Dynamics of Ecosystem Water Use Efficiency in Citrus Orchards of Central India Using Eddy Covariance and Landsat Measurements

Peddinti, Srinivasa Rao; Kambhammettu, B.V.N.P.; Rodda, Suraj Reddy; Thumaty, Kiran Chand; Suradhaniwar, Saurabh

doi:10.1007/s10021-019-00416-3

Cited by 21 publications

(15 citation statements)

References 68 publications

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“…Chardonnay potted plants subjected to varying soil water availability. Similarly, Peddinti et al [58] found a good correlation between VIs derived from visible, near-infrared, and shortwave-infrared bands with WUE i , thus corroborating our results.…”

Section: Potential Of the Thermal And Spectral Vegetation Indices To ...supporting

confidence: 92%

Water Stress Impacts on Grapevines (Vitis vinifera L.) in Hot Environments: Physiological and Spectral Responses

Cogato

Jewan

et al. 2022

Agronomy

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The projected increase in temperature and water scarcity represents a challenge for winegrowers due to changing climatic conditions. Although heat and drought often occur concurrently in nature, there is still little known about the effects of water stress (WS) on grapevines in hot environments. This study aimed to assess whether the grapevine’s physiological and spectral responses to WS in hot environments differ from those expected under lower temperatures. Therefore, we propose an integrated approach to assess the physiological, thermal, and spectral response of two grapevine varieties (Vitis vinifera L.), Grenache and Shiraz, to WS in a hot environment. In a controlled environment room (CER), we imposed high-temperature conditions (TMIN 30 °C–TMAX 40 °C) and compared the performance of well-watered (WW) and WS-ed potted own-rooted Shiraz and Grenache grapevines (SH_WW, SH_WS, GR_WW, and GR_WS, respectively). We monitored the vines’ physiological, spectral, and thermal trends from the stress imposition to the recovery after re-watering. Then, we performed a correlation analysis between the physiological parameters and the spectral and thermal vegetation indices (VIs). Finally, we looked for the best-fitting models to predict the physiological parameters based on the spectral VIs. The results showed that GR_WS was more negatively impacted than SH_WS in terms of net photosynthesis (Pn, GR-WS = 1.14 μmol·CO2 m−2·s−1; SH-WS = 3.64 μmol·CO2 m−2·s−1), leaf transpiration rate (E, GR-WS = 1.02 mmol·H2O m−2·s−1; SH-WS = 1.75 mmol·H2O m−2·s−1), and stomatal conductance (gs, GR-WS = 0.04 mol·H2O m−2·s−1; SH-WS = 0.11 mol·H2O m−2·s−1). The intrinsic water-use efficiency (WUEi = Pn/gs) of GR_WS (26.04 μmol·CO2 mol−1 H2O) was lower than SH_WS (34.23 μmol·CO2 mol−1 H2O) and comparable to that of SH_WW (26.31 μmol·CO2 mol−1 H2O). SH_WS was not unaffected by water stress except for E. After stress, Pn, gs, and E of GR_WS did not recover, as they were significantly lower than the other treatments. The correlation analysis showed that the anthocyanin Gitelson (AntGitelson) and the green normalised difference vegetation index (GNDVI) had significant negative correlations with stem water potential (stem), Pn, gs, and E and positive correlation with WUEi. In contrast, the photochemical reflectance index (PRI), the water index (WI), and the normalised difference infrared index (NDII) showed an opposite trend. Finally, the crop water stress (CWSI) had significant negative correlations with the stem in both varieties. Our findings help unravel the behaviour of vines under WS in hot environments and suggest instrumental approaches to help the winegrowers managing abiotic stress.

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Section: Potential Of the Thermal And Spectral Vegetation Indices To ...supporting

confidence: 92%

Water Stress Impacts on Grapevines (Vitis vinifera L.) in Hot Environments: Physiological and Spectral Responses

Cogato

Jewan

et al. 2022

Agronomy

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“…We then performed quality checks on the flux data using two tests: a steady state test and the developed turbulent condition test considering 1‐min‐averaged time series data (Foken & Wichura, 1996). We used REddyproc package coded in open source “R” environment along with a MATLAB script to perform primary data processing that includes (a) removal of negative CO 2 fluxes, (b) friction velocity correction, and (c) uncertainty analysis (Peddinti et al, 2019; Rodda et al, 2016). The measured fluxes were normalized with the source strength “Q” for ease in comparisons.…”

Section: Methodsmentioning

confidence: 99%

Sensitivity of Analytical Flux Footprint Models in Diverse Source‐Receptor Configurations: A Field Experimental Study

