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Dataset of photosynthesis and photosynthetic factors measurements of greenhouse tomato.

ABSTRACT: This article presents a set of photosynthetic responses and related environmental variables from greenhouse tomato plants (Lycopersicum esculentum). The dataset was obtained by direct in situ measurement of functional leaves of 100 different plants with a portable photosynthesis system. The measurements were taken for six hours at different time intervals in 16 different days during the cycle of November 2019 to January 2020. This dataset can be used to understand the physiology of greenhouse tomato plants and for the design of photosynthesis forecasting models. It is also useful in precision agriculture and for designing automatic controllers for optimal temperature, humidity, and CO2 enrichment, with the purpose of maximizing productivity and avoiding waste of resources. Additionally, this dataset can be used for research in time series analysis.

SUBMITTER: Manjarrez-Sanchez J

PROVIDER: S-EPMC7494668 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Dataset of photosynthesis and photosynthetic factors measurements of greenhouse tomato.

Manjarrez-Sanchez Jorge J Martinez-Carrillo Gerardo G

Data in brief 20200903

This article presents a set of photosynthetic responses and related environmental variables from greenhouse tomato plants (Lycopersicum esculentum). The dataset was obtained by direct in situ measurement of functional leaves of 100 different plants with a portable photosynthesis system. The measurements were taken for six hours at different time intervals in 16 different days during the cycle of November 2019 to January 2020. This dataset can be used to understand the physiology of greenhouse to ...[more]

PMID: 32984468

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Project description:BackgroundOver the past decades, the structural and functional genomics of rice have been deeply studied, and high density of molecular genetic markers have been developed. However, the genetic variation in leaf photosynthesis, the most important trait for rice yield improvement, was rarely studied. The lack of photosynthesis phenotyping tools is one of the bottlenecks, as traditional direct photosynthesis measurements are very low-throughput, and recently developed high-throughput methods are indirect measurements. Hence, there is an urgent need for a fast, accurate and direct measurement approach.ResultWe reported a fast photosynthesis measurement (FPM) method for phenotyping photosynthetic capacity of rice, which measures photosynthesis of excised tillers in environment-controlled lab conditions. The light response curves measured using FPM approach coped well with that the curves measured using traditional gas exchange approach. Importantly, the FPM technique achieved an average throughput of 5.4 light response curves per hour, which was 3 times faster than the 1.8 light response curves per hour using the traditional method. Tillers sampled at early morning had the highest photosynthesis, stomatal conductance and the lowest variability. In addition, even 12 h after sampling, there was no significant difference of photosynthesis rate between excised tillers and in situ. We finally investigated the genetic variations of photosynthetic traits across 568 F2 lines using the FPM technique and discussed the logistics of screening several hundred samples per day per instrumental unit using FPM to generate a wealth of photosynthetic phenotypic data, which might help to improve the selection power in large populations of rice with the ultimate aim of improving yield through improved photosynthesis.ConclusionsHere we developed a high-throughput method that can measure the rice leaf photosynthetic capacity approximately 10 times faster than traditional gas exchange approaches. Importantly, this method can overcome measurement errors caused by environmental heterogeneity under field conditions, and it is possible to measure 12 or more hours per day under lab conditions.

Project description:Combining information of plant physiological processes with climate control systems can improve control accuracy in controlled environments as greenhouses and plant factories. Through that, resource optimization can be achieved. To predict the plant physiological processes and implement them in control actions of interest, a reliable monitoring system and a capable control system are needed. In this paper, we focused on the option to use real-time crop monitoring for precision climate control in greenhouses. For that, we studied the processes and external factors influencing leaf net CO2 assimilation rate (AL , µmol CO2 m-2 s-1) as possible variables of a plant performance indicator. While measured greenhouse environmental variables such as light, temperature, or humidity showed a direct relation between AL and light-quantum yield of photosystem II (Φ2), we defined three objectives: (1) to explore the relationship between climate variables and AL , as well as Φ2; (2) create a simple and reliable method for real-time prediction of AL with continuously Φ2 measurements; and (3) calibrate parameters to predict chloroplast electron transport rate as input in AL modelling. Due to practical obstacles in measuring CO2 gas-exchange in commercial production, we explored a method to predict AL by measuring Φ2 of leaves in a commercial hydroponic greenhouse tomato crop ("Pureza"). We calculated AL with two different approaches based on either the negative exponential response model with simplified biochemical equations (marked as Model I) or the non-rectangular hyperbola full biochemical photosynthetic models (marked as Model II). Using Model I can only be used to predict AL with large uncertainty (R2 0.64; RMSE 2.21), while using Φ2 as input to Model II could be used to improve the prediction accuracy of AL (R2 0.71; RMSE 1.98). Our results suggests that (1) Φ2 light signals can be used to predict net photosynthesis rate with high accuracy; (2) a parameterized photosynthetic electron transport rate model is suitable predicting measured electron transport rate (J) and AL . The system can be used as decision support system (DSS) for plant and crop performance monitoring when leaf-dynamics are up-scaled to the plant or crop level.

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Dataset of photosynthesis and photosynthetic factors measurements of greenhouse tomato.

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Dataset of photosynthesis and photosynthetic factors measurements of greenhouse tomato.

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