Modelling evapotranspiration of soilless cut roses "Red Naomi" based on climatic and crop predictors

Original PaperThis study aimed to estimate the daily crop evapotranspiration (ETc) of soilless cut ‘Red Naomi’ roses, cultivated in a commercial glass greenhouse, using climatic and crop predictors. A multiple stepwise regression technique was applied for estimating ETc using the daily relative humidity, stem leaf area and number of leaves of the bended stems. The model explained 90% of the daily ETc variability (R2 = 0.90, n = 33, P < 0.0001) measured by weighing lysimeters. The mean relative difference between the observed and the estimated daily ETc was 9.1%. The methodology revealed a high accuracy and precision in the estimation of daily ETcinfo:eu-repo/semantics/publishedVersio

Retrieval of maize leaf area index using hyperspectral and multispectral data

Field spectra acquired from a handheld spectroradiometer and Sentinel-2 images spectra were used to investigate the applicability of hyperspectral and multispectral data in retrieving the maize leaf area index in low-input crop systems, with high spatial and intra-annual variability, and low yield, in southern Mozambique, during three years. Seventeen vegetation indices, comprising two and three band indices, and nine machine learning regression algorithms (MLRA) were tested for the statistical approach while five cost functions were tested in the look-up-table (LUT) inversion approach. The three band vegetation indices were selected, specifically the modified difference index (mDId: 725; 715; 565) for the hyperspectral dataset and the modified simple ratio (mSRc: 740; 705; 865) for the multispectral dataset of field spectra and the three band spectral index (TBSIb: 665; 865; 783) for the Sentinel-2 dataset. The relevant vector machine was the selected MLRA for the two datasets of field spectra (multispectral and hyperspectral) while the support vector machine was selected for the Sentinel-2 data. When using the LUT inversion technique, the minimum contrast estimation and the Bhattacharyya divergence cost functions were the best performing. The vegetation indices outperformed the other two approaches, with the TBSIb as the most accurate index (RMSE = 0.35). At the field scale, spectral data from Sentinel-2 can accurately retrieve the maize leaf area index in the study areainfo:eu-repo/semantics/publishedVersio

Estimation of grapevine predawn leaf water potential based on hyperspectral reflectance data in Douro wine region

Hyperspectral data collected through a handheld spectroradiometer (400-1010 nm) were tested for assessing the grapevine predawn leaf water potential (ѱpd) measured by a Scholander chamber in two test sites of Douro wine region. The study was implemented in 2017, being a year with very hot and dry summer, conditions prone to severe water shortage. Three grapevine cultivars, 'Touriga Nacional', 'Touriga Franca' and 'Tinta Barroca' were sampled both in rainfed and irrigated vineyards, with a total of 325 plants assessed in four post-flowering dates. A large set of vegetation indices computed with the hyperspectral data and optimized for the ѱpd values, as well as structural variables, were used as predictors in the model. From a total of 631 possible predictors, four variables were selected based on a stepwise forward procedure and the Wald statistics: irrigation treatment, test site, Anthocyanin Reflectance Index Optimized (ARIopt_656,647) and Normalized Ratio Index (NRI711,700). An ordinal logistic regression model was calibrated using 70 % of the dataset randomly selected and the 30 of the remaining observations where used in model validation. The overall model accuracy obtained with the validation dataset was 73.2 %, with the class of ѱpd corresponding to the high-water deficit presenting a positive prediction value of 79.3 %. The accuracy and operability of this predictive model indicates good perspectives for its use in the monitoring of grapevine water status, and to support the irrigation tasks

A review of strategies, methods and technologies to reduce non-beneficial consumptive water use on farms considering the FAO56 methods

In the past few decades, research has developed a multitude of strategies, methods and technologies to reduce consumptive water use on farms for adaptation to the increasing incidence of water scarcity, agricultural droughts and multi-sectoral competition for water. The adoption of these water-saving practices implies accurate quantification of crop water requirements with the FAO56 crop coefficient approach, under diverse water availability and management practices. This paper critically reviews notions and means for maintaining high levels of water consumed through transpiration, land and water productivity, and for minimizing non-beneficial water consumption at farm level. Literature published on sound and quantified experimentation was used to evaluate water-saving practices related to irrigation methods, irrigation management and scheduling, crop management, remote sensing, plant conditioners, mulching, soil management and micro-climate regulation. Summary tables were developed on the benefits of these practices, their effects on non-beneficial water consumption, crop yields and crop water productivity, and the directions for adjustment of FAO56 crop coefficients when they are adopted. The main message is that on-farm application of these practices can result in water savings to a limited extent (usually<20%) compared to sound conventional practices, however this may translate into large volumes of water at catchment scale. The need to streamline data collection internationally was identified due to the insufficient number of sound field experiments and modelling work on the FAO56 crop water requirements that would allow an improved use of crop coefficients for different field conditions and practices. Optimization is required for the application of some practices that involve a large number of possible combinations (e.g. wetted area in micro-irrigation, row spacing and orientation, plant density, different types of mulching, in-field water harvesting) and for strategies such as deficit irrigation that aim at balancing water productivity, the economics of production, infrastructural and irrigation system requirements. Further research is required on promising technologies such as plant and soil conditioners, and remote sensing applicationsinfo:eu-repo/semantics/publishedVersio

