Integrated physiological and agronomic modelling of N capture and use within the plant

Abstract

Today farmers have several constraints to take into account in managing their crops: (i) competitiveness: productivity must be maintained or increased whereas inputs must be decreased, (ii) the environmental consequences of cultural practices: pesticide and fertilizer use must be decreased, and (iii) product quality must be improved and nitrogen nutrition is an important factor in harvest quality. These new constraints sometimes conflict: maximum yield is often obtained with large amounts of N, increasing the risks of N leaching. The determination of rates and dates for nitrogen application must become more precise in this context. Tools are required for the forecasting of crop requirements, the diagnosis of N deficiencies during the crop cycle and breeding of new adapted varieties. Models and diagnosis indicators have been developed to meet these needs, but those relating to nitrogen are often based on empirical relationships. Moreover, the available models and indicators often fail to account for cultivar-specific responses. The improvement of agronomic tools and the breeding of new varieties adapted to new cropping systems should be based on a thorough understanding of the key metabolic processes involved, and the relative contributions of these processes to yield determination in conditions of fluctuating N supply. For both purposes, more information is required about plant and crop N economy. In this paper, the way in which N absorption and use within the plant and crop, plant responses to deficiencies and excesses of nitrogen are taken into account in major agronomic models is described first. The level of sophistication of the modules comprising these models depends on operational objectives. Secondly, the ways in which the most recent molecular plant physiology findings can, and indeed should, be integrated into models at the crop and crop cycle levels are described. The potential value of this approach for improving current agronomic models and diagnostic tools, and for breeding more efficient varieties is also discussed.