Plants are constantly subjected to variations in their surrounding environment, which affect their functioning in different ways. The influence of environmental factors on the physiology of plants depends on several factors including the intensity, duration and frequency of the variation of the external stimulus. Water deficit is one of the main limiting factors for agricultural production worldwide and affects many physiological processes in plants. The aim of this study was to analyse the effects of different rates of induced water deficit on the leaf photosynthetic responses of soybean (Glycine max L.) and cowpea (Vigna unguiculata L.). The plants were subjected to two types of water deficit induction: a rapid induction (RD) by which detached leaves were dehydrated by the exposure to air under controlled conditions and a slow induction (SD) by suspending irrigation under greenhouse conditions. The leaf gas exchange, chlorophyll (Chl) a fluorescence, and relative water content (RWC) were analysed throughout the water-deficit induction. V. unguiculata and G. max demonstrated similar dehydration as the soil water percentage declined under SD, with V. unguiculata showing a greater stomatal sensitivity to reductions in the RWC. V. unguiculata plants were more sensitive to water deficit, as determined by all of the physiological parameters when subjected to RD, and the net photosynthetic rate (P N ) was sharply reduced in the early stages of dehydration. After the plants exposed to the SD treatment were rehydrated, V. unguiculata recovered 65% of the P N in relation to the values measured under the control conditions (initial watering state), whereas G. max recovered only 10% of the P N . Thus, the better stomatal control of V. unguiculata could enable the maintenance of the RWC and a more efficient recovery of the P N than G. max.

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Because of the complexity of plant responses to water deficit, researchers have attempted to identify simplified models to understand critical aspects of the problem by searching for single indicators that would enable evaluations of the effects of environmental changes on the entire plant. However, this reductionist approach, which is often used in plant sciences, makes it difficult to distinguish systemic emergent behaviours. Currently, a new class of models and epistemology have called attention to the fundamental properties of complex systems. These properties, termed 'emergent', are observed at a large scale of the system (top hierarchical level) but cannot be observed or inferred from smaller scales of observation in the same system. We propose that multivariate statistical analysis can provide a suitable tool to quantify global responses to water deficit, allowing a specific and partially quantitative assessment of emergent properties. Based on an experimental study, our results showed that the classical approach of the individual analysis of different data sets might provide different interpretations for the observed effects of water deficit. These results support the hypothesis that a cross-scale multivariate analysis is an appropriate method to establish models for systemic understanding of the interactions between plants and their changing environment.

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