Development of a transpiration model for precise tomato (Solanum lycopersicum L.) irrigation control under various environmental conditions in greenhouse

Micromachined needle-like calorimetric flow sensor for sap flux density measurement in the xylem of plants

Miniaturized silicon thermal probes for plant's sap flow measurement, or micro sap flow sensors, have advantages in minimum invasiveness, low power consumption, and fast responses. Practical applications in sap flow measurement has been demonstrated with the single-probe silicon micro sensors. However, the sensors could not detect flow directions and require estimating zero sap flow output that leads to significant source of uncertainty. Furthermore, silicon-needles would break easily during the insertion into plants. We present the first three-element micro thermal sap flow sensor packaged on a durable printed circuit board needle that can measure bidirectional flows with improved dynamics and precision. The performance of the newly designed calorimetric flow sensor was confirmed through precision calibration and field test on tomato stems. A calibration curve for a tomato stem was obtained with a sensitivity of 0.299 K/(µL mm-2 s-1) under the maximum temperature increase of 4.61 K. Results from the field test for one month revealed a correlation between the measured sap flux density and related conditions such as solar radiation, vapor pressure deficit, sunshade and irrigation. The developed sensor will contribute to practical long-term sap flow monitoring for small and delicate plants with minimal physical invasion.

Simulation of Daily Transpiration of Tomatoes Grown in Venlo-Type Greenhouse Substrates

An appropriate water supply strategy is imperative for obtaining tomatoes of a high yield and quality; the lack of one has caused resource wastage and quality deterioration. To determine the suitable irrigation amount and simulate daily transpiration under these optimal irrigation conditions, a two-year greenhouse cultivation experiment was conducted over 2022-2023. Commencing at anthesis, three distinct irrigation gradients were triggered and designated as irrigation controls with the lower limits set at 80% (T1), 70% (T2), and 60% (T3) of the substrate water-holding capacity. We determined the optimal irrigation amount by ranking the treatments using the TOPSIS method, balancing the tomato yield and quality. A segmented daily transpiration model under optimal irrigation conditions driven by crop and environmental factors was established using the Marquardt method and data from 2022, and the model was validated using data from 2023. The results indicated that T2 was the optimal irrigation amount, with the water use efficiency increased by 18.0%, but with a 10.9% decrease in yield, while the quality indices improved significantly. The R2 values of the segmented model in the flowering and fruit-setting stage and the picking stage were 0.92 and 0.86, respectively, which could provide support for optimized water management for tomato planting in greenhouse substrate cultivation.

Methods for Functional Physiological Phenotyping and High-Order Data Quantification

This chapter presents the application of Plantarray, a high-throughput platform commercially available for noninvasive monitoring of plant functional physiology phenotyping (FPP). The platform continuously measures water flux in the soil-plant-atmosphere for each plant in dynamic environments. To better interpret the massive phenotypic data acquired with FPP, several quantitative analysis methods were demonstrated for various types of data. Simple mathematical models were utilized to fit characteristic parameters of plant transpiration response to drought stress. Additionally, ecophysiological models were employed to quantify the sensitivity of transpiration to radiation and vapor pressure deficit (VPD) as component traits and predict more complex higher-order traits. The established protocols provide a tangible tool for integrating FPP and model analysis to address complex traits.

The Role of Plant Growth Regulators in Modulating Root Architecture and Tolerance to High-Nitrate Stress in Tomato

Plant growth regulators are known to exert strong influences on plant performance under abiotic stress, including exposure to high nitrate, as occurs commonly in intensive vegetable production. However, direct comparative evaluations of growth regulators under otherwise identical conditions in major crop species are scarce. In this study, tomato (Solanum lycopersicum L.) was used as a model crop, and the roles of four common exogenously applied plant growth regulators (MT, melatonin; SA, salicylic acid; HA, humic acid; SNP, sodium nitroprusside) in regulating crop growth were studied under high-nitrate stress. We provide a particular focus on root system architecture and root physiological responses. Our data show that all four growth regulators improve tomato tolerance under high nitrate, but that this occurs to differing extents and via differing mechanisms. Optimal concentrations of MT, SA, HA, and SNP were 50 μmol L^-1, 25 μmol L^-1, 25 mg L^-1, and 50 μmol L^-1, respectively. MT and SNP produced the strongest effects. MT enhanced root growth while SNP enhanced above-ground growth. Growth of coarse and thin lateral roots was significantly improved. Furthermore, an enhancement of root vitality and metabolism, improved integrity of root cell membranes, and an increase in antioxidant enzyme activities were found, but regulatory mechanisms were different for each growth regulator. Our results show that in particular the application of MT and SNP can improve growth of tomato in intensive vegetable production under high-nitrate stress and that root growth stimulation is of special importance in procuring these beneficial effects.