Inundation, excessive precipitation, or inadequate field drainage can cause waterlogging of cultivated land. It is anticipated that climate change will increase the frequency, intensity, and unpredictability of flooding events. This stress affects 10–15 million hectares of wheat every year, resulting in 20–50% yield losses. Since this crop greatly sustains a population’s food demands, providing ca. 20% of the world’s energy and protein diets requirements, it is crucial to understand changes in soil and plant physiology under excess water conditions. Variations in redox potential, pH, nutrient availability, and electrical conductivity of waterlogged soil will be addressed, as well as their impacts in major plant responses, such as root system and plant development. Waterlogging effects at the leaf level will also be addressed, with a particular focus on gas exchanges, photosynthetic pigments, soluble sugars, membrane integrity, lipids, and oxidative stress.

Nitrogen use efficiency (NUE) is a central issue to address regarding the nitrogen (N) uptake by crops, and can be improved by applying the correct dose of fertilizers at specific points in the fields according to the plants status. The N nutrition index (NNI) was developed to diagnose plant N status. However, its determination requires destructive, time-consuming measurements of plant N content (PNC) and plant dry matter (PDM). To overcome logistical and economic problems, it is necessary to assesses crop NNI rapidly and non-destructively. According to the literature which we reviewed, it, as well as PNC and PDM, can be estimated using vegetation indices obtained from remote sensing. While sensory techniques are useful for measuring PNC, crop growth models estimate crop N requirements. Research has indicated that the accuracy of the estimate is increased through the integration of remote sensing data to periodically update the model, considering the spatial variability in the plot. However, this combination of data presents some difficulties. On one hand, at the level of remote sensing is the identification of the most appropriate sensor for each situation, and on the other hand, at the level of crop growth models is the estimation of the needs of crops in the interest stages of growth. The methods used to couple remote sensing data with the needs of crops estimated by crop growth models must be very well calibrated, especially for the crop parameters and for the environment around this crop. Therefore, this paper reviews currently available information from Google Scholar and ScienceDirect to identify studies relevant to crops N nutrition status, to assess crop NNI through non-destructive methods, and to integrate the remote sensing data on crop models from which the cited articles were selected. Finally, we discuss further research on PNC determination via remote sensing and algorithms to help farmers with field application. Although some knowledge about this determination is still necessary, we can define three guidelines to aid in choosing a correct platform.

SUMMARY. Triticale, a hybrid of wheat and rye, is a cereal grain that displays a nutritive value similar to wheat and a resistance to hard agronomic conditions