Evaluation of crop coefficient and evapotranspiration data for sugar beets from landsat surface reflectances using micrometeorological measurements and weighing lysimetry

In California and other agricultural regions that are facing challenges with water scarcity, accurate estimates of crop evapotranspiration (ETc) can support agricultural entities in ongoing efforts to improve on-farm water use efficiency. Remote sensing approaches for calculating ETc can be used to support wide area mapping of crop coefficients and ETc with the goal of increasing access to spatially and temporally distributed information for these variables, and advancing the use of evapotranspiration (ET) data in irrigation scheduling and management. We briefly review past work on the derivation of crop coefficients and ETc data from satellite-derived vegetation indices (VI) and evaluate the accuracy of a VI-based approach for calculation of ETc using a well instrumented, drip irrigated sugar beet (Beta vulgaris) field in the California Central Valley as a demonstration case. Sugar beets are grown around the world for sugar production, and are also being evaluated in California as a potential biofuel crop as well as for their ability to scavenge nitrogen from the soil, with important potential benefits for reduction of nitrate leaching from agricultural fields during the winter months. In this study, we evaluated the accuracy of ETc data from the Satellite Irrigation Management Support (SIMS) framework for sugar beets using ET data from a weighing lysimeter and a flux station instrumented with micrometeorological instrumentation. We used the Allen and Pereira (A&P) approach, which was developed to estimate single and basal crop coefficients from crop fractional cover (fc) and height, and combined with satellite-derived fc data and grass reference ET (ETo) data as implemented within SIMS to estimate daily ETc from SIMS (ETc-SIMS) for the sugar beet crop. The accuracy of the daily ETc-SIMS data was evaluated against daily actual ET data from the weighing lysimeter (ETa-lys) and actual ET calculated using an energy balance approach from micrometeorological instrumentation (ETa-eb). Over the course of the 181-day production cycle, ETc-SIMS totaled 737.1 mm, which was within 7.7% of total ETa-lys and 3.7% of ETa-eb. On a daily timestep, SIMS mean bias error was −0.31 mm/day relative to ETa-lys, and 0.15 mm/day relative to ETa-eb. The results from this study highlight the potential utility of applying satellite-based fc data coupled with the A&P approach to estimate ETc for drip-irrigated crops

Estimation of actual crop coefficients using remotely sensed vegetation indices and soil water balance modelled data

A new procedure is proposed for estimating actual basal crop coefficients from vegetation indices (Kcb VI) considering a density coefficient (Kd) and a crop coefficient for bare soil. Kd is computed using the fraction of ground cover by vegetation (fc VI), which is also estimated from vegetation indices derived from remote sensing. A combined approach for estimating actual crop coefficients from vegetation indices (Kc VI) is also proposed by integrating the Kcb VI with the soil evaporation coefficient (Ke) derived from the soil water balance model SIMDualKc. Results for maize, barley and an olive orchard have shown that the approaches for estimating both fc VI and Kcb VI compared well with results obtained using the SIMDualKc model after calibration with ground observation data. For the crops studied, the correlation coefficients relative to comparing the actual Kcb VI and Kc VI with actual Kcb and Kc obtained with SIMDualKc were larger than 0.73 and 0.71, respectively. The corresponding regression coefficients were close to 1.0. The methodology herein presented and discussed allowed for obtaining information for the whole crop season, including periods when vegetation cover is incomplete, as the initial and development stages. Results show that the proposed methods are adequate for supporting irrigation managementinfo:eu-repo/semantics/publishedVersio

Inhibitory effect of combinations of digoxin and endogenous cardiotonic steroids on Na+/K+-ATPase activity in human kidney membrane preparation

