Climate change has increased the severity of drought episodes by further reducing precipitation in vulnerable zones. Drought induces a substantial decrease in agricultural water, reducing crop yields. Consequently, addressing water consumption can increase farmers’ profits. This work describes lab-to-field research in Zea mays, using two biostimulants: glycine betaine (GB) and L-pyroglutamic acid (PG). The biostimulant optimal dosages were selected using a hydroponic system with 20% polyethylene glycol and nursery experiments under water-deficit irrigation. The established dosages were evaluated in field trials in which irrigation was reduced by 20%. Laboratory biostimulant optimisation showed in stressed treated seedlings (GB 0.1 mM; PG 1 mM) an increased dry weight, relative growth rate and water use efficiency, reducing seedling growth loss between 65 and 85%, respectively. Field trials using a GB-optimised dosage showed an increase in plants’ growth, grain yield and flour Ca content. In addition, grain flour carbohydrate content and protein remained similar to control well-watered plants. Finally, the economic aspects of biostimulant treatments, water consumption, water sources (ground vs. desalinated) and grain biomass were addressed. Overall, GB treatment demonstrated to be a valuable tool to reduce water consumption and improve farmers’ earnings.

Biostimulants became a hotspot in the fight to alleviate the consequences of abiotic stresses in crops. Due to their complex nature, it is challenging to obtain stable and reproducible final products and more challenging to define their mechanism of action. As an alternative, small molecule-based biostimulants, such as polyamines have promoted plant growth and improved stress tolerance. However, profound research about their mechanisms of action is still missing. To go further, we tested the effect of putrescine (Put) and its precursor ornithine (Orn) and degradation product 1,3-diaminopropane (DAP) at two different concentrations (0.1 and 1 mM) as a seed priming on in vitro Arabidopsis seedlings grown under optimal growth conditions, osmotic or salt stress. None of the primings affected the growth of the seedlings in optimal conditions but altered the metabolism of the plants. Under stress conditions, almost all primed plants grew better and improved their greenness. Only Orn-primed plants showed different plant responses. Interestingly, the metabolic analysis revealed the implication of the N- acetylornithine and Orn and polyamine conjugation as the leading player regulating growth and development under control and stress conditions. We corroborated polyamines as very powerful small molecule-based biostimulants to alleviate the adverse abiotic stress effects.

The estimation of plant growth is a challenging but key issue that may help us to understand crop vs. environment interactions. To perform precise and high-throughput analysis of plant growth in field conditions, remote sensing using LiDAR and unmanned aerial vehicles (UAV) has been developed, in addition to other approaches. Although there are software tools for the processing of LiDAR data in general, there are no specialized tools for the automatic extraction of experimental field blocks with crops that represent specific “points of interest”. Our tool aims to detect precisely individual field plots, small experimental plots (in our case 10 m${}^2$) which in agricultural research represent the treatment of a single plant or one genotype in a breeding trial. Cutting out points belonging to the specific field plots allows the user to measure automatically their growth characteristics, such as plant height or plot biomass. For this purpose, new method of edge detection was combined with Fourier transformation to find individual field plots. In our case study with winter wheat, two UAV flight levels (20 and 40 m above ground) and two canopy surface modelling methods (raw points and B-spline) were tested. At a flight level of 20 m, our algorithm reached a $0.78$ to $0.79$ correlation with LiDAR measurement with manual validation (RMSE = $0.19$) for both methods. The algorithm, in the Python 3 programming language, is designed as open-source and is freely available publicly, including the latest updates.