reported to be one of the most influential parameters on N losses before 7 yr of age in a sensitivity analysis of Agricultural Production Systems sIMulator (APSIM)‐Oil palm simulation model (Pardon et al., 2017). Moreover, in a range of models compared, N fixation was always modeled with constant fixation rates (Pardon et al., 2016), while in the field, legumes usually have the capacity to regulate their N provision by fostering N fixation or N uptake from soil, depending on soil mineral N content (Giller & Fairhurst, 2003).…”

Section: Methodsmentioning

confidence: 99%

“…To calibrate the N 2 O emission modules we used the factors and classes defined in Stehfest and Bouwman (2006) model of N 2 O emissions. Finally, we used a dataset of 58,500 simulations (Pardon et al., 2017), from the APSIM‐Oil palm process‐based model (Huth, Banabas, Nelson, & Webb, 2014), for the calibration of the Palm N Uptake module and estimation of evapotranspiration in the Soil Water Budget module. The APSIM‐Oil Palm was the only process‐based model validated for oil palm production which also included a prediction of N fluxes and evapotranspiration.…”

Section: Methodsmentioning

confidence: 99%

IN‐Palm: An agri‐environmental indicator to assess nitrogen losses in oil palm plantations

et al. 2020

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Oil palm (Elaeis guineensis Jacq.) is currently cultivated on 19 million ha, and palm oil represents more than one-third of the global vegetable oil market. Addition of nitrogen (N) via legume cover crop and fertilizers is a common practice in industrial oil palm plantations, however, there is a tendency for N loss, thus contributing significantly to environmental effects. To improve the sustainability of palm oil production, it is crucial to determine which management practices minimize N losses.Continuous field measurements would be cost-prohibiting as a monitoring tool, and in the case of oil palm, available models do not account for all the potential nitrogen inputs and losses or management practices. In this context, we developed IN-Palm, a model to help managers and scientists estimate N losses to the environment and identify best management practices. The main challenge was to build the model in a context of knowledge scarcity. Given these objectives and constraints, we developed an agri-environmental indicator, using the INDIGO method and fuzzy decision trees. We validated the N leaching module of IN-Palm against field data from Sumatra, Indonesia. IN-Palm is implemented in an Excel file and uses 21 readily available input variables to compute 17 modules. It estimates annual emissions and scores for each N-loss pathway and provides recommendations to reduce N losses.IN-Palm predictions of N leaching were acceptable according to several statistics, with a tendency to underestimate nitrogen leaching. Thus, we highlighted necessary improvements to increase IN-Palm precision before use in plantations.

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“…However, the collection of such field data is still limited (e.g. Pardon et al 2016aPardon et al , 2016bPardon et al , 2017. Furthermore, a discrepancy between respective field data and world mean values is a source of uncertainty.…”

Section: Calculation Of the N Footprintmentioning

confidence: 99%

Concealed nitrogen footprint in protein-free foods: an empirical example using oil palm products

Hayashi

Oita

Nishina

2020

Environ. Res. Lett.

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The agro-food system satisfying human food demand releases heavy nitrogen (N) loads into the environment. The N footprint is an indicator of N loads from individual consumption of food as well as energy. A bottom-up approach called the 'N-calculator method' calculates the food N footprint using the N content in consumed foods, such that the N footprint of protein-free foods is treated as zero. This method underestimates the N footprint of protein-free foods, such as oil and sugar, when the source crops require N input in production. In this study, we propose a substitution factor, the virtual nitrogen factor for protein-free foods (VNFree), defined as the potential N load per unit weight of consumed food, to explicitly calculate the production N footprint. Oil palm and its products, palm oil (PO) and palm kernel oil (PKO), were chosen for this case study of protein-free foods. Global mean VNFree values of PO and PKO obtained by averaging national-scale data of the three countries with the largest production (Indonesia, Malaysia, and Thailand) were 0.0241 and 0.0037 kg N kg -1 oil, respectively. The 6.5-times difference in VNFree values was attributed to the difference in oil yield. The food N footprint of PO and PKO calculated here represented less than 2% of the previously reported total food N footprints of several countries. However, oil palm products are also used for industry, and the chemical fertilizer consumption for oil palm accounted for only 8%-12% of that of all oil and sugar crops. The protein-free N footprint of all these products will be much larger. We expect that the current N-calculator method as a bottom-up approach will be improved by expanding the VNFree concept, which enables the calculation of the concealed N footprint in protein-free products, including all uses of oil and sugar crops.

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Yield and nitrogen losses in oil palm plantations: Main drivers and management trade-offs determined using simulation

Cited by 26 publications

References 32 publications

Ecohydrological changes after tropical forest conversion to oil palm

Ecohydrological changes after tropical forest conversion to oil palm

IN‐Palm: An agri‐environmental indicator to assess nitrogen losses in oil palm plantations

Concealed nitrogen footprint in protein-free foods: an empirical example using oil palm products

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