Modelling Evapotranspiration of Container Crops for Irrigation Scheduling

Bacci, L.; Battista, Piero; Cardarelli, Mariateresa; Carmassi, Giulia; Rouphael, Youssef; Incrocci, Luca; Malorgio, Fernando; Pardossi, Alberto; Rapi, B.; Colla, G.

doi:10.5772/18697

Cited by 11 publications

(6 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results of this study indicated that the humidity and wind velocity have very low influence in the greenhouse ET model. These results confirm the possibility to simplify the ET model, as suggested by Carmassi et al [48], Marfa et al [49] and Bacci et al [50].…”

Section: Discussionsupporting

confidence: 88%

Effect of Environmental Measurement Uncertainty on Prediction of Evapotranspiration

Chen¹,

Chen

2018

Atmosphere

View full text Add to dashboard Cite

Evapotranspiration (ET) is a typical biological environmental process to influence leaf temperature, crop water requirement, and greenhouse microclimate. Affecting factors of ET include air temperature, air relative humidity, wind speed, solar radiation, longwave radiation, soil moisture, CO2 concentration, and crop state. In this study, two ET models of indoor cultivation commonly adopted in literature were selected to evaluate the effect of the performance of sensors on the model uncertainty. The method of the International Organization for Standardization, Guides to the expression of Uncertainty in Measurement (ISO GUM) was adopted. The result indicated that the performance of leaf area index (LAI) and solar radiation (Is) sensors were primary sources of uncertainty. The uncertainty of ET models due to sensor performance needs to be considered. To ensure the predictive ability for applying the ET model for crops irrigation management and greenhouse environmental control, the improvements in the measurement of environmental variables for calculating ET would be of particular importance. The method of this study can be used for evaluating the uncertainty of ET models that calculate ET based on environmental variables measured by meteorological sensors or the remote sensing technique.

show abstract

Section: Discussionsupporting

confidence: 88%

Effect of Environmental Measurement Uncertainty on Prediction of Evapotranspiration

Chen¹,

Chen

2018

Atmosphere

View full text Add to dashboard Cite

show abstract

“…These models take into account greenhouse climate variables such as radiation and vapor pressure deficit and crop measurements such as the Leaf Area Index (LAI) or stomatal resistance [28,[33][34][35][36][37][38][39]. In some cases, a multiple linear regression of ET against vapor pressure deficit, as well as outside or inside solar radiation has been proposed for irrigation management in greenhouse crops [40]. For tomatoes, it was shown in [28] that the Baille model in [33] simplifies to:…”

Section: The Standard Models and Beyondmentioning

confidence: 99%

Bayesian Sigmoid-Type Time Series Forecasting with Missing Data for Greenhouse Crops

Kocian

Carmassi

Cela

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

This paper follows an integrated approach of Internet of Things based sensing and machine learning for crop growth prediction in agriculture. A Dynamic Bayesian Network (DBN) relates crop growth associated measurement data to environmental control data via hidden states. The measurement data, having (non-linear) sigmoid-type dynamics, are instances of the two classes observed and missing, respectively. Considering that the time series of the logistic sigmoid function is the solution to a reciprocal linear dynamic model, the exact expectation-maximization algorithm can be applied to infer the hidden states and to learn the parameters of the model. At iterative convergence, the parameter estimates are then used to derive a predictor of the measurement data several days ahead. To evaluate the performance of the proposed DBN, we followed three cultivation cycles of micro-tomatoes (MicroTom) in a mini-greenhouse. The environmental parameters were temperature, converted into Growing Degree Days (GDD), and the solar irradiance, both at a daily granularity. The measurement data were Leaf Area Index (LAI) and Evapotranspiration (ET). Although measurement data were only available scarcely, it turned out that high quality measurement data predictions were possible up to three weeks ahead.

show abstract

“…Irrigation of container plants, however, has been under increasing scrutiny, which is due to the following reasons: First, freshwater is an irreplaceable natural resource [3], but container plant production is one of the heaviest consumers. Irrigation of container-grown vegetables and nursery crops uses up to 1500 mm [4] and 2900 mm freshwater [5], respectively. Production of 212,055 hectare of container horticultural crops in open field used 776 billion liters of water annually in the United States (U.S.) [6].…”

Section: Introductionmentioning

confidence: 99%

Daily Water Requirement of Container Grown Davallia bullata and Nephrolepis exaltata and Implication in Irrigation Practices

Beeson

Kjelgren

Chen

2020

Water

View full text Add to dashboard Cite

Container crop production has become increasingly popular, but daily water requirements of those crops from transplanting to marketable or harvestable stages are largely unavailable. To address this concern, daily water consumption of two container-grown fern species, Davallia bullata and Nephrolepis exaltata from initial transplanting to marketable size were studied using a canopy closure model. Daily actual evapotranspiration (ETA) of D. bullata ranged from 4.6 mL to 76.5 mL with an average of 29.0 mL per plant per day. The mean cumulative ETA was 13.2 L during 431 days of production spanning from 8 November 2006 to 4 February 2008. Two crops of N. exaltata were produced. Daily ETA per N. exaltata plant produced in crop 1 varied from 19.0 to 241.2 mL with an average of 69.5 mL, and daily ETA of crop 2 differed from 5.7 to 136.8 mL with a mean of 74.0 mL. Both crops had a cumulative ETA of 9.4 L. Such differences in daily ETA and cumulative ETA between the two fern species raised further concern of irrigation practices in commercial foliage plant production as multiple species are commonly produced in one greenhouse and share the same irrigation schedule. Comparing daily ETA and cumulative ETA values of the ferns with the other studied foliage plants indicated that daily ETA and cumulative ETA are species specific. Therefore, to improve irrigation efficiency, daily ETA and cumulative ETA values of major container-grown plants should be established. Implementing the research-based daily ETA and cumulative ETA in container plant production should reduce irrigation water leaching and runoff and conserving freshwater resources.

show abstract

Modelling Evapotranspiration of Container Crops for Irrigation Scheduling

Cited by 11 publications

References 39 publications

Effect of Environmental Measurement Uncertainty on Prediction of Evapotranspiration

Effect of Environmental Measurement Uncertainty on Prediction of Evapotranspiration

Bayesian Sigmoid-Type Time Series Forecasting with Missing Data for Greenhouse Crops

Daily Water Requirement of Container Grown Davallia bullata and Nephrolepis exaltata and Implication in Irrigation Practices

Contact Info

Product

Resources

About