**5. Concluding remarks**

In this chapter, we showed that fractal, compositional and machine learning models are promising alternatives to former empirical and mechanistic models to diagnose soil quality and plant nutrition at local scale and conduct side-by-side comparisons. Fractal kinetics confirmed that organic matter decomposition rates are controlled by protection mechanisms developing during organic matter transformation in soils. Site-specific coefficients can be assigned to decomposition rates under soil management practices. Compositional Data Analysis accounted for the special geometry of *D*-part compositions using log ratio transformations to tackle numerical bias before running numerical analyses. Machine learning methods can handle large and diversified datasets acquired through close collaboration between stakeholders.The CoDa methods can be combined with machine learning methods to diagnose nutrient imbalance and rank nutrients in the order of their limitation to yield by side-by-side comparison with successful neighbors.

This paper emphasized the need to change paradigm from the regional to the local scale to diagnose soil quality and plant nutrients and customize recommendations. Local features can be assembled in large and diversified numbers to address trustful feature combinations, then carved to a minimum data set impacting system's productivity and sustainability. Large and diversified data sets can be processed by methods of machine learning and compositional data analysis to reach the field or subfield scale. This requires collecting data uniformly and a close collaboration between stakeholders.
