Modelling of the nitrogen budget of oil palm plantations to help reduce losses to the environment. Case study in Sumatra, Indonesia

Abstract : Humanity faces the challenges of urgently decreasing the environmental impact of agriculture, shifting diets and increasing food production. Oil palm is a tropical perennial crop emblematic of these challenges. While its cultivation can be associated with environmental impacts, oil palm can produce 3 to 7 t of edible oil ha-1 in optimal conditions, which is 7 to 10 fold higher than in annual oil crops. In this context, improving palm oil production sustainability is crucial for both reducing negative environmental impacts and ensuring food security. Application of synthetic nitrogen (N) fertilisers was identified as a major source of environmental impacts associated with the cultivation of oil palm. Life cycle assessments of palm oil have already been performed to help quantify impacts and identify potential improvements of management practices. However, the only available emission models to estimate N losses to environment are generally valid for annual crops and temperate climate conditions. The use of such general models in life cycle assessment may lead to very uncertain results or to low sensitivity of assessments to management practices. The overall objective of this research work was to help identify management practices to reduce N losses in the environment. The core of the work was hence to develop a model that estimates all N losses in oil palm plantations, while being sensitive to management practices. The study focused on N fluxes in industrial oil palm plantations on mineral soils. We performed four steps in order to complete the objectives of this research work. First, we conducted a literature review of all the existing knowledge about N fluxes and losses in plantations. Second, we compared 11 existing models that may be used to predict N losses in plantations. Third, we performed an in-depth Morris’s sensitivity analysis of one of the models, the APSIM-Oil palm process-based model. Fourth, we used all the information identified in the previous chapters, together with expert knowledge, to build IN-Palm, an agri-environmental indicator for N losses in oil palm plantations. We used the INDIGO® method and the fuzzy decision tree modelling approach to develop IN-Palm, and we validated this indicator using a field dataset of N leaching from a plantation in Sumatra, Indonesia. Our literature review and model comparison showed that oil palm peculiarities may impact significantly N dynamics and losses. We identified research gaps and uncertainties about N losses, their drivers and the modelling of oil palm peculiarities. We identified the main drivers of N losses and yield in the APSIM-Oil palm processbased model. We built IN-Palm, which uses 21 readily available input variables to estimate each N loss pathway. IN-Palm predictions of N leaching were acceptable, and IN-Palm has shown efficient to help testing management changes. This research constitutes a comprehensive synthesis of the available knowledge and models for N fluxes and losses in oil palm plantations. One of the main results is a novel agri-environmental indicator, IN-Palm, operationally-oriented, sensitive to local practices and environmental conditions, as well as potentially useable as an emission model for holistic approaches such as life cycle assessment. The INDIGO® method and fuzzy decision tree modelling approach were shown to be very well adapted for building agri-environmental indicators in contexts of knowledge scarcity. This indicator can be a useful base for further research about using agrienvironmental indicators to reduce uncertainty in life cycle assessment, and for future adaptations for other tropical perennial crops.
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Submitted on : Thursday, May 23, 2019 - 9:48:44 AM
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Lénaïc Pardon. Modelling of the nitrogen budget of oil palm plantations to help reduce losses to the environment. Case study in Sumatra, Indonesia. Soil study. Institut agronomique, vétérinaire et forestier de France; James Cook university of North Queensland, 2017. English. ⟨NNT : 2017IAVF0018⟩. ⟨tel-02137458⟩



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