Dataset Information

Crop Management in Controlled Environment Agriculture (CEA) Systems Using Predictive Mathematical Models.

ABSTRACT: Proximal sensors in controlled environment agriculture (CEA) are used to monitor plant growth, yield, and water consumption with non-destructive technologies. Rapid and continuous monitoring of environmental and crop parameters may be used to develop mathematical models to predict crop response to microclimatic changes. Here, we applied the energy cascade model (MEC) on green- and red-leaf butterhead lettuce (Lactuca sativa L. var. capitata). We tooled up the model to describe the changing leaf functional efficiency during the growing period. We validated the model on an independent dataset with two different vapor pressure deficit (VPD) levels, corresponding to nominal (low VPD) and off-nominal (high VPD) conditions. Under low VPD, the modified model accurately predicted the transpiration rate (RMSE = 0.10 Lm-2), edible biomass (RMSE = 6.87 g m-2), net-photosynthesis (rBIAS = 34%), and stomatal conductance (rBIAS = 39%). Under high VPD, the model overestimated photosynthesis and stomatal conductance (rBIAS = 76-68%). This inconsistency is likely due to the empirical nature of the original model, which was designed for nominal conditions. Here, applications of the modified model are discussed, and possible improvements are suggested based on plant morpho-physiological changes occurring in sub-optimal scenarios.

SUBMITTER: Amitrano C

PROVIDER: S-EPMC7308940 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Crop Management in Controlled Environment Agriculture (CEA) Systems Using Predictive Mathematical Models.

Amitrano Chiara C Chirico Giovanni Battista GB De Pascale Stefania S Rouphael Youssef Y De Micco Veronica V

Sensors (Basel, Switzerland) 20200531 11

Proximal sensors in controlled environment agriculture (CEA) are used to monitor plant growth, yield, and water consumption with non-destructive technologies. Rapid and continuous monitoring of environmental and crop parameters may be used to develop mathematical models to predict crop response to microclimatic changes. Here, we applied the energy cascade model (MEC) on green- and red-leaf butterhead lettuce (<i>Lactuca sativa</i> L. var. <i>capitata</i>). We tooled up the model to describe the ...[more]

PMID: 32486394

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Project description:Microbe-plant interactions in the root zone not only shape crop performance in soil but also in hydroponic cultivation systems. The biological and physicochemical properties of the plant-growing medium determine the root-associated microbial community and influence bacterial inoculation effectiveness, which affects plant growth. This study investigated the combined impact of plant-growing media composition and bacterial community inoculation on the root-associated bacterial community of hydroponically grown lettuce (Lactuca sativa L.). Ten plant-growing media were composed of varying raw materials, including black peat, white peat, coir pith, wood fibre, composted bark, green waste compost, perlite and sand. In addition, five different bacterial community inocula (BCI S1-5) were collected from the roots of lettuce obtained at different farms. After inoculation and cultivation inside a vertical farm, lettuce root-associated bacterial community structures, diversity and compositions were determined by evaluating 16S rRNA gene sequences. The study revealed distinct bacterial community structures among experimental replicates, highlighting the influence of raw material variations on root-associated bacterial communities, even at the batch level. However, bacterial community inoculation allowed modulation of the root-associated bacterial communities independently from the plant-growing medium composition. Bacterial diversity was identified as a key determinant of plant growth performance with green waste compost introducing Bacilli and Actinobacteria, and bacterial community inoculum S3 introducing Pseudomonas, which positively correlated with plant growth. These findings challenge the prevailing notion of hydroponic cultivation systems as sterile environments and highlight the significance of proper plant-growing media raw material selection and bacterial community inoculation in shaping root-associated microbiomes that provide stability through microbial diversity. This study supports the concept of creating bacterially enhanced plant-growing media to promote plant growth in controlled environment agriculture.

Dataset Information

Crop Management in Controlled Environment Agriculture (CEA) Systems Using Predictive Mathematical Models.

Publications

Crop Management in Controlled Environment Agriculture (CEA) Systems Using Predictive Mathematical Models.

Similar Datasets

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