Development of an optimality-based model to investigate the contribution of abiotic limiting factors to the current global yield gap

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INRAE presentation

The French National Research Institute for Agriculture, Food, and Environment (INRAE) is a major player in research and innovation. It is a community of 12,000 people with 272 research, experimental research, and support units located in 18 regional centres throughout France. Internationally, INRAE is among the top research organisations in the agricultural and food sciences, plant and animal sciences, as well as in ecology and environmental science. It is the world’s leading research organisation specialising in agriculture, food and the environment. INRAE’s goal is to be a key player in the transitions necessary to address major global challenges. Faced with a growing world population, climate change, resource scarcity, and declining biodiversity, the Institute has a major role to play in building solutions and supporting the necessary acceleration of agricultural, food and environmental transitions.

Work environment, missions and activities

-Vous serez accueilli(e) au sein de :
l’unité ISPA (Interactions Sol Plante Atmosphère), qui associe INRAE et Bordeaux Sciences Agro, sur le site INRAE de Bordeaux (Villenave d’Ornon). Vous ferez parti(e) de l’équipe AGROFOR (Modélisation multi-échelles en agronomie et foresterie) qui regroupe chercheurs, ingénieurs et doctorants travaillant sur l’impact des pratiques agricoles/sylvicoles sur le fonctionnement des écosystèmes à larges échelles spatiales.
-Contexte scientifique :
“The future of agriculture faces two great challenges: meeting the substantial increases in food demand while decreasing its global environmental footprint” (Mueller et al., 2012). Several strategies should be combined to take up these challenges, among which, closing the yield gap, defined as the mismatch between potential yield (i.e. a yield only determined by genotype, plant population density, and location-specific solar radiation and temperature regimes) and actual yield. Factors limiting the yield encompass abiotic (nutrients, water) and biotic factors (pest, diseases, weeds). Yield achieved at a given field is dependent to the use of different resources, such as nitrogen (N) and phosphorus (P) fertilizers and water (H2O) when supplied through irrigation. The use of resources is far from efficient. E.g. while global soil P inputs through chemical and organic fertilizers are larger than the P contained in global harvest, around 30% of the cropland area has a negative local soil P budget (MacDonald et al., 2011). Nutrient input in excess could trigger losses to the environment while nutrient and water limitation could reduce yield. Improving the resource use efficiency is one way to increase yield while limiting the environmental footprint of agriculture. However, an in-depth understanding of the main abiotic factors driving the current yield gap at the global scale is missing in the literature. It is nevertheless required to improve resource management and to understand how yield can evolve in the future under different socio-economic and climatic scenarios.
GPCROP is a spatial explicit model (half-degree latitude x longitude resolution) that simulates the potential growth for maize and how the P limitation can have an effect on this potential growth (Ringeval et al., 2025). The model explicitly represents key mechanisms such as the replenishment of the soil P solution by more stable soil P pools, the diffusion of P in soil, and plant adjustments to P limitation. Plant adjustments are mainly represented through an optimized plant daily allocation of carbon (C) and P among plant organs (root, leaf, grain). Trade-off in allocation between root and leaf as function of resource limitation is one component of what is called “eco-evolutionary optimality principles”. These principles are in particular based on the concept that organisms adjust to their environment on short (eco-physiological, days to months) timescale (Harrison et al., 2021). Thanks to these additional assumptions, optimality principles
allows to reduce the number of uncertain parameters needed in the current terrestrial biosphere models, and thus, are a promising strategy to make their simulations much less uncertain.
-Vous serez plus particulièrement en charge de :
The M2 internship will aim to develop GPCROP to further incorporate the N limitation. Water will be considered as non-limiting in the framework of this internship. Model development will be done through a parsimonious-parameter approach by mimicking what was done for P but by adapting it to N cycle specificity (in particular soil N dynamics). Implementation will focus on soil N dynamics, N root uptake, and allocation between plant organs. The allocation following N limitation will be evaluated against a long-term field trial in France with different N treatments (Plénet, 1995). While this is a well proven dataset, it has the specificity to provide both measurements over the course of the growing season (vs. “only” at harvest time in most long-term field trials) and for roots, which are valuable to evaluate the plant adjustments simulated. Long-term field trial at Broadbalk (Van Grinsven et al., 2022) with combination of 6 N and 3 P treatments will be used to assess NxP limitations simulated. A network of long-term field trials with N treatments (Van Grinsven et al., 2022) will be used to evaluate N limitations simulated under different environments. At the end of the internship, global simulations to estimate the N, P and NxP limitation of the potential yield under irrigated conditions could be performed.

A proposal to get a funding for a PhD related to this internship is currently under submission. The PhD would aim to consider P, N and H2O limitations together, following the further implementation within GPCROP of an eco-evolutionary optimality-based approach applied to the C assimilation–H2O transpiration trade-off (Stocker et al., 2020).
The internship could focus on H2O limitation (instead of N limitation), as function of the M2 student interest.The internship could be done in french or in english

Training and skills

-Formation recommandée : Master 2
-Connaissances souhaitées : En écophysiologie
-Expérience appréciée : Langage de programmation Python, système Linux
-Aptitudes recherchées : Fort intérêt pour la programmation/modélisation

INRAE's life quality

By joining INRAE, you can benefit from, depending on the type and duration of your contract:

- up to 30 days of annual leave + up to 15 additional days off (for full-time employees);
- access to the French public healthcare system;
- 50% of the cost of your supplementary health insurance covered by INRAE;
- childcare and parenting support;
- training and career guidance;
- social assistance and financial support;
- holiday vouchers and accommodation at preferential rates;
- sports and cultural activities;
- subsidised staff restaurants.

How to apply

I send my CV and my motivation letter