2020

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Flux footprint models simulate the source area of scalar fluxes from a measurement site but are hindered by the assumptions that are rarely satisfied in real field conditions. We conducted artificial tracer (CO2) experiments in unstable conditions over an open field, to evaluate three analytical footprint models (Kormann and Meixner‐KM, Hsieh‐HS, and Schuepp‐SP) under variable source‐receptor settings. Experimental configurations include (i) source length (point to semi‐infinite), (ii) emission height (below and above zero‐plane displacement), (iii) measurement height (1.5 and 2.8 m above ground level), and (iv) offset from the principal wind direction (±60°). Eddy covariance (EC) fluxes were measured using one source‐multiple sampling system approach. Results indicate that KM model is in good agreement with the EC measurements under ideal conditions (R2 = 0.81, root‐mean‐square error (RMSE) = 0.008 m−1) compared to HS (R2 = 0.60, RMSE = 0.014 m−1) and SP (R2 = 0.72, RMSE = 0.009 m−1) models. The KM model captured both footprint maximum and its location. Uncertainties in the model estimates were ascertained by considering random ensembles of wind speed, friction velocity, and Obukhov length. The KM model has resulted in the least uncertainty band containing all the flux observations. Error propagation into model simulations resulting from a progressive deviation of the underlying assumptions was assessed. Model‐to‐measurement discrepancies were positive (flux overestimation) for offset deviations up to ±30° and negative beyond. Modeled fluxes were sensitive to source length and emission height in comparison to the offset deviation. Overall, the KM model showed the least error (9.6 ± 8.12%) for different source‐receptor deviations. Study findings can help in evaluating and improving the analytical models for use under nonideal source‐receptor configurations and scaling measurements for different applications.

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“…Average daily maximum temperatures in the area range from 32 to 42 °C, occasionally rising to 48 °C, during summers, while average daily minimum temperature ranges from 15 to 24 °C during winters. The selected study area is also one of the largest Citrus growing regions of central India [ 43 ].…”

Section: Methodsmentioning

confidence: 99%

Time Series Forecasting of Univariate Agrometeorological Data: A Comparative Performance Evaluation via One-Step and Multi-Step Ahead Forecasting Strategies

Suradhaniwar

Kar

Durbha

et al. 2021

Sensors

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High-frequency monitoring of agrometeorological parameters is quintessential in the domain of Precision Agriculture (PA), where timeliness of collected observations and the ability to generate ahead-of-time predictions can substantially impact the crop yield. In this context, state-of-the-art internet-of-things (IoT)-based sensing platforms are often employed to generate, pre-process and assimilate real-time data from heterogeneous sensors and streaming data sources. Simultaneously, Time-Series Forecasting Algorithms (TSFAs) are responsible for generating reliable forecasts with a pre-defined forecast horizon and confidence. These TSFAs often rely on modelling the correlation between endogenous variables, the impact of exogenous variables on latent form and structural properties of data such as autocorrelation, periodicity, trend, pattern, and causality to approximate the model parameters. Traditionally, TSFAs such as the Holt–Winters (HW) and Autoregressive family of models (ARIMA) apply a linear and parametric approach towards model approximation, whilst models like Support Vector Regression (SVRs) and Neural Networks (NNs) adhere to a non-linear, non-parametric approach for modelling the historical data. Recently, Deep-Learning-based TSFAs such as Recurrent Neural Networks (RNNs), and Long-Short-Term-Memory (LSTMS) have gained popularity due to their capability to model long sequences of highly non-linear and stochastic data effectively. However, the evolution of TSFAs for predicting agrometeorological parameters pivots around one-step-ahead forecasting, which often overestimates the performance metrics defined for validating forecast capabilities of potential TSFAs. Hence, this paper attempts to evaluate and compare the performance of different machine learning (ML) and deep learning (DL) based TSFAs under one-step and multi-step-ahead forecast scenarios, thereby estimating the generalization capabilities of TSFA models over unseen data. The data used in this study are collected from an Automatic Weather Station (AWS), sampled at an interval of 15 min, and range over one month. Temperature (T) and Humidity (H) observations from the AWS are further converted into univariate, supervised time-series diurnal data profiles. Finally, walk-forward validation is used to evaluate recursive one-step-ahead forecasts until the desired prediction horizon is achieved. The results show that the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and SVR models outperform their DL-based counterparts in one-step and multi-step ahead settings with a fixed forecast horizon. This work aims to present a baseline comparison between different TSFAs to assist the process of model selection and facilitate rapid ahead-of-time forecasting for end-user applications.

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Dynamics of Ecosystem Water Use Efficiency in Citrus Orchards of Central India Using Eddy Covariance and Landsat Measurements

Cited by 21 publications

References 68 publications

Water Stress Impacts on Grapevines (Vitis vinifera L.) in Hot Environments: Physiological and Spectral Responses

Water Stress Impacts on Grapevines (Vitis vinifera L.) in Hot Environments: Physiological and Spectral Responses

Sensitivity of Analytical Flux Footprint Models in Diverse Source‐Receptor Configurations: A Field Experimental Study

Time Series Forecasting of Univariate Agrometeorological Data: A Comparative Performance Evaluation via One-Step and Multi-Step Ahead Forecasting Strategies

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