AbstractAimsCardiac glycosides have been extensively used in the treatment of congestive heart failure for more than 200years. Recently, cardenolides and bufadienolides were isolated from mammalian tissue and are considered as a new class of steroidal hormones. The aim of the present work was to characterize the interaction between the most clinical used cardiac glycoside digoxin and the cardiac glycosides known to exist endogenously, i.e., ouabain, marinobufagin and telocinobufagin, on human kidney Na+/K+-ATPase.Main methodsInhibition of Na+/K+-ATPase activity from crude membrane preparations of human kidney was performed using increasing concentrations of the drugs alone or mixtures of ouabain:digoxin, telocinobufagin:digoxin and marinobufagin:digoxin in a fixed ratio 1:4, 2:3 and 3:2, respectively. The colorimetric method of Fiske and Subbarow was used to measure the inorganic phosphate released.Key findingsAnalyses of inhibition curves showed that the experimental curves for all combinations were superimposed on the theoretical additive curves indicating that an additive effect occurs among distinct cardenolides and bufadienolides combinations on the human α1β1 Na+/K+-ATPase protomer.SignificanceConsidering the extensive use of digoxin in the treatment of heart failure and the recent findings that endogenous cardiac glycosides may have altered levels in many diseases, including heart failure, the demonstration of additive effect between cardiac glycosides can help in the understanding of recent clinical observations, including that lower than usual doses of cardiac glycosides are necessary for decreasing mortality in these patients

Evapotranspiration and crop coefficients for a super intensive olive orchard. An application of SIMDualKc and METRIC models using ground and satellite observations

The estimation of crop evapotranspiration (ETc) from the reference evapotranspiration (ETo) and a standard crop coefficient (Kc) in olive orchards requires that the latter be adjusted to planting density and height. The use of the dual Kc approach may be the best solution because the basal crop coefficient Kcb represents plant transpiration and the evaporation coefficient reproduces the soil coverage conditions and the frequency of wettings. To support related computations for a super intensive olive orchard, the model SIMDualKc was adopted because it uses the dual Kc approach. Alternatively, to consider the physical characteristics of the vegetation, the satellite-based surface energy balance model METRIC™ – Mapping EvapoTranspiration at high Resolution using Internalized Calibration – was used to estimate ETc and to derive crop coefficients. Both approaches were compared in this study. SIMDualKc model was calibrated and validated using sap-flow measurements of the transpiration for 2011 and 2012. In addition, eddy covariance estimation of ETc was also used. In the current study, METRIC™ was applied to Landsat images from 2011 to 2012. Adaptations for incomplete cover woody crops were required to parameterize METRIC. It was observed that ETc obtained from both approaches was similar and that crop coefficients derived from both models showed similar patterns throughout the year. Although the two models use distinct approaches, their results are comparable and they are complementary in spatial and temporal scalesinfo:eu-repo/semantics/publishedVersio

Visualisation of trust and quality information for geospatial dataset selection and use:Drawing trust presentation comparisons with B2C e-Commerce

The evaluation of geospatial data quality and trustworthiness presents a major challenge to geospatial data users when making a dataset selection decision. Part of the problem arises from the inconsistent and patchy nature of data quality information, which makes intercomparison very difficult. Over recent years, the production and availability of geospatial data has significantly increased, facilitated by the recent explosion of Web-based catalogues, portals, standards and services, and by initiatives such as INSPIRE and GEOSS. Despite this significant growth in availability of geospatial data and the fact that geospatial datasets can, in many respects, be considered commercial products that are available for purchase online, consumer trust has to date received relatively little attention in the GIS domain. In this paper, we discuss how concepts of trust, trust models, and trust indicators (largely derived from B2C e-Commerce) apply to the GIS domain and to geospatial data selection and use. Our research aim is to support data users in more efficient and effective geospatial dataset selection on the basis of quality, trustworthiness and fitness for purpose. To achieve this, we propose a GEO label – a decision support mechanism that visually summarises availability of key geospatial data informational aspects. We also present a Web service that was developed to support generation of dynamic GEO label representations for datasets by combining producer metadata (from standard catalogues or other published locations) with structured user feedback

Assessing the utility of geospatial technologies to investigate environmental change within lake systems

Over 50% of the world's population live within 3. km of rivers and lakes highlighting the on-going importance of freshwater resources to human health and societal well-being. Whilst covering c. 3.5% of the Earth's non-glaciated land mass, trends in the environmental quality of the world's standing waters (natural lakes and reservoirs) are poorly understood, at least in comparison with rivers, and so evaluation of their current condition and sensitivity to change are global priorities. Here it is argued that a geospatial approach harnessing existing global datasets, along with new generation remote sensing products, offers the basis to characterise trajectories of change in lake properties e.g., water quality, physical structure, hydrological regime and ecological behaviour. This approach furthermore provides the evidence base to understand the relative importance of climatic forcing and/or changing catchment processes, e.g. land cover and soil moisture data, which coupled with climate data provide the basis to model regional water balance and runoff estimates over time. Using examples derived primarily from the Danube Basin but also other parts of the World, we demonstrate the power of the approach and its utility to assess the sensitivity of lake systems to environmental change, and hence better manage these key resources